diff --git a/addheader.yml b/addheader.yml index 18fd9b80..f82da9d2 100644 --- a/addheader.yml +++ b/addheader.yml @@ -13,5 +13,5 @@ path_exclude: - '.jupyter_cache' verbose: 1 sep-len: 80 -jupyter: '_src.ipynb' -... \ No newline at end of file +jupyter: '.ipynb' +... diff --git a/file_header.txt b/file_header.txt index 2a5d95aa..dd7718c8 100644 --- a/file_header.txt +++ b/file_header.txt @@ -2,9 +2,9 @@ The Institute for the Design of Advanced Energy Systems Integrated Platform Framework (IDAES IP) was produced under the DOE Institute for the Design of Advanced Energy Systems (IDAES). -Copyright (c) 2018-2023 by the software owners: The Regents of the +Copyright (c) 2018-2025 by the software owners: The Regents of the University of California, through Lawrence Berkeley National Laboratory, National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia University Research Corporation, et al. All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md -for full copyright and license information. \ No newline at end of file +for full copyright and license information. diff --git a/idaes_examples/notebooks/_config.yml b/idaes_examples/notebooks/_config.yml index 1f273dce..0300fb91 100644 --- a/idaes_examples/notebooks/_config.yml +++ b/idaes_examples/notebooks/_config.yml @@ -1,4 +1,5 @@ author: The IDAES Team +copyright: "2018-2025" bibtex_bibfiles: - references.bib exclude_patterns: diff --git a/idaes_examples/notebooks/_dev/notebooks/cache.ipynb b/idaes_examples/notebooks/_dev/notebooks/cache.ipynb index 74e2c269..e0e7c425 100644 --- a/idaes_examples/notebooks/_dev/notebooks/cache.ipynb +++ b/idaes_examples/notebooks/_dev/notebooks/cache.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ac23bafd", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 24, diff --git a/idaes_examples/notebooks/_dev/notebooks/ex/notebook_tags_example.ipynb b/idaes_examples/notebooks/_dev/notebooks/ex/notebook_tags_example.ipynb index b2287906..7cfe7821 100644 --- a/idaes_examples/notebooks/_dev/notebooks/ex/notebook_tags_example.ipynb +++ b/idaes_examples/notebooks/_dev/notebooks/ex/notebook_tags_example.ipynb @@ -17,12 +17,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/dae/petsc_chem.ipynb b/idaes_examples/notebooks/docs/dae/petsc_chem.ipynb index 44969c96..e4d9e317 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_chem.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_chem.ipynb @@ -3,6 +3,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6cabc606", "metadata": { "tags": [ "header", @@ -16,17 +17,19 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, { "cell_type": "markdown", + "id": "28bb63c9", "metadata": {}, "source": [ "# PETSc Time-stepping Solver -- Chemical Akzo Nobel Example\n", @@ -41,6 +44,7 @@ }, { "cell_type": "markdown", + "id": "42025a07", "metadata": {}, "source": [ "## Prerequisites\n", @@ -50,6 +54,7 @@ }, { "cell_type": "markdown", + "id": "070fa782", "metadata": {}, "source": [ "## Imports\n", @@ -60,6 +65,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "ca3afd41", "metadata": {}, "outputs": [], "source": [ @@ -73,6 +79,7 @@ }, { "cell_type": "markdown", + "id": "b1872b90", "metadata": {}, "source": [ "## Set Up the Model\n", @@ -83,6 +90,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "0629b344", "metadata": {}, "outputs": [], "source": [ @@ -93,6 +101,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "f27c92dc", "metadata": {}, "outputs": [], "source": [ @@ -102,6 +111,7 @@ }, { "cell_type": "markdown", + "id": "f453bfd4", "metadata": {}, "source": [ "The variables y1 to y6 represent concentrations of chemical species. The values returned by the function above are the correct solution at t = 180. These values can be used to verify the solver results. The Pyomo model is ``m``. We are mainly interested in the y variables. The variables y1 to y5 are differential variables and y6 is an algebraic variable. Initial conditions are required for y1 through y5, and the initial values of the other variables can be calculated from there. The variables y1 through y5 at t = 0 are: ``m.y[0, 1]`` to ``m.y[0, 5]`` and y6 is ``m.y6[0]``. The variables at the final state are ``m.y[180, 1]`` to ``m.y[180, 5]`` and ``m.y6[180]``. The variable y6 is indexed differently because we want to treat it differently than the differential variables." @@ -110,6 +120,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "16c95b9d", "metadata": {}, "outputs": [], "source": [ @@ -124,6 +135,7 @@ }, { "cell_type": "markdown", + "id": "5471f384", "metadata": {}, "source": [ "## Solve\n", @@ -134,6 +146,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "8a34145e", "metadata": {}, "outputs": [], "source": [ @@ -144,6 +157,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "87b95521", "metadata": {}, "outputs": [], "source": [ @@ -173,6 +187,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "513f4f32", "metadata": {}, "outputs": [], "source": [ @@ -187,6 +202,7 @@ }, { "cell_type": "markdown", + "id": "ebfda339", "metadata": {}, "source": [ "## Plot Results Stored in Model\n", @@ -197,6 +213,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "36b069ea", "metadata": {}, "outputs": [], "source": [ @@ -213,6 +230,7 @@ }, { "cell_type": "markdown", + "id": "e9a21252", "metadata": {}, "source": [ "## Plot Trajectory" @@ -221,6 +239,7 @@ { "cell_type": "code", "execution_count": 9, + "id": "ab7e20c6", "metadata": {}, "outputs": [], "source": [ @@ -240,6 +259,7 @@ { "cell_type": "code", "execution_count": 10, + "id": "5bb9a56d", "metadata": {}, "outputs": [], "source": [ @@ -254,6 +274,7 @@ { "cell_type": "code", "execution_count": 11, + "id": "425ccec5", "metadata": {}, "outputs": [], "source": [ @@ -267,6 +288,7 @@ }, { "cell_type": "markdown", + "id": "42511c06", "metadata": {}, "source": [ "## Interpolate Trajectory\n", @@ -277,6 +299,7 @@ { "cell_type": "code", "execution_count": 12, + "id": "a34aca76", "metadata": {}, "outputs": [], "source": [ @@ -287,6 +310,7 @@ { "cell_type": "code", "execution_count": 13, + "id": "d060fdcd", "metadata": {}, "outputs": [], "source": [ @@ -307,6 +331,7 @@ { "cell_type": "code", "execution_count": 14, + "id": "f8a01476", "metadata": {}, "outputs": [], "source": [ @@ -321,6 +346,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a4c8d973", "metadata": {}, "outputs": [], "source": [] diff --git a/idaes_examples/notebooks/docs/dae/petsc_chem_doc.ipynb b/idaes_examples/notebooks/docs/dae/petsc_chem_doc.ipynb index f36b14c1..75737586 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_chem_doc.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_chem_doc.ipynb @@ -16,966 +16,165 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PETSc Time-stepping Solver -- Chemical Akzo Nobel Example\n", - "Author: John Eslick \n", - "Maintainer: John Eslick \n", - "Updated: 2023-06-01 \n", - "\n", - "This example provides an overview of the PETSc time-stepping solver utilities in IDAES, which can be used to solve systems of differential algebraic equations (DAEs). PETSc is a solver suite developed primarily by Argonne National Lab (https://petsc.org/release/). IDAES provides a wrapper for PETSc (https://github.com/IDAES/idaes-ext/tree/main/petsc) that uses the AMPL solver interface (https://ampl.com/resources/learn-more/hooking-your-solver-to-ampl/) and utility functions that allow Pyomo and Pyomo.DAE (https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html) problems to be solved using PETSc.\n", - "\n", - "This demonstration problem describes a set of chemical reactions in a reactor. A full description of the problem is available at https://archimede.dm.uniba.it/~testset/report/chemakzo.pdf. This is part of a test set which can be found at https://archimede.uniba.it/~testset/." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "The PETSc solver is an extra download for IDAES, which can be downloaded using the command ```idaes get-extensions --extra petsc```, if it is not installed already. See the IDAES solver documentation for more information (https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports\n", - "\n", - "Import the modules that will be used. Numpy and matplotlib are used to make some plots, ```idaes.core.solvers.petsc``` contains the PETSc utilities, and ```idaes.core.solvers.features``` contains the example model used here." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import pyomo.environ as pyo\n", - "import idaes.core.solvers.petsc as petsc # petsc utilities module\n", - "from idaes.core.solvers.features import dae # DAE example/test problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Up the Model\n", - "\n", - "The model in this example is used for basic solver testing, so it is provided as part of an IDAES solver testing module. The model implementation is standard Pyomo.DAE, and nothing special needs to be done in the model to use the PETSc solver. The IDAES utilities for the PETSc solver will take the discretized Pyomo model and integrate between discrete time points to fill in the solution. To integrate over the entire time domain (as we will do here), you can discretize time using one time element, in which case, the problem will just contain the initial and final points. The intermediate solutions can be read from the trajectory data saved by the solver. The trajectory data can be used for analysis, or interpolation can be used to initialize a Pyomo problem before solving the fully time discretized problem. Integrating over the entire time domain is fastest with a coarsely discretized model (ideally just a single finite element in time) because the model is smaller and there are fewer calls to the integrator. This can be a good way to start testing a new dynamic IDAES model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# To see the example problem code, uncomment the line below and execute this cell.\n", - "# ??dae" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the model and known solution for y variables at t=180 minutes.\n", - "m, y1, y2, y3, y4, y5, y6 = dae(nfe=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The variables y1 to y6 represent concentrations of chemical species. The values returned by the function above are the correct solution at t = 180. These values can be used to verify the solver results. The Pyomo model is ``m``. We are mainly interested in the y variables. The variables y1 to y5 are differential variables and y6 is an algebraic variable. Initial conditions are required for y1 through y5, and the initial values of the other variables can be calculated from there. The variables y1 through y5 at t = 0 are: ``m.y[0, 1]`` to ``m.y[0, 5]`` and y6 is ``m.y6[0]``. The variables at the final state are ``m.y[180, 1]`` to ``m.y[180, 5]`` and ``m.y6[180]``. The variable y6 is indexed differently because we want to treat it differently than the differential variables." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "at t = 0:\n", - " y1 = 0.444\n", - " y2 = 0.00123\n", - " y3 = 0.0\n", - " y4 = 0.007\n", - " y5 = 0.0\n" - ] - } - ], - "source": [ - "# See the initial conditions:\n", - "print(\"at t = 0:\")\n", - "print(f\" y1 = {pyo.value(m.y[0, 1])}\")\n", - "print(f\" y2 = {pyo.value(m.y[0, 2])}\")\n", - "print(f\" y3 = {pyo.value(m.y[0, 3])}\")\n", - "print(f\" y4 = {pyo.value(m.y[0, 4])}\")\n", - "print(f\" y5 = {pyo.value(m.y[0, 5])}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve\n", - "\n", - "The ``petsc_dae_by_time_element()`` function is used to solve Pyomo.DAE discretized Pyomo problem with the PETSc time-stepping solver by integrating between discrete time points. In this case there is only one time element." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# To see the docs, uncomment the line below and execute this cell.\n", - "# ?petsc.petsc_dae_by_time_element" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: DAE: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp79_vpohc.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of constraints: 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of variables: 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 30 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: 0 SNES Function norm 4.725472106218e+00 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: 1 SNES Function norm 6.033402274321e-03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: 2 SNES Function norm 5.511108092160e-19 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: SNESConvergedReason = SNES_CONVERGED_FNORM_ABS, in 2 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: SNES_CONVERGED_FNORM_ABS\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: DAE: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp86gcsgoo.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of constraints: 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: Number of variables: 17\n" - ] - }, - { - "name": "stdout", - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: 74 TS dt 16.8503 time 180.\n" - ] - }, + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PETSc Time-stepping Solver -- Chemical Akzo Nobel Example\n", + "Author: John Eslick \n", + "Maintainer: John Eslick \n", + "Updated: 2023-06-01 \n", + "\n", + "This example provides an overview of the PETSc time-stepping solver utilities in IDAES, which can be used to solve systems of differential algebraic equations (DAEs). PETSc is a solver suite developed primarily by Argonne National Lab (https://petsc.org/release/). IDAES provides a wrapper for PETSc (https://github.com/IDAES/idaes-ext/tree/main/petsc) that uses the AMPL solver interface (https://ampl.com/resources/learn-more/hooking-your-solver-to-ampl/) and utility functions that allow Pyomo and Pyomo.DAE (https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html) problems to be solved using PETSc.\n", + "\n", + "This demonstration problem describes a set of chemical reactions in a reactor. A full description of the problem is available at https://archimede.dm.uniba.it/~testset/report/chemakzo.pdf. This is part of a test set which can be found at https://archimede.uniba.it/~testset/." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "The PETSc solver is an extra download for IDAES, which can be downloaded using the command ```idaes get-extensions --extra petsc```, if it is not installed already. See the IDAES solver documentation for more information (https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "Import the modules that will be used. Numpy and matplotlib are used to make some plots, ```idaes.core.solvers.petsc``` contains the PETSc utilities, and ```idaes.core.solvers.features``` contains the example model used here." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyomo.environ as pyo\n", + "import idaes.core.solvers.petsc as petsc # petsc utilities module\n", + "from idaes.core.solvers.features import dae # DAE example/test problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Up the Model\n", + "\n", + "The model in this example is used for basic solver testing, so it is provided as part of an IDAES solver testing module. The model implementation is standard Pyomo.DAE, and nothing special needs to be done in the model to use the PETSc solver. The IDAES utilities for the PETSc solver will take the discretized Pyomo model and integrate between discrete time points to fill in the solution. To integrate over the entire time domain (as we will do here), you can discretize time using one time element, in which case, the problem will just contain the initial and final points. The intermediate solutions can be read from the trajectory data saved by the solver. The trajectory data can be used for analysis, or interpolation can be used to initialize a Pyomo problem before solving the fully time discretized problem. Integrating over the entire time domain is fastest with a coarsely discretized model (ideally just a single finite element in time) because the model is smaller and there are fewer calls to the integrator. This can be a good way to start testing a new dynamic IDAES model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# To see the example problem code, uncomment the line below and execute this cell.\n", + "# ??dae" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the model and known solution for y variables at t=180 minutes.\n", + "m, y1, y2, y3, y4, y5, y6 = dae(nfe=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variables y1 to y6 represent concentrations of chemical species. The values returned by the function above are the correct solution at t = 180. These values can be used to verify the solver results. The Pyomo model is ``m``. We are mainly interested in the y variables. The variables y1 to y5 are differential variables and y6 is an algebraic variable. Initial conditions are required for y1 through y5, and the initial values of the other variables can be calculated from there. The variables y1 through y5 at t = 0 are: ``m.y[0, 1]`` to ``m.y[0, 5]`` and y6 is ``m.y6[0]``. The variables at the final state are ``m.y[180, 1]`` to ``m.y[180, 5]`` and ``m.y6[180]``. The variable y6 is indexed differently because we want to treat it differently than the differential variables." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n" + "at t = 0:\n", + " y1 = 0.444\n", + " y2 = 0.00123\n", + " y3 = 0.0\n", + " y4 = 0.007\n", + " y5 = 0.0\n" ] - }, + } + ], + "source": [ + "# See the initial conditions:\n", + "print(\"at t = 0:\")\n", + "print(f\" y1 = {pyo.value(m.y[0, 1])}\")\n", + "print(f\" y2 = {pyo.value(m.y[0, 2])}\")\n", + "print(f\" y3 = {pyo.value(m.y[0, 3])}\")\n", + "print(f\" y4 = {pyo.value(m.y[0, 4])}\")\n", + "print(f\" y5 = {pyo.value(m.y[0, 5])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve\n", + "\n", + "The ``petsc_dae_by_time_element()`` function is used to solve Pyomo.DAE discretized Pyomo problem with the PETSc time-stepping solver by integrating between discrete time points. In this case there is only one time element." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# To see the docs, uncomment the line below and execute this cell.\n", + "# ?petsc.petsc_dae_by_time_element" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:22:07 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n" + "ename": "RuntimeError", + "evalue": "No PETSc executable found.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# The command below will solve the problem. In this case, we want to read the saved\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;66;03m# trajectory for each time element in the Pyomo.DAE problem (in this case there is\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# only 1) so we will need to provide solver options to save the trajectory to the PETSc\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 6\u001b[39m \u001b[38;5;66;03m# information written by PETSc and resave it as json. This allows us to cleanly read\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;66;03m# the trajectory data for multiple time elements.\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m result = \u001b[43mpetsc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpetsc_dae_by_time_element\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m=\u001b[49m\u001b[43mm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 12\u001b[39m \u001b[43m \u001b[49m\u001b[43mbetween\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfirst\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mt\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlast\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 13\u001b[39m \u001b[43m \u001b[49m\u001b[43mts_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 14\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcn\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Crank–Nicolson\u001b[39;49;00m\n\u001b[32m 15\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_adapt_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbasic\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_dt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 17\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_save_trajectory\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 18\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 19\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 20\u001b[39m tj = result.trajectory\n\u001b[32m 21\u001b[39m res = result.results\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/solvers/petsc.py:592\u001b[39m, in \u001b[36mpetsc_dae_by_time_element\u001b[39m\u001b[34m(m, time, timevar, initial_constraints, initial_variables, detect_initial, skip_initial, initial_solver, initial_solver_options, ts_options, keepfiles, symbolic_solver_labels, between, interpolate, calculate_derivatives, previous_trajectory, representative_time, snes_options)\u001b[39m\n\u001b[32m 590\u001b[39m _sub_problem_scaling_suffix(m, t_block)\n\u001b[32m 591\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m idaeslog.solver_log(solve_log, idaeslog.INFO) \u001b[38;5;28;01mas\u001b[39;00m slc:\n\u001b[32m--> \u001b[39m\u001b[32m592\u001b[39m res = \u001b[43minitial_solver_obj\u001b[49m\u001b[43m.\u001b[49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[43mt_block\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[43mtee\u001b[49m\u001b[43m=\u001b[49m\u001b[43mslc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 595\u001b[39m \u001b[43m \u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m=\u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 596\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 597\u001b[39m res_list.append(res)\n\u001b[32m 599\u001b[39m tprev = t0\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/base/solvers.py:534\u001b[39m, in \u001b[36mOptSolver.solve\u001b[39m\u001b[34m(self, *args, **kwds)\u001b[39m\n\u001b[32m 531\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msolve\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwds):\n\u001b[32m 532\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Solve the problem\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 536\u001b[39m \u001b[38;5;66;03m# If the inputs are models, then validate that they have been\u001b[39;00m\n\u001b[32m 537\u001b[39m \u001b[38;5;66;03m# constructed! Collect suffix names to try and import from solution.\u001b[39;00m\n\u001b[32m 538\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 539\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyomo\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mblock\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BlockData\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/solvers/plugins/solvers/ASL.py:119\u001b[39m, in \u001b[36mASL.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mavailable\u001b[39m(\u001b[38;5;28mself\u001b[39m, exception_flag=\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m119\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 120\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 121\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.version() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:134\u001b[39m, in \u001b[36mSystemCallSolver.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 132\u001b[39m cm = nullcontext() \u001b[38;5;28;01mif\u001b[39;00m exception_flag \u001b[38;5;28;01melse\u001b[39;00m LoggingIntercept()\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m cm:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m ans = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[32m 136\u001b[39m ans = \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:205\u001b[39m, in \u001b[36mSystemCallSolver.executable\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 198\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mexecutable\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 199\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 200\u001b[39m \u001b[33;03m Returns the executable used by this solver.\u001b[39;00m\n\u001b[32m 201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 203\u001b[39m \u001b[38;5;28mself\u001b[39m._user_executable\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m._user_executable \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m205\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_default_executable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 206\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/solvers/petsc.py:124\u001b[39m, in \u001b[36mPetsc._default_executable\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 122\u001b[39m executable = Executable(\u001b[33m\"\u001b[39m\u001b[33mpetsc\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m executable:\n\u001b[32m--> \u001b[39m\u001b[32m124\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo PETSc executable found.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 125\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m executable.path()\n", + "\u001b[31mRuntimeError\u001b[39m: No PETSc executable found." ] } ], @@ -1005,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1029,24 +228,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_chem_doc_15_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "a = plt.plot(m.t, [pyo.value(m.y[t, 1]) for t in m.t], label=\"y1\")\n", "a = plt.plot(m.t, [pyo.value(m.y[t, 2]) for t in m.t], label=\"y2\")\n", @@ -1068,24 +252,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_chem_doc_18_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# 2 and 4 are pretty low concentration, so plot those so we can see better\n", "a = plt.plot(tj.time, tj.get_vec(m.y[180, 2]), label=\"y2\")\n", @@ -1131,24 +285,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_chem_doc_19_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# 2 seems to have some fast dynamics so plot a shorter time\n", "a = plt.plot(tj.vecs[\"_time\"], tj.vecs[str(m.y[180, 2])], label=\"y2\")\n", @@ -1169,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1179,24 +318,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_chem_doc_23_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "a = plt.plot(tji.time, tji.get_vec(m.y[180, 2]), label=\"y2 interpolate dt=1\")\n", "a = plt.plot(tj.time, tj.get_vec(m.y[180, 2]), label=\"y2 original\")\n", @@ -1265,9 +374,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/dae/petsc_chem_test.ipynb b/idaes_examples/notebooks/docs/dae/petsc_chem_test.ipynb index c92befe9..fecec24b 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_chem_test.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_chem_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -160,7 +161,7 @@ " time=m.t,\n", " between=[m.t.first(), m.t.last()],\n", " ts_options={\n", - " \"--ts_type\": \"cn\", # Crank–Nicolson\n", + " \"--ts_type\": \"cn\", # Crank\u2013Nicolson\n", " \"--ts_adapt_type\": \"basic\",\n", " \"--ts_dt\": 0.01,\n", " \"--ts_save_trajectory\": 1,\n", @@ -347,4 +348,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/dae/petsc_chem_usr.ipynb b/idaes_examples/notebooks/docs/dae/petsc_chem_usr.ipynb index c92befe9..fecec24b 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_chem_usr.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_chem_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -160,7 +161,7 @@ " time=m.t,\n", " between=[m.t.first(), m.t.last()],\n", " ts_options={\n", - " \"--ts_type\": \"cn\", # Crank–Nicolson\n", + " \"--ts_type\": \"cn\", # Crank\u2013Nicolson\n", " \"--ts_adapt_type\": \"basic\",\n", " \"--ts_dt\": 0.01,\n", " \"--ts_save_trajectory\": 1,\n", @@ -347,4 +348,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/dae/petsc_pid.ipynb b/idaes_examples/notebooks/docs/dae/petsc_pid.ipynb index b00e4c6b..19ff7ae8 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_pid.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_pid.ipynb @@ -3,6 +3,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "54f17eaa", "metadata": { "pycharm": { "is_executing": true @@ -19,17 +20,19 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, { "cell_type": "markdown", + "id": "75972e4c", "metadata": {}, "source": [ "# PETSc Time-stepping Solver -- PID Control and Steam Example\n", @@ -52,6 +55,7 @@ }, { "cell_type": "markdown", + "id": "b1de1dfa", "metadata": {}, "source": [ "## Prerequisites\n", @@ -63,6 +67,7 @@ }, { "cell_type": "markdown", + "id": "45d10140", "metadata": {}, "source": [ "## Imports" @@ -71,6 +76,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "67b6281f", "metadata": {}, "outputs": [], "source": [ @@ -86,6 +92,7 @@ }, { "cell_type": "markdown", + "id": "06aa08d6", "metadata": {}, "source": [ "## Model Set Up" @@ -94,6 +101,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "36710288", "metadata": {}, "outputs": [], "source": [ @@ -104,6 +112,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "4c19a5af", "metadata": {}, "outputs": [], "source": [ @@ -116,6 +125,7 @@ }, { "cell_type": "markdown", + "id": "755fd8cf", "metadata": {}, "source": [ "## Solve" @@ -124,6 +134,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "592dbbf0", "metadata": {}, "outputs": [], "source": [ @@ -143,6 +154,7 @@ }, { "cell_type": "markdown", + "id": "2802f9b9", "metadata": {}, "source": [ "## Plot Trajectory\n", @@ -153,6 +165,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "545c6d71", "metadata": {}, "outputs": [], "source": [ @@ -164,6 +177,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "c3ef512e", "metadata": {}, "outputs": [], "source": [ @@ -174,6 +188,7 @@ }, { "cell_type": "markdown", + "id": "27c53e18", "metadata": {}, "source": [ "## Model a ramp in inlet pressure\n", @@ -184,6 +199,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "cbf65cfa", "metadata": {}, "outputs": [], "source": [ @@ -221,6 +237,7 @@ { "cell_type": "code", "execution_count": 9, + "id": "7a189bcf", "metadata": {}, "outputs": [], "source": [ @@ -243,6 +260,7 @@ { "cell_type": "code", "execution_count": 10, + "id": "2117da8a", "metadata": {}, "outputs": [], "source": [ @@ -256,6 +274,7 @@ { "cell_type": "code", "execution_count": 11, + "id": "8a2c76dd", "metadata": {}, "outputs": [], "source": [ @@ -267,6 +286,7 @@ { "cell_type": "code", "execution_count": 12, + "id": "446d614d", "metadata": {}, "outputs": [], "source": [ @@ -277,6 +297,7 @@ }, { "cell_type": "markdown", + "id": "bfe890a2", "metadata": {}, "source": [ "## Model a ramp in inlet pressure (again)\n", @@ -287,6 +308,7 @@ { "cell_type": "code", "execution_count": 13, + "id": "e73f85c0", "metadata": {}, "outputs": [], "source": [ @@ -327,6 +349,7 @@ { "cell_type": "code", "execution_count": 14, + "id": "86db8473", "metadata": {}, "outputs": [], "source": [ @@ -349,6 +372,7 @@ { "cell_type": "code", "execution_count": 15, + "id": "f4bc39df", "metadata": {}, "outputs": [], "source": [ @@ -362,6 +386,7 @@ { "cell_type": "code", "execution_count": 16, + "id": "94bd28cf", "metadata": {}, "outputs": [], "source": [ @@ -373,6 +398,7 @@ { "cell_type": "code", "execution_count": 17, + "id": "a1edacc4", "metadata": {}, "outputs": [], "source": [ @@ -384,6 +410,7 @@ { "cell_type": "code", "execution_count": null, + "id": "75977f30", "metadata": {}, "outputs": [], "source": [] diff --git a/idaes_examples/notebooks/docs/dae/petsc_pid_doc.ipynb b/idaes_examples/notebooks/docs/dae/petsc_pid_doc.ipynb index 9fc799cf..2f4dcc5e 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_pid_doc.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_pid_doc.ipynb @@ -19,12 +19,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -110,28 +111,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:31:29 [INFO] idaes.init.fs.valve_1: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:30:28 [INFO] idaes.init.fs.valve_1: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:31:29 [INFO] idaes.init.fs.tank.control_volume: Initialization Complete\n" + "2025-03-17 17:30:28 [INFO] idaes.init.fs.tank.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:31:29 [INFO] idaes.init.fs.tank: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:30:28 [INFO] idaes.init.fs.tank: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:31:29 [INFO] idaes.init.fs.valve_2: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:30:28 [INFO] idaes.init.fs.valve_2: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -156,4481 +157,226 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: DAE: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp8y_z4ws2.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of constraints: 28\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of variables: 28\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 83 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 0 SNES Function norm 5.783088779321e+05 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 1 SNES Function norm 4.315325974705e+05 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 2 SNES Function norm 7.639793381288e+04 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 3 SNES Function norm 5.928557930246e+03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 4 SNES Function norm 5.008361736228e+01 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 5 SNES Function norm 3.698195211732e-03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 6 SNES Function norm 3.725294295263e-09 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: SNESConvergedReason = SNES_CONVERGED_FNORM_RELATIVE, in 6 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: SNES_CONVERGED_FNORM_RELATIVE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: DAE: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp3aw8uqg4.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of constraints: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of variables: 34\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 91 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of derivatives: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of differential vars: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 24\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of state vars: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:30 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.025 time 0.025\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 2 TS dt 0.0154252 time 0.0381739\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 3 TS dt 0.0133373 time 0.0510085\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 4 TS dt 0.0136013 time 0.0643458\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 5 TS dt 0.0141735 time 0.0779471\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 6 TS dt 0.0146392 time 0.0921205\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 7 TS dt 0.0152517 time 0.10676\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 8 TS dt 0.0158389 time 0.122011\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 9 TS dt 0.0165162 time 0.13785\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 10 TS dt 0.0172093 time 0.154367\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 11 TS dt 0.0179712 time 0.171576\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 12 TS dt 0.0187679 time 0.189547\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 13 TS dt 0.0196222 time 0.208315\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 14 TS dt 0.020514 time 0.227937\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 15 TS dt 0.0214472 time 0.248451\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 16 TS dt 0.022401 time 0.269898\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:33 [INFO] idaes.solve.petsc-dae: 17 TS dt 0.0233619 time 0.292299\n" - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:35 [INFO] idaes.solve.petsc-dae: 83 TS dt 2.39766 time 7.20469\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:35 [INFO] idaes.solve.petsc-dae: 84 TS dt 2.39766 time 9.60234\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:35 [INFO] idaes.solve.petsc-dae: 85 TS dt 23.9766 time 12.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:35 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:35 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n" - ] - } - ], - "source": [ - "result = petsc.petsc_dae_by_time_element(\n", - " m,\n", - " time=m.fs.time,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\",\n", - " \"--ts_dt\": 0.1,\n", - " \"--ts_monitor\": \"\", # set initial step to 0.1\n", - " \"--ts_save_trajectory\": 1,\n", - " },\n", - ")\n", - "tj = result.trajectory # trajectroy data\n", - "res = result.results # solver status list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Trajectory\n", - "\n", - "At the initial conditions the valve is fully open. At t=0, the controller is activated and the controller adjusts the opening of valve 1 to keep the tank pressure at the setpoint of 300 kPa." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_pid_doc_11_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[12]))\n", - "a = plt.ylabel(\"valve 1 fraction open\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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p3pZKpUJ8fLxZG5PJhIyMDKlNfHw83NzczNrk5+ejsLDQbP6Xo2vpqbpcVY/6JqPM1RAREXUusg7/paamYtSoUYiMjERlZSU+//xz7Ny5E9u2bcOZM2fw+eef48EHH0RAQACOHDmC2bNn45577kG/fv0AACNGjEBcXByeeuopLFq0CMXFxXjttdcwY8YMqNVqAMDzzz+PDz/8EHPnzsWUKVOwfft2rFu3Dps2bZLqSElJwaRJkzB48GAMGTIE77//PqqrqzF58mQAgFarxdSpU5GSkgJ/f39oNBq88MIL0Ol0uOOOO9r/xN0kP083uLspUddoQrG+DlEBXnKXRERE1Hm009WIbZoyZYqIiooSKpVKBAYGiuHDh4tvvvlGCCFEYWGhuOeee4S/v79Qq9UiJiZGzJkzp9XljOfOnROjRo0SHh4eokuXLuJ3v/udaGxsNGuzY8cOMWDAAKFSqcRtt90mVq1a1aqWDz74QERGRgqVSiWGDBkivvvuO7P9tbW14v/+7/+En5+f8PT0FA8//LC4ePGiVZ9X7iUVhBDi/sU7RNS8jWLv6cuy1UBERNSRWPr9rRBC8EZw7cRgMECr1UKv18s2v2rCyu+w5/QV/OXR/hg7KFyWGoiIiDoSS7+/HW5OFdlXqJZXABIREdkDQ5WTCZOuAORaVURERLbEUOVkQrlWFRERkV0wVDmZlrWqGKqIiIhsi6HKyfw0p4rDf0RERLbEUOVkQq/eVLmiphG1DVwAlIiIyFYYqpyMxt0N3urmNV+L2FtFRERkMwxVTijk6ryqYs6rIiIishmGKicUymUViIiIbI6hygmFsqeKiIjI5hiqnFDLFYBFDFVEREQ2w1DlhH7qqeLwHxERka0wVDmhEC4ASkREZHMMVU4ojLeqISIisjmGKifU0lOlr21ETUOTzNUQERF1DgxVTujaBUB5BSAREZFtMFQ5Kc6rIiIisi2GKicVylBFRERkUwxVTipEw2UViIiIbImhykmxp4qIiMi2GKqcVMjVVdU5UZ2IiMg2GKqcFHuqiIiIbIuhykm1XP1XbGCoIiIisgWGKifV0lNVVt2AukajzNUQERF1fAxVTkrr4QZ3t+a//hL2VhEREd0yhionpVAoEHp1snpRBUMVERHRrWKocmIta1Wxp4qIiOjWMVQ5sVBOViciIrIZhionJl0ByGUViIiIbhlDlRP7aa0q3qqGiIjoVjFUOTGuqk5ERGQ7DFVOjKuqExER2Q5DlRNrmVN1qaoejUaTzNUQERF1bAxVTszfUwWVixJCAKWV9XKXQ0RE1KExVDkxpVKBII0aAHCxgpPViYiIbgVDlZPjWlVERES2wVDl5HgFIBERkW0wVDk5XgFIRERkGwxVTq7l/n/sqSIiIro1DFVOjnOqiIiIbIOhysnx/n9ERES2wVDl5EKvTlQvMdTBaBIyV0NERNRxuVrT2GQyITMzE99++y3Onz+PmpoaBAYGYuDAgUhMTERERIS96iQ7CfRRw0WpQJNJ4EpVPYKuzrEiIiIi61jUU1VbW4s//vGPiIiIwIMPPogtW7agoqICLi4uOH36NBYsWIDo6Gg8+OCD+O677yx+8xUrVqBfv37QaDTQaDTQ6XTYsmVLq3ZCCIwaNQoKhQL//ve/zfYVFhYiOTkZnp6eCAoKwpw5c9DU1GTWZufOnRg0aBDUajViYmKwevXqVu+xbNkydOvWDe7u7khISMD+/fvN9tfV1WHGjBkICAiAt7c3xo0bh5KSEos/q6NyUSoQ5NO8AGgRhwCJiIhumkWhqkePHjhy5Ag+/fRTGAwGZGVl4V//+hf+8Y9/YPPmzSgsLMSZM2dw991347HHHsOnn35q0ZuHh4dj4cKFyMnJQXZ2NoYNG4bRo0fj6NGjZu3ef/99KBSKVq83Go1ITk5GQ0MD9u7di88++wyrV6/G/PnzpTYFBQVITk7G/fffj9zcXMyaNQvTpk3Dtm3bpDZr165FSkoKFixYgIMHD6J///5ISkpCaWmp1Gb27Nn4+uuvsX79emRmZqKoqAhjx4616HM6Os6rIiIisgFhgWPHjlnSTAghRENDgzh9+rTF7X/Oz89PrFy5Unp+6NAh0bVrV3Hx4kUBQGzYsEHat3nzZqFUKkVxcbG0bcWKFUKj0Yj6+nohhBBz584VvXv3NnuP8ePHi6SkJOn5kCFDxIwZM6TnRqNRhIWFibS0NCGEEBUVFcLNzU2sX79eanP8+HEBQGRlZVn82fR6vQAg9Hq9xa9pD9P/kS2i5m0Uq3aflbsUIiIih2Pp97dFPVW9evWyOKS5ubnh9ttvtzrcGY1GrFmzBtXV1dDpdACAmpoaPPHEE1i2bBlCQkJavSYrKwt9+/ZFcHCwtC0pKQkGg0Hq7crKykJiYqLZ65KSkpCVlQUAaGhoQE5OjlkbpVKJxMREqU1OTg4aGxvN2vTs2RORkZFSm7bU19fDYDCYPRxRiKZ5svpFLqtARER006yaqH6tmpoaFBYWoqGhwWx7v379rDpOXl4edDod6urq4O3tjQ0bNiAuLg5A85DbnXfeidGjR7f52uLiYrNABUB6Xlxc/IttDAYDamtrUV5eDqPR2GabEydOSMdQqVTw9fVt1ablfdqSlpaGN9988wZnQH4h2uY5VRz+IyIiunlWh6pLly5h8uTJbU4oB5p7nKwRGxuL3Nxc6PV6fPnll5g0aRIyMzNx+vRpbN++HYcOHbK2RIeRmpqKlJQU6bnBYHDIKyRb7v/HW9UQERHdPKtD1axZs1BRUYF9+/bhvvvuw4YNG1BSUoI//vGPePfdd60uQKVSISYmBgAQHx+PAwcOYMmSJfDw8MCZM2da9Q6NGzcOd999N3bu3ImQkJBWV+m1XJHXMlwYEhLS6iq9kpISaDQaeHh4wMXFBS4uLm22ufYYDQ0NqKioMKvn2jZtUavVUKvVlp8MmYRyojoREdEts3rxz+3bt+Mvf/kLBg8eDKVSiaioKDz55JNYtGgR0tLSbrkgk8mE+vp6vPzyyzhy5Ahyc3OlBwC89957WLVqFQBAp9MhLy/P7Cq99PR0aDQaaQhRp9MhIyPD7D3S09OleVsqlQrx8fFmbUwmEzIyMqQ28fHxcHNzM2uTn5+PwsJCqU1Hdu39/4TgAqBEREQ3w+qequrqagQFBQEA/Pz8cOnSJfTo0QN9+/bFwYMHrTpWamoqRo0ahcjISFRWVuLzzz/Hzp07sW3bNoSEhLTZCxQZGYno6GgAwIgRIxAXF4ennnoKixYtQnFxMV577TXMmDFD6iF6/vnn8eGHH2Lu3LmYMmUKtm/fjnXr1mHTpk3SMVNSUjBp0iQMHjwYQ4YMwfvvv4/q6mpMnjwZAKDVajF16lSkpKTA398fGo0GL7zwAnQ6He644w5rT6HDCb4aqhqMJpRVNyDA2/F714iIiByN1aEqNjYW+fn56NatG/r374+PP/4Y3bp1w0cffYTQ0FCrjlVaWoqJEyfi4sWL0Gq16NevH7Zt24YHHnjAote7uLhg48aNmD59OnQ6Hby8vDBp0iS89dZbUpvo6Ghs2rQJs2fPxpIlSxAeHo6VK1ciKSlJajN+/HhcunQJ8+fPR3FxMQYMGICtW7eaTV5/7733oFQqMW7cONTX1yMpKQnLly+36vM6KpWrEl281bhcVY+L+jqGKiIiopugEFaO9/zjH/9AY2MjJk+ejJycHIwcORJlZWVQqVRYvXo1xo8fb69aOzyDwQCtVgu9Xg+NRiN3OWYe+mA38n7UY+XEwUiMC77xC4iIiJyEpd/fVvdUPfnkk9LP8fHxOH/+PE6cOIHIyEh06dLl5qol2YVo3ZH3o55rVREREd0kiyeqV1dXY/r06ejatSsCAwPx2GOP4dKlS/D09MSgQYMYqDq4MOkKwFqZKyEiIuqYLA5Vr7/+Ov7+97/j17/+NZ544gls374dzz77rD1ro3bEtaqIiIhujcXDfxs2bMCqVavw29/+FgAwceJE3HHHHWhqaoKr600vzE4OomWtqosVDFVEREQ3w+Keqh9++AFDhw6Vnres3VRUVGSXwqh9hbQM/3FOFRER0U2xOFSZTCa4ubmZbXN1dbX6tjTkmMKk4b9aLgBKRER0EywetxNCYPjw4WZDfTU1NXjooYegUqmkbdYuAEqOIUjTvDZVXaMJ+tpG+HqqbvAKIiIiupbFoWrBggWtto0ePdqmxZB83N1cEOClwpXqBhRV1DFUERERWemWQhV1LiFad1ypbsBFfS3iwhxrcVIiIiJHZ/UNlanzCuVkdSIioptmUagaOXIkvvvuuxu2q6ysxJ/+9CcsW7bslguj9hfaMlmdyyoQERFZzaLhv9/+9rcYN24ctFotHnroIQwePBhhYWFwd3dHeXk5jh07ht27d2Pz5s1ITk7G4sWL7V032UHLsgpcAJSIiMh6FoWqqVOn4sknn8T69euxdu1afPLJJ9Dr9QAAhUKBuLg4JCUl4cCBA+jVq5ddCyb7CfNtGf7jrWqIiIisZfFEdbVajSeffFK6obJer0dtbS0CAgJarV9FHVOIhsN/REREN+um7y+j1Wqh1WptWQvJLPSa4T8hBBQKhcwVERERdRy8+o8kLXOqahuN0Nc2ylwNERFRx8JQRRJ3Nxf4ezUv+snJ6kRERNZhqCIzPw0BcrI6ERGRNRiqyEwol1UgIiK6KTcVqioqKrBy5UqkpqairKwMQPONlH/88UebFkftjwuAEhER3Ryrr/47cuQIEhMTodVqce7cOTzzzDPw9/fHV199hcLCQvztb3+zR53UTkKvrlVVxOE/IiIiq1jdU5WSkoKnn34ap06dgru7u7T9wQcfxK5du2xaHLU/afiPPVVERERWsTpUHThwAM8991yr7V27dkVxcbFNiiL5SMN/7KkiIiKyitWhSq1Ww2AwtNp+8uRJBAYG2qQokk+YFKqaFwAlIiIiy1gdqn7zm9/grbfeQmNj8+KQCoUChYWFmDdvHsaNG2fzAql9BWvVAID6JhPKa7gAKBERkaWsDlXvvvsuqqqqEBQUhNraWtx7772IiYmBj48P3n77bXvUSO1I7eqCLt7NC4AWVXAIkIiIyFJWX/2n1WqRnp6OPXv24PDhw6iqqsKgQYOQmJhoj/pIBqFaD1yuasBFfR36dOX9HYmIiCxhVahqbGyEh4cHcnNzMXToUAwdOtRedZGMQrXuyPtRz8nqREREVrBq+M/NzQ2RkZEwGo32qoccQJhv82T1Ii6rQEREZDGr51S9+uqreOWVV6SV1KnzCfPl/f+IiIisZfWcqg8//BCnT59GWFgYoqKi4OXlZbb/4MGDNiuO5NGyVhUnqhMREVnO6lA1ZswYO5RBjqSlp4rDf0RERJazOlQtWLDAHnWQA2mZU1ViqIPRJOCiVMhcERERkeOzek4VdX5BPu5wUSrQZBK4VFkvdzlEREQdgtWhSqlUwsXF5boP6vhclAoE+zSvrF7EyepEREQWsXr4b8OGDWbPGxsbcejQIXz22Wd48803bVYYySvM1wNF+joUVdRiUKSf3OUQERE5PKtD1ejRo1tte+SRR9C7d2+sXbsWU6dOtUlhJK9QXw/gfDmvACQiIrKQzeZU3XHHHcjIyLDV4UhmvAKQiIjIOjYJVbW1tVi6dCm6du1qi8ORA+h69QpALgBKRERkGauH//z8/KBQ/HSJvRAClZWV8PT0xD/+8Q+bFkfyCdPyVjVERETWsDpUvffee2ahSqlUIjAwEAkJCfDz44TmzuKn+/+xp4qIiMgSVoeqp59+2g5lkKNpGf67Ut2AukYj3N24XAYREdEvsXpO1datW7F7927p+bJlyzBgwAA88cQTKC8vt2lxJB+Nhys8Vc1Bir1VREREN2Z1qJozZw4MBgMAIC8vDykpKXjwwQdRUFCAlJQUq461YsUK9OvXDxqNBhqNBjqdDlu2bJH2P/fcc7j99tvh4eGBwMBAjB49GidOnDA7RmFhIZKTk+Hp6YmgoCDMmTMHTU1NZm127tyJQYMGQa1WIyYmBqtXr25Vy7Jly9CtWze4u7sjISEB+/fvN9tfV1eHGTNmICAgAN7e3hg3bhxKSkqs+rwdiUKhuGYIkPOqiIiIbsTqUFVQUIC4uDgAwL/+9S889NBDeOedd7Bs2TKzQGSJ8PBwLFy4EDk5OcjOzsawYcMwevRoHD16FAAQHx+PVatW4fjx49i2bRuEEBgxYgSMRiMAwGg0Ijk5GQ0NDdi7dy8+++wzrF69GvPnzzerNzk5Gffffz9yc3Mxa9YsTJs2Ddu2bZParF27FikpKViwYAEOHjyI/v37IykpCaWlpVKb2bNn4+uvv8b69euRmZmJoqIijB071trT16FIoYpXABIREd2YsJKfn584evSoEEKIoUOHio8//lgIIURBQYHw8PCw9nBtHn/lypVt7jt8+LAAIE6fPi2EEGLz5s1CqVSK4uJiqc2KFSuERqMR9fX1Qggh5s6dK3r37m12nPHjx4ukpCTp+ZAhQ8SMGTOk50ajUYSFhYm0tDQhhBAVFRXCzc1NrF+/Xmpz/PhxAUBkZWVZ/Nn0er0AIPR6vcWvkdPL/zosouZtFH/5Jl/uUoiIiGRj6fe31T1Vd911F1JSUvCHP/wB+/fvR3JyMgDg5MmTCA8Pv+lwZzQasWbNGlRXV0On07XaX11djVWrViE6OhoREREAgKysLPTt2xfBwcFSu6SkJBgMBqm3KysrC4mJiWbHSkpKQlZWFgCgoaEBOTk5Zm2USiUSExOlNjk5OWhsbDRr07NnT0RGRkpt2lJfXw+DwWD26Eh+WlaBPVVEREQ3YnWo+vDDD+Hq6oovv/wSK1askBb83LJlC0aOHGl1AXl5efD29oZarcbzzz+PDRs2SMOLALB8+XJ4e3vD29sbW7ZsQXp6OlQqFQCguLjYLFABkJ4XFxf/YhuDwYDa2lpcvnwZRqOxzTbXHkOlUsHX1/e6bdqSlpYGrVYrPVrCYEfR1Y/Df0RERJayekmFyMhIbNy4sdX2995776YKiI2NRW5uLvR6Pb788ktMmjQJmZmZUrCaMGECHnjgAVy8eBF//vOf8eijj2LPnj1wd3e/qfdrT6mpqWaT9w0GQ4cKVi1zqn4sZ6giIiK6Eat7qg4ePIi8vDzp+X/+8x+MGTMGr7zyChoaGqwuQKVSISYmBvHx8UhLS0P//v2xZMkSab9Wq0X37t1xzz334Msvv8SJEyewYcMGAEBISEirK/BanoeEhPxiG41GAw8PD3Tp0gUuLi5ttrn2GA0NDaioqLhum7ao1WrpysaWR0fSVZqoXgeTSchcDRERkWOzOlQ999xzOHnyJADg7NmzeOyxx+Dp6Yn169dj7ty5t1yQyWRCfX19m/uEEBBCSPt1Oh3y8vLMrtJLT0+HRqORerp0Ol2rGz2np6dL87ZUKhXi4+PN2phMJmRkZEht4uPj4ebmZtYmPz8fhYWFbc7/6ixCtO5QKoCGJhMuV7f9d0JERERXWTsDXqPRSFffLVy4UIwYMUIIIcTu3btFeHi4Vcd6+eWXRWZmpigoKBBHjhwRL7/8slAoFOKbb74RZ86cEe+8847Izs4W58+fF3v27BEPPfSQ8Pf3FyUlJUIIIZqamkSfPn3EiBEjRG5urti6dasIDAwUqamp0nucPXtWeHp6ijlz5ojjx4+LZcuWCRcXF7F161apzZo1a4RarRarV68Wx44dE88++6zw9fU1u6rw+eefF5GRkWL79u0iOztb6HQ6odPprPq8He3qPyGEuOOd/4moeRvFocJyuUshIiKShaXf31bPqRJCwGQyAQD+97//4de//jUAICIiApcvX7bqWKWlpZg4cSIuXrwIrVaLfv36Ydu2bXjggQdQVFSEb7/9Fu+//z7Ky8sRHByMe+65B3v37kVQUBAAwMXFBRs3bsT06dOh0+ng5eWFSZMm4a233pLeIzo6Gps2bcLs2bOxZMkShIeHY+XKlUhKSpLajB8/HpcuXcL8+fNRXFyMAQMGYOvWrWaT19977z0olUqMGzcO9fX1SEpKwvLly609fR1OmK8HLurr8GN5LQZE+MpdDhERkcNSCCGsmiwzbNgwREREIDExEVOnTsWxY8cQExODzMxMTJo0CefOnbNTqR2fwWCAVquFXq/vMPOrXvziEP57uAivPtgLz9xzm9zlEBERtTtLv7+tnlP1/vvv4+DBg5g5cyZeffVVxMTEAAC+/PJL3HnnnTdfMTmklmUVfuRaVURERL/I6uG/fv36mV3912Lx4sVwcXGxSVHkOFquAPyByyoQERH9Iqt7qgCgoqICK1euRGpqKsrKygAAx44dM7sKjzoH9lQRERFZxuqeqiNHjmD48OHw9fXFuXPn8Mwzz8Df3x9fffUVCgsL8be//c0edZJMwqWeqhqZKyEiInJsVvdUpaSkYPLkyTh16pTZquYPPvggdu3aZdPiSH4tPVWVdU0w1DXKXA0REZHjsjpUHThwAM8991yr7V27dv3F++BRx+SpcoWfpxsA3q6GiIjol1gdqtRqNQwGQ6vtJ0+eRGBgoE2KIscizatiqCIiIrouq0PVb37zG7z11ltobGweClIoFCgsLMS8efMwbtw4mxdI8gv39QTAyepERES/xOpQ9e6776KqqgpBQUGora3Fvffei5iYGPj4+ODtt9+2R40ks5aeKk5WJyIiuj6rr/7TarVIT0/Hnj17cPjwYVRVVWHQoEFITEy0R33kALhWFRER0Y1ZFaoaGxvh4eGB3NxcDB06FEOHDrVXXeRAwrlWFRER0Q1ZNfzn5uaGyMhIGI1Ge9VDDijCv3lOFXuqiIiIrs/qOVWvvvoqXnnlFWklder8WuZUlVU3oLq+SeZqiIiIHJPVc6o+/PBDnD59GmFhYYiKioKXl5fZ/oMHD9qsOHIMGnc3aD3coK9txI8VtegR7CN3SURERA7H6lA1ZswYO5RBji7czwP62kb8UF7DUEVERNQGq0PVggUL7FEHObhwPw8cLTLgQhnnVREREbXF6lDVIjs7G8ePHwcAxMXFIT4+3mZFkeMJ92uZrM61qoiIiNpidaj64Ycf8Pjjj2PPnj3w9fUFAFRUVODOO+/EmjVrEB4ebusayQG0LKvAnioiIqK2WX3137Rp09DY2Ijjx4+jrKwMZWVlOH78OEwmE6ZNm2aPGskBSD1VFeypIiIiaovVPVWZmZnYu3cvYmNjpW2xsbH44IMPcPfdd9u0OHIckVfXqmJPFRERUdus7qmKiIiQbqZ8LaPRiLCwMJsURY6nZfhPX9sIQ13rv38iIiJnZ3WoWrx4MV544QVkZ2dL27Kzs/HSSy/hz3/+s02LI8fhpXZFgJcKAHChjEOAREREP6cQQghrXuDn54eamho0NTXB1bV59LDl558vBMpV180ZDAZotVro9XpoNBq5y7Ha6GV7cPhCBT56Mh4j+4TIXQ4REVG7sPT72+o5Ve+///6t1EUdWISfBw5fqOCyCkRERG2wOlRNmjTJHnVQB9ByY+VCDv8RERG1YvWcKnJeEX4tVwAyVBEREf0cQxVZTFpWoZzLKhAREf0cQxVZ7Ke1qmpgMll1fQMREVGnx1BFFgv1dYeLUoH6JhMuVdXLXQ4REZFDsTpU7dix47r7li1bdkvFkGNzc1EizNcdACerExER/ZzVoWrs2LHIyclptX3JkiVITU21SVHkuFqGAAuvMFQRERFd66ZWVB81ahROnDghbXv33Xcxf/58bNq0yabFkeOJ5LIKREREbbJ6napp06ahrKwMiYmJ2L17N9auXYt33nkHmzdvxtChQ+1RIzmQCH8uq0BERNQWq0MVAMydOxdXrlzB4MGDYTQasW3bNtxxxx22ro0cEHuqiIiI2mZRqFq6dGmrbV27doWnpyfuuece7N+/H/v37wcAvPjii7atkBxKS6g6z1BFRERkxqIbKkdHR1t2MIUCZ8+eveWiOquOfkNlANDXNKL/W98AAI69lQRP1U11dhIREXUYNr2hckFBgc0Ko45N6+kGrYcb9LWNOH+lBr1CO2Y4JCIisjUu/klWiwq4OgTIZRWIiIgkVo/dGI1GrF69GhkZGSgtLYXJZDLbv337dpsVR44pKsALR37Qo7CsWu5SiIiIHIbVoeqll17C6tWrkZycjD59+kChUNijLnJgUf7sqSIiIvo5q0PVmjVrsG7dOjz44IP2qIc6gMgALqtARET0c1bPqVKpVIiJibFHLdRBtPRUnbvC4T8iIqIWVoeq3/3ud1iyZAksWImBOqluXbwAAD+W16KhyXSD1kRERM7B6lC1e/du/POf/8Ttt9+Ohx56CGPHjjV7WGPFihXo168fNBoNNBoNdDodtmzZAgAoKyvDCy+8gNjYWHh4eCAyMhIvvvgi9Hq92TEKCwuRnJwMT09PBAUFYc6cOWhqajJrs3PnTgwaNAhqtRoxMTFYvXp1q1qWLVuGbt26wd3dHQkJCdJipi3q6uowY8YMBAQEwNvbG+PGjUNJSYlVn7ezCPJRw8PNBSYB/FDOIUAiIiLgJkKVr68vHn74Ydx7773o0qULtFqt2cMa4eHhWLhwIXJycpCdnY1hw4Zh9OjROHr0KIqKilBUVIQ///nP+P7777F69Wps3boVU6dOlV5vNBqRnJyMhoYG7N27F5999hlWr16N+fPnS20KCgqQnJyM+++/H7m5uZg1axamTZuGbdu2SW3Wrl2LlJQULFiwAAcPHkT//v2RlJSE0tJSqc3s2bPx9ddfY/369cjMzERRUZHVIbKzUCgU0rIKHAIkIiK6SjgYPz8/sXLlyjb3rVu3TqhUKtHY2CiEEGLz5s1CqVSK4uJiqc2KFSuERqMR9fX1Qggh5s6dK3r37m12nPHjx4ukpCTp+ZAhQ8SMGTOk50ajUYSFhYm0tDQhhBAVFRXCzc1NrF+/Xmpz/PhxAUBkZWVd97PU1dUJvV4vPS5cuCAACL1eb+npcFjP/z1bRM3bKP767Vm5SyEiIrIrvV5v0fe3wyz+aTQasWbNGlRXV0On07XZpmV5eFfX5osWs7Ky0LdvXwQHB0ttkpKSYDAYcPToUalNYmKi2XGSkpKQlZUFAGhoaEBOTo5ZG6VSicTERKlNTk4OGhsbzdr07NkTkZGRUpu2pKWlmfXiRUREWHNKHFpUQPO8KvZUERERNbupG7d9+eWXWLduHQoLC9HQ0GC27+DBg1YdKy8vDzqdDnV1dfD29saGDRsQFxfXqt3ly5fxhz/8Ac8++6y0rbi42CxQAZCeFxcX/2Ibg8GA2tpalJeXw2g0ttnmxIkT0jFUKhV8fX1btWl5n7akpqYiJSVFem4wGDpNsIru0jL8xzlVREREwE3MqVq6dCkmT56M4OBgHDp0CEOGDEFAQADOnj2LUaNGWV1AbGwscnNzsW/fPkyfPh2TJk3CsWPHzNoYDAYkJycjLi4Ob7zxhtXvIRe1Wi1Nwm95dBZST9Vl9lQREREBNxGqli9fjk8++QQffPABVCoV5s6di/T09DavzLNEy7pX8fHxSEtLQ//+/bFkyRJpf2VlJUaOHAkfHx9s2LABbm5u0r6QkJBWV+C1PA8JCfnFNhqNBh4eHujSpQtcXFzabHPtMRoaGlBRUXHdNs4m+uqyCj+U13BZBSIiItxEqCosLMSdd94JAPDw8EBlZSUA4KmnnsIXX3xxywWZTCbU19cDaO6hGjFiBFQqFf773//C3d3drK1Op0NeXp7ZVXrp6enQaDTSEKJOp0NGRobZ69LT06V5WyqVCvHx8WZtTCYTMjIypDbx8fFwc3Mza5Ofn4/CwsLrzv/q7IJ81PBUNS+rcIHLKhAREVkfqkJCQlBWVgYAiIyMxHfffQegeekCYeWCoKmpqdi1axfOnTuHvLw8pKamYufOnZgwYYIUqKqrq/HXv/4VBoMBxcXFKC4uhtFoBACMGDECcXFxeOqpp3D48GFs27YNr732GmbMmAG1Wg0AeP7553H27FnMnTsXJ06cwPLly7Fu3TrMnj1bqiMlJQWffvopPvvsMxw/fhzTp09HdXU1Jk+eDADQarWYOnUqUlJSsGPHDuTk5GDy5MnQ6XS44447rD2FnYJCoZB6qwoucQiQiIjI6iUVpk6dKt544w0hhBAffvih8PDwEImJicLX11dMmTLFqmNNmTJFREVFCZVKJQIDA8Xw4cPFN998I4QQYseOHQJAm4+CggLpGOfOnROjRo0SHh4eokuXLuJ3v/udtORCix07dogBAwYIlUolbrvtNrFq1apWtXzwwQciMjJSqFQqMWTIEPHdd9+Z7a+trRX/93//J/z8/ISnp6d4+OGHxcWLF636vJZektlRzPhnjoiat1F8knlG7lKIiIjsxtLvb4UQ1nUvmUwmmEwmaVmDNWvWYO/evejevTuee+45qFQqm4a+zsRgMECr1UpLQ3R0f/kmH0u3n8bjQyKRNrav3OUQERHZhaXf31YvqfDDDz+YLQvw2GOP4bHHHoMQAhcuXEBkZOTNVUwdTss9AAsuV8lcCRERkfysnlMVHR2NS5cutdpeVlaG6OhomxRFHUPLnKpzlzlRnYiIyOpQJYSAQqFotb2qqqrV1XnUud3WxRsAUGyoQ3V90w1aExERdW4WD/+1rAyuUCjw+uuvw9PTU9pnNBqxb98+DBgwwOYFkuPSerohwEuFK9UNKLhcjT5drbuhNhERUWdicag6dOgQgOaeqry8PLMJ6SqVCv3798fvf/9721dIDu22QC9cqW7AmUtVDFVEROTULA5VO3bsAABMnjwZS5Ys6RRXr9Gtuz3QGwfOleMM16oiIiInZ/XVf6tWrbJHHdRB3R7YPK/q7CVeAUhERM7N6onqRNe6LbD5CkD2VBERkbNjqKJb0tJTVXC5CiaTdbcpIiIi6kwYquiWhPt5wM1FgbpGE4r0tXKXQ0REJBuGKrolri5KdAtoHgI8Xcp5VURE5LwYquiWxQQ1DwEyVBERkTNjqKJb1v1qqDrDKwCJiMiJMVTRLbudPVVEREQMVXTrWob/TpVWQQheAUhERM6JoYpu2e2B3lAogIqaRlypbpC7HCIiIlkwVNEtc3dzQbifBwDgVAmHAImIyDkxVJFN9AjyAQCcLq2UuRIiIiJ5MFSRTXQPbg5VJ9lTRUREToqhimyiR3DzZPWTJeypIiIi58RQRTbRQ+qpquQVgERE5JQYqsgmWq4ALK9pxOUqXgFIRETOh6GKbMJD5YJIf08AwCkOARIRkRNiqCKb6X71CsB8hioiInJCDFVkMz1DroaqYoYqIiJyPgxVZDOxV0PVCYYqIiJyQgxVZDO9Qn+6AtBk4hWARETkXBiqyGa6BXhB5apETYMRP5TXyl0OERFRu2KoIptxdVEiJrB5EdDjxQaZqyEiImpfDFVkU5ysTkREzoqhimyqV6gGAHD8InuqiIjIuTBUkU3FhTWHqmMMVURE5GQYqsimWnqqzl+pQVV9k8zVEBERtR+GKrIpfy8VQjTuAIAT7K0iIiInwlBFNtcyBMh5VURE5EwYqsjmWhYB5bwqIiJyJgxVZHN9wrQAgKNFDFVEROQ8GKrI5npfDVUnLlai0WiSuRoiIqL2wVBFNhfh7wEfd1c0GE04VVIldzlERETtgqGKbE6hUEhDgN8X6WWuhoiIqH0wVJFd9OnafAXg0R8ZqoiIyDkwVJFd9Ona3FN1hKGKiIicBEMV2UXfq6HqWJGBk9WJiMgpyBqqVqxYgX79+kGj0UCj0UCn02HLli3S/k8++QT33XcfNBoNFAoFKioqWh2jrKwMEyZMgEajga+vL6ZOnYqqKvPJ0UeOHMHdd98Nd3d3REREYNGiRa2Os379evTs2RPu7u7o27cvNm/ebLZfCIH58+cjNDQUHh4eSExMxKlTp2xzIjqhbgFe8HF3RX2TCfnFlXKXQ0REZHeyhqrw8HAsXLgQOTk5yM7OxrBhwzB69GgcPXoUAFBTU4ORI0filVdeue4xJkyYgKNHjyI9PR0bN27Erl278Oyzz0r7DQYDRowYgaioKOTk5GDx4sV444038Mknn0ht9u7di8cffxxTp07FoUOHMGbMGIwZMwbff/+91GbRokVYunQpPvroI+zbtw9eXl5ISkpCXV2dHc5Mx6dUKtAv/OoQ4A8cAiQiIicgHIyfn59YuXKl2bYdO3YIAKK8vNxs+7FjxwQAceDAAWnbli1bhEKhED/++KMQQojly5cLPz8/UV9fL7WZN2+eiI2NlZ4/+uijIjk52ezYCQkJ4rnnnhNCCGEymURISIhYvHixtL+iokKo1WrxxRdfXPez1NXVCb1eLz0uXLggAAi9Xm/h2ejYFm45LqLmbRTzvjwsdylEREQ3Ta/XW/T97TBzqoxGI9asWYPq6mrodDqLXpOVlQVfX18MHjxY2paYmAilUol9+/ZJbe655x6oVCqpTVJSEvLz81FeXi61SUxMNDt2UlISsrKyAAAFBQUoLi42a6PVapGQkCC1aUtaWhq0Wq30iIiIsOhzdRb9r/ZUHWZPFREROQHZQ1VeXh68vb2hVqvx/PPPY8OGDYiLi7PotcXFxQgKCjLb5urqCn9/fxQXF0ttgoODzdq0PL9Rm2v3X/u6ttq0JTU1FXq9XnpcuHDBos/VWQyI8AMA5BcbUNPQJHM1RERE9iV7qIqNjUVubi727duH6dOnY9KkSTh27JjcZdmEWq2WJuG3PJxJiNYdoVp3mATnVRERUecne6hSqVSIiYlBfHw80tLS0L9/fyxZssSi14aEhKC0tNRsW1NTE8rKyhASEiK1KSkpMWvT8vxGba7df+3r2mpDbRsY6QsAOFRYIWsdRERE9iZ7qPo5k8mE+vp6i9rqdDpUVFQgJydH2rZ9+3aYTCYkJCRIbXbt2oXGxkapTXp6OmJjY+Hn5ye1ycjIMDt2enq6NLcrOjoaISEhZm0MBgP27dtn8fwvZzXw6hDgocJymSshIiKyL1lDVWpqKnbt2oVz584hLy8Pqamp2LlzJyZMmACgeS5Tbm4uTp8+DaB5/lVubi7KysoAAL169cLIkSPxzDPPYP/+/dizZw9mzpyJxx57DGFhYQCAJ554AiqVClOnTsXRo0exdu1aLFmyBCkpKVIdL730ErZu3Yp3330XJ06cwBtvvIHs7GzMnDkTQPO97GbNmoU//vGP+O9//4u8vDxMnDgRYWFhGDNmTDuesY6npafqYGEFhBDyFkNERGRP7XMxYtumTJkioqKihEqlEoGBgWL48OHim2++kfYvWLBAAGj1WLVqldTmypUr4vHHHxfe3t5Co9GIyZMni8rKSrP3OXz4sLjrrruEWq0WXbt2FQsXLmxVy7p160SPHj2ESqUSvXv3Fps2bTLbbzKZxOuvvy6Cg4OFWq0Ww4cPF/n5+VZ9XksvyexMahuaRMwrm0TUvI2i8Eq13OUQERFZzdLvb4UQ7D5oLwaDAVqtFnq93qkmrT+8fA8OFVbgL4/2x9hB4XKXQ0REZBVLv78dbk4VdT6Do5rnVWWf57wqIiLqvBiqyO4Gd/MHAGSfK5O5EiIiIvthqCK7i7/aU3WypAoVNQ0yV0NERGQfDFVkd1281bitixcA4MA5DgESEVHnxFBF7SLhtuYhwH1nr8hcCRERkX0wVFG7uOO2AADAvgLOqyIios6JoYraRUJ0c6g6WqSHoa7xBq2JiIg6HoYqahchWnd0C/CESQAH2FtFRESdEEMVtZuWIcC9ZzivioiIOh+GKmo3Q2O6AAD2nL4scyVERES2x1BF7aYlVJ0orkRpZZ3M1RAREdkWQxW1G38vFXqHNd8zae9pDgESEVHnwlBF7equ7s29Vd+e4hAgERF1LgxV1K7u6R4IAMg8eQkmk5C5GiIiItthqKJ2NbibHzxVLrhcVY9jFw1yl0NERGQzDFXUrtSuLtKE9Z35pTJXQ0REZDsMVdTu7ottHgLckX9J5kqIiIhsh6GK2t39sUEAgEOF5bhSVS9zNURERLbBUEXtLszXA73DNDAJIOMEhwCJiKhzYKgiWYyICwEApB8rkbkSIiIi22CoIlk8EBcMAPj21CXUNhhlroaIiOjWMVSRLHqF+iDC3wN1jSZeBUhERJ0CQxXJQqFQ4MG+oQCAjXkXZa6GiIjo1jFUkWx+3TcMALD9eClqGppkroaIiOjWMFSRbPp01SDS3xO1jUZs51WARETUwTFUkWwUCgV+3a95CPDfh4pkroaIiOjWMFSRrB4e2BVA8y1ruBAoERF1ZAxVJKvuwT7oF65Fk0ng68PsrSIioo6LoYpkN/Zqb9X6nB8ghJC5GiIiopvDUEWyGz2gK1SuShwtMuDID3q5yyEiIropDFUkOz8vFR7s03zbms/3FcpcDRER0c1hqCKH8ERCFADgv4eLoK9tlLkaIiIi6zFUkUP4VTc/9Aj2Rm2jEesOXJC7HCIiIqsxVJFDUCgUmHpXNABg1Z4CNBlNMldERERkHYYqchijB3RFgJcKRfo6bP6+WO5yiIiIrMJQRQ7D3c0FT+ma51Yt33EaJhOXVyAioo6DoYocytN3doO32hUniivxzbESucshIiKyGEMVORRfTxWevrMbAGBJxin2VhERUYfBUEUOZ+pd0fBWu+L4RQP+c/hHucshIiKyCEMVORw/LxWm33c7AGDx1nzUNRplroiIiOjGGKrIIU29KxpdfT1QpK/DR5ln5C6HiIjohhiqyCG5u7ng5VE9AQDLd5zBmUtVMldERET0yxiqyGH9ul8o7osNRIPRhFe+yuOkdSIicmiyhqoVK1agX79+0Gg00Gg00Ol02LJli7S/rq4OM2bMQEBAALy9vTFu3DiUlJhfZl9YWIjk5GR4enoiKCgIc+bMQVNTk1mbnTt3YtCgQVCr1YiJicHq1atb1bJs2TJ069YN7u7uSEhIwP79+832W1IL2ZZCocAfRveBh5sL9hWU4dNvz8pdEhER0XXJGqrCw8OxcOFC5OTkIDs7G8OGDcPo0aNx9OhRAMDs2bPx9ddfY/369cjMzERRURHGjh0rvd5oNCI5ORkNDQ3Yu3cvPvvsM6xevRrz58+X2hQUFCA5ORn3338/cnNzMWvWLEybNg3btm2T2qxduxYpKSlYsGABDh48iP79+yMpKQmlpaVSmxvVQvYR4e+JBQ/FAQAWb8tH7oUKeQsiIiK6DoUQwqHGVPz9/bF48WI88sgjCAwMxOeff45HHnkEAHDixAn06tULWVlZuOOOO7Blyxb8+te/RlFREYKDgwEAH330EebNm4dLly5BpVJh3rx52LRpE77//nvpPR577DFUVFRg69atAICEhAT86le/wocffggAMJlMiIiIwAsvvICXX34Zer3+hrW0pb6+HvX19dJzg8GAiIgI6PV6aDQa25+8TkoIgZmfH8KmvIsI0bjjPzOHIljjLndZnYbRJFDbaERtQ/OjrsmIhiYTGowmNLb8aTRd3SZabWs0CpiEgNEk0GQSMJkEjKL5zyZT83aTuGZfy0MImETz368QgEDznybR8icAXNMG+MX2Lb/Jmlte/Vnahmu2CbNt1/4GFGi98eft2jr+zbrl19/ay6/W4FBfAUS37MXh3fFg31CbHtNgMECr1d7w+9vVpu96C4xGI9avX4/q6mrodDrk5OSgsbERiYmJUpuePXsiMjJSCjJZWVno27evFKgAICkpCdOnT8fRo0cxcOBAZGVlmR2jpc2sWbMAAA0NDcjJyUFqaqq0X6lUIjExEVlZWQBgUS1tSUtLw5tvvnnL58bZKRQKvDO2L04UG3DmUjWmfZaNtc/dAU+Vw/zzlU19kxFXqhpQVt0AQ20jDHWNMNQ2QS/93AhDXRMMtY2orG9CbYMRNQ3Nf9Y2GlHTYER9E29eTUSdR0VNo2zvLfu3Ul5eHnQ6Herq6uDt7Y0NGzYgLi4Oubm5UKlU8PX1NWsfHByM4uLmm+0WFxebBaqW/S37fqmNwWBAbW0tysvLYTQa22xz4sQJ6Rg3qqUtqampSElJkZ639FSR9bQeblj19BCMWb4HeT/q8fSqA1j19K/gpZb9n7Bd1DUacVFfh6KKWvxYUYuLFXW4XFWPy1X1uFLVgMtV9bhUVY/KuqYbH8xCCgXg4eYCtasSqqsPNxclVC7mP7u5KqFyUUjbXJVKuCgBl5Y/FYqffm5jm1KpgKtSAaWi+aFQQPpToVBAcbUW5TU/t2z/qV3zz811K6BUAAo07wMAxc8+18+3/rydQnHNvlavu7Z96ze49r0sO8/WvcL641v5AqJOJibIW7b3lv0bKTY2Frm5udDr9fjyyy8xadIkZGZmyl2WTajVaqjVarnL6DQiAzzx10mDMfGv+7G/oAxPr9qPTycOhq+nSu7SrGYyCRQb6nDucjXOXanB+SvVOHelGkUVzUHqSnWDxcdyVSrg76WC1sMNWg83aDzcoHF3hablubsbNB6u8Fa7wVPlAg+VCzzcXKSfPVWu8HBzgbub0uovfCIi+onsoUqlUiEmJgYAEB8fjwMHDmDJkiUYP348GhoaUFFRYdZDVFJSgpCQEABASEhIq6v0Wq7Iu7bNz6/SKykpgUajgYeHB1xcXODi4tJmm2uPcaNaqH0MjPTD36YOwcS/7seBc+UYvWwPVk4cjO7BPnKX1qb6JiMKLlfjVEkVTpVU4mRJFc5ersL5KzU3HHbzcHNBmK87wnw9EKb1QJBGjQAvFbr4qNHFW40u3ip08VZD6+HGMERE5ABkD1U/ZzKZUF9fj/j4eLi5uSEjIwPjxo0DAOTn56OwsBA6nQ4AoNPp8Pbbb6O0tBRBQUEAgPT0dGg0GsTFxUltNm/ebPYe6enp0jFUKhXi4+ORkZGBMWPGSDVkZGRg5syZAGBRLdR+Bkb6Yd3zOkz7LBvnr9TgoQ934/cjYjF5aDRclPKECyEEivR1OHHRgOMXDTh+sRInig04d6UGxuusr+WqVCDC3xPdAjwRFeCFbgGeCPfzRKivO7r6ejAsERF1MLJe/ZeamopRo0YhMjISlZWV+Pzzz/GnP/0J27ZtwwMPPIDp06dj8+bNWL16NTQaDV544QUAwN69ewE0T24fMGAAwsLCsGjRIhQXF+Opp57CtGnT8M477wBoXlKhT58+mDFjBqZMmYLt27fjxRdfxKZNm5CUlASgeUmFSZMm4eOPP8aQIUPw/vvvY926dThx4oQ01+pGtVjC0qsHyDJXqurx0ppc7D59GQDQO0yD343ogftjg+waRhqaTDhdWoVjFw04VmTAsYt6HL9YCX1t25MjfdSu6B7sjR7BPuge7IPbA71wWxdvhPm6w9WF6+8SETm6DnH1X2lpKSZOnIiLFy9Cq9WiX79+UqACgPfeew9KpRLjxo1DfX09kpKSsHz5cun1Li4u2LhxI6ZPnw6dTgcvLy9MmjQJb731ltQmOjoamzZtwuzZs7FkyRKEh4dj5cqVUqACgPHjx+PSpUuYP38+iouLMWDAAGzdutVs8vqNaqH2F+Ctxt+nDsGaAxfwzqbjOFpkwJTV2egZ4oNH4sPxmwFhCPK5+aUXWuY9nSypRH5xJU5cfZwurUSjsfX/i7gqFYgJ8kavUA16hfogNkSD2GAfBGvU7HEiInICDrdOVWfGnir7uVJVj0++PYu/7T2P2kajtD0myBu/6uaPmCBvRPp7ItBHLU3KbjQK1F1dVuByVT2K9XUoqazDD+W1OHupGucuV5sd61o+7q6IC9UgLkyD3mFa9Ar1QUyQN9SuLu31kYmIqJ1Y+v3NUNWOGKrsr7y6AV8fKcK/Dv6IwzZYfd1VqUC3Ll6IDfFBr5Dm3qeeIT4I9/Ng7xMRkZNgqHJADFXtq7y6AfvPlSH3QgUKr9TgfFk1yqsbUdfYvPClq1LRvJyAygX+XiqEaNwRrHFHmK87ort44bZAb4T7ecCN856IiJxah5hTRWRPfl4qJPUOQVJvLntBRET2x/8FJyIiIrIBhioiIiIiG2CoIiIiIrIBhioiIiIiG2CoIiIiIrIBhioiIiIiG2CoIiIiIrIBhioiIiIiG2CoIiIiIrIBhioiIiIiG2CoIiIiIrIBhioiIiIiG2CoIiIiIrIBhioiIiIiG3CVuwBnIoQAABgMBpkrISIiIku1fG+3fI9fD0NVO6qsrAQAREREyFwJERERWauyshJarfa6+xXiRrGLbMZkMqGoqAg+Pj5QKBQ2O67BYEBERAQuXLgAjUZjs+N2VjxfluO5shzPleV4rizHc2U5e54rIQQqKysRFhYGpfL6M6fYU9WOlEolwsPD7XZ8jUbD/+iswPNlOZ4ry/FcWY7nynI8V5az17n6pR6qFpyoTkRERGQDDFVERERENsBQ1Qmo1WosWLAAarVa7lI6BJ4vy/FcWY7nynI8V5bjubKcI5wrTlQnIiIisgH2VBERERHZAEMVERERkQ0wVBERERHZAEMVERERkQ0wVHUCy5YtQ7du3eDu7o6EhATs379f7pIcTlpaGn71q1/Bx8cHQUFBGDNmDPLz8+Uuq0NYuHAhFAoFZs2aJXcpDunHH3/Ek08+iYCAAHh4eKBv377Izs6WuyyHZDQa8frrryM6OhoeHh64/fbb8Yc//OGG91NzBrt27cJDDz2EsLAwKBQK/Pvf/zbbL4TA/PnzERoaCg8PDyQmJuLUqVPyFCuzXzpXjY2NmDdvHvr27QsvLy+EhYVh4sSJKCoqapfaGKo6uLVr1yIlJQULFizAwYMH0b9/fyQlJaG0tFTu0hxKZmYmZsyYge+++w7p6elobGzEiBEjUF1dLXdpDu3AgQP4+OOP0a9fP7lLcUjl5eUYOnQo3NzcsGXLFhw7dgzvvvsu/Pz85C7NIf3pT3/CihUr8OGHH+L48eP405/+hEWLFuGDDz6QuzTZVVdXo3///li2bFmb+xctWoSlS5fio48+wr59++Dl5YWkpCTU1dW1c6Xy+6VzVVNTg4MHD+L111/HwYMH8dVXXyE/Px+/+c1v2qc4QR3akCFDxIwZM6TnRqNRhIWFibS0NBmrcnylpaUCgMjMzJS7FIdVWVkpunfvLtLT08W9994rXnrpJblLcjjz5s0Td911l9xldBjJycliypQpZtvGjh0rJkyYIFNFjgmA2LBhg/TcZDKJkJAQsXjxYmlbRUWFUKvV4osvvpChQsfx83PVlv379wsA4vz583avhz1VHVhDQwNycnKQmJgobVMqlUhMTERWVpaMlTk+vV4PAPD395e5Esc1Y8YMJCcnm/37InP//e9/MXjwYPz2t79FUFAQBg4ciE8//VTushzWnXfeiYyMDJw8eRIAcPjwYezevRujRo2SuTLHVlBQgOLiYrP/FrVaLRISEvi73gJ6vR4KhQK+vr52fy/eULkDu3z5MoxGI4KDg822BwcH48SJEzJV5fhMJhNmzZqFoUOHok+fPnKX45DWrFmDgwcP4sCBA3KX4tDOnj2LFStWICUlBa+88goOHDiAF198ESqVCpMmTZK7PIfz8ssvw2AwoGfPnnBxcYHRaMTbb7+NCRMmyF2aQysuLgaANn/Xt+yjttXV1WHevHl4/PHH2+WG1AxV5HRmzJiB77//Hrt375a7FId04cIFvPTSS0hPT4e7u7vc5Tg0k8mEwYMH45133gEADBw4EN9//z0++ugjhqo2rFu3Dv/85z/x+eefo3fv3sjNzcWsWbMQFhbG80U219jYiEcffRRCCKxYsaJd3pPDfx1Yly5d4OLigpKSErPtJSUlCAkJkakqxzZz5kxs3LgRO3bsQHh4uNzlOKScnByUlpZi0KBBcHV1haurKzIzM7F06VK4urrCaDTKXaLDCA0NRVxcnNm2Xr16obCwUKaKHNucOXPw8ssv47HHHkPfvn3x1FNPYfbs2UhLS5O7NIfW8vucv+st1xKozp8/j/T09HbppQIYqjo0lUqF+Ph4ZGRkSNtMJhMyMjKg0+lkrMzxCCEwc+ZMbNiwAdu3b0d0dLTcJTms4cOHIy8vD7m5udJj8ODBmDBhAnJzc+Hi4iJ3iQ5j6NChrZbmOHnyJKKiomSqyLHV1NRAqTT/2nFxcYHJZJKpoo4hOjoaISEhZr/rDQYD9u3bx9/1bWgJVKdOncL//vc/BAQEtNt7c/ivg0tJScGkSZMwePBgDBkyBO+//z6qq6sxefJkuUtzKDNmzMDnn3+O//znP/Dx8ZHmIWi1Wnh4eMhcnWPx8fFpNdfMy8sLAQEBnIP2M7Nnz8add96Jd955B48++ij279+PTz75BJ988oncpTmkhx56CG+//TYiIyPRu3dvHDp0CH/5y18wZcoUuUuTXVVVFU6fPi09LygoQG5uLvz9/REZGYlZs2bhj3/8I7p3747o6Gi8/vrrCAsLw5gxY+QrWia/dK5CQ0PxyCOP4ODBg9i4cSOMRqP0+97f3x8qlcq+xdn9+kKyuw8++EBERkYKlUolhgwZIr777ju5S3I4ANp8rFq1Su7SOgQuqXB9X3/9tejTp49Qq9WiZ8+e4pNPPpG7JIdlMBjESy+9JCIjI4W7u7u47bbbxKuvvirq6+vlLk12O3bsaPN31KRJk4QQzcsqvP766yI4OFio1WoxfPhwkZ+fL2/RMvmlc1VQUHDd3/c7duywe20KIbiULREREdGt4pwqIiIiIhtgqCIiIiKyAYYqIiIiIhtgqCIiIiKyAYYqIiIiIhtgqCIiIiKyAYYqIiIiIhtgqCIiIiKyAYYqInJKO3fuhEKhQEVFhSzvn5GRgV69ell0g+qtW7diwIABvEcekYNjqCKiTu++++7DrFmzzLbdeeeduHjxIrRarSw1zZ07F6+99ppFN6geOXIk3Nzc8M9//rMdKiOim8VQRUROSaVSISQkBAqFot3fe/fu3Thz5gzGjRtn8WuefvppLF261I5VEdGtYqgiok7t6aefRmZmJpYsWQKFQgGFQoFz5861Gv5bvXo1fH19sXHjRsTGxsLT0xOPPPIIampq8Nlnn6Fbt27w8/PDiy++aDZkV19fj9///vfo2rUrvLy8kJCQgJ07d/5iTWvWrMEDDzwAd3d3advhw4dx//33w8fHBxqNBvHx8cjOzpb2P/TQQ8jOzsaZM2dsen6IyHZc5S6AiMielixZgpMnT6JPnz546623AACBgYE4d+5cq7Y1NTVYunQp1qxZg8rKSowdOxYPP/wwfH19sXnzZpw9exbjxo3D0KFDMX78eADAzJkzcezYMaxZswZhYWHYsGEDRo4ciby8PHTv3r3Nmr799ls88cQTZtsmTJiAgQMHYsWKFXBxcUFubi7c3Nyk/ZGRkQgODsa3336L22+/3UZnh4hsiaGKiDo1rVYLlUoFT09PhISE/GLbxsZGrFixQgotjzzyCP7+97+jpKQE3t7eiIuLw/33348dO3Zg/PjxKCwsxKpVq1BYWIiwsDAAwO9//3ts3boVq1atwjvvvNPm+5w/f15q36KwsBBz5sxBz549AaDNQBYWFobz589bfQ6IqH0wVBERXeXp6WnWCxQcHIxu3brB29vbbFtpaSkAIC8vD0ajET169DA7Tn19PQICAq77PrW1tWZDfwCQkpKCadOm4e9//zsSExPx29/+tlWPlIeHB2pqam768xGRfTFUERFdde1wGwAoFIo2t7UsbVBVVQUXFxfk5OS0uorv2iD2c126dEF5ebnZtjfeeANPPPEENm3ahC1btmDBggVYs2YNHn74YalNWVkZAgMDb+qzEZH9MVQRUaenUqksWg/KWgMHDoTRaERpaSnuvvtuq1537NixVtt79OiBHj16YPbs2Xj88cexatUqKVTV1dXhzJkzGDhwoM3qJyLb4tV/RNTpdevWDfv27cO5c+dw+fJlmy2i2aNHD0yYMAETJ07EV199hYKCAuzfvx9paWnYtGnTdV+XlJSE3bt3S89ra2sxc+ZM7Ny5E+fPn8eePXtw4MAB9OrVS2rz3XffQa1WQ6fT2aR2IrI9hioi6vR+//vfw8XFBXFxcQgMDERhYaHNjr1q1SpMnDgRv/vd7xAbG4sxY8bgwIEDiIyMvO5rJkyYgKNHjyI/Px8A4OLigitXrmDixIno0aMHHn30UYwaNQpvvvmm9JovvvgCEyZMgKenp81qJyLbUgghhNxFEBE5mzlz5sBgMODjjz++YdvLly8jNjYW2dnZiI6ObofqiOhmsKeKiEgGr776KqKioiwaijx37hyWL1/OQEXk4NhTRURERGQD7KkiIiIisgGGKiIiIiIbYKgiIiIisgGGKiIiIiIbYKgiIiIisgGGKiIiIiIbYKgiIiIisgGGKiIiIiIbYKgiIiIisoH/D15hb6MIILzzAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_pid_doc_12_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[12].pressure))\n", - "a = plt.ylabel(\"tank pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model a ramp in inlet pressure\n", - "\n", - "Next we show how to add an explicit time variable and ramp the inlet pressure from 500 kPa to 600 kPa between 10 and 12 seconds." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.init.fs.valve_1: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.init.fs.tank.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.init.fs.tank: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.init.fs.valve_2: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], - "source": [ - "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", - "\n", - "m = pid.create_model(\n", - " time_set=[0, 24],\n", - " nfe=1,\n", - " calc_integ=True,\n", - ")\n", - "# time_var will be an explicit time variable we can use in constraints.\n", - "m.fs.time_var = pyo.Var(m.fs.time)\n", - "\n", - "# We'll add a constraint to calculate the inlet pressure based on time,\n", - "# so we need to unfix pressure.\n", - "m.fs.valve_1.control_volume.properties_in[0].pressure.unfix()\n", - "m.fs.valve_1.control_volume.properties_in[24].pressure.unfix()\n", - "\n", - "# The solver will directly set the time variable for the DAE solve, but\n", - "# solving the initial conditions is just a system of nonlinear equations,\n", - "# so we need to fix the initial time.\n", - "m.fs.time_var[0].fix(m.fs.time.first())\n", - "\n", - "\n", - "# We could break up the time domain and solve this in pieces, but creative use\n", - "# of min and max will let us create the ramping function we want.\n", - "# From 10s to 12s ramp inlet pressure from 500,000 Pa to 600,000 Pa\n", - "@m.fs.Constraint(m.fs.time)\n", - "def inlet_pressure_eqn(b, t):\n", - " return b.valve_1.control_volume.properties_in[t].pressure == smooth_min(\n", - " 600000, smooth_max(500000, 50000 * (b.time_var[t] - 10) + 500000)\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: DAE: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmpntswp4hz.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of constraints: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of variables: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 89 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 0 SNES Function norm 5.783088779321e+05 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 1 SNES Function norm 4.315325974705e+05 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 2 SNES Function norm 7.639793381288e+04 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 3 SNES Function norm 5.928557930246e+03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 4 SNES Function norm 5.008361736230e+01 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 5 SNES Function norm 3.698195222021e-03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: 6 SNES Function norm 3.725635339799e-09 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: SNESConvergedReason = SNES_CONVERGED_FNORM_RELATIVE, in 6 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: SNES_CONVERGED_FNORM_RELATIVE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: DAE: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmpzdyo3gs9.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of constraints: 30\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of variables: 36\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 98 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:37 [INFO] idaes.solve.petsc-dae: Explicit time variable: 1\n" - 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"text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 156 TS dt 0.158824 time 13.5566\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 157 TS dt 0.191975 time 13.7154\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 158 TS dt 0.229408 time 13.9074\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 159 TS dt 0.269761 time 14.1368\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 160 TS dt 0.32234 time 14.4066\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 161 TS dt 0.405611 time 14.7289\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 162 TS dt 0.558837 time 15.1345\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:46 [INFO] idaes.solve.petsc-dae: 163 TS dt 0.890135 time 15.6934\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:47 [INFO] idaes.solve.petsc-dae: 164 TS dt 1.79623 time 16.5835\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:47 [INFO] idaes.solve.petsc-dae: 165 TS dt 2.81014 time 18.3797\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:47 [INFO] idaes.solve.petsc-dae: 166 TS dt 2.81014 time 21.1899\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:47 [INFO] idaes.solve.petsc-dae: 167 TS dt 28.1014 time 24.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:47 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:47 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n" - ] - } - ], - "source": [ - "# Solve the new problem. Notice the new argument specifying the explicit time variable.\n", - "result = petsc.petsc_dae_by_time_element(\n", - " m,\n", - " time=m.fs.time,\n", - " timevar=m.fs.time_var,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\",\n", - " \"--ts_dt\": 0.1,\n", - " \"--ts_monitor\": \"\", # set initial step to 0.1\n", - " \"--ts_save_trajectory\": 1,\n", - " },\n", - ")\n", - "tj = result.trajectory # trajectroy data\n", - "res = result.results # solver status list" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_pid_doc_16_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "a = plt.plot(\n", - " tj.time, tj.get_vec(m.fs.valve_1.control_volume.properties_in[24].pressure)\n", - ")\n", - "a = plt.ylabel(\"inlet pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_pid_doc_17_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[24]))\n", - "a = plt.ylabel(\"valve 1 fraction open\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_pid_doc_18_0.png" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", - "a = plt.ylabel(\"tank pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model a ramp in inlet pressure (again)\n", - "\n", - "Here we repeat the ramp from the previous simulation in a different way. In this case we do the integration in three parts. 1) Constant pressure at 500 kPa to 10 s 2) ramp from 500 to 600 kPa from 10 to 12 s. 3) Constant pressure at 600 kPa from 12 to 24 s." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.init.fs.valve_1: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.init.fs.tank.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.init.fs.tank: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.init.fs.valve_2: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], - "source": [ - "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", - "\n", - "m = pid.create_model(\n", - " time_set=[0, 10, 12, 24],\n", - " nfe=3,\n", - " calc_integ=True,\n", - ")\n", - "# time_var will be an explicit time variable we can use in constraints.\n", - "m.fs.time_var = pyo.Var(m.fs.time)\n", - "\n", - "# We'll add a constraint to calculate the inlet pressure from 10 to 12s. The rest of the\n", - "# time pressure will be fixed. For the time section from 10 to 12s, the constraints are\n", - "# defined by time 12; this means the pressure at time 12 should be unfixed and the\n", - "# pressure constraint should be active. At all other times, pressure should be fixed and\n", - "# the pressure constraint should be deactivated.\n", - "m.fs.valve_1.control_volume.properties_in[0].pressure.fix(500000)\n", - "m.fs.valve_1.control_volume.properties_in[10].pressure.fix(500000)\n", - "m.fs.valve_1.control_volume.properties_in[12].pressure.set_value(600000)\n", - "m.fs.valve_1.control_volume.properties_in[12].pressure.unfix()\n", - "m.fs.valve_1.control_volume.properties_in[24].pressure.fix(600000)\n", - "\n", - "\n", - "@m.fs.Constraint(m.fs.time)\n", - "def inlet_pressure_eqn(b, t):\n", - " return (\n", - " b.valve_1.control_volume.properties_in[t].pressure\n", - " == 50000 * (b.time_var[t] - 10) + 500000\n", - " )\n", - "\n", - "\n", - "m.fs.inlet_pressure_eqn.deactivate()\n", - "m.fs.inlet_pressure_eqn[12].activate()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: DAE: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp7g4th5iv.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of constraints: 28\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of variables: 28\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 83 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 0 SNES Function norm 5.783088779321e+05 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 1 SNES Function norm 4.315325974705e+05 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 2 SNES Function norm 7.639793381288e+04 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 3 SNES Function norm 5.928557930246e+03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 4 SNES Function norm 5.008361736228e+01 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 5 SNES Function norm 3.698195211732e-03 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: 6 SNES Function norm 3.725294295263e-09 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: SNESConvergedReason = SNES_CONVERGED_FNORM_RELATIVE, in 6 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: SNES_CONVERGED_FNORM_RELATIVE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'dae_suffix' that contains 1 component\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: DAE: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmprvabt9ma.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of constraints: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of variables: 34\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 91 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:48 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:49 [INFO] idaes.solve.petsc-dae: Number of derivatives: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:49 [INFO] idaes.solve.petsc-dae: Number of differential vars: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:49 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 24\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:49 [INFO] idaes.solve.petsc-dae: Number of state vars: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:49 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:53 [INFO] idaes.solve.petsc-dae: 51 TS dt 0.0364218 time 1.28554\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:53 [INFO] idaes.solve.petsc-dae: 52 TS dt 0.0374622 time 1.32196\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:53 [INFO] idaes.solve.petsc-dae: 53 TS dt 0.0386159 time 1.35942\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 54 TS dt 0.0398987 time 1.39804\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 55 TS dt 0.0413295 time 1.43793\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 56 TS dt 0.0429313 time 1.47926\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 57 TS dt 0.0447325 time 1.5222\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 58 TS dt 0.0467682 time 1.56693\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 59 TS dt 0.0490823 time 1.6137\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 60 TS dt 0.0517305 time 1.66278\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 61 TS dt 0.0547839 time 1.71451\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 62 TS dt 0.0583343 time 1.76929\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 63 TS dt 0.0625012 time 1.82763\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 64 TS dt 0.0674415 time 1.89013\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 65 TS dt 0.0733598 time 1.95757\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 66 TS dt 0.0805186 time 2.03093\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 67 TS dt 0.0892348 time 2.11145\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 68 TS dt 0.0998324 time 2.20068\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 69 TS dt 0.112487 time 2.30052\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 70 TS dt 0.12691 time 2.413\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 71 TS dt 0.142117 time 2.53991\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 72 TS dt 0.157007 time 2.68203\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 73 TS dt 0.171623 time 2.83904\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 74 TS dt 0.187368 time 3.01066\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 75 TS dt 0.206382 time 3.19803\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 76 TS dt 0.231364 time 3.40441\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 77 TS dt 0.266076 time 3.63577\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 78 TS dt 0.316702 time 3.90185\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 79 TS dt 0.394852 time 4.21855\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 80 TS dt 0.525171 time 4.6134\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 81 TS dt 0.767756 time 5.13857\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 82 TS dt 1.29836 time 5.90633\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 83 TS dt 2.79531 time 7.20469\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: 84 TS dt 8.85294 time 10.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:54 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: DAE: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp4d4exz1y.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of constraints: 30\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of variables: 36\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 98 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Explicit time variable: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of derivatives: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of differential vars: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 25\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of state vars: 30\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 10.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 10.1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 2 TS dt 0.0448184 time 10.1371\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 3 TS dt 0.0362261 time 10.1719\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 4 TS dt 0.037926 time 10.2081\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 5 TS dt 0.040655 time 10.246\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 6 TS dt 0.0432601 time 10.2867\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 7 TS dt 0.0460301 time 10.3299\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 8 TS dt 0.0483791 time 10.376\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 9 TS dt 0.0503537 time 10.4243\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 10 TS dt 0.0518023 time 10.4747\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 11 TS dt 0.0529149 time 10.5265\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 12 TS dt 0.0538116 time 10.5794\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 13 TS dt 0.0546803 time 10.6332\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 14 TS dt 0.0556299 time 10.6879\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 15 TS dt 0.056762 time 10.7435\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 16 TS dt 0.0581408 time 10.8003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 17 TS dt 0.0598299 time 10.8584\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 18 TS dt 0.0618893 time 10.9183\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:55 [INFO] idaes.solve.petsc-dae: 19 TS dt 0.0643931 time 10.9802\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 20 TS dt 0.0674341 time 11.0445\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 21 TS dt 0.0711384 time 11.112\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 22 TS dt 0.0756776 time 11.1831\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 23 TS dt 0.0812903 time 11.2588\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 24 TS dt 0.0883011 time 11.3401\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 25 TS dt 0.09713 time 11.4284\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 26 TS dt 0.108211 time 11.5255\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 27 TS dt 0.121606 time 11.6337\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 28 TS dt 0.122333 time 11.7553\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 29 TS dt 0.122333 time 11.8777\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 30 TS dt 0.159258 time 12.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'dae_suffix' that contains 1 component\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: DAE: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dkgun\\AppData\\Local\\Temp\\tmp1qggvz23.pyomo.nl\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of constraints: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of variables: 34\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 91 \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of derivatives: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of differential vars: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 24\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of state vars: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 12.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 12.1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:56 [INFO] idaes.solve.petsc-dae: 2 TS dt 0.0494004 time 12.1411\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 3 TS dt 0.0407055 time 12.1802\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 4 TS dt 0.0422725 time 12.2209\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 5 TS dt 0.0447524 time 12.2632\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 6 TS dt 0.0468318 time 12.3079\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 7 TS dt 0.0490217 time 12.3548\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 8 TS dt 0.0508279 time 12.4038\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 9 TS dt 0.0525655 time 12.4546\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 10 TS dt 0.0541373 time 12.5072\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 11 TS dt 0.055779 time 12.5613\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 12 TS dt 0.057531 time 12.6171\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 13 TS dt 0.0595583 time 12.6746\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 14 TS dt 0.061941 time 12.7342\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 15 TS dt 0.064821 time 12.7961\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 16 TS dt 0.0683275 time 12.8609\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 17 TS dt 0.0726534 time 12.9293\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 18 TS dt 0.0780452 time 13.0019\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 19 TS dt 0.0848677 time 13.08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 20 TS dt 0.093653 time 13.1648\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 21 TS dt 0.105212 time 13.2585\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 22 TS dt 0.120767 time 13.3637\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 23 TS dt 0.142036 time 13.4845\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 24 TS dt 0.170717 time 13.6265\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 25 TS dt 0.206234 time 13.7972\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 26 TS dt 0.244736 time 14.0034\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 27 TS dt 0.288411 time 14.2482\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 28 TS dt 0.350093 time 14.5366\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 29 TS dt 0.453668 time 14.8867\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 30 TS dt 0.655245 time 15.3404\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 31 TS dt 1.1261 time 15.9956\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 32 TS dt 2.56902 time 17.1217\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 33 TS dt 4.30929 time 19.6907\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: 34 TS dt 26.3639 time 24.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:31:57 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n" + "ename": "RuntimeError", + "evalue": "No PETSc executable found.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m result = \u001b[43mpetsc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpetsc_dae_by_time_element\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m=\u001b[49m\u001b[43mm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mts_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbeuler\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_dt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_monitor\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# set initial step to 0.1\u001b[39;49;00m\n\u001b[32m 8\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_save_trajectory\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 11\u001b[39m tj = result.trajectory \u001b[38;5;66;03m# trajectroy data\u001b[39;00m\n\u001b[32m 12\u001b[39m res = result.results \u001b[38;5;66;03m# solver status list\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/solvers/petsc.py:592\u001b[39m, in \u001b[36mpetsc_dae_by_time_element\u001b[39m\u001b[34m(m, time, timevar, initial_constraints, initial_variables, detect_initial, skip_initial, initial_solver, initial_solver_options, ts_options, keepfiles, symbolic_solver_labels, between, interpolate, calculate_derivatives, previous_trajectory, representative_time, snes_options)\u001b[39m\n\u001b[32m 590\u001b[39m _sub_problem_scaling_suffix(m, t_block)\n\u001b[32m 591\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m idaeslog.solver_log(solve_log, idaeslog.INFO) \u001b[38;5;28;01mas\u001b[39;00m slc:\n\u001b[32m--> \u001b[39m\u001b[32m592\u001b[39m res = \u001b[43minitial_solver_obj\u001b[49m\u001b[43m.\u001b[49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 593\u001b[39m \u001b[43m \u001b[49m\u001b[43mt_block\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 594\u001b[39m \u001b[43m \u001b[49m\u001b[43mtee\u001b[49m\u001b[43m=\u001b[49m\u001b[43mslc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 595\u001b[39m \u001b[43m \u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m=\u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 596\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 597\u001b[39m res_list.append(res)\n\u001b[32m 599\u001b[39m tprev = t0\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/base/solvers.py:534\u001b[39m, in \u001b[36mOptSolver.solve\u001b[39m\u001b[34m(self, *args, **kwds)\u001b[39m\n\u001b[32m 531\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msolve\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwds):\n\u001b[32m 532\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Solve the problem\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 536\u001b[39m \u001b[38;5;66;03m# If the inputs are models, then validate that they have been\u001b[39;00m\n\u001b[32m 537\u001b[39m \u001b[38;5;66;03m# constructed! Collect suffix names to try and import from solution.\u001b[39;00m\n\u001b[32m 538\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 539\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyomo\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mblock\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BlockData\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/solvers/plugins/solvers/ASL.py:119\u001b[39m, in \u001b[36mASL.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mavailable\u001b[39m(\u001b[38;5;28mself\u001b[39m, exception_flag=\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m119\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 120\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 121\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.version() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:134\u001b[39m, in \u001b[36mSystemCallSolver.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 132\u001b[39m cm = nullcontext() \u001b[38;5;28;01mif\u001b[39;00m exception_flag \u001b[38;5;28;01melse\u001b[39;00m LoggingIntercept()\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m cm:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m ans = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[32m 136\u001b[39m ans = \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:205\u001b[39m, in \u001b[36mSystemCallSolver.executable\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 198\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mexecutable\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 199\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 200\u001b[39m \u001b[33;03m Returns the executable used by this solver.\u001b[39;00m\n\u001b[32m 201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 203\u001b[39m \u001b[38;5;28mself\u001b[39m._user_executable\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m._user_executable \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m205\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_default_executable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 206\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/solvers/petsc.py:124\u001b[39m, in \u001b[36mPetsc._default_executable\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 122\u001b[39m executable = Executable(\u001b[33m\"\u001b[39m\u001b[33mpetsc\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m executable:\n\u001b[32m--> \u001b[39m\u001b[32m124\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo PETSc executable found.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 125\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m executable.path()\n", + "\u001b[31mRuntimeError\u001b[39m: No PETSc executable found." ] } ], + "source": [ + "result = petsc.petsc_dae_by_time_element(\n", + " m,\n", + " time=m.fs.time,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\",\n", + " \"--ts_dt\": 0.1,\n", + " \"--ts_monitor\": \"\", # set initial step to 0.1\n", + " \"--ts_save_trajectory\": 1,\n", + " },\n", + ")\n", + "tj = result.trajectory # trajectroy data\n", + "res = result.results # solver status list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Trajectory\n", + "\n", + "At the initial conditions the valve is fully open. At t=0, the controller is activated and the controller adjusts the opening of valve 1 to keep the tank pressure at the setpoint of 300 kPa." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[12]))\n", + "a = plt.ylabel(\"valve 1 fraction open\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[12].pressure))\n", + "a = plt.ylabel(\"tank pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model a ramp in inlet pressure\n", + "\n", + "Next we show how to add an explicit time variable and ramp the inlet pressure from 500 kPa to 600 kPa between 10 and 12 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", + "\n", + "m = pid.create_model(\n", + " time_set=[0, 24],\n", + " nfe=1,\n", + " calc_integ=True,\n", + ")\n", + "# time_var will be an explicit time variable we can use in constraints.\n", + "m.fs.time_var = pyo.Var(m.fs.time)\n", + "\n", + "# We'll add a constraint to calculate the inlet pressure based on time,\n", + "# so we need to unfix pressure.\n", + "m.fs.valve_1.control_volume.properties_in[0].pressure.unfix()\n", + "m.fs.valve_1.control_volume.properties_in[24].pressure.unfix()\n", + "\n", + "# The solver will directly set the time variable for the DAE solve, but\n", + "# solving the initial conditions is just a system of nonlinear equations,\n", + "# so we need to fix the initial time.\n", + "m.fs.time_var[0].fix(m.fs.time.first())\n", + "\n", + "\n", + "# We could break up the time domain and solve this in pieces, but creative use\n", + "# of min and max will let us create the ramping function we want.\n", + "# From 10s to 12s ramp inlet pressure from 500,000 Pa to 600,000 Pa\n", + "@m.fs.Constraint(m.fs.time)\n", + "def inlet_pressure_eqn(b, t):\n", + " return b.valve_1.control_volume.properties_in[t].pressure == smooth_min(\n", + " 600000, smooth_max(500000, 50000 * (b.time_var[t] - 10) + 500000)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the new problem. Notice the new argument specifying the explicit time variable.\n", + "result = petsc.petsc_dae_by_time_element(\n", + " m,\n", + " time=m.fs.time,\n", + " timevar=m.fs.time_var,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\",\n", + " \"--ts_dt\": 0.1,\n", + " \"--ts_monitor\": \"\", # set initial step to 0.1\n", + " \"--ts_save_trajectory\": 1,\n", + " },\n", + ")\n", + "tj = result.trajectory # trajectroy data\n", + "res = result.results # solver status list" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(\n", + " tj.time, tj.get_vec(m.fs.valve_1.control_volume.properties_in[24].pressure)\n", + ")\n", + "a = plt.ylabel(\"inlet pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[24]))\n", + "a = plt.ylabel(\"valve 1 fraction open\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", + "a = plt.ylabel(\"tank pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model a ramp in inlet pressure (again)\n", + "\n", + "Here we repeat the ramp from the previous simulation in a different way. In this case we do the integration in three parts. 1) Constant pressure at 500 kPa to 10 s 2) ramp from 500 to 600 kPa from 10 to 12 s. 3) Constant pressure at 600 kPa from 12 to 24 s." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", + "\n", + "m = pid.create_model(\n", + " time_set=[0, 10, 12, 24],\n", + " nfe=3,\n", + " calc_integ=True,\n", + ")\n", + "# time_var will be an explicit time variable we can use in constraints.\n", + "m.fs.time_var = pyo.Var(m.fs.time)\n", + "\n", + "# We'll add a constraint to calculate the inlet pressure from 10 to 12s. The rest of the\n", + "# time pressure will be fixed. For the time section from 10 to 12s, the constraints are\n", + "# defined by time 12; this means the pressure at time 12 should be unfixed and the\n", + "# pressure constraint should be active. At all other times, pressure should be fixed and\n", + "# the pressure constraint should be deactivated.\n", + "m.fs.valve_1.control_volume.properties_in[0].pressure.fix(500000)\n", + "m.fs.valve_1.control_volume.properties_in[10].pressure.fix(500000)\n", + "m.fs.valve_1.control_volume.properties_in[12].pressure.set_value(600000)\n", + "m.fs.valve_1.control_volume.properties_in[12].pressure.unfix()\n", + "m.fs.valve_1.control_volume.properties_in[24].pressure.fix(600000)\n", + "\n", + "\n", + "@m.fs.Constraint(m.fs.time)\n", + "def inlet_pressure_eqn(b, t):\n", + " return (\n", + " b.valve_1.control_volume.properties_in[t].pressure\n", + " == 50000 * (b.time_var[t] - 10) + 500000\n", + " )\n", + "\n", + "\n", + "m.fs.inlet_pressure_eqn.deactivate()\n", + "m.fs.inlet_pressure_eqn[12].activate()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], "source": [ "# Solve the new problem. Notice the argument specifying the explicit time variable.\n", "result = petsc.petsc_dae_by_time_element(\n", @@ -4652,22 +398,7 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
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", - "text/plain": [ - "
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" - ] - }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\dae\\petsc_pid_doc_24_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", "a = plt.ylabel(\"tank pressure (Pa)\")\n", @@ -4757,7 +458,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/dae/petsc_pid_usr.ipynb b/idaes_examples/notebooks/docs/dae/petsc_pid_usr.ipynb index 0a43bf12..a0f52288 100644 --- a/idaes_examples/notebooks/docs/dae/petsc_pid_usr.ipynb +++ b/idaes_examples/notebooks/docs/dae/petsc_pid_usr.ipynb @@ -1,418 +1,419 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "pycharm": { - "is_executing": true + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "pycharm": { + "is_executing": true + }, + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PETSc Time-stepping Solver -- PID Control and Steam Example\n", - "Author: John Eslick \n", - "Maintainer: John Eslick \n", - "Updated: 2023-06-01\n", - "\n", - "This example provides an overview of the PETSc time-stepping solver utilities in IDAES, which can be used to solve systems of differential algebraic equations (DAEs). PETSc is a solver suite developed primarily by Argonne National Lab (https://petsc.org/release/). IDAES provides a wrapper for PETSc (https://github.com/IDAES/idaes-ext/tree/main/petsc) that uses the AMPL solver interface (https://ampl.com/resources/learn-more/hooking-your-solver-to-ampl/) and utility functions that allow Pyomo and Pyomo.DAE (https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html) problems to be solved using PETSc.\n", - "\n", - "This demonstration uses the IDAES PID controller model and a flowsheet arranged like so:\n", - "\n", - "```\n", - " \n", - "->--|><|------[]------|><|-->-\n", - " valve_1 tank valve_2 \n", - "```\n", - "\n", - "where the tank pressure is controlled by the opening of valve_1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "The PETSc solver is an extra download for IDAES, which can be downloaded using the command ```idaes get-extensions --extra petsc```, if it is not installed already. See the IDAES solver documentation for more information (https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html).\n", - "\n", - "You may want to review the [\"PETSc Time-stepping Solver -- Chemical Akzo Nobel Example\"](petsc_chem_example_usr.ipynb) notebook first." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import pyomo.environ as pyo\n", - "import pyomo.dae as pyodae\n", - "import idaes.core.solvers.petsc as petsc # petsc utilities module\n", - "import idaes_examples.mod.dae.petsc.pid_steam_tank as pid\n", - "from idaes.core.util.math import smooth_max, smooth_min" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Set Up" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# to see the model code uncomment the line below\n", - "# ??pid" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = pid.create_model(\n", - " time_set=[0, 12],\n", - " nfe=1,\n", - " calc_integ=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "result = petsc.petsc_dae_by_time_element(\n", - " m,\n", - " time=m.fs.time,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\",\n", - " \"--ts_dt\": 0.1,\n", - " \"--ts_monitor\": \"\", # set initial step to 0.1\n", - " \"--ts_save_trajectory\": 1,\n", - " },\n", - ")\n", - "tj = result.trajectory # trajectroy data\n", - "res = result.results # solver status list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Trajectory\n", - "\n", - "At the initial conditions the valve is fully open. At t=0, the controller is activated and the controller adjusts the opening of valve 1 to keep the tank pressure at the setpoint of 300 kPa." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[12]))\n", - "a = plt.ylabel(\"valve 1 fraction open\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[12].pressure))\n", - "a = plt.ylabel(\"tank pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model a ramp in inlet pressure\n", - "\n", - "Next we show how to add an explicit time variable and ramp the inlet pressure from 500 kPa to 600 kPa between 10 and 12 seconds." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", - "\n", - "m = pid.create_model(\n", - " time_set=[0, 24],\n", - " nfe=1,\n", - " calc_integ=True,\n", - ")\n", - "# time_var will be an explicit time variable we can use in constraints.\n", - "m.fs.time_var = pyo.Var(m.fs.time)\n", - "\n", - "# We'll add a constraint to calculate the inlet pressure based on time,\n", - "# so we need to unfix pressure.\n", - "m.fs.valve_1.control_volume.properties_in[0].pressure.unfix()\n", - "m.fs.valve_1.control_volume.properties_in[24].pressure.unfix()\n", - "\n", - "# The solver will directly set the time variable for the DAE solve, but\n", - "# solving the initial conditions is just a system of nonlinear equations,\n", - "# so we need to fix the initial time.\n", - "m.fs.time_var[0].fix(m.fs.time.first())\n", - "\n", - "\n", - "# We could break up the time domain and solve this in pieces, but creative use\n", - "# of min and max will let us create the ramping function we want.\n", - "# From 10s to 12s ramp inlet pressure from 500,000 Pa to 600,000 Pa\n", - "@m.fs.Constraint(m.fs.time)\n", - "def inlet_pressure_eqn(b, t):\n", - " return b.valve_1.control_volume.properties_in[t].pressure == smooth_min(\n", - " 600000, smooth_max(500000, 50000 * (b.time_var[t] - 10) + 500000)\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the new problem. Notice the new argument specifying the explicit time variable.\n", - "result = petsc.petsc_dae_by_time_element(\n", - " m,\n", - " time=m.fs.time,\n", - " timevar=m.fs.time_var,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\",\n", - " \"--ts_dt\": 0.1,\n", - " \"--ts_monitor\": \"\", # set initial step to 0.1\n", - " \"--ts_save_trajectory\": 1,\n", - " },\n", - ")\n", - "tj = result.trajectory # trajectroy data\n", - "res = result.results # solver status list" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(\n", - " tj.time, tj.get_vec(m.fs.valve_1.control_volume.properties_in[24].pressure)\n", - ")\n", - "a = plt.ylabel(\"inlet pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[24]))\n", - "a = plt.ylabel(\"valve 1 fraction open\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", - "a = plt.ylabel(\"tank pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model a ramp in inlet pressure (again)\n", - "\n", - "Here we repeat the ramp from the previous simulation in a different way. In this case we do the integration in three parts. 1) Constant pressure at 500 kPa to 10 s 2) ramp from 500 to 600 kPa from 10 to 12 s. 3) Constant pressure at 600 kPa from 12 to 24 s." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", - "\n", - "m = pid.create_model(\n", - " time_set=[0, 10, 12, 24],\n", - " nfe=3,\n", - " calc_integ=True,\n", - ")\n", - "# time_var will be an explicit time variable we can use in constraints.\n", - "m.fs.time_var = pyo.Var(m.fs.time)\n", - "\n", - "# We'll add a constraint to calculate the inlet pressure from 10 to 12s. The rest of the\n", - "# time pressure will be fixed. For the time section from 10 to 12s, the constraints are\n", - "# defined by time 12; this means the pressure at time 12 should be unfixed and the\n", - "# pressure constraint should be active. At all other times, pressure should be fixed and\n", - "# the pressure constraint should be deactivated.\n", - "m.fs.valve_1.control_volume.properties_in[0].pressure.fix(500000)\n", - "m.fs.valve_1.control_volume.properties_in[10].pressure.fix(500000)\n", - "m.fs.valve_1.control_volume.properties_in[12].pressure.set_value(600000)\n", - "m.fs.valve_1.control_volume.properties_in[12].pressure.unfix()\n", - "m.fs.valve_1.control_volume.properties_in[24].pressure.fix(600000)\n", - "\n", - "\n", - "@m.fs.Constraint(m.fs.time)\n", - "def inlet_pressure_eqn(b, t):\n", - " return (\n", - " b.valve_1.control_volume.properties_in[t].pressure\n", - " == 50000 * (b.time_var[t] - 10) + 500000\n", - " )\n", - "\n", - "\n", - "m.fs.inlet_pressure_eqn.deactivate()\n", - "m.fs.inlet_pressure_eqn[12].activate()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the new problem. Notice the argument specifying the explicit time variable.\n", - "result = petsc.petsc_dae_by_time_element(\n", - " m,\n", - " time=m.fs.time,\n", - " timevar=m.fs.time_var,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\",\n", - " \"--ts_dt\": 0.1,\n", - " \"--ts_monitor\": \"\", # set initial step to 0.1\n", - " \"--ts_save_trajectory\": 1,\n", - " },\n", - ")\n", - "tj = result.trajectory # trajectroy data\n", - "res = result.results # solver status list" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(\n", - " tj.time, tj.get_vec(m.fs.valve_1.control_volume.properties_in[24].pressure)\n", - ")\n", - "a = plt.ylabel(\"inlet pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[24]))\n", - "a = plt.ylabel(\"valve 1 fraction open\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", - "a = plt.ylabel(\"tank pressure (Pa)\")\n", - "a = plt.xlabel(\"time (s)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "idaes": { - "skip": [ - "test" - ] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PETSc Time-stepping Solver -- PID Control and Steam Example\n", + "Author: John Eslick \n", + "Maintainer: John Eslick \n", + "Updated: 2023-06-01\n", + "\n", + "This example provides an overview of the PETSc time-stepping solver utilities in IDAES, which can be used to solve systems of differential algebraic equations (DAEs). PETSc is a solver suite developed primarily by Argonne National Lab (https://petsc.org/release/). IDAES provides a wrapper for PETSc (https://github.com/IDAES/idaes-ext/tree/main/petsc) that uses the AMPL solver interface (https://ampl.com/resources/learn-more/hooking-your-solver-to-ampl/) and utility functions that allow Pyomo and Pyomo.DAE (https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html) problems to be solved using PETSc.\n", + "\n", + "This demonstration uses the IDAES PID controller model and a flowsheet arranged like so:\n", + "\n", + "```\n", + " \n", + "->--|><|------[]------|><|-->-\n", + " valve_1 tank valve_2 \n", + "```\n", + "\n", + "where the tank pressure is controlled by the opening of valve_1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "The PETSc solver is an extra download for IDAES, which can be downloaded using the command ```idaes get-extensions --extra petsc```, if it is not installed already. See the IDAES solver documentation for more information (https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html).\n", + "\n", + "You may want to review the [\"PETSc Time-stepping Solver -- Chemical Akzo Nobel Example\"](petsc_chem_example_usr.ipynb) notebook first." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyomo.environ as pyo\n", + "import pyomo.dae as pyodae\n", + "import idaes.core.solvers.petsc as petsc # petsc utilities module\n", + "import idaes_examples.mod.dae.petsc.pid_steam_tank as pid\n", + "from idaes.core.util.math import smooth_max, smooth_min" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Set Up" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# to see the model code uncomment the line below\n", + "# ??pid" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = pid.create_model(\n", + " time_set=[0, 12],\n", + " nfe=1,\n", + " calc_integ=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "result = petsc.petsc_dae_by_time_element(\n", + " m,\n", + " time=m.fs.time,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\",\n", + " \"--ts_dt\": 0.1,\n", + " \"--ts_monitor\": \"\", # set initial step to 0.1\n", + " \"--ts_save_trajectory\": 1,\n", + " },\n", + ")\n", + "tj = result.trajectory # trajectroy data\n", + "res = result.results # solver status list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Trajectory\n", + "\n", + "At the initial conditions the valve is fully open. At t=0, the controller is activated and the controller adjusts the opening of valve 1 to keep the tank pressure at the setpoint of 300 kPa." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[12]))\n", + "a = plt.ylabel(\"valve 1 fraction open\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[12].pressure))\n", + "a = plt.ylabel(\"tank pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model a ramp in inlet pressure\n", + "\n", + "Next we show how to add an explicit time variable and ramp the inlet pressure from 500 kPa to 600 kPa between 10 and 12 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", + "\n", + "m = pid.create_model(\n", + " time_set=[0, 24],\n", + " nfe=1,\n", + " calc_integ=True,\n", + ")\n", + "# time_var will be an explicit time variable we can use in constraints.\n", + "m.fs.time_var = pyo.Var(m.fs.time)\n", + "\n", + "# We'll add a constraint to calculate the inlet pressure based on time,\n", + "# so we need to unfix pressure.\n", + "m.fs.valve_1.control_volume.properties_in[0].pressure.unfix()\n", + "m.fs.valve_1.control_volume.properties_in[24].pressure.unfix()\n", + "\n", + "# The solver will directly set the time variable for the DAE solve, but\n", + "# solving the initial conditions is just a system of nonlinear equations,\n", + "# so we need to fix the initial time.\n", + "m.fs.time_var[0].fix(m.fs.time.first())\n", + "\n", + "\n", + "# We could break up the time domain and solve this in pieces, but creative use\n", + "# of min and max will let us create the ramping function we want.\n", + "# From 10s to 12s ramp inlet pressure from 500,000 Pa to 600,000 Pa\n", + "@m.fs.Constraint(m.fs.time)\n", + "def inlet_pressure_eqn(b, t):\n", + " return b.valve_1.control_volume.properties_in[t].pressure == smooth_min(\n", + " 600000, smooth_max(500000, 50000 * (b.time_var[t] - 10) + 500000)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the new problem. Notice the new argument specifying the explicit time variable.\n", + "result = petsc.petsc_dae_by_time_element(\n", + " m,\n", + " time=m.fs.time,\n", + " timevar=m.fs.time_var,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\",\n", + " \"--ts_dt\": 0.1,\n", + " \"--ts_monitor\": \"\", # set initial step to 0.1\n", + " \"--ts_save_trajectory\": 1,\n", + " },\n", + ")\n", + "tj = result.trajectory # trajectroy data\n", + "res = result.results # solver status list" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(\n", + " tj.time, tj.get_vec(m.fs.valve_1.control_volume.properties_in[24].pressure)\n", + ")\n", + "a = plt.ylabel(\"inlet pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[24]))\n", + "a = plt.ylabel(\"valve 1 fraction open\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", + "a = plt.ylabel(\"tank pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model a ramp in inlet pressure (again)\n", + "\n", + "Here we repeat the ramp from the previous simulation in a different way. In this case we do the integration in three parts. 1) Constant pressure at 500 kPa to 10 s 2) ramp from 500 to 600 kPa from 10 to 12 s. 3) Constant pressure at 600 kPa from 12 to 24 s." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new copy of the model that runs to 24 seconds, and add a constraint.\n", + "\n", + "m = pid.create_model(\n", + " time_set=[0, 10, 12, 24],\n", + " nfe=3,\n", + " calc_integ=True,\n", + ")\n", + "# time_var will be an explicit time variable we can use in constraints.\n", + "m.fs.time_var = pyo.Var(m.fs.time)\n", + "\n", + "# We'll add a constraint to calculate the inlet pressure from 10 to 12s. The rest of the\n", + "# time pressure will be fixed. For the time section from 10 to 12s, the constraints are\n", + "# defined by time 12; this means the pressure at time 12 should be unfixed and the\n", + "# pressure constraint should be active. At all other times, pressure should be fixed and\n", + "# the pressure constraint should be deactivated.\n", + "m.fs.valve_1.control_volume.properties_in[0].pressure.fix(500000)\n", + "m.fs.valve_1.control_volume.properties_in[10].pressure.fix(500000)\n", + "m.fs.valve_1.control_volume.properties_in[12].pressure.set_value(600000)\n", + "m.fs.valve_1.control_volume.properties_in[12].pressure.unfix()\n", + "m.fs.valve_1.control_volume.properties_in[24].pressure.fix(600000)\n", + "\n", + "\n", + "@m.fs.Constraint(m.fs.time)\n", + "def inlet_pressure_eqn(b, t):\n", + " return (\n", + " b.valve_1.control_volume.properties_in[t].pressure\n", + " == 50000 * (b.time_var[t] - 10) + 500000\n", + " )\n", + "\n", + "\n", + "m.fs.inlet_pressure_eqn.deactivate()\n", + "m.fs.inlet_pressure_eqn[12].activate()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the new problem. Notice the argument specifying the explicit time variable.\n", + "result = petsc.petsc_dae_by_time_element(\n", + " m,\n", + " time=m.fs.time,\n", + " timevar=m.fs.time_var,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\",\n", + " \"--ts_dt\": 0.1,\n", + " \"--ts_monitor\": \"\", # set initial step to 0.1\n", + " \"--ts_save_trajectory\": 1,\n", + " },\n", + ")\n", + "tj = result.trajectory # trajectroy data\n", + "res = result.results # solver status list" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(\n", + " tj.time, tj.get_vec(m.fs.valve_1.control_volume.properties_in[24].pressure)\n", + ")\n", + "a = plt.ylabel(\"inlet pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.valve_1.valve_opening[24]))\n", + "a = plt.ylabel(\"valve 1 fraction open\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "a = plt.plot(tj.time, tj.get_vec(m.fs.tank.control_volume.properties_out[24].pressure))\n", + "a = plt.ylabel(\"tank pressure (Pa)\")\n", + "a = plt.xlabel(\"time (s)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "idaes": { + "skip": [ + "test" + ] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter.ipynb b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter.ipynb index e2039fd0..56fc9743 100644 --- a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_doc.ipynb b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_doc.ipynb index 340fdfcb..5e080691 100644 --- a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_doc.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -124,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -162,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -222,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -289,7 +290,13 @@ "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", - "EXIT: Maximum Number of Iterations Exceeded.\n", + "EXIT: Maximum Number of Iterations Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", @@ -300,10 +307,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 5, 'Number of variables': 3, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Maximum Number of Iterations Exceeded.', 'Termination condition': 'maxIterations', 'Id': 400, 'Error rc': 0, 'Time': 0.004778385162353516}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 5, 'Number of variables': 3, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Maximum Number of Iterations Exceeded.', 'Termination condition': 'maxIterations', 'Id': 400, 'Error rc': 0, 'Time': 0.008158445358276367}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -361,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -391,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -472,7 +479,7 @@ "Number of equality constraint Jacobian evaluations = 17\n", "Number of inequality constraint Jacobian evaluations = 17\n", "Number of Lagrangian Hessian evaluations = 16\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n" @@ -481,10 +488,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 5, 'Number of variables': 3, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.04887509346008301}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 5, 'Number of variables': 3, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0076329708099365234}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -505,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -537,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -584,36 +591,52 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Searching for Candidate Equations\n", - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Building MILP model.\n", - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Solving Candidates MILP model.\n", - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Searching for Irreducible Degenerate Sets\n", - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Building MILP model to compute irreducible degenerate set.\n", - "Solving MILP 1 of 2.\n", - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Solving IDS MILP for constraint con2.\n", - "Solving MILP 2 of 2.\n", - "2023-11-17 14:33:20 [INFO] idaes.core.util.model_diagnostics: Solving IDS MILP for constraint con5.\n", - "====================================================================================\n", - "Irreducible Degenerate Sets\n", - "\n", - " Irreducible Degenerate Set 0\n", - " nu Constraint Name\n", - " 1.0 con2\n", - " -1.0 con5\n", - "\n", - " Irreducible Degenerate Set 1\n", - " nu Constraint Name\n", - " -1.0 con2\n", - " 1.0 con5\n", - "\n", - "====================================================================================\n" + "2025-03-17 17:30:31 [INFO] idaes.core.util.model_diagnostics: Searching for Candidate Equations\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:31 [INFO] idaes.core.util.model_diagnostics: Building MILP model.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:31 [INFO] idaes.core.util.model_diagnostics: Solving Candidates MILP model.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Could not locate the 'scip' executable or the older 'scipampl'\n", + "executable, which is required for solver scip\n" + ] + }, + { + "ename": "ApplicationError", + "evalue": "No executable found for solver 'scip'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mApplicationError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m dh = dt.prepare_degeneracy_hunter()\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mdh\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreport_irreducible_degenerate_sets\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/util/model_diagnostics.py:2806\u001b[39m, in \u001b[36mDegeneracyHunter2.report_irreducible_degenerate_sets\u001b[39m\u001b[34m(self, stream, tee)\u001b[39m\n\u001b[32m 2803\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2804\u001b[39m stream = sys.stdout\n\u001b[32m-> \u001b[39m\u001b[32m2806\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfind_irreducible_degenerate_sets\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2808\u001b[39m stream.write(\u001b[33m\"\u001b[39m\u001b[33m=\u001b[39m\u001b[33m\"\u001b[39m * MAX_STR_LENGTH + \u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 2809\u001b[39m stream.write(\u001b[33m\"\u001b[39m\u001b[33mIrreducible Degenerate Sets\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/util/model_diagnostics.py:2765\u001b[39m, in \u001b[36mDegeneracyHunter2.find_irreducible_degenerate_sets\u001b[39m\u001b[34m(self, tee)\u001b[39m\n\u001b[32m 2763\u001b[39m _log.info(\u001b[33m\"\u001b[39m\u001b[33mSearching for Candidate Equations\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2764\u001b[39m \u001b[38;5;28mself\u001b[39m._prepare_candidates_milp()\n\u001b[32m-> \u001b[39m\u001b[32m2765\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_solve_candidates_milp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2767\u001b[39m \u001b[38;5;66;03m# Find irreducible degenerate sets\u001b[39;00m\n\u001b[32m 2768\u001b[39m \u001b[38;5;66;03m# Check if degenerate_set is not empty\u001b[39;00m\n\u001b[32m 2769\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.degenerate_set:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/util/model_diagnostics.py:2635\u001b[39m, in \u001b[36mDegeneracyHunter2._solve_candidates_milp\u001b[39m\u001b[34m(self, tee)\u001b[39m\n\u001b[32m 2626\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Solve MILP to generate set of candidate equations\u001b[39;00m\n\u001b[32m 2627\u001b[39m \n\u001b[32m 2628\u001b[39m \u001b[33;03mArguments:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 2631\u001b[39m \n\u001b[32m 2632\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 2633\u001b[39m _log.info(\u001b[33m\"\u001b[39m\u001b[33mSolving Candidates MILP model.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2635\u001b[39m results = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcandidates_milp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtee\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2637\u001b[39m \u001b[38;5;28mself\u001b[39m.degenerate_set = {}\n\u001b[32m 2639\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m check_optimal_termination(results):\n\u001b[32m 2640\u001b[39m \u001b[38;5;66;03m# We found a degenerate set\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/base/solvers.py:534\u001b[39m, in \u001b[36mOptSolver.solve\u001b[39m\u001b[34m(self, *args, **kwds)\u001b[39m\n\u001b[32m 531\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msolve\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwds):\n\u001b[32m 532\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Solve the problem\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 536\u001b[39m \u001b[38;5;66;03m# If the inputs are models, then validate that they have been\u001b[39;00m\n\u001b[32m 537\u001b[39m \u001b[38;5;66;03m# constructed! Collect suffix names to try and import from solution.\u001b[39;00m\n\u001b[32m 538\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 539\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyomo\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mblock\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BlockData\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:140\u001b[39m, in \u001b[36mSystemCallSolver.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 138\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exception_flag:\n\u001b[32m 139\u001b[39m msg = \u001b[33m\"\u001b[39m\u001b[33mNo executable found for solver \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ApplicationError(msg % \u001b[38;5;28mself\u001b[39m.name)\n\u001b[32m 141\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 142\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[31mApplicationError\u001b[39m: No executable found for solver 'scip'" ] } ], @@ -828,7 +851,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_test.ipynb b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_test.ipynb index c8c70c3a..48ba8c09 100644 --- a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_test.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -868,4 +869,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_usr.ipynb b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_usr.ipynb index 340fdfcb..0795bdfa 100644 --- a/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_usr.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/degeneracy_hunter_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -833,4 +834,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox.ipynb b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox.ipynb index 88a87541..af9b74a2 100755 --- a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox.ipynb @@ -17,12 +17,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_doc.ipynb b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_doc.ipynb index d1cc0390..bda9c751 100644 --- a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_doc.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -71,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "tags": [ "solution" @@ -85,7 +86,7 @@ "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", "\n", "class DiagnosticsToolbox(builtins.object)\n", - " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | DiagnosticsToolbox(model: pyomo.core.base.block.BlockData, **kwargs)\n", " | \n", " | The IDAES Model DiagnosticsToolbox.\n", " | \n", @@ -129,66 +130,136 @@ " | \n", " | Keyword Arguments\n", " | -----------------\n", - " | variable_bounds_absolute_tolerance: float, default=0.0001\n", + " | variable_bounds_absolute_tolerance: NonNegativeFloat, default=0.0001\n", + " | \n", " | Absolute tolerance for considering a variable to be close to its\n", " | bounds.\n", " | \n", - " | variable_bounds_relative_tolerance: float, default=0.0001\n", + " | variable_bounds_relative_tolerance: NonNegativeFloat, default=0.0001\n", + " | \n", " | Relative tolerance for considering a variable to be close to its\n", " | bounds.\n", " | \n", - " | variable_bounds_violation_tolerance: float, default=0\n", + " | variable_bounds_violation_tolerance: NonNegativeFloat, default=0\n", + " | \n", " | Absolute tolerance for considering a variable to violate its bounds.\n", " | Some solvers relax bounds on variables thus allowing a small violation\n", " | to be considered acceptable.\n", " | \n", - " | constraint_residual_tolerance: float, default=1e-05\n", + " | constraint_residual_tolerance: NonNegativeFloat, default=1e-05\n", + " | \n", " | Absolute tolerance to use when checking constraint residuals.\n", " | \n", - " | variable_large_value_tolerance: float, default=10000.0\n", + " | constraint_term_mismatch_tolerance: NonNegativeFloat, default=1000000.0\n", + " | \n", + " | Magnitude difference to use when checking for mismatched additive\n", + " | terms in constraints.\n", + " | \n", + " | constraint_term_cancellation_tolerance: NonNegativeFloat, default=0.0001\n", + " | \n", + " | Absolute tolerance to use when checking for canceling additive terms\n", + " | in constraints.\n", + " | \n", + " | max_canceling_terms: NonNegativeInt, default=5\n", + " | \n", + " | Maximum number of terms to consider when looking for canceling\n", + " | combinations in expressions.\n", + " | \n", + " | constraint_term_zero_tolerance: NonNegativeFloat, default=1e-10\n", + " | \n", + " | Absolute tolerance to use when determining if a constraint term is\n", + " | equal to zero.\n", + " | \n", + " | variable_large_value_tolerance: NonNegativeFloat, default=10000.0\n", + " | \n", " | Absolute tolerance for considering a value to be large.\n", " | \n", - " | variable_small_value_tolerance: float, default=0.0001\n", + " | variable_small_value_tolerance: NonNegativeFloat, default=0.0001\n", + " | \n", " | Absolute tolerance for considering a value to be small.\n", " | \n", - " | variable_zero_value_tolerance: float, default=1e-08\n", + " | variable_zero_value_tolerance: NonNegativeFloat, default=1e-08\n", + " | \n", " | Absolute tolerance for considering a value to be near to zero.\n", " | \n", - " | jacobian_large_value_caution: float, default=10000.0\n", + " | jacobian_large_value_caution: NonNegativeFloat, default=10000.0\n", + " | \n", " | Tolerance for raising a caution for large Jacobian values.\n", " | \n", - " | jacobian_large_value_warning: float, default=100000000.0\n", + " | jacobian_large_value_warning: NonNegativeFloat, default=100000000.0\n", + " | \n", " | Tolerance for raising a warning for large Jacobian values.\n", " | \n", - " | jacobian_small_value_caution: float, default=0.0001\n", + " | jacobian_small_value_caution: NonNegativeFloat, default=0.0001\n", + " | \n", " | Tolerance for raising a caution for small Jacobian values.\n", " | \n", - " | jacobian_small_value_warning: float, default=1e-08\n", + " | jacobian_small_value_warning: NonNegativeFloat, default=1e-08\n", + " | \n", " | Tolerance for raising a warning for small Jacobian values.\n", " | \n", " | warn_for_evaluation_error_at_bounds: bool, default=True\n", + " | \n", " | If False, warnings will not be generated for things like log(x) with x\n", " | >= 0\n", " | \n", + " | parallel_component_tolerance: NonNegativeFloat, default=1e-08\n", + " | \n", + " | Tolerance for identifying near-parallel Jacobian rows/columns\n", + " | \n", + " | absolute_feasibility_tolerance: NonNegativeFloat, default=1e-06\n", + " | \n", + " | Feasibility tolerance for identifying infeasible constraints and\n", + " | bounds\n", + " | \n", " | Methods defined here:\n", " | \n", - " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | __init__(self, model: pyomo.core.base.block.BlockData, **kwargs)\n", " | Initialize self. See help(type(self)) for accurate signature.\n", " | \n", - " | assert_no_numerical_warnings(self)\n", + " | assert_no_numerical_warnings(self, ignore_parallel_components=False)\n", " | Checks for numerical warnings in the model and raises an AssertionError\n", " | if any are found.\n", " | \n", + " | Args:\n", + " | ignore_parallel_components - ignore checks for parallel components\n", + " | \n", " | Raises:\n", " | AssertionError if any warnings are identified by numerical analysis.\n", " | \n", - " | assert_no_structural_warnings(self)\n", + " | assert_no_structural_warnings(self, ignore_evaluation_errors: bool = False, ignore_unit_consistency: bool = False)\n", " | Checks for structural warnings in the model and raises an AssertionError\n", " | if any are found.\n", " | \n", + " | Args:\n", + " | ignore_evaluation_errors - ignore potential evaluation error warnings\n", + " | ignore_unit_consistency - ignore unit consistency warnings\n", + " | \n", " | Raises:\n", " | AssertionError if any warnings are identified by structural analysis.\n", " | \n", + " | compute_infeasibility_explanation(self, stream=None, solver=None, tee=False)\n", + " | This function attempts to determine why a given model is infeasible. It deploys\n", + " | two main algorithms:\n", + " | \n", + " | 1. Relaxes the constraints of the problem, and reports to the user\n", + " | some sets of constraints and variable bounds, which when relaxed, creates a\n", + " | feasible model.\n", + " | 2. Uses the information collected from (1) to attempt to compute a Minimal\n", + " | Infeasible System (MIS), which is a set of constraints and variable bounds\n", + " | which appear to be in conflict with each other. It is minimal in the sense\n", + " | that removing any single constraint or variable bound would result in a\n", + " | feasible subsystem.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | solver: A pyomo solver object or a string for SolverFactory\n", + " | (default = get_solver())\n", + " | tee: Display intermediate solves conducted (False)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", " | display_components_with_inconsistent_units(self, stream=None)\n", " | Prints a list of all Constraints, Expressions and Objectives in the\n", " | model with inconsistent units of measurement.\n", @@ -199,6 +270,22 @@ " | Returns:\n", " | None\n", " | \n", + " | display_constraints_with_canceling_terms(self, stream=None)\n", + " | Display constraints in model which contain additive terms which potentially cancel each other.\n", + " | \n", + " | Note that this method looks a the current state of the constraint, and will flag terms as\n", + " | cancelling if you have a form A == B + C where C is significantly smaller than A and B. In some\n", + " | cases this behavior is intended, as C is a correction term which happens to be very\n", + " | small at the current state. However, you should review these constraints to determine whether\n", + " | the correction term is important for the situation you are modeling and consider removing the\n", + " | term if it will never be significant.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", " | display_constraints_with_extreme_jacobians(self, stream=None)\n", " | Prints the constraints associated with rows in the Jacobian with extreme\n", " | L2 norms. This often indicates poorly scaled constraints.\n", @@ -221,6 +308,24 @@ " | Returns:\n", " | None\n", " | \n", + " | display_constraints_with_mismatched_terms(self, stream=None)\n", + " | Display constraints in model which contain additive terms of significantly different magnitude.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_no_free_variables(self, stream=None)\n", + " | Display constraints in model which contain no free variables.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", " | display_external_variables(self, stream=None)\n", " | Prints a list of variables that appear within activated Constraints in the\n", " | model but are not contained within the model themselves.\n", @@ -244,6 +349,24 @@ " | Returns:\n", " | None\n", " | \n", + " | display_near_parallel_constraints(self, stream=None)\n", + " | Display near-parallel (duplicate) constraints in model.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_near_parallel_variables(self, stream=None)\n", + " | Display near-parallel (duplicate) variables in model.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", " | display_overconstrained_set(self, stream=None)\n", " | Prints the variables and constraints in the over-constrained sub-problem\n", " | from a Dulmage-Mendelsohn partitioning.\n", @@ -267,6 +390,25 @@ " | Returns:\n", " | None\n", " | \n", + " | display_problematic_constraint_terms(self, constraint, max_cancellations: int = 5, stream=None)\n", + " | Display a summary of potentially problematic terms in a given constraint.\n", + " | \n", + " | Note that this method looks a the current state of the constraint, and will flag terms as\n", + " | cancelling if you have a form A == B + C where C is significantly smaller than A and B. In some\n", + " | cases this behavior is intended, as C is a correction term which happens to be very\n", + " | small at the current state. However, you should review these constraints to determine whether\n", + " | the correction term is important for the situation you are modeling and consider removing the\n", + " | term if it will never be significant.\n", + " | \n", + " | Args:\n", + " | constraint: ConstraintData object to be examined\n", + " | max_cancellations: maximum number of cancellations per node before termination.\n", + " | None = find all cancellations.\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", " | display_underconstrained_set(self, stream=None)\n", " | Prints the variables and constraints in the under-constrained sub-problem\n", " | from a Dulmage-Mendelsohn partitioning.\n", @@ -350,6 +492,17 @@ " | Returns:\n", " | None\n", " | \n", + " | display_variables_with_none_value_in_activated_constraints(self, stream=None)\n", + " | Prints a list of variables with values of None that are present in the\n", + " | mathematical program generated to solve the model. This list includes only\n", + " | variables in active constraints that are reachable through active blocks.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", " | display_variables_with_value_near_zero(self, stream=None)\n", " | Prints a list of variables with a value close to zero. The tolerance\n", " | for determining what is close to zero can be set in the class configuration\n", @@ -384,18 +537,23 @@ " | Keyword Arguments\n", " | -----------------\n", " | solver: str, default='scip'\n", + " | \n", " | MILP solver to use for finding irreducible degenerate sets.\n", " | \n", " | solver_options: optional\n", + " | \n", " | Options to pass to MILP solver.\n", " | \n", - " | M: float, default=100000.0\n", + " | M: NonNegativeFloat, default=100000.0\n", + " | \n", " | Maximum value for nu in MILP models.\n", " | \n", - " | m_small: float, default=1e-05\n", + " | m_small: NonNegativeFloat, default=1e-05\n", + " | \n", " | Smallest value for nu to be considered non-zero in MILP models.\n", " | \n", - " | trivial_constraint_tolerance: float, default=1e-06\n", + " | trivial_constraint_tolerance: NonNegativeFloat, default=1e-06\n", + " | \n", " | Tolerance for identifying non-zero rows in Jacobian.\n", " | \n", " | prepare_svd_toolbox(self, **kwargs)\n", @@ -411,21 +569,26 @@ " | Keyword Arguments\n", " | -----------------\n", " | number_of_smallest_singular_values: PositiveInt, optional\n", + " | \n", " | Number of smallest singular values to compute\n", " | \n", - " | svd_callback: svd_callback_validator, default=\n", + " | svd_callback: svd_callback_validator, default=\n", + " | \n", " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", " | should take the Jacobian and number of singular values to compute as\n", " | options, plus any method specific arguments, and should return the u,\n", " | s and v matrices as numpy arrays.\n", " | \n", " | svd_callback_arguments: dict, optional\n", + " | \n", " | Optional arguments to pass to SVD callback (default = None)\n", " | \n", - " | singular_value_tolerance: float, default=1e-06\n", + " | singular_value_tolerance: NonNegativeFloat, default=1e-06\n", + " | \n", " | Tolerance for defining a small singular value\n", " | \n", - " | size_cutoff_in_singular_vector: float, default=0.1\n", + " | size_cutoff_in_singular_vector: NonNegativeFloat, default=0.1\n", + " | \n", " | Size below which to ignore constraints and variables in the singular\n", " | vector\n", " | \n", @@ -466,10 +629,10 @@ " | Data descriptors defined here:\n", " | \n", " | __dict__\n", - " | dictionary for instance variables (if defined)\n", + " | dictionary for instance variables\n", " | \n", " | __weakref__\n", - " | list of weak references to the object (if defined)\n", + " | list of weak references to the object\n", "\n" ] } @@ -491,7 +654,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -534,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "tags": [ "solution" @@ -559,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "tags": [ "solution" @@ -640,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": { "scrolled": false, "tags": [ @@ -683,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": { "scrolled": true, "tags": [ @@ -783,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": { "tags": [ "solution" @@ -838,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": { "scrolled": true, "tags": [ @@ -906,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": { "tags": [ "solution" @@ -957,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "metadata": { "scrolled": true, "tags": [ @@ -1024,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": { "tags": [ "solution" @@ -1106,10 +1269,17 @@ "Number of equality constraint Jacobian evaluations = 17\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 15\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: infeasible\n", @@ -1120,10 +1290,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.008040428161621094}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 24, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1152,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 15, "metadata": { "tags": [ "solution" @@ -1187,6 +1357,7 @@ "Suggested next steps:\n", "\n", " display_constraints_with_large_residuals()\n", + " compute_infeasibility_explanation()\n", " display_variables_at_or_outside_bounds()\n", "\n", "====================================================================================\n" @@ -1215,7 +1386,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 16, "metadata": { "tags": [ "solution" @@ -1262,7 +1433,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 17, "metadata": { "tags": [ "solution" @@ -1311,7 +1482,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 18, "metadata": { "tags": [ "solution" @@ -1341,7 +1512,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 19, "metadata": { "scrolled": true, "tags": [ @@ -1404,7 +1575,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 20, "metadata": { "tags": [ "solution" @@ -1474,19 +1645,20 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", - "EXIT: Optimal Solution Found.\n" + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] }, { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.009382247924804688}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 36, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1520,7 +1692,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "metadata": { "tags": [ "solution" @@ -1554,8 +1726,9 @@ "Suggested next steps:\n", "\n", " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", + "\n", " prepare_degeneracy_hunter()\n", + " prepare_svd_toolbox()\n", "\n", "====================================================================================\n" ] @@ -1579,7 +1752,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 22, "metadata": { "tags": [ "solution" @@ -1616,7 +1789,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1643,7 +1816,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 24, "metadata": { "tags": [ "solution" @@ -1720,10 +1893,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.003998279571533203}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 43, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1754,7 +1927,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 25, "metadata": { "tags": [ "solution" @@ -1786,8 +1959,9 @@ "Suggested next steps:\n", "\n", " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", + "\n", " prepare_degeneracy_hunter()\n", + " prepare_svd_toolbox()\n", "\n", "====================================================================================\n" ] @@ -1830,7 +2004,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_exercise.ipynb b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_exercise.ipynb index aa02ff4f..2840f6f3 100644 --- a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_exercise.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_exercise.ipynb @@ -1,788 +1,789 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IDAES Model Diagnostics Toolbox Tutorial\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-10-31 \n", - "\n", - "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", - "\n", - "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", - "\n", - "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", - "\n", - "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the DiagnosticsToolbox in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util import DiagnosticsToolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the help() function for more information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", - "\n", - "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", - "\n", - "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "\n", - "m = pyo.ConcreteModel()\n", - "\n", - "m.v1 = pyo.Var(units=pyo.units.m)\n", - "m.v2 = pyo.Var(units=pyo.units.m)\n", - "m.v3 = pyo.Var(bounds=(0, 5))\n", - "m.v4 = pyo.Var()\n", - "m.v5 = pyo.Var(bounds=(0, 10))\n", - "m.v6 = pyo.Var()\n", - "m.v7 = pyo.Var(\n", - " units=pyo.units.m, bounds=(0, 1)\n", - ") # Poorly scaled variable with lower bound\n", - "m.v8 = pyo.Var() # unused variable\n", - "\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", - "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", - "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", - "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", - "\n", - "m.v4.fix(2)\n", - "m.v5.fix(2)\n", - "m.v6.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create an instance of the Diagnostics Toolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the report_structural_issues() method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", - "\n", - "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", - "\n", - "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", - "\n", - "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", - "\n", - "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", - "\n", - "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", - "\n", - "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", - "\n", - "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the display_components_with_inconsistent_units() method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", - "\n", - "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", - "\n", - "
\n", - "Warning:\n", - "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the incorrect Constraint\n", - "m.del_component(m.c1)\n", - "\n", - "# Re-create the Constraint with the correct units\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Warning:\n", - "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", - " \n", - "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", - "
\n", - "\n", - "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the report_structural_issues() method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", - "\n", - "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.display_overconstrained_set() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the display_overconstrained_set() method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", - "\n", - "``c2: v3 == v4 + v5``\n", - "\n", - "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", - "\n", - "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", - "\n", - "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Unfix v4\n", - "\n", - "# Then call the report_structural_issues() method again" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the under-constrained set in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display the under-constrained set" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", - "\n", - "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Fix v2 = 5\n", - "\n", - "# Then re-run report_structural_issues() method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", - "\n", - "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a solver object\n", - "\n", - "# Try to solve the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", - "\n", - "
\n", - "Warning:\n", - "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the report_numerical_issues method in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for numerical issues" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", - "\n", - "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", - "\n", - "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the constraint with a large residual in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display constraint with large residual" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", - "\n", - "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", - "
\n", - "\n", - "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the variables with bounds violations.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display the variables with bounds violations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", - "\n", - "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", - " \n", - "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", - "
\n", - "\n", - "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Update the bounds on v3 in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Update bounds for v3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", - "\n", - "
\n", - "Warning:\n", - "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check to see if there are any new structural issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for new structural issues" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Re-solve the model in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Re-solve the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", - "\n", - "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", - "\n", - "
\n", - "Warning:\n", - "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", - " \n", - "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", - "
\n", - "\n", - "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional numerical issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for additional numerical issues" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional information about variables with extreme values.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display variable with extreme value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", - "\n", - "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", - "\n", - "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", - "\n", - "m.scaling_factor[m.v7] = 1e8\n", - "m.scaling_factor[m.c4] = 1e8\n", - "\n", - "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", - "scaled_model = scaling.create_using(m, rename=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Solve the scaled model and check to see how many iterations are required.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Solve scaled model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", - "\n", - "
\n", - "Warning:\n", - "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", - "
\n", - "\n", - "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", - "\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a new diagnostics toolbox for scaled model\n", - "\n", - "# Report numerical issues for scaled model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", - "\n", - "
\n", - "Warning:\n", - "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", - "
\n", - "\n", - "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Model Diagnostics Toolbox Tutorial\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-10-31 \n", + "\n", + "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", + "\n", + "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", + "\n", + "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", + "\n", + "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the DiagnosticsToolbox in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util import DiagnosticsToolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the help() function for more information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", + "\n", + "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", + "\n", + "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "\n", + "m = pyo.ConcreteModel()\n", + "\n", + "m.v1 = pyo.Var(units=pyo.units.m)\n", + "m.v2 = pyo.Var(units=pyo.units.m)\n", + "m.v3 = pyo.Var(bounds=(0, 5))\n", + "m.v4 = pyo.Var()\n", + "m.v5 = pyo.Var(bounds=(0, 10))\n", + "m.v6 = pyo.Var()\n", + "m.v7 = pyo.Var(\n", + " units=pyo.units.m, bounds=(0, 1)\n", + ") # Poorly scaled variable with lower bound\n", + "m.v8 = pyo.Var() # unused variable\n", + "\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", + "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", + "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", + "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", + "\n", + "m.v4.fix(2)\n", + "m.v5.fix(2)\n", + "m.v6.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create an instance of the Diagnostics Toolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the report_structural_issues() method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", + "\n", + "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", + "\n", + "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", + "\n", + "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", + "\n", + "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", + "\n", + "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", + "\n", + "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", + "\n", + "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the display_components_with_inconsistent_units() method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", + "\n", + "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", + "\n", + "
\n", + "Warning:\n", + "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the incorrect Constraint\n", + "m.del_component(m.c1)\n", + "\n", + "# Re-create the Constraint with the correct units\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Warning:\n", + "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", + " \n", + "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", + "
\n", + "\n", + "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the report_structural_issues() method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", + "\n", + "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.display_overconstrained_set() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the display_overconstrained_set() method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", + "\n", + "``c2: v3 == v4 + v5``\n", + "\n", + "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", + "\n", + "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", + "\n", + "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Unfix v4\n", + "\n", + "# Then call the report_structural_issues() method again" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the under-constrained set in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display the under-constrained set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", + "\n", + "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Fix v2 = 5\n", + "\n", + "# Then re-run report_structural_issues() method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", + "\n", + "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a solver object\n", + "\n", + "# Try to solve the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", + "\n", + "
\n", + "Warning:\n", + "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the report_numerical_issues method in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for numerical issues" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", + "\n", + "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", + "\n", + "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the constraint with a large residual in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display constraint with large residual" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", + "\n", + "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", + "
\n", + "\n", + "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the variables with bounds violations.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display the variables with bounds violations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", + "\n", + "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", + " \n", + "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", + "
\n", + "\n", + "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Update the bounds on v3 in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Update bounds for v3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", + "\n", + "
\n", + "Warning:\n", + "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check to see if there are any new structural issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for new structural issues" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Re-solve the model in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Re-solve the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", + "\n", + "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", + "\n", + "
\n", + "Warning:\n", + "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", + " \n", + "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", + "
\n", + "\n", + "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional numerical issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for additional numerical issues" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional information about variables with extreme values.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display variable with extreme value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", + "\n", + "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", + "\n", + "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", + "\n", + "m.scaling_factor[m.v7] = 1e8\n", + "m.scaling_factor[m.c4] = 1e8\n", + "\n", + "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", + "scaled_model = scaling.create_using(m, rename=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Solve the scaled model and check to see how many iterations are required.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Solve scaled model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", + "\n", + "
\n", + "Warning:\n", + "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", + "
\n", + "\n", + "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", + "\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a new diagnostics toolbox for scaled model\n", + "\n", + "# Report numerical issues for scaled model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", + "\n", + "
\n", + "Warning:\n", + "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", + "
\n", + "\n", + "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_solution.ipynb b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_solution.ipynb index 688e5148..2dc54029 100644 --- a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_solution.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_solution.ipynb @@ -1,2106 +1,2107 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IDAES Model Diagnostics Toolbox Tutorial\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-10-31 \n", - "\n", - "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", - "\n", - "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", - "\n", - "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", - "\n", - "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the DiagnosticsToolbox in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util import DiagnosticsToolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the help() function for more information" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", - "\n", - "class DiagnosticsToolbox(builtins.object)\n", - " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", - " | \n", - " | The IDAES Model DiagnosticsToolbox.\n", - " | \n", - " | To get started:\n", - " | \n", - " | 1. Create an instance of your model (this does not need to be initialized yet).\n", - " | 2. Fix variables until you have 0 degrees of freedom. Many of these tools presume\n", - " | a square model, and a square model should always be the foundation of any more\n", - " | advanced model.\n", - " | 3. Create an instance of the DiagnosticsToolbox and provide the model to debug as\n", - " | the model argument.\n", - " | 4. Call the ``report_structural_issues()`` method.\n", - " | \n", - " | Model diagnostics is an iterative process and you will likely need to run these\n", - " | tools multiple times to resolve all issues. After making a change to your model,\n", - " | you should always start from the beginning again to ensure the change did not\n", - " | introduce any new issues; i.e., always start from the report_structural_issues()\n", - " | method.\n", - " | \n", - " | Note that structural checks do not require the model to be initialized, thus users\n", - " | should start with these. Numerical checks require at least a partial solution to the\n", - " | model and should only be run once all structural issues have been resolved.\n", - " | \n", - " | Report methods will print a summary containing three parts:\n", - " | \n", - " | 1. Warnings - these are critical issues that should be resolved before continuing.\n", - " | For each warning, a method will be suggested in the Next Steps section to get\n", - " | additional information.\n", - " | 2. Cautions - these are things that could be correct but could also be the source of\n", - " | solver issues. Not all cautions need to be addressed, but users should investigate\n", - " | each one to ensure that the behavior is correct and that they will not be the source\n", - " | of difficulties later. Methods exist to provide more information on all cautions,\n", - " | but these will not appear in the Next Steps section.\n", - " | 3. Next Steps - these are recommended methods to call from the DiagnosticsToolbox to\n", - " | get further information on warnings. If no warnings are found, this will suggest\n", - " | the next report method to call.\n", - " | \n", - " | Args:\n", - " | \n", - " | model: model to be diagnosed. The DiagnosticsToolbox does not support indexed Blocks.\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | variable_bounds_absolute_tolerance: float, default=0.0001\n", - " | Absolute tolerance for considering a variable to be close to its\n", - " | bounds.\n", - " | \n", - " | variable_bounds_relative_tolerance: float, default=0.0001\n", - " | Relative tolerance for considering a variable to be close to its\n", - " | bounds.\n", - " | \n", - " | variable_bounds_violation_tolerance: float, default=0\n", - " | Absolute tolerance for considering a variable to violate its bounds.\n", - " | Some solvers relax bounds on variables thus allowing a small violation\n", - " | to be considered acceptable.\n", - " | \n", - " | constraint_residual_tolerance: float, default=1e-05\n", - " | Absolute tolerance to use when checking constraint residuals.\n", - " | \n", - " | variable_large_value_tolerance: float, default=10000.0\n", - " | Absolute tolerance for considering a value to be large.\n", - " | \n", - " | variable_small_value_tolerance: float, default=0.0001\n", - " | Absolute tolerance for considering a value to be small.\n", - " | \n", - " | variable_zero_value_tolerance: float, default=1e-08\n", - " | Absolute tolerance for considering a value to be near to zero.\n", - " | \n", - " | jacobian_large_value_caution: float, default=10000.0\n", - " | Tolerance for raising a caution for large Jacobian values.\n", - " | \n", - " | jacobian_large_value_warning: float, default=100000000.0\n", - " | Tolerance for raising a warning for large Jacobian values.\n", - " | \n", - " | jacobian_small_value_caution: float, default=0.0001\n", - " | Tolerance for raising a caution for small Jacobian values.\n", - " | \n", - " | jacobian_small_value_warning: float, default=1e-08\n", - " | Tolerance for raising a warning for small Jacobian values.\n", - " | \n", - " | warn_for_evaluation_error_at_bounds: bool, default=True\n", - " | If False, warnings will not be generated for things like log(x) with x\n", - " | >= 0\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " | \n", - " | assert_no_numerical_warnings(self)\n", - " | Checks for numerical warnings in the model and raises an AssertionError\n", - " | if any are found.\n", - " | \n", - " | Raises:\n", - " | AssertionError if any warnings are identified by numerical analysis.\n", - " | \n", - " | assert_no_structural_warnings(self)\n", - " | Checks for structural warnings in the model and raises an AssertionError\n", - " | if any are found.\n", - " | \n", - " | Raises:\n", - " | AssertionError if any warnings are identified by structural analysis.\n", - " | \n", - " | display_components_with_inconsistent_units(self, stream=None)\n", - " | Prints a list of all Constraints, Expressions and Objectives in the\n", - " | model with inconsistent units of measurement.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_constraints_with_extreme_jacobians(self, stream=None)\n", - " | Prints the constraints associated with rows in the Jacobian with extreme\n", - " | L2 norms. This often indicates poorly scaled constraints.\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_constraints_with_large_residuals(self, stream=None)\n", - " | Prints a list of Constraints with residuals greater than a specified tolerance.\n", - " | Tolerance can be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_external_variables(self, stream=None)\n", - " | Prints a list of variables that appear within activated Constraints in the\n", - " | model but are not contained within the model themselves.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_extreme_jacobian_entries(self, stream=None)\n", - " | Prints variables and constraints associated with entries in the Jacobian with extreme\n", - " | values. This can be indicative of poor scaling, especially for isolated terms (e.g.\n", - " | variables which appear only in one term of a single constraint).\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_overconstrained_set(self, stream=None)\n", - " | Prints the variables and constraints in the over-constrained sub-problem\n", - " | from a Dulmage-Mendelsohn partitioning.\n", - " | \n", - " | This can be used to identify the over-defined part of a model and thus\n", - " | where constraints must be removed or variables unfixed.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_potential_evaluation_errors(self, stream=None)\n", - " | Prints constraints that may be prone to evaluation errors\n", - " | (e.g., log of a negative number) based on variable bounds.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_underconstrained_set(self, stream=None)\n", - " | Prints the variables and constraints in the under-constrained sub-problem\n", - " | from a Dulmage-Mendelsohn partitioning.\n", - " | \n", - " | This can be used to identify the under-defined part of a model and thus\n", - " | where additional information (fixed variables or constraints) are required.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_unused_variables(self, stream=None)\n", - " | Prints a list of variables that do not appear in any activated Constraints.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_at_or_outside_bounds(self, stream=None)\n", - " | Prints a list of variables with values that fall at or outside the bounds\n", - " | on the variable.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_fixed_to_zero(self, stream=None)\n", - " | Prints a list of variables that are fixed to an absolute value of 0.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_near_bounds(self, stream=None)\n", - " | Prints a list of variables with values close to their bounds. Tolerance can\n", - " | be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_extreme_jacobians(self, stream=None)\n", - " | Prints the variables associated with columns in the Jacobian with extreme\n", - " | L2 norms. This often indicates poorly scaled variables.\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_extreme_values(self, stream=None)\n", - " | Prints a list of variables with extreme values.\n", - " | \n", - " | Tolerances can be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_none_value(self, stream=None)\n", - " | Prints a list of variables with a value of None.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_value_near_zero(self, stream=None)\n", - " | Prints a list of variables with a value close to zero. The tolerance\n", - " | for determining what is close to zero can be set in the class configuration\n", - " | options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | get_dulmage_mendelsohn_partition(self)\n", - " | Performs a Dulmage-Mendelsohn partitioning on the model and returns\n", - " | the over- and under-constrained sub-problems.\n", - " | \n", - " | Returns:\n", - " | list-of-lists variables in each independent block of the under-constrained set\n", - " | list-of-lists constraints in each independent block of the under-constrained set\n", - " | list-of-lists variables in each independent block of the over-constrained set\n", - " | list-of-lists constraints in each independent block of the over-constrained set\n", - " | \n", - " | prepare_degeneracy_hunter(self, **kwargs)\n", - " | Create an instance of the DegeneracyHunter and store as self.degeneracy_hunter.\n", - " | \n", - " | After creating an instance of the toolbox, call\n", - " | report_irreducible_degenerate_sets.\n", - " | \n", - " | Returns:\n", - " | \n", - " | Instance of DegeneracyHunter\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | solver: str, default='scip'\n", - " | MILP solver to use for finding irreducible degenerate sets.\n", - " | \n", - " | solver_options: optional\n", - " | Options to pass to MILP solver.\n", - " | \n", - " | M: float, default=100000.0\n", - " | Maximum value for nu in MILP models.\n", - " | \n", - " | m_small: float, default=1e-05\n", - " | Smallest value for nu to be considered non-zero in MILP models.\n", - " | \n", - " | trivial_constraint_tolerance: float, default=1e-06\n", - " | Tolerance for identifying non-zero rows in Jacobian.\n", - " | \n", - " | prepare_svd_toolbox(self, **kwargs)\n", - " | Create an instance of the SVDToolbox and store as self.svd_toolbox.\n", - " | \n", - " | After creating an instance of the toolbox, call\n", - " | display_underdetermined_variables_and_constraints().\n", - " | \n", - " | Returns:\n", - " | \n", - " | Instance of SVDToolbox\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | number_of_smallest_singular_values: PositiveInt, optional\n", - " | Number of smallest singular values to compute\n", - " | \n", - " | svd_callback: svd_callback_validator, default=\n", - " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", - " | should take the Jacobian and number of singular values to compute as\n", - " | options, plus any method specific arguments, and should return the u,\n", - " | s and v matrices as numpy arrays.\n", - " | \n", - " | svd_callback_arguments: dict, optional\n", - " | Optional arguments to pass to SVD callback (default = None)\n", - " | \n", - " | singular_value_tolerance: float, default=1e-06\n", - " | Tolerance for defining a small singular value\n", - " | \n", - " | size_cutoff_in_singular_vector: float, default=0.1\n", - " | Size below which to ignore constraints and variables in the singular\n", - " | vector\n", - " | \n", - " | report_numerical_issues(self, stream=None)\n", - " | Generates a summary report of any numerical issues identified in the model provided\n", - " | and suggest next steps for debugging model.\n", - " | \n", - " | Numerical checks should only be performed once all structural issues have been resolved,\n", - " | and require that at least a partial solution to the model is available.\n", - " | \n", - " | Args:\n", - " | stream: I/O object to write report to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | report_structural_issues(self, stream=None)\n", - " | Generates a summary report of any structural issues identified in the model provided\n", - " | and suggests next steps for debugging the model.\n", - " | \n", - " | This should be the first method called when debugging a model and after any change\n", - " | is made to the model. These checks can be run before trying to initialize and solve\n", - " | the model.\n", - " | \n", - " | Args:\n", - " | stream: I/O object to write report to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Readonly properties defined here:\n", - " | \n", - " | model\n", - " | Model currently being diagnosed.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables (if defined)\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object (if defined)\n", - "\n" - ] - } - ], - "source": [ - "help(DiagnosticsToolbox)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", - "\n", - "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", - "\n", - "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "\n", - "m = pyo.ConcreteModel()\n", - "\n", - "m.v1 = pyo.Var(units=pyo.units.m)\n", - "m.v2 = pyo.Var(units=pyo.units.m)\n", - "m.v3 = pyo.Var(bounds=(0, 5))\n", - "m.v4 = pyo.Var()\n", - "m.v5 = pyo.Var(bounds=(0, 10))\n", - "m.v6 = pyo.Var()\n", - "m.v7 = pyo.Var(\n", - " units=pyo.units.m, bounds=(0, 1)\n", - ") # Poorly scaled variable with lower bound\n", - "m.v8 = pyo.Var() # unused variable\n", - "\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", - "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", - "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", - "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", - "\n", - "m.v4.fix(2)\n", - "m.v5.fix(2)\n", - "m.v6.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create an instance of the Diagnostics Toolbox" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the report_structural_issues() method" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Component with inconsistent units\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 1 variables, 2 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_components_with_inconsistent_units()\n", - " display_underconstrained_set()\n", - " display_overconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", - "\n", - "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", - "\n", - "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", - "\n", - "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", - "\n", - "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", - "\n", - "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", - "\n", - "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", - "\n", - "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the display_components_with_inconsistent_units() method" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following component(s) have unit consistency issues:\n", - "\n", - " c1\n", - "\n", - "For more details on unit inconsistencies, import the assert_units_consistent method\n", - "from pyomo.util.check_units\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_components_with_inconsistent_units()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", - "\n", - "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", - "\n", - "
\n", - "Warning:\n", - "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the incorrect Constraint\n", - "m.del_component(m.c1)\n", - "\n", - "# Re-create the Constraint with the correct units\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Warning:\n", - "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", - " \n", - "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", - "
\n", - "\n", - "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the report_structural_issues() method" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 1 variables, 2 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_underconstrained_set()\n", - " display_overconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", - "\n", - "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.display_overconstrained_set() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the display_overconstrained_set() method" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Dulmage-Mendelsohn Over-Constrained Set\n", - "\n", - " Independent Block 0:\n", - "\n", - " Variables:\n", - "\n", - " v3\n", - "\n", - " Constraints:\n", - "\n", - " c2\n", - " c3\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", - "\n", - "``c2: v3 == v4 + v5``\n", - "\n", - "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", - "\n", - "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", - "\n", - "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Unfix v4\n", - "\n", - "# Then call the report_structural_issues() method again" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 5 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 2 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Degree of Freedom\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 0 variables, 0 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_underconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.v4.unfix()\n", - "\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the under-constrained set in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display the under-constrained set" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Dulmage-Mendelsohn Under-Constrained Set\n", - "\n", - " Independent Block 0:\n", - "\n", - " Variables:\n", - "\n", - " v2\n", - " v1\n", - " v7\n", - "\n", - " Constraints:\n", - "\n", - " c1\n", - " c4\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_underconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", - "\n", - "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Fix v2 = 5\n", - "\n", - "# Then re-run report_structural_issues() method" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.v2.fix(5)\n", - "\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", - "\n", - "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a solver object\n", - "\n", - "# Try to solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.39e+01 1.50e+02 -1.0 6.00e+00 - 7.16e-01 4.93e-03h 1\n", - " 2 0.0000000e+00 1.39e+01 3.03e+06 -1.0 5.97e+00 - 1.00e+00 4.95e-05h 1\n", - " 3r 0.0000000e+00 1.39e+01 1.00e+03 1.1 0.00e+00 - 0.00e+00 2.47e-07R 2\n", - " 4r 0.0000000e+00 4.19e+00 9.42e+02 1.1 3.50e+03 - 4.02e-01 3.37e-03f 1\n", - " 5r 0.0000000e+00 2.12e+00 8.72e+02 1.1 5.89e+01 - 4.35e-01 7.06e-02f 1\n", - " 6r 0.0000000e+00 6.74e-01 6.06e+02 1.1 5.29e+00 - 9.93e-03 3.98e-01f 1\n", - " 7r 0.0000000e+00 6.80e-01 3.14e+02 0.4 2.05e-01 - 1.00e+00 1.03e-01f 1\n", - " 8r 0.0000000e+00 6.69e-01 2.78e-05 0.4 2.58e-02 - 1.00e+00 1.00e+00f 1\n", - " 9r 0.0000000e+00 6.67e-01 7.56e+00 -1.7 8.13e-03 - 9.93e-01 9.96e-01f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10r 0.0000000e+00 6.67e-01 2.23e-07 -1.7 4.13e-05 - 1.00e+00 1.00e+00f 1\n", - " 11r 0.0000000e+00 6.67e-01 6.73e-01 -3.7 6.61e-05 - 1.00e+00 1.00e+00f 1\n", - " 12r 0.0000000e+00 6.67e-01 1.91e-09 -3.7 1.48e-09 - 1.00e+00 1.00e+00h 1\n", - " 13r 0.0000000e+00 6.67e-01 2.69e+00 -8.4 5.74e-07 - 1.00e+00 9.26e-01f 1\n", - " 14r 0.0000000e+00 6.67e-01 7.65e+01 -8.4 4.23e-08 - 8.68e-01 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 14\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 3.2644919411246030e-04 3.2644919411246030e-04\n", - "Constraint violation....: 6.6666666333656233e-01 6.6666666333656233e-01\n", - "Complementarity.........: 4.6615546565561981e-09 4.6615546565561981e-09\n", - "Overall NLP error.......: 6.6666666333656233e-01 6.6666666333656233e-01\n", - "\n", - "\n", - "Number of objective function evaluations = 18\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 18\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 17\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 15\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: infeasible\n", - " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", - " point. Problem may be infeasible.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", - "\n", - "
\n", - "Warning:\n", - "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the report_numerical_issues method in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for numerical issues" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.700E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", - " WARNING: 1 Variable at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 2 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Variable with None value\n", - " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_constraints_with_large_residuals()\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", - "\n", - "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", - "\n", - "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the constraint with a large residual in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display constraint with large residual" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following constraint(s) have large residuals (>1.0E-05):\n", - "\n", - " c2: 6.66667E-01\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_constraints_with_large_residuals()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", - "\n", - "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", - "
\n", - "\n", - "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the variables with bounds violations.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display the variables with bounds violations" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", - "\n", - " v3 (free): value=0.0 bounds=(0, 5)\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_at_or_outside_bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", - "\n", - "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", - " \n", - "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", - "
\n", - "\n", - "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Update the bounds on v3 in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Update bounds for v3" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.v3.setlb(-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", - "\n", - "
\n", - "Warning:\n", - "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check to see if there are any new structural issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for new structural issues" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Re-solve the model in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Re-solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 6.67e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 6.66e-03 2.97e+00 -1.0 2.00e+00 - 7.17e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 6.27e-05 9.38e+00 -1.0 2.00e-02 - 1.00e+00 9.91e-01h 1\n", - " 3 0.0000000e+00 8.88e-16 1.13e-12 -1.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 8.8817841970012523e-16 8.8817841970012523e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 8.8817841970012523e-16 8.8817841970012523e-16\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", - "\n", - "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", - "\n", - "
\n", - "Warning:\n", - "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", - " \n", - "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", - "
\n", - "\n", - "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional numerical issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for additional numerical issues" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.700E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Variable with None value\n", - " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", - " prepare_degeneracy_hunter()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional information about variables with extreme values.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display variable with extreme value" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have extreme values (<1.0E-04 or > 1.0E+04):\n", - "\n", - " v7: 4.9999999999999945e-08\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_with_extreme_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", - "\n", - "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", - "\n", - "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", - "\n", - "m.scaling_factor[m.v7] = 1e8\n", - "m.scaling_factor[m.c4] = 1e8\n", - "\n", - "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", - "scaled_model = scaling.create_using(m, rename=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Solve the scaled model and check to see how many iterations are required.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Solve scaled model" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 5.33e-15 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 5.3290705182007514e-15 5.3290705182007514e-15\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.3290705182007514e-15 5.3290705182007514e-15\n", - "\n", - "\n", - "Number of objective function evaluations = 1\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 1\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 1\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Model Diagnostics Toolbox Tutorial\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-10-31 \n", + "\n", + "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", + "\n", + "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", + "\n", + "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", + "\n", + "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the DiagnosticsToolbox in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util import DiagnosticsToolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the help() function for more information" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", + "\n", + "class DiagnosticsToolbox(builtins.object)\n", + " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | \n", + " | The IDAES Model DiagnosticsToolbox.\n", + " | \n", + " | To get started:\n", + " | \n", + " | 1. Create an instance of your model (this does not need to be initialized yet).\n", + " | 2. Fix variables until you have 0 degrees of freedom. Many of these tools presume\n", + " | a square model, and a square model should always be the foundation of any more\n", + " | advanced model.\n", + " | 3. Create an instance of the DiagnosticsToolbox and provide the model to debug as\n", + " | the model argument.\n", + " | 4. Call the ``report_structural_issues()`` method.\n", + " | \n", + " | Model diagnostics is an iterative process and you will likely need to run these\n", + " | tools multiple times to resolve all issues. After making a change to your model,\n", + " | you should always start from the beginning again to ensure the change did not\n", + " | introduce any new issues; i.e., always start from the report_structural_issues()\n", + " | method.\n", + " | \n", + " | Note that structural checks do not require the model to be initialized, thus users\n", + " | should start with these. Numerical checks require at least a partial solution to the\n", + " | model and should only be run once all structural issues have been resolved.\n", + " | \n", + " | Report methods will print a summary containing three parts:\n", + " | \n", + " | 1. Warnings - these are critical issues that should be resolved before continuing.\n", + " | For each warning, a method will be suggested in the Next Steps section to get\n", + " | additional information.\n", + " | 2. Cautions - these are things that could be correct but could also be the source of\n", + " | solver issues. Not all cautions need to be addressed, but users should investigate\n", + " | each one to ensure that the behavior is correct and that they will not be the source\n", + " | of difficulties later. Methods exist to provide more information on all cautions,\n", + " | but these will not appear in the Next Steps section.\n", + " | 3. Next Steps - these are recommended methods to call from the DiagnosticsToolbox to\n", + " | get further information on warnings. If no warnings are found, this will suggest\n", + " | the next report method to call.\n", + " | \n", + " | Args:\n", + " | \n", + " | model: model to be diagnosed. The DiagnosticsToolbox does not support indexed Blocks.\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | variable_bounds_absolute_tolerance: float, default=0.0001\n", + " | Absolute tolerance for considering a variable to be close to its\n", + " | bounds.\n", + " | \n", + " | variable_bounds_relative_tolerance: float, default=0.0001\n", + " | Relative tolerance for considering a variable to be close to its\n", + " | bounds.\n", + " | \n", + " | variable_bounds_violation_tolerance: float, default=0\n", + " | Absolute tolerance for considering a variable to violate its bounds.\n", + " | Some solvers relax bounds on variables thus allowing a small violation\n", + " | to be considered acceptable.\n", + " | \n", + " | constraint_residual_tolerance: float, default=1e-05\n", + " | Absolute tolerance to use when checking constraint residuals.\n", + " | \n", + " | variable_large_value_tolerance: float, default=10000.0\n", + " | Absolute tolerance for considering a value to be large.\n", + " | \n", + " | variable_small_value_tolerance: float, default=0.0001\n", + " | Absolute tolerance for considering a value to be small.\n", + " | \n", + " | variable_zero_value_tolerance: float, default=1e-08\n", + " | Absolute tolerance for considering a value to be near to zero.\n", + " | \n", + " | jacobian_large_value_caution: float, default=10000.0\n", + " | Tolerance for raising a caution for large Jacobian values.\n", + " | \n", + " | jacobian_large_value_warning: float, default=100000000.0\n", + " | Tolerance for raising a warning for large Jacobian values.\n", + " | \n", + " | jacobian_small_value_caution: float, default=0.0001\n", + " | Tolerance for raising a caution for small Jacobian values.\n", + " | \n", + " | jacobian_small_value_warning: float, default=1e-08\n", + " | Tolerance for raising a warning for small Jacobian values.\n", + " | \n", + " | warn_for_evaluation_error_at_bounds: bool, default=True\n", + " | If False, warnings will not be generated for things like log(x) with x\n", + " | >= 0\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | assert_no_numerical_warnings(self)\n", + " | Checks for numerical warnings in the model and raises an AssertionError\n", + " | if any are found.\n", + " | \n", + " | Raises:\n", + " | AssertionError if any warnings are identified by numerical analysis.\n", + " | \n", + " | assert_no_structural_warnings(self)\n", + " | Checks for structural warnings in the model and raises an AssertionError\n", + " | if any are found.\n", + " | \n", + " | Raises:\n", + " | AssertionError if any warnings are identified by structural analysis.\n", + " | \n", + " | display_components_with_inconsistent_units(self, stream=None)\n", + " | Prints a list of all Constraints, Expressions and Objectives in the\n", + " | model with inconsistent units of measurement.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_extreme_jacobians(self, stream=None)\n", + " | Prints the constraints associated with rows in the Jacobian with extreme\n", + " | L2 norms. This often indicates poorly scaled constraints.\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_large_residuals(self, stream=None)\n", + " | Prints a list of Constraints with residuals greater than a specified tolerance.\n", + " | Tolerance can be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_external_variables(self, stream=None)\n", + " | Prints a list of variables that appear within activated Constraints in the\n", + " | model but are not contained within the model themselves.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_extreme_jacobian_entries(self, stream=None)\n", + " | Prints variables and constraints associated with entries in the Jacobian with extreme\n", + " | values. This can be indicative of poor scaling, especially for isolated terms (e.g.\n", + " | variables which appear only in one term of a single constraint).\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_overconstrained_set(self, stream=None)\n", + " | Prints the variables and constraints in the over-constrained sub-problem\n", + " | from a Dulmage-Mendelsohn partitioning.\n", + " | \n", + " | This can be used to identify the over-defined part of a model and thus\n", + " | where constraints must be removed or variables unfixed.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_potential_evaluation_errors(self, stream=None)\n", + " | Prints constraints that may be prone to evaluation errors\n", + " | (e.g., log of a negative number) based on variable bounds.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_underconstrained_set(self, stream=None)\n", + " | Prints the variables and constraints in the under-constrained sub-problem\n", + " | from a Dulmage-Mendelsohn partitioning.\n", + " | \n", + " | This can be used to identify the under-defined part of a model and thus\n", + " | where additional information (fixed variables or constraints) are required.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_unused_variables(self, stream=None)\n", + " | Prints a list of variables that do not appear in any activated Constraints.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_at_or_outside_bounds(self, stream=None)\n", + " | Prints a list of variables with values that fall at or outside the bounds\n", + " | on the variable.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_fixed_to_zero(self, stream=None)\n", + " | Prints a list of variables that are fixed to an absolute value of 0.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_near_bounds(self, stream=None)\n", + " | Prints a list of variables with values close to their bounds. Tolerance can\n", + " | be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_extreme_jacobians(self, stream=None)\n", + " | Prints the variables associated with columns in the Jacobian with extreme\n", + " | L2 norms. This often indicates poorly scaled variables.\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_extreme_values(self, stream=None)\n", + " | Prints a list of variables with extreme values.\n", + " | \n", + " | Tolerances can be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_none_value(self, stream=None)\n", + " | Prints a list of variables with a value of None.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_value_near_zero(self, stream=None)\n", + " | Prints a list of variables with a value close to zero. The tolerance\n", + " | for determining what is close to zero can be set in the class configuration\n", + " | options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | get_dulmage_mendelsohn_partition(self)\n", + " | Performs a Dulmage-Mendelsohn partitioning on the model and returns\n", + " | the over- and under-constrained sub-problems.\n", + " | \n", + " | Returns:\n", + " | list-of-lists variables in each independent block of the under-constrained set\n", + " | list-of-lists constraints in each independent block of the under-constrained set\n", + " | list-of-lists variables in each independent block of the over-constrained set\n", + " | list-of-lists constraints in each independent block of the over-constrained set\n", + " | \n", + " | prepare_degeneracy_hunter(self, **kwargs)\n", + " | Create an instance of the DegeneracyHunter and store as self.degeneracy_hunter.\n", + " | \n", + " | After creating an instance of the toolbox, call\n", + " | report_irreducible_degenerate_sets.\n", + " | \n", + " | Returns:\n", + " | \n", + " | Instance of DegeneracyHunter\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | solver: str, default='scip'\n", + " | MILP solver to use for finding irreducible degenerate sets.\n", + " | \n", + " | solver_options: optional\n", + " | Options to pass to MILP solver.\n", + " | \n", + " | M: float, default=100000.0\n", + " | Maximum value for nu in MILP models.\n", + " | \n", + " | m_small: float, default=1e-05\n", + " | Smallest value for nu to be considered non-zero in MILP models.\n", + " | \n", + " | trivial_constraint_tolerance: float, default=1e-06\n", + " | Tolerance for identifying non-zero rows in Jacobian.\n", + " | \n", + " | prepare_svd_toolbox(self, **kwargs)\n", + " | Create an instance of the SVDToolbox and store as self.svd_toolbox.\n", + " | \n", + " | After creating an instance of the toolbox, call\n", + " | display_underdetermined_variables_and_constraints().\n", + " | \n", + " | Returns:\n", + " | \n", + " | Instance of SVDToolbox\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | number_of_smallest_singular_values: PositiveInt, optional\n", + " | Number of smallest singular values to compute\n", + " | \n", + " | svd_callback: svd_callback_validator, default=\n", + " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", + " | should take the Jacobian and number of singular values to compute as\n", + " | options, plus any method specific arguments, and should return the u,\n", + " | s and v matrices as numpy arrays.\n", + " | \n", + " | svd_callback_arguments: dict, optional\n", + " | Optional arguments to pass to SVD callback (default = None)\n", + " | \n", + " | singular_value_tolerance: float, default=1e-06\n", + " | Tolerance for defining a small singular value\n", + " | \n", + " | size_cutoff_in_singular_vector: float, default=0.1\n", + " | Size below which to ignore constraints and variables in the singular\n", + " | vector\n", + " | \n", + " | report_numerical_issues(self, stream=None)\n", + " | Generates a summary report of any numerical issues identified in the model provided\n", + " | and suggest next steps for debugging model.\n", + " | \n", + " | Numerical checks should only be performed once all structural issues have been resolved,\n", + " | and require that at least a partial solution to the model is available.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | report_structural_issues(self, stream=None)\n", + " | Generates a summary report of any structural issues identified in the model provided\n", + " | and suggests next steps for debugging the model.\n", + " | \n", + " | This should be the first method called when debugging a model and after any change\n", + " | is made to the model. These checks can be run before trying to initialize and solve\n", + " | the model.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties defined here:\n", + " | \n", + " | model\n", + " | Model currently being diagnosed.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + "\n" + ] + } + ], + "source": [ + "help(DiagnosticsToolbox)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", + "\n", + "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", + "\n", + "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "\n", + "m = pyo.ConcreteModel()\n", + "\n", + "m.v1 = pyo.Var(units=pyo.units.m)\n", + "m.v2 = pyo.Var(units=pyo.units.m)\n", + "m.v3 = pyo.Var(bounds=(0, 5))\n", + "m.v4 = pyo.Var()\n", + "m.v5 = pyo.Var(bounds=(0, 10))\n", + "m.v6 = pyo.Var()\n", + "m.v7 = pyo.Var(\n", + " units=pyo.units.m, bounds=(0, 1)\n", + ") # Poorly scaled variable with lower bound\n", + "m.v8 = pyo.Var() # unused variable\n", + "\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", + "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", + "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", + "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", + "\n", + "m.v4.fix(2)\n", + "m.v5.fix(2)\n", + "m.v6.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create an instance of the Diagnostics Toolbox" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the report_structural_issues() method" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Component with inconsistent units\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 1 variables, 2 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", + "\n", + "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", + "\n", + "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", + "\n", + "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", + "\n", + "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", + "\n", + "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", + "\n", + "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", + "\n", + "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the display_components_with_inconsistent_units() method" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following component(s) have unit consistency issues:\n", + "\n", + " c1\n", + "\n", + "For more details on unit inconsistencies, import the assert_units_consistent method\n", + "from pyomo.util.check_units\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_components_with_inconsistent_units()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", + "\n", + "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", + "\n", + "
\n", + "Warning:\n", + "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the incorrect Constraint\n", + "m.del_component(m.c1)\n", + "\n", + "# Re-create the Constraint with the correct units\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Warning:\n", + "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", + " \n", + "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", + "
\n", + "\n", + "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the report_structural_issues() method" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 1 variables, 2 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", + "\n", + "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.display_overconstrained_set() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the display_overconstrained_set() method" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Over-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " v3\n", + "\n", + " Constraints:\n", + "\n", + " c2\n", + " c3\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_overconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", + "\n", + "``c2: v3 == v4 + v5``\n", + "\n", + "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", + "\n", + "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", + "\n", + "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Unfix v4\n", + "\n", + "# Then call the report_structural_issues() method again" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 5 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 2 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Degree of Freedom\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 0 variables, 0 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_underconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.v4.unfix()\n", + "\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the under-constrained set in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display the under-constrained set" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Under-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " v2\n", + " v1\n", + " v7\n", + "\n", + " Constraints:\n", + "\n", + " c1\n", + " c4\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_underconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", + "\n", + "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Fix v2 = 5\n", + "\n", + "# Then re-run report_structural_issues() method" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.v2.fix(5)\n", + "\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", + "\n", + "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a solver object\n", + "\n", + "# Try to solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.39e+01 1.50e+02 -1.0 6.00e+00 - 7.16e-01 4.93e-03h 1\n", + " 2 0.0000000e+00 1.39e+01 3.03e+06 -1.0 5.97e+00 - 1.00e+00 4.95e-05h 1\n", + " 3r 0.0000000e+00 1.39e+01 1.00e+03 1.1 0.00e+00 - 0.00e+00 2.47e-07R 2\n", + " 4r 0.0000000e+00 4.19e+00 9.42e+02 1.1 3.50e+03 - 4.02e-01 3.37e-03f 1\n", + " 5r 0.0000000e+00 2.12e+00 8.72e+02 1.1 5.89e+01 - 4.35e-01 7.06e-02f 1\n", + " 6r 0.0000000e+00 6.74e-01 6.06e+02 1.1 5.29e+00 - 9.93e-03 3.98e-01f 1\n", + " 7r 0.0000000e+00 6.80e-01 3.14e+02 0.4 2.05e-01 - 1.00e+00 1.03e-01f 1\n", + " 8r 0.0000000e+00 6.69e-01 2.78e-05 0.4 2.58e-02 - 1.00e+00 1.00e+00f 1\n", + " 9r 0.0000000e+00 6.67e-01 7.56e+00 -1.7 8.13e-03 - 9.93e-01 9.96e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 0.0000000e+00 6.67e-01 2.23e-07 -1.7 4.13e-05 - 1.00e+00 1.00e+00f 1\n", + " 11r 0.0000000e+00 6.67e-01 6.73e-01 -3.7 6.61e-05 - 1.00e+00 1.00e+00f 1\n", + " 12r 0.0000000e+00 6.67e-01 1.91e-09 -3.7 1.48e-09 - 1.00e+00 1.00e+00h 1\n", + " 13r 0.0000000e+00 6.67e-01 2.69e+00 -8.4 5.74e-07 - 1.00e+00 9.26e-01f 1\n", + " 14r 0.0000000e+00 6.67e-01 7.65e+01 -8.4 4.23e-08 - 8.68e-01 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 3.2644919411246030e-04 3.2644919411246030e-04\n", + "Constraint violation....: 6.6666666333656233e-01 6.6666666333656233e-01\n", + "Complementarity.........: 4.6615546565561981e-09 4.6615546565561981e-09\n", + "Overall NLP error.......: 6.6666666333656233e-01 6.6666666333656233e-01\n", + "\n", + "\n", + "Number of objective function evaluations = 18\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 18\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 17\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 15\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: infeasible\n", + " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", + " point. Problem may be infeasible.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", + "\n", + "
\n", + "Warning:\n", + "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the report_numerical_issues method in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for numerical issues" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.700E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", + " WARNING: 1 Variable at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 2 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Variable with None value\n", + " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", + "\n", + "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", + "\n", + "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the constraint with a large residual in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display constraint with large residual" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following constraint(s) have large residuals (>1.0E-05):\n", + "\n", + " c2: 6.66667E-01\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_constraints_with_large_residuals()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", + "\n", + "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", + "
\n", + "\n", + "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the variables with bounds violations.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display the variables with bounds violations" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", + "\n", + " v3 (free): value=0.0 bounds=(0, 5)\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_at_or_outside_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", + "\n", + "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", + " \n", + "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", + "
\n", + "\n", + "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Update the bounds on v3 in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Update bounds for v3" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.v3.setlb(-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", + "\n", + "
\n", + "Warning:\n", + "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check to see if there are any new structural issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for new structural issues" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Re-solve the model in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Re-solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.67e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 6.66e-03 2.97e+00 -1.0 2.00e+00 - 7.17e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 6.27e-05 9.38e+00 -1.0 2.00e-02 - 1.00e+00 9.91e-01h 1\n", + " 3 0.0000000e+00 8.88e-16 1.13e-12 -1.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 8.8817841970012523e-16 8.8817841970012523e-16\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 8.8817841970012523e-16 8.8817841970012523e-16\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", + "\n", + "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", + "\n", + "
\n", + "Warning:\n", + "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", + " \n", + "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", + "
\n", + "\n", + "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional numerical issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for additional numerical issues" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.700E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Variable with None value\n", + " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " If you still have issues converging your model consider:\n", + " prepare_svd_toolbox()\n", + " prepare_degeneracy_hunter()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional information about variables with extreme values.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display variable with extreme value" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have extreme values (<1.0E-04 or > 1.0E+04):\n", + "\n", + " v7: 4.9999999999999945e-08\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_with_extreme_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", + "\n", + "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", + "\n", + "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", + "\n", + "m.scaling_factor[m.v7] = 1e8\n", + "m.scaling_factor[m.c4] = 1e8\n", + "\n", + "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", + "scaled_model = scaling.create_using(m, rename=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Solve the scaled model and check to see how many iterations are required.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Solve scaled model" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.33e-15 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.3290705182007514e-15 5.3290705182007514e-15\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.3290705182007514e-15 5.3290705182007514e-15\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(scaled_model, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", + "\n", + "
\n", + "Warning:\n", + "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", + "
\n", + "\n", + "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", + "\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a new diagnostics toolbox for scaled model\n", + "\n", + "# Report numerical issues for scaled model" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.800E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with None value\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " If you still have issues converging your model consider:\n", + " prepare_svd_toolbox()\n", + " prepare_degeneracy_hunter()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt_scaled = DiagnosticsToolbox(scaled_model)\n", + "dt_scaled.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", + "\n", + "
\n", + "Warning:\n", + "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", + "
\n", + "\n", + "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." + ] } - ], - "source": [ - "solver.solve(scaled_model, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", - "\n", - "
\n", - "Warning:\n", - "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", - "
\n", - "\n", - "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", - "\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a new diagnostics toolbox for scaled model\n", - "\n", - "# Report numerical issues for scaled model" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.800E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "3 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with None value\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", - " prepare_degeneracy_hunter()\n", - "\n", - "====================================================================================\n" - ] + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" } - ], - "source": [ - "dt_scaled = DiagnosticsToolbox(scaled_model)\n", - "dt_scaled.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", - "\n", - "
\n", - "Warning:\n", - "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", - "
\n", - "\n", - "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_test.ipynb b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_test.ipynb index 740f40fe..f8e8ba5a 100644 --- a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_test.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_test.ipynb @@ -1,1868 +1,1869 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IDAES Model Diagnostics Toolbox Tutorial\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-10-31 \n", - "\n", - "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", - "\n", - "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", - "\n", - "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", - "\n", - "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the DiagnosticsToolbox in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util import DiagnosticsToolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", - "\n", - "class DiagnosticsToolbox(builtins.object)\n", - " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", - " | \n", - " | The IDAES Model DiagnosticsToolbox.\n", - " | \n", - " | To get started:\n", - " | \n", - " | 1. Create an instance of your model (this does not need to be initialized yet).\n", - " | 2. Fix variables until you have 0 degrees of freedom. Many of these tools presume\n", - " | a square model, and a square model should always be the foundation of any more\n", - " | advanced model.\n", - " | 3. Create an instance of the DiagnosticsToolbox and provide the model to debug as\n", - " | the model argument.\n", - " | 4. Call the ``report_structural_issues()`` method.\n", - " | \n", - " | Model diagnostics is an iterative process and you will likely need to run these\n", - " | tools multiple times to resolve all issues. After making a change to your model,\n", - " | you should always start from the beginning again to ensure the change did not\n", - " | introduce any new issues; i.e., always start from the report_structural_issues()\n", - " | method.\n", - " | \n", - " | Note that structural checks do not require the model to be initialized, thus users\n", - " | should start with these. Numerical checks require at least a partial solution to the\n", - " | model and should only be run once all structural issues have been resolved.\n", - " | \n", - " | Report methods will print a summary containing three parts:\n", - " | \n", - " | 1. Warnings - these are critical issues that should be resolved before continuing.\n", - " | For each warning, a method will be suggested in the Next Steps section to get\n", - " | additional information.\n", - " | 2. Cautions - these are things that could be correct but could also be the source of\n", - " | solver issues. Not all cautions need to be addressed, but users should investigate\n", - " | each one to ensure that the behavior is correct and that they will not be the source\n", - " | of difficulties later. Methods exist to provide more information on all cautions,\n", - " | but these will not appear in the Next Steps section.\n", - " | 3. Next Steps - these are recommended methods to call from the DiagnosticsToolbox to\n", - " | get further information on warnings. If no warnings are found, this will suggest\n", - " | the next report method to call.\n", - " | \n", - " | Args:\n", - " | \n", - " | model: model to be diagnosed. The DiagnosticsToolbox does not support indexed Blocks.\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | variable_bounds_absolute_tolerance: float, default=0.0001\n", - " | Absolute tolerance for considering a variable to be close to its\n", - " | bounds.\n", - " | \n", - " | variable_bounds_relative_tolerance: float, default=0.0001\n", - " | Relative tolerance for considering a variable to be close to its\n", - " | bounds.\n", - " | \n", - " | variable_bounds_violation_tolerance: float, default=0\n", - " | Absolute tolerance for considering a variable to violate its bounds.\n", - " | Some solvers relax bounds on variables thus allowing a small violation\n", - " | to be considered acceptable.\n", - " | \n", - " | constraint_residual_tolerance: float, default=1e-05\n", - " | Absolute tolerance to use when checking constraint residuals.\n", - " | \n", - " | variable_large_value_tolerance: float, default=10000.0\n", - " | Absolute tolerance for considering a value to be large.\n", - " | \n", - " | variable_small_value_tolerance: float, default=0.0001\n", - " | Absolute tolerance for considering a value to be small.\n", - " | \n", - " | variable_zero_value_tolerance: float, default=1e-08\n", - " | Absolute tolerance for considering a value to be near to zero.\n", - " | \n", - " | jacobian_large_value_caution: float, default=10000.0\n", - " | Tolerance for raising a caution for large Jacobian values.\n", - " | \n", - " | jacobian_large_value_warning: float, default=100000000.0\n", - " | Tolerance for raising a warning for large Jacobian values.\n", - " | \n", - " | jacobian_small_value_caution: float, default=0.0001\n", - " | Tolerance for raising a caution for small Jacobian values.\n", - " | \n", - " | jacobian_small_value_warning: float, default=1e-08\n", - " | Tolerance for raising a warning for small Jacobian values.\n", - " | \n", - " | warn_for_evaluation_error_at_bounds: bool, default=True\n", - " | If False, warnings will not be generated for things like log(x) with x\n", - " | >= 0\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " | \n", - " | assert_no_numerical_warnings(self)\n", - " | Checks for numerical warnings in the model and raises an AssertionError\n", - " | if any are found.\n", - " | \n", - " | Raises:\n", - " | AssertionError if any warnings are identified by numerical analysis.\n", - " | \n", - " | assert_no_structural_warnings(self)\n", - " | Checks for structural warnings in the model and raises an AssertionError\n", - " | if any are found.\n", - " | \n", - " | Raises:\n", - " | AssertionError if any warnings are identified by structural analysis.\n", - " | \n", - " | display_components_with_inconsistent_units(self, stream=None)\n", - " | Prints a list of all Constraints, Expressions and Objectives in the\n", - " | model with inconsistent units of measurement.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_constraints_with_extreme_jacobians(self, stream=None)\n", - " | Prints the constraints associated with rows in the Jacobian with extreme\n", - " | L2 norms. This often indicates poorly scaled constraints.\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_constraints_with_large_residuals(self, stream=None)\n", - " | Prints a list of Constraints with residuals greater than a specified tolerance.\n", - " | Tolerance can be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_external_variables(self, stream=None)\n", - " | Prints a list of variables that appear within activated Constraints in the\n", - " | model but are not contained within the model themselves.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_extreme_jacobian_entries(self, stream=None)\n", - " | Prints variables and constraints associated with entries in the Jacobian with extreme\n", - " | values. This can be indicative of poor scaling, especially for isolated terms (e.g.\n", - " | variables which appear only in one term of a single constraint).\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_overconstrained_set(self, stream=None)\n", - " | Prints the variables and constraints in the over-constrained sub-problem\n", - " | from a Dulmage-Mendelsohn partitioning.\n", - " | \n", - " | This can be used to identify the over-defined part of a model and thus\n", - " | where constraints must be removed or variables unfixed.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_potential_evaluation_errors(self, stream=None)\n", - " | Prints constraints that may be prone to evaluation errors\n", - " | (e.g., log of a negative number) based on variable bounds.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_underconstrained_set(self, stream=None)\n", - " | Prints the variables and constraints in the under-constrained sub-problem\n", - " | from a Dulmage-Mendelsohn partitioning.\n", - " | \n", - " | This can be used to identify the under-defined part of a model and thus\n", - " | where additional information (fixed variables or constraints) are required.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_unused_variables(self, stream=None)\n", - " | Prints a list of variables that do not appear in any activated Constraints.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_at_or_outside_bounds(self, stream=None)\n", - " | Prints a list of variables with values that fall at or outside the bounds\n", - " | on the variable.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_fixed_to_zero(self, stream=None)\n", - " | Prints a list of variables that are fixed to an absolute value of 0.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_near_bounds(self, stream=None)\n", - " | Prints a list of variables with values close to their bounds. Tolerance can\n", - " | be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_extreme_jacobians(self, stream=None)\n", - " | Prints the variables associated with columns in the Jacobian with extreme\n", - " | L2 norms. This often indicates poorly scaled variables.\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_extreme_values(self, stream=None)\n", - " | Prints a list of variables with extreme values.\n", - " | \n", - " | Tolerances can be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_none_value(self, stream=None)\n", - " | Prints a list of variables with a value of None.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_value_near_zero(self, stream=None)\n", - " | Prints a list of variables with a value close to zero. The tolerance\n", - " | for determining what is close to zero can be set in the class configuration\n", - " | options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | get_dulmage_mendelsohn_partition(self)\n", - " | Performs a Dulmage-Mendelsohn partitioning on the model and returns\n", - " | the over- and under-constrained sub-problems.\n", - " | \n", - " | Returns:\n", - " | list-of-lists variables in each independent block of the under-constrained set\n", - " | list-of-lists constraints in each independent block of the under-constrained set\n", - " | list-of-lists variables in each independent block of the over-constrained set\n", - " | list-of-lists constraints in each independent block of the over-constrained set\n", - " | \n", - " | prepare_degeneracy_hunter(self, **kwargs)\n", - " | Create an instance of the DegeneracyHunter and store as self.degeneracy_hunter.\n", - " | \n", - " | After creating an instance of the toolbox, call\n", - " | report_irreducible_degenerate_sets.\n", - " | \n", - " | Returns:\n", - " | \n", - " | Instance of DegeneracyHunter\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | solver: str, default='scip'\n", - " | MILP solver to use for finding irreducible degenerate sets.\n", - " | \n", - " | solver_options: optional\n", - " | Options to pass to MILP solver.\n", - " | \n", - " | M: float, default=100000.0\n", - " | Maximum value for nu in MILP models.\n", - " | \n", - " | m_small: float, default=1e-05\n", - " | Smallest value for nu to be considered non-zero in MILP models.\n", - " | \n", - " | trivial_constraint_tolerance: float, default=1e-06\n", - " | Tolerance for identifying non-zero rows in Jacobian.\n", - " | \n", - " | prepare_svd_toolbox(self, **kwargs)\n", - " | Create an instance of the SVDToolbox and store as self.svd_toolbox.\n", - " | \n", - " | After creating an instance of the toolbox, call\n", - " | display_underdetermined_variables_and_constraints().\n", - " | \n", - " | Returns:\n", - " | \n", - " | Instance of SVDToolbox\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | number_of_smallest_singular_values: PositiveInt, optional\n", - " | Number of smallest singular values to compute\n", - " | \n", - " | svd_callback: svd_callback_validator, default=\n", - " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", - " | should take the Jacobian and number of singular values to compute as\n", - " | options, plus any method specific arguments, and should return the u,\n", - " | s and v matrices as numpy arrays.\n", - " | \n", - " | svd_callback_arguments: dict, optional\n", - " | Optional arguments to pass to SVD callback (default = None)\n", - " | \n", - " | singular_value_tolerance: float, default=1e-06\n", - " | Tolerance for defining a small singular value\n", - " | \n", - " | size_cutoff_in_singular_vector: float, default=0.1\n", - " | Size below which to ignore constraints and variables in the singular\n", - " | vector\n", - " | \n", - " | report_numerical_issues(self, stream=None)\n", - " | Generates a summary report of any numerical issues identified in the model provided\n", - " | and suggest next steps for debugging model.\n", - " | \n", - " | Numerical checks should only be performed once all structural issues have been resolved,\n", - " | and require that at least a partial solution to the model is available.\n", - " | \n", - " | Args:\n", - " | stream: I/O object to write report to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | report_structural_issues(self, stream=None)\n", - " | Generates a summary report of any structural issues identified in the model provided\n", - " | and suggests next steps for debugging the model.\n", - " | \n", - " | This should be the first method called when debugging a model and after any change\n", - " | is made to the model. These checks can be run before trying to initialize and solve\n", - " | the model.\n", - " | \n", - " | Args:\n", - " | stream: I/O object to write report to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Readonly properties defined here:\n", - " | \n", - " | model\n", - " | Model currently being diagnosed.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables (if defined)\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object (if defined)\n", - "\n" - ] - } - ], - "source": [ - "help(DiagnosticsToolbox)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", - "\n", - "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", - "\n", - "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "\n", - "m = pyo.ConcreteModel()\n", - "\n", - "m.v1 = pyo.Var(units=pyo.units.m)\n", - "m.v2 = pyo.Var(units=pyo.units.m)\n", - "m.v3 = pyo.Var(bounds=(0, 5))\n", - "m.v4 = pyo.Var()\n", - "m.v5 = pyo.Var(bounds=(0, 10))\n", - "m.v6 = pyo.Var()\n", - "m.v7 = pyo.Var(\n", - " units=pyo.units.m, bounds=(0, 1)\n", - ") # Poorly scaled variable with lower bound\n", - "m.v8 = pyo.Var() # unused variable\n", - "\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", - "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", - "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", - "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", - "\n", - "m.v4.fix(2)\n", - "m.v5.fix(2)\n", - "m.v6.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Component with inconsistent units\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 1 variables, 2 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_components_with_inconsistent_units()\n", - " display_underconstrained_set()\n", - " display_overconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", - "\n", - "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", - "\n", - "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", - "\n", - "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", - "\n", - "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", - "\n", - "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", - "\n", - "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", - "\n", - "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false, - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Model Diagnostics Toolbox Tutorial\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-10-31 \n", + "\n", + "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", + "\n", + "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", + "\n", + "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", + "\n", + "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the DiagnosticsToolbox in the cell below.\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following component(s) have unit consistency issues:\n", - "\n", - " c1\n", - "\n", - "For more details on unit inconsistencies, import the assert_units_consistent method\n", - "from pyomo.util.check_units\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_components_with_inconsistent_units()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", - "\n", - "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", - "\n", - "
\n", - "Warning:\n", - "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the incorrect Constraint\n", - "m.del_component(m.c1)\n", - "\n", - "# Re-create the Constraint with the correct units\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Warning:\n", - "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", - " \n", - "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", - "
\n", - "\n", - "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util import DiagnosticsToolbox" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 1 variables, 2 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_underconstrained_set()\n", - " display_overconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", - "\n", - "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.display_overconstrained_set() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Dulmage-Mendelsohn Over-Constrained Set\n", - "\n", - " Independent Block 0:\n", - "\n", - " Variables:\n", - "\n", - " v3\n", - "\n", - " Constraints:\n", - "\n", - " c2\n", - " c3\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", - "\n", - "``c2: v3 == v4 + v5``\n", - "\n", - "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", - "\n", - "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", - "\n", - "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", + "\n", + "class DiagnosticsToolbox(builtins.object)\n", + " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | \n", + " | The IDAES Model DiagnosticsToolbox.\n", + " | \n", + " | To get started:\n", + " | \n", + " | 1. Create an instance of your model (this does not need to be initialized yet).\n", + " | 2. Fix variables until you have 0 degrees of freedom. Many of these tools presume\n", + " | a square model, and a square model should always be the foundation of any more\n", + " | advanced model.\n", + " | 3. Create an instance of the DiagnosticsToolbox and provide the model to debug as\n", + " | the model argument.\n", + " | 4. Call the ``report_structural_issues()`` method.\n", + " | \n", + " | Model diagnostics is an iterative process and you will likely need to run these\n", + " | tools multiple times to resolve all issues. After making a change to your model,\n", + " | you should always start from the beginning again to ensure the change did not\n", + " | introduce any new issues; i.e., always start from the report_structural_issues()\n", + " | method.\n", + " | \n", + " | Note that structural checks do not require the model to be initialized, thus users\n", + " | should start with these. Numerical checks require at least a partial solution to the\n", + " | model and should only be run once all structural issues have been resolved.\n", + " | \n", + " | Report methods will print a summary containing three parts:\n", + " | \n", + " | 1. Warnings - these are critical issues that should be resolved before continuing.\n", + " | For each warning, a method will be suggested in the Next Steps section to get\n", + " | additional information.\n", + " | 2. Cautions - these are things that could be correct but could also be the source of\n", + " | solver issues. Not all cautions need to be addressed, but users should investigate\n", + " | each one to ensure that the behavior is correct and that they will not be the source\n", + " | of difficulties later. Methods exist to provide more information on all cautions,\n", + " | but these will not appear in the Next Steps section.\n", + " | 3. Next Steps - these are recommended methods to call from the DiagnosticsToolbox to\n", + " | get further information on warnings. If no warnings are found, this will suggest\n", + " | the next report method to call.\n", + " | \n", + " | Args:\n", + " | \n", + " | model: model to be diagnosed. The DiagnosticsToolbox does not support indexed Blocks.\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | variable_bounds_absolute_tolerance: float, default=0.0001\n", + " | Absolute tolerance for considering a variable to be close to its\n", + " | bounds.\n", + " | \n", + " | variable_bounds_relative_tolerance: float, default=0.0001\n", + " | Relative tolerance for considering a variable to be close to its\n", + " | bounds.\n", + " | \n", + " | variable_bounds_violation_tolerance: float, default=0\n", + " | Absolute tolerance for considering a variable to violate its bounds.\n", + " | Some solvers relax bounds on variables thus allowing a small violation\n", + " | to be considered acceptable.\n", + " | \n", + " | constraint_residual_tolerance: float, default=1e-05\n", + " | Absolute tolerance to use when checking constraint residuals.\n", + " | \n", + " | variable_large_value_tolerance: float, default=10000.0\n", + " | Absolute tolerance for considering a value to be large.\n", + " | \n", + " | variable_small_value_tolerance: float, default=0.0001\n", + " | Absolute tolerance for considering a value to be small.\n", + " | \n", + " | variable_zero_value_tolerance: float, default=1e-08\n", + " | Absolute tolerance for considering a value to be near to zero.\n", + " | \n", + " | jacobian_large_value_caution: float, default=10000.0\n", + " | Tolerance for raising a caution for large Jacobian values.\n", + " | \n", + " | jacobian_large_value_warning: float, default=100000000.0\n", + " | Tolerance for raising a warning for large Jacobian values.\n", + " | \n", + " | jacobian_small_value_caution: float, default=0.0001\n", + " | Tolerance for raising a caution for small Jacobian values.\n", + " | \n", + " | jacobian_small_value_warning: float, default=1e-08\n", + " | Tolerance for raising a warning for small Jacobian values.\n", + " | \n", + " | warn_for_evaluation_error_at_bounds: bool, default=True\n", + " | If False, warnings will not be generated for things like log(x) with x\n", + " | >= 0\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | assert_no_numerical_warnings(self)\n", + " | Checks for numerical warnings in the model and raises an AssertionError\n", + " | if any are found.\n", + " | \n", + " | Raises:\n", + " | AssertionError if any warnings are identified by numerical analysis.\n", + " | \n", + " | assert_no_structural_warnings(self)\n", + " | Checks for structural warnings in the model and raises an AssertionError\n", + " | if any are found.\n", + " | \n", + " | Raises:\n", + " | AssertionError if any warnings are identified by structural analysis.\n", + " | \n", + " | display_components_with_inconsistent_units(self, stream=None)\n", + " | Prints a list of all Constraints, Expressions and Objectives in the\n", + " | model with inconsistent units of measurement.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_extreme_jacobians(self, stream=None)\n", + " | Prints the constraints associated with rows in the Jacobian with extreme\n", + " | L2 norms. This often indicates poorly scaled constraints.\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_large_residuals(self, stream=None)\n", + " | Prints a list of Constraints with residuals greater than a specified tolerance.\n", + " | Tolerance can be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_external_variables(self, stream=None)\n", + " | Prints a list of variables that appear within activated Constraints in the\n", + " | model but are not contained within the model themselves.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_extreme_jacobian_entries(self, stream=None)\n", + " | Prints variables and constraints associated with entries in the Jacobian with extreme\n", + " | values. This can be indicative of poor scaling, especially for isolated terms (e.g.\n", + " | variables which appear only in one term of a single constraint).\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_overconstrained_set(self, stream=None)\n", + " | Prints the variables and constraints in the over-constrained sub-problem\n", + " | from a Dulmage-Mendelsohn partitioning.\n", + " | \n", + " | This can be used to identify the over-defined part of a model and thus\n", + " | where constraints must be removed or variables unfixed.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_potential_evaluation_errors(self, stream=None)\n", + " | Prints constraints that may be prone to evaluation errors\n", + " | (e.g., log of a negative number) based on variable bounds.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_underconstrained_set(self, stream=None)\n", + " | Prints the variables and constraints in the under-constrained sub-problem\n", + " | from a Dulmage-Mendelsohn partitioning.\n", + " | \n", + " | This can be used to identify the under-defined part of a model and thus\n", + " | where additional information (fixed variables or constraints) are required.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_unused_variables(self, stream=None)\n", + " | Prints a list of variables that do not appear in any activated Constraints.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_at_or_outside_bounds(self, stream=None)\n", + " | Prints a list of variables with values that fall at or outside the bounds\n", + " | on the variable.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_fixed_to_zero(self, stream=None)\n", + " | Prints a list of variables that are fixed to an absolute value of 0.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_near_bounds(self, stream=None)\n", + " | Prints a list of variables with values close to their bounds. Tolerance can\n", + " | be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_extreme_jacobians(self, stream=None)\n", + " | Prints the variables associated with columns in the Jacobian with extreme\n", + " | L2 norms. This often indicates poorly scaled variables.\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_extreme_values(self, stream=None)\n", + " | Prints a list of variables with extreme values.\n", + " | \n", + " | Tolerances can be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_none_value(self, stream=None)\n", + " | Prints a list of variables with a value of None.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_value_near_zero(self, stream=None)\n", + " | Prints a list of variables with a value close to zero. The tolerance\n", + " | for determining what is close to zero can be set in the class configuration\n", + " | options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | get_dulmage_mendelsohn_partition(self)\n", + " | Performs a Dulmage-Mendelsohn partitioning on the model and returns\n", + " | the over- and under-constrained sub-problems.\n", + " | \n", + " | Returns:\n", + " | list-of-lists variables in each independent block of the under-constrained set\n", + " | list-of-lists constraints in each independent block of the under-constrained set\n", + " | list-of-lists variables in each independent block of the over-constrained set\n", + " | list-of-lists constraints in each independent block of the over-constrained set\n", + " | \n", + " | prepare_degeneracy_hunter(self, **kwargs)\n", + " | Create an instance of the DegeneracyHunter and store as self.degeneracy_hunter.\n", + " | \n", + " | After creating an instance of the toolbox, call\n", + " | report_irreducible_degenerate_sets.\n", + " | \n", + " | Returns:\n", + " | \n", + " | Instance of DegeneracyHunter\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | solver: str, default='scip'\n", + " | MILP solver to use for finding irreducible degenerate sets.\n", + " | \n", + " | solver_options: optional\n", + " | Options to pass to MILP solver.\n", + " | \n", + " | M: float, default=100000.0\n", + " | Maximum value for nu in MILP models.\n", + " | \n", + " | m_small: float, default=1e-05\n", + " | Smallest value for nu to be considered non-zero in MILP models.\n", + " | \n", + " | trivial_constraint_tolerance: float, default=1e-06\n", + " | Tolerance for identifying non-zero rows in Jacobian.\n", + " | \n", + " | prepare_svd_toolbox(self, **kwargs)\n", + " | Create an instance of the SVDToolbox and store as self.svd_toolbox.\n", + " | \n", + " | After creating an instance of the toolbox, call\n", + " | display_underdetermined_variables_and_constraints().\n", + " | \n", + " | Returns:\n", + " | \n", + " | Instance of SVDToolbox\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | number_of_smallest_singular_values: PositiveInt, optional\n", + " | Number of smallest singular values to compute\n", + " | \n", + " | svd_callback: svd_callback_validator, default=\n", + " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", + " | should take the Jacobian and number of singular values to compute as\n", + " | options, plus any method specific arguments, and should return the u,\n", + " | s and v matrices as numpy arrays.\n", + " | \n", + " | svd_callback_arguments: dict, optional\n", + " | Optional arguments to pass to SVD callback (default = None)\n", + " | \n", + " | singular_value_tolerance: float, default=1e-06\n", + " | Tolerance for defining a small singular value\n", + " | \n", + " | size_cutoff_in_singular_vector: float, default=0.1\n", + " | Size below which to ignore constraints and variables in the singular\n", + " | vector\n", + " | \n", + " | report_numerical_issues(self, stream=None)\n", + " | Generates a summary report of any numerical issues identified in the model provided\n", + " | and suggest next steps for debugging model.\n", + " | \n", + " | Numerical checks should only be performed once all structural issues have been resolved,\n", + " | and require that at least a partial solution to the model is available.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | report_structural_issues(self, stream=None)\n", + " | Generates a summary report of any structural issues identified in the model provided\n", + " | and suggests next steps for debugging the model.\n", + " | \n", + " | This should be the first method called when debugging a model and after any change\n", + " | is made to the model. These checks can be run before trying to initialize and solve\n", + " | the model.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties defined here:\n", + " | \n", + " | model\n", + " | Model currently being diagnosed.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + "\n" + ] + } + ], + "source": [ + "help(DiagnosticsToolbox)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 5 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 2 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Degree of Freedom\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 0 variables, 0 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_underconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.v4.unfix()\n", - "\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the under-constrained set in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", + "\n", + "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", + "\n", + "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Dulmage-Mendelsohn Under-Constrained Set\n", - "\n", - " Independent Block 0:\n", - "\n", - " Variables:\n", - "\n", - " v2\n", - " v1\n", - " v7\n", - "\n", - " Constraints:\n", - "\n", - " c1\n", - " c4\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_underconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", - "\n", - "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "\n", + "m = pyo.ConcreteModel()\n", + "\n", + "m.v1 = pyo.Var(units=pyo.units.m)\n", + "m.v2 = pyo.Var(units=pyo.units.m)\n", + "m.v3 = pyo.Var(bounds=(0, 5))\n", + "m.v4 = pyo.Var()\n", + "m.v5 = pyo.Var(bounds=(0, 10))\n", + "m.v6 = pyo.Var()\n", + "m.v7 = pyo.Var(\n", + " units=pyo.units.m, bounds=(0, 1)\n", + ") # Poorly scaled variable with lower bound\n", + "m.v8 = pyo.Var() # unused variable\n", + "\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", + "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", + "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", + "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", + "\n", + "m.v4.fix(2)\n", + "m.v5.fix(2)\n", + "m.v6.fix(0)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.v2.fix(5)\n", - "\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", - "\n", - "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.39e+01 1.50e+02 -1.0 6.00e+00 - 7.16e-01 4.93e-03h 1\n", - " 2 0.0000000e+00 1.39e+01 3.03e+06 -1.0 5.97e+00 - 1.00e+00 4.95e-05h 1\n", - " 3r 0.0000000e+00 1.39e+01 1.00e+03 1.1 0.00e+00 - 0.00e+00 2.47e-07R 2\n", - " 4r 0.0000000e+00 4.19e+00 9.42e+02 1.1 3.50e+03 - 4.02e-01 3.37e-03f 1\n", - " 5r 0.0000000e+00 2.12e+00 8.72e+02 1.1 5.89e+01 - 4.35e-01 7.06e-02f 1\n", - " 6r 0.0000000e+00 6.74e-01 6.06e+02 1.1 5.29e+00 - 9.93e-03 3.98e-01f 1\n", - " 7r 0.0000000e+00 6.80e-01 3.14e+02 0.4 2.05e-01 - 1.00e+00 1.03e-01f 1\n", - " 8r 0.0000000e+00 6.69e-01 2.78e-05 0.4 2.58e-02 - 1.00e+00 1.00e+00f 1\n", - " 9r 0.0000000e+00 6.67e-01 7.56e+00 -1.7 8.13e-03 - 9.93e-01 9.96e-01f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10r 0.0000000e+00 6.67e-01 2.23e-07 -1.7 4.13e-05 - 1.00e+00 1.00e+00f 1\n", - " 11r 0.0000000e+00 6.67e-01 6.73e-01 -3.7 6.61e-05 - 1.00e+00 1.00e+00f 1\n", - " 12r 0.0000000e+00 6.67e-01 1.91e-09 -3.7 1.48e-09 - 1.00e+00 1.00e+00h 1\n", - " 13r 0.0000000e+00 6.67e-01 2.69e+00 -8.4 5.74e-07 - 1.00e+00 9.26e-01f 1\n", - " 14r 0.0000000e+00 6.67e-01 7.65e+01 -8.4 4.23e-08 - 8.68e-01 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 14\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 3.2644919411246030e-04 3.2644919411246030e-04\n", - "Constraint violation....: 6.6666666333656233e-01 6.6666666333656233e-01\n", - "Complementarity.........: 4.6615546565561981e-09 4.6615546565561981e-09\n", - "Overall NLP error.......: 6.6666666333656233e-01 6.6666666333656233e-01\n", - "\n", - "\n", - "Number of objective function evaluations = 18\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 18\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 17\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 15\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: infeasible\n", - " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", - " point. Problem may be infeasible.\n" - ] + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(m)" + ] }, { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", - "\n", - "
\n", - "Warning:\n", - "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the report_numerical_issues method in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.700E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", - " WARNING: 1 Variable at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 2 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Variable with None value\n", - " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_constraints_with_large_residuals()\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", - "\n", - "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", - "\n", - "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the constraint with a large residual in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Component with inconsistent units\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 1 variables, 2 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following constraint(s) have large residuals (>1.0E-05):\n", - "\n", - " c2: 6.66667E-01\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_constraints_with_large_residuals()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", - "\n", - "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", - "
\n", - "\n", - "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the variables with bounds violations.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", + "\n", + "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", + "\n", + "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", + "\n", + "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", + "\n", + "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", + "\n", + "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", + "\n", + "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", + "\n", + "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", - "\n", - " v3 (free): value=0.0 bounds=(0, 5)\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_at_or_outside_bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", - "\n", - "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", - " \n", - "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", - "
\n", - "\n", - "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Update the bounds on v3 in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.v3.setlb(-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", - "\n", - "
\n", - "Warning:\n", - "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check to see if there are any new structural issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following component(s) have unit consistency issues:\n", + "\n", + " c1\n", + "\n", + "For more details on unit inconsistencies, import the assert_units_consistent method\n", + "from pyomo.util.check_units\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_components_with_inconsistent_units()" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Re-solve the model in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", + "\n", + "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", + "\n", + "
\n", + "Warning:\n", + "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 6.67e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 6.66e-03 2.97e+00 -1.0 2.00e+00 - 7.17e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 6.27e-05 9.38e+00 -1.0 2.00e-02 - 1.00e+00 9.91e-01h 1\n", - " 3 0.0000000e+00 8.88e-16 1.13e-12 -1.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 8.8817841970012523e-16 8.8817841970012523e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 8.8817841970012523e-16 8.8817841970012523e-16\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the incorrect Constraint\n", + "m.del_component(m.c1)\n", + "\n", + "# Re-create the Constraint with the correct units\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" + ] }, { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Warning:\n", + "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", + " \n", + "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", + "
\n", + "\n", + "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", - "\n", - "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", - "\n", - "
\n", - "Warning:\n", - "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", - " \n", - "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", - "
\n", - "\n", - "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional numerical issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.700E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Variable with None value\n", - " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", - " prepare_degeneracy_hunter()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional information about variables with extreme values.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 1 variables, 2 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have extreme values (<1.0E-04 or > 1.0E+04):\n", - "\n", - " v7: 4.9999999999999945e-08\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_with_extreme_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", - "\n", - "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", - "\n", - "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", - "\n", - "m.scaling_factor[m.v7] = 1e8\n", - "m.scaling_factor[m.c4] = 1e8\n", - "\n", - "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", - "scaled_model = scaling.create_using(m, rename=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Solve the scaled model and check to see how many iterations are required.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", + "\n", + "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.display_overconstrained_set() in the cell below.\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 5.33e-15 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 5.3290705182007514e-15 5.3290705182007514e-15\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.3290705182007514e-15 5.3290705182007514e-15\n", - "\n", - "\n", - "Number of objective function evaluations = 1\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 1\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 1\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Over-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " v3\n", + "\n", + " Constraints:\n", + "\n", + " c2\n", + " c3\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_overconstrained_set()" + ] }, { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", + "\n", + "``c2: v3 == v4 + v5``\n", + "\n", + "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", + "\n", + "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", + "\n", + "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", + "
" ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(scaled_model, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from pyomo.environ import assert_optimal_termination\n", - "\n", - "res = solver.solve(scaled_model, tee=False)\n", - "assert_optimal_termination(res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", - "\n", - "
\n", - "Warning:\n", - "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", - "
\n", - "\n", - "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", - "\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 5 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 2 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Degree of Freedom\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 0 variables, 0 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_underconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.v4.unfix()\n", + "\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the under-constrained set in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Under-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " v2\n", + " v1\n", + " v7\n", + "\n", + " Constraints:\n", + "\n", + " c1\n", + " c4\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_underconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", + "\n", + "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.v2.fix(5)\n", + "\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", + "\n", + "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.39e+01 1.50e+02 -1.0 6.00e+00 - 7.16e-01 4.93e-03h 1\n", + " 2 0.0000000e+00 1.39e+01 3.03e+06 -1.0 5.97e+00 - 1.00e+00 4.95e-05h 1\n", + " 3r 0.0000000e+00 1.39e+01 1.00e+03 1.1 0.00e+00 - 0.00e+00 2.47e-07R 2\n", + " 4r 0.0000000e+00 4.19e+00 9.42e+02 1.1 3.50e+03 - 4.02e-01 3.37e-03f 1\n", + " 5r 0.0000000e+00 2.12e+00 8.72e+02 1.1 5.89e+01 - 4.35e-01 7.06e-02f 1\n", + " 6r 0.0000000e+00 6.74e-01 6.06e+02 1.1 5.29e+00 - 9.93e-03 3.98e-01f 1\n", + " 7r 0.0000000e+00 6.80e-01 3.14e+02 0.4 2.05e-01 - 1.00e+00 1.03e-01f 1\n", + " 8r 0.0000000e+00 6.69e-01 2.78e-05 0.4 2.58e-02 - 1.00e+00 1.00e+00f 1\n", + " 9r 0.0000000e+00 6.67e-01 7.56e+00 -1.7 8.13e-03 - 9.93e-01 9.96e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 0.0000000e+00 6.67e-01 2.23e-07 -1.7 4.13e-05 - 1.00e+00 1.00e+00f 1\n", + " 11r 0.0000000e+00 6.67e-01 6.73e-01 -3.7 6.61e-05 - 1.00e+00 1.00e+00f 1\n", + " 12r 0.0000000e+00 6.67e-01 1.91e-09 -3.7 1.48e-09 - 1.00e+00 1.00e+00h 1\n", + " 13r 0.0000000e+00 6.67e-01 2.69e+00 -8.4 5.74e-07 - 1.00e+00 9.26e-01f 1\n", + " 14r 0.0000000e+00 6.67e-01 7.65e+01 -8.4 4.23e-08 - 8.68e-01 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 3.2644919411246030e-04 3.2644919411246030e-04\n", + "Constraint violation....: 6.6666666333656233e-01 6.6666666333656233e-01\n", + "Complementarity.........: 4.6615546565561981e-09 4.6615546565561981e-09\n", + "Overall NLP error.......: 6.6666666333656233e-01 6.6666666333656233e-01\n", + "\n", + "\n", + "Number of objective function evaluations = 18\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 18\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 17\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 15\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: infeasible\n", + " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", + " point. Problem may be infeasible.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", + "\n", + "
\n", + "Warning:\n", + "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the report_numerical_issues method in the cell below.\n", + "
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.800E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "3 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with None value\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", - " prepare_degeneracy_hunter()\n", - "\n", - "====================================================================================\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.700E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", + " WARNING: 1 Variable at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 2 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Variable with None value\n", + " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", + "\n", + "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", + "\n", + "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the constraint with a large residual in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following constraint(s) have large residuals (>1.0E-05):\n", + "\n", + " c2: 6.66667E-01\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_constraints_with_large_residuals()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", + "\n", + "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", + "
\n", + "\n", + "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the variables with bounds violations.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", + "\n", + " v3 (free): value=0.0 bounds=(0, 5)\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_at_or_outside_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", + "\n", + "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", + " \n", + "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", + "
\n", + "\n", + "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Update the bounds on v3 in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.v3.setlb(-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", + "\n", + "
\n", + "Warning:\n", + "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check to see if there are any new structural issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Re-solve the model in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.67e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 6.66e-03 2.97e+00 -1.0 2.00e+00 - 7.17e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 6.27e-05 9.38e+00 -1.0 2.00e-02 - 1.00e+00 9.91e-01h 1\n", + " 3 0.0000000e+00 8.88e-16 1.13e-12 -1.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 8.8817841970012523e-16 8.8817841970012523e-16\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 8.8817841970012523e-16 8.8817841970012523e-16\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", + "\n", + "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", + "\n", + "
\n", + "Warning:\n", + "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", + " \n", + "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", + "
\n", + "\n", + "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional numerical issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.700E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Variable with None value\n", + " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " If you still have issues converging your model consider:\n", + " prepare_svd_toolbox()\n", + " prepare_degeneracy_hunter()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional information about variables with extreme values.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have extreme values (<1.0E-04 or > 1.0E+04):\n", + "\n", + " v7: 4.9999999999999945e-08\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_with_extreme_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", + "\n", + "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", + "\n", + "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", + "\n", + "m.scaling_factor[m.v7] = 1e8\n", + "m.scaling_factor[m.c4] = 1e8\n", + "\n", + "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", + "scaled_model = scaling.create_using(m, rename=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Solve the scaled model and check to see how many iterations are required.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.33e-15 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.3290705182007514e-15 5.3290705182007514e-15\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.3290705182007514e-15 5.3290705182007514e-15\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(scaled_model, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from pyomo.environ import assert_optimal_termination\n", + "\n", + "res = solver.solve(scaled_model, tee=False)\n", + "assert_optimal_termination(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", + "\n", + "
\n", + "Warning:\n", + "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", + "
\n", + "\n", + "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", + "\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.800E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with None value\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " If you still have issues converging your model consider:\n", + " prepare_svd_toolbox()\n", + " prepare_degeneracy_hunter()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt_scaled = DiagnosticsToolbox(scaled_model)\n", + "dt_scaled.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", + "\n", + "
\n", + "Warning:\n", + "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", + "
\n", + "\n", + "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "dt.assert_no_structural_warnings()\n", + "dt.assert_no_numerical_warnings()" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" } - ], - "source": [ - "dt_scaled = DiagnosticsToolbox(scaled_model)\n", - "dt_scaled.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", - "\n", - "
\n", - "Warning:\n", - "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", - "
\n", - "\n", - "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "dt.assert_no_structural_warnings()\n", - "dt.assert_no_numerical_warnings()" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_usr.ipynb b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_usr.ipynb index 688e5148..2dc54029 100644 --- a/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_usr.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/diagnostics_toolbox_usr.ipynb @@ -1,2106 +1,2107 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IDAES Model Diagnostics Toolbox Tutorial\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-10-31 \n", - "\n", - "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", - "\n", - "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", - "\n", - "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", - "\n", - "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the DiagnosticsToolbox in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util import DiagnosticsToolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the help() function for more information" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", - "\n", - "class DiagnosticsToolbox(builtins.object)\n", - " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", - " | \n", - " | The IDAES Model DiagnosticsToolbox.\n", - " | \n", - " | To get started:\n", - " | \n", - " | 1. Create an instance of your model (this does not need to be initialized yet).\n", - " | 2. Fix variables until you have 0 degrees of freedom. Many of these tools presume\n", - " | a square model, and a square model should always be the foundation of any more\n", - " | advanced model.\n", - " | 3. Create an instance of the DiagnosticsToolbox and provide the model to debug as\n", - " | the model argument.\n", - " | 4. Call the ``report_structural_issues()`` method.\n", - " | \n", - " | Model diagnostics is an iterative process and you will likely need to run these\n", - " | tools multiple times to resolve all issues. After making a change to your model,\n", - " | you should always start from the beginning again to ensure the change did not\n", - " | introduce any new issues; i.e., always start from the report_structural_issues()\n", - " | method.\n", - " | \n", - " | Note that structural checks do not require the model to be initialized, thus users\n", - " | should start with these. Numerical checks require at least a partial solution to the\n", - " | model and should only be run once all structural issues have been resolved.\n", - " | \n", - " | Report methods will print a summary containing three parts:\n", - " | \n", - " | 1. Warnings - these are critical issues that should be resolved before continuing.\n", - " | For each warning, a method will be suggested in the Next Steps section to get\n", - " | additional information.\n", - " | 2. Cautions - these are things that could be correct but could also be the source of\n", - " | solver issues. Not all cautions need to be addressed, but users should investigate\n", - " | each one to ensure that the behavior is correct and that they will not be the source\n", - " | of difficulties later. Methods exist to provide more information on all cautions,\n", - " | but these will not appear in the Next Steps section.\n", - " | 3. Next Steps - these are recommended methods to call from the DiagnosticsToolbox to\n", - " | get further information on warnings. If no warnings are found, this will suggest\n", - " | the next report method to call.\n", - " | \n", - " | Args:\n", - " | \n", - " | model: model to be diagnosed. The DiagnosticsToolbox does not support indexed Blocks.\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | variable_bounds_absolute_tolerance: float, default=0.0001\n", - " | Absolute tolerance for considering a variable to be close to its\n", - " | bounds.\n", - " | \n", - " | variable_bounds_relative_tolerance: float, default=0.0001\n", - " | Relative tolerance for considering a variable to be close to its\n", - " | bounds.\n", - " | \n", - " | variable_bounds_violation_tolerance: float, default=0\n", - " | Absolute tolerance for considering a variable to violate its bounds.\n", - " | Some solvers relax bounds on variables thus allowing a small violation\n", - " | to be considered acceptable.\n", - " | \n", - " | constraint_residual_tolerance: float, default=1e-05\n", - " | Absolute tolerance to use when checking constraint residuals.\n", - " | \n", - " | variable_large_value_tolerance: float, default=10000.0\n", - " | Absolute tolerance for considering a value to be large.\n", - " | \n", - " | variable_small_value_tolerance: float, default=0.0001\n", - " | Absolute tolerance for considering a value to be small.\n", - " | \n", - " | variable_zero_value_tolerance: float, default=1e-08\n", - " | Absolute tolerance for considering a value to be near to zero.\n", - " | \n", - " | jacobian_large_value_caution: float, default=10000.0\n", - " | Tolerance for raising a caution for large Jacobian values.\n", - " | \n", - " | jacobian_large_value_warning: float, default=100000000.0\n", - " | Tolerance for raising a warning for large Jacobian values.\n", - " | \n", - " | jacobian_small_value_caution: float, default=0.0001\n", - " | Tolerance for raising a caution for small Jacobian values.\n", - " | \n", - " | jacobian_small_value_warning: float, default=1e-08\n", - " | Tolerance for raising a warning for small Jacobian values.\n", - " | \n", - " | warn_for_evaluation_error_at_bounds: bool, default=True\n", - " | If False, warnings will not be generated for things like log(x) with x\n", - " | >= 0\n", - " | \n", - " | Methods defined here:\n", - " | \n", - " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", - " | Initialize self. See help(type(self)) for accurate signature.\n", - " | \n", - " | assert_no_numerical_warnings(self)\n", - " | Checks for numerical warnings in the model and raises an AssertionError\n", - " | if any are found.\n", - " | \n", - " | Raises:\n", - " | AssertionError if any warnings are identified by numerical analysis.\n", - " | \n", - " | assert_no_structural_warnings(self)\n", - " | Checks for structural warnings in the model and raises an AssertionError\n", - " | if any are found.\n", - " | \n", - " | Raises:\n", - " | AssertionError if any warnings are identified by structural analysis.\n", - " | \n", - " | display_components_with_inconsistent_units(self, stream=None)\n", - " | Prints a list of all Constraints, Expressions and Objectives in the\n", - " | model with inconsistent units of measurement.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_constraints_with_extreme_jacobians(self, stream=None)\n", - " | Prints the constraints associated with rows in the Jacobian with extreme\n", - " | L2 norms. This often indicates poorly scaled constraints.\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_constraints_with_large_residuals(self, stream=None)\n", - " | Prints a list of Constraints with residuals greater than a specified tolerance.\n", - " | Tolerance can be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_external_variables(self, stream=None)\n", - " | Prints a list of variables that appear within activated Constraints in the\n", - " | model but are not contained within the model themselves.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_extreme_jacobian_entries(self, stream=None)\n", - " | Prints variables and constraints associated with entries in the Jacobian with extreme\n", - " | values. This can be indicative of poor scaling, especially for isolated terms (e.g.\n", - " | variables which appear only in one term of a single constraint).\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_overconstrained_set(self, stream=None)\n", - " | Prints the variables and constraints in the over-constrained sub-problem\n", - " | from a Dulmage-Mendelsohn partitioning.\n", - " | \n", - " | This can be used to identify the over-defined part of a model and thus\n", - " | where constraints must be removed or variables unfixed.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_potential_evaluation_errors(self, stream=None)\n", - " | Prints constraints that may be prone to evaluation errors\n", - " | (e.g., log of a negative number) based on variable bounds.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_underconstrained_set(self, stream=None)\n", - " | Prints the variables and constraints in the under-constrained sub-problem\n", - " | from a Dulmage-Mendelsohn partitioning.\n", - " | \n", - " | This can be used to identify the under-defined part of a model and thus\n", - " | where additional information (fixed variables or constraints) are required.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_unused_variables(self, stream=None)\n", - " | Prints a list of variables that do not appear in any activated Constraints.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_at_or_outside_bounds(self, stream=None)\n", - " | Prints a list of variables with values that fall at or outside the bounds\n", - " | on the variable.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_fixed_to_zero(self, stream=None)\n", - " | Prints a list of variables that are fixed to an absolute value of 0.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_near_bounds(self, stream=None)\n", - " | Prints a list of variables with values close to their bounds. Tolerance can\n", - " | be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_extreme_jacobians(self, stream=None)\n", - " | Prints the variables associated with columns in the Jacobian with extreme\n", - " | L2 norms. This often indicates poorly scaled variables.\n", - " | \n", - " | Tolerances can be set via the DiagnosticsToolbox config.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the output to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_extreme_values(self, stream=None)\n", - " | Prints a list of variables with extreme values.\n", - " | \n", - " | Tolerances can be set in the class configuration options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_none_value(self, stream=None)\n", - " | Prints a list of variables with a value of None.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | display_variables_with_value_near_zero(self, stream=None)\n", - " | Prints a list of variables with a value close to zero. The tolerance\n", - " | for determining what is close to zero can be set in the class configuration\n", - " | options.\n", - " | \n", - " | Args:\n", - " | stream: an I/O object to write the list to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | get_dulmage_mendelsohn_partition(self)\n", - " | Performs a Dulmage-Mendelsohn partitioning on the model and returns\n", - " | the over- and under-constrained sub-problems.\n", - " | \n", - " | Returns:\n", - " | list-of-lists variables in each independent block of the under-constrained set\n", - " | list-of-lists constraints in each independent block of the under-constrained set\n", - " | list-of-lists variables in each independent block of the over-constrained set\n", - " | list-of-lists constraints in each independent block of the over-constrained set\n", - " | \n", - " | prepare_degeneracy_hunter(self, **kwargs)\n", - " | Create an instance of the DegeneracyHunter and store as self.degeneracy_hunter.\n", - " | \n", - " | After creating an instance of the toolbox, call\n", - " | report_irreducible_degenerate_sets.\n", - " | \n", - " | Returns:\n", - " | \n", - " | Instance of DegeneracyHunter\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | solver: str, default='scip'\n", - " | MILP solver to use for finding irreducible degenerate sets.\n", - " | \n", - " | solver_options: optional\n", - " | Options to pass to MILP solver.\n", - " | \n", - " | M: float, default=100000.0\n", - " | Maximum value for nu in MILP models.\n", - " | \n", - " | m_small: float, default=1e-05\n", - " | Smallest value for nu to be considered non-zero in MILP models.\n", - " | \n", - " | trivial_constraint_tolerance: float, default=1e-06\n", - " | Tolerance for identifying non-zero rows in Jacobian.\n", - " | \n", - " | prepare_svd_toolbox(self, **kwargs)\n", - " | Create an instance of the SVDToolbox and store as self.svd_toolbox.\n", - " | \n", - " | After creating an instance of the toolbox, call\n", - " | display_underdetermined_variables_and_constraints().\n", - " | \n", - " | Returns:\n", - " | \n", - " | Instance of SVDToolbox\n", - " | \n", - " | Keyword Arguments\n", - " | -----------------\n", - " | number_of_smallest_singular_values: PositiveInt, optional\n", - " | Number of smallest singular values to compute\n", - " | \n", - " | svd_callback: svd_callback_validator, default=\n", - " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", - " | should take the Jacobian and number of singular values to compute as\n", - " | options, plus any method specific arguments, and should return the u,\n", - " | s and v matrices as numpy arrays.\n", - " | \n", - " | svd_callback_arguments: dict, optional\n", - " | Optional arguments to pass to SVD callback (default = None)\n", - " | \n", - " | singular_value_tolerance: float, default=1e-06\n", - " | Tolerance for defining a small singular value\n", - " | \n", - " | size_cutoff_in_singular_vector: float, default=0.1\n", - " | Size below which to ignore constraints and variables in the singular\n", - " | vector\n", - " | \n", - " | report_numerical_issues(self, stream=None)\n", - " | Generates a summary report of any numerical issues identified in the model provided\n", - " | and suggest next steps for debugging model.\n", - " | \n", - " | Numerical checks should only be performed once all structural issues have been resolved,\n", - " | and require that at least a partial solution to the model is available.\n", - " | \n", - " | Args:\n", - " | stream: I/O object to write report to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | report_structural_issues(self, stream=None)\n", - " | Generates a summary report of any structural issues identified in the model provided\n", - " | and suggests next steps for debugging the model.\n", - " | \n", - " | This should be the first method called when debugging a model and after any change\n", - " | is made to the model. These checks can be run before trying to initialize and solve\n", - " | the model.\n", - " | \n", - " | Args:\n", - " | stream: I/O object to write report to (default = stdout)\n", - " | \n", - " | Returns:\n", - " | None\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Readonly properties defined here:\n", - " | \n", - " | model\n", - " | Model currently being diagnosed.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables (if defined)\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object (if defined)\n", - "\n" - ] - } - ], - "source": [ - "help(DiagnosticsToolbox)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", - "\n", - "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", - "\n", - "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "\n", - "m = pyo.ConcreteModel()\n", - "\n", - "m.v1 = pyo.Var(units=pyo.units.m)\n", - "m.v2 = pyo.Var(units=pyo.units.m)\n", - "m.v3 = pyo.Var(bounds=(0, 5))\n", - "m.v4 = pyo.Var()\n", - "m.v5 = pyo.Var(bounds=(0, 10))\n", - "m.v6 = pyo.Var()\n", - "m.v7 = pyo.Var(\n", - " units=pyo.units.m, bounds=(0, 1)\n", - ") # Poorly scaled variable with lower bound\n", - "m.v8 = pyo.Var() # unused variable\n", - "\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", - "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", - "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", - "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", - "\n", - "m.v4.fix(2)\n", - "m.v5.fix(2)\n", - "m.v6.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create an instance of the Diagnostics Toolbox" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the report_structural_issues() method" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Component with inconsistent units\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 1 variables, 2 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_components_with_inconsistent_units()\n", - " display_underconstrained_set()\n", - " display_overconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", - "\n", - "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", - "\n", - "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", - "\n", - "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", - "\n", - "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", - "\n", - "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", - "\n", - "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", - "\n", - "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the display_components_with_inconsistent_units() method" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following component(s) have unit consistency issues:\n", - "\n", - " c1\n", - "\n", - "For more details on unit inconsistencies, import the assert_units_consistent method\n", - "from pyomo.util.check_units\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_components_with_inconsistent_units()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", - "\n", - "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", - "\n", - "
\n", - "Warning:\n", - "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the incorrect Constraint\n", - "m.del_component(m.c1)\n", - "\n", - "# Re-create the Constraint with the correct units\n", - "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Warning:\n", - "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", - " \n", - "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", - "
\n", - "\n", - "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the report_structural_issues() method" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 1 variables, 2 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_underconstrained_set()\n", - " display_overconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", - "\n", - "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call dt.display_overconstrained_set() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Call the display_overconstrained_set() method" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Dulmage-Mendelsohn Over-Constrained Set\n", - "\n", - " Independent Block 0:\n", - "\n", - " Variables:\n", - "\n", - " v3\n", - "\n", - " Constraints:\n", - "\n", - " c2\n", - " c3\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", - "\n", - "``c2: v3 == v4 + v5``\n", - "\n", - "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", - "\n", - "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", - "\n", - "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Unfix v4\n", - "\n", - "# Then call the report_structural_issues() method again" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 5 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 2 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Degree of Freedom\n", - " WARNING: Structural singularity found\n", - " Under-Constrained Set: 3 variables, 2 constraints\n", - " Over-Constrained Set: 0 variables, 0 constraints\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_underconstrained_set()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.v4.unfix()\n", - "\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the under-constrained set in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display the under-constrained set" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Dulmage-Mendelsohn Under-Constrained Set\n", - "\n", - " Independent Block 0:\n", - "\n", - " Variables:\n", - "\n", - " v2\n", - " v1\n", - " v7\n", - "\n", - " Constraints:\n", - "\n", - " c1\n", - " c4\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_underconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", - "\n", - "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Fix v2 = 5\n", - "\n", - "# Then re-run report_structural_issues() method" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.v2.fix(5)\n", - "\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", - "\n", - "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a solver object\n", - "\n", - "# Try to solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.39e+01 1.50e+02 -1.0 6.00e+00 - 7.16e-01 4.93e-03h 1\n", - " 2 0.0000000e+00 1.39e+01 3.03e+06 -1.0 5.97e+00 - 1.00e+00 4.95e-05h 1\n", - " 3r 0.0000000e+00 1.39e+01 1.00e+03 1.1 0.00e+00 - 0.00e+00 2.47e-07R 2\n", - " 4r 0.0000000e+00 4.19e+00 9.42e+02 1.1 3.50e+03 - 4.02e-01 3.37e-03f 1\n", - " 5r 0.0000000e+00 2.12e+00 8.72e+02 1.1 5.89e+01 - 4.35e-01 7.06e-02f 1\n", - " 6r 0.0000000e+00 6.74e-01 6.06e+02 1.1 5.29e+00 - 9.93e-03 3.98e-01f 1\n", - " 7r 0.0000000e+00 6.80e-01 3.14e+02 0.4 2.05e-01 - 1.00e+00 1.03e-01f 1\n", - " 8r 0.0000000e+00 6.69e-01 2.78e-05 0.4 2.58e-02 - 1.00e+00 1.00e+00f 1\n", - " 9r 0.0000000e+00 6.67e-01 7.56e+00 -1.7 8.13e-03 - 9.93e-01 9.96e-01f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10r 0.0000000e+00 6.67e-01 2.23e-07 -1.7 4.13e-05 - 1.00e+00 1.00e+00f 1\n", - " 11r 0.0000000e+00 6.67e-01 6.73e-01 -3.7 6.61e-05 - 1.00e+00 1.00e+00f 1\n", - " 12r 0.0000000e+00 6.67e-01 1.91e-09 -3.7 1.48e-09 - 1.00e+00 1.00e+00h 1\n", - " 13r 0.0000000e+00 6.67e-01 2.69e+00 -8.4 5.74e-07 - 1.00e+00 9.26e-01f 1\n", - " 14r 0.0000000e+00 6.67e-01 7.65e+01 -8.4 4.23e-08 - 8.68e-01 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 14\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 3.2644919411246030e-04 3.2644919411246030e-04\n", - "Constraint violation....: 6.6666666333656233e-01 6.6666666333656233e-01\n", - "Complementarity.........: 4.6615546565561981e-09 4.6615546565561981e-09\n", - "Overall NLP error.......: 6.6666666333656233e-01 6.6666666333656233e-01\n", - "\n", - "\n", - "Number of objective function evaluations = 18\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 18\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 17\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 15\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: infeasible\n", - " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", - " point. Problem may be infeasible.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", - "\n", - "
\n", - "Warning:\n", - "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the report_numerical_issues method in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for numerical issues" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.700E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", - " WARNING: 1 Variable at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 2 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Variable with None value\n", - " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_constraints_with_large_residuals()\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", - "\n", - "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", - "\n", - "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the constraint with a large residual in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display constraint with large residual" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following constraint(s) have large residuals (>1.0E-05):\n", - "\n", - " c2: 6.66667E-01\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_constraints_with_large_residuals()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", - "\n", - "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", - "
\n", - "\n", - "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Display the variables with bounds violations.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display the variables with bounds violations" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", - "\n", - " v3 (free): value=0.0 bounds=(0, 5)\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_at_or_outside_bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", - "\n", - "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", - "\n", - "
\n", - "Warning:\n", - "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", - " \n", - "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", - "
\n", - "\n", - "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Update the bounds on v3 in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Update bounds for v3" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.v3.setlb(-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", - "\n", - "
\n", - "Warning:\n", - "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check to see if there are any new structural issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for new structural issues" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 1 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 4 (External: 0)\n", - " Free Variables with only lower bounds: 0\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 3 (External: 0)\n", - " Activated Equality Constraints: 4 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 Cautions\n", - "\n", - " Caution: 1 variable fixed to 0\n", - " Caution: 1 unused variable (0 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Re-solve the model in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Re-solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 6.67e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 6.66e-03 2.97e+00 -1.0 2.00e+00 - 7.17e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 6.27e-05 9.38e+00 -1.0 2.00e-02 - 1.00e+00 9.91e-01h 1\n", - " 3 0.0000000e+00 8.88e-16 1.13e-12 -1.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 8.8817841970012523e-16 8.8817841970012523e-16\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 8.8817841970012523e-16 8.8817841970012523e-16\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", - "\n", - "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", - "\n", - "
\n", - "Warning:\n", - "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", - " \n", - "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", - "
\n", - "\n", - "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional numerical issues in the cell below.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Check for additional numerical issues" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.700E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Variable with None value\n", - " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", - " prepare_degeneracy_hunter()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Check for additional information about variables with extreme values.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Display variable with extreme value" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have extreme values (<1.0E-04 or > 1.0E+04):\n", - "\n", - " v7: 4.9999999999999945e-08\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_with_extreme_values()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", - "\n", - "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", - "\n", - "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", - "\n", - "m.scaling_factor[m.v7] = 1e8\n", - "m.scaling_factor[m.c4] = 1e8\n", - "\n", - "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", - "scaled_model = scaling.create_using(m, rename=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Solve the scaled model and check to see how many iterations are required.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Solve scaled model" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 0\n", - "\n", - "Total number of variables............................: 4\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 4\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 5.33e-15 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 5.3290705182007514e-15 5.3290705182007514e-15\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.3290705182007514e-15 5.3290705182007514e-15\n", - "\n", - "\n", - "Number of objective function evaluations = 1\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 1\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 1\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Model Diagnostics Toolbox Tutorial\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-10-31 \n", + "\n", + "As you have likely discovered already, developing and solving models in an equation-oriented (EO) environment can be challenging and often takes a significant amount of effort. There are many pitfalls and mistakes that can be encountered when developing a model which can greatly impact the solvability and robustness of the final problem.\n", + "\n", + "Model diagnosis and debugging is often more of an art than a science, and it generally relies on significant experience and understanding both of general EO modeling techniques and the specific model and problem being solved. To assist with this process, IDAES has developed a model diagnostics toolbox that brings together a large number of tools for identifying potential issues in a model to help guide the user through the process of finding and resolving these issues. Note however that whilst these tools can help identify the presence of an issue, remedying the issue always requires some degree of engineering knowledge about the system being modeled, and thus it is ultimately up to the user to find a solution to the problem.\n", + "\n", + "This tutorial will take you through using the {py:class}`DiagnosticsToolbox ` to debug a number of issues in a simple Pyomo model and to take it from initially reporting a possible infeasible solution to returning the correct solution.\n", + "\n", + "To get started, the ``DiagnosticsToolbox`` can be imported from ``idaes.core.util``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the DiagnosticsToolbox in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util import DiagnosticsToolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get some information on where to start, try using the Python ``help()`` function to see the documentation for the ``DiagnosticsToolbox``.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call `help(DiagnosticsToolbox)` to see some more information on the toolbox and some instructions on how to get started.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the help() function for more information" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class DiagnosticsToolbox in module idaes.core.util.model_diagnostics:\n", + "\n", + "class DiagnosticsToolbox(builtins.object)\n", + " | DiagnosticsToolbox(model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | \n", + " | The IDAES Model DiagnosticsToolbox.\n", + " | \n", + " | To get started:\n", + " | \n", + " | 1. Create an instance of your model (this does not need to be initialized yet).\n", + " | 2. Fix variables until you have 0 degrees of freedom. Many of these tools presume\n", + " | a square model, and a square model should always be the foundation of any more\n", + " | advanced model.\n", + " | 3. Create an instance of the DiagnosticsToolbox and provide the model to debug as\n", + " | the model argument.\n", + " | 4. Call the ``report_structural_issues()`` method.\n", + " | \n", + " | Model diagnostics is an iterative process and you will likely need to run these\n", + " | tools multiple times to resolve all issues. After making a change to your model,\n", + " | you should always start from the beginning again to ensure the change did not\n", + " | introduce any new issues; i.e., always start from the report_structural_issues()\n", + " | method.\n", + " | \n", + " | Note that structural checks do not require the model to be initialized, thus users\n", + " | should start with these. Numerical checks require at least a partial solution to the\n", + " | model and should only be run once all structural issues have been resolved.\n", + " | \n", + " | Report methods will print a summary containing three parts:\n", + " | \n", + " | 1. Warnings - these are critical issues that should be resolved before continuing.\n", + " | For each warning, a method will be suggested in the Next Steps section to get\n", + " | additional information.\n", + " | 2. Cautions - these are things that could be correct but could also be the source of\n", + " | solver issues. Not all cautions need to be addressed, but users should investigate\n", + " | each one to ensure that the behavior is correct and that they will not be the source\n", + " | of difficulties later. Methods exist to provide more information on all cautions,\n", + " | but these will not appear in the Next Steps section.\n", + " | 3. Next Steps - these are recommended methods to call from the DiagnosticsToolbox to\n", + " | get further information on warnings. If no warnings are found, this will suggest\n", + " | the next report method to call.\n", + " | \n", + " | Args:\n", + " | \n", + " | model: model to be diagnosed. The DiagnosticsToolbox does not support indexed Blocks.\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | variable_bounds_absolute_tolerance: float, default=0.0001\n", + " | Absolute tolerance for considering a variable to be close to its\n", + " | bounds.\n", + " | \n", + " | variable_bounds_relative_tolerance: float, default=0.0001\n", + " | Relative tolerance for considering a variable to be close to its\n", + " | bounds.\n", + " | \n", + " | variable_bounds_violation_tolerance: float, default=0\n", + " | Absolute tolerance for considering a variable to violate its bounds.\n", + " | Some solvers relax bounds on variables thus allowing a small violation\n", + " | to be considered acceptable.\n", + " | \n", + " | constraint_residual_tolerance: float, default=1e-05\n", + " | Absolute tolerance to use when checking constraint residuals.\n", + " | \n", + " | variable_large_value_tolerance: float, default=10000.0\n", + " | Absolute tolerance for considering a value to be large.\n", + " | \n", + " | variable_small_value_tolerance: float, default=0.0001\n", + " | Absolute tolerance for considering a value to be small.\n", + " | \n", + " | variable_zero_value_tolerance: float, default=1e-08\n", + " | Absolute tolerance for considering a value to be near to zero.\n", + " | \n", + " | jacobian_large_value_caution: float, default=10000.0\n", + " | Tolerance for raising a caution for large Jacobian values.\n", + " | \n", + " | jacobian_large_value_warning: float, default=100000000.0\n", + " | Tolerance for raising a warning for large Jacobian values.\n", + " | \n", + " | jacobian_small_value_caution: float, default=0.0001\n", + " | Tolerance for raising a caution for small Jacobian values.\n", + " | \n", + " | jacobian_small_value_warning: float, default=1e-08\n", + " | Tolerance for raising a warning for small Jacobian values.\n", + " | \n", + " | warn_for_evaluation_error_at_bounds: bool, default=True\n", + " | If False, warnings will not be generated for things like log(x) with x\n", + " | >= 0\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, model: pyomo.core.base.block._BlockData, **kwargs)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | assert_no_numerical_warnings(self)\n", + " | Checks for numerical warnings in the model and raises an AssertionError\n", + " | if any are found.\n", + " | \n", + " | Raises:\n", + " | AssertionError if any warnings are identified by numerical analysis.\n", + " | \n", + " | assert_no_structural_warnings(self)\n", + " | Checks for structural warnings in the model and raises an AssertionError\n", + " | if any are found.\n", + " | \n", + " | Raises:\n", + " | AssertionError if any warnings are identified by structural analysis.\n", + " | \n", + " | display_components_with_inconsistent_units(self, stream=None)\n", + " | Prints a list of all Constraints, Expressions and Objectives in the\n", + " | model with inconsistent units of measurement.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_extreme_jacobians(self, stream=None)\n", + " | Prints the constraints associated with rows in the Jacobian with extreme\n", + " | L2 norms. This often indicates poorly scaled constraints.\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_constraints_with_large_residuals(self, stream=None)\n", + " | Prints a list of Constraints with residuals greater than a specified tolerance.\n", + " | Tolerance can be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_external_variables(self, stream=None)\n", + " | Prints a list of variables that appear within activated Constraints in the\n", + " | model but are not contained within the model themselves.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_extreme_jacobian_entries(self, stream=None)\n", + " | Prints variables and constraints associated with entries in the Jacobian with extreme\n", + " | values. This can be indicative of poor scaling, especially for isolated terms (e.g.\n", + " | variables which appear only in one term of a single constraint).\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_overconstrained_set(self, stream=None)\n", + " | Prints the variables and constraints in the over-constrained sub-problem\n", + " | from a Dulmage-Mendelsohn partitioning.\n", + " | \n", + " | This can be used to identify the over-defined part of a model and thus\n", + " | where constraints must be removed or variables unfixed.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_potential_evaluation_errors(self, stream=None)\n", + " | Prints constraints that may be prone to evaluation errors\n", + " | (e.g., log of a negative number) based on variable bounds.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_underconstrained_set(self, stream=None)\n", + " | Prints the variables and constraints in the under-constrained sub-problem\n", + " | from a Dulmage-Mendelsohn partitioning.\n", + " | \n", + " | This can be used to identify the under-defined part of a model and thus\n", + " | where additional information (fixed variables or constraints) are required.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_unused_variables(self, stream=None)\n", + " | Prints a list of variables that do not appear in any activated Constraints.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_at_or_outside_bounds(self, stream=None)\n", + " | Prints a list of variables with values that fall at or outside the bounds\n", + " | on the variable.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_fixed_to_zero(self, stream=None)\n", + " | Prints a list of variables that are fixed to an absolute value of 0.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_near_bounds(self, stream=None)\n", + " | Prints a list of variables with values close to their bounds. Tolerance can\n", + " | be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_extreme_jacobians(self, stream=None)\n", + " | Prints the variables associated with columns in the Jacobian with extreme\n", + " | L2 norms. This often indicates poorly scaled variables.\n", + " | \n", + " | Tolerances can be set via the DiagnosticsToolbox config.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the output to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_extreme_values(self, stream=None)\n", + " | Prints a list of variables with extreme values.\n", + " | \n", + " | Tolerances can be set in the class configuration options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_none_value(self, stream=None)\n", + " | Prints a list of variables with a value of None.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | display_variables_with_value_near_zero(self, stream=None)\n", + " | Prints a list of variables with a value close to zero. The tolerance\n", + " | for determining what is close to zero can be set in the class configuration\n", + " | options.\n", + " | \n", + " | Args:\n", + " | stream: an I/O object to write the list to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | get_dulmage_mendelsohn_partition(self)\n", + " | Performs a Dulmage-Mendelsohn partitioning on the model and returns\n", + " | the over- and under-constrained sub-problems.\n", + " | \n", + " | Returns:\n", + " | list-of-lists variables in each independent block of the under-constrained set\n", + " | list-of-lists constraints in each independent block of the under-constrained set\n", + " | list-of-lists variables in each independent block of the over-constrained set\n", + " | list-of-lists constraints in each independent block of the over-constrained set\n", + " | \n", + " | prepare_degeneracy_hunter(self, **kwargs)\n", + " | Create an instance of the DegeneracyHunter and store as self.degeneracy_hunter.\n", + " | \n", + " | After creating an instance of the toolbox, call\n", + " | report_irreducible_degenerate_sets.\n", + " | \n", + " | Returns:\n", + " | \n", + " | Instance of DegeneracyHunter\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | solver: str, default='scip'\n", + " | MILP solver to use for finding irreducible degenerate sets.\n", + " | \n", + " | solver_options: optional\n", + " | Options to pass to MILP solver.\n", + " | \n", + " | M: float, default=100000.0\n", + " | Maximum value for nu in MILP models.\n", + " | \n", + " | m_small: float, default=1e-05\n", + " | Smallest value for nu to be considered non-zero in MILP models.\n", + " | \n", + " | trivial_constraint_tolerance: float, default=1e-06\n", + " | Tolerance for identifying non-zero rows in Jacobian.\n", + " | \n", + " | prepare_svd_toolbox(self, **kwargs)\n", + " | Create an instance of the SVDToolbox and store as self.svd_toolbox.\n", + " | \n", + " | After creating an instance of the toolbox, call\n", + " | display_underdetermined_variables_and_constraints().\n", + " | \n", + " | Returns:\n", + " | \n", + " | Instance of SVDToolbox\n", + " | \n", + " | Keyword Arguments\n", + " | -----------------\n", + " | number_of_smallest_singular_values: PositiveInt, optional\n", + " | Number of smallest singular values to compute\n", + " | \n", + " | svd_callback: svd_callback_validator, default=\n", + " | Callback to SVD method of choice (default = svd_dense). Callbacks\n", + " | should take the Jacobian and number of singular values to compute as\n", + " | options, plus any method specific arguments, and should return the u,\n", + " | s and v matrices as numpy arrays.\n", + " | \n", + " | svd_callback_arguments: dict, optional\n", + " | Optional arguments to pass to SVD callback (default = None)\n", + " | \n", + " | singular_value_tolerance: float, default=1e-06\n", + " | Tolerance for defining a small singular value\n", + " | \n", + " | size_cutoff_in_singular_vector: float, default=0.1\n", + " | Size below which to ignore constraints and variables in the singular\n", + " | vector\n", + " | \n", + " | report_numerical_issues(self, stream=None)\n", + " | Generates a summary report of any numerical issues identified in the model provided\n", + " | and suggest next steps for debugging model.\n", + " | \n", + " | Numerical checks should only be performed once all structural issues have been resolved,\n", + " | and require that at least a partial solution to the model is available.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | report_structural_issues(self, stream=None)\n", + " | Generates a summary report of any structural issues identified in the model provided\n", + " | and suggests next steps for debugging the model.\n", + " | \n", + " | This should be the first method called when debugging a model and after any change\n", + " | is made to the model. These checks can be run before trying to initialize and solve\n", + " | the model.\n", + " | \n", + " | Args:\n", + " | stream: I/O object to write report to (default = stdout)\n", + " | \n", + " | Returns:\n", + " | None\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties defined here:\n", + " | \n", + " | model\n", + " | Model currently being diagnosed.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + "\n" + ] + } + ], + "source": [ + "help(DiagnosticsToolbox)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``help()`` function gives us a lot of information on the ``DiagnosticsToolbox`` and all the methods that it supports (and there are many). However, the important part to start with are the four steps outlined at the top of the doc string that tell us how to get started.\n", + "\n", + "Firstly, we need a model to test (and, for this tutorial at least, one that has a wide range of issues that we need to fix before it will solve). We then also need to fix some variables so that we have 0 degrees of freedom in our model. Whilst our ultimate goal is generally optimization (and thus a system with 1 or more degrees of freedom), all models conceptually derive from a square model representing a nominal state. If this nominal state is not well-posed, then any issues present will also be present in the resulting optimization (even if adding degrees of freedom means that the model is now easier to solve).\n", + "\n", + "The cell below contains a demonstration model for this tutorial that contains a number of issues that we will resolve using the ``DiagnosticsToolbox``." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "\n", + "m = pyo.ConcreteModel()\n", + "\n", + "m.v1 = pyo.Var(units=pyo.units.m)\n", + "m.v2 = pyo.Var(units=pyo.units.m)\n", + "m.v3 = pyo.Var(bounds=(0, 5))\n", + "m.v4 = pyo.Var()\n", + "m.v5 = pyo.Var(bounds=(0, 10))\n", + "m.v6 = pyo.Var()\n", + "m.v7 = pyo.Var(\n", + " units=pyo.units.m, bounds=(0, 1)\n", + ") # Poorly scaled variable with lower bound\n", + "m.v8 = pyo.Var() # unused variable\n", + "\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10) # Unit consistency issue\n", + "m.c2 = pyo.Constraint(expr=m.v3 == m.v4 + m.v5)\n", + "m.c3 = pyo.Constraint(expr=2 * m.v3 == 3 * m.v4 + 4 * m.v5 + m.v6)\n", + "m.c4 = pyo.Constraint(expr=m.v7 == 1e-8 * m.v1) # Poorly scaled constraint\n", + "\n", + "m.v4.fix(2)\n", + "m.v5.fix(2)\n", + "m.v6.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the instructions tell us to create an instance of the ``DiagnosticsToolbox`` and to pass the model we wish to examine as an argument.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create an instance of the DiagnosticsToolbox: dt = DiagnosticsToolbox(m)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create an instance of the Diagnostics Toolbox" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the instructions tell us to run the ``report_structural_issues()`` method. Structural issues represent issues that exist solely in the form of the model equations and thus do not depend on the current value of any of the variables. This is useful as it means we can check for these before we even call a solver, which can be critical as sometimes these issues will cause a solver to fail without providing a useful solution.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the report_structural_issues() method" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Component with inconsistent units\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 1 variables, 2 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``report_structural_issues()`` method, we can see that it provides a fairly short summary containing 4 sections.\n", + "\n", + "1. The first section is a summary of the size of the model, indicating things like the number of variables and constraints. The size of the model is often important for judging how difficult it will be to solve, and this information can also be useful for comparison to what is being sent to the solver. Most solvers will report the size of the model in their output logs, and if there is a difference between what is reported here and by the solver, then you should probably look into what is happening. This section also notes some things such as if you have any deactivated Blocks, Constraints or Objectives, or if you have variables which appear in the constraints that are not part of the model; these are not necessarily wrong but it is easy to have accidentally deactivated something you did not intend to so you should always check to see that these are expected.\n", + "\n", + "2. The second section provides a summary of any critical structural issues that were found - in this case we can see that there are 2 warnings we are going to need to look into. Warnings represent issues that need to be addressed before moving on as these will likely cause the solver to fail or give an incorrect answer.\n", + "\n", + "3. The third section lists a summary of any cautions that are found. Cautions represent issues that may or may not be problematic; in many cases these might be expected behaviors or borderline issues. However, these could also represent conceptual issues that should be addressed, so users should take the time to investigate these and determine if they need to be fixed or not.\n", + "\n", + "4. Finally, there is a section that suggests the next steps to take to help guide you through the model diagnosis process. If any warnings were identified, this section will list methods that can help you get more information on each specific problem, and if no warnings are found then it will guide you onto the next step in the model diagnosis workflow.\n", + "\n", + "**Note:** there are methods available to help investigate cautions as well, but these will not show up in the next steps in order to avoid cluttering the output. You can get more information on the available methods for investigating cautions via the documentation or ``help()`` function.\n", + "\n", + "In our current model, we have 2 critical issues (warnings) that we need to look into and resolve. The order in which we resolve these will generally not matter, but be aware that these can often be interrelated - fixing one warning might resolve other warnings as well (or create new ones), and sometimes you will need to look at multiple issues together to find the overall root cause.\n", + "\n", + "To start with, let us look at the unit consistency issue. From the \"Next Steps\" section above, the toolbox is suggesting we run the ``display_components_with_inconsistent_units()`` method for more information.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the `display_components_with_inconsistent_units()` method from the DiagnosticsToolbox to see more information on which constraint is causing the unit consistency issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the display_components_with_inconsistent_units() method" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following component(s) have unit consistency issues:\n", + "\n", + " c1\n", + "\n", + "For more details on unit inconsistencies, import the assert_units_consistent method\n", + "from pyomo.util.check_units\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_components_with_inconsistent_units()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that the issue lies in constraint ``c1``. If we go back and look at this constraint, we can see that it says ``v1 + v2 == 10``. ``v1`` and ``v2`` both have units of ``m`` which is consistent, but the constant in the expression (right hand side) is unitless. Thus, we need to correct this so that the right hand side has units for the constraint to be consistent.\n", + "\n", + "The cell below shows how to delete a constraint and replace it with a new one with the correct units.\n", + "\n", + "
\n", + "Warning:\n", + "Deleting components can cause unexpected issues if something else in a model is using that component (e.g., deleting a variable which is used in a constraint). You should always be careful when deleting Pyomo components and make sure you only delete components that are not used elsewhere.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete the incorrect Constraint\n", + "m.del_component(m.c1)\n", + "\n", + "# Re-create the Constraint with the correct units\n", + "m.c1 = pyo.Constraint(expr=m.v1 + m.v2 == 10 * pyo.units.m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Warning:\n", + "Fixing issues in models is often an iterative process requiring trial and error. You might also have some results from a model before running the diagnostics tools and the changes you make during debugging may make it difficult to replicate those results afterwards.\n", + " \n", + "It is strongly recommended that you keep a record of the changes you make at each step and why, along with a Git hash (or similar version control marker) corresponding to these changes. This will allow you see what changes and why, and give you a way to go back to previous iterations if the current approach does not work out. The IDAES documentation contains recommendations on how to keep and maintain a modeling logbook.\n", + "
\n", + "\n", + "Now, re-run the ``report_structural_issues()`` method and see if this change has fixed the unit consistency issue.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the report_structural_issues() method" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 1 variables, 2 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The unit consistency issue has been resolved by the changes above, so now we need to look at the structural singularity. A structural singularity occurs when one sub-part of the model is over-constrained (negative degrees of freedom), which generally means another part is under-constrained (positive degrees of freedom, assuming that there are 0 degrees of freedom overall).\n", + "\n", + "The toolbox is suggesting we use the ``display_overconstrained_set()`` and ``display_underconstrained_set()`` methods to get more information on the singularity; for now, let us start with the over-constrained set.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call dt.display_overconstrained_set() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Call the display_overconstrained_set() method" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Over-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " v3\n", + "\n", + " Constraints:\n", + "\n", + " c2\n", + " c3\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_overconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the output above, the toolbox is telling us that we have two constraints (``c2`` and ``c3``) which only contain a single unfixed variable (``v3``); thus in this part of the model we have -1 degree of freedom and the model is not well defined (structurally singular). If we go back and look at these constraints, we can see the that the constraints are:\n", + "\n", + "``c2: v3 == v4 + v5``\n", + "\n", + "``c3: 2*v3 == 3*v4 + 4*v5 + v6``\n", + "\n", + "We can see that in addition to ``v3`` these constraints actually contain 3 other variables (``v4``, ``v5`` and ``v6``), however these are all variables we fixed to get our initial zero degrees of freedom. It looks like we have either accidentally fixed one too many variables or written one too many constraints.\n", + "\n", + "For this example, let us assume that ``v4`` was not supposed to be fixed and unfix it.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Resolve the structural singularity and then call dt.report_structural_issues() in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Unfix v4\n", + "\n", + "# Then call the report_structural_issues() method again" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 5 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 2 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Degree of Freedom\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 3 variables, 2 constraints\n", + " Over-Constrained Set: 0 variables, 0 constraints\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_underconstrained_set()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.v4.unfix()\n", + "\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the over-constrained set is now empty (0 variables and 0 constraints) but the under-constrained set still has 3 variables and only 2 constraints. We can also see that there is a new warning about having 1 degree of freedom in the model, however this should not be surprising as we have just unfixed ``v4`` to resolve the over-constrained set so we have added a degree of freedom to the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the under-constrained set in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display the under-constrained set" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Under-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " v2\n", + " v1\n", + " v7\n", + "\n", + " Constraints:\n", + "\n", + " c1\n", + " c4\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_underconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the output from the ``display_underconstrained_set()`` method, we can see that we have two constraints, ``c1`` and ``c4``, which contain three unfixed variables, ``v1``, ``v2`` and ``v7``. Thus, we have one degree of freedom that needs to be addressed. To fix this, we could either fix one of the variables shown or add an additional equality constraint to the model.\n", + "\n", + "For this example let's fix ``v2`` to a value of 5 and then re-run the ``report_structural_issues()`` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fix v2 to a value of 5 and then re-run dt.report_structural_issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Fix v2 = 5\n", + "\n", + "# Then re-run report_structural_issues() method" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.v2.fix(5)\n", + "\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is now telling us that no warnings were found, so we have resolved all the structural issues (for now at least). The toolbox is telling us that there are also 2 non-critical issues (cautions) that we should look at; one about an unused variable and one about a variable fixed to zero. If you wish, you can look into identifying and fixing these yourself, however for this example we will move on to the next step (remember that the toolbox has methods to display more details for each of these which you can find in the documentation or from the ``help()`` function).\n", + "\n", + "For the Next Steps section, the toolbox is recommending we try to solve our model and then check for numerical issues.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the Pyomo SolverFactory to create an instance of IPOPT and then try to solve the model. Make sure to set \"tee=True\" as this is going to fail (and it is always good practice to review the solver logs).\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a solver object\n", + "\n", + "# Try to solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.39e+01 1.50e+02 -1.0 6.00e+00 - 7.16e-01 4.93e-03h 1\n", + " 2 0.0000000e+00 1.39e+01 3.03e+06 -1.0 5.97e+00 - 1.00e+00 4.95e-05h 1\n", + " 3r 0.0000000e+00 1.39e+01 1.00e+03 1.1 0.00e+00 - 0.00e+00 2.47e-07R 2\n", + " 4r 0.0000000e+00 4.19e+00 9.42e+02 1.1 3.50e+03 - 4.02e-01 3.37e-03f 1\n", + " 5r 0.0000000e+00 2.12e+00 8.72e+02 1.1 5.89e+01 - 4.35e-01 7.06e-02f 1\n", + " 6r 0.0000000e+00 6.74e-01 6.06e+02 1.1 5.29e+00 - 9.93e-03 3.98e-01f 1\n", + " 7r 0.0000000e+00 6.80e-01 3.14e+02 0.4 2.05e-01 - 1.00e+00 1.03e-01f 1\n", + " 8r 0.0000000e+00 6.69e-01 2.78e-05 0.4 2.58e-02 - 1.00e+00 1.00e+00f 1\n", + " 9r 0.0000000e+00 6.67e-01 7.56e+00 -1.7 8.13e-03 - 9.93e-01 9.96e-01f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 0.0000000e+00 6.67e-01 2.23e-07 -1.7 4.13e-05 - 1.00e+00 1.00e+00f 1\n", + " 11r 0.0000000e+00 6.67e-01 6.73e-01 -3.7 6.61e-05 - 1.00e+00 1.00e+00f 1\n", + " 12r 0.0000000e+00 6.67e-01 1.91e-09 -3.7 1.48e-09 - 1.00e+00 1.00e+00h 1\n", + " 13r 0.0000000e+00 6.67e-01 2.69e+00 -8.4 5.74e-07 - 1.00e+00 9.26e-01f 1\n", + " 14r 0.0000000e+00 6.67e-01 7.65e+01 -8.4 4.23e-08 - 8.68e-01 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 3.2644919411246030e-04 3.2644919411246030e-04\n", + "Constraint violation....: 6.6666666333656233e-01 6.6666666333656233e-01\n", + "Complementarity.........: 4.6615546565561981e-09 4.6615546565561981e-09\n", + "Overall NLP error.......: 6.6666666333656233e-01 6.6666666333656233e-01\n", + "\n", + "\n", + "Number of objective function evaluations = 18\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 18\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 17\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 15\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: infeasible\n", + " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", + " point. Problem may be infeasible.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.007064104080200195}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As hinted at above, IPOPT has returned a warning that the problem may be infeasible. Before moving on however, it is always good practice to look over the solver outputs and see what it is telling you.\n", + "\n", + "
\n", + "Warning:\n", + "A lot of useful information is contained in the solver logs which is extremely useful when diagnosing modeling issues. Each solver has its own way of reporting output and its own specific behavior, so you will need to learn to interpret the output of each solver you use. The IDAES Documentation contains some guidance on interpreting output logs for a few common solvers.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the report_numerical_issues method in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for numerical issues" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.700E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", + " WARNING: 1 Variable at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 2 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Variable with None value\n", + " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``report_numerical_issues()`` provides a summary similar to that which we saw for the structural issues. Firstly, it reports to us the Jacobian condition number for our problem which can give us an idea of how well-scaled the problem is, followed by a list of warnings, cautions and suggested next steps.\n", + "\n", + "Unsurprisingly, we are seeing a warning about a constraint with a large residual which we would expect when a solver reports a potentially infeasible problem. We are also seeing a warning about a variable with bound violations which might be contributing to the potential infeasibility.\n", + "\n", + "For the next steps, the toolbox is suggesting some new methods to get more information on these issues; let us start by looking at the constraints with large residuals.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the constraint with a large residual in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display constraint with large residual" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following constraint(s) have large residuals (>1.0E-05):\n", + "\n", + " c2: 6.66667E-01\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_constraints_with_large_residuals()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that the constraint which failed to converge is ``c2``, however this is generally only part of the story. Solvers work by trying to minimize the infeasibility in the model (residual of the constraints), which generally means they push any infeasibility onto the least sensitive constraint in the problem. Thus, the constraint which shows the infeasibility is often not the root cause of the problem, but only the symptom of the underlying issue.\n", + "\n", + "If we look back at the constraints, we can see that the same variables also appear in ``c3`` and that some of these have bounds, all of which could be contributing to the infeasibility. In this case the solver tried to minimize the residual in all the constraints and ended up pushing all the issues off onto ``c2``.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with solver issues such as this, you should always remember that the obvious symptoms are often just the tip of the iceberg and that the real issue generally lies somewhere else; the challenge is tracing the symptoms back to their ultimate source.\n", + "
\n", + "\n", + "Next, let us take a look at the variables at or outside their bounds as well. When a solver reports an potentially infeasible solution, the most common cause is unexpected bounds violations so you should always check these first.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Display the variables with bounds violations.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display the variables with bounds violations" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", + "\n", + " v3 (free): value=0.0 bounds=(0, 5)\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_at_or_outside_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is telling us that ``v3`` is the variable with a potential issue. It is also showing us the current value and bounds for ``v3`` as well as if it is a fixed or free variable, which will be useful for diagnosing the issues.\n", + "\n", + "We can see that ``v3`` is a free variable with bounds between 0 and 5 and a current value of 0. As ``v3`` is a free variable, this suggests that the solver has pushed the value to the bound where it cannot go any further, and this might be part of the cause of our infeasibility.\n", + "\n", + "
\n", + "Warning:\n", + "When dealing with bounds violations you should always start by understanding why the bounds exist and what they mean - in many cases a bound indicates the range over which the model can be trusted and that going beyond this may result in unexpected behavior due to extrapolation.\n", + " \n", + "Never arbitrarily change a bound just because it is causing your model to be infeasible without understanding the consequences of this decision. Often, a bound violation is an indication that you need to re-think some of the constraints in your model to find alternatives which are valid in the actual range of values you are trying to solve for.\n", + "
\n", + "\n", + "For this example, let us assume that we made a mistake with the bounds on ``v3`` and set the lower bound to be -5.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Update the bounds on v3 in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Update bounds for v3" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.v3.setlb(-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fixed the bounds issues, we should check whether our model is now feasible. However, before we continue we should recognize that we have just made a structural change to the model. If we were not careful, this could have introduced new structural issues to the model, so we should start from the beginning just to be sure.\n", + "\n", + "
\n", + "Warning:\n", + "In general, you should always start from the beginning of the model diagnosis workflow after you make any change to the model. Remember to also record these changes in your log book in case something unexpected happens so that you can revert any changes that cause problems.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check to see if there are any new structural issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for new structural issues" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 4 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 3 (External: 0)\n", + " Activated Equality Constraints: 4 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 Cautions\n", + "\n", + " Caution: 1 variable fixed to 0\n", + " Caution: 1 unused variable (0 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our change has not introduced any new structural issues, so we can move on and try to solve the model again.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Re-solve the model in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Re-solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.67e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 6.66e-03 2.97e+00 -1.0 2.00e+00 - 7.17e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 6.27e-05 9.38e+00 -1.0 2.00e-02 - 1.00e+00 9.91e-01h 1\n", + " 3 0.0000000e+00 8.88e-16 1.13e-12 -1.0 1.88e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 8.8817841970012523e-16 8.8817841970012523e-16\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 8.8817841970012523e-16 8.8817841970012523e-16\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.02317023277282715}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IPOPT should have returned optimal solution now, so it looks like those bounds were what was causing the model to be infeasible. At this point, the model is now solving (for the current values at least), so you might think that the model is now ready for optimization.\n", + "\n", + "However, if we look at the solver logs we can see that it took around 3 iterations for IPOPT to solve our model (depending on minor variations in computer architecture). For a model this simple, we would generally expect it to solve in only 1 iteration so there is still some room for improvement.\n", + "\n", + "
\n", + "Warning:\n", + "You should keep in mind that just because you get an optimal solution does not mean that your model is robust and free of issues.\n", + " \n", + "You should always take the time to look over the solver logs to look for signs of trouble, even if you get an optimal solution. While you might get an optimal solution for the current state, there may be advance warning signs of issues that will cause problems later when you try to solve the model at a different state.\n", + "
\n", + "\n", + "Let us run the ``report_numerical_issues`` method again to see if there are any other problems we need to address.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional numerical issues in the cell below.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Check for additional numerical issues" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.700E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Variable with None value\n", + " Caution: 1 extreme Jacobian Entry (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " If you still have issues converging your model consider:\n", + " prepare_svd_toolbox()\n", + " prepare_degeneracy_hunter()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The toolbox is not reporting any warnings which is good, however there are still 5 numerical cautions that it has identified which might be contributing to the larger than expected number of iterations. As mentioned earlier, the toolbox does not suggest methods for investigating these, but there are methods available. For example, we can look at the variable with an extreme value using the `display_variables_with_extreme_values()` method.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Check for additional information about variables with extreme values.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Display variable with extreme value" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have extreme values (<1.0E-04 or > 1.0E+04):\n", + "\n", + " v7: 4.9999999999999945e-08\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_with_extreme_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that ``v7`` is potentially causing problems due to having a very small value (on the order of magnitude of the solver tolerance). This can be especially problematic for interior point solvers like IPOPT if there is a lower bound of 0 (which there is in this case). IPOPT tries to avoid bounds and thus perturbs solutions away from these if it gets too close, which can cause convergence to be slow (or fail) if the solution lies close to the bound.\n", + "\n", + "We can address this by scaling the variable so that the value of the scaled variable is large enough that the solution is not close to the lower bound. Additionally, we should look at any constraint that ``v7`` appears in (in this case ``c4``) and ensure that those constraints are well scaled as well (so that a residual of 1e-6 is reasonable for the terms involved).\n", + "\n", + "For this case, we can set a scaling factor of 1e8 for both ``v7`` and ``c4`` as shown below. Note that we also need to apply Pyomo's scaling transformation to create a new scaled model to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "m.scaling_factor = pyo.Suffix(direction=pyo.Suffix.EXPORT)\n", + "\n", + "m.scaling_factor[m.v7] = 1e8\n", + "m.scaling_factor[m.c4] = 1e8\n", + "\n", + "scaling = pyo.TransformationFactory(\"core.scale_model\")\n", + "scaled_model = scaling.create_using(m, rename=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a scaled model, we can try to solve it and hopefully see better convergence than the unscaled model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Solve the scaled model and check to see how many iterations are required.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Solve scaled model" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 4\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 4\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.33e-15 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.3290705182007514e-15 5.3290705182007514e-15\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.3290705182007514e-15 5.3290705182007514e-15\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 4, 'Number of variables': 4, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0058002471923828125}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(scaled_model, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", + "\n", + "
\n", + "Warning:\n", + "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", + "
\n", + "\n", + "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", + "\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a new diagnostics toolbox for scaled model\n", + "\n", + "# Report numerical issues for scaled model" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.800E+01\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", + " Caution: 1 Variable with None value\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " If you still have issues converging your model consider:\n", + " prepare_svd_toolbox()\n", + " prepare_degeneracy_hunter()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt_scaled = DiagnosticsToolbox(scaled_model)\n", + "dt_scaled.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", + "\n", + "
\n", + "Warning:\n", + "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", + "
\n", + "\n", + "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." + ] } - ], - "source": [ - "solver.solve(scaled_model, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the scaled model solved in 0 iterations (indicating that it already had the right solution). However, had we done this to the unscaled model we would have found it required 2-3 iterations again due to IPOPT perturbing the initial (correct) solution away from the bounds.\n", - "\n", - "
\n", - "Warning:\n", - "Normally in these cases we would need to map the solution from the scaled model back to the unscaled model so we can view the results. In this case, we are not actually interested in the solution so we move on with the model diagnosis.\n", - "
\n", - "\n", - "Now that we have fixed the scaling issues, we can go back to the ``DiagnosticsToolbox`` and see if we still have any warnings. Note however that we need to look at the scaled model now rather than the original model, so we need to create a new instance of the ``DiagnosticsToolbox`` with the scaled model as the ``model`` argument.\n", - "\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a new instance of the DiagnosticsToolbox and check the scaled model for issues.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a new diagnostics toolbox for scaled model\n", - "\n", - "# Report numerical issues for scaled model" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.800E+01\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "3 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 1 Variable with value close to zero (tol=1.0E-08)\n", - " Caution: 1 Variable with None value\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " If you still have issues converging your model consider:\n", - " prepare_svd_toolbox()\n", - " prepare_degeneracy_hunter()\n", - "\n", - "====================================================================================\n" - ] + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" } - ], - "source": [ - "dt_scaled = DiagnosticsToolbox(scaled_model)\n", - "dt_scaled.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that applying scaling addressed two of the cautions we had before (the variable with an extreme value and an associated large value in the model Jacobian). Whilst we were able to solve the unscaled model in this case, this is in part because it was a simple linear model. In more complex, non-linear models, scaling becomes much more important and often depends strongly on the current state of the model. That is, you can often find cases where the unscaled (or poorly scaled) model solves for a limited range of conditions but fails to solve if you move too far away for the current state. Whilst you might be able to solve the model at the current state, you should always check the solver logs and numerical cautions for advanced warning signs of scaling issues that might manifest later when you try to solve the model for a different state (e.g., during optimization).\n", - "\n", - "
\n", - "Warning:\n", - "By their nature, numerical issues depend on the current values of the variables in the model, and thus may remain hidden until someone tries to solve the model close to where the issue exists. For this reason, the full model diagnostics workflow contains steps to run the numerical checks across a wide range of variable values to try to ensure that no issues remain hidden. This is beyond the scope of this tutorial however.\n", - "
\n", - "\n", - "At this point, we have addressed all the issues that were preventing us from solving the demonstration model and so reached the end of this tutorial. For cases where we are still having trouble solving the model, we can see that the toolbox is suggesting additional methods for further debugging and these advanced features will be the focus of separate tutorials." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/diagnostics/structural_singularity.ipynb b/idaes_examples/notebooks/docs/diagnostics/structural_singularity.ipynb index 6142a0e1..71bd02bf 100644 --- a/idaes_examples/notebooks/docs/diagnostics/structural_singularity.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/structural_singularity.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "1032c7a0", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/idaes_examples/notebooks/docs/diagnostics/structural_singularity_doc.ipynb b/idaes_examples/notebooks/docs/diagnostics/structural_singularity_doc.ipynb index 29eb4cc5..2ef6d85e 100644 --- a/idaes_examples/notebooks/docs/diagnostics/structural_singularity_doc.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/structural_singularity_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,9 +91,357 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constructing a steady model to initialize the dynamic model\n", + "Initializing steady model\n", + "2025-03-17 17:30:38 [INFO] idaes.init.fs.MB: Initialize Thermophysical Properties\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:38 [INFO] idaes.init.fs.MB: Initialize Hydrodynamics\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:38 [INFO] idaes.init.fs.MB: Initialize Mass Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:38 [INFO] idaes.init.fs.MB: Initialize Energy Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2670\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 966\n", + "\n", + "Total number of variables............................: 850\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 850\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.39e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.49e-08 0.00e+00 -8.6 9.36e-06 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving square problem with dynamic model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 32700\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 11506\n", + "\n", + "Total number of variables............................: 10200\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 10200\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e-08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.627\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing steady model\n", + "2025-03-17 17:30:40 [INFO] idaes.init.fs.MB: Initialize Thermophysical Properties\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:40 [INFO] idaes.init.fs.MB: Initialize Hydrodynamics\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:40 [INFO] idaes.init.fs.MB: Initialize Mass Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:30:40 [INFO] idaes.init.fs.MB: Initialize Energy Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2670\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 966\n", + "\n", + "Total number of variables............................: 850\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 850\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.39e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.49e-08 0.00e+00 -8.6 9.36e-06 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2670\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 966\n", + "\n", + "Total number of variables............................: 850\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 850\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 7.76e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.42e+03 0.00e+00 -1.0 5.19e+05 - 1.00e+00 1.00e+00h 1\n", + " 2 0.0000000e+00 1.58e+02 0.00e+00 -1.0 1.03e+05 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 3.69e-02 0.00e+00 -1.7 6.88e+02 - 1.00e+00 1.00e+00h 1\n", + " 4 0.0000000e+00 1.49e-08 0.00e+00 -5.7 4.14e-01 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 4\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.0841176845133305e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.0841176845133305e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 5\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 5\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 5\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 4\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.020\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + } + ], "source": [ "from idaes_examples.mod.diagnostics.gas_solid_contactors.model import make_model\n", "import logging\n", @@ -94,9 +468,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degrees of freedom: 10\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Has large residuals: False\n" + ] + } + ], "source": [ "# Import some useful utilities from the model_statistics module.\n", "# Degrees of freedom and constraint residuals are always good things to check before\n", @@ -118,9 +507,236 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: max_iter=20\n", + "print_user_options=yes\n", + "option_file_name=/tmp/tmp7prq37mb_ipopt.opt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Using option file \"/tmp/tmp7prq37mb_ipopt.opt\".\n", + "\n", + "\n", + "List of user-set options:\n", + "\n", + " Name Value used\n", + " max_iter = 20 yes\n", + " option_file_name = /tmp/tmp7prq37mb_ipopt.opt yes\n", + " print_info_string = yes yes\n", + " print_user_options = yes yes\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 32820\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 12126\n", + "\n", + "Total number of variables............................: 10220\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 10210\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 1.7682026e+04 1.49e-08 2.93e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 1.7330638e+04 8.08e+01 3.86e+03 -1.0 5.38e+02 -4.0 1.00e+00 1.25e-01f 4 Nh LTmaxTmaxTmaxTmax\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 1.7264117e+04 2.62e+02 2.39e+04 -1.0 2.13e+01 -1.8 1.00e+00 1.00e+00f 1 LMcqsNj L\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3 1.7284985e+04 9.61e+01 1.96e+06 -1.0 5.31e+00 0.5 1.00e+00 1.00e+00h 1 qLa\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 4 1.7289252e+04 1.01e+02 1.07e+06 -1.0 2.23e+00 1.8 1.00e+00 1.00e+00h 1 qsL\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MA27BD returned iflag=-4 and requires more memory.\n", + " Increase liw from 751170 to 1502340 and la from 3161950 to 6630972 and factorize again.\n", + " 5 1.7280273e+04 4.18e+02 4.82e+06 -1.0 1.42e+01 1.3 1.00e+00 1.00e+00h 1 qLa\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 6 1.7280273e+04 2.26e+04 4.82e+06 -1.0 8.19e+23 - 1.00e+00 7.45e-09h 28 STmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmaxTmax\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 7 1.7280273e+04 2.26e+04 4.82e+06 -1.0 1.74e+00 2.6 1.00e+00 1.53e-05h 17 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error in an AMPL evaluation. Run with \"halt_on_ampl_error yes\" to see details.\n", + "Warning: SOC step rejected due to evaluation error\n", + " 8r 1.7280273e+04 2.26e+04 9.99e+02 3.4 0.00e+00 2.2 0.00e+00 4.77e-07R 22 \n", + " 9r 1.7280639e+04 1.13e+03 1.61e+02 3.4 2.62e+06 - 1.00e+00 9.90e-04f 1 \n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 1.7291982e+04 1.05e+02 1.63e+00 3.4 3.51e+02 - 1.00e+00 9.97e-01f 1 Nhj \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 11 1.7292223e+04 1.05e+02 2.46e+04 -1.0 2.17e+00 1.7 1.00e+00 6.25e-02h 5 L\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12 1.7292424e+04 1.05e+02 3.05e+04 -1.0 4.75e+00 1.2 1.00e+00 3.12e-02h 6 La\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 13 1.7293185e+04 1.05e+02 2.06e+05 -1.0 2.82e+00 1.6 1.00e+00 1.25e-01h 4 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 14 1.7294516e+04 1.05e+02 9.35e+05 -1.0 1.32e+00 2.1 1.00e+00 2.50e-01h 3 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 15 1.7298381e+04 1.06e+02 2.59e+06 -1.0 2.96e-01 3.4 1.00e+00 1.00e+00h 1 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 16 1.7298544e+04 1.06e+02 1.03e+05 -1.0 2.71e-02 4.7 1.00e+00 1.00e+00h 1 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 17 1.7298562e+04 1.06e+02 1.16e+04 -1.0 1.02e-02 6.1 1.00e+00 1.00e+00h 1 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 18 1.7298579e+04 1.06e+02 1.03e+04 -1.0 8.38e-03 6.5 1.00e+00 1.00e+00H 1 sL\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 19 1.7298580e+04 1.06e+02 1.06e+04 -1.0 8.01e-02 6.0 1.00e+00 3.91e-03h 9 LaS\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 1.7298595e+04 1.06e+02 1.34e+04 -1.0 6.95e-03 6.4 1.00e+00 5.00e-01h 2 LaS\n", + "\n", + "Number of Iterations....: 20\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.7298595037806357e+04 1.7298595037806357e+04\n", + "Dual infeasibility......: 1.3366913073223608e+04 1.3366913073223608e+04\n", + "Constraint violation....: 6.2500000000000000e-02 1.0604230125733011e+02\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8011633535594157e+01 1.3366913073223608e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 90\n", + "Number of objective gradient evaluations = 20\n", + "Number of equality constraint evaluations = 123\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 22\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 20\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 52.652\n", + "Total CPU secs in NLP function evaluations = 0.207\n", + "\n", + "EXIT: Maximum Number of Iterations Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged successfully: False\n" + ] + } + ], "source": [ "# Import pyomo.environ for access to solvers\n", "import pyomo.environ as pyo\n", @@ -153,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -174,9 +790,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 387 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 10200 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 212 (External: 0)\n", + " Activated Equality Constraints: 10200 (Deactivated: 10)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 1 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 WARNINGS\n", + "\n", + " WARNING: 4457 Components with inconsistent units\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 130 variables, 120 constraints\n", + " Over-Constrained Set: 61 variables, 71 constraints\n", + " WARNING: Found 14300 potential evaluation errors.\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 143 unused variables (3 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + " display_potential_evaluation_errors()\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "from idaes.core.util.model_diagnostics import DiagnosticsToolbox\n", "\n", @@ -200,9 +859,428 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Under-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.0].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.1].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.2].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.3].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.4].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.5].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.6].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.7].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.8].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.9].flow_mass\n", + " fs.MB.solid_phase._flow_terms[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.0,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.1,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.2,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.3,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.4,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.5,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.6,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.7,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.8,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.9,Sol]\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.0,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.0,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.0,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.0]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.1,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.1,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.1,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.1]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.2,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.2,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.2,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.2]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.3,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.3,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.3,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.4,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.4,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.4,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.5,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.5,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.5,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.5]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.6,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.6,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.6,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.6]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.7,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.7,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.7,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.7]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.8,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.8,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.8,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.8]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.9,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.9,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.9,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.9]\n", + "\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Over-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.0].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.0].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.0]\n", + " fs.MB.solid_phase.properties[0.0,0.0].dens_mass_particle\n", + " fs.MB.bed_area\n", + " fs.MB.solid_phase.properties[0.0,0.1].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.1].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.1]\n", + " fs.MB.solid_phase.properties[0.0,0.1].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.2].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.2].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.2]\n", + " fs.MB.solid_phase.properties[0.0,0.2].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.3].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.3].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.4].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.4].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.4]\n", + " fs.MB.solid_phase.properties[0.0,0.4].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.5].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.5].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.5]\n", + " fs.MB.solid_phase.properties[0.0,0.5].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.6].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.6].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.6]\n", + " fs.MB.solid_phase.properties[0.0,0.6].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.7].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.7].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.7]\n", + " fs.MB.solid_phase.properties[0.0,0.7].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.8].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.8].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.8]\n", + " fs.MB.solid_phase.properties[0.0,0.8].dens_mass_particle\n", + " fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.9]\n", + " fs.MB.solid_phase.properties[0.0,0.9].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.0]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.bed_area_eqn\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.1]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.2]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.5]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.6]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.7]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.8]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.9]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.1].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.2].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.3].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.4].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.5].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.6].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.7].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.8].density_skeletal_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.9].density_skeletal_constraint\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "dt.display_underconstrained_set()\n", "dt.display_overconstrained_set()" @@ -227,9 +1305,534 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 1 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 847 (External: 847)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 103 (External: 103)\n", + " Activated Equality Constraints: 847 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 WARNINGS\n", + "\n", + " WARNING: 325 Components with inconsistent units\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 130 variables, 120 constraints\n", + " Over-Constrained Set: 60 variables, 70 constraints\n", + " WARNING: Found 1300 potential evaluation errors.\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 Cautions\n", + "\n", + " No cautions found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_underconstrained_set()\n", + " display_overconstrained_set()\n", + " display_potential_evaluation_errors()\n", + "\n", + "====================================================================================\n", + "====================================================================================\n", + "Dulmage-Mendelsohn Under-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.0].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.1].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.2].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.3].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.4].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.5].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.6].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.7].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.8].flow_mass\n", + " fs.MB.solid_phase.properties[0.0,0.9].flow_mass\n", + " fs.MB.solid_phase._flow_terms[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.0,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.1,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.2,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.3,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.4,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.5,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.6,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.7,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.8,Sol]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase._flow_terms[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase._enthalpy_flow[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_accumulation[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.energy_accumulation[0.0,0.9,Sol]\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.0,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.0,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.0,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.0,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.0]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.1,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.1,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.1,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.1,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.1]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.2,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.2,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.2,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.2,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.2]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.3,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.3,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.3,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.3,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.4,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.4,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.4,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.4,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.5,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.5,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.5,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.5,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.5]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.6,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.6,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.6,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.6,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.6]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.7,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.7,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.7,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.7,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.7]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.8,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.8,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.8,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.8,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.8]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_linking_constraints[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_linking_constraint[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_flow_dx_disc_eq[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_flow_dx_disc_eq[0.0,0.9,Sol]\n", + " fs.MB.solid_phase.material_balances[0.0,0.9,Fe2O3]\n", + " fs.MB.solid_phase.material_balances[0.0,0.9,Fe3O4]\n", + " fs.MB.solid_phase.material_balances[0.0,0.9,Al2O3]\n", + " fs.MB.solid_phase.enthalpy_balances[0.0,0.9]\n", + "\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Dulmage-Mendelsohn Over-Constrained Set\n", + "\n", + " Independent Block 0:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.0].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.0].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.0]\n", + " fs.MB.solid_phase.properties[0.0,0.0].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.0,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.0,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.0]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.0,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.0].density_skeletal_constraint\n", + "\n", + " Independent Block 1:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.1].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.1].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.1]\n", + " fs.MB.solid_phase.properties[0.0,0.1].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.1,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.1,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.1]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.1,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.1].density_skeletal_constraint\n", + "\n", + " Independent Block 2:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.2].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.2].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.2]\n", + " fs.MB.solid_phase.properties[0.0,0.2].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.2,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.2,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.2]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.2,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.2].density_skeletal_constraint\n", + "\n", + " Independent Block 3:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.3].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.3].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.3,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.3,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.3]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.3,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.3].density_skeletal_constraint\n", + "\n", + " Independent Block 4:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.4].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.4].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.4]\n", + " fs.MB.solid_phase.properties[0.0,0.4].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.4,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.4,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.4,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.4].density_skeletal_constraint\n", + "\n", + " Independent Block 5:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.5].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.5].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.5]\n", + " fs.MB.solid_phase.properties[0.0,0.5].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.5,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.5,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.5]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.5,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.5].density_skeletal_constraint\n", + "\n", + " Independent Block 6:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.6].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.6].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.6]\n", + " fs.MB.solid_phase.properties[0.0,0.6].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.6,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.6,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.6]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.6,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.6].density_skeletal_constraint\n", + "\n", + " Independent Block 7:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.7].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.7].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.7]\n", + " fs.MB.solid_phase.properties[0.0,0.7].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.7,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.7,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.7]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.7,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.7].density_skeletal_constraint\n", + "\n", + " Independent Block 8:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.8].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.8].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.8]\n", + " fs.MB.solid_phase.properties[0.0,0.8].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.8,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.8,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.8]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.8,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.8].density_skeletal_constraint\n", + "\n", + " Independent Block 9:\n", + "\n", + " Variables:\n", + "\n", + " fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe3O4]\n", + " fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].dens_mass_skeletal\n", + " fs.MB.solid_phase.area[0.0,0.9]\n", + " fs.MB.solid_phase.properties[0.0,0.9].dens_mass_particle\n", + "\n", + " Constraints:\n", + "\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.9,Sol,Fe3O4]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.9,Sol,Al2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].sum_component_eqn\n", + " fs.MB.solid_phase.properties[0.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase_area[0.0,0.9]\n", + " fs.MB.solid_phase.material_holdup_calculation[0.0,0.9,Sol,Fe2O3]\n", + " fs.MB.solid_phase.properties[0.0,0.9].density_skeletal_constraint\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "# We've included a utility function to extract the subsystem of variables and equations\n", "# at a specified point in time. If you are dealing with a process flowsheet, here you\n", @@ -264,9 +1867,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sum_component_eqn : Size=1, Index=None, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " None : 100.0 : 100.0*(fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe2O3] + fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe3O4] + fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Al2O3]) : 100.0 : True\n", + "{Member of material_holdup_calculation} : Material holdup calculations\n", + " Size=363, Index=fs._time*fs.MB.solid_length_domain*fs.solid_properties._phase_component_set, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " (0.0, 0.9, 'Sol', 'Fe3O4') : 0.0 : fs.MB.solid_phase.material_holdup[0.0,0.9,Sol,Fe3O4] - fs.MB.solid_phase.area[0.0,0.9]*1*(fs.MB.solid_phase.properties[0.0,0.9].dens_mass_particle*fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe3O4]) : 0.0 : True\n" + ] + } + ], "source": [ "model.fs.MB.solid_phase.properties[0, 0.9].sum_component_eqn.pprint()\n", "model.fs.MB.solid_phase.material_holdup_calculation[0, 0.9, \"Sol\", \"Fe3O4\"].pprint()" @@ -281,9 +1898,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "density_particle_constraint : Size=1, Index=None, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " None : 0.0 : fs.MB.solid_phase.properties[0.0,0.9].dens_mass_particle - (1 - fs.solid_properties.particle_porosity)*fs.MB.solid_phase.properties[0.0,0.9].dens_mass_skeletal : 0.0 : True\n" + ] + } + ], "source": [ "model.fs.MB.solid_phase.properties[0, 0.9].density_particle_constraint.pprint()" ] @@ -302,9 +1929,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "density_skeletal_constraint : Size=1, Index=None, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " None : 1.0 : fs.MB.solid_phase.properties[0.0,0.9].dens_mass_skeletal*(0.00019047619047619048*fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe2O3] + 0.0002*fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Fe3O4] + 0.00025081514923501377*fs.MB.solid_phase.properties[0.0,0.9].mass_frac_comp[Al2O3]) : 1.0 : True\n" + ] + } + ], "source": [ "model.fs.MB.solid_phase.properties[0, 0.9].density_skeletal_constraint.pprint()" ] @@ -340,9 +1977,357 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constructing a steady model to initialize the dynamic model\n", + "Initializing steady model\n", + "2025-03-17 17:31:48 [INFO] idaes.init.fs.MB: Initialize Thermophysical Properties\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:31:48 [INFO] idaes.init.fs.MB: Initialize Hydrodynamics\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:31:48 [INFO] idaes.init.fs.MB: Initialize Mass Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:31:48 [INFO] idaes.init.fs.MB: Initialize Energy Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2670\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 966\n", + "\n", + "Total number of variables............................: 850\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 850\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.39e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.49e-08 0.00e+00 -8.6 9.36e-06 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving square problem with dynamic model\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 32700\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 11506\n", + "\n", + "Total number of variables............................: 10200\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 10200\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e-08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.539\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing steady model\n", + "2025-03-17 17:31:50 [INFO] idaes.init.fs.MB: Initialize Thermophysical Properties\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:31:50 [INFO] idaes.init.fs.MB: Initialize Hydrodynamics\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:31:50 [INFO] idaes.init.fs.MB: Initialize Mass Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:31:50 [INFO] idaes.init.fs.MB: Initialize Energy Balances\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2670\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 966\n", + "\n", + "Total number of variables............................: 850\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 850\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.39e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.49e-08 0.00e+00 -8.6 9.36e-06 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.2223608791828156e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2670\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 966\n", + "\n", + "Total number of variables............................: 850\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 850\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 7.76e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.42e+03 0.00e+00 -1.0 5.19e+05 - 1.00e+00 1.00e+00h 1\n", + " 2 0.0000000e+00 1.58e+02 0.00e+00 -1.0 1.03e+05 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 3.69e-02 0.00e+00 -1.7 6.88e+02 - 1.00e+00 1.00e+00h 1\n", + " 4 0.0000000e+00 1.49e-08 0.00e+00 -5.7 4.14e-01 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 4\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.0841176845133305e-09 1.4901161193847656e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.0841176845133305e-09 1.4901161193847656e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 5\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 5\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 5\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 4\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + } + ], "source": [ "model2 = make_model()\n", "# Make the model square while we try to fix the structural singularity\n", @@ -360,7 +2345,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -380,9 +2365,259 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constraints containing fs.solid_properties.particle_porosity:\n", + " fs.MB.solid_phase.properties[0.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[0.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[30.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[60.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[90.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[120.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[150.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[180.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[210.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[240.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[270.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.0].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.1].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.2].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.3].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.4].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.5].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.6].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.7].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.8].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,0.9].density_particle_constraint\n", + " fs.MB.solid_phase.properties[300.0,1.0].density_particle_constraint\n", + " fs.MB.solid_phase.reactions[0.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[0.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[30.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[60.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[90.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[120.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[150.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[180.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[210.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[240.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[270.0,1.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.0].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.1].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.2].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.3].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.4].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.5].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.6].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.7].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.8].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,0.9].gen_rate_expression[R1]\n", + " fs.MB.solid_phase.reactions[300.0,1.0].gen_rate_expression[R1]\n" + ] + } + ], "source": [ "from pyomo.contrib.incidence_analysis import IncidenceGraphInterface\n", "\n", @@ -402,7 +2637,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -434,7 +2669,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -459,9 +2694,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 387 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 10321 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 211 (External: 0)\n", + " Activated Equality Constraints: 10321 (Deactivated: 10)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 1 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 4457 Components with inconsistent units\n", + " WARNING: Found 14300 potential evaluation errors.\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 144 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_potential_evaluation_errors()\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "# Construct a new diagnostics toolbox\n", "dt = DiagnosticsToolbox(model2)\n", @@ -477,9 +2750,244 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: max_iter=20\n", + "print_user_options=yes\n", + "option_file_name=/tmp/tmpj0gx5c3z_ipopt.opt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Using option file \"/tmp/tmpj0gx5c3z_ipopt.opt\".\n", + "\n", + "\n", + "List of user-set options:\n", + "\n", + " Name Value used\n", + " max_iter = 20 yes\n", + " option_file_name = /tmp/tmpj0gx5c3z_ipopt.opt yes\n", + " print_info_string = yes yes\n", + " print_user_options = yes yes\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 33545\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 12940\n", + "\n", + "Total number of variables............................: 10341\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 10331\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 1.7682026e+04 2.99e+00 2.93e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 1.7521239e+04 8.45e+01 1.80e+03 -1.0 4.99e+02 -4.0 1.00e+00 1.25e-01f 4 McqNh LTmaxTmaxTmaxTmax\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 1.7535291e+04 5.53e+01 1.28e+05 -1.0 4.80e+00 -0.9 1.00e+00 1.00e+00h 1 LDj L\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3 1.7533314e+04 5.91e+01 9.39e+04 -1.0 2.85e+00 0.5 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 4 1.7531376e+04 6.18e+01 1.63e+05 -1.0 1.97e+00 0.9 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 5 1.7532039e+04 5.98e+01 2.44e+05 -1.0 7.29e+00 1.3 1.00e+00 2.50e-01h 3 lTmax\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 6 1.7531943e+04 5.92e+01 1.07e+04 -1.0 8.42e-01 1.7 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 7 1.7531610e+04 5.78e+01 4.39e+03 -1.0 2.44e-01 1.3 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8 1.7530730e+04 5.82e+01 1.35e+04 -1.0 3.83e-01 0.8 1.00e+00 1.00e+00f 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 9 1.7530159e+04 7.00e+01 2.32e+05 -1.0 7.76e-01 0.3 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 1.7530383e+04 7.07e+01 2.15e+04 -1.0 2.31e-01 0.7 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 11 1.7532473e+04 7.05e+01 8.42e+03 -1.0 5.56e-01 0.3 1.00e+00 1.00e+00h 1 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12 1.7532758e+04 7.03e+01 8.16e+03 -1.0 1.85e+00 -0.2 1.00e+00 3.12e-02h 6 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 13 1.7532965e+04 7.02e+01 8.09e+03 -1.0 5.55e+00 -0.7 1.00e+00 7.81e-03h 8 lTmax\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 14 1.7533286e+04 7.00e+01 7.84e+03 -1.0 2.09e+00 -0.3 1.00e+00 3.12e-02h 6 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 15 1.7533543e+04 6.97e+01 7.38e+03 -1.0 7.88e-01 0.2 1.00e+00 6.25e-02h 5 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 16 1.7533908e+04 6.95e+01 7.14e+03 -1.0 2.37e+00 -0.3 1.00e+00 3.12e-02h 6 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 17 1.7534200e+04 6.93e+01 6.71e+03 -1.0 8.93e-01 0.1 1.00e+00 6.25e-02h 5 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 18 1.7534615e+04 6.91e+01 6.49e+03 -1.0 2.69e+00 -0.4 1.00e+00 3.12e-02h 6 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 19 1.7534947e+04 6.88e+01 6.10e+03 -1.0 1.01e+00 0.1 1.00e+00 6.25e-02h 5 l\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 1.7535213e+04 6.85e+01 5.41e+03 -1.0 3.84e-01 0.5 1.00e+00 1.25e-01h 4 l\n", + "\n", + "Number of Iterations....: 20\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.7535212526993204e+04 1.7535212526993204e+04\n", + "Dual infeasibility......: 5.4114139205253450e+03 5.4114139205253450e+03\n", + "Constraint violation....: 7.6798113076064212e-03 6.8492626244787971e+01\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.3869144373417086e+01 5.4114139205253450e+03\n", + "\n", + "\n", + "Number of objective function evaluations = 73\n", + "Number of objective gradient evaluations = 21\n", + "Number of equality constraint evaluations = 79\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 21\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 20\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 14.038\n", + "Total CPU secs in NLP function evaluations = 0.175\n", + "\n", + "EXIT: Maximum Number of Iterations Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged successfully: False\n" + ] + } + ], "source": [ "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", @@ -504,9 +3012,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: Undefined (Exactly Singular)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "4 WARNINGS\n", + "\n", + " WARNING: 1290 Constraints with large residuals (>1.0E-05)\n", + " WARNING: 77 Variables with extreme Jacobian values (<1.0E-08 or >1.0E+08)\n", + " WARNING: 77 Constraints with extreme Jacobian values (<1.0E-08 or >1.0E+08)\n", + " WARNING: 11 pairs of constraints are parallel (to tolerance 1.0E-08)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "9 Cautions\n", + "\n", + " Caution: 219 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 1314 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 99 Variables with None value\n", + " Caution: 356 Constraints with mismatched terms\n", + " Caution: 1654 Constraints with potential cancellation of terms\n", + " Caution: 10 Constraints with no free variables\n", + " Caution: 1619 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 1870 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 3554 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " compute_infeasibility_explanation()\n", + " display_variables_with_extreme_jacobians()\n", + " display_constraints_with_extreme_jacobians()\n", + " display_near_parallel_constraints()\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", @@ -523,9 +3074,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following pairs of constraints are nearly parallel:\n", + "\n", + " fs.MB.solid_super_vel[0.0], fs.MB.density_flowrate_constraint[0.0,1.0]\n", + " fs.MB.solid_super_vel[30.0], fs.MB.density_flowrate_constraint[30.0,1.0]\n", + " fs.MB.solid_super_vel[60.0], fs.MB.density_flowrate_constraint[60.0,1.0]\n", + " fs.MB.solid_super_vel[90.0], fs.MB.density_flowrate_constraint[90.0,1.0]\n", + " fs.MB.solid_super_vel[120.0], fs.MB.density_flowrate_constraint[120.0,1.0]\n", + " fs.MB.solid_super_vel[150.0], fs.MB.density_flowrate_constraint[150.0,1.0]\n", + " fs.MB.solid_super_vel[180.0], fs.MB.density_flowrate_constraint[180.0,1.0]\n", + " fs.MB.solid_super_vel[210.0], fs.MB.density_flowrate_constraint[210.0,1.0]\n", + " fs.MB.solid_super_vel[240.0], fs.MB.density_flowrate_constraint[240.0,1.0]\n", + " fs.MB.solid_super_vel[270.0], fs.MB.density_flowrate_constraint[270.0,1.0]\n", + " fs.MB.solid_super_vel[300.0], fs.MB.density_flowrate_constraint[300.0,1.0]\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "dt.display_near_parallel_constraints()" ] @@ -539,9 +3113,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of solid_super_vel} : Solid superficial velocity\n", + " Size=11, Index=fs._time, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " 0.0 : 0.0 : fs.MB.velocity_superficial_solid[0.0]*fs.MB.bed_area*fs.MB.solid_phase.properties[0.0,1.0].dens_mass_particle - fs.MB.solid_phase.properties[0.0,1.0].flow_mass : 0.0 : True\n" + ] + } + ], "source": [ "model2.fs.MB.solid_super_vel[0].pprint()" ] @@ -555,7 +3140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -571,9 +3156,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 387 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 10321 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 211 (External: 0)\n", + " Activated Equality Constraints: 10310 (Deactivated: 21)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 1 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "4 WARNINGS\n", + "\n", + " WARNING: 11 Degrees of Freedom\n", + " WARNING: 4457 Components with inconsistent units\n", + " WARNING: Structural singularity found\n", + " Under-Constrained Set: 8881 variables, 8870 constraints\n", + " Over-Constrained Set: 0 variables, 0 constraints\n", + " WARNING: Found 14300 potential evaluation errors.\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 144 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_underconstrained_set()\n", + " display_potential_evaluation_errors()\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "dt = DiagnosticsToolbox(model2)\n", "dt.report_structural_issues()" @@ -588,7 +3216,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -604,9 +3232,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 387 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 10310 (External: 0)\n", + " Free Variables with only lower bounds: 0\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 222 (External: 0)\n", + " Activated Equality Constraints: 10310 (Deactivated: 21)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 1 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 4457 Components with inconsistent units\n", + " WARNING: Found 14300 potential evaluation errors.\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 144 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_components_with_inconsistent_units()\n", + " display_potential_evaluation_errors()\n", + "\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 7.617E+25\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 WARNINGS\n", + "\n", + " WARNING: 1279 Constraints with large residuals (>1.0E-05)\n", + " WARNING: 77 Variables with extreme Jacobian values (<1.0E-08 or >1.0E+08)\n", + " WARNING: 77 Constraints with extreme Jacobian values (<1.0E-08 or >1.0E+08)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "9 Cautions\n", + "\n", + " Caution: 219 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 1314 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 99 Variables with None value\n", + " Caution: 356 Constraints with mismatched terms\n", + " Caution: 1654 Constraints with potential cancellation of terms\n", + " Caution: 10 Constraints with no free variables\n", + " Caution: 1619 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 1859 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 3543 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " compute_infeasibility_explanation()\n", + " display_variables_with_extreme_jacobians()\n", + " display_constraints_with_extreme_jacobians()\n", + "\n", + "====================================================================================\n" + ] + } + ], "source": [ "dt = DiagnosticsToolbox(model2)\n", "dt.report_structural_issues()\n", @@ -622,9 +3328,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: max_iter=20\n", + "print_user_options=yes\n", + "option_file_name=/tmp/tmpq3s1keqc_ipopt.opt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Using option file \"/tmp/tmpq3s1keqc_ipopt.opt\".\n", + "\n", + "\n", + "List of user-set options:\n", + "\n", + " Name Value used\n", + " max_iter = 20 yes\n", + " option_file_name = /tmp/tmpq3s1keqc_ipopt.opt yes\n", + " print_info_string = yes yes\n", + " print_user_options = yes yes\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 33480\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 12896\n", + "\n", + "Total number of variables............................: 10330\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 10320\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 1.7535213e+04 6.85e+01 2.93e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.4256949e+03 1.01e+03 1.33e+07 -1.0 3.98e+05 - 1.00e+00 1.00e+00f 1 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 4.4845871e+03 1.00e+00 5.34e+04 -1.0 6.13e+03 - 1.00e+00 1.00e+00h 1 Nhj \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3 4.4844740e+03 2.80e-04 2.63e+01 -1.0 2.07e+02 - 1.00e+00 1.00e+00h 1 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 4 4.4844740e+03 2.98e-08 1.19e-07 -5.7 5.10e-03 - 1.00e+00 1.00e+00h 1 \n", + "\n", + "Number of Iterations....: 4\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.4844740157746410e+03 4.4844740157746410e+03\n", + "Dual infeasibility......: 1.1920928955078125e-07 1.1920928955078125e-07\n", + "Constraint violation....: 1.6975718608591706e-09 2.9802322387695312e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.6975718608591706e-09 1.1920928955078125e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 5\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 5\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 5\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 4\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 3.445\n", + "Total CPU secs in NLP function evaluations = 0.023\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged successfully: True\n" + ] + } + ], "source": [ "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", @@ -693,7 +3520,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/diagnostics/structural_singularity_test.ipynb b/idaes_examples/notebooks/docs/diagnostics/structural_singularity_test.ipynb index 29eb4cc5..8e0cdda0 100644 --- a/idaes_examples/notebooks/docs/diagnostics/structural_singularity_test.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/structural_singularity_test.ipynb @@ -1,701 +1,727 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Debugging a Structural Singularity\n", - "===========================\n", - "Author: Robert Parker\\\n", - "Maintainer: Robert Parker\\\n", - "Updated: 2024-06-10\n", - "\n", - "In this tutorial, we will use the [IDAES Diagnostics Toolbox](https://idaes-pse.readthedocs.io/en/2.4.0/explanations/model_diagnostics/index.html#diagnostics-toolbox)\n", - "to diagnose and fix a structural singularity that is preventing us from solving an optimization problem." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "# Constructing the model\n", - "\n", - "Suppose a collaborator has given us a model to work with. They give us a square model and tell us what the degrees of freedom are. We construct an optimization problem and try to solve it. In this tutorial, we don't want to worry too much about the details that go into constructing the model. This has been provided in the `idaes_examples.mod.diagnostics.gas_solid_contactors.model` module.\n", - "\n", - "## Model details (OKAY TO SKIP)\n", - "\n", - "The model we are trying to optimize is a dynamic model of a moving bed chemical looping combustion reactor. The model has been described by [Okoli et al.][1] and [Parker and Biegler][2]. This is a gas-solid reactor with counter-current flow. The degrees of freedom are gas and solid inlet flow rates, and we are trying to minimize the deviation from a desired operating point via a least-squares objective function.\n", - "\n", - "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", - "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", - "\n", - "Again, we don't want to worry too much about the model. The `make_model` function will construct the optimization problem that we want to solve, and whenever we do something model-specific, we will explicitly make note of it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trying to solve the original model\n", - "\n", - "With that out of the way, let's construct the model and try to solve it!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.diagnostics.gas_solid_contactors.model import make_model\n", - "import logging\n", - "\n", - "# We'll turn off IDAES logging. This is not recommended in general, but this is an old model\n", - "# (from IDAES 1.7) that has been ported to work with the current version of IDAES. It generates\n", - "# a lot of warnings.\n", - "logging.getLogger(\"idaes\").setLevel(logging.CRITICAL)\n", - "# We'll also turn off Pyomo logging. This will suppress unit inconsistency warnings later,\n", - "# which otherwise flood our console and slow down this notebook. We have unit inconsistencies\n", - "# as, in IDAES 1.7, we didn't rigorously enforce that models use units.\n", - "logging.getLogger(\"pyomo\").setLevel(logging.CRITICAL)\n", - "\n", - "# This constructs a dynamic model with degrees of freedom and an objective function.\n", - "model = make_model()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before trying to solve the model, let's make sure it conforms to our expectations, i.e. it (a) has degrees of freedom and (b) is well-initialized to a feasible point." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import some useful utilities from the model_statistics module.\n", - "# Degrees of freedom and constraint residuals are always good things to check before\n", - "# trying to solve a simulation or optimization problem.\n", - "from idaes.core.util.model_statistics import degrees_of_freedom, large_residuals_set\n", - "\n", - "dof = degrees_of_freedom(model)\n", - "print(f\"Degrees of freedom: {dof}\")\n", - "has_large_residuals = bool(large_residuals_set(model, tol=1e-5))\n", - "print(f\"Has large residuals: {has_large_residuals}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above `make_model` function, the model has been \"solved\" to arrive at a feasible point, then degrees of freedom have been unfixed and an objective function has been added to give us an optimization problem. This looks good so far, so let's try to solve the optimization problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import pyomo.environ for access to solvers\n", - "import pyomo.environ as pyo\n", - "\n", - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.options[\"max_iter\"] = 20\n", - "solver.options[\"print_user_options\"] = \"yes\"\n", - "solver.options[\"OF_print_info_string\"] = \"yes\"\n", - "res = solver.solve(model, tee=True)\n", - "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IPOPT fails to solve the optimization problem... You can try increasing the iteration limit, but it is very unlikely that this model will ever solve. A telltale sign that something is wrong with our model is the persistence of regularization coefficients, that is, numbers in the `lg(rg)` column of the IPOPT log. These coefficients can have multiple causes. One is that the constraint Jacobian (partial derivative matrix) is singular, which indicates a problem with our model. We have set the `print_info_string` option in IPOPT to display \"diagnostic tags\" to help interpret these regularization coefficients. The \"L\" and \"l\" diagnostic tags, which appear repeatedly, indicate that the Jacobian is singular. For more information on IPOPT diagnostic tags, see the IPOPT [documentation](https://coin-or.github.io/Ipopt/OUTPUT.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Debugging the original model\n", - "\n", - "Let's run the diagnostics toolbox on the model and see what it has to say.\n", - "\n", - "For good practice, we'll first make sure the model we're debugging is square. Remember that we're assuming we already know how to toggle degrees of freedom in our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix gas and solid flow rates at their respective inlets\n", - "model.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", - "model.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", - "# Part of our optimization problem was a set of constraints to enforce piecewise\n", - "# constant control inputs. We need to deactivate these as well.\n", - "model.piecewise_constant_constraints.deactivate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run the diagnostics toolbox." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util.model_diagnostics import DiagnosticsToolbox\n", - "\n", - "dt = DiagnosticsToolbox(model)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the warnings we got:\n", - "- Inconsistent units\n", - "- Structural singularity\n", - "- Potential evaluation errors\n", - "\n", - "We'll ignore the inconsistent units. The property package and unit model here were extracted from IDAES 1.7, before we rigorously enforced that all models use units. The potential evaluation errors we see here may be worth looking into, but looking at the failing IPOPT log above, we don't notice any evaluation errors. (If evaluation errors occurred in IPOPT, we would see a message like \"Error in AMPL evaluation\" in the IPOPT iteration log, which we don't see here.) The structural singularity looks like the most promising avenue to debug, especially as the IPOPT log displays persistent regularization coefficients that appear to be caused by a singular Jacobian.\n", - "\n", - "Let's follow the toolbox's advice and display the under and over-constrained sets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.display_underconstrained_set()\n", - "dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Over and under-constrained subsystems\n", - "\n", - "Structural singularities are characterized by the [Dulmage-Mendelson decomposition][3], which partitions a system into minimal over and under-constrained subsystems. These subsystems contain the potentially unmatched constraints and variables, respectively. Here, \"unmatched\" effectively means \"causing a singularity\". [Pothen and Fan][4] give a good overview of the Dulmage-Mendelsohn decomposition and [Parker et al.][5] give several examples.\n", - "\n", - "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", - "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", - "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n", - "\n", - "The most straightforward way to fix a structural singularity is to fix variables that are in the under-constrained system and deactivate constraints in the over-constrained subsystem. However, this may not be applicable for every model. For example, we may need to add variables and constraints instead. What over and under-constrained subsystems are telling us is that something is wrong with our modeling assumptions. The particular fix that is appropriate will depend heavily on the model.\n", - "\n", - "If the above output gives us any clues, we can go ahead and start trying to fix things. However, suppose it doesn't. A good strategy is to try to break down the model into smaller, square subsystems that we think should be nonsingular. For a dynamic model like this one, a good candidate is the subsystem of variables and equations at each point in time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We've included a utility function to extract the subsystem of variables and equations\n", - "# at a specified point in time. If you are dealing with a process flowsheet, here you\n", - "# may want to extract each unit model individually.\n", - "from idaes_examples.mod.diagnostics.util import get_subsystem_at_time\n", - "\n", - "# TemporarySubsystemManager is used to temporarily fix some variables to make sure\n", - "# we're debugging a square subsystem.\n", - "from pyomo.util.subsystems import TemporarySubsystemManager\n", - "\n", - "# Let's start with t=0. Really, we'd probably want to do this in a loop and try all time points.\n", - "t0 = model.fs.time.first()\n", - "t_block, inputs = get_subsystem_at_time(model, model.fs.time, t0)\n", - "# We'll temporarily fix the \"inputs\" to make sure we have a square system while debugging\n", - "with TemporarySubsystemManager(to_fix=inputs):\n", - " dt = DiagnosticsToolbox(t_block)\n", - " dt.report_structural_issues()\n", - " dt.display_underconstrained_set()\n", - " dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These over and under-constrained subsystems aren't much smaller, but now the over-constrained system decomposes into 10 small, independent blocks. These should be easier to debug.\n", - "\n", - "## Debugging the over-constrained subsystem\n", - "\n", - "To debug the over-constrained subsystem, we look for a constraint that is not calculating any of the variables in the subsystem. The \"odd constraint out\" here seems to be the mass fraction sum, `sum_component_eqn`. This must \"solve for\" one of the mass fractions, which means one of the `material_holdup_calculation` equations must \"solve for\" particle density rather than mass fraction. If we want to see what variables are contained in one of these constraints, we can always `pprint` it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fs.MB.solid_phase.properties[0, 0.9].sum_component_eqn.pprint()\n", - "model.fs.MB.solid_phase.material_holdup_calculation[0, 0.9, \"Sol\", \"Fe3O4\"].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If one of these `material_holdup_calculation` equations is solving for particle density, then that means that `density_particle_constraint` is not actually solving for density. Maybe `density_particle_constraint` is over-determining our system?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fs.MB.solid_phase.properties[0, 0.9].density_particle_constraint.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this looks like a very reasonable constraint. After some thought, which admittedly requires some knowledge of the process we are modeling, we decide that the right approach is to make particle porosity a variable. We have assumed that porosity is constant, but this overconstrained subsystem is telling us that this assumption is not valid.\n", - "\n", - "### How did we figure this out? (OKAY TO SKIP)\n", - "Adding a variable (including by unfixing a parameter) to an over-constraining constraint will often remove that constraint from the over-constrained subsystem. But how did we know that this was the right thing to do? If you just care about using the diagnostics toolbox to extract as much information about a singularity as possible, you can skip this section. But if you are curious how we determined that particle porosity should not be constant, read on.\n", - "\n", - "`dens_mass_skeletal` is determined purely by the composition of solid, which is made up of Fe2O3, Fe3O4, and inert Ti2O3. We can view the `density_skeletal_constraint` as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fs.MB.solid_phase.properties[0, 0.9].density_skeletal_constraint.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we assume a constant particle porosity, this gives us a particle porosity that is also uniquely determined by the solid composition by the above `density_particle_constraint`:\n", - "```\n", - "dens_mass_particle = (1 - porosity) * dens_mass_skeletal\n", - "```\n", - "But the composition of the solid is determined by the (somewhat misnamed) `material_holdup_calculation` constraints. While the name of these constraints implies they \"calculate holdups,\" material holdups at $t=0$ are fixed as initial conditions (because holdups are the differential variables with respect to time in this model). At other time points, we assume that holdups are specified by differential and discretization equations of the model. This means that the `material_holdup_calculation` constraints actually calculate the solid phase mass fractions *from* the holdups. But as we hinted at above, the 4-by-4 system of holdup calculation constraints, `sum_component_eqn` (which simply constrains the sum of mass fractions to be one), mass fractions, and `dens_mass_particle`, uniquely solve for `dens_mass_particle` *as well as* the mass fractions. But if the holdup variables can be used to solve for the mass fractions, they *also* solve for `dens_mass_skeletal`. So both sides of `density_particle_constraint` are already uniquely determined! This implies that we don't need this constraint at all, but we also know that this constraint has to hold. Something has to give. With this in mind, we actually have several options for how to resolve this overspecification:\n", - "1. Remove `density_particle_constraint`. Then we would have `dens_mass_particle` and `dens_mass_skeletal`, with no relationship between them. This would leave us with a mathematically sound model, but with densities that contradict constant particle porosity that we have assumed (which is used elsewhere in the reaction rate calculation equations).\n", - "2. Remove the constraints that calculate skeletal density from composition.\n", - "3. Relax particle porosity from a parameter to a variable.\n", - "\n", - "Options 2 and 3 are equally valid. We've chosen option 3, meaning we assume that the particle \"evolves\" with a density that is well determined from its constituent species, rather than changing density to accommodate whatever mass it accumulates via reaction without altering its volume. This exercise should remind us that all mathematical modeling is somewhat of an art. In the process of choosing the \"least bad\" model, it is fairly easy to over or under-specify something by making the wrong combination of assumptions, and the Dulmage-Mendelsohn decomposition is a great tool for detecting when this has happened.\n", - "\n", - "## Debugging the under-constrained subsystem\n", - "\n", - "The under-constrained system does not decompose into independent subsystems, making it more difficult to debug. However, by inspection, we notice that the same constraints and variables seem to be repeated at each point in the length domain. For each point in space, the \"odd variable out\" seems to be the total flow rate `flow_mass`. Using some intuition about this particular process model, we may conclude that this variable should be calculated from the solid phase velocity, which is constant. We expect an equation that looks like\n", - "```\n", - "flow_mass == velocity * area * density\n", - "```\n", - "\n", - "But this equation isn't here... so we need to add it.\n", - "\n", - "# Fixing the model\n", - "\n", - "We'll start by creating a fresh copy of the model, so we don't accidentally rely on IPOPT's point of termination." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2 = make_model()\n", - "# Make the model square while we try to fix the structural singularity\n", - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.deactivate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding a new particle porosity variable" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.particle_porosity = pyo.Var(\n", - " model2.fs.time,\n", - " model2.fs.MB.length_domain,\n", - " initialize=model2.fs.solid_properties.particle_porosity.value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to replace the old particle porosity parameter with this new variable. Luckily, the old parameter is actually implemented as a fixed variable, so we can easily identify all the constraints it participates in with `IncidenceGraphInterface`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.contrib.incidence_analysis import IncidenceGraphInterface\n", - "\n", - "igraph = IncidenceGraphInterface(model2, include_fixed=True)\n", - "porosity_param = model2.fs.solid_properties.particle_porosity\n", - "print(f\"Constraints containing {porosity_param.name}:\")\n", - "for con in igraph.get_adjacent_to(porosity_param):\n", - " print(f\" {con.name}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Particle porosity only appears in two constraints: the density constraint we saw above, and the reaction rate equation. We can replace particle porosity in these constraints using Pyomo's `replace_expressions` function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.core.expr import replace_expressions\n", - "\n", - "for t, x in model2.fs.time * model2.fs.MB.length_domain:\n", - " substitution_map = {id(porosity_param): model2.fs.MB.particle_porosity[t, x]}\n", - " sp = model2.fs.MB.solid_phase\n", - " cons = [\n", - " sp.properties[t, x].density_particle_constraint,\n", - " sp.reactions[t, x].gen_rate_expression[\"R1\"],\n", - " ]\n", - " for con in cons:\n", - " con.set_value(\n", - " replace_expressions(\n", - " con.expr, substitution_map, descend_into_named_expressions=True\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have added a new `particle_porosity` variable, and are using it in the relevant locations. Now we can move on to adding the missing constraint.\n", - "\n", - "## Adding a new density-flow rate constraint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@model2.fs.MB.Constraint(model2.fs.time, model2.fs.MB.length_domain)\n", - "def density_flowrate_constraint(mb, t, x):\n", - " return (\n", - " mb.velocity_superficial_solid[t]\n", - " * mb.bed_area\n", - " * mb.solid_phase.properties[t, x].dens_mass_particle\n", - " == mb.solid_phase.properties[t, x].flow_mass\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Testing the new model\n", - "\n", - "Let's see if these changes have fixed our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Construct a new diagnostics toolbox\n", - "dt = DiagnosticsToolbox(model2)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The structural singularity seems to be gone! Let's unfix our degrees of freedom and see if we can solve." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", - "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", - "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.activate()\n", - "\n", - "res = solver.solve(model2, tee=True)\n", - "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This doesn't look much better. What's going on? I thought we just fixed the issue?\n", - "\n", - "# Debugging the model, take two\n", - "\n", - "Let's check the diagnostics toolbox for numerical issues." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.deactivate()\n", - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks like we have \"parallel constraints\", which are another form of singularity. Let's follow the toolbox's advice to see what they are." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.display_near_parallel_constraints()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`density_flowrate_constraint` is the constraint that we added. What is `solid_super_vel`?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.solid_super_vel[0].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the same as the constraint we just added! Looks like that constraint already existed at the solid inlet. We can easily deactivate the new constraints at this point in the length domain:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.density_flowrate_constraint[:, 1.0].deactivate();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But now we have removed constraints from a square model, and expect to have degrees of freedom. Let's see what the diagnostics toolbox has to say." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(model2)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this doesn't help us very much. We have some extraneous degrees of freedom, but with 8881 variables in the under-constrained subsystem, it will be difficult to tell what they are. After some thought (and model-specific intuition), we land on the conclusion that maybe we need to fix particle porosity at the solid inlet. Here, total flow rate is specified, and the `solid_super_vel` equation is using it to compute velocity. So we need `dens_mass_particle` to be known, which means we need `particle_porosity` to be fixed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.particle_porosity[:, 1.0].fix();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run the diagnostics toolbox as a sanity check." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(model2)\n", - "dt.report_structural_issues()\n", - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks good! Now we can release our degrees of freedom and try to solve again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", - "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", - "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.activate()\n", - "\n", - "res = solver.solve(model2, tee=True)\n", - "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It worked! For the simple optimization problem we have set up, this solve looks a lot more like what we expect.\n", - "\n", - "# Takeaways from this tutorial\n", - "What have we learned?\n", - "1. IPOPT using non-zero regularization coefficients hints at a singular Jacobian (especially when \"L\"/\"l\" diagnostic tags are present).\n", - "2. When this happens, start by calling `report_structural_issues` to check for a structural singularity. If this looks good, call `report_numerical_issues` to check for a numerical singularity.\n", - "3. When debugging a structural singularity, decomposing a problem into subsystems that each should be nonsingular (e.g. unit models or points in time) is very useful.\n", - "4. The solution to a structural singularity is often to relax a fixed parameter, add a constraint that was forgotten, remove a constraint that was redundant, or fix an extraneous degree of freedom.\n", - "5. Model-specific intuition is usually necessary to diagnose and fix modeling issues. (If you're an algorithm developer, learn about the models you're using! If you don't understand your models, you don't understand your algorithms!)\n", - "6. A modeling issue doesn't necessarily have a unique solution. This is especially true when the issue involves invalid assumptions.\n", - "7. Debugging is an iterative process — fixing one issue can introduce another." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# References\n", - "\n", - "[[1]] Okoli et al., \"A framework for the optimization of chemical looping combustion processes\". *Powder Tech*, 2020.\n", - "\n", - "[[2]] Parker and Biegler, \"Dynamic modeling and nonlinear model predictive control of a moving bed chemical looping combustion reactor\". *IFAC PapersOnline*, 2022.\n", - "\n", - "[[3]] Dulmage and Mendelsohn, \"Coverings of bipartite graphs\". *Can J. Math.*, 1958.\n", - "\n", - "[[4]] Pothen and Fan, \"Computing the block triangular form of a sparse matrix\". *ACM Trans. Math. Softw.*, 1990.\n", - "\n", - "[[5]] Parker et al., \"Applications of the Dulmage-Mendelsohn decomposition for debugging nonlinear optimization problems\". *Comp. Chem. Eng.*, 2023.\n", - "\n", - "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", - "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", - "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", - "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", - "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Debugging a Structural Singularity\n", + "===========================\n", + "Author: Robert Parker\\\n", + "Maintainer: Robert Parker\\\n", + "Updated: 2024-06-10\n", + "\n", + "In this tutorial, we will use the [IDAES Diagnostics Toolbox](https://idaes-pse.readthedocs.io/en/2.4.0/explanations/model_diagnostics/index.html#diagnostics-toolbox)\n", + "to diagnose and fix a structural singularity that is preventing us from solving an optimization problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Constructing the model\n", + "\n", + "Suppose a collaborator has given us a model to work with. They give us a square model and tell us what the degrees of freedom are. We construct an optimization problem and try to solve it. In this tutorial, we don't want to worry too much about the details that go into constructing the model. This has been provided in the `idaes_examples.mod.diagnostics.gas_solid_contactors.model` module.\n", + "\n", + "## Model details (OKAY TO SKIP)\n", + "\n", + "The model we are trying to optimize is a dynamic model of a moving bed chemical looping combustion reactor. The model has been described by [Okoli et al.][1] and [Parker and Biegler][2]. This is a gas-solid reactor with counter-current flow. The degrees of freedom are gas and solid inlet flow rates, and we are trying to minimize the deviation from a desired operating point via a least-squares objective function.\n", + "\n", + "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", + "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", + "\n", + "Again, we don't want to worry too much about the model. The `make_model` function will construct the optimization problem that we want to solve, and whenever we do something model-specific, we will explicitly make note of it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trying to solve the original model\n", + "\n", + "With that out of the way, let's construct the model and try to solve it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.diagnostics.gas_solid_contactors.model import make_model\n", + "import logging\n", + "\n", + "# We'll turn off IDAES logging. This is not recommended in general, but this is an old model\n", + "# (from IDAES 1.7) that has been ported to work with the current version of IDAES. It generates\n", + "# a lot of warnings.\n", + "logging.getLogger(\"idaes\").setLevel(logging.CRITICAL)\n", + "# We'll also turn off Pyomo logging. This will suppress unit inconsistency warnings later,\n", + "# which otherwise flood our console and slow down this notebook. We have unit inconsistencies\n", + "# as, in IDAES 1.7, we didn't rigorously enforce that models use units.\n", + "logging.getLogger(\"pyomo\").setLevel(logging.CRITICAL)\n", + "\n", + "# This constructs a dynamic model with degrees of freedom and an objective function.\n", + "model = make_model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before trying to solve the model, let's make sure it conforms to our expectations, i.e. it (a) has degrees of freedom and (b) is well-initialized to a feasible point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import some useful utilities from the model_statistics module.\n", + "# Degrees of freedom and constraint residuals are always good things to check before\n", + "# trying to solve a simulation or optimization problem.\n", + "from idaes.core.util.model_statistics import degrees_of_freedom, large_residuals_set\n", + "\n", + "dof = degrees_of_freedom(model)\n", + "print(f\"Degrees of freedom: {dof}\")\n", + "has_large_residuals = bool(large_residuals_set(model, tol=1e-5))\n", + "print(f\"Has large residuals: {has_large_residuals}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above `make_model` function, the model has been \"solved\" to arrive at a feasible point, then degrees of freedom have been unfixed and an objective function has been added to give us an optimization problem. This looks good so far, so let's try to solve the optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import pyomo.environ for access to solvers\n", + "import pyomo.environ as pyo\n", + "\n", + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.options[\"max_iter\"] = 20\n", + "solver.options[\"print_user_options\"] = \"yes\"\n", + "solver.options[\"OF_print_info_string\"] = \"yes\"\n", + "res = solver.solve(model, tee=True)\n", + "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IPOPT fails to solve the optimization problem... You can try increasing the iteration limit, but it is very unlikely that this model will ever solve. A telltale sign that something is wrong with our model is the persistence of regularization coefficients, that is, numbers in the `lg(rg)` column of the IPOPT log. These coefficients can have multiple causes. One is that the constraint Jacobian (partial derivative matrix) is singular, which indicates a problem with our model. We have set the `print_info_string` option in IPOPT to display \"diagnostic tags\" to help interpret these regularization coefficients. The \"L\" and \"l\" diagnostic tags, which appear repeatedly, indicate that the Jacobian is singular. For more information on IPOPT diagnostic tags, see the IPOPT [documentation](https://coin-or.github.io/Ipopt/OUTPUT.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Debugging the original model\n", + "\n", + "Let's run the diagnostics toolbox on the model and see what it has to say.\n", + "\n", + "For good practice, we'll first make sure the model we're debugging is square. Remember that we're assuming we already know how to toggle degrees of freedom in our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix gas and solid flow rates at their respective inlets\n", + "model.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", + "model.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", + "# Part of our optimization problem was a set of constraints to enforce piecewise\n", + "# constant control inputs. We need to deactivate these as well.\n", + "model.piecewise_constant_constraints.deactivate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run the diagnostics toolbox." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.model_diagnostics import DiagnosticsToolbox\n", + "\n", + "dt = DiagnosticsToolbox(model)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the warnings we got:\n", + "- Inconsistent units\n", + "- Structural singularity\n", + "- Potential evaluation errors\n", + "\n", + "We'll ignore the inconsistent units. The property package and unit model here were extracted from IDAES 1.7, before we rigorously enforced that all models use units. The potential evaluation errors we see here may be worth looking into, but looking at the failing IPOPT log above, we don't notice any evaluation errors. (If evaluation errors occurred in IPOPT, we would see a message like \"Error in AMPL evaluation\" in the IPOPT iteration log, which we don't see here.) The structural singularity looks like the most promising avenue to debug, especially as the IPOPT log displays persistent regularization coefficients that appear to be caused by a singular Jacobian.\n", + "\n", + "Let's follow the toolbox's advice and display the under and over-constrained sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt.display_underconstrained_set()\n", + "dt.display_overconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Over and under-constrained subsystems\n", + "\n", + "Structural singularities are characterized by the [Dulmage-Mendelson decomposition][3], which partitions a system into minimal over and under-constrained subsystems. These subsystems contain the potentially unmatched constraints and variables, respectively. Here, \"unmatched\" effectively means \"causing a singularity\". [Pothen and Fan][4] give a good overview of the Dulmage-Mendelsohn decomposition and [Parker et al.][5] give several examples.\n", + "\n", + "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", + "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", + "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n", + "\n", + "The most straightforward way to fix a structural singularity is to fix variables that are in the under-constrained system and deactivate constraints in the over-constrained subsystem. However, this may not be applicable for every model. For example, we may need to add variables and constraints instead. What over and under-constrained subsystems are telling us is that something is wrong with our modeling assumptions. The particular fix that is appropriate will depend heavily on the model.\n", + "\n", + "If the above output gives us any clues, we can go ahead and start trying to fix things. However, suppose it doesn't. A good strategy is to try to break down the model into smaller, square subsystems that we think should be nonsingular. For a dynamic model like this one, a good candidate is the subsystem of variables and equations at each point in time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We've included a utility function to extract the subsystem of variables and equations\n", + "# at a specified point in time. If you are dealing with a process flowsheet, here you\n", + "# may want to extract each unit model individually.\n", + "from idaes_examples.mod.diagnostics.util import get_subsystem_at_time\n", + "\n", + "# TemporarySubsystemManager is used to temporarily fix some variables to make sure\n", + "# we're debugging a square subsystem.\n", + "from pyomo.util.subsystems import TemporarySubsystemManager\n", + "\n", + "# Let's start with t=0. Really, we'd probably want to do this in a loop and try all time points.\n", + "t0 = model.fs.time.first()\n", + "t_block, inputs = get_subsystem_at_time(model, model.fs.time, t0)\n", + "# We'll temporarily fix the \"inputs\" to make sure we have a square system while debugging\n", + "with TemporarySubsystemManager(to_fix=inputs):\n", + " dt = DiagnosticsToolbox(t_block)\n", + " dt.report_structural_issues()\n", + " dt.display_underconstrained_set()\n", + " dt.display_overconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These over and under-constrained subsystems aren't much smaller, but now the over-constrained system decomposes into 10 small, independent blocks. These should be easier to debug.\n", + "\n", + "## Debugging the over-constrained subsystem\n", + "\n", + "To debug the over-constrained subsystem, we look for a constraint that is not calculating any of the variables in the subsystem. The \"odd constraint out\" here seems to be the mass fraction sum, `sum_component_eqn`. This must \"solve for\" one of the mass fractions, which means one of the `material_holdup_calculation` equations must \"solve for\" particle density rather than mass fraction. If we want to see what variables are contained in one of these constraints, we can always `pprint` it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fs.MB.solid_phase.properties[0, 0.9].sum_component_eqn.pprint()\n", + "model.fs.MB.solid_phase.material_holdup_calculation[0, 0.9, \"Sol\", \"Fe3O4\"].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one of these `material_holdup_calculation` equations is solving for particle density, then that means that `density_particle_constraint` is not actually solving for density. Maybe `density_particle_constraint` is over-determining our system?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fs.MB.solid_phase.properties[0, 0.9].density_particle_constraint.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this looks like a very reasonable constraint. After some thought, which admittedly requires some knowledge of the process we are modeling, we decide that the right approach is to make particle porosity a variable. We have assumed that porosity is constant, but this overconstrained subsystem is telling us that this assumption is not valid.\n", + "\n", + "### How did we figure this out? (OKAY TO SKIP)\n", + "Adding a variable (including by unfixing a parameter) to an over-constraining constraint will often remove that constraint from the over-constrained subsystem. But how did we know that this was the right thing to do? If you just care about using the diagnostics toolbox to extract as much information about a singularity as possible, you can skip this section. But if you are curious how we determined that particle porosity should not be constant, read on.\n", + "\n", + "`dens_mass_skeletal` is determined purely by the composition of solid, which is made up of Fe2O3, Fe3O4, and inert Ti2O3. We can view the `density_skeletal_constraint` as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fs.MB.solid_phase.properties[0, 0.9].density_skeletal_constraint.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we assume a constant particle porosity, this gives us a particle porosity that is also uniquely determined by the solid composition by the above `density_particle_constraint`:\n", + "```\n", + "dens_mass_particle = (1 - porosity) * dens_mass_skeletal\n", + "```\n", + "But the composition of the solid is determined by the (somewhat misnamed) `material_holdup_calculation` constraints. While the name of these constraints implies they \"calculate holdups,\" material holdups at $t=0$ are fixed as initial conditions (because holdups are the differential variables with respect to time in this model). At other time points, we assume that holdups are specified by differential and discretization equations of the model. This means that the `material_holdup_calculation` constraints actually calculate the solid phase mass fractions *from* the holdups. But as we hinted at above, the 4-by-4 system of holdup calculation constraints, `sum_component_eqn` (which simply constrains the sum of mass fractions to be one), mass fractions, and `dens_mass_particle`, uniquely solve for `dens_mass_particle` *as well as* the mass fractions. But if the holdup variables can be used to solve for the mass fractions, they *also* solve for `dens_mass_skeletal`. So both sides of `density_particle_constraint` are already uniquely determined! This implies that we don't need this constraint at all, but we also know that this constraint has to hold. Something has to give. With this in mind, we actually have several options for how to resolve this overspecification:\n", + "1. Remove `density_particle_constraint`. Then we would have `dens_mass_particle` and `dens_mass_skeletal`, with no relationship between them. This would leave us with a mathematically sound model, but with densities that contradict constant particle porosity that we have assumed (which is used elsewhere in the reaction rate calculation equations).\n", + "2. Remove the constraints that calculate skeletal density from composition.\n", + "3. Relax particle porosity from a parameter to a variable.\n", + "\n", + "Options 2 and 3 are equally valid. We've chosen option 3, meaning we assume that the particle \"evolves\" with a density that is well determined from its constituent species, rather than changing density to accommodate whatever mass it accumulates via reaction without altering its volume. This exercise should remind us that all mathematical modeling is somewhat of an art. In the process of choosing the \"least bad\" model, it is fairly easy to over or under-specify something by making the wrong combination of assumptions, and the Dulmage-Mendelsohn decomposition is a great tool for detecting when this has happened.\n", + "\n", + "## Debugging the under-constrained subsystem\n", + "\n", + "The under-constrained system does not decompose into independent subsystems, making it more difficult to debug. However, by inspection, we notice that the same constraints and variables seem to be repeated at each point in the length domain. For each point in space, the \"odd variable out\" seems to be the total flow rate `flow_mass`. Using some intuition about this particular process model, we may conclude that this variable should be calculated from the solid phase velocity, which is constant. We expect an equation that looks like\n", + "```\n", + "flow_mass == velocity * area * density\n", + "```\n", + "\n", + "But this equation isn't here... so we need to add it.\n", + "\n", + "# Fixing the model\n", + "\n", + "We'll start by creating a fresh copy of the model, so we don't accidentally rely on IPOPT's point of termination." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2 = make_model()\n", + "# Make the model square while we try to fix the structural singularity\n", + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.deactivate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding a new particle porosity variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.particle_porosity = pyo.Var(\n", + " model2.fs.time,\n", + " model2.fs.MB.length_domain,\n", + " initialize=model2.fs.solid_properties.particle_porosity.value,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to replace the old particle porosity parameter with this new variable. Luckily, the old parameter is actually implemented as a fixed variable, so we can easily identify all the constraints it participates in with `IncidenceGraphInterface`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.contrib.incidence_analysis import IncidenceGraphInterface\n", + "\n", + "igraph = IncidenceGraphInterface(model2, include_fixed=True)\n", + "porosity_param = model2.fs.solid_properties.particle_porosity\n", + "print(f\"Constraints containing {porosity_param.name}:\")\n", + "for con in igraph.get_adjacent_to(porosity_param):\n", + " print(f\" {con.name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Particle porosity only appears in two constraints: the density constraint we saw above, and the reaction rate equation. We can replace particle porosity in these constraints using Pyomo's `replace_expressions` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.core.expr import replace_expressions\n", + "\n", + "for t, x in model2.fs.time * model2.fs.MB.length_domain:\n", + " substitution_map = {id(porosity_param): model2.fs.MB.particle_porosity[t, x]}\n", + " sp = model2.fs.MB.solid_phase\n", + " cons = [\n", + " sp.properties[t, x].density_particle_constraint,\n", + " sp.reactions[t, x].gen_rate_expression[\"R1\"],\n", + " ]\n", + " for con in cons:\n", + " con.set_value(\n", + " replace_expressions(\n", + " con.expr, substitution_map, descend_into_named_expressions=True\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have added a new `particle_porosity` variable, and are using it in the relevant locations. Now we can move on to adding the missing constraint.\n", + "\n", + "## Adding a new density-flow rate constraint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@model2.fs.MB.Constraint(model2.fs.time, model2.fs.MB.length_domain)\n", + "def density_flowrate_constraint(mb, t, x):\n", + " return (\n", + " mb.velocity_superficial_solid[t]\n", + " * mb.bed_area\n", + " * mb.solid_phase.properties[t, x].dens_mass_particle\n", + " == mb.solid_phase.properties[t, x].flow_mass\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the new model\n", + "\n", + "Let's see if these changes have fixed our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a new diagnostics toolbox\n", + "dt = DiagnosticsToolbox(model2)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The structural singularity seems to be gone! Let's unfix our degrees of freedom and see if we can solve." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", + "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", + "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.activate()\n", + "\n", + "res = solver.solve(model2, tee=True)\n", + "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This doesn't look much better. What's going on? I thought we just fixed the issue?\n", + "\n", + "# Debugging the model, take two\n", + "\n", + "Let's check the diagnostics toolbox for numerical issues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.deactivate()\n", + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like we have \"parallel constraints\", which are another form of singularity. Let's follow the toolbox's advice to see what they are." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt.display_near_parallel_constraints()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`density_flowrate_constraint` is the constraint that we added. What is `solid_super_vel`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.solid_super_vel[0].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same as the constraint we just added! Looks like that constraint already existed at the solid inlet. We can easily deactivate the new constraints at this point in the length domain:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.density_flowrate_constraint[:, 1.0].deactivate();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But now we have removed constraints from a square model, and expect to have degrees of freedom. Let's see what the diagnostics toolbox has to say." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(model2)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this doesn't help us very much. We have some extraneous degrees of freedom, but with 8881 variables in the under-constrained subsystem, it will be difficult to tell what they are. After some thought (and model-specific intuition), we land on the conclusion that maybe we need to fix particle porosity at the solid inlet. Here, total flow rate is specified, and the `solid_super_vel` equation is using it to compute velocity. So we need `dens_mass_particle` to be known, which means we need `particle_porosity` to be fixed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.particle_porosity[:, 1.0].fix();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's run the diagnostics toolbox as a sanity check." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(model2)\n", + "dt.report_structural_issues()\n", + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Now we can release our degrees of freedom and try to solve again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", + "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", + "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.activate()\n", + "\n", + "res = solver.solve(model2, tee=True)\n", + "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It worked! For the simple optimization problem we have set up, this solve looks a lot more like what we expect.\n", + "\n", + "# Takeaways from this tutorial\n", + "What have we learned?\n", + "1. IPOPT using non-zero regularization coefficients hints at a singular Jacobian (especially when \"L\"/\"l\" diagnostic tags are present).\n", + "2. When this happens, start by calling `report_structural_issues` to check for a structural singularity. If this looks good, call `report_numerical_issues` to check for a numerical singularity.\n", + "3. When debugging a structural singularity, decomposing a problem into subsystems that each should be nonsingular (e.g. unit models or points in time) is very useful.\n", + "4. The solution to a structural singularity is often to relax a fixed parameter, add a constraint that was forgotten, remove a constraint that was redundant, or fix an extraneous degree of freedom.\n", + "5. Model-specific intuition is usually necessary to diagnose and fix modeling issues. (If you're an algorithm developer, learn about the models you're using! If you don't understand your models, you don't understand your algorithms!)\n", + "6. A modeling issue doesn't necessarily have a unique solution. This is especially true when the issue involves invalid assumptions.\n", + "7. Debugging is an iterative process — fixing one issue can introduce another." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "[[1]] Okoli et al., \"A framework for the optimization of chemical looping combustion processes\". *Powder Tech*, 2020.\n", + "\n", + "[[2]] Parker and Biegler, \"Dynamic modeling and nonlinear model predictive control of a moving bed chemical looping combustion reactor\". *IFAC PapersOnline*, 2022.\n", + "\n", + "[[3]] Dulmage and Mendelsohn, \"Coverings of bipartite graphs\". *Can J. Math.*, 1958.\n", + "\n", + "[[4]] Pothen and Fan, \"Computing the block triangular form of a sparse matrix\". *ACM Trans. Math. Softw.*, 1990.\n", + "\n", + "[[5]] Parker et al., \"Applications of the Dulmage-Mendelsohn decomposition for debugging nonlinear optimization problems\". *Comp. Chem. Eng.*, 2023.\n", + "\n", + "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", + "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", + "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", + "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", + "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/diagnostics/structural_singularity_usr.ipynb b/idaes_examples/notebooks/docs/diagnostics/structural_singularity_usr.ipynb index 29eb4cc5..8e0cdda0 100644 --- a/idaes_examples/notebooks/docs/diagnostics/structural_singularity_usr.ipynb +++ b/idaes_examples/notebooks/docs/diagnostics/structural_singularity_usr.ipynb @@ -1,701 +1,727 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Debugging a Structural Singularity\n", - "===========================\n", - "Author: Robert Parker\\\n", - "Maintainer: Robert Parker\\\n", - "Updated: 2024-06-10\n", - "\n", - "In this tutorial, we will use the [IDAES Diagnostics Toolbox](https://idaes-pse.readthedocs.io/en/2.4.0/explanations/model_diagnostics/index.html#diagnostics-toolbox)\n", - "to diagnose and fix a structural singularity that is preventing us from solving an optimization problem." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "# Constructing the model\n", - "\n", - "Suppose a collaborator has given us a model to work with. They give us a square model and tell us what the degrees of freedom are. We construct an optimization problem and try to solve it. In this tutorial, we don't want to worry too much about the details that go into constructing the model. This has been provided in the `idaes_examples.mod.diagnostics.gas_solid_contactors.model` module.\n", - "\n", - "## Model details (OKAY TO SKIP)\n", - "\n", - "The model we are trying to optimize is a dynamic model of a moving bed chemical looping combustion reactor. The model has been described by [Okoli et al.][1] and [Parker and Biegler][2]. This is a gas-solid reactor with counter-current flow. The degrees of freedom are gas and solid inlet flow rates, and we are trying to minimize the deviation from a desired operating point via a least-squares objective function.\n", - "\n", - "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", - "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", - "\n", - "Again, we don't want to worry too much about the model. The `make_model` function will construct the optimization problem that we want to solve, and whenever we do something model-specific, we will explicitly make note of it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trying to solve the original model\n", - "\n", - "With that out of the way, let's construct the model and try to solve it!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.diagnostics.gas_solid_contactors.model import make_model\n", - "import logging\n", - "\n", - "# We'll turn off IDAES logging. This is not recommended in general, but this is an old model\n", - "# (from IDAES 1.7) that has been ported to work with the current version of IDAES. It generates\n", - "# a lot of warnings.\n", - "logging.getLogger(\"idaes\").setLevel(logging.CRITICAL)\n", - "# We'll also turn off Pyomo logging. This will suppress unit inconsistency warnings later,\n", - "# which otherwise flood our console and slow down this notebook. We have unit inconsistencies\n", - "# as, in IDAES 1.7, we didn't rigorously enforce that models use units.\n", - "logging.getLogger(\"pyomo\").setLevel(logging.CRITICAL)\n", - "\n", - "# This constructs a dynamic model with degrees of freedom and an objective function.\n", - "model = make_model()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before trying to solve the model, let's make sure it conforms to our expectations, i.e. it (a) has degrees of freedom and (b) is well-initialized to a feasible point." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import some useful utilities from the model_statistics module.\n", - "# Degrees of freedom and constraint residuals are always good things to check before\n", - "# trying to solve a simulation or optimization problem.\n", - "from idaes.core.util.model_statistics import degrees_of_freedom, large_residuals_set\n", - "\n", - "dof = degrees_of_freedom(model)\n", - "print(f\"Degrees of freedom: {dof}\")\n", - "has_large_residuals = bool(large_residuals_set(model, tol=1e-5))\n", - "print(f\"Has large residuals: {has_large_residuals}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above `make_model` function, the model has been \"solved\" to arrive at a feasible point, then degrees of freedom have been unfixed and an objective function has been added to give us an optimization problem. This looks good so far, so let's try to solve the optimization problem." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import pyomo.environ for access to solvers\n", - "import pyomo.environ as pyo\n", - "\n", - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.options[\"max_iter\"] = 20\n", - "solver.options[\"print_user_options\"] = \"yes\"\n", - "solver.options[\"OF_print_info_string\"] = \"yes\"\n", - "res = solver.solve(model, tee=True)\n", - "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IPOPT fails to solve the optimization problem... You can try increasing the iteration limit, but it is very unlikely that this model will ever solve. A telltale sign that something is wrong with our model is the persistence of regularization coefficients, that is, numbers in the `lg(rg)` column of the IPOPT log. These coefficients can have multiple causes. One is that the constraint Jacobian (partial derivative matrix) is singular, which indicates a problem with our model. We have set the `print_info_string` option in IPOPT to display \"diagnostic tags\" to help interpret these regularization coefficients. The \"L\" and \"l\" diagnostic tags, which appear repeatedly, indicate that the Jacobian is singular. For more information on IPOPT diagnostic tags, see the IPOPT [documentation](https://coin-or.github.io/Ipopt/OUTPUT.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Debugging the original model\n", - "\n", - "Let's run the diagnostics toolbox on the model and see what it has to say.\n", - "\n", - "For good practice, we'll first make sure the model we're debugging is square. Remember that we're assuming we already know how to toggle degrees of freedom in our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix gas and solid flow rates at their respective inlets\n", - "model.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", - "model.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", - "# Part of our optimization problem was a set of constraints to enforce piecewise\n", - "# constant control inputs. We need to deactivate these as well.\n", - "model.piecewise_constant_constraints.deactivate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run the diagnostics toolbox." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util.model_diagnostics import DiagnosticsToolbox\n", - "\n", - "dt = DiagnosticsToolbox(model)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the warnings we got:\n", - "- Inconsistent units\n", - "- Structural singularity\n", - "- Potential evaluation errors\n", - "\n", - "We'll ignore the inconsistent units. The property package and unit model here were extracted from IDAES 1.7, before we rigorously enforced that all models use units. The potential evaluation errors we see here may be worth looking into, but looking at the failing IPOPT log above, we don't notice any evaluation errors. (If evaluation errors occurred in IPOPT, we would see a message like \"Error in AMPL evaluation\" in the IPOPT iteration log, which we don't see here.) The structural singularity looks like the most promising avenue to debug, especially as the IPOPT log displays persistent regularization coefficients that appear to be caused by a singular Jacobian.\n", - "\n", - "Let's follow the toolbox's advice and display the under and over-constrained sets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.display_underconstrained_set()\n", - "dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Over and under-constrained subsystems\n", - "\n", - "Structural singularities are characterized by the [Dulmage-Mendelson decomposition][3], which partitions a system into minimal over and under-constrained subsystems. These subsystems contain the potentially unmatched constraints and variables, respectively. Here, \"unmatched\" effectively means \"causing a singularity\". [Pothen and Fan][4] give a good overview of the Dulmage-Mendelsohn decomposition and [Parker et al.][5] give several examples.\n", - "\n", - "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", - "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", - "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n", - "\n", - "The most straightforward way to fix a structural singularity is to fix variables that are in the under-constrained system and deactivate constraints in the over-constrained subsystem. However, this may not be applicable for every model. For example, we may need to add variables and constraints instead. What over and under-constrained subsystems are telling us is that something is wrong with our modeling assumptions. The particular fix that is appropriate will depend heavily on the model.\n", - "\n", - "If the above output gives us any clues, we can go ahead and start trying to fix things. However, suppose it doesn't. A good strategy is to try to break down the model into smaller, square subsystems that we think should be nonsingular. For a dynamic model like this one, a good candidate is the subsystem of variables and equations at each point in time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We've included a utility function to extract the subsystem of variables and equations\n", - "# at a specified point in time. If you are dealing with a process flowsheet, here you\n", - "# may want to extract each unit model individually.\n", - "from idaes_examples.mod.diagnostics.util import get_subsystem_at_time\n", - "\n", - "# TemporarySubsystemManager is used to temporarily fix some variables to make sure\n", - "# we're debugging a square subsystem.\n", - "from pyomo.util.subsystems import TemporarySubsystemManager\n", - "\n", - "# Let's start with t=0. Really, we'd probably want to do this in a loop and try all time points.\n", - "t0 = model.fs.time.first()\n", - "t_block, inputs = get_subsystem_at_time(model, model.fs.time, t0)\n", - "# We'll temporarily fix the \"inputs\" to make sure we have a square system while debugging\n", - "with TemporarySubsystemManager(to_fix=inputs):\n", - " dt = DiagnosticsToolbox(t_block)\n", - " dt.report_structural_issues()\n", - " dt.display_underconstrained_set()\n", - " dt.display_overconstrained_set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These over and under-constrained subsystems aren't much smaller, but now the over-constrained system decomposes into 10 small, independent blocks. These should be easier to debug.\n", - "\n", - "## Debugging the over-constrained subsystem\n", - "\n", - "To debug the over-constrained subsystem, we look for a constraint that is not calculating any of the variables in the subsystem. The \"odd constraint out\" here seems to be the mass fraction sum, `sum_component_eqn`. This must \"solve for\" one of the mass fractions, which means one of the `material_holdup_calculation` equations must \"solve for\" particle density rather than mass fraction. If we want to see what variables are contained in one of these constraints, we can always `pprint` it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fs.MB.solid_phase.properties[0, 0.9].sum_component_eqn.pprint()\n", - "model.fs.MB.solid_phase.material_holdup_calculation[0, 0.9, \"Sol\", \"Fe3O4\"].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If one of these `material_holdup_calculation` equations is solving for particle density, then that means that `density_particle_constraint` is not actually solving for density. Maybe `density_particle_constraint` is over-determining our system?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fs.MB.solid_phase.properties[0, 0.9].density_particle_constraint.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this looks like a very reasonable constraint. After some thought, which admittedly requires some knowledge of the process we are modeling, we decide that the right approach is to make particle porosity a variable. We have assumed that porosity is constant, but this overconstrained subsystem is telling us that this assumption is not valid.\n", - "\n", - "### How did we figure this out? (OKAY TO SKIP)\n", - "Adding a variable (including by unfixing a parameter) to an over-constraining constraint will often remove that constraint from the over-constrained subsystem. But how did we know that this was the right thing to do? If you just care about using the diagnostics toolbox to extract as much information about a singularity as possible, you can skip this section. But if you are curious how we determined that particle porosity should not be constant, read on.\n", - "\n", - "`dens_mass_skeletal` is determined purely by the composition of solid, which is made up of Fe2O3, Fe3O4, and inert Ti2O3. We can view the `density_skeletal_constraint` as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fs.MB.solid_phase.properties[0, 0.9].density_skeletal_constraint.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we assume a constant particle porosity, this gives us a particle porosity that is also uniquely determined by the solid composition by the above `density_particle_constraint`:\n", - "```\n", - "dens_mass_particle = (1 - porosity) * dens_mass_skeletal\n", - "```\n", - "But the composition of the solid is determined by the (somewhat misnamed) `material_holdup_calculation` constraints. While the name of these constraints implies they \"calculate holdups,\" material holdups at $t=0$ are fixed as initial conditions (because holdups are the differential variables with respect to time in this model). At other time points, we assume that holdups are specified by differential and discretization equations of the model. This means that the `material_holdup_calculation` constraints actually calculate the solid phase mass fractions *from* the holdups. But as we hinted at above, the 4-by-4 system of holdup calculation constraints, `sum_component_eqn` (which simply constrains the sum of mass fractions to be one), mass fractions, and `dens_mass_particle`, uniquely solve for `dens_mass_particle` *as well as* the mass fractions. But if the holdup variables can be used to solve for the mass fractions, they *also* solve for `dens_mass_skeletal`. So both sides of `density_particle_constraint` are already uniquely determined! This implies that we don't need this constraint at all, but we also know that this constraint has to hold. Something has to give. With this in mind, we actually have several options for how to resolve this overspecification:\n", - "1. Remove `density_particle_constraint`. Then we would have `dens_mass_particle` and `dens_mass_skeletal`, with no relationship between them. This would leave us with a mathematically sound model, but with densities that contradict constant particle porosity that we have assumed (which is used elsewhere in the reaction rate calculation equations).\n", - "2. Remove the constraints that calculate skeletal density from composition.\n", - "3. Relax particle porosity from a parameter to a variable.\n", - "\n", - "Options 2 and 3 are equally valid. We've chosen option 3, meaning we assume that the particle \"evolves\" with a density that is well determined from its constituent species, rather than changing density to accommodate whatever mass it accumulates via reaction without altering its volume. This exercise should remind us that all mathematical modeling is somewhat of an art. In the process of choosing the \"least bad\" model, it is fairly easy to over or under-specify something by making the wrong combination of assumptions, and the Dulmage-Mendelsohn decomposition is a great tool for detecting when this has happened.\n", - "\n", - "## Debugging the under-constrained subsystem\n", - "\n", - "The under-constrained system does not decompose into independent subsystems, making it more difficult to debug. However, by inspection, we notice that the same constraints and variables seem to be repeated at each point in the length domain. For each point in space, the \"odd variable out\" seems to be the total flow rate `flow_mass`. Using some intuition about this particular process model, we may conclude that this variable should be calculated from the solid phase velocity, which is constant. We expect an equation that looks like\n", - "```\n", - "flow_mass == velocity * area * density\n", - "```\n", - "\n", - "But this equation isn't here... so we need to add it.\n", - "\n", - "# Fixing the model\n", - "\n", - "We'll start by creating a fresh copy of the model, so we don't accidentally rely on IPOPT's point of termination." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2 = make_model()\n", - "# Make the model square while we try to fix the structural singularity\n", - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.deactivate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding a new particle porosity variable" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.particle_porosity = pyo.Var(\n", - " model2.fs.time,\n", - " model2.fs.MB.length_domain,\n", - " initialize=model2.fs.solid_properties.particle_porosity.value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to replace the old particle porosity parameter with this new variable. Luckily, the old parameter is actually implemented as a fixed variable, so we can easily identify all the constraints it participates in with `IncidenceGraphInterface`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.contrib.incidence_analysis import IncidenceGraphInterface\n", - "\n", - "igraph = IncidenceGraphInterface(model2, include_fixed=True)\n", - "porosity_param = model2.fs.solid_properties.particle_porosity\n", - "print(f\"Constraints containing {porosity_param.name}:\")\n", - "for con in igraph.get_adjacent_to(porosity_param):\n", - " print(f\" {con.name}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Particle porosity only appears in two constraints: the density constraint we saw above, and the reaction rate equation. We can replace particle porosity in these constraints using Pyomo's `replace_expressions` function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.core.expr import replace_expressions\n", - "\n", - "for t, x in model2.fs.time * model2.fs.MB.length_domain:\n", - " substitution_map = {id(porosity_param): model2.fs.MB.particle_porosity[t, x]}\n", - " sp = model2.fs.MB.solid_phase\n", - " cons = [\n", - " sp.properties[t, x].density_particle_constraint,\n", - " sp.reactions[t, x].gen_rate_expression[\"R1\"],\n", - " ]\n", - " for con in cons:\n", - " con.set_value(\n", - " replace_expressions(\n", - " con.expr, substitution_map, descend_into_named_expressions=True\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have added a new `particle_porosity` variable, and are using it in the relevant locations. Now we can move on to adding the missing constraint.\n", - "\n", - "## Adding a new density-flow rate constraint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@model2.fs.MB.Constraint(model2.fs.time, model2.fs.MB.length_domain)\n", - "def density_flowrate_constraint(mb, t, x):\n", - " return (\n", - " mb.velocity_superficial_solid[t]\n", - " * mb.bed_area\n", - " * mb.solid_phase.properties[t, x].dens_mass_particle\n", - " == mb.solid_phase.properties[t, x].flow_mass\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Testing the new model\n", - "\n", - "Let's see if these changes have fixed our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Construct a new diagnostics toolbox\n", - "dt = DiagnosticsToolbox(model2)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The structural singularity seems to be gone! Let's unfix our degrees of freedom and see if we can solve." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", - "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", - "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.activate()\n", - "\n", - "res = solver.solve(model2, tee=True)\n", - "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This doesn't look much better. What's going on? I thought we just fixed the issue?\n", - "\n", - "# Debugging the model, take two\n", - "\n", - "Let's check the diagnostics toolbox for numerical issues." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.deactivate()\n", - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks like we have \"parallel constraints\", which are another form of singularity. Let's follow the toolbox's advice to see what they are." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.display_near_parallel_constraints()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`density_flowrate_constraint` is the constraint that we added. What is `solid_super_vel`?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.solid_super_vel[0].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the same as the constraint we just added! Looks like that constraint already existed at the solid inlet. We can easily deactivate the new constraints at this point in the length domain:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.density_flowrate_constraint[:, 1.0].deactivate();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But now we have removed constraints from a square model, and expect to have degrees of freedom. Let's see what the diagnostics toolbox has to say." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(model2)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But this doesn't help us very much. We have some extraneous degrees of freedom, but with 8881 variables in the under-constrained subsystem, it will be difficult to tell what they are. After some thought (and model-specific intuition), we land on the conclusion that maybe we need to fix particle porosity at the solid inlet. Here, total flow rate is specified, and the `solid_super_vel` equation is using it to compute velocity. So we need `dens_mass_particle` to be known, which means we need `particle_porosity` to be fixed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.particle_porosity[:, 1.0].fix();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run the diagnostics toolbox as a sanity check." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt = DiagnosticsToolbox(model2)\n", - "dt.report_structural_issues()\n", - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks good! Now we can release our degrees of freedom and try to solve again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", - "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", - "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", - "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", - "model2.piecewise_constant_constraints.activate()\n", - "\n", - "res = solver.solve(model2, tee=True)\n", - "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It worked! For the simple optimization problem we have set up, this solve looks a lot more like what we expect.\n", - "\n", - "# Takeaways from this tutorial\n", - "What have we learned?\n", - "1. IPOPT using non-zero regularization coefficients hints at a singular Jacobian (especially when \"L\"/\"l\" diagnostic tags are present).\n", - "2. When this happens, start by calling `report_structural_issues` to check for a structural singularity. If this looks good, call `report_numerical_issues` to check for a numerical singularity.\n", - "3. When debugging a structural singularity, decomposing a problem into subsystems that each should be nonsingular (e.g. unit models or points in time) is very useful.\n", - "4. The solution to a structural singularity is often to relax a fixed parameter, add a constraint that was forgotten, remove a constraint that was redundant, or fix an extraneous degree of freedom.\n", - "5. Model-specific intuition is usually necessary to diagnose and fix modeling issues. (If you're an algorithm developer, learn about the models you're using! If you don't understand your models, you don't understand your algorithms!)\n", - "6. A modeling issue doesn't necessarily have a unique solution. This is especially true when the issue involves invalid assumptions.\n", - "7. Debugging is an iterative process — fixing one issue can introduce another." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# References\n", - "\n", - "[[1]] Okoli et al., \"A framework for the optimization of chemical looping combustion processes\". *Powder Tech*, 2020.\n", - "\n", - "[[2]] Parker and Biegler, \"Dynamic modeling and nonlinear model predictive control of a moving bed chemical looping combustion reactor\". *IFAC PapersOnline*, 2022.\n", - "\n", - "[[3]] Dulmage and Mendelsohn, \"Coverings of bipartite graphs\". *Can J. Math.*, 1958.\n", - "\n", - "[[4]] Pothen and Fan, \"Computing the block triangular form of a sparse matrix\". *ACM Trans. Math. Softw.*, 1990.\n", - "\n", - "[[5]] Parker et al., \"Applications of the Dulmage-Mendelsohn decomposition for debugging nonlinear optimization problems\". *Comp. Chem. Eng.*, 2023.\n", - "\n", - "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", - "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", - "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", - "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", - "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Debugging a Structural Singularity\n", + "===========================\n", + "Author: Robert Parker\\\n", + "Maintainer: Robert Parker\\\n", + "Updated: 2024-06-10\n", + "\n", + "In this tutorial, we will use the [IDAES Diagnostics Toolbox](https://idaes-pse.readthedocs.io/en/2.4.0/explanations/model_diagnostics/index.html#diagnostics-toolbox)\n", + "to diagnose and fix a structural singularity that is preventing us from solving an optimization problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Constructing the model\n", + "\n", + "Suppose a collaborator has given us a model to work with. They give us a square model and tell us what the degrees of freedom are. We construct an optimization problem and try to solve it. In this tutorial, we don't want to worry too much about the details that go into constructing the model. This has been provided in the `idaes_examples.mod.diagnostics.gas_solid_contactors.model` module.\n", + "\n", + "## Model details (OKAY TO SKIP)\n", + "\n", + "The model we are trying to optimize is a dynamic model of a moving bed chemical looping combustion reactor. The model has been described by [Okoli et al.][1] and [Parker and Biegler][2]. This is a gas-solid reactor with counter-current flow. The degrees of freedom are gas and solid inlet flow rates, and we are trying to minimize the deviation from a desired operating point via a least-squares objective function.\n", + "\n", + "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", + "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", + "\n", + "Again, we don't want to worry too much about the model. The `make_model` function will construct the optimization problem that we want to solve, and whenever we do something model-specific, we will explicitly make note of it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trying to solve the original model\n", + "\n", + "With that out of the way, let's construct the model and try to solve it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.diagnostics.gas_solid_contactors.model import make_model\n", + "import logging\n", + "\n", + "# We'll turn off IDAES logging. This is not recommended in general, but this is an old model\n", + "# (from IDAES 1.7) that has been ported to work with the current version of IDAES. It generates\n", + "# a lot of warnings.\n", + "logging.getLogger(\"idaes\").setLevel(logging.CRITICAL)\n", + "# We'll also turn off Pyomo logging. This will suppress unit inconsistency warnings later,\n", + "# which otherwise flood our console and slow down this notebook. We have unit inconsistencies\n", + "# as, in IDAES 1.7, we didn't rigorously enforce that models use units.\n", + "logging.getLogger(\"pyomo\").setLevel(logging.CRITICAL)\n", + "\n", + "# This constructs a dynamic model with degrees of freedom and an objective function.\n", + "model = make_model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before trying to solve the model, let's make sure it conforms to our expectations, i.e. it (a) has degrees of freedom and (b) is well-initialized to a feasible point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import some useful utilities from the model_statistics module.\n", + "# Degrees of freedom and constraint residuals are always good things to check before\n", + "# trying to solve a simulation or optimization problem.\n", + "from idaes.core.util.model_statistics import degrees_of_freedom, large_residuals_set\n", + "\n", + "dof = degrees_of_freedom(model)\n", + "print(f\"Degrees of freedom: {dof}\")\n", + "has_large_residuals = bool(large_residuals_set(model, tol=1e-5))\n", + "print(f\"Has large residuals: {has_large_residuals}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above `make_model` function, the model has been \"solved\" to arrive at a feasible point, then degrees of freedom have been unfixed and an objective function has been added to give us an optimization problem. This looks good so far, so let's try to solve the optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import pyomo.environ for access to solvers\n", + "import pyomo.environ as pyo\n", + "\n", + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.options[\"max_iter\"] = 20\n", + "solver.options[\"print_user_options\"] = \"yes\"\n", + "solver.options[\"OF_print_info_string\"] = \"yes\"\n", + "res = solver.solve(model, tee=True)\n", + "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IPOPT fails to solve the optimization problem... You can try increasing the iteration limit, but it is very unlikely that this model will ever solve. A telltale sign that something is wrong with our model is the persistence of regularization coefficients, that is, numbers in the `lg(rg)` column of the IPOPT log. These coefficients can have multiple causes. One is that the constraint Jacobian (partial derivative matrix) is singular, which indicates a problem with our model. We have set the `print_info_string` option in IPOPT to display \"diagnostic tags\" to help interpret these regularization coefficients. The \"L\" and \"l\" diagnostic tags, which appear repeatedly, indicate that the Jacobian is singular. For more information on IPOPT diagnostic tags, see the IPOPT [documentation](https://coin-or.github.io/Ipopt/OUTPUT.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Debugging the original model\n", + "\n", + "Let's run the diagnostics toolbox on the model and see what it has to say.\n", + "\n", + "For good practice, we'll first make sure the model we're debugging is square. Remember that we're assuming we already know how to toggle degrees of freedom in our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix gas and solid flow rates at their respective inlets\n", + "model.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", + "model.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", + "# Part of our optimization problem was a set of constraints to enforce piecewise\n", + "# constant control inputs. We need to deactivate these as well.\n", + "model.piecewise_constant_constraints.deactivate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run the diagnostics toolbox." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.model_diagnostics import DiagnosticsToolbox\n", + "\n", + "dt = DiagnosticsToolbox(model)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the warnings we got:\n", + "- Inconsistent units\n", + "- Structural singularity\n", + "- Potential evaluation errors\n", + "\n", + "We'll ignore the inconsistent units. The property package and unit model here were extracted from IDAES 1.7, before we rigorously enforced that all models use units. The potential evaluation errors we see here may be worth looking into, but looking at the failing IPOPT log above, we don't notice any evaluation errors. (If evaluation errors occurred in IPOPT, we would see a message like \"Error in AMPL evaluation\" in the IPOPT iteration log, which we don't see here.) The structural singularity looks like the most promising avenue to debug, especially as the IPOPT log displays persistent regularization coefficients that appear to be caused by a singular Jacobian.\n", + "\n", + "Let's follow the toolbox's advice and display the under and over-constrained sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt.display_underconstrained_set()\n", + "dt.display_overconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Over and under-constrained subsystems\n", + "\n", + "Structural singularities are characterized by the [Dulmage-Mendelson decomposition][3], which partitions a system into minimal over and under-constrained subsystems. These subsystems contain the potentially unmatched constraints and variables, respectively. Here, \"unmatched\" effectively means \"causing a singularity\". [Pothen and Fan][4] give a good overview of the Dulmage-Mendelsohn decomposition and [Parker et al.][5] give several examples.\n", + "\n", + "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", + "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", + "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n", + "\n", + "The most straightforward way to fix a structural singularity is to fix variables that are in the under-constrained system and deactivate constraints in the over-constrained subsystem. However, this may not be applicable for every model. For example, we may need to add variables and constraints instead. What over and under-constrained subsystems are telling us is that something is wrong with our modeling assumptions. The particular fix that is appropriate will depend heavily on the model.\n", + "\n", + "If the above output gives us any clues, we can go ahead and start trying to fix things. However, suppose it doesn't. A good strategy is to try to break down the model into smaller, square subsystems that we think should be nonsingular. For a dynamic model like this one, a good candidate is the subsystem of variables and equations at each point in time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We've included a utility function to extract the subsystem of variables and equations\n", + "# at a specified point in time. If you are dealing with a process flowsheet, here you\n", + "# may want to extract each unit model individually.\n", + "from idaes_examples.mod.diagnostics.util import get_subsystem_at_time\n", + "\n", + "# TemporarySubsystemManager is used to temporarily fix some variables to make sure\n", + "# we're debugging a square subsystem.\n", + "from pyomo.util.subsystems import TemporarySubsystemManager\n", + "\n", + "# Let's start with t=0. Really, we'd probably want to do this in a loop and try all time points.\n", + "t0 = model.fs.time.first()\n", + "t_block, inputs = get_subsystem_at_time(model, model.fs.time, t0)\n", + "# We'll temporarily fix the \"inputs\" to make sure we have a square system while debugging\n", + "with TemporarySubsystemManager(to_fix=inputs):\n", + " dt = DiagnosticsToolbox(t_block)\n", + " dt.report_structural_issues()\n", + " dt.display_underconstrained_set()\n", + " dt.display_overconstrained_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These over and under-constrained subsystems aren't much smaller, but now the over-constrained system decomposes into 10 small, independent blocks. These should be easier to debug.\n", + "\n", + "## Debugging the over-constrained subsystem\n", + "\n", + "To debug the over-constrained subsystem, we look for a constraint that is not calculating any of the variables in the subsystem. The \"odd constraint out\" here seems to be the mass fraction sum, `sum_component_eqn`. This must \"solve for\" one of the mass fractions, which means one of the `material_holdup_calculation` equations must \"solve for\" particle density rather than mass fraction. If we want to see what variables are contained in one of these constraints, we can always `pprint` it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fs.MB.solid_phase.properties[0, 0.9].sum_component_eqn.pprint()\n", + "model.fs.MB.solid_phase.material_holdup_calculation[0, 0.9, \"Sol\", \"Fe3O4\"].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If one of these `material_holdup_calculation` equations is solving for particle density, then that means that `density_particle_constraint` is not actually solving for density. Maybe `density_particle_constraint` is over-determining our system?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fs.MB.solid_phase.properties[0, 0.9].density_particle_constraint.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this looks like a very reasonable constraint. After some thought, which admittedly requires some knowledge of the process we are modeling, we decide that the right approach is to make particle porosity a variable. We have assumed that porosity is constant, but this overconstrained subsystem is telling us that this assumption is not valid.\n", + "\n", + "### How did we figure this out? (OKAY TO SKIP)\n", + "Adding a variable (including by unfixing a parameter) to an over-constraining constraint will often remove that constraint from the over-constrained subsystem. But how did we know that this was the right thing to do? If you just care about using the diagnostics toolbox to extract as much information about a singularity as possible, you can skip this section. But if you are curious how we determined that particle porosity should not be constant, read on.\n", + "\n", + "`dens_mass_skeletal` is determined purely by the composition of solid, which is made up of Fe2O3, Fe3O4, and inert Ti2O3. We can view the `density_skeletal_constraint` as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fs.MB.solid_phase.properties[0, 0.9].density_skeletal_constraint.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we assume a constant particle porosity, this gives us a particle porosity that is also uniquely determined by the solid composition by the above `density_particle_constraint`:\n", + "```\n", + "dens_mass_particle = (1 - porosity) * dens_mass_skeletal\n", + "```\n", + "But the composition of the solid is determined by the (somewhat misnamed) `material_holdup_calculation` constraints. While the name of these constraints implies they \"calculate holdups,\" material holdups at $t=0$ are fixed as initial conditions (because holdups are the differential variables with respect to time in this model). At other time points, we assume that holdups are specified by differential and discretization equations of the model. This means that the `material_holdup_calculation` constraints actually calculate the solid phase mass fractions *from* the holdups. But as we hinted at above, the 4-by-4 system of holdup calculation constraints, `sum_component_eqn` (which simply constrains the sum of mass fractions to be one), mass fractions, and `dens_mass_particle`, uniquely solve for `dens_mass_particle` *as well as* the mass fractions. But if the holdup variables can be used to solve for the mass fractions, they *also* solve for `dens_mass_skeletal`. So both sides of `density_particle_constraint` are already uniquely determined! This implies that we don't need this constraint at all, but we also know that this constraint has to hold. Something has to give. With this in mind, we actually have several options for how to resolve this overspecification:\n", + "1. Remove `density_particle_constraint`. Then we would have `dens_mass_particle` and `dens_mass_skeletal`, with no relationship between them. This would leave us with a mathematically sound model, but with densities that contradict constant particle porosity that we have assumed (which is used elsewhere in the reaction rate calculation equations).\n", + "2. Remove the constraints that calculate skeletal density from composition.\n", + "3. Relax particle porosity from a parameter to a variable.\n", + "\n", + "Options 2 and 3 are equally valid. We've chosen option 3, meaning we assume that the particle \"evolves\" with a density that is well determined from its constituent species, rather than changing density to accommodate whatever mass it accumulates via reaction without altering its volume. This exercise should remind us that all mathematical modeling is somewhat of an art. In the process of choosing the \"least bad\" model, it is fairly easy to over or under-specify something by making the wrong combination of assumptions, and the Dulmage-Mendelsohn decomposition is a great tool for detecting when this has happened.\n", + "\n", + "## Debugging the under-constrained subsystem\n", + "\n", + "The under-constrained system does not decompose into independent subsystems, making it more difficult to debug. However, by inspection, we notice that the same constraints and variables seem to be repeated at each point in the length domain. For each point in space, the \"odd variable out\" seems to be the total flow rate `flow_mass`. Using some intuition about this particular process model, we may conclude that this variable should be calculated from the solid phase velocity, which is constant. We expect an equation that looks like\n", + "```\n", + "flow_mass == velocity * area * density\n", + "```\n", + "\n", + "But this equation isn't here... so we need to add it.\n", + "\n", + "# Fixing the model\n", + "\n", + "We'll start by creating a fresh copy of the model, so we don't accidentally rely on IPOPT's point of termination." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2 = make_model()\n", + "# Make the model square while we try to fix the structural singularity\n", + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.deactivate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding a new particle porosity variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.particle_porosity = pyo.Var(\n", + " model2.fs.time,\n", + " model2.fs.MB.length_domain,\n", + " initialize=model2.fs.solid_properties.particle_porosity.value,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to replace the old particle porosity parameter with this new variable. Luckily, the old parameter is actually implemented as a fixed variable, so we can easily identify all the constraints it participates in with `IncidenceGraphInterface`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.contrib.incidence_analysis import IncidenceGraphInterface\n", + "\n", + "igraph = IncidenceGraphInterface(model2, include_fixed=True)\n", + "porosity_param = model2.fs.solid_properties.particle_porosity\n", + "print(f\"Constraints containing {porosity_param.name}:\")\n", + "for con in igraph.get_adjacent_to(porosity_param):\n", + " print(f\" {con.name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Particle porosity only appears in two constraints: the density constraint we saw above, and the reaction rate equation. We can replace particle porosity in these constraints using Pyomo's `replace_expressions` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.core.expr import replace_expressions\n", + "\n", + "for t, x in model2.fs.time * model2.fs.MB.length_domain:\n", + " substitution_map = {id(porosity_param): model2.fs.MB.particle_porosity[t, x]}\n", + " sp = model2.fs.MB.solid_phase\n", + " cons = [\n", + " sp.properties[t, x].density_particle_constraint,\n", + " sp.reactions[t, x].gen_rate_expression[\"R1\"],\n", + " ]\n", + " for con in cons:\n", + " con.set_value(\n", + " replace_expressions(\n", + " con.expr, substitution_map, descend_into_named_expressions=True\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have added a new `particle_porosity` variable, and are using it in the relevant locations. Now we can move on to adding the missing constraint.\n", + "\n", + "## Adding a new density-flow rate constraint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@model2.fs.MB.Constraint(model2.fs.time, model2.fs.MB.length_domain)\n", + "def density_flowrate_constraint(mb, t, x):\n", + " return (\n", + " mb.velocity_superficial_solid[t]\n", + " * mb.bed_area\n", + " * mb.solid_phase.properties[t, x].dens_mass_particle\n", + " == mb.solid_phase.properties[t, x].flow_mass\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the new model\n", + "\n", + "Let's see if these changes have fixed our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a new diagnostics toolbox\n", + "dt = DiagnosticsToolbox(model2)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The structural singularity seems to be gone! Let's unfix our degrees of freedom and see if we can solve." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", + "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", + "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.activate()\n", + "\n", + "res = solver.solve(model2, tee=True)\n", + "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This doesn't look much better. What's going on? I thought we just fixed the issue?\n", + "\n", + "# Debugging the model, take two\n", + "\n", + "Let's check the diagnostics toolbox for numerical issues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.deactivate()\n", + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like we have \"parallel constraints\", which are another form of singularity. Let's follow the toolbox's advice to see what they are." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt.display_near_parallel_constraints()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`density_flowrate_constraint` is the constraint that we added. What is `solid_super_vel`?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.solid_super_vel[0].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same as the constraint we just added! Looks like that constraint already existed at the solid inlet. We can easily deactivate the new constraints at this point in the length domain:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.density_flowrate_constraint[:, 1.0].deactivate();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But now we have removed constraints from a square model, and expect to have degrees of freedom. Let's see what the diagnostics toolbox has to say." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(model2)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this doesn't help us very much. We have some extraneous degrees of freedom, but with 8881 variables in the under-constrained subsystem, it will be difficult to tell what they are. After some thought (and model-specific intuition), we land on the conclusion that maybe we need to fix particle porosity at the solid inlet. Here, total flow rate is specified, and the `solid_super_vel` equation is using it to compute velocity. So we need `dens_mass_particle` to be known, which means we need `particle_porosity` to be fixed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.particle_porosity[:, 1.0].fix();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's run the diagnostics toolbox as a sanity check." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt = DiagnosticsToolbox(model2)\n", + "dt.report_structural_issues()\n", + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Now we can release our degrees of freedom and try to solve again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model2.fs.MB.gas_phase.properties[:, 0].flow_mol.unfix()\n", + "model2.fs.MB.gas_phase.properties[0, 0].flow_mol.fix()\n", + "model2.fs.MB.solid_phase.properties[:, 1].flow_mass.unfix()\n", + "model2.fs.MB.solid_phase.properties[0, 1].flow_mass.fix()\n", + "model2.piecewise_constant_constraints.activate()\n", + "\n", + "res = solver.solve(model2, tee=True)\n", + "print(f\"Converged successfully: {pyo.check_optimal_termination(res)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It worked! For the simple optimization problem we have set up, this solve looks a lot more like what we expect.\n", + "\n", + "# Takeaways from this tutorial\n", + "What have we learned?\n", + "1. IPOPT using non-zero regularization coefficients hints at a singular Jacobian (especially when \"L\"/\"l\" diagnostic tags are present).\n", + "2. When this happens, start by calling `report_structural_issues` to check for a structural singularity. If this looks good, call `report_numerical_issues` to check for a numerical singularity.\n", + "3. When debugging a structural singularity, decomposing a problem into subsystems that each should be nonsingular (e.g. unit models or points in time) is very useful.\n", + "4. The solution to a structural singularity is often to relax a fixed parameter, add a constraint that was forgotten, remove a constraint that was redundant, or fix an extraneous degree of freedom.\n", + "5. Model-specific intuition is usually necessary to diagnose and fix modeling issues. (If you're an algorithm developer, learn about the models you're using! If you don't understand your models, you don't understand your algorithms!)\n", + "6. A modeling issue doesn't necessarily have a unique solution. This is especially true when the issue involves invalid assumptions.\n", + "7. Debugging is an iterative process — fixing one issue can introduce another." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "[[1]] Okoli et al., \"A framework for the optimization of chemical looping combustion processes\". *Powder Tech*, 2020.\n", + "\n", + "[[2]] Parker and Biegler, \"Dynamic modeling and nonlinear model predictive control of a moving bed chemical looping combustion reactor\". *IFAC PapersOnline*, 2022.\n", + "\n", + "[[3]] Dulmage and Mendelsohn, \"Coverings of bipartite graphs\". *Can J. Math.*, 1958.\n", + "\n", + "[[4]] Pothen and Fan, \"Computing the block triangular form of a sparse matrix\". *ACM Trans. Math. Softw.*, 1990.\n", + "\n", + "[[5]] Parker et al., \"Applications of the Dulmage-Mendelsohn decomposition for debugging nonlinear optimization problems\". *Comp. Chem. Eng.*, 2023.\n", + "\n", + "[1]: https://www.sciencedirect.com/science/article/pii/S0032591019302803\n", + "[2]: https://www.sciencedirect.com/science/article/pii/S2405896322008825\n", + "[3]: https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/coverings-of-bipartite-graphs/413735C5888AB542B92D0C4F402800B1\n", + "[4]: https://dl.acm.org/doi/10.1145/98267.98287\n", + "[5]: https://www.sciencedirect.com/science/article/pii/S0098135423002533\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing.ipynb index 656dc674..8cbcaa64 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_doc.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_doc.ipynb index 0a6a239c..d775891c 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_doc.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -120,476 +121,610 @@ "fs.F101\n", "fs.S101\n", "fs.C101\n", - "fs.M101\n" + "fs.M101\n", + "2025-03-17 17:33:04 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - 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"2023-11-02 10:23:48 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:48 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:48 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:48 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:49 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - 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"2023-11-02 10:23:50 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:50 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:51 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:52 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:52 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:52 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:52 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { @@ -603,21 +738,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:52 [INFO] idaes.init.fs.F102.control_volume: Initialization Complete\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:52 [INFO] idaes.init.fs.F102: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:06 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Solving flowsheet...\n", + "Solving flowsheet..." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "\n" ] }, @@ -664,26 +806,32 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 6.60e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 0 0.0000000e+00 6.60e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 1 0.0000000e+00 8.69e+03 1.42e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", " 2 0.0000000e+00 3.05e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.58e+03 1.55e+05 -1.0 1.41e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.49e+02 8.87e+08 -1.0 8.43e+03 - 1.00e+00 9.57e-01h 1\n", + " 3 0.0000000e+00 1.58e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.49e+02 8.87e+08 -1.0 8.42e+03 - 1.00e+00 9.57e-01h 1\n", " 5 0.0000000e+00 4.25e+03 2.87e+10 -1.0 8.02e+02 - 1.00e+00 9.90e-01h 1\n", " 6 0.0000000e+00 2.25e+03 1.51e+10 -1.0 8.39e+00 - 1.00e+00 1.00e+00h 1\n", " 7 0.0000000e+00 2.27e+01 1.40e+08 -1.0 2.45e-03 - 1.00e+00 1.00e+00f 1\n", " 8 0.0000000e+00 2.45e-03 1.23e+04 -1.0 2.38e-05 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 3.06e-01 -2.5 9.06e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 3.73e-08 3.38e-01 -2.5 1.06e-07 - 1.00e+00 1.00e+00h 1\n", "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", "\n", "Number of Iterations....: 9\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 2.8284422320850682e+05 2.8284422320850682e+05\n", - "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Dual infeasibility......: 2.8284425441187131e+05 2.8284425441187131e+05\n", + "Constraint violation....: 5.8207660913467407e-11 3.7252902984619141e-08\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 2.8284422320850682e+05\n", + "Overall NLP error.......: 5.8207660913467407e-11 2.8284425441187131e+05\n", "\n", "\n", "Number of objective function evaluations = 11\n", @@ -693,8 +841,8 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.001\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -975,13 +1123,25 @@ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 0.0000000e+00 1.00e+05 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 0.0000000e+00 8.81e+04 5.49e+03 -1.0 9.20e+04 - 2.62e-01 1.20e-01h 1\n", - " 2 0.0000000e+00 6.34e+04 2.46e+03 -1.0 8.10e+04 - 5.83e-01 2.87e-01h 1\n", + " 2 0.0000000e+00 6.34e+04 2.46e+03 -1.0 8.10e+04 - 5.83e-01 2.87e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 3 0.0000000e+00 3.40e+04 5.65e+03 -1.0 5.80e+04 - 5.72e-01 4.86e-01h 1\n", " 4 0.0000000e+00 2.33e+04 5.28e+05 -1.0 3.01e+04 - 7.04e-01 5.09e-01h 1\n", " 5 0.0000000e+00 1.13e+04 2.99e+09 -1.0 1.49e+04 - 8.02e-01 9.65e-01h 1\n", " 6 0.0000000e+00 5.50e+03 1.54e+09 -1.0 8.18e+02 - 9.90e-01 6.10e-01h 1\n", " 7 0.0000000e+00 5.23e+03 1.44e+09 -1.0 3.84e+02 - 9.92e-01 5.07e-02h 1\n", - " 8 0.0000000e+00 5.22e+03 1.44e+09 -1.0 3.70e+02 - 1.00e+00 5.85e-04h 1\n", + " 8 0.0000000e+00 5.22e+03 1.44e+09 -1.0 3.70e+02 - 1.00e+00 5.85e-04h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 9r 0.0000000e+00 5.22e+03 1.00e+03 2.3 0.00e+00 - 0.00e+00 3.66e-07R 5\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10r 0.0000000e+00 6.47e+04 1.34e+04 2.3 1.78e+05 - 2.30e-03 1.09e-03f 1\n", @@ -1026,17 +1186,17 @@ " 46 0.0000000e+00 7.56e+00 5.75e+06 -1.0 1.54e+01 - 1.00e+00 5.00e-01h 2\n", " 47 0.0000000e+00 3.06e+01 1.36e+07 -1.0 1.62e+00 - 1.00e+00 1.00e+00h 1\n", " 48 0.0000000e+00 3.18e-04 1.31e+03 -1.0 3.34e+01 - 1.00e+00 1.00e+00h 1\n", - " 49 0.0000000e+00 2.24e-08 3.21e-01 -3.8 1.06e-07 - 1.00e+00 1.00e+00h 1\n", + " 49 0.0000000e+00 2.16e-07 7.98e+00 -3.8 6.86e-06 - 1.00e+00 1.00e+00h 1\n", "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", "\n", "Number of Iterations....: 49\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5041240304748950e+04 1.5041240304748950e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Dual infeasibility......: 1.5041240458817709e+04 1.5041240458817709e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.1571759134531021e-07\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5041240304748950e+04\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5041240458817709e+04\n", "\n", "\n", "Number of objective function evaluations = 74\n", @@ -1046,8 +1206,8 @@ "Number of equality constraint Jacobian evaluations = 51\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 49\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.061\n", - "Total CPU secs in NLP function evaluations = 0.003\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.040\n", + "Total CPU secs in NLP function evaluations = 0.004\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -1090,61 +1250,54 @@ "output_type": "stream", "text": [ "Setting inputs...\n", - "\n" + "\n", + "Initializing flowsheet...\n", + "\n", + "Limiting Wegstein tear to 3 iterations to obtain initial solution, if not converged IPOPT will pick up and continue.\n", + "\n", + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Initializing flowsheet...\n", - "\n", - "Limiting Wegstein tear to 3 iterations to obtain initial solution, if not converged IPOPT will pick up and continue.\n", - "\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: Wegstein failed to converge in 3 iterations\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Solving flowsheet...\n", - "\n", - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", "component keys that are not exported as part of the NL file. Skipping.\n" ] }, @@ -1152,355 +1305,865 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Adding distillation column and resolving flowsheet...\n", - "\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", - "contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Complete.\n", - "\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", - "contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:07 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:08 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:33:08 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:08 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:33:08 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] - } - ], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "Costs in $1000:\n" + "2025-03-17 17:33:08 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { - "data": { - "text/html": [ - "
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\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Wegstein failed to converge in 3 iterations\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:09 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:09 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:09 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:09 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:09 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - 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Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding distillation column and resolving flowsheet...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Complete.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + } + ], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Costs in $1000:\n" + ] + }, + { + "data": { + "text/html": [ + "
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Equipmentfs.H101fs.R101fs.F101fs.F102
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", 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", 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", 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", 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"text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\flowsheets\\hda_flowsheet_with_costing_doc_25_2.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1860,9 +2511,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb index feceb84b..586036df 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb @@ -1,562 +1,563 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Costing\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and a comparison with the HDA with Distillation.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", + "- Define and add costing blocks using the IDAES Process Costing Framework\n", + "- Fomulate and solve a process economics optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", + "\n", + "- `hda_flowsheets_for_costing_notebook.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and run HDA Flowsheets\n", + "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Source file for prebuilt flowsheets\n", + "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", + "\n", + "# Build hda model with second flash unit and return model object\n", + "m = hda_with_flash(tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IDAES Process Costing Framework\n", + "IDAES provides a library of capital costing correlations based on those in the following source:\n", + "\n", + "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", + "\n", + "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Operating Cost Equations\n", + "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Required imports\n", + "from pyomo.environ import Expression\n", + "\n", + "# Operating costs for HDA with second flash (model m)\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Capital Costing\n", + "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import costing methods - classes, heaters, vessels, compressors, columns\n", + "from idaes.models.costing.SSLW import (\n", + " SSLWCosting,\n", + " SSLWCostingData,\n", + ")\n", + "from idaes.core import UnitModelCostingBlock\n", + "\n", + "# Costing block\n", + "m.fs.costing = SSLWCosting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", + "\n", + "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", + "\n", + "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flexibility of Costing Block Definitions\n", + "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", + "\n", + "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define costing for the heater unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " HeaterMaterial,\n", + " HeaterSource,\n", + ")\n", + "\n", + "# Costing for heater - m.fs.H101\n", + "m.fs.H101.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=m.fs.costing,\n", + " costing_method=SSLWCostingData.cost_fired_heater,\n", + " costing_method_arguments={\n", + " \"material_type\": HeaterMaterial.CarbonSteel,\n", + " \"heat_source\": HeaterSource.Fuel,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", + "\n", + "All vessels are assigned costing succinctly via a loop below - users may define each block individually if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " VesselMaterial,\n", + " TrayType,\n", + " TrayMaterial,\n", + ")\n", + "\n", + "from idaes.core.util.constants import Constants\n", + "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", + "from idaes.models.unit_models import StoichiometricReactor, Flash\n", + "\n", + "# Map unit models to unit classes\n", + "# Will pass to unit_mapping which calls costing methods based on unit class\n", + "unit_class_mapping = {\n", + " m.fs.R101: StoichiometricReactor,\n", + " m.fs.F101: Flash,\n", + " m.fs.F102: Flash,\n", + "}\n", + "\n", + "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", + "\n", + "# Loop over units\n", + "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", + " # Get correct unit class for unit model\n", + " unit_class = unit_class_mapping[unit]\n", + "\n", + " # Add dimension variables and constraint if they don't exist\n", + " if not hasattr(unit, \"diameter\"):\n", + " unit.diameter = Var(initialize=1, units=pyunits.m)\n", + " if not hasattr(unit, \"length\"):\n", + " unit.length = Var(initialize=1, units=pyunits.m)\n", + " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", + " unit.volume_eq = Constraint(\n", + " expr=unit.volume[0] == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", + " )\n", + " else: # fix diameter directly\n", + " unit.diameter.fix(0.2214 * pyunits.m)\n", + " # Either way, fix L/D to calculate L from D\n", + " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", + "\n", + " # Define vessel costing\n", + " unit.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", + " costing_method=SSLWCostingData.unit_mapping[\n", + " unit_class\n", + " ], # e.g. cost_vertical_vessel()\n", + " costing_method_arguments={\n", + " \"material_type\": VesselMaterial.CarbonSteel,\n", + " \"shell_thickness\": 1.25 * pyunits.inch,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Flowsheet Costing Blocks\n", + "Now, we may solve the full flowsheet for all costing blocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Eefine solver\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Check that the degrees of freedom is zero\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Check physical units consistency, solve and check solver status\n", + "from pyomo.environ import TerminationCondition\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)\n", + "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare equipment purchase costs (actual capital costs)\n", + "\n", + "two_flash_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", + "}\n", + "distillation_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", + "}\n", + "\n", + "two_flash_capdf = pd.DataFrame(\n", + " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", + ").set_index(\"Equipment\")\n", + "distillation_capdf = pd.DataFrame(\n", + " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", + ").set_index(\"Equipment\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "# Sort according to an easier order to view\n", + "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(capcosts) # view dataframe before plotting\n", + "\n", + "capplot = capcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare operating costs (per year)\n", + "\n", + "two_flash_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", + "}\n", + "distillation_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", + "}\n", + "\n", + "two_flash_opdf = pd.DataFrame(\n", + " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", + ").set_index(\"Utilities\")\n", + "distillation_opdf = pd.DataFrame(\n", + " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", + ").set_index(\"Utilities\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(opcosts) # view dataframe before plotting\n", + "\n", + "opplot = opcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare total costs (capital costs and operating costs)\n", + "\n", + "two_flash_totcost = {\n", + " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", + " \"operating\": value(m.fs.operating_cost) / 1e3,\n", + "}\n", + "distillation_totcost = {\n", + " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", + " \"operating\": value(n.fs.operating_cost) / 1e3,\n", + "}\n", + "\n", + "two_flash_totdf = pd.DataFrame(\n", + " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", + ").set_index(\"Costs\")\n", + "distillation_totdf = pd.DataFrame(\n", + " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", + ").set_index(\"Costs\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(totcosts) # view dataframe before plotting\n", + "\n", + "totplot = totcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", + "two_flash_prod = value(\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", + ")\n", + "distillation_cost = value(\n", + " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", + ")\n", + "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", + "\n", + "print(\n", + " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")\n", + "print(\n", + " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Costing\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and a comparison with the HDA with Distillation.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", - "- Define and add costing blocks using the IDAES Process Costing Framework\n", - "- Fomulate and solve a process economics optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", - "\n", - "- `hda_flowsheets_for_costing_notebook.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and run HDA Flowsheets\n", - "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Source file for prebuilt flowsheets\n", - "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", - "\n", - "# Build hda model with second flash unit and return model object\n", - "m = hda_with_flash(tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IDAES Process Costing Framework\n", - "IDAES provides a library of capital costing correlations based on those in the following source:\n", - "\n", - "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", - "\n", - "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Operating Cost Equations\n", - "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Required imports\n", - "from pyomo.environ import Expression\n", - "\n", - "# Operating costs for HDA with second flash (model m)\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Capital Costing\n", - "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import costing methods - classes, heaters, vessels, compressors, columns\n", - "from idaes.models.costing.SSLW import (\n", - " SSLWCosting,\n", - " SSLWCostingData,\n", - ")\n", - "from idaes.core import UnitModelCostingBlock\n", - "\n", - "# Costing block\n", - "m.fs.costing = SSLWCosting()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", - "\n", - "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", - "\n", - "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flexibility of Costing Block Definitions\n", - "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", - "\n", - "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define costing for the heater unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " HeaterMaterial,\n", - " HeaterSource,\n", - ")\n", - "\n", - "# Costing for heater - m.fs.H101\n", - "m.fs.H101.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=m.fs.costing,\n", - " costing_method=SSLWCostingData.cost_fired_heater,\n", - " costing_method_arguments={\n", - " \"material_type\": HeaterMaterial.CarbonSteel,\n", - " \"heat_source\": HeaterSource.Fuel,\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", - "\n", - "All vessels are assigned costing succinctly via a loop below - users may define each block individually if desired:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " VesselMaterial,\n", - " TrayType,\n", - " TrayMaterial,\n", - ")\n", - "\n", - "from idaes.core.util.constants import Constants\n", - "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", - "from idaes.models.unit_models import StoichiometricReactor, Flash\n", - "\n", - "# Map unit models to unit classes\n", - "# Will pass to unit_mapping which calls costing methods based on unit class\n", - "unit_class_mapping = {\n", - " m.fs.R101: StoichiometricReactor,\n", - " m.fs.F101: Flash,\n", - " m.fs.F102: Flash,\n", - "}\n", - "\n", - "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", - "\n", - "# Loop over units\n", - "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", - " # Get correct unit class for unit model\n", - " unit_class = unit_class_mapping[unit]\n", - "\n", - " # Add dimension variables and constraint if they don't exist\n", - " if not hasattr(unit, \"diameter\"):\n", - " unit.diameter = Var(initialize=1, units=pyunits.m)\n", - " if not hasattr(unit, \"length\"):\n", - " unit.length = Var(initialize=1, units=pyunits.m)\n", - " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", - " unit.volume_eq = Constraint(\n", - " expr=unit.volume[0] == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", - " )\n", - " else: # fix diameter directly\n", - " unit.diameter.fix(0.2214 * pyunits.m)\n", - " # Either way, fix L/D to calculate L from D\n", - " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", - "\n", - " # Define vessel costing\n", - " unit.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", - " costing_method=SSLWCostingData.unit_mapping[\n", - " unit_class\n", - " ], # e.g. cost_vertical_vessel()\n", - " costing_method_arguments={\n", - " \"material_type\": VesselMaterial.CarbonSteel,\n", - " \"shell_thickness\": 1.25 * pyunits.inch,\n", - " },\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve Flowsheet Costing Blocks\n", - "Now, we may solve the full flowsheet for all costing blocks:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Eefine solver\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Check that the degrees of freedom is zero\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check physical units consistency, solve and check solver status\n", - "from pyomo.environ import TerminationCondition\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)\n", - "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare equipment purchase costs (actual capital costs)\n", - "\n", - "two_flash_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", - "}\n", - "distillation_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", - "}\n", - "\n", - "two_flash_capdf = pd.DataFrame(\n", - " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", - ").set_index(\"Equipment\")\n", - "distillation_capdf = pd.DataFrame(\n", - " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", - ").set_index(\"Equipment\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "# Sort according to an easier order to view\n", - "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(capcosts) # view dataframe before plotting\n", - "\n", - "capplot = capcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare operating costs (per year)\n", - "\n", - "two_flash_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", - "}\n", - "distillation_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", - "}\n", - "\n", - "two_flash_opdf = pd.DataFrame(\n", - " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", - ").set_index(\"Utilities\")\n", - "distillation_opdf = pd.DataFrame(\n", - " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", - ").set_index(\"Utilities\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(opcosts) # view dataframe before plotting\n", - "\n", - "opplot = opcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare total costs (capital costs and operating costs)\n", - "\n", - "two_flash_totcost = {\n", - " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", - " \"operating\": value(m.fs.operating_cost) / 1e3,\n", - "}\n", - "distillation_totcost = {\n", - " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", - " \"operating\": value(n.fs.operating_cost) / 1e3,\n", - "}\n", - "\n", - "two_flash_totdf = pd.DataFrame(\n", - " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", - ").set_index(\"Costs\")\n", - "distillation_totdf = pd.DataFrame(\n", - " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", - ").set_index(\"Costs\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(totcosts) # view dataframe before plotting\n", - "\n", - "totplot = totcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", - "two_flash_prod = value(\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", - ")\n", - "distillation_cost = value(\n", - " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", - ")\n", - "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", - "\n", - "print(\n", - " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")\n", - "print(\n", - " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb index 8c04460d..1dc29b42 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb @@ -1,562 +1,563 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Costing\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and a comparison with the HDA with Distillation.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", + "- Define and add costing blocks using the IDAES Process Costing Framework\n", + "- Fomulate and solve a process economics optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", + "\n", + "- `hda_flowsheets_for_costing_notebook.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and run HDA Flowsheets\n", + "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Source file for prebuilt flowsheets\n", + "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", + "\n", + "# Build hda model with second flash unit and return model object\n", + "m = hda_with_flash(tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IDAES Process Costing Framework\n", + "IDAES provides a library of capital costing correlations based on those in the following source:\n", + "\n", + "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", + "\n", + "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Operating Cost Equations\n", + "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Required imports\n", + "from pyomo.environ import Expression\n", + "\n", + "# Operating costs for HDA with second flash (model m)\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Capital Costing\n", + "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import costing methods - classes, heaters, vessels, compressors, columns\n", + "from idaes.models.costing.SSLW import (\n", + " SSLWCosting,\n", + " SSLWCostingData,\n", + ")\n", + "from idaes.core import UnitModelCostingBlock\n", + "\n", + "# Costing block\n", + "m.fs.costing = SSLWCosting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", + "\n", + "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", + "\n", + "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flexibility of Costing Block Definitions\n", + "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", + "\n", + "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define costing for the heater unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " HeaterMaterial,\n", + " HeaterSource,\n", + ")\n", + "\n", + "# Costing for heater - m.fs.H101\n", + "m.fs.H101.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=m.fs.costing,\n", + " costing_method=SSLWCostingData.cost_fired_heater,\n", + " costing_method_arguments={\n", + " \"material_type\": HeaterMaterial.CarbonSteel,\n", + " \"heat_source\": HeaterSource.Fuel,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", + "\n", + "All vessels are assigned costing succinctly via a loop below - users may define each block individually if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " VesselMaterial,\n", + " TrayType,\n", + " TrayMaterial,\n", + ")\n", + "\n", + "from idaes.core.util.constants import Constants\n", + "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", + "from idaes.models.unit_models import StoichiometricReactor, Flash\n", + "\n", + "# Map unit models to unit classes\n", + "# Will pass to unit_mapping which calls costing methods based on unit class\n", + "unit_class_mapping = {\n", + " m.fs.R101: StoichiometricReactor,\n", + " m.fs.F101: Flash,\n", + " m.fs.F102: Flash,\n", + "}\n", + "\n", + "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", + "\n", + "# Loop over units\n", + "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", + " # Get correct unit class for unit model\n", + " unit_class = unit_class_mapping[unit]\n", + "\n", + " # Add dimension variables and constraint if they don't exist\n", + " if not hasattr(unit, \"diameter\"):\n", + " unit.diameter = Var(initialize=1, units=pyunits.m)\n", + " if not hasattr(unit, \"length\"):\n", + " unit.length = Var(initialize=1, units=pyunits.m)\n", + " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", + " unit.volume_eq = Constraint(\n", + " expr=unit.volume[0] == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", + " )\n", + " else: # fix diameter directly\n", + " unit.diameter.fix(0.2214 * pyunits.m)\n", + " # Either way, fix L/D to calculate L from D\n", + " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", + "\n", + " # Define vessel costing\n", + " unit.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", + " costing_method=SSLWCostingData.unit_mapping[\n", + " unit_class\n", + " ], # e.g. cost_vertical_vessel()\n", + " costing_method_arguments={\n", + " \"material_type\": VesselMaterial.CarbonSteel,\n", + " \"shell_thickness\": 1.25 * pyunits.inch,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Flowsheet Costing Blocks\n", + "Now, we may solve the full flowsheet for all costing blocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Eefine solver\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Check that the degrees of freedom is zero\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Check physical units consistency, solve and check solver status\n", + "from pyomo.environ import TerminationCondition\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)\n", + "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare equipment purchase costs (actual capital costs)\n", + "\n", + "two_flash_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", + "}\n", + "distillation_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", + "}\n", + "\n", + "two_flash_capdf = pd.DataFrame(\n", + " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", + ").set_index(\"Equipment\")\n", + "distillation_capdf = pd.DataFrame(\n", + " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", + ").set_index(\"Equipment\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "# Sort according to an easier order to view\n", + "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(capcosts) # view dataframe before plotting\n", + "\n", + "capplot = capcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare operating costs (per year)\n", + "\n", + "two_flash_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", + "}\n", + "distillation_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", + "}\n", + "\n", + "two_flash_opdf = pd.DataFrame(\n", + " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", + ").set_index(\"Utilities\")\n", + "distillation_opdf = pd.DataFrame(\n", + " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", + ").set_index(\"Utilities\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(opcosts) # view dataframe before plotting\n", + "\n", + "opplot = opcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare total costs (capital costs and operating costs)\n", + "\n", + "two_flash_totcost = {\n", + " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", + " \"operating\": value(m.fs.operating_cost) / 1e3,\n", + "}\n", + "distillation_totcost = {\n", + " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", + " \"operating\": value(n.fs.operating_cost) / 1e3,\n", + "}\n", + "\n", + "two_flash_totdf = pd.DataFrame(\n", + " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", + ").set_index(\"Costs\")\n", + "distillation_totdf = pd.DataFrame(\n", + " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", + ").set_index(\"Costs\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(totcosts) # view dataframe before plotting\n", + "\n", + "totplot = totcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", + "two_flash_prod = value(\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", + ")\n", + "distillation_cost = value(\n", + " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", + ")\n", + "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", + "\n", + "print(\n", + " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")\n", + "print(\n", + " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Costing\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and a comparison with the HDA with Distillation.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", - "- Define and add costing blocks using the IDAES Process Costing Framework\n", - "- Fomulate and solve a process economics optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", - "\n", - "- `hda_flowsheets_for_costing_notebook.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and run HDA Flowsheets\n", - "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Source file for prebuilt flowsheets\n", - "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", - "\n", - "# Build hda model with second flash unit and return model object\n", - "m = hda_with_flash(tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IDAES Process Costing Framework\n", - "IDAES provides a library of capital costing correlations based on those in the following source:\n", - "\n", - "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", - "\n", - "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Operating Cost Equations\n", - "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Required imports\n", - "from pyomo.environ import Expression\n", - "\n", - "# Operating costs for HDA with second flash (model m)\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Capital Costing\n", - "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import costing methods - classes, heaters, vessels, compressors, columns\n", - "from idaes.models.costing.SSLW import (\n", - " SSLWCosting,\n", - " SSLWCostingData,\n", - ")\n", - "from idaes.core import UnitModelCostingBlock\n", - "\n", - "# Costing block\n", - "m.fs.costing = SSLWCosting()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", - "\n", - "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", - "\n", - "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flexibility of Costing Block Definitions\n", - "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", - "\n", - "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define costing for the heater unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " HeaterMaterial,\n", - " HeaterSource,\n", - ")\n", - "\n", - "# Costing for heater - m.fs.H101\n", - "m.fs.H101.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=m.fs.costing,\n", - " costing_method=SSLWCostingData.cost_fired_heater,\n", - " costing_method_arguments={\n", - " \"material_type\": HeaterMaterial.CarbonSteel,\n", - " \"heat_source\": HeaterSource.Fuel,\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", - "\n", - "All vessels are assigned costing succinctly via a loop below - users may define each block individually if desired:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " VesselMaterial,\n", - " TrayType,\n", - " TrayMaterial,\n", - ")\n", - "\n", - "from idaes.core.util.constants import Constants\n", - "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", - "from idaes.models.unit_models import StoichiometricReactor, Flash\n", - "\n", - "# Map unit models to unit classes\n", - "# Will pass to unit_mapping which calls costing methods based on unit class\n", - "unit_class_mapping = {\n", - " m.fs.R101: StoichiometricReactor,\n", - " m.fs.F101: Flash,\n", - " m.fs.F102: Flash,\n", - "}\n", - "\n", - "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", - "\n", - "# Loop over units\n", - "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", - " # Get correct unit class for unit model\n", - " unit_class = unit_class_mapping[unit]\n", - "\n", - " # Add dimension variables and constraint if they don't exist\n", - " if not hasattr(unit, \"diameter\"):\n", - " unit.diameter = Var(initialize=1, units=pyunits.m)\n", - " if not hasattr(unit, \"length\"):\n", - " unit.length = Var(initialize=1, units=pyunits.m)\n", - " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", - " unit.volume_eq = Constraint(\n", - " expr=unit.volume[0] == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", - " )\n", - " else: # fix diameter directly\n", - " unit.diameter.fix(0.2214 * pyunits.m)\n", - " # Either way, fix L/D to calculate L from D\n", - " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", - "\n", - " # Define vessel costing\n", - " unit.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", - " costing_method=SSLWCostingData.unit_mapping[\n", - " unit_class\n", - " ], # e.g. cost_vertical_vessel()\n", - " costing_method_arguments={\n", - " \"material_type\": VesselMaterial.CarbonSteel,\n", - " \"shell_thickness\": 1.25 * pyunits.inch,\n", - " },\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve Flowsheet Costing Blocks\n", - "Now, we may solve the full flowsheet for all costing blocks:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Eefine solver\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Check that the degrees of freedom is zero\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check physical units consistency, solve and check solver status\n", - "from pyomo.environ import TerminationCondition\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)\n", - "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare equipment purchase costs (actual capital costs)\n", - "\n", - "two_flash_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", - "}\n", - "distillation_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", - "}\n", - "\n", - "two_flash_capdf = pd.DataFrame(\n", - " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", - ").set_index(\"Equipment\")\n", - "distillation_capdf = pd.DataFrame(\n", - " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", - ").set_index(\"Equipment\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "# Sort according to an easier order to view\n", - "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(capcosts) # view dataframe before plotting\n", - "\n", - "capplot = capcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare operating costs (per year)\n", - "\n", - "two_flash_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", - "}\n", - "distillation_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", - "}\n", - "\n", - "two_flash_opdf = pd.DataFrame(\n", - " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", - ").set_index(\"Utilities\")\n", - "distillation_opdf = pd.DataFrame(\n", - " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", - ").set_index(\"Utilities\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(opcosts) # view dataframe before plotting\n", - "\n", - "opplot = opcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare total costs (capital costs and operating costs)\n", - "\n", - "two_flash_totcost = {\n", - " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", - " \"operating\": value(m.fs.operating_cost) / 1e3,\n", - "}\n", - "distillation_totcost = {\n", - " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", - " \"operating\": value(n.fs.operating_cost) / 1e3,\n", - "}\n", - "\n", - "two_flash_totdf = pd.DataFrame(\n", - " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", - ").set_index(\"Costs\")\n", - "distillation_totdf = pd.DataFrame(\n", - " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", - ").set_index(\"Costs\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(totcosts) # view dataframe before plotting\n", - "\n", - "totplot = totcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", - "two_flash_prod = value(\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", - ")\n", - "distillation_cost = value(\n", - " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", - ")\n", - "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", - "\n", - "print(\n", - " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")\n", - "print(\n", - " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb index 44e6cf27..61ef1614 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb index 1e23d2be..afa973ec 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -144,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "tags": [ "solution" @@ -175,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -261,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -310,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -342,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "tags": [ "solution" @@ -368,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -408,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -437,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": { "tags": [ "solution" @@ -489,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -528,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": { "tags": [ "solution" @@ -555,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -578,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": { "tags": [ "solution" @@ -599,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -620,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -643,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -671,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -695,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -754,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -780,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": { "tags": [ "solution" @@ -804,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -824,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -844,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -864,41 +865,203 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor 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"stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" ] } ], @@ -932,7 +1095,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 32, "metadata": { "tags": [ "solution" @@ -965,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -989,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1014,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1046,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1078,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1102,7 +1265,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 38, "metadata": { "scrolled": false }, @@ -1111,154 +1274,832 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + "2025-03-17 17:33:16 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:16 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:17 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:18 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - 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\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", - "component keys that are not exported as part of the NL file. Skipping.\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", "tol=1e-06\n", "max_iter=200\n", @@ -1304,21 +2145,27 @@ " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.98e-08 1.44e-01 -3.8 1.38e-07 - 1.00e+00 1.00e+00h 1\n", "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", "\n", "Number of Iterations....: 9\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Dual infeasibility......: 1.5042487594757811e+04 1.5042487594757811e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.9802322387695312e-08\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487594757811e+04\n", "\n", "\n", "Number of objective function evaluations = 11\n", @@ -1328,652 +2175,4264 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n", "Total CPU secs in NLP function evaluations = 0.001\n", "\n", "EXIT: Optimal Solution Found.\n" ] - } - ], - "source": [ - "# Create the solver object\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add distillation column \n", - "\n", - "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", - "\n", - "In the following, we will\n", - "- Add the distillation column \n", - "- Connect it to the heater \n", - "- Add the necessary equality constraints\n", - "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", - "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", - "- Scale the control volume heat variables to help convergence\n", - "- Initialize the distillation block.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ + } + ], + "source": [ + "# Create the solver object\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add distillation column \n", + "\n", + "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", + "\n", + "In the following, we will\n", + "- Add the distillation column \n", + "- Connect it to the heater \n", + "- Add the necessary equality constraints\n", + "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", + "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", + "- Scale the control volume heat variables to help convergence\n", + "- Initialize the distillation block.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:19 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n" + ] + }, + { + "name": "stdout", + "output_type": 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idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + 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idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:20 [INFO] 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idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": 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{ + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] 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+ { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] 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idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:21 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + "2025-03-17 17:33:22 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" ] } ], @@ -2024,7 +6483,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -2059,7 +6518,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2067,9 +6526,21 @@ "output_type": "stream", "text": [ "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", "tol=1e-06\n", "max_iter=200\n", @@ -2119,17 +6590,17 @@ " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 4.10e-02 -3.8 3.45e-08 - 1.00e+00 1.00e+00h 1\n", "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", "\n", "Number of Iterations....: 9\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Dual infeasibility......: 1.5042524550088077e+04 1.5042524550088077e+04\n", "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042524550088077e+04\n", "\n", "\n", "Number of objective function evaluations = 11\n", @@ -2139,8 +6610,8 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", - "Total CPU secs in NLP function evaluations = 0.013\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.029\n", + "Total CPU secs in NLP function evaluations = 0.006\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2148,10 +6619,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0450441837310791}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 53, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2171,25 +6642,25 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total cost = $ 442301.47075252194\n", - "operating cost = $ 427596.73056805483\n", - "capital cost = $ 14704.740184467111\n", + "total cost = $ 442301.47075252153\n", + "operating cost = $ 427596.7305680539\n", + "capital cost = $ 14704.740184467615\n", "\n", - "Distillate flowrate = 0.16196898920633368 mol/s\n", - "Benzene purity = 89.4916166580088 %\n", - "Residue flowrate = 0.10515007120697904 mol/s\n", - "Toluene purity = 43.32260291055251 %\n", + "Distillate flowrate = 0.16196898920633576 mol/s\n", + "Benzene purity = 89.49161665800844 %\n", + "Residue flowrate = 0.10515007120697695 mol/s\n", + "Toluene purity = 43.322602910552874 %\n", "\n", "Conversion = 75.0 %\n", "\n", - "Overhead benzene loss in F101 = 42.161938483603194 %\n" + "Overhead benzene loss in F101 = 42.16193848360323 %\n" ] } ], @@ -2235,7 +6706,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2284,7 +6755,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -2338,7 +6809,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2414,7 +6885,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -2430,7 +6901,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -2457,7 +6928,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 49, "metadata": { "tags": [ "solution" @@ -2486,7 +6957,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -2528,7 +6999,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 51, "metadata": { "tags": [ "solution" @@ -2554,7 +7025,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -2579,7 +7050,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 53, "metadata": { "tags": [ "solution" @@ -2600,7 +7071,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -2621,7 +7092,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2629,9 +7100,21 @@ "output_type": "stream", "text": [ "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", - "component keys that are not exported as part of the NL file. Skipping.\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", "tol=1e-06\n", "max_iter=200\n", @@ -2683,20 +7166,44 @@ " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", @@ -2706,18 +7213,18 @@ " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + " 31 4.3883992e+05 3.65e-07 1.12e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 1.49e-08 5.89e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 33\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", - "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", - "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", + "Objective...............: 4.3883989842628768e+02 4.3883989842628769e+05\n", + "Dual infeasibility......: 5.8892458138371715e-07 5.8892458138371710e-04\n", + "Constraint violation....: 2.9103830456733704e-11 1.4901161193847656e-08\n", + "Complementarity.........: 9.0909948039807953e-09 9.0909948039807953e-06\n", + "Overall NLP error.......: 9.0909948039807953e-09 5.8892458138371710e-04\n", "\n", "\n", "Number of objective function evaluations = 35\n", @@ -2727,8 +7234,8 @@ "Number of equality constraint Jacobian evaluations = 34\n", "Number of inequality constraint Jacobian evaluations = 34\n", "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", - "Total CPU secs in NLP function evaluations = 0.020\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.095\n", + "Total CPU secs in NLP function evaluations = 0.014\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2749,25 +7256,25 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total cost = $ 438839.898426286\n", - "operating cost = $ 408883.5314830889\n", - "capital cost = $ 29956.3669431971\n", + "total cost = $ 438839.8984262877\n", + "operating cost = $ 408883.53148309066\n", + "capital cost = $ 29956.366943197056\n", "\n", - "Distillate flowrate = 0.1799999900263989 mol/s\n", - "Benzene purity = 98.99999900049086 %\n", + "Distillate flowrate = 0.17999999002639894 mol/s\n", + "Benzene purity = 98.99999900049087 %\n", "Residue flowrate = 0.1085161642426372 mol/s\n", - "Toluene purity = 15.676178086213548 %\n", + "Toluene purity = 15.676178086212905 %\n", "\n", - "Conversion = 93.38705916369427 %\n", + "Conversion = 93.38705916369437 %\n", "\n", - "Overhead benzene loss in F101 = 17.34061793115618 %\n" + "Overhead benzene loss in F101 = 17.340617931156153 %\n" ] } ], @@ -2813,7 +7320,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -2822,13 +7329,13 @@ "text": [ "Optimal Values\n", "\n", - "H101 outlet temperature = 568.923204295196 K\n", + "H101 outlet temperature = 568.9232042951957 K\n", "\n", - "R101 outlet temperature = 790.3655425698853 K\n", + "R101 outlet temperature = 790.3655425698856 K\n", "\n", "F101 outlet temperature = 298.0 K\n", "\n", - "H102 outlet temperature = 368.7414339952852 K\n" + "H102 outlet temperature = 368.74143399528595 K\n" ] } ], @@ -2880,7 +7387,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb index e44357de..fd5b112c 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb @@ -1,2857 +1,2858 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", - "\n", - "![](HDA_flowsheet_distillation.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Translator block\n", - "\n", - "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", - "\n", - "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "- Flash\n", - "- Separator (splitter) \n", - "- PressureChanger\n", - "- Translator (to switch from one property package to another)\n", - "- TrayColumn (distillation column)\n", - "- CondenserType (Type of the overhead condenser: complete or partial)\n", - "- TemperatureSpec (Temperature specification inside the condenser)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " CSTR,\n", - " Flash,\n", - " Translator,\n", - ")\n", - "\n", - "from idaes.models_extra.column_models import TrayColumn\n", - "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.core.util.scaling as iscale\n", - "from idaes.core.util.exceptions import InitializationError\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction packages\n", - "\n", - "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "\n", - "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Pyomo Concrete Model to contain the problem\n", - "m = ConcreteModel()\n", - "\n", - "# Add a steady state flowsheet block to the model\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now add the thermophysical and reaction packages to the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Property package for benzene, toluene, hydrogen, methane mixture\n", - "m.fs.BTHM_params = HDAParameterBlock()\n", - "\n", - "# Property package for the benzene-toluene mixture\n", - "m.fs.BT_params = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", - ")\n", - "\n", - "# Reaction package for the HDA reaction\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.BTHM_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the mixer M101 to the flowsheet\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.BTHM_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "# Adding the heater H101 to the flowsheet\n", - "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.BTHM_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the flash tank F101 to the flowsheet\n", - "m.fs.F101 = Flash(\n", - " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", - ")\n", - "\n", - "# Adding the splitter S101 to the flowsheet\n", - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", - ")\n", - "\n", - "# Adding the compressor C101 to the flowsheet\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.BTHM_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remark\n", - "\n", - "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Add translator block to convert between property packages\n", - "m.fs.translator = Translator(\n", - " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translator block constraints\n", - "\n", - "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", - "\n", - "For this process, five constraints are required based on the state variables used in the outgoing process.\n", - "\n", - "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", - "- Temperature of the inlet and outlet streams must be the same.\n", - "- Pressure of the inlet and outgoing streams must be the same\n", - "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", - "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", - "m.fs.translator.eq_total_flow = Constraint(\n", - " expr=m.fs.translator.outlet.flow_mol[0]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - ")\n", - "\n", - "# Add constraint: Outlet temperature = Inlet temperature\n", - "m.fs.translator.eq_temperature = Constraint(\n", - " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Remaining constraints on the translator block\n", - "\n", - "# Add constraint: Benzene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "# Add constraint: Toluene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet_distillation.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", - "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Appending additional constraints to the model\n", - "\n", - "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", - "\n", - "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", - "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", - "\n", - "We add the constraint to the model as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the conversion variables using 'Var'\n", - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "# Append the constraint to the model\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions and Initializing the flowsheet\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H101\n", - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for the reactor R101 to the following conditions:\n", - "
    \n", - "
  • `conversion` = 0.75
    • \n", - "
  • `heat_duty` = 0
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "\n", - "# Fix the pressure drop in the flash F101\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the split fraction of the 'purge' stream from S101\n", - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "\n", - "# Fix the pressure of the outlet from the compressor C101\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H102\n", - "m.fs.H102.outlet.temperature.fix(375)\n", - "\n", - "# Fix the pressure drop in the heater H102\n", - "m.fs.H102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" - ] - } - ], - "source": [ - "# Set scaling factors for heat duty, reaction extent and volume\n", - "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", - "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.H101)\n", - "iscale.calculate_scaling_factors(m.fs.R101)\n", - "iscale.calculate_scaling_factors(m.fs.F101)\n", - "iscale.calculate_scaling_factors(m.fs.H102)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Check the degrees of freedom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization\n", - "\n", - "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", - "\n", - "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303.2},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " # Try initializing using default initializer,\n", - " # if it fails (probably due to scaling) try for the second time\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" - ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Create the solver object\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add distillation column \n", - "\n", - "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", - "\n", - "In the following, we will\n", - "- Add the distillation column \n", - "- Connect it to the heater \n", - "- Add the necessary equality constraints\n", - "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", - "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", - "- Scale the control volume heat variables to help convergence\n", - "- Initialize the distillation block.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], - "source": [ - "# Add distillation column to the flowsheet\n", - "m.fs.D101 = TrayColumn(\n", - " number_of_trays=10,\n", - " feed_tray_location=5,\n", - " condenser_type=CondenserType.totalCondenser,\n", - " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", - " property_package=m.fs.BT_params,\n", - ")\n", - "\n", - "# Connect the outlet from the heater H102 to the distillation column\n", - "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", - "\n", - "# Add the necessary equality constraints\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "\n", - "# Propagate the state\n", - "propagate_state(m.fs.s11)\n", - "\n", - "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", - "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", - "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", - "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", - "\n", - "# set scaling factors\n", - "# Set scaling factors for heat duty\n", - "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.D101)\n", - "\n", - "# Initialize the distillation column\n", - "m.fs.D101.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute capital and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Expression to compute the total cooling cost\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", - " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", - ")\n", - "\n", - "# Expression to compute the total heating cost\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", - " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", - ")\n", - "\n", - "# Expression to compute the total operating cost\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")\n", - "\n", - "# Expression to compute the total capital cost\n", - "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve the entire flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", - "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", - "Total CPU secs in NLP function evaluations = 0.013\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252194\n", - "operating cost = $ 427596.73056805483\n", - "capital cost = $ 14704.740184467111\n", - "\n", - "Distillate flowrate = 0.16196898920633368 mol/s\n", - "Benzene purity = 89.4916166580088 %\n", - "Residue flowrate = 0.10515007120697904 mol/s\n", - "Toluene purity = 43.32260291055251 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603194 %\n" - ] - } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 outlet temperature\n", - "- F101 outlet temperature\n", - "- H102 outlet temperature\n", - "- Condenser pressure\n", - "- reflux ratio\n", - "- boilup ratio\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.conversion.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.D101.condenser.condenser_pressure.unfix()\n", - "m.fs.D101.condenser.reflux_ratio.unfix()\n", - "m.fs.D101.reboiler.boilup_ratio.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 900] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - H102 outlet temperature [350, 400] K\n", - " - D101 condenser pressure [101325, 150000] Pa\n", - " - D101 reflux ratio [0.1, 5]\n", - " - D101 boilup ratio [0.1, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# Set bounds on the temperature of the outlet from H101\n", - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)\n", - "\n", - "# Set bounds on the temperature of the outlet from R101\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(900)\n", - "\n", - "# Set bounds on the volume of the reactor R101\n", - "m.fs.R101.volume[0].setlb(0)\n", - "\n", - "# Set bounds on the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "\n", - "# Set bounds on the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature[0].setlb(350)\n", - "m.fs.H102.outlet.temperature[0].setub(400)\n", - "\n", - "# Set bounds on the pressure inside the condenser\n", - "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", - "m.fs.D101.condenser.condenser_pressure.setub(150000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "\n", - "\n", - "# Todo: Set bounds on the boilup ratio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that the overhead loss of benzene from F101 <= 20%\n", - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(\n", - " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", - "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", - "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", - "Total CPU secs in NLP function evaluations = 0.020\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.898426286\n", - "operating cost = $ 408883.5314830889\n", - "capital cost = $ 29956.3669431971\n", - "\n", - "Distillate flowrate = 0.1799999900263989 mol/s\n", - "Benzene purity = 98.99999900049086 %\n", - "Residue flowrate = 0.1085161642426372 mol/s\n", - "Toluene purity = 15.676178086213548 %\n", - "\n", - "Conversion = 93.38705916369427 %\n", - "\n", - "Overhead benzene loss in F101 = 17.34061793115618 %\n" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", + "\n", + "![](HDA_flowsheet_distillation.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translator block\n", + "\n", + "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", + "\n", + "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "- Flash\n", + "- Separator (splitter) \n", + "- PressureChanger\n", + "- Translator (to switch from one property package to another)\n", + "- TrayColumn (distillation column)\n", + "- CondenserType (Type of the overhead condenser: complete or partial)\n", + "- TemperatureSpec (Temperature specification inside the condenser)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " CSTR,\n", + " Flash,\n", + " Translator,\n", + ")\n", + "\n", + "from idaes.models_extra.column_models import TrayColumn\n", + "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction packages\n", + "\n", + "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "\n", + "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Pyomo Concrete Model to contain the problem\n", + "m = ConcreteModel()\n", + "\n", + "# Add a steady state flowsheet block to the model\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now add the thermophysical and reaction packages to the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Property package for benzene, toluene, hydrogen, methane mixture\n", + "m.fs.BTHM_params = HDAParameterBlock()\n", + "\n", + "# Property package for the benzene-toluene mixture\n", + "m.fs.BT_params = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", + ")\n", + "\n", + "# Reaction package for the HDA reaction\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.BTHM_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the mixer M101 to the flowsheet\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.BTHM_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "# Adding the heater H101 to the flowsheet\n", + "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.BTHM_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the flash tank F101 to the flowsheet\n", + "m.fs.F101 = Flash(\n", + " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", + ")\n", + "\n", + "# Adding the splitter S101 to the flowsheet\n", + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", + ")\n", + "\n", + "# Adding the compressor C101 to the flowsheet\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.BTHM_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remark\n", + "\n", + "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Add translator block to convert between property packages\n", + "m.fs.translator = Translator(\n", + " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translator block constraints\n", + "\n", + "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", + "\n", + "For this process, five constraints are required based on the state variables used in the outgoing process.\n", + "\n", + "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", + "- Temperature of the inlet and outlet streams must be the same.\n", + "- Pressure of the inlet and outgoing streams must be the same\n", + "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", + "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", + "m.fs.translator.eq_total_flow = Constraint(\n", + " expr=m.fs.translator.outlet.flow_mol[0]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + ")\n", + "\n", + "# Add constraint: Outlet temperature = Inlet temperature\n", + "m.fs.translator.eq_temperature = Constraint(\n", + " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Remaining constraints on the translator block\n", + "\n", + "# Add constraint: Benzene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "# Add constraint: Toluene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet_distillation.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", + "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appending additional constraints to the model\n", + "\n", + "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", + "\n", + "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", + "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", + "\n", + "We add the constraint to the model as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the conversion variables using 'Var'\n", + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "# Append the constraint to the model\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions and Initializing the flowsheet\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H101\n", + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for the reactor R101 to the following conditions:\n", + "
    \n", + "
  • `conversion` = 0.75
    • \n", + "
  • `heat_duty` = 0
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "\n", + "# Fix the pressure drop in the flash F101\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the split fraction of the 'purge' stream from S101\n", + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "\n", + "# Fix the pressure of the outlet from the compressor C101\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H102\n", + "m.fs.H102.outlet.temperature.fix(375)\n", + "\n", + "# Fix the pressure drop in the heater H102\n", + "m.fs.H102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], + "source": [ + "# Set scaling factors for heat duty, reaction extent and volume\n", + "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", + "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.H101)\n", + "iscale.calculate_scaling_factors(m.fs.R101)\n", + "iscale.calculate_scaling_factors(m.fs.F101)\n", + "iscale.calculate_scaling_factors(m.fs.H102)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Check the degrees of freedom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization\n", + "\n", + "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", + "\n", + "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303.2},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " # Try initializing using default initializer,\n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Create the solver object\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add distillation column \n", + "\n", + "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", + "\n", + "In the following, we will\n", + "- Add the distillation column \n", + "- Connect it to the heater \n", + "- Add the necessary equality constraints\n", + "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", + "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", + "- Scale the control volume heat variables to help convergence\n", + "- Initialize the distillation block.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], + "source": [ + "# Add distillation column to the flowsheet\n", + "m.fs.D101 = TrayColumn(\n", + " number_of_trays=10,\n", + " feed_tray_location=5,\n", + " condenser_type=CondenserType.totalCondenser,\n", + " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", + " property_package=m.fs.BT_params,\n", + ")\n", + "\n", + "# Connect the outlet from the heater H102 to the distillation column\n", + "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", + "\n", + "# Add the necessary equality constraints\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "\n", + "# Propagate the state\n", + "propagate_state(m.fs.s11)\n", + "\n", + "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", + "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", + "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", + "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", + "\n", + "# set scaling factors\n", + "# Set scaling factors for heat duty\n", + "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.D101)\n", + "\n", + "# Initialize the distillation column\n", + "m.fs.D101.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute capital and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Expression to compute the total cooling cost\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", + " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", + ")\n", + "\n", + "# Expression to compute the total heating cost\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", + " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", + ")\n", + "\n", + "# Expression to compute the total operating cost\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")\n", + "\n", + "# Expression to compute the total capital cost\n", + "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the entire flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", + "Total CPU secs in NLP function evaluations = 0.013\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 outlet temperature\n", + "- F101 outlet temperature\n", + "- H102 outlet temperature\n", + "- Condenser pressure\n", + "- reflux ratio\n", + "- boilup ratio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.conversion.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.D101.condenser.condenser_pressure.unfix()\n", + "m.fs.D101.condenser.reflux_ratio.unfix()\n", + "m.fs.D101.reboiler.boilup_ratio.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 900] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - H102 outlet temperature [350, 400] K\n", + " - D101 condenser pressure [101325, 150000] Pa\n", + " - D101 reflux ratio [0.1, 5]\n", + " - D101 boilup ratio [0.1, 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Set bounds on the temperature of the outlet from H101\n", + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)\n", + "\n", + "# Set bounds on the temperature of the outlet from R101\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(900)\n", + "\n", + "# Set bounds on the volume of the reactor R101\n", + "m.fs.R101.volume[0].setlb(0)\n", + "\n", + "# Set bounds on the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "\n", + "# Set bounds on the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature[0].setlb(350)\n", + "m.fs.H102.outlet.temperature[0].setub(400)\n", + "\n", + "# Set bounds on the pressure inside the condenser\n", + "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", + "m.fs.D101.condenser.condenser_pressure.setub(150000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "\n", + "\n", + "# Todo: Set bounds on the boilup ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure that the overhead loss of benzene from F101 <= 20%\n", + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(\n", + " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key Takeaways\n", + "\n", + "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", + "\n", + "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", + "\n", + "\n", + "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." + ] } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.923204295196 K\n", - "\n", - "R101 outlet temperature = 790.3655425698853 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.7414339952852 K\n" - ] + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Key Takeaways\n", - "\n", - "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", - "\n", - "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", - "\n", - "\n", - "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 86d59dd3..121bd803 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -1,3024 +1,3025 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", - "\n", - "![](HDA_flowsheet_distillation.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Translator block\n", - "\n", - "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", - "\n", - "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "- Flash\n", - "- Separator (splitter) \n", - "- PressureChanger\n", - "- Translator (to switch from one property package to another)\n", - "- TrayColumn (distillation column)\n", - "- CondenserType (Type of the overhead condenser: complete or partial)\n", - "- TemperatureSpec (Temperature specification inside the condenser)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " CSTR,\n", - " Flash,\n", - " Translator,\n", - ")\n", - "\n", - "from idaes.models_extra.column_models import TrayColumn\n", - "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.core.util.scaling as iscale\n", - "from idaes.core.util.exceptions import InitializationError\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction packages\n", - "\n", - "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "\n", - "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Pyomo Concrete Model to contain the problem\n", - "m = ConcreteModel()\n", - "\n", - "# Add a steady state flowsheet block to the model\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now add the thermophysical and reaction packages to the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Property package for benzene, toluene, hydrogen, methane mixture\n", - "m.fs.BTHM_params = HDAParameterBlock()\n", - "\n", - "# Property package for the benzene-toluene mixture\n", - "m.fs.BT_params = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", - ")\n", - "\n", - "# Reaction package for the HDA reaction\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.BTHM_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the mixer M101 to the flowsheet\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.BTHM_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "# Adding the heater H101 to the flowsheet\n", - "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.BTHM_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = CSTR(\n", - " property_package=m.fs.BTHM_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the flash tank F101 to the flowsheet\n", - "m.fs.F101 = Flash(\n", - " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", - ")\n", - "\n", - "# Adding the splitter S101 to the flowsheet\n", - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", - ")\n", - "\n", - "# Adding the compressor C101 to the flowsheet\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.BTHM_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remark\n", - "\n", - "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Add translator block to convert between property packages\n", - "m.fs.translator = Translator(\n", - " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translator block constraints\n", - "\n", - "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", - "\n", - "For this process, five constraints are required based on the state variables used in the outgoing process.\n", - "\n", - "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", - "- Temperature of the inlet and outlet streams must be the same.\n", - "- Pressure of the inlet and outgoing streams must be the same\n", - "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", - "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", - "m.fs.translator.eq_total_flow = Constraint(\n", - " expr=m.fs.translator.outlet.flow_mol[0]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - ")\n", - "\n", - "# Add constraint: Outlet temperature = Inlet temperature\n", - "m.fs.translator.eq_temperature = Constraint(\n", - " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", - "m.fs.translator.eq_pressure = Constraint(\n", - " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Remaining constraints on the translator block\n", - "\n", - "# Add constraint: Benzene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "# Add constraint: Toluene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet\n", - "m.fs.H102 = Heater(\n", - " property_package=m.fs.BT_params,\n", - " has_pressure_change=True,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet_distillation.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", - "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Appending additional constraints to the model\n", - "\n", - "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", - "\n", - "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", - "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", - "\n", - "We add the constraint to the model as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the conversion variables using 'Var'\n", - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "# Append the constraint to the model\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions and Initializing the flowsheet\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H101\n", - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for the reactor R101 to the following conditions:\n", - "
    \n", - "
  • `conversion` = 0.75
    • \n", - "
  • `heat_duty` = 0
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "m.fs.R101.conversion.fix(0.75)\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "\n", - "# Fix the pressure drop in the flash F101\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the split fraction of the 'purge' stream from S101\n", - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "\n", - "# Fix the pressure of the outlet from the compressor C101\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H102\n", - "m.fs.H102.outlet.temperature.fix(375)\n", - "\n", - "# Fix the pressure drop in the heater H102\n", - "m.fs.H102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" - ] - } - ], - "source": [ - "# Set scaling factors for heat duty, reaction extent and volume\n", - "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", - "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.H101)\n", - "iscale.calculate_scaling_factors(m.fs.R101)\n", - "iscale.calculate_scaling_factors(m.fs.F101)\n", - "iscale.calculate_scaling_factors(m.fs.H102)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Check the degrees of freedom" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# Todo: Check the degrees of freedom\n", - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization\n", - "\n", - "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", - "\n", - "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303.2},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " # Try initializing using default initializer,\n", - " # if it fails (probably due to scaling) try for the second time\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" - ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Create the solver object\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add distillation column \n", - "\n", - "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", - "\n", - "In the following, we will\n", - "- Add the distillation column \n", - "- Connect it to the heater \n", - "- Add the necessary equality constraints\n", - "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", - "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", - "- Scale the control volume heat variables to help convergence\n", - "- Initialize the distillation block.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], - "source": [ - "# Add distillation column to the flowsheet\n", - "m.fs.D101 = TrayColumn(\n", - " number_of_trays=10,\n", - " feed_tray_location=5,\n", - " condenser_type=CondenserType.totalCondenser,\n", - " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", - " property_package=m.fs.BT_params,\n", - ")\n", - "\n", - "# Connect the outlet from the heater H102 to the distillation column\n", - "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", - "\n", - "# Add the necessary equality constraints\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "\n", - "# Propagate the state\n", - "propagate_state(m.fs.s11)\n", - "\n", - "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", - "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", - "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", - "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", - "\n", - "# set scaling factors\n", - "# Set scaling factors for heat duty\n", - "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.D101)\n", - "\n", - "# Initialize the distillation column\n", - "m.fs.D101.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute capital and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Expression to compute the total cooling cost\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", - " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", - ")\n", - "\n", - "# Expression to compute the total heating cost\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", - " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", - ")\n", - "\n", - "# Expression to compute the total operating cost\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")\n", - "\n", - "# Expression to compute the total capital cost\n", - "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve the entire flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", - "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", - "Total CPU secs in NLP function evaluations = 0.013\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252194\n", - "operating cost = $ 427596.73056805483\n", - "capital cost = $ 14704.740184467111\n", - "\n", - "Distillate flowrate = 0.16196898920633368 mol/s\n", - "Benzene purity = 89.4916166580088 %\n", - "Residue flowrate = 0.10515007120697904 mol/s\n", - "Toluene purity = 43.32260291055251 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603194 %\n" - ] - } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 outlet temperature\n", - "- F101 outlet temperature\n", - "- H102 outlet temperature\n", - "- Condenser pressure\n", - "- reflux ratio\n", - "- boilup ratio\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.conversion.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.D101.condenser.condenser_pressure.unfix()\n", - "m.fs.D101.condenser.reflux_ratio.unfix()\n", - "m.fs.D101.reboiler.boilup_ratio.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 900] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - H102 outlet temperature [350, 400] K\n", - " - D101 condenser pressure [101325, 150000] Pa\n", - " - D101 reflux ratio [0.1, 5]\n", - " - D101 boilup ratio [0.1, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# Set bounds on the temperature of the outlet from H101\n", - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)\n", - "\n", - "# Set bounds on the temperature of the outlet from R101\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(900)\n", - "\n", - "# Set bounds on the volume of the reactor R101\n", - "m.fs.R101.volume[0].setlb(0)\n", - "\n", - "# Set bounds on the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "\n", - "# Set bounds on the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature[0].setlb(350)\n", - "m.fs.H102.outlet.temperature[0].setub(400)\n", - "\n", - "# Set bounds on the pressure inside the condenser\n", - "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", - "m.fs.D101.condenser.condenser_pressure.setub(150000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "\n", - "\n", - "# Todo: Set bounds on the boilup ratio" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", - "m.fs.D101.condenser.reflux_ratio.setub(5)\n", - "\n", - "# Todo: Set bounds on the boilup ratio\n", - "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", - "m.fs.D101.reboiler.boilup_ratio.setub(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that the overhead loss of benzene from F101 <= 20%\n", - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(\n", - " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", - "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", - "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", - "Total CPU secs in NLP function evaluations = 0.020\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.898426286\n", - "operating cost = $ 408883.5314830889\n", - "capital cost = $ 29956.3669431971\n", - "\n", - "Distillate flowrate = 0.1799999900263989 mol/s\n", - "Benzene purity = 98.99999900049086 %\n", - "Residue flowrate = 0.1085161642426372 mol/s\n", - "Toluene purity = 15.676178086213548 %\n", - "\n", - "Conversion = 93.38705916369427 %\n", - "\n", - "Overhead benzene loss in F101 = 17.34061793115618 %\n" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", + "\n", + "![](HDA_flowsheet_distillation.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translator block\n", + "\n", + "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", + "\n", + "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "- Flash\n", + "- Separator (splitter) \n", + "- PressureChanger\n", + "- Translator (to switch from one property package to another)\n", + "- TrayColumn (distillation column)\n", + "- CondenserType (Type of the overhead condenser: complete or partial)\n", + "- TemperatureSpec (Temperature specification inside the condenser)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " CSTR,\n", + " Flash,\n", + " Translator,\n", + ")\n", + "\n", + "from idaes.models_extra.column_models import TrayColumn\n", + "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction packages\n", + "\n", + "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "\n", + "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Pyomo Concrete Model to contain the problem\n", + "m = ConcreteModel()\n", + "\n", + "# Add a steady state flowsheet block to the model\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now add the thermophysical and reaction packages to the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Property package for benzene, toluene, hydrogen, methane mixture\n", + "m.fs.BTHM_params = HDAParameterBlock()\n", + "\n", + "# Property package for the benzene-toluene mixture\n", + "m.fs.BT_params = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", + ")\n", + "\n", + "# Reaction package for the HDA reaction\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.BTHM_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the mixer M101 to the flowsheet\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.BTHM_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "# Adding the heater H101 to the flowsheet\n", + "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.BTHM_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = CSTR(\n", + " property_package=m.fs.BTHM_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the flash tank F101 to the flowsheet\n", + "m.fs.F101 = Flash(\n", + " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", + ")\n", + "\n", + "# Adding the splitter S101 to the flowsheet\n", + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", + ")\n", + "\n", + "# Adding the compressor C101 to the flowsheet\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.BTHM_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remark\n", + "\n", + "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Add translator block to convert between property packages\n", + "m.fs.translator = Translator(\n", + " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translator block constraints\n", + "\n", + "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", + "\n", + "For this process, five constraints are required based on the state variables used in the outgoing process.\n", + "\n", + "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", + "- Temperature of the inlet and outlet streams must be the same.\n", + "- Pressure of the inlet and outgoing streams must be the same\n", + "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", + "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", + "m.fs.translator.eq_total_flow = Constraint(\n", + " expr=m.fs.translator.outlet.flow_mol[0]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + ")\n", + "\n", + "# Add constraint: Outlet temperature = Inlet temperature\n", + "m.fs.translator.eq_temperature = Constraint(\n", + " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", + "m.fs.translator.eq_pressure = Constraint(\n", + " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Remaining constraints on the translator block\n", + "\n", + "# Add constraint: Benzene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "# Add constraint: Toluene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet\n", + "m.fs.H102 = Heater(\n", + " property_package=m.fs.BT_params,\n", + " has_pressure_change=True,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet_distillation.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", + "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appending additional constraints to the model\n", + "\n", + "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", + "\n", + "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", + "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", + "\n", + "We add the constraint to the model as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the conversion variables using 'Var'\n", + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "# Append the constraint to the model\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions and Initializing the flowsheet\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H101\n", + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for the reactor R101 to the following conditions:\n", + "
    \n", + "
  • `conversion` = 0.75
    • \n", + "
  • `heat_duty` = 0
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "m.fs.R101.conversion.fix(0.75)\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "\n", + "# Fix the pressure drop in the flash F101\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the split fraction of the 'purge' stream from S101\n", + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "\n", + "# Fix the pressure of the outlet from the compressor C101\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H102\n", + "m.fs.H102.outlet.temperature.fix(375)\n", + "\n", + "# Fix the pressure drop in the heater H102\n", + "m.fs.H102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], + "source": [ + "# Set scaling factors for heat duty, reaction extent and volume\n", + "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", + "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.H101)\n", + "iscale.calculate_scaling_factors(m.fs.R101)\n", + "iscale.calculate_scaling_factors(m.fs.F101)\n", + "iscale.calculate_scaling_factors(m.fs.H102)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Check the degrees of freedom" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "# Todo: Check the degrees of freedom\n", + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization\n", + "\n", + "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", + "\n", + "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303.2},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " # Try initializing using default initializer,\n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Create the solver object\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add distillation column \n", + "\n", + "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", + "\n", + "In the following, we will\n", + "- Add the distillation column \n", + "- Connect it to the heater \n", + "- Add the necessary equality constraints\n", + "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", + "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", + "- Scale the control volume heat variables to help convergence\n", + "- Initialize the distillation block.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] 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idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] 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Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], + "source": [ + "# Add distillation column to the flowsheet\n", + "m.fs.D101 = TrayColumn(\n", + " number_of_trays=10,\n", + " feed_tray_location=5,\n", + " condenser_type=CondenserType.totalCondenser,\n", + " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", + " property_package=m.fs.BT_params,\n", + ")\n", + "\n", + "# Connect the outlet from the heater H102 to the distillation column\n", + "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", + "\n", + "# Add the necessary equality constraints\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "\n", + "# Propagate the state\n", + "propagate_state(m.fs.s11)\n", + "\n", + "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", + "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", + "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", + "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", + "\n", + "# set scaling factors\n", + "# Set scaling factors for heat duty\n", + "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.D101)\n", + "\n", + "# Initialize the distillation column\n", + "m.fs.D101.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute capital and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Expression to compute the total cooling cost\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", + " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", + ")\n", + "\n", + "# Expression to compute the total heating cost\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", + " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", + ")\n", + "\n", + "# Expression to compute the total operating cost\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")\n", + "\n", + "# Expression to compute the total capital cost\n", + "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the entire flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", + "Total CPU secs in NLP function evaluations = 0.013\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 outlet temperature\n", + "- F101 outlet temperature\n", + "- H102 outlet temperature\n", + "- Condenser pressure\n", + "- reflux ratio\n", + "- boilup ratio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.conversion.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.D101.condenser.condenser_pressure.unfix()\n", + "m.fs.D101.condenser.reflux_ratio.unfix()\n", + "m.fs.D101.reboiler.boilup_ratio.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 900] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - H102 outlet temperature [350, 400] K\n", + " - D101 condenser pressure [101325, 150000] Pa\n", + " - D101 reflux ratio [0.1, 5]\n", + " - D101 boilup ratio [0.1, 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Set bounds on the temperature of the outlet from H101\n", + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)\n", + "\n", + "# Set bounds on the temperature of the outlet from R101\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(900)\n", + "\n", + "# Set bounds on the volume of the reactor R101\n", + "m.fs.R101.volume[0].setlb(0)\n", + "\n", + "# Set bounds on the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "\n", + "# Set bounds on the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature[0].setlb(350)\n", + "m.fs.H102.outlet.temperature[0].setub(400)\n", + "\n", + "# Set bounds on the pressure inside the condenser\n", + "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", + "m.fs.D101.condenser.condenser_pressure.setub(150000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "\n", + "\n", + "# Todo: Set bounds on the boilup ratio" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", + "m.fs.D101.condenser.reflux_ratio.setub(5)\n", + "\n", + "# Todo: Set bounds on the boilup ratio\n", + "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", + "m.fs.D101.reboiler.boilup_ratio.setub(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure that the overhead loss of benzene from F101 <= 20%\n", + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(\n", + " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key Takeaways\n", + "\n", + "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", + "\n", + "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", + "\n", + "\n", + "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." + ] } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.923204295196 K\n", - "\n", - "R101 outlet temperature = 790.3655425698853 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.7414339952852 K\n" - ] + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Key Takeaways\n", - "\n", - "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", - "\n", - "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", - "\n", - "\n", - "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb index ba91dc8e..3aaf3b21 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_test.ipynb @@ -1,3033 +1,3034 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", - "\n", - "![](HDA_flowsheet_distillation.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Translator block\n", - "\n", - "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", - "\n", - "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "- Flash\n", - "- Separator (splitter) \n", - "- PressureChanger\n", - "- Translator (to switch from one property package to another)\n", - "- TrayColumn (distillation column)\n", - "- CondenserType (Type of the overhead condenser: complete or partial)\n", - "- TemperatureSpec (Temperature specification inside the condenser)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " CSTR,\n", - " Flash,\n", - " Translator,\n", - ")\n", - "\n", - "from idaes.models_extra.column_models import TrayColumn\n", - "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.core.util.scaling as iscale\n", - "from idaes.core.util.exceptions import InitializationError\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction packages\n", - "\n", - "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "\n", - "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Pyomo Concrete Model to contain the problem\n", - "m = ConcreteModel()\n", - "\n", - "# Add a steady state flowsheet block to the model\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now add the thermophysical and reaction packages to the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Property package for benzene, toluene, hydrogen, methane mixture\n", - "m.fs.BTHM_params = HDAParameterBlock()\n", - "\n", - "# Property package for the benzene-toluene mixture\n", - "m.fs.BT_params = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", - ")\n", - "\n", - "# Reaction package for the HDA reaction\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.BTHM_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the mixer M101 to the flowsheet\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.BTHM_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "# Adding the heater H101 to the flowsheet\n", - "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.BTHM_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = CSTR(\n", - " property_package=m.fs.BTHM_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the flash tank F101 to the flowsheet\n", - "m.fs.F101 = Flash(\n", - " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", - ")\n", - "\n", - "# Adding the splitter S101 to the flowsheet\n", - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", - ")\n", - "\n", - "# Adding the compressor C101 to the flowsheet\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.BTHM_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remark\n", - "\n", - "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Add translator block to convert between property packages\n", - "m.fs.translator = Translator(\n", - " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translator block constraints\n", - "\n", - "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", - "\n", - "For this process, five constraints are required based on the state variables used in the outgoing process.\n", - "\n", - "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", - "- Temperature of the inlet and outlet streams must be the same.\n", - "- Pressure of the inlet and outgoing streams must be the same\n", - "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", - "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", - "m.fs.translator.eq_total_flow = Constraint(\n", - " expr=m.fs.translator.outlet.flow_mol[0]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - ")\n", - "\n", - "# Add constraint: Outlet temperature = Inlet temperature\n", - "m.fs.translator.eq_temperature = Constraint(\n", - " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", - "m.fs.translator.eq_pressure = Constraint(\n", - " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Remaining constraints on the translator block\n", - "\n", - "# Add constraint: Benzene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "# Add constraint: Toluene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet\n", - "m.fs.H102 = Heater(\n", - " property_package=m.fs.BT_params,\n", - " has_pressure_change=True,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet_distillation.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", - "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Appending additional constraints to the model\n", - "\n", - "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", - "\n", - "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", - "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", - "\n", - "We add the constraint to the model as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the conversion variables using 'Var'\n", - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "# Append the constraint to the model\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions and Initializing the flowsheet\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 29" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H101\n", - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for the reactor R101 to the following conditions:\n", - "
    \n", - "
  • `conversion` = 0.75
    • \n", - "
  • `heat_duty` = 0
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "m.fs.R101.conversion.fix(0.75)\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "\n", - "# Fix the pressure drop in the flash F101\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the split fraction of the 'purge' stream from S101\n", - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "\n", - "# Fix the pressure of the outlet from the compressor C101\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H102\n", - "m.fs.H102.outlet.temperature.fix(375)\n", - "\n", - "# Fix the pressure drop in the heater H102\n", - "m.fs.H102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" - ] - } - ], - "source": [ - "# Set scaling factors for heat duty, reaction extent and volume\n", - "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", - "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.H101)\n", - "iscale.calculate_scaling_factors(m.fs.R101)\n", - "iscale.calculate_scaling_factors(m.fs.F101)\n", - "iscale.calculate_scaling_factors(m.fs.H102)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# Todo: Check the degrees of freedom\n", - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization\n", - "\n", - "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", - "\n", - "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303.2},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " # Try initializing using default initializer,\n", - " # if it fails (probably due to scaling) try for the second time\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" - ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Create the solver object\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add distillation column \n", - "\n", - "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", - "\n", - "In the following, we will\n", - "- Add the distillation column \n", - "- Connect it to the heater \n", - "- Add the necessary equality constraints\n", - "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", - "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", - "- Scale the control volume heat variables to help convergence\n", - "- Initialize the distillation block.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], - "source": [ - "# Add distillation column to the flowsheet\n", - "m.fs.D101 = TrayColumn(\n", - " number_of_trays=10,\n", - " feed_tray_location=5,\n", - " condenser_type=CondenserType.totalCondenser,\n", - " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", - " property_package=m.fs.BT_params,\n", - ")\n", - "\n", - "# Connect the outlet from the heater H102 to the distillation column\n", - "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", - "\n", - "# Add the necessary equality constraints\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "\n", - "# Propagate the state\n", - "propagate_state(m.fs.s11)\n", - "\n", - "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", - "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", - "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", - "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", - "\n", - "# set scaling factors\n", - "# Set scaling factors for heat duty\n", - "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.D101)\n", - "\n", - "# Initialize the distillation column\n", - "m.fs.D101.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute capital and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Expression to compute the total cooling cost\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", - " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", - ")\n", - "\n", - "# Expression to compute the total heating cost\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", - " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", - ")\n", - "\n", - "# Expression to compute the total operating cost\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")\n", - "\n", - "# Expression to compute the total capital cost\n", - "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve the entire flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check that the degrees of freedom is zero\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", - "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", - "Total CPU secs in NLP function evaluations = 0.013\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252194\n", - "operating cost = $ 427596.73056805483\n", - "capital cost = $ 14704.740184467111\n", - "\n", - "Distillate flowrate = 0.16196898920633368 mol/s\n", - "Benzene purity = 89.4916166580088 %\n", - "Residue flowrate = 0.10515007120697904 mol/s\n", - "Toluene purity = 43.32260291055251 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603194 %\n" - ] - } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "427596.73056805483\n", - "14704.740184467111\n" - ] - } - ], - "source": [ - "import pytest\n", - "\n", - "print(value(m.fs.operating_cost))\n", - "assert value(m.fs.operating_cost) == pytest.approx(427596.731, abs=100)\n", - "print(value(m.fs.capital_cost))\n", - "assert value(m.fs.capital_cost) == pytest.approx(14704.740, abs=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 outlet temperature\n", - "- F101 outlet temperature\n", - "- H102 outlet temperature\n", - "- Condenser pressure\n", - "- reflux ratio\n", - "- boilup ratio\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.conversion.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.D101.condenser.condenser_pressure.unfix()\n", - "m.fs.D101.condenser.reflux_ratio.unfix()\n", - "m.fs.D101.reboiler.boilup_ratio.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 900] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - H102 outlet temperature [350, 400] K\n", - " - D101 condenser pressure [101325, 150000] Pa\n", - " - D101 reflux ratio [0.1, 5]\n", - " - D101 boilup ratio [0.1, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# Set bounds on the temperature of the outlet from H101\n", - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)\n", - "\n", - "# Set bounds on the temperature of the outlet from R101\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(900)\n", - "\n", - "# Set bounds on the volume of the reactor R101\n", - "m.fs.R101.volume[0].setlb(0)\n", - "\n", - "# Set bounds on the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "\n", - "# Set bounds on the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature[0].setlb(350)\n", - "m.fs.H102.outlet.temperature[0].setub(400)\n", - "\n", - "# Set bounds on the pressure inside the condenser\n", - "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", - "m.fs.D101.condenser.condenser_pressure.setub(150000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", - "m.fs.D101.condenser.reflux_ratio.setub(5)\n", - "\n", - "# Todo: Set bounds on the boilup ratio\n", - "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", - "m.fs.D101.reboiler.boilup_ratio.setub(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that the overhead loss of benzene from F101 <= 20%\n", - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(\n", - " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", - "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", - "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", - "Total CPU secs in NLP function evaluations = 0.020\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.898426286\n", - "operating cost = $ 408883.5314830889\n", - "capital cost = $ 29956.3669431971\n", - "\n", - "Distillate flowrate = 0.1799999900263989 mol/s\n", - "Benzene purity = 98.99999900049086 %\n", - "Residue flowrate = 0.1085161642426372 mol/s\n", - "Toluene purity = 15.676178086213548 %\n", - "\n", - "Conversion = 93.38705916369427 %\n", - "\n", - "Overhead benzene loss in F101 = 17.34061793115618 %\n" - ] - } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "408883.5314830889\n", - "29956.3669431971\n" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", + "\n", + "![](HDA_flowsheet_distillation.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translator block\n", + "\n", + "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", + "\n", + "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "- Flash\n", + "- Separator (splitter) \n", + "- PressureChanger\n", + "- Translator (to switch from one property package to another)\n", + "- TrayColumn (distillation column)\n", + "- CondenserType (Type of the overhead condenser: complete or partial)\n", + "- TemperatureSpec (Temperature specification inside the condenser)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " CSTR,\n", + " Flash,\n", + " Translator,\n", + ")\n", + "\n", + "from idaes.models_extra.column_models import TrayColumn\n", + "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction packages\n", + "\n", + "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "\n", + "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Pyomo Concrete Model to contain the problem\n", + "m = ConcreteModel()\n", + "\n", + "# Add a steady state flowsheet block to the model\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now add the thermophysical and reaction packages to the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Property package for benzene, toluene, hydrogen, methane mixture\n", + "m.fs.BTHM_params = HDAParameterBlock()\n", + "\n", + "# Property package for the benzene-toluene mixture\n", + "m.fs.BT_params = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", + ")\n", + "\n", + "# Reaction package for the HDA reaction\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.BTHM_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the mixer M101 to the flowsheet\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.BTHM_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "# Adding the heater H101 to the flowsheet\n", + "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.BTHM_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = CSTR(\n", + " property_package=m.fs.BTHM_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the flash tank F101 to the flowsheet\n", + "m.fs.F101 = Flash(\n", + " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", + ")\n", + "\n", + "# Adding the splitter S101 to the flowsheet\n", + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", + ")\n", + "\n", + "# Adding the compressor C101 to the flowsheet\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.BTHM_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remark\n", + "\n", + "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Add translator block to convert between property packages\n", + "m.fs.translator = Translator(\n", + " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translator block constraints\n", + "\n", + "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", + "\n", + "For this process, five constraints are required based on the state variables used in the outgoing process.\n", + "\n", + "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", + "- Temperature of the inlet and outlet streams must be the same.\n", + "- Pressure of the inlet and outgoing streams must be the same\n", + "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", + "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", + "m.fs.translator.eq_total_flow = Constraint(\n", + " expr=m.fs.translator.outlet.flow_mol[0]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + ")\n", + "\n", + "# Add constraint: Outlet temperature = Inlet temperature\n", + "m.fs.translator.eq_temperature = Constraint(\n", + " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", + "m.fs.translator.eq_pressure = Constraint(\n", + " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Remaining constraints on the translator block\n", + "\n", + "# Add constraint: Benzene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "# Add constraint: Toluene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet\n", + "m.fs.H102 = Heater(\n", + " property_package=m.fs.BT_params,\n", + " has_pressure_change=True,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet_distillation.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", + "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appending additional constraints to the model\n", + "\n", + "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", + "\n", + "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", + "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", + "\n", + "We add the constraint to the model as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the conversion variables using 'Var'\n", + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "# Append the constraint to the model\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions and Initializing the flowsheet\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 29" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H101\n", + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for the reactor R101 to the following conditions:\n", + "
    \n", + "
  • `conversion` = 0.75
    • \n", + "
  • `heat_duty` = 0
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "m.fs.R101.conversion.fix(0.75)\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "\n", + "# Fix the pressure drop in the flash F101\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the split fraction of the 'purge' stream from S101\n", + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "\n", + "# Fix the pressure of the outlet from the compressor C101\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H102\n", + "m.fs.H102.outlet.temperature.fix(375)\n", + "\n", + "# Fix the pressure drop in the heater H102\n", + "m.fs.H102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], + "source": [ + "# Set scaling factors for heat duty, reaction extent and volume\n", + "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", + "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.H101)\n", + "iscale.calculate_scaling_factors(m.fs.R101)\n", + "iscale.calculate_scaling_factors(m.fs.F101)\n", + "iscale.calculate_scaling_factors(m.fs.H102)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "# Todo: Check the degrees of freedom\n", + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization\n", + "\n", + "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", + "\n", + "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303.2},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " # Try initializing using default initializer,\n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Create the solver object\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add distillation column \n", + "\n", + "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", + "\n", + "In the following, we will\n", + "- Add the distillation column \n", + "- Connect it to the heater \n", + "- Add the necessary equality constraints\n", + "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", + "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", + "- Scale the control volume heat variables to help convergence\n", + "- Initialize the distillation block.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], + "source": [ + "# Add distillation column to the flowsheet\n", + "m.fs.D101 = TrayColumn(\n", + " number_of_trays=10,\n", + " feed_tray_location=5,\n", + " condenser_type=CondenserType.totalCondenser,\n", + " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", + " property_package=m.fs.BT_params,\n", + ")\n", + "\n", + "# Connect the outlet from the heater H102 to the distillation column\n", + "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", + "\n", + "# Add the necessary equality constraints\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "\n", + "# Propagate the state\n", + "propagate_state(m.fs.s11)\n", + "\n", + "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", + "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", + "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", + "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", + "\n", + "# set scaling factors\n", + "# Set scaling factors for heat duty\n", + "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.D101)\n", + "\n", + "# Initialize the distillation column\n", + "m.fs.D101.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute capital and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Expression to compute the total cooling cost\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", + " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", + ")\n", + "\n", + "# Expression to compute the total heating cost\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", + " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", + ")\n", + "\n", + "# Expression to compute the total operating cost\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")\n", + "\n", + "# Expression to compute the total capital cost\n", + "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the entire flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check that the degrees of freedom is zero\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", + "Total CPU secs in NLP function evaluations = 0.013\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427596.73056805483\n", + "14704.740184467111\n" + ] + } + ], + "source": [ + "import pytest\n", + "\n", + "print(value(m.fs.operating_cost))\n", + "assert value(m.fs.operating_cost) == pytest.approx(427596.731, abs=100)\n", + "print(value(m.fs.capital_cost))\n", + "assert value(m.fs.capital_cost) == pytest.approx(14704.740, abs=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 outlet temperature\n", + "- F101 outlet temperature\n", + "- H102 outlet temperature\n", + "- Condenser pressure\n", + "- reflux ratio\n", + "- boilup ratio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.conversion.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.D101.condenser.condenser_pressure.unfix()\n", + "m.fs.D101.condenser.reflux_ratio.unfix()\n", + "m.fs.D101.reboiler.boilup_ratio.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 900] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - H102 outlet temperature [350, 400] K\n", + " - D101 condenser pressure [101325, 150000] Pa\n", + " - D101 reflux ratio [0.1, 5]\n", + " - D101 boilup ratio [0.1, 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Set bounds on the temperature of the outlet from H101\n", + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)\n", + "\n", + "# Set bounds on the temperature of the outlet from R101\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(900)\n", + "\n", + "# Set bounds on the volume of the reactor R101\n", + "m.fs.R101.volume[0].setlb(0)\n", + "\n", + "# Set bounds on the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "\n", + "# Set bounds on the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature[0].setlb(350)\n", + "m.fs.H102.outlet.temperature[0].setub(400)\n", + "\n", + "# Set bounds on the pressure inside the condenser\n", + "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", + "m.fs.D101.condenser.condenser_pressure.setub(150000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", + "m.fs.D101.condenser.reflux_ratio.setub(5)\n", + "\n", + "# Todo: Set bounds on the boilup ratio\n", + "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", + "m.fs.D101.reboiler.boilup_ratio.setub(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure that the overhead loss of benzene from F101 <= 20%\n", + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(\n", + " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "408883.5314830889\n", + "29956.3669431971\n" + ] + } + ], + "source": [ + "import pytest\n", + "\n", + "print(value(m.fs.operating_cost))\n", + "print(value(m.fs.capital_cost))\n", + "\n", + "assert value(m.fs.operating_cost) == pytest.approx(408883.531, abs=100)\n", + "assert value(m.fs.capital_cost) == pytest.approx(29956.367, abs=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key Takeaways\n", + "\n", + "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", + "\n", + "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", + "\n", + "\n", + "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." + ] } - ], - "source": [ - "import pytest\n", - "\n", - "print(value(m.fs.operating_cost))\n", - "print(value(m.fs.capital_cost))\n", - "\n", - "assert value(m.fs.operating_cost) == pytest.approx(408883.531, abs=100)\n", - "assert value(m.fs.capital_cost) == pytest.approx(29956.367, abs=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.923204295196 K\n", - "\n", - "R101 outlet temperature = 790.3655425698853 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.7414339952852 K\n" - ] + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Key Takeaways\n", - "\n", - "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", - "\n", - "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", - "\n", - "\n", - "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb index 86d59dd3..121bd803 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_usr.ipynb @@ -1,3024 +1,3025 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Note\n", - "\n", - "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", - "\n", - "![](HDA_flowsheet_distillation.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Translator block\n", - "\n", - "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", - "\n", - "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Import the above mentioned tools from pyomo.network\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "- Flash\n", - "- Separator (splitter) \n", - "- PressureChanger\n", - "- Translator (to switch from one property package to another)\n", - "- TrayColumn (distillation column)\n", - "- CondenserType (Type of the overhead condenser: complete or partial)\n", - "- TemperatureSpec (Temperature specification inside the condenser)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " CSTR,\n", - " Flash,\n", - " Translator,\n", - ")\n", - "\n", - "from idaes.models_extra.column_models import TrayColumn\n", - "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.core.util.scaling as iscale\n", - "from idaes.core.util.exceptions import InitializationError\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction packages\n", - "\n", - "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "\n", - "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Pyomo Concrete Model to contain the problem\n", - "m = ConcreteModel()\n", - "\n", - "# Add a steady state flowsheet block to the model\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now add the thermophysical and reaction packages to the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Property package for benzene, toluene, hydrogen, methane mixture\n", - "m.fs.BTHM_params = HDAParameterBlock()\n", - "\n", - "# Property package for the benzene-toluene mixture\n", - "m.fs.BT_params = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", - ")\n", - "\n", - "# Reaction package for the HDA reaction\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.BTHM_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the mixer M101 to the flowsheet\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.BTHM_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "# Adding the heater H101 to the flowsheet\n", - "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.BTHM_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = CSTR(\n", - " property_package=m.fs.BTHM_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding the flash tank F101 to the flowsheet\n", - "m.fs.F101 = Flash(\n", - " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", - ")\n", - "\n", - "# Adding the splitter S101 to the flowsheet\n", - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", - ")\n", - "\n", - "# Adding the compressor C101 to the flowsheet\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.BTHM_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remark\n", - "\n", - "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Add translator block to convert between property packages\n", - "m.fs.translator = Translator(\n", - " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translator block constraints\n", - "\n", - "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", - "\n", - "For this process, five constraints are required based on the state variables used in the outgoing process.\n", - "\n", - "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", - "- Temperature of the inlet and outlet streams must be the same.\n", - "- Pressure of the inlet and outgoing streams must be the same\n", - "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", - "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", - "m.fs.translator.eq_total_flow = Constraint(\n", - " expr=m.fs.translator.outlet.flow_mol[0]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - ")\n", - "\n", - "# Add constraint: Outlet temperature = Inlet temperature\n", - "m.fs.translator.eq_temperature = Constraint(\n", - " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", - "m.fs.translator.eq_pressure = Constraint(\n", - " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Remaining constraints on the translator block\n", - "\n", - "# Add constraint: Benzene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "# Add constraint: Toluene mole fraction definition\n", - "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", - " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", - " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " / (\n", - " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", - " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add the Heater H102 to the flowsheet\n", - "m.fs.H102 = Heater(\n", - " property_package=m.fs.BT_params,\n", - " has_pressure_change=True,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet_distillation.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", - "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Appending additional constraints to the model\n", - "\n", - "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", - "\n", - "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", - "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", - "\n", - "We add the constraint to the model as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the conversion variables using 'Var'\n", - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "# Append the constraint to the model\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions and Initializing the flowsheet\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H101\n", - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for the reactor R101 to the following conditions:\n", - "
    \n", - "
  • `conversion` = 0.75
    • \n", - "
  • `heat_duty` = 0
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Fix the 'conversion' of the reactor R101\n", - "m.fs.R101.conversion.fix(0.75)\n", - "\n", - "# Todo: Fix the 'heat_duty' of the reactor R101\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "\n", - "# Fix the pressure drop in the flash F101\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the split fraction of the 'purge' stream from S101\n", - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "\n", - "# Fix the pressure of the outlet from the compressor C101\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the temperature of the outlet from the heater H102\n", - "m.fs.H102.outlet.temperature.fix(375)\n", - "\n", - "# Fix the pressure drop in the heater H102\n", - "m.fs.H102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" - ] - } - ], - "source": [ - "# Set scaling factors for heat duty, reaction extent and volume\n", - "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", - "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", - "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.H101)\n", - "iscale.calculate_scaling_factors(m.fs.R101)\n", - "iscale.calculate_scaling_factors(m.fs.F101)\n", - "iscale.calculate_scaling_factors(m.fs.H102)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Check the degrees of freedom" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "# Todo: Check the degrees of freedom\n", - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization\n", - "\n", - "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", - "\n", - "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303.2},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " # Try initializing using default initializer,\n", - " # if it fails (probably due to scaling) try for the second time\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", - "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", - "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", - "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" - ] - } - ], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Create the solver object\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add distillation column \n", - "\n", - "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", - "\n", - "In the following, we will\n", - "- Add the distillation column \n", - "- Connect it to the heater \n", - "- Add the necessary equality constraints\n", - "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", - "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", - "- Scale the control volume heat variables to help convergence\n", - "- Initialize the distillation block.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], - "source": [ - "# Add distillation column to the flowsheet\n", - "m.fs.D101 = TrayColumn(\n", - " number_of_trays=10,\n", - " feed_tray_location=5,\n", - " condenser_type=CondenserType.totalCondenser,\n", - " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", - " property_package=m.fs.BT_params,\n", - ")\n", - "\n", - "# Connect the outlet from the heater H102 to the distillation column\n", - "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", - "\n", - "# Add the necessary equality constraints\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "\n", - "# Propagate the state\n", - "propagate_state(m.fs.s11)\n", - "\n", - "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", - "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", - "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", - "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", - "\n", - "# set scaling factors\n", - "# Set scaling factors for heat duty\n", - "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", - "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", - "\n", - "# Set the scaling factors for the remaining variables and all constraints\n", - "iscale.calculate_scaling_factors(m.fs.D101)\n", - "\n", - "# Initialize the distillation column\n", - "m.fs.D101.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute capital and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# Expression to compute the total cooling cost\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", - " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", - ")\n", - "\n", - "# Expression to compute the total heating cost\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", - " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", - ")\n", - "\n", - "# Expression to compute the total operating cost\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")\n", - "\n", - "# Expression to compute the total capital cost\n", - "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve the entire flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", - "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", - "Total CPU secs in NLP function evaluations = 0.013\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252194\n", - "operating cost = $ 427596.73056805483\n", - "capital cost = $ 14704.740184467111\n", - "\n", - "Distillate flowrate = 0.16196898920633368 mol/s\n", - "Benzene purity = 89.4916166580088 %\n", - "Residue flowrate = 0.10515007120697904 mol/s\n", - "Toluene purity = 43.32260291055251 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603194 %\n" - ] - } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the state of the streams entering and leaving the reactor R101" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 outlet temperature\n", - "- F101 outlet temperature\n", - "- H102 outlet temperature\n", - "- Condenser pressure\n", - "- reflux ratio\n", - "- boilup ratio\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.conversion.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.D101.condenser.condenser_pressure.unfix()\n", - "m.fs.D101.condenser.reflux_ratio.unfix()\n", - "m.fs.D101.reboiler.boilup_ratio.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 900] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - H102 outlet temperature [350, 400] K\n", - " - D101 condenser pressure [101325, 150000] Pa\n", - " - D101 reflux ratio [0.1, 5]\n", - " - D101 boilup ratio [0.1, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# Set bounds on the temperature of the outlet from H101\n", - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)\n", - "\n", - "# Set bounds on the temperature of the outlet from R101\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(900)\n", - "\n", - "# Set bounds on the volume of the reactor R101\n", - "m.fs.R101.volume[0].setlb(0)\n", - "\n", - "# Set bounds on the temperature of the vapor outlet from F101\n", - "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "\n", - "# Set bounds on the temperature of the outlet from H102\n", - "m.fs.H102.outlet.temperature[0].setlb(350)\n", - "m.fs.H102.outlet.temperature[0].setub(400)\n", - "\n", - "# Set bounds on the pressure inside the condenser\n", - "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", - "m.fs.D101.condenser.condenser_pressure.setub(150000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "\n", - "\n", - "# Todo: Set bounds on the boilup ratio" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set bounds on the reflux ratio\n", - "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", - "m.fs.D101.condenser.reflux_ratio.setub(5)\n", - "\n", - "# Todo: Set bounds on the boilup ratio\n", - "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", - "m.fs.D101.reboiler.boilup_ratio.setub(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Ensure that the overhead loss of benzene from F101 <= 20%\n", - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(\n", - " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", - "that are not Var, Constraint, Objective, or the model. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", - "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", - "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", - "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", - "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", - "Total CPU secs in NLP function evaluations = 0.020\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.898426286\n", - "operating cost = $ 408883.5314830889\n", - "capital cost = $ 29956.3669431971\n", - "\n", - "Distillate flowrate = 0.1799999900263989 mol/s\n", - "Benzene purity = 98.99999900049086 %\n", - "Residue flowrate = 0.1085161642426372 mol/s\n", - "Toluene purity = 15.676178086213548 %\n", - "\n", - "Conversion = 93.38705916369427 %\n", - "\n", - "Overhead benzene loss in F101 = 17.34061793115618 %\n" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Note\n", + "\n", + "This tutorial will be similar to the HDA flowsheet tutorial in the Tutorials section, except that we use a distillation column instead of a second flash (F102) to produce benzene and toluene products.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, we use a flash tank, F101, to separate out the non-condensibles, and a distillation column, D101, to further separate the benzene-toluene mixture to improve the benzene purity. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be purged. We will assume ideal gas behavior for this flowsheet. The properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "We will be using two thermodynamic packages: one (first in the list above) containing all four components (i.e., toluene, hydrogen, benzene, and methane) and the other (second in the list above) containing benzene and toluene only. The latter is needed to simplify the VLE calculations in the distillation column model. \n", + "\n", + "![](HDA_flowsheet_distillation.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translator block\n", + "\n", + "Benzene and toluene are separated by distillation, so the process involves phase equilibrium and two-phase flow conditions. However, the presence of hydrogen and methane complicates the calculations. This is because, hydrogen and methane are non-condensable under all conditions of interest; ergo, a vapor phase will always be present, and the mixture bubble point is extremely low. To simplify the phase equilibrium calculations, hydrogen and methane will be considered completely as non-condensable and insoluble in the liquid outlet from the flash F101.\n", + "\n", + "Since no hydrogen and methane will be present in the unit operations following the flash, a different component list can be used to simplify the property calculations. IDAES supports the definition of multiple property packages within a single flowsheet via `Translator` blocks. `Translator` blocks convert between different property calculations, component lists, and equations of state. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Import `Arc` and `SequentialDecomposition` tools from `pyomo.network`\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Import the above mentioned tools from pyomo.network\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From IDAES, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "- Flash\n", + "- Separator (splitter) \n", + "- PressureChanger\n", + "- Translator (to switch from one property package to another)\n", + "- TrayColumn (distillation column)\n", + "- CondenserType (Type of the overhead condenser: complete or partial)\n", + "- TemperatureSpec (Temperature specification inside the condenser)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " CSTR,\n", + " Flash,\n", + " Translator,\n", + ")\n", + "\n", + "from idaes.models_extra.column_models import TrayColumn\n", + "from idaes.models_extra.column_models.condenser import CondenserType, TemperatureSpec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility tools to put together the flowsheet and calculate the degrees of freedom\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "import idaes.core.util.scaling as iscale\n", + "from idaes.core.util.exceptions import InitializationError\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction packages\n", + "\n", + "Finally, we import the thermophysical (`ideal_VLE.py` and `BTXParameterBlock`) packages and reaction package (`reaction.py`) for the HDA process. We have created custom thermophysical packages that assume ideal gas behavior with support for VLE. The reaction package consists of the stochiometric coefficients for the reaction, heat of reaction, and kinetic information (Arrhenius constant and activation energy). " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_reaction as reaction_props\n", + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "\n", + "from idaes_examples.mod.hda.hda_ideal_VLE import HDAParameterBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Pyomo Concrete Model to contain the problem\n", + "m = ConcreteModel()\n", + "\n", + "# Add a steady state flowsheet block to the model\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now add the thermophysical and reaction packages to the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Property package for benzene, toluene, hydrogen, methane mixture\n", + "m.fs.BTHM_params = HDAParameterBlock()\n", + "\n", + "# Property package for the benzene-toluene mixture\n", + "m.fs.BT_params = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\"\n", + ")\n", + "\n", + "# Reaction package for the HDA reaction\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.BTHM_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition, the Mixer unit model needs a `list` consisting of the inlets (toluene feed, hydrogen feed and vapor recycle streams in this case). " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the mixer M101 to the flowsheet\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.BTHM_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "# Adding the heater H101 to the flowsheet\n", + "m.fs.H101 = Heater(property_package=m.fs.BTHM_params, has_phase_equilibrium=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the CSTR (assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.BTHM_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = CSTR(\n", + " property_package=m.fs.BTHM_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash (assign the name F101), Splitter (assign the name S101) and PressureChanger (assign the name C101)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Adding the flash tank F101 to the flowsheet\n", + "m.fs.F101 = Flash(\n", + " property_package=m.fs.BTHM_params, has_heat_transfer=True, has_pressure_change=True\n", + ")\n", + "\n", + "# Adding the splitter S101 to the flowsheet\n", + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.BTHM_params, outlet_list=[\"purge\", \"recycle\"]\n", + ")\n", + "\n", + "# Adding the compressor C101 to the flowsheet\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.BTHM_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remark\n", + "\n", + "Currently, the `SequentialDecomposition()` tool, which we will later be using to initialize the flowsheet, does not support the distillation column model. Thus, we will first simulate the flowsheet without the distillation column. After it converges, we will then add the distillation column, initialize it, and simulate the entire flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, we use the `m.fs.BTHM_params` package, which contains all the four species, for the reactor loop, and the simpler `m.fs.BT_params` for unit operations following the flash (i.e., heater H102 and the distillation column D101). We define a `Translator` block to link the source property package and the package it is to be translated to in the following manner:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Add translator block to convert between property packages\n", + "m.fs.translator = Translator(\n", + " inlet_property_package=m.fs.BTHM_params, outlet_property_package=m.fs.BT_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Translator block constraints\n", + "\n", + "The `Translator` block needs to know how to translate between the two property packages. This must be custom coded for each application because of the generality of the IDAES framework.\n", + "\n", + "For this process, five constraints are required based on the state variables used in the outgoing process.\n", + "\n", + "- Since we assumed that only benzene and toluene are present in the liquid phase, the total molar flowrate must be the sum of molar flowrates of benzene and toluene, respectively.\n", + "- Temperature of the inlet and outlet streams must be the same.\n", + "- Pressure of the inlet and outgoing streams must be the same\n", + "- The mole fraction of benzene in the outgoing stream is the ratio of the molar flowrate of liquid benzene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet.\n", + "- The mole fraction of toluene in the outgoing stream is the ratio of the molar flowrate of liquid toluene in the inlet to the sum of molar flowrates of liquid benzene and toluene in the inlet." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Add constraint: Total flow = benzene flow + toluene flow (molar)\n", + "m.fs.translator.eq_total_flow = Constraint(\n", + " expr=m.fs.translator.outlet.flow_mol[0]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + ")\n", + "\n", + "# Add constraint: Outlet temperature = Inlet temperature\n", + "m.fs.translator.eq_temperature = Constraint(\n", + " expr=m.fs.translator.outlet.temperature[0] == m.fs.translator.inlet.temperature[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, note that the variable flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Add the constraint to ensure that the outlet pressure is the same as the inlet pressure\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add constraint: Outlet pressure = Inlet pressure\n", + "m.fs.translator.eq_pressure = Constraint(\n", + " expr=m.fs.translator.outlet.pressure[0] == m.fs.translator.inlet.pressure[0]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Remaining constraints on the translator block\n", + "\n", + "# Add constraint: Benzene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_benzene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"benzene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "# Add constraint: Toluene mole fraction definition\n", + "m.fs.translator.eq_mole_frac_toluene = Constraint(\n", + " expr=m.fs.translator.outlet.mole_frac_comp[0, \"toluene\"]\n", + " == m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " / (\n", + " m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"benzene\"]\n", + " + m.fs.translator.inlet.flow_mol_phase_comp[0, \"Liq\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Finally, let us add the Heater H102 in the same way as H101 but pass the m.fs.BT_params thermodynamic package. We will add the distillation column after converging the flowsheet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add the Heater H102 to the flowsheet\n", + "m.fs.H102 = Heater(\n", + " property_package=m.fs.BT_params,\n", + " has_pressure_change=True,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added the initial set of unit models to the flowsheet. However, we have not yet specified how the units are connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer (M101) to the inlet of the heater (H101). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet_distillation.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the units as shown below. Notice how the outlet names are different for the flash tank as it has a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10a = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.translator.inlet)\n", + "m.fs.s10b = Arc(source=m.fs.translator.outlet, destination=m.fs.H102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Appending additional constraints to the model\n", + "\n", + "Now, we will see how we can add additional constraints to the model using `Constraint` from Pyomo.\n", + "\n", + "Consider the reactor R101. By default, the conversion of a component is not calculated when we simulate the flowsheet. If we are interested either in specifying or constraining the conversion value, we can add the following constraint to calculate the conversion:\n", + "$$ \\text{Conversion of toluene} = \\frac{\\text{molar flow of toluene in the inlet} - \\text{molar flow of toluene in the outlet}}{\\text{molar flow of toluene in the inlet}} $$ \n", + "\n", + "We add the constraint to the model as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the conversion variables using 'Var'\n", + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "# Append the constraint to the model\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions and Initializing the flowsheet\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H101\n", + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for the reactor R101 to the following conditions:\n", + "
    \n", + "
  • `conversion` = 0.75
    • \n", + "
  • `heat_duty` = 0
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Fix the 'conversion' of the reactor R101\n", + "m.fs.R101.conversion.fix(0.75)\n", + "\n", + "# Todo: Fix the 'heat_duty' of the reactor R101\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "\n", + "# Fix the pressure drop in the flash F101\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the split fraction of the purge stream from the splitter S101 and the outlet pressure from the compressor C101" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the split fraction of the 'purge' stream from S101\n", + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "\n", + "# Fix the pressure of the outlet from the compressor C101\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let us fix the temperature of the outlet from H102 and the pressure drop in H102 as the following" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the temperature of the outlet from the heater H102\n", + "m.fs.H102.outlet.temperature.fix(375)\n", + "\n", + "# Fix the pressure drop in the heater H102\n", + "m.fs.H102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To avoid convergence issues associated with poorly scaled variables and/or constraints, we scale the variables and constraints corresponding to the heaters H101 and H102, flash F101 and the reactor R101. Scaling factors for the flow rates, temperature, pressure, etc. have been defined in the property package: `ideal_VLE.py` file. Here, we set scaling factors only for the heat duty of the heater, the reaction extent, heat duty and volume of the reactor." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:14 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], + "source": [ + "# Set scaling factors for heat duty, reaction extent and volume\n", + "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.rate_reaction_extent, 1)\n", + "iscale.set_scaling_factor(m.fs.R101.control_volume.volume, 1)\n", + "iscale.set_scaling_factor(m.fs.F101.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.H102.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.H101)\n", + "iscale.calculate_scaling_factors(m.fs.R101)\n", + "iscale.calculate_scaling_factors(m.fs.F101)\n", + "iscale.calculate_scaling_factors(m.fs.H102)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Check the degrees of freedom" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "# Todo: Check the degrees of freedom\n", + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization\n", + "\n", + "This subsection will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "Let us first create an object for the `SequentialDecomposition` and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet (s03 in the Figure above). We will need to provide a reasonable guess for this.\n", + "\n", + "For the initial guess, we assume that the flowrate of the recycle stream (s09) is zero. Consequently, the flow rate of the stream s03 is simply the sum of the flowrates of the toluene feed and hydrogen feed streams. Further, since the temperature and the pressure of both the toluene and hydrogen feed streams are the same, we specify their values as the initial guess for the temperature and pressure of the stream s03." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303.2},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. For the initialization, we will import a Block Triangularization Initializer which decomposes the model into a set of subproblems. These subproblems are solved using a block triangularization transformation before applying a simple Newton or user-selected solver. Methods such as block triangularization often solve faster and yield more reliable behavior than heuristic methods, but sometime struggle to decompose models with strongly coupled equations (e.g. column models, systems with counter-current flow, vapor-liquid equilibrium)." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " # Try initializing using default initializer,\n", + " # if it fails (probably due to scaling) try for the second time\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 3 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:14 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:16 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:18 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:19 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:20 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:21 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:22 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 50 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:24 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:25 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:29 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n", + "2024-08-28 18:38:30 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - .\n", + "2024-08-28 18:38:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - .\n", + "2024-08-28 18:38:33 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - .\n" + ] + } + ], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 6\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.42e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.41e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.24e-08 4.99e-01 -3.8 5.92e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042487592972509e+04 1.5042487592972509e+04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042487592972509e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Create the solver object\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add distillation column \n", + "\n", + "As mentioned earlier, the `SequentialDecomposition` tool currently does not support the distillation column model. Thus, we have not included the distillation column in the flowsheet. Now that we have a converged flowsheet, we will add the distillation column and simulate the entire flowsheet. \n", + "\n", + "In the following, we will\n", + "- Add the distillation column \n", + "- Connect it to the heater \n", + "- Add the necessary equality constraints\n", + "- Propagate the state variable information from the outlet of the heater to the inlet of the distillation column \n", + "- Fix the degrees of freedom of the distillation block (reflux ratio, boilup ratio, and condenser pressure)\n", + "- Scale the control volume heat variables to help convergence\n", + "- Initialize the distillation block.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] 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idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] 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Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2024-08-28 18:38:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2024-08-28 18:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2024-08-28 18:38:42 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:44 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:46 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:48 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:50 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2024-08-28 18:38:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:52 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:53 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:54 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:55 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:56 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2024-08-28 18:38:57 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2024-08-28 18:38:59 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:00 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:01 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:02 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2024-08-28 18:39:03 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], + "source": [ + "# Add distillation column to the flowsheet\n", + "m.fs.D101 = TrayColumn(\n", + " number_of_trays=10,\n", + " feed_tray_location=5,\n", + " condenser_type=CondenserType.totalCondenser,\n", + " condenser_temperature_spec=TemperatureSpec.atBubblePoint,\n", + " property_package=m.fs.BT_params,\n", + ")\n", + "\n", + "# Connect the outlet from the heater H102 to the distillation column\n", + "m.fs.s11 = Arc(source=m.fs.H102.outlet, destination=m.fs.D101.feed)\n", + "\n", + "# Add the necessary equality constraints\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "\n", + "# Propagate the state\n", + "propagate_state(m.fs.s11)\n", + "\n", + "# Fix the reflux ratio, boilup ratio, and the condenser pressure\n", + "m.fs.D101.condenser.reflux_ratio.fix(0.5)\n", + "m.fs.D101.reboiler.boilup_ratio.fix(0.5)\n", + "m.fs.D101.condenser.condenser_pressure.fix(150000)\n", + "\n", + "# set scaling factors\n", + "# Set scaling factors for heat duty\n", + "iscale.set_scaling_factor(m.fs.D101.condenser.control_volume.heat, 1e-2)\n", + "iscale.set_scaling_factor(m.fs.D101.reboiler.control_volume.heat, 1e-2)\n", + "\n", + "# Set the scaling factors for the remaining variables and all constraints\n", + "iscale.calculate_scaling_factors(m.fs.D101)\n", + "\n", + "# Initialize the distillation column\n", + "m.fs.D101.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute capital and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allow us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# Expression to compute the total cooling cost\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.25e-7 * (-m.fs.F101.heat_duty[0])\n", + " + 0.2e-7 * (-m.fs.D101.condenser.heat_duty[0])\n", + ")\n", + "\n", + "# Expression to compute the total heating cost\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + " + 1.2e-7 * m.fs.H102.heat_duty[0]\n", + " + 1.9e-7 * m.fs.D101.reboiler.heat_duty[0]\n", + ")\n", + "\n", + "# Expression to compute the total operating cost\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")\n", + "\n", + "# Expression to compute the total capital cost\n", + "m.fs.capital_cost = Expression(expr=1e5 * m.fs.R101.volume[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve the entire flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 7\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 6.64e-03 -3.8 2.00e-07 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042483516409773e+04 1.5042483516409773e+04\n", + "Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9103830456733704e-11 1.5042483516409773e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.083\n", + "Total CPU secs in NLP function evaluations = 0.013\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.2022566795349121}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "How much is the total cost (operating cost + capital cost), operating cost, capital cost, benzene purity in the distillate from the distilation column, and conversion of toluene in the reactor?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252194\n", + "operating cost = $ 427596.73056805483\n", + "capital cost = $ 14704.740184467111\n", + "\n", + "Distillate flowrate = 0.16196898920633368 mol/s\n", + "Benzene purity = 89.4916166580088 %\n", + "Residue flowrate = 0.10515007120697904 mol/s\n", + "Toluene purity = 43.32260291055251 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603194 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the state of the streams entering and leaving the reactor R101" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $442,297 per year. We are producing 0.162 mol/s of benzene at a purity of 89.5%. However, we are losing around 43.3% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.18 mol/s of benzene as distillate i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in the distillate is at least 99%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 outlet temperature\n", + "- F101 outlet temperature\n", + "- H102 outlet temperature\n", + "- Condenser pressure\n", + "- reflux ratio\n", + "- boilup ratio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost + m.fs.capital_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.conversion.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.D101.condenser.condenser_pressure.unfix()\n", + "m.fs.D101.condenser.reflux_ratio.unfix()\n", + "m.fs.D101.reboiler.boilup_ratio.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable: the temperature of the outlet from H102\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 900] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - H102 outlet temperature [350, 400] K\n", + " - D101 condenser pressure [101325, 150000] Pa\n", + " - D101 reflux ratio [0.1, 5]\n", + " - D101 boilup ratio [0.1, 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Set bounds on the temperature of the outlet from H101\n", + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)\n", + "\n", + "# Set bounds on the temperature of the outlet from R101\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(900)\n", + "\n", + "# Set bounds on the volume of the reactor R101\n", + "m.fs.R101.volume[0].setlb(0)\n", + "\n", + "# Set bounds on the temperature of the vapor outlet from F101\n", + "m.fs.F101.vap_outlet.temperature[0].setlb(298)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "\n", + "# Set bounds on the temperature of the outlet from H102\n", + "m.fs.H102.outlet.temperature[0].setlb(350)\n", + "m.fs.H102.outlet.temperature[0].setub(400)\n", + "\n", + "# Set bounds on the pressure inside the condenser\n", + "m.fs.D101.condenser.condenser_pressure.setlb(101325)\n", + "m.fs.D101.condenser.condenser_pressure.setub(150000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the bounds for the D101 reflux ratio and boilup ratio.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "\n", + "\n", + "# Todo: Set bounds on the boilup ratio" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set bounds on the reflux ratio\n", + "m.fs.D101.condenser.reflux_ratio.setlb(0.1)\n", + "m.fs.D101.condenser.reflux_ratio.setub(5)\n", + "\n", + "# Todo: Set bounds on the boilup ratio\n", + "m.fs.D101.reboiler.boilup_ratio.setlb(0.1)\n", + "m.fs.D101.reboiler.boilup_ratio.setub(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, distillate flowrate and its purity. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 % of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure that the overhead loss of benzene from F101 <= 20%\n", + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.18 mol/s of benzene in the product stream which is the distillate of D101. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(expr=m.fs.D101.condenser.distillate.flow_mol[0] >= 0.18)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the distillate such that it is at least greater than 99%. " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(\n", + " expr=m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"] >= 0.99\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 3\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 150 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.48e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.50e-07 7.79e-06 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.47e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 2.24e-08 1.46e-07 -8.0 5.42e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842628603e+02 4.3883989842628600e+05\n", + "Dual infeasibility......: 1.4600704448671754e-07 1.4600704448671753e-04\n", + "Constraint violation....: 2.9103830456733704e-11 2.2351741790771484e-08\n", + "Complementarity.........: 9.0909948039799681e-09 9.0909948039799686e-06\n", + "Overall NLP error.......: 9.0909948039799681e-09 1.4600704448671753e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.164\n", + "Total CPU secs in NLP function evaluations = 0.020\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.898426286\n", + "operating cost = $ 408883.5314830889\n", + "capital cost = $ 29956.3669431971\n", + "\n", + "Distillate flowrate = 0.1799999900263989 mol/s\n", + "Benzene purity = 98.99999900049086 %\n", + "Residue flowrate = 0.1085161642426372 mol/s\n", + "Toluene purity = 15.676178086213548 %\n", + "\n", + "Conversion = 93.38705916369427 %\n", + "\n", + "Overhead benzene loss in F101 = 17.34061793115618 %\n" + ] + } + ], + "source": [ + "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "print(\"capital cost = $\", value(m.fs.capital_cost))\n", + "print()\n", + "print(\n", + " \"Distillate flowrate = \",\n", + " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", + " \"mol/s\",\n", + ")\n", + "print(\n", + " \"Benzene purity = \",\n", + " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", + " \"%\",\n", + ")\n", + "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", + "print(\n", + " \"Toluene purity = \",\n", + " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", + " \"%\",\n", + ")\n", + "print()\n", + "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", + "print()\n", + "print(\n", + " \"Overhead benzene loss in F101 = \",\n", + " 100\n", + " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", + " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", + " \"%\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.923204295196 K\n", + "\n", + "R101 outlet temperature = 790.3655425698853 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.7414339952852 K\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Key Takeaways\n", + "\n", + "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", + "\n", + "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", + "\n", + "\n", + "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." + ] } - ], - "source": [ - "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "print(\"capital cost = $\", value(m.fs.capital_cost))\n", - "print()\n", - "print(\n", - " \"Distillate flowrate = \",\n", - " value(m.fs.D101.condenser.distillate.flow_mol[0]()),\n", - " \"mol/s\",\n", - ")\n", - "print(\n", - " \"Benzene purity = \",\n", - " 100 * value(m.fs.D101.condenser.distillate.mole_frac_comp[0, \"benzene\"]),\n", - " \"%\",\n", - ")\n", - "print(\"Residue flowrate = \", value(m.fs.D101.reboiler.bottoms.flow_mol[0]()), \"mol/s\")\n", - "print(\n", - " \"Toluene purity = \",\n", - " 100 * value(m.fs.D101.reboiler.bottoms.mole_frac_comp[0, \"toluene\"]),\n", - " \"%\",\n", - ")\n", - "print()\n", - "print(\"Conversion = \", 100 * value(m.fs.R101.conversion), \"%\")\n", - "print()\n", - "print(\n", - " \"Overhead benzene loss in F101 = \",\n", - " 100\n", - " * value(m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"])\n", - " / value(m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]),\n", - " \"%\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.923204295196 K\n", - "\n", - "R101 outlet temperature = 790.3655425698853 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.7414339952852 K\n" - ] + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"H102 outlet temperature = \", value(m.fs.H102.outlet.temperature[0]), \"K\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Key Takeaways\n", - "\n", - "Observe that the optimization was able to reduce the yearly operating cost from \\\\$427,593 to \\\\$408,342 (~4.5%). However, the amortized capital cost more than doubled from \\\\$14,704 to \\\\$29,927 due to the need to increase the conversion in the reactor (from 75% to 93%) to meet the production and purity constraints. \n", - "\n", - "Further, observe that the product flow rate and product purity are at their minimum values (0.18 mol/s and 99%, respectively). This is expected as increasing recovery would require more energy and cost to purify the product.\n", - "\n", - "\n", - "Finally, observe that the operating temperature of the flash (F101) is almost at its lower bound. This helps in minimizing the amount of benzene in the vapor stream leaving the flash." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py index e365fcd9..e960ed9c 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheets_for_costing_notebook.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Flowsheets for HDA with Flash and HDA with Distillation for costing notebook. diff --git a/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet.py b/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet.py index db61295e..c5459dc3 100644 --- a/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet.py +++ b/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Task: IDAES Support for ARPE-E Differentiate diff --git a/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet_w_recycle.py b/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet_w_recycle.py index 8d5cfd7c..691140c7 100644 --- a/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet_w_recycle.py +++ b/idaes_examples/notebooks/docs/flowsheets/methanol_flowsheet_w_recycle.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Task: IDAES Support for ARPE-E Differentiate diff --git a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis.ipynb b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis.ipynb index 44462419..fceaba76 100644 --- a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_doc.ipynb b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_doc.ipynb index 6e83e8ed..c157bca9 100644 --- a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_doc.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -100,11 +101,7 @@ ] }, "execution_count": 2, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\flowsheets\\methanol_synthesis_doc_4_0.png" - } - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -255,511 +252,511 @@ "DOF after streams specified: 9\n", "DOF after units specified: 0\n", "\n", - "2023-11-02 10:29:50 [INFO] idaes.init.fs.H2.properties: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H2.properties: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.H2.properties: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H2.properties: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.H2.properties: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H2.properties: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.H2: Initialization Complete.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H2: Initialization Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.CO.properties: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CO.properties: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.CO.properties: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CO.properties: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.CO.properties: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CO.properties: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.CO: Initialization Complete.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CO: Initialization Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.H2_WGS_state: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.H2_WGS_state: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.H2_WGS_state: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.H2_WGS_state: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.CO_WGS_state: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.CO_WGS_state: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.CO_WGS_state: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.CO_WGS_state: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:50 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.control_volume.properties_in: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.control_volume.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.control_volume.properties_out: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.control_volume.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.properties_isentropic: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.properties_isentropic: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.T101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.T101: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H102.control_volume.properties_in: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H102.control_volume.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H102.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H102.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H102.control_volume.properties_out: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H102.control_volume.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H102.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H102.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_in: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_in: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_in: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_in: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_in: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_in: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_in: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_out: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_out: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_out: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_out: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_out: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_out: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_out: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:51 [INFO] idaes.init.fs.EXHAUST.properties: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.EXHAUST.properties: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.EXHAUST.properties: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.EXHAUST.properties: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.EXHAUST.properties: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.EXHAUST.properties: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.EXHAUST: Initialization Complete.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.EXHAUST: Initialization Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH.properties: Starting initialization\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH.properties: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH.properties: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH.properties: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH.properties: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH.properties: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH.properties: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH.properties: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH.properties: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH.properties: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH.properties: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH.properties: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:52 [INFO] idaes.init.fs.CH3OH: Initialization Complete.\n" + "2025-03-17 17:33:26 [INFO] idaes.init.fs.CH3OH: Initialization Complete.\n" ] }, { @@ -768,144 +765,14 @@ "text": [ "DOF before solve: 0\n", "\n", - "Solving initial problem...\n", - "WARNING: model contains export suffix\n", - "'fs.CH3OH.properties[0.0].scaling_factor' that contains 7 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.EXHAUST.properties[0.0].scaling_factor' that contains 4 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.F101.split.scaling_factor' that\n", - "contains 24 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.properties_isentropic[0.0].scaling_factor' that contains 8 component\n", - "keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "Solving initial problem...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 98\n", "component keys that are not exported as part of the NL file. Skipping.\n" ] }, @@ -913,144 +780,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.C101.control_volume.properties_out[0.0].scaling_factor' that contains 8\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.C101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.mixed_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.CO_WGS_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.H2_WGS_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.CO.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H2.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH3OH.scaling_factor' that contains 11 component keys\n", - "that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH3OH.scaling_factor' that contains 23 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.H2.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.CO.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH4.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 160 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -1105,16 +836,16 @@ " 0 0.0000000e+00 2.79e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 0.0000000e+00 2.79e+02 2.77e+00 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n", " 2 0.0000000e+00 2.77e+00 1.21e+00 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n", - " 3 0.0000000e+00 3.68e-08 1.00e+03 -1.0 1.76e-06 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 3.41e-08 1.00e+03 -1.0 1.76e-06 - 9.90e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 3\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.2293436154280945e-12 3.6787241697311401e-08\n", + "Constraint violation....: 1.2412789903351633e-12 3.4109689295291901e-08\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.2293436154280945e-12 3.6787241697311401e-08\n", + "Overall NLP error.......: 1.2412789903351633e-12 3.4109689295291901e-08\n", "\n", "\n", "Number of objective function evaluations = 4\n", @@ -1166,361 +897,101 @@ "output_type": "stream", "text": [ "\n", - "Solving with costing...\n", - "WARNING: model contains export suffix\n", - "'fs.CH3OH.properties[0.0].scaling_factor' that contains 7 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" + "Solving with costing...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.EXHAUST.properties[0.0].scaling_factor' that contains 4 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 98\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.F101.split.scaling_factor' that\n", - "contains 24 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 160 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "Ipopt 3.13.2: tol=1e-06\n", + "max_iter=100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: tol=1e-06\n", - "max_iter=100\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 971\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 611\n", - "\n", - "Total number of variables............................: 319\n", - " variables with only lower bounds: 43\n", - " variables with lower and upper bounds: 255\n", - " variables with only upper bounds: 1\n", - "Total number of equality constraints.................: 319\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 -2.8492051e+07 9.10e+04 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -2.9068962e+07 5.76e+04 6.45e+01 -1.0 1.20e+05 - 5.03e-02 9.90e-01h 1\n", - " 2 -2.9074767e+07 5.71e+02 9.98e+00 -1.0 5.72e+04 - 9.81e-01 9.90e-01h 1\n", - " 3 -2.9074825e+07 5.22e-05 1.00e+03 -1.0 5.67e+02 - 9.90e-01 1.00e+00h 1\n", - " 4 -2.9074825e+07 1.86e-09 9.90e+04 -1.0 5.22e-05 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 4\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: -4.4948186430824144e+01 -2.9074824816033211e+07\n", - "Dual infeasibility......: 8.3561161967102299e-07 5.4051705720246346e-01\n", - "Constraint violation....: 4.5474735088646412e-12 1.8626451492309570e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 4.5474735088646412e-12 5.4051705720246346e-01\n", - "\n", - "\n", - "Number of objective function evaluations = 5\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 5\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 5\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 4\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 971\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 611\n", + "\n", + "Total number of variables............................: 319\n", + " variables with only lower bounds: 43\n", + " variables with lower and upper bounds: 255\n", + " variables with only upper bounds: 1\n", + "Total number of equality constraints.................: 319\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 -2.8492051e+07 9.10e+04 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -2.9068962e+07 5.76e+04 6.45e+01 -1.0 1.20e+05 - 5.03e-02 9.90e-01h 1\n", + " 2 -2.9074767e+07 5.71e+02 9.98e+00 -1.0 5.72e+04 - 9.81e-01 9.90e-01h 1\n", + " 3 -2.9074825e+07 5.22e-05 1.00e+03 -1.0 5.67e+02 - 9.90e-01 1.00e+00h 1\n", + " 4 -2.9074825e+07 1.16e-09 9.90e+04 -1.0 5.22e-05 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 4\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: -4.4948186430824137e+01 -2.9074824816033222e+07\n", + "Dual infeasibility......: 4.4769483402316870e-07 2.8959230402542491e-01\n", + "Constraint violation....: 4.5474735088646412e-12 1.1641532182693481e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 4.5474735088646412e-12 2.8959230402542491e-01\n", + "\n", + "\n", + "Number of objective function evaluations = 5\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 5\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 5\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 4\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" ] }, { @@ -1530,7 +1001,7 @@ "Initial solution process results:\n", "\n", "\n", - "Extent of reaction: 237.60047790000002\n", + "Extent of reaction: 237.6004779\n", "Stoichiometry of each component normalized by the extent:\n", "CH4 : 0.0\n", "H2 : -2.0\n", @@ -1541,28 +1012,34 @@ "Reaction conversion: 0.75\n", "Reactor duty (MW): -45.21917830318435\n", "Duty from Reaction (MW)): 21.536107316856\n", - "Turbine work (MW): -0.9593346445867593\n", + "Turbine work (MW): -0.9593346445867499\n", "Mixer outlet temperature (C)): 20.051714213753257\n", "Compressor outlet temperature (C)): 20.051714213753314\n", "Compressor outlet pressure (Pa)): 5100000.0\n", "Heater outlet temperature (C)): 215.0\n", "Reactor outlet temperature (C)): 234.0\n", - "Turbine outlet temperature (C)): 192.87815244243234\n", + "Turbine outlet temperature (C)): 192.8781524424324\n", "Turbine outlet pressure (Pa)): 3100000.0\n", "Cooler outlet temperature (C)): 134.0\n", "Flash outlet temperature (C)): 134.0\n", - "Methanol recovery(%): 60.004430129216814\n", - "annualized capital cost ($/year) = 219790.50447043404\n", - "operating cost ($/year) = 380701687.4964808\n", - "sales ($/year) = 64685201172.19813\n", + "Methanol recovery(%): 60.004430129216836\n", + "annualized capital cost ($/year) = 219790.5044704343\n", + "operating cost ($/year) = 380701687.4964806\n", + "sales ($/year) = 64685201172.198135\n", "raw materials cost ($/year) = 35229454878.16397\n", - "revenue (1000$/year)= 29074824.816033203\n", + "revenue (1000$/year)= 29074824.81603321\n", "\n", "\n", "====================================================================================\n", "Unit : fs.H2 Time: 0.0\n", "------------------------------------------------------------------------------------\n", - " Stream Table\n", + " Stream Table\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Units Outlet \n", " Total Molar Flowrate mole / second 637.20\n", " Total Mole Fraction CH4 dimensionless 1.0000e-06\n", @@ -1647,278 +1124,30 @@ "text": [ "\n", "Solving optimization problem...\n", - "WARNING: model contains export suffix\n", - "'fs.CH3OH.properties[0.0].scaling_factor' that contains 7 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.EXHAUST.properties[0.0].scaling_factor' that contains 4 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 96\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.F101.split.scaling_factor' that\n", - "contains 24 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 160 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "Ipopt 3.13.2: tol=1e-06\n", + "max_iter=100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.properties_isentropic[0.0].scaling_factor' that contains 8 component\n", - "keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.control_volume.properties_in[0.0].scaling_factor' that contains 8\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.reactions[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.C101.control_volume.properties_out[0.0].scaling_factor' that contains 8\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.C101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.mixed_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.CO_WGS_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.H2_WGS_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.CO.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H2.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH3OH.scaling_factor' that contains 11 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.H2.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CO.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH4.scaling_factor' that contains 8 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH3OH.scaling_factor' that contains 23 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.H2.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.CO.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH4.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: tol=1e-06\n", - "max_iter=100\n", "\n", "\n", "******************************************************************************\n", @@ -1968,52 +1197,63 @@ " 9 -2.8910081e+07 8.51e+04 2.07e+06 -1.0 1.31e+06 - 4.38e-01 9.05e-01h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 -2.8914184e+07 7.59e+04 7.45e+06 -1.0 9.51e+04 - 9.00e-01 1.08e-01h 1\n", - " 11 -2.8947637e+07 1.17e+03 1.82e+05 -1.0 8.76e+04 - 9.09e-01 9.90e-01h 1\n", - " 12 -2.8897906e+07 2.27e+01 4.44e+06 -1.0 6.65e+03 - 9.89e-01 9.92e-01h 1\n", - " 13 -3.9607639e+07 3.82e+04 7.42e+07 -1.0 5.09e+06 - 8.24e-02 1.77e-01f 3\n", - " 14 -5.3973065e+07 7.32e+04 5.80e+07 -1.0 1.89e+06 - 6.48e-01 1.00e+00F 1\n", - " 15 -5.8002084e+07 1.39e+05 2.82e+06 -1.0 1.54e+06 - 6.75e-01 1.00e+00f 1\n", - " 16 -5.8861909e+07 1.30e+05 2.75e+06 -1.0 3.00e+06 - 2.44e-02 7.50e-02f 1\n", - " 17 -6.8252381e+07 1.25e+05 2.72e+06 -1.0 6.30e+07 - 8.98e-03 4.63e-02f 2\n", - " 18 -6.7691432e+07 5.47e+02 2.17e+06 -1.0 1.32e+04 -4.0 2.04e-01 1.00e+00h 1\n", - " 19 -6.7689998e+07 4.76e-01 2.15e+04 -1.0 1.89e+02 -4.5 9.90e-01 1.00e+00h 1\n", + " 11 -2.8947704e+07 1.17e+03 1.82e+05 -1.0 8.76e+04 - 9.09e-01 9.90e-01h 1\n", + " 12 -2.8956418e+07 1.33e+01 1.66e+06 -1.0 6.65e+03 - 9.89e-01 9.92e-01h 1\n", + " 13 -3.9651704e+07 3.83e+04 1.08e+09 -1.0 4.89e+06 - 4.90e-01 1.84e-01f 3\n", + " 14 -6.4336952e+07 5.29e+05 4.65e+08 -1.0 5.88e+06 - 3.52e-01 4.50e-01F 1\n", + " 15 -6.3892166e+07 4.90e+05 4.29e+08 -1.0 1.30e+06 - 6.84e-01 7.50e-02h 1\n", + " 16 -6.1563748e+07 1.84e+04 2.19e+06 -1.0 1.44e+06 - 2.70e-02 1.00e+00h 1\n", + " 17 -6.9341130e+07 1.74e+04 1.16e+06 -1.0 4.39e+07 - 2.12e-01 5.31e-02f 2\n", + " 18 -7.2756566e+07 1.93e+04 1.09e+06 -1.0 6.62e+07 - 5.91e-03 1.78e-02f 2\n", + " 19 -7.6032508e+07 2.89e+04 8.76e+05 -1.0 6.78e+07 - 7.77e-02 1.76e-02f 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 -6.7690000e+07 1.68e-08 2.33e+01 -1.0 2.45e-01 -5.0 9.99e-01 1.00e+00h 1\n", - " 21 -7.7035618e+07 3.05e+04 5.41e+04 -3.8 4.62e+08 - 6.81e-03 6.33e-03f 1\n", - " 22 -7.7132115e+07 3.01e+04 6.44e+06 -3.8 3.55e+06 - 6.66e-01 1.74e-02h 1\n", - " 23 -7.7647090e+07 3.09e+04 8.42e+06 -3.8 3.42e+06 - 8.94e-01 1.00e-01h 1\n", - " 24 -7.7699683e+07 8.17e+01 3.90e+03 -3.8 2.07e+04 - 9.89e-01 1.00e+00h 1\n", - " 25 -7.7699749e+07 7.87e+01 3.80e+03 -3.8 5.54e+07 - 2.54e-02 8.20e-04h 2\n", - " 26 -7.7699755e+07 3.08e+00 3.03e-01 -3.8 2.79e+05 - 1.00e+00 1.00e+00H 1\n", - " 27 -7.7700174e+07 1.09e+02 5.94e-03 -3.8 3.03e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 -7.7700167e+07 3.00e-02 4.25e-06 -3.8 1.14e+04 - 1.00e+00 1.00e+00h 1\n", - " 29 -7.7700500e+07 3.89e+00 3.43e+02 -5.7 9.59e+04 - 9.77e-01 1.00e+00h 1\n", + " 20 -7.7298453e+07 4.32e+04 3.37e+06 -1.0 3.71e+06 - 9.90e-01 2.02e-01h 1\n", + " 21 -7.7128685e+07 6.83e+03 5.30e+04 -1.0 4.84e+05 - 9.99e-01 1.00e+00h 1\n", + " 22 -7.7533095e+07 4.65e+03 1.55e+03 -1.0 3.06e+05 - 1.00e+00 1.00e+00h 1\n", + " 23 -7.7681920e+07 4.79e+02 5.51e+05 -1.7 6.74e+04 - 1.00e+00 9.31e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 24 -7.7673164e+07 1.14e+00 2.46e+01 -1.7 3.30e+04 - 1.00e+00 1.00e+00f 1\n", + " 25 -7.7696657e+07 3.44e+00 2.10e+02 -2.5 1.14e+04 - 1.00e+00 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 26 -7.7696184e+07 3.81e+00 1.12e-03 -2.5 6.24e+04 - 1.00e+00 1.00e+00h 1\n", + " 27 -7.7699949e+07 5.40e+00 1.26e+04 -5.7 7.58e+04 - 9.49e-01 9.94e-01h 1\n", + " 28 -7.7700458e+07 8.81e+01 1.03e+03 -5.7 3.66e+05 - 9.25e-01 9.72e-01h 1\n", + " 29 -7.7700530e+07 1.74e+01 1.01e+01 -5.7 6.58e+04 - 1.00e+00 8.03e-01h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 -7.7700531e+07 4.15e-01 1.39e-03 -5.7 2.29e+04 - 1.00e+00 9.07e-01h 1\n", - " 31 -7.7700531e+07 4.98e-04 3.89e-08 -5.7 9.80e+02 - 1.00e+00 1.00e+00f 1\n", - " 32 -7.7700536e+07 9.48e-04 2.13e-01 -8.6 1.62e+03 - 1.00e+00 9.99e-01h 1\n", - " 33 -7.7700536e+07 5.01e-09 1.08e-10 -8.6 1.88e+00 - 1.00e+00 1.00e+00f 1\n", - " 34 -7.7700536e+07 8.38e-09 6.97e-11 -10.9 2.16e+00 - 1.00e+00 1.00e+00h 1\n", + " 30 -7.7700531e+07 2.06e-04 4.19e-06 -5.7 1.25e+03 - 1.00e+00 1.00e+00h 1\n", + " 31 -7.7700536e+07 9.48e-04 1.29e-01 -8.6 1.69e+03 - 1.00e+00 9.99e-01h 1\n", + " 32 -7.7700536e+07 5.24e-09 5.34e-11 -8.6 1.52e+00 - 1.00e+00 1.00e+00f 1\n", + " 33 -7.7700536e+07 7.45e-09 6.97e-11 -10.9 2.16e+00 - 1.00e+00 1.00e+00h 1\n", "\n", - "Number of Iterations....: 34\n", + "Number of Iterations....: 33\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: -1.2012103932708919e+02 -7.7700535938862875e+07\n", - "Dual infeasibility......: 6.9703904435575171e-11 4.5088110809028537e-05\n", - "Constraint violation....: 8.7311491370201111e-11 8.3819031715393066e-09\n", - "Complementarity.........: 1.4074777602064815e-11 9.1042982064351105e-06\n", - "Overall NLP error.......: 8.7311491370201111e-11 4.5088110809028537e-05\n", - "\n", - "\n", - "Number of objective function evaluations = 47\n", - "Number of objective gradient evaluations = 35\n", - "Number of equality constraint evaluations = 47\n", - "Number of inequality constraint evaluations = 47\n", - "Number of equality constraint Jacobian evaluations = 35\n", - "Number of inequality constraint Jacobian evaluations = 35\n", - "Number of Lagrangian Hessian evaluations = 34\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.022\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Objective...............: -1.2012103932708921e+02 -7.7700535938862905e+07\n", + "Dual infeasibility......: 6.9708615944535924e-11 4.5091158458078059e-05\n", + "Constraint violation....: 8.7311491370201111e-11 7.4505805969238281e-09\n", + "Complementarity.........: 1.4074777632696104e-11 9.1042982262490221e-06\n", + "Overall NLP error.......: 8.7311491370201111e-11 4.5091158458078059e-05\n", + "\n", + "\n", + "Number of objective function evaluations = 46\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 46\n", + "Number of inequality constraint evaluations = 46\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.024\n", + "Total CPU secs in NLP function evaluations = 0.004\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2027,31 +1267,31 @@ "\n", "Extent of reaction: 269.280544787992\n", "Stoichiometry of each component normalized by the extent:\n", - "CH4 : -0.0\n", + "CH4 : 0.0\n", "H2 : -2.0\n", "CH3OH : 1.0\n", "CO : -1.0\n", "These coefficients should follow 1*CO + 2*H2 => 1*CH3OH\n", "\n", "Reaction conversion: 0.8500000099999546\n", - "Reactor duty (MW): -51.363573577545786\n", + "Reactor duty (MW): -51.36357357754578\n", "Duty from Reaction (MW)): 24.407588579583596\n", - "Turbine work (MW): -1.9904899177794766\n", - "Mixer outlet temperature (C)): 20.0517142137536\n", - "Compressor outlet temperature (C)): 20.051714213753428\n", + "Turbine work (MW): -1.99048991777949\n", + "Mixer outlet temperature (C)): 20.051714213753428\n", + "Compressor outlet temperature (C)): 20.05171421375337\n", "Compressor outlet pressure (Pa)): 5100000.0\n", "Heater outlet temperature (C)): 215.0\n", "Reactor outlet temperature (C)): 231.85000468716584\n", - "Turbine outlet temperature (C)): 139.85888172675635\n", - "Turbine outlet pressure (Pa)): 1427653.3547820912\n", + "Turbine outlet temperature (C)): 139.85888172675521\n", + "Turbine outlet pressure (Pa)): 1427653.3547820929\n", "Cooler outlet temperature (C)): 52.56999709299214\n", "Flash outlet temperature (C)): 134.0\n", - "Methanol recovery(%): 92.80355474657543\n", - "annualized capital cost ($/year) = 235547.18924473223\n", - "operating cost ($/year) = 451663512.6847628\n", - "sales ($/year) = 113381889876.90083\n", + "Methanol recovery(%): 92.80355474657544\n", + "annualized capital cost ($/year) = 235547.1892447323\n", + "operating cost ($/year) = 451663512.6847631\n", + "sales ($/year) = 113381889876.90086\n", "raw materials cost ($/year) = 35229454878.16397\n", - "revenue (1000$/year)= 77700535.93886286\n", + "revenue (1000$/year)= 77700535.93886289\n", "\n", "\n", "====================================================================================\n", @@ -2066,7 +1306,13 @@ " Total Mole Fraction CH3OH dimensionless 1.0000e-06\n", " Molar Enthalpy joule / mole -142.40\n", " Pressure pascal 3.0000e+06\n", - "====================================================================================\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "====================================================================================\n", "Unit : fs.CO Time: 0.0\n", @@ -2207,11 +1453,7 @@ ] }, "execution_count": 8, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\flowsheets\\methanol_synthesis_doc_18_0.png" - } - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -2322,7 +1564,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Unit degrees of freedom\n", + "Unit degrees of freedom" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "M101 0\n", "C101 1\n", "H101 1\n", @@ -2331,13 +1580,7 @@ "H102 1\n", "F101 2\n", "M102 0\n", - "S101 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "S101 1\n", "Total DOF: 24\n", "DOF after streams specified: 10\n", "DOF after units specified: 0\n", @@ -2358,9 +1601,7 @@ "Initial DOF = 0\n", "Solving fs.H2\n", "DOF = 0\n", - "Solving fs.CO\n", - "DOF = 0\n", - "Solving fs.C101\n" + "Solving fs.CO\n" ] }, { @@ -2368,15 +1609,17 @@ "output_type": "stream", "text": [ "DOF = 0\n", - "Solving fs.M101\n", - "DOF = 0\n", - "Solving fs.H101\n" + "Solving fs.C101\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "DOF = 0\n", + "Solving fs.M101\n", + "DOF = 0\n", + "Solving fs.H101\n", "DOF = 0\n", "Solving fs.R101\n", "DOF = 0\n", @@ -2390,45 +1633,27 @@ "DOF = 0\n", "Solving fs.H102\n", "DOF = 0\n", - "Solving fs.F101\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Solving fs.F101\n", + "DOF = 0\n", + "Solving fs.S101\n", "DOF = 0\n", - "Solving fs.S101\n" + "Solving fs.CH3OH\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "DOF = 0\n", - "Solving fs.CH3OH\n", "DOF = 0\n", "Solving fs.EXHAUST\n", "DOF = 0\n", - "Solving fs.M102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Solving fs.M102\n", "DOF = 0\n", "Solving fs.H2\n", "DOF = 0\n", "Solving fs.CO\n", "DOF = 0\n", - "Solving fs.M101\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Solving fs.M101\n", "DOF = 0\n", "Solving fs.EXHAUST\n", "DOF = 0\n", @@ -2444,342 +1669,16 @@ "DOF before solve: 0\n", "\n", "Solving initial problem...\n", - "WARNING: model contains export suffix\n", - "'fs.CH3OH.properties[0.0].scaling_factor' that contains 7 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.EXHAUST.properties[0.0].scaling_factor' that contains 4 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.S101.recycle_state[0.0].scaling_factor' that contains 4 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.S101.purge_state[0.0].scaling_factor' that contains 4 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 98\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.S101.mixed_state[0.0].scaling_factor' that contains 4 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.F101.split.scaling_factor' that\n", - "contains 24 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 13\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.properties_isentropic[0.0].scaling_factor' that contains 8 component\n", - "keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.T101.control_volume.properties_in[0.0].scaling_factor' that contains 8\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.T101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.reactions[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 7\n", - "component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.H2_WGS_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.CO.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H2.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH3OH.scaling_factor' that contains 11 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.H2.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CO.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH4.scaling_factor' that contains 8 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH3OH.scaling_factor' that contains 23 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.H2.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.CO.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH4.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 190 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -2836,9 +1735,9 @@ " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 1.0099088928127794e+05 1.0099088928127794e+05\n", - "Constraint violation....: 2.4013852159823137e-10 7.4442941695451736e-06\n", + "Constraint violation....: 2.4014227693119351e-10 7.4444105848670006e-06\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.4013852159823137e-10 1.0099088928127794e+05\n", + "Overall NLP error.......: 2.4014227693119351e-10 1.0099088928127794e+05\n", "\n", "\n", "Number of objective function evaluations = 4\n", @@ -2848,8 +1747,8 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.002\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.001\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2883,360 +1782,28 @@ "Now that we have a well-initialized and solved flowsheet, we can add process economics and optimize the revenue. We utilize IDAES costing tools to calculate reactor and flash vessel capital cost, and implement surrogate models to account for heat exchanger capital costs, reactor operating costs and utility costs for heating, cooling and electricity. As before, revenue is determined from total liquid methanol sales, operating costs, annualized capital costs and feed raw material costs. The flowsheet report method returns key process results, which are updated for new results with the presence of a recycle stream:" ] }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.CH3OH.properties[0.0].scaling_factor' that contains 7 component keys that\n", - "are not exported as part of the NL file. 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Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH4.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 190 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -3244,13 +1811,7 @@ "output_type": "stream", "text": [ "Ipopt 3.13.2: tol=1e-06\n", - "max_iter=100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "max_iter=100\n", "\n", "\n", "******************************************************************************\n", @@ -3292,17 +1853,16 @@ " 1 -2.9074832e+07 5.76e+04 4.22e+03 -1.0 1.20e+05 - 5.03e-02 9.90e-01h 1\n", " 2 -2.9080637e+07 5.71e+02 3.82e+03 -1.0 5.72e+04 - 9.81e-01 9.90e-01h 1\n", " 3 -2.9080695e+07 5.22e-05 1.00e+03 -1.0 5.67e+02 - 9.90e-01 1.00e+00h 1\n", - " 4 -2.9080695e+07 6.98e-10 9.90e+04 -1.0 5.22e-05 - 9.90e-01 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + " 4 -2.9080695e+07 1.40e-09 9.90e+04 -1.0 5.22e-05 - 9.90e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 4\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: -4.4953175283791907e+01 -2.9080695361147862e+07\n", - "Dual infeasibility......: 9.9999999999985807e+06 6.4691081725719443e+12\n", - "Constraint violation....: 4.5474735088646412e-12 6.9849193096160889e-10\n", + "Objective...............: -4.4953175283791850e+01 -2.9080695361147840e+07\n", + "Dual infeasibility......: 2.1493237944298540e-06 1.3904208124051489e+00\n", + "Constraint violation....: 4.5474735088646412e-12 1.3969838619232178e-09\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 4.5474735088646412e-12 6.4691081725719443e+12\n", + "Overall NLP error.......: 4.5474735088646412e-12 1.3904208124051489e+00\n", "\n", "\n", "Number of objective function evaluations = 5\n", @@ -3312,8 +1872,8 @@ "Number of equality constraint Jacobian evaluations = 5\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 4\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.013\n", + "Total CPU secs in NLP function evaluations = 0.002\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -3334,27 +1894,27 @@ "These coefficients should follow 1*CO + 2*H2 => 1*CH3OH\n", "\n", "Reaction conversion: 0.75\n", - "Reactor duty (MW): -45.22029794711383\n", + "Reactor duty (MW): -45.22029794711382\n", "Duty from Reaction (MW)): 21.536645732999325\n", - "Compressor work (MW): 4.9022090500807066e-15\n", - "Turbine work (MW): -0.95937850239144\n", - "Feed Mixer outlet temperature (C)): 20.051714213753144\n", - "Recycle Mixer outlet temperature (C)): 20.056485612776044\n", - "Feed Compressor outlet temperature (C)): 20.056485612776157\n", + "Compressor work (MW): -5.562384174525762e-19\n", + "Turbine work (MW): -0.9593785023914313\n", + "Feed Mixer outlet temperature (C)): 20.051714213753314\n", + "Recycle Mixer outlet temperature (C)): 20.056485612776214\n", + "Feed Compressor outlet temperature (C)): 20.056485612776328\n", "Feed Compressor outlet pressure (Pa)): 5100000.0\n", "Heater outlet temperature (C)): 215.0\n", "Reactor outlet temperature (C)): 234.0\n", - "Turbine outlet temperature (C)): 192.87840947667905\n", + "Turbine outlet temperature (C)): 192.87840947667956\n", "Turbine outlet pressure (Pa)): 3100000.0\n", "Cooler outlet temperature (C)): 134.0\n", "Flash outlet temperature (C)): 134.0\n", "Purge percentage (amount of vapor vented to exhaust): 99.99 %\n", - "Methanol recovery(%): 60.00598493491174\n", - "annualized capital cost ($/year) = 219794.2325658716\n", - "operating cost ($/year) = 380711692.18370014\n", - "sales ($/year) = 64691081725.72809\n", + "Methanol recovery(%): 60.005984934911716\n", + "annualized capital cost ($/year) = 219794.23256587196\n", + "operating cost ($/year) = 380711692.1836997\n", + "sales ($/year) = 64691081725.72806\n", "raw materials cost ($/year) = 35229454878.16397\n", - "revenue (1000$/year)= 29080695.36114785\n", + "revenue (1000$/year)= 29080695.36114782\n", "\n", "\n", "====================================================================================\n", @@ -3444,179 +2004,7 @@ "text": [ "\n", "Solving optimization problem...\n", - "WARNING: model contains export suffix\n", - "'fs.CH3OH.properties[0.0].scaling_factor' that contains 7 component keys that\n", - "are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.M101.H2_WGS_state[0.0].scaling_factor' that contains 6 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.CO.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H2.properties[0.0].scaling_factor'\n", - "that contains 15 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH3OH.scaling_factor' that contains 11 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.H2.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CO.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_vapor.CH4.scaling_factor' that contains 8 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH3OH.scaling_factor' that contains 23 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.H2.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.thermo_params_VLE.CO.scaling_factor'\n", - "that contains 8 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.thermo_params_VLE.CH4.scaling_factor' that contains 8 component keys that\n", - "are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 190 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -3769,13 +2021,7 @@ "output_type": "stream", "text": [ "Ipopt 3.13.2: tol=1e-06\n", - "max_iter=100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "max_iter=100\n", "\n", "\n", "******************************************************************************\n", @@ -3815,7 +2061,13 @@ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 -2.8497829e+07 2.79e+04 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 -2.8431118e+07 2.74e+04 9.77e+01 -1.0 8.82e+06 - 4.99e-02 1.94e-02h 1\n", - " 2 -2.8430711e+07 2.74e+04 7.81e+02 -1.0 8.52e+06 - 3.76e-02 1.99e-04h 1\n", + " 2 -2.8430711e+07 2.74e+04 7.81e+02 -1.0 8.52e+06 - 3.76e-02 1.99e-04h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 3 -2.8524881e+07 2.48e+04 1.61e+05 -1.0 6.46e+06 - 2.49e-04 9.25e-02f 1\n", " 4 -2.8526701e+07 2.48e+04 1.61e+05 -1.0 5.76e+06 - 5.84e-02 9.84e-04h 1\n", " 5 -2.8554187e+07 2.48e+04 1.60e+05 -1.0 1.49e+07 - 3.31e-03 8.96e-04h 1\n", @@ -3830,49 +2082,55 @@ " 13 -4.3718128e+07 4.53e+04 1.94e+06 -1.0 3.57e+06 - 5.80e-01 3.23e-04h 1\n", " 14 -4.3760522e+07 4.51e+04 4.15e+07 -1.0 3.56e+06 - 4.25e-01 5.93e-03h 1\n", " 15 -4.4646098e+07 7.99e+04 5.37e+07 -1.0 3.54e+06 - 6.75e-01 1.23e-01h 4\n", - " 16 -6.6609948e+07 4.70e+04 6.17e+06 -1.0 3.14e+06 - 7.83e-01 9.90e-01H 1\n", - " 17 -6.5995574e+07 4.70e+04 3.11e+06 -1.0 2.01e+07 - 3.61e-03 3.25e-03h 5\n", - " 18 -6.5244336e+07 4.71e+04 3.13e+07 -1.0 1.74e+07 - 4.59e-05 4.34e-03h 5\n", - " 19 -6.4062606e+07 2.26e+03 9.67e+06 -1.0 7.97e+04 - 3.33e-04 9.87e-01h 1\n", + " 16 -6.6609954e+07 4.70e+04 6.17e+06 -1.0 3.14e+06 - 7.83e-01 9.90e-01H 1\n", + " 17 -6.5995579e+07 4.70e+04 3.11e+06 -1.0 2.01e+07 - 3.61e-03 3.25e-03h 5\n", + " 18 -6.5244340e+07 4.71e+04 3.13e+07 -1.0 1.74e+07 - 4.59e-05 4.34e-03h 5\n", + " 19 -6.4062612e+07 2.26e+03 9.67e+06 -1.0 7.97e+04 - 3.33e-04 9.87e-01h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 -6.4049002e+07 1.77e+03 8.54e+03 -1.0 2.65e+03 - 9.89e-01 1.00e+00h 1\n", - " 21 -6.5078492e+07 1.26e+03 4.14e+05 -1.0 1.04e+05 - 9.50e-01 9.92e-01f 1\n", - " 22 -7.5331202e+07 1.38e+05 3.86e+06 -1.0 1.20e+06 - 2.40e-01 1.00e+00f 1\n", - " 23 -7.4656453e+07 9.99e+04 2.12e+06 -1.0 1.07e+05 -4.0 9.90e-01 2.77e-01h 1\n", - " 24 -7.4647249e+07 9.94e+04 2.08e+06 -1.0 7.74e+04 -4.5 5.39e-01 5.21e-03h 1\n", - " 25 -7.4611354e+07 9.73e+04 2.02e+06 -1.0 7.24e+04 -5.0 1.00e+00 2.07e-02h 1\n", - " 26 -7.4417188e+07 8.61e+04 1.78e+06 -1.0 7.10e+04 -5.4 5.90e-01 1.15e-01h 1\n", - " 27 -7.4386526e+07 8.46e+04 1.75e+06 -1.0 7.83e+04 -5.9 1.23e-02 1.80e-02h 1\n", - " 28 -7.2736872e+07 3.01e+02 1.72e+06 -1.0 1.09e+05 -6.4 1.06e-04 1.00e+00h 1\n", - " 29 -7.2723082e+07 1.43e-01 6.52e+02 -1.0 5.95e+02 -6.9 1.00e+00 1.00e+00h 1\n", + " 20 -6.4049009e+07 1.77e+03 8.54e+03 -1.0 2.65e+03 - 9.89e-01 1.00e+00h 1\n", + " 21 -6.5078499e+07 1.26e+03 4.14e+05 -1.0 1.04e+05 - 9.50e-01 9.92e-01f 1\n", + " 22 -7.5331204e+07 1.38e+05 3.86e+06 -1.0 1.20e+06 - 2.40e-01 1.00e+00f 1\n", + " 23 -7.4656456e+07 9.99e+04 2.12e+06 -1.0 1.07e+05 -4.0 9.90e-01 2.77e-01h 1\n", + " 24 -7.4647252e+07 9.94e+04 2.08e+06 -1.0 7.74e+04 -4.5 5.39e-01 5.21e-03h 1\n", + " 25 -7.4611357e+07 9.73e+04 2.02e+06 -1.0 7.24e+04 -5.0 1.00e+00 2.07e-02h 1\n", + " 26 -7.4417191e+07 8.61e+04 1.78e+06 -1.0 7.10e+04 -5.4 5.90e-01 1.15e-01h 1\n", + " 27 -7.4386529e+07 8.46e+04 1.75e+06 -1.0 7.83e+04 -5.9 1.23e-02 1.80e-02h 1\n", + " 28 -7.2736876e+07 3.01e+02 1.72e+06 -1.0 1.09e+05 -6.4 1.06e-04 1.00e+00h 1\n", + " 29 -7.2723086e+07 1.43e-01 6.52e+02 -1.0 5.95e+02 -6.9 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 -7.2723222e+07 7.89e-05 2.09e+01 -1.7 1.63e+02 -7.3 1.00e+00 1.00e+00f 1\n", - " 31 -8.6816123e+07 8.68e+03 9.78e+05 -2.5 5.17e+06 - 2.86e-02 5.98e-01f 1\n", - " 32 -8.6799976e+07 8.39e+03 5.89e+05 -2.5 1.65e+04 -7.8 9.53e-01 3.34e-02h 1\n", - " 33 -8.6300379e+07 1.51e+01 7.15e+04 -2.5 2.25e+04 -8.3 8.28e-02 1.00e+00h 1\n", - " 34 -8.6315273e+07 4.51e-03 1.67e+00 -2.5 3.37e+03 -8.8 1.00e+00 1.00e+00h 1\n", - " 35 -8.6355832e+07 4.54e-02 7.17e-02 -2.5 1.03e+04 -9.2 1.00e+00 1.00e+00f 1\n" + " 30 -7.2723226e+07 7.89e-05 2.09e+01 -1.7 1.63e+02 -7.3 1.00e+00 1.00e+00f 1\n", + " 31 -8.6816186e+07 8.68e+03 9.78e+05 -2.5 5.17e+06 - 2.86e-02 5.98e-01f 1\n", + " 32 -8.6800038e+07 8.39e+03 5.89e+05 -2.5 1.65e+04 -7.8 9.53e-01 3.34e-02h 1\n", + " 33 -8.6300445e+07 1.51e+01 7.15e+04 -2.5 2.25e+04 -8.3 8.28e-02 1.00e+00h 1\n", + " 34 -8.6315340e+07 4.51e-03 1.67e+00 -2.5 3.37e+03 -8.8 1.00e+00 1.00e+00h 1\n", + " 35 -8.6355898e+07 4.54e-02 7.17e-02 -2.5 1.03e+04 -9.2 1.00e+00 1.00e+00f 1\n", + " 36 -8.6484852e+07 3.89e-01 3.04e+03 -5.7 3.04e+04 -9.7 9.89e-01 1.00e+00f 1\n", + " 37 -8.6863673e+07 3.35e+00 6.38e-01 -5.7 9.16e+04 -10.2 1.00e+00 9.94e-01f 1\n", + " 38 -8.7407084e+07 7.89e+00 3.54e-01 -5.7 2.74e+05 -10.7 1.00e+00 4.78e-01f 1\n", + " 39 -9.0728134e+07 2.04e+02 2.21e+00 -5.7 8.01e+05 -11.2 1.00e+00 1.00e+00f 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 36 -8.6484786e+07 3.89e-01 3.04e+03 -5.7 3.04e+04 -9.7 9.89e-01 1.00e+00f 1\n", - " 37 -8.6863607e+07 3.35e+00 6.38e-01 -5.7 9.16e+04 -10.2 1.00e+00 9.94e-01f 1\n", - " 38 -8.7407007e+07 7.89e+00 3.54e-01 -5.7 2.74e+05 -10.7 1.00e+00 4.78e-01f 1\n", - " 39 -9.0728057e+07 2.04e+02 2.21e+00 -5.7 8.01e+05 -11.2 1.00e+00 1.00e+00f 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 40 -9.9495392e+07 6.13e+02 3.85e+00 -5.7 2.39e+06 -11.6 1.00e+00 9.06e-01f 1\n", - " 41 -9.9495416e+07 6.13e+02 3.85e+00 -5.7 5.53e+06 -12.1 1.07e-01 1.10e-06h 1\n", - " 42 -1.0301582e+08 3.81e+04 2.43e+01 -5.7 7.11e+06 -12.6 3.99e-01 1.44e-01f 1\n", - " 43 -1.0301583e+08 3.81e+04 3.36e+01 -5.7 1.39e+06 - 6.86e-01 1.25e-06h 2\n", + " 40 -9.9495400e+07 6.13e+02 3.85e+00 -5.7 2.39e+06 -11.6 1.00e+00 9.06e-01f 1\n", + " 41 -9.9495423e+07 6.13e+02 3.85e+00 -5.7 5.53e+06 -12.1 1.07e-01 1.10e-06h 1\n", + " 42 -1.0301584e+08 3.81e+04 2.43e+01 -5.7 7.11e+06 -12.6 3.99e-01 1.44e-01f 1\n", + " 43 -1.0301584e+08 3.81e+04 3.36e+01 -5.7 1.39e+06 - 6.86e-01 1.25e-06h 2\n", " 44 -1.0436645e+08 3.42e+05 1.56e+01 -5.7 2.02e+06 - 1.00e+00 1.00e+00h 1\n", - " 45 -1.0441853e+08 3.22e+05 1.44e+01 -5.7 4.91e+06 - 9.43e-01 7.95e-02h 1\n", - " 46 -1.0439096e+08 2.72e+05 1.22e+01 -5.7 1.25e+07 - 2.29e-01 1.53e-01h 1\n", - " 47 -1.0436186e+08 2.08e+05 9.28e+00 -5.7 6.35e+05 - 1.00e+00 2.38e-01h 1\n", + " 45 -1.0441854e+08 3.22e+05 1.44e+01 -5.7 4.91e+06 - 9.43e-01 7.95e-02h 1\n", + " 46 -1.0439096e+08 2.72e+05 1.22e+01 -5.7 1.25e+07 - 2.29e-01 1.54e-01h 1\n", + " 47 -1.0436186e+08 2.07e+05 9.28e+00 -5.7 6.35e+05 - 1.00e+00 2.38e-01h 1\n", " 48 -1.0427962e+08 1.89e+04 8.38e-01 -5.7 2.21e+05 - 1.00e+00 9.09e-01h 1\n", - " 49 -1.0427372e+08 1.22e+01 8.52e-02 -5.7 2.01e+04 - 1.00e+00 1.00e+00h 1\n", + " 49 -1.0427372e+08 1.22e+01 8.52e-02 -5.7 2.01e+04 - 1.00e+00 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 50 -1.0427510e+08 3.31e+02 1.55e+00 -5.7 5.86e+04 -13.1 1.00e+00 9.55e-01h 1\n", " 51 -1.0427510e+08 2.94e+02 5.87e+01 -5.7 2.24e+04 - 7.65e-02 1.13e-01f 1\n", @@ -3881,8 +2139,8 @@ " 54 -1.0427839e+08 2.40e+03 4.05e+01 -5.7 1.34e+05 - 4.18e-01 2.55e-01h 1\n", " 55 -1.0427605e+08 1.01e+03 2.12e+01 -5.7 9.49e+04 - 4.92e-01 1.00e+00h 1\n", " 56 -1.0427605e+08 6.84e-03 5.30e+01 -5.7 1.43e+03 - 2.80e-01 1.00e+00h 1\n", - " 57 -1.0427605e+08 6.92e-04 5.08e-03 -5.7 1.97e+02 - 1.00e+00 1.00e+00h 1\n", - " 58 -1.0427606e+08 1.10e-02 4.86e+00 -5.7 3.32e+02 -10.4 4.14e-01 1.00e+00h 1\n", + " 57 -1.0427605e+08 6.93e-04 5.08e-03 -5.7 1.97e+02 - 1.00e+00 1.00e+00h 1\n", + " 58 -1.0427606e+08 1.10e-02 4.86e+00 -5.7 3.31e+02 -10.4 4.14e-01 1.00e+00h 1\n", " 59 -1.0427605e+08 2.11e-03 5.96e-01 -5.7 2.09e+02 - 2.89e-01 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 60 -1.0427605e+08 7.75e-05 1.67e-01 -5.7 3.44e+02 - 5.92e-01 1.00e+00h 1\n", @@ -3899,17 +2157,17 @@ " 70 -1.0427883e+08 1.43e-01 9.49e-05 -5.7 1.47e+04 - 1.00e+00 1.00e+00h 1\n", " 71 -1.0427883e+08 7.08e-03 1.45e-08 -5.7 2.57e+03 - 1.00e+00 1.00e+00h 1\n", " 72 -1.0427884e+08 5.67e-04 5.09e-06 -8.6 1.47e+03 - 1.00e+00 1.00e+00h 1\n", - " 73 -1.0427884e+08 1.86e-09 3.49e-13 -8.6 6.91e-01 - 1.00e+00 1.00e+00h 1\n", - " 74 -1.0427884e+08 9.31e-10 9.31e-12 -10.9 1.99e+00 - 1.00e+00 1.00e+00h 1\n", + " 73 -1.0427884e+08 1.86e-09 4.62e-13 -8.6 6.91e-01 - 1.00e+00 1.00e+00h 1\n", + " 74 -1.0427884e+08 1.40e-09 9.30e-12 -10.9 1.99e+00 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 74\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: -1.6119507851269793e+02 -1.0427883997850099e+08\n", - "Dual infeasibility......: 9.3066982868688585e-12 6.0206037947252707e-06\n", - "Constraint violation....: 1.0186340659856796e-10 9.3132257461547852e-10\n", - "Complementarity.........: 1.4067955349081809e-11 9.1007124920134500e-06\n", - "Overall NLP error.......: 1.0186340659856796e-10 9.1007124920134500e-06\n", + "Objective...............: -1.6119507851269799e+02 -1.0427883997850099e+08\n", + "Dual infeasibility......: 9.3031457019390660e-12 6.0183055891049595e-06\n", + "Constraint violation....: 1.0186340659856796e-10 1.3969838619232178e-09\n", + "Complementarity.........: 1.4067955349081809e-11 9.1007124920134466e-06\n", + "Overall NLP error.......: 1.0186340659856796e-10 9.1007124920134466e-06\n", "\n", "\n", "Number of objective function evaluations = 109\n", @@ -3920,7 +2178,7 @@ "Number of inequality constraint Jacobian evaluations = 75\n", "Number of Lagrangian Hessian evaluations = 74\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.060\n", - "Total CPU secs in NLP function evaluations = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.009\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -3941,24 +2199,24 @@ "These coefficients should follow 1*CO + 2*H2 => 1*CH3OH\n", "\n", "Reaction conversion: 0.8500000099996615\n", - "Reactor duty (MW): -59.34022107299144\n", + "Reactor duty (MW): -59.34022107299143\n", "Duty from Reaction (MW)): 28.216865165267237\n", - "Compressor work (MW): 8.44420706806932e-25\n", - "Turbine work (MW): -2.491301208383534\n", - "Feed Mixer outlet temperature (C)): 20.051714213753428\n", - "Recycle Mixer outlet temperature (C)): 41.54321437801781\n", - "Feed Compressor outlet temperature (C)): 41.54321437801781\n", + "Compressor work (MW): 3.5544149580620033e-25\n", + "Turbine work (MW): -2.4913012083835593\n", + "Feed Mixer outlet temperature (C)): 20.0517142137536\n", + "Recycle Mixer outlet temperature (C)): 41.54321437801798\n", + "Feed Compressor outlet temperature (C)): 41.54321437801798\n", "Feed Compressor outlet pressure (Pa)): 5100000.0\n", "Heater outlet temperature (C)): 215.0\n", "Reactor outlet temperature (C)): 231.85000478420352\n", - "Turbine outlet temperature (C)): 141.50037862881612\n", - "Turbine outlet pressure (Pa)): 1487177.2483577346\n", + "Turbine outlet temperature (C)): 141.50037862881595\n", + "Turbine outlet pressure (Pa)): 1487177.2483577342\n", "Cooler outlet temperature (C)): 52.56999699056837\n", "Flash outlet temperature (C)): 134.0\n", "Purge percentage (amount of vapor vented to exhaust): 9.999999000026644 %\n", - "Methanol recovery(%): 92.05882105498138\n", - "annualized capital cost ($/year) = 259559.90821304667\n", - "operating cost ($/year) = 525130020.7095513\n", + "Methanol recovery(%): 92.0588210549814\n", + "annualized capital cost ($/year) = 259559.90821304618\n", + "operating cost ($/year) = 525130020.7095519\n", "sales ($/year) = 140033684437.28275\n", "raw materials cost ($/year) = 35229454878.16397\n", "revenue (1000$/year)= 104278839.978501\n", @@ -4118,9 +2376,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_test.ipynb index a7ec0962..02baea16 100644 --- a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -730,4 +731,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_usr.ipynb index 86592750..6e4ec273 100644 --- a/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/methanol_synthesis_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -626,4 +627,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/solver_captured.py b/idaes_examples/notebooks/docs/flowsheets/solver_captured.py index faaab841..1e4af4b7 100644 --- a/idaes_examples/notebooks/docs/flowsheets/solver_captured.py +++ b/idaes_examples/notebooks/docs/flowsheets/solver_captured.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Captured solver diff --git a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption.ipynb b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption.ipynb index bdb4a02b..0a91523e 100644 --- a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_doc.ipynb b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_doc.ipynb index 31202935..8aa8f6f5 100644 --- a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_doc.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -364,16 +365,70 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-10-26 15:28:28 [INFO] idaes.init.fs.tsa: Starting fixed bed TSA initialization\n", - "2023-10-26 15:28:45 [INFO] idaes.init.fs.tsa.heating: Starting initialization of heating step.\n", - "2023-10-26 15:28:47 [INFO] idaes.init.fs.tsa.heating: Initialization of heating step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:01 [INFO] idaes.init.fs.tsa.cooling: Starting initialization of cooling step.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.cooling: Initialization of cooling step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Starting initialization of pressurization step.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Initialization of pressurization step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.adsorption: Starting initialization of adsorption step.\n", - "2023-10-26 15:29:04 [INFO] idaes.init.fs.tsa.adsorption: Initialization of adsorption step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:13 [INFO] idaes.init.fs.tsa: Initialization of fixed bed TSA model completed optimal - Optimal Solution Found.\n" + "2025-03-17 17:33:31 [INFO] idaes.init.fs.tsa: Starting fixed bed TSA initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:32 [INFO] idaes.init.fs.tsa.heating: Starting initialization of heating step.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:32 [INFO] idaes.init.fs.tsa.heating: Initialization of heating step completed optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:33 [INFO] idaes.init.fs.tsa.cooling: Starting initialization of cooling step.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:33 [INFO] idaes.init.fs.tsa.cooling: Initialization of cooling step completed optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:33 [INFO] idaes.init.fs.tsa.pressurization: Starting initialization of pressurization step.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:33 [INFO] idaes.init.fs.tsa.pressurization: Initialization of pressurization step completed optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:33 [INFO] idaes.init.fs.tsa.adsorption: Starting initialization of adsorption step.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:33 [INFO] idaes.init.fs.tsa.adsorption: Initialization of adsorption step completed optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:33:34 [INFO] idaes.init.fs.tsa: Initialization of fixed bed TSA model completed optimal - Optimal Solution Found.\n" ] } ], @@ -403,14 +458,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'fs.tsa.cooling.scaling_factor' that\n", - "contains 4 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix 'fs.tsa.heating.scaling_factor' that\n", - "contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix 'fs.tsa.scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 9\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", "tol=1e-06\n", "\n", @@ -451,18 +506,30 @@ "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 0.0000000e+00 3.63e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.06e+00 3.00e+03 -1.0 4.96e+00 - 9.90e-01 9.90e-01h 1\n", + " 1 0.0000000e+00 1.06e+00 3.00e+03 -1.0 4.96e+00 - 9.90e-01 9.90e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 2 0.0000000e+00 4.90e-03 4.83e+03 -1.0 4.91e+00 - 9.90e-01 9.96e-01h 1\n", - " 3 0.0000000e+00 2.44e-07 4.53e+00 -1.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 2.78e-07 4.53e+00 -1.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Number of Iterations....: 3\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.0710209608078003e-07 2.4400780240796394e-07\n", + "Constraint violation....: 1.0803341865539551e-07 2.7837979033051852e-07\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.0710209608078003e-07 2.4400780240796394e-07\n", + "Overall NLP error.......: 1.0803341865539551e-07 2.7837979033051852e-07\n", "\n", "\n", "Number of objective function evaluations = 4\n", @@ -472,8 +539,8 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 3.208\n", - "Total CPU secs in NLP function evaluations = 0.089\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.464\n", + "Total CPU secs in NLP function evaluations = 0.013\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -499,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -543,6 +610,198 @@ " Concentration of CO2 emitted to atmosphere [ppm] 13803.\n", "====================================================================================\n" ] + }, + { + "data": { + "text/html": [ + "
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Value
Adsorption temperature [K]310.000000
Desorption temperature [K]430.000000
Heating temperature [K]440.000000
Cooling temperature [K]300.000000
Column diameter [m]0.030000
Column length [m]1.200000
Column volume [m3]0.000848
CO2 mole fraction at feed [%]12.000000
Feed flow rate [mol/s]0.009600
Feed velocity [m/s]0.500077
Minimum fluidization velocity [m/s]1.520736
Time of heating step [h]0.370300
Time of cooling step [h]0.208258
Time of pressurization step [h]0.005110
Time of adsorption step [h]0.252212
Cycle time [h]0.835880
Purity [-]0.902192
Recovery [-]0.898726
Productivity [kg CO2/ton/h]84.085281
Specific energy [MJ/kg CO2]3.653238
Heat duty per bed [MW]0.000051
Heat duty total [MW]0.000166
Pressure drop [Pa]5263.600021
Number of beds3.248381
CO2 captured in one cycle per bed [kg/cycle]0.042210
Cycles per year10479.973784
Total CO2 captured per year [tonne/year]1.436938
Amount of flue gas processed per year [Gmol/year]0.000303
Amount of flue gas processed per year (target) [Gmol/year]0.000303
Amount of CO2 to atmosphere [mol/s]0.000117
Concentration of CO2 emitted to atmosphere [ppm]13802.815041
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" + ], + "text/plain": [ + " Value\n", + "Adsorption temperature [K] 310.000000\n", + "Desorption temperature [K] 430.000000\n", + "Heating temperature [K] 440.000000\n", + "Cooling temperature [K] 300.000000\n", + "Column diameter [m] 0.030000\n", + "Column length [m] 1.200000\n", + "Column volume [m3] 0.000848\n", + "CO2 mole fraction at feed [%] 12.000000\n", + "Feed flow rate [mol/s] 0.009600\n", + "Feed velocity [m/s] 0.500077\n", + "Minimum fluidization velocity [m/s] 1.520736\n", + "Time of heating step [h] 0.370300\n", + "Time of cooling step [h] 0.208258\n", + "Time of pressurization step [h] 0.005110\n", + "Time of adsorption step [h] 0.252212\n", + "Cycle time [h] 0.835880\n", + "Purity [-] 0.902192\n", + "Recovery [-] 0.898726\n", + "Productivity [kg CO2/ton/h] 84.085281\n", + "Specific energy [MJ/kg CO2] 3.653238\n", + "Heat duty per bed [MW] 0.000051\n", + "Heat duty total [MW] 0.000166\n", + "Pressure drop [Pa] 5263.600021\n", + "Number of beds 3.248381\n", + "CO2 captured in one cycle per bed [kg/cycle] 0.042210\n", + "Cycles per year 10479.973784\n", + "Total CO2 captured per year [tonne/year] 1.436938\n", + "Amount of flue gas processed per year [Gmol/year] 0.000303\n", + "Amount of flue gas processed per year (target) ... 0.000303\n", + "Amount of CO2 to atmosphere [mol/s] 0.000117\n", + "Concentration of CO2 emitted to atmosphere [ppm] 13802.815041" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -561,12 +820,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -605,9 +864,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_test.ipynb index 022ff95a..d6c9b797 100644 --- a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_test.ipynb @@ -1,646 +1,647 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# TSA Adsorption Cycle for Carbon Capture\n", - "\n", - "\n", - "Maintainer: Daison Yancy Caballero and Alexander Noring \n", - "Author: Daison Yancy Caballero and Alexander Noring \n", - "Updated: 2023-11-13 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Demonstrate the use of the IDAES fixed bed temperature swing adsorption (TSA) 0D unit model\n", - "- Initialize the IDAES fixed bed TSA 0D unit model\n", - "- Simulate the IDAES fixed bed TSA 0D unit model by solving a square problem\n", - "- Generate and analyze results\n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This Jupyter notebook shows the simulation of a fixed bed TSA cycle for carbon capture by using the fixed bed TSA 0D unit model in IDAES. The fixed bed TSA model consists of a 0D equilibrium-based shortcut model composed of four steps a) heating, b) cooling, c) pressurization, and d) adsorption. Note that the equations in the IDAES fixed bed TSA 0D unit model and the input specifications used in this tutorial for the feed stream have been taken from Joss et al. 2015.\n", - "\n", - "\n", - "#### A diagram of the TSA adsorption cycle is given below: \n", - "\n", - "![](tsa_cycle.svg)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Import Libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import Pyomo packages \n", - "\n", - "We will need the following components from the pyomo libraries.\n", - "\n", - "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- SolverFactory (to set up the solver that will solve the problem)\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- Objective (to declare an objective function)\n", - "- minimize (to minimize an objective function)\n", - "- value (to return the numerical value of an Pyomo objects such as variables, constraints or expressions)\n", - "- units (to handle units in Pyomo and IDAES)\n", - "- check_optimal_termination (this method returns the solution status from solver)\n", - "\n", - "For further details on these components, please refer to the Pyomo documentation:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# python libraries\n", - "import os\n", - "\n", - "# pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " TransformationFactory,\n", - " SolverFactory,\n", - " Constraint,\n", - " Var,\n", - " Objective,\n", - " minimize,\n", - " value,\n", - " units,\n", - " check_optimal_termination,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES core components\n", - "\n", - "To build, initialize, and solve IDAES flowsheets we will need the following core components/utilities:\n", - "\n", - "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", - "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", - "- FixedBedTSA0D (fixed bed TSA model unit model)\n", - "- util (some utility functions in IDAES)\n", - "- idaeslog (it's used to set output messages like warnings or errors)\n", - "\n", - "For further details on these components, please refer to the IDAES documentation:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# import IDAES core libraries\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "import idaes.core.util as iutil\n", - "import idaes.logger as idaeslog\n", - "\n", - "# import tsa unit model\n", - "from idaes.models_extra.temperature_swing_adsorption import (\n", - " FixedBedTSA0D,\n", - " FixedBedTSA0DInitializer,\n", - " Adsorbent,\n", - ")\n", - "from idaes.models_extra.temperature_swing_adsorption.util import (\n", - " tsa_summary,\n", - " plot_tsa_profiles,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Constructing the Flowsheet\n", - "\n", - "First, let's create a ConcreteModel and attach the flowsheet block to it." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# create concrete model\n", - "m = ConcreteModel()\n", - "\n", - "# create flowsheet\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1: Adding the TSA Unit Model\n", - "\n", - "Now, we will be adding the fixed bed temperature swing adsorption (TSA) cycle model (assigned a name tsa).\n", - "\n", - "The TSA unit model builds variables, constraints and expressions for a solid sorbent based TSA capture system. This IDAES model can take up to 11 config arguments:\n", - "\n", - "1. `dynamic`: to set up the model as steady state. The IDAES fixed bed TSA 0D\n", - " unit model only supports steady state as the dynamic nature of the adsorption\n", - " cycle is handled in internal blocks for each cycle step of the unit. This\n", - " config argument is used to enable the TSA unit model to connect with other\n", - " IDAES unit models.\n", - "2. `adsorbent`: to set up the adsorbent to be used in the fixed bed TSA system. \n", - " Supported values currently are `Adsorbent.zeolite_13x`, `Adsorbent.mmen_mg_mof_74`, and `Adsorbent.polystyrene_amine`.\n", - "3. `number_of_beds`: to set up the number of beds to be used in the unit model.\n", - " This config argument accepts either an `int` (model assumes a fixed number of beds) or `None` (model calculates the number of beds).\n", - "4. `compressor`: indicates whether a compressor unit should be added to the\n", - " fixed bed TSA system to calculate the energy required to overcome\n", - " the pressure drop in the system. Supported values are `True` and `False`.\n", - "5. `compressor_properties`: indicates a property package to use in the compressor unit model.\n", - "6. `steam_calculation`: indicates whether a method to estimate the steam flow rate\n", - " required in the desorption step should be included. Supported values are: `SteamCalculationType.none`,\n", - " steam calculation method is not included. `SteamCalculationType.simplified`, a surrogate model is used\n", - " to estimate the mass flow rate of steam. `SteamCalculationType.rigorous`, a heater unit model is\n", - " included in the TSA system assuming total saturation.\n", - "7. `steam_properties`: indicates a property package to use for rigorous steam calculations. Currently, only the iapws95 property package is supported.\n", - "8. `transformation_method`: to set up the discretization method to be use for the time\n", - " domain. The discretization method must be a method recognized by the\n", - " Pyomo `TransformationFactory`. Supported values are `dae.finite_difference` and\n", - " `dae.collocation`.\n", - "9. `transformation_scheme`: to set up the scheme to use when discretizing the time domain.\n", - " Supported values are: `TransformationScheme.backward` and `TransformationScheme.forward` for finite difference transformation\n", - " method. `TransformationScheme.lagrangeRadau` for collocation transformation method.\n", - "10. `finite_elements`: to set up the number of finite elements to use when discretizing\n", - " the time domain.\n", - "11. `collocation_points`: to set up the number of collocation points to use per finite element\n", - " when the discretization method is `dae.collocation`.\n", - " \n", - "
\n", - "Note: a default value defined in the IDAES unit class is used for\n", - " a config argument when no value is passed in the time the unit model\n", - " is called.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# add tsa unit\n", - "m.fs.tsa = FixedBedTSA0D(adsorbent=Adsorbent.zeolite_13x, number_of_beds=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2: Fix Specifications of Feed Stream in TSA Unit\n", - "\n", - "The inlet specifications of the TSA unit are fixed to match the exhaust gas stream (stream 8) of case B31B in the NETL baseline report, which is a exhaust gas stream after 90% carbon capture by means of a solvent-based capture system." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# fix inlet conditions of tsa unit - baseline case from Joss et al. 2015\n", - "flue_gas = {\n", - " \"flow_mol_comp\": {\n", - " \"H2O\": 0.0,\n", - " \"CO2\": 0.00960 * 0.12,\n", - " \"N2\": 0.00960 * 0.88,\n", - " \"O2\": 0.0,\n", - " },\n", - " \"temperature\": 300.0,\n", - " \"pressure\": 1.0e5,\n", - "}\n", - "for i in m.fs.tsa.component_list:\n", - " m.fs.tsa.inlet.flow_mol_comp[:, i].fix(flue_gas[\"flow_mol_comp\"][i])\n", - "m.fs.tsa.inlet.temperature.fix(flue_gas[\"temperature\"])\n", - "m.fs.tsa.inlet.pressure.fix(flue_gas[\"pressure\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3: Fix DOF of TSA unit\n", - "\n", - "The degrees of freedom of the TSA unit model are: adsorption and desorption temperatures, temperatures of heating and cooling fluids, column diameter, and column height. These variables must be fixed to solve a square problem." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The DOF of the TSA unit is 0\n" - ] - } - ], - "source": [ - "# fix design and operating variables of tsa unit - baseline case from Joss et al. 2015\n", - "m.fs.tsa.temperature_desorption.fix(430)\n", - "m.fs.tsa.temperature_adsorption.fix(310)\n", - "m.fs.tsa.temperature_heating.fix(440)\n", - "m.fs.tsa.temperature_cooling.fix(300)\n", - "m.fs.tsa.bed_diameter.fix(3 / 100)\n", - "m.fs.tsa.bed_height.fix(1.2)\n", - "\n", - "\n", - "# check the degrees of freedom\n", - "DOF = degrees_of_freedom(m)\n", - "print(f\"The DOF of the TSA unit is {DOF}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4: Scaling Unit Models\n", - "\n", - "Creating well scaled models is important for increasing the efficiency and reliability of solvers. Depending on unit models, variables and constraints are often badly scaled. IDAES unit models contain a method to scale variables and constraints to improve solver convergence. To apply the scaled factors defined in each unit model, we need to call the IDAES method `calculate_scaling_factors` in `idaes.core.util.scaling`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# scaling factors\n", - "iutil.scaling.calculate_scaling_factors(m.fs.tsa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.5: Define Solver and Solver Options\n", - "\n", - "We select the solver that we will be using to initialize and solve the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# define solver options\n", - "solver_options = {\n", - " \"nlp_scaling_method\": \"user-scaling\",\n", - " \"tol\": 1e-6,\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6: Initialization of Unit Models\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models. To initialize the TSA unit model, call the method `m.fs.tsa.initialize()`.\n", - "\n", - "
\n", - "Note: initialize methods in IDAES unit models solve a square problem,\n", - " so the user needs to be sure that the degrees of freedom of the unit being\n", - " initialized are zero.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-10-26 15:28:28 [INFO] idaes.init.fs.tsa: Starting fixed bed TSA initialization\n", - "2023-10-26 15:28:45 [INFO] idaes.init.fs.tsa.heating: Starting initialization of heating step.\n", - "2023-10-26 15:28:47 [INFO] idaes.init.fs.tsa.heating: Initialization of heating step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:01 [INFO] idaes.init.fs.tsa.cooling: Starting initialization of cooling step.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.cooling: Initialization of cooling step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Starting initialization of pressurization step.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Initialization of pressurization step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.adsorption: Starting initialization of adsorption step.\n", - "2023-10-26 15:29:04 [INFO] idaes.init.fs.tsa.adsorption: Initialization of adsorption step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:13 [INFO] idaes.init.fs.tsa: Initialization of fixed bed TSA model completed optimal - Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# initialize tsa unit\n", - "initializer = FixedBedTSA0DInitializer(\n", - " output_level=idaeslog.INFO, solver_options=solver_options\n", - ")\n", - "initializer.initialize(m.fs.tsa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Solve the TSA Unit Model\n", - "\n", - "Now, we can simulate the TSA unit model by solving a square problem. For this, we need to set up the solver by using the Pyomo component `SolverFactory`. We will be using the solver and solver options defined during the initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# TSA Adsorption Cycle for Carbon Capture\n", + "\n", + "\n", + "Maintainer: Daison Yancy Caballero and Alexander Noring \n", + "Author: Daison Yancy Caballero and Alexander Noring \n", + "Updated: 2023-11-13 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Demonstrate the use of the IDAES fixed bed temperature swing adsorption (TSA) 0D unit model\n", + "- Initialize the IDAES fixed bed TSA 0D unit model\n", + "- Simulate the IDAES fixed bed TSA 0D unit model by solving a square problem\n", + "- Generate and analyze results\n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This Jupyter notebook shows the simulation of a fixed bed TSA cycle for carbon capture by using the fixed bed TSA 0D unit model in IDAES. The fixed bed TSA model consists of a 0D equilibrium-based shortcut model composed of four steps a) heating, b) cooling, c) pressurization, and d) adsorption. Note that the equations in the IDAES fixed bed TSA 0D unit model and the input specifications used in this tutorial for the feed stream have been taken from Joss et al. 2015.\n", + "\n", + "\n", + "#### A diagram of the TSA adsorption cycle is given below: \n", + "\n", + "![](tsa_cycle.svg)\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.tsa.cooling.scaling_factor' that\n", - "contains 4 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix 'fs.tsa.heating.scaling_factor' that\n", - "contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix 'fs.tsa.scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", - "tol=1e-06\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 19132\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 70375\n", - "\n", - "Total number of variables............................: 2815\n", - " variables with only lower bounds: 5\n", - " variables with lower and upper bounds: 605\n", - " variables with only upper bounds: 1\n", - "Total number of equality constraints.................: 2815\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.63e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.06e+00 3.00e+03 -1.0 4.96e+00 - 9.90e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 4.90e-03 4.83e+03 -1.0 4.91e+00 - 9.90e-01 9.96e-01h 1\n", - " 3 0.0000000e+00 2.44e-07 4.53e+00 -1.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.0710209608078003e-07 2.4400780240796394e-07\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.0710209608078003e-07 2.4400780240796394e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 3.208\n", - "Total CPU secs in NLP function evaluations = 0.089\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# set up solver to solve flowsheet\n", - "solver = SolverFactory(\"ipopt\")\n", - "solver.options = solver_options\n", - "\n", - "# solve flowsheet\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Viewing the Simulation Results\n", - "\n", - "We will call some utility methods defined in the TSA unit model to get displayed some key variables." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Import Libraries" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Summary - tsa\n", - "------------------------------------------------------------------------------------ Value \n", - " Adsorption temperature [K] 310.00\n", - " Desorption temperature [K] 430.00\n", - " Heating temperature [K] 440.00\n", - " Cooling temperature [K] 300.00\n", - " Column diameter [m] 0.030000\n", - " Column length [m] 1.2000\n", - " Column volume [m3] 0.00084823\n", - " CO2 mole fraction at feed [%] 12.000\n", - " Feed flow rate [mol/s] 0.0096000\n", - " Feed velocity [m/s] 0.50008\n", - " Minimum fluidization velocity [m/s] 1.5207\n", - " Time of heating step [h] 0.37030\n", - " Time of cooling step [h] 0.20826\n", - " Time of pressurization step [h] 0.0051098\n", - " Time of adsorption step [h] 0.25221\n", - " Cycle time [h] 0.83588\n", - " Purity [-] 0.90219\n", - " Recovery [-] 0.89873\n", - " Productivity [kg CO2/ton/h] 84.085\n", - " Specific energy [MJ/kg CO2] 3.6532\n", - " Heat duty per bed [MW] 5.1244e-05\n", - " Heat duty total [MW] 0.00016646\n", - " Pressure drop [Pa] 5263.6\n", - " Number of beds 3.2484\n", - " CO2 captured in one cycle per bed [kg/cycle] 0.042210\n", - " Cycles per year 10480.\n", - " Total CO2 captured per year [tonne/year] 1.4369\n", - " Amount of flue gas processed per year [Gmol/year] 0.00030275\n", - " Amount of flue gas processed per year (target) [Gmol/year] 0.00030275\n", - " Amount of CO2 to atmosphere [mol/s] 0.00011667\n", - " Concentration of CO2 emitted to atmosphere [ppm] 13803.\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "# summary tsa\n", - "tsa_summary(m.fs.tsa)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "assert value(m.fs.tsa.purity) == pytest.approx(0.90219, abs=1e-5)\n", - "assert value(m.fs.tsa.recovery) == pytest.approx(0.89873, abs=1e-5)\n", - "assert value(m.fs.tsa.specific_energy) == pytest.approx(3.6532, abs=1e-4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Plotting Profiles\n", - "\n", - "Call plots method in the FixedBedTSA0D model to generate profiles of temperature, pressure and $\\mathrm{CO_{2}}$ concentration at the outlet of the column." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Pyomo packages \n", + "\n", + "We will need the following components from the pyomo libraries.\n", + "\n", + "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- SolverFactory (to set up the solver that will solve the problem)\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- Objective (to declare an objective function)\n", + "- minimize (to minimize an objective function)\n", + "- value (to return the numerical value of an Pyomo objects such as variables, constraints or expressions)\n", + "- units (to handle units in Pyomo and IDAES)\n", + "- check_optimal_termination (this method returns the solution status from solver)\n", + "\n", + "For further details on these components, please refer to the Pyomo documentation:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# python libraries\n", + "import os\n", + "\n", + "# pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " TransformationFactory,\n", + " SolverFactory,\n", + " Constraint,\n", + " Var,\n", + " Objective,\n", + " minimize,\n", + " value,\n", + " units,\n", + " check_optimal_termination,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES core components\n", + "\n", + "To build, initialize, and solve IDAES flowsheets we will need the following core components/utilities:\n", + "\n", + "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", + "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", + "- FixedBedTSA0D (fixed bed TSA model unit model)\n", + "- util (some utility functions in IDAES)\n", + "- idaeslog (it's used to set output messages like warnings or errors)\n", + "\n", + "For further details on these components, please refer to the IDAES documentation:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# import IDAES core libraries\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import idaes.core.util as iutil\n", + "import idaes.logger as idaeslog\n", + "\n", + "# import tsa unit model\n", + "from idaes.models_extra.temperature_swing_adsorption import (\n", + " FixedBedTSA0D,\n", + " FixedBedTSA0DInitializer,\n", + " Adsorbent,\n", + ")\n", + "from idaes.models_extra.temperature_swing_adsorption.util import (\n", + " tsa_summary,\n", + " plot_tsa_profiles,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Constructing the Flowsheet\n", + "\n", + "First, let's create a ConcreteModel and attach the flowsheet block to it." + ] + }, { - "data": { - "image/png": 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" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# create concrete model\n", + "m = ConcreteModel()\n", + "\n", + "# create flowsheet\n", + "m.fs = FlowsheetBlock(dynamic=False)" ] - }, - "metadata": {}, - "output_type": "display_data" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1: Adding the TSA Unit Model\n", + "\n", + "Now, we will be adding the fixed bed temperature swing adsorption (TSA) cycle model (assigned a name tsa).\n", + "\n", + "The TSA unit model builds variables, constraints and expressions for a solid sorbent based TSA capture system. This IDAES model can take up to 11 config arguments:\n", + "\n", + "1. `dynamic`: to set up the model as steady state. The IDAES fixed bed TSA 0D\n", + " unit model only supports steady state as the dynamic nature of the adsorption\n", + " cycle is handled in internal blocks for each cycle step of the unit. This\n", + " config argument is used to enable the TSA unit model to connect with other\n", + " IDAES unit models.\n", + "2. `adsorbent`: to set up the adsorbent to be used in the fixed bed TSA system. \n", + " Supported values currently are `Adsorbent.zeolite_13x`, `Adsorbent.mmen_mg_mof_74`, and `Adsorbent.polystyrene_amine`.\n", + "3. `number_of_beds`: to set up the number of beds to be used in the unit model.\n", + " This config argument accepts either an `int` (model assumes a fixed number of beds) or `None` (model calculates the number of beds).\n", + "4. `compressor`: indicates whether a compressor unit should be added to the\n", + " fixed bed TSA system to calculate the energy required to overcome\n", + " the pressure drop in the system. Supported values are `True` and `False`.\n", + "5. `compressor_properties`: indicates a property package to use in the compressor unit model.\n", + "6. `steam_calculation`: indicates whether a method to estimate the steam flow rate\n", + " required in the desorption step should be included. Supported values are: `SteamCalculationType.none`,\n", + " steam calculation method is not included. `SteamCalculationType.simplified`, a surrogate model is used\n", + " to estimate the mass flow rate of steam. `SteamCalculationType.rigorous`, a heater unit model is\n", + " included in the TSA system assuming total saturation.\n", + "7. `steam_properties`: indicates a property package to use for rigorous steam calculations. Currently, only the iapws95 property package is supported.\n", + "8. `transformation_method`: to set up the discretization method to be use for the time\n", + " domain. The discretization method must be a method recognized by the\n", + " Pyomo `TransformationFactory`. Supported values are `dae.finite_difference` and\n", + " `dae.collocation`.\n", + "9. `transformation_scheme`: to set up the scheme to use when discretizing the time domain.\n", + " Supported values are: `TransformationScheme.backward` and `TransformationScheme.forward` for finite difference transformation\n", + " method. `TransformationScheme.lagrangeRadau` for collocation transformation method.\n", + "10. `finite_elements`: to set up the number of finite elements to use when discretizing\n", + " the time domain.\n", + "11. `collocation_points`: to set up the number of collocation points to use per finite element\n", + " when the discretization method is `dae.collocation`.\n", + " \n", + "
\n", + "Note: a default value defined in the IDAES unit class is used for\n", + " a config argument when no value is passed in the time the unit model\n", + " is called.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# add tsa unit\n", + "m.fs.tsa = FixedBedTSA0D(adsorbent=Adsorbent.zeolite_13x, number_of_beds=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2: Fix Specifications of Feed Stream in TSA Unit\n", + "\n", + "The inlet specifications of the TSA unit are fixed to match the exhaust gas stream (stream 8) of case B31B in the NETL baseline report, which is a exhaust gas stream after 90% carbon capture by means of a solvent-based capture system." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# fix inlet conditions of tsa unit - baseline case from Joss et al. 2015\n", + "flue_gas = {\n", + " \"flow_mol_comp\": {\n", + " \"H2O\": 0.0,\n", + " \"CO2\": 0.00960 * 0.12,\n", + " \"N2\": 0.00960 * 0.88,\n", + " \"O2\": 0.0,\n", + " },\n", + " \"temperature\": 300.0,\n", + " \"pressure\": 1.0e5,\n", + "}\n", + "for i in m.fs.tsa.component_list:\n", + " m.fs.tsa.inlet.flow_mol_comp[:, i].fix(flue_gas[\"flow_mol_comp\"][i])\n", + "m.fs.tsa.inlet.temperature.fix(flue_gas[\"temperature\"])\n", + "m.fs.tsa.inlet.pressure.fix(flue_gas[\"pressure\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3: Fix DOF of TSA unit\n", + "\n", + "The degrees of freedom of the TSA unit model are: adsorption and desorption temperatures, temperatures of heating and cooling fluids, column diameter, and column height. These variables must be fixed to solve a square problem." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The DOF of the TSA unit is 0\n" + ] + } + ], + "source": [ + "# fix design and operating variables of tsa unit - baseline case from Joss et al. 2015\n", + "m.fs.tsa.temperature_desorption.fix(430)\n", + "m.fs.tsa.temperature_adsorption.fix(310)\n", + "m.fs.tsa.temperature_heating.fix(440)\n", + "m.fs.tsa.temperature_cooling.fix(300)\n", + "m.fs.tsa.bed_diameter.fix(3 / 100)\n", + "m.fs.tsa.bed_height.fix(1.2)\n", + "\n", + "\n", + "# check the degrees of freedom\n", + "DOF = degrees_of_freedom(m)\n", + "print(f\"The DOF of the TSA unit is {DOF}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4: Scaling Unit Models\n", + "\n", + "Creating well scaled models is important for increasing the efficiency and reliability of solvers. Depending on unit models, variables and constraints are often badly scaled. IDAES unit models contain a method to scale variables and constraints to improve solver convergence. To apply the scaled factors defined in each unit model, we need to call the IDAES method `calculate_scaling_factors` in `idaes.core.util.scaling`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# scaling factors\n", + "iutil.scaling.calculate_scaling_factors(m.fs.tsa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5: Define Solver and Solver Options\n", + "\n", + "We select the solver that we will be using to initialize and solve the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# define solver options\n", + "solver_options = {\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + " \"tol\": 1e-6,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6: Initialization of Unit Models\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models. To initialize the TSA unit model, call the method `m.fs.tsa.initialize()`.\n", + "\n", + "
\n", + "Note: initialize methods in IDAES unit models solve a square problem,\n", + " so the user needs to be sure that the degrees of freedom of the unit being\n", + " initialized are zero.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-26 15:28:28 [INFO] idaes.init.fs.tsa: Starting fixed bed TSA initialization\n", + "2023-10-26 15:28:45 [INFO] idaes.init.fs.tsa.heating: Starting initialization of heating step.\n", + "2023-10-26 15:28:47 [INFO] idaes.init.fs.tsa.heating: Initialization of heating step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:01 [INFO] idaes.init.fs.tsa.cooling: Starting initialization of cooling step.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.cooling: Initialization of cooling step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Starting initialization of pressurization step.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Initialization of pressurization step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.adsorption: Starting initialization of adsorption step.\n", + "2023-10-26 15:29:04 [INFO] idaes.init.fs.tsa.adsorption: Initialization of adsorption step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:13 [INFO] idaes.init.fs.tsa: Initialization of fixed bed TSA model completed optimal - Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# initialize tsa unit\n", + "initializer = FixedBedTSA0DInitializer(\n", + " output_level=idaeslog.INFO, solver_options=solver_options\n", + ")\n", + "initializer.initialize(m.fs.tsa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Solve the TSA Unit Model\n", + "\n", + "Now, we can simulate the TSA unit model by solving a square problem. For this, we need to set up the solver by using the Pyomo component `SolverFactory`. We will be using the solver and solver options defined during the initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'fs.tsa.cooling.scaling_factor' that\n", + "contains 4 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix 'fs.tsa.heating.scaling_factor' that\n", + "contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix 'fs.tsa.scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", + "tol=1e-06\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 19132\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 70375\n", + "\n", + "Total number of variables............................: 2815\n", + " variables with only lower bounds: 5\n", + " variables with lower and upper bounds: 605\n", + " variables with only upper bounds: 1\n", + "Total number of equality constraints.................: 2815\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.63e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.06e+00 3.00e+03 -1.0 4.96e+00 - 9.90e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 4.90e-03 4.83e+03 -1.0 4.91e+00 - 9.90e-01 9.96e-01h 1\n", + " 3 0.0000000e+00 2.44e-07 4.53e+00 -1.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.0710209608078003e-07 2.4400780240796394e-07\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.0710209608078003e-07 2.4400780240796394e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 3.208\n", + "Total CPU secs in NLP function evaluations = 0.089\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# set up solver to solve flowsheet\n", + "solver = SolverFactory(\"ipopt\")\n", + "solver.options = solver_options\n", + "\n", + "# solve flowsheet\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Viewing the Simulation Results\n", + "\n", + "We will call some utility methods defined in the TSA unit model to get displayed some key variables." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Summary - tsa\n", + "------------------------------------------------------------------------------------ Value \n", + " Adsorption temperature [K] 310.00\n", + " Desorption temperature [K] 430.00\n", + " Heating temperature [K] 440.00\n", + " Cooling temperature [K] 300.00\n", + " Column diameter [m] 0.030000\n", + " Column length [m] 1.2000\n", + " Column volume [m3] 0.00084823\n", + " CO2 mole fraction at feed [%] 12.000\n", + " Feed flow rate [mol/s] 0.0096000\n", + " Feed velocity [m/s] 0.50008\n", + " Minimum fluidization velocity [m/s] 1.5207\n", + " Time of heating step [h] 0.37030\n", + " Time of cooling step [h] 0.20826\n", + " Time of pressurization step [h] 0.0051098\n", + " Time of adsorption step [h] 0.25221\n", + " Cycle time [h] 0.83588\n", + " Purity [-] 0.90219\n", + " Recovery [-] 0.89873\n", + " Productivity [kg CO2/ton/h] 84.085\n", + " Specific energy [MJ/kg CO2] 3.6532\n", + " Heat duty per bed [MW] 5.1244e-05\n", + " Heat duty total [MW] 0.00016646\n", + " Pressure drop [Pa] 5263.6\n", + " Number of beds 3.2484\n", + " CO2 captured in one cycle per bed [kg/cycle] 0.042210\n", + " Cycles per year 10480.\n", + " Total CO2 captured per year [tonne/year] 1.4369\n", + " Amount of flue gas processed per year [Gmol/year] 0.00030275\n", + " Amount of flue gas processed per year (target) [Gmol/year] 0.00030275\n", + " Amount of CO2 to atmosphere [mol/s] 0.00011667\n", + " Concentration of CO2 emitted to atmosphere [ppm] 13803.\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "# summary tsa\n", + "tsa_summary(m.fs.tsa)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "assert value(m.fs.tsa.purity) == pytest.approx(0.90219, abs=1e-5)\n", + "assert value(m.fs.tsa.recovery) == pytest.approx(0.89873, abs=1e-5)\n", + "assert value(m.fs.tsa.specific_energy) == pytest.approx(3.6532, abs=1e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plotting Profiles\n", + "\n", + "Call plots method in the FixedBedTSA0D model to generate profiles of temperature, pressure and $\\mathrm{CO_{2}}$ concentration at the outlet of the column." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# profiles\n", + "plot_tsa_profiles(m.fs.tsa)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# profiles\n", - "plot_tsa_profiles(m.fs.tsa)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_usr.ipynb index 31202935..a2ad2989 100644 --- a/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/temperature_swing_adsorption/temperature_swing_adsorption_usr.ipynb @@ -1,613 +1,614 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# TSA Adsorption Cycle for Carbon Capture\n", - "\n", - "\n", - "Maintainer: Daison Yancy Caballero and Alexander Noring \n", - "Author: Daison Yancy Caballero and Alexander Noring \n", - "Updated: 2023-11-13 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Demonstrate the use of the IDAES fixed bed temperature swing adsorption (TSA) 0D unit model\n", - "- Initialize the IDAES fixed bed TSA 0D unit model\n", - "- Simulate the IDAES fixed bed TSA 0D unit model by solving a square problem\n", - "- Generate and analyze results\n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This Jupyter notebook shows the simulation of a fixed bed TSA cycle for carbon capture by using the fixed bed TSA 0D unit model in IDAES. The fixed bed TSA model consists of a 0D equilibrium-based shortcut model composed of four steps a) heating, b) cooling, c) pressurization, and d) adsorption. Note that the equations in the IDAES fixed bed TSA 0D unit model and the input specifications used in this tutorial for the feed stream have been taken from Joss et al. 2015.\n", - "\n", - "\n", - "#### A diagram of the TSA adsorption cycle is given below: \n", - "\n", - "![](tsa_cycle.svg)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Import Libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import Pyomo packages \n", - "\n", - "We will need the following components from the pyomo libraries.\n", - "\n", - "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- SolverFactory (to set up the solver that will solve the problem)\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- Objective (to declare an objective function)\n", - "- minimize (to minimize an objective function)\n", - "- value (to return the numerical value of an Pyomo objects such as variables, constraints or expressions)\n", - "- units (to handle units in Pyomo and IDAES)\n", - "- check_optimal_termination (this method returns the solution status from solver)\n", - "\n", - "For further details on these components, please refer to the Pyomo documentation:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# python libraries\n", - "import os\n", - "\n", - "# pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " TransformationFactory,\n", - " SolverFactory,\n", - " Constraint,\n", - " Var,\n", - " Objective,\n", - " minimize,\n", - " value,\n", - " units,\n", - " check_optimal_termination,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES core components\n", - "\n", - "To build, initialize, and solve IDAES flowsheets we will need the following core components/utilities:\n", - "\n", - "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", - "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", - "- FixedBedTSA0D (fixed bed TSA model unit model)\n", - "- util (some utility functions in IDAES)\n", - "- idaeslog (it's used to set output messages like warnings or errors)\n", - "\n", - "For further details on these components, please refer to the IDAES documentation:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# import IDAES core libraries\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "import idaes.core.util as iutil\n", - "import idaes.logger as idaeslog\n", - "\n", - "# import tsa unit model\n", - "from idaes.models_extra.temperature_swing_adsorption import (\n", - " FixedBedTSA0D,\n", - " FixedBedTSA0DInitializer,\n", - " Adsorbent,\n", - ")\n", - "from idaes.models_extra.temperature_swing_adsorption.util import (\n", - " tsa_summary,\n", - " plot_tsa_profiles,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Constructing the Flowsheet\n", - "\n", - "First, let's create a ConcreteModel and attach the flowsheet block to it." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# create concrete model\n", - "m = ConcreteModel()\n", - "\n", - "# create flowsheet\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1: Adding the TSA Unit Model\n", - "\n", - "Now, we will be adding the fixed bed temperature swing adsorption (TSA) cycle model (assigned a name tsa).\n", - "\n", - "The TSA unit model builds variables, constraints and expressions for a solid sorbent based TSA capture system. This IDAES model can take up to 11 config arguments:\n", - "\n", - "1. `dynamic`: to set up the model as steady state. The IDAES fixed bed TSA 0D\n", - " unit model only supports steady state as the dynamic nature of the adsorption\n", - " cycle is handled in internal blocks for each cycle step of the unit. This\n", - " config argument is used to enable the TSA unit model to connect with other\n", - " IDAES unit models.\n", - "2. `adsorbent`: to set up the adsorbent to be used in the fixed bed TSA system. \n", - " Supported values currently are `Adsorbent.zeolite_13x`, `Adsorbent.mmen_mg_mof_74`, and `Adsorbent.polystyrene_amine`.\n", - "3. `number_of_beds`: to set up the number of beds to be used in the unit model.\n", - " This config argument accepts either an `int` (model assumes a fixed number of beds) or `None` (model calculates the number of beds).\n", - "4. `compressor`: indicates whether a compressor unit should be added to the\n", - " fixed bed TSA system to calculate the energy required to overcome\n", - " the pressure drop in the system. Supported values are `True` and `False`.\n", - "5. `compressor_properties`: indicates a property package to use in the compressor unit model.\n", - "6. `steam_calculation`: indicates whether a method to estimate the steam flow rate\n", - " required in the desorption step should be included. Supported values are: `SteamCalculationType.none`,\n", - " steam calculation method is not included. `SteamCalculationType.simplified`, a surrogate model is used\n", - " to estimate the mass flow rate of steam. `SteamCalculationType.rigorous`, a heater unit model is\n", - " included in the TSA system assuming total saturation.\n", - "7. `steam_properties`: indicates a property package to use for rigorous steam calculations. Currently, only the iapws95 property package is supported.\n", - "8. `transformation_method`: to set up the discretization method to be use for the time\n", - " domain. The discretization method must be a method recognized by the\n", - " Pyomo `TransformationFactory`. Supported values are `dae.finite_difference` and\n", - " `dae.collocation`.\n", - "9. `transformation_scheme`: to set up the scheme to use when discretizing the time domain.\n", - " Supported values are: `TransformationScheme.backward` and `TransformationScheme.forward` for finite difference transformation\n", - " method. `TransformationScheme.lagrangeRadau` for collocation transformation method.\n", - "10. `finite_elements`: to set up the number of finite elements to use when discretizing\n", - " the time domain.\n", - "11. `collocation_points`: to set up the number of collocation points to use per finite element\n", - " when the discretization method is `dae.collocation`.\n", - " \n", - "
\n", - "Note: a default value defined in the IDAES unit class is used for\n", - " a config argument when no value is passed in the time the unit model\n", - " is called.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# add tsa unit\n", - "m.fs.tsa = FixedBedTSA0D(adsorbent=Adsorbent.zeolite_13x, number_of_beds=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2: Fix Specifications of Feed Stream in TSA Unit\n", - "\n", - "The inlet specifications of the TSA unit are fixed to match the exhaust gas stream (stream 8) of case B31B in the NETL baseline report, which is a exhaust gas stream after 90% carbon capture by means of a solvent-based capture system." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# fix inlet conditions of tsa unit - baseline case from Joss et al. 2015\n", - "flue_gas = {\n", - " \"flow_mol_comp\": {\n", - " \"H2O\": 0.0,\n", - " \"CO2\": 0.00960 * 0.12,\n", - " \"N2\": 0.00960 * 0.88,\n", - " \"O2\": 0.0,\n", - " },\n", - " \"temperature\": 300.0,\n", - " \"pressure\": 1.0e5,\n", - "}\n", - "for i in m.fs.tsa.component_list:\n", - " m.fs.tsa.inlet.flow_mol_comp[:, i].fix(flue_gas[\"flow_mol_comp\"][i])\n", - "m.fs.tsa.inlet.temperature.fix(flue_gas[\"temperature\"])\n", - "m.fs.tsa.inlet.pressure.fix(flue_gas[\"pressure\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3: Fix DOF of TSA unit\n", - "\n", - "The degrees of freedom of the TSA unit model are: adsorption and desorption temperatures, temperatures of heating and cooling fluids, column diameter, and column height. These variables must be fixed to solve a square problem." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The DOF of the TSA unit is 0\n" - ] - } - ], - "source": [ - "# fix design and operating variables of tsa unit - baseline case from Joss et al. 2015\n", - "m.fs.tsa.temperature_desorption.fix(430)\n", - "m.fs.tsa.temperature_adsorption.fix(310)\n", - "m.fs.tsa.temperature_heating.fix(440)\n", - "m.fs.tsa.temperature_cooling.fix(300)\n", - "m.fs.tsa.bed_diameter.fix(3 / 100)\n", - "m.fs.tsa.bed_height.fix(1.2)\n", - "\n", - "\n", - "# check the degrees of freedom\n", - "DOF = degrees_of_freedom(m)\n", - "print(f\"The DOF of the TSA unit is {DOF}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4: Scaling Unit Models\n", - "\n", - "Creating well scaled models is important for increasing the efficiency and reliability of solvers. Depending on unit models, variables and constraints are often badly scaled. IDAES unit models contain a method to scale variables and constraints to improve solver convergence. To apply the scaled factors defined in each unit model, we need to call the IDAES method `calculate_scaling_factors` in `idaes.core.util.scaling`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# scaling factors\n", - "iutil.scaling.calculate_scaling_factors(m.fs.tsa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.5: Define Solver and Solver Options\n", - "\n", - "We select the solver that we will be using to initialize and solve the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# define solver options\n", - "solver_options = {\n", - " \"nlp_scaling_method\": \"user-scaling\",\n", - " \"tol\": 1e-6,\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6: Initialization of Unit Models\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models. To initialize the TSA unit model, call the method `m.fs.tsa.initialize()`.\n", - "\n", - "
\n", - "Note: initialize methods in IDAES unit models solve a square problem,\n", - " so the user needs to be sure that the degrees of freedom of the unit being\n", - " initialized are zero.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-10-26 15:28:28 [INFO] idaes.init.fs.tsa: Starting fixed bed TSA initialization\n", - "2023-10-26 15:28:45 [INFO] idaes.init.fs.tsa.heating: Starting initialization of heating step.\n", - "2023-10-26 15:28:47 [INFO] idaes.init.fs.tsa.heating: Initialization of heating step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:01 [INFO] idaes.init.fs.tsa.cooling: Starting initialization of cooling step.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.cooling: Initialization of cooling step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Starting initialization of pressurization step.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Initialization of pressurization step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.adsorption: Starting initialization of adsorption step.\n", - "2023-10-26 15:29:04 [INFO] idaes.init.fs.tsa.adsorption: Initialization of adsorption step completed optimal - Optimal Solution Found.\n", - "2023-10-26 15:29:13 [INFO] idaes.init.fs.tsa: Initialization of fixed bed TSA model completed optimal - Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# initialize tsa unit\n", - "initializer = FixedBedTSA0DInitializer(\n", - " output_level=idaeslog.INFO, solver_options=solver_options\n", - ")\n", - "initializer.initialize(m.fs.tsa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Solve the TSA Unit Model\n", - "\n", - "Now, we can simulate the TSA unit model by solving a square problem. For this, we need to set up the solver by using the Pyomo component `SolverFactory`. We will be using the solver and solver options defined during the initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# TSA Adsorption Cycle for Carbon Capture\n", + "\n", + "\n", + "Maintainer: Daison Yancy Caballero and Alexander Noring \n", + "Author: Daison Yancy Caballero and Alexander Noring \n", + "Updated: 2023-11-13 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Demonstrate the use of the IDAES fixed bed temperature swing adsorption (TSA) 0D unit model\n", + "- Initialize the IDAES fixed bed TSA 0D unit model\n", + "- Simulate the IDAES fixed bed TSA 0D unit model by solving a square problem\n", + "- Generate and analyze results\n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This Jupyter notebook shows the simulation of a fixed bed TSA cycle for carbon capture by using the fixed bed TSA 0D unit model in IDAES. The fixed bed TSA model consists of a 0D equilibrium-based shortcut model composed of four steps a) heating, b) cooling, c) pressurization, and d) adsorption. Note that the equations in the IDAES fixed bed TSA 0D unit model and the input specifications used in this tutorial for the feed stream have been taken from Joss et al. 2015.\n", + "\n", + "\n", + "#### A diagram of the TSA adsorption cycle is given below: \n", + "\n", + "![](tsa_cycle.svg)\n" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.tsa.cooling.scaling_factor' that\n", - "contains 4 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix 'fs.tsa.heating.scaling_factor' that\n", - "contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix 'fs.tsa.scaling_factor' that contains 12\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", - "tol=1e-06\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 19132\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 70375\n", - "\n", - "Total number of variables............................: 2815\n", - " variables with only lower bounds: 5\n", - " variables with lower and upper bounds: 605\n", - " variables with only upper bounds: 1\n", - "Total number of equality constraints.................: 2815\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.63e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.06e+00 3.00e+03 -1.0 4.96e+00 - 9.90e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 4.90e-03 4.83e+03 -1.0 4.91e+00 - 9.90e-01 9.96e-01h 1\n", - " 3 0.0000000e+00 2.44e-07 4.53e+00 -1.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.0710209608078003e-07 2.4400780240796394e-07\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.0710209608078003e-07 2.4400780240796394e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 3.208\n", - "Total CPU secs in NLP function evaluations = 0.089\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# set up solver to solve flowsheet\n", - "solver = SolverFactory(\"ipopt\")\n", - "solver.options = solver_options\n", - "\n", - "# solve flowsheet\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Viewing the Simulation Results\n", - "\n", - "We will call some utility methods defined in the TSA unit model to get displayed some key variables." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Import Libraries" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Summary - tsa\n", - "------------------------------------------------------------------------------------ Value \n", - " Adsorption temperature [K] 310.00\n", - " Desorption temperature [K] 430.00\n", - " Heating temperature [K] 440.00\n", - " Cooling temperature [K] 300.00\n", - " Column diameter [m] 0.030000\n", - " Column length [m] 1.2000\n", - " Column volume [m3] 0.00084823\n", - " CO2 mole fraction at feed [%] 12.000\n", - " Feed flow rate [mol/s] 0.0096000\n", - " Feed velocity [m/s] 0.50008\n", - " Minimum fluidization velocity [m/s] 1.5207\n", - " Time of heating step [h] 0.37030\n", - " Time of cooling step [h] 0.20826\n", - " Time of pressurization step [h] 0.0051098\n", - " Time of adsorption step [h] 0.25221\n", - " Cycle time [h] 0.83588\n", - " Purity [-] 0.90219\n", - " Recovery [-] 0.89873\n", - " Productivity [kg CO2/ton/h] 84.085\n", - " Specific energy [MJ/kg CO2] 3.6532\n", - " Heat duty per bed [MW] 5.1244e-05\n", - " Heat duty total [MW] 0.00016646\n", - " Pressure drop [Pa] 5263.6\n", - " Number of beds 3.2484\n", - " CO2 captured in one cycle per bed [kg/cycle] 0.042210\n", - " Cycles per year 10480.\n", - " Total CO2 captured per year [tonne/year] 1.4369\n", - " Amount of flue gas processed per year [Gmol/year] 0.00030275\n", - " Amount of flue gas processed per year (target) [Gmol/year] 0.00030275\n", - " Amount of CO2 to atmosphere [mol/s] 0.00011667\n", - " Concentration of CO2 emitted to atmosphere [ppm] 13803.\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "# summary tsa\n", - "tsa_summary(m.fs.tsa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Plotting Profiles\n", - "\n", - "Call plots method in the FixedBedTSA0D model to generate profiles of temperature, pressure and $\\mathrm{CO_{2}}$ concentration at the outlet of the column." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Pyomo packages \n", + "\n", + "We will need the following components from the pyomo libraries.\n", + "\n", + "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- SolverFactory (to set up the solver that will solve the problem)\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- Objective (to declare an objective function)\n", + "- minimize (to minimize an objective function)\n", + "- value (to return the numerical value of an Pyomo objects such as variables, constraints or expressions)\n", + "- units (to handle units in Pyomo and IDAES)\n", + "- check_optimal_termination (this method returns the solution status from solver)\n", + "\n", + "For further details on these components, please refer to the Pyomo documentation:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# python libraries\n", + "import os\n", + "\n", + "# pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " TransformationFactory,\n", + " SolverFactory,\n", + " Constraint,\n", + " Var,\n", + " Objective,\n", + " minimize,\n", + " value,\n", + " units,\n", + " check_optimal_termination,\n", + ")" + ] + }, { - "data": { - "image/png": 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES core components\n", + "\n", + "To build, initialize, and solve IDAES flowsheets we will need the following core components/utilities:\n", + "\n", + "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", + "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", + "- FixedBedTSA0D (fixed bed TSA model unit model)\n", + "- util (some utility functions in IDAES)\n", + "- idaeslog (it's used to set output messages like warnings or errors)\n", + "\n", + "For further details on these components, please refer to the IDAES documentation:" ] - }, - "metadata": {}, - "output_type": "display_data" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# import IDAES core libraries\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import idaes.core.util as iutil\n", + "import idaes.logger as idaeslog\n", + "\n", + "# import tsa unit model\n", + "from idaes.models_extra.temperature_swing_adsorption import (\n", + " FixedBedTSA0D,\n", + " FixedBedTSA0DInitializer,\n", + " Adsorbent,\n", + ")\n", + "from idaes.models_extra.temperature_swing_adsorption.util import (\n", + " tsa_summary,\n", + " plot_tsa_profiles,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Constructing the Flowsheet\n", + "\n", + "First, let's create a ConcreteModel and attach the flowsheet block to it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# create concrete model\n", + "m = ConcreteModel()\n", + "\n", + "# create flowsheet\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1: Adding the TSA Unit Model\n", + "\n", + "Now, we will be adding the fixed bed temperature swing adsorption (TSA) cycle model (assigned a name tsa).\n", + "\n", + "The TSA unit model builds variables, constraints and expressions for a solid sorbent based TSA capture system. This IDAES model can take up to 11 config arguments:\n", + "\n", + "1. `dynamic`: to set up the model as steady state. The IDAES fixed bed TSA 0D\n", + " unit model only supports steady state as the dynamic nature of the adsorption\n", + " cycle is handled in internal blocks for each cycle step of the unit. This\n", + " config argument is used to enable the TSA unit model to connect with other\n", + " IDAES unit models.\n", + "2. `adsorbent`: to set up the adsorbent to be used in the fixed bed TSA system. \n", + " Supported values currently are `Adsorbent.zeolite_13x`, `Adsorbent.mmen_mg_mof_74`, and `Adsorbent.polystyrene_amine`.\n", + "3. `number_of_beds`: to set up the number of beds to be used in the unit model.\n", + " This config argument accepts either an `int` (model assumes a fixed number of beds) or `None` (model calculates the number of beds).\n", + "4. `compressor`: indicates whether a compressor unit should be added to the\n", + " fixed bed TSA system to calculate the energy required to overcome\n", + " the pressure drop in the system. Supported values are `True` and `False`.\n", + "5. `compressor_properties`: indicates a property package to use in the compressor unit model.\n", + "6. `steam_calculation`: indicates whether a method to estimate the steam flow rate\n", + " required in the desorption step should be included. Supported values are: `SteamCalculationType.none`,\n", + " steam calculation method is not included. `SteamCalculationType.simplified`, a surrogate model is used\n", + " to estimate the mass flow rate of steam. `SteamCalculationType.rigorous`, a heater unit model is\n", + " included in the TSA system assuming total saturation.\n", + "7. `steam_properties`: indicates a property package to use for rigorous steam calculations. Currently, only the iapws95 property package is supported.\n", + "8. `transformation_method`: to set up the discretization method to be use for the time\n", + " domain. The discretization method must be a method recognized by the\n", + " Pyomo `TransformationFactory`. Supported values are `dae.finite_difference` and\n", + " `dae.collocation`.\n", + "9. `transformation_scheme`: to set up the scheme to use when discretizing the time domain.\n", + " Supported values are: `TransformationScheme.backward` and `TransformationScheme.forward` for finite difference transformation\n", + " method. `TransformationScheme.lagrangeRadau` for collocation transformation method.\n", + "10. `finite_elements`: to set up the number of finite elements to use when discretizing\n", + " the time domain.\n", + "11. `collocation_points`: to set up the number of collocation points to use per finite element\n", + " when the discretization method is `dae.collocation`.\n", + " \n", + "
\n", + "Note: a default value defined in the IDAES unit class is used for\n", + " a config argument when no value is passed in the time the unit model\n", + " is called.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# add tsa unit\n", + "m.fs.tsa = FixedBedTSA0D(adsorbent=Adsorbent.zeolite_13x, number_of_beds=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2: Fix Specifications of Feed Stream in TSA Unit\n", + "\n", + "The inlet specifications of the TSA unit are fixed to match the exhaust gas stream (stream 8) of case B31B in the NETL baseline report, which is a exhaust gas stream after 90% carbon capture by means of a solvent-based capture system." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# fix inlet conditions of tsa unit - baseline case from Joss et al. 2015\n", + "flue_gas = {\n", + " \"flow_mol_comp\": {\n", + " \"H2O\": 0.0,\n", + " \"CO2\": 0.00960 * 0.12,\n", + " \"N2\": 0.00960 * 0.88,\n", + " \"O2\": 0.0,\n", + " },\n", + " \"temperature\": 300.0,\n", + " \"pressure\": 1.0e5,\n", + "}\n", + "for i in m.fs.tsa.component_list:\n", + " m.fs.tsa.inlet.flow_mol_comp[:, i].fix(flue_gas[\"flow_mol_comp\"][i])\n", + "m.fs.tsa.inlet.temperature.fix(flue_gas[\"temperature\"])\n", + "m.fs.tsa.inlet.pressure.fix(flue_gas[\"pressure\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3: Fix DOF of TSA unit\n", + "\n", + "The degrees of freedom of the TSA unit model are: adsorption and desorption temperatures, temperatures of heating and cooling fluids, column diameter, and column height. These variables must be fixed to solve a square problem." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The DOF of the TSA unit is 0\n" + ] + } + ], + "source": [ + "# fix design and operating variables of tsa unit - baseline case from Joss et al. 2015\n", + "m.fs.tsa.temperature_desorption.fix(430)\n", + "m.fs.tsa.temperature_adsorption.fix(310)\n", + "m.fs.tsa.temperature_heating.fix(440)\n", + "m.fs.tsa.temperature_cooling.fix(300)\n", + "m.fs.tsa.bed_diameter.fix(3 / 100)\n", + "m.fs.tsa.bed_height.fix(1.2)\n", + "\n", + "\n", + "# check the degrees of freedom\n", + "DOF = degrees_of_freedom(m)\n", + "print(f\"The DOF of the TSA unit is {DOF}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4: Scaling Unit Models\n", + "\n", + "Creating well scaled models is important for increasing the efficiency and reliability of solvers. Depending on unit models, variables and constraints are often badly scaled. IDAES unit models contain a method to scale variables and constraints to improve solver convergence. To apply the scaled factors defined in each unit model, we need to call the IDAES method `calculate_scaling_factors` in `idaes.core.util.scaling`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# scaling factors\n", + "iutil.scaling.calculate_scaling_factors(m.fs.tsa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5: Define Solver and Solver Options\n", + "\n", + "We select the solver that we will be using to initialize and solve the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# define solver options\n", + "solver_options = {\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + " \"tol\": 1e-6,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6: Initialization of Unit Models\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models. To initialize the TSA unit model, call the method `m.fs.tsa.initialize()`.\n", + "\n", + "
\n", + "Note: initialize methods in IDAES unit models solve a square problem,\n", + " so the user needs to be sure that the degrees of freedom of the unit being\n", + " initialized are zero.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-10-26 15:28:28 [INFO] idaes.init.fs.tsa: Starting fixed bed TSA initialization\n", + "2023-10-26 15:28:45 [INFO] idaes.init.fs.tsa.heating: Starting initialization of heating step.\n", + "2023-10-26 15:28:47 [INFO] idaes.init.fs.tsa.heating: Initialization of heating step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:01 [INFO] idaes.init.fs.tsa.cooling: Starting initialization of cooling step.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.cooling: Initialization of cooling step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Starting initialization of pressurization step.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.pressurization: Initialization of pressurization step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:03 [INFO] idaes.init.fs.tsa.adsorption: Starting initialization of adsorption step.\n", + "2023-10-26 15:29:04 [INFO] idaes.init.fs.tsa.adsorption: Initialization of adsorption step completed optimal - Optimal Solution Found.\n", + "2023-10-26 15:29:13 [INFO] idaes.init.fs.tsa: Initialization of fixed bed TSA model completed optimal - Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# initialize tsa unit\n", + "initializer = FixedBedTSA0DInitializer(\n", + " output_level=idaeslog.INFO, solver_options=solver_options\n", + ")\n", + "initializer.initialize(m.fs.tsa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Solve the TSA Unit Model\n", + "\n", + "Now, we can simulate the TSA unit model by solving a square problem. For this, we need to set up the solver by using the Pyomo component `SolverFactory`. We will be using the solver and solver options defined during the initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'fs.tsa.cooling.scaling_factor' that\n", + "contains 4 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix 'fs.tsa.heating.scaling_factor' that\n", + "contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix 'fs.tsa.scaling_factor' that contains 12\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", + "tol=1e-06\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 19132\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 70375\n", + "\n", + "Total number of variables............................: 2815\n", + " variables with only lower bounds: 5\n", + " variables with lower and upper bounds: 605\n", + " variables with only upper bounds: 1\n", + "Total number of equality constraints.................: 2815\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.63e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.06e+00 3.00e+03 -1.0 4.96e+00 - 9.90e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 4.90e-03 4.83e+03 -1.0 4.91e+00 - 9.90e-01 9.96e-01h 1\n", + " 3 0.0000000e+00 2.44e-07 4.53e+00 -1.0 2.25e-02 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.0710209608078003e-07 2.4400780240796394e-07\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.0710209608078003e-07 2.4400780240796394e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 3.208\n", + "Total CPU secs in NLP function evaluations = 0.089\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# set up solver to solve flowsheet\n", + "solver = SolverFactory(\"ipopt\")\n", + "solver.options = solver_options\n", + "\n", + "# solve flowsheet\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Viewing the Simulation Results\n", + "\n", + "We will call some utility methods defined in the TSA unit model to get displayed some key variables." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Summary - tsa\n", + "------------------------------------------------------------------------------------ Value \n", + " Adsorption temperature [K] 310.00\n", + " Desorption temperature [K] 430.00\n", + " Heating temperature [K] 440.00\n", + " Cooling temperature [K] 300.00\n", + " Column diameter [m] 0.030000\n", + " Column length [m] 1.2000\n", + " Column volume [m3] 0.00084823\n", + " CO2 mole fraction at feed [%] 12.000\n", + " Feed flow rate [mol/s] 0.0096000\n", + " Feed velocity [m/s] 0.50008\n", + " Minimum fluidization velocity [m/s] 1.5207\n", + " Time of heating step [h] 0.37030\n", + " Time of cooling step [h] 0.20826\n", + " Time of pressurization step [h] 0.0051098\n", + " Time of adsorption step [h] 0.25221\n", + " Cycle time [h] 0.83588\n", + " Purity [-] 0.90219\n", + " Recovery [-] 0.89873\n", + " Productivity [kg CO2/ton/h] 84.085\n", + " Specific energy [MJ/kg CO2] 3.6532\n", + " Heat duty per bed [MW] 5.1244e-05\n", + " Heat duty total [MW] 0.00016646\n", + " Pressure drop [Pa] 5263.6\n", + " Number of beds 3.2484\n", + " CO2 captured in one cycle per bed [kg/cycle] 0.042210\n", + " Cycles per year 10480.\n", + " Total CO2 captured per year [tonne/year] 1.4369\n", + " Amount of flue gas processed per year [Gmol/year] 0.00030275\n", + " Amount of flue gas processed per year (target) [Gmol/year] 0.00030275\n", + " Amount of CO2 to atmosphere [mol/s] 0.00011667\n", + " Concentration of CO2 emitted to atmosphere [ppm] 13803.\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "# summary tsa\n", + "tsa_summary(m.fs.tsa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Plotting Profiles\n", + "\n", + "Call plots method in the FixedBedTSA0D model to generate profiles of temperature, pressure and $\\mathrm{CO_{2}}$ concentration at the outlet of the column." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# profiles\n", + "plot_tsa_profiles(m.fs.tsa)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# profiles\n", - "plot_tsa_profiles(m.fs.tsa)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb index a9b7b17b..d926bdd0 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_doc.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_doc.ipynb index 863667af..9d534d71 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_doc.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -727,15 +728,26 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: You're using the deprecated parmest interface\n", + "(model_function, data, theta_names). This interface will be removed in a\n", + "future release, please update to the new parmest interface using experiment\n", + "lists. (deprecated in 6.7.2) (called from /home/dang/miniforge3/envs/idaes_ex\n", + "amples_py3.11/lib/python3.11/functools.py:946)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_11124\\1110609025.py:44: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + "/tmp/ipykernel_291399/1110609025.py:44: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_11124\\426137296.py:7: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + "/tmp/ipykernel_291399/426137296.py:7: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " float(data[\"vap_benzene\"])\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_11124\\426137296.py:10: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", + "/tmp/ipykernel_291399/426137296.py:10: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " float(data[\"liq_benzene\"])\n" ] }, @@ -797,11 +809,11 @@ "Number of Iterations....: 11\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 5.0749685783046434e+00 5.0749685783046434e+00\n", - "Dual infeasibility......: 2.1827409324437497e-10 2.1827409324437497e-10\n", + "Objective...............: 5.0749685783046621e+00 5.0749685783046621e+00\n", + "Dual infeasibility......: 2.1827345486613581e-10 2.1827345486613581e-10\n", "Constraint violation....: 1.6625508263665860e-10 4.7832145355641842e-08\n", - "Complementarity.........: 2.5076274461651402e-09 2.5076274461651402e-09\n", - "Overall NLP error.......: 2.5076274461651402e-09 4.7832145355641842e-08\n", + "Complementarity.........: 2.5076274461738773e-09 2.5076274461738773e-09\n", + "Overall NLP error.......: 2.5076274461738773e-09 4.7832145355641842e-08\n", "\n", "\n", "Number of objective function evaluations = 12\n", @@ -811,8 +823,8 @@ "Number of equality constraint Jacobian evaluations = 12\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 11\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", - "Total CPU secs in NLP function evaluations = 0.010\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.014\n", + "Total CPU secs in NLP function evaluations = 0.006\n", "\n", "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" @@ -849,8 +861,8 @@ "The SSE at the optimal solution is 0.000507\n", "\n", "The values for the parameters are as follows:\n", - "fs.properties.tau[benzene,toluene] = -0.8987624036283798\n", - "fs.properties.tau[toluene,benzene] = 1.4104861099366137\n" + "fs.properties.tau[benzene,toluene] = -0.8987624036283826\n", + "fs.properties.tau[toluene,benzene] = 1.4104861099366197\n" ] } ], @@ -918,7 +930,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_exercise.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_exercise.ipynb index 23756bea..8fd0a5e7 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_exercise.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_exercise.ipynb @@ -1,417 +1,417 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameter Estimation Using the NRTL State Block\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", - "\n", - "We will complete the following tasks:\n", - "* Set up a method to return an initialized model\n", - "* Set up the parameter estimation problem using `parmest`\n", - "* Analyze the results\n", - "* Demonstrate advanced features using `parmest`\n", - "\n", - "## Key links to documentation:\n", - "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import ConcreteModel from pyomo.environ\n", - "\n", - "# Todo: import FlowsheetBlock from idaes.core" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import the parameter block used in this module and the idaes logger. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pyomo.contrib.parmest.parmest as parmest\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up an initialized model\n", - "\n", - "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "def NRTL_model(data):\n", - "\n", - " # Todo: Create a ConcreteModel object\n", - "\n", - " # Todo: Create FlowsheetBlock object\n", - "\n", - " # Todo: Create a properties parameter object with the following options:\n", - " # \"valid_phase\": ('Liq', 'Vap')\n", - " # \"activity_coeff_model\": 'NRTL'\n", - "\n", - " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", - "\n", - " # Fix the state variables on the state block\n", - " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", - " m.fs.state_block.flow_mol.fix(1)\n", - " m.fs.state_block.temperature.fix(368)\n", - " m.fs.state_block.pressure.fix(101325)\n", - " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", - " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", - "\n", - " # Fix NRTL specific parameters.\n", - "\n", - " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", - " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", - " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", - "\n", - " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", - " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", - " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", - "\n", - " # Initialize the flash unit\n", - " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", - "\n", - " # Fix at actual temperature\n", - " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "\n", - " # Set bounds on variables to be estimated\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", - "\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", - "\n", - " # Return initialized flash model\n", - " return m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parameter estimation using parmest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", - "\n", - "* List of variable names to be estimated\n", - "* Dataset\n", - "* Expression to compute the sum of squared errors\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", - "\n", - "* fs.properties.tau['benzene', 'toluene']\n", - "* fs.properties.tau['toluene', 'benzene']\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Create a list of vars to estimate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool supports the following data formats:\n", - "- pandas dataframe\n", - "- list of dictionaries\n", - "- list of json file names.\n", - "\n", - "Please see the documentation for more details. \n", - "\n", - "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Load data from csv\n", - "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", - "\n", - "# Display the dataset\n", - "display(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Complete the following cell by adding an expression to compute the sum of square errors. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create method to return an expression that computes the sum of squared error\n", - "def SSE(m, data):\n", - " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", - " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", - "\n", - " return expr * 1e4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note:\n", - "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", - "# Initialize a parameter estimation object\n", - "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", - "\n", - "# Run parameter estimation using all data\n", - "obj_value, parameters = pest.theta_est()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", - "print()\n", - "print(\"The values for the parameters are as follows:\")\n", - "for k, v in parameters.items():\n", - " print(k, \"=\", v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Advanced options for parmest: bootstrapping\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", - "\n", - "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", - "# plot results along with confidence regions\n", - "\n", - "# Uncomment the following code:\n", - "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", - "# display(bootstrap_theta)" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameter Estimation Using the NRTL State Block\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", + "\n", + "We will complete the following tasks:\n", + "* Set up a method to return an initialized model\n", + "* Set up the parameter estimation problem using `parmest`\n", + "* Analyze the results\n", + "* Demonstrate advanced features using `parmest`\n", + "\n", + "## Key links to documentation:\n", + "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import ConcreteModel from pyomo.environ\n", + "\n", + "# Todo: import FlowsheetBlock from idaes.core" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import the parameter block used in this module and the idaes logger. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pyomo.contrib.parmest.parmest as parmest\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up an initialized model\n", + "\n", + "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "def NRTL_model(data):\n", + "\n", + " # Todo: Create a ConcreteModel object\n", + "\n", + " # Todo: Create FlowsheetBlock object\n", + "\n", + " # Todo: Create a properties parameter object with the following options:\n", + " # \"valid_phase\": ('Liq', 'Vap')\n", + " # \"activity_coeff_model\": 'NRTL'\n", + "\n", + " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", + "\n", + " # Fix the state variables on the state block\n", + " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", + " m.fs.state_block.flow_mol.fix(1)\n", + " m.fs.state_block.temperature.fix(368)\n", + " m.fs.state_block.pressure.fix(101325)\n", + " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", + " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", + "\n", + " # Fix NRTL specific parameters.\n", + "\n", + " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", + " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", + " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", + "\n", + " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", + " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", + " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", + "\n", + " # Initialize the flash unit\n", + " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", + "\n", + " # Fix at actual temperature\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + "\n", + " # Set bounds on variables to be estimated\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", + "\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", + "\n", + " # Return initialized flash model\n", + " return m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter estimation using parmest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", + "\n", + "* List of variable names to be estimated\n", + "* Dataset\n", + "* Expression to compute the sum of squared errors\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", + "\n", + "* fs.properties.tau['benzene', 'toluene']\n", + "* fs.properties.tau['toluene', 'benzene']\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Create a list of vars to estimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool supports the following data formats:\n", + "- pandas dataframe\n", + "- list of dictionaries\n", + "- list of json file names.\n", + "\n", + "Please see the documentation for more details. \n", + "\n", + "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load data from csv\n", + "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", + "\n", + "# Display the dataset\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Complete the following cell by adding an expression to compute the sum of square errors. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create method to return an expression that computes the sum of squared error\n", + "def SSE(m, data):\n", + " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", + " # and vapor phase. For example, the squared error for the vapor phase is:\n", + " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", + "\n", + " return expr * 1e4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note:\n", + "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", + "# Initialize a parameter estimation object\n", + "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", + "\n", + "# Run parameter estimation using all data\n", + "obj_value, parameters = pest.theta_est()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", + "print()\n", + "print(\"The values for the parameters are as follows:\")\n", + "for k, v in parameters.items():\n", + " print(k, \"=\", v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Advanced options for parmest: bootstrapping\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", + "\n", + "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", + "# plot results along with confidence regions\n", + "\n", + "# Uncomment the following code:\n", + "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", + "# display(bootstrap_theta)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_solution.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_solution.ipynb index 5bdce840..5895980e 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_solution.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_solution.ipynb @@ -1,542 +1,542 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameter Estimation Using the NRTL State Block\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", - "\n", - "We will complete the following tasks:\n", - "* Set up a method to return an initialized model\n", - "* Set up the parameter estimation problem using `parmest`\n", - "* Analyze the results\n", - "* Demonstrate advanced features using `parmest`\n", - "\n", - "## Key links to documentation:\n", - "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import ConcreteModel from pyomo.environ\n", - "\n", - "# Todo: import FlowsheetBlock from idaes.core" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import ConcreteModel from pyomo.environ\n", - "from pyomo.environ import ConcreteModel, value\n", - "\n", - "# Todo: import FlowsheetBlock from idaes.core\n", - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import the parameter block used in this module and the idaes logger. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pyomo.contrib.parmest.parmest as parmest\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up an initialized model\n", - "\n", - "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "def NRTL_model(data):\n", - "\n", - " # Todo: Create a ConcreteModel object\n", - "\n", - " # Todo: Create FlowsheetBlock object\n", - "\n", - " # Todo: Create a properties parameter object with the following options:\n", - " # \"valid_phase\": ('Liq', 'Vap')\n", - " # \"activity_coeff_model\": 'NRTL'\n", - "\n", - " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", - "\n", - " # Fix the state variables on the state block\n", - " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", - " m.fs.state_block.flow_mol.fix(1)\n", - " m.fs.state_block.temperature.fix(368)\n", - " m.fs.state_block.pressure.fix(101325)\n", - " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", - " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", - "\n", - " # Fix NRTL specific parameters.\n", - "\n", - " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", - " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", - " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", - "\n", - " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", - " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", - " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", - "\n", - " # Initialize the flash unit\n", - " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", - "\n", - " # Fix at actual temperature\n", - " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "\n", - " # Set bounds on variables to be estimated\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", - "\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", - "\n", - " # Return initialized flash model\n", - " return m" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "def NRTL_model(data):\n", - "\n", - " # Todo: Create a ConcreteModel object\n", - " m = ConcreteModel()\n", - "\n", - " # Todo: Create FlowsheetBlock object\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Todo: Create a properties parameter object with the following options:\n", - " # \"valid_phase\": ('Liq', 'Vap')\n", - " # \"activity_coeff_model\": 'NRTL'\n", - " m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"NRTL\"\n", - " )\n", - " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", - "\n", - " # Fix the state variables on the state block\n", - " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", - "\n", - " m.fs.state_block.flow_mol.fix(1)\n", - " m.fs.state_block.temperature.fix(368)\n", - " m.fs.state_block.pressure.fix(101325)\n", - " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", - " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", - "\n", - " # Fix NRTL specific parameters.\n", - "\n", - " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", - " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", - " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", - "\n", - " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", - " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", - " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", - "\n", - " # Initialize the flash unit\n", - " m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n", - "\n", - " # Fix at actual temperature\n", - " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "\n", - " # Set bounds on variables to be estimated\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", - "\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", - "\n", - " # Return initialized flash model\n", - " return m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parameter estimation using parmest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", - "\n", - "* List of variable names to be estimated\n", - "* Dataset\n", - "* Expression to compute the sum of squared errors\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", - "\n", - "* fs.properties.tau['benzene', 'toluene']\n", - "* fs.properties.tau['toluene', 'benzene']\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Create a list of vars to estimate" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Create a list of vars to estimate\n", - "variable_name = [\n", - " \"fs.properties.tau['benzene', 'toluene']\",\n", - " \"fs.properties.tau['toluene', 'benzene']\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool supports the following data formats:\n", - "- pandas dataframe\n", - "- list of dictionaries\n", - "- list of json file names.\n", - "\n", - "Please see the documentation for more details. \n", - "\n", - "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Load data from csv\n", - "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", - "\n", - "# Display the dataset\n", - "display(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Complete the following cell by adding an expression to compute the sum of square errors. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create method to return an expression that computes the sum of squared error\n", - "def SSE(m, data):\n", - " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", - " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", - "\n", - " return expr * 1e4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create method to return an expression that computes the sum of squared error\n", - "def SSE(m, data):\n", - " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", - " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", - " expr = (\n", - " float(data[\"vap_benzene\"])\n", - " - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n", - " ) ** 2 + (\n", - " float(data[\"liq_benzene\"])\n", - " - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n", - " ) ** 2\n", - " return expr * 1e4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note:\n", - "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", - "# Initialize a parameter estimation object\n", - "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", - "\n", - "# Run parameter estimation using all data\n", - "obj_value, parameters = pest.theta_est()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", - "print()\n", - "print(\"The values for the parameters are as follows:\")\n", - "for k, v in parameters.items():\n", - " print(k, \"=\", v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Advanced options for parmest: bootstrapping\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", - "\n", - "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", - "# plot results along with confidence regions\n", - "\n", - "# Uncomment the following code:\n", - "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", - "# display(bootstrap_theta)" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameter Estimation Using the NRTL State Block\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", + "\n", + "We will complete the following tasks:\n", + "* Set up a method to return an initialized model\n", + "* Set up the parameter estimation problem using `parmest`\n", + "* Analyze the results\n", + "* Demonstrate advanced features using `parmest`\n", + "\n", + "## Key links to documentation:\n", + "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import ConcreteModel from pyomo.environ\n", + "\n", + "# Todo: import FlowsheetBlock from idaes.core" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import ConcreteModel from pyomo.environ\n", + "from pyomo.environ import ConcreteModel, value\n", + "\n", + "# Todo: import FlowsheetBlock from idaes.core\n", + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import the parameter block used in this module and the idaes logger. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pyomo.contrib.parmest.parmest as parmest\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up an initialized model\n", + "\n", + "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "def NRTL_model(data):\n", + "\n", + " # Todo: Create a ConcreteModel object\n", + "\n", + " # Todo: Create FlowsheetBlock object\n", + "\n", + " # Todo: Create a properties parameter object with the following options:\n", + " # \"valid_phase\": ('Liq', 'Vap')\n", + " # \"activity_coeff_model\": 'NRTL'\n", + "\n", + " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", + "\n", + " # Fix the state variables on the state block\n", + " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", + " m.fs.state_block.flow_mol.fix(1)\n", + " m.fs.state_block.temperature.fix(368)\n", + " m.fs.state_block.pressure.fix(101325)\n", + " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", + " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", + "\n", + " # Fix NRTL specific parameters.\n", + "\n", + " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", + " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", + " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", + "\n", + " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", + " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", + " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", + "\n", + " # Initialize the flash unit\n", + " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", + "\n", + " # Fix at actual temperature\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + "\n", + " # Set bounds on variables to be estimated\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", + "\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", + "\n", + " # Return initialized flash model\n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "def NRTL_model(data):\n", + "\n", + " # Todo: Create a ConcreteModel object\n", + " m = ConcreteModel()\n", + "\n", + " # Todo: Create FlowsheetBlock object\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Todo: Create a properties parameter object with the following options:\n", + " # \"valid_phase\": ('Liq', 'Vap')\n", + " # \"activity_coeff_model\": 'NRTL'\n", + " m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"NRTL\"\n", + " )\n", + " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", + "\n", + " # Fix the state variables on the state block\n", + " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", + "\n", + " m.fs.state_block.flow_mol.fix(1)\n", + " m.fs.state_block.temperature.fix(368)\n", + " m.fs.state_block.pressure.fix(101325)\n", + " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", + " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", + "\n", + " # Fix NRTL specific parameters.\n", + "\n", + " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", + " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", + " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", + "\n", + " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", + " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", + " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", + "\n", + " # Initialize the flash unit\n", + " m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n", + "\n", + " # Fix at actual temperature\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + "\n", + " # Set bounds on variables to be estimated\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", + "\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", + "\n", + " # Return initialized flash model\n", + " return m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter estimation using parmest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", + "\n", + "* List of variable names to be estimated\n", + "* Dataset\n", + "* Expression to compute the sum of squared errors\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", + "\n", + "* fs.properties.tau['benzene', 'toluene']\n", + "* fs.properties.tau['toluene', 'benzene']\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Create a list of vars to estimate" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Create a list of vars to estimate\n", + "variable_name = [\n", + " \"fs.properties.tau['benzene', 'toluene']\",\n", + " \"fs.properties.tau['toluene', 'benzene']\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool supports the following data formats:\n", + "- pandas dataframe\n", + "- list of dictionaries\n", + "- list of json file names.\n", + "\n", + "Please see the documentation for more details. \n", + "\n", + "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load data from csv\n", + "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", + "\n", + "# Display the dataset\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Complete the following cell by adding an expression to compute the sum of square errors. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create method to return an expression that computes the sum of squared error\n", + "def SSE(m, data):\n", + " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", + " # and vapor phase. For example, the squared error for the vapor phase is:\n", + " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", + "\n", + " return expr * 1e4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create method to return an expression that computes the sum of squared error\n", + "def SSE(m, data):\n", + " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", + " # and vapor phase. For example, the squared error for the vapor phase is:\n", + " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", + " expr = (\n", + " float(data[\"vap_benzene\"])\n", + " - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n", + " ) ** 2 + (\n", + " float(data[\"liq_benzene\"])\n", + " - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n", + " ) ** 2\n", + " return expr * 1e4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note:\n", + "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", + "# Initialize a parameter estimation object\n", + "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", + "\n", + "# Run parameter estimation using all data\n", + "obj_value, parameters = pest.theta_est()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", + "print()\n", + "print(\"The values for the parameters are as follows:\")\n", + "for k, v in parameters.items():\n", + " print(k, \"=\", v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Advanced options for parmest: bootstrapping\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", + "\n", + "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", + "# plot results along with confidence regions\n", + "\n", + "# Uncomment the following code:\n", + "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", + "# display(bootstrap_theta)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_test.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_test.ipynb index 7d253335..3de45dd6 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_test.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_test.ipynb @@ -1,487 +1,487 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameter Estimation Using the NRTL State Block\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", - "\n", - "We will complete the following tasks:\n", - "* Set up a method to return an initialized model\n", - "* Set up the parameter estimation problem using `parmest`\n", - "* Analyze the results\n", - "* Demonstrate advanced features using `parmest`\n", - "\n", - "## Key links to documentation:\n", - "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import ConcreteModel from pyomo.environ\n", - "from pyomo.environ import ConcreteModel, value\n", - "\n", - "# Todo: import FlowsheetBlock from idaes.core\n", - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import the parameter block used in this module and the idaes logger. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pyomo.contrib.parmest.parmest as parmest\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up an initialized model\n", - "\n", - "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "def NRTL_model(data):\n", - "\n", - " # Todo: Create a ConcreteModel object\n", - " m = ConcreteModel()\n", - "\n", - " # Todo: Create FlowsheetBlock object\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Todo: Create a properties parameter object with the following options:\n", - " # \"valid_phase\": ('Liq', 'Vap')\n", - " # \"activity_coeff_model\": 'NRTL'\n", - " m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"NRTL\"\n", - " )\n", - " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", - "\n", - " # Fix the state variables on the state block\n", - " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", - "\n", - " m.fs.state_block.flow_mol.fix(1)\n", - " m.fs.state_block.temperature.fix(368)\n", - " m.fs.state_block.pressure.fix(101325)\n", - " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", - " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", - "\n", - " # Fix NRTL specific parameters.\n", - "\n", - " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", - " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", - " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", - "\n", - " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", - " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", - " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", - "\n", - " # Initialize the flash unit\n", - " m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n", - "\n", - " # Fix at actual temperature\n", - " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "\n", - " # Set bounds on variables to be estimated\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", - "\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", - "\n", - " # Return initialized flash model\n", - " return m" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "import pytest\n", - "\n", - "# Testing the initialized model\n", - "test_data = {\"temperature\": 368}\n", - "\n", - "m = NRTL_model(test_data)\n", - "\n", - "# Check that degrees of freedom is 0\n", - "assert degrees_of_freedom(m) == 0\n", - "\n", - "# Check for output values\n", - "assert value(m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]) == pytest.approx(\n", - " 0.389, abs=1e-2\n", - ")\n", - "assert value(m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]) == pytest.approx(\n", - " 0.610, abs=1e-2\n", - ")\n", - "\n", - "assert value(m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"toluene\"]) == pytest.approx(\n", - " 0.610, abs=1e-2\n", - ")\n", - "assert value(m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"toluene\"]) == pytest.approx(\n", - " 0.394, abs=1e-2\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parameter estimation using parmest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", - "\n", - "* List of variable names to be estimated\n", - "* Dataset\n", - "* Expression to compute the sum of squared errors\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", - "\n", - "* fs.properties.tau['benzene', 'toluene']\n", - "* fs.properties.tau['toluene', 'benzene']\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Create a list of vars to estimate\n", - "variable_name = [\n", - " \"fs.properties.tau['benzene', 'toluene']\",\n", - " \"fs.properties.tau['toluene', 'benzene']\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool supports the following data formats:\n", - "- pandas dataframe\n", - "- list of dictionaries\n", - "- list of json file names.\n", - "\n", - "Please see the documentation for more details. \n", - "\n", - "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Load data from csv\n", - "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", - "\n", - "# Display the dataset\n", - "display(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Complete the following cell by adding an expression to compute the sum of square errors. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create method to return an expression that computes the sum of squared error\n", - "def SSE(m, data):\n", - " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", - " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", - " expr = (\n", - " float(data[\"vap_benzene\"])\n", - " - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n", - " ) ** 2 + (\n", - " float(data[\"liq_benzene\"])\n", - " - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n", - " ) ** 2\n", - " return expr * 1e4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note:\n", - "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", - "# Initialize a parameter estimation object\n", - "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", - "\n", - "# Run parameter estimation using all data\n", - "obj_value, parameters = pest.theta_est()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for values of the parameter estimation problem\n", - "assert obj_value == pytest.approx(5.07496, 1e-3)\n", - "assert parameters[\"fs.properties.tau[benzene,toluene]\"] == pytest.approx(-0.89876, 1e-3)\n", - "assert parameters[\"fs.properties.tau[toluene,benzene]\"] == pytest.approx(1.41048, 1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", - "print()\n", - "print(\"The values for the parameters are as follows:\")\n", - "for k, v in parameters.items():\n", - " print(k, \"=\", v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Advanced options for parmest: bootstrapping\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", - "\n", - "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", - "# plot results along with confidence regions\n", - "\n", - "# Uncomment the following code:\n", - "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", - "# display(bootstrap_theta)" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameter Estimation Using the NRTL State Block\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", + "\n", + "We will complete the following tasks:\n", + "* Set up a method to return an initialized model\n", + "* Set up the parameter estimation problem using `parmest`\n", + "* Analyze the results\n", + "* Demonstrate advanced features using `parmest`\n", + "\n", + "## Key links to documentation:\n", + "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import ConcreteModel from pyomo.environ\n", + "from pyomo.environ import ConcreteModel, value\n", + "\n", + "# Todo: import FlowsheetBlock from idaes.core\n", + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import the parameter block used in this module and the idaes logger. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pyomo.contrib.parmest.parmest as parmest\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up an initialized model\n", + "\n", + "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "def NRTL_model(data):\n", + "\n", + " # Todo: Create a ConcreteModel object\n", + " m = ConcreteModel()\n", + "\n", + " # Todo: Create FlowsheetBlock object\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Todo: Create a properties parameter object with the following options:\n", + " # \"valid_phase\": ('Liq', 'Vap')\n", + " # \"activity_coeff_model\": 'NRTL'\n", + " m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"NRTL\"\n", + " )\n", + " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", + "\n", + " # Fix the state variables on the state block\n", + " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", + "\n", + " m.fs.state_block.flow_mol.fix(1)\n", + " m.fs.state_block.temperature.fix(368)\n", + " m.fs.state_block.pressure.fix(101325)\n", + " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", + " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", + "\n", + " # Fix NRTL specific parameters.\n", + "\n", + " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", + " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", + " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", + "\n", + " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", + " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", + " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", + "\n", + " # Initialize the flash unit\n", + " m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n", + "\n", + " # Fix at actual temperature\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + "\n", + " # Set bounds on variables to be estimated\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", + "\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", + "\n", + " # Return initialized flash model\n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import pytest\n", + "\n", + "# Testing the initialized model\n", + "test_data = {\"temperature\": 368}\n", + "\n", + "m = NRTL_model(test_data)\n", + "\n", + "# Check that degrees of freedom is 0\n", + "assert degrees_of_freedom(m) == 0\n", + "\n", + "# Check for output values\n", + "assert value(m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]) == pytest.approx(\n", + " 0.389, abs=1e-2\n", + ")\n", + "assert value(m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]) == pytest.approx(\n", + " 0.610, abs=1e-2\n", + ")\n", + "\n", + "assert value(m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"toluene\"]) == pytest.approx(\n", + " 0.610, abs=1e-2\n", + ")\n", + "assert value(m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"toluene\"]) == pytest.approx(\n", + " 0.394, abs=1e-2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter estimation using parmest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", + "\n", + "* List of variable names to be estimated\n", + "* Dataset\n", + "* Expression to compute the sum of squared errors\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", + "\n", + "* fs.properties.tau['benzene', 'toluene']\n", + "* fs.properties.tau['toluene', 'benzene']\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Create a list of vars to estimate\n", + "variable_name = [\n", + " \"fs.properties.tau['benzene', 'toluene']\",\n", + " \"fs.properties.tau['toluene', 'benzene']\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool supports the following data formats:\n", + "- pandas dataframe\n", + "- list of dictionaries\n", + "- list of json file names.\n", + "\n", + "Please see the documentation for more details. \n", + "\n", + "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load data from csv\n", + "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", + "\n", + "# Display the dataset\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Complete the following cell by adding an expression to compute the sum of square errors. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create method to return an expression that computes the sum of squared error\n", + "def SSE(m, data):\n", + " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", + " # and vapor phase. For example, the squared error for the vapor phase is:\n", + " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", + " expr = (\n", + " float(data[\"vap_benzene\"])\n", + " - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n", + " ) ** 2 + (\n", + " float(data[\"liq_benzene\"])\n", + " - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n", + " ) ** 2\n", + " return expr * 1e4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note:\n", + "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", + "# Initialize a parameter estimation object\n", + "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", + "\n", + "# Run parameter estimation using all data\n", + "obj_value, parameters = pest.theta_est()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for values of the parameter estimation problem\n", + "assert obj_value == pytest.approx(5.07496, 1e-3)\n", + "assert parameters[\"fs.properties.tau[benzene,toluene]\"] == pytest.approx(-0.89876, 1e-3)\n", + "assert parameters[\"fs.properties.tau[toluene,benzene]\"] == pytest.approx(1.41048, 1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", + "print()\n", + "print(\"The values for the parameters are as follows:\")\n", + "for k, v in parameters.items():\n", + " print(k, \"=\", v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Advanced options for parmest: bootstrapping\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", + "\n", + "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", + "# plot results along with confidence regions\n", + "\n", + "# Uncomment the following code:\n", + "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", + "# display(bootstrap_theta)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_usr.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_usr.ipynb index 5bdce840..5895980e 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_usr.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_state_block_usr.ipynb @@ -1,542 +1,542 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameter Estimation Using the NRTL State Block\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", - "\n", - "We will complete the following tasks:\n", - "* Set up a method to return an initialized model\n", - "* Set up the parameter estimation problem using `parmest`\n", - "* Analyze the results\n", - "* Demonstrate advanced features using `parmest`\n", - "\n", - "## Key links to documentation:\n", - "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import ConcreteModel from pyomo.environ\n", - "\n", - "# Todo: import FlowsheetBlock from idaes.core" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import ConcreteModel from pyomo.environ\n", - "from pyomo.environ import ConcreteModel, value\n", - "\n", - "# Todo: import FlowsheetBlock from idaes.core\n", - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import the parameter block used in this module and the idaes logger. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pyomo.contrib.parmest.parmest as parmest\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up an initialized model\n", - "\n", - "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "def NRTL_model(data):\n", - "\n", - " # Todo: Create a ConcreteModel object\n", - "\n", - " # Todo: Create FlowsheetBlock object\n", - "\n", - " # Todo: Create a properties parameter object with the following options:\n", - " # \"valid_phase\": ('Liq', 'Vap')\n", - " # \"activity_coeff_model\": 'NRTL'\n", - "\n", - " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", - "\n", - " # Fix the state variables on the state block\n", - " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", - " m.fs.state_block.flow_mol.fix(1)\n", - " m.fs.state_block.temperature.fix(368)\n", - " m.fs.state_block.pressure.fix(101325)\n", - " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", - " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", - "\n", - " # Fix NRTL specific parameters.\n", - "\n", - " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", - " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", - " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", - "\n", - " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", - " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", - " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", - "\n", - " # Initialize the flash unit\n", - " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", - "\n", - " # Fix at actual temperature\n", - " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "\n", - " # Set bounds on variables to be estimated\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", - "\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", - "\n", - " # Return initialized flash model\n", - " return m" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "def NRTL_model(data):\n", - "\n", - " # Todo: Create a ConcreteModel object\n", - " m = ConcreteModel()\n", - "\n", - " # Todo: Create FlowsheetBlock object\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Todo: Create a properties parameter object with the following options:\n", - " # \"valid_phase\": ('Liq', 'Vap')\n", - " # \"activity_coeff_model\": 'NRTL'\n", - " m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"NRTL\"\n", - " )\n", - " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", - "\n", - " # Fix the state variables on the state block\n", - " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", - "\n", - " m.fs.state_block.flow_mol.fix(1)\n", - " m.fs.state_block.temperature.fix(368)\n", - " m.fs.state_block.pressure.fix(101325)\n", - " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", - " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", - "\n", - " # Fix NRTL specific parameters.\n", - "\n", - " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", - " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", - " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", - "\n", - " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", - " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", - " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", - "\n", - " # Initialize the flash unit\n", - " m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n", - "\n", - " # Fix at actual temperature\n", - " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", - "\n", - " # Set bounds on variables to be estimated\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", - " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", - "\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", - " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", - "\n", - " # Return initialized flash model\n", - " return m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parameter estimation using parmest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", - "\n", - "* List of variable names to be estimated\n", - "* Dataset\n", - "* Expression to compute the sum of squared errors\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", - "\n", - "* fs.properties.tau['benzene', 'toluene']\n", - "* fs.properties.tau['toluene', 'benzene']\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Create a list of vars to estimate" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Create a list of vars to estimate\n", - "variable_name = [\n", - " \"fs.properties.tau['benzene', 'toluene']\",\n", - " \"fs.properties.tau['toluene', 'benzene']\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool supports the following data formats:\n", - "- pandas dataframe\n", - "- list of dictionaries\n", - "- list of json file names.\n", - "\n", - "Please see the documentation for more details. \n", - "\n", - "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Load data from csv\n", - "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", - "\n", - "# Display the dataset\n", - "display(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Complete the following cell by adding an expression to compute the sum of square errors. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create method to return an expression that computes the sum of squared error\n", - "def SSE(m, data):\n", - " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", - " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", - "\n", - " return expr * 1e4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create method to return an expression that computes the sum of squared error\n", - "def SSE(m, data):\n", - " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", - " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", - " expr = (\n", - " float(data[\"vap_benzene\"])\n", - " - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n", - " ) ** 2 + (\n", - " float(data[\"liq_benzene\"])\n", - " - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n", - " ) ** 2\n", - " return expr * 1e4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note:\n", - "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "\n", - "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", - "# Initialize a parameter estimation object\n", - "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", - "\n", - "# Run parameter estimation using all data\n", - "obj_value, parameters = pest.theta_est()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", - "print()\n", - "print(\"The values for the parameters are as follows:\")\n", - "for k, v in parameters.items():\n", - " print(k, \"=\", v)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Advanced options for parmest: bootstrapping\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", - "\n", - "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", - "# plot results along with confidence regions\n", - "\n", - "# Uncomment the following code:\n", - "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", - "# display(bootstrap_theta)" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameter Estimation Using the NRTL State Block\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we use Pyomo's `parmest` tool in conjunction with IDAES models for parameter estimation. We demonstrate these tools by estimating the parameters associated with the NRTL property model for a benzene-toluene mixture. The NRTL model has 2 sets of parameters: the non-randomness parameter (`alpha_ij`) and the binary interaction parameter (`tau_ij`), where `i` and `j` are the pure component species. In this example, we only estimate the binary interaction parameter (`tau_ij`) for a given dataset. When estimating parameters associated with the property package, IDAES provides the flexibility of doing the parameter estimation by just using the state block or by using a unit model with a specified property package. This module will demonstrate parameter estimation by using only the state block. \n", + "\n", + "We will complete the following tasks:\n", + "* Set up a method to return an initialized model\n", + "* Set up the parameter estimation problem using `parmest`\n", + "* Analyze the results\n", + "* Demonstrate advanced features using `parmest`\n", + "\n", + "## Key links to documentation:\n", + "* NRTL Model - https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "* parmest - https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/index.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "import `ConcreteModel` from Pyomo and `FlowsheetBlock` from IDAES. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import ConcreteModel from pyomo.environ\n", + "\n", + "# Todo: import FlowsheetBlock from idaes.core" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import ConcreteModel from pyomo.environ\n", + "from pyomo.environ import ConcreteModel, value\n", + "\n", + "# Todo: import FlowsheetBlock from idaes.core\n", + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import the parameter block used in this module and the idaes logger. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we import `parmest` from Pyomo and the `pandas` package. We need `pandas` as `parmest` uses `pandas.dataframe` for handling the input data and the results." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pyomo.contrib.parmest.parmest as parmest\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up an initialized model\n", + "\n", + "We need to provide a method that returns an initialized model to the `parmest` tool in Pyomo." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Using what you have learned from previous modules, fill in the missing code below to return an initialized IDAES model. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "def NRTL_model(data):\n", + "\n", + " # Todo: Create a ConcreteModel object\n", + "\n", + " # Todo: Create FlowsheetBlock object\n", + "\n", + " # Todo: Create a properties parameter object with the following options:\n", + " # \"valid_phase\": ('Liq', 'Vap')\n", + " # \"activity_coeff_model\": 'NRTL'\n", + "\n", + " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", + "\n", + " # Fix the state variables on the state block\n", + " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", + " m.fs.state_block.flow_mol.fix(1)\n", + " m.fs.state_block.temperature.fix(368)\n", + " m.fs.state_block.pressure.fix(101325)\n", + " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", + " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", + "\n", + " # Fix NRTL specific parameters.\n", + "\n", + " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", + " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", + " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", + "\n", + " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", + " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", + " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", + "\n", + " # Initialize the flash unit\n", + " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", + "\n", + " # Fix at actual temperature\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + "\n", + " # Set bounds on variables to be estimated\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", + "\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", + "\n", + " # Return initialized flash model\n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "def NRTL_model(data):\n", + "\n", + " # Todo: Create a ConcreteModel object\n", + " m = ConcreteModel()\n", + "\n", + " # Todo: Create FlowsheetBlock object\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Todo: Create a properties parameter object with the following options:\n", + " # \"valid_phase\": ('Liq', 'Vap')\n", + " # \"activity_coeff_model\": 'NRTL'\n", + " m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"NRTL\"\n", + " )\n", + " m.fs.state_block = m.fs.properties.build_state_block(defined_state=True)\n", + "\n", + " # Fix the state variables on the state block\n", + " # hint: state variables exist on the state block i.e. on m.fs.state_block\n", + "\n", + " m.fs.state_block.flow_mol.fix(1)\n", + " m.fs.state_block.temperature.fix(368)\n", + " m.fs.state_block.pressure.fix(101325)\n", + " m.fs.state_block.mole_frac_comp[\"benzene\"].fix(0.5)\n", + " m.fs.state_block.mole_frac_comp[\"toluene\"].fix(0.5)\n", + "\n", + " # Fix NRTL specific parameters.\n", + "\n", + " # non-randomness parameter - alpha_ij (set at 0.3, 0 if i=j)\n", + " m.fs.properties.alpha[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.alpha[\"benzene\", \"toluene\"].fix(0.3)\n", + " m.fs.properties.alpha[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.alpha[\"toluene\", \"benzene\"].fix(0.3)\n", + "\n", + " # binary interaction parameter - tau_ij (0 if i=j, else to be estimated later but fixing to initialize)\n", + " m.fs.properties.tau[\"benzene\", \"benzene\"].fix(0)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].fix(-0.9)\n", + " m.fs.properties.tau[\"toluene\", \"toluene\"].fix(0)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].fix(1.4)\n", + "\n", + " # Initialize the flash unit\n", + " m.fs.state_block.initialize(outlvl=idaeslog.INFO_LOW)\n", + "\n", + " # Fix at actual temperature\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + "\n", + " # Set bounds on variables to be estimated\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", + " m.fs.properties.tau[\"benzene\", \"toluene\"].setub(5)\n", + "\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setlb(-5)\n", + " m.fs.properties.tau[\"toluene\", \"benzene\"].setub(5)\n", + "\n", + " # Return initialized flash model\n", + " return m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter estimation using parmest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to providing a method to return an initialized model, the `parmest` tool needs the following:\n", + "\n", + "* List of variable names to be estimated\n", + "* Dataset\n", + "* Expression to compute the sum of squared errors\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we only estimate the binary interaction parameter (`tau_ij`). Given that this variable is usually indexed as `tau_ij = Var(component_list, component_list)`, there are 2*2=4 degrees of freedom. However, when i=j, the binary interaction parameter is 0. Therefore, in this problem, we estimate the binary interaction parameter for the following variables only:\n", + "\n", + "* fs.properties.tau['benzene', 'toluene']\n", + "* fs.properties.tau['toluene', 'benzene']\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Create a list called `variable_name` with the above-mentioned variables declared as strings.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Create a list of vars to estimate" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Create a list of vars to estimate\n", + "variable_name = [\n", + " \"fs.properties.tau['benzene', 'toluene']\",\n", + " \"fs.properties.tau['toluene', 'benzene']\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool supports the following data formats:\n", + "- pandas dataframe\n", + "- list of dictionaries\n", + "- list of json file names.\n", + "\n", + "Please see the documentation for more details. \n", + "\n", + "For this example, we load data from the csv file `BT_NRTL_dataset.csv`. The dataset consists of fifty data points which provide the mole fraction of benzene in the vapor and liquid phase as a function of temperature. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load data from csv\n", + "data = pd.read_csv(\"BT_NRTL_dataset.csv\")\n", + "\n", + "# Display the dataset\n", + "display(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to provide a method to return an expression to compute the sum of squared errors that will be used as the objective in solving the parameter estimation problem. For this problem, the error will be computed for the mole fraction of benzene in the vapor and liquid phase between the model prediction and data. \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Complete the following cell by adding an expression to compute the sum of square errors. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create method to return an expression that computes the sum of squared error\n", + "def SSE(m, data):\n", + " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", + " # and vapor phase. For example, the squared error for the vapor phase is:\n", + " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", + "\n", + " return expr * 1e4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create method to return an expression that computes the sum of squared error\n", + "def SSE(m, data):\n", + " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", + " # and vapor phase. For example, the squared error for the vapor phase is:\n", + " # (float(data[\"vap_benzene\"]) - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"])**2\n", + " expr = (\n", + " float(data[\"vap_benzene\"])\n", + " - m.fs.state_block.mole_frac_phase_comp[\"Vap\", \"benzene\"]\n", + " ) ** 2 + (\n", + " float(data[\"liq_benzene\"])\n", + " - m.fs.state_block.mole_frac_phase_comp[\"Liq\", \"benzene\"]\n", + " ) ** 2\n", + " return expr * 1e4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note:\n", + "Notice that we have scaled the expression up by a factor of 10000 as the SSE computed here will be an extremely small number given that we are using the difference in mole fraction in our expression. This will help in using a well-scaled objective to improve solve robustness when using IPOPT. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to set up the parameter estimation problem. We will create a parameter estimation object called `pest`. As shown below, we pass the method that returns an initialized model, data, variable_name, and the SSE expression to the Estimator method. `tee=True` will print the solver output after solving the parameter estimation problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "idaeslog.getIdaesLogger(\"core.property_meta\").setLevel(logging.ERROR)\n", + "# Initialize a parameter estimation object\n", + "pest = parmest.Estimator(NRTL_model, data, variable_name, SSE, tee=True)\n", + "\n", + "# Run parameter estimation using all data\n", + "obj_value, parameters = pest.theta_est()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will notice that the resulting parameter estimation problem will have 1102 variables and 1100 constraints. Let us display the results by running the next cell. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"The SSE at the optimal solution is %0.6f\" % (obj_value * 1e-4))\n", + "print()\n", + "print(\"The values for the parameters are as follows:\")\n", + "for k, v in parameters.items():\n", + " print(k, \"=\", v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the data that was provided, we have estimated the binary interaction parameters in the NRTL model for a benzene-toluene mixture. Although the dataset that was provided was temperature dependent, in this example we have estimated a single value that fits best for all temperatures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Advanced options for parmest: bootstrapping\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyomo's `parmest` tool allows for bootstrapping where the parameter estimation is repeated over `n` samples with resampling from the original data set. Parameter estimation with bootstrap resampling can be used to identify confidence regions around each parameter estimate. This analysis can be slow given the increased number of model instances that need to be solved. Please refer to https://pyomo.readthedocs.io/en/stable/contributed_packages/parmest/driver.html for more details. \n", + "\n", + "For the example above, the bootstrapping can be run by uncommenting the code in the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run parameter estimation using bootstrap resample of the data (10 samples),\n", + "# plot results along with confidence regions\n", + "\n", + "# Uncomment the following code:\n", + "# bootstrap_theta = pest.theta_est_bootstrap(4)\n", + "# display(bootstrap_theta)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb index 495b6540..4cd9f8c5 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_doc.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_doc.ipynb index edb05ee6..294ed3ea 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_doc.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -187,7 +188,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.flash.inlet.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -695,9 +701,11 @@ " # and vapor phase. For example, the squared error for the vapor phase is:\n", " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", " expr = (\n", - " float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"vap_benzene\"])\n", + " - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2 + (\n", - " float(data[\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"liq_benzene\"])\n", + " - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2\n", " return expr * 1e4" ] @@ -725,15 +733,14 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_36352\\1862275024.py:45: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_36352\\2860104238.py:7: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_36352\\2860104238.py:9: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " float(data[\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n" + "WARNING: DEPRECATED: You're using the deprecated parmest interface\n", + "(model_function, data, theta_names). This interface will be removed in a\n", + "future release, please update to the new parmest interface using experiment\n", + "lists. (deprecated in 6.7.2) (called from /home/dang/miniforge3/envs/idaes_ex\n", + "amples_py3.11/lib/python3.11/functools.py:946)\n" ] }, { @@ -789,16 +796,16 @@ " 9 5.0749679e+00 5.85e-02 7.21e-04 -3.8 8.43e+00 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 10 5.0749686e+00 5.59e-04 1.05e-05 -5.7 9.63e-01 - 1.00e+00 1.00e+00h 1\n", - " 11 5.0749686e+00 3.98e-08 1.56e-09 -8.6 7.56e-03 - 1.00e+00 1.00e+00h 1\n", + " 11 5.0749686e+00 3.99e-08 1.56e-09 -8.6 7.56e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 11\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 5.0749685783045084e+00 5.0749685783045084e+00\n", - "Dual infeasibility......: 1.5648775501801708e-09 1.5648775501801708e-09\n", - "Constraint violation....: 1.3843631310512158e-10 3.9843143895268440e-08\n", - "Complementarity.........: 2.5074825419922871e-09 2.5074825419922871e-09\n", - "Overall NLP error.......: 2.5074825419922871e-09 3.9843143895268440e-08\n", + "Objective...............: 5.0749685783045351e+00 5.0749685783045351e+00\n", + "Dual infeasibility......: 1.5648777722895738e-09 1.5648777722895738e-09\n", + "Constraint violation....: 1.3853747226705444e-10 3.9872247725725174e-08\n", + "Complementarity.........: 2.5074825420127933e-09 2.5074825420127933e-09\n", + "Overall NLP error.......: 2.5074825420127933e-09 3.9872247725725174e-08\n", "\n", "\n", "Number of objective function evaluations = 12\n", @@ -808,8 +815,8 @@ "Number of equality constraint Jacobian evaluations = 12\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 11\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.053\n", - "Total CPU secs in NLP function evaluations = 0.010\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.047\n", + "Total CPU secs in NLP function evaluations = 0.017\n", "\n", "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" @@ -845,8 +852,8 @@ "The SSE at the optimal solution is 0.000507\n", "\n", "The values for the parameters are as follows:\n", - "fs.properties.tau[benzene,toluene] = -0.8987624039723903\n", - "fs.properties.tau[toluene,benzene] = 1.410486110660486\n" + "fs.properties.tau[benzene,toluene] = -0.8987624039724009\n", + "fs.properties.tau[toluene,benzene] = 1.41048611066051\n" ] } ], @@ -915,9 +922,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_exercise.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_exercise.ipynb index 9ebb2bbd..8edb8894 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_exercise.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_exercise.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -180,7 +181,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.state_block.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -293,7 +299,7 @@ "def SSE(m, data):\n", " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", + " # (float(data.iloc[0][\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", "\n", " return expr * 1e4" ] @@ -407,9 +413,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_solution.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_solution.ipynb index d70ff27d..6864c791 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_solution.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_solution.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -200,7 +201,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.state_block.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -267,7 +273,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.flash.inlet.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -397,7 +408,7 @@ "def SSE(m, data):\n", " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", + " # (float(data.iloc[0][\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", "\n", " return expr * 1e4" ] @@ -418,9 +429,11 @@ " # and vapor phase. For example, the squared error for the vapor phase is:\n", " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", " expr = (\n", - " float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"vap_benzene\"])\n", + " - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2 + (\n", - " float(data[\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"liq_benzene\"])\n", + " - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2\n", " return expr * 1e4" ] @@ -534,9 +547,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_test.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_test.ipynb index 7add77ff..6a00b230 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_test.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -187,7 +188,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.flash.inlet.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -343,9 +349,11 @@ " # and vapor phase. For example, the squared error for the vapor phase is:\n", " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", " expr = (\n", - " float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"vap_benzene\"])\n", + " - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2 + (\n", - " float(data[\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"liq_benzene\"])\n", + " - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2\n", " return expr * 1e4" ] @@ -475,9 +483,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_usr.ipynb b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_usr.ipynb index d70ff27d..6864c791 100644 --- a/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_usr.ipynb +++ b/idaes_examples/notebooks/docs/param_est/parameter_estimation_nrtl_using_unit_model_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -200,7 +201,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.state_block.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.state_block.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -267,7 +273,12 @@ " m.fs.flash.initialize(outlvl=idaeslog.INFO_LOW)\n", "\n", " # Fix at actual temperature\n", - " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.flash.inlet.temperature.fix(float(data[\"temperature\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.flash.inlet.temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", "\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.tau[\"benzene\", \"toluene\"].setlb(-5)\n", @@ -397,7 +408,7 @@ "def SSE(m, data):\n", " # Todo: Add expression for computing the sum of squared errors in mole fraction of benzene in the liquid\n", " # and vapor phase. For example, the squared error for the vapor phase is:\n", - " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", + " # (float(data.iloc[0][\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", "\n", " return expr * 1e4" ] @@ -418,9 +429,11 @@ " # and vapor phase. For example, the squared error for the vapor phase is:\n", " # (float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"])**2\n", " expr = (\n", - " float(data[\"vap_benzene\"]) - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"vap_benzene\"])\n", + " - m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2 + (\n", - " float(data[\"liq_benzene\"]) - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", + " float(data.iloc[0][\"liq_benzene\"])\n", + " - m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]\n", " ) ** 2\n", " return expr * 1e4" ] @@ -534,9 +547,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb index 20fa0c26..f4fd666f 100644 --- a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc.ipynb @@ -17,12 +17,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_doc.ipynb b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_doc.ipynb index 7127641c..60561b67 100644 --- a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_doc.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -139,16 +140,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-07-25 03:12:25 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n", + "2025-03-17 17:34:04 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", "tol=1e-06\n", "max_iter=200\n", "linear_solver=ma57\n", "ma57_pivtol=1e-05\n", "ma57_pivtolmax=0.1\n", - "option_file_name=C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\n", + "option_file_name=/tmp/tmp00x0xxe1_ipopt.opt\n", "\n", - "Using option file \"C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\".\n", + "Using option file \"/tmp/tmp00x0xxe1_ipopt.opt\".\n", "\n", "\n", "******************************************************************************\n", @@ -186,10 +193,22 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Reallocating memory for MA57: lfact (111709)\n", " 1 0.0000000e+00 3.49e-01 1.12e+04 -1.0 3.06e+03 - 9.90e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " 3 0.0000000e+00 2.95e-07 9.98e+02 -1.0 3.74e+01 - 9.90e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 3\n", @@ -197,9 +216,9 @@ " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.9462398742907681e-07 2.9462398742907681e-07\n", + "Constraint violation....: 2.9462262318702415e-07 2.9462262318702415e-07\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9462398742907681e-07 2.9462398742907681e-07\n", + "Overall NLP error.......: 2.9462262318702415e-07 2.9462262318702415e-07\n", "\n", "\n", "Number of objective function evaluations = 4\n", @@ -209,8 +228,8 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.085\n", - "Total CPU secs in NLP function evaluations = 1.396\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.056\n", + "Total CPU secs in NLP function evaluations = 0.537\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2819,7 +2838,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -2828,7 +2847,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2956,9 +2975,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_test.ipynb b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_test.ipynb index 7127641c..32fa7cf7 100644 --- a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_test.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_test.ipynb @@ -1,2964 +1,2965 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NGCC Baseline and Turndown\n", - "Maintainer: Javal Vyas \n", - "Author: John Eslick \n", - "Updated: 2024-07-25 \n", - "\n", - "This notebook runs a series of net electric power outputs from 650 MW to 160 MW (about 100% to 25%) for an NGCC with 97% CO2 capture. The NGCC model is based on the NETL report \"Cost and Performance Baseline for Fossil Energy Plants Volume 1, Bituminous Coal and Natural Gas to Electricity.\" Sept 2019, Case B31B [resource](https://www.osti.gov/servlets/purl/1893822). Another valuable resource for gaining a deeper understanding of the mathematical model would be the publication referenced [here](https://www.sciencedirect.com/science/article/pii/S1750583617302414). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports\n", - "\n", - "Import the modules that will be used." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.core.display import SVG\n", - "import pyomo.environ as pyo\n", - "import idaes\n", - "from idaes.core.solvers import use_idaes_solver_configuration_defaults\n", - "import idaes.core.util.scaling as iscale\n", - "import idaes.core.util as iutil\n", - "from idaes_examples.mod.power_gen import ngcc\n", - "import idaes.logger as idaeslog\n", - "import pytest\n", - "import logging\n", - "\n", - "logging.getLogger(\"pyomo\").setLevel(logging.ERROR)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make Output Directories\n", - "\n", - "This notebook can produce a large number of output files. To make it easier to manage, some subdirectories are used to organize output. This ensures that the directories exist." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def make_directory(path):\n", - " \"\"\"Make a directory if it doesn't exist\"\"\"\n", - " try:\n", - " os.mkdir(path)\n", - " except FileExistsError:\n", - " pass\n", - "\n", - "\n", - "make_directory(\"data\")\n", - "make_directory(\"data_pfds\")\n", - "make_directory(\"data_tabulated\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Global Solver Settings\n", - "\n", - "Use the IDAES configuration system for solver settings. These will apply to all Ipopt instances created, including the ones created in initialization methods." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "use_idaes_solver_configuration_defaults()\n", - "idaes.cfg.ipopt.options.nlp_scaling_method = \"user-scaling\"\n", - "idaes.cfg.ipopt.options.linear_solver = \"ma57\"\n", - "idaes.cfg.ipopt.options.OF_ma57_automatic_scaling = \"yes\"\n", - "idaes.cfg.ipopt.options.ma57_pivtol = 1e-5\n", - "idaes.cfg.ipopt.options.ma57_pivtolmax = 0.1\n", - "solver = pyo.SolverFactory(\"ipopt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the NGCC model\n", - "\n", - "Create the NGCC model and initialize it or read the saved initialization if available. The base initialized NGCC model is configured to match the baseline report with 90% capture using a Cansolv system." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-07-25 03:12:25 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n", - "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", - "tol=1e-06\n", - "max_iter=200\n", - "linear_solver=ma57\n", - "ma57_pivtol=1e-05\n", - "ma57_pivtolmax=0.1\n", - "option_file_name=C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\n", - "\n", - "Using option file \"C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\".\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma57.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7661\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 5948\n", - "\n", - "Total number of variables............................: 2404\n", - " variables with only lower bounds: 87\n", - " variables with lower and upper bounds: 1447\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 2404\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "Reallocating memory for MA57: lfact (111709)\n", - " 1 0.0000000e+00 3.49e-01 1.12e+04 -1.0 3.06e+03 - 9.90e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n", - " 3 0.0000000e+00 2.95e-07 9.98e+02 -1.0 3.74e+01 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.9462398742907681e-07 2.9462398742907681e-07\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9462398742907681e-07 2.9462398742907681e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.085\n", - "Total CPU secs in NLP function evaluations = 1.396\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "m = pyo.ConcreteModel()\n", - "m.fs = ngcc.NgccFlowsheet(dynamic=False)\n", - "iscale.calculate_scaling_factors(m)\n", - "m.fs.initialize(\n", - " load_from=\"ngcc_init.json.gz\",\n", - " save_to=\"ngcc_init.json.gz\",\n", - " outlvl=idaeslog.INFO_HIGH,\n", - ")\n", - "res = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show PFDs with baseline results\n", - "\n", - "This displays PFDs in the notebook, and saves them to files. The full NGCC model is too big to show well in a single PFD, so it is broken into the three main sections, gas turbine, heat recovery steam generator (HRSG), and steam turbine." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NGCC Baseline and Turndown\n", + "Maintainer: Javal Vyas \n", + "Author: John Eslick \n", + "Updated: 2024-07-25 \n", + "\n", + "This notebook runs a series of net electric power outputs from 650 MW to 160 MW (about 100% to 25%) for an NGCC with 97% CO2 capture. The NGCC model is based on the NETL report \"Cost and Performance Baseline for Fossil Energy Plants Volume 1, Bituminous Coal and Natural Gas to Electricity.\" Sept 2019, Case B31B [resource](https://www.osti.gov/servlets/purl/1893822). Another valuable resource for gaining a deeper understanding of the mathematical model would be the publication referenced [here](https://www.sciencedirect.com/science/article/pii/S1750583617302414). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "Import the modules that will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.core.display import SVG\n", + "import pyomo.environ as pyo\n", + "import idaes\n", + "from idaes.core.solvers import use_idaes_solver_configuration_defaults\n", + "import idaes.core.util.scaling as iscale\n", + "import idaes.core.util as iutil\n", + "from idaes_examples.mod.power_gen import ngcc\n", + "import idaes.logger as idaeslog\n", + "import pytest\n", + "import logging\n", + "\n", + "logging.getLogger(\"pyomo\").setLevel(logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make Output Directories\n", + "\n", + "This notebook can produce a large number of output files. To make it easier to manage, some subdirectories are used to organize output. This ensures that the directories exist." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Gas Turbine Section\n", - "\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def make_directory(path):\n", + " \"\"\"Make a directory if it doesn't exist\"\"\"\n", + " try:\n", + " os.mkdir(path)\n", + " except FileExistsError:\n", + " pass\n", + "\n", + "\n", + "make_directory(\"data\")\n", + "make_directory(\"data_pfds\")\n", + "make_directory(\"data_tabulated\")" + ] }, { - "data": { - "image/svg+xml": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " image/svg+xml\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " cmp1\n", - " cmb1\n", - " gts1\n", - " inject1\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " gts2\n", - " \n", - " \n", - " gts3\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " splt1\n", - " mx1\n", - " mx2\n", - " mx3\n", - " Blade Cooling Air\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " NGPreheater\n", - " \n", - " To HRSG\n", - " \n", - " \n", - " \n", - " \n", - " air01\n", - " \n", - " \n", - " air03\n", - " \n", - " \n", - " air02\n", - " \n", - " \n", - " air04\n", - " \n", - " \n", - " fuel01\n", - " \n", - " \n", - " fuel02\n", - " \n", - " \n", - " \n", - " \n", - " st02\n", - " st01\n", - " \n", - " \n", - " air05\n", - " \n", - " \n", - " air06\n", - " \n", - " \n", - " g02\n", - " \n", - " \n", - " g01\n", - " \n", - " \n", - " air09\n", - " \n", - " \n", - " air10\n", - " \n", - " \n", - " air07\n", - " \n", - " \n", - " g03\n", - " \n", - " \n", - " g04\n", - " \n", - " \n", - " g05\n", - " \n", - " \n", - " g06\n", - " \n", - " \n", - " g07\n", - " \n", - " \n", - " g08\n", - " \n", - " \n", - " air08\n", - " \n", - " \n", - " Summary\n", - " total GT power:\n", - " 476.99 MW\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " air01\n", - " \n", - " \n", - " \n", - " air02\n", - " \n", - " \n", - " \n", - " air03\n", - " \n", - " \n", - " \n", - " air04\n", - " \n", - " \n", - " \n", - " fuel01\n", - " \n", - " \n", - " \n", - " fuel02\n", - " \n", - " \n", - " feed_air1\n", - " \n", - " \n", - " st02\n", - " \n", - " \n", - " \n", - " st01\n", - " \n", - " \n", - " \n", - " air05\n", - " \n", - " \n", - " \n", - " air06\n", - " \n", - " \n", - " exhaust_1\n", - " \n", - " \n", - " g01\n", - " \n", - " \n", - " \n", - " g02\n", - " \n", - " \n", - " \n", - " air09\n", - " \n", - " \n", - " \n", - " air10\n", - " \n", - " \n", - " \n", - " air07\n", - " \n", - " \n", - " \n", - " g03\n", - " \n", - " \n", - " \n", - " g04\n", - " \n", - " \n", - " \n", - " g05\n", - " \n", - " \n", - " \n", - " g07\n", - " \n", - " \n", - " \n", - " g06\n", - " \n", - " \n", - " \n", - " g08\n", - " \n", - " \n", - " \n", - " air08\n", - " \n", - " \n", - " \n", - " \n", - " 299.82 K\n", - " 25.946 kg/s\n", - " 31.026 bar\n", - " 1.000%\n", - " 93.100%\n", - " 0.000%\n", - " 1.600%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 3.200%\n", - " yC2H6:\n", - " 0.700%\n", - " yC3H8:\n", - " 0.400%\n", - " yC4H10:\n", - " \n", - " \n", - " \n", - " 335.99 K\n", - " 18.526 kg/s\n", - " 43.355 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 457.27 K\n", - " 18.526 kg/s\n", - " 43.355 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " NG PreharerUses Hot WaterFrom HRSG\n", - " \n", - " \n", - " 448.75 K\n", - " 25.946 kg/s\n", - " 31.026 bar\n", - " 1.000%\n", - " 93.100%\n", - " 0.000%\n", - " 1.600%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 3.200%\n", - " yC2H6:\n", - " 0.700%\n", - " yC3H8:\n", - " 0.400%\n", - " yC4H10:\n", - " \n", - " \n", - " power:\n", - " 481.28 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 84.02%\n", - " isentr. head:\n", - " 367.27 kJ/kg\n", - " inlet vol. flow:\n", - " 883.2 m**3/s\n", - " \n", - " 288.15 K\n", - " 1100.984 kg/s\n", - " 1.034 bar\n", - " 0.030%\n", - " 0.990%\n", - " 0.920%\n", - " 20.740%\n", - " 77.320%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 288.17 K\n", - " 1100.984 kg/s\n", - " 1.099 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.64 K\n", - " 1100.984 kg/s\n", - " 19.226 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.64 K\n", - " 1008.614 kg/s\n", - " 19.226 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 691.89 K\n", - " 1034.561 kg/s\n", - " 19.226 bar\n", - " 0.070%\n", - " 3.842%\n", - " 19.884%\n", - " 74.195%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 0.132%\n", - " yC2H6:\n", - " 0.029%\n", - " yC3H8:\n", - " 0.017%\n", - " yC4H10:\n", - " \n", - " \n", - " \n", - " 1641.38 K\n", - " 1034.691 kg/s\n", - " 18.265 bar\n", - " 4.324%\n", - " 9.217%\n", - " 0.000%\n", - " 11.471%\n", - " 74.106%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 0.000%\n", - " yC2H6:\n", - " 0.000%\n", - " yC3H8:\n", - " 0.000%\n", - " yC4H10:\n", - " 0.881%\n", - " yAr:\n", - " \n", - " \n", - " \n", - " power:\n", - " -374.58 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 88.53%\n", - " isentr. head:\n", - " -408.95 kJ/kg\n", - " inlet vol. flow:\n", - " 273.6 m**3/s\n", - " \n", - " \n", - " \n", - " 898.00 K\n", - " 1127.060 kg/s\n", - " 1.100 bar\n", - " 3.978%\n", - " 8.554%\n", - " 0.884%\n", - " 12.219%\n", - " 74.365%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " 899.61 K\n", - " 1116.809 kg/s\n", - " 1.100 bar\n", - " 4.014%\n", - " 8.622%\n", - " 0.884%\n", - " 12.142%\n", - " 74.339%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1094.58 K\n", - " 1116.809 kg/s\n", - " 2.799 bar\n", - " 4.014%\n", - " 8.622%\n", - " 0.884%\n", - " 12.142%\n", - " 74.339%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.76 K\n", - " 14.769 kg/s\n", - " 2.799 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1099.28 K\n", - " 1102.041 kg/s\n", - " 2.799 bar\n", - " 4.066%\n", - " 8.723%\n", - " 0.883%\n", - " 12.028%\n", - " 74.299%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1329.41 K\n", - " 1102.041 kg/s\n", - " 7.137 bar\n", - " 4.066%\n", - " 8.723%\n", - " 0.883%\n", - " 12.028%\n", - " 74.299%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1365.44 K\n", - " 1034.691 kg/s\n", - " 7.137 bar\n", - " 4.324%\n", - " 9.217%\n", - " 0.881%\n", - " 11.471%\n", - " 74.106%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " power:\n", - " -264.25 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 88.19%\n", - " isentr. head:\n", - " -268.31 kJ/kg\n", - " inlet vol. flow:\n", - " 1280.2 m**3/s\n", - " \n", - " \n", - " \n", - " 709.77 K\n", - " 10.250 kg/s\n", - " 1.100 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 85.0%\n", - " opening:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 85.0%\n", - " opening:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 85.0%\n", - " opening:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.73 K\n", - " 67.350 kg/s\n", - " 7.137 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " power:\n", - " -319.43 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 88.35%\n", - " isentr. head:\n", - " -328.07 kJ/kg\n", - " inlet vol. flow:\n", - " 602.3 m**3/s\n", - " \n", - " \n", - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Global Solver Settings\n", + "\n", + "Use the IDAES configuration system for solver settings. These will apply to all Ipopt instances created, including the ones created in initialization methods." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "use_idaes_solver_configuration_defaults()\n", + "idaes.cfg.ipopt.options.nlp_scaling_method = \"user-scaling\"\n", + "idaes.cfg.ipopt.options.linear_solver = \"ma57\"\n", + "idaes.cfg.ipopt.options.OF_ma57_automatic_scaling = \"yes\"\n", + "idaes.cfg.ipopt.options.ma57_pivtol = 1e-5\n", + "idaes.cfg.ipopt.options.ma57_pivtolmax = 0.1\n", + "solver = pyo.SolverFactory(\"ipopt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the NGCC model\n", + "\n", + "Create the NGCC model and initialize it or read the saved initialization if available. The base initialized NGCC model is configured to match the baseline report with 90% capture using a Cansolv system." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-07-25 03:12:25 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n", + "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", + "tol=1e-06\n", + "max_iter=200\n", + "linear_solver=ma57\n", + "ma57_pivtol=1e-05\n", + "ma57_pivtolmax=0.1\n", + "option_file_name=C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\n", + "\n", + "Using option file \"C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\".\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma57.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7661\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 5948\n", + "\n", + "Total number of variables............................: 2404\n", + " variables with only lower bounds: 87\n", + " variables with lower and upper bounds: 1447\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 2404\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "Reallocating memory for MA57: lfact (111709)\n", + " 1 0.0000000e+00 3.49e-01 1.12e+04 -1.0 3.06e+03 - 9.90e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n", + " 3 0.0000000e+00 2.95e-07 9.98e+02 -1.0 3.74e+01 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 2.9462398742907681e-07 2.9462398742907681e-07\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9462398742907681e-07 2.9462398742907681e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.085\n", + "Total CPU secs in NLP function evaluations = 1.396\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "m = pyo.ConcreteModel()\n", + "m.fs = ngcc.NgccFlowsheet(dynamic=False)\n", + "iscale.calculate_scaling_factors(m)\n", + "m.fs.initialize(\n", + " load_from=\"ngcc_init.json.gz\",\n", + " save_to=\"ngcc_init.json.gz\",\n", + " outlvl=idaeslog.INFO_HIGH,\n", + ")\n", + "res = solver.solve(m, tee=True)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "HRSG Section\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show PFDs with baseline results\n", + "\n", + "This displays PFDs in the notebook, and saves them to files. The full NGCC model is too big to show well in a single PFD, so it is broken into the three main sections, gas turbine, heat recovery steam generator (HRSG), and steam turbine." + ] }, { - "data": { - "image/svg+xml": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " image/svg+xml\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " IPSH1\n", - " IPSH2\n", - " IPSH3\n", - " HPSH3\n", - " HPSH2\n", - " HPSH1\n", - " HPEVAP\n", - " HPECON5\n", - " LPECON\n", - " LPEVAP\n", - " LPDRUM\n", - " HPSH4\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " HPECON4\n", - " HPECON3\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " LPSH1\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Gas Turbine Exhaust\n", - " HP Steam\n", - " IP Steam\n", - " \n", - " Feedwater\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " HPECON1\n", - " \n", - " \n", - " \n", - " \n", - " HPECON2\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " IPECON1\n", - " IPECON2\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " IPEVAP\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Cold Reheat\n", - " \n", - " \n", - " \n", - " \n", - " From HP ECON2\n", - " To HP ECON3\n", - " \n", - " \n", - " LP Steam\n", - " \n", - " \n", - " \n", - " Mixer1\n", - " \n", - " LP_FGsplit\n", - " \n", - " \n", - " \n", - " \n", - " LP_Mixer2\n", - " IPPump\n", - " HPPump\n", - " IP_Mixer1\n", - " IP_Splitter2\n", - " To Ejector\n", - " To Reclaimer\n", - " To Dryer\n", - " To NG Preheater\n", - " From NG Preheater\n", - " To Stack or Capture\n", - " IP_Splitter1\n", - " Splitter1\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " SOEC Makeup\n", - " \n", - " \n", - " \n", - " \n", - " lp01\n", - " \n", - " \n", - " \n", - " lp02\n", - " \n", - " \n", - " \n", - " lp03\n", - " \n", - " \n", - " \n", - " lp04\n", - " \n", - " \n", - " \n", - " lp12\n", - " \n", - " \n", - " \n", - " lp05\n", - " \n", - " \n", - " \n", - " lp13\n", - " \n", - " \n", - " \n", - " g30\n", - " \n", - " \n", - " \n", - " g19\n", - " \n", - " \n", - " \n", - " hp03\n", - " \n", - " \n", - " \n", - " hp04\n", - " \n", - " \n", - " \n", - " hp05\n", - " \n", - " \n", - " \n", - " g18\n", - " \n", - " \n", - " \n", - " hp06\n", - " \n", - " \n", - " \n", - " hp06b\n", - " \n", - " \n", - " \n", - " g15\n", - " \n", - " \n", - " \n", - " ip06\n", - " \n", - " \n", - " \n", - " g14\n", - " \n", - " \n", - " \n", - " g17\n", - " \n", - " \n", - " \n", - " g16\n", - " \n", - " \n", - " \n", - " hp07\n", - " \n", - " \n", - " \n", - " hp08\n", - " \n", - " \n", - " \n", - " hp09\n", - " \n", - " \n", - " \n", - " hp10\n", - " \n", - " \n", - " \n", - " hp11\n", - " \n", - " \n", - " \n", - " g13\n", - " \n", - " \n", - " \n", - " g12\n", - " \n", - " \n", - " \n", - " g11\n", - " \n", - " \n", - " \n", - " g08\n", - " \n", - " \n", - " \n", - " g09\n", - " \n", - " \n", - " \n", - " ip10\n", - " \n", - " \n", - " \n", - " ip09\n", - " \n", - " \n", - " \n", - " ip08\n", - " \n", - " \n", - " \n", - " ip07\n", - " \n", - " \n", - " \n", - " g10\n", - " \n", - " \n", - " \n", - " g28\n", - " \n", - " \n", - " \n", - " ip11\n", - " \n", - " \n", - " \n", - " ip012\n", - " \n", - " \n", - " \n", - " ip013\n", - " \n", - " \n", - " \n", - " ip14\n", - " \n", - " \n", - " \n", - " ip015\n", - " \n", - " \n", - " \n", - " lp09\n", - " \n", - " \n", - " \n", - " lp08\n", - " \n", - " \n", - " \n", - " lp06\n", - " \n", - " \n", - " \n", - " hp01\n", - " \n", - " \n", - " \n", - " hp02\n", - " \n", - " \n", - " \n", - " hp03\n", - " \n", - " \n", - " \n", - " ip01\n", - " \n", - " \n", - " \n", - " ip02\n", - " \n", - " \n", - " \n", - " ip03\n", - " \n", - " \n", - " \n", - " g25\n", - " \n", - " \n", - " \n", - " g26\n", - " \n", - " \n", - " \n", - " g27\n", - " \n", - " \n", - " \n", - " ip05\n", - " \n", - " \n", - " \n", - " ip04\n", - " \n", - " \n", - " \n", - " g24\n", - " \n", - " \n", - " \n", - " g23\n", - " \n", - " \n", - " \n", - " g29\n", - " \n", - " \n", - " \n", - " lp10\n", - " \n", - " \n", - " \n", - " lp11\n", - " \n", - " \n", - " \n", - " g21\n", - " \n", - " \n", - " \n", - " g20\n", - " \n", - " \n", - " \n", - " g22\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " lp01\n", - " \n", - " \n", - " lp02\n", - " \n", - " \n", - " lp03\n", - " \n", - " \n", - " lp04\n", - " \n", - " \n", - " lp12\n", - " \n", - " \n", - " \n", - " lp05\n", - " \n", - " lp13\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " g30\n", - " \n", - " g29\n", - " g28\n", - " \n", - " \n", - " ip11\n", - " \n", - " ip06\n", - " g17\n", - " \n", - " \n", - " g16\n", - " g15\n", - " \n", - " \n", - " g14\n", - " hp07\n", - " \n", - " \n", - " hp08\n", - " hp06b\n", - " \n", - " hp06\n", - " \n", - " g19\n", - " \n", - " hp03\n", - " \n", - " g18\n", - " hp04\n", - " hp05\n", - " hp09\n", - " hp10\n", - " hp11\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " g12\n", - " g13\n", - " g11\n", - " g08\n", - " g09\n", - " ip08\n", - " ip09\n", - " ip10\n", - " ip07\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " g10\n", - " \n", - " ip14\n", - " ip13\n", - " ip12\n", - " ip15\n", - " \n", - " \n", - " \n", - " \n", - " ip06\n", - " \n", - " ip08\n", - " ip09\n", - " \n", - " \n", - " \n", - " hp01\n", - " hp02\n", - " hp03\n", - " \n", - " \n", - " \n", - " g27\n", - " g26\n", - " ip02\n", - " ip01\n", - " ip03\n", - " \n", - " \n", - " \n", - " \n", - " g25\n", - " \n", - " g24\n", - " \n", - " ip05\n", - " \n", - " ip04\n", - " \n", - " g23\n", - " \n", - " \n", - " g22\n", - " \n", - " lp11\n", - " lp10\n", - " g21\n", - " g20\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 382.51 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 180.904 kg/s\n", - " 356.59 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 410.40 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 0.000 kg/s\n", - " 443.56 K\n", - " 8.000 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 18.526 kg/s\n", - " 335.99 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 199.430 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 22.669 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 199.430 kg/s\n", - " 394.15 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 556.520 kg/s\n", - " 547.38 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 199.430 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 180.904 kg/s\n", - " 399.98 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 40.345 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 40.345 kg/s\n", - " 436.13 K\n", - " 43.850 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 40.345 kg/s\n", - " 457.27 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 22.669 kg/s\n", - " 557.10 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 556.520 kg/s\n", - " 557.25 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 556.520 kg/s\n", - " 557.25 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 552.32 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 510.22 K\n", - " 42.352 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 176.761 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 18.526 kg/s\n", - " 457.27 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 479.13 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 439.77 K\n", - " 244.000 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 457.27 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 520.59 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 516.28 K\n", - " 1.012 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 514.35 K\n", - " 1.011 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 511.29 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 504.84 K\n", - " 243.913 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 508.62 K\n", - " 243.829 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 157.430 kg/s\n", - " 855.94 K\n", - " 30.909 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 157.430 kg/s\n", - " 710.16 K\n", - " 33.408 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 157.430 kg/s\n", - " 600.93 K\n", - " 34.177 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 556.91 K\n", - " 42.146 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 135.612 kg/s\n", - " 610.47 K\n", - " 34.177 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 527.32 K\n", - " 42.352 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 610.47 K\n", - " 34.177 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 858.53 K\n", - " 172.428 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 898.00 K\n", - " 1.100 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 880.11 K\n", - " 1.098 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 735.19 K\n", - " 173.171 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 796.53 K\n", - " 172.830 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 786.52 K\n", - " 1.092 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 576.08 K\n", - " 1.047 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 577.92 K\n", - " 1.081 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 755.13 K\n", - " 1.083 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 840.55 K\n", - " 1.096 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 557.25 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 568.07 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 529.72 K\n", - " 243.746 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 544.93 K\n", - " 243.667 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 557.78 K\n", - " 243.589 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 627.24 K\n", - " 173.589 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 659.11 K\n", - " 173.415 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 584.88 K\n", - " 1.081 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 723.83 K\n", - " 1.081 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 557.34 K\n", - " 173.589 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 820.55 K\n", - " 1.094 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Gas Turbine Section\n", + "\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " image/svg+xml\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " cmp1\n", + " cmb1\n", + " gts1\n", + " inject1\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " gts2\n", + " \n", + " \n", + " gts3\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " splt1\n", + " mx1\n", + " mx2\n", + " mx3\n", + " Blade Cooling Air\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " NGPreheater\n", + " \n", + " To HRSG\n", + " \n", + " \n", + " \n", + " \n", + " air01\n", + " \n", + " \n", + " air03\n", + " \n", + " \n", + " air02\n", + " \n", + " \n", + " air04\n", + " \n", + " \n", + " fuel01\n", + " \n", + " \n", + " fuel02\n", + " \n", + " \n", + " \n", + " \n", + " st02\n", + " st01\n", + " \n", + " \n", + " air05\n", + " \n", + " \n", + " air06\n", + " \n", + " \n", + " g02\n", + " \n", + " \n", + " g01\n", + " \n", + " \n", + " air09\n", + " \n", + " \n", + " air10\n", + " \n", + " \n", + " air07\n", + " \n", + " \n", + " g03\n", + " \n", + " \n", + " g04\n", + " \n", + " \n", + " g05\n", + " \n", + " \n", + " g06\n", + " \n", + " \n", + " g07\n", + " \n", + " \n", + " g08\n", + " \n", + " \n", + " air08\n", + " \n", + " \n", + " Summary\n", + " total GT power:\n", + " 476.99 MW\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " air01\n", + " \n", + " \n", + " \n", + " air02\n", + " \n", + " \n", + " \n", + " air03\n", + " \n", + " \n", + " \n", + " air04\n", + " \n", + " \n", + " \n", + " fuel01\n", + " \n", + " \n", + " \n", + " fuel02\n", + " \n", + " \n", + " feed_air1\n", + " \n", + " \n", + " st02\n", + " \n", + " \n", + " \n", + " st01\n", + " \n", + " \n", + " \n", + " air05\n", + " \n", + " \n", + " \n", + " air06\n", + " \n", + " \n", + " exhaust_1\n", + " \n", + " \n", + " g01\n", + " \n", + " \n", + " \n", + " g02\n", + " \n", + " \n", + " \n", + " air09\n", + " \n", + " \n", + " \n", + " air10\n", + " \n", + " \n", + " \n", + " air07\n", + " \n", + " \n", + " \n", + " g03\n", + " \n", + " \n", + " \n", + " g04\n", + " \n", + " \n", + " \n", + " g05\n", + " \n", + " \n", + " \n", + " g07\n", + " \n", + " \n", + " \n", + " g06\n", + " \n", + " \n", + " \n", + " g08\n", + " \n", + " \n", + " \n", + " air08\n", + " \n", + " \n", + " \n", + " \n", + " 299.82 K\n", + " 25.946 kg/s\n", + " 31.026 bar\n", + " 1.000%\n", + " 93.100%\n", + " 0.000%\n", + " 1.600%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yCH4:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " 3.200%\n", + " yC2H6:\n", + " 0.700%\n", + " yC3H8:\n", + " 0.400%\n", + " yC4H10:\n", + " \n", + " \n", + " \n", + " 335.99 K\n", + " 18.526 kg/s\n", + " 43.355 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 457.27 K\n", + " 18.526 kg/s\n", + " 43.355 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " NG PreharerUses Hot WaterFrom HRSG\n", + " \n", + " \n", + " 448.75 K\n", + " 25.946 kg/s\n", + " 31.026 bar\n", + " 1.000%\n", + " 93.100%\n", + " 0.000%\n", + " 1.600%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yCH4:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " 3.200%\n", + " yC2H6:\n", + " 0.700%\n", + " yC3H8:\n", + " 0.400%\n", + " yC4H10:\n", + " \n", + " \n", + " power:\n", + " 481.28 MW\n", + " \n", + " \n", + " isentr. efficiency:\n", + " 84.02%\n", + " isentr. head:\n", + " 367.27 kJ/kg\n", + " inlet vol. flow:\n", + " 883.2 m**3/s\n", + " \n", + " 288.15 K\n", + " 1100.984 kg/s\n", + " 1.034 bar\n", + " 0.030%\n", + " 0.990%\n", + " 0.920%\n", + " 20.740%\n", + " 77.320%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yH2O:\n", + " yAr:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 288.17 K\n", + " 1100.984 kg/s\n", + " 1.099 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 709.64 K\n", + " 1100.984 kg/s\n", + " 19.226 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 709.64 K\n", + " 1008.614 kg/s\n", + " 19.226 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 691.89 K\n", + " 1034.561 kg/s\n", + " 19.226 bar\n", + " 0.070%\n", + " 3.842%\n", + " 19.884%\n", + " 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kg/s\n", + " 610.47 K\n", + " 34.177 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 858.53 K\n", + " 172.428 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:\n", + " 1113.040 kg/s\n", + " 898.00 K\n", + " 1.100 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:\n", + " 1113.040 kg/s\n", + " 880.11 K\n", + " 1.098 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 735.19 K\n", + " 173.171 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 796.53 K\n", + " 172.830 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 786.52 K\n", + " 1.092 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:\n", + " 1113.040 kg/s\n", + " 576.08 K\n", + " 1.047 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:\n", + " 1113.040 kg/s\n", + " 577.92 K\n", + " 1.081 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 755.13 K\n", + " 1.083 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 840.55 K\n", + " 1.096 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:\n", + " 1113.040 kg/s\n", + " 557.25 K\n", + " 1.046 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:\n", + " 1113.040 kg/s\n", + " 568.07 K\n", + " 1.046 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 529.72 K\n", + " 243.746 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 544.93 K\n", + " 243.667 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 557.78 K\n", + " 243.589 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 627.24 K\n", + " 173.589 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 659.11 K\n", + " 173.415 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 584.88 K\n", + " 1.081 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 723.83 K\n", + " 1.081 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 557.34 K\n", + " 173.589 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 820.55 K\n", + " 1.094 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Steam Turbine Section\n", + "\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " image/svg+xml\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " HRSG\n", + " Cold Flue Gas\n", + " Gas Turbine Exhaust\n", + " Makeup Water\n", + " HP\n", + " IP\n", + " LP\n", + " Condensate Pump\n", + " Condenser\n", + " \n", + " \n", + " Cold Reheat\n", + " Hot Reheat\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " From Dryer\n", + " From Reclaimer\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " To Reclaimer\n", + " To Dryer\n", + " To Ejector\n", + " \n", + " \n", + " \n", + " \n", + " 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581.07 K\n", + " 4.592 bar\n", + " 55.522 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 136.416 kg/s\n", + " 610.47 K\n", + " 34.177 bar\n", + " 55.412 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 111.286 kg/s\n", + " 577.72 K\n", + " 4.592 bar\n", + " 55.397 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 180.904 kg/s\n", + " 356.59 K\n", + " 6.550 bar\n", + " 6.304 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 0.001 kg/s\n", + " 487.00 K\n", + " 20.000 bar\n", + " 50.496 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 0.000 kg/s\n", + " 476.00 K\n", + " 16.000 bar\n", + " 50.393 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 111.286 kg/s\n", + " 316.88 K\n", + " 0.090 bar\n", + " 3.299 kJ/mol\n", + " 0.000\n", 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display(SVG(m.fs.hrsg.write_pfd()))\n", + " print(\"\\n\\nSteam Turbine Section\\n\")\n", + " display(SVG(m.fs.st.write_pfd()))\n", + "\n", + "\n", + "display_pfd()\n", + "\n", + "m.fs.gt.write_pfd(fname=\"data_pfds/gt_baseline.svg\")\n", + "m.fs.hrsg.write_pfd(fname=\"data_pfds/hrsg_baseline.svg\")\n", + "m.fs.st.write_pfd(fname=\"data_pfds/st_baseline.svg\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Steam Turbine Section\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test key model outputs against NETL baseline" + ] }, { - "data": { - "image/svg+xml": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " image/svg+xml\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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t11\n", - " \n", - " \n", - " \n", - " t15\n", - " \n", - " \n", - " \n", - " t14\n", - " \n", - " \n", - " \n", - " t13\n", - " \n", - " \n", - " \n", - " t10\n", - " \n", - " \n", - " \n", - " t09\n", - " \n", - " \n", - " \n", - " t13\n", - " \n", - " \n", - " \n", - " t04\n", - " \n", - " \n", - " \n", - " t05\n", - " \n", - " \n", - " \n", - " t03\n", - " \n", - " \n", - " \n", - " t06\n", - " \n", - " \n", - " \n", - " t07\n", - " \n", - " \n", - " \n", - " t08\n", - " \n", - " \n", - " \n", - " t12\n", - " \n", - " \n", - " \n", - " cw01\n", - " \n", - " \n", - " \n", - " cw02\n", - " \n", - " \n", - " \n", - " t18\n", - " \n", - " \n", - " \n", - " t17\n", - " \n", - " \n", - " \n", - " t16\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " t01\n", - " \n", - " t02\n", - " \n", - " \n", - " t11\n", - " \n", - " \n", - " t15\n", - " \n", - " \n", - " t14\n", - " \n", - " \n", - " t10\n", - " \n", - " \n", - " t09\n", - " \n", - " \n", - " t13\n", - " \n", - " \n", - " t04\n", - " \n", - " \n", - " t05\n", - " \n", - " \n", - " t08\n", - " \n", - " \n", - " t12\n", - " \n", - " \n", - " cw01\n", - " \n", - " \n", - " cw02\n", - " \n", - " \n", - " t16\n", - " \n", - " \n", - " t18\n", - " \n", - " \n", - " t17\n", - " \n", - " t03\n", - " \n", - " t06\n", - " \n", - " t07\n", - " \n", - " \n", - " \n", - " 136.416 kg/s\n", - " 858.53 K\n", - " 172.428 bar\n", - " 63.487 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 157.430 kg/s\n", - " 855.94 K\n", - " 30.909 bar\n", - " 65.630 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 0.806 kg/s\n", - " 306.25 K\n", - " 1.013 bar\n", - " 2.500 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 3603.054 kg/s\n", - " 289.70 K\n", - " 5.000 bar\n", - " 1.260 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 111.286 kg/s\n", - " 316.88 K\n", - " 0.090 bar\n", - " 45.114 kJ/mol\n", - " 0.968\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 22.669 kg/s\n", - " 557.10 K\n", - " 6.550 bar\n", - " 54.533 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 0.002 kg/s\n", - " 577.72 K\n", - " 4.592 bar\n", - " 55.397 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 68.812 kg/s\n", - " 577.72 K\n", - " 4.592 bar\n", - " 55.397 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 157.430 kg/s\n", - " 581.07 K\n", - " 4.592 bar\n", - " 55.522 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 136.416 kg/s\n", - " 610.47 K\n", - " 34.177 bar\n", - " 55.412 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 111.286 kg/s\n", - " 577.72 K\n", - " 4.592 bar\n", - " 55.397 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 180.904 kg/s\n", - " 356.59 K\n", - " 6.550 bar\n", - " 6.304 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 0.001 kg/s\n", - " 487.00 K\n", - " 20.000 bar\n", - " 50.496 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 0.000 kg/s\n", - " 476.00 K\n", - " 16.000 bar\n", - " 50.393 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 111.286 kg/s\n", - " 316.88 K\n", - " 0.090 bar\n", - " 3.299 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 3603.054 kg/s\n", - " 306.85 K\n", - " 5.000 bar\n", - " 2.552 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 68.812 kg/s\n", - " 420.51 K\n", - " 4.592 bar\n", - " 11.184 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 112.092 kg/s\n", - " 316.80 K\n", - " 0.090 bar\n", - " 3.293 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 112.092 kg/s\n", - " 316.86 K\n", - " 6.550 bar\n", - " 3.308 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - "" + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Assert results approximately agree with baseline reoprt\n", + "assert pyo.value(m.fs.net_power_mw[0]) == pytest.approx(646)\n", + "assert pyo.value(m.fs.gross_power[0]) == pytest.approx(-690e6, rel=0.001)\n", + "assert pyo.value(100 * m.fs.lhv_efficiency[0]) == pytest.approx(52.8, abs=0.1)\n", + "assert pyo.value(\n", + " m.fs.total_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", + ") == pytest.approx(37.2799, rel=0.01)\n", + "assert pyo.value(m.fs.fuel_cost_rate[0] / m.fs.net_power_mw[0]) == pytest.approx(\n", + " 31.6462, rel=0.01\n", + ")\n", + "assert pyo.value(\n", + " m.fs.other_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", + ") == pytest.approx(5.63373, rel=0.01)\n", + "assert pyo.value(m.fs.gt.gt_power[0]) == pytest.approx(-477e6, rel=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "\n", + "variables = [\"net_power\", \"gross_power\", \"gt_power\"]\n", + "netl_baseline = [646, 690, 477]\n", + "idaes_prediction = [\n", + " pyo.value(m.fs.net_power_mw[0]),\n", + " -pyo.value(m.fs.gross_power[0]) * 1e-6,\n", + " -pyo.value(m.fs.gt.gt_power[0]) * 1e-6,\n", + "]\n", + "\n", + "label_location = np.arange(len(variables))\n", + "\n", + "width = 0.4\n", + "\n", + "fig, ax = plt.subplots()\n", + "netl_data = ax.bar(variables, netl_baseline, label=\"NETL Baseline\")\n", + "idaes_sim = ax.bar(\n", + " label_location + (width / 2), idaes_prediction, width, label=\"IDAES Prediction\"\n", + ")\n", + "\n", + "ax.set_ylabel(\"Power (MW)\")\n", + "ax.set_xticks(label_location)\n", + "ax.set_xticklabels(variables)\n", + "ax.legend()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def display_pfd():\n", - " print(\"\\n\\nGas Turbine Section\\n\")\n", - " display(SVG(m.fs.gt.write_pfd()))\n", - " print(\"\\n\\nHRSG Section\\n\")\n", - " display(SVG(m.fs.hrsg.write_pfd()))\n", - " print(\"\\n\\nSteam Turbine Section\\n\")\n", - " display(SVG(m.fs.st.write_pfd()))\n", - "\n", - "\n", - "display_pfd()\n", - "\n", - "m.fs.gt.write_pfd(fname=\"data_pfds/gt_baseline.svg\")\n", - "m.fs.hrsg.write_pfd(fname=\"data_pfds/hrsg_baseline.svg\")\n", - "m.fs.st.write_pfd(fname=\"data_pfds/st_baseline.svg\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test key model outputs against NETL baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Assert results approximately agree with baseline reoprt\n", - "assert pyo.value(m.fs.net_power_mw[0]) == pytest.approx(646)\n", - "assert pyo.value(m.fs.gross_power[0]) == pytest.approx(-690e6, rel=0.001)\n", - "assert pyo.value(100 * m.fs.lhv_efficiency[0]) == pytest.approx(52.8, abs=0.1)\n", - "assert pyo.value(\n", - " m.fs.total_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", - ") == pytest.approx(37.2799, rel=0.01)\n", - "assert pyo.value(m.fs.fuel_cost_rate[0] / m.fs.net_power_mw[0]) == pytest.approx(\n", - " 31.6462, rel=0.01\n", - ")\n", - "assert pyo.value(\n", - " m.fs.other_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", - ") == pytest.approx(5.63373, rel=0.01)\n", - "assert pyo.value(m.fs.gt.gt_power[0]) == pytest.approx(-477e6, rel=0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run turndown cases 5 MW interval\n", + "\n", + "Here we set the CO2 capture rate to 97% and set the specific reboiler duty to PZ advanced solvent system. The minimum power is 160 MW net, which corresponds to a bit under 25%. This is roughly the minimum load for the NGCC modeled. Results are tabulated for tags in the tags_output tag group in a Pandas data frame. \n", + "\n", + "To run the series, change run_series to True. Running the turndown series takes a while, unless previous saved results are available. " ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "run_series = False\n", + "if run_series:\n", + " idaes.cfg.ipopt.options.tol = 1e-6\n", + " idaes.cfg.ipopt.options.max_iter = 50\n", + " solver = pyo.SolverFactory(\"ipopt\")\n", + "\n", + " m.fs.cap_specific_reboiler_duty.fix(2.4e6)\n", + " m.fs.cap_fraction.fix(0.97)\n", + " powers = np.linspace(650, 160, int((650 - 160) / 5) + 1)\n", + " powers = list(powers)\n", + " powers.insert(1, 646)\n", + "\n", + " df = pd.DataFrame(columns=m.fs.tags_output.table_heading())\n", + "\n", + " for p in powers:\n", + " print(\"Simulation for net power = \", p)\n", + " fname = f\"data/ngcc_{int(p)}.json.gz\"\n", + " if os.path.exists(fname):\n", + " iutil.from_json(m, fname=fname, wts=iutil.StoreSpec(suffix=False))\n", + " else:\n", + " m.fs.net_power_mw.fix(p)\n", + " res = solver.solve(m, tee=False, symbolic_solver_labels=True)\n", + " if not pyo.check_optimal_termination(res):\n", + " break\n", + " iutil.to_json(m, fname=fname)\n", + " df.loc[m.fs.tags_output[\"net_power\"].value] = m.fs.tags_output.table_row(\n", + " numeric=True\n", + " )\n", + " if abs(p - 650) < 0.1:\n", + " m.fs.gt.streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_gt.csv\"\n", + " )\n", + " m.fs.st.steam_streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_st.csv\"\n", + " )\n", + " m.fs.hrsg.steam_streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_hrsg_steam.csv\"\n", + " )\n", + " m.fs.hrsg.flue_gas_streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_hrsg_gas.csv\"\n", + " )\n", + " df.to_csv(\"data_tabulated/ngcc.csv\")\n", + "\n", + " # Display the results from the run stored in a pandas dataframe\n", + " pd.set_option(\"display.max_rows\", None)\n", + " pd.set_option(\"display.max_columns\", None)\n", + " display(df)\n", + "\n", + " # Plot results\n", + " plt.plot(df[\"net_power (MW)\"], df[\"lhv_efficiency (%)\"])\n", + " plt.grid()\n", + " plt.xlabel(\"Net Power (MW)\")\n", + " plt.ylabel(\"LHV Efficiency (%)\")\n", + " plt.title(\"Net Power vs. Efficiency\")\n", + " plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "\n", - "variables = [\"net_power\", \"gross_power\", \"gt_power\"]\n", - "netl_baseline = [646, 690, 477]\n", - "idaes_prediction = [\n", - " pyo.value(m.fs.net_power_mw[0]),\n", - " -pyo.value(m.fs.gross_power[0]) * 1e-6,\n", - " -pyo.value(m.fs.gt.gt_power[0]) * 1e-6,\n", - "]\n", - "\n", - "label_location = np.arange(len(variables))\n", - "\n", - "width = 0.4\n", - "\n", - "fig, ax = plt.subplots()\n", - "netl_data = ax.bar(variables, netl_baseline, label=\"NETL Baseline\")\n", - "idaes_sim = ax.bar(\n", - " label_location + (width / 2), idaes_prediction, width, label=\"IDAES Prediction\"\n", - ")\n", - "\n", - "ax.set_ylabel(\"Power (MW)\")\n", - "ax.set_xticks(label_location)\n", - "ax.set_xticklabels(variables)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run turndown cases 5 MW interval\n", - "\n", - "Here we set the CO2 capture rate to 97% and set the specific reboiler duty to PZ advanced solvent system. The minimum power is 160 MW net, which corresponds to a bit under 25%. This is roughly the minimum load for the NGCC modeled. Results are tabulated for tags in the tags_output tag group in a Pandas data frame. \n", - "\n", - "To run the series, change run_series to True. Running the turndown series takes a while, unless previous saved results are available. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "run_series = False\n", - "if run_series:\n", - " idaes.cfg.ipopt.options.tol = 1e-6\n", - " idaes.cfg.ipopt.options.max_iter = 50\n", - " solver = pyo.SolverFactory(\"ipopt\")\n", - "\n", - " m.fs.cap_specific_reboiler_duty.fix(2.4e6)\n", - " m.fs.cap_fraction.fix(0.97)\n", - " powers = np.linspace(650, 160, int((650 - 160) / 5) + 1)\n", - " powers = list(powers)\n", - " powers.insert(1, 646)\n", - "\n", - " df = pd.DataFrame(columns=m.fs.tags_output.table_heading())\n", - "\n", - " for p in powers:\n", - " print(\"Simulation for net power = \", p)\n", - " fname = f\"data/ngcc_{int(p)}.json.gz\"\n", - " if os.path.exists(fname):\n", - " iutil.from_json(m, fname=fname, wts=iutil.StoreSpec(suffix=False))\n", - " else:\n", - " m.fs.net_power_mw.fix(p)\n", - " res = solver.solve(m, tee=False, symbolic_solver_labels=True)\n", - " if not pyo.check_optimal_termination(res):\n", - " break\n", - " iutil.to_json(m, fname=fname)\n", - " df.loc[m.fs.tags_output[\"net_power\"].value] = m.fs.tags_output.table_row(\n", - " numeric=True\n", - " )\n", - " if abs(p - 650) < 0.1:\n", - " m.fs.gt.streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_gt.csv\"\n", - " )\n", - " m.fs.st.steam_streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_st.csv\"\n", - " )\n", - " m.fs.hrsg.steam_streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_hrsg_steam.csv\"\n", - " )\n", - " m.fs.hrsg.flue_gas_streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_hrsg_gas.csv\"\n", - " )\n", - " df.to_csv(\"data_tabulated/ngcc.csv\")\n", - "\n", - " # Display the results from the run stored in a pandas dataframe\n", - " pd.set_option(\"display.max_rows\", None)\n", - " pd.set_option(\"display.max_columns\", None)\n", - " display(df)\n", - "\n", - " # Plot results\n", - " plt.plot(df[\"net_power (MW)\"], df[\"lhv_efficiency (%)\"])\n", - " plt.grid()\n", - " plt.xlabel(\"Net Power (MW)\")\n", - " plt.ylabel(\"LHV Efficiency (%)\")\n", - " plt.title(\"Net Power vs. Efficiency\")\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_usr.ipynb b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_usr.ipynb index 7127641c..32fa7cf7 100644 --- a/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_usr.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/ngcc/ngcc_usr.ipynb @@ -1,2964 +1,2965 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NGCC Baseline and Turndown\n", - "Maintainer: Javal Vyas \n", - "Author: John Eslick \n", - "Updated: 2024-07-25 \n", - "\n", - "This notebook runs a series of net electric power outputs from 650 MW to 160 MW (about 100% to 25%) for an NGCC with 97% CO2 capture. The NGCC model is based on the NETL report \"Cost and Performance Baseline for Fossil Energy Plants Volume 1, Bituminous Coal and Natural Gas to Electricity.\" Sept 2019, Case B31B [resource](https://www.osti.gov/servlets/purl/1893822). Another valuable resource for gaining a deeper understanding of the mathematical model would be the publication referenced [here](https://www.sciencedirect.com/science/article/pii/S1750583617302414). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports\n", - "\n", - "Import the modules that will be used." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.core.display import SVG\n", - "import pyomo.environ as pyo\n", - "import idaes\n", - "from idaes.core.solvers import use_idaes_solver_configuration_defaults\n", - "import idaes.core.util.scaling as iscale\n", - "import idaes.core.util as iutil\n", - "from idaes_examples.mod.power_gen import ngcc\n", - "import idaes.logger as idaeslog\n", - "import pytest\n", - "import logging\n", - "\n", - "logging.getLogger(\"pyomo\").setLevel(logging.ERROR)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make Output Directories\n", - "\n", - "This notebook can produce a large number of output files. To make it easier to manage, some subdirectories are used to organize output. This ensures that the directories exist." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def make_directory(path):\n", - " \"\"\"Make a directory if it doesn't exist\"\"\"\n", - " try:\n", - " os.mkdir(path)\n", - " except FileExistsError:\n", - " pass\n", - "\n", - "\n", - "make_directory(\"data\")\n", - "make_directory(\"data_pfds\")\n", - "make_directory(\"data_tabulated\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Global Solver Settings\n", - "\n", - "Use the IDAES configuration system for solver settings. These will apply to all Ipopt instances created, including the ones created in initialization methods." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "use_idaes_solver_configuration_defaults()\n", - "idaes.cfg.ipopt.options.nlp_scaling_method = \"user-scaling\"\n", - "idaes.cfg.ipopt.options.linear_solver = \"ma57\"\n", - "idaes.cfg.ipopt.options.OF_ma57_automatic_scaling = \"yes\"\n", - "idaes.cfg.ipopt.options.ma57_pivtol = 1e-5\n", - "idaes.cfg.ipopt.options.ma57_pivtolmax = 0.1\n", - "solver = pyo.SolverFactory(\"ipopt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the NGCC model\n", - "\n", - "Create the NGCC model and initialize it or read the saved initialization if available. The base initialized NGCC model is configured to match the baseline report with 90% capture using a Cansolv system." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-07-25 03:12:25 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n", - "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", - "tol=1e-06\n", - "max_iter=200\n", - "linear_solver=ma57\n", - "ma57_pivtol=1e-05\n", - "ma57_pivtolmax=0.1\n", - "option_file_name=C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\n", - "\n", - "Using option file \"C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\".\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma57.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 7661\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 5948\n", - "\n", - "Total number of variables............................: 2404\n", - " variables with only lower bounds: 87\n", - " variables with lower and upper bounds: 1447\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 2404\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "Reallocating memory for MA57: lfact (111709)\n", - " 1 0.0000000e+00 3.49e-01 1.12e+04 -1.0 3.06e+03 - 9.90e-01 9.90e-01h 1\n", - " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n", - " 3 0.0000000e+00 2.95e-07 9.98e+02 -1.0 3.74e+01 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.9462398742907681e-07 2.9462398742907681e-07\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9462398742907681e-07 2.9462398742907681e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.085\n", - "Total CPU secs in NLP function evaluations = 1.396\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "m = pyo.ConcreteModel()\n", - "m.fs = ngcc.NgccFlowsheet(dynamic=False)\n", - "iscale.calculate_scaling_factors(m)\n", - "m.fs.initialize(\n", - " load_from=\"ngcc_init.json.gz\",\n", - " save_to=\"ngcc_init.json.gz\",\n", - " outlvl=idaeslog.INFO_HIGH,\n", - ")\n", - "res = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show PFDs with baseline results\n", - "\n", - "This displays PFDs in the notebook, and saves them to files. The full NGCC model is too big to show well in a single PFD, so it is broken into the three main sections, gas turbine, heat recovery steam generator (HRSG), and steam turbine." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NGCC Baseline and Turndown\n", + "Maintainer: Javal Vyas \n", + "Author: John Eslick \n", + "Updated: 2024-07-25 \n", + "\n", + "This notebook runs a series of net electric power outputs from 650 MW to 160 MW (about 100% to 25%) for an NGCC with 97% CO2 capture. The NGCC model is based on the NETL report \"Cost and Performance Baseline for Fossil Energy Plants Volume 1, Bituminous Coal and Natural Gas to Electricity.\" Sept 2019, Case B31B [resource](https://www.osti.gov/servlets/purl/1893822). Another valuable resource for gaining a deeper understanding of the mathematical model would be the publication referenced [here](https://www.sciencedirect.com/science/article/pii/S1750583617302414). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "Import the modules that will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.core.display import SVG\n", + "import pyomo.environ as pyo\n", + "import idaes\n", + "from idaes.core.solvers import use_idaes_solver_configuration_defaults\n", + "import idaes.core.util.scaling as iscale\n", + "import idaes.core.util as iutil\n", + "from idaes_examples.mod.power_gen import ngcc\n", + "import idaes.logger as idaeslog\n", + "import pytest\n", + "import logging\n", + "\n", + "logging.getLogger(\"pyomo\").setLevel(logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make Output Directories\n", + "\n", + "This notebook can produce a large number of output files. To make it easier to manage, some subdirectories are used to organize output. This ensures that the directories exist." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Gas Turbine Section\n", - "\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def make_directory(path):\n", + " \"\"\"Make a directory if it doesn't exist\"\"\"\n", + " try:\n", + " os.mkdir(path)\n", + " except FileExistsError:\n", + " pass\n", + "\n", + "\n", + "make_directory(\"data\")\n", + "make_directory(\"data_pfds\")\n", + "make_directory(\"data_tabulated\")" + ] }, { - "data": { - "image/svg+xml": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " image/svg+xml\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " cmp1\n", - " cmb1\n", - " gts1\n", - " inject1\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " gts2\n", - " \n", - " \n", - " gts3\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " splt1\n", - " mx1\n", - " mx2\n", - " mx3\n", - " Blade Cooling Air\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " NGPreheater\n", - " \n", - " To HRSG\n", - " \n", - " \n", - " \n", - " \n", - " air01\n", - " \n", - " \n", - " air03\n", - " \n", - " \n", - " air02\n", - " \n", - " \n", - " air04\n", - " \n", - " \n", - " fuel01\n", - " \n", - " \n", - " fuel02\n", - " \n", - " \n", - " \n", - " \n", - " st02\n", - " st01\n", - " \n", - " \n", - " air05\n", - " \n", - " \n", - " air06\n", - " \n", - " \n", - " g02\n", - " \n", - " \n", - " g01\n", - " \n", - " \n", - " air09\n", - " \n", - " \n", - " air10\n", - " \n", - " \n", - " air07\n", - " \n", - " \n", - " g03\n", - " \n", - " \n", - " g04\n", - " \n", - " \n", - " g05\n", - " \n", - " \n", - " g06\n", - " \n", - " \n", - " g07\n", - " \n", - " \n", - " g08\n", - " \n", - " \n", - " air08\n", - " \n", - " \n", - " Summary\n", - " total GT power:\n", - " 476.99 MW\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " air01\n", - " \n", - " \n", - " \n", - " air02\n", - " \n", - " \n", - " \n", - " air03\n", - " \n", - " \n", - " \n", - " air04\n", - " \n", - " \n", - " \n", - " fuel01\n", - " \n", - " \n", - " \n", - " fuel02\n", - " \n", - " \n", - " feed_air1\n", - " \n", - " \n", - " st02\n", - " \n", - " \n", - " \n", - " st01\n", - " \n", - " \n", - " \n", - " air05\n", - " \n", - " \n", - " \n", - " air06\n", - " \n", - " \n", - " exhaust_1\n", - " \n", - " \n", - " g01\n", - " \n", - " \n", - " \n", - " g02\n", - " \n", - " \n", - " \n", - " air09\n", - " \n", - " \n", - " \n", - " air10\n", - " \n", - " \n", - " \n", - " air07\n", - " \n", - " \n", - " \n", - " g03\n", - " \n", - " \n", - " \n", - " g04\n", - " \n", - " \n", - " \n", - " g05\n", - " \n", - " \n", - " \n", - " g07\n", - " \n", - " \n", - " \n", - " g06\n", - " \n", - " \n", - " \n", - " g08\n", - " \n", - " \n", - " \n", - " air08\n", - " \n", - " \n", - " \n", - " \n", - " 299.82 K\n", - " 25.946 kg/s\n", - " 31.026 bar\n", - " 1.000%\n", - " 93.100%\n", - " 0.000%\n", - " 1.600%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 3.200%\n", - " yC2H6:\n", - " 0.700%\n", - " yC3H8:\n", - " 0.400%\n", - " yC4H10:\n", - " \n", - " \n", - " \n", - " 335.99 K\n", - " 18.526 kg/s\n", - " 43.355 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 457.27 K\n", - " 18.526 kg/s\n", - " 43.355 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " NG PreharerUses Hot WaterFrom HRSG\n", - " \n", - " \n", - " 448.75 K\n", - " 25.946 kg/s\n", - " 31.026 bar\n", - " 1.000%\n", - " 93.100%\n", - " 0.000%\n", - " 1.600%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 3.200%\n", - " yC2H6:\n", - " 0.700%\n", - " yC3H8:\n", - " 0.400%\n", - " yC4H10:\n", - " \n", - " \n", - " power:\n", - " 481.28 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 84.02%\n", - " isentr. head:\n", - " 367.27 kJ/kg\n", - " inlet vol. flow:\n", - " 883.2 m**3/s\n", - " \n", - " 288.15 K\n", - " 1100.984 kg/s\n", - " 1.034 bar\n", - " 0.030%\n", - " 0.990%\n", - " 0.920%\n", - " 20.740%\n", - " 77.320%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 288.17 K\n", - " 1100.984 kg/s\n", - " 1.099 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.64 K\n", - " 1100.984 kg/s\n", - " 19.226 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.64 K\n", - " 1008.614 kg/s\n", - " 19.226 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 691.89 K\n", - " 1034.561 kg/s\n", - " 19.226 bar\n", - " 0.070%\n", - " 3.842%\n", - " 19.884%\n", - " 74.195%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 0.132%\n", - " yC2H6:\n", - " 0.029%\n", - " yC3H8:\n", - " 0.017%\n", - " yC4H10:\n", - " \n", - " \n", - " \n", - " 1641.38 K\n", - " 1034.691 kg/s\n", - " 18.265 bar\n", - " 4.324%\n", - " 9.217%\n", - " 0.000%\n", - " 11.471%\n", - " 74.106%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yCH4:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " 0.000%\n", - " yC2H6:\n", - " 0.000%\n", - " yC3H8:\n", - " 0.000%\n", - " yC4H10:\n", - " 0.881%\n", - " yAr:\n", - " \n", - " \n", - " \n", - " power:\n", - " -374.58 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 88.53%\n", - " isentr. head:\n", - " -408.95 kJ/kg\n", - " inlet vol. flow:\n", - " 273.6 m**3/s\n", - " \n", - " \n", - " \n", - " 898.00 K\n", - " 1127.060 kg/s\n", - " 1.100 bar\n", - " 3.978%\n", - " 8.554%\n", - " 0.884%\n", - " 12.219%\n", - " 74.365%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " 899.61 K\n", - " 1116.809 kg/s\n", - " 1.100 bar\n", - " 4.014%\n", - " 8.622%\n", - " 0.884%\n", - " 12.142%\n", - " 74.339%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1094.58 K\n", - " 1116.809 kg/s\n", - " 2.799 bar\n", - " 4.014%\n", - " 8.622%\n", - " 0.884%\n", - " 12.142%\n", - " 74.339%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.76 K\n", - " 14.769 kg/s\n", - " 2.799 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1099.28 K\n", - " 1102.041 kg/s\n", - " 2.799 bar\n", - " 4.066%\n", - " 8.723%\n", - " 0.883%\n", - " 12.028%\n", - " 74.299%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1329.41 K\n", - " 1102.041 kg/s\n", - " 7.137 bar\n", - " 4.066%\n", - " 8.723%\n", - " 0.883%\n", - " 12.028%\n", - " 74.299%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1365.44 K\n", - " 1034.691 kg/s\n", - " 7.137 bar\n", - " 4.324%\n", - " 9.217%\n", - " 0.881%\n", - " 11.471%\n", - " 74.106%\n", - " T:\n", - " F:\n", - " P:\n", - " yCO2:\n", - " yH2O:\n", - " yAr:\n", - " yO2:\n", - " yN2:\n", - " \n", - " \n", - " \n", - " \n", - " power:\n", - " -264.25 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 88.19%\n", - " isentr. head:\n", - " -268.31 kJ/kg\n", - " inlet vol. flow:\n", - " 1280.2 m**3/s\n", - " \n", - " \n", - " \n", - " 709.77 K\n", - " 10.250 kg/s\n", - " 1.100 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 85.0%\n", - " opening:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 85.0%\n", - " opening:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 85.0%\n", - " opening:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 709.73 K\n", - " 67.350 kg/s\n", - " 7.137 bar\n", - " T:\n", - " F:\n", - " P:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " power:\n", - " -319.43 MW\n", - " \n", - " \n", - " isentr. efficiency:\n", - " 88.35%\n", - " isentr. head:\n", - " -328.07 kJ/kg\n", - " inlet vol. flow:\n", - " 602.3 m**3/s\n", - " \n", - " \n", - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Global Solver Settings\n", + "\n", + "Use the IDAES configuration system for solver settings. These will apply to all Ipopt instances created, including the ones created in initialization methods." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "use_idaes_solver_configuration_defaults()\n", + "idaes.cfg.ipopt.options.nlp_scaling_method = \"user-scaling\"\n", + "idaes.cfg.ipopt.options.linear_solver = \"ma57\"\n", + "idaes.cfg.ipopt.options.OF_ma57_automatic_scaling = \"yes\"\n", + "idaes.cfg.ipopt.options.ma57_pivtol = 1e-5\n", + "idaes.cfg.ipopt.options.ma57_pivtolmax = 0.1\n", + "solver = pyo.SolverFactory(\"ipopt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the NGCC model\n", + "\n", + "Create the NGCC model and initialize it or read the saved initialization if available. The base initialized NGCC model is configured to match the baseline report with 90% capture using a Cansolv system." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-07-25 03:12:25 [INFO] idaes.init.fs: NGCC load initial from ngcc_init.json.gz\n", + "Ipopt 3.13.2: nlp_scaling_method=user-scaling\n", + "tol=1e-06\n", + "max_iter=200\n", + "linear_solver=ma57\n", + "ma57_pivtol=1e-05\n", + "ma57_pivtolmax=0.1\n", + "option_file_name=C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\n", + "\n", + "Using option file \"C:\\Users\\javal\\AppData\\Local\\Temp\\tmpa9m4gkwo_ipopt.opt\".\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma57.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 7661\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 5948\n", + "\n", + "Total number of variables............................: 2404\n", + " variables with only lower bounds: 87\n", + " variables with lower and upper bounds: 1447\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 2404\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.50e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "Reallocating memory for MA57: lfact (111709)\n", + " 1 0.0000000e+00 3.49e-01 1.12e+04 -1.0 3.06e+03 - 9.90e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 3.15e-03 5.15e+02 -1.0 3.02e+03 - 9.89e-01 9.91e-01h 1\n", + " 3 0.0000000e+00 2.95e-07 9.98e+02 -1.0 3.74e+01 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 2.9462398742907681e-07 2.9462398742907681e-07\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.9462398742907681e-07 2.9462398742907681e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.085\n", + "Total CPU secs in NLP function evaluations = 1.396\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "m = pyo.ConcreteModel()\n", + "m.fs = ngcc.NgccFlowsheet(dynamic=False)\n", + "iscale.calculate_scaling_factors(m)\n", + "m.fs.initialize(\n", + " load_from=\"ngcc_init.json.gz\",\n", + " save_to=\"ngcc_init.json.gz\",\n", + " outlvl=idaeslog.INFO_HIGH,\n", + ")\n", + "res = solver.solve(m, tee=True)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "HRSG Section\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show PFDs with baseline results\n", + "\n", + "This displays PFDs in the notebook, and saves them to files. The full NGCC model is too big to show well in a single PFD, so it is broken into the three main sections, gas turbine, heat recovery steam generator (HRSG), and steam turbine." + ] }, { - "data": { - "image/svg+xml": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " image/svg+xml\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " IPSH1\n", - " IPSH2\n", - " IPSH3\n", - " HPSH3\n", - " HPSH2\n", - " HPSH1\n", - " HPEVAP\n", - " HPECON5\n", - " LPECON\n", - " LPEVAP\n", - " LPDRUM\n", - " HPSH4\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " HPECON4\n", - " HPECON3\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " LPSH1\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Gas Turbine Exhaust\n", - " HP Steam\n", - " IP Steam\n", - " \n", - " Feedwater\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " HPECON1\n", - " \n", - " \n", - " \n", - " \n", - " HPECON2\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " IPECON1\n", - " IPECON2\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " IPEVAP\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Cold Reheat\n", - " \n", - " \n", - " \n", - " \n", - " From HP ECON2\n", - " To HP ECON3\n", - " \n", - " \n", - " LP Steam\n", - " \n", - " \n", - " \n", - " Mixer1\n", - " \n", - " LP_FGsplit\n", - " \n", - " \n", - " \n", - " \n", - " LP_Mixer2\n", - " IPPump\n", - " HPPump\n", - " IP_Mixer1\n", - " IP_Splitter2\n", - " To Ejector\n", - " To Reclaimer\n", - " To Dryer\n", - " To NG Preheater\n", - " From NG Preheater\n", - " To Stack or Capture\n", - " IP_Splitter1\n", - " Splitter1\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " SOEC Makeup\n", - " \n", - " \n", - " \n", - " \n", - " lp01\n", - " \n", - " \n", - " \n", - " lp02\n", - " \n", - " \n", - " \n", - " lp03\n", - " \n", - " \n", - " \n", - " lp04\n", - " \n", - " \n", - " \n", - " lp12\n", - " \n", - " \n", - " \n", - " lp05\n", - " \n", - " \n", - " \n", - " lp13\n", - " \n", - " \n", - " \n", - " g30\n", - " \n", - " \n", - " \n", - " g19\n", - " \n", - " \n", - " \n", - " hp03\n", - " \n", - " \n", - " \n", - " hp04\n", - " \n", - " \n", - " \n", - " hp05\n", - " \n", - " \n", - " \n", - " g18\n", - " \n", - " \n", - " \n", - " hp06\n", - " \n", - " \n", - " \n", - " hp06b\n", - " \n", - " \n", - " \n", - " g15\n", - " \n", - " \n", - " \n", - " ip06\n", - " \n", - " \n", - " \n", - " g14\n", - " \n", - " \n", - " \n", - " g17\n", - " \n", - " \n", - " \n", - " g16\n", - " \n", - " \n", - " \n", - " hp07\n", - " \n", - " \n", - " \n", - " hp08\n", - " \n", - " \n", - " \n", - " hp09\n", - " \n", - " \n", - " \n", - " hp10\n", - " \n", - " \n", - " \n", - " hp11\n", - " \n", - " \n", - " \n", - " g13\n", - " \n", - " \n", - " \n", - " g12\n", - " \n", - " \n", - " \n", - " g11\n", - " \n", - " \n", - " \n", - " g08\n", - " \n", - " \n", - " \n", - " g09\n", - " \n", - " \n", - " \n", - " ip10\n", - " \n", - " \n", - " \n", - " ip09\n", - " \n", - " \n", - " \n", - " ip08\n", - " \n", - " \n", - " \n", - " ip07\n", - " \n", - " \n", - " \n", - " g10\n", - " \n", - " \n", - " \n", - " g28\n", - " \n", - " \n", - " \n", - " ip11\n", - " \n", - " \n", - " \n", - " ip012\n", - " \n", - " \n", - " \n", - " ip013\n", - " \n", - " \n", - " \n", - " ip14\n", - " \n", - " \n", - " \n", - " ip015\n", - " \n", - " \n", - " \n", - " lp09\n", - " \n", - " \n", - " \n", - " lp08\n", - " \n", - " \n", - " \n", - " lp06\n", - " \n", - " \n", - " \n", - " hp01\n", - " \n", - " \n", - " \n", - " hp02\n", - " \n", - " \n", - " \n", - " hp03\n", - " \n", - " \n", - " \n", - " ip01\n", - " \n", - " \n", - " \n", - " ip02\n", - " \n", - " \n", - " \n", - " ip03\n", - " \n", - " \n", - " \n", - " g25\n", - " \n", - " \n", - " \n", - " g26\n", - " \n", - " \n", - " \n", - " g27\n", - " \n", - " \n", - " \n", - " ip05\n", - " \n", - " \n", - " \n", - " ip04\n", - " \n", - " \n", - " \n", - " g24\n", - " \n", - " \n", - " \n", - " g23\n", - " \n", - " \n", - " \n", - " g29\n", - " \n", - " \n", - " \n", - " lp10\n", - " \n", - " \n", - " \n", - " lp11\n", - " \n", - " \n", - " \n", - " g21\n", - " \n", - " \n", - " \n", - " g20\n", - " \n", - " \n", - " \n", - " g22\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " lp01\n", - " \n", - " \n", - " lp02\n", - " \n", - " \n", - " lp03\n", - " \n", - " \n", - " lp04\n", - " \n", - " \n", - " lp12\n", - " \n", - " \n", - " \n", - " lp05\n", - " \n", - " lp13\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " g30\n", - " \n", - " g29\n", - " g28\n", - " \n", - " \n", - " ip11\n", - " \n", - " ip06\n", - " g17\n", - " \n", - " \n", - " g16\n", - " g15\n", - " \n", - " \n", - " g14\n", - " hp07\n", - " \n", - " \n", - " hp08\n", - " hp06b\n", - " \n", - " hp06\n", - " \n", - " g19\n", - " \n", - " hp03\n", - " \n", - " g18\n", - " hp04\n", - " hp05\n", - " hp09\n", - " hp10\n", - " hp11\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " g12\n", - " g13\n", - " g11\n", - " g08\n", - " g09\n", - " ip08\n", - " ip09\n", - " ip10\n", - " ip07\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " g10\n", - " \n", - " ip14\n", - " ip13\n", - " ip12\n", - " ip15\n", - " \n", - " \n", - " \n", - " \n", - " ip06\n", - " \n", - " ip08\n", - " ip09\n", - " \n", - " \n", - " \n", - " hp01\n", - " hp02\n", - " hp03\n", - " \n", - " \n", - " \n", - " g27\n", - " g26\n", - " ip02\n", - " ip01\n", - " ip03\n", - " \n", - " \n", - " \n", - " \n", - " g25\n", - " \n", - " g24\n", - " \n", - " ip05\n", - " \n", - " ip04\n", - " \n", - " g23\n", - " \n", - " \n", - " g22\n", - " \n", - " lp11\n", - " lp10\n", - " g21\n", - " g20\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 382.51 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 180.904 kg/s\n", - " 356.59 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 410.40 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 0.000 kg/s\n", - " 443.56 K\n", - " 8.000 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 18.526 kg/s\n", - " 335.99 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 199.430 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 22.669 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 199.430 kg/s\n", - " 394.15 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 556.520 kg/s\n", - " 547.38 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 199.430 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 180.904 kg/s\n", - " 399.98 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 40.345 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 40.345 kg/s\n", - " 436.13 K\n", - " 43.850 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 40.345 kg/s\n", - " 457.27 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 22.669 kg/s\n", - " 557.10 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 556.520 kg/s\n", - " 557.25 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 556.520 kg/s\n", - " 557.25 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 552.32 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 510.22 K\n", - " 42.352 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 176.761 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 18.526 kg/s\n", - " 457.27 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 435.43 K\n", - " 6.550 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 479.13 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 439.77 K\n", - " 244.000 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 457.27 K\n", - " 43.355 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 520.59 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 516.28 K\n", - " 1.012 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 514.35 K\n", - " 1.011 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 511.29 K\n", - " 1.010 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 504.84 K\n", - " 243.913 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 508.62 K\n", - " 243.829 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 157.430 kg/s\n", - " 855.94 K\n", - " 30.909 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 157.430 kg/s\n", - " 710.16 K\n", - " 33.408 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 157.430 kg/s\n", - " 600.93 K\n", - " 34.177 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 556.91 K\n", - " 42.146 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 135.612 kg/s\n", - " 610.47 K\n", - " 34.177 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 21.818 kg/s\n", - " 527.32 K\n", - " 42.352 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 610.47 K\n", - " 34.177 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 858.53 K\n", - " 172.428 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 898.00 K\n", - " 1.100 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 880.11 K\n", - " 1.098 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 735.19 K\n", - " 173.171 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 796.53 K\n", - " 172.830 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 786.52 K\n", - " 1.092 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 576.08 K\n", - " 1.047 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 577.92 K\n", - " 1.081 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 755.13 K\n", - " 1.083 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 840.55 K\n", - " 1.096 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 557.25 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:\n", - " 1113.040 kg/s\n", - " 568.07 K\n", - " 1.046 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 529.72 K\n", - " 243.746 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 544.93 K\n", - " 243.667 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 557.78 K\n", - " 243.589 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 627.24 K\n", - " 173.589 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 659.11 K\n", - " 173.415 bar\n", - " ?\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 584.88 K\n", - " 1.081 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " T:P:\n", - " 723.83 K\n", - " 1.081 bar\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " F:T:P:x:\n", - " 136.416 kg/s\n", - " 557.34 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" \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " air01\n", + " \n", + " \n", + " \n", + " air02\n", + " \n", + " \n", + " \n", + " air03\n", + " \n", + " \n", + " \n", + " air04\n", + " \n", + " \n", + " \n", + " fuel01\n", + " \n", + " \n", + " \n", + " fuel02\n", + " \n", + " \n", + " feed_air1\n", + " \n", + " \n", + " st02\n", + " \n", + " \n", + " \n", + " st01\n", + " \n", + " \n", + " \n", + " air05\n", + " \n", + " \n", + " \n", + " air06\n", + " \n", + " \n", + " exhaust_1\n", + " \n", + " \n", + " g01\n", + " \n", + " \n", + " \n", + " g02\n", + " \n", + " \n", + " \n", + " air09\n", + " \n", + " \n", + " \n", + " air10\n", + " \n", + " \n", + " \n", + " air07\n", + " \n", + " \n", + " \n", + " g03\n", + " \n", + " \n", + " \n", + " g04\n", + " \n", + " \n", + " \n", + " g05\n", + " \n", + " \n", + " \n", + " g07\n", + " \n", + " \n", + " \n", + " g06\n", + " \n", + " \n", + " \n", + " g08\n", + " \n", + " \n", + " \n", + " air08\n", + " \n", + " \n", + " \n", + " \n", + " 299.82 K\n", + " 25.946 kg/s\n", + " 31.026 bar\n", + " 1.000%\n", + " 93.100%\n", + " 0.000%\n", + " 1.600%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yCH4:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " 3.200%\n", + " yC2H6:\n", + " 0.700%\n", + " yC3H8:\n", + " 0.400%\n", + " yC4H10:\n", + " \n", + " \n", + " \n", + " 335.99 K\n", + " 18.526 kg/s\n", + " 43.355 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 457.27 K\n", + " 18.526 kg/s\n", + " 43.355 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " NG PreharerUses Hot WaterFrom HRSG\n", + " \n", + " \n", + " 448.75 K\n", + " 25.946 kg/s\n", + " 31.026 bar\n", + " 1.000%\n", + " 93.100%\n", + " 0.000%\n", + " 1.600%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yCH4:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " 3.200%\n", + " yC2H6:\n", + " 0.700%\n", + " yC3H8:\n", + " 0.400%\n", + " yC4H10:\n", + " \n", + " \n", + " power:\n", + " 481.28 MW\n", + " \n", + " \n", + " isentr. efficiency:\n", + " 84.02%\n", + " isentr. head:\n", + " 367.27 kJ/kg\n", + " inlet vol. flow:\n", + " 883.2 m**3/s\n", + " \n", + " 288.15 K\n", + " 1100.984 kg/s\n", + " 1.034 bar\n", + " 0.030%\n", + " 0.990%\n", + " 0.920%\n", + " 20.740%\n", + " 77.320%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yH2O:\n", + " yAr:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 288.17 K\n", + " 1100.984 kg/s\n", + " 1.099 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 709.64 K\n", + " 1100.984 kg/s\n", + " 19.226 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 709.64 K\n", + " 1008.614 kg/s\n", + " 19.226 bar\n", + " T:\n", + " F:\n", + " P:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 691.89 K\n", + " 1034.561 kg/s\n", + " 19.226 bar\n", + " 0.070%\n", + " 3.842%\n", + " 19.884%\n", + " 74.195%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yCH4:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " 0.132%\n", + " yC2H6:\n", + " 0.029%\n", + " yC3H8:\n", + " 0.017%\n", + " yC4H10:\n", + " \n", + " \n", + " \n", + " 1641.38 K\n", + " 1034.691 kg/s\n", + " 18.265 bar\n", + " 4.324%\n", + " 9.217%\n", + " 0.000%\n", + " 11.471%\n", + " 74.106%\n", + " T:\n", + " F:\n", + " P:\n", + " yCO2:\n", + " yH2O:\n", + " yCH4:\n", + " yO2:\n", + " yN2:\n", + " \n", + " \n", + " 0.000%\n", + " yC2H6:\n", + " 0.000%\n", + " yC3H8:\n", + " 0.000%\n", + " yC4H10:\n", + " 0.881%\n", + " yAr:\n", + " \n", + " \n", + " \n", + " power:\n", + " -374.58 MW\n", + " \n", + " \n", + " isentr. efficiency:\n", + " 88.53%\n", + " isentr. head:\n", + " -408.95 kJ/kg\n", + " inlet vol. flow:\n", + " 273.6 m**3/s\n", + " \n", + " \n", + " \n", + " 898.00 K\n", + " 1127.060 kg/s\n", + " 1.100 bar\n", + " 3.978%\n", + " 8.554%\n", + " 0.884%\n", + " 12.219%\n", + " 74.365%\n", + " T:\n", + " 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+ " 243.589 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 627.24 K\n", + " 173.589 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 659.11 K\n", + " 173.415 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 584.88 K\n", + " 1.081 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 723.83 K\n", + " 1.081 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " F:T:P:x:\n", + " 136.416 kg/s\n", + " 557.34 K\n", + " 173.589 bar\n", + " ?\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " T:P:\n", + " 820.55 K\n", + " 1.094 bar\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Steam Turbine Section\n", + "\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " image/svg+xml\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " HRSG\n", + " Cold Flue Gas\n", + " Gas Turbine Exhaust\n", + " Makeup Water\n", + " HP\n", + " IP\n", + " LP\n", + " Condensate Pump\n", + " Condenser\n", + " \n", + " \n", + " Cold Reheat\n", + " Hot Reheat\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " From Dryer\n", + " From Reclaimer\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " To Reclaimer\n", + " To Dryer\n", + " To Ejector\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " From NG Preheater\n", + " To NG Preheater\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " CaptureReboiler\n", + " \n", + " \n", + " \n", + " \n", + " To SOEC\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " t01\n", + " \n", + " \n", + " \n", + " t02\n", + " \n", + " \n", + " \n", + " t11\n", + " \n", + " \n", + " \n", + " t15\n", + " \n", + " \n", + " \n", + " t14\n", + " \n", + " \n", + " \n", + " t13\n", + " \n", + " \n", + " \n", + " t10\n", + " \n", + " \n", + " \n", + " t09\n", + " \n", + " \n", + " \n", + " t13\n", + " \n", + " \n", + " \n", + " t04\n", + " \n", + " \n", + " \n", + " t05\n", + " \n", + " \n", + " \n", + " t03\n", + " \n", + " \n", + " \n", + " t06\n", + " \n", + " \n", + " \n", + " t07\n", + " \n", + " \n", + " \n", + " t08\n", + " \n", + " \n", + " \n", + " t12\n", + " \n", + " \n", + " \n", + " cw01\n", + " \n", + " \n", + " \n", + " cw02\n", + " \n", + " \n", + " \n", + " t18\n", + " \n", + " \n", + " \n", + " t17\n", + " \n", + " \n", + " \n", + " t16\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " t01\n", + " \n", + " t02\n", + " \n", + " \n", + " t11\n", + " \n", + " \n", + " t15\n", + " \n", + " \n", + " t14\n", + " \n", + " \n", + " t10\n", + " \n", + " \n", + " t09\n", + " \n", + " \n", + " t13\n", + " \n", + " \n", + " t04\n", + " \n", + " \n", + " t05\n", + " \n", + " \n", + " t08\n", + " \n", + " \n", + " t12\n", + " \n", + " \n", + " cw01\n", + " \n", + " \n", + " cw02\n", + " \n", + " \n", + " t16\n", + " \n", + " \n", + " t18\n", + " \n", + " \n", + " t17\n", + " \n", + " t03\n", + " \n", + " t06\n", + " \n", + " t07\n", + " \n", + " \n", + " \n", + " 136.416 kg/s\n", + " 858.53 K\n", + " 172.428 bar\n", + " 63.487 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 157.430 kg/s\n", + " 855.94 K\n", + " 30.909 bar\n", + " 65.630 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 0.806 kg/s\n", + " 306.25 K\n", + " 1.013 bar\n", + " 2.500 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 3603.054 kg/s\n", + " 289.70 K\n", + " 5.000 bar\n", + " 1.260 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 111.286 kg/s\n", + " 316.88 K\n", + " 0.090 bar\n", + " 45.114 kJ/mol\n", + " 0.968\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 22.669 kg/s\n", + " 557.10 K\n", + " 6.550 bar\n", + " 54.533 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 0.002 kg/s\n", + " 577.72 K\n", + " 4.592 bar\n", + " 55.397 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 68.812 kg/s\n", + " 577.72 K\n", + " 4.592 bar\n", + " 55.397 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 157.430 kg/s\n", + " 581.07 K\n", + " 4.592 bar\n", + " 55.522 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 136.416 kg/s\n", + " 610.47 K\n", + " 34.177 bar\n", + " 55.412 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 111.286 kg/s\n", + " 577.72 K\n", + " 4.592 bar\n", + " 55.397 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 180.904 kg/s\n", + " 356.59 K\n", + " 6.550 bar\n", + " 6.304 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 0.001 kg/s\n", + " 487.00 K\n", + " 20.000 bar\n", + " 50.496 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 0.000 kg/s\n", + " 476.00 K\n", + " 16.000 bar\n", + " 50.393 kJ/mol\n", + " 1.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 111.286 kg/s\n", + " 316.88 K\n", + " 0.090 bar\n", + " 3.299 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " 3603.054 kg/s\n", + " 306.85 K\n", + " 5.000 bar\n", + " 2.552 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 68.812 kg/s\n", + " 420.51 K\n", + " 4.592 bar\n", + " 11.184 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 112.092 kg/s\n", + " 316.80 K\n", + " 0.090 bar\n", + " 3.293 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + " \n", + " 112.092 kg/s\n", + " 316.86 K\n", + " 6.550 bar\n", + " 3.308 kJ/mol\n", + " 0.000\n", + " \n", + " \n", + " F:T:P:H:X:\n", + " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "def display_pfd():\n", + " print(\"\\n\\nGas Turbine Section\\n\")\n", + " display(SVG(m.fs.gt.write_pfd()))\n", + " print(\"\\n\\nHRSG Section\\n\")\n", + " display(SVG(m.fs.hrsg.write_pfd()))\n", + " print(\"\\n\\nSteam Turbine Section\\n\")\n", + " display(SVG(m.fs.st.write_pfd()))\n", + "\n", + "\n", + "display_pfd()\n", + "\n", + "m.fs.gt.write_pfd(fname=\"data_pfds/gt_baseline.svg\")\n", + "m.fs.hrsg.write_pfd(fname=\"data_pfds/hrsg_baseline.svg\")\n", + "m.fs.st.write_pfd(fname=\"data_pfds/st_baseline.svg\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Steam Turbine Section\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test key model outputs against NETL baseline" + ] }, { - "data": { - "image/svg+xml": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " image/svg+xml\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " HRSG\n", - " Cold Flue Gas\n", - " Gas Turbine Exhaust\n", - " Makeup Water\n", - " HP\n", - " IP\n", - " LP\n", - " Condensate Pump\n", - " Condenser\n", - " \n", - " \n", - " Cold Reheat\n", - " Hot Reheat\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " From Dryer\n", - " From Reclaimer\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " To Reclaimer\n", - " To Dryer\n", - " To Ejector\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " From NG Preheater\n", - " To NG Preheater\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " CaptureReboiler\n", - " \n", - " \n", - " \n", - " \n", - " To SOEC\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " t01\n", - " \n", - " \n", - " \n", - " t02\n", - " \n", - " \n", - " \n", - " t11\n", - " \n", - " \n", - " \n", - " t15\n", - " \n", - " \n", - " \n", - " t14\n", - " \n", - " \n", - " \n", - " t13\n", - " \n", - " \n", - " \n", - " t10\n", - " \n", - " \n", - " \n", - " t09\n", - " \n", - " \n", - " \n", - " t13\n", - " \n", - " \n", - " \n", - " t04\n", - " \n", - " \n", - " \n", - " t05\n", - " \n", - " \n", - " \n", - " t03\n", - " \n", - " \n", - " \n", - " t06\n", - " \n", - " \n", - " \n", - " t07\n", - " \n", - " \n", - " \n", - " t08\n", - " \n", - " \n", - " \n", - " t12\n", - " \n", - " \n", - " \n", - " cw01\n", - " \n", - " \n", - " \n", - " cw02\n", - " \n", - " \n", - " \n", - " t18\n", - " \n", - " \n", - " \n", - " t17\n", - " \n", - " \n", - " \n", - " t16\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " t01\n", - " \n", - " t02\n", - " \n", - " \n", - " t11\n", - " \n", - " \n", - " t15\n", - " \n", - " \n", - " t14\n", - " \n", - " \n", - " t10\n", - " \n", - " \n", - " t09\n", - " \n", - " \n", - " t13\n", - " \n", - " \n", - " t04\n", - " \n", - " \n", - " t05\n", - " \n", - " \n", - " t08\n", - " \n", - " \n", - " t12\n", - " \n", - " \n", - " cw01\n", - " \n", - " \n", - " cw02\n", - " \n", - " \n", - " t16\n", - " \n", - " \n", - " t18\n", - " \n", - " \n", - " t17\n", - " \n", - " t03\n", - " \n", - " t06\n", - " \n", - " t07\n", - " \n", - " \n", - " \n", - " 136.416 kg/s\n", - " 858.53 K\n", - " 172.428 bar\n", - " 63.487 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 157.430 kg/s\n", - " 855.94 K\n", - " 30.909 bar\n", - " 65.630 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 0.806 kg/s\n", - " 306.25 K\n", - " 1.013 bar\n", - " 2.500 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 3603.054 kg/s\n", - " 289.70 K\n", - " 5.000 bar\n", - " 1.260 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 111.286 kg/s\n", - " 316.88 K\n", - " 0.090 bar\n", - " 45.114 kJ/mol\n", - " 0.968\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 22.669 kg/s\n", - " 557.10 K\n", - " 6.550 bar\n", - " 54.533 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 0.002 kg/s\n", - " 577.72 K\n", - " 4.592 bar\n", - " 55.397 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 68.812 kg/s\n", - " 577.72 K\n", - " 4.592 bar\n", - " 55.397 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 157.430 kg/s\n", - " 581.07 K\n", - " 4.592 bar\n", - " 55.522 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 136.416 kg/s\n", - " 610.47 K\n", - " 34.177 bar\n", - " 55.412 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 111.286 kg/s\n", - " 577.72 K\n", - " 4.592 bar\n", - " 55.397 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 180.904 kg/s\n", - " 356.59 K\n", - " 6.550 bar\n", - " 6.304 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 0.001 kg/s\n", - " 487.00 K\n", - " 20.000 bar\n", - " 50.496 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 0.000 kg/s\n", - " 476.00 K\n", - " 16.000 bar\n", - " 50.393 kJ/mol\n", - " 1.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 111.286 kg/s\n", - " 316.88 K\n", - " 0.090 bar\n", - " 3.299 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " 3603.054 kg/s\n", - " 306.85 K\n", - " 5.000 bar\n", - " 2.552 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 68.812 kg/s\n", - " 420.51 K\n", - " 4.592 bar\n", - " 11.184 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 112.092 kg/s\n", - " 316.80 K\n", - " 0.090 bar\n", - " 3.293 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - " \n", - " 112.092 kg/s\n", - " 316.86 K\n", - " 6.550 bar\n", - " 3.308 kJ/mol\n", - " 0.000\n", - " \n", - " \n", - " F:T:P:H:X:\n", - " \n", - " \n", - "" + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Assert results approximately agree with baseline reoprt\n", + "assert pyo.value(m.fs.net_power_mw[0]) == pytest.approx(646)\n", + "assert pyo.value(m.fs.gross_power[0]) == pytest.approx(-690e6, rel=0.001)\n", + "assert pyo.value(100 * m.fs.lhv_efficiency[0]) == pytest.approx(52.8, abs=0.1)\n", + "assert pyo.value(\n", + " m.fs.total_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", + ") == pytest.approx(37.2799, rel=0.01)\n", + "assert pyo.value(m.fs.fuel_cost_rate[0] / m.fs.net_power_mw[0]) == pytest.approx(\n", + " 31.6462, rel=0.01\n", + ")\n", + "assert pyo.value(\n", + " m.fs.other_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", + ") == pytest.approx(5.63373, rel=0.01)\n", + "assert pyo.value(m.fs.gt.gt_power[0]) == pytest.approx(-477e6, rel=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MGbi4uGDNmjX48ssv0aNHDwD3AlPz5s0fuO9du3bh8OHDyMzMxFNPPQUAaNmypdSuVquhUCig0WgeuI3du3cjPT0d2dnZcHV1BQB88cUXeOaZZ5CamiqNg6qoqEB8fDyaNGkCABg2bBh2796Njz76qFqvU23wyg4REf2tCCGgUCik+bVr12Lw4MEwMbn39/+QIUNw8OBBnD9/Xm+9bt264fjx43rT2LFjpfaPPvoIOp0OK1euxDPPPIOVK1fC09MT6enpD61n+fLlsLa2hoWFBUaPHo2JEydi3LhxUnuHDh30+p84cQJ79+6FtbW1NHl6egK4d9Xk/PnzKC0thZ+fn7SOnZ0dnn766QfWcPz4cTRv3lwKOrWRmZkJV1dXKegAgLe3N2xsbJCZmSktc3d3l4IOADg7OyMvL6/W+60OXtmpZ+7vbTZ0CQZ3wfw1Q5dgWDV8qoSI6ldmZqY0jub69evYuHEjysrKsGLFCqlPeXk51q5dq3e1wcrKCq1bt37otu3t7fHyyy/j5Zdfxpw5c9C+fXssWLDgvk9yVQoPD8f06dNhYWEBZ2fnKk+K/fVpp6KiIvTv3x8ff/xxlW05Ozvj3LlzD63xfiwsLGq8Tm0plUq9eYVCgYqKinrdJ6/sEBHR38aePXuQnp6OsLAwAMC6devQvHlznDhxQu+KzcKFCxEfH4/y8vJa78vU1BStWrV65NNYarUarVu3RrNmze77SPxfPffcczh9+jTc3d3RunVrvcnKygqtWrWCUqlESkqKtM6NGzdw5syZB26zbdu2+O233x7Yx9TU9JGvhZeXFy5duoRLly5JyzIyMpCfnw9vb+9HHld9YtghIiJZKikpgU6nw+XLl3H06FHMmTMHAwYMQL9+/TB8+HAAwJo1a/DSSy/h2Wef1ZtGjhyJ33//XfoMnT9v789T5RNQCQkJGDp0KBISEnDmzBlkZWVhwYIF2LJlCwYMGFCnxxUZGYnr169jyJAhSE1Nxfnz57F9+3a8/vrrKC8vh7W1NUaOHIkpU6Zgz549OHXq1AM/W6hSUFAQunbtirCwMOzcuRPZ2dnYunWrdPzu7u4oKirC7t278fvvv+P27dtVthESEgIfHx+Eh4fj6NGjOHz4MIYPH46goCB07NixTl+DmmLYISIiWdq2bRucnZ3h7u6O3r17Y+/evVi6dCl+/PFHGBsbIy0tDSdOnJCu8vyZWq1Gjx49sGbNmirb+/MUGBgI4N7YFEtLS7zzzjvw9fWFv78/vv32W6xevbrKQOfH5eLigoMHD6K8vBy9evWCj48PoqKiYGNjIwWaTz75BP/4xz/Qv39/hISEIDAwsMrYn7/64Ycf0KlTJwwZMgTe3t549913pas5Xbp0wdixYzF48GA4ODjoDZiupFAo8OOPP8LW1hZdu3ZFSEgIWrZsiW+++aZOj782FOJxn5GTgcLCQqjVahQUFEClUtXptjlmh2N2DD1mh+cgz8GanIPFxcXIzs6Gh4cHzM3N67Eooup52DlZ3fdvXtkhIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEioir4oC41FnVxLjLsEBGRxNjYGAD0vnWbyJAqP8Dwr18zURP8biwiIpKYmJjA0tIS165dg1KprNbXFxDVByEEbt++jby8PNjY2EhBvDYYdoiISKJQKODs7Izs7GxcvHjR0OUQwcbGBhqN5rG2wbBDRER6TE1N0aZNG97KIoNTKpWPdUWnEsMOERFVYWRkxK+LINngzVgiIiKSNYYdIiIikjWGHSIiIpI1g4edy5cvY+jQobC3t4eFhQV8fHxw5MgRqV0IgZkzZ8LZ2RkWFhYICQnB2bNn9bZx/fp1hIeHQ6VSwcbGBiNHjkRRUVFDHwoRERE1QgYNOzdu3EBAQACUSiW2bt2KjIwMLFy4ELa2tlKf+fPnY+nSpVi5ciVSUlJgZWWF0NBQFBcXS33Cw8Nx+vRp7Ny5EwkJCdi/fz/GjBljiEMiIiKiRsagT2N9/PHHcHV1RVxcnLTMw8ND+rcQAosXL8aMGTMwYMAAAMAXX3wBJycnbNq0Ca+++ioyMzOxbds2pKamomPHjgCAZcuW4fnnn8eCBQvg4uLSsAdFREREjYpBr+z89NNP6NixI15++WU4Ojqiffv2WLVqldSenZ0NnU6HkJAQaZlarYafnx+SkpIAAElJSbCxsZGCDgCEhITAyMgIKSkp991vSUkJCgsL9SYiIiKSJ4OGnV9//RUrVqxAmzZtsH37dowbNw4TJkzA559/DgDQ6XQAACcnJ731nJycpDadTgdHR0e9dhMTE9jZ2Ul9/mru3LlQq9XS5OrqWteHRkRERI2EQcNORUUFnnvuOcyZMwft27fHmDFjMHr0aKxcubJe9xsdHY2CggJpunTpUr3uj4iIiAzHoGHH2dkZ3t7eesu8vLyQk5MDANJ3YeTm5ur1yc3Nldo0Gg3y8vL02u/evYvr168/8Ls0zMzMoFKp9CYiIiKSJ4OGnYCAAGRlZektO3PmDNzc3ADcG6ys0Wiwe/duqb2wsBApKSnQarUAAK1Wi/z8fKSlpUl99uzZg4qKCvj5+TXAURAREVFjZtCnsSZOnIguXbpgzpw5eOWVV3D48GH85z//wX/+8x8A9759NyoqCrNnz0abNm3g4eGBf/3rX3BxccHAgQMB3LsS1Lt3b+n2V1lZGcaPH49XX32VT2IRERGRYcNOp06dsHHjRkRHR2PWrFnw8PDA4sWLER4eLvV59913cevWLYwZMwb5+fkIDAzEtm3b9L6gbt26dRg/fjx69OgBIyMjhIWFYenSpYY4JCIiImpkFEIIYegiDK2wsBBqtRoFBQV1Pn7H/b3Ndbq9J9EF89cMXYJhxRQYdPc8B3kOGvocJKov1X3/NvjXRRARERHVJ4YdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYOGnZiYGCgUCr3J09NTai8uLkZkZCTs7e1hbW2NsLAw5Obm6m0jJycHffv2haWlJRwdHTFlyhTcvXu3oQ+FiIiIGikTQxfwzDPPYNeuXdK8icn/lzRx4kRs3rwZ3333HdRqNcaPH49Bgwbh4MGDAIDy8nL07dsXGo0Ghw4dwtWrVzF8+HAolUrMmTOnwY+FiIiIGh+Dhx0TExNoNJoqywsKCrBmzRqsX78e3bt3BwDExcXBy8sLycnJ8Pf3x44dO5CRkYFdu3bByckJvr6++PDDDzF16lTExMTA1NS0oQ+HiIiIGhmDj9k5e/YsXFxc0LJlS4SHhyMnJwcAkJaWhrKyMoSEhEh9PT090aJFCyQlJQEAkpKS4OPjAycnJ6lPaGgoCgsLcfr06Qfus6SkBIWFhXoTERERyZNBw46fnx/i4+Oxbds2rFixAtnZ2fjHP/6BmzdvQqfTwdTUFDY2NnrrODk5QafTAQB0Op1e0Klsr2x7kLlz50KtVkuTq6tr3R4YERERNRoGvY3Vp08f6d9t27aFn58f3Nzc8O2338LCwqLe9hsdHY1JkyZJ84WFhQw8REREMmXw21h/ZmNjg6eeegrnzp2DRqNBaWkp8vPz9frk5uZKY3w0Gk2Vp7Mq5+83DqiSmZkZVCqV3kRERETy1KjCTlFREc6fPw9nZ2d06NABSqUSu3fvltqzsrKQk5MDrVYLANBqtUhPT0deXp7UZ+fOnVCpVPD29m7w+omIiKjxMehtrMmTJ6N///5wc3PDlStX8P7778PY2BhDhgyBWq3GyJEjMWnSJNjZ2UGlUuGtt96CVquFv78/AKBXr17w9vbGsGHDMH/+fOh0OsyYMQORkZEwMzMz5KERERFRI2HQsPPbb79hyJAh+OOPP+Dg4IDAwEAkJyfDwcEBALBo0SIYGRkhLCwMJSUlCA0NxfLly6X1jY2NkZCQgHHjxkGr1cLKygoRERGYNWuWoQ6JiIiIGhmDhp2vv/76oe3m5uaIjY1FbGzsA/u4ublhy5YtdV0aERERyUSjGrNDREREVNcYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWDP6t50REJG/u7202dAkGd8H8NUOXYFgxBQbdPa/sEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrJnUdIX8/Hxs3LgRBw4cwMWLF3H79m04ODigffv2CA0NRZcuXeqjTiIiIqJaqfaVnStXrmDUqFFwdnbG7NmzcefOHfj6+qJHjx5o3rw59u7di549e8Lb2xvffPNNfdZMREREVG3VvrLTvn17REREIC0tDd7e3vftc+fOHWzatAmLFy/GpUuXMHny5DorlIiIiKg2qh12MjIyYG9v/9A+FhYWGDJkCIYMGYI//vjjsYsjIiIielzVvo1lb2+PkpKSam/4UcGIiIiIqCHUaICyWq2GVqtFt27d0K1bN/j7+0OpVNZXbURERESPrUaPnq9cuRJubm5Yu3YtgoKCYGNjg549e2Lu3LlITk5GeXl5fdVJREREVCs1CjsjRoxAfHw8Lly4gHPnzmHZsmVwcXHBypUrERAQAFtbW/Tt27e+aiUiIiKqsRp/zk6lli1bomXLlnjjjTeQnZ2NNWvWYNmyZdi2bVtd1kdERET0WGoVdnJycrB3714kJiYiMTERv//+O/z9/TF58mQEBQXVdY1EREREtVajsPPGG28gMTER169fR0BAAP7xj39gzJgx6NSpE0xMan2RiIiIiKje1CihxMfHo0WLFpg+fTp69OiB9u3bQ6FQ1FdtRERERI+tRmEnMzNTun21cOFClJSUIDAwEEFBQQgODsZzzz0HIyN+tygRERE1HjVKJk8//TTGjh2Lr7/+GjqdDgcPHsTzzz+Pw4cPo1+/frCzs0O/fv3qq1YiIiKiGnusgTbe3t6wt7eHra0tbG1t8fXXX2Pr1q11VRsRERHRY6tx2MnLy0NiYqJ0O+vMmTMwNTVF586dMXHiRHTr1q0+6iQiIiKqlRrdxvLy8oKzszOGDRuG9PR0vPTSS9ixYwdu3LiBffv2ISYmptaPns+bNw8KhQJRUVHSsuLiYkRGRsLe3h7W1tYICwtDbm6u3no5OTno27cvLC0t4ejoiClTpuDu3bu1qoGIiIjkp0ZXdgYOHIhu3bohMDAQlpaWdVZEamoqPvvsM7Rt21Zv+cSJE7F582Z89913UKvVGD9+PAYNGoSDBw8CAMrLy9G3b19oNBocOnQIV69exfDhw6FUKjFnzpw6q4+IiIieXDW6sjN37lz06tWrToNOUVERwsPDsWrVKtja2krLCwoKsGbNGnz66afo3r07OnTogLi4OBw6dAjJyckAgB07diAjIwNffvklfH190adPH3z44YeIjY1FaWlpndVIRERET64aXdmZNWtWtfrNnDmz2tuMjIxE3759ERISgtmzZ0vL09LSUFZWhpCQEGmZp6cnWrRogaSkJPj7+yMpKQk+Pj5wcnKS+oSGhmLcuHE4ffo02rdvf999lpSUoKSkRJovLCysdr1ERET0ZKlR2ImJiYGLiwscHR0hhLhvH4VCUe2w8/XXX+Po0aNITU2t0qbT6WBqagobGxu95U5OTtDpdFKfPwedyvbKtgeZO3cuPvjgg2rVSERERE+2GoWdPn36YM+ePejYsSPeeOMN9OvXr9YfInjp0iW8/fbb2LlzJ8zNzWu1jdqKjo7GpEmTpPnCwkK4uro2aA1ERETUMGqUVDZv3ozz58/Dz88PU6ZMQbNmzTB16lRkZWXVeMdpaWnIy8vDc889BxMTE5iYmGDfvn1YunQpTExM4OTkhNLSUuTn5+utl5ubC41GAwDQaDRVns6qnK/scz9mZmZQqVR6ExEREclTjS/LuLi4IDo6GllZWfjmm2+Ql5eHTp06ISAgAHfu3Kn2dnr06IH09HQcP35cmjp27Ijw8HDp30qlErt375bWycrKQk5ODrRaLQBAq9UiPT0deXl5Up+dO3dCpVLB29u7podGREREMvRYn6DcqVMnXLhwARkZGTh27BjKyspgYWFRrXWbNGmCZ599Vm+ZlZUV7O3tpeUjR47EpEmTYGdnB5VKhbfeegtarRb+/v4AgF69esHb2xvDhg3D/PnzodPpMGPGDERGRsLMzOxxDo2IiIhkolYDbpKSkjB69GhoNBosW7YMERERuHLlSp3fDlq0aBH69euHsLAwdO3aFRqNBhs2bJDajY2NkZCQAGNjY2i1WgwdOhTDhw+v9lNjREREJH81urIzf/58xMfH4/fff0d4eDgOHDhQ5YMAH0diYqLevLm5OWJjYxEbG/vAddzc3LBly5Y6q4GIiIjkpUZh57333kOLFi3wyiuvQKFQID4+/r79Pv3007qojYiIiOix1SjsdO3aFQqFAqdPn35gH4VC8dhFEREREdWVGoWdv95mIiIiImrsaveJgERERERPiGqHnXnz5uH27dvV6puSkoLNmzfXuigiIiKiulLtsJORkQE3Nzf885//xNatW3Ht2jWp7e7duzh58iSWL1+OLl26YPDgwWjSpEm9FExERERUE9Ues/PFF1/gxIkT+Pe//43XXnsNhYWFMDY2hpmZmXTFp3379hg1ahRGjBjR4N93RURERHQ/NRqg3K5dO6xatQqfffYZTp48iYsXL+LOnTto2rQpfH190bRp0/qqk4iIiKhWavV1EUZGRvD19YWvr28dl0NERERUt/g0FhEREckaww4RERHJGsMOERERyRrDDhEREclajcNOWVkZTExMcOrUqfqoh4iIiKhO1TjsKJVKtGjRAuXl5fVRDxEREVGdqtVtrOnTp2PatGm4fv16XddDREREVKdq9Tk7//73v3Hu3Dm4uLjAzc0NVlZWeu1Hjx6tk+KIiIiIHletws7AgQPruAwiIiKi+lGrsPP+++/XdR1ERERE9aLWj57n5+dj9erViI6OlsbuHD16FJcvX66z4oiIiIgeV62u7Jw8eRIhISFQq9W4cOECRo8eDTs7O2zYsAE5OTn44osv6rpOIiIiolqp1ZWdSZMmYcSIETh79izMzc2l5c8//zz2799fZ8URERERPa5ahZ3U1FS8+eabVZY3a9YMOp3usYsiIiIiqiu1CjtmZmYoLCyssvzMmTNwcHB47KKIiIiI6kqtws4LL7yAWbNmoaysDACgUCiQk5ODqVOnIiwsrE4LJCIiInoctQo7CxcuRFFRERwdHXHnzh0EBQWhdevWaNKkCT766KO6rpGIiIio1mr1NJZarcbOnTvxv//9DydPnkRRURGee+45hISE1HV9RERERI+lVmGnuLgY5ubmCAwMRGBgYF3XRERERFRnahV2bGxs0LlzZwQFBaFbt27QarWwsLCo69qIiIiIHlutxuzs2rULvXv3RkpKCl544QXY2toiMDAQ06dPx86dO+u6RiIiIqJaq1XYCQwMxLRp07Bjxw7k5+dj7969aN26NebPn4/evXvXdY1EREREtVar21jAvc/USUxMlKaSkhL069cPwcHBdVgeERER0eOpVdhp1qwZ7ty5g+DgYAQHB2Pq1Klo27YtFApFXddHRERE9FhqdRvLwcEBt2/fhk6ng06nQ25uLu7cuVPXtRERERE9tlqFnePHj0On0+G9995DSUkJpk2bhqZNm6JLly6YPn16XddIREREVGu1HrNjY2ODF154AQEBAejSpQt+/PFHfPXVV0hJSeGnKBMREVGjUauws2HDBmlgckZGBuzs7BAYGIiFCxciKCiormskIiIiqrVahZ2xY8eia9euGDNmDIKCguDj41PXdRERERHViVqFnby8vLqug4iIiKhe1HrMTnl5OTZt2oTMzEwAgLe3NwYMGABjY+M6K46IiIjocdXqaaxz587By8sLw4cPx4YNG7BhwwYMGzYMzzzzDM6fP1/t7axYsQJt27aFSqWCSqWCVqvF1q1bpfbi4mJERkbC3t4e1tbWCAsLQ25urt42cnJy0LdvX1haWsLR0RFTpkzB3bt3a3NYREREJEO1CjsTJkxAq1atcOnSJRw9ehRHjx5FTk4OPDw8MGHChGpvp3nz5pg3bx7S0tJw5MgRdO/eHQMGDMDp06cBABMnTsTPP/+M7777Dvv27cOVK1cwaNAgaf3y8nL07dsXpaWlOHToED7//HPEx8dj5syZtTksIiIikqFa3cbat28fkpOTYWdnJy2zt7fHvHnzEBAQUO3t9O/fX2/+o48+wooVK5CcnIzmzZtjzZo1WL9+Pbp37w4AiIuLg5eXF5KTk+Hv748dO3YgIyMDu3btgpOTE3x9ffHhhx9i6tSpiImJgampaW0Oj4iIiGSkVld2zMzMcPPmzSrLi4qKah0wysvL8fXXX+PWrVvQarVIS0tDWVkZQkJCpD6enp5o0aIFkpKSAABJSUnw8fGBk5OT1Cc0NBSFhYXS1aH7KSkpQWFhod5ERERE8lSrsNOvXz+MGTMGKSkpEEJACIHk5GSMHTsWL7zwQo22lZ6eDmtra5iZmWHs2LHYuHEjvL29odPpYGpqChsbG73+Tk5O0Ol0AACdTqcXdCrbK9seZO7cuVCr1dLk6upao5qJiIjoyVGrsLN06VK0atUKWq0W5ubmMDc3R0BAAFq3bo0lS5bUaFtPP/00jh8/jpSUFIwbNw4RERHIyMioTVnVFh0djYKCAmm6dOlSve6PiIiIDKdWY3ZsbGzw448/4ty5c9Kj515eXmjdunWNt2Vqaiqt16FDB6SmpmLJkiUYPHgwSktLkZ+fr3d1Jzc3FxqNBgCg0Whw+PBhve1VPq1V2ed+zMzMYGZmVuNaiYiI6MlToys7FRUV+PjjjxEQEIBOnTph9erVCAkJQf/+/WsVdB60j5KSEnTo0AFKpRK7d++W2rKyspCTkwOtVgsA0Gq1SE9P1/uQw507d0KlUsHb27tO6iEiIqInW42u7Hz00UeIiYlBSEgILCwssGTJEuTl5WHt2rW12nl0dDT69OmDFi1a4ObNm1i/fj0SExOxfft2qNVqjBw5EpMmTYKdnR1UKhXeeustaLVa+Pv7AwB69eoFb29vDBs2DPPnz4dOp8OMGTMQGRnJKzdEREQEoIZh54svvsDy5cvx5ptvAgB27dqFvn37YvXq1TAyqvnwn7y8PAwfPhxXr16FWq1G27ZtsX37dvTs2RMAsGjRIhgZGSEsLAwlJSUIDQ3F8uXLpfWNjY2RkJCAcePGQavVwsrKChEREZg1a1aNayEiIiJ5qlHYycnJwfPPPy/Nh4SEQKFQ4MqVK2jevHmNd75mzZqHtpubmyM2NhaxsbEP7OPm5oYtW7bUeN9ERET091CjyzF3796Fubm53jKlUomysrI6LYqIiIiortToyo4QAiNGjNAbD1NcXIyxY8fCyspKWrZhw4a6q5CIiIjoMdQo7ERERFRZNnTo0DorhoiIiKiu1SjsxMXF1VcdRERERPWiVp+gTERERPSkYNghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIlkzaNiZO3cuOnXqhCZNmsDR0REDBw5EVlaWXp/i4mJERkbC3t4e1tbWCAsLQ25url6fnJwc9O3bF5aWlnB0dMSUKVNw9+7dhjwUIiIiaqQMGnb27duHyMhIJCcnY+fOnSgrK0OvXr1w69Ytqc/EiRPx888/47vvvsO+fftw5coVDBo0SGovLy9H3759UVpaikOHDuHzzz9HfHw8Zs6caYhDIiIiokbGxJA737Ztm958fHw8HB0dkZaWhq5du6KgoABr1qzB+vXr0b17dwBAXFwcvLy8kJycDH9/f+zYsQMZGRnYtWsXnJyc4Ovriw8//BBTp05FTEwMTE1NDXFoRERE1Eg0qjE7BQUFAAA7OzsAQFpaGsrKyhASEiL18fT0RIsWLZCUlAQASEpKgo+PD5ycnKQ+oaGhKCwsxOnTp++7n5KSEhQWFupNREREJE+NJuxUVFQgKioKAQEBePbZZwEAOp0OpqamsLGx0evr5OQEnU4n9flz0Klsr2y7n7lz50KtVkuTq6trHR8NERERNRaNJuxERkbi1KlT+Prrr+t9X9HR0SgoKJCmS5cu1fs+iYiIyDAMOman0vjx45GQkID9+/ejefPm0nKNRoPS0lLk5+frXd3Jzc2FRqOR+hw+fFhve5VPa1X2+SszMzOYmZnV8VEQERFRY2TQKztCCIwfPx4bN27Enj174OHhodfeoUMHKJVK7N69W1qWlZWFnJwcaLVaAIBWq0V6ejry8vKkPjt37oRKpYK3t3fDHAgRERE1Wga9shMZGYn169fjxx9/RJMmTaQxNmq1GhYWFlCr1Rg5ciQmTZoEOzs7qFQqvPXWW9BqtfD39wcA9OrVC97e3hg2bBjmz58PnU6HGTNmIDIykldviIiIyLBhZ8WKFQCA4OBgveVxcXEYMWIEAGDRokUwMjJCWFgYSkpKEBoaiuXLl0t9jY2NkZCQgHHjxkGr1cLKygoRERGYNWtWQx0GERERNWIGDTtCiEf2MTc3R2xsLGJjYx/Yx83NDVu2bKnL0oiIiEgmGs3TWERERET1gWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkzaBhZ//+/ejfvz9cXFygUCiwadMmvXYhBGbOnAlnZ2dYWFggJCQEZ8+e1etz/fp1hIeHQ6VSwcbGBiNHjkRRUVEDHgURERE1ZgYNO7du3UK7du0QGxt73/b58+dj6dKlWLlyJVJSUmBlZYXQ0FAUFxdLfcLDw3H69Gns3LkTCQkJ2L9/P8aMGdNQh0BERESNnIkhd96nTx/06dPnvm1CCCxevBgzZszAgAEDAABffPEFnJycsGnTJrz66qvIzMzEtm3bkJqaio4dOwIAli1bhueffx4LFiyAi4tLgx0LERERNU6NdsxOdnY2dDodQkJCpGVqtRp+fn5ISkoCACQlJcHGxkYKOgAQEhICIyMjpKSkPHDbJSUlKCws1JuIiIhInhpt2NHpdAAAJycnveVOTk5Sm06ng6Ojo167iYkJ7OzspD73M3fuXKjVamlydXWt4+qJiIiosWi0Yac+RUdHo6CgQJouXbpk6JKIiIionjTasKPRaAAAubm5estzc3OlNo1Gg7y8PL32u3fv4vr161Kf+zEzM4NKpdKbiIiISJ4abdjx8PCARqPB7t27pWWFhYVISUmBVqsFAGi1WuTn5yMtLU3qs2fPHlRUVMDPz6/BayYiIqLGx6BPYxUVFeHcuXPSfHZ2No4fPw47Ozu0aNECUVFRmD17Ntq0aQMPDw/861//gouLCwYOHAgA8PLyQu/evTF69GisXLkSZWVlGD9+PF599VU+iUVEREQADBx2jhw5gm7duknzkyZNAgBEREQgPj4e7777Lm7duoUxY8YgPz8fgYGB2LZtG8zNzaV11q1bh/Hjx6NHjx4wMjJCWFgYli5d2uDHQkRERI2TQcNOcHAwhBAPbFcoFJg1axZmzZr1wD52dnZYv359fZRHREREMtBox+wQERER1QWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjXZhJ3Y2Fi4u7vD3Nwcfn5+OHz4sKFLIiIiokZAFmHnm2++waRJk/D+++/j6NGjaNeuHUJDQ5GXl2fo0oiIiMjAZBF2Pv30U4wePRqvv/46vL29sXLlSlhaWmLt2rWGLo2IiIgMzMTQBTyu0tJSpKWlITo6WlpmZGSEkJAQJCUl3XedkpISlJSUSPMFBQUAgMLCwjqvr6Lkdp1v80lTqBCGLsGw6uG8qgmegzwHeQ4aHs/B+jkHK9+3hXj46/vEh53ff/8d5eXlcHJy0lvu5OSEX3755b7rzJ07Fx988EGV5a6urvVS49+d2tAFGNq8v/0rYHB/+58Az0GD+9v/BOr5HLx58ybU6gfv44kPO7URHR2NSZMmSfMVFRW4fv067O3toVAoDFiZ/BQWFsLV1RWXLl2CSqUydDn0N8RzkAyN52D9EULg5s2bcHFxeWi/Jz7sNG3aFMbGxsjNzdVbnpubC41Gc991zMzMYGZmprfMxsamvkokACqViv/JyaB4DpKh8RysHw+7olPpiR+gbGpqig4dOmD37t3SsoqKCuzevRtardaAlREREVFj8MRf2QGASZMmISIiAh07dkTnzp2xePFi3Lp1C6+//rqhSyMiIiIDk0XYGTx4MK5du4aZM2dCp9PB19cX27ZtqzJomRqemZkZ3n///Sq3DYkaCs9BMjSeg4anEI96XouIiIjoCfbEj9khIiIiehiGHSIiIpI1hh0iIiKSNYYdIiIikjWGHaoiJiYGvr6+hi6DiKhBubu7Y/HixYYug+oBww79bZSWlhq6hL89/gwerry8HBUVFYYug2SmrKzM0CUYHMOODAUHB2PChAl49913YWdnB41Gg5iYGKk9Pz8fo0aNgoODA1QqFbp3744TJ04AAOLj4/HBBx/gxIkTUCgUUCgUiI+Pf+Q+FQoFVqxYgT59+sDCwgItW7bE999/r9cnPT0d3bt3h4WFBezt7TFmzBgUFRUBAE6dOgUjIyNcu3YNAHD9+nUYGRnh1VdfldafPXs2AgMDpflTp06hT58+sLa2hpOTE4YNG4bff/9d73UYP348oqKi0LRpU4SGhtb4taSHu3nzJsLDw2FlZQVnZ2csWrQIwcHBiIqKAnDvL+UPP/wQw4cPh0qlwpgxYwAAP/zwA5555hmYmZnB3d0dCxcu1Nvu8uXL0aZNG5ibm8PJyQkvvfSS1Pb999/Dx8dHOo9CQkJw69atR9Y6YsQIDBw4EB988IF07o8dO1YvgJWUlGDChAlwdHSEubk5AgMDkZqaKrV37NgRCxYskOYHDhwIpVIpnce//fYbFAoFzp07J21v8uTJaNasGaysrODn54fExERp/fj4eNjY2OCnn36Ct7c3zMzMkJOTU81Xn2rqYedrcHAwLl68iIkTJ0q/+x6l8ue3adMm6XwNDQ3FpUuX9PqtWLECrVq1gqmpKZ5++mn897//ldomT56Mfv36SfOLFy+GQqHAtm3bpGWtW7fG6tWrpfnVq1fDy8sL5ubm8PT0xPLly6W2CxcuQKFQ4JtvvkFQUBDMzc2xbt26Wr1esiJIdoKCgoRKpRIxMTHizJkz4vPPPxcKhULs2LFDCCFESEiI6N+/v0hNTRVnzpwR77zzjrC3txd//PGHuH37tnjnnXfEM888I65evSquXr0qbt++/ch9AhD29vZi1apVIisrS8yYMUMYGxuLjIwMIYQQRUVFwtnZWQwaNEikp6eL3bt3Cw8PDxERESGEEKKiokI0bdpUfPfdd0IIITZt2iSaNm0qNBqNtI+QkBAxffp0IYQQN27cEA4ODiI6OlpkZmaKo0ePip49e4pu3brpvQ7W1tZiypQp4pdffhG//PJLnby+9P9GjRol3NzcxK5du0R6erp48cUXRZMmTcTbb78thBDCzc1NqFQqsWDBAnHu3Dlx7tw5ceTIEWFkZCRmzZolsrKyRFxcnLCwsBBxcXFCCCFSU1OFsbGxWL9+vbhw4YI4evSoWLJkiRBCiCtXrggTExPx6aefiuzsbHHy5EkRGxsrbt68+chaIyIihLW1tRg8eLA4deqUSEhIEA4ODmLatGlSnwkTJggXFxexZcsWcfr0aRERESFsbW3FH3/8IYQQYtKkSaJv375CiHvnrJ2dnWjatKnYunWrEEKIL7/8UjRr1kzv9enSpYvYv3+/OHfunPjkk0+EmZmZOHPmjBBCiLi4OKFUKkWXLl3EwYMHxS+//CJu3br1eD8UeqCHna9//PGHaN68uZg1a5b0u+9RKn9+HTt2FIcOHRJHjhwRnTt3Fl26dJH6bNiwQSiVShEbGyuysrLEwoULhbGxsdizZ48QQoiffvpJqNVqcffuXSGEEAMHDhRNmzYVU6dOFUII8dtvvwkA4uzZs0KIe+eYs7Oz+OGHH8Svv/4qfvjhB2FnZyfi4+OFEEJkZ2cLAMLd3V3qc+XKlTp9HZ9EDDsyFBQUJAIDA/WWderUSUydOlUcOHBAqFQqUVxcrNfeqlUr8dlnnwkhhHj//fdFu3btarRPAGLs2LF6y/z8/MS4ceOEEEL85z//Eba2tqKoqEhq37x5szAyMhI6nU4IIcSgQYNEZGSkEEKIqKgoMWXKFGFraysyMzNFaWmpsLS0lALbhx9+KHr16qW3v0uXLgkAIisrS3od2rdvX6PjoOorLCwUSqVSCqhCCJGfny8sLS31ws7AgQP11nvttddEz5499ZZNmTJFeHt7CyGE+OGHH4RKpRKFhYVV9pmWliYAiAsXLtS43oiICGFnZ6cXJlasWCGsra1FeXm5KCoqEkqlUqxbt05qLy0tFS4uLmL+/PlCCP03puPHjwuNRiPefvtt6Y1p1KhR4rXXXhNCCHHx4kVhbGwsLl++rFdHjx49RHR0tBDi3pslAHH8+PEaHw/VTHXP10WLFlV7m5U/v+TkZGlZZmamACBSUlKEEEJ06dJFjB49Wm+9l19+WTz//PNCiHt/uBkZGYnU1FQpQM+dO1f4+fkJIaoG6FatWon169frbe/DDz8UWq1WCPH/YWfx4sXVPo6/A97Gkqm2bdvqzTs7OyMvLw8nTpxAUVER7O3tYW1tLU3Z2dk4f/78Y+3zr1+8qtVqkZmZCQDIzMxEu3btYGVlJbUHBASgoqICWVlZAICgoCDpEv++ffvQvXt3dO3aFYmJiUhNTUVZWRkCAgIAACdOnMDevXv1jsHT0xMA9I6jQ4cOj3VM9GC//vorysrK0LlzZ2mZWq3G008/rdevY8eOevOZmZnSz7FSQEAAzp49i/LycvTs2RNubm5o2bIlhg0bhnXr1uH27dsAgHbt2qFHjx7w8fHByy+/jFWrVuHGjRvVrrldu3awtLSU5rVaLYqKinDp0iWcP39e7xwDAKVSic6dO0vn8T/+8Q/cvHkTx44dw759+xAUFITg4GC98zY4OBjAvdu25eXleOqpp/TO03379umdo6amplX+v1Ldq+75WlMmJibo1KmTNO/p6QkbGxu93333O98r221sbNCuXTskJiYiPT0dpqamGDNmDI4dO4aioiLpPAOAW7du4fz58xg5cqTeOTV79uwqv7//+v/u704W341FVSmVSr15hUKBiooKFBUVwdnZWW/cQCUbG5uGKe4BKu+dnz17FhkZGQgMDMQvv/yCxMRE3LhxAx07dpTeqIqKitC/f398/PHHVbbj7Ows/fvP4YoMo6Y/gyZNmuDo0aNITEzEjh07MHPmTMTExCA1NRU2NjbYuXMnDh06hB07dmDZsmWYPn06UlJS4OHhUU9H8P/+/MaUlJSEnj17omvXrhg8eDDOnDmDs2fPSm9MRUVFMDY2RlpaGoyNjfW2Y21tLf3bwsKiWuNDSL4qA7OZmRmCgoJgZ2cHLy8v/O9//8O+ffvwzjvvAIA0NmzVqlXw8/PT28ZfzzH+7tPHKzt/M8899xx0Oh1MTEzQunVrvalp06YA7v2lWV5eXuNtJycnV5n38vICAHh5eeHEiRN6A0kPHjwIIyMj6S8rHx8f2NraYvbs2fD19YW1tTWCg4Oxb98+JCYmSn8xVx7H6dOn4e7uXuU4+J+8YbRs2RJKpVJvAG9BQQHOnDnz0PW8vLxw8OBBvWUHDx7EU089Jf3CNjExQUhICObPn4+TJ0/iwoUL2LNnD4B7wT0gIAAffPABjh07BlNTU2zcuLFaNZ84cQJ37tyR5pOTk2FtbQ1XV1dpAOmfaysrK0Nqaiq8vb2lZUFBQdi7dy/279+P4OBg6Y3po48+grOzM5566ikAQPv27VFeXo68vLwq56hGo6lWvVR3qnO+1uZ33927d3HkyBFpPisrC/n5+Xq/++53vv/1nPrf//6H3bt3S7/ngoOD8dVXX+HMmTPSMicnJ7i4uODXX3+tck41RNh/ohn6PhrVvaCgIOkedKUBAwaIiIgIUVFRIQIDA0W7du3E9u3bRXZ2tjh48KCYNm2aSE1NFUIIsW7dOmFlZSWOHTsmrl27VmV8z/0AEE2bNhVr1qwRWVlZYubMmcLIyEicPn1aCCHErVu3hLOzswgLCxPp6eliz549omXLltIA5UoDBw4UxsbG0hiI8vJyYWtrK4yNjcW2bdukfpcvXxYODg7ipZdeEocPHxbnzp0T27ZtEyNGjJAG+t3vdaC6NWrUKOHh4SH27NkjTp06JcLCwkSTJk1EVFSUEOL+YyDS0tL0BijHx8frDVD++eefxZIlS8SxY8fEhQsXxPLly4WRkZE4deqUSE5OFh999JFITU0VFy9eFN9++60wNTUVW7ZseWStlQOUhwwZIk6fPi02b94snJycxHvvvSf1efvtt4WLi4vYunWr3gDl69evS302bdokjI2N9QbPv/3228LY2Fi8+uqrevsMDw/XGyiakpIi5syZIxISEoQQ98Z8qNXqmrzk9Bgedb727NlTvPDCC+K3334T165de+T2Kgcod+7cWSQnJ4sjR44If39/4e/vL/XZuHGjUCqVYvny5eLMmTPSAOW9e/dKfa5fvy6MjIyEsbGxyMzMlNYzNjYWzs7OevtctWqVsLCwEEuWLBFZWVni5MmTYu3atWLhwoVCiP8fs3Ps2LHHfLXkhWFHhh4WdoS4N1DvrbfeEi4uLkKpVApXV1cRHh4ucnJyhBBCFBcXi7CwMGFjYyMASG9CDwNAxMbGip49ewozMzPh7u4uvvnmG70+J0+eFN26dRPm5ubCzs5OjB49uspTNIsWLRIApKdbKms3MTGp0vfMmTPixRdfFDY2NsLCwkJ4enqKqKgoUVFR8cDXgepWYWGheO2114SlpaXQaDTi008/FZ07d5YCxIMGfH7//ffC29tbKJVK0aJFC/HJJ59IbQcOHBBBQUHC1tZWWFhYiLZt20rnUkZGhggNDRUODg7CzMxMPPXUU2LZsmXVqjUiIkIMGDBAzJw5U9jb2wtra2sxevRovTB/584d8dZbb4mmTZsKMzMzERAQIA4fPqy3nT/++EMoFAoxePBgadnGjRsFALFy5Uq9vqWlpWLmzJnC3d1dKJVK4ezsLF588UVx8uRJIQTDTkN71PmalJQk2rZtK8zMzER1rgVU/vx++OEH0bJlS2FmZiZCQkLExYsX9fotX75ctGzZUiiVSvHUU0+JL774osq22rVrpxegK8+zvwZoIe79Qerr6ytMTU2Fra2t6Nq1q9iwYYMQgmHnQRRCCGG460okFwqFAhs3bsTAgQMNXQoZ0K1bt9CsWTMsXLgQI0eONHQ5ekaMGIH8/Hxs2rTJ0KVQI/G452t8fDyioqKQn59f98VRneIAZSKqtWPHjuGXX35B586dUVBQgFmzZgEABgwYYODKiKri+fr3xbBDj7Ru3Tq8+eab921zc3PD6dOnG7giakwWLFiArKwsmJqaokOHDjhw4IA02L0h/fkJp7/aunVrA1ZCjVlNztc+ffrgwIED922bNm0aXFxc6rNUqkO8jUWPdPPmTeTm5t63TalUws3NrYErIqqq8isa7qdZs2awsLBowGpIDi5fvqz39N6f2dnZwc7OroErotpi2CEiIiJZ4+fsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGs/R8sVPMaSvX2DQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "\n", + "variables = [\"net_power\", \"gross_power\", \"gt_power\"]\n", + "netl_baseline = [646, 690, 477]\n", + "idaes_prediction = [\n", + " pyo.value(m.fs.net_power_mw[0]),\n", + " -pyo.value(m.fs.gross_power[0]) * 1e-6,\n", + " -pyo.value(m.fs.gt.gt_power[0]) * 1e-6,\n", + "]\n", + "\n", + "label_location = np.arange(len(variables))\n", + "\n", + "width = 0.4\n", + "\n", + "fig, ax = plt.subplots()\n", + "netl_data = ax.bar(variables, netl_baseline, label=\"NETL Baseline\")\n", + "idaes_sim = ax.bar(\n", + " label_location + (width / 2), idaes_prediction, width, label=\"IDAES Prediction\"\n", + ")\n", + "\n", + "ax.set_ylabel(\"Power (MW)\")\n", + "ax.set_xticks(label_location)\n", + "ax.set_xticklabels(variables)\n", + "ax.legend()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def display_pfd():\n", - " print(\"\\n\\nGas Turbine Section\\n\")\n", - " display(SVG(m.fs.gt.write_pfd()))\n", - " print(\"\\n\\nHRSG Section\\n\")\n", - " display(SVG(m.fs.hrsg.write_pfd()))\n", - " print(\"\\n\\nSteam Turbine Section\\n\")\n", - " display(SVG(m.fs.st.write_pfd()))\n", - "\n", - "\n", - "display_pfd()\n", - "\n", - "m.fs.gt.write_pfd(fname=\"data_pfds/gt_baseline.svg\")\n", - "m.fs.hrsg.write_pfd(fname=\"data_pfds/hrsg_baseline.svg\")\n", - "m.fs.st.write_pfd(fname=\"data_pfds/st_baseline.svg\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test key model outputs against NETL baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Assert results approximately agree with baseline reoprt\n", - "assert pyo.value(m.fs.net_power_mw[0]) == pytest.approx(646)\n", - "assert pyo.value(m.fs.gross_power[0]) == pytest.approx(-690e6, rel=0.001)\n", - "assert pyo.value(100 * m.fs.lhv_efficiency[0]) == pytest.approx(52.8, abs=0.1)\n", - "assert pyo.value(\n", - " m.fs.total_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", - ") == pytest.approx(37.2799, rel=0.01)\n", - "assert pyo.value(m.fs.fuel_cost_rate[0] / m.fs.net_power_mw[0]) == pytest.approx(\n", - " 31.6462, rel=0.01\n", - ")\n", - "assert pyo.value(\n", - " m.fs.other_variable_cost_rate[0] / m.fs.net_power_mw[0]\n", - ") == pytest.approx(5.63373, rel=0.01)\n", - "assert pyo.value(m.fs.gt.gt_power[0]) == pytest.approx(-477e6, rel=0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run turndown cases 5 MW interval\n", + "\n", + "Here we set the CO2 capture rate to 97% and set the specific reboiler duty to PZ advanced solvent system. The minimum power is 160 MW net, which corresponds to a bit under 25%. This is roughly the minimum load for the NGCC modeled. Results are tabulated for tags in the tags_output tag group in a Pandas data frame. \n", + "\n", + "To run the series, change run_series to True. Running the turndown series takes a while, unless previous saved results are available. " ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "run_series = False\n", + "if run_series:\n", + " idaes.cfg.ipopt.options.tol = 1e-6\n", + " idaes.cfg.ipopt.options.max_iter = 50\n", + " solver = pyo.SolverFactory(\"ipopt\")\n", + "\n", + " m.fs.cap_specific_reboiler_duty.fix(2.4e6)\n", + " m.fs.cap_fraction.fix(0.97)\n", + " powers = np.linspace(650, 160, int((650 - 160) / 5) + 1)\n", + " powers = list(powers)\n", + " powers.insert(1, 646)\n", + "\n", + " df = pd.DataFrame(columns=m.fs.tags_output.table_heading())\n", + "\n", + " for p in powers:\n", + " print(\"Simulation for net power = \", p)\n", + " fname = f\"data/ngcc_{int(p)}.json.gz\"\n", + " if os.path.exists(fname):\n", + " iutil.from_json(m, fname=fname, wts=iutil.StoreSpec(suffix=False))\n", + " else:\n", + " m.fs.net_power_mw.fix(p)\n", + " res = solver.solve(m, tee=False, symbolic_solver_labels=True)\n", + " if not pyo.check_optimal_termination(res):\n", + " break\n", + " iutil.to_json(m, fname=fname)\n", + " df.loc[m.fs.tags_output[\"net_power\"].value] = m.fs.tags_output.table_row(\n", + " numeric=True\n", + " )\n", + " if abs(p - 650) < 0.1:\n", + " m.fs.gt.streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_gt.csv\"\n", + " )\n", + " m.fs.st.steam_streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_st.csv\"\n", + " )\n", + " m.fs.hrsg.steam_streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_hrsg_steam.csv\"\n", + " )\n", + " m.fs.hrsg.flue_gas_streams_dataframe().to_csv(\n", + " \"data_tabulated/ngcc_stream_650mw_hrsg_gas.csv\"\n", + " )\n", + " df.to_csv(\"data_tabulated/ngcc.csv\")\n", + "\n", + " # Display the results from the run stored in a pandas dataframe\n", + " pd.set_option(\"display.max_rows\", None)\n", + " pd.set_option(\"display.max_columns\", None)\n", + " display(df)\n", + "\n", + " # Plot results\n", + " plt.plot(df[\"net_power (MW)\"], df[\"lhv_efficiency (%)\"])\n", + " plt.grid()\n", + " plt.xlabel(\"Net Power (MW)\")\n", + " plt.ylabel(\"LHV Efficiency (%)\")\n", + " plt.title(\"Net Power vs. Efficiency\")\n", + " plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "\n", - "variables = [\"net_power\", \"gross_power\", \"gt_power\"]\n", - "netl_baseline = [646, 690, 477]\n", - "idaes_prediction = [\n", - " pyo.value(m.fs.net_power_mw[0]),\n", - " -pyo.value(m.fs.gross_power[0]) * 1e-6,\n", - " -pyo.value(m.fs.gt.gt_power[0]) * 1e-6,\n", - "]\n", - "\n", - "label_location = np.arange(len(variables))\n", - "\n", - "width = 0.4\n", - "\n", - "fig, ax = plt.subplots()\n", - "netl_data = ax.bar(variables, netl_baseline, label=\"NETL Baseline\")\n", - "idaes_sim = ax.bar(\n", - " label_location + (width / 2), idaes_prediction, width, label=\"IDAES Prediction\"\n", - ")\n", - "\n", - "ax.set_ylabel(\"Power (MW)\")\n", - "ax.set_xticks(label_location)\n", - "ax.set_xticklabels(variables)\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run turndown cases 5 MW interval\n", - "\n", - "Here we set the CO2 capture rate to 97% and set the specific reboiler duty to PZ advanced solvent system. The minimum power is 160 MW net, which corresponds to a bit under 25%. This is roughly the minimum load for the NGCC modeled. Results are tabulated for tags in the tags_output tag group in a Pandas data frame. \n", - "\n", - "To run the series, change run_series to True. Running the turndown series takes a while, unless previous saved results are available. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "run_series = False\n", - "if run_series:\n", - " idaes.cfg.ipopt.options.tol = 1e-6\n", - " idaes.cfg.ipopt.options.max_iter = 50\n", - " solver = pyo.SolverFactory(\"ipopt\")\n", - "\n", - " m.fs.cap_specific_reboiler_duty.fix(2.4e6)\n", - " m.fs.cap_fraction.fix(0.97)\n", - " powers = np.linspace(650, 160, int((650 - 160) / 5) + 1)\n", - " powers = list(powers)\n", - " powers.insert(1, 646)\n", - "\n", - " df = pd.DataFrame(columns=m.fs.tags_output.table_heading())\n", - "\n", - " for p in powers:\n", - " print(\"Simulation for net power = \", p)\n", - " fname = f\"data/ngcc_{int(p)}.json.gz\"\n", - " if os.path.exists(fname):\n", - " iutil.from_json(m, fname=fname, wts=iutil.StoreSpec(suffix=False))\n", - " else:\n", - " m.fs.net_power_mw.fix(p)\n", - " res = solver.solve(m, tee=False, symbolic_solver_labels=True)\n", - " if not pyo.check_optimal_termination(res):\n", - " break\n", - " iutil.to_json(m, fname=fname)\n", - " df.loc[m.fs.tags_output[\"net_power\"].value] = m.fs.tags_output.table_row(\n", - " numeric=True\n", - " )\n", - " if abs(p - 650) < 0.1:\n", - " m.fs.gt.streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_gt.csv\"\n", - " )\n", - " m.fs.st.steam_streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_st.csv\"\n", - " )\n", - " m.fs.hrsg.steam_streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_hrsg_steam.csv\"\n", - " )\n", - " m.fs.hrsg.flue_gas_streams_dataframe().to_csv(\n", - " \"data_tabulated/ngcc_stream_650mw_hrsg_gas.csv\"\n", - " )\n", - " df.to_csv(\"data_tabulated/ngcc.csv\")\n", - "\n", - " # Display the results from the run stored in a pandas dataframe\n", - " pd.set_option(\"display.max_rows\", None)\n", - " pd.set_option(\"display.max_columns\", None)\n", - " display(df)\n", - "\n", - " # Plot results\n", - " plt.plot(df[\"net_power (MW)\"], df[\"lhv_efficiency (%)\"])\n", - " plt.grid()\n", - " plt.xlabel(\"Net Power (MW)\")\n", - " plt.ylabel(\"LHV Efficiency (%)\")\n", - " plt.title(\"Net Power vs. Efficiency\")\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control.ipynb b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control.ipynb index 978d3c90..a52f87de 100644 --- a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "77b107c3", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_doc.ipynb b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_doc.ipynb index b22f6611..544e193e 100644 --- a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_doc.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_doc.ipynb @@ -3,6 +3,33 @@ { "cell_type": "code", "execution_count": 1, + "id": "1529f66a", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -129,15 +156,42 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-04-24 16:44:46 [WARNING] idaes.models.properties.modular_properties.transport_properties.no_method: Skipping construction of thermal conductivity for phase Liq\n", - "2024-04-24 16:44:46 [WARNING] idaes.models.properties.modular_properties.transport_properties.no_method: Skipping construction of dynamic viscosity for phase Liq\n" + "2025-03-17 17:34:10 [WARNING] idaes.models.properties.modular_properties.transport_properties.no_method: Skipping construction of thermal conductivity for phase Liq\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:10 [WARNING] idaes.models.properties.modular_properties.transport_properties.no_method: Skipping construction of dynamic viscosity for phase Liq\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:11 [INFO] idaes.models.unit_models.heat_exchanger_1D: For cold_side, a BACKWARD scheme was chosen to discretize the length domain. However, this scheme is not an upwind scheme for countercurrent flow, and as a result may run into numerical stability issues. To avoid this, use a FORWARD scheme (which may result in energy conservation issues for coarse discretizations) or use a high-order collocation method.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:12 [INFO] idaes.models.unit_models.heat_exchanger_1D: For cold_side, a BACKWARD scheme was chosen to discretize the length domain. However, this scheme is not an upwind scheme for countercurrent flow, and as a result may run into numerical stability issues. To avoid this, use a FORWARD scheme (which may result in energy conservation issues for coarse discretizations) or use a high-order collocation method.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:13 [INFO] idaes.models.unit_models.heat_exchanger_1D: For cold_side, a BACKWARD scheme was chosen to discretize the length domain. However, this scheme is not an upwind scheme for countercurrent flow, and as a result may run into numerical stability issues. To avoid this, use a FORWARD scheme (which may result in energy conservation issues for coarse discretizations) or use a high-order collocation method.\n" ] } ], @@ -200,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -410,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -428,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -450,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -471,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -915,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -963,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -998,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1030,607 +1084,549 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-04-24 16:45:08 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 243\n", - "2024-04-24 16:45:08 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Ipopt 3.13.2: constr_viol_tol=1e-08\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: nlp_scaling_method=user-scaling\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: linear_solver=ma57\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: max_iter=300\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: tol=1e-08\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: halt_on_ampl_error=no\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: option_file_name=C:\\Users\\dallan\\AppData\\Local\\Temp\\tmphl5vnriw_ipopt.opt\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Using option file \"C:\\Users\\dallan\\AppData\\Local\\Temp\\tmphl5vnriw_ipopt.opt\".\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: ******************************************************************************\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: For more information visit http://projects.coin-or.org/Ipopt\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: This version of Ipopt was compiled from source code available at\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: for large-scale scientific computation. All technical papers, sales and\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: publicity material resulting from use of the HSL codes within IPOPT must\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: contain the following acknowledgement:\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: HSL, a collection of Fortran codes for large-scale scientific\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: computation. See http://www.hsl.rl.ac.uk.\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: ******************************************************************************\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: This is Ipopt version 3.13.2, running with linear solver ma57.\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of nonzeros in equality constraint Jacobian...: 15011\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of nonzeros in inequality constraint Jacobian.: 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of nonzeros in Lagrangian Hessian.............: 9356\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Total number of variables............................: 3864\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: variables with only lower bounds: 667\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: variables with lower and upper bounds: 1495\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: variables with only upper bounds: 31\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Total number of equality constraints.................: 3864\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Total number of inequality constraints...............: 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: inequality constraints with only lower bounds: 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: inequality constraints with lower and upper bounds: 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: inequality constraints with only upper bounds: 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: 0 0.0000000e+00 6.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Reallocating memory for MA57: lfact (331665)\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: 1 0.0000000e+00 9.77e-01 1.41e+02 -1.0 5.95e+00 - 8.13e-01 9.85e-01h 1\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: 2 0.0000000e+00 9.25e-03 1.71e+02 -1.0 8.74e-02 - 9.90e-01 9.90e-01h 1\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: 3 0.0000000e+00 3.08e-05 2.80e+04 -1.0 6.27e-03 - 9.91e-01 9.97e-01h 1\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: 4 0.0000000e+00 3.05e-11 4.27e+03 -1.0 7.30e-04 - 1.00e+00 1.00e+00h 1\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of Iterations....: 4\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: (scaled) (unscaled)\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Constraint violation....: 3.0518305330767825e-11 3.0518305330767825e-11\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Overall NLP error.......: 3.0518305330767825e-11 3.0518305330767825e-11\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of objective function evaluations = 5\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of objective gradient evaluations = 5\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of equality constraint evaluations = 5\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of inequality constraint evaluations = 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of equality constraint Jacobian evaluations = 5\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of inequality constraint Jacobian evaluations = 0\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Number of Lagrangian Hessian evaluations = 4\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Total CPU secs in IPOPT (w/o function evaluations) = 0.260\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: Total CPU secs in NLP function evaluations = 0.041\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: \n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: EXIT: Optimal Solution Found.\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_nan\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_nan\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_ext\n", - "2024-04-24 16:45:14 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_ext\n", - "2024-04-24 16:45:22 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 93\n", - "2024-04-24 16:45:22 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n", - "2024-04-24 16:45:22 [INFO] idaes.solve.petsc-dae: Solver log file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmp69gau4jt_petsc_ts.log'\n", - "2024-04-24 16:45:22 [INFO] idaes.solve.petsc-dae: Solver solution file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpva9yq47l.pyomo.sol'\n", - "2024-04-24 16:45:22 [INFO] idaes.solve.petsc-dae: Solver problem files: ('C:\\\\Users\\\\dallan\\\\AppData\\\\Local\\\\Temp\\\\tmpva9yq47l.pyomo.nl',)\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: DAE: 1\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpva9yq47l.pyomo.nl\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of constraints: 3920\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 1795\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 2125\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of variables: 4007\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 15554 \n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of derivatives: 87\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of differential vars: 87\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 3833\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of state vars: 3920\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 0.\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 0.1\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 2 TS dt 0.100412 time 0.2\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 3 TS dt 1.00412 time 0.300412\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 4 TS dt 10.0412 time 1.30453\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 5 TS dt 21.1594 time 11.3457\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 6 TS dt 34.1485 time 32.5051\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 7 TS dt 56.7094 time 66.6536\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 8 TS dt 103.25 time 123.363\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 9 TS dt 205.123 time 226.612\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 10 TS dt 309.964 time 431.736\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 11 TS dt 432.696 time 741.7\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 12 TS dt 621.577 time 1174.4\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 13 TS dt 902.014 time 1795.97\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 14 TS dt 902.014 time 2697.99\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: 15 TS dt 2466.59 time 3600.\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_nan\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_nan\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_ext\n", - "2024-04-24 16:45:23 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_ext\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 170\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Solver log file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpn0tqghes_petsc_ts.log'\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Solver solution file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpk0c1pdqp.pyomo.sol'\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Solver problem files: ('C:\\\\Users\\\\dallan\\\\AppData\\\\Local\\\\Temp\\\\tmpk0c1pdqp.pyomo.nl',)\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: DAE: 1\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpk0c1pdqp.pyomo.nl\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of constraints: 3920\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 1795\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 2125\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of variables: 4007\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 15554 \n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of derivatives: 87\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of differential vars: 87\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 3833\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of state vars: 3920\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 3600.\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 3600.1\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: 2 TS dt 0.00199239 time 3600.1\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: 3 TS dt 0.0199239 time 3600.1\n", - "2024-04-24 16:45:28 [INFO] idaes.solve.petsc-dae: 4 TS dt 0.199239 time 3600.12\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 5 TS dt 0.697133 time 3600.32\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 6 TS dt 0.866231 time 3601.02\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 7 TS dt 1.13013 time 3601.89\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 8 TS dt 1.179 time 3603.02\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 9 TS dt 1.3192 time 3604.19\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 10 TS dt 1.33644 time 3605.51\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 11 TS dt 1.45894 time 3606.85\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 12 TS dt 1.46098 time 3608.31\n", - "2024-04-24 16:45:29 [INFO] idaes.solve.petsc-dae: 13 TS dt 1.37246 time 3609.77\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 14 TS dt 1.48172 time 3611.14\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 15 TS dt 1.2735 time 3612.35\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 16 TS dt 1.52602 time 3613.63\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 17 TS dt 1.64143 time 3615.15\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 18 TS dt 1.5531 time 3616.8\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 19 TS dt 1.61899 time 3618.35\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 20 TS dt 1.8938 time 3619.97\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 21 TS dt 2.14371 time 3621.86\n", - "2024-04-24 16:45:30 [INFO] idaes.solve.petsc-dae: 22 TS dt 1.95341 time 3624.01\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 23 TS dt 0.593203 time 3624.34\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 24 TS dt 2.07741 time 3624.94\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 25 TS dt 1.8343 time 3626.78\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 26 TS dt 2.34236 time 3628.62\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 27 TS dt 2.70968 time 3630.96\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 28 TS dt 3.17793 time 3633.67\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 29 TS dt 2.08994 time 3635.54\n", - "2024-04-24 16:45:31 [INFO] idaes.solve.petsc-dae: 30 TS dt 1.32652 time 3637.01\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 31 TS dt 3.93769 time 3638.34\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 32 TS dt 3.47399 time 3641.79\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 33 TS dt 4.44515 time 3645.27\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 34 TS dt 4.54859 time 3649.71\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 35 TS dt 5.11148 time 3654.26\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 36 TS dt 5.37965 time 3659.37\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 37 TS dt 5.84736 time 3664.75\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 38 TS dt 6.21996 time 3670.6\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 39 TS dt 6.69274 time 3676.82\n", - "2024-04-24 16:45:32 [INFO] idaes.solve.petsc-dae: 40 TS dt 7.14526 time 3683.51\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 41 TS dt 7.65699 time 3690.66\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 42 TS dt 8.18317 time 3698.31\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 43 TS dt 8.74315 time 3706.5\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 44 TS dt 9.27856 time 3715.24\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 45 TS dt 9.68853 time 3724.52\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 46 TS dt 9.77885 time 3734.21\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 47 TS dt 9.92111 time 3743.99\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 48 TS dt 8.33461 time 3752.18\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 49 TS dt 8.96337 time 3760.51\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 50 TS dt 9.29772 time 3769.48\n", - "2024-04-24 16:45:33 [INFO] idaes.solve.petsc-dae: 51 TS dt 9.60548 time 3778.77\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 52 TS dt 11.0593 time 3788.38\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 53 TS dt 9.88706 time 3797.26\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 54 TS dt 9.88436 time 3807.15\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 55 TS dt 7.55235 time 3814.54\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 56 TS dt 9.65705 time 3822.09\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 57 TS dt 11.0499 time 3831.75\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 58 TS dt 12.2975 time 3842.8\n", - "2024-04-24 16:45:34 [INFO] idaes.solve.petsc-dae: 59 TS dt 12.6609 time 3855.1\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: 60 TS dt 13.9163 time 3859.75\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: 61 TS dt 10.4683 time 3870.54\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: 62 TS dt 9.49383 time 3881.01\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: 63 TS dt 9.49383 time 3890.51\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: 64 TS dt 14.0081 time 3900.\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_nan\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_nan\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_ext\n", - "2024-04-24 16:45:35 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_ext\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 170\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Solver log file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmp3wnf4q2o_petsc_ts.log'\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Solver solution file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpcy91h9f0.pyomo.sol'\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Solver problem files: ('C:\\\\Users\\\\dallan\\\\AppData\\\\Local\\\\Temp\\\\tmpcy91h9f0.pyomo.nl',)\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: DAE: 1\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpcy91h9f0.pyomo.nl\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of constraints: 3920\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 1795\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 2125\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of variables: 4007\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 15554 \n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of derivatives: 87\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of differential vars: 87\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 3833\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of state vars: 3920\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 3900.\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 3900.1\n", - "2024-04-24 16:45:40 [INFO] idaes.solve.petsc-dae: 2 TS dt 1. time 3900.2\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 3 TS dt 2.86361 time 3901.2\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 4 TS dt 3.11474 time 3904.06\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 5 TS dt 3.95219 time 3907.18\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 6 TS dt 4.26539 time 3911.13\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 7 TS dt 4.68321 time 3915.4\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 8 TS dt 4.75843 time 3920.08\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 9 TS dt 4.77214 time 3924.84\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 10 TS dt 4.57975 time 3929.61\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 11 TS dt 5.10332 time 3934.19\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 12 TS dt 5.83254 time 3939.29\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 13 TS dt 6.72867 time 3945.13\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 14 TS dt 7.50262 time 3951.85\n", - "2024-04-24 16:45:41 [INFO] idaes.solve.petsc-dae: 15 TS dt 8.77609 time 3959.36\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 16 TS dt 9.95946 time 3968.13\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 17 TS dt 11.4862 time 3978.09\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 18 TS dt 13.2173 time 3989.58\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 19 TS dt 15.4588 time 4002.8\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 20 TS dt 18.2767 time 4018.25\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 21 TS dt 21.855 time 4036.53\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 22 TS dt 25.9265 time 4058.39\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 23 TS dt 29.7008 time 4084.31\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 24 TS dt 32.3941 time 4114.01\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 25 TS dt 34.1498 time 4146.41\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 26 TS dt 34.3963 time 4180.56\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 27 TS dt 37.1487 time 4214.95\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 28 TS dt 37.9342 time 4252.1\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 29 TS dt 40.3768 time 4290.04\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 30 TS dt 42.4218 time 4330.41\n", - "2024-04-24 16:45:42 [INFO] idaes.solve.petsc-dae: 31 TS dt 45.0908 time 4372.84\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 32 TS dt 47.3702 time 4417.93\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 33 TS dt 49.135 time 4465.3\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 34 TS dt 49.6503 time 4514.43\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 35 TS dt 51.6702 time 4564.08\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 36 TS dt 53.1554 time 4615.75\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 37 TS dt 54.6803 time 4668.91\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 38 TS dt 61.8766 time 4723.59\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 39 TS dt 62.3182 time 4785.46\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 40 TS dt 78.6399 time 4847.78\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 41 TS dt 82.7293 time 4926.42\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 42 TS dt 90.4869 time 5009.15\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 43 TS dt 89.7949 time 5099.64\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 44 TS dt 85.7202 time 5189.43\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 45 TS dt 88.4188 time 5275.15\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 46 TS dt 91.9337 time 5363.57\n", - "2024-04-24 16:45:43 [INFO] idaes.solve.petsc-dae: 47 TS dt 90.9639 time 5455.51\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 48 TS dt 99.5246 time 5546.47\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 49 TS dt 108.67 time 5645.99\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 50 TS dt 120.748 time 5754.66\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 51 TS dt 132.47 time 5875.41\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 52 TS dt 145.985 time 6007.88\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 53 TS dt 160.665 time 6153.87\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 54 TS dt 177.438 time 6314.53\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 55 TS dt 192.168 time 6491.97\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 56 TS dt 221.56 time 6684.14\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 57 TS dt 250.729 time 6905.7\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 58 TS dt 292.659 time 7156.43\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 59 TS dt 342.344 time 7449.09\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 60 TS dt 396.307 time 7791.43\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 61 TS dt 387.849 time 8187.74\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 62 TS dt 347.808 time 8474.05\n", - "2024-04-24 16:45:44 [INFO] idaes.solve.petsc-dae: 63 TS dt 289.931 time 8626.27\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 64 TS dt 219.289 time 8708.2\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 65 TS dt 202.344 time 8927.49\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 66 TS dt 211.265 time 9055.82\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 67 TS dt 43.6911 time 9087.46\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 68 TS dt 97.1116 time 9131.15\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 69 TS dt 116.121 time 9228.26\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 70 TS dt 163.226 time 9344.39\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 71 TS dt 202.871 time 9507.61\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 72 TS dt 262.974 time 9710.48\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 73 TS dt 295.993 time 9973.46\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 74 TS dt 275.494 time 10269.4\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 75 TS dt 277.528 time 10544.9\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 76 TS dt 277.528 time 10822.5\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: 77 TS dt 405.82 time 11100.\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_nan\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_nan\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_ext\n", - "2024-04-24 16:45:45 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_ext\n", - "2024-04-24 16:45:50 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 170\n", - "2024-04-24 16:45:50 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n", - "2024-04-24 16:45:50 [INFO] idaes.solve.petsc-dae: Solver log file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmp89d3otha_petsc_ts.log'\n", - "2024-04-24 16:45:50 [INFO] idaes.solve.petsc-dae: Solver solution file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpumhqgnkw.pyomo.sol'\n", - "2024-04-24 16:45:50 [INFO] idaes.solve.petsc-dae: Solver problem files: ('C:\\\\Users\\\\dallan\\\\AppData\\\\Local\\\\Temp\\\\tmpumhqgnkw.pyomo.nl',)\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: DAE: 1\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpumhqgnkw.pyomo.nl\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of constraints: 3920\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 1795\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 2125\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of variables: 4007\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 15554 \n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of derivatives: 87\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of differential vars: 87\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 3833\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of state vars: 3920\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 11100.\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 11100.1\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 2 TS dt 0.00392524 time 11100.1\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 3 TS dt 0.0392524 time 11100.1\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 4 TS dt 0.392524 time 11100.1\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 5 TS dt 0.760703 time 11100.5\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 6 TS dt 1.01241 time 11101.3\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 7 TS dt 0.967184 time 11102.2\n", - "2024-04-24 16:45:51 [INFO] idaes.solve.petsc-dae: 8 TS dt 0.999746 time 11103.2\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 9 TS dt 0.924929 time 11104.\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 10 TS dt 0.866639 time 11104.9\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 11 TS dt 0.915231 time 11105.8\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 12 TS dt 0.858768 time 11106.7\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 13 TS dt 1.16546 time 11107.6\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 14 TS dt 1.0729 time 11108.7\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 15 TS dt 1.40736 time 11109.8\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 16 TS dt 1.47697 time 11111.2\n", - "2024-04-24 16:45:52 [INFO] idaes.solve.petsc-dae: 17 TS dt 1.55373 time 11112.7\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 18 TS dt 1.4277 time 11114.\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 19 TS dt 1.07175 time 11115.\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 20 TS dt 1.32123 time 11116.1\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 21 TS dt 1.26966 time 11117.4\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 22 TS dt 0.929849 time 11118.2\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 23 TS dt 0.37561 time 11118.5\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 24 TS dt 1.3944 time 11118.9\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 25 TS dt 1.43845 time 11120.3\n", - "2024-04-24 16:45:53 [INFO] idaes.solve.petsc-dae: 26 TS dt 1.90206 time 11121.7\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 27 TS dt 1.81077 time 11123.6\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 28 TS dt 1.79649 time 11125.4\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 29 TS dt 1.70313 time 11127.2\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 30 TS dt 1.8855 time 11128.9\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 31 TS dt 1.85638 time 11130.8\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 32 TS dt 1.85523 time 11132.7\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 33 TS dt 1.73671 time 11134.5\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 34 TS dt 1.64306 time 11136.2\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 35 TS dt 1.52316 time 11137.9\n", - "2024-04-24 16:45:54 [INFO] idaes.solve.petsc-dae: 36 TS dt 1.41285 time 11139.2\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 37 TS dt 1.57227 time 11139.7\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 38 TS dt 0.96963 time 11140.2\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 39 TS dt 0.78644 time 11140.8\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 40 TS dt 0.705073 time 11141.5\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 41 TS dt 0.621216 time 11142.\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 42 TS dt 0.62459 time 11142.6\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 43 TS dt 0.63664 time 11143.2\n", - "2024-04-24 16:45:55 [INFO] idaes.solve.petsc-dae: 44 TS dt 0.690959 time 11143.5\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 45 TS dt 0.430777 time 11144.\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 46 TS dt 0.410148 time 11144.4\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 47 TS dt 0.856631 time 11144.9\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 48 TS dt 0.949106 time 11145.1\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 49 TS dt 0.570639 time 11145.5\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 50 TS dt 0.657937 time 11146.1\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 51 TS dt 0.756228 time 11146.8\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 52 TS dt 0.960609 time 11147.5\n", - "2024-04-24 16:45:56 [INFO] idaes.solve.petsc-dae: 53 TS dt 1.03942 time 11148.2\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 54 TS dt 0.73841 time 11148.8\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 55 TS dt 0.723736 time 11149.6\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 56 TS dt 1.3136 time 11150.3\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 57 TS dt 1.44703 time 11151.6\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 58 TS dt 1.78016 time 11153.1\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 59 TS dt 1.79591 time 11154.8\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 60 TS dt 1.70487 time 11156.6\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 61 TS dt 1.63852 time 11157.6\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 62 TS dt 0.894779 time 11158.4\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 63 TS dt 0.904928 time 11159.3\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 64 TS dt 1.51786 time 11160.2\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 65 TS dt 1.79934 time 11161.7\n", - "2024-04-24 16:45:57 [INFO] idaes.solve.petsc-dae: 66 TS dt 2.19124 time 11163.5\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 67 TS dt 2.39088 time 11165.7\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 68 TS dt 2.77663 time 11168.1\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 69 TS dt 2.98602 time 11170.9\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 70 TS dt 3.19428 time 11173.9\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 71 TS dt 3.17037 time 11177.1\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 72 TS dt 3.2767 time 11180.2\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 73 TS dt 3.70518 time 11183.5\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 74 TS dt 3.9439 time 11187.2\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 75 TS dt 4.1184 time 11191.1\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 76 TS dt 4.51077 time 11195.3\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 77 TS dt 4.73 time 11199.8\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 78 TS dt 4.95553 time 11204.5\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 79 TS dt 5.05272 time 11209.5\n", - "2024-04-24 16:45:58 [INFO] idaes.solve.petsc-dae: 80 TS dt 5.07776 time 11214.5\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 81 TS dt 5.05502 time 11219.6\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 82 TS dt 5.05021 time 11224.6\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 83 TS dt 5.29358 time 11229.7\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 84 TS dt 5.52422 time 11235.\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 85 TS dt 5.70998 time 11240.5\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 86 TS dt 5.77308 time 11246.2\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 87 TS dt 5.68832 time 11252.\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 88 TS dt 5.21191 time 11257.7\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 89 TS dt 5.52452 time 11262.9\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 90 TS dt 5.08208 time 11267.8\n", - "2024-04-24 16:45:59 [INFO] idaes.solve.petsc-dae: 91 TS dt 6.0966 time 11269.7\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 92 TS dt 4.47115 time 11271.1\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 93 TS dt 4.46473 time 11271.8\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 94 TS dt 1.64611 time 11273.5\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 95 TS dt 3.11947 time 11275.2\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 96 TS dt 3.69342 time 11278.3\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 97 TS dt 4.85562 time 11282.\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 98 TS dt 4.86391 time 11286.8\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 99 TS dt 4.74083 time 11290.1\n", - "2024-04-24 16:46:00 [INFO] idaes.solve.petsc-dae: 100 TS dt 3.88706 time 11291.9\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 101 TS dt 2.36784 time 11293.1\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 102 TS dt 3.04988 time 11295.5\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 103 TS dt 3.79675 time 11297.4\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 104 TS dt 3.41211 time 11299.4\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 105 TS dt 5.09485 time 11302.8\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 106 TS dt 6.30524 time 11307.9\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 107 TS dt 7.14277 time 11314.2\n", - "2024-04-24 16:46:01 [INFO] idaes.solve.petsc-dae: 108 TS dt 6.97261 time 11321.4\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 109 TS dt 6.92383 time 11328.4\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 110 TS dt 6.31828 time 11335.3\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 111 TS dt 5.49724 time 11340.5\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 112 TS dt 4.90343 time 11345.5\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 113 TS dt 5.24546 time 11350.4\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 114 TS dt 5.37157 time 11355.6\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 115 TS dt 5.08981 time 11361.\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 116 TS dt 5.15951 time 11366.1\n", - "2024-04-24 16:46:02 [INFO] idaes.solve.petsc-dae: 117 TS dt 4.68969 time 11371.2\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 118 TS dt 4.69922 time 11375.9\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 119 TS dt 4.35928 time 11380.6\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 120 TS dt 4.01346 time 11385.\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 121 TS dt 3.60641 time 11388.5\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 122 TS dt 3.18203 time 11391.7\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 123 TS dt 2.78627 time 11394.4\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 124 TS dt 1.58852 time 11396.8\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 125 TS dt 1.58852 time 11398.4\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: 126 TS dt 1.90429 time 11400.\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_nan\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_nan\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_ext\n", - "2024-04-24 16:46:03 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_ext\n", - "2024-04-24 16:46:08 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 170\n", - "2024-04-24 16:46:08 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Solver log file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpendl44cj_petsc_ts.log'\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Solver solution file: 'C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpson30_9f.pyomo.sol'\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Solver problem files: ('C:\\\\Users\\\\dallan\\\\AppData\\\\Local\\\\Temp\\\\tmpson30_9f.pyomo.nl',)\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Called fg_read, err: 0 (0 is good)\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: DAE: 1\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Reading nl file: C:\\Users\\dallan\\AppData\\Local\\Temp\\tmpson30_9f.pyomo.nl\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of constraints: 3920\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of nonlinear constraints: 1795\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of linear constraints: 2125\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of inequalities: 0\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of variables: 4007\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of integers: 0\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of binary: 0\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of objectives: 0 (Ignoring)\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of non-zeros in Jacobian: 15554 \n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Explicit time variable: 0\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of derivatives: 87\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of differential vars: 87\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of algebraic vars: 3833\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of state vars: 3920\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: Number of degrees of freedom: 0\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: ---------------------------------------------------\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 0 TS dt 0.1 time 11400.\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 1 TS dt 0.1 time 11400.1\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 2 TS dt 1. time 11400.2\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 3 TS dt 1.73678 time 11401.2\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 4 TS dt 2.23341 time 11402.9\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 5 TS dt 2.51853 time 11405.2\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 6 TS dt 2.72979 time 11407.7\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 7 TS dt 2.94331 time 11410.4\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 8 TS dt 3.1084 time 11413.4\n", - "2024-04-24 16:46:09 [INFO] idaes.solve.petsc-dae: 9 TS dt 3.33766 time 11416.5\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 10 TS dt 3.65145 time 11419.8\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 11 TS dt 3.92962 time 11423.5\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 12 TS dt 4.4225 time 11427.4\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 13 TS dt 4.87494 time 11431.8\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 14 TS dt 5.42871 time 11436.7\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 15 TS dt 5.98195 time 11442.1\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 16 TS dt 6.92855 time 11448.1\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 17 TS dt 8.07333 time 11455.\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 18 TS dt 9.63979 time 11463.1\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 19 TS dt 11.702 time 11472.7\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 20 TS dt 14.5291 time 11484.4\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 21 TS dt 18.1896 time 11499.\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 22 TS dt 22.203 time 11517.2\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 23 TS dt 25.5942 time 11539.4\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 24 TS dt 28.4541 time 11565.\n", - "2024-04-24 16:46:10 [INFO] idaes.solve.petsc-dae: 25 TS dt 30.7952 time 11593.4\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 26 TS dt 32.9926 time 11624.2\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 27 TS dt 34.8988 time 11657.2\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 28 TS dt 37.5398 time 11692.1\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 29 TS dt 41.8031 time 11729.6\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 30 TS dt 47.4503 time 11771.4\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 31 TS dt 53.673 time 11818.9\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 32 TS dt 60.8933 time 11872.6\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 33 TS dt 69.4519 time 11933.5\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 34 TS dt 77.8654 time 12002.9\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 35 TS dt 85.3528 time 12080.8\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 36 TS dt 92.584 time 12166.1\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 37 TS dt 99.6879 time 12258.7\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 38 TS dt 107.395 time 12358.4\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 39 TS dt 115.883 time 12465.8\n", - "2024-04-24 16:46:11 [INFO] idaes.solve.petsc-dae: 40 TS dt 125.805 time 12581.7\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 41 TS dt 136.484 time 12707.5\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 42 TS dt 146.675 time 12844.\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 43 TS dt 154.432 time 12990.6\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 44 TS dt 158.051 time 13145.1\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 45 TS dt 156.722 time 13303.1\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 46 TS dt 161.592 time 13459.8\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 47 TS dt 169.876 time 13621.4\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 48 TS dt 175.501 time 13791.3\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 49 TS dt 177.773 time 13966.8\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 50 TS dt 195.829 time 14144.6\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 51 TS dt 209.215 time 14340.4\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 52 TS dt 228.235 time 14549.6\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 53 TS dt 245.428 time 14777.9\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 54 TS dt 265.796 time 15023.3\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 55 TS dt 286.41 time 15289.1\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 56 TS dt 309.614 time 15575.5\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 57 TS dt 336.559 time 15885.1\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 58 TS dt 370.237 time 16221.7\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 59 TS dt 418.152 time 16591.9\n", - "2024-04-24 16:46:12 [INFO] idaes.solve.petsc-dae: 60 TS dt 475.299 time 17010.1\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 61 TS dt 548.284 time 17485.4\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 62 TS dt 640.182 time 18033.6\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 63 TS dt 756.084 time 18673.8\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 64 TS dt 900.068 time 19429.9\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 65 TS dt 935.01 time 20330.\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 66 TS dt 935.01 time 21265.\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: 67 TS dt 1415.01 time 22200.\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: TSConvergedReason = TS_CONVERGED_TIME\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: TS_CONVERGED_TIME\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_nan\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_nan\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_l_ext\n", - "2024-04-24 16:46:13 [INFO] idaes.solve.petsc-dae: addfunc: duplicate function cubic_root_h_ext\n" + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: WARNING: model contains export suffix 'scaling_factor' that contains 243\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Ipopt 3.13.2: constr_viol_tol=1e-08\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: nlp_scaling_method=user-scaling\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: linear_solver=ma57\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: max_iter=300\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: tol=1e-08\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: halt_on_ampl_error=no\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: option_file_name=/tmp/tmpm77l7ma2_ipopt.opt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Using option file \"/tmp/tmpm77l7ma2_ipopt.opt\".\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: ******************************************************************************\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: For more information visit http://projects.coin-or.org/Ipopt\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: This version of Ipopt was compiled from source code available at\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: for large-scale scientific computation. All technical papers, sales and\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: publicity material resulting from use of the HSL codes within IPOPT must\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: contain the following acknowledgement:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: HSL, a collection of Fortran codes for large-scale scientific\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: computation. See http://www.hsl.rl.ac.uk.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: ******************************************************************************\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: This is Ipopt version 3.13.2, running with linear solver ma57.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of nonzeros in equality constraint Jacobian...: 15011\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of nonzeros in inequality constraint Jacobian.: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of nonzeros in Lagrangian Hessian.............: 9356\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Total number of variables............................: 3864\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: variables with only lower bounds: 667\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: variables with lower and upper bounds: 1495\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: variables with only upper bounds: 31\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Total number of equality constraints.................: 3864\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Total number of inequality constraints...............: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: inequality constraints with only lower bounds: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: inequality constraints with lower and upper bounds: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: inequality constraints with only upper bounds: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: 0 0.0000000e+00 6.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Reallocating memory for MA57: lfact (331665)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: 1 0.0000000e+00 9.77e-01 1.41e+02 -1.0 5.95e+00 - 8.13e-01 9.85e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: 2 0.0000000e+00 9.25e-03 1.71e+02 -1.0 8.74e-02 - 9.90e-01 9.90e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: 3 0.0000000e+00 3.08e-05 2.80e+04 -1.0 6.27e-03 - 9.91e-01 9.97e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: 4 0.0000000e+00 3.05e-11 4.27e+03 -1.0 7.30e-04 - 1.00e+00 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of Iterations....: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: (scaled) (unscaled)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Constraint violation....: 3.0518305330767825e-11 3.0518305330767825e-11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Overall NLP error.......: 3.0518305330767825e-11 3.0518305330767825e-11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of objective function evaluations = 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of objective gradient evaluations = 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of equality constraint evaluations = 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of inequality constraint evaluations = 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of equality constraint Jacobian evaluations = 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of inequality constraint Jacobian evaluations = 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Number of Lagrangian Hessian evaluations = 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Total CPU secs in IPOPT (w/o function evaluations) = 0.205\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: Total CPU secs in NLP function evaluations = 0.030\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:34:20 [INFO] idaes.solve.petsc-dae: EXIT: Optimal Solution Found.\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "No PETSc executable found.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m idaeslog.solver_log.tee = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m results = \u001b[43mpetsc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpetsc_dae_by_time_element\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m=\u001b[49m\u001b[43mm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeepfiles\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[43mts_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbeuler\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_dt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_rtol\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1e-3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"--ts_adapt_clip\":\"0.001,3600\",\u001b[39;49;00m\n\u001b[32m 12\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"--ksp_monitor\":\"\",\u001b[39;49;00m\n\u001b[32m 13\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_adapt_dt_min\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1e-3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 14\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_adapt_dt_max\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m3600\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 15\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--snes_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnewtontr\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"--ts_max_reject\": 200,\u001b[39;49;00m\n\u001b[32m 17\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"--snes_monitor\":\"\",\u001b[39;49;00m\n\u001b[32m 18\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_monitor\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 19\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_save_trajectory\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 20\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_trajectory_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mvisualization\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 21\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_max_snes_failures\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m25\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 22\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# \"--show_cl\":\"\",\u001b[39;49;00m\n\u001b[32m 23\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m-snes_max_it\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m50\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 24\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m-snes_rtol\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 25\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m-snes_stol\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 26\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m-snes_atol\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1e-6\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 27\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 28\u001b[39m \u001b[43m \u001b[49m\u001b[43mskip_initial\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 29\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_solver\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mipopt\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 30\u001b[39m \u001b[43m \u001b[49m\u001b[43minitial_solver_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 31\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mconstr_viol_tol\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1e-8\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 32\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnlp_scaling_method\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser-scaling\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 33\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlinear_solver\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mma57\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 34\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mOF_ma57_automatic_scaling\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43myes\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 35\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmax_iter\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m300\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 36\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtol\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1e-8\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 37\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhalt_on_ampl_error\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mno\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 38\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 39\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 40\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results.results:\n\u001b[32m 41\u001b[39m pyo.assert_optimal_termination(result)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/solvers/petsc.py:640\u001b[39m, in \u001b[36mpetsc_dae_by_time_element\u001b[39m\u001b[34m(m, time, timevar, initial_constraints, initial_variables, detect_initial, skip_initial, initial_solver, initial_solver_options, ts_options, keepfiles, symbolic_solver_labels, between, interpolate, calculate_derivatives, previous_trajectory, representative_time, snes_options)\u001b[39m\n\u001b[32m 638\u001b[39m _copy_time(time_vars, tprev, t)\n\u001b[32m 639\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m idaeslog.solver_log(solve_log, idaeslog.INFO) \u001b[38;5;28;01mas\u001b[39;00m slc:\n\u001b[32m--> \u001b[39m\u001b[32m640\u001b[39m res = \u001b[43msolver_dae\u001b[49m\u001b[43m.\u001b[49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 641\u001b[39m \u001b[43m \u001b[49m\u001b[43mt_block\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 642\u001b[39m \u001b[43m \u001b[49m\u001b[43mtee\u001b[49m\u001b[43m=\u001b[49m\u001b[43mslc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtee\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 643\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeepfiles\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeepfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 644\u001b[39m \u001b[43m \u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m=\u001b[49m\u001b[43msymbolic_solver_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 645\u001b[39m \u001b[43m \u001b[49m\u001b[43mexport_nonlinear_variables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdifferential_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 646\u001b[39m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_init_time\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtprev\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m--ts_max_time\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 647\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 648\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m save_trajectory:\n\u001b[32m 649\u001b[39m tj_prev = tj\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/base/solvers.py:534\u001b[39m, in \u001b[36mOptSolver.solve\u001b[39m\u001b[34m(self, *args, **kwds)\u001b[39m\n\u001b[32m 531\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msolve\u001b[39m(\u001b[38;5;28mself\u001b[39m, *args, **kwds):\n\u001b[32m 532\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Solve the problem\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m534\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 536\u001b[39m \u001b[38;5;66;03m# If the inputs are models, then validate that they have been\u001b[39;00m\n\u001b[32m 537\u001b[39m \u001b[38;5;66;03m# constructed! Collect suffix names to try and import from solution.\u001b[39;00m\n\u001b[32m 538\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 539\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyomo\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mblock\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BlockData\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/solvers/plugins/solvers/ASL.py:119\u001b[39m, in \u001b[36mASL.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 118\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mavailable\u001b[39m(\u001b[38;5;28mself\u001b[39m, exception_flag=\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m119\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mavailable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexception_flag\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 120\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 121\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.version() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:134\u001b[39m, in \u001b[36mSystemCallSolver.available\u001b[39m\u001b[34m(self, exception_flag)\u001b[39m\n\u001b[32m 132\u001b[39m cm = nullcontext() \u001b[38;5;28;01mif\u001b[39;00m exception_flag \u001b[38;5;28;01melse\u001b[39;00m LoggingIntercept()\n\u001b[32m 133\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m cm:\n\u001b[32m--> \u001b[39m\u001b[32m134\u001b[39m ans = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mexecutable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[32m 136\u001b[39m ans = \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/pyomo/opt/solver/shellcmd.py:205\u001b[39m, in \u001b[36mSystemCallSolver.executable\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 198\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mexecutable\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 199\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 200\u001b[39m \u001b[33;03m Returns the executable used by this solver.\u001b[39;00m\n\u001b[32m 201\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 203\u001b[39m \u001b[38;5;28mself\u001b[39m._user_executable\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m._user_executable \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m205\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_default_executable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 206\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/idaes/core/solvers/petsc.py:124\u001b[39m, in \u001b[36mPetsc._default_executable\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 122\u001b[39m executable = Executable(\u001b[33m\"\u001b[39m\u001b[33mpetsc\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m executable:\n\u001b[32m--> \u001b[39m\u001b[32m124\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo PETSc executable found.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 125\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m executable.path()\n", + "\u001b[31mRuntimeError\u001b[39m: No PETSc executable found." ] } ], @@ -2588,7 +2584,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_test.ipynb b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_test.ipynb index cf20e2f6..95d4fa31 100644 --- a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_test.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_test.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "bcfb2e3e", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -2643,4 +2670,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_usr.ipynb b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_usr.ipynb index b22f6611..ac5f39a2 100644 --- a/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_usr.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/solid_oxide_cell/soc_pid_control_usr.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "812d65fc", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -2593,4 +2620,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant.ipynb index 93ab7f29..a239c59b 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant.ipynb @@ -1,160 +1,187 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical Power Plant Flowsheet Example\n", - "Maintainer: Andrew Lee \n", - "Author: John Eslick \n", - "\n", - "\n", - "## 1. Introduction\n", - "\n", - "\n", - "This example is to demonstrate a supercritical pulverized coal power plant model. The power plant consists of two major sub-systems (or flowsheets), a boiler heat exchanger network and a steam cycle. This jupyter notebook provides the workflow to import the steam cycle flowsheet, import the boiler heat exchanger network, connect and run both the flowsheets, and display the main results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Model Description\n", - "\n", - "The case study demonstrated here is for a ~620MW gross power output. The process flow diagram is shown in section 3 of this jupyter notebook. Figure 1 shows the boiler heat exchanger network, while, figure 2 shows the steam cycle system. \n", - "\n", - "The streams connecting both the flowsheets are: \n", - "  a) The main steam: that connects the boiler attemperator to the throttle valves of the high pressure turbine \n", - "  b) The cold reheat: that connects the final stage of the high pressure turbine to the boiler reheater \n", - "  c) The hot reheat: that connects the boiler reheater to the intermediate pressure turbine \n", - "  d) The main feed water: that connects the last feed water heater to the boiler economizer \n", - " \n", - "To get a more detailed description of the power plant flowsheet, review the ```SCPC_full_plant.py``` file. For details in terms of specific power plant units (for example dimensions, parameters, and variables), more information can be found at ```supercritical_steam_cycle.py``` and ```boiler_subflowsheet.py```.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Process Flow Diagram (PFD)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import SVG, display\n", - "\n", - "display(\n", - " \"Boiler subsystem PFD\",\n", - " SVG(filename=\"Boiler_scpc_PFD.svg\"),\n", - " \"Steam Cycle subsystem PFD\",\n", - " SVG(filename=\"supercritical_steam_cycle.svg\"),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Run power plant model example\n", - "\n", - "This example runs the main ``SCPC_full_plant.py`` script, which, imports two flowsheets (steam cycle and boiler heat exchanger network), builds arcs to connect both flowsheets, and run the full power plant model." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# import SCPC power plant\n", - "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", - "# and run SCPC plant.\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", - " main,\n", - ")\n", - "\n", - "m, res = main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Creating a PFD with results and a stream table\n", - "\n", - "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pkg_resources\n", - "import pyomo.environ as pyo\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", - " pfd_result,\n", - ")\n", - "from idaes.core.util.tables import create_stream_table_dataframe\n", - "\n", - "# Create stream results as Pandas dataframe\n", - "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", - "# Create a new PFD with simulation results\n", - "init_pfd = pkg_resources.resource_string(\n", - " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", - " \"supercritical_steam_cycle.svg\",\n", - ")\n", - "res_pfd = pfd_result(m, df, svg=init_pfd)\n", - "# Display PFD with results.\n", - "display(SVG(res_pfd))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Display the stream table.\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "3ef393f7", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical Power Plant Flowsheet Example\n", + "Maintainer: Andrew Lee \n", + "Author: John Eslick \n", + "\n", + "\n", + "## 1. Introduction\n", + "\n", + "\n", + "This example is to demonstrate a supercritical pulverized coal power plant model. The power plant consists of two major sub-systems (or flowsheets), a boiler heat exchanger network and a steam cycle. This jupyter notebook provides the workflow to import the steam cycle flowsheet, import the boiler heat exchanger network, connect and run both the flowsheets, and display the main results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Model Description\n", + "\n", + "The case study demonstrated here is for a ~620MW gross power output. The process flow diagram is shown in section 3 of this jupyter notebook. Figure 1 shows the boiler heat exchanger network, while, figure 2 shows the steam cycle system. \n", + "\n", + "The streams connecting both the flowsheets are: \n", + "  a) The main steam: that connects the boiler attemperator to the throttle valves of the high pressure turbine \n", + "  b) The cold reheat: that connects the final stage of the high pressure turbine to the boiler reheater \n", + "  c) The hot reheat: that connects the boiler reheater to the intermediate pressure turbine \n", + "  d) The main feed water: that connects the last feed water heater to the boiler economizer \n", + " \n", + "To get a more detailed description of the power plant flowsheet, review the ```SCPC_full_plant.py``` file. For details in terms of specific power plant units (for example dimensions, parameters, and variables), more information can be found at ```supercritical_steam_cycle.py``` and ```boiler_subflowsheet.py```.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Process Flow Diagram (PFD)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import SVG, display\n", + "\n", + "display(\n", + " \"Boiler subsystem PFD\",\n", + " SVG(filename=\"Boiler_scpc_PFD.svg\"),\n", + " \"Steam Cycle subsystem PFD\",\n", + " SVG(filename=\"supercritical_steam_cycle.svg\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Run power plant model example\n", + "\n", + "This example runs the main ``SCPC_full_plant.py`` script, which, imports two flowsheets (steam cycle and boiler heat exchanger network), builds arcs to connect both flowsheets, and run the full power plant model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# import SCPC power plant\n", + "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", + "# and run SCPC plant.\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", + " main,\n", + ")\n", + "\n", + "m, res = main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Creating a PFD with results and a stream table\n", + "\n", + "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pkg_resources\n", + "import pyomo.environ as pyo\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", + " pfd_result,\n", + ")\n", + "from idaes.core.util.tables import create_stream_table_dataframe\n", + "\n", + "# Create stream results as Pandas dataframe\n", + "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", + "# Create a new PFD with simulation results\n", + "init_pfd = pkg_resources.resource_string(\n", + " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", + " \"supercritical_steam_cycle.svg\",\n", + ")\n", + "res_pfd = pfd_result(m, df, svg=init_pfd)\n", + "# Display PFD with results.\n", + "display(SVG(res_pfd))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Display the stream table.\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_doc.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_doc.ipynb index 81222d52..88acf944 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_doc.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_doc.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6eb5978e", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -41,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -2128,11 +2155,7 @@ "" ] }, - "metadata": { - "filenames": { - "image/svg+xml": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\power_gen\\supercritical\\supercritical_power_plant_doc_3_1.svg" - } - }, + "metadata": {}, "output_type": "display_data" }, { @@ -3095,11 +3118,7 @@ "" ] }, - "metadata": { - "filenames": { - "image/svg+xml": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\power_gen\\supercritical\\supercritical_power_plant_doc_3_3.svg" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3125,7 +3144,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -3134,3534 +3153,3476 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:39 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:25:39 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:25:40 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:25:40 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:30 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:40 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:30 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:40 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:30 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:30 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:30 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:30 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:42 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:32 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:33 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:33 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:43 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:33 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:45 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:33 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:45 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:33 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:45 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:33 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:45 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:34 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[1].control_volume.work\n" + "2025-03-17 17:34:34 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[2].control_volume.work\n" + "2025-03-17 17:34:34 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[3].control_volume.work\n" + "2025-03-17 17:34:34 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[4].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[1].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[1].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[2].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[2].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[3].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[3].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[4].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[4].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[1].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[5].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[2].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:47 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[6].control_volume.work\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[3].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - 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"2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.condense.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.bfp.control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.hot_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.bfpt.control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.cold_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.condense.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.condense.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.hot_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.cold_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.hot_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.cold_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.hot_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.cold_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.hot_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.cold_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.hot_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.cold_side.heat\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.area\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.Steam Cycle Model: Starting initialization\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" + "2025-03-17 17:34:35 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" + "2025-03-17 17:34:36 [INFO] idaes.init.Steam Cycle Model: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:48 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:49 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:49 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:49 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:49 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:49 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:50 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:50 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:51 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:51 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:36 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:52 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:53 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:53 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:53 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:53 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:53 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:54 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:54 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:55 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:55 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:56 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:57 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:37 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:57 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:38 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:57 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:38 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:57 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:38 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:58 [INFO] idaes.init.Steam Cycle Model: Full turbine solve complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:38 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix\n" + "2025-03-17 17:34:38 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: 'fs.bfpt.control_volume.properties_out[0.0].scaling_factor' that contains 60\n" + "2025-03-17 17:34:38 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:38 [INFO] idaes.init.Steam Cycle Model: Full turbine solve complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix 'scaling_factor' that contains 3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: 'fs.bfpt.control_volume.properties_in[0.0].scaling_factor' that contains 63\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix 'scaling_factor' that contains 120 keys\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: tol=1e-06\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: max_iter=200\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: tol=1e-06\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: For more information visit http://projects.coin-or.org/Ipopt\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: For more information visit http://projects.coin-or.org/Ipopt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled from source code available at\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled from source code available at\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: for large-scale scientific computation. All technical papers, sales and\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: publicity material resulting from use of the HSL codes within IPOPT must\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: for large-scale scientific computation. All technical papers, sales and\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: contain the following acknowledgement:\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: publicity material resulting from use of the HSL codes within IPOPT must\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: HSL, a collection of Fortran codes for large-scale scientific\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: contain the following acknowledgement:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: computation. See http://www.hsl.rl.ac.uk.\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: HSL, a collection of Fortran codes for large-scale scientific\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: computation. See http://www.hsl.rl.ac.uk.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: This is Ipopt version 3.13.2, running with linear solver ma27.\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: This is Ipopt version 3.13.2, running with linear solver ma27.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in equality constraint Jacobian...: 9\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in inequality constraint Jacobian.: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in equality constraint Jacobian...: 9\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in Lagrangian Hessian.............: 4\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in inequality constraint Jacobian.: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in Lagrangian Hessian.............: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Total number of variables............................: 5\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: variables with only lower bounds: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Total number of variables............................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: variables with lower and upper bounds: 2\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: variables with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: variables with only upper bounds: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: variables with lower and upper bounds: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Total number of equality constraints.................: 5\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: variables with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Total number of inequality constraints...............: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Total number of equality constraints.................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only lower bounds: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Total number of inequality constraints...............: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with lower and upper bounds: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only upper bounds: 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with lower and upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: 0 0.0000000e+00 5.46e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: 1 0.0000000e+00 5.55e-17 1.00e-07 -1.0 5.46e+07 - 9.90e-01 1.00e+00h 1\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: 0 0.0000000e+00 5.46e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: 1 0.0000000e+00 5.55e-17 1.00e-07 -1.0 5.46e+07 - 9.90e-01 1.00e+00h 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of Iterations....: 1\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of Iterations....: 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: (scaled) (unscaled)\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: (scaled) (unscaled)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Constraint violation....: 5.5511151231257827e-17 5.5511151231257827e-17\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Constraint violation....: 5.5511151231257827e-17 5.5511151231257827e-17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Overall NLP error.......: 5.5511151231257827e-17 5.5511151231257827e-17\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Overall NLP error.......: 5.5511151231257827e-17 5.5511151231257827e-17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of objective function evaluations = 2\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of objective gradient evaluations = 2\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of objective function evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint evaluations = 2\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of objective gradient evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint evaluations = 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint Jacobian evaluations = 2\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint Jacobian evaluations = 0\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint Jacobian evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Number of Lagrangian Hessian evaluations = 1\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint Jacobian evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Number of Lagrangian Hessian evaluations = 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in NLP function evaluations = 0.000\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in NLP function evaluations = 0.000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [DEBUG] idaes.solve.fs.bfpt: EXIT: Optimal Solution Found.\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:38 [DEBUG] idaes.solve.fs.bfpt: EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.condenser.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.condenser.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.condenser.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.hotwell: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.condenser.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.fwh1.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.hotwell: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [WARNING] idaes.init.fs.fwh1: The steam sat. temperature (329.33327413754273) is near the feedwater inlet temperature (299.90239563835314)\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.fwh1.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [WARNING] idaes.init.fs.fwh1: The steam sat. temperature (329.33327413754296) is near the feedwater inlet temperature (299.9023956383419)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.fwh1.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:59 [INFO] idaes.init.fs.fwh1.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh1.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh1: Condensing hot side inlet delta T = 12.513326095276463\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh1: Condensing hot side outlet delta T = 29.430878499189813\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1: Condensing hot side inlet delta T = 12.513326095291399\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh1: Steam Flow = 1345.0635216258854\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1: Condensing hot side outlet delta T = 29.430878499201064\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh1: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1: Steam Flow = 1345.0635216261717\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: 'fs.fwh1_pump.control_volume.properties_out[0.0].scaling_factor' that contains\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix 'scaling_factor' that contains 3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: 60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix 'scaling_factor' that contains 120 keys\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: 'fs.fwh1_pump.control_volume.properties_in[0.0].scaling_factor' that contains\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: 63 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: tol=1e-06\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: tol=1e-06\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: max_iter=200\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: For more information visit http://projects.coin-or.org/Ipopt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: For more information visit http://projects.coin-or.org/Ipopt\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled from source code available at\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled from source code available at\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: for large-scale scientific computation. All technical papers, sales and\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: for large-scale scientific computation. All technical papers, sales and\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: publicity material resulting from use of the HSL codes within IPOPT must\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: publicity material resulting from use of the HSL codes within IPOPT must\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: contain the following acknowledgement:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: contain the following acknowledgement:\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: HSL, a collection of Fortran codes for large-scale scientific\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: HSL, a collection of Fortran codes for large-scale scientific\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: computation. See http://www.hsl.rl.ac.uk.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: computation. See http://www.hsl.rl.ac.uk.\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: This is Ipopt version 3.13.2, running with linear solver ma27.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: This is Ipopt version 3.13.2, running with linear solver ma27.\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in equality constraint Jacobian...: 9\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in equality constraint Jacobian...: 9\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in inequality constraint Jacobian.: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in inequality constraint Jacobian.: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in Lagrangian Hessian.............: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in Lagrangian Hessian.............: 4\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of variables............................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of variables............................: 5\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only lower bounds: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: variables with lower and upper bounds: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: variables with lower and upper bounds: 2\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only upper bounds: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of equality constraints.................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of equality constraints.................: 5\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of inequality constraints...............: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of inequality constraints...............: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only lower bounds: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with lower and upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with lower and upper bounds: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only upper bounds: 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: 0 0.0000000e+00 5.66e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: 0 0.0000000e+00 5.66e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: 1 0.0000000e+00 1.11e-16 2.21e-07 -1.0 5.66e+06 - 9.90e-01 1.00e+00h 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: 1 0.0000000e+00 1.11e-16 2.21e-07 -1.0 5.66e+06 - 9.90e-01 1.00e+00h 1\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Iterations....: 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Iterations....: 1\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: (scaled) (unscaled)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: (scaled) (unscaled)\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Constraint violation....: 1.1102230246251565e-16 1.1102230246251565e-16\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Constraint violation....: 1.1102230246251565e-16 1.1102230246251565e-16\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective function evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective function evaluations = 2\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective gradient evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective gradient evaluations = 2\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint evaluations = 2\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint evaluations = 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint Jacobian evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint Jacobian evaluations = 2\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint Jacobian evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint Jacobian evaluations = 0\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Lagrangian Hessian evaluations = 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Lagrangian Hessian evaluations = 1\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in NLP function evaluations = 0.000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in NLP function evaluations = 0.000\n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:39 [DEBUG] idaes.solve.fs.fwh1_pump: EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [DEBUG] idaes.solve.fs.fwh1_pump: EXIT: Optimal Solution Found.\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh1_return: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh1_return: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [WARNING] idaes.init.fs.fwh2: The steam sat. temperature (335.2272258893377) is near the feedwater inlet temperature (318.02261253782166)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [WARNING] idaes.init.fs.fwh2: The steam sat. temperature (335.227225889338) is near the feedwater inlet temperature (318.02261253783706)\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:00 [INFO] idaes.init.fs.fwh2.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2: Condensing hot side inlet delta T = 12.73124007739836\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2: Condensing hot side inlet delta T = 12.731240077383344\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2: Condensing hot side outlet delta T = 17.002372103630485\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2: Condensing hot side outlet delta T = 17.00237210361631\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2: Steam Flow = 217.13965467977735\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2: Steam Flow = 217.13965467965426\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh2: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:01 [INFO] idaes.init.fs.fwh2: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [WARNING] idaes.init.fs.fwh3: The steam sat. temperature (347.7738554943195) is near the feedwater inlet temperature (323.03655083894466)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [WARNING] idaes.init.fs.fwh3: The steam sat. temperature (347.7738554943195) is near the feedwater inlet temperature (323.03655083895876)\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:34:39 [INFO] idaes.init.fs.fwh3.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:02 [INFO] idaes.init.fs.fwh3.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh3: Condensing hot side inlet delta T = 20.206912020999226\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh3: Condensing hot side inlet delta T = 20.206912020985854\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh3: Condensing hot side outlet delta T = 24.5035955629366\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh3: Condensing hot side outlet delta T = 24.503595562923753\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh3: Steam Flow = 217.4462771554057\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh3: Steam Flow = 217.44627715533093\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh3: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh3: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:04 [INFO] idaes.init.fs.fwh4.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh4.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4: Condensing hot side inlet delta T = 39.43016001679878\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh4: Condensing hot side inlet delta T = 39.4301600167788\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4: Condensing hot side outlet delta T = 47.80805362178066\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh4: Condensing hot side outlet delta T = 47.808053621766945\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4: Steam Flow = 247.42787053680715\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh4: Steam Flow = 247.42787053670986\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh4: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh4: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh5_da: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh5_da: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh6.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh6.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh6.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh6.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh6.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:05 [INFO] idaes.init.fs.fwh6.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh6.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh6.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:40 [INFO] idaes.init.fs.fwh6.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6: Condensing hot side inlet delta T = 45.08557769763464\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6: Condensing hot side inlet delta T = 45.08557769758456\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6: Condensing hot side outlet delta T = 72.43686375400229\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6: Condensing hot side outlet delta T = 72.43686375394775\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6: Steam Flow = 2128.5569356298247\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6: Steam Flow = 2128.5569356289825\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh6: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh6: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh7.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh7.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:06 [INFO] idaes.init.fs.fwh7.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7: Condensing hot side inlet delta T = 72.40778629498362\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7: Condensing hot side inlet delta T = 72.40778629492878\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7: Condensing hot side outlet delta T = 98.78550984878194\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7: Condensing hot side outlet delta T = 98.78550984873016\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7: Steam Flow = 3749.0680255320203\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7: Steam Flow = 3749.068025531304\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh7: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh7: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh8.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:07 [INFO] idaes.init.fs.fwh8.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8: Condensing hot side inlet delta T = 99.32852730884954\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8: Condensing hot side inlet delta T = 99.32852730878224\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8: Condensing hot side outlet delta T = 108.51918961637729\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8: Condensing hot side outlet delta T = 108.51918961631927\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8: Steam Flow = 1487.8775467645535\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8: Steam Flow = 1487.8775467636813\n" + "2025-03-17 17:34:41 [INFO] idaes.init.fs.fwh8: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:08 [INFO] idaes.init.fs.fwh8: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:43 [INFO] idaes.init.Steam Cycle Model: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:12 [INFO] idaes.init.Steam Cycle Model: Initialization Complete: optimal - Optimal Solution Found\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 13500\n", + "keys that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.hot_side.properties_out[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2341\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1021\n", + "\n", + "Total number of variables............................: 858\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 444\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 858\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.73e-09 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.9117155615240335e-11 3.7252902984619141e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.9117155615240335e-11 3.7252902984619141e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.154\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.cold_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "57072.525483603706\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.cold_side.properties_in[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:34:43 [INFO] idaes.init.fs.ECON.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.hot_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:34:43 [INFO] idaes.init.fs.ECON.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.hot_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.ECON.hot_side.properties_out: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.ECON.hot_side.properties_out: fs.ECON.hot_side.properties_out State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.cold_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.ECON.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.hot_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.ECON: fs.ECON Initialisation Step 1 Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.ECON.hot_side.properties_in: fs.ECON.hot_side.properties_in State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.ECON: fs.ECON Initialisation Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.cold_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.PrSH.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.hot_side.properties_out[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.PrSH.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.PrSH.hot_side.properties_out: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.cold_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.PrSH.hot_side.properties_out: fs.PrSH.hot_side.properties_out State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.cold_side.properties_in[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.PrSH.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.hot_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.PrSH: fs.PrSH Initialisation Step 1 Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.hot_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.FSH.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.drain_mix.steam_state[0.0].scaling_factor' that contains 60 component\n", - "keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.FSH.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.condense.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.FSH.hot_side.properties_in: fs.FSH.hot_side.properties_in State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh6.cooling.cold_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.FSH: fs.FSH Initialisation Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh6.cooling.hot_side.properties_out[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.RH.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh6.cooling.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. 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Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.Spl1.outlet_2_state: Initialisation Complete, skipped.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.bfp.control_volume.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.Spl1.outlet_2_state: fs.Spl1.outlet_2_state State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh5_da.mixed_state[0.0].scaling_factor' that contains 60 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.Spl1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh5_da.feedwater_state[0.0].scaling_factor' that contains 60 component\n", - "keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.Spl1.mixed_state: fs.Spl1.mixed_state State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh5_da.drain_state[0.0].scaling_factor' that contains 60 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.mix1.Reheat_out_state: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh5_da.steam_state[0.0].scaling_factor' that contains 60 component keys\n", - "that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:44 [INFO] idaes.init.fs.mix1.PrSH_out_state: Initialisation Complete, optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh4.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh4.condense.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 13500\n", + "keys that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh4.condense.cold_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh4.condense.hot_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3045\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1592\n", + "\n", + "Exception of type: TOO_FEW_DOF in file \"IpIpoptApplication.cpp\" at line 926:\n", + " Exception message: status != TOO_FEW_DEGREES_OF_FREEDOM evaluated false: Too few degrees of freedom (rethrown)!\n", + "\n", + "EXIT: Problem has too few degrees of freedom.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh4.condense.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"Steam Cycle Model\";\n", + " - termination condition: other\n", + " - message from solver: Too few degrees of freedom (rethrown)!\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "unfix inlet conditions, degreeso of freedom = 0\n", + "connecting flowsheets, degrees of freedom = 0\n", + "solving full plant model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.cooling.cold_side.properties_in[0.0].scaling_factor' that contains 60\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 10\n", "component keys that are not exported as part of the NL file. Skipping.\n" ] }, @@ -6669,6500 +6630,127 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.cooling.hot_side.properties_out[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 13500\n", + "keys that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.cooling.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "Ipopt 3.13.2: tol=1e-06\n", + "linear_solver=ma27\n", + "max_iter=40\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3579\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2246\n", + "\n", + "Total number of variables............................: 1195\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1195\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 8.59e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.desuperheat.cold_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + " 1 0.0000000e+00 5.94e+06 0.00e+00 -1.0 8.21e+07 - 1.00e+00 1.00e+00h 1\n", + " 2 0.0000000e+00 9.93e+04 0.00e+00 -1.0 4.31e+06 - 1.00e+00 1.00e+00h 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.desuperheat.cold_side.properties_in[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + " 3 0.0000000e+00 7.63e+01 0.00e+00 -1.0 1.34e+05 - 1.00e+00 1.00e+00h 1\n", + " 4 0.0000000e+00 3.91e-05 0.00e+00 -3.8 1.80e+01 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 4\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 7.8780398382605199e-07 3.9115548133850098e-05\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 7.8780398382605199e-07 3.9115548133850098e-05\n", + "\n", + "\n", + "Number of objective function evaluations = 5\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 5\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 5\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 4\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.163\n", + "Total CPU secs in NLP function evaluations = 0.588\n", + "\n", + "EXIT: Optimal Solution Found.\n" ] - }, + } + ], + "source": [ + "# import SCPC power plant\n", + "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", + "# and run SCPC plant.\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", + " main,\n", + ")\n", + "\n", + "m, res = main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Creating a PFD with results and a stream table\n", + "\n", + "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.desuperheat.hot_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh3.desuperheat.hot_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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"Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.throttle_valve[1].control_volume.properties_out[0.0].scaling_factor'\n", - "that contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.throttle_valve[1].control_volume.properties_in[0.0].scaling_factor'\n", - "that contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_4_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_3_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_2_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_1_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.mixed_state[0.0].scaling_factor' that contains 62\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 2341\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1021\n", - "\n", - "Total number of variables............................: 858\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 444\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 858\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 2.79e-09 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 9.9134922493249178e-11 2.7939677238464351e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 9.9134922493249178e-11 2.7939677238464351e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 1\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 1\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 1\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.307\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "57072.525483603706\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON.cold_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON.hot_side.properties_out: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON.hot_side.properties_out: fs.ECON.hot_side.properties_out State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON.hot_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON: fs.ECON Initialisation Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON.hot_side.properties_in: fs.ECON.hot_side.properties_in State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.ECON: fs.ECON Initialisation Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:15 [INFO] idaes.init.fs.PrSH.cold_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH.hot_side.properties_out: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH.hot_side.properties_out: fs.PrSH.hot_side.properties_out State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH.hot_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH: fs.PrSH Initialisation Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH.hot_side.properties_in: fs.PrSH.hot_side.properties_in State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.PrSH: fs.PrSH Initialisation Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH.cold_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH.hot_side.properties_out: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH.hot_side.properties_out: fs.FSH.hot_side.properties_out State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH.hot_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH: fs.FSH Initialisation Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH.hot_side.properties_in: fs.FSH.hot_side.properties_in State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.FSH: fs.FSH Initialisation Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:16 [INFO] idaes.init.fs.RH.cold_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH.hot_side.properties_in: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH.hot_side.properties_out: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH.hot_side.properties_out: fs.RH.hot_side.properties_out State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH.hot_side: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH: fs.RH Initialisation Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH.hot_side.properties_in: fs.RH.hot_side.properties_in State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.RH: fs.RH Initialisation Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.PlSH.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.PlSH: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Water_wall.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Water_wall: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1.mixed_state: Initialisation Complete, skipped.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1.outlet_1_state: Initialisation Complete, skipped.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1.outlet_1_state: fs.Spl1.outlet_1_state State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1.outlet_2_state: Initialisation Complete, skipped.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1.outlet_2_state: fs.Spl1.outlet_2_state State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.Spl1.mixed_state: fs.Spl1.mixed_state State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1.Reheat_out_state: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1.PrSH_out_state: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1.mixed_state: Initialisation Complete, optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1.mixed_state: fs.mix1.mixed_state State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1.Reheat_out_state: fs.mix1.Reheat_out_state State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.mix1.PrSH_out_state: fs.mix1.PrSH_out_state State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:26:17 [INFO] idaes.init.fs.ATMP1: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "initialization done\n", - "solving square problem disconnected\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3045\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1592\n", - "\n", - "Exception of type: TOO_FEW_DOF in file \"IpIpoptApplication.cpp\" at line 926:\n", - " Exception message: status != TOO_FEW_DEGREES_OF_FREEDOM evaluated false: Too few degrees of freedom (rethrown)!\n", - "\n", - "EXIT: Problem has too few degrees of freedom.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"Steam Cycle Model\";\n", - " - termination condition: other\n", - " - message from solver: Too few degrees of freedom (rethrown)!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unfix inlet conditions, degreeso of freedom = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connecting flowsheets, degrees of freedom = 0\n", - "solving full plant model\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_3_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_2_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_1_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.mixed_state[0.0].scaling_factor' that contains 61\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: tol=1e-06\n", - "linear_solver=ma27\n", - "max_iter=40\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3579\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2246\n", - "\n", - "Total number of variables............................: 1195\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1195\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 8.59e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1 0.0000000e+00 5.94e+06 0.00e+00 -1.0 8.21e+07 - 1.00e+00 1.00e+00h 1\n", - " 2 0.0000000e+00 9.93e+04 0.00e+00 -1.0 4.31e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 3 0.0000000e+00 7.63e+01 0.00e+00 -1.0 1.34e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 4 0.0000000e+00 3.91e-05 0.00e+00 -3.8 1.80e+01 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 4\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.9103830456733704e-09 3.9085745811462402e-05\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.9103830456733704e-09 3.9085745811462402e-05\n", - "\n", - "\n", - "Number of objective function evaluations = 5\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 5\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 5\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 4\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.342\n", - "Total CPU secs in NLP function evaluations = 1.169\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# import SCPC power plant\n", - "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", - "# and run SCPC plant.\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", - " main,\n", - ")\n", - "\n", - "m, res = main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Creating a PFD with results and a stream table\n", - "\n", - "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_35844\\2286906919.py:1: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", - " import pkg_resources\n" + "/tmp/ipykernel_294031/2286906919.py:1: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + " import pkg_resources\n" ] }, { @@ -14116,11 +7704,7 @@ "" ] }, - "metadata": { - "filenames": { - "image/svg+xml": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\power_gen\\supercritical\\supercritical_power_plant_doc_7_1.svg" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -14146,7 +7730,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -14519,7 +8103,7 @@ " 19911.433775\n", " 358.709816\n", " 896.029914\n", - " 4059473.408394\n", + " 4059473.408393\n", " 1.0\n", " 67143.632291\n", " \n", @@ -14676,7 +8260,7 @@ "FWH8_DRN 8629988.197215 0.0 22326.428163 \n", "MAKEUP_01 101325 0.0 2500 \n", "RHT_COLD 4418553.956974 1.0 54662.948278 \n", - "RHT_HOT 4059473.408394 1.0 67143.632291 \n", + "RHT_HOT 4059473.408393 1.0 67143.632291 \n", "STEAM_LP 338388.603252 1.0 54195.400951 \n", "STEAM_MAIN 24230000.0 0.0 62710.01 \n", "THRTL1 23161159.682041 0.0 62710.01 \n", @@ -14686,7 +8270,7 @@ "condenser_mix_to_condenser 3878.882993 0.968709 44615.952422 " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -14720,7 +8304,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_test.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_test.ipynb index c9851e9c..8553676d 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_test.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_test.ipynb @@ -1,160 +1,187 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical Power Plant Flowsheet Example\n", - "Maintainer: Andrew Lee \n", - "Author: John Eslick \n", - "\n", - "\n", - "## 1. Introduction\n", - "\n", - "\n", - "This example is to demonstrate a supercritical pulverized coal power plant model. The power plant consists of two major sub-systems (or flowsheets), a boiler heat exchanger network and a steam cycle. This jupyter notebook provides the workflow to import the steam cycle flowsheet, import the boiler heat exchanger network, connect and run both the flowsheets, and display the main results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Model Description\n", - "\n", - "The case study demonstrated here is for a ~620MW gross power output. The process flow diagram is shown in section 3 of this jupyter notebook. Figure 1 shows the boiler heat exchanger network, while, figure 2 shows the steam cycle system. \n", - "\n", - "The streams connecting both the flowsheets are: \n", - "  a) The main steam: that connects the boiler attemperator to the throttle valves of the high pressure turbine \n", - "  b) The cold reheat: that connects the final stage of the high pressure turbine to the boiler reheater \n", - "  c) The hot reheat: that connects the boiler reheater to the intermediate pressure turbine \n", - "  d) The main feed water: that connects the last feed water heater to the boiler economizer \n", - " \n", - "To get a more detailed description of the power plant flowsheet, review the ```SCPC_full_plant.py``` file. For details in terms of specific power plant units (for example dimensions, parameters, and variables), more information can be found at ```supercritical_steam_cycle.py``` and ```boiler_subflowsheet.py```.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Process Flow Diagram (PFD)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import SVG, display\n", - "\n", - "display(\n", - " \"Boiler subsystem PFD\",\n", - " SVG(filename=\"Boiler_scpc_PFD.svg\"),\n", - " \"Steam Cycle subsystem PFD\",\n", - " SVG(filename=\"supercritical_steam_cycle.svg\"),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Run power plant model example\n", - "\n", - "This example runs the main ``SCPC_full_plant.py`` script, which, imports two flowsheets (steam cycle and boiler heat exchanger network), builds arcs to connect both flowsheets, and run the full power plant model." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# import SCPC power plant\n", - "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", - "# and run SCPC plant.\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", - " main,\n", - ")\n", - "\n", - "m, res = main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Creating a PFD with results and a stream table\n", - "\n", - "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pkg_resources\n", - "import pyomo.environ as pyo\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", - " pfd_result,\n", - ")\n", - "from idaes.core.util.tables import create_stream_table_dataframe\n", - "\n", - "# Create stream results as Pandas dataframe\n", - "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", - "# Create a new PFD with simulation results\n", - "init_pfd = pkg_resources.resource_string(\n", - " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", - " \"supercritical_steam_cycle.svg\",\n", - ")\n", - "res_pfd = pfd_result(m, df, svg=init_pfd)\n", - "# Display PFD with results.\n", - "display(SVG(res_pfd))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Display the stream table.\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "c40351ff", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical Power Plant Flowsheet Example\n", + "Maintainer: Andrew Lee \n", + "Author: John Eslick \n", + "\n", + "\n", + "## 1. Introduction\n", + "\n", + "\n", + "This example is to demonstrate a supercritical pulverized coal power plant model. The power plant consists of two major sub-systems (or flowsheets), a boiler heat exchanger network and a steam cycle. This jupyter notebook provides the workflow to import the steam cycle flowsheet, import the boiler heat exchanger network, connect and run both the flowsheets, and display the main results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Model Description\n", + "\n", + "The case study demonstrated here is for a ~620MW gross power output. The process flow diagram is shown in section 3 of this jupyter notebook. Figure 1 shows the boiler heat exchanger network, while, figure 2 shows the steam cycle system. \n", + "\n", + "The streams connecting both the flowsheets are: \n", + "  a) The main steam: that connects the boiler attemperator to the throttle valves of the high pressure turbine \n", + "  b) The cold reheat: that connects the final stage of the high pressure turbine to the boiler reheater \n", + "  c) The hot reheat: that connects the boiler reheater to the intermediate pressure turbine \n", + "  d) The main feed water: that connects the last feed water heater to the boiler economizer \n", + " \n", + "To get a more detailed description of the power plant flowsheet, review the ```SCPC_full_plant.py``` file. For details in terms of specific power plant units (for example dimensions, parameters, and variables), more information can be found at ```supercritical_steam_cycle.py``` and ```boiler_subflowsheet.py```.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Process Flow Diagram (PFD)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import SVG, display\n", + "\n", + "display(\n", + " \"Boiler subsystem PFD\",\n", + " SVG(filename=\"Boiler_scpc_PFD.svg\"),\n", + " \"Steam Cycle subsystem PFD\",\n", + " SVG(filename=\"supercritical_steam_cycle.svg\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Run power plant model example\n", + "\n", + "This example runs the main ``SCPC_full_plant.py`` script, which, imports two flowsheets (steam cycle and boiler heat exchanger network), builds arcs to connect both flowsheets, and run the full power plant model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# import SCPC power plant\n", + "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", + "# and run SCPC plant.\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", + " main,\n", + ")\n", + "\n", + "m, res = main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Creating a PFD with results and a stream table\n", + "\n", + "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pkg_resources\n", + "import pyomo.environ as pyo\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", + " pfd_result,\n", + ")\n", + "from idaes.core.util.tables import create_stream_table_dataframe\n", + "\n", + "# Create stream results as Pandas dataframe\n", + "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", + "# Create a new PFD with simulation results\n", + "init_pfd = pkg_resources.resource_string(\n", + " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", + " \"supercritical_steam_cycle.svg\",\n", + ")\n", + "res_pfd = pfd_result(m, df, svg=init_pfd)\n", + "# Display PFD with results.\n", + "display(SVG(res_pfd))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Display the stream table.\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_usr.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_usr.ipynb index c9851e9c..968d5a64 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_usr.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_power_plant_usr.ipynb @@ -1,160 +1,187 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical Power Plant Flowsheet Example\n", - "Maintainer: Andrew Lee \n", - "Author: John Eslick \n", - "\n", - "\n", - "## 1. Introduction\n", - "\n", - "\n", - "This example is to demonstrate a supercritical pulverized coal power plant model. The power plant consists of two major sub-systems (or flowsheets), a boiler heat exchanger network and a steam cycle. This jupyter notebook provides the workflow to import the steam cycle flowsheet, import the boiler heat exchanger network, connect and run both the flowsheets, and display the main results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Model Description\n", - "\n", - "The case study demonstrated here is for a ~620MW gross power output. The process flow diagram is shown in section 3 of this jupyter notebook. Figure 1 shows the boiler heat exchanger network, while, figure 2 shows the steam cycle system. \n", - "\n", - "The streams connecting both the flowsheets are: \n", - "  a) The main steam: that connects the boiler attemperator to the throttle valves of the high pressure turbine \n", - "  b) The cold reheat: that connects the final stage of the high pressure turbine to the boiler reheater \n", - "  c) The hot reheat: that connects the boiler reheater to the intermediate pressure turbine \n", - "  d) The main feed water: that connects the last feed water heater to the boiler economizer \n", - " \n", - "To get a more detailed description of the power plant flowsheet, review the ```SCPC_full_plant.py``` file. For details in terms of specific power plant units (for example dimensions, parameters, and variables), more information can be found at ```supercritical_steam_cycle.py``` and ```boiler_subflowsheet.py```.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Process Flow Diagram (PFD)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import SVG, display\n", - "\n", - "display(\n", - " \"Boiler subsystem PFD\",\n", - " SVG(filename=\"Boiler_scpc_PFD.svg\"),\n", - " \"Steam Cycle subsystem PFD\",\n", - " SVG(filename=\"supercritical_steam_cycle.svg\"),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Run power plant model example\n", - "\n", - "This example runs the main ``SCPC_full_plant.py`` script, which, imports two flowsheets (steam cycle and boiler heat exchanger network), builds arcs to connect both flowsheets, and run the full power plant model." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# import SCPC power plant\n", - "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", - "# and run SCPC plant.\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", - " main,\n", - ")\n", - "\n", - "m, res = main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Creating a PFD with results and a stream table\n", - "\n", - "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pkg_resources\n", - "import pyomo.environ as pyo\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", - " pfd_result,\n", - ")\n", - "from idaes.core.util.tables import create_stream_table_dataframe\n", - "\n", - "# Create stream results as Pandas dataframe\n", - "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", - "# Create a new PFD with simulation results\n", - "init_pfd = pkg_resources.resource_string(\n", - " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", - " \"supercritical_steam_cycle.svg\",\n", - ")\n", - "res_pfd = pfd_result(m, df, svg=init_pfd)\n", - "# Display PFD with results.\n", - "display(SVG(res_pfd))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Display the stream table.\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "2fa4a417", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical Power Plant Flowsheet Example\n", + "Maintainer: Andrew Lee \n", + "Author: John Eslick \n", + "\n", + "\n", + "## 1. Introduction\n", + "\n", + "\n", + "This example is to demonstrate a supercritical pulverized coal power plant model. The power plant consists of two major sub-systems (or flowsheets), a boiler heat exchanger network and a steam cycle. This jupyter notebook provides the workflow to import the steam cycle flowsheet, import the boiler heat exchanger network, connect and run both the flowsheets, and display the main results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Model Description\n", + "\n", + "The case study demonstrated here is for a ~620MW gross power output. The process flow diagram is shown in section 3 of this jupyter notebook. Figure 1 shows the boiler heat exchanger network, while, figure 2 shows the steam cycle system. \n", + "\n", + "The streams connecting both the flowsheets are: \n", + "  a) The main steam: that connects the boiler attemperator to the throttle valves of the high pressure turbine \n", + "  b) The cold reheat: that connects the final stage of the high pressure turbine to the boiler reheater \n", + "  c) The hot reheat: that connects the boiler reheater to the intermediate pressure turbine \n", + "  d) The main feed water: that connects the last feed water heater to the boiler economizer \n", + " \n", + "To get a more detailed description of the power plant flowsheet, review the ```SCPC_full_plant.py``` file. For details in terms of specific power plant units (for example dimensions, parameters, and variables), more information can be found at ```supercritical_steam_cycle.py``` and ```boiler_subflowsheet.py```.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Process Flow Diagram (PFD)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import SVG, display\n", + "\n", + "display(\n", + " \"Boiler subsystem PFD\",\n", + " SVG(filename=\"Boiler_scpc_PFD.svg\"),\n", + " \"Steam Cycle subsystem PFD\",\n", + " SVG(filename=\"supercritical_steam_cycle.svg\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Run power plant model example\n", + "\n", + "This example runs the main ``SCPC_full_plant.py`` script, which, imports two flowsheets (steam cycle and boiler heat exchanger network), builds arcs to connect both flowsheets, and run the full power plant model." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# import SCPC power plant\n", + "# initialize steam cycle, initialize boiler heat exchanger network, connect both flowsheets,\n", + "# and run SCPC plant.\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_power_plant.SCPC_full_plant import (\n", + " main,\n", + ")\n", + "\n", + "m, res = main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Creating a PFD with results and a stream table\n", + "\n", + "The steam cycle results can be displayed on the PFD and as a stream table, by running the following cells." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pkg_resources\n", + "import pyomo.environ as pyo\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", + " pfd_result,\n", + ")\n", + "from idaes.core.util.tables import create_stream_table_dataframe\n", + "\n", + "# Create stream results as Pandas dataframe\n", + "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", + "# Create a new PFD with simulation results\n", + "init_pfd = pkg_resources.resource_string(\n", + " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", + " \"supercritical_steam_cycle.svg\",\n", + ")\n", + "res_pfd = pfd_result(m, df, svg=init_pfd)\n", + "# Display PFD with results.\n", + "display(SVG(res_pfd))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Display the stream table.\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle.ipynb index 56bbd4ee..fdec1d64 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle.ipynb @@ -1,224 +1,251 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical Steam Cycle Example\n", - "Maintainer: Andrew Lee \n", - "Author: Andrew Lee \n", - "\n", - "This example uses Jupyter Lab or Jupyter notebook, and demonstrates a supercritical pulverized coal (SCPC) steam cycle model. See the ```supercritical_steam_cycle.py``` to see more information on how to assemble a power plant model flowsheet. Code comments in that file will guide you through the process." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model Description\n", - "\n", - "The example model doesn't represent any particular power plant, but should be a reasonable approximation of a typical plant. The gross power output is about 620 MW. The process flow diagram (PFD) can be shown using the code below. The initial PFD contains spaces for model results, to be filled in later.\n", - "\n", - "To get a more detailed look at the model structure, you may find it useful to review ```supercritical_steam_cycle.py``` first. Although there is no detailed boiler model, there are constraints in the model to complete the steam loop through the boiler and calculate boiler heat input to the steam cycle. The efficiency calculation for the steam cycle doesn't account for heat loss in the boiler, which would be a result of a more detailed boiler model." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# pkg_resources is used here to get the svg information from the\n", - "# installed IDAES package\n", - "\n", - "import pkg_resources\n", - "from IPython.display import SVG, display\n", - "\n", - "# Get the contents of the PFD (which is an svg file)\n", - "init_pfd = pkg_resources.resource_string(\n", - " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", - " \"supercritical_steam_cycle.svg\",\n", - ")\n", - "\n", - "# Make the svg contents into an SVG object and display it.\n", - "display(SVG(init_pfd))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize the steam cycle flowsheet\n", - "\n", - "This example is part of the ```idaes``` package, which you should have installed. To run the example, the example flowsheet is imported from the ```idaes``` package. When you write your own model, you can import and run it in whatever way is appropriate for you. The Pyomo environment is also imported as ```pyo```, providing easy access to Pyomo functions and classes.\n", - "\n", - "The supercritical flowsheet example main function returns a Pyomo concrete mode (m) and a solver object (solver). The model is also initialized by the ```main()``` function." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", - " main,\n", - " pfd_result,\n", - ")\n", - "from idaes.core.util.tables import create_stream_table_dataframe\n", - "\n", - "m, solver = main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Inside the model, there is a subblock ```fs```. This is an IDAES flowsheet model, which contains the supercritical steam cycle model. In the flowsheet, the model called ```turb``` is a multistage turbine model. The turbine model contains an expression for total power, ```power```. In this case the model is steady-state, but all IDAES models allow for dynamic simulation, and contain time indexes. Power is indexed by time, and only the \"0\" time point exists. By convention, in the IDAES framework, power going into a model is positive, so power produced by the turbine is negative. \n", - "\n", - "The property package used for this model uses SI (mks) units of measure, so the power is in Watts. Here a function is defined which can be used to report power output in MW." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a function to report gross power output in MW\n", - "def gross_power_mw(model):\n", - " # pyo.value(m.fs.turb.power[0]) is the power consumed in Watts\n", - " return -pyo.value(model.fs.turb.power[0]) / 1e6\n", - "\n", - "\n", - "# Show the gross power\n", - "gross_power_mw(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Change the model inputs\n", - "\n", - "The turbine in this example simulates partial arc admission with four arcs, so there are four throttle valves. For this example, we will close one of the valves to 25% open, and observe the result." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.turb.throttle_valve[1].valve_opening[:].value = 0.25" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we re-solve the model using the solver created by the ```supercritical_steam_cycle.py``` script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can check the gross power output again." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gross_power_mw(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a PFD with results and a stream table\n", - "\n", - "A more detailed look at the model results can be obtained by creating a stream table and putting key results on the PFD. Of course, any unit model or stream result can be obtained from the model." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Pandas dataframe with stream results\n", - "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", - "\n", - "# Create a new PFD with simulation results\n", - "res_pfd = pfd_result(m, df, svg=init_pfd)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Display PFD with results.\n", - "display(SVG(res_pfd))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Display the stream table.\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "fa5b8340", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical Steam Cycle Example\n", + "Maintainer: Andrew Lee \n", + "Author: Andrew Lee \n", + "\n", + "This example uses Jupyter Lab or Jupyter notebook, and demonstrates a supercritical pulverized coal (SCPC) steam cycle model. See the ```supercritical_steam_cycle.py``` to see more information on how to assemble a power plant model flowsheet. Code comments in that file will guide you through the process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Description\n", + "\n", + "The example model doesn't represent any particular power plant, but should be a reasonable approximation of a typical plant. The gross power output is about 620 MW. The process flow diagram (PFD) can be shown using the code below. The initial PFD contains spaces for model results, to be filled in later.\n", + "\n", + "To get a more detailed look at the model structure, you may find it useful to review ```supercritical_steam_cycle.py``` first. Although there is no detailed boiler model, there are constraints in the model to complete the steam loop through the boiler and calculate boiler heat input to the steam cycle. The efficiency calculation for the steam cycle doesn't account for heat loss in the boiler, which would be a result of a more detailed boiler model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# pkg_resources is used here to get the svg information from the\n", + "# installed IDAES package\n", + "\n", + "import pkg_resources\n", + "from IPython.display import SVG, display\n", + "\n", + "# Get the contents of the PFD (which is an svg file)\n", + "init_pfd = pkg_resources.resource_string(\n", + " \"idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle\",\n", + " \"supercritical_steam_cycle.svg\",\n", + ")\n", + "\n", + "# Make the svg contents into an SVG object and display it.\n", + "display(SVG(init_pfd))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize the steam cycle flowsheet\n", + "\n", + "This example is part of the ```idaes``` package, which you should have installed. To run the example, the example flowsheet is imported from the ```idaes``` package. When you write your own model, you can import and run it in whatever way is appropriate for you. The Pyomo environment is also imported as ```pyo```, providing easy access to Pyomo functions and classes.\n", + "\n", + "The supercritical flowsheet example main function returns a Pyomo concrete mode (m) and a solver object (solver). The model is also initialized by the ```main()``` function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", + " main,\n", + " pfd_result,\n", + ")\n", + "from idaes.core.util.tables import create_stream_table_dataframe\n", + "\n", + "m, solver = main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inside the model, there is a subblock ```fs```. This is an IDAES flowsheet model, which contains the supercritical steam cycle model. In the flowsheet, the model called ```turb``` is a multistage turbine model. The turbine model contains an expression for total power, ```power```. In this case the model is steady-state, but all IDAES models allow for dynamic simulation, and contain time indexes. Power is indexed by time, and only the \"0\" time point exists. By convention, in the IDAES framework, power going into a model is positive, so power produced by the turbine is negative. \n", + "\n", + "The property package used for this model uses SI (mks) units of measure, so the power is in Watts. Here a function is defined which can be used to report power output in MW." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a function to report gross power output in MW\n", + "def gross_power_mw(model):\n", + " # pyo.value(m.fs.turb.power[0]) is the power consumed in Watts\n", + " return -pyo.value(model.fs.turb.power[0]) / 1e6\n", + "\n", + "\n", + "# Show the gross power\n", + "gross_power_mw(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Change the model inputs\n", + "\n", + "The turbine in this example simulates partial arc admission with four arcs, so there are four throttle valves. For this example, we will close one of the valves to 25% open, and observe the result." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.turb.throttle_valve[1].valve_opening[:].value = 0.25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we re-solve the model using the solver created by the ```supercritical_steam_cycle.py``` script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can check the gross power output again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gross_power_mw(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a PFD with results and a stream table\n", + "\n", + "A more detailed look at the model results can be obtained by creating a stream table and putting key results on the PFD. Of course, any unit model or stream result can be obtained from the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Pandas dataframe with stream results\n", + "df = create_stream_table_dataframe(streams=m._streams, orient=\"index\")\n", + "\n", + "# Create a new PFD with simulation results\n", + "res_pfd = pfd_result(m, df, svg=init_pfd)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Display PFD with results.\n", + "display(SVG(res_pfd))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Display the stream table.\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_doc.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_doc.ipynb index 75428839..aa9cd93a 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_doc.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_doc.ipynb @@ -1,5 +1,31 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -24,14 +50,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_36220\\1084714406.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", + "/tmp/ipykernel_294708/1084714406.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " import pkg_resources\n" ] }, @@ -986,11 +1012,7 @@ "" ] }, - "metadata": { - "filenames": { - "image/svg+xml": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\power_gen\\supercritical\\supercritical_steam_cycle_doc_2_1.svg" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1024,2980 +1046,3104 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:19 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:20 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:50 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:20 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:51 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:20 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:51 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:20 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:51 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:20 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:51 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:51 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:52 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:22 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:23 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:23 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:23 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:23 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:23 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:23 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:24 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:53 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:24 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:54 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:24 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:54 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:24 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" + "2025-03-17 17:34:54 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[1].control_volume.work\n" + "2025-03-17 17:34:54 [WARNING] idaes.models.properties.general_helmholtz.helmholtz_state: Helmholtz EoS packages using Mixed phase representation ignore the 'has_phase_equilibrium' configuration argument. However, setting this to True can result in errors when constructing material balances due to only having a single phase (thus phase transfer terms cannot be constructed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[2].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[1].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[3].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[2].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[4].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[3].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[1].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.inlet_stage[4].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[2].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[1].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[3].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[2].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[4].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[3].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[5].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[4].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[6].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[5].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[7].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[6].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.ip_stages[1].control_volume.work\n" + "2025-03-17 17:34:55 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.turb.hp_stages[7].control_volume.work\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - 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"2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.cold_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.hot_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.desuperheat.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.cold_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh6.cooling.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.hot_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.condense.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.cold_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.hot_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.desuperheat.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.cold_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh7.cooling.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.hot_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.condense.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.cold_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.hot_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.desuperheat.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.cold_side.heat\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.hot_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.overall_heat_transfer_coefficient[0.0]\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.cold_side.heat\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.area\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.overall_heat_transfer_coefficient[0.0]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.Steam Cycle Model: Starting initialization\n" + "2025-03-17 17:34:56 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.fwh8.cooling.area\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.Steam Cycle Model: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:28 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:29 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:29 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:56 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:30 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:30 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:30 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:57 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.inlet_split: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[1]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[2]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[3]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization started\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:31 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.throttle_valve[4]: Steam valve initialization complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:32 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.inlet_stage[1]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:32 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.inlet_stage[2]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:32 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.inlet_stage[3]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:32 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.inlet_stage[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:32 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.inlet_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:32 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.hp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:33 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.hp_split[7]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:33 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.ip_split[5]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:34 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.ip_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:34 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.lp_split[4]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:34 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:58 [INFO] idaes.init.fs.turb.lp_split[8]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:34 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.turb.lp_split[10]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:34 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.turb.lp_split[11]: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [INFO] idaes.init.Steam Cycle Model: Full turbine solve complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.turb.outlet_stage: Initialization Complete (Outlet Stage): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix\n" + "2025-03-17 17:34:59 [INFO] idaes.init.Steam Cycle Model: Full turbine solve complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: 'fs.bfpt.control_volume.properties_out[0.0].scaling_factor' that contains 60\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix 'scaling_factor' that contains 3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: WARNING: model contains export suffix 'scaling_factor' that contains 120 keys\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: 'fs.bfpt.control_volume.properties_in[0.0].scaling_factor' that contains 63\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: tol=1e-06\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: tol=1e-06\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: max_iter=200\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: For more information visit http://projects.coin-or.org/Ipopt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: For more information visit http://projects.coin-or.org/Ipopt\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled from source code available at\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled from source code available at\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: for large-scale scientific computation. All technical papers, sales and\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: for large-scale scientific computation. All technical papers, sales and\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: publicity material resulting from use of the HSL codes within IPOPT must\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: publicity material resulting from use of the HSL codes within IPOPT must\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: contain the following acknowledgement:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: contain the following acknowledgement:\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: HSL, a collection of Fortran codes for large-scale scientific\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: HSL, a collection of Fortran codes for large-scale scientific\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: computation. See http://www.hsl.rl.ac.uk.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: computation. See http://www.hsl.rl.ac.uk.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: ******************************************************************************\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: This is Ipopt version 3.13.2, running with linear solver ma27.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: This is Ipopt version 3.13.2, running with linear solver ma27.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in equality constraint Jacobian...: 9\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in equality constraint Jacobian...: 9\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in inequality constraint Jacobian.: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in inequality constraint Jacobian.: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in Lagrangian Hessian.............: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of nonzeros in Lagrangian Hessian.............: 4\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Total number of variables............................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Total number of variables............................: 5\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: variables with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: variables with only lower bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: variables with lower and upper bounds: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: variables with lower and upper bounds: 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: variables with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: variables with only upper bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Total number of equality constraints.................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Total number of equality constraints.................: 5\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Total number of inequality constraints...............: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Total number of inequality constraints...............: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only lower bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with lower and upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with lower and upper bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: inequality constraints with only upper bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: 0 0.0000000e+00 5.46e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: 0 0.0000000e+00 5.46e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: 1 0.0000000e+00 5.55e-17 1.00e-07 -1.0 5.46e+07 - 9.90e-01 1.00e+00h 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: 1 0.0000000e+00 5.55e-17 1.00e-07 -1.0 5.46e+07 - 9.90e-01 1.00e+00h 1\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of Iterations....: 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of Iterations....: 1\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: (scaled) (unscaled)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: (scaled) (unscaled)\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Constraint violation....: 5.5511151231257827e-17 5.5511151231257827e-17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Constraint violation....: 5.5511151231257827e-17 5.5511151231257827e-17\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Overall NLP error.......: 5.5511151231257827e-17 5.5511151231257827e-17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Overall NLP error.......: 5.5511151231257827e-17 5.5511151231257827e-17\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of objective function evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of objective function evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of objective gradient evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of objective gradient evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint evaluations = 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint Jacobian evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of equality constraint Jacobian evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint Jacobian evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of inequality constraint Jacobian evaluations = 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Number of Lagrangian Hessian evaluations = 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Number of Lagrangian Hessian evaluations = 1\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in NLP function evaluations = 0.000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: Total CPU secs in NLP function evaluations = 0.000\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.bfpt: EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:35 [DEBUG] idaes.solve.fs.bfpt: EXIT: Optimal Solution Found.\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.condenser.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.condenser.hot_side: Initialization Complete\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.condenser.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.condenser.cold_side: Initialization Complete\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.hotwell: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.hotwell: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [WARNING] idaes.init.fs.fwh1: The steam sat. temperature (329.33327413754296) is near the feedwater inlet temperature (299.9023956383419)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [WARNING] idaes.init.fs.fwh1: The steam sat. temperature (329.33327413754273) is near the feedwater inlet temperature (299.90239563835314)\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1: Condensing hot side inlet delta T = 12.513326095291399\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1: Condensing hot side inlet delta T = 12.513326095276463\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1: Condensing hot side outlet delta T = 29.430878499201064\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1: Condensing hot side outlet delta T = 29.430878499189813\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1: Steam Flow = 1345.0635216261717\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1: Steam Flow = 1345.0635216258854\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix 'scaling_factor' that contains 3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: 'fs.fwh1_pump.control_volume.properties_out[0.0].scaling_factor' that contains\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix 'scaling_factor' that contains 120 keys\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: 60 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: WARNING: model contains export suffix\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: 'fs.fwh1_pump.control_volume.properties_in[0.0].scaling_factor' that contains\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: tol=1e-06\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: 63 component keys that are not exported as part of the NL file. Skipping.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: tol=1e-06\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: max_iter=200\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: For more information visit http://projects.coin-or.org/Ipopt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled from source code available at\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: For more information visit http://projects.coin-or.org/Ipopt\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled from source code available at\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: for large-scale scientific computation. All technical papers, sales and\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: publicity material resulting from use of the HSL codes within IPOPT must\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: contain the following acknowledgement:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: for large-scale scientific computation. All technical papers, sales and\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: HSL, a collection of Fortran codes for large-scale scientific\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: publicity material resulting from use of the HSL codes within IPOPT must\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: computation. See http://www.hsl.rl.ac.uk.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: contain the following acknowledgement:\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: HSL, a collection of Fortran codes for large-scale scientific\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: computation. See http://www.hsl.rl.ac.uk.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: This is Ipopt version 3.13.2, running with linear solver ma27.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: ******************************************************************************\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in equality constraint Jacobian...: 9\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: This is Ipopt version 3.13.2, running with linear solver ma27.\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in inequality constraint Jacobian.: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in Lagrangian Hessian.............: 4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in equality constraint Jacobian...: 9\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in inequality constraint Jacobian.: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of variables............................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of nonzeros in Lagrangian Hessian.............: 4\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: variables with lower and upper bounds: 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of variables............................: 5\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only lower bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of equality constraints.................: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: variables with lower and upper bounds: 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of inequality constraints...............: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: variables with only upper bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only lower bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of equality constraints.................: 5\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with lower and upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Total number of inequality constraints...............: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only upper bounds: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only lower bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with lower and upper bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: inequality constraints with only upper bounds: 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: 0 0.0000000e+00 5.66e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: 1 0.0000000e+00 1.11e-16 2.21e-07 -1.0 5.66e+06 - 9.90e-01 1.00e+00h 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: 0 0.0000000e+00 5.66e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Iterations....: 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: 1 0.0000000e+00 1.11e-16 2.21e-07 -1.0 5.66e+06 - 9.90e-01 1.00e+00h 1\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: (scaled) (unscaled)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Iterations....: 1\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: (scaled) (unscaled)\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Constraint violation....: 1.1102230246251565e-16 1.1102230246251565e-16\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Constraint violation....: 1.1102230246251565e-16 1.1102230246251565e-16\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective function evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective gradient evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective function evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of objective gradient evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint Jacobian evaluations = 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint Jacobian evaluations = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint evaluations = 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Lagrangian Hessian evaluations = 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of equality constraint Jacobian evaluations = 2\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of inequality constraint Jacobian evaluations = 0\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in NLP function evaluations = 0.000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Number of Lagrangian Hessian evaluations = 1\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n" + "2025-03-17 17:34:59 [DEBUG] idaes.solve.fs.fwh1_pump: EXIT: Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: Total CPU secs in NLP function evaluations = 0.000\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh1_return: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: \n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh2.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [DEBUG] idaes.solve.fs.fwh1_pump: EXIT: Optimal Solution Found.\n" + "2025-03-17 17:34:59 [INFO] idaes.init.fs.fwh2.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh1_return: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh2.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh2.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:35:00 [WARNING] idaes.init.fs.fwh2: The steam sat. temperature (335.2272258893377) is near the feedwater inlet temperature (318.02261253782166)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh2.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh2.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [WARNING] idaes.init.fs.fwh2: The steam sat. temperature (335.227225889338) is near the feedwater inlet temperature (318.02261253783706)\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh2.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:36 [INFO] idaes.init.fs.fwh2.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2: Condensing hot side inlet delta T = 12.73124007739836\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2: Condensing hot side outlet delta T = 17.002372103630485\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2: Steam Flow = 217.13965467977735\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2: Condensing hot side inlet delta T = 12.731240077383344\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh2: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2: Condensing hot side outlet delta T = 17.00237210361631\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2: Steam Flow = 217.13965467965426\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh2: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh3.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:37 [INFO] idaes.init.fs.fwh3.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:35:00 [WARNING] idaes.init.fs.fwh3: The steam sat. temperature (347.7738554943195) is near the feedwater inlet temperature (323.03655083894466)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [WARNING] idaes.init.fs.fwh3: The steam sat. temperature (347.7738554943195) is near the feedwater inlet temperature (323.03655083895876)\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:35:00 [INFO] idaes.init.fs.fwh3.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh3: Condensing hot side inlet delta T = 20.206912020999226\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh3: Condensing hot side outlet delta T = 24.5035955629366\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:38 [INFO] idaes.init.fs.fwh3.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh3: Steam Flow = 217.4462771554057\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:39 [INFO] idaes.init.fs.fwh3: Condensing hot side inlet delta T = 20.206912020985854\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh3: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:39 [INFO] idaes.init.fs.fwh3: Condensing hot side outlet delta T = 24.503595562923753\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:39 [INFO] idaes.init.fs.fwh3: Steam Flow = 217.44627715533093\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:39 [INFO] idaes.init.fs.fwh3: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:39 [INFO] idaes.init.fs.fwh4.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:39 [INFO] idaes.init.fs.fwh4.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4: Condensing hot side inlet delta T = 39.43016001679878\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4: Condensing hot side outlet delta T = 47.80805362178066\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4: Steam Flow = 247.42787053680715\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4: Condensing hot side inlet delta T = 39.4301600167788\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh4: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4: Condensing hot side outlet delta T = 47.808053621766945\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh5_da: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4: Steam Flow = 247.42787053670986\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh4: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh5_da: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh6.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:40 [INFO] idaes.init.fs.fwh6.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6: Condensing hot side inlet delta T = 45.08557769763464\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6: Condensing hot side outlet delta T = 72.43686375400229\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6: Steam Flow = 2128.5569356298247\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6: Condensing hot side inlet delta T = 45.08557769758456\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh6: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6: Condensing hot side outlet delta T = 72.43686375394775\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6: Steam Flow = 2128.5569356289825\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh6: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.drain_mix: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:41 [INFO] idaes.init.fs.fwh7.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:01 [INFO] idaes.init.fs.fwh7.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh7.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh7: Condensing hot side inlet delta T = 72.40778629498362\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh7: Condensing hot side outlet delta T = 98.78550984878194\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh7: Steam Flow = 3749.0680255320203\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7: Condensing hot side inlet delta T = 72.40778629492878\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh7: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7: Condensing hot side outlet delta T = 98.78550984873016\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.desuperheat.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7: Steam Flow = 3749.068025531304\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.desuperheat.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh7: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.desuperheat.hot_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.condense.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.desuperheat.cold_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.condense.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.desuperheat: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.condense: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.condense.hot_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.condense.cold_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.cooling.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.condense: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.cooling.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.condense: Initialization Complete (w/ extraction calc): optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8.cooling: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.cooling.hot_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8: Condensing hot side inlet delta T = 99.32852730884954\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.cooling.cold_side: Initialization Complete\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8: Condensing hot side outlet delta T = 108.51918961637729\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8.cooling: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8: Steam Flow = 1487.8775467645535\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8: Condensing hot side inlet delta T = 99.32852730878224\n" + "2025-03-17 17:35:02 [INFO] idaes.init.fs.fwh8: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8: Condensing hot side outlet delta T = 108.51918961631927\n" + "2025-03-17 17:35:03 [INFO] idaes.init.Steam Cycle Model: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8: Steam Flow = 1487.8775467636813\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:42 [INFO] idaes.init.fs.fwh8: Initialization Complete: optimal - Optimal Solution Found\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 13500\n", + "keys that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:45 [INFO] idaes.init.Steam Cycle Model: Initialization Complete: optimal - Optimal Solution Found\n" + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2341\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1021\n", + "\n", + "Total number of variables............................: 858\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 444\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 858\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.73e-09 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.9117155615240335e-11 3.7252902984619141e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.9117155615240335e-11 3.7252902984619141e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.163\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" ] - }, + } + ], + "source": [ + "import pyomo.environ as pyo\n", + "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", + " main,\n", + " pfd_result,\n", + ")\n", + "from idaes.core.util.tables import create_stream_table_dataframe\n", + "\n", + "m, solver = main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inside the model, there is a subblock ```fs```. This is an IDAES flowsheet model, which contains the supercritical steam cycle model. In the flowsheet, the model called ```turb``` is a multistage turbine model. The turbine model contains an expression for total power, ```power```. In this case the model is steady-state, but all IDAES models allow for dynamic simulation, and contain time indexes. Power is indexed by time, and only the \"0\" time point exists. By convention, in the IDAES framework, power going into a model is positive, so power produced by the turbine is negative. \n", + "\n", + "The property package used for this model uses SI (mks) units of measure, so the power is in Watts. Here a function is defined which can be used to report power output in MW." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, + "data": { + "text/plain": [ + "622.3884026414312" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define a function to report gross power output in MW\n", + "def gross_power_mw(model):\n", + " # pyo.value(m.fs.turb.power[0]) is the power consumed in Watts\n", + " return -pyo.value(model.fs.turb.power[0]) / 1e6\n", + "\n", + "\n", + "# Show the gross power\n", + "gross_power_mw(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Change the model inputs\n", + "\n", + "The turbine in this example simulates partial arc admission with four arcs, so there are four throttle valves. For this example, we will close one of the valves to 25% open, and observe the result." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.turb.throttle_valve[1].valve_opening[:].value = 0.25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we re-solve the model using the solver created by the ```supercritical_steam_cycle.py``` script." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.hot_side.properties_out[0.0].scaling_factor' that contains 60\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 11\n", "component keys that are not exported as part of the NL file. Skipping.\n" ] }, @@ -4005,4650 +4151,236 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 13500\n", + "keys that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.cold_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.cold_side.properties_in[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2341\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1021\n", + "\n", + "Total number of variables............................: 858\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 444\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 858\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.51e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.hot_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + " 1 0.0000000e+00 3.46e-01 8.48e+01 -1.0 2.63e+07 - 9.82e-01 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.desuperheat.hot_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + " 2 0.0000000e+00 3.41e-01 8.75e+01 -1.0 2.61e+07 - 9.83e-01 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + " 3 0.0000000e+00 3.35e-01 8.51e+01 -1.0 2.59e+07 - 9.88e-01 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.cold_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + " 4 0.0000000e+00 3.30e-01 8.25e+01 -1.0 2.56e+07 - 9.88e-01 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.hot_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + " 5 0.0000000e+00 3.25e-01 7.99e+01 -1.0 2.54e+07 - 9.92e-01 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.condense.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + " 6 0.0000000e+00 3.20e-01 7.74e+01 -1.0 2.52e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + " 7 0.0000000e+00 3.15e-01 7.50e+01 -1.0 2.50e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.cold_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + " 8 0.0000000e+00 3.10e-01 7.26e+01 -1.0 2.48e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.hot_side.properties_out[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + " 9 0.0000000e+00 3.05e-01 7.03e+01 -1.7 2.46e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.cooling.hot_side.properties_in[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. Skipping.\n" + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 0.0000000e+00 3.00e-01 6.80e+01 -1.7 2.44e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.cold_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + " 11 0.0000000e+00 2.96e-01 6.58e+01 -1.7 2.42e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.cold_side.properties_in[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + " 12 0.0000000e+00 2.91e-01 6.37e+01 -1.7 2.40e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.hot_side.properties_out[0.0].scaling_factor' that\n", - "contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" + " 13 0.0000000e+00 2.87e-01 6.17e+01 -1.7 2.37e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.desuperheat.hot_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" + " 18 0.0000000e+00 2.65e-01 5.22e+01 -1.7 2.27e+07 - 1.00e+00 1.56e-02h 7\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.condense.cold_side.properties_in[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. Skipping.\n" + " 19 0.0000000e+00 5.87e+01 3.75e+03 -1.7 2.25e+07 - 1.00e+00 1.00e+00w 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 0.0000000e+00 2.58e+01 8.26e+01 -1.7 7.28e+06 - 1.00e+00 1.00e+00w 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh7.condense.hot_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.mixed_state[0.0].scaling_factor' that contains 62\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 2341\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1021\n", - "\n", - "Total number of variables............................: 858\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 444\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 858\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 2.79e-09 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "\n", - "Number of Iterations....: 0\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 9.9134922493249178e-11 2.7939677238464351e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 9.9134922493249178e-11 2.7939677238464351e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 1\n", - "Number of objective gradient evaluations = 1\n", - "Number of equality constraint evaluations = 1\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 1\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.297\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "import pyomo.environ as pyo\n", - "from idaes.models_extra.power_generation.flowsheets.supercritical_steam_cycle import (\n", - " main,\n", - " pfd_result,\n", - ")\n", - "from idaes.core.util.tables import create_stream_table_dataframe\n", - "\n", - "m, solver = main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Inside the model, there is a subblock ```fs```. This is an IDAES flowsheet model, which contains the supercritical steam cycle model. In the flowsheet, the model called ```turb``` is a multistage turbine model. The turbine model contains an expression for total power, ```power```. In this case the model is steady-state, but all IDAES models allow for dynamic simulation, and contain time indexes. Power is indexed by time, and only the \"0\" time point exists. By convention, in the IDAES framework, power going into a model is positive, so power produced by the turbine is negative. \n", - "\n", - "The property package used for this model uses SI (mks) units of measure, so the power is in Watts. Here a function is defined which can be used to report power output in MW." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "622.3884026414165" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Define a function to report gross power output in MW\n", - "def gross_power_mw(model):\n", - " # pyo.value(m.fs.turb.power[0]) is the power consumed in Watts\n", - " return -pyo.value(model.fs.turb.power[0]) / 1e6\n", - "\n", - "\n", - "# Show the gross power\n", - "gross_power_mw(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Change the model inputs\n", - "\n", - "The turbine in this example simulates partial arc admission with four arcs, so there are four throttle valves. For this example, we will close one of the valves to 25% open, and observe the result." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.turb.throttle_valve[1].valve_opening[:].value = 0.25" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we re-solve the model using the solver created by the ```supercritical_steam_cycle.py``` script." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.fwh8.cooling.cold_side.properties_out[0.0].scaling_factor' that contains\n", - "60 component keys that are not exported as part of the NL file. 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"Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.throttle_valve[1].control_volume.properties_out[0.0].scaling_factor'\n", - "that contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.throttle_valve[1].control_volume.properties_in[0.0].scaling_factor'\n", - "that contains 60 component keys that are not exported as part of the NL file.\n", - "Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.outlet_4_state[0.0].scaling_factor' that contains 60\n", - "component keys that are not exported as part of the NL file. 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Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.turb.inlet_split.mixed_state[0.0].scaling_factor' that contains 62\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 2341\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1021\n", - "\n", - "Total number of variables............................: 858\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 444\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 858\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.51e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1 0.0000000e+00 3.46e-01 8.48e+01 -1.0 2.63e+07 - 9.82e-01 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2 0.0000000e+00 3.41e-01 8.75e+01 -1.0 2.61e+07 - 9.83e-01 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 3 0.0000000e+00 3.35e-01 8.51e+01 -1.0 2.59e+07 - 9.88e-01 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 4 0.0000000e+00 3.30e-01 8.25e+01 -1.0 2.56e+07 - 9.88e-01 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 5 0.0000000e+00 3.25e-01 7.99e+01 -1.0 2.54e+07 - 9.92e-01 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 6 0.0000000e+00 3.20e-01 7.74e+01 -1.0 2.52e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 7 0.0000000e+00 3.15e-01 7.50e+01 -1.0 2.50e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 8 0.0000000e+00 3.10e-01 7.26e+01 -1.0 2.48e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 9 0.0000000e+00 3.05e-01 7.03e+01 -1.7 2.46e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 0.0000000e+00 3.00e-01 6.80e+01 -1.7 2.44e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 11 0.0000000e+00 2.96e-01 6.58e+01 -1.7 2.42e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 12 0.0000000e+00 2.91e-01 6.37e+01 -1.7 2.40e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 13 0.0000000e+00 2.87e-01 6.17e+01 -1.7 2.37e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 14 0.0000000e+00 2.82e-01 5.96e+01 -1.7 2.35e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 15 0.0000000e+00 2.78e-01 5.77e+01 -1.7 2.33e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 16 0.0000000e+00 2.73e-01 5.58e+01 -1.7 2.31e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 17 0.0000000e+00 2.69e-01 5.39e+01 -1.7 2.29e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 18 0.0000000e+00 2.65e-01 5.22e+01 -1.7 2.27e+07 - 1.00e+00 1.56e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 19 0.0000000e+00 5.87e+01 3.75e+03 -1.7 2.25e+07 - 1.00e+00 1.00e+00w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 0.0000000e+00 2.58e+01 8.24e+01 -1.7 7.28e+06 - 1.00e+00 1.00e+00w 1\n", - " 21 0.0000000e+00 1.46e-01 5.49e-01 -1.7 5.67e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 22 0.0000000e+00 4.29e-06 4.85e-05 -1.7 3.35e+03 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 22\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 3.6219717003405094e-08 4.2896717786788940e-06\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.6219717003405094e-08 4.2896717786788940e-06\n", - "\n", - "\n", - "Number of objective function evaluations = 203\n", - "Number of objective gradient evaluations = 23\n", - "Number of equality constraint evaluations = 203\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 23\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 22\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.413\n", - "Total CPU secs in NLP function evaluations = 58.164\n", - "\n", - "EXIT: Optimal Solution Found.\n" + " 21 0.0000000e+00 1.46e-01 5.49e-01 -1.7 5.67e+05 - 1.00e+00 1.00e+00h 1\n", + " 22 0.0000000e+00 4.29e-06 4.86e-05 -1.7 3.35e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 22\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.6219717003405094e-08 4.2943283915519714e-06\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.6219717003405094e-08 4.2943283915519714e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 203\n", + "Number of objective gradient evaluations = 23\n", + "Number of equality constraint evaluations = 203\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 23\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 22\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.221\n", + "Total CPU secs in NLP function evaluations = 32.398\n", + "\n", + "EXIT: Optimal Solution Found.\n" ] }, { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 858, 'Number of variables': 858, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 58.77723503112793}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 858, 'Number of variables': 858, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 32.85140347480774}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -8666,16 +4398,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "594.6634894062614" + "594.6634894062742" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -8695,7 +4427,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -8708,7 +4440,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -9262,7 +4994,7 @@ " T:\n", " P:\n", " x:\n", - " 0.000\n", + " -0.000\n", " F:\n", " \n", " \n", @@ -9662,11 +5394,7 @@ "" ] }, - "metadata": { - "filenames": { - "image/svg+xml": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\power_gen\\supercritical\\supercritical_steam_cycle_doc_15_0.svg" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -9677,7 +5405,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -10029,8 +5757,8 @@ " \n", " \n", " MAKEUP_01\n", - " 0.0\n", - " 0.0\n", + " -0.0\n", + " -0.0\n", " 306.248085\n", " 101325\n", " 0.0\n", @@ -10158,7 +5886,7 @@ "FWH6_DRN 3913.89602 70.509886 449.769309 \n", "FWH7_DRN 3041.163589 54.787377 512.845012 \n", "FWH8_DRN 1785.002753 32.157303 547.774255 \n", - "MAKEUP_01 0.0 0.0 306.248085 \n", + "MAKEUP_01 -0.0 -0.0 306.248085 \n", "RHT_COLD 20490.063288 369.133981 597.344823 \n", "RHT_HOT 20490.063288 369.133981 866.0 \n", "STEAM_LP 16866.348 303.851779 523.019743 \n", @@ -10217,7 +5945,7 @@ "condenser_mix_to_condenser 3903.244367 0.95998 44236.940998 " ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -10251,7 +5979,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_test.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_test.ipynb index afb2be97..9bafa66f 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_test.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_test.ipynb @@ -1,5 +1,31 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_usr.ipynb b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_usr.ipynb index afb2be97..9bafa66f 100644 --- a/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_usr.ipynb +++ b/idaes_examples/notebooks/docs/power_gen/supercritical/supercritical_steam_cycle_usr.ipynb @@ -1,5 +1,31 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb index 70fc2e64..87d4b8a1 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.ipynb index fa2a58ca..81606c90 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -146,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "c:\\users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\mod\\properties\\thermophysical_property_example.py\n" + "/home/dang/src/dangunter/examples/idaes_examples/mod/properties/thermophysical_property_example.py\n" ] } ], @@ -1072,7 +1073,16 @@ "cell_type": "code", "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Params with units must be mutable. Converting Param\n", + "'fs.thermo_props.mw_comp' to mutable.\n" + ] + } + ], "source": [ "m = ConcreteModel()\n", "\n", @@ -1236,14 +1246,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:26 [INFO] idaes.init.fs.state: Properties Initialized optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:41 [INFO] idaes.init.fs.state: Properties Initialized optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:26 [INFO] idaes.init.fs.state: Initialization Complete\n" + "2025-03-17 17:35:41 [INFO] idaes.init.fs.state: Initialization Complete\n" ] }, { @@ -1460,9 +1470,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb index 41a3b535..b72cf89b 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -34,7 +35,7 @@ "Maintainer: Andrew Lee \n", "Updated: 2023-06-01 \n", "\n", - "Calculation of thermophysical, transport and reaction properties form a key part of any process model, and it is important that these calculations are both accurate and tractable in order for the overall problem to be solved correctly. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property “packages” to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating thermophysical and transport properties within the IDAES Core Modeling Framework.\n", + "Calculation of thermophysical, transport and reaction properties form a key part of any process model, and it is important that these calculations are both accurate and tractable in order for the overall problem to be solved correctly. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property \u201cpackages\u201d to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating thermophysical and transport properties within the IDAES Core Modeling Framework.\n", "\n", "## What is a Property?\n", "\n", @@ -50,7 +51,7 @@ "* transport properties such as viscosity and thermal conductivity\n", "* rates of reaction and chemical equilibria\n", "\n", - "The definition and calculation of all of these is defined via “property packages”, which contain all the variables and constraints associated with calculating these properties.\n", + "The definition and calculation of all of these is defined via \u201cproperty packages\u201d, which contain all the variables and constraints associated with calculating these properties.\n", "\n", "
\n", "Note:\n", @@ -67,19 +68,19 @@ "\n", "## What Properties do I Need?\n", "\n", - "An important aspect of the IDAES Core Modeling Framework is that a modeler only needs to provide calculations for those properties that they will use within their process. Put another way, modelers do not need to include calculations for a property that they are not going to use in their model – this allows modelers to avoid introducing unnecessary complexity into their models to calculate a property they do not actually need. When combined with flexibility elsewhere in the modeling framework to control which equations are written in the unit models, this can even allow users to avoid calculating properties that would normally be considered mandatory – for example, a property package for a conceptual design flowsheet which does not include energy or momentum balances would not need to define specific enthalpy or even temperature and pressure as these will not be required by the unit models.\n", + "An important aspect of the IDAES Core Modeling Framework is that a modeler only needs to provide calculations for those properties that they will use within their process. Put another way, modelers do not need to include calculations for a property that they are not going to use in their model \u2013 this allows modelers to avoid introducing unnecessary complexity into their models to calculate a property they do not actually need. When combined with flexibility elsewhere in the modeling framework to control which equations are written in the unit models, this can even allow users to avoid calculating properties that would normally be considered mandatory \u2013 for example, a property package for a conceptual design flowsheet which does not include energy or momentum balances would not need to define specific enthalpy or even temperature and pressure as these will not be required by the unit models.\n", "\n", "This then raises the question of how do you know what properties you will need, especially if you are using models from a library you did not write yourself. To answer this, you should refer to the model documentation, and you can also use the [IDAES Properties Interrogator tool](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/interrogator.html) to analyze your flowsheet and determine what properties are required.\n", "\n", "## Thermophysical Properties and Reaction Properties\n", "\n", - "Within the IDAES Core Modeling Framework, properties are divided into two classifications; thermophysical properties and reaction properties. Reaction properties are those properties related to chemical reactions (both equilibrium and rate-based, but not phase equilibrium) that occur within the system , whilst thermophysical properties include those properties related to thermodynamic relationships (including phase equilibrium) and transport properties. The reason for this separation is that thermophysical properties are required by all unit operations in a process (and need to be consistent with each other), whilst reaction properties are generally only required in specific unit operations identified as “reactors” (and each reactor may have a different set of chemical reactions occurring in it). Thus, reaction properties are separated from the thermophysical property calculations to allow for modular implementation in only specific reactor units. This tutorial only deals with thermophysical properties, and reaction properties will be dealt with in a later tutorial.\n", + "Within the IDAES Core Modeling Framework, properties are divided into two classifications; thermophysical properties and reaction properties. Reaction properties are those properties related to chemical reactions (both equilibrium and rate-based, but not phase equilibrium) that occur within the system , whilst thermophysical properties include those properties related to thermodynamic relationships (including phase equilibrium) and transport properties. The reason for this separation is that thermophysical properties are required by all unit operations in a process (and need to be consistent with each other), whilst reaction properties are generally only required in specific unit operations identified as \u201creactors\u201d (and each reactor may have a different set of chemical reactions occurring in it). Thus, reaction properties are separated from the thermophysical property calculations to allow for modular implementation in only specific reactor units. This tutorial only deals with thermophysical properties, and reaction properties will be dealt with in a later tutorial.\n", "\n", - "## What is a Property “Package”?\n", + "## What is a Property \u201cPackage\u201d?\n", "\n", - "Generally, properties (both thermophysical and reaction) are calculated using correlations that depend on some set of parameters (be they physical constants or empirical parameters). These parameters are constant across all instances of a property calculation in a flowsheet (i.e. each StateBlock uses the same parameters), it makes sense to store these parameters in a single, central location that all StateBlocks can refer to as necessary. Thus, the IDAES modeling framework has “Parameter Blocks” which are attached to the flowsheet to contain all the global parameters associated with a given set of property calculations.\n", + "Generally, properties (both thermophysical and reaction) are calculated using correlations that depend on some set of parameters (be they physical constants or empirical parameters). These parameters are constant across all instances of a property calculation in a flowsheet (i.e. each StateBlock uses the same parameters), it makes sense to store these parameters in a single, central location that all StateBlocks can refer to as necessary. Thus, the IDAES modeling framework has \u201cParameter Blocks\u201d which are attached to the flowsheet to contain all the global parameters associated with a given set of property calculations.\n", "\n", - "Thus, the calculations of thermophysical properties within the IDAES modeling framework is achieved using a “package” of three related modeling components (or classes); the Physical Parameter Block, the State Block and the State Block Data classes. Each of these will be discussed further in the next section as we develop an example property package.\n", + "Thus, the calculations of thermophysical properties within the IDAES modeling framework is achieved using a \u201cpackage\u201d of three related modeling components (or classes); the Physical Parameter Block, the State Block and the State Block Data classes. Each of these will be discussed further in the next section as we develop an example property package.\n", "\n", "At a deeper level, the calculation for many thermophysical properties is a self-contained correlation that is more or less independent of the other properties around it. Thus, each set of thermophysical property calculations is a package of user-chosen sub-models for each property of interest to the user.\n", "\n", @@ -232,7 +233,7 @@ "\n", "The first step is to define the units of measurement for the property package, which will in turn be inherited by any unit model using this property package. Units of measurement for the property package are defined by setting units for the 7 base measurement quantities; time, length, mass, amount, temperature, current and luminous intensity (as current and luminous intensity are generally of lesser importance in process systems engineering, specifying units for these base quantities is optional). Within IDAES, units are specified using Pyomo's units of measurement features, which can be imported from `pyomo.environ`. For this example, the units of measurement features were given the name `pyunits` for clarity.\n", "\n", - "The units of measurement for all other quantities in the model can then be derived from these base quantities; for example the units of energy are `mass*length^2/time^2`. The framework expects all quantities in the property package to use these base units – the Pyomo units of measurement conversion tools can be used if conversion between different sets of units are required.\n", + "The units of measurement for all other quantities in the model can then be derived from these base quantities; for example the units of energy are `mass*length^2/time^2`. The framework expects all quantities in the property package to use these base units \u2013 the Pyomo units of measurement conversion tools can be used if conversion between different sets of units are required.\n", "\n", "In order to set the base units, we need to create a dictionary which has each of the base quantities as a key, and provide a Pyomo recognized unit as the value as shown below." ] @@ -258,7 +259,7 @@ "source": [ "## Step 2: Define Supported Properties\n", "\n", - "The next step is to provide some metadata defining what properties are supported by the property package (including state variables). The first purpose of this metadata is to record a summary of what properties are supported to help user identify whether a given property package is suitable for their needs. The second purpose of the metadata is to allow us to simplify our property calculations by only construction those properties that are actually required by a given unit operation – a property package needs to support all the properties required by a process flowsheet, but not all of those properties are required in every unit operation. Thus, the IDAES modeling framework supports a “build-on-demand” approach for properties, such that only those properties that are required are constructed at any given point.\n", + "The next step is to provide some metadata defining what properties are supported by the property package (including state variables). The first purpose of this metadata is to record a summary of what properties are supported to help user identify whether a given property package is suitable for their needs. The second purpose of the metadata is to allow us to simplify our property calculations by only construction those properties that are actually required by a given unit operation \u2013 a property package needs to support all the properties required by a process flowsheet, but not all of those properties are required in every unit operation. Thus, the IDAES modeling framework supports a \u201cbuild-on-demand\u201d approach for properties, such that only those properties that are required are constructed at any given point.\n", "\n", "This is achieved through the use of the properties metadata, where for each property supported by the property package the user needs to define a `method` argument. This argument can take one of two forms:\n", "\n", @@ -327,9 +328,9 @@ "
\n", "Param or Var:\n", "\n", - "The most obvious way to declare a \"parameter\" in a model would appear to be to use the Pyomo `Param` object. However, modelers should be aware the `Param` objects are never seen by the solver (they are converted to fixed floating point numbers by the solver writer). This means that it is not possible to use a solver to find the value for a parameter – i.e., it is not possible to use `Param` objects in a parameter estimation problem.\n", + "The most obvious way to declare a \"parameter\" in a model would appear to be to use the Pyomo `Param` object. However, modelers should be aware the `Param` objects are never seen by the solver (they are converted to fixed floating point numbers by the solver writer). This means that it is not possible to use a solver to find the value for a parameter \u2013 i.e., it is not possible to use `Param` objects in a parameter estimation problem.\n", "\n", - "Instead, modelers should use fixed `Var` objects for any parameter that may need to be estimated at some point. Within IDAES, this means that most “parameters” are in fact declared as `Var` objects, with `Param` objects used only for parameters with well-known values (for example critical pressures and temperatures or molecular weights).\n", + "Instead, modelers should use fixed `Var` objects for any parameter that may need to be estimated at some point. Within IDAES, this means that most \u201cparameters\u201d are in fact declared as `Var` objects, with `Param` objects used only for parameters with well-known values (for example critical pressures and temperatures or molecular weights).\n", "
\n", "\n", "For this example, the first parameters we need to define are the reference state for our property calculations along with the molecular weights of each of the components of interest. These are fixed parameters that should not be estimated by parameter estimation, so we create Pyomo `Param` objects to represent each of these, as shown below. When we declare a `Param`, we also need to define a default value and the units of measurement for each parameter. Note that the units of measurement for these parameters does not necessarily need to match those defined in the properties metadata, but if they are not consistent then a unit conversion will be required at some point when calculating property values.\n", @@ -371,15 +372,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For this example, we also need to define the parameter associated with calculating the specific enthalpy of each component. As mentioned before, we will use the correlation proposed in “The Properties of Gases and Liquids, 4th Edition” by Reid, Prausnitz and Polling (1987), which has the form:\n", + "For this example, we also need to define the parameter associated with calculating the specific enthalpy of each component. As mentioned before, we will use the correlation proposed in \u201cThe Properties of Gases and Liquids, 4th Edition\u201d by Reid, Prausnitz and Polling (1987), which has the form:\n", "\n", "\\begin{equation*}\n", - "h_j – h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", + "h_j \u2013 h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", "\\end{equation*}\n", "\n", - "where $h_{j, ref}$ is the standard heat of formation of component $j$ in the vapor phase, and $A_j$, $B_j$, $C_j$, and $D_j$ are component-specific parameters in the correlation. At first glance, one might ask if we could declare a single object indexed by the list `[“A”, “B”, “C”, “D”]` and component to represent all the parameters as a single object; however it must be noted that the parameters $A$, $B$, $C$, and $D$ all have different units. Thus, we need to declare separate objects for each of $A$, $B$, $C$, and $D$ (along with $h_{ref}$) which are indexed by component so that we can assign the correct units to each.\n", + "where $h_{j, ref}$ is the standard heat of formation of component $j$ in the vapor phase, and $A_j$, $B_j$, $C_j$, and $D_j$ are component-specific parameters in the correlation. At first glance, one might ask if we could declare a single object indexed by the list `[\u201cA\u201d, \u201cB\u201d, \u201cC\u201d, \u201cD\u201d]` and component to represent all the parameters as a single object; however it must be noted that the parameters $A$, $B$, $C$, and $D$ all have different units. Thus, we need to declare separate objects for each of $A$, $B$, $C$, and $D$ (along with $h_{ref}$) which are indexed by component so that we can assign the correct units to each.\n", "\n", - "However, these parameters are mostly empirical and are values that we may wish to estimate at some point, thus we will declare these as Pyomo `Var` objects rather than `Param` objects, which also means that we must `fix` the value of these parameters when we construct the property package. This is shown in the code below – note that each parameters (`Var` object is fixed immediately after it is declared)." + "However, these parameters are mostly empirical and are values that we may wish to estimate at some point, thus we will declare these as Pyomo `Var` objects rather than `Param` objects, which also means that we must `fix` the value of these parameters when we construct the property package. This is shown in the code below \u2013 note that each parameters (`Var` object is fixed immediately after it is declared)." ] }, { @@ -478,9 +479,9 @@ "\n", "First, we need to declare our new class and give it a unique name. In this example, we will call our new class `HDAParameterBlock`. The first two lines of the example below show how we declare our new class using the `declare_process_block_decorator` and inheriting from the `PhysicalParameterBlock` base class from the IDAES Core model libraries. Inheriting from the `PhysicalParameterBlock` brings us access to all the necessary features required by the IDAES modeling framework, whilst the `declare_process_block_class` decorator performs some boilerplate operations to replicate the expected object structure of Pyomo. Further details on these components can be found in the IDAES documentation.\n", "\n", - "Next, we need to set up any configuration arguments we need for the property package. This is done using Pyomo “Config Blocks” which provide a convenient way of declaring, organizing and documenting configuration arguments. To begin with, we can inherit from the `CONFIG` block declared in the `PhysicalParameterBlock` base class, which provides all the arguments that the IDAES modeling framework expects to be present. Modelers can then add additional configuration arguments to provide users with options when constructing their property packages, however we will not cover that in this tutorial.\n", + "Next, we need to set up any configuration arguments we need for the property package. This is done using Pyomo \u201cConfig Blocks\u201d which provide a convenient way of declaring, organizing and documenting configuration arguments. To begin with, we can inherit from the `CONFIG` block declared in the `PhysicalParameterBlock` base class, which provides all the arguments that the IDAES modeling framework expects to be present. Modelers can then add additional configuration arguments to provide users with options when constructing their property packages, however we will not cover that in this tutorial.\n", "\n", - "The most significant part of any IDAES model class is the `build` method, which contains the instructions on how to construct an instance of the desired model and all IDAES models are expected to have a `build` method. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from – this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `PhysicalParameterBlock` needs to contain a pointer to the related `StateBlock` (which we will look at next) – this is used to allow us to build instances of the `StateBlock` by only knowing the `PhysicalParameterBlock` we wish to use. To do this, we create an attribute named `_state_block_class` attached to our class with a pointer to the `StateBlock` class; in this case `self._state_block_class = HDAStateBlock`, where `HDAStateBlock` is the name of the yet to be declared `StateBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", + "The most significant part of any IDAES model class is the `build` method, which contains the instructions on how to construct an instance of the desired model and all IDAES models are expected to have a `build` method. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from \u2013 this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `PhysicalParameterBlock` needs to contain a pointer to the related `StateBlock` (which we will look at next) \u2013 this is used to allow us to build instances of the `StateBlock` by only knowing the `PhysicalParameterBlock` we wish to use. To do this, we create an attribute named `_state_block_class` attached to our class with a pointer to the `StateBlock` class; in this case `self._state_block_class = HDAStateBlock`, where `HDAStateBlock` is the name of the yet to be declared `StateBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", "\n", "The final step in creating the `PhysicalParameterBlock` class is to declare a `classmethod` named `define_metadata` which takes two arguments; a class (`cls`) and an instance of that class (`obj`). This method in turn needs to call two pre-defined methods (inherited from the underlying base classes):\n", "\n", @@ -526,13 +527,13 @@ "\n", "After the `Physical Parameter Block` class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet. Unlike other models however, creating a `State Block` actually required us to write two `classes`. In short, indexed Pyomo object components (e.g. `Vars` and `Blocks`) actually consist of two objects: an `IndexedComponent` object which serves as a container for multiple `ComponentData` objects which represent the component at each point in the indexing set. For example, a `Var` indexed by the `Set` `[1, 2, 3, 4]` actually consists of a single `IndexedVar` object which contains 4 `VarData` objects. Normally this behavior is hidden behind the `declare_process_block_data` decorator which handles the details of this structure (as a side note, unindexed components similarly involve two classes but this is hidden by the use of multiple inheritance.)\n", "\n", - "Normally, when we write models in IDAES, we are concerned only with the `ComponentData` object – i.e., the instructions on how to build an instance of the model at each indexed point (hence the naming convention used when declaring classes). However, State Blocks are slightly different in that we always expect State Blocks to be indexed (they will always be indexed by time, at a minimum). Due to this, we often want to perform actions on all the elements of the indexed `State Block` at once (rather than element by element), such as during initialization. Thus, we have a need to write methods that are attached to the `IndexedStateBlock` in addition to the normal methods for the `StateBlockData` object. Fortunately, the `declare_process_block_data` decorator facilitates this for us, but it does mean we need to declare two classes when creating State Blocks.\n", + "Normally, when we write models in IDAES, we are concerned only with the `ComponentData` object \u2013 i.e., the instructions on how to build an instance of the model at each indexed point (hence the naming convention used when declaring classes). However, State Blocks are slightly different in that we always expect State Blocks to be indexed (they will always be indexed by time, at a minimum). Due to this, we often want to perform actions on all the elements of the indexed `State Block` at once (rather than element by element), such as during initialization. Thus, we have a need to write methods that are attached to the `IndexedStateBlock` in addition to the normal methods for the `StateBlockData` object. Fortunately, the `declare_process_block_data` decorator facilitates this for us, but it does mean we need to declare two classes when creating State Blocks.\n", "\n", "For this example, we will begin by describing the content of the `StateBlockData` objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. After that, we will discuss how to create the class that contains methods to be applied to the `IndexedStateBlock` as a whole.\n", "\n", "## Step 5: Declare State Variables\n", "\n", - "The first step in defining a `State Block` is to create the “state variables” which will be used to define the “state” of the material at any given point. The concept of a “state variable” in IDAES is much the same as the concept in thermodynamics, with the exception that we include extensive flow information in the state definition in IDAES. In short, the “state variables” should be sufficient to fully define the state of the material (both extensive and intensive), and should result in a `State Block` with zero degrees of freedom if all the state variables are fixed.\n", + "The first step in defining a `State Block` is to create the \u201cstate variables\u201d which will be used to define the \u201cstate\u201d of the material at any given point. The concept of a \u201cstate variable\u201d in IDAES is much the same as the concept in thermodynamics, with the exception that we include extensive flow information in the state definition in IDAES. In short, the \u201cstate variables\u201d should be sufficient to fully define the state of the material (both extensive and intensive), and should result in a `State Block` with zero degrees of freedom if all the state variables are fixed.\n", "\n", "For this example, our state variables will be:\n", "\n", @@ -614,7 +615,7 @@ "2. by using an `Expression`, or,\n", "3. by using a `Reference`.\n", "\n", - "The different between the first two options is that an `Expression` does not appear in the problem passed to the solver – the `Expression` can be evaluated by the user and included in constraints in the same way as a variable, but when the problem is passed to the solver the `Expression` object is substituted for the expression it represents wherever it appears in the model. This means that there are fewer variables and constraints in the problem the solver sees, but that the constraints that do appear are more complex. There is no simple answer to which approach is best, and different applications may see better results with one form or the other. The third option, using a `Reference` is for cases where a property already exists elsewhere in the model, and we just want to create a local copy of the same object. In terms of properties, this most often occurs with fixed quantities which are declared in the Physical Parameter Block such as molecular weights. For the purposes of this example, we will demonstrate all of these approaches. \n", + "The different between the first two options is that an `Expression` does not appear in the problem passed to the solver \u2013 the `Expression` can be evaluated by the user and included in constraints in the same way as a variable, but when the problem is passed to the solver the `Expression` object is substituted for the expression it represents wherever it appears in the model. This means that there are fewer variables and constraints in the problem the solver sees, but that the constraints that do appear are more complex. There is no simple answer to which approach is best, and different applications may see better results with one form or the other. The third option, using a `Reference` is for cases where a property already exists elsewhere in the model, and we just want to create a local copy of the same object. In terms of properties, this most often occurs with fixed quantities which are declared in the Physical Parameter Block such as molecular weights. For the purposes of this example, we will demonstrate all of these approaches. \n", "\n", "You may recall from the initial problem statement that we have three properties of interest in this example:\n", "\n", @@ -658,7 +659,7 @@ "where $x_j$ is the mole fraction of component $j$. Recall that for this example we are using the following correlation for the component specific enthalpies.\n", "\n", "\\begin{equation*}\n", - "h_j – h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", + "h_j \u2013 h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", "\\end{equation*}\n", "\n", "For the specific enthalpy, we will create a Pyomo `Expression` rather than a `Var` and `Constraint`. In practice, this is much like creating a `Constraint`. However, rather than returning an equality between two expressions, an `Expression` requires a single numerical expression that can be used to compute the quantity of interest.\n", @@ -736,16 +737,16 @@ "\n", "### Writing the Initialization Routine\n", "\n", - "For initializing State Blocks, the first step is to get our model to a state where it has no degrees of freedom. As mentioned earlier, fixing all of the state variables should be sufficient to fully define the state of the material – i.e. no degrees of freedom. Additionally, we want to initialize our State Block at a set of conditions that are good initial guesses for the final state of the model once it is finally solved. Of course, the State Block has no way of knowing what these initial values should be, so we depend on the unit model (or the end-user) to provide us with a set of initial values to use – this is done through a `dict` which we generally call `state_args` where the keys are the names of the state variables and the values are the initial guesses.\n", + "For initializing State Blocks, the first step is to get our model to a state where it has no degrees of freedom. As mentioned earlier, fixing all of the state variables should be sufficient to fully define the state of the material \u2013 i.e. no degrees of freedom. Additionally, we want to initialize our State Block at a set of conditions that are good initial guesses for the final state of the model once it is finally solved. Of course, the State Block has no way of knowing what these initial values should be, so we depend on the unit model (or the end-user) to provide us with a set of initial values to use \u2013 this is done through a `dict` which we generally call `state_args` where the keys are the names of the state variables and the values are the initial guesses.\n", "\n", - "Before we start fixing the state variables, there is the possibility that all the state variables have already been fixed (e.g. by a the unit model during its own initialization routine). To allow us to save some time, we include a `state_vars_fixed` argument in our State Block initialization methods that lets the unit model tell us if the state variables are already fixed – if this is `True` then we know we can skip the step of checking the state variables ourselves. If `state_vars_fixed is False` however, then we need to go and fix all the state variables as part of our initialization routine. To save us the effort of having to code all of this ourselves, the IDAES toolkit contains a utility method named `fix_state_vars` (which we imported earlier), which takes the `state_args` `dict` and then iterates through all the state variables as defined by the State Block (using the `dict` we declared earlier in the `return_state_var_dict` sub-method). This method iterates through all the defined state variables and does the following:\n", + "Before we start fixing the state variables, there is the possibility that all the state variables have already been fixed (e.g. by a the unit model during its own initialization routine). To allow us to save some time, we include a `state_vars_fixed` argument in our State Block initialization methods that lets the unit model tell us if the state variables are already fixed \u2013 if this is `True` then we know we can skip the step of checking the state variables ourselves. If `state_vars_fixed is False` however, then we need to go and fix all the state variables as part of our initialization routine. To save us the effort of having to code all of this ourselves, the IDAES toolkit contains a utility method named `fix_state_vars` (which we imported earlier), which takes the `state_args` `dict` and then iterates through all the state variables as defined by the State Block (using the `dict` we declared earlier in the `return_state_var_dict` sub-method). This method iterates through all the defined state variables and does the following:\n", "\n", "1. If the variable is already fixed, it records this and does nothing. If a variable is already fixed, we assume it was fixed for a reason and that we should not change its value.\n", "2. If the variable is not fixed, this is recorded and the method then checks the `state_args` dict for an initial guess for the variable. If a value is found, the variable is fixed to this value; otherwise, the variable is fixed to its current value.\n", "\n", "Finally, the `fix_state_vars` method returns a `dict` that records which variables were fixed by the method, so that we can later reverse these changes. In the example below, we refer to this `dict` as `flags`.\n", "\n", - "At this point, all the state variables should now be fixed, but once again we have a small catch – if we fix all the state variables then we have a situation similar to the inlet of a unit where we cannot write a constraint on the sum of mole fractions and still solve the model. Thus we need to deactivate this constraint if it exists (remembering that this constraint will not exist in all State Blocks). We know that this constraint will only exist if `defined_state is False`, so we start by writing an `IF` statement to check for this and then use the Pyomo `deactivate()` method to deactivate the constraint (remembering that we will need to reactivate it later).\n", + "At this point, all the state variables should now be fixed, but once again we have a small catch \u2013 if we fix all the state variables then we have a situation similar to the inlet of a unit where we cannot write a constraint on the sum of mole fractions and still solve the model. Thus we need to deactivate this constraint if it exists (remembering that this constraint will not exist in all State Blocks). We know that this constraint will only exist if `defined_state is False`, so we start by writing an `IF` statement to check for this and then use the Pyomo `deactivate()` method to deactivate the constraint (remembering that we will need to reactivate it later).\n", "\n", "Before we move on however, it is probably a good idea to check the degrees of freedom to be sure that they are zero (as expected). There are a number of ways things could go wrong (e.g., the unit model said the state variables were fixed when not all of them were, or we missed a constraint we need to deactivate), so a quick check now might save someone a lot of pain in the future. We can use the IDAES `degrees_of_freedom` method to check the degrees of freedom of our State Block, and if this is not zero raise an `Exception` to let the user know something went wrong." ] @@ -788,7 +789,7 @@ "\n", "Before we call the solver, we first need to make sure there is actually something to solve; depending on how the State Block is written (e.g. build-on-demand properties and use of `Expressions`), it is sometimes possible that there are actually no `Constraints` to be solved in the model. If we try to send a problem like that to a solver, we will likely get back an error message which is not what we want to see. Whilst we know that our State Block will always contain at least one constraint (for mixture density), we will add a check here anyway to show how it is done. First ,we create a counter to keep track of the number of unfixed variables in the system, `free_vars`. Then we iterate over all the elements of the `blk` (the `IndexedStateBlock`) and check how many free variables are in each. We use the `number_unfixed_variables()` method from the `idaes.core.util.model_statistics` module to do this, and add the result `free_vars` for each element. If the final value of `free_vars` is not zero, then we know there is something to solve for and we can proceed to call a solver; otherwise we know that we can skip this step.\n", "\n", - "In order to solve the entire `IndexedStateBlock`, we need to do things slightly differently than normal. The standard Pyomo `SolverFactory` cannot be applied to indexed blocks, so instead we use the IDAES `solve_indexed_block` method (imported from `idaes.core.initialization`) which puts a wrapper around the indexed block so that we can use Pyomo’s solver interface. In order to use this method, we need to provide a Pyomo `SolverFactory` object (called `solver` here, which also includes any attached solver options) along with the `blk` we wish to solve and where to send the solver results (the `tee` argument). Additionally, we want the user to have the ability to control the output from the solver through the IDAES logger interface, which we do by wrapping the solver call with the following line of code:\n", + "In order to solve the entire `IndexedStateBlock`, we need to do things slightly differently than normal. The standard Pyomo `SolverFactory` cannot be applied to indexed blocks, so instead we use the IDAES `solve_indexed_block` method (imported from `idaes.core.initialization`) which puts a wrapper around the indexed block so that we can use Pyomo\u2019s solver interface. In order to use this method, we need to provide a Pyomo `SolverFactory` object (called `solver` here, which also includes any attached solver options) along with the `blk` we wish to solve and where to send the solver results (the `tee` argument). Additionally, we want the user to have the ability to control the output from the solver through the IDAES logger interface, which we do by wrapping the solver call with the following line of code:\n", "\n", "```\n", "with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc:\n", @@ -796,7 +797,7 @@ "\n", "where `idaeslog` is an instance of the IDAES logger. Note that we send the solver output to this logger by setting `tee=slc`. In this way, all the output from the solver is passed into the logger allowing users to easily control the output level without needing to send additional arguments to the initialization methods.\n", "\n", - "If all goes well, the solver will successfully initialize our model and we can move on. However, sometimes the solver will fail catastrophically, in which case we need to make sure that our initialization routine can attempt to recover. In order to do this, we wrap the solver call within a Python `try/except` statement. This way, if the solver fails badly and returns an `Exception`, we can capture this and decide how to process – otherwise the execution of our model would terminate with the exception from the solver. In the case we encounter an `Exception` here, we will record `results=None` and try to continue with initializing our model in the hope that we can recover.\n", + "If all goes well, the solver will successfully initialize our model and we can move on. However, sometimes the solver will fail catastrophically, in which case we need to make sure that our initialization routine can attempt to recover. In order to do this, we wrap the solver call within a Python `try/except` statement. This way, if the solver fails badly and returns an `Exception`, we can capture this and decide how to process \u2013 otherwise the execution of our model would terminate with the exception from the solver. In the case we encounter an `Exception` here, we will record `results=None` and try to continue with initializing our model in the hope that we can recover.\n", "\n", "Finally, it is useful to provide the user with some feedback on how the initialization is proceeding. In the last lines below, we send a message to the IDAES logger with a message saying that the initialization step has been completed and append the final solver status." ] @@ -900,7 +901,7 @@ "As the name suggests, the `initialize` method is used to run the initialization routine for the State Block, and this is where we will use the `prepare_state`, `initialize_state` and `restore_state` methods we wrote previously. The `initialize` method requires the following arguments to be declared:\n", "\n", "* `blk`: this will be a pointer to an instance of the State Block to be initialized.\n", - "* `state_args`: this is used to pass the ‘dict’ of initial guesses to the initialization routine. This should default to `None` if not provided. The `fix_state_vars` method will interpret a value of `None` as no guesses provided as use the current values instead.\n", + "* `state_args`: this is used to pass the \u2018dict\u2019 of initial guesses to the initialization routine. This should default to `None` if not provided. The `fix_state_vars` method will interpret a value of `None` as no guesses provided as use the current values instead.\n", "* `solver`: this argument is used to allow tell the State Block to use a specific solver during initialization, and should be a string recognized by the Pyomo `SolverFactory`. We generally set this to `None` in order to signify that IDAES Should use the default solver (which is IPOPT).\n", "* `optarg`: this argument is used to set any solver options the user desires. Again this is generally set to `None` to indicate that the default solver settings should be used.\n", "* `state_vars_fixed`: argument to allow the unit model to inform the State Block that the state variables are already fixed. This should default to `False`.\n", @@ -959,13 +960,13 @@ "source": [ "### The StateBlockData class\n", "\n", - "Finally, we can build the `StateBlockData` class, which we will call `HDAStateBlockData`. First, we use the `declare_process_block_class` decorator but this time we provide two arguments. The first argument is the name of the class that will be automatically constructed for us (`HDAStateBlock`) whilst the second argument is a reference to the class we wish to use as the base when building the `IndexedHDAStateBlock` class – i.e. the `_HDAStateBlock` class we just declared. Then, we declare our new `HDAStateBlockData` class and inherit from the IDAES `StateBlockData` base class.\n", + "Finally, we can build the `StateBlockData` class, which we will call `HDAStateBlockData`. First, we use the `declare_process_block_class` decorator but this time we provide two arguments. The first argument is the name of the class that will be automatically constructed for us (`HDAStateBlock`) whilst the second argument is a reference to the class we wish to use as the base when building the `IndexedHDAStateBlock` class \u2013 i.e. the `_HDAStateBlock` class we just declared. Then, we declare our new `HDAStateBlockData` class and inherit from the IDAES `StateBlockData` base class.\n", "\n", "As usual, the first thing we need to define in our new class is a `build` method, where we will provide the instructions for constructing our property model, and once again the first thing we should do is call `super().build()` to construct all the underlying components defined by the parent class. After this, we can call the methods we wrote earlier to construct the state variables and the add the calculations for the properties of interest.\n", "\n", - "However, if you recall from when we defined the properties metadata at the beginning of the example, we decided that the specific molar enthalpy of the mixture would be a “build-on-demand” property (i.e., we provided a specific method in the properties metadata rather than `None`). Thus, we do not want to call the method to construct the specific molar enthalpy as part of the `build` method, meaning that we only call the `add_state_variables`, `add_mole_fraction_constraint` and `add_molecular_weight_and_density` as in the `build` method.\n", + "However, if you recall from when we defined the properties metadata at the beginning of the example, we decided that the specific molar enthalpy of the mixture would be a \u201cbuild-on-demand\u201d property (i.e., we provided a specific method in the properties metadata rather than `None`). Thus, we do not want to call the method to construct the specific molar enthalpy as part of the `build` method, meaning that we only call the `add_state_variables`, `add_mole_fraction_constraint` and `add_molecular_weight_and_density` as in the `build` method.\n", "\n", - "To add the specific molar enthalpy calculation as a “build-on-demand” property, we instead declare a separate method with the name we provided in the properties metadata. Whenever the specific molar enthalpy is required by a unit model it will check to see if the property already exists, and if not it will look up the properties metadata and call the method listed there; i.e. `_enth_mol` in this case. Thus, we declare another method on our `HDAStateBlockData` class named `enth_mol` which takes only the class instance as an argument (`self`), and then call the `add_enth_mol` method we created earlier to construct the required `Expression`.\n", + "To add the specific molar enthalpy calculation as a \u201cbuild-on-demand\u201d property, we instead declare a separate method with the name we provided in the properties metadata. Whenever the specific molar enthalpy is required by a unit model it will check to see if the property already exists, and if not it will look up the properties metadata and call the method listed there; i.e. `_enth_mol` in this case. Thus, we declare another method on our `HDAStateBlockData` class named `enth_mol` which takes only the class instance as an argument (`self`), and then call the `add_enth_mol` method we created earlier to construct the required `Expression`.\n", "\n", "That is all we need to do in order to construct the variables and constraints we need for the property calculations. However, there are a number of other things we need to define in our State Block Data class. In order to provide much of the flexibility present in the IDAES modeling framework, we defer making decisions on much of the form of the overall model for as long as possible. However, these decisions need to be made at some point, and the State Block Data is where this finally occurs.\n", "\n", @@ -983,7 +984,7 @@ "* `default_material_balance_type` should return an instance of the IDAES `MaterialBalanceType` `Enum` (imported from `idaes.core`).\n", "* `default_energy_balance_type` should return an instance of the IDAES `EnergyBalanceType` `Enum` (imported from `idaes.core`).\n", "\n", - "Finally, we need to specify the basis of the material flow terms (mass, mole or other). This is used to automatically convert between different bases as required (e.g. a user can define a custom mass transfer term on a molar basis whilst using a mass basis for the actual material balance). Note that automatic conversion only works for mass and molar basis; the “other” basis is used to indicate forms which cannot be easily converted (i.e., the modeler needs to handle this manually). To define the material flow term basis we define a final method named `get_material_flow_basis` which returns an instance of the IDAES `MaterialFlowBasis` `Enum` (again imported from `idaes.core`)." + "Finally, we need to specify the basis of the material flow terms (mass, mole or other). This is used to automatically convert between different bases as required (e.g. a user can define a custom mass transfer term on a molar basis whilst using a mass basis for the actual material balance). Note that automatic conversion only works for mass and molar basis; the \u201cother\u201d basis is used to indicate forms which cannot be easily converted (i.e., the modeler needs to handle this manually). To define the material flow term basis we define a final method named `get_material_flow_basis` which returns an instance of the IDAES `MaterialFlowBasis` `Enum` (again imported from `idaes.core`)." ] }, { @@ -1079,7 +1080,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now have an instance of our new State Block in our flowsheet, so let’s display it and see what it contains." + "We now have an instance of our new State Block in our flowsheet, so let\u2019s display it and see what it contains." ] }, { @@ -1095,7 +1096,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that our State Block contains a single point in time, which in turn contains the five variables. These are our four state variables (molar flow rate, component mole fraction, temperature and pressure) as well as the mixture density. We also have a single constraint, which is the ideal gas equation used to calculate density. Note that we don’t see the component molecular weights as they are `Params` (`References` take on the appearance of the component being referenced) or the molar enthalpy as it is an `Expression`, not a variable (plus it hasn’t been constructed yet as we haven’t asked for it).\n", + "We can see that our State Block contains a single point in time, which in turn contains the five variables. These are our four state variables (molar flow rate, component mole fraction, temperature and pressure) as well as the mixture density. We also have a single constraint, which is the ideal gas equation used to calculate density. Note that we don\u2019t see the component molecular weights as they are `Params` (`References` take on the appearance of the component being referenced) or the molar enthalpy as it is an `Expression`, not a variable (plus it hasn\u2019t been constructed yet as we haven\u2019t asked for it).\n", "\n", "Next, let us check the degrees of freedom in our State Block." ] @@ -1131,7 +1132,7 @@ "source": [ "This is unexpected: the `degrees_of_freedom` method is saying there are only 2 degrees of freedom in our State Block, but there are 8 state variables.\n", "\n", - "However, if we think about the constraints we have written, we are only actually using 2 of the state variables in any constraint (temperature and pressure appear in the ideal gas equation). The molar flowrate and component mole fractions are not actually used anywhere in our model, so they have been excluded from the degrees of freedom calculation. In Pyomo terminology, these variables are “Stale”, and they will not be sent to the solver when it is called. Thus, the two degrees of freedom is in fact correct.\n", + "However, if we think about the constraints we have written, we are only actually using 2 of the state variables in any constraint (temperature and pressure appear in the ideal gas equation). The molar flowrate and component mole fractions are not actually used anywhere in our model, so they have been excluded from the degrees of freedom calculation. In Pyomo terminology, these variables are \u201cStale\u201d, and they will not be sent to the solver when it is called. Thus, the two degrees of freedom is in fact correct.\n", "\n", "Note that this is only the case because our property package is so simple. Also, the specific enthalpy calculation depends on the component mole fractions, so whilst we could solve the State Block by only specifying temperature and pressure, the value of the specific molar enthalpy would be meaningless.\n", "\n", @@ -1158,7 +1159,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have fixed the values for all the state variables, we would expect that the degrees of freedom should be zero (even though we fixed all 8 variables, only temperature and pressure actually contribute to the degrees of freedom). Let’s check this to be sure." + "Now that we have fixed the values for all the state variables, we would expect that the degrees of freedom should be zero (even though we fixed all 8 variables, only temperature and pressure actually contribute to the degrees of freedom). Let\u2019s check this to be sure." ] }, { @@ -1406,4 +1407,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb index 9196bb04..710b7f16 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -34,7 +35,7 @@ "Maintainer: Andrew Lee \n", "Updated: 2023-06-01 \n", "\n", - "Calculation of thermophysical, transport and reaction properties form a key part of any process model, and it is important that these calculations are both accurate and tractable in order for the overall problem to be solved correctly. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property “packages” to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating thermophysical and transport properties within the IDAES Core Modeling Framework.\n", + "Calculation of thermophysical, transport and reaction properties form a key part of any process model, and it is important that these calculations are both accurate and tractable in order for the overall problem to be solved correctly. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property \u201cpackages\u201d to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating thermophysical and transport properties within the IDAES Core Modeling Framework.\n", "\n", "## What is a Property?\n", "\n", @@ -50,7 +51,7 @@ "* transport properties such as viscosity and thermal conductivity\n", "* rates of reaction and chemical equilibria\n", "\n", - "The definition and calculation of all of these is defined via “property packages”, which contain all the variables and constraints associated with calculating these properties.\n", + "The definition and calculation of all of these is defined via \u201cproperty packages\u201d, which contain all the variables and constraints associated with calculating these properties.\n", "\n", "
\n", "Note:\n", @@ -67,19 +68,19 @@ "\n", "## What Properties do I Need?\n", "\n", - "An important aspect of the IDAES Core Modeling Framework is that a modeler only needs to provide calculations for those properties that they will use within their process. Put another way, modelers do not need to include calculations for a property that they are not going to use in their model – this allows modelers to avoid introducing unnecessary complexity into their models to calculate a property they do not actually need. When combined with flexibility elsewhere in the modeling framework to control which equations are written in the unit models, this can even allow users to avoid calculating properties that would normally be considered mandatory – for example, a property package for a conceptual design flowsheet which does not include energy or momentum balances would not need to define specific enthalpy or even temperature and pressure as these will not be required by the unit models.\n", + "An important aspect of the IDAES Core Modeling Framework is that a modeler only needs to provide calculations for those properties that they will use within their process. Put another way, modelers do not need to include calculations for a property that they are not going to use in their model \u2013 this allows modelers to avoid introducing unnecessary complexity into their models to calculate a property they do not actually need. When combined with flexibility elsewhere in the modeling framework to control which equations are written in the unit models, this can even allow users to avoid calculating properties that would normally be considered mandatory \u2013 for example, a property package for a conceptual design flowsheet which does not include energy or momentum balances would not need to define specific enthalpy or even temperature and pressure as these will not be required by the unit models.\n", "\n", "This then raises the question of how do you know what properties you will need, especially if you are using models from a library you did not write yourself. To answer this, you should refer to the model documentation, and you can also use the [IDAES Properties Interrogator tool](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/interrogator.html) to analyze your flowsheet and determine what properties are required.\n", "\n", "## Thermophysical Properties and Reaction Properties\n", "\n", - "Within the IDAES Core Modeling Framework, properties are divided into two classifications; thermophysical properties and reaction properties. Reaction properties are those properties related to chemical reactions (both equilibrium and rate-based, but not phase equilibrium) that occur within the system , whilst thermophysical properties include those properties related to thermodynamic relationships (including phase equilibrium) and transport properties. The reason for this separation is that thermophysical properties are required by all unit operations in a process (and need to be consistent with each other), whilst reaction properties are generally only required in specific unit operations identified as “reactors” (and each reactor may have a different set of chemical reactions occurring in it). Thus, reaction properties are separated from the thermophysical property calculations to allow for modular implementation in only specific reactor units. This tutorial only deals with thermophysical properties, and reaction properties will be dealt with in a later tutorial.\n", + "Within the IDAES Core Modeling Framework, properties are divided into two classifications; thermophysical properties and reaction properties. Reaction properties are those properties related to chemical reactions (both equilibrium and rate-based, but not phase equilibrium) that occur within the system , whilst thermophysical properties include those properties related to thermodynamic relationships (including phase equilibrium) and transport properties. The reason for this separation is that thermophysical properties are required by all unit operations in a process (and need to be consistent with each other), whilst reaction properties are generally only required in specific unit operations identified as \u201creactors\u201d (and each reactor may have a different set of chemical reactions occurring in it). Thus, reaction properties are separated from the thermophysical property calculations to allow for modular implementation in only specific reactor units. This tutorial only deals with thermophysical properties, and reaction properties will be dealt with in a later tutorial.\n", "\n", - "## What is a Property “Package”?\n", + "## What is a Property \u201cPackage\u201d?\n", "\n", - "Generally, properties (both thermophysical and reaction) are calculated using correlations that depend on some set of parameters (be they physical constants or empirical parameters). These parameters are constant across all instances of a property calculation in a flowsheet (i.e. each StateBlock uses the same parameters), it makes sense to store these parameters in a single, central location that all StateBlocks can refer to as necessary. Thus, the IDAES modeling framework has “Parameter Blocks” which are attached to the flowsheet to contain all the global parameters associated with a given set of property calculations.\n", + "Generally, properties (both thermophysical and reaction) are calculated using correlations that depend on some set of parameters (be they physical constants or empirical parameters). These parameters are constant across all instances of a property calculation in a flowsheet (i.e. each StateBlock uses the same parameters), it makes sense to store these parameters in a single, central location that all StateBlocks can refer to as necessary. Thus, the IDAES modeling framework has \u201cParameter Blocks\u201d which are attached to the flowsheet to contain all the global parameters associated with a given set of property calculations.\n", "\n", - "Thus, the calculations of thermophysical properties within the IDAES modeling framework is achieved using a “package” of three related modeling components (or classes); the Physical Parameter Block, the State Block and the State Block Data classes. Each of these will be discussed further in the next section as we develop an example property package.\n", + "Thus, the calculations of thermophysical properties within the IDAES modeling framework is achieved using a \u201cpackage\u201d of three related modeling components (or classes); the Physical Parameter Block, the State Block and the State Block Data classes. Each of these will be discussed further in the next section as we develop an example property package.\n", "\n", "At a deeper level, the calculation for many thermophysical properties is a self-contained correlation that is more or less independent of the other properties around it. Thus, each set of thermophysical property calculations is a package of user-chosen sub-models for each property of interest to the user.\n", "\n", @@ -232,7 +233,7 @@ "\n", "The first step is to define the units of measurement for the property package, which will in turn be inherited by any unit model using this property package. Units of measurement for the property package are defined by setting units for the 7 base measurement quantities; time, length, mass, amount, temperature, current and luminous intensity (as current and luminous intensity are generally of lesser importance in process systems engineering, specifying units for these base quantities is optional). Within IDAES, units are specified using Pyomo's units of measurement features, which can be imported from `pyomo.environ`. For this example, the units of measurement features were given the name `pyunits` for clarity.\n", "\n", - "The units of measurement for all other quantities in the model can then be derived from these base quantities; for example the units of energy are `mass*length^2/time^2`. The framework expects all quantities in the property package to use these base units – the Pyomo units of measurement conversion tools can be used if conversion between different sets of units are required.\n", + "The units of measurement for all other quantities in the model can then be derived from these base quantities; for example the units of energy are `mass*length^2/time^2`. The framework expects all quantities in the property package to use these base units \u2013 the Pyomo units of measurement conversion tools can be used if conversion between different sets of units are required.\n", "\n", "In order to set the base units, we need to create a dictionary which has each of the base quantities as a key, and provide a Pyomo recognized unit as the value as shown below." ] @@ -258,7 +259,7 @@ "source": [ "## Step 2: Define Supported Properties\n", "\n", - "The next step is to provide some metadata defining what properties are supported by the property package (including state variables). The first purpose of this metadata is to record a summary of what properties are supported to help user identify whether a given property package is suitable for their needs. The second purpose of the metadata is to allow us to simplify our property calculations by only construction those properties that are actually required by a given unit operation – a property package needs to support all the properties required by a process flowsheet, but not all of those properties are required in every unit operation. Thus, the IDAES modeling framework supports a “build-on-demand” approach for properties, such that only those properties that are required are constructed at any given point.\n", + "The next step is to provide some metadata defining what properties are supported by the property package (including state variables). The first purpose of this metadata is to record a summary of what properties are supported to help user identify whether a given property package is suitable for their needs. The second purpose of the metadata is to allow us to simplify our property calculations by only construction those properties that are actually required by a given unit operation \u2013 a property package needs to support all the properties required by a process flowsheet, but not all of those properties are required in every unit operation. Thus, the IDAES modeling framework supports a \u201cbuild-on-demand\u201d approach for properties, such that only those properties that are required are constructed at any given point.\n", "\n", "This is achieved through the use of the properties metadata, where for each property supported by the property package the user needs to define a `method` argument. This argument can take one of two forms:\n", "\n", @@ -327,9 +328,9 @@ "
\n", "Param or Var:\n", "\n", - "The most obvious way to declare a \"parameter\" in a model would appear to be to use the Pyomo `Param` object. However, modelers should be aware the `Param` objects are never seen by the solver (they are converted to fixed floating point numbers by the solver writer). This means that it is not possible to use a solver to find the value for a parameter – i.e., it is not possible to use `Param` objects in a parameter estimation problem.\n", + "The most obvious way to declare a \"parameter\" in a model would appear to be to use the Pyomo `Param` object. However, modelers should be aware the `Param` objects are never seen by the solver (they are converted to fixed floating point numbers by the solver writer). This means that it is not possible to use a solver to find the value for a parameter \u2013 i.e., it is not possible to use `Param` objects in a parameter estimation problem.\n", "\n", - "Instead, modelers should use fixed `Var` objects for any parameter that may need to be estimated at some point. Within IDAES, this means that most “parameters” are in fact declared as `Var` objects, with `Param` objects used only for parameters with well-known values (for example critical pressures and temperatures or molecular weights).\n", + "Instead, modelers should use fixed `Var` objects for any parameter that may need to be estimated at some point. Within IDAES, this means that most \u201cparameters\u201d are in fact declared as `Var` objects, with `Param` objects used only for parameters with well-known values (for example critical pressures and temperatures or molecular weights).\n", "
\n", "\n", "For this example, the first parameters we need to define are the reference state for our property calculations along with the molecular weights of each of the components of interest. These are fixed parameters that should not be estimated by parameter estimation, so we create Pyomo `Param` objects to represent each of these, as shown below. When we declare a `Param`, we also need to define a default value and the units of measurement for each parameter. Note that the units of measurement for these parameters does not necessarily need to match those defined in the properties metadata, but if they are not consistent then a unit conversion will be required at some point when calculating property values.\n", @@ -371,15 +372,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For this example, we also need to define the parameter associated with calculating the specific enthalpy of each component. As mentioned before, we will use the correlation proposed in “The Properties of Gases and Liquids, 4th Edition” by Reid, Prausnitz and Polling (1987), which has the form:\n", + "For this example, we also need to define the parameter associated with calculating the specific enthalpy of each component. As mentioned before, we will use the correlation proposed in \u201cThe Properties of Gases and Liquids, 4th Edition\u201d by Reid, Prausnitz and Polling (1987), which has the form:\n", "\n", "\\begin{equation*}\n", - "h_j – h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", + "h_j \u2013 h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", "\\end{equation*}\n", "\n", - "where $h_{j, ref}$ is the standard heat of formation of component $j$ in the vapor phase, and $A_j$, $B_j$, $C_j$, and $D_j$ are component-specific parameters in the correlation. At first glance, one might ask if we could declare a single object indexed by the list `[“A”, “B”, “C”, “D”]` and component to represent all the parameters as a single object; however it must be noted that the parameters $A$, $B$, $C$, and $D$ all have different units. Thus, we need to declare separate objects for each of $A$, $B$, $C$, and $D$ (along with $h_{ref}$) which are indexed by component so that we can assign the correct units to each.\n", + "where $h_{j, ref}$ is the standard heat of formation of component $j$ in the vapor phase, and $A_j$, $B_j$, $C_j$, and $D_j$ are component-specific parameters in the correlation. At first glance, one might ask if we could declare a single object indexed by the list `[\u201cA\u201d, \u201cB\u201d, \u201cC\u201d, \u201cD\u201d]` and component to represent all the parameters as a single object; however it must be noted that the parameters $A$, $B$, $C$, and $D$ all have different units. Thus, we need to declare separate objects for each of $A$, $B$, $C$, and $D$ (along with $h_{ref}$) which are indexed by component so that we can assign the correct units to each.\n", "\n", - "However, these parameters are mostly empirical and are values that we may wish to estimate at some point, thus we will declare these as Pyomo `Var` objects rather than `Param` objects, which also means that we must `fix` the value of these parameters when we construct the property package. This is shown in the code below – note that each parameters (`Var` object is fixed immediately after it is declared)." + "However, these parameters are mostly empirical and are values that we may wish to estimate at some point, thus we will declare these as Pyomo `Var` objects rather than `Param` objects, which also means that we must `fix` the value of these parameters when we construct the property package. This is shown in the code below \u2013 note that each parameters (`Var` object is fixed immediately after it is declared)." ] }, { @@ -478,9 +479,9 @@ "\n", "First, we need to declare our new class and give it a unique name. In this example, we will call our new class `HDAParameterBlock`. The first two lines of the example below show how we declare our new class using the `declare_process_block_decorator` and inheriting from the `PhysicalParameterBlock` base class from the IDAES Core model libraries. Inheriting from the `PhysicalParameterBlock` brings us access to all the necessary features required by the IDAES modeling framework, whilst the `declare_process_block_class` decorator performs some boilerplate operations to replicate the expected object structure of Pyomo. Further details on these components can be found in the IDAES documentation.\n", "\n", - "Next, we need to set up any configuration arguments we need for the property package. This is done using Pyomo “Config Blocks” which provide a convenient way of declaring, organizing and documenting configuration arguments. To begin with, we can inherit from the `CONFIG` block declared in the `PhysicalParameterBlock` base class, which provides all the arguments that the IDAES modeling framework expects to be present. Modelers can then add additional configuration arguments to provide users with options when constructing their property packages, however we will not cover that in this tutorial.\n", + "Next, we need to set up any configuration arguments we need for the property package. This is done using Pyomo \u201cConfig Blocks\u201d which provide a convenient way of declaring, organizing and documenting configuration arguments. To begin with, we can inherit from the `CONFIG` block declared in the `PhysicalParameterBlock` base class, which provides all the arguments that the IDAES modeling framework expects to be present. Modelers can then add additional configuration arguments to provide users with options when constructing their property packages, however we will not cover that in this tutorial.\n", "\n", - "The most significant part of any IDAES model class is the `build` method, which contains the instructions on how to construct an instance of the desired model and all IDAES models are expected to have a `build` method. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from – this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `PhysicalParameterBlock` needs to contain a pointer to the related `StateBlock` (which we will look at next) – this is used to allow us to build instances of the `StateBlock` by only knowing the `PhysicalParameterBlock` we wish to use. To do this, we create an attribute named `_state_block_class` attached to our class with a pointer to the `StateBlock` class; in this case `self._state_block_class = HDAStateBlock`, where `HDAStateBlock` is the name of the yet to be declared `StateBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", + "The most significant part of any IDAES model class is the `build` method, which contains the instructions on how to construct an instance of the desired model and all IDAES models are expected to have a `build` method. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from \u2013 this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `PhysicalParameterBlock` needs to contain a pointer to the related `StateBlock` (which we will look at next) \u2013 this is used to allow us to build instances of the `StateBlock` by only knowing the `PhysicalParameterBlock` we wish to use. To do this, we create an attribute named `_state_block_class` attached to our class with a pointer to the `StateBlock` class; in this case `self._state_block_class = HDAStateBlock`, where `HDAStateBlock` is the name of the yet to be declared `StateBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", "\n", "The final step in creating the `PhysicalParameterBlock` class is to declare a `classmethod` named `define_metadata` which takes two arguments; a class (`cls`) and an instance of that class (`obj`). This method in turn needs to call two pre-defined methods (inherited from the underlying base classes):\n", "\n", @@ -526,13 +527,13 @@ "\n", "After the `Physical Parameter Block` class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet. Unlike other models however, creating a `State Block` actually required us to write two `classes`. In short, indexed Pyomo object components (e.g. `Vars` and `Blocks`) actually consist of two objects: an `IndexedComponent` object which serves as a container for multiple `ComponentData` objects which represent the component at each point in the indexing set. For example, a `Var` indexed by the `Set` `[1, 2, 3, 4]` actually consists of a single `IndexedVar` object which contains 4 `VarData` objects. Normally this behavior is hidden behind the `declare_process_block_data` decorator which handles the details of this structure (as a side note, unindexed components similarly involve two classes but this is hidden by the use of multiple inheritance.)\n", "\n", - "Normally, when we write models in IDAES, we are concerned only with the `ComponentData` object – i.e., the instructions on how to build an instance of the model at each indexed point (hence the naming convention used when declaring classes). However, State Blocks are slightly different in that we always expect State Blocks to be indexed (they will always be indexed by time, at a minimum). Due to this, we often want to perform actions on all the elements of the indexed `State Block` at once (rather than element by element), such as during initialization. Thus, we have a need to write methods that are attached to the `IndexedStateBlock` in addition to the normal methods for the `StateBlockData` object. Fortunately, the `declare_process_block_data` decorator facilitates this for us, but it does mean we need to declare two classes when creating State Blocks.\n", + "Normally, when we write models in IDAES, we are concerned only with the `ComponentData` object \u2013 i.e., the instructions on how to build an instance of the model at each indexed point (hence the naming convention used when declaring classes). However, State Blocks are slightly different in that we always expect State Blocks to be indexed (they will always be indexed by time, at a minimum). Due to this, we often want to perform actions on all the elements of the indexed `State Block` at once (rather than element by element), such as during initialization. Thus, we have a need to write methods that are attached to the `IndexedStateBlock` in addition to the normal methods for the `StateBlockData` object. Fortunately, the `declare_process_block_data` decorator facilitates this for us, but it does mean we need to declare two classes when creating State Blocks.\n", "\n", "For this example, we will begin by describing the content of the `StateBlockData` objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. After that, we will discuss how to create the class that contains methods to be applied to the `IndexedStateBlock` as a whole.\n", "\n", "## Step 5: Declare State Variables\n", "\n", - "The first step in defining a `State Block` is to create the “state variables” which will be used to define the “state” of the material at any given point. The concept of a “state variable” in IDAES is much the same as the concept in thermodynamics, with the exception that we include extensive flow information in the state definition in IDAES. In short, the “state variables” should be sufficient to fully define the state of the material (both extensive and intensive), and should result in a `State Block` with zero degrees of freedom if all the state variables are fixed.\n", + "The first step in defining a `State Block` is to create the \u201cstate variables\u201d which will be used to define the \u201cstate\u201d of the material at any given point. The concept of a \u201cstate variable\u201d in IDAES is much the same as the concept in thermodynamics, with the exception that we include extensive flow information in the state definition in IDAES. In short, the \u201cstate variables\u201d should be sufficient to fully define the state of the material (both extensive and intensive), and should result in a `State Block` with zero degrees of freedom if all the state variables are fixed.\n", "\n", "For this example, our state variables will be:\n", "\n", @@ -614,7 +615,7 @@ "2. by using an `Expression`, or,\n", "3. by using a `Reference`.\n", "\n", - "The different between the first two options is that an `Expression` does not appear in the problem passed to the solver – the `Expression` can be evaluated by the user and included in constraints in the same way as a variable, but when the problem is passed to the solver the `Expression` object is substituted for the expression it represents wherever it appears in the model. This means that there are fewer variables and constraints in the problem the solver sees, but that the constraints that do appear are more complex. There is no simple answer to which approach is best, and different applications may see better results with one form or the other. The third option, using a `Reference` is for cases where a property already exists elsewhere in the model, and we just want to create a local copy of the same object. In terms of properties, this most often occurs with fixed quantities which are declared in the Physical Parameter Block such as molecular weights. For the purposes of this example, we will demonstrate all of these approaches. \n", + "The different between the first two options is that an `Expression` does not appear in the problem passed to the solver \u2013 the `Expression` can be evaluated by the user and included in constraints in the same way as a variable, but when the problem is passed to the solver the `Expression` object is substituted for the expression it represents wherever it appears in the model. This means that there are fewer variables and constraints in the problem the solver sees, but that the constraints that do appear are more complex. There is no simple answer to which approach is best, and different applications may see better results with one form or the other. The third option, using a `Reference` is for cases where a property already exists elsewhere in the model, and we just want to create a local copy of the same object. In terms of properties, this most often occurs with fixed quantities which are declared in the Physical Parameter Block such as molecular weights. For the purposes of this example, we will demonstrate all of these approaches. \n", "\n", "You may recall from the initial problem statement that we have three properties of interest in this example:\n", "\n", @@ -658,7 +659,7 @@ "where $x_j$ is the mole fraction of component $j$. Recall that for this example we are using the following correlation for the component specific enthalpies.\n", "\n", "\\begin{equation*}\n", - "h_j – h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", + "h_j \u2013 h_{j, ref}= A_j \\times (T-T_{ref}) + \\frac{B_j}{2}\\times (T^2-T_{ref}^2) + \\frac{C_j}{3}\\times (T^3-T_{ref}^3) + \\frac{D_j}{4}\\times (T^4-T_{ref}^4)\n", "\\end{equation*}\n", "\n", "For the specific enthalpy, we will create a Pyomo `Expression` rather than a `Var` and `Constraint`. In practice, this is much like creating a `Constraint`. However, rather than returning an equality between two expressions, an `Expression` requires a single numerical expression that can be used to compute the quantity of interest.\n", @@ -736,16 +737,16 @@ "\n", "### Writing the Initialization Routine\n", "\n", - "For initializing State Blocks, the first step is to get our model to a state where it has no degrees of freedom. As mentioned earlier, fixing all of the state variables should be sufficient to fully define the state of the material – i.e. no degrees of freedom. Additionally, we want to initialize our State Block at a set of conditions that are good initial guesses for the final state of the model once it is finally solved. Of course, the State Block has no way of knowing what these initial values should be, so we depend on the unit model (or the end-user) to provide us with a set of initial values to use – this is done through a `dict` which we generally call `state_args` where the keys are the names of the state variables and the values are the initial guesses.\n", + "For initializing State Blocks, the first step is to get our model to a state where it has no degrees of freedom. As mentioned earlier, fixing all of the state variables should be sufficient to fully define the state of the material \u2013 i.e. no degrees of freedom. Additionally, we want to initialize our State Block at a set of conditions that are good initial guesses for the final state of the model once it is finally solved. Of course, the State Block has no way of knowing what these initial values should be, so we depend on the unit model (or the end-user) to provide us with a set of initial values to use \u2013 this is done through a `dict` which we generally call `state_args` where the keys are the names of the state variables and the values are the initial guesses.\n", "\n", - "Before we start fixing the state variables, there is the possibility that all the state variables have already been fixed (e.g. by a the unit model during its own initialization routine). To allow us to save some time, we include a `state_vars_fixed` argument in our State Block initialization methods that lets the unit model tell us if the state variables are already fixed – if this is `True` then we know we can skip the step of checking the state variables ourselves. If `state_vars_fixed is False` however, then we need to go and fix all the state variables as part of our initialization routine. To save us the effort of having to code all of this ourselves, the IDAES toolkit contains a utility method named `fix_state_vars` (which we imported earlier), which takes the `state_args` `dict` and then iterates through all the state variables as defined by the State Block (using the `dict` we declared earlier in the `return_state_var_dict` sub-method). This method iterates through all the defined state variables and does the following:\n", + "Before we start fixing the state variables, there is the possibility that all the state variables have already been fixed (e.g. by a the unit model during its own initialization routine). To allow us to save some time, we include a `state_vars_fixed` argument in our State Block initialization methods that lets the unit model tell us if the state variables are already fixed \u2013 if this is `True` then we know we can skip the step of checking the state variables ourselves. If `state_vars_fixed is False` however, then we need to go and fix all the state variables as part of our initialization routine. To save us the effort of having to code all of this ourselves, the IDAES toolkit contains a utility method named `fix_state_vars` (which we imported earlier), which takes the `state_args` `dict` and then iterates through all the state variables as defined by the State Block (using the `dict` we declared earlier in the `return_state_var_dict` sub-method). This method iterates through all the defined state variables and does the following:\n", "\n", "1. If the variable is already fixed, it records this and does nothing. If a variable is already fixed, we assume it was fixed for a reason and that we should not change its value.\n", "2. If the variable is not fixed, this is recorded and the method then checks the `state_args` dict for an initial guess for the variable. If a value is found, the variable is fixed to this value; otherwise, the variable is fixed to its current value.\n", "\n", "Finally, the `fix_state_vars` method returns a `dict` that records which variables were fixed by the method, so that we can later reverse these changes. In the example below, we refer to this `dict` as `flags`.\n", "\n", - "At this point, all the state variables should now be fixed, but once again we have a small catch – if we fix all the state variables then we have a situation similar to the inlet of a unit where we cannot write a constraint on the sum of mole fractions and still solve the model. Thus we need to deactivate this constraint if it exists (remembering that this constraint will not exist in all State Blocks). We know that this constraint will only exist if `defined_state is False`, so we start by writing an `IF` statement to check for this and then use the Pyomo `deactivate()` method to deactivate the constraint (remembering that we will need to reactivate it later).\n", + "At this point, all the state variables should now be fixed, but once again we have a small catch \u2013 if we fix all the state variables then we have a situation similar to the inlet of a unit where we cannot write a constraint on the sum of mole fractions and still solve the model. Thus we need to deactivate this constraint if it exists (remembering that this constraint will not exist in all State Blocks). We know that this constraint will only exist if `defined_state is False`, so we start by writing an `IF` statement to check for this and then use the Pyomo `deactivate()` method to deactivate the constraint (remembering that we will need to reactivate it later).\n", "\n", "Before we move on however, it is probably a good idea to check the degrees of freedom to be sure that they are zero (as expected). There are a number of ways things could go wrong (e.g., the unit model said the state variables were fixed when not all of them were, or we missed a constraint we need to deactivate), so a quick check now might save someone a lot of pain in the future. We can use the IDAES `degrees_of_freedom` method to check the degrees of freedom of our State Block, and if this is not zero raise an `Exception` to let the user know something went wrong." ] @@ -788,7 +789,7 @@ "\n", "Before we call the solver, we first need to make sure there is actually something to solve; depending on how the State Block is written (e.g. build-on-demand properties and use of `Expressions`), it is sometimes possible that there are actually no `Constraints` to be solved in the model. If we try to send a problem like that to a solver, we will likely get back an error message which is not what we want to see. Whilst we know that our State Block will always contain at least one constraint (for mixture density), we will add a check here anyway to show how it is done. First ,we create a counter to keep track of the number of unfixed variables in the system, `free_vars`. Then we iterate over all the elements of the `blk` (the `IndexedStateBlock`) and check how many free variables are in each. We use the `number_unfixed_variables()` method from the `idaes.core.util.model_statistics` module to do this, and add the result `free_vars` for each element. If the final value of `free_vars` is not zero, then we know there is something to solve for and we can proceed to call a solver; otherwise we know that we can skip this step.\n", "\n", - "In order to solve the entire `IndexedStateBlock`, we need to do things slightly differently than normal. The standard Pyomo `SolverFactory` cannot be applied to indexed blocks, so instead we use the IDAES `solve_indexed_block` method (imported from `idaes.core.initialization`) which puts a wrapper around the indexed block so that we can use Pyomo’s solver interface. In order to use this method, we need to provide a Pyomo `SolverFactory` object (called `solver` here, which also includes any attached solver options) along with the `blk` we wish to solve and where to send the solver results (the `tee` argument). Additionally, we want the user to have the ability to control the output from the solver through the IDAES logger interface, which we do by wrapping the solver call with the following line of code:\n", + "In order to solve the entire `IndexedStateBlock`, we need to do things slightly differently than normal. The standard Pyomo `SolverFactory` cannot be applied to indexed blocks, so instead we use the IDAES `solve_indexed_block` method (imported from `idaes.core.initialization`) which puts a wrapper around the indexed block so that we can use Pyomo\u2019s solver interface. In order to use this method, we need to provide a Pyomo `SolverFactory` object (called `solver` here, which also includes any attached solver options) along with the `blk` we wish to solve and where to send the solver results (the `tee` argument). Additionally, we want the user to have the ability to control the output from the solver through the IDAES logger interface, which we do by wrapping the solver call with the following line of code:\n", "\n", "```\n", "with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc:\n", @@ -796,7 +797,7 @@ "\n", "where `idaeslog` is an instance of the IDAES logger. Note that we send the solver output to this logger by setting `tee=slc`. In this way, all the output from the solver is passed into the logger allowing users to easily control the output level without needing to send additional arguments to the initialization methods.\n", "\n", - "If all goes well, the solver will successfully initialize our model and we can move on. However, sometimes the solver will fail catastrophically, in which case we need to make sure that our initialization routine can attempt to recover. In order to do this, we wrap the solver call within a Python `try/except` statement. This way, if the solver fails badly and returns an `Exception`, we can capture this and decide how to process – otherwise the execution of our model would terminate with the exception from the solver. In the case we encounter an `Exception` here, we will record `results=None` and try to continue with initializing our model in the hope that we can recover.\n", + "If all goes well, the solver will successfully initialize our model and we can move on. However, sometimes the solver will fail catastrophically, in which case we need to make sure that our initialization routine can attempt to recover. In order to do this, we wrap the solver call within a Python `try/except` statement. This way, if the solver fails badly and returns an `Exception`, we can capture this and decide how to process \u2013 otherwise the execution of our model would terminate with the exception from the solver. In the case we encounter an `Exception` here, we will record `results=None` and try to continue with initializing our model in the hope that we can recover.\n", "\n", "Finally, it is useful to provide the user with some feedback on how the initialization is proceeding. In the last lines below, we send a message to the IDAES logger with a message saying that the initialization step has been completed and append the final solver status." ] @@ -900,7 +901,7 @@ "As the name suggests, the `initialize` method is used to run the initialization routine for the State Block, and this is where we will use the `prepare_state`, `initialize_state` and `restore_state` methods we wrote previously. The `initialize` method requires the following arguments to be declared:\n", "\n", "* `blk`: this will be a pointer to an instance of the State Block to be initialized.\n", - "* `state_args`: this is used to pass the ‘dict’ of initial guesses to the initialization routine. This should default to `None` if not provided. The `fix_state_vars` method will interpret a value of `None` as no guesses provided as use the current values instead.\n", + "* `state_args`: this is used to pass the \u2018dict\u2019 of initial guesses to the initialization routine. This should default to `None` if not provided. The `fix_state_vars` method will interpret a value of `None` as no guesses provided as use the current values instead.\n", "* `solver`: this argument is used to allow tell the State Block to use a specific solver during initialization, and should be a string recognized by the Pyomo `SolverFactory`. We generally set this to `None` in order to signify that IDAES Should use the default solver (which is IPOPT).\n", "* `optarg`: this argument is used to set any solver options the user desires. Again this is generally set to `None` to indicate that the default solver settings should be used.\n", "* `state_vars_fixed`: argument to allow the unit model to inform the State Block that the state variables are already fixed. This should default to `False`.\n", @@ -959,13 +960,13 @@ "source": [ "### The StateBlockData class\n", "\n", - "Finally, we can build the `StateBlockData` class, which we will call `HDAStateBlockData`. First, we use the `declare_process_block_class` decorator but this time we provide two arguments. The first argument is the name of the class that will be automatically constructed for us (`HDAStateBlock`) whilst the second argument is a reference to the class we wish to use as the base when building the `IndexedHDAStateBlock` class – i.e. the `_HDAStateBlock` class we just declared. Then, we declare our new `HDAStateBlockData` class and inherit from the IDAES `StateBlockData` base class.\n", + "Finally, we can build the `StateBlockData` class, which we will call `HDAStateBlockData`. First, we use the `declare_process_block_class` decorator but this time we provide two arguments. The first argument is the name of the class that will be automatically constructed for us (`HDAStateBlock`) whilst the second argument is a reference to the class we wish to use as the base when building the `IndexedHDAStateBlock` class \u2013 i.e. the `_HDAStateBlock` class we just declared. Then, we declare our new `HDAStateBlockData` class and inherit from the IDAES `StateBlockData` base class.\n", "\n", "As usual, the first thing we need to define in our new class is a `build` method, where we will provide the instructions for constructing our property model, and once again the first thing we should do is call `super().build()` to construct all the underlying components defined by the parent class. After this, we can call the methods we wrote earlier to construct the state variables and the add the calculations for the properties of interest.\n", "\n", - "However, if you recall from when we defined the properties metadata at the beginning of the example, we decided that the specific molar enthalpy of the mixture would be a “build-on-demand” property (i.e., we provided a specific method in the properties metadata rather than `None`). Thus, we do not want to call the method to construct the specific molar enthalpy as part of the `build` method, meaning that we only call the `add_state_variables`, `add_mole_fraction_constraint` and `add_molecular_weight_and_density` as in the `build` method.\n", + "However, if you recall from when we defined the properties metadata at the beginning of the example, we decided that the specific molar enthalpy of the mixture would be a \u201cbuild-on-demand\u201d property (i.e., we provided a specific method in the properties metadata rather than `None`). Thus, we do not want to call the method to construct the specific molar enthalpy as part of the `build` method, meaning that we only call the `add_state_variables`, `add_mole_fraction_constraint` and `add_molecular_weight_and_density` as in the `build` method.\n", "\n", - "To add the specific molar enthalpy calculation as a “build-on-demand” property, we instead declare a separate method with the name we provided in the properties metadata. Whenever the specific molar enthalpy is required by a unit model it will check to see if the property already exists, and if not it will look up the properties metadata and call the method listed there; i.e. `_enth_mol` in this case. Thus, we declare another method on our `HDAStateBlockData` class named `enth_mol` which takes only the class instance as an argument (`self`), and then call the `add_enth_mol` method we created earlier to construct the required `Expression`.\n", + "To add the specific molar enthalpy calculation as a \u201cbuild-on-demand\u201d property, we instead declare a separate method with the name we provided in the properties metadata. Whenever the specific molar enthalpy is required by a unit model it will check to see if the property already exists, and if not it will look up the properties metadata and call the method listed there; i.e. `_enth_mol` in this case. Thus, we declare another method on our `HDAStateBlockData` class named `enth_mol` which takes only the class instance as an argument (`self`), and then call the `add_enth_mol` method we created earlier to construct the required `Expression`.\n", "\n", "That is all we need to do in order to construct the variables and constraints we need for the property calculations. However, there are a number of other things we need to define in our State Block Data class. In order to provide much of the flexibility present in the IDAES modeling framework, we defer making decisions on much of the form of the overall model for as long as possible. However, these decisions need to be made at some point, and the State Block Data is where this finally occurs.\n", "\n", @@ -983,7 +984,7 @@ "* `default_material_balance_type` should return an instance of the IDAES `MaterialBalanceType` `Enum` (imported from `idaes.core`).\n", "* `default_energy_balance_type` should return an instance of the IDAES `EnergyBalanceType` `Enum` (imported from `idaes.core`).\n", "\n", - "Finally, we need to specify the basis of the material flow terms (mass, mole or other). This is used to automatically convert between different bases as required (e.g. a user can define a custom mass transfer term on a molar basis whilst using a mass basis for the actual material balance). Note that automatic conversion only works for mass and molar basis; the “other” basis is used to indicate forms which cannot be easily converted (i.e., the modeler needs to handle this manually). To define the material flow term basis we define a final method named `get_material_flow_basis` which returns an instance of the IDAES `MaterialFlowBasis` `Enum` (again imported from `idaes.core`)." + "Finally, we need to specify the basis of the material flow terms (mass, mole or other). This is used to automatically convert between different bases as required (e.g. a user can define a custom mass transfer term on a molar basis whilst using a mass basis for the actual material balance). Note that automatic conversion only works for mass and molar basis; the \u201cother\u201d basis is used to indicate forms which cannot be easily converted (i.e., the modeler needs to handle this manually). To define the material flow term basis we define a final method named `get_material_flow_basis` which returns an instance of the IDAES `MaterialFlowBasis` `Enum` (again imported from `idaes.core`)." ] }, { @@ -1079,7 +1080,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now have an instance of our new State Block in our flowsheet, so let’s display it and see what it contains." + "We now have an instance of our new State Block in our flowsheet, so let\u2019s display it and see what it contains." ] }, { @@ -1095,7 +1096,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that our State Block contains a single point in time, which in turn contains the five variables. These are our four state variables (molar flow rate, component mole fraction, temperature and pressure) as well as the mixture density. We also have a single constraint, which is the ideal gas equation used to calculate density. Note that we don’t see the component molecular weights as they are `Params` (`References` take on the appearance of the component being referenced) or the molar enthalpy as it is an `Expression`, not a variable (plus it hasn’t been constructed yet as we haven’t asked for it).\n", + "We can see that our State Block contains a single point in time, which in turn contains the five variables. These are our four state variables (molar flow rate, component mole fraction, temperature and pressure) as well as the mixture density. We also have a single constraint, which is the ideal gas equation used to calculate density. Note that we don\u2019t see the component molecular weights as they are `Params` (`References` take on the appearance of the component being referenced) or the molar enthalpy as it is an `Expression`, not a variable (plus it hasn\u2019t been constructed yet as we haven\u2019t asked for it).\n", "\n", "Next, let us check the degrees of freedom in our State Block." ] @@ -1115,7 +1116,7 @@ "source": [ "This is unexpected: the `degrees_of_freedom` method is saying there are only 2 degrees of freedom in our State Block, but there are 8 state variables.\n", "\n", - "However, if we think about the constraints we have written, we are only actually using 2 of the state variables in any constraint (temperature and pressure appear in the ideal gas equation). The molar flowrate and component mole fractions are not actually used anywhere in our model, so they have been excluded from the degrees of freedom calculation. In Pyomo terminology, these variables are “Stale”, and they will not be sent to the solver when it is called. Thus, the two degrees of freedom is in fact correct.\n", + "However, if we think about the constraints we have written, we are only actually using 2 of the state variables in any constraint (temperature and pressure appear in the ideal gas equation). The molar flowrate and component mole fractions are not actually used anywhere in our model, so they have been excluded from the degrees of freedom calculation. In Pyomo terminology, these variables are \u201cStale\u201d, and they will not be sent to the solver when it is called. Thus, the two degrees of freedom is in fact correct.\n", "\n", "Note that this is only the case because our property package is so simple. Also, the specific enthalpy calculation depends on the component mole fractions, so whilst we could solve the State Block by only specifying temperature and pressure, the value of the specific molar enthalpy would be meaningless.\n", "\n", @@ -1142,7 +1143,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have fixed the values for all the state variables, we would expect that the degrees of freedom should be zero (even though we fixed all 8 variables, only temperature and pressure actually contribute to the degrees of freedom). Let’s check this to be sure." + "Now that we have fixed the values for all the state variables, we would expect that the degrees of freedom should be zero (even though we fixed all 8 variables, only temperature and pressure actually contribute to the degrees of freedom). Let\u2019s check this to be sure." ] }, { @@ -1364,4 +1365,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages.ipynb index 5a825d3e..80414f05 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_doc.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_doc.ipynb index de3ceedd..50f8ca36 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_doc.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -116,7 +117,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "c:\\users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\mod\\properties\\reaction_property_example.py\n" + "/home/dang/src/dangunter/examples/idaes_examples/mod/properties/reaction_property_example.py\n" ] } ], @@ -577,7 +578,16 @@ "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Params with units must be mutable. Converting Param\n", + "'fs.thermo_params.mw_comp' to mutable.\n" + ] + } + ], "source": [ "m = ConcreteModel()\n", "\n", @@ -668,35 +678,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:12 [INFO] idaes.init.fs.reactor.control_volume.properties_in: Properties Initialized optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:43 [INFO] idaes.init.fs.reactor.control_volume.properties_in: Properties Initialized optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:12 [INFO] idaes.init.fs.reactor.control_volume.properties_out: Properties Initialized optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:43 [INFO] idaes.init.fs.reactor.control_volume.properties_out: Properties Initialized optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:12 [INFO] idaes.init.fs.reactor.control_volume.reactions: Initialization Complete.\n" + "2025-03-17 17:35:43 [INFO] idaes.init.fs.reactor.control_volume.reactions: Initialization Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:12 [INFO] idaes.init.fs.reactor.control_volume: Initialization Complete\n" + "2025-03-17 17:35:43 [INFO] idaes.init.fs.reactor.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:12 [INFO] idaes.init.fs.reactor: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:35:43 [INFO] idaes.init.fs.reactor: Initialization Complete: optimal - Optimal Solution Found\n" ] }, { @@ -762,7 +772,7 @@ "Number of equality constraint Jacobian evaluations = 1\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n" @@ -880,7 +890,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_test.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_test.ipynb index e9f5b157..864875ba 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_test.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_test.ipynb @@ -1,791 +1,792 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reaction Property Packages in IDAES\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "
\n", - "Note:\n", - "Reaction property packages are closely related to, and dependent on, thermophysical property packages and it is advised the readers start with understanding these.\n", - "
\n", - "\n", - "Similar to thermophysical property packages, reaction property packages in IDAES are used to define the set of parameters, variables and constraints associated with a specific set of chemical reactions that a user wishes to model. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property \u201cpackages\u201d to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating reaction properties within the IDAES Core Modeling Framework.\n", - "\n", - "## Relationship with Thermophysical Property Packages\n", - "\n", - "Reaction properties depend on the state of the system, such as the temperature, pressure and composition of the material. All of these properties are defined in the thermophysical property package, thus reaction property packages are closely tied to thermophysical property packages; indeed, a given reaction package is often tied to a single specific thermophysical property package. Reaction packages need to be used with a thermophysical property package which defines the expected set of components and the expected forms and units for the state variables.\n", - "\n", - "As such, developers of reaction packages should have a specific thermophysical property package in mind when developing a reaction property package, and to tailor the reaction package to the thermophysical property package.\n", - "\n", - "## Types of Reactions\n", - "\n", - "Within the IDAES Core Modeling Framework, chemical reactions are divided into two categories:\n", - "\n", - "1. Equilibrium based reactions, where extent of reaction is determined by satisfying a constraint relating the concentration of species within the system, and\n", - "2. Rate based reactions, where extent of reaction depends on some characteristic of the reactor unit. Despite the name, this category is also used to represent stoichiometric and yield based reactions.\n", - "\n", - "## Steps in Creating a Reaction Property Package\n", - "\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **equilibrium reactions** of interest.\n", - "4. Defining the **equilibrium reactions** of interest.\n", - "5. Defining the **parameters** related to the reactions of interest.\n", - "6. Creating **variables and constraints** to describe the reactions of interest.\n", - "7. Creating an **initialization routine** for the reaction property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial Example\n", - "\n", - "For this tutorial, we will be building a upon the property package from the thermophysical property example. In that example, we constructed a thermophysical property package that could be used to model a process for the hydrodealkylation of toluene to form benzene. This process involves five key chemical species:\n", - "\n", - "* toluene\n", - "* benzene\n", - "* hydrogen\n", - "* methane\n", - "* diphenyl\n", - "\n", - "In this tutorial, we will write a reaction property package to define the reactions associated with the HDA process:\n", - "\n", - "$$\n", - "\\text{Toluene} + \\text{Hydrogen} \\rightarrow \\text{Benzene} + \\text{Methane}\n", - "$$\n", - "$$\n", - "2 \\text{Benzene} \\rightleftharpoons \\text{Hydrogen} + \\text{Diphenyl}\n", - "$$\n", - "\n", - "## A Note on this Tutorial\n", - "\n", - "The `build` methods in the reaction property package classes are generally written as a single, long method. However, to break the code into manageable pieces for discussion, in this tutorial we will create a number of smaller sub-methods that will then be called as part of the `build` method. This is done entirely for presentation purposes, and model developers should not feel compelled to write their models this way." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "An example of how the example in this tutorial would be written without sub-methods can be found in the module `idaes_examples.mod.properties.reaction_property_example`. To locate this file on your system, you can use the following code snippet:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from idaes_examples.mod.properties import reaction_property_example as example\n", - "import inspect\n", - "\n", - "print(inspect.getabsfile(example))\n", - "# To print the file contents, uncomment the following line\n", - "# print(''.join(inspect.getsourcelines(example)[0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Components of a Reaction Property Package\n", - "\n", - "Similar to thermophysical property packages, reaction property packages consist of three parts, which are written as Python `classes`. These components are:\n", - "\n", - "* The `Reaction Parameter Block` class, which contains all the global parameters associated with the reaction property package,\n", - "* The `Reaction Block Data` class, which contains the instructions on how to calculate all the properties at a given state, and,\n", - "* The `Reaction Block` class, which is used to construct indexed sets of `Reaction Block Data` objects and contains methods for acting on all multiple `Reaction Block Data` objects at once (such as initialization).\n", - "\n", - "It is not necessary to understand the reason for the distinction between the `Reaction Block` and `Reaction Block Data` classes. Suffice to say that this is due to the need to replicate the underlying component structure of Pyomo, and that the `Reaction Block` represents the indexed `Block` representing a set of states across a given indexing set (most often time), and the `Reaction Block Data` represents the individual elements of the indexed `Block`.\n", - "\n", - "## Importing Libraries\n", - "\n", - "Before we begin writing the actual `classes` however, we need to import all the necessary components from the Pyomo and IDAES modeling libraries. To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries.\n", - "\n", - "Rather than describe the purpose of all of these here, we shall just import all of them here and discuss their use as they arise in the example." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Pyomo libraries\n", - "from pyomo.environ import Constraint, exp, Param, Set, units as pyunits, Var\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " MaterialFlowBasis,\n", - " ReactionParameterBlock,\n", - " ReactionBlockDataBase,\n", - " ReactionBlockBase,\n", - ")\n", - "from idaes.core.util.constants import Constants as const\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The Reaction Parameter Block\n", - "\n", - "We will begin by constructing the `Reaction Parameter Block` for our example. This serves as the central point of reference for all aspects of the reaction property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What reaction properties are supported and how they are implemented\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated `Reaction Block` class, so that construction of the `Reaction Block` components can be automated from the `Reaction Parameter Block`\n", - "\n", - "## Step 1: Define Units of Measurement and Property Metadata\n", - "\n", - "The first step is to define the units of measurement for the property package, which will in turn be inherited by any unit model using this property package. The IDAES Core Modeling Framework requires that the units of measurement defined for a reaction property package be identical to those used in the thermophysical property package it is associated with (this is to avoid any chance of confusion regarding units when setting up the balance equations).\n", - "\n", - "In order to set the base units, we use the same approach as for thermophysical property packages; we create a dictionary which has each of the base quantities as a key, and provide a Pyomo recognized unit as the value as shown in the cell below.\n", - "\n", - "Much like thermophysical property packages, we also need to define metadata regarding the reaction properties supported by our package. For this example, we have three supported properties:\n", - "\n", - "* a rate constant (`k_rxn`),\n", - "* an equilibrium constant (`k_eq`), and\n", - "* a reaction rate term (`rate_reaction`).\n", - "\n", - "The cell below shows how to define the units of measurement and properties metadata for this example." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "units_metadata = {\n", - " \"time\": pyunits.s,\n", - " \"length\": pyunits.m,\n", - " \"mass\": pyunits.kg,\n", - " \"amount\": pyunits.mol,\n", - " \"temperature\": pyunits.K,\n", - "}\n", - "\n", - "properties_metadata = {\n", - " \"k_rxn\": {\"method\": None},\n", - " \"k_eq\": {\"method\": None},\n", - " \"reaction_rate\": {\"method\": None},\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to define the rate-based reactions of interest and the associated stoichiometry. For this, we need to define two things:\n", - "\n", - "* a `Set` of names for the rate-based reaction, and\n", - "* a `dict` containing the stoichiometric coefficients for all the rate-based reactions.\n", - "\n", - "In this example, we have only one rate-based reaction, which is the conversion of toluene to benzene; we will call this reaction `R1`. Thus, we create a Pyomo `Set` component and initialize it with the list of rate-based reactions (`[\u201cR1\u201d]`) as shown in the following cell.\n", - "\n", - "Next, we create a `dict` object for the stoichiometric coefficients. This `dict` needs to provide coefficients for all combinations of reaction, phase and component present in the system, even for those components which do not take part in a reaction. This is required as `ControlVolumes` create generation terms for all reaction-phase-component combinations, and need a stoichiometric coefficient for each of these. In this `dict`, the keys need to have the form of a tuple with three parts:\n", - "\n", - "1. the reaction name,\n", - "2. the phase name, and\n", - "3. the component name,\n", - "\n", - "whilst the value is the stoichiometric coefficient for that key combination. See the example in the cell below; in this example we have 1 reaction (`R1`), 1 phase (`Vap`, as defined in the thermophysical property package) and 5 components (`benzene`, `toluene`, `hydrogen`, `methane` and `diphenyl`), thus the resulting dict has `1x1x5` entries." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def define_kinetic_reactions(self):\n", - " # Rate Reaction Index\n", - " self.rate_reaction_idx = Set(initialize=[\"R1\"])\n", - "\n", - " # Rate Reaction Stoichiometry\n", - " self.rate_reaction_stoichiometry = {\n", - " (\"R1\", \"Vap\", \"benzene\"): 1,\n", - " (\"R1\", \"Vap\", \"toluene\"): -1,\n", - " (\"R1\", \"Vap\", \"hydrogen\"): -1,\n", - " (\"R1\", \"Vap\", \"methane\"): 1,\n", - " (\"R1\", \"Vap\", \"diphenyl\"): 0,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to do the same thing for the equilibrium-based reactions. The format is the same as for rate-based reactions, as shown in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def define_equilibrium_reactions(self):\n", - " # Equilibrium Reaction Index\n", - " self.equilibrium_reaction_idx = Set(initialize=[\"E1\"])\n", - "\n", - " # Equilibrium Reaction Stoichiometry\n", - " self.equilibrium_reaction_stoichiometry = {\n", - " (\"E1\", \"Vap\", \"benzene\"): -2,\n", - " (\"E1\", \"Vap\", \"toluene\"): 0,\n", - " (\"E1\", \"Vap\", \"hydrogen\"): 1,\n", - " (\"E1\", \"Vap\", \"methane\"): 0,\n", - " (\"E1\", \"Vap\", \"diphenyl\"): 1,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to define any global parameters related to the reactions of interest. For this example, we will assume that the rate-based reactions follow the Arrhenius equation, thus we need to declare a pre-exponential factor ($A=1.25\\times10^{-9} \\text{ mol}/\\text{m}^3/\\text{s}/\\text{Pa}^2$) and an activation energy parameter ($E_a=3800 \\text{ J}/\\text{mol}$). We will not declare any parameters for the equilibrium-based reactions at this point; this will be done in the individual `ReactionBlocks`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def define_parameters(self):\n", - " # Arrhenius Constant\n", - " self.arrhenius = Param(\n", - " default=1.25e-9,\n", - " doc=\"Arrhenius constant\",\n", - " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", - " )\n", - "\n", - " # Activation Energy\n", - " self.energy_activation = Param(\n", - " default=3800, doc=\"Activation energy\", units=pyunits.J / pyunits.mol\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Declaring the Reaction Parameter Block\n", - "\n", - "Now that the various parts of the Reaction Parameter Block have been declared, we can assemble the actual `class` that will assemble these components in a flowsheet. The steps for declaring a new `ReactionParameterBlock` class which are:\n", - "\n", - "1. Declaring the new class and inheriting from the `ReactionParameterBlock` base class\n", - "3. Writing the `build` method for our `class`\n", - "4. Creating a `define_metadata` method for the class.\n", - "\n", - "Each of these steps are shown in the code example below.\n", - "\n", - "First, we need to declare our new class and give it a unique name. In this example, we will call our new class `HDAReactionParameterBlock`. The first two lines of the example below show how we declare our new class using the `declare_process_block_decorator` and inheriting from the `ReactionParameterBlock` base class from the IDAES Core model libraries. Inheriting from the `ReactionParameterBlock` brings us access to all the necessary features required by the IDAES modeling framework, whilst the `declare_process_block_class` decorator performs some boilerplate operations to replicate the expected object structure of Pyomo. Further details on these components can be found in the IDAES documentation.\n", - "\n", - "Next, we need to declare the `build` method that will be used to construct our Reaction Parameter Block. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from \u2013 this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `ReactionParameterBlock` needs to contain a pointer to the related `ReactionBlock` (which we will look at next) \u2013 this is used to allow us to build instances of the `ReactionBlock` by only knowing the `ReactionParameterBlock` we wish to use. To do this, we create an attribute named `_reaction_block_class` attached to our class with a pointer to the `ReactionBlock` class; in this case `self._reaction_block_class = HDAReactionBlock`, where `HDAReactionBlock` is the name of the yet to be declared `ReactionBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", - "\n", - "The final step in creating the `ReactionParameterBlock` class is to declare a `classmethod` named `define_metadata` which takes two arguments; a class (`cls`) and an instance of that class (`obj`). This method in turn needs to call two pre-defined methods (inherited from the underlying base classes):\n", - "\n", - "* `obj.add_properties()` is used to set the metadata regarding the supported reaction properties, and here we pass the `properties_metadata` dict we created earlier as an argument.\n", - "* `obj.add_default_units()` sets the default units metadata for the reaction property package, and here we pass the `units_metadata` `dict ` we created earlier as an argument." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"HDAReactionParameterBlock\")\n", - "class HDAReactionParameterData(ReactionParameterBlock):\n", - " \"\"\"\n", - " Reaction Parameter Block Class\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(HDAReactionParameterData, self).build()\n", - "\n", - " self._reaction_block_class = HDAReactionBlock\n", - "\n", - " define_kinetic_reactions(self)\n", - " define_equilibrium_reactions(self)\n", - " define_parameters(self)\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(properties_metadata)\n", - " obj.add_default_units(units_metadata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reaction Block\n", - "\n", - "After the Reaction Parameter Block class has been created, the next step is to write the code necessary to create the Reaction Blocks that will be used through out the flowsheet. Similar to State Blocks for thermophysical properties, Reaction Blocks also require two Python `classes` to construct (for a discussion on why, see the related example for creating custom thermophysical property packages).\n", - "\n", - "For this example, we will begin by describing the content of the `ReactionBlockData` objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. After that, we will discuss how to create the class that contains methods to be applied to the `IndexedReactionBlock` as a whole.\n", - "\n", - "## State Variables\n", - "\n", - "Like thermophysical property calculations, reaction properties also depend on the material state variables such as temperature and pressure. However, the state variables are declared as a part of the State Block, and it does not make sense to duplicate them here. Due to this, Reaction Blocks are always associated with a State Block representing the material at the same point in space and time, and the Reaction Block contains a pointer to the equivalent State Block. This allows the Reaction Block to access the state variables, and any other thermophysical property the State Block supports, in order to perform reaction property calculations. The State Block can be accessed using the `state_ref` property of the Reaction Block.\n", - "\n", - "\n", - "## Step 1. Define Property Variables\n", - "\n", - "The first thing we need to do when creating our Reaction Block is create Pyomo components to represent the properties of interest. In this example, we have three properties we need to define:\n", - "\n", - "1. the rate constant for the rate-based reaction: `k_rxn`,\n", - "2. a variable for the rate of reaction at the current state, `rate_reaction`, and\n", - "3. the equilibrium constant for the equilibrium-based reaction, `k_eq`.\n", - "\n", - "The declaration of these is shown in the cell below. Note that for this example we are assuming the equilibrium constant does not vary, and have thus declared it as a Pyomo `Param`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def define_variables_and_parameters(self):\n", - " self.k_rxn = Var(\n", - " initialize=7e-10,\n", - " doc=\"Rate constant\",\n", - " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", - " )\n", - "\n", - " self.reaction_rate = Var(\n", - " self.params.rate_reaction_idx,\n", - " initialize=0,\n", - " doc=\"Rate of reaction\",\n", - " units=pyunits.mol / pyunits.m**3 / pyunits.s,\n", - " )\n", - "\n", - " self.k_eq = Param(initialize=10000, doc=\"Equlibrium constant\", units=pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2. Define Constraints for the Rate-Based Reactions\n", - "\n", - "Next, we need to define the `Constraints` which describe the rate-based reaction. First, we use the Arrhenius equation to calculate the rate constant, $k_{rxn} = A \\times e^{-E_a/(RT)}$. For this calculation, $A$ and $E_a$ come from the associated Reaction Parameter Block (`self.params`), $T$ comes from the associated State Block (`self.state_ref.temperature`) and the gas constant $R$ can be found in the IDAES `Constants` class.\n", - "\n", - "After the rate constant, we need to declare the form of the rate expression as well. In this case, we are dealing with a gas phase reaction so $r = k_{rxn} \\times x_{toluene} \\times x_{hydrogen} \\times P^2$, where $P$ is the system pressure. $x_{toluene}$, $x_{hydrogen}$ and $P$ are all state variables, and can be accessed from the associated State Block.\n", - "\n", - "The cell below shows how we declare these two constraints." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def define_rate_expression(self):\n", - " self.arrhenius_equation = Constraint(\n", - " expr=self.k_rxn\n", - " == self.params.arrhenius\n", - " * exp(\n", - " -self.params.energy_activation\n", - " / (const.gas_constant * self.state_ref.temperature)\n", - " )\n", - " )\n", - "\n", - " def rate_rule(b, r):\n", - " return b.reaction_rate[r] == (\n", - " b.k_rxn\n", - " * b.state_ref.mole_frac_comp[\"toluene\"]\n", - " * b.state_ref.mole_frac_comp[\"hydrogen\"]\n", - " * b.state_ref.pressure**2\n", - " )\n", - "\n", - " self.rate_expression = Constraint(self.params.rate_reaction_idx, rule=rate_rule)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3. Define Constraints for the Equilibrium-Based Reactions\n", - "\n", - "Similar to those for the rate-based reactions, we also need to define constraints for the equilibrium-based reactions in the system. In this case, the constraint will take the form of an equality that will force the compositions in the system to satisfy the given equilibrium constant. For this example, we have the following equilibrium constraint:\n", - "\n", - "$$\n", - "k_{eq} = \\frac{x_{diphenyl} \\times x_{hydrogen} \\times P^2}{x_{benzene} \\times P}\n", - "$$\n", - "\n", - "Note that $P$ appears in both the numerator and denominator to make it clear that this is a ratio of partial pressures, and because we will rearrange this constraint when creating the actual Pyomo component in order to avoid potential singularities. This is shown in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def define_equilibrium_expression(self):\n", - " self.equilibrium_constraint = Constraint(\n", - " expr=self.k_eq\n", - " * self.state_ref.mole_frac_comp[\"benzene\"]\n", - " * self.state_ref.pressure\n", - " == self.state_ref.mole_frac_comp[\"diphenyl\"]\n", - " * self.state_ref.mole_frac_comp[\"hydrogen\"]\n", - " * self.state_ref.pressure**2\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating the Reaction Block class\n", - "\n", - "These are all the variables and constraints that need to be declared for this example. All that remains is to declare the `_ReactionBlock` and `ReactionBlock` classes to complete our reaction property package. This process is much the same as for the thermophysical property package example, and will only be covered briefly here.\n", - "\n", - "## The `_ReactionBlock` class\n", - "\n", - "For this example, the `_ReactionBlock` class is very simple, and contains only a placeholder `initialize` method. As all the state variables are held separately in the associated State Block and the constraints within the reaction package are fairly simple, it is sufficient to not initialize the reaction properties before solving. However, a placeholder method still needs to be created, as the IDAES framework assumes all components will have an `initialize` method; however, this method need only consist of a `pass` statement.\n", - "\n", - "
\n", - "Note:\n", - "In more complex reaction systems, it is likely that a proper initialization routine would need to be implemented. Developers of these should be aware that equilibrium constraints will need to be deactivated during initialization, as the state variables (i.e. compositions) will be fixed in the State Block. Thus, trying to solve for the system with equilibrium constraints present will result in an over-specified problem.\n", - "
\n", - "\n", - "## The `ReactionBlock` class\n", - "\n", - "Once the `_ReactionBlock` class has been declared, the overall `ReactionBlock` class can be declared as shown below. Once again, we define a `build` method which calls the sub-methods we created earlier in order to construct an instance of the Reaction Block. The `ReactionBlock` class also needs to define a `get_reaction_rate_basis` method, which should return an instance of the `MaterialFlowBasis` `Enum`; this is used by the IDAES framework to determine if conversion between mass and mole basis is required." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "class _HDAReactionBlock(ReactionBlockBase):\n", - " def initialize(blk, outlvl=idaeslog.NOTSET, **kwargs):\n", - " init_log = idaeslog.getInitLogger(blk.name, outlvl, tag=\"properties\")\n", - " init_log.info(\"Initialization Complete.\")\n", - "\n", - "\n", - "@declare_process_block_class(\"HDAReactionBlock\", block_class=_HDAReactionBlock)\n", - "class HDAReactionBlockData(ReactionBlockDataBase):\n", - " def build(self):\n", - "\n", - " super(HDAReactionBlockData, self).build()\n", - "\n", - " define_variables_and_parameters(self)\n", - " define_rate_expression(self)\n", - " define_equilibrium_expression(self)\n", - "\n", - " def get_reaction_rate_basis(b):\n", - " return MaterialFlowBasis.molar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demonstration\n", - "\n", - "In order to demonstrate our new Reaction Property package in practice, we will now use it to build and solve a CSTR. First, we will need to import some more components from Pyomo and IDAES to use when building the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.models.unit_models import CSTR\n", - "\n", - "from idaes_examples.mod.properties.thermophysical_property_example import (\n", - " HDAParameterBlock,\n", - ")\n", - "\n", - "from idaes.core.util.model_statistics import degrees_of_freedom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we can construct a Pyomo `ConcreteModel` and IDAES `FlowsheetBlock` as usual. We then attach an instance of the associated thermophysical property package (imported as `HDAParameterBlock`) and our new reaction property package to the flowsheet, and then construct a CSTR using these property packages." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "m.fs.thermo_params = HDAParameterBlock()\n", - "m.fs.reaction_params = HDAReactionParameterBlock(property_package=m.fs.thermo_params)\n", - "\n", - "m.fs.reactor = CSTR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, we should see no errors when constructing the flowsheet above. To be sure, let us print the degrees of freedom in our flowsheet model as shown below; we should see 9 degrees of freedom." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert degrees_of_freedom(m) == 9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 9 degrees of freedom are the flowrate, temperature, pressure and mole fractions (5) of the inlet stream, as well as the reactor volume. We will fix them to some default values as shown below. Once we are done, we will also print the degrees of freedom again to ensure we have fixed enough variables." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.reactor.inlet.flow_mol.fix(100)\n", - "m.fs.reactor.inlet.temperature.fix(500)\n", - "m.fs.reactor.inlet.pressure.fix(350000)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"benzene\"].fix(0.1)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"toluene\"].fix(0.4)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"hydrogen\"].fix(0.4)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"methane\"].fix(0.1)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"diphenyl\"].fix(0.0)\n", - "\n", - "m.fs.reactor.volume.fix(1)\n", - "\n", - "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have defined our example problem, we can initialize and solve the flowsheet. This is done in the cell below, which should result in an optimal solution." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.reactor.initialize(\n", - " state_args={\n", - " \"flow_mol\": 100,\n", - " \"mole_frac_comp\": {\n", - " \"benzene\": 0.15,\n", - " \"toluene\": 0.35,\n", - " \"hydrogen\": 0.35,\n", - " \"methane\": 0.15,\n", - " \"diphenyl\": 0.01,\n", - " },\n", - " \"temperature\": 600,\n", - " \"pressure\": 350000,\n", - " }\n", - ")\n", - "\n", - "solver = get_solver()\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from pyomo.environ import TerminationCondition, SolverStatus\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal\n", - "assert results.solver.status == SolverStatus.ok" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that our model has solved, let us use the `report()` method for the CSRT to have a look at what happened in the reactor. We should see that the outlet mole fraction of benzene is now around 0.160 and the mole fraction of diphenyl is 0.014; thus, our reactor has successfully generated benzene from toluene. In the process, the reaction has also generated a lot of heat, which has raised the temperature of the gas from 500 K to 790.2 K." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.reactor.report()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import value\n", - "\n", - "assert value(m.fs.reactor.outlet.flow_mol[0]) == pytest.approx(100, abs=1e-3)\n", - "assert value(m.fs.reactor.outlet.temperature[0]) == pytest.approx(790.212, abs=1e-3)\n", - "assert value(m.fs.reactor.outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", - " 0.159626, abs=1e-6\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, as a quick check of model consistency, let us assert that the units of measurement in our model are consistent (the units of the rate constant and pre-exponential factor are rather complex)." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "assert_units_consistent(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Concluding Remarks\n", - "\n", - "The above example has hopefully introduced you to the basic requirements for creating your own custom reaction property packages. However, it is probably clear that it requires a significant amount of effort to write your own property packages, thus users are encouraged to look into the IDAES Modular Reactions Framework if they are not already familiar with this.\n", - "\n", - "The IDAES Modular Reactions Framework is designed to automatically generate user-defined reaction property packages for common reaction forms based on a single configuration file. Users provide a list of reactions of interest (both rate- and equilibrium-based), and select from a library of common reaction forms, and the Modular Reaction Framework then does the hard work of assembling the necessary code to construct the desired model." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reaction Property Packages in IDAES\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "
\n", + "Note:\n", + "Reaction property packages are closely related to, and dependent on, thermophysical property packages and it is advised the readers start with understanding these.\n", + "
\n", + "\n", + "Similar to thermophysical property packages, reaction property packages in IDAES are used to define the set of parameters, variables and constraints associated with a specific set of chemical reactions that a user wishes to model. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property “packages” to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating reaction properties within the IDAES Core Modeling Framework.\n", + "\n", + "## Relationship with Thermophysical Property Packages\n", + "\n", + "Reaction properties depend on the state of the system, such as the temperature, pressure and composition of the material. All of these properties are defined in the thermophysical property package, thus reaction property packages are closely tied to thermophysical property packages; indeed, a given reaction package is often tied to a single specific thermophysical property package. Reaction packages need to be used with a thermophysical property package which defines the expected set of components and the expected forms and units for the state variables.\n", + "\n", + "As such, developers of reaction packages should have a specific thermophysical property package in mind when developing a reaction property package, and to tailor the reaction package to the thermophysical property package.\n", + "\n", + "## Types of Reactions\n", + "\n", + "Within the IDAES Core Modeling Framework, chemical reactions are divided into two categories:\n", + "\n", + "1. Equilibrium based reactions, where extent of reaction is determined by satisfying a constraint relating the concentration of species within the system, and\n", + "2. Rate based reactions, where extent of reaction depends on some characteristic of the reactor unit. Despite the name, this category is also used to represent stoichiometric and yield based reactions.\n", + "\n", + "## Steps in Creating a Reaction Property Package\n", + "\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **equilibrium reactions** of interest.\n", + "4. Defining the **equilibrium reactions** of interest.\n", + "5. Defining the **parameters** related to the reactions of interest.\n", + "6. Creating **variables and constraints** to describe the reactions of interest.\n", + "7. Creating an **initialization routine** for the reaction property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial Example\n", + "\n", + "For this tutorial, we will be building a upon the property package from the thermophysical property example. In that example, we constructed a thermophysical property package that could be used to model a process for the hydrodealkylation of toluene to form benzene. This process involves five key chemical species:\n", + "\n", + "* toluene\n", + "* benzene\n", + "* hydrogen\n", + "* methane\n", + "* diphenyl\n", + "\n", + "In this tutorial, we will write a reaction property package to define the reactions associated with the HDA process:\n", + "\n", + "$$\n", + "\\text{Toluene} + \\text{Hydrogen} \\rightarrow \\text{Benzene} + \\text{Methane}\n", + "$$\n", + "$$\n", + "2 \\text{Benzene} \\rightleftharpoons \\text{Hydrogen} + \\text{Diphenyl}\n", + "$$\n", + "\n", + "## A Note on this Tutorial\n", + "\n", + "The `build` methods in the reaction property package classes are generally written as a single, long method. However, to break the code into manageable pieces for discussion, in this tutorial we will create a number of smaller sub-methods that will then be called as part of the `build` method. This is done entirely for presentation purposes, and model developers should not feel compelled to write their models this way." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "An example of how the example in this tutorial would be written without sub-methods can be found in the module `idaes_examples.mod.properties.reaction_property_example`. To locate this file on your system, you can use the following code snippet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from idaes_examples.mod.properties import reaction_property_example as example\n", + "import inspect\n", + "\n", + "print(inspect.getabsfile(example))\n", + "# To print the file contents, uncomment the following line\n", + "# print(''.join(inspect.getsourcelines(example)[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Components of a Reaction Property Package\n", + "\n", + "Similar to thermophysical property packages, reaction property packages consist of three parts, which are written as Python `classes`. These components are:\n", + "\n", + "* The `Reaction Parameter Block` class, which contains all the global parameters associated with the reaction property package,\n", + "* The `Reaction Block Data` class, which contains the instructions on how to calculate all the properties at a given state, and,\n", + "* The `Reaction Block` class, which is used to construct indexed sets of `Reaction Block Data` objects and contains methods for acting on all multiple `Reaction Block Data` objects at once (such as initialization).\n", + "\n", + "It is not necessary to understand the reason for the distinction between the `Reaction Block` and `Reaction Block Data` classes. Suffice to say that this is due to the need to replicate the underlying component structure of Pyomo, and that the `Reaction Block` represents the indexed `Block` representing a set of states across a given indexing set (most often time), and the `Reaction Block Data` represents the individual elements of the indexed `Block`.\n", + "\n", + "## Importing Libraries\n", + "\n", + "Before we begin writing the actual `classes` however, we need to import all the necessary components from the Pyomo and IDAES modeling libraries. To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries.\n", + "\n", + "Rather than describe the purpose of all of these here, we shall just import all of them here and discuss their use as they arise in the example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, exp, Param, Set, units as pyunits, Var\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " MaterialFlowBasis,\n", + " ReactionParameterBlock,\n", + " ReactionBlockDataBase,\n", + " ReactionBlockBase,\n", + ")\n", + "from idaes.core.util.constants import Constants as const\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Reaction Parameter Block\n", + "\n", + "We will begin by constructing the `Reaction Parameter Block` for our example. This serves as the central point of reference for all aspects of the reaction property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What reaction properties are supported and how they are implemented\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated `Reaction Block` class, so that construction of the `Reaction Block` components can be automated from the `Reaction Parameter Block`\n", + "\n", + "## Step 1: Define Units of Measurement and Property Metadata\n", + "\n", + "The first step is to define the units of measurement for the property package, which will in turn be inherited by any unit model using this property package. The IDAES Core Modeling Framework requires that the units of measurement defined for a reaction property package be identical to those used in the thermophysical property package it is associated with (this is to avoid any chance of confusion regarding units when setting up the balance equations).\n", + "\n", + "In order to set the base units, we use the same approach as for thermophysical property packages; we create a dictionary which has each of the base quantities as a key, and provide a Pyomo recognized unit as the value as shown in the cell below.\n", + "\n", + "Much like thermophysical property packages, we also need to define metadata regarding the reaction properties supported by our package. For this example, we have three supported properties:\n", + "\n", + "* a rate constant (`k_rxn`),\n", + "* an equilibrium constant (`k_eq`), and\n", + "* a reaction rate term (`rate_reaction`).\n", + "\n", + "The cell below shows how to define the units of measurement and properties metadata for this example." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "units_metadata = {\n", + " \"time\": pyunits.s,\n", + " \"length\": pyunits.m,\n", + " \"mass\": pyunits.kg,\n", + " \"amount\": pyunits.mol,\n", + " \"temperature\": pyunits.K,\n", + "}\n", + "\n", + "properties_metadata = {\n", + " \"k_rxn\": {\"method\": None},\n", + " \"k_eq\": {\"method\": None},\n", + " \"reaction_rate\": {\"method\": None},\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to define the rate-based reactions of interest and the associated stoichiometry. For this, we need to define two things:\n", + "\n", + "* a `Set` of names for the rate-based reaction, and\n", + "* a `dict` containing the stoichiometric coefficients for all the rate-based reactions.\n", + "\n", + "In this example, we have only one rate-based reaction, which is the conversion of toluene to benzene; we will call this reaction `R1`. Thus, we create a Pyomo `Set` component and initialize it with the list of rate-based reactions (`[“R1”]`) as shown in the following cell.\n", + "\n", + "Next, we create a `dict` object for the stoichiometric coefficients. This `dict` needs to provide coefficients for all combinations of reaction, phase and component present in the system, even for those components which do not take part in a reaction. This is required as `ControlVolumes` create generation terms for all reaction-phase-component combinations, and need a stoichiometric coefficient for each of these. In this `dict`, the keys need to have the form of a tuple with three parts:\n", + "\n", + "1. the reaction name,\n", + "2. the phase name, and\n", + "3. the component name,\n", + "\n", + "whilst the value is the stoichiometric coefficient for that key combination. See the example in the cell below; in this example we have 1 reaction (`R1`), 1 phase (`Vap`, as defined in the thermophysical property package) and 5 components (`benzene`, `toluene`, `hydrogen`, `methane` and `diphenyl`), thus the resulting dict has `1x1x5` entries." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def define_kinetic_reactions(self):\n", + " # Rate Reaction Index\n", + " self.rate_reaction_idx = Set(initialize=[\"R1\"])\n", + "\n", + " # Rate Reaction Stoichiometry\n", + " self.rate_reaction_stoichiometry = {\n", + " (\"R1\", \"Vap\", \"benzene\"): 1,\n", + " (\"R1\", \"Vap\", \"toluene\"): -1,\n", + " (\"R1\", \"Vap\", \"hydrogen\"): -1,\n", + " (\"R1\", \"Vap\", \"methane\"): 1,\n", + " (\"R1\", \"Vap\", \"diphenyl\"): 0,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to do the same thing for the equilibrium-based reactions. The format is the same as for rate-based reactions, as shown in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def define_equilibrium_reactions(self):\n", + " # Equilibrium Reaction Index\n", + " self.equilibrium_reaction_idx = Set(initialize=[\"E1\"])\n", + "\n", + " # Equilibrium Reaction Stoichiometry\n", + " self.equilibrium_reaction_stoichiometry = {\n", + " (\"E1\", \"Vap\", \"benzene\"): -2,\n", + " (\"E1\", \"Vap\", \"toluene\"): 0,\n", + " (\"E1\", \"Vap\", \"hydrogen\"): 1,\n", + " (\"E1\", \"Vap\", \"methane\"): 0,\n", + " (\"E1\", \"Vap\", \"diphenyl\"): 1,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to define any global parameters related to the reactions of interest. For this example, we will assume that the rate-based reactions follow the Arrhenius equation, thus we need to declare a pre-exponential factor ($A=1.25\\times10^{-9} \\text{ mol}/\\text{m}^3/\\text{s}/\\text{Pa}^2$) and an activation energy parameter ($E_a=3800 \\text{ J}/\\text{mol}$). We will not declare any parameters for the equilibrium-based reactions at this point; this will be done in the individual `ReactionBlocks`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def define_parameters(self):\n", + " # Arrhenius Constant\n", + " self.arrhenius = Param(\n", + " default=1.25e-9,\n", + " doc=\"Arrhenius constant\",\n", + " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", + " )\n", + "\n", + " # Activation Energy\n", + " self.energy_activation = Param(\n", + " default=3800, doc=\"Activation energy\", units=pyunits.J / pyunits.mol\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Declaring the Reaction Parameter Block\n", + "\n", + "Now that the various parts of the Reaction Parameter Block have been declared, we can assemble the actual `class` that will assemble these components in a flowsheet. The steps for declaring a new `ReactionParameterBlock` class which are:\n", + "\n", + "1. Declaring the new class and inheriting from the `ReactionParameterBlock` base class\n", + "3. Writing the `build` method for our `class`\n", + "4. Creating a `define_metadata` method for the class.\n", + "\n", + "Each of these steps are shown in the code example below.\n", + "\n", + "First, we need to declare our new class and give it a unique name. In this example, we will call our new class `HDAReactionParameterBlock`. The first two lines of the example below show how we declare our new class using the `declare_process_block_decorator` and inheriting from the `ReactionParameterBlock` base class from the IDAES Core model libraries. Inheriting from the `ReactionParameterBlock` brings us access to all the necessary features required by the IDAES modeling framework, whilst the `declare_process_block_class` decorator performs some boilerplate operations to replicate the expected object structure of Pyomo. Further details on these components can be found in the IDAES documentation.\n", + "\n", + "Next, we need to declare the `build` method that will be used to construct our Reaction Parameter Block. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from – this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `ReactionParameterBlock` needs to contain a pointer to the related `ReactionBlock` (which we will look at next) – this is used to allow us to build instances of the `ReactionBlock` by only knowing the `ReactionParameterBlock` we wish to use. To do this, we create an attribute named `_reaction_block_class` attached to our class with a pointer to the `ReactionBlock` class; in this case `self._reaction_block_class = HDAReactionBlock`, where `HDAReactionBlock` is the name of the yet to be declared `ReactionBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", + "\n", + "The final step in creating the `ReactionParameterBlock` class is to declare a `classmethod` named `define_metadata` which takes two arguments; a class (`cls`) and an instance of that class (`obj`). This method in turn needs to call two pre-defined methods (inherited from the underlying base classes):\n", + "\n", + "* `obj.add_properties()` is used to set the metadata regarding the supported reaction properties, and here we pass the `properties_metadata` dict we created earlier as an argument.\n", + "* `obj.add_default_units()` sets the default units metadata for the reaction property package, and here we pass the `units_metadata` `dict ` we created earlier as an argument." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"HDAReactionParameterBlock\")\n", + "class HDAReactionParameterData(ReactionParameterBlock):\n", + " \"\"\"\n", + " Reaction Parameter Block Class\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(HDAReactionParameterData, self).build()\n", + "\n", + " self._reaction_block_class = HDAReactionBlock\n", + "\n", + " define_kinetic_reactions(self)\n", + " define_equilibrium_reactions(self)\n", + " define_parameters(self)\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(properties_metadata)\n", + " obj.add_default_units(units_metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reaction Block\n", + "\n", + "After the Reaction Parameter Block class has been created, the next step is to write the code necessary to create the Reaction Blocks that will be used through out the flowsheet. Similar to State Blocks for thermophysical properties, Reaction Blocks also require two Python `classes` to construct (for a discussion on why, see the related example for creating custom thermophysical property packages).\n", + "\n", + "For this example, we will begin by describing the content of the `ReactionBlockData` objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. After that, we will discuss how to create the class that contains methods to be applied to the `IndexedReactionBlock` as a whole.\n", + "\n", + "## State Variables\n", + "\n", + "Like thermophysical property calculations, reaction properties also depend on the material state variables such as temperature and pressure. However, the state variables are declared as a part of the State Block, and it does not make sense to duplicate them here. Due to this, Reaction Blocks are always associated with a State Block representing the material at the same point in space and time, and the Reaction Block contains a pointer to the equivalent State Block. This allows the Reaction Block to access the state variables, and any other thermophysical property the State Block supports, in order to perform reaction property calculations. The State Block can be accessed using the `state_ref` property of the Reaction Block.\n", + "\n", + "\n", + "## Step 1. Define Property Variables\n", + "\n", + "The first thing we need to do when creating our Reaction Block is create Pyomo components to represent the properties of interest. In this example, we have three properties we need to define:\n", + "\n", + "1. the rate constant for the rate-based reaction: `k_rxn`,\n", + "2. a variable for the rate of reaction at the current state, `rate_reaction`, and\n", + "3. the equilibrium constant for the equilibrium-based reaction, `k_eq`.\n", + "\n", + "The declaration of these is shown in the cell below. Note that for this example we are assuming the equilibrium constant does not vary, and have thus declared it as a Pyomo `Param`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def define_variables_and_parameters(self):\n", + " self.k_rxn = Var(\n", + " initialize=7e-10,\n", + " doc=\"Rate constant\",\n", + " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", + " )\n", + "\n", + " self.reaction_rate = Var(\n", + " self.params.rate_reaction_idx,\n", + " initialize=0,\n", + " doc=\"Rate of reaction\",\n", + " units=pyunits.mol / pyunits.m**3 / pyunits.s,\n", + " )\n", + "\n", + " self.k_eq = Param(initialize=10000, doc=\"Equlibrium constant\", units=pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Define Constraints for the Rate-Based Reactions\n", + "\n", + "Next, we need to define the `Constraints` which describe the rate-based reaction. First, we use the Arrhenius equation to calculate the rate constant, $k_{rxn} = A \\times e^{-E_a/(RT)}$. For this calculation, $A$ and $E_a$ come from the associated Reaction Parameter Block (`self.params`), $T$ comes from the associated State Block (`self.state_ref.temperature`) and the gas constant $R$ can be found in the IDAES `Constants` class.\n", + "\n", + "After the rate constant, we need to declare the form of the rate expression as well. In this case, we are dealing with a gas phase reaction so $r = k_{rxn} \\times x_{toluene} \\times x_{hydrogen} \\times P^2$, where $P$ is the system pressure. $x_{toluene}$, $x_{hydrogen}$ and $P$ are all state variables, and can be accessed from the associated State Block.\n", + "\n", + "The cell below shows how we declare these two constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def define_rate_expression(self):\n", + " self.arrhenius_equation = Constraint(\n", + " expr=self.k_rxn\n", + " == self.params.arrhenius\n", + " * exp(\n", + " -self.params.energy_activation\n", + " / (const.gas_constant * self.state_ref.temperature)\n", + " )\n", + " )\n", + "\n", + " def rate_rule(b, r):\n", + " return b.reaction_rate[r] == (\n", + " b.k_rxn\n", + " * b.state_ref.mole_frac_comp[\"toluene\"]\n", + " * b.state_ref.mole_frac_comp[\"hydrogen\"]\n", + " * b.state_ref.pressure**2\n", + " )\n", + "\n", + " self.rate_expression = Constraint(self.params.rate_reaction_idx, rule=rate_rule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Define Constraints for the Equilibrium-Based Reactions\n", + "\n", + "Similar to those for the rate-based reactions, we also need to define constraints for the equilibrium-based reactions in the system. In this case, the constraint will take the form of an equality that will force the compositions in the system to satisfy the given equilibrium constant. For this example, we have the following equilibrium constraint:\n", + "\n", + "$$\n", + "k_{eq} = \\frac{x_{diphenyl} \\times x_{hydrogen} \\times P^2}{x_{benzene} \\times P}\n", + "$$\n", + "\n", + "Note that $P$ appears in both the numerator and denominator to make it clear that this is a ratio of partial pressures, and because we will rearrange this constraint when creating the actual Pyomo component in order to avoid potential singularities. This is shown in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def define_equilibrium_expression(self):\n", + " self.equilibrium_constraint = Constraint(\n", + " expr=self.k_eq\n", + " * self.state_ref.mole_frac_comp[\"benzene\"]\n", + " * self.state_ref.pressure\n", + " == self.state_ref.mole_frac_comp[\"diphenyl\"]\n", + " * self.state_ref.mole_frac_comp[\"hydrogen\"]\n", + " * self.state_ref.pressure**2\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating the Reaction Block class\n", + "\n", + "These are all the variables and constraints that need to be declared for this example. All that remains is to declare the `_ReactionBlock` and `ReactionBlock` classes to complete our reaction property package. This process is much the same as for the thermophysical property package example, and will only be covered briefly here.\n", + "\n", + "## The `_ReactionBlock` class\n", + "\n", + "For this example, the `_ReactionBlock` class is very simple, and contains only a placeholder `initialize` method. As all the state variables are held separately in the associated State Block and the constraints within the reaction package are fairly simple, it is sufficient to not initialize the reaction properties before solving. However, a placeholder method still needs to be created, as the IDAES framework assumes all components will have an `initialize` method; however, this method need only consist of a `pass` statement.\n", + "\n", + "
\n", + "Note:\n", + "In more complex reaction systems, it is likely that a proper initialization routine would need to be implemented. Developers of these should be aware that equilibrium constraints will need to be deactivated during initialization, as the state variables (i.e. compositions) will be fixed in the State Block. Thus, trying to solve for the system with equilibrium constraints present will result in an over-specified problem.\n", + "
\n", + "\n", + "## The `ReactionBlock` class\n", + "\n", + "Once the `_ReactionBlock` class has been declared, the overall `ReactionBlock` class can be declared as shown below. Once again, we define a `build` method which calls the sub-methods we created earlier in order to construct an instance of the Reaction Block. The `ReactionBlock` class also needs to define a `get_reaction_rate_basis` method, which should return an instance of the `MaterialFlowBasis` `Enum`; this is used by the IDAES framework to determine if conversion between mass and mole basis is required." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class _HDAReactionBlock(ReactionBlockBase):\n", + " def initialize(blk, outlvl=idaeslog.NOTSET, **kwargs):\n", + " init_log = idaeslog.getInitLogger(blk.name, outlvl, tag=\"properties\")\n", + " init_log.info(\"Initialization Complete.\")\n", + "\n", + "\n", + "@declare_process_block_class(\"HDAReactionBlock\", block_class=_HDAReactionBlock)\n", + "class HDAReactionBlockData(ReactionBlockDataBase):\n", + " def build(self):\n", + "\n", + " super(HDAReactionBlockData, self).build()\n", + "\n", + " define_variables_and_parameters(self)\n", + " define_rate_expression(self)\n", + " define_equilibrium_expression(self)\n", + "\n", + " def get_reaction_rate_basis(b):\n", + " return MaterialFlowBasis.molar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demonstration\n", + "\n", + "In order to demonstrate our new Reaction Property package in practice, we will now use it to build and solve a CSTR. First, we will need to import some more components from Pyomo and IDAES to use when building the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.models.unit_models import CSTR\n", + "\n", + "from idaes_examples.mod.properties.thermophysical_property_example import (\n", + " HDAParameterBlock,\n", + ")\n", + "\n", + "from idaes.core.util.model_statistics import degrees_of_freedom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we can construct a Pyomo `ConcreteModel` and IDAES `FlowsheetBlock` as usual. We then attach an instance of the associated thermophysical property package (imported as `HDAParameterBlock`) and our new reaction property package to the flowsheet, and then construct a CSTR using these property packages." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "m.fs.thermo_params = HDAParameterBlock()\n", + "m.fs.reaction_params = HDAReactionParameterBlock(property_package=m.fs.thermo_params)\n", + "\n", + "m.fs.reactor = CSTR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If all went well, we should see no errors when constructing the flowsheet above. To be sure, let us print the degrees of freedom in our flowsheet model as shown below; we should see 9 degrees of freedom." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert degrees_of_freedom(m) == 9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 9 degrees of freedom are the flowrate, temperature, pressure and mole fractions (5) of the inlet stream, as well as the reactor volume. We will fix them to some default values as shown below. Once we are done, we will also print the degrees of freedom again to ensure we have fixed enough variables." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.reactor.inlet.flow_mol.fix(100)\n", + "m.fs.reactor.inlet.temperature.fix(500)\n", + "m.fs.reactor.inlet.pressure.fix(350000)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"benzene\"].fix(0.1)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"toluene\"].fix(0.4)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"hydrogen\"].fix(0.4)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"methane\"].fix(0.1)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"diphenyl\"].fix(0.0)\n", + "\n", + "m.fs.reactor.volume.fix(1)\n", + "\n", + "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have defined our example problem, we can initialize and solve the flowsheet. This is done in the cell below, which should result in an optimal solution." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.reactor.initialize(\n", + " state_args={\n", + " \"flow_mol\": 100,\n", + " \"mole_frac_comp\": {\n", + " \"benzene\": 0.15,\n", + " \"toluene\": 0.35,\n", + " \"hydrogen\": 0.35,\n", + " \"methane\": 0.15,\n", + " \"diphenyl\": 0.01,\n", + " },\n", + " \"temperature\": 600,\n", + " \"pressure\": 350000,\n", + " }\n", + ")\n", + "\n", + "solver = get_solver()\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from pyomo.environ import TerminationCondition, SolverStatus\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal\n", + "assert results.solver.status == SolverStatus.ok" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that our model has solved, let us use the `report()` method for the CSRT to have a look at what happened in the reactor. We should see that the outlet mole fraction of benzene is now around 0.160 and the mole fraction of diphenyl is 0.014; thus, our reactor has successfully generated benzene from toluene. In the process, the reaction has also generated a lot of heat, which has raised the temperature of the gas from 500 K to 790.2 K." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.reactor.report()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import value\n", + "\n", + "assert value(m.fs.reactor.outlet.flow_mol[0]) == pytest.approx(100, abs=1e-3)\n", + "assert value(m.fs.reactor.outlet.temperature[0]) == pytest.approx(790.212, abs=1e-3)\n", + "assert value(m.fs.reactor.outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", + " 0.159626, abs=1e-6\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, as a quick check of model consistency, let us assert that the units of measurement in our model are consistent (the units of the rate constant and pre-exponential factor are rather complex)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "assert_units_consistent(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Concluding Remarks\n", + "\n", + "The above example has hopefully introduced you to the basic requirements for creating your own custom reaction property packages. However, it is probably clear that it requires a significant amount of effort to write your own property packages, thus users are encouraged to look into the IDAES Modular Reactions Framework if they are not already familiar with this.\n", + "\n", + "The IDAES Modular Reactions Framework is designed to automatically generate user-defined reaction property packages for common reaction forms based on a single configuration file. Users provide a list of reactions of interest (both rate- and equilibrium-based), and select from a library of common reaction forms, and the Modular Reaction Framework then does the hard work of assembling the necessary code to construct the desired model." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_usr.ipynb b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_usr.ipynb index 00d5caa2..0ea48296 100644 --- a/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_usr.ipynb +++ b/idaes_examples/notebooks/docs/properties/custom/custom_reaction_property_packages_usr.ipynb @@ -1,729 +1,730 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reaction Property Packages in IDAES\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "
\n", - "Note:\n", - "Reaction property packages are closely related to, and dependent on, thermophysical property packages and it is advised the readers start with understanding these.\n", - "
\n", - "\n", - "Similar to thermophysical property packages, reaction property packages in IDAES are used to define the set of parameters, variables and constraints associated with a specific set of chemical reactions that a user wishes to model. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property \u201cpackages\u201d to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating reaction properties within the IDAES Core Modeling Framework.\n", - "\n", - "## Relationship with Thermophysical Property Packages\n", - "\n", - "Reaction properties depend on the state of the system, such as the temperature, pressure and composition of the material. All of these properties are defined in the thermophysical property package, thus reaction property packages are closely tied to thermophysical property packages; indeed, a given reaction package is often tied to a single specific thermophysical property package. Reaction packages need to be used with a thermophysical property package which defines the expected set of components and the expected forms and units for the state variables.\n", - "\n", - "As such, developers of reaction packages should have a specific thermophysical property package in mind when developing a reaction property package, and to tailor the reaction package to the thermophysical property package.\n", - "\n", - "## Types of Reactions\n", - "\n", - "Within the IDAES Core Modeling Framework, chemical reactions are divided into two categories:\n", - "\n", - "1. Equilibrium based reactions, where extent of reaction is determined by satisfying a constraint relating the concentration of species within the system, and\n", - "2. Rate based reactions, where extent of reaction depends on some characteristic of the reactor unit. Despite the name, this category is also used to represent stoichiometric and yield based reactions.\n", - "\n", - "## Steps in Creating a Reaction Property Package\n", - "\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **equilibrium reactions** of interest.\n", - "4. Defining the **equilibrium reactions** of interest.\n", - "5. Defining the **parameters** related to the reactions of interest.\n", - "6. Creating **variables and constraints** to describe the reactions of interest.\n", - "7. Creating an **initialization routine** for the reaction property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial Example\n", - "\n", - "For this tutorial, we will be building a upon the property package from the thermophysical property example. In that example, we constructed a thermophysical property package that could be used to model a process for the hydrodealkylation of toluene to form benzene. This process involves five key chemical species:\n", - "\n", - "* toluene\n", - "* benzene\n", - "* hydrogen\n", - "* methane\n", - "* diphenyl\n", - "\n", - "In this tutorial, we will write a reaction property package to define the reactions associated with the HDA process:\n", - "\n", - "$$\n", - "\\text{Toluene} + \\text{Hydrogen} \\rightarrow \\text{Benzene} + \\text{Methane}\n", - "$$\n", - "$$\n", - "2 \\text{Benzene} \\rightleftharpoons \\text{Hydrogen} + \\text{Diphenyl}\n", - "$$\n", - "\n", - "## A Note on this Tutorial\n", - "\n", - "The `build` methods in the reaction property package classes are generally written as a single, long method. However, to break the code into manageable pieces for discussion, in this tutorial we will create a number of smaller sub-methods that will then be called as part of the `build` method. This is done entirely for presentation purposes, and model developers should not feel compelled to write their models this way." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "An example of how the example in this tutorial would be written without sub-methods can be found in the module `idaes_examples.mod.properties.reaction_property_example`. To locate this file on your system, you can use the following code snippet:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from idaes_examples.mod.properties import reaction_property_example as example\n", - "import inspect\n", - "\n", - "print(inspect.getabsfile(example))\n", - "# To print the file contents, uncomment the following line\n", - "# print(''.join(inspect.getsourcelines(example)[0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Components of a Reaction Property Package\n", - "\n", - "Similar to thermophysical property packages, reaction property packages consist of three parts, which are written as Python `classes`. These components are:\n", - "\n", - "* The `Reaction Parameter Block` class, which contains all the global parameters associated with the reaction property package,\n", - "* The `Reaction Block Data` class, which contains the instructions on how to calculate all the properties at a given state, and,\n", - "* The `Reaction Block` class, which is used to construct indexed sets of `Reaction Block Data` objects and contains methods for acting on all multiple `Reaction Block Data` objects at once (such as initialization).\n", - "\n", - "It is not necessary to understand the reason for the distinction between the `Reaction Block` and `Reaction Block Data` classes. Suffice to say that this is due to the need to replicate the underlying component structure of Pyomo, and that the `Reaction Block` represents the indexed `Block` representing a set of states across a given indexing set (most often time), and the `Reaction Block Data` represents the individual elements of the indexed `Block`.\n", - "\n", - "## Importing Libraries\n", - "\n", - "Before we begin writing the actual `classes` however, we need to import all the necessary components from the Pyomo and IDAES modeling libraries. To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries.\n", - "\n", - "Rather than describe the purpose of all of these here, we shall just import all of them here and discuss their use as they arise in the example." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Pyomo libraries\n", - "from pyomo.environ import Constraint, exp, Param, Set, units as pyunits, Var\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " MaterialFlowBasis,\n", - " ReactionParameterBlock,\n", - " ReactionBlockDataBase,\n", - " ReactionBlockBase,\n", - ")\n", - "from idaes.core.util.constants import Constants as const\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The Reaction Parameter Block\n", - "\n", - "We will begin by constructing the `Reaction Parameter Block` for our example. This serves as the central point of reference for all aspects of the reaction property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What reaction properties are supported and how they are implemented\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated `Reaction Block` class, so that construction of the `Reaction Block` components can be automated from the `Reaction Parameter Block`\n", - "\n", - "## Step 1: Define Units of Measurement and Property Metadata\n", - "\n", - "The first step is to define the units of measurement for the property package, which will in turn be inherited by any unit model using this property package. The IDAES Core Modeling Framework requires that the units of measurement defined for a reaction property package be identical to those used in the thermophysical property package it is associated with (this is to avoid any chance of confusion regarding units when setting up the balance equations).\n", - "\n", - "In order to set the base units, we use the same approach as for thermophysical property packages; we create a dictionary which has each of the base quantities as a key, and provide a Pyomo recognized unit as the value as shown in the cell below.\n", - "\n", - "Much like thermophysical property packages, we also need to define metadata regarding the reaction properties supported by our package. For this example, we have three supported properties:\n", - "\n", - "* a rate constant (`k_rxn`),\n", - "* an equilibrium constant (`k_eq`), and\n", - "* a reaction rate term (`rate_reaction`).\n", - "\n", - "The cell below shows how to define the units of measurement and properties metadata for this example." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "units_metadata = {\n", - " \"time\": pyunits.s,\n", - " \"length\": pyunits.m,\n", - " \"mass\": pyunits.kg,\n", - " \"amount\": pyunits.mol,\n", - " \"temperature\": pyunits.K,\n", - "}\n", - "\n", - "properties_metadata = {\n", - " \"k_rxn\": {\"method\": None},\n", - " \"k_eq\": {\"method\": None},\n", - " \"reaction_rate\": {\"method\": None},\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to define the rate-based reactions of interest and the associated stoichiometry. For this, we need to define two things:\n", - "\n", - "* a `Set` of names for the rate-based reaction, and\n", - "* a `dict` containing the stoichiometric coefficients for all the rate-based reactions.\n", - "\n", - "In this example, we have only one rate-based reaction, which is the conversion of toluene to benzene; we will call this reaction `R1`. Thus, we create a Pyomo `Set` component and initialize it with the list of rate-based reactions (`[\u201cR1\u201d]`) as shown in the following cell.\n", - "\n", - "Next, we create a `dict` object for the stoichiometric coefficients. This `dict` needs to provide coefficients for all combinations of reaction, phase and component present in the system, even for those components which do not take part in a reaction. This is required as `ControlVolumes` create generation terms for all reaction-phase-component combinations, and need a stoichiometric coefficient for each of these. In this `dict`, the keys need to have the form of a tuple with three parts:\n", - "\n", - "1. the reaction name,\n", - "2. the phase name, and\n", - "3. the component name,\n", - "\n", - "whilst the value is the stoichiometric coefficient for that key combination. See the example in the cell below; in this example we have 1 reaction (`R1`), 1 phase (`Vap`, as defined in the thermophysical property package) and 5 components (`benzene`, `toluene`, `hydrogen`, `methane` and `diphenyl`), thus the resulting dict has `1x1x5` entries." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def define_kinetic_reactions(self):\n", - " # Rate Reaction Index\n", - " self.rate_reaction_idx = Set(initialize=[\"R1\"])\n", - "\n", - " # Rate Reaction Stoichiometry\n", - " self.rate_reaction_stoichiometry = {\n", - " (\"R1\", \"Vap\", \"benzene\"): 1,\n", - " (\"R1\", \"Vap\", \"toluene\"): -1,\n", - " (\"R1\", \"Vap\", \"hydrogen\"): -1,\n", - " (\"R1\", \"Vap\", \"methane\"): 1,\n", - " (\"R1\", \"Vap\", \"diphenyl\"): 0,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to do the same thing for the equilibrium-based reactions. The format is the same as for rate-based reactions, as shown in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def define_equilibrium_reactions(self):\n", - " # Equilibrium Reaction Index\n", - " self.equilibrium_reaction_idx = Set(initialize=[\"E1\"])\n", - "\n", - " # Equilibrium Reaction Stoichiometry\n", - " self.equilibrium_reaction_stoichiometry = {\n", - " (\"E1\", \"Vap\", \"benzene\"): -2,\n", - " (\"E1\", \"Vap\", \"toluene\"): 0,\n", - " (\"E1\", \"Vap\", \"hydrogen\"): 1,\n", - " (\"E1\", \"Vap\", \"methane\"): 0,\n", - " (\"E1\", \"Vap\", \"diphenyl\"): 1,\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to define any global parameters related to the reactions of interest. For this example, we will assume that the rate-based reactions follow the Arrhenius equation, thus we need to declare a pre-exponential factor ($A=1.25\\times10^{-9} \\text{ mol}/\\text{m}^3/\\text{s}/\\text{Pa}^2$) and an activation energy parameter ($E_a=3800 \\text{ J}/\\text{mol}$). We will not declare any parameters for the equilibrium-based reactions at this point; this will be done in the individual `ReactionBlocks`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def define_parameters(self):\n", - " # Arrhenius Constant\n", - " self.arrhenius = Param(\n", - " default=1.25e-9,\n", - " doc=\"Arrhenius constant\",\n", - " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", - " )\n", - "\n", - " # Activation Energy\n", - " self.energy_activation = Param(\n", - " default=3800, doc=\"Activation energy\", units=pyunits.J / pyunits.mol\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Declaring the Reaction Parameter Block\n", - "\n", - "Now that the various parts of the Reaction Parameter Block have been declared, we can assemble the actual `class` that will assemble these components in a flowsheet. The steps for declaring a new `ReactionParameterBlock` class which are:\n", - "\n", - "1. Declaring the new class and inheriting from the `ReactionParameterBlock` base class\n", - "3. Writing the `build` method for our `class`\n", - "4. Creating a `define_metadata` method for the class.\n", - "\n", - "Each of these steps are shown in the code example below.\n", - "\n", - "First, we need to declare our new class and give it a unique name. In this example, we will call our new class `HDAReactionParameterBlock`. The first two lines of the example below show how we declare our new class using the `declare_process_block_decorator` and inheriting from the `ReactionParameterBlock` base class from the IDAES Core model libraries. Inheriting from the `ReactionParameterBlock` brings us access to all the necessary features required by the IDAES modeling framework, whilst the `declare_process_block_class` decorator performs some boilerplate operations to replicate the expected object structure of Pyomo. Further details on these components can be found in the IDAES documentation.\n", - "\n", - "Next, we need to declare the `build` method that will be used to construct our Reaction Parameter Block. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from \u2013 this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `ReactionParameterBlock` needs to contain a pointer to the related `ReactionBlock` (which we will look at next) \u2013 this is used to allow us to build instances of the `ReactionBlock` by only knowing the `ReactionParameterBlock` we wish to use. To do this, we create an attribute named `_reaction_block_class` attached to our class with a pointer to the `ReactionBlock` class; in this case `self._reaction_block_class = HDAReactionBlock`, where `HDAReactionBlock` is the name of the yet to be declared `ReactionBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", - "\n", - "The final step in creating the `ReactionParameterBlock` class is to declare a `classmethod` named `define_metadata` which takes two arguments; a class (`cls`) and an instance of that class (`obj`). This method in turn needs to call two pre-defined methods (inherited from the underlying base classes):\n", - "\n", - "* `obj.add_properties()` is used to set the metadata regarding the supported reaction properties, and here we pass the `properties_metadata` dict we created earlier as an argument.\n", - "* `obj.add_default_units()` sets the default units metadata for the reaction property package, and here we pass the `units_metadata` `dict ` we created earlier as an argument." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"HDAReactionParameterBlock\")\n", - "class HDAReactionParameterData(ReactionParameterBlock):\n", - " \"\"\"\n", - " Reaction Parameter Block Class\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(HDAReactionParameterData, self).build()\n", - "\n", - " self._reaction_block_class = HDAReactionBlock\n", - "\n", - " define_kinetic_reactions(self)\n", - " define_equilibrium_reactions(self)\n", - " define_parameters(self)\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(properties_metadata)\n", - " obj.add_default_units(units_metadata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reaction Block\n", - "\n", - "After the Reaction Parameter Block class has been created, the next step is to write the code necessary to create the Reaction Blocks that will be used through out the flowsheet. Similar to State Blocks for thermophysical properties, Reaction Blocks also require two Python `classes` to construct (for a discussion on why, see the related example for creating custom thermophysical property packages).\n", - "\n", - "For this example, we will begin by describing the content of the `ReactionBlockData` objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. After that, we will discuss how to create the class that contains methods to be applied to the `IndexedReactionBlock` as a whole.\n", - "\n", - "## State Variables\n", - "\n", - "Like thermophysical property calculations, reaction properties also depend on the material state variables such as temperature and pressure. However, the state variables are declared as a part of the State Block, and it does not make sense to duplicate them here. Due to this, Reaction Blocks are always associated with a State Block representing the material at the same point in space and time, and the Reaction Block contains a pointer to the equivalent State Block. This allows the Reaction Block to access the state variables, and any other thermophysical property the State Block supports, in order to perform reaction property calculations. The State Block can be accessed using the `state_ref` property of the Reaction Block.\n", - "\n", - "\n", - "## Step 1. Define Property Variables\n", - "\n", - "The first thing we need to do when creating our Reaction Block is create Pyomo components to represent the properties of interest. In this example, we have three properties we need to define:\n", - "\n", - "1. the rate constant for the rate-based reaction: `k_rxn`,\n", - "2. a variable for the rate of reaction at the current state, `rate_reaction`, and\n", - "3. the equilibrium constant for the equilibrium-based reaction, `k_eq`.\n", - "\n", - "The declaration of these is shown in the cell below. Note that for this example we are assuming the equilibrium constant does not vary, and have thus declared it as a Pyomo `Param`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def define_variables_and_parameters(self):\n", - " self.k_rxn = Var(\n", - " initialize=7e-10,\n", - " doc=\"Rate constant\",\n", - " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", - " )\n", - "\n", - " self.reaction_rate = Var(\n", - " self.params.rate_reaction_idx,\n", - " initialize=0,\n", - " doc=\"Rate of reaction\",\n", - " units=pyunits.mol / pyunits.m**3 / pyunits.s,\n", - " )\n", - "\n", - " self.k_eq = Param(initialize=10000, doc=\"Equlibrium constant\", units=pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2. Define Constraints for the Rate-Based Reactions\n", - "\n", - "Next, we need to define the `Constraints` which describe the rate-based reaction. First, we use the Arrhenius equation to calculate the rate constant, $k_{rxn} = A \\times e^{-E_a/(RT)}$. For this calculation, $A$ and $E_a$ come from the associated Reaction Parameter Block (`self.params`), $T$ comes from the associated State Block (`self.state_ref.temperature`) and the gas constant $R$ can be found in the IDAES `Constants` class.\n", - "\n", - "After the rate constant, we need to declare the form of the rate expression as well. In this case, we are dealing with a gas phase reaction so $r = k_{rxn} \\times x_{toluene} \\times x_{hydrogen} \\times P^2$, where $P$ is the system pressure. $x_{toluene}$, $x_{hydrogen}$ and $P$ are all state variables, and can be accessed from the associated State Block.\n", - "\n", - "The cell below shows how we declare these two constraints." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def define_rate_expression(self):\n", - " self.arrhenius_equation = Constraint(\n", - " expr=self.k_rxn\n", - " == self.params.arrhenius\n", - " * exp(\n", - " -self.params.energy_activation\n", - " / (const.gas_constant * self.state_ref.temperature)\n", - " )\n", - " )\n", - "\n", - " def rate_rule(b, r):\n", - " return b.reaction_rate[r] == (\n", - " b.k_rxn\n", - " * b.state_ref.mole_frac_comp[\"toluene\"]\n", - " * b.state_ref.mole_frac_comp[\"hydrogen\"]\n", - " * b.state_ref.pressure**2\n", - " )\n", - "\n", - " self.rate_expression = Constraint(self.params.rate_reaction_idx, rule=rate_rule)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3. Define Constraints for the Equilibrium-Based Reactions\n", - "\n", - "Similar to those for the rate-based reactions, we also need to define constraints for the equilibrium-based reactions in the system. In this case, the constraint will take the form of an equality that will force the compositions in the system to satisfy the given equilibrium constant. For this example, we have the following equilibrium constraint:\n", - "\n", - "$$\n", - "k_{eq} = \\frac{x_{diphenyl} \\times x_{hydrogen} \\times P^2}{x_{benzene} \\times P}\n", - "$$\n", - "\n", - "Note that $P$ appears in both the numerator and denominator to make it clear that this is a ratio of partial pressures, and because we will rearrange this constraint when creating the actual Pyomo component in order to avoid potential singularities. This is shown in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def define_equilibrium_expression(self):\n", - " self.equilibrium_constraint = Constraint(\n", - " expr=self.k_eq\n", - " * self.state_ref.mole_frac_comp[\"benzene\"]\n", - " * self.state_ref.pressure\n", - " == self.state_ref.mole_frac_comp[\"diphenyl\"]\n", - " * self.state_ref.mole_frac_comp[\"hydrogen\"]\n", - " * self.state_ref.pressure**2\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating the Reaction Block class\n", - "\n", - "These are all the variables and constraints that need to be declared for this example. All that remains is to declare the `_ReactionBlock` and `ReactionBlock` classes to complete our reaction property package. This process is much the same as for the thermophysical property package example, and will only be covered briefly here.\n", - "\n", - "## The `_ReactionBlock` class\n", - "\n", - "For this example, the `_ReactionBlock` class is very simple, and contains only a placeholder `initialize` method. As all the state variables are held separately in the associated State Block and the constraints within the reaction package are fairly simple, it is sufficient to not initialize the reaction properties before solving. However, a placeholder method still needs to be created, as the IDAES framework assumes all components will have an `initialize` method; however, this method need only consist of a `pass` statement.\n", - "\n", - "
\n", - "Note:\n", - "In more complex reaction systems, it is likely that a proper initialization routine would need to be implemented. Developers of these should be aware that equilibrium constraints will need to be deactivated during initialization, as the state variables (i.e. compositions) will be fixed in the State Block. Thus, trying to solve for the system with equilibrium constraints present will result in an over-specified problem.\n", - "
\n", - "\n", - "## The `ReactionBlock` class\n", - "\n", - "Once the `_ReactionBlock` class has been declared, the overall `ReactionBlock` class can be declared as shown below. Once again, we define a `build` method which calls the sub-methods we created earlier in order to construct an instance of the Reaction Block. The `ReactionBlock` class also needs to define a `get_reaction_rate_basis` method, which should return an instance of the `MaterialFlowBasis` `Enum`; this is used by the IDAES framework to determine if conversion between mass and mole basis is required." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "class _HDAReactionBlock(ReactionBlockBase):\n", - " def initialize(blk, outlvl=idaeslog.NOTSET, **kwargs):\n", - " init_log = idaeslog.getInitLogger(blk.name, outlvl, tag=\"properties\")\n", - " init_log.info(\"Initialization Complete.\")\n", - "\n", - "\n", - "@declare_process_block_class(\"HDAReactionBlock\", block_class=_HDAReactionBlock)\n", - "class HDAReactionBlockData(ReactionBlockDataBase):\n", - " def build(self):\n", - "\n", - " super(HDAReactionBlockData, self).build()\n", - "\n", - " define_variables_and_parameters(self)\n", - " define_rate_expression(self)\n", - " define_equilibrium_expression(self)\n", - "\n", - " def get_reaction_rate_basis(b):\n", - " return MaterialFlowBasis.molar" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demonstration\n", - "\n", - "In order to demonstrate our new Reaction Property package in practice, we will now use it to build and solve a CSTR. First, we will need to import some more components from Pyomo and IDAES to use when building the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.models.unit_models import CSTR\n", - "\n", - "from idaes_examples.mod.properties.thermophysical_property_example import (\n", - " HDAParameterBlock,\n", - ")\n", - "\n", - "from idaes.core.util.model_statistics import degrees_of_freedom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we can construct a Pyomo `ConcreteModel` and IDAES `FlowsheetBlock` as usual. We then attach an instance of the associated thermophysical property package (imported as `HDAParameterBlock`) and our new reaction property package to the flowsheet, and then construct a CSTR using these property packages." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "m.fs.thermo_params = HDAParameterBlock()\n", - "m.fs.reaction_params = HDAReactionParameterBlock(property_package=m.fs.thermo_params)\n", - "\n", - "m.fs.reactor = CSTR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all went well, we should see no errors when constructing the flowsheet above. To be sure, let us print the degrees of freedom in our flowsheet model as shown below; we should see 9 degrees of freedom." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 9 degrees of freedom are the flowrate, temperature, pressure and mole fractions (5) of the inlet stream, as well as the reactor volume. We will fix them to some default values as shown below. Once we are done, we will also print the degrees of freedom again to ensure we have fixed enough variables." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.reactor.inlet.flow_mol.fix(100)\n", - "m.fs.reactor.inlet.temperature.fix(500)\n", - "m.fs.reactor.inlet.pressure.fix(350000)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"benzene\"].fix(0.1)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"toluene\"].fix(0.4)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"hydrogen\"].fix(0.4)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"methane\"].fix(0.1)\n", - "m.fs.reactor.inlet.mole_frac_comp[0, \"diphenyl\"].fix(0.0)\n", - "\n", - "m.fs.reactor.volume.fix(1)\n", - "\n", - "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have defined our example problem, we can initialize and solve the flowsheet. This is done in the cell below, which should result in an optimal solution." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.reactor.initialize(\n", - " state_args={\n", - " \"flow_mol\": 100,\n", - " \"mole_frac_comp\": {\n", - " \"benzene\": 0.15,\n", - " \"toluene\": 0.35,\n", - " \"hydrogen\": 0.35,\n", - " \"methane\": 0.15,\n", - " \"diphenyl\": 0.01,\n", - " },\n", - " \"temperature\": 600,\n", - " \"pressure\": 350000,\n", - " }\n", - ")\n", - "\n", - "solver = get_solver()\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that our model has solved, let us use the `report()` method for the CSRT to have a look at what happened in the reactor. We should see that the outlet mole fraction of benzene is now around 0.160 and the mole fraction of diphenyl is 0.014; thus, our reactor has successfully generated benzene from toluene. In the process, the reaction has also generated a lot of heat, which has raised the temperature of the gas from 500 K to 790.2 K." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.reactor.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, as a quick check of model consistency, let us assert that the units of measurement in our model are consistent (the units of the rate constant and pre-exponential factor are rather complex)." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "assert_units_consistent(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Concluding Remarks\n", - "\n", - "The above example has hopefully introduced you to the basic requirements for creating your own custom reaction property packages. However, it is probably clear that it requires a significant amount of effort to write your own property packages, thus users are encouraged to look into the IDAES Modular Reactions Framework if they are not already familiar with this.\n", - "\n", - "The IDAES Modular Reactions Framework is designed to automatically generate user-defined reaction property packages for common reaction forms based on a single configuration file. Users provide a list of reactions of interest (both rate- and equilibrium-based), and select from a library of common reaction forms, and the Modular Reaction Framework then does the hard work of assembling the necessary code to construct the desired model." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reaction Property Packages in IDAES\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "
\n", + "Note:\n", + "Reaction property packages are closely related to, and dependent on, thermophysical property packages and it is advised the readers start with understanding these.\n", + "
\n", + "\n", + "Similar to thermophysical property packages, reaction property packages in IDAES are used to define the set of parameters, variables and constraints associated with a specific set of chemical reactions that a user wishes to model. One of the features of the IDAES Integrated Platform is the ability for modelers to create their own property “packages” to calculate these properties, allowing them to customize the level of complexity and rigor to suit each application. This tutorial will introduce you to the basics of creating property packages for calculating reaction properties within the IDAES Core Modeling Framework.\n", + "\n", + "## Relationship with Thermophysical Property Packages\n", + "\n", + "Reaction properties depend on the state of the system, such as the temperature, pressure and composition of the material. All of these properties are defined in the thermophysical property package, thus reaction property packages are closely tied to thermophysical property packages; indeed, a given reaction package is often tied to a single specific thermophysical property package. Reaction packages need to be used with a thermophysical property package which defines the expected set of components and the expected forms and units for the state variables.\n", + "\n", + "As such, developers of reaction packages should have a specific thermophysical property package in mind when developing a reaction property package, and to tailor the reaction package to the thermophysical property package.\n", + "\n", + "## Types of Reactions\n", + "\n", + "Within the IDAES Core Modeling Framework, chemical reactions are divided into two categories:\n", + "\n", + "1. Equilibrium based reactions, where extent of reaction is determined by satisfying a constraint relating the concentration of species within the system, and\n", + "2. Rate based reactions, where extent of reaction depends on some characteristic of the reactor unit. Despite the name, this category is also used to represent stoichiometric and yield based reactions.\n", + "\n", + "## Steps in Creating a Reaction Property Package\n", + "\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **equilibrium reactions** of interest.\n", + "4. Defining the **equilibrium reactions** of interest.\n", + "5. Defining the **parameters** related to the reactions of interest.\n", + "6. Creating **variables and constraints** to describe the reactions of interest.\n", + "7. Creating an **initialization routine** for the reaction property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial Example\n", + "\n", + "For this tutorial, we will be building a upon the property package from the thermophysical property example. In that example, we constructed a thermophysical property package that could be used to model a process for the hydrodealkylation of toluene to form benzene. This process involves five key chemical species:\n", + "\n", + "* toluene\n", + "* benzene\n", + "* hydrogen\n", + "* methane\n", + "* diphenyl\n", + "\n", + "In this tutorial, we will write a reaction property package to define the reactions associated with the HDA process:\n", + "\n", + "$$\n", + "\\text{Toluene} + \\text{Hydrogen} \\rightarrow \\text{Benzene} + \\text{Methane}\n", + "$$\n", + "$$\n", + "2 \\text{Benzene} \\rightleftharpoons \\text{Hydrogen} + \\text{Diphenyl}\n", + "$$\n", + "\n", + "## A Note on this Tutorial\n", + "\n", + "The `build` methods in the reaction property package classes are generally written as a single, long method. However, to break the code into manageable pieces for discussion, in this tutorial we will create a number of smaller sub-methods that will then be called as part of the `build` method. This is done entirely for presentation purposes, and model developers should not feel compelled to write their models this way." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "An example of how the example in this tutorial would be written without sub-methods can be found in the module `idaes_examples.mod.properties.reaction_property_example`. To locate this file on your system, you can use the following code snippet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from idaes_examples.mod.properties import reaction_property_example as example\n", + "import inspect\n", + "\n", + "print(inspect.getabsfile(example))\n", + "# To print the file contents, uncomment the following line\n", + "# print(''.join(inspect.getsourcelines(example)[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Components of a Reaction Property Package\n", + "\n", + "Similar to thermophysical property packages, reaction property packages consist of three parts, which are written as Python `classes`. These components are:\n", + "\n", + "* The `Reaction Parameter Block` class, which contains all the global parameters associated with the reaction property package,\n", + "* The `Reaction Block Data` class, which contains the instructions on how to calculate all the properties at a given state, and,\n", + "* The `Reaction Block` class, which is used to construct indexed sets of `Reaction Block Data` objects and contains methods for acting on all multiple `Reaction Block Data` objects at once (such as initialization).\n", + "\n", + "It is not necessary to understand the reason for the distinction between the `Reaction Block` and `Reaction Block Data` classes. Suffice to say that this is due to the need to replicate the underlying component structure of Pyomo, and that the `Reaction Block` represents the indexed `Block` representing a set of states across a given indexing set (most often time), and the `Reaction Block Data` represents the individual elements of the indexed `Block`.\n", + "\n", + "## Importing Libraries\n", + "\n", + "Before we begin writing the actual `classes` however, we need to import all the necessary components from the Pyomo and IDAES modeling libraries. To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries.\n", + "\n", + "Rather than describe the purpose of all of these here, we shall just import all of them here and discuss their use as they arise in the example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, exp, Param, Set, units as pyunits, Var\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " MaterialFlowBasis,\n", + " ReactionParameterBlock,\n", + " ReactionBlockDataBase,\n", + " ReactionBlockBase,\n", + ")\n", + "from idaes.core.util.constants import Constants as const\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Reaction Parameter Block\n", + "\n", + "We will begin by constructing the `Reaction Parameter Block` for our example. This serves as the central point of reference for all aspects of the reaction property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What reaction properties are supported and how they are implemented\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated `Reaction Block` class, so that construction of the `Reaction Block` components can be automated from the `Reaction Parameter Block`\n", + "\n", + "## Step 1: Define Units of Measurement and Property Metadata\n", + "\n", + "The first step is to define the units of measurement for the property package, which will in turn be inherited by any unit model using this property package. The IDAES Core Modeling Framework requires that the units of measurement defined for a reaction property package be identical to those used in the thermophysical property package it is associated with (this is to avoid any chance of confusion regarding units when setting up the balance equations).\n", + "\n", + "In order to set the base units, we use the same approach as for thermophysical property packages; we create a dictionary which has each of the base quantities as a key, and provide a Pyomo recognized unit as the value as shown in the cell below.\n", + "\n", + "Much like thermophysical property packages, we also need to define metadata regarding the reaction properties supported by our package. For this example, we have three supported properties:\n", + "\n", + "* a rate constant (`k_rxn`),\n", + "* an equilibrium constant (`k_eq`), and\n", + "* a reaction rate term (`rate_reaction`).\n", + "\n", + "The cell below shows how to define the units of measurement and properties metadata for this example." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "units_metadata = {\n", + " \"time\": pyunits.s,\n", + " \"length\": pyunits.m,\n", + " \"mass\": pyunits.kg,\n", + " \"amount\": pyunits.mol,\n", + " \"temperature\": pyunits.K,\n", + "}\n", + "\n", + "properties_metadata = {\n", + " \"k_rxn\": {\"method\": None},\n", + " \"k_eq\": {\"method\": None},\n", + " \"reaction_rate\": {\"method\": None},\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to define the rate-based reactions of interest and the associated stoichiometry. For this, we need to define two things:\n", + "\n", + "* a `Set` of names for the rate-based reaction, and\n", + "* a `dict` containing the stoichiometric coefficients for all the rate-based reactions.\n", + "\n", + "In this example, we have only one rate-based reaction, which is the conversion of toluene to benzene; we will call this reaction `R1`. Thus, we create a Pyomo `Set` component and initialize it with the list of rate-based reactions (`[“R1”]`) as shown in the following cell.\n", + "\n", + "Next, we create a `dict` object for the stoichiometric coefficients. This `dict` needs to provide coefficients for all combinations of reaction, phase and component present in the system, even for those components which do not take part in a reaction. This is required as `ControlVolumes` create generation terms for all reaction-phase-component combinations, and need a stoichiometric coefficient for each of these. In this `dict`, the keys need to have the form of a tuple with three parts:\n", + "\n", + "1. the reaction name,\n", + "2. the phase name, and\n", + "3. the component name,\n", + "\n", + "whilst the value is the stoichiometric coefficient for that key combination. See the example in the cell below; in this example we have 1 reaction (`R1`), 1 phase (`Vap`, as defined in the thermophysical property package) and 5 components (`benzene`, `toluene`, `hydrogen`, `methane` and `diphenyl`), thus the resulting dict has `1x1x5` entries." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def define_kinetic_reactions(self):\n", + " # Rate Reaction Index\n", + " self.rate_reaction_idx = Set(initialize=[\"R1\"])\n", + "\n", + " # Rate Reaction Stoichiometry\n", + " self.rate_reaction_stoichiometry = {\n", + " (\"R1\", \"Vap\", \"benzene\"): 1,\n", + " (\"R1\", \"Vap\", \"toluene\"): -1,\n", + " (\"R1\", \"Vap\", \"hydrogen\"): -1,\n", + " (\"R1\", \"Vap\", \"methane\"): 1,\n", + " (\"R1\", \"Vap\", \"diphenyl\"): 0,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to do the same thing for the equilibrium-based reactions. The format is the same as for rate-based reactions, as shown in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def define_equilibrium_reactions(self):\n", + " # Equilibrium Reaction Index\n", + " self.equilibrium_reaction_idx = Set(initialize=[\"E1\"])\n", + "\n", + " # Equilibrium Reaction Stoichiometry\n", + " self.equilibrium_reaction_stoichiometry = {\n", + " (\"E1\", \"Vap\", \"benzene\"): -2,\n", + " (\"E1\", \"Vap\", \"toluene\"): 0,\n", + " (\"E1\", \"Vap\", \"hydrogen\"): 1,\n", + " (\"E1\", \"Vap\", \"methane\"): 0,\n", + " (\"E1\", \"Vap\", \"diphenyl\"): 1,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to define any global parameters related to the reactions of interest. For this example, we will assume that the rate-based reactions follow the Arrhenius equation, thus we need to declare a pre-exponential factor ($A=1.25\\times10^{-9} \\text{ mol}/\\text{m}^3/\\text{s}/\\text{Pa}^2$) and an activation energy parameter ($E_a=3800 \\text{ J}/\\text{mol}$). We will not declare any parameters for the equilibrium-based reactions at this point; this will be done in the individual `ReactionBlocks`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def define_parameters(self):\n", + " # Arrhenius Constant\n", + " self.arrhenius = Param(\n", + " default=1.25e-9,\n", + " doc=\"Arrhenius constant\",\n", + " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", + " )\n", + "\n", + " # Activation Energy\n", + " self.energy_activation = Param(\n", + " default=3800, doc=\"Activation energy\", units=pyunits.J / pyunits.mol\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Declaring the Reaction Parameter Block\n", + "\n", + "Now that the various parts of the Reaction Parameter Block have been declared, we can assemble the actual `class` that will assemble these components in a flowsheet. The steps for declaring a new `ReactionParameterBlock` class which are:\n", + "\n", + "1. Declaring the new class and inheriting from the `ReactionParameterBlock` base class\n", + "3. Writing the `build` method for our `class`\n", + "4. Creating a `define_metadata` method for the class.\n", + "\n", + "Each of these steps are shown in the code example below.\n", + "\n", + "First, we need to declare our new class and give it a unique name. In this example, we will call our new class `HDAReactionParameterBlock`. The first two lines of the example below show how we declare our new class using the `declare_process_block_decorator` and inheriting from the `ReactionParameterBlock` base class from the IDAES Core model libraries. Inheriting from the `ReactionParameterBlock` brings us access to all the necessary features required by the IDAES modeling framework, whilst the `declare_process_block_class` decorator performs some boilerplate operations to replicate the expected object structure of Pyomo. Further details on these components can be found in the IDAES documentation.\n", + "\n", + "Next, we need to declare the `build` method that will be used to construct our Reaction Parameter Block. The first step in any `build` method is to call `super().build()`, which will trigger the `build` method of the base class that the current class inherits from – this is important since this is how we automate construction of any underlying components required by the modeling framework and ensure that everything integrates smoothly. Next, a `ReactionParameterBlock` needs to contain a pointer to the related `ReactionBlock` (which we will look at next) – this is used to allow us to build instances of the `ReactionBlock` by only knowing the `ReactionParameterBlock` we wish to use. To do this, we create an attribute named `_reaction_block_class` attached to our class with a pointer to the `ReactionBlock` class; in this case `self._reaction_block_class = HDAReactionBlock`, where `HDAReactionBlock` is the name of the yet to be declared `ReactionBlock`. Finally, the `build` method needs to construct the actual parameters required for the property package, which we do here by calling the sub-methods written previously.\n", + "\n", + "The final step in creating the `ReactionParameterBlock` class is to declare a `classmethod` named `define_metadata` which takes two arguments; a class (`cls`) and an instance of that class (`obj`). This method in turn needs to call two pre-defined methods (inherited from the underlying base classes):\n", + "\n", + "* `obj.add_properties()` is used to set the metadata regarding the supported reaction properties, and here we pass the `properties_metadata` dict we created earlier as an argument.\n", + "* `obj.add_default_units()` sets the default units metadata for the reaction property package, and here we pass the `units_metadata` `dict ` we created earlier as an argument." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"HDAReactionParameterBlock\")\n", + "class HDAReactionParameterData(ReactionParameterBlock):\n", + " \"\"\"\n", + " Reaction Parameter Block Class\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(HDAReactionParameterData, self).build()\n", + "\n", + " self._reaction_block_class = HDAReactionBlock\n", + "\n", + " define_kinetic_reactions(self)\n", + " define_equilibrium_reactions(self)\n", + " define_parameters(self)\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(properties_metadata)\n", + " obj.add_default_units(units_metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reaction Block\n", + "\n", + "After the Reaction Parameter Block class has been created, the next step is to write the code necessary to create the Reaction Blocks that will be used through out the flowsheet. Similar to State Blocks for thermophysical properties, Reaction Blocks also require two Python `classes` to construct (for a discussion on why, see the related example for creating custom thermophysical property packages).\n", + "\n", + "For this example, we will begin by describing the content of the `ReactionBlockData` objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. After that, we will discuss how to create the class that contains methods to be applied to the `IndexedReactionBlock` as a whole.\n", + "\n", + "## State Variables\n", + "\n", + "Like thermophysical property calculations, reaction properties also depend on the material state variables such as temperature and pressure. However, the state variables are declared as a part of the State Block, and it does not make sense to duplicate them here. Due to this, Reaction Blocks are always associated with a State Block representing the material at the same point in space and time, and the Reaction Block contains a pointer to the equivalent State Block. This allows the Reaction Block to access the state variables, and any other thermophysical property the State Block supports, in order to perform reaction property calculations. The State Block can be accessed using the `state_ref` property of the Reaction Block.\n", + "\n", + "\n", + "## Step 1. Define Property Variables\n", + "\n", + "The first thing we need to do when creating our Reaction Block is create Pyomo components to represent the properties of interest. In this example, we have three properties we need to define:\n", + "\n", + "1. the rate constant for the rate-based reaction: `k_rxn`,\n", + "2. a variable for the rate of reaction at the current state, `rate_reaction`, and\n", + "3. the equilibrium constant for the equilibrium-based reaction, `k_eq`.\n", + "\n", + "The declaration of these is shown in the cell below. Note that for this example we are assuming the equilibrium constant does not vary, and have thus declared it as a Pyomo `Param`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def define_variables_and_parameters(self):\n", + " self.k_rxn = Var(\n", + " initialize=7e-10,\n", + " doc=\"Rate constant\",\n", + " units=pyunits.mol / pyunits.m**3 / pyunits.s / pyunits.Pa**2,\n", + " )\n", + "\n", + " self.reaction_rate = Var(\n", + " self.params.rate_reaction_idx,\n", + " initialize=0,\n", + " doc=\"Rate of reaction\",\n", + " units=pyunits.mol / pyunits.m**3 / pyunits.s,\n", + " )\n", + "\n", + " self.k_eq = Param(initialize=10000, doc=\"Equlibrium constant\", units=pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2. Define Constraints for the Rate-Based Reactions\n", + "\n", + "Next, we need to define the `Constraints` which describe the rate-based reaction. First, we use the Arrhenius equation to calculate the rate constant, $k_{rxn} = A \\times e^{-E_a/(RT)}$. For this calculation, $A$ and $E_a$ come from the associated Reaction Parameter Block (`self.params`), $T$ comes from the associated State Block (`self.state_ref.temperature`) and the gas constant $R$ can be found in the IDAES `Constants` class.\n", + "\n", + "After the rate constant, we need to declare the form of the rate expression as well. In this case, we are dealing with a gas phase reaction so $r = k_{rxn} \\times x_{toluene} \\times x_{hydrogen} \\times P^2$, where $P$ is the system pressure. $x_{toluene}$, $x_{hydrogen}$ and $P$ are all state variables, and can be accessed from the associated State Block.\n", + "\n", + "The cell below shows how we declare these two constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def define_rate_expression(self):\n", + " self.arrhenius_equation = Constraint(\n", + " expr=self.k_rxn\n", + " == self.params.arrhenius\n", + " * exp(\n", + " -self.params.energy_activation\n", + " / (const.gas_constant * self.state_ref.temperature)\n", + " )\n", + " )\n", + "\n", + " def rate_rule(b, r):\n", + " return b.reaction_rate[r] == (\n", + " b.k_rxn\n", + " * b.state_ref.mole_frac_comp[\"toluene\"]\n", + " * b.state_ref.mole_frac_comp[\"hydrogen\"]\n", + " * b.state_ref.pressure**2\n", + " )\n", + "\n", + " self.rate_expression = Constraint(self.params.rate_reaction_idx, rule=rate_rule)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3. Define Constraints for the Equilibrium-Based Reactions\n", + "\n", + "Similar to those for the rate-based reactions, we also need to define constraints for the equilibrium-based reactions in the system. In this case, the constraint will take the form of an equality that will force the compositions in the system to satisfy the given equilibrium constant. For this example, we have the following equilibrium constraint:\n", + "\n", + "$$\n", + "k_{eq} = \\frac{x_{diphenyl} \\times x_{hydrogen} \\times P^2}{x_{benzene} \\times P}\n", + "$$\n", + "\n", + "Note that $P$ appears in both the numerator and denominator to make it clear that this is a ratio of partial pressures, and because we will rearrange this constraint when creating the actual Pyomo component in order to avoid potential singularities. This is shown in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def define_equilibrium_expression(self):\n", + " self.equilibrium_constraint = Constraint(\n", + " expr=self.k_eq\n", + " * self.state_ref.mole_frac_comp[\"benzene\"]\n", + " * self.state_ref.pressure\n", + " == self.state_ref.mole_frac_comp[\"diphenyl\"]\n", + " * self.state_ref.mole_frac_comp[\"hydrogen\"]\n", + " * self.state_ref.pressure**2\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating the Reaction Block class\n", + "\n", + "These are all the variables and constraints that need to be declared for this example. All that remains is to declare the `_ReactionBlock` and `ReactionBlock` classes to complete our reaction property package. This process is much the same as for the thermophysical property package example, and will only be covered briefly here.\n", + "\n", + "## The `_ReactionBlock` class\n", + "\n", + "For this example, the `_ReactionBlock` class is very simple, and contains only a placeholder `initialize` method. As all the state variables are held separately in the associated State Block and the constraints within the reaction package are fairly simple, it is sufficient to not initialize the reaction properties before solving. However, a placeholder method still needs to be created, as the IDAES framework assumes all components will have an `initialize` method; however, this method need only consist of a `pass` statement.\n", + "\n", + "
\n", + "Note:\n", + "In more complex reaction systems, it is likely that a proper initialization routine would need to be implemented. Developers of these should be aware that equilibrium constraints will need to be deactivated during initialization, as the state variables (i.e. compositions) will be fixed in the State Block. Thus, trying to solve for the system with equilibrium constraints present will result in an over-specified problem.\n", + "
\n", + "\n", + "## The `ReactionBlock` class\n", + "\n", + "Once the `_ReactionBlock` class has been declared, the overall `ReactionBlock` class can be declared as shown below. Once again, we define a `build` method which calls the sub-methods we created earlier in order to construct an instance of the Reaction Block. The `ReactionBlock` class also needs to define a `get_reaction_rate_basis` method, which should return an instance of the `MaterialFlowBasis` `Enum`; this is used by the IDAES framework to determine if conversion between mass and mole basis is required." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class _HDAReactionBlock(ReactionBlockBase):\n", + " def initialize(blk, outlvl=idaeslog.NOTSET, **kwargs):\n", + " init_log = idaeslog.getInitLogger(blk.name, outlvl, tag=\"properties\")\n", + " init_log.info(\"Initialization Complete.\")\n", + "\n", + "\n", + "@declare_process_block_class(\"HDAReactionBlock\", block_class=_HDAReactionBlock)\n", + "class HDAReactionBlockData(ReactionBlockDataBase):\n", + " def build(self):\n", + "\n", + " super(HDAReactionBlockData, self).build()\n", + "\n", + " define_variables_and_parameters(self)\n", + " define_rate_expression(self)\n", + " define_equilibrium_expression(self)\n", + "\n", + " def get_reaction_rate_basis(b):\n", + " return MaterialFlowBasis.molar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demonstration\n", + "\n", + "In order to demonstrate our new Reaction Property package in practice, we will now use it to build and solve a CSTR. First, we will need to import some more components from Pyomo and IDAES to use when building the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.models.unit_models import CSTR\n", + "\n", + "from idaes_examples.mod.properties.thermophysical_property_example import (\n", + " HDAParameterBlock,\n", + ")\n", + "\n", + "from idaes.core.util.model_statistics import degrees_of_freedom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we can construct a Pyomo `ConcreteModel` and IDAES `FlowsheetBlock` as usual. We then attach an instance of the associated thermophysical property package (imported as `HDAParameterBlock`) and our new reaction property package to the flowsheet, and then construct a CSTR using these property packages." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "m.fs.thermo_params = HDAParameterBlock()\n", + "m.fs.reaction_params = HDAReactionParameterBlock(property_package=m.fs.thermo_params)\n", + "\n", + "m.fs.reactor = CSTR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If all went well, we should see no errors when constructing the flowsheet above. To be sure, let us print the degrees of freedom in our flowsheet model as shown below; we should see 9 degrees of freedom." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 9 degrees of freedom are the flowrate, temperature, pressure and mole fractions (5) of the inlet stream, as well as the reactor volume. We will fix them to some default values as shown below. Once we are done, we will also print the degrees of freedom again to ensure we have fixed enough variables." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.reactor.inlet.flow_mol.fix(100)\n", + "m.fs.reactor.inlet.temperature.fix(500)\n", + "m.fs.reactor.inlet.pressure.fix(350000)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"benzene\"].fix(0.1)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"toluene\"].fix(0.4)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"hydrogen\"].fix(0.4)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"methane\"].fix(0.1)\n", + "m.fs.reactor.inlet.mole_frac_comp[0, \"diphenyl\"].fix(0.0)\n", + "\n", + "m.fs.reactor.volume.fix(1)\n", + "\n", + "print(\"Degrees of Freedom: \", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have defined our example problem, we can initialize and solve the flowsheet. This is done in the cell below, which should result in an optimal solution." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.reactor.initialize(\n", + " state_args={\n", + " \"flow_mol\": 100,\n", + " \"mole_frac_comp\": {\n", + " \"benzene\": 0.15,\n", + " \"toluene\": 0.35,\n", + " \"hydrogen\": 0.35,\n", + " \"methane\": 0.15,\n", + " \"diphenyl\": 0.01,\n", + " },\n", + " \"temperature\": 600,\n", + " \"pressure\": 350000,\n", + " }\n", + ")\n", + "\n", + "solver = get_solver()\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that our model has solved, let us use the `report()` method for the CSRT to have a look at what happened in the reactor. We should see that the outlet mole fraction of benzene is now around 0.160 and the mole fraction of diphenyl is 0.014; thus, our reactor has successfully generated benzene from toluene. In the process, the reaction has also generated a lot of heat, which has raised the temperature of the gas from 500 K to 790.2 K." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.reactor.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, as a quick check of model consistency, let us assert that the units of measurement in our model are consistent (the units of the rate constant and pre-exponential factor are rather complex)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "assert_units_consistent(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Concluding Remarks\n", + "\n", + "The above example has hopefully introduced you to the basic requirements for creating your own custom reaction property packages. However, it is probably clear that it requires a significant amount of effort to write your own property packages, thus users are encouraged to look into the IDAES Modular Reactions Framework if they are not already familiar with this.\n", + "\n", + "The IDAES Modular Reactions Framework is designed to automatically generate user-defined reaction property packages for common reaction forms based on a single configuration file. Users provide a list of reactions of interest (both rate- and equilibrium-based), and select from a library of common reaction forms, and the Modular Reaction Framework then does the hard work of assembling the necessary code to construct the desired model." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams.ipynb b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams.ipynb index b7e1a3cb..d526fb88 100644 --- a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams.ipynb +++ b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_doc.ipynb b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_doc.ipynb index 91726cfe..31196d2d 100644 --- a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_doc.ipynb +++ b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -307,29 +308,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:15 [WARNING] idaes.core.util.scaling: Missing scaling factor for props[1].mole_frac_comp\n" + "2025-03-17 17:35:46 [WARNING] idaes.core.util.scaling: Missing scaling factor for props[1].mole_frac_comp\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:35:46 [WARNING] idaes.core.util.scaling: Missing scaling factor for props[1].mole_frac_comp\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:15 [WARNING] idaes.core.util.scaling: Missing scaling factor for props[1].mole_frac_comp\n" + "2025-03-17 17:35:46 [INFO] idaes.init.props: Property package initialization: optimal - Solved To Acceptable Level..\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:15 [INFO] idaes.init.props: Property package initialization: optimal - Optimal Solution Found.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -337,8 +346,16 @@ "output_type": "stream", "text": [ "Case: 1 Optimal. benzene x = 0.99\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -346,8 +363,16 @@ "output_type": "stream", "text": [ "Case: 2 Optimal. benzene x = 0.95\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -355,8 +380,16 @@ "output_type": "stream", "text": [ "Case: 3 Optimal. benzene x = 0.91\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -364,8 +397,16 @@ "output_type": "stream", "text": [ "Case: 4 Optimal. benzene x = 0.87\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -373,8 +414,16 @@ "output_type": "stream", "text": [ "Case: 5 Optimal. benzene x = 0.83\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -382,24 +431,33 @@ "output_type": "stream", "text": [ "Case: 6 Optimal. benzene x = 0.79\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Case: 7 Optimal. benzene x = 0.74" + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "Case: 7 Optimal. benzene x = 0.74\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -407,8 +465,16 @@ "output_type": "stream", "text": [ "Case: 8 Optimal. benzene x = 0.70\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -416,8 +482,16 @@ "output_type": "stream", "text": [ "Case: 9 Optimal. benzene x = 0.66\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -425,8 +499,16 @@ "output_type": "stream", "text": [ "Case: 10 Optimal. benzene x = 0.62\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -434,8 +516,16 @@ "output_type": "stream", "text": [ "Case: 11 Optimal. benzene x = 0.58\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -443,8 +533,16 @@ "output_type": "stream", "text": [ "Case: 12 Optimal. benzene x = 0.54\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -452,17 +550,40 @@ "output_type": "stream", "text": [ "Case: 13 Optimal. benzene x = 0.50\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Case: 14 Optimal. benzene x = 0.46\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "Case: 14 Optimal. benzene x = 0.46" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -470,8 +591,16 @@ "output_type": "stream", "text": [ "Case: 15 Optimal. benzene x = 0.42\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -479,17 +608,40 @@ "output_type": "stream", "text": [ "Case: 16 Optimal. benzene x = 0.38\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Case: 17 Optimal. benzene x = 0.34\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Case: 17 Optimal. benzene x = 0.34" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -497,8 +649,16 @@ "output_type": "stream", "text": [ "Case: 18 Optimal. benzene x = 0.30\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -506,8 +666,16 @@ "output_type": "stream", "text": [ "Case: 19 Optimal. benzene x = 0.26\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -515,8 +683,16 @@ "output_type": "stream", "text": [ "Case: 20 Optimal. benzene x = 0.21\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -524,8 +700,16 @@ "output_type": "stream", "text": [ "Case: 21 Optimal. benzene x = 0.17\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -533,39 +717,50 @@ "output_type": "stream", "text": [ "Case: 22 Optimal. benzene x = 0.13\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Case: 23 Optimal. benzene x = 0.09\n" + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "Case: 23 Optimal. benzene x = 0.09\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Case: 24 Optimal. benzene x = 0.05" + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "WARNING: model contains export suffix 'props[1].scaling_factor' that contains\n", - "9 component keys that are not exported as part of the NL file. Skipping.\n" + "Case: 24 Optimal. benzene x = 0.05\n", + "WARNING: model contains export suffix 'scaling_factor' that contains 5\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 4 keys\n", + "that are not Var, Constraint, Objective, or the model. Skipping.\n" ] }, { @@ -577,16 +772,12 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\properties\\dictionary_txy_diagrams_doc_14_32.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -630,7 +821,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_test.ipynb b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_test.ipynb index 4117f832..c0366e0f 100644 --- a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_test.ipynb +++ b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_usr.ipynb b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_usr.ipynb index 4117f832..c0366e0f 100644 --- a/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_usr.ipynb +++ b/idaes_examples/notebooks/docs/properties/dictionary_txy_diagrams_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb index 1d0fcf3e..e8817fc0 100644 --- a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb +++ b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_doc.ipynb b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_doc.ipynb index ea87d247..6d0ce6f6 100644 --- a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_doc.ipynb +++ b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -161,9 +162,16 @@ " m.fs.state_block = m.fs.properties.build_state_block([1], defined_state=True)\n", "\n", " m.fs.state_block[1].flow_mol.fix(1)\n", - " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", + " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", + " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " x = float(data.iloc[0][\"x_carbon_dioxide\"]) + 0.5\n", + " m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " m.fs.state_block[1].pressure.fix(float(data.iloc[0][\"pressure\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(1 - x)\n", " m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].fix(x)\n", "\n", @@ -177,15 +185,30 @@ " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", "\n", " # Fix the state variables on the state block\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", + " float(data[\"x_bmimPF6\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", + " float(data[\"x_carbon_dioxide\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", + " float(data.iloc[0][\"x_bmimPF6\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", + " float(data.iloc[0][\"x_carbon_dioxide\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(\n", + " float(data.iloc[0][\"x_bmimPF6\"])\n", + " )\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", + "\n", " m.fs.state_block[1].pressure.unfix()\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", - " float(data[\"x_bmimPF6\"])\n", - " )\n", - " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", - " float(data[\"x_carbon_dioxide\"])\n", - " )\n", - " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", " m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].unfix()\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.PR_kappa[\"bmimPF6\", \"carbon_dioxide\"].setlb(-5)\n", @@ -208,15 +231,13 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:59 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -233,42 +254,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:59 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:59 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:29:59 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] } ], @@ -328,7 +349,7 @@ "outputs": [], "source": [ "def SSE(m, data):\n", - " expr = (float(data[\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n", + " expr = (float(data.iloc[0][\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n", " return expr * 1e-7" ] }, @@ -344,86 +365,83 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "WARNING: DEPRECATED: You're using the deprecated parmest interface\n", + "(model_function, data, theta_names). This interface will be removed in a\n", + "future release, please update to the new parmest interface using experiment\n", + "lists. (deprecated in 6.7.2) (called from /home/dang/miniforge3/envs/idaes_ex\n", + "amples_py3.11/lib/python3.11/functools.py:946)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING (W1002): Setting Var\n", - "'fs.state_block[1].log_mole_frac_tbub[Vap,Liq,carbon_dioxide]' to a numeric\n", - "value `4.301303339264284e-06` outside the bounds (None, 0).\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "WARNING (W1002): Setting Var\n", + "'fs.state_block[1].log_mole_frac_tbub[Vap,Liq,carbon_dioxide]' to a numeric\n", + "value `4.301303339264284e-06` outside the bounds (None, 0).\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:12: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:13: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:14: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:49 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -440,65 +458,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:29: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:31: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " float(data[\"x_bmimPF6\"])\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:34: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " float(data[\"x_carbon_dioxide\"])\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\3856510393.py:36: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_28652\\1809745473.py:2: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", - " expr = (float(data[\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -515,49 +517,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:00 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -574,49 +576,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -633,49 +635,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -692,49 +694,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:01 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:50 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -751,49 +753,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -810,49 +812,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -869,49 +871,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:02 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -928,49 +930,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -987,49 +989,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:51 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1046,49 +1048,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1105,49 +1107,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:03 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1164,49 +1166,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1223,49 +1225,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:04 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1282,49 +1284,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:52 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1341,49 +1343,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Starting initialization\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Starting initialization\n" ] }, { @@ -1400,42 +1402,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:05 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:35:53 [INFO] idaes.init.fs.state_block: Property package initialization: optimal - Optimal Solution Found.\n" ] }, { @@ -1479,7 +1481,7 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 5.00e-01 6.99e-14 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 0 0.0000000e+00 5.00e-01 1.22e-15 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 1.1422488e+01 2.60e-01 2.37e+03 -1.0 3.35e+04 - 3.39e-01 6.89e-01h 1\n", " 2 2.8748813e+01 1.27e-01 1.10e+03 -1.0 1.36e+04 - 8.22e-02 9.88e-01h 1\n", " 3 2.9813930e+01 1.87e-01 5.94e+02 -1.0 5.01e+02 - 8.73e-01 9.90e-01h 1\n", @@ -1488,19 +1490,19 @@ " 6 2.9283589e+01 1.44e-04 9.56e+04 -1.0 3.48e+02 - 9.90e-01 1.00e+00h 1\n", " 7 2.9283603e+01 7.59e-08 9.12e+02 -1.0 5.97e-01 - 9.90e-01 1.00e+00h 1\n", " 8 2.9282891e+01 3.35e-07 1.47e+04 -2.5 1.24e+02 - 9.98e-01 1.00e+00f 1\n", - " 9 2.9282892e+01 2.21e-12 4.97e-08 -2.5 2.39e-01 - 1.00e+00 1.00e+00h 1\n", + " 9 2.9282892e+01 1.75e-12 4.97e-08 -2.5 2.39e-01 - 1.00e+00 1.00e+00h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.9282891e+01 2.85e-10 3.05e+00 -8.6 3.61e+00 - 1.00e+00 1.00e+00h 1\n", - " 11 2.9282891e+01 2.72e-12 3.60e-12 -8.6 2.03e-04 - 1.00e+00 1.00e+00h 1\n", + " 10 2.9282891e+01 2.88e-10 3.05e+00 -8.6 3.61e+00 - 1.00e+00 1.00e+00h 1\n", + " 11 2.9282891e+01 4.31e-12 2.31e-12 -8.6 2.03e-04 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 11\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 2.9282891309640156e+01 2.9282891309640156e+01\n", - "Dual infeasibility......: 3.6021722623181066e-12 3.6021722623181066e-12\n", - "Constraint violation....: 2.7191470654339899e-12 2.7191470654339899e-12\n", - "Complementarity.........: 2.5059037693947522e-09 2.5059037693947522e-09\n", - "Overall NLP error.......: 2.5059037693947522e-09 2.5059037693947522e-09\n", + "Objective...............: 2.9282891309644025e+01 2.9282891309644025e+01\n", + "Dual infeasibility......: 2.3095432991869018e-12 2.3095432991869018e-12\n", + "Constraint violation....: 4.3063395653366371e-12 4.3063395653366371e-12\n", + "Complementarity.........: 2.5059037695121854e-09 2.5059037695121854e-09\n", + "Overall NLP error.......: 2.5059037695121854e-09 2.5059037695121854e-09\n", "\n", "\n", "Number of objective function evaluations = 12\n", @@ -1510,8 +1512,8 @@ "Number of equality constraint Jacobian evaluations = 12\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 11\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.048\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.013\n", + "Total CPU secs in NLP function evaluations = 0.068\n", "\n", "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" @@ -1545,8 +1547,8 @@ "The SSE at the optimal solution is 29.282891\n", "\n", "The values for the parameters are as follows:\n", - "fs.properties.PR_kappa[bmimPF6,carbon_dioxide] = -0.4071428400296551\n", - "fs.properties.PR_kappa[carbon_dioxide,bmimPF6] = 0.020593684002515204\n" + "fs.properties.PR_kappa[bmimPF6,carbon_dioxide] = -0.40714284002998746\n", + "fs.properties.PR_kappa[carbon_dioxide,bmimPF6] = 0.020593684002508692\n" ] } ], @@ -1589,9 +1591,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_test.ipynb b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_test.ipynb index 4d620a5a..75a53718 100644 --- a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_test.ipynb +++ b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -57,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -161,9 +162,16 @@ " m.fs.state_block = m.fs.properties.build_state_block([1], defined_state=True)\n", "\n", " m.fs.state_block[1].flow_mol.fix(1)\n", - " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", + " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", + " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " x = float(data.iloc[0][\"x_carbon_dioxide\"]) + 0.5\n", + " m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " m.fs.state_block[1].pressure.fix(float(data.iloc[0][\"pressure\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(1 - x)\n", " m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].fix(x)\n", "\n", @@ -177,15 +185,30 @@ " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", "\n", " # Fix the state variables on the state block\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", + " float(data[\"x_bmimPF6\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", + " float(data[\"x_carbon_dioxide\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", + " float(data.iloc[0][\"x_bmimPF6\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", + " float(data.iloc[0][\"x_carbon_dioxide\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(\n", + " float(data.iloc[0][\"x_bmimPF6\"])\n", + " )\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", + "\n", " m.fs.state_block[1].pressure.unfix()\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", - " float(data[\"x_bmimPF6\"])\n", - " )\n", - " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", - " float(data[\"x_carbon_dioxide\"])\n", - " )\n", - " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", " m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].unfix()\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.PR_kappa[\"bmimPF6\", \"carbon_dioxide\"].setlb(-5)\n", @@ -207,10 +230,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from idaes.core.util.model_statistics import degrees_of_freedom\n", @@ -242,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -263,12 +284,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def SSE(m, data):\n", - " expr = (float(data[\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n", + " expr = (float(data.iloc[0][\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n", " return expr * 1e-7" ] }, @@ -283,10 +304,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "pest = parmest.Estimator(PR_model, data, variable_name, SSE, tee=True)\n", @@ -305,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -347,9 +366,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_usr.ipynb b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_usr.ipynb index 4d620a5a..75a53718 100644 --- a/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_usr.ipynb +++ b/idaes_examples/notebooks/docs/properties/parameter_estimation_pr_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -57,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -161,9 +162,16 @@ " m.fs.state_block = m.fs.properties.build_state_block([1], defined_state=True)\n", "\n", " m.fs.state_block[1].flow_mol.fix(1)\n", - " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " x = float(data[\"x_carbon_dioxide\"]) + 0.5\n", + " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", + " m.fs.state_block[1].pressure.fix(float(data[\"pressure\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " x = float(data.iloc[0][\"x_carbon_dioxide\"]) + 0.5\n", + " m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " m.fs.state_block[1].pressure.fix(float(data.iloc[0][\"pressure\"]))\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(1 - x)\n", " m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].fix(x)\n", "\n", @@ -177,15 +185,30 @@ " m.fs.state_block.initialize(outlvl=idaeslog.INFO)\n", "\n", " # Fix the state variables on the state block\n", + " if isinstance(data, dict) or isinstance(data, pd.Series):\n", + " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", + " float(data[\"x_bmimPF6\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", + " float(data[\"x_carbon_dioxide\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", + " elif isinstance(data, pd.DataFrame):\n", + " m.fs.state_block[1].temperature.fix(float(data.iloc[0][\"temperature\"]))\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", + " float(data.iloc[0][\"x_bmimPF6\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", + " float(data.iloc[0][\"x_carbon_dioxide\"])\n", + " )\n", + " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(\n", + " float(data.iloc[0][\"x_bmimPF6\"])\n", + " )\n", + " else:\n", + " raise ValueError(\"Unrecognized data type.\")\n", + "\n", " m.fs.state_block[1].pressure.unfix()\n", - " m.fs.state_block[1].temperature.fix(float(data[\"temperature\"]))\n", - " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"bmimPF6\"].fix(\n", - " float(data[\"x_bmimPF6\"])\n", - " )\n", - " m.fs.state_block[1].mole_frac_phase_comp[\"Liq\", \"carbon_dioxide\"].fix(\n", - " float(data[\"x_carbon_dioxide\"])\n", - " )\n", - " m.fs.state_block[1].mole_frac_comp[\"bmimPF6\"].fix(float(data[\"x_bmimPF6\"]))\n", " m.fs.state_block[1].mole_frac_comp[\"carbon_dioxide\"].unfix()\n", " # Set bounds on variables to be estimated\n", " m.fs.properties.PR_kappa[\"bmimPF6\", \"carbon_dioxide\"].setlb(-5)\n", @@ -207,10 +230,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from idaes.core.util.model_statistics import degrees_of_freedom\n", @@ -242,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -263,12 +284,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def SSE(m, data):\n", - " expr = (float(data[\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n", + " expr = (float(data.iloc[0][\"pressure\"]) - m.fs.state_block[1].pressure) ** 2\n", " return expr * 1e-7" ] }, @@ -283,10 +304,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "pest = parmest.Estimator(PR_model, data, variable_name, SSE, tee=True)\n", @@ -305,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -347,9 +366,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/scaling/scaler_workshop.ipynb b/idaes_examples/notebooks/docs/scaling/scaler_workshop.ipynb index be3c5350..acdd77e9 100644 --- a/idaes_examples/notebooks/docs/scaling/scaler_workshop.ipynb +++ b/idaes_examples/notebooks/docs/scaling/scaler_workshop.ipynb @@ -17,12 +17,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/scaling/scaler_workshop_doc.ipynb b/idaes_examples/notebooks/docs/scaling/scaler_workshop_doc.ipynb index a7ba87bb..b7198926 100644 --- a/idaes_examples/notebooks/docs/scaling/scaler_workshop_doc.ipynb +++ b/idaes_examples/notebooks/docs/scaling/scaler_workshop_doc.ipynb @@ -1,1912 +1,1937 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to Create Scaler Objects in IDAES\n", - "\n", - "Author: Andrew Lee\n", - "Maintainer: Doug Allan\n", - "Updated: 2024-10-24\n", - "\n", - "## Introduction\n", - "\n", - "
\n", - "NOTE All the suggestions in this introduction should be viewed as \"rules-of-thumb\" and not taken as absolute guidance. There are many cases where alternative approaches may give as-good or better results and you should always consider the meaning of the scaling factors you are applying and how they affect the solver's behavior. \n", - "
\n", - "\n", - "Solving general non-linear problems has always been challenging, and is highly dependent on how well scaled the model is. In many cases, as much time (or more) is spent trying to improve the model formulation and scaling as was spent writing the original model. To assist molders with this task, IDAES has implemented a Scaling Toolbox which contains a number of useful tools for common scaling techniques as well as a standard interface and form for how to write scaling routines.\n", - "\n", - "The goal of this workshop is to take you through the process of writing a general-purpose, modular scaling routine for an equilibrium reactor example. By the end of this exercise you should:\n", - "\n", - "* understand the ``CustomScalerBase`` class and how to apply the tools it contains,\n", - "* understand how to use ``CustomScalerBase`` to set up a modular scaling routine for a model,\n", - "* understand how to use the Diagnostics Toolbox to check for scaling issues in a model.\n", - "\n", - "## How to Write a Scaling Routine\n", - "\n", - "
The golden rule when developing a scaling routine to a model is to always think about what you are doing and why. Bad scaling is often worse than no scaling at all, so assigning arbitrary scaling factors should be avoided. Always start by taking the time to look over the model you want to scale and understand what variables and constraints are present. For variables, you should ask yourself what the expected range of magnitudes will be; assigning an arbitrary default value should be avoided. For constraints you should ask yourself what the expected magnitude of each additive term will be, how much these vary from each other, and which term is likely to be most significant in terms of variation (partial derivatives).
\n", - "\n", - "
\n", - "NOTE Different solvers behave in different ways, and you may find cases where tuning scaling for one solver results in worse performance for another.\n", - " \n", - "You should always consider the end-goal when writing a Scaler; if you are writing a routine for a specific application and solver then you may wish to tune the scaling factors for best performance, however if you are writing a general-purpose Scaler then you should aim for scaling that will work for a wide range of conditions and solver.\n", - "
\n", - "\n", - "Below are some general suggestions for developing scaling routines.\n", - " \n", - "* Order of magnitude estimates are generally good enough (and often better than exact values).\n", - "* Start with what you know the most about, and work out from there.\n", - "* If in doubt, start by scaling variables first, and then scale constraints based on the variable scaling.\n", - "* Be judicious when applying scaling factors for things you are uncertain about. If in doubt, leave a component unscaled and see what the model diagnostics have to say.\n", - "* Make use of the modular nature of IDAES when writing scaling routines. A unit model developer might not know the expected magnitude of the thermophysical properties they get from a property package, but there should be a scaling routine for the property package that they can call to provide these.\n", - "\n", - "
\n", - "NOTE When dealing with systems of partial differential algebraic equations (PDAEs), such as dynamic systems or those with spatial variation, it is important to consider how scaling may change across the discretized domain. In many of these types of models, you will find significant changes in scale across a small portion of the domain; for example a dynamic model of a step disturbance will show an initial equilibrium state followed by a rapid change in system conditions until a new equilibrium is established. To complicate things further, the location of this ramp can often move significantly with minor changes in system conditions, thus you should not presume that the ramp will remain in the same place.\n", - " \n", - "As a general rule, for scaling PDAE systems with significant changes, you should focus on finding a set of scaling factors that is suitable for the ramp region as this is the part of the model which will be hardest to solve.\n", - "
\n", - "\n", - "### IDAES Scaling Interface and Toolbox\n", - "\n", - "IDAES uses a class-based interface for defining scaling routines, where model developers can create ``Scaler`` objects which define a scaling routine suitable for a type of model or specific application. All models (both those in the IDAES model libraries and user-developed models) should have one or more ``Scaler`` classes defined for them that can be used to apply scaling routines to the model. To assist end-users in identifying a suitable ``Scaler`` for a model, all IDAES models have a ``default_scaler`` attribute which can be set to point to a ``Scaler`` object suitable for that model. Model developers should endeavor to create a reliable, general-purpose ``Scaler`` for each model they create and assign this as the default ``Scaler``. We will demonstrate how to do this at the end of this workshop.\n", - "\n", - "\n", - "## Step 1: Set Up Test Case(s)\n", - "\n", - "Whilst it is possible to develop a scaling routine by looking only at the model code and the resulting variables and constraints, in order to test it we will need one or more test cases to run. These test cases are important for both checking the that ``Scaler`` code runs as expected, and that it also improves the scaling of the model. The more test cases you can check against, the more confident you can be that the ``Scaler`` you have written is suitable for a wide range of applications.\n", - "\n", - "For this example we will develop a general purpose ``Scaler`` for the ``EquilibriumReactor`` model from the core IDAES model library using the saponification property and reaction packages as a test case. The code below imports the necessary packages and creates a function that will build and initialize our test case." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel, Constraint, units, Var\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.unit_models.equilibrium_reactor import (\n", - " EquilibriumReactor,\n", - ")\n", - "from idaes.models.properties.examples.saponification_thermo import (\n", - " SaponificationParameterBlock,\n", - ")\n", - "from idaes.models.properties.examples.saponification_reactions import (\n", - " SaponificationReactionParameterBlock,\n", - ")\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.initialization import BlockTriangularizationInitializer\n", - "from idaes.core.util import DiagnosticsToolbox\n", - "\n", - "\n", - "def build_model():\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " m.fs.properties = SaponificationParameterBlock()\n", - " m.fs.reactions = SaponificationReactionParameterBlock(\n", - " property_package=m.fs.properties\n", - " )\n", - "\n", - " m.fs.equil = EquilibriumReactor(\n", - " property_package=m.fs.properties,\n", - " reaction_package=m.fs.reactions,\n", - " has_equilibrium_reactions=False,\n", - " has_heat_transfer=True,\n", - " has_heat_of_reaction=True,\n", - " has_pressure_change=True,\n", - " )\n", - "\n", - " m.fs.equil.inlet.flow_vol[0].fix(1.0e-03 * units.m**3 / units.s)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"H2O\"].fix(55388.0 * units.mol / units.m**3)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"NaOH\"].fix(100.0 * units.mol / units.m**3)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"EthylAcetate\"].fix(\n", - " 100.0 * units.mol / units.m**3\n", - " )\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"SodiumAcetate\"].fix(0.0 * units.mol / units.m**3)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"Ethanol\"].fix(0.0 * units.mol / units.m**3)\n", - "\n", - " m.fs.equil.inlet.temperature[0].fix(303.15 * units.K)\n", - " m.fs.equil.inlet.pressure[0].fix(101325.0 * units.Pa)\n", - "\n", - " m.fs.equil.heat_duty.fix(0 * units.W)\n", - " m.fs.equil.deltaP.fix(0 * units.Pa)\n", - "\n", - " initializer = BlockTriangularizationInitializer()\n", - " initializer.initialize(m.fs.equil)\n", - "\n", - " return m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we move on to try to solve the model or develop a ``Scaler``, we should first check to make sure the model is well-posed and that there are not any structural issues that will prevent us from solving the model. The code below creates an instance of the IDAES Diagnostics Toolbox and runs the ``report_structural_issues`` method to ensure there are no warnings." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 5 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 16 (External: 0)\n", - " Free Variables with only lower bounds: 6\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 2\n", - " Fixed Variables in Activated Constraints: 10 (External: 0)\n", - " Activated Equality Constraints: 16 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 Cautions\n", - "\n", - " Caution: 4 variables fixed to 0\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m = build_model()\n", - "\n", - "dt = DiagnosticsToolbox(model=m.fs.equil)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Make sure base model is constructed properly\n", - "dt.assert_no_structural_warnings()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to fully test our new ``Scaler`` it is also useful to test how the model responds to perturbations in the state. In many ways, this is the real test of a scaling routine as it is easy to write something that gets good scaling for a known state (e.g., auto-scalers), but what we really need is a routine that can get good scaling across a range of conditions.\n", - "\n", - "The cell below creates a function that perturbs the state of our model significantly. Note that the volumetric flowrate has been increased by two orders of magnitude, the inlet concentrations have changed significantly, and we have also made a small change to the temperature." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver(\n", - " \"ipopt_v2\", writer_config={\"scale_model\": True, \"linear_presolve\": True}\n", - ")\n", - "\n", - "\n", - "def perturb_model(m):\n", - " m.fs.equil.inlet.flow_vol.fix(1 * units.m**3 / units.s)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"NaOH\"].fix(200.0 * units.mol / units.m**3)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"EthylAcetate\"].fix(\n", - " 100.0 * units.mol / units.m**3\n", - " )\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"SodiumAcetate\"].fix(50 * units.mol / units.m**3)\n", - " m.fs.equil.inlet.conc_mol_comp[0, \"Ethanol\"].fix(1e-8 * units.mol / units.m**3)\n", - "\n", - " m.fs.equil.inlet.temperature.fix(320 * units.K)\n", - " solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets apply this perturbation to our example model and see how well it solves." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: linear_solver=ma57\n", - "max_iter=200\n", - "nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma57.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 21\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 9\n", - "\n", - "Total number of variables............................: 8\n", - " variables with only lower bounds: 5\n", - " variables with lower and upper bounds: 1\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 8\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 9.09e+07 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "Reallocating memory for MA57: lfact (247)\n", - " 1r 0.0000000e+00 9.09e+07 9.99e+02 2.5 0.00e+00 - 0.00e+00 7.73e-09R 9\n", - " 2r 0.0000000e+00 8.42e+07 8.24e+03 2.5 7.20e+02 - 1.64e-02 2.59e-02f 1\n", - " 3r 0.0000000e+00 8.37e+07 7.72e+03 1.8 8.82e+04 - 5.56e-04 3.77e-05f 1\n", - " 4r 0.0000000e+00 3.76e+07 2.65e+04 1.8 1.13e+03 0.0 1.27e-01 1.63e-01f 1\n", - " 5r 0.0000000e+00 3.60e+07 2.30e+04 1.8 6.83e+01 1.3 7.53e-02 1.45e-01f 1\n", - " 6r 0.0000000e+00 4.17e+07 1.77e+04 1.8 2.10e+02 0.9 7.11e-02 1.47e-01f 1\n", - " 7r 0.0000000e+00 4.08e+07 1.75e+04 1.8 3.95e+02 0.4 2.35e-01 8.19e-03f 1\n", - " 8r 0.0000000e+00 3.13e+07 1.75e+04 1.8 1.12e+03 -0.1 3.57e-01 3.16e-02f 1\n", - " 9r 0.0000000e+00 7.77e+06 1.74e+04 1.8 1.00e+04 - 1.43e-02 9.06e-03f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10r 0.0000000e+00 7.36e+06 1.70e+04 1.8 2.14e+02 - 2.20e-01 2.38e-02f 1\n", - " 11r 0.0000000e+00 5.93e+06 1.67e+04 1.8 1.72e+02 - 7.89e-01 1.95e-01f 1\n", - " 12r 0.0000000e+00 1.54e+06 3.54e+04 1.8 1.06e+02 - 8.80e-01 7.41e-01f 1\n", - " 13r 0.0000000e+00 1.21e+06 2.79e+04 1.8 4.60e+00 - 1.00e+00 2.12e-01h 1\n", - " 14r 0.0000000e+00 3.31e+03 4.79e+01 1.8 2.39e+00 - 1.00e+00 1.00e+00f 1\n", - " 15r 0.0000000e+00 2.85e+03 7.72e+02 -0.2 2.01e+00 - 9.81e-01 9.09e-01f 1\n", - " 16r 0.0000000e+00 2.47e+03 6.60e+02 -0.2 9.87e-01 - 1.00e+00 1.48e-01f 1\n", - " 17r 0.0000000e+00 3.18e-01 8.47e+01 -0.2 1.39e-01 - 1.00e+00 1.00e+00f 1\n", - " 18r 0.0000000e+00 3.18e-01 6.25e+01 -0.2 1.96e-01 - 1.00e+00 1.00e+00f 1\n", - " 19r 0.0000000e+00 3.18e-01 5.46e+00 -0.2 3.26e-02 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20r 0.0000000e+00 3.18e-01 1.44e+02 -1.6 2.34e-01 - 1.00e+00 1.00e+00f 1\n", - " 21r 0.0000000e+00 3.18e-01 1.45e+01 -1.6 9.26e-02 - 9.13e-01 1.00e+00f 1\n", - " 22r 0.0000000e+00 3.18e-01 1.46e+01 -1.6 1.71e-01 - 1.00e+00 1.25e-01f 4\n", - " 23r 0.0000000e+00 3.18e-01 1.44e+01 -1.6 1.24e-01 - 1.00e+00 1.56e-02h 7\n", - " 24r 0.0000000e+00 3.18e-01 1.41e+01 -1.6 1.27e-01 - 1.00e+00 1.56e-02h 7\n", - " 25r 0.0000000e+00 3.18e-01 1.39e+01 -1.6 1.24e-01 - 1.00e+00 1.56e-02h 7\n", - " 26r 0.0000000e+00 3.18e-01 1.37e+01 -1.6 1.22e-01 - 1.00e+00 1.56e-02h 7\n", - " 27r 0.0000000e+00 3.18e-01 1.35e+01 -1.6 1.20e-01 - 1.00e+00 1.56e-02h 7\n", - " 28r 0.0000000e+00 3.18e-01 1.33e+01 -1.6 1.18e-01 - 1.00e+00 1.56e-02h 7\n", - " 29r 0.0000000e+00 3.18e-01 1.31e+01 -1.6 1.17e-01 - 1.00e+00 1.56e-02h 7\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30r 0.0000000e+00 3.18e-01 1.29e+01 -1.6 1.15e-01 - 1.00e+00 1.56e-02h 7\n", - " 31r 0.0000000e+00 3.18e-01 1.28e+01 -1.6 1.13e-01 - 1.00e+00 1.56e-02h 7\n", - " 32r 0.0000000e+00 3.18e-01 4.28e+01 -1.6 1.11e-01 - 1.00e+00 1.00e+00w 1\n", - " 33r 0.0000000e+00 3.18e-01 1.43e-03 -1.6 2.09e-05 - 1.00e+00 1.00e+00w 1\n", - " 34r 0.0000000e+00 3.18e-01 1.37e+01 -3.7 6.94e-02 - 1.00e+00 1.00e+00f 1\n", - " 35r 0.0000000e+00 3.17e-01 3.73e+04 -3.7 3.39e+00 - 1.44e-01 1.00e+00f 1\n", - " 36r 0.0000000e+00 3.17e-01 6.78e+03 -3.7 5.99e-01 - 1.00e+00 1.00e+00f 1\n", - " 37r 0.0000000e+00 3.17e-01 7.66e+00 -3.7 4.92e-03 - 1.00e+00 1.00e+00h 1\n", - " 38r 0.0000000e+00 3.17e-01 1.65e-04 -3.7 9.43e-05 - 1.00e+00 1.00e+00h 1\n", - " 39r 0.0000000e+00 3.17e-01 1.30e+00 -5.6 9.94e-04 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 40r 0.0000000e+00 3.11e-01 6.82e+04 -5.6 3.21e+01 - 1.41e-01 1.00e+00f 1\n", - " 41r 0.0000000e+00 3.11e-01 1.17e+01 -5.6 4.97e-03 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 41\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 2.2783833299154238e-01 2.2783833299154238e-01\n", - "Constraint violation....: 3.1132475345243688e-01 3.1132475345243688e-01\n", - "Complementarity.........: 2.7808801399127131e-06 2.7808801399127131e-06\n", - "Overall NLP error.......: 3.1132475345243688e-01 3.1132475345243688e-01\n", - "\n", - "\n", - "Number of objective function evaluations = 109\n", - "Number of objective gradient evaluations = 3\n", - "Number of equality constraint evaluations = 109\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 44\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 42\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n" - ] - } - ], - "source": [ - "perturb_model(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As can be seen from the solver logs, IPOPT was unable to find a feasible solution to this problem, and went into restoration from the first iteration. However, there is no reason the perturbed conditions should not be feasible (you can verify this with the `infeasibility_explainer` in the Diagnostics Toolbox if you desire).\n", - "\n", - "There are a few reasons for this, most of which can be resolved by providing better scaling for the model. One of the reasons is because we have a number of concentrations approaching zero which results in a number of very small numbers appearing in the problem.\n", - "\n", - "A bigger issue however is the fact that in our initial model we are feeding reactants in stoichiometric amounts (1:1) meaning that both reactant concentrations go to zero at equilibrium. This results in the Jacobian for the reaction rate constraint becoming singular; with `rate = K_rxn * [NaOH] * [EthylAcetate]` if both concentrations go to zero then the partial derivative of the reaction rate with respect to each concentration is also 0, and thus our solver has no idea of what direction to move when trying to converge the problem. Whilst scaling can help work around this, this is ultimately an indication that our problem is not well formulated. In practice, an Equilibrium reactor model is not well suited for systems involving irreversible rate-based reactions as it requires concentrations to be driven to zero, and is an especially poor choice for stoichiometric feeds." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Understanding the Model\n", - "\n", - "Now that we have a test case (or multiple test cases), we can start planning out the new scaling routine. As our goal is to estimate scaling factors for as many of the variables and constraints in the model as possible, the first step is to understand what variables and constraints may be present in the model. Note that we need to be careful to check for all variables and constraints that may exist under different configuration options, and not just those that appear in the our test case(s).\n", - "\n", - "Given the modular nature of IDAES, we need to also make a distinction between those variables and constraints we have direct knowledge of, and those that are created via modular sub-models that we do not know the details of. The most common examples of modular sub-models are the ``StateBlocks`` and ``ReactionBlocks`` created by the associated property packages; we know that these exist and we create these in our models, but we do not know what variables and constraints they may construct. On the other hand, we also have variables and constraints that we construct directly in our model. For the purposes of this we include those variables and constraints constructed by ``ControlVolumes`` as being directly construed; whilst the ``ControlVolume`` might automate the details for us, we directly call methods on the ``ControlVolume`` to create these variables and constraints and we know what they will be based on the instructions we give.\n", - "\n", - "For our example of the ``EquilibriumReactor``, let us take a look at the code in the ``build`` method, which has been copied below for convenience:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def build(self):\n", - " \"\"\"\n", - " Begin building model.\n", - "\n", - " Args:\n", - " None\n", - "\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - " # Call UnitModel.build to setup dynamics\n", - " super(EquilibriumReactorData, self).build()\n", - "\n", - " # Build Control Volume\n", - " self.control_volume = ControlVolume0DBlock(\n", - " dynamic=self.config.dynamic, # Config block forces this to be False\n", - " has_holdup=self.config.has_holdup, # Config block forces this to be False\n", - " property_package=self.config.property_package,\n", - " property_package_args=self.config.property_package_args,\n", - " reaction_package=self.config.reaction_package,\n", - " reaction_package_args=self.config.reaction_package_args,\n", - " )\n", - "\n", - " # No need for control volume geometry\n", - "\n", - " self.control_volume.add_state_blocks(\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium\n", - " )\n", - "\n", - " self.control_volume.add_reaction_blocks(\n", - " has_equilibrium=self.config.has_equilibrium_reactions\n", - " )\n", - "\n", - " self.control_volume.add_material_balances(\n", - " balance_type=self.config.material_balance_type,\n", - " has_rate_reactions=self.config.has_rate_reactions,\n", - " has_equilibrium_reactions=self.config.has_equilibrium_reactions,\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", - " )\n", - "\n", - " self.control_volume.add_energy_balances(\n", - " balance_type=self.config.energy_balance_type,\n", - " has_heat_of_reaction=self.config.has_heat_of_reaction,\n", - " has_heat_transfer=self.config.has_heat_transfer,\n", - " )\n", - "\n", - " self.control_volume.add_momentum_balances(\n", - " balance_type=self.config.momentum_balance_type,\n", - " has_pressure_change=self.config.has_pressure_change,\n", - " )\n", - "\n", - " # Add Ports\n", - " self.add_inlet_port()\n", - " self.add_outlet_port()\n", - "\n", - " if self.config.has_rate_reactions:\n", - " # Add equilibrium reactor performance equation\n", - " @self.Constraint(\n", - " self.flowsheet().time,\n", - " self.config.reaction_package.rate_reaction_idx,\n", - " doc=\"Rate reaction equilibrium constraint\",\n", - " )\n", - " def rate_reaction_constraint(b, t, r):\n", - " # Set kinetic reaction rates to zero\n", - " return b.control_volume.reactions[t].reaction_rate[r] == 0\n", - "\n", - " # Set references to balance terms at unit level\n", - " if (\n", - " self.config.has_heat_transfer is True\n", - " and self.config.energy_balance_type != EnergyBalanceType.none\n", - " ):\n", - " self.heat_duty = Reference(self.control_volume.heat[:])\n", - "\n", - " if (\n", - " self.config.has_pressure_change is True\n", - " and self.config.momentum_balance_type != MomentumBalanceType.none\n", - " ):\n", - " self.deltaP = Reference(self.control_volume.deltaP[:])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we look through the code in the ``build`` method, we can see that the model contains a single 0D Control Volume with ``StateBlocks``, a ``ReactionBlock``, material, energy and momentum balances and one additional constraint (``rate_reaction_constraint``). Thus, we have the following components that need to be scaled:\n", - "\n", - "3 Sub-Models:\n", - "\n", - "1. The inlet state sub-model (``model.control_volume.properties_in``)\n", - "2. The outlet state sub-model (``model.control_volume.properties_out``)\n", - "3. The reaction sub-model (``model.control_volume.reactions``)\n", - "\n", - "Unit Model Variables (from control volume options):\n", - "\n", - "1. Rate-based reaction extent and generation terms\n", - "2. Equilibrium-based reaction extent and generation terms\n", - "3. Inherent reaction extent and generation terms (no explicit argument, but determined by properties)\n", - "4. Phase equilibrium generation terms\n", - "5. Energy balance heat term\n", - "6. Energy balance heats of reaction\n", - "7. Pressure drop\n", - "\n", - "Unit Model Constraints (from control volume + 1 in the ``build`` method):\n", - "\n", - "1. Material balance constraints\n", - "2. Reaction stoichiometry constraints\n", - "3. Energy balance constraints\n", - "4. Pressure balance constraints\n", - "5. ``rate_reaction_constraint``\n", - "\n", - "When writing our ``Scaler`` we will need to consider all of these to determine how best to estimate scaling factors. Before starting however, we should check the numerical diagnostics for each case study, both to see what scaling issues currently exist and to establish a baseline for comparison once we have a proposed ``Scaler`` for our model.\n", - "\n", - "The cell below calls the ``report_numerical_issues`` method for the unscaled test case." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 1.540E+12\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "7 Cautions\n", - "\n", - " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 4 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", - " Caution: 1 Constraint with mismatched terms\n", - " Caution: 3 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 2 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 6 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_constraints_with_large_residuals()\n", - " compute_infeasibility_explanation()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the results of the diagnostics, we can see that the test case is not particularly well scaled. The Jacobian condition number is rather large (1e12), and the diagnostics are reporting a number of variables with extremely large or small values, and 3 variables and 2 constraints with poorly scaled Jacobians. As we develop our new ``Scaler`` for the ``EquilibriumReactor`` we will hopefully see these improve.\n", - "\n", - "We can also use the Diagnostics Toolbox to further explore these issues to get a better idea of which variables and constraints might be causing issues. For example, lets display the set of variables and constraints with extreme Jacobian norms." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) are associated with extreme Jacobian values (<1.0E-04 or>1.0E+04):\n", - "\n", - " fs.equil.control_volume.properties_out[0.0].flow_vol: 9.427E+07\n", - " fs.equil.control_volume.properties_out[0.0].temperature: 4.172E+06\n", - " fs.equil.control_volume.rate_reaction_extent[0.0,R1]: 4.900E+04\n", - "\n", - "====================================================================================\n", - "====================================================================================\n", - "The following constraint(s) are associated with extreme Jacobian values (<1.0E-04 or>1.0E+04):\n", - "\n", - " fs.equil.control_volume.enthalpy_balances[0.0]: 9.436E+07\n", - " fs.equil.control_volume.material_balances[0.0,Liq,H2O]: 5.539E+04\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_with_extreme_jacobians()\n", - "dt.display_constraints_with_extreme_jacobians()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These diagnostics can help give us an idea of what may be causing problems in our model. From the output above, we can see that the variables with large Jacobian norms (i.e., high sensitivities) are the outlet flow rate and temperature, as well as the rate-based extent of reaction. We can also see that the constraints with large Jacobian norms are the enthalpy balance and H20 material balance for the reactor. However, caution must be used when interpreting these in isolation, as understanding what these mean is often complicated and initial impressions may be misleading. To get a better picture of what is contributing to extreme Jacobian values you should make use of the tools in the diagnostics ``SVDToolbox``, however that is a topic for another example.\n", - "\n", - "For example, one might wonder why the volumetric flow rate at the outlet of the reactor is so important as it is effectively determined by the inlet flow rate (due to the water balance effectively conserving volume). However, it is important to remember that the Jacobian does not consider the value of the variable, but rather its partial derivatives. Thus, it is important to compare the list of variables and constraints with large Jacobian norms and think about how those intersect.\n", - "\n", - "Let's start by taking a look at the H2O material balance. The cell below prints the constraint expression in a compact form that only shows top level ``Expressions`` rather than expanding these to show the full expression tree." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.equil.control_volume.properties_in[0.0].flow_vol*fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] - fs.equil.control_volume.properties_out[0.0].flow_vol*fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] + fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] == 0" - ] - } - ], - "source": [ - "from idaes.core.util.misc import print_compact_form\n", - "\n", - "print_compact_form(m.fs.equil.control_volume.material_balances[0, \"Liq\", \"H2O\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at how the outlet volumetric flowrate appears in the H2O balance equation above, it can be seen that the volumetric flow term is multiplied by the molar concentration of water, $F \\times C_{H2O}$. Whilst $C_{H2O}$ is assumed to be constant in this model (and equal to the molar density of pure water at ambient conditions), this means that the partial derivative of the constraint term with respect to flow is $\\frac{\\partial F C_{H2O}}{\\partial F} = C_{H2O}$; given that $C_{H2O}$ is equal to 5.5E4 mol/liter, you can quickly see why it is being identified as an issue.\n", - "\n", - "If we look at the energy balance, we will find that it is similar." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.properties.dens_mol*fs.properties.cp_mol*fs.equil.control_volume.properties_in[0.0].flow_vol*(fs.equil.control_volume.properties_in[0.0].temperature - fs.properties.temperature_ref) - fs.properties.dens_mol*fs.properties.cp_mol*fs.equil.control_volume.properties_out[0.0].flow_vol*(fs.equil.control_volume.properties_out[0.0].temperature - fs.properties.temperature_ref) + fs.equil.control_volume.heat[0.0] + fs.equil.control_volume.heat_of_reaction[0.0] == 0" - ] - } - ], - "source": [ - "print_compact_form(m.fs.equil.control_volume.enthalpy_balances[0.0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Whilst a bit harder to read due to the size of the constraint, you can see that it involves the term $\\rho \\times c_p \\times F \\times (T - T_{ref})$, where $c_p$ is the specific molar heat capacity of the solution, $T$ is temperature and $T_{ref}$ is the reference temperature. Given that $\\rho$ is of order 1E4 (a) and $c_p \\times (T-T_{ref})$ is of order 1E3, this means that the partial derivative with respect to the volumetric flowrate is even larger than that for the H2O balance. This also explains the appearance of the outlet temperature as well, as we can see that it is multiplied by a number of large values as well and thus has a large partial derivative.\n", - "\n", - "It is also important to mention that having a large value in the Jacobian does not mean a variable is \"important\" (and conversely a small value is not unimportant). What is important is how sensitive the constraint residual is to that change in variable, which is often difficult to assess from the Jacobian alone (which is where the ``SVDToolbox`` can assist).\n", - "\n", - "\n", - "## Step 3: Creating a New Scaler Class\n", - "\n", - "To create a new scaling routine for the equilibrium reactor, we start by creating a new ``Scaler`` class which inherits from the ``CustomScalerBase`` class in ``idaes.core.scaling``. The ``CustomScalerBase`` class contains a number of useful methods to help us in developing our scaling routine, including some placeholder methods for implementing a standard scaling workflow and helper methods for doing common tasks.\n", - "\n", - "The cell below shows how to create our new class which we will name ``EquilibriumReactorScaler`` as well as two key methods we will fill out as part of this workshop." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.scaling import CustomScalerBase\n", - "\n", - "\n", - "class EquilibriumReactorScaler(CustomScalerBase):\n", - " def variable_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Empty method for now\n", - " pass\n", - "\n", - " def constraint_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Empty method for now\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``variable_scaling_routine`` and ``constraint_scaling_routine`` methods are used to implement subroutines for scaling the variables and constraints in the model respectively. Separately, there is a ``scale_model`` method that will call each of these in sequence in order to scale an entire model by applying the following steps:\n", - "\n", - "1. apply variable scaling routine,\n", - "2. apply first stage scaling fill-in,\n", - "3. apply constraint scaling routine,\n", - "4. apply second stage scaling fill-in.\n", - "\n", - "The second and fourth steps are intended to allow users to provide methods to fill in missing scaling information that was not provided by the first and second steps, or to provide a way to update the scaling factors with more information.\n", - "\n", - "Both the ``variable_scaling_routine`` and ``constraint_scaling_routine`` are user-facing methods and take three arguments.\n", - "\n", - "1. The model to be scaled.\n", - "2. An argument indicating whether to overwrite any existing scaling factors. Generally we assume that any existing scaling factors were provided by the user for a reason, so by default we set this to ``False``. However, there will likely be cases where a user wants to overwrite their existing scaling factors so this argument exists to let us pass on those instructions.\n", - "3. A mapping of user-provided ``Scalers`` to use when scaling submodels.\n", - "\n", - "## Step 4: Apply Scaling to Sub-Models\n", - "\n", - "First, lets look at how to scale the property and reaction sub-models. As these are modular packages, we do not know what variables and constraints may be in them, so we cannot (and should not) scale any of these directly. However, we can (hopefully) assume that there are ``Scalers`` available for these sub-models, either through default ``Scalers`` associated with the property packages or provided by the user. Thus, what we want to do here is to call the variable and constraint scaling routines from the ``Scaler`` associated with each sub-model, which we can do using the ``call_submodel_scaler_method`` method from the ``CustomScalerBase`` class.\n", - "\n", - "The cell below prints the doc-string for the ``call_submodel_scaler_method`` method so we can see what the expected arguments are." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function call_submodel_scaler_method in module idaes.core.scaling.custom_scaler_base:\n", - "\n", - "call_submodel_scaler_method(self, submodel, method: str, submodel_scalers: pyomo.common.collections.component_map.ComponentMap = None, overwrite: bool = False)\n", - " Call scaling method for submodel.\n", - " \n", - " Scaler for submodel is taken from submodel_scalers if present, otherwise the\n", - " default scaler for the submodel is used.\n", - " \n", - " Args:\n", - " submodel: submodel to be scaled\n", - " submodel_scalers: user provided ComponentMap of Scalers to use for submodels\n", - " method: name of method to call from submodel (as string)\n", - " overwrite: whether to overwrite existing scaling factors\n", - " \n", - " Returns:\n", - " None\n", - "\n" - ] - } - ], - "source": [ - "help(CustomScalerBase.call_submodel_scaler_method)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that ``call_submodel_scaler_method`` takes 4 arguments:\n", - "\n", - "1. ``submodel`` is the submodel we want to scale. \n", - "2. The ``submodel_scalers`` argument should be passed through from the ``variable_scaling_routine`` or ``constraint_scaling_routine`` method.\n", - "3. The name of the method we want to call from the ``Scaler`` when we get it - this will normally be either ``variable_scaling_routine`` (if we are scaling variables) or ``constraint_scaling_routine`` (if we are doing constraints).\n", - "4. The ``overwrite`` argument should also be passed through from the ``variable_scaling_routine`` or ``constraint_scaling_routine`` method.\n", - "\n", - "For the Equilibrium Reactor, we have three submodels to scale; inlet state, outlet state and reactions. As mentioned in the introduction, when developing scaling routines always start with the things you have the most information about. In this case, we likely know the most about the inlet state; either it is a defined feed state (like in our test case) or we have some idea of the state (and scaling) from propagating values from an upstream operation. So, to apply variable scaling to the inlet state we would do the following:\n", - "\n", - "```python\n", - "self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - ")\n", - "```\n", - "\n", - "Once we have an idea of scaling for the inlet we can use that information to try to estimate scaling for the outlet state. The default assumption is that the scaling of the outlet will be similar to that of the inlet, so the easy path is to copy scaling from the inlet state to the outlet. However, we know that something must change between inlet and outlet (as otherwise this unit operation is doing nothing) so we should always stop and think about whether we can try to estimate these changes. For example, in a pressure changer we know, or be able to estimate, the pressure change across the unit and thus be able to change the scaling of pressure between the inlet and outlet. However, keep in mind that over-scaling can make things worse so be judicious when deciding whether to adjust scaling based on estimates.\n", - "\n", - "In regards to this, Equilibrium Reactors are one of the more challenging units to scale, as it is very hard to know what the outlet flows and concentrations will be without knowing what the reactions are (and even if you know the reactions it is often hard to know the equilibrium state). In most cases, we have no reliable way to estimate the outlet flowrate and concentrations, so this is best left to the user to provide. In the case of temperature and pressure, whilst we may expect these to change but any change will generally be 1-2 orders of magnitude less than the inlet state and thus the overall scale of these will likely remain similar. Thus, for the Equilibrium Reactor it is probably sufficient to just scale the outlet state based on the inlet state.\n", - "\n", - "The ``CustomScalerBase`` class has a method for propagating scaling factors for state variables from one state to another called ``propagate_state_scaling`` as see below." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function propagate_state_scaling in module idaes.core.scaling.custom_scaler_base:\n", - "\n", - "propagate_state_scaling(self, target_state, source_state, overwrite: bool = False)\n", - " Propagate scaling of state variables from one StateBlock to another.\n", - " \n", - " Indexing of target and source StateBlocks must match.\n", - " \n", - " Args:\n", - " target_state: StateBlock to set scaling factors on\n", - " source_state: StateBlock to use as source for scaling factors\n", - " overwrite: whether to overwrite existing scaling factors\n", - " \n", - " Returns:\n", - " None\n", - "\n" - ] - } - ], - "source": [ - "help(CustomScalerBase.propagate_state_scaling)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we can see that ``propagate_state_scaling`` takes three arguments; the ``StateBlock`` we want to apply scaling to, the ``StateBlock`` we want to use as the source for the scaling factors, and the ``overwrite`` argument. Thus, we can propagate scaling from the inlet state to the outlet state as shown below.\n", - "\n", - "```python\n", - "self.propagate_state_scaling(\n", - " target_state=model.control_volume.properties_out,\n", - " source_state=model.control_volume.properties_in,\n", - " overwrite=overwrite,\n", - ")\n", - "```\n", - "\n", - "This only propagates scaling factors for the state variables, however, so we should then call the ``Scaler`` for the outlet state block to scale any remaining variables and constraints (which will hopefully make use of the scaling factors for the state variables we just propagated).\n", - "\n", - "We can then move on to scaling the ``ReactionBlock``. ``ReactionBlocks`` are slightly unusual in that they rely heavily on the state variables defined in a separate ``StateBlock`` - in this case the outlet state block. As we just applied a ``Scaler`` to the outlet state block, we can assume that all of the necessary variables have been scaled so all we need to do now is call a ``Scaler`` for the ``ReactionBlock``.\n", - "\n", - "All of this is shown in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class EquilibriumReactorScaler(CustomScalerBase):\n", - " def variable_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.propagate_state_scaling(\n", - " target_state=model.control_volume.properties_out,\n", - " source_state=model.control_volume.properties_in,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " def constraint_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Empty method for now\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then take a similar approach for the constraint scaling routine as shown below. Note that there is no need for a propagation step here as the residual of a constraint is derived from the value of the variables (which we handled in the variable scaling step)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "class EquilibriumReactorScaler(CustomScalerBase):\n", - " def variable_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.propagate_state_scaling(\n", - " target_state=model.control_volume.properties_out,\n", - " source_state=model.control_volume.properties_in,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " def constraint_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets do a quick check to see if our new scaler works and how it has affected the model scaling. The cell below creates a function that builds a new instance of the model (to avoid contamination from previous model runs then creates an instance of our new scaler and applies it to the model. We then solve the scaled model (adding scaling changes constraint residuals so we want to solve to the scaled state). Finally, the function prints a report of the scaling factors in the model and calls the ``report_numerical_issues`` method from the Diagnostics Toolbox." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import check_optimal_termination, TransformationFactory\n", - "\n", - "from idaes.core.scaling import report_scaling_factors\n", - "\n", - "\n", - "def check_scaling(tee=False):\n", - " # Build new instance of model\n", - " m = build_model()\n", - "\n", - " # Apply scaler to model\n", - " scaler = EquilibriumReactorScaler()\n", - " scaler.scale_model(m.fs.equil)\n", - "\n", - " # Solve scaled model\n", - " results = solver.solve(m, tee=tee)\n", - " if check_optimal_termination(results):\n", - " print(\"\\nModel Solved\\n\")\n", - " else:\n", - " print(\"\\nModel Failed to Converge!\\n\")\n", - "\n", - " # Print report of scaling factors\n", - " report_scaling_factors(m.fs.equil, descend_into=True)\n", - "\n", - " # Show numerical issues report\n", - " sm = TransformationFactory(\"core.scale_model\").create_using(m, rename=False)\n", - "\n", - " dt = DiagnosticsToolbox(model=sm.fs.equil)\n", - " dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets run the ``check_scaling`` function and see how the model scaling has changed." - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Create Scaler Objects in IDAES\n", + "\n", + "Author: Andrew Lee\n", + "Maintainer: Doug Allan\n", + "Updated: 2024-10-24\n", + "\n", + "## Introduction\n", + "\n", + "
\n", + "NOTE All the suggestions in this introduction should be viewed as \"rules-of-thumb\" and not taken as absolute guidance. There are many cases where alternative approaches may give as-good or better results and you should always consider the meaning of the scaling factors you are applying and how they affect the solver's behavior. \n", + "
\n", + "\n", + "Solving general non-linear problems has always been challenging, and is highly dependent on how well scaled the model is. In many cases, as much time (or more) is spent trying to improve the model formulation and scaling as was spent writing the original model. To assist molders with this task, IDAES has implemented a Scaling Toolbox which contains a number of useful tools for common scaling techniques as well as a standard interface and form for how to write scaling routines.\n", + "\n", + "The goal of this workshop is to take you through the process of writing a general-purpose, modular scaling routine for an equilibrium reactor example. By the end of this exercise you should:\n", + "\n", + "* understand the ``CustomScalerBase`` class and how to apply the tools it contains,\n", + "* understand how to use ``CustomScalerBase`` to set up a modular scaling routine for a model,\n", + "* understand how to use the Diagnostics Toolbox to check for scaling issues in a model.\n", + "\n", + "## How to Write a Scaling Routine\n", + "\n", + "
The golden rule when developing a scaling routine to a model is to always think about what you are doing and why. Bad scaling is often worse than no scaling at all, so assigning arbitrary scaling factors should be avoided. Always start by taking the time to look over the model you want to scale and understand what variables and constraints are present. For variables, you should ask yourself what the expected range of magnitudes will be; assigning an arbitrary default value should be avoided. For constraints you should ask yourself what the expected magnitude of each additive term will be, how much these vary from each other, and which term is likely to be most significant in terms of variation (partial derivatives).
\n", + "\n", + "
\n", + "NOTE Different solvers behave in different ways, and you may find cases where tuning scaling for one solver results in worse performance for another.\n", + " \n", + "You should always consider the end-goal when writing a Scaler; if you are writing a routine for a specific application and solver then you may wish to tune the scaling factors for best performance, however if you are writing a general-purpose Scaler then you should aim for scaling that will work for a wide range of conditions and solver.\n", + "
\n", + "\n", + "Below are some general suggestions for developing scaling routines.\n", + " \n", + "* Order of magnitude estimates are generally good enough (and often better than exact values).\n", + "* Start with what you know the most about, and work out from there.\n", + "* If in doubt, start by scaling variables first, and then scale constraints based on the variable scaling.\n", + "* Be judicious when applying scaling factors for things you are uncertain about. If in doubt, leave a component unscaled and see what the model diagnostics have to say.\n", + "* Make use of the modular nature of IDAES when writing scaling routines. A unit model developer might not know the expected magnitude of the thermophysical properties they get from a property package, but there should be a scaling routine for the property package that they can call to provide these.\n", + "\n", + "
\n", + "NOTE When dealing with systems of partial differential algebraic equations (PDAEs), such as dynamic systems or those with spatial variation, it is important to consider how scaling may change across the discretized domain. In many of these types of models, you will find significant changes in scale across a small portion of the domain; for example a dynamic model of a step disturbance will show an initial equilibrium state followed by a rapid change in system conditions until a new equilibrium is established. To complicate things further, the location of this ramp can often move significantly with minor changes in system conditions, thus you should not presume that the ramp will remain in the same place.\n", + " \n", + "As a general rule, for scaling PDAE systems with significant changes, you should focus on finding a set of scaling factors that is suitable for the ramp region as this is the part of the model which will be hardest to solve.\n", + "
\n", + "\n", + "### IDAES Scaling Interface and Toolbox\n", + "\n", + "IDAES uses a class-based interface for defining scaling routines, where model developers can create ``Scaler`` objects which define a scaling routine suitable for a type of model or specific application. All models (both those in the IDAES model libraries and user-developed models) should have one or more ``Scaler`` classes defined for them that can be used to apply scaling routines to the model. To assist end-users in identifying a suitable ``Scaler`` for a model, all IDAES models have a ``default_scaler`` attribute which can be set to point to a ``Scaler`` object suitable for that model. Model developers should endeavor to create a reliable, general-purpose ``Scaler`` for each model they create and assign this as the default ``Scaler``. We will demonstrate how to do this at the end of this workshop.\n", + "\n", + "\n", + "## Step 1: Set Up Test Case(s)\n", + "\n", + "Whilst it is possible to develop a scaling routine by looking only at the model code and the resulting variables and constraints, in order to test it we will need one or more test cases to run. These test cases are important for both checking the that ``Scaler`` code runs as expected, and that it also improves the scaling of the model. The more test cases you can check against, the more confident you can be that the ``Scaler`` you have written is suitable for a wide range of applications.\n", + "\n", + "For this example we will develop a general purpose ``Scaler`` for the ``EquilibriumReactor`` model from the core IDAES model library using the saponification property and reaction packages as a test case. The code below imports the necessary packages and creates a function that will build and initialize our test case." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel, Constraint, units, Var\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.unit_models.equilibrium_reactor import (\n", + " EquilibriumReactor,\n", + ")\n", + "from idaes.models.properties.examples.saponification_thermo import (\n", + " SaponificationParameterBlock,\n", + ")\n", + "from idaes.models.properties.examples.saponification_reactions import (\n", + " SaponificationReactionParameterBlock,\n", + ")\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.initialization import BlockTriangularizationInitializer\n", + "from idaes.core.util import DiagnosticsToolbox\n", + "\n", + "\n", + "def build_model():\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " m.fs.properties = SaponificationParameterBlock()\n", + " m.fs.reactions = SaponificationReactionParameterBlock(\n", + " property_package=m.fs.properties\n", + " )\n", + "\n", + " m.fs.equil = EquilibriumReactor(\n", + " property_package=m.fs.properties,\n", + " reaction_package=m.fs.reactions,\n", + " has_equilibrium_reactions=False,\n", + " has_heat_transfer=True,\n", + " has_heat_of_reaction=True,\n", + " has_pressure_change=True,\n", + " )\n", + "\n", + " m.fs.equil.inlet.flow_vol[0].fix(1.0e-03 * units.m**3 / units.s)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"H2O\"].fix(55388.0 * units.mol / units.m**3)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"NaOH\"].fix(100.0 * units.mol / units.m**3)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"EthylAcetate\"].fix(\n", + " 100.0 * units.mol / units.m**3\n", + " )\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"SodiumAcetate\"].fix(0.0 * units.mol / units.m**3)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"Ethanol\"].fix(0.0 * units.mol / units.m**3)\n", + "\n", + " m.fs.equil.inlet.temperature[0].fix(303.15 * units.K)\n", + " m.fs.equil.inlet.pressure[0].fix(101325.0 * units.Pa)\n", + "\n", + " m.fs.equil.heat_duty.fix(0 * units.W)\n", + " m.fs.equil.deltaP.fix(0 * units.Pa)\n", + "\n", + " initializer = BlockTriangularizationInitializer()\n", + " initializer.initialize(m.fs.equil)\n", + "\n", + " return m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we move on to try to solve the model or develop a ``Scaler``, we should first check to make sure the model is well-posed and that there are not any structural issues that will prevent us from solving the model. The code below creates an instance of the IDAES Diagnostics Toolbox and runs the ``report_structural_issues`` method to ensure there are no warnings." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Solved\n", - "\n", - "Scaling Factors for fs.equil\n", - "\n", - "Variable Scaling Factor Value Scaled Value\n", - "fs.equil.control_volume.properties_in[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[NaOH] 1.000E-02 1.000E+02 1.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 1.000E+02 1.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[Ethanol] 1.000E-02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].temperature 3.219E-03 3.031E+02 9.759E-01\n", - "fs.equil.control_volume.properties_in[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", - "fs.equil.control_volume.properties_out[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[NaOH] 1.000E-02 6.250E-02 6.250E-04\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 6.250E-02 6.250E-04\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 9.994E+01 9.994E-01\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[Ethanol] 1.000E-02 9.994E+01 9.994E-01\n", - "fs.equil.control_volume.properties_out[0.0].temperature 3.219E-03 3.043E+02 9.796E-01\n", - "fs.equil.control_volume.properties_out[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", - "fs.equil.control_volume.heat[0.0] None 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.deltaP[0.0] None 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] None 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,NaOH] None -9.994E-02 -9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,EthylAcetate] None -9.994E-02 -9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,SodiumAcetate] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,Ethanol] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.rate_reaction_extent[0.0,R1] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.reactions[0.0].reaction_rate[R1] 1.000E+02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.reactions[0.0].k_rxn 5.424E+00 1.304E-01 7.075E-01\n", - "\n", - "Constraint Scaling Factor\n", - "fs.equil.rate_reaction_constraint[0.0,R1] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,H2O] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,NaOH] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,EthylAcetate] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,SodiumAcetate] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,Ethanol] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,H2O] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,NaOH] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,EthylAcetate] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,SodiumAcetate] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,Ethanol] None\n", - "fs.equil.control_volume.enthalpy_balances[0.0] None\n", - "fs.equil.control_volume.pressure_balance[0.0] None\n", - "fs.equil.control_volume.properties_out[0.0].conc_water_eqn 1.000E-04\n", - "fs.equil.control_volume.reactions[0.0].rate_expression[R1] 5.424E-04\n", - "fs.equil.control_volume.reactions[0.0].arrhenius_eqn 5.424E+00\n", - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 3.022E+09\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 4 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 2 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 6 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "check_scaling()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 5 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 16 (External: 0)\n", + " Free Variables with only lower bounds: 6\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 2\n", + " Fixed Variables in Activated Constraints: 10 (External: 0)\n", + " Activated Equality Constraints: 16 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 4 variables fixed to 0\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m = build_model()\n", + "\n", + "dt = DiagnosticsToolbox(model=m.fs.equil)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure base model is constructed properly\n", + "dt.assert_no_structural_warnings()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to fully test our new ``Scaler`` it is also useful to test how the model responds to perturbations in the state. In many ways, this is the real test of a scaling routine as it is easy to write something that gets good scaling for a known state (e.g., auto-scalers), but what we really need is a routine that can get good scaling across a range of conditions.\n", + "\n", + "The cell below creates a function that perturbs the state of our model significantly. Note that the volumetric flowrate has been increased by two orders of magnitude, the inlet concentrations have changed significantly, and we have also made a small change to the temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver(\n", + " \"ipopt_v2\", writer_config={\"scale_model\": True, \"linear_presolve\": True}\n", + ")\n", + "\n", + "\n", + "def perturb_model(m):\n", + " m.fs.equil.inlet.flow_vol.fix(1 * units.m**3 / units.s)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"NaOH\"].fix(200.0 * units.mol / units.m**3)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"EthylAcetate\"].fix(\n", + " 100.0 * units.mol / units.m**3\n", + " )\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"SodiumAcetate\"].fix(50 * units.mol / units.m**3)\n", + " m.fs.equil.inlet.conc_mol_comp[0, \"Ethanol\"].fix(1e-8 * units.mol / units.m**3)\n", + "\n", + " m.fs.equil.inlet.temperature.fix(320 * units.K)\n", + " solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets apply this perturbation to our example model and see how well it solves." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the scaling factor report, we can see that by calling the submodel scalers we have already scaled many of the variables in our problem, as well as three of the constraints. If we look at the \"Scaled Value\" column for the variables, we can also see that most of the scaled values are close to 1 (the few outliers might be things we want to look into more later on).\n", - "\n", - "From the numerical diagnostics, we can see that the Jacobian condition number has decreased by a few orders of magnitude, although it is still large, whilst we still have a number of potential issues with individual variables and constraints. All up though, this appears to be a step in the right direction." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: linear_solver=ma57\n", + "max_iter=200\n", + "nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma57.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 21\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 9\n", + "\n", + "Total number of variables............................: 8\n", + " variables with only lower bounds: 5\n", + " variables with lower and upper bounds: 1\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 8\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.09e+07 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "Reallocating memory for MA57: lfact (247)\n", + " 1r 0.0000000e+00 9.09e+07 9.99e+02 2.5 0.00e+00 - 0.00e+00 7.73e-09R 9\n", + " 2r 0.0000000e+00 8.42e+07 8.24e+03 2.5 7.20e+02 - 1.64e-02 2.59e-02f 1\n", + " 3r 0.0000000e+00 8.37e+07 7.72e+03 1.8 8.82e+04 - 5.56e-04 3.77e-05f 1\n", + " 4r 0.0000000e+00 3.76e+07 2.65e+04 1.8 1.13e+03 0.0 1.27e-01 1.63e-01f 1\n", + " 5r 0.0000000e+00 3.60e+07 2.30e+04 1.8 6.83e+01 1.3 7.53e-02 1.45e-01f 1\n", + " 6r 0.0000000e+00 4.17e+07 1.77e+04 1.8 2.10e+02 0.9 7.11e-02 1.47e-01f 1\n", + " 7r 0.0000000e+00 4.08e+07 1.75e+04 1.8 3.95e+02 0.4 2.35e-01 8.19e-03f 1\n", + " 8r 0.0000000e+00 3.13e+07 1.75e+04 1.8 1.12e+03 -0.1 3.57e-01 3.16e-02f 1\n", + " 9r 0.0000000e+00 7.77e+06 1.74e+04 1.8 1.00e+04 - 1.43e-02 9.06e-03f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 0.0000000e+00 7.36e+06 1.70e+04 1.8 2.14e+02 - 2.20e-01 2.38e-02f 1\n", + " 11r 0.0000000e+00 5.93e+06 1.67e+04 1.8 1.72e+02 - 7.89e-01 1.95e-01f 1\n", + " 12r 0.0000000e+00 1.54e+06 3.54e+04 1.8 1.06e+02 - 8.80e-01 7.41e-01f 1\n", + " 13r 0.0000000e+00 1.21e+06 2.79e+04 1.8 4.60e+00 - 1.00e+00 2.12e-01h 1\n", + " 14r 0.0000000e+00 3.31e+03 4.79e+01 1.8 2.39e+00 - 1.00e+00 1.00e+00f 1\n", + " 15r 0.0000000e+00 2.85e+03 7.72e+02 -0.2 2.01e+00 - 9.81e-01 9.09e-01f 1\n", + " 16r 0.0000000e+00 2.47e+03 6.60e+02 -0.2 9.87e-01 - 1.00e+00 1.48e-01f 1\n", + " 17r 0.0000000e+00 3.18e-01 8.47e+01 -0.2 1.39e-01 - 1.00e+00 1.00e+00f 1\n", + " 18r 0.0000000e+00 3.18e-01 6.25e+01 -0.2 1.96e-01 - 1.00e+00 1.00e+00f 1\n", + " 19r 0.0000000e+00 3.18e-01 5.46e+00 -0.2 3.26e-02 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20r 0.0000000e+00 3.18e-01 1.44e+02 -1.6 2.34e-01 - 1.00e+00 1.00e+00f 1\n", + " 21r 0.0000000e+00 3.18e-01 1.45e+01 -1.6 9.26e-02 - 9.13e-01 1.00e+00f 1\n", + " 22r 0.0000000e+00 3.18e-01 1.46e+01 -1.6 1.71e-01 - 1.00e+00 1.25e-01f 4\n", + " 23r 0.0000000e+00 3.18e-01 1.44e+01 -1.6 1.24e-01 - 1.00e+00 1.56e-02h 7\n", + " 24r 0.0000000e+00 3.18e-01 1.41e+01 -1.6 1.27e-01 - 1.00e+00 1.56e-02h 7\n", + " 25r 0.0000000e+00 3.18e-01 1.39e+01 -1.6 1.24e-01 - 1.00e+00 1.56e-02h 7\n", + " 26r 0.0000000e+00 3.18e-01 1.37e+01 -1.6 1.22e-01 - 1.00e+00 1.56e-02h 7\n", + " 27r 0.0000000e+00 3.18e-01 1.35e+01 -1.6 1.20e-01 - 1.00e+00 1.56e-02h 7\n", + " 28r 0.0000000e+00 3.18e-01 1.33e+01 -1.6 1.18e-01 - 1.00e+00 1.56e-02h 7\n", + " 29r 0.0000000e+00 3.18e-01 1.31e+01 -1.6 1.17e-01 - 1.00e+00 1.56e-02h 7\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30r 0.0000000e+00 3.18e-01 1.29e+01 -1.6 1.15e-01 - 1.00e+00 1.56e-02h 7\n", + " 31r 0.0000000e+00 3.18e-01 1.28e+01 -1.6 1.13e-01 - 1.00e+00 1.56e-02h 7\n", + " 32r 0.0000000e+00 3.18e-01 4.28e+01 -1.6 1.11e-01 - 1.00e+00 1.00e+00w 1\n", + " 33r 0.0000000e+00 3.18e-01 1.43e-03 -1.6 2.09e-05 - 1.00e+00 1.00e+00w 1\n", + " 34r 0.0000000e+00 3.18e-01 1.37e+01 -3.7 6.94e-02 - 1.00e+00 1.00e+00f 1\n", + " 35r 0.0000000e+00 3.17e-01 3.73e+04 -3.7 3.39e+00 - 1.44e-01 1.00e+00f 1\n", + " 36r 0.0000000e+00 3.17e-01 6.78e+03 -3.7 5.99e-01 - 1.00e+00 1.00e+00f 1\n", + " 37r 0.0000000e+00 3.17e-01 7.66e+00 -3.7 4.92e-03 - 1.00e+00 1.00e+00h 1\n", + " 38r 0.0000000e+00 3.17e-01 1.65e-04 -3.7 9.43e-05 - 1.00e+00 1.00e+00h 1\n", + " 39r 0.0000000e+00 3.17e-01 1.30e+00 -5.6 9.94e-04 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 40r 0.0000000e+00 3.11e-01 6.82e+04 -5.6 3.21e+01 - 1.41e-01 1.00e+00f 1\n", + " 41r 0.0000000e+00 3.11e-01 1.17e+01 -5.6 4.97e-03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 41\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 2.2783833299154238e-01 2.2783833299154238e-01\n", + "Constraint violation....: 3.1132475345243688e-01 3.1132475345243688e-01\n", + "Complementarity.........: 2.7808801399127131e-06 2.7808801399127131e-06\n", + "Overall NLP error.......: 3.1132475345243688e-01 3.1132475345243688e-01\n", + "\n", + "\n", + "Number of objective function evaluations = 109\n", + "Number of objective gradient evaluations = 3\n", + "Number of equality constraint evaluations = 109\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 44\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 42\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.014\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n" + ] + } + ], + "source": [ + "perturb_model(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As can be seen from the solver logs, IPOPT was unable to find a feasible solution to this problem, and went into restoration from the first iteration. However, there is no reason the perturbed conditions should not be feasible (you can verify this with the `infeasibility_explainer` in the Diagnostics Toolbox if you desire).\n", + "\n", + "There are a few reasons for this, most of which can be resolved by providing better scaling for the model. One of the reasons is because we have a number of concentrations approaching zero which results in a number of very small numbers appearing in the problem.\n", + "\n", + "A bigger issue however is the fact that in our initial model we are feeding reactants in stoichiometric amounts (1:1) meaning that both reactant concentrations go to zero at equilibrium. This results in the Jacobian for the reaction rate constraint becoming singular; with `rate = K_rxn * [NaOH] * [EthylAcetate]` if both concentrations go to zero then the partial derivative of the reaction rate with respect to each concentration is also 0, and thus our solver has no idea of what direction to move when trying to converge the problem. Whilst scaling can help work around this, this is ultimately an indication that our problem is not well formulated. In practice, an Equilibrium reactor model is not well suited for systems involving irreversible rate-based reactions as it requires concentrations to be driven to zero, and is an especially poor choice for stoichiometric feeds." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Understanding the Model\n", + "\n", + "Now that we have a test case (or multiple test cases), we can start planning out the new scaling routine. As our goal is to estimate scaling factors for as many of the variables and constraints in the model as possible, the first step is to understand what variables and constraints may be present in the model. Note that we need to be careful to check for all variables and constraints that may exist under different configuration options, and not just those that appear in the our test case(s).\n", + "\n", + "Given the modular nature of IDAES, we need to also make a distinction between those variables and constraints we have direct knowledge of, and those that are created via modular sub-models that we do not know the details of. The most common examples of modular sub-models are the ``StateBlocks`` and ``ReactionBlocks`` created by the associated property packages; we know that these exist and we create these in our models, but we do not know what variables and constraints they may construct. On the other hand, we also have variables and constraints that we construct directly in our model. For the purposes of this we include those variables and constraints constructed by ``ControlVolumes`` as being directly construed; whilst the ``ControlVolume`` might automate the details for us, we directly call methods on the ``ControlVolume`` to create these variables and constraints and we know what they will be based on the instructions we give.\n", + "\n", + "For our example of the ``EquilibriumReactor``, let us take a look at the code in the ``build`` method, which has been copied below for convenience:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def build(self):\n", + " \"\"\"\n", + " Begin building model.\n", + "\n", + " Args:\n", + " None\n", + "\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " # Call UnitModel.build to setup dynamics\n", + " super(EquilibriumReactorData, self).build()\n", + "\n", + " # Build Control Volume\n", + " self.control_volume = ControlVolume0DBlock(\n", + " dynamic=self.config.dynamic, # Config block forces this to be False\n", + " has_holdup=self.config.has_holdup, # Config block forces this to be False\n", + " property_package=self.config.property_package,\n", + " property_package_args=self.config.property_package_args,\n", + " reaction_package=self.config.reaction_package,\n", + " reaction_package_args=self.config.reaction_package_args,\n", + " )\n", + "\n", + " # No need for control volume geometry\n", + "\n", + " self.control_volume.add_state_blocks(\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium\n", + " )\n", + "\n", + " self.control_volume.add_reaction_blocks(\n", + " has_equilibrium=self.config.has_equilibrium_reactions\n", + " )\n", + "\n", + " self.control_volume.add_material_balances(\n", + " balance_type=self.config.material_balance_type,\n", + " has_rate_reactions=self.config.has_rate_reactions,\n", + " has_equilibrium_reactions=self.config.has_equilibrium_reactions,\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", + " )\n", + "\n", + " self.control_volume.add_energy_balances(\n", + " balance_type=self.config.energy_balance_type,\n", + " has_heat_of_reaction=self.config.has_heat_of_reaction,\n", + " has_heat_transfer=self.config.has_heat_transfer,\n", + " )\n", + "\n", + " self.control_volume.add_momentum_balances(\n", + " balance_type=self.config.momentum_balance_type,\n", + " has_pressure_change=self.config.has_pressure_change,\n", + " )\n", + "\n", + " # Add Ports\n", + " self.add_inlet_port()\n", + " self.add_outlet_port()\n", + "\n", + " if self.config.has_rate_reactions:\n", + " # Add equilibrium reactor performance equation\n", + " @self.Constraint(\n", + " self.flowsheet().time,\n", + " self.config.reaction_package.rate_reaction_idx,\n", + " doc=\"Rate reaction equilibrium constraint\",\n", + " )\n", + " def rate_reaction_constraint(b, t, r):\n", + " # Set kinetic reaction rates to zero\n", + " return b.control_volume.reactions[t].reaction_rate[r] == 0\n", + "\n", + " # Set references to balance terms at unit level\n", + " if (\n", + " self.config.has_heat_transfer is True\n", + " and self.config.energy_balance_type != EnergyBalanceType.none\n", + " ):\n", + " self.heat_duty = Reference(self.control_volume.heat[:])\n", + "\n", + " if (\n", + " self.config.has_pressure_change is True\n", + " and self.config.momentum_balance_type != MomentumBalanceType.none\n", + " ):\n", + " self.deltaP = Reference(self.control_volume.deltaP[:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we look through the code in the ``build`` method, we can see that the model contains a single 0D Control Volume with ``StateBlocks``, a ``ReactionBlock``, material, energy and momentum balances and one additional constraint (``rate_reaction_constraint``). Thus, we have the following components that need to be scaled:\n", + "\n", + "3 Sub-Models:\n", + "\n", + "1. The inlet state sub-model (``model.control_volume.properties_in``)\n", + "2. The outlet state sub-model (``model.control_volume.properties_out``)\n", + "3. The reaction sub-model (``model.control_volume.reactions``)\n", + "\n", + "Unit Model Variables (from control volume options):\n", + "\n", + "1. Rate-based reaction extent and generation terms\n", + "2. Equilibrium-based reaction extent and generation terms\n", + "3. Inherent reaction extent and generation terms (no explicit argument, but determined by properties)\n", + "4. Phase equilibrium generation terms\n", + "5. Energy balance heat term\n", + "6. Energy balance heats of reaction\n", + "7. Pressure drop\n", + "\n", + "Unit Model Constraints (from control volume + 1 in the ``build`` method):\n", + "\n", + "1. Material balance constraints\n", + "2. Reaction stoichiometry constraints\n", + "3. Energy balance constraints\n", + "4. Pressure balance constraints\n", + "5. ``rate_reaction_constraint``\n", + "\n", + "When writing our ``Scaler`` we will need to consider all of these to determine how best to estimate scaling factors. Before starting however, we should check the numerical diagnostics for each case study, both to see what scaling issues currently exist and to establish a baseline for comparison once we have a proposed ``Scaler`` for our model.\n", + "\n", + "The cell below calls the ``report_numerical_issues`` method for the unscaled test case." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Apply Variable Scaling\n", - "\n", - "Next, we need to look at scaling the variables and constraints that make up the unit model itself. From a conceptual standpoint, it is generally easiest to start with the variables as we generally have at least some idea of the magnitude of these.\n", - "\n", - "For the equilibrium reactor, we have the following variables we need to scale:\n", - "\n", - "1. Rate-based reaction extent and generation terms\n", - "2. Equilibrium-based reaction extent and generation terms\n", - "3. Inherent reaction extent and generation terms\n", - "4. Phase equilibrium generation terms\n", - "5. Energy balance heat term\n", - "6. Energy balance heats of reaction\n", - "7. Pressure drop\n", - "\n", - "Many of these are hard to know a priori - anything related to a reaction is very hard to know without knowing the reaction behavior. Considering that the equilibrium reactor is modular, we have little to no way of knowing these in the general case (and even in the specific test case it is hard enough). We can assume that the reaction package will scale all of its variables (i.e., rate and equilibrium constants, and reaction rates), however it is hard to project these to unit model scaling.\n", - "\n", - "For a CSTR we can say that ``extent = volume*rate`` and thus estimate scaling, but this does not work for equilibrium systems where 1) volume is undefined, 2) reaction rate at the outlet state is being driven to zero to satisfy equilibrium, and 3) extent is solved implicitly to satisfy the need for reaction rate to equal zero.\n", - "\n", - "Considering that a bad guess is often worse than no guess, we will not scale these right now - it is important to remember that our goal is to improve the overall scaling so if we do not know how to scale something it is generally best to leave it unscaled. We might come back to these later if necessary, but for now we will leave these either for the user to provide based on knowledge of their system, or for automated fill-in using some autoscaler.\n", - "\n", - "For the heat and deltaP terms, these are dependent on extensive variables in each case study and we have no way of knowing their exact values. However, we can probably take a good guess at order-of-magnitude using engineering knowledge; heat duties are generally approximately one order of magnitude smaller than the enthalpy flows,\n", - "and pressure drops are generally on the order of 0.1 bar.\n", - "\n", - "To apply scaling for the pressure drop term, we can make use of the ``scale_variable_by_units`` method in ``CustomScalerBase``. This method looks up the units of measurement for the variable, and then loops in the class attribute ``UNIT_SCALING_FACTORS`` dictionary to find an equivalent unit for the quantity of interest and an associated scaling factor. If a scaling factor is found, it is converted as necessary; e.g., in this case pressure is defined in ``Pa`` but we can set the default scaling factor in ``bar`` and it will be converted as appropriate. The code required to do this is below.\n", - "\n", - "```python\n", - "UNIT_SCALING_FACTORS = {\n", - " # \"QuantityName: (reference units, scaling factor)\n", - " \"Pressure Change\": (units.bar, 10),\n", - "}\n", - "\n", - "def variable_scaling_routine(*args, **kwargs):\n", - " if hasattr(model.control_volume, \"deltaP\"):\n", - " for t in model.flowsheet().time:\n", - " self.scale_variable_by_units(\n", - " model.control_volume.deltaP[t],\n", - " overwrite=overwrite\n", - " )\n", - "```\n", - "\n", - "There are a few things to note here:\n", - "\n", - "1. As we expect the pressure drop to be on the order of 0.1 bar, we need to set a scaling factor of 10 for quantities with units of pressure. Also note that the key ``\"Pressure Change\"`` is for documentation purposes only and is not actually used by the code (but must be there). \n", - "\n", - "
\n", - "NOTE We cannot distinguish between different quantities with the same apparent units (e.g., we cannot distinguish between an absolute pressure and a pressure change).\n", - "
\n", - "\n", - "2. Note that scaling is applied to elements of indexed components and not to the indexed component as a whole, and thus we need to use a ``for`` loop to iterate over the time index. This is done to force modelers to consider how the scaling of a variable or constraint will vary over the indexed domain, and try to discourage automatically setting a single scaling factor for all points.\n", - "3. Pressure change is a configuration argument in our unit model, and thus may not be present in all cases. Therefore, we need the ``hasattr`` check to see if we need to scale ``deltaP`` or not.\n", - "\n", - "For the case of the heat duty, we want to scale based on the incoming enthalpy flow which means we first need to get the expected magnitude of the enthalpy flow. For that, we can use the ``get_expression_nominal_values`` method in ``CustomScalerBase`` which uses an expression walker to go through an expression to return a list of the expected magnitude (or nominal value) of all additive terms in the expression based on the scaling factors for the variables involved.\n", - "\n", - "We can get an expression for the enthalpy flow term using the ``get_enthalpy_flow_terms`` method from the associated ``StateBlock``. We should assume this expression might contain multiple terms, so we should sum all the values returned to get the overall magnitude of the enthalpy flow term. Once we have this, we can then get the scaling factor for the heat duty by ``sf = abs(1/(0.1*enthalpy_flow))`` - note that the tools insist on scaling factors being positive (for sanity) and thus we need the absolute value here in case enthalpy flow is negative (which is not uncommon for enthalpy). The code to do this is shown below.\n", - "\n", - "```python\n", - "if hasattr(model.control_volume, \"heat\"):\n", - " for t in model.flowsheet().time:\n", - " h_in = 0\n", - " for p in model.control_volume.properties_in.phase_list:\n", - " # The expression for enthalpy flow might include multiple terms,\n", - " # so we will sum over all the terms provided\n", - " h_in += sum(\n", - " self.get_expression_nominal_values(\n", - " model.control_volume.properties_in[t].get_enthalpy_flow_terms(p)\n", - " )\n", - " )\n", - " # Scale for heat is general one order of magnitude less than enthalpy flow\n", - " self.set_variable_scaling_factor(model.control_volume.heat[t], abs(1 / (0.1 * h_in)))\n", - "```\n", - "\n", - "Putting all of this together results in the code below for our ``EquilibriumReactorScaler`` class." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 1.540E+12\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: 1 Constraint with large residuals (>1.0E-05)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "7 Cautions\n", + "\n", + " Caution: 1 Variable with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 4 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", + " Caution: 1 Constraint with mismatched terms\n", + " Caution: 3 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 2 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 6 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " compute_infeasibility_explanation()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the results of the diagnostics, we can see that the test case is not particularly well scaled. The Jacobian condition number is rather large (1e12), and the diagnostics are reporting a number of variables with extremely large or small values, and 3 variables and 2 constraints with poorly scaled Jacobians. As we develop our new ``Scaler`` for the ``EquilibriumReactor`` we will hopefully see these improve.\n", + "\n", + "We can also use the Diagnostics Toolbox to further explore these issues to get a better idea of which variables and constraints might be causing issues. For example, lets display the set of variables and constraints with extreme Jacobian norms." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import units\n", - "\n", - "\n", - "class EquilibriumReactorScaler(CustomScalerBase):\n", - " # =======================================================================================\n", - " # New Code\n", - " UNIT_SCALING_FACTORS = {\n", - " # \"QuantityName: (reference units, scaling factor)\n", - " \"Pressure Change\": (units.bar, 10),\n", - " }\n", - " # =======================================================================================\n", - "\n", - " def variable_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.propagate_state_scaling(\n", - " target_state=model.control_volume.properties_out,\n", - " source_state=model.control_volume.properties_in,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " # =======================================================================================\n", - " # New Code\n", - "\n", - " # Pressure drop - optional\n", - " if hasattr(model.control_volume, \"deltaP\"):\n", - " for t in model.flowsheet().time:\n", - " self.scale_variable_by_units(\n", - " model.control_volume.deltaP[t], overwrite=overwrite\n", - " )\n", - "\n", - " # Heat transfer - optional\n", - " # Scale heat based on enthalpy flow entering reactor\n", - " if hasattr(model.control_volume, \"heat\"):\n", - " for t in model.flowsheet().time:\n", - " h_in = 0\n", - " for p in model.control_volume.properties_in.phase_list:\n", - " # The expression for enthalpy flow might include multiple terms,\n", - " # so we will sum over all the terms provided\n", - " h_in += sum(\n", - " self.get_expression_nominal_values(\n", - " model.control_volume.properties_in[\n", - " t\n", - " ].get_enthalpy_flow_terms(p)\n", - " )\n", - " )\n", - " # Scale for heat is generally one order of magnitude less than enthalpy flow\n", - " self.set_variable_scaling_factor(\n", - " model.control_volume.heat[t], abs(1 / (0.1 * h_in))\n", - " )\n", - " # =======================================================================================\n", - "\n", - " def constraint_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) are associated with extreme Jacobian values (<1.0E-04 or>1.0E+04):\n", + "\n", + " fs.equil.control_volume.properties_out[0.0].flow_vol: 9.427E+07\n", + " fs.equil.control_volume.properties_out[0.0].temperature: 4.172E+06\n", + " fs.equil.control_volume.rate_reaction_extent[0.0,R1]: 4.900E+04\n", + "\n", + "====================================================================================\n", + "====================================================================================\n", + "The following constraint(s) are associated with extreme Jacobian values (<1.0E-04 or>1.0E+04):\n", + "\n", + " fs.equil.control_volume.enthalpy_balances[0.0]: 9.436E+07\n", + " fs.equil.control_volume.material_balances[0.0,Liq,H2O]: 5.539E+04\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_with_extreme_jacobians()\n", + "dt.display_constraints_with_extreme_jacobians()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These diagnostics can help give us an idea of what may be causing problems in our model. From the output above, we can see that the variables with large Jacobian norms (i.e., high sensitivities) are the outlet flow rate and temperature, as well as the rate-based extent of reaction. We can also see that the constraints with large Jacobian norms are the enthalpy balance and H20 material balance for the reactor. However, caution must be used when interpreting these in isolation, as understanding what these mean is often complicated and initial impressions may be misleading. To get a better picture of what is contributing to extreme Jacobian values you should make use of the tools in the diagnostics ``SVDToolbox``, however that is a topic for another example.\n", + "\n", + "For example, one might wonder why the volumetric flow rate at the outlet of the reactor is so important as it is effectively determined by the inlet flow rate (due to the water balance effectively conserving volume). However, it is important to remember that the Jacobian does not consider the value of the variable, but rather its partial derivatives. Thus, it is important to compare the list of variables and constraints with large Jacobian norms and think about how those intersect.\n", + "\n", + "Let's start by taking a look at the H2O material balance. The cell below prints the constraint expression in a compact form that only shows top level ``Expressions`` rather than expanding these to show the full expression tree." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once again, lets run the ``check_scaling`` function and see how we are going." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.equil.control_volume.properties_in[0.0].flow_vol*fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] - fs.equil.control_volume.properties_out[0.0].flow_vol*fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] + fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] == 0" + ] + } + ], + "source": [ + "from idaes.core.util.misc import print_compact_form\n", + "\n", + "print_compact_form(m.fs.equil.control_volume.material_balances[0, \"Liq\", \"H2O\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at how the outlet volumetric flowrate appears in the H2O balance equation above, it can be seen that the volumetric flow term is multiplied by the molar concentration of water, $F \\times C_{H2O}$. Whilst $C_{H2O}$ is assumed to be constant in this model (and equal to the molar density of pure water at ambient conditions), this means that the partial derivative of the constraint term with respect to flow is $\\frac{\\partial F C_{H2O}}{\\partial F} = C_{H2O}$; given that $C_{H2O}$ is equal to 5.5E4 mol/liter, you can quickly see why it is being identified as an issue.\n", + "\n", + "If we look at the energy balance, we will find that it is similar." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Solved\n", - "\n", - "Scaling Factors for fs.equil\n", - "\n", - "Variable Scaling Factor Value Scaled Value\n", - "fs.equil.control_volume.properties_in[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[NaOH] 1.000E-02 1.000E+02 1.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 1.000E+02 1.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[Ethanol] 1.000E-02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].temperature 3.219E-03 3.031E+02 9.759E-01\n", - "fs.equil.control_volume.properties_in[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", - "fs.equil.control_volume.properties_out[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[NaOH] 1.000E-02 6.250E-02 6.250E-04\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 6.250E-02 6.250E-04\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 9.994E+01 9.994E-01\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[Ethanol] 1.000E-02 9.994E+01 9.994E-01\n", - "fs.equil.control_volume.properties_out[0.0].temperature 3.219E-03 3.043E+02 9.796E-01\n", - "fs.equil.control_volume.properties_out[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", - "fs.equil.control_volume.heat[0.0] 4.794E-04 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.deltaP[0.0] 1.000E-04 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] None 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,NaOH] None -9.994E-02 -9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,EthylAcetate] None -9.994E-02 -9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,SodiumAcetate] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,Ethanol] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.rate_reaction_extent[0.0,R1] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.reactions[0.0].reaction_rate[R1] 1.000E+02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.reactions[0.0].k_rxn 5.424E+00 1.304E-01 7.075E-01\n", - "\n", - "Constraint Scaling Factor\n", - "fs.equil.rate_reaction_constraint[0.0,R1] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,H2O] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,NaOH] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,EthylAcetate] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,SodiumAcetate] None\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,Ethanol] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,H2O] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,NaOH] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,EthylAcetate] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,SodiumAcetate] None\n", - "fs.equil.control_volume.material_balances[0.0,Liq,Ethanol] None\n", - "fs.equil.control_volume.enthalpy_balances[0.0] None\n", - "fs.equil.control_volume.pressure_balance[0.0] None\n", - "fs.equil.control_volume.properties_out[0.0].conc_water_eqn 1.000E-04\n", - "fs.equil.control_volume.reactions[0.0].rate_expression[R1] 5.424E-04\n", - "fs.equil.control_volume.reactions[0.0].arrhenius_eqn 5.424E+00\n", - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 3.022E+09\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "5 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 4 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 2 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 6 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "check_scaling()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.properties.dens_mol*fs.properties.cp_mol*fs.equil.control_volume.properties_in[0.0].flow_vol*(fs.equil.control_volume.properties_in[0.0].temperature - fs.properties.temperature_ref) - fs.properties.dens_mol*fs.properties.cp_mol*fs.equil.control_volume.properties_out[0.0].flow_vol*(fs.equil.control_volume.properties_out[0.0].temperature - fs.properties.temperature_ref) + fs.equil.control_volume.heat[0.0] + fs.equil.control_volume.heat_of_reaction[0.0] == 0" + ] + } + ], + "source": [ + "print_compact_form(m.fs.equil.control_volume.enthalpy_balances[0.0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whilst a bit harder to read due to the size of the constraint, you can see that it involves the term $\\rho \\times c_p \\times F \\times (T - T_{ref})$, where $c_p$ is the specific molar heat capacity of the solution, $T$ is temperature and $T_{ref}$ is the reference temperature. Given that $\\rho$ is of order 1E4 (a) and $c_p \\times (T-T_{ref})$ is of order 1E3, this means that the partial derivative with respect to the volumetric flowrate is even larger than that for the H2O balance. This also explains the appearance of the outlet temperature as well, as we can see that it is multiplied by a number of large values as well and thus has a large partial derivative.\n", + "\n", + "It is also important to mention that having a large value in the Jacobian does not mean a variable is \"important\" (and conversely a small value is not unimportant). What is important is how sensitive the constraint residual is to that change in variable, which is often difficult to assess from the Jacobian alone (which is where the ``SVDToolbox`` can assist).\n", + "\n", + "\n", + "## Step 3: Creating a New Scaler Class\n", + "\n", + "To create a new scaling routine for the equilibrium reactor, we start by creating a new ``Scaler`` class which inherits from the ``CustomScalerBase`` class in ``idaes.core.scaling``. The ``CustomScalerBase`` class contains a number of useful methods to help us in developing our scaling routine, including some placeholder methods for implementing a standard scaling workflow and helper methods for doing common tasks.\n", + "\n", + "The cell below shows how to create our new class which we will name ``EquilibriumReactorScaler`` as well as two key methods we will fill out as part of this workshop." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.scaling import CustomScalerBase\n", + "\n", + "\n", + "class EquilibriumReactorScaler(CustomScalerBase):\n", + " def variable_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Empty method for now\n", + " pass\n", + "\n", + " def constraint_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Empty method for now\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ``variable_scaling_routine`` and ``constraint_scaling_routine`` methods are used to implement subroutines for scaling the variables and constraints in the model respectively. Separately, there is a ``scale_model`` method that will call each of these in sequence in order to scale an entire model by applying the following steps:\n", + "\n", + "1. apply variable scaling routine,\n", + "2. apply first stage scaling fill-in,\n", + "3. apply constraint scaling routine,\n", + "4. apply second stage scaling fill-in.\n", + "\n", + "The second and fourth steps are intended to allow users to provide methods to fill in missing scaling information that was not provided by the first and second steps, or to provide a way to update the scaling factors with more information.\n", + "\n", + "Both the ``variable_scaling_routine`` and ``constraint_scaling_routine`` are user-facing methods and take three arguments.\n", + "\n", + "1. The model to be scaled.\n", + "2. An argument indicating whether to overwrite any existing scaling factors. Generally we assume that any existing scaling factors were provided by the user for a reason, so by default we set this to ``False``. However, there will likely be cases where a user wants to overwrite their existing scaling factors so this argument exists to let us pass on those instructions.\n", + "3. A mapping of user-provided ``Scalers`` to use when scaling submodels.\n", + "\n", + "## Step 4: Apply Scaling to Sub-Models\n", + "\n", + "First, lets look at how to scale the property and reaction sub-models. As these are modular packages, we do not know what variables and constraints may be in them, so we cannot (and should not) scale any of these directly. However, we can (hopefully) assume that there are ``Scalers`` available for these sub-models, either through default ``Scalers`` associated with the property packages or provided by the user. Thus, what we want to do here is to call the variable and constraint scaling routines from the ``Scaler`` associated with each sub-model, which we can do using the ``call_submodel_scaler_method`` method from the ``CustomScalerBase`` class.\n", + "\n", + "The cell below prints the doc-string for the ``call_submodel_scaler_method`` method so we can see what the expected arguments are." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our updates have resulted in scaling factors for ``heat`` and ``deltaP`` appearing in the scaling report which is good, but comparing the diagnostics from the previous step we can see that the Jacobian condition number has not changed. Does this mean we did something wrong?\n", - "\n", - "The answer is no - when we add a scaling factor to a variable, wherever that variable appears in a constraint it is replaced with ``sf*v_scaled``. Given that ``v_scaled = v/sf``, this means that for variables which only appear linearly in constraints then the partial derivative with respect to the scaled variable does not change either; thus the Jacobian is unaffected by scaling only the linear variables. In the case of this example, it turns out that almost all the variables appear linearly and thus we see no change in the Jacobian condition number.\n", - "\n", - "
\n", - "NOTE It is important to note that partial scaling of a model (e.g., variables only) can often appear worse than that of the unscaled model. Generally, it is best to wait until you have scaled both variables and constraints to make a decision on whether your attempts at scaling have made the problem better or worse, and you should not be discouraged if things look worse while in an intermediate state.\n", - "
\n", - "\n", - "\n", - "## Step 6: Apply Constraint Scaling\n", - "\n", - "Now that we have scaled all the variables that we can (for now at least), we can move on to scaling constraints. The advantage of scaling all the variables first means that now we have an idea of the expected magnitude for all terms in the constraints which we can use to estimate scaling factors. For the Equilibrium reactor model, we need to scale all the constraints in the control volume, as well as the unit level constraint equating all reaction rates to zero.\n", - "\n", - "There are many approaches to estimating scaling for constraints, and different approaches are better suited to certain situations. ``CustomScalerBase`` contains a ``scale_constraint_by_nominal_value`` method which can be used to automatically implement a number of common approaches to save you the effort of having to manually implement these yourself. As of writing, the approaches (or schemes) supported are:\n", - "\n", - "1. ``ConstraintScalingScheme.inverseMaximum`` - scale the constraint based on the term with the largest absolute expected magnitude. This is scheme is useful for cases where most terms have similar magnitudes and is a good initial point to start.\n", - "2. ``ConstraintScalingScheme.inverseMinimum`` - scale the constraint based on the term with the smallest absolute expected magnitude. This scheme is similar to the inverse maximum scheme and is useful for cases where you have a constraint with a number of smaller terms mixed with a few larger terms, or cases where the smaller term is expected to be most significant. This scheme should be used carefully however as it can result in large scaling factors making convergence of larger terms difficult.\n", - "3. ``ConstraintScalingScheme.harmonicMean`` - scale the constraint using the harmonic mean of the absolute expected magnitude of all terms (``sf = sum(1/abs(nominal value))``). This scheme is most useful when you have a constraint with terms with a mix of expected magnitudes where you need to find a balance between the large and small terms.\n", - "4. ``ConstraintScalingScheme.inverseSum`` - scale the constraint using the sum of the absolute expected magnitudes of all terms. Situationally useful for cases with terms of mixed magnitudes.\n", - "5. ``ConstraintScalingScheme.inverseRSS`` - scale the constraint using the root sum of squares of the absolute expected magnitudes of all terms. Situationally useful for cases with terms of mixed magnitudes.\n", - "\n", - "``CustomScalerBase`` also contains a ``scale_constraint_by_nominal_derivative_norm`` method that can scale a constraint based on an estimate of the Jacobian norm associated with that constraint which can be useful for cases where you want to focus on the Jacobian scaling.\n", - "\n", - "
\n", - "NOTE The solver you intend to use may impact which approach provides the best scaling for a given model. For example, IPOPT has very good internal Jacobian scaling (when using the `gradient-based` scaling option), and thus benefits the most from focusing on scaling the constraint residual magnitudes as opposed to the Jacobian.\n", - "
\n", - "\n", - "For this workshop, we will start by just using ``ConstraintScalingScheme.inverseMaximum`` to get a starting point and to see if further scaling is required. We can apply this scheme to scale all the constraints in the control volume using the code below.\n", - "\n", - "```python\n", - "for c in model.control_volume.component_data_objects(\n", - " Constraint, descend_into=False\n", - "):\n", - " self.scale_constraint_by_nominal_value(\n", - " c,\n", - " scheme=ConstraintScalingScheme.inverseMaximum,\n", - " overwrite=overwrite,\n", - " )\n", - "```\n", - "\n", - "Adding this and a similar approach to scale the unit level constraint gives us the code below for our ``EquilibriumreactorScaler`` class." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function call_submodel_scaler_method in module idaes.core.scaling.custom_scaler_base:\n", + "\n", + "call_submodel_scaler_method(self, submodel, method: str, submodel_scalers: pyomo.common.collections.component_map.ComponentMap = None, overwrite: bool = False)\n", + " Call scaling method for submodel.\n", + " \n", + " Scaler for submodel is taken from submodel_scalers if present, otherwise the\n", + " default scaler for the submodel is used.\n", + " \n", + " Args:\n", + " submodel: submodel to be scaled\n", + " submodel_scalers: user provided ComponentMap of Scalers to use for submodels\n", + " method: name of method to call from submodel (as string)\n", + " overwrite: whether to overwrite existing scaling factors\n", + " \n", + " Returns:\n", + " None\n", + "\n" + ] + } + ], + "source": [ + "help(CustomScalerBase.call_submodel_scaler_method)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that ``call_submodel_scaler_method`` takes 4 arguments:\n", + "\n", + "1. ``submodel`` is the submodel we want to scale. \n", + "2. The ``submodel_scalers`` argument should be passed through from the ``variable_scaling_routine`` or ``constraint_scaling_routine`` method.\n", + "3. The name of the method we want to call from the ``Scaler`` when we get it - this will normally be either ``variable_scaling_routine`` (if we are scaling variables) or ``constraint_scaling_routine`` (if we are doing constraints).\n", + "4. The ``overwrite`` argument should also be passed through from the ``variable_scaling_routine`` or ``constraint_scaling_routine`` method.\n", + "\n", + "For the Equilibrium Reactor, we have three submodels to scale; inlet state, outlet state and reactions. As mentioned in the introduction, when developing scaling routines always start with the things you have the most information about. In this case, we likely know the most about the inlet state; either it is a defined feed state (like in our test case) or we have some idea of the state (and scaling) from propagating values from an upstream operation. So, to apply variable scaling to the inlet state we would do the following:\n", + "\n", + "```python\n", + "self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + ")\n", + "```\n", + "\n", + "Once we have an idea of scaling for the inlet we can use that information to try to estimate scaling for the outlet state. The default assumption is that the scaling of the outlet will be similar to that of the inlet, so the easy path is to copy scaling from the inlet state to the outlet. However, we know that something must change between inlet and outlet (as otherwise this unit operation is doing nothing) so we should always stop and think about whether we can try to estimate these changes. For example, in a pressure changer we know, or be able to estimate, the pressure change across the unit and thus be able to change the scaling of pressure between the inlet and outlet. However, keep in mind that over-scaling can make things worse so be judicious when deciding whether to adjust scaling based on estimates.\n", + "\n", + "In regards to this, Equilibrium Reactors are one of the more challenging units to scale, as it is very hard to know what the outlet flows and concentrations will be without knowing what the reactions are (and even if you know the reactions it is often hard to know the equilibrium state). In most cases, we have no reliable way to estimate the outlet flowrate and concentrations, so this is best left to the user to provide. In the case of temperature and pressure, whilst we may expect these to change but any change will generally be 1-2 orders of magnitude less than the inlet state and thus the overall scale of these will likely remain similar. Thus, for the Equilibrium Reactor it is probably sufficient to just scale the outlet state based on the inlet state.\n", + "\n", + "The ``CustomScalerBase`` class has a method for propagating scaling factors for state variables from one state to another called ``propagate_state_scaling`` as see below." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.scaling import ConstraintScalingScheme\n", - "\n", - "\n", - "class EquilibriumReactorScaler(CustomScalerBase):\n", - " UNIT_SCALING_FACTORS = {\n", - " # \"QuantityName: (reference units, scaling factor)\n", - " \"Pressure Change\": (units.bar, 10),\n", - " }\n", - "\n", - " def variable_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.propagate_state_scaling(\n", - " target_state=model.control_volume.properties_out,\n", - " source_state=model.control_volume.properties_in,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"variable_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " # Pressure drop - optional\n", - " if hasattr(model.control_volume, \"deltaP\"):\n", - " for t in model.flowsheet().time:\n", - " self.scale_variable_by_units(\n", - " model.control_volume.deltaP[t], overwrite=overwrite\n", - " )\n", - "\n", - " # Heat transfer - optional\n", - " # Scale heat based on enthalpy flow entering reactor\n", - " if hasattr(model.control_volume, \"heat\"):\n", - " for t in model.flowsheet().time:\n", - " h_in = 0\n", - " for p in model.control_volume.properties_in.phase_list:\n", - " # The expression for enthalpy flow might include multiple terms,\n", - " # so we will sum over all the terms provided\n", - " h_in += sum(\n", - " self.get_expression_nominal_values(\n", - " model.control_volume.properties_in[\n", - " t\n", - " ].get_enthalpy_flow_terms(p)\n", - " )\n", - " )\n", - " # Scale for heat is generally one order of magnitude less than enthalpy flow\n", - " self.set_variable_scaling_factor(\n", - " model.control_volume.heat[t], abs(1 / (0.1 * h_in))\n", - " )\n", - "\n", - " def constraint_scaling_routine(\n", - " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", - " ):\n", - " # Call scaling methods for sub-models\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_in,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.properties_out,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - " self.call_submodel_scaler_method(\n", - " submodel=model.control_volume.reactions,\n", - " method=\"constraint_scaling_routine\",\n", - " submodel_scalers=submodel_scalers,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " # =======================================================================================\n", - " # New Code\n", - " # Scale control volume constraints\n", - " for c in model.control_volume.component_data_objects(\n", - " Constraint, descend_into=False\n", - " ):\n", - " self.scale_constraint_by_nominal_value(\n", - " c,\n", - " scheme=ConstraintScalingScheme.inverseMaximum,\n", - " overwrite=overwrite,\n", - " )\n", - "\n", - " # Scale unit level constraints\n", - " if hasattr(model, \"rate_reaction_constraint\"):\n", - " for c in model.rate_reaction_constraint.values():\n", - " self.scale_constraint_by_nominal_value(\n", - " c,\n", - " scheme=ConstraintScalingScheme.inverseMaximum,\n", - " overwrite=overwrite,\n", - " )\n", - " # =======================================================================================" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function propagate_state_scaling in module idaes.core.scaling.custom_scaler_base:\n", + "\n", + "propagate_state_scaling(self, target_state, source_state, overwrite: bool = False)\n", + " Propagate scaling of state variables from one StateBlock to another.\n", + " \n", + " Indexing of target and source StateBlocks must match.\n", + " \n", + " Args:\n", + " target_state: StateBlock to set scaling factors on\n", + " source_state: StateBlock to use as source for scaling factors\n", + " overwrite: whether to overwrite existing scaling factors\n", + " \n", + " Returns:\n", + " None\n", + "\n" + ] + } + ], + "source": [ + "help(CustomScalerBase.propagate_state_scaling)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see that ``propagate_state_scaling`` takes three arguments; the ``StateBlock`` we want to apply scaling to, the ``StateBlock`` we want to use as the source for the scaling factors, and the ``overwrite`` argument. Thus, we can propagate scaling from the inlet state to the outlet state as shown below.\n", + "\n", + "```python\n", + "self.propagate_state_scaling(\n", + " target_state=model.control_volume.properties_out,\n", + " source_state=model.control_volume.properties_in,\n", + " overwrite=overwrite,\n", + ")\n", + "```\n", + "\n", + "This only propagates scaling factors for the state variables, however, so we should then call the ``Scaler`` for the outlet state block to scale any remaining variables and constraints (which will hopefully make use of the scaling factors for the state variables we just propagated).\n", + "\n", + "We can then move on to scaling the ``ReactionBlock``. ``ReactionBlocks`` are slightly unusual in that they rely heavily on the state variables defined in a separate ``StateBlock`` - in this case the outlet state block. As we just applied a ``Scaler`` to the outlet state block, we can assume that all of the necessary variables have been scaled so all we need to do now is call a ``Scaler`` for the ``ReactionBlock``.\n", + "\n", + "All of this is shown in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class EquilibriumReactorScaler(CustomScalerBase):\n", + " def variable_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.propagate_state_scaling(\n", + " target_state=model.control_volume.properties_out,\n", + " source_state=model.control_volume.properties_in,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " def constraint_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Empty method for now\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then take a similar approach for the constraint scaling routine as shown below. Note that there is no need for a propagation step here as the residual of a constraint is derived from the value of the variables (which we handled in the variable scaling step)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class EquilibriumReactorScaler(CustomScalerBase):\n", + " def variable_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.propagate_state_scaling(\n", + " target_state=model.control_volume.properties_out,\n", + " source_state=model.control_volume.properties_in,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " def constraint_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets do a quick check to see if our new scaler works and how it has affected the model scaling. The cell below creates a function that builds a new instance of the model (to avoid contamination from previous model runs then creates an instance of our new scaler and applies it to the model. We then solve the scaled model (adding scaling changes constraint residuals so we want to solve to the scaled state). Finally, the function prints a report of the scaling factors in the model and calls the ``report_numerical_issues`` method from the Diagnostics Toolbox." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import check_optimal_termination, TransformationFactory\n", + "\n", + "from idaes.core.scaling import report_scaling_factors\n", + "\n", + "\n", + "def check_scaling(tee=False):\n", + " # Build new instance of model\n", + " m = build_model()\n", + "\n", + " # Apply scaler to model\n", + " scaler = EquilibriumReactorScaler()\n", + " scaler.scale_model(m.fs.equil)\n", + "\n", + " # Solve scaled model\n", + " results = solver.solve(m, tee=tee)\n", + " if check_optimal_termination(results):\n", + " print(\"\\nModel Solved\\n\")\n", + " else:\n", + " print(\"\\nModel Failed to Converge!\\n\")\n", + "\n", + " # Print report of scaling factors\n", + " report_scaling_factors(m.fs.equil, descend_into=True)\n", + "\n", + " # Show numerical issues report\n", + " sm = TransformationFactory(\"core.scale_model\").create_using(m, rename=False)\n", + "\n", + " dt = DiagnosticsToolbox(model=sm.fs.equil)\n", + " dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets run the ``check_scaling`` function and see how the model scaling has changed." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once again, let us use the ``check_scaling`` function to see how our ``Scaler`` performs." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model Solved\n", + "\n", + "Scaling Factors for fs.equil\n", + "\n", + "Variable Scaling Factor Value Scaled Value\n", + "fs.equil.control_volume.properties_in[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[NaOH] 1.000E-02 1.000E+02 1.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 1.000E+02 1.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[Ethanol] 1.000E-02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].temperature 3.219E-03 3.031E+02 9.759E-01\n", + "fs.equil.control_volume.properties_in[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", + "fs.equil.control_volume.properties_out[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[NaOH] 1.000E-02 6.250E-02 6.250E-04\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 6.250E-02 6.250E-04\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 9.994E+01 9.994E-01\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[Ethanol] 1.000E-02 9.994E+01 9.994E-01\n", + "fs.equil.control_volume.properties_out[0.0].temperature 3.219E-03 3.043E+02 9.796E-01\n", + "fs.equil.control_volume.properties_out[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", + "fs.equil.control_volume.heat[0.0] None 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.deltaP[0.0] None 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] None 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,NaOH] None -9.994E-02 -9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,EthylAcetate] None -9.994E-02 -9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,SodiumAcetate] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,Ethanol] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.rate_reaction_extent[0.0,R1] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.reactions[0.0].reaction_rate[R1] 1.000E+02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.reactions[0.0].k_rxn 5.424E+00 1.304E-01 7.075E-01\n", + "\n", + "Constraint Scaling Factor\n", + "fs.equil.rate_reaction_constraint[0.0,R1] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,H2O] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,NaOH] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,EthylAcetate] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,SodiumAcetate] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,Ethanol] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,H2O] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,NaOH] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,EthylAcetate] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,SodiumAcetate] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,Ethanol] None\n", + "fs.equil.control_volume.enthalpy_balances[0.0] None\n", + "fs.equil.control_volume.pressure_balance[0.0] None\n", + "fs.equil.control_volume.properties_out[0.0].conc_water_eqn 1.000E-04\n", + "fs.equil.control_volume.reactions[0.0].rate_expression[R1] 5.424E-04\n", + "fs.equil.control_volume.reactions[0.0].arrhenius_eqn 5.424E+00\n", + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 3.022E+09\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 4 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 2 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 6 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "check_scaling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the scaling factor report, we can see that by calling the submodel scalers we have already scaled many of the variables in our problem, as well as three of the constraints. If we look at the \"Scaled Value\" column for the variables, we can also see that most of the scaled values are close to 1 (the few outliers might be things we want to look into more later on).\n", + "\n", + "From the numerical diagnostics, we can see that the Jacobian condition number has decreased by a few orders of magnitude, although it is still large, whilst we still have a number of potential issues with individual variables and constraints. All up though, this appears to be a step in the right direction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Apply Variable Scaling\n", + "\n", + "Next, we need to look at scaling the variables and constraints that make up the unit model itself. From a conceptual standpoint, it is generally easiest to start with the variables as we generally have at least some idea of the magnitude of these.\n", + "\n", + "For the equilibrium reactor, we have the following variables we need to scale:\n", + "\n", + "1. Rate-based reaction extent and generation terms\n", + "2. Equilibrium-based reaction extent and generation terms\n", + "3. Inherent reaction extent and generation terms\n", + "4. Phase equilibrium generation terms\n", + "5. Energy balance heat term\n", + "6. Energy balance heats of reaction\n", + "7. Pressure drop\n", + "\n", + "Many of these are hard to know a priori - anything related to a reaction is very hard to know without knowing the reaction behavior. Considering that the equilibrium reactor is modular, we have little to no way of knowing these in the general case (and even in the specific test case it is hard enough). We can assume that the reaction package will scale all of its variables (i.e., rate and equilibrium constants, and reaction rates), however it is hard to project these to unit model scaling.\n", + "\n", + "For a CSTR we can say that ``extent = volume*rate`` and thus estimate scaling, but this does not work for equilibrium systems where 1) volume is undefined, 2) reaction rate at the outlet state is being driven to zero to satisfy equilibrium, and 3) extent is solved implicitly to satisfy the need for reaction rate to equal zero.\n", + "\n", + "Considering that a bad guess is often worse than no guess, we will not scale these right now - it is important to remember that our goal is to improve the overall scaling so if we do not know how to scale something it is generally best to leave it unscaled. We might come back to these later if necessary, but for now we will leave these either for the user to provide based on knowledge of their system, or for automated fill-in using some autoscaler.\n", + "\n", + "For the heat and deltaP terms, these are dependent on extensive variables in each case study and we have no way of knowing their exact values. However, we can probably take a good guess at order-of-magnitude using engineering knowledge; heat duties are generally approximately one order of magnitude smaller than the enthalpy flows,\n", + "and pressure drops are generally on the order of 0.1 bar.\n", + "\n", + "To apply scaling for the pressure drop term, we can make use of the ``scale_variable_by_units`` method in ``CustomScalerBase``. This method looks up the units of measurement for the variable, and then loops in the class attribute ``UNIT_SCALING_FACTORS`` dictionary to find an equivalent unit for the quantity of interest and an associated scaling factor. If a scaling factor is found, it is converted as necessary; e.g., in this case pressure is defined in ``Pa`` but we can set the default scaling factor in ``bar`` and it will be converted as appropriate. The code required to do this is below.\n", + "\n", + "```python\n", + "UNIT_SCALING_FACTORS = {\n", + " # \"QuantityName: (reference units, scaling factor)\n", + " \"Pressure Change\": (units.bar, 10),\n", + "}\n", + "\n", + "def variable_scaling_routine(*args, **kwargs):\n", + " if hasattr(model.control_volume, \"deltaP\"):\n", + " for t in model.flowsheet().time:\n", + " self.scale_variable_by_units(\n", + " model.control_volume.deltaP[t],\n", + " overwrite=overwrite\n", + " )\n", + "```\n", + "\n", + "There are a few things to note here:\n", + "\n", + "1. As we expect the pressure drop to be on the order of 0.1 bar, we need to set a scaling factor of 10 for quantities with units of pressure. Also note that the key ``\"Pressure Change\"`` is for documentation purposes only and is not actually used by the code (but must be there). \n", + "\n", + "
\n", + "NOTE We cannot distinguish between different quantities with the same apparent units (e.g., we cannot distinguish between an absolute pressure and a pressure change).\n", + "
\n", + "\n", + "2. Note that scaling is applied to elements of indexed components and not to the indexed component as a whole, and thus we need to use a ``for`` loop to iterate over the time index. This is done to force modelers to consider how the scaling of a variable or constraint will vary over the indexed domain, and try to discourage automatically setting a single scaling factor for all points.\n", + "3. Pressure change is a configuration argument in our unit model, and thus may not be present in all cases. Therefore, we need the ``hasattr`` check to see if we need to scale ``deltaP`` or not.\n", + "\n", + "For the case of the heat duty, we want to scale based on the incoming enthalpy flow which means we first need to get the expected magnitude of the enthalpy flow. For that, we can use the ``get_expression_nominal_values`` method in ``CustomScalerBase`` which uses an expression walker to go through an expression to return a list of the expected magnitude (or nominal value) of all additive terms in the expression based on the scaling factors for the variables involved.\n", + "\n", + "We can get an expression for the enthalpy flow term using the ``get_enthalpy_flow_terms`` method from the associated ``StateBlock``. We should assume this expression might contain multiple terms, so we should sum all the values returned to get the overall magnitude of the enthalpy flow term. Once we have this, we can then get the scaling factor for the heat duty by ``sf = abs(1/(0.1*enthalpy_flow))`` - note that the tools insist on scaling factors being positive (for sanity) and thus we need the absolute value here in case enthalpy flow is negative (which is not uncommon for enthalpy). The code to do this is shown below.\n", + "\n", + "```python\n", + "if hasattr(model.control_volume, \"heat\"):\n", + " for t in model.flowsheet().time:\n", + " h_in = 0\n", + " for p in model.control_volume.properties_in.phase_list:\n", + " # The expression for enthalpy flow might include multiple terms,\n", + " # so we will sum over all the terms provided\n", + " h_in += sum(\n", + " self.get_expression_nominal_values(\n", + " model.control_volume.properties_in[t].get_enthalpy_flow_terms(p)\n", + " )\n", + " )\n", + " # Scale for heat is general one order of magnitude less than enthalpy flow\n", + " self.set_variable_scaling_factor(model.control_volume.heat[t], abs(1 / (0.1 * h_in)))\n", + "```\n", + "\n", + "Putting all of this together results in the code below for our ``EquilibriumReactorScaler`` class." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import units\n", + "\n", + "\n", + "class EquilibriumReactorScaler(CustomScalerBase):\n", + " # =======================================================================================\n", + " # New Code\n", + " UNIT_SCALING_FACTORS = {\n", + " # \"QuantityName: (reference units, scaling factor)\n", + " \"Pressure Change\": (units.bar, 10),\n", + " }\n", + " # =======================================================================================\n", + "\n", + " def variable_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.propagate_state_scaling(\n", + " target_state=model.control_volume.properties_out,\n", + " source_state=model.control_volume.properties_in,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " # =======================================================================================\n", + " # New Code\n", + "\n", + " # Pressure drop - optional\n", + " if hasattr(model.control_volume, \"deltaP\"):\n", + " for t in model.flowsheet().time:\n", + " self.scale_variable_by_units(\n", + " model.control_volume.deltaP[t], overwrite=overwrite\n", + " )\n", + "\n", + " # Heat transfer - optional\n", + " # Scale heat based on enthalpy flow entering reactor\n", + " if hasattr(model.control_volume, \"heat\"):\n", + " for t in model.flowsheet().time:\n", + " h_in = 0\n", + " for p in model.control_volume.properties_in.phase_list:\n", + " # The expression for enthalpy flow might include multiple terms,\n", + " # so we will sum over all the terms provided\n", + " h_in += sum(\n", + " self.get_expression_nominal_values(\n", + " model.control_volume.properties_in[\n", + " t\n", + " ].get_enthalpy_flow_terms(p)\n", + " )\n", + " )\n", + " # Scale for heat is generally one order of magnitude less than enthalpy flow\n", + " self.set_variable_scaling_factor(\n", + " model.control_volume.heat[t], abs(1 / (0.1 * h_in))\n", + " )\n", + " # =======================================================================================\n", + "\n", + " def constraint_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, lets run the ``check_scaling`` function and see how we are going." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model Solved\n", - "\n", - "Scaling Factors for fs.equil\n", - "\n", - "Variable Scaling Factor Value Scaled Value\n", - "fs.equil.control_volume.properties_in[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[NaOH] 1.000E-02 1.000E+02 1.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 1.000E+02 1.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[Ethanol] 1.000E-02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.properties_in[0.0].temperature 3.219E-03 3.031E+02 9.759E-01\n", - "fs.equil.control_volume.properties_in[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", - "fs.equil.control_volume.properties_out[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[NaOH] 1.000E-02 6.250E-02 6.250E-04\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 6.250E-02 6.250E-04\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 9.994E+01 9.994E-01\n", - "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[Ethanol] 1.000E-02 9.994E+01 9.994E-01\n", - "fs.equil.control_volume.properties_out[0.0].temperature 3.219E-03 3.043E+02 9.796E-01\n", - "fs.equil.control_volume.properties_out[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", - "fs.equil.control_volume.heat[0.0] 4.794E-04 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.deltaP[0.0] 1.000E-04 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] None 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,NaOH] None -9.994E-02 -9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,EthylAcetate] None -9.994E-02 -9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,SodiumAcetate] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,Ethanol] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.rate_reaction_extent[0.0,R1] None 9.994E-02 9.994E-02\n", - "fs.equil.control_volume.reactions[0.0].reaction_rate[R1] 1.000E+02 0.000E+00 0.000E+00\n", - "fs.equil.control_volume.reactions[0.0].k_rxn 5.424E+00 1.304E-01 7.075E-01\n", - "\n", - "Constraint Scaling Factor\n", - "fs.equil.rate_reaction_constraint[0.0,R1] 1.000E+02\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,H2O] 1.000E+00\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,NaOH] 1.000E+01\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,EthylAcetate] 1.000E+01\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,SodiumAcetate] 1.000E+01\n", - "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,Ethanol] 1.000E+01\n", - "fs.equil.control_volume.material_balances[0.0,Liq,H2O] 1.000E-02\n", - "fs.equil.control_volume.material_balances[0.0,Liq,NaOH] 1.000E+00\n", - "fs.equil.control_volume.material_balances[0.0,Liq,EthylAcetate] 1.000E+00\n", - "fs.equil.control_volume.material_balances[0.0,Liq,SodiumAcetate] 1.000E+00\n", - "fs.equil.control_volume.material_balances[0.0,Liq,Ethanol] 1.000E+00\n", - "fs.equil.control_volume.enthalpy_balances[0.0] 7.715E-08\n", - "fs.equil.control_volume.pressure_balance[0.0] 9.869E-06\n", - "fs.equil.control_volume.properties_out[0.0].conc_water_eqn 1.000E-04\n", - "fs.equil.control_volume.reactions[0.0].rate_expression[R1] 5.424E-04\n", - "fs.equil.control_volume.reactions[0.0].arrhenius_eqn 5.424E+00\n", - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 7.182E+04\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "3 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 2 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "check_scaling()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model Solved\n", + "\n", + "Scaling Factors for fs.equil\n", + "\n", + "Variable Scaling Factor Value Scaled Value\n", + "fs.equil.control_volume.properties_in[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[NaOH] 1.000E-02 1.000E+02 1.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 1.000E+02 1.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[Ethanol] 1.000E-02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].temperature 3.219E-03 3.031E+02 9.759E-01\n", + "fs.equil.control_volume.properties_in[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", + "fs.equil.control_volume.properties_out[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[NaOH] 1.000E-02 6.250E-02 6.250E-04\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 6.250E-02 6.250E-04\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 9.994E+01 9.994E-01\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[Ethanol] 1.000E-02 9.994E+01 9.994E-01\n", + "fs.equil.control_volume.properties_out[0.0].temperature 3.219E-03 3.043E+02 9.796E-01\n", + "fs.equil.control_volume.properties_out[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", + "fs.equil.control_volume.heat[0.0] 4.794E-04 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.deltaP[0.0] 1.000E-04 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] None 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,NaOH] None -9.994E-02 -9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,EthylAcetate] None -9.994E-02 -9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,SodiumAcetate] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,Ethanol] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.rate_reaction_extent[0.0,R1] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.reactions[0.0].reaction_rate[R1] 1.000E+02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.reactions[0.0].k_rxn 5.424E+00 1.304E-01 7.075E-01\n", + "\n", + "Constraint Scaling Factor\n", + "fs.equil.rate_reaction_constraint[0.0,R1] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,H2O] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,NaOH] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,EthylAcetate] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,SodiumAcetate] None\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,Ethanol] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,H2O] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,NaOH] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,EthylAcetate] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,SodiumAcetate] None\n", + "fs.equil.control_volume.material_balances[0.0,Liq,Ethanol] None\n", + "fs.equil.control_volume.enthalpy_balances[0.0] None\n", + "fs.equil.control_volume.pressure_balance[0.0] None\n", + "fs.equil.control_volume.properties_out[0.0].conc_water_eqn 1.000E-04\n", + "fs.equil.control_volume.reactions[0.0].rate_expression[R1] 5.424E-04\n", + "fs.equil.control_volume.reactions[0.0].arrhenius_eqn 5.424E+00\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the results of ``check_scaling`` we can see that we now have scaling factors for almost all the variables and constraints in the model (the only exceptions being the reaction related variables we left unscaled earlier). More importantly, we can see that the Jacobian condition number is now down to ``7.2E4`` from the original ``1.5E12`` which is an impressive improvement (and for not a lot of effort on our part). We can also see that the numerical diagnostics are no longer reporting any variables or constraints with extreme Jacobians (there are 2 individual entries that are a bit large, but it appears they are not having a big impact on the condition number).\n", - "\n", - "We do see that there are a number of variables with values close to ``0`` which we should be wary of, but in this case it is due to the case study we are using. Here we are using an equilibrium reactor to drive a rate-based reaction to completion, which necessitates that at least one reactant have a concentration of zero as well as the reaction rate for all reactions. Thus, for this case these are unavoidable. As mentioned earlier, we really should be asking whether an Equilibrium Reactor is well suited for the reaction model we have here, and a Stoichiometric Reactor would probably have been a better choice (or a better reaction package which use reversible reactions with equilibrium).\n", - "\n", - "\n", - "## Step 7: Review Scaling Routine\n", - "\n", - "We now have a new ``Scaler`` for an equilibrium reactor that uses the modular nature of IDAES to implement a general purpose scaling routine (or so we hope at least). So, does this mean we are done?\n", - "\n", - "No, or not yet at least.\n", - "\n", - "We should always take a step back and ask ourselves if what we have is good enough and see if we can see any areas where we might be able to do better, or places where edge cases might exist. As a starting point, let us first see how we compare to an autoscaling routine using the model Jacobian. We can use the ``AutoScaler.scale_model`` method for this as shown below." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 3.022E+09\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "5 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 4 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 2 Constraints with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 6 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "check_scaling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our updates have resulted in scaling factors for ``heat`` and ``deltaP`` appearing in the scaling report which is good, but comparing the diagnostics from the previous step we can see that the Jacobian condition number has not changed. Does this mean we did something wrong?\n", + "\n", + "The answer is no - when we add a scaling factor to a variable, wherever that variable appears in a constraint it is replaced with ``sf*v_scaled``. Given that ``v_scaled = v/sf``, this means that for variables which only appear linearly in constraints then the partial derivative with respect to the scaled variable does not change either; thus the Jacobian is unaffected by scaling only the linear variables. In the case of this example, it turns out that almost all the variables appear linearly and thus we see no change in the Jacobian condition number.\n", + "\n", + "
\n", + "NOTE It is important to note that partial scaling of a model (e.g., variables only) can often appear worse than that of the unscaled model. Generally, it is best to wait until you have scaled both variables and constraints to make a decision on whether your attempts at scaling have made the problem better or worse, and you should not be discouraged if things look worse while in an intermediate state.\n", + "
\n", + "\n", + "\n", + "## Step 6: Apply Constraint Scaling\n", + "\n", + "Now that we have scaled all the variables that we can (for now at least), we can move on to scaling constraints. The advantage of scaling all the variables first means that now we have an idea of the expected magnitude for all terms in the constraints which we can use to estimate scaling factors. For the Equilibrium reactor model, we need to scale all the constraints in the control volume, as well as the unit level constraint equating all reaction rates to zero.\n", + "\n", + "There are many approaches to estimating scaling for constraints, and different approaches are better suited to certain situations. ``CustomScalerBase`` contains a ``scale_constraint_by_nominal_value`` method which can be used to automatically implement a number of common approaches to save you the effort of having to manually implement these yourself. As of writing, the approaches (or schemes) supported are:\n", + "\n", + "1. ``ConstraintScalingScheme.inverseMaximum`` - scale the constraint based on the term with the largest absolute expected magnitude. This is scheme is useful for cases where most terms have similar magnitudes and is a good initial point to start.\n", + "2. ``ConstraintScalingScheme.inverseMinimum`` - scale the constraint based on the term with the smallest absolute expected magnitude. This scheme is similar to the inverse maximum scheme and is useful for cases where you have a constraint with a number of smaller terms mixed with a few larger terms, or cases where the smaller term is expected to be most significant. This scheme should be used carefully however as it can result in large scaling factors making convergence of larger terms difficult.\n", + "3. ``ConstraintScalingScheme.harmonicMean`` - scale the constraint using the harmonic mean of the absolute expected magnitude of all terms (``sf = sum(1/abs(nominal value))``). This scheme is most useful when you have a constraint with terms with a mix of expected magnitudes where you need to find a balance between the large and small terms.\n", + "4. ``ConstraintScalingScheme.inverseSum`` - scale the constraint using the sum of the absolute expected magnitudes of all terms. Situationally useful for cases with terms of mixed magnitudes.\n", + "5. ``ConstraintScalingScheme.inverseRSS`` - scale the constraint using the root sum of squares of the absolute expected magnitudes of all terms. Situationally useful for cases with terms of mixed magnitudes.\n", + "\n", + "``CustomScalerBase`` also contains a ``scale_constraint_by_nominal_derivative_norm`` method that can scale a constraint based on an estimate of the Jacobian norm associated with that constraint which can be useful for cases where you want to focus on the Jacobian scaling.\n", + "\n", + "
\n", + "NOTE The solver you intend to use may impact which approach provides the best scaling for a given model. For example, IPOPT has very good internal Jacobian scaling (when using the `gradient-based` scaling option), and thus benefits the most from focusing on scaling the constraint residual magnitudes as opposed to the Jacobian.\n", + "
\n", + "\n", + "For this workshop, we will start by just using ``ConstraintScalingScheme.inverseMaximum`` to get a starting point and to see if further scaling is required. We can apply this scheme to scale all the constraints in the control volume using the code below.\n", + "\n", + "```python\n", + "for c in model.control_volume.component_data_objects(\n", + " Constraint, descend_into=False\n", + "):\n", + " self.scale_constraint_by_nominal_value(\n", + " c,\n", + " scheme=ConstraintScalingScheme.inverseMaximum,\n", + " overwrite=overwrite,\n", + " )\n", + "```\n", + "\n", + "Adding this and a similar approach to scale the unit level constraint gives us the code below for our ``EquilibriumreactorScaler`` class." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.scaling import ConstraintScalingScheme\n", + "\n", + "\n", + "class EquilibriumReactorScaler(CustomScalerBase):\n", + " UNIT_SCALING_FACTORS = {\n", + " # \"QuantityName: (reference units, scaling factor)\n", + " \"Pressure Change\": (units.bar, 10),\n", + " }\n", + "\n", + " def variable_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.propagate_state_scaling(\n", + " target_state=model.control_volume.properties_out,\n", + " source_state=model.control_volume.properties_in,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"variable_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " # Pressure drop - optional\n", + " if hasattr(model.control_volume, \"deltaP\"):\n", + " for t in model.flowsheet().time:\n", + " self.scale_variable_by_units(\n", + " model.control_volume.deltaP[t], overwrite=overwrite\n", + " )\n", + "\n", + " # Heat transfer - optional\n", + " # Scale heat based on enthalpy flow entering reactor\n", + " if hasattr(model.control_volume, \"heat\"):\n", + " for t in model.flowsheet().time:\n", + " h_in = 0\n", + " for p in model.control_volume.properties_in.phase_list:\n", + " # The expression for enthalpy flow might include multiple terms,\n", + " # so we will sum over all the terms provided\n", + " h_in += sum(\n", + " self.get_expression_nominal_values(\n", + " model.control_volume.properties_in[\n", + " t\n", + " ].get_enthalpy_flow_terms(p)\n", + " )\n", + " )\n", + " # Scale for heat is generally one order of magnitude less than enthalpy flow\n", + " self.set_variable_scaling_factor(\n", + " model.control_volume.heat[t], abs(1 / (0.1 * h_in))\n", + " )\n", + "\n", + " def constraint_scaling_routine(\n", + " self, model, overwrite: bool = False, submodel_scalers: dict = None\n", + " ):\n", + " # Call scaling methods for sub-models\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_in,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.properties_out,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + " self.call_submodel_scaler_method(\n", + " submodel=model.control_volume.reactions,\n", + " method=\"constraint_scaling_routine\",\n", + " submodel_scalers=submodel_scalers,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " # =======================================================================================\n", + " # New Code\n", + " # Scale control volume constraints\n", + " for c in model.control_volume.component_data_objects(\n", + " Constraint, descend_into=False\n", + " ):\n", + " self.scale_constraint_by_nominal_value(\n", + " c,\n", + " scheme=ConstraintScalingScheme.inverseMaximum,\n", + " overwrite=overwrite,\n", + " )\n", + "\n", + " # Scale unit level constraints\n", + " if hasattr(model, \"rate_reaction_constraint\"):\n", + " for c in model.rate_reaction_constraint.values():\n", + " self.scale_constraint_by_nominal_value(\n", + " c,\n", + " scheme=ConstraintScalingScheme.inverseMaximum,\n", + " overwrite=overwrite,\n", + " )\n", + " # =======================================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, let us use the ``check_scaling`` function to see how our ``Scaler`` performs." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'scaling_factor' that contains 10\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "\n", - "Model Solved\n", - "\n", - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 3.863E+06\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 WARNINGS\n", - "\n", - " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "4 Cautions\n", - "\n", - " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 2 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", - " Caution: 7 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "from idaes.core.scaling import AutoScaler\n", - "\n", - "m = build_model()\n", - "\n", - "scaler = EquilibriumReactorScaler()\n", - "autoscaler = AutoScaler()\n", - "\n", - "autoscaler.scale_model(m)\n", - "\n", - "solver = get_solver(\n", - " \"ipopt_v2\", writer_config={\"scale_model\": True, \"linear_presolve\": True}\n", - ")\n", - "results = solver.solve(m)\n", - "\n", - "if check_optimal_termination(results):\n", - " print(\"\\nModel Solved\\n\")\n", - "else:\n", - " print(\"\\nModel Failed to Converge!\\n\")\n", - "\n", - "sm = TransformationFactory(\"core.scale_model\").create_using(m, rename=False)\n", - "\n", - "dt = DiagnosticsToolbox(model=sm.fs.equil)\n", - "dt.report_numerical_issues()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model Solved\n", + "\n", + "Scaling Factors for fs.equil\n", + "\n", + "Variable Scaling Factor Value Scaled Value\n", + "fs.equil.control_volume.properties_in[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[NaOH] 1.000E-02 1.000E+02 1.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 1.000E+02 1.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].conc_mol_comp[Ethanol] 1.000E-02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.properties_in[0.0].temperature 3.219E-03 3.031E+02 9.759E-01\n", + "fs.equil.control_volume.properties_in[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", + "fs.equil.control_volume.properties_out[0.0].flow_vol 1.000E+02 1.000E-03 1.000E-01\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[H2O] 1.000E-04 5.539E+04 5.539E+00\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[NaOH] 1.000E-02 6.250E-02 6.250E-04\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[EthylAcetate] 1.000E-02 6.250E-02 6.250E-04\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[SodiumAcetate] 1.000E-02 9.994E+01 9.994E-01\n", + "fs.equil.control_volume.properties_out[0.0].conc_mol_comp[Ethanol] 1.000E-02 9.994E+01 9.994E-01\n", + "fs.equil.control_volume.properties_out[0.0].temperature 3.219E-03 3.043E+02 9.796E-01\n", + "fs.equil.control_volume.properties_out[0.0].pressure 1.000E-05 1.013E+05 1.013E+00\n", + "fs.equil.control_volume.heat[0.0] 4.794E-04 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.deltaP[0.0] 1.000E-04 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,H2O] None 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,NaOH] None -9.994E-02 -9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,EthylAcetate] None -9.994E-02 -9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,SodiumAcetate] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.rate_reaction_generation[0.0,Liq,Ethanol] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.rate_reaction_extent[0.0,R1] None 9.994E-02 9.994E-02\n", + "fs.equil.control_volume.reactions[0.0].reaction_rate[R1] 1.000E+02 0.000E+00 0.000E+00\n", + "fs.equil.control_volume.reactions[0.0].k_rxn 5.424E+00 1.304E-01 7.075E-01\n", + "\n", + "Constraint Scaling Factor\n", + "fs.equil.rate_reaction_constraint[0.0,R1] 1.000E+02\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,H2O] 1.000E+00\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,NaOH] 1.000E+01\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,EthylAcetate] 1.000E+01\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,SodiumAcetate] 1.000E+01\n", + "fs.equil.control_volume.rate_reaction_stoichiometry_constraint[0.0,Liq,Ethanol] 1.000E+01\n", + "fs.equil.control_volume.material_balances[0.0,Liq,H2O] 1.000E-02\n", + "fs.equil.control_volume.material_balances[0.0,Liq,NaOH] 1.000E+00\n", + "fs.equil.control_volume.material_balances[0.0,Liq,EthylAcetate] 1.000E+00\n", + "fs.equil.control_volume.material_balances[0.0,Liq,SodiumAcetate] 1.000E+00\n", + "fs.equil.control_volume.material_balances[0.0,Liq,Ethanol] 1.000E+00\n", + "fs.equil.control_volume.enthalpy_balances[0.0] 7.715E-08\n", + "fs.equil.control_volume.pressure_balance[0.0] 9.869E-06\n", + "fs.equil.control_volume.properties_out[0.0].conc_water_eqn 1.000E-04\n", + "fs.equil.control_volume.reactions[0.0].rate_expression[R1] 5.424E-04\n", + "fs.equil.control_volume.reactions[0.0].arrhenius_eqn 5.424E+00\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that our ``EquilibriumReactorScaling`` routine actually results in a lower Jacobian condition number than the ``AutoScaler`` approach, so that is a sign we are doing things right. It is not unusual to see that we can get better scaling with a manual, magnitude based approach than an autoscaler as the autoscaler focuses solely on the Jacobian and thus often over-scales the problem.\n", - "\n", - "However, we might be able to do better by using other constraint scaling schemes, but before we start experimenting we should stop and think about what sort of scaling might make sense for each constraint. We should always also keep in the back of our minds whether additional work is worth the effort, and if we risk over-tuning the scaling for the specific property package we have.\n", - "\n", - "Fortunately, the model in this example is fairly simple and we do not have too many constraints to consider. Firstly, we have the unit-level constraint that says that `rate_reaction == 0` for all rate-based reactions. When considering scaling of a constraint we should ignore any 0 terms, thus this constraint has only 1 term and so we should scale based on this. If we use the ``scale_constraint_by_nominal_value`` method for this it will ignore the zero for us, the scheme used does not actually matter as there is only one term to consider.\n", - "\n", - "Next, we have the balance equations which all have the form `0 == In - Out + Gen` - note the equilibrium reactor does not support dynamics so we don't need to think about that. Generation terms can vary a lot, but we basically have two possible cases:\n", - "\n", - "1. one term is negligible compared to the other 2, so we should scale based on one of the significant\n", - "terms, or\n", - "2. all three terms are of similar significance (e.g., inlet and gen are of similar scale and outlet\n", - "is ~inletx2). Here we could scale based on the harmonic mean, by the maximum term is probably not bad either.\n", - "\n", - "So, in short the maximum magnitude is probably the best general-purpose scale for these constraints.\n", - "\n", - "Finally, we have stoichiometric constraints with the form `G[j, r] == n[j, r]*X[r]` where ``G`` is generation, ``X`` is extent and ``n`` is the stoichiometric coefficient (i.e., a constant) - these are simple ``A=B`` constraints, so scaling by maximum magnitude is equivalent to other methods (as there are only two terms which will take the same value, all schemes will give the same result in the end).\n", - "\n", - "So, for the equilibrium reactor at least, we are probably best leaving things as they are.\n", - "\n", - "However, there is one important test left. The whole purpose of a scaling routine is to allow us to perturb the model and solve it at the new state so we should test to confirm that our new ``Scaler`` has improved the performance of our solver when solving for the perturbed state we tried earlier. This also lets us see how the new ``Scaler`` will look for a user trying to apply the tool, which we can see below." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 7.182E+04\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 2 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "check_scaling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the results of ``check_scaling`` we can see that we now have scaling factors for almost all the variables and constraints in the model (the only exceptions being the reaction related variables we left unscaled earlier). More importantly, we can see that the Jacobian condition number is now down to ``7.2E4`` from the original ``1.5E12`` which is an impressive improvement (and for not a lot of effort on our part). We can also see that the numerical diagnostics are no longer reporting any variables or constraints with extreme Jacobians (there are 2 individual entries that are a bit large, but it appears they are not having a big impact on the condition number).\n", + "\n", + "We do see that there are a number of variables with values close to ``0`` which we should be wary of, but in this case it is due to the case study we are using. Here we are using an equilibrium reactor to drive a rate-based reaction to completion, which necessitates that at least one reactant have a concentration of zero as well as the reaction rate for all reactions. Thus, for this case these are unavoidable. As mentioned earlier, we really should be asking whether an Equilibrium Reactor is well suited for the reaction model we have here, and a Stoichiometric Reactor would probably have been a better choice (or a better reaction package which use reversible reactions with equilibrium).\n", + "\n", + "\n", + "## Step 7: Review Scaling Routine\n", + "\n", + "We now have a new ``Scaler`` for an equilibrium reactor that uses the modular nature of IDAES to implement a general purpose scaling routine (or so we hope at least). So, does this mean we are done?\n", + "\n", + "No, or not yet at least.\n", + "\n", + "We should always take a step back and ask ourselves if what we have is good enough and see if we can see any areas where we might be able to do better, or places where edge cases might exist. As a starting point, let us first see how we compare to an autoscaling routine using the model Jacobian. We can use the ``AutoScaler.scale_model`` method for this as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: linear_solver=ma57\n", - "max_iter=200\n", - "nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma57.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 21\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 9\n", - "\n", - "Total number of variables............................: 8\n", - " variables with only lower bounds: 5\n", - " variables with lower and upper bounds: 1\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 8\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 5.53e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - "Reallocating memory for MA57: lfact (247)\n", - " 1 0.0000000e+00 5.53e+02 1.20e+00 -1.0 9.95e+02 - 2.00e-05 1.96e-05h 1\n", - " 2 0.0000000e+00 5.53e+02 1.20e+00 -1.0 9.57e+02 - 2.06e-05 2.00e-05h 1\n", - " 3 0.0000000e+00 5.53e+02 7.36e+01 -1.0 9.25e+02 - 4.36e-04 4.06e-05h 1\n", - " 4 0.0000000e+00 5.53e+02 3.34e+05 -1.0 8.55e+02 - 2.41e-04 1.21e-03f 1\n", - " 5 0.0000000e+00 5.40e+02 6.59e+03 -1.0 9.98e+01 - 2.25e-04 2.34e-02f 1\n", - " 6 0.0000000e+00 5.24e+02 1.11e+08 -1.0 9.74e+01 - 2.54e-02 2.84e-02f 1\n", - " 7 0.0000000e+00 2.36e+02 2.03e+06 -1.0 9.47e+01 - 7.09e-02 5.49e-01h 1\n", - " 8 0.0000000e+00 8.62e+01 6.37e+10 -1.0 4.27e+01 - 8.23e-03 6.35e-01h 1\n", - " 9 0.0000000e+00 1.96e+00 4.93e+10 -1.0 1.56e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 0.0000000e+00 2.05e-04 2.15e+09 -1.0 3.70e-02 - 1.00e+00 1.00e+00h 1\n", - " 11 0.0000000e+00 6.28e-15 2.56e+05 -1.0 2.05e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 11\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 6.2780236478193237e-15 6.2780236478193237e-15\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 6.2780236478193237e-15 6.2780236478193237e-15\n", - "\n", - "\n", - "Number of objective function evaluations = 12\n", - "Number of objective gradient evaluations = 12\n", - "Number of equality constraint evaluations = 12\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 12\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 11\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "m = build_model()\n", - "\n", - "scaler = EquilibriumReactorScaler()\n", - "scaler.scale_model(m.fs.equil)\n", - "\n", - "perturb_model(m)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'scaling_factor' that contains 10\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that by applying our new ``EquilibriumReactorScaler`` we are now able to use IPOPT to solve for the perturbation, and that it reaches an optimal solution in 11 iterations. Looking at the solver logs we can see that the solver step lengths (``alpha_du`` and ``alpha_pr``) are rather small for the first iterations but the number of line searches (``ls``) is 1 for all iterations. This indicates that IPOPT is pushing up against some bound or constraint and cannot make full steps, but in this case it is due to the fact that to achieve equilibrium for an irreversible reaction at least one concentration must be driven to zero (and is why an EquibriumReactor is probably not a good choice for this test case). However, the fact that our ``Scaler`` let us solve for this challenging test case is probably a good sign.\n", - "\n", - "\n", - "## Step 8: Finishing Up\n", - "\n", - "Ideally, we would have more than one test case to apply our ``Scaler`` to put it through its paces and ensure it is robust across a wide range of conditions. However, for the purposes of this workshop we will move on.\n", - "\n", - "Once you are satisfied that your ``Scaler`` is ready, you can start applying it to actual problems of interest. For those modelers developing new unit and property models, you should assign your new ``Scaler`` as the default scaler for that unit model. You can do this by setting the ``default_scaler`` attribute on your model to point to the new ``Scaler`` as shown below.\n", - "\n", - "```python\n", - "@declare_process_block_class(\"EquilibriumReactor\")\n", - "class EquilibriumReactorData(UnitModelBlockData):\n", - " \"\"\"\n", - " Standard Equilibrium Reactor Unit Model Class\n", - " \"\"\"\n", - "\n", - " # Setting the default_scaler attribute\n", - " default_scaler = EquilibriumReactorScaler\n", - "```\n", - "\n", - "With that, we have finished this workshop on developing ``Scaler`` classes. Hopefully you now know enough to begin writing ``Scalers`` for your own models, and have gained some insight into how to think about developing scaling routines and the tools available to help you." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model Solved\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 3.863E+06\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 WARNINGS\n", + "\n", + " WARNING: 2 Variables at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "4 Cautions\n", + "\n", + " Caution: 2 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 6 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 2 Variables with extreme Jacobian values (<1.0E-04 or >1.0E+04)\n", + " Caution: 7 extreme Jacobian Entries (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" + ], + "source": [ + "from idaes.core.scaling import AutoScaler\n", + "\n", + "m = build_model()\n", + "\n", + "scaler = EquilibriumReactorScaler()\n", + "autoscaler = AutoScaler()\n", + "\n", + "autoscaler.scale_model(m)\n", + "\n", + "solver = get_solver(\n", + " \"ipopt_v2\", writer_config={\"scale_model\": True, \"linear_presolve\": True}\n", + ")\n", + "results = solver.solve(m)\n", + "\n", + "if check_optimal_termination(results):\n", + " print(\"\\nModel Solved\\n\")\n", + "else:\n", + " print(\"\\nModel Failed to Converge!\\n\")\n", + "\n", + "sm = TransformationFactory(\"core.scale_model\").create_using(m, rename=False)\n", + "\n", + "dt = DiagnosticsToolbox(model=sm.fs.equil)\n", + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that our ``EquilibriumReactorScaling`` routine actually results in a lower Jacobian condition number than the ``AutoScaler`` approach, so that is a sign we are doing things right. It is not unusual to see that we can get better scaling with a manual, magnitude based approach than an autoscaler as the autoscaler focuses solely on the Jacobian and thus often over-scales the problem.\n", + "\n", + "However, we might be able to do better by using other constraint scaling schemes, but before we start experimenting we should stop and think about what sort of scaling might make sense for each constraint. We should always also keep in the back of our minds whether additional work is worth the effort, and if we risk over-tuning the scaling for the specific property package we have.\n", + "\n", + "Fortunately, the model in this example is fairly simple and we do not have too many constraints to consider. Firstly, we have the unit-level constraint that says that `rate_reaction == 0` for all rate-based reactions. When considering scaling of a constraint we should ignore any 0 terms, thus this constraint has only 1 term and so we should scale based on this. If we use the ``scale_constraint_by_nominal_value`` method for this it will ignore the zero for us, the scheme used does not actually matter as there is only one term to consider.\n", + "\n", + "Next, we have the balance equations which all have the form `0 == In - Out + Gen` - note the equilibrium reactor does not support dynamics so we don't need to think about that. Generation terms can vary a lot, but we basically have two possible cases:\n", + "\n", + "1. one term is negligible compared to the other 2, so we should scale based on one of the significant\n", + "terms, or\n", + "2. all three terms are of similar significance (e.g., inlet and gen are of similar scale and outlet\n", + "is ~inletx2). Here we could scale based on the harmonic mean, by the maximum term is probably not bad either.\n", + "\n", + "So, in short the maximum magnitude is probably the best general-purpose scale for these constraints.\n", + "\n", + "Finally, we have stoichiometric constraints with the form `G[j, r] == n[j, r]*X[r]` where ``G`` is generation, ``X`` is extent and ``n`` is the stoichiometric coefficient (i.e., a constant) - these are simple ``A=B`` constraints, so scaling by maximum magnitude is equivalent to other methods (as there are only two terms which will take the same value, all schemes will give the same result in the end).\n", + "\n", + "So, for the equilibrium reactor at least, we are probably best leaving things as they are.\n", + "\n", + "However, there is one important test left. The whole purpose of a scaling routine is to allow us to perturb the model and solve it at the new state so we should test to confirm that our new ``Scaler`` has improved the performance of our solver when solving for the perturbed state we tried earlier. This also lets us see how the new ``Scaler`` will look for a user trying to apply the tool, which we can see below." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: linear_solver=ma57\n", + "max_iter=200\n", + "nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma57.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 21\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 9\n", + "\n", + "Total number of variables............................: 8\n", + " variables with only lower bounds: 5\n", + " variables with lower and upper bounds: 1\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 8\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.53e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "Reallocating memory for MA57: lfact (247)\n", + " 1 0.0000000e+00 5.53e+02 1.20e+00 -1.0 9.95e+02 - 2.00e-05 1.96e-05h 1\n", + " 2 0.0000000e+00 5.53e+02 1.20e+00 -1.0 9.57e+02 - 2.06e-05 2.00e-05h 1\n", + " 3 0.0000000e+00 5.53e+02 7.36e+01 -1.0 9.25e+02 - 4.36e-04 4.06e-05h 1\n", + " 4 0.0000000e+00 5.53e+02 3.34e+05 -1.0 8.55e+02 - 2.41e-04 1.21e-03f 1\n", + " 5 0.0000000e+00 5.40e+02 6.59e+03 -1.0 9.98e+01 - 2.25e-04 2.34e-02f 1\n", + " 6 0.0000000e+00 5.24e+02 1.11e+08 -1.0 9.74e+01 - 2.54e-02 2.84e-02f 1\n", + " 7 0.0000000e+00 2.36e+02 2.03e+06 -1.0 9.47e+01 - 7.09e-02 5.49e-01h 1\n", + " 8 0.0000000e+00 8.62e+01 6.37e+10 -1.0 4.27e+01 - 8.23e-03 6.35e-01h 1\n", + " 9 0.0000000e+00 1.96e+00 4.93e+10 -1.0 1.56e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 0.0000000e+00 2.05e-04 2.15e+09 -1.0 3.70e-02 - 1.00e+00 1.00e+00h 1\n", + " 11 0.0000000e+00 6.28e-15 2.56e+05 -1.0 2.05e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 11\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 6.2780236478193237e-15 6.2780236478193237e-15\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 6.2780236478193237e-15 6.2780236478193237e-15\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 12\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 12\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 11\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] } + ], + "source": [ + "m = build_model()\n", + "\n", + "scaler = EquilibriumReactorScaler()\n", + "scaler.scale_model(m.fs.equil)\n", + "\n", + "perturb_model(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that by applying our new ``EquilibriumReactorScaler`` we are now able to use IPOPT to solve for the perturbation, and that it reaches an optimal solution in 11 iterations. Looking at the solver logs we can see that the solver step lengths (``alpha_du`` and ``alpha_pr``) are rather small for the first iterations but the number of line searches (``ls``) is 1 for all iterations. This indicates that IPOPT is pushing up against some bound or constraint and cannot make full steps, but in this case it is due to the fact that to achieve equilibrium for an irreversible reaction at least one concentration must be driven to zero (and is why an EquibriumReactor is probably not a good choice for this test case). However, the fact that our ``Scaler`` let us solve for this challenging test case is probably a good sign.\n", + "\n", + "\n", + "## Step 8: Finishing Up\n", + "\n", + "Ideally, we would have more than one test case to apply our ``Scaler`` to put it through its paces and ensure it is robust across a wide range of conditions. However, for the purposes of this workshop we will move on.\n", + "\n", + "Once you are satisfied that your ``Scaler`` is ready, you can start applying it to actual problems of interest. For those modelers developing new unit and property models, you should assign your new ``Scaler`` as the default scaler for that unit model. You can do this by setting the ``default_scaler`` attribute on your model to point to the new ``Scaler`` as shown below.\n", + "\n", + "```python\n", + "@declare_process_block_class(\"EquilibriumReactor\")\n", + "class EquilibriumReactorData(UnitModelBlockData):\n", + " \"\"\"\n", + " Standard Equilibrium Reactor Unit Model Class\n", + " \"\"\"\n", + "\n", + " # Setting the default_scaler attribute\n", + " default_scaler = EquilibriumReactorScaler\n", + "```\n", + "\n", + "With that, we have finished this workshop on developing ``Scaler`` classes. Hopefully you now know enough to begin writing ``Scalers`` for your own models, and have gained some insight into how to think about developing scaling routines and the tools available to help you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/scaling/scaler_workshop_test.ipynb b/idaes_examples/notebooks/docs/scaling/scaler_workshop_test.ipynb index fddb4930..91f656bf 100644 --- a/idaes_examples/notebooks/docs/scaling/scaler_workshop_test.ipynb +++ b/idaes_examples/notebooks/docs/scaling/scaler_workshop_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/scaling/scaler_workshop_usr.ipynb b/idaes_examples/notebooks/docs/scaling/scaler_workshop_usr.ipynb index a7ba87bb..71ae61db 100644 --- a/idaes_examples/notebooks/docs/scaling/scaler_workshop_usr.ipynb +++ b/idaes_examples/notebooks/docs/scaling/scaler_workshop_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization.ipynb index 24ac1767..5c38b15e 100644 --- a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization.ipynb @@ -1,496 +1,497 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Autothermal Reformer Flowsheet Optimization with ALAMO Surrogate Object\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the ALAMO surrogate trainer and IDAES Python wrapper. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Problem Statement \n", - "\n", - "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", - "\n", - "## 2.1. Main Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "## 2.2. Main Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training and Validating Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import the required Python, Pyomo and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " SolverFactory,\n", - " value,\n", - " Var,\n", - " Constraint,\n", - " Set,\n", - " Objective,\n", - " maximize,\n", - ")\n", - "from pyomo.common.timing import TicTocTimer\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1 Importing Training and Validation Datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Auto-reformer training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", - "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(\n", - " data, 0.8, seed=n_data\n", - ") # seed=100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Training Surrogates with ALAMO" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis term forms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO naturally seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", - "\n", - "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# capture long output (not required to use surrogate API)\n", - "from io import StringIO\n", - "import sys\n", - "\n", - "stream = StringIO()\n", - "oldstdout = sys.stdout\n", - "sys.stdout = stream\n", - "\n", - "# Create ALAMO trainer object\n", - "trainer = AlamoTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - ")\n", - "\n", - "# Set ALAMO options\n", - "trainer.config.constant = True\n", - "trainer.config.linfcns = True\n", - "trainer.config.multi2power = [1, 2]\n", - "trainer.config.monomialpower = [2, 3]\n", - "trainer.config.ratiopower = [1, 2]\n", - "trainer.config.maxterms = [10] * len(output_labels) # max for each surrogate\n", - "trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", - "trainer.config.overwrite_files = True\n", - "\n", - "# Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", - "has_alamo = True\n", - "try:\n", - " success, alm_surr, msg = trainer.train_surrogate()\n", - "except FileNotFoundError as err:\n", - " if \"Could not find ALAMO\" in str(err):\n", - " print(\"ALAMO not found. You must install ALAMO to use this notebook\")\n", - " has_alamo = False\n", - " else:\n", - " raise\n", - "\n", - "if has_alamo:\n", - " # save model to JSON\n", - " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", - "\n", - " # create callable surrogate object\n", - "\n", - " surrogate_expressions = trainer._results[\"Model\"]\n", - " input_labels = trainer._input_labels\n", - " output_labels = trainer._output_labels\n", - " xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", - " input_bounds = {\n", - " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", - " }\n", - "\n", - " alm_surr = AlamoSurrogate(\n", - " surrogate_expressions, input_labels, output_labels, input_bounds\n", - " )\n", - "\n", - " # revert back to normal output capture\n", - " sys.stdout = oldstdout\n", - "\n", - " # display first 50 lines and last 50 lines of output\n", - " celloutput = stream.getvalue().split(\"\\n\")\n", - " for line in celloutput[:50]:\n", - " print(line)\n", - " print(\".\")\n", - " print(\".\")\n", - " print(\".\")\n", - " for line in celloutput[-50:]:\n", - " print(line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Visualizing surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "if has_alamo:\n", - " # visualize with IDAES surrogate plotting tools\n", - " surrogate_scatter2D(alm_surr, data_training, filename=\"alamo_train_scatter2D.pdf\")\n", - " surrogate_parity(alm_surr, data_training, filename=\"alamo_train_parity.pdf\")\n", - " surrogate_residual(alm_surr, data_training, filename=\"alamo_train_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "if has_alamo:\n", - " # visualize with IDAES surrogate plotting tools\n", - " surrogate_scatter2D(alm_surr, data_validation, filename=\"alamo_val_scatter2D.pdf\")\n", - " surrogate_parity(alm_surr, data_validation, filename=\"alamo_val_parity.pdf\")\n", - " surrogate_residual(alm_surr, data_validation, filename=\"alamo_val_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. IDAES Flowsheet Integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build an IDAES flowsheet and import the surrogate model object. A single ALAMO model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "if has_alamo:\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C4H10,\n", - " m.fs.C3H8,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # ALAMO\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - "\n", - " # fix input values and solve flowsheet\n", - " m.fs.bypass_frac.fix(0.5)\n", - " m.fs.ng_steam_ratio.fix(1)\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " results = solver.solve(m, tee=True)\n", - "else:\n", - " status_obj = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print some model results:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "if has_alamo:\n", - " print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", - " print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", - " print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", - " print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", - " print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", - " print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", - " print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", - " print(\"Mole Fraction CO = \", value(m.fs.CO))\n", - " print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", - " print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", - " print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", - " print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", - " print(\"Mole Fraction O2 = \", value(m.fs.O2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Optimizing the Autothermal Reformer\n", - "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", - "\n", - "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "if has_alamo:\n", - " # unfix input values and add the objective/constraint to the model\n", - " m.fs.bypass_frac.unfix()\n", - " m.fs.ng_steam_ratio.unfix()\n", - " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - " # solve the model\n", - " tmr = TicTocTimer()\n", - " status = solver.solve(m, tee=True)\n", - " solve_time = tmr.toc(\"solve\")\n", - "\n", - " # print and check results\n", - " assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", - " assert value(m.fs.N2 <= 0.4 + 1e-8)\n", - " print(\"Model status: \", status)\n", - " print(\"Solve time: \", solve_time)\n", - " for var in inputs:\n", - " print(var.name, \": \", value(var))\n", - " for var in outputs:\n", - " print(var.name, \": \", value(var))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autothermal Reformer Flowsheet Optimization with ALAMO Surrogate Object\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the ALAMO surrogate trainer and IDAES Python wrapper. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Statement \n", + "\n", + "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", + "\n", + "## 2.1. Main Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "## 2.2. Main Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training and Validating Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import the required Python, Pyomo and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " SolverFactory,\n", + " value,\n", + " Var,\n", + " Constraint,\n", + " Set,\n", + " Objective,\n", + " maximize,\n", + ")\n", + "from pyomo.common.timing import TicTocTimer\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 Importing Training and Validation Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", + "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") # seed=100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Training Surrogates with ALAMO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis term forms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO naturally seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# capture long output (not required to use surrogate API)\n", + "from io import StringIO\n", + "import sys\n", + "\n", + "stream = StringIO()\n", + "oldstdout = sys.stdout\n", + "sys.stdout = stream\n", + "\n", + "# Create ALAMO trainer object\n", + "trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "# Set ALAMO options\n", + "trainer.config.constant = True\n", + "trainer.config.linfcns = True\n", + "trainer.config.multi2power = [1, 2]\n", + "trainer.config.monomialpower = [2, 3]\n", + "trainer.config.ratiopower = [1, 2]\n", + "trainer.config.maxterms = [10] * len(output_labels) # max for each surrogate\n", + "trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + "trainer.config.overwrite_files = True\n", + "\n", + "# Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + "has_alamo = True\n", + "try:\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "except FileNotFoundError as err:\n", + " if \"Could not find ALAMO\" in str(err):\n", + " print(\"ALAMO not found. You must install ALAMO to use this notebook\")\n", + " has_alamo = False\n", + " else:\n", + " raise\n", + "\n", + "if has_alamo:\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + "\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n", + "\n", + " # revert back to normal output capture\n", + " sys.stdout = oldstdout\n", + "\n", + " # display first 50 lines and last 50 lines of output\n", + " celloutput = stream.getvalue().split(\"\\n\")\n", + " for line in celloutput[:50]:\n", + " print(line)\n", + " print(\".\")\n", + " print(\".\")\n", + " print(\".\")\n", + " for line in celloutput[-50:]:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Visualizing surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "if has_alamo:\n", + " # visualize with IDAES surrogate plotting tools\n", + " surrogate_scatter2D(alm_surr, data_training, filename=\"alamo_train_scatter2D.pdf\")\n", + " surrogate_parity(alm_surr, data_training, filename=\"alamo_train_parity.pdf\")\n", + " surrogate_residual(alm_surr, data_training, filename=\"alamo_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "if has_alamo:\n", + " # visualize with IDAES surrogate plotting tools\n", + " surrogate_scatter2D(alm_surr, data_validation, filename=\"alamo_val_scatter2D.pdf\")\n", + " surrogate_parity(alm_surr, data_validation, filename=\"alamo_val_parity.pdf\")\n", + " surrogate_residual(alm_surr, data_validation, filename=\"alamo_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. IDAES Flowsheet Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build an IDAES flowsheet and import the surrogate model object. A single ALAMO model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "if has_alamo:\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C4H10,\n", + " m.fs.C3H8,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # ALAMO\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + "\n", + " # fix input values and solve flowsheet\n", + " m.fs.bypass_frac.fix(0.5)\n", + " m.fs.ng_steam_ratio.fix(1)\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " results = solver.solve(m, tee=True)\n", + "else:\n", + " status_obj = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print some model results:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "if has_alamo:\n", + " print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", + " print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", + " print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", + " print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", + " print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", + " print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", + " print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", + " print(\"Mole Fraction CO = \", value(m.fs.CO))\n", + " print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", + " print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", + " print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", + " print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", + " print(\"Mole Fraction O2 = \", value(m.fs.O2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Optimizing the Autothermal Reformer\n", + "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", + "\n", + "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "if has_alamo:\n", + " # unfix input values and add the objective/constraint to the model\n", + " m.fs.bypass_frac.unfix()\n", + " m.fs.ng_steam_ratio.unfix()\n", + " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + " # solve the model\n", + " tmr = TicTocTimer()\n", + " status = solver.solve(m, tee=True)\n", + " solve_time = tmr.toc(\"solve\")\n", + "\n", + " # print and check results\n", + " assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", + " assert value(m.fs.N2 <= 0.4 + 1e-8)\n", + " print(\"Model status: \", status)\n", + " print(\"Solve time: \", solve_time)\n", + " for var in inputs:\n", + " print(var.name, \": \", value(var))\n", + " for var in outputs:\n", + " print(var.name, \": \", value(var))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_doc.ipynb index f2ffdcad..a76f214b 100644 --- a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -69,11 +70,7 @@ ] }, "execution_count": 2, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\alamo\\alamo_flowsheet_optimization_doc_3_0.png" - } - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -162,7 +159,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\miniconda3\\envs\\idaes-examples-py311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } @@ -513,7 +510,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb index f8fda916..a12cc805 100644 --- a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_usr.ipynb index f8fda916..a12cc805 100644 --- a/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb index 0344ee98..0fa61fe2 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb index 4f5ec2c3..57a2505e 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -70,7 +71,19 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Image\n", "from pathlib import Path\n", @@ -107,7 +120,337 @@ "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-17 17:36:02.752208: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-17 17:36:02.752913: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:36:02.756005: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:36:02.762878: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742258162.773996 296077 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742258162.777031 296077 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742258162.785759 296077 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258162.785777 296077 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258162.785778 296077 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258162.785779 296077 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-17 17:36:02.789997: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading existing surrogate models and training missing models.\n", + "Any training output will print below; otherwise, models will be loaded without any further output.\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [1.133636 0.172046] \n", + "Mean: [[0.712175]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output Steam_Flow trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [2.098572 0.044889] \n", + "Mean: [[24489.420238]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output Reformer_Duty trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n", + "\n", + "Final results\n", + "================\n", + "Theta: [5.205204 0.045084] \n", + "Mean: [[0.00325]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output AR trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [5.721231 0.130094] \n", + "Mean: [[0.007353]] \n", + "Regularization parameter: 1e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C2H6 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [6.24093 0.001815] \n", + "Mean: [[0.001768]] \n", + "Regularization parameter: 1.0000000001536558e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:06 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C3H8 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [2.535505 0.898683] \n", + "Mean: [[0.001241]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:06 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C4H10 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [10.72192 0.029391] \n", + "Mean: [[0.19263]] \n", + "Regularization parameter: 1e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:06 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CH4 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [4.566243 0.09761 ] \n", + "Mean: [[0.086864]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:07 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CO trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [3.42649 0.167599] \n", + "Mean: [[0.036997]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:07 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CO2 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [6.661825 0.01018 ] \n", + "Mean: [[0.239706]] \n", + "Regularization parameter: 1.0000000002854947e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:07 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output H2 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n", + "\n", + "Final results\n", + "================\n", + "Theta: [0.588033 0.111373] \n", + "Mean: [[0.031395]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:08 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output H2O trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [5.878418 0.019023] \n", + "Mean: [[0.283429]] \n", + "Regularization parameter: 1.000000000001e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:08 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output N2 trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "Optimizing kriging parameters using L-BFGS-B algorithm...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final results\n", + "================\n", + "Theta: [0.653803 0.123706] \n", + "Mean: [[0.]] \n", + "Regularization parameter: 1.5856961192994845e-06\n", + "\n", + "Results saved in solution.pickle\n", + "2025-03-17 17:36:08 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output O2 trained successfully\n" + ] + } + ], "source": [ "from idaes_examples.mod.surrogates.AR_training_methods import (\n", " train_load_surrogates,\n", @@ -284,7 +627,2294 @@ "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.221739 1.021053] pysmo_kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:36:08 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.21e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.21e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.221739 1.021053] pysmo_poly\n", + "2025-03-17 17:36:09 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.21e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.21e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.221739 1.021053] pysmo_rbf\n", + "2025-03-17 17:36:09 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.22e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.22e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.302899 1.168421] pysmo_kriging\n", + "2025-03-17 17:36:09 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.13e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.13e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.302899 1.168421] pysmo_poly\n", + "2025-03-17 17:36:09 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.13e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.13e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.302899 1.168421] pysmo_rbf\n", + "2025-03-17 17:36:09 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.13e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.13e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.546377 0.884211] pysmo_kriging\n", + "2025-03-17 17:36:09 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 8.20e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 8.20e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.546377 0.884211] pysmo_poly\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 8.21e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 8.21e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.546377 0.884211] pysmo_rbf\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 8.21e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 8.21e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.678261 1.084211] pysmo_kriging\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.19e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.19e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.678261 1.084211] pysmo_poly\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.19e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.19e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.678261 1.084211] pysmo_rbf\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.19e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.19e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.657971 1.063158] pysmo_kriging\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.78e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.78e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.657971 1.063158] pysmo_poly\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.78e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.78e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.657971 1.063158] pysmo_rbf\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.78e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.78e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.12029 0.863158] pysmo_kriging\n", + "2025-03-17 17:36:10 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.30e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.30e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.12029 0.863158] pysmo_poly\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.30e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.30e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.12029 0.863158] pysmo_rbf\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 2.30e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 2.30e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.333333 1. ] pysmo_kriging\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.74e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.74e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.333333 1. ] pysmo_poly\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.74e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.74e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.333333 1. ] pysmo_rbf\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.74e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.74e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.373913 0.926316] pysmo_kriging\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.49e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.373913 0.926316] pysmo_poly\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.49e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.373913 0.926316] pysmo_rbf\n", + "2025-03-17 17:36:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.48e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.48e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.515942 1.2 ] pysmo_kriging\n", + "2025-03-17 17:36:12 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.26e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.26e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.515942 1.2 ] pysmo_poly\n", + "2025-03-17 17:36:12 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.26e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.11e-16 0.00e+00 -1.0 1.26e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1102230246251565e-16 1.1102230246251565e-16\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1102230246251565e-16 1.1102230246251565e-16\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.515942 1.2 ] pysmo_rbf\n", + "2025-03-17 17:36:12 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.26e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 1.26e+04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.688406 1.147368] pysmo_kriging\n", + "2025-03-17 17:36:12 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.23e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.23e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.688406 1.147368] pysmo_poly\n", + "2025-03-17 17:36:12 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.25e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.25e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.688406 1.147368] pysmo_rbf\n", + "2025-03-17 17:36:12 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 13\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 13\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.24e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 0.00e+00 0.00e+00 -1.0 5.24e+03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + } + ], "source": [ "# Import Auto-reformer training data\n", "import numpy as np\n", @@ -359,7 +2989,77 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Mole fraction predictions displayed with absolute error:\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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IkSMoLi6Gnp4eX17f61uzC6kmxphCmYGBQaP/dmu7nrK6detW799Dbm4uKioq+G0dHR0YGRkpHDd79mwcPnwYW7duhbu7u9w+HR2dWucvqhrKX/3DB/DsedUW9Na3j6gHBUik2TIyMoKNjQ2uXr1a73FXr16FnZ2dwj/x/Px8BAUFIS8vD2fOnGnQ0ODajB8/Hv/73/9gYGCAsWPHKgRAL0omkwEAJkyYUGcLl5ub2wvfp+Y/awDIy8tDQEAADA0NsWTJEjg7O0NbWxuXLl3Cp59+ytetOagruG3om2nfvn3l8kdqU9trJJPJ0K1bN6xatarWc6rnmLRkjX19zczMag1Su3TpgsuXL6O8vLzO6QyuXr0KTU1NdOjQgb8WwAVlbdq0Uab6Shs1apRcq25ISIhCkvvixYvx448/4ttvv8XEiRMVrmFjY4PHjx8rlKelpQGAwv+cqteptt/DJ0+eQFdXt9bfQaIeFCCRZu3111/Hhg0bcPbsWbk5RaqcOXMG9+7dw3vvvSdXXlZWhuDgYCQlJeH48eNwcXFpdB3Gjx+PhQsXIi0trd7k47Zt2+Lq1auQyWRyQVRVN1XNJNYqVSOepFJpvZ94m0JUVBRycnLwxx9/oG/fvnx51ciuKlWtItevX6+3jg399GthYQFdXV3cunVLYd/NmzchFAqbTeDh7OyMK1euYODAgfU+v7Zt20Imk+HOnTtyLTe1Pcfa7gFwr2/NuYSqa+jrW/W7Vtfra25uLtd69CI6d+6MPXv2KJS//vrrOH/+PHbt2oUJEyYo7L937x7OnDmDwMBAPijo3LkzAO73r1u3biqpX12+//57ucCuZjBTNZ/XvHnzap0AE+DmHjt58iQKCgrkPqD9+++//P7qqv6uqlqVa+6rrZyoD+UgkWbt448/ho6ODt577z0+P6FKbm4uZs6cCV1dXXz88cd8uVQqxdixY/l/zs+bGO55nJ2dsWbNGixfvpwfnVKbYcOGIT09XW5UlUQiwdq1a6Gvr4+AgIBaz9PQ0MCbb76JPXv24Pr16wr7X2Qo+/NUtRpUbyWoqKhQWBLB09MTTk5OWLNmDfLy8uT2VT+36k235jG13Xfw4MH4888/5Ya6Z2RkYPv27ejdu/dzu3VeljFjxuDx48fYsGGDwr7S0lIUFxcDAIKCggAAP/zwg9wxDZn5evDgwTAwMMDy5csVZlqu+fpWdXvWx8bGBh4eHtiyZYvcz+L69es4evQoP3mjKvj7++PJkydyw98B4L333oOlpSU+/vhjhX1lZWWYMmUKGGNYuHAhX+7l5QUtLS3ExsaqrH518fLyQmBgIP+o/iFq586dmDNnDt555506Ww4B4K233oJUKpXrGi8vL8fmzZvh6+urEOTHxcVBIBDU+j/p0qVL6NmzpwqeGVEVakEizVqHDh2wZcsWvPPOO+jWrZvCTNrZ2dnYsWOHXN7HRx99hP379yM4OBi5ubn45Zdf5K5Z26fZ56lviH6Vd999F+vXr8fkyZMRFxcHR0dH7N69G9HR0VizZk29CavffvstTp48CV9fX8yYMQMuLi7Izc3FpUuXcPz4cYV5U1SlZ8+eMDExQUhICObMmQOBQIBt27YpdKsIhUL873//Q3BwMDw8PDBlyhTY2Njg5s2buHHjBo4cOQKAe9MBuETlIUOGQENDA+PGjav13l9//TWOHTuG3r174/3334dIJML69etRXl4uN6+Vuk2cOBG///47Zs6ciZMnT6JXr16QSqW4efMmfv/9dxw5cgQ9evSAh4cH3n77bfz444/Iz89Hz549ceLECSQnJz/3HoaGhli9ejWmT58Ob29vjB8/HiYmJrhy5QpKSkr4ubS8vLywc+dOhIWFwdvbG/r6+ggODq71mitXrkRQUBD8/f0xbdo0fpi/kZGRSpeDee211yASiXD8+HG5+YDMzMywe/duvPbaa/D09FSYSTs5ORn/93//JxcUaGtrY/DgwTh+/Ljc/EgA1x23f/9+AEBycjLy8/Px9ddfAwDc3d0VXoczZ87UusyOm5tbvV3WMTExmDRpEszMzDBw4ED8+uuvcvt79uzJDyzw9fXF6NGjsWDBAmRmZqJ9+/bYsmUL//+ppmPHjqFXr15yU0sAXOCUm5uL4cOH11kvogZqGTtHiJKuXr3K3n77bWZjY8M0NTWZtbU1e/vtt2sdDlw1XLuux/PUNfNyTahlJu2MjAw2ZcoUZm5uzrS0tFi3bt3khl5XP7fmzMoZGRksNDSU2dvb889x4MCB7KeffnpunZ83k3ZAQABzdXWtdV90dDTz8/NjOjo6zNbWln3yySfsyJEjDAA7efKk3LFnz55lgwYNYgYGBkxPT4+5ubmxtWvX8vslEgmbPXs2s7CwYAKBQO71ru05X7p0iQ0ZMoTp6+szXV1d1r9/f4WZrauGxF+8eFGuvGo4d8061tTQn2d9r1FFRQX77rvvmKurKxOLxczExIR5eXmxxYsXs/z8fP640tJSNmfOHGZmZsb09PRYcHAwe/jw4XOH+VfZv38/69mzJ9PR0WGGhobMx8eH7dixg99fVFTExo8fz4yNjRkAfqh7bcP8GWPs+PHjrFevXvz1goODWUJCQoNen7rqWJs33niDDRw4sNZ9KSkpbMaMGczBwYFpamoyc3Nz9sYbbyhMm1Dljz/+YAKBgD148KDW+tT2CAkJ4Y973jD/581oXt99anuNS0tL2X/+8x9mbW3NxGIx8/b2ZocPH1a4bl5eHtPS0lKYcZwxxj799FPm4OAgN6UDUT8BYyoYLkAIIeSVdebMGfTr1w83b97kE64bSyqVwsXFBWPGjMHSpUtVVEP1W7NmDVasWIE7d+7IJWKXl5fD0dER8+fPb1BLNXl5KAeJEELIC+nTpw8GDx6skq5RDQ0NLFmyBOHh4SgqKlJB7dSvsrISq1atwhdffKEwSm3z5s3Q1NRUmIeKqB+1IBFCCCGE1EAtSIQQQgghNVCARAghhBBSAwVIhBBCCCE1UIBECCGEEFIDTRTZSDKZDKmpqTAwMKDFBQkhhJAWgjGGwsJC2Nra1ru2JgVIjZSamtps1ooihBBCiHIePnxY76LIFCA1UtWyEQ8fPmw2a0YRQgghpH4FBQWwt7evd/kngAKkRqvqVjM0NKQAiRBCCGlhnpceQ0naSgoPD4eLiwu8vb3VXRVCCCGENBGaSbuRCgoKYGRkhPz8fGpBIoQQQlqIhr5/UwsSIYQQQkgNFCARQgghhNRAARIhhBBCSA0UIBFCCCGE1EABEiGEEEJIDRQgKYmG+RNCCCGtHw3zbyQa5k8IIYS0PDTMnxBCCCGkkShAIoQQQgipgQIkQgghhJAaKEAihBB1kJQDUom6a0EIqcMrESCNHDkSJiYmeOuttxT2OTo6ws3NDR4eHujfv78aakcIeeUkHQW+7wys9QTSr6u7NoSQWrwSAdLcuXOxdevWOvefO3cO8fHxOHny5EusFSHklSOTASeXA9vHAKW5QN59YONg4OYhddeMEFLDKxEg9evXDwYGBuquBiHkVVaSywVGp74FwACvKYBTAFBZDPw2Hoj+P4BmXSGk2VB7gHT69GkEBwfD1tYWAoEA+/btUzgmPDwcjo6O0NbWhq+vL2JiYlR2f4FAgICAAHh7e+PXX39V2XUJIYSXGg/8FAAkHwNE2sCICCB4DTBhD9BjKgAGHFsI/PkBIKlQc2UJIQAgUncFiouL4e7ujqlTp2LUqFEK+3fu3ImwsDBERETA19cXa9aswZAhQ3Dr1i1YWloCADw8PCCRKCY7Hj16FLa2tvXe/+zZs7Czs0NaWhoCAwPRrVs3uLm5qebJEULI5V+Ag2GAtBwwcQTG/gJYd+P2aWgCr60CLDoDh+cD8b8AuXe5Y/TM1FptQl51ag+QgoKCEBQUVOf+VatWYcaMGZgyZQoAICIiAn/99Rc2bdqE+fPnAwDi4+MbfX87OzsAgI2NDYYNG4ZLly7VGiCVl5ejvLyc3y4oKGj0PQkhr4DKMuDvT4BLW7jtjkOBkRGAjon8cQIB4PseYOoM7J4CPDgHbOgPjP8dsOz88utNCAHQDLrY6lNRUYG4uDgEBgbyZUKhEIGBgTh//vwLX7+4uBiFhYUAgKKiIvzzzz9wdXWt9djly5fDyMiIf9jb27/w/QkhrVTeA2Dz0KfBkQDo/wUwbodicFRdh0Bg2jGulSnvPrBxEHD7+MuqMSGkhmYdIGVnZ0MqlcLKykqu3MrKCunp6Q2+TmBgIEaPHo1Dhw6hTZs2fHCVkZGB3r17w93dHX5+fpg0aVKdi9AuWLAA+fn5/OPhw4eNf2KEkNYr+QSwvi+QepkLiCbsBgI+BoQN+Hdr2RmY/g/QthdQXgBsHw1ciKDkbULUQO1dbC/D8eO1fwpr164drly50qBriMViiMVihIeHIzw8HFKpVJVVJIS0dDIZcPZ74J9lABhg2x0YsxUwdlDuOnpmwMR9wMEPuZykw58CWTeBYSu5nCVCyEvRrFuQzM3NoaGhgYyMDLnyjIwMWFtbq6VOoaGhSEhIwMWLF9Vyf0JIM1Saxw3V/+drAAzwDAGmHFY+OKoi0gKGrwMGfw1AAMRtBn4ZxU0VQAh5KZp1gKSlpQUvLy+cOHGCL5PJZDhx4gT8/f3VUqfw8HC4uLjU2RVHniovBK7tBooy1V0TQppW+jXgp35A0t+Ahhh4Yx3wxg+ApvaLXVcgAHrOBt7eAWjpAymngZ8DgexklVSbEFI/AWPq7dwuKipCcjL3B9+9e3esWrUK/fv3h6mpKRwcHLBz506EhIRg/fr18PHxwZo1a/D777/j5s2bCrlJL1NBQQGMjIyQn58PQ0NDtdWjWcq6Bfz2DpBzG9AyAPr+B/CbBYjE6q4ZIap15TfgwDxAUsq1Fo3ZBth6qP4+6deBHeOA/IeAthHXddeun+rvQ8groKHv32oPkKKiompdAy0kJASRkZEAgHXr1mHlypVIT0+Hh4cHfvjhB/j6+r7kmnKq5yAlJSVRgFRTwn5g3yygoggQagKySq7cxJHrLuj8OvfJmJCWTFIOHF4AxG7kttsHAqM2ALqmTXfPokzug8ejGECgAbz236eTTBJClNFiAqSWilqQapBJgX+WAmdXc9ttewNvbQKSjwMnFgNFT/PIHPsAQ5c/myiPkJYm/zHw+yTgcSwAARDwKfdoyCi1F1VZBuyfDVz7ndv2nQkMXgZovBLjbQhRCQqQmhgFSNWU5AK7pwJ3ny726/8BEPjVsxE35UVc4HRuLTebMASA5yRgwJeAvoW6ak2I8u6e4n7XS7K5rq5RPwMdB7/cOjAGnPme+0ACAM4DgdGbufoQQp6LAqQmQl1sNaTGAzsnAvkPAE1d4I21QLe3aj/2yX3g+CLgxl5uW8uAmx/GdyblJ5HmjTEgeg1wYgnAZFwL6JhtgKmT+uqUsB/Y+x5QWQKYdwLG/waYtlNffQhpIShAamLUggQgfjs3V4ukDDBxerrGVNfnn3f/PLfuVFo8t23i9DQ/6TXKTyLNT1k+sO994OZBbtvjHeC17wFNHfXWC+A+oOx4GyhMBXRMub9Bx17qrhUhzRoFSE3slQ6QJBXAkc+Aixu47Q6DgVE/1b+MQk0yGXBlRy35Sd82LMgi5GXISAB2TgBy7wAaWkDQCsBrcvMK5AvSgN/e5mbuFmoCwWuA7hPUXStCmq2Gvn8363mQmqNXfh6kgjRgy+vPgqOA+cDbO5ULjgAuobX7O8DsOKDPR9z8MffOAOv7AAfmAkVZqq87Icq4thv4eSAXHBm2AaYeBnpMaV7BEQAY2gCTDwGuI7lRo3+GAke/4AZOEEIajVqQGumVbEG6fx7YFcK1+IiNuFajTkNVc+2a+UliQ6Dvx9wq55SfRF4mSQVw7Evg3whuu11/4M2N3BIgzRljQNS3wKlvue2OQcCbGwCxgXrrRRpGWglUFAOVpVxeGf99MVBR8uz7ytKn+0rq+L4UEOsDvrOA9gObX0DfDFAXWxN7pQIkxoCYDcCRBYBMAlh0Acb9Cpg5q/5e9889zU96ukaeiRMwZBnQaRj9oZOmV5DGfQh4+C+33ec/QP/PAKGGeuuljGu7uZwpaTlg6colbzd2yRPCYYwLYBocrJTIH1fX95WlT7eLuf+tqmbvC/T/HGgXoPprt2AUIDWxVyZAqijhErGv/sZtu47iRqqJ9ZvunjIZcGU7N2KoKj/JqS8wZDnlJ5Gmc+8ssGsKUJzJtZCOjAA6D1N3rRrnUSy3NlxRBqBnAYzbDtj7qLtWzVfuXSBuC5fHVVfLDHtJXZYCDUBLjxsEoKnLPbR0a3yvA2jq1VL+9PHwX+Diz9wAGoDL7+z/OdBWPUt0NTcUIDWRV2qY/5N7XIJq+jXuj3bQEsA/9OW15JQXPp0/aR33aVgg5OZP6v8FzZ9EVIcx4Pw64Ngi7k3Q0hUYu61pWkhfpvxH3PIk6de4BPM31gHuY9Vdq+ZDKgFuHwEubgTunHj+8VWEIi440dR5GpRU/76ugKYB+6q+19BSzf/YgjTg7CogLhKQVnBlzgO5QKmN14tfvwWjAKmJtfoWpOQTwJ5pQOkTQNecm4jOqa966vLkHvfmlbCP2xYbAgGfAD7vcaueE9JY5YVcUnPCn9y221jg9TXcm1VrUF7EzZVUNUVBn4+4DxgvY9bv5qowHbi0lQscCh4/LRRw+TquIwFt4xqBS7XWHC29ZxPgthR5D4HTK4H4X59143UM4rqObdzUWzc1oQCpibXaAIkx7lPHiaUAGGDryX2aNmqj7pop5ieZtuPmT6L8JNIYWbe4FtLsJG54/NDlgPf01ve7JJM9XQZoFbfdJRgYuZ57s39VMAaknObWzrv517NAQdeMmxLBa4p6J/18GXJTgFMruHQJJuPKurzBBUqWXdRbt5eMAqQm1ioDpLICbqHZqk+bnpOAoJWAprZ661VdrflJAdybm5WreutGWo4be4E/P+AWVTawBcZsBexb+dQd8TuAA3O47hYbd2DcDsDITt21alqlT7jnHbsJyLn9rNzelwuGu7zRvP6/vQzZt7nRjtf3AGAABEDXN4F+CwDz9uqu3UtBAVITa3UBUtYtbqXwnNtcH/iwldyEeM1VeSFwZhVwPvxZfpLXZK5/Xc9c3bUjzZW0Ejj+FZdzBHDJq29tfnVy2h5c4P7OS7IBfWvg7e2AXSvMR3kcB1zcxAUBklKuTEsfcBsD9JhGgz0AbhLUqG+AxAPctkAIuL/NpS+YOKq1ak2NAqQm0iqTtBP2cy1HVZ+mx24D2vRQd60a5sk94NjCZzkklJ9E6lKYAeyeAtyP5rZ7zeMWTNYQqbVaL92T+1zydmYCINIGRvwP6DpK3bV6cRUlwPXdXNJ11TJGAJd07z2Vyy+jOaEUpV0BTn4DJB3mtoUirtux78fNI7WiCVCA1MRaRQuSTPo0N2E1t922N5eMrW+p3no1xr1oLj8p/Sq3bdoOGLwM6BTU+nJKiPLunwd2TQaK0rlFkkf8CLi8oe5aqU9ZAbBnOjeKCwD6fcZ9sGiJfytZSVwXWvx2oDyfK9PQAlxGAN7TuO60lvi8XrZHscDJZcCdf7htDS2uVb7PR4CBtVqrpmoUIDWxFh8gleQCu6cCd09y2/4fAIGLW/anaZmU+yd5Ygk3lw0AtOsHDPmG8pNeVYxxM2If/eLZJKdjtwHmHdRdM/WTSbnW16ruxq5vAsPDm8civM8jreRyJS9u5JYoqmLiyCVcd59AXe2Ndf8c8M8y4P5ZblukzeVr9f6w1bymFCA1sRYdIKXGAzsnAvkPuKGrb6wFur2l7lqpTnkhcOb7p/lJFZSf9KoqL+KSkq/v4ba7vgkE/9C0k5y2RHFbgL/CuADSzoubVLK5thjkP+KG51/a+myQhkAIdBzK5RY5D3i1pzBQFcaAlFNcoPQohivT1OOWfuo5G9A1VW/9XhAFSE2sxQZI8du5mbElZdwyHuN+bb2tKwr5SUZP85Pepfyk1i47mRvCn5XI5VQMXsb9c6eultqlnAF+n8iN+jK0A97ewY10aw5kMq7bJ3YjlydTNURd34obaesZAhjbq7eOrRVjQPJx4J+vn+V1iQ0Bv/cB//cBbSO1Vq+xKEBqYi0uQJJUAEc+Ay5u4LY7DOEWm9UxVmu1XgrKT3q1JB4A9s4CKgq5kVqjI2mJhYbIucMlb2cncS3LozYAXV5XX32Kc4DL24C4zdyHnSqOfbjcos6vt7xJG1sqxoBbh7hk7ozrXJm2Mdea5DuzxbXKUoDURFrkKLaaC3AGzAcCPn21mqIpP6n1k0q4QQfRa7jttr24IfwGVmqtVotSmseN9LvzDwABELiIG+33sj5IMAY8jOHWEUvY92yJDLER4DEe6DEVsOj4cupCFMlkQOKfwMnlQPYtrkzXjMtP6jGtxcxATwFSE2sxLUj3z3PBUVEG909m1E9Ap6HqrpX6lBVwMwrL5SdN4WaTpfyklqsoi3tjr0rY9f8ACPyKWhgaQyrhWlyrWpvd3waC/w8QiZvunuWFwNXfudFoVS0UAGDbnXvj7fpmi3nzfSXIpMC13cCpb7mFfgGuy7PPR1y+Z1P+rqgABUhNrNkHSIwBMRuAIwu45EtLF2DsLy1/AU5VyU3h8pMS93PblJ/Ucj28CPw+CShM5RJJh69rHfP6qFvMBuDvT7kFfO39uHxFVX+IyLjBjUS7upObhw0ARDpcQOQ9tXVOYtmaSCXAlR3cEib5D7gyQzug738AjwnN9n8pBUhNrFkHSBUlXCL21d+4bddR3Ei1FtZP/FLcO/s0P+kat23qDAxZxo2Kofyk5o0xrivm8AJAVgmYd+Q+BFh0UnfNWo87/wC/T+bmFzJ2AN7eCVi5vNg1JeXcwImLG4GHF56Vm3XgutA83gZ0TF7sHuTlklRw+WKn/8t9UAG435eA+dwEnc1s+hgKkJpYsw2QntzjRu+kXwMEGsCgJYB/KL3Z10cm5Va6PrEEKM7iytr1f5qf9IJvBqRpVJQAB+dxLQ8A4DKcm8OHZkpWvawkYPsY4EkKt1zHW5uAjkOUv05uCpdwffkXoCSHKxOKgM6vcd1oTn3p/1RLV1nG/YzPrHqW62nWnguUuo4ChBrqrd9TFCA1sWYZICWfAPZM44bq6ppzs2I79VV3rVqOsgJu/qQLP9bIT/oc0DNTd+2ITMpNEJh3n5vkNOM6fQh4WUpyuW7Me2e4v4vBX3NDvZ/3msukQNIRboh+8glwi6OC64bxmswN02+ucy6Rxqso4XLYzq4BSnO5MovO3IK4Xd5Q+wAhCpCqGTlyJKKiojBw4EDs3r1bbl9KSgqmTp2KjIwMaGho4MKFC9DT03vuNZtVgMQYl3h8YikABth6crMFt9J1dJpcbflJVi7cp12hxtOvmtW+r/bQqLEtd7xI/hyN2q6hUe3Yhhxf20Pj6bFPtwVC+Tcyxri8NGnF08fT72WVXAAirXj6tbJGebV9svqOa0B5Y+5VNf9NFT0Lbgi/Y++X9qvxSpNUAIf+A1zawm17hgDD/lt7nklhBjeZY1wkUPDoWbnzQG6Ifochza7bhTSB8kJuJvtza4Gyp8vAWHXjBsWocZoVCpCqiYqKQmFhIbZs2aIQIAUEBODrr79Gnz59kJubC0NDQ4hEz//DbTYBUlkBt9DszYPctuckIGgloKmtvjq1FilnuCT3qvyklqwqWJJJuYCjpXPsw43INLRVd01eLYwBF/4HHP2cC1gd+wBjtnIzKzPGtTBd3Mj9P5JJuHN0TLmlP3pM4eYgI6+e0jxu5PCF/3HzkwHcB/kBn3NB80sOlChAqiEqKgrr1q2TC5Bu3LiBuXPn4vjx40pfr1kESFm3gN/eAXJucwsLDlvJNVsT1ZFJuUTusnzuH77C42m3T/VtmYQLQuS2JU+Pk9ZxnRrnS2s5X1bL+VKJ/P7GEoq43yENTa4Fq+p7DU0lyquuoVXtesqWV7u2sMZ9qt+PPgCoV9IRYPc07s3OxIn7vxO//dncOAC3SGyPaVx+GP28CMB11Ub/HxDzE1BZwpXZ+3GB0ktMB2no+7fa2zhPnz6NlStXIi4uDmlpadi7dy9GjBghd0x4eDhWrlyJ9PR0uLu7Y+3atfDx8Xnhe9++fRv6+voIDg7G48eP8dZbb+Gzzz574eu+FIkHgL0zuaGxBrZcl1qbHuquVesj1ADaBai7Fg0nk9UfoFV11dUMQih/hyij4xBg2lFgx1guefv4Iq5cSx9wG8MFRtZd1VtH0vzomgKDFnM5g2fXcKNQH14AtgRzrZEDvgAc/NRdS57aA6Ti4mK4u7tj6tSpGDVKce6SnTt3IiwsDBEREfD19cWaNWswZMgQ3Lp1C5aWlgAADw8PSCQShXOPHj0KW9u6m+AlEgnOnDmD+Ph4WFpaYujQofD29sagQYNU9wRVTSblZgs+u5rbbtuby8PQt1BrtUgzIRQCQi0AzXP+EdKKWLkAM05yH9RKsrluNLexNJKQPJ++JTD0G26pkjPfc7lq984Am4YA7QO5HKVmMAeW2gOkoKAgBAUF1bl/1apVmDFjBqZMmQIAiIiIwF9//YVNmzZh/vz5AID4+PhG3dvOzg49evSAvT230OGwYcMQHx9fa4BUXl6O8vJyfrugoKBR93whJbnc6J27J7lt/w+AwMWU7EgIUQ89c2DC7ucfR0htDG2A1/4L9JoDnF4JXP6VWxw3+TjQaRgXKFl3U1v1mvViXBUVFYiLi0NgYCBfJhQKERgYiPPnz7/w9b29vZGZmYknT55AJpPh9OnT6NKlS63HLl++HEZGRvyjKqh6aVLjgfUBXHCkqQu8uZGb0JCCI0IIIS2ZsQM3mfHsWG5pG4GQWxw3ojfXwqQmzTpAys7OhlQqhZWV/GKTVlZWSE9Pb/B1AgMDMXr0aBw6dAht2rThgyuRSIRvvvkGffv2hZubGzp06IDXX6999eoFCxYgPz+ffzx8+LDxT0xZ8Tu4psf8B1xC5PTjQLe3Xt79CSGEkKZm2g4YGQG8/y+33AwEgJP6ckBfieaH+kapPa+Lr4pYLIZYLEZ4eDjCw8MhlUpVWcXaSSqAI589WzSywxBuaLOOcdPfmxBCCFEHi47cjO0DFwEmbdVWjWbdgmRubg4NDQ1kZGTIlWdkZMDaWj2zr4aGhiIhIQEXL15s2hsVpAFbXn8WHAXMB97+jYIjQgghrwY1BkdAMw+QtLS04OXlhRMnTvBlMpkMJ06cgL+/v1rqFB4eDhcXF3h7ezfdTe6fB34KAB7+y83i/PZOoP8CtU/PTgghhLwq1N7FVlRUhOTkZH47JSUF8fHxMDU1hYODA8LCwhASEoIePXrAx8cHa9asQXFxMT+q7WULDQ1FaGgoP9GUSvGrk8/n5q2xdOFWJzdzVu19CCGEEFIvtQdIsbGx6N+/P78dFhYGAAgJCUFkZCTGjh2LrKwsLFy4EOnp6fDw8MDhw4cVErdbjUexXHDkOorL6hfrq7tGhBBCyCvnlVlqRFWqJ2knJSWpfqmRihLgxl7AYzzNbkwIIYSoGK3F1sSaxVpshBBCCFFKQ9+/KetXSS8lSZsQQgghakUtSI1ELUiEEEJIy0MtSIQQQgghjUQBkpKoi40QQghp/aiLrZGoi40QQghpeaiLjRBCCCGkkShAIoQQQgipgQIkJVEOEiGEENL6UQ5SI1EOEiGEENLyUA4SIYQQQkgjUYBECCGEEFIDBUiEEEIIITVQgKQkStImhBBCWj9K0m4kStImhBBCWh5K0iaEEEIIaSQKkAghhBBCaqAAiRBCCCGkBgqQCCGEEEJqoACJEEIIIaQGCpCURMP8CSGEkNaPhvk3Eg3zJ4QQQloeGuZPCCGEENJIFCARQgghhNRAARIhhBBCSA0UIBFCCCGE1PBKBEgjR46EiYkJ3nrrLbnyW7duwcPDg3/o6Ohg37596qkkIYQQQpqNV2IUW1RUFAoLC7Flyxbs3r271mOKiorg6OiI+/fvQ09P77nXpFFshBBCSMtDo9iq6devHwwMDOo9Zv/+/Rg4cGCDgiNCCCGEtG5qD5BOnz6N4OBg2NraQiAQ1NrFFR4eDkdHR2hra8PX1xcxMTEqr8fvv/+OsWPHqvy6hBBCCGl51B4gFRcXw93dHeHh4bXu37lzJ8LCwrBo0SJcunQJ7u7uGDJkCDIzM/ljPDw80LVrV4VHampqg+pQUFCAc+fOYdiwYSp5ToQQQghp2UTqrkBQUBCCgoLq3L9q1SrMmDEDU6ZMAQBERETgr7/+wqZNmzB//nwAQHx8/AvV4c8//8TgwYOhra1d5zHl5eUoLy/ntwsKCl7onoQQQghpvtTeglSfiooKxMXFITAwkC8TCoUIDAzE+fPnVXafhnSvLV++HEZGRvzD3t5eZfcnhBBCSPPSrAOk7OxsSKVSWFlZyZVbWVkhPT29wdcJDAzE6NGjcejQIbRp00YuuMrPz0dMTAyGDBlS7zUWLFiA/Px8/vHw4UPlngwhhBBCWgy1d7G9DMePH69zn5GRETIyMp57DbFYDLFYjPDwcISHh0MqlaqyioQQQghpRpp1C5K5uTk0NDQUApiMjAxYW1urpU6hoaFISEjAxYsX1XJ/QgghhDS9Zh0gaWlpwcvLCydOnODLZDIZTpw4AX9/f7XUKTw8HC4uLvD29lbL/QkhhBDS9NTexVZUVITk5GR+OyUlBfHx8TA1NYWDgwPCwsIQEhKCHj16wMfHB2vWrEFxcTE/qu1lCw0NRWhoKD8TJyGEEEJaH7UHSLGxsejfvz+/HRYWBgAICQlBZGQkxo4di6ysLCxcuBDp6enw8PDA4cOHFRK3XxbKQSKEEEJav1diLbamQGuxEUIIIS0PrcVGCCGEENJIFCApiZK0CSGEkNaPutgaibrYCCGEkJaHutgIIYQQQhqJAiQlURcbIYQQ0vpRF1sjURcbIYQQ0vJQFxshhBBCSCNRgEQIIYQQUgMFSEqiHCRCCCGk9aMcpEaiHCRCCCGk5aEcJEIIIYSQRqIAiRBCCCGkBgqQCCGEEEJqoABJSZSkTQghhLR+lKTdSJSkTQghhLQ8lKRNCCGEENJIFCARQgghhNRAARIhhBBCSA0UIBFCCCGE1EABEiGEEEJIDRQgKYmG+RNCCCGtHw3zbyQa5k8IIYS0PDTMnxBCCCGkkShAIoQQQgipgQIkQgghhJAaXokAaeTIkTAxMcFbb72lsG/16tVwdXWFi4sL5syZA0rJIoQQQsgrESDNnTsXW7duVSjPysrCunXrEBcXh2vXriEuLg4XLlxQQw0JIYQQ0py8EgFSv379YGBgUOs+iUSCsrIyVFZWorKyEpaWli+5doQQQghpbpQKkCorKyESiXD9+nWVVeD06dMIDg6Gra0tBAIB9u3bp3BMeHg4HB0doa2tDV9fX8TExKjk3hYWFvjPf/4DBwcH2NraIjAwEM7Oziq5NiGEEEJaLqUCJE1NTTg4OEAqlaqsAsXFxXB3d0d4eHit+3fu3ImwsDAsWrQIly5dgru7O4YMGYLMzEz+GA8PD3Tt2lXhkZqaWu+9nzx5goMHD+LevXt4/Pgxzp07h9OnT6vsuRFCCCGkZRIpe8Lnn3+Ozz77DNu2bYOpqekLVyAoKAhBQUF17l+1ahVmzJiBKVOmAAAiIiLw119/YdOmTZg/fz4AID4+vlH3Pn78ONq3b88/j9deew0XLlxA3759FY4tLy9HeXk5v11QUNCoexJCCCGk+VM6QFq3bh2Sk5Nha2uLtm3bQk9PT27/pUuXVFa5iooKxMXFYcGCBXyZUChEYGAgzp8//8LXt7e3x7lz51BWVgZNTU1ERUXh3XffrfXY5cuXY/HixS98T0IIIYQ0f0oHSCNGjGiCatQuOzsbUqkUVlZWcuVWVla4efNmg68TGBiIK1euoLi4GG3atMGuXbvg7+8PPz8/DBs2DN27d4dQKMTAgQPxxhtv1HqNBQsWICwsjN8uKCiAvb19454YIYQQQpo1pQOkRYsWNUU9mtTx48fr3Lds2TIsW7bsudcQi8UQi8UIDw9HeHi4SvOwCCGEENK8KB0gVYmLi0NiYiIAwNXVFd27d1dZpaqYm5tDQ0MDGRkZcuUZGRmwtrZW+f0aIjQ0FKGhofxid4QQQghpfZSeBykzMxMDBgyAt7c35syZgzlz5sDLywsDBw5EVlaWSiunpaUFLy8vnDhxgi+TyWQ4ceIE/P39VXqvhgoPD4eLiwu8vb3Vcn9CCCGEND2lA6TZs2ejsLAQN27cQG5uLnJzc3H9+nUUFBRgzpw5SlegqKgI8fHx/Ei0lJQUxMfH48GDBwCAsLAwbNiwAVu2bEFiYiJmzZqF4uJiflTbyxYaGoqEhARcvHhRLfcnhBBCSNMTMCUXHzMyMsLx48cVWlBiYmIwePBg5OXlKVWBqKgo9O/fX6E8JCQEkZGRALiRcytXrkR6ejo8PDzwww8/wNfXV6n7qFpVF1t+fj4MDQ3VWhdCCCGENExD37+VzkGSyWTQ1NRUKNfU1IRMJlP2cujXr99zF4j94IMP8MEHHyh97aZASdqEEEJI66d0C9Lw4cORl5eHHTt2wNbWFgDw+PFjvPPOOzAxMcHevXubpKLNDbUgEUIIIS1PQ9+/lc5BWrduHQoKCuDo6AhnZ2c4OzvDyckJBQUFWLt27QtVuiWgJG1CCCGk9VO6BQkAGGM4fvw4P1ljly5dEBgYqPLKNWfUgkQIaS6kUikqKyvVXQ1CmgVNTU1oaGjUub9JcpAqKyuho6OD+Ph4DBo0CIMGDVLmdEIIISrEGEN6errSg2MIae2MjY1hbW0NgUDQ6GsoFSBpamrCwcHhlU5QpiRtQkhzURUcWVpaQldX94XeDAhpDRhjKCkpQWZmJgDAxsam0ddSuott48aN+OOPP7Bt2zaYmpo2+sYtHXWxEULUSSqVIikpCZaWljAzM1N3dQhpVnJycpCZmYmOHTsqdLc12TD/devWITk5Gba2tmjbti309PTk9l+6dEnZSxJCCFFSVc6Rrq6ummtCSPNT9XdRWVlZbz5SfZQOkEaMGNGoGxFCCFE96lYjRJEq/i6UCpAkEgkEAgGmTp2KNm3avPDNWyLKQSKEEEJaP6XmQRKJRFi5ciUkEklT1afZo7XYCCGEkNZP6YkiBwwYgFOnTjVFXQghhLwisrKyMGvWLDg4OEAsFsPa2hpDhgxBdHS0Su8TFRUFgUBQ7yMqKkql92yIr776ir+/SCSCo6MjPvzwQxQVFWHp0qWwsbFBbm6u3DlXrlyBWCzGwYMHAQCnTp3CgAEDYGpqCl1dXXTo0AEhISGoqKgA8Oy5m5iYoKysTO5aFy9e5O9fnVQqxerVq9GtWzdoa2vDxMQEQUFBSv1cIiMjYWxsLFeWmJgIe3t7jB49GhUVFYiMjOTvLxQKYWNjg7Fjx/IL1VeXnJyMqVOn8r8rdnZ2GDhwIH799dcmbbBROgcpKCgI8+fPx7Vr1+Dl5aWQpP3GG2+orHKEEEJapzfffBMVFRXYsmUL2rVrh4yMDJw4cQI5OTmNuh5jDFKpFCKR/Ntaz549kZaWxm/PnTsXBQUF2Lx5M1+mrhHZrq6uOH78OCQSCaKjozF16lSUlJQgPDwcBw4cQGhoKHbs2AGASzYOCQnBhAkT8PrrryMhIQFDhw7F7Nmz8cMPP0BHRwe3b9/Gnj17FFJADAwMsHfvXrz99tt82caNG+Hg4CAXkDDGMG7cOBw/fhwrV67EwIEDUVBQgPDwcPTr1w+7du1qVB7yxYsXERQUhJEjR2L9+vUQCrm2GUNDQ9y6dQuMMaSkpOD999/H6NGj8e+///LnxsTEIDAwEK6urggPD0fnzp0BALGxsQgPD0fXrl3h7u6udJ0ahClJIBDU+RAKhcpersXKz89nAFh+fr66q0IIeQWVlpayhIQEVlpaqu6qKO3JkycMAIuKiqp1f0pKCgPALl++rHDOyZMnGWOMnTx5kgFghw4dYp6enkxTU5OdPHmSBQQEsNDQUDZ37lxmZmbG+vXrJ3ftkJAQNnz4cMYYY6dOnWIikYilpaXJHTN37lzWu3dvxhhjmzdvZkZGRmzv3r2sffv2TCwWs8GDB7MHDx7InbNv3z7WvXt3JhaLmZOTE/vqq69YZWVlna/BokWLmLu7u1zZjBkzmLW1NWOMscTERKatrc127drFH9+2bVv+PWf16tXM0dGxzutXf42++OILFhgYyJeXlJQwIyMj9uWXX7LqYcBvv/3GALD9+/crXGvUqFHMzMyMFRUV1XtPxp69ZowxduLECaavr88++eSTOo+p8sMPP8i9r8pkMtalSxfm5eXFpFJprfeSyWS1ltf399HQ92+lu9hkMlmdj1chcZnWYiOENFeMMZRUSNTyYEpMqaevrw99fX3s27cP5eXlL/Sc58+fj2+//RaJiYlwc3MDAGzZsgVaWlqIjo5GREREnef27dsX7dq1w7Zt2/iyyspK/Prrr5g6dSpfVlJSgmXLlmHr1q2Ijo5GXl4exo0bx+8/c+YMJk2ahLlz5yIhIQHr169HZGQkli1bptRz0dHR4bvHOnfujOXLl2PWrFk4cuQIli9fjs2bN/Pz9lhbWyMtLQ2nT59+7nUnTpyIM2fO8K1Fe/bsgaOjIzw9PeWO2759Ozp27Ijg4GCFa3z00UfIycnBsWPHGvx89u7di9deew1ffPEFvvvuu3qPzczMxN69e6GhocEPy4+Pj0diYiL+85//8K1ONTXlKE6lu9hedaGhoQgNDeUnmiKEkOaitFIKl4VH1HLvhCVDoKvVsLcUkUiEyMhIzJgxAxEREfD09ERAQADGjRvHBzkNtWTJEoVlrzp06IAVK1Y06Pxp06Zh8+bN+PjjjwEABw4cQFlZGcaMGcMfU1lZiXXr1sHX1xcAF4B16dIFMTEx8PHxweLFizF//nyEhIQAANq1a4elS5fik08+waJFixpUj7i4OGzfvh0DBgzgy+bOnYs///wTw4YNw+zZs9G/f39+3+jRo3HkyBEEBATA2toafn5+GDhwICZNmqQw+aGlpSWCgoIQGRmJhQsXYtOmTXIBYJWkpCR06dKl1vpVlSclJTXo+RQVFWH06NH47LPP8Omnn9Z6TH5+PvT19fnZrwFgzpw5fOpO1b06derEn5OZmYl27drx2ytWrMD777/foDopq8EtSMOGDUN+fj6//e2338qt/5OTkwMXFxeVVo4QQkjr9OabbyI1NRX79+/H0KFDERUVBU9PT0RGRip1nR49eiiUeXl5Nfj8yZMnIzk5GRcuXADAJRiPGTNGLr9WJBLJ9Rp07twZxsbGSExMBMAlTy9ZsoRvGdPX18eMGTOQlpbGv/HX5tq1a9DX14eOjg58fHzg7++PdevW8fsFAgE+//xzyGQyfPHFF3LnamhoYPPmzXj06BFWrFgBOzs7fPPNN3B1dZXLuaoydepUREZG4u7duzh//jzeeeedWuukTEtgfXR0dDBo0CBs2LCBf51qMjAwQHx8PGJjY/H999/D09Pzua1uZmZmiI+PR3x8PIyNjfkWt6bQ4BakI0eOyDWFfvPNNxgzZgyfqS6RSHDr1i2VV5AQQkjD6GhqIGHJELXdW1na2tr8wudffvklpk+fjkWLFuHMmTMA5N+sq2YOr6nmQKG6yupiaWmJ4OBgbN68GU5OTvj777+VHtVWVFSExYsXY9SoUQr7tLW16zyvU6dO2L9/P0QiEWxtbaGlpaVwTFXSec3k8yp2dnaYOHEiJk6ciKVLl6Jjx46IiIjA4sWL5Y4LCgrCu+++i2nTpiE4OLjW5Wk6duxYZzBTVd6xY8c6n091Ghoa2LdvH0aNGoX+/fvj5MmTCq1TQqEQ7du3B8C1UN25cwezZs3iuzw7dOgAALh16xa6d+/OX7fqnLpeE1VpcAtSzahSVVEmIYQQ1RAIBNDVEqnloYpcEBcXFxQXF8PCwgIA5FpC4uPjX/j6dZk+fTp27tyJn376Cc7OzujVq5fcfolEgtjYWH771q1byMvL49/wPT09cevWLbRv317hUVfuDABoaWmhffv2cHR0rDU4UpaJiQlsbGxQXFyssE8kEmHSpEmIioqqtXsNAMaNG4fbt2/jwIEDCvu+//57mJmZKXRn1kcsFuOPP/6At7c3+vfvj4SEhHqPnz9/Pnbu3MkvWda9e3d07twZ//3vfyGTyRp8X1VROkmbEEIIeRE5OTkYMGAAfvnlF1y9ehUpKSnYtWsXVqxYgeHDh0NHRwd+fn588vWpU6cUuphUaciQITA0NMTXX3+NKVOmKOzX1NTE7Nmz8e+//yIuLg6TJ0+Gn58ffHx8AAALFy7E1q1bsXjxYty4cQOJiYn47bff5Oo8adIkLFiwQGV1Xr9+PWbNmoWjR4/izp07uHHjBj799FPcuHGj1iRrAFi6dCmysrIwZEjtrYzjxo3DyJEjERISgo0bN+LevXu4evUq3nvvPezfvx8///yzUq1zABck7dmzB76+vujfvz9u3LhR57H29vYYOXIkFi5cCIAL+Ddv3oxbt26hV69e2L9/P27fvo2EhAREREQgKyur0eusNUSDA6TaJpSiNYAIIYQoS19fH76+vli9ejX69u2Lrl274ssvv8SMGTP4HJxNmzZBIpHAy8sL8+bNw9dff91k9REKhZg8eTKkUikmTZqksF9XVxeffvopxo8fj169ekFfXx87d+7k9w8ZMgQHDx7E0aNH4e3tDT8/P6xevRpt27blj3nw4EGtuUGN5ePjg6KiIsycOROurq4ICAjAhQsXsG/fPgQEBNR6jpaWFszNzet87xYIBPj999/x2WefYfXq1ejUqRP69OmD+/fvIyoqqtFrsWppaWH37t3o2bMn+vfvj+vXr9d57Icffoi//voLMTExAAA/Pz/ExcWhU6dOCA0NhYuLC3r27IkdO3Zg9erVmDVrVqPq1BAC1sC+MqFQiKCgIIjFYgBcpv+AAQP4aLK8vByHDx9u9UP9q6/FlpSUhPz8fIURA4QQ0tTKysqQkpICJyenevNcSMNMmzYNWVlZ2L9/v1x5ZGQk5s2bJzcoiTR/9f19VI1Cf977d4MznKqGL1aZMGGCwjG1Rd6tDQ3zJ4SQ1iM/Px/Xrl3D9u3bFYIj8mprcIBUfVp2QgghpDUYPnw4YmJiMHPmTKUSkF9lrq6uuH//fq371q9fX+cUAi1Ng7vYiLyGNtERQkhToC42oi7379+vc9oFKysrGBgYvOQaKXqpXWyEEEIIIdWTz1szGuZPCCGEEFLDKxEgjRw5EiYmJnjrrbcU9v33v/+Fq6srunbtil9++UUNtSOEEEJIc/NKBEhz587F1q1bFcqrRi7ExcXh4sWLWLduHQ3lJIQQQkjjAqRt27ahV69esLW15TPZ16xZgz///FOllVOVfv361Zo0lpiYCH9/f2hra0NHRwfu7u44fPiwGmpICCGEkOZE6QDpf//7H8LCwjBs2DDk5eXxE0MaGxtjzZo1Slfg9OnTCA4Ohq2tLQQCAfbt26dwTHh4OBwdHaGtrQ1fX19+hs0X1bVrV0RFRSEvLw9PnjxBVFQUHj9+rJJrE0IIIaTlUjpAWrt2LTZs2IDPP/9cbg2UHj164Nq1a0pXoLi4GO7u7ggPD691/86dOxEWFoZFixbh0qVLcHd3x5AhQ5CZmckf4+Hhga5duyo8UlNT6723i4sL5syZgwEDBmDUqFHw8/Nr0nVdCCGEENIyKB0gpaSkoHv37grlYrG41hWEnycoKAhff/01Ro4cWev+VatWYcaMGZgyZQpcXFwQEREBXV1dbNq0iT8mPj4e169fV3jY2to+9/7vvfceLl26hJMnT0JTUxMdOnSo9bjy8nIUFBTIPQghhDROVlYWZs2aBQcHB4jFYlhbW2PIkCGIjo5W6X2ioqL4tUTrekRFRan0ng3x1Vdf8fcXiURwdHTEhx9+iKKiIixduhQ2NjbIzc2VO+fKlSsQi8U4ePAgAODUqVMYMGAATE1Noauriw4dOiAkJAQVFRUAnj13ExMTlJWVyV3r4sWLta6xKpVKsXr1anTr1g3a2towMTFBUFCQUj+XyMhI/tpCoRBt2rTBlClT5Bo2avYYnTp1Cpqamjh79qzctYqLi9GuXTv85z//afD9VUXpAMnJyQnx8fEK5YcPH0aXLl1UUSdeRUUF4uLiEBgYyJcJhUIEBgbi/PnzKrlH1Q/s1q1biImJqXOV4+XLl8PIyIh/2Nvbq+T+hBDyKnrzzTdx+fJlbNmyBUlJSdi/fz/69euHnJycRl2PMQaJRKJQ3rNnT6SlpfGPMWPGYOjQoXJlPXv2fNGn0yiurq5IS0vDvXv38N133+Gnn37CRx99hAULFsDe3h6hoaH8sZWVlQgJCcGECRPw+uuvIyEhAUOHDkWPHj1w+vRpXLt2DWvXroWWlpbCmqgGBgbYu3evXNnGjRvh4OAgV8YYw7hx47BkyRLMnTsXiYmJiIqKgr29Pfr161drCkxdDA0NkZaWhkePHmHDhg34+++/MXHixDqPDwgIwOzZszF58mS5xpZPPvkEOjo6TbpYcZ2YkjZs2MDs7OzYb7/9xvT09NiOHTvY119/zX//IgCwvXv38tuPHz9mANi5c+fkjvv444+Zj49Pg687cOBAZm5uznR0dJidnZ3c9fz8/FiXLl1Yjx49WGxsbJ3XKCsrY/n5+fzj4cOHDADLz89v+BMkhBAVKS0tZQkJCay0tFTdVVHakydPGAAWFRVV6/6UlBQGgF2+fFnhnJMnTzLGGDt58iQDwA4dOsQ8PT2ZpqYmO3nyJAsICGChoaFs7ty5zMzMjPXr10/u2iEhIWz48OGMMcZOnTrFRCIRS0tLkztm7ty5rHfv3owxxjZv3syMjIzY3r17Wfv27ZlYLGaDBw9mDx48kDtn3759rHv37kwsFjMnJyf21VdfscrKyjpfg0WLFjF3d3e5shkzZjBra2vGGGOJiYlMW1ub7dq1iz++bdu2/HvO6tWrmaOjY53Xr/4affHFFywwMJAvLykpYUZGRuzLL79k1cOA3377jQFg+/fvV7jWqFGjmJmZGSsqKqr3now9e82qW7ZsGRMKhaykpIQxpvh+zxj3O92lSxcWGhrKGGPsn3/+YVpaWvW+N9elvr+P/Pz8Br1/Kz2T9vTp06Gjo4MvvvgCJSUlGD9+PGxtbfF///d/GDdunKriNpU6fvx4nfsa2hIlFoshFosRHh6O8PBwhQidEELUjjGgskQ999bUBWp019RFX18f+vr62LdvH/z8/CAWixt92/nz5+O///0v2rVrBxMTEwDAli1bMGvWrOd2C/Xt2xft2rXDtm3b8PHHHwPgWmp+/fVXrFixgj+upKQEy5Ytw9atW6GlpYX3338f48aN469/5swZTJo0CT/88AP69OmDO3fu4N133wUALFq0qMHPRUdHh+8e69y5M5YvX45Zs2bBwMAAy5cvx+HDh/mlMaytrZGWlobTp0+jb9++9V534sSJWLlyJR48eAAHBwfs2bMHjo6O8PT0lDtu+/bt6NixI4KDgxWu8dFHH+GPP/7AsWPHMGLEiAY/p+rPTSaT1drKV0VbWxtbt25Fz549MWjQIMybNw+fffYZvLy8lL6fKjRqqZF33nkH77zzDkpKSlBUVARLS0tV1wsAYG5uDg0NDWRkZMiVZ2RkwNrauknu+TyhoaEIDQ3l13IhhJBmo7IE+Ob5uZdN4rNUQEuvQYeKRCJERkZixowZiIiIgKenJwICAjBu3Di4ubkpddslS5YoLDLboUMHuQCnPtOmTcPmzZv5AOnAgQMoKyvDmDFj+GMqKyuxbt06+Pr6AuACsC5duiAmJgY+Pj5YvHgx5s+fj5CQEABAu3btsHTpUnzyyScNDpDi4uKwfft2DBgwgC+bO3cu/vzzTwwbNgyzZ89G//79+X2jR4/GkSNHEBAQAGtra/j5+WHgwIGYNGmSwvpilpaWCAoKQmRkJBYuXIhNmzZh6tSpCnVISkqqM1WmqjwpKalBz6e627dvIyIiAj169HjuOm09evTAggULMGrUKHTv3h2ff/650vdTFaVzkAYMGMBPpqirq8sHRwUFBXI/WFXQ0tKCl5cXTpw4wZfJZDKcOHEC/v7+Kr1XQ4WHh8PFxQXe3t5quT8hhLQGb775JlJTU7F//34MHToUUVFR8PT0RGRkpFLX6dGjh0KZMi0OkydPRnJyMi5cuACASzAeM2YM9PSeBXsikUjuf37nzp1hbGyMxMREAFzy9JIlS/iWMX19fcyYMQNpaWkoKam7Re/atWvQ19eHjo4OfHx84O/vj3Xr1vH7BQIBPv/8c8hkMnzxxRdy52poaGDz5s149OgRVqxYATs7O3zzzTd8XlNNU6dORWRkJO7evYvz58/jnXfeqbVOTEXr1+fn50NfXx+6urro1KkTrKys8Ouvvzbo3C+//BIymQzz58+HSKS+JWOVvnNUVBTfBFhdWVkZzpw5o3QFioqKkJyczG+npKQgPj4epqamcHBwQFhYGEJCQtCjRw/4+PhgzZo1KC4uxpQpU5S+lypQCxIhpNnS1OVactR1byVpa2tj0KBBGDRoEL788ktMnz4dixYt4t9Lqr9Z17V6fPVApr6yulhaWiI4OBibN2+Gk5MT/v77b6VHtRUVFWHx4sUYNWqUwr6aK8lX16lTJ+zfvx8ikQi2trbQ0tJSOKYqQKgrULCzs8PEiRMxceJELF26FB07dkRERAQWL14sd1xQUBDeffddTJs2DcHBwTAzM1O4VseOHfmgr6aq8o4dO9b5fKozMDDApUuXIBQKYWNjAx0dnQadBzz/Ob8sDb771atX+e8TEhKQnp7Ob0ulUhw+fBh2dnZKVyA2Nlau2TAsLAwAEBISgsjISIwdOxZZWVlYuHAh0tPT4eHhgcOHD8PKykrpe6kC5SARQpotgaDB3VzNkYuLC/bt2wcLCwsAQFpaGj+tTG2jp1Vl+vTpePvtt9GmTRs4OzujV69ecvslEgliY2Ph4+MDgBv1nJeXx3c7eXp64tatW2jfvr1S99XS0lL6nPqYmJjAxsam1il3RCIRJk2ahBUrVuDvv/+u9fxx48Zh/PjxOHDggEIe0vfffw8zMzOF7sy6CIVClT43dWhwgOTh4cHPa1BbV5qOjg7Wrl2rdAX69ev33Ca9Dz74AB988IHS124K1IJECCEvJicnB6NHj8bUqVPh5uYGAwMDxMbGYsWKFRg+fDh0dHTg5+eHb7/9Fk5OTsjMzFToYlKlIUOGwNDQEF9//TWWLFmisF9TUxOzZ8/GDz/8AJFIhA8++AB+fn58wLRw4UK8/vrrcHBwwFtvvQWhUIgrV67g+vXr/PD0SZMmwc7ODsuXL1dJndevX4/4+HiMHDkSzs7OKCsrw9atW3Hjxo0634uXLl2Kjz/+uNbWI4ALkHbt2oWQkBCsXLkSAwcOREFBAcLDw7F//37s2rVLqda556nqMaquQ4cOKr3Hi2hwgJSSkgLGGNq1a4eYmBg+wge4KNjS0pJmoSaEEPJc+vr68PX1xerVq3Hnzh1UVlbC3t4eM2bMwGeffQYA2LRpE6ZNmwYvLy906tQJK1aswODBg5ukPkKhEJMnT8Y333yDSZMmKezX1dXFp59+ivHjx+Px48fo06cPNm7cyO8fMmQIDh48iCVLluC7776DpqYmOnfujOnTp/PHPHjwAEKh6taH9/HxwdmzZzFz5kykpqZCX18frq6u2LdvHwICAmo9R0tLC+bm5nVeUyAQ4Pfff8eaNWuwevVqvP/++9DW1oa/vz+ioqIUWtZeVFWPUXVnzpxB7969VXqfxhIwVWVkvSKqd7ElJSUhPz9fYcQAIYQ0tbKyMqSkpMDJyanePBfSMNOmTUNWVhb2798vVx4ZGYl58+bxg5NIy1Df30dVD9Dz3r+VzoDaunVrvftri75bE+piI4SQ1iM/Px/Xrl3D9u3bFYIj8mpTOkCaO3eu3HZlZSVKSkqgpaUFXV3dVh8gEUIIaT2GDx+OmJgYzJw5s8EJyK86V1dX3L9/v9Z969evr3MKgZZG6QDpyZMnCmW3b9/GrFmz+Im2WjMaxUYIIa3H84b0T548GZMnT34pdWkpDh06VOe0C+oaYd4UVJaDFBsbiwkTJuDmzZuquFyz19A+TEIIaQqUg0RI3VSRg6SylHqRSITUVDVNUEYIIYQQokJKd7HVTGJjjCEtLQ3r1q1T+RBAQgghhBB1UDpAqrmKr0AggIWFBQYMGIDvv/9eVfVqtigHiRBCCGn9lA6QZDJZU9SjxaBh/oQQQkjrp7ppPQkhhBBCWokGtSDVNh14XVatWtXoyhBCCCGENAcNakG6fPlygx5NudoyIYSQ1iMrKwuzZs2Cg4MDxGIxrK2tMWTIEERHR6v0PlFRUfxC63U9njcXUlP46quv+PuLRCI4Ojriww8/RFFREZYuXQobGxvk5ubKnXPlyhWIxWIcPHgQAHDq1CkMGDAApqam0NXVRYcOHRASEoKKigoAz567iYkJysrK5K518eJF/v7VSaVSrF69Gt26dYO2tjZMTEwQFBSk1M8lMjKSv7ZQKISNjQ3Gjh2LBw8eyB3Xr18/uZ+DlZUVRo8eLTcJ5b1792r9mU2YMKHB9WmsBrUgnTx5sqnr0WJQkjYhhLy4N998ExUVFdiyZQvatWuHjIwMnDhxAjk5OY26HmMMUqkUIpH821rPnj2RlpbGb8+dOxcFBQXYvHkzX2Zqatq4J/GCXF1dcfz4cUgkEkRHR2Pq1KkoKSlBeHg4Dhw4gNDQUOzYsQMAt2pFSEgIJkyYgNdffx0JCQkYOnQoZs+ejR9++AE6Ojq4ffs29uzZo/D+ZGBggL179+Ltt9/myzZu3AgHBwe5oIUxhnHjxuH48eNYuXIlBg4ciIKCAoSHh6Nfv37YtWuXwkCtuhgaGuLWrVtgjCElJQXvv/8+Ro8ejX///VfuuBkzZmDJkiVgjOH+/fuYN28eJkyYgDNnzsgdd/z4cbi6uvLbOjo6DarHC2Ev4OHDh+zhw4cvcokWKz8/nwFg+fn56q4KIeQVVFpayhISElhpaam6q6K0J0+eMAAsKiqq1v0pKSkMALt8+bLCOSdPnmSMMXby5EkGgB06dIh5enoyTU1NdvLkSRYQEMBCQ0PZ3LlzmZmZGevXr5/ctUNCQtjw4cMZY4ydOnWKiUQilpaWJnfM3LlzWe/evRljjG3evJkZGRmxvXv3svbt2zOxWMwGDx7MHjx4IHfOvn37WPfu3ZlYLGZOTk7sq6++YpWVlXW+BosWLWLu7u5yZTNmzGDW1taMMcYSExOZtrY227VrF39827Zt+fec1atXM0dHxzqvX/01+uKLL1hgYCBfXlJSwoyMjNiXX37JqocBv/32GwPA9u/fr3CtUaNGMTMzM1ZUVFTvPRl79ppV98MPPyi8ZwYEBLC5c+fKHbdt2zamq6vLb9f2u9AQ9f19NPT9W+kkbZlMhiVLlsDIyAht27ZF27ZtYWxsjKVLl77yI9wIIUSdGGMoqSxRy4MpsSiDvr4+9PX1sW/fPpSXl7/Qc54/fz6+/fZbJCYmws3NDQCwZcsWaGlpITo6GhEREXWe27dvX7Rr1w7btm3jyyorK/Hrr79i6tSpfFlJSQmWLVuGrVu3Ijo6Gnl5eRg3bhy//8yZM5g0aRLmzp2LhIQErF+/HpGRkVi2bJlSz0VHR4fvHuvcuTOWL1+OWbNm4ciRI1i+fDk2b97Mz/xsbW2NtLQ0nD59+rnXnThxIs6cOcO3Fu3ZsweOjo7w9PSUO2779u3o2LEjgoODFa7x0UcfIScnB8eOHVPqOQFAZmYm9u7dCw0NDWhoaNR5XG5uLn7//Xf4+voqfY+moPQw/88//xwbN27Et99+y08MefbsWXz11VcoKytT+heCEEKIapRKSuG7XT1vLv+O/xe6mroNOlYkEiEyMhIzZsxAREQEPD09ERAQgHHjxvFBTkMtWbJEYZHZDh06YMWKFQ06f9q0adi8eTO/luiBAwdQVlaGMWPG8MdUVlZi3bp1/Bv3li1b0KVLF8TExMDHxweLFy/G/PnzERISAgBo164dli5dik8++QSLFi1qUD3i4uKwfft2DBgwgC+bO3cu/vzzTwwbNgyzZ89G//79+X2jR4/GkSNHEBAQAGtra/j5+WHgwIGYNGmSwvIZlpaWCAoKQmRkJBYuXIhNmzbJBYBVkpKS0KVLl1rrV1WelJTUoOeTn58PfX19LmgvKQEAzJkzB3p6enLH/fjjj/j555/54zp27IgjR44oXK9nz54QCp+16Zw5cwbdu3dvUF0aS+kWpC1btuDnn3/GrFmz4ObmBjc3N7z//vvYsGEDIiMjm6CKhBBCWps333wTqamp2L9/P4YOHYqoqCh4enoq/T7So0cPhTIvL68Gnz958mQkJyfjwoULALgE4zFjxsi9kYtEInh7e/PbnTt3hrGxMRITEwFwydNLlizhW8b09fUxY8YMpKWl8cFBba5duwZ9fX3o6OjAx8cH/v7+WLduHb9fIBDg888/h0wmwxdffCF3roaGBjZv3oxHjx5hxYoVsLOzwzfffANXV1e5nKsqU6dORWRkJO7evYvz58/jnXfeqbVOyrQE1sfAwADx8fGIjY3F999/D09Pz1obUN555x3Ex8fjypUrOHv2LNq3b4/BgwejsLBQ7ridO3ciPj6ef7i4uKiknvVRugUpNzcXnTt3Vijv3LmzQsY9IYSQl0dHpIN/x//7/AOb6N7K0tbWxqBBgzBo0CB8+eWXmD59OhYtWsQn6FZ/s65r9fiaLRJ1ldXF0tISwcHB2Lx5M5ycnPD3338rPaqtqKgIixcvxqhRoxT21beQcKdOnbB//36IRCLY2tpCS0tL4ZiqpPOayedV7OzsMHHiREycOBFLly5Fx44dERERgcWLF8sdFxQUhHfffRfTpk1DcHAwzMzMFK7VsWNHPuirqaq8Y8eOdT6f6oRCIdq3bw+Aa326c+cOZs2aJdedCQBGRkb8ce3bt8fGjRthY2ODnTt3Yvr06fxx9vb2/HEvi9ItSO7u7nIRbpV169bB3d1dJZUihBCiPIFAAF1NXbU8ag4XbwwXFxcUFxfDwsICAORaQppyGpnp06dj586d+Omnn+Ds7KywrqhEIkFsbCy/fevWLeTl5fHdTp6enrh16xbat2+v8KjeLVSTlpYW2rdvD0dHx1qDI2WZmJjAxsYGxcXFCvtEIhEmTZqEqKioWrvXAGDcuHG4ffs2Dhw4oLDv+++/h5mZmUJ3ZkPNnz8fO3fuxKVLl+o9ripHqbS0tFH3USWlW5BWrFiB1157DcePH4e/vz8A4Pz583j48CEOHTqk8go2NzTMnxBCXkxOTg5Gjx6NqVOnws3NDQYGBoiNjcWKFSswfPhw6OjowM/PD99++y2cnJyQmZmp0MWkSkOGDIGhoSG+/vprLFmyRGG/pqYmP5xeJBLhgw8+gJ+fH3x8fAAACxcuxOuvvw4HBwe89dZbEAqFuHLlCq5fv46vv/4aADBp0iTY2dlh+fLlKqnz+vXrER8fj5EjR8LZ2RllZWXYunUrbty4gbVr19Z6ztKlS/Hxxx/X2noEcAHSrl27EBISojDMf//+/di1a5dSrXPV2dvbY+TIkVi4cCE/jxPAJcCnp6cDADIyMrB06VJoa2tj8ODBjbqPKindghQQEICkpCSMHDkSeXl5yMvLw6hRo3Dr1i306dOnKerYrISGhiIhIQEXL15Ud1UIIaRF0tfXh6+vL1avXo2+ffuia9eu+PLLLzFjxgy+h2LTpk2QSCTw8vLCvHnz+ECjKQiFQkyePBlSqRSTJk1S2K+rq4tPP/0U48ePR69evaCvr4+dO3fy+4cMGYKDBw/i6NGj8Pb2hp+fH1avXo22bdvyxzx48KDW3KDG8vHxQVFREWbOnAlXV1cEBATgwoUL2LdvHwICAmo9R0tLC+bm5nW29gkEAvz+++/47LPPsHr1anTq1Al9+vTB/fv3ERUV1eA5kOry4Ycf4q+//kJMTAxftmHDBtjY2MDGxgb9+/dHdnY2Dh06hE6dOr3QvVRBwFSVkfWKqVqsNj8/X2HEACGENLWysjKkpKTAycmp3jwX0jDTpk1DVlYW9u/fL1ceGRmJefPmIS8vTz0VI41S399HQ9+/lW5BOnz4MM6ePctvh4eHw8PDA+PHj8eTJ0+UvRwhhBCiNvn5+Th79iy2b9+O2bNnq7s6pBlROkD6+OOPUVBQAIAbohgWFoZhw4YhJSVFqUVtCSGEEHUbPnw4Bg8ejJkzZzY6AflV4+rqKjelQfXHr7/+qu7qqYzSSdopKSn8/AN79uxBcHAwvvnmG1y6dAnDhg1TeQUJIYSQpvK8If2TJ0/G5MmTX0pdWopDhw7VOe2ClZXVS65N01G6BUlLS4uf+Or48eN8prmpqSnfstScPHz4EP369YOLiwvc3Nywa9cuuf0HDx5Ep06d0KFDB/z8889qqiUhhBDSMrRt27bWKQ3at28PAwMDdVdPZZRuQerduzfCwsLQq1cvxMTE8Jn8SUlJaNOmjcor+KJEIhHWrFkDDw8PpKenw8vLC8OGDYOenh4kEgnCwsJw8uRJGBkZwcvLCyNHjqxzCCQhhBBCXg1KtyCtW7cOIpEIu3fvxv/+9z/Y2dkBAP7++28MHTpU5RV8UTY2NvDw8ADALe5nbm7Oz/gdExMDV1dX2NnZQV9fH0FBQTh69Kgaa0sIIcqhgciEKFLF34XSAZKDgwMOHjyIK1euYNq0aXz56tWr8cMPPyhdgdOnTyM4OBi2trYQCATYt2+fwjHh4eFwdHSEtrY2fH195eZQUEZcXBykUins7e0BAKmpqXyAB3BTtj9+/LhR1yaEkJdJU1MTAOpd64uQV1XV30XV30ljKN3FBgBSqRR79+7l12bp0qULRowYUedaMfUpLi6Gu7s7pk6dWus6Njt37kRYWBgiIiLg6+uLNWvWYMiQIbh16xYsLS0BAB4eHpBIJArnHj16FLa2tgC4NeQmTZqEDRs2KF1HQghpbjQ0NGBsbIzMzEwA3GSGqljug5CWjDGGkpISZGZmwtjYmF+6pDGUjmhu3LiB4OBgZGRk8DNdfvfdd7CwsMCBAwfQtWtXpa4XFBSEoKCgOvevWrUKM2bMwJQpUwAAERER+Ouvv7Bp0ybMnz8fwPPX6CkvL8eIESMwf/589OzZky+3tbWVazF6/PgxP3V8bdcoLy/nt5tjQjoh5NVibW0NAHyQRAjhGBsb838fjaV0gDR9+nR07doVcXFxMDExAQA8efIEkydPxrvvvotz5869UIWqq6ioQFxcHBYsWMCXCYVCBAYG4vz58w26BmMMkydPxoABAzBx4kS5fT4+Prh+/ToeP34MIyMj/P333/jyyy9rvc7y5csVVkcmhBB1EggEsLGxgaWlZZ3Drgl51Whqar5Qy1EVpQOk+Ph4xMbG8sERwK0gvGzZMnh7e79wharLzs6GVCpVmFfBysoKN2/ebNA1oqOjsXPnTri5ufH5Tdu2bUO3bt0gEonw/fffo3///pDJZPjkk0/qHMG2YMEChIWFYcOGDdiwYQOkUimSk5Nf6PkRQogqaGhoqOQNgRDyjNIBUseOHZGRkQFXV1e58szMTLRv315lFVOV3r17QyaT1bn/jTfewBtvvPHc64jFYojFYnz00Uf46KOP+LVcCCGEENL6NGgUW0FBAf9Yvnw55syZg927d+PRo0d49OgRdu/ejXnz5uG7775TaeXMzc2hoaGBjIwMufKMjIwX7ltsrPDwcLi4uKi8tYwQQgghzYeANWCyAKFQKDc6ouqUqrLq21KptPGVEQiwd+9ejBgxgi/z9fWFj48P1q5dCwCQyWRwcHDABx98wCdpq0NDVwMmhBBCSPPR0PfvBnWxnTx5UmUVq6moqEgulyclJQXx8fEwNTWFg4MDwsLCEBISgh49esDHxwdr1qxBcXExP6qNEEIIIUTVGhQgBQQENOhi169fV7oCsbGx6N+/P78dFhYGAAgJCUFkZCTGjh2LrKwsLFy4EOnp6fDw8MDhw4fVtiBeeHg4wsPDX6iljBBCCCHNW4O62OpTWFiIHTt24Oeff+Znqn4VUBcbIYQQ0vI09P1b6aVGqpw+fRohISGwsbHBf//7XwwYMAAXLlxo7OVaDErSJoQQQlo/pVqQ0tPTERkZiY0bN6KgoABjxoxBREQErly5AhcXl6asZ7NDLUiEEEJIy6PyFqTg4GB06tQJV69exZo1a5CamsqPLCOEEEIIaU0aPFHk33//jTlz5mDWrFno0KFDU9apWaMkbUIIIaT1a3AL0tmzZ1FYWAgvLy/4+vpi3bp1yM7Obsq6NUuhoaFISEjAxYsX1V0VQgghhDSRBgdIfn5+2LBhA9LS0vDee+/ht99+g62tLWQyGY4dO4bCwsKmrCchhBBCyEvzQsP8b926hY0bN2Lbtm3Iy8vDoEGDsH//flXWr9miJG1CCCGk5WnyYf4A0KlTJ6xYsQKPHj3Cjh07XuRSLQYN8yeEEEJavxeeKPJVRS1IhBBCSMvzUlqQCCGEEEJaIwqQCCGEEEJqoACJEEIIIaQGCpCUREnahBBCSOtHSdqNREnahBBCSMtDSdqEEEIIIY1EARIhhBBCSA0UIBFCCCGE1EABEiGEEEJIDRQgEUIIIYTUQAGSkmiYPyGEENL60TD/RqJh/oQQQkjLQ8P8CSGEEEIaiQIkQgghhJAaKEAihBBCCKmBAiRCCCGEkBpafYD08OFD9OvXDy4uLnBzc8OuXbvk9o8cORImJiZ466231FRDQgghhDQ3rT5AEolEWLNmDRISEnD06FHMmzcPxcXF/P65c+di69ataqwhIYQQQpqbVh8g2djYwMPDAwBgbW0Nc3Nz5Obm8vv79esHAwMDNdWOEEIIIc2R2gOk06dPIzg4GLa2thAIBNi3b5/CMeHh4XB0dIS2tjZ8fX0RExPTqHvFxcVBKpXC3t7+BWtNCCGEkNZM7QFScXEx3N3dER4eXuv+nTt3IiwsDIsWLcKlS5fg7u6OIUOGIDMzkz/Gw8MDXbt2VXikpqbyx+Tm5mLSpEn46aefmvw5EUIIIaRlE6m7AkFBQQgKCqpz/6pVqzBjxgxMmTIFABAREYG//voLmzZtwvz58wEA8fHx9d6jvLwcI0aMwPz589GzZ89G1bO8vBzl5eX8dkFBQaOuQwghhJDmT+0tSPWpqKhAXFwcAgMD+TKhUIjAwECcP3++QddgjGHy5MkYMGAAJk6c2Oi6LF++HEZGRvyDuukIIYSQ1qtZB0jZ2dmQSqWwsrKSK7eyskJ6enqDrhEdHY2dO3di37598PDwgIeHB65du8bvDwwMxOjRo3Ho0CG0adOmzsBrwYIFyM/P5x8PHz5s/BMjhBBCSLOm9i62pta7d2/IZLI69x8/frxB1xGLxRCLxQgPD0d4eDikUqmqqkgIIYSQZqZZtyCZm5tDQ0MDGRkZcuUZGRmwtrZWS51CQ0ORkJCAixcvquX+hBBCCGl6zTpA0tLSgpeXF06cOMGXyWQynDhxAv7+/mqpU3h4OFxcXODt7a2W+xNCCCGk6am9i62oqAjJycn8dkpKCuLj42FqagoHBweEhYUhJCQEPXr0gI+PD9asWYPi4mJ+VNvLFhoaitDQUBQUFMDIyEgtdSCEEEJI01J7gBQbG4v+/fvz22FhYQCAkJAQREZGYuzYscjKysLChQuRnp4ODw8PHD58WCFx+2WhHCRCCCGk9RMwxpi6K9ESVbUg5efnw9DQUN3VIYQQQkgDNPT9u1nnIBFCCCGEqAMFSEqiJG1CCCGk9aMutkaiLjZCCCGk5aEuNkIIIYSQRqIASUnUxUYIIYS0ftTF1kjUxUYIIYS0PNTFRgghhBDSSBQgEUIIIYTUQAGSkigHiRBCCGn9KAepkSgHiRBCCGl5KAeJEEIIIaSRKEAihBBCCKmBAiRCCCGEkBooQFISJWkTQgghrR8laTcSJWkTQgghLQ8laRNCCCGENBIFSIQQQgghNVCARAghhBBSAwVIhBBCCCE1UIBECCGEEFIDBUhKomH+hBBCSOtHw/wbiYb5E0IIIS0PDfMnhBBCCGkkCpAIIYQQQmqgAIkQQgghpAYKkAghhBBCamj1AdLDhw/Rr18/uLi4wM3NDbt27WrQPkIIIYS8ulr9KLa0tDRkZGTAw8MD6enp8PLyQlJSEvT09Ord9zw0io0QQghpeRr6/i16iXVSCxsbG9jY2AAArK2tYW5ujtzcXOjp6dW7jxBCCCGvLrV3sZ0+fRrBwcGwtbWFQCDAvn37FI4JDw+Ho6MjtLW14evri5iYmEbdKy4uDlKpFPb29krtI4QQQsirRe0BUnFxMdzd3REeHl7r/p07dyIsLAyLFi3CpUuX4O7ujiFDhiAzM5M/xsPDA127dlV4pKam8sfk5uZi0qRJ+OmnnxTuUd8+QgghhLx6mlUOkkAgwN69ezFixAi+zNfXF97e3li3bh0AQCaTwd7eHrNnz8b8+fMbdN3y8nIMGjQIM2bMwMSJExu8r+Zx5eXl/HZBQQHs7e0pB4kQQghpQVrFTNoVFRWIi4tDYGAgXyYUChEYGIjz58836BqMMUyePBkDBgxQCIDq21fT8uXLYWRkxD+oK44QQghpvZp1gJSdnQ2pVAorKyu5cisrK6SnpzfoGtHR0di5cyf27dsHDw8PeHh44Nq1a8/dV9OCBQuQn5/PPx4+fPhiT44QQgghzVarH8XWu3dvyGQypffVJBaLIRaLER4ejvDwcEilUlVWkxBCCCHNSLNuQTI3N4eGhgYyMjLkyjMyMmBtba2WOoWGhiIhIQEXL15Uy/0JIYQQ0vSadYCkpaUFLy8vnDhxgi+TyWQ4ceIE/P391VKn8PBwuLi4wNvbWy33J4QQQkjTU3sXW1FREZKTk/ntlJQUxMfHw9TUFA4ODggLC0NISAh69OgBHx8frFmzBsXFxZgyZYpa6hsaGorQ0FA+C54QQl5FJRUSxKTk4vzdHEikDN6OJvB2NIWZvljdVSNEJdQeIMXGxqJ///78dlhYGAAgJCQEkZGRGDt2LLKysrBw4UKkp6fDw8MDhw8fVkjcflkoB4kQ8ioql0gR/yAP5+7k4NydbMQ/zEOl9NksMRvPpgAA2lvqw8fJFL5OpvB2NIWtsY66qkzIC2lW8yC1JLQWGyGkNZPKGG6k5iM6mQuILt7LRVml/KAWW2Nt2Le9jErBE+Rm2+PeYyuAackdY2+qAx9HM/g4mcDHyQyOZroQCAQv86kQIofWYmsi1IJECGmNGGO4nVmEc8nZiL6Tgwt3c1BYJpE7xlxfC/7O5ujlbAZfJ2Nsub0Ke27v4XYaAKYummir5wodaWfkZLVF8iNjPMwtxcPcR9hz6REAwMJAzLcw+TiZoqOlAYRCCpgaq6RCgpvphUhILUBCWgESUgtQUFaJHm1N0Ku9OfzbmcHSUFvd1WyRqAWpkagFiRDS0j3MLUF0cvbTbrMcZBeVy+030BbBr50ZejqboaezOTpa6UMgEKBcWo5PTn2Cfx7+AwEEGOgwENdzriO9WH5+OgMtQzjre0Bb0glZmW2R8EATFVL5txwjHU14Oz4LmFxtDSHSaNbjh9SCMYaswnLceBoEJaQVIDGtACnZxXj2Ls4gEOVDqJkHmVQPTKIPyLTR3tKA/xn6tTOFsa5Wfbdq9Rr6/k0BUiNRgEQIaWkyC8tw/k4OziXnIPpONh49KZXbr60phLejKfydzdDL2bzWYKWgogBz/pmDuIw4aAo1saLvCgS2DQRjDPcL7uNC2gWcTz2PmPQYFFUWyZ1rq2cLZ4Pu0KrojIzMNrhyX4KSCvnWeF0tDXi1NeFzmNztjaGtqdE0L0gzJZUxpGQX4Ua1VqHEtAJkF1XIHyiohIb2YxgaP4a+0WOUa6SgVPZE7hAm0wCT6oNJ9PmvZjqmcDa1QTdrW3jZt4WdgQXMdMxgLDaGSNj6O5YoQGpiFCARQpq7/JJKXEjJwfk7OYhOzsbtTPmARSQUwMPeGD3bm6Onsxm6OxhDLKo7GMkqycLM4zOR9CQJ+pr6+GHAD/C2rn3KE4lMghs5N3Ah9QLOp53HlawrkMieddkJIEAn085w1u8OUUUnpKZbITalCAU1uvW0NITwsDeGz9MWJs+2JtAXt5438eLyp11k1VqGbqUXKOR7AQwaWrmwtsyEgdFjVIhSkFN5DzImH2CKBCJY6VkhrzwPxZXFStVFAAGMxMYw0zaFmY4ZTJ9+NdN+9n31rzqilpmATwFSE6meg5SUlEQBEiGk2SipkCD23hNE38nG+Ts5uP44H7Jq/+EFAsDV1hA9nc3h72wGH0dT6DUw2LhfcB/vHXsPj4sew0zbDBGDItDZtHPD61ZZgtiMWL6FKTkvWW6/WEOM7hbd0c6gO0TlHZGSaoyYlDyFbj8NoQBdbQ2fBkxm8HY0aRFdRowxZBaWy+UKJaQV4F5O9S6yZ3TFEjjY5MDQ+DEqNe8hozwJhZV5CsdZ6FjA3cIdbhZucLNwg4uZCx+4lEnKkFuWi9yyXOSU5iC3LBf389KRmJmGlCfpyCzJQQXLh0BUDIFGMQQC5cIBXZFurYGTmbYZTHW4r2baZjDTMYOhlmGzSc6nAKmJUQsSIUTdKiQyxD/Mw7k72TiXnIPLD5/IDb0HAGcLPfR0Nkev9mbwdTKDiZ7ywcSN7Bt4/8T7yC3Lhb2BPdYPWg97gxdbsDurJAsX0i5wj9QLyCzNlNtvLDaGr7Uv2ht2h6C0I2491kRMSq5CtyAAdLY24FuYfBxN1Z6ULJHKkJJdLBcIJaQWIKe4otbjLQ010c6mFEbGqZBq3kdGxS08KLoLGZNvRdIUaqKLWRe4mbvB3dId7ubusNazfqHA42FuCc7dyUZ0chbO3XuA3LJcCDSKIBAVQaBRBG1xCSxMKqCvWwahqBgl0jzklOagQlb7c6mLSCB6bjBVVW6ibQJNoWajn9PzUIDUxChA4khkEpRKShUflaWokFXAQtcCbfTbwEhMk2oS8qKkMoaE1ALuDe1ODi6m5KK0Ur6Lxc5Yh0vIbc8l5Vq9YLBwPvU85p2chxJJCbqYdsGPgT/CXMf8ha5ZE2MMKfkpOJ92HhdSLyAmPQYlkhK5Y+wN7OFv44+ORp6QFTvj6sNKxKTk4E6WYjeSk7kefBxN+aCpjYlOk7VeFJVLcCtdPhC6mV6IconiOp9CAeBsoY8ONhowMUmHVOsesipu4+aT6yioKFA43kbPBm4WbnwLURfTLtDSaLrWMsYYkjOL+Lmuzt/JUejyNNPTgm87U/Rw0kUnO0BHu1SulSqnLEeuxSqnLAeFFYVK18VIbARTbVO87/4+hjoNVdVTBEABUpNp6i42iVQGiYxBImOQShmkjEEik0EqY5BIGfdVxn2tevD7Zc/2y/htGcqllXzQUiYtRZm0DGWSUpRLSlEmK0O5tBTl0jJUyspQLitDxdNH5dOHhJVDwqq+cg8pyiBFORgkz39SAHQ09GGjZ4t2xg6wN7CHnb4d2hi0QRuDNrDVs4WmRtN9WiCkpWKM4U5WET8X0YW7ucgvrZQ7xkxPi0uqfppH5GCqunmGDqccxoKzCyCRSeBr44s1/dZAX0tfJdeuT6WsEtezr+N86nlcSLuAq1lXIa2WayMUCOFi6gJ/W390MfZCWWEbxN0vQkxKLhLTCxS6rGyMtPlgydfJFM4W+kq/RowxZBSUIyEtXy4Yup9bUnsXmZYGutgYoou1PizM8sC07iGr8jau51zF3fy7Csdra2jDxcwF7hbucLdwRzeLbrDUtVSqjqpWPSA/dycHMbUE5LZG2vB3NueDchsjxbykCmkFHyzlluYqBlHVgqknZU/kftbf9P4Gwc7BKn1eFCA1saZqQZr7+1nsv54MgbACAkEFIKyAQPj0q6CS335W9pxjBBUQCJt+zibGBIBMC4xpcV9lmgDTgEAzH0JRUb3nCiCAla4V2hi0kQuc2uhzX820zZpN3zUhTe1hbgmXVP30TSmrsMbQe7EIvlVD79uboZOVQZP8ffya+Cu+i/kODAxDHIfgm97fNGnrRX2KKork8pdqBhjaGtrwsvKCv60/XE17IC/PFLH38hGTkoOrj/Ihkcm/zZnpacG7WgtTFxtDaFSbi0kileFudrFCvlBuHV1k1obacLE1hIuNIRwsGCB+gPSym7iacxXXs6/Xmixtb2AvlzvU0aRjk3YrqUKFRIYrj/Jw7mnAfvlBHiqk8i1lTuZ6clMKKLv0jIzJkF+ezwdOTkZOsNC1UOXToACpqTVVgBT06/t4JDmjsuvJYUJoQAyhQAwNiCGCGBoCMUQCbYieftUUiqEp0IamUBtaQjG0hNrQ0tCBlkAbYpE2xEJtiDV0oC3SgY6GNsQiXehoaENLQwxNDSE0hAKIhAJoCAWQyhjuZhfjZno2bmbfx8OiR5AKcyDUyoVQMxcCzVwItXIhEFbWW21tDW25wMlO344Pnuz07aCrqds0rxchL0FWYTnO383BuafzET3Ile9aEouqDb1vb46uTTxPEGMMay+vxYZrGwAAb3d+G/N95kMoaD5zE2UUZzzLX0q7gOzSbLn9ptqm8LXxhb+NPzzMvZGWo41/U3IRk5KLSw+eKHR/GYhF6OFoAgsDMW6mF+JmeiEqauki0xAK4GyhBxcbQ7jYGqKTtT60dNJxvzgRV7Ou4krWFTwofKBwnq5IF93Mu/HdZd0susFU21S1L4oalFZIEXs/l59H69qjPNSIRdHZ2oBv3fRxMoWBtvqDQAqQmlhTBUjLzi/HXykHoCPS4R6aOtAV6T7brv54zj4dkfx+TaGmWltipDKGB7klSMooxO2MQiRlFOFWRgFScjMg4QOnHAi0nkBYFTyJ8p87ssJU21QucKrehWelawUN4as1hwpp3iokMly8l4sTiZk4m5yFpAzFoffu9sbo5WwGf2dzeLatf+i9KklkEiy9sBR/3P4DAPCBxwd41+3dZt2CyxhDcl4y3x0XmxGLUol8IrejoSP8bPzgZ+sHD3Mv3MuS8QFT7L0nKCpXTBXQe9pFVtUy5GJrCFPDctzKu44rWVdwNesqEnISFO4FAE5GTnwitZu5G9obt38l/g/ll1YiJiWXz1+6mS6fe6QhFKCbnRF6Pg32vdqaqGWOKwqQmhglaauORCrD/dwSJKVzQVNSJhdApWQXo1JaCYFm3tPg6Qnf6qQpfgKhVi5kgpJ6ry0SimCjZyPX4lS9C4+Sx8nL8KS4AlFJmTiemInTt7JQWO0NWSAAXGwM+W4JbydTtczzUyYpwyenP8HJhychFAjxpd+XeKvjWy+9Hi+qUlqJK1lXuITvtAu4nn1dbjSYUCBEV/Ou8Lfxh5+NH1xNu+FOVhn+TeHyuzpbG8DFxhA2Rpq4lXcTV7Ou8q1DqcWpCvcz0DTgu8ncLNzQzbwb/V95KquwHBfucq1L5+9k416O/P9rLQ0hPNsa86Ms3doYQ/MlzKJOAVIToXmQXp5KqQz3sou5oCmjELczuQDqXnbxs5wCYemz1ibNXGjr5EFXLx8CUS5KWTZkz0kiN9A0qLPrzlbfVm05F6Rlq0quPp6YiROJGYi7/0Su68FcXwsDOluiXydL+Ldr3NB7VSqoKMDsE7NxKfMStIRaWNF3BQa2HajWOqlKQUUBLqZfxIVUrjvuXsE9uf06Ih30sOoBf1t/WOhY4Go2FxAl5iQqDGUXQID2Ju251qGnydSORo7NqvuxOXucV4pzydl8jl1GgXx+na6WBnycTPkPCy42hk2yTh8FSE2MWpDUp0LCzTFSvasuKbMQ93NKIJXrAJdBICqAUCsXenr5MDUqgrZuHphGDoqkmSiozK33PgIIYKVnhTb6beBv64/X270OW33bpn1ypMWqlMpwMSWXC4puZuB+jU/Lna0NENjFCgO7WMK9jXGzWaA1syQTM4/PxO0nt2GgaYAfBvyAHtY91F2tJpNWlMYle6edx79p/yK3rO7/A8ZiY7lE6q5mXV/KKL5XAWMMKdnFiH7aunT+Tg6elMjnoxrramL+0M4Y5+Og0ntTgNTEKEBqfsolUtzN4gKnpKeB0+2MwjqH4UJQARPDItiYl8DYsBBa2k8gEeagQJKO9JLUWnMLvKy8ENwuGIMcB8FQi37ur7r6us60NITwczZDYBdLDOhsiTYmzW8wwb38e3jv2HtILU6FuY45IgIj0Mm0k7qr9dLImAy3n9zm85fyy/Phau7Ktw7ZG9g36/yr1kQmY7iZXshPKfDv3RwUV0gRMcELQ7taq/ReFCA1MQqQWo6ySimSM4v4LrqqVqeHT+oInACY6WuinSVgbVYMsV4mHleex43cS2DgTtASaqGffT8EOwejl20vmsfpFcF1nRXjRGIGTiRmIvZ+rkLXWf9OlhjYxQp9Opg3eBkPdbiefR3vH38fT8qfoK1hW0QERqCNQRt1V4sQAFyL7LXH+ehgqa/ykW8UIDUxCpBavtIKLnBKyih8mhjOfV/bUgYAYGxQhDb2t1Ck+S9yKp4N5TUWG2Oo41AEOwejm3k3+sTZylTvOvvnZoZComlnawMM7MIFRR7NqOusPucen8O8qHkolZTCxcwFPw78EWY6ZuquFiEvBQVITYwCpNaruFzCB063M4uQmFaAuPtPUFJRNeEmg1CcCgPzq9AwjEcl8vlzHQ0d8Vq71/B6u9fp07iKVS12GpsRC0dDRwQ7BzfZxHp5JRWIupWF44kZOJWUhcKyltV1Vp9Ddw/h87OfQ8Ik8LPxw5r+a6CnqafuahHy0lCA1MQoQHq1VEpluPooHxfu5uD8nRzE3s9FWaUMgBQaesnQNLoMTYMEQPhs1Et3y+4Idg7G4LaDadhvIzDGcCfvDqJTo3H28VlcyrgkN6rI0dAR87zmYYD9gBdutXte15mZnhb6d7ZEYBdL9O5goZZh+KrwS8Iv+O7idwCAIMcgLOu9jLqHySuHAqQmQsP8CcAlhF99lI/zd7iAKe7BE1TISiEyuA5Nw8vQ0LvDT3CpIRDB16o3xnQegb5t+tIbUj0KKgrwb9q/iH7MBUUZJRly+231bNHDugfOPDqDJ+VPAACelp4I6xEGdwt3pe5VKX02YeOJxLq7zgZ0toKHvbHcUhQtDWMMP1z+AT9f+xkAML7zeHzq8ykNTyevJAqQmhi1IJHqyiqliH+YxwVMd3MQn3ofTO8SNI0uQ0M7nT9OE/pwMwnAeJeRGOTs88rnK8mYDIm5iYh+HI3ox9G4knVFbqFKsYYYPax7oLdtb/Sy6wVHQ0cIBAIUVRRh0/VN2JawDWXSMgDA4LaDMddzLhwM6x4SXNV1duJmJqJuZcp1nWlqCODXzgyBXawwoLMl7E1bVtdZXSQyCZacX4K9yXsBAHM952Ja12mv/O8eeXVRgNTEKEAi9SmrlOLS/Sc4fzcHUSnxSC45BaFBPISaz6beF0rM4aTTF685vobXXLrB1lhxFezWKLcsF+dSzyH6cTTOpZ5TmIfGycgJvWx7obddb3hZeUFbpF3ntdKL0/Fj/I/Yl7wPDAwioQhjO43Fe27vwUTbBABwJ6sIJxIzcDwxE3H3n8jNlWWqx406C+xiiT4dW27XWV1KJaX45NQniHoUBaFAiIV+C/FmxzfVXS1C1IoCpCZGARJRRkmFBBfvZWNv4inEZB1DgcZlCKrlK0lLHGAg9UVPq0AEtG8Lf2czWBnWHRi0JBKZBFezriI6lWslSshJ4KdLALiFPP1s/NDLrhd62fWCnb6d0vdIepKEVXGrEP04GgCgraGHDuI3kHq/B+5ly08+18mq2qizFt51Vp/88nzM/mc2LmdehlhDjBV9V2CAwwB1V4sQtaMAqYlRgEReRFZRASLjD+Dow7+RXn4VeJqvxJgGJEWdIMn3hL3YE37trOHvbAa/dqawNGg5AVN6cTrXbZYajQupF1BYKb9oZWfTzuhlywVEHhYeL5yXlV9SiaikTPx+4x9cLf4VED8GAMgqjSDJGQJv80AEdrbGwC5WrabrrD4ZxRmYeXwmkvOSYaBpgLUD18LLykvd1SKkWaAAqYlRgERUJaskC38kHcDe2/vxuOQOX86kOqgscIMkvzukpW3R3tIAfu1M4d/OHH7tTGGmL1ZjreVVSCsQlxHHB0XJecly+43ERuhp2xO9bHuhp21PWOhavPA9q7rOuFFn1bvOZDC2uA5NiyMoYzkAgE4mnRDmFYaedj1f+L7N3d38u5h5bCbSitNgoWOBiEER6GjSUd3VIqTZoADpqYcPH2LixInIzMyESCTCl19+idGjRwMA8vLyEBgYCIlEAolEgrlz52LGjBkNui4FSKQpJD1JwsG7B3Hwzl/IKs3ky2UVpqjM90BlvidYpTkAoKOVPvzbmcHf2Qy+Ti9/wdMHBQ9w9vFZRKdG42L6RbmlWYQCIbqZd0Mvu17obdsbLmYu0BBq1Hs9qYyhqEyCgrJKFJZJUFQuQeHT7wvLKlFQJkFhmQT5pRW4cDcXKdnFcud3tNLHwC5WCOxiCQ97E0hYBbYnbseGqxv4Fqyetj0R5hXWapfTuJZ1De+feB955XlwNHRExKCIRnVZEtKaUYD0VFpaGjIyMuDh4YH09HR4eXkhKSkJenp6kEqlKC8vh66uLoqLi9G1a1fExsbCzOz5M8pSgESaklQmxcWMizhw5wCO3z+OEsmzIeiaEkcUZrtDUuAGJn02wV9nawP4O5vBvx0XMBnpqnY6gZLKElxMv8gHRQ8LH8rtN9M2R3cLP7gYe8NR1wMyqS4f3BSVSVBYLh/oVAU/RU+/L66Q1nHn2lWNOhvY2bLerrO8sjz8dO0n7Li5AxKZBAIIEOwcjNndZ8NaT7VrPKnT2cdnERYVhlJJKbqadUV4YDhMtU3VXS1Cmh0KkOrg7u6OgwcPwt7eXq48NzcXnp6eiI2Nhbm5+XOvQwESeVlKKktw8uFJHLh7AOdTz0PGZAAAITRgKXJH2RMPPHzsBLBnAZFAALjYGPItTN5OpjCsZT0jxhjKJTK+1aZ6wFJQWomUwmQkFcTifsllZElugkFS7WQNCMqdUFnUEeUFHSArtwbw4gnPWiIhDLVFMNDWhIG2iHuINaFf9b22JjpbG6BPB3Ol1mh6WPgQay+txd/3/gbATSEwocsETOs2DQZaBi9cb3U6ePcgvjz7JSRMgp62PbG632roarb+XCtCGqPFBEinT5/GypUrERcXh7S0NOzduxcjRoyQOyY8PBwrV65Eeno63N3dsXbtWvj4+Ch9r7i4OISEhOD69et8WV5eHgICAnD79m2sXLkSoaGhDboWBUhEHbJLs3Ho7iEcvHsQibmJfLmepj5cDHtDq9Qbtx6YIyVLfj05oQBwtTWCnlijWvcVFwhVSqv9CxCWQKSXDA39JIj0kiDULJC7jqzCBJLiTpAUdYS0xBmQyedB6Wpp8EGMvpgLaAyrBzpPv+f2aSoEQvraIohF9XfFvahrWdfwfdz3iMuIA8CtpTfTfSbGdBzTIifx3HpjK1bGrgQABDkFYVkvmh2bkPq0mADp77//RnR0NLy8vDBq1CiFAGnnzp2YNGkSIiIi4OvrizVr1mDXrl24desWLC0tAQAeHh6QSCQK1z569ChsbW0BcC1Effr0wYYNG9Czp2KiZkZGBkaNGoU//vgDVlZWz603BUhE3ZKfJOPg3YP4K+UvpBc/m4zSVs8W/dsMhTn8cSdVt9Z8nWdkEGo/hkg/CWKD24D4Pj+iDgCE0IK5qAvstbujg0EPtNF3gKGOJgzEmnIBT1XQI9JoGTMzM8Zw6tEprI5bjbv5dwEA9gb2mOs5F4PbDm4RkygyxrDm0hpsur4JADChywR87P0xzY5NyHO0mACpOoFAoBAg+fr6wtvbG+vWrQMAyGQy2NvbY/bs2Zg/f36DrlteXo5BgwZhxowZmDhxYp3Hvf/++xgwYADeeuutWq9RXl7ObxcUFMDe3p4CJKJ2MiZDbHosDtw9gGP3j6G48lkw5GrmimDnYHQ364e76QJupJdGIe6XXEZifiyu5cQgvyJP7nrORs7cnES2veBp5VnvRI0tnUQmwd7kvQi/HI6cMm7Em5u5Gz7q8RE8rTzVXLu6SWQSfHXuK/x5508ANDs2IcpoFQFSRUUFdHV1sXv3brmgKSQkBHl5efjzzz+fe03GGMaPH49OnTrhq6++ktuXkZEBXV1dGBgYID8/H7169cKOHTvQrVs3het89dVXWLx4sUI5BUikOSmVlCLqYRQO3j2I6MfR/LIdGgIN+Nn4IbcsV65rDgD0NfWfTdRo2ws2+jZqqLl6lVSWYMuNLdh8YzM/Gq+/fX986PUhnIyc1Fw7eaWSUnx86mOcenQKGgINLPJfhJEdRqq7WoS0GK0iQEpNTYWdnR3OnTsHf39//rhPPvkEp06dwr///vvca549exZ9+/aFm5sbX7Zt2zZ069YNMTExePfd/2/v3oOirBc/jr9hE0REQQwMRRPN4qool1EayzAN0zOV5YljajpdzFWj9dRgZTrnqGQqOcB663TKKZ30iE5qqRFeUH+ZKKLgBT2gphmoeUG8u/D7wxMTa+Ul8VnYz2uGf56FZz/w6Oxnv/v9Pt9XqKqqoqqqCrPZzKuvvvqb59EIktQ1P1/4mVUHV7G8eDm7ft5V47HgZsE83PLa/mYR90bQwFVzVuDaPalm7pjJkv1LqKyqxORi4tkOzzK843Cae9x48UZtO3PpDCOzR5J/PB93kzvTHpnGo4GPGh1LpE652YJUvzYe+g0PP/wwlZWVv/lYTEwM+fn5N3Ued3d33N3dsVqtWK1WbLZbW5Iscrf5evgyMHggA4MHUnK6hPVH1uPr4Uu3gG4O8WLviO5tdC/ju45nUPAgPsz7kHWH17GwaCHLi5czNGwog0MGG7Y6rPRcKcOzhlN8phgvNy+s8VYi/SINySLiDBx6Nl/z5s0xmUyUlZXVOF5WVkaLFsbcv8RsNrN7925yc3MNeX6R2xHkHcTQsKH8pd1fVI5uQpB3EOmPpfPv3v8mzDeM81fPY8230ndpXzL3ZXK18vpFIbWp5HQJg1YOovhMMX4efsx7Yp7KkUgtc+iC5ObmRpcuXcjOzq4+VllZSXZ2do2P3O4mq9VKSEgI0dHRhjy/iNw90S2imf/kfKZ2n0rLxi05fuE4E76bwHPLnyPnSA53Y4bCzuM7GbxqMKXnSrm/yf181uczHvB5oNafV8TZGT4HqaKigv/+99q+TZGRkaSmptKjRw+aNWtG69atWbhwIUOGDGHOnDnExMQwY8YMFi1axN69e29qOX5t0TJ/Eedy2XaZhUULmbNzDmcunQEgpkUMligLob6htfKcG45sYMz6MVy4eoHw5uFY4634NPSplecScRZ1ZpL2unXr6NGjx3XHhwwZwqeffgpARkZG9Y0iO3XqRFpaGrGxsXc56TW/noO0b98+FSQRJ1N+uZx/FfyL+bvnc7nyMgB92vZhdOfRd3Tfs+XFy3lv03tcrbpKXMs4Uh9J1d2xRe6AOlOQ6iqNIIk4t6MVR8nYnsHykuUANHBtwN8e+hsvR7xMU/emf+rc83bNY9rWaQD0DerLP+L+oZWGIneIClItU0ESEYA9P+9h+rbpfP/TtduONHFrwisRr5D4UCJuJrdbOldVVRUfbvuQT3Z9AsDgkMGMiRqju2OL3EEqSLVEH7GJiL2qqio2Hd1E6rZU9p/aD0DLxi0ZFTmKhLYJN1VwrlReYcL/TWBZ8TIALF0sDA0bWqu5RZyRClIt0wiSiNizVdpYVryMjO0ZHLtwDIAQ3xDGdBlDzH2/v8H2+Svn+fv6v7Phxw2YXExM6DaBp9o/dZdSizgXFaRapoIkIr/nwtULfL77cz4u/Lh6b7zurbrzRuc3aO/Tvsb3nr54GvMaMzuP76ShqSHTHpnGI4GPGBFbxCmoINUSfcQmIjfr5MWTzN4xm/8U/YerVVdxdXHl6fZPM6LTCPwa+VF6rpRXs16l5EwJTdyaYI230smvk9GxReo1FaRaphEkEblZB88cJG17GlmHsgDwuMeDvz74V1YeWEnZ+TL8G/kz5/E5tPNuZ3BSkfpPBamWqSCJyK3KP5bP9K3TyT+eX30sqGkQcx6fQwtPY7ZPEnE2Kki1TAVJRG5HVVUVa35Yw6wds2jWsBkfdP8A74beRscScRo3+/p9z13MVC/8eg6SiMitcnFxIb5NPPFt4o2OIiJ/QCNIt0kjSCIiInXPzb5+6/asIiIiInZUkERERETsqCCJiIiI2FFBukVWq5WQkBCio6ONjiIiIiK1RJO0b5MmaYuIiNQ9mqQtIiIicptUkERERETsqCCJiIiI2FFBEhEREbGjgiQiIiJiRwXpFmmZv4iISP2nZf63Scv8RURE6h4t8xcRERG5TfcYHaCu+mXgrby83OAkIiIicrN+ed2+0QdoKki36ezZswAEBgYanERERERu1dmzZ2natOnvPq45SLepsrKSo0eP4uXlhYuLyx07b3l5OYGBgRw+fFhzmxyErolj0fVwLLoejkXX48aqqqo4e/YsAQEBuLr+/kwjjSDdJldXV1q1alVr52/SpIn+cTsYXRPHouvhWHQ9HIuuxx/7o5GjX2iStoiIiIgdFSQREREROypIDsbd3Z3x48fj7u5udBT5H10Tx6Lr4Vh0PRyLrsedo0naIiIiInY0giQiIiJiRwVJRERExI4KkoiIiIgdFSQREREROypIDsZqtXL//ffTsGFDYmNj2bJli9GRnFJKSgrR0dF4eXnh5+fHU089RVFRkdGx5H/ef/99XFxcSEpKMjqKU/vxxx954YUX8PX1xcPDg/DwcLZu3Wp0LKdks9kYN24cbdu2xcPDg3bt2vHPf/7zhvuNye9TQXIgCxcuxGKxMH78ePLy8ujYsSO9e/fm2LFjRkdzOuvXr8dsNrN582aysrK4cuUKvXr14ty5c0ZHc3q5ubnMmTOHiIgIo6M4tVOnThEXF0eDBg1YuXIlu3fvZvr06fj4+BgdzSlNmTKFWbNmkZGRwZ49e5gyZQoffPAB6enpRkers7TM34HExsYSHR1NRkYGcG2/t8DAQEaNGkVycrLB6Zzb8ePH8fPzY/369XTv3t3oOE6roqKCzp07M3PmTCZOnEinTp2YMWOG0bGcUnJyMps2bWLDhg1GRxGgb9+++Pv78/HHH1cf69+/Px4eHnz++ecGJqu7NILkIC5fvsy2bdvo2bNn9TFXV1d69uzJd999Z2AyAThz5gwAzZo1MziJczObzTz55JM1/p+IMZYtW0ZUVBTPPfccfn5+REZG8tFHHxkdy2l169aN7Oxs9u3bB8COHTvYuHEjCQkJBieru7RZrYM4ceIENpsNf3//Gsf9/f3Zu3evQakEro3kJSUlERcXR1hYmNFxnNYXX3xBXl4eubm5RkcRoKSkhFmzZmGxWHj77bfJzc1l9OjRuLm5MWTIEKPjOZ3k5GTKy8t56KGHMJlM2Gw2Jk2axMCBA42OVmepIIncgNlsprCwkI0bNxodxWkdPnyY119/naysLBo2bGh0HOHaG4eoqCgmT54MQGRkJIWFhcyePVsFyQCLFi1i/vz5LFiwgNDQUPLz80lKSiIgIEDX4zapIDmI5s2bYzKZKCsrq3G8rKyMFi1aGJRKRo4cyYoVK8jJyaFVq1ZGx3Fa27Zt49ixY3Tu3Ln6mM1mIycnh4yMDC5duoTJZDIwofO57777CAkJqXEsODiYzMxMgxI5tzfffJPk5GSef/55AMLDwzl06BApKSkqSLdJc5AchJubG126dCE7O7v6WGVlJdnZ2XTt2tXAZM6pqqqKkSNHsnTpUtasWUPbtm2NjuTU4uPjKSgoID8/v/orKiqKgQMHkp+fr3JkgLi4uOtufbFv3z7atGljUCLndv78eVxda76km0wmKisrDUpU92kEyYFYLBaGDBlCVFQUMTExzJgxg3PnzjF06FCjozkds9nMggUL+PLLL/Hy8qK0tBSApk2b4uHhYXA65+Pl5XXd/C9PT098fX01L8wgb7zxBt26dWPy5MkMGDCALVu2MHfuXObOnWt0NKfUr18/Jk2aROvWrQkNDWX79u2kpqYybNgwo6PVWVrm72AyMjKYOnUqpaWldOrUibS0NGJjY42O5XRcXFx+8/gnn3zCiy++eHfDyG969NFHtczfYCtWrGDs2LHs37+ftm3bYrFYePnll42O5ZTOnj3LuHHjWLp0KceOHSMgIIDExETee+893NzcjI5XJ6kgiYiIiNjRHCQREREROypIIiIiInZUkERERETsqCCJiIiI2FFBEhEREbGjgiQiIiJiRwVJRERExI4KkoiIiIgdFSQRqVdKS0sZNWoUQUFBuLu7ExgYSL9+/WrscygiciPai01E6o2DBw8SFxeHt7c3U6dOJTw8nCtXrrB69WrMZjN79+41OqKI1BEaQRKRemPEiBG4uLiwZcsW+vfvT4cOHQgNDcVisbB582YAUlNTCQ8Px9PTk8DAQEaMGEFFRUX1OQ4dOkS/fv3w8fHB09OT0NBQvv766+rHCwsLSUhIoHHjxvj7+zNo0CBOnDhR/fjixYsJDw/Hw8MDX19fevbsyblz5+7eH0FE7ggVJBGpF06ePMmqVaswm814enpe97i3tzcArq6upKWlsWvXLubNm8eaNWt46623qr/PbDZz6dIlcnJyKCgoYMqUKTRu3BiA06dP89hjjxEZGcnWrVtZtWoVZWVlDBgwAICffvqJxMREhg0bxp49e1i3bh3PPPMM2vJSpO7RZrUiUi9s2bKF2NhYlixZwtNPP33TP7d48WKGDx9ePQoUERFB//79GT9+/HXfO3HiRDZs2MDq1aurjx05coTAwECKioqoqKigS5cuHDx4kDZt2vz5X0pEDKM5SCJSL9zse71vv/2WlJQU9u7dS3l5OVevXuXixYucP3+eRo0aMXr0aF577TW++eYbevbsSf/+/YmIiABgx44drF27tnpE6deKi4vp1asX8fHxhIeH07t3b3r16sWzzz6Lj4/PHf1dRaT26SM2EakXHnjgAVxcXP5wIvbBgwfp27cvERERZGZmsm3bNqxWKwCXL18G4KWXXqKkpIRBgwZRUFBAVFQU6enpAFRUVNCvXz/y8/NrfO3fv5/u3btjMpnIyspi5cqVhISEkJ6ezoMPPsiBAwdq/w8gIneUPmITkXojISGBgoICioqKrpuHdPr0abKzs0lMTOTixYu4ul57fzhx4kTGjRvHqVOnqucp/drYsWP56quv2LlzJ++88w6ZmZkUFhZyzz03HoC32Wy0adMGi8WCxWK5I7+jiNwdGkESkXrDarVis9mIiYkhMzOT/fv3s2fPHtLS0ujatSvt27fnypUrpKenU1JSwmeffcbs2bNrnCMpKYnVq1dz4MAB8vLyWLt2LcHBwcC1CdwnT54kMTGR3NxciouLWb16NUOHDsVms/H9998zefJktm7dyg8//MCSJUs4fvx49c+LSN2hgiQi9UZQUBB5eXn06NGDMWPGEBYWxuOPP052djazZs2iY8eOpKamMmXKFMLCwpg/fz4pKSk1zmGz2TCbzQQHB/PEE0/QoUMHZs6cCUBAQACbNm3CZrPRq1cvwsPDSUpKwtvbG1dXV5o0aUJOTg59+vShQ4cOvPvuu0yfPp2EhAQj/hwi8ifoIzYREREROxpBEhEREbGjgiQiIiJiRwVJRERExI4KkoiIiIgdFSQREREROypIIiIiInZUkERERETsqCCJiIiI2FFBEhEREbGjgiQiIiJiRwVJRERExI4KkoiIiIid/wcD+QVExi5XLgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib import pyplot as plt\n", "\n", @@ -581,7 +3281,277 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SurrType.PYSMO_KRG\n", + "2025-03-17 17:36:14 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=kriging\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 39\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 3\n", + "\n", + "Total number of variables............................: 15\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 -0.0000000e+00 4.20e+01 2.26e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -2.2208184e-01 8.99e+00 1.45e-02 -1.7 6.94e+02 - 9.39e-01 1.00e+00h 1\n", + " 2 -3.1500983e-01 4.55e+02 3.16e-02 -2.5 6.08e+03 - 9.48e-01 1.00e+00h 1\n", + " 3 -3.2646827e-01 1.71e+03 1.30e-02 -2.5 1.72e+04 - 5.42e-01 4.89e-01h 1\n", + " 4 -3.2727204e-01 2.22e+02 6.79e-03 -2.5 9.03e+03 - 1.00e+00 1.00e+00h 1\n", + " 5 -3.2603541e-01 3.23e+01 3.16e-04 -2.5 1.31e+02 - 1.00e+00 1.00e+00h 1\n", + " 6 -3.2609765e-01 3.96e-01 3.86e-06 -2.5 1.83e+02 - 1.00e+00 1.00e+00h 1\n", + " 7 -3.3095969e-01 6.95e+01 4.43e-03 -3.8 3.80e+03 - 9.20e-01 1.00e+00h 1\n", + " 8 -3.3153543e-01 3.65e+00 8.14e-04 -3.8 1.12e+03 - 1.00e+00 1.00e+00h 1\n", + " 9 -3.3138139e-01 4.61e-01 1.14e-05 -3.8 2.96e+02 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 -3.3138774e-01 3.05e-04 6.91e-09 -3.8 1.03e+01 - 1.00e+00 1.00e+00h 1\n", + " 11 -3.3167316e-01 1.22e-01 5.16e-04 -5.7 1.82e+02 - 1.00e+00 9.83e-01h 1\n", + " 12 -3.3168105e-01 7.41e-04 7.25e-09 -5.7 1.43e+01 - 1.00e+00 1.00e+00h 1\n", + " 13 -3.3168473e-01 3.41e-05 7.91e-09 -8.6 1.82e+00 - 1.00e+00 1.00e+00h 1\n", + " 14 -3.3168473e-01 1.72e-09 2.50e-14 -8.6 2.68e-03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: -3.3168473018874750e-01 -3.3168473018874750e-01\n", + "Dual infeasibility......: 2.5035529205297280e-14 2.5035529205297280e-14\n", + "Constraint violation....: 6.1212990624078506e-12 1.7244019545614719e-09\n", + "Complementarity.........: 2.5059041410567415e-09 2.5059041410567415e-09\n", + "Overall NLP error.......: 2.5059041410567415e-09 2.5059041410567415e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 15\n", + "Number of objective gradient evaluations = 15\n", + "Number of equality constraint evaluations = 15\n", + "Number of inequality constraint evaluations = 15\n", + "Number of equality constraint Jacobian evaluations = 15\n", + "Number of inequality constraint Jacobian evaluations = 15\n", + "Number of Lagrangian Hessian evaluations = 14\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.004\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SurrType.PYSMO_PLY\n", + "2025-03-17 17:36:14 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 39\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 3\n", + "\n", + "Total number of variables............................: 15\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 -0.0000000e+00 5.35e+01 3.24e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -2.2227750e-01 1.30e+01 1.45e-02 -1.7 6.89e+02 - 9.38e-01 1.00e+00h 1\n", + " 2 -3.1492876e-01 5.64e+02 3.41e-02 -2.5 6.01e+03 - 9.71e-01 1.00e+00h 1\n", + " 3 -3.2714017e-01 2.01e+03 1.99e-02 -2.5 1.39e+04 - 6.00e-01 7.01e-01h 1\n", + " 4 -3.2662426e-01 2.91e+02 5.57e-03 -2.5 7.48e+03 - 1.00e+00 1.00e+00h 1\n", + " 5 -3.2618524e-01 1.47e+01 1.24e-04 -2.5 4.56e+02 - 1.00e+00 1.00e+00h 1\n", + " 6 -3.2612971e-01 4.24e-02 9.16e-07 -2.5 8.41e+01 - 1.00e+00 1.00e+00h 1\n", + " 7 -3.3096494e-01 8.05e+01 4.63e-03 -3.8 3.74e+03 - 9.24e-01 1.00e+00h 1\n", + " 8 -3.3151074e-01 7.83e+00 1.23e-04 -3.8 1.13e+03 - 1.00e+00 1.00e+00h 1\n", + " 9 -3.3138534e-01 5.65e-01 1.09e-05 -3.8 2.55e+02 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 -3.3139357e-01 1.08e-03 3.11e-08 -3.8 1.28e+01 - 1.00e+00 1.00e+00h 1\n", + " 11 -3.3167896e-01 1.27e-01 8.04e-04 -5.7 1.96e+02 - 1.00e+00 9.72e-01h 1\n", + " 12 -3.3168859e-01 1.17e-03 2.28e-08 -5.7 1.38e+01 - 1.00e+00 1.00e+00h 1\n", + " 13 -3.3169228e-01 3.87e-07 1.13e-09 -8.6 1.95e+00 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 13\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: -3.3169227859422723e-01 -3.3169227859422723e-01\n", + "Dual infeasibility......: 1.1339429495425323e-09 1.1339429495425323e-09\n", + "Constraint violation....: 1.4249529091406254e-09 3.8666985346935689e-07\n", + "Complementarity.........: 3.9898113808548269e-09 3.9898113808548269e-09\n", + "Overall NLP error.......: 3.9898113808548269e-09 3.8666985346935689e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 14\n", + "Number of objective gradient evaluations = 14\n", + "Number of equality constraint evaluations = 14\n", + "Number of inequality constraint evaluations = 14\n", + "Number of equality constraint Jacobian evaluations = 14\n", + "Number of inequality constraint Jacobian evaluations = 14\n", + "Number of Lagrangian Hessian evaluations = 13\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SurrType.PYSMO_RBF\n", + "2025-03-17 17:36:14 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=rbf\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 39\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 3\n", + "\n", + "Total number of variables............................: 15\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 13\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 -0.0000000e+00 4.58e+01 2.28e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -2.2187269e-01 1.44e+01 1.33e-02 -1.7 6.80e+02 - 9.44e-01 1.00e+00h 1\n", + " 2 -3.1833654e-01 6.49e+02 1.65e-02 -2.5 6.42e+03 - 9.23e-01 1.00e+00h 1\n", + " 3 -3.2904216e-01 1.98e+03 3.27e-03 -2.5 1.61e+04 - 6.11e-01 5.97e-01h 1\n", + " 4 -3.2728772e-01 2.07e+02 3.54e-03 -2.5 8.41e+03 - 1.00e+00 1.00e+00h 1\n", + " 5 -3.2617335e-01 1.27e+01 3.00e-04 -2.5 7.83e+02 - 1.00e+00 1.00e+00h 1\n", + " 6 -3.2612431e-01 5.10e-03 1.28e-06 -2.5 4.03e+01 - 1.00e+00 1.00e+00h 1\n", + " 7 -3.3094622e-01 5.19e+01 4.80e-03 -3.8 3.78e+03 - 9.21e-01 1.00e+00h 1\n", + " 8 -3.3154557e-01 3.78e+00 1.51e-03 -3.8 1.17e+03 - 1.00e+00 9.89e-01h 1\n", + " 9 -3.3138015e-01 4.26e-01 1.61e-05 -3.8 3.24e+02 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 -3.3138995e-01 5.21e-04 8.48e-08 -3.8 1.39e+01 - 1.00e+00 1.00e+00h 1\n", + " 11 -3.3167385e-01 1.35e-01 8.06e-04 -5.7 1.76e+02 - 1.00e+00 9.73e-01h 1\n", + " 12 -3.3168390e-01 1.87e-04 4.24e-08 -5.7 1.52e+01 - 1.00e+00 1.00e+00h 1\n", + " 13 -3.3168758e-01 3.70e-05 1.36e-09 -8.6 1.69e+00 - 1.00e+00 1.00e+00h 1\n", + " 14 -3.3168758e-01 2.31e-09 2.50e-14 -8.6 3.09e-03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: -3.3168758389152364e-01 -3.3168758389152364e-01\n", + "Dual infeasibility......: 2.5035529205297280e-14 2.5035529205297280e-14\n", + "Constraint violation....: 8.5709850027361010e-12 2.3137545213103294e-09\n", + "Complementarity.........: 2.5059042476426688e-09 2.5059042476426688e-09\n", + "Overall NLP error.......: 2.5059042476426688e-09 2.5059042476426688e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 15\n", + "Number of objective gradient evaluations = 15\n", + "Number of equality constraint evaluations = 15\n", + "Number of inequality constraint evaluations = 15\n", + "Number of equality constraint Jacobian evaluations = 15\n", + "Number of inequality constraint Jacobian evaluations = 15\n", + "Number of Lagrangian Hessian evaluations = 14\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + } + ], "source": [ "# create list objects to store data, run optimization\n", "results = {}\n", @@ -597,7 +3567,169 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_296077/4045444663.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n", + "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n", + "A typical example is when you are setting values in a column of a DataFrame, like:\n", + "\n", + "df[\"col\"][row_indexer] = value\n", + "\n", + "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + " df[j][i] = results[(i, j)]\n" + ] + }, + { + "data": { + "text/html": [ + "
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SurrType.PYSMO_KRGSurrType.PYSMO_PLYSurrType.PYSMO_RBF
fs.bypass_frac0.10.10.1
fs.ng_steam_ratio1.1245231.1264511.124305
fs.steam_flowrate1.226471.2285921.226153
fs.reformer_duty39080.24972839131.2664339062.177089
fs.AR0.0041070.0041070.004107
fs.C2H60.0005180.0005190.000545
fs.C3H80.0001140.0001140.000119
fs.C4H100.0000650.0000650.000068
fs.CH40.0161930.01620.016991
fs.CO0.1048390.1044190.104856
fs.CO20.0535340.0535610.053528
fs.H20.3316850.3316920.331688
fs.H2O0.148950.1490560.148918
fs.N20.340.340.34
fs.O20.0-0.00.0
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" + ], + "text/plain": [ + " SurrType.PYSMO_KRG SurrType.PYSMO_PLY SurrType.PYSMO_RBF\n", + "fs.bypass_frac 0.1 0.1 0.1\n", + "fs.ng_steam_ratio 1.124523 1.126451 1.124305\n", + "fs.steam_flowrate 1.22647 1.228592 1.226153\n", + "fs.reformer_duty 39080.249728 39131.26643 39062.177089\n", + "fs.AR 0.004107 0.004107 0.004107\n", + "fs.C2H6 0.000518 0.000519 0.000545\n", + "fs.C3H8 0.000114 0.000114 0.000119\n", + "fs.C4H10 0.000065 0.000065 0.000068\n", + "fs.CH4 0.016193 0.0162 0.016991\n", + "fs.CO 0.104839 0.104419 0.104856\n", + "fs.CO2 0.053534 0.053561 0.053528\n", + "fs.H2 0.331685 0.331692 0.331688\n", + "fs.H2O 0.14895 0.149056 0.148918\n", + "fs.N2 0.34 0.34 0.34\n", + "fs.O2 0.0 -0.0 0.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# print results as a table\n", "df_index = []\n", @@ -639,7 +3771,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb index 24abb8ba..6eb6ec73 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_test.ipynb @@ -1,647 +1,648 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ML/AI Best Practices: \"Selecting Surrogate Model Form/Size for Optimization\"\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "In this notebook we demonstrate the use of model and solver statistics to select the best surrogate model. For this purpose we trained (offline) different models with ALAMO, PySMO for three basis forms, and TensorFlow Keras. The surrogates are imported into the notebook, and the IDAES flowsheet is constructed and solved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the ALAMO, PySMO and Keras surrogate trainers, and compares key indicators of model performance. In this notebook, IPOPT will be run with statistics using ALAMO, PySMO Polynomial, PySMO RBF, PySMO Kriging and Keras surrogate models to assess each model type for flowsheet integration and tractability." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Problem Statement \n", + "\n", + "Within the context of a larger Natural Gas Fuel Cell (NGFC) system, the autothermal reformer unit is used to generate syngas from air, steam, and natural gas. Two input variables are considered for this example (reformer bypass fraction and fuel to steam ratio). The reformer bypass fraction (also called internal reformation percentage) plays an important role in the syngas final composition and it is typically controlled in this process. The fuel to steam ratio is an important variable that affects the final syngas reaction and heat duty required by the reactor. The syngas is then used as fuel by a solid-oxide fuel cell (SOFC) to generate electricity and heat. \n", + "\n", + "The autothermal reformer is typically modeled using the IDAES Gibbs reactor and this reactor is robust once it is initialized; however, the overall model robustness is affected due to several components present in the reaction, scaling issues for the largrangean multipliers, and Gibbs free energy minimization formulation. Substituting rigorously trained and validated surrogates in lieu of rigorous unit model equations increases the robustness of the problem.\n", + "\n", + "### 2.1. Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "### 2.2. Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\".\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previous Jupyter Notebooks demonstrated the workflow to import data, train surrogate models using [ALAMO](alamo/alamo_flowsheet_optimization_src_test.ipynb), [PySMO](pysmo/pysmo_flowsheet_optimization_src_test.ipynb) and Keras, and develop IDAES's validation plots. To keep this notebook simple, this notebook simply loads the surrogate models trained off line.\n", + "\n", + "Note that the training/loading method includes a \"retrain\" argument in case the user wants to retrain all surrogate models. Since the retrain method runs ALAMO, PySMO (Polynomial, Radial Basis Functions, and Kriging basis types) and Keras, it takes about an 1 hr to complete the training for all models.\n", + "\n", + "Each run will overwrite the serialized JSON files for previously trained surrogates if retraining is enforced. To retrain individual surrogates, simply delete the desired JSON before running this notebook (for Keras, delete the folder `keras_surrogate/`)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from idaes_examples.mod.surrogates.AR_training_methods import (\n", + " train_load_surrogates,\n", + " SurrType,\n", + ")\n", + "\n", + "trained_surr = train_load_surrogates(retrain=False)\n", + "# setting retrain to True will take ~ 1 hour to run, best to load if possible\n", + "# setting retrain to False will only generate missing surrogates (only if JSON/folder doesn't exist)\n", + "# this method trains surrogates and serializes to JSON\n", + "# The return value is a set of surrogate types (instances of SurrType) that were trained\n", + "\n", + "# imports to capture long output\n", + "from io import StringIO\n", + "import sys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step builds an IDAES flowsheet and imports the surrogate model objects. As shown in the prior three examples, a single model object accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component. While the serialization method and file structure differs slightly between the ALAMO, PySMO and Keras Python Wrappers, the three are imported similarly into IDAES flowsheets as shown below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build IDAES Flowsheet\n", + "\n", + "This method builds an instance of the IDAES flowsheet model and solves the flowsheet using IPOPT. The method allows users to select a case and the surrogate model type to be used (i.e., alamo, pysmo, keras). The case argument consists of a list with values for the input variables (in this order, bypass split fraction and natural gas to steam ratio). Then the method fixes the input variables values to solve a square problem with IPOPT. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import IDAES and Pyomo libraries\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value, Var, Constraint, Set\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "\n", + "def build_flowsheet(case, surrogate_type: SurrType = None):\n", + " print(case, \" \", surrogate_type.value)\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C3H8,\n", + " m.fs.C4H10,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # call correct PySMO object to use below (will let us avoid nested switches)\n", + "\n", + " # capture long output from loading surrogates (don't need to print it)\n", + " stream = StringIO()\n", + " oldstdout = sys.stdout\n", + " sys.stdout = stream\n", + "\n", + " if surrogate_type == SurrType.ALAMO:\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " elif surrogate_type == SurrType.KERAS:\n", + " keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " elif SurrType.is_pysmo(\n", + " surrogate_type\n", + " ): # surrogate is one of the three pysmo basis options\n", + " surrogate = PysmoSurrogate.load_from_file(\n", + " surrogate_type.value + \"_surrogate.json\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " else:\n", + " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", + "\n", + " # revert to standard output\n", + " sys.stdout = oldstdout\n", + "\n", + " # fix input values and solve flowsheet\n", + " m.fs.bypass_frac.fix(case[0])\n", + " m.fs.ng_steam_ratio.fix(case[1])\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " try: # attempt to solve problem\n", + " results = solver.solve(m, tee=True)\n", + " except: # retry solving one more time\n", + " results = solver.solve(m, tee=True)\n", + "\n", + " return (\n", + " value(m.fs.steam_flowrate),\n", + " value(m.fs.reformer_duty),\n", + " value(m.fs.C2H6),\n", + " value(m.fs.CH4),\n", + " value(m.fs.H2),\n", + " value(m.fs.O2),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Model Size/Form Comparison\n", + "\n", + "As mentioned above, as part of best practices the IDAES ML/AI demonstration includes the analysis of model/solver statistics and performance to determine the best surrogate model, including model size, model form, model trainer, etc. This section provides the rigorous analysis of solver performance comparing different surrogate models (ALAMO, PySMO polynomial, PysMO RBF, and PySMO Kriging).\n", + "\n", + "To obtain the results, we run the flowsheet for ten different simulation cases for each surrogate model type. Since the simulation cases are obtained from the training data set we can compare model performance (absolute error of measurement vs predicted output values)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "csv_data = pd.read_csv(r\"reformer-data.csv\") # 2800 data points\n", + "\n", + "# extracting 10 data points out of 2800 data points, randomly selecting 10 cases to run\n", + "case_data = csv_data.sample(n=10)\n", + "\n", + "# selecting columns that correspond to Input Variables\n", + "inputs = np.array(case_data.iloc[:, :2])\n", + "\n", + "# selecting columns that correspond to Output Variables\n", + "cols = [\"Steam_Flow\", \"Reformer_Duty\", \"C2H6\", \"CH4\", \"H2\", \"O2\"]\n", + "outputs = np.array(case_data[cols])\n", + "\n", + "# For results comparison with minimum memory usage we will extract the values to plot on each pass\n", + "# note that the entire model could be returned and saved on each loop if desired\n", + "\n", + "# create empty dictionaries so we may easily index results as we save them\n", + "# for convenience while plotting, each output variable has its own dictionary\n", + "# indexed by (case number, trainer type)\n", + "# trainers = [\"alamo\", \"pysmo_poly\", \"pysmo_rbf\", \"pysmo_krig\", \"keras\"]\n", + "# temporarily remove keras\n", + "trainers = list(trained_surr - {SurrType.KERAS})\n", + "\n", + "cases = range(len(inputs))\n", + "steam_flow_error = {}\n", + "reformer_duty_error = {}\n", + "conc_C2H6 = {}\n", + "conc_CH4 = {}\n", + "conc_H2 = {}\n", + "conc_O2 = {}\n", + "\n", + "# run flowsheet for each trainer and save results\n", + "i = 0\n", + "for case in inputs: # each case is a value pair (bypass_frac, ng_steam_ratio)\n", + " i = i + 1\n", + " for trainer in trainers:\n", + " [\n", + " sf,\n", + " rd,\n", + " eth,\n", + " meth,\n", + " hyd,\n", + " oxy,\n", + " ] = build_flowsheet(case, surrogate_type=trainer)\n", + " steam_flow_error[(i, trainer)] = abs(\n", + " (sf - value(outputs[i - 1, 0])) / value(outputs[i - 1, 0])\n", + " )\n", + " reformer_duty_error[(i, trainer)] = abs(\n", + " (rd - value(outputs[i - 1, 1])) / value(outputs[i - 1, 1])\n", + " )\n", + " conc_C2H6[(i, trainer)] = abs(eth - value(outputs[i - 1, 2]))\n", + " conc_CH4[(i, trainer)] = abs(meth - value(outputs[i - 1, 3]))\n", + " conc_H2[(i, trainer)] = abs(hyd - value(outputs[i - 1, 4]))\n", + " conc_O2[(i, trainer)] = abs(oxy - value(outputs[i - 1, 5]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize these results by plotting a graph for each of the quantities above, creating a data series for each surrogate trainer. Some data series may overlay if values are identical for all cases:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "# create figure/axes for each plot sequentially, plotting each trainer as a separate data series\n", + "\n", + "# Steam Flow Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " sf = [steam_flow_error[(i, j)] for (i, j) in steam_flow_error if j == trainer]\n", + " ax.plot(cases, sf, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Relative Error\")\n", + "ax.set_title(\"Steam Flow Prediction\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# Reformer Duty Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " rd = [reformer_duty_error[(i, j)] for (i, j) in reformer_duty_error if j == trainer]\n", + " ax.plot(cases, rd, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Relative Error\")\n", + "ax.set_title(\"Reformer Duty Prediction\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# C2H6 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " eth = [conc_C2H6[(i, j)] for (i, j) in conc_C2H6 if j == trainer]\n", + " ax.plot(cases, eth, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"C2H6 Mole Fraction Prediction (O(1E-2))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"Mole fraction predictions displayed with absolute error:\")\n", + "print()\n", + "\n", + "# CH4 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " meth = [conc_CH4[(i, j)] for (i, j) in conc_CH4 if j == trainer]\n", + " ax.plot(cases, meth, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"CH4 Mole Fraction Prediction (O(1E-1))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# H2 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " hyd = [conc_H2[(i, j)] for (i, j) in conc_H2 if j == trainer]\n", + " ax.plot(cases, hyd, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"H2 Mole Fraction Prediction (O(1E-1))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# O2 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " oxy = [conc_O2[(i, j)] for (i, j) in conc_O2 if j == trainer]\n", + " ax.plot(cases, oxy, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"O2 Mole Fraction Prediction (O(1E-20))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Comparing Surrogate Optimization\n", + "Extending this analysis, we will run a single optimization scenario for each surrogate model and compare results. As in previous examples detailing workflows for [ALAMO](alamo_flowsheet_optimization_src_test.ipynb), [PySMO](pysmo_flowsheet_optimization_src_test.ipynb) and [Keras](keras_flowsheet_optimization_src_test.ipynb), we will optimize hydrogen production while restricting nitrogen below 34 mol% in the product stream." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Import additional Pyomo libraries\n", + "from pyomo.environ import Objective, maximize\n", + "\n", + "\n", + "def run_optimization(surrogate_type=None):\n", + " print(surrogate_type)\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C3H8,\n", + " m.fs.C4H10,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # call correct PySMO object to use below (will let us avoid nested switches)\n", + "\n", + " # capture long output from loading surrogates (don't need to print it)\n", + " stream = StringIO()\n", + " oldstdout = sys.stdout\n", + " sys.stdout = stream\n", + "\n", + " if surrogate_type == SurrType.ALAMO:\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " elif surrogate_type == SurrType.KERAS:\n", + " keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " elif SurrType.is_pysmo(\n", + " surrogate_type\n", + " ): # surrogate is one of the three pysmo basis options\n", + " surrogate = PysmoSurrogate.load_from_file(\n", + " surrogate_type.value + \"_surrogate.json\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " else:\n", + " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", + "\n", + " # revert to standard output\n", + " sys.stdout = oldstdout\n", + "\n", + " # unfix input values and add the objective/constraint to the model\n", + " m.fs.bypass_frac.unfix()\n", + " m.fs.ng_steam_ratio.unfix()\n", + " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " try: # attempt to solve problem\n", + " results = solver.solve(m, tee=True)\n", + " except: # retry solving one more time\n", + " results = solver.solve(m, tee=True)\n", + "\n", + " return inputs, outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create list objects to store data, run optimization\n", + "results = {}\n", + "for trainer in trainers:\n", + " inputs, outputs = run_optimization(trainer)\n", + " for var in inputs:\n", + " results[(var.name, trainer)] = value(var)\n", + " for var in outputs:\n", + " results[(var.name, trainer)] = value(var)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# print results as a table\n", + "df_index = []\n", + "for var in inputs:\n", + " df_index.append(var.name)\n", + "for var in outputs:\n", + " df_index.append(var.name)\n", + "df_cols = trainers\n", + "\n", + "df = pd.DataFrame(index=df_index, columns=df_cols)\n", + "for i in df_index:\n", + " for j in df_cols:\n", + " df[j][i] = results[(i, j)]\n", + "\n", + "df # display results table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ML/AI Best Practices: \"Selecting Surrogate Model Form/Size for Optimization\"\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "In this notebook we demonstrate the use of model and solver statistics to select the best surrogate model. For this purpose we trained (offline) different models with ALAMO, PySMO for three basis forms, and TensorFlow Keras. The surrogates are imported into the notebook, and the IDAES flowsheet is constructed and solved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the ALAMO, PySMO and Keras surrogate trainers, and compares key indicators of model performance. In this notebook, IPOPT will be run with statistics using ALAMO, PySMO Polynomial, PySMO RBF, PySMO Kriging and Keras surrogate models to assess each model type for flowsheet integration and tractability." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Problem Statement \n", - "\n", - "Within the context of a larger Natural Gas Fuel Cell (NGFC) system, the autothermal reformer unit is used to generate syngas from air, steam, and natural gas. Two input variables are considered for this example (reformer bypass fraction and fuel to steam ratio). The reformer bypass fraction (also called internal reformation percentage) plays an important role in the syngas final composition and it is typically controlled in this process. The fuel to steam ratio is an important variable that affects the final syngas reaction and heat duty required by the reactor. The syngas is then used as fuel by a solid-oxide fuel cell (SOFC) to generate electricity and heat. \n", - "\n", - "The autothermal reformer is typically modeled using the IDAES Gibbs reactor and this reactor is robust once it is initialized; however, the overall model robustness is affected due to several components present in the reaction, scaling issues for the largrangean multipliers, and Gibbs free energy minimization formulation. Substituting rigorously trained and validated surrogates in lieu of rigorous unit model equations increases the robustness of the problem.\n", - "\n", - "### 2.1. Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "### 2.2. Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\".\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Previous Jupyter Notebooks demonstrated the workflow to import data, train surrogate models using [ALAMO](alamo/alamo_flowsheet_optimization_src_test.ipynb), [PySMO](pysmo/pysmo_flowsheet_optimization_src_test.ipynb) and Keras, and develop IDAES's validation plots. To keep this notebook simple, this notebook simply loads the surrogate models trained off line.\n", - "\n", - "Note that the training/loading method includes a \"retrain\" argument in case the user wants to retrain all surrogate models. Since the retrain method runs ALAMO, PySMO (Polynomial, Radial Basis Functions, and Kriging basis types) and Keras, it takes about an 1 hr to complete the training for all models.\n", - "\n", - "Each run will overwrite the serialized JSON files for previously trained surrogates if retraining is enforced. To retrain individual surrogates, simply delete the desired JSON before running this notebook (for Keras, delete the folder `keras_surrogate/`)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from idaes_examples.mod.surrogates.AR_training_methods import (\n", - " train_load_surrogates,\n", - " SurrType,\n", - ")\n", - "\n", - "trained_surr = train_load_surrogates(retrain=False)\n", - "# setting retrain to True will take ~ 1 hour to run, best to load if possible\n", - "# setting retrain to False will only generate missing surrogates (only if JSON/folder doesn't exist)\n", - "# this method trains surrogates and serializes to JSON\n", - "# The return value is a set of surrogate types (instances of SurrType) that were trained\n", - "\n", - "# imports to capture long output\n", - "from io import StringIO\n", - "import sys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This step builds an IDAES flowsheet and imports the surrogate model objects. As shown in the prior three examples, a single model object accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component. While the serialization method and file structure differs slightly between the ALAMO, PySMO and Keras Python Wrappers, the three are imported similarly into IDAES flowsheets as shown below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build IDAES Flowsheet\n", - "\n", - "This method builds an instance of the IDAES flowsheet model and solves the flowsheet using IPOPT. The method allows users to select a case and the surrogate model type to be used (i.e., alamo, pysmo, keras). The case argument consists of a list with values for the input variables (in this order, bypass split fraction and natural gas to steam ratio). Then the method fixes the input variables values to solve a square problem with IPOPT. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import IDAES and Pyomo libraries\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value, Var, Constraint, Set\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "\n", - "def build_flowsheet(case, surrogate_type: SurrType = None):\n", - " print(case, \" \", surrogate_type.value)\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C3H8,\n", - " m.fs.C4H10,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # call correct PySMO object to use below (will let us avoid nested switches)\n", - "\n", - " # capture long output from loading surrogates (don't need to print it)\n", - " stream = StringIO()\n", - " oldstdout = sys.stdout\n", - " sys.stdout = stream\n", - "\n", - " if surrogate_type == SurrType.ALAMO:\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - " elif SurrType.is_pysmo(\n", - " surrogate_type\n", - " ): # surrogate is one of the three pysmo basis options\n", - " surrogate = PysmoSurrogate.load_from_file(\n", - " surrogate_type.value + \"_surrogate.json\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " else:\n", - " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", - "\n", - " # revert to standard output\n", - " sys.stdout = oldstdout\n", - "\n", - " # fix input values and solve flowsheet\n", - " m.fs.bypass_frac.fix(case[0])\n", - " m.fs.ng_steam_ratio.fix(case[1])\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " try: # attempt to solve problem\n", - " results = solver.solve(m, tee=True)\n", - " except: # retry solving one more time\n", - " results = solver.solve(m, tee=True)\n", - "\n", - " return (\n", - " value(m.fs.steam_flowrate),\n", - " value(m.fs.reformer_duty),\n", - " value(m.fs.C2H6),\n", - " value(m.fs.CH4),\n", - " value(m.fs.H2),\n", - " value(m.fs.O2),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Model Size/Form Comparison\n", - "\n", - "As mentioned above, as part of best practices the IDAES ML/AI demonstration includes the analysis of model/solver statistics and performance to determine the best surrogate model, including model size, model form, model trainer, etc. This section provides the rigorous analysis of solver performance comparing different surrogate models (ALAMO, PySMO polynomial, PysMO RBF, and PySMO Kriging).\n", - "\n", - "To obtain the results, we run the flowsheet for ten different simulation cases for each surrogate model type. Since the simulation cases are obtained from the training data set we can compare model performance (absolute error of measurement vs predicted output values)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Import Auto-reformer training data\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "csv_data = pd.read_csv(r\"reformer-data.csv\") # 2800 data points\n", - "\n", - "# extracting 10 data points out of 2800 data points, randomly selecting 10 cases to run\n", - "case_data = csv_data.sample(n=10)\n", - "\n", - "# selecting columns that correspond to Input Variables\n", - "inputs = np.array(case_data.iloc[:, :2])\n", - "\n", - "# selecting columns that correspond to Output Variables\n", - "cols = [\"Steam_Flow\", \"Reformer_Duty\", \"C2H6\", \"CH4\", \"H2\", \"O2\"]\n", - "outputs = np.array(case_data[cols])\n", - "\n", - "# For results comparison with minimum memory usage we will extract the values to plot on each pass\n", - "# note that the entire model could be returned and saved on each loop if desired\n", - "\n", - "# create empty dictionaries so we may easily index results as we save them\n", - "# for convenience while plotting, each output variable has its own dictionary\n", - "# indexed by (case number, trainer type)\n", - "# trainers = [\"alamo\", \"pysmo_poly\", \"pysmo_rbf\", \"pysmo_krig\", \"keras\"]\n", - "# temporarily remove keras\n", - "trainers = list(trained_surr - {SurrType.KERAS})\n", - "\n", - "cases = range(len(inputs))\n", - "steam_flow_error = {}\n", - "reformer_duty_error = {}\n", - "conc_C2H6 = {}\n", - "conc_CH4 = {}\n", - "conc_H2 = {}\n", - "conc_O2 = {}\n", - "\n", - "# run flowsheet for each trainer and save results\n", - "i = 0\n", - "for case in inputs: # each case is a value pair (bypass_frac, ng_steam_ratio)\n", - " i = i + 1\n", - " for trainer in trainers:\n", - " [\n", - " sf,\n", - " rd,\n", - " eth,\n", - " meth,\n", - " hyd,\n", - " oxy,\n", - " ] = build_flowsheet(case, surrogate_type=trainer)\n", - " steam_flow_error[(i, trainer)] = abs(\n", - " (sf - value(outputs[i - 1, 0])) / value(outputs[i - 1, 0])\n", - " )\n", - " reformer_duty_error[(i, trainer)] = abs(\n", - " (rd - value(outputs[i - 1, 1])) / value(outputs[i - 1, 1])\n", - " )\n", - " conc_C2H6[(i, trainer)] = abs(eth - value(outputs[i - 1, 2]))\n", - " conc_CH4[(i, trainer)] = abs(meth - value(outputs[i - 1, 3]))\n", - " conc_H2[(i, trainer)] = abs(hyd - value(outputs[i - 1, 4]))\n", - " conc_O2[(i, trainer)] = abs(oxy - value(outputs[i - 1, 5]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize these results by plotting a graph for each of the quantities above, creating a data series for each surrogate trainer. Some data series may overlay if values are identical for all cases:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "# create figure/axes for each plot sequentially, plotting each trainer as a separate data series\n", - "\n", - "# Steam Flow Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " sf = [steam_flow_error[(i, j)] for (i, j) in steam_flow_error if j == trainer]\n", - " ax.plot(cases, sf, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Relative Error\")\n", - "ax.set_title(\"Steam Flow Prediction\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# Reformer Duty Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " rd = [reformer_duty_error[(i, j)] for (i, j) in reformer_duty_error if j == trainer]\n", - " ax.plot(cases, rd, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Relative Error\")\n", - "ax.set_title(\"Reformer Duty Prediction\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# C2H6 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " eth = [conc_C2H6[(i, j)] for (i, j) in conc_C2H6 if j == trainer]\n", - " ax.plot(cases, eth, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"C2H6 Mole Fraction Prediction (O(1E-2))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "print()\n", - "print(\"Mole fraction predictions displayed with absolute error:\")\n", - "print()\n", - "\n", - "# CH4 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " meth = [conc_CH4[(i, j)] for (i, j) in conc_CH4 if j == trainer]\n", - " ax.plot(cases, meth, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"CH4 Mole Fraction Prediction (O(1E-1))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# H2 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " hyd = [conc_H2[(i, j)] for (i, j) in conc_H2 if j == trainer]\n", - " ax.plot(cases, hyd, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"H2 Mole Fraction Prediction (O(1E-1))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# O2 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " oxy = [conc_O2[(i, j)] for (i, j) in conc_O2 if j == trainer]\n", - " ax.plot(cases, oxy, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"O2 Mole Fraction Prediction (O(1E-20))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.3 Comparing Surrogate Optimization\n", - "Extending this analysis, we will run a single optimization scenario for each surrogate model and compare results. As in previous examples detailing workflows for [ALAMO](alamo_flowsheet_optimization_src_test.ipynb), [PySMO](pysmo_flowsheet_optimization_src_test.ipynb) and [Keras](keras_flowsheet_optimization_src_test.ipynb), we will optimize hydrogen production while restricting nitrogen below 34 mol% in the product stream." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Import additional Pyomo libraries\n", - "from pyomo.environ import Objective, maximize\n", - "\n", - "\n", - "def run_optimization(surrogate_type=None):\n", - " print(surrogate_type)\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C3H8,\n", - " m.fs.C4H10,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # call correct PySMO object to use below (will let us avoid nested switches)\n", - "\n", - " # capture long output from loading surrogates (don't need to print it)\n", - " stream = StringIO()\n", - " oldstdout = sys.stdout\n", - " sys.stdout = stream\n", - "\n", - " if surrogate_type == SurrType.ALAMO:\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - " elif SurrType.is_pysmo(\n", - " surrogate_type\n", - " ): # surrogate is one of the three pysmo basis options\n", - " surrogate = PysmoSurrogate.load_from_file(\n", - " surrogate_type.value + \"_surrogate.json\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " else:\n", - " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", - "\n", - " # revert to standard output\n", - " sys.stdout = oldstdout\n", - "\n", - " # unfix input values and add the objective/constraint to the model\n", - " m.fs.bypass_frac.unfix()\n", - " m.fs.ng_steam_ratio.unfix()\n", - " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " try: # attempt to solve problem\n", - " results = solver.solve(m, tee=True)\n", - " except: # retry solving one more time\n", - " results = solver.solve(m, tee=True)\n", - "\n", - " return inputs, outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# create list objects to store data, run optimization\n", - "results = {}\n", - "for trainer in trainers:\n", - " inputs, outputs = run_optimization(trainer)\n", - " for var in inputs:\n", - " results[(var.name, trainer)] = value(var)\n", - " for var in outputs:\n", - " results[(var.name, trainer)] = value(var)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# print results as a table\n", - "df_index = []\n", - "for var in inputs:\n", - " df_index.append(var.name)\n", - "for var in outputs:\n", - " df_index.append(var.name)\n", - "df_cols = trainers\n", - "\n", - "df = pd.DataFrame(index=df_index, columns=df_cols)\n", - "for i in df_index:\n", - " for j in df_cols:\n", - " df[j][i] = results[(i, j)]\n", - "\n", - "df # display results table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb index dc5a8e7d..b199a819 100644 --- a/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/best_practices_optimization_usr.ipynb @@ -1,647 +1,648 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ML/AI Best Practices: \"Selecting Surrogate Model Form/Size for Optimization\"\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "In this notebook we demonstrate the use of model and solver statistics to select the best surrogate model. For this purpose we trained (offline) different models with ALAMO, PySMO for three basis forms, and TensorFlow Keras. The surrogates are imported into the notebook, and the IDAES flowsheet is constructed and solved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the ALAMO, PySMO and Keras surrogate trainers, and compares key indicators of model performance. In this notebook, IPOPT will be run with statistics using ALAMO, PySMO Polynomial, PySMO RBF, PySMO Kriging and Keras surrogate models to assess each model type for flowsheet integration and tractability." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Problem Statement \n", + "\n", + "Within the context of a larger Natural Gas Fuel Cell (NGFC) system, the autothermal reformer unit is used to generate syngas from air, steam, and natural gas. Two input variables are considered for this example (reformer bypass fraction and fuel to steam ratio). The reformer bypass fraction (also called internal reformation percentage) plays an important role in the syngas final composition and it is typically controlled in this process. The fuel to steam ratio is an important variable that affects the final syngas reaction and heat duty required by the reactor. The syngas is then used as fuel by a solid-oxide fuel cell (SOFC) to generate electricity and heat. \n", + "\n", + "The autothermal reformer is typically modeled using the IDAES Gibbs reactor and this reactor is robust once it is initialized; however, the overall model robustness is affected due to several components present in the reaction, scaling issues for the largrangean multipliers, and Gibbs free energy minimization formulation. Substituting rigorously trained and validated surrogates in lieu of rigorous unit model equations increases the robustness of the problem.\n", + "\n", + "### 2.1. Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "### 2.2. Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\".\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previous Jupyter Notebooks demonstrated the workflow to import data, train surrogate models using [ALAMO](alamo/alamo_flowsheet_optimization_src_usr.ipynb), [PySMO](pysmo/pysmo_flowsheet_optimization_src_usr.ipynb) and Keras, and develop IDAES's validation plots. To keep this notebook simple, this notebook simply loads the surrogate models trained off line.\n", + "\n", + "Note that the training/loading method includes a \"retrain\" argument in case the user wants to retrain all surrogate models. Since the retrain method runs ALAMO, PySMO (Polynomial, Radial Basis Functions, and Kriging basis types) and Keras, it takes about an 1 hr to complete the training for all models.\n", + "\n", + "Each run will overwrite the serialized JSON files for previously trained surrogates if retraining is enforced. To retrain individual surrogates, simply delete the desired JSON before running this notebook (for Keras, delete the folder `keras_surrogate/`)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from idaes_examples.mod.surrogates.AR_training_methods import (\n", + " train_load_surrogates,\n", + " SurrType,\n", + ")\n", + "\n", + "trained_surr = train_load_surrogates(retrain=False)\n", + "# setting retrain to True will take ~ 1 hour to run, best to load if possible\n", + "# setting retrain to False will only generate missing surrogates (only if JSON/folder doesn't exist)\n", + "# this method trains surrogates and serializes to JSON\n", + "# The return value is a set of surrogate types (instances of SurrType) that were trained\n", + "\n", + "# imports to capture long output\n", + "from io import StringIO\n", + "import sys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step builds an IDAES flowsheet and imports the surrogate model objects. As shown in the prior three examples, a single model object accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component. While the serialization method and file structure differs slightly between the ALAMO, PySMO and Keras Python Wrappers, the three are imported similarly into IDAES flowsheets as shown below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build IDAES Flowsheet\n", + "\n", + "This method builds an instance of the IDAES flowsheet model and solves the flowsheet using IPOPT. The method allows users to select a case and the surrogate model type to be used (i.e., alamo, pysmo, keras). The case argument consists of a list with values for the input variables (in this order, bypass split fraction and natural gas to steam ratio). Then the method fixes the input variables values to solve a square problem with IPOPT. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import IDAES and Pyomo libraries\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value, Var, Constraint, Set\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "\n", + "def build_flowsheet(case, surrogate_type: SurrType = None):\n", + " print(case, \" \", surrogate_type.value)\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C3H8,\n", + " m.fs.C4H10,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # call correct PySMO object to use below (will let us avoid nested switches)\n", + "\n", + " # capture long output from loading surrogates (don't need to print it)\n", + " stream = StringIO()\n", + " oldstdout = sys.stdout\n", + " sys.stdout = stream\n", + "\n", + " if surrogate_type == SurrType.ALAMO:\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " elif surrogate_type == SurrType.KERAS:\n", + " keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " elif SurrType.is_pysmo(\n", + " surrogate_type\n", + " ): # surrogate is one of the three pysmo basis options\n", + " surrogate = PysmoSurrogate.load_from_file(\n", + " surrogate_type.value + \"_surrogate.json\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " else:\n", + " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", + "\n", + " # revert to standard output\n", + " sys.stdout = oldstdout\n", + "\n", + " # fix input values and solve flowsheet\n", + " m.fs.bypass_frac.fix(case[0])\n", + " m.fs.ng_steam_ratio.fix(case[1])\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " try: # attempt to solve problem\n", + " results = solver.solve(m, tee=True)\n", + " except: # retry solving one more time\n", + " results = solver.solve(m, tee=True)\n", + "\n", + " return (\n", + " value(m.fs.steam_flowrate),\n", + " value(m.fs.reformer_duty),\n", + " value(m.fs.C2H6),\n", + " value(m.fs.CH4),\n", + " value(m.fs.H2),\n", + " value(m.fs.O2),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Model Size/Form Comparison\n", + "\n", + "As mentioned above, as part of best practices the IDAES ML/AI demonstration includes the analysis of model/solver statistics and performance to determine the best surrogate model, including model size, model form, model trainer, etc. This section provides the rigorous analysis of solver performance comparing different surrogate models (ALAMO, PySMO polynomial, PysMO RBF, and PySMO Kriging).\n", + "\n", + "To obtain the results, we run the flowsheet for ten different simulation cases for each surrogate model type. Since the simulation cases are obtained from the training data set we can compare model performance (absolute error of measurement vs predicted output values)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "csv_data = pd.read_csv(r\"reformer-data.csv\") # 2800 data points\n", + "\n", + "# extracting 10 data points out of 2800 data points, randomly selecting 10 cases to run\n", + "case_data = csv_data.sample(n=10)\n", + "\n", + "# selecting columns that correspond to Input Variables\n", + "inputs = np.array(case_data.iloc[:, :2])\n", + "\n", + "# selecting columns that correspond to Output Variables\n", + "cols = [\"Steam_Flow\", \"Reformer_Duty\", \"C2H6\", \"CH4\", \"H2\", \"O2\"]\n", + "outputs = np.array(case_data[cols])\n", + "\n", + "# For results comparison with minimum memory usage we will extract the values to plot on each pass\n", + "# note that the entire model could be returned and saved on each loop if desired\n", + "\n", + "# create empty dictionaries so we may easily index results as we save them\n", + "# for convenience while plotting, each output variable has its own dictionary\n", + "# indexed by (case number, trainer type)\n", + "# trainers = [\"alamo\", \"pysmo_poly\", \"pysmo_rbf\", \"pysmo_krig\", \"keras\"]\n", + "# temporarily remove keras\n", + "trainers = list(trained_surr - {SurrType.KERAS})\n", + "\n", + "cases = range(len(inputs))\n", + "steam_flow_error = {}\n", + "reformer_duty_error = {}\n", + "conc_C2H6 = {}\n", + "conc_CH4 = {}\n", + "conc_H2 = {}\n", + "conc_O2 = {}\n", + "\n", + "# run flowsheet for each trainer and save results\n", + "i = 0\n", + "for case in inputs: # each case is a value pair (bypass_frac, ng_steam_ratio)\n", + " i = i + 1\n", + " for trainer in trainers:\n", + " [\n", + " sf,\n", + " rd,\n", + " eth,\n", + " meth,\n", + " hyd,\n", + " oxy,\n", + " ] = build_flowsheet(case, surrogate_type=trainer)\n", + " steam_flow_error[(i, trainer)] = abs(\n", + " (sf - value(outputs[i - 1, 0])) / value(outputs[i - 1, 0])\n", + " )\n", + " reformer_duty_error[(i, trainer)] = abs(\n", + " (rd - value(outputs[i - 1, 1])) / value(outputs[i - 1, 1])\n", + " )\n", + " conc_C2H6[(i, trainer)] = abs(eth - value(outputs[i - 1, 2]))\n", + " conc_CH4[(i, trainer)] = abs(meth - value(outputs[i - 1, 3]))\n", + " conc_H2[(i, trainer)] = abs(hyd - value(outputs[i - 1, 4]))\n", + " conc_O2[(i, trainer)] = abs(oxy - value(outputs[i - 1, 5]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize these results by plotting a graph for each of the quantities above, creating a data series for each surrogate trainer. Some data series may overlay if values are identical for all cases:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "# create figure/axes for each plot sequentially, plotting each trainer as a separate data series\n", + "\n", + "# Steam Flow Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " sf = [steam_flow_error[(i, j)] for (i, j) in steam_flow_error if j == trainer]\n", + " ax.plot(cases, sf, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Relative Error\")\n", + "ax.set_title(\"Steam Flow Prediction\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# Reformer Duty Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " rd = [reformer_duty_error[(i, j)] for (i, j) in reformer_duty_error if j == trainer]\n", + " ax.plot(cases, rd, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Relative Error\")\n", + "ax.set_title(\"Reformer Duty Prediction\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# C2H6 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " eth = [conc_C2H6[(i, j)] for (i, j) in conc_C2H6 if j == trainer]\n", + " ax.plot(cases, eth, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"C2H6 Mole Fraction Prediction (O(1E-2))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "print()\n", + "print(\"Mole fraction predictions displayed with absolute error:\")\n", + "print()\n", + "\n", + "# CH4 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " meth = [conc_CH4[(i, j)] for (i, j) in conc_CH4 if j == trainer]\n", + " ax.plot(cases, meth, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"CH4 Mole Fraction Prediction (O(1E-1))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# H2 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " hyd = [conc_H2[(i, j)] for (i, j) in conc_H2 if j == trainer]\n", + " ax.plot(cases, hyd, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"H2 Mole Fraction Prediction (O(1E-1))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n", + "# O2 Mole Fraction Prediction\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "for trainer in trainers:\n", + " # pick out the points that use that trainer and plot them against case number\n", + " oxy = [conc_O2[(i, j)] for (i, j) in conc_O2 if j == trainer]\n", + " ax.plot(cases, oxy, label=trainer)\n", + "# add info to plot\n", + "ax.set_xlabel(\"Cases\")\n", + "ax.set_ylabel(\"Absolute Error\")\n", + "ax.set_title(\"O2 Mole Fraction Prediction (O(1E-20))\")\n", + "ax.legend()\n", + "plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Comparing Surrogate Optimization\n", + "Extending this analysis, we will run a single optimization scenario for each surrogate model and compare results. As in previous examples detailing workflows for [ALAMO](alamo_flowsheet_optimization_src_usr.ipynb), [PySMO](pysmo_flowsheet_optimization_src_usr.ipynb) and [Keras](keras_flowsheet_optimization_src_usr.ipynb), we will optimize hydrogen production while restricting nitrogen below 34 mol% in the product stream." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Import additional Pyomo libraries\n", + "from pyomo.environ import Objective, maximize\n", + "\n", + "\n", + "def run_optimization(surrogate_type=None):\n", + " print(surrogate_type)\n", + " # create the IDAES model and flowsheet\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # create flowsheet input variables\n", + " m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + " )\n", + " m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + " )\n", + "\n", + " # create flowsheet output variables\n", + " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + " # create input and output variable object lists for flowsheet\n", + " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + " outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C3H8,\n", + " m.fs.C4H10,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + " ]\n", + "\n", + " # create the Pyomo/IDAES block that corresponds to the surrogate\n", + " # call correct PySMO object to use below (will let us avoid nested switches)\n", + "\n", + " # capture long output from loading surrogates (don't need to print it)\n", + " stream = StringIO()\n", + " oldstdout = sys.stdout\n", + " sys.stdout = stream\n", + "\n", + " if surrogate_type == SurrType.ALAMO:\n", + " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " elif surrogate_type == SurrType.KERAS:\n", + " keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " elif SurrType.is_pysmo(\n", + " surrogate_type\n", + " ): # surrogate is one of the three pysmo basis options\n", + " surrogate = PysmoSurrogate.load_from_file(\n", + " surrogate_type.value + \"_surrogate.json\"\n", + " )\n", + " m.fs.surrogate = SurrogateBlock()\n", + " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + " else:\n", + " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", + "\n", + " # revert to standard output\n", + " sys.stdout = oldstdout\n", + "\n", + " # unfix input values and add the objective/constraint to the model\n", + " m.fs.bypass_frac.unfix()\n", + " m.fs.ng_steam_ratio.unfix()\n", + " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + " solver = SolverFactory(\"ipopt\")\n", + " try: # attempt to solve problem\n", + " results = solver.solve(m, tee=True)\n", + " except: # retry solving one more time\n", + " results = solver.solve(m, tee=True)\n", + "\n", + " return inputs, outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create list objects to store data, run optimization\n", + "results = {}\n", + "for trainer in trainers:\n", + " inputs, outputs = run_optimization(trainer)\n", + " for var in inputs:\n", + " results[(var.name, trainer)] = value(var)\n", + " for var in outputs:\n", + " results[(var.name, trainer)] = value(var)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# print results as a table\n", + "df_index = []\n", + "for var in inputs:\n", + " df_index.append(var.name)\n", + "for var in outputs:\n", + " df_index.append(var.name)\n", + "df_cols = trainers\n", + "\n", + "df = pd.DataFrame(index=df_index, columns=df_cols)\n", + "for i in df_index:\n", + " for j in df_cols:\n", + " df[j][i] = results[(i, j)]\n", + "\n", + "df # display results table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ML/AI Best Practices: \"Selecting Surrogate Model Form/Size for Optimization\"\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "In this notebook we demonstrate the use of model and solver statistics to select the best surrogate model. For this purpose we trained (offline) different models with ALAMO, PySMO for three basis forms, and TensorFlow Keras. The surrogates are imported into the notebook, and the IDAES flowsheet is constructed and solved." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the ALAMO, PySMO and Keras surrogate trainers, and compares key indicators of model performance. In this notebook, IPOPT will be run with statistics using ALAMO, PySMO Polynomial, PySMO RBF, PySMO Kriging and Keras surrogate models to assess each model type for flowsheet integration and tractability." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Problem Statement \n", - "\n", - "Within the context of a larger Natural Gas Fuel Cell (NGFC) system, the autothermal reformer unit is used to generate syngas from air, steam, and natural gas. Two input variables are considered for this example (reformer bypass fraction and fuel to steam ratio). The reformer bypass fraction (also called internal reformation percentage) plays an important role in the syngas final composition and it is typically controlled in this process. The fuel to steam ratio is an important variable that affects the final syngas reaction and heat duty required by the reactor. The syngas is then used as fuel by a solid-oxide fuel cell (SOFC) to generate electricity and heat. \n", - "\n", - "The autothermal reformer is typically modeled using the IDAES Gibbs reactor and this reactor is robust once it is initialized; however, the overall model robustness is affected due to several components present in the reaction, scaling issues for the largrangean multipliers, and Gibbs free energy minimization formulation. Substituting rigorously trained and validated surrogates in lieu of rigorous unit model equations increases the robustness of the problem.\n", - "\n", - "### 2.1. Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "### 2.2. Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\".\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Previous Jupyter Notebooks demonstrated the workflow to import data, train surrogate models using [ALAMO](alamo/alamo_flowsheet_optimization_src_usr.ipynb), [PySMO](pysmo/pysmo_flowsheet_optimization_src_usr.ipynb) and Keras, and develop IDAES's validation plots. To keep this notebook simple, this notebook simply loads the surrogate models trained off line.\n", - "\n", - "Note that the training/loading method includes a \"retrain\" argument in case the user wants to retrain all surrogate models. Since the retrain method runs ALAMO, PySMO (Polynomial, Radial Basis Functions, and Kriging basis types) and Keras, it takes about an 1 hr to complete the training for all models.\n", - "\n", - "Each run will overwrite the serialized JSON files for previously trained surrogates if retraining is enforced. To retrain individual surrogates, simply delete the desired JSON before running this notebook (for Keras, delete the folder `keras_surrogate/`)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from idaes_examples.mod.surrogates.AR_training_methods import (\n", - " train_load_surrogates,\n", - " SurrType,\n", - ")\n", - "\n", - "trained_surr = train_load_surrogates(retrain=False)\n", - "# setting retrain to True will take ~ 1 hour to run, best to load if possible\n", - "# setting retrain to False will only generate missing surrogates (only if JSON/folder doesn't exist)\n", - "# this method trains surrogates and serializes to JSON\n", - "# The return value is a set of surrogate types (instances of SurrType) that were trained\n", - "\n", - "# imports to capture long output\n", - "from io import StringIO\n", - "import sys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This step builds an IDAES flowsheet and imports the surrogate model objects. As shown in the prior three examples, a single model object accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component. While the serialization method and file structure differs slightly between the ALAMO, PySMO and Keras Python Wrappers, the three are imported similarly into IDAES flowsheets as shown below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build IDAES Flowsheet\n", - "\n", - "This method builds an instance of the IDAES flowsheet model and solves the flowsheet using IPOPT. The method allows users to select a case and the surrogate model type to be used (i.e., alamo, pysmo, keras). The case argument consists of a list with values for the input variables (in this order, bypass split fraction and natural gas to steam ratio). Then the method fixes the input variables values to solve a square problem with IPOPT. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import IDAES and Pyomo libraries\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value, Var, Constraint, Set\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "\n", - "def build_flowsheet(case, surrogate_type: SurrType = None):\n", - " print(case, \" \", surrogate_type.value)\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C3H8,\n", - " m.fs.C4H10,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # call correct PySMO object to use below (will let us avoid nested switches)\n", - "\n", - " # capture long output from loading surrogates (don't need to print it)\n", - " stream = StringIO()\n", - " oldstdout = sys.stdout\n", - " sys.stdout = stream\n", - "\n", - " if surrogate_type == SurrType.ALAMO:\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - " elif SurrType.is_pysmo(\n", - " surrogate_type\n", - " ): # surrogate is one of the three pysmo basis options\n", - " surrogate = PysmoSurrogate.load_from_file(\n", - " surrogate_type.value + \"_surrogate.json\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " else:\n", - " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", - "\n", - " # revert to standard output\n", - " sys.stdout = oldstdout\n", - "\n", - " # fix input values and solve flowsheet\n", - " m.fs.bypass_frac.fix(case[0])\n", - " m.fs.ng_steam_ratio.fix(case[1])\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " try: # attempt to solve problem\n", - " results = solver.solve(m, tee=True)\n", - " except: # retry solving one more time\n", - " results = solver.solve(m, tee=True)\n", - "\n", - " return (\n", - " value(m.fs.steam_flowrate),\n", - " value(m.fs.reformer_duty),\n", - " value(m.fs.C2H6),\n", - " value(m.fs.CH4),\n", - " value(m.fs.H2),\n", - " value(m.fs.O2),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Model Size/Form Comparison\n", - "\n", - "As mentioned above, as part of best practices the IDAES ML/AI demonstration includes the analysis of model/solver statistics and performance to determine the best surrogate model, including model size, model form, model trainer, etc. This section provides the rigorous analysis of solver performance comparing different surrogate models (ALAMO, PySMO polynomial, PysMO RBF, and PySMO Kriging).\n", - "\n", - "To obtain the results, we run the flowsheet for ten different simulation cases for each surrogate model type. Since the simulation cases are obtained from the training data set we can compare model performance (absolute error of measurement vs predicted output values)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Import Auto-reformer training data\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "csv_data = pd.read_csv(r\"reformer-data.csv\") # 2800 data points\n", - "\n", - "# extracting 10 data points out of 2800 data points, randomly selecting 10 cases to run\n", - "case_data = csv_data.sample(n=10)\n", - "\n", - "# selecting columns that correspond to Input Variables\n", - "inputs = np.array(case_data.iloc[:, :2])\n", - "\n", - "# selecting columns that correspond to Output Variables\n", - "cols = [\"Steam_Flow\", \"Reformer_Duty\", \"C2H6\", \"CH4\", \"H2\", \"O2\"]\n", - "outputs = np.array(case_data[cols])\n", - "\n", - "# For results comparison with minimum memory usage we will extract the values to plot on each pass\n", - "# note that the entire model could be returned and saved on each loop if desired\n", - "\n", - "# create empty dictionaries so we may easily index results as we save them\n", - "# for convenience while plotting, each output variable has its own dictionary\n", - "# indexed by (case number, trainer type)\n", - "# trainers = [\"alamo\", \"pysmo_poly\", \"pysmo_rbf\", \"pysmo_krig\", \"keras\"]\n", - "# temporarily remove keras\n", - "trainers = list(trained_surr - {SurrType.KERAS})\n", - "\n", - "cases = range(len(inputs))\n", - "steam_flow_error = {}\n", - "reformer_duty_error = {}\n", - "conc_C2H6 = {}\n", - "conc_CH4 = {}\n", - "conc_H2 = {}\n", - "conc_O2 = {}\n", - "\n", - "# run flowsheet for each trainer and save results\n", - "i = 0\n", - "for case in inputs: # each case is a value pair (bypass_frac, ng_steam_ratio)\n", - " i = i + 1\n", - " for trainer in trainers:\n", - " [\n", - " sf,\n", - " rd,\n", - " eth,\n", - " meth,\n", - " hyd,\n", - " oxy,\n", - " ] = build_flowsheet(case, surrogate_type=trainer)\n", - " steam_flow_error[(i, trainer)] = abs(\n", - " (sf - value(outputs[i - 1, 0])) / value(outputs[i - 1, 0])\n", - " )\n", - " reformer_duty_error[(i, trainer)] = abs(\n", - " (rd - value(outputs[i - 1, 1])) / value(outputs[i - 1, 1])\n", - " )\n", - " conc_C2H6[(i, trainer)] = abs(eth - value(outputs[i - 1, 2]))\n", - " conc_CH4[(i, trainer)] = abs(meth - value(outputs[i - 1, 3]))\n", - " conc_H2[(i, trainer)] = abs(hyd - value(outputs[i - 1, 4]))\n", - " conc_O2[(i, trainer)] = abs(oxy - value(outputs[i - 1, 5]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can visualize these results by plotting a graph for each of the quantities above, creating a data series for each surrogate trainer. Some data series may overlay if values are identical for all cases:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "# create figure/axes for each plot sequentially, plotting each trainer as a separate data series\n", - "\n", - "# Steam Flow Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " sf = [steam_flow_error[(i, j)] for (i, j) in steam_flow_error if j == trainer]\n", - " ax.plot(cases, sf, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Relative Error\")\n", - "ax.set_title(\"Steam Flow Prediction\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# Reformer Duty Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " rd = [reformer_duty_error[(i, j)] for (i, j) in reformer_duty_error if j == trainer]\n", - " ax.plot(cases, rd, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Relative Error\")\n", - "ax.set_title(\"Reformer Duty Prediction\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# C2H6 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " eth = [conc_C2H6[(i, j)] for (i, j) in conc_C2H6 if j == trainer]\n", - " ax.plot(cases, eth, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"C2H6 Mole Fraction Prediction (O(1E-2))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "print()\n", - "print(\"Mole fraction predictions displayed with absolute error:\")\n", - "print()\n", - "\n", - "# CH4 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " meth = [conc_CH4[(i, j)] for (i, j) in conc_CH4 if j == trainer]\n", - " ax.plot(cases, meth, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"CH4 Mole Fraction Prediction (O(1E-1))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# H2 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " hyd = [conc_H2[(i, j)] for (i, j) in conc_H2 if j == trainer]\n", - " ax.plot(cases, hyd, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"H2 Mole Fraction Prediction (O(1E-1))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()\n", - "\n", - "# O2 Mole Fraction Prediction\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot()\n", - "for trainer in trainers:\n", - " # pick out the points that use that trainer and plot them against case number\n", - " oxy = [conc_O2[(i, j)] for (i, j) in conc_O2 if j == trainer]\n", - " ax.plot(cases, oxy, label=trainer)\n", - "# add info to plot\n", - "ax.set_xlabel(\"Cases\")\n", - "ax.set_ylabel(\"Absolute Error\")\n", - "ax.set_title(\"O2 Mole Fraction Prediction (O(1E-20))\")\n", - "ax.legend()\n", - "plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.3 Comparing Surrogate Optimization\n", - "Extending this analysis, we will run a single optimization scenario for each surrogate model and compare results. As in previous examples detailing workflows for [ALAMO](alamo_flowsheet_optimization_src_usr.ipynb), [PySMO](pysmo_flowsheet_optimization_src_usr.ipynb) and [Keras](keras_flowsheet_optimization_src_usr.ipynb), we will optimize hydrogen production while restricting nitrogen below 34 mol% in the product stream." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Import additional Pyomo libraries\n", - "from pyomo.environ import Objective, maximize\n", - "\n", - "\n", - "def run_optimization(surrogate_type=None):\n", - " print(surrogate_type)\n", - " # create the IDAES model and flowsheet\n", - " m = ConcreteModel()\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # create flowsheet input variables\n", - " m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - " )\n", - " m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - " )\n", - "\n", - " # create flowsheet output variables\n", - " m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - " m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - " m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - " m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - " m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - " m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - " m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - " m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - " m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - " m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - " m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - " m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - " m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - " # create input and output variable object lists for flowsheet\n", - " inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - " outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C3H8,\n", - " m.fs.C4H10,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - " ]\n", - "\n", - " # create the Pyomo/IDAES block that corresponds to the surrogate\n", - " # call correct PySMO object to use below (will let us avoid nested switches)\n", - "\n", - " # capture long output from loading surrogates (don't need to print it)\n", - " stream = StringIO()\n", - " oldstdout = sys.stdout\n", - " sys.stdout = stream\n", - "\n", - " if surrogate_type == SurrType.ALAMO:\n", - " surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " elif surrogate_type == SurrType.KERAS:\n", - " keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - " elif SurrType.is_pysmo(\n", - " surrogate_type\n", - " ): # surrogate is one of the three pysmo basis options\n", - " surrogate = PysmoSurrogate.load_from_file(\n", - " surrogate_type.value + \"_surrogate.json\"\n", - " )\n", - " m.fs.surrogate = SurrogateBlock()\n", - " m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - " else:\n", - " raise ValueError(f\"Unknown surrogate type: {surrogate_type}\")\n", - "\n", - " # revert to standard output\n", - " sys.stdout = oldstdout\n", - "\n", - " # unfix input values and add the objective/constraint to the model\n", - " m.fs.bypass_frac.unfix()\n", - " m.fs.ng_steam_ratio.unfix()\n", - " m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - " m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - " solver = SolverFactory(\"ipopt\")\n", - " try: # attempt to solve problem\n", - " results = solver.solve(m, tee=True)\n", - " except: # retry solving one more time\n", - " results = solver.solve(m, tee=True)\n", - "\n", - " return inputs, outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# create list objects to store data, run optimization\n", - "results = {}\n", - "for trainer in trainers:\n", - " inputs, outputs = run_optimization(trainer)\n", - " for var in inputs:\n", - " results[(var.name, trainer)] = value(var)\n", - " for var in outputs:\n", - " results[(var.name, trainer)] = value(var)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# print results as a table\n", - "df_index = []\n", - "for var in inputs:\n", - " df_index.append(var.name)\n", - "for var in outputs:\n", - " df_index.append(var.name)\n", - "df_cols = trainers\n", - "\n", - "df = pd.DataFrame(index=df_index, columns=df_cols)\n", - "for i in df_index:\n", - " for j in df_cols:\n", - " df[j][i] = results[(i, j)]\n", - "\n", - "df # display results table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb index 8a16cf25..d7739732 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb index bae4c938..7a02d4dc 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -60,7 +61,19 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Image\n", "from pathlib import Path\n", @@ -91,7 +104,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-17 17:36:17.904242: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-17 17:36:17.904968: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:36:17.908041: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:36:17.914657: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742258177.926239 296211 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742258177.929301 296211 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742258177.938642 296211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258177.938663 296211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258177.938664 296211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258177.938665 296211 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-17 17:36:17.942645: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "# Import statements\n", "import os\n", @@ -155,7 +188,16 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ], "source": [ "# Import Auto-reformer training data\n", "np.set_printoptions(precision=6, suppress=True)\n", @@ -207,7 +249,226 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n", + "2025-03-17 17:36:20.210719: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 502ms/step - loss: 0.3816 - mae: 0.5276 - mse: 0.3816\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 173ms/step - loss: 0.3746 - mae: 0.5232 - mse: 0.3746 - val_loss: 0.3230 - val_mae: 0.4945 - val_mse: 0.3230\n", + "Epoch 2/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.3142 - mae: 0.4739 - mse: 0.3142\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.3106 - mae: 0.4715 - mse: 0.3106 - val_loss: 0.2687 - val_mae: 0.4451 - val_mse: 0.2687\n", + "Epoch 3/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.2590 - mae: 0.4252 - mse: 0.2590\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m 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0.1783\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.1801 - mae: 0.3442 - mse: 0.1801 - val_loss: 0.1562 - val_mae: 0.3199 - val_mse: 0.1562\n", + "Epoch 6/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.1495 - mae: 0.3074 - mse: 0.1495\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.1519 - mae: 0.3108 - mse: 0.1519 - val_loss: 0.1310 - val_mae: 0.2865 - val_mse: 0.1310\n", + "Epoch 7/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1266 - mae: 0.2789 - mse: 0.1266\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.1291 - mae: 0.2831 - mse: 0.1291 - val_loss: 0.1104 - val_mae: 0.2588 - val_mse: 0.1104\n", + "Epoch 8/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.1088 - mae: 0.2571 - mse: 0.1088\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.1112 - mae: 0.2614 - mse: 0.1112 - val_loss: 0.0941 - val_mae: 0.2388 - val_mse: 0.0941\n", + "Epoch 9/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0954 - mae: 0.2407 - mse: 0.0954\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0974 - mae: 0.2444 - mse: 0.0974 - val_loss: 0.0815 - val_mae: 0.2233 - val_mse: 0.0815\n", + "Epoch 10/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0857 - mae: 0.2297 - mse: 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"\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0743 - mae: 0.2161 - mse: 0.0743\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0747 - mae: 0.2164 - mse: 0.0747 - val_loss: 0.0604 - val_mae: 0.1982 - val_mse: 0.0604\n", + "Epoch 13/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0710 - mae: 0.2117 - mse: 0.0710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.0708 - mae: 0.2113 - mse: 0.0708 - val_loss: 0.0565 - val_mae: 0.1926 - val_mse: 0.0565\n", + "Epoch 14/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0683 - mae: 0.2075 - mse: 0.0683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0677 - mae: 0.2065 - mse: 0.0677 - val_loss: 0.0531 - val_mae: 0.1876 - val_mse: 0.0531\n", + "Epoch 15/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0659 - mae: 0.2029 - mse: 0.0659\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.0649 - mae: 0.2015 - mse: 0.0649 - val_loss: 0.0501 - val_mae: 0.1823 - val_mse: 0.0501\n", + "Epoch 16/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0633 - mae: 0.1980 - mse: 0.0633\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.0621 - mae: 0.1961 - mse: 0.0621 - val_loss: 0.0473 - val_mae: 0.1765 - val_mse: 0.0473\n", + "Epoch 17/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0608 - mae: 0.1928 - mse: 0.0608\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0595 - mae: 0.1906 - mse: 0.0595 - val_loss: 0.0447 - val_mae: 0.1704 - val_mse: 0.0447\n", + "Epoch 18/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0583 - mae: 0.1873 - mse: 0.0583\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 0.0570 - mae: 0.1849 - mse: 0.0570 - val_loss: 0.0425 - val_mae: 0.1642 - val_mse: 0.0425\n", + "Epoch 19/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0560 - mae: 0.1823 - mse: 0.0560\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0547 - mae: 0.1795 - mse: 0.0547 - val_loss: 0.0405 - val_mae: 0.1580 - val_mse: 0.0405\n", + "Epoch 20/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0540 - mae: 0.1776 - mse: 0.0540\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0527 - mae: 0.1743 - mse: 0.0527 - val_loss: 0.0388 - val_mae: 0.1521 - val_mse: 0.0388\n", + "Epoch 21/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0522 - mae: 0.1730 - mse: 0.0522\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0509 - mae: 0.1695 - mse: 0.0509 - val_loss: 0.0374 - val_mae: 0.1468 - val_mse: 0.0374\n", + "Epoch 22/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0505 - mae: 0.1689 - mse: 0.0505\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0493 - mae: 0.1650 - mse: 0.0493 - val_loss: 0.0361 - val_mae: 0.1423 - val_mse: 0.0361\n", + "Epoch 23/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0490 - mae: 0.1653 - mse: 0.0490\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0478 - mae: 0.1611 - mse: 0.0478 - val_loss: 0.0350 - val_mae: 0.1386 - val_mse: 0.0350\n", + "Epoch 24/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0475 - mae: 0.1620 - mse: 0.0475\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.0464 - mae: 0.1576 - mse: 0.0464 - val_loss: 0.0339 - val_mae: 0.1358 - val_mse: 0.0339\n", + "Epoch 25/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0461 - mae: 0.1589 - mse: 0.0461\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0450 - mae: 0.1544 - mse: 0.0450 - val_loss: 0.0330 - val_mae: 0.1337 - val_mse: 0.0330\n", + ".\n", + ".\n", + ".\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 8.1064e-05 - mae: 0.0064 - mse: 8.1064e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 8.4865e-05 - mae: 0.0066 - mse: 8.4865e-05 - val_loss: 6.2869e-05 - val_mae: 0.0058 - val_mse: 6.2869e-05\n", + "Epoch 977/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 8.0814e-05 - mae: 0.0064 - mse: 8.0814e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.4609e-05 - mae: 0.0066 - mse: 8.4609e-05 - val_loss: 6.2697e-05 - val_mae: 0.0058 - val_mse: 6.2697e-05\n", + "Epoch 978/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 8.0564e-05 - mae: 0.0063 - mse: 8.0564e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 8.4354e-05 - mae: 0.0066 - mse: 8.4354e-05 - val_loss: 6.2527e-05 - val_mae: 0.0058 - val_mse: 6.2527e-05\n", + "Epoch 979/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 8.0317e-05 - mae: 0.0063 - mse: 8.0317e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 8.4101e-05 - mae: 0.0066 - mse: 8.4101e-05 - val_loss: 6.2357e-05 - val_mae: 0.0058 - val_mse: 6.2357e-05\n", + "Epoch 980/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 8.0070e-05 - mae: 0.0063 - mse: 8.0070e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 8.3850e-05 - mae: 0.0066 - mse: 8.3850e-05 - val_loss: 6.2189e-05 - val_mae: 0.0058 - val_mse: 6.2189e-05\n", + "Epoch 981/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 7.9825e-05 - mae: 0.0063 - mse: 7.9825e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 8.3599e-05 - mae: 0.0066 - mse: 8.3599e-05 - val_loss: 6.2021e-05 - val_mae: 0.0057 - val_mse: 6.2021e-05\n", + "Epoch 982/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 7.9581e-05 - mae: 0.0063 - mse: 7.9581e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 8.3350e-05 - mae: 0.0066 - mse: 8.3350e-05 - val_loss: 6.1855e-05 - val_mae: 0.0057 - val_mse: 6.1855e-05\n", + "Epoch 983/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 7.9339e-05 - mae: 0.0063 - mse: 7.9339e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.3103e-05 - mae: 0.0066 - mse: 8.3103e-05 - val_loss: 6.1689e-05 - val_mae: 0.0057 - val_mse: 6.1689e-05\n", + "Epoch 984/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 7.9098e-05 - mae: 0.0063 - mse: 7.9098e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.2857e-05 - mae: 0.0066 - mse: 8.2857e-05 - val_loss: 6.1524e-05 - val_mae: 0.0057 - val_mse: 6.1524e-05\n", + "Epoch 985/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 7.8858e-05 - mae: 0.0063 - mse: 7.8858e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 8.2611e-05 - mae: 0.0066 - mse: 8.2611e-05 - val_loss: 6.1360e-05 - val_mae: 0.0057 - val_mse: 6.1360e-05\n", + "Epoch 986/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.8620e-05 - mae: 0.0063 - mse: 7.8620e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 8.2368e-05 - mae: 0.0065 - mse: 8.2368e-05 - val_loss: 6.1197e-05 - val_mae: 0.0057 - val_mse: 6.1197e-05\n", + "Epoch 987/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.8383e-05 - mae: 0.0063 - mse: 7.8383e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 8.2126e-05 - mae: 0.0065 - mse: 8.2126e-05 - val_loss: 6.1035e-05 - val_mae: 0.0057 - val_mse: 6.1035e-05\n", + "Epoch 988/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 7.8147e-05 - mae: 0.0062 - mse: 7.8147e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.1885e-05 - mae: 0.0065 - mse: 8.1885e-05 - val_loss: 6.0874e-05 - val_mae: 0.0057 - val_mse: 6.0874e-05\n", + "Epoch 989/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 7.7912e-05 - mae: 0.0062 - mse: 7.7912e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 8.1645e-05 - mae: 0.0065 - mse: 8.1645e-05 - val_loss: 6.0714e-05 - val_mae: 0.0057 - val_mse: 6.0714e-05\n", + "Epoch 990/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 7.7679e-05 - mae: 0.0062 - mse: 7.7679e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.1406e-05 - mae: 0.0065 - mse: 8.1406e-05 - val_loss: 6.0554e-05 - val_mae: 0.0057 - val_mse: 6.0554e-05\n", + "Epoch 991/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.7447e-05 - mae: 0.0062 - mse: 7.7447e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 8.1169e-05 - mae: 0.0065 - mse: 8.1169e-05 - val_loss: 6.0395e-05 - val_mae: 0.0057 - val_mse: 6.0395e-05\n", + "Epoch 992/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 7.7216e-05 - mae: 0.0062 - mse: 7.7216e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 8.0933e-05 - mae: 0.0065 - mse: 8.0933e-05 - val_loss: 6.0238e-05 - val_mae: 0.0057 - val_mse: 6.0238e-05\n", + "Epoch 993/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 7.6987e-05 - mae: 0.0062 - mse: 7.6987e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0699e-05 - mae: 0.0065 - mse: 8.0699e-05 - val_loss: 6.0081e-05 - val_mae: 0.0057 - val_mse: 6.0081e-05\n", + "Epoch 994/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 7.6759e-05 - mae: 0.0062 - mse: 7.6759e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 8.0465e-05 - mae: 0.0065 - mse: 8.0465e-05 - val_loss: 5.9925e-05 - val_mae: 0.0057 - val_mse: 5.9925e-05\n", + "Epoch 995/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 7.6532e-05 - mae: 0.0062 - mse: 7.6532e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0233e-05 - mae: 0.0065 - mse: 8.0233e-05 - val_loss: 5.9769e-05 - val_mae: 0.0057 - val_mse: 5.9769e-05\n", + "Epoch 996/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.6306e-05 - mae: 0.0062 - mse: 7.6306e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 8.0002e-05 - mae: 0.0065 - mse: 8.0002e-05 - val_loss: 5.9615e-05 - val_mae: 0.0057 - val_mse: 5.9615e-05\n", + "Epoch 997/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 7.6081e-05 - mae: 0.0062 - mse: 7.6081e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 7.9772e-05 - mae: 0.0064 - mse: 7.9772e-05 - val_loss: 5.9461e-05 - val_mae: 0.0056 - val_mse: 5.9461e-05\n", + "Epoch 998/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.5858e-05 - mae: 0.0062 - mse: 7.5858e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 7.9543e-05 - mae: 0.0064 - mse: 7.9543e-05 - val_loss: 5.9309e-05 - val_mae: 0.0056 - val_mse: 5.9309e-05\n", + "Epoch 999/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.5636e-05 - mae: 0.0061 - mse: 7.5636e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 7.9316e-05 - mae: 0.0064 - mse: 7.9316e-05 - val_loss: 5.9157e-05 - val_mae: 0.0056 - val_mse: 5.9157e-05\n", + "Epoch 1000/1000\n", + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 7.5415e-05 - mae: 0.0061 - mse: 7.5415e-05\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 7.9090e-05 - mae: 0.0064 - mse: 7.9090e-05 - val_loss: 5.9005e-05 - val_mae: 0.0056 - val_mse: 5.9005e-05\n", + "\n" + ] + } + ], "source": [ "# capture long output (not required to use surrogate API)\n", "from io import StringIO\n", @@ -306,7 +567,741 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/3\u001b[0m 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", 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", 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", 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", 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", 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", 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", 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", 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", 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25+7du7h79670e1FRkd6yctKwYUPExcVxrgcREZERbBpE3bx5E2VlZfDz89M57ufnhzNnzlT7mNzc3GrL5+bmSvdrj+krU9n58+exZs0aLF++XDpWr149rFixAo899hhcXFywY8cOREdHIy0tTW8gtXjxYixYsKCGFssXAyQiIiLjOP3qvKtXr2LQoEF47rnnMHHiROl4o0aNdHq8evTogWvXrmHZsmV6g6hZs2bpPKaoqAhBQUGWqzwRERHZjE2DqEaNGsHV1RV5eXk6x/Py8uDv71/tY/z9/Wssr/2Zl5enMy8hLy+vytyma9euYcCAAejVq5dBcx3Cw8Oxf/9+vfd7eHjAw8Oj1vM4Iy4NJyIiR2PTIMrd3R1hYWFIT09HdHQ0gPKJ5enp6YiLi6v2MREREUhPT0d8fLx0bP/+/YiIiAAABAcHw9/fH+np6VLQVFRUhCNHjmDy5MnSY65evYoBAwYgLCwMmzdvhotL7QsVjx8/7hQTRq9cAc6dA0JDgWbNHvx8nLhORESOyObDeQkJCYiJiUH37t3Rs2dPJCUloaSkBOPHjwcAjBs3Dk2bNsXixYsBAFOnTkW/fv2wYsUKREVFYevWrfjpp5+kniSFQoH4+Hi89dZbCA0NRXBwMObOnYvAwEApULt69Sr69++PFi1aYPny5bhx44ZUH21P1pYtW+Du7o6uXbsCAHbu3IlNmzbh//7v/6z10tjExo1AbCyg0QAuLsCGDcCECQ92TrktDWevGJnC3d3drOWIyP7ZPIgaOXIkbty4gcTEROTm5qJLly7Yu3evNDH80qVLOr1EvXr1QmpqKubMmYPZs2cjNDQUaWlp6Nixo1Rm+vTpKCkpQWxsLAoLC9G7d2/s3bsXnp6eAMp7rs6fP4/z58+jWaWulooZHxYtWoSLFy+iTp06aNeuHbZt24Znn33Wki+HTV258mcABZT/nDQJUKnM0yMlB+wVI1NVXsV67ZoLsrLqIDj4PgIDy//TMAAnci42zxPlyOwlT5TWgQPAwIHVH+/f3/Tzyim/jpzqQvbLEj22RCQfhn5+2zxjOclHaGj5B0JFrq5A69a2qQ+RHOnrsb1yxbb1IiLrYxBFkmbNyr9Ru7qW/+7qCqxf7zhDeUTmcO7cnwGUVlkZcP68bepDRLZj8zlRJC8TJpTPgTp/vrwHigEUkS5tj23FQIo9tkTOiT1RVEWzZuVzoBhAEVXFHlsi0mJPFFkcl4aTo2GPLREBDKLICuS8NFytro+Cgobw9c2HUlls9ecn+9WsGYMnImfHIIqsQhsg2XppeMXerqNHu2LXriEQwgUKhQZDh36Bbt2OVSlHRCRnTCBsO8wTZUH2lifK0q5cAVq0qDohNzvbut/o8/PzkZ19Hz17NoFGo6hQF4EjR66jZcs6vOAQkV1gAmHLYJ4okh25LA1v2LAhior8dAKo8rooUFzsxwsNEdkNuW2r5WwYRJHVyCmZp5zqQkRE9olBFFmNnJaGy6kuRERknzixnKxKTkvD5VQXIiKyPwyiyOrktDRcTnUhIiL7wuE8IiIiIhMwiCIiIiIyAYMoIiIiO8VttWyLc6KIiIjsVOVttarDjOWWwyCKiIjIjlUMkK5cKU9sHBrKRTPWwOE8IiIiB7BxY/nWWgMHlv/cuNHWNXJ8DKKIiIjs3JUrf27uDpT/nDSp/DhZDoMoIiIiOyeXvUmdDYMoIiIiO8f9QG2DQRQREZGd436gtsHVeURERA6A+4FaH4MoIpKt/Px85r8hMgL3A7UuBlFEJEv5+flYu3ZtreXi4uIYSBGRTXBOFBHJUk09UKaUIyIyNwZRRGQX1Or6yMpqCbW6vq2rQkQEgMN5RGQHjh7til27hkAIFygUGgwd+gW6dTtm62oRkZNjTxQRyZpaXV8KoABACBfs2jWEPVJEZHMMoohI1goKGkoBlJYQLigo8LVRjYiIyjGIIiJZ8/XNh0Khu5+FQqGBr2+BjWpERFSOQRQRyZpSWYyhQ7+QAintnCilstjGNSMiZ8eJ5UQkS+7u7tK/u3U7hpCQ8ygo8IWvb4FOAFWxHBGRNTGIIiJZatiwIeLi4pixnIhki8N5RERERCZgTxQRyRK3fSEiuWNPFBHJUuVhPH0Zy7ntCxHZCnuiiEj2mLHc+vLz8zkfjagWDKKISNb0ZSwPCTnPNAcWwqFUIsNwOI+IZI0Zy62PQ6lEhmFPFJmEXf1kLdqM5RUDKWYstx4OpRLpxyCKjMaufrImbcbyyh/kHMqzPA6lEtWMQRQZrbqu/oKChvD1zde5sLKrn8ylpozlZDk1DaXyPSBiEEUPiF391ucsQ6mVt3NRKour/eDmti+Ww6FUopoxiCKTsavf+ioPperrBXSEoVRu+2J7HEolqhmDKDIZu/qtr2JAUVMvoKMMpTJAsj0OpRLpxxQHZDJtV39F7Oq3Dn29gJWXoBOZorqh1ODgi1UCKA6lkrNjTxSZjF39tsNeQLIkDqUSGYZBFD0QdvXbBif8kqUxQCKqHYfzyGjs6rc9bS+gdjiVvYBERNbHnigyGrv65YG9gEREtsUgikzCAEke9OVOIiIiy+NwHpEdMXSIlEOpRESWx54oIjvCoVQiIvlgEEVkZxggERHJA4fziIiIiEzAIIqIiIjIBLIIotatW4eWLVvC09MT4eHh+OGHH2osv337drRr1w6enp7o1KkT9uzZo3O/EAKJiYkICAiAl5cXIiMjce7cOen+7OxsTJgwAcHBwfDy8kJISAjmzZtXZZ7JiRMn0KdPH3h6eiIoKAhLly41X6OJiIjIrtk8iNq2bRsSEhIwb948HD16FJ07d4ZKpcL169erLX/48GGMHj0aEyZMwLFjxxAdHY3o6GicPHlSKrN06VKsXr0aycnJOHLkCLy9vaFSqXDnzh0AwJkzZ6DRaLB+/Xr8+uuveO+995CcnIzZs2dL5ygqKsKTTz6JFi1aIDMzE8uWLcP8+fOxYcMGy74gREREZB+EjfXs2VNMmTJF+r2srEwEBgaKxYsXV1t+xIgRIioqSudYeHi4mDRpkhBCCI1GI/z9/cWyZcuk+wsLC4WHh4f45JNP9NZj6dKlIjg4WPr9/fffFw899JC4e/eudGzGjBmibdu2BrdNrVYLAEKtVhv8GCIiIrItQz+/bdoTVVpaiszMTERGRkrHXFxcEBkZiYyMjGofk5GRoVMeAFQqlVQ+KysLubm5OmWUSiXCw8P1nhMA1Go1fH19dZ6nb9++Ovl2VCoVzp49iz/++KPac9y9exdFRUU6NyIiInJMNg2ibt68ibKyMvj5+ekc9/PzQ25ubrWPyc3NrbG89qcx5zx//jzWrFmDSZMm1fo8FZ+jssWLF0OpVEq3oKCgassRERGR/bP5nChbu3r1KgYNGoTnnnsOEydOfKBzzZo1C2q1WrpdvnzZTLUkIiIiubFpENWoUSO4uroiLy9P53heXh78/f2rfYy/v3+N5bU/DTnntWvXMGDAAPTq1avKhHF9z1PxOSrz8PCAj4+Pzo2IiIgck02DKHd3d4SFhSE9PV06ptFokJ6ejoiIiGofExERoVMeAPbv3y+VDw4Ohr+/v06ZoqIiHDlyROecV69eRf/+/REWFobNmzfDxUX3pYiIiMC3336Le/fu6TxP27Zt8dBDD5neaCIiInIMVprortfWrVuFh4eHSElJEadOnRKxsbGiQYMGIjc3VwghxNixY8XMmTOl8ocOHRJ16tQRy5cvF6dPnxbz5s0Tbm5u4pdffpHKLFmyRDRo0EB8/vnn4sSJE2L48OEiODhY/Pe//xVCCHHlyhXRunVr8fjjj4srV66InJwc6aZVWFgo/Pz8xNixY8XJkyfF1q1bRd26dcX69esNbhtX5xEREdkfQz+/bb533siRI3Hjxg0kJiYiNzcXXbp0wd69e6VJ3JcuXdLpJerVqxdSU1MxZ84czJ49G6GhoUhLS0PHjh2lMtOnT0dJSQliY2NRWFiI3r17Y+/evfD09ARQ3qN0/vx5nD9/Hs2aNdOpjxACQPmKvn379mHKlCkICwtDo0aNkJiYiNjYWEu/JERERGQHFEIbNZDZFRUVQalUQq1Wc34UERGRnTD089vpV+cRERERmYJBFBEREZEJbD4nipxDfn6+tMHztWsuyMqqg+Dg+wgM1AAoX6nZsGFDW1aRiIjIKAyiyOLy8/Oxdu1aAMDRo12xa9cQCOEChUKDoUO/QLduxwAAcXFxDKSIiMhucDiPLE7bA6VW15cCKAAQwgW7dg2BWl1fpxw5hytXgAMHyn8SEdkjBlFkNQUFDVF5z2shXFBQ4KvnEeRo8vPzkZOTgxUrCtGihcDAgUCLFgIrVhQiJycH+fn5tq4iEZHBOJxHVuPrmw+FQqMTSCkUGvj6FtiwVmQt2mFdtbo+kpLiIYQCAKDRKDBtmg+uXt0EpbKYw7pEZDfYE0VWo1QWY+jQL6BQlE8m186JUiqLbVwzsgbtcG1tPZIc1iUie8GeKLKqbt2OISTkPAoKfOHrW8AAygmxR5KIHAV7osjqlMpiBAdfZADlpNgjSUSOgj1RRGR17JEkIkfAIIoszt3d3ahyTMzpHJTKYgZPRGTXGESRxTVs2BBxcXE1ThjWBkbOnpjzyhXg3DkgNBRo1szWtSEiopowiCKrMDTgqS0xZ0jIeSiVxQ61gkvb85aa6oXp05XQaBRwcRFYulSNMWP+y543IiKZYhBFslTTMnhHGgJyptxJxg7rEhHJHYMokiVnWQZvSO4kR+l5M2ZYl4jIHjCIIlnSLoOvPCfKkXqhKnKWoJEBEhE5EgZRJFvOtAze2YJGIiJHwCCKZM2ZlsE7U9BIROQIGEQ5KEvnWmIuJ8twpqCRiMjeMYhyQJbOtWTJ83MFFxER2QsGUQ7I0rmWLHl+ruCyHCbyJCIyLwZRDszSuZYsdX5nCpAs3fPGRJ5ERJbDIMqBWXrZvLMsy7ckS/a8OVMiTyIiW2AQ5cAsvWyey/LNw1IBjDMl8iQisgUGUQ7O0svmuSxf/thjSERkGS61FyF7p1QWIzj4osUCHEufnx6MtsdQoShPP8EeQ3ImV64ABw6U/yQyN/ZEETkB9hiSM+GCCrIWBlEOyNIrvpjLyT4xkSc5Ay6oIGtiEOWALJ1ribmciEiuuKCCrIlBlIOydADDAImI5IwLKsgaOLGcyEFx2JWcGRdUkDWwJ4rIQXHYlZwdF1SQpTGIInJgDJDI2XFBBVkSh/OIiIiITMAgioiIiMgEDKKIiMhhcEEFWRPnRBHVQpv9GACuXXNBVlYdBAffR2Bg+aofTs4mkg8uqCBrYhBFVANt9mMAOHq0K3btGgIhXKTl0t26HQMAZj8mkhH+XyRr4XAeUQ2032bV6vpSAAWUZz7etWsI1Or6OuWIiMh5MIgiMkBNW0gQEZFzYhBFZADtFhIVcQsJIiLnxiCKyADcQoKIiCrjxHIiA3ELCSIiqohBFJERuIUEERFpcTiPiIiIyAQMoohqwOzHRESkD4fziGrA7MdERKQPgyiiWjBAIiKi6nA4j4iIiMgEDKKIiIj+58oV4MCB8p9EtWEQRURETi0/Px85OTlYsaIQLVoIDBwItGghsGJFIXJycpCfn2/rKpJMcU4UkZnl5+dLE9GvXXNBVlYdBAffR2BgebZzTkQnko/8/HysXbsWanV9JCXFQwgFAECjUWDaNB9cvboJSmUx4uLi+P+WqmAQRWRG2gsyABw92hW7dg2BEC7SNjHduh0DAF6QiWRC+4Wnpk3GlcriGlfokvPicB6RGWkvtGp1fSmAAsovxrt2DYFaXV+nHBHJAzcZJ1MwiCKygJq+1RKR/HCTcTIFh/OILED7rbZiIMVvtUTVu3IFOHcOCA0FmjWzXT24yTgZiz1RRBbAb7VENZPrijilshjBwRf5f5UMYvMgat26dWjZsiU8PT0RHh6OH374ocby27dvR7t27eDp6YlOnTphz549OvcLIZCYmIiAgAB4eXkhMjIS586d0ynz9ttvo1evXqhbty4aNGhQ7fMoFIoqt61btz5QWx2J9gKo78YlweXfauPjkxATk4L4+CRpUjmRs9MuwFi27BNMm+YDjUZ3RdyyZZ9g7dq1vI6Q7Nl0OG/btm1ISEhAcnIywsPDkZSUBJVKhbNnz6JJkyZVyh8+fBijR4/G4sWLMWTIEKSmpiI6OhpHjx5Fx44dAQBLly7F6tWrsWXLFgQHB2Pu3LlQqVQ4deoUPD09AZRP6n3uuecQERGBjRs36q3f5s2bMWjQIOl3fQGXs6m4Ag0on0RdUNAQvr75Ot/euAKt/Fstv9ES6eKKOHIUNg2iVq5ciYkTJ2L8+PEAgOTkZOzevRubNm3CzJkzq5RftWoVBg0ahGnTpgEAFi1ahP3792Pt2rVITk6GEAJJSUmYM2cOhg8fDgD48MMP4efnh7S0NIwaNQoAsGDBAgBASkpKjfVr0KAB/P39zdVch1HxwlbTMn5eAImoJnKYO+ju7m7WcuRcbBZElZaWIjMzE7NmzZKOubi4IDIyEhkZGdU+JiMjAwkJCTrHVCoV0tLSAABZWVnIzc1FZGSkdL9SqUR4eDgyMjKkIMpQU6ZMwcsvv4xWrVrhlVdewfjx46FQKPSWv3v3Lu7evSv9XlRUZNTz2Rt9y/hDQs47be8LL8hEhtPOHaz8Rcya14+GDRsiLi6uxi99TJBL+tgsiLp58ybKysrg5+enc9zPzw9nzpyp9jG5ubnVls/NzZXu1x7TV8ZQCxcuxMCBA1G3bl3s27cPf/3rX3Hr1i289tpreh+zePFiqZfLGdTWFe+MeEEmMo4cVsTJ8f9jWVkZ7t27Z+tqOCw3Nze4uro+8HmY4kCPuXPnSv/u2rUrSkpKsGzZshqDqFmzZun0lBUVFSEoKMii9bQlOXTFy5EcL8hEcsa5g38SQiA3NxeFhYW2rorD007ZqWmEqTY2C6IaNWoEV1dX5OXl6RzPy8vTOw/J39+/xvLan3l5eQgICNAp06VLlweqb3h4OBYtWoS7d+/Cw8Oj2jIeHh5673NEcuiKJyKqiVxyUBlKG0A1adIEdevWfaAPeKqeEAK3b9/G9evXAUAnXjCWzYIod3d3hIWFIT09HdHR0QAAjUaD9PR0xMXFVfuYiIgIpKenIz4+Xjq2f/9+REREAACCg4Ph7++P9PR0KWgqKirCkSNHMHny5Aeq7/Hjx/HQQw85VZBkCDl0xRMRVaTdBDw11QvTpyuh0Sjg4iKwdKkaY8b8V7ZD6mVlZVIAJcf6ORIvLy8AwPXr19GkSROTh/ZsOpyXkJCAmJgYdO/eHT179kRSUhJKSkqk1Xrjxo1D06ZNsXjxYgDA1KlT0a9fP6xYsQJRUVHYunUrfvrpJ2zYsAFAeW6n+Ph4vPXWWwgNDZVSHAQGBkqBGgBcunQJBQUFuHTpEsrKynD8+HEAQOvWrVGvXj3s2rULeXl5ePTRR+Hp6Yn9+/fjnXfewRtvvGHV18desCueiIxhyQUY2hQsanV9JCXFQwjdHFRXr26CUlksyxQs2jlQdevWtXFNnIP2db537559BlEjR47EjRs3kJiYiNzcXHTp0gV79+6VJoZfunQJLi5/zrfp1asXUlNTMWfOHMyePRuhoaFIS0uTckQBwPTp01FSUoLY2FgUFhaid+/e2Lt3r5QjCgASExOxZcsW6feuXbsCAA4cOID+/fvDzc0N69atw9/+9jcIIdC6dWspHQNxBRoRPRhLLsBwhBxUHMKzDnO8zgohhDBDXagaRUVFUCqVUKvV8PHxsXV1zErbXa6PXLvLicix5eTkYMOGDRV6onQXvsTHJ0GpLEZsbOwDzYWxhDt37iArKwvBwcE6X/zJMmp6vQ39/ObqPDIJAyQikjMufCFrMHrvvC+++AKJiYk4dOgQAODrr7/G4MGDMWjQIGluEhERka1x/0rrefHFF6V9Zt3c3ODn54cnnngCmzZtgkajMfg8KSkpdrXFmlFB1Pr16/GXv/wFe/bsweDBg/GPf/wD0dHRaNq0KVq2bIn4+HisWrXKUnUlIiIyilJZjODgi07RA2XrjeEHDRqEnJwcZGdn49///jcGDBiAqVOnYsiQIbh//75Fn9tWjBrOW716Nd5//31MnDgRBw4cwODBg7FixQr89a9/BQA8+uijWLp0KaZOnWqRyhIREVFVlTeG18eSqxI9PDykfI1NmzZFt27d8Oijj+Lxxx9HSkoKXn75ZaxcuRKbN2/G77//Dl9fXwwdOhRLly5FvXr18M0330ir87WTvufNm4f58+fjo48+wqpVq3D27Fl4e3tj4MCBSEpKQpMmTSzSFkMZ1ROVlZUFlUoFABgwYADKysrQt29f6f7+/fvj4sWL5q0hERER1cjQ1YbWXpU4cOBAdO7cGTt37gRQvkfu6tWr8euvv2LLli34+uuvMX36dADlK/CTkpLg4+Mj9Z5pUwvdu3cPixYtws8//4y0tDRkZ2fjxRdftGpbqmNUT1TDhg1x8eJFNG/eHNeuXcP9+/dx6dIlKcXAxYsX4evra5GKEhER1YYpWOSnXbt2OHHiBADoJMtu2bIl3nrrLbzyyit4//334e7uDqVSCYVCUWXnkpdeekn6d6tWrbB69Wr06NEDt27dQr169azSjuoYFUQNHz4cEyZMQExMDP71r39h3LhxeP311+Hi4gKFQoFp06bhySeftFRdiYiIasRNwOVHCCENz3311VdYvHgxzpw5g6KiIty/fx937tzB7du3a0wympmZifnz5+Pnn3/GH3/8IU1Wv3TpEtq3b2+VdlTHqCDq3XffRWlpKbZu3YpevXphzZo1WL16NYYPH4579+6hX79+UnZxIiIiW2CAJC+nT59GcHAwsrOzMWTIEEyePBlvv/02fH19cfDgQUyYMAGlpaV6g6iSkhKoVCqoVCp8/PHHaNy4MS5dugSVSmXzpKlGBVHe3t5V0hi88cYbiIuLw71791C/fn2zVo6IiIjs19dff41ffvkFf/vb35CZmQmNRoMVK1ZIu5F8+umnOuXd3d1RVlamc+zMmTPIz8/HkiVLEBQUBAD46aefrNOAWpgl2aanpyezq1KNmOGciMix3b17F7m5uSgrK0NeXh727t2LxYsXY8iQIRg3bhxOnjyJe/fuYc2aNRg6dCgOHTqE5ORknXO0bNkSt27dQnp6Ojp37oy6deuiefPmcHd3x5o1a/DKK6/g5MmTWLRokY1aqcuoICohIcGgcitXrjSpMuSY5LD0loiILGvv3r0ICAhAnTp18NBDD6Fz585YvXo1YmJi4OLigs6dO2PlypV49913MWvWLPTt2xeLFy/GuHHjpHP06tULr7zyCkaOHIn8/HwpxUFKSgpmz56N1atXo1u3bli+fDmGDRtmw9aWM2rvvAEDBuj8fvDgQYSFhcHLy+vPEyoU+Prrr81XQzvmyHvnGUO7l1Vt5LiXlRyxV4/IMT3I3nn8smo8q++dd+DAAZ3f69evj9TUVLRq1cqY05CTU6vro6CgIXx9850ii7A5Vb5Q6nsteaEkci5clWgb3ICYrOro0a5VNgTlflaGq3iBrOm1tPWKFUdVsRfw2jUXZGXVQXDwfQQGli+35ocU2RL/9qyPQRRZjVpdX/rQBwAhXLBr1xCEhJxnj5SR+FpaX8VewJoCWPYCEjkPo7Z9IXoQBQUNpQ99LSFcUFDALPfG4mtpfdoeKH0BrFpdX6ccETk+o3qitGnbtYQQOHPmDG7duqVz/JFHHnnwmpHD8fXNh0Kh0fnwVyg08PUtsGGt7BNfS9upKYBlLyCRczEqiOrSpQsUCgUqLugbMmQIAEjHFQpFlURZRACgVBZj6NAvqgyD8IPHeHwtbYcBLBFpGRVEZWVlWaoe5MAqbvTZrdsxhIScR0GBL3x9C3Q+9LkhqHFqei3JchjAEpGWUUFUixYtLFUPcmBcems5SmUxP7xtgAEsEQEmrM4TQiA7OxtBQUGoU6cOSktL8dlnn+Hu3bsYPHgwGjVqZIl6kp1jgGQehvbWsVfP8hjAEpFRQdTZs2ehUqlw+fJltGrVCvv27cNzzz2HM2fOQAiBunXr4vDhwwgNDbVUfYmcGnv1iMgZffPNNxgwYAD++OMPNGjQwKDHtGzZEvHx8YiPj7dYvYxKcTBjxgx07twZx48fx5AhQxAVFYVmzZrhjz/+QEFBASIiIrBw4UJL1ZWIUB5IBQQE6L0xgLIM9gIS6ffiiy9CoVDglVdeqXLflClToFAo8OKLL1q/YhZmVE/U4cOHsW/fPnTq1AlvvfUWVq1ahQ0bNsDNzQ0AMHPmTIwePdoiFSUisiX2AhLVLCgoCFu3bsV7770n7al7584dpKamonnz5jaunWUY1RN169Yt+PqWJ/Pz9vaGt7e3zoaxQUFByMvLM28NiYhkgr2ARPp169YNQUFB2Llzp3Rs586daN68Obp27Sodu3v3Ll577TU0adIEnp6e6N27N3788Uedc+3Zswdt2rSBl5cXBgwYgOzs7CrPd/DgQfTp0wdeXl4ICgrCa6+9hpKSEou1rzpGBVGBgYG4dOmS9PvSpUvRpEkT6fcbN27goYceMl/tiIiIyGhXrgAHDpT/tKaXXnoJmzdvln7ftGkTxo8fr1Nm+vTp2LFjB7Zs2YKjR4+idevWUKlUKCgoz7V2+fJlPP300xg6dCiOHz+Ol19+GTNnztQ5x4ULFzBo0CA888wzOHHiBLZt24aDBw8iLi7O8o2swKggKjIyEmfOnJF+nzx5MurXry/9vm/fPnTr1s18tSMiIiKjbNwItGgBDBxY/nPjRus99wsvvICDBw/i4sWLuHjxIg4dOoQXXnhBur+kpAQffPABli1bhqeeegrt27fH3//+d3h5eWHj/yr6wQcfICQkBCtWrEDbtm3x/PPPV5lPtXjxYjz//POIj49HaGgoevXqhdWrV+PDDz/EnTt3rNZeo+ZEJScn13j/qFGjEBMT80AVIiIikrP8/HzZzo27cgWIjQU0mvLfNRpg0iRApQKaNbP88zdu3BhRUVFISUmBEAJRUVE6qY8uXLiAe/fu4bHHHpOOubm5oWfPnjh9+jQA4PTp0wgPD9c5b0REhM7vP//8M06cOIGPP/5YOiaEgEajQVZWFh5++GFLNK8Ko4Kor7/+GnFxcfj+++/h4+Ojc59arUZUVBSSk5N15kkRERE5ivz8fKxdu1b6Xa2uj4KChvD1zdfJGxYXF2eTQOrcuT8DKK2yMuD8eesEUUD5kJ52WG3dunUWeY5bt25h0qRJeO2116rcZ81J7EYFUUlJSZg4cWKVAAoAlEolJk2ahJUrV6JPnz5mqyAREZFcVOyBOnq0a5Xtf7p1O1alnDWFhgIuLrqBlKsr0Lq19eowaNAglJaWQqFQQKVS6dwXEhICd3d3HDp0SNoF5d69e/jxxx+lfE4PP/ww/vWvf+k87vvvv9f5vVu3bjh16hRaW7Nh1TBqTtTPP/+MQYMG6b3/ySefRGZm5gNXiojIFPn5+cjJydF7y8/Pt3UVyUGo1fWlAAoAhHDBrl1DoFbXr+WRltWsGbBhQ3ngBJT/XL/eer1Q5c/pitOnT+PUqVNw1Vbkf7y9vTF58mRMmzYNe/fuxalTpzBx4kTcvn0bEyZMAAC88sorOHfuHKZNm4azZ88iNTUVKSkpOueZMWMGDh8+jLi4OBw/fhznzp3D559/bvWJ5Ub1ROXl5Uk5oao9WZ06uHHjxgNXiojIWHIfZiHHUlDQUAqgtIRwQUGBr823A5owoXwO1Pnz5T1Q1gygtKobsdJasmQJNBoNxo4di+LiYnTv3h1ffvmltLq/efPm2LFjB/72t79hzZo16NmzJ9555x289NJL0jkeeeQR/Oc//8Gbb76JPn36QAiBkJAQjBw50uJtq8ioIKpp06Y4efKk3u6zEydOcD4UEdmE3IdZyLH4+uZDodDoBFIKhQa+vgU2rNWfmjWzbvBUuaeosrS0NOnfnp6eWL16NVavXq23/JAhQzBkyBCdY5VTJfTo0QP79u3Te47qckuZm1HDeYMHD8bcuXOrXT743//+F/PmzavSaCIia5LrMAs5FqWyGEOHfgGFonzykTZYt3UvFFmXUT1Rc+bMwc6dO9GmTRvExcWhbdu2AIAzZ85g3bp1KCsrw5tvvmmRihIRGULOwyxk/woLC6V/d+t2DE2a5OLy5eYICrqEZs1ydMpxZMbxGRVE+fn54fDhw5g8eTJmzZoFIQQASDPw161bBz8/P4tUlIjIEHIfZiH7dv/+fenfNQ0bVyxHjsuoIAoAWrRogT179uCPP/7A+fPnIYRAaGgot3shIlnQDrNU/nBjLxSZg3Zxlb5h45CQ81Aqi2tchEWOw+ggSuuhhx5Cjx49zFkXIiKz6NbtGEJCzqOgwBe+vgUMoMhslEolgNqHjbXlTKEd5SHLMsfrbHIQRUQkZ+UfZAyeyDIsMWys7b26ffs2vLy8HriOVLPbt28DwAP1GjKIIiKH4O7ubtZyRDWxxLCxq6srGjRogOvXrwMA6tatC4VCYa4q0/8IIXD79m1cv34dDRo0qJIQ1BgMoojIITRs2BBxcXGy3RiWHI8lho39/f0BQAqkyHIaNGggvd6mYhBFRA6DARJZm7mHjRUKBQICAtCkSRPcu3fPbOclXW5ubg/UA6XFIIqIiKrIz89nr141rDVs7OrqapYPebIsBlFERKSD+xDqx2FjqohBFBER6eA+hDVjgERaRu2dR0REzoP7EBLVjEEUERFVq6aEkkTEIIqIiPTQJpSsiPsQEv2JQRQREVVLm1BSG0hxH0IiXZxYTkREenEfQiL9GEQR2Rnm7yFr4z6EZCn2fj1jEEVkRyrn79HHGfP3kPlwH0KyBke4njGIIrIjhublcdb8PWQeTChJ1uAI1zMGUUR2TF8maaIHZekAyd6Hccj87PF6xiCKyE7VlEmaSM64rQxVZq/XMwZRRHZIXybpkJDzVv0Gx94EMgW3laGK5HI9MwWDKCI7VFMmaWtddBxhUijZlj1/eNKDKywsBFD79aywsBABAQE2qGHtbJ5sc926dWjZsiU8PT0RHh6OH374ocby27dvR7t27eDp6YlOnTphz549OvcLIZCYmIiAgAB4eXkhMjIS586d0ynz9ttvo1evXqhbty4aNGhQ7fNcunQJUVFRqFu3Lpo0aYJp06bh/v37D9RWInORQyZpR5gUSrbFbWWcm/YztbbrmZw/e20aRG3btg0JCQmYN28ejh49is6dO0OlUuH69evVlj98+DBGjx6NCRMm4NixY4iOjkZ0dDROnjwplVm6dClWr16N5ORkHDlyBN7e3lCpVLhz545UprS0FM899xwmT55c7fOUlZUhKioKpaWlOHz4MLZs2YKUlBQkJiaa9wUgMpEcM0mr1fWRldWSm9OSweTwZYBsT47XM0PZdDhv5cqVmDhxIsaPHw8ASE5Oxu7du7Fp0ybMnDmzSvlVq1Zh0KBBmDZtGgBg0aJF2L9/P9auXYvk5GQIIZCUlIQ5c+Zg+PDhAIAPP/wQfn5+SEtLw6hRowAACxYsAACkpKRUW699+/bh1KlT+Oqrr+Dn54cuXbpg0aJFmDFjBubPn8/cKGQzFf/2asokbe2/UXudFEq2pf3wrPy3Yw8fnvTg3NzcdH4XQvenvnJyYrMgqrS0FJmZmZg1a5Z0zMXFBZGRkcjIyKj2MRkZGUhISNA5plKpkJaWBgDIyspCbm4uIiMjpfuVSiXCw8ORkZEhBVG1ycjIQKdOneDn56fzPJMnT8avv/6Krl27GtpMIrOSY/4ezmuhB8FtZZyXUqkE8Oc15M/BMd1riLacHNksiLp58ybKysp0AhUA8PPzw5kzZ6p9TG5ubrXlc3Nzpfu1x/SVMYS+56n4HNW5e/cu7t69K/1eVFRk8HMSGUpuk7TlMMmd7Bu3lXFu9nwNsfnEckeyePFiKJVK6RYUFGTrKhFZHOe1kLG4rQxVZM/XEJv1RDVq1Aiurq7Iy8vTOZ6Xlwd/f/9qH+Pv719jee3PvLw8neWQeXl56NKli8F18/f3r7JKUPu8+uoGALNmzdIZbiwqKmIgRQ6P81rIWMYOSzMfmWOz52uIzYIod3d3hIWFIT09HdHR0QAAjUaD9PR0xMXFVfuYiIgIpKenIz4+Xjq2f/9+REREAACCg4Ph7++P9PR0KWgqKirCkSNH9K7E0/c8b7/9Nq5fv44mTZpIz+Pj44P27dvrfZyHhwc8PDwMfh4ieybXSe5kHwwNepjd3HE5wjXEpqvzEhISEBMTg+7du6Nnz55ISkpCSUmJtFpv3LhxaNq0KRYvXgwAmDp1Kvr164cVK1YgKioKW7duxU8//YQNGzYAABQKBeLj4/HWW28hNDQUwcHBmDt3LgIDA6VADSjPAVVQUIBLly6hrKwMx48fBwC0bt0a9erVw5NPPon27dtj7NixWLp0KXJzczFnzhxMmTKFQRLR/8hxkjs5HmY3d1yOcA2xaRA1cuRI3LhxA4mJicjNzUWXLl2wd+9eaRL3pUuX4OLy57StXr16ITU1FXPmzMHs2bMRGhqKtLQ0dOzYUSozffp0lJSUIDY2FoWFhejduzf27t0LT09PqUxiYiK2bNki/a5dbXfgwAH0798frq6u+OKLLzB58mRERETA29sbMTExWLhwoaVfEiK7IueLGzkWrgJ1TPZ+DVEIUTkjA5lLUVERlEol1Go1fHx8bF0dIiK7k5OTgw0bNiArqyW2bImpcn9MTAqCgy8iNjZWtluDkP0x9PObq/OIiEj27HkFFzkubkBMRESyZ88ruJyJs62kZBBFRER2gdnN5a3ySkp9HGklJYMoAuB83x6IyD4xu7l8Vf4M0ZeOwpFWUjKIIqf89kBE9oHZze2Ts2xKziCKDP5W4EjfHojIPjhCLiFn40zpKBhEERGRrDFAsi/2vKGwsZjigIiIiMzGmdJRsCeKiIjICFyIUzNnSkfBIIqIiMhAXIhjGGdJR8EgiqrQtyyViMjZOeMyfkNVXiGpLx2FI62kZBBFOpxlWSoR0YPi9VKXM66kZBBF0reC2palWuvbA+cbEJHcOdMyfmM427WZQRRJ3x4OHADee6/qstTHHotB//7W+c/B+QZEZA+caRk/6ccgigCUB0iPPgq4uACaCitTXV2B8PCGsFa8wsSfRGQPtMv4KwZSjrqMn/RjniiSNGsGbNhQHjgB5T/Xry8/TkREf9Iu49fmQ3LkZfykH3uiSMeECYBKBZw/D7RuzQCKiEgfZ1nGT/oxiKIqmjVj8EREVB1nXMZP+jGIIlljzioikhNnXMZP+jGIItliDhYikiMGSKTFieUkS/pysKjV9W1cMyIionLsiSJZ0c4jqC0Hi5znGzBZKBGRc1AIIYStK+GoioqKoFQqoVar4ePjY+vq2I38/HxkZ99Hz55NoNEopOOurgJHjlxHy5Z1ZBuEMFkoEZH9M/Tzm8N5JDsNGzZEWJgfNmxQVMpZpUBYmJ+sgw8mCyUich4cziPZYs4qIiKSMwZRJGv2nrOKKRqIiMxHbnNOGUQRWQhTNBARmY8c55xyThSRBTBFAxGReclxzimDKCILqClFAxEROQYGUUQW4OubL+3urqVQaODrW2CjGhERORa1uj6yslratIefc6KIzEibBFSpLMbQoV9UmROlnVwu52ShRERyJ5c5pwyiiMyo8uakiYk3kJ1dBy1b3kdgYA8APZixnIjoAeibcxoSct7qq6AZRBGZWcUAKSAACAuzYWWIiBxMbduCWRPnRBEREZHdkNOcU/ZEERHZIbklHSSyNDnOOeUGxBbEDYiJyBLkmHSQyBoqfnm4ds2lwpzT8p4pc315MPTzmz1RRER2pnIPlL7thbjRNTkauc05ZRBFRGTH5LLUm8gZcWI5EZGd4vZCRLbFnigiIjslp6XeZDtcZGA7DKKIiOyUdql3xUCK2ws5Fy4ysC0O5xER2SntUm9tzpzKS73J8VW3yKC6/eS4yMAy2BNFZGPsiqcH0a3bMYSEnEdBgS98fQsYQDkxSywy4PWpZgyiiGyIXfFkisrJBJXK4mqDJ2507TwssZ8cr0+1YxBlJ/htwDEZ2sXOrniqqPJG19XhNcG5WGKRAa9PtWMQZQf4bYCIKuP/daqIiwxsgxPL7QC/DTgPfZNCiYhqYo1FBrw+VcWeKCKZYOZpInoQllxkwOtT9dgTRSQDzDxNRKaobpFBcPDFKgHUgywy4PVJP/ZEEcmApTJPy21BgtzqQ2TvrLHIgJnx9WMQRSQDlpgUKrcFCXKrD5GjsPT/F05a149BFJENabvYtZNCK8850H7LM6UrXm4LEuRWHyIt9pBWz5LXJ0fBIMoOqdX1UVDQEL6++U7flWrvKnfFJybeQHZ2HbRseR+BgT0A9DDbBVxufzdyqw85J/aQ6mfN65O9YhBlBypG+TWtkHDmbwP2rOIFKCAACAsz/3PIbWWN3OpDzqu6veeqC+6dtYfUGtcne8Ygyg5ovw1kZ9/HwoVNIIQCQPnEvt27hyIxMRwtW9Zx6m8DpJ8ltoNwpPoQaTG4J2MxxYGdaNiwIYqK/KDRKHSOl5UpUFzsxwCK9KppZQ3rQ1SOy/jJFAyi7EhoKOBS6R1zdQVat7ZNfcg+aFfWVGTLlTVyqw8RwOCeTMMgyo40awZs2FAeOAHlP9evLz9eWX5+PnJycvTe8vPzrVt5shlrbAdhz/UhAhjck2lkMSdq3bp1WLZsGXJzc9G5c2esWbMGPXv21Ft++/btmDt3LrKzsxEaGop3330XgwcPlu4XQmDevHn4+9//jsLCQjz22GP44IMPEBoaKpUpKCjAq6++il27dsHFxQXPPPMMVq1ahXr16gEAsrOzERwcXOW5MzIy8Oijj5qx9caZMAFQqYDz58t7oPQFUFxtQhUXGtS0HYS1FiTIrT5EFdW2jJ+oOjYPorZt24aEhAQkJycjPDwcSUlJUKlUOHv2LJo0aVKl/OHDhzF69GgsXrwYQ4YMQWpqKqKjo3H06FF07NgRALB06VKsXr0aW7ZsQXBwMObOnQuVSoVTp07B09MTAPD8888jJycH+/fvx7179zB+/HjExsYiNTVV5/m++uordOjQQfpdDkFHs2bVB09azMdDgHUyGdtzfeSGuYpsz5J7z5FjUgghhC0rEB4ejh49ekg9JxqNBkFBQXj11Vcxc+bMKuVHjhyJkpISfPHFF9KxRx99FF26dEFycjKEEAgMDMTrr7+ON954AwCgVqvh5+eHlJQUjBo1CqdPn0b79u3x448/onv37gCAvXv3YvDgwbhy5QoCAwOlnqhjx46hS5cuJrWtqKgISqUSarUaPj4+Jp3DFDk5OdiwYUOt5WJjYxEQEGCFGhFRTSr3HutbZs/eY/Njzz1Vx9DPb5v2RJWWliIzMxOzZs2Sjrm4uCAyMhIZGRnVPiYjIwMJCQk6x1QqFdLS0gAAWVlZyM3NRWRkpHS/UqlEeHg4MjIyMGrUKGRkZKBBgwZSAAUAkZGRcHFxwZEjR/CXv/xFOj5s2DDcuXMHbdq0wfTp0zFs2DC97bl79y7u3r0r/V5UVGTYC0FETq1iD1RNy+zZe2x+7CGlB2HTIOrmzZsoKyuDn5+fznE/Pz+cOXOm2sfk5uZWWz43N1e6X3uspjKVhwrr1KkDX19fqUy9evWwYsUKPPbYY3BxccGOHTsQHR2NtLQ0vYHU4sWLsWDBAkOaTkRUBXNo2QYDJDKVzedEyVWjRo10erx69OiBa9euYdmyZXqDqFmzZuk8pqioCEFBQRava224vQaRfahpmT3/7xLJj02DqEaNGsHV1RV5eXk6x/Py8uDv71/tY/z9/Wssr/2Zl5enM98nLy9Pmtvk7++P69ev65zj/v37KCgo0Pu8QPn8rf379+u938PDAx4eHnrvtwVm4CWyH9pl9hUDKS6zJ5Ivm+aJcnd3R1hYGNLT06VjGo0G6enpiIiIqPYxEREROuUBYP/+/VL54OBg+Pv765QpKirCkSNHpDIREREoLCxEZmamVObrr7+GRqNBeHi43voeP37criZiMwMvkX1hDi0i+2Lz4byEhATExMSge/fu6NmzJ5KSklBSUoLx48cDAMaNG4emTZti8eLFAICpU6eiX79+WLFiBaKiorB161b89NNP0mo0hUKB+Ph4vPXWWwgNDZVSHAQGBiI6OhoA8PDDD2PQoEGYOHEikpOTce/ePcTFxWHUqFEIDAwEAGzZsgXu7u7o2rUrAGDnzp3YtGkT/u///s/Kr5DxtHl2ahsaYD4eeeDSdqqIy+yJ7IfNg6iRI0fixo0bSExMRG5uLrp06YK9e/dKE8MvXboElwp7nfTq1QupqamYM2cOZs+ejdDQUKSlpUk5ogBg+vTpKCkpQWxsLAoLC9G7d2/s3btXyhEFAB9//DHi4uLw+OOPS8k2V69erVO3RYsW4eLFi6hTpw7atWuHbdu24dlnn7XwK/LgKm5Y/NFHQme/PVdXgVdffYobFssEl1ebl6MEpEplMYMnIjtg8zxRjsxWeaIq2rgRmDQJKCv7c5uYCRNsUhWqBnN6mY8951piME0kL3aRJ4osz5BtYkg+uJLSdPaca4m5iojsE4MoJ1DbNjEkD1xJaR72mmuJARKR/bHp6jwiKseVlOZT04IKIiJzYk8UkQzIJcmipSdmW2PiN3MtEZG1MIgikgE5fPBbemK2tSZ+a3MtVR4alfNQHtmWo6zqJOtjEEUkA3L44Lf0xGxrTvxmriUyFFdG0oNgEEVkQxUTntb0wW/NxKiWnphtrYnfzLVEhqgctOvrIZXjqk6yPQZRRDYkx6Xtlp6fZanzGxpoMlM/6cMVstZn70OpDKKIbExuFwhLz8+y1PnlGJCS/bDX1Bj2zBGGUhlEEZEOS8/PsuT55XqhJfmTywpZZ2LoEKmch1IZRBGZmb13TwOWn5jNid8kN3JYIUv2h0EUkRk5Qve0lqUnZnPiN8mJHFbIajnCFzFnwSCKyIxMWekjlwumpSdmc+I3yZ0cekgd6YuYsexx71AGUUQWYshKHzldMC09MdvZJn7LJTimmlUO2vX1kForuHeEeUKmsNeVkQyiiCzA0JU+cstRY41AzRlYKzs7PTi5B/f22DtjLHteGckgisgCTFnpY6/fxKgqa2Znpwcn10DWWa4J9rwy0qX2IkRkLO1Kn4pqWumj75uYWl3f4nUly+H7SqZyhr8d7RBpbddLOc+TZE8UkQUYu9LHnr+JkX58X8lUzvC3U3EotWnTIsyYoURZmQKurgLvvluEMWNGy37uIIMoB8VJrbZnzEof5qhxTHxfyVT2+rdj7GeP9t+vvw6MHAmcPw+0bq1As2YNADSwbGXNgEGUA5LTii9nY+pKHznlqCHz4ftKprLHv50H/exp1qz8Zk8YRDkgZ10iKwcPstJHDjlqyPz4vpIxKn7BqulvR47zhJzxs4dBFJGZGdO7J7ccNWQZzM5OhpJ7ygXSxSCKyIZ4wXRMzM5OD4L/3+0Hgygn4AzJ2izJ0pP0ecF0PAyOiZzjs4dBlINzlmRtlmLvk/QrBoDXrrkgK6sOgoPvIzCwPCcLP8gth68rOTNn+exhEOXA7DmVvlzIbVsWY1QMAGu6oMk1ACQi++RMnz0MohyYMyRrsyZ7+2alDexqu6BZMwBkz5jjsuSwN/Pe2Rdn+uxhEOWAKqfS15esjZNaDWfP36zkckFjz5jjsuSGy9zM2X4442cPgygH5Aip9OWisLAQQO2BSGFhIQICAmxQw9rJJfOxHHvGyDwsueEyN3O2H8742cMgykHZeyp9ubh//z6A2gMRbTk5klvmY7n0jJH5WbLH1p57g52Js332MIhyAvaYSl9ulMpiPPLICfz8c2cACgACjzxywm4u3nLKmi2XnjEyP0sGyAy+7Y8zfPYwiCIygFpdHydOPILyAAoAFDhx4hEMHPi13VzA5ZI1W249Y2Q+lgyQ7Tn45sR4x8UgiqgGbm5uAGr/FqwtR4aRU88YmY8lA2R7Db7tPdcc1YxBFFENlEolgNq/BWvLyYnctx6RS8+Yqa5cAc6dA0JDHX/IwhiWDJDtMfi251xzVDsGUUQGsMdvwdx6xPy0wzKpqV6YPl0JjUYBFxeBpUvVGDPmv3w9/8eSAbI9B9/2lmuOascgishA9vgtWE4f6HLvGauNdlhGra6PpKR4CFE+P06jUWDaNB9cvboJSmWxXQzLmLsXzZLvrb3/3WjZ6+pCzueqGYMoohpUvjDr+xYs9wu4HNh7z5i23rXNj5PrsIwle9Es+d7a+9+NljGrC+USuHA+V+0YRBHVwFEu4HIh59fJ0N4Ze1wlZo1eNEu+t3L+uzGUoX83cgpcDP1CINcvDtbAIIqoFo5wAafqmdI7Y8r8OFtPQrf3XjRHYOjfjZwnouurizNjEEVETulBemcMmR9nzUnojtyL5kiMnVcpp4nocqqLnDCIIiKn9KC9MzWtErPG8Jm1etHowZg6r1JOE9HlVBe5zBfTYhBFRE7NEr0zlh4+s3QvGpmPqfMq5bTNjVzqIqf5YloMoojIqVmyd8ZSw2eW7EUj8zPlA11OQ69yqYscJ7oziCIip2dI74wx+Yq0F3FLD5/J5cONzE9OQ69yqovcMIgiIkLtvTPGDMvk5ORIxyw5fMYPN8dm66HXil8caqqLM+fJYxBFRGQgU+dZWHL4zNy9aGRbckrwK/c8eXJIucAgiojIzpmzF41sS27vlVz/JuSScoFBFBE5JWfrnZHrhyFVxfeqZnJKucAgioickiW/8TtbgEZkTXJJuQAwiCIiJ2apb/yWHpJhkEbOSPv3XNuqVGv+3SuEEMJqz+ZkioqKoFQqoVar4ePjY+vqEJEDkVvmZiJrqJipf8YMJcrKFHB1FXj3XfNup2To5zeDKAtiEEVERGQZV64A588DrVubf2NvQz+/OZxHREREdqdZM/MHT8Zyqb0IEREREVXGIIqIiIjIBAyiiIiIiEzAIIqIiIjIBAyiiIiIiEwgiyBq3bp1aNmyJTw9PREeHo4ffvihxvLbt29Hu3bt4OnpiU6dOmHPnj069wshkJiYiICAAHh5eSEyMhLnzp3TKVNQUIDnn38ePj4+aNCgASZMmIBbt27plDlx4gT69OkDT09PBAUFYenSpeZpMBEREdk9mwdR27ZtQ0JCAubNm4ejR4+ic+fOUKlUuH79erXlDx8+jNGjR2PChAk4duwYoqOjER0djZMnT0plli5ditWrVyM5ORlHjhyBt7c3VCoV7ty5I5V5/vnn8euvv2L//v344osv8O233yI2Nla6v6ioCE8++SRatGiBzMxMLFu2DPPnz8eGDRss92IQERGR/RA21rNnTzFlyhTp97KyMhEYGCgWL15cbfkRI0aIqKgonWPh4eFi0qRJQgghNBqN8Pf3F8uWLZPuLywsFB4eHuKTTz4RQghx6tQpAUD8+OOPUpl///vfQqFQiKtXrwohhHj//ffFQw89JO7evSuVmTFjhmjbtq3BbVOr1QKAUKvVBj+GiIiIbMvQz2+b9kSVlpYiMzMTkZGR0jEXFxdERkYiIyOj2sdkZGTolAcAlUollc/KykJubq5OGaVSifDwcKlMRkYGGjRogO7du0tlIiMj4eLigiNHjkhl+vbtq7MHj0qlwtmzZ/HHH39UW7e7d++iqKhI50ZERESOyaZB1M2bN1FWVgY/Pz+d435+fsjNza32Mbm5uTWW1/6srUyTJk107q9Tpw58fX11ylR3jorPUdnixYuhVCqlW1BQUPUNJyIiIrtn8zlRjmTWrFlQq9XS7fLly7auEhEREVmITffOa9SoEVxdXZGXl6dzPC8vD/7+/tU+xt/fv8by2p95eXkICAjQKdOlSxepTOWJ6/fv30dBQYHOeap7norPUZmHhwc8PDyk38X/9nbmsB4REZH90H5uaz/H9bLKDK0a9OzZU8TFxUm/l5WViaZNm9Y4sXzIkCE6xyIiIqpMLF++fLl0v1qtrnZi+U8//SSV+fLLL6udWF5aWiqVmTVrllETyy9fviwA8MYbb7zxxhtvdni7fPlyjZ/zCiFqC7Msa9u2bYiJicH69evRs2dPJCUl4dNPP8WZM2fg5+eHcePGoWnTpli8eDGA8hQH/fr1w5IlSxAVFYWtW7finXfewdGjR9GxY0cAwLvvvoslS5Zgy5YtCA4Oxty5c3HixAmcOnUKnp6eAICnnnoKeXl5SE5Oxr179zB+/Hh0794dqampAAC1Wo22bdviySefxIwZM3Dy5Em89NJLeO+993RSIdREo9Hg2rVrqF+/PhQKhdles6KiIgQFBeHy5cvw8fEx23nlwtHbBzh+Gx29fYDjt5Hts3+O3kZLtk8IgeLiYgQGBsLFpYaZTwZ3q1jQmjVrRPPmzYW7u7vo2bOn+P7776X7+vXrJ2JiYnTKf/rpp6JNmzbC3d1ddOjQQezevVvnfo1GI+bOnSv8/PyEh4eHePzxx8XZs2d1yuTn54vRo0eLevXqCR8fHzF+/HhRXFysU+bnn38WvXv3Fh4eHqJp06ZiyZIl5m24iRw9dYKjt08Ix2+jo7dPCMdvI9tn/xy9jXJon817osh4RUVFUCqVUKvVDvvtwpHbBzh+Gx29fYDjt5Hts3+O3kY5tI+r84iIiIhMwCDKDnl4eGDevHk6KwEdiaO3D3D8Njp6+wDHbyPbZ/8cvY1yaB+H84iIiIhMwJ4oIiIiIhMwiCIiIiIyAYMoIiIiIhMwiCIiIiIyAYMomVi3bh1atmwJT09PhIeH44cffqixfFJSEtq2bQsvLy8EBQXhb3/7G+7cufNA57Qkc7dv/vz5UCgUOrd27dpZuhl6GdO+e/fuYeHChQgJCYGnpyc6d+6MvXv3PtA5rcHcbZTTe/jtt99i6NChCAwMhEKhQFpaWq2P+eabb9CtWzd4eHigdevWSElJqVJGLu+hJdpnz+9fTk4OxowZgzZt2sDFxQXx8fHVltu+fTvatWsHT09PdOrUCXv27DF/5Q1kiTampKRUeQ+1u3pYm7Ht27lzJ5544gk0btwYPj4+iIiIwJdfflmlnKX/DzKIkoFt27YhISEB8+bNw9GjR9G5c2eoVKoqmyRrpaamYubMmZg3bx5Onz6NjRs3Ytu2bZg9e7bJ57QkS7QPADp06ICcnBzpdvDgQWs0pwpj2zdnzhysX78ea9aswalTp/DKK6/gL3/5C44dO2byOS3NEm0E5PMelpSUoHPnzli3bp1B5bOyshAVFYUBAwbg+PHjiI+Px8svv6xzEZfTe2iJ9gH2+/7dvXsXjRs3xpw5c9C5c+dqyxw+fBijR4/GhAkTcOzYMURHRyM6OhonT540Z9UNZok2AoCPj4/Oe3jx4kVzVdkoxrbv22+/xRNPPIE9e/YgMzMTAwYMwNChQ61/HbVZrnSS9OzZU0yZMkX6vaysTAQGBurdhHnKlCli4MCBOscSEhLEY489ZvI5LckS7Zs3b57o3LmzReprLGPbFxAQINauXatz7OmnnxbPP/+8yee0NEu0UU7vYUUAxGeffVZjmenTp4sOHTroHBs5cqRQqVTS73J7D7XM1T57fv8q6tevn5g6dWqV4yNGjBBRUVE6x8LDw6XN7m3JXG3cvHmzUCqVZquXuRjbPq327duLBQsWSL9b4/8ge6JsrLS0FJmZmYiMjJSOubi4IDIyEhkZGdU+plevXsjMzJS6JX///Xfs2bMHgwcPNvmclmKJ9mmdO3cOgYGBaNWqFZ5//nlcunTJcg3Rw5T23b17t0qXuZeXl/QtXk7vn6n1qa2NWnJ4D02RkZGh83oAgEqlkl4Pub2HxqqtfVr2+v4ZwtDXwN7dunULLVq0QFBQEIYPH45ff/3V1lUyiUajQXFxMXx9fQFY7/8ggygbu3nzJsrKyuDn56dz3M/PD7m5udU+ZsyYMVi4cCF69+4NNzc3hISEoH///tJwlynntBRLtA8AwsPDkZKSgr179+KDDz5AVlYW+vTpg+LiYou2pzJT2qdSqbBy5UqcO3cOGo0G+/fvx86dO5GTk2PyOS3JEm0E5PMemiI3N7fa16OoqAj//e9/ZfceGqu29gH2/f4ZQt9rYA/vn6Hatm2LTZs24fPPP8c//vEPaDQa9OrVC1euXLF11Yy2fPly3Lp1CyNGjABgvesogyg79M033+Cdd97B+++/j6NHj2Lnzp3YvXs3Fi1aZOuqmYUh7Xvqqafw3HPP4ZFHHoFKpcKePXtQWFiITz/91IY1N8yqVasQGhqKdu3awd3dHXFxcRg/fjxcXBznv6MhbbTn95D4/jmCiIgIjBs3Dl26dEG/fv2wc+dONG7cGOvXr7d11YySmpqKBQsW4NNPP0WTJk2s+tx1rPpsVEWjRo3g6uqKvLw8neN5eXnw9/ev9jFz587F2LFj8fLLLwMAOnXqhJKSEsTGxuLNN9806ZyWYon2VRdsNGjQAG3atMH58+fN34gamNK+xo0bIy0tDXfu3EF+fj4CAwMxc+ZMtGrVyuRzWpIl2lgdW72HpvD396/29fDx8YGXlxdcXV1l9R4aq7b2Vcee3j9D6HsN7OH9M5Wbmxu6du1qV+/h1q1b8fLLL2P79u06Q3fWuo46zldfO+Xu7o6wsDCkp6dLxzQaDdLT0xEREVHtY27fvl0lkHB1dQUACCFMOqelWKJ91bl16xYuXLiAgIAAM9XcMA/yWnt6eqJp06a4f/8+duzYgeHDhz/wOS3BEm2sjq3eQ1NERETovB4AsH//fun1kNt7aKza2lcde3r/DGHKa2DvysrK8Msvv9jNe/jJJ59g/Pjx+OSTTxAVFaVzn9X+D5ptijqZbOvWrcLDw0OkpKSIU6dOidjYWNGgQQORm5srhBBi7NixYubMmVL5efPmifr164tPPvlE/P7772Lfvn0iJCREjBgxwuBz2nv7Xn/9dfHNN9+IrKwscejQIREZGSkaNWokrl+/Lvv2ff/992LHjh3iwoUL4ttvvxUDBw4UwcHB4o8//jD4nNZmiTbK6T0sLi4Wx44dE8eOHRMAxMqVK8WxY8fExYsXhRBCzJw5U4wdO1Yq//vvv4u6deuKadOmidOnT4t169YJV1dXsXfvXqmMnN5DS7TPnt8/IYRUPiwsTIwZM0YcO3ZM/Prrr9L9hw4dEnXq1BHLly8Xp0+fFvPmzRNubm7il19+sWrbtCzRxgULFogvv/xSXLhwQWRmZopRo0YJT09PnTLWYmz7Pv74Y1GnTh2xbt06kZOTI90KCwulMtb4P8ggSibWrFkjmjdvLtzd3UXPnj3F999/L93Xr18/ERMTI/1+7949MX/+fBESEiI8PT1FUFCQ+Otf/6rzAVXbOa3N3O0bOXKkCAgIEO7u7qJp06Zi5MiR4vz581ZskS5j2vfNN9+Ihx9+WHh4eIiGDRuKsWPHiqtXrxp1Tlswdxvl9B4eOHBAAKhy07YpJiZG9OvXr8pjunTpItzd3UWrVq3E5s2bq5xXLu+hJdpn7+9fdeVbtGihU+bTTz8Vbdq0Ee7u7qJDhw5i9+7d1mlQNSzRxvj4eOnv08/PTwwePFgcPXrUeo2qwNj29evXr8byWpb+P6gQQs/4CBERERHpxTlRRERERCZgEEVERERkAgZRRERERCZgEEVERERkAgZRRERERCZgEEVERERkAgZRRERERCZgEEVE5ES++eYbKBQKFBYW2roqRHaPQRQRWcSLL74IhUKBJUuW6BxPS0uDQqGQfhdC4O9//zsiIiLg4+ODevXqoUOHDpg6darBG6Hevn0bs2bNQkhICDw9PdG4cWP069cPn3/+uVSmZcuWSEpKMkvbLE372ikUCri5uSE4OBjTp0/HnTt3jDpP//79ER8fr3OsV69eyMnJgVKpNGONiZwTgygishhPT0+8++67+OOPP6q9XwiBMWPG4LXXXsPgwYOxb98+nDp1Chs3boSnpyfeeustg57nlVdewc6dO7FmzRqcOXMGe/fuxbPPPov8/HxzNseqBg0ahJycHPz+++947733sH79esybN++Bz+vu7g5/f3+dQJaITGTWTWSIiP4nJiZGDBkyRLRr105MmzZNOv7ZZ58J7aXnk08+EQDE559/Xu05NBqNQc+lVCpFSkqK3vur22dL67vvvhO9e/cWnp6eolmzZuLVV18Vt27dku7/8MMPRVhYmKhXr57w8/MTo0ePFnl5edL92j2/9u7dK7p06SI8PT3FgAEDRF5entizZ49o166dqF+/vhg9erQoKSkxqD0xMTFi+PDhOseefvpp0bVrV+n3mzdvilGjRonAwEDh5eUlOnbsKFJTU3XOUbnNWVlZUn0r7kX5z3/+U7Rv3164u7uLFi1aiOXLlxtUTyJnx54oIrIYV1dXvPPOO1izZg2uXLlS5f5PPvkEbdu2xbBhw6p9vKG9Jf7+/tizZw+Ki4urvX/nzp1o1qwZFi5ciJycHOTk5AAALly4gEGDBuGZZ57BiRMnsG3bNhw8eBBxcXHSY+/du4dFixbh559/RlpaGrKzs/Hiiy9WeY758+dj7dq1OHz4MC5fvowRI0YgKSkJqamp2L17N/bt24c1a9YY1J7KTp48icOHD8Pd3V06dufOHYSFhWH37t04efIkYmNjMXbsWPzwww8AgFWrViEiIgITJ06U2hwUFFTl3JmZmRgxYgRGjRqFX375BfPnz8fcuXORkpJiUl2JnIqtozgickwVe1MeffRR8dJLLwkhdHui2rVrJ4YNG6bzuKlTpwpvb2/h7e0tmjZtatBz/ec//xHNmjUTbm5uonv37iI+Pl4cPHhQp0yLFi3Ee++9p3NswoQJIjY2VufYd999J1xcXMR///vfap/rxx9/FABEcXGxEOLPnqivvvpKKrN48WIBQFy4cEE6NmnSJKFSqQxqT0xMjHB1dRXe3t7Cw8NDABAuLi7in//8Z42Pi4qKEq+//rr0e79+/cTUqVN1ylTuiRozZox44okndMpMmzZNtG/f3qC6Ejkz9kQRkcW9++672LJlC06fPl1r2TfffBPHjx9HYmIibt26ZdD5+/bti99//x3p6el49tln8euvv6JPnz5YtGhRjY/7+eefkZKSgnr16kk3lUoFjUaDrKwsAOU9NUOHDkXz5s1Rv3599OvXDwBw6dIlnXM98sgj0r/9/PxQt25dtGrVSufY9evXDWoPAAwYMADHjx/HkSNHEBMTg/Hjx+OZZ56R7i8rK8OiRYvQqVMn+Pr6ol69evjyyy+r1Ks2p0+fxmOPPaZz7LHHHsO5c+dQVlZm1LmInA2DKCKyuL59+0KlUmHWrFk6x0NDQ3H27FmdY40bN0br1q3RpEkTo57Dzc0Nffr0wYwZM7Bv3z4sXLgQixYtQmlpqd7H3Lp1C5MmTcLx48el288//4xz584hJCQEJSUlUKlU8PHxwccff4wff/wRn332GQBUOa+bm5v0b+2quooUCgU0Go3B7fH29kbr1q3RuXNnbNq0CUeOHMHGjRul+5ctW4ZVq1ZhxowZOHDgAI4fPw6VSlVje4nIvOrYugJE5ByWLFmCLl26oG3bttKx0aNHY8yYMfj8888xfPhwsz5f+/btcf/+fdy5cwfu7u5wd3ev0rPSrVs3nDp1Cq1bt672HL/88gvy8/OxZMkSaT7RTz/9ZNZ6GsLFxQWzZ89GQkICxowZAy8vLxw6dAjDhw/HCy+8AADQaDT47bff0L59e+lx1bW5socffhiHDh3SOXbo0CG0adMGrq6u5m8MkQNhTxQRWUWnTp3w/PPPY/Xq1dKxUaNG4dlnn8WoUaOwcOFCHDlyBNnZ2fjPf/6Dbdu2Gfwh3r9/f6xfvx6ZmZnIzs7Gnj17MHv2bAwYMAA+Pj4AyvNEffvtt7h69Spu3rwJAJgxYwYOHz6MuLg4HD9+HOfOncPnn38uTSxv3rw53N3dsWbNGvz+++/417/+VesQoaU899xzcHV1xbp16wCU9+Lt378fhw8fxunTpzFp0iTk5eXpPKZly5bSa3rz5s1qe8Jef/11pKenY9GiRfjtt9+wZcsWrF27Fm+88YZV2kVkzxhEEZHVLFy4UOeDXKFQYNu2bUhKSsKePXvw+OOPo23btnjppZcQFBSEgwcPGnRelUqFLVu24Mknn8TDDz+MV199FSqVCp9++qnOc2dnZyMkJASNGzcGUD6P6T//+Q9+++039OnTB127dkViYiICAwMBlA8tpqSkYPv27Wjfvj2WLFmC5cuXm/EVMVydOnUQFxeHpUuXoqSkBHPmzEG3bt2gUqnQv39/+Pv7Izo6Wucxb7zxBlxdXdG+fXs0bty42vlS3bp1w6effoqtW7eiY8eOSExMxMKFC6tdgUhEuhRCCGHrShARERHZG/ZEEREREZmAQRQRyV7FFASVb999952tq2eUS5cu1dgeY1MUEJHtcDiPiGSvpo2ImzZtCi8vLyvW5sHcv38f2dnZeu9v2bIl6tThwmkie8AgioiIiMgEHM4jIiIiMgGDKCIiIiITMIgiIiIiMgGDKCIiIiITMIgiIiIiMgGDKCIiIiITMIgiIiIiMgGDKCIiIiIT/D/WosFbclneLwAAAABJRU5ErkJggg==", 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", 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", 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KyGQyqSVJ7fHHH5fed+3aFatXr0b//v1RUlKCDh06mOQ6iIiIGsPsLUCrVq3CU089henTp6NXr17YsGED2rdvj82bN2stP2zYMDz44IPo2bMnAgMD8fzzzyMkJATff/+9Rjn1ox71y9nZ2RSX02oJIaRHWocOHcL9998PHx8fODo6YvLkySgoKEBZWVm950hLS0NsbCw6d+4MR0dHDB06FABw5coVo9efiIhaJqVSadByhmLWAFRRUYG0tDRERUVJ2+RyOaKionD06NEGjxdCICUlBZmZmRgyZIjGvtTUVLi7u6N79+6YOXMmCgoKdJ6nvLwcRUVFGi9Lc+7cOQQEBODSpUsYPXo0QkJCsGfPHqSlpWHt2rUA6n80V1paiujoaHTs2BGffPIJjh8/ji+++KLB44iIqG1r7Ez+pp7x36yPwG7evImqqip4eHhobPfw8MD58+d1HqdUKuHj44Py8nJYWVlh3bp1GDFihLQ/JiYG48ePR0BAAC5evIjXXnsNI0eOxNGjR7WuJ5WYmIglS5YY7sJame+++w6//PILXnjhBaSlpUGlUuGdd96BXF6dj//5z39qlLexsUFVVZXGtvPnz6OgoADLli2Dn58fAODnn382zQUQEVGL1djRxaYehWz2PkBN4ejoiJMnT6KkpAQpKSlISEhA165dMWzYMADAxIkTpbJ9+vRBSEgIAgMDkZqaivvvv7/O+ebNm4eEhATpc1FRkfRLvK0pLy9HXl6exjD4xMREjB49GlOmTMHp06dRWVmJ999/H7Gxsfjhhx+wYcMGjXP4+/tL9z40NBTt27dH586dYWNjg/fffx/PPPMMTp8+jaVLl5rpKomIqKVo7Lxxpp5fzqyPwNzc3GBlZYX8/HyN7fn5+XU62NYkl8sRFBSEsLAwvPjii3jooYeQmJios3zXrl3h5uaGrKwsrfttbW3RsWNHjVdblZycDC8vL/j7+yMmJgaHDx/G6tWr8a9//QtWVlYIDQ3FqlWrsHz5ctx111345JNP6tzbQYMG4ZlnnkFcXBw6deqEFStWoFOnTti6dSs+++wz9OrVC8uWLcPKlSvNdJVERET1M2sLkI2NDfr164eUlBSMGzcOAKBSqZCSkoL4+PhGn0elUqG8vFzn/pycHBQUFMDLy6u5VW4WGxsbg5bT19atW7F169YGy73wwgt44YUXNLZNnjxZ4/P69euxfv16jW2TJk3CpEmTNLYJIZpWWSIiIiMy+yOwhIQETJ06FRERERgwYACSkpJQWloqTbY3ZcoU+Pj4SK0QiYmJiIiIQGBgIMrLy3Hw4EFs375d+mVcUlKCJUuWYMKECfD09MTFixcxd+5cBAUFaQyTNwdXV1fEx8e3uAXhiIiITEWpdERhoStcXAqgUBSbrR5mD0BxcXG4ceMGFi5ciLy8PISFhSE5OVnqGH3lyhWpMy5QPdro2WefRU5ODuzt7dGjRw/s2LEDcXFxAAArKytkZGRg27ZtuHXrFry9vfHAAw9g6dKlLWIuIIYbIiJqq7Qt93Tz5k3pfXp6X+zfPxpCyCGTqRAbewDh4SdMXU0AgEzwGUUdRUVFUCgUUCqVdfoD/fnnn8jOzkZAQADs7OzMVEPLwftNRNQ6NLTck1LpiKSkORDiTqOGTKbCnDlJUCiKDbIMVH2/v2szewsQERERtX43btyod39hoatG+AEAIeS4556pGDbM9E9IGICIiIio2RqayNDFpQAymUojBFlZAZGRrjBH7xCzL4VBREREbZ9CUYyQkAwAd3rePPYY4OtrnvowABEREZHRKZWOyMgIASCTtu3YAeTkmKc+DEBERERkdNr6AFVVATrmKDY6BiAiIiIyOnUfoJqsrICgIPPUhwGIDCY1NRUymQy3bt1q9DH+/v5ISkoyWp2IiKhlUCiKERt7AHJ5dR8gKyvggw/YB4hMYNq0aZDJZHjmmWfq7Js1axZkMhmmTZtm+ooREVGrV3s195wcLxw5cjdycu4sQxUefgKHDmXh8GHg0iXgiSdMXMkaOAzewvj5+WHXrl149913YW9vD6B6ssGdO3eic+fOZq4dERG1Vu7u7tL7L74Yi1OnQlHd4VkgNPQUHnzwXwCAkBAXswx7r40tQBYmPDwcfn5+2Lt3r7Rt79696Ny5M/r27SttKy8vx+zZs+Hu7g47OzsMHjwYx48f1zjXwYMH0a1bN9jb2+O+++7DpUuX6nzf999/j3vvvRf29vbw8/PD7NmzUVpaarTrIyIi88rJ8aoRfgBAhlOnQjVagloCBiAzyskBDh82/RDAxx9/HFu2bJE+b968WVp8Vm3u3LnYs2cPtm3bhvT0dGkx2cLCQgDA1atXMX78eMTGxuLkyZN48skn8eqrr2qc4+LFi4iJicGECROQkZGB3bt34/vvv0d8fLzxL5KIiExKvQbYlStdUHOoezUZrl7trFHO3BiAzGTTJqBLF2D48Oo/N20y3Xc/9thj+P7773H58mVcvnwZP/zwAx577DFpf2lpKdavX4+3334bI0eORK9evbBx40bY29tj018VXb9+PQIDA/HOO++ge/fuePTRR+v0H0pMTMSjjz6KOXPmIDg4GIMGDcLq1avx8ccf488//zTdBRMRkcl07nwZNSc7rCbg53fFHNXRiX2AzCAnB5gxA1D9NRpQpQKefhqIjjZNb/hOnTph1KhR2Lp1K4QQGDVqFNzc3KT9Fy9eRGVlJe655x5pm7W1NQYMGIBz584BAM6dO4fIyEiN8w4cOFDj86lTp5CRkYFPPvlE2iaEgEqlQnZ2Nnr27GmMyyMiIjPy9c1FaOipOn2AfH1zzV01DQxAZnDhwp3wo6aeDMpUwwEff/xx6VHU2rVrjfIdJSUlePrppzF79uw6+9jhmoio7XrwwX+hf/9juHq1M/z8rrS48AMwAJlFcDAgl2uGIFNPBhUTE4OKigrIZDJER0dr7AsMDISNjQ1++OEHdOnSBUD1InfHjx/HnDlzAAA9e/bEvn37NI778ccfNT6Hh4fj7NmzCDLXLFdERGQ2vr65LTL4qLEPkBn4+gIfflgdegDzTAZlZWWFc+fO4ezZs7BSV+QvDg4OmDlzJl5++WUkJyfj7NmzeOqpp1BWVoYn/pq04ZlnnsGFCxfw8ssvIzMzEzt37sTWrVs1zvPKK6/gyJEjiI+Px8mTJ3HhwgX861//YidoIiIyO7YAmckTT1T3+cnKqm75McdMmB07dtS5b9myZVCpVJg8eTKKi4sRERGBr7/+Gs7OzgCqH2Ht2bMHL7zwAt5//30MGDAAb731Fh5//HHpHCEhIfi///s/zJ8/H/feey+EEAgMDERcXJzRr42IiKg+MiFE7a7aFq+oqAgKhQJKpbJOSPjzzz+RnZ2NgIAA2NnZmamGloP3m4iodSgoKMCaNWsaLBcfHw9XI82EWN/v79rYAkRERETN5urqivj4+Hrn+bGxsTFa+NEXAxAREREZREsJN43BTtBERERkcRiAiIiIyODMtdxTYzEANRH7jpsG7zMRUetjzuWeGosBSE/W1tYAgLKyMjPXxDKo77P6vhMRUcuma7mnltYSxE7QerKysoKTkxOuX78OAGjfvj1kstqr3lJzCSFQVlaG69evw8nJqc5kjURE1DK1hOWeGoMBqAk8PT0BQApBZDxOTk7S/SYiopavJSz31BgMQE0gk8ng5eUFd3d3VFZWmrs6bZa1tTVbfoiIzKCgoKDJ8/mol3t6+unqlh9zLPfUGAxAzWBlZcVf0ERE1KYYYkbnlrDcU0MYgIiIiEhSX8uPPuV8fVtm8FHjKDAiIiLSSal0RHa2P5RKR3NXxaDYAkRERERapaf3xf79oyGEHDKZCrGxBxAefsLc1TIItgARERFRHUqloxR+AEAIOfbvH91mWoIYgIiIiKiOwkJXKfyoCSFHYaGLmWpkWAxAREREVIeLSwFkMs0ZDWUyFVxcCs1UI8NiACIiIqI6FIpixMYekEKQug+QQlFs5poZBjtBExERkcTGxkZ6Hx5+AoGBWSgsdIGLS6FG+KlZrjViACIiIiKJq6sr4uPjmzwTdGvBAERERGShmrPkRWvHAERERGSBDLHkRWvWIjpBr127Fv7+/rCzs0NkZCSOHTums+zevXsREREBJycnODg4ICwsDNu3b9coI4TAwoUL4eXlBXt7e0RFReHChQvGvgwiIqJWo3bLj64Znxu7NEZrY/YWoN27dyMhIQEbNmxAZGQkkpKSEB0djczMTLi7u9cp7+Ligvnz56NHjx6wsbHBgQMHMH36dLi7uyM6OhoAsGLFCqxevRrbtm1DQEAAFixYgOjoaJw9exZ2dnamvkQiIqIWrS3P+KyL2VuAVq1ahaeeegrTp09Hr169sGHDBrRv3x6bN2/WWn7YsGF48MEH0bNnTwQGBuL5559HSEgIvv/+ewDVrT9JSUl4/fXXMXbsWISEhODjjz/GtWvX8OWXX5rwyoiIiFquW7duAWh4xmd1ubbGrAGooqICaWlpiIqKkrbJ5XJERUXh6NGjDR4vhEBKSgoyMzMxZMgQAEB2djby8vI0zqlQKBAZGanznOXl5SgqKtJ4ERERtWW3b98G0PCMz+pybY1ZH4HdvHkTVVVV8PDw0Nju4eGB8+fP6zxOqVTCx8cH5eXlsLKywrp16zBixAgAQF5ennSO2udU76stMTERS5Ysac6lEBERtUi6Rnr98ccfAO7M+FwzBLWlGZ91MXsfoKZwdHTEyZMnUVJSgpSUFCQkJKBr164YNmxYk843b948JCQkSJ+Liorg5+dnoNoSERGZR2NGeikUxQgJycCpU6EAZAAEQkIy2syMz7qYNQC5ubnBysoK+fn5Gtvz8/Ph6emp8zi5XI6goCAAQFhYGM6dO4fExEQMGzZMOi4/Px9eXl4a5wwLC9N6PltbW9ja2jbzaoiIiFoWbSO9Cgtd4eJSIAUcpdIRGRkhqA4/ACBDRkYIhg//rk2HILP2AbKxsUG/fv2QkpIibVOpVEhJScHAgQMbfR6VSoXy8nIAQEBAADw9PTXOWVRUhJ9++kmvcxIREbUl6el9kZQ0B9u2TUVS0hykp/cF0HAfIGtra5PX1RTM/ggsISEBU6dORUREBAYMGICkpCSUlpZi+vTpAIApU6bAx8cHiYmJAKr760RERCAwMBDl5eU4ePAgtm/fjvXr1wMAZDIZ5syZgzfeeAPBwcHSMHhvb2+MGzfOXJdJRERkcg2N9AoMzGqwD5BCoTB5vU3B7AEoLi4ON27cwMKFC5GXl4ewsDAkJydLnZivXLkCufzOf5TS0lI8++yzyMnJgb29PXr06IEdO3YgLi5OKjN37lyUlpZixowZuHXrFgYPHozk5GTOAURERBalMSO9AgIuIzb2QJ15gNry4y8AkAkhhLkr0dIUFRVBoVBAqVSiY8eO5q4OERFRk/zyyy/Yu3cvcnK88NFHT+FOPx8AEHjyyY3w9c0FoO4fVHfV99a0FIY+v7/N3gJERERExlVZaQvN8AMAMlRW2gAA7rvvPgQHB9c5jouhEhERUavVUD8fZ2dnjZHTlsDsS2EQERGRcahHcCkUxYiNPQCZTAUAdfr5tNWRXvVhCxAREVEbVXMEV3j4CQQGZmnt59NWR3rVhwGIiIjIQigUxW1+dFdj8REYERFRG2VjY2PQcm0JW4CIiIjaKFdXV8THx2tdDFWtLY/0qg8DEBERURtmieGmMfgIjIiIiCwOAxARERFZHAYgIiIisjgMQERERGRxGICIiIjI4jAAERERkcVhACIiIiKLwwBEREREFocBiIiIiCwOAxARERFZHAYgIiIisjgMQERERGRxGICIiIjI4jAAERERkcVhACIiImqhcnKAw4er/yTDYgAiIiJqQQoKCpCbm4t33rmFLl0Ehg8HunQReOedW8jNzUVBQYG5q9gmtDN3BYiIiKhaQUEB1qxZA6XSEUlJcyCEDACgUsnw8ssd8fvvm6FQFCM+Ph6urq5mrm3rxhYgIiKiFqKiogIAUFjoCiE0f0ULIUdhoYtGOWo6BiAiIqIWxsWlAICq1lYVXFwKzVGdNokBiIiIqEWSaX6S6ShGTcIARERE1EIolUoA1Y/Aagegmo/A1OWo6RiAiIiIWojKykoA1Y/AZDLNR2Ay2Z1HYOpy1HQMQERERC2MQlGM2NgDUgiSyVSIjT0AhaLYzDVrOzgMnoiIyIxycoALF4DgYM3t4eEnEBiYhcJCF7i4FDL8GBgDEBERkZls2gTMmAGoVIBcDixZojm3j0JRrDX4tGvHX9/NxUdgREREJlZQUIC0tHzMmCGg+qurj0oFLFrkBaXSscHjnZycjFtBC8AISUREZELq2Z6zs/2hUk3V2KdSyVBY6MLHXSbAFiAiIiITunHjBgDA2rocgKi1V8DamrM8mwIDEBERkQmph7BXVtqi9lw/gAyVlTYNnsPGpuEyVD8+AiMiIjID9Vw/Ndf8qjnXz3333Yfg2kPDUB1+uBBq8zEAERERmYF6rp/9+0dDCHmduX6cnZ3h5eVl5lq2XS3iEdjatWvh7+8POzs7REZG4tixYzrLbty4Effeey+cnZ3h7OyMqKioOuWnTZsGmUym8YqJiTH2ZRAREWmVkwMcPlz9Z03h4ScwZ04Spk7dijlzkhAefsI8FbRAZg9Au3fvRkJCAhYtWoT09HSEhoYiOjoa169f11o+NTUVkyZNwuHDh3H06FH4+fnhgQcewO+//65RLiYmBrm5udLr008/NcXlEBERAage7ZWbm4t33rmFLl0Ehg8HunQR2LHDVqOcQlGMgIDLHPllYjIhRO0u6CYVGRmJ/v37Y82aNQAAlUoFPz8/PPfcc3j11VcbPL6qqgrOzs5Ys2YNpkyZAqC6BejWrVv48ssvm1SnoqIiKBQKKJVKdOzYsUnnICIiy6Ue6q5UOuLdd19Azc7OMpkKc+YkNRh4xo8fjz59+hi5pm2LPr+/zdoCVFFRgbS0NERFRUnb5HI5oqKicPTo0Uado6ysDJWVlXBxcdHYnpqaCnd3d3Tv3h0zZ85EQUGBznOUl5ejqKhI40VERNRU6qcYV6/6Qduq7lev+jZ4Ds72bFxmDUA3b95EVVUVPDw8NLZ7eHggLy+vUed45ZVX4O3trRGiYmJi8PHHHyMlJQXLly/H//3f/2HkyJGoqqrSeo7ExEQoFArp5efn1/SLIiIii3f79u1mn8Pd3d0ANSFdWnW8XLZsGXbt2oXU1FTY2dlJ2ydOnCi979OnD0JCQhAYGIjU1FTcf//9dc4zb948JCQkSJ+LiooYgoiIqMmKi6sfbzk5/YHqyQ5rtgIJODndAgDcfffdCAkJqXM8h7obn1kDkJubG6ysrJCfn6+xPT8/H56envUeu3LlSixbtgyHDh3S+sNTU9euXeHm5oasrCytAcjW1ha2trZajiQiItKfugWoockO7e3tOdTdTMz6CMzGxgb9+vVDSkqKtE2lUiElJQUDBw7UedyKFSuwdOlSJCcnIyIiosHvycnJQUFBAX/IiIjIpNSTHdZUc7JDMh+zD4NPSEjAxo0bsW3bNpw7dw4zZ85EaWkppk+fDgCYMmUK5s2bJ5Vfvnw5FixYgM2bN8Pf3x95eXnIy8tDSUkJAKCkpAQvv/wyfvzxR1y6dAkpKSkYO3YsgoKCEB0dbZZrJCIiy6Se7FAdgmpPdkjmY/Y+QHFxcbhx4wYWLlyIvLw8hIWFITk5WeoYfeXKFcjld3La+vXrUVFRgYceekjjPIsWLcLixYthZWWFjIwMbNu2Dbdu3YK3tzceeOABLF26lI+5iIjI5MLDTyAwMAuFhS5wcSlk+GkhzB6AACA+Ph7x8fFa96Wmpmp8vnTpUr3nsre3x9dff22gmhEREemvvLxcy9bafYF0lSNTaBEBiIiIqC1xcHCQ3qen962z3pd6yYua5ci0zN4HiIiIqK1xdHQEACiVjlL4AaonQdy/fzSUSkeNcmR6DEBEREQGZm1tDQAoLHSVwo+aEHIUFrpolCPTYwAiIiIyMIVCAaDhYfDqcmR6DEBERERGwmHwLRc7QRMRERkRh8G3TAxARERERqZQFDP4tDB8BEZERKSHnBzg8OHqP3WxsbFp1LkaW44Mjy1AREREjbRpEzBjBqBSAXI58OGHwBNP1C3n6uqK+Ph4VFRU6DwXV3w3LwYgIiKiBhQUFODSpduYMcMdKlX1jM4qFfD00wJhYdfh79+uTphhuGnZGICIiIjqUVBQgDVr1iA72x8q1VSNfVVVMrz//r8REHAZ8fHxDD2tCPsAERER1UP9GKuhOX3qe9xFLQ8DEBERUSNwTp+2hY/AiIiIGolz+rQdDEBERER64Jw+bQMfgREREZHFYQAiIiIii8MARERERBaHAYiIiKgeXNaibWInaCIionpwWYu2iQGIiIioAQw3bQ8fgREREZHFYQsQERERqtf84mMuy8EAREREFiknB7hwAQgOBuztqxc8VVMqHVFY6AoXlwKNSQ+54GnbwQBEREQWQ93Ks3OnPebOVUClkkEuF1i48H9SmfT0vti/fzSEkEvrfYWHnwDABU/bEvYBIiIii1BQUN3K8/bbn+LllztCpZIBAFQqGZYs8YZS6Qil0lEKPwAghBz794+GUulozqqTEbAFiIiILIK69aaw0FUKOGpCyFFY6AJApnMf1/9qWxiAiIjIori4FEAmU2kEHZlMBReXQum9rn3UdvARGBERWRSFohixsQcgk6kAQOrno17lXdc+alvYAkRERG2OtiHtN2/elN6Hh5+Au3serlzpjM6dr8DXN1djX2BgFgoLXeDiUsjw00YxABERUZui7uyspjmkvXpbfSO9AEitQdR2MQAREVGbUrPlR1vQCQzM0jrSKzAwq8HQwwVP2w4GICIiapN0DWmfMGFPvSO97rvvPgQHB9c5H2eCblsYgIiIqE3SNdwdEPWO9OrUqRO8vLxMWVUyA44CIyKiNkk93L0mmUwFP7+cekd6OTk5mbqqZAZsASIiojZJPaS9dh8ghaKYI72IAYiIiNqu+oIOR3pZNj4CIyKiNkWpVGrZKjN5PahlaxEBaO3atfD394ednR0iIyNx7NgxnWU3btyIe++9F87OznB2dkZUVFSd8kIILFy4EF5eXrC3t0dUVBQuXLhg7MsgIqIWQAghvU9P74ukpDnYtm0qkpLmID29b4PHc6i7ZTD7I7Ddu3cjISEBGzZsQGRkJJKSkhAdHY3MzEy4u7vXKZ+amopJkyZh0KBBsLOzw/Lly/HAAw/gzJkz8PHxAQCsWLECq1evxrZt2xAQEIAFCxYgOjoaZ8+ehZ2dnakvkYiITEjdiVnXMHj1fD/jx4+Hm5ubxrEc6m45zN4CtGrVKjz11FOYPn06evXqhQ0bNqB9+/bYvHmz1vKffPIJnn32WYSFhaFHjx746KOPoFKpkJKSAqA6+SclJeH111/H2LFjERISgo8//hjXrl3Dl19+acIrIyIic6p/1XfAzc0NXl5eGi+GH8th1gBUUVGBtLQ0REVFSdvkcjmioqJw9OjRRp2jrKwMlZWVcHGp/oHOzs5GXl6exjkVCgUiIyN1nrO8vBxFRUUaLyIiat10DYPnyu4EmDkA3bx5E1VVVfDw8NDY7uHhgby8vEad45VXXoG3t7cUeNTH6XPOxMREKBQK6eXn56fvpRARUQvDld2pPmbvA9Qcy5Ytw65du5Camtqsvj3z5s1DQkKC9LmoqIghiIioDeB8P6SLWQOQm5sbrKyskJ+fr7E9Pz8fnp6e9R67cuVKLFu2DIcOHUJISIi0XX1cfn6+xlTm+fn5CAsL03ouW1tb2NraNvEqiIioJeN8P6SNQR6BZWdn4/bt23ofZ2Njg379+kkdmAFIHZoHDhyo87gVK1Zg6dKlSE5ORkREhMa+gIAAeHp6apyzqKgIP/30U73nJCKitqGxw9g53N2yGaQFqHv37jh16hR69uyp97EJCQmYOnUqIiIiMGDAACQlJaG0tBTTp08HAEyZMgU+Pj5ITEwEACxfvhwLFy7Ezp074e/vL/Xr6dChAzp06ACZTIY5c+bgjTfeQHBwsDQM3tvbG+PGjTPE5RIRUQvm6uqK+Ph4VFRU6CzD4e6kVwAaP3681u1VVVWYPXs2HB0dAQB79+5t9Dnj4uJw48YNLFy4EHl5eQgLC0NycrLUifnKlSuQy+80VK1fvx4VFRV46KGHNM6zaNEiLF68GAAwd+5clJaWYsaMGbh16xYGDx6M5ORkzgFERGQhGG6oITJRc8rMBsjlcgwZMgQBAQEa2z/++GOMGTNGmnxqy5YtBq2kqRUVFUGhUECpVKJjx47mrg4RERE1gj6/v/UKQLt27cLLL7+Mf/zjH9IjKgCwtrbGqVOn0KtXr6bXugVhACIiImp99Pn9rdcjsIkTJ+Luu+/GY489hgMHDuCjjz6Cs7NzsypLRESWraCggP11yOT07gTt7++P//znP1iyZAlCQ0OxceNGyGRcZZeIiPRXUFCANWvWSJ+VSkcUFrrCxaVAY+h6fHw8QxAZVJNGgcnlcixZsgQjRozAlClTUFVVZeh6ERGRBbh+/br0Pj29r7R4qXrW5vDwE1I5BiAypGbNAzR48GBkZGQgPT0dgYGBhqoTERFZCPUcckqlI/bti62zcrtS6ahRjshQmj0RYocOHRAaGsqZlImISG/FxdWPua5e9QOg2Z1CCDmuXvXVKEdkKHo9Auvbt2+j+vukp6c3uUJERGQ51C07ZWX2Wvert7MFiAxNrwBUcyZlIQQSExPxzDPPwMXFxdD1IiIiC9K+/f/02k7UXHoFoEWLFml8fuedd/D888+ja9euBq0UERFZFj+/qwAEaj4Gk8lU8PPLMVudqG0zyGKoREREzaFQFGPMmP2QyVQAII0C4yruZCwGWQyViIioKaytraX34eEnEBiYhcJCF7i4FGqEn5rliAyBAYiIiMxGvYakmkJRrLXVp3Y5oubSKwCtXr1a4/Pt27exdetWuLm5aWyfPXt282tGRERtXmODDQMQGZpeAejdd9/V+Ozp6Ynt27drbJPJZAxARETUKDY2NgYtR9RYeq0Gbym4GjwRkelwMVQyFH1+f+s1Cuy7775Dr169UFRUVGefUqlE79698d///le/2hIRkUVzdXWFl5eXzhfDDxmDXgEoKSkJTz31lNZUpVAo8PTTT2PVqlUGqxwRERGRMegVgE6dOoWYmBid+x944AGkpaU1u1JERERExqRXAMrPz693LoZ27drhxo0bza4UERERkTHpNQrMx8cHp0+fRlBQkNb9GRkZ8PLyMkjFiIio9WGHZmot9ApAf/vb37BgwQLExMTAzs5OY9///vc/LFq0CKNHjzZoBYmIqHUoKCjAmjVrpM9KpSMKC13h4lKgMblhfHw8QxCZnV4B6PXXX8fevXvRrVs3xMfHo3v37gCA8+fPY+3ataiqqsL8+fONUlEiImrZarb8pKf3xf79oyGEXFrXKzz8RJ1yROaiVwDy8PDAkSNHMHPmTMybNw/qKYRkMhmio6Oxdu1aeHh4GKWiRETUMtR8zHXtmhzZ2e0QEHAbNjY3AVS3/KjDDwAIIcf+/aMRGJjFxU2pxdB7LbAuXbrg4MGD+OOPP5CVlQUhBIKDg+Hs7GyM+hERUQtS8zFX3VaenxAeDhQWukrhR00IOQoLXRiAqMVo8mKozs7O6N+/vyHrQkRELZy65ae+Vh4XlwLIZCqNECSTqeDiUmiWOhNpo9cweCIiIqDhVp7Y2AOQyVQAIPUBYusPtSRNbgEiIiLLo1QqAaDBVp7w8BMIDMxCYaELXFwKGX6oxWELEBERNVplZSUAQKEoRkhIBgD1etoCISEZGkFHoShGQMBlhh9qkRiAiIhIb0qlIzIyQgDI/toiQ0ZGCJRKxwaPtbGxMWrdiBqDj8CIiEhvDfUBuu+++xAcHFznOM4ETS0FAxAREemtoT5Azs7OXBqJWjQ+AiMiokZr1676380NjfRSlyNqqfgTSkREjebk5CS9r2+kV81yRC0RAxARETWZQlHMUV7UKjEAERFZsJrremlTu9NyY0dwcaQXtXQMQEREFqrmul5A9dD2wkJXuLgUaLTqxMfHSyHI1dUV8fHxeoUmopaIAYiIyELVDDF1FzY9gPDwE3XKAWC4oTaBo8CIiCycroVNGzOpIVFrxQBERGTh6pvUkKitMnsAWrt2Lfz9/WFnZ4fIyEgcO3ZMZ9kzZ85gwoQJ8Pf3h0wmQ1JSUp0yixcvhkwm03j16NHDiFdARNS6qSc1rKnmpIZEbZFZA9Du3buRkJCARYsWIT09HaGhoYiOjsb169e1li8rK0PXrl2xbNkyeHp66jxv7969kZubK72+//57Y10CEVGr19CkhkRtkVk7Qa9atQpPPfUUpk+fDgDYsGEDvvrqK2zevBmvvvpqnfL9+/dH//79AUDrfrV27drVG5CIiEhTfZMaErVFZgtAFRUVSEtLw7x586RtcrkcUVFROHr0aLPOfeHCBXh7e8POzg4DBw5EYmIiOnfurLN8eXk5ysvLpc9FRUXN+n4iIlOrbz4fpVIJIUSd2Zlv3ryp8ZmTGpIlMVsAunnzJqqqquDh4aGx3cPDA+fPn2/yeSMjI7F161Z0794dubm5WLJkCe69916cPn0ajo7aRzQkJiZiyZIlTf5OIiJzqj2fj6FxUkNqi9rcPEAjR46U3oeEhCAyMhJdunTBP//5TzzxxBNaj5k3bx4SEhKkz0VFRfDz8zN6XYmIDKF2y4+uCQ3rM378eLi5udXZzkkNqa0yWwByc3ODlZUV8vPzNbbn5+cbtP+Ok5MTunXrhqysLJ1lbG1tYWtra7DvJCIyl/omNAR0hyM3Nzd4eXmZo8pEZmG2UWA2Njbo168fUlJSpG0qlQopKSkYOHCgwb6npKQEFy9e5P/YRNRmKZXKv/6sf0LD9PS+SEqag23bpiIpaQ7S0/uarc5E5mbWR2AJCQmYOnUqIiIiMGDAACQlJaG0tFQaFTZlyhT4+PggMTERQHUz79mzZ6X3v//+O06ePIkOHTogKCgIAPDSSy8hNjYWXbp0wbVr17Bo0SJYWVlh0qRJ5rlIIiID0tbZWT11SEMTGmoLR4GBWez4TBbJrAEoLi4ON27cwMKFC5GXl4ewsDAkJydLHaOvXLkCufzO/8zXrl1D3753/sWycuVKrFy5EkOHDkVqaioAICcnB5MmTUJBQQE6deqEwYMH48cff0SnTp1Mem1ERIbWUGdna+tyAAKArMZWAWvrinrDEQMQWSKzd4KOj49HfHy81n3qUKPm7+8PIUS959u1a5ehqkZE1KI01Nm5stIWmuEHAGSorLSRZnuuGYI42zNZMrMHICIi0p+2zs6BgVk6Q456tufax7D1hywVAxARUSujq7PznDlJCAnJwKlToahuCRIICcmQQk59sz1zrh+yNAxAREStjK7+PFev+iIjIwR3HoPJkJERguHDv4NCUYz77rsPwcHBdc7HuX7IEjEAERG1Mrr68wCyejs6Ozs7c0oQor8wABERtTC61vVSr92lqz+Pn9/Vejs6W1tbm+YCiFoBBiAiohak9lB3XTM36+rPU19HZ4VCYdqLIWrBGICIiFqQmi0/DS1roW31dnZ0JmocBiAiohZI10gv9czNtRcvvXXrFm7fvg1ra2utLT3s6EykiQGIiKgFamjm5tqLl7JzM5F+zLYYKhER6aYe6VUTZ24mMhwGICKiFkg90ksdgjhzM5Fh8REYEZEZNDTUHai/QzMRNQ8DEBGRiTW0qntN2kZ6EVHzMQARETWTrtYctdojsBpa1b0xOKSdqHkYgIiImqGxrTnx8fFah6HXN9dP7aHuahzSTtR8DEBERM1QX8tPQ+Uamuun9lB3IjIcjgIjIjKT+ub6ISLjYgAiIjITzvVDZD4MQEREZsK5fojMh32AiIiaQalUanzOyfHClStd0LnzZfj65mqU09afh3P9EJkHAxARUTNUVlZK77/4YixOnQoFIAMgEBp6Cg8++K865WoPYdc11w+HuhMZDwMQEZEB5OR41Qg/ACDDqVOh6N//mEZLEAC4uroiPj5er7mDiMiwGICIiP6i74SGANCuXfVfo1eudMGd8KMmw9WrneHrmyuVU2O4ITIvBiAiIjR9QkMnJycAQOfOlwEIaIYgAT+/KxrliKhl4CgwIiI0b0JDAPD1zUVo6ClUhyBA3Qeo9uMvImoZ2AJERGQgDz74L/TvfwxXr3aGn98Vhh+iFowBiIjIgHx9cxl8iFoBPgIjItJCqXREdrY/lErHess1dqg6h7QTtSxsASIiqqW+Fdpr45B2otaJAYiIqIaGVmjXhuGGqPXhIzAiohq4QjuRZWALEBG1SfpOaqjuo6Neob1mCKq5Qjv78hC1DQxARNTmNGVSw5p9eXx8ivDKKwpUVclgZSWwfHkRHnlkEvvyELUhDEBE1OY0dVJDdbh58UUgLg7IygKCgmTw9XUC4GTYShKRWTEAEVGbp1Q6orDQFS4uBTo7Mtfm61v9IqK2iQGIiNo0fYa0E5Hl4CgwImpzlErlX39qH9KuntxQXY6ILA8DEBG1OZWVlQAaHtKuLkdElsfsAWjt2rXw9/eHnZ0dIiMjcezYMZ1lz5w5gwkTJsDf3x8ymQxJSUnNPicRtV3qIe011RzSTkSWy6wBaPfu3UhISMCiRYuQnp6O0NBQREdH4/r161rLl5WVoWvXrli2bBk8PT0Nck4iarsUimKEhGQAEH9tEQgJyWh0R2giarvMGoBWrVqFp556CtOnT0evXr2wYcMGtG/fHps3b9Zavn///nj77bcxceJE2NraGuScRNT2tGtXPb5DqXRERkYIANlfe2TIyAiR+gCpyxGR5TFbAKqoqEBaWhqioqLuVEYuR1RUFI4ePdpizklErY+TkxOAhvsAqcsRkeUx2z9/bt68iaqqKnh4eGhs9/DwwPnz5016zvLycpSXl0ufi4qKmvT9RNSyNLSsBRFZLrN3gm4JEhMToVAopJefn5+5q0REBqBQFCM29oDUEVo9DxD7ABGR2VqA3NzcYGVlhfz8fI3t+fn5Ojs4G+uc8+bNQ0JCgvS5qKiIIYjIhPRduLQhNRcsDQ8/gcDALBQWusDFpVAj/HBhUyLLZbYAZGNjg379+iElJQXjxo0DAKhUKqSkpCA+Pt6k57S1tdXZqZqIjKv2wqW6lq2ouXBpQ2oubKoLFzYlsmxmHQKRkJCAqVOnIiIiAgMGDEBSUhJKS0sxffp0AMCUKVPg4+ODxMREANWdnM+ePSu9//3333Hy5El06NABQUFBjTonEbUsNUNKfctWNHaBUzWGGyKqj1kDUFxcHG7cuIGFCxciLy8PYWFhSE5OljoxX7lyBXL5nW5K165dQ9++faXPK1euxMqVKzF06FCkpqY26pxE1DLpWrYiMDCLfXaIyOBkQgjRcDHLUlRUBIVCAaVSiY4dO5q7OkRthra+Pjdv3sTevXuRne2Pbdum1jlm6tStCAi4jBkzZsDLy8tUVSWiVkif39+cBYyITKJ2X5/aOGSdiEyJw+CJyCRqt/wolY7IzvaXZmXmkHUiMiW2ABGRyenq7FzfkHUiIkNiACIivdU3b49SqYQQos4yEzdv3vxrf/2dndUvIiJjYgAiIr001JenIfWtz1Vf8OGkhURkSAxARKSTrlFbjaVtUsOGOjuPHz8ebm5uGufhpIVEZGgMQESkVXNbenT181F3dq69Tx2Q3NzcONydiIyOAYiItGrszMvaWnka6ufDzs5EZG4MQETUZLpaeRrTz0dXZ2f29SEiU2AAIqImqa+Vpyn9fAD29SEi02EAIrJwFy9eRFlZWZ3tf/zxR73H1dfKExBwmf18iKhFYwAismAXL17Ejh07mnRsQ6087OdDRC0Zl8IgsmC1W35ycrxw5MjdyMlpuHWmMUtXKBTFCAi4XCf8sJ8PEZkbW4CILFhJSYn0/osvxuLUqVAAMgACoaGn8OCD/5L2axvtFR5+AvHxwSgqcoe//214e/eHUtlN60zQauznQ0QtAQMQkQWrrKwEUN3ycyf8AIAMp06Fon//Y/D1zdU52gsAwsPdNQIN+/YQUWvAAERE+PXXbrgTftRkuHAhGI6OJdi/PxZCVO8XQo6vvorFwoWR8Pdvx9YcImqVGICICB06aO+g7OBQ8tdoL81wVFUlQ3GxB5h9iKi1YgAiamXqW4kdaFofm+7dL+DgQQHNViCB7t0vAADkcgGV6s4+KysgKEivryAialEYgIhakcauzxUfH69XCFIoijFmzH6d8/a89NIFvPNON1RVVYefDz4AfH2bfBlERGbHAETUijR2fa7GlrOzs5Pe1zdvz4QJt/Dcc0BWVnXLD8MPEbV2DEBEFszPz0/js671ufz8/ODlxeBDRG0HJ0IkakWUSmWtz47IzvaHUulYbzldGjshIScuJKK2hi1ARK2Iet4eQPdK7LXL1cfV1RXx8fEG71RNRNTSMQARtUL1rcSu75pbDDdEZIn4CIyoFapvJXYiImoYAxBRK6Reib2mmiuxExFR/RiAiFqRdu2qn1orFMUICckAIP7aIxASkiE9/lKXIyIi7fi3JJERGXrWZvUK60qlIzIyQlBz8dKMjBAMH/4dFIpinSuxExFRNQYgIiMx1qzNQP19gPTtBE1EZIn4CIzISK5fv67xWdecPbXL1Uc9H09DfYA4bw8RUf3YAkRkJLdv35bep6f3xb59o1H9bw4Vxoy5M2dPzXINqTlvj49PEV55RYGqKhmsrASWLy/CI49M4rw9RESNwABEZGRKpWON8AMAcuzb17Q5e4A78/a8+CIQF6den0sGX18nAE4GqjURUdvGAETUSE3t0Hz1qh/qPm2W4+pVXygU55pVJ19frs9FRNQUDEBEjWDMDs1ERGR67ARN1Ag3btzQ+KyrQ3PtcgDg53cVd+brqSaTqeDnl2PwehIRUeOwBYioEQoL78ywXN8ipDXLqSkUxRgzZn+dYzhcnYjIfBiAiBpBPVKrukNzLNQTEAohx759sVKH5pojuqytraX34eEnEBiYhcJCF7i4FGqEn5rliIjINBiAiGrQ1dG5uLg6sGRmBuPO7MtqMmRmBmPAgHSNrZ06ddL4rFAUa231qV2OiIiMjwGI6C+N6ehcUuKodXtpaYc622rO2aML5+whIjKPFtEJeu3atfD394ednR0iIyNx7Nixest/9tln6NGjB+zs7NCnTx8cPHhQY/+0adMgk8k0XjExMca8BGoD6gsqat26/YraHZoBgeDgC1rLu7q6wsvLS+eL4YeIyDzMHoB2796NhIQELFq0COnp6QgNDUV0dLTO5QGOHDmCSZMm4YknnsCJEycwbtw4jBs3DqdPn9YoFxMTg9zcXOn16aefmuJyqAUpKCjQ+Bmo/SooKKj3eG0jvXx9cxEaego1V2EPDT0FX99c410IEREZnEwIUfufsyYVGRmJ/v37S48eVCoV/Pz88Nxzz+HVV1+tUz4uLg6lpaU4cOCAtO3uu+9GWFgYNmzYAKC6BejWrVv48ssvm1SnoqIiKBQKKJVKdOzYsUnnIPNqyrw9ubm5+PDDDwHUP9ILAHJyvHD1amf4+V3RCD8jRozAoEGDDHw1RETUGPr8/jZrC1BFRQXS0tIQFRUlbZPL5YiKisLRo0e1HnP06FGN8gAQHR1dp3xqairc3d3RvXt3zJw5s95/7ZeXl6OoqEjjRa2DrlaezMzMRh2vraVRqXSUwg9QPdJr//7RdVqCBg78qU7Lj4uLSzOuhoiITMWsnaBv3ryJqqoqeHh4aGz38PDA+fPntR6Tl5entXxeXp70OSYmBuPHj0dAQAAuXryI1157DSNHjsTRo0dhZWVV55yJiYlYsmSJAa6ITKmxrTxAdYvNlStd0LnzZY3Qom0h0sJCVyn8qAkhR2GhS4Nz93BEFxFR69AmR4FNnDhRet+nTx+EhIQgMDAQqampuP/+++uUnzdvHhISEqTPRUVF8PPzM0ldqem0zc5cWOgKF5cCjaDyxRdjcepUKKqHr1f32XnwwX/pPK+LSwFkMpVGCJLJVHBxqZ7kcPz48XBzc6tzHEd0ERG1HmYNQG5ubrCyskJ+fr7G9vz8fHh6emo9xtPTU6/yANC1a1e4ubkhKytLawCytbWFra1tE66AzKmyslJ6r6vPTk6OV43wAwAynDoViv79j+nsuKxQFCM29oDOmZvd3Nzg5eVl5KsjIiJjMmsfIBsbG/Tr1w8pKSnSNpVKhZSUFAwcOFDrMQMHDtQoDwDffvutzvIAkJOTg4KCAv7SaqPUszNr67Nz5UoXaJu48OrVznXOY2NjI70PDz+BOXOSMHXqVsyZk6TRAbpmOSIiap3M/ggsISEBU6dORUREBAYMGICkpCSUlpZi+vTpAIApU6bAx8cHiYmJAIDnn38eQ4cOxTvvvINRo0Zh165d+Pnnn6XROyUlJViyZAkmTJgAT09PXLx4EXPnzkVQUBCio6PNdp1kPFev+qF2yBFCjqtXfdG582VUD1mvuV/Az+9KnfNw4kIiIsth9gAUFxeHGzduYOHChcjLy0NYWBiSk5Oljs5XrlyBXH6noWrQoEHYuXMnXn/9dbz22msIDg7Gl19+ibvuugsAYGVlhYyMDGzbtg23bt2Ct7c3HnjgASxdupSPuVoAXUtNqBk6YKjn7andB0jX4y+GGyIiy2D2eYBaIs4DZByNHbX18MMPw8nJqc722uHol19+wd69e6FUOuLdd+dA84muCi+8kCT129E1b09cXBx69OjRxCsiIqKWRJ/f32ZvASLL0ZilJgDgn//8p859NScuVFMoijFmzAHs2zca1SFIhTFjDmiMBPP1zdXa6sNh60RElokBiFokXUPaa4aodu3u/PiGh59AYGAWCgtd4OJSqHHMiBEjEBAQUOc72J+HiMhyMQBRi9PQMhRq7u7uGp8VimKtExV2796dQYeIiDQwAFGTXLx4EWVlZTr3t2/fHoGBgXqfV9cyFIGBWXXCDUdtERFRUzEAkd4uXryIHTt2NFjuscce0zsE6bsMBcMNERE1hVknQqTWqb6Wn6aUq0m9DEVNNZehICIiMgS2AFG9Tpw4geJizZaXmgvPAro7LDdE23ENLUNBRERkCAxApHNywvPnz+M///mP9DkzMwgXLnRDcHAlunev3pae3rfO8HNtHZYBzSUk6uvoXN+ILiIiIkNgALJwjZ2c8KOPpiMnp3rJiZ9/joCv71X8/e+f1wg/ACDHvn3aOywDdzotX7p0G//4hzuEqF6eQgg5DhyI1ThO14gursNFRESGwABk4RozOWFmZpAUfqrJkJPjhx9/7I+63ciq1+BSKM5pPZerqysyMgCVZjcfqFQyDB36BAYM0N1viCO6iIjIUBiAqEEXLnSDthXV8/M9m3S+4GBALtcMQVZWQL9+Cnh5KZpcTyIiosbiKDALUVBQgNzc3DqvmzdvapRTKh2Rne0PpdJR2hYc/CuqV1SvSaBPn1/qbJfJVPDzy6m3Lr6+wIcfVoceoPrPDz6o3k5ERGQKbAGyALX7+egataWrY3L37lnw9b1a4zGYgK/vVfTt+wuEaKdzxFb79u111umJJ4DoaCArCwgKYvghIiLTYgCyADX7+egKOQ3NwPzkk1uQmRmErKxgBAVdQPfuWQCqR2zFxMhga9sbfn7l8PQMABDQqJmgfX0ZfIiIyDwYgFohXcPW1XR1Fq4v5DRmBubu3bOk4FNT794KDBnStTmXREREZFIMQK1MYx9nxcfH1wlB9YUc9QzMNfc3dgZmR0fHBssQERG1JAxALVTNxUbz8trhyhVbdO5cjqqqy1KZ+iYTvHHjRp0AVF/IaWgG5sjISK19ehwdHdG3b1+DXz8REZExMQC1QDUXG60bci4gPLzhVdMrKyvrnLehkPPGGwFYuPAGLl1qB3//2/D27g+gP+ffISKiNocByIxq9uW5dk2O7Ox2CAi4jdLS3wE0v8+ONvUtM+Hm5gYvLw/062foKyUiImpZGIDMpGZfnrqtPD8hPLz+PjvW1uWonoOn5gSFAtbWDc/srGuZCSIiIkvBAGQCNVdUP3euAzIyOiIoqHpF9fpaeeoLOZWVttA2O3NlZd21shq7fhbX2SIiIkvBAGRkJ06cwL59+wAAX3wxFqdOhaI6uAQiNLQDwsJO6WzlqS6nPeToM2pLvQhpU4bOExERtUUMQEambvnJyfGqEX4AQIZTp0LRq9eZeoNMU0dt1cZwQ0REdAcDkIlcudIF2lpzCgtd6w0y9e2rr0Nzu3b8T0tERKQLf0uaSOfOl6GtP4+f3xX4+ubqDDL1hRxAd4dmd3d341wIERFRG8AAZCK+vrlaFxT19c0FUP/ILF37Ro4cCT8/vzrb2Z+HiIiofgxAJqJUOuL3331Rsw/Q77/7Qql0rHdI+n333QdnZ+c62xuz2CgRERFpxwBkIk2duLB3795szSEiIjIwBiATaWjYekREBMLDwzWO4aMsIiIi42AAMjL1SukNDVv39vaGl5eXOatKRERkMWRCCGHuSrQ0RUVFUCgUUCqV6NixY7PPV3Mm6OvXbfD77/bw8fkf3N2rJybkiupERETNp8/vb7YAmQDDDRERUcsib7gIERERUdvCAEREREQWhwGIiIiILA4DEBEREVkcBiAiIiKyOAxAREREZHEYgIiIiMjitIgAtHbtWvj7+8POzg6RkZE4duxYveU/++wz9OjRA3Z2dujTpw8OHjyosV8IgYULF8LLywv29vaIiorChQsXjHkJRERE1IqYPQDt3r0bCQkJWLRoEdLT0xEaGoro6Ghcv35da/kjR45g0qRJeOKJJ3DixAmMGzcO48aNw+nTp6UyK1aswOrVq7Fhwwb89NNPcHBwQHR0NP78809TXRYRERG1YGZfCiMyMhL9+/fHmjVrAAAqlQp+fn547rnn8Oqrr9YpHxcXh9LSUhw4cEDadvfddyMsLAwbNmyAEALe3t548cUX8dJLLwEAlEolPDw8sHXrVkycOLHBOhl6KQwiIiIyPn1+f5u1BaiiogJpaWmIioqStsnlckRFReHo0aNajzl69KhGeQCIjo6WymdnZyMvL0+jjEKhQGRkpM5zlpeXo6ioSONFREREbZdZ1wK7efMmqqqq4OHhobHdw8MD58+f13pMXl6e1vJ5eXnSfvU2XWVqS0xMxJIlS+psZxAiIiJqPdS/txvzcIuLoQKYN28eEhISpM+///47evXqBT8/PzPWioiIiJqiuLgYCoWi3jJmDUBubm6wsrJCfn6+xvb8/Hx4enpqPcbT07Pe8uo/8/Pz4eXlpVEmLCxM6zltbW1ha2srfe7QoQOuXr0KR0dHyGQyva+rqKgIfn5+uHr1qkX3IeJ9uIP3ohrvQzXehzt4L6rxPlRr7n0QQqC4uBje3t4NljVrALKxsUG/fv2QkpKCcePGAajuBJ2SkoL4+HitxwwcOBApKSmYM2eOtO3bb7/FwIEDAQABAQHw9PRESkqKFHiKiorw008/YebMmY2ql1wuh6+vb5OvS61jx44W/YOsxvtwB+9FNd6HarwPd/BeVON9qNac+9BQy4+a2R+BJSQkYOrUqYiIiMCAAQOQlJSE0tJSTJ8+HQAwZcoU+Pj4IDExEQDw/PPPY+jQoXjnnXcwatQo7Nq1Cz///DM+/PBDAIBMJsOcOXPwxhtvIDg4GAEBAViwYAG8vb2lkEVERESWzewBKC4uDjdu3MDChQuRl5eHsLAwJCcnS52Yr1y5Arn8zmC1QYMGYefOnXj99dfx2muvITg4GF9++SXuuusuqczcuXNRWlqKGTNm4NatWxg8eDCSk5NhZ2dn8usjIiKilsfsAQgA4uPjdT7ySk1NrbPt73//O/7+97/rPJ9MJsM//vEP/OMf/zBUFfVia2uLRYsWafQrskS8D3fwXlTjfajG+3AH70U13odqprwPZp8IkYiIiMjUzL4UBhEREZGpMQARERGRxWEAIiIiIovDAEREREQWhwGoidauXQt/f3/Y2dkhMjISx44d01n2zJkzmDBhAvz9/SGTyZCUlGS6ihqZPvdh48aNuPfee+Hs7AxnZ2dERUXVW7410ec+7N27FxEREXBycoKDgwPCwsKwfft2E9bWuPS5FzXt2rULMpmszczXpc992Lp1K2QymcarrUzboe/Pw61btzBr1ix4eXnB1tYW3bp1w8GDB01UW+PS514MGzaszs+ETCbDqFGjTFhj49D3ZyIpKQndu3eHvb09/Pz88MILL+DPP/9sfkUE6W3Xrl3CxsZGbN68WZw5c0Y89dRTwsnJSeTn52stf+zYMfHSSy+JTz/9VHh6eop3333XtBU2En3vwyOPPCLWrl0rTpw4Ic6dOyemTZsmFAqFyMnJMXHNDUvf+3D48GGxd+9ecfbsWZGVlSWSkpKElZWVSE5ONnHNDU/fe6GWnZ0tfHx8xL333ivGjh1rmsoakb73YcuWLaJjx44iNzdXeuXl5Zm41oan730oLy8XERER4m9/+5v4/vvvRXZ2tkhNTRUnT540cc0NT997UVBQoPHzcPr0aWFlZSW2bNli2oobmL734ZNPPhG2trbik08+EdnZ2eLrr78WXl5e4oUXXmh2XRiAmmDAgAFi1qxZ0ueqqirh7e0tEhMTGzy2S5cubSYANec+CCHE7du3haOjo9i2bZuxqmgSzb0PQgjRt29f8frrrxujeibVlHtx+/ZtMWjQIPHRRx+JqVOntokApO992LJli1AoFCaqnenoex/Wr18vunbtKioqKkxVRZNp7t8T7777rnB0dBQlJSXGqqJJ6HsfZs2aJYYPH66xLSEhQdxzzz3NrgsfgempoqICaWlpiIqKkrbJ5XJERUXh6NGjZqyZaRniPpSVlaGyshIuLi7GqqbRNfc+CCGQkpKCzMxMDBkyxJhVNbqm3ot//OMfcHd3xxNPPGGKahpdU+9DSUkJunTpAj8/P4wdOxZnzpwxRXWNpin3Yd++fRg4cCBmzZoFDw8P3HXXXXjrrbdQVVVlqmobhSH+vty0aRMmTpwIBwcHY1XT6JpyHwYNGoS0tDTpMdlvv/2GgwcP4m9/+1uz69MiZoJuTW7evImqqippqQ41Dw8PnD9/3ky1Mj1D3IdXXnkF3t7eGv8ztDZNvQ9KpRI+Pj4oLy+HlZUV1q1bhxEjRhi7ukbVlHvx/fffY9OmTTh58qQJamgaTbkP3bt3x+bNmxESEgKlUomVK1di0KBBOHPmjEEWZjaHptyH3377Dd999x0effRRHDx4EFlZWXj22WdRWVmJRYsWmaLaRtHcvy+PHTuG06dPY9OmTcaqokk05T488sgjuHnzJgYPHgwhBG7fvo1nnnkGr732WrPrwwBEZrFs2TLs2rULqampbaazpz4cHR1x8uRJlJSUICUlBQkJCejatSuGDRtm7qqZTHFxMSZPnoyNGzfCzc3N3NUxq4EDB2LgwIHS50GDBqFnz5744IMPsHTpUjPWzLRUKhXc3d3x4YcfwsrKCv369cPvv/+Ot99+u1UHoObatGkT+vTpgwEDBpi7KiaXmpqKt956C+vWrUNkZCSysrLw/PPPY+nSpViwYEGzzs0ApCc3NzdYWVkhPz9fY3t+fj48PT3NVCvTa859WLlyJZYtW4ZDhw4hJCTEmNU0uqbeB7lcjqCgIABAWFgYzp07h8TExFYdgPS9FxcvXsSlS5cQGxsrbVOpVACAdu3aITMzE4GBgcattBEY4u8Ia2tr9O3bF1lZWcaookk05T54eXnB2toaVlZW0raePXsiLy8PFRUVsLGxMWqdjaU5PxOlpaXYtWuX2da2NKSm3IcFCxZg8uTJePLJJwEAffr0kRY7nz9/vsZi6fpiHyA92djYoF+/fkhJSZG2qVQqpKSkaPwLrq1r6n1YsWIFli5diuTkZERERJiiqkZlqJ8HlUqF8vJyY1TRZPS9Fz169MAvv/yCkydPSq8xY8bgvvvuw8mTJ+Hn52fK6huMIX4mqqqq8Msvv8DLy8tY1TS6ptyHe+65B1lZWVIQBoBff/0VXl5erTb8AM37mfjss89QXl6Oxx57zNjVNLqm3IeysrI6IUcdkEVzlzJtdjdqC7Rr1y5ha2srtm7dKs6ePStmzJghnJycpGGrkydPFq+++qpUvry8XJw4cUKcOHFCeHl5iZdeekmcOHFCXLhwwVyXYBD63odly5YJGxsb8fnnn2sM7ywuLjbXJRiEvvfhrbfeEt988424ePGiOHv2rFi5cqVo166d2Lhxo7kuwWD0vRe1tZVRYPrehyVLloivv/5aXLx4UaSlpYmJEycKOzs7cebMGXNdgkHoex+uXLkiHB0dRXx8vMjMzBQHDhwQ7u7u4o033jDXJRhMU//fGDx4sIiLizN1dY1G3/uwaNEi4ejoKD799FPx22+/iW+++UYEBgaKhx9+uNl1YQBqovfff1907txZ2NjYiAEDBogff/xR2jd06FAxdepU6XN2drYAUOc1dOhQ01fcwPS5D126dNF6HxYtWmT6ihuYPvdh/vz5IigoSNjZ2QlnZ2cxcOBAsWvXLjPU2jj0uRe1tZUAJIR+92HOnDlSWQ8PD/G3v/1NpKenm6HWhqfvz8ORI0dEZGSksLW1FV27dhVvvvmmuH37tolrbRz63ovz588LAOKbb74xcU2NS5/7UFlZKRYvXiwCAwOFnZ2d8PPzE88++6z4448/ml0PmRDNbUMiIiIial3YB4iIiIgsDgMQERERWRwGICIiIrI4DEBERERkcRiAiIiIyOIwABEREZHFYQAiIiIii8MARERkRtOmTcO4cePMXQ0ii8MARERaTZs2DTKZTHq5uroiJiYGGRkZ5q6aQdS8NvVr8ODBRvu+S5cuQSaT4eTJkxrb33vvPWzdutVo30tE2jEAEZFOMTExyM3NRW5uLlJSUtCuXTuMHj3a3NUymC1btkjXl5ubi3379mktV1lZabQ6KBQKODk5Ge38RKQdAxAR6WRrawtPT094enoiLCwMr776Kq5evYobN25g+PDhiI+P1yh/48YN2NjYSKs9+/v7Y+nSpZg0aRIcHBzg4+ODtWvXahyzatUq9OnTBw4ODvDz88Ozzz6LkpISaf/ly5cRGxsLZ2dnODg4oHfv3jh48CAA4I8//sCjjz6KTp06wd7eHsHBwdiyZUujr8/JyUm6Pk9PT7i4uEgtNbt378bQoUNhZ2eHTz75BAUFBZg0aRJ8fHzQvn179OnTB59++qnG+VQqFVasWIGgoCDY2tqic+fOePPNNwEAAQEBAIC+fftCJpNh2LBhAOo+AisvL8fs2bPh7u4OOzs7DB48GMePH5f2p6amQiaTISUlBREREWjfvj0GDRqEzMzMRl83ETEAEVEjlZSUYMeOHQgKCoKrqyuefPJJ7Ny5E+Xl5VKZHTt2wMfHB8OHD5e2vf322wgNDcWJEyfw6quv4vnnn8e3334r7ZfL5Vi9ejXOnDmDbdu24bvvvsPcuXOl/bNmzUJ5eTn+85//4JdffsHy5cvRoUMHAMCCBQtw9uxZ/Pvf/8a5c+ewfv16uLm5GeR61XU9d+4coqOj8eeff6Jfv3746quvcPr0acyYMQOTJ0/GsWPHpGPmzZuHZcuWSfXauXMnPDw8AEAqd+jQIeTm5mLv3r1av3fu3LnYs2cPtm3bhvT0dAQFBSE6OhqFhYUa5ebPn4933nkHP//8M9q1a4fHH3/cINdNZDGavZwqEbVJU6dOFVZWVsLBwUE4ODgIAMLLy0ukpaUJIYT43//+J5ydncXu3bulY0JCQsTixYulz126dBExMTEa542LixMjR47U+b2fffaZcHV1lT736dNH45w1xcbGiunTpzfp+gAIOzs76focHBzEF198IbKzswUAkZSU1OA5Ro0aJV588UUhhBBFRUXC1tZWbNy4UWtZ9XlPnDihsX3q1Kli7NixQgghSkpKhLW1tfjkk0+k/RUVFcLb21usWLFCCCHE4cOHBQBx6NAhqcxXX30lAIj//e9/+twCIovGFiAi0um+++7DyZMncfLkSRw7dgzR0dEYOXIkLl++DDs7O0yePBmbN28GAKSnp+P06dOYNm2axjkGDhxY5/O5c+ekz4cOHcL9998PHx8fODo6YvLkySgoKEBZWRkAYPbs2XjjjTdwzz33YNGiRRqdsGfOnIldu3YhLCwMc+fOxZEjR/S6vnfffVe6vpMnT2LEiBHSvoiICI2yVVVVWLp0Kfr06QMXFxd06NABX3/9Na5cuQIAOHfuHMrLy3H//ffrVYeaLl68iMrKStxzzz3SNmtrawwYMEDjngFASEiI9N7LywsAcP369SZ/N5GlYQAiIp0cHBwQFBSEoKAg9O/fHx999BFKS0uxceNGAMCTTz6Jb7/9Fjk5OdiyZQuGDx+OLl26NPr8ly5dwujRoxESEoI9e/YgLS1N6iNUUVEhfcdvv/2GyZMn45dffkFERATef/99AJDC2AsvvIBr167h/vvvx0svvdTo7/f09JSuLygoCA4ODhrXXtPbb7+N9957D6+88goOHz6MkydPIjo6Wqqnvb19o7/XEKytraX3MpkMQHUfJCJqHAYgImo0mUwGuVyO//3vfwCAPn36ICIiAhs3bsTOnTu19kP58ccf63zu2bMnACAtLQ0qlQrvvPMO7r77bnTr1g3Xrl2rcw4/Pz8888wz2Lt3L1588UUpgAFAp06dMHXqVOzYsQNJSUn48MMPDXnJkh9++AFjx47FY489htDQUHTt2hW//vqrtD84OBj29vZSB/DabGxsAFS3JOkSGBgIGxsb/PDDD9K2yspKHD9+HL169TLQlRARALQzdwWIqOUqLy9HXl4egOoRV2vWrEFJSQliY2OlMk8++STi4+Ph4OCABx98sM45fvjhB6xYsQLjxo3Dt99+i88++wxfffUVACAoKAiVlZV4//33ERsbix9++AEbNmzQOH7OnDkYOXIkunXrhj/++AOHDx+WAtTChQvRr18/9O7dG+Xl5Thw4IC0z9CCg4Px+eef48iRI3B2dsaqVauQn58vBRM7Ozu88sormDt3LmxsbHDPPffgxo0bOHPmDJ544gm4u7vD3t4eycnJ8PX1hZ2dHRQKhcZ3ODg4YObMmXj55Zfh4uKCzp07Y8WKFSgrK8MTTzxhlOsislRsASIinZKTk+Hl5QUvLy9ERkbi+PHj+Oyzz6Qh3AAwadIktGvXDpMmTYKdnV2dc7z44ov4+eef0bdvX7zxxhtYtWoVoqOjAQChoaFYtWoVli9fjrvuuguffPIJEhMTNY6vqqrCrFmz0LNnT8TExKBbt25Yt24dgOpWlXnz5iEkJARDhgyBlZUVdu3aZZR78frrryM8PBzR0dEYNmwYPD0968zgvGDBArz44otYuHAhevbsibi4OKlfTrt27bB69Wp88MEH8Pb2xtixY7V+z7JlyzBhwgRMnjwZ4eHhyMrKwtdffw1nZ2ejXBeRpZIJIYS5K0FErdelS5cQGBiI48ePIzw8XGOfv78/5syZgzlz5pinckREOvARGBE1SWVlJQoKCvD666/j7rvvrhN+iIhaMj4CI6Im+eGHH+Dl5YXjx4/X6bdjbm+99RY6dOig9TVy5EhzV4+IWgA+AiOiNqewsLDOzMlq9vb28PHxMXGNiKilYQAiIiIii8NHYERERGRxGICIiIjI4jAAERERkcVhACIiIiKLwwBEREREFocBiIiIiCwOAxARERFZHAYgIiIisjj/D7K6Xz0XSj/vAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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BQQGGDRuGN954A8uXL0fr1q3x448/IioqCuXl5XUGKoVCAalUCqlUis8++wxubm4oLCyEVCpFeXm53uokIiLjV11djfXr1+Pu3bvKtmeffRZhYWEiVtV4DFRNQCKR6O22m1gOHjyIX3/9FW+++SaOHz+O6upqrFmzRrmS/eeff67S38bGBlVVVSpt58+fx59//okVK1bAx8cHQM3fTIiIqHm5efMmNm/erNI2a9YstGrVSqSKdMdARWrKysogk8lQVVWFoqIiZGZmIjExEcOGDcO4ceNw+vRpVFRUYMOGDXj++efx008/ITk5WeUYvr6+uHfvHrKzs9GzZ084ODigffv2sLGxwYYNGzB16lScPn0aS5cuFeksiYhIDAkJCSo/e3l5YdKkSSb/+i4+5UdqMjMz4eXlBV9fXwwePBiHDh1CUlIS9u7dC0tLS/Ts2RNr167Fe++9h+7du+Ozzz5Tm3QfFhaGqVOnIjIyEm5ubli5ciXc3Nywfft27N69G926dcOKFSuwevVqkc6SiIia0l9//aUWpkaOHInJkyebfJgC+C6/RuO7/Ohh/HMnItLsu+++w/fff6/SNmPGDLRp06bJa+G7/IiIiMjkJCQkqIWp+Pj4esNUejoQFlbz3VRwDhURERHp3Z9//omNGzeqtA0YMAD9+/d/5L4rVgA5OTXfhw83VIX6xUBFREREepWSkoILFy6otM2fP7/BK5/HxNSEqZgYQ1RnGLzlR0RERHohCAISEhLUwlR8fLxKmHrULb3hw4EjR0xndArgCBURERHpwYkTJ/C///1Ppe2ll17C448/rtbXFG/pPQoDFREREenk4eUQAGDhwoV1vj7GFG/pPQoDFRERETXK/fv3sWrVKrX2+Pj4evcbPtx8RqZqMVARERGR1pKTk1FUVKTSFhkZiYCAAJEqEhcDFREREWlF0y2+uLg4s1jxvLH4lB+JYsKECYiIiFD+PGDAAMyZM0enY+rjGEREVLfr169rDFPx8fHNOkwBHKGih0yYMAE7duwAAFhbW6N9+/YYN24cFixYACsrw/3rsmfPHlhbWzeo73fffYeBAwfizp07cHFxadQxiIhIO5qCVFRUFNq1a4f09JpJ5gMHAocO1Uw2N7c5Uo/CQEVqBg8ejG3btqGsrAz79+/H9OnTYW1tjdjYWJV+5eXlsLGx0ctntm7d2iiOQURE6uoalapVuwzCr78C9+6Z13IIDcVbfqTG1tYWnp6e6NChA9544w2Eh4cjPT1deZtu+fLl8Pb2RpcuXQAAV69exb///W+4uLigdevWGDFiBAoKCpTHq6qqQnR0NFxcXNCmTRvMnz8fD7+T++HbdWVlZXj77bfh4+MDW1tbdO7cGR999BEKCgowcOBAAECrVq0gkUgwYcIEjce4c+cOxo0bh1atWsHBwQFDhgzBpUuXlNu3b98OFxcXZGVloWvXrmjRogUGDx6MGzduKPt899136NOnDxwdHeHi4oInn3wSv//+u56uNBGRcTtx4oRamHJzc1N7ii8mBggNBWbNqvluTsshNBRHqOiR7O3t8eeffwIAsrOz4eTkhAMHDgAAKioqIJVKERoaisOHD8PKygrLli3D4MGD8csvv8DGxgZr1qzB9u3bsXXrVnTt2hVr1qzBV199haeffrrOzxw3bhxycnKQlJSEnj17Ij8/H7du3YKPjw++/PJLjBw5EhcuXICTk1OdrzKYMGECLl26hPT0dDg5OeHtt9/Gc889h7NnzypvDd6/fx+rV6/GJ598AgsLC4wZMwbz5s3DZ599hsrKSkRERGDSpEnYtWsXysvLcfTo0WY/T4CImgdNo1Lz5s2Do6OjWrs5LoOgLQYqqpMgCMjOzkZWVhZmzpyJ4uJiODo64sMPP1Te6vv0009RXV2NDz/8UBk0tm3bBhcXF3z33XcYNGgQ1q9fj9jYWLz44osAah61zcrKqvNzL168iM8//xwHDhxAeHg4AKBjx47K7bW39tzd3VXmUP1TbZD66aefEBYWBgD47LPP4OPjg7S0NLz88ssAagJhcnIyOnXqBACYMWMGlixZAgCQy+UoLS3FsGHDlNu7du2q/YUkIjIhFRUVePfdd9XaH7W2VHPHQGXkaif6NeUEv4yMDLRo0QIVFRWorq7GK6+8gsWLF2P69Ono0aOHyrypU6dO4fLly2jZsqXKMR48eIArV66gtLQUN27cQEhIiHKblZUVgoOD1W771Tp58iQsLS0b9Ebyupw7dw5WVlYqn9umTRt06dIF586dU7Y5ODgowxIAeHl54ebNmwBqgtuECRMglUrx7LPPIjw8HP/+97/h5eXV6LqIiIzZli1bVKY9AEBoaCgGDRokUkWmg4HKyInxvqOBAwdi8+bNsLGxgbe3t8rTfQ8P9d67dw9BQUH47LPP1I7j5ubWqM9v6NvI9eHhpwIlEolK0Nu2bRtmzZqFzMxMpKamYuHChThw4AD69u3bZDUSETUFTbf4Fi1aBAsLTrduCF4lI1c70a8pJ/g5Ojqic+fOaN++/SOXSnjiiSdw6dIluLu7o3Pnzipfzs7OcHZ2hpeXF3Jzc5X7VFZW4vjx43Ues0ePHqiursb333+vcXvtCFlVVVWdx+jatSsqKytVPvfPP//EhQsX0K1bt3rP6WG9evVCbGwsjhw5gu7du2Pnzp1a7U9EZMxu3bpV51N8DFMNxytl5IYPB44cMd7Jfq+++ipcXV0xYsQIHD58GPn5+fjuu+8wa9YsXLt2DQAwe/ZsrFixAmlpaTh//jymTZuGkpKSOo/p6+uL8ePH47XXXkNaWprymJ9//jkAoEOHDpBIJMjIyEBxcTHu3bundgx/f3+MGDECkyZNwo8//ohTp05hzJgxaNu2LUaMGNGgc8vPz0dsbCxycnLw+++/45tvvsGlS5c4j4qIzEZCQgI2bdqk0jZs2DDOl2oEBirSiYODA3744Qe0b98eL774Irp27YqoqCg8ePAATk5OAIC5c+di7NixGD9+PEJDQ9GyZUu88MIL9R538+bNeOmllzBt2jQEBARg0qRJUCgUAIC2bdsiISEBMTEx8PDwwIwZMzQeY9u2bQgKCsKwYcMQGhoKQRCwf//+Bi/+6eDggPPnz2PkyJF47LHHMHnyZEyfPh1TpkzR4goRERmnukalgoKCRKjG9EmEumYGU73kcjmcnZ1RWlqqDA61Hjx4gPz8fPj5+cHOzk6kCqmp8c+diEzBr7/+ij179qi1N5dRqfp+f+uCk9KJiIiaCU2jUlOmTIGnp6cI1ZgX3vIjIiIyc4Ig1HmLT1OYSk8HwsJqvlPDcISKiIjIjO3duxcnT55UaXN2dlZ5VdfDxFiyx9QxUBEREZkpTaNSMTExsLW1rXe/mJi/F5WmhmGgMiDO929e+OdNRMZCoVBg9erVau0NnXjOd/Npj4HKAP754t2mXPWbxHX//n0A6quvExE1peXLl6OyslKlja+PMTwGKgOwtLSEi4uL8p1wDg4OyhcHk/kRBAH379/HzZs34eLiAktLS7FLIqJmStMtvri4OP4OagIMVAZS+9REbagi8+fi4sJHj4lIFIWFhdi2bZtae3NZW8oYMFAZiEQigZeXF9zd3VFRUSF2OWRg1tbWHJkiIlFoGpV65ZVX4O/vL0I1zRcDlYFZWlryFy0RERlEXWtLUdNjoCIiIjIxR44cwYEDB9TaGabEw0BFRERkQjSNSs2ZMwfOzs4iVEO1GKiIiIhMQEVFBd599121do5KGQcGKiIiIiO3evVqKBQKlbYWLTpg7twJ4hREahioiIiIjJimW3zvvPMOrKz4K9yY8E+DiIjICBUXF+P9999Xa+ctPuNkIXYBmzZtgq+vL+zs7BASEoKjR4/W23/37t0ICAiAnZ0devTogf3796ts37NnDwYNGoQ2bdpAIpGovWEbAB48eIDp06ejTZs2aNGiBUaOHImioiJ9nhYREVGjJSQkqIWpp556imHKiIkaqFJTUxEdHY34+HicOHECPXv2hFQqrXN18SNHjmD06NGIiopCXl4eIiIiEBERgdOnTyv7KBQK9OvXD++9916dn/vmm2/if//7H3bv3o3vv/8e169fx4svvqj38yMiItJWXWtLDRw4UIRqqKEkgiAIYn14SEgIevfujY0bNwIAqqur4ePjg5kzZyImJkatf2RkJBQKBTIyMpRtffv2RWBgIJKTk1X6FhQUwM/PD3l5eQgMDFS2l5aWws3NDTt37sRLL70EADh//jy6du2KnJwc9O3bt0G1y+VyODs7o7S0FE5OTtqeOhERkYq8vDykp6ertXNUSr8M9ftbtDlU5eXlOH78OGJjY5VtFhYWCA8PR05OjsZ9cnJyEB0drdImlUqRlpbW4M89fvw4KioqEB4ermwLCAhA+/bt6w1UZWVlKCsrU/4sl8sb/JlERET10TQqNX78ePj6+jZ9MdQoot3yu3XrFqqqquDh4aHS7uHhAZlMpnEfmUymVf+6jmFjYwMXFxetjpOYmAhnZ2fll4+PT4M/k4iISBNBEOq8xccwZVr4lF8DxcbGqoyOyeVyhioiImqU9HQgMzMFHh4X1LbxFp9pEi1Qubq6wtLSUu3puqKiInh6emrcx9PTU6v+dR2jvLwcJSUlKqNUjzqOra0tbG1tG/w5REREdcnLS8BDN1zw1ltvwcHBQZyCSGei3fKzsbFBUFAQsrOzlW3V1dXIzs5GaGioxn1CQ0NV+gPAgQMH6uyvSVBQEKytrVWOc+HCBRQWFmp1HCIiIm3du3evzlt8DFOmTdRbftHR0Rg/fjyCg4PRp08frF+/HgqFAhMnTgQAjBs3Dm3btkViYiIAYPbs2ejfvz/WrFmDoUOHIiUlBceOHcOWLVuUx7x9+zYKCwtx/fp1ADVhCagZmfL09ISzszOioqIQHR2N1q1bw8nJCTNnzkRoaGiDn/AjIiLSlqYg1alTJ4wZM0aEakjfRA1UkZGRKC4uRlxcHGQyGQIDA5GZmamceF5YWAgLi78H0cLCwrBz504sXLgQCxYsgL+/P9LS0tC9e3dln/T0dGUgA4BRo0YBqEn/ixcvBgCsW7cOFhYWGDlyJMrKyiCVSjWuRktERKQPmsJUXFwcJBKJCNWQIYi6DpUp4zpURET0KGfPnsXu3bvV2jnxXDxmtw4VERGROdM0KnX58lN4+WWueG6OGKiIiIj0rK6J52S+GKiIiIh0lJ4OrFgBjBy5Hffu/a62nWHK/DFQERER6WjFCkAqTcC9e6rtjz32OkaPbitOUdSkGKiIiIh0UF5eDqk0Ua2do1LNCwMVERFRI2maKwUwTDVHDFRERESNoClMxcTE8DVlzRQDFRERkRYKCwuxbds2tXaOSjVvDFREREQNpGlUytHREfPmzROhGjImDFREREQNwLWlqD4MVERERPXYv38/fv75Z7V2hin6JwYqIiKiOmgalXr22WcRFhYmQjVkzBioiIiIHiIIApYsWaLWzlEpqgsDFRER0T9wbSlqDAYqIiKi/09TmJo+fTpcXV1FqIZMCQMVERE1e3K5HOvWrVNr56gUNRQDFRERNWu8xUf6wEBFRETNlqYwtWjRIlhYWIhQDZkyBioiImp2Tp06hbS0NLV2jkpRYzFQERFRs6JpVKpLly4YNWqUCNWQuWCgIiKiZoOvjyFDYaAiIiKz99FHH+HatWtq7QxTpC8MVEREZNY0jUpFRkYiICBAhGrIXDFQERGRWaqsrMTy5cvV2jkqRYbAQEVERGaHa0tRU2OgIiIis6IpTL311ltwcHAQoRpqLhioiIjILFy/fh0ffPCBWjtHpagpMFAREZHJ0zQqZWFhgUWLFolQDTVHDFRERGTSuLYUGQMGKiIiMknffvstfvrpJ7V2hikSAwMVERGZHE2jUk899RQGDhwoQjVEAF+nTURERi09HQgLq/kuCEKdt/gYpkhMHKEiIiKjtmIFkJMD5OUlIC9PfTtv8ZExYKAiIiKjFhNTE6YeNmnSJHh7e4tQEZE6BioiIjJa9+7dQ17eGrV2jkqRsWGgIiIio8TXx5ApYaAiIiKjoylMvfPOO7Cy4q8tMk78N5OIiIzGL7/8gq+++kqtnaNSZOwYqIiIyChoGpVyd3fHG2+8IUI1RNphoCIiIlGkp9csiTBwIGBjw9fHkGljoCIiIlGsWAH07p0EG5s7atsYpsjUMFAREZEopFL1Uanhw4ejV69eIlRDpBsGKiIialJVVVVYtmyZWjtHpciUMVAREVGT4dpSZK4YqIiIqEloClOzZ8+Gi4tL0xdDpGcMVEREZFBXr17F1q1b1do5KkXmhIGKiIgMhrf4qLlgoCIiIoPQFKbi4uIgkUhEqIbIsCzELmDTpk3w9fWFnZ0dQkJCcPTo0Xr77969GwEBAbCzs0OPHj2wf/9+le2CICAuLg5eXl6wt7dHeHg4Ll26pNLn4sWLGDFiBFxdXeHk5IR+/frh0KFDej83IqLmIj0dCAur+f75559rDFPx8fEMU2S2RA1UqampiI6ORnx8PE6cOIGePXtCKpXi5s2bGvsfOXIEo0ePRlRUFPLy8hAREYGIiAicPn1a2WflypVISkpCcnIycnNz4ejoCKlUigcPHij7DBs2DJWVlTh48CCOHz+Onj17YtiwYZDJZAY/ZyIic7RiBZCTA+TlJeDcuXMq24qLO/MWH5k9iSAIglgfHhISgt69e2Pjxo0AgOrqavj4+GDmzJmIiYlR6x8ZGQmFQoGMjAxlW9++fREYGIjk5GQIggBvb2/MnTsX8+bNAwCUlpbCw8MD27dvx6hRo3Dr1i24ubnhhx9+wL/+9S8AwN27d+Hk5IQDBw4gPDy8QbXL5XI4OzujtLQUTk5Oul4KIiKTlp5eE6YelpISj/feA4YPF6EoIg0M9ftbtBGq8vJyHD9+XCXAWFhYIDw8HDk5ORr3ycnJUQs8UqlU2T8/Px8ymUylj7OzM0JCQpR92rRpgy5duuDjjz+GQqFAZWUl/vvf/8Ld3R1BQUF11ltWVga5XK7yRURENXOlNIWp+Ph4nDvHMEXNg2iT0m/duoWqqip4eHiotHt4eOD8+fMa95HJZBr7196qq/1eXx+JRIJvv/0WERERaNmyJSwsLODu7o7MzEy0atWqznoTExPrfFqFiKi50vT/xVGjRqFLly4iVEMkHtEnpTc1QRAwffp0uLu74/Dhwzh69CgiIiLw/PPP48aNG3XuFxsbi9LSUuXX1atXm7BqIiLjUDv5/Msv79Y58Zxhipoj0UaoXF1dYWlpiaKiIpX2oqIieHp6atzH09Oz3v6134uKiuDl5aXSJzAwEABw8OBBZGRk4M6dO8p7p++//z4OHDiAHTt2aJy7BQC2trawtbXV/kSJiMzIihU1LzX+x7NASpx4Ts2ZaCNUNjY2CAoKQnZ2trKturoa2dnZCA0N1bhPaGioSn8AOHDggLK/n58fPD09VfrI5XLk5uYq+9y/fx9AzXytf7KwsEB1dbXuJ0ZEZMakUvVRqZiYGIYpavZEXdgzOjoa48ePR3BwMPr06YP169dDoVBg4sSJAIBx48ahbdu2SExMBFDzzqf+/ftjzZo1GDp0KFJSUnDs2DFs2bIFQM38qDlz5mDZsmXw9/eHn58fFi1aBG9vb0RERACoCWWtWrXC+PHjERcXB3t7e3zwwQfIz8/H0KFDRbkORETGLjc3F5mZmWrtDFJENUQNVJGRkSguLkZcXBxkMhkCAwORmZmpnFReWFioMpIUFhaGnTt3YuHChViwYAH8/f2RlpaG7t27K/vMnz8fCoUCkydPRklJCfr164fMzEzY2dkBqLnVmJmZiXfeeQdPP/00Kioq8Pjjj2Pv3r3o2bNn014AIiITwNfHED2aqOtQmTKuQ0VEzUFdE8+JTJWhfn/zXX5ERKSGo1JE2mGgIiIiFZrCVHBwMOeZEtWDgYqIiADUPGm9dOlStXaOShE9GgMVERHxFh+RjhioiIiaOU1haurUqWqv8SKiujFQERE1U9euXcNHH32k1s5RKSLtMVARETVDvMVHpF9aBypLS0vcuHED7u7uKu1//vkn3N3dUVVVpbfiiIhI/zSFqbi4OEgkEhGqITIPWgequtYBLSsrg42Njc4FERGRYXz55Zc4reGtxhyVItJdgwNVUlISgJr35X344Ydo0aKFcltVVRV++OEHBAQE6L9CIiLSmaZRKUdHR8ybN0+EaojMT4MD1bp16wDUjFAlJyfD0tJSuc3Gxga+vr5ITk7Wf4VERKQTvj6GyPAaHKjy8/MBAAMHDsSePXvQqlUrgxVFRES648Rzoqaj9RyqQ4cOGaIOIiLSI01hasSIEQgMDGz6YoiaAa0D1WuvvVbv9q1btza6GCIi0s39+/exatUqtXaOShEZltaB6s6dOyo/V1RU4PTp0ygpKcHTTz+tt8KIiEg7vMVHJB6tA9VXX32l1lZdXY033ngDnTp10ktRRESkHU1h6q233oKDg4MI1RA1PxKhroWltHThwgUMGDAAN27c0MfhjJ5cLoezszNKS0vh5OQkdjlE1Ezl5eUhPT1drZ2jUkSaGer3t95ePXPlyhVUVlbq63BERPQIvMVHZDy0DlTR0dEqPwuCgBs3bmDfvn0YP3683gojIiJ16enAihWAVMq1pYiMidaBKi8vT+VnCwsLuLm5Yc2aNY98ApCIiHTzf/+3GlKpQq2dYYpIXFyHiojIRCQkJMDWVrWtR48eePHFF8UpiIiUGj2H6ubNm7hw4QIAoEuXLnB3d9dbUURE9DdBELBkyRK1do5KERkPrQOVXC7H9OnTsWvXLlRXVwMALC0tERkZiU2bNsHZ2VnvRRIRNVeceE5kGiy03WHSpEnIzc3Fvn37UFJSgpKSEmRkZODYsWOYMmWKIWokImqWNIWpiRMnMkwRGSGt16FydHREVlYW+vXrp9J++PBhDB48GAqF+mRJc8R1qIhI32qf4Js16wYuXNiitr1Xr3gMHy5CYURmxGjWoWrTpo3G23rOzs5o1aqVXooiImqOapdD+P/TU1VwVIrIuGkdqBYuXIjo6Gh88skn8PT0BADIZDK89dZbWLRokd4LJCJqLjStLbVo0SJYWGg9O4OImpjWt/x69eqFy5cvo6ysDO3btwcAFBYWwtbWFv7+/ip9T5w4ob9KjQxv+RGRvnzxxRc4c+aMWjtHpYj0z2hu+Y0YMQISiURvBRARNWd8io/IPOjt5cjNDUeoiEhXmsIUgxSRYRnq97fWN+Y7duyIP//8U629pKQEHTt21EtRRETmLCEhQWOYysqKR3q6CAURkc60DlQFBQWoqqpSay8rK8O1a9f0UhQRkbnSFKQGDhyIrKx45OTUPOlHRKanwXOo0v/x16asrCyVpROqqqqQnZ0NPz8//VZHRGQG0tOB1asf4Jln3lPbVnuLLyamJkzFxDR1dUSkDw2eQ1X72K5EIsHDu1hbW8PX1xdr1qzBsGHD9F+lEeIcKiJqKE48JzIeoj/lV/vePj8/P/z8889wdXXVWxFEROZKU5h68803+RcxIjOj9bIJ+fn5hqiDiMisHD9+HBkZGWrtHJUiMk9aB6olS5bUuz0uLq7RxRARmQPe4iNqfrQOVF999ZXKzxUVFcjPz4eVlRU6derEQEVEzRrXliJqnrQOVHl5eWptcrkcEyZMwAsvvKCXooiITA1HpYiaN72tlP7rr7/i+eefR0FBgT4OZ/T4lB8R1dIUplq3bo2ZM2eKUA0R1Uf0p/wepbS0FKWlpfo6HBGR0RMEQeO8Uo5KETU/WgeqpKQklZ8FQcCNGzfwySefYMiQIXorjIjImPEWHxH9k9aBat26dSo/W1hYwM3NDePHj0dsbKzeCiMiMlaawtSoUaPQpUsXEaohImPAdaiIiBqoqKgIycnJau0clSKiRs2hKikpweXLlwEAnTt3houLiz5rIiIyOrzFR0T10SpQFRQUYPr06cjKylK+z08ikWDw4MHYuHEjfH19DVEjEZGoNIWpd955B1ZWenuuh4hMXIP/b3D16lX07dsX1tbWWLp0Kbp27QoAOHv2LDZv3ozQ0FD8/PPPaNeuncGKJSJqSnv37sXJkyfV2jkqRUQPa/A6VFFRUbh8+TKysrJgZ2ensu2vv/7C4MGD4e/vjw8//NAghRobrkNFZN54i4/IPIm+DlVmZiZSU1PVwhQA2NvbY+nSpRg1apTeCiMiEgtfH0NE2rJoaMdbt27VO0eqY8eOuH37ttYFbNq0Cb6+vrCzs0NISAiOHj1ab//du3cjICAAdnZ26NGjB/bv36+yXRAExMXFwcvLC/b29ggPD8elS5fUjrNv3z6EhITA3t4erVq1QkREhNa1E5F5SUhIYJgiokZpcKDy8vLC2bNn69x++vRpeHp6avXhqampiI6ORnx8PE6cOIGePXtCKpXi5s2bGvsfOXIEo0ePRlRUFPLy8hAREYGIiAicPn1a2WflypVISkpCcnIycnNz4ejoCKlUigcPHij7fPnllxg7diwmTpyIU6dO4aeffsIrr7yiVe1EZD7S0zWPSgUGBjJMEVGDNHgO1Zw5c3Dw4EFkZ2fDzc1NZdvNmzfx7LPPYuDAgVi/fn2DPzwkJAS9e/fGxo0bAQDV1dXw8fHBzJkzERMTo9Y/MjISCoUCGRkZyra+ffsiMDAQycnJEAQB3t7emDt3LubNmweg5pU4Hh4e2L59O0aNGoXKykr4+voiISEBUVFRDa71YZxDRWQeysvLkZiYqNaelRWPmBhgxQogJgYYPlyE4ohI7wz1+7vBI1Tx8fF48OABOnXqhGnTpiEpKQn/+c9/MHXqVHTu3Bl//fUX4uLiGvzB5eXlOH78OMLDw/8uxsIC4eHhyMnJ0bhPTk6OSn8AkEqlyv75+fmQyWQqfZydnRESEqLsc+LECfzxxx+wsLBAr1694OXlhSFDhqiMcmlSVlYGuVyu8kVEpi0hIeGRYSonp+Y7EVF9GjwpvVWrVsjNzcWCBQuQkpKCkpISAICLiwteeeUVvPvuu2jdunWDP/jWrVuoqqqCh4eHSruHhwfOnz+vcR+ZTKaxv0wmU26vbaurz2+//QYAWLx4MdauXQtfX1+sWbMGAwYMwMWLF+s8h8TExDqf+iEi06Ppv+dp06bBzc0N/7zLVztCRURUH61WpWvVqhU2b96M999/H8XFxQAANzc3SCQSgxRnCNXV1QBqFuUbOXIkAGDbtm1o164ddu/ejSlTpmjcLzY2FtHR0cqf5XI5fHx8DF8wEenVr7/+ij179qi1a5orNXw4b/URUcM0aplfiUQCd3d3nT7Y1dUVlpaWKCoqUmkvKiqqc3K7p6dnvf1rvxcVFcHLy0ulT2BgIAAo27t166bcbmtri44dO6KwsLDOem1tbWFra9vAsyMiY8S1pYjIUBo8h0rfbGxsEBQUhOzsbGVbdXU1srOzERoaqnGf0NBQlf4AcODAAWV/Pz8/eHp6qvSRy+XIzc1V9gkKCoKtrS0uXLig7FNRUYGCggJ06NBBb+dHRMZFU5iKi4tjmCIivRD1RVTR0dEYP348goOD0adPH6xfvx4KhQITJ04EAIwbNw5t27ZVThqdPXs2+vfvjzVr1mDo0KFISUnBsWPHsGXLFgA1I2dz5szBsmXL4O/vDz8/PyxatAje3t7KdaacnJwwdepUxMfHw8fHBx06dMCqVasAAC+//HLTXwQiMqjly5ejsrJSrZ1Bioj0SdRAFRkZieLiYsTFxUEmkyEwMBCZmZnKSeWFhYWwsPh7EC0sLAw7d+7EwoULsWDBAvj7+yMtLQ3du3dX9pk/fz4UCgUmT56MkpIS9OvXD5mZmSorvK9atQpWVlYYO3Ys/vrrL4SEhODgwYNo1apV0508ERkcb/ERUVNp8DpUpIrrUBEZN654TkSaiPouv6SkpAYfcNasWY0uhohIVxyVIiIxNGiEys/Pr2EHk0iU6zyZO45QERkfTWFqxIgRyqd8iYhEHaHKz8/X2wcSEenbn3/+qXyF1T9xVIqImkqjJ6WXl5cjPz8fnTp1gpWVqHPbiagZ4y0+IjIGWieh+/fvY+bMmdixYwcA4OLFi+jYsSNmzpyJtm3banypMRGRvqWnA3l56mEqJiaGi/ASUZPTemHP2NhYnDp1Ct99953KUgTh4eFITU3Va3FERJrs3btXY5iKj49nmCIiUWg9QpWWlobU1FT07dtX5R1+jz/+OK5cuaLX4oiIHsZbfERkjLQOVMXFxRrf46dQKEzqJclEZHq4thQRGSutA1VwcDD27duHmTNnAoAyRH344Yd1voOPiEgXHJUiImOndaB69913MWTIEJw9exaVlZX4z3/+g7Nnz+LIkSP4/vvvDVEjETVjmsJUx44dMXbsWBGqISLSTOtJ6f369cPJkydRWVmJHj164JtvvoG7uztycnIQFBRkiBqJqBlKS6vUGKZ69YpnmCIio8N3+TUSV0onMhze4iMiQxF1pXS5XN7gAzJcEJEuNIWpSZMmwdvbW4RqiIgapkGBysXFpcFP8FVVVelUEBE1T5cuXcLOnTvV2jkqRUSmoEGB6tChQ8p/LigoQExMDCZMmKB8qi8nJwc7duxAYmKiYaokIrPGW3xEZOq0nkP1zDPP4PXXX8fo0aNV2nfu3IktW7bgu+++02d9RotzqIj0Q1OYiouL47p2RGQQhvr9rXWgcnBwwKlTp+Dv76/SfvHiRQQGBuL+/ft6K86YMVAR6Wbz5s24efOmWjtHpYjIkAz1+1vrZRN8fHzwwQcfqLV/+OGH8PHx0UtRRGTeEhISGKaIyKxovbDnunXrMHLkSHz99dcICQkBABw9ehSXLl3Cl19+qfcCici88PUxRGSOGrUO1bVr1/D+++/j/PnzAICuXbti6tSpzWqEirf8iBouPR3Iy+PEcyISn6jrUD2sXbt2ePfdd/VWBBGZN01h6rnnnkPv3r1FqIaISP8aFahKSkrw0Ucf4dy5cwCAxx9/HK+99hqcnZ31WhwRmba7d+9i7dq1au0clSIic6P1Lb9jx45BKpXC3t4effr0AQD8/PPP+Ouvv/DNN9/giSeeMEihxoa3/Ijqx7WliMgYGc2yCf/617/QuXNnfPDBB7CyqhngqqysxOuvv47ffvsNP/zwg96KM2YMVER10xSm5s+fD3t7exGqISL6m9EEKnt7e+Tl5SEgIECl/ezZswgODuY6VETN2I8//ojs7Gy1do5KEZGxMJpJ6U5OTigsLFQLVFevXkXLli31VhgRmRbe4iOi5kzrQBUZGYmoqCisXr0aYWFhAICffvoJb731ltrraIioeeDaUkTU3GkdqFavXg2JRIJx48ahsrISAGBtbY033ngDK1as0HuBRGS8OCpFRFSjUQt7AsD9+/dx5coVAECnTp3g4OCg18KMHedQUXOnKUz5+/vjlVdeEaEaIqKGMZo5VLUcHBzQo0cPvRVCRKahuroaS5cuVWvnqBQRNWcNDlSvvfZag/pt3bq10cUQkXGr6xZfeTnDFBE1bw0OVNu3b0eHDh3Qq1cvNPIuIRGZME1hKjd3Ir7+uj1CQ0UoiIjIiDQ4UL3xxhvYtWsX8vPzMXHiRIwZMwatW7c2ZG1EZAR27vwdly5tV2uPj49HejpQUgLExDR5WURERkWrSellZWXYs2cPtm7diiNHjmDo0KGIiorCoEGDIJFIDFmn0eGkdGoO+BQfEZkbo1kpvdbvv/+O7du34+OPP0ZlZSXOnDmDFi1a6K0wY8dAReZOU5hatGgRLCwsRKiGiEg/jO4pPwsLC0gkEgiCgKqqKr0VRETi2r59O37//Xe19qyseHBgiohIM63+qllWVoZdu3bh2WefxWOPPYZff/0VGzduRGFhYbManSIyVwkJCXWGKc6TIiKqW4NHqKZNm4aUlBT4+Pjgtddew65du+Dq6mrI2oioCdX3+hiOTBER1a/Bc6gsLCzQvn179OrVq94J6Hv27NFbccaMc6jIXHDiORE1J6LPoRo3blyze5KPyJylpwN5eephasCAAejfv78IFRERmS6tFvYkIvNw//595OWtUmvnqBQRUeM0+ik/IjJNvMVHRKR/DFREzYimMPXmm29yHiARkY4YqIiagdzcXGRmZqq1c1SKiEg/GKiIzBxv8RERGR4DFZEZq29tKSIi0h8GKiIzxFEpIqKmxUBFZGY0hSlXV1dMnz5dhGqIiJoHo3ht/KZNm+Dr6ws7OzuEhITg6NGj9fbfvXs3AgICYGdnhx49emD//v0q2wVBQFxcHLy8vGBvb4/w8HBcunRJ47HKysoQGBgIiUSCkydP6uuUiJqcIAgaw1SvXvEMU0REBiZ6oEpNTUV0dDTi4+Nx4sQJ9OzZE1KpFDdv3tTY/8iRIxg9ejSioqKQl5eHiIgIRERE4PTp08o+K1euRFJSEpKTk5GbmwtHR0dIpVI8ePBA7Xjz58+Ht7e3wc6PqCkkJCRgyZIlau3x8fEYPlyEgoiImpkGv8vPUEJCQtC7d29s3LgRAFBdXQ0fHx/MnDkTMRpebx8ZGQmFQoGMjAxlW9++fREYGIjk5GQIggBvb2/MnTsX8+bNAwCUlpbCw8MD27dvx6hRo5T7ff3114iOjsaXX36Jxx9/HHl5eQgMDGxQ3XyXHxkLTaNSo0aNQpcuXUSohojIuBnq97eoI1Tl5eU4fvw4wsPDlW0WFhYIDw9HTk6Oxn1ycnJU+gOAVCpV9s/Pz4dMJlPp4+zsjJCQEJVjFhUVYdKkSfjkk0/g4ODwyFrLysogl8tVvojEdO3atTqf4mOYIiJqWqIGqlu3bqGqqgoeHh4q7R4eHpDJZBr3kclk9fav/V5fH0EQMGHCBEydOhXBwcENqjUxMRHOzs7KLx8fnwbtR2QICQkJ+Oijj9Ta+RQfEZE4muVTfhs2bMDdu3cRGxvb4H1iY2MRHR2t/FkulzNUkSg0jUq98847sLJqlv85ExEZBVH/D+zq6gpLS0sUFRWptBcVFcHT01PjPp6envX2r/1eVFQELy8vlT6186MOHjyInJwc2NraqhwnODgYr776Knbs2KH2uba2tmr9iZrS9u3b8fvvv6u1c1SKiEh8ot7ys7GxQVBQELKzs5Vt1dXVyM7ORmhoqMZ9QkNDVfoDwIEDB5T9/fz84OnpqdJHLpcjNzdX2ScpKQmnTp3CyZMncfLkSeWyC6mpqVi+fLlez5FIHxISEhimiIiMmOj3CKKjozF+/HgEBwejT58+WL9+PRQKBSZOnAgAGDduHNq2bYvExEQAwOzZs9G/f3+sWbMGQ4cORUpKCo4dO4YtW7YAACQSCebMmYNly5bB398ffn5+WLRoEby9vREREQEAaN++vUoNLVq0AAB06tQJ7dq1a6IzJ2qYhr4+Jj0dWLECiIkBl0ogImpiogeqyMhIFBcXIy4uDjKZDIGBgcjMzFROKi8sLISFxd8DaWFhYdi5cycWLlyIBQsWwN/fH2lpaejevbuyz/z586FQKDB58mSUlJSgX79+yMzMhJ2dXZOfH1Fjafv6mBUrgJycmu8MVERETUv0dahMFdehIkPSFKYCAwMxYsSIOvfhCBUR0aMZ6ve36CNURPS3Bw8e4L333lNrb8hcqeHDGaSIiMTCQEVkJLS9xUdERMaDgYrICGgKU9OnT4erq6sI1RARkbYYqIhE9PPPPyuX7fgnjkoREZkWUdehImrOEhIS6g1T6elAWFjNdyIiMm4coSISQUPWluIyCEREpoOBiqgJaTPxPCbm72UQiIjIuDFQETURbZ/i4zIIRESmg4GKyMAEQcCSJUvU2jnxnIjIfDBQERkQ15YiImoe+JQfkYFoClM+Ps8jKyueT+4REZkZjlAR6ZlMJsN///tftfb4+HiEhfHJPSIic8RARaRHj7rFxyf3iIjMEwMVkZ5oClPffhuLw4dtlD/zyT0iIvPEQEWko127duHixYtq7VlZ8RyJIiJqJhioiHRQ3y0+PshHRNR8MFARNVJDXh9DRETNAwMVkZa4thQRET2MgYpIC5rCVMeOHTF27FgRqiEiImPBQEXUAGlpFTh16l21do5KERERwEBF9Ei8xUdERI/CQEVUD01hatKkSfD29hahGiIiMlYMVEQanDp1CmlpaWrtHJUiIiJNGKiIHsJbfEREpC0GKqJ/0BSm4uLiIJFIRKiGiIhMBQMVEYAlS5ZAEAS1do5KERFRQzBQUbPHW3xERKQrBipq1vj6GCIi0gcGKmqWOCpFRET6xEBFzY6mMDVo0CCEhoaKUA0REZkDBipqNu7cuYOkpCS19vLyeDBLERGRLhioqFmo6xbf4sUMU0REpDsGKjJ7msLU/PnzceCAPUJDgZgYEYoiIiKzwkBFZiszMxO5ublq7bUTz4cPr/kiIiLSFQMVmSU+xUdERE2JgYrMDteWIiKipsZARWaDo1JERCQWC7ELINIHTWHq5s0O6NVLNUylpwNhYTXfiYiI9IUjVGTSqqqqsGzZMrX2ukalVqwAcnJqvnNCOhER6QsDFZmsxtzii4mpCVNcKoGIiPSJgYpMkqYw9dprr8HHx6fe/bhUAhERGQIDFZmUK1eu4NNPP1Vr58RzIiISEwMVmQw+xUdERMaKgYqMWnp6zZwnqVQ9TMXFxUEikYhQFRERkSoGKjJqhw59BKn0mlo7R6WIiMiYMFCR0UpISICLi3o7wxQRERkbBioySnx9DBERmRIGKjIq9U08r51PFRPDpQ+IiMi4MFCR0dAUpoYMGYI+ffoA4CrnRERkvIziXX6bNm2Cr68v7OzsEBISgqNHj9bbf/fu3QgICICdnR169OiB/fv3q2wXBAFxcXHw8vKCvb09wsPDcenSJeX2goICREVFwc/PD/b29ujUqRPi4+NRXl5ukPOj+ikUijpv8dWGKaBmZCo0lKucExGR8RE9UKWmpiI6Ohrx8fE4ceIEevbsCalUips3b2rsf+TIEYwePRpRUVHIy8tDREQEIiIicPr0aWWflStXIikpCcnJycjNzYWjoyOkUikePHgAADh//jyqq6vx3//+F2fOnMG6deuQnJyMBQsWNMk5098SEhKwevVqtXZN86WGDweOHOHoFBERGR+JIAiCmAWEhISgd+/e2LhxIwCguroaPj4+mDlzJmI0DEVERkZCoVAgIyND2da3b18EBgYiOTkZgiDA29sbc+fOxbx58wAApaWl8PDwwPbt2zFq1CiNdaxatQqbN2/Gb7/91qC65XI5nJ2dUVpaCicnJ21Pu9lLTwfy8tRHpebNmwdHR0cRKiIioubAUL+/RR2hKi8vx/HjxxEeHq5ss7CwQHh4OHJycjTuk5OTo9IfAKRSqbJ/fn4+ZDKZSh9nZ2eEhITUeUygJnS1bt26zu1lZWWQy+UqX9Q4hw8f1him4uPjGaaIiMgkiRqobt26haqqKnh4eKi0e3h4QCaTadxHJpPV27/2uzbHvHz5MjZs2IApU6bUWWtiYiKcnZ2VX496CS9plpCQgIMHD6q1c0kEIiIyZc3+Kb8//vgDgwcPxssvv4xJkybV2S82NhbR0dHKn+VyOUOVlri2FBERmStRA5WrqyssLS1RVFSk0l5UVARPT0+N+3h6etbbv/Z7UVERvLy8VPoEBgaq7Hf9+nUMHDgQYWFh2LJlS7212trawtbWtkHnRar4UmMiIjJ3ot7ys7GxQVBQELKzs5Vt1dXVyM7ORmhoqMZ9QkNDVfoDwIEDB5T9/fz84OnpqdJHLpcjNzdX5Zh//PEHBgwYgKCgIGzbtg0WFqI/8Gh23nlHc5jy8PBgmCIiIrMi+i2/6OhojB8/HsHBwejTpw/Wr18PhUKBiRMnAgDGjRuHtm3bIjExEQAwe/Zs9O/fH2vWrMHQoUORkpKCY8eOKUeYJBIJ5syZg2XLlsHf3x9+fn5YtGgRvL29ERERAeDvMNWhQwesXr0axcXFynrqGhkj7QiCABubJWrtDFJERGSORA9UkZGRKC4uRlxcHGQyGQIDA5GZmamcVF5YWKgyehQWFoadO3di4cKFWLBgAfz9/ZGWlobu3bsr+8yfPx8KhQKTJ09GSUkJ+vXrh8zMTNjZ2QGoGdG6fPkyLl++jHbt2qnUI/IqEmaBt/iIiKi5EX0dKlPFdag00xSmxowZg06dOolQDRERkSpD/f4WfYSKzMP169fxwQcfqLVzVIqIiJoDBirSGW/xERFRc8dARTrRFKYWLlwIS0tLEaohIiISBwMVaS09Hdi//wt4eZ1R28ZRKSIiao4YqEhreXkJ+MeaqUq9ejFMERFR88RARY+Ung68/TYgCMDo0eq3+LKy4pGTA4SGAsOHi1AgERGRyBio6JFWrABGjap74nmvXjV9YmKauDAiIiIjwUBFjySVqoep8PBwPPnkkwBqRqU4MkVERM0ZX2BHdSorK9P4FF9WVrwyTBERERFHqKgOda0tlZUVz1t7RERED2GgIjWawtTcuXPRokULcFUEIiIidQxUpHT+/HmkpqaqtXNtKSIiovoxUBEAvj6GiIhIFwxUpDFMMUgRERE1HANVM/bpp5/iypUrau0MU0RERNphoGpG0tP/XoAzL099VEom64bNm18WoTIiIiLTxnWompH584GcHEFjmFq9Oh6tWzNMERERNQZHqMzcP0elNL2HD6hZW+rePeDQoSYujoiIyEwwUJm5FSuAnBzNt/iioqLQrl07vouPiIhIRxJBEASxizBFcrkczs7OKC0thZOTk9jlqPjnqFR5uRxnzqxT68OJ50RE1BwZ6vc3R6jMUH2jUgDDFBERkb4xUJmhup7iW7hwISwtLUWoiIiIyLwxUJmZy5cvIy/vM7V2jkoREREZDgOVGdG04nlhYTBGjBgqQjVERETNBwOVmeDrY4iIiMTDQGXisrOz8eOPP6q1M0wRERE1HQYqE1S7LIJUqj4qNW7cOPj5+YlQFRERUfPFQGWC3nuvClLpMrX2Xr3iwSxFRETU9BioTMzmzZsxaNBNtfbFi+MRGgoMHy5CUURERM0cA5WRq729N3AgYGOjfovv7bffxjff2CE0lK+OISIiEgsDlZFbsQI4efI+pNJVattqJ54PH86RKSIiIjExUBmhf76L75VX0iGV5qls79evH5555hmRqiMiIqKHMVAZobffBs6f1/z6mLi4OEgkEhGqIiIiorpYiF0AqWvR4hYWL1YNUw8etESvXvH43/8kCAurGcUiIiIi48ARKiNz7do1DBv2kUrbjz9Ow7ffuiE0tObnnJyaW4KcN0VERGQcGKiMzNGjR1V+jo+PR3o6oFD8/RRf7fwqIiIiMg4SQRAEsYswRXK5HM7OzigtLYWTk5Pejnv9+nUcPHgQTz75JFc8JyIi0jND/f7mCJUREQQB3t7eGDNmjNilEBERkRY4KZ2IiIhIRwxURoTLIRAREZkmBioiIiIiHTFQGZn0dHCdKSIiIhPDQGVkVqz4e50pIiIiMg0MVEYmJgYIDeU6U0RERKaEyyYYmeHDuQI6ERGRqeEIFREREZGOGKiIiIiIdMRARURERKQjowhUmzZtgq+vL+zs7BASEqL2guCH7d69GwEBAbCzs0OPHj2wf/9+le2CICAuLg5eXl6wt7dHeHg4Ll26pNLn9u3bePXVV+Hk5AQXFxdERUXh3r17ej83IiIiMn+iB6rU1FRER0cjPj4eJ06cQM+ePSGVSnHz5k2N/Y8cOYLRo0cjKioKeXl5iIiIQEREBE6fPq3ss3LlSiQlJSE5ORm5ublwdHSEVCrFgwcPlH1effVVnDlzBgcOHEBGRgZ++OEHTJ482eDnS0REROZHIgiCIGYBISEh6N27NzZu3AgAqK6uho+PD2bOnIkYDWsHREZGQqFQICMjQ9nWt29fBAYGIjk5WfmC4blz52LevHkAgNLSUnh4eGD79u0YNWoUzp07h27duuHnn39GcHAwACAzMxPPPfccrl27Bm9v70fWbai3VRMREZHhGOr3t6gjVOXl5Th+/DjCw8OVbRYWFggPD0dOTo7GfXJyclT6A4BUKlX2z8/Ph0wmU+nj7OyMkJAQZZ+cnBy4uLgowxQAhIeHw8LCArm5uXo7PyIiImoeRF2H6tatW6iqqoKHh4dKu4eHB86fP69xH5lMprG/TCZTbq9tq6+Pu7u7ynYrKyu0bt1a2edhZWVlKCsrU/4sl8sfdXpERETUTIg+h8pUJCYmwtnZWfnl4+MjdklERERkJEQNVK6urrC0tERRUZFKe1FRETw9PTXu4+npWW//2u+P6vPwpPfKykrcvn27zs+NjY1FaWmp8uvq1asNPEsiIiIyd6IGKhsbGwQFBSE7O1vZVl1djezsbISGhmrcJzQ0VKU/ABw4cEDZ38/PD56enip95HI5cnNzlX1CQ0NRUlKC48ePK/scPHgQ1dXVCAkJ0fi5tra2cHJyUvkiIiIiAozgXX7R0dEYP348goOD0adPH6xfvx4KhQITJ04EAIwbNw5t27ZFYmIiAGD27Nno378/1qxZg6FDhyIlJQXHjh3Dli1bAAASiQRz5szBsmXL4O/vDz8/PyxatAje3t6IiIgAAHTt2hWDBw/GpEmTkJycjIqKCsyYMQOjRo1q0BN+RERERP8keqCKjIxEcXEx4uLiIJPJEBgYiMzMTOWk8sLCQlhY/D2QFhYWhp07d2LhwoVYsGAB/P39kZaWhu7duyv7zJ8/HwqFApMnT0ZJSQn69euHzMxM2NnZKft89tlnmDFjBp555hlYWFhg5MiRSEpKaroTJyIiIrMh+jpUpqq0tBQuLi64evUqb/8RERGZCLlcDh8fH5SUlMDZ2VlvxxV9hMpU3b17FwD4tB8REZEJunv3rl4DFUeoGqm6uhrXr19Hy5YtIZFIxC7HJNX+LYGjfI3Ha6g7XkP94HXUHa+hfjzqOgqCgLt378Lb21tlSpGuOELVSBYWFmjXrp3YZZgFPjWpO15D3fEa6gevo+54DfWjvuuoz5GpWlzYk4iIiEhHDFREREREOmKgItHY2toiPj4etra2YpdisngNdcdrqB+8jrrjNdQPsa4jJ6UTERER6YgjVEREREQ6YqAiIiIi0hEDFREREZGOGKiIiIiIdMRARY22adMm+Pr6ws7ODiEhITh69Gi9/Xfv3o2AgADY2dmhR48e2L9/v8p2QRAQFxcHLy8v2NvbIzw8HJcuXVJuLygoQFRUFPz8/GBvb49OnTohPj4e5eXlBjm/ptDU1/CfysrKEBgYCIlEgpMnT+rrlEQh1nXct28fQkJCYG9vj1atWiEiIkKfp9WkxLiGFy9exIgRI+Dq6gonJyf069cPhw4d0vu5NSV9X8c9e/Zg0KBBaNOmTZ3/rT548ADTp09HmzZt0KJFC4wcORJFRUX6PK0m1dTX8Pbt25g5cya6dOkCe3t7tG/fHrNmzUJpaal2hQtEjZCSkiLY2NgIW7duFc6cOSNMmjRJcHFxEYqKijT2/+mnnwRLS0th5cqVwtmzZ4WFCxcK1tbWwq+//qrss2LFCsHZ2VlIS0sTTp06JQwfPlzw8/MT/vrrL0EQBOHrr78WJkyYIGRlZQlXrlwR9u7dK7i7uwtz585tknPWNzGu4T/NmjVLGDJkiABAyMvLM9RpGpxY1/GLL74QWrVqJWzevFm4cOGCcObMGSE1NdXg52sIYl1Df39/4bnnnhNOnTolXLx4UZg2bZrg4OAg3Lhxw+DnbAiGuI4ff/yxkJCQIHzwwQd1/rc6depUwcfHR8jOzhaOHTsm9O3bVwgLCzPUaRqUGNfw119/FV588UUhPT1duHz5spCdnS34+/sLI0eO1Kp2BipqlD59+gjTp09X/lxVVSV4e3sLiYmJGvv/+9//FoYOHarSFhISIkyZMkUQBEGorq4WPD09hVWrVim3l5SUCLa2tsKuXbvqrGPlypWCn5+fLqciGjGv4f79+4WAgADhzJkzJh+oxLiOFRUVQtu2bYUPP/xQ36cjCjGuYXFxsQBA+OGHH5R95HK5AEA4cOCA3s6tKen7Ov5Tfn6+xv9WS0pKBGtra2H37t3KtnPnzgkAhJycHB3ORhxiXENNPv/8c8HGxkaoqKhocO285UdaKy8vx/HjxxEeHq5ss7CwQHh4OHJycjTuk5OTo9IfAKRSqbJ/fn4+ZDKZSh9nZ2eEhITUeUwAKC0tRevWrXU5HVGIeQ2LioowadIkfPLJJ3BwcNDnaTU5sa7jiRMn8Mcff8DCwgK9evWCl5cXhgwZgtOnT+v7FA1OrGvYpk0bdOnSBR9//DEUCgUqKyvx3//+F+7u7ggKCtL3aRqcIa5jQxw/fhwVFRUqxwkICED79u21Oo4xEOsaalJaWgonJydYWTX8lccMVKS1W7duoaqqCh4eHirtHh4ekMlkGveRyWT19q/9rs0xL1++jA0bNmDKlCmNOg8xiXUNBUHAhAkTMHXqVAQHB+vlXMQk1nX87bffAACLFy/GwoULkZGRgVatWmHAgAG4ffu27ifWhMS6hhKJBN9++y3y8vLQsmVL2NnZYe3atcjMzESrVq30cm5NyRDXsSFkMhlsbGzg4uKi03GMgVjXUFMdS5cuxeTJk7Xaj4GKTNIff/yBwYMH4+WXX8akSZPELsdkbNiwAXfv3kVsbKzYpZi06upqAMA777yDkSNHIigoCNu2bYNEIsHu3btFrs40CIKA6dOnw93dHYcPH8bRo0cRERGB559/Hjdu3BC7PGqm5HI5hg4dim7dumHx4sVa7ctARVpzdXWFpaWl2lMkRUVF8PT01LiPp6dnvf1rvzfkmNevX8fAgQMRFhaGLVu26HQuYhHrGh48eBA5OTmwtbWFlZUVOnfuDAAIDg7G+PHjdT+xJibWdfTy8gIAdOvWTbnd1tYWHTt2RGFhoQ5n1PTE/HcxIyMDKSkpePLJJ/HEE0/g/fffh729PXbs2KGXc2tKhriODeHp6Yny8nKUlJTodBxjINY1rHX37l0MHjwYLVu2xFdffQVra2ut9megIq3Z2NggKCgI2dnZyrbq6mpkZ2cjNDRU4z6hoaEq/QHgwIEDyv5+fn7w9PRU6SOXy5Gbm6tyzD/++AMDBgxQjghYWJjmv8JiXcOkpCScOnUKJ0+exMmTJ5WPF6empmL58uV6PcemINZ1DAoKgq2tLS5cuKDsU1FRgYKCAnTo0EFv59cUxLqG9+/fBwC1/4YtLCyUI4CmxBDXsSGCgoJgbW2tcpwLFy6gsLBQq+MYA7GuIVDz7+egQYNgY2OD9PR02NnZaX8CDZ6+TvQPKSkpgq2trbB9+3bh7NmzwuTJkwUXFxdBJpMJgiAIY8eOFWJiYpT9f/rpJ8HKykpYvXq1cO7cOSE+Pl7jY9YuLi7C3r17hV9++UUYMWKEymPW165dEzp37iw888wzwrVr14QbN24ov0yRGNfwYdo89WKsxLqOs2fPFtq2bStkZWUJ58+fF6KiogR3d3fh9u3bTXfyeiLGNSwuLhbatGkjvPjii8LJkyeFCxcuCPPmzROsra2FkydPNu0F0BNDXMc///xTyMvLE/bt2ycAEFJSUoS8vDyV/+9NnTpVaN++vXDw4EHh2LFjQmhoqBAaGtp0J65HYlzD0tJSISQkROjRo4dw+fJlld8tlZWVDa6dgYoabcOGDUL79u0FGxsboU+fPsL//d//Kbf1799fGD9+vEr/zz//XHjssccEGxsb4fHHHxf27dunsr26ulpYtGiR4OHhIdja2grPPPOMcOHCBeX2bdu2CQA0fpmqpr6GDzOHQCUI4lzH8vJyYe7cuYK7u7vQsmVLITw8XDh9+rTBztHQxLiGP//8szBo0CChdevWQsuWLYW+ffsK+/fvN9g5NgV9X8e6/r8XHx+v7PPXX38J06ZNE1q1aiU4ODgIL7zwgsn+RVMQmv4aHjp0qM7fLfn5+Q2uWyIIgqD9uBYRERER1TLNCShERERERoSBioiIiEhHDFREREREOmKgIiIiItIRAxURERGRjhioiIiIiHTEQEVERESkIwYqIiIiIh0xUBGRWZLJZJg5cyY6duwIW1tb+Pj44Pnnn0d2djZu376NmTNnokuXLrC3t0f79u0xa9YslJaWKvcvKCiARCLByZMn1Y49YMAAzJkzR6Xt3LlzGD58OJydneHo6IjevXub3IuSiajxrMQugIhI3woKCvDkk0/CxcUFq1atQo8ePVBRUYGsrCxMnz4dX3zxBa5fv47Vq1ejW7du+P333zF16lRcv34dX3zxhdafd+XKFfTr1w9RUVFISEiAk5MTzpw507gXrBKRSeKrZ4jI7Dz33HP45ZdfcOHCBTg6OqpsKykpgYuLi9o+u3fvxpgxY6BQKGBlZYWCggL4+fkhLy8PgYGBKn0HDBiAwMBArF+/HgAwatQoWFtb45NPPjHQGRGRseMtPyIyK7dv30ZmZiamT5+uFqYAaAxTAFBaWgonJydYWWk3cF9dXY19+/bhscceg1Qqhbu7O0JCQpCWltaI6onIVDFQEZFZuXz5MgRBQEBAQIP3uXXrFpYuXYrJkyerbQsLC0OLFi1Uvg4fPqzcfvPmTdy7dw8rVqzA4MGD8c033+CFF17Aiy++iO+//14v50RExo9zqIjIrGg7i0Eul2Po0KHo1q0bFi9erLY9NTUVXbt2VWl79dVXlf9cXV0NABgxYgTefPNNAEBgYCCOHDmC5ORk9O/fX8szICJTxEBFRGbF398fEokE58+ff2Tfu3fvYvDgwWjZsiW++uorWFtbq/Xx8fFB586dVdrs7e2V/+zq6gorKyt069ZNpU/Xrl3x448/NvIsiMjU8JYfEZmV1q1bQyqVYtOmTVAoFGrbS0pKANSMTA0aNAg2NjZIT09v9BN5NjY26N27Ny5cuKDSfvHiRXTo0KFRxyQi08NARURmZ9OmTaiqqkKfPn3w5Zdf4tKlSzh37hySkpIQGhqqDFMKhQIfffQR5HI5ZDIZZDIZqqqqtP68t956C6mpqfjggw9w+fJlbNy4Ef/73/8wbdo0A5wdERkj3vIjIrPTsWNHnDhxAsuXL8fcuXNx48YNuLm5ISgoCJs3b8aJEyeQm5sLAGq38/Lz8+Hr66vV573wwgtITk5GYmIiZs2ahS5duuDLL79Ev3799HVKRGTkuA4VERERkY54y4+IiIhIRwxURERERDpioCIiIiLSEQMVERERkY4YqIiIiIh0xEBFREREpCMGKiIiIiIdMVARERER6YiBioiIiEhHDFREREREOmKgIiIiItIRAxURERGRjv4feDFNtaSlCTIAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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Pa/To0Zo7d64SEhL8WqYtW7bo1KlTGjx4sHtZu3bt1LRpU3388cd+fW0AsEF6dp47pBQrNEb7so87U6AQQotKBSzcnOFO0mEuKXVYJ43o3tQvr7Vnzx4ZY9SuXbty17v33nvVu3dvXXXVVeWu17t3b4WFeebR/Px8de3atcJlOnjwoCIiItwhqVijRo108ODBCm8HAAJVi7jaCnPJI6yEu1xqHsdcXf5GUDmL6m7uM8acdZ3ly5drzZo1+uKLL8667sKFC3Xeeed5LBszZozP5QOAUJQYG63UYZ300JIdKjRG4S6XHh/WkW6fakBQOYvymvv88Q967rnnyuVy6dtvvy1znTVr1igtLa1EC8e1116rfv36ae3ate5lycnJat26tcd6xdPbV1RCQoJOnjypI0eOeLxmVlaW37udAMAWI7o3Vf828dqXfVzN42oRUqoJY1TOori579f82dxXv359paSkaO7cucrLyyvx+JEjRzRlyhR99dVX2rZtm/tHkp566inNnz+/ysvUrVs31axZ0+PS6F27dikjI0O9evWq8tcDAFslxkarV6sGhJRqRIvKWTjR3Dd37lz16dNHPXr00MyZM9W5c2f98ssvWrVqlebNm6dvvvmm1JaMpk2bqkWLFl6/3v/+9z9lZGTowIEDkk6HEOl0S0pCQoJiY2N18803a/Lkyapfv75iYmJ01113qVevXlzxAwDwK4JKBVR3c1/Lli21detW/fnPf9Z9992nzMxMxcfHq1u3bpo3b16Vv97y5cs1fvx4998jR46UJE2bNk3Tp0+XdLq1JiwsTNdee60KCgqUkpKiZ599tsrLAgDAr7lMRUZvWio3N1exsbHKyclRTEyMx2MnTpxQenq6WrRooaioKIdKGNo4BgCA0pR3/j4TY1QAAIC1CCoAAMBaBBUAAGAtggoAALBW0AeVAB4rHPDY9wCAygraoFKzZk1Jp2/eB2cU7/viYwEAgLeCdh6V8PBw1atXT4cOHZIk1apVSy6X6yzPQlUwxuj48eM6dOiQ6tWrp/DwcKeLBAAIUEEbVCS5Z28tDiuoXvXq1eNeQACASgnqoOJyuZSYmKiGDRvq1KlTThcnpNSsWZOWFABApTkaVKZPn64ZM2Z4LGvbtm25dw72RXh4OCdNAAACkOMtKh06dNAHH3zg/rtGDceLBAAALOF4KqhRowbjGAAAQKkcvzx59+7dSkpKUsuWLTVmzBhlZGSUuW5BQYFyc3M9fgAAQPByNKj07NlTCxYs0MqVKzVv3jylp6erX79+Onr0aKnrp6amKjY21v2TnJxczSUGAADVyWUsmj70yJEjatasmZ588kndfPPNJR4vKChQQUGB++/c3FwlJydX6DbRAADADrm5uYqNja3Q+dvxMSq/Vq9ePbVp00Z79uwp9fHIyEhFRkZWc6kAAIBTHB+j8mvHjh1TWlqaEhMTnS4KAACwgKNB5f7779e6deu0b98+bdq0Sddcc43Cw8M1atQoJ4sFAAAs4WjXzw8//KBRo0bpp59+Unx8vPr27atPPvlE8fHxThYLAABYwtGg8sYbbzj58gAAwHJWjVEBAAD4NYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYKKAzJz8rUpLVuZOflOFwUAAKvVcLoAoWbh5gxNXbJdRUYKc0mpwzppRPemThcLAAAr0aJSjTJz8t0hRZKKjPTQkh20rAAAUAaCSjVKz85zh5RihcZoX/ZxZwoEAIDlCCrVqEVcbYW5PJeFu1xqHlfLmQIBAGA5gko1SoyNVuqwTgp3nU4r4S6XHh/WUYmx0Q6XDAAAOzGYtpqN6N5U/dvEa1/2cTWPq0VIAQCgHAQVByTGRhNQAACoALp+AACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC1rgsqsWbPkcrk0adIkp4sCAAAsYUVQ2bx5s55//nl17tzZ6aIAAACLOB5Ujh07pjFjxuiFF17QOeec43RxAACARRwPKhMmTNAVV1yhwYMHn3XdgoIC5ebmevwAAIDgVcPJF3/jjTe0detWbd68uULrp6amasaMGX4uFQAAsIVjLSr79+/XPffco1dffVVRUVEVes7UqVOVk5Pj/tm/f7+fSwkAAJzkMsYYJ1542bJluuaaaxQeHu5eVlhYKJfLpbCwMBUUFHg8Vprc3FzFxsYqJydHMTEx/i4yAACoAt6cvx3r+rn44ou1fft2j2Xjx49Xu3bt9OCDD541pAAAgODnWFCpW7euOnbs6LGsdu3aatCgQYnlAAAgNDl+1Q8AAEBZHL3q50xr1651uggAAMAitKgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoALASpk5+dqUlq3MnHyniwLAQTWcLgAAnGnh5gxNXbJdRUYKc0mpwzppRPemThcLgANoUQFglcycfHdIkaQiIz20ZActK0CIIqgAsEp6dp47pBQrNEb7so87UyAAjiKoALBKi7jaCnN5Lgt3udQ8rpYzBQLgKIIKAKskxkYrdVgnhbtOp5Vwl0uPD+uoxNhoh0sGwAkMpgVgnRHdm6p/m3jtyz6u5nG1CClACCOoALBSYmw0AQUAXT8AAMBeBJUyMNkUAADOo+unFEw2BQCAHWhROQOTTQEAYA+CyhmYbAoAAHsQVM7AZFMAANiDoHIGJpsCAMAeDKYtBZNNAQBgB4JKGZhsCgAA59H1AwAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWo4GlXnz5qlz586KiYlRTEyMevXqpf/+979OFgkAAFjE0aDSpEkTzZo1S1u2bNHnn3+uiy66SFdddZV27tzpZLEAAIAlXMYYc/bVqk/9+vX117/+VTfffPNZ183NzVVsbKxycnIUExNTDaUDAACV5c3525oJ3woLC7Vo0SLl5eWpV69epa5TUFCggoIC99+5ubnVVTwAAOAAn4LKZ599po8//lgHDx6UJCUkJKhXr17q0aOH19vavn27evXqpRMnTqhOnTpaunSp2rdvX+q6qampmjFjhi9FBgAAAcirrp9Dhw7p2muv1caNG9W0aVM1atRIkpSVlaWMjAz16dNHb731lho2bFjhApw8eVIZGRnKycnR4sWL9c9//lPr1q0rNayU1qKSnJxM1w8AAAHEm64fr4LKddddpwMHDmj+/Plq27atx2O7du3STTfdpKSkJC1atMi3kksaPHiwWrVqpeeff/6s6zJGBQCAwOO3MSrvvfee1q9fXyKkSFLbtm31j3/8QwMHDvSqsGcqKiryaDUBAAChy6ugEhkZWe4A1qNHjyoyMrLC25s6daqGDBmipk2b6ujRo3rttde0du1avffee94UCwAABCmv5lEZMWKExo0bp6VLl3oEltzcXC1dulTjx4/XqFGjKry9Q4cO6YYbblDbtm118cUXa/PmzXrvvfd0ySWXeFMsAAAQpLxqUXnyySdVVFSkkSNH6pdfflFERISk0wNia9SooZtvvllPPPFEhbf34osveldaAAAQUnya8C03N1dbtmzxuDy5W7du1T6glcG0AAAEHr9P+BYTE6NBgwb5VDgAAICKqtJ7/WRlZWnmzJlVuUkAABDCqjSoHDx4kJljAQBAlfGq6+err74q9/Fdu3ZVqjAAAAC/5lVQ6dq1q1wul0obf1u83OVyVVnhAABAaPMqqNSvX19/+ctfdPHFF5f6+M6dOzV06NAqKVigyczJV3p2nlrE1VZibLTTxQEAICh4FVS6deumAwcOqFmzZqU+fuTIkVJbW4Ldws0Zmrpku4qMFOaSUod10ojuTR0rD6EJABAsvAoqd9xxh/Ly8sp8vGnTppo/f36lCxVIMnPy3SFFkoqM9NCSHerfJt6RkGBbaAIAoDK8CirXXHNNuY+fc845GjduXKUKFGjSs/PcIaVYoTHal3282oOKbaEJAIDKqtLLk0NRi7jaCjtj/HC4y6XmcbWqvSzlhSYAAAKRVy0qkydPrtB6Tz75pE+FCUSJsdFKHdZJDy3ZoUJjFO5y6fFhHR1pwSgOTb8OK06FJgAAqoJXQeWLL77w+HvDhg3q1q2boqP/76Qcipcnj+jeVP3bxGtf9nE1j6vlWDeLTaEJAICq4NNNCYvVrVtXX375pVq2bFmVZaowbkpYusycfMdDEwAAZfH7TQlht8TYaAIKACAoMJgWAABYi6ACAACsVambEhpj9O233+rYsWMeyzt37lz5kgUxZo4FAKBivBpMGxYWVqGbEhYWFlZpIcsSiINpmTkWABDq/DaYNj09vVIFC3XMHAsAgHe8Cipl3YwQFWPTdPsAAASCKhlMe9FFF+n777+vik0FNZum2wcAIBB41aKyfPnyUpevX79e77zzjpKTkyVJV155ZeVLFoSYORYAAO9U2WBa9wYZTHtWzBwLAAhl3py/ver6SUlJ0ZAhQ3Tw4EEVFRW5f8LDw7Vjxw4VFRVVW0gJZImx0erVqgEhBQCAs/AqqPz3v//VxRdfrAsvvFDvvPOOv8oEAAAgyYfBtPfee6+WL1+uBx98ULfffruOHz/uj3IBAAD4dtVP165d9fnnn8vlcqlr167ljlkBAADwlc93T46OjtZzzz2n5cuX68MPP1RcXFxVlgsAAMC7FpU1a9aoffv2ys3NdS+78sor9dRTTykyMlIdOnTQRx99VOWFBAAAocmroDJnzhzdeuutpV5KFBsbq9tvv11PPvlklRUOAACENq+CypdffqnLLruszMcvvfRSbdmypdKFAgAAkLwMKllZWapZs2aZj9eoUUOHDx+udKEAAAAkL4NK48aNtWPHjjIf/+qrr5SYmFjpQiG4ZObka1NatjJz8p0uCgAgwHh11c/ll1+uRx55RJdddpmioqI8HsvPz9e0adP029/+tkoLiMC2cHOGpi7ZriIjhbmk1GGdNKJ7U6eLBQAIEF7d6ycrK0sXXHCBwsPDNXHiRLVt21aS9O2332ru3LkqLCzU1q1b1ahRI78V+NcC9V4/oSIzJ199Zq1R0a/+w8JdLm2YMojbBwBACPPm/O1Vi0qjRo20adMm/f73v9fUqVPdE725XC6lpKRo7ty51RZSYL/07DyPkCJJhcZoX/ZxggoAoEK8nvCtWbNmWrFihX7++Wft2bNHxhide+65Ouecc/xRPgSwFnG1FeZSiRaV5nG1nCsUACCg+DSFviSdc8456t69u3r06EFIQakSY6OVOqyTwl0uSadDyuPDOtKaAgCoMJ+n0AcqYkT3purfJl77so+reVwtQgoAwCsEFfhdYmw0AQUA4BOfu34AAAD8jaACAACsRVABAADWIqgAAABrEVSqCPezAQCg6nHVTxXgfjYAAPgHLSqVlJmT7w4p0ulZWB9asoOWFQAAqgBBpZLKu58NAACoHIJKJRXfz+bXuJ8NAABVg6BSSdzPBgAA/2EwbRXgfjYAAPiHoy0qqamp6t69u+rWrauGDRvq6quv1q5du5wsks8SY6PVq1UDQgoAAFXI0aCybt06TZgwQZ988olWrVqlU6dO6dJLL1VeXp6TxQIAAJZwGWPM2VerHocPH1bDhg21bt069e/f/6zr5+bmKjY2Vjk5OYqJiamGEgIAgMry5vxt1RiVnJwcSVL9+vVLfbygoEAFBQXuv3Nzc6ulXAAAwBnWXPVTVFSkSZMmqU+fPurYsWOp66Smpio2Ntb9k5ycXM2lBAAA1cmarp/f//73+u9//6sNGzaoSZMmpa5TWotKcnIyXT8AAASQgOv6mThxot555x2tX7++zJAiSZGRkYqMjKzGkgEAACc5GlSMMbrrrru0dOlSrV27Vi1atHCyOAAAwDKOBpUJEybotdde09tvv626devq4MGDkqTY2FhFRzMfSXXJzMlXenaeWsTVZh4YAIBVHB2j4nK5Sl0+f/583XjjjWd9PpcnV97CzRnuuz+HuaTUYZ00ontTp4sFAAhiATNGxZJxvCErMyffHVIkqchIDy3Zof5t4mlZAQBYwZrLk1H90rPz3CGlWKEx2pd93JkCAQBwBoJKCGsRV1thZ/S+hbtcah5Xy5kCAQBwBoJKCEuMjVbqsE4K//9jhcJdLj0+rCPdPgAAa1gxjwqcM6J7U/VvE6992cfVPK4WIQUAYBWCCpQYG01AAQBYia4fAABgLYIKAACwFkEFAABYi6ACVKHMnHxtSstWZk6+00UBgKDAYFqginA7AgCoerSoAFWgrNsR0LICAJVDUAGqgK23I6ArCkCgo+sHqALFtyP4dVhx+nYEdEUBCAa0qCCk+KuFwbbbEdAVBSBY0KKCkOHvFgabbkdQXlcUsxADCCS0qCAkVFcLQ2JstHq1auB4GODO2ACCBUEFIcHWwa7+YltXFAD4iq4fhAQbB7v6m01dUQDgK1pUEBJCtYXBlq4oAPAVLSoIGbQwAEDgIaggpCTGRhNQACCA0PUDAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCqyTmZOvTWnZyszJd7ooAACH1XC6AMCvLdycoalLtqvISGEuKXVYJ43o3tTpYgEAHEKLCqyRmZPvDimSVGSkh5bsoGUFAEIYQQXWSM/Oc4eUYoXGaF/2cWcKBABwHEEF1mgRV1thLs9l4S6XmsfVcqZAAADHEVRgzeDVxNhopQ7rpHDX6bQS7nLp8WEdlRgb7Wi5AADOYTBtiLNt8OqI7k3Vv0289mUfV/O4WoQUAAhxtKiEMFsHrybGRqtXqwaEFAAAQSWUMXgVAGA7gkoIY/AqbGbL2CkAziKohDAGr8JWCzdnqM+sNRr9wqfqM2uNFm7OcLpIABziMsaYs69mp9zcXMXGxionJ0cxMTFOFydgZebkM3gV1sjMyVefWWs8uiXDXS5tmDKI/08gSHhz/uaqHygxNjpgTwCZOflKz85Ti7jaAVsHeCpv7BTHGAg9BBUELNsurUbVKB47dWaLCmOngNDEGBUEJFsvrUblMXYKwK/RohKEQqE7hO6B4MbEfwCKEVSCTKh0h9A9EPwCeeyUbULhywuCl6NdP+vXr9fQoUOVlJQkl8ulZcuWOVmcgBdK3SGJsdG65vzGHsuuPj+JD2HgDFzqjUDnaFDJy8tTly5dNHfuXCeLETRCaabZzJx8Lf3iR49ly744EJShDPBVKH15QfBytOtnyJAhGjJkiJNFCCqh1B3CGBXg7HifIBgE1FU/BQUFys3N9fjB/wmlqyWY/h84O94nCAYBFVRSU1MVGxvr/klOTna6SNYZ0b2pNkwZpNdv/Y02TBkUlANppdAKZYCveJ8gGFgzhb7L5dLSpUt19dVXl7lOQUGBCgoK3H/n5uYqOTmZKfRDGNP/A2fH+wS2Cdop9CMjIxUZGel0MaodlxaWzd+XsLLvEQy41BuBLKCCSigKlXlRbMS+BwDnOTpG5dixY9q2bZu2bdsmSUpPT9e2bduUkcF1/hKXFjqJfQ8Apz8LN6VlO/rZ52iLyueff65Bgwa5/548ebIkady4cVqwYIFDpbIHlxY6h30PINTZ0qrsaFAZOHCgLBnLa6VQmhfFNux7AKGsrFbl/m3iq/3LWkBdnhxquLTQOex7AKHMppnOGUxrOe4i6xz2fWDhCi2g6tjUqkxQCQBcWugc9n1gqI6+dIIQQklxq/JDS3ao0BhHW5WtmfDNF95MGAPAWf460Wfm5KvPrDUlvvltmDKoyl7H30GIEARb+WuywKCd8A2oLE4IVcebfenLib6i2/f3FVr+HlRoy5UVlcH7KnjZ0KpMUEHICIYTgi282Ze+nOi92b6/+9J9DUIVOXnbdGWFr3hfwd+46gchgQncqo63+9Lbqwe83b6/r9Dy5Q7ECzdnqM+sNRr9wqfqM2uNFm4ufRJLm66s8AXvK1QHWlQQEpjArep4uy+9bfHw5Vj58wotbwcVetNKYtOVFb7gfYXqQFBBSAj0E4JNvN2X3p7ofT1W/uxL9yYIeXPytunKCl/wvkJ1IKggJAT6CcEmvuxLb070th6rigYhb0/egTxfj63HCsGFy5MRUvx1qV0o8ve+DORjtXBzRomTt5MDTP19VU4gHys4w5vzN0EFAPzAlpM3V+XARt6cv7nqBwD8IDE2Wr1aNXA0pHBVDoIBQaWKZObka1NaNh8AQYbjikAW6Jc/AxKDaasETavBieMaWEJpdtSK1pWrchAMaFGpJJpWgxPHNbBUdIK1YOBNXX2dDI+WRNiEFpVKYsKj4MRxDRzBMA19RflSV28vf+ZO1LANQaWSaFoNThzXwBFKodLXulZ0DpjqCH10qcJbdP1Ukr/vMwJncFwDhy/34glU/q6rr4NvK9pVRJcqfEGLShUI5JklUTaOa2AIpdlR/V1XX1oSvWkhCaXWL1QdJnwDEBRsmWCtOvizrt7MqpuZk68+s9aUCDYbpgwqtVzero/g5c35mxYVhJRQG8QXSvX19qaE3u4bm/ZlIN6AUQqt1i9UHYIKQkaoDeILtfp6w9t9E4r70ujsje2+dBX50qVqU0gMdIG4L+n6QUCr6Jsu1JqcQ62+3qC7ony+hDh/3oAxFEOiv9i0L+n6QUhgEF/ZQq2+3vB231THvrTlW251zNPibXmmvLXd3bZTZKQpS7YH5Rw5/hbI8w1xeTICkreXOYbSJaxS6NXXG97uG3/vS5tm1fX18mR/3YBxy/c/l+iAMkba+v3PVfo6gayil4YH8n2fCCoISN6+6UJtXhSmTi+bt/vGn/87ts0rYlvALWtkQuAOWKha3oTcFnG1dcahlUs667G14TOBrh9YpyLN4NU1iC+Q2Th1ui283Tf++t+xrYuuuq7KqWhX14XN68slebSquCR1a35OlZYnEFVJV86ZyeUMtnwmEFSCkC393b6o6BvD1w9Uf17WaSObpk63jbf/C/7437HxVg3+DvTenPwSY6M17ILGemvrj+5lwy5oHLT/k97wZaxVad1oZa1v02cCQSXI2JKAfeHtG8PGFpJADYm2fbMPFbbOK+KvQO/tezwzJ19Lv/jRY9myLw7o/pS2ju8jf6noZ4i3Ibd2RHipy2tFlD4CxKbPBIJKELEpAfvClzeGTS0kgRwSbfxmHypsDNz+YuMVVzbxtrXJm5Cbd7Kw1OXHTxaVutymzwQG0waRQB7VLdk3kM8btg2K9JYvA0ZtGGQXLPx11YxtvB3QGcifCd7y5TNkRPem2jBlkF6/9TfaMGVQuV+MvN2XNl2AQItKELEpAfui+I3x628UNjSDV0QwfPPz5pt9ILcewTLlDOi0tWvMH3z9DKloq7Iv+9KW1j6CShAJljd18aWHgXQJYqCHxGIV+dAL9C5GOMfbAZ2SPSdLf6uOzxBf9qUN3esElSATyG/q4hNg8fvUKHBOgMESEisiGFqP4AxfT8Y2nCz9rbo+QwJxXxJUglAg/iNKgX8CDOSQ6I1gaT1C9QulQO+LUPkM8RZBBdaojhOgvy8fDtSQ6A1ONqgMTsblC4XPEG8RVGANf58AfRkAGqjzovgbJxtUBidjeMNlyrqZQgDw5jbRCByZOflVfgLMzMlXn1lrSrTWbJgyqMzX4MoWAPAPb87fzKMSAEJtvgp/zCnh7RwzgT4vSrFQ+98BEHzo+rEc3+qrhrfjXwJ9YK/E/w4QCkKhe5oWFYsFy7d6G3g7y6KvM2La0oLh6/+OLeUHcHYLN2eoz6w1Gv3Cp+oza40Wbs5wukh+QYuKxYLhW71NvBkA6svAXptaMHz537Gp/ADKF0oTLxJULMZ8FVXPm6sNvAk2tn1oePu/Y1v5AVvZ0tUSSl9k6fqxmE03hQpVFR3Ya9sNIb3937Gt/ICNbOpqCaUbNtKiYjnmqwgMNrZ+efO/Y2P5AV/5o9XDtlbHUJp4kaASAJgcyX62fmj4886qgI38NdbKxq6WUPkiS1AJALb0iaJ8gf6hEejlB/zZ6mFrq2MofJElqFiOKzECi78/NLhXEVA2X1o9KvqeotXROQQVi9nWJwpnEVqB8nnb6uHte4pWR2dw1Y/FuBIDxZj8Dzg7b6528/U95Y9bfJxZLiZd9ESLisVs7RNF9bNxIB9go4q2etj4nqLVtHRWtKjMnTtXzZs3V1RUlHr27KnPPvvM6SJZgXlUUCzQp/QHqlNFWj1sm4eEVtOyOd6isnDhQk2ePFnPPfecevbsqTlz5iglJUW7du1Sw4YNnS6e4+gTDW7+HMjHtzOgbImx0brm/MZ6a+uP7mVXn5/k2GesjS08tnAZY8zZV/Ofnj17qnv37nrmmWckSUVFRUpOTtZdd92lKVOmlPvc3NxcxcbGKicnRzExMdVRXKDK+BIkMnPyKzylf59Za0p0G26YMijkP/QAyb73iG3l8Tdvzt+Odv2cPHlSW7Zs0eDBg93LwsLCNHjwYH388ccl1i8oKFBubq7HDxCI/D2Qj4HYQPlse4/Q1V82R7t+srOzVVhYqEaNGnksb9Sokb799tsS66empmrGjBnVVTzAb/zdzMtAbKB8Nr5H6OovnRWDaStq6tSpysnJcf/s37/f6SIBPvH3QD6+nQHls/U94u/LnwORoy0qcXFxCg8PV1ZWlsfyrKwsJSQklFg/MjJSkZGR1VU8wG+qY5ZLvp0B5eM9EhgcDSoRERHq1q2bVq9erauvvlrS6cG0q1ev1sSJE50sGuB31fEhyZT4QPl4j9jP8cuTJ0+erHHjxunCCy9Ujx49NGfOHOXl5Wn8+PFOFw3wOz4kAaB8jgeVESNG6PDhw3r00Ud18OBBde3aVStXriwxwBYAAIQex+dRqQzmUQEAIPAEzDwqAAAA5SGoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWcnwK/coonlQ3NzfX4ZIAAICKKj5vV2Ry/IAOKkePHpUkJScnO1wSAADgraNHjyo2NrbcdQL6Xj9FRUU6cOCA6tatK5fLVaXbzs3NVXJysvbv3x+U9xGifoEv2OsY7PWTgr+O1C/w+auOxhgdPXpUSUlJCgsrfxRKQLeohIWFqUmTJn59jZiYmKD9B5SoXzAI9joGe/2k4K8j9Qt8/qjj2VpSijGYFgAAWIugAgAArEVQKUNkZKSmTZumyMhIp4viF9Qv8AV7HYO9flLw15H6BT4b6hjQg2kBAEBwo0UFAABYi6ACAACsRVABAADWIqgAAABrhUxQmTt3rpo3b66oqCj17NlTn332Wbnrz5kzR23btlV0dLSSk5N177336sSJE5Xapr9VdR2nT58ul8vl8dOuXTt/V6NM3tTv1KlTmjlzplq1aqWoqCh16dJFK1eurNQ2/a2q62fT8Vu/fr2GDh2qpKQkuVwuLVu27KzPWbt2rS644AJFRkaqdevWWrBgQYl1bDp+/qhjIB/DzMxMjR49Wm3atFFYWJgmTZpU6nqLFi1Su3btFBUVpU6dOmnFihVVX/gK8Ef9FixYUOL4RUVF+acCFeBtHZcsWaJLLrlE8fHxiomJUa9evfTee++VWM/v70MTAt544w0TERFhXnrpJbNz505z6623mnr16pmsrKxS13/11VdNZGSkefXVV016erp57733TGJiorn33nt93qa/+aOO06ZNMx06dDCZmZnun8OHD1dXlTx4W78//OEPJikpybz77rsmLS3NPPvssyYqKsps3brV5236kz/qZ9PxW7FihXn44YfNkiVLjCSzdOnSctffu3evqVWrlpk8ebL5+uuvzdNPP23Cw8PNypUr3evYdPyM8U8dA/kYpqenm7vvvtu8/PLLpmvXruaee+4psc7GjRtNeHi4+ctf/mK+/vpr88c//tHUrFnTbN++3T+VKIc/6jd//nwTExPjcfwOHjzonwpUgLd1vOeee8zs2bPNZ599Zr777jszdepUU7NmzWr/HA2JoNKjRw8zYcIE99+FhYUmKSnJpKamlrr+hAkTzEUXXeSxbPLkyaZPnz4+b9Pf/FHHadOmmS5duvilvN7ytn6JiYnmmWee8Vg2bNgwM2bMGJ+36U/+qJ9Nx+/XKvIB+Yc//MF06NDBY9mIESNMSkqK+2+bjt+ZqqqOgXwMf23AgAGlnsiHDx9urrjiCo9lPXv2NLfffnslS1g5VVW/+fPnm9jY2CorV1Xyto7F2rdvb2bMmOH+uzreh0Hf9XPy5Elt2bJFgwcPdi8LCwvT4MGD9fHHH5f6nN69e2vLli3u5qu9e/dqxYoVuvzyy33epj/5o47Fdu/eraSkJLVs2VJjxoxRRkaG/ypSBl/qV1BQUKKJNTo6Whs2bPB5m/7ij/oVs+H4+eLjjz/22B+SlJKS4t4fNh0/X52tjsUC9RhWREX3QSA7duyYmjVrpuTkZF111VXauXOn00XyWVFRkY4ePar69etLqr73YdAHlezsbBUWFqpRo0Yeyxs1aqSDBw+W+pzRo0dr5syZ6tu3r2rWrKlWrVpp4MCBeuihh3zepj/5o46S1LNnTy1YsEArV67UvHnzlJ6ern79+uno0aN+rc+ZfKlfSkqKnnzySe3evVtFRUVatWqVlixZoszMTJ+36S/+qJ9kz/HzxcGDB0vdH7m5ucrPz7fq+PnqbHWUAvsYVkRZ+yBQjuHZtG3bVi+99JLefvttvfLKKyoqKlLv3r31ww8/OF00nzzxxBM6duyYhg8fLqn6PkeDPqj4Yu3atXr88cf17LPPauvWrVqyZIneffddPfbYY04XrcpUpI5DhgzR9ddfr86dOyslJUUrVqzQkSNH9OabbzpY8or5+9//rnPPPVft2rVTRESEJk6cqPHjx5/1duKBoiL1C+Tjh9M4hoGtV69euuGGG9S1a1cNGDBAS5YsUXx8vJ5//nmni+a11157TTNmzNCbb76phg0bVutr16jWV3NAXFycwsPDlZWV5bE8KytLCQkJpT7nkUce0dixY3XLLbdIkjp16qS8vDzddtttevjhh33apj/5o46lndDr1aunNm3aaM+ePVVfiXL4Ur/4+HgtW7ZMJ06c0E8//aSkpCRNmTJFLVu29Hmb/uKP+pXGqePni4SEhFL3R0xMjKKjoxUeHm7N8fPV2epYmkA6hhVR1j4IlGPorZo1a+r8888PuOP3xhtv6JZbbtGiRYs8unmq63M0OL5eliMiIkLdunXT6tWr3cuKioq0evVq9erVq9TnHD9+vMSJOjw8XJJkjPFpm/7kjzqW5tixY0pLS1NiYmIVlbxiKrO/o6Ki1LhxY/3yyy966623dNVVV1V6m1XNH/UrjVPHzxe9evXy2B+StGrVKvf+sOn4+epsdSxNIB3DivBlHwSywsJCbd++PaCO3+uvv67x48fr9ddf1xVXXOHxWLW9D6tsWK7F3njjDRMZGWkWLFhgvv76a3PbbbeZevXquS8TGzt2rJkyZYp7/WnTppm6deua119/3ezdu9e8//77plWrVmb48OEV3mZ180cd77vvPrN27VqTnp5uNm7caAYPHmzi4uLMoUOHrK/fJ598Yt566y2TlpZm1q9fby666CLTokUL8/PPP1d4m9XJH/Wz6fgdPXrUfPHFF+aLL74wksyTTz5pvvjiC/P9998bY4yZMmWKGTt2rHv94kt3H3jgAfPNN9+YuXPnlnp5si3Hzxj/1DGQj6Exxr1+t27dzOjRo80XX3xhdu7c6X5848aNpkaNGuaJJ54w33zzjZk2bZpjlyf7o34zZsww7733nklLSzNbtmwxI0eONFFRUR7rVCdv6/jqq6+aGjVqmLlz53pcYn3kyBH3OtXxPgyJoGKMMU8//bRp2rSpiYiIMD169DCffPKJ+7EBAwaYcePGuf8+deqUmT59umnVqpWJiooyycnJ5s477/Q4CZxtm06o6jqOGDHCJCYmmoiICNO4cWMzYsQIs2fPnmqskSdv6rd27Vpz3nnnmcjISNOgQQMzduxY8+OPP3q1zepW1fWz6fh9+OGHRlKJn+I6jRs3zgwYMKDEc7p27WoiIiJMy5Ytzfz580ts16bj5486BvoxLG39Zs2aeazz5ptvmjZt2piIiAjToUMH8+6771ZPhc7gj/pNmjTJ/f/ZqFEjc/nll3vMQVLdvK3jgAEDyl2/mL/fhy5jymjnBwAAcFjQj1EBAACBi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAVLG1a9fK5XLpyJEjThcFCHgEFSCA3XjjjXK5XJo1a5bH8mXLlsnlcrn/NsbohRdeUK9evRQTE6M6deqoQ4cOuueeeyp8g7Tjx49r6tSpatWqlaKiohQfH68BAwbo7bffdq/TvHlzzZkzp0rq5m/F+87lcqlmzZpq0aKF/vCHP+jEiRNebWfgwIGaNGmSx7LevXsrMzNTsbGxVVhiIDQRVIAAFxUVpdmzZ+vnn38u9XFjjEaPHq27775bl19+ud5//319/fXXevHFFxUVFaU//elPFXqdO+64Q0uWLNHTTz+tb7/9VitXrtR1112nn376qSqrU60uu+wyZWZmau/evXrqqaf0/PPPa9q0aZXebkREhBISEjzCIgAfVemE/ACq1bhx48xvf/tb065dO/PAAw+4ly9dutQUv71ff/11I8m8/fbbpW6jqKioQq8VGxtrFixYUObjpd0XpNhHH31k+vbta6KiokyTJk3MXXfdZY4dO+Z+/F//+pfp1q2bqVOnjmnUqJEZNWqUycrKcj9efI+SlStXmq5du5qoqCgzaNAgk5WVZVasWGHatWtn6tata0aNGmXy8vIqVJ9x48aZq666ymPZsGHDzPnnn+/+Ozs724wcOdIkJSWZ6Oho07FjR/Paa695bOPMOqenp7vL++t7Zy1evNi0b9/eREREmGbNmpknnniiQuUEQh0tKkCACw8P1+OPP66nn35aP/zwQ4nHX3/9dbVt21ZXXnllqc+v6Lf+hIQErVixQkePHi318SVLlqhJkyaaOXOmMjMzlZmZKUlKS0vTZZddpmuvvVZfffWVFi5cqA0bNmjixInu5546dUqPPfaYvvzySy1btkz79u3TjTfeWOI1pk+frmeeeUabNm3S/v37NXz4cM2ZM0evvfaa3n33Xb3//vt6+umnK1SfM+3YsUObNm1SRESEe9mJEyfUrVs3vfvuu9qxY4duu+02jR07Vp999pkk6e9//7t69eqlW2+91V3n5OTkEtvesmWLhg8frpEjR2r79u2aPn26HnnkES1YsMCnsgIhxemkBMB3v24V+M1vfmNuuukmY4xni0q7du3MlVde6fG8e+65x9SuXdvUrl3bNG7cuEKvtW7dOtOkSRNTs2ZNc+GFF5pJkyaZDRs2eKzTrFkz89RTT3ksu/nmm81tt93mseyjjz4yYWFhJj8/v9TX2rx5s5Fkjh49aoz5vxaVDz74wL1OamqqkWTS0tLcy26//XaTkpJSofqMGzfOhIeHm9q1a5vIyEgjyYSFhZnFixeX+7wrrrjC3Hfffe6/BwwYYO655x6Pdc5sURk9erS55JJLPNZ54IEHTPv27StUViCU0aICBInZs2fr5Zdf1jfffHPWdR9++GFt27ZNjz76qI4dO1ah7ffv31979+7V6tWrdd1112nnzp3q16+fHnvssXKf9+WXX2rBggWqU6eO+yclJUVFRUVKT0+XdLrFYejQoWratKnq1q2rAQMGSJIyMjI8ttW5c2f3740aNVKtWrXUsmVLj2WHDh2qUH0kadCgQdq2bZs+/fRTjRs3TuPHj9e1117rfrywsFCPPfaYOnXqpPr166tOnTp67733SpTrbL755hv16dPHY1mfPn20e/duFRYWerUtINQQVIAg0b9/f6WkpGjq1Kkey88991zt2rXLY1l8fLxat26thg0bevUaNWvWVL9+/fTggw/q/fff18yZM/XYY4/p5MmTZT7n2LFjuv3227Vt2zb3z5dffqndu3erVatWysvLU0pKimJiYvTqq69q8+bNWrp0qSSV2G7NmjXdvxdfrfNrLpdLRUVFFa5P7dq11bp1a3Xp0kUvvfSSPv30U7344ovux//617/q73//ux588EF9+OGH2rZtm1JSUsqtL4CqVcPpAgCoOrNmzVLXrl3Vtm1b97JRo0Zp9OjRevvtt3XVVVdV6eu1b99ev/zyi06cOKGIiAhFRESUaCG44IIL9PXXX6t169albmP79u366aefNGvWLPf4js8//7xKy1kRYWFheuihhzR58mSNHj1a0dHR2rhxo6666ir97ne/kyQVFRXpu+++U/v27d3PK63OZzrvvPO0ceNGj2UbN25UmzZtFB4eXvWVAYIILSpAEOnUqZPGjBmjf/zjH+5lI0eO1HXXXaeRI0dq5syZ+vTTT7Vv3z6tW7dOCxcurPCJcuDAgXr++ee1ZcsW7du3TytWrNBDDz2kQYMGKSYmRtLpeVTWr1+vH3/8UdnZ2ZKkBx98UJs2bdLEiRO1bds27d69W2+//bZ7MG3Tpk0VERGhp59+Wnv37tXy5cvP2p3kL9dff73Cw8M1d+5cSadbo1atWqVNmzbpm2++0e23366srCyP5zRv3ty9T7Ozs0tt0bnvvvu0evVqPfbYY/ruu+/08ssv65lnntH9999fLfUCAhlBBQgyM2fO9DhZulwuLVy4UHPmzNGKFSt08cUXq23btrrpppuUnJysDRs2VGi7KSkpevnll3XppZfqvPPO01133aWUlBS9+eabHq+9b98+tWrVSvHx8ZJOjytZt26dvvvuO/Xr10/nn3++Hn30USUlJUk63Q21YMECLVq0SO3bt9esWbP0xBNPVOEeqbgaNWpo4sSJ+stf/qK8vDz98Y9/1AUXXKCUlBQNHDhQCQkJuvrqqz2ec//99ys8PFzt27dXfHx8qeNXLrjgAr355pt644031LFjRz366KOaOXNmqVc2AfDkMsYYpwsBAABQGlpUAACAtQgqACTJ4/LhM38++ugjp4vnlYyMjHLr4+3lxQCcQ9cPAEkq9+aEjRs3VnR0dDWWpnJ++eUX7du3r8zHmzdvrho1uOgRCAQEFQAAYC26fgAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAa/0/JkLAN7EtDhYAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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bLBaLcySq2G233eb8d7t27TR79mxddNFFOnLkiJo0aeKR4ygNI0gAAI+x2fKVmLhKFsvJyb3Fc5C4vFY3GWOcl8w+/PBD/eUvf1GrVq0UHBys4cOHKycnR0ePHi13G1u2bFFiYqLatGmj4OBg9enTR5K0b9++Gq+/PIwgAQA8Ki4uQzExu5WbG6rQ0FzCUR22fft2RUdHa+/evRo4cKDuuusuPf744woNDdX69es1atQoFRYWlnkH8YKCAiUkJCghIUGvv/66mjVrpn379ikhIcHr32gkIAEAapzVanV5bbPllxqM3Puh9vroo4/07bff6h//+Ie2bNkih8OhZ599Vj4+Jy9Ovfnmmy79rVarioqKXNp27NihnJwczZw5U5GRkZKkr776yjMHcBoEJABAjQsLC1NycjJ30q6jjh8/rqysLJev+c+YMUMDBw7ULbfcom3btunEiROaM2eOEhMTtWHDBs2bN89lG1FRUTpy5IjWrVunLl26qHHjxmrTpo2sVqvmzJmjO++8U9u2bdP06dO9dJSumIMEAPCIsLAwtWjRoswfwlHttWbNGrVo0UJRUVHq37+/Pv74Y82ePVvvvvuufH191aVLF82aNUtPPvmkzjvvPL3++uuaMWOGyzZ69uypO++8U4MHD1azZs301FNPqVmzZlq4cKHeeustderUSTNnztQzzzzjpaN0ZTHGGG8XURfl5eXJZrPJbrcrJCTE2+UAAGqxY8eOKTMzU9HR0QoICKj0+t6+D1JdU975rujnN5fYAACVcuodnX/7zUeZmY0UHf2nWrY8+c00LpVVPy5Reh4BCQBQYaeOZJT3yBBGMqof59OzmIMEAKiw4hGMsh4ZUvzwWW9/RRs4UwQkAECl8cgQ1HcEJABApRU/MuRUPDIE9QkBCQBQaTwyBPUdk7QBAFXCI0NQnxGQAABVVtYjQ4C6jktsAAAAbghIAIAKq+jDZHnoLCrqk08+kcVi0eHDhyu8TlRUlFJTU2usJolLbACASuCOzg3PrbfeqkWLFmnMmDElHkB7991364UXXtCIESO0cOFC7xRYQwhIAIBKIfw0PJGRkVqyZImee+45BQYGSjr5vLPFixerTZs2Xq6uZnCJDQAAlCsuLk6RkZFavny5s2358uVq06aNunXr5mw7fvy47r33XjVv3lwBAQHq1auXvvzyS5dtrV69Wuecc44CAwN1+eWXa+/evSX2t379evXu3VuBgYGKjIzUvffeq4KCgho7vtIQkAAAqGN++UX6+OOTvz3ltttu04IFC5yvX3nlFY0cOdKlz4MPPqhly5Zp0aJFSk9PV/v27ZWQkKDc3JM3EP3555913XXXKTExUVu3btXtt9+uhx9+2GUbe/bsUf/+/XX99dfrm2++0dKlS7V+/XolJyfX/EGegoAEAEAd8vLLUtu20hVXnPz98sue2e/NN9+s9evX66efftJPP/2kDRs26Oabb3YuLygo0Isvvqinn35aV111lTp16qR//vOfCgwM1Mv/V+SLL76omJgYPfvss+rQoYNuuukm3XrrrS77mTFjhm666SalpKQoNjZWPXv21OzZs/Xqq6/q2LFjnjlYMQcJAIA645dfpNGjJcf/PeXF4ZDGjJESEqTWrWt2382aNdOAAQO0cOFCGWM0YMAANW3a1Ll8z549OnHihC699FJnm5+fny6++GJt375dkrR9+3Z1797dZbs9evRwef3111/rm2++0euvv+5sM8bI4XAoMzNT5557bk0cXgkEJAAA6ohdu/4XjooVFUm7d9d8QJJOXmYrvtQ1d+7cGtnHkSNHNGbMGN17770llnlyQjgBCQCAOiI2VvLxcQ1Jvr5S+/ae2X///v1VWFgoi8WihIQEl2UxMTGyWq3asGGD2rZtK0k6ceKEvvzyS6WkpEiSzj33XP373/92We/zzz93eR0XF6fvv/9e7T11UGVgDhIAAHVE69bSSy+dDEXSyd/z53tm9Ojk/ny1fft2ff/99/ItLuL/BAUF6a677tIDDzygNWvW6Pvvv9cdd9yho0ePatSoUZKkO++8U7t27dIDDzygnTt3avHixSXun/TQQw9p48aNSk5O1tatW7Vr1y69++67Hp+kzQgSAAB1yKhRJ+cc7d59cuTIU+GoWEhISJnLZs6cKYfDoeHDhys/P18XXnih3n//fZ199tmSTl4iW7Zsmf7xj39ozpw5uvjii/XEE0/otttuc27jggsu0H//+19NmDBBvXv3ljFGMTExGjx4cI0f26ksxhjj0T3WE3l5ebLZbLLb7eW+WQAAOHbsmDIzMxUdHa2AgABvl1PvlXe+K/r5zSU2AAAANwQkAAAANwQkAAAANwQkAAAANwQkAAA8hO9FeUZ1nGcCEgAANczPz0+SdPToUS9X0jAUn+fi814V3AcJAIAa5uvrq7POOksHDhyQJDVu3FgWi8XLVdU/xhgdPXpUBw4c0FlnnVXiZpaVQUACAMADIiIiJMkZklBzzjrrLOf5rioCEgAAHmCxWNSiRQs1b95cJ06c8HY59Zafn98ZjRwVIyABAOBBvr6+1fIBjprFJG0AAAA3BCQAAAA3BCQAAAA3BCQAAAA3BCQAAAA3BCQAAAA3BCQAAAA3BCQAAAA33CgSAACUKicnR4WFhWUut1qtCgsL82BFnkNAAgAAJeTk5CgtLe20/ZKTk+tlSOISGwAAKMF95MhuD1ZmZpTs9uBy+9UXXg9Ic+fOVVRUlAICAtS9e3d98cUX5fZ/66231LFjRwUEBOj888/X6tWrXZYbYzR58mS1aNFCgYGB6tevn3bt2uXS54cfftC1116rpk2bKiQkRL169dLHH39c7ccGAEB9kJ7eTampKVq0aIRSU1OUnt7N2yXVOK8GpKVLl2rcuHGaMmWK0tPT1aVLFyUkJOjAgQOl9t+4caOGDh2qUaNGKSMjQ0lJSUpKStK2bducfZ566inNnj1b8+bN0+bNmxUUFKSEhAQdO3bM2WfgwIH6888/9dFHH2nLli3q0qWLBg4cqKysrBo/ZgAA6hK7PVgrVw6UMScjgzE+WrlyYImRpPrGqwFp1qxZuuOOOzRy5Eh16tRJ8+bNU+PGjfXKK6+U2v/5559X//799cADD+jcc8/V9OnTFRcX57xGaoxRamqqJk6cqGuvvVYXXHCBXn31Vf32229asWKFJOnQoUPatWuXHn74YV1wwQWKjY3VzJkzdfToUZegBQAApNzcMGc4KmaMj3JzQ71UkWd4LSAVFhZqy5Yt6tev3/+K8fFRv379tGnTplLX2bRpk0t/SUpISHD2z8zMVFZWlksfm82m7t27O/uEhYWpQ4cOevXVV1VQUKA///xT8+fPV/PmzRUfH19mvcePH1deXp7LDwAA9V1oaI4sFodLm8XiUGhorpcq8gyvBaRDhw6pqKhI4eHhLu3h4eFlXurKysoqt3/x7/L6WCwWffjhh8rIyFBwcLACAgI0a9YsrVmzRmeffXaZ9c6YMUM2m835ExkZWbkDBgCgDrLZ8pWYuMoZkiwWhxITV8lmy/dyZTWrwX3N3xiju+++W82bN9dnn32mwMBA/b//9/+UmJioL7/8Ui1atCh1vfHjx2vcuHHO13l5eYQkAECDEBeXoZiY3crNDVVoaG69D0eSFwNS06ZN5evrq+zsbJf27OxsRURElLpOREREuf2Lf2dnZ7sEnezsbHXt2lWS9NFHH2nVqlX6/fffFRISIkl64YUXtHbtWi1atEgPP/xwqfv29/eXv79/5Q8UAIA6yGq1ury22fJLDUbu/eoLrwUkq9Wq+Ph4rVu3TklJSZIkh8OhdevWKTk5udR1evTooXXr1iklJcXZtnbtWvXo0UOSFB0drYiICK1bt84ZiPLy8rR582bdddddkqSjR49KOjnf6VQ+Pj5yOFyvsQIA0FCFhYUpOTmZO2l7w7hx4zRixAhdeOGFuvjii5WamqqCggKNHDlSknTLLbeoVatWmjFjhiRp7Nix6tOnj5599lkNGDBAS5Ys0VdffaWXXnpJ0sn5RSkpKXrssccUGxur6OhoTZo0SS1btnSGsB49eujss8/WiBEjNHnyZAUGBuqf//ynMjMzNWDAAK+cBwAAaqP6Gn4qwqsBafDgwTp48KAmT56srKwsde3aVWvWrHFOst63b5/LSE/Pnj21ePFiTZw4UY888ohiY2O1YsUKnXfeec4+Dz74oAoKCjR69GgdPnxYvXr10po1axQQECDp5KW9NWvWaMKECbriiit04sQJde7cWe+++666dOni2RMAAABqJYsxxni7iLooLy9PNptNdrvdOZcJAADUbhX9/Pb6o0YAAABqGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAGwISAACAm0beLgAAACAnJ0eFhYVlLrdarQoLC/NYPQQkAADgVTk5OUpLSzttv+TkZI+FJC6xAQAAr3IfObLbg5WZGSW7PbjcfjWJESQAAFBrpKd308qVA2WMjywWhxITVykuLsPjdTCCBAAAagW7PdgZjiTJGB+tXDmwxEiSJxCQAABArZCbG+YMR8WM8VFubqjHayEgAQCAWiE0NEcWi8OlzWJxKDQ01+O1EJAAAECtYLPlKzFxlTMkFc9BstnyPV4Lk7QBAECtEReXoZiY3crNDVVoaK5XwpFEQAIAAF5mtVpdXtts+aUGI/d+NYmABAAAvCosLEzJycncSRsAAOBUngw/FcEkbQAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADcEJAAAADeNvF0ApJycHBUWFpa53Gq1KiwszIMVAQDQsBGQvCwnJ0dpaWmn7ZecnExIAgDAQ7jE5mXljRxVpR8AADhzBCQAAAA3BCQAAAA3BCQAAAA3Xg9Ic+fOVVRUlAICAtS9e3d98cUX5fZ/66231LFjRwUEBOj888/X6tWrXZYbYzR58mS1aNFCgYGB6tevn3bt2lViO++99566d++uwMBAnX322UpKSqrOw6oyuz1YmZlRstuDvV0KAAANllcD0tKlSzVu3DhNmTJF6enp6tKlixISEnTgwIFS+2/cuFFDhw7VqFGjlJGRoaSkJCUlJWnbtm3OPk899ZRmz56tefPmafPmzQoKClJCQoKOHTvm7LNs2TINHz5cI0eO1Ndff60NGzZo2LBhNX68p5Oe3k2pqSlatGiEUlNTlJ7ezdslAQDQIFmMMcZbO+/evbsuuugi59fcHQ6HIiMjdc899+jhhx8u0X/w4MEqKCjQqlWrnG2XXHKJunbtqnnz5skYo5YtW+q+++7T/fffL0my2+0KDw/XwoULNWTIEP3555+KiorStGnTNGrUqCrXnpeXJ5vNJrvdrpCQkCpvZ//+/XrppZdktwcrNTVFxvwvs1osDqWkpMpmy9fo0aPVokWLKu8HAABU/PPbayNIhYWF2rJli/r16/e/Ynx81K9fP23atKnUdTZt2uTSX5ISEhKc/TMzM5WVleXSx2azqXv37s4+6enp+vXXX+Xj46Nu3bqpRYsWuuqqq1xGoTzJarVKknJzw1zCkSQZ46Pc3FCXfgAAoOZ57UaRhw4dUlFRkcLDw13aw8PDtWPHjlLXycrKKrV/VlaWc3lxW1l9fvzxR0nS1KlTNWvWLEVFRenZZ59V37599cMPPyg0NLTUfR8/flzHjx93vs7Ly6vooZYrLCxMycnJ2rv3T732mpHDYXEu8/U1uueeqxQV1YibRAIA4EFen6TtaQ6HQ5I0YcIEXX/99YqPj9eCBQtksVj01ltvlbnejBkzZLPZnD+RkZHVVlNYWJji48P10ksW+fqebPP1lebPtyg+PpxwBACAh3ltBKlp06by9fVVdna2S3t2drYiIiJKXSciIqLc/sW/s7OzXebrZGdnq2vXrpLkbO/UqZNzub+/v9q1a6d9+/aVWe/48eM1btw45+u8vLxqDUmSNGqUlJAg7d4ttW8vtW5drZsHKoRnAwKAFwOS1WpVfHy81q1b5/yKvcPh0Lp165ScnFzqOj169NC6deuUkpLibFu7dq169OghSYqOjlZERITWrVvnDER5eXnavHmz7rrrLklSfHy8/P39tXPnTvXq1UuSdOLECe3du1dt27Yts15/f3/5+/uf4VGfXuvWBCN4j/uzAe32YOXmhik0NEc2W76znWcDAqjvvPqw2nHjxmnEiBG68MILdfHFFys1NVUFBQUaOXKkJOmWW25Rq1atNGPGDEnS2LFj1adPHz377LMaMGCAlixZoq+++kovvfSSJMlisSglJUWPPfaYYmNjFR0drUmTJqlly5bOEBYSEqI777xTU6ZMUWRkpNq2baunn35aknTjjTd6/iQAtcipI0fp6d20cuVAGeMji8WhxMRViovLKNEPAOojrwakwYMH6+DBg5o8ebKysrLUtWtXrVmzxjnJet++ffLx+d80qZ49e2rx4sWaOHGiHnnkEcXGxmrFihU677zznH0efPBBFRQUaPTo0Tp8+LB69eqlNWvWKCAgwNnn6aefVqNGjTR8+HD98ccf6t69uz766COdffbZnjt4oBaz24Od4Ug6+Y3KlSsHKiZmt8tIEgDUV169D1JdVl33QQJqk+L7cmVmRmnRohEllo8YsVDR0T9xXy4AdVatvw8SgNorNDRHFovDpc1icSg0NNdLFQGAZxGQAJRgs+UrMXGVMyQVz0Hi8hqAhsKrc5AA1F5xcRmKidmt3NxQhYbmEo4ANCgEJABlstnyCUYAGiQusQFwqugz/3g2IID6jhEkAE7FzwbkTtoAGjoCEgAXhB8A4BIbAABACQQkAAAANwQkAAAAN8xBAuBVOTk5TAoHUOsQkAB4TU5OjtLS0pyv7fZg5eaGKTQ0x+X+S8nJyYQkAB5FQALgNaeOHKWnd9PKlQNljI/z0SZxcRkl+gGAJzAHCYDX2e3BznAkScb4aOXKgbLbg71cGYCGioAEwOtyc8Oc4aiYMT7KzQ31UkUAGrpKBaRffvlFhw4dcr7+7LPPdNNNN6l37966+eabtWnTpmovEED9FxqaI4vF4dJmsTgUGprrpYoANHSVCkjXX3+9Pv/8c0nSu+++q759++rIkSO69NJLdfToUfXp00erVq2qkUIB1F82W74SE1c5Q1LxHCQelAvAWyo1Sfu7775T586dJUkzZszQE088oYceesi5PC0tTZMnT9bAgQOrt0oA9V5cXIZiYnYrNzdUoaG5hCMAXlWpEaRGjRopP//kH63MzExdddVVLsuvuuoq7dy5s/qqA9Cg2Gz5io7+iXAEwOsqFZD69OmjN954Q5LUrVs3ffLJJy7LP/74Y7Vq1araigNQv1mt1mrtBwDVpVKX2GbOnKnevXvrt99+U69evTRhwgR9+eWXOvfcc7Vz504tXbpU8+bNq6laAdQzYWFhSk5O5k7aAGodizHGVGaFPXv2aOLEiXrvvfd05MgRSScvvV100UV64IEHlJSUVBN11jp5eXmy2Wyy2+0KCQnxdjkAAKACKvr5XemAVMwYowMHDsjhcKhp06by8/OrcrF1EQEJAIC6p6Kf31V+1IjFYlF4eHhVVwcAAKi1KhWQxo0bV6F+s2bNqlIxAAAAtUGlAlJGRobL6/Xr1ys+Pl6BgYHONovFUj2VAQAAeEmlAtLHH3/s8jo4OFiLFy9Wu3btqrUoAAAAb+JhtQAAAG4ISAAAAG4ISAAAAG4qNQfpm2++cXltjNGOHTucN4wsdsEFF5x5ZQAAAF5SqRtF+vj4yGKxqLRVitstFouKioqqtcjaiBtFAgBQ99TIjSIzMzPPuDAAAIDarlIBqW3btjVVBwAAQK1RpUnaDoejzPZ9+/adUUEAAADeVqmAlJeXp0GDBikoKEjh4eGaPHmyy3yjgwcPKjo6utqLBAAA8KRKXWKbNGmSvv76a7322ms6fPiwHnvsMaWnp2v58uWyWq2SVOoEbgAAgLqkUiNIK1as0Pz583XDDTfo9ttv11dffaWDBw8qMTFRx48fl8Sz2AAAQN1XqYB08OBBl4naTZs21Ycffqj8/HxdffXVOnr0aLUXCAAA4GmVCkht2rTR9u3bXdqCg4P1wQcf6I8//tDf/va3ai0OAADAGyoVkK688kotWLCgRHuTJk30/vvvKyAgoNoKAwAA8JZKTdJ+9NFHtX///lKXBQcHa+3atUpPT6+WwgAAALylUiNIGRkZuuGGG5SXl1dimd1u1yWXXMIkbaAWycnJ0f79+8v8ycnJ8XaJAFArVWoEKTU1VXfccUepzy6x2WwaM2aMnnvuOV122WXVViCAqsnJyVFaWtpp+yUnJyssLMwDFQFA3VGpEaSvv/5a/fv3L3P5X//6V23ZsuWMiwJw5goLC6u1HwA0JJUKSNnZ2fLz8ytzeaNGjXTw4MEzLgpA9bPbg5WZGSW7PdjbpQBArVepS2ytWrXStm3b1L59+1KXf/PNN2rRokW1FAag+qSnd9PKlQNljI8sFocSE1cpLi7D22UBQK1VqRGkq6++WpMmTdKxY8dKLPvjjz80ZcoUDRw4sNqKA3Dm7PZgZziSJGN8tHLlQEaSAKAclRpBmjhxopYvX65zzjlHycnJ6tChgyRpx44dmjt3roqKijRhwoQaKRRA1eTmhjnDUTFjfJSbGyqbLd9LVQFA7VapgBQeHq6NGzfqrrvu0vjx450PprVYLEpISNDcuXMVHh5eI4UCqJrQ0BxZLA6XkGSxOBQamuvFqgCgdqtUQJKktm3bavXq1fr999+1e/duGWMUGxurs88+uybqA3CGbLZ8JSauKjEHidEjAChbpQNSsbPPPlsXXXRRddYCoBpZrVbnv+PiMhQTs1u5uaEKDc11CUen9gMAnFTlgASgdgsLC1NycnK59zmyWq3cJBIASkFAAuoxwg8AVE2lvuYPAADQEBCQAAAA3BCQAAAA3BCQAAAA3DBJGxWSk5PDt6EAAA0GAQmnlZOTo7S0tNP2S05OJiQBAOoFAhJOq7yRo6r0Q+UxggcAnkVAAmo5RvAAwPOYpA3UcozgAYDnEZCAOsZuD1ZmZpTs9mBvlwIA9RaX2IA6JD29m1auHChjfGSxOJSYuEpxcRneLgsA6h1GkFBpjGB4h90e7AxHkmSMj1auHMj/DgBQA2pFQJo7d66ioqIUEBCg7t2764svvii3/1tvvaWOHTsqICBA559/vlavXu2y3BijyZMnq0WLFgoMDFS/fv20a9euUrd1/Phxde3aVRaLRVu3bq2uQ6q30tO7KTU1RYsWjVBqaorS07t5u6QGIzc3zBmOihnjo9zcUC9VBAD1l9cD0tKlSzVu3DhNmTJF6enp6tKlixISEnTgwIFS+2/cuFFDhw7VqFGjlJGRoaSkJCUlJWnbtm3OPk899ZRmz56tefPmafPmzQoKClJCQoKOHTtWYnsPPvigWrZsWWPHVx9YrVZJpx/BKO6HmhEamiOLxeHSZrE4FBqa66WKAKD+8npAmjVrlu644w6NHDlSnTp10rx589S4cWO98sorpfZ//vnn1b9/fz3wwAM699xzNX36dMXFxTm/Bm2MUWpqqiZOnKhrr71WF1xwgV599VX99ttvWrFihcu2/vOf/+iDDz7QM888U9OHWaeFhYUpOTlZPXuOKHUE49JLR/AVcw+w2fKVmLjKGZKK5yDZbPlergwA6h+vTtIuLCzUli1bNH78eGebj4+P+vXrp02bNpW6zqZNmzRu3DiXtoSEBGf4yczMVFZWlvr16+dcbrPZ1L17d23atElDhgyRJGVnZ+uOO+7QihUr1Lhx49PWevz4cR0/ftz5Oi8vr8LHWR+EhYXpkkskHx/Jccoghq+v1L17mMhGNefUkbm4uAzFxOxWbm6oQkNzXcIRI3gAUH28GpAOHTqkoqIihYeHu7SHh4drx44dpa6TlZVVav+srCzn8uK2svoYY3Trrbfqzjvv1IUXXqi9e/eettYZM2Zo2rRpFTqu+qp1a+mll6QxY6SiopPhaP78k+2oOcUjeNxJGwA8p0F+zX/OnDnKz893Gbk6nfHjx7uMXOXl5SkyMrImyqvVRo2SEhKk3bul9u0JR55C+AEAz/LqHKSmTZvK19dX2dnZLu3Z2dmKiIgodZ2IiIhy+xf/Lq/PRx99pE2bNsnf31+NGjVS+/btJUkXXnihRowYUep+/f39FRIS4vLTULVuLfXtSzgCANRfXg1IVqtV8fHxWrdunbPN4XBo3bp16tGjR6nr9OjRw6W/JK1du9bZPzo6WhERES598vLytHnzZmef2bNn6+uvv9bWrVu1detW520Cli5dqscff7xajxEAANQ9Xr/ENm7cOI0YMUIXXnihLr74YqWmpqqgoEAjR46UJN1yyy1q1aqVZsyYIUkaO3as+vTpo2effVYDBgzQkiVL9NVXX+mll16SJFksFqWkpOixxx5TbGysoqOjNWnSJLVs2VJJSUmSpDZt2rjU0KRJE0lSTEyMWjMsAgBAg+f1gDR48GAdPHhQkydPVlZWlrp27ao1a9Y4J1nv27dPPj7/G+jq2bOnFi9erIkTJ+qRRx5RbGysVqxYofPOO8/Z58EHH1RBQYFGjx6tw4cPq1evXlqzZo0CAgI8fnwAAKDusRhjjLeLqIvy8vJks9lkt9sb9HwkAADqkop+fnv9RpEAAAC1DQEJAADADQEJAADADQEJAADADQEJAADADQEJAADADQEJAADAjddvFImqy8nJ4QnvAADUAAJSHZWTk6O0tLTT9ktOTiYkAQBQSVxiq6PKGzmqSj8AAPA/BCQAAAA3BCQAAAA3BCQAAAA3BCQAAAA3BKR6wm4PVmZmlOz2YG+XAgBAncfX/OuB9PRuWrlyoIzxkcXiUGLiKsXFZXi7LAAA6ixGkOooq9Uq6eTIUXE4kiRjfLRy5UDnSFJxPwAAUHEWY4zxdhF1UV5enmw2m+x2u0JCQrxSQ05Ojj7+WLrxxpI3gnz77Rz17StuEgkAwCkq+vnNJbY6LCwsTJdcIvn4SA7H/9p9faXu3cNENgIAoGq4xFbHtW4tvfTSyVAknfw9f/7JdgAAUDWMINUDo0ZJCQnS7t1S+/aEIwC1Ew/YRl1CQKonWrcmGAGovdwfsG23Bys3N0yhoTmy2fKd7dX1gO1Tw9hvv/koM7ORoqP/VMuWJ+cjEMZwOgQkAECNO3XkqLxbk1THA7ZPDWPl7au6whjqJwISABdcBkFNKuvWJDExu11Gks5E8fv3dPuqjjCG+ouABMDJ05dBUFJ9D6i5uWHOwFLMGB/l5oZWW0Dyxr5Q/xCQADh58jIISmoIATU0NEcWi8MluFgsDoWG5tbpfaH+4Wv+AEo43R3aUTPcA2pqaooWLRqh1NQUpad3K7VfXWOz5SsxcZUslpOTpYvDd02M6HhyX6h/GEECUAKXJrzLE/N0vCkuLkMxMbuVmxuq0NDcGj0mT+4L9QsBCUAJXJrwroYQUG22fI8diyf3hfqDS2wASuDShHcVB9RT1fWAWtEHZ/OAbdQWjCABKBWXJrynOKC6T5Kvy/8bhIWFKTk52SPf0COMoToQkACUiUsT3lMfA6qnvnnnyTCG+ouABMCJ//KuXQioVUf4wZkiIAFw4r+8vcvTAZXnlQFlsxhjjLeLqIvy8vJks9lkt9sVEhLi7XIA1BOeupM2zytDQ1XRz29GkACgFvFUGOF5ZUD5+Jo/ADRg5d1zCWjICEgA0IDVx3suAdWBgAQADRg3BQVKxxwkAGjg6uM9l4AzRUACAHDPJcANl9gAAADcEJAAoAHirulA+bjEBgANEHdNB8pHQAKABorwA5SNS2wAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuCEgAAABuakVAmjt3rqKiohQQEKDu3bvriy++KLf/W2+9pY4dOyogIEDnn3++Vq9e7bLcGKPJkyerRYsWCgwMVL9+/bRr1y7n8r1792rUqFGKjo5WYGCgYmJiNGXKFBUWFtbI8QEAgLrF6wFp6dKlGjdunKZMmaL09HR16dJFCQkJOnDgQKn9N27cqKFDh2rUqFHKyMhQUlKSkpKStG3bNmefp556SrNnz9a8efO0efNmBQUFKSEhQceOHZMk7dixQw6HQ/Pnz9d3332n5557TvPmzdMjjzzikWMGAAC1m8UYY7xZQPfu3XXRRRcpLS1NkuRwOBQZGal77rlHDz/8cIn+gwcPVkFBgVatWuVsu+SSS9S1a1fNmzdPxhi1bNlS9913n+6//35Jkt1uV3h4uBYuXKghQ4aUWsfTTz+tF198UT/++GOF6s7Ly5PNZpPdbldISEhlDxsAAHhBRT+/vTqCVFhYqC1btqhfv37ONh8fH/Xr10+bNm0qdZ1Nmza59JekhIQEZ//MzExlZWW59LHZbOrevXuZ25ROhqjQ0NAylx8/flx5eXkuPwAAoH7yakA6dOiQioqKFB4e7tIeHh6urKysUtfJysoqt3/x78psc/fu3ZozZ47GjBlTZq0zZsyQzWZz/kRGRpZ/cAAAoM7y+hwkb/v111/Vv39/3XjjjbrjjjvK7Dd+/HjZ7Xbnz88//+zBKgEAgCd5NSA1bdpUvr6+ys7OdmnPzs5WREREqetERESU27/4d0W2+dtvv+nyyy9Xz5499dJLL5Vbq7+/v0JCQlx+AABA/eTVgGS1WhUfH69169Y52xwOh9atW6cePXqUuk6PHj1c+kvS2rVrnf2jo6MVERHh0icvL0+bN2922eavv/6qvn37Kj4+XgsWLJCPT4MfTAMAAP+nkbcLGDdunEaMGKELL7xQF198sVJTU1VQUKCRI0dKkm655Ra1atVKM2bMkCSNHTtWffr00bPPPqsBAwZoyZIl+uqrr5wjQBaLRSkpKXrssccUGxur6OhoTZo0SS1btlRSUpKk/4Wjtm3b6plnntHBgwed9ZQ1cgUAABoOrwekwYMH6+DBg5o8ebKysrLUtWtXrVmzxjnJet++fS6jOz179tTixYs1ceJEPfLII4qNjdWKFSt03nnnOfs8+OCDKigo0OjRo3X48GH16tVLa9asUUBAgKSTI067d+/W7t271bp1a5d6vHzXAwAA6rycnJxyb75stVoVFhbmwYoqz+v3QaqruA8SAAAl5eTkOO9tWJ7k5GSvhKQ6cR8kAABQv1T0sV21/fFeBCQAAAA3BCQAAAA3BCQAAFBj7PZgZWZGyW4P9nYpleL1b7EBAID6KT29m1auHChjfGSxOJSYuEpxcRneLqtCGEECAADVzm4PdoYjSTLGRytXDqwzI0kEJAAAUO1yc8Oc4aiYMT7KzQ31UkWVQ0ACAADVxmq1SpJCQ3NksThcllksDoWG5rr0q62YgwQAAKpNWFiYkpOTVVhYqFat8vTQQzYVFVnk62v05JN5GjZsKHfSrs+4kzYAAKf3yy/S7t1S+/aS29O9vKKin9+MIAEAgBrTunXtCEaVxRwkAAAANwQkAAAANwQkAAAANwQkAAAANwQkAAAANwQkAAAANwQkAAAAN9wHCQAAeF1OTo4KCwvLXO7pu28TkAAAgFfl5OQoLS3ttP2Sk5M9FpK4xAYAALyqvJGjqvSrDgQkAAAANwQkAAAANwQkAAAANwQkAAAANwQkAAAANwQkAABQq9jtwcrMjJLdHuy1GrgPEgAA8KqjR486/52e3k0rVw6UMT6yWBxKTFyluLiMEv1qGiNIAADAqxo3bizp5MhRcTiSJGN8tHLlQOdIUnE/TyAgAQCAWiE3N8wZjooZ46Pc3FCP10JAAgAAtUJoaI4sFodLm8XiUGhorsdrISABAIBawWbLV2LiKmdIKp6DZLPle7wWJmkDAIBaIy4uQzExu5WbG6rQ0FyvhCOJgAQAAGoZmy3fa8GoGJfYAAAA3BCQAACAV1mtVpfXZd0o0r1fTeISGwAA8KqwsDAlJyersLBQixcH6tFHbXI4LPLxMXrqKbuGDftDVqtVYWFhHqvJYowxHttbPZKXlyebzSa73a6QkBBvlwOc1i+/SLt2SbGxUuvW3q4GAEr65RepbVvJcco3/X19pb17q+/vVkU/v7nEBjQAL7988o/OFVec/P3yy96uCABK2rXLNRxJUlGRtHu352shIAH13C+/SKNH/++PjsMhjRlzsh0AapPYWMnHLZn4+krt23u+FgISUI/l5OTo889zSv0vss2bc5STk+OdwgCgFK1bSy+9dDIUSSd/z5/vnWkBTNIG6qmcnBylpaXJbg+WxZLi8nwji8WhDRsWadu2fCUnJ3t04iMAlGfUKCkh4eRltfbtvTdnkhEkoJ4qLCyUdPpb9xf3A4DaonVrqW9f736hhBEkoAGoLbfuB4C6goAENBC14db9AFBXcIkNAADADQEJAADADQEJAADADQEJAADADQEJqKcq+tRrTz4dGwDqCr7FBtRTpz4duyyefjo2ANQVBCSgHiP8AEDVcIkNAADADQEJAADADQEJAADADQEJAADADQEJAADADQEJAADADQEJAADADQEJAADADQEJAADADXfSriJjjCQpLy/Py5UAAICKKv7cLv4cLwsBqYry8/MlSZGRkV6uBAAAVFZ+fr5sNluZyy3mdBEKpXI4HPrtt98UHBwsi8VyRtvKy8tTZGSkfv75Z4WEhFRThXUL54BzIHEOJM5BQz9+iXMg1ew5MMYoPz9fLVu2lI9P2TONGEGqIh8fH7Vu3bpatxkSEtJg/89QjHPAOZA4BxLnoKEfv8Q5kGruHJQ3clSMSdoAAABuCEgAAABuCEi1gL+/v6ZMmSJ/f39vl+I1nAPOgcQ5kDgHDf34Jc6BVDvOAZO0AQAA3DCCBAAA4IaABAAA4IaABAAA4IaABAAA4IaAVAPmzp2rqKgoBQQEqHv37vriiy/K7Z+amqoOHTooMDBQkZGR+sc//qFjx445l0+dOlUWi8Xlp2PHjjV9GGekMufgxIkTevTRRxUTE6OAgAB16dJFa9asOaNt1gbVfQ7q2vvg008/VWJiolq2bCmLxaIVK1acdp1PPvlEcXFx8vf3V/v27bVw4cISferS+6AmzkF9fx/s379fw4YN0znnnCMfHx+lpKSU2u+tt95Sx44dFRAQoPPPP1+rV6+u/uKrQU0c/8KFC0u8BwICAmrmAKpBZc/B8uXLdeWVV6pZs2YKCQlRjx499P7775foV9N/CwhI1Wzp0qUaN26cpkyZovT0dHXp0kUJCQk6cOBAqf0XL16shx9+WFOmTNH27dv18ssva+nSpXrkkUdc+nXu3Fn79+93/qxfv94Th1MllT0HEydO1Pz58zVnzhx9//33uvPOO/W3v/1NGRkZVd6mt9XEOZDq1vugoKBAXbp00dy5cyvUPzMzUwMGDNDll1+urVu3KiUlRbfffrvLH8a69j6oiXMg1e/3wfHjx9WsWTNNnDhRXbp0KbXPxo0bNXToUI0aNUoZGRlKSkpSUlKStm3bVp2lV4uaOH7p5B2mT30P/PTTT9VVcrWr7Dn49NNPdeWVV2r16tXasmWLLr/8ciUmJnr+M8GgWl188cXm7rvvdr4uKioyLVu2NDNmzCi1/913322uuOIKl7Zx48aZSy+91Pl6ypQppkuXLjVSb02o7Dlo0aKFSUtLc2m77rrrzE033VTlbXpbTZyDuvY+OJUk884775Tb58EHHzSdO3d2aRs8eLBJSEhwvq5r74NTVdc5qO/vg1P16dPHjB07tkT7oEGDzIABA1zaunfvbsaMGXOGFdas6jr+BQsWGJvNVm11eVJlz0GxTp06mWnTpjlfe+JvASNI1aiwsFBbtmxRv379nG0+Pj7q16+fNm3aVOo6PXv21JYtW5xDgz/++KNWr16tq6++2qXfrl271LJlS7Vr10433XST9u3bV3MHcgaqcg6OHz9eYng4MDDQ+V/FVdmmN9XEOShWV94HVbFp0yaXcyZJCQkJznNW194HVXG6c1CsPr8PKqKi56k+O3LkiNq2bavIyEhde+21+u6777xdUo1xOBzKz89XaGioJM/9LSAgVaNDhw6pqKhI4eHhLu3h4eHKysoqdZ1hw4bp0UcfVa9eveTn56eYmBj17dvX5RJb9+7dtXDhQq1Zs0YvvviiMjMz1bt3b+Xn59fo8VRFVc5BQkKCZs2apV27dsnhcGjt2rVavny59u/fX+VtelNNnAOpbr0PqiIrK6vUc5aXl6c//vijzr0PquJ050Cq/++DiijrPNWX98HpdOjQQa+88oreffdd/etf/5LD4VDPnj31yy+/eLu0GvHMM8/oyJEjGjRokCTPfSYQkLzsk08+0RNPPKEXXnhB6enpWr58ud577z1Nnz7d2eeqq67SjTfeqAsuuEAJCQlavXq1Dh8+rDfffNOLlVef559/XrGxserYsaOsVquSk5M1cuRI+fg0nLdnRc5BfX8foGJ4H6BHjx665ZZb1LVrV/Xp00fLly9Xs2bNNH/+fG+XVu0WL16sadOm6c0331Tz5s09uu+G8wnkAU2bNpWvr6+ys7Nd2rOzsxUREVHqOpMmTdLw4cN1++236/zzz9ff/vY3PfHEE5oxY4YcDkep65x11lk655xztHv37mo/hjNVlXPQrFkzrVixQgUFBfrpp5+0Y8cONWnSRO3atavyNr2pJs5BaWrz+6AqIiIiSj1nISEhCgwMrHPvg6o43TkoTX17H1REWeepvrwPKsvPz0/dunWrd++BJUuW6Pbbb9ebb77pcjnNU38LCEjVyGq1Kj4+XuvWrXO2ORwOrVu3Tj169Ch1naNHj5YYKfH19ZUkmTIek3fkyBHt2bNHLVq0qKbKq09VzkGxgIAAtWrVSn/++aeWLVuma6+99oy36Q01cQ5KU5vfB1XRo0cPl3MmSWvXrnWes7r2PqiK052D0tS390FFVOU81WdFRUX69ttv69V74I033tDIkSP1xhtvaMCAAS7LPPa3oNqme8MYY8ySJUuMv7+/Wbhwofn+++/N6NGjzVlnnWWysrKMMcYMHz7cPPzww87+U6ZMMcHBweaNN94wP/74o/nggw9MTEyMGTRokLPPfffdZz755BOTmZlpNmzYYPr162eaNm1qDhw44PHjq4jKnoPPP//cLFu2zOzZs8d8+umn5oorrjDR0dHm999/r/A2a5uaOAd17X2Qn59vMjIyTEZGhpFkZs2aZTIyMsxPP/1kjDHm4YcfNsOHD3f2//HHH03jxo3NAw88YLZv327mzp1rfH19zZo1a5x96tr7oCbOQX1/HxhjnP3j4+PNsGHDTEZGhvnuu++cyzds2GAaNWpknnnmGbN9+3YzZcoU4+fnZ7799luPHltF1MTxT5s2zbz//vtmz549ZsuWLWbIkCEmICDApU9tUtlz8Prrr5tGjRqZuXPnmv379zt/Dh8+7Ozjib8FBKQaMGfOHNOmTRtjtVrNxRdfbD7//HPnsj59+pgRI0Y4X584ccJMnTrVxMTEmICAABMZGWn+/ve/u3wwDh482LRo0cJYrVbTqlUrM3jwYLN7924PHlHlVeYcfPLJJ+bcc881/v7+JiwszAwfPtz8+uuvldpmbVTd56CuvQ8+/vhjI6nET/FxjxgxwvTp06fEOl27djVWq9W0a9fOLFiwoMR269L7oCbOQUN4H5TWv23bti593nzzTXPOOecYq9VqOnfubN577z3PHFAl1cTxp6SkOP8/EB4ebq6++mqTnp7uuYOqpMqegz59+pTbv1hN/y2wGFPGdRwAAIAGijlIAAAAbghIAAAAbghIAAAAbghIAAAAbghIAAAAbghIAAAAbghIAAAAbghIAFBPfPLJJ7JYLDp8+LC3SwHqPAISgEq79dZbZbFYNHPmTJf2FStWyGKxOF8bY/TPf/5TPXr0UEhIiJo0aaLOnTtr7NixFX6w5tGjRzV+/HjFxMQoICBAzZo1U58+ffTuu+86+0RFRSk1NbVajq2mFZ87i8UiPz8/RUdH68EHH9SxY8cqtZ2+ffsqJSXFpa1nz57av3+/bDZbNVYMNEwEJABVEhAQoCeffFK///57qcuNMRo2bJjuvfdeXX311frggw/0/fff6+WXX1ZAQIAee+yxCu3nzjvv1PLlyzVnzhzt2LFDa9as0Q033KCcnJzqPByP6t+/v/bv368ff/xRzz33nObPn68pU6ac8XatVqsiIiJcQiqAKqrWB5cAaBBGjBhhBg4caDp27GgeeOABZ/s777xjiv+svPHGG0aSeffdd0vdhsPhqNC+bDabWbhwYZnLS3tuU7HPPvvM9OrVywQEBJjWrVube+65xxw5csS5/NVXXzXx8fGmSZMmJjw83AwdOtRkZ2c7lxc/Q2rNmjWma9euJiAgwFx++eUmOzvbrF692nTs2NEEBweboUOHmoKCggodz4gRI8y1117r0nbdddeZbt26OV8fOnTIDBkyxLRs2dIEBgaa8847zyxevNhlG+7HnJmZ6az31Gc5vv3226ZTp07GarWatm3bmmeeeaZCdQINHSNIAKrE19dXTzzxhObMmaNffvmlxPI33nhDHTp00DXXXFPq+hUd5YiIiNDq1auVn59f6vLly5erdevWevTRR7V//37t379fkrRnzx71799f119/vb755hstXbpU69evV3JysnPdEydOaPr06fr666+1YsUK7d27V7feemuJfUydOlVpaWnauHGjfv75Zw0aNEipqalavHix3nvvPX3wwQeaM2dOhY7H3bZt27Rx40ZZrVZn27FjxxQfH6/33ntP27Zt0+jRozV8+HB98cUXkqTnn39ePXr00B133OE85sjIyBLb3rJliwYNGqQhQ4bo22+/1dSpUzVp0iQtXLiwSrUCDYq3ExqAuufUUZBLLrnE3HbbbcYY1xGkjh07mmuuucZlvbFjx5qgoCATFBRkWrVqVaF9/fe//zWtW7c2fn5+5sILLzQpKSlm/fr1Ln3atm1rnnvuOZe2UaNGmdGjR7u0ffbZZ8bHx8f88ccfpe7ryy+/NJJMfn6+MeZ/I0gffvihs8+MGTOMJLNnzx5n25gxY0xCQkKFjmfEiBHG19fXBAUFGX9/fyPJ+Pj4mLfffrvc9QYMGGDuu+8+5+s+ffqYsWPHuvRxH0EaNmyYufLKK136PPDAA6ZTp04VqhVoyBhBAnBGnnzySS1atEjbt28/bd8JEyZo69atmjx5so4cOVKh7V922WX68ccftW7dOt1www367rvv1Lt3b02fPr3c9b7++mstXLhQTZo0cf4kJCTI4XAoMzNT0skRlsTERLVp00bBwcHq06ePJGnfvn0u27rggguc/w4PD1fjxo3Vrl07l7YDBw5U6Hgk6fLLL9fWrVu1efNmjRgxQiNHjtT111/vXF5UVKTp06fr/PPPV2hoqJo0aaL333+/RF2ns337dl166aUubZdeeql27dqloqKiSm0LaGgISADOyGWXXaaEhASNHz/epT02NlY7d+50aWvWrJnat2+v5s2bV2offn5+6t27tx566CF98MEHevTRRzV9+nQVFhaWuc6RI0c0ZswYbd261fnz9ddfa9euXYqJiVFBQYESEhIUEhKi119/XV9++aXeeecdSSqxXT8/P+e/i799diqLxSKHw1Hh4wkKClL79u3VpUsXvfLKK9q8ebNefvll5/Knn35azz//vB566CF9/PHH2rp1qxISEso9XgDVq5G3CwBQ982cOVNdu3ZVhw4dnG1Dhw7VsGHD9O677+raa6+t1v116tRJf/75p44dOyar1Sqr1VpiRCQuLk7ff/+92rdvX+o2vv32W+Xk5GjmzJnO+TtfffVVtdZZET4+PnrkkUc0btw4DRs2TIGBgdqwYYOuvfZa3XzzzZIkh8OhH374QZ06dXKuV9oxuzv33HO1YcMGl7YNGzbonHPOka+vb/UfDFCPMIIE4Iydf/75uummmzR79mxn25AhQ3TDDTdoyJAhevTRR7V582bt3btX//3vf7V06dIKf0D37dtX8+fP15YtW7R3716tXr1ajzzyiC6//HKFhIRIOnkfpE8//VS//vqrDh06JEl66KGHtHHjRiUnJ2vr1q3atWuX3n33Xeck7TZt2shqtWrOnDn68ccf9e9///u0l+1qyo033ihfX1/NnTtX0snRt7Vr12rjxo3avn27xowZo+zsbJd1oqKinOf00KFDpY5g3XfffVq3bp2mT5+uH374QYsWLVJaWpruv/9+jxwXUJcRkABUi0cffdTlQ9pisWjp0qVKTU3V6tWr9Ze//EUdOnTQbbfdpsjISK1fv75C201ISNCiRYv017/+Veeee67uueceJSQk6M0333TZ9969exUTE6NmzZpJOjlv6L///a9++OEH9e7dW926ddPkyZPVsmVLSScv9y1cuFBvvfWWOnXqpJkzZ+qZZ56pxjNScY0aNVJycrKeeuopFRQUaOLEiYqLi1NCQoL69u2riIgIJSUluaxz//33y9fXV506dVKzZs1KnZ8UFxenN998U0uWLNF5552nyZMn69FHHy31m3oAXFmMMcbbRQAAANQmjCABAAC4ISAB8KpTv4bv/vPZZ595u7xK2bdvX7nHU9mv6QPwHi6xAfCq8h5a26pVKwUGBnqwmjPz559/au/evWUuj4qKUqNGfHkYqAsISAAAAG64xAYAAOCGgAQAAOCGgAQAAOCGgAQAAOCGgAQAAOCGgAQAAOCGgAQAAOCGgAQAAODm/wNtlzA89LJumwAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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klMnUsLbOMVCNiKihEEJIt8z279+PF154AS1btoSlpSXeeOMNZGdno6CgoNJ9JCcnIzAwEK1bt4alpSX8/PwAANevX9d5/SvDgETUwCmVeQgM3COFpNIxSOw9IqKnde7cObRp0wZXr17F0KFD0bVrV2zbtg3JycmIjY0FUPltv/z8fAQEBMDKygrffPMNfv31V2zfvv2J2+kDb7ERNVAKhUL62cvrFFxdLyEnxxrW1jka4ejxckREVfW///0Pv//+O6ZPn47k5GSo1WosXboUcvmjvpfvvvtOo7xCoUBJSYnGsvPnzyM7OxuLFi2Ck5MTAODkyZP6acATMCARNVA2NjYIDw/nTNpE9NQKCwuRmZmp8Zh/VFQUhg4dijFjxuDMmTMoLi5GTEwMAgMDceTIEaxZs0ZjHy4uLrh//z4SExPh6ekJc3NztG7dGgqFAjExMZg0aRLOnDmDhQsXGqiVmniLjagBs7GxgYODQ4UvhiMiqoqEhAQ4ODjAxcUFgwYNwoEDB/DZZ59h586dMDIygqenJ5YtW4ZPPvkEnTt3xjfffIOoqCiNffTp0weTJk1CSEgIbG1t8emnn8LW1hYbN27E1q1b4eHhgUWLFmHJkiUGaqUmmRBCGLoS9VFubi6USiVUKhWsrKwMXR0iIqrDHj58iPT0dLRp0wampqbV3t7Q8yDVN5Wd76p+fhv8FltsbCwWL16MzMxMeHp6IiYmBr169Sq37B9//IE5c+YgOTkZ165dw/LlyzUeKwSAn3/+GYsXL0ZycjIyMjKwfft2BAUFaZQRQmDu3Ln44osvcO/ePTz77LNYvXo13NzcdNRKIiKimuMtc/0z6C22LVu2ICIiAnPnzkVKSgo8PT0REBCA27dvl1u+oKAAbdu2xaJFi8rMo1AqPz8fnp6e0uj58nz66af47LPPsGbNGhw/fhwWFhYICAjAw4cPa6VdREREtY23zPXLoLfYfHx80LNnT6nbUK1Ww8nJCVOmTMH7779f6bYuLi6YNm1amR6kx8lksjI9SEIIODo64p133pFm8VSpVLCzs8PGjRsxYsSIKtWdt9iIiKiqnvYWG1VPbdxiM1gPUlFREZKTk+Hv7/9PZeRy+Pv7IykpSWfHTU9PR2ZmpsZxlUolfHx8Kj1uYWEhcnNzNV5ERETUMBksIN29exclJSWws7PTWG5nZ4fMzEydHbd039U9blRUFJRKpfQqna+BiIiIGh4+5l9FkZGRUKlU0uvGjRuGrhIRERHpiMECUvPmzWFkZISsrCyN5VlZWRUOwK4Npfuu7nFNTExgZWWl8SIiIqKGyWABSaFQwNvbG4mJidIytVqNxMRE+Pr66uy4bdq0gb29vcZxc3Nzcfz4cZ0el4iIiOoPg86DFBERgdDQUPTo0QO9evVCdHQ08vPzERYWBgAYM2YMWrZsKc3GWVRUhLNnz0o///nnn0hNTUXTpk3Rrl07AMD9+/dx6dIl6Rjp6elITU2FtbU1WrduDZlMhmnTpuHDDz+Em5sb2rRpg9mzZ8PR0bHMfElERETUSAkDi4mJEa1btxYKhUL06tVLHDt2TFrn5+cnQkNDpffp6ekCQJmXn5+fVObAgQPllnl8P2q1WsyePVvY2dkJExMT8cILL4i0tLRq1VulUgkAQqVS1bTpRETUSDx48ECcPXtWPHjwwNBVqXNKP7f/+uuvKm/j7Owsli9fXuH6ys53VT+/DT5IOzw8HNeuXUNhYSGOHz8OHx8fad3BgwexceNG6b2LiwuEEGVeBw8elMr079+/3DKP70cmk2HBggXIzMzEw4cPsX//frRv314PrSUiIqpf3nzzTchkMkyaNKnMusmTJ0Mmk+HNN9/Uf8V0zOABiYiIiOo2JycnbN68GQ8ePJCWPXz4EHFxcWjdurUBa6Y7DEhERERUKS8vLzg5OSE+Pl5aFh8fj9atW6N79+7SssLCQrz99tto0aIFTE1N8dxzz+HXX3/V2NfevXvRvn17mJmZ4fnnn8fVq1fLHO/w4cPo27cvzMzM4OTkhLfffhv5+fk6a195GJCIiIjqmZs3gQMHHv2rL2+99RY2bNggvV+/fr30UFWp//znP9i2bRs2bdqElJQUtGvXDgEBAcjJyQEA3LhxA8OGDUNgYCBSU1Mxbty4Ml8tdvnyZQwaNAjBwcE4ffo0tmzZgsOHDyM8PFz3jXwMAxIREVE9sm4d4OwMDBjw6N916/Rz3Ndffx2HDx/GtWvXcO3aNRw5cgSvv/66tD4/Px+rV6/G4sWLMXjwYHh4eOCLL76AmZkZ1v3/Sq5evRqurq5YunQpOnTogNGjR5cZvxQVFYXRo0dj2rRpcHNzQ58+ffDZZ5/hyy+/1OuXyhv0MX8iIiKqups3gQkTALX60Xu1Gpg4EQgIAFq10u2xbW1tMWTIEGzcuBFCCAwZMgTNmzeX1l++fBnFxcV49tlnpWXGxsbo1asXzp07BwA4d+6cxsNYAMrMQfjbb7/h9OnT+Oabb6RlQgio1Wqkp6ejY8eOumheGQxIRERE9cTFi/+Eo1IlJcClS7oPSMCj22ylt7piY2N1coz79+9j4sSJePvtt8us0+eAcAYkIiKiesLNDZDLNUOSkRHw/+dK1rlBgwahqKgIMpkMAQEBGutcXV2hUChw5MgRODs7AwCKi4vx66+/Ytq0aQCAjh07YteuXRrbHTt2TOO9l5cXzp49K00AbSgcg0RERFRPtGoFrF37KBQBj/79/HP99B49Op4Rzp07h7Nnz8KotBL/n4WFBf79739jxowZSEhIwNmzZzF+/HgUFBRg7NixAIBJkybh4sWLmDFjBtLS0hAXF6cxTyEAvPfeezh69CjCw8ORmpqKixcvYufOnXofpM0eJCIionpk7NhHY44uXXrUc6SvcFSqsi9rX7RoEdRqNd544w3k5eWhR48e+PHHH/HMM88AeHSLbNu2bZg+fTpiYmLQq1cvfPzxx3jrrbekfXTt2hWHDh3CBx98gL59+0IIAVdXV4SEhOi8bY+TCSGEXo/YQOTm5kKpVEKlUlV6sRARET18+BDp6elo06YNTE1NDV2dBq+y813Vz2/eYiMiIiLSwoBEREREpIUBiYiIiEgLAxIRERGRFgYkIiIiPeFzUfpRG+eZAYmIiEjHjI2NAQAFBQUGrknjUHqeS897TXAeJCIiIh0zMjJCs2bNcPv2bQCAubk5ZDKZgWvV8AghUFBQgNu3b6NZs2ZlJrOsDgYkIiIiPbC3twcAKSSR7jRr1kw63zXFgERERKQHMpkMDg4OaNGiBYqLiw1dnQbL2Nj4qXqOSjEgERER6ZGRkVGtfICTbnGQNhEREZEWBiQiIiIiLQxIRERERFoYkIiIiIi0MCARERERaWFAIiIiItLCgERERESkhQGJiIiISAsDEhEREZEWBiQiIiIiLQxIDcTNm8CBA4/+JSIioqfDgFSPZWdnIyMjA0uX3oOzs8CAAYCzs8DSpfeQkZGB7OxsQ1eRiIioXuKX1dZT2dnZWLlyJVQqS0RHT4MQMgCAWi3DjBlW+PPP9VAq8xAeHg4bGxsD15aIiKh+YQ9SPVVUVAQAyMmxgRCav0Yh5MjJsdYoR0RERFXHgFTPWVtnQyZTayyTydSwts4xUI2IiIjqvzoRkGJjY+Hi4gJTU1P4+PjgxIkTFZb9448/EBwcDBcXF8hkMkRHR9don/3794dMJtN4TZo0qTabpRdKZR4CA/dIIUkmUyMwcA+UyjwD14yIiKj+MvgYpC1btiAiIgJr1qyBj48PoqOjERAQgLS0NLRo0aJM+YKCArRt2xavvfYapk+f/lT7HD9+PBYsWCC9Nzc3r/0G6oGX1ym4ul5CTo41rK1zGI6IiIieksF7kJYtW4bx48cjLCwMHh4eWLNmDczNzbF+/fpyy/fs2ROLFy/GiBEjYGJi8lT7NDc3h729vfSysrKq9fbpi1KZhzZtrjEcERER1QKDBqSioiIkJyfD399fWiaXy+Hv74+kpCSd7/Obb75B8+bN0blzZ0RGRqKgoKDC/RYWFiI3N1fjRURERA2TQW+x3b17FyUlJbCzs9NYbmdnh/Pnz+t0n6NGjYKzszMcHR1x+vRpvPfee0hLS0N8fHy5+42KisL8+fNrVCciIiKqXww+BslQJkyYIP3cpUsXODg44IUXXsDly5fh6upapnxkZCQiIiKk97m5uXByctJLXcujUChqtRwRERH9w6ABqXnz5jAyMkJWVpbG8qysLNjb2+t1nz4+PgCAS5culRuQTExMKhzzZAg2NjYIDw+vdJ4jhULBSSKJiIhqwKBjkBQKBby9vZGYmCgtU6vVSExMhK+vr173mZqaCgBwcHCo0XENwcbGBg4ODhW+GI6IiIhqxuC32CIiIhAaGooePXqgV69eiI6ORn5+PsLCwgAAY8aMQcuWLREVFQXg0SDss2fPSj//+eefSE1NRdOmTdGuXbsq7fPy5cuIi4vDSy+9BBsbG5w+fRrTp09Hv3790LVrVwOcBSIiIqpLDB6QQkJCcOfOHcyZMweZmZno1q0bEhISpEHW169fh1z+T0fXrVu30L17d+n9kiVLsGTJEvj5+eHgwYNV2qdCocD+/ful4OTk5ITg4GDMmjVLfw0nIiKiOksmhBCGrkR9lJubC6VSCZVKVa/nTyIiImpMqvr5bfCJIomIiIjqGgYkIiIiIi0MSERERERaGJCIiIiItDAgEREREWlhQCIiIiLSwoBEREREpIUBiYiIiEgLAxIRERGRFgYkIiIiIi0MSERERERaGJCIiIiItDAgEREREWlhQCIiIiLSwoBEREREpIUBiart5k3gwIFH/xIRETVEDEhULevWAc7OwIABj/5dt87QNSIiIqp9TQxdAaofsrOzcfXq35gwoQXUahkAQK0GJk4U6NbtNlxcmsDGxsbAtaTakJ2djaKiogrXKxQK/q6JqMFjQKInys7OxsqVK5Ge7gK1OlRjXUmJDDEx/0WbNtcQHh7OD856rvR3XUqlskROjg2srbOhVOZJy/m7JqKGjgGJnqi0N8HaOhsymRpC/HNnViZTw9o6R6Mc1V+P/w5TUrpj9+6hEEIOmUyNwMA98PI6VaYcEVFDxDFIVGVKZR4CA/dAJlMDgPSh+XjPAjUMKpWlFI4AQAg5du8eCpXK0sA1IyLSD/YgUbV4eZ2Cq+sl5ORYw9o6h+GogcrJsdHoKQQehaScHGv+zomoUWBAompTKvP4IdnAPel2KhFRQ8dbbERUBm+nElFjxx4kIioXb6cSUWPGgEREFeLtVCJqrHiLjZ5IoVDUajmqu/i7JiJ6RCaEEIauRH2Um5sLpVIJlUoFKysrQ1dH5zi7cuPB3zURNWRV/fzmLTaqEn4gNh78XRMR8RYbERERURkMSERERERaGJCIiIiItDAgEREREWnhIG0iIiIyuMefoL11S4709CZo0+ZvODo+mtFf30/Q1okepNjYWLi4uMDU1BQ+Pj44ceJEhWX/+OMPBAcHw8XFBTKZDNHR0TXa58OHDzF58mTY2NigadOmCA4ORlZWVm02i4iIiKogOzsbK1euxNq1azFp0q/o2dMWr71mg549bTFp0q9Yu3YtVq5ciezsbL3VyeABacuWLYiIiMDcuXORkpICT09PBAQE4Pbt2+WWLygoQNu2bbFo0SLY29vXeJ/Tp0/H7t27sXXrVhw6dAi3bt3CsGHDdNJGIiIiqlhpz5FKZYndu4dKX5QthBy7dw+FSmWpUU4fDB6Qli1bhvHjxyMsLAweHh5Ys2YNzM3NsX79+nLL9+zZE4sXL8aIESNgYmJSo32qVCqsW7cOy5Ytw4ABA+Dt7Y0NGzbg6NGjOHbsmM7aSkRERBXLybGRwlEpIeTIybHWe10MGpCKioqQnJwMf39/aZlcLoe/vz+SkpJ0ts/k5GQUFxdrlHF3d0fr1q0rPG5hYSFyc3M1XkRERFR7rK2zIZOpNZbJZGpYW+fovS4GDUh3795FSUkJ7OzsNJbb2dkhMzNTZ/vMzMyEQqFAs2bNqnzcqKgoKJVK6eXk5FSj+hEREVH5lMo8BAbukUKSTKZGYOAeg3xpNp9iq6LIyEhERERI73NzcxmSiIiIapmX1ym4ul5CTo41rK1zDBKOAAMHpObNm8PIyKjM02NZWVkVDsCujX3a29ujqKgI9+7d0+hFquy4JiYmFY55IiIiopq7d+9eOUtl5ZZzcHDQeX0AA99iUygU8Pb2RmJiorRMrVYjMTERvr6+Otunt7c3jI2NNcqkpaXh+vXrNT4uERER1czff/8t/ZyS0h3R0dOwaVMooqOnISWle7nldM3gt9giIiIQGhqKHj16oFevXoiOjkZ+fj7CwsIAAGPGjEHLli0RFRUF4NEg7LNnz0o///nnn0hNTUXTpk3Rrl27Ku1TqVRi7NixiIiIgLW1NaysrDBlyhT4+vqid+/eBjgLREREjZexsTGAih/zd3W9BKUyTyqnDwYPSCEhIbhz5w7mzJmDzMxMdOvWDQkJCdIg6+vXr0Mu/6ej69atW+je/Z80uWTJEixZsgR+fn44ePBglfYJAMuXL4dcLkdwcDAKCwsREBCAVatW6afRREREJFEqlQAqf8xfqcyTyumDTAgh9Ha0BiQ3NxdKpRIqlQpWVlaGrg41cHVtCn7Sncd/1+Wpzd+1Po9FVJmMjAysXbsWKpUloqOnaYQkmUyNadOioVTmYcKECU89Bqmqn98G70EiosqVTsEPPLo3X9r9XPr4q5fXKQBAeHg4P8zqucd/15Wpjd+1Po9FVFWlj/lr/53jY/5EVMaTpuAvvTevzyn4STe0f4cqlSVycmxgbZ2t8QFRG79rfR6LqDr4mD8RVcuT7s1Tw1JZb2F9PhZRVTwab2TYv2sG/y42IqqaujQFP+nWk76ws74ei6giCoWiVsvVBvYgEdUTdenePOmWPnsL2TNJdYGNjQ3Cw8Pr1EMDDEhE9UhduTdPulXaW6j9JI8uegv1eSyiytS1hwF4i42onlEq89CmzTWGowZMn1/YWZe+HJSoLmEPEhFRHaTP3kL2TFJtawhzbDEgEdVxdXHwIumG9u+woid5auN3rc9jUePSUObYYkAiquPq4uBF0g19/q55XZGuNJQ5thiQiOoBfkg1Hvr8XfO6Il2rz3NscZA2ERER1br6PscWAxIRERHVusrm2KoPGJCIiIio1tX32f8ZkIiIiKjW1fc5tjhIm4iIiHSiPs+xxYBEREREtaahzLHFgERERHrREGZXpidrKHNsMSARNWCPfyDduiVHenoTtGnzNxwdH40JqA9/pKhhaCizK1PVNITfIQMSUQP1+AdSZZO18QOJ9KGhzK5MjQcDElEDVfpBU9Fkba6ul6BU5vEDifSuPs+uTI0HH/MnauDq+2Rt1LDU99mVqfFgQCJq4Or7ZG3UsDCwU33BgETUwNX3ydqoYWFgp/qCY5CIGoH6PFkbNSylgV17DBKvSaprGJCIGomKJmsj0jd9BHbOuURPiwGJiIh0Tp+zK3POJaoNDEhEDVRVP2jq+nT/1DDoc3ZlzrlEtYEBqY65eRO4eBFwcwNatTJ0bag+ayjT/VPDYYhrjXMuUU0xINUBpffK4+LM8J//KKFWyyCXC3z6qQqjRj3ghxjVGK8basyeNEkqUWUYkAys9F65SmWJ6OhpEEIGAFCrZZgxwwp//rkeSmUe75UTEVVTZXMuMSDRk3AeJAMrvf3xpMnTeK+ciKh6OOcSPQ0GpDqC/yETEdUuTpJKT4O32OoITp5GRFT7OEkq1RQDUh3C/5CJiJ6ePudcooarTgSk2NhYLF68GJmZmfD09ERMTAx69epVYfmtW7di9uzZuHr1Ktzc3PDJJ5/gpZdektZnZWXhvffew08//YR79+6hX79+iImJgZubm1Smf//+OHTokMZ+J06ciDVr1tR+A6uBsx0TET0dTnFBtcHgY5C2bNmCiIgIzJ07FykpKfD09ERAQABu375dbvmjR49i5MiRGDt2LE6dOoWgoCAEBQXhzJkzAAAhBIKCgnDlyhXs3LkTp06dgrOzM/z9/ZGfn6+xr/HjxyMjI0N6ffrppzpvLxER6Z6NjQ0cHBwqfDEc0ZMYPCAtW7YM48ePR1hYGDw8PLBmzRqYm5tj/fr15ZZfsWIFBg0ahBkzZqBjx45YuHAhvLy8pGnlL168iGPHjmH16tXo2bMnOnTogNWrV+PBgwf49ttvNfZlbm4Oe3t76WVlZaXz9hIREVHdZ9CAVFRUhOTkZPj7+0vL5HI5/P39kZSUVO42SUlJGuUBICAgQCpfWFgIADA1NdXYp4mJCQ4fPqyx3TfffIPmzZujc+fOiIyMREFBQYV1LSwsRG5ursarNvDrIIiIiOoeg45Bunv3LkpKSmBnZ6ex3M7ODufPny93m8zMzHLLZ2ZmAgDc3d3RunVrREZG4vPPP4eFhQWWL1+OmzdvIiMjQ9pm1KhRcHZ2hqOjI06fPo333nsPaWlpiI+PL/e4UVFRmD9//tM0t1y8V05ERFT31IlB2rXJ2NgY8fHxGDt2LKytrWFkZAR/f38MHjwYQgip3IQJE6Sfu3TpAgcHB7zwwgu4fPkyXF1dy+w3MjISERER0vvc3Fw4OTnVSp0ZfoiIiOoWgwak5s2bw8jICFlZWRrLs7KyYG9vX+429vb2Tyzv7e2N1NRUqFQqFBUVwdbWFj4+PujRo0eFdfHx8QEAXLp0qdyAZGJiAhMTkyq3jYiIiOovg45BUigU8Pb2RmJiorRMrVYjMTERvr6+5W7j6+urUR4A9u3bV255pVIJW1tbXLx4ESdPnsQrr7xSYV1SU1MBAA4ODjVoCRERETUkBr/FFhERgdDQUPTo0QO9evVCdHQ08vPzERYWBgAYM2YMWrZsiaioKADA1KlT4efnh6VLl2LIkCHYvHkzTp48ibVr10r73Lp1K2xtbdG6dWv8/vvvmDp1KoKCgjBw4EAAwOXLlxEXF4eXXnoJNjY2OH36NKZPn45+/fqha9eu+j8JREREVKcYPCCFhITgzp07mDNnDjIzM9GtWzckJCRIA7GvX78Oufyfjq4+ffogLi4Os2bNwsyZM+Hm5oYdO3agc+fOUpmMjAxEREQgKysLDg4OGDNmDGbPni2tVygU2L9/vxTGnJycEBwcjFmzZumv4URERFRnycTjI5epynJzc6FUKqFSqTh/EhERUT1R1c9vg08USURERFTXMCARERERaWFAIiIiItLCgERERESkpUYBaevWrRg2bBg6d+6Mzp07Y9iwYfj+++9ru25EREREBlGtgKRWqxESEoKQkBCcPXsW7dq1Q7t27fDHH38gJCQEI0aMAB+KIyIiovquWvMgrVixAvv378euXbswdOhQjXW7du1CWFgYVqxYgWnTptVmHYmIiIj0qlo9SBs2bMDixYvLhCMAePnll/Hpp59i/fr1tVY5IiIiIkOoVkC6ePEi/P39K1zv7++PixcvPnWliIiIiAypWgHJzMwM9+7dq3B9bm4uTE1Nn7ZORERERAZVrYDk6+uL1atXV7g+NjYWvr6+T10pIiIiIkOq1iDtDz74AP3790d2djbeffdduLu7QwiBc+fOYenSpdi5cycOHDigq7oSERER6UW1AlKfPn2wZcsWTJgwAdu2bdNY98wzz+Dbb7/Fs88+W6sVJKKGLTs7G0VFRQCAW7fkSE9vgjZt/oajoxoAoFAoYGNjY8gqElEjJBM1mLiooKAAP/74ozQgu3379hg4cCDMzc1rvYJ1VVW/DZiIKpadnY2VK1cCAFJSumP37qEQQg6ZTI3AwD3w8joFAAgPD2dIIqJaUdXP72r1IP3vf/9DeHg4jh07hn/9618a61QqFTp16oQ1a9agb9++Nas1ETUqpT1HKpWlFI4AQAg5du8eClfXS1Aq86RyRET6Uq1B2tHR0Rg/fny5iUupVGLixIlYtmxZrVWOiBqHnBwbKRyVEkKOnBxrA9WIiBq7agWk3377DYMGDapw/cCBA5GcnPzUlSKixsXaOhsymVpjmUymhrV1joFqRESNXbUCUlZWFoyNjStc36RJE9y5c+epK0VEjYtSmYfAwD1SSCodg6RU5hm4ZkTUWFVrDFLLli1x5swZtGvXrtz1p0+fhoODQ61UjIgMw1BPlXl5nYKr6yXk5FjD2jqH4YiIDKpaAemll17C7NmzMWjQoDIzZj948ABz584t93vaiKh+MPRTZUplHoMREdUJ1QpIs2bNQnx8PNq3b4/w8HB06NABAHD+/HnExsaipKQEH3zwgU4qSkS6x6fKiIgeqVZAsrOzw9GjR/Hvf/8bkZGRKJ1CSSaTISAgALGxsbCzs9NJRYlIfyp7qqw2e3gUCkWtliMiqi3VCkgA4OzsjL179+Kvv/7CpUuXIISAm5sbnnnmGV3Uj4gMoPSpssdDki6eKrOxsUF4eHilPVKcSZuIDKHaAanUM888g549e9ZmXYiojih9qkx7DJIuxgcx/BBRXVTjgEREDRufKiOixowBiYgqxKfKiKixqtZEkURERESNAQMSEUn4VBkR0SO8xUZEEj5VRkT0CAMSEWlg+CEi4i02IiIiojIYkIiIiIi0MCARERERaWFAIiIiItLCgERERESkpU4EpNjYWLi4uMDU1BQ+Pj44ceJEpeW3bt0Kd3d3mJqaokuXLti7d6/G+qysLLz55ptwdHSEubk5Bg0ahIsXL2qUefjwISZPngwbGxs0bdoUwcHByMrKqvW2ERERUf1j8IC0ZcsWREREYO7cuUhJSYGnpycCAgJw+/btcssfPXoUI0eOxNixY3Hq1CkEBQUhKCgIZ86cAQAIIRAUFIQrV65g586dOHXqFJydneHv74/8/HxpP9OnT8fu3buxdetWHDp0CLdu3cKwYcP00mYiIiKq22RCCGHICvj4+KBnz55YuXIlAECtVsPJyQlTpkzB+++/X6Z8SEgI8vPzsWfPHmlZ79690a1bN6xZswYXLlxAhw4dcObMGXTq1Enap729PT7++GOMGzcOKpUKtra2iIuLw6uvvgoAOH/+PDp27IikpCT07t37ifXOzc2FUqmESqWClZVVbZwKIiIi0rGqfn4btAepqKgIycnJ8Pf3l5bJ5XL4+/sjKSmp3G2SkpI0ygNAQECAVL6wsBAAYGpqqrFPExMTHD58GACQnJyM4uJijf24u7ujdevWFR63sLAQubm5Gi8iIiJqmAwakO7evYuSkhLY2dlpLLezs0NmZma522RmZlZavjToREZG4q+//kJRURE++eQT3Lx5ExkZGdI+FAoFmjVrVuXjRkVFQalUSi8nJ6eaNJmIiIjqAYOPQaptxsbGiI+Px4ULF2BtbQ1zc3McOHAAgwcPhlxe8+ZGRkZCpVJJrxs3btRirYmIiKguMeh3sTVv3hxGRkZlnh7LysqCvb19udvY29s/sby3tzdSU1OhUqlQVFQEW1tb+Pj4oEePHtI+ioqKcO/ePY1epMqOa2JiAhMTk5o0k4iIiOoZg/YgKRQKeHt7IzExUVqmVquRmJgIX1/fcrfx9fXVKA8A+/btK7e8UqmEra0tLl68iJMnT+KVV14B8ChAGRsba+wnLS0N169fr/C4RERE1HgYtAcJACIiIhAaGooePXqgV69eiI6ORn5+PsLCwgAAY8aMQcuWLREVFQUAmDp1Kvz8/LB06VIMGTIEmzdvxsmTJ7F27Vppn1u3boWtrS1at26N33//HVOnTkVQUBAGDhwI4FFwGjt2LCIiImBtbQ0rKytMmTIFvr6+VXqCjYiIiBo2gwekkJAQ3LlzB3PmzEFmZia6deuGhIQEaSD29evXNcYO9enTB3FxcZg1axZmzpwJNzc37NixA507d5bKZGRkICIiAllZWXBwcMCYMWMwe/ZsjeMuX74ccrkcwcHBKCwsREBAAFatWqWfRhMREVGdZvB5kOorzoNERERU/9SLeZCIiIiI6iIGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItdSIgxcbGwsXFBaampvDx8cGJEycqLb9161a4u7vD1NQUXbp0wd69ezXW379/H+Hh4WjVqhXMzMzg4eGBNWvWaJTp378/ZDKZxmvSpEm13jYiIiKqfwwekLZs2YKIiAjMnTsXKSkp8PT0REBAAG7fvl1u+aNHj2LkyJEYO3YsTp06haCgIAQFBeHMmTNSmYiICCQkJODrr7/GuXPnMG3aNISHh2PXrl0a+xo/fjwyMjKk16effqrTthIREVH9IBNCCENWwMfHBz179sTKlSsBAGq1Gk5OTpgyZQref//9MuVDQkKQn5+PPXv2SMt69+6Nbt26Sb1EnTt3RkhICGbPni2V8fb2xuDBg/Hhhx8CeNSD1K1bN0RHR9eo3rm5uVAqlVCpVLCysqrRPoiIiEi/qvr5bdAepKKiIiQnJ8Pf319aJpfL4e/vj6SkpHK3SUpK0igPAAEBARrl+/Tpg127duHPP/+EEAIHDhzAhQsXMHDgQI3tvvnmGzRv3hydO3dGZGQkCgoKKqxrYWEhcnNzNV5ERETUMDUx5MHv3r2LkpIS2NnZaSy3s7PD+fPny90mMzOz3PKZmZnS+5iYGEyYMAGtWrVCkyZNIJfL8cUXX6Bfv35SmVGjRsHZ2RmOjo44ffo03nvvPaSlpSE+Pr7c40ZFRWH+/Pk1bSoRERHVIwYNSLoSExODY8eOYdeuXXB2dsbPP/+MyZMnw9HRUep9mjBhglS+S5cucHBwwAsvvIDLly/D1dW1zD4jIyMREREhvc/NzYWTk5PuG0NERER6Z9CA1Lx5cxgZGSErK0tjeVZWFuzt7cvdxt7evtLyDx48wMyZM7F9+3YMGTIEANC1a1ekpqZiyZIlZW7PlfLx8QEAXLp0qdyAZGJiAhMTk+o1kIiIiOolg45BUigU8Pb2RmJiorRMrVYjMTERvr6+5W7j6+urUR4A9u3bJ5UvLi5GcXEx5HLNphkZGUGtVldYl9TUVACAg4NDTZpCREREDYjBb7FFREQgNDQUPXr0QK9evRAdHY38/HyEhYUBAMaMGYOWLVsiKioKADB16lT4+flh6dKlGDJkCDZv3oyTJ09i7dq1AAArKyv4+flhxowZMDMzg7OzMw4dOoQvv/wSy5YtAwBcvnwZcXFxeOmll2BjY4PTp09j+vTp6NevH7p27WqYE0FERER1hsEDUkhICO7cuYM5c+YgMzMT3bp1Q0JCgjQQ+/r16xq9QX369EFcXBxmzZqFmTNnws3NDTt27EDnzp2lMps3b0ZkZCRGjx6NnJwcODs746OPPpImglQoFNi/f78UxpycnBAcHIxZs2bpt/FERERUJxl8HqT6ivMgERER1T/1Yh4kIiIiorqIAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkZY6EZBiY2Ph4uICU1NT+Pj44MSJE5WW37p1K9zd3WFqaoouXbpg7969Guvv37+P8PBwtGrVCmZmZvDw8MCaNWs0yjx8+BCTJ0+GjY0NmjZtiuDgYGRlZdV624iIiKj+MXhA2rJlCyIiIjB37lykpKTA09MTAQEBuH37drnljx49ipEjR2Ls2LE4deoUgoKCEBQUhDNnzkhlIiIikJCQgK+//hrnzp3DtGnTEB4ejl27dkllpk+fjt27d2Pr1q04dOgQbt26hWHDhum8vURERFT3yYQQwpAV8PHxQc+ePbFy5UoAgFqthpOTE6ZMmYL333+/TPmQkBDk5+djz5490rLevXujW7duUi9R586dERISgtmzZ0tlvL29MXjwYHz44YdQqVSwtbVFXFwcXn31VQDA+fPn0bFjRyQlJaF3795PrHdubi6USiVUKhWsrKye6hwQERGRflT189ugPUhFRUVITk6Gv7+/tEwul8Pf3x9JSUnlbpOUlKRRHgACAgI0yvfp0we7du3Cn3/+CSEEDhw4gAsXLmDgwIEAgOTkZBQXF2vsx93dHa1bt67wuIWFhcjNzdV4ERERUcNk0IB09+5dlJSUwM7OTmO5nZ0dMjMzy90mMzPzieVjYmLg4eGBVq1aQaFQYNCgQYiNjUW/fv2kfSgUCjRr1qzKx42KioJSqZReTk5O1W0uERER1RMGH4OkCzExMTh27Bh27dqF5ORkLF26FJMnT8b+/ftrvM/IyEioVCrpdePGjVqsMREREdUlTQx58ObNm8PIyKjM02NZWVmwt7cvdxt7e/tKyz948AAzZ87E9u3bMWTIEABA165dkZqaiiVLlsDf3x/29vYoKirCvXv3NHqRKjuuiYkJTExMatpUIiIiqkcM2oOkUCjg7e2NxMREaZlarUZiYiJ8fX3L3cbX11ejPADs27dPKl9cXIzi4mLI5ZpNMzIyglqtBvBowLaxsbHGftLS0nD9+vUKj0tERESNh0F7kIBHj+SHhoaiR48e6NWrF6Kjo5Gfn4+wsDAAwJgxY9CyZUtERUUBAKZOnQo/Pz8sXboUQ4YMwebNm3Hy5EmsXbsWAGBlZQU/Pz/MmDEDZmZmcHZ2xqFDh/Dll19i2bJlAAClUomxY8ciIiIC1tbWsLKywpQpU+Dr61ulJ9iIiIiodmVnZ6OoqKjC9QqFAjY2Nnqrj8EDUkhICO7cuYM5c+YgMzMT3bp1Q0JCgjQQ+/r16xq9QX369EFcXBxmzZqFmTNnws3NDTt27EDnzp2lMps3b0ZkZCRGjx6NnJwcODs746OPPsKkSZOkMsuXL4dcLkdwcDAKCwsREBCAVatW6a/hREREBOBROCqd7qcy4eHhegtJBp8Hqb7iPEhERES1IyMjQ7oTVJkJEybAwcHhqY5VL+ZBIiIiItKmUlkiPd0FKpWlwepg8FtsRERERKVSUrpj9+6hEEIOmUyNwMA98PI6pfd6sAeJiIiI6gSVylIKRwAghBy7dw81SE8SAxIRERHVCTk5NlI4KiWEHDk51nqvCwMSERER1QnW1tmQydQay2QyNaytc/ReFwYkIiIiqhOUyjwEBu6RQlLpGCSlMk/vdeEgbSIiIjIohUIh/ezldQqurpeQk2MNa+scjXD0eDldY0AiIiIig7KxsUF4eDhn0iYiIiJ6nD7DT1VwDBIRERGRFgYkIiIiIi0MSERERERaGJCIiIiItDAgEREREWlhQCIiIiLSwoBEREREpIXzIBEREVG5srOz69TkjfrEgERERERlZGdnY+XKlU8sFx4e3iBDEm+xERERURmV9RzVpFx9w4BEREREpIUBiYiIiJ5IpbJEeroLVCpLQ1dFLzgGiYiIqqUxD9xtrFJSumP37qEQQg6ZTI3AwD3w8jpl6GrpFAMSERFVWWMfuNsYqVSWUjgCACHk2L17KFxdL0GpzDNw7XSHt9iIiKjKGvvA3cYoJ8dGCkelhJAjJ8faQDXSDwYkIiIiqpC1dTZkMrXGMplMDWvrHAPVSD8YkIiIiKgMhUIBAFAq8xAYuEcKSaVjkEpvr5WWa2g4BomIiGpMpbJETo4NrK2zG/R4lMbIxsYG4eHh0u3SOXPu4OrVJnBx+RuOjj0B9GzQA/IZkIiIqEYa45NNjc3j4cfBAfD2NmBl9Iy32IiIqNoqerKpscyRQw0fAxIREVVbY32yiRoPBiQiIqqy0gG5T3qyqaEO3KXGg2OQiIioyh4fuNuyZS7ee0+JkhIZjIwEPvkkF6NGjWzQA3ep8ZAJIYShK1Ef5ebmQqlUQqVSwcrKytDVISIyiJs3gUuXgHbtgFatDF0boier6uc3e5CIiKjGWrViMKKGqU6MQYqNjYWLiwtMTU3h4+ODEydOVFp+69atcHd3h6mpKbp06YK9e/dqrJfJZOW+Fi9eLJVxcXEps37RokU6aR8RET36HreMjIwKX9nZ2YauIpHE4D1IW7ZsQUREBNasWQMfHx9ER0cjICAAaWlpaNGiRZnyR48exciRIxEVFYWhQ4ciLi4OQUFBSElJQefOnQEAGRkZGtv897//xdixYxEcHKyxfMGCBRg/frz03tKSj6cSEekCv+SW6huD9yAtW7YM48ePR1hYGDw8PLBmzRqYm5tj/fr15ZZfsWIFBg0ahBkzZqBjx45YuHAhvLy8NP7Ds7e313jt3LkTzz//PNq2bauxL0tLS41yFhYWOm0rEVFjxS+5pfrGoAGpqKgIycnJ8Pf3l5bJ5XL4+/sjKSmp3G2SkpI0ygNAQEBAheWzsrLwww8/YOzYsWXWLVq0CDY2NujevTsWL16Mv//++ylaQ0RERA2FQW+x3b17FyUlJbCzs9NYbmdnh/Pnz5e7TWZmZrnlMzMzyy2/adMmWFpaYtiwYRrL3377bXh5ecHa2hpHjx5FZGQkMjIysGzZsnL3U1hYiMLCQul9bm7uE9tHRERE9ZPBxyDp2vr16zF69GiYmppqLI+IiJB+7tq1KxQKBSZOnIioqCiYmJiU2U9UVBTmz5+v8/oSERGR4Rn0Flvz5s1hZGSErKwsjeVZWVmwt7cvdxt7e/sql//ll1+QlpaGcePGPbEuPj4++Pvvv3H16tVy10dGRkKlUkmvGzduPHGfRERUPpXKEunpLvzuNqqzDBqQFAoFvL29kZiYKC1Tq9VITEyEr69vudv4+vpqlAeAffv2lVt+3bp18Pb2hqen5xPrkpqaCrlcXu6TcwBgYmICKysrjRcREVVfSkp3REdPw6ZNoYiOnoaUlO6GrhJRGQa/xRYREYHQ0FD06NEDvXr1QnR0NPLz8xEWFgYAGDNmDFq2bImoqCgAwNSpU+Hn54elS5diyJAh2Lx5M06ePIm1a9dq7Dc3Nxdbt27F0qVLyxwzKSkJx48fx/PPPw9LS0skJSVh+vTpeP311/HMM8/ovtFERI2USmWJ3buHSl90K4Qcu3cPhavrJSiVeQauHdE/DB6QQkJCcOfOHcyZMweZmZno1q0bEhISpIHY169fh1z+T0dXnz59EBcXh1mzZmHmzJlwc3PDjh07pDmQSm3evBlCCIwcObLMMU1MTLB582bMmzcPhYWFaNOmDaZPn64xLomIiGpP6ZfX5uTYSOGolBBy5ORYQ6nM45fcUp3B72KrIX4XGxFR9WRnZ+Pq1b/Rq1cLqNUyabmRkcDx47fh4tKEk0SSzlX189vgE0USEVHjYGNjA29vO6xdK4OR0aNlRkbA55/L4O1tx3BEdYrBb7EREVHjMnYsEBAAXLoEtGvHL7uluokBiYiI9K5VKwYjqtt4i42IiIhICwMSERERkRYGJCIiIiItDEhEREREWhiQiIiIiLQwIBERERFpYUAiIiIi0sKARERERKSFAYmIiIhICwMSERERkRYGJCIiIiIt/C62GhJCAAByc3MNXBMiIiKqqtLP7dLP8YowINVQXl4eAMDJycnANSEiIqLqysvLg1KprHC9TDwpQlG51Go1bt26BUtLS8hksqfaV25uLpycnHDjxg1YWVnVUg3rF54DngOA5wDgOQB4Dhp7+wHdngMhBPLy8uDo6Ai5vOKRRuxBqiG5XI5WrVrV6j6trKwa7X8MpXgOeA4AngOA5wDgOWjs7Qd0dw4q6zkqxUHaRERERFoYkIiIiIi0MCDVASYmJpg7dy5MTEwMXRWD4TngOQB4DgCeA4DnoLG3H6gb54CDtImIiIi0sAeJiIiISAsDEhEREZEWBiQiIiIiLQxIRERERFoYkHQkNjYWLi4uMDU1hY+PD06cOFFp+ejoaHTo0AFmZmZwcnLC9OnT8fDhQ2n9vHnzIJPJNF7u7u66bkaNVaf9xcXFWLBgAVxdXWFqagpPT08kJCQ81T7rgto+B/XtGvj5558RGBgIR0dHyGQy7Nix44nbHDx4EF5eXjAxMUG7du2wcePGMmXq03Wgi3PQ0K+DjIwMjBo1Cu3bt4dcLse0adPKLbd161a4u7vD1NQUXbp0wd69e2u/8rVEF+dg48aNZa4DU1NT3TSgFlT3HMTHx+PFF1+Era0trKys4Ovrix9//LFMOV3+PWBA0oEtW7YgIiICc+fORUpKCjw9PREQEIDbt2+XWz4uLg7vv/8+5s6di3PnzmHdunXYsmULZs6cqVGuU6dOyMjIkF6HDx/WR3OqrbrtnzVrFj7//HPExMTg7NmzmDRpEv71r3/h1KlTNd6noeniHAD15xoAgPz8fHh6eiI2NrZK5dPT0zFkyBA8//zzSE1NxbRp0zBu3DiNP4r17TrQxTkAGvZ1UFhYCFtbW8yaNQuenp7lljl69ChGjhyJsWPH4tSpUwgKCkJQUBDOnDlTm1WvNbo4B8CjWaYfvw6uXbtWW1WuddU9Bz///DNefPFF7N27F8nJyXj++ecRGBio388FQbWuV69eYvLkydL7kpIS4ejoKKKiosotP3nyZDFgwACNZREREeLZZ5+V3s+dO1d4enrqpL61rbrtd3BwECtXrtRYNmzYMDF69Oga79PQdHEO6tM1oA2A2L59e6Vl/vOf/4hOnTppLAsJCREBAQHS+/p2HTyuts5BQ78OHufn5yemTp1aZvnw4cPFkCFDNJb5+PiIiRMnPmUNda+2zsGGDRuEUqmstXrpU3XPQSkPDw8xf/586b2u/x6wB6mWFRUVITk5Gf7+/tIyuVwOf39/JCUllbtNnz59kJycLHUNXrlyBXv37sVLL72kUe7ixYtwdHRE27ZtMXr0aFy/fl13DamhmrS/sLCwTNewmZmZ9H/FNdmnIeniHJSqD9dATSUlJWmcMwAICAiQzll9uw5q4knnoFRDvg6qoqrnqaG7f/8+nJ2d4eTkhFdeeQV//PGHoaukM2q1Gnl5ebC2tgagn78HDEi17O7duygpKYGdnZ3Gcjs7O2RmZpa7zahRo7BgwQI899xzMDY2hqurK/r3769xi83HxwcbN25EQkICVq9ejfT0dPTt2xd5eXk6bU911aT9AQEBWLZsGS5evAi1Wo19+/YhPj4eGRkZNd6nIeniHAD15xqoqczMzHLPWW5uLh48eFDvroOaeNI5ABr+dVAVFZ2nhnIdVEWHDh2wfv167Ny5E19//TXUajX69OmDmzdvGrpqOrFkyRLcv38fw4cPB6CfzwUGpDrg4MGD+Pjjj7Fq1SqkpKQgPj4eP/zwAxYuXCiVGTx4MF577TV07doVAQEB2Lt3L+7du4fvvvvOgDWvHStWrICbmxvc3d2hUCgQHh6OsLAwyOWN5/KsyjloyNcAVR2vAwIAX19fjBkzBt26dYOfnx/i4+Nha2uLzz//3NBVq3VxcXGYP38+vvvuO7Ro0UJvx208n0B60rx5cxgZGSErK0tjeVZWFuzt7cvdZvbs2XjjjTcwbtw4dOnSBf/617/w8ccfIyoqCmq1utxtmjVrhvbt2+PSpUu13oanUZP229raYseOHcjPz8e1a9dw/vx5NG3aFG3btq3xPg1JF+egPHX1Gqgpe3v7cs+ZlZUVzMzM6t11UBNPOgflaWjXQVVUdJ4aynVQE8bGxujevXuDuw42b96McePG4bvvvtO4naaPvwcMSLVMoVDA29sbiYmJ0jK1Wo3ExET4+vqWu01BQUGZ3hIjIyMAgKjgq/Lu37+Py5cvw8HBoZZqXjtq0v5SpqamaNmyJf7++29s27YNr7zyylPv0xB0cQ7KU1evgZry9fXVOGcAsG/fPumc1bfroCaedA7K09Cug6qoyXlq6EpKSvD77783qOvg22+/RVhYGL799lsMGTJEY51e/h7UylBv0rB582ZhYmIiNm7cKM6ePSsmTJggmjVrJjIzM4UQQrzxxhvi/fffl8rPnTtXWFpaim+//VZcuXJF/PTTT8LV1VUMHz5cKvPOO++IgwcPivT0dHHkyBHh7+8vmjdvLm7fvq339j1Jddt/7NgxsW3bNnH58mXx888/iwEDBog2bdqIv/76q8r7rGt0cQ7q0zUghBB5eXni1KlT4tSpUwKAWLZsmTh16pS4du2aEEKI999/X7zxxhtS+StXrghzc3MxY8YMce7cOREbGyuMjIxEQkKCVKa+XQe6OAcN/ToQQkjlvb29xahRo8SpU6fEH3/8Ia0/cuSIaNKkiViyZIk4d+6cmDt3rjA2Nha///67XttWVbo4B/Pnzxc//vijuHz5skhOThYjRowQpqamGmXqkuqeg2+++UY0adJExMbGioyMDOl17949qYyu/x4wIOlITEyMaN26tVAoFKJXr17i2LFj0jo/Pz8RGhoqvS8uLhbz5s0Trq6uwtTUVDg5OYn/+7//0/hwDAkJEQ4ODkKhUIiWLVuKkJAQcenSJT22qHqq0/6DBw+Kjh07ChMTE2FjYyPeeOMN8eeff1Zrn3VRbZ+D+nYNHDhwQAAo8yptd2hoqPDz8yuzTbdu3YRCoRBt27YVGzZsKLPf+nQd6OIcNIbroLzyzs7OGmW+++470b59e6FQKESnTp3EDz/8oJ8G1YAuzsG0adOk/w7s7OzESy+9JFJSUvTXqGqq7jnw8/OrtHwpXf49kAlRwT0cIiIiokaKY5CIiIiItDAgEREREWlhQCIiIiLSwoBEREREpIUBiYiIiEgLAxIRERGRFgYkIiIiIi0MSEREDcTBgwchk8lw7949Q1eFqN5jQCKianvzzTchk8mwaNEijeU7duyATCaT3gsh8MUXX8DX1xdWVlZo2rQpOnXqhKlTp1b5SzULCgoQGRkJV1dXmJqawtbWFn5+fti5c6dUxsXFBdHR0bXSNl0rPXcymQzGxsZo06YN/vOf/+Dhw4fV2k///v0xbdo0jWV9+vRBRkYGlEplLdaYqHFiQCKiGjE1NcUnn3yCv/76q9z1QgiMGjUKb7/9Nl566SX89NNPOHv2LNatWwdTU1N8+OGHVTrOpEmTEB8fj5iYGJw/fx4JCQl49dVXkZ2dXZvN0atBgwYhIyMDV65cwfLly/H5559j7ty5T71fhUIBe3t7jZBKRDVUa19aQkSNRmhoqBg6dKhwd3cXM2bMkJZv375dlP5Z+fbbbwUAsXPnznL3oVarq3QspVIpNm7cWOH68r6zqdQvv/winnvuOWFqaipatWolpkyZIu7fvy+t//LLL4W3t7do2rSpsLOzEyNHjhRZWVnS+tLvj0pISBDdunUTpqam4vnnnxdZWVli7969wt3dXVhaWoqRI0eK/Pz8KrUnNDRUvPLKKxrLhg0bJrp37y69v3v3rhgxYoRwdHQUZmZmonPnziIuLk5jH9ptTk9Pl+r7+Pc4fv/998LDw0MoFArh7OwslixZUqV6EjV27EEiohoxMjLCxx9/jJiYGNy8ebPM+m+//RYdOnTAyy+/XO72Ve3lsLe3x969e5GXl1fu+vj4eLRq1QoLFixARkYGMjIyAACXL1/GoEGDEBwcjNOnT2PLli04fPgwwsPDpW2Li4uxcOFC/Pbbb9ixYweuXr2KN998s8wx5s2bh5UrV+Lo0aO4ceMGhg8fjujoaMTFxeGHH37ATz/9hJiYmCq1R9uZM2dw9OhRKBQKadnDhw/h7e2NH374AWfOnMGECRPwxhtv4MSJEwCAFStWwNfXF+PHj5fa7OTkVGbfycnJGD58OEaMGIHff/8d8+bNw+zZs7Fx48Ya1ZWoUTF0QiOi+ufxXpDevXuLt956Swih2YPk7u4uXn75ZY3tpk6dKiwsLISFhYVo2bJllY516NAh0apVK2FsbCx69Oghpk2bJg4fPqxRxtnZWSxfvlxj2dixY8WECRM0lv3yyy9CLpeLBw8elHusX3/9VQAQeXl5Qoh/epD2798vlYmKihIAxOXLl6VlEydOFAEBAVVqT2hoqDAyMhIWFhbCxMREABByuVx8//33lW43ZMgQ8c4770jv/fz8xNSpUzXKaPcgjRo1Srz44osaZWbMmCE8PDyqVFeixow9SET0VD755BNs2rQJ586de2LZDz74AKmpqZgzZw7u379fpf3369cPV65cQWJiIl599VX88ccf6Nu3LxYuXFjpdr/99hs2btyIpk2bSq+AgACo1Wqkp6cDeNTDEhgYiNatW8PS0hJ+fn4AgOvXr2vsq2vXrtLPdnZ2MDc3R9u2bTWW3b59u0rtAYDnn38eqampOH78OEJDQxEWFobg4GBpfUlJCRYuXIguXbrA2toaTZs2xY8//limXk9y7tw5PPvssxrLnn32WVy8eBElJSXV2hdRY8OARERPpV+/fggICEBkZKTGcjc3N6SlpWkss7W1Rbt27dCiRYtqHcPY2Bh9+/bFe++9h59++gkLFizAwoULUVRUVOE29+/fx8SJE5Gamiq9fvvtN1y8eBGurq7Iz89HQEAArKys8M033+DXX3/F9u3bAaDMfo2NjaWfS58+e5xMJoNara5yeywsLNCuXTt4enpi/fr1OH78ONatWyetX7x4MVasWIH33nsPBw4cQGpqKgICAiptLxHVriaGrgAR1X+LFi1Ct27d0KFDB2nZyJEjMWrUKOzcuROvvPJKrR7Pw8MDf//9Nx4+fAiFQgGFQlGmR8TLywtnz55Fu3btyt3H77//juzsbCxatEgav3Py5MlarWdVyOVyzJw5ExERERg1ahTMzMxw5MgRvPLKK3j99dcBAGq1GhcuXICHh4e0XXlt1taxY0ccOXJEY9mRI0fQvn17GBkZ1X5jiBoQ9iAR0VPr0qULRo8ejc8++0xaNmLECLz66qsYMWIEFixYgOPHj+Pq1as4dOgQtmzZUuUP6P79++Pzzz9HcnIyrl69ir1792LmzJl4/vnnYWVlBeDRPEg///wz/vzzT9y9excA8N577+Ho0aMIDw9HamoqLl68iJ07d0qDtFu3bg2FQoGYmBhcuXIFu3bteuJtO1157bXXYGRkhNjYWACPet/27duHo0eP4ty5c5g4cSKysrI0tnFxcZHO6d27d8vtwXrnnXeQmJiIhQsX4sKFC9i0aRNWrlyJd999Vy/tIqrPGJCIqFYsWLBA40NaJpNhy5YtiI6Oxt69e/HCCy+gQ4cOeOutt+Dk5ITDhw9Xab8BAQHYtGkTBg4ciI4dO2LKlCkICAjAd999p3Hsq1evwtXVFba2tgAejRs6dOgQLly4gL59+6J79+6YM2cOHB0dATy63bdx40Zs3boVHh4eWLRoEZYsWVKLZ6TqmjRpgvDwcHz66afIz8/HrFmz4OXlhYCAAPTv3x/29vYICgrS2Obdd9+FkZERPDw8YGtrW+74JC8vL3z33XfYvHkzOnfujDlz5mDBggXlPqlHRJpkQghh6EoQERER1SXsQSIiIiLSwoBERAb1+GP42q9ffvnF0NWrluvXr1fanuo+pk9EhsNbbERkUJV9aW3Lli1hZmamx9o8nb///htXr16tcL2LiwuaNOHDw0T1AQMSERERkRbeYiMiIiLSwoBEREREpIUBiYiIiEgLAxIRERGRFgYkIiIiIi0MSERERERaGJCIiIiItDAgEREREWn5f0Ium90BDH9mAAAAAElFTkSuQmCC", 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o27cv7t27h9atWzfKeVSGAYnq7NGu3qFD+2Dv3qEQBDlkMh1CQ5OQlJQBwHhdvUREZBoEQRBvmR08eBBxcXE4f/48CgoK8PDhQ/z555+4f/9+tRNkpqWlYcGCBfjpp5/w+++/iwO/r169iu7duzfKeVSGAYnq7NEuXB+fDHh4ZCE/3w52dvlQqQorbUdERM3PuXPn0KlTJ2RnZ2Po0KGYOHEi3nnnHdjZ2eGHH35AVFQUSkpKqgxIRUVFCA4ORnBwMLZv34527drh6tWrCA4OlvxnCAMSPTaVqlAvGBERUfN36NAh/Pzzz/j3v/+NtLQ06HQ6LF++HHJ52fDm//f//p9ee6VSidLSUr1l58+fh0ajwZIlS+Dm5gYAOHXqVOOcQA1MYpD22rVr4e7uDktLS/j7++PEiRPVtt+5cye6du0KS0tL9OrVC/v27dNb/+icDeWfkJCQSvdVXFwMb29vyGQyZGZmNtQpUQPg/EpERKahuLgYOTk5uHHjBtLT0/Huu+/ipZdewtChQzFq1Ch4enriwYMHWL16NX777Td8+umnWL9+vd4+3N3dce/ePaSkpODOnTu4f/8+OnToAKVSKW739ddfY9GiRRKdpT7JA9KOHTsQGxuL+fPnIz09Hb1790ZwcDDy8vIqbX/s2DGMGDECUVFRyMjIQHh4OMLDw3HmzBm9diEhIeJI+Vu3buHzzz+vdH8zZsyAi4tLg58XPZ5Nm4COHYFBg8p+3bRJ6oqIiFqu5ORkODs7w93dHSEhITh8+DBWrVqFr776CgqFAr1798aKFSuwdOlS9OzZE9u3b0dcXJzePgIDAzFhwgRERESgXbt2eO+999CuXTts3boVO3fuRPfu3bFkyRK8//77Ep2lPpkgCIKUBfj7+6Nv377ioF+dTgc3NzdMnjwZM2fOrNA+IiICRUVFSEpKEpf1798f3t7eYlodM2YM7t69iz179lR77G+//RaxsbHYtWsXevTogYyMDHh7e9eq7oKCAqhUKmi1Wtja2tbuZJuJW7duYcOGDTW2Gz9+PJydneu8/+vXy0LRo1MIKBRAdjbnVyKipunPP//E5cuX0alTJ1haWtZ5e6nnQWpqqrvetf35LekYpJKSEqSlpWHWrFniMrlcjqCgIKSmpla6TWpqKmJjY/WWBQcHVwhDR44cgaOjI9q2bYtBgwZh8eLFen9ocnNzMW7cOOzZs6fa0fXliouLUVxcLH4vKCiozSlSHWk0Ghw/Duh0+v+Dl5YCP/6ogZUV+D8/EbU49vb2iI6Olmwm7ZZI0oB0584dlJaWwsnJSW+5k5MTzp8/X+k2OTk5lbbPyckRv4eEhODll19Gp06dcOnSJcyePRtDhgxBamoqFAoFBEHAmDFjMGHCBPj5+SE7O7vGWuPi4rBw4cK6nyTVWvm/kLRaG8hkMRCEv+4Ay2Q6HD26DWfOFPJfSETUIvHvvcYl+RgkYxg+fDhefPFF9OrVC+Hh4UhKSsLJkydx5MgRAMDq1atRWFio13NVk1mzZkGr1Yqfa9euGal606dUKhu0XbnyfxmpVIUIC0uCTFZ2j00m0yEsLEl8Uk7qRz+JiKj5k7QHycHBAQqFArm5uXrLc3NzK8y0WU6tVtepPVA2M6eDgwOysrIwePBgHDp0CKmpqbCwsNBr5+fnh5EjR2Lbtm0V9mFhYVGhfUvVGF291c2vREREZGySBiSlUglfX1+kpKQgPDwcQNkg7ZSUFERHR1e6TUBAAFJSUvSmND9w4AACAgKqPM7169eh0WjEAcOrVq3C4sWLxfU3b95EcHAwduzYAX9//8c/sRagMbp6Ob8SERFJRfKJImNjYzF69Gj4+fmhX79+iI+PR1FRkfjW31GjRsHV1VV8XHDq1KkYMGAAli9fjtDQUCQmJuLUqVPiU1X37t3DwoULMWzYMKjValy6dAkzZsyAp6cngoODAQAdOnTQq6H8XS8eHh5oz8ekiIiIWjzJA1JERARu376NefPmIScnB97e3khOThYHYl+9elWclRMom0chISEBc+fOxezZs+Hl5YU9e/agZ8+eAACFQoHTp09j27ZtuHv3LlxcXPDCCy9g0aJFvEVGREREtSL5PEhNVUueB8lYjD2/EhGRVB53HiSqm4aYB6lZPsVGRERE9DgYkMhkGGv6ACIiMl1HjhyBTCbD3bt3a72Nu7s74uPjjVYTYAJjkIjKcaZYIiLTM2bMGGzbtg1vvPFGhRfQTpo0CR9++CFGjx6NrVu3SlOgkTAgkUlh+CEiMj1ubm5ITEzEBx98ACsrKwBl43wSEhIqPBneXPAWGxEREVXLx8cHbm5u2L17t7hs9+7d6NChA/r06SMuKy4uxpQpU+Do6AhLS0s8/fTTOHnypN6+9u3bhyeeeAJWVlZ47rnnKn3d1w8//IBnnnkGVlZWcHNzw5QpU1BUVGS086sMAxIREVETc/06cPhw2a+N5bXXXsOWLVvE75s3bxbnLCw3Y8YM7Nq1C9u2bUN6ero4B2F+fj4A4Nq1a3j55ZcRFhaGzMxMvP7665g5c6bePi5duoSQkBAMGzYMp0+fxo4dO/DDDz9UOYG0sTAgERERNSGbNgEdOwKDBpX9umlT4xz31VdfxQ8//IArV67gypUrOHr0KF599VVxfVFREdatW4dly5ZhyJAh6N69OzZu3AgrKyts+r8i161bBw8PDyxfvhxdunTByJEjMWbMGL3jxMXFYeTIkYiJiYGXlxcCAwOxatUqfPLJJ/jzzz8b52TBMUhERERNxvXrwPjxgK7sXd7Q6YA33gCCgwFjvwiiXbt2CA0NxdatWyEIAkJDQ+Hg4CCuv3TpEh48eICnnnpKXGZubo5+/frh3LlzAIBz585VeKWX4avCfvrpJ5w+fRrbt28XlwmCAJ1Oh8uXL6Nbt27GOL0KGJCIiIiaiIsX/wpH5UpLgaws4wckoOw2W/mtrrVr1xrlGPfu3cMbb7yBKVOmVFjXmAPCGZCIiIiaCC8vQC7XD0kKBeDp2TjHDwkJQUlJCWQymfh+03IeHh5QKpU4evQoOnbsCAB48OABTp48Kb5gvlu3bvj666/1tjt+/Ljedx8fH/zyyy/wbKyTqgLHIBERETUR7dsDGzaUhSKg7NePPmqc3qOy4ylw7tw5/PLLL1CUF/F/rK2tMXHiREyfPh3Jycn45ZdfMG7cONy/fx9RUVEAgAkTJuDixYuYPn06Lly4gISEhArzJ7311ls4duwYoqOjkZmZiYsXL+Krr75q9EHa7EEiIiJqQqKiysYcZWWV9Rw1VjgqV937y5YsWQKdTofIyEgUFhbCz88P+/fvR9u2bQGU3SLbtWsX/v3vf2P16tXo168f3n33Xbz22mviPp588kn87//+L+bMmYNnnnkGgiDAw8MDERERRj+3R/FltfXEl9USEVFt8WW1jYsvqyUiIiIyAgYkIiIiIgMcg0RkRBqNRnz57s2bcly+bIZOnR7CxaXsERS+fJeIyDQxIBEZiUajwZo1awAA6el9sHfvUAiCHDKZDmFhSfDxyQAAREdHMyQREZkY3mIjMpLyniOt1kYMRwAgCHLs3TsUWq2NXjsiav74XFTjaIjrzIBEZGT5+fZiOConCHLk59tJVBERNTZzc3MAwP379yWupGUov87l170+eIuNyMjs7DSQyXR6IUkm08HOLl/CqoioMSkUCrRp0wZ5eXkAgFatWkEmk0lcVfMjCALu37+PvLw8tGnTpsJklnXBgERkZCpVIcLCkiqMQVKpCqUujYgakVqtBgAxJJHxtGnTRrze9cWARNQIfHwy4OGRhfx8O9jZ5TMcEbVAMpkMzs7OcHR0xIMHD6Qup9kyNzd/rJ6jcgxIRI1EpSpkMCIiKBSKBvkBTsbFQdpEREREBhiQiIxEqVQ2aDsiImo8vMVGZCT29vaIjo6udp4jzqRNRGSaGJCIjIjhh4ioaeItNiIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkQEGJCIiIiIDJhGQ1q5dC3d3d1haWsLf3x8nTpyotv3OnTvRtWtXWFpaolevXti3b5/e+jFjxkAmk+l9QkJCxPXZ2dmIiopCp06dYGVlBQ8PD8yfP7/aV0IQERFRyyF5QNqxYwdiY2Mxf/58pKeno3fv3ggODkZeXl6l7Y8dO4YRI0YgKioKGRkZCA8PR3h4OM6cOaPXLiQkBLdu3RI/n3/+ubju/Pnz0Ol0+Oijj3D27Fl88MEHWL9+PWbPnm3UcyUiIqKmQSYIgiBlAf7+/ujbty/WrFkDANDpdHBzc8PkyZMxc+bMCu0jIiJQVFSEpKQkcVn//v3h7e2N9evXAyjrQbp79y727NlT6zqWLVuGdevW4bfffqtV+4KCAqhUKmi1Wtja2tb6OERERCSd2v78lrQHqaSkBGlpaQgKChKXyeVyBAUFITU1tdJtUlNT9doDQHBwcIX2R44cgaOjI7p06YKJEydCo9FUW4tWq4WdnV09z4SIiIiaEzMpD37nzh2UlpbCyclJb7mTkxPOnz9f6TY5OTmVts/JyRG/h4SE4OWXX0anTp1w6dIlzJ49G0OGDEFqaioUCkWFfWZlZWH16tV4//33q6y1uLgYxcXF4veCgoJanSMRERE1PZIGJGMZPny4+PtevXrhySefhIeHB44cOYLBgwfrtb1x4wZCQkLwz3/+E+PGjatyn3FxcVi4cKHRaiYiIiLTIektNgcHBygUCuTm5uotz83NhVqtrnQbtVpdp/YA0LlzZzg4OCArK0tv+c2bN/Hcc88hMDAQGzZsqLbWWbNmQavVip9r165V256IiIiaLkkDklKphK+vL1JSUsRlOp0OKSkpCAgIqHSbgIAAvfYAcODAgSrbA8D169eh0Wjg7OwsLrtx4wYGDhwIX19fbNmyBXJ59ZfCwsICtra2eh8iIiJqniS/xRYbG4vRo0fDz88P/fr1Q3x8PIqKijB27FgAwKhRo+Dq6oq4uDgAwNSpUzFgwAAsX74coaGhSExMxKlTp8QeoHv37mHhwoUYNmwY1Go1Ll26hBkzZsDT0xPBwcEA/gpHHTt2xPvvv4/bt2+L9VTXE0VEREQtg+QBKSIiArdv38a8efOQk5MDb29vJCcniwOxr169qte7ExgYiISEBMydOxezZ8+Gl5cX9uzZg549ewIAFAoFTp8+jW3btuHu3btwcXHBCy+8gEWLFsHCwgJAWY9TVlYWsrKy0L59e716JJ71gIiIiEyA5PMgNVWcB4mIiKjpaRLzIBERERGZIgYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZMBM6gII0Gg0KCkpqXK9UqmEvb19I1ZERETUsjEgSUyj0WDNmjU1touOjmZIIiIiaiS8xSax6nqO6tOOiIiIHh8DEhEREZEBBiQiIiIiAwxIRERERAZMIiCtXbsW7u7usLS0hL+/P06cOFFt+507d6Jr166wtLREr169sG/fPr31Y8aMgUwm0/uEhITotcnPz8fIkSNha2uLNm3aICoqCvfu3Wvwc6srrdYGly+7Q6u1kboUIiKiFkvygLRjxw7ExsZi/vz5SE9PR+/evREcHIy8vLxK2x87dgwjRoxAVFQUMjIyEB4ejvDwcJw5c0avXUhICG7duiV+Pv/8c731I0eOxNmzZ3HgwAEkJSXhu+++w/jx4412nrWRnt4H8fEx2LZtNOLjY5Ce3kfSeoiIiFoqyQPSihUrMG7cOIwdOxbdu3fH+vXr0apVK2zevLnS9itXrkRISAimT5+Obt26YdGiRfDx8anwqLyFhQXUarX4adu2rbju3LlzSE5Oxscffwx/f388/fTTWL16NRITE3Hz5k2jnm9VtFob7N07FIJQ9p9EEOTYu3coe5KIiIgkIGlAKikpQVpaGoKCgsRlcrkcQUFBSE1NrXSb1NRUvfYAEBwcXKH9kSNH4OjoiC5dumDixInQaDR6+2jTpg38/PzEZUFBQZDL5fjxxx8rPW5xcTEKCgr0Pg1BqVQCAPLz7cVwVE4Q5MjPt9NrR0RERMYn6USRd+7cQWlpKZycnPSWOzk54fz585Vuk5OTU2n7nJwc8XtISAhefvlldOrUCZcuXcLs2bMxZMgQpKamQqFQICcnB46Ojnr7MDMzg52dnd5+HhUXF4eFCxfW5zSrZW9vj+joaGRnP8SnnwrQ6WTiOoVCwOTJQ+DubsZJIomIiBpRs5xJe/jw4eLve/XqhSeffBIeHh44cuQIBg8eXK99zpo1C7GxseL3goICuLm5PXatQFlIsrcHNmwA3ngDKC0FFArgo49k8PV1qnkHRERE1KAkDUgODg5QKBTIzc3VW56bmwu1Wl3pNmq1uk7tAaBz585wcHBAVlYWBg8eDLVaXWEQ+MOHD5Gfn1/lfiwsLGBhYVGb06q3qCggOBjIygI8PYH27Y16OCIiIqqCpGOQlEolfH19kZKSIi7T6XRISUlBQEBApdsEBATotQeAAwcOVNkeAK5fvw6NRgNnZ2dxH3fv3kVaWprY5tChQ9DpdPD393+cU3ps7dsDAwcyHBEREUlJ8qfYYmNjsXHjRmzbtg3nzp3DxIkTUVRUhLFjxwIARo0ahVmzZontp06diuTkZCxfvhznz5/HggULcOrUKURHRwMA7t27h+nTp+P48ePIzs5GSkoKXnrpJXh6eiI4OBgA0K1bN4SEhGDcuHE4ceIEjh49iujoaAwfPhwuLi6NfxGIiIjIpEg+BikiIgK3b9/GvHnzkJOTA29vbyQnJ4sDsa9evQq5/K8cFxgYiISEBMydOxezZ8+Gl5cX9uzZg549ewIAFAoFTp8+jW3btuHu3btwcXHBCy+8gEWLFundItu+fTuio6MxePBgyOVyDBs2DKtWrWrckyciIiKTJBMEQZC6iKaooKAAKpUKWq0Wtra2UpdDRHWk0WhQUlJS5XqlUsmnR4maodr+/Ja8B4mIqLFpNJoKk8tWJjo6miGJqIWSfAwSEVFjq67nqD7tiKj5YUAiIiIiMsCAREQtnlZrg8uX3fnuQyIScQwSEbVo6el9xBdFy2Q6hIUlwccnQ+qyiEhiDEhEZNKM+bSZVmsjhiOg7AXRe/cOhYdHFlSqwnrtk4iaBwYkIjJZxn7aLD/fXgxH5QRBjvx8OwYkohaOY5CIyGQZ+2kzOzsNZDKd3jKZTAc7u/x67Y+Img8GJCJqcZRKJQBApSpEWFiSGJLKxyCV9x6VtyOiloe32IioRfPxyYCHRxby8+1gZ5fPW2tEBIABiYiaEK3WBvn59rCz0zxWkDG8JadSFVa6P04USdRyMSARUZPAx/GJqDFxDBIRmbyqHsdvqIkdOVEkERliDxIRmTxjPo7Pnikiqgx7kIjIZJU/RVbT4/j1fdrM2D1TRNR0sQeJiEyWvb09oqOjUVJSAlfXArz1lgqlpTIoFAKWLi3AK6+MeKyZtDlRJBFVhQGJiExaefiZNg2IiACysgBPTxnat28DoM1j7bu8Z+rRkMSJIokI4C02ImpC2rcHBg4s+/VxcKJIIqqJTBAEQeoimqKCggKoVCpotVrY2tpKXQ4R1dGjL8G9eVOO7GwzuLs/hItLWVh6nFt3RGS6avvzm7fYiKhFejT8ODsDvr4SFkNEJoe32IiIiIgMMCARERERGeAtNiJqth4dZ1QZjjMioqowIBFRs6TRaLBmzZoa20VHRzMkEVEFvMVGRM1SdT1H9WlHRC0LAxIRERGRAQYkIiIiIgMMSETUImi1Nrh82Z0voiWiWuEgbSJq9tLT+2Dv3qEQBLn4OhEfnwypyyIiE8YeJCJq1rRaGzEcAYAgyLF371D2JBFRtRiQiKhZy8+3F8NROUGQIz/fTqKKiKgpYEAiomZJqVQCAOzsNJDJdHrrZDId7Ozy9doRET2KY5CIqFmyt7dHdHQ0SkpK4OpagLfeUqG0VAaFQsDSpQV45ZURnEmbiKokEwRBkLqIpqigoAAqlQparRa2trZSl0NENbh+HcjKAjw9gfbtpa6GiKRS25/f7EEiohahfXsGIyKqPY5BIiIiIjIgeUBau3Yt3N3dYWlpCX9/f5w4caLa9jt37kTXrl1haWmJXr16Yd++fVW2nTBhAmQyGeLj4/WW//rrr3jppZfg4OAAW1tbPP300zh8+HBDnA4RERE1A5IGpB07diA2Nhbz589Heno6evfujeDgYOTl5VXa/tixYxgxYgSioqKQkZGB8PBwhIeH48yZMxXafvnllzh+/DhcXFwqrBs6dCgePnyIQ4cOIS0tDb1798bQoUORk5PT4OdIRERETY+kg7T9/f3Rt29frFmzBgCg0+ng5uaGyZMnY+bMmRXaR0REoKioCElJSeKy/v37w9vbG+vXrxeX3bhxA/7+/ti/fz9CQ0MRExODmJgYAMCdO3fQrl07fPfdd3jmmWcAAIWFhbC1tcWBAwcQFBRUq9o5SJuIiKjpqe3Pb8l6kEpKSpCWlqYXSORyOYKCgpCamlrpNqmpqRUCTHBwsF57nU6HyMhITJ8+HT169KiwD3t7e3Tp0gWffPIJioqK8PDhQ3z00UdwdHSEr69vlfUWFxejoKBA70NERETNk2QB6c6dOygtLYWTk5PecicnpypvdeXk5NTYfunSpTAzM8OUKVMq3YdMJsPBgweRkZEBGxsbWFpaYsWKFUhOTkbbtm2rrDcuLg4qlUr8uLm51fZUiYiIqImRfJB2Q0pLS8PKlSuxdetWyGSyStsIgoBJkybB0dER33//PU6cOIHw8HCEhYXh1q1bVe571qxZ0Gq14ufatWvGOg0iIiKSmGQBycHBAQqFArm5uXrLc3NzoVarK91GrVZX2/77779HXl4eOnToADMzM5iZmeHKlSuYNm0a3N3dAQCHDh1CUlISEhMT8dRTT8HHxwcffvghrKyssG3btirrtbCwgK2trd6HiIiImifJApJSqYSvry9SUlLEZTqdDikpKQgICKh0m4CAAL32AHDgwAGxfWRkJE6fPo3MzEzx4+LigunTp2P//v0AgPv37wMoG+/0KLlcDp1O/31NRERE1DJJOpN2bGwsRo8eDT8/P/Tr1w/x8fEoKirC2LFjAQCjRo2Cq6sr4uLiAABTp07FgAEDsHz5coSGhiIxMRGnTp3Chg0bAJQNwDZ8r5K5uTnUajW6dOkCoCxktW3bFqNHj8a8efNgZWWFjRs34vLlywgNDW3EsyciIiJTJWlAioiIwO3btzFv3jzk5OTA29sbycnJ4kDsq1ev6vX0BAYGIiEhAXPnzsXs2bPh5eWFPXv2oGfPnrU+poODA5KTkzFnzhwMGjQIDx48QI8ePfDVV1+hd+/eDX6ORERE1PTUaR6kBw8eYM6cOdi9ezfs7OwwYcIEvPbaa+L63NxcuLi4oLS01CjFmhLOg0RERNT0GGUepHfeeQeffPIJJkyYgBdeeAGxsbF444039NpIOO8kERERUYOo0y227du34+OPP8bQoUMBAGPGjMGQIUMwduxYbN68GQCqfLyeiIiIqKmoUw/SjRs39Mb7eHp64siRIzh27BgiIyNbxK01IiIiav7qFJDUajUuXbqkt8zV1RWHDx/GyZMnMWbMmIasjYiIiEgSdQpIgwYNQkJCQoXlLi4uOHToEC5fvtxghRERERFJpU5jkN5++22cP3++0nWurq743//9Xxw4cKBBCiMiIiKSSp0e86e/8DF/IiKipscoj/mX27lzJ15++WX07NkTPXv2xMsvv4wvvvii3sUSERERmZI6BSSdToeIiAhERETgl19+gaenJzw9PXH27FlERERg+PDhnAeJiIiImrw6jUFauXIlDh48iK+//lqcC6nc119/jbFjx2LlypWIiYlpyBqJiOpFo9GgpKSkyvVKpbLC+xuJiIA6jkF68sknERMTo/d6kUdt2rQJK1euxOnTpxusQFPFMUhEpk2j0WDNmjU1touOjmZIImpBjDIG6eLFiwgKCqpyfVBQEC5evFiXXRIRGUV1PUf1aUdELUudApKVlRXu3r1b5fqCggJYWlo+bk1ERA1Oq7XB5cvu0GptpC6FiJqAOo1BCggIwLp167Bu3bpK169duxYBAQENUhgRUUNJT++DvXuHQhDkkMl0CAtLgo9PhtRlEZEJq1NAmjNnDgYOHAiNRoM333wTXbt2hSAIOHfuHJYvX46vvvoKhw8fNlatRER1ptXaiOEIAARBjr17h8LDIwsqVaHE1RGRqapTQAoMDMSOHTswfvx47Nq1S29d27Zt8fnnn+Opp55q0AKJiB5Hfr69GI7KCYIc+fl2DEhEVKU6BSQA+Pvf/47g4GDs379fHJD9xBNP4IUXXkCrVq0avEAiosdhZ6eBTKbTC0kymQ52dvkSVkVEpq5OAenQoUOIjo7G8ePH8fe//11vnVarRY8ePbB+/Xo888wzDVokEVF9qVSFCAtLqjAGib1HRFSdOgWk+Ph4jBs3rtJ5A1QqFd544w2sWLGCAYmIJKdUKsXf+/hkwMMjC/n5drCzy9cLR4+2IyIqV6eJIjt27Ijk5GR069at0vXnz5/HCy+8gKtXrzZYgaaKE0USmT7OpE1Ehmr787tOPUi5ubkwNzevemdmZrh9+3ZddklEZDQMP0RUX3WaKNLV1RVnzpypcv3p06fh7Oz82EURERERSalOAelvf/sb3n77bfz5558V1v3xxx+YP39+hZfYEhERETU1dRqDlJubCx8fHygUCkRHR6NLly4AysYerV27FqWlpUhPT4eTk5PRCjYVHINERETU9BhlDJKTkxOOHTuGiRMnYtasWSjPVjKZDMHBwVi7dm2LCEdERETUvNV5osiOHTti3759+P3335GVlQVBEODl5YW2bdsaoz4iIiKiRlfngFSubdu26Nu3b0PWQkRERGQS6jRIm4iIiKglYEAiIiIiMsCARERERGSAAYmIiIjIAAMSERERkQEGJCIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMiB5QFq7di3c3d1haWkJf39/nDhxotr2O3fuRNeuXWFpaYlevXph3759VbadMGECZDIZ4uPjK6z75ptv4O/vDysrK7Rt2xbh4eGPeSZERETUXEgakHbs2IHY2FjMnz8f6enp6N27N4KDg5GXl1dp+2PHjmHEiBGIiopCRkYGwsPDER4ejjNnzlRo++WXX+L48eNwcXGpsG7Xrl2IjIzE2LFj8dNPP+Ho0aN45ZVXGvz8iIiIqGmSCYIgSHVwf39/9O3bF2vWrAEA6HQ6uLm5YfLkyZg5c2aF9hERESgqKkJSUpK4rH///vD29sb69evFZTdu3IC/vz/279+P0NBQxMTEICYmBgDw8OFDuLu7Y+HChYiKiqp37QUFBVCpVNBqtbC1ta33foiIiKjx1Pbnt2Q9SCUlJUhLS0NQUNBfxcjlCAoKQmpqaqXbpKam6rUHgODgYL32Op0OkZGRmD59Onr06FFhH+np6bhx4wbkcjn69OkDZ2dnDBkypNJeqEcVFxejoKBA70NERETNk2QB6c6dOygtLYWTk5PecicnJ+Tk5FS6TU5OTo3tly5dCjMzM0yZMqXSffz2228AgAULFmDu3LlISkpC27ZtMXDgQOTn51dZb1xcHFQqlfhxc3Or1XkSERFR0yP5IO2GlJaWhpUrV2Lr1q2QyWSVttHpdACAOXPmYNiwYfD19cWWLVsgk8mwc+fOKvc9a9YsaLVa8XPt2jWjnAMRERFJT7KA5ODgAIVCgdzcXL3lubm5UKvVlW6jVqurbf/9998jLy8PHTp0gJmZGczMzHDlyhVMmzYN7u7uAABnZ2cAQPfu3cV9WFhYoHPnzrh69WqV9VpYWMDW1lbvQ0RERM2TZAFJqVTC19cXKSkp4jKdToeUlBQEBARUuk1AQIBeewA4cOCA2D4yMhKnT59GZmam+HFxccH06dOxf/9+AICvry8sLCxw4cIFcR8PHjxAdnY2Onbs2NCnSURERE2QmZQHj42NxejRo+Hn54d+/fohPj4eRUVFGDt2LABg1KhRcHV1RVxcHABg6tSpGDBgAJYvX47Q0FAkJibi1KlT2LBhAwDA3t4e9vb2escwNzeHWq1Gly5dAAC2traYMGEC5s+fDzc3N3Ts2BHLli0DAPzzn/9srFMnIiIiEyZpQIqIiMDt27cxb9485OTkwNvbG8nJyeJA7KtXr0Iu/6uTKzAwEAkJCZg7dy5mz54NLy8v7NmzBz179qzTcZctWwYzMzNERkbijz/+gL+/Pw4dOoS2bds26PkRERFR0yTpPEhNGedBIiIianpMfh4kIiIiIlPFgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiA5I+5k9ERA1Do9GgpKSkyvVKpbLCPHFEVDUGJCKiJk6j0WDNmjU1touOjmZIIqol3mIjImriDHuOtFobXL7sDq3Wptp2RFQ19iARETUj6el9sHfvUAiCHDKZDmFhSfDxyZC6LKImhz1IRETNhFZrI4YjABAEOfbuHVqhJ4mIasaARETUTOTn24vhqJwgyJGfbydRRURNFwMSEVEzYWengUym01smk+lgZ5cvUUVETRcDEhFRM6FSFSIsLEkMSeVjkFSqQokrI2p6OEibiKgZ8fHJgIdHFvLz7WBnl89wRFRPDEhERE2cUqnU+65SFVYajAzb1QYnoKSWigGJiKiJs7e3R3R0dIMHGU5A2XRcvw5cvAh4eQHt20tdTfPAgERE1AwYI6DUdmJJTkApjfLevYQEK8yYoYJOJ4NcLuC997R45ZU/2Lv3mBiQiIioVrRaG+Tn28POTsOxTRIr793Tam0QHx8DQZABAHQ6GaZPt8WNG5uhUhWyd+8xMCAREVGNOEO3aSnvtatu7iuVqpC9e4+Bj/kTEVG1OEO36eLcV8bDgERERNXiDN2mi3NfGQ9vsRERUbXKeykeDUnspTAdnPvKONiDRERE1WIvhelTqQrRqdMV/jdpQOxBIiKiSj06sWR1vRT1mYCSyNQxIBERUaWMNQElUVPAgERERFVi+DFNte21Y+9e/TEgERE9Br7igaTA3j3jY0AiIqqnTZuA8eMBnQ6Qy4ENG4CoKKmropaC4ce4ZIIgCFIX0RQVFBRApVJBq9XC1tZW6nKIqJFdvw507FgWjsopFEB2NnuSjK38HWRVYc8JVae2P7/Zg0REVA8XL+qHIwAoLQWyshiQjKn8HWQ14TvI6HFxHiQionrw8iq7rfYohQLw9JSmnpaitu8W4zvI6HExIBER1ZFGo4FCcQvvvXcXCkXZKAWFQsDSpXehUNyCRqORuEIiely8xUZEVAeGt3imTLERJ0+8d68QGzaULectnsah1dogP98ednYaziJNDYoBiYioDgxv3ahUhZX+YOYtHuNLT++DvXuHQhDk4utPfHwypC6LmgneYiMioiZHq7URwxEACIIce/cOhVZrI3Fl1FwwIBERUZOTn28vhqNygiBHfr6dRBVRc2MSAWnt2rVwd3eHpaUl/P39ceLEiWrb79y5E127doWlpSV69eqFffv2Vdl2woQJkMlkiI+Pr3R9cXExvL29IZPJkJmZ+RhnQUREjcXOTgOZTH+eBZlMBzu7fIkqouZG8oC0Y8cOxMbGYv78+UhPT0fv3r0RHByMvLy8StsfO3YMI0aMQFRUFDIyMhAeHo7w8HCcOXOmQtsvv/wSx48fh4uLS5XHnzFjRrXriYjIdJS/W0ylKkRYWJIYksrHIJWPB+M7yOhxST6Ttr+/P/r27Ss+FaLT6eDm5obJkydj5syZFdpHRESgqKgISUlJ4rL+/fvD29sb69evF5fduHED/v7+2L9/P0JDQxETE4OYmBi9fX377beIjY3Frl270KNHD2RkZMDb27tWdXMmbaKW6datW9hQ/qhaNcaPHw9nZ+dGqKii5v5+uEdn0r55U47sbDO4uz+Ei0tZWOJM2lSdJjGTdklJCdLS0jBr1ixxmVwuR1BQEFJTUyvdJjU1FbGxsXrLgoODsWfPHvG7TqdDZGQkpk+fjh49elS6n9zcXIwbNw579uxBq1ataqy1uLgYxcXF4veCgoIatyEiamwt4f1wj4YfZ2fA11fCYqjZkvQW2507d1BaWgonJye95U5OTsjJyal0m5ycnBrbL126FGZmZpgyZUql+xAEAWPGjMGECRPg5+dXq1rj4uKgUqnEj5ubW622I6Lmpba3bqS4xXP9+l/hCCj79Y03ypYTUd00u3mQ0tLSsHLlSqSnp0Mmk1XaZvXq1SgsLNTruarJrFmz9HquCgoKGJKIWiB7e3tER0eb3MtSNRoNjh8HdDr945aWAj/+qIGVFd/+TlQXkgYkBwcHKBQK5Obm6i3Pzc2FWq2udBu1Wl1t+++//x55eXno0KGDuL60tBTTpk1DfHw8srOzcejQIaSmpsLCwkJvP35+fhg5ciS2bdtW4bgWFhYV2hNRy2RqQaN8dm+t1gYyWYze4+8ymQ5Hj27DmTOFnN2bqA4kvcWmVCrh6+uLlJQUcZlOp0NKSgoCAgIq3SYgIECvPQAcOHBAbB8ZGYnTp08jMzNT/Li4uGD69OnYv38/AGDVqlX46aefxPXl0wTs2LED77zzjjFOlYjIaMp7s2p6souzexPVnuS32GJjYzF69Gj4+fmhX79+iI+PR1FREcaOHQsAGDVqFFxdXREXFwcAmDp1KgYMGIDly5cjNDQUiYmJOHXqlPhUib29fYV/IZmbm0OtVqNLly4AoNe7BACtW7cGAHh4eKB9c3zkg4haDB+fDHh4ZInvh+P7yYjqR/KAFBERgdu3b2PevHnIycmBt7c3kpOTxYHYV69ehVz+V0dXYGAgEhISMHfuXMyePRteXl7Ys2cPevbsKdUpEBGZlKreD0dEtSf5PEhNFedBIiJT0RTmZiIyFbX9+S35TNpEREREpoYBiYiIiMiA5GOQiIiIGsujrympDF9TQuUYkIiImjhTnt3blJTPF1UTzhelr7m/268qDEhERE2cqc7ubWpqOw8U54v6S0t4t19VGJCIiJqBlh5+qGFpNBpkZz/E+PGO0OnKXttV9m4/Ad7eeXB3N2v2f+YYkIiIiEhUfivy8mV36HSj9daVlsqwevW36NTpSrO/Fcmn2IiIqEXSam1w+bI7tFobqUsxKeW3GO3sNOJra8rJZDrY2eXrtWuu2INEREQtTnp6H+zdOxSCIBffWefjkyF1WSal/N1+hteppczSzoBEREQtilZrI/7QBwBBkGPv3qHw8MhqMT/8a6slv9uPAYmIiFqU/Hx7MRyVEwQ58vPtWlQAqK2W+m4/jkEiIqIWoXweqJrG1rT0+aKoDHuQiIioRXh0vihX1wK89ZYKpaUyKBQCli4twCuvjOB8USRiQCIiohajPPxMmwZERABZWYCnpwzt27cB0EbK0sjEMCAREVGL1L59y3p1Rm3x1TVlGJCIiIhIxFfXlGFAIiIiIj3NPfzUBp9iIyIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiAwxIRERERAYYkIiIiIgMMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiAwxIRERERAYYkIiIiIgMMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyIBJBKS1a9fC3d0dlpaW8Pf3x4kTJ6ptv3PnTnTt2hWWlpbo1asX9u3bV2XbCRMmQCaTIT4+XlyWnZ2NqKgodOrUCVZWVvDw8MD8+fNRUlLSUKdERERETZjkAWnHjh2IjY3F/PnzkZ6ejt69eyM4OBh5eXmVtj927BhGjBiBqKgoZGRkIDw8HOHh4Thz5kyFtl9++SWOHz8OFxcXveXnz5+HTqfDRx99hLNnz+KDDz7A+vXrMXv2bKOcIxERETUtMkEQBCkL8Pf3R9++fbFmzRoAgE6ng5ubGyZPnoyZM2dWaB8REYGioiIkJSWJy/r37w9vb2+sX79eXHbjxg34+/tj//79CA0NRUxMDGJiYqqsY9myZVi3bh1+++23WtVdUFAAlUoFrVYLW1vbWp4tERERSam2P78l7UEqKSlBWloagoKCxGVyuRxBQUFITU2tdJvU1FS99gAQHBys116n0yEyMhLTp09Hjx49alWLVquFnZ1dleuLi4tRUFCg9yEiIqLmSdKAdOfOHZSWlsLJyUlvuZOTE3JycirdJicnp8b2S5cuhZmZGaZMmVKrOrKysrB69Wq88cYbVbaJi4uDSqUSP25ubrXaNxERETU9ko9BamhpaWlYuXIltm7dCplMVmP7GzduICQkBP/85z8xbty4KtvNmjULWq1W/Fy7dq0hyyYiIiITImlAcnBwgEKhQG5urt7y3NxcqNXqSrdRq9XVtv/++++Rl5eHDh06wMzMDGZmZrhy5QqmTZsGd3d3ve1u3ryJ5557DoGBgdiwYUO1tVpYWMDW1lbvQ0RERM2TpAFJqVTC19cXKSkp4jKdToeUlBQEBARUuk1AQIBeewA4cOCA2D4yMhKnT59GZmam+HFxccH06dOxf/9+cZsbN25g4MCB8PX1xZYtWyCXN7vONCIiIqonM6kLiI2NxejRo+Hn54d+/fohPj4eRUVFGDt2LABg1KhRcHV1RVxcHABg6tSpGDBgAJYvX47Q0FAkJibi1KlTYg+Qvb097O3t9Y5hbm4OtVqNLl26APgrHHXs2BHvv/8+bt++LbatqueKiIiIWg7JA1JERARu376NefPmIScnB97e3khOThYHYl+9elWvdycwMBAJCQmYO3cuZs+eDS8vL+zZswc9e/as9TEPHDiArKwsZGVloX379nrrJJ71gIiIiEyA5PMgNVWcB4mIiKjpaRLzIBERERGZIgYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERERlgQCIiIiIywIBEREREZIABiYiIiMgAAxIRERGRAQYkIiIiIgMMSEREREQGGJCIiIiIDDAgERERGbh+HTh8uOxXapkYkIiIiABoNBrcunULy5ffRceOAgYNAjp2FLB8+V3cunULGo1G6hKpEZlJXQAREZHUNBoN1qxZA63WBvHxMRAEGQBAp5Nh+nRb3LixGSpVIaKjo2Fvby9xtdQYGJCIiKjFKykpAQDk59tDEPRvrgiCHPn5dlCpCsV2VD8ajabaa6hUKk0mgDIgERER/R87Ow1kMp1eSJLJdLCzy5ewquahvJeunFZrg/x8e9jZaaBSFYrLTaWXjgGJiIjo/6hUhQgLS8LevUMhCHLIZDqEhSXp/QCn+nm05yg9vU+Fa+zjk1GhnZQYkIiIiB7h45MBD48s5Ofbwc4un+GogWm1NmI4AspuYe7dOxQeHlkmda0ZkIiIiAyoVIUm9cO6OalpnJep4GP+RERE1GjKx3k9yhTHeTEgERERUaMpH+dVHpJMdZwXb7EREVGLp1QqG7QdVa+mcV7XrwMXLwJeXkD79tLUyIBEREQtnr29PaKjo5vMHD3NQVXjvDZsKMV//iNAp5NBLhfw3ntavPLKH41+/RmQiIiIAIYfI6tN71vZTOYuJjGTOQMSERERGV11vXR37tzB7t27TWomcwYkIiIiahQ19f6Y0kzmfIqNiIiITIIpPeHGHiQiIiIyGaYyk7lJ9CCtXbsW7u7usLS0hL+/P06cOFFt+507d6Jr166wtLREr169sG/fvirbTpgwATKZDPHx8XrL8/PzMXLkSNja2qJNmzaIiorCvXv3GuJ0iIiI6DGoVIXo1OmKpHMjSR6QduzYgdjYWMyfPx/p6eno3bs3goODkZeXV2n7Y8eOYcSIEYiKikJGRgbCw8MRHh6OM2fOVGj75Zdf4vjx43BxcamwbuTIkTh79iwOHDiApKQkfPfddxg/fnyDnx8RERE1PZIHpBUrVmDcuHEYO3YsunfvjvXr16NVq1bYvHlzpe1XrlyJkJAQTJ8+Hd26dcOiRYvg4+ODNWvW6LW7ceMGJk+ejO3bt8Pc3Fxv3blz55CcnIyPP/4Y/v7+ePrpp7F69WokJibi5s2bRjtXIiIiahokDUglJSVIS0tDUFCQuEwulyMoKAipqamVbpOamqrXHgCCg4P12ut0OkRGRmL69Ono0aNHpfto06YN/Pz8xGVBQUGQy+X48ccfKz1ucXExCgoK9D5ERET0+ExxJnNJB2nfuXMHpaWlcHJy0lvu5OSE8+fPV7pNTk5Ope1zcnLE70uXLoWZmRmmTJlS5T4cHR31lpmZmcHOzk5vP4+Ki4vDwoULazwnIiIiqhtTnMm82T3FlpaWhpUrVyI9PR0ymazB9jtr1izExsaK3wsKCuDm5tZg+yciImrJTG0mc0lvsTk4OEChUCA3N1dveW5uLtRqdaXbqNXqatt///33yMvLQ4cOHWBmZgYzMzNcuXIF06ZNg7u7u7gPw0HgDx8+RH5+fpXHtbCwgK2trd6HiIiImidJA5JSqYSvry9SUlLEZTqdDikpKQgICKh0m4CAAL32AHDgwAGxfWRkJE6fPo3MzEzx4+LigunTp2P//v3iPu7evYu0tDRxH4cOHYJOp4O/v39DnyYRERE1MZLfYouNjcXo0aPh5+eHfv36IT4+HkVFRRg7diwAYNSoUXB1dUVcXBwAYOrUqRgwYACWL1+O0NBQJCYm4tSpU9iwYQOAsi46w246c3NzqNVqdOnSBQDQrVs3hISEYNy4cVi/fj0ePHiA6OhoDB8+vNIpAYiIiKhlkTwgRURE4Pbt25g3bx5ycnLg7e2N5ORkcSD21atXIZf/1dEVGBiIhIQEzJ07F7Nnz4aXlxf27NmDnj171um427dvR3R0NAYPHgy5XI5hw4Zh1apVDXpuRERE1DTJBEEQpC6iKSooKIBKpYJWq+V4JCIioiaitj+/JZ8okoiIiMjUMCARERERGWBAIiIiIjLAgERERERkQPKn2Jqq8rHtfCcbERFR01H+c7umZ9QYkOqpsLAQAPi6ESIioiaosLAQKpWqyvV8zL+edDodbt68CRsbm0rf+Vb+rrZr165xGoBK8PrUjNeoZrxGNeM1qhmvUc2a0zUSBAGFhYVwcXHRm2fREHuQ6kkul6N9+/Y1tuN726rH61MzXqOa8RrVjNeoZrxGNWsu16i6nqNyHKRNREREZIABiYiIiMgAA5KRWFhYYP78+bCwsJC6FJPE61MzXqOa8RrVjNeoZrxGNWuJ14iDtImIiIgMsAeJiIiIyAADEhEREZEBBiQiIiIiAwxIRERERAYYkOpp7dq1cHd3h6WlJfz9/XHixIkq2549exbDhg2Du7s7ZDIZ4uPjG69QCdXlGm3cuBHPPPMM2rZti7Zt2yIoKKja9s1FXa7R7t274efnhzZt2sDa2hre3t749NNPG7FaadTlGj0qMTERMpkM4eHhxi3QBNTlGm3duhUymUzvY2lp2YjVSqOuf47u3r2LSZMmwdnZGRYWFnjiiSewb9++RqpWGnW5RgMHDqzw50gmkyE0NLQRKzYygeosMTFRUCqVwubNm4WzZ88K48aNE9q0aSPk5uZW2v7EiRPCm2++KXz++eeCWq0WPvjgg8YtWAJ1vUavvPKKsHbtWiEjI0M4d+6cMGbMGEGlUgnXr19v5MobT12v0eHDh4Xdu3cLv/zyi5CVlSXEx8cLCoVCSE5ObuTKG09dr1G5y5cvC66ursIzzzwjvPTSS41TrETqeo22bNki2NraCrdu3RI/OTk5jVx146rrNSouLhb8/PyEv/3tb8IPP/wgXL58WThy5IiQmZnZyJU3nrpeI41Go/dn6MyZM4JCoRC2bNnSuIUbEQNSPfTr10+YNGmS+L20tFRwcXER4uLiaty2Y8eOLSIgPc41EgRBePjwoWBjYyNs27bNWCVK7nGvkSAIQp8+fYS5c+caozyTUJ9r9PDhQyEwMFD4+OOPhdGjRzf7gFTXa7RlyxZBpVI1UnWmoa7XaN26dULnzp2FkpKSxipRco/799EHH3wg2NjYCPfu3TNWiY2Ot9jqqKSkBGlpaQgKChKXyeVyBAUFITU1VcLKTEdDXKP79+/jwYMHsLOzM1aZknrcayQIAlJSUnDhwgU8++yzxixVMvW9Rv/5z3/g6OiIqKioxihTUvW9Rvfu3UPHjh3h5uaGl156CWfPnm2MciVRn2v09ddfIyAgAJMmTYKTkxN69uyJd999F6WlpY1VdqNqiL+zN23ahOHDh8Pa2tpYZTY6BqQ6unPnDkpLS+Hk5KS33MnJCTk5ORJVZVoa4hq99dZbcHFx0fsftjmp7zXSarVo3bo1lEolQkNDsXr1ajz//PPGLlcS9blGP/zwAzZt2oSNGzc2RomSq8816tKlCzZv3oyvvvoKn332GXQ6HQIDA3H9+vXGKLnR1eca/fbbb/jiiy9QWlqKffv24e2338by5cuxePHixii50T3u39knTpzAmTNn8PrrrxurREmYSV0AkaElS5YgMTERR44caRGDR+vCxsYGmZmZuHfvHlJSUhAbG4vOnTtj4MCBUpcmucLCQkRGRmLjxo1wcHCQuhyTFRAQgICAAPF7YGAgunXrho8++giLFi2SsDLTodPp4OjoiA0bNkChUMDX1xc3btzAsmXLMH/+fKnLMzmbNm1Cr1690K9fP6lLaVAMSHXk4OAAhUKB3NxcveW5ublQq9USVWVaHucavf/++1iyZAkOHjyIJ5980phlSqq+10gul8PT0xMA4O3tjXPnziEuLq5ZBqS6XqNLly4hOzsbYWFh4jKdTgcAMDMzw4ULF+Dh4WHcohtZQ/x9ZG5ujj59+iArK8sYJUquPtfI2dkZ5ubmUCgU4rJu3bohJycHJSUlUCqVRq25sT3On6OioiIkJibiP//5jzFLlARvsdWRUqmEr68vUlJSxGU6nQ4pKSl6/ypryep7jd577z0sWrQIycnJ8PPza4xSJdNQf450Oh2Ki4uNUaLk6nqNunbtip9//hmZmZni58UXX8Rzzz2HzMxMuLm5NWb5jaIh/hyVlpbi559/hrOzs7HKlFR9rtFTTz2FrKwsMWADwK+//gpnZ+dmF46Ax/tztHPnThQXF+PVV181dpmNT+pR4k1RYmKiYGFhIWzdulX45ZdfhPHjxwtt2rQRH5WNjIwUZs6cKbYvLi4WMjIyhIyMDMHZ2Vl48803hYyMDOHixYtSnYLR1fUaLVmyRFAqlcIXX3yh9+hoYWGhVKdgdHW9Ru+++67w3//+V7h06ZLwyy+/CO+//75gZmYmbNy4UapTMLq6XiNDLeEptrpeo4ULFwr79+8XLl26JKSlpQnDhw8XLC0thbNnz0p1CkZX12t09epVwcbGRoiOjhYuXLggJCUlCY6OjsLixYulOgWjq+//a08//bQQERHR2OU2Cgakelq9erXQoUMHQalUCv369ROOHz8urhswYIAwevRo8fvly5cFABU+AwYMaPzCG1FdrlHHjh0rvUbz589v/MIbUV2u0Zw5cwRPT0/B0tJSaNu2rRAQECAkJiZKUHXjqss1MtQSApIg1O0axcTEiG2dnJyEv/3tb0J6eroEVTeuuv45OnbsmODv7y9YWFgInTt3Ft555x3h4cOHjVx146rrNTp//rwAQPjvf//byJU2DpkgCIJEnVdEREREJoljkIiIiIgMMCARERERGWBAIiIiIjLAgERERERkgAGJiIiIyAADEhEREZEBBiQiIiIiAwxIREQmbsyYMQgPD5e6DKIWhQGJiOptzJgxkMlk4sfe3h4hISE4ffq01KU1iEfPrfzz9NNPG+142dnZkMlkyMzM1Fu+cuVKbN261WjHJaKKGJCI6LGEhITg1q1buHXrFlJSUmBmZoahQ4dKXVaD2bJli3h+t27dwtdff11puwcPHhitBpVKhTZt2hht/0RUEQMSET0WCwsLqNVqqNVqeHt7Y+bMmbh27Rpu376NQYMGITo6Wq/97du3oVQqxTeHu7u7Y9GiRRgxYgSsra3h6uqKtWvX6m2zYsUK9OrVC9bW1nBzc8P//M//4N69e+L6K1euICwsDG3btoW1tTV69OiBffv2AQB+//13jBw5Eu3atYOVlRW8vLywZcuWWp9fmzZtxPNTq9Wws7MTe3p27NiBAQMGwNLSEtu3b4dGo8GIESPg6uqKVq1aoVevXvj888/19qfT6fDee+/B09MTFhYW6NChA9555x0AQKdOnQAAffr0gUwmw8CBAwFUvMVWXFyMKVOmwNHREZaWlnj66adx8uRJcf2RI0cgk8mQkpICPz8/tGrVCoGBgbhw4UKtz5uopWNAIqIGc+/ePXz22Wfw9PSEvb09Xn/9dSQkJKC4uFhs89lnn8HV1RWDBg0Sly1btgy9e/dGRkYGZs6cialTp+LAgQPierlcjlWrVuHs2bPYtm0bDh06hBkzZojrJ02ahOLiYnz33Xf4+eefsXTpUrRu3RoA8Pbbb+OXX37Bt99+i3PnzmHdunVwcHBokPMtr/XcuXMIDg7Gn3/+CV9fX3zzzTc4c+YMxo8fj8jISJw4cULcZtasWViyZIlYV0JCApycnABAbHfw4EHcunULu3fvrvS4M2bMwK5du7Bt2zakp6fD09MTwcHByM/P12s3Z84cLF++HKdOnYKZmRlee+21BjlvohZB6rflElHTNXr0aEGhUAjW1taCtbW1AEBwdnYW0tLSBEEQhD/++ENo27atsGPHDnGbJ598UliwYIH4vWPHjkJISIjefiMiIoQhQ4ZUedydO3cK9vb24vdevXrp7fNRYWFhwtixY+t1fgAES0tL8fysra2FL7/8Urh8+bIAQIiPj69xH6GhocK0adMEQRCEgoICwcLCQti4cWOlbcv3m5GRobd89OjRwksvvSQIgiDcu3dPMDc3F7Zv3y6uLykpEVxcXIT33ntPEARBOHz4sABAOHjwoNjmm2++EQAIf/zxR10uAVGLxR4kInoszz33HDIzM5GZmYkTJ04gODgYQ4YMwZUrV2BpaYnIyEhs3rwZAJCeno4zZ85gzJgxevsICAio8P3cuXPi94MHD2Lw4MFwdXWFjY0NIiMjodFocP/+fQDAlClTsHjxYjz11FOYP3++3iDxiRMnIjExEd7e3pgxYwaOHTtWp/P74IMPxPPLzMzE888/L67z8/PTa1taWopFixahV69esLOzQ+vWrbF//35cvXoVAHDu3DkUFxdj8ODBdarhUZcuXcKDBw/w1FNPicvMzc3Rr18/vWsGAE8++aT4e2dnZwBAXl5evY9N1JIwIBHRY7G2toanpyc8PT3Rt29ffPzxxygqKsLGjRsBAK+//joOHDiA69evY8uWLRg0aBA6duxY6/1nZ2dj6NChePLJJ7Fr1y6kpaWJY5RKSkrEY/z222+IjIzEzz//DD8/P6xevRoAxLD273//Gzdv3sTgwYPx5ptv1vr4arVaPD9PT09YW1vrnfujli1bhpUrV+Ktt97C4cOHkZmZieDgYLFOKyurWh+3IZibm4u/l8lkAMrGQBFRzRiQiKhByWQyyOVy/PHHHwCAXr16wc/PDxs3bkRCQkKl42COHz9e4Xu3bt0AAGlpadDpdFi+fDn69++PJ554Ajdv3qywDzc3N0yYMAG7d+/GtGnTxIAGAO3atcPo0aPx2WefIT4+Hhs2bGjIUxYdPXoUL730El599VX07t0bnTt3xq+//iqu9/LygpWVlThA3ZBSqQRQ1hNVFQ8PDyiVShw9elRc9uDBA5w8eRLdu3dvoDMhIjOpCyCipq24uBg5OTkAyp4YW7NmDe7du4ewsDCxzeuvv47o6GhYW1vj73//e4V9HD16FO+99x7Cw8Nx4MAB7Ny5E9988w0AwNPTEw8ePMDq1asRFhaGo0ePYv369Xrbx8TEYMiQIXjiiSfw+++/4/Dhw2LAmjdvHnx9fdGjRw8UFxcjKSlJXNfQvLy88MUXX+DYsWNo27YtVqxYgdzcXDG4WFpa4q233sKMGTOgVCrx1FNP4fbt2zh79iyioqLg6OgIKysrJCcno3379rC0tIRKpdI7hrW1NSZOnIjp06fDzs4OHTp0wHvvvYf79+8jKirKKOdF1BKxB4mIHktycjKcnZ3h7OwMf39/nDx5Ejt37hQfUQeAESNGwMzMDCNGjIClpWWFfUybNg2nTp1Cnz59sHjxYqxYsQLBwcEAgN69e2PFihVYunQpevbsie3btyMuLk5v+9LSUkyaNAndunVDSEgInnjiCXz44YcAynplZs2ahSeffBLPPvssFAoFEhMTjXIt5s6dCx8fHwQHB2PgwIFQq9UVZsB+++23MW3aNMybNw/dunVDRESEOC7IzMwMq1atwkcffQQXFxe89NJLlR5nyZIlGDZsGCIjI+Hj44OsrCzs378fbdu2Ncp5EbVEMkEQBKmLIKLmLTs7Gx4eHjh58iR8fHz01rm7uyMmJgYxMTHSFEdEVAneYiMio3nw4AE0Gg3mzp2L/v37VwhHRESmirfYiMhojh49CmdnZ5w8ebLCuCGpvfvuu2jdunWlnyFDhkhdHhFJjLfYiKhFys/PrzDzdDkrKyu4uro2ckVEZEoYkIiIiIgM8BYbERERkQEGJCIiIiIDDEhEREREBhiQiIiIiAwwIBEREREZYEAiIiIiMsCARERERGSAAYmIiIjIwP8HjdIeIxViIt4AAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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6w48kGYaXtm4dwpkgAEC1ZBiG85LW9u3bdccdd6hJkyYKDAzUyJEjlZ2drYsXL5Z5jLS0NCUkJKhZs2YKDAxUnz59JEknTpyo8vqvhQDkJjk5oc7wU8QwvJSTE+KhigAAKN2hQ4fUokULHT9+XEOGDFGHDh20adMmpaWladmyZZLKvix34cIFDRgwQEFBQXrrrbe0d+9evfvuu9f8nLtwCayKWa1WSVJISLYsFodLCLJYHAoJyXFpBwCAp/3tb3/TF198oaefflppaWlyOBx65ZVX5OV1+XfY//zP/7i0t1qtKiwsdNn29ddfKzs7W/PmzVNkZKQk6Z///Kd7OnAdCEBVLDQ0VImJicrPz1eTJrmaMsWmwkKLvL0NzZ+fqwceGMGToAEAHpOXl6fMzEyX2+CTk5M1ZMgQjRo1SgcPHlRBQYGWLFmihIQE7dq1SytWrHA5RlRUlM6fP6/U1FR17NhRdevWVbNmzWS1WrVkyRI99thjOnjwoGbPnu2hXhbHJTA3CA0NVUREhJ55JljHj1u0Y4d0/LhFzzwTrIiICMIPAMBjUlJSFBERoaioKA0cOFA7duzQ4sWL9Ze//EXe3t7q2LGjFi5cqPnz5+vWW2/VW2+9peTkZJdj9OjRQ4899piGDRumhg0basGCBWrYsKFWr16tt99+W+3atdO8efP08ssve6iXxVkMwzA8XUR1k5ubK5vNJrvdrqCgIE+XAwCoxi5duqSMjAy1aNFCfn5+5f68p58DVNOUNd7l+f3NJTAAADzoyqkSpWGqROUjAAEA4GGEG/djDhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAAKgSO3fulMVi0U8//XTdn4mKitKiRYuqrKYiBCAAAExqzJgxslgseuyxx4rtmzBhgiwWi8aMGeP+wtygWgSgZcuWKSoqSn5+furWrZv27NlTatvNmzcrLi5OwcHBCggIUExMjNauXVtq+8cee0wWi8UtaRIAgJomMjJSGzZs0M8//+zcdunSJa1fv17NmjXzYGVVy+MBaOPGjUpKStLMmTO1b98+dezYUQMGDNDp06dLbB8SEqLp06dr9+7dOnDggMaOHauxY8fqww8/LNb23Xff1WeffabGjRtXdTcAAKiRYmNjFRkZqc2bNzu3bd68Wc2aNVOnTp2c2/Ly8jRx4kQ1atRIfn5+6tmzp/bu3etyrA8++EA33XST/P39dfvtt+v48ePFvu+TTz5Rr1695O/vr8jISE2cOFEXLlyosv6VxuMBaOHChXr00Uc1duxYtWvXTitWrFDdunX15ptvlti+b9++uueee9S2bVtFR0frqaeeUocOHfTJJ5+4tPv+++/15JNP6q233pKPj487ugIAwA07eVLasePyn+7y8MMPa9WqVc73b775psaOHevS5rnnntOmTZu0Zs0a7du3T61atdKAAQOUk5MjSfruu+/0m9/8RgkJCUpPT9e4ceM0depUl2McO3ZMAwcO1L333qsDBw5o48aN+uSTT5SYmFj1nbyKRwNQfn6+0tLSFB8f79zm5eWl+Ph47d69+5qfNwxDqampOnz4sHr37u3c7nA4NHLkSE2ePFm33HLLNY+Tl5en3NxclxcAAO72xhtS8+ZSv36X/3zjDfd870MPPaRPPvlE3377rb799lvt2rVLDz30kHP/hQsXtHz5cr300ksaNGiQ2rVrp5UrV8rf319v/LvI5cuXKzo6Wq+88opuvvlmPfjgg8XmDyUnJ+vBBx/UpEmT1Lp1a/Xo0UOLFy/Wn/70J126dMk9nf03j64Gf/bsWRUWFiosLMxle1hYmL7++utSP2e329WkSRPl5eXJ29tbr7/+uvr37+/cP3/+fNWpU0cTJ068rjqSk5P1wgsvVKwTAABUgpMnpfHjJYfj8nuHQ/rtb6UBA6SmTav2uxs2bKjBgwdr9erVMgxDgwcPVoMGDZz7jx07poKCAt12223ObT4+PuratasOHTokSTp06JC6devmctzu3bu7vP+///s/HThwQG+99ZZzm2EYcjgcysjIUNu2bauieyXyaACqqMDAQKWnp+v8+fNKTU1VUlKSWrZsqb59+yotLU2vvfaa9u3bJ4vFcl3HmzZtmpKSkpzvc3NzFRkZWVXlAwBQzJEj/wk/RQoLpaNHqz4ASZcvgxVdilq2bFmVfMf58+f129/+tsQTFO6ecO3RANSgQQN5e3srKyvLZXtWVpbCw8NL/ZyXl5datWolSYqJidGhQ4eUnJysvn376h//+IdOnz7tMpCFhYV65plntGjRohInZPn6+srX17dyOgUAQAW0bi15ebmGIG9v6d+/7qrcwIEDlZ+fL4vFogEDBrjsi46OltVq1a5du9S8eXNJUkFBgfbu3atJkyZJktq2bastW7a4fO6zzz5zeR8bG6uvvvrK+Tvckzw6B8hqtapz585KTU11bnM4HEpNTS122qwsDodDeXl5kqSRI0fqwIEDSk9Pd74aN26syZMnl3inGAAA1UHTptIf/3g59EiX//zDH9xz9ufy93nr0KFD+uqrr+RdVMS/BQQE6PHHH9fkyZOVkpKir776So8++qguXryoRx55RNLlx84cOXJEkydP1uHDh7V+/XqtXr3a5ThTpkzRp59+qsTERKWnp+vIkSP6y1/+4pFJ0B6/BJaUlKTRo0crLi5OXbt21aJFi3ThwgXn7PNRo0apSZMmSk5OlnR5vk5cXJyio6OVl5enDz74QGvXrtXy5cslSaGhoQoNDXX5Dh8fH4WHh+vmm292b+cAACiHRx65POfn6NHLZ37cFX6KBAUFlbpv3rx5zpuMzp07p7i4OH344YeqX7++pMuXsDZt2qSnn35aS5YsUdeuXfXiiy/q4Ycfdh6jQ4cO+vvf/67p06erV69eMgxD0dHRGjZsWJX37WoeD0DDhg3TmTNnNGPGDGVmZiomJkYpKSnOidEnTpyQl9d/TlRduHBBTzzxhE6ePCl/f3+1adNG69at88jgAQBQ2Zo2dV/wufoMzdXee+89589+fn5avHixFi9eXGr7IUOGaMiQIS7brr6dvkuXLvroo49KPUZJU1WqgsUwDMMt31SD5ObmymazyW63l5mGAQC4dOmSMjIy1KJFC/n5+Xm6nFqvrPEuz+9vjz8IEQAAwN0IQAAAwHQIQAAAwHQIQAAAVAKm1LpHZY0zAQgAgBtQtOD2xYsXPVyJORSN840udO7x2+ABAKjJvL29FRwcrNOnT0uS6tate91LMeH6GYahixcv6vTp0woODi72sMbyIgABAHCDipZvKgpBqDrBwcFlLpd1vQhAAADcIIvFooiICDVq1EgFBQWeLqfW8vHxueEzP0UIQAAAVBJvb+9K+wWNqsUkaAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDo8CRrFZGdnKz8/v9T9VqtVoaGhbqwIAIDKRQCCi+zsbC1dutT53m4PVE5OqEJCsmWznXNuT0xMJAQBAGosAhBcXHnmZ9++Ttq6dYgMw0sWi0MJCdsUG7u/WDsAAGoa5gChRHZ7oDP8SJJheGnr1iGy2wM9XBkAADeOAIQS5eSEOsNPEcPwUk5OiIcqAgCg8hCAUKKQkGxZLA6XbRaLQyEhOR6qCACAykMAQolstnNKSNjmDEFFc4CunAgNAEBNxSRolCo2dr+io48qJydEISE5Nxx+uL0eAFBdEIBQJpvtXKWc9eH2egBAdUIAggur1Vqp7Ypwez0AoDohAMFFaGioEhMTq+xSVWm310dHH2V+EQDAbQhAKKYqL0GVdXs9AQgA4C7cBQa34vZ6AEB1QACCW3F7PQCgOuASGNyusm+vBwCgvAhA8IjKur0eAICK4BIY3KKqbq8HAKAiOAMEt6jq2+sBACgPAhDchnADAKguuAQGAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEVIKTJ6UdOy7/CQCo/ghAQAVlZ2fr1KlTeuWVn9S8uaF+/aTmzQ298spPOnXqlLKzsz1dIgCgFDwHCKiA7OxsLV26VHZ7oBYtmiTDsEiSHA6LJk8O0vffvymb7ZwSExN5/hEAVEOcAQIqoOiJ1jk5oTIM1/+MDMNLOTkhLu0AANULAQi4ASEh2bJYHC7bLBaHQkJyPFQRAOB6EICAG2CznVNCwjZnCLJYHEpI2MZK9wBQzTEHCLhBsbH7FR19VDk5IQoJySH8AEANQAACKoHNdo7gAwA1CJfAAKAUPN8JqL0IQABQgjfekJo317+f73T5PYDagwAEVIDVaq3Udqg+srOzlZaWpfHjDTn+fYOfwyH99reG0tKyeMAlUEswBwiogNDQUCUmJpb5nB+r1cpDEGuYogdcZmREyeEY7bKvsNCiJUv+qhYtvuUBl0AtQAACKohfgLVPUaAter7TlQ+5vPL5TjzgEqj5qsUlsGXLlikqKkp+fn7q1q2b9uzZU2rbzZs3Ky4uTsHBwQoICFBMTIzWrl3r3F9QUKApU6aoffv2CggIUOPGjTVq1Cj98MMP7ugKAA+o7MnKPN8JqP08fgZo48aNSkpK0ooVK9StWzctWrRIAwYM0OHDh9WoUaNi7UNCQjR9+nS1adNGVqtV27Zt09ixY9WoUSMNGDBAFy9e1L59+/T888+rY8eO+vHHH/XUU09p6NCh+uc//+mBHgKoCtnZ2crPz9f69f567jmbHA6LvLwMLVhg1wMP/HzDlyB5vhNQu1kMwzA8WUC3bt3UpUsXLV26VJLkcDgUGRmpJ598UlOnTr2uY8TGxmrw4MGaPXt2ifv37t2rrl276ttvv1WzZs2uebzc3FzZbDbZ7XYFBQVdf2cAuEXxxWhdL1VNmrSoQovRnjp1Sn/84x+v2W78+PGKiIioUO0Aqk55fn979BJYfn6+0tLSFB8f79zm5eWl+Ph47d69+5qfNwxDqampOnz4sHr37l1qO7vdLovFouDg4BL35+XlKTc31+UFoPpiMVoAN8qjAejs2bMqLCxUWFiYy/awsDBlZmaW+jm73a569erJarVq8ODBWrJkifr3719i20uXLmnKlCkaMWJEqWkwOTlZNpvN+YqMjKx4pwC4DYvRAqioajEJurwCAwOVnp6uvXv3au7cuUpKStLOnTuLtSsoKND9998vwzC0fPnyUo83bdo02e125+u7776rwuoBVBYmKwOoKI9Ogm7QoIG8vb2VlZXlsj0rK0vh4eGlfs7Ly0utWrWSJMXExOjQoUNKTk5W3759nW2Kws+3336rv/3tb2VeC/T19ZWvr++NdQbADTl5UjpyRGrdWmra9Po/V5mTlXnAJWAeHg1AVqtVnTt3Vmpqqu6++25JlydBp6amKjEx8bqP43A4lJeX53xfFH6OHDmiHTt28LwWoJqqrDu5KmsxWh5wCZiHx2+DT0pK0ujRoxUXF6euXbtq0aJFunDhgsaOHStJGjVqlJo0aaLk5GRJl+frxMXFKTo6Wnl5efrggw+0du1a5yWugoIC3Xfffdq3b5+2bdumwsJC53yikJAQ/p8bUE0Uv5PLIklyOCyaPDlI33//ZoXu5LpRhBvAHDwegIYNG6YzZ85oxowZyszMVExMjFJSUpwTo0+cOCEvr/9MVbpw4YKeeOIJnTx5Uv7+/mrTpo3WrVunYcOGSZK+//57bdmyRdLly2NX2rFjh8tlMgCecz13ctls57iTC0CV8HgAkqTExMRSL3ldPbl5zpw5mjNnTqnHioqKkocfbQSgHK617ERJmKsD4EZViwAEwLyK7uTaunWIDMPruu7kunquzg8/eCkjo45atPhFjRtfviOMuToAykIAAuBxN3InV2kTqAGgLAQgANVCee7kqq4TqAHUHDXyQYgAzI2lMADcKAIQgBqLpTAAVBQBCIBHVMadXCyFAaCimAMEwCMq66nLlbkUBgDzIAAB8JjKmqBcWUthADAPLoEBAADTIQABAADTIQABqHFYCgPAjbIYLJxVTG5urmw2m+x2u4KCgjxdDoASZGdnlzqB2m63yzAMBQcHs0wGYCLl+f3NJGgANVJpASY7O1sbN26UJO3b16nYGmOxsfsliadEAybHJTAAtUrRWSG7PdAZfqTLT4jeunWI7PZAl3YAzIkzQABqpbKWyahpt8xfebmPS3pA5SAAAahV7Ha7pMvLZEgOuZ7o/s8yGXa7XREREW6vr7yKFn6VuKQHVCYugQGoVQoKCq54Z3HZZ7GU1q764pIeUDUIQABqpZycUF0dgK5cKb6mKeuSHoDyIwABqJVq20rxta0/gKcRgADUSrVtpfja1h/A05gEDaBWqVPnP/+slbVS/JXtaoqy+gOgfGrevwAAUIbg4GCX96WtFH91u5qitP4AKB8ugQEAANMhAAGoVWrbQqm1rT9AdcFiqCVgMVSgZitroVSp5j05ubb1B6gqLIYKwNQIAwCuhQAEANXYlUthSJefCJ2TE6qQkGyXydAshQGUDwEIAKqxKy99lbUWGEthAOXDJGgAqAGutRYYgPIhAAFADcBaYEDlIgABQA3AWmBA5SIAAUANwFpgQOViEjQA1BCsBQZUHgIQANQgrAUGVA4ugQFANcZSGEDV4AwQAFRjoaGhSkxMZCkMoJIRgACgmiPcAJWPS2AAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0uA0eAEwqOzub5wvBtAhAAGBC2dnZWrp06TXbJSYmEoJQK3EJDABM6OozP3Z7oDIyomS3B5bZDqgtOAMEACa3b18nbd06RIbhJYvFoYSEbYqN3e/psoAqxRkgADAxuz3QGX4kyTC8tHXrkGJngoDahgAEACaWkxPqDD9FDMNLOTkhHqoIcA8CEACYWEhItiwWh8s2i8WhkJAcD1UEuAcBCABMzGY7p4SEbc4QVDQHyGY75+HKgKrFJGgAMLnY2P2Kjj6qnJwQhYTkEH5gCgQgADAhq9Xq8t5mO1di8Lm6HVBbEIAAwIRCQ0OVmJjIk6BhWgQgADApwg3MrFpMgl62bJmioqLk5+enbt26ac+ePaW23bx5s+Li4hQcHKyAgADFxMRo7dq1Lm0Mw9CMGTMUEREhf39/xcfH68iRI1XdDQAAUEN4PABt3LhRSUlJmjlzpvbt26eOHTtqwIABOn36dIntQ0JCNH36dO3evVsHDhzQ2LFjNXbsWH344YfONgsWLNDixYu1YsUKff755woICNCAAQN06dIld3ULAABUYxbDMAxPFtCtWzd16dLFuSifw+FQZGSknnzySU2dOvW6jhEbG6vBgwdr9uzZMgxDjRs31jPPPKNnn31WkmS32xUWFqbVq1dr+PDh1zxebm6ubDab7Ha7goKCKt45AADgNuX5/e3RM0D5+flKS0tTfHy8c5uXl5fi4+O1e/fua37eMAylpqbq8OHD6t27tyQpIyNDmZmZLse02Wzq1q1bqcfMy8tTbm6uywsAANReHg1AZ8+eVWFhocLCwly2h4WFKTMzs9TP2e121atXT1arVYMHD9aSJUvUv39/SXJ+rjzHTE5Ols1mc74iIyNvpFsAAKCa8/gcoIoIDAxUenq69u7dq7lz5yopKUk7d+6s8PGmTZsmu93ufH333XeVVywAAKh2PHobfIMGDeTt7a2srCyX7VlZWQoPDy/1c15eXmrVqpUkKSYmRocOHVJycrL69u3r/FxWVpYiIiJcjhkTE1Pi8Xx9feXr63uDvQEAADWFR88AWa1Wde7cWampqc5tDodDqamp6t69+3Ufx+FwKC8vT5LUokULhYeHuxwzNzdXn3/+ebmOCQAAai+PPwgxKSlJo0ePVlxcnLp27apFixbpwoULGjt2rCRp1KhRatKkiZKTkyVdnq8TFxen6Oho5eXl6YMPPtDatWu1fPlySZLFYtGkSZM0Z84ctW7dWi1atNDzzz+vxo0b6+677/ZUNwEAQDXi8QA0bNgwnTlzRjNmzFBmZqZiYmKUkpLinMR84sQJeXn950TVhQsX9MQTT+jkyZPy9/dXmzZttG7dOg0bNszZ5rnnntOFCxc0fvx4/fTTT+rZs6dSUlLk5+fn9v4BAIDqx+PPAaqOeA4QAAA1T415DhAAAIAnlDsAffDBBxo3bpyee+45ff311y77fvzxR/Xr16/SigMAAKgK5QpA69ev19ChQ5WZmandu3erU6dOeuutt5z78/Pz9fe//73SiwQAAKhM5ZoE/dJLL2nhwoWaOHGiJOl//ud/9PDDD+vSpUt65JFHqqRAAACAylauAHTkyBElJCQ4399///1q2LChhg4dqoKCAt1zzz2VXiAAAEBlK1cACgoKUlZWllq0aOHcdvvtt2vbtm0aMmSITp48WekFAgAAVLZyzQHq2rWr/vrXvxbb3qdPH23dulWLFi2qrLoAAACqTLkC0NNPP13qwwT79u2rrVu3atSoUZVSGAAAQFXhQYgl4EGIAADUPOX5/V2uOUBeXl6yWCxltrFYLPrll1/Kc1gAAAC3KlcAevfdd0vdt3v3bi1evFgOh+OGiwIAAKhK5QpAd911V7Fthw8f1tSpU7V161Y9+OCD+v3vf19pxQEAAFSFCq8F9sMPP+jRRx9V+/bt9csvvyg9PV1r1qxR8+bNK7M+AACASleuM0CSZLfb9eKLL2rJkiWKiYlRamqqevXqVRW1AQBMJjs7W/n5+aXut1qtCg0NdWNFqK3KFYAWLFig+fPnKzw8XH/+859LvCQGAEBFZGdna+nSpddsl5iYSAjCDStXAJo6dar8/f3VqlUrrVmzRmvWrCmx3ebNmyulOACAeZR15qci7YCylCsAjRo16pq3wQMAUN1waQ1XK1cAWr16dRWVAQCAK7s9UDk5oQoJyZbNdq7Cx+HSGkpS7knQAABUtX37Omnr1iEyDC9ZLA4lJGxTbOz+Ch2LS2soSYVvgwcAoCrY7YHO8CNJhuGlrVuHyG4P9HBlqE0IQACAaiUnJ9QZfooYhpdyckI8VBFqIwIQAKBaCQnJlsXiuqySxeJQSEhOpRzfbg9URkYUZ5RMjjlAAIBqwWq1SpJstnNKSNhWbA5Q0UToonYVUZlzi1CzEYAAANVCaGioEhMTnZORZ8w4o+PH6ygq6hc1btxFUpcbul29tLlF0dFHb+guM9RMBCAAQLVxZbiJiJA6d77+z5b2rJ+zZ89KKntuEQHIfAhAAIAa73qe9VM0t+jKEHTl3KIbubSGmocABACo8a7nGT422znNnPmDZs9uosJCi7y9Dc2fn6sHHhjBk6BNiAAEADCN8eO99cgjFh09KrVqZVHTpsGSgj1cFTyBAAQAMJWmTS+/YG48BwgAUOvwrB9cC2eAAAC1Cs/6wfXgDBAAoNZgHTFcLwIQAKDWYB0xXC8CEACgxit6hs+11hHjWT8owhwgAECNd+UyGk2a5GrKFBvP+kGZLIZhGJ4uorrJzc2VzWaT3W5XUFCQp8sBAJTTyZP697N+uOXdTMrz+5szQACAWodn/eBamAMEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMx+MBaNmyZYqKipKfn5+6deumPXv2lNp25cqV6tWrl+rXr6/69esrPj6+WPvz588rMTFRTZs2lb+/v9q1a6cVK1ZUdTcAAEAN4tEAtHHjRiUlJWnmzJnat2+fOnbsqAEDBuj06dMltt+5c6dGjBihHTt2aPfu3YqMjNSdd96p77//3tkmKSlJKSkpWrdunQ4dOqRJkyYpMTFRW7ZscVe3AABANWcxDMPw1Jd369ZNXbp00dKlSyVJDodDkZGRevLJJzV16tRrfr6wsFD169fX0qVLNWrUKEnSrbfeqmHDhun55593tuvcubMGDRqkOXPmXFddubm5stlsstvtCgoKqkDPAACAu5Xn97fHzgDl5+crLS1N8fHx/ynGy0vx8fHavXv3dR3j4sWLKigoUEhIiHNbjx49tGXLFn3//fcyDEM7duzQN998ozvvvLPU4+Tl5Sk3N9flBQAAai+PBaCzZ8+qsLBQYWFhLtvDwsKUmZl5XceYMmWKGjdu7BKilixZonbt2qlp06ayWq0aOHCgli1bpt69e5d6nOTkZNlsNucrMjKyYp0CAAA1gscnQVfUvHnztGHDBr377rvy8/Nzbl+yZIk+++wzbdmyRWlpaXrllVc0YcIEbd++vdRjTZs2TXa73fn67rvv3NEFAADgIXU89cUNGjSQt7e3srKyXLZnZWUpPDy8zM++/PLLmjdvnrZv364OHTo4t//888/6r//6L7377rsaPHiwJKlDhw5KT0/Xyy+/7HKm6Eq+vr7y9fW9wR4BAICawmNngKxWqzp37qzU1FTnNofDodTUVHXv3r3Uzy1YsECzZ89WSkqK4uLiXPYVFBSooKBAXl6u3fL29pbD4ajcDgAAgBrLY2eApMu3rI8ePVpxcXHq2rWrFi1apAsXLmjs2LGSpFGjRqlJkyZKTk6WJM2fP18zZszQ+vXrFRUV5ZwrVK9ePdWrV09BQUHq06ePJk+eLH9/fzVv3lx///vf9ac//UkLFy70WD8BAED14tEANGzYMJ05c0YzZsxQZmamYmJilJKS4pwYfeLECZezOcuXL1d+fr7uu+8+l+PMnDlTs2bNkiRt2LBB06ZN04MPPqicnBw1b95cc+fO1WOPPea2fgEAgOrNo88Bqq54DhAAADVPjXgOEAAAgKcQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOnU8XQBAACguOzsbOXn55e632q1KjQ01I0V1S4EIAAAqpns7GwtXbr0mu0SExMJQRXEJTAAAKqZss78VKQdiiMAAQBQzdntgcrIiJLdHujpUmoNLoEBAFCN7dvXSVu3DpFheMlicSghYZtiY/d7uqwajzNAAABUU3Z7oDP8SJJheGnr1iGcCaoEBCAAAKqpnJxQZ/gpYhheyskJ8VBFtQcBCACAaiokJFsWi8Nlm8XiUEhIjocqqj0IQAAAVFM22zklJGxzhqCiOUA22zkPV1bzMQkaAIBqxmq1On+Ojd2v6OijyskJUUhIjkv4ubIdyocABABANRMaGqrExESeBF2FCEAAAFRDhJuqxRwgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOh4PQMuWLVNUVJT8/PzUrVs37dmzp9S2K1euVK9evVS/fn3Vr19f8fHxJbY/dOiQhg4dKpvNpoCAAHXp0kUnTpyoym4AAIAaxKMBaOPGjUpKStLMmTO1b98+dezYUQMGDNDp06dLbL9z506NGDFCO3bs0O7duxUZGak777xT33//vbPNsWPH1LNnT7Vp00Y7d+7UgQMH9Pzzz8vPz89d3QIAANWcxTAMw1Nf3q1bN3Xp0kVLly6VJDkcDkVGRurJJ5/U1KlTr/n5wsJC1a9fX0uXLtWoUaMkScOHD5ePj4/Wrl1b4bpyc3Nls9lkt9sVFBRU4eMAAAD3Kc/vb4+dAcrPz1daWpri4+P/U4yXl+Lj47V79+7rOsbFixdVUFCgkJDLq+I6HA69//77uummmzRgwAA1atRI3bp103vvvVfmcfLy8pSbm+vyAgAAtZfHAtDZs2dVWFiosLAwl+1hYWHKzMy8rmNMmTJFjRs3doao06dP6/z585o3b54GDhyojz76SPfcc49+85vf6O9//3upx0lOTpbNZnO+IiMjK94xAABQ7dXYpTDmzZunDRs2aOfOnc75PQ7H5dVy77rrLj399NOSpJiYGH366adasWKF+vTpU+Kxpk2bpqSkJOf73NxcQhAAALWYxwJQgwYN5O3traysLJftWVlZCg8PL/OzL7/8subNm6ft27erQ4cOLsesU6eO2rVr59K+bdu2+uSTT0o9nq+vr3x9fSvQCwAAUBN57BKY1WpV586dlZqa6tzmcDiUmpqq7t27l/q5BQsWaPbs2UpJSVFcXFyxY3bp0kWHDx922f7NN9+oefPmldsBAABQY3n0ElhSUpJGjx6tuLg4de3aVYsWLdKFCxc0duxYSdKoUaPUpEkTJScnS5Lmz5+vGTNmaP369YqKinLOFapXr57q1asnSZo8ebKGDRum3r176/bbb1dKSoq2bt2qnTt3eqSPAACg+vFoABo2bJjOnDmjGTNmKDMzUzExMUpJSXFOjD5x4oS8vP5zkmr58uXKz8/Xfffd53KcmTNnatasWZKke+65RytWrFBycrImTpyom2++WZs2bVLPnj3d1i8AAFC9efQ5QNUVzwECANRW2dnZys/PL3W/1WpVaGioGyuqPOX5/V1j7wIDAADlk52d7Xz4cFkSExNrbAi6Xh5fCwwAALhHWWd+KtKuJiMAAQBgUnZ7oDIyomS3B3q6FLfjEhgAACa0b18nbd06RIbhJYvFoYSEbYqN3e/pstyGM0AAAJiM3R7oDD+SZBhe2rp1iKnOBBGAAAAwmZycUGf4KWIYXsrJCfFQRe5HAAIAwGRCQrJlsThctlksDoWE5HioIvcjAAEAYDI22zklJGxzhqCiOUA22zkPV+Y+TIIGAMAkrFar8+fY2P2Kjj6qnJwQhYTkuISfK9vVVgQgAABMIjQ0VImJibX2SdDlQQACAMBEzBBurgdzgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOmwGCoAALhh2dnZNWqVeQIQAAC4IdnZ2Vq6dOk12yUmJlabEMQlMAAAcEPKOvNTkXbuQAACAACVym4PVEZGlOz2QE+XUiougQEAgEqzb18nbd06RIbhJYvFoYSEbYqN3e/psorhDBAAAKgUdnugM/xIkmF4aevWIdXyTBABCAAAVIqcnFBn+CliGF7KyQnxUEWlIwABAIBKERKSLYvF4bLNYnEoJCTHQxWVjgAEAAAqhc12TgkJ25whqGgOkM12zsOVFcckaAAAcEOsVqvz59jY/YqOPqqcnBCFhOS4hJ8r23kaAQgAANyQ0NBQJSYm8iRoAABgLtUp3FwP5gABAADT4QwQAACoUtVxoVQCEAAAqDLVdaFULoEBAIAqc/WZn9LWCXP3QqmcAQIAAG5RndYJ4wwQAACoctVtnTACEAAAqHLVbZ0wAhAAAKhy1W2dMAIQAACoctVtnTAmQQMAALcoa50wdyMAAQCAKnP1Aqg227kSg4+7F0olAAEAgCpTXRdKJQABAIAqVR0XSmUSNAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB2eBF0CwzAkSbm5uR6uBAAAXK+i39tFv8fLQgAqwblzlxdpi4yM9HAlAACgvM6dOyebzVZmG4txPTHJZBwOh3744QcFBgbKYrEU25+bm6vIyEh99913CgoK8kCF1R9jdG2MUdkYn2tjjK6NMbq22jRGhmHo3Llzaty4sby8yp7lwxmgEnh5ealp06bXbBcUFFTj/7JUNcbo2hijsjE+18YYXRtjdG21ZYyudeanCJOgAQCA6RCAAACA6RCAKsDX11czZ86Ur6+vp0upthija2OMysb4XBtjdG2M0bWZdYyYBA0AAEyHM0AAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEClWLZsmaKiouTn56du3bppz549pbb98ssvde+99yoqKkoWi0WLFi1yX6EeVJ4xWrlypXr16qX69eurfv36io+PL7N9bVCe8dm8ebPi4uIUHBysgIAAxcTEaO3atW6s1jPKM0ZX2rBhgywWi+6+++6qLbAaKM8YrV69WhaLxeXl5+fnxmo9o7x/j3766SdNmDBBERER8vX11U033aQPPvjATdV6RnnGqG/fvsX+HlksFg0ePNiNFbuBgWI2bNhgWK1W48033zS+/PJL49FHHzWCg4ONrKysEtvv2bPHePbZZ40///nPRnh4uPHqq6+6t2APKO8YPfDAA8ayZcuM/fv3G4cOHTLGjBlj2Gw24+TJk26u3D3KOz47duwwNm/ebHz11VfG0aNHjUWLFhne3t5GSkqKmyt3n/KOUZGMjAyjSZMmRq9evYy77rrLPcV6SHnHaNWqVUZQUJBx6tQp5yszM9PNVbtXeccoLy/PiIuLM379618bn3zyiZGRkWHs3LnTSE9Pd3Pl7lPeMcrOznb5O3Tw4EHD29vbWLVqlXsLr2IEoBJ07drVmDBhgvN9YWGh0bhxYyM5Ofman23evLkpAtCNjJFhGMYvv/xiBAYGGmvWrKmqEj3qRsfHMAyjU6dOxu9+97uqKK9aqMgY/fLLL0aPHj2M//7v/zZGjx5d6wNQecdo1apVhs1mc1N11UN5x2j58uVGy5Ytjfz8fHeV6HE3+u/Rq6++agQGBhrnz5+vqhI9gktgV8nPz1daWpri4+Od27y8vBQfH6/du3d7sLLqozLG6OLFiyooKFBISEhVlekxNzo+hmEoNTVVhw8fVu/evauyVI+p6Bj9/ve/V6NGjfTII4+4o0yPqugYnT9/Xs2bN1dkZKTuuusuffnll+4o1yMqMkZbtmxR9+7dNWHCBIWFhenWW2/Viy++qMLCQneV7VaV8e/1G2+8oeHDhysgIKCqyvQIAtBVzp49q8LCQoWFhblsDwsLU2Zmpoeqql4qY4ymTJmixo0bu/xHWVtUdHzsdrvq1asnq9WqwYMHa8mSJerfv39Vl+sRFRmjTz75RG+88YZWrlzpjhI9riJjdPPNN+vNN9/UX/7yF61bt04Oh0M9evTQyZMn3VGy21VkjP71r3/pnXfeUWFhoT744AM9//zzeuWVVzRnzhx3lOx2N/rv9Z49e3Tw4EGNGzeuqkr0GFaDh9vNmzdPGzZs0M6dO00xQfN6BQYGKj09XefPn1dqaqqSkpLUsmVL9e3b19Oledy5c+c0cuRIrVy5Ug0aNPB0OdVW9+7d1b17d+f7Hj16qG3btvrDH/6g2bNne7Cy6sPhcKhRo0b64x//KG9vb3Xu3Fnff/+9XnrpJc2cOdPT5VU7b7zxhtq3b6+uXbt6upRKRwC6SoMGDeTt7a2srCyX7VlZWQoPD/dQVdXLjYzRyy+/rHnz5mn79u3q0KFDVZbpMRUdHy8vL7Vq1UqSFBMTo0OHDik5OblWBqDyjtGxY8d0/PhxJSQkOLc5HA5JUp06dXT48GFFR0dXbdFuVhn/Fvn4+KhTp046evRoVZTocRUZo4iICPn4+Mjb29u5rW3btsrMzFR+fr6sVmuV1uxuN/L36MKFC9qwYYN+//vfV2WJHsMlsKtYrVZ17txZqampzm0Oh0Opqaku/8/KzCo6RgsWLNDs2bOVkpKiuLg4d5TqEZX1d8jhcCgvL68qSvS48o5RmzZt9MUXXyg9Pd35Gjp0qG6//Xalp6crMjLSneW7RWX8PSosLNQXX3yhiIiIqirToyoyRrfddpuOHj3qDNCS9M033ygiIqLWhR/pxv4evf3228rLy9NDDz1U1WV6hqdnYVdHGzZsMHx9fY3Vq1cbX331lTF+/HgjODjYeTvpyJEjjalTpzrb5+XlGfv37zf2799vREREGM8++6yxf/9+48iRI57qQpUr7xjNmzfPsFqtxjvvvONye+W5c+c81YUqVd7xefHFF42PPvrIOHbsmPHVV18ZL7/8slGnTh1j5cqVnupClSvvGF3NDHeBlXeMXnjhBePDDz80jh07ZqSlpRnDhw83/Pz8jC+//NJTXahy5R2jEydOGIGBgUZiYqJx+PBhY9u2bUajRo2MOXPmeKoLVa6i/6317NnTGDZsmLvLdRsCUCmWLFliNGvWzLBarUbXrl2Nzz77zLmvT58+xujRo53vMzIyDEnFXn369HF/4W5UnjFq3rx5iWM0c+ZM9xfuJuUZn+nTpxutWrUy/Pz8jPr16xvdu3c3NmzY4IGq3as8Y3Q1MwQgwyjfGE2aNMnZNiwszPj1r39t7Nu3zwNVu1d5/x59+umnRrdu3QxfX1+jZcuWxty5c41ffvnFzVW7V3nH6OuvvzYkGR999JGbK3Ufi2EYhodOPgEAAHgEc4AAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAAIDpEIAAwIPGjBmju+++29NlAKZDAAJQojFjxshisThfoaGhGjhwoA4cOODp0irFlX0revXs2bPKvu/48eOyWCxKT0932f7aa69p9erVVfa9AEpGAAJQqoEDB+rUqVM6deqUUlNTVadOHQ0ZMsTTZVWaVatWOft36tQpbdmypcR2BQUFVVaDzWZTcHBwlR0fQMkIQABK5evrq/DwcIWHhysmJkZTp07Vd999pzNnzqhfv35KTEx0aX/mzBlZrVbnytNRUVGaPXu2RowYoYCAADVp0kTLli1z+czChQvVvn17BQQEKDIyUk888YTOnz/v3P/tt98qISFB9evXV0BAgG655RZ98MEHkqQff/xRDz74oBo2bCh/f3+1bt1aq1atuu7+BQcHO/sXHh6ukJAQ55majRs3qk+fPvLz89Nbb72l7OxsjRgxQk2aNFHdunXVvn17/fnPf3Y5nsPh0IIFC9SqVSv5+vqqWbNmmjt3riSpRYsWkqROnTrJYrGob9++kopfAsvLy9PEiRPVqFEj+fn5qWfPntq7d69z/86dO2WxWJSamqq4uDjVrVtXPXr00OHDh6+73wAIQACu0/nz57Vu3Tq1atVKoaGhGjdunNavX6+8vDxnm3Xr1qlJkybq16+fc9tLL72kjh07av/+/Zo6daqeeuopffzxx879Xl5eWrx4sb788kutWbNGf/vb3/Tcc88590+YMEF5eXn63//9X33xxReaP3++6tWrJ0l6/vnn9dVXX+mvf/2rDh06pOXLl6tBgwaV0t+iWg8dOqQBAwbo0qVL6ty5s95//30dPHhQ48eP18iRI7Vnzx7nZ6ZNm6Z58+Y561q/fr3CwsIkydlu+/btOnXqlDZv3lzi9z733HPatGmT1qxZo3379qlVq1YaMGCAcnJyXNpNnz5dr7zyiv75z3+qTp06evjhhyul34BpeHo1VgDV0+jRow1vb28jICDACAgIMCQZERERRlpammEYhvHzzz8b9evXNzZu3Oj8TIcOHYxZs2Y53zdv3twYOHCgy3GHDRtmDBo0qNTvffvtt43Q0FDn+/bt27sc80oJCQnG2LFjK9Q/SYafn5+zfwEBAca7775rZGRkGJKMRYsWXfMYgwcPNp555hnDMAwjNzfX8PX1NVauXFli26Lj7t+/32X7lavanz9/3vDx8THeeust5/78/HyjcePGxoIFCwzDMIwdO3YYkozt27c727z//vuGJOPnn38uzxAApsYZIACluv3225Wenq709HTt2bNHAwYM0KBBg/Ttt9/Kz89PI0eO1JtvvilJ2rdvnw4ePKgxY8a4HKN79+7F3h86dMj5fvv27brjjjvUpEkTBQYGauTIkcrOztbFixclSRMnTtScOXN02223aebMmS6TsB9//HFt2LBBMTExeu655/Tpp5+Wq3+vvvqqs3/p6enq37+/c19cXJxL28LCQs2ePVvt27dXSEiI6tWrpw8//FAnTpyQJB06dEh5eXm64447ylXDlY4dO6aCggLddtttzm0+Pj7q2rWry5hJUocOHZw/R0RESJJOnz5d4e8GzIYABKBUAQEBatWqlVq1aqUuXbrov//7v3XhwgWtXLlSkjRu3Dh9/PHHOnnypFatWqV+/fqpefPm133848ePa8iQIerQoYM2bdqktLQ05xyh/Px853f861//0siRI/XFF18oLi5OS5YskSRnGHv66af1ww8/6I477tCzzz573d8fHh7u7F+rVq0UEBDg0vcrvfTSS3rttdc0ZcoU7dixQ+np6RowYICzTn9//+v+3srg4+Pj/NlisUi6PAcJwPUhAAG4bhaLRV5eXvr5558lSe3bt1dcXJxWrlyp9evXlzgP5bPPPiv2vm3btpKktLQ0ORwOvfLKK/rVr36lm266ST/88EOxY0RGRuqxxx7T5s2b9cwzzzgDmCQ1bNhQo0eP1rp167Ro0SL98Y9/rMwuO+3atUt33XWXHnroIXXs2FEtW7bUN99849zfunVr+fv7OyeAX81qtUq6fCapNNHR0bJardq1a5dzW0FBgfbu3at27dpVUk8ASFIdTxcAoPrKy8tTZmampMt3XC1dulTnz59XQkKCs824ceOUmJiogIAA3XPPPcWOsWvXLi1YsEB33323Pv74Y7399tt6//33JUmtWrVSQUGBlixZooSEBO3atUsrVqxw+fykSZM0aNAg3XTTTfrxxx+1Y8cOZ4CaMWOGOnfurFtuuUV5eXnatm2bc19la926td555x19+umnql+/vhYuXKisrCxnMPHz89OUKVP03HPPyWq16rbbbtOZM2f05Zdf6pFHHlGjRo3k7++vlJQUNW3aVH5+frLZbC7fERAQoMcff1yTJ09WSEiImjVrpgULFujixYt65JFHqqRfgFlxBghAqVJSUhQREaGIiAh169ZNe/fu1dtvv+28hVuSRowYoTp16mjEiBHy8/MrdoxnnnlG//znP9WpUyfNmTNHCxcu1IABAyRJHTt21MKFCzV//nzdeuuteuutt5ScnOzy+cLCQk2YMEFt27bVwIEDddNNN+n111+XdPmsyrRp09ShQwf17t1b3t7e2rBhQ5WMxe9+9zvFxsZqwIAB6tu3r8LDw4s9wfn555/XM888oxkzZqht27YaNmyYc15OnTp1tHjxYv3hD39Q48aNddddd5X4PfPmzdO9996rkSNHKjY2VkePHtWHH36o+vXrV0m/ALOyGIZheLoIADXX8ePHFR0drb179yo2NtZlX1RUlCZNmqRJkyZ5pjgAKAWXwABUSEFBgbKzs/W73/1Ov/rVr4qFHwCozrgEBqBCdu3apYiICO3du7fYvB1Pe/HFF1WvXr0SX4MGDfJ0eQCqAS6BAah1cnJyij05uYi/v7+aNGni5ooAVDcEIAAAYDpcAgMAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKbz/wG0vzFTG9753AAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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C7dq1w5o1a566TUFBAWJjYzF+/Phn7r9hw4aoU6cOrl69Wm4fCwsL1Zlojx/aEEKguLi4xh9CCK3qLE/fvn3Rrl07bN++HQBgYmKCtWvX4vz589i0aRMOHjyImTNnAgC6deuGTz75BPb29sjKykJWVhY++OADAI8S/aJFi/Dbb78hPj4eGRkZCAoKqpIaiYhIv/3666/4+OOPVc/lPkFJ5y6EWFZWhqKioqf22bp1K4qKijB69Ohn7u/69eu4c+dOtV7Bt6SkBBEREdW2//KEhYXB3Ny8SvbVvHlznDlzBgAQEhKiam/QoAEWL16Md955B59//jnMzc2hVCqhUCjUDjQfN26c6n83bNgQa9euRefOnZGfnw9bW9sqqZOIiPSLEAKff/45/vvf/6ra+vXrh4YNG8pYlcwBKCwsDAMGDICXlxfu3buHmJgYJCUlISEhAQAQGBiIunXrqoWL9evXIyAgQO1KzPn5+ViwYAGGDx8ONzc3pKWlYebMmWjcuDH8/PxqbFz6SAihutbN/v37ERERgUuXLiEvLw8PHz5EYWEh7t+//9QLQJ48eRLz58/Hb7/9hrt376oOrM7MzKzQcV1ERGRYMjIysGnTJknbtGnT1M7qloOsAejWrVsIDAxEVlYWlEol2rZti4SEBPTr1w/Aox9OExPpKl1qaioOHz6MH3/8UW1/pqamOHPmDDZt2oScnBx4eHigf//+WLRoUbVOtZmZmSEsLKza9v+0960qFy9ehLe3NzIyMvDqq69i8uTJWLJkCZycnHD48GGMHz8excXF5QaggoIC+Pn5wc/PD9988w2cnZ2RmZkJPz8/ybWbiIjIOPzzn//E//73P9VzBwcHTJ8+XWcuLCtrAFq/fv1TX09KSlJra9asWbnHvlhZWalmj2qSQqGosqUoORw8eBBnz57Fe++9h5MnT6KsrAyrVq1Shc9vv/1W0t/c3FztrLpLly7hzp07iIyMVJ1Fd+LEiZoZABER6YzS0lIsXrxY0ubu7o5JkyYBAHbuBCIjgdBQYPBgOSp8ROeOAaLqVVRUhOzsbJSWluLmzZvYu3cvIiIi8OqrryIwMBDnzp1DSUkJ/vnPf2LQoEE4cuQI1q1bJ9lHgwYNkJ+fjwMHDqBdu3awtraGl5cXzM3N8c9//hPvvPMOzp07h0WLFsk0SiIiksPly5exefNmSds//vEPeHl5qZ5HRgIpKY/+lDMAyX4WGNWsvXv3wt3dHQ0aNIC/vz8SExOxdu1a7NixA6ampmjXrh1Wr16NZcuWoXXr1vjmm2/UjsHq1q0b3nnnHbzxxhtwdnbG8uXL4ezsjI0bN2Lr1q1o2bIlIiMjsXLlSplGSURENS0yMlIt/ISHh0vCD/Bo5sfH59GfclKIqjqX2oDk5eVBqVQiNzdX7ZT4wsJCpKenw9vbG5aWljJVSDWNnzsRkWYPHz7EkiVLJG0NGzbEmDFjaryWp/1+P4kzQERERFQpZ86cUQs/b7/9tiT87NwJdOv26E9dwmOAiIiISGsLFixQawsPD1c7y0tXjvl5EgMQERERVVhxcbHasaGtW7fG8OHDNfYPDf3rrC9dwgBEREREFfLLL79g9+7dkrbg4GDUqVOn3G0GD9atmZ/HGICIiIjomTQtec2bN0+GSqoGAxARERGV68GDB1i+fLmkrVOnThg4cKBMFVUNBiAiIiLS6PDhwzhw4ICkLSQkBEqlUqaKqg4DEBEREakxtCWvJzEAERERkUp+fj5WrVolaevZsyf69u0rU0XVgxdCpCoXFBSEgIAA1fPevXsjJCTkufZZFfsgIqKn27dvn1r4mTFjhsGFH4AzQEYlKCgImzZtAgCYmZnBy8sLgYGBmDVrFmrVqr6/Ctu3b4eZmVmF+iYlJaFPnz64e/cuHBwcKrUPIiLSnqEveT2JAcjI+Pv7Izo6GkVFRdi9ezeCg4NhZmaGsLAwSb/i4mKYm5tXyXs6OTnpxD6IiEhdTk4O1qxZI2nr168funXrJlNFNYNLYEbGwsICbm5uqF+/PiZPngxfX1/s3LlTtWy1ZMkSeHh4oFmzZgCAa9eu4fXXX4eDgwOcnJwwZMgQZGRkqPZXWlqK999/Hw4ODqhduzZmzpyJJ++v++TyVVFRET766CN4enrCwsICjRs3xvr165GRkYE+ffoAABwdHaFQKBAUFKRxH3fv3kVgYCAcHR1hbW2NAQMG4MqVK6rXN27cCAcHByQkJKBFixawtbWFv78/srKyVH2SkpLw4osvwsbGBg4ODujevTv++OOPKvp/mohI933//fdq4eejjz4y+PADMAAZPSsrKxQXFwMADhw4gNTUVOzbtw+7du1CSUkJ/Pz8YGdnh59++glHjhxRBYnH26xatQobN27Ehg0bcPjwYfzvf/9DXFzcU98zMDAQmzdvxtq1a3Hx4kX861//gq2tLTw9PfHdd98BAFJTU5GVlaX2xXwsKCgIJ06cwM6dO5GSkgIhBF555RWUlJSo+ty/fx8rV67Ev//9bxw6dAiZmZn44IMPADy6e3FAQAB69eqFM2fOICUlBZMmTVK7hw0RkaFasGABfv31V0nbvHnzYGlpKVNFNYtLYEZKCIEDBw4gISEB06ZNw+3bt2FjY4OvvvpKtfT1n//8B2VlZfjqq69UwSA6OhoODg5ISkpC//798cknnyAsLAzDhg0DAKxbtw4JCQnlvu/ly5fx7bffYt++ffD19QUANGzYUPX646UuFxcXyTFAf3flyhXs3LkTR44cUf1XyjfffANPT0/Ex8fjtddeAwCUlJRg3bp1aNSoEQBg6tSpWLhwIQAgLy8Pubm5ePXVV1Wvt2jRQvv/I4mI9Mzt27fx+eefS9peffVVdOzYUaaK5MEZIBnt3Al06/boz5qya9cu2NrawtLSEgMGDMAbb7yB+fPnAwDatGkjOe7nt99+w9WrV2FnZwdbW1vY2trCyckJhYWFSEtLQ25uLrKystClSxfVNrVq1UKnTp3Kff/Tp0/D1NQUvXr1qvQYLl68iFq1aknet3bt2mjWrBkuXryoarO2tlaFGwBwd3fHrVu3ADwKWkFBQfDz88OgQYOwZs0ayfIYEZEh+vbbb9XCT1hYmNGFH4AzQLKKjARSUh79WVM3iuvTpw+ioqJgbm4ODw8PydlfNjY2kr75+fno2LEjvvnmG7X9ODs7V+r9raysKrVdZTx51phCoZAcnxQdHY3p06dj79692LJlC+bMmYN9+/aha9euNVYjEVFNEEKoZsD/zpDP8noWzgDJKDQU8PF59GdNsbGxQePGjeHl5fXMU99feOEFXLlyBS4uLmjcuLHkoVQqoVQq4e7ujuPHj6u2efjwIU6ePFnuPtu0aYOysjIkJydrfP3xDFRpaWm5+2jRogUePnwoed87d+4gNTUVLVu2fOqYntShQweEhYXh6NGjaN26NWJiYrTanohI112+fFkt/AwfPtyoww/AACSrwYOBo0drbvZHW2+99Rbq1KmDIUOG4KeffkJ6ejqSkpIwffp0XL9+HQDw7rvvIjIyEvHx8bh06RKmTJmCnJyccvfZoEEDjB07FuPGjUN8fLxqn99++y0AoH79+lAoFNi1axdu376N/Px8tX00adIEQ4YMwcSJE3H48GH89ttvGD16NOrWrYshQ4ZUaGzp6ekICwtDSkoK/vjjD/z444+4cuUKjwMiIoOyYMECbN68WdI2a9YstG7dWqaKdAcDEJXL2toahw4dgpeXF4YNG4YWLVpg/PjxKCwshL29PYBHVwgdM2YMxo4dCx8fH9jZ2WHo0KFP3W9UVBRGjBiBKVOmoHnz5pg4cSIKCgoAAHXr1sWCBQsQGhoKV1dXTJ06VeM+oqOj0bFjR7z66qvw8fGBEAK7d++u8MUSra2tcenSJQwfPhxNmzbFpEmTEBwcjLfffluL/4eIiHSTEELjhQ0TEuZhzx5eVBYAFOLJi7YQ8vLyoFQqkZubq/qhf6ywsBDp6enw9vY2mlMFiZ87EemPM2fOqF2OxM/PD++/3xUpKY8OvTh6VKbiqtnTfr+fxIOgiYiIDISmWZ85c+bA1NQUoaGPTrqpyeNOdRkDEBERkZ4rLS3F4sWL1dr/fqDz4MG6e8ypHBiAiIiI9Njx48exd+9eSVtAQADatWsnU0X6gQGIiIhIT2la8goPD+dtfSqAAaiSeOy4ceHnTUS6pKSkBEuXLlVrN/Zr+2iDAUhLj0+zvn//fo1e1Zjkdf/+fQDqV5cmIqppiYmJOHTokKRt5MiRaNasmUwV6ScGIC2ZmprCwcFBdU8pa2trTjUaMCEE7t+/j1u3bsHBwQGmpqZyl0RERoxLXlVH1gAUFRWFqKgoZGRkAABatWqF8PBwDBgwQGP/jRs34h//+IekzcLCAoWFharnQgjMmzcPX375JXJyctC9e3dERUWhSZMmVVa3m5sbAKhCEBk+BwcH1edORFTTCgsLsWzZMrV2LnlVnqwBqF69eoiMjESTJk0ghMCmTZswZMgQnDp1Cq1atdK4jb29PVJTU1XPn0y9y5cvx9q1a7Fp0yZ4e3tj7ty58PPzw4ULF6rsAnYKhQLu7u5wcXFBSUlJleyTdJeZmRlnfohINj/88ANOnDghaRs7diwaNGggT0EGQtYANGjQIMnzJUuWICoqCseOHSs3ACkUinL/S1wIgU8++QRz5sxR3RPq66+/hqurK+Lj4zFy5Mgqrd/U1JQ/jEREVG00LXlx1qdq6My9wEpLSxEbG4uCggL4+PiU2y8/Px/169eHp6cnhgwZgvPnz6teS09PR3Z2Nnx9fVVtSqUSXbp0QUpKSrn7LCoqQl5enuRBREQkl/z8fLXwY2Njw/BThWQ/CPrs2bPw8fFBYWEhbG1tERcXh5YtW2rs26xZM2zYsAFt27ZFbm4uVq5ciW7duuH8+fOoV68esrOzAQCurq6S7VxdXVWvaRIREaExZRMREdW0b7/9FhcvXpS0TZo0Ce7u7jJVZJhkvxlqcXExMjMzkZubi23btuGrr75CcnJyuSHo70pKStCiRQuMGjUKixYtwtGjR9G9e3fcuHFD8hfl9ddfh0KhwJYtWzTup6ioCEVFRarneXl58PT0rNDN1IiIiKoKl7yej17dDNXc3ByNGzcGAHTs2BG//PIL1qxZg3/961/P3NbMzAwdOnTA1atXAfx1dtbNmzclAejmzZto3759ufuxsLCAhYXFc4yCiIio8u7evYu1a9dK2tzc3PD222/LVJHhkz0APamsrEwyG/M0paWlOHv2LF555RUAgLe3N9zc3HDgwAFV4MnLy8Px48cxefLk6iqZiIio0jZs2IBr165J2qZOnYratWvLVJFxkDUAhYWFYcCAAfDy8sK9e/cQExODpKQkJCQkAAACAwNRt25dREREAAAWLlyIrl27onHjxsjJycGKFSvwxx9/YMKECQAenSEWEhKCxYsXo0mTJqrT4D08PBAQECDXMImIiDTikpd8ZA1At27dQmBgILKysqBUKtG2bVskJCSgX79+AIDMzEyYmPx1otrdu3cxceJEZGdnw9HRER07dsTRo0clxwvNnDkTBQUFmDRpEnJyctCjRw/s3bu3yq4BRERE9Lxu3bqFqKgoSVuTJk3w5ptvylSR8ZH9IGhdpM1BVERERNpYs2YNcnJyJG3vvfcef2+qgF4dBE1ERGQsuOSlOxiAiIiIqtm1a9ewYcMGSVv79u1Vdy2gmscAREREVI0WLVqEsrIySduHH34Ia2trmSoigAGIiIioWgghsHDhQrV2LnnpBgYgIiKiKnb16lV88803krZu3bqpznIm+TEAERERVSFNBzqHhYXB3NxchmqoPAxAREREVYBLXvqFAYiIiOg5nT9/Htu2bZO0+fr6onv37jJVRM/CAERERPQcNC15zZ49G7Vq8SdWl/HTISIiqoSysjIsWrRIrZ1LXvqBAYiIiEhLJ06cwA8//CBpGzx4MDp06CBTRaQtBiAiIiItaFrymjt3ruTm3aT7GICIiIgqoKSkBEuXLlVr55KXfmIAIiIieoZDhw4hMTFR0vb666+jRYsWMlVEz4sBiIiI6Ck0LXmFh4dDoVDIUA1VFQYgIiIiDYqKihAZGanWziUvw8AARERE9IS9e/fi+PHjkrbAwEB4e3vLVBFVNQYgIiKiv9G05MVZH8PDAERERASgoKAAK1eulLRZWlrio48+kqkiqk4MQEREZPS+++47nDt3TtI2YcIE1K1bV6aKqLoxABERkVHjkpdxYgAiIiKjlJOTgzVr1kjanJ2dMWXKFJkqoprEAEREREZn06ZNyMjIkLQFBwejTp068hRENY4BiIiIjAqXvAhgACIiIiNx+/ZtfP7555I2b29vBAYGylQRyYkBiIiIDN5nn32G//73v5K2kJAQKJVKmSoiuTEAERGRQeOSF2nCAERERAbpzz//xFdffSVpa9OmDYYNGyZTRaRLGICIiMjgREREoLi4WNL2wQcfwMbGRqaKSNcwABERkUHhkhdVBAMQEREZhN9//x3//ve/JW1du3aFn5+fTBWRLjOR882joqLQtm1b2Nvbw97eHj4+PtizZ0+5/b/88kv07NkTjo6OcHR0hK+vL37++WdJn6CgICgUCsnD39+/uodCREQyWrBggVr4CQ0NZfihcskagOrVq4fIyEicPHkSJ06cQN++fTFkyBCcP39eY/+kpCSMGjUKiYmJSElJgaenJ/r3748///xT0s/f3x9ZWVmqx+bNm2tiOEREVMOEEOUueVlYWMhQEekLhRBCyF3E3zk5OWHFihUYP378M/uWlpbC0dERn376qepCVkFBQcjJyUF8fHyla8jLy4NSqURubi7s7e0rvR8iIqo+Fy9exLfffitp69u3L3r27ClTRSQ3bX6/deYYoNLSUmzduhUFBQXw8fGp0Db3799HSUkJnJycJO1JSUlwcXGBo6Mj+vbti8WLF6N27drl7qeoqAhFRUWq53l5eZUbBBER1QhNsz6zZ89GrVo687NGOk72vylnz56Fj48PCgsLYWtri7i4OLRs2bJC23700Ufw8PCAr6+vqs3f3x/Dhg2Dt7c30tLSMGvWLAwYMAApKSkwNTXVuJ+IiAiNXyYiItItZWVlWLRokVo7z/Iibcm+BFZcXIzMzEzk5uZi27Zt+Oqrr5CcnPzMEBQZGYnly5cjKSkJbdu2Lbff77//jkaNGmH//v14+eWXNfbRNAPk6enJJTAiIh3y66+/4vvvv5e0DRw4EJ06dZKpItI1erUEZm5ujsaNGwMAOnbsiF9++QVr1qzBv/71r3K3WblyJSIjI7F///6nhh8AaNiwIerUqYOrV6+WG4AsLCx4sBwRkQ7TNEs/d+5cmJjIei4P6THZA9CTysrKJLMxT1q+fDmWLFmChISECqX+69ev486dO3B3d6/KMomIqAY8fPgQS5YsUWvnkhc9L1kDUFhYGAYMGAAvLy/cu3cPMTExSEpKQkJCAgAgMDAQdevWRUREBABg2bJlCA8PR0xMDBo0aIDs7GwAgK2tLWxtbZGfn48FCxZg+PDhcHNzQ1paGmbOnInGjRvzWhBERHrmyJEj2L9/v6RtxIgRaNWqlUwVkSGRNQDdunULgYGByMrKglKpRNu2bZGQkIB+/foBADIzMyXTm1FRUSguLsaIESMk+5k3bx7mz58PU1NTnDlzBps2bUJOTg48PDzQv39/LFq0iEtcRER6RNOSV3h4OBQKhQzVkCGS/SBoXcTrABERyaO4uFg16/93XPKiitCrg6CJiIgAYN++fTh69KikbfTo0WjUqJFMFZEhYwAiIiLZccmLahoDEBERyeb+/ftYsWKFpM3U1BRz5syRqSIyFgxAREQki/j4ePz222+StnHjxsHT01OmisiYMAAREVGNK+8O7kQ1hQGIiIhqTF5eHj7++GNJm6OjI6ZPny5TRWSsGICIiKhGfPPNN7h69aqkbfLkyXBxcZGpIjJmDEBERFTtuORFuoYBiIiIqs2dO3fw6aefStq8vLzwj3/8Q6aKiB5hACIiomqxbt063Lx5U9I2ffp0ODo6ylQR0V8YgIiIqMpxyYt0HQMQERFVmaysLHzxxReStpYtW+K1116TqSIizRiAiIioSqxYsQL379+XtM2YMQO2trYyVURUPgYgIiJ6blzyIn3DAERERJWWkZGBTZs2Sdo6deqEgQMHylQRUcUwABERUaVomvX56KOPYGlpKUM1RNphACIiIq0IIbBw4UK1di55kT5hACIiogpLTU1FbGyspK1Xr17o3bu3PAURVRIDEBERVYimJa9Zs2bBzMxMhmqIng8DEBERPVVZWRkWLVqk1s4lL9JnDEBERFSu06dPY8eOHZI2f39/dOnSRaaKiKoGAxAREWmkaclrzpw5MDU1laEaoqrFAERERBIPHz7EkiVL1Nq55EWGhAGIiIhUfvjhB5w4cULSNmzYMLRp00amioiqBwMQEREB0Lzk1b59ONq0UchQDVH1YgAiIjJyRUVFiIyMVGvnkhcZMgYgIiIjFhsbi9TUVEnboEGD8MILL8hUEVHNYAAiIjJSmpa8wsPDoVBwyYsMHwMQEZGRyc/Px6pVq9TaueRFxoQBiIjIiHzxxRfIysqStL3xxhto3ry5TBURyYMBiIjISGha8uKsDxkrEznfPCoqCm3btoW9vT3s7e3h4+ODPXv2PHWbrVu3onnz5rC0tESbNm2we/duyetCCISHh8Pd3R1WVlbw9fXFlStXqnMYREQ67e7duww/RE+QNQDVq1cPkZGROHnyJE6cOIG+fftiyJAhOH/+vMb+R48exahRozB+/HicOnUKAQEBCAgIwLlz51R9li9fjrVr12LdunU4fvw4bGxs4Ofnh8LCwpoaFhGRzlixYgXWrl0raRs7dizDDxk9hRBCaLOBqakpsrKy4OLiImm/c+cOXFxcUFpa+lwFOTk5YcWKFRg/frzaa2+88QYKCgqwa9cuVVvXrl3Rvn17rFu3DkIIeHh4YMaMGfjggw8AALm5uXB1dcXGjRsxcuTICtWQl5cHpVKJ3Nxc2NvbP9d4iIjkwlkfMjba/H5rPQNUXl4qKiqCubm5trtTKS0tRWxsLAoKCuDj46OxT0pKCnx9fSVtfn5+SElJAQCkp6cjOztb0kepVKJLly6qPkREhi47O5vhh+gZKnwQ9OMpVIVCga+++gq2traq10pLS3Ho0KFKnUVw9uxZ+Pj4oLCwELa2toiLi0PLli019s3Ozoarq6ukzdXVFdnZ2arXH7eV10eToqIiFBUVqZ7n5eVpPQ4iIl2gKfi8/fbbcHNzk6EaIt1V4QD08ccfA3g0A7Ru3TqYmpqqXjM3N0eDBg2wbt06rQto1qwZTp8+jdzcXGzbtg1jx45FcnJyuSGoOkRERGj8R4OISJ9w1oeo4iocgNLT0wEAffr0wfbt2+Ho6FglBZibm6Nx48YAgI4dO+KXX37BmjVr8K9//Uutr5ubG27evClpu3nzpuq/bB7/efPmTbi7u0v6tG/fvtwawsLC8P7776ue5+XlwdPTs9JjIiKqCTt3ApGRwNSpf+DKlY2S16ysrDBz5kx5CiPSA1ofA5SYmFhl4UeTsrIyyXLU3/n4+ODAgQOStn379qmOGfL29oabm5ukT15eHo4fP17ucUUAYGFhoToV//GDiEjXRUYCfn4L1MLP9OnTGX6InkHrCyGOGzfuqa9v2LChwvsKCwvDgAED4OXlhXv37iEmJgZJSUlISEgAAAQGBqJu3bqIiIgAALz77rvo1asXVq1ahYEDByI2NhYnTpzAF198AeDR8UkhISFYvHgxmjRpAm9vb8ydOxceHh4ICAjQdqhERDrNz49LXkSVpXUAunv3ruR5SUkJzp07h5ycHPTt21erfd26dQuBgYHIysqCUqlE27ZtkZCQgH79+gEAMjMzYWLy1yRVt27dEBMTgzlz5mDWrFlo0qQJ4uPj0bp1a1WfmTNnoqCgAJMmTUJOTg569OiBvXv3wtLSUtuhEhHppEuXLmHLli2SNnd3d0yaNEmmioj0j9bXAdKkrKwMkydPRqNGjQxi2pXXASIiXaXpQOcZM2ZIzswlMlbVeh0gjTsxMcH777+vOlOMiIiqlhCi3LO8GH6ItFdlN0NNS0vDw4cPq2p3RET0/3766SccPHhQ0ubk5IRp06bJVBGR/tM6AP39dHHg0X+VZGVl4YcffsDYsWOrrDAiItK85DVz5kxYWVnJUA2R4dA6AJ06dUry3MTEBM7Ozli1atUzzxAjIiJ1j6/nExoKDB78qK2srAyLFi1S68uzvIiqRpUcBG1oeBA0EdWkbt2AlBTAxwc4ehT44YcfcOLECUkfb29vBAYGylQhkX7Q5ve70scA3bp1C6mpqQAe3c7iybvDExFRxYSG/jUDpGnJa9asWTAzM5OhMiLDpfVZYHl5eRgzZgw8PDzQq1cv9OrVC3Xr1sXo0aORm5tbHTUSERm0wYOBQ4ce4tQpzWd5MfwQVT2tA9DEiRNx/Phx/PDDD8jJyUFOTg527dqFEydO4O23366OGomIDFp0dDSWLFkiaWvfvj2P9yGqRlofA2RjY4OEhAT06NFD0v7TTz/B398fBQUFVVqgHHgMEBHVFE1LXnPnzpVcBZ+IKqZaL4RYu3ZtKJVKtXalUlmtN0klIjIkDx48KPfChs8KPzt3PjpweufO6qqOyPBpfRD0nDlz8P777+Pf//433NzcAADZ2dn48MMPMXfu3CovkIjI0KxcuVJttrxLly7w9/ev0PaRkY/OGouM/Ou0eSLSjtZLYB06dMDVq1dRVFQELy8vAI9uWmphYYEmTZpI+v76669VV2kN4hIYEVUXTbM+4eHhUCgUFd6HpusGEVE1nwY/ZMgQrb6oRET06B9mTfdLrMyBzoMHM/gQPS9eCFEDzgARUVXSNOvj6+uL7t27y1ANkeGq1oOgGzZsiDt37qi15+TkoGHDhtrujojIoJV3oDPDD5G8tF4Cy8jIQGlpqVp7UVERrl+/XiVFERHpu9u3b+Pzzz9Xa+e1fYh0Q4UD0M6/nW+ZkJAgORW+tLQUBw4cgLe3d9VWR0SkhzTN+gwdOhRt27aVoRoi0qTCASggIAAAoFAoMHbsWMlrZmZmaNCgAVatWlWlxRER6ZvylryISLdUOACVlZUBeHRH4l9++QV16tSptqKIiPRNZmYmoqOj1doZfoh0k9bHAKWnp1dHHUREekvTrM/o0aPRqFEjGaohoorQOgAtXLjwqa+Hh4dXuhgiIn3DJS8i/aR1AIqLi5M8LykpQXp6OmrVqoVGjRoxABGRUUhNTUVsbKxaO8MPkX7QOgCdOnVKrS0vLw9BQUEYOnRolRRFRCS3p91uQtOsz8SJE+Hh4VFD1RHR86qyK0GfPXsWgwYNQkZGRlXsTla8EjQRdev26IajPj7A0aN/tXPJi0h3Veu9wMqTm5uL3NzcqtodEZGsQkP/mgECgJMnT2LXrl1q/Rh+iPST1gFo7dq1kudCCGRlZeHf//43BgwYUGWFERHJ6e83HNU06zNt2jQ4OTnVcFVEVFW0DkBP3s3YxMQEzs7OGDt2LMLCwqqsMCIiXcAlLyLDxOsAERFpcPDgQfz0009q7Qw/RIahUscA5eTk4OrVqwCAxo0bw8HBoSprIiKSlaZZnxkzZsDW1laGaoioOpho0zkjIwMDBw5EnTp10KVLF3Tp0gV16tTBq6++ahBnfxGRcRNClLvkxfBDZFgqPAN07do1dO3aFWZmZli0aBFatGgBALhw4QKioqLg4+ODX375BfXq1au2YomIqss333yjmtn+Oy55ERmmCs8AzZ8/H82aNcOVK1cQFhaGgIAABAQEYNasWbh8+TKaNm2K+fPna/XmERER6Ny5M+zs7ODi4oKAgACkpqY+dZvevXtDoVCoPQYOHKjqExQUpPa6v7+/VrURkfFYsGCBWvj56KOPGH6IDFiFZ4D27t2LLVu2wNLSUu01KysrLFq0CCNHjtTqzZOTkxEcHIzOnTvj4cOHmDVrFvr3748LFy7AxsZG4zbbt29HcXGx6vmdO3fQrl07vPbaa5J+/v7+kjszW1hYaFUbERm+kpISLF26VK2dwYfI8FU4AP33v/9FgwYNyn29YcOG+N///qfVm+/du1fyfOPGjXBxccHJkyfx0ksvadzmyetuxMbGwtraWi0AWVhYwM3NTat6iMh4aDrWx9TUFHPmzJGhGiKqaRUOQO7u7rhw4UK5x/icO3fuuQPH4ytJa3NxsfXr12PkyJFqM0ZJSUlwcXGBo6Mj+vbti8WLF6N27drPVR8RGQZN4Wf27NmoVavKLo5PRDquwvcCCwkJwcGDB3HgwAE4OztLXrt16xb69euHPn364JNPPqlUIWVlZRg8eDBycnJw+PDhCm3z888/o0uXLjh+/DhefPFFVfvjWSFvb2+kpaVh1qxZsLW1RUpKCkxNTdX2U1RUhKKiItXzvLw8eHp68l5gRAbm/v37WLFihVo7l7yIDIM29wKrcAC6e/cuunTpguzsbIwePRrNmzeHEAIXL15ETEwM3NzccOzYsUpfGn7y5MnYs2cPDh8+XOEzyd5++22kpKTgzJkzT+33+++/o1GjRti/fz9efvlltdfnz5+v8b8IGYCIDIem77i7uzsmTZokQzVEVB2qJQABj0LQrFmzsGXLFuTk5AAAHBwc8Prrr2Pp0qWVDj9Tp07Fjh07cOjQIXh7e1dom4KCAnh4eGDhwoV49913n9nf2dkZixcvxttvv632GmeAiAybpvATHh4OhUKher5z5183P318DzAi0i/VFoAeE0Lg9u3bAB4Fi7//I6LtfqZNm4a4uDgkJSWhSZMmFd5248aNeOedd/Dnn38+89ie69evw8vLC/Hx8RhcgX/ZtPk/kIh01927d9Vu4AxoXvLq1g1ISQF8fICjR2uiOiKqatr8flfqiD+FQgEXF5dKFfd3wcHBiImJwY4dO2BnZ4fs7GwAgFKphJWVFQAgMDAQdevWRUREhGTb9evXIyAgQC385OfnY8GCBRg+fDjc3NyQlpaGmTNnonHjxvDz83vumolIP2ia9WnVqhVGjBihsX9o6F8zQERk+GQ95SEqKgrAo4sb/l10dDSCgoIAAJmZmTAxkV6vMTU1FYcPH8aPP/6otk9TU1OcOXMGmzZtQk5ODjw8PNC/f38sWrSI1wIiMhKVuYP74MFc+iIyJpVaAjN0XAIj0k83btzAl19+qdbOs7yIjEO1L4EREekaTbM+PXr00HjmJxERAxAR6b3KLHkRkXGrUADSdBZFeaZPn17pYoiItHHlyhXExMSotTP8ENGzVOgYoIpem0ehUOD3339/7qLkxmOAiHSfplmfgQMHolOnTjJUQ0S6oMqPAUpPT6+SwoiIqgKXvIjoeVX6GKDi4mKkp6ejUaNGvIEgEdWIX3/9Fd9//71aO8MPEWlL6+Ry//59TJs2DZs2bQIAXL58GQ0bNsS0adNQt25dhPIqYkRUDTTN+owaNQpNmzaVoRoi0ncmz+4iFRYWht9++w1JSUmwtLRUtfv6+mLLli1VWhwREVD+khfDDxFVltYzQPHx8diyZQu6du0quQdYq1atkJaWVqXFEZFxS0xMxKFDh9TaueRFRM9L6wB0+/ZtjfcBKygoqPRNUYmInqRp1mfChAmoW7euDNUQkaHRegmsU6dO+OGHH1TPH4eer776Cj4+PlVXGREZrfKWvBh+iKiqaD0DtHTpUgwYMAAXLlzAw4cPsWbNGly4cAFHjx5FcnJyddRIREbim2++wdWrV9XaueRFRFVN6wDUo0cPnD59GpGRkWjTpg1+/PFHvPDCC0hJSUGbNm2qo0YiMgKaZn2mTZsGJycnGaohIkNXqQv4NGrUSOMdl4mItCWEwMKFC9XaO3SYB2YfIqouFQpAeXl5Fd4hbx1BRBX18ccfa/z3hUteRFTdKhSAHBwcKnyGV2lp6XMVRETGQdOS14wZM2BraytDNURkbCoUgBITE1X/OyMjA6GhoQgKClKd9ZWSkoJNmzYhIiKieqokIoPx8OFDLFmyRK2dsz5EVJMqdDf4v3v55ZcxYcIEjBo1StIeExODL774AklJSVVZnyx4N3ii6qFp1gdg+CGiqqHN77fWAcja2hq//fYbmjRpImm/fPky2rdvj/v372tfsY5hACKqeprCT1hYGMzNzWWohogMkTa/31pfCNHT01PjGWBfffUVPD09td0dERm4Bw8elHthQ4YfIpKL1qfBf/zxxxg+fDj27NmDLl26AAB+/vlnXLlyBd99912VF0hE+ktT8KlVqxZmz54tQzVERH/RegkMAK5fv47PP/8cly5dAgC0aNEC77zzjsHMAHEJjOj5aQo/c+fOhYmJ1hPPREQVUq3HABkDBiCiysvJycGaNWvU2nmgMxFVN21+vyt1JeicnBysX78eFy9eBAC0atUK48aNg1KprMzuiMhAaJr1qVu3LiZMmCBDNURE5dN6BujEiRPw8/ODlZUVXnzxRQDAL7/8ggcPHqjuC6bvOANEpD1N4Sc8PLzCF1ElInpe1boE1rNnTzRu3BhffvklatV6NIH08OFDTJgwAb///jsOHTpU+cp1BAMQUcXduHFD45mhXPIioppWrQHIysoKp06dQvPmzSXtFy5cQKdOnXgdICIjomnWp23bthg6dKgM1RCRsavWY4Ds7e2RmZmpFoCuXbsGOzs7bXdHRHqqvGv7EBHpA60D0BtvvIHx48dj5cqV6NatGwDgyJEj+PDDD9Vuj0FEhufy5cvYvHmzWjvDDxHpE60D0MqVK6FQKBAYGIiHDx8CAMzMzDB58mRERkZWeYFEpDs0zfr07t0bvXr1kqEaIqLKq/R1gO7fv4+0tDQAQKNGjWBtbV2lhcmJxwARqeOSFxHpumq9F9hj1tbWaNOmDdq0aVPp8BMREYHOnTvDzs4OLi4uCAgIQGpq6lO32bhxIxQKheRhaWkp6SOEQHh4ONzd3WFlZQVfX19cuXKlUjUSGbsTJ04w/BCRwanwEti4ceMq1G/Dhg0VfvPk5GQEBwejc+fOePjwIWbNmoX+/fvjwoULsLGxKXc7e3t7SVB68jojy5cvx9q1a7Fp0yZ4e3tj7ty58PPzw4ULF9TCEhGVT1PwGTJkCNq3b1/zxRARVaEKB6CNGzeifv366NChA6rq7hl79+5Vew8XFxecPHkSL730UrnbKRQKuLm5aXxNCIFPPvkEc+bMwZAhQwAAX3/9NVxdXREfH4+RI0dWSe1Eho6zPkRkyCocgCZPnozNmzcjPT0d//jHPzB69Gg4OTlVaTG5ubkA8Mz95ufno379+igrK8MLL7yApUuXolWrVgCA9PR0ZGdnw9fXV9VfqVSiS5cuSElJYQAi+n87dwKRkUBoKDB48F/t+/fvx5EjR9T6M/wQkSGp8DFAn332GbKysjBz5kx8//338PT0xOuvv46EhIQqmREqKytDSEgIunfvjtatW5fbr1mzZtiwYQN27NiB//znPygrK0O3bt1w/fp1AEB2djYAwNXVVbKdq6ur6rUnFRUVIS8vT/IgMnSRkUBKyqM/H1uwYIFa+BkzZgzDDxEZHK0OgrawsMCoUaOwb98+XLhwAa1atcKUKVPQoEED5OfnP1chwcHBOHfuHGJjY5/az8fHB4GBgWjfvj169eqF7du3w9nZGf/6178q/d4RERFQKpWqh6enZ6X3RaQvQkMBH59HfwLlL3k1bNiwhisjIqp+lT4LzMTEBAqFAkIIlJaWPlcRU6dOxa5du5CYmIh69eppta2ZmRk6dOiAq1evAoDq2KCbN29K+t28ebPc44bCwsKQm5urely7dq0SoyDSL4MHA0ePAoWF3/J4HyIyOloFoKKiImzevBn9+vVD06ZNcfbsWXz66afIzMyEra2t1m8uhMDUqVMRFxeHgwcPwtvbW+t9lJaW4uzZs3B3dwcAeHt7w83NDQcOHFD1ycvLw/Hjx+Hj46NxHxYWFrC3t5c8iIzBggULcPHiRUnbO++8w/BDRAavwgdBT5kyBbGxsfD09MS4ceOwefNm1KlT57nePDg4GDExMdixYwfs7OxUx+golUpYWVkBAAIDA1G3bl1EREQAABYuXIiuXbuicePGyMnJwYoVK/DHH39gwoQJAB6dIRYSEoLFixejSZMmqtPgPTw8EBAQ8Fz1EhkKIQQWLlyo1s7gQ0TGosIBaN26dfDy8kLDhg2RnJyM5ORkjf22b99e4TePiooC8OhS+n8XHR2NoKAgAEBmZiZMTP6aqLp79y4mTpyI7OxsODo6omPHjjh69Chatmyp6jNz5kwUFBRg0qRJyMnJQY8ePbB3715eA4gIwKZNm5CRkaHWzvBDRMakwrfCCAoKUrvgoCbR0dHPXZTceCsMMlSajvV57733+PeciAyCNr/fWl0IkYj0U2lpKRYvXqzWzlkfIjJWWt8Nnoj0y+rVq3Hv3j21doYfIjJmDEBEBkzTkldoaCgsLCxkqIaISHcwABEZoKKiIkT+/RLP/4+zPkREjzAAERkYTbM+tra2mDFjhgzVEBHpJgYgIgOiKfzMmTMHpqamMlRDRKS7GICIDMC9e/ewevVqtXYueRERacYARKTnNM36eHt7IzAwUIZqiIj0AwMQkR7TFH7Cw8MrdNFSIiJjxgBEpIf++9//4rPPPlNr55IXEVHFMAAR6RlNsz4vvPACBg0aJEM1RET6iQGISI9oCj+c9SEi0h4DEJEeyMzM1HijYYYfIqLKYQAi0nGaZn1efvll9OjRQ4ZqiIgMAwMQkQ7jkhcRUfVgACLSQRcuXMDWrVvV2hl+iIiqBgMQkY7RNOszdOhQtG3bVoZqiIgMEwMQkcx27gQiI4HQUODUKS55ERHVBAYgIplFRgJlZcdw6lSC2msMP0RE1YMBiEhmfn7qsz6BgYHw9vaWoRoiIuPAAEQkI57lRUQkDwYgIhkcOnQIiYmJau0MP0RENYMBiKiGaZr1CQ4ORp06dWSohojIOJnIXQCRIZo9G7Cze/TnY0KIcpe8GH6IiGqWQggh5C5C1+Tl5UGpVCI3Nxf29vZyl0N6yM4OyM8HLC2BDh2AN9/8Hnfu/KrWj0teRERVR5vfby6BEVUDf39g+3bA2vrRWV537khfnzFjBmxtbeUpjoiIGICIqsOffwJClGH69EVqr3HWh4hIfgxARNVg1Kgd8PM7LWkzNTXFnDlz5CmIiIgkGICIqpimA51DQ0NhYWEhQzVERKQJAxBRFSktLcXixYvV2rnkRUSkexiAiKrATz/9hIMHD0ra+vXrh27duslUERERPQ0DENFz0rTkNXfuXJiY8DJbRES6StZ/oSMiItC5c2fY2dnBxcUFAQEBSE1Nfeo2X375JXr27AlHR0c4OjrC19cXP//8s6RPUFAQFAqF5OHv71+dQyEjVFJSUu6FDRl+iIh0m6wzQMnJyQgODkbnzp3x8OFDzJo1C/3798eFCxdgY2OjcZukpCSMGjUK3bp1g6WlJZYtW4b+/fvj/PnzqFu3rqqfv78/oqOjVc95ACpVpb179+L48eOStoCAALRr106mioiISBs6dSXo27dvw8XFBcnJyXjppZcqtE1paSkcHR3x6aefIjAwEMCjGaCcnBzEx8dXqg5eCZqeRtOsT3h4OBQKhQzVEBHRY9r8fuvUPH1ubi4AwMnJqcLb3L9/HyUlJWrbJCUlwcXFBc2aNcPkyZNx58lL8f5NUVER8vLyJA+iJz148KDcJS+GHyIi/aIzM0BlZWUYPHgwcnJycPjw4QpvN2XKFCQkJOD8+fOwtLQEAMTGxsLa2hre3t5IS0vDrFmzYGtri5SUFJiamqrtY/78+Rp/2DgDRI9t27YN58+fl7SNGjUKTZs2lakiIiJ6kjYzQDoTgCZPnow9e/bg8OHDqFevXoW2iYyMxPLly5GUlIS2bduW2+/3339Ho0aNsH//frz88stqrxcVFaGoqEj1PC8vD56engxARmznTiAyEggNBU6d0jzrQ0REukXvlsCmTp2KXbt2ITExscLhZ+XKlYiMjMSPP/741PADAA0bNkSdOnVw9epVja9bWFjA3t5e8iDjFhkJnD17j+GHiMhAyXoWmBAC06ZNQ1xcHJKSkuDt7V2h7ZYvX44lS5YgISEBnTp1emb/69ev486dO3B3d3/ekslIDB++EX5+f0jagoKCUL9+fZkqIiKiqiRrAAoODkZMTAx27NgBOzs7ZGdnAwCUSiWsrKwAAIGBgahbty4iIiIAAMuWLUN4eDhiYmLQoEED1Ta2trawtbVFfn4+FixYgOHDh8PNzQ1paWmYOXMmGjduDD8/P3kGSnqlvAOdiYjIcMi6BBYVFYXc3Fz07t0b7u7uqseWLVtUfTIzM5GVlSXZpri4GCNGjJBss3LlSgCP7rh95swZDB48GE2bNsX48ePRsWNH/PTTT7wWED3VvXv31MJPrVq1GH6IiAyQzhwErUt4HSDjs2PHDpw+fVrSNn36dDg6OspTEBERaU2b32/eC4yMHpe8iIiMDwMQGa27d+9i7dq1krYOHTpg8ODBMlVEREQ1hQGIjFJMTAyuXLkiaXv//fdhZ2cnU0VERFSTGIDI6HDJi4iIGIDIaNy8eRPr1q2TtHXv3h2+vr4yVURERHJhACKj8OWXX+LGjRuStpkzZ6quN0VERMaFAYgMHpe8iIjoSQxAZLCuX7+O9evXS9p8fX3RvXt3mSoiIiJdwQBEBumTTz5Bbm6upC0sLAzm5uYyVURERLqEAYgMihACCxcuVGvnkhcREf0dAxAZjLS0NPznP/+RtL366qvo2LGjTBUREZGuYgAig7Bo0SKUlZVJ2mbPno1atfhXnIiI1PHXgfRaWVkZFi1apNbOJS8iInoaBiDSWxcvXsS3334raRsxYgRatWolU0VERKQvGIBIL2m6ts/cuXNhYmIiQzVERKRvGIBIr5SWlmLx4sVq7VzyIiIibTAAkd749ddf8f3330va3nrrLTRu3FimioiISF8xAJFe0LTkFR4eDoVCIUM1RESk7xiASKeVlJRg6dKlkjZbW1vMmDFDpoqIiMgQMACRzjp69Cj27dsnaRs3bhw8PT1lqoiIiAwFAxDpJN7BnYiIqhMDEOmUwsJCLFu2TNLm7u6OSZMmyVQREREZIgYg0hkHDhzA4cOHJW3vvPMOXF1dZaqIiIgMFQMQ6QQueRERUU1iACJZ5efnY9WqVZK2Jk2a4M0335SpIiIiMgYMQCSb77//Hr/++qukbdq0aXBycpKpIiIiMhYMQCQLLnkREZGcGICoRuXk5GDNmjWStnbt2iEgIECegoiIyCgxAFGN+fbbb3Hx4kVJ23vvvQd7e3uZKiIiImPFAEQ1gkteRESkSxiAqFrdvn0bn3/+uaSta9eu8PPzk6kiIiIiwETON4+IiEDnzp1hZ2cHFxcXBAQEIDU19Znbbd26Fc2bN4elpSXatGmD3bt3S14XQiA8PBzu7u6wsrKCr68vrly5Ul3DIA127gRCQjaohZ8PP/yQ4YeIiGQnawBKTk5GcHAwjh07hn379qGkpAT9+/dHQUFBudscPXoUo0aNwvjx43Hq1CkEBAQgICAA586dU/VZvnw51q5di3Xr1uH48eOwsbGBn58fCgsLa2JYBODUqQVwdLwmaZs3bx6sra1lqoiIiOgvCiGEkLuIx27fvg0XFxckJyfjpZde0tjnjTfeQEFBAXbt2qVq69q1K9q3b49169ZBCAEPDw/MmDEDH3zwAQAgNzcXrq6u2LhxI0aOHPnMOvLy8qBUKpGbm8sDdLWk6Syvvn37omfPnjJVRERExkKb329ZZ4CelJubCwBPvRBeSkoKfH19JW1+fn5ISUkBAKSnpyM7O1vSR6lUokuXLqo+TyoqKkJeXp7kQdo7ePCgWviZNWsWww8REekcnTkIuqysDCEhIejevTtat25dbr/s7Gy1m2O6uroiOztb9frjtvL6PCkiIkLjWUpUMUIILFy4UNLWoEEDjB07VqaKiIiInk5nAlBwcDDOnTundjfwmhAWFob3339f9TwvLw+enp41Xoc++u9//4vPPvtM0jZ27Fg0aNBAnoKIiIgqQCcC0NSpU7Fr1y4cOnQI9erVe2pfNzc33Lx5U9J28+ZNuLm5qV5/3Obu7i7p0759e437tLCwgIWFxXOMwDilp6fj66+/lrTNmTMHpqamMlVERERUMbIeAySEwNSpUxEXF4eDBw/C29v7mdv4+PjgwIEDkrZ9+/bBx8cHAODt7Q03NzdJn7y8PBw/flzVh56PEAI///yzJPzcvNkC8+bNY/ghIiK9IOsMUHBwMGJiYrBjxw7Y2dmpjtFRKpWwsrICAAQGBqJu3bqIiIgAALz77rvo1asXVq1ahYEDByI2NhYnTpzAF198AQBQKBQICQnB4sWL0aRJE3h7e2Pu3Lnw8PDg/aaqQGFhIXbu3Km6pYW5uQP27RuLkBAHeQsjIiLSgqwBKCoqCgDQu3dvSXt0dDSCgoIAAJmZmTAx+Wuiqlu3boiJicGcOXMwa9YsNGnSBPHx8ZIDp2fOnImCggJMmjQJOTk56NGjB/bu3QtLS8tqH5Mh+/PPP7Ft2zbk5OTAxMQE/fr1Q5cuXRAWppC7NCIiIq3o1HWAdAWvAyQlhMCxY8ewf/9+lJWVwcHBASNGjEDdunXlLo2IiEhFm99vnTgImnTX/fv3sWPHDly+fBkA0LJlSwwaNIizaUREpNcYgKhcmZmZ+O6775CXlwdTU1P4+fmhU6dOUCi45EVERPqNAYjUCCFw+PBhJCYmQgiB2rVrY8SIEapLDBAREek7BiCSKCgoQFxcHNLS0gAAbdq0wcCBA3mdJCIiMigMQKSSnp6O7du3Iz8/H7Vq1cIrr7yC9u3bc8mLiIgMDgMQoaysDIcOHUJycjIAwNnZGSNGjICLi4vMlREREVUPBiAjd+/ePWzfvh0ZGRkAgA4dOmDAgAEwMzOTtzAiIqJqxABkxK5evYq4uDjcv38fZmZmePXVV9G2bVu5yyIiIqp2DEBGqLS0FImJiThy5AgAwNXVFa+99hpq164tc2VEREQ1gwHIyOTm5uK7777DtWvXAACdOnWCn58fatXiXwUiIjIe/NUzIqmpqdixYwcePHgACwsLDB48GC1btpS7LCIiohrHAGQESktLsX//fhw7dgwA4OHhgREjRsDR0VHmyoiIiOTBAGTg7t69i23btuHGjRsAgK5du8LX1xempqYyV0ZERCQfBiADduHCBezcuRNFRUWwtLREQEAAmjVrJndZREREsmMAMkAPHz5EQkICTpw4AQDw9PTE8OHDoVQqZa6MiIhINzAAGZg7d+5g69atuHnzJgCge/fu6NOnD5e8iIiI/oYByICcPXsWu3btQnFxMaytrTF06FA0btxY7rKIiIh0DgOQASgpKcGePXtw6tQpAECDBg0wbNgw2NnZyVwZERGRbmIA0nO3bt3Ctm3bcPv2bQBAr1698NJLL8HExETmyoiIiHQXA5CeEkLg9OnT2L17Nx4+fAhbW1sMGzYM3t7ecpdGRESk8xiA9FBRURF2796NM2fOAAAaNmyIoUOHwtbWVubKiIiI9AMDkJ7Jzs7Gtm3bcOfOHSgUCvTp0wc9evSAQqGQuzQiIiK9wQCkJ4QQOHnyJPbu3YvS0lLY29tj+PDh8PLykrs0IiIivcMApAcKCwvx/fff48KFCwCApk2bYsiQIbC2tpa5MiIiIv3EAKTj/vzzT3z33Xe4e/cuTExM4Ovri65du3LJi4iI6DkwAOkoIQSOHz+Offv2oaysDA4ODhgxYgTq1q0rd2lERER6jwFIBz148AA7duxAamoqAKBFixYYPHgwLC0tZa6MiIjIMDAA6Zhr165h27ZtyMvLg6mpKfr374/OnTtzyYuIiKgKMQDpCCEEjhw5goMHD0IIAScnJ4wYMQLu7u5yl0ZERGRwGIB0QEFBAeLj43H16lUAQOvWrfHqq6/CwsJC5sqIiIgMEwOQzDIyMrB9+3bcu3cPtWrVwoABA9ChQwcueREREVUjWe+YeejQIQwaNAgeHh5QKBSIj49/av+goCAoFAq1R6tWrVR95s+fr/Z68+bNq3kk2isrK0NycjK+/vpr3Lt3D3Xq1MHEiRPxwgsvMPwQERFVM1lngAoKCtCuXTuMGzcOw4YNe2b/NWvWIDIyUvX84cOHaNeuHV577TVJv1atWmH//v2q57Vq6dZE17179xAXF4f09HQAQPv27TFgwACYm5vLXBkREZFxkDUZDBgwAAMGDKhwf6VSCaVSqXoeHx+Pu3fv4h//+IekX61ateDm5lZldValtLQ0xMXFoaCgAGZmZhg4cCDatWsnd1lERERGRbemRrS0fv16+Pr6on79+pL2K1euwMPDA5aWlvDx8UFERMRT75lVVFSEoqIi1fO8vLxqqfenn37CwYMHAQCurq4YMWIE6tSpUy3vRUREROWT9Rig53Hjxg3s2bMHEyZMkLR36dIFGzduxN69exEVFYX09HT07NkT9+7dK3dfERERqtklpVIJT0/PaqnZ0dERAHDtWke4uY1n+CEiIpKJQggh5C4CABQKBeLi4hAQEFCh/hEREVi1ahVu3Ljx1GNncnJyUL9+faxevRrjx4/X2EfTDJCnpydyc3Nhb2+v1Tiexd//TyQk1IWPD3D0aJXumoiIyKjl5eVBqVRW6PdbL5fAhBDYsGEDxowZ88wDhx0cHNC0aVPVNXY0sbCwqLFr7kyZUhd5eUBoaI28HREREWmgl0tgycnJuHr1arkzOn+Xn5+PtLQ0nbmi8uDBj2Z+Bg+WuxIiIiLjJWsAys/Px+nTp3H69GkAQHp6Ok6fPo3MzEwAQFhYGAIDA9W2W79+Pbp06YLWrVurvfbBBx8gOTkZGRkZOHr0KIYOHQpTU1OMGjWqWsdCRERE+kPWJbATJ06gT58+qufvv/8+AGDs2LHYuHEjsrKyVGHosdzcXHz33XdYs2aNxn1ev34do0aNwp07d+Ds7IwePXrg2LFjcHZ2rr6BEBERkV7RmYOgdYk2B1ERERGRbtDm91svjwEiIiIieh4MQERERGR0GICIiIjI6DAAERERkdFhACIiIiKjwwBERERERocBiIiIiIwOAxAREREZHQYgIiIiMjoMQERERGR0ZL0XmK56fHeQvLw8mSshIiKiinr8u12Ru3wxAGlw7949AICnp6fMlRAREZG27t27B6VS+dQ+vBmqBmVlZbhx4wbs7OygUCiqdN95eXnw9PTEtWvXDPJGq4Y+PoBjNASGPj7A8Mdo6OMDOMbKEELg3r178PDwgInJ04/y4QyQBiYmJqhXr161voe9vb3B/oUGDH98AMdoCAx9fIDhj9HQxwdwjNp61szPYzwImoiIiIwOAxAREREZHQagGmZhYYF58+bBwsJC7lKqhaGPD+AYDYGhjw8w/DEa+vgAjrG68SBoIiIiMjqcASIiIiKjwwBERERERocBiIiIiIwOAxAREREZHQag53Do0CEMGjQIHh4eUCgUiI+Pf2r/oKAgKBQKtUerVq1UfebPn6/2evPmzat5JJpFRESgc+fOsLOzg4uLCwICApCamvrM7bZu3YrmzZvD0tISbdq0we7duyWvCyEQHh4Od3d3WFlZwdfXF1euXKmuYTxVZcb45ZdfomfPnnB0dISjoyN8fX3x888/S/po+qz9/f2rcygaVWZ8GzduVKvd0tJS0kffP8PevXtr/C4OHDhQ1UdXPkMAiIqKQtu2bVUXi/Px8cGePXueuo0+fQ+1HZ8+fQcf03aM+vY91HZ8uvAdZAB6DgUFBWjXrh0+++yzCvVfs2YNsrKyVI9r167ByckJr732mqRfq1atJP0OHz5cHeU/U3JyMoKDg3Hs2DHs27cPJSUl6N+/PwoKCsrd5ujRoxg1ahTGjx+PU6dOISAgAAEBATh37pyqz/Lly7F27VqsW7cOx48fh42NDfz8/FBYWFgTw5KozBiTkpIwatQoJCYmIiUlBZ6enujfvz/+/PNPST9/f3/J57h58+bqHo6ayowPeHRV1r/X/scff0he1/fPcPv27ZLxnTt3DqampmrfRV34DAGgXr16iIyMxMmTJ3HixAn07dsXQ4YMwfnz5zX217fvobbj06fv4GPajhHQr++htuPTie+goCoBQMTFxWm1TVxcnFAoFCIjI0PVNm/ePNGuXbuqLa6K3Lp1SwAQycnJ5fZ5/fXXxcCBAyVtXbp0EW+//bYQQoiysjLh5uYmVqxYoXo9JydHWFhYiM2bN1dP4VqoyBif9PDhQ2FnZyc2bdqkahs7dqwYMmRINVT4fCoyvujoaKFUKst93RA/w48//ljY2dmJ/Px8VZuufoaPOTo6iq+++krja/r+PRTi6eN7kj59B//uaWPU9++hENp9hnJ8BzkDJKP169fD19cX9evXl7RfuXIFHh4eaNiwId566y1kZmbKVKFUbm4uAMDJyancPikpKfD19ZW0+fn5ISUlBQCQnp6O7OxsSR+lUokuXbqo+sipImN80v3791FSUqK2TVJSElxcXNCsWTNMnjwZd+7cqdJaK6Oi48vPz0f9+vXh6emp9l9xhvgZrl+/HiNHjoSNjY2kXRc/w9LSUsTGxqKgoAA+Pj4a++jz97Ai43uSPn0HgYqPUV+/h5X5DOX4DvJmqDK5ceMG9uzZg5iYGEl7ly5dsHHjRjRr1gxZWVlYsGABevbsiXPnzsHOzk6maoGysjKEhISge/fuaN26dbn9srOz4erqKmlzdXVFdna26vXHbeX1kUtFx/ikjz76CB4eHpJ/iPz9/TFs2DB4e3sjLS0Ns2bNwoABA5CSkgJTU9PqKP+ZKjq+Zs2aYcOGDWjbti1yc3OxcuVKdOvWDefPn0e9evUM7jP8+eefce7cOaxfv17Srmuf4dmzZ+Hj44PCwkLY2toiLi4OLVu21NhXH7+H2ozvSfryHdRmjPr4PazsZyjbd7Da5paMDLRcAlu6dKmoXbu2KCoqemq/u3fvCnt7+wpPI1aXd955R9SvX19cu3btqf3MzMxETEyMpO2zzz4TLi4uQgghjhw5IgCIGzduSPq89tpr4vXXX6/aorVU0TH+XUREhHB0dBS//fbbU/ulpaUJAGL//v3PW2alVWZ8QghRXFwsGjVqJObMmSOEMLzPcNKkSaJNmzbP7Cf3Z1hUVCSuXLkiTpw4IUJDQ0WdOnXE+fPnNfbVx++hNuP7O336DlZ2jELox/ewsuOT6zvIJTAZCCGwYcMGjBkzBubm5k/t6+DggKZNm+Lq1as1VJ26qVOnYteuXUhMTES9evWe2tfNzQ03b96UtN28eRNubm6q1x+3lddHDtqM8bGVK1ciMjISP/74I9q2bfvUvg0bNkSdOnVk+xwrM77HzMzM0KFDB1XthvQZFhQUIDY2FuPHj39mX7k/Q3NzczRu3BgdO3ZEREQE2rVrhzVr1mjsq4/fQ23G95g+fQeByo3xMX34HlZmfHJ+BxmAZJCcnIyrV69W6APPz89HWloa3N3da6AyKSEEpk6diri4OBw8eBDe3t7P3MbHxwcHDhyQtO3bt0+1Duzt7Q03NzdJn7y8PBw/frzCa8VVqTJjBB6dfbFo0SLs3bsXnTp1emb/69ev486dOzX+OVZ2fH9XWlqKs2fPqmo3lM8QeHSqeFFREUaPHv3MvnJ9huUpKytDUVGRxtf07XuoydPGB+jPd/BpnjXGv9P176EmFRmfrN/BKplHMlL37t0Tp06dEqdOnRIAxOrVq8WpU6fEH3/8IYQQIjQ0VIwZM0Ztu9GjR4suXbpo3OeMGTNEUlKSSE9PF0eOHBG+vr6iTp064tatW9U6Fk0mT54slEqlSEpKEllZWarH/fv3VX3GjBkjQkNDVc+PHDkiatWqJVauXCkuXrwo5s2bJ8zMzMTZs2dVfSIjI4WDg4PYsWOHOHPmjBgyZIjw9vYWDx48qNHxCVG5MUZGRgpzc3Oxbds2yTb37t0TQjz6e/HBBx+IlJQUkZ6eLvbv3y9eeOEF0aRJE1FYWKjz41uwYIFISEgQaWlp4uTJk2LkyJHC0tJSMpWt75/hYz169BBvvPGGWrsufYZCPPq3JDk5WaSnp4szZ86I0NBQoVAoxI8//iiE0P/vobbj06fvYGXHqG/fQ23H95ic30EGoOeQmJgoAKg9xo4dK4R4dApfr169JNvk5OQIKysr8cUXX2jc5xtvvCHc3d2Fubm5qFu3rnjjjTfE1atXq3kkmmkaGwARHR2t6tOrVy/VeB/79ttvRdOmTYW5ublo1aqV+OGHHySvl5WViblz5wpXV1dhYWEhXn75ZZGamloDI1JXmTHWr19f4zbz5s0TQghx//590b9/f+Hs7CzMzMxE/fr1xcSJE0V2dnbNDk5UbnwhISHCy8tLmJubC1dXV/HKK6+IX3/9VbJfff8MhRDi0qVLAoDqH+i/06XPUAghxo0bJ+rXry/Mzc2Fs7OzePnllyV16/v3UNvx6dN38DFtx6hv38PK/B2V+zuoEEKIqplLIiIiItIPPAaIiIiIjA4DEBERERkdBiAiIiIyOgxAREREZHQYgIiIiMjoMAARERGR0WEAIiIiIqPDAEREREQaHTp0CIMGDYKHhwcUCgXi4+Or9f0iIiLQuXNn2NnZwcXFBQEBAUhNTZX0KSwsRHBwMGrXrg1bW1sMHz5c7Z5oFcEAREQG6dq1axg3bhw8PDxgbm6O+vXr491338WdO3cAACUlJfjoo4/Qpk0b2NjYwMPDA4GBgbhx44bMlRPpjoKCArRr1w6fffZZjbxfcnIygoODcezYMezbtw8lJSXo378/CgoKVH3ee+89fP/999i6dSuSk5Nx48YNDBs2TOv34pWgicjg/P777/Dx8UHTpk2xePFieHt74/z58/jwww9RXFyMY8eOwdTUFCNGjMDEiRPRrl073L17F++++y5KS0tx4sQJuYdApHMUCgXi4uIQEBCgaisqKsLs2bOxefNm5OTkoHXr1li2bBl69+5dJe95+/ZtuLi4IDk5GS+99BJyc3Ph7OyMmJgYjBgxAgBw6dIltGjRAikpKejatWuF912rSiokItIhwcHBMDc3x48//ggrKysAgJeXFzp06IBGjRph9uzZiIqKwr59+yTbffrpp3jxxReRmZkJLy8vOUon0itTp07FhQsXEBsbCw8PD8TFxcHf3x9nz55FkyZNnnv/ubm5AAAnJycAwMmTJ1FSUgJfX19Vn+bNm8PLy0vrAMQlMCIyKP/73/+QkJCAKVOmqMLPY25ubnjrrbewZcsWaJr8zs3NhUKhgIODQw1VS6S/MjMzER0dja1bt6Jnz55o1KgRPvjgA/To0QPR0dHPvf+ysjKEhISge/fuaN26NQAgOzsb5ubmat9RV1dXZGdna7V/BiAiMihXrlyBEAItWrTQ+HqLFi1w9+5d3L59W9JeWFiIjz76CKNGjYK9vX1NlEqk186ePYvS0lI0bdoUtra2qkdycjLS0tIAPFqeUigUT32EhoZq3H9wcDDOnTuH2NjYaqmfS2BEZJC0ObyxpKQEr7/+OoQQiIqKqsaqiAxHfn4+TE1NcfLkSZiamkpes7W1BQA0bNgQFy9efOp+ateurdY2depU7Nq1C4cOHUK9evVU7W5ubiguLkZOTo5kFujmzZtwc3PTqn4GICIyKI0bN4ZCocDFixcxdOhQtdcvXrwIR0dHODs7A/gr/Pzxxx84ePAgZ3+IKqhDhw4oLS3FrVu30LNnT419zM3N0bx58wrvUwiBadOmIS4uDklJSfD29pa83rFjR5iZmeHAgQMYPnw4ACA1NRWZmZnw8fHRqn6eBUZEBsfPzw/nz5/HlStXJMcBZWdno1GjRggMDERUVJQq/Fy5cgWJiYmqUEREj+Tn5+Pq1asAHgWe1atXo0+fPnBycoKXlxdGjx6NI0eOYNWqVejQoQNu376NAwcOoG3bthg4cKDW7zdlyhTExMRgx44daNasmapdqVSqvsuTJ0/G7t27sXHjRtjb22PatGkAgKNHj2r3ZoKIyMBcvnxZ1KlTR/Ts2VMkJyeLzMxMsWfPHtG6dWvRpEkTcefOHVFcXCwGDx4s6tWrJ06fPi2ysrJUj6KiIrmHQKQTEhMTBQC1x9ixY4UQQhQXF4vw8HDRoEEDYWZmJtzd3cXQoUPFmTNnKvV+mt4LgIiOjlb1efDggZgyZYpwdHQU1tbWYujQoSIrK0vr9+IMEBEZpD/++APz5s3D3r178b///Q9ubm4ICAjAvHnzULt2bWRkZKhNrz+WmJhYZdcxISLdxABERERERoenwRMREZHRYQAiIiIio8MAREREREaHAYiIiIiMDgMQERERGR0GICIiIjI6DEBERERkdBiAiIiIyOgwABEREZHRYQAiIiIio8MAREREREaHAYiIiIiMzv8BY4LAtvOCEwYAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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lJaVMnebNmyskJERdu3bVlClTlJ+fr61bt9Z+ZwEA9UZWXpESV3yrUnPxdamRpqzY67aZII8NQIsXL1bTpk01bNgwp/JBgwbpnnvuUbdu3RQfH681a9bo1KlTWr58eYVtJSYmKi8vz7EdPnzYxb2/yB3TgMuXL1enTp0UGRmp++67TwsXLpQxpty6RUVFWrJkiSTJ29u71voIAKh/MnILHeHnkhJjdCj3jFv645E3QjTGaOHChbr//vuv+MPctGlTdezYUQcPHqywjo+Pj3x8fGq6m5eVuj3TkYS9bFLy8CiN7N3K5Z+7YMEC3XfffZKkgQMHKi8vT59//rn69+/vqHPTTTfJy8tLZ86ckTFGPXv21G233ebyvgEA6q82gY3lZZNTCLLbbIoIdM999TxyBujzzz/XwYMH9eCDD16xbkFBgdLT0+vUYxrcNQ24f/9+bdu2TaNHj5Z0cbH4yJEjtWDBAqd6qamp2r17tz788EO1b99eKSkpatiwoUv7BgCo30ID/JQ8PEr2///ecXabTbOGd1VogJ9b+uPWGaCCggKnmZmMjAzt2bNHzZo1U6tWrZSYmKijR486TsNcsmDBAsXExKhr165l2nzmmWc0ZMgQtW7dWseOHVNSUpLsdrvjR78uuNw0oCu/CAsWLNCFCxcUFhbmKDPGyMfHR3PnznWUhYeHq0OHDurQoYMuXLigu+66S3v37q31WTIAQP0ysncr3doxSIdyzygisJHbwo/k5hmgHTt2qEePHurRo4ckKSEhQT169NDUqVMlSVlZWWWu4MrLy9OHH35Y4ezPkSNHNHr0aEVGRmrEiBFq3ry5vvrqKwUFBbl2MFVwaRrwl1w9DXjhwgUtWbJEf/nLX7Rnzx7H9vXXXyssLExLly4td7+7775bDRo00N/+9jeX9Q0AYB2hAX6KbdfcreFHcvMMUP/+/StcgCup3CuUAgICdOZMxQumli1bVhNdc6lL04BTVuxViTG1Mg34z3/+Uz///LMefPBBBQQEOL33+9//XgsWLNDAgQPL7Gez2fTkk09q2rRpeuSRR3gGGgCgXvDINUD1wcjerbRp8gAtfehGbZo8wOULoC/dM+nX4Ue6GIB27NhR4f2Pxo0bp/PnzzudJgMAwJN55FVg9UVogF+tTQH+4x//qPC9Pn36OGbiypuRa9Sokf7v//7PZX0DAKC2MQMEAAAshwAEAAAshwAEAAAshwAEAAAshwBUTZe7fN9qOBYAAE9DAKqiS4+EuNy9iKzm0rHgcRkAAE/BZfBVZLfb1bRpUx0/flzSxUvEbTbbFfaqn4wxOnPmjI4fP66mTZvKbre7u0sAAFQKAagaQkJCJMkRgqyuadOmjmMCAIAnIABVg81mU2hoqFq0aKHz58+7uztu1bBhQ2Z+AAAehwB0Fex2Oz/+AAB4IBZBAwAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQBgcVl5RdqcnqusvCJ3d6XW8CwwAAAsLHV7phJXfKtSI3nZpOThURrZu5W7u+VyzAABAGBRWXlFjvAjSaVGmrJiryVmgghAAABYVEZuoSP8XFJijA7lnnFPh2oRAQgAAItqE9hYXjbnMrvNpojARu7pUC0iAAEAYFGhAX5KHh4lu+1iCrLbbJo1vKtCA/zc3DPXYxE0AAAWNrJ3K93aMUiHcs8oIrCRJcKPRAACAMDyQgP8LBN8LuEUGAAAsBwCEAAAsBwCEFAPWPEurgBwNVgDBHg4q97FFQCuBjNAgAez8l1cAeBqEIAAD2blu7gCwNUgAAEezMp3cQWAq0EAAjyYle/iCgBXg0XQgIez6l1cAeBquHUGaOPGjRoyZIjCwsJks9m0cuXKy9bfsGGDbDZbmS07O9up3rx58xQRESFfX1/FxMRo27ZtLhwF4H6hAX6Kbdec8AMAleTWAFRYWKjo6GjNmzevSvvt379fWVlZjq1FixaO91JTU5WQkKCkpCTt2rVL0dHRio+P1/Hjx2u6+wAAwEO59RTYoEGDNGjQoCrv16JFCzVt2rTc91577TU99NBDmjBhgiRp/vz5Wr16tRYuXKjJkydfTXcBAEA94ZGLoLt3767Q0FDdfvvt+vLLLx3l586d086dOxUXF+co8/LyUlxcnLZs2VJhe8XFxcrPz3faAABA/eVRASg0NFTz58/Xhx9+qA8//FDh4eHq37+/du3aJUnKzc1VSUmJgoODnfYLDg4us07ol5KTkxUQEODYwsPDXToOAADgXh51FVhkZKQiIyMdr2+66Salp6fr9ddf1zvvvFPtdhMTE5WQkOB4nZ+fTwgCAKAe86gAVJ4+ffpo06ZNkqTAwEDZ7Xbl5OQ41cnJyVFISEiFbfj4+MjHx8el/QQAAHWHR50CK8+ePXsUGhoqSfL29lbPnj2VlpbmeL+0tFRpaWmKjY11VxcBAEAd49YZoIKCAh08eNDxOiMjQ3v27FGzZs3UqlUrJSYm6ujRo1qyZIkkafbs2WrTpo26dOmis2fP6u9//7s+++wzrVu3ztFGQkKCxo0bp169eqlPnz6aPXu2CgsLHVeFAQAAuDUA7dixQwMGDHC8vrQOZ9y4cUpJSVFWVpYyMzMd7587d05PP/20jh49qkaNGqlbt2769NNPndoYOXKkTpw4oalTpyo7O1vdu3fX2rVryyyMBgAA1mUzxpgrV7OW/Px8BQQEKC8vT/7+/u7uDgAAqISq/H57/BogAACAqiIAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAARaWlVekzem5ysorcndXAKBWNXB3BwC4R+r2TCWu+FalRvKyScnDozSydyt3dwsAagUzQIAFZeUVOcKPJJUaacqKvcwEAbAMAhBgQRm5hY7wc0mJMTqUe8Y9HQKAWkYAAiyoTWBjedmcy+w2myICG7mnQwBQywhAgAWFBvgpeXiU7LaLKchus2nW8K4KDfBzc88AoHawCBqwqJG9W+nWjkE6lHtGEYGNCD8ALIUABFhYaIAfwQeAJXEKDAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWI5bA9DGjRs1ZMgQhYWFyWazaeXKlZetv2LFCt1+++0KCgqSv7+/YmNj9cknnzjVmTZtmmw2m9PWqVMnF44CAAB4GrcGoMLCQkVHR2vevHmVqr9x40bdfvvtWrNmjXbu3KkBAwZoyJAh2r17t1O9Ll26KCsry7Ft2rTJFd0HAAAeyq0PQx00aJAGDRpU6fqzZ892ej1r1iytWrVK//jHP9SjRw9HeYMGDRQSElJT3QQAAPWMR68BKi0t1enTp9WsWTOn8gMHDigsLExt27bVvffeq8zMzMu2U1xcrPz8fKcNAADUXx4dgF599VUVFBRoxIgRjrKYmBilpKRo7dq1evPNN5WRkaG+ffvq9OnTFbaTnJysgIAAxxYeHl4b3QcAAG5iM8YYd3dCkmw2mz766CMNGzasUvXfe+89PfTQQ1q1apXi4uIqrHfq1Cm1bt1ar732mh588MFy6xQXF6u4uNjxOj8/X+Hh4crLy5O/v3+VxgEAANwjPz9fAQEBlfr9dusaoOpatmyZJk6cqPfff/+y4UeSmjZtqo4dO+rgwYMV1vHx8ZGPj09NdxMAANRRHncKbOnSpZowYYKWLl2qwYMHX7F+QUGB0tPTFRoaWgu9AwAAnsCtM0AFBQVOMzMZGRnas2ePmjVrplatWikxMVFHjx7VkiVLJF087TVu3Di98cYbiomJUXZ2tiTJz89PAQEBkqRnnnlGQ4YMUevWrXXs2DElJSXJbrdr9OjRtT9AAABQJ7l1BmjHjh3q0aOH4xL2hIQE9ejRQ1OnTpUkZWVlOV3B9d///d+6cOGC/vjHPyo0NNSxPfXUU446R44c0ejRoxUZGakRI0aoefPm+uqrrxQUFFS7gwMAAHVWnVkEXZdUZREVAACoG6ry++1xa4AAAACuFgEIAABYDgHIA2TlFWlzeq6y8orc3RUAAOoFj7wPkJWkbs9U4opvVWokL5uUPDxKI3u3cne3AADwaMwA1WFZeUWO8CNJpUaasmIvM0EAAFwlAlAdlpFb6Ag/l5QYo0O5Z9zTIQAA6gkCUB3WJrCxvGzOZXabTRGBjdzTIQAA6gkCUB0WGuCn5OFRstsupiC7zaZZw7sqNMDPzT0DAMCzsQi6jhvZu5Vu7RikQ7lnFBHYiPADAEANIAB5gNAAP4IPAAA1iFNgAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAOAGPOQYcC8ugweAWsZDjgH3YwYIAGoRDzkG6gYCEADUInc95JhTboAzToGhjKy8ImXkFqpNYGPuQA3UsEsPOf5lCHL1Q4455QaUxQwQnKRuz9TNL32mMW9v1c0vfabU7Znu7hJQr9T2Q4455QaUjxkgOFT0h/LWjkHMBAE1qDYfcny5U278/xpWRgCCA38ogdpTWw85dscpN8ATcAoMDpf+UP4SfygBz1bbp9wAT8EMEBwu/aGcsmKvSozhDyVQT9TmKTfAUxCA4IQ/lED9VFun3ABPQQBCGfyhBADUd6wBAgAAlkMAAuoQ7tYLALWDU2BAHcHdegGg9jADBNQB3K0XAGoXAQioA9z1gEwAsCoCEFAHcBNKAKhdBCCgDuBuvQBQu1gEDdQR3IQSAGoPAQioQ7gJJQDUDk6BAQDKxX2pUJ8xAwQAKIP7UqG+YwYIAOCE+1LBCtwagDZu3KghQ4YoLCxMNptNK1euvOI+GzZs0A033CAfHx+1b99eKSkpZerMmzdPERER8vX1VUxMjLZt21bznQeAeor7UsEK3BqACgsLFR0drXnz5lWqfkZGhgYPHqwBAwZoz549mjRpkiZOnKhPPvnEUSc1NVUJCQlKSkrSrl27FB0drfj4eB0/ftxVwwCAeoX7UsEKbMYYc+Vqrmez2fTRRx9p2LBhFdZ57rnntHr1au3du9dRNmrUKJ06dUpr166VJMXExKh3796aO3euJKm0tFTh4eF64oknNHny5Er1JT8/XwEBAcrLy5O/v3/1BwUAHip1e6amrNirEmMc96ViDRB+LSuvSBm5hWoT2LhOXMFald9vj1oEvWXLFsXFxTmVxcfHa9KkSZKkc+fOaefOnUpMTHS87+Xlpbi4OG3ZsqXCdouLi1VcXOx4nZ+fX7MdBwAPw32pcCWevlDeoxZBZ2dnKzg42KksODhY+fn5KioqUm5urkpKSsqtk52dXWG7ycnJCggIcGzh4eEu6T8AeJLQAD/FtmtO+EEZ9WGhvEcFIFdJTExUXl6eYzt8+LC7uwQAQJ1VHxbKe9QpsJCQEOXk5DiV5eTkyN/fX35+frLb7bLb7eXWCQkJqbBdHx8f+fj4uKTPAADUN5cWyv8yBHnaQnmPmgGKjY1VWlqaU9n69esVGxsrSfL29lbPnj2d6pSWliotLc1RBwAAXJ368ABnt84AFRQU6ODBg47XGRkZ2rNnj5o1a6ZWrVopMTFRR48e1ZIlSyRJjz76qObOnas///nPeuCBB/TZZ59p+fLlWr16taONhIQEjRs3Tr169VKfPn00e/ZsFRYWasKECbU+PgAA6itPXyjv1gC0Y8cODRgwwPE6ISFBkjRu3DilpKQoKytLmZmZjvfbtGmj1atX609/+pPeeOMNXXfddfr73/+u+Ph4R52RI0fqxIkTmjp1qrKzs9W9e3etXbu2zMJoAABwdTz5Ac515j5AdQn3AQIAwPNU5ffbo9YAAQAAz5aVV6TN6bluv2Teo64CAwAAnqsu3TyRGSAAAOByde3miQQgAADgcnXt5okEIAAA4HKXbp74S+68eWKNBaCzZ8/q1VdfranmAABAPVLXbp5YpcvgT5w4oa1bt8rb21u33Xab7Ha7zp8/r7/97W9KTk7WhQsXlJub68r+1gougwcAwDWy8opcdvPEqvx+V/oqsE2bNum3v/2t8vPzZbPZ1KtXLy1atEjDhg1TgwYNNG3aNI0bN+6qOw8AAOqvunLzxEqfAnv++ed155136ptvvlFCQoK2b9+uu+66S7NmzdL333+vRx99VH5+7h8QAADAlVT6FFjz5s31xRdfqHPnzioqKlKTJk20YsUKDR061NV9rHWcAgMAwPO45E7QP//8swIDAyVJfn5+atSokbp27Xp1PQUAAHCDKt0J+vvvv1d2drYkyRij/fv3q7Cw0KlOt27daq53AAAALlDpU2BeXl6y2Wy6XHWbzaaSkpIa65y7cAoMAADP45KrwDIyMq5Y5/Tp05VtDgAAwG0qHYBat25dbvnp06e1dOlSLViwQDt27KgXM0AAgOrLyitSRm6h2gQ2rhOXOwPlqfbT4Ddu3KgFCxboww8/VFhYmIYPH665c+fWZN8AAB6mLj3tG7icKgWg7OxspaSkaMGCBcrPz9eIESNUXFyslStXqnPnzq7qIwDAA1T0tO9bOwYxE4Q6p9KXwQ8ZMkSRkZH65ptvNHv2bB07dkxz5sxxZd8AAB6krj3tG7icSs8A/e///q+efPJJPfbYY+rQoYMr+wQA8ECXnvb9yxDkzqd9A5dT6RmgTZs26fTp0+rZs6diYmI0d+7cevHgUwBAzahrT/sGLqdKT4OXpMLCQqWmpmrhwoXatm2bSkpK9Nprr+mBBx7QNddc46p+1iruAwQA1efKp30Dl1OV3+8qB6Bf2r9/vxYsWKB33nlHp06d0u23366PP/64us3VGQQgAABcw5W3SXDJs8DKExkZqVdeeUVHjhzR0qVLr6YpAABQz6Vuz9TNL32mMW9v1c0vfabU7Zlu68tVzQDVV8wAAQBQs7LyinTzS5+VWSS/afKAGpsJqrUZIAAAgMqoa7dJIAABAACXu3SbhF9y520SCEAAAMDl6tptEqr9LDAAAICqGNm7lW7tGFQnbpNAAAIAALUmNMCvTtwfilNgAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAACPlZVXpM3pucrKK3J3V+BhuBM0AMAjpW7PVOKKb1VqJC+blDw8SiN7t3J3t+Ah6sQM0Lx58xQRESFfX1/FxMRo27ZtFdbt37+/bDZbmW3w4MGOOuPHjy/z/sCBA2tjKACAWpCVV+QIP5JUaqQpK/YyE4RKc/sMUGpqqhISEjR//nzFxMRo9uzZio+P1/79+9WiRYsy9VesWKFz5845Xp88eVLR0dG65557nOoNHDhQixYtcrz28fFx3SAAALUqI7fQEX4uKTFGh3LP1InnTKHuc/sM0GuvvaaHHnpIEyZMUOfOnTV//nw1atRICxcuLLd+s2bNFBIS4tjWr1+vRo0alQlAPj4+TvWuvfba2hgOAKAWtAlsLC+bc5ndZlNEYCP3dAgex60B6Ny5c9q5c6fi4uIcZV5eXoqLi9OWLVsq1caCBQs0atQoNW7c2Kl8w4YNatGihSIjI/XYY4/p5MmTFbZRXFys/Px8pw3wJCwEhdWEBvgpeXiU7LaLKchus2nW8K7M/qDS3HoKLDc3VyUlJQoODnYqDw4O1g8//HDF/bdt26a9e/dqwYIFTuUDBw7U8OHD1aZNG6Wnp2vKlCkaNGiQtmzZIrvdXqad5ORkTZ8+/eoGA7gJC0FhVSN7t9KtHYN0KPeMIgIbEX5QJW5fA3Q1FixYoKioKPXp08epfNSoUY5/R0VFqVu3bmrXrp02bNig2267rUw7iYmJSkhIcLzOz89XeHi46zoO1JCKFoLe2jGIHwNYQmiAH991VItbT4EFBgbKbrcrJyfHqTwnJ0chISGX3bewsFDLli3Tgw8+eMXPadu2rQIDA3Xw4MFy3/fx8ZG/v7/TBniCyy0EBQBUzK0ByNvbWz179lRaWpqjrLS0VGlpaYqNjb3svu+//76Ki4t13333XfFzjhw5opMnTyo0NPSq+wzUJSwEBYDqcftVYAkJCXr77be1ePFi7du3T4899pgKCws1YcIESdLYsWOVmJhYZr8FCxZo2LBhat68uVN5QUGBnn32WX311Vc6dOiQ0tLSNHToULVv317x8fG1MiagtrAQFACqx+1rgEaOHKkTJ05o6tSpys7OVvfu3bV27VrHwujMzEx5eTnntP3792vTpk1at25dmfbsdru++eYbLV68WKdOnVJYWJjuuOMOzZw5k3sBoV5iISgAVJ3NGGOuXM1a8vPzFRAQoLy8PNYDAQDgIary++32U2AAAAC1jQAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAKhRWXlF2pyeq6y8Ind3BaiQ258FBgCoP1K3ZypxxbcqNZKXTUoeHqWRvVu5u1tAGcwAAQBqRFZekSP8SFKpkaas2MtMEOokAhAAoEZk5BY6ws8lJcboUO4Z93QIuAwCEACgRrQJbCwvm3OZ3WZTRGAj93QIuAwCEACgRoQG+Cl5eJTstospyG6zadbwrgoN8HNzz4CyWAQNAKgxI3u30q0dg3Qo94wiAhsRflBnEYAAADUqNMCP4IM6j1NgAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcghAAADAcupEAJo3b54iIiLk6+urmJgYbdu2rcK6KSkpstlsTpuvr69THWOMpk6dqtDQUPn5+SkuLk4HDhxw9TAAAICHcHsASk1NVUJCgpKSkrRr1y5FR0crPj5ex48fr3Aff39/ZWVlObaffvrJ6f1XXnlFf/3rXzV//nxt3bpVjRs3Vnx8vM6ePevq4QAAAA/g9gD02muv6aGHHtKECRPUuXNnzZ8/X40aNdLChQsr3MdmsykkJMSxBQcHO94zxmj27Nl6/vnnNXToUHXr1k1LlizRsWPHtHLlyloYEQAAqOvcGoDOnTunnTt3Ki4uzlHm5eWluLg4bdmypcL9CgoK1Lp1a4WHh2vo0KH67rvvHO9lZGQoOzvbqc2AgADFxMRU2GZxcbHy8/OdNgAAUH+5NQDl5uaqpKTEaQZHkoKDg5WdnV3uPpGRkVq4cKFWrVqld999V6Wlpbrpppt05MgRSXLsV5U2k5OTFRAQ4NjCw8OvdmgAAKAOc/spsKqKjY3V2LFj1b17d/Xr108rVqxQUFCQ3nrrrWq3mZiYqLy8PMd2+PDhGuwxAACoa9wagAIDA2W325WTk+NUnpOTo5CQkEq10bBhQ/Xo0UMHDx6UJMd+VWnTx8dH/v7+ThsAAKi/3BqAvL291bNnT6WlpTnKSktLlZaWptjY2Eq1UVJSom+//VahoaGSpDZt2igkJMSpzfz8fG3durXSbQIAgPqtgbs7kJCQoHHjxqlXr17q06ePZs+ercLCQk2YMEGSNHbsWLVs2VLJycmSpBkzZujGG29U+/btderUKf3Xf/2XfvrpJ02cOFHSxSvEJk2apBdffFEdOnRQmzZt9MILLygsLEzDhg1z1zABAEAd4vYANHLkSJ04cUJTp05Vdna2unfvrrVr1zoWMWdmZsrL6/9NVP3888966KGHlJ2drWuvvVY9e/bU5s2b1blzZ0edP//5zyosLNTDDz+sU6dO6ZZbbtHatWvL3DARAABYk80YY9zdibomPz9fAQEBysvLYz0QAAAeoiq/3x53FRgAAMDVIgABAADLIQABAADLIQABAADLIQDVsqy8Im1Oz1VWXpG7uwIAgGW5/TJ4K0ndnqnEFd+q1EheNil5eJRG9m7l7m4BAGA5zADVkqy8Ikf4kaRSI01ZsZeZIAAA3IAAVEsycgsd4eeSEmN0KPeMezoEAICFEYBqSZvAxvKyOZfZbTZFBDZyT4cAALAwAlAtCQ3wU/LwKNltF1OQ3WbTrOFdFRrg5+aeAQBgPSyCrkUje7fSrR2DdCj3jCICGxF+AABwEwJQLQsN8CP4AADgZpwCAwAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAiwsK69Im9NzlZVX5O6uAECtauDuDgBwj9TtmUpc8a1KjeRlk5KHR2lk71bu7hYA1ApmgAALysorcoQfSSo10pQVe5kJAmAZBCDAgjJyCx3h55ISY3Qo94x7OgQAtYwABFhQm8DG8rI5l9ltNkUENnJPhwCglhGAAAsKDfBT8vAo2W0XU5DdZtOs4V0VGuDn5p4BQO2oEwFo3rx5ioiIkK+vr2JiYrRt27YK67799tvq27evrr32Wl177bWKi4srU3/8+PGy2WxO28CBA109DMCjjOzdSpsmD9DSh27UpskDWAANwFLcHoBSU1OVkJCgpKQk7dq1S9HR0YqPj9fx48fLrb9hwwaNHj1a//rXv7RlyxaFh4frjjvu0NGjR53qDRw4UFlZWY5t6dKltTEcwKOEBvgptl1zZn4AWI7NGGOuXM11YmJi1Lt3b82dO1eSVFpaqvDwcD3xxBOaPHnyFfcvKSnRtddeq7lz52rs2LGSLs4AnTp1SitXrqxWn/Lz8xUQEKC8vDz5+/tXqw0AAFC7qvL77dYZoHPnzmnnzp2Ki4tzlHl5eSkuLk5btmypVBtnzpzR+fPn1axZM6fyDRs2qEWLFoqMjNRjjz2mkydPVthGcXGx8vPznTYAAFB/uTUA5ebmqqSkRMHBwU7lwcHBys7OrlQbzz33nMLCwpxC1MCBA7VkyRKlpaXp5Zdf1ueff65BgwappKSk3DaSk5MVEBDg2MLDw6s/KAAAUOd59J2gX3rpJS1btkwbNmyQr6+vo3zUqFGOf0dFRalbt25q166dNmzYoNtuu61MO4mJiUpISHC8zs/PJwQBAFCPuXUGKDAwUHa7XTk5OU7lOTk5CgkJuey+r776ql566SWtW7dO3bp1u2zdtm3bKjAwUAcPHiz3fR8fH/n7+zttAACg/nJrAPL29lbPnj2VlpbmKCstLVVaWppiY2Mr3O+VV17RzJkztXbtWvXq1euKn3PkyBGdPHlSoaGhNdJvAADg2dx+GXxCQoLefvttLV68WPv27dNjjz2mwsJCTZgwQZI0duxYJSYmOuq//PLLeuGFF7Rw4UJFREQoOztb2dnZKigokCQVFBTo2Wef1VdffaVDhw4pLS1NQ4cOVfv27RUfH++WMQIAgLrF7WuARo4cqRMnTmjq1KnKzs5W9+7dtXbtWsfC6MzMTHl5/b+c9uabb+rcuXO6++67ndpJSkrStGnTZLfb9c0332jx4sU6deqUwsLCdMcdd2jmzJny8fGp1bEBAIC6ye33AaqLuA8QAACex2PuAwQAAOAOBCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAAGA5BCAAtSIrr0ib03OVlVfk7q4AgBq4uwMA6r/U7ZlKXPGtSo3kZZOSh0dpZO9W7u4WAAtjBgiAS2XlFTnCjySVGmnKir3MBAFwKwIQAJfKyC10hJ9LSozRodwz7ukQAIgABMDF2gQ2lpfNucxusykisJF7OgQAIgABcLHQAD8lD4+S3XYxBdltNs0a3lWhAX5u7hkAK2MRNACXG9m7lW7tGKRDuWcUEdiI8APA7QhAAGpFaIAfwQdAncEpMAAAYDkEIAAAYDkEIAAAYDl1IgDNmzdPERER8vX1VUxMjLZt23bZ+u+//746deokX19fRUVFac2aNU7vG2M0depUhYaGys/PT3FxcTpw4IArhwAAADyI2wNQamqqEhISlJSUpF27dik6Olrx8fE6fvx4ufU3b96s0aNH68EHH9Tu3bs1bNgwDRs2THv37nXUeeWVV/TXv/5V8+fP19atW9W4cWPFx8fr7NmztTUsAABQh9mMMebK1VwnJiZGvXv31ty5cyVJpaWlCg8P1xNPPKHJkyeXqT9y5EgVFhbqn//8p6PsxhtvVPfu3TV//nwZYxQWFqann35azzzzjCQpLy9PwcHBSklJ0ahRo67Yp/z8fAUEBCgvL0/+/v41NFIAAOBKVfn9dusM0Llz57Rz507FxcU5yry8vBQXF6ctW7aUu8+WLVuc6ktSfHy8o35GRoays7Od6gQEBCgmJqbCNgEAgLW49T5Aubm5KikpUXBwsFN5cHCwfvjhh3L3yc7OLrd+dna24/1LZRXV+bXi4mIVFxc7Xufn51dtIAAAwKO4fQ1QXZCcnKyAgADHFh4e7u4uAQAAF3JrAAoMDJTdbldOTo5TeU5OjkJCQsrdJyQk5LL1L/1vVdpMTExUXl6eYzt8+HC1xgMAADyDWwOQt7e3evbsqbS0NEdZaWmp0tLSFBsbW+4+sbGxTvUlaf369Y76bdq0UUhIiFOd/Px8bd26tcI2fXx85O/v77QBAID6y+3PAktISNC4cePUq1cv9enTR7Nnz1ZhYaEmTJggSRo7dqxatmyp5ORkSdJTTz2lfv366S9/+YsGDx6sZcuWaceOHfrv//5vSZLNZtOkSZP04osvqkOHDmrTpo1eeOEFhYWFadiwYZXq06UL41gLBACA57j0u12pC9xNHTBnzhzTqlUr4+3tbfr06WO++uorx3v9+vUz48aNc6q/fPly07FjR+Pt7W26dOliVq9e7fR+aWmpeeGFF0xwcLDx8fExt912m9m/f3+l+3P48GEjiY2NjY2Njc0Dt8OHD1/xt97t9wGqi0pLS3Xs2DFdc801stlsV9VWfn6+wsPDdfjwYcueWuMYcAwkjoHEMZA4BhLHwJXjN8bo9OnTCgsLk5fX5Vf5uP0UWF3k5eWl6667rkbbZG0Rx0DiGEgcA4ljIHEMJI6Bq8YfEBBQqXpcBg8AACyHAAQAACyHAORiPj4+SkpKko+Pj7u74jYcA46BxDGQOAYSx0DiGNSV8bMIGgAAWA4zQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQNUwb948RUREyNfXVzExMdq2bdtl68+ePVuRkZHy8/NTeHi4/vSnP+ns2bOO96dNmyabzea0derUydXDuCpVOQbnz5/XjBkz1K5dO/n6+io6Olpr1669qjbdrabH72nfgY0bN2rIkCEKCwuTzWbTypUrr7jPhg0bdMMNN8jHx0ft27dXSkpKmTqe9B1wxTGo79+DrKwsjRkzRh07dpSXl5cmTZpUbr33339fnTp1kq+vr6KiorRmzZqa73wNccUxSElJKfM98PX1dc0AakBVj8GKFSt0++23KygoSP7+/oqNjdUnn3xSpp6r/x4QgKooNTVVCQkJSkpK0q5duxQdHa34+HgdP3683PrvvfeeJk+erKSkJO3bt08LFixQamqqpkyZ4lSvS5cuysrKcmybNm2qjeFUS1WPwfPPP6+33npLc+bM0ffff69HH31Ud911l3bv3l3tNt3JFeOXPOs7UFhYqOjoaM2bN69S9TMyMjR48GANGDBAe/bs0aRJkzRx4kSnP3qe9B2QXHMMpPr9PSguLlZQUJCef/55RUdHl1tn8+bNGj16tB588EHt3r1bw4YN07Bhw7R3796a7HqNccUxkC7eJfmX34Offvqpprpc46p6DDZu3Kjbb79da9as0c6dOzVgwAANGTKk9n8TKv2EUBhjjOnTp4/54x//6HhdUlJiwsLCTHJycrn1//jHP5rf/OY3TmUJCQnm5ptvdrxOSkoy0dHRLumvK1T1GISGhpq5c+c6lQ0fPtzce++91W7TnVwxfk/7DvySJPPRRx9dts6f//xn06VLF6eykSNHmvj4eMdrT/oO/FpNHYP6/j34pX79+pmnnnqqTPmIESPM4MGDncpiYmLMI488cpU9dL2aOgaLFi0yAQEBNdav2lTVY3BJ586dzfTp0x2va+PvATNAVXDu3Dnt3LlTcXFxjjIvLy/FxcVpy5Yt5e5z0003aefOnY6pux9//FFr1qzRnXfe6VTvwIEDCgsLU9u2bXXvvfcqMzPTdQO5CtU5BsXFxWWmb/38/Bz/ZVudNt3FFeO/xFO+A9WxZcsWp2MmSfHx8Y5j5knfgeq60jG4pD5/DyqjssepvisoKFDr1q0VHh6uoUOH6rvvvnN3l1ymtLRUp0+fVrNmzSTV3t8DAlAV5ObmqqSkRMHBwU7lwcHBys7OLnefMWPGaMaMGbrlllvUsGFDtWvXTv3793c6BRYTE6OUlBStXbtWb775pjIyMtS3b1+dPn3apeOpjuocg/j4eL322ms6cOCASktLtX79eq1YsUJZWVnVbtNdXDF+ybO+A9WRnZ1d7jHLz89XUVGRR30HqutKx0Cq/9+DyqjoONWX70FlREZGauHChVq1apXeffddlZaW6qabbtKRI0fc3TWXePXVV1VQUKARI0ZIqr3fBAKQi23YsEGzZs3S3/72N+3atUsrVqzQ6tWrNXPmTEedQYMG6Z577lG3bt0UHx+vNWvW6NSpU1q+fLkbe15z3njjDXXo0EGdOnWSt7e3Hn/8cU2YMEFeXtb4+lVm/PX9O4DK4XsASYqNjdXYsWPVvXt39evXTytWrFBQUJDeeustd3etxr333nuaPn26li9frhYtWtTqZ1vjF6iGBAYGym63Kycnx6k8JydHISEh5e7zwgsv6P7779fEiRMVFRWlu+66S7NmzVJycrJKS0vL3adp06bq2LGjDh48WONjuFrVOQZBQUFauXKlCgsL9dNPP+mHH35QkyZN1LZt22q36S6uGH956vJ3oDpCQkLKPWb+/v7y8/PzqO9AdV3pGJSnvn0PKqOi41RfvgfV0bBhQ/Xo0aPefQ+WLVumiRMnavny5U6nu2rr7wEBqAq8vb3Vs2dPpaWlOcpKS0uVlpam2NjYcvc5c+ZMmZkOu90uSTIVPIatoKBA6enpCg0NraGe15zqHINLfH191bJlS124cEEffvihhg4detVt1jZXjL88dfk7UB2xsbFOx0yS1q9f7zhmnvQdqK4rHYPy1LfvQWVU5zjVdyUlJfr222/r1fdg6dKlmjBhgpYuXarBgwc7vVdrfw9qbDm1RSxbtsz4+PiYlJQU8/3335uHH37YNG3a1GRnZxtjjLn//vvN5MmTHfWTkpLMNddcY5YuXWp+/PFHs27dOtOuXTszYsQIR52nn37abNiwwWRkZJgvv/zSxMXFmcDAQHP8+PFaH19lVPUYfPXVV+bDDz806enpZuPGjeY3v/mNadOmjfn5558r3WZd4orxe9p34PTp02b37t1m9+7dRpJ57bXXzO7du81PP/1kjDFm8uTJ5v7773fU//HHH02jRo3Ms88+a/bt22fmzZtn7Ha7Wbt2raOOJ30HjHHNMajv3wNjjKN+z549zZgxY8zu3bvNd99953j/yy+/NA0aNDCvvvqq2bdvn0lKSjINGzY03377ba2OrbJccQymT59uPvnkE5Oenm527txpRo0aZXx9fZ3q1CVVPQb/8z//Yxo0aGDmzZtnsrKyHNupU6ccdWrj7wEBqBrmzJljWrVqZby9vU2fPn3MV1995XivX79+Zty4cY7X58+fN9OmTTPt2rUzvr6+Jjw83PzhD39w+vEbOXKkCQ0NNd7e3qZly5Zm5MiR5uDBg7U4oqqryjHYsGGDuf76642Pj49p3ry5uf/++83Ro0er1GZdU9Pj97TvwL/+9S8jqcx2adzjxo0z/fr1K7NP9+7djbe3t2nbtq1ZtGhRmXY96TvgimNghe9BefVbt27tVGf58uWmY8eOxtvb23Tp0sWsXr26dgZUDa44BpMmTXL8/yA4ONjceeedZteuXbU3qCqq6jHo16/fZetf4uq/BzZjKjgPAwAAUE+xBggAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAAFgOAQgAPMCGDRtks9l06tQpd3cFqBcIQACcjB8/XjabTS+99JJT+cqVK2Wz2RyvjTF6++23FRsbK39/fzVp0kRdunTRU089VemHNp45c0aJiYlq166dfH19FRQUpH79+mnVqlWOOhEREZo9e3aNjM3VLh07m82mhg0bqk2bNvrzn/+ss2fPVqmd/v37a9KkSU5lN910k7KyshQQEFCDPQasiwAEoAxfX1+9/PLL+vnnn8t93xijMWPG6Mknn9Sdd96pdevW6fvvv9eCBQvk6+urF198sVKf8+ijj2rFihWaM2eOfvjhB61du1Z33323Tp48WZPDqVUDBw5UVlaWfvzxR73++ut66623lJSUdNXtent7KyQkxCmEArgKNfpgDQAeb9y4cea3v/2t6dSpk3n22Wcd5R999JG59Cdj6dKlRpJZtWpVuW2UlpZW6rMCAgJMSkpKhe+X98ygS7744gtzyy23GF9fX3PdddeZJ554whQUFDjeX7JkienZs6dp0qSJCQ4ONqNHjzY5OTmO9y89v2jt2rWme/fuxtfX1wwYMMDk5OSYNWvWmE6dOplrrrnGjB492hQWFlZqPOPGjTNDhw51Khs+fLjp0aOH43Vubq4ZNWqUCQsLM35+fqZr167mvffec2rj12POyMhw9PeXzxH84IMPTOfOnY23t7dp3bq1efXVVyvVTwDGMAMEoAy73a5Zs2Zpzpw5OnLkSJn3ly5dqsjISP3ud78rd//KzlKEhIRozZo1On36dLnvr1ixQtddd51mzJihrKwsZWVlSZLS09M1cOBA/f73v9c333yj1NRUbdq0SY8//rhj3/Pnz2vmzJn6+uuvtXLlSh06dEjjx48v8xnTpk3T3LlztXnzZh0+fFgjRozQ7Nmz9d5772n16tVat26d5syZU6nx/NrevXu1efNmeXt7O8rOnj2rnj17avXq1dq7d68efvhh3X///dq2bZsk6Y033lBsbKweeughx5jDw8PLtL1z506NGDFCo0aN0rfffqtp06bphRdeUEpKSrX6CliOuxMYgLrll7MYN954o3nggQeMMc4zQJ06dTK/+93vnPZ76qmnTOPGjU3jxo1Ny5YtK/VZn3/+ubnuuutMw4YNTa9evcykSZPMpk2bnOq0bt3avP76605lDz74oHn44Yedyr744gvj5eVlioqKyv2s7du3G0nm9OnTxpj/NwP06aefOuokJycbSSY9Pd1R9sgjj5j4+PhKjWfcuHHGbrebxo0bGx8fHyPJeHl5mQ8++OCy+w0ePNg8/fTTjtf9+vUzTz31lFOdX88AjRkzxtx+++1OdZ599lnTuXPnSvUVsDpmgABU6OWXX9bixYu1b9++K9b9j//4D+3Zs0dTp05VQUFBpdq/9dZb9eOPPyotLU133323vvvuO/Xt21czZ8687H5ff/21UlJS1KRJE8cWHx+v0tJSZWRkSLo4QzJkyBC1atVK11xzjfr16ydJyszMdGqrW7dujn8HBwerUaNGatu2rVPZ8ePHKzUeSRowYID27NmjrVu3aty4cZowYYJ+//vfO94vKSnRzJkzFRUVpWbNmqlJkyb65JNPyvTrSvbt26ebb77Zqezmm2/WgQMHVFJSUqW2ACsiAAGo0K233qr4+HglJiY6lXfo0EH79+93KgsKClL79u3VokWLKn1Gw4YN1bdvXz333HNat26dZsyYoZkzZ+rcuXMV7lNQUKBHHnlEe/bscWxff/21Dhw4oHbt2qmwsFDx8fHy9/fX//zP/2j79u366KOPJKlMuw0bNnT8+9LVW79ks9lUWlpa6fE0btxY7du3V3R0tBYuXKitW7dqwYIFjvf/67/+S2+88Yaee+45/etf/9KePXsUHx9/2fECqHkN3N0BAHXbSy+9pO7duysyMtJRNnr0aI0ZM0arVq3S0KFDa/TzOnfurAsXLujs2bPy9vaWt7d3mRmNG264Qd9//73at29fbhvffvutTp48qZdeesmxfmbHjh012s/K8PLy0pQpU5SQkKAxY8bIz89PX375pYYOHar77rtPklRaWqp///vf6ty5s2O/8sb8a9dff72+/PJLp7Ivv/xSHTt2lN1ur/nBAPUMM0AALisqKkr33nuv/vrXvzrKRo0apbvvvlujRo3SjBkztHXrVh06dEiff/65UlNTK/0D3L9/f7311lvauXOnDh06pDVr1mjKlCkaMGCA/P39JV28D9DGjRt19OhR5ebmSpKee+45bd68WY8//rj27NmjAwcOaNWqVY5F0K1atZK3t7fmzJmjH3/8UR9//PEVT6u5yj333CO73a558+ZJujh7tn79em3evFn79u3TI488opycHKd9IiIiHMc0Nze33Bmop59+WmlpaZo5c6b+/e9/a/HixZo7d66eeeaZWhkX4OkIQACuaMaMGU4/wjabTampqZo9e7bWrFmj2267TZGRkXrggQcUHh6uTZs2Vard+Ph4LV68WHfccYeuv/56PfHEE4qPj9fy5cudPvvQoUNq166dgoKCJF1ct/P555/r3//+t/r27asePXpo6tSpCgsLk3TxdFxKSoref/99de7cWS+99JJeffXVGjwildegQQM9/vjjeuWVV1RYWKjnn39eN9xwg+Lj49W/f3+FhIRo2LBhTvs888wzstvt6ty5s4KCgspdH3TDDTdo+fLlWrZsmbp27aqpU6dqxowZ5V7pBqAsmzHGuLsTAAAAtYkZIAAAYDkEIAAu88vL1H+9ffHFF+7uXpVkZmZedjxVvYwdgHtxCgyAy1zuoagtW7aUn59fLfbm6ly4cEGHDh2q8P2IiAg1aMCFtYCnIAABAADL4RQYAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwnP8PyEQkbvPxHp4AAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# visualize with IDAES surrogate plotting tools\n", "surrogate_scatter2D(\n", @@ -364,7 +2093,79 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2567\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 80\n", + "\n", + "Total number of variables............................: 231\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 192\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 231\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.02e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.37e+03 4.81e+01 -1.0 2.05e+04 - 1.35e-02 6.69e-01f 1\n", + " 2 0.0000000e+00 5.15e-02 8.03e+00 -1.0 7.48e+03 - 8.25e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 3.65e-05 2.29e+00 -1.0 1.89e+02 - 6.49e-01 1.00e+00h 1\n", + " 4 0.0000000e+00 1.82e-12 5.38e-16 -1.0 1.51e-01 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 4\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 6.0616859024126514e-15 1.8189894035458565e-12\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 6.0616859024126514e-15 1.8189894035458565e-12\n", + "\n", + "\n", + "Number of objective function evaluations = 5\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 5\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 5\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 4\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + } + ], "source": [ "# create the IDAES model and flowsheet\n", "m = ConcreteModel()\n", @@ -443,7 +2244,27 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Steam flowrate = 0.6068368589186967\n", + "Reformer duty = 20991.593615005655\n", + "Mole Fraction Ar = 0.0036876627716784078\n", + "Mole Fraction C2H6 = 0.0041922326063124704\n", + "Mole Fraction C3H8 = 0.000527814905073567\n", + "Mole Fraction C4H10 = 0.0009169578471303844\n", + "Mole Fraction CH4 = 0.12842550728190893\n", + "Mole Fraction CO = 0.09715611233240133\n", + "Mole Fraction CO2 = 0.04595020144165076\n", + "Mole Fraction H2 = 0.2943636734533869\n", + "Mole Fraction H2O = 0.1200063881622875\n", + "Mole Fraction N2 = 0.3067149530501456\n", + "Mole Fraction O2 = 2.4843636525024052e-20\n" + ] + } + ], "source": [ "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", @@ -474,7 +2295,132 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2569\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 80\n", + "\n", + "Total number of variables............................: 233\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 194\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 231\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 -2.9436367e-01 1.82e-12 7.28e-04 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -2.9565413e-01 1.38e-04 9.97e-03 -1.0 2.30e+02 - 1.00e+00 1.00e+00f 1\n", + " 2 -2.9616875e-01 3.54e-05 4.93e-03 -2.5 1.30e+02 - 1.00e+00 1.00e+00h 1\n", + " 3 -3.0856563e-01 1.70e-02 1.13e-02 -3.8 3.16e+03 - 8.54e-01 1.00e+00f 1\n", + " 4 -3.2143524e-01 2.18e-02 8.85e-03 -3.8 6.31e+03 - 1.00e+00 6.80e-01h 1\n", + " 5 -3.2594841e-01 1.17e-02 4.78e-02 -3.8 3.81e+03 - 1.00e+00 5.71e-01h 1\n", + " 6 -3.2913975e-01 1.75e-02 8.33e-02 -3.8 4.13e+03 - 8.12e-01 1.00e+00h 1\n", + " 7 -3.2865783e-01 1.30e-03 1.59e-04 -3.8 8.47e+02 - 1.00e+00 1.00e+00h 1\n", + " 8 -3.3140527e-01 5.99e-03 7.08e-03 -5.7 3.65e+03 - 7.63e-01 7.29e-01h 1\n", + " 9 -3.3257325e-01 4.36e-03 5.17e-02 -5.7 2.76e+03 - 9.76e-01 4.27e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 -3.3247988e-01 1.96e-05 4.33e-02 -5.7 1.10e+02 - 8.60e-01 1.00e+00h 1\n", + " 11 -3.3249189e-01 1.38e-07 2.73e-07 -5.7 1.37e+01 - 1.00e+00 1.00e+00h 1\n", + " 12 -3.3250034e-01 1.45e-08 4.60e-04 -8.6 5.26e+00 - 1.00e+00 9.68e-01h 1\n", + " 13 -3.3250046e-01 6.69e-11 2.72e-01 -8.6 1.60e-01 - 5.13e-02 1.00e+00f 1\n", + " 14 -3.3250046e-01 3.64e-12 2.50e-14 -8.6 1.86e-03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: -3.3250045579433241e-01 -3.3250045579433241e-01\n", + "Dual infeasibility......: 2.5035529205297280e-14 2.5035529205297280e-14\n", + "Constraint violation....: 1.2123371804825303e-14 3.6379788070917130e-12\n", + "Complementarity.........: 3.0722667263320647e-09 3.0722667263320647e-09\n", + "Overall NLP error.......: 3.0722667263320647e-09 3.0722667263320647e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 15\n", + "Number of objective gradient evaluations = 15\n", + "Number of equality constraint evaluations = 15\n", + "Number of inequality constraint evaluations = 15\n", + "Number of equality constraint Jacobian evaluations = 15\n", + "Number of inequality constraint Jacobian evaluations = 15\n", + "Number of Lagrangian Hessian evaluations = 14\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.013\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[+ 0.03] solve\n", + "Model status: \n", + "Problem: \n", + "- Lower bound: -inf\n", + " Upper bound: inf\n", + " Number of objectives: 1\n", + " Number of constraints: 232\n", + " Number of variables: 233\n", + " Sense: unknown\n", + "Solver: \n", + "- Status: ok\n", + " Message: Ipopt 3.13.2\\x3a Optimal Solution Found\n", + " Termination condition: optimal\n", + " Id: 0\n", + " Error rc: 0\n", + " Time: 0.01901984214782715\n", + "Solution: \n", + "- number of solutions: 0\n", + " number of solutions displayed: 0\n", + "\n", + "Solve time: 0.027978026002529077\n", + "fs.bypass_frac : 0.10000020212075882\n", + "fs.ng_steam_ratio : 1.100676931942783\n", + "fs.steam_flowrate : 1.1878835613476406\n", + "fs.reformer_duty : 38002.3828518229\n", + "fs.AR : 0.004116895599526309\n", + "fs.C2H6 : 0.0004913978369500205\n", + "fs.C4H10 : 0.00012539103921606313\n", + "fs.C3H8 : 6.923814505429928e-05\n", + "fs.CH4 : 0.017457976165817648\n", + "fs.CO : 0.10608503398940847\n", + "fs.CO2 : 0.05310177709899404\n", + "fs.H2 : 0.3325004557943324\n", + "fs.H2O : 0.14644614887662047\n", + "fs.N2 : 0.3400000016914794\n", + "fs.O2 : 3.2942822113301266e-20\n" + ] + } + ], "source": [ "# unfix input values and add the objective/constraint to the model\n", "m.fs.bypass_frac.unfix()\n", @@ -522,7 +2468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb index bae4c938..11d56f16 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_test.ipynb @@ -1,530 +1,531 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autothermal Reformer Flowsheet Optimization with OMLT (TensorFlow Keras) Surrogate Object\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the OMLT package utilizing TensorFlow Keras neural networks. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Statement \n", + "\n", + "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", + "\n", + "## 2.1. Main Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "## 2.2. Main Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training and Validating Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import the required Python, Pyomo and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " SolverFactory,\n", + " value,\n", + " Var,\n", + " Constraint,\n", + " Set,\n", + " Objective,\n", + " maximize,\n", + ")\n", + "from pyomo.common.timing import TicTocTimer\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import (\n", + " KerasSurrogate,\n", + " save_keras_json_hd5,\n", + " load_keras_json_hd5,\n", + ")\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 Importing Training and Validation Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", + "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") # seed=100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Training Surrogates with TensorFlow Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "- Activation function: relu, sigmoid, ***tanh***\n", + "- Optimizer: ***Adam***, RMSprop, SGD\n", + "- Number of hidden layers: 1, ***2***, 4\n", + "- Number of neurons per layer: 10, 20, ***40***\n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# capture long output (not required to use surrogate API)\n", + "from io import StringIO\n", + "import sys\n", + "\n", + "stream = StringIO()\n", + "oldstdout = sys.stdout\n", + "sys.stdout = stream\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 2, 40\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(\n", + " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", + ")\n", + "\n", + "# save model to JSON and create callable surrogate object\n", + "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + ")\n", + "\n", + "# revert back to normal output capture\n", + "sys.stdout = oldstdout\n", + "\n", + "# display first 50 lines and last 50 lines of output\n", + "celloutput = stream.getvalue().split(\"\\n\")\n", + "for line in celloutput[:50]:\n", + " print(line)\n", + "print(\".\")\n", + "print(\".\")\n", + "print(\".\")\n", + "for line in celloutput[-50:]:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Visualizing surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity, and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates fit the data. Then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(\n", + " keras_surrogate, data_training, filename=\"keras_train_scatter2D.pdf\"\n", + ")\n", + "surrogate_parity(keras_surrogate, data_training, filename=\"keras_train_parity.pdf\")\n", + "surrogate_residual(keras_surrogate, data_training, filename=\"keras_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(\n", + " keras_surrogate, data_validation, filename=\"keras_val_scatter2D.pdf\"\n", + ")\n", + "surrogate_parity(keras_surrogate, data_validation, filename=\"keras_val_parity.pdf\")\n", + "surrogate_residual(keras_surrogate, data_validation, filename=\"keras_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. IDAES Flowsheet Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build an IDAES flowsheet and import the surrogate model object. A single Keras neural network model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create the IDAES model and flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "# create flowsheet input variables\n", + "m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + ")\n", + "m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + ")\n", + "\n", + "# create flowsheet output variables\n", + "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + "# create input and output variable object lists for flowsheet\n", + "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + "outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C4H10,\n", + " m.fs.C3H8,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + "]\n", + "\n", + "# create the Pyomo/IDAES block that corresponds to the surrogate\n", + "# Keras\n", + "keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + ")\n", + "m.fs.surrogate = SurrogateBlock()\n", + "m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + ")\n", + "\n", + "# fix input values and solve flowsheet\n", + "m.fs.bypass_frac.fix(0.5)\n", + "m.fs.ng_steam_ratio.fix(1)\n", + "\n", + "solver = SolverFactory(\"ipopt\")\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print some model results:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", + "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", + "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", + "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", + "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", + "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", + "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", + "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", + "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", + "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", + "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", + "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", + "print(\"Mole Fraction O2 = \", value(m.fs.O2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Optimizing the Autothermal Reformer\n", + "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", + "\n", + "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# unfix input values and add the objective/constraint to the model\n", + "m.fs.bypass_frac.unfix()\n", + "m.fs.ng_steam_ratio.unfix()\n", + "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + "# solve the model\n", + "tmr = TicTocTimer()\n", + "status = solver.solve(m, tee=True)\n", + "solve_time = tmr.toc(\"solve\")\n", + "\n", + "# print and check results\n", + "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", + "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", + "print(\"Model status: \", status)\n", + "print(\"Solve time: \", solve_time)\n", + "for var in inputs:\n", + " print(var.name, \": \", value(var))\n", + "for var in outputs:\n", + " print(var.name, \": \", value(var))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Autothermal Reformer Flowsheet Optimization with OMLT (TensorFlow Keras) Surrogate Object\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the OMLT package utilizing TensorFlow Keras neural networks. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Problem Statement \n", - "\n", - "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", - "\n", - "## 2.1. Main Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "## 2.2. Main Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training and Validating Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import the required Python, Pyomo and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import random as rn\n", - "import tensorflow as tf\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " SolverFactory,\n", - " value,\n", - " Var,\n", - " Constraint,\n", - " Set,\n", - " Objective,\n", - " maximize,\n", - ")\n", - "from pyomo.common.timing import TicTocTimer\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", - "from idaes.core.surrogate.keras_surrogate import (\n", - " KerasSurrogate,\n", - " save_keras_json_hd5,\n", - " load_keras_json_hd5,\n", - ")\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# fix environment variables to ensure consist neural network training\n", - "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", - "np.random.seed(46)\n", - "rn.seed(1342)\n", - "tf.random.set_seed(62)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1 Importing Training and Validation Datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Auto-reformer training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", - "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(\n", - " data, 0.8, seed=n_data\n", - ") # seed=100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Training Surrogates with TensorFlow Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", - "\n", - "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", - "\n", - "- Activation function: relu, sigmoid, ***tanh***\n", - "- Optimizer: ***Adam***, RMSprop, SGD\n", - "- Number of hidden layers: 1, ***2***, 4\n", - "- Number of neurons per layer: 10, 20, ***40***\n", - "\n", - "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", - "\n", - "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# capture long output (not required to use surrogate API)\n", - "from io import StringIO\n", - "import sys\n", - "\n", - "stream = StringIO()\n", - "oldstdout = sys.stdout\n", - "sys.stdout = stream\n", - "\n", - "# selected settings for regression (best fit from options above)\n", - "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 2, 40\n", - "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", - "\n", - "# Create data objects for training using scalar normalization\n", - "n_inputs = len(input_labels)\n", - "n_outputs = len(output_labels)\n", - "x = input_data\n", - "y = output_data\n", - "\n", - "input_scaler = None\n", - "output_scaler = None\n", - "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", - "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", - "x = input_scaler.scale(x)\n", - "y = output_scaler.scale(y)\n", - "x = x.to_numpy()\n", - "y = y.to_numpy()\n", - "\n", - "# Create Keras Sequential object and build neural network\n", - "model = tf.keras.Sequential()\n", - "model.add(\n", - " tf.keras.layers.Dense(\n", - " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", - " )\n", - ")\n", - "for i in range(1, n_hidden_layers):\n", - " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", - "model.add(tf.keras.layers.Dense(units=n_outputs))\n", - "\n", - "# Train surrogate (calls optimizer on neural network and solves for weights)\n", - "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", - "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", - " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", - ")\n", - "history = model.fit(\n", - " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", - ")\n", - "\n", - "# save model to JSON and create callable surrogate object\n", - "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "\n", - "keras_surrogate = KerasSurrogate(\n", - " model,\n", - " input_labels=list(input_labels),\n", - " output_labels=list(output_labels),\n", - " input_bounds=input_bounds,\n", - " input_scaler=input_scaler,\n", - " output_scaler=output_scaler,\n", - ")\n", - "keras_surrogate.save_to_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - ")\n", - "\n", - "# revert back to normal output capture\n", - "sys.stdout = oldstdout\n", - "\n", - "# display first 50 lines and last 50 lines of output\n", - "celloutput = stream.getvalue().split(\"\\n\")\n", - "for line in celloutput[:50]:\n", - " print(line)\n", - "print(\".\")\n", - "print(\".\")\n", - "print(\".\")\n", - "for line in celloutput[-50:]:\n", - " print(line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Visualizing surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity, and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates fit the data. Then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(\n", - " keras_surrogate, data_training, filename=\"keras_train_scatter2D.pdf\"\n", - ")\n", - "surrogate_parity(keras_surrogate, data_training, filename=\"keras_train_parity.pdf\")\n", - "surrogate_residual(keras_surrogate, data_training, filename=\"keras_train_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(\n", - " keras_surrogate, data_validation, filename=\"keras_val_scatter2D.pdf\"\n", - ")\n", - "surrogate_parity(keras_surrogate, data_validation, filename=\"keras_val_parity.pdf\")\n", - "surrogate_residual(keras_surrogate, data_validation, filename=\"keras_val_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. IDAES Flowsheet Integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build an IDAES flowsheet and import the surrogate model object. A single Keras neural network model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# create the IDAES model and flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "# create flowsheet input variables\n", - "m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - ")\n", - "m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - ")\n", - "\n", - "# create flowsheet output variables\n", - "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - "# create input and output variable object lists for flowsheet\n", - "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - "outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C4H10,\n", - " m.fs.C3H8,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - "]\n", - "\n", - "# create the Pyomo/IDAES block that corresponds to the surrogate\n", - "# Keras\n", - "keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - ")\n", - "m.fs.surrogate = SurrogateBlock()\n", - "m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - ")\n", - "\n", - "# fix input values and solve flowsheet\n", - "m.fs.bypass_frac.fix(0.5)\n", - "m.fs.ng_steam_ratio.fix(1)\n", - "\n", - "solver = SolverFactory(\"ipopt\")\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print some model results:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", - "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", - "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", - "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", - "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", - "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", - "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", - "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", - "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", - "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", - "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", - "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", - "print(\"Mole Fraction O2 = \", value(m.fs.O2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Optimizing the Autothermal Reformer\n", - "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", - "\n", - "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# unfix input values and add the objective/constraint to the model\n", - "m.fs.bypass_frac.unfix()\n", - "m.fs.ng_steam_ratio.unfix()\n", - "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - "# solve the model\n", - "tmr = TicTocTimer()\n", - "status = solver.solve(m, tee=True)\n", - "solve_time = tmr.toc(\"solve\")\n", - "\n", - "# print and check results\n", - "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", - "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", - "print(\"Model status: \", status)\n", - "print(\"Solve time: \", solve_time)\n", - "for var in inputs:\n", - " print(var.name, \": \", value(var))\n", - "for var in outputs:\n", - " print(var.name, \": \", value(var))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb index bae4c938..11d56f16 100644 --- a/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/omlt/keras_flowsheet_optimization_usr.ipynb @@ -1,530 +1,531 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autothermal Reformer Flowsheet Optimization with OMLT (TensorFlow Keras) Surrogate Object\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the OMLT package utilizing TensorFlow Keras neural networks. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Statement \n", + "\n", + "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", + "\n", + "## 2.1. Main Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "## 2.2. Main Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training and Validating Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import the required Python, Pyomo and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " SolverFactory,\n", + " value,\n", + " Var,\n", + " Constraint,\n", + " Set,\n", + " Objective,\n", + " maximize,\n", + ")\n", + "from pyomo.common.timing import TicTocTimer\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import (\n", + " KerasSurrogate,\n", + " save_keras_json_hd5,\n", + " load_keras_json_hd5,\n", + ")\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 Importing Training and Validation Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", + "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") # seed=100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Training Surrogates with TensorFlow Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "- Activation function: relu, sigmoid, ***tanh***\n", + "- Optimizer: ***Adam***, RMSprop, SGD\n", + "- Number of hidden layers: 1, ***2***, 4\n", + "- Number of neurons per layer: 10, 20, ***40***\n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# capture long output (not required to use surrogate API)\n", + "from io import StringIO\n", + "import sys\n", + "\n", + "stream = StringIO()\n", + "oldstdout = sys.stdout\n", + "sys.stdout = stream\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 2, 40\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(\n", + " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", + ")\n", + "\n", + "# save model to JSON and create callable surrogate object\n", + "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + ")\n", + "\n", + "# revert back to normal output capture\n", + "sys.stdout = oldstdout\n", + "\n", + "# display first 50 lines and last 50 lines of output\n", + "celloutput = stream.getvalue().split(\"\\n\")\n", + "for line in celloutput[:50]:\n", + " print(line)\n", + "print(\".\")\n", + "print(\".\")\n", + "print(\".\")\n", + "for line in celloutput[-50:]:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Visualizing surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity, and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates fit the data. Then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(\n", + " keras_surrogate, data_training, filename=\"keras_train_scatter2D.pdf\"\n", + ")\n", + "surrogate_parity(keras_surrogate, data_training, filename=\"keras_train_parity.pdf\")\n", + "surrogate_residual(keras_surrogate, data_training, filename=\"keras_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(\n", + " keras_surrogate, data_validation, filename=\"keras_val_scatter2D.pdf\"\n", + ")\n", + "surrogate_parity(keras_surrogate, data_validation, filename=\"keras_val_parity.pdf\")\n", + "surrogate_residual(keras_surrogate, data_validation, filename=\"keras_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. IDAES Flowsheet Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build an IDAES flowsheet and import the surrogate model object. A single Keras neural network model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# create the IDAES model and flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "# create flowsheet input variables\n", + "m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + ")\n", + "m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + ")\n", + "\n", + "# create flowsheet output variables\n", + "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + "# create input and output variable object lists for flowsheet\n", + "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + "outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C4H10,\n", + " m.fs.C3H8,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + "]\n", + "\n", + "# create the Pyomo/IDAES block that corresponds to the surrogate\n", + "# Keras\n", + "keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", + ")\n", + "m.fs.surrogate = SurrogateBlock()\n", + "m.fs.surrogate.build_model(\n", + " keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + ")\n", + "\n", + "# fix input values and solve flowsheet\n", + "m.fs.bypass_frac.fix(0.5)\n", + "m.fs.ng_steam_ratio.fix(1)\n", + "\n", + "solver = SolverFactory(\"ipopt\")\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print some model results:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", + "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", + "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", + "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", + "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", + "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", + "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", + "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", + "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", + "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", + "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", + "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", + "print(\"Mole Fraction O2 = \", value(m.fs.O2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Optimizing the Autothermal Reformer\n", + "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", + "\n", + "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# unfix input values and add the objective/constraint to the model\n", + "m.fs.bypass_frac.unfix()\n", + "m.fs.ng_steam_ratio.unfix()\n", + "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + "# solve the model\n", + "tmr = TicTocTimer()\n", + "status = solver.solve(m, tee=True)\n", + "solve_time = tmr.toc(\"solve\")\n", + "\n", + "# print and check results\n", + "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", + "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", + "print(\"Model status: \", status)\n", + "print(\"Solve time: \", solve_time)\n", + "for var in inputs:\n", + " print(var.name, \": \", value(var))\n", + "for var in outputs:\n", + " print(var.name, \": \", value(var))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Autothermal Reformer Flowsheet Optimization with OMLT (TensorFlow Keras) Surrogate Object\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the OMLT package utilizing TensorFlow Keras neural networks. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Problem Statement \n", - "\n", - "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", - "\n", - "## 2.1. Main Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "## 2.2. Main Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training and Validating Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import the required Python, Pyomo and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import random as rn\n", - "import tensorflow as tf\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " SolverFactory,\n", - " value,\n", - " Var,\n", - " Constraint,\n", - " Set,\n", - " Objective,\n", - " maximize,\n", - ")\n", - "from pyomo.common.timing import TicTocTimer\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", - "from idaes.core.surrogate.keras_surrogate import (\n", - " KerasSurrogate,\n", - " save_keras_json_hd5,\n", - " load_keras_json_hd5,\n", - ")\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# fix environment variables to ensure consist neural network training\n", - "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", - "np.random.seed(46)\n", - "rn.seed(1342)\n", - "tf.random.set_seed(62)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1 Importing Training and Validation Datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Auto-reformer training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", - "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(\n", - " data, 0.8, seed=n_data\n", - ") # seed=100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Training Surrogates with TensorFlow Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", - "\n", - "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", - "\n", - "- Activation function: relu, sigmoid, ***tanh***\n", - "- Optimizer: ***Adam***, RMSprop, SGD\n", - "- Number of hidden layers: 1, ***2***, 4\n", - "- Number of neurons per layer: 10, 20, ***40***\n", - "\n", - "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", - "\n", - "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# capture long output (not required to use surrogate API)\n", - "from io import StringIO\n", - "import sys\n", - "\n", - "stream = StringIO()\n", - "oldstdout = sys.stdout\n", - "sys.stdout = stream\n", - "\n", - "# selected settings for regression (best fit from options above)\n", - "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 2, 40\n", - "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", - "\n", - "# Create data objects for training using scalar normalization\n", - "n_inputs = len(input_labels)\n", - "n_outputs = len(output_labels)\n", - "x = input_data\n", - "y = output_data\n", - "\n", - "input_scaler = None\n", - "output_scaler = None\n", - "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", - "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", - "x = input_scaler.scale(x)\n", - "y = output_scaler.scale(y)\n", - "x = x.to_numpy()\n", - "y = y.to_numpy()\n", - "\n", - "# Create Keras Sequential object and build neural network\n", - "model = tf.keras.Sequential()\n", - "model.add(\n", - " tf.keras.layers.Dense(\n", - " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", - " )\n", - ")\n", - "for i in range(1, n_hidden_layers):\n", - " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", - "model.add(tf.keras.layers.Dense(units=n_outputs))\n", - "\n", - "# Train surrogate (calls optimizer on neural network and solves for weights)\n", - "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", - "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", - " \".mdl_wts.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", - ")\n", - "history = model.fit(\n", - " x=x, y=y, validation_split=0.2, verbose=1, epochs=1000, callbacks=[mcp_save]\n", - ")\n", - "\n", - "# save model to JSON and create callable surrogate object\n", - "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "\n", - "keras_surrogate = KerasSurrogate(\n", - " model,\n", - " input_labels=list(input_labels),\n", - " output_labels=list(output_labels),\n", - " input_bounds=input_bounds,\n", - " input_scaler=input_scaler,\n", - " output_scaler=output_scaler,\n", - ")\n", - "keras_surrogate.save_to_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - ")\n", - "\n", - "# revert back to normal output capture\n", - "sys.stdout = oldstdout\n", - "\n", - "# display first 50 lines and last 50 lines of output\n", - "celloutput = stream.getvalue().split(\"\\n\")\n", - "for line in celloutput[:50]:\n", - " print(line)\n", - "print(\".\")\n", - "print(\".\")\n", - "print(\".\")\n", - "for line in celloutput[-50:]:\n", - " print(line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Visualizing surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity, and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates fit the data. Then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(\n", - " keras_surrogate, data_training, filename=\"keras_train_scatter2D.pdf\"\n", - ")\n", - "surrogate_parity(keras_surrogate, data_training, filename=\"keras_train_parity.pdf\")\n", - "surrogate_residual(keras_surrogate, data_training, filename=\"keras_train_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(\n", - " keras_surrogate, data_validation, filename=\"keras_val_scatter2D.pdf\"\n", - ")\n", - "surrogate_parity(keras_surrogate, data_validation, filename=\"keras_val_parity.pdf\")\n", - "surrogate_residual(keras_surrogate, data_validation, filename=\"keras_val_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. IDAES Flowsheet Integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build an IDAES flowsheet and import the surrogate model object. A single Keras neural network model accounts for all input and output variables, and the JSON model serialized earlier may be imported into a single SurrogateBlock() component." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# create the IDAES model and flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "# create flowsheet input variables\n", - "m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - ")\n", - "m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - ")\n", - "\n", - "# create flowsheet output variables\n", - "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - "# create input and output variable object lists for flowsheet\n", - "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - "outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C4H10,\n", - " m.fs.C3H8,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - "]\n", - "\n", - "# create the Pyomo/IDAES block that corresponds to the surrogate\n", - "# Keras\n", - "keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"keras_surrogate\", keras_model_name=\"keras_model\"\n", - ")\n", - "m.fs.surrogate = SurrogateBlock()\n", - "m.fs.surrogate.build_model(\n", - " keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - ")\n", - "\n", - "# fix input values and solve flowsheet\n", - "m.fs.bypass_frac.fix(0.5)\n", - "m.fs.ng_steam_ratio.fix(1)\n", - "\n", - "solver = SolverFactory(\"ipopt\")\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print some model results:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", - "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", - "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", - "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", - "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", - "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", - "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", - "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", - "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", - "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", - "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", - "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", - "print(\"Mole Fraction O2 = \", value(m.fs.O2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Optimizing the Autothermal Reformer\n", - "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", - "\n", - "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# unfix input values and add the objective/constraint to the model\n", - "m.fs.bypass_frac.unfix()\n", - "m.fs.ng_steam_ratio.unfix()\n", - "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - "# solve the model\n", - "tmr = TicTocTimer()\n", - "status = solver.solve(m, tee=True)\n", - "solve_time = tmr.toc(\"solve\")\n", - "\n", - "# print and check results\n", - "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", - "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", - "print(\"Model status: \", status)\n", - "print(\"Solve time: \", solve_time)\n", - "for var in inputs:\n", - " print(var.name, \": \", value(var))\n", - "for var in outputs:\n", - " print(var.name, \": \", value(var))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras b/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras index 55b0068b..55a164bf 100644 Binary files a/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras and b/idaes_examples/notebooks/docs/surrogates/omlt/keras_surrogate/keras_model.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics.ipynb index 60c8f350..081f1c08 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_doc.ipynb index 7a3c6798..65686cda 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -207,16 +208,12 @@ "outputs": [ { "data": { - "image/png": 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", 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q6xrCNDMAiAyEDyCEOruMyipOqLdrmd3byipOqLMroq52AoClCB9ACNXWN/c44/FZRlKDr0219c3WTQoAIgzhAwih8xf6Dh4DGQcAdkT4AEIobbQzpOMAwI4IH0AIzcxOVobbqb4eqHXo2lMvM7OTrZwWAEQUwgcQQsPiHCpdkiNJPQJI9/vSJTnU+wAQ0wgfQIgtzM3QllVT5XEHXlrxuJ3asmoqdT4AxDyKjAFDYGFuhhbkeKhwCgC9IHwAQ2RYnEPeL6WEexqIEZTzRzQhfABAlKOcP6IN93wAQBSjnD+iEeEDAKIU5fwRrQgfABClKOePaEX4AIAoRTl/RCvCBwBEKcr5I1oRPgAgSlHOH9GK8AEAUYpy/ohWhA8AiGKU80c0osgYAEQ5yvkj2sRc+KAEMQA7opw/oklMhQ9KEAMAEH4xc88HJYgBAIgMMRE+KEEMAEDkiInwQQliAAAiR0yED0oQAwAQOWIifFCCGACAyBET4YMSxAAARI6YCB+UIAYAIHLERPiQKEEMAECkiKkiY5QgBgAg/GIqfEiUIAYAINxi5rILAACIDIQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALBUzPV2ARBbOrsMzSSBCEP4AGBblXUNKqs4oQZfm39bhtup0iU5WpibEcaZAbGNyy4AbKmyrkFrXz4aEDwkqdHXprUvH1VlXUOYZgaA8AHAdjq7jMoqTsj0sq97W1nFCXV29TYCwFAjfACwndr65h5nPD7LSGrwtam2vtm6SQHwI3wAsJ3zF/oOHgMZByC0CB8AbCdttDOk4wCEFuEDgO3MzE5Whtupvh6odejaUy8zs5OtnBaAPyJ8ALCdYXEOlS7JkaQeAaT7femSHOp9AGFC+ABgSwtzM7Rl1VR53IGXVjxup7asmkqdDyCMKDIGwLYW5mZoQY6HCqdAhCF8ALC1YXEOeb+UEu5pIAZQyr//CB8AAAwSpfyDwz0fAAAMAqX8g0f4AABggCjlPzCEDwAABohS/gND+AAAYIAo5T8whA8AAAaIUv4DQ/gAAGCAKOU/MEGFj/Lycs2YMUOjR49WWlqali1bppMnTwaMaWtrU1FRkVJSUpSUlKTCwkI1NTWFdNIAAEQCSvkPTFDho7q6WkVFRTpw4IB2796tK1eu6O6779alS5f8Y9avX6+Kigrt2LFD1dXVOnfunJYvXx7yiQMAEAko5R88hzFmwM//fPjhh0pLS1N1dbXuuusu+Xw+paamatu2bbrnnnskSe+++65uu+021dTUaNasWTc8Zmtrq9xut3w+n1wu10CnBgCApWK9wmkw39+DqnDq8/kkScnJ165lHTlyRFeuXFF+fr5/zKRJk5SVldVn+Ghvb1d7e3vA5AEAiDaU8u+/Ad9w2tXVpUcffVRf/epXlZubK0lqbGxUQkKCxowZEzA2PT1djY2NvR6nvLxcbrfb/8rMzBzolBAlOruMak5/rJ8d+4NqTn9M8R0AiDEDPvNRVFSkuro67d+/f1ATKCkpUXFxsf99a2srAcTG6H8AABjQmY+HH35YP//5z/XWW29pwoQJ/u0ej0cdHR1qaWkJGN/U1CSPx9PrsRITE+VyuQJesCf6HwAApCDDhzFGDz/8sHbu3Kk9e/YoOzs7YP+0adMUHx+vqqoq/7aTJ0/qzJkz8nq9oZkxohL9DwAA3YK67FJUVKRt27bpZz/7mUaPHu2/j8PtdmvEiBFyu91as2aNiouLlZycLJfLpXXr1snr9fbrSRfYVzD9D7hhCwDsLajwsWXLFknS3LlzA7Zv3bpV999/vyRp06ZNiouLU2Fhodrb21VQUKBnn302JJNF9KL/AQCgW1Dhoz8lQZxOpzZv3qzNmzcPeFKwH/ofAAC60dsFlqD/AQCgG+EDlqD/AQCgG+EDlqH/AQBAGmR5dSBYC3MztCDHE9P9DwAg1hE+YDn6HwBAbOOyCwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivLqQJTo7DL0xAFgC4QPIApU1jWorOKEGnxt/m0ZbqdKl+TQDRhA1OGyCxDhKusatPblowHBQ5IafW1a+/JRVdY1hGlmADAwhA8ggnV2GZVVnJDpZV/3trKKE+rs6m0EAEQmwgcQwWrrm3uc8fgsI6nB16ba+mbrJgUAg0T4ACLY+Qt9B4+BjAOASED4ACJY2mhnSMcBQCQgfAARbGZ2sjLcTvX1QK1D1556mZmdbOW0AGBQCB9ABBsW51DpkhxJ6hFAut+XLsmh3geAqEL4ACLcwtwMbVk1VR534KUVj9upLaumUucDQNShyBgQBRbmZmhBjocKpwBsgfABRIlhcQ55v5QS7mkgBlDKH0ON8AEA8KOUP6zAPR8AAEmU8od1CB8AAEr5w1KEDwAApfxhKcIHAIBS/rAU4QMAQCl/WIrwAQCglD8sRfgAAFDKH5YifAAAJFHKH9ahyBgAwI9S/rAC4QMAEIBS/hhqhI9BoP8BAADBI3wMEP0PAAAYGG44HQD6HwAAMHCEjyDR/wAAgMEhfASJ/gcAAAwO4SNI9D8AAGBwCB9Bov8BAACDQ/gIEv0PAAAYHMJHkOh/AADA4BA+BoD+BwAADBxFxgaI/gcAAAwM4WMQ6H8AAEDwuOwCAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJaivDqAQevsMvQ5AtBvhA8Ag1JZ16CyihNq8LX5t2W4nSpdkkOHZwC94rILgAGrrGvQ2pePBgQPSWr0tWnty0dVWdcQppkBiGRBh49f/epXWrJkicaPHy+Hw6HXXnstYL8xRt/97neVkZGhESNGKD8/X++//36o5gsgQnR2GZVVnJDpZV/3trKKE+rs6m0EgFgWdPi4dOmS7rjjDm3evLnX/f/8z/+sH//4x3ruued08OBBjRo1SgUFBWpra+t1PIDoVFvf3OOMx2cZSQ2+NtXWN1s3KQBRIeh7PhYtWqRFixb1us8Yo6efflqPP/64li5dKkl66aWXlJ6ertdee03f+MY3BjdbABHj/IX+/R+K/o4DEDtCes9HfX29GhsblZ+f79/mdruVl5enmpqaXj/T3t6u1tbWgBeAyJc22hnScQBiR0jDR2NjoyQpPT09YHt6erp/3+eVl5fL7Xb7X5mZmaGcEoAhMjM7WRlup/p6oNaha0+9zMxOtnJaAKJA2J92KSkpkc/n87/Onj0b7ikB6IdhcQ6VLsmRpB4BpPt96ZIc6n0A6CGk4cPj8UiSmpqaArY3NTX5931eYmKiXC5XwAtAdFiYm6Etq6bK4w68tOJxO7Vl1VTqfADoVUiLjGVnZ8vj8aiqqkpTpkyRJLW2turgwYNau3ZtKH8UgAixMDdDC3I8VDgF0G9Bh4+LFy/q1KlT/vf19fU6duyYkpOTlZWVpUcffVRPPvmkbrnlFmVnZ+uJJ57Q+PHjtWzZslDOG0AEGRbnkPdLKeGeBoAoEXT4OHz4sP70T//U/764uFiStHr1ar344ov6h3/4B126dEkPPvigWlpa9LWvfU2VlZVyOrnjHQAwOPQRsgeHMSaiyg+2trbK7XbL5/Nx/wcAwI8+QpEtmO/vsD/tAgDAjdBHyF4IHwCAiEYfIfshfAAAIhp9hOyH8AEAiGj0EbIfwgcAIKLRR8h+CB8AgIhGHyH7IXwAACIafYTsh/ABAIh49BGyl5D2dgEAYKjQR8g+CB82RQliAHZEHyF7IHzYECWIAQCRjHs+bIYSxACASEf4sBFKEAMAogHhw0YoQQwAiAaEDxuhBDEAIBoQPmyEEsQAgGhA+LARShADAKIB4cNGKEEMAIgGhA+boQQxACDSUWTMhihBDACIZIQPm6IEMQCgW6S13CB8AABgY5HYcoN7PgAAsKlIbblB+AAAwIYiueUG4QMAABuK5JYbhA8AAGwokltuED4AALChSG65QfgAAMCGIrnlBuEDAAAbiuSWG4QPAABsKlJbblBkDAAAG4vElhuEDwAAbC7SWm4QPoDPiLT+BwBgR4QP4I8isf8BANgRN5wCitz+BwBgR4QPxLxI7n8AAHZE+EDMi+T+BwBgR4QPxLxI7n8AAHZE+EDMi+T+BwBgR4QPxLxI7n8AAHZE+EDMi+T+BwBgR4QPQJHb/wAA7IgiY8AfRWL/AwCwI8IH8BmR1v8A9kUpf8QywgcAWIxS/oh13PMBABailD9A+AAAy1DKH7iG8AEAFqGUP3AN4QMALEIpf+AawgcAWIRS/sA1hA8AsAil/IFrCB8AYBFK+QPXED4AwEKU8gcoMgYAlqOUP2JdxIcPShADsCNK+SOWRXT4oAQxAAD2E7H3fOw+0UgJYgAAbChiw8dTu96lBDEAADYUseGjqbW9z32UIAYAIHpFbPjoD0oQAwAQfaI6fFCCGACA6BOx4SPdlUgJYgAAbChiw8eGRZMkUYIYAAC7GbLwsXnzZt18881yOp3Ky8tTbW1tUJ9fkOOhBDEAADY0JEXGXnnlFRUXF+u5555TXl6enn76aRUUFOjkyZNKS0vr93EoQQwAgP04jDEhL5aRl5enGTNm6JlnnpEkdXV1KTMzU+vWrdOGDRuu+9nW1la53W75fD65XK5QTw0AAAyBYL6/Q37mo6OjQ0eOHFFJSYl/W1xcnPLz81VTU9NjfHt7u9rb/7+mh8/nk3TtlwAAANGh+3u7P+c0Qh4+PvroI3V2dio9PT1ge3p6ut59990e48vLy1VWVtZje2ZmZqinBgAAhtiFCxfkdruvOybsjeVKSkpUXFzsf9/V1aXm5malpKTI4YiseztaW1uVmZmps2fPckkojFiH8GMNwo81CD/WIJAxRhcuXND48eNvODbk4WPcuHEaNmyYmpqaArY3NTXJ4/H0GJ+YmKjExMSAbWPGjAn1tELK5XLxX7QIwDqEH2sQfqxB+LEG/+9GZzy6hfxR24SEBE2bNk1VVVX+bV1dXaqqqpLX6w31jwMAAFFmSC67FBcXa/Xq1Zo+fbpmzpypp59+WpcuXdIDDzwwFD8OAABEkSEJHytWrNCHH36o7373u2psbNSUKVNUWVnZ4ybUaJOYmKjS0tIel4lgLdYh/FiD8GMNwo81GLghqfMBAADQl4jt7QIAAOyJ8AEAACxF+AAAAJYifAAAAEvFfPgoLy/XjBkzNHr0aKWlpWnZsmU6efJkwJjGxkb91V/9lTwej0aNGqWpU6fqP//zPwPGNDc367777pPL5dKYMWO0Zs0aXbx40cpfJWpt2bJFkydP9hfq8Xq92rVrl39/W1ubioqKlJKSoqSkJBUWFvYoYnfmzBktXrxYI0eOVFpamv7+7/9eV69etfpXiWrXW4fm5matW7dOt956q0aMGKGsrCx9+9vf9vdi6sY6DM6N/i10M8Zo0aJFcjgceu211wL2sQaD0581qKmp0bx58zRq1Ci5XC7ddddd+vTTT/37+T7oBxPjCgoKzNatW01dXZ05duyY+frXv26ysrLMxYsX/WMWLFhgZsyYYQ4ePGhOnz5tvve975m4uDhz9OhR/5iFCxeaO+64wxw4cMDs27fPTJw40axcuTIcv1LUef31180vfvEL895775mTJ0+a73znOyY+Pt7U1dUZY4z51re+ZTIzM01VVZU5fPiwmTVrlpk9e7b/81evXjW5ubkmPz/fvP322+aNN94w48aNMyUlJeH6laLS9dbh+PHjZvny5eb11183p06dMlVVVeaWW24xhYWF/s+zDoN3o38L3X74wx+aRYsWGUlm586d/u2sweDdaA1+85vfGJfLZcrLy01dXZ159913zSuvvGLa2tr8x+D74MZiPnx83vnz540kU11d7d82atQo89JLLwWMS05ONs8//7wxxpgTJ04YSebQoUP+/bt27TIOh8P84Q9/sGbiNjN27Fjz05/+1LS0tJj4+HizY8cO/7533nnHSDI1NTXGGGPeeOMNExcXZxobG/1jtmzZYlwul2lvb7d87nbSvQ69efXVV01CQoK5cuWKMYZ1GCqfX4O3337bfOELXzANDQ09wgdrMDQ+uwZ5eXnm8ccf73Ms3wf9E/OXXT6v+zRycnKyf9vs2bP1yiuvqLm5WV1dXdq+fbva2to0d+5cSddOwY0ZM0bTp0/3fyY/P19xcXE6ePCgpfOPdp2dndq+fbsuXbokr9erI0eO6MqVK8rPz/ePmTRpkrKyslRTUyPp2t//9ttvDyhiV1BQoNbWVv3ud7+z/Hewg8+vQ298Pp9cLpeGD79Wq5B1CK3e1uDy5cu69957tXnz5l57ZbEGofX5NTh//rwOHjyotLQ0zZ49W+np6fqTP/kT7d+/3/8Zvg/6J+xdbSNJV1eXHn30UX31q19Vbm6uf/urr76qFStWKCUlRcOHD9fIkSO1c+dOTZw4UdK1e0LS0tICjjV8+HAlJyersbHR0t8hWh0/flxer1dtbW1KSkrSzp07lZOTo2PHjikhIaFHs8H09HT/37axsbFH9dzu9/z9g9PXOnzeRx99pO9973t68MEH/dtYh9C43hqsX79es2fP1tKlS3v9LGsQGn2twYEDByRJGzdu1A9+8ANNmTJFL730kubPn6+6ujrdcsstfB/0E+HjM4qKilRXVxeQYiXpiSeeUEtLi/77v/9b48aN02uvvaa//Mu/1L59+3T77beHabb2cuutt+rYsWPy+Xz6j//4D61evVrV1dXhnlbM6WsdPhtAWltbtXjxYuXk5Gjjxo3hm6xN9bUGp06d0p49e/T222+He4q219cadHV1SZK++c1v+nuV3XnnnaqqqtK//uu/qry8PJzTjiqEjz96+OGH9fOf/1y/+tWvNGHCBP/206dP65lnnlFdXZ2+8pWvSJLuuOMO7du3T5s3b9Zzzz0nj8ej8+fPBxzv6tWram5u7vXUKHpKSEjwn0maNm2aDh06pB/96EdasWKFOjo61NLSEnD2o6mpyf+39Xg8qq2tDThe99Mw/P2D09c6/OQnP5EkXbhwQQsXLtTo0aO1c+dOxcfH+z/LOoRGX2swYsQInT59usdZwMLCQs2ZM0d79+5lDUKkrzXYsGGDJPU4G3jbbbfpzJkzksT3QT/F/D0fxhg9/PDD2rlzp/bs2aPs7OyA/ZcvX5YkxcUF/qmGDRvmT8Fer1ctLS06cuSIf/+ePXvU1dWlvLy8If4N7Kmrq0vt7e2aNm2a4uPjVVVV5d938uRJnTlzxn8d3Ov16vjx4wH/4Hfv3i2Xy9XrJQP0X/c6SNfOeNx9991KSEjQ66+/LqfTGTCWdRga3WuwYcMG/fa3v9WxY8f8L0natGmTtm7dKok1GCrda3DzzTdr/PjxPcoxvPfee7rpppsk8X3Qb+G+4zXc1q5da9xut9m7d69paGjwvy5fvmyMMaajo8NMnDjRzJkzxxw8eNCcOnXK/OAHPzAOh8P84he/8B9n4cKF5s477zQHDx40+/fvN7fccguPVvXThg0bTHV1tamvrze//e1vzYYNG4zD4TC//OUvjTHXHrXNysoye/bsMYcPHzZer9d4vV7/57sfL7z77rvNsWPHTGVlpUlNTeXxwiBdbx18Pp/Jy8szt99+uzl16lTAv5WrV68aY1iHULjRv4XPUx+P2rIGA3ejNdi0aZNxuVxmx44d5v333zePP/64cTqd5tSpU/5j8H1wYzEfPiT1+tq6dat/zHvvvWeWL19u0tLSzMiRI83kyZN7PHr78ccfm5UrV5qkpCTjcrnMAw88YC5cuGDxbxOd/uZv/sbcdNNNJiEhwaSmppr58+cH/I/tp59+ah566CEzduxYM3LkSPPnf/7npqGhIeAYv//9782iRYvMiBEjzLhx48xjjz3mfwQU/XO9dXjrrbf6/LdSX1/vPwbrMDg3+rfweZ8PH8awBoPVnzUoLy83EyZMMCNHjjRer9fs27cvYD/fBzfmMMaYcJxxAQAAsSnm7/kAAADWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AHEoI0bN8rhcPhfqampmjdvnvbt2xfuqQGIAYQPIEaNGDFCNTU1qqmp0ZYtW/Txxx/7W4MDwFCiqy0Qo+Li4jRr1iz/+5kzZ+rmm2/Wc889p2eeeSZgrDFGHR0dSkxMtHqaQfn00081YsSIcE8DwA1w5gOAJCkrK0upqamqr6/X/fffr9zcXL3xxhu64447lJiYqIqKCklSTU2N5s2bp1GjRsntduvee+/t0UL8qaee0sSJE+V0OpWamqr8/HzV19f3a//evXvlcDh0+PDhgGMuW7ZMc+fO9b/fuHGjkpKSVFtbK6/XK6fTqc2bN0uS3nnnHS1dulRut1ujRo3S4sWLdfr06aH4swEYAMIHAElSa2urPv74Y40fP16SdO7cOX3729/W+vXrVVlZqSlTpqimpkZz586V2+3WK6+8on/5l3/RoUOHtHTpUv9xXnrpJT3xxBNas2aNKisr9dOf/lRTpkxRa2trv/YHo6OjQ/fee69WrVqlXbt26e6779b//u//avbs2WpubtaLL76obdu26cMPP9T8+fPV3t4emj8WgEHhsgsQw65evSpJ+uCDD/TYY4+ps7NT99xzj/793/9dn3zyiXbt2qW8vDz/+DVr1mj69On6r//6LzkcDknS7bff7j9L8vWvf121tbWaPHmySkpK/J/7bDi50f5gXLlyRf/4j/+oFStW+LetXr1aycnJ2r17t5xOpyRp9uzZ+uIXv6gXXnhBDz300IB+FoDQ4cwHEKMuXbqk+Ph4xcfHKzs7W2+99ZaeeeYZFRQUSJJSUlICgsfly5f161//Wn/xF3+hzs5OXb16VVevXtWXv/xlZWZm6tChQ5KkqVOn6u2331ZxcbH279+vK1euBPzcG+0P1uLFiwPe//KXv9Sf/dmfafjw4f45jh07Vnfeead/jgDCi/ABxKgRI0bo0KFDOnz4sH7/+9/ro48+UlFRkX9/enp6wPhPPvlEnZ2dWr9+vT+0dL/OnDmjs2fPSpLuv/9+bdq0SW+++abmzJmj1NRUPfLII/r000/7tT8YI0eOVFJSUsC2jz76SE8//XSPOe7bt88/RwDhxWUXIEbFxcVp+vTpfe7vvqzSbcyYMXI4HPrOd76jZcuW9Rg/btw4/3EfeeQRPfLII/rDH/6g7du3a8OGDRo3bpyeeOKJG+7vvlTS0dERcPxPPvmkx5w+/16SkpOTtXjx4l4vr4wePbrP3xeAdQgfAPpl1KhR8nq9euedd/Tkk0/26zNf+MIX9Nhjj2nbtm165513+rV/woQJkq49sTJ79mxJ185mHD16VNOmTbvhz8zPz1ddXZ3uvPNODRs2rL+/HgALET4A9Nv3v/99zZs3TytWrNA3vvENjR07Vh988IF2796tBx54QHPnztU3v/lNjR07VrNmzdLYsWP161//Wv/zP//jPxNxo/0TJkxQXl6eysrK5Ha7NXz4cP3TP/2T3G53v+ZYVlamGTNmqKCgQA8++KDS09PV2Nio6upqzZkzRytXrhyyvw+A/iF8AOi32bNna//+/SotLdUDDzygjo4OTZgwQfPnz9fEiRP9Y55//nk9//zzunz5sr74xS9q06ZNWrNmTb/2S9K//du/6W//9m91//33y+Px6Mknn9T27dvV0tJywzlOnDhRtbW1evzxx/XQQw/p4sWLysjI0F133aXJkycPyd8FQHAcxhgT7kkAAIDYwdMuAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFjq/wDcm+X0t+qLHgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\pysmo\\pysmo_basics_doc_14_0.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -340,7 +337,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\AppData\\Local\\Temp\\ipykernel_31700\\142152307.py:6: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", + "/tmp/ipykernel_360653/142152307.py:6: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", " ax.scatter3D(\n" ] }, @@ -356,16 +353,12 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\pysmo\\pysmo_basics_doc_20_2.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -443,8 +436,6 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; previous file will be overwritten.\n", - "\n", "No iterations will be run.\n", "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", @@ -644,16 +635,12 @@ "outputs": [ { "data": { - "image/png": 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", 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1.3203209127587061 | 7.936658808585865e+18 | 1762.2922695772181 | 300000.87994025816 | 6.661357686349637e-11\n", - "0.001 | 0.0002 | 1.320453829126744 | 7.936658808585865e+18 | 1762.2922695772181 | 150000.93996799184 | 3.330689945357609e-11\n", - "0.001 | 0.0005 | 1.3206180157500875 | 7.936658808585865e+18 | 1762.2922695772181 | 60000.975986938625 | 1.3322893008136078e-11\n", - "0.001 | 0.00075 | 1.3206637488446602 | 7.936658808585865e+18 | 1762.2922695772181 | 40000.98399132156 | 8.882002686965498e-12\n", - "0.001 | 0.001 | 1.3206877851838068 | 7.936658808585865e+18 | 1762.2922695772181 | 30000.987993464787 | 6.661557526369496e-12\n", - "0.001 | 0.002 | 1.3207246211571246 | 7.936658808585865e+18 | 1762.2922695772181 | 15000.993996724195 | 3.3308897854853903e-12\n", - "0.001 | 0.005 | 1.3207441010685603 | 7.936658808585865e+18 | 1762.2922695772181 | 6000.997598686373 | 1.3324891409563772e-12\n", - "0.001 | 0.0075 | 1.3207457685491724 | 7.936658808585865e+18 | 1762.2922695772181 | 4000.9983991229724 | 8.884001088389431e-13\n", - "0.001 | 0.01 | 1.3207447379585568 | 7.936658808585865e+18 | 1762.2922695772181 | 3000.998799341891 | 6.663555927803635e-13\n", - "0.001 | 0.02 | 1.320733880721723 | 7.936658808585865e+18 | 1762.2922695772181 | 1500.9993996709732 | 3.332888186926504e-13\n", - "0.001 | 0.05 | 1.320692242454931 | 7.936658808585865e+18 | 1762.2922695772181 | 600.999759868342 | 1.334487542400047e-13\n", - "0.001 | 0.075 | 1.3206569542287017 | 7.936658808585865e+18 | 1762.2922695772181 | 400.9998399122189 | 8.903985102830944e-14\n", - "0.001 | 0.1 | 1.320622227525564 | 7.936658808585865e+18 | 1762.2922695772181 | 300.99987993416397 | 6.68353994224633e-14\n", - "0.001 | 0.2 | 1.3204909253945512 | 7.936658808585865e+18 | 1762.2922695772181 | 150.9999399670793 | 3.352872201369357e-14\n", - "0.001 | 0.5 | 1.3201734897566477 | 7.936658808585865e+18 | 1762.2922695772181 | 60.99997598683129 | 1.354471556843235e-14\n", - "0.001 | 0.75 | 1.3199938819612356 | 7.936658808585865e+18 | 1762.2922695772181 | 40.999983991220795 | 9.10382524726323e-15\n", - "0.001 | 1 | 1.3198876280547236 | 7.936658808585865e+18 | 1762.2922695772181 | 30.999987993415566 | 6.883380086678673e-15\n", - "0.002 | 1e-05 | 1.362387822278568 | 5.782309804024167e+18 | 1283.9306959886815 | 2999996.19772378 | 6.661329705001728e-10\n", - "0.002 | 2e-05 | 1.343994630873922 | 5.782309804024167e+18 | 1283.9306959886815 | 1499998.5987316754 | 3.3306659624347544e-10\n", - "0.002 | 5e-05 | 1.3272246485698753 | 5.782309804024167e+18 | 1283.9306959886815 | 600000.0394937922 | 1.3322677172440228e-10\n", - "0.002 | 7.5e-05 | 1.3235440099208564 | 5.782309804024167e+18 | 1283.9306959886815 | 400000.3596555643 | 8.881792182959021e-11\n", - "0.002 | 0.0001 | 1.3220037168855518 | 5.782309804024167e+18 | 1283.9306959886815 | 300000.51974200836 | 6.66134968834183e-11\n", - "0.002 | 0.0002 | 1.3204880868279008 | 5.782309804024167e+18 | 1283.9306959886815 | 150000.75986912134 | 3.330685946359354e-11\n", - "0.002 | 0.0005 | 1.3203538501482557 | 5.782309804024167e+18 | 1283.9306959886815 | 60000.903947554114 | 1.3322877012179405e-11\n", - "0.002 | 0.00075 | 1.3204389361297426 | 5.782309804024167e+18 | 1283.9306959886815 | 40000.935965030716 | 8.881992022986721e-12\n", - "0.002 | 0.001 | 1.320500039765387 | 5.782309804024167e+18 | 1283.9306959886815 | 30000.95197376373 | 6.661549528389206e-12\n", - "0.002 | 0.002 | 1.320615846455827 | 5.782309804024167e+18 | 1283.9306959886815 | 15000.975986864492 | 3.330885786493208e-12\n", - "0.002 | 0.005 | 1.3206969267656394 | 5.782309804024167e+18 | 1283.9306959886815 | 6000.990394744541 | 1.3324875413599593e-12\n", - "0.002 | 0.0075 | 1.3207137929011277 | 5.782309804024167e+18 | 1283.9306959886815 | 4000.9935964959222 | 8.883990424415172e-13\n", - "0.002 | 0.01 | 1.3207205715037762 | 5.782309804024167e+18 | 1283.9306959886815 | 3000.9951973718266 | 6.663547929823436e-13\n", - "0.002 | 0.02 | 1.3207216922930982 | 5.782309804024167e+18 | 1283.9306959886815 | 1500.997598685877 | 3.3328841879362624e-13\n", - "0.002 | 0.05 | 1.320687393868504 | 5.782309804024167e+18 | 1283.9306959886815 | 600.9990394743187 | 1.334485942803984e-13\n", - "0.002 | 0.075 | 1.320653752364362 | 5.782309804024167e+18 | 1283.9306959886815 | 400.9993596495438 | 8.90397443885735e-14\n", - "0.002 | 0.1 | 1.3206198511578064 | 5.782309804024167e+18 | 1283.9306959886815 | 300.9995197371585 | 6.683531944266152e-14\n", - "0.002 | 0.2 | 1.320489789943987 | 5.782309804024167e+18 | 1283.9306959886815 | 150.9997598685783 | 3.352868202379307e-14\n", - "0.002 | 0.5 | 1.3201731005146329 | 5.782309804024167e+18 | 1283.9306959886815 | 60.99990394743119 | 1.3544699572472217e-14\n", - "0.002 | 0.75 | 1.3199936589276795 | 5.782309804024167e+18 | 1283.9306959886815 | 40.99993596495411 | 9.103814583289818e-15\n", - "0.002 | 1 | 1.319887488406783 | 5.782309804024167e+18 | 1283.9306959886815 | 30.99995197371558 | 6.88337208869862e-15\n", - "0.005 | 1e-05 | 1.398281393989088 | 4.7315568285138035e+19 | 10506.166666676816 | 2999970.9840130205 | 6.661273719317286e-10\n", - "0.005 | 2e-05 | 1.388722431615681 | 4.7315568285138035e+19 | 10506.166666676816 | 1499985.9920165162 | 3.330637969903885e-10\n", - "0.005 | 5e-05 | 1.3683630926262451 | 4.7315568285138035e+19 | 10506.166666676816 | 599994.9967708938 | 1.3322565201498855e-10\n", - "0.005 | 7.5e-05 | 1.3574022549722211 | 4.7315568285138035e+19 | 10506.166666676816 | 399996.9978457239 | 8.881717535785236e-11\n", - "0.005 | 0.0001 | 1.349656526521845 | 4.7315568285138035e+19 | 10506.166666676816 | 299997.99838577345 | 6.661293702986925e-11\n", - "0.005 | 0.0002 | 1.3338899892280682 | 4.7315568285138035e+19 | 10506.166666676816 | 149999.49919096037 | 3.330657953680935e-11\n", - "0.005 | 0.0005 | 1.3231314606154727 | 4.7315568285138035e+19 | 10506.166666676816 | 60000.399676413464 | 1.3322765041493204e-11\n", - "0.005 | 0.00075 | 1.3213658709059117 | 4.7315568285138035e+19 | 10506.166666676816 | 40000.599784124155 | 8.88191737583014e-12\n", - "0.005 | 0.001 | 1.3207434325086935 | 4.7315568285138035e+19 | 10506.166666676816 | 30000.699838091627 | 6.661493543023506e-12\n", - "0.005 | 0.002 | 1.3203109441737189 | 4.7315568285138035e+19 | 10506.166666676816 | 15000.84991903379 | 3.3308577938115457e-12\n", - "0.005 | 0.005 | 1.3204473592564139 | 4.7315568285138035e+19 | 10506.166666676816 | 6000.939967612918 | 1.3324763442874405e-12\n", - "0.005 | 0.0075 | 1.3205266147948784 | 4.7315568285138035e+19 | 10506.166666676816 | 4000.95997840818 | 8.883915777265062e-13\n", - "0.005 | 0.01 | 1.3205722511348426 | 4.7315568285138035e+19 | 10506.166666676816 | 3000.9699838059914 | 6.66349194446079e-13\n", - "0.005 | 0.02 | 1.3206416674457822 | 4.7315568285138035e+19 | 10506.166666676816 | 1500.9849919029054 | 3.3328561952548196e-13\n", - "0.005 | 0.05 | 1.3206543062552778 | 4.7315568285138035e+19 | 10506.166666676816 | 600.9939967611574 | 1.3344747457314674e-13\n", - "0.005 | 0.075 | 1.3206317178276619 | 4.7315568285138035e+19 | 10506.166666676816 | 400.9959978407689 | 8.903899791707224e-14\n", - "0.005 | 0.1 | 1.3206034290426678 | 4.7315568285138035e+19 | 10506.166666676816 | 300.9969983805781 | 6.683475958903575e-14\n", - "0.005 | 0.2 | 1.3204818942961338 | 4.7315568285138035e+19 | 10506.166666676816 | 150.99849919028685 | 3.3528402096979903e-14\n", - "0.005 | 0.5 | 1.3201703838941503 | 4.7315568285138035e+19 | 10506.166666676816 | 60.99939967611459 | 1.3544587601746947e-14\n", - "0.005 | 0.75 | 1.3199921011576614 | 4.7315568285138035e+19 | 10506.166666676816 | 40.999599784076366 | 9.103739936139636e-15\n", - "0.005 | 1 | 1.3198865127506896 | 4.7315568285138035e+19 | 10506.166666676816 | 30.999699838057264 | 6.883316103335982e-15\n", - "0.0075 | 1e-05 | 1.404300593878447 | 1.7372731761309637e+18 | 385.75213604085417 | 2999933.4645628342 | 6.661190409402349e-10\n", - "0.0075 | 2e-05 | 1.3994032633183124 | 1.7372731761309637e+18 | 385.75213604085417 | 1499967.232282145 | 3.3305963149258154e-10\n", - "0.0075 | 5e-05 | 1.386805647947958 | 1.7372731761309637e+18 | 385.75213604085417 | 599987.4928880802 | 1.332239858182938e-10\n", - "0.0075 | 7.5e-05 | 1.3783815287748937 | 1.7372731761309637e+18 | 385.75213604085417 | 399991.9952527292 | 8.881606455906726e-11\n", - "0.0075 | 0.0001 | 1.3714017808048378 | 1.7372731761309637e+18 | 385.75213604085417 | 299994.24643613514 | 6.661210392969411e-11\n", - "0.0075 | 0.0002 | 1.352783451657262 | 1.7372731761309637e+18 | 385.75213604085417 | 149997.6232191162 | 3.330616298738236e-11\n", - "0.0075 | 0.0005 | 1.3320471009302473 | 1.7372731761309637e+18 | 385.75213604085417 | 59999.64928707162 | 1.3322598421588254e-11\n", - "0.0075 | 0.00075 | 1.3264972569267337 | 1.7372731761309637e+18 | 385.75213604085417 | 40000.09952468137 | 8.881806295919807e-12\n", - "0.0075 | 0.001 | 1.3239147582316242 | 1.7372731761309637e+18 | 385.75213604085417 | 30000.32464348631 | 6.6614102330855985e-12\n", - "0.0075 | 0.002 | 1.3209497744222265 | 1.7372731761309637e+18 | 385.75213604085417 | 15000.66232173369 | 3.33081613884316e-12\n", - "0.0075 | 0.005 | 1.32030667526022 | 1.7372731761309637e+18 | 385.75213604085417 | 6000.864928690638 | 1.332459682299589e-12\n", - "0.0075 | 0.0075 | 1.3203572891448485 | 1.7372731761309637e+18 | 385.75213604085417 | 4000.9099524600265 | 8.883804697346124e-13\n", - "0.0075 | 0.01 | 1.3204149453606089 | 1.7372731761309637e+18 | 385.75213604085417 | 3000.932464345096 | 6.663408634522074e-13\n", - "0.0075 | 0.02 | 1.3205391985944162 | 1.7372731761309637e+18 | 385.75213604085417 | 1500.9662321724334 | 3.332814540285408e-13\n", - "0.0075 | 0.05 | 1.3206077953595612 | 1.7372731761309637e+18 | 385.75213604085417 | 600.9864928689655 | 1.3344580837436958e-13\n", - "0.0075 | 0.075 | 1.320600144100872 | 1.7372731761309637e+18 | 385.75213604085417 | 400.9909952459681 | 8.90378871178861e-14\n", - "0.0075 | 0.1 | 1.320579675219363 | 1.7372731761309637e+18 | 385.75213604085417 | 300.99324643447636 | 6.683392648964589e-14\n", - "0.0075 | 0.2 | 1.3204703143593972 | 1.7372731761309637e+18 | 385.75213604085417 | 150.99662321723713 | 3.352798554728523e-14\n", - "0.0075 | 0.5 | 1.320166367422988 | 1.7372731761309637e+18 | 385.75213604085417 | 60.998649286894484 | 1.3544420981869028e-14\n", - "0.0075 | 0.75 | 1.3199897942613739 | 1.7372731761309637e+18 | 385.75213604085417 | 40.99909952459634 | 9.103628856221033e-15\n", - "0.0075 | 1 | 1.3198850669656137 | 1.7372731761309637e+18 | 385.75213604085417 | 30.9993246434473 | 6.8832327933970424e-15\n", - "0.01 | 1e-05 | 1.4061992061470945 | 9.15881260810045e+18 | 2033.66492714806 | 2999880.9389639026 | 6.661073779143717e-10\n", - "0.01 | 2e-05 | 1.4035355143448425 | 9.15881260810045e+18 | 2033.66492714806 | 1499940.969350007 | 3.330537999501908e-10\n", - "0.01 | 5e-05 | 1.395627182515116 | 9.15881260810045e+18 | 2033.66492714806 | 599976.9877232183 | 1.3322165320311236e-10\n", - "0.01 | 7.5e-05 | 1.389760075244399 | 9.15881260810045e+18 | 2033.66492714806 | 399984.9918138873 | 8.881450948325649e-11\n", - "0.01 | 0.0001 | 1.3845164268287486 | 9.15881260810045e+18 | 2033.66492714806 | 299988.99385887664 | 6.66109376232519e-11\n", - "0.01 | 0.0002 | 1.368340200656151 | 9.15881260810045e+18 | 2033.66492714806 | 149994.9969292092 | 3.3305579833877544e-11\n", - "0.01 | 0.0005 | 1.3439837129673218 | 9.15881260810045e+18 | 2033.66492714806 | 59998.598771298515 | 1.3322365160228448e-11\n", - "0.01 | 0.00075 | 1.335093711991605 | 9.15881260810045e+18 | 2033.66492714806 | 39999.399180849905 | 8.881650788350438e-12\n", - "0.01 | 0.001 | 1.3301839804871138 | 9.15881260810045e+18 | 2033.66492714806 | 29999.799385624003 | 6.661293602411079e-12\n", - "0.01 | 0.002 | 1.3231293127499864 | 9.15881260810045e+18 | 2033.66492714806 | 15000.399692799341 | 3.3307578235051907e-12\n", - "0.01 | 0.005 | 1.3204830758443515 | 9.15881260810045e+18 | 2033.66492714806 | 6000.7598771195135 | 1.3324363561649818e-12\n", - "0.01 | 0.0075 | 1.3203100371670242 | 9.15881260810045e+18 | 2033.66492714806 | 4000.839918079025 | 8.883649189781518e-13\n", - "0.01 | 0.01 | 1.320308943205694 | 9.15881260810045e+18 | 2033.66492714806 | 3000.879938558849 | 6.663292003847519e-13\n", - "0.01 | 0.02 | 1.3204273423452193 | 9.15881260810045e+18 | 2033.66492714806 | 1500.9399692794002 | 3.3327562249483305e-13\n", - "0.01 | 0.05 | 1.3205480387004698 | 9.15881260810045e+18 | 2033.66492714806 | 600.9759877117588 | 1.3344347576088794e-13\n", - "0.01 | 0.075 | 1.3205583477737104 | 9.15881260810045e+18 | 2033.66492714806 | 400.9839918078323 | 8.903633204223211e-14\n", - "0.01 | 0.1 | 1.320547778680883 | 9.15881260810045e+18 | 2033.66492714806 | 300.98799385587427 | 6.683276018290535e-14\n", - "0.01 | 0.2 | 1.3204544411843475 | 9.15881260810045e+18 | 2033.66492714806 | 150.99399692793642 | 3.3527402393915034e-14\n", - "0.01 | 0.5 | 1.3201607967208802 | 9.15881260810045e+18 | 2033.66492714806 | 60.99759877117422 | 1.3544187720520954e-14\n", - "0.01 | 0.75 | 1.3199865871021215 | 9.15881260810045e+18 | 2033.66492714806 | 40.99839918078277 | 9.103473348655637e-15\n", - "0.01 | 1 | 1.3198830550759415 | 9.15881260810045e+18 | 2033.66492714806 | 30.998799385587116 | 6.8831161627229944e-15\n", - "0.02 | 1e-05 | 1.3991967821771687 | 4.596172752109427e+18 | 1020.5553629093316 | 2999520.8077985244 | 6.660274127320341e-10\n", - "0.02 | 2e-05 | 1.4033527820009084 | 4.596172752109427e+18 | 1020.5553629093316 | 1499760.9038967583 | 3.3301381738776355e-10\n", - "0.02 | 5e-05 | 1.4040547659095226 | 4.596172752109427e+18 | 1020.5553629093316 | 599904.9615311394 | 1.3320566017574796e-10\n", - "0.02 | 7.5e-05 | 1.4028711082098146 | 4.596172752109427e+18 | 1020.5553629093316 | 399936.9743506041 | 8.880384746459227e-11\n", - "0.02 | 0.0001 | 1.4014091897441256 | 4.596172752109427e+18 | 1020.5553629093316 | 299952.98076160415 | 6.660294110929591e-11\n", - "0.02 | 0.0002 | 1.3952513762994323 | 4.596172752109427e+18 | 1020.5553629093316 | 149976.99038130237 | 3.3301581577061505e-11\n", - "0.02 | 0.0005 | 1.3796701173934471 | 4.596172752109427e+18 | 1020.5553629093316 | 59991.39615212075 | 1.3320765857498696e-11\n", - "0.02 | 0.00075 | 1.3699232085655064 | 4.596172752109427e+18 | 1020.5553629093316 | 39994.5974347125 | 8.880584586526408e-12\n", - "0.02 | 0.001 | 1.362292110296485 | 4.596172752109427e+18 | 1020.5553629093316 | 29996.198076027966 | 6.660493951044614e-12\n", - "0.02 | 0.002 | 1.3439496124690997 | 4.596172752109427e+18 | 1020.5553629093316 | 14998.599038011473 | 3.330357997824212e-12\n", - "0.02 | 0.005 | 1.3272089427378684 | 4.596172752109427e+18 | 1020.5553629093316 | 6000.039615202919 | 1.332276425892269e-12\n", - "0.02 | 0.0075 | 1.323532461862641 | 4.596172752109427e+18 | 1020.5553629093316 | 4000.3597434684293 | 8.88258298796447e-13\n", - "0.02 | 0.01 | 1.3219923714906017 | 4.596172752109427e+18 | 1020.5553629093316 | 3000.5198076010715 | 6.662492352485109e-13\n", - "0.02 | 0.02 | 1.3204676186157385 | 4.596172752109427e+18 | 1020.5553629093316 | 1500.7599038004946 | 3.332356399267088e-13\n", - "0.02 | 0.05 | 1.3202928659769584 | 4.596172752109427e+18 | 1020.5553629093316 | 600.9039615201905 | 1.334274827336369e-13\n", - "0.02 | 0.075 | 1.3203431410941724 | 4.596172752109427e+18 | 1020.5553629093316 | 400.9359743467912 | 8.902567002406575e-14\n", - "0.02 | 0.1 | 1.320369937260628 | 4.596172752109427e+18 | 1020.5553629093316 | 300.95198076008995 | 6.68247636692798e-14\n", - "0.02 | 0.2 | 1.320356005679441 | 4.596172752109427e+18 | 1020.5553629093316 | 150.97599038004486 | 3.352340413710239e-14\n", - "0.02 | 0.5 | 1.3201242272878075 | 4.596172752109427e+18 | 1020.5553629093316 | 60.990396152017844 | 1.3542588417795952e-14\n", - "0.02 | 0.75 | 1.3199652973344642 | 4.596172752109427e+18 | 1020.5553629093316 | 40.99359743467856 | 9.102407146838978e-15\n", - "0.02 | 1 | 1.3198696409051938 | 4.596172752109427e+18 | 1020.5553629093316 | 30.99519807600895 | 6.882316511360498e-15\n", - "0.05 | 1e-05 | 1.1802626763387565 | 2.292482447793158e+19 | 5090.333594178005 | 2997002.1306760353 | 6.654681540654373e-10\n", - "0.05 | 2e-05 | 1.2638668839634342 | 2.292482447793158e+19 | 5090.333594178005 | 1498501.5652584448 | 3.327341880373524e-10\n", - "0.05 | 5e-05 | 1.3399914336674565 | 2.292482447793158e+19 | 5090.333594178005 | 599401.2260877036 | 1.3309380843822352e-10\n", - "0.05 | 7.5e-05 | 1.3607917455523473 | 2.292482447793158e+19 | 5090.333594178005 | 399601.150725315 | 8.872927964039045e-11\n", - "0.05 | 0.0001 | 1.3717399149142016 | 2.292482447793158e+19 | 5090.333594178005 | 299701.11304101965 | 6.654701524078536e-11\n", - "0.05 | 0.0002 | 1.388357196418246 | 2.292482447793158e+19 | 5090.333594178005 | 149851.0565198782 | 3.3273618642554895e-11\n", - "0.05 | 0.0005 | 1.3960809195486679 | 2.292482447793158e+19 | 5090.333594178005 | 59941.02260797523 | 1.330958068379023e-11\n", - "0.05 | 0.00075 | 1.395825918790288 | 2.292482447793158e+19 | 5090.333594178005 | 39961.015071850336 | 8.87312780403223e-12\n", - "0.05 | 0.001 | 1.3944270862078088 | 2.292482447793158e+19 | 5090.333594178005 | 29971.011303897183 | 6.6549013641774975e-12\n", - "0.05 | 0.002 | 1.3868038656182429 | 2.292482447793158e+19 | 5090.333594178005 | 14986.005651941321 | 3.327561704389597e-12\n", - "0.05 | 0.005 | 1.3676102367336447 | 2.292482447793158e+19 | 5090.333594178005 | 5995.002260777155 | 1.331157908518933e-12\n", - "0.05 | 0.0075 | 1.3569104532323966 | 2.292482447793158e+19 | 5090.333594178005 | 3997.0015071830744 | 8.875126205472204e-13\n", - "0.05 | 0.01 | 1.3492964425395066 | 2.292482447793158e+19 | 5090.333594178005 | 2998.001130387283 | 6.656899765616415e-13\n", - "0.05 | 0.02 | 1.3337278030826976 | 2.292482447793158e+19 | 5090.333594178005 | 1499.500565193526 | 3.3295601058325763e-13\n", - "0.05 | 0.05 | 1.3230623528240189 | 2.292482447793158e+19 | 5090.333594178005 | 600.4002260774155 | 1.333156309962592e-13\n", - "0.05 | 0.075 | 1.3212917568278908 | 2.292482447793158e+19 | 5090.333594178005 | 400.6001507182743 | 8.895110219914721e-14\n", - "0.05 | 0.1 | 1.3206495137172813 | 2.292482447793158e+19 | 5090.333594178005 | 300.70011303870496 | 6.67688378005915e-14\n", - "0.05 | 0.2 | 1.3201079927565518 | 2.292482447793158e+19 | 5090.333594178005 | 150.85005651935137 | 3.349544120275802e-14\n", - "0.05 | 0.5 | 1.3199430633401974 | 2.292482447793158e+19 | 5090.333594178005 | 60.940022607740424 | 1.3531403244058199e-14\n", - "0.05 | 0.75 | 1.3198490982380757 | 2.292482447793158e+19 | 5090.333594178005 | 40.96001507182699 | 9.094950364347151e-15\n", - "0.05 | 1 | 1.3197937467706273 | 2.292482447793158e+19 | 5090.333594178005 | 30.970011303870223 | 6.876723924491617e-15\n", - "0.075 | 1e-05 | 1.0289125856997683 | 2.5655434447895905e+18 | 569.6650806163085 | 2993261.340844146 | 6.646375318651079e-10\n", - "0.075 | 2e-05 | 1.06833574230173 | 2.5655434447895905e+18 | 569.6650806163085 | 1496631.170356869 | 3.323188769403782e-10\n", - "0.075 | 5e-05 | 1.1788194485853987 | 2.5655434447895905e+18 | 569.6650806163085 | 598653.0681396328 | 1.3292768400222262e-10\n", - "0.075 | 7.5e-05 | 1.229861581346619 | 2.5655434447895905e+18 | 569.6650806163085 | 399102.3787592882 | 8.861853001624636e-11\n", - "0.075 | 0.0001 | 1.2625315302908773 | 2.5655434447895905e+18 | 569.6650806163085 | 299327.0340661838 | 6.646395302260717e-11\n", - "0.075 | 0.0002 | 1.3241269630070216 | 2.5655434447895905e+18 | 569.6650806163085 | 149664.01703276392 | 3.323208753353322e-11\n", - "0.075 | 0.0005 | 1.3699287056814906 | 2.5655434447895905e+18 | 569.6650806163085 | 59866.20681304865 | 1.3292968240163605e-11\n", - "0.075 | 0.00075 | 1.3804567145238036 | 2.5655434447895905e+18 | 569.6650806163085 | 39911.137875334636 | 8.862052841637133e-12\n", - "0.075 | 0.001 | 1.3852806670848645 | 2.5655434447895905e+18 | 569.6650806163085 | 29933.60340651576 | 6.646595142382363e-12\n", - "0.075 | 0.002 | 1.389766784950012 | 2.5655434447895905e+18 | 569.6650806163085 | 14967.301703250861 | 3.3234085934920857e-12\n" + "0.001 | 1e-05 | 1.3301889998089138 | 5.157729967665246e+18 | 1145.246112980224 | 2999999.7995568505 | 6.66133770267774e-10\n", + "0.001 | 2e-05 | 1.3231321486415055 | 5.157729967665246e+18 | 1145.246112980224 | 1500000.3997263806 | 3.330669961446332e-10\n", + "0.001 | 5e-05 | 1.320488243926721 | 5.157729967665246e+18 | 1145.246112980224 | 600000.759879188 | 1.3322693168209288e-10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.075 | 0.005 | 1.3829509021399737 | 2.5655434447895905e+18 | 569.6650806163085 | 5987.520681298484 | 1.3294966641593762e-12\n", - "0.075 | 0.0075 | 1.3758254841268682 | 2.5655434447895905e+18 | 569.6650806163085 | 3992.013787532067 | 8.864051243078357e-13\n", - "0.075 | 0.01 | 1.3694975238618983 | 2.5655434447895905e+18 | 569.6650806163085 | 2994.2603406490575 | 6.648593543821096e-13\n", - "0.075 | 0.02 | 1.3518655278680678 | 2.5655434447895905e+18 | 569.6650806163085 | 1497.630170324489 | 3.325406994935085e-13\n", - "0.075 | 0.05 | 1.3317271553627905 | 2.5655434447895905e+18 | 569.6650806163085 | 599.6520681297745 | 1.3314950656035374e-13\n", - "0.075 | 0.075 | 1.3262949589526654 | 2.5655434447895905e+18 | 569.6650806163085 | 400.1013787531858 | 8.884035257521146e-14\n", - "0.075 | 0.1 | 1.3237555826770928 | 2.5655434447895905e+18 | 569.6650806163085 | 300.3260340648898 | 6.668577558263996e-14\n", - "0.075 | 0.2 | 1.3207755491672837 | 2.5655434447895905e+18 | 569.6650806163085 | 150.6630170324437 | 3.345391009378222e-14\n", - "0.075 | 0.5 | 1.3198839335916897 | 2.5655434447895905e+18 | 569.6650806163085 | 60.86520681297757 | 1.3514790800467928e-14\n", - "0.075 | 0.75 | 1.3197723726186081 | 2.5655434447895905e+18 | 569.6650806163085 | 40.91013787531835 | 9.083875401953622e-15\n", - "0.075 | 1 | 1.3197346497546036 | 2.5655434447895905e+18 | 569.6650806163085 | 30.932603406488766 | 6.868417702696476e-15\n", - "0.1 | 1e-05 | 1.0798981068160698 | 1.4534741232350866e+18 | 322.73608746249107 | 2988038.7413779353 | 6.634778818299514e-10\n", - "0.1 | 2e-05 | 1.034411147287902 | 1.4534741232350866e+18 | 322.73608746249107 | 1494019.8706289497 | 3.3173905192395153e-10\n", - "0.1 | 5e-05 | 1.0485665416883234 | 1.4534741232350866e+18 | 322.73608746249107 | 597608.5482298866 | 1.3269575399152668e-10\n", - "0.1 | 7.5e-05 | 1.0859812540943814 | 1.4534741232350866e+18 | 322.73608746249107 | 398406.0321496727 | 8.846391000842339e-11\n", - "0.1 | 0.0001 | 1.1199271289309896 | 1.4534741232350866e+18 | 322.73608746249107 | 298804.7741134506 | 6.634798801773436e-11\n", - "0.1 | 0.0002 | 1.2092033599799092 | 1.4534741232350866e+18 | 322.73608746249107 | 149402.88705538897 | 3.317410503087292e-11\n", - "0.1 | 0.0005 | 1.305803183492356 | 1.4534741232350866e+18 | 322.73608746249107 | 59761.75482211882 | 1.3269775239103958e-11\n", - "0.1 | 0.00075 | 1.3347573633949794 | 1.4534741232350866e+18 | 322.73608746249107 | 39841.5032147429 | 8.846590840936952e-12\n", - "0.1 | 0.001 | 1.3503821090957047 | 1.4534741232350866e+18 | 322.73608746249107 | 29881.37741105147 | 6.634998641852679e-12\n", - "0.1 | 0.002 | 1.374194887133274 | 1.4534741232350866e+18 | 322.73608746249107 | 14941.188705527122 | 3.3176103432291097e-12\n", - "0.1 | 0.005 | 1.3835828525725824 | 1.4534741232350866e+18 | 322.73608746249107 | 5977.075482208381 | 1.327177364054051e-12\n", - "0.1 | 0.0075 | 1.381710463377333 | 1.4534741232350866e+18 | 322.73608746249107 | 3985.050321472298 | 8.848589242376854e-13\n", - "0.1 | 0.01 | 1.378484030173927 | 1.4534741232350866e+18 | 322.73608746249107 | 2989.0377411040554 | 6.63699704329458e-13\n", - "0.1 | 0.02 | 1.365360961026239 | 1.4534741232350866e+18 | 322.73608746249107 | 1495.018870551993 | 3.319608744671838e-13\n", - "0.1 | 0.05 | 1.3428726584962276 | 1.4534741232350866e+18 | 322.73608746249107 | 598.607548220793 | 1.329175765498276e-13\n", - "0.1 | 0.075 | 1.3344025162997288 | 1.4534741232350866e+18 | 322.73608746249107 | 399.4050321471928 | 8.868573256819285e-14\n", - "0.1 | 0.1 | 1.3296947739898706 | 1.4534741232350866e+18 | 322.73608746249107 | 299.80377411039274 | 6.656981057737548e-14\n", - "0.1 | 0.2 | 1.3228751118106652 | 1.4534741232350866e+18 | 322.73608746249107 | 150.40188705519589 | 3.339592759115015e-14\n", - "0.1 | 0.5 | 1.320130553011318 | 1.4534741232350866e+18 | 322.73608746249107 | 60.76075482207832 | 1.3491597799415071e-14\n", - "0.1 | 0.75 | 1.319826836642748 | 1.4534741232350866e+18 | 322.73608746249107 | 40.84050321471888 | 9.068413401251725e-15\n", - "0.1 | 1 | 1.3197458467857655 | 1.4534741232350866e+18 | 322.73608746249107 | 30.880377411039163 | 6.856821202170052e-15\n", - "0.2 | 1e-05 | 1.034878071426437 | 8.562996015832429e+17 | 190.13670673101288 | 2952676.851384146 | 6.556259649368781e-10\n", - "0.2 | 2e-05 | 1.099054270537908 | 8.562996015832429e+17 | 190.13670673101288 | 1476338.9256587334 | 3.278130934833386e-10\n", - "0.2 | 5e-05 | 1.118668157567818 | 8.562996015832429e+17 | 190.13670673101288 | 590536.170254422 | 1.3112537061808416e-10\n", - "0.2 | 7.5e-05 | 1.106463291346616 | 8.562996015832429e+17 | 190.13670673101288 | 393691.1135038531 | 8.741698776045872e-11\n", - "0.2 | 0.0001 | 1.091806769704812 | 8.562996015832429e+17 | 190.13670673101288 | 295268.58512797963 | 6.556279633151521e-11\n", - "0.2 | 0.0002 | 1.0475901909841485 | 8.562996015832429e+17 | 190.13670673101288 | 147634.79256333216 | 3.278150918791404e-11\n", - "0.2 | 0.0005 | 1.0237060377513256 | 8.562996015832429e+17 | 190.13670673101288 | 59054.517025191155 | 1.3112736901897105e-11\n", - "0.2 | 0.00075 | 1.04286126411308 | 8.562996015832429e+17 | 190.13670673101288 | 39370.011350120825 | 8.741898616131577e-12\n", - "0.2 | 0.001 | 1.0675482361224131 | 8.562996015832429e+17 | 190.13670673101288 | 29527.758512602293 | 6.556479473252506e-12\n", - "0.2 | 0.002 | 1.150251960798731 | 8.562996015832429e+17 | 190.13670673101288 | 14764.379256294229 | 3.2783507589271796e-12\n", - "0.2 | 0.005 | 1.2613461172689404 | 8.562996015832429e+17 | 190.13670673101288 | 5906.351702517787 | 1.311473530333848e-12\n", - "0.2 | 0.0075 | 1.2986164432111689 | 8.562996015832429e+17 | 190.13670673101288 | 3937.9011350116157 | 8.743897017574866e-13\n", - "0.2 | 0.01 | 1.3193653278045645 | 8.562996015832429e+17 | 190.13670673101288 | 2953.6758512586707 | 6.558477874693371e-13\n", - "0.2 | 0.02 | 1.3511101649720016 | 8.562996015832429e+17 | 190.13670673101288 | 1477.3379256293263 | 3.2803491603712903e-13\n", - "0.2 | 0.05 | 1.361365740618009 | 8.562996015832429e+17 | 190.13670673101288 | 591.5351702517382 | 1.3134719317780835e-13\n", - "0.2 | 0.075 | 1.3577244256580792 | 8.562996015832429e+17 | 190.13670673101288 | 394.69011350115363 | 8.763881032017942e-14\n", - "0.2 | 0.1 | 1.35321206907301 | 8.562996015832429e+17 | 190.13670673101288 | 296.26758512586605 | 6.578461889136601e-14\n", - "0.2 | 0.2 | 1.3396807959929746 | 8.562996015832429e+17 | 190.13670673101288 | 148.6337925629331 | 3.300333174814554e-14\n", - "0.2 | 0.5 | 1.3258785979549303 | 8.562996015832429e+17 | 190.13670673101288 | 60.05351702517299 | 1.3334559462213177e-14\n", - "0.2 | 0.75 | 1.3227927985529944 | 8.562996015832429e+17 | 190.13670673101288 | 40.369011350115315 | 8.96372117645046e-15\n", - "0.2 | 1 | 1.321535717952325 | 8.562996015832429e+17 | 190.13670673101288 | 30.5267585125865 | 6.778302033569106e-15\n", - "0.5 | 1e-05 | 0.24096794492402857 | 980258980477233.9 | 0.21766121804428137 | 2725466.7390964394 | 6.051751853189823e-10\n", - "0.5 | 2e-05 | 0.24490838803503873 | 980258980477233.9 | 0.21766121804428137 | 1362733.8714523853 | 3.025877041046033e-10\n", - "0.5 | 5e-05 | 0.26603719418936833 | 980258980477233.9 | 0.21766121804428137 | 545094.1490353851 | 1.2103521496950822e-10\n", - "0.5 | 7.5e-05 | 0.3041435170925932 | 980258980477233.9 | 0.21766121804428137 | 363396.432758199 | 8.069021734296e-11\n", - "0.5 | 0.0001 | 0.3466455959864901 | 980258980477233.9 | 0.21766121804428137 | 272547.57459426456 | 6.051771852405897e-11\n", - "0.5 | 0.0002 | 0.48965105730966685 | 980258980477233.9 | 0.21766121804428137 | 136274.2873159813 | 3.025897028851727e-11\n", - "0.5 | 0.0005 | 0.7000258094502446 | 980258980477233.9 | 0.21766121804428137 | 54510.31493093633 | 1.2103721343178792e-11\n", - "0.5 | 0.00075 | 0.7803152404537564 | 980258980477233.9 | 0.21766121804428137 | 36340.54328796332 | 8.069221577136814e-12\n", - "0.5 | 0.001 | 0.8291864833507758 | 980258980477233.9 | 0.21766121804428137 | 27255.657466223165 | 6.0519716940595025e-12\n", - "0.5 | 0.002 | 0.9148557625310408 | 980258980477233.9 | 0.21766121804428137 | 13628.32873330063 | 3.0260968693741908e-12\n", - "0.5 | 0.005 | 0.9605054768523522 | 980258980477233.9 | 0.21766121804428137 | 5451.931493366453 | 1.21057197452289e-12\n", - "0.5 | 0.0075 | 0.9664986040888508 | 980258980477233.9 | 0.21766121804428137 | 3634.9543289176663 | 8.071219978850555e-13\n", - "0.5 | 0.01 | 0.9715770597539694 | 980258980477233.9 | 0.21766121804428137 | 2726.465746690786 | 6.053970095655861e-13\n", - "0.5 | 0.02 | 1.0046221692212978 | 980258980477233.9 | 0.21766121804428137 | 1363.7328733473048 | 3.0280952708568005e-13\n", - "0.5 | 0.05 | 1.1047795554227993 | 980258980477233.9 | 0.21766121804428137 | 546.0931493393778 | 1.2125703759732826e-13\n", - "0.5 | 0.075 | 1.158142451657815 | 980258980477233.9 | 0.21766121804428137 | 364.395432892986 | 8.091203993320883e-14\n", - "0.5 | 0.1 | 1.1943040342674953 | 980258980477233.9 | 0.21766121804428137 | 273.5465746697651 | 6.073954110114357e-14\n", - "0.5 | 0.2 | 1.2650403625958067 | 980258980477233.9 | 0.21766121804428137 | 137.27328733490157 | 3.048079285303852e-14\n", - "0.5 | 0.5 | 1.3129959855689677 | 980258980477233.9 | 0.21766121804428137 | 55.50931493396519 | 1.2325543904171442e-14\n", - "0.5 | 0.75 | 1.320563153538016 | 980258980477233.9 | 0.21766121804428137 | 37.3395432893108 | 8.29104413775612e-15\n", - "0.5 | 1 | 1.322880946564286 | 980258980477233.9 | 0.21766121804428137 | 28.254657466983357 | 6.273794254548405e-15\n", - "0.75 | 1e-05 | 0.2224499701913163 | 3532627464900.7993 | 0.0007844008697912129 | 2442727.9074404994 | 5.423945531469741e-10\n", - "0.75 | 2e-05 | 0.20530606055267492 | 3532627464900.7993 | 0.0007844008697912129 | 1221364.876011224 | 2.711974813632221e-10\n", - "0.75 | 5e-05 | 0.2085533760821295 | 3532627464900.7993 | 0.0007844008697912129 | 488546.6517436233 | 1.084791482738597e-10\n", - "0.75 | 7.5e-05 | 0.21981047089552586 | 3532627464900.7993 | 0.0007844008697912129 | 325698.1161762267 | 7.23195095311772e-11\n", - "0.75 | 0.0001 | 0.22929183435173858 | 3532627464900.7993 | 0.0007844008697912129 | 244273.84276196134 | 5.4239688909598923e-11\n", - "0.75 | 0.0002 | 0.25206556710849465 | 3532627464900.7993 | 0.0007844008697912129 | 122137.4256038074 | 2.7119956414757816e-11\n", - "0.75 | 0.0005 | 0.29114658168992563 | 3532627464900.7993 | 0.0007844008697912129 | 48855.571254952665 | 1.084811601769268e-11\n", - "0.75 | 0.00075 | 0.32523987895143874 | 3532627464900.7993 | 0.0007844008697912129 | 32570.714320096464 | 7.232151393331879e-12\n", - "0.75 | 0.001 | 0.35990828966432387 | 3532627464900.7993 | 0.0007844008697912129 | 24428.285796379587 | 5.424169068652859e-12\n", - "0.75 | 0.002 | 0.4758051392903338 | 3532627464900.7993 | 0.0007844008697912129 | 12214.642940416235 | 2.7121955660050457e-12\n", - "0.75 | 0.005 | 0.6515260879776209 | 3532627464900.7993 | 0.0007844008697912129 | 4886.457186300838 | 1.0850114554152496e-12\n", - "0.75 | 0.0075 | 0.7197356993725796 | 3532627464900.7993 | 0.0007844008697912129 | 3257.9714590352405 | 7.234149854785078e-13\n", - "0.75 | 0.01 | 0.7617582568840171 | 3532627464900.7993 | 0.0007844008697912129 | 2443.7285948394756 | 5.426167503851333e-13\n", - "0.75 | 0.02 | 0.8408992984214783 | 3532627464900.7993 | 0.0007844008697912129 | 1222.3642978419875 | 2.714193975887874e-13\n", - "0.75 | 0.05 | 0.9210906761308861 | 3532627464900.7993 | 0.0007844008697912129 | 489.5457192381444 | 1.0870098582097407e-13\n", - "0.75 | 0.075 | 0.9624492708631208 | 3532627464900.7993 | 0.0007844008697912129 | 326.69714617377673 | 7.254133875229146e-14\n", - "0.75 | 0.1 | 0.9968207350967749 | 3532627464900.7993 | 0.0007844008697912129 | 245.2728596359627 | 5.4461515216699997e-14\n", - "0.75 | 0.2 | 1.0921914353907192 | 3532627464900.7993 | 0.0007844008697912129 | 123.13642982220418 | 2.734177991175017e-14\n", - "0.75 | 0.5 | 1.2113887072395224 | 3532627464900.7993 | 0.0007844008697912129 | 49.85457192989512 | 1.1069938727880118e-14\n", - "0.75 | 0.75 | 1.2502765485666594 | 3532627464900.7993 | 0.0007844008697912129 | 33.56971462008021 | 7.453974020261758e-15\n", - "0.75 | 1 | 1.2715113409155712 | 3532627464900.7993 | 0.0007844008697912129 | 25.427285965116454 | 5.645991666440076e-15\n", - "1.0 | 1e-05 | 0.3201790446663786 | 63914578843.30032 | 1.4191887408210385e-05 | 2130369.1131565426 | 4.730369680753338e-10\n", - "1.0 | 2e-05 | 0.30074023142892836 | 63914578843.30032 | 1.4191887408210385e-05 | 1065202.8089900897 | 2.3652253688723805e-10\n", - "1.0 | 5e-05 | 0.26987296047072223 | 63914578843.30032 | 1.4191887408210385e-05 | 426085.98427822563 | 9.461009404315171e-11\n", - "1.0 | 7.5e-05 | 0.255971387187336 | 63914578843.30032 | 1.4191887408210385e-05 | 284058.28740671236 | 6.307361020290444e-11\n", - "1.0 | 0.0001 | 0.24652041427480517 | 63914578843.30032 | 1.4191887408210385e-05 | 213044.20226336323 | 4.730531572313695e-11\n", - "1.0 | 0.0002 | 0.2298542106226902 | 63914578843.30032 | 1.4191887408210385e-05 | 106522.77866349413 | 2.365280830385211e-11\n", - "1.0 | 0.0005 | 0.23919407237621151 | 63914578843.30032 | 1.4191887408210385e-05 | 42609.754073134274 | 9.461266009121844e-12\n", - "1.0 | 0.00075 | 0.25539979901780313 | 63914578843.30032 | 1.4191887408210385e-05 | 28406.842361022733 | 6.307586089220936e-12\n", - "1.0 | 0.001 | 0.2695794334088568 | 63914578843.30032 | 1.4191887408210385e-05 | 21305.384137872177 | 4.730745603669856e-12\n", - "1.0 | 0.002 | 0.31063876807450386 | 63914578843.30032 | 1.4191887408210385e-05 | 10653.193844259556 | 2.3654842183383887e-12\n", - "1.0 | 0.005 | 0.3936609595496504 | 63914578843.30032 | 1.4191887408210385e-05 | 4261.877963782238 | 9.46327008706724e-13\n", - "1.0 | 0.0075 | 0.4469915900961469 | 63914578843.30032 | 1.4191887408210385e-05 | 2841.5853723107916 | 6.309587013554977e-13\n", - "1.0 | 0.01 | 0.49016736300399305 | 63914578843.30032 | 1.4191887408210385e-05 | 2131.4390529041125 | 4.732745424238766e-13\n", - "1.0 | 0.02 | 0.6017532446914196 | 63914578843.30032 | 1.4191887408210385e-05 | 1066.21954420533 | 2.3674829745641947e-13\n", - "1.0 | 0.05 | 0.7405769938099301 | 63914578843.30032 | 1.4191887408210385e-05 | 427.0878219429178 | 9.48325466916073e-14\n", - "1.0 | 0.075 | 0.7971634633262175 | 63914578843.30032 | 1.4191887408210385e-05 | 285.05854859317236 | 6.32957128028738e-14\n", - "1.0 | 0.1 | 0.8372760302121777 | 63914578843.30032 | 1.4191887408210385e-05 | 214.04391168158926 | 4.752729580594678e-14\n", - "1.0 | 0.2 | 0.9403435142235629 | 63914578843.30032 | 1.4191887408210385e-05 | 107.52195601832736 | 2.387467024485609e-14\n", - "1.0 | 0.5 | 1.0888716539372494 | 63914578843.30032 | 1.4191887408210385e-05 | 43.60878244993884 | 9.68309487035831e-15\n", - "1.0 | 0.75 | 1.1499813978771314 | 63914578843.30032 | 1.4191887408210385e-05 | 29.40585497293815 | 6.529411449948819e-15\n", - "1.0 | 1 | 1.1884279444711843 | 63914578843.30032 | 1.4191887408210385e-05 | 22.304391232070714 | 4.952569739218474e-15\n", - "2.0 | 1e-05 | 0.3771752708028818 | 4995157.201079173 | 1.1091477072520501e-09 | 931785.0004902228 | 2.0689783230892163e-10\n", - "2.0 | 2e-05 | 0.3655473904437711 | 4995157.201079173 | 1.1091477072520501e-09 | 513815.8738667831 | 1.1409004271695957e-10\n", - "2.0 | 5e-05 | 0.3556659370171325 | 4995157.201079173 | 1.1091477072520501e-09 | 219045.86744179655 | 4.8637953096574494e-11\n", - "2.0 | 7.5e-05 | 0.3535036870211835 | 4995157.201079173 | 1.1091477072520501e-09 | 148197.11674353815 | 3.290637023834767e-11\n", - "2.0 | 0.0001 | 0.3528173534980046 | 4995157.201079173 | 1.1091477072520501e-09 | 111978.62460515015 | 2.4864249460498955e-11\n", - "2.0 | 0.0002 | 0.35343475255308104 | 4995157.201079173 | 1.1091477072520501e-09 | 56624.48346399592 | 1.2573161059846943e-11\n", - "2.0 | 0.0005 | 0.3550242415453222 | 4995157.201079173 | 1.1091477072520501e-09 | 22805.49604335586 | 5.063837359066316e-12\n", - "2.0 | 0.00075 | 0.35371635738709434 | 4995157.201079173 | 1.1091477072520501e-09 | 15227.168148581388 | 3.3811105356787747e-12\n", - "2.0 | 0.001 | 0.35159094176904937 | 4995157.201079173 | 1.1091477072520501e-09 | 11429.335035838723 | 2.537822182588628e-12\n", - "2.0 | 0.002 | 0.3437425980245058 | 4995157.201079173 | 1.1091477072520501e-09 | 5721.711678609065 | 1.2704752091716875e-12\n", - "2.0 | 0.005 | 0.3384378232843943 | 4995157.201079173 | 1.1091477072520501e-09 | 2290.858149957779 | 5.086726928466632e-13\n", - "2.0 | 0.0075 | 0.34349118443995413 | 4995157.201079173 | 1.1091477072520501e-09 | 1527.8054038403636 | 3.3924094729806145e-13\n", - "2.0 | 0.01 | 0.35145093865438215 | 4995157.201079173 | 1.1091477072520501e-09 | 1146.1915618887438 | 2.545056525279907e-13\n", - "2.0 | 0.02 | 0.3879545312712362 | 4995157.201079173 | 1.1091477072520501e-09 | 573.6614252411312 | 1.2737842452839735e-13\n", - "2.0 | 0.05 | 0.47107485080566824 | 4995157.201079173 | 1.1091477072520501e-09 | 230.08032761773362 | 5.108809544690143e-14\n", - "2.0 | 0.075 | 0.5172647100633155 | 4995157.201079173 | 1.1091477072520501e-09 | 153.72255305569738 | 3.413326356131949e-14\n", - "2.0 | 0.1 | 0.5526131904880445 | 4995157.201079173 | 1.1091477072520501e-09 | 115.54279030330187 | 2.5655653224832402e-14\n", - "2.0 | 0.2 | 0.6463104722195465 | 4995157.201079173 | 1.1091477072520501e-09 | 58.27205179784529 | 1.2938994719623518e-14\n", - "2.0 | 0.5 | 0.7972439807925051 | 4995157.201079173 | 1.1091477072520501e-09 | 23.908978317115814 | 5.308859644585121e-15\n", - "2.0 | 0.75 | 0.8774340306364916 | 4995157.201079173 | 1.1091477072520501e-09 | 16.272675559455422 | 3.613259815672492e-15\n", - "2.0 | 1 | 0.9386486865700188 | 4995157.201079173 | 1.1091477072520501e-09 | 12.454515425128454 | 2.765457957105356e-15\n", - "5.0 | 1e-05 | 0.7290963744458004 | 555.636804006467 | 1.23376154627423e-13 | 554.9108780960391 | 1.2321496669543722e-13\n", - "5.0 | 2e-05 | 0.729116226423653 | 555.636804006467 | 1.23376154627423e-13 | 554.1868499306469 | 1.230542001474981e-13\n", - "5.0 | 5e-05 | 0.7291757523892822 | 555.636804006467 | 1.23376154627423e-13 | 552.0260777777667 | 1.225744123484788e-13\n", - "5.0 | 7.5e-05 | 0.729225323038207 | 555.636804006467 | 1.23376154627423e-13 | 550.2382870339197 | 1.2217744305907268e-13\n", - "5.0 | 0.0001 | 0.7292748625029514 | 555.636804006467 | 1.23376154627423e-13 | 548.462059660397 | 1.217830413536618e-13\n", - "5.0 | 0.0002 | 0.7294727088701933 | 555.636804006467 | 1.23376154627423e-13 | 541.470568779268 | 1.2023061852312455e-13\n", - "5.0 | 0.0005 | 0.7300632679255015 | 555.636804006467 | 1.23376154627423e-13 | 521.5279990898952 | 1.1580247851525786e-13\n", - "5.0 | 0.00075 | 0.7305520056062911 | 555.636804006467 | 1.23376154627423e-13 | 505.99987151524925 | 1.1235454156272012e-13\n", - "5.0 | 0.001 | 0.731037681432382 | 555.636804006467 | 1.23376154627423e-13 | 491.37136147682907 | 1.0910635983059726e-13\n", - "5.0 | 0.002 | 0.7329502858896307 | 555.636804006467 | 1.23376154627423e-13 | 440.4522822110097 | 9.780005299187204e-14\n", - "5.0 | 0.005 | 0.7384152522426066 | 555.636804006467 | 1.23376154627423e-13 | 336.0726653659447 | 7.462312220728345e-14\n", - "5.0 | 0.0075 | 0.742685910400422 | 555.636804006467 | 1.23376154627423e-13 | 280.7085388287958 | 6.232981660332277e-14\n", - "5.0 | 0.01 | 0.7467295294070299 | 555.636804006467 | 1.23376154627423e-13 | 241.04573230445237 | 5.3522904398406983e-14\n", - "5.0 | 0.02 | 0.7610911745523715 | 555.636804006467 | 1.23376154627423e-13 | 154.16834802636305 | 3.4232249929458513e-14\n", - "5.0 | 0.05 | 0.7938460924485602 | 555.636804006467 | 1.23376154627423e-13 | 74.4352551993876 | 1.6527946833241902e-14\n", - "5.0 | 0.075 | 0.815067856911206 | 555.636804006467 | 1.23376154627423e-13 | 52.21726682813808 | 1.1594562383118863e-14\n", - "5.0 | 0.1 | 0.8333792535955515 | 555.636804006467 | 1.23376154627423e-13 | 40.32070591377204 | 8.952995214921886e-15\n", - "5.0 | 0.2 | 0.8919888448352009 | 555.636804006467 | 1.23376154627423e-13 | 21.382869766048398 | 4.747950869365613e-15\n", - "5.0 | 0.5 | 1.0120030229593937 | 555.636804006467 | 1.23376154627423e-13 | 9.336977975122132 | 2.0732255856797125e-15\n", - "5.0 | 0.75 | 1.0827038377848375 | 555.636804006467 | 1.23376154627423e-13 | 6.585973686347976 | 1.4623799252317884e-15\n", - "5.0 | 1 | 1.1385693673981578 | 555.636804006467 | 1.23376154627423e-13 | 5.2000553828745195 | 1.1546442430786551e-15\n", - "7.5 | 1e-05 | 1.1579727162941067 | 65.42062274366073 | 1.4526296331065664e-14 | 65.40750197167736 | 1.4523382934434306e-14\n", - "7.5 | 2e-05 | 1.157977684282372 | 65.42062274366073 | 1.4526296331065664e-14 | 65.39438654331288 | 1.452047072432469e-14\n", - "7.5 | 5e-05 | 1.1579925874856083 | 65.42062274366073 | 1.4526296331065664e-14 | 65.35507228730188 | 1.4511741205880808e-14\n", - "7.5 | 7.5e-05 | 1.1580050059490283 | 65.42062274366073 | 1.4526296331065664e-14 | 65.32234705876328 | 1.4504474745438874e-14\n", - "7.5 | 0.0001 | 1.1580174236191763 | 65.42062274366073 | 1.4526296331065664e-14 | 65.28965509555218 | 1.449721567138344e-14\n", - "7.5 | 0.0002 | 1.1580670863672333 | 65.42062274366073 | 1.4526296331065664e-14 | 65.1592188836095 | 1.446825301423471e-14\n", - "7.5 | 0.0005 | 1.158215998465057 | 65.42062274366073 | 1.4526296331065664e-14 | 64.77106671380919 | 1.4382065919040608e-14\n", - "7.5 | 0.00075 | 1.1583400046414278 | 65.42062274366073 | 1.4526296331065664e-14 | 64.45117618545733 | 1.431103595305346e-14\n", - "7.5 | 0.001 | 1.1584639315271195 | 65.42062274366073 | 1.4526296331065664e-14 | 64.13447892974611 | 1.424071503602822e-14\n", - "7.5 | 0.002 | 1.1589588465897578 | 65.42062274366073 | 1.4526296331065664e-14 | 62.89868505684161 | 1.3966313673750365e-14\n", - "7.5 | 0.005 | 1.1604360020625992 | 65.42062274366073 | 1.4526296331065664e-14 | 59.46547650580346 | 1.320398823740986e-14\n", - "7.5 | 0.0075 | 1.161658314493383 | 65.42062274366073 | 1.4526296331065664e-14 | 56.88254043457721 | 1.2630461217927815e-14\n", - "7.5 | 0.01 | 1.162872831495904 | 65.42062274366073 | 1.4526296331065664e-14 | 54.518170529658384 | 1.2105465636493481e-14\n", - "7.5 | 0.02 | 1.1676546346371224 | 65.42062274366073 | 1.4526296331065664e-14 | 46.77181528449087 | 1.0385429246471316e-14\n", - "7.5 | 0.05 | 1.1813220595206992 | 65.42062274366073 | 1.4526296331065664e-14 | 32.913913723848474 | 7.3083569693485e-15\n", - "7.5 | 0.075 | 1.192025682675384 | 65.42062274366073 | 1.4526296331065664e-14 | 26.48423006114886 | 5.8806804006714365e-15\n", - "7.5 | 0.1 | 1.2021902002798601 | 65.42062274366073 | 1.4526296331065664e-14 | 22.210881996573836 | 4.931806517965728e-15\n", - "7.5 | 0.2 | 1.2385142386896435 | 65.42062274366073 | 1.4526296331065664e-14 | 13.695471638717006 | 3.0410055892808887e-15\n", - "7.5 | 0.5 | 1.3202321940460664 | 65.42062274366073 | 1.4526296331065664e-14 | 6.759169742060505 | 1.5008371749970505e-15\n", - "7.5 | 0.75 | 1.3692070811077144 | 65.42062274366073 | 1.4526296331065664e-14 | 4.957375592564133 | 1.1007585049158958e-15\n", - "7.5 | 1 | 1.4074503211973 | 65.42062274366073 | 1.4526296331065664e-14 | 4.014324376831694 | 8.91359070294516e-16\n" + "0.001 | 7.5e-05 | 1.3203184940698338 | 5.157729967665246e+18 | 1145.246112980224 | 400000.8399173096 | 8.88180284691197e-11\n", + "0.001 | 0.0001 | 1.3203209127586129 | 5.157729967665246e+18 | 1145.246112980224 | 300000.8799380822 | 6.661357686301321e-11\n", + "0.001 | 0.0002 | 1.3204538291266175 | 5.157729967665246e+18 | 1145.246112980224 | 150000.93996807796 | 3.330689945359521e-11\n", + "0.001 | 0.0005 | 1.3206180157499612 | 5.157729967665246e+18 | 1145.246112980224 | 60000.97598693792 | 1.3322893008135921e-11\n", + "0.001 | 0.00075 | 1.3206637488446704 | 5.157729967665246e+18 | 1145.246112980224 | 40000.983991246234 | 8.882002686948772e-12\n", + "0.001 | 0.001 | 1.3206877851838226 | 5.157729967665246e+18 | 1145.246112980224 | 30000.98799345863 | 6.66155752636813e-12\n", + "0.001 | 0.002 | 1.320724621157106 | 5.157729967665246e+18 | 1145.246112980224 | 15000.993996717698 | 3.3308897854839476e-12\n", + "0.001 | 0.005 | 1.3207441010685657 | 5.157729967665246e+18 | 1145.246112980224 | 6000.997598684582 | 1.3324891409559796e-12\n", + "0.001 | 0.0075 | 1.3207457685491917 | 5.157729967665246e+18 | 1145.246112980224 | 4000.99839912232 | 8.884001088387982e-13\n", + "0.001 | 0.01 | 1.320744737958569 | 5.157729967665246e+18 | 1145.246112980224 | 3000.998799341891 | 6.663555927803635e-13\n", + "0.001 | 0.02 | 1.3207338807217237 | 5.157729967665246e+18 | 1145.246112980224 | 1500.9993996709493 | 3.332888186926451e-13\n", + "0.001 | 0.05 | 1.3206922424549334 | 5.157729967665246e+18 | 1145.246112980224 | 600.9997598683192 | 1.3344875423999962e-13\n", + "0.001 | 0.075 | 1.3206569542287017 | 5.157729967665246e+18 | 1145.246112980224 | 400.99983991221217 | 8.903985102830795e-14\n", + "0.001 | 0.1 | 1.320622227525565 | 5.157729967665246e+18 | 1145.246112980224 | 300.99987993415647 | 6.683539942246163e-14\n", + "0.001 | 0.2 | 1.320490925394551 | 5.157729967665246e+18 | 1145.246112980224 | 150.9999399670778 | 3.3528722013693237e-14\n", + "0.001 | 0.5 | 1.3201734897566475 | 5.157729967665246e+18 | 1145.246112980224 | 60.99997598683111 | 1.354471556843231e-14\n", + "0.001 | 0.75 | 1.3199938819612358 | 5.157729967665246e+18 | 1145.246112980224 | 40.99998399122079 | 9.103825247263228e-15\n", + "0.001 | 1 | 1.3198876280547236 | 5.157729967665246e+18 | 1145.246112980224 | 30.99998799341556 | 6.883380086678672e-15\n", + "0.002 | 1e-05 | 1.3623878222762442 | 5.267415428535686e+18 | 1169.601177805221 | 2999996.197724369 | 6.661329705003036e-10\n", + "0.002 | 2e-05 | 1.3439946308776922 | 5.267415428535686e+18 | 1169.601177805221 | 1499998.5987321674 | 3.330665962435847e-10\n", + "0.002 | 5e-05 | 1.3272246485702754 | 5.267415428535686e+18 | 1169.601177805221 | 600000.0394937723 | 1.3322677172439786e-10\n", + "0.002 | 7.5e-05 | 1.3235440099210103 | 5.267415428535686e+18 | 1169.601177805221 | 400000.3596555092 | 8.881792182957797e-11\n", + "0.002 | 0.0001 | 1.3220037168858698 | 5.267415428535686e+18 | 1169.601177805221 | 300000.5197420093 | 6.661349688341851e-11\n", + "0.002 | 0.0002 | 1.320488086827574 | 5.267415428535686e+18 | 1169.601177805221 | 150000.75986911857 | 3.3306859463592924e-11\n", + "0.002 | 0.0005 | 1.3203538501482435 | 5.267415428535686e+18 | 1169.601177805221 | 60000.90394755304 | 1.3322877012179166e-11\n", + "0.002 | 0.00075 | 1.3204389361297004 | 5.267415428535686e+18 | 1169.601177805221 | 40000.935965030905 | 8.881992022986763e-12\n", + "0.002 | 0.001 | 1.3205000397653752 | 5.267415428535686e+18 | 1169.601177805221 | 30000.951973763542 | 6.661549528389164e-12\n", + "0.002 | 0.002 | 1.3206158464557667 | 5.267415428535686e+18 | 1169.601177805221 | 15000.975986864521 | 3.3308857864932143e-12\n", + "0.002 | 0.005 | 1.3206969267656237 | 5.267415428535686e+18 | 1169.601177805221 | 6000.990394744528 | 1.3324875413599564e-12\n", + "0.002 | 0.0075 | 1.320713792901136 | 5.267415428535686e+18 | 1169.601177805221 | 4000.9935964959072 | 8.883990424415139e-13\n", + "0.002 | 0.01 | 1.320720571503768 | 5.267415428535686e+18 | 1169.601177805221 | 3000.9951973718244 | 6.663547929823431e-13\n", + "0.002 | 0.02 | 1.3207216922930962 | 5.267415428535686e+18 | 1169.601177805221 | 1500.9975986858778 | 3.3328841879362644e-13\n", + "0.002 | 0.05 | 1.3206873938685024 | 5.267415428535686e+18 | 1169.601177805221 | 600.9990394743188 | 1.3344859428039842e-13\n", + "0.002 | 0.075 | 1.3206537523643624 | 5.267415428535686e+18 | 1169.601177805221 | 400.99935964954386 | 8.903974438857351e-14\n", + "0.002 | 0.1 | 1.3206198511578058 | 5.267415428535686e+18 | 1169.601177805221 | 300.9995197371585 | 6.683531944266152e-14\n", + "0.002 | 0.2 | 1.3204897899439867 | 5.267415428535686e+18 | 1169.601177805221 | 150.99975986857828 | 3.352868202379306e-14\n", + "0.002 | 0.5 | 1.3201731005146327 | 5.267415428535686e+18 | 1169.601177805221 | 60.99990394743119 | 1.3544699572472217e-14\n", + "0.002 | 0.75 | 1.3199936589276795 | 5.267415428535686e+18 | 1169.601177805221 | 40.99993596495411 | 9.103814583289818e-15\n", + "0.002 | 1 | 1.3198874884067833 | 5.267415428535686e+18 | 1169.601177805221 | 30.99995197371557 | 6.883372088698619e-15\n", + "0.005 | 1e-05 | 1.398281393975959 | 4.737718716102057e+19 | 10519.848805628078 | 2999970.984013021 | 6.661273719317287e-10\n", + "0.005 | 2e-05 | 1.3887224316240243 | 4.737718716102057e+19 | 10519.848805628078 | 1499985.9920165162 | 3.330637969903885e-10\n", + "0.005 | 5e-05 | 1.3683630926273394 | 4.737718716102057e+19 | 10519.848805628078 | 599994.9967708938 | 1.3322565201498855e-10\n", + "0.005 | 7.5e-05 | 1.3574022549715115 | 4.737718716102057e+19 | 10519.848805628078 | 399996.9978457238 | 8.881717535785234e-11\n", + "0.005 | 0.0001 | 1.349656526521127 | 4.737718716102057e+19 | 10519.848805628078 | 299997.9983857735 | 6.661293702986926e-11\n", + "0.005 | 0.0002 | 1.3338899892282914 | 4.737718716102057e+19 | 10519.848805628078 | 149999.49919096043 | 3.330657953680936e-11\n", + "0.005 | 0.0005 | 1.32313146061549 | 4.737718716102057e+19 | 10519.848805628078 | 60000.399676413464 | 1.3322765041493204e-11\n", + "0.005 | 0.00075 | 1.3213658709059986 | 4.737718716102057e+19 | 10519.848805628078 | 40000.59978412415 | 8.881917375830139e-12\n", + "0.005 | 0.001 | 1.3207434325086527 | 4.737718716102057e+19 | 10519.848805628078 | 30000.69983809163 | 6.661493543023507e-12\n", + "0.005 | 0.002 | 1.3203109441737066 | 4.737718716102057e+19 | 10519.848805628078 | 15000.849919033793 | 3.3308577938115465e-12\n", + "0.005 | 0.005 | 1.3204473592563999 | 4.737718716102057e+19 | 10519.848805628078 | 6000.939967612918 | 1.3324763442874405e-12\n", + "0.005 | 0.0075 | 1.3205266147948884 | 4.737718716102057e+19 | 10519.848805628078 | 4000.95997840818 | 8.883915777265062e-13\n", + "0.005 | 0.01 | 1.320572251134835 | 4.737718716102057e+19 | 10519.848805628078 | 3000.9699838059914 | 6.66349194446079e-13\n", + "0.005 | 0.02 | 1.3206416674457788 | 4.737718716102057e+19 | 10519.848805628078 | 1500.984991902906 | 3.3328561952548206e-13\n", + "0.005 | 0.05 | 1.320654306255278 | 4.737718716102057e+19 | 10519.848805628078 | 600.9939967611572 | 1.334474745731467e-13\n", + "0.005 | 0.075 | 1.3206317178276614 | 4.737718716102057e+19 | 10519.848805628078 | 400.9959978407689 | 8.903899791707224e-14\n", + "0.005 | 0.1 | 1.3206034290426676 | 4.737718716102057e+19 | 10519.848805628078 | 300.9969983805781 | 6.683475958903575e-14\n", + "0.005 | 0.2 | 1.3204818942961338 | 4.737718716102057e+19 | 10519.848805628078 | 150.99849919028682 | 3.35284020969799e-14\n", + "0.005 | 0.5 | 1.3201703838941494 | 4.737718716102057e+19 | 10519.848805628078 | 60.99939967611469 | 1.354458760174697e-14\n", + "0.005 | 0.75 | 1.3199921011576619 | 4.737718716102057e+19 | 10519.848805628078 | 40.99959978407637 | 9.103739936139637e-15\n", + "0.005 | 1 | 1.3198865127506894 | 4.737718716102057e+19 | 10519.848805628078 | 30.999699838057264 | 6.883316103335982e-15\n", + "0.0075 | 1e-05 | 1.4043005938705444 | 1.7372731761309476e+18 | 385.7521360408506 | 2999933.4645628347 | 6.66119040940235e-10\n", + "0.0075 | 2e-05 | 1.3994032633189917 | 1.7372731761309476e+18 | 385.7521360408506 | 1499967.232282145 | 3.3305963149258154e-10\n", + "0.0075 | 5e-05 | 1.3868056479481081 | 1.7372731761309476e+18 | 385.7521360408506 | 599987.4928880802 | 1.332239858182938e-10\n", + "0.0075 | 7.5e-05 | 1.3783815287733572 | 1.7372731761309476e+18 | 385.7521360408506 | 399991.9952527292 | 8.881606455906726e-11\n", + "0.0075 | 0.0001 | 1.3714017808043564 | 1.7372731761309476e+18 | 385.7521360408506 | 299994.24643613514 | 6.661210392969411e-11\n", + "0.0075 | 0.0002 | 1.3527834516570942 | 1.7372731761309476e+18 | 385.7521360408506 | 149997.62321911624 | 3.330616298738237e-11\n", + "0.0075 | 0.0005 | 1.3320471009304675 | 1.7372731761309476e+18 | 385.7521360408506 | 59999.64928707162 | 1.3322598421588254e-11\n", + "0.0075 | 0.00075 | 1.3264972569268536 | 1.7372731761309476e+18 | 385.7521360408506 | 40000.09952468137 | 8.881806295919807e-12\n", + "0.0075 | 0.001 | 1.3239147582316049 | 1.7372731761309476e+18 | 385.7521360408506 | 30000.324643486318 | 6.6614102330856e-12\n", + "0.0075 | 0.002 | 1.3209497744222536 | 1.7372731761309476e+18 | 385.7521360408506 | 15000.66232173369 | 3.33081613884316e-12\n", + "0.0075 | 0.005 | 1.3203066752602197 | 1.7372731761309476e+18 | 385.7521360408506 | 6000.864928690636 | 1.3324596822995884e-12\n", + "0.0075 | 0.0075 | 1.3203572891448458 | 1.7372731761309476e+18 | 385.7521360408506 | 4000.9099524600265 | 8.883804697346124e-13\n", + "0.0075 | 0.01 | 1.3204149453606102 | 1.7372731761309476e+18 | 385.7521360408506 | 3000.9324643450964 | 6.663408634522075e-13\n", + "0.0075 | 0.02 | 1.3205391985944166 | 1.7372731761309476e+18 | 385.7521360408506 | 1500.9662321724334 | 3.332814540285408e-13\n", + "0.0075 | 0.05 | 1.3206077953595614 | 1.7372731761309476e+18 | 385.7521360408506 | 600.9864928689656 | 1.334458083743696e-13\n", + "0.0075 | 0.075 | 1.3206001441008735 | 1.7372731761309476e+18 | 385.7521360408506 | 400.9909952459681 | 8.90378871178861e-14\n", + "0.0075 | 0.1 | 1.3205796752193628 | 1.7372731761309476e+18 | 385.7521360408506 | 300.9932464344763 | 6.683392648964588e-14\n", + "0.0075 | 0.2 | 1.3204703143593972 | 1.7372731761309476e+18 | 385.7521360408506 | 150.99662321723713 | 3.352798554728523e-14\n", + "0.0075 | 0.5 | 1.320166367422988 | 1.7372731761309476e+18 | 385.7521360408506 | 60.9986492868945 | 1.3544420981869032e-14\n", + "0.0075 | 0.75 | 1.3199897942613736 | 1.7372731761309476e+18 | 385.7521360408506 | 40.99909952459634 | 9.103628856221033e-15\n", + "0.0075 | 1 | 1.3198850669656137 | 1.7372731761309476e+18 | 385.7521360408506 | 30.9993246434473 | 6.8832327933970424e-15\n", + "0.01 | 1e-05 | 1.406199206150202 | 9.15881273337224e+18 | 2033.664954963985 | 2999880.9389639026 | 6.661073779143717e-10\n", + "0.01 | 2e-05 | 1.4035355143548909 | 9.15881273337224e+18 | 2033.664954963985 | 1499940.969350007 | 3.330537999501908e-10\n", + "0.01 | 5e-05 | 1.395627182514651 | 9.15881273337224e+18 | 2033.664954963985 | 599976.9877232183 | 1.3322165320311236e-10\n", + "0.01 | 7.5e-05 | 1.3897600752448445 | 9.15881273337224e+18 | 2033.664954963985 | 399984.99181388726 | 8.881450948325648e-11\n", + "0.01 | 0.0001 | 1.384516426828877 | 9.15881273337224e+18 | 2033.664954963985 | 299988.99385887675 | 6.661093762325193e-11\n", + "0.01 | 0.0002 | 1.3683402006562813 | 9.15881273337224e+18 | 2033.664954963985 | 149994.99692920924 | 3.330557983387755e-11\n", + "0.01 | 0.0005 | 1.3439837129674972 | 9.15881273337224e+18 | 2033.664954963985 | 59998.59877129853 | 1.3322365160228451e-11\n", + "0.01 | 0.00075 | 1.3350937119914852 | 9.15881273337224e+18 | 2033.664954963985 | 39999.399180849905 | 8.881650788350438e-12\n", + "0.01 | 0.001 | 1.3301839804871247 | 9.15881273337224e+18 | 2033.664954963985 | 29999.799385623995 | 6.661293602411077e-12\n", + "0.01 | 0.002 | 1.3231293127499324 | 9.15881273337224e+18 | 2033.664954963985 | 15000.399692799341 | 3.3307578235051907e-12\n", + "0.01 | 0.005 | 1.3204830758443646 | 9.15881273337224e+18 | 2033.664954963985 | 6000.759877119514 | 1.332436356164982e-12\n", + "0.01 | 0.0075 | 1.32031003716702 | 9.15881273337224e+18 | 2033.664954963985 | 4000.839918079025 | 8.883649189781518e-13\n", + "0.01 | 0.01 | 1.3203089432056967 | 9.15881273337224e+18 | 2033.664954963985 | 3000.8799385588486 | 6.663292003847518e-13\n", + "0.01 | 0.02 | 1.3204273423452233 | 9.15881273337224e+18 | 2033.664954963985 | 1500.9399692794002 | 3.3327562249483305e-13\n", + "0.01 | 0.05 | 1.3205480387004707 | 9.15881273337224e+18 | 2033.664954963985 | 600.9759877117588 | 1.3344347576088794e-13\n", + "0.01 | 0.075 | 1.3205583477737106 | 9.15881273337224e+18 | 2033.664954963985 | 400.98399180783207 | 8.903633204223206e-14\n", + "0.01 | 0.1 | 1.320547778680883 | 9.15881273337224e+18 | 2033.664954963985 | 300.98799385587427 | 6.683276018290535e-14\n", + "0.01 | 0.2 | 1.320454441184348 | 9.15881273337224e+18 | 2033.664954963985 | 150.9939969279356 | 3.352740239391485e-14\n", + "0.01 | 0.5 | 1.3201607967208797 | 9.15881273337224e+18 | 2033.664954963985 | 60.99759877117423 | 1.3544187720520958e-14\n", + "0.01 | 0.75 | 1.3199865871021212 | 9.15881273337224e+18 | 2033.664954963985 | 40.99839918078277 | 9.103473348655637e-15\n", + "0.01 | 1 | 1.3198830550759415 | 9.15881273337224e+18 | 2033.664954963985 | 30.998799385587105 | 6.883116162722992e-15\n", + "0.02 | 1e-05 | 1.3991967821727724 | 4.596172751931884e+18 | 1020.5553628699091 | 2999520.8077985262 | 6.660274127320345e-10\n", + "0.02 | 2e-05 | 1.4033527820031186 | 4.596172751931884e+18 | 1020.5553628699091 | 1499760.9038967583 | 3.3301381738776355e-10\n", + "0.02 | 5e-05 | 1.404054765908641 | 4.596172751931884e+18 | 1020.5553628699091 | 599904.9615311394 | 1.3320566017574796e-10\n", + "0.02 | 7.5e-05 | 1.4028711082085463 | 4.596172751931884e+18 | 1020.5553628699091 | 399936.9743506041 | 8.880384746459227e-11\n", + "0.02 | 0.0001 | 1.40140918974444 | 4.596172751931884e+18 | 1020.5553628699091 | 299952.98076160415 | 6.660294110929591e-11\n", + "0.02 | 0.0002 | 1.3952513762990684 | 4.596172751931884e+18 | 1020.5553628699091 | 149976.99038130237 | 3.3301581577061505e-11\n", + "0.02 | 0.0005 | 1.3796701173933874 | 4.596172751931884e+18 | 1020.5553628699091 | 59991.39615212076 | 1.3320765857498698e-11\n", + "0.02 | 0.00075 | 1.3699232085654953 | 4.596172751931884e+18 | 1020.5553628699091 | 39994.5974347125 | 8.880584586526408e-12\n", + "0.02 | 0.001 | 1.362292110296499 | 4.596172751931884e+18 | 1020.5553628699091 | 29996.198076027962 | 6.660493951044613e-12\n", + "0.02 | 0.002 | 1.3439496124691088 | 4.596172751931884e+18 | 1020.5553628699091 | 14998.599038011469 | 3.3303579978242113e-12\n", + "0.02 | 0.005 | 1.3272089427378742 | 4.596172751931884e+18 | 1020.5553628699091 | 6000.039615202918 | 1.3322764258922688e-12\n", + "0.02 | 0.0075 | 1.3235324618626423 | 4.596172751931884e+18 | 1020.5553628699091 | 4000.3597434684316 | 8.882582987964475e-13\n", + "0.02 | 0.01 | 1.3219923714906012 | 4.596172751931884e+18 | 1020.5553628699091 | 3000.519807601072 | 6.66249235248511e-13\n", + "0.02 | 0.02 | 1.3204676186157391 | 4.596172751931884e+18 | 1020.5553628699091 | 1500.7599038004946 | 3.332356399267088e-13\n", + "0.02 | 0.05 | 1.3202928659769575 | 4.596172751931884e+18 | 1020.5553628699091 | 600.9039615201903 | 1.3342748273363686e-13\n", + "0.02 | 0.075 | 1.3203431410941726 | 4.596172751931884e+18 | 1020.5553628699091 | 400.9359743467913 | 8.902567002406576e-14\n", + "0.02 | 0.1 | 1.3203699372606292 | 4.596172751931884e+18 | 1020.5553628699091 | 300.95198076008984 | 6.682476366927977e-14\n", + "0.02 | 0.2 | 1.320356005679441 | 4.596172751931884e+18 | 1020.5553628699091 | 150.97599038004486 | 3.352340413710239e-14\n", + "0.02 | 0.5 | 1.3201242272878078 | 4.596172751931884e+18 | 1020.5553628699091 | 60.990396152017844 | 1.3542588417795952e-14\n", + "0.02 | 0.75 | 1.319965297334464 | 4.596172751931884e+18 | 1020.5553628699091 | 40.99359743467855 | 9.102407146838975e-15\n", + "0.02 | 1 | 1.3198696409051942 | 4.596172751931884e+18 | 1020.5553628699091 | 30.99519807600895 | 6.882316511360498e-15\n", + "0.05 | 1e-05 | 1.1802626763433055 | 1.7515416781593348e+19 | 3889.2037993661584 | 2997002.130677457 | 6.65468154065753e-10\n", + "0.05 | 2e-05 | 1.2638668839596363 | 1.7515416781593348e+19 | 3889.2037993661584 | 1498501.5652596476 | 3.3273418803761946e-10\n", + "0.05 | 5e-05 | 1.3399914336691139 | 1.7515416781593348e+19 | 3889.2037993661584 | 599401.2260877149 | 1.3309380843822602e-10\n", + "0.05 | 7.5e-05 | 1.3607917455534582 | 1.7515416781593348e+19 | 3889.2037993661584 | 399601.1507253162 | 8.872927964039072e-11\n", + "0.05 | 0.0001 | 1.371739914914181 | 1.7515416781593348e+19 | 3889.2037993661584 | 299701.11304101977 | 6.654701524078538e-11\n", + "0.05 | 0.0002 | 1.3883571964181647 | 1.7515416781593348e+19 | 3889.2037993661584 | 149851.05651988334 | 3.327361864255603e-11\n", + "0.05 | 0.0005 | 1.3960809195486918 | 1.7515416781593348e+19 | 3889.2037993661584 | 59941.022607974846 | 1.3309580683790145e-11\n", + "0.05 | 0.00075 | 1.3958259187903233 | 1.7515416781593348e+19 | 3889.2037993661584 | 39961.015071849826 | 8.873127804032116e-12\n", + "0.05 | 0.001 | 1.3944270862078145 | 1.7515416781593348e+19 | 3889.2037993661584 | 29971.011303897136 | 6.654901364177487e-12\n", + "0.05 | 0.002 | 1.3868038656181922 | 1.7515416781593348e+19 | 3889.2037993661584 | 14986.00565194129 | 3.32756170438959e-12\n", + "0.05 | 0.005 | 1.3676102367336342 | 1.7515416781593348e+19 | 3889.2037993661584 | 5995.002260777159 | 1.3311579085189338e-12\n", + "0.05 | 0.0075 | 1.356910453232406 | 1.7515416781593348e+19 | 3889.2037993661584 | 3997.001507183075 | 8.875126205472205e-13\n", + "0.05 | 0.01 | 1.3492964425395064 | 1.7515416781593348e+19 | 3889.2037993661584 | 2998.001130387282 | 6.656899765616413e-13\n", + "0.05 | 0.02 | 1.3337278030826965 | 1.7515416781593348e+19 | 3889.2037993661584 | 1499.5005651935264 | 3.3295601058325773e-13\n", + "0.05 | 0.05 | 1.3230623528240175 | 1.7515416781593348e+19 | 3889.2037993661584 | 600.4002260774154 | 1.3331563099625918e-13\n", + "0.05 | 0.075 | 1.3212917568278915 | 1.7515416781593348e+19 | 3889.2037993661584 | 400.6001507182744 | 8.895110219914723e-14\n", + "0.05 | 0.1 | 1.320649513717282 | 1.7515416781593348e+19 | 3889.2037993661584 | 300.7001130387049 | 6.676883780059148e-14\n", + "0.05 | 0.2 | 1.320107992756552 | 1.7515416781593348e+19 | 3889.2037993661584 | 150.85005651935137 | 3.349544120275802e-14\n", + "0.05 | 0.5 | 1.3199430633401976 | 1.7515416781593348e+19 | 3889.2037993661584 | 60.940022607740424 | 1.3531403244058199e-14\n", + "0.05 | 0.75 | 1.3198490982380753 | 1.7515416781593348e+19 | 3889.2037993661584 | 40.96001507182699 | 9.094950364347151e-15\n", + "0.05 | 1 | 1.3197937467706276 | 1.7515416781593348e+19 | 3889.2037993661584 | 30.970011303870226 | 6.876723924491618e-15\n", + "0.075 | 1e-05 | 1.0289125856927244 | 2.5655434459999447e+18 | 569.6650808850611 | 2993261.3408441492 | 6.646375318651086e-10\n", + "0.075 | 2e-05 | 1.0683357423022957 | 2.5655434459999447e+18 | 569.6650808850611 | 1496631.170356881 | 3.323188769403809e-10\n", + "0.075 | 5e-05 | 1.1788194485860952 | 2.5655434459999447e+18 | 569.6650808850611 | 598653.068139632 | 1.3292768400222243e-10\n", + "0.075 | 7.5e-05 | 1.2298615813471019 | 2.5655434459999447e+18 | 569.6650808850611 | 399102.3787592882 | 8.861853001624636e-11\n", + "0.075 | 0.0001 | 1.2625315302909002 | 2.5655434459999447e+18 | 569.6650808850611 | 299327.0340661838 | 6.646395302260717e-11\n", + "0.075 | 0.0002 | 1.3241269630073698 | 2.5655434459999447e+18 | 569.6650808850611 | 149664.01703276392 | 3.323208753353322e-11\n", + "0.075 | 0.0005 | 1.369928705681422 | 2.5655434459999447e+18 | 569.6650808850611 | 59866.20681304865 | 1.3292968240163605e-11\n", + "0.075 | 0.00075 | 1.380456714523796 | 2.5655434459999447e+18 | 569.6650808850611 | 39911.13787533464 | 8.862052841637134e-12\n", + "0.075 | 0.001 | 1.3852806670847033 | 2.5655434459999447e+18 | 569.6650808850611 | 29933.60340651576 | 6.646595142382363e-12\n", + "0.075 | 0.002 | 1.389766784950075 | 2.5655434459999447e+18 | 569.6650808850611 | 14967.301703250858 | 3.323408593492085e-12\n", + "0.075 | 0.005 | 1.382950902139967 | 2.5655434459999447e+18 | 569.6650808850611 | 5987.5206812984825 | 1.3294966641593758e-12\n", + "0.075 | 0.0075 | 1.3758254841268711 | 2.5655434459999447e+18 | 569.6650808850611 | 3992.0137875320675 | 8.864051243078358e-13\n", + "0.075 | 0.01 | 1.3694975238618996 | 2.5655434459999447e+18 | 569.6650808850611 | 2994.260340649059 | 6.648593543821099e-13\n", + "0.075 | 0.02 | 1.3518655278680658 | 2.5655434459999447e+18 | 569.6650808850611 | 1497.630170324489 | 3.325406994935085e-13\n", + "0.075 | 0.05 | 1.3317271553627898 | 2.5655434459999447e+18 | 569.6650808850611 | 599.6520681297744 | 1.3314950656035372e-13\n", + "0.075 | 0.075 | 1.3262949589526674 | 2.5655434459999447e+18 | 569.6650808850611 | 400.10137875318566 | 8.884035257521142e-14\n", + "0.075 | 0.1 | 1.3237555826770926 | 2.5655434459999447e+18 | 569.6650808850611 | 300.3260340648898 | 6.668577558263996e-14\n", + "0.075 | 0.2 | 1.3207755491672832 | 2.5655434459999447e+18 | 569.6650808850611 | 150.66301703244358 | 3.34539100937822e-14\n", + "0.075 | 0.5 | 1.3198839335916897 | 2.5655434459999447e+18 | 569.6650808850611 | 60.865206812977554 | 1.3514790800467925e-14\n", + "0.075 | 0.75 | 1.3197723726186084 | 2.5655434459999447e+18 | 569.6650808850611 | 40.910137875318284 | 9.083875401953608e-15\n", + "0.075 | 1 | 1.3197346497546039 | 2.5655434459999447e+18 | 569.6650808850611 | 30.93260340648876 | 6.868417702696474e-15\n", + "0.1 | 1e-05 | 1.0798981068166194 | 2.667779301865279e+21 | 592366.0011098518 | 2988038.7413794813 | 6.634778818302947e-10\n", + "0.1 | 2e-05 | 1.03441114728845 | 2.667779301865279e+21 | 592366.0011098518 | 1494019.8706298168 | 3.3173905192414405e-10\n", + "0.1 | 5e-05 | 1.0485665416885148 | 2.667779301865279e+21 | 592366.0011098518 | 597608.548229899 | 1.3269575399152945e-10\n", + "0.1 | 7.5e-05 | 1.08598125409383 | 2.667779301865279e+21 | 592366.0011098518 | 398406.0321496369 | 8.846391000841544e-11\n", + "0.1 | 0.0001 | 1.1199271289317363 | 2.667779301865279e+21 | 592366.0011098518 | 298804.7741134555 | 6.634798801773545e-11\n", + "0.1 | 0.0002 | 1.2092033599798173 | 2.667779301865279e+21 | 592366.0011098518 | 149402.8870553795 | 3.317410503087082e-11\n", + "0.1 | 0.0005 | 1.3058031834924426 | 2.667779301865279e+21 | 592366.0011098518 | 59761.754822120776 | 1.3269775239104392e-11\n", + "0.1 | 0.00075 | 1.334757363394958 | 2.667779301865279e+21 | 592366.0011098518 | 39841.50321474247 | 8.846590840936857e-12\n", + "0.1 | 0.001 | 1.3503821090956738 | 2.667779301865279e+21 | 592366.0011098518 | 29881.377411051322 | 6.634998641852646e-12\n", + "0.1 | 0.002 | 1.3741948871332732 | 2.667779301865279e+21 | 592366.0011098518 | 14941.188705527115 | 3.317610343229108e-12\n", + "0.1 | 0.005 | 1.3835828525726024 | 2.667779301865279e+21 | 592366.0011098518 | 5977.075482208369 | 1.3271773640540483e-12\n", + "0.1 | 0.0075 | 1.3817104633773307 | 2.667779301865279e+21 | 592366.0011098518 | 3985.050321472293 | 8.848589242376843e-13\n", + "0.1 | 0.01 | 1.378484030173926 | 2.667779301865279e+21 | 592366.0011098518 | 2989.0377411040545 | 6.636997043294578e-13\n", + "0.1 | 0.02 | 1.365360961026242 | 2.667779301865279e+21 | 592366.0011098518 | 1495.0188705519927 | 3.3196087446718374e-13\n", + "0.1 | 0.05 | 1.342872658496226 | 2.667779301865279e+21 | 592366.0011098518 | 598.6075482207931 | 1.3291757654982763e-13\n", + "0.1 | 0.075 | 1.3344025162997275 | 2.667779301865279e+21 | 592366.0011098518 | 399.4050321471928 | 8.868573256819285e-14\n", + "0.1 | 0.1 | 1.3296947739898701 | 2.667779301865279e+21 | 592366.0011098518 | 299.8037741103928 | 6.65698105773755e-14\n", + "0.1 | 0.2 | 1.3228751118106665 | 2.667779301865279e+21 | 592366.0011098518 | 150.40188705519586 | 3.3395927591150145e-14\n", + "0.1 | 0.5 | 1.3201305530113177 | 2.667779301865279e+21 | 592366.0011098518 | 60.76075482207833 | 1.3491597799415074e-14\n", + "0.1 | 0.75 | 1.3198268366427475 | 2.667779301865279e+21 | 592366.0011098518 | 40.840503214718886 | 9.068413401251726e-15\n", + "0.1 | 1 | 1.3197458467857652 | 2.667779301865279e+21 | 592366.0011098518 | 30.88037741103915 | 6.856821202170049e-15\n", + "0.2 | 1e-05 | 1.0348780714189445 | 6.679884723262186e+17 | 148.32323643215042 | 2952676.8513839184 | 6.556259649368276e-10\n", + "0.2 | 2e-05 | 1.0990542705423856 | 6.679884723262186e+17 | 148.32323643215042 | 1476338.9256587664 | 3.2781309348334595e-10\n", + "0.2 | 5e-05 | 1.118668157565593 | 6.679884723262186e+17 | 148.32323643215042 | 590536.1702544192 | 1.3112537061808354e-10\n", + "0.2 | 7.5e-05 | 1.1064632913469394 | 6.679884723262186e+17 | 148.32323643215042 | 393691.1135038526 | 8.741698776045861e-11\n", + "0.2 | 0.0001 | 1.0918067697059484 | 6.679884723262186e+17 | 148.32323643215042 | 295268.5851280461 | 6.556279633152997e-11\n", + "0.2 | 0.0002 | 1.0475901909840273 | 6.679884723262186e+17 | 148.32323643215042 | 147634.79256327386 | 3.2781509187901095e-11\n", + "0.2 | 0.0005 | 1.023706037751357 | 6.679884723262186e+17 | 148.32323643215042 | 59054.517025191824 | 1.3112736901897254e-11\n", + "0.2 | 0.00075 | 1.0428612641129864 | 6.679884723262186e+17 | 148.32323643215042 | 39370.01135012085 | 8.741898616131582e-12\n", + "0.2 | 0.001 | 1.0675482361223787 | 6.679884723262186e+17 | 148.32323643215042 | 29527.75851260228 | 6.556479473252503e-12\n", + "0.2 | 0.002 | 1.1502519607987045 | 6.679884723262186e+17 | 148.32323643215042 | 14764.379256294176 | 3.278350758927168e-12\n", + "0.2 | 0.005 | 1.26134611726893 | 6.679884723262186e+17 | 148.32323643215042 | 5906.351702517757 | 1.3114735303338414e-12\n", + "0.2 | 0.0075 | 1.2986164432111627 | 6.679884723262186e+17 | 148.32323643215042 | 3937.9011350116025 | 8.743897017574836e-13\n", + "0.2 | 0.01 | 1.319365327804564 | 6.679884723262186e+17 | 148.32323643215042 | 2953.6758512586593 | 6.558477874693346e-13\n", + "0.2 | 0.02 | 1.3511101649720025 | 6.679884723262186e+17 | 148.32323643215042 | 1477.3379256293263 | 3.2803491603712903e-13\n", + "0.2 | 0.05 | 1.361365740618009 | 6.679884723262186e+17 | 148.32323643215042 | 591.5351702517387 | 1.3134719317780845e-13\n", + "0.2 | 0.075 | 1.3577244256580787 | 6.679884723262186e+17 | 148.32323643215042 | 394.69011350115375 | 8.763881032017945e-14\n", + "0.2 | 0.1 | 1.3532120690730098 | 6.679884723262186e+17 | 148.32323643215042 | 296.26758512586605 | 6.578461889136601e-14\n", + "0.2 | 0.2 | 1.3396807959929744 | 6.679884723262186e+17 | 148.32323643215042 | 148.63379256293302 | 3.300333174814552e-14\n", + "0.2 | 0.5 | 1.3258785979549308 | 6.679884723262186e+17 | 148.32323643215042 | 60.05351702517303 | 1.3334559462213187e-14\n", + "0.2 | 0.75 | 1.3227927985529941 | 6.679884723262186e+17 | 148.32323643215042 | 40.36901135011532 | 8.963721176450461e-15\n", + "0.2 | 1 | 1.321535717952325 | 6.679884723262186e+17 | 148.32323643215042 | 30.526758512586515 | 6.778302033569109e-15\n", + "0.5 | 1e-05 | 0.24096794492337217 | 980292850693723.0 | 0.21766873874312043 | 2725466.739096163 | 6.051751853189209e-10\n", + "0.5 | 2e-05 | 0.2449083880337301 | 980292850693723.0 | 0.21766873874312043 | 1362733.8714525797 | 3.0258770410464647e-10\n", + "0.5 | 5e-05 | 0.26603719418924143 | 980292850693723.0 | 0.21766873874312043 | 545094.1490353391 | 1.21035214969498e-10\n", + "0.5 | 7.5e-05 | 0.3041435170921793 | 980292850693723.0 | 0.21766873874312043 | 363396.43275823985 | 8.069021734296907e-11\n", + "0.5 | 0.0001 | 0.34664559598668254 | 980292850693723.0 | 0.21766873874312043 | 272547.574594265 | 6.051771852405908e-11\n", + "0.5 | 0.0002 | 0.48965105730970193 | 980292850693723.0 | 0.21766873874312043 | 136274.28731598076 | 3.0258970288517154e-11\n", + "0.5 | 0.0005 | 0.7000258094502043 | 980292850693723.0 | 0.21766873874312043 | 54510.31493093597 | 1.2103721343178713e-11\n", + "0.5 | 0.00075 | 0.7803152404537448 | 980292850693723.0 | 0.21766873874312043 | 36340.54328796344 | 8.06922157713684e-12\n", + "0.5 | 0.001 | 0.8291864833507464 | 980292850693723.0 | 0.21766873874312043 | 27255.65746622313 | 6.0519716940594945e-12\n", + "0.5 | 0.002 | 0.9148557625310191 | 980292850693723.0 | 0.21766873874312043 | 13628.328733300632 | 3.026096869374191e-12\n", + "0.5 | 0.005 | 0.9605054768523503 | 980292850693723.0 | 0.21766873874312043 | 5451.931493366455 | 1.2105719745228904e-12\n", + "0.5 | 0.0075 | 0.9664986040888477 | 980292850693723.0 | 0.21766873874312043 | 3634.9543289176677 | 8.071219978850558e-13\n", + "0.5 | 0.01 | 0.971577059753968 | 980292850693723.0 | 0.21766873874312043 | 2726.4657466907856 | 6.05397009565586e-13\n", + "0.5 | 0.02 | 1.0046221692212927 | 980292850693723.0 | 0.21766873874312043 | 1363.7328733473057 | 3.0280952708568026e-13\n", + "0.5 | 0.05 | 1.1047795554227975 | 980292850693723.0 | 0.21766873874312043 | 546.0931493393776 | 1.2125703759732823e-13\n", + "0.5 | 0.075 | 1.1581424516578147 | 980292850693723.0 | 0.21766873874312043 | 364.39543289298604 | 8.091203993320884e-14\n", + "0.5 | 0.1 | 1.1943040342674953 | 980292850693723.0 | 0.21766873874312043 | 273.5465746697651 | 6.073954110114357e-14\n", + "0.5 | 0.2 | 1.2650403625958075 | 980292850693723.0 | 0.21766873874312043 | 137.2732873349015 | 3.048079285303851e-14\n", + "0.5 | 0.5 | 1.3129959855689668 | 980292850693723.0 | 0.21766873874312043 | 55.50931493396521 | 1.2325543904171445e-14\n", + "0.5 | 0.75 | 1.3205631535380165 | 980292850693723.0 | 0.21766873874312043 | 37.33954328931077 | 8.291044137756114e-15\n", + "0.5 | 1 | 1.3228809465642857 | 980292850693723.0 | 0.21766873874312043 | 28.254657466983346 | 6.273794254548403e-15\n", + "0.75 | 1e-05 | 0.2224499701965531 | 3532632123678.7227 | 0.0007844019042477163 | 2442727.9074419998 | 5.423945531473073e-10\n", + "0.75 | 2e-05 | 0.2053060605543837 | 3532632123678.7227 | 0.0007844019042477163 | 1221364.876010962 | 2.711974813631639e-10\n", + "0.75 | 5e-05 | 0.20855337608222344 | 3532632123678.7227 | 0.0007844019042477163 | 488546.6517437474 | 1.0847914827388725e-10\n", + "0.75 | 7.5e-05 | 0.21981047089574937 | 3532632123678.7227 | 0.0007844019042477163 | 325698.11617629393 | 7.231950953119213e-11\n", + "0.75 | 0.0001 | 0.22929183435175596 | 3532632123678.7227 | 0.0007844019042477163 | 244273.84276198 | 5.4239688909603066e-11\n", + "0.75 | 0.0002 | 0.25206556710851546 | 3532632123678.7227 | 0.0007844019042477163 | 122137.42560380595 | 2.7119956414757496e-11\n", + "0.75 | 0.0005 | 0.29114658168989516 | 3532632123678.7227 | 0.0007844019042477163 | 48855.57125495263 | 1.0848116017692672e-11\n", + "0.75 | 0.00075 | 0.3252398789514181 | 3532632123678.7227 | 0.0007844019042477163 | 32570.71432009598 | 7.2321513933317716e-12\n", + "0.75 | 0.001 | 0.35990828966428157 | 3532632123678.7227 | 0.0007844019042477163 | 24428.28579637967 | 5.424169068652878e-12\n", + "0.75 | 0.002 | 0.47580513929031976 | 3532632123678.7227 | 0.0007844019042477163 | 12214.642940416195 | 2.712195566005037e-12\n", + "0.75 | 0.005 | 0.6515260879776209 | 3532632123678.7227 | 0.0007844019042477163 | 4886.457186300838 | 1.0850114554152496e-12\n", + "0.75 | 0.0075 | 0.7197356993725887 | 3532632123678.7227 | 0.0007844019042477163 | 3257.971459035242 | 7.234149854785081e-13\n", + "0.75 | 0.01 | 0.7617582568840177 | 3532632123678.7227 | 0.0007844019042477163 | 2443.7285948394738 | 5.426167503851329e-13\n", + "0.75 | 0.02 | 0.8408992984214797 | 3532632123678.7227 | 0.0007844019042477163 | 1222.3642978419878 | 2.7141939758878747e-13\n", + "0.75 | 0.05 | 0.9210906761308845 | 3532632123678.7227 | 0.0007844019042477163 | 489.54571923814456 | 1.0870098582097411e-13\n", + "0.75 | 0.075 | 0.96244927086312 | 3532632123678.7227 | 0.0007844019042477163 | 326.697146173777 | 7.254133875229152e-14\n", + "0.75 | 0.1 | 0.9968207350967746 | 3532632123678.7227 | 0.0007844019042477163 | 245.2728596359628 | 5.4461515216700016e-14\n", + "0.75 | 0.2 | 1.0921914353907194 | 3532632123678.7227 | 0.0007844019042477163 | 123.1364298222042 | 2.7341779911750176e-14\n", + "0.75 | 0.5 | 1.2113887072395224 | 3532632123678.7227 | 0.0007844019042477163 | 49.85457192989512 | 1.1069938727880118e-14\n", + "0.75 | 0.75 | 1.2502765485666594 | 3532632123678.7227 | 0.0007844019042477163 | 33.56971462008021 | 7.453974020261758e-15\n", + "0.75 | 1 | 1.2715113409155705 | 3532632123678.7227 | 0.0007844019042477163 | 25.42728596511644 | 5.645991666440073e-15\n", + "1.0 | 1e-05 | 0.32017904466762515 | 63914577491.36427 | 1.4191887108020279e-05 | 2130369.1131594917 | 4.730369680759886e-10\n", + "1.0 | 2e-05 | 0.3007402314260465 | 63914577491.36427 | 1.4191887108020279e-05 | 1065202.808990499 | 2.3652253688732894e-10\n", + "1.0 | 5e-05 | 0.26987296047005466 | 63914577491.36427 | 1.4191887108020279e-05 | 426085.98427838425 | 9.461009404318693e-11\n", + "1.0 | 7.5e-05 | 0.2559713871873844 | 63914577491.36427 | 1.4191887108020279e-05 | 284058.2874067469 | 6.307361020291211e-11\n", + "1.0 | 0.0001 | 0.2465204142745105 | 63914577491.36427 | 1.4191887108020279e-05 | 213044.20226335054 | 4.730531572313413e-11\n", + "1.0 | 0.0002 | 0.22985421062259584 | 63914577491.36427 | 1.4191887108020279e-05 | 106522.77866349614 | 2.3652808303852554e-11\n", + "1.0 | 0.0005 | 0.2391940723762629 | 63914577491.36427 | 1.4191887108020279e-05 | 42609.75407313465 | 9.461266009121928e-12\n", + "1.0 | 0.00075 | 0.2553997990178182 | 63914577491.36427 | 1.4191887108020279e-05 | 28406.842361022937 | 6.3075860892209815e-12\n", + "1.0 | 0.001 | 0.2695794334088796 | 63914577491.36427 | 1.4191887108020279e-05 | 21305.38413787239 | 4.730745603669903e-12\n", + "1.0 | 0.002 | 0.31063876807450047 | 63914577491.36427 | 1.4191887108020279e-05 | 10653.193844259604 | 2.3654842183383992e-12\n", + "1.0 | 0.005 | 0.3936609595496581 | 63914577491.36427 | 1.4191887108020279e-05 | 4261.877963782244 | 9.463270087067252e-13\n", + "1.0 | 0.0075 | 0.4469915900961414 | 63914577491.36427 | 1.4191887108020279e-05 | 2841.585372310787 | 6.309587013554967e-13\n", + "1.0 | 0.01 | 0.4901673630039956 | 63914577491.36427 | 1.4191887108020279e-05 | 2131.439052904112 | 4.732745424238765e-13\n", + "1.0 | 0.02 | 0.60175324469142 | 63914577491.36427 | 1.4191887108020279e-05 | 1066.2195442053296 | 2.3674829745641937e-13\n", + "1.0 | 0.05 | 0.7405769938099307 | 63914577491.36427 | 1.4191887108020279e-05 | 427.08782194291774 | 9.483254669160729e-14\n", + "1.0 | 0.075 | 0.797163463326217 | 63914577491.36427 | 1.4191887108020279e-05 | 285.05854859317225 | 6.329571280287377e-14\n", + "1.0 | 0.1 | 0.8372760302121772 | 63914577491.36427 | 1.4191887108020279e-05 | 214.04391168158918 | 4.752729580594676e-14\n", + "1.0 | 0.2 | 0.9403435142235637 | 63914577491.36427 | 1.4191887108020279e-05 | 107.52195601832739 | 2.3874670244856097e-14\n", + "1.0 | 0.5 | 1.0888716539372494 | 63914577491.36427 | 1.4191887108020279e-05 | 43.60878244993882 | 9.683094870358305e-15\n", + "1.0 | 0.75 | 1.1499813978771312 | 63914577491.36427 | 1.4191887108020279e-05 | 29.40585497293814 | 6.5294114499488166e-15\n", + "1.0 | 1 | 1.1884279444711843 | 63914577491.36427 | 1.4191887108020279e-05 | 22.30439123207071 | 4.952569739218473e-15\n", + "2.0 | 1e-05 | 0.37717527080114094 | 4995157.201110629 | 1.1091477072590349e-09 | 931785.0004887689 | 2.068978323085988e-10\n", + "2.0 | 2e-05 | 0.36554739044363094 | 4995157.201110629 | 1.1091477072590349e-09 | 513815.87386685994 | 1.1409004271697663e-10\n", + "2.0 | 5e-05 | 0.3556659370173608 | 4995157.201110629 | 1.1091477072590349e-09 | 219045.86744165132 | 4.8637953096542247e-11\n", + "2.0 | 7.5e-05 | 0.3535036870210737 | 4995157.201110629 | 1.1091477072590349e-09 | 148197.11674354723 | 3.290637023834969e-11\n", + "2.0 | 0.0001 | 0.35281735349796495 | 4995157.201110629 | 1.1091477072590349e-09 | 111978.62460516892 | 2.4864249460503123e-11\n", + "2.0 | 0.0002 | 0.3534347525529885 | 4995157.201110629 | 1.1091477072590349e-09 | 56624.48346399062 | 1.2573161059845765e-11\n", + "2.0 | 0.0005 | 0.35502424154537043 | 4995157.201110629 | 1.1091477072590349e-09 | 22805.49604335498 | 5.063837359066121e-12\n", + "2.0 | 0.00075 | 0.3537163573870454 | 4995157.201110629 | 1.1091477072590349e-09 | 15227.1681485824 | 3.3811105356789993e-12\n", + "2.0 | 0.001 | 0.35159094176904615 | 4995157.201110629 | 1.1091477072590349e-09 | 11429.335035838845 | 2.537822182588655e-12\n", + "2.0 | 0.002 | 0.3437425980245005 | 4995157.201110629 | 1.1091477072590349e-09 | 5721.711678609094 | 1.270475209171694e-12\n", + "2.0 | 0.005 | 0.3384378232843908 | 4995157.201110629 | 1.1091477072590349e-09 | 2290.8581499577735 | 5.08672692846662e-13\n", + "2.0 | 0.0075 | 0.3434911844399528 | 4995157.201110629 | 1.1091477072590349e-09 | 1527.8054038403657 | 3.392409472980619e-13\n", + "2.0 | 0.01 | 0.3514509386543829 | 4995157.201110629 | 1.1091477072590349e-09 | 1146.1915618887404 | 2.545056525279899e-13\n", + "2.0 | 0.02 | 0.38795453127123614 | 4995157.201110629 | 1.1091477072590349e-09 | 573.6614252411312 | 1.2737842452839735e-13\n", + "2.0 | 0.05 | 0.47107485080566847 | 4995157.201110629 | 1.1091477072590349e-09 | 230.08032761773353 | 5.108809544690141e-14\n", + "2.0 | 0.075 | 0.5172647100633151 | 4995157.201110629 | 1.1091477072590349e-09 | 153.7225530556975 | 3.4133263561319513e-14\n", + "2.0 | 0.1 | 0.5526131904880447 | 4995157.201110629 | 1.1091477072590349e-09 | 115.54279030330179 | 2.5655653224832383e-14\n", + "2.0 | 0.2 | 0.646310472219547 | 4995157.201110629 | 1.1091477072590349e-09 | 58.27205179784526 | 1.293899471962351e-14\n", + "2.0 | 0.5 | 0.797243980792505 | 4995157.201110629 | 1.1091477072590349e-09 | 23.908978317115814 | 5.308859644585121e-15\n", + "2.0 | 0.75 | 0.8774340306364914 | 4995157.201110629 | 1.1091477072590349e-09 | 16.272675559455415 | 3.6132598156724904e-15\n", + "2.0 | 1 | 0.9386486865700185 | 4995157.201110629 | 1.1091477072590349e-09 | 12.454515425128456 | 2.7654579571053563e-15\n", + "5.0 | 1e-05 | 0.729096374445801 | 555.6368040064594 | 1.2337615462742134e-13 | 554.9108780960305 | 1.232149666954353e-13\n", + "5.0 | 2e-05 | 0.7291162264236533 | 555.6368040064594 | 1.2337615462742134e-13 | 554.186849930652 | 1.2305420014749923e-13\n", + "5.0 | 5e-05 | 0.7291757523892828 | 555.6368040064594 | 1.2337615462742134e-13 | 552.0260777777625 | 1.2257441234847787e-13\n", + "5.0 | 7.5e-05 | 0.729225323038207 | 555.6368040064594 | 1.2337615462742134e-13 | 550.23828703392 | 1.2217744305907273e-13\n", + "5.0 | 0.0001 | 0.7292748625029511 | 555.6368040064594 | 1.2337615462742134e-13 | 548.4620596604069 | 1.21783041353664e-13\n", + "5.0 | 0.0002 | 0.7294727088701931 | 555.6368040064594 | 1.2337615462742134e-13 | 541.4705687792607 | 1.2023061852312293e-13\n", + "5.0 | 0.0005 | 0.7300632679255016 | 555.6368040064594 | 1.2337615462742134e-13 | 521.5279990898956 | 1.1580247851525796e-13\n", + "5.0 | 0.00075 | 0.7305520056062914 | 555.6368040064594 | 1.2337615462742134e-13 | 505.99987151524476 | 1.1235454156271913e-13\n", + "5.0 | 0.001 | 0.7310376814323817 | 555.6368040064594 | 1.2337615462742134e-13 | 491.37136147683685 | 1.0910635983059899e-13\n", + "5.0 | 0.002 | 0.7329502858896297 | 555.6368040064594 | 1.2337615462742134e-13 | 440.4522822110072 | 9.780005299187148e-14\n", + "5.0 | 0.005 | 0.7384152522426068 | 555.6368040064594 | 1.2337615462742134e-13 | 336.0726653659441 | 7.462312220728331e-14\n", + "5.0 | 0.0075 | 0.7426859104004216 | 555.6368040064594 | 1.2337615462742134e-13 | 280.7085388287946 | 6.23298166033225e-14\n", + "5.0 | 0.01 | 0.7467295294070295 | 555.6368040064594 | 1.2337615462742134e-13 | 241.04573230445232 | 5.352290439840697e-14\n", + "5.0 | 0.02 | 0.7610911745523715 | 555.6368040064594 | 1.2337615462742134e-13 | 154.16834802636362 | 3.423224992945864e-14\n", + "5.0 | 0.05 | 0.7938460924485602 | 555.6368040064594 | 1.2337615462742134e-13 | 74.43525519938748 | 1.6527946833241876e-14\n", + "5.0 | 0.075 | 0.8150678569112061 | 555.6368040064594 | 1.2337615462742134e-13 | 52.21726682813804 | 1.1594562383118853e-14\n", + "5.0 | 0.1 | 0.8333792535955517 | 555.6368040064594 | 1.2337615462742134e-13 | 40.320705913772024 | 8.952995214921882e-15\n", + "5.0 | 0.2 | 0.8919888448352014 | 555.6368040064594 | 1.2337615462742134e-13 | 21.382869766048398 | 4.747950869365613e-15\n", + "5.0 | 0.5 | 1.012003022959394 | 555.6368040064594 | 1.2337615462742134e-13 | 9.33697797512213 | 2.073225585679712e-15\n", + "5.0 | 0.75 | 1.0827038377848375 | 555.6368040064594 | 1.2337615462742134e-13 | 6.585973686347976 | 1.4623799252317884e-15\n", + "5.0 | 1 | 1.138569367398157 | 555.6368040064594 | 1.2337615462742134e-13 | 5.200055382874517 | 1.1546442430786545e-15\n", + "7.5 | 1e-05 | 1.1579727162941067 | 65.42062274366074 | 1.4526296331065668e-14 | 65.40750197167768 | 1.4523382934434376e-14\n", + "7.5 | 2e-05 | 1.1579776842823726 | 65.42062274366074 | 1.4526296331065668e-14 | 65.39438654331282 | 1.452047072432468e-14\n", + "7.5 | 5e-05 | 1.1579925874856085 | 65.42062274366074 | 1.4526296331065668e-14 | 65.35507228730187 | 1.4511741205880805e-14\n", + "7.5 | 7.5e-05 | 1.1580050059490286 | 65.42062274366074 | 1.4526296331065668e-14 | 65.32234705876313 | 1.450447474543884e-14\n", + "7.5 | 0.0001 | 1.1580174236191758 | 65.42062274366074 | 1.4526296331065668e-14 | 65.28965509555222 | 1.449721567138345e-14\n", + "7.5 | 0.0002 | 1.1580670863672333 | 65.42062274366074 | 1.4526296331065668e-14 | 65.15921888360964 | 1.4468253014234742e-14\n", + "7.5 | 0.0005 | 1.1582159984650569 | 65.42062274366074 | 1.4526296331065668e-14 | 64.77106671380928 | 1.4382065919040627e-14\n", + "7.5 | 0.00075 | 1.1583400046414276 | 65.42062274366074 | 1.4526296331065668e-14 | 64.45117618545744 | 1.4311035953053484e-14\n", + "7.5 | 0.001 | 1.1584639315271186 | 65.42062274366074 | 1.4526296331065668e-14 | 64.1344789297461 | 1.4240715036028216e-14\n", + "7.5 | 0.002 | 1.158958846589758 | 65.42062274366074 | 1.4526296331065668e-14 | 62.89868505684164 | 1.3966313673750373e-14\n", + "7.5 | 0.005 | 1.1604360020625986 | 65.42062274366074 | 1.4526296331065668e-14 | 59.46547650580331 | 1.3203988237409827e-14\n", + "7.5 | 0.0075 | 1.1616583144933827 | 65.42062274366074 | 1.4526296331065668e-14 | 56.88254043457733 | 1.2630461217927842e-14\n", + "7.5 | 0.01 | 1.162872831495904 | 65.42062274366074 | 1.4526296331065668e-14 | 54.518170529658406 | 1.2105465636493486e-14\n", + "7.5 | 0.02 | 1.1676546346371226 | 65.42062274366074 | 1.4526296331065668e-14 | 46.77181528449076 | 1.0385429246471292e-14\n", + "7.5 | 0.05 | 1.181322059520699 | 65.42062274366074 | 1.4526296331065668e-14 | 32.91391372384855 | 7.308356969348518e-15\n", + "7.5 | 0.075 | 1.192025682675384 | 65.42062274366074 | 1.4526296331065668e-14 | 26.4842300611489 | 5.880680400671445e-15\n", + "7.5 | 0.1 | 1.20219020027986 | 65.42062274366074 | 1.4526296331065668e-14 | 22.21088199657384 | 4.931806517965729e-15\n", + "7.5 | 0.2 | 1.238514238689643 | 65.42062274366074 | 1.4526296331065668e-14 | 13.695471638717029 | 3.041005589280894e-15\n", + "7.5 | 0.5 | 1.3202321940460673 | 65.42062274366074 | 1.4526296331065668e-14 | 6.759169742060504 | 1.5008371749970503e-15\n", + "7.5 | 0.75 | 1.369207081107715 | 65.42062274366074 | 1.4526296331065668e-14 | 4.957375592564134 | 1.100758504915896e-15\n", + "7.5 | 1 | 1.4074503211973002 | 65.42062274366074 | 1.4526296331065668e-14 | 4.014324376831696 | 8.913590702945164e-16\n", + "10.0 | 1e-05 | 1.4570968886841436 | 19.175864900543594 | 4.257897345936977e-15 | 19.174489304841277 | 4.2575919023327196e-15\n", + "10.0 | 2e-05 | 1.4570993187331152 | 19.175864900543594 | 4.257897345936977e-15 | 19.1731139173404 | 4.257286504958469e-15\n", + "10.0 | 5e-05 | 1.4571066086154156 | 19.175864900543594 | 4.257897345936977e-15 | 19.16898900357348 | 4.256370590110743e-15\n", + "10.0 | 7.5e-05 | 1.4571126832141401 | 19.175864900543594 | 4.257897345936977e-15 | 19.1655530055741 | 4.2556076452924475e-15\n", + "10.0 | 0.0001 | 1.4571187575372517 | 19.175864900543594 | 4.257897345936977e-15 | 19.162118306915016 | 4.2548449889856545e-15\n", + "10.0 | 0.0002 | 1.4571430520739155 | 19.175864900543594 | 4.257897345936977e-15 | 19.1483924909525 | 4.251797245602984e-15\n", + "10.0 | 0.0005 | 1.4572159092372137 | 19.175864900543594 | 4.257897345936977e-15 | 19.10733923905199 | 4.242681592503847e-15\n", + "10.0 | 0.00075 | 1.4572765932520457 | 19.175864900543594 | 4.257897345936977e-15 | 19.07326980753783 | 4.2351166590432654e-15\n", + "10.0 | 0.001 | 1.4573372497498749 | 19.175864900543594 | 4.257897345936977e-15 | 19.03932834030532 | 4.227580139361047e-15\n", + "10.0 | 0.002 | 1.457579600899744 | 19.175864900543594 | 4.257897345936977e-15 | 18.904827802739533 | 4.197715020635047e-15\n", + "10.0 | 0.005 | 1.4583040248763588 | 19.175864900543594 | 4.257897345936977e-15 | 18.513096874683363 | 4.110733281477899e-15\n", + "10.0 | 0.0075 | 1.4589047145245595 | 19.175864900543594 | 4.257897345936977e-15 | 18.19951378363078 | 4.0411038479139585e-15\n", + "10.0 | 0.01 | 1.4595026976623577 | 19.175864900543594 | 4.257897345936977e-15 | 17.89696296052528 | 3.973924069927754e-15\n", + "10.0 | 0.02 | 1.4618679084524944 | 19.175864900543594 | 4.257897345936977e-15 | 16.78620389115526 | 3.727286011202593e-15\n", + "10.0 | 0.05 | 1.468716436218725 | 19.175864900543594 | 4.257897345936977e-15 | 14.185809264834273 | 3.149882413751975e-15\n", + "10.0 | 0.075 | 1.4741565641494006 | 19.175864900543594 | 4.257897345936977e-15 | 12.59424951147126 | 2.7964851571019044e-15\n", + "10.0 | 0.1 | 1.4793710196810035 | 19.175864900543594 | 4.257897345936977e-15 | 11.34551972268524 | 2.5192114444927947e-15\n", + "10.0 | 0.2 | 1.4982457886579086 | 19.175864900543594 | 4.257897345936977e-15 | 8.230533331790692 | 1.827545521979766e-15\n", + "10.0 | 0.5 | 1.5410098659533926 | 19.175864900543594 | 4.257897345936977e-15 | 4.798974397071007 | 1.065586374042972e-15\n", + "10.0 | 0.75 | 1.5663574352182952 | 19.175864900543594 | 4.257897345936977e-15 | 3.7223151344244276 | 8.265199934297368e-16\n", + "10.0 | 1 | 1.585871685281188 | 19.175864900543594 | 4.257897345936977e-15 | 3.121161313222283 | 6.930370307017337e-16\n", + "20.0 | 1e-05 | 1.7286486756519488 | 2.142916234875077 | 4.758229887602721e-16 | 2.1428984173511356 | 4.758190324752078e-16\n", + "20.0 | 2e-05 | 1.728648764407431 | 2.142916234875077 | 4.758229887602721e-16 | 2.142880600382718 | 4.758150763134945e-16\n", + "20.0 | 5e-05 | 1.728649030664948 | 2.142916234875077 | 4.758229887602721e-16 | 2.1428271528103546 | 4.758032085684049e-16\n", + "20.0 | 7.5e-05 | 1.7286492525359831 | 2.142916234875077 | 4.758229887602721e-16 | 2.1427826169852677 | 4.757933196287185e-16\n", + "20.0 | 0.0001 | 1.7286494743977214 | 2.142916234875077 | 4.758229887602721e-16 | 2.1427380846311563 | 4.757834314597434e-16\n", + "20.0 | 0.0002 | 1.7286503617517073 | 2.142916234875077 | 4.758229887602721e-16 | 2.142559989916349 | 4.757438864891548e-16\n", + "20.0 | 0.0005 | 1.7286530229215007 | 2.142916234875077 | 4.758229887602721e-16 | 2.1420260386871077 | 4.756253254994087e-16\n", + "20.0 | 0.00075 | 1.7286552395412749 | 2.142916234875077 | 4.758229887602721e-16 | 2.1415814604086196 | 4.755266092912035e-16\n", + "20.0 | 0.001 | 1.7286574552328235 | 2.142916234875077 | 4.758229887602721e-16 | 2.141137228134383 | 4.754279699113757e-16\n", + "20.0 | 0.002 | 1.728666308728265 | 2.142916234875077 | 4.758229887602721e-16 | 2.13936375101392 | 4.750341788848189e-16\n", + "20.0 | 0.005 | 1.7286927805265957 | 2.142916234875077 | 4.758229887602721e-16 | 2.1340762407576848 | 4.738601157589361e-16\n", + "20.0 | 0.0075 | 1.728714739279412 | 2.142916234875077 | 4.758229887602721e-16 | 2.129707326476959 | 4.72890021913521e-16\n", + "20.0 | 0.01 | 1.7287366067447665 | 2.142916234875077 | 4.758229887602721e-16 | 2.1253719446187826 | 4.719273737616231e-16\n", + "20.0 | 0.02 | 1.7288231748695113 | 2.142916234875077 | 4.758229887602721e-16 | 2.1083581370698807 | 4.681495495861567e-16\n", + "20.0 | 0.05 | 1.7290745149199331 | 2.142916234875077 | 4.758229887602721e-16 | 2.0602694484403052 | 4.574717157180397e-16\n", + "20.0 | 0.075 | 1.7292748667352156 | 2.142916234875077 | 4.758229887602721e-16 | 2.023271926743741 | 4.492566156297209e-16\n", + "20.0 | 0.1 | 1.7294674543189976 | 2.142916234875077 | 4.758229887602721e-16 | 1.988769361423232 | 4.4159550714422835e-16\n", + "20.0 | 0.2 | 1.730168466353089 | 2.142916234875077 | 4.758229887602721e-16 | 1.8712610423214961 | 4.155034188538789e-16\n", + "20.0 | 0.5 | 1.73177251697718 | 2.142916234875077 | 4.758229887602721e-16 | 1.64227232787048 | 3.6465771022131223e-16\n", + "20.0 | 0.75 | 1.732728835501288 | 2.142916234875077 | 4.758229887602721e-16 | 1.526875789694727 | 3.3903453149236083e-16\n", + "20.0 | 1 | 1.7334658393718354 | 2.142916234875077 | 4.758229887602721e-16 | 1.446630124465455 | 3.212164144595808e-16\n", + "50.0 | 1e-05 | 1.739141140397846 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013610614050856 | 2.2234682126700225e-16\n", + "50.0 | 2e-05 | 1.739141140461825 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013610477854813 | 2.223468182428426e-16\n", + "50.0 | 5e-05 | 1.7391411406537571 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013610069283048 | 2.2234680917072697e-16\n", + "50.0 | 7.5e-05 | 1.7391411408136916 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013609728825315 | 2.223468016110467e-16\n", + "50.0 | 0.0001 | 1.7391411409736184 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013609388384612 | 2.2234679405174456e-16\n", + "50.0 | 0.0002 | 1.739141141613244 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013608026792107 | 2.2234676381831757e-16\n", + "50.0 | 0.0005 | 1.7391411435313564 | 1.001361075024962 | 2.223468242912223e-16 | 1.00136039436488 | 2.223466731543233e-16\n", + "50.0 | 0.00075 | 1.739141145128904 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013600542900682 | 2.223465976425461e-16\n", + "50.0 | 0.001 | 1.739141146725655 | 1.001361075024962 | 2.223468242912223e-16 | 1.00135971438524 | 2.223465221685128e-16\n", + "50.0 | 0.002 | 1.7391411531046919 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013583564632107 | 2.223462206492523e-16\n", + "50.0 | 0.005 | 1.7391411721656662 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013542989220954 | 2.2234531969413837e-16\n", + "50.0 | 0.0075 | 1.7391411879631302 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013509361089248 | 2.2234457299961647e-16\n", + "50.0 | 0.01 | 1.7391412036824194 | 1.001361075024962 | 2.223468242912223e-16 | 1.0013475899545612 | 2.223438300040928e-16\n", + "50.0 | 0.02 | 1.7391412657893077 | 1.001361075024962 | 2.223468242912223e-16 | 1.001334369473718 | 2.2234089446764705e-16\n", + "50.0 | 0.05 | 1.7391414450146112 | 1.001361075024962 | 2.223468242912223e-16 | 1.001296219922522 | 2.2233242356562365e-16\n", + "50.0 | 0.075 | 1.7391415867303397 | 1.001361075024962 | 2.223468242912223e-16 | 1.001266056190751 | 2.223257258717195e-16\n", + "50.0 | 0.1 | 1.739141722006669 | 1.001361075024962 | 2.223468242912223e-16 | 1.0012372643858902 | 2.223193328067841e-16\n", + "50.0 | 0.2 | 1.739142206764682 | 1.001361075024962 | 2.223468242912223e-16 | 1.0011341005544732 | 2.2229642583459456e-16\n", + "50.0 | 0.5 | 1.7391432733304772 | 1.001361075024962 | 2.223468242912223e-16 | 1.0009071775605214 | 2.2224603880805413e-16\n", + "50.0 | 0.75 | 1.7391438828572479 | 1.001361075024962 | 2.223468242912223e-16 | 1.0007775303834385 | 2.222172513518391e-16\n", + "50.0 | 1 | 1.7391443400312512 | 1.001361075024962 | 2.223468242912223e-16 | 1.000680306025596 | 2.221956632077129e-16\n", + "75.0 | 1e-05 | 1.7391475402804037 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001490198573 | 2.2204463801408666e-16\n", + "75.0 | 2e-05 | 1.7391475402804104 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001490183668 | 2.220446380137557e-16\n", + "75.0 | 5e-05 | 1.7391475402804306 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001490138963 | 2.2204463801276305e-16\n", + "75.0 | 7.5e-05 | 1.7391475402804475 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001490101718 | 2.2204463801193603e-16\n", + "75.0 | 0.0001 | 1.7391475402804637 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001490064465 | 2.2204463801110886e-16\n", + "75.0 | 0.0002 | 1.73914754028053 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001489915489 | 2.220446380078009e-16\n", + "75.0 | 0.0005 | 1.7391475402807286 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001489468737 | 2.2204463799788104e-16\n", + "75.0 | 0.00075 | 1.7391475402808942 | 1.0000001490213473 | 2.220446380144175e-16 | 1.000000148909665 | 2.2204463798961905e-16\n", + "75.0 | 0.001 | 1.73914754028106 | 1.0000001490213473 | 2.220446380144175e-16 | 1.000000148872475 | 2.220446379813612e-16\n", + "75.0 | 0.002 | 1.739147540281721 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001487238992 | 2.2204463794837075e-16\n", + "75.0 | 0.005 | 1.739147540283696 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001482799472 | 2.220446378497936e-16\n", + "75.0 | 0.0075 | 1.7391475402853334 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001479120069 | 2.2204463776809444e-16\n", + "75.0 | 0.01 | 1.7391475402869627 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001475458882 | 2.2204463768679976e-16\n", + "75.0 | 0.02 | 1.7391475402933994 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001460993597 | 2.220446373656059e-16\n", + "75.0 | 0.05 | 1.7391475403119734 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001419250921 | 2.220446364387323e-16\n", + "75.0 | 0.075 | 1.7391475403266607 | 1.0000001490213473 | 2.220446380144175e-16 | 1.000000138624508 | 2.2204463570585544e-16\n", + "75.0 | 0.1 | 1.7391475403406798 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001354739514 | 2.220446350062913e-16\n", + "75.0 | 0.2 | 1.739147540390914 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000001241844543 | 2.220446324995194e-16\n", + "75.0 | 0.5 | 1.7391475405014305 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000000993475624 | 2.2204462698462155e-16\n", + "75.0 | 0.75 | 1.739147540564583 | 1.0000001490213473 | 2.220446380144175e-16 | 1.0000000851550528 | 2.2204462383325136e-16\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "10.0 | 1e-05 | 1.4570968886841438 | 19.17586490054359 | 4.257897345936976e-15 | 19.174489304841284 | 4.257591902332721e-15\n", - "10.0 | 2e-05 | 1.4570993187331154 | 19.17586490054359 | 4.257897345936976e-15 | 19.173113917340388 | 4.257286504958466e-15\n", - "10.0 | 5e-05 | 1.4571066086154152 | 19.17586490054359 | 4.257897345936976e-15 | 19.168989003573486 | 4.256370590110744e-15\n", - "10.0 | 7.5e-05 | 1.4571126832141397 | 19.17586490054359 | 4.257897345936976e-15 | 19.16555300557406 | 4.255607645292439e-15\n", - "10.0 | 0.0001 | 1.457118757537252 | 19.17586490054359 | 4.257897345936976e-15 | 19.162118306915016 | 4.2548449889856545e-15\n", - "10.0 | 0.0002 | 1.4571430520739157 | 19.17586490054359 | 4.257897345936976e-15 | 19.148392490952492 | 4.251797245602982e-15\n", - 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1.0000000000004219 | 2.22044604925125e-16\n", - "100.0 | 0.02 | 1.7391475409434953 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000004174 | 2.22044604925124e-16\n", - "100.0 | 0.05 | 1.7391475409434953 | 1.000000000000426 | 2.220446049251259e-16 | 1.000000000000406 | 2.2204460492512144e-16\n", + "100.0 | 0.02 | 1.739147540943495 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000004174 | 2.22044604925124e-16\n", + "100.0 | 0.05 | 1.7391475409434949 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000004057 | 2.220446049251214e-16\n", "100.0 | 0.075 | 1.7391475409434953 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000003963 | 2.220446049251193e-16\n", - "100.0 | 0.1 | 1.7391475409434949 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000003872 | 2.220446049251173e-16\n", - "100.0 | 0.2 | 1.739147540943496 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000003548 | 2.220446049251101e-16\n", - "100.0 | 0.5 | 1.739147540943496 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000002842 | 2.220446049250944e-16\n", - "100.0 | 0.75 | 1.7391475409434956 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000002436 | 2.220446049250854e-16\n", - "100.0 | 1 | 1.7391475409434956 | 1.000000000000426 | 2.220446049251259e-16 | 1.000000000000213 | 2.220446049250786e-16\n", + "100.0 | 0.1 | 1.739147540943495 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000003872 | 2.220446049251173e-16\n", + "100.0 | 0.2 | 1.7391475409434958 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000003548 | 2.220446049251101e-16\n", + "100.0 | 0.5 | 1.7391475409434956 | 1.000000000000426 | 2.220446049251259e-16 | 1.000000000000284 | 2.2204460492509437e-16\n", + "100.0 | 0.75 | 1.7391475409434958 | 1.000000000000426 | 2.220446049251259e-16 | 1.0000000000002436 | 2.220446049250854e-16\n", + "100.0 | 1 | 1.739147540943496 | 1.000000000000426 | 2.220446049251259e-16 | 1.000000000000213 | 2.220446049250786e-16\n", "200.0 | 1e-05 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000002 | 2.2204460492503136e-16\n", "200.0 | 2e-05 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000002 | 2.2204460492503136e-16\n", - "200.0 | 5e-05 | 1.7391475409434967 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 5e-05 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "200.0 | 7.5e-05 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.0001 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.0002 | 1.7391475409434969 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.0001 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000002 | 2.2204460492503136e-16\n", + "200.0 | 0.0002 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000002 | 2.2204460492503136e-16\n", "200.0 | 0.0005 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.00075 | 1.7391475409434967 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.001 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.002 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.005 | 1.7391475409434969 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.00075 | 1.7391475409434969 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.001 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000007 | 2.2204460492503146e-16\n", + "200.0 | 0.002 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.005 | 1.7391475409434969 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000007 | 2.2204460492503146e-16\n", "200.0 | 0.0075 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", - "200.0 | 0.01 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.01 | 1.7391475409434967 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", "200.0 | 0.02 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", "200.0 | 0.05 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", "200.0 | 0.075 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0 | 2.220446049250313e-16\n", - "200.0 | 0.1 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000007 | 2.2204460492503146e-16\n", - "200.0 | 0.2 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.1 | 1.7391475409434973 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000007 | 2.2204460492503146e-16\n", + "200.0 | 0.2 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", "200.0 | 0.5 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0 | 2.220446049250313e-16\n", - "200.0 | 0.75 | 1.739147540943497 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000002 | 2.2204460492503136e-16\n", - "200.0 | 1 | 1.7391475409434967 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 0.75 | 1.7391475409434967 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", + "200.0 | 1 | 1.7391475409434969 | 1.0000000000000004 | 2.220446049250314e-16 | 1.0000000000000004 | 2.220446049250314e-16\n", "500.0 | 1e-05 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "500.0 | 2e-05 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "500.0 | 5e-05 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", @@ -1343,7 +1342,13 @@ "500.0 | 0.0001 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "500.0 | 0.0002 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "500.0 | 0.0005 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", - "500.0 | 0.00075 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", + "500.0 | 0.00075 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "500.0 | 0.001 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "500.0 | 0.002 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "500.0 | 0.005 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", @@ -1368,7 +1373,13 @@ "1000.0 | 0.001 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "1000.0 | 0.002 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "1000.0 | 0.005 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", - "1000.0 | 0.0075 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", + "1000.0 | 0.0075 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "1000.0 | 0.01 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "1000.0 | 0.02 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", "1000.0 | 0.05 | 1.739147540943497 | 1.0 | 2.220446049250313e-16 | 1.0 | 2.220446049250313e-16\n", @@ -1397,14 +1408,14 @@ "RBF Expression:\n", "--------------------------\n", "\n", - "4.038157591240296 + 237.8499310618983*(-1551.5244569618876*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.5518182560031254)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.2731190901874679)**2)**0.5)**2) - 334.71891273048857*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.4300326512391472)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8610629174234938)**2)**0.5)**2) + 235.58220304204985*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.11035664436222248)**2)**0.5)**2) + 27.886153309561678*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.6822668136056202)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8414199967160326)**2)**0.5)**2) - 1415.9829578091171*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.13520535030959882)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5811256814195772)**2)**0.5)**2) - 990.5466782519585*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.908216841775921)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.20132180755786522)**2)**0.5)**2) - 343.10909375147367*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.18501228557936236)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.09666279198240106)**2)**0.5)**2) - 723.9922784849874*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.22021941796179972)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 1.0)**2)**0.5)**2) - 8.525226879211669*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.8266365897830592)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.16264387978612238)**2)**0.5)**2) + 255.07386589590683*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.831288905218487)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.26866011456766936)**2)**0.5)**2) + 528.0399010886766*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.43739443999592303)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.9599385359524552)**2)**0.5)**2) + 432.74002291691215*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.8327482651149929)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3330572615505772)**2)**0.5)**2) - 761.4482839044426*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.17485274298531528)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3711736045917253)**2)**0.5)**2) + 854.4206928222548*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.0009932894634358945)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.2461895993357224)**2)**0.5)**2) + 158.4825871693355*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.8102253955808789)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546)**2)**0.5)**2) - 2542.5018400558793*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.6086081776493459)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.6109286075828757)**2)**0.5)**2) - 1366.554383948263*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.1028770983224648)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.38063145554753214)**2)**0.5)**2) + 342.1406582093882*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.0325313822086792)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.9084335925839453)**2)**0.5)**2) + 107.6091733488148*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 1.0)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.046386199083146756)**2)**0.5)**2) + 49.67120676760413*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.9074219145138307)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5830026053427076)**2)**0.5)**2) + 897.3018668528205*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.7557460774371298)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.6383110566137417)**2)**0.5)**2) - 8.627269390497531*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.5911925356865033)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.005381283583344741)**2)**0.5)**2) - 136.91234827602625*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.21031274399063923)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5495609776179707)**2)**0.5)**2) + 735.7407100245284*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.7830308832306688)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.24439265066740792)**2)**0.5)**2) + 541.730933783887*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.28803140536417743)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8689715765112118)**2)**0.5)**2) - 45.921399758259014*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.993941455353761)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.9026639746834018)**2)**0.5)**2) + 1712.6788445573948*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.36343808704287006)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.6057949629205177)**2)**0.5)**2) + 3266.9168170263265*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.3586109792561233)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3372905989731323)**2)**0.5)**2) - 827.2715689260567*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.17758838147122427)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.23089727866378493)**2)**0.5)**2) + 919.0325530603195*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.04112204875223326)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5088149538788017)**2)**0.5)**2))\n", + "4.038157591240296 + 237.8499310618983*(-1551.5244569790484*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.5518182560031254)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.2731190901874679)**2)**0.5)**2) - 334.71891270994945*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.4300326512391472)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8610629174234938)**2)**0.5)**2) + 235.58220304447389*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.11035664436222248)**2)**0.5)**2) + 27.886153320078392*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.6822668136056202)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8414199967160326)**2)**0.5)**2) - 1415.9829577991513*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.13520535030959882)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5811256814195772)**2)**0.5)**2) - 990.5466783129937*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.908216841775921)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.20132180755786522)**2)**0.5)**2) - 343.1090937504871*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.18501228557936236)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.09666279198240106)**2)**0.5)**2) - 723.9922784938293*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.22021941796179972)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 1.0)**2)**0.5)**2) - 8.525226892380312*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.8266365897830592)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.16264387978612238)**2)**0.5)**2) + 255.07386589846564*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.831288905218487)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.26866011456766936)**2)**0.5)**2) + 528.0399010835685*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.43739443999592303)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.9599385359524552)**2)**0.5)**2) + 432.74002294811885*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.8327482651149929)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3330572615505772)**2)**0.5)**2) - 761.4482839023253*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.17485274298531528)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3711736045917253)**2)**0.5)**2) + 854.4206928285184*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.0009932894634358945)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.2461895993357224)**2)**0.5)**2) + 158.48258716555927*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.8102253955808789)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546)**2)**0.5)**2) - 2542.501840064855*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.6086081776493459)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.6109286075828757)**2)**0.5)**2) - 1366.5543839471993*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.1028770983224648)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.38063145554753214)**2)**0.5)**2) + 342.14065821289296*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.0325313822086792)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.9084335925839453)**2)**0.5)**2) + 107.60917336635657*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 1.0)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.046386199083146756)**2)**0.5)**2) + 49.67120678278616*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.9074219145138307)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5830026053427076)**2)**0.5)**2) + 897.3018668200407*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.7557460774371298)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.6383110566137417)**2)**0.5)**2) - 8.627269388010859*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.5911925356865033)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.005381283583344741)**2)**0.5)**2) - 136.91234826302025*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.21031274399063923)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5495609776179707)**2)**0.5)**2) + 735.7407100718409*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.7830308832306688)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.24439265066740792)**2)**0.5)**2) + 541.7309337858117*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.28803140536417743)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8689715765112118)**2)**0.5)**2) - 45.92139975993581*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.993941455353761)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.9026639746834018)**2)**0.5)**2) + 1712.6788445330471*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.36343808704287006)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.6057949629205177)**2)**0.5)**2) + 3266.9168170510325*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.3586109792561233)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3372905989731323)**2)**0.5)**2) - 827.2715689473107*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.17758838147122427)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.23089727866378493)**2)**0.5)**2) + 919.0325530451682*exp(- (0.75*(((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.04112204875223326)**2 + ((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5088149538788017)**2)**0.5)**2))\n", "--------------------------\n", "\n", "Model training errors:\n", "-----------------------\n", - "Mean Squared Error (MSE) : 0.0004443809827437673\n", - "Root Mean Squared Error (RMSE) : 0.021080345887669095\n", - "Goodness of fit (R2) : 0.9918215668806757\n", + "Mean Squared Error (MSE) : 0.00044438098273198273\n", + "Root Mean Squared Error (RMSE) : 0.02108034588738958\n", + "Goodness of fit (R2) : 0.9918215668808925\n", "\n", "========================================================================================================================" ] @@ -1436,8 +1447,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "R2: 0.9918215668806757 \n", - "RMSE: 0.021080345887669095\n" + "R2: 0.9918215668808925 \n", + "RMSE: 0.02108034588738958\n" ] } ], @@ -1472,7 +1483,7 @@ "text": [ "[[61.06401281]\n", " [12.7142377 ]\n", - " [50.97501439]]\n" + " [50.9750144 ]]\n" ] } ], @@ -1616,8 +1627,8 @@ "\n", "Final results\n", "================\n", - "Theta: [6.01576437 0.22794622] \n", - "Mean: [[386.67466251]] \n", + "Theta: [6.01392028 0.22771839] \n", + "Mean: [[386.90347574]] \n", "Regularization parameter: 1.000000000001e-06\n", "\n", "Results saved in solution.pickle\n" @@ -1630,23 +1641,23 @@ "========================================================================================================================\n", "Results of Kriging run:\n", "\n", - "Kriging mean : [[386.67466251]]\n", - "Kriging variance : [[70460.78166121]]\n", - "Kriging weights : [6.01576437 0.22794622]\n", + "Kriging mean : [[386.90347574]]\n", + "Kriging variance : [[70556.80108849]]\n", + "Kriging weights : [6.01392028 0.22771839]\n", "Regularization parameter : 1.000000000001e-06\n", "Number of terms in Kriging model : 31\n", "\n", "Kriging Expression:\n", "--------------------\n", "\n", - "386.6746625106164 + (3469.0208871846553*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.5518182560031254)**2 + 0.22794621767031822*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.2731190901874679)**2)) - 141877.18052533641*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.4300326512391472)**2 + 0.22794621767031822*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8610629174234938)**2)) + 89858.53563122451*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084)**2 + 0.22794621767031822*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.11035664436222248)**2)) - 17331.16845172411*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.6822668136056202)**2 + 0.22794621767031822*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.8414199967160326)**2)) + 70866.88925537094*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.13520535030959882)**2 + 0.22794621767031822*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5811256814195772)**2)) + 84227.85506583657*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.908216841775921)**2 + 0.22794621767031822*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.20132180755786522)**2)) + 12240.144907206297*exp(- (6.01576436749808*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.18501228557936236)**2 + 0.22794621767031822*((IndexedParam[1] - 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4.929217157135412)/14.643030012320084 - 0.3586109792561233)**2 + 0.22771839133441968*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.3372905989731323)**2)) - 47543.8977778852*exp(- (6.013920280600496*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.17758838147122427)**2 + 0.22771839133441968*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.23089727866378493)**2)) + 108516.00992591679*exp(- (6.013920280600496*((IndexedParam[0] + 4.929217157135412)/14.643030012320084 - 0.04112204875223326)**2 + 0.22771839133441968*((IndexedParam[1] - 0.22882456869508516)/14.45053220191546 - 0.5088149538788017)**2)))\n", "--------------------------\n", "\n", "Model training errors:\n", "-----------------------\n", - "Mean Squared Error (MSE) : 0.005821749035738542\n", - "Root Mean Squared Error (RMSE) : 0.07630038686493366\n", - "Goodness of fit (R2) : 0.999998106078119\n", + "Mean Squared Error (MSE) : 0.005830984939233206\n", + "Root Mean Squared Error (RMSE) : 0.07636088618679858\n", + "Goodness of fit (R2) : 0.99999810307351\n", "\n", "========================================================================================================================" ] @@ -1687,8 +1698,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "R2: 0.999998106078119 \n", - "RMSE: 0.07630038686493366\n" + "R2: 0.99999810307351 \n", + "RMSE: 0.07636088618679858\n" ] } ], @@ -1721,9 +1732,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[57.63225481]\n", - " [ 4.44461901]\n", - " [50.8024123 ]]\n" + "[[57.63168118]\n", + " [ 4.44415424]\n", + " [50.80252545]]\n" ] } ], @@ -1751,7 +1762,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "3469.0208871846553*exp(- (6.01576436749808*((x[1] + 4.929217157135412)/14.643030012320084 - 0.5518182560031254)**2 + 0.22794621767031822*((x[2] - 0.22882456869508516)/14.45053220191546 - 0.2731190901874679)**2)) - 141877.18052533641*exp(- (6.01576436749808*((x[1] + 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(6.013920280600496*((x[1] + 4.929217157135412)/14.643030012320084 - 0.3586109792561233)**2 + 0.22771839133441968*((x[2] - 0.22882456869508516)/14.45053220191546 - 0.3372905989731323)**2)) - 47543.8977778852*exp(- (6.013920280600496*((x[1] + 4.929217157135412)/14.643030012320084 - 0.17758838147122427)**2 + 0.22771839133441968*((x[2] - 0.22882456869508516)/14.45053220191546 - 0.23089727866378493)**2)) + 108516.00992591679*exp(- (6.013920280600496*((x[1] + 4.929217157135412)/14.643030012320084 - 0.04112204875223326)**2 + 0.22771839133441968*((x[2] - 0.22882456869508516)/14.45053220191546 - 0.5088149538788017)**2)) + 386.9034757421604\n" ] } ], @@ -1796,9 +1807,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_test.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_test.ipynb index 16ec6887..0b7a066f 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_test.ipynb @@ -1,867 +1,868 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PySMO Tutorial\n", - "Author: Mayo Amusat \n", - "Maintainer: Mayo Amusat \n", - "Updated: 2023-06-01 \n", - "\n", - "**Python-based Surrogate Modelling Objects** (PySMO) provides tools for generating different types of reduced order models. PySMO currently provides tools for sampling and surrogate model generation.\n", - "\n", - "## Installation\n", - "\n", - "**PySMO** is installed by default as part of IDAES. For instructions on installing IDAES, see the [online documentation](https://idaes-pse.readthedocs.io/en/stable/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## One-Shot Sampling with PySMO\n", - "\n", - "The PySMO package offers five common sampling methods for one-shot design:\n", - "\n", - "* Latin Hypercube Sampling (LHS)\n", - "* Full-Factorial Sampling\n", - "* Halton Sampling\n", - "* Hammersley Sampling\n", - "* Centroidal voronoi tessellation (CVT) sampling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySMO provides two modes for data sampling: creation and selection.\n", - "- In creation mode, PySMO creates a specified number of sample points from the bounds provided by the user.\n", - "- In selection mode, PySMO selects a specified number of data points from a user-supplied dataset or file." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating samples: \n", - "For demonstration purposes, let us consider a problem for which we need twenty-five (25) samples of temperature and pressure from within the ranges T = 273K - 373K, P = 1 MPa - 50 MPa. Let us generate these samples in PySMO." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Import PySMO's sampling tool\n", - "For this demonstration, we will attempt to generate the samples using the Hammersley sampling method." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.surrogate.pysmo.sampling import HammersleySampling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Specify sampling information and initialize class\n", - "\n", - "All the sampling tools (except full-factorial sampling) require the same keyword arguments:\n", - "\n", - " - data_input : must be a list of lists containing the problem bounds (when creating points), \n", - " or an input dataset (when selecting points from a dataset)\n", - " - number_of_samples : number of samples to be created or selected.\n", - " - sampling_type : \"creation\" or \"selection\".\n", - "\n", - "For full factorial sampling, the user needs to enter a list of points in each dimension in place of the number of samples. Full-factorial sampling requires other inputs - details may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/sampling/pysmo_uniform.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For our example, we will create the bounds and then initialize the class with the number of samples." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "bounds_info = [[273, 1], [373, 50]]\n", - "init_data = HammersleySampling(\n", - " data_input=bounds_info, number_of_samples=25, sampling_type=\"creation\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Create the samples\n", - "The samples are created by calling the ``sample_points`` method on the initialized class." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "samples = init_data.sample_points()\n", - "print(samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simple as that, the samples have been created!\n", - "\n", - "Now, let us visualize the samples in a 2-D plot." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Visualize samples with matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "plt.plot(samples[:, 0], samples[:, 1], \"o\")\n", - "plt.xlabel(r\"Temperature\", fontsize=11)\n", - "plt.xlabel(r\"Pressure\", fontsize=11)\n", - "plt.xlim(272, 374)\n", - "plt.ylim(0, 50)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating surrogates with PySMO\n", - "\n", - "PySMO currently provides tools for generating three types of surrogates:\n", - "\n", - "- Polynomial surrogates\n", - "- Radial basis function (RBF) surrogates, and\n", - "- Kriging surrogates\n", - "\n", - "Details about thee various methods may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating polynomial models\n", - "\n", - "The ``PolynomialRegression`` class trains polynomial models from data.\n", - "\n", - "As an example, let us generate a surrogate for the Brainin function. \n", - "\n", - "The true Brainin function is given by the expression:\n", - " \n", - " \\begin{gather}\n", - "\\hat{y}(x_{1},x_{2})=\\left(x_{2}-\\frac{5.1x_{1}^{2}}{4\\pi^{2}}+\\frac{5x_{1}}{\\pi}-6\\right)^{2}+10\\left[\\left(1-\\frac{1}{8\\pi}\\right)\\cos\\left(x_{1}\\right)+1\\right]+5x_{1}\\nonumber \\\\\n", - "x_{1}\\in\\left[-5,10\\right];x_{2}\\in\\left[0,15\\right]\n", - "\\end{gather}\n", - "\n", - "We have generated 30 points from the function and saved the information in a text file called \"brainin_30.txt\". We will use this data to train a simple polynomial model. The data is in XY format, with the outputs $y$ in the third column." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Import and visualize the data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", - "print(brainin_data, \"\\n\\nDataset shape:\", brainin_data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us visualize the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import pyplot as plt\n", - "\n", - "fig1 = plt.figure(figsize=(6, 4), tight_layout=True)\n", - "ax = fig1.add_subplot(111, projection=\"3d\")\n", - "ax.scatter3D(\n", - " brainin_data[:, 0], brainin_data[:, 1], brainin_data[:, 2], cmap=brainin_data[:, 2]\n", - ")\n", - "ax.set_xlabel(r\"$x_{1}$\", fontsize=11)\n", - "ax.set_ylabel(r\"$x_{2}$\", fontsize=11)\n", - "ax.set_zlabel(r\"$y$\", fontsize=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Import the polynomial model tool" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.surrogate.pysmo.polynomial_regression import PolynomialRegression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Specify the regression settings and initialize the PolynomialRegression class\n", - " \n", - "The PolynomialRegression class takes a keyword arguments:\n", - "\n", - " - original_data_input : The dataset for regression training. training_data is expected to contain xy_data, \n", - " with the output values (y) in the last column.\n", - " - regression_data_input : same as above\n", - " - maximum_polynomial_order : maximum order of the polynomial to be generated \n", - "\n", - "It also takes a number of optional arguments:\n", - "\n", - " - multinomials : True/False option for specifying second-order bi-variate terms. default is False\n", - " - training_split : The training/cross-validation split of training data. Must be between 0 and 1. \n", - " Default is 0.75\n", - " - fname : Filename for saving results (.pickle extension). \n", - " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", - " should be overwritten." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example, let us consider a 4th order polynomial with interaction terms. We will split the data 80/20 between training and cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "poly_class = PolynomialRegression(\n", - " original_data_input=brainin_data,\n", - " regression_data_input=brainin_data,\n", - " maximum_polynomial_order=4,\n", - " multinomials=1,\n", - " training_split=0.8,\n", - " number_of_crossvalidations=10,\n", - " overwrite=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Extract variable names\n", - "Next, we extract Pyomo variable names from the dataset. This should be done always." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "vars = poly_class.get_feature_vector()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can view the variables using Pyomo's pprint function:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "vars.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 5: Specify additional regression terms, if required.\n", - "\n", - "This is one of the unique features of PySMO - it allows the user to specify additional regression features if they want.\n", - "The additional features must be specified in terms of the Pyomo variables created when calling the ``get_feature_vector()`` \n", - "\n", - "For this example, let us create three additional features: $x_{1}^{2}x_{2}^{2}$, $exp(x_1)$ and $exp(x_2)$. We do this by calling the ``set_additional_terms`` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import exp\n", - "\n", - "poly_class.set_additional_terms(\n", - " [vars[0] * vars[0] * vars[1] * vars[1], exp(vars[0]), exp(vars[1])]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it - those features will now exist in the model. \n", - "\n", - "Note that ``set_additional_terms`` an optional call - the regression process works just fine without it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 6: Train the surrogate and view results\n", - "Next, we train the polynomial surrogate by calling ``training``:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "poly_class.training()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The polynomial model seems to fit well based on the $R^2$. It should be noted that the metrics are only an indication of how well of how well the model fit the training data - the user needs to verify the model's performance on a test data set if possible.\n", - "\n", - "We can view the parity and residual plots for the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "poly_class.parity_residual_plots()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySMO is also able to compute the confidence intervals on the regression coefficients obtained by calling ``confint_regression()``. This is left as an exercise for the user.\n", - "\n", - "#### Step 8 (Optional): Generate Pyomo expression \n", - "\n", - "If the user wishes, they can generate the Pyomo expression for the polynomial fit using PySMO's ``generate_expression``. To do this, the user must pass in a list of Pyomo variables corresponding to each variable in the input dataset. \n", - "\n", - "As a demonstration, let us create the variables $x_1$ and $x_2$ and generate the pyomo expression based on them:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import Var, ConcreteModel\n", - "\n", - "m = ConcreteModel()\n", - "m.x = Var([1, 2])\n", - "print(poly_class.generate_expression([m.x[1], m.x[2]]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 9 (Optional): Predict output at any unsampled point\n", - "\n", - "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. \n", - "\n", - "Let us evaluate the surrogate at three points:\n", - "\n", - "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", - "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", - "- $x_{1}=-2$, $x_{2}=3$. (true function value: 50.8899)\n", - "\n", - "We will pass the points in as an array." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", - "ys = poly_class.predict_output(unsampled_points)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model performs fairly well in predicting the value at two of our sampled points but is off on the value at [-3, 2]. For better model performance, additional training data is needed in this region. We will leave this to the user to try.\n", - "\n", - "Further information about using PySMO's polynomial regression tool can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_polyregression.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating RBF models\n", - "\n", - "The ``RadialBasisFunction`` class trains RBF models from data. For details about RBF models, the user should consult the documentation.\n", - "\n", - "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Import the data and the RBF tool" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", - "from idaes.core.surrogate.pysmo.radial_basis_function import RadialBasisFunctions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Specify the RBF settings and initialize the RadialBasisFunctions class\n", - " \n", - "The RadialBasisFunctions class takes a number of keyword arguments:\n", - "\n", - " - XY_data : The dataset forRBF training. training_data is expected to contain xy_data, \n", - " with the output values (y) in the last column.\n", - "\n", - "\n", - "It also takes a number of optional arguments:\n", - "\n", - " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", - " - basis_function : Basis function transformation to be applied to the training data. PySMO offers \n", - " six basis function types including the Gaussian and Spline transformations. User \n", - " should consult documentation for full list of options. \n", - " - fname : Filename for saving (.pickle extension)\n", - " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", - " should be overwritten." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this demonstration, we will train an RBF model with a Gaussian basis function:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "rbf_class = RadialBasisFunctions(\n", - " XY_data=brainin_data, basis_function=\"gaussian\", overwrite=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Extract variable names\n", - "Next, we extract Pyomo variable names from the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "vars = rbf_class.get_feature_vector()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Train the RBF surrogate\n", - "Next, we train the RBF surrogate by calling ``training``:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "rbf_class.training()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 5: View model metrics\n", - " \n", - "We can view the Root Mean Square Error (RMSE) and $R^2$ values of the RBF fit based on the training data:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"R2: \", rbf_class.R2, \"\\nRMSE: \", rbf_class.rmse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", - "\n", - "\n", - "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. We do this by calling the function ``predict_output``.\n", - "\n", - "Let us again evaluate the RBF surrogate at the same set of points we considered for the polynomial model:\n", - "\n", - "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", - "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", - "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", - "ys = rbf_class.predict_output(unsampled_points)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results from the RBF surrogate are similar to those obtained from the polynomial.\n", - "\n", - "\n", - "For RBF models, the Pyomo expression is generated by calling ``generate_expression`` on the results object, while parity plots may be viewed with the ``parity_residual_plots`` method.\n", - "\n", - "Further information about using PySMO's RBF tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_radialbasisfunctions.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training Kriging models\n", - "\n", - "The ``KrigingModel`` class trains Kriging from data. For details about Kriging models, users should consult the documentation.\n", - "\n", - "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Load the data and import the Kriging tool" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", - "from idaes.core.surrogate.pysmo.kriging import KrigingModel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Specify the Kriging settings and initialize the KrigingModel class\n", - " \n", - "The KrigingModel class takes a number of keyword arguments:\n", - "\n", - " - XY_data : The dataset for Kriging training. training_data is expected to contain xy_data, \n", - " with the output values (y) in the last column.\n", - "\n", - "\n", - "It also takes a number of optional arguments:\n", - "\n", - " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", - " - numerical_gradients : Boolean variable which determines whether numerical gradients are used when\n", - " solving the max. likelihood optimization problem. Default is True.\n", - " - fname : Filename for saving (.pickle extension)\n", - " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", - " should be overwritten." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "For this demonstration, we will train a Kriging model with regularization:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "krg_class = KrigingModel(XY_data=brainin_data, overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Extract variable names (optional)\n", - "Next, we extract Pyomo variable names from the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "vars = krg_class.get_feature_vector()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Train the Kriging surrogate\n", - "Next, we train the RBF surrogate by calling ``training``:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "krg_class.training()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The returned information correspond to the Kriging model parameters.\n", - "\n", - "As we can see, the optimization problem was solved using SciPy's L-BFGS algorithm which makes use of gradients. A different algorithm (Basinhopping) is used when no numerical gradients are computed (when numerical_gradients is set to False). The user should try this." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 5: View model metrics\n", - " \n", - "We can view the RMSE and $R^2$ values of the Kriging fit based on the training data:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"R2: \", krg_class.training_R2, \"\\nRMSE: \", krg_class.training_rmse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", - "\n", - "\n", - "Again, based on the model we trained, we evaluate the surrogate at a set of off-design points:\n", - "\n", - "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", - "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", - "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)\n", - "\n", - "We do this by calling the function ``predict_output``:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", - "ys = krg_class.predict_output(unsampled_points)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Kriging model performs very well, predicting all three points fairly accurately. \n", - "\n", - "For Kriging models, the Pyomo expression is generated by calling ``generate_expression`` on the results object: " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "print(krg_class.generate_expression([m.x[1], m.x[2]]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, expressing a Kriging model algebraically is pretty complicated!\n", - "\n", - "Parity plots for the Kriging model may be viewed with the ``parity_residual_plots`` method.\n", - "\n", - "Further information about using PySMO's Kriging tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_kriging.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "PySMO allows IDAES users to sample design spaces and generate different types of surrogate models. Further information about PySMO's capabilities may be found in the documentation." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PySMO Tutorial\n", + "Author: Mayo Amusat \n", + "Maintainer: Mayo Amusat \n", + "Updated: 2023-06-01 \n", + "\n", + "**Python-based Surrogate Modelling Objects** (PySMO) provides tools for generating different types of reduced order models. PySMO currently provides tools for sampling and surrogate model generation.\n", + "\n", + "## Installation\n", + "\n", + "**PySMO** is installed by default as part of IDAES. For instructions on installing IDAES, see the [online documentation](https://idaes-pse.readthedocs.io/en/stable/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One-Shot Sampling with PySMO\n", + "\n", + "The PySMO package offers five common sampling methods for one-shot design:\n", + "\n", + "* Latin Hypercube Sampling (LHS)\n", + "* Full-Factorial Sampling\n", + "* Halton Sampling\n", + "* Hammersley Sampling\n", + "* Centroidal voronoi tessellation (CVT) sampling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PySMO provides two modes for data sampling: creation and selection.\n", + "- In creation mode, PySMO creates a specified number of sample points from the bounds provided by the user.\n", + "- In selection mode, PySMO selects a specified number of data points from a user-supplied dataset or file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating samples: \n", + "For demonstration purposes, let us consider a problem for which we need twenty-five (25) samples of temperature and pressure from within the ranges T = 273K - 373K, P = 1 MPa - 50 MPa. Let us generate these samples in PySMO." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Import PySMO's sampling tool\n", + "For this demonstration, we will attempt to generate the samples using the Hammersley sampling method." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.surrogate.pysmo.sampling import HammersleySampling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Specify sampling information and initialize class\n", + "\n", + "All the sampling tools (except full-factorial sampling) require the same keyword arguments:\n", + "\n", + " - data_input : must be a list of lists containing the problem bounds (when creating points), \n", + " or an input dataset (when selecting points from a dataset)\n", + " - number_of_samples : number of samples to be created or selected.\n", + " - sampling_type : \"creation\" or \"selection\".\n", + "\n", + "For full factorial sampling, the user needs to enter a list of points in each dimension in place of the number of samples. Full-factorial sampling requires other inputs - details may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/sampling/pysmo_uniform.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our example, we will create the bounds and then initialize the class with the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "bounds_info = [[273, 1], [373, 50]]\n", + "init_data = HammersleySampling(\n", + " data_input=bounds_info, number_of_samples=25, sampling_type=\"creation\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Create the samples\n", + "The samples are created by calling the ``sample_points`` method on the initialized class." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "samples = init_data.sample_points()\n", + "print(samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple as that, the samples have been created!\n", + "\n", + "Now, let us visualize the samples in a 2-D plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Visualize samples with matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.plot(samples[:, 0], samples[:, 1], \"o\")\n", + "plt.xlabel(r\"Temperature\", fontsize=11)\n", + "plt.xlabel(r\"Pressure\", fontsize=11)\n", + "plt.xlim(272, 374)\n", + "plt.ylim(0, 50)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating surrogates with PySMO\n", + "\n", + "PySMO currently provides tools for generating three types of surrogates:\n", + "\n", + "- Polynomial surrogates\n", + "- Radial basis function (RBF) surrogates, and\n", + "- Kriging surrogates\n", + "\n", + "Details about thee various methods may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating polynomial models\n", + "\n", + "The ``PolynomialRegression`` class trains polynomial models from data.\n", + "\n", + "As an example, let us generate a surrogate for the Brainin function. \n", + "\n", + "The true Brainin function is given by the expression:\n", + " \n", + " \\begin{gather}\n", + "\\hat{y}(x_{1},x_{2})=\\left(x_{2}-\\frac{5.1x_{1}^{2}}{4\\pi^{2}}+\\frac{5x_{1}}{\\pi}-6\\right)^{2}+10\\left[\\left(1-\\frac{1}{8\\pi}\\right)\\cos\\left(x_{1}\\right)+1\\right]+5x_{1}\\nonumber \\\\\n", + "x_{1}\\in\\left[-5,10\\right];x_{2}\\in\\left[0,15\\right]\n", + "\\end{gather}\n", + "\n", + "We have generated 30 points from the function and saved the information in a text file called \"brainin_30.txt\". We will use this data to train a simple polynomial model. The data is in XY format, with the outputs $y$ in the third column." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Import and visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", + "print(brainin_data, \"\\n\\nDataset shape:\", brainin_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us visualize the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import pyplot as plt\n", + "\n", + "fig1 = plt.figure(figsize=(6, 4), tight_layout=True)\n", + "ax = fig1.add_subplot(111, projection=\"3d\")\n", + "ax.scatter3D(\n", + " brainin_data[:, 0], brainin_data[:, 1], brainin_data[:, 2], cmap=brainin_data[:, 2]\n", + ")\n", + "ax.set_xlabel(r\"$x_{1}$\", fontsize=11)\n", + "ax.set_ylabel(r\"$x_{2}$\", fontsize=11)\n", + "ax.set_zlabel(r\"$y$\", fontsize=11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Import the polynomial model tool" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.surrogate.pysmo.polynomial_regression import PolynomialRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Specify the regression settings and initialize the PolynomialRegression class\n", + " \n", + "The PolynomialRegression class takes a keyword arguments:\n", + "\n", + " - original_data_input : The dataset for regression training. training_data is expected to contain xy_data, \n", + " with the output values (y) in the last column.\n", + " - regression_data_input : same as above\n", + " - maximum_polynomial_order : maximum order of the polynomial to be generated \n", + "\n", + "It also takes a number of optional arguments:\n", + "\n", + " - multinomials : True/False option for specifying second-order bi-variate terms. default is False\n", + " - training_split : The training/cross-validation split of training data. Must be between 0 and 1. \n", + " Default is 0.75\n", + " - fname : Filename for saving results (.pickle extension). \n", + " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", + " should be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example, let us consider a 4th order polynomial with interaction terms. We will split the data 80/20 between training and cross-validation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "poly_class = PolynomialRegression(\n", + " original_data_input=brainin_data,\n", + " regression_data_input=brainin_data,\n", + " maximum_polynomial_order=4,\n", + " multinomials=1,\n", + " training_split=0.8,\n", + " number_of_crossvalidations=10,\n", + " overwrite=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Extract variable names\n", + "Next, we extract Pyomo variable names from the dataset. This should be done always." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "vars = poly_class.get_feature_vector()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view the variables using Pyomo's pprint function:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "vars.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 5: Specify additional regression terms, if required.\n", + "\n", + "This is one of the unique features of PySMO - it allows the user to specify additional regression features if they want.\n", + "The additional features must be specified in terms of the Pyomo variables created when calling the ``get_feature_vector()`` \n", + "\n", + "For this example, let us create three additional features: $x_{1}^{2}x_{2}^{2}$, $exp(x_1)$ and $exp(x_2)$. We do this by calling the ``set_additional_terms`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import exp\n", + "\n", + "poly_class.set_additional_terms(\n", + " [vars[0] * vars[0] * vars[1] * vars[1], exp(vars[0]), exp(vars[1])]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it - those features will now exist in the model. \n", + "\n", + "Note that ``set_additional_terms`` an optional call - the regression process works just fine without it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 6: Train the surrogate and view results\n", + "Next, we train the polynomial surrogate by calling ``training``:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "poly_class.training()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The polynomial model seems to fit well based on the $R^2$. It should be noted that the metrics are only an indication of how well of how well the model fit the training data - the user needs to verify the model's performance on a test data set if possible.\n", + "\n", + "We can view the parity and residual plots for the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "poly_class.parity_residual_plots()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PySMO is also able to compute the confidence intervals on the regression coefficients obtained by calling ``confint_regression()``. This is left as an exercise for the user.\n", + "\n", + "#### Step 8 (Optional): Generate Pyomo expression \n", + "\n", + "If the user wishes, they can generate the Pyomo expression for the polynomial fit using PySMO's ``generate_expression``. To do this, the user must pass in a list of Pyomo variables corresponding to each variable in the input dataset. \n", + "\n", + "As a demonstration, let us create the variables $x_1$ and $x_2$ and generate the pyomo expression based on them:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import Var, ConcreteModel\n", + "\n", + "m = ConcreteModel()\n", + "m.x = Var([1, 2])\n", + "print(poly_class.generate_expression([m.x[1], m.x[2]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 9 (Optional): Predict output at any unsampled point\n", + "\n", + "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. \n", + "\n", + "Let us evaluate the surrogate at three points:\n", + "\n", + "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", + "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", + "- $x_{1}=-2$, $x_{2}=3$. (true function value: 50.8899)\n", + "\n", + "We will pass the points in as an array." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", + "ys = poly_class.predict_output(unsampled_points)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model performs fairly well in predicting the value at two of our sampled points but is off on the value at [-3, 2]. For better model performance, additional training data is needed in this region. We will leave this to the user to try.\n", + "\n", + "Further information about using PySMO's polynomial regression tool can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_polyregression.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating RBF models\n", + "\n", + "The ``RadialBasisFunction`` class trains RBF models from data. For details about RBF models, the user should consult the documentation.\n", + "\n", + "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Import the data and the RBF tool" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", + "from idaes.core.surrogate.pysmo.radial_basis_function import RadialBasisFunctions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Specify the RBF settings and initialize the RadialBasisFunctions class\n", + " \n", + "The RadialBasisFunctions class takes a number of keyword arguments:\n", + "\n", + " - XY_data : The dataset forRBF training. training_data is expected to contain xy_data, \n", + " with the output values (y) in the last column.\n", + "\n", + "\n", + "It also takes a number of optional arguments:\n", + "\n", + " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", + " - basis_function : Basis function transformation to be applied to the training data. PySMO offers \n", + " six basis function types including the Gaussian and Spline transformations. User \n", + " should consult documentation for full list of options. \n", + " - fname : Filename for saving (.pickle extension)\n", + " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", + " should be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this demonstration, we will train an RBF model with a Gaussian basis function:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "rbf_class = RadialBasisFunctions(\n", + " XY_data=brainin_data, basis_function=\"gaussian\", overwrite=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Extract variable names\n", + "Next, we extract Pyomo variable names from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "vars = rbf_class.get_feature_vector()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Train the RBF surrogate\n", + "Next, we train the RBF surrogate by calling ``training``:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "rbf_class.training()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 5: View model metrics\n", + " \n", + "We can view the Root Mean Square Error (RMSE) and $R^2$ values of the RBF fit based on the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"R2: \", rbf_class.R2, \"\\nRMSE: \", rbf_class.rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", + "\n", + "\n", + "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. We do this by calling the function ``predict_output``.\n", + "\n", + "Let us again evaluate the RBF surrogate at the same set of points we considered for the polynomial model:\n", + "\n", + "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", + "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", + "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", + "ys = rbf_class.predict_output(unsampled_points)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results from the RBF surrogate are similar to those obtained from the polynomial.\n", + "\n", + "\n", + "For RBF models, the Pyomo expression is generated by calling ``generate_expression`` on the results object, while parity plots may be viewed with the ``parity_residual_plots`` method.\n", + "\n", + "Further information about using PySMO's RBF tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_radialbasisfunctions.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training Kriging models\n", + "\n", + "The ``KrigingModel`` class trains Kriging from data. For details about Kriging models, users should consult the documentation.\n", + "\n", + "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Load the data and import the Kriging tool" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", + "from idaes.core.surrogate.pysmo.kriging import KrigingModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Specify the Kriging settings and initialize the KrigingModel class\n", + " \n", + "The KrigingModel class takes a number of keyword arguments:\n", + "\n", + " - XY_data : The dataset for Kriging training. training_data is expected to contain xy_data, \n", + " with the output values (y) in the last column.\n", + "\n", + "\n", + "It also takes a number of optional arguments:\n", + "\n", + " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", + " - numerical_gradients : Boolean variable which determines whether numerical gradients are used when\n", + " solving the max. likelihood optimization problem. Default is True.\n", + " - fname : Filename for saving (.pickle extension)\n", + " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", + " should be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "For this demonstration, we will train a Kriging model with regularization:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "krg_class = KrigingModel(XY_data=brainin_data, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Extract variable names (optional)\n", + "Next, we extract Pyomo variable names from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "vars = krg_class.get_feature_vector()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Train the Kriging surrogate\n", + "Next, we train the RBF surrogate by calling ``training``:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "krg_class.training()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The returned information correspond to the Kriging model parameters.\n", + "\n", + "As we can see, the optimization problem was solved using SciPy's L-BFGS algorithm which makes use of gradients. A different algorithm (Basinhopping) is used when no numerical gradients are computed (when numerical_gradients is set to False). The user should try this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 5: View model metrics\n", + " \n", + "We can view the RMSE and $R^2$ values of the Kriging fit based on the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"R2: \", krg_class.training_R2, \"\\nRMSE: \", krg_class.training_rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", + "\n", + "\n", + "Again, based on the model we trained, we evaluate the surrogate at a set of off-design points:\n", + "\n", + "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", + "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", + "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)\n", + "\n", + "We do this by calling the function ``predict_output``:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", + "ys = krg_class.predict_output(unsampled_points)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Kriging model performs very well, predicting all three points fairly accurately. \n", + "\n", + "For Kriging models, the Pyomo expression is generated by calling ``generate_expression`` on the results object: " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "print(krg_class.generate_expression([m.x[1], m.x[2]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, expressing a Kriging model algebraically is pretty complicated!\n", + "\n", + "Parity plots for the Kriging model may be viewed with the ``parity_residual_plots`` method.\n", + "\n", + "Further information about using PySMO's Kriging tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_kriging.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "PySMO allows IDAES users to sample design spaces and generate different types of surrogate models. Further information about PySMO's capabilities may be found in the documentation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_usr.ipynb index 16ec6887..0b7a066f 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_basics_usr.ipynb @@ -1,867 +1,868 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PySMO Tutorial\n", - "Author: Mayo Amusat \n", - "Maintainer: Mayo Amusat \n", - "Updated: 2023-06-01 \n", - "\n", - "**Python-based Surrogate Modelling Objects** (PySMO) provides tools for generating different types of reduced order models. PySMO currently provides tools for sampling and surrogate model generation.\n", - "\n", - "## Installation\n", - "\n", - "**PySMO** is installed by default as part of IDAES. For instructions on installing IDAES, see the [online documentation](https://idaes-pse.readthedocs.io/en/stable/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## One-Shot Sampling with PySMO\n", - "\n", - "The PySMO package offers five common sampling methods for one-shot design:\n", - "\n", - "* Latin Hypercube Sampling (LHS)\n", - "* Full-Factorial Sampling\n", - "* Halton Sampling\n", - "* Hammersley Sampling\n", - "* Centroidal voronoi tessellation (CVT) sampling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySMO provides two modes for data sampling: creation and selection.\n", - "- In creation mode, PySMO creates a specified number of sample points from the bounds provided by the user.\n", - "- In selection mode, PySMO selects a specified number of data points from a user-supplied dataset or file." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating samples: \n", - "For demonstration purposes, let us consider a problem for which we need twenty-five (25) samples of temperature and pressure from within the ranges T = 273K - 373K, P = 1 MPa - 50 MPa. Let us generate these samples in PySMO." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Import PySMO's sampling tool\n", - "For this demonstration, we will attempt to generate the samples using the Hammersley sampling method." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.surrogate.pysmo.sampling import HammersleySampling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Specify sampling information and initialize class\n", - "\n", - "All the sampling tools (except full-factorial sampling) require the same keyword arguments:\n", - "\n", - " - data_input : must be a list of lists containing the problem bounds (when creating points), \n", - " or an input dataset (when selecting points from a dataset)\n", - " - number_of_samples : number of samples to be created or selected.\n", - " - sampling_type : \"creation\" or \"selection\".\n", - "\n", - "For full factorial sampling, the user needs to enter a list of points in each dimension in place of the number of samples. Full-factorial sampling requires other inputs - details may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/sampling/pysmo_uniform.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For our example, we will create the bounds and then initialize the class with the number of samples." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "bounds_info = [[273, 1], [373, 50]]\n", - "init_data = HammersleySampling(\n", - " data_input=bounds_info, number_of_samples=25, sampling_type=\"creation\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Create the samples\n", - "The samples are created by calling the ``sample_points`` method on the initialized class." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "samples = init_data.sample_points()\n", - "print(samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simple as that, the samples have been created!\n", - "\n", - "Now, let us visualize the samples in a 2-D plot." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Visualize samples with matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "plt.plot(samples[:, 0], samples[:, 1], \"o\")\n", - "plt.xlabel(r\"Temperature\", fontsize=11)\n", - "plt.xlabel(r\"Pressure\", fontsize=11)\n", - "plt.xlim(272, 374)\n", - "plt.ylim(0, 50)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating surrogates with PySMO\n", - "\n", - "PySMO currently provides tools for generating three types of surrogates:\n", - "\n", - "- Polynomial surrogates\n", - "- Radial basis function (RBF) surrogates, and\n", - "- Kriging surrogates\n", - "\n", - "Details about thee various methods may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating polynomial models\n", - "\n", - "The ``PolynomialRegression`` class trains polynomial models from data.\n", - "\n", - "As an example, let us generate a surrogate for the Brainin function. \n", - "\n", - "The true Brainin function is given by the expression:\n", - " \n", - " \\begin{gather}\n", - "\\hat{y}(x_{1},x_{2})=\\left(x_{2}-\\frac{5.1x_{1}^{2}}{4\\pi^{2}}+\\frac{5x_{1}}{\\pi}-6\\right)^{2}+10\\left[\\left(1-\\frac{1}{8\\pi}\\right)\\cos\\left(x_{1}\\right)+1\\right]+5x_{1}\\nonumber \\\\\n", - "x_{1}\\in\\left[-5,10\\right];x_{2}\\in\\left[0,15\\right]\n", - "\\end{gather}\n", - "\n", - "We have generated 30 points from the function and saved the information in a text file called \"brainin_30.txt\". We will use this data to train a simple polynomial model. The data is in XY format, with the outputs $y$ in the third column." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Import and visualize the data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", - "print(brainin_data, \"\\n\\nDataset shape:\", brainin_data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us visualize the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import pyplot as plt\n", - "\n", - "fig1 = plt.figure(figsize=(6, 4), tight_layout=True)\n", - "ax = fig1.add_subplot(111, projection=\"3d\")\n", - "ax.scatter3D(\n", - " brainin_data[:, 0], brainin_data[:, 1], brainin_data[:, 2], cmap=brainin_data[:, 2]\n", - ")\n", - "ax.set_xlabel(r\"$x_{1}$\", fontsize=11)\n", - "ax.set_ylabel(r\"$x_{2}$\", fontsize=11)\n", - "ax.set_zlabel(r\"$y$\", fontsize=11)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Import the polynomial model tool" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.surrogate.pysmo.polynomial_regression import PolynomialRegression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Specify the regression settings and initialize the PolynomialRegression class\n", - " \n", - "The PolynomialRegression class takes a keyword arguments:\n", - "\n", - " - original_data_input : The dataset for regression training. training_data is expected to contain xy_data, \n", - " with the output values (y) in the last column.\n", - " - regression_data_input : same as above\n", - " - maximum_polynomial_order : maximum order of the polynomial to be generated \n", - "\n", - "It also takes a number of optional arguments:\n", - "\n", - " - multinomials : True/False option for specifying second-order bi-variate terms. default is False\n", - " - training_split : The training/cross-validation split of training data. Must be between 0 and 1. \n", - " Default is 0.75\n", - " - fname : Filename for saving results (.pickle extension). \n", - " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", - " should be overwritten." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this example, let us consider a 4th order polynomial with interaction terms. We will split the data 80/20 between training and cross-validation." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "poly_class = PolynomialRegression(\n", - " original_data_input=brainin_data,\n", - " regression_data_input=brainin_data,\n", - " maximum_polynomial_order=4,\n", - " multinomials=1,\n", - " training_split=0.8,\n", - " number_of_crossvalidations=10,\n", - " overwrite=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Extract variable names\n", - "Next, we extract Pyomo variable names from the dataset. This should be done always." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "vars = poly_class.get_feature_vector()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can view the variables using Pyomo's pprint function:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "vars.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 5: Specify additional regression terms, if required.\n", - "\n", - "This is one of the unique features of PySMO - it allows the user to specify additional regression features if they want.\n", - "The additional features must be specified in terms of the Pyomo variables created when calling the ``get_feature_vector()`` \n", - "\n", - "For this example, let us create three additional features: $x_{1}^{2}x_{2}^{2}$, $exp(x_1)$ and $exp(x_2)$. We do this by calling the ``set_additional_terms`` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import exp\n", - "\n", - "poly_class.set_additional_terms(\n", - " [vars[0] * vars[0] * vars[1] * vars[1], exp(vars[0]), exp(vars[1])]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it - those features will now exist in the model. \n", - "\n", - "Note that ``set_additional_terms`` an optional call - the regression process works just fine without it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 6: Train the surrogate and view results\n", - "Next, we train the polynomial surrogate by calling ``training``:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "poly_class.training()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The polynomial model seems to fit well based on the $R^2$. It should be noted that the metrics are only an indication of how well of how well the model fit the training data - the user needs to verify the model's performance on a test data set if possible.\n", - "\n", - "We can view the parity and residual plots for the fit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "poly_class.parity_residual_plots()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PySMO is also able to compute the confidence intervals on the regression coefficients obtained by calling ``confint_regression()``. This is left as an exercise for the user.\n", - "\n", - "#### Step 8 (Optional): Generate Pyomo expression \n", - "\n", - "If the user wishes, they can generate the Pyomo expression for the polynomial fit using PySMO's ``generate_expression``. To do this, the user must pass in a list of Pyomo variables corresponding to each variable in the input dataset. \n", - "\n", - "As a demonstration, let us create the variables $x_1$ and $x_2$ and generate the pyomo expression based on them:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import Var, ConcreteModel\n", - "\n", - "m = ConcreteModel()\n", - "m.x = Var([1, 2])\n", - "print(poly_class.generate_expression([m.x[1], m.x[2]]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 9 (Optional): Predict output at any unsampled point\n", - "\n", - "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. \n", - "\n", - "Let us evaluate the surrogate at three points:\n", - "\n", - "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", - "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", - "- $x_{1}=-2$, $x_{2}=3$. (true function value: 50.8899)\n", - "\n", - "We will pass the points in as an array." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", - "ys = poly_class.predict_output(unsampled_points)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model performs fairly well in predicting the value at two of our sampled points but is off on the value at [-3, 2]. For better model performance, additional training data is needed in this region. We will leave this to the user to try.\n", - "\n", - "Further information about using PySMO's polynomial regression tool can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_polyregression.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating RBF models\n", - "\n", - "The ``RadialBasisFunction`` class trains RBF models from data. For details about RBF models, the user should consult the documentation.\n", - "\n", - "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Import the data and the RBF tool" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", - "from idaes.core.surrogate.pysmo.radial_basis_function import RadialBasisFunctions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Specify the RBF settings and initialize the RadialBasisFunctions class\n", - " \n", - "The RadialBasisFunctions class takes a number of keyword arguments:\n", - "\n", - " - XY_data : The dataset forRBF training. training_data is expected to contain xy_data, \n", - " with the output values (y) in the last column.\n", - "\n", - "\n", - "It also takes a number of optional arguments:\n", - "\n", - " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", - " - basis_function : Basis function transformation to be applied to the training data. PySMO offers \n", - " six basis function types including the Gaussian and Spline transformations. User \n", - " should consult documentation for full list of options. \n", - " - fname : Filename for saving (.pickle extension)\n", - " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", - " should be overwritten." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this demonstration, we will train an RBF model with a Gaussian basis function:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "rbf_class = RadialBasisFunctions(\n", - " XY_data=brainin_data, basis_function=\"gaussian\", overwrite=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Extract variable names\n", - "Next, we extract Pyomo variable names from the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "vars = rbf_class.get_feature_vector()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Train the RBF surrogate\n", - "Next, we train the RBF surrogate by calling ``training``:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "rbf_class.training()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 5: View model metrics\n", - " \n", - "We can view the Root Mean Square Error (RMSE) and $R^2$ values of the RBF fit based on the training data:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"R2: \", rbf_class.R2, \"\\nRMSE: \", rbf_class.rmse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", - "\n", - "\n", - "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. We do this by calling the function ``predict_output``.\n", - "\n", - "Let us again evaluate the RBF surrogate at the same set of points we considered for the polynomial model:\n", - "\n", - "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", - "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", - "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", - "ys = rbf_class.predict_output(unsampled_points)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results from the RBF surrogate are similar to those obtained from the polynomial.\n", - "\n", - "\n", - "For RBF models, the Pyomo expression is generated by calling ``generate_expression`` on the results object, while parity plots may be viewed with the ``parity_residual_plots`` method.\n", - "\n", - "Further information about using PySMO's RBF tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_radialbasisfunctions.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training Kriging models\n", - "\n", - "The ``KrigingModel`` class trains Kriging from data. For details about Kriging models, users should consult the documentation.\n", - "\n", - "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 1: Load the data and import the Kriging tool" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", - "from idaes.core.surrogate.pysmo.kriging import KrigingModel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 2: Specify the Kriging settings and initialize the KrigingModel class\n", - " \n", - "The KrigingModel class takes a number of keyword arguments:\n", - "\n", - " - XY_data : The dataset for Kriging training. training_data is expected to contain xy_data, \n", - " with the output values (y) in the last column.\n", - "\n", - "\n", - "It also takes a number of optional arguments:\n", - "\n", - " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", - " - numerical_gradients : Boolean variable which determines whether numerical gradients are used when\n", - " solving the max. likelihood optimization problem. Default is True.\n", - " - fname : Filename for saving (.pickle extension)\n", - " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", - " should be overwritten." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "For this demonstration, we will train a Kriging model with regularization:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "krg_class = KrigingModel(XY_data=brainin_data, overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 3: Extract variable names (optional)\n", - "Next, we extract Pyomo variable names from the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "vars = krg_class.get_feature_vector()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 4: Train the Kriging surrogate\n", - "Next, we train the RBF surrogate by calling ``training``:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "krg_class.training()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The returned information correspond to the Kriging model parameters.\n", - "\n", - "As we can see, the optimization problem was solved using SciPy's L-BFGS algorithm which makes use of gradients. A different algorithm (Basinhopping) is used when no numerical gradients are computed (when numerical_gradients is set to False). The user should try this." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 5: View model metrics\n", - " \n", - "We can view the RMSE and $R^2$ values of the Kriging fit based on the training data:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"R2: \", krg_class.training_R2, \"\\nRMSE: \", krg_class.training_rmse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", - "\n", - "\n", - "Again, based on the model we trained, we evaluate the surrogate at a set of off-design points:\n", - "\n", - "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", - "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", - "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)\n", - "\n", - "We do this by calling the function ``predict_output``:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", - "ys = krg_class.predict_output(unsampled_points)\n", - "print(ys)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Kriging model performs very well, predicting all three points fairly accurately. \n", - "\n", - "For Kriging models, the Pyomo expression is generated by calling ``generate_expression`` on the results object: " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "print(krg_class.generate_expression([m.x[1], m.x[2]]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, expressing a Kriging model algebraically is pretty complicated!\n", - "\n", - "Parity plots for the Kriging model may be viewed with the ``parity_residual_plots`` method.\n", - "\n", - "Further information about using PySMO's Kriging tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_kriging.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "PySMO allows IDAES users to sample design spaces and generate different types of surrogate models. Further information about PySMO's capabilities may be found in the documentation." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PySMO Tutorial\n", + "Author: Mayo Amusat \n", + "Maintainer: Mayo Amusat \n", + "Updated: 2023-06-01 \n", + "\n", + "**Python-based Surrogate Modelling Objects** (PySMO) provides tools for generating different types of reduced order models. PySMO currently provides tools for sampling and surrogate model generation.\n", + "\n", + "## Installation\n", + "\n", + "**PySMO** is installed by default as part of IDAES. For instructions on installing IDAES, see the [online documentation](https://idaes-pse.readthedocs.io/en/stable/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One-Shot Sampling with PySMO\n", + "\n", + "The PySMO package offers five common sampling methods for one-shot design:\n", + "\n", + "* Latin Hypercube Sampling (LHS)\n", + "* Full-Factorial Sampling\n", + "* Halton Sampling\n", + "* Hammersley Sampling\n", + "* Centroidal voronoi tessellation (CVT) sampling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PySMO provides two modes for data sampling: creation and selection.\n", + "- In creation mode, PySMO creates a specified number of sample points from the bounds provided by the user.\n", + "- In selection mode, PySMO selects a specified number of data points from a user-supplied dataset or file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating samples: \n", + "For demonstration purposes, let us consider a problem for which we need twenty-five (25) samples of temperature and pressure from within the ranges T = 273K - 373K, P = 1 MPa - 50 MPa. Let us generate these samples in PySMO." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Import PySMO's sampling tool\n", + "For this demonstration, we will attempt to generate the samples using the Hammersley sampling method." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.surrogate.pysmo.sampling import HammersleySampling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Specify sampling information and initialize class\n", + "\n", + "All the sampling tools (except full-factorial sampling) require the same keyword arguments:\n", + "\n", + " - data_input : must be a list of lists containing the problem bounds (when creating points), \n", + " or an input dataset (when selecting points from a dataset)\n", + " - number_of_samples : number of samples to be created or selected.\n", + " - sampling_type : \"creation\" or \"selection\".\n", + "\n", + "For full factorial sampling, the user needs to enter a list of points in each dimension in place of the number of samples. Full-factorial sampling requires other inputs - details may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/sampling/pysmo_uniform.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our example, we will create the bounds and then initialize the class with the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "bounds_info = [[273, 1], [373, 50]]\n", + "init_data = HammersleySampling(\n", + " data_input=bounds_info, number_of_samples=25, sampling_type=\"creation\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Create the samples\n", + "The samples are created by calling the ``sample_points`` method on the initialized class." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "samples = init_data.sample_points()\n", + "print(samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple as that, the samples have been created!\n", + "\n", + "Now, let us visualize the samples in a 2-D plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Visualize samples with matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.plot(samples[:, 0], samples[:, 1], \"o\")\n", + "plt.xlabel(r\"Temperature\", fontsize=11)\n", + "plt.xlabel(r\"Pressure\", fontsize=11)\n", + "plt.xlim(272, 374)\n", + "plt.ylim(0, 50)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating surrogates with PySMO\n", + "\n", + "PySMO currently provides tools for generating three types of surrogates:\n", + "\n", + "- Polynomial surrogates\n", + "- Radial basis function (RBF) surrogates, and\n", + "- Kriging surrogates\n", + "\n", + "Details about thee various methods may be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating polynomial models\n", + "\n", + "The ``PolynomialRegression`` class trains polynomial models from data.\n", + "\n", + "As an example, let us generate a surrogate for the Brainin function. \n", + "\n", + "The true Brainin function is given by the expression:\n", + " \n", + " \\begin{gather}\n", + "\\hat{y}(x_{1},x_{2})=\\left(x_{2}-\\frac{5.1x_{1}^{2}}{4\\pi^{2}}+\\frac{5x_{1}}{\\pi}-6\\right)^{2}+10\\left[\\left(1-\\frac{1}{8\\pi}\\right)\\cos\\left(x_{1}\\right)+1\\right]+5x_{1}\\nonumber \\\\\n", + "x_{1}\\in\\left[-5,10\\right];x_{2}\\in\\left[0,15\\right]\n", + "\\end{gather}\n", + "\n", + "We have generated 30 points from the function and saved the information in a text file called \"brainin_30.txt\". We will use this data to train a simple polynomial model. The data is in XY format, with the outputs $y$ in the third column." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Import and visualize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", + "print(brainin_data, \"\\n\\nDataset shape:\", brainin_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us visualize the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import pyplot as plt\n", + "\n", + "fig1 = plt.figure(figsize=(6, 4), tight_layout=True)\n", + "ax = fig1.add_subplot(111, projection=\"3d\")\n", + "ax.scatter3D(\n", + " brainin_data[:, 0], brainin_data[:, 1], brainin_data[:, 2], cmap=brainin_data[:, 2]\n", + ")\n", + "ax.set_xlabel(r\"$x_{1}$\", fontsize=11)\n", + "ax.set_ylabel(r\"$x_{2}$\", fontsize=11)\n", + "ax.set_zlabel(r\"$y$\", fontsize=11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Import the polynomial model tool" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.surrogate.pysmo.polynomial_regression import PolynomialRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Specify the regression settings and initialize the PolynomialRegression class\n", + " \n", + "The PolynomialRegression class takes a keyword arguments:\n", + "\n", + " - original_data_input : The dataset for regression training. training_data is expected to contain xy_data, \n", + " with the output values (y) in the last column.\n", + " - regression_data_input : same as above\n", + " - maximum_polynomial_order : maximum order of the polynomial to be generated \n", + "\n", + "It also takes a number of optional arguments:\n", + "\n", + " - multinomials : True/False option for specifying second-order bi-variate terms. default is False\n", + " - training_split : The training/cross-validation split of training data. Must be between 0 and 1. \n", + " Default is 0.75\n", + " - fname : Filename for saving results (.pickle extension). \n", + " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", + " should be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example, let us consider a 4th order polynomial with interaction terms. We will split the data 80/20 between training and cross-validation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "poly_class = PolynomialRegression(\n", + " original_data_input=brainin_data,\n", + " regression_data_input=brainin_data,\n", + " maximum_polynomial_order=4,\n", + " multinomials=1,\n", + " training_split=0.8,\n", + " number_of_crossvalidations=10,\n", + " overwrite=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Extract variable names\n", + "Next, we extract Pyomo variable names from the dataset. This should be done always." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "vars = poly_class.get_feature_vector()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view the variables using Pyomo's pprint function:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "vars.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 5: Specify additional regression terms, if required.\n", + "\n", + "This is one of the unique features of PySMO - it allows the user to specify additional regression features if they want.\n", + "The additional features must be specified in terms of the Pyomo variables created when calling the ``get_feature_vector()`` \n", + "\n", + "For this example, let us create three additional features: $x_{1}^{2}x_{2}^{2}$, $exp(x_1)$ and $exp(x_2)$. We do this by calling the ``set_additional_terms`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import exp\n", + "\n", + "poly_class.set_additional_terms(\n", + " [vars[0] * vars[0] * vars[1] * vars[1], exp(vars[0]), exp(vars[1])]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it - those features will now exist in the model. \n", + "\n", + "Note that ``set_additional_terms`` an optional call - the regression process works just fine without it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 6: Train the surrogate and view results\n", + "Next, we train the polynomial surrogate by calling ``training``:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "poly_class.training()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The polynomial model seems to fit well based on the $R^2$. It should be noted that the metrics are only an indication of how well of how well the model fit the training data - the user needs to verify the model's performance on a test data set if possible.\n", + "\n", + "We can view the parity and residual plots for the fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "poly_class.parity_residual_plots()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PySMO is also able to compute the confidence intervals on the regression coefficients obtained by calling ``confint_regression()``. This is left as an exercise for the user.\n", + "\n", + "#### Step 8 (Optional): Generate Pyomo expression \n", + "\n", + "If the user wishes, they can generate the Pyomo expression for the polynomial fit using PySMO's ``generate_expression``. To do this, the user must pass in a list of Pyomo variables corresponding to each variable in the input dataset. \n", + "\n", + "As a demonstration, let us create the variables $x_1$ and $x_2$ and generate the pyomo expression based on them:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import Var, ConcreteModel\n", + "\n", + "m = ConcreteModel()\n", + "m.x = Var([1, 2])\n", + "print(poly_class.generate_expression([m.x[1], m.x[2]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 9 (Optional): Predict output at any unsampled point\n", + "\n", + "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. \n", + "\n", + "Let us evaluate the surrogate at three points:\n", + "\n", + "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", + "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", + "- $x_{1}=-2$, $x_{2}=3$. (true function value: 50.8899)\n", + "\n", + "We will pass the points in as an array." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", + "ys = poly_class.predict_output(unsampled_points)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model performs fairly well in predicting the value at two of our sampled points but is off on the value at [-3, 2]. For better model performance, additional training data is needed in this region. We will leave this to the user to try.\n", + "\n", + "Further information about using PySMO's polynomial regression tool can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_polyregression.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating RBF models\n", + "\n", + "The ``RadialBasisFunction`` class trains RBF models from data. For details about RBF models, the user should consult the documentation.\n", + "\n", + "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Import the data and the RBF tool" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", + "from idaes.core.surrogate.pysmo.radial_basis_function import RadialBasisFunctions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Specify the RBF settings and initialize the RadialBasisFunctions class\n", + " \n", + "The RadialBasisFunctions class takes a number of keyword arguments:\n", + "\n", + " - XY_data : The dataset forRBF training. training_data is expected to contain xy_data, \n", + " with the output values (y) in the last column.\n", + "\n", + "\n", + "It also takes a number of optional arguments:\n", + "\n", + " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", + " - basis_function : Basis function transformation to be applied to the training data. PySMO offers \n", + " six basis function types including the Gaussian and Spline transformations. User \n", + " should consult documentation for full list of options. \n", + " - fname : Filename for saving (.pickle extension)\n", + " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", + " should be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this demonstration, we will train an RBF model with a Gaussian basis function:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "rbf_class = RadialBasisFunctions(\n", + " XY_data=brainin_data, basis_function=\"gaussian\", overwrite=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Extract variable names\n", + "Next, we extract Pyomo variable names from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "vars = rbf_class.get_feature_vector()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Train the RBF surrogate\n", + "Next, we train the RBF surrogate by calling ``training``:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "rbf_class.training()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 5: View model metrics\n", + " \n", + "We can view the Root Mean Square Error (RMSE) and $R^2$ values of the RBF fit based on the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"R2: \", rbf_class.R2, \"\\nRMSE: \", rbf_class.rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", + "\n", + "\n", + "Based on the model we trained, we can predict the surrogate value at any previously unsampled point. We do this by calling the function ``predict_output``.\n", + "\n", + "Let us again evaluate the RBF surrogate at the same set of points we considered for the polynomial model:\n", + "\n", + "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", + "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", + "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", + "ys = rbf_class.predict_output(unsampled_points)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results from the RBF surrogate are similar to those obtained from the polynomial.\n", + "\n", + "\n", + "For RBF models, the Pyomo expression is generated by calling ``generate_expression`` on the results object, while parity plots may be viewed with the ``parity_residual_plots`` method.\n", + "\n", + "Further information about using PySMO's RBF tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_radialbasisfunctions.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training Kriging models\n", + "\n", + "The ``KrigingModel`` class trains Kriging from data. For details about Kriging models, users should consult the documentation.\n", + "\n", + "As an example, we will again consider the Brainin function. The same dataset loaded previously will be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Load the data and import the Kriging tool" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "brainin_data = np.loadtxt(\"brainin_30.txt\")\n", + "from idaes.core.surrogate.pysmo.kriging import KrigingModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 2: Specify the Kriging settings and initialize the KrigingModel class\n", + " \n", + "The KrigingModel class takes a number of keyword arguments:\n", + "\n", + " - XY_data : The dataset for Kriging training. training_data is expected to contain xy_data, \n", + " with the output values (y) in the last column.\n", + "\n", + "\n", + "It also takes a number of optional arguments:\n", + "\n", + " - regularization : Boolean variable determining whether regularization is done. Default is True.\n", + " - numerical_gradients : Boolean variable which determines whether numerical gradients are used when\n", + " solving the max. likelihood optimization problem. Default is True.\n", + " - fname : Filename for saving (.pickle extension)\n", + " - overwrite : Option determining whether any existing file with the same name supplied in 'fname' \n", + " should be overwritten." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "For this demonstration, we will train a Kriging model with regularization:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "krg_class = KrigingModel(XY_data=brainin_data, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 3: Extract variable names (optional)\n", + "Next, we extract Pyomo variable names from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "vars = krg_class.get_feature_vector()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 4: Train the Kriging surrogate\n", + "Next, we train the RBF surrogate by calling ``training``:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "krg_class.training()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The returned information correspond to the Kriging model parameters.\n", + "\n", + "As we can see, the optimization problem was solved using SciPy's L-BFGS algorithm which makes use of gradients. A different algorithm (Basinhopping) is used when no numerical gradients are computed (when numerical_gradients is set to False). The user should try this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 5: View model metrics\n", + " \n", + "We can view the RMSE and $R^2$ values of the Kriging fit based on the training data:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"R2: \", krg_class.training_R2, \"\\nRMSE: \", krg_class.training_rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 6 (Optional): Generate Pyomo expression output at any unsampled point\n", + "\n", + "\n", + "Again, based on the model we trained, we evaluate the surrogate at a set of off-design points:\n", + "\n", + "- $x_{1}=5$, $x_{2}=8$ (true function value: 57.9908)\n", + "- $x_{1}=-3$, $x_{2}=10$ (true function value: 4.2461)\n", + "- $x_{1}=-2$, $x_{2}=3$ (true function value: 50.8899)\n", + "\n", + "We do this by calling the function ``predict_output``:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "unsampled_points = np.array([[5, 8], [-3, 10], [-2, 3]])\n", + "ys = krg_class.predict_output(unsampled_points)\n", + "print(ys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Kriging model performs very well, predicting all three points fairly accurately. \n", + "\n", + "For Kriging models, the Pyomo expression is generated by calling ``generate_expression`` on the results object: " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "print(krg_class.generate_expression([m.x[1], m.x[2]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, expressing a Kriging model algebraically is pretty complicated!\n", + "\n", + "Parity plots for the Kriging model may be viewed with the ``parity_residual_plots`` method.\n", + "\n", + "Further information about using PySMO's Kriging tool and features can be found in the [documentation](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/api/pysmo/pysmo_kriging.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "PySMO allows IDAES users to sample design spaces and generate different types of surrogate models. Further information about PySMO's capabilities may be found in the documentation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization.ipynb index 513f47d8..929cf81f 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization.ipynb @@ -1,478 +1,479 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Autothermal Reformer Flowsheet Optimization with PySMO Surrogate Object\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "## 1. Introduction\n", - "\n", - "This example demonstrates autothermal reformer optimization leveraging the PySMO Polynomial surrogate trainer. Other than the specific training method syntax, this workflow is identical for PySMO RBF and PySMO Kriging surrogate models. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Problem Statement \n", - "\n", - "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", - "\n", - "## 2.1. Main Inputs: \n", - "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", - "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", - "\n", - "## 2.2. Main Outputs:\n", - "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", - "- Reformer duty (kW) - required energy input to AR unit\n", - "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"AR_PFD.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Training and Validating Surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's import the required Python, Pyomo and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " SolverFactory,\n", - " value,\n", - " Var,\n", - " Constraint,\n", - " Set,\n", - " Objective,\n", - " maximize,\n", - ")\n", - "from pyomo.common.timing import TicTocTimer\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1 Importing Training and Validation Datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Auto-reformer training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", - "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:]\n", - "\n", - "# Define labels, and split training and validation data\n", - "# note that PySMO requires that labels are passed as string lists\n", - "input_labels = list(input_data.columns)\n", - "output_labels = list(output_data.columns)\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(\n", - " data, 0.8, seed=n_data\n", - ") # seed=100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Training Surrogates with PySMO" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 6th order polynomial as well as a variable product, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", - "\n", - "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# capture long output (not required to use surrogate API)\n", - "from io import StringIO\n", - "import sys\n", - "\n", - "stream = StringIO()\n", - "oldstdout = sys.stdout\n", - "sys.stdout = stream\n", - "\n", - "# Create PySMO trainer object\n", - "trainer = PysmoPolyTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - ")\n", - "\n", - "# Set PySMO options\n", - "trainer.config.maximum_polynomial_order = 6\n", - "trainer.config.multinomials = True\n", - "trainer.config.training_split = 0.8\n", - "trainer.config.number_of_crossvalidations = 10\n", - "\n", - "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", - "poly_train = trainer.train_surrogate()\n", - "\n", - "# create callable surrogate object\n", - "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", - "\n", - "# save model to JSON\n", - "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)\n", - "\n", - "# revert back to normal output capture\n", - "sys.stdout = oldstdout\n", - "\n", - "# display first 50 lines and last 50 lines of output\n", - "celloutput = stream.getvalue().split(\"\\n\")\n", - "for line in celloutput[:50]:\n", - " print(line)\n", - "print(\".\")\n", - "print(\".\")\n", - "print(\".\")\n", - "for line in celloutput[-50:]:\n", - " print(line)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Visualizing surrogates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. IDAES Flowsheet Integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.1 Build and Run IDAES Flowsheet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build an IDAES flowsheet and import the surrogate model object. Each output variable has a unique PySMO model expression, and the surrogate expressions may be added to the model via an indexed Constraint() component." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# create the IDAES model and flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "# create flowsheet input variables\n", - "m.fs.bypass_frac = Var(\n", - " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", - ")\n", - "m.fs.ng_steam_ratio = Var(\n", - " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", - ")\n", - "\n", - "# create flowsheet output variables\n", - "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", - "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", - "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", - "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", - "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", - "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", - "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", - "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", - "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", - "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", - "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", - "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", - "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", - "\n", - "# create input and output variable object lists for flowsheet\n", - "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", - "outputs = [\n", - " m.fs.steam_flowrate,\n", - " m.fs.reformer_duty,\n", - " m.fs.AR,\n", - " m.fs.C2H6,\n", - " m.fs.C4H10,\n", - " m.fs.C3H8,\n", - " m.fs.CH4,\n", - " m.fs.CO,\n", - " m.fs.CO2,\n", - " m.fs.H2,\n", - " m.fs.H2O,\n", - " m.fs.N2,\n", - " m.fs.O2,\n", - "]\n", - "\n", - "# create the Pyomo/IDAES block that corresponds to the surrogate\n", - "# PySMO\n", - "\n", - "# capture long output (not required to use surrogate API)\n", - "stream = StringIO()\n", - "oldstdout = sys.stdout\n", - "sys.stdout = stream\n", - "\n", - "surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", - "m.fs.surrogate = SurrogateBlock(concrete=True)\n", - "m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", - "\n", - "# revert back to normal output capture - don't need to print PySMO load output\n", - "sys.stdout = oldstdout\n", - "\n", - "# fix input values and solve flowsheet\n", - "m.fs.bypass_frac.fix(0.5)\n", - "m.fs.ng_steam_ratio.fix(1)\n", - "\n", - "solver = SolverFactory(\"ipopt\")\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's print some model results:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", - "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", - "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", - "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", - "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", - "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", - "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", - "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", - "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", - "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", - "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", - "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", - "print(\"Mole Fraction O2 = \", value(m.fs.O2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4.2 Optimizing the Autothermal Reformer\n", - "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", - "\n", - "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# unfix input values and add the objective/constraint to the model\n", - "m.fs.bypass_frac.unfix()\n", - "m.fs.ng_steam_ratio.unfix()\n", - "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", - "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", - "\n", - "# solve the model\n", - "tmr = TicTocTimer()\n", - "status = solver.solve(m, tee=True)\n", - "solve_time = tmr.toc(\"solve\")\n", - "\n", - "# print and check results\n", - "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", - "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", - "print(\"Model status: \", status)\n", - "print(\"Solve time: \", solve_time)\n", - "for var in inputs:\n", - " print(var.name, \": \", value(var))\n", - "for var in outputs:\n", - " print(var.name, \": \", value(var))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autothermal Reformer Flowsheet Optimization with PySMO Surrogate Object\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "## 1. Introduction\n", + "\n", + "This example demonstrates autothermal reformer optimization leveraging the PySMO Polynomial surrogate trainer. Other than the specific training method syntax, this workflow is identical for PySMO RBF and PySMO Kriging surrogate models. In this notebook, sampled simulation data will be used to train and validate a surrogate model. IDAES surrogate plotting tools will be utilized to visualize the surrogates on training and validation data. Once validated, integration of the surrogate into an IDAES flowsheet will be demonstrated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Statement \n", + "\n", + "Within the context of a larger NGFC system, the autothermal reformer generates syngas from air, steam and natural gas for use in a solid-oxide fuel cell (SOFC).\n", + "\n", + "## 2.1. Main Inputs: \n", + "- Bypass fraction (dimensionless) - split fraction of natural gas to bypass AR unit and feed directly to the power island\n", + "- NG-Steam Ratio (dimensionless) - proportion of natural relative to steam fed into AR unit operation\n", + "\n", + "## 2.2. Main Outputs:\n", + "- Steam flowrate (kg/s) - inlet steam fed to AR unit\n", + "- Reformer duty (kW) - required energy input to AR unit\n", + "- Composition (dimensionless) - outlet mole fractions of components (Ar, C2H6, C3H8, C4H10, CH4, CO, CO2, H2, H2O, N2, O2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"AR_PFD.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training and Validating Surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import the required Python, Pyomo and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " SolverFactory,\n", + " value,\n", + " Var,\n", + " Constraint,\n", + " Set,\n", + " Objective,\n", + " maximize,\n", + ")\n", + "from pyomo.common.timing import TicTocTimer\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 Importing Training and Validation Datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we read the dataset from the CSV file located in this directory. 2800 data points were simulated from a rigorous IDAES NGFC flowsheet using a grid sampling method. For simplicity and to reduce training runtime, this example randomly selects 100 data points to use for training/validation. The data is separated using an 80/20 split into training and validation data using the IDAES `split_training_validation()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Auto-reformer training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"reformer-data.csv\")) # 2800 data points\n", + "data = csv_data.sample(n=100) # randomly sample points for training/validation\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:]\n", + "\n", + "# Define labels, and split training and validation data\n", + "# note that PySMO requires that labels are passed as string lists\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns)\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") # seed=100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Training Surrogates with PySMO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 6th order polynomial as well as a variable product, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# capture long output (not required to use surrogate API)\n", + "from io import StringIO\n", + "import sys\n", + "\n", + "stream = StringIO()\n", + "oldstdout = sys.stdout\n", + "sys.stdout = stream\n", + "\n", + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 6\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [0.1, 0.8], [0.8, 1.2]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)\n", + "\n", + "# revert back to normal output capture\n", + "sys.stdout = oldstdout\n", + "\n", + "# display first 50 lines and last 50 lines of output\n", + "celloutput = stream.getvalue().split(\"\\n\")\n", + "for line in celloutput[:50]:\n", + " print(line)\n", + "print(\".\")\n", + "print(\".\")\n", + "print(\".\")\n", + "for line in celloutput[-50:]:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Visualizing surrogates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. IDAES Flowsheet Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Build and Run IDAES Flowsheet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build an IDAES flowsheet and import the surrogate model object. Each output variable has a unique PySMO model expression, and the surrogate expressions may be added to the model via an indexed Constraint() component." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# create the IDAES model and flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "# create flowsheet input variables\n", + "m.fs.bypass_frac = Var(\n", + " initialize=0.80, bounds=[0.1, 0.8], doc=\"natural gas bypass fraction\"\n", + ")\n", + "m.fs.ng_steam_ratio = Var(\n", + " initialize=0.80, bounds=[0.8, 1.2], doc=\"natural gas to steam ratio\"\n", + ")\n", + "\n", + "# create flowsheet output variables\n", + "m.fs.steam_flowrate = Var(initialize=0.2, doc=\"steam flowrate\")\n", + "m.fs.reformer_duty = Var(initialize=10000, doc=\"reformer heat duty\")\n", + "m.fs.AR = Var(initialize=0, doc=\"AR fraction\")\n", + "m.fs.C2H6 = Var(initialize=0, doc=\"C2H6 fraction\")\n", + "m.fs.C3H8 = Var(initialize=0, doc=\"C3H8 fraction\")\n", + "m.fs.C4H10 = Var(initialize=0, doc=\"C4H10 fraction\")\n", + "m.fs.CH4 = Var(initialize=0, doc=\"CH4 fraction\")\n", + "m.fs.CO = Var(initialize=0, doc=\"CO fraction\")\n", + "m.fs.CO2 = Var(initialize=0, doc=\"CO2 fraction\")\n", + "m.fs.H2 = Var(initialize=0, doc=\"H2 fraction\")\n", + "m.fs.H2O = Var(initialize=0, doc=\"H2O fraction\")\n", + "m.fs.N2 = Var(initialize=0, doc=\"N2 fraction\")\n", + "m.fs.O2 = Var(initialize=0, doc=\"O2 fraction\")\n", + "\n", + "# create input and output variable object lists for flowsheet\n", + "inputs = [m.fs.bypass_frac, m.fs.ng_steam_ratio]\n", + "outputs = [\n", + " m.fs.steam_flowrate,\n", + " m.fs.reformer_duty,\n", + " m.fs.AR,\n", + " m.fs.C2H6,\n", + " m.fs.C4H10,\n", + " m.fs.C3H8,\n", + " m.fs.CH4,\n", + " m.fs.CO,\n", + " m.fs.CO2,\n", + " m.fs.H2,\n", + " m.fs.H2O,\n", + " m.fs.N2,\n", + " m.fs.O2,\n", + "]\n", + "\n", + "# create the Pyomo/IDAES block that corresponds to the surrogate\n", + "# PySMO\n", + "\n", + "# capture long output (not required to use surrogate API)\n", + "stream = StringIO()\n", + "oldstdout = sys.stdout\n", + "sys.stdout = stream\n", + "\n", + "surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + "m.fs.surrogate = SurrogateBlock(concrete=True)\n", + "m.fs.surrogate.build_model(surrogate, input_vars=inputs, output_vars=outputs)\n", + "\n", + "# revert back to normal output capture - don't need to print PySMO load output\n", + "sys.stdout = oldstdout\n", + "\n", + "# fix input values and solve flowsheet\n", + "m.fs.bypass_frac.fix(0.5)\n", + "m.fs.ng_steam_ratio.fix(1)\n", + "\n", + "solver = SolverFactory(\"ipopt\")\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print some model results:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Steam flowrate = \", value(m.fs.steam_flowrate))\n", + "print(\"Reformer duty = \", value(m.fs.reformer_duty))\n", + "print(\"Mole Fraction Ar = \", value(m.fs.AR))\n", + "print(\"Mole Fraction C2H6 = \", value(m.fs.C2H6))\n", + "print(\"Mole Fraction C3H8 = \", value(m.fs.C3H8))\n", + "print(\"Mole Fraction C4H10 = \", value(m.fs.C4H10))\n", + "print(\"Mole Fraction CH4 = \", value(m.fs.CH4))\n", + "print(\"Mole Fraction CO = \", value(m.fs.CO))\n", + "print(\"Mole Fraction CO2 = \", value(m.fs.CO2))\n", + "print(\"Mole Fraction H2 = \", value(m.fs.H2))\n", + "print(\"Mole Fraction H2O = \", value(m.fs.H2O))\n", + "print(\"Mole Fraction N2 = \", value(m.fs.N2))\n", + "print(\"Mole Fraction O2 = \", value(m.fs.O2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Optimizing the Autothermal Reformer\n", + "Extending this example, we will unfix the input variables and optimize hydrogen production. We will restrict nitrogen below 34 mol% of the product stream and leave all other variables unfixed.\n", + "\n", + "Above, variable values are called in reference to actual objects names; however, as shown below this may be done much more compactly by calling the list objects we created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# unfix input values and add the objective/constraint to the model\n", + "m.fs.bypass_frac.unfix()\n", + "m.fs.ng_steam_ratio.unfix()\n", + "m.fs.obj = Objective(expr=m.fs.H2, sense=maximize)\n", + "m.fs.con = Constraint(expr=m.fs.N2 <= 0.34)\n", + "\n", + "# solve the model\n", + "tmr = TicTocTimer()\n", + "status = solver.solve(m, tee=True)\n", + "solve_time = tmr.toc(\"solve\")\n", + "\n", + "# print and check results\n", + "assert abs(value(m.fs.H2) - 0.33) <= 0.01\n", + "assert value(m.fs.N2 <= 0.4 + 1e-8)\n", + "print(\"Model status: \", status)\n", + "print(\"Solve time: \", solve_time)\n", + "for var in inputs:\n", + " print(var.name, \": \", value(var))\n", + "for var in outputs:\n", + " print(var.name, \": \", value(var))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_doc.ipynb index b5eec52c..05f2722b 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -69,11 +70,7 @@ ] }, "execution_count": 2, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\surrogates\\pysmo\\pysmo_flowsheet_optimization_doc_3_0.png" - } - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -162,7 +159,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\dkgun\\miniconda3\\envs\\idaes-examples-py311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", " return bound(*args, **kwds)\n" ] } @@ -214,91 +211,98 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:19 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output Steam_Flow trained successfully\n" + "2025-03-17 17:37:56 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output Steam_Flow trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:22 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output Reformer_Duty trained successfully\n" + "2025-03-17 17:37:57 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output Reformer_Duty trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:25 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output AR trained successfully\n" + "2025-03-17 17:37:58 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output AR trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C2H6 trained successfully\n" + "2025-03-17 17:37:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C2H6 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:30 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C3H8 trained successfully\n" + "2025-03-17 17:38:00 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C3H8 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:33 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C4H10 trained successfully\n" + "2025-03-17 17:38:01 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output C4H10 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:35 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CH4 trained successfully\n" + "2025-03-17 17:38:01 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CH4 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:38 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CO trained successfully\n" + "2025-03-17 17:38:02 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CO trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:41 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CO2 trained successfully\n" + "2025-03-17 17:38:03 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output CO2 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:43 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output H2 trained successfully\n" + "2025-03-17 17:38:04 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output H2 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:47 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output H2O trained successfully\n" + "2025-03-17 17:38:05 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output H2O trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:49 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output N2 trained successfully\n" + "2025-03-17 17:38:06 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output N2 trained successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:22:52 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output O2 trained successfully\n" + "2025-03-17 17:38:07 [WARNING] idaes.core.surrogate.pysmo.polynomial_regression: Polynomial regression generates poor fit for the dataset\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:07 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output O2 trained successfully\n" ] }, { @@ -318,29 +322,27 @@ "Number of adaptive samples (no_adaptive_samples) set at 4\n", "Maximum number of iterations (Max_iter) set at: 0\n", "\n", - "Initial surrogate model is of order 4 with a cross-val error of 0.000000\n", + "Initial surrogate model is of order 3 with a cross-val error of 0.000000\n", "Initial Regression Model Performance:\n", - "Order: 4 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000\n", + "Order: 3 / MAE: 0.000000 / MSE: 0.000000 / R^2: 1.000000 / Adjusted R^2: 1.000000\n", "\n", "Polynomial regression generates a good surrogate model for the input data.\n", "\n", "-------------------------------------------------\n", "-------------------------------------------------\n", "Best solution found: \n", - "Order: 4 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000\n", + "Order: 3 / MAE: 0.000000 / MSE: 0.000000 / R_sq: 1.000000 / Adjusted R^2: 1.000000\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | 0.0\n", + "k | -0.0\n", "(x_ 1 )^ 1 | 0.0\n", - "(x_ 2 )^ 1 | 1.21186\n", - "(x_ 1 )^ 2 | -0.0\n", - "(x_ 2 )^ 2 | 3e-06\n", - "(x_ 1 )^ 3 | 0.0\n", - "(x_ 2 )^ 3 | -2e-06\n", - "(x_ 1 )^ 4 | -0.0\n", - "(x_ 2 )^ 4 | 0.0\n", + "(x_ 2 )^ 1 | 1.211862\n", + "(x_ 1 )^ 2 | 0.0\n", + "(x_ 2 )^ 2 | -0.0\n", + "(x_ 1 )^ 3 | -0.0\n", + "(x_ 2 )^ 3 | 0.0\n", "x_ 1 .x_ 2 | -1.211862\n", "\n", "Results saved in solution.pickle\n", @@ -355,23 +357,25 @@ "\n", "max_fraction_training_samples set at 0.5\n", "Number of adaptive samples (no_adaptive_samples) set at 4\n", + "Maximum number of iterations (Max_iter) set at: 0\n", + "\n", ".\n", ".\n", ".\n", - "k | 23.343737\n", - "(x_ 1 )^ 1 | -0.03395\n", - "(x_ 2 )^ 1 | -139.706887\n", - "(x_ 1 )^ 2 | -0.458656\n", - "(x_ 2 )^ 2 | 353.282249\n", - "(x_ 1 )^ 3 | 1.34249\n", - "(x_ 2 )^ 3 | -475.264806\n", - "(x_ 1 )^ 4 | -2.542639\n", - "(x_ 2 )^ 4 | 358.569677\n", - "(x_ 1 )^ 5 | 2.410967\n", - "(x_ 2 )^ 5 | -143.84727\n", - "(x_ 1 )^ 6 | -1.054685\n", - "(x_ 2 )^ 6 | 23.972175\n", - "x_ 1 .x_ 2 | 0.042895\n", + "k | -11.119438\n", + "(x_ 1 )^ 1 | -0.13808\n", + "(x_ 2 )^ 1 | 66.536719\n", + "(x_ 1 )^ 2 | 0.178502\n", + "(x_ 2 )^ 2 | -159.241311\n", + "(x_ 1 )^ 3 | -0.603785\n", + "(x_ 2 )^ 3 | 201.872038\n", + "(x_ 1 )^ 4 | 0.548697\n", + "(x_ 2 )^ 4 | -143.104903\n", + "(x_ 1 )^ 5 | -0.024305\n", + "(x_ 2 )^ 5 | 53.783788\n", + "(x_ 1 )^ 6 | -0.308294\n", + "(x_ 2 )^ 6 | -8.371776\n", + "x_ 1 .x_ 2 | 0.044791\n", "\n", "Results saved in solution.pickle\n", "\n", @@ -389,34 +393,26 @@ "\n", "Initial surrogate model is of order 1 with a cross-val error of 0.000000\n", "Initial Regression Model Performance:\n", - "Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: -5233897914.466867 / Adjusted R^2: 0.000000\n", + "Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R^2: -1234394494.826904 / Adjusted R^2: 0.000000\n", "\n", "Polynomial regression performs poorly for this dataset.\n", "\n", "-------------------------------------------------\n", "-------------------------------------------------\n", "Best solution found: \n", - "Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: -5233897914.466867 / Adjusted R^2: 0.000000\n", + "Order: 1 / MAE: 0.000000 / MSE: 0.000000 / R_sq: -1234394494.826904 / Adjusted R^2: 0.000000\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -0.0\n", - "(x_ 1 )^ 1 | 0.0\n", - "(x_ 2 )^ 1 | 0.0\n", - "x_ 1 .x_ 2 | -0.0\n", + "k | 0.0\n", + "(x_ 1 )^ 1 | -0.0\n", + "(x_ 2 )^ 1 | -0.0\n", + "x_ 1 .x_ 2 | 0.0\n", "\n", "Results saved in solution.pickle\n", "\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dkgun\\miniconda3\\envs\\idaes-examples-py311\\Lib\\site-packages\\idaes\\core\\surrogate\\pysmo\\polynomial_regression.py:1415: UserWarning: Polynomial regression generates poor fit for the dataset\n", - " warnings.warn(\n" - ] } ], "source": [ @@ -489,913 +485,688 @@ "outputs": [ { "data": { - "image/png": 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evXvDUJmdjRs3aj3OzMy0boNs7NixGOzcOQiC4AKFQoOEhK8RG3vc3s0iIiILsvdUhP79+2PDhg2oqqrCtWvXsGfPHkyZMgXbt2/HV199BTc32c1QqZPj3ZFMqNV+YrABAEFwwc6dgxAVlQOlssTOrSMiIkuQwlQET09PNGnSBAAQFhaG2NhYPPbYY+jbty82btyIcePGISUlBRs2bMCFCxegUqmQkJCAxYsXo0GDBsjMzMSYMWMA/D3hd/bs2ZgzZw4+/vhjvPfee8jOzoavry/69OmD1NRUg6uYbUG2S8HlrqgoQAw21QTBBUVF+gsSEhGRvBg7xcDWUxH69OmD9u3bIz09HcC9ysDLly/H6dOnsWnTJnz77beYNm0aAKBr165ITU2Fv78/8vLykJeXh9dffx3AvWXy8+bNwy+//IIvv/wSFy9exOjRo216L7qw58ZOVKpCKBQarYCjUGigUhXZsVVEROQsWrVqhV9//RUAMHXqVPH5yMhIvP3225gwYQJWrVoFDw8PKJVKKBQKsQeo2osvvij+uUWLFli+fDkeffRR3L59Gw0aNLDJfejCnhs7USpLkJDwNRSKexO6qufccEiKiIhsQRAEcZhp//796Nu3L8LCwuDn54eRI0eisLAQZWVlBq+RlZWFhIQEREREwM/PD7169QIArcVA9sCeGzuKjT2OqKgcFBWpoFIVST7Y2HtSHBERWc6ZM2fQvHlzXLx4EYMGDcLEiRMxf/58qFQqHDx4EGPHjkVFRYXeysylpaWIj49HfHw8tmzZgsaNG+Py5cuIj4+3+4pfhhsbq7nTqVJZojPU6NoR1Z6kMCmOiIgs49tvv8XJkyfx6quvIisrCxqNBkuXLoWLy70Bnc8++0zreA8Pj1pbIpw9exaFhYVYtGgRwsPDAdwr8SIFDDc2FhAQgMTERNn1gOiqz1NUFACVqlArnNk7rRMRkbby8nLk5+drLQVfuHAhBg0ahBdeeAGnTp1CZWUl3n//fSQkJODQoUNYvXq11jUiIyNx+/ZtZGRkoH379vDx8UFERAQ8PDzw/vvvY8KECTh16hTmzZtnp7vUxnBjB1ILLqZifR4iIvnYs2cPQkJC4ObmhkaNGqF9+/ZYvnw5Ro0aBRcXF7Rv3x4pKSl45513kJycjJ49e2LhwoV44YUXxGt07doVEyZMwNChQ1FYWCguBd+4cSP+/e9/Y/ny5YiNjcW7776Lp556yo53e49CMFRFzwEVFxdDqVRCrVbD39/f3s2Rjby8PKxZswZqtR9SU6fWWuU1dWoqlMoSjB8/HiEhIXZsKRGRZd25cwe5ublo3rw5vLy8TDqXQ/qmMfRZm/L9zZ4bMomh+jxSnxBNRGRrcp2KIHcMN2QS1uchIjINg4vtsc4NmYT1eYiISOrYc0Mmk1t9HiIici4MN2QUudbnISIi58NwQ0bhpDgiIpILhhsyGoMLERHJAScUExERkUNhuCEiIiKHwnBDREREZsnMzIRCocCtW7eMPicyMhKpqalWaxPAcEMmKCwsRF5ent6fwsJCezeRiIjuM3r0aCgUCkyYMKHWa5MmTYJCocDo0aNt3zAr44RiMgr3RyEikqfw8HCkpaVh2bJl8Pb2BnBvD6etW7ciIiLCzq2zDvbckBZ9vTN//vmn1nFqtR9ycyOhVvtpPW9oqTgREdlebGwswsPDkZ6eLj6Xnp6OiIgIxMTEiM+Vl5dj8uTJCAoKgpeXF7p3746jR49qXWv37t144IEH4O3tjccffxwXL16s9X4HDx5Ejx494O3tjfDwcEyePBmlpaVWuz9d2HNDImN7Z44di8HOnYMgCC7i9guxscdt0EIiInn74w/g99+Bli2Bpk1t974vvvgiNmzYgOHDhwMA1q9fjzFjxiAzM1M8Ztq0adixYwc2bdqEZs2aYfHixYiPj0dOTg5UKhWuXLmCIUOGYNKkSRg/fjx+/vlnvPbaa1rvc/78efTv3x9vv/021q9fjxs3biAxMRGJiYnYsGGDze6XPTckqtnroqt3Rq32E4MNcG9H8J07B9XqwSEiIm3r1gHNmgF9+tz733XrbPfeI0aMwMGDB3Hp0iVcunQJhw4dwogRI8TXS0tL8cEHH2DJkiUYMGAAHnroIaxduxbe3t5Y938N/eCDDxAVFYWlS5fiwQcfxPDhw2vN11m4cCGGDx+OqVOnomXLlujatSuWL1+OzZs3486dOza7X/bckE76emeKigK0dgQH7gWcoiIV95giItLjjz+A8eMBzb09h6HRAP/6FxAfb5senMaNG2PgwIHYuHEjBEHAwIEDERgYKL5+/vx5VFZWolu3buJz7u7u6NSpE86cOQMAOHPmDDp37qx13S5dumg9/uWXX/Drr79iy5Yt4nOCIECj0SA3NxetW7e2xu3VwnBDtejrnYmKyoFKVQiFQqMVcBQKDVSqIns1l4hI8n7//e9gU62qCsjJsd3w1IsvvojExEQAwMqVK63yHrdv38a//vUvTJ48udZrtpy8zGEpqqWu3pmEhK+hUNz7W1rdq8NeGyIi/Vq2BFxqfOO6ugLR0bZrQ//+/VFRUYHKykrEx8drvRYVFQUPDw8cOnRIfK6yshJHjx7FQw89BABo3bo1jhw5onXejz/+qPU4NjYWv/32G6Kjo2v92HJjZfbcUC119c7Exh5HVFQOiopUUKmKGGyIiOrQtCmwZs29oaiqqnvB5sMPbTup2NXVVRxicnV11XrN19cXEydOxBtvvAGVSoWIiAgsXrwYZWVlGDt2LABgwoQJWLp0Kd544w2MGzcOWVlZ2Lhxo9Z13nzzTTz22GNITEzEuHHj4Ovri99++w379u0zasGKpbDnhmoxpndGqSxB8+aXagUbWyZzIiI5GTsWuHgROHDg3v/+X2awKX9/f/j7++t8bdGiRXjmmWcwcuRIxMbGIicnB3v37kWjRo0A3BtW2rFjB7788ku0b98eq1evxoIFC7Su0a5dO/zvf//DuXPn0KNHD8TExGDWrFkIDQ21+r3dTyEIgmDTd7Sz4uJiKJVKqNVqvb9gZ5WXl4c1a9aIj9VqP529M0OGDNGaiFbNw8ODBfyIyOHcuXMHubm5aN68Oby8vOzdHIdm6LM25fubw1IkqtnrolSW6BxyCg0NZYghIiLJYrghUUBAABITEw1WGWbvDBERSR3DjR0UFhZKNkAwuBARkdwx3NgYN6AkIiKyLq6WsjFjtjjQdRwREdmXk62/sQtLfcbsubEjR9mAUsrDbERE9eXu7g4AKCsrg7e3t51b49iqv0tq1uExFcONnRja4kCpLEFBQYHO86QWFDjMRkSOztXVFQ0bNsT169cBAD4+PlAoFHZulePRaDS4ceMGfHx84OZWv3jCcGMndW1xkJ6ervdcKQUFXcNsRUUBUKkKtZaRc5iNiOSsSZMmACAGHLIOFxcXRERE1Ds8MtzYiTEbUMotKDjKMBsRUU0KhQIhISEICgpCZWWlvZvjsDw8POBScxMuMzDc2En1Fgc1w0B1iJFbUKhrmI2IyBG4urrWez4IWR/DjR3p24BSjkGhrmE2IiIiW2G4sTFjtjiQY1AwZpiNiIjIFhhubMzQFgcFBQVIT0+XZVCoa5iNiIjIVhhu7KCulU5yDQr6htmIiIhsieHGDvQVvbu/to1cgoKxO4nXPI6IiMhaGG5szNiid4A8ggJ3EiciIqlhuLExY4veDRkyBIGBgbXOl2JQkFp7iIjIuTHc2JGhWjaBgYEICQmxcwuJiIjkh7uC24m+WjY1dwcnIiIi0zDc2ImhWjZERERkPoYbO6muZXM/qdeyISIikgOGGzuprmVTHXDkUsuGiIhI6jih2I7kUsuGiIhIThhubIxF7+RNXwHGalJcqk9E5GwYbmyMRe/ky9gCjImJifz9ERHZEcONHfCLT56MLcBoKLgSEZH1MdyQ0Tgk8zdDBRiJiMi+GG7IKByS+Zu+AoxRUTmcFE5EJAFcCk5G0TUkk5sbWauisjMMybAAIxGRtLHnhkzm7EMy1QUY7w84LMBIRCQddu25+e6775CQkIDQ0FAoFAp8+eWXdZ6TmZmJ2NhYeHp6Ijo6Ghs3brR6O+lvzrInVmFhIfLy8rR+CgoKALAAIxGR1Nm156a0tBTt27fHiy++iCFDhtR5fG5uLgYOHIgJEyZgy5YtyMjIwLhx4xASEoL4+HgbtJgMDck4ype7MfOLWICRiEi67BpuBgwYgAEDBhh9/OrVq9G8eXMsXboUANC6dWscPHgQy5YtY7ixEH0roqp7LZxhSMbYJd8swEhEJE2ymnNz+PBhxMXFaT0XHx+PqVOn2qdBDsaYHovqIZmac24ctefC0PyiIUOGIDAwUOt4Z1oOT0QkVbIKN/n5+QgODtZ6Ljg4GMXFxfjrr7/g7e1d65zy8nKUl5eLj4uLiy3aJn09Hbdu3cLdu3fh7u4OpVJZ63V9X4L2rCVjbI+FswzJ1LXkOzAwECEhIXZuJRER1SSrcGOOhQsXYu7cuVa5trG1X/SpWRNGSrVk6loR5QxDMs4wv4iIyBHJKtw0adIE165d03ru2rVr8Pf319lrAwDJyclISkoSHxcXFyM8PNwi7TG2p8PYMv1SKe9fV4+FruEYwPGGZJxhfhERkSOSVbjp0qULdu/erfXcvn370KVLF73neHp6wtPT09pN09vTYW5NGHvWkqmrx8JZhmOcbX4REZGjsGu4uX37NnJycsTHubm5OHHiBFQqFSIiIpCcnIyrV69i8+bNAIAJEyZgxYoVmDZtGl588UV8++23+Oyzz7Br1y573QIA/T0dQUH5ZpXpt3d5f/ZY/M1Z5hcRETkSuxbx+/nnnxETE4OYmBgAQFJSEmJiYjBr1iwAQF5eHi5fviwe37x5c+zatQv79u1D+/btsXTpUnz00Ud2Xwaur6fjypUIs8r027u8v7MXqas5b0ipLEHz5pdq3b8jzS8iInIkdu256d27NwRB0Pu6rurDvXv3xvHj0ir1r6+nIzz8slk9IFLoOXHmHouAgAAkJibadQd07sBORGQ+Wc25kSp9czOaNs0za86GveZ6lJWV1WqHrveseZwjsmdwkNKqOSIiOWK4sRB9PR3m9oDYo+fEx8dH67G+1Vo1jyPLksqqOSIiuWK4qQddczN0hRBja8IYez1bzPVw9p2/pYK/ByIi0zHc1IOhuRnmVCiWwlwPwP6rtSxNrvNXHO33QERkKww39aTvS9HcOjBS+JJ1pMq8cp6/4ki/ByIiW7LrUnCSpurVWveTa50bXfNXcnMjoVb7GTxOChzp90BEZEvsuaFaHLUyr9zmrzjq74GIyNoYbkgnR6tzI9f5K472eyAisgWGGxJJabWWpclp/ooj/x6IiGyB4YZEUlmtZQ1SqPpsLEf+PRAR2QLDDWlx1C9Muc1fcdTfAxGRLTDckNMwNH+loKCg1vHsHSEikieGG3Joxs5fSU9P13m+FOvfEBGRYQw35ND0zV8pKCjQCjTcv4mIyHEw3JDDq6vnRW71b4iIyDBWKCanpq/+Tc0KxkREJB8MN+TUDNW/ISIieWK4IafG/ZuIiBwPww05ter6N9UBR+r1b4iIqG6cUExOj/s3ERE5FoYbckrcv4mIyHEx3JBsFBYWWmy/Je7fRETkuBhuSBYKCwuxYsWKOo8zpaIwgwsRkWPihGKShZo9LGq1H3JzI2vVo2FFYSIiYs8NyQ4rChMRkSEMNzJhyfkmcqavonBUVA5XOREREQCGG1mwxnwTuTJUUZjhhoiIAM65kQXON/kbKwoTEVFd2HMjM84+36S6onDNz4C9NkREVI3hRkY43+QeVhQmIiJDGG5kxJnnm7CiMBERGYvhRkaq55vcH3CcZb4JKwoTEZGxGG5kxNnnmzC4EBGRMRhuZIbzTUzHGkFERM6F4cZKLPmFyvkm5mONICIi58NwYwWW/kLlfBPz6aoRVFQUAJWqUCsg1jyOvT1ERPLFcGMF5n6hGsIv0voztkaQFHp7GK6IiMzHcGNlzl50TypMqRFkjXBqCimEKyIiOWO4sSIW3ZMOc2sE2SOc2jtcERHJHcONFTlz0T2pMadGkBTCKXv+iIhMx40zrYibPEpHdY2g6t+HMTWCDIVTW9AXrmpumEpERNrYc2NFzl50T2pMrRFk74rQ7PkjIjIPw42VseiefdWnRpC9w6m9wxURkVwx3FgBi+5JR31rBNkznNo7XBERyRXDjRU4atE9S9ZesWUdF1OvI6VwKreeP9bnISIpYLixEkf7B9yStVekXsfF3uFUSuHKFFL/vRKR82C4IaNYsvaKHOq42PPL197hylxy+L0SkXNguCGTWbL2Cuu46Ca14GIq/l6JyJ4YbsgklixsJ4UiebbgbPNQnOX3SkTSxXBDJrFk7RVnqOPijPNQnOH3SkTSxgrFZBJLVl12hgrOuuah5OZG1qoy7EjzUJzh90pE0sZwQyYxZxsDW1xLDo4di0Fq6lRs2jQKqalTcexYjL2bZBXO9nslIunhsBSZzJK1V+RWx8VczjYPxVl+r0QkTQw3ZBRL1l6Rax2X+nCGeSjO+HslImliuCGjWLL2ilzruNSHM+wT5Yy/VyKSJoYbMpolv5Sc7QvOWfaJcrbfKxFJE8MNOQQ51JLhPBQiIttguCHZk3ItGc5DISKyPYYbkj0p72nEeShERLZnVriJiIhA79690atXL/Tu3RtRUVGWbhdRLfqGngoKCsQ/S3FPIwYXIiLbMivcLFiwAN999x3eeecdvPTSSwgLC0OvXr3EsNOyZUtLt5OcnDFDT85WS4aIiHQzq0LxiBEjsGbNGpw7dw5Xr17FkiVLAAAvv/wyWrVqZfL1Vq5cicjISHh5eaFz5844cuSIweNTU1Px4IMPwtvbG+Hh4Xj11Vdx584dc26FZMKYbQwM1ZIhIiLnYfacm7KyMhw8eBCZmZk4cOAAjh8/jjZt2qB3794mXWfbtm1ISkrC6tWr0blzZ6SmpiI+Ph7Z2dkICgqqdfzWrVsxffp0rF+/Hl27dsW5c+cwevRoKBQKpKSkmHs7JCP6hp6coZYMII+VYZai717VajUqKyvh5uaGhg0b1nrdkT4DIjKdWeGma9euOH78OFq3bo3evXtj+vTp6NmzJxo1amTytVJSUvDSSy9hzJgxAIDVq1dj165dWL9+PaZPn17r+B9++AHdunXD888/DwCIjIzEsGHD8NNPP5lzKyQzdQ09OXotGSmvDLM0Y+9VH0f4DIjIPGaFm7Nnz8LX1xetWrVCq1at0Lp1a7OCTUVFBbKyspCcnCw+5+Ligri4OBw+fFjnOV27dsUnn3yCI0eOoFOnTrhw4QJ2796NkSNH6jy+vLwc5eXl4uPi4mKT20nSUdc2Bo5eS0bKK8Mszdh7deTPgIjMY1a4KSwsxMmTJ5GZmYm9e/dixowZ8PDwQK9evfD444/jpZdeMuo6BQUFqKqqQnBwsNbzwcHBOHv2rM5znn/+eRQUFKB79+4QBAF3797FhAkT8O9//1vn8QsXLsTcuXNNu0GSLGOGnpylloy1V4ZJafhL371KcXUcEdmfWeFGoVCgXbt2aNeuHV555RVkZWVhxYoV2LJlC7Zt22Z0uDFHZmYmFixYgFWrVqFz587IycnBlClTMG/ePMycObPW8cnJyUhKShIfFxcXIzw83GrtI+uqa+hpyJAhCAwMrHWeo83BsPbKMCkNf+m716CgfK6OIyKdzAo3x44dQ2ZmJjIzM3Hw4EGUlJSgbdu2eOWVV9CrVy+jrxMYGAhXV1dcu3ZN6/lr166hSZMmOs+ZOXMmRo4ciXHjxgEA2rZti9LSUowfPx4zZsyAi4v2kIWnpyc8PT1NvEOSMkNDT4GBgQgJCbFj62zD2ruMS2n4S9+9Xr4c4fA7rRORecwKN506dUJMTAx69eqFl156CT179oRSqTT5Oh4eHujQoQMyMjIwePBgAIBGo0FGRgYSExN1nlNWVlYrwLi6ugIABEEwuQ0kD9zGQJstV4bZe+hH371GRFx2itVxRGQ6s8JNUVER/P39LdKApKQkjBo1Ch07dkSnTp2QmpqK0tJScfXUCy+8gLCwMCxcuBAAkJCQgJSUFMTExIjDUjNnzkRCQoIYcsjxcBsDbbZaGSaFwoj67rVp0zyDn4FarXaKXjwiqs2scFMdbLKysnDmzBkAwEMPPYTY2FiTrzV06FDcuHEDs2bNQn5+Ph555BHs2bNHnGR8+fJlrZ6at956CwqFAm+99RauXr2Kxo0bIyEhAfPnzzfnVkhGnCW4GMsWK8OsPfxlLH33Ght7HEFB+bh8OQIREZfRtGmeeE5lZaXN2kdE0mJWuLl+/TqGDh2K//3vf2IBrVu3buHxxx9HWloaGjdubNL1EhMT9Q5DZWZmajfYzQ2zZ8/G7NmzzWk6kazZenjOnoURjblXQ0Nmbm7cF5jIWZn1t/+VV17B7du3cfr0abRu3RoA8Ntvv2HUqFGYPHkyPv30U4s2kojusfXwnKnDX5ZcPq7vXgsKCpCenl7nkJmuysVE5BzMCjd79uzB/v37xWAD3BuWWrlyJZ544gmLNY6IarP18Jyxw1/WWD5u6DipDJkRkfSYFW40Gg3c3d1rPe/u7g6NRlPvRhHZm5QK2NmDOcNftl4+7ix7iRGR6cwKN3369MGUKVPw6aefIjQ0FABw9epVvPrqq+jbt69FG0hka1IqYGcv9R3+ssXycWfYS4yIzGNWuFmxYgWeeuopREZGitV+r1y5gjZt2uCTTz6xaAOJbE1KBezsydzgZsvl446+lxgRmcescBMeHo5jx45h//794h5QrVu3RlxcnEUbR2Rv9i5gJ0fWngvDgo5EVBez10oqFAr069cP/fr1s2R7iCRDCgXs5Mjac2FY0JGI6mJ0uFm+fLnRF508ebJZjSGSEmdfjWPupGpbzIVhcCEiQ4wON8uWLTPqOIVCwXBDDsGZV+PUd1I158IQkT0ZHW5yc3Ot2Q4iyXHm1TjmTKrmXBgikgqT5txoNJpaO3ITOTL2QBg/qZpzYYhIKkwKN+7u7sjLy0NQUBAA4I033kBycjJUKpVVGkdkD+yB+Jupk6oZXIhICkwKN4IgaD3+8MMPMXHiRIYbcijsgfibs0+qJiJ5qte2uTXDDpGjcIbgYgxnnlRNRPLFCTREVItarQbw96RqheLennE1J1VXH0dEJCUm99zMmjULPj4+AO6tlJg/fz6USqXWMSkpKZZpHRHZRWVlpfjn2NjjCArKx+XLEYiIuIymTfN0HkdEJBUmhZuePXsiOztbfNy1a1dcuHBB6xiFQmGZlhGR3bi5/f1Pg6HVUvcfR0QkFSb9y5SZmWmlZhCRlDRs2BBA3aulqo8jIpISq8658ff3r9WzQ0TyYWi1FBGRVFk13HA1FZG8Va+Wuh9XSxGR1HG1FBHpVddqKSIiKeJsQCIyiFtQEJHcMNwQUS3cgoKI5Myq4YbLwonkiVtQEJGcWTXccEIxkXwxuBCRXFl1QvF///tfhIWFWfMtiIiIiLSY1XMjCAK2b9+OAwcO4Pr169BotJeKpqenAwC6d+9e/xYSERERmcCscDN16lR8+OGHePzxxxEcHMy5NURERCQZZoWbjz/+GOnp6XjyySct3R4iIskoLCzkpGoiGTIr3CiVSrRo0cLSbSEikozCwkKsWLGizuMSExMZcIgkxqwJxXPmzMHcuXPx119/Wbo9RESSULPHRq32Q25uJNRqP4PHEZH9mdVz8+yzz+LTTz9FUFAQIiMj4e7urvX6sWPHLNI4IiIpOHYsRtwdvXoLitjY4/ZuFhHpYVa4GTVqFLKysjBixAhOKCZyUJxvco9a7ScGG+Derug7dw5CVFQOt6Igkiizws2uXbuwd+9eLvUmclCcb/K3oqIAMdhUEwQXFBWpGG6IJMqsOTfh4eHw9/e3dFuISCI43+RvKlWhuCt6NYVCA5WqyE4tIqK6mBVuli5dimnTpuHixYsWbg4RSc2xYzFITZ2KTZtGITV1Ko4di7F3k2xKqSxBQsLXYsCpnnPDXhsi6TJrWGrEiBEoKytDVFQUfHx8ak0oLiri/6MhkhJz589wvsk9sbHHERWVg6IiFVSqIqe6dyI5MivcpKamWrgZRGQt9Zk/48zzTTw8PLQeK5UlOu+55nFEZH9mr5YiInnQNX+mqCgAKlWh1pe1rp6d6vkm9wccZ5lvEhAQgMTERK4YI5Ihs8LN/e7cuVPrLz8nGxNJk6n1Wqrnm9Q8x9F7baoxuBDJk1nhprS0FG+++SY+++wzFBYW1nq9qqqq3g0jIssyd/4M55sQkdyYtVpq2rRp+Pbbb/HBBx/A09MTH330EebOnYvQ0FBs3rzZ0m0kIgswNH+mJl3zTZo3v1Qr2HC+CRFJkVk9Nzt37sTmzZvRu3dvjBkzBj169EB0dDSaNWuGLVu2YPjw4ZZuJxHVkynzZxxxvgkrLhM5D7PCTVFRkbgruL+/v7j0u3v37pg4caLlWkdEFmPq/BlH+qJnxWUi52JWuGnRogVyc3MRERGBVq1a4bPPPkOnTp2wc+dONGzY0MJNJCJLcdb5M+auGGNvD5E8mRVuxowZg19++QW9evXC9OnTkZCQgBUrVqCyshIpKSmWbiMR1QPrtWgzdsUYe3uI5MuscPPqq6+Kf46Li8PZs2eRlZWF6OhotGvXzmKNI6L6c8T5M+YyZcVYfeoDWRJ7j4hMZ5E6N82aNUOzZs0s0R4isgJ++d1jbsVlU+sDWQp7j4jMY9ZS8KqqKsybNw9hYWFo0KABLly4AACYOXMm1q1bZ9EGEhFZijk7fOvr7am5Q7o1cHd2IvOYFW7mz5+PjRs3YvHixVrj9G3atMFHH31kscYREVmSOTt8m1IfyJqcfXd2IlOYNSy1efNmrFmzBn379sWECRPE59u3b4+zZ89arHFERJZm6ooxKeyvxd3ZiUxjVs/N1atXER0dXet5jUaDysrKejeKiMiS6lNx2ZzeHkuTSu8RkVyY1XPz0EMP4fvvv681iXj79u2IiWFXKRFJS31XjNm7PpAUeo+I5MSscDNr1iyMGjUKV69ehUajQXp6OrKzs7F582Z8/fXXlm4jEVG9mbqaSEr1gZx9d3YiU5kVbp5++mns3LkT//nPf+Dr64tZs2YhNjYWO3fuRL9+/SzdRiIiq9JXS2bo0KGorKyEm5ubzurrtqwxY+/eIyI5MbvOTY8ePbBv3z5LtoWIyOakXEtGSr1HRHJi9t5SR48erfUX/datW4iNjRXr3hARSZ1UKhHrwurSROYxK9xcvHgRVVVVtZ4vLy/H1atX690oIiJ7sFclYkMYXIhMZ1K4+eqrr8Q/7927F0qlUnxcVVWFjIwMREZGWqxxRES2wloyRI7DpHAzePBgAIBCocCoUaO0XnN3d0dkZCSWLl1qscYREdmKuftOEZH0mFTET6PRQKPRICIiAtevXxcfazQalJeXIzs7G4MGDTK5EStXrkRkZCS8vLzQuXNnHDlyxODxt27dwqRJkxASEgJPT0888MAD2L17t8nvS0RUzZx9p4hImkzquTl8+DAKCwuRm5srPrd582bMnj0bpaWlGDx4MN5//314enoafc1t27YhKSkJq1evRufOnZGamor4+HhkZ2cjKCio1vEVFRXo168fgoKCsH37doSFheHSpUs6l2kSkfTpW4ZdzVYTZllLhshxmBRu5s6di8cff1zsnTl58iTGjh2L0aNHo3Xr1liyZAlCQ0MxZ84co6+ZkpKCl156CWPGjAEArF69Grt27cL69esxffr0WsevX78eRUVF+OGHH+Du7g4AnOdDJFNSW4bNWjJEjsGkYalffvkFffv2FR+npaWhc+fOWLt2LZKSkrB8+XJ89tlnRl+voqICWVlZiIuL+7tBLi6Ii4vD4cOHdZ7z1VdfoUuXLpg0aRKCg4PRpk0bLFiwQOfqLeDeCq7i4mKtHyKSBl3LsHNzI6FW+xk8zpLqs+8UEUmTST03N2/eRHBwsPj4f//7HwYMGCA+fvTRR3HlyhWjr1dQUICqqiqtawJAcHCw3t3FL1y4gG+//RbDhw/H7t27kZOTg5dffhmVlZWYPXt2reMXLlyIuXPnGt0mIrIPey3DZi0ZIsdjUrgJDg5Gbm4uwsPDUVFRgWPHjmkFh5KSEnGoyFo0Gg2CgoKwZs0auLq6okOHDrh69SqWLFmiM9wkJycjKSlJfFxcXIzw8HCrtpGITGPvZdgMLkSOxaRw8+STT2L69Ol455138OWXX8LHxwc9evQQX//1118RFRVl9PUCAwPh6uqKa9euaT1/7do1NGnSROc5ISEhcHd3h6urq/hc69atkZ+fj4qKilpdx56eniZNcCYi2+MybCKyJJPm3MybNw9ubm7o1asX1q5di7Vr12qFifXr1+OJJ54w+noeHh7o0KEDMjIyxOc0Gg0yMjLQpUsXned069YNOTk50Gj+XrJ57tw5hISEcEycSKa4DJuILMmkcBMYGIjvvvsON2/exM2bN/GPf/xD6/XPP/9c59CQIUlJSVi7di02bdqEM2fOYOLEiSgtLRVXT73wwgtITk4Wj584cSKKioowZcoUnDt3Drt27cKCBQswadIkk96XiKSjehl2dcDhMmwiqg+z9pa6f9uF+6lUKpOvNXToUNy4cQOzZs1Cfn4+HnnkEezZs0ecZHz58mW4uPydwcLDw7F37168+uqraNeuHcLCwjBlyhS8+eab5twKEUkEl2ETkaUoBEEQ7N0IWyouLoZSqYRarYa/v7+9m0Pk1KRW54aIpMuU72+zem6IiCyBy7CJyBoYbojIrhhciMjSTJpQTERERCR1DDdERETkUBhuiIiIyKEw3BAREZFDYbghIiIih8JwQ0RERA6FS8GJSHYKCwttUhvHVu9DRJbFcENEsmKrqsasnkwkXxyWIiJZqdmTolb7ITc3Emq1n8HjpPo+RGR57LkhItk6diwGO3cOgiC4iDuJx8Yel+37EJFlMNwQkSyp1X5i4AAAQXDBzp2DEBWVA6WyBAUFBTrPM3WeTF3vQ0TSw3BDRLJUVBQgBo5qguCCoiIVlMoSpKen6z3XlHkydb0PEUkPww0RyZJKVQiFQqMVPBQKDVSqIvGxWu2HoqIAqFSFWkHElHkyxryPI+DKMHIkDDdEJEtKZQkSEr6uNRemOsRYap5MXe/jCLgyjBwNww0RyVZs7HFEReWgqEgFlapIDByWniej730cha6VYfXt8SKyJ4YbIpIVDw8PrcdKZUmtsGGJeTLGvI+u4+SOK8PIETDcEJGsBAQEIDExUWcvQkFBAdLT0y0yT8bQ+1RztHkoXBlGjoLhhohkp65AYal5Mo4UXIzBlWHkKBhuiMghOfo8GWuQ48owrvIiXRhuiMhhOOs8GUuR28owrvIifRhuiMhhOOM8GUuTU48XV3mRPgw3RORQHC242GLYxRF6vLjKi+7HcENEJFG2GnaRe48XV3lRTQw3REQSZcthF6kGF2NwlRfVxHBDRCQDHHbRT46rvMi6GG6IiCTO1GEXZ1seLbdVXmR9DDdERBJnyrCLLZdHSylEyWmVF1kfww0RkcSZMuxiq3k6Uqgx4wirvMg6GG6IiCTO3GEXS83T0dVDU1BQoPXYHjVm5L7Ki6yH4YaISAZMHXax1PJoY3po7DnZmcGFdHGp+xAiIrIHXcMuzZtfqhVOdA27GJqnYwpdw1y5uZFQq/3Ex7pCVPXrRPbAnhsiIomqz7BLXfN0ag4r1XU9QHcPTaNGN1ljhiSH4YaISMLMHXapa55Oenq63nN1TQLW10MzduxHrDFDksNwQ0TkoOqap2PKJGB9w1yVlR6sMUOSw3BDRORAjF0ebeokYEPDXM2bX2KNGZIUhhsiIgcSEBCAESNGoKysrNZrN2/exIEDB8xaSVXXMBdrzJCUMNwQETmQwsJCfPLJJwaPMXejSUPDXEOGDEFgYKDW8awxQ/bCcENE5ECMqVBsSsVjY4e5QkNDGWRIMhhuiIgclL55NXUNMd26dQshISEAWAWY5InhhojIAdU1ryY29jiCgvJx5UoEwsMvo2nTPPHcu3fval2LwYXkhuGGiMgB1TWvxtBqKXd3d3s0mchiGG6IiByQoXk1dfXqKJVKezVbJ10bd96Pw2JUE8MNEZEDMjSvJjc3UjZbJhizcSegu6oyOS+GGyIiB6Vv6bYpq6XszZjVX7qOI+fGcENE5ECMWbpd12opqTK1qjI5L4YbIiIHYmjpdkFBgbhhZl37TkmNOVWVyXkx3BARORh9c0+MLcgnxS0TzK2qTM6J4YaIyEnIuSBfXfOECgoKdJ4n1fsh62K4ISJyInL9oq9rnlD1cJsuXEnlfBhuiIhIFuqaJ8SVVFSN4YaIiCTL2HlCXElF92O4ISJyInKr9mvM6i+upKKaGG6IiJyEXKv91tUWrqSimlzqPoSIiByBrmq/ubmRUKv9DB4nddUrqe4n1YrLZBvsuSEickKONEdFrhWXyXoYboiInIwjzlGRW8Vlsi6GGyIiJ+Moc1TkXHGZrIvhhojIyUh1V3BTV3LJueIyWRfDDRGRkzF1jootlo+bu5KLwYV0kUS4WblyJZYsWYL8/Hy0b98e77//Pjp16lTneWlpaRg2bBiefvppfPnll9ZvKBGRgzB2joqtlo/rWsnFasNkLruHm23btiEpKQmrV69G586dkZqaivj4eGRnZyMoKEjveRcvXsTrr7+OHj162LC1RETyZc4cFXuEDkdayUX2Yfdwk5KSgpdeegljxowBAKxevRq7du3C+vXrMX36dJ3nVFVVYfjw4Zg7dy6+//573Lp1y4YtJiKSp/rOUbFF6DB1JZfcKi6Tbdg13FRUVCArKwvJycnicy4uLoiLi8Phw4f1nvef//wHQUFBGDt2LL7//nuD71FeXo7y8nLxcXFxcf0bTkQkU+Z+0dtq+bgpK7nkWnGZrM+uFYoLCgpQVVWF4OBgreeDg4ORn5+v85yDBw9i3bp1WLt2rVHvsXDhQiiVSvEnPDy83u0mInI2hkKHJZlSbdhRKy5T/dl9WMoUJSUlGDlyJNauXYvAwECjzklOTkZSUpL4uLi4mAGHiMhEtlo+bm61Yc7TofvZNdwEBgbC1dUV165d03r+2rVraNKkSa3jz58/j4sXLyIhIUF8TqO5l/Dd3NyQnZ2NqKgorXM8PT3h6elphdYTETkPW25xYOxKLrVa/X//a3jITK1WIyQkxOLtJOmya7jx8PBAhw4dkJGRgcGDBwO4F1YyMjKQmJhY6/hWrVrh5MmTWs+99dZbKCkpwXvvvcceGSIiK7LmFgfmrOSqrKwEUPc8nerjyHnYfVgqKSkJo0aNQseOHdGpUyekpqaitLRUXD31wgsvICwsDAsXLoSXlxfatGmjdX7Dhg0BoNbzRERUf7ba4sDQSq5bt27h7t27cHd3R0VFBfLy8gAAN2/eBHBvyAzQQHsaqf0rLpP92D3cDB06FDdu3MCsWbOQn5+PRx55BHv27BEnGV++fBkuLnad90xE5LRsucWBrmsUFhbis88+M+JshfYjhZ7DyCnYPdwA95bp6RqGAoDMzEyD527cuNHyDSIiIpE9l1EbU0SwqCgANcONHDcCJcuRRLghIiKqi74VUXWt5HJz41eds+F4DxERSZ6+FVFqtZ+4kqu6Pk7NlVzVczPJeTDOEhGR5NW1IsqaK7lIfhhuiIhI8owpImitlVwkPww3REQkeXUVERwyZIjOyvXcONM5MdwQEZEsGBp6CgwMZBViEjHcEBGRZBlbRLB6KwZd57PnxvkoBEEQ7N0IWyouLoZSqYRarYa/v7+9m0NERHUoLCzUW7nYmAJ/iYmJDDgOwJTvb/bcEBGRpBkbTHQV+AOAP//8s1Y4Yo+OY2O4ISIi2dNX4A8A0tPTdZ5jzx4dfb1R1Ri+6ofhhoiIZE1fgb+oqBwolSV6e3QMhQtrKiwsxIoVK+o8jsNp5mO4ISIiWTNU4O/8+Wi9PTr2Ysx+WQCH0+qD4YaIiGRNX4E/d/cKgz06UiC34TS54N5SREQka/r2lqqs9NTboyMFhvbLqn49NzdSfFzNXsNpcsKeGyIikj1dBf7Uar86t2ywJ7kNp8kJe26IiEiWdBX4a978kjjkVNdu4fZWPZx2P0PDaTV7cEg/9twQEZEsBQQEIDExsdYwTUFBgThfRcq7hevbL8vQcJqU2i9lDDdERCRbuibWGrtlgxR2C7f2cJo59XQcoQYPww0RETkUfT0697PnF3Rd4auuHdALCgr0Xvf+ezKnno6j1OBhuCEiIocj5S/e+g6n6VsiDmiHDmPr6dx/nDnnSBHDDRERkY3VZzgNMD10GKqno48550gFww0REZEEGBpOu79Xx9TQUdf2FJY6R0oYboiIiKzAnIm5dQ2nmRM6DNXTseQ5UsJwQ0REZGHWmphrTujQtz2FodVX5pwjJSziR0REZGG6JuZaYisFfYX/DIUOc4oZSr0AYl3Yc0NERGRFlpyYW9cycX3MKWYo5QKIdWG4ISIishJrTMw1NnSYU8xQTgUQDWG4ISIishJLTcw1J3SYU8xQ6gUQjcVwQ0REZCWWmphrbugwJ4RIPbgYg+GGiIjISsydI6OLI4QOW2G4ISIisiI5T8yVK4YbIiIiC3OUiblyxXBDRERkYY4yMVeuGG6IiIisgMHFflihmIiIiBwKww0RERE5FIYbIiIicigMN0RERORQGG6IiIjIoXC1FBERkZMpLCx06GXqDDdEREROpLCwECtWrKjzuMTERNkGHA5LEREROZGaPTZqtR9ycyOhVvsZPE5O2HNDRETkpI4di6m1qWds7HF7N6veGG6IiIickFrtJwYbABAEF+zcOQhRUTk698GS0zwdhhsiIiInVFQUIAabaoLggqIiVa1wY+w8nWeffRYNGzas9bytgw/DDRERkRNSqQqhUGi0Ao5CoYFKVVTrWF3zdIqKAqBSFWoFoc8++0zv+9lygjLDDRERkRNSKkuQkPB1rTk3uoak7lfXPB19wceWE5QZboiIiJzIrVu3xD/Hxh5HVFQOiopUUKmKtMLIrVu3EBISonVuXfN0pDJBmUvBiYiInIhCodD1rFHHGZqnoy/41FxibgvsuSEiInIiSqVS/LOhnpb7j6tmaJ6OKROUrY09N0RERE7InJ6W6nk6CoUGALTm6VQHn/vpm6Bsbey5ISIickLm9rTom6dj7gRla2C4ISIickKmLAX38PDQeqxUlugMLYYmKNsSww0REZETMqWnJSAgAImJiTqXc6vVamzbtk3rurquUTMgWRPDDRERkZMypadFXwG+kJAQvcGnGisUExERkdUYO8RkSk+LVPaUqsZwQ0RE5EQMDTFVk9ImmOZguCEiInIycg4uxmCdGyIiInIoDDdERETkUCQRblauXInIyEh4eXmhc+fOOHLkiN5j165dix49eqBRo0Zo1KgR4uLiDB5PREREzsXu4Wbbtm1ISkrC7NmzcezYMbRv3x7x8fG4fv26zuMzMzMxbNgwHDhwAIcPH0Z4eDieeOIJXL161cYtJyIiIilSCIIg2LMBnTt3xqOPPooVK1YAADQaDcLDw/HKK69g+vTpdZ5fVVWFRo0aYcWKFXjhhRfqPL64uBhKpRJqtRr+/v71bj8RERFZnynf33btuamoqEBWVhbi4uLE51xcXBAXF4fDhw8bdY2ysjJUVlZCpVLpfL28vBzFxcVaP0REROS47BpuCgoKUFVVheDgYK3ng4ODkZ+fb9Q13nzzTYSGhmoFpPstXLgQSqVS/AkPD693u4mIiEi67D7npj4WLVqEtLQ0fPHFF/Dy8tJ5THJyMtRqtfhz5coVG7eSiIiIbMmuRfwCAwPh6uqKa9euaT1/7do1NGnSxOC57777LhYtWoT9+/ejXbt2eo/z9PSEp6enRdpLRERE0mfXcOPh4YEOHTogIyMDgwcPBnBvQnFGRgYSExP1nrd48WLMnz8fe/fuRceOHU16z+r505x7Q0REJB/V39tGrYMS7CwtLU3w9PQUNm7cKPz222/C+PHjhYYNGwr5+fmCIAjCyJEjhenTp4vHL1q0SPDw8BC2b98u5OXliT8lJSVGvd+VK1cEAPzhD3/4wx/+8EeGP1euXKnzu97ue0sNHToUN27cwKxZs5Cfn49HHnkEe/bsEScZX758GS4uf08N+uCDD1BRUYF//vOfWteZPXs25syZU+f7hYaG4sqVK/Dz84NCobDovRQXFyM8PBxXrlxxymXmzn7/AD8DZ79/gJ8B79+57x+w3mcgCAJKSkoQGhpa57F2r3PjSJy9ho6z3z/Az8DZ7x/gZ8D7d+77B6TxGch6tRQRERFRTQw3RERE5FAYbizI09MTs2fPdtql585+/wA/A2e/f4CfAe/fue8fkMZnwDk3RERE5FDYc0NEREQOheGGiIiIHArDDRERETkUhhsiIiJyKAw3Jlq5ciUiIyPh5eWFzp0748iRI3qPPX36NJ555hlERkZCoVAgNTXVdg21ElPuf+3atejRowcaNWqERo0aIS4uzuDxcmHKZ5Ceno6OHTuiYcOG8PX1xSOPPIKPP/7Yhq21PFPu/35paWlQKBTiPnJyZspnsHHjRigUCq0fLy8vG7bW8kz9b+DWrVuYNGkSQkJC4OnpiQceeAC7d++2UWstz5T77927d63fv0KhwMCBA23YYssz9b+B1NRUPPjgg/D29kZ4eDheffVV3Llzx3oNNHkzKCeWlpYmeHh4COvXrxdOnz4tvPTSS0LDhg2Fa9eu6Tz+yJEjwuuvvy58+umnQpMmTYRly5bZtsEWZur9P//888LKlSuF48ePC2fOnBFGjx4tKJVK4Y8//rBxyy3H1M/gwIEDQnp6uvDbb78JOTk5QmpqquDq6irs2bPHxi23DFPvv1pubq4QFhYm9OjRQ3j66adt01grMfUz2LBhg+Dv76+1F1713nlyZOr9l5eXCx07dhSefPJJ4eDBg0Jubq6QmZkpnDhxwsYttwxT77+wsFDrd3/q1CnB1dVV2LBhg20bbkGmfgZbtmwRPD09hS1btgi5ubnC3r17hZCQEOHVV1+1WhsZbkzQqVMnYdKkSeLjqqoqITQ0VFi4cGGd5zZr1kz24aY+9y8IgnD37l3Bz89P2LRpk7WaaHX1/QwEQRBiYmKEt956yxrNszpz7v/u3btC165dhY8++kgYNWqU7MONqZ/Bhg0bBKVSaaPWWZ+p9//BBx8ILVq0ECoqKmzVRKuq778By5YtE/z8/ITbt29bq4lWZ+pnMGnSJKFPnz5azyUlJQndunWzWhs5LGWkiooKZGVlIS4uTnzOxcUFcXFxOHz4sB1bZhuWuP+ysjJUVlZCpVJZq5lWVd/PQBAEZGRkIDs7Gz179rRmU63C3Pv/z3/+g6CgIIwdO9YWzbQqcz+D27dvo1mzZggPD8fTTz+N06dP26K5FmfO/X/11Vfo0qULJk2ahODgYLRp0wYLFixAVVWVrZptMZb4d3DdunV47rnn4Ovra61mWpU5n0HXrl2RlZUlDl1duHABu3fvxpNPPmm1dtp9V3C5KCgoQFVVlbhbebXg4GCcPXvWTq2yHUvc/5tvvonQ0FCtvxRyYu5noFarERYWhvLycri6umLVqlXo16+ftZtrcebc/8GDB7Fu3TqcOHHCBi20PnM+gwcffBDr169Hu3btoFar8e6776Jr1644ffo0mjZtaotmW4w593/hwgV8++23GD58OHbv3o2cnBy8/PLLqKysxOzZs23RbIup77+DR44cwalTp7Bu3TprNdHqzPkMnn/+eRQUFKB79+4QBAF3797FhAkT8O9//9tq7WS4IZtYtGgR0tLSkJmZKfvJlKby8/PDiRMncPv2bWRkZCApKQktWrRA79697d00qyopKcHIkSOxdu1aBAYG2rs5dtOlSxd06dJFfNy1a1e0bt0aH374IebNm2fHltmGRqNBUFAQ1qxZA1dXV3To0AFXr17FkiVLZBdu6mvdunVo27YtOnXqZO+m2FRmZiYWLFiAVatWoXPnzsjJycGUKVMwb948zJw50yrvyXBjpMDAQLi6uuLatWtaz1+7dg1NmjSxU6tspz73/+6772LRokXYv38/2rVrZ81mWpW5n4GLiwuio6MBAI888gjOnDmDhQsXyi7cmHr/58+fx8WLF5GQkCA+p9FoAABubm7Izs5GVFSUdRttYZb4d8Dd3R0xMTHIycmxRhOtypz7DwkJgbu7O1xdXcXnWrdujfz8fFRUVMDDw8Oqbbak+vz+S0tLkZaWhv/85z/WbKLVmfMZzJw5EyNHjsS4ceMAAG3btkVpaSnGjx+PGTNmwMXF8jNkOOfGSB4eHujQoQMyMjLE5zQaDTIyMrT+X5mjMvf+Fy9ejHnz5mHPnj3o2LGjLZpqNZb6b0Cj0aC8vNwaTbQqU++/VatWOHnyJE6cOCH+PPXUU3j88cdx4sQJhIeH27L5FmGJ/waqqqpw8uRJhISEWKuZVmPO/Xfr1g05OTlisAWAc+fOISQkRFbBBqjf7//zzz9HeXk5RowYYe1mWpU5n0FZWVmtAFMddgVrbW9ptanKDigtLU3w9PQUNm7cKPz222/C+PHjhYYNG4rLOkeOHClMnz5dPL68vFw4fvy4cPz4cSEkJER4/fXXhePHjwu///67vW6hXky9/0WLFgkeHh7C9u3btZZClpSU2OsW6s3Uz2DBggXCN998I5w/f1747bffhHfffVdwc3MT1q5da69bqBdT778mR1gtZepnMHfuXGHv3r3C+fPnhaysLOG5554TvLy8hNOnT9vrFurF1Pu/fPmy4OfnJyQmJgrZ2dnC119/LQQFBQlvv/22vW6hXsz9O9C9e3dh6NChtm6uVZj6GcyePVvw8/MTPv30U+HChQvCN998I0RFRQnPPvus1drIcGOi999/X4iIiBA8PDyETp06CT/++KP4Wq9evYRRo0aJj3NzcwUAtX569epl+4ZbiCn336xZM533P3v2bNs33IJM+QxmzJghREdHC15eXkKjRo2ELl26CGlpaXZoteWYcv81OUK4EQTTPoOpU6eKxwYHBwtPPvmkcOzYMTu02nJM/W/ghx9+EDp37ix4enoKLVq0EObPny/cvXvXxq22HFPv/+zZswIA4ZtvvrFxS63HlM+gsrJSmDNnjhAVFSV4eXkJ4eHhwssvvyzcvHnTau1TCIK1+oSIiIiIbI9zboiIiMihMNwQERGRQ2G4ISIiIofCcENEREQOheGGiIiIHArDDRERETkUhhsiIiJyKAw3RERWNHr0aAwePNjezSByKgw3RE5q9OjRUCgU4k9AQAD69++PX3/91d5Ns4j77636p3v37lZ7v4sXL0KhUODEiRNaz7/33nvYuHGj1d6XiGpjuCFyYv3790deXh7y8vKQkZEBNzc3DBo0yN7NspgNGzaI95eXl4evvvpK53GVlZVWa4NSqUTDhg2tdn0iqo3hhsiJeXp6okmTJmjSpAkeeeQRTJ8+HVeuXMGNGzfQp08fJCYmah1/48YNeHh4iDsCR0ZGYt68eRg2bBh8fX0RFhaGlStXap2TkpKCtm3bwtfXF+Hh4Xj55Zdx+/Zt8fVLly4hISEBjRo1gq+vLx5++GHs3r0bAHDz5k0MHz4cjRs3hre3N1q2bIkNGzYYfX8NGzYU769JkyZQqVRiD8u2bdvQq1cveHl5YcuWLSgsLMSwYcMQFhYGHx8ftG3bFp9++qnW9TQaDRYvXozo6Gh4enoiIiIC8+fPBwA0b94cABATEwOFQoHevXsDqD0sVV5ejsmTJyMoKAheXl7o3r07jh49Kr6emZkJhUKBjIwMdOzYET4+PujatSuys7ONvm8iZ8dwQ0QAgNu3b+OTTz5BdHQ0AgICMG7cOGzduhXl5eXiMZ988gnCwsLQp08f8bklS5agffv2OH78OKZPn44pU6Zg37594usuLi5Yvnw5Tp8+jU2bNuHbb7/FtGnTxNcnTZqE8vJyfPfddzh58iTeeecdNGjQAAAwc+ZM/Pbbb/jvf/+LM2fO4IMPPkBgYKBF7re6rWfOnEF8fDzu3LmDDh06YNeuXTh16hTGjx+PkSNH4siRI+I5ycnJWLRokdiurVu3Ijg4GADE4/bv34+8vDykp6frfN9p06Zhx44d2LRpE44dO4bo6GjEx8ejqKhI67gZM2Zg6dKl+Pnnn+Hm5oYXX3zRIvdN5BSstiUnEUnaqFGjBFdXV8HX11fw9fUVAAghISFCVlaWIAiC8NdffwmNGjUStm3bJp7Trl07Yc6cOeLjZs2aCf3799e67tChQ4UBAwbofd/PP/9cCAgIEB+3bdtW65r3S0hIEMaMGWPW/QEQvLy8xPvz9fUVvvjiCyE3N1cAIKSmptZ5jYEDBwqvvfaaIAiCUFxcLHh6egpr167VeWz1dY8fP671/P07od++fVtwd3cXtmzZIr5eUVEhhIaGCosXLxYEQRAOHDggABD2798vHrNr1y4BgPDXX3+Z8hEQOS323BA5sccffxwnTpzAiRMncOTIEcTHx2PAgAG4dOkSvLy8MHLkSKxfvx4AcOzYMZw6dQqjR4/WukaXLl1qPT5z5oz4eP/+/ejbty/CwsLg5+eHkSNHorCwEGVlZQCAyZMn4+2330a3bt0we/ZsrQnNEydORFpaGh555BFMmzYNP/zwg0n3t2zZMvH+Tpw4gX79+omvdezYUevYqqoqzJs3D23btoVKpUKDBg2wd+9eXL58GQBw5swZlJeXo2/fvia14X7nz59HZWUlunXrJj7n7u6OTp06aX1mANCuXTvxzyEhIQCA69evm/3eRM6E4YbIifn6+iI6OhrR0dF49NFH8dFHH6G0tBRr164FAIwbNw779u3DH3/8gQ0bNqBPnz5o1qyZ0de/ePEiBg0ahHbt2mHHjh3IysoS5+RUVFSI73HhwgWMHDkSJ0+eRMeOHfH+++8DgBi0Xn31Vfz555/o27cvXn/9daPfv0mTJuL9RUdHw9fXV+ve77dkyRK89957ePPNN3HgwAGcOHEC8fHxYju9vb2Nfl9LcHd3F/+sUCgA3JvzQ0R1Y7ghIpFCoYCLiwv++usvAEDbtm3RsWNHrF27Flu3btU57+PHH3+s9bh169YAgKysLGg0GixduhSPPfYYHnjgAfz555+1rhEeHo4JEyYgPT0dr732mhiuAKBx48YYNWoUPvnkE6SmpmLNmjWWvGXRoUOH8PTTT2PEiBFo3749WrRogXPnzomvt2zZEt7e3uJk6po8PDwA3OsB0icqKgoeHh44dOiQ+FxlZSWOHj2Khx56yEJ3QkRu9m4AEdlPeXk58vPzAdxbmbRixQrcvn0bCQkJ4jHjxo1DYmIifH198Y9//KPWNQ4dOoTFixdj8ODB2LdvHz7//HPs2rULABAdHY3Kykq8//77SEhIwKFDh7B69Wqt86dOnYoBAwbggQcewM2bN3HgwAExHM2aNQsdOnTAww8/jPLycnz99dfia5bWsmVLbN++HT/88AMaNWqElJQUXLt2TQwdXl5eePPNNzFt2jR4eHigW7duuHHjBk6fPo2xY8ciKCgI3t7e2LNnD5o2bQovLy8olUqt9/D19cXEiRPxxhtvQKVSISIiAosXL0ZZWRnGjh1rlfsickbsuSFyYnv27EFISAhCQkLQuXNnHD16FJ9//rm4jBkAhg0bBjc3NwwbNgxeXl61rvHaa6/h559/RkxMDN5++22kpKQgPj4eANC+fXukpKTgnXfeQZs2bbBlyxYsXLhQ6/yqqipMmjQJrVu3Rv/+/fHAAw9g1apVAO71hiQnJ6Ndu3bo2bMnXF1dkZaWZpXP4q233kJsbCzi4+PRu3dvNGnSpFZl4ZkzZ+K1117DrFmz0Lp1awwdOlScB+Pm5obly5fjww8/RGhoKJ5++mmd77No0SI888wzGDlyJGJjY5GTk4O9e/eiUaNGVrkvImekEARBsHcjiEi6Ll68iKioKBw9ehSxsbFar0VGRmLq1KmYOnWqfRpHRKQDh6WISKfKykoUFhbirbfewmOPPVYr2BARSRWHpYhIp0OHDiEkJARHjx6tNU/G3hYsWIAGDRro/BkwYIC9m0dEdsZhKSKSnaKioloVfat5e3sjLCzMxi0iIilhuCEiIiKHwmEpIiIicigMN0RERORQGG6IiIjIoTDcEBERkUNhuCEiIiKHwnBDREREDoXhhoiIiBwKww0RERE5lP8PXNQPIjpHwq8AAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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FC/HSSy/h7bffhlKphEqlgkKhEHvGKr3wwgvi/++77z6sXr0a999/P27cuIEmTZqYpR2GsEfLzqlUpYiO3gWFQgsA4hwtDhsSEZE5CIIgDgV+8cUXePTRR9GmTRs0bdoUo0ePRmFhIW7evFnrPo4cOYLo6Gi0a9cOTZs2RXh4OADg4sWLkte/LuzRIgQF5cHf/yyKijzg4VHEIIuIiMzm1KlT8PPzw/nz5zF48GC8/PLLePPNN+Hh4YHvvvsO48aNw+3btw1mxC8rK0NkZCQiIyOxefNmtGrVChcvXkRkZKQsLuhioEUA7vZsMcAiIiJz+vLLL3HixAn8+9//xpEjR6DVarF8+XIx3cPHH3+sU16pVKKiokJn2a+//orCwkIsWrQIvr6+AIDDhw+bpwFG4NChnVIqlSYtR0REVJvy8nKo1Wr8+eefOHr0KN566y0MHToUgwcPRmxsLDp27Ig7d+5gzZo1+P333/HBBx8gNTVVZx8dOnTAjRs3kJOTg2vXruHmzZto164dlEqluN3OnTuxYMECC7WyJvZo2akWLVogISGBmeGJiMgssrOz4e3tjUaNGqF58+bo3bs3Vq9ejbi4ODg4OKB3795YsWIFFi9ejOnTp+Ohhx5CcnIyYmNjxX2EhYXhpZdewvDhw1FYWCimd0hLS8OMGTOwevVqBAUFYdmyZRgyZIgFW/sPhSAIgqUrYa9KSkqgUqmg0Wjg7u5u6eqQTPFWSUQEALdu3UJ+fj78/Pzg4uJS7+0tnUfL2tT2etfn99viPVrr1q3D0qVLoVar0bt3b6xZswb9+vUzWH7btm2YNWsWzp8/j4CAACxevBhPPvmkuF4QBMyZMwfvvfceiouL8cADD+Cdd95BQEBAjX2Vl5cjJCQEx48fR15eHgIDAwEA+/fvx8qVK/Hjjz+ipKQEAQEBmDJlCp577jlx27S0NDFxWiVnZ2fcunXrHl8RkoK1Biv8YiQiU+FIhmVYNNDaunUrkpKSkJqaipCQEKSkpCAyMhKnT5/Wm831wIEDGDlyJJKTkzF48GBkZGQgJiYGR48eRY8ePQAAS5YswerVq5Geng4/Pz/MmjULkZGROHnyZI2IdOrUqfDx8cHx48drHKdXr154/fXX4enpiV27diE2NhYqlQqDBw8Wy7m7u+P06dPi88rLU0lerDlYqf6FaOielHK4soaI5E9u33H2wKKB1ooVKzBhwgSxZyg1NRW7d+/Gxo0bMW3atBrlV61ahUGDBmHKlCkAgAULFmDfvn1Yu3YtUlNTIQgCUlJSMHPmTAwdOhQA8P7778PT0xNZWVkYMWKEuK/PPvsMe/fuxfbt2/HZZ5/pHGfGjBk6zydNmoS9e/ciMzNTJ9DSlzSN5MdWgpXa7klZlbX23hER2SKLBVq3b9/GkSNHMH36dHGZg4MDIiIikJubq3eb3NxcJCUl6SyLjIxEVlYWACA/Px9qtRoRERHiepVKhZCQEOTm5oqBVkFBASZMmICsrCyDeTmq02g06Nq1q86yGzduoH379tBqtQgKCsJbb72F7t27G9xHeXk5ysvLxeclJSVGHZtMx9hgRW6MvSelNffeERHZIould7h27RoqKirg6emps9zT0xNqtVrvNmq1utbylf/WVkYQBIwZMwYvvfQS+vbta1RdP/74Yxw6dEhnTlbnzp2xceNG7NixAx9++CG0Wi3CwsLwxx9/GNxPcnIyVCqV+KjM90HmYShY0WiaWrhmdTP2npT6eu/y8zvUaKPce++IiGyFxSfDm9uaNWtQWlqq05NWm6+++gpjx47Fe++9p9NbFRoaitDQUPF5WFgYunbtivXr1xvM3zF9+nSdHrmSkhIGW2ZUW7Ai92StDbknpbX23hER2RKL9Wi1bNkSjo6OKCgo0FleUFBgcN6Tl5dXreUr/62tzJdffonc3Fw4OzujUaNG6NixIwCgb9++iIuL09nu66+/RnR0NFauXKmTx0MfJycn9OnTB2fPnjVYxtnZGe7u7joPMp/KYKUqa7mBdn3vSWnNvXdERLbEYoGWUqlEcHAwcnJyxGVarRY5OTk6PUVVhYaG6pQHgH379onl/fz84OXlpVOmpKQEBw8eFMusXr0ax48fx7Fjx3Ds2DHs2bMHwN0rIN98801xu/379yMqKgqLFy9GfHx8ne2pqKjAiRMn4O3tbeQrQOZm7TfQDgrKQ2JiCuLi0pCYmFJr75SxQ41ERCQtiw4dJiUlIS4uDn379kW/fv2QkpKCsrIycS5UbGws2rRpg+TkZAB3r/4LDw/H8uXLERUVhS1btuDw4cN49913Ady9CjAxMRELFy5EQECAmN7Bx8cHMTExAIB27drp1KFJkyYAAH9/f7Rt2xbA3eHCwYMHY9KkSXj66afF+V1KpRIeHnd/qObPn4/+/fujY8eOKC4uxtKlS3HhwgWMHz9e2heN7om13UC7+i2QDN2Tsnq5hgw1GotXNRIRGc+igdbw4cNx9epVzJ49G2q1GoGBgcjOzhYns1+8eFG8sSRwdx5URkYGZs6ciRkzZiAgIABZWVliDi3gbm6ssrIyxMfHo7i4GAMGDEB2dna9suimp6fj5s2bSE5OFoM8AAgPD8f+/fsBANevX8eECROgVqvRvHlzBAcH48CBA+jWrds9vipkag0NVuSgoQkGK3vvqs/RutfAklc1EpEc7d+/HwMHDsT169fRrFkzo7bp0KEDEhMTkZiYKGndeAseC+IteMzn3LlzuHnzpsH1jRs3hr+/vxlrJI3Lly+LPbxAZc6wmr138fHxDRrm1r//mjnJGrp/IjLsXm/BY0ljxoxBeno6XnzxxRo3ip44cSLefvttxMXFIS0trUH7lyLQsplb8BBJrbCwEB9++KH43FBwYAu9MObsveNVjURUH76+vtiyZQtWrlwJV1dXAHeDmYyMjBrTemwJAy2yeVWH3WoLDmwht5S57mVmbAJVueH8MiLLCQoKwrlz55CZmSneOzgzMxPt2rWDn5+fWK68vBxTpkzBli1bUFJSgr59+2LlypW4//77xTJ79uxBYmIiLl26hP79+9fIGgAA3333HaZPn47Dhw+jZcuW+Ne//oXk5GS4ublJ39gqGGiR3bDW4KC+zBEoWGNOMs4vI9L1xx/AmTNAQADwv2vBJPfCCy9g06ZNYqC1ceNGjB07Vpz/DNyda719+3akp6ejffv2WLJkCSIjI3H27Fl4eHjg0qVLeOqppzBx4kTEx8fj8OHDeO2113SOc+7cOQwaNAgLFy7Exo0bcfXqVSQkJCAhIQGbNm0yT2P/x2LpHYjMjSkPTMcac5Ixaz7RPzZsANq3Bx555O6/GzaY57jPP/88vvvuO1y4cAEXLlzA999/j+eff15cX1ZWhnfeeQdLly7FE088gW7duuG9996Dq6srNvyvku+88w78/f2xfPlydO7cGc899xzGjBmjc5zk5GQ899xzSExMREBAAMLCwrB69Wq8//77uHXrlnka+z/s0SK7IWXKA3sj1VWN5sL5ZWTP/vgDiI8HtP87V9JqgRdfBCIjpe/ZatWqFaKiopCWlgZBEBAVFYWWLVuK68+dO4c7d+7ggQceEJc5OTmhX79+OHXqFADg1KlTCAkJ0dlv9fybx48fx08//YTNmzeLywRBgFarRX5+fo17F0uJgRbZDWsPDuTG2nKSVZLTEDLnjJElnDnzT5BVqaICOHvWPEOIL7zwAhISEgAA69atk+QYN27cwIsvvohXX321xjpzT7xnoEV2xVqDA7mw5pxkleQyv4xzxshSAgIABwfdYMvREfjfHekkN2jQINy+fRsKhQKRkZE66/z9/aFUKvH999+jffv2AIA7d+7g0KFDYhqGrl27YufOnTrb/fDDDzrPg4KCcPLkSfE2e5bEQIvsjqHggOpmrqsapSSXIWR9c8b0pR3hnDEytbZtgXffvTtcWFFxN8hav958E+IdHR3FYUBHR0eddW5ubnj55ZcxZcoUeHh4oF27dliyZAlu3ryJcePGAQBeeuklLF++HFOmTMH48eNx5MiRGvm3Xn/9dfTv3x8JCQkYP3483NzccPLkSezbt8+oExxTYqBFNs/Y3hU598LIiZyDKGPIZQi5uLhY/H9tc8aKi4uZ/JVMbty4u3Oyzp6925NlriCrUm1JPhctWgStVovRo0ejtLQUffv2xeeff47mzZsDuDv0t337dvz73//GmjVr0K9fP7z11lt44YUXxH306tULX3/9Nd544w08+OCDEAQB/v7+GD58uORtq46Z4S2ImeHNR05zYeRUF3siddb8+jpx4gQyMzOh0TRFSkpijR62xMQUqFSleOqpp9CzZ0/J60PyZ82Z4a0RM8MT1YNcAhfOy7Ecuc4vk8ucMSKSBgMtIj2k6nXivBzLkev8MiencgACAEWVpQKcnPg3QGQLGGgRVVO910mqeyMylxMBwJ07ztANsgBAgTt3OGeQyBYw0CKqxhz3RpRTLid7Ya4Aur7kchUkEUmDgRaRAVIGQ5yXY37mCKDrM+Ts5OQEoO6rICvLEVXiNWzmYarXmYEWkQFSBkPsxbAcqQLo+l7ooFKpxGW1JdKtWo7sW2XQffPmTbi6ulq4Nrbv5s2bAO79ZIeBFpEBUgRDlbmT6urFYO4k6UgVQNf3Qge5XgVJ8uXo6IhmzZrhypUrAIDGjRtDoag+v4/ulSAIuHnzJq5cuYJmzZrVSKpaXwy0iAyQIrHl33//Lf4/KCgPrVurcelSO/j6XkTbtpf1liPTMkdvojEXOsj1KkjSTy7577y8vABADLZIOs2aNRNf73vBQIuoFqa+N2LVLujafow5L0c6UmeGr8/QJIMo6yCn/HcKhQLe3t5o3bo17ty5I+mx7JmTk9M992RVYqBFVAdT3huxcr5NXT/GnJcjLSlvLs4LHWyPsRdImDP/naOjo8kCAZIWAy2iasxxb0T+GFueVDcXl9OFDnIZ7rI1hubfEenDQIvIAuT0Y2wvzHVzcbnctFpOw122hImGqb4YaBFVY47b5Mjlx9iemHPyuZRDk8bi7Z5Mj4mGqSEYaBHVQsqzVzn8GNsbKXtu5Jyugb0wpsEhf2oIBlpEBkhx9irnH2O6N3JN18BeGNPhkD81BAMtIgOkOHuV648x6VffyeRyfN/YC2M6HPKnhmCgRWSAVGevcvwxpppsZTI5e2HuXdUe5tqG/NkTTfow0CIygGev9s1WJpPz7/jesSea7gUDLaJacMK6bWloXilrn0zOv+N7V98gijnMqBIDLaJqOGHdNjV0KNBaJ5Pz79hybGXYmUyDgRZRNRwmsE0NHQq01snk/Du2HDnesocsh4EWkR788bFt9RkKtObJ5Pw7Np17GQrkLXvsGwMtIrIr9R0K5GRyupehQGuf30f3joEWEdmVhgwFcjK5fWvosLNU8/s40d66MNAiIrti7FAgJ5OTPvXpoZJifh8n2lsfBlpEZFeMHQpsyGRy9jTYtvr2UEkxv89W8rvZEwZaRGR3jB0KrE9QxJ4G21ffHiqp5/dx/pd1YKBFRHZB6qFA9jTYvoYMO0t1yx5rze9mjyweaK1btw5Lly6FWq1G7969sWbNGvTr189g+W3btmHWrFk4f/48AgICsHjxYjz55JPiekEQMGfOHLz33nsoLi7GAw88gHfeeQcBAQE19lVeXo6QkBAcP34ceXl5CAwMFNf99NNPmDhxIg4dOoRWrVrhlVdewdSpU+tVFyKSD3PmlWJPg22Scti5vqw1vxtgf0PsFg20tm7diqSkJKSmpiIkJAQpKSmIjIzE6dOn0bp16xrlDxw4gJEjRyI5ORmDBw9GRkYGYmJicPToUfTo0QMAsGTJEqxevRrp6enw8/PDrFmzEBkZiZMnT8LFxUVnf1OnToWPjw+OHz+us7ykpASPP/44IiIikJqaihMnTuCFF15As2bNEB8fb3RdiEhezPHlzZ4G05Drj7EUw84NYa353exxiN2igdaKFSswYcIEjB07FgCQmpqK3bt3Y+PGjZg2bVqN8qtWrcKgQYMwZcoUAMCCBQuwb98+rF27FqmpqRAEASkpKZg5cyaGDh0KAHj//ffh6emJrKwsjBgxQtzXZ599hr1792L79u347LPPdI6zefNm3L59Gxs3boRSqUT37t1x7NgxrFixQgy06qoLyYtcv7TJ9lhzT4OU6vMZlNuPsRyvQLXW/G72OMRusUDr9u3bOHLkCKZPny4uc3BwQEREBHJzc/Vuk5ubi6SkJJ1lkZGRyMrKAgDk5+dDrVYjIiJCXK9SqRASEoLc3Fwx0CooKMCECROQlZWFxo0b6z3OQw89pPOhiYyMxOLFi3H9+nU0b968zrroU15ejvLycvF5SUmJwbJkOnL70ibbZq09DVKq72dQbj/Gcr2dkbXnd7OXIXaLBVrXrl1DRUUFPD09dZZ7enri119/1buNWq3WW16tVovrK5cZKiMIAsaMGYOXXnoJffv2xfnz5/Uex8/Pr8Y+Ktc1b968zrrok5ycjHnz5hlcT9KQ25c22TZr7WmQ0r18BuXyYyyXkzA59q41hD0NsVt8Mry5rVmzBqWlpTo9aeYyffp0nV6wkpIS+Pr6mr0e9kwuX9pkezQajfj/2noaNBoNvL29LVFFWajPZ9CefoyNJdfetfqypyF2iwVaLVu2hKOjIwoKCnSWFxQUwMvLS+82Xl5etZav/LegoEDni6ygoEC8ovDLL79Ebm4unJ2ddfbTt29fPPfcc0hPTzd4nKrHqKsu+jg7O9c4LpkPv7RJSoIg6FmqMLKcfajvZ9CefozrQ+5BlDHsaYjdoe4i0lAqlQgODkZOTo64TKvVIicnB6GhoXq3CQ0N1SkPAPv27RPL+/n5wcvLS6dMSUkJDh48KJZZvXo1jh8/jmPHjuHYsWPYs2cPgLtXQL755pvicb755hvcuXNH5zidO3dG8+bNjaoLyU9tX9pE96pZs2bi/48e7YOUlESkp8chJSURR4/20VvO3tT3M1j5Y1yVrf4Y25vKIfbK99eWh9gtOnSYlJSEuLg49O3bF/369UNKSgrKysrEqxBjY2PRpk0bJCcnAwAmTZqE8PBwLF++HFFRUdiyZQsOHz6Md999FwCgUCiQmJiIhQsXIiAgQEzv4OPjg5iYGABAu3btdOrQpEkTAIC/vz/atm0LABg1ahTmzZuHcePG4fXXX8fPP/+MVatWYeXKleJ2ddWF5MeezqDIcthzalh9P4Oc72bbrH0yv7EsGmgNHz4cV69exezZs6FWqxEYGIjs7GxxkvnFixfh4PDPBzIsLAwZGRmYOXMmZsyYgYCAAGRlZenkrZo6dSrKysoQHx+P4uJiDBgwANnZ2TVyaNVGpVJh7969mDhxIoKDg9GyZUvMnj1bTO1gbF1IXvilTebA4S7DGvIZtJcfY3thK5P568Pik+ETEhKQkJCgd93+/ftrLHv22Wfx7LPPGtyfQqHA/PnzMX/+fKOO36FDB71zJnr16oVvv/221m3rqgvJD7+0SWrsOa2dMZ9Be/wxthe2Mpm/PiweaBFJjV/aZE7sOa2pvp9Be/wxtif29r4x0CKbxy9tMjf2nOpqyGeQn0eyFQy0yC5Y85c2bx9kHW7evKnz3FCvTfVy9oJ/o2SvGGgRyRhvH2Q9qt/Oy1D2c323/SKSEk/WLIuBFpGM8fZB1ol3ICC54Mma5THQIrIS/PG2DsyjRXLCkzXLY6BFZAX44209mEeL5Iona5bBQIvICvDH23owj5ZpcF6RafFkzXIYaJFVsrcvYf54Ww/m0bp3nFdkejxZsxwGWmR17PFLmD/e1oV5tO4N5xWZHk/WLIeBFlkdY79cbe1LmD/e907KnlDegUAanFdkGtZ8smbtIxgMtOyYtf/xVjJ0tmsL+ONtOlL3hPIOBKbHeUWmZY0na7YwgsFAy07Zwh8vYPtnu/zxNh0OR1kfziu6d9Z+smYLn1sGWnbKFv547eVsl0GU6UkRoNvKyYuccF7RvbOlkzVrPbFmoEVW+8fLs11qCKkCdFs4eZEba55XJCfWEETVxZpPrBlo2SmNRvO/f2v/49VoNPD29rZkVQ3i2S41hDkCdGs9eZEja5xXRKZnzSfWDnUXIVt0584dALX/8VYtJ0eVZ7sKhRYAeLZLRqkM0KsyZYBu6ORFo2lqkv3bA33zivz8LtT4bMt1XhGZntSfWymxR8vOWWOvUNUv19rOdvklTPpIPRxlzWfecmFL84rINKx5GJmBlp1TqUrRq9dPOH68NwAFAAG9ev0k6z9efgnTvZJyOMoaT14aQur0MPz8UnXWOozMQMvOaTRN8dNPvXA3yAIABX76qRceeeRLWf8R80uY6stcl7lb85m3sXiFJZmLtaenABho2a1Gje6+9XUNc1SWI7J25uwJtdYzb2PxCksyF1sYweCvqJ1q1qwZgLqHOSrLEdkCKb+MbeHMuyF4hSVJrb6fW7nd9YSBlp2zh2EOInOwhTPv+rLm3EZkm6oPaxvqbTXnsDYDLTvFK/eITM+Wgihj8ApLkpuqJzq19baac1ibgZadssezbyIyLXu5wpKsj5x6Wxlo2TEGUUR0L+oz9UBu82bItsmpt5WBFhERNZgxV1gyHQSZm5x6W3kLHiIiqpf63iJHXzqI/PwONW5LxHQQZCpyukUbe7SIiKhe7mWOJ9NBkLnIJZ8dAy0iIqq3hgzxyWmCMtkHQ/nszIlDh0REZBa1TVAmMgVjUxKZM3URe7SIiMgs5DRBmWyTHFMXMdAiIiKz4J0oyBzkduUqAy0iPZjzh0gacpmgTGQuDLSIqmHOHyLTstcbbhMBDLSIajA2lw9z/hAZR47zZojMhYEWUR0M3f2diIzHIMp2capF7RhoEdWCyRWJiAzjVIu6WTyP1rp169ChQwe4uLggJCQEP/74Y63lt23bhi5dusDFxQU9e/bEnj17dNYLgoDZs2fD29sbrq6uiIiIwJkzZ3TKDBkyBO3atYOLiwu8vb0xevRo/PXXX+L6uXPnQqFQ1Hi4ubmJZdLS0mqsd3FxMcErQnJhKLli9duGEFUqLCzE5cuXDT4KCwstXUUik+Ltlepm0R6trVu3IikpCampqQgJCUFKSgoiIyNx+vRptG7dukb5AwcOYOTIkUhOTsbgwYORkZGBmJgYHD16FD169AAALFmyBKtXr0Z6ejr8/Pwwa9YsREZG4uTJk2IgNHDgQMyYMQPe3t74888/MXnyZDzzzDM4cOAAAGDy5Ml46aWXdI796KOP4v7779dZ5u7ujtOnT4vPFQqFSV8fsiw53f2dDJPLsAXP7MnecQRAP4sGWitWrMCECRMwduxYAEBqaip2796NjRs3Ytq0aTXKr1q1CoMGDcKUKVMAAAsWLMC+ffuwdu1apKamQhAEpKSkYObMmRg6dCgA4P3334enpyeysrIwYsQIAMC///1vcZ/t27fHtGnTEBMTgzt37sDJyQlNmjRBkyZNxDLHjx/HyZMnkZqaqlMfhUIBLy8v074oJBtMrih/cgpu9J3Z65vbZ89n9mS7eHslwyw2dHj79m0cOXIEERER/1TGwQERERHIzc3Vu01ubq5OeQCIjIwUy+fn50OtVuuUUalUCAkJMbjPoqIibN68GWFhYXByctJb5j//+Q86deqEBx98UGf5jRs30L59e/j6+mLo0KH45Zdf6m44WQ053f2d9JPrsMXRo32QkpKI9PQ4pKQk4ujRPmY9PpG58fZKhlmsR+vatWuoqKiAp6enznJPT0/8+uuverdRq9V6y6vVanF95TJDZSq9/vrrWLt2LW7evIn+/ftj165deo9569YtbN68uUYPW+fOnbFx40b06tULGo0Gy5YtQ1hYGH755Re0bdtW777Ky8tRXl4uPi8pKdFbjiyrai6f2pIrMuePvMhl2IJn9mSPOAJgmN1edThlyhSMGzcOFy5cwLx58xAbG4tdu3bVmGf1ySefoLS0FHFxcTrLQ0NDERoaKj4PCwtD165dsX79eixYsEDvMZOTkzFv3jzTN4ZMijl/rI+cghvO7SN7xNsrGWaxQKtly5ZwdHREQUGBzvKCggKD8568vLxqLV/5b0FBAby9vXXKBAYG1jh+y5Yt0alTJ3Tt2hW+vr744YcfdIIn4O6w4eDBg2v0klXn5OSEPn364OzZswbLTJ8+HUlJSeLzkpIS+Pr61rpfsgwGUdZFTsENz+zJ2jX0AhPeXkk/iwVaSqUSwcHByMnJQUxMDABAq9UiJycHCQkJercJDQ1FTk4OEhMTxWX79u0TgyM/Pz94eXkhJydHDKxKSkpw8OBBvPzyywbrotXenYNTdVgPuDvn66uvvsLOnTvrbE9FRQVOnDiBJ5980mAZZ2dnODs717kvIqofOQU3PLMna1bfC0x4e6W6WXToMCkpCXFxcejbty/69euHlJQUlJWViVchxsbGok2bNkhOTgYATJo0CeHh4Vi+fDmioqKwZcsWHD58GO+++y6Au1cBJiYmYuHChQgICBDTO/j4+IjB3MGDB3Ho0CEMGDAAzZs3x7lz5zBr1iz4+/vX6M3auHEjvL298cQTT9So+/z589G/f3907NgRxcXFWLp0KS5cuIDx48dL+IoRkT5yC254Zk/Wqr5Xz3KqRd0sGmgNHz4cV69exezZs6FWqxEYGIjs7GxxmO7ixYtwcPjnDDUsLAwZGRmYOXMmZsyYgYCAAGRlZYk5tABg6tSpKCsrQ3x8PIqLizFgwABkZ2eLObQaN26MzMxMzJkzB2VlZfD29sagQYMwc+ZMnd4mrVaLtLQ0jBkzBo6OjjXqfv36dUyYMAFqtRrNmzdHcHAwDhw4gG7dukn1chFRLSwd3PDMnmyNsReY2HMQZQyFIAiCpSthr0pKSqBSqaDRaODu7m7p6hBZHTnl0aqsD8/syZpdvnwZ7777LjSapkhJSawxHJ+YmAKVqhTx8fE6c6HtTX1+v+32qkMisn5yG7aQWxDFwI/qS6PRAKj7AhONRmPXgVZ9MNAiIqvGQEE/ufX2kXW4c+cOgLsXmABa6OY1/+cCk8pyDWFvJwAMtIiIbBBvCUT3TjevpClu51v9BMDQ36UtnQAw0CIisnFyyZpP1qOoqAWqB1qmyE1XNbCv7e/Slk4ALHavQyIikp6hrPnV7wdJBACNGt3tf6nMTVdV1dx0leUayp7+LhloERHZMN7sl+qjWbNmAP7JTVcZbFXPTVdZrqHs6e+SQ4dERDZMTlnzybpImZvOnv4uGWgREdkwuWXNt1b2dqVcJUOJd02xX3v5u2SgRWRm9vqFTZZj6az51s6eUmUYe+cCU9zhwF7+LhloEZmRPX1hk2XxlkCmY0+pMsydBFiqHjM5YaBFZEb29IVNliW3rPm2wh5SZUj5N2HOHjO5YKBFZCH28IVNlsUgyrQMpSTw9z9r870ypmKPJwAMtIgsgF/YRNanrvv/kXFsKYgyBgMtG8JJ1taDX9hE1seeUhKQ6TDQshGcZF07uQWh/MImsj72lJKATIeBlo3gJGvD5BiE8gubyDrZS0oCMh0GWjaIk6x1GRtcmjsI5Rc2kXVgqgy6Fwy0bAwnWdfNUG+fOfALm8j62OOVcmQ6DLRsDCdZ187SvX38wiayTvxMUkMx0LIxnGRtmFx6+6T+wpbbxH8iInvGQMvGcJK1YfXp7bPWYEWOE/+JiOwZAy0bxEnW+hnb22fNwQqvPiUikhcGWjaCk6zrZmxvn60EK5aej0ZERAy0bAYnWRtWNbisrbdPXxAqh2ClIcOYcpmPRkRk7xhokc1raBAqh2ClocOYvPqUiEgeGGjZCGueV2QODWmzHIKVhg5j8upTIiJ5YKBlI2xlXpGcyC1Yqc8wJq8+JSKSBwZaNkgO84psgZyClYYMY/LqUyIiy2OgZWPkMK/IlsglWDF2GJNXnxIRyQsDLRsjh3lF1k6OwYqxw5i8+pSISF4YaNkYuc0rskZyDFbqM4zJIIqISD4YaNkYOc0rsmZyDFbkMoxJRJZlrbcIs1cMtGwQf5BthxyHMYnIcqqn8jF0hbm9pvKRIwZaNoI/yLZJjsOYRGQ5Vb8LarvCnKl85IOBlo3gD7Lt4ntGRNXxCnPrwUDLhvAHmYjIPvAKc+vhUHcRIiIikpPKK8yr4hXm8sRAi4iIyMpUXmFeGWzxCnP54tAhERGRFeIV5tbB4j1a69atQ4cOHeDi4oKQkBD8+OOPtZbftm0bunTpAhcXF/Ts2RN79uzRWS8IAmbPng1vb2+4uroiIiICZ86c0SkzZMgQtGvXDi4uLvD29sbo0aPx119/ievPnz8PhUJR4/HDDz/Uqy5EJL3CwkJcvnzZ4KOwsNDSVSSSjEpVCj+/CwyyZMyiPVpbt25FUlISUlNTERISgpSUFERGRuL06dNo3bp1jfIHDhzAyJEjkZycjMGDByMjIwMxMTE4evQoevToAQBYsmQJVq9ejfT0dPj5+WHWrFmIjIzEyZMn4eLiAgAYOHAgZsyYAW9vb/z555+YPHkynnnmGRw4cEDneF988QW6d+8uPq862dyYuhCRtKrnFDLEXnMKMbGl7TE2RQ9T+ciHQhAEwVIHDwkJwf333y9+UWq1Wvj6+uKVV17BtGnTapQfPnw4ysrKsGvXLnFZ//79ERgYiNTUVAiCAB8fH7z22muYPHkyAECj0cDT0xNpaWkYMWKE3nrs3LkTMTExKC8vh5OTE86fPw8/Pz/k5eUhMDBQ7zZ11cUYJSUlUKlU0Gg0cHd3N2obIvrH5cuX8e6774rPDSVvjI+Ph7e3tyWqaDEMQm0XA2jLq8/vt8V6tG7fvo0jR45g+vTp4jIHBwdEREQgNzdX7za5ublISkrSWRYZGYmsrCwAQH5+PtRqNSIiIsT1KpUKISEhyM3N1RtoFRUVYfPmzQgLC4OTk5POuiFDhuDWrVvo1KkTpk6diiFDhhhdF33Ky8tRXl4uPi8pKTFYlojqp7bkjfao+g+xoSCUiS2tD4Mo62KxOVrXrl1DRUUFPD09dZZ7enpCrVbr3UatVtdavvJfY/b5+uuvw83NDS1atMDFixexY8cOcV2TJk2wfPlybNu2Dbt378aAAQMQExODnTt3Gl0XfZKTk6FSqcSHr6+vwbJEZDxDyRs1mqYWrpk8HD3aBykpiUhPj0NKSiKOHu1j6SoR2Q2TBVq3bt3CsmXLTLU7yU2ZMgV5eXnYu3cvHB0dERsbi8pR1JYtWyIpKUkc2ly0aBGef/55LF269J6OOX36dGg0GvFx6dIlUzSFyO7VlrzR3jEIJbKsegVaV69exa5du7B3715UVFQAAO7cuYNVq1ahQ4cOWLRokdH7atmyJRwdHVFQUKCzvKCgAF5eXnq38fLyqrV85b/G7LNly5bo1KkTHnvsMWzZsgV79uypcVVhVSEhITh79qzRddHH2dkZ7u7uOg8iundM3mgYg1AiyzI60Pruu+8QEBCAIUOG4IknnkBYWBhOnjyJ7t27Y/369Zg7d269emiUSiWCg4ORk5MjLtNqtcjJyUFoaKjebUJDQ3XKA8C+ffvE8n5+fvDy8tIpU1JSgoMHDxrcZ+VxAejMn6ru2LFjOpNp66oLEZkPkzcaxiCUyLKMngw/c+ZMPPnkk5gxYwbS09OxfPly/Otf/8Jbb72FZ555pkEHT0pKQlxcHPr27Yt+/fohJSUFZWVlGDt2LAAgNjYWbdq0QXJyMgBg0qRJCA8Px/LlyxEVFYUtW7bg8OHD4lVHCoUCiYmJWLhwIQICAsT0Dj4+PoiJiQEAHDx4EIcOHcKAAQPQvHlznDt3DrNmzYK/v78YJKWnp0OpVKJPn7vzGDIzM7Fx40b85z//EeteV12IyLzkkLxRjleDVQah1S8UYBBKZB5GB1onTpzA22+/jW7dumH+/PlYsWIFlixZgqFDhzb44MOHD8fVq1cxe/ZsqNVqBAYGIjs7W5xkfvHiRTg4/NPpFhYWhoyMDMycORMzZsxAQEAAsrKydPJWTZ06FWVlZYiPj0dxcTEGDBiA7OxsMYdW48aNkZmZiTlz5qCsrAze3t4YNGgQZs6cCWdnZ3E/CxYswIULF9CoUSN06dIFW7du1QkojakLEUmreq4glapUbwBhjpxCck6nIIcglMheGZ1Hy8HBAWq1Wkwk2rRpUxw7dgz+/v6SVtCWMY8W0b2TSy+S3HJ6yTnwI7J2kuXROnnypJi+QBAEnD59GmVlZTplevXqVc/qEhE1nByDBDnk9GrRogUSEhJkEYQS2bN6BVqPPvooqnaADR48WGe9QqEQr0YkIrJHhtIp+PufNfuQHYMoIsszOtDKz8+vs0xpKcf9ici+1ZZOgXOjiOyP0YFW+/bt9S4vLS3FRx99hA0bNuDw4cPs0SIiu1aZTqFqsMV0CkT2q8GZ4b/55hvExcXB29sby5Ytw8CBA2tN+ElEZA+Y04uIqqrXHC21Wo20tDRs2LABJSUlGDZsGMrLy5GVlYVu3bpJVUciIqvCdApEVMnoQCs6OhrffPMNoqKikJKSgkGDBsHR0RGpqalS1o/I5sglHQGZlpxyehGRfBgdaH322Wd49dVX8fLLLyMgIEDKOhHZLOY2sl1Mp0BE+hgdaH333XfYsGEDgoOD0bVrV4wePRojRoyQsm5ENqf6j7ChpJa1/ViTfDGIIqLqjA60+vfvj/79+yMlJQVbt27Fxo0bkZSUBK1Wi3379sHX1xdNmzaVsq5EIlsYfpNDUksiIpJWvSbDA4CbmxteeOEFvPDCCzh9+jQ2bNiARYsWYdq0aXjsscewc+dOKepJJLKF4Tc5JbUkIiLpNDi9AwB07twZS5YswR9//IGPPvrIVHUiqpW+4bf8/A7QaJrWWk5OaktqSUREtqPePVr6ODo6IiYmBjExMabYHZHRrHX4jUktiYjsg0kCLbJ9cpwTZc3Db5VJLasHiXKvNxER1Q8DLaqTXOdEWfs95ZjUkojI9jHQojrJNSWBNQ6/MaklEZF9YaBF9SKnOVHWOPzGpJZERPaFgRYZTY5zoqxx+I1BFBGR/WCgRUaTy5woDr8REZG1YKBFRpPLnChrH36T4xWcREQkDQZaZLT6zImSOpiw1kDEHFdwMpAjIpIPBlpUL8bMiZJrOgg5kPoKTr72RETywkCL6lTfOVFyTQchN1JcwWnsa2rvrz0Rkbkw0KI63cucKDmlg5ATc13BaSjIJevEYWEi68NAi4zSkC9vOaaDkAtzXMHJINe2cFiYyDo51F2EqGFqCybsXeUVnFWZ8gpOQ0GuRtPUJPsn89M3JJ+f36HGe8phYSJ5YY8WSUYu6SDkSOqs9nLJeUbSYG8lkfVgoEWSscZb5JiTlFntGeTaLg7JE1kXBlokKWu8RY6UzJXVnkGudWjI5Hb2VhJZFwZaZHK8RY5hUme1r/qa1hbk2uNrLzcNndzO3koi68JAi0zO2m+RIzUp283X3no0NN8ceyuJrAsDLZIEf8gth6+99anv5HYOyRNZD6Z3ICKyIGNTcegbkvfzu1AjyOKwMJG8sEeLiMiCjJ3czmFhIuvEQIuIyILqM7mdQRSR9eHQIRGRBVVObq+8UwAntxPZFvZoERFZGCe3E9kuBlpERBbAfHNE9sHiQ4fr1q1Dhw4d4OLigpCQEPz444+1lt+2bRu6dOkCFxcX9OzZE3v27NFZLwgCZs+eDW9vb7i6uiIiIgJnzpzRKTNkyBC0a9cOLi4u8Pb2xujRo/HXX3+J6/fv34+hQ4fC29sbbm5uCAwMxObNm3X2kZaWBoVCofNwcXG5x1eDiOxF5eT2+Ph4g4/qyUqJyPpYNNDaunUrkpKSMGfOHBw9ehS9e/dGZGQkrly5orf8gQMHMHLkSIwbNw55eXmIiYlBTEwMfv75Z7HMkiVLsHr1aqSmpuLgwYNwc3NDZGQkbt26JZYZOHAgPv74Y5w+fRrbt2/HuXPn8Mwzz+gcp1evXti+fTt++uknjB07FrGxsdi1a5dOfdzd3XH58mXxceHCBRO/QkRky1q0aAFvb2+DDwZZRNZPIQiCYKmDh4SE4P777xdvQ6HVauHr64tXXnkF06ZNq1F++PDhKCsr0wl4+vfvj8DAQKSmpkIQBPj4+OC1117D5MmTAQAajQaenp5IS0vDiBEj9NZj586diImJQXl5OZycnPSWiYqKgqenJzZu3Ajgbo9WYmIiiouLG9z+kpISqFQqaDQauLu7N3g/REREZD71+f22WI/W7du3ceTIEURERPxTGQcHREREIDc3V+82ubm5OuUBIDIyUiyfn58PtVqtU0alUiEkJMTgPouKirB582aEhYUZDLKAuwGbh4eHzrIbN26gffv28PX1xdChQ/HLL7/U2uby8nKUlJToPIiIiMh2WSzQunbtGioqKuDp6amz3NPTE2q1Wu82arW61vKV/xqzz9dffx1ubm5o0aIFLl68iB07dhis68cff4xDhw5h7Nix4rLOnTtj48aN2LFjBz788ENotVqEhYXhjz/+MLif5ORkqFQq8eHr62uwLBEREVk/i0+Gt5QpU6YgLy8Pe/fuhaOjI2JjY6FvFPWrr77C2LFj8d5776F79+7i8tDQUMTGxiIwMBDh4eHIzMxEq1atsH79eoPHnD59OjQajfi4dOmSJG0jIiKyV4WFhTrzp6s/CgsLzVofi6V3aNmyJRwdHVFQUKCzvKCgAF5eXnq38fLyqrV85b8FBQXw9vbWKRMYGFjj+C1btkSnTp3QtWtX+Pr64ocffkBoaKhY5uuvv0Z0dDRWrlyJ2NjYWtvj5OSEPn364OzZswbLODs7w9nZudb9EBERUcMUFhaK875rY84rei3Wo6VUKhEcHIycnBxxmVarRU5Ojk6wU1VoaKhOeQDYt2+fWN7Pzw9eXl46ZUpKSnDw4EGD+6w8LnB3DlWl/fv3IyoqCosXL0Z8fHyd7amoqMCJEyd0AjwiIiIyn+r3AtVomiI/v0ONm7TXds9QU7NowtKkpCTExcWhb9++6NevH1JSUlBWVibOhYqNjUWbNm2QnJwMAJg0aRLCw8OxfPlyREVFYcuWLTh8+DDeffddAIBCoUBiYiIWLlyIgIAA+Pn5YdasWfDx8UFMTAwA4ODBgzh06BAGDBiA5s2b49y5c5g1axb8/f3FYOyrr77C4MGDMWnSJDz99NPi/C6lUilOiJ8/fz769++Pjh07ori4GEuXLsWFCxcwfvx4c76EREREpMfRo33w6aeDIQgO4q2tgoLyzF4PiwZaw4cPx9WrVzF79myo1WoEBgYiOztbnMx+8eJFODj80+kWFhaGjIwMzJw5EzNmzEBAQACysrLQo0cPsczUqVNRVlaG+Ph4FBcXY8CAAcjOzhaTiTZu3BiZmZmYM2cOysrK4O3tjUGDBmHmzJnisF56ejpu3ryJ5ORkMcgDgPDwcOzfvx8AcP36dUyYMAFqtRrNmzdHcHAwDhw4gG7dukn9shEREVEtNJqmYpAFAILggE8/HQx//7Nmv8WVRfNo2Tvm0SIiIjKdy5cv491330V+fgekp8fVWB8XlwY/vwuIj4+/p6k+VpFHi4iIiEgKHh6FUCi0OssUCi08PIrMXhcGWkRERGRTVKpSREfvEoOtyjla5h42BCw8R4uIiIhICkFBefD3P4uiIg94eBRZJMgCGGgRERGRjVAqlTrPVapSvQFW9XJSYqBFRERENqFFixZISEioNU+WUqk0W7JSgIEWERER2RBzBlHG4GR4IiIiIokw0CIiIiKSCAMtIiIiIokw0CIiIiKSCAMtIiIiIokw0CIiIiKSCAMtIiIiIokwjxbZhcLCQlklsCMiIvvAQItsXmFhIdauXVtnuYSEBAZbRERkUhw6JJtXvSdLo2mK/PwO0Gia1lqOiIjoXrFHi+zK0aN98OmngyEIDlAotIiO3oWgoDxLV4uIiGwUe7TIbmg0TcUgCwAEwQGffjq4Rs8WERGRqTDQIrtRVNRCDLIqCYIDioo8LFQjIiKydQy0yG54eBRCodDqLFMotPDwKLJQjYiIyNYx0CK7oVKVIjp6lxhsVc7RUqlKLVwzIiKyVZwMT3YlKCgP/v5nUVTkAQ+PIgZZREQkKQZaZPOUSqXOc5WqVG+AVb0cERHRvWKgRTavRYsWSEhIYGZ4IiIyOwZaZBcYRBERkSVwMjwRERGRRBhoEREREUmEgRYRERGRRBhoEREREUmEgRYRERGRRBhoEREREUmE6R2IiEyksLCQ+dqISAcDLSIiEygsLMTatWvrLJeQkMBgi8iOcOiQiMgEqvdkaTRNkZ/fARpN01rLEZFtY48WEZGJHT3aB59+OhiC4ACFQovo6F0ICsqzdLWIyALYo0VEZEIaTVMxyAIAQXDAp58OrtGzRUT2gYEWEZEJFRW1EIOsSoLggKIiDwvViIgsiYEWEZEJeXgUQqHQ6ixTKLTw8CiyUI2IyJIYaBERmZBKVYro6F1isFU5R0ulKrVwzYjIEiweaK1btw4dOnSAi4sLQkJC8OOPP9Zaftu2bejSpQtcXFzQs2dP7NmzR2e9IAiYPXs2vL294erqioiICJw5c0anzJAhQ9CuXTu4uLjA29sbo0ePxl9//aVT5qeffsKDDz4IFxcX+Pr6YsmSJfWuCxHZp6CgPCQmpiAuLg2JiSmcCE9kxywaaG3duhVJSUmYM2cOjh49it69eyMyMhJXrlzRW/7AgQMYOXIkxo0bh7y8PMTExCAmJgY///yzWGbJkiVYvXo1UlNTcfDgQbi5uSEyMhK3bt0SywwcOBAff/wxTp8+je3bt+PcuXN45plnxPUlJSV4/PHH0b59exw5cgRLly7F3Llz8e6779arLkRkP5RKpc5zlaoUfn4XavRkVS9HRLZNIQiCYKmDh4SE4P777xeT/Gm1Wvj6+uKVV17BtGnTapQfPnw4ysrKsGvXLnFZ//79ERgYiNTUVAiCAB8fH7z22muYPHkyAECj0cDT0xNpaWkYMWKE3nrs3LkTMTExKC8vh5OTE9555x288cYbUKvV4pfitGnTkJWVhV9//dWouhijpKQEKpUKGo0G7u7uRm1DRPIldWZ4Zp4nkof6/H5bLI/W7du3ceTIEUyfPl1c5uDggIiICOTm5urdJjc3F0lJSTrLIiMjkZWVBQDIz8+HWq1GRESEuF6lUiEkJAS5ubl6A62ioiJs3rwZYWFhcHJyEo/z0EMP6Zx5RkZGYvHixbh+/TqaN29eZ130KS8vR3l5ufi8pKTEYFkisj5SBjnMPE9knSw2dHjt2jVUVFTA09NTZ7mnpyfUarXebdRqda3lK/81Zp+vv/463Nzc0KJFC1y8eBE7duyo8zhVj1FXXfRJTk6GSqUSH76+vgbLEhFVZWxGeWaeJ5IXi0+Gt5QpU6YgLy8Pe/fuhaOjI2JjYyH1KOr06dOh0WjEx6VLlyQ9HhHZLkO3+CEiebHY0GHLli3h6OiIgoICneUFBQXw8vLSu42Xl1et5Sv/LSgogLe3t06ZwMDAGsdv2bIlOnXqhK5du8LX1xc//PADQkNDDR6n6jHqqos+zs7OcHZ2NrieiMgYvMUPkfWwWI+WUqlEcHAwcnJyxGVarRY5OTkIDQ3Vu01oaKhOeQDYt2+fWN7Pzw9eXl46ZUpKSnDw4EGD+6w8LgBx/lRoaCi++eYb3LlzR+c4nTt3RvPmzY2qCxGRFHiLHyLrYtGhw6SkJLz33ntIT0/HqVOn8PLLL6OsrAxjx44FAMTGxupMlp80aRKys7OxfPly/Prrr5g7dy4OHz6MhIQEAIBCoUBiYiIWLlyInTt34sSJE4iNjYWPjw9iYmIAAAcPHsTatWtx7NgxXLhwAV9++SVGjhwJf39/MUgaNWoUlEolxo0bh19++QVbt27FqlWrdCa/11UXIiIp8BY/RNbFYkOHwN0UCVevXsXs2bOhVqsRGBiI7OxscZL5xYsX4eDwzxdKWFgYMjIyMHPmTMyYMQMBAQHIyspCjx49xDJTp05FWVkZ4uPjUVxcjAEDBiA7OxsuLi4AgMaNGyMzMxNz5sxBWVkZvL29MWjQIMycOVMc1lOpVNi7dy8mTpyI4OBgtGzZErNnz0Z8fHy96kJEZGqVt/ipGmzxFj9E8mXRPFr2jnm0iGyLlHmuLl++LCZNrm2OVnx8vM4cVSIyPavIo0VEZEukznNVNa9fUFAe/P3PoqjIAx4eRTrZ55l5nkheGGgREZlA9Z4sjaYpiopawMOjUCcQamieqxYtWiAhIYGZ4YmsDAMtIiITkyr9AoMoIutjtwlLiYikwPQLRFQVAy0iIhNi+gUiqoqBFhGRCVWmX6iK6ReI7BcDLSIiE1KpShEdvUsMtirnaFWdEE9E9oOT4YmITKy29AtEZF8YaBERmUD1/FUqVaneAIt5rojsCwMtIiITYJ4rItKHgRYRkYkwiCKi6jgZnoiIiEgiDLSIiIiIJMJAi4iIiEgiDLSIiIiIJMJAi4iIiEgiDLSIiIiIJMJAi4iIiEgiDLSIiIiIJMJAi4iIiEgiDLSIiIiIJMJb8JBVKiws5D3liIhI9hhokdUpLCzE2rVrxecaTVMUFbWAh0chVKpScXlCQgKDLSIisigGWmR1qvZkHT3aB59+OhiC4ACFQovo6F0ICsqrUY6IiMgSOEeLrJZG01QMsgBAEBzw6aeDodE0tXDNiIiI7mKPFlmtoqIWYpBVSRAcUFTkoTOEaG84f42obvyckLkw0CKr5eFRCIVCqxNsKRRaeHgUWbBWlsX5a0R1q/45MYSfEzIFBlpktVSqUkRH76oxR8uee7M4f42obsb+/fNzQqbAQIusWlBQHvz9z6KoyAMeHkV2HWRVZWj+mr//Wb5GRNUY6vklMgUGWmT1VKpSfjlWw/lrRMapreeXyBR41SFZHaVSadJytqhy/lpV9j5/jag6XrlM5sAeLbI6LVq0QEJCAq8YqgXnrxHVjT2/ZA4MtMgq2XMQZSzOXyOqHa9cJnPg0CGRDVOpSuHnd4FBFpEelT2/lcPs7PklKbBHi8iGcP4aUd2q/v3X1vPLzwmZgkIQBMHSlbBXJSUlUKlU0Gg0cHd3t3R1yEYw4zVR3fg5oXtRn99v9mgR2Rj+OBDVjZ8TMhfO0SIiIiKSiMUDrXXr1qFDhw5wcXFBSEgIfvzxx1rLb9u2DV26dIGLiwt69uyJPXv26KwXBAGzZ8+Gt7c3XF1dERERgTNnzojrz58/j3HjxsHPzw+urq7w9/fHnDlzdLqQ586dC4VCUePh5uYmlklLS6ux3sXFxUSvChEREdkCiwZaW7duRVJSEubMmYOjR4+id+/eiIyMxJUrV/SWP3DgAEaOHIlx48YhLy8PMTExiImJwc8//yyWWbJkCVavXo3U1FQcPHgQbm5uiIyMxK1btwAAv/76K7RaLdavX49ffvkFK1euRGpqKmbMmCHuY/Lkybh8+bLOo1u3bnj22Wd16uPu7q5T5sKFCxK8SkRERGS1BAvq16+fMHHiRPF5RUWF4OPjIyQnJ+stP2zYMCEqKkpnWUhIiPDiiy8KgiAIWq1W8PLyEpYuXSquLy4uFpydnYWPPvrIYD2WLFki+Pn5GVx/7NgxAYDwzTffiMs2bdokqFSqWttXF41GIwAQNBrNPe2HiIiIzKc+v98W69G6ffs2jhw5goiICHGZg4MDIiIikJubq3eb3NxcnfIAEBkZKZbPz8+HWq3WKaNSqRASEmJwnwCg0Wjg4eFhcP1//vMfdOrUCQ8++KDO8hs3bqB9+/bw9fXF0KFD8csvvxhuMIDy8nKUlJToPIiIiMh2WSzQunbtGioqKuDp6amz3NPTE2q1Wu82arW61vKV/9Znn2fPnsWaNWvw4osv6l1/69YtbN68GePGjdNZ3rlzZ2zcuBE7duzAhx9+CK1Wi7CwMPzxxx8GWgwkJydDpVKJD19fX4NliYiIyPpZfDK8Jf35558YNGgQnn32WUyYMEFvmU8++QSlpaWIi4vTWR4aGorY2FgEBgYiPDwcmZmZaNWqFdavX2/weNOnT4dGoxEfly5dMml7iIiISF4sFmi1bNkSjo6OKCgo0FleUFAALy8vvdt4eXnVWr7yX2P2+ddff2HgwIEICwvDu+++a7Ce//nPfzB48OAavWTVOTk5oU+fPjh79qzBMs7OznB3d9d5EBERke2yWKClVCoRHByMnJwccZlWq0VOTg5CQ0P1bhMaGqpTHgD27dsnlvfz84OXl5dOmZKSEhw8eFBnn3/++ScefvhhBAcHY9OmTXBw0P8y5Ofn46uvvqoxbKhPRUUFTpw4AW9v7zrLEhERkX2waGb4pKQkxMXFoW/fvujXrx9SUlJQVlaGsWPHAgBiY2PRpk0bJCcnAwAmTZqE8PBwLF++HFFRUdiyZQsOHz4s9kgpFAokJiZi4cKFCAgIgJ+fH2bNmgUfHx/ExMQA+CfIat++PZYtW4arV6+K9ane67Vx40Z4e3vjiSeeqFH3+fPno3///ujYsSOKi4uxdOlSXLhwAePHj5fipSIiIiIrZNFAa/jw4bh69Spmz54NtVqNwMBAZGdni8N0Fy9e1OltCgsLQ0ZGBmbOnIkZM2YgICAAWVlZ6NGjh1hm6tSpKCsrQ3x8PIqLizFgwABkZ2eLyUT37duHs2fP4uzZs2jbtq1OfYQqt33UarVIS0vDmDFj4OjoWKPu169fx4QJE6BWq9G8eXMEBwfjwIED6Natm0lfIyIiIrJevKm0BfGm0kRERNanPr/fdn3VIREREZGUGGgRERERScSic7SIiO5VYWGhzk3hq1MqlWjRooUZa0REpmTtn3EGWkRktQoLC7F27VrxuUbTFEVFLeDhUQiVqlRcnpCQIOsvYiLSr/pn3BA5f8YZaBGR1ap6lnv0aB98+ulgCIIDFAotoqN3ISgor0Y5IrIexn525fwZ5xwtIrJ6Gk1TMcgCAEFwwKefDoZG09TCNSMie8dAi4isXlFRCzHIqiQIDigq8rBQjYhIChpNU+Tnd7CqkygOHRKR1fPwKIRCodUJthQKLTw8iixYKyIypdqmB8gZe7SIyOqpVKWIjt4FhUILAOKXcNUJ8URkvax5egB7tIjIJgQF5cHf/yyKijzg4VHEIIvIhtQ2PUDun3UGWkRkM1SqUtl/6RJR/Vnz9AAOHRKR1VIqlSYtR0TyUvnZrWt6gJw/47yptAXxptJE987as0YTUe2qfsb/+ssB5883QocOf8PH527QZYnPeH1+vzl0SERWjUEUkW2r+hn39gaCgy1YmQbg0CERERGRRBhoEREREUmEQ4dERCbC+WJEVB0DLSIiEygsLMTatWvrLJeQkMBgi8iOcOiQiMgEqvdkGbonW209XkRke9ijRURkYtZ6TzYiMj32aBERmZA135ONiEyPgRYRkQnVdk82IrI/DLSIiEyo8p5sVVnLPdmIyPQYaBERmVBd92QjIvvCyfBERCYWFJQHf/+zKCrygIdHEYMsIjvGQIuIyASUSqXOc5WqVG+AVb0cEdk2BlpERCbQokULJCQkMDM8EelgoEVEZCIMooioOk6GJyIiIpIIAy0iIiIiiTDQIiIiIpIIAy0iIiIiiTDQIiIiIpIIAy0iIiIiiTDQIiIiIpIIAy0iIiIiiTDQIiIiIpIIM8NbkCAIAICSkhIL14SIiIiMVfm7Xfk7XhsGWhZUWnr3hrO+vr4WrgkRERHVV2lpKVQqVa1lFIIx4RhJQqvV4q+//kLTpk2hUChMuu+SkhL4+vri0qVLcHd3N+m+5YDts3623kZbbx9g+21k+6yfVG0UBAGlpaXw8fGBg0Pts7DYo2VBDg4OaNu2raTHcHd3t9kPEMD22QJbb6Ottw+w/TayfdZPijbW1ZNViZPhiYiIiCTCQIuIiIhIIgy0bJSzszPmzJkDZ2dnS1dFEmyf9bP1Ntp6+wDbbyPbZ/3k0EZOhiciIiKSCHu0iIiIiCTCQIuIiIhIIgy0iIiIiCTCQIuIiIhIIgy0rMS6devQoUMHuLi4ICQkBD/++GOt5VNSUtC5c2e4urrC19cX//73v3Hr1q172qfUTN3GuXPnQqFQ6Dy6dOkidTMMqk/77ty5g/nz58Pf3x8uLi7o3bs3srOz72mfUjN1++T0/n3zzTeIjo6Gj48PFAoFsrKy6txm//79CAoKgrOzMzp27Ii0tLQaZeT0/knRRmt+Dy9fvoxRo0ahU6dOcHBwQGJiot5y27ZtQ5cuXeDi4oKePXtiz549pq+8EaRoX1paWo33z8XFRZoGGKG+bczMzMRjjz2GVq1awd3dHaGhofj8889rlJP6c8hAywps3boVSUlJmDNnDo4ePYrevXsjMjISV65c0Vs+IyMD06ZNw5w5c3Dq1Cls2LABW7duxYwZMxq8T6lJ0UYA6N69Oy5fviw+vvvuO3M0p4b6tm/mzJlYv3491qxZg5MnT+Kll17Cv/71L+Tl5TV4n1KSon2AfN6/srIy9O7dG+vWrTOqfH5+PqKiojBw4EAcO3YMiYmJGD9+vM6XvJzeP0CaNgLW+x6Wl5ejVatWmDlzJnr37q23zIEDBzBy5EiMGzcOeXl5iImJQUxMDH7++WdTVt0oUrQPuJtRver7d+HCBVNVud7q28ZvvvkGjz32GPbs2YMjR45g4MCBiI6ONv/3qECy169fP2HixIni84qKCsHHx0dITk7WW37ixInCI488orMsKSlJeOCBBxq8T6lJ0cY5c+YIvXv3lqS+9VXf9nl7ewtr167VWfbUU08Jzz33XIP3KSUp2ien968qAMInn3xSa5mpU6cK3bt311k2fPhwITIyUnwup/evOlO10Zrfw6rCw8OFSZMm1Vg+bNgwISoqSmdZSEiI8OKLL95jDe+Nqdq3adMmQaVSmaxeplTfNlbq1q2bMG/ePPG5OT6H7NGSudu3b+PIkSOIiIgQlzk4OCAiIgK5ubl6twkLC8ORI0fE7s/ff/8de/bswZNPPtngfUpJijZWOnPmDHx8fHDffffhueeew8WLF6VriAENaV95eXmNLnpXV1exN0BO76EU7askh/evIXJzc3VeDwCIjIwUXw85vX8NVVcbK1nre2gMY18Da3bjxg20b98evr6+GDp0KH755RdLV6nBtFotSktL4eHhAcB8n0MGWjJ37do1VFRUwNPTU2e5p6cn1Gq13m1GjRqF+fPnY8CAAXBycoK/vz8efvhhcVitIfuUkhRtBICQkBCkpaUhOzsb77zzDvLz8/Hggw+itLRU0vZU15D2RUZGYsWKFThz5gy0Wi327duHzMxMXL58ucH7lIoU7QPk8/41hFqt1vt6lJSU4P/9v/8nq/evoepqI2Dd76ExDL0G1vIe1qVz587YuHEjduzYgQ8//BBarRZhYWH4448/LF21Blm2bBlu3LiBYcOGATDf9ygDLRu0f/9+vPXWW3j77bdx9OhRZGZmYvfu3ViwYIGlq2YyxrTxiSeewLPPPotevXohMjISe/bsQXFxMT7++GML1tw4q1atQkBAALp06QKlUomEhASMHTsWDg628ZE1pn3W/P7RXXwPrVtoaChiY2MRGBiI8PBwZGZmolWrVli/fr2lq1ZvGRkZmDdvHj7++GO0bt3arMduZNajUb21bNkSjo6OKCgo0FleUFAALy8vvdvMmjULo0ePxvjx4wEAPXv2RFlZGeLj4/HGG280aJ9SkqKN+gKSZs2aoVOnTjh79qzpG1GLhrSvVatWyMrKwq1bt1BYWAgfHx9MmzYN9913X4P3KRUp2qePpd6/hvDy8tL7eri7u8PV1RWOjo6yef8aqq426mNN76ExDL0G1vIe1peTkxP69Oljde/fli1bMH78eGzbtk1nmNBc36O2cXpsw5RKJYKDg5GTkyMu02q1yMnJQWhoqN5tbt68WSPQcHR0BAAIgtCgfUpJijbqc+PGDZw7dw7e3t4mqrlx7uX1dnFxQZs2bfD3339j+/btGDp06D3v09SkaJ8+lnr/GiI0NFTn9QCAffv2ia+HnN6/hqqrjfpY03tojIa8BtasoqICJ06csKr376OPPsLYsWPx0UcfISoqSmed2T6HJptWT5LZsmWL4OzsLKSlpQknT54U4uPjhWbNmglqtVoQBEEYPXq0MG3aNLH8nDlzhKZNmwofffSR8Pvvvwt79+4V/P39hWHDhhm9T3OToo2vvfaasH//fiE/P1/4/vvvhYiICKFly5bClStXZN++H374Qdi+fbtw7tw54ZtvvhEeeeQRwc/PT7h+/brR+zQnKdonp/evtLRUyMvLE/Ly8gQAwooVK4S8vDzhwoULgiAIwrRp04TRo0eL5X///XehcePGwpQpU4RTp04J69atExwdHYXs7GyxjJzeP0GQpo3W/B4KgiCWDw4OFkaNGiXk5eUJv/zyi7j++++/Fxo1aiQsW7ZMOHXqlDBnzhzByclJOHHihFnbJgjStG/evHnC559/Lpw7d044cuSIMGLECMHFxUWnjDnVt42bN28WGjVqJKxbt064fPmy+CguLhbLmONzyEDLSqxZs0Zo166doFQqhX79+gk//PCDuC48PFyIi4sTn9+5c0eYO3eu4O/vL7i4uAi+vr7C//3f/+n8iNW1T0swdRuHDx8ueHt7C0qlUmjTpo0wfPhw4ezZs2Zska76tG///v1C165dBWdnZ6FFixbC6NGjhT///LNe+zQ3U7dPTu/fV199JQCo8ahsU1xcnBAeHl5jm8DAQEGpVAr33XefsGnTphr7ldP7J0Ubrf091Fe+ffv2OmU+/vhjoVOnToJSqRS6d+8u7N692zwNqkaK9iUmJop/n56ensKTTz4pHD161HyNqqa+bQwPD6+1fCWpP4cKQTAwzkJERERE94RztIiIiIgkwkCLiIiISCIMtIiIiIgkwkCLiIiISCIMtIiIiIgkwkCLiIiISCIMtIiIiIgkwkCLiIh07N+/HwqFAsXFxZauCpHVY6BFRBYzZswYKBQKLFq0SGd5VlYWFAqF+FwQBLz33nsIDQ2Fu7s7mjRpgu7du2PSpElG3+D25s2bmD59Ovz9/eHi4oJWrVohPDwcO3bsEMt06NABKSkpJmmb1CpfO4VCAScnJ/j5+WHq1Km4detWvfbz8MMPIzExUWdZWFgYLl++DJVKZcIaE9knBlpEZFEuLi5YvHgxrl+/rne9IAgYNWoUXn31VTz55JPYu3cvTp48iQ0bNsDFxQULFy406jgvvfQSMjMzsWbNGvz666/Izs7GM888g8LCQlM2x6wGDRqEy5cv4/fff8fKlSuxfv16zJkz5573q1Qq4eXlpRPsElEDmfSGPkRE9RAXFycMHjxY6NKlizBlyhRx+SeffCJUfj199NFHAgBhx44deveh1WqNOpZKpRLS0tIMrtd3X7RK3377rTBgwADBxcVFaNu2rfDKK68IN27cENe///77QnBwsNCkSRPB09NTGDlypFBQUCCur7xHW3Z2thAYGCi4uLgIAwcOFAoKCoQ9e/YIXbp0EZo2bSqMHDlSKCsrM6o9cXFxwtChQ3WWPfXUU0KfPn3E59euXRNGjBgh+Pj4CK6urkKPHj2EjIwMnX1Ub3N+fr5Y36r3Dv3vf/8rdOvWTVAqlUL79u2FZcuWGVVPInvHHi0isihHR0e89dZbWLNmDf74448a6z/66CN07twZQ4YM0bu9sb0uXl5e2LNnD0pLS/Wuz8zMRNu2bTF//nxcvnwZly9fBgCcO3cOgwYNwtNPP42ffvoJW7duxXfffYeEhARx2zt37mDBggU4fvw4srKycP78eYwZM6bGMebOnYu1a9fiwIEDuHTpEoYNG4aUlBRkZGRg9+7d2Lt3L9asWWNUe6r7+eefceDAASiVSnHZrVu3EBwcjN27d+Pnn39GfHw8Ro8ejR9//BEAsGrVKoSGhmLChAlim319fWvs+8iRIxg2bBhGjBiBEydOYO7cuZg1axbS0tIaVFciu2LpSI+I7FfVXpn+/fsLL7zwgiAIuj1aXbp0EYYMGaKz3aRJkwQ3NzfBzc1NaNOmjVHH+vrrr4W2bdsKTk5OQt++fYXExEThu+++0ynTvn17YeXKlTrLxo0bJ8THx+ss+/bbbwUHBwfh//2//6f3WIcOHRIACKWlpYIg/NOj9cUXX4hlkpOTBQDCuXPnxGUvvviiEBkZaVR74uLiBEdHR8HNzU1wdnYWAAgODg7Cf//731q3i4qKEl577TXxeXh4uDBp0iSdMtV7tEaNGiU89thjOmWmTJkidOvWzai6Etkz9mgRkSwsXrwY6enpOHXqVJ1l33jjDRw7dgyzZ8/GjRs3jNr/Qw89hN9//x05OTl45pln8Msvv+DBBx/EggULat3u+PHjSEtLQ5MmTcRHZGQktFot8vPzAdzt8YmOjka7du3QtGlThIeHAwAuXryos69evXqJ//f09ETjxo1x33336Sy7cuWKUe0BgIEDB+LYsWM4ePAg4uLiMHbsWDz99NPi+oqKCixYsAA9e/aEh4cHmjRpgs8//7xGvepy6tQpPPDAAzrLHnjgAZw5cwYVFRX12heRvWGgRUSy8NBDDyEyMhLTp0/XWR4QEIDTp0/rLGvVqhU6duyI1q1b1+sYTk5OePDBB/H6669j7969mD9/PhYsWIDbt28b3ObGjRt48cUXcezYMfFx/PhxnDlzBv7+/igrK0NkZCTc3d2xefNmHDp0CJ988gkA1Nivk5OT+P/KqwWrUigU0Gq1RrfHzc0NHTt2RO/evbFx40YcPHgQGzZsENcvXboUq1atwuuvv46vvvoKx44dQ2RkZK3tJSLTamTpChARVVq0aBECAwPRuXNncdnIkSMxatQo7NixA0OHDjXp8bp164a///4bt27dglKphFKprNFDExQUhJMnT6Jjx45693HixAkUFhZi0aJF4vymw4cPm7SexnBwcMCMGTOQlJSEUaNGwdXVFd9//z2GDh2K559/HgCg1Wrx22+/oVu3buJ2+tpcXdeuXfH999/rLPv+++/RqVMnODo6mr4xRDaEPVpEJBs9e/bEc889h9WrV4vLRowYgWeeeQYjRozA/PnzcfDgQZw/fx5ff/01tm7davQP/cMPP4z169fjyJEjOH/+PPbs2YMZM2Zg4MCBcHd3B3A3j9Y333yDP//8E9euXQMAvP766zhw4AASEhJw7NgxnDlzBjt27BAnw7dr1w5KpRJr1qzB77//jp07d9Y5HCmVZ599Fo6Ojli3bh2Au72B+/btw4EDB3Dq1Cm8+OKLKCgo0NmmQ4cO4mt67do1vT1qr732GnJycrBgwQL89ttvSE9Px9q1azF58mSztIvImjHQIiJZmT9/vs6PvUKhwNatW5GSkoI9e/bg0UcfRefOnfHCCy/A19cX3333nVH7jYyMRHp6Oh5//HF07doVr7zyCiIjI/Hxxx/rHPv8+fPw9/dHq1atANydV/X111/jt99+w4MPPog+ffpg9uzZ8PHxAXB3GDMtLQ3btm1Dt27dsGjRIixbtsyEr4jxGjVqhISEBCxZsgRlZWWYOXMmgoKCEBkZiYcffhheXl6IiYnR2Wby5MlwdHREt27d0KpVK73zt4KCgvDxxx9jy5Yt6NGjB2bPno358+frvbKSiHQpBEEQLF0JIiIiIlvEHi0iIiIiiTDQIiKbUDX9QvXHt99+a+nq1cvFixdrbU990zMQkeVw6JCIbEJtN5du06YNXF1dzVibe/P333/j/PnzBtd36NABjRrxonEia8BAi4iIiEgiHDokIiIikggDLSIiIiKJMNAiIiIikggDLSIiIiKJMNAiIiIikggDLSIiIiKJMNAiIiIikggDLSIiIiKJ/H/QhoXrafEnJQAAAABJRU5ErkJggg==", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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LIZNpdEKQTKaBUllik/qwB4gA1G09oX2ktX//ftx7770IDg6Gp6cnJk6ciOLiYlRWVpq8R3Z2NhISEtClSxd4enpi6NChAIALFy5YvP5ERGTfFIpyJCTshUxWN+lGOwbIFo+/APYA0f+cOnUKoaGhOHfuHMaMGYPp06dj6dKlUCqV+Oabb/DEE0+gurra6ArYFRUViI+PR3x8PN5//3106tQJFy5cQHx8vNUf6RERkX2Kjj6KsLA8lJQooVSW2Cz8AAxABOCrr77CTz/9hOeffx7Z2dnQaDR4/fXX4eRU10H4z3/+U6e8XC5HbW2tzrHTp0+juLgYy5Ytg0qlAgD88MMP1mkAERHZLblcrvNeoSg3GHzql7M0BiAHU1VVhYKCAp1p8KmpqRgzZgwmTZqE48ePo6amBmvXrkVCQgK+/fZbbNy4UeceISEhuH79OjIzMxEZGYn27dujS5cukMvlWLt2LZ5++mkcP34cixcvtlEriYjIXvj4+CApKcnuVoLmGCAHk5GRgcDAQISEhGDkyJH4+uuvsWbNGnz66adwdnZGZGQkVq9ejeXLl+OOO+7A+++/j9TUVJ17DBo0CE8//TQSExPRqVMnrFixAp06dcK2bdvw4YcfolevXli2bBlWrVplo1YSEZE98fHxQWBgoNGXtcMPAMiEEMLqn1rP+vXrsXLlShQUFCAyMhJr167FgAEDDJbdtWsXXnvtNeTl5aGmpgYRERF44YUXMHHiRKnMlClTkJ6ernNdfHw8MjIyGlWfsrIyKBQKqNVqdOzYUefcjRs3kJ+fj9DQULi5uZnVTntYB6i1ac73TURE1mXrxX5N/f6uz+aPwHbu3Ink5GRs3LgRsbGxSEtLQ3x8PHJzcw0uoKdUKjF37lz07NkTcrkce/fuxdSpU+Hn54f4+HipnHbFYy1XV1ertMcUe+0GJCIiaq7W9o98mweg1atX46mnnpJWF964cSM+++wzbNmyBXPmzNErf8899+i8f+6555Ceno5vvvlGJwDduuKxPbGH/9GJiIhamqE9v0pKfKBUFusMeraXmcE2DUDV1dXIzs5GSkqKdMzJyQlxcXE4ePBgg9cLIfDVV18hNzcXy5cv1zmXlZUFPz8/eHt7Y/jw4ViyZInR8FFVVYWqqirpfVlZWRNbRERERPa055cxNh0EXVRUhNraWvj7++sc9/f3R0FBgdHr1Go1OnToALlcjtGjR2Pt2rUYMWKEdH7kyJF45513kJmZieXLl+M///kPRo0apTd1Wys1NRUKhUJ6aadxExERkXmM7flVf/d3W7P5I7Cm8PT0RE5OjjQVOzk5Gd26dZMej40fP14q27t3b/Tp0wdhYWHIysrCvffeq3e/lJQUJCcnS+/LysoYgoiIiJrA1J5ftlz4sD6bBiBfX184OzujsLBQ53hhYaHJ8TtOTk4IDw8HAERFReHUqVNITU3VGx+k1a1bN/j6+iIvL89gAHJ1dbWLQdJEREStnb3t+WWMTR+ByeVy9OvXD5mZmdIxjUaDzMxMDBw4sNH30Wg0OmN46rt06RKKi4sRGBjYrPoSERGRafa255cxNn8ElpycjMmTJyMmJgYDBgxAWloaKioqpFlhkyZNQnBwsLQYX2pqKmJiYhAWFoaqqirs27cP7777LjZs2AAAuH79OhYtWoSHH34YAQEBOHv2LGbPno3w8HCdWWJERERkGfa055cxNg9AiYmJuHr1KubPn4+CggJERUUhIyNDGhh94cIFaU8qoG7TzWeeeQaXLl2Cu7s7evbsiffeew+JiYkAAGdnZxw7dgzp6ekoLS1FUFAQ7rvvPixevJiPuYiIiCzEXvf8MsYuVoK2N5ZaCbqty8rKwrBhw3Dt2jV4eXk16pqQkBDMmjULs2bNMnie3zcRUevRmlaC5l5gDmTKlCmQyWR4+umn9c7NmDEDMpkMU6ZMsX7FiIioTbDHPb+MYQByMCqVCjt27MDvv/8uHbtx4wa2b9+OLl262LBmRERE1sMA5GCio6OhUqmwa9cu6diuXbvQpUsX9O3bVzpWVVWFmTNnws/PD25ubhg8eDAOHz6sc699+/ahe/fucHd3x7Bhw3Du3Dm9z/vmm28wZMgQuLu7Q6VSYebMmaioqLBY+4iIiBqDAciGLl0Cvv667r/W9Je//EVno9gtW7ZIs+60Zs+ejY8//hjp6ek4cuSINIuupKRuHYeLFy/ioYceQkJCAnJycvDkk0/q7d129uxZjBw5Eg8//DCOHTuGnTt34ptvvkFSUpLlG0lERGQCA5CNbN4MdO0KDB9e99/Nm6332Y8//ji++eYbnD9/HufPn8e3336Lxx9/XDpfUVGBDRs2YOXKlRg1ahR69eqFTZs2wd3dHZv/V9ENGzYgLCwMr7/+Onr06IHHHntMb/xQamoqHnvsMcyaNQsREREYNGgQ1qxZg3feeQc3btywXoOJiIjqsfk0eEd06RIwbRqgqVsjChoN8Ne/AvHxQOfOlv/8Tp06YfTo0di2bRuEEBg9ejR8fX2l82fPnkVNTQ3uuusu6ZiLiwsGDBiAU6dOAQBOnTqF2NhYnfvWX7zyxx9/xLFjx/D+++9Lx4QQ0Gg0yM/Px2233WaJ5hERETWIAcgGzpz5I/xo1dYCeXnWCUBA3WMw7aOo9evXW+Qzrl+/jr/+9a+YOXOm3jkOuCYiIltiALKBiAjAyUk3BDk7A//b3swqRo4cierqashkMr0VssPCwiCXy/Htt9+ia9euAICamhocPnxYWq/ntttuw+7du3Wu++6773TeR0dH4+TJk9K+bURERPaCY4BsoHNn4O2360IPUPffv//der0/dZ/pjFOnTuHkyZNw1lbkfzw8PDB9+nS89NJLyMjIwMmTJ/HUU0+hsrISTzzxBADg6aefxpkzZ/DSSy8hNzcX27dvx7Zt23Tu8/LLL+PAgQNISkpCTk4Ozpw5g08//ZSDoImIyObYA2QjTzxRN+YnL6+u58ea4UfL1CqZy5Ytg0ajwcSJE1FeXo6YmBh8/vnn8Pb2BlD3COvjjz/G888/j7Vr12LAgAF47bXX8Je//EW6R58+ffCf//wHc+fOxZAhQyCEQFhYmLRtCRERtT62Xu25pXArDAO4FYb94PdNRGQbhoKOWq3Gzp07G7w2KSnJJiHInK0w2ANEREREOoqLi7Fu3boGy6nVnigp8YFSWayz8ampHiJ7wQBEREREOvR7fvSDzpEjfbFnzxgI4QSZTIOEhL2Ijj5qi+o2CQMQERER6VCr1dLPhoJOWFiedAwAhHDCnj1jEBaWp9MTZM84C4yIiIh01NTUAKjr+TEUdC5eVEnHtIRwQkmJ0up1bSoGoCbi2HHr4PdMRGQ7JSU+BoMOICCT6a7oK5NpoFSWWLF2zcMAZCYXFxcAQGVlpY1r4hi037P2eyciIutxcakCUP8fogJeXqVISNgrhSDto7HW8vgL4Bggszk7O8PLywtXrlwBALRv3x4ymczGtWp7hBCorKzElStX4OXlpbdYIxERWV5NjSuA+r/jZKipkSM6+ijCwvJQUqKEUlnSqsIPwADUJAEBAQAghSCyHC8vL+n7JiIi61IqiyGTaXQeg936qEuhKDcYfORyudXq2FQMQE0gk8kQGBgIPz8/aaAYtTwXFxf2/BAR2UC7dnXxQKEoR0LCXr1ZYNrQM2LECISGhupc21pWgmYAagZnZ2f+giYiojbHy8tL+tnUo67Q0FAEBgbaoIbNxwBEREREOuo/wmrNj7qMYQAiIiIiHT4+PkhKSmoTm54awwBEREREelpzuGkMrgNEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORwOAuMiIjIQRUXF7fpqe6mMAARERE5oOLiYqxbt67BcklJSW0yBPERGBERkQOq3/OjVnsiPz8EarWnyXJtBXuAiIiIHNyRI331NjyNjj5q62pZFHuAiIiIHJha7SmFHwAQwgl79ozR6wlqaxiAiIiIHFhJiY8UfrSEcEJJidJGNbIOBiAiIiIHplQWQybT6ByTyTRQKktsVCPrYAAiIiJyYApFORIS9kohSDsGSKEot3HNLIuDoImIiBxcdPRRhIXloaRECaWypM2HH8BOeoDWr1+PkJAQuLm5ITY2FocOHTJadteuXYiJiYGXlxc8PDwQFRWFd999V6eMEALz589HYGAg3N3dERcXhzNnzli6GURERK2GXC7Xea9QlCM09Lxe+Klfrq2weQ/Qzp07kZycjI0bNyI2NhZpaWmIj49Hbm4u/Pz89MorlUrMnTsXPXv2hFwux969ezF16lT4+fkhPj4eALBixQqsWbMG6enpCA0Nxbx58xAfH4+TJ0/Czc3N2k0kIiKyOz4+PkhKSnLYlaBlQghhywrExsaif//+0mqUGo0GKpUKzz77LObMmdOoe0RHR2P06NFYvHgxhBAICgrCCy+8gBdffBEAoFar4e/vj23btmH8+PEN3q+srAwKhQJqtRodO3ZseuOIiIjIasz5/W3TR2DV1dXIzs5GXFycdMzJyQlxcXE4ePBgg9cLIZCZmYnc3FzcfffdAID8/HwUFBTo3FOhUCA2NtboPauqqlBWVqbzIiIiorbLpgGoqKgItbW18Pf31znu7++PgoICo9ep1Wp06NABcrkco0ePxtq1azFixAgAkK4z556pqalQKBTSS6VSNadZREREZOfsYhC0uTw9PZGTk4PDhw9j6dKlSE5ORlZWVpPvl5KSArVaLb0uXrzYcpUlIiIiu2PTQdC+vr5wdnZGYWGhzvHCwkIEBAQYvc7JyQnh4eEAgKioKJw6dQqpqam45557pOsKCwsRGBioc8+oqCiD93N1dYWrq2szW0NERESthU17gORyOfr164fMzEzpmEajQWZmJgYOHNjo+2g0GlRVVQEAQkNDERAQoHPPsrIyfP/992bdk4iIiNoum0+DT05OxuTJkxETE4MBAwYgLS0NFRUVmDp1KgBg0qRJCA4ORmpqKoC68ToxMTEICwtDVVUV9u3bh3fffRcbNmwAAMhkMsyaNQtLlixBRESENA0+KCgI48aNs1UziYiIyI7YPAAlJibi6tWrmD9/PgoKChAVFYWMjAxpEPOFCxfg5PRHR1VFRQWeeeYZXLp0Ce7u7ujZsyfee+89JCYmSmVmz56NiooKTJs2DaWlpRg8eDAyMjK4BhAREREBsIN1gOwR1wEiIqLWpri42GEXNdQy5/e3zXuAiIiIqHmKi4ulBYVNSUpKavMhqLFa5TR4IiIi+kP9nh+12hP5+SFQqz1NlnNk7AEiIiJqQ44c6Ys9e8ZACCfIZBokJOxFdPRRW1fL7rAHiIiIqI1Qqz2l8AMAQjhhz54xej1BxABERETUZpSU+EjhR0sIJ5SUKG1UI/vFAERERNRGKJXFkMk0OsdkMg2UyhIb1ch+MQARERG1EQpFORIS9kohSDsGSKEot3HN7A8HQRMREbUh0dFHERaWh5ISJZTKEoYfIxiAiIiIWjm5XK7zXqEoNxh86pdzZAxARERErYixFZ8TExNRU1ODdu3awcvLS++8I6wEbQ4GICIiolaCKz63HA6CJiIiaiW44nPLYQ8QERFRK8QVn5uHPUBEREStDFd8bj4GICIiolaGKz43HwMQERFRK8MVn5uPAYiIiKiV4YrPzcdB0ERERK0QV3xuHgYgIiIiO2NsscPS0lKd91zxuekYgIiIiOxIYxc7TExMhEKh0DvOFZ8bhwGIiIjIjhha7LCkxAdKZbFOb49CoUBgYKC1q9dmMAARERHZKS52aDmcBUZERGSHuNihZbEHiIiIyEYMDXYuKioCYHqxQ874aj4GICIiIhtoaLCzdrHDW0MQFztsOXwERkREZAMN7ezOxQ4tiz1ARERENmZssDMXO7Qc9gARERHZUEODnRWKcoSGntcLP1zssHnYA0RERGRDDQ12fuihh+Dr66tznosdNh8DEBERkQ01NNjZ19eXCx5aAB+BERER2RAHO9sGe4CIiIhsjIOdrY8BiIiIyAbqD2Lmzu7WxQBERERkAz4+PkhKStJbD+hWHOxsOQxARERENsJwYzscBE1EREQOhwGIiIiIHA4DEBERETkcuwhA69evR0hICNzc3BAbG4tDhw4ZLbtp0yYMGTIE3t7e8Pb2RlxcnF75KVOmQCaT6bxGjhxp6WYQERFRK2HzALRz504kJydjwYIFOHLkCCIjIxEfH48rV64YLJ+VlYUJEybg66+/xsGDB6FSqXDffffh119/1Sk3cuRIXL58WXp98MEH1mgOERERtQIyIYSwZQViY2PRv39/rFu3DgCg0WigUqnw7LPPYs6cOQ1eX1tbC29vb6xbtw6TJk0CUNcDVFpaik8++aRJdSorK4NCoYBarUbHjh2bdA8iIiKyLnN+f9u0B6i6uhrZ2dmIi4uTjjk5OSEuLg4HDx5s1D0qKytRU1MDpVKpczwrKwt+fn7o0aMHpk+fjuLiYqP3qKqqQllZmc6LiIiI2i6bBqCioiLU1tbC399f57i/vz8KCgoadY+XX34ZQUFBOiFq5MiReOedd5CZmYnly5fjP//5D0aNGoXa2lqD90hNTYVCoZBeKpWq6Y0iIiIiu9eqF0JctmwZduzYgaysLLi5uUnHx48fL/3cu3dv9OnTB2FhYcjKysK9996rd5+UlBQkJydL78vKyhiCiIiI2jCb9gD5+vrC2dkZhYWFOscLCwsREBBg8tpVq1Zh2bJl+OKLL9CnTx+TZbt16wZfX1/k5eUZPO/q6oqOHTvqvIiIiKjtsmkAksvl6NevHzIzM6VjGo0GmZmZGDhwoNHrVqxYgcWLFyMjIwMxMTENfs6lS5dQXFyMwMDAFqk3ERERtW42nwafnJyMTZs2IT09HadOncL06dNRUVGBqVOnAgAmTZqElJQUqfzy5csxb948bNmyBSEhISgoKEBBQQGuX78OALh+/TpeeuklfPfddzh37hwyMzPxwAMPIDw8HPHx8TZpIxEREdkXm48BSkxMxNWrVzF//nwUFBQgKioKGRkZ0sDoCxcuwMnpj5y2YcMGVFdX409/+pPOfRYsWICFCxfC2dkZx44dQ3p6OkpLSxEUFIT77rsPixcvhqurq1XbRkRERPbJ5usA2SOuA0RERNT6tJp1gIiIiIhsgQGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhMAARERGRw2EAIiIiIofDAEREREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhtLN1BYiIiGyhuLgY1dXVRs/L5XL4+PhYsUZkTQxARETkcIqLi7Fu3boGyyUlJTEEtVF8BEZERA6nfs+PWu2J/PwQqNWeJstR28EeICIicmhHjvTFnj1jIIQTZDINEhL2Ijr6qK2rRRbGHiAiInJYarWnFH4AQAgn7NkzRq8niNoeBiAiInJYJSU+UvjREsIJJSVKG9WIrIUBiIiIHJZSWQyZTKNzTCbTQKkssVGNyFo4BoiIiNosY1Pdi4qKAAAKRTkSEvbqjQFSKMqtXVWyMgYgIiJqkxo71T06+ijCwvJQUqKEUlnC8OMgGICIiKhNMjTVvaTEB0plsV7IUSjKDQYfuVxu0TqS7TAAERFRm6RWq6WfTU11HzFiBEJDQ/Wu50rQbRsDEBERtUklJXUDmY1NdQ8Ly5N6fQIDA21WT7INzgIjIqI26ebNmwAanuquLUeOhQGIiIjapMrKSgCAi0sVAFHvrICLS7VOOXIsDEBERNQm1dbWAgBKS70ByOqdlaG01EunHDkWjgEiIqJWz9B6PxUVFTaqDbUGDEBERNSqNbTej5fXNdQ9Aru1F0jAy6sUAODs7GzJ6pGd4iMwIiJq1Qyt95OfHyJtaFpT4wpDj8BqaurW+Gnfvr0Vakn2hj1ARETUZhha7ycsLA8ymUZnJtit+30Z2iqD2j72ABERUZtgbL0fAEhI2AtAu+mp7n5f7AFyTAxARETUJjS03o/sf0/BZPWehnl6elqjemRnGICIiKhV0255oVQWQybT6JyTyTRwcak22DOkHSPUrh1HgzgiBiAiImrVampqANRtaGroUVdNjavJniEvLy/rVZbshl0EoPXr1yMkJARubm6IjY3FoUOHjJbdtGkThgwZAm9vb3h7eyMuLk6vvBAC8+fPR2BgINzd3REXF4czZ85YuhlERGRBxcXFuHz5st7r2rVrOuXqP+oy1jOkHQRNjsnm/X47d+5EcnIyNm7ciNjYWKSlpSE+Ph65ubnw8/PTK5+VlYUJEyZg0KBBcHNzw/Lly3HffffhxIkTCA4OBgCsWLECa9asQXp6OkJDQzFv3jzEx8fj5MmTcHNzs3YTiYiomRpa6wcwPgh61qw0JCTs1Zsdph0ETY5JJoSov0GKVcXGxqJ///7SH2yNRgOVSoVnn30Wc+bMafD62tpaeHt7Y926dZg0aRKEEAgKCsILL7yAF198EUDd82F/f39s27YN48ePb/CeZWVlUCgUUKvV6NixY/MaSEREzXb58mW8/fbb0nu12hMlJT5QKoulIJOfH4L09Ml6106evA2hoef/d40SSmWJTviZNm0ad4NvI8z5/d0iPUD5+flQqVRmDySrrq5GdnY2UlJSpGNOTk6Ii4vDwYMHG3WPyspK1NTUQKlUSnUpKChAXFycVEahUCA2NhYHDx40GICqqqpQVVUlvS8rKzOrHUREZD2G1vqJjj4qPeoytt6PQlFusNdHLpdbre5kP1okAPXo0QM//vgjbrvtNrOuKyoqQm1tLfz9/XWO+/v74/Tp0426x8svv4ygoCAp8BQUFEj3qH9P7bn6UlNTsWjRIrPqTkRE1mfsMVdYWJ40CNrYo66HHnoIvr6+OveTy+Xw8fGxejvI9swKQA899JDB47W1tZg5c6a0lsKuXbuaX7NGWLZsGXbs2IGsrKxmje1JSUlBcnKy9L6srAwqlaolqkhERC3I1Fo/CkU5oqOPIiwsz+CjLl9fXz7qIolZAeiTTz7B3XffjdDQUL1zHTp0gEKhMOvDfX194ezsjMLCQp3jhYWFCAgIMHntqlWrsGzZMuzfvx99+vSRjmuvKyws1PmDXlhYiKioKIP3cnV1haurq1l1JyIi62voMRfAR13UOGYFoO3bt+Oll17C5MmTMXXqVOn4e++9h6VLl6JXr15mfbhcLke/fv2QmZmJcePGAagbBJ2ZmYmkpCSj161YsQJLly7F559/jpiYGJ1zoaGhCAgIQGZmphR4ysrK8P3332P69Olm1Y+IiOxLUx5zAXzURfrMCkDjx4/HnXfeiccffxx79+7FP/7xD3h7ezerAsnJyZg8eTJiYmIwYMAApKWloaKiQgpYkyZNQnBwMFJTUwEAy5cvx/z587F9+3aEhIRI43o6dOiADh06QCaTYdasWViyZAkiIiKkafBBQUFSyCIiotaLj7moJZg9CDokJAT//e9/sWjRIkRGRmLTpk2Q1d9YxQyJiYm4evUq5s+fj4KCAkRFRSEjI0MaxHzhwgU4Of3R1blhwwZUV1fjT3/6k859FixYgIULFwIAZs+ejYqKCkybNg2lpaUYPHgwMjIyuAYQEVErVf/xFR9zUXM1ax2gb775BpMmTcL58+fx008/mf0IzF5xHSAiIvtTXFyM6upqo+f5mIustg7Q4MGDcezYMZw9exZhYWHNuRUREZFJDDfUkpq9DlCHDh0QGRnZEnUhIiIisgqzAlDfvn0bNd7nyJEjTa4QERERkaWZFYBunUUlhEBqaiqefvppaRsKIiIiotagWYOgPT098eOPP6Jbt24tWSeb4yBoIiKi1sec399OJs8SERERtUEMQERERORwGICIiIjI4Zg1CHrNmjU672/evIlt27bp7bsyc+bM5teMiIiIyELMGgRtaBd4vRvKZPjll1+aVSlb4yBoIiKi1sdiK0Hn5+c3q2JERORYuH0F2SuzAtBXX32FpKQkfPfdd3rJSq1WY9CgQdi4cSOGDBnSopUkIqLWp7i4GOvWrWuwXFJSEkMQWZ1ZASgtLQ1PPfWUwW4lhUKBv/71r1i9ejUDEBGRAzHWy1NUVKTzXq32REmJD5TKYp2d3E31EBFZilkB6Mcff8Ty5cuNnr/vvvuwatWqZleKiIhah8b28hw50hd79oyBEE6QyTRISNiL6OijVqghkWFmTYMvLCyEi4uL0fPt2rXD1atXm10pIiJqHer33qjVnsjPD4Fa7alzTBt+AEAIJ+zZM0anDJG1mdUDFBwcjOPHjyM8PNzg+WPHjiEwMLBFKkZERK2LsV6ekhIfKfxoCeGEkhKlzqMwImsyqwfo/vvvx7x583Djxg29c7///jsWLFiAMWPGtFjliIjIvqnV6v/913gvj1JZDJlMo3OdTKaBUlli9foSaZkVgF599VWUlJSge/fuWLFiBT799FN8+umnWL58OXr06IGSkhLMnTvXUnUlIiI7U1NTAwAN9vIkJOyVQpC2d4i9P2RLZj0C8/f3x4EDBzB9+nSkpKRAu4aiTCZDfHw81q9fD39/f4tUlIiI7JdSWQxAA91/V//RyxMdfRRhYXkoKVFCqSxh+CGbMysAAUDXrl2xb98+XLt2DXl5eRBCICIiAt7e3paoHxERtRoy3Xe6b6FQlBsMPnK53JKVIjLI7ACk5e3tjf79+7dkXYiIqJUqKfFB/QB06yOwYcOGISIiQu86rgRNttLkAERERNSuXd2vEe1A51vHAd060LlTp06cJUx2hQGIiIgaxdCKzzdv3gQAaaBz/Wnw2kdeXl5e1q4ukUkMQEREJDG2rUVpaSn++c9/mryWA52pNWEAIiIiAI3f1gIwvq8XBzpTa8EAREREAIArV67ovDcWckzt6/XQQw/B19dX5z4c6Ez2iAGIiIgA1D3m0jIWcoyt+BwWlgeFohy+vr4c7EytglkrQRMRUdulXdVZrfbE7t0JBre1MLXiM1FrwgBEREQ6Ll5UwdCaPhcvdua+XtRm8BEYEZEDMjTbq7y8bpxPZaW7wWsqK90bnO5O1FowABEROZiGZnu1b/+7yeOc7k5tAQMQEZGDqd/zU3+2l0p1EYDArY/BZDINVKpL0ntOd6fWjgGIiMjBNGa219ixe4w+5rrzzjvRp08fvftyuju1JgxAREQORrt9hakp7aYec3Xp0oVT3anVYwAiInJQpqa0ax9xGXrM5efnZ60qElkMAxARkYNycalC/bE+gICLS90YoWHDhiEiIkLnGj7moraCAYiIyEHV1Lii/no/gAw1NXUDmb29vfmoi9osLoRIROSguKghOTKbB6D169cjJCQEbm5uiI2NxaFDh4yWPXHiBB5++GGEhIRAJpMhLS1Nr8zChQshk8l0Xj179rRgC4iIWhcXFxcAkBY11Iag+rO9tOWI2iKbPgLbuXMnkpOTsXHjRsTGxiItLQ3x8fHIzc01OMiusrIS3bp1wyOPPILnn3/e6H1vv/127N+/X3rfrh2f9BERaSkUCulnU7O9bi1H1NbYNBmsXr0aTz31FKZOnQoA2LhxIz777DNs2bIFc+bM0Svfv39/9O/fHwAMntdq164dAgICLFNpIiI7Y2hbi1vVH7hcf7FCLmpIjshmAai6uhrZ2dlISUmRjjk5OSEuLg4HDx5s1r3PnDmDoKAguLm5YeDAgUhNTUWXLl2Mlq+qqkJVVZX0vqysrFmfT0RkLQ1ta6GVlJQkhSAfHx8kJSWZFZqI2hqbBaCioiLU1tbC399f57i/vz9Onz7d5PvGxsZi27Zt6NGjBy5fvoxFixZhyJAhOH78ODw9PQ1ek5qaikWLFjX5M4mIbKWhbS2MlWO4IUfX5gbHjBo1Svq5T58+iI2NRdeuXfHPf/4TTzzxhMFrUlJSkJycLL0vKyuDSqWyeF2JiFqSsW0tiEifzQKQr68vnJ2dUVhYqHO8sLCwRcfveHl5oXv37sjLyzNaxtXVFa6uri32mURE1mZqWwvu1k6kz2bT4OVyOfr164fMzEzpmEajQWZmJgYOHNhin3P9+nWcPXuWi3kRUZtmalsLItJn03WAkpOTsWnTJqSnp+PUqVOYPn06KioqpFlhkyZN0hkkXV1djZycHOTk5KC6uhq//vorcnJydHp3XnzxRfznP//BuXPncODAATz44INwdnbGhAkTrN4+IiJL0+7s3tCihrfuAE9ENh4DlJiYiKtXr2L+/PkoKChAVFQUMjIypIHRFy5cgJPTHxntt99+Q9++faX3q1atwqpVqzB06FBkZWUBAC5duoQJEyaguLgYnTp1wuDBg/Hdd9+hU6dOVm0bEZE1aHd21y5qWH8MkPbxl7YcEdWx+SDopKQkJCUlGTynDTVaISEhEEKYvN+OHTtaqmpERK2O9q/IBv6qJHJ4Nt8Kg4iImk67XYV2EPQff63XDYJWqz11yhFRHQYgIqJWTLtdRUODoLmtBZEuBiAiojaAO7sTmYcBiIioDWhoZ3ci0mXzQdBERNQyTO3sTkS6GICIiFox7uxO1DQMQERErRh3didqGgYgIqJWjuGGyHwcBE1EREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhcBo8EVEzFBcXcw0eolaIAYiIqImKi4uxbt066b1a7YmSEh8olcU6qzEnJSUxBBHZGQYgIqImurXn58iRvtizZwyEcJI2Io2OPqpXjojsA8cAERE1kVqt/t9/PaXwAwBCOGHPnjFQqz11yhGR/WAAIiJqopqaGgBASYmPFH60hHBCSYlSpxwR2Q8+AiMiaoCxgc6//vorAECpLAagge6/KTVQKksAAOXl+ruzE5FtMQAREZlQf6CzcTLdd7e8vXnzZstWioiajY/AiIhMqN/zo1Z7Ij8/RBrfA9Q9AqsfgG59BEZE9oc9QEREjWRsppeLSxUAAd0QJODiwtlfRPaKPUBERI1gaqZXTY0r6vcAATLU1MgBAO3a8d+aRPaG/68kIvofQ4Odi4qKAJie6aVUFkMm0+icl8n+GATt6ekJIrIvDEBE5FCMzegqLS3FP//5T6PXmQo5CkU5EhL26j0e064G7eLi0vINIaJmYQAiIofR+Bld+ttaNBRyoqOPIiws7389QiU6W2EoFAqLtIeImo4BiIgchqEZXYb27jI22NlUyAEgBaX65HK5ZRpERE3GAEREDslYyDE22DksLE8KOIZCTmJiosGeHu4GT2SfGICIqE0yNaDZVMgxNdhZoSjHQw89BF9fX53zDDlErQ8DEBG1OfXH+ug+6mrejC5fX18EBgZapyFEZDEMQETU5tza82PoUVdYWF6TZ3QRUdvAAEREbZaxR12zZqU1eUYXEbUNDEBE1GaZetTFGV1Ejo0BiIjarIbG83BGF5HjYgAiojbn4sWLANDgeJ5Bgwbhjjvu0LmWIYfIMTAAEVGbc+PGDenn6Oij8PAox5kzEYiIOIMePfKkc66urpzRReSgGICIyO4Z279LrVajpqYG7dq1g5eXl3S8vPyPx1r/+tcD+PHHSAAy/PBDf0RG/ogHH/zUCrUmIntm8wC0fv16rFy5EgUFBYiMjMTatWsxYMAAg2VPnDiB+fPnIzs7G+fPn8cbb7yBWbNmNeueRGTfTK/pY3p21qVLgVL4qSPDjz9Gon//Q+jc+TI3KSVyYE4NF7GcnTt3Ijk5GQsWLMCRI0cQGRmJ+Ph4XLlyxWD5yspKdOvWDcuWLUNAQECL3JOI7Fv9NX3S0mYhPX0y0tJm4ciRviavvXChK/4IP1oyXLzYBQDQoUOHFq4tEbUWNg1Aq1evxlNPPYWpU6eiV69e2LhxI9q3b48tW7YYLN+/f3+sXLkS48ePh6ura4vck4haB2Nr+qjVntL5/PwQ6T0AdOlyHoCodycBleqClWpNRPbKZo/AqqurkZ2djZSUFOmYk5MT4uLicPDgQaves6qqClVVVdL7srKyJn0+EVmOqTV9zp4NN7ixaefOlxEZ+eMtj8EEIiN/ROfOl23SBiKyHzYLQEVFRaitrYW/v7/OcX9/f5w+fdqq90xNTcWiRYua9JlEZB3G1vRxcak2uXv7gw9+iv79D+HixS5QqS4w/BARABs/ArMXKSkpUKvV0ku7hggR2Q/tmj4ymQYApJ6emhpXoz1DWp07X8bAgd/rhZ/27dtbvuJEZJds1gPk6+sLZ2dnFBYW6hwvLCw0OsDZUvd0dXU1OqaIiOyHoe0r1GpPk6s9Dxs2DN7e3nr3at++PcLCwqxWdyKyLzbrAZLL5ejXrx8yMzOlYxqNBpmZmRg4cKDd3JOI7ItCUY7Q0PPSFHhjPUPa8xEREejdu7fei+GHyLHZdB2g5ORkTJ48GTExMRgwYADS0tJQUVGBqVOnAgAmTZqE4OBgpKamAqgb5Hzy5Enp519//RU5OTno0KEDwsPDG3VPImpdGrP5qKmNTbl5KREZYtMAlJiYiKtXr2L+/PkoKChAVFQUMjIypEHMFy5cgJPTH51Uv/32G/r2/WPdj1WrVmHVqlUYOnQosrKyGnVPIrItY6s6a9Xfi8vHxwdJSUlmrQRt7F5ERFoyIUT9RTIcXllZGRQKBdRqNTp27Gjr6hC1GfVXdTYmKSmJwYWIzGbO72/OAiMiq6m/IruhxQsNlSMiamk23wuMiBzHzZs3pZ+PHOlrcPHC+uWIiCyBAYiImszc8TxaarUndu9OgHafrvqLFxIRWRoDEBE1SXPG81y8qEL9TUqFcMLFi52hUJxqyWoSERnEMUBE1CS//PKLzntj43nqlyMisgfsASKiJikuLpZ+PnKkL3bvHoO6f1NpMHbsH+N5bi2npVJdBKCB7r/BNFCpLlmyykREEvYAEVGT1NbWAtCO59GGHwBwwu7dY6SeIG25WykU5Rg7di/qQhCgDU0c/0NE1sIeICJqltzc7tD/t5QTcnMjMGDAEZ2jLi4u0s+mVm++tRwRkSUwABFRk2dz1V2rNHJcv3ynTp103isU5QZ7feqXIyJqaQxARA6uuasz+/iUGCzv46M/9sfUthZa3L6CiKyBAYjIwdUPI2q1J0pKfKBUFuv0zhgLLT16/Ix9+/QHNPfoccZgeYYbIrIHDEBEJDG1OnN9zs7OAP4Y0Fx/Fpg2PGnLERHZEwYgIgJQ1/OjDT9Aw6sz39qTY2pAM3t8iMgeMQAREQCgpMRHCj9aQjihpERpMAB169ZN572xAc31yxER2QMGICICACiVxZDJNDohSCbTQKk0NsiZA5qJqPViACJqY5o6pV2hKEdCwl69MUCmFidkuCGi1ooBiKgNae6UdlNjeYiI2hIGIKI2pClT2uVyuc41xsby1C9HRNSaMQARtVGNndLOsTxE5IgYgIhaKUNjfYqKigA0b0o7EZEjYAAiaoUaGutj7pR2IiJHU38LZyJqBa5evarzXq32RH5+CNRqTwB/TGm/lakp7UREjoY9QEStUE1NjfTzkSN99bahiI4+avaUdiIiR8IARNSKqdWet4QfAHDC7t11Y304pZ2IyDgGICI70NTFCy9eVEH/SbYTLl7sDIXiFKe0ExEZwQBEZGPNXbzQlGHDhiEiIkLnGKe0ExExABHZXFMWL9RSqS4CEABk0jGZTAOV6hIAwNvbG4GBgRapNxFRa8YARGRHGrt4oZZCUY6xY/dwsDMRkZkYgIjshDmLF7Zr98f/dU0Ndr61HBER/YF/OxLZCXMWL/Tz89N5b2ywc/1yRERUhwGIqAmaOmvLFO3ihbeGIGOLF3L/LiKi5mEAIjJTY2dtJSYmQqFQ6B03FkwUinKzFi9kuCEiajoGICIzNXbW1s6dO43ew9iUdi5eSERkHQxARGYqLS2Vfm5o1lZjprTXX5SQixcSEVkeAxCRCYbG+pw/fx6AdhuKBGjX4Kk/a6uxU9o5noeIyPoYgIiMaGisT902FDKdY0LUbUMBXGr0lHaA43mIiKyNAYgcnrEZXUVFRU2+pzlT2omIyPrsIgCtX78eK1euREFBASIjI7F27VoMGDDAaPkPP/wQ8+bNw7lz5xAREYHly5fj/vvvl85PmTIF6enpOtfEx8cjIyPDYm2g1qmxM7oA/fE8Xl7XUH8bCkDAy6sUnp7XGz2lnYiIrM/mAWjnzp1ITk7Gxo0bERsbi7S0NMTHxyM3N9fgIm4HDhzAhAkTkJqaijFjxmD79u0YN24cjhw5gjvuuEMqN3LkSGzdulV67+rqapX2kP0y1NOTn5+v897YoGVD43m8va+h/iMwQIaaGrnZU9qJiMi6ZEIIYcsKxMbGon///tK/wjUaDVQqFZ599lnMmTNHr3xiYiIqKiqwd+9e6didd96JqKgobNy4EUBdD1BpaSk++eSTJtWprKwMCoUCarUaHTt2bNI9yL40pqfH2KBltdoTaWmz9HpznnjiH9i8+Um947NmpUlBpy5Q6U9pnzZtGjcpJSJqYeb8/nYyedbCqqurkZ2djbi4OOmYk5MT4uLicPDgQYPXHDx4UKc8UPd4q375rKws+Pn5oUePHpg+fTqKi4tbvgFkd4qLi3H58mW912+//aZTTq32RH5+CNRqT+n97t0JeoOWtT1Chsbz1NTIkZCwFzKZBgAM9vIoFOUIDT2v1/PDKe1ERLZl00dgRUVFqK2thb+/v85xf39/nD592uA1BQUFBssXFBRI70eOHImHHnoIoaGhOHv2LF555RWMGjUKBw8ehLOzs949q6qqUFVVJb0vKytrTrPIRho7nsdQT49cXgVjM7pUqktGx/OEhp43unDhiBEjEBoaqvf5nNJORGR7Nh8DZAnjx4+Xfu7duzf69OmDsLAwZGVl4d5779Urn5qaikWLFlmzimQBV65c0XlvaDyPsR3XR43aZ/S+DY3nMbZwYY8ePRh0iIjslE0DkK+vL5ydnVFYWKhzvLCwEAEBAQavCQgIMKs8AHTr1g2+vr7Iy8szGIBSUlKQnJwsvS8rK4NKpTKnKWQHbt68Kf1sbDyPscdZ7dtXAtBA96mwBirVJQCmt6gwtOcXe3mIiOybTQOQXC5Hv379kJmZiXHjxgGoGwSdmZmJpKQkg9cMHDgQmZmZmDVrlnTsyy+/xMCBA41+zqVLl1BcXGx00KmrqytnibUhplZoNrbjukp1CWPH7sXu3WNQF4I0GDv2j16eYcOGISIiQu+zGHSIiFonmz8CS05OxuTJkxETE4MBAwYgLS0NFRUVmDp1KgBg0qRJCA4ORmpqKgDgueeew9ChQ/H6669j9OjR2LFjB3744Qe8/fbbAIDr169j0aJFePjhhxEQEICzZ89i9uzZCA8PR3x8vM3aSeYztkChlrHwYWqF5jvuOGX0cZapXh4/Pz/O2iIiakNsHoASExNx9epVzJ8/HwUFBYiKikJGRoY00PnChQtwcvrjX+uDBg3C9u3b8eqrr+KVV15BREQEPvnkE2kNIGdnZxw7dgzp6ekoLS1FUFAQ7rvvPixevJi9PK1IYwc0G9tV3RRTQcfYeJ5OnTqZ9RlERGTfbB6AgLpfYsYeeWVlZekde+SRR/DII48YLO/u7o7PP/+8JatHNlC/56cxu6prqVQXYWo8D2A86HA8DxGRY7CLAERkSmN3VddSKMo5noeIiExiACK7ZmzauqFd1V1cXKSfOZ6HiIhMYQAiu2bOrur1x+lwPA8RERnDAEQtxtSsrdLSUshkMr3xNVrGHj8Zm7ZuaFd1Hx8fJCUlNWnmGBERORYGIGoRjZ21ZYqhGV3m7qrOcENERI3BAEQtojHbUDR0zljPjanxPERERE3BAERmO3v2LCorK3WOHTt2TPrZ1Kytxs7oqr9burHxPNxVnYiImoIBiMxy9uxZvPfee0bPm9qGAkCjZ3RxPA8REVkSAxCZpf5GtJcuBeLCha7o0uU8One+bHIbCg+P3xs9owvgeB4iIrIcBiAyy607rv/rXw/gxx8jURd4BCIjf4RSWWTwuuJib6hUlxo9o4uIiMiSnBouQqTv0qXAW8IPAMjw44+RuHbN22D52tp20owumUxTd0UDM7qIiIgshT1A1CQ//9wd9R91aXuC6l63nhPo3v0MAM7oIiIi+8AARE3SocN1g8eDgi5DiB/1Ho117nxZKsMZXUREZGsMQG2IqZWYgZadNdWjx8/Yt69+T48GPXqcwYABR9C//yFcvNgFKtUFnfATGxuLyMhIi9aNiIioIQxAbURjV2I2tNpyU9TtuL7H6I7rnTtf1gk+WhEREdyIlIiIbI4BqJUx1stTVKQ7+8rc1ZYby83NTfrZ1HieyMhIhIWF6Vzbvn17vWNERES2wADUijS2l6exqy03Rf0AY2w8z5AhQ/hIi4iI7BYDkJ0y1NOTn5/f4HVqtWejV1tuCq7QTEREbQEDkB1qbE9P/VWYAaCkxMes1ZabguGGiIhaOwYgGzK0qaj2+K0MBR1DqzA/+OCnUCqLudoyERFRAxiAbKShTUW1DAWd/v0PGVyFuX//Q+jc+TISEvbqjQHigoNERER/YACyAkPjeX799Ved94Z6eYxtN+Hm9jsMrcJ88WIXdO58mastExERNYAByMIaM57H2OMsY9tNVFW5wNB2EyrVBekdV1smIiIyjgHIwur3/NRfn8dYL0///ofQoYPhnpuGtpsYNmwYIiIi9K7j7CwiIqI6DEBWZGh9nhs3XGHscVavXicNbDchGtxuws/Pj6stExERmeDUcBFqCcbW5/HxKULd46xb1T3O0m43IZNpANTN5ho7do/OdhMDB36vt+VEp06dLN0cIiKiVo09QFZibH0eubwGkZHGH2eZGtB85513ok+fPjr35GMuIiKihjEAWYmp9XkefPBTo4+zAOMDmsPDw/moi4iIqAkYgKxEoSg3uT6Psd3T77rrLvj7++sd58aiRERETccAZEWmHmcNGzYM3t7eOuUZcoiIiCyDAcjC6q+7Y+xx1u23386xO0RERFbCAGRh3D2diIjI/jAAWQHDDRERkX3hOkBERETkcBiAiIiIyOEwABEREZHDYQAiIiIih8MARERERA7HLgLQ+vXrERISAjc3N8TGxuLQoUMmy3/44Yfo2bMn3Nzc0Lt3b+zbt0/nvBAC8+fPR2BgINzd3REXF4czZ85YsglERETUitg8AO3cuRPJyclYsGABjhw5gsjISMTHx+PKlSsGyx84cAATJkzAE088gaNHj2LcuHEYN24cjh8/LpVZsWIF1qxZg40bN+L777+Hh4cH4uPjcePGDWs1i4iIiOyYTAghbFmB2NhY9O/fH+vWrQMAaDQaqFQqPPvss5gzZ45e+cTERFRUVGDv3r3SsTvvvBNRUVHYuHEjhBAICgrCCy+8gBdffBEAoFar4e/vj23btmH8+PEN1qmsrAwKhQJqtRodO3ZsoZYSERGRJZnz+9umPUDV1dXIzs5GXFycdMzJyQlxcXE4ePCgwWsOHjyoUx4A4uPjpfL5+fkoKCjQKaNQKBAbG2v0nlVVVSgrK9N5ERERUdtl05Wgi4qKUFtbq7fbub+/P06fPm3wmoKCAoPlCwoKpPPaY8bK1JeamopFixbpHWcQIiIiaj20v7cb83CLW2EASElJQXJysvT+119/Ra9evaBSqWxYKyIiImqK8vJyKBQKk2VsGoB8fX3h7OyMwsJCneOFhYUICAgweE1AQIDJ8tr/FhYWIjAwUKdMVFSUwXu6urrC1dVVet+hQwdcvHgRnp6ekMlkZrfLlLKyMqhUKly8eNEhxxex/Y7dfoDfgaO3H+B3wPZbrv1CCJSXlyMoKKjBsjYNQHK5HP369UNmZibGjRsHoG4QdGZmJpKSkgxeM3DgQGRmZmLWrFnSsS+//BIDBw4EAISGhiIgIACZmZlS4CkrK8P333+P6dOnN6peTk5O6Ny5c5Pb1RgdO3Z0yD/4Wmy/Y7cf4Hfg6O0H+B2w/ZZpf0M9P1o2fwSWnJyMyZMnIyYmBgMGDEBaWhoqKiowdepUAMCkSZMQHByM1NRUAMBzzz2HoUOH4vXXX8fo0aOxY8cO/PDDD3j77bcBADKZDLNmzcKSJUsQERGB0NBQzJs3D0FBQVLIIiIiIsdm8wCUmJiIq1evYv78+SgoKEBUVBQyMjKkQcwXLlyAk9Mfk9UGDRqE7du349VXX8Urr7yCiIgIfPLJJ7jjjjukMrNnz0ZFRQWmTZuG0tJSDB48GBkZGXBzc7N6+4iIiMj+2DwAAUBSUpLRR15ZWVl6xx555BE88sgjRu8nk8nwt7/9DX/7299aqootxtXVFQsWLNAZc+RI2H7Hbj/A78DR2w/wO2D77aP9Nl8IkYiIiMjabL4VBhEREZG1MQARERGRw2EAIiIiIofDAEREREQOhwGoha1fvx4hISFwc3NDbGwsDh06ZLTsiRMn8PDDDyMkJAQymQxpaWnWq6gFmfMdbNq0CUOGDIG3tze8vb0RFxdnsnxrYE77d+3ahZiYGHh5ecHDwwNRUVF49913rVhbyzDnO7jVjh07IJPJWv2aXea0f9u2bZDJZDqvtrBkh7l/BkpLSzFjxgwEBgbC1dUV3bt3x759+6xU25ZnTvvvuecevT8DMpkMo0ePtmKNW5a5//unpaWhR48ecHd3h0qlwvPPP48bN25YtpKCWsyOHTuEXC4XW7ZsESdOnBBPPfWU8PLyEoWFhQbLHzp0SLz44ovigw8+EAEBAeKNN96wboUtwNzv4M9//rNYv369OHr0qDh16pSYMmWKUCgU4tKlS1auecswt/1ff/212LVrlzh58qTIy8sTaWlpwtnZWWRkZFi55i3H3O9AKz8/XwQHB4shQ4aIBx54wDqVtQBz279161bRsWNHcfnyZelVUFBg5Vq3LHO/g6qqKhETEyPuv/9+8c0334j8/HyRlZUlcnJyrFzzlmFu+4uLi3X+9z9+/LhwdnYWW7dutW7FW4i57X///feFq6ureP/990V+fr74/PPPRWBgoHj++ectWk8GoBY0YMAAMWPGDOl9bW2tCAoKEqmpqQ1e27Vr1zYRgJrzHQghxM2bN4Wnp6dIT0+3VBUtqrntF0KIvn37ildffdUS1bOKpnwHN2/eFIMGDRL/+Mc/xOTJk1t1ADK3/Vu3bhUKhcJKtbMOc7+DDRs2iG7duonq6mprVdGimvv3wBtvvCE8PT3F9evXLVVFizK3/TNmzBDDhw/XOZacnCzuuusui9aTj8BaSHV1NbKzsxEXFycdc3JyQlxcHA4ePGjDmllPS3wHlZWVqKmpgVKptFQ1Laa57RdCIDMzE7m5ubj77rstWVWLaep38Le//Q1+fn544oknrFFNi2lq+69fv46uXbtCpVLhgQcewIkTJ6xRXYtoynewe/duDBw4EDNmzIC/vz/uuOMOvPbaa6itrbVWtVtMS/w9uHnzZowfPx4eHh6WqqbFNKX9gwYNQnZ2tvSY7JdffsG+fftw//33W7SudrESdFtQVFSE2tpaaQsPLX9/f5w+fdpGtbKulvgOXn75ZQQFBen8n6e1aGr71Wo1goODUVVVBWdnZ7z11lsYMWKEpatrEU35Dr755hts3rwZOTk5VqihZTWl/T169MCWLVvQp08fqNVqrFq1CoMGDcKJEycsvimzJTTlO/jll1/w1Vdf4bHHHsO+ffuQl5eHZ555BjU1NViwYIE1qt1imvv34KFDh3D8+HFs3rzZUlW0qKa0/89//jOKioowePBgCCFw8+ZNPP3003jllVcsWlcGILIby5Ytw44dO5CVldUmBoE2lqenJ3JycnD9+nVkZmYiOTkZ3bp1wz333GPrqllceXk5Jk6ciE2bNsHX19fW1bGJgQMHYuDAgdL7QYMG4bbbbsPf//53LF682IY1sx6NRgM/Pz+8/fbbcHZ2Rr9+/fDrr79i5cqVrS4ANdfmzZvRu3dvDBgwwNZVsZqsrCy89tpreOuttxAbG4u8vDw899xzWLx4MebNm2exz2UAaiG+vr5wdnZGYWGhzvHCwkIEBATYqFbW1ZzvYNWqVVi2bBn279+PPn36WLKaFtPU9js5OSE8PBwAEBUVhVOnTiE1NbVVBiBzv4OzZ8/i3LlzSEhIkI5pNBoAQLt27ZCbm4uwsDDLVroFtcTfAy4uLujbty/y8vIsUUWLa8p3EBgYCBcXFzg7O0vHbrvtNhQUFKC6uhpyudyidW5JzfkzUFFRgR07dtjlPpaN1ZT2z5s3DxMnTsSTTz4JAOjdu7e0ofncuXN1NkRvSRwD1ELkcjn69euHzMxM6ZhGo0FmZqbOv+7asqZ+BytWrMDixYuRkZGBmJgYa1TVIlrqz4BGo0FVVZUlqmhx5n4HPXv2xE8//YScnBzpNXbsWAwbNgw5OTlQqVTWrH6ztcSfgdraWvz0008IDAy0VDUtqinfwV133YW8vDwp/ALAzz//jMDAwFYVfoDm/Rn48MMPUVVVhccff9zS1bSYprS/srJSL+Row7Cw5HalFh1i7WB27NghXF1dxbZt28TJkyfFtGnThJeXlzSldeLEiWLOnDlS+aqqKnH06FFx9OhRERgYKF588UVx9OhRcebMGVs1odnM/Q6WLVsm5HK5+Oijj3SmgZaXl9uqCc1ibvtfe+018cUXX4izZ8+KkydPilWrVol27dqJTZs22aoJzWbud1Bfa58FZm77Fy1aJD7//HNx9uxZkZ2dLcaPHy/c3NzEiRMnbNWEZjP3O7hw4YLw9PQUSUlJIjc3V+zdu1f4+fmJJUuW2KoJzdLU/w8MHjxYJCYmWru6Lc7c9i9YsEB4enqKDz74QPzyyy/iiy++EGFhYeLRRx+1aD0ZgFrY2rVrRZcuXYRcLhcDBgwQ3333nXRu6NChYvLkydL7/Px8AUDvNXToUOtXvAWZ8x107drV4HewYMEC61e8hZjT/rlz54rw8HDh5uYmvL29xcCBA8WOHTtsUOuWZc53UF9rD0BCmNf+WbNmSWX9/f3F/fffL44cOWKDWrcsc/8MHDhwQMTGxgpXV1fRrVs3sXTpUnHz5k0r17rlmNv+06dPCwDiiy++sHJNLcOc9tfU1IiFCxeKsLAw4ebmJlQqlXjmmWfEtWvXLFpHmRCW7F8iIiIisj8cA0REREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhMAARERGRw2EAIiKyoSlTpmDcuHG2rgaRw2EAIiKDpkyZAplMJr18fHwwcuRIHDt2zNZVaxG3tk37Gjx4sMU+79y5c5DJZMjJydE5/uabb2Lbtm0W+1wiMowBiIiMGjlyJC5fvozLly8jMzMT7dq1w5gxY2xdrRazdetWqX2XL1/G7t27DZarqamxWB0UCgW8vLwsdn8iMowBiIiMcnV1RUBAAAICAhAVFYU5c+bg4sWLuHr1KoYPH46kpCSd8levXoVcLpd2gg4JCcHixYsxYcIEeHh4IDg4GOvXr9e5ZvXq1ejduzc8PDygUqnwzDPP4Pr169L58+fPIyEhAd7e3vDw8MDtt9+Offv2AQCuXbuGxx57DJ06dYK7uzsiIiKwdevWRrfPy8tLal9AQACUSqXUU7Nz504MHToUbm5ueP/991FcXIwJEyYgODgY7du3R+/evfHBBx/o3E+j0WDFihUIDw+Hq6srunTpgqVLlwIAQkNDAQB9+/aFTCbDPffcA0D/EVhVVRVmzpwJPz8/uLm5YfDgwTh8+LB0PisrCzKZDJmZmYiJiUH79u0xaNAg5ObmNrrdRMQARESNdP36dbz33nsIDw+Hj48PnnzySWzfvh1VVVVSmffeew/BwcEYPny4dGzlypWIjIzE0aNHMWfOHDz33HP48ssvpfNOTk5Ys2YNTpw4gfT0dHz11VeYPXu2dH7GjBmoqqrCf//7X/z0009Yvnw5OnToAACYN28eTp48iX//+984deoUNmzYAF9f3xZpr7aup06dQnx8PG7cuIF+/frhs88+w/HjxzFt2jRMnDgRhw4dkq5JSUnBsmXLpHpt374d/v7+ACCV279/Py5fvoxdu3YZ/NzZs2fj448/Rnp6Oo4cOYLw8HDEx8ejpKREp9zcuXPx+uuv44cffkC7du3wl7/8pUXaTeQwLLrVKhG1WpMnTxbOzs7Cw8NDeHh4CAAiMDBQZGdnCyGE+P3334W3t7fYuXOndE2fPn3EwoULpfddu3YVI0eO1LlvYmKiGDVqlNHP/fDDD4WPj4/0vnfv3jr3vFVCQoKYOnVqk9oHQLi5uUnt8/DwEP/6179Efn6+ACDS0tIavMfo0aPFCy+8IIQQoqysTLi6uopNmzYZLKu979GjR3WOT548WTzwwANCCCGuX78uXFxcxPvvvy+dr66uFkFBQWLFihVCCCG+/vprAUDs379fKvPZZ58JAOL333835ysgcmjsASIio4YNG4acnBzk5OTg0KFDiI+Px6hRo3D+/Hm4ublh4sSJ2LJlCwDgyJEjOH78OKZMmaJzj4EDB+q9P3XqlPR+//79uPfeexEcHAxPT09MnDgRxcXFqKysBADMnDkTS5YswV133YUFCxboDMKePn06duzYgaioKMyePRsHDhwwq31vvPGG1L6cnByMGDFCOhcTE6NTtra2FosXL0bv3r2hVCrRoUMHfP7557hw4QIA4NSpU6iqqsK9995rVh1udfbsWdTU1OCuu+6Sjrm4uGDAgAE63xkA9OnTR/o5MDAQAHDlypUmfzaRo2EAIiKjPDw8EB4ejvDwcPTv3x//+Mc/UFFRgU2bNgEAnnzySXz55Ze4dOkStm7diuHDh6Nr166Nvv+5c+cwZswY9OnTBx9//DGys7OlMULV1dXSZ/zyyy+YOHEifvrpJ8TExGDt2rUAIIWx559/Hr/99hvuvfdevPjii43+/ICAAKl94eHh8PDw0Gn7rVauXIk333wTL7/8Mr7++mvk5OQgPj5eqqe7u3ujP7cluLi4SD/LZDIAdWOQiKhxGICIqNFkMhmcnJzw+++/AwB69+6NmJgYbNq0Cdu3bzc4DuW7777Te3/bbbcBALKzs6HRaPD666/jzjvvRPfu3fHbb7/p3UOlUuHpp5/Grl278MILL0gBDAA6deqEyZMn47333kNaWhrefvvtlmyy5Ntvv8UDDzyAxx9/HJGRkejWrRt+/vln6XxERATc3d2lAeD1yeVyAHU9ScaEhYVBLpfj22+/lY7V1NTg8OHD6NWrVwu1hIgAoJ2tK0BE9quqqgoFBQUA6mZcrVu3DtevX0dCQoJU5sknn0RSUhI8PDzw4IMP6t3j22+/xYoVKzBu3Dh8+eWX+PDDD/HZZ58BAMLDw1FTU4O1a9ciISEB3377LTZu3Khz/axZszBq1Ch0794d165dw9dffy0FqPnz56Nfv364/fbbUVVVhb1790rnWlpERAQ++ugjHDhwAN7e3li9ejUKCwulYOLm5oaXX34Zs2fPhlwux1133YWrV6/ixIkTeOKJJ+Dn5wd3d3dkZGSgc+fOcHNzg0Kh0PkMDw8PTJ8+HS+99BKUSiW6dOmCFStWoLKyEk888YRF2kXkqNgDRERGZWRkIDAwEIGBgYiNjcXhw4fx4YcfSlO4AWDChAlo164dJkyYADc3N717vPDCC/jhhx/Qt29fLFmyBKtXr0Z8fDwAIDIyEqtXr8by5ctxxx134P3330dqaqrO9bW1tZgxYwZuu+02jBw5Et27d8dbb70FoK5XJSUlBX369MHdd98NZ2dn7NixwyLfxauvvoro6GjEx8fjnnvuQUBAgN4KzvPmzcMLL7yA+fPn47bbbkNiYqI0Lqddu3ZYs2YN/v73vyMoKAgPPPCAwc9ZtmwZHn74YUycOBHR0dHIy8vD559/Dm9vb4u0i8hRyYQQwtaVIKLW69y5cwgLC8Phw4cRHR2tcy4kJASzZs3CrFmzbFM5IiIj+AiMiJqkpqYGxcXFePXVV3HnnXfqhR8iInvGR2BE1CTffvstAgMDcfjwYb1xO7b22muvoUOHDgZfo0aNsnX1iMgO8BEYEbU5JSUleisna7m7uyM4ONjKNSIie8MARERERA6Hj8CIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORw/h+mtgpEsPC5SQAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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nn8DV1RVXr15FZGQkQkNDxYBNRETGbf36Gzh+fBEAwMLCBzNnPouAAIWLqgZFQ1lQUBCuXbuGmJgYaLVauLm5ITU1VbxQ//z585L7AHp7e2P16tWIjo7G5MmT4eLigpSUFPTo0UPsM2HCBBQVFSE8PBz5+fno378/UlNTYWlpWe26vv76a9y+fRvx8fFiIAQAHx8f7Nq1CwBw48YNjBo1ClqtFi1atICHhwfS09PRrVu3xzwrlbt7966knvoSFRWlt9v+dOnSBUeOHAEAREREiO0dOnTA9OnT8c477+Dzzz+Hubk5bG1toVKpdKaz3377bfF/P/nkk1i0aBH69OmDW7duicGNiIiM08KFC1FQUCA+b9HCHqGhytVTEypBEASlizBWhYWFsLW1RUFBgc5UZnFxMXJycuDs7CwGytLS0gYRykJDQ5Gfn4+UlBSd14KCgpCdnY1jx47hxx9/RHx8PE6ePInCwkLcu3cPxcXFKCoqgrW1NZKSkhAREaFz788DBw5g6tSpOHz4MG7cuIHy8nLcvn0bx44dq9NQXB/k/rsTEdGjpaSU4fDh6ZI2jcYfb79dvZUWaqKqv9+Pg/e+bEDMzMyq/a1RfR9XX06cOAFnZ2fk5uZi0KBBePfddzFjxgy0bNkSv/76K8LCwlBaWlrp+nFFRUXw8/ODn58fVq1ahdatW+P8+fPw8/NDaWmp3uokIqKGY/nyndBqd0vawsLCxMuSGgqGsgZEpVLpbRpRCT/99BOOHj2Kf//73zhw4ADKy8sxb948cYp63bp1kv7m5uY6txs6efIk/vzzT8ycOVNcUmT//v318waIiMjgxMXF6bTFxMTo5Vro+mZ065RR/SgpKYFWq8WlS5dw8OBBfPrppxg8eDAGDRqE4OBgdOrUCXfv3sXixYvxxx9/4JtvvkFCQoJkHx06dMCtW7eQlpaG69ev4/bt22jXrh3Mzc3F7TZv3oxPPvlEoXdJRERKyc/Plw1ksbGxDTKQAQxlVEdSU1Ph6OiIDh06YODAgdi5cycWLVqETZs2wdTUFL169cL8+fMxa9Ys9OjRA6tWrdK5Xs7b2xvvvPMOgoKC0Lp1a8yePRutW7dGUlIS1q9fj27dumHmzJmYO3euQu+SiIiUEBcXh88++0zSlpUVhNjYWIUq0g9e6K+gml7oT40f/7sTEVVNbnRs27ZYTJqEelv2ghf6ExERkdG6ePEivvrqK5322NhYNPABMhFDGRERERk0udGx4OBgODs7K1BN3WEoIyIiIoNV2cX8jRFDGRERERmcgwcP4n//+59Oe2MNZABDGRERERkYudGx9957D61bt1agmvrDUEZEREQGQRAETJs2Tafd3T0WjTyPAWAoIyIiIgOwdetW7Nu3T6e9MU9XPoyhjIiIiBQlN10ZGRmJZs2aKVCNchjKiIiISBFlZWWYPn26TrsxjY49iKGMGqzQ0FDk5+cjJSUFAPDss8/Czc0NCxcurPU+9bEPIiJ6tLlz56KoqEjSZm7eAlFRHypUkfIYykjvQkND8fXXXwMAzMzM0K5dOwQHB2Py5Mlo0qTufuU2btwIMzOzavXdtWsXBgwYgBs3bsDOzq5W+yAiotqRm66MioqCubm5AtUYDoYyqhMDBw7EypUrUVJSgq1bt+L999+HmZkZoqKiJP1KS0v19n/Cli1bGsQ+iIhIXlFREebOnavTbqzTlQ8zUboAapwsLCygVqvRvn17vPvuu/D19cXmzZsRGhqKwMBAzJgxA05OTnB1dQUAXLhwAa+//jrs7OzQsmVLDB48GLm5ueL+ysrKEBkZCTs7O7Rq1QoTJkyAIAiSYz777LOIiIgQn5eUlGDixInQaDSwsLBAp06d8NVXXyE3NxcDBgwAALRo0QIqlQqhoaGy+7hx4waCg4PRokULWFtb44UXXsDp06fF15OSkmBnZ4dt27aha9euaNq0KQYOHIgrV66IfXbt2oW+ffvCxsYGdnZ2+L//+z+cO3dOT2eaiKhhiIuL0wlkTZu2ZyB7AEMZ1QsrKyuUlpYCANLS0nDq1Cns2LEDW7Zswd27d+Hn54dmzZrhl19+wW+//SaGm4pt5s2bh6SkJCQmJuLXX3/FX3/9he+++67KYwYHB2PNmjVYtGgRTpw4geXLl6Np06bQaDTYsGEDAODUqVO4cuUKPvvsM9l9hIaGYv/+/di8eTMyMjIgCAJefPFF3L17V+xz+/ZtzJ07F9988w12796N8+fPY/z48QCAe/fuITAwED4+Pjhy5AgyMjIQHh4OlUr12OeUiKihkJuujImJwbhxofVfjAHj9CXVKUEQkJaWhm3btmHMmDG4du0abGxssGLFCnHa8ttvv0V5eTlWrFghhpWVK1fCzs4Ou3btwr/+9S8sXLgQUVFReOWVVwAACQkJ2LZtW6XH/f3337Fu3Trs2LEDvr6+AIAnn3xSfL1imrJNmzaSa8oedPr0aWzevBm//fYbvL29AQCrVq2CRqNBSkoKXnvtNQDA3bt3kZCQgI4dOwIAPvjgA3Hxw8LCQhQUFGDQoEHi6127dq35iSQiaoAuX76ML7/8Uqedo2PyOFJmJDZvBry97/+sD1u2bEHTpk1haWmJF154AUFBQZg6dSoAoGfPnpLryA4fPowzZ86gWbNmaNq0KZo2bYqWLVuiuLgYZ8+eRUFBAa5cuQJPT09xmyZNmqB3796VHv/QoUMwNTWFj49Prd/DiRMn0KRJE8lxW7VqBVdXV5w4cUJss7a2FgMXADg6OuLq1asA7oe/0NBQ+Pn54aWXXsJnn30mmdokImqs4uLidALZxYtucHdnIKsMR8qMxMyZQEbG/Z8BAXV/vAEDBmDZsmUwNzeHk5OT5FuXNjY2kr63bt2Ch4cHVq1apbOf2t7nzMrKqlbb1cbD39ZUqVSS691WrlyJDz/8EKmpqUhOTkZ0dDR27NiBfv361VuNRET1SW66kqNjj8aRMiMxaRLg5XX/Z32wsbFBp06d0K5du0cug/HUU0/h9OnTaNOmDTp16iR52NrawtbWFo6OjsjMzBS3uXfvHg4cOFDpPnv27Iny8nL8/PPPsq9XjNSVlZVVuo+uXbvi3r17kuP++eefOHXqFLp161ble3qYu7s7oqKikJ6ejh49emD16tU12p6IqCE4evQoA9ljYCgzEgEBQHp6/YyS1dSbb74Je3t7DB48GL/88gtycnKwa9cufPjhh7h48SIAYOzYsZg5cyZSUlJw8uRJvPfee8jPz690nx06dEBISAjefvttpKSkiPtct24dAKB9+/ZQqVTYsmULrl27hlu3bunsw8XFBYMHD8aoUaPw66+/4vDhw3jrrbfQtm1bDB48uFrvLScnB1FRUcjIyMC5c+ewfft2nD59mteVEVGjExcXh40bN0raXnzxRQayGmAoI8VZW1tj9+7daNeuHV555RV07doVYWFhKC4uRvPmzQEA48aNw/DhwxESEgIvLy80a9YML7/8cpX7XbZsGYYMGYL33nsPXbp0wahRo8TVo9u2bYu4uDhMmjQJDg4O+OCDD2T3sXLlSnh4eGDQoEHw8vKCIAjYunVrtReYtba2xsmTJ/Hqq6+ic+fOCA8Px/vvv4/Ro0fX4AwRERm2ykbH+vTpo0A1DZdKeHixJ6o3hYWFsLW1RUFBgRg+KhQXFyMnJwfOzs6wtLRUqEKqb/zvTkQNyfr163H8+HGd9sY+OlbV3+/HwQv9iYiIqMbkRscOHHgTI0d2UqCaxoGhjIiIiKpNEARxLcYHubvHopEPkNU5hjIiIiKqFrnRMeB+IDPEL5I1NLzQn4iIiB5JLpClp4czkOkRR8oMHL+HYVz435uIDE1paSni4+N12ufOjcXNmwoU1IgxlBmoiiUXbt++Xa+r05OyKm7AbmpqqnAlRESVT1fOnRuLDz+s52KMAEOZgTI1NYWdnZ14D0Vra2vxZt3UOJWXl+PatWuwtrZ+5F0QiIjqmlwgS0ubgN27rXhBfx3hJ78BU6vVACAGM2r8TExM0K5dOwZwIlLMX3/9hcWLF+u0b9sWW2+36jNWXDxWQdVdfK6srAx3796tx8pIKebm5jAx4fdviEgZlU1XNvbFYGuKi8caMVNTU15jREREdUoukM2dG4NVqzhyX18YyoiIiIxYVlYWNm/erNM+d24sVq0Cl7uoRwxlRERERqqy6covv2QgUwJDGRERkRGSC2QVC8HyEjJlMJQREREZkf/+9784duyYTjsv5lee4l/zWrp0KTp06ABLS0t4enpi7969VfZfv349unTpAktLS/Ts2RNbt26VvC4IAmJiYuDo6AgrKyv4+vri9OnTsvsqKSmBm5sbVCoVDh06JHntyJEjePrpp2FpaQmNRoPZs2fXuBYiIiJDEhcXpxPINBoNA5mBUDSUJScnIzIyErGxsTh48CB69eoFPz+/StflSk9Px7BhwxAWFoasrCwEBgYiMDAQ2dnZYp/Zs2dj0aJFSEhIQGZmJmxsbODn54fi4mKd/U2YMAFOTk467YWFhfjXv/6F9u3b48CBA5gzZw6mTp2KL774oka1EBERGQq56crY2Fi8/fbbClRDchRdp8zT0xN9+vTBkiVLANxf0Vyj0WDMmDGYJLNCXVBQEIqKirBlyxaxrV+/fnBzc0NCQgIEQYCTkxPGjRuH8ePHAwAKCgrg4OCApKQkDB06VNzuhx9+QGRkJDZs2IDu3bsjKysLbm5uAIBly5bh448/hlarhbm5OQBg0qRJSElJwcmTJ6tVS3XU1TonREREFSq7mH/btlikp9dzMY1EXf39VmykrLS0FAcOHICvr+/fxZiYwNfXFxkZGbLbZGRkSPoDgJ+fn9g/JycHWq1W0sfW1haenp6Sfebl5WHUqFH45ptvYG1tLXucZ555RgxkFcc5deoUbty4Ua1a5JSUlKCwsFDyICIiqitygezoUR+sWcPV+Q2RYhf6X79+HWVlZXBwcJC0Ozg4iKNRD9NqtbL9tVqt+HpFW2V9BEFAaGgo3nnnHfTu3Ru5ubmyx3F2dtbZR8VrLVq0eGQtcuLj4yv9FwsREZG+CIKAadOm6bTz2jHDZnTfvly8eDFu3ryJqKioej92VFQUIiMjxeeFhYXQaDT1XgcRETVelf3j392dgczQKRbK7O3tYWpqiry8PEl7Xl6eeCPuh6nV6ir7V/zMy8uDo6OjpE/F9WI//fQTMjIyYGFhIdlP79698eabb+Lrr7+u9DgPHuNRtcixsLDQOS4REZG+yAWyN998E506dVKgGqopxa4pMzc3h4eHB9LS0sS28vJypKWlwcvLS3YbLy8vSX8A2LFjh9jf2dkZarVa0qewsBCZmZlin0WLFuHw4cM4dOgQDh06JC5jkZycjBkzZojH2b17t+Qm4Dt27ICrqytatGhRrVqIiIjqS3FxcaWLwTKQNRyKTl9GRkYiJCQEvXv3Rt++fbFw4UIUFRVhxIgRAIDg4GC0bdsW8fHxAICxY8fCx8cH8+bNg7+/P9auXYv9+/eLS1WoVCpERERg+vTpcHFxgbOzM6ZMmQInJycEBgYCANq1ayepoWnTpgCAjh074oknngAAvPHGG4iLi0NYWBgmTpyI7OxsfPbZZ1iwYIG43aNqISIiqg9VTVfyNkkNi6KhLCgoCNeuXUNMTAy0Wi3c3NyQmpoqXkB//vx5mJj8PZjn7e2N1atXIzo6GpMnT4aLiwtSUlLQo0cPsc+ECRNQVFSE8PBw5Ofno3///khNTYWlpWW167K1tcX27dvx/vvvw8PDA/b29oiJiUF4eHiNaiEiIqpLcoHs3//+N5dZaqAUXafM2HGdMiIiqo0LFy4gMTFRp51rj9WPuvr7bXTfviQiImrIqloMlmuPNWwMZURERA2EXCCbMmUKTExMwCXIGj6GMiIiIgO3c+dO7N69W6edi8E2LgxlREREBqyy6UoGssaHoYyIiMhAVbb2GJe6aJwYyoiIiAzMwoULUVBQoNPO0bHGjaGMiIjIgHC60ngxlBERERkIuUDGMGY8GMqIiIgUVtnoWGkpA5kxYSgjIiJSkFwg69mzJ1555RUFqiElMZQREREpQBAETJs2Taed05XGi6GMiIionlU2XenuzkBmzBjKiIiI6pFcIHN2fg3Bwd0UqIYMCUMZERFRPbhz5w5mz56t087pSqrAUEZERFTHuPYYVQdDGRERUR2SC2QRERGwtbVVoBoyZAxlREREdSA3Nxdff/21TjtHx6gyDGVERER6xulKqg2GMiIiIj2SC2RTpkyBiYmJAtVQQ8JQRkREpAepqanIzMzUaefoGFUXQxkREdFj4nQl6QPHUomIiB6DXCCbOjUW27YxkFHNcKSMiIioFmbPno07d+7otJeWxsLLC5g0SYGiqEFjKCMiIqohTldSXeD0JRERUQ3IBbJt22J5M3F6bBwpIyIiqoaqRsc4QEb6wFBGRET0CHKB7B//+AdefvllBaqhxoqhjIiIqBKCIGDatGk67e7usQgIUKAgatQYyoiIiGTwYn6qbwxlRERED5ELZEOGDEH37t0VqIaMBUMZERHR/3f79m3MmTNHp52jY1QfGMqIiIjA6UpSHkMZEREZPblAFhERAVtbWwWqIWPFUEZEREbrjz/+wDfffKPTztExUgJDGRERGSVOV5Kh4W2WiIjI6MjfKmkKb5VEiuJIGRERGY3U1FRkZmbqtPNWSWQIGMqIiMgocLqSDB1DGRERNXpygYxhjAwNQxkRETVa06ZNgyAIOu0MZGSIFL/Qf+nSpejQoQMsLS3h6emJvXv3Vtl//fr16NKlCywtLdGzZ09s3bpV8rogCIiJiYGjoyOsrKzg6+uL06dPS/oEBASgXbt2sLS0hKOjI4YPH47Lly+Lr0+dOhUqlUrnYWNjI/ZJSkrSed3S0lIPZ4SIiPQhLi6OgYwaFEVDWXJyMiIjIxEbG4uDBw+iV69e8PPzw9WrV2X7p6enY9iwYQgLC0NWVhYCAwMRGBiI7Oxssc/s2bOxaNEiJCQkIDMzEzY2NvDz80NxcbHYZ8CAAVi3bh1OnTqFDRs24OzZsxgyZIj4+vjx43HlyhXJo1u3bnjttdck9TRv3lzS59y5c3o+Q0REVBuVTVcykJEhUwly/4yoJ56enujTpw+WLFkCACgvL4dGo8GYMWMwadIknf5BQUEoKirCli1bxLZ+/frBzc0NCQkJEAQBTk5OGDduHMaPHw8AKCgogIODA5KSkjB06FDZOjZv3ozAwECUlJTAzMxM5/XDhw/Dzc0Nu3fvxtNPPw3g/khZREQE8vPza/3+CwsLYWtri4KCAjRv3rzW+yEiovsqu5h/27ZYpKfXczHUaNXV32/FRspKS0tx4MAB+Pr6/l2MiQl8fX2RkZEhu01GRoakPwD4+fmJ/XNycqDVaiV9bG1t4enpWek+//rrL6xatQre3t6ygQwAVqxYgc6dO4uBrMKtW7fQvn17aDQaDB48GMeOHavyPZeUlKCwsFDyICIi/ZALZHZ2XbFtWyxk/p1PZHAUC2XXr19HWVkZHBwcJO0ODg7QarWy22i12ir7V/yszj4nTpwIGxsbtGrVCufPn8emTZtkj1lcXIxVq1YhLCxM0u7q6orExERs2rQJ3377LcrLy+Ht7Y2LFy9W+p7j4+Nha2srPjQaTaV9iYioegRBkA1k7u6xGDv2daSnAwEBChRGVEOKX+ivlI8++ghZWVnYvn07TE1NERwcLHtB6HfffYebN28iJCRE0u7l5YXg4GC4ubnBx8cHGzduROvWrbF8+fJKjxkVFYWCggLxceHCBb2/LyIiYxIXF4dp06bptMfGxjKIUYOj2JIY9vb2MDU1RV5enqQ9Ly8ParVadhu1Wl1l/4qfeXl5cHR0lPRxc3PTOb69vT06d+6Mrl27QqPRYM+ePfDy8pL0W7FiBQYNGqQz+vYwMzMzuLu748yZM5X2sbCwgIWFRZX7ISKi6pEbHRsyZAi6d++uQDVEj0+xkTJzc3N4eHggLS1NbCsvL0daWppOMKrg5eUl6Q8AO3bsEPs7OztDrVZL+hQWFiIzM7PSfVYcF7h/zdeDcnJysHPnTp2pSzllZWU4evSoJAwSEZH+3b59u5J7V8YykFGDpujisZGRkQgJCUHv3r3Rt29fLFy4EEVFRRgxYgQAIDg4GG3btkV8fDwAYOzYsfDx8cG8efPg7++PtWvXYv/+/fjiiy8AACqVChEREZg+fTpcXFzg7OyMKVOmwMnJCYGBgQCAzMxM7Nu3D/3790eLFi1w9uxZTJkyBR07dtQJbomJiXB0dMQLL7ygU/u0adPQr18/dOrUCfn5+ZgzZw7OnTuHkSNH1uEZIyIyblV9u5IX81NDp2goCwoKwrVr1xATEwOtVgs3NzekpqaKU4Xnz5+Hicnfg3ne3t5YvXo1oqOjMXnyZLi4uCAlJQU9evQQ+0yYMAFFRUUIDw9Hfn4++vfvj9TUVHFhV2tra2zcuBGxsbEoKiqCo6MjBg4ciOjoaMnUYnl5OZKSkhAaGgpTU1Od2m/cuIFRo0ZBq9WiRYsW8PDwQHp6Orp161ZXp4uIyKjJBbL58yPQvbstl7ugRkHRdcqMHdcpIyJ6tD/++APffPONTru7eyxmzgQmTeK3K6l+1dXfb977koiIDFZl05UVK/MzjFFjwlBGREQGZ/NmICtLN5BNmTJFclkLUWPCUEZERAYlNTUVWVmZOu3u7rFgHqPGjKGMiIgMxqOmK4kaM4YyIiIyCHKBjGGMjAlDGRERKWr27Nm4c+eOTjsDGRkbhjIiIlIMpyuJ/sZQRkREiqjsVklcCJaMFUMZERHVK94qiUgeQxkREdUbuUDm7u6OgIAAcMaSjB1XfCEiojonCEKl05UBXJafCABHyoiIqI5VNl25dm0sZs2q52KIDBhDGRER1Rm5QDZ06FC4urpyupLoIQxlRESkd7dv38acOXN02rnUBVHlGMqIiEivuPYYUe0wlBERkd7IBbLIyEg0a9ZMgWqIGhaGMiIiemw5OTn4z3/+o9PO0TGi6qvxkhimpqa4evWqTvuff/4JU1NTvRRFREQNR1xcnGwg27aNgYyoJmo8UiYIgmx7SUkJzM3NH7sgIiJqOOSmK93cpmDWLBOuzk9UQ9UOZYsWLQIAqFQqrFixAk2bNhVfKysrw+7du9GlSxf9V0hERAbnp59+wi+//KLTXjFdOXhwfVdE1PBVO5QtWLAAwP2RsoSEBMlUpbm5OTp06ICEhAT9V0hERAaF364kqhvVDmU5OTkAgAEDBmDjxo1o0aJFnRVFRESGSf7elbHgnZKIHl+NrynbuXNnXdRBREQG7Msvv8Tly5d12hnIiPSnxqHs7bffrvL1xMTEWhdDRESGR250zMbGBuPHj1egGqLGq8ah7MaNG5Lnd+/eRXZ2NvLz8/Hcc8/prTAiIlIepyuJ6k+NQ9l3332n01ZeXo53330XHTt21EtRRESkLF7MT1T/VEJlC4/V0KlTp/Dss8/iypUr+tidUSgsLIStrS0KCgrQvHlzpcshIgIgH8ieeeYZDBgwQIFqiAxPXf391tttls6ePYt79+7pa3dERFTPBEHAtGnTdNo5OkZUP2ocyiIjIyXPBUHAlStX8P333yMkJERvhRERUf2pbLpy27ZYMJMR1Y8ah7KsrCzJcxMTE7Ru3Rrz5s175DcziYjI8MgFMheXUCxZ0p63SiKqR3q7poxqjteUEZGS7ty5g9mzZ+u0c7qSqGoGd03Z1atXcerUKQCAq6sr2rRpo7eiiIiobvHblUSGp8ahrLCwEO+//z7WrFmD8vJyAICpqSmCgoKwdOlS2Nra6r1IIiLSH7lANn78eNjY2ChQDRFVMKnpBqNGjUJmZia+//575OfnIz8/H1u2bMH+/fsxevTouqiRiIj04NKlS5UuBstARqS8Gl9TZmNjg23btqF///6S9l9++QUDBw5EUVGRXgtszHhNGRHVl8qmK7k6P1HNGcw1Za1atZKdorS1tUWLFi30UhQREemPXCCLiYmBSqVSoBoiqkyNpy+jo6MRGRkJrVYrtmm1Wnz00UeYMmWKXosjIqLa27dvn2wgi42NZSAjMkA1nr50d3fHmTNnUFJSgnbt2gEAzp8/DwsLC7i4uEj6Hjx4UH+VNkKcviSiusJvVxLVHYOZvhw8eDD/hUVEZMDkAtm2bbFIT1egGCKqthqHsqlTp+q1gKVLl2LOnDnQarXo1asXFi9ejL59+1baf/369ZgyZQpyc3Ph4uKCWbNm4cUXXxRfFwQBsbGx+PLLL5Gfn4//+7//w7JlyySjeAEBATh06BCuXr2KFi1awNfXF7NmzYKTkxMAIDc3F87OzjrHzsjIQL9+/apdCxFRfVq9ejVOnz6t075mTSxk1oglIgNT42vKnnzySfz555867fn5+XjyySdrtK/k5GRERkYiNjYWBw8eRK9eveDn54erV6/K9k9PT8ewYcMQFhaGrKwsBAYGIjAwENnZ2WKf2bNnY9GiRUhISEBmZiZsbGzg5+eH4uJisc+AAQOwbt06nDp1Chs2bMDZs2cxZMgQneP9+OOPuHLlivjw8PCoUS1ERPUlLi5OJ5C1b98esbGxOHkS/IYlUQNQ42vKTExMoNVqdVbwz8vLg0ajQWlpabX35enpiT59+mDJkiUAgPLycmg0GowZMwaTZG64FhQUhKKiImzZskVs69evH9zc3JCQkABBEODk5IRx48Zh/PjxAICCggI4ODggKSkJQ4cOla1j8+bNCAwMRElJCczMzMSRsqysLLi5uclu86haqoPXlBGRPlR2MT8R1Q3FrynbvHmz+L+3bdsmWRajrKwMaWlpslN+lSktLcWBAwcQFRUltpmYmMDX1xcZGRmy22RkZCAyMlLS5ufnh5SUFABATk4OtFotfH19xddtbW3h6emJjIwM2VD2119/YdWqVfD29oaZmZnktYCAABQXF6Nz586YMGECAh74p+ajapFTUlKCkpIS8XlhYWGlfYmIHoUX8xM1LtUOZYGBgQAAlUqFkJAQyWtmZmbo0KED5s2bV+0DX79+HWVlZXBwcJC0Ozg44OTJk7LbaLVa2f4Vy3NU/KyqT4WJEydiyZIluH37Nvr16ycZ8WratCnmzZuH//u//4OJiQk2bNiAwMBApKSkiMHsUbXIiY+Pr/RDlIioJuQ+S/75z3/C29tbgWqISB+qHcoq7nPp7OyMffv2wd7evs6Kqg8fffQRwsLCcO7cOcTFxSE4OBhbtmyBSqWCvb29ZBSsT58+uHz5MubMmSMZLaupqKgoyX4LCwuh0Wge630QkXERBAHTpk3TaefoGFHDV+NvX+bk5OjlwPb29jA1NUVeXp6kPS8vD2q1WnYbtVpdZf+Kn3l5eXB0dJT0efjaMHt7e9jb26Nz587o2rUrNBoN9uzZAy8vL9lje3p6YseOHdWuRY6FhQUsLCwqfZ2IqCqcriRq3GocyuT+hfagmJiYau3H3NwcHh4eSEtLE6dGy8vLkZaWhg8++EB2Gy8vL6SlpSEiIkJs27FjhxiknJ2doVarkZaWJoawwsJCZGZm4t133620lopRwAev93rYoUOHJEHvUbUQEemTXCDbs2ck3n23rQLVEFFdqHEo++677yTP7969i5ycHDRp0gQdO3asdigDgMjISISEhKB3797o27cvFi5ciKKiIowYMQIAEBwcjLZt2yI+Ph4AMHbsWPj4+GDevHnw9/fH2rVrsX//fnzxxRcA7l/vFhERgenTp8PFxQXOzs6YMmUKnJycxOCXmZmJffv2oX///mjRogXOnj2LKVOmoGPHjmKg+vrrr2Fubg53d3cAwMaNG5GYmIgVK1aItT+qFiIifSguLsasWbN02jk6RtT41DiUZWVl6bQVFhYiNDQUL7/8co32FRQUhGvXriEmJgZarRZubm5ITU0VL6A/f/48TEz+XkrN29sbq1evRnR0NCZPngwXFxekpKSgR48eYp8JEyagqKgI4eHhyM/PR//+/ZGamgpLS0sAgLW1NTZu3IjY2FgUFRXB0dERAwcORHR0tGRq8ZNPPsG5c+fQpEkTdOnSBcnJyZK1zKpTCxHR4+B0JZFxqfE6ZZU5evQoXnrpJeTm5upjd0aB65QRUWXkAtmECRNgZWWlQDVE9CDF1yl7lIKCAhQUFOhrd0RERkmr1WL58uU67RwdI2r8ahzKFi1aJHkuCAKuXLmCb775Bi+88ILeCiMiMjacriQybjUOZQsWLJA8NzExQevWrRESEiJZnZ+IiKpPLpDFxMRApVIpUA0RKUGxdcqIiAg4ePAg/ve//+m0c3SMyPjU6pqy/Px8nDlzBgDQqVMn2NnZ6bMmIiKjwOlKInpQjUJZbm4u3n//fWzbtg0VX9pUqVQYOHAglixZgg4dOtRFjUREjY5cIGMYIzJu1Q5lFy5cQL9+/WBmZoZPPvkEXbt2BQAcP34cy5Ytg5eXF/bt24cnnniizoolImroUlJScPjwYZ12BjIiqvY6ZWFhYThz5gy2bdsmLsRa4c6dOxg4cCBcXFwkq95T1bhOGZFxkRsda97cBf/+9xsKVENEtaX4OmWpqalITk7WCWQAYGVlhU8++QRDhw7VW2FERI3F5s1AVhanK4moatUOZdevX6/ymrEnn3wSf/31lz5qIiJqND799FPcvXtXp52BjIgeVu1Q5ujoiOPHj1d6zVh2djbUarXeCiMiaujkpitfeuklPPXUUwpUQ0SGzuTRXe4LDAzE+PHjce3aNZ3Xrl69iokTJyIwMFCftRERNUiCIFT67UoGMiKqTLUv9L9x4wY8PT2h1Wrx1ltvoUuXLhAEASdOnMDq1auhVquxZ88etGzZsq5rbjR4oT9R48O1x4gaP8Uv9G/RogUyMzMxefJkrF27Fvn5+QAAOzs7vPHGG/j0008ZyIjIqMkFsvDwcDg6OipQDRE1NNUeKXuQIAjiNGbr1q15b7Za4kgZUeNQUlKCmTNn6rRzdIyocVJ8pOxBKpUKbdq00VsRREQNFacriUhfahXKiIhIPpBNnDhRdj1HIqJHYSgjIqqhq1evYtmyZTrtHB0josfBUEZEVAOcriSiusJQRkRUTXKBLCYmhl92IiK9qFYoW7RoUbV3+OGHH9a6GCIiQ5SdnY0NGzbotHN0jIj0qVqhbMGCBdXamUqlYigjokZFbnSspMQa/fp9pEA1RNSYVSuU5eTk1HUdREQGp7JbJRER1YVaX1NWWlqKnJwcdOzYEU2a8NI0Imo8vv/+e+zfv1+nnYGMiOpStW9IXuH27dsICwuDtbU1unfvjvPnzwMAxowZI7uiNRFRQxIXF6cTyNzd3RnIiKjO1TiURUVF4fDhw9i1a5dkgURfX18kJyfrtTgiovpU2XRlQECAAtUQkbGp8bxjSkoKkpOT0a9fP8nXwLt3746zZ8/qtTgiovqwYMECFBYW6rRzdIyI6lONQ9m1a9dk73tZVFTEtXqIqMGRGx175ZVX0LNnTwWqISJjVuPpy969e+P7778Xn1cEsRUrVsDLy0t/lRER1SFBECqdrmQgIyIl1Hik7NNPP8ULL7yA48eP4969e/jss89w/PhxpKen4+eff66LGomI9Iq3SiIiQ1TjkbL+/fvj0KFDuHfvHnr27Int27ejTZs2yMjIgIeHR13USESkN3KB7N1332UgIyLFqQRBEJQuwlgVFhbC1tYWBQUFaN68udLlEDVqJSUlssv2MIwRUU3V1d/vak1fyn0rqTIMF0RkaDhdSUQNQbVCmZ2dXbW/WVlWVvZYBRER6ZNcIJs4caJknUUiIkNQrVC2c+dO8X/n5uZi0qRJCA0NFb9tmZGRga+//hrx8fF1UyURUQ1dvXoVy5Yt02nn6BgRGaoaX1P2/PPPY+TIkRg2bJikffXq1fjiiy+wa9cufdbXqPGaMqK6welKIqpLdfX3u8ahzNraGocPH4aLi4uk/ffff4ebmxtu376tt+IaO4YyIv2TC2QxMTFc3JqI9Kau/n7XeEkMjUaDL7/8Uqd9xYoV0Gg0eimKiKimsrOzK10MloGMiBqCGi8eu2DBArz66qv44Ycf4OnpCQDYu3cvTp8+jQ0bNui9QCKiR5ELYzY2Nhg/frwC1RAR1U6t1im7ePEiPv/8c5w8eRIA0LVrV7zzzjscKashTl8SPb7KRseIiOqKwUxfAsATTzyBTz/9FBs3bsTGjRsxY8aMWgeypUuXokOHDrC0tISnpyf27t1bZf/169ejS5cusLS0RM+ePbF161bJ64IgICYmBo6OjrCysoKvry9Onz4t6RMQEIB27drB0tISjo6OGD58OC5fviy+vmvXLgwePBiOjo6wsbGBm5sbVq1aJdlHUlISVCqV5MGv2BPVn++//56BjIgalVqFsvz8fMybNw8jR47EyJEjsWDBAhQUFNR4P8nJyYiMjERsbCwOHjyIXr16wc/PD1evXpXtn56ejmHDhiEsLAxZWVkIDAxEYGAgsrOzxT6zZ8/GokWLkJCQgMzMTNjY2MDPzw/FxcVinwEDBmDdunU4deoUNmzYgLNnz2LIkCGS4/zjH//Ahg0bcOTIEYwYMQLBwcHYsmWLpJ7mzZvjypUr4uPcuXM1PgdEVHNxcXHYv3+/pO2pp55iICOiBq3G05f79++Hn58frKys0LdvXwDAvn37cOfOHWzfvh1PPfVUtffl6emJPn36YMmSJQCA8vJyaDQajBkzBpMmTdLpHxQUhKKiIkk46tevH9zc3JCQkABBEODk5IRx48aJ15IUFBTAwcEBSUlJGDp0qGwdmzdvRmBgIEpKSmBmZibbx9/fHw4ODkhMTARwf6QsIiIC+fn51X6/D+P0JVHNcXSMiJRmMNOX//73vxEQEIDc3Fxx+jInJweDBg1CREREtfdTWlqKAwcOwNfX9+9iTEzg6+uLjIwM2W0yMjIk/QHAz89P7J+TkwOtVivpY2trC09Pz0r3+ddff2HVqlXw9vauNJAB98Ndy5YtJW23bt1C+/btodFoMHjwYBw7dqzK91xSUoLCwkLJg4iqZ/78+QxkRNSo1TiU7d+/HxMnTkSTJn9/cbNJkyaYMGGCznRCVa5fv46ysjI4ODhI2h0cHKDVamW30Wq1Vfav+FmdfU6cOBE2NjZo1aoVzp8/j02bNlVa67p167Bv3z6MGDFCbHN1dUViYiI2bdqEb7/9FuXl5fD29sbFixcr3U98fDxsbW3FB78YQVQ9cXFxuHnzpqTtlVdeYSAjokalxqGsefPmOH/+vE77hQsX0KxZM70UVR8++ugjZGVlYfv27TA1NUVwcDDkZnJ37tyJESNG4Msvv0T37t3Fdi8vLwQHB8PNzQ0+Pj7YuHEjWrdujeXLl1d6zKioKBQUFIiPCxcu1Ml7I2osBEGodHSsZ8+eClRERFR3arxOWVBQEMLCwjB37lx4e3sDAH777Td89NFHOrdeqoq9vT1MTU2Rl5cnac/Ly4NarZbdRq1WV9m/4mdeXh4cHR0lfdzc3HSOb29vj86dO6Nr167QaDTYs2ePeD9PAPj555/x0ksvYcGCBQgODq7y/ZiZmcHd3R1nzpyptI+FhQUsLCyq3A8R3cdbJRGRsanxSNncuXPxyiuvIDg4GB06dECHDh0QGhqKIUOGYNasWdXej7m5OTw8PJCWlia2lZeXIy0tTRKMHuTl5SXpDwA7duwQ+zs7O0OtVkv6FBYWIjMzs9J9VhwXuH/NV4Vdu3bB398fs2bNQnh4+CPfT1lZGY4ePSoJg0RUO3KB7N1332UgI6JGrcYjZebm5vjss88QHx+Ps2fPAgA6duwIa2vrGh88MjISISEh6N27N/r27YuFCxeiqKhIvHYrODgYbdu2RXx8PABg7Nix8PHxwbx58+Dv74+1a9di//79+OKLLwAAKpUKERERmD59OlxcXODs7IwpU6bAyckJgYGBAIDMzEzs27cP/fv3R4sWLXD27FlMmTIFHTt2FIPbzp07MWjQIIwdOxavvvqqeD2aubm5eLH/tGnT0K9fP3Tq1An5+fmYM2cOzp07h5EjR9b4PBDRfSUlJZg5c6ZOO8MYERmDGoeyCtbW1o99TUdQUBCuXbuGmJgYaLVauLm5ITU1VbxQ//z58zAx+Xswz9vbG6tXr0Z0dDQmT54MFxcXpKSkoEePHmKfCRMmoKioCOHh4cjPz0f//v2RmpoqLuxqbW2NjRs3IjY2FkVFRXB0dMTAgQMRHR0tTi1+/fXXuH37NuLj48VACAA+Pj7YtWsXAODGjRsYNWoUtFotWrRoAQ8PD6Snp6Nbt26PdU6IjBWnK4nI2FV7nbK33367WjusWMeLHo3rlBHdJxfIJk2axGswicgg1dXf72qPlCUlJaF9+/Zwd3eX/ZYiEVFNXb16FcuWLdNp5+gYERmjaoeyd999F2vWrEFOTg5GjBiBt956S2cxVSKi6uJ0JRGRVI1us1RSUoKNGzciMTER6enp8Pf3R1hYGP71r39BpVLVZZ2NEqcvyVjJBbKYmBh+jhBRg1BXf79rfO/LCufOnUNSUhL+85//4N69ezh27BiaNm2qt8KMAUMZGZujR49i48aNOu0cHSOihkTxa8oeZmJiApVKBUEQUFZWpreCiKhxkhsds7Gxwfjx4xWohojI8NQolD04ffnrr79i0KBBWLJkCQYOHChZuoKI6EG8kTgR0aNVO5S99957WLt2LTQaDd5++22sWbMG9vb2dVkbETVwW7ZswYEDB3TaGciIiHRV+5oyExMTtGvXDu7u7lVejCt3vQjJ4zVl1JjJjY55eHhg0KBBClRDRKQ/il9TFhwczG9GEVG1cLqSiKjmarR4LBFRVebNm4dbt27ptDOQERE9Wq2/fUlE9CC50bFXXnnlse+RS0RkLBjKiOixCIKAadOm6bRzdIyIqGYYyoio1nirJCIi/WEoI6JakQtkv/76LnbsaKNANUREDR9DGRHVSGlpKeLj43Xa166NxaxZChRERNRIMJQRUbVVNl1ZWhqLEyfquRgiokaGoYyIqkUukEVFRcHc3FyBaoiIGh+GMiKq0l9//YXFixfrtPNifiIi/WIoI6JK8duVRET1h6GMiGTJBbKYmBjebo2IqI4wlBGRxJkzZ7Bq1Sqddo6OERHVLYYyIhLJjY517NgRb731lgLVEBEZF4YyIgIgH8g4OkZEVH8YyoiM3C+//IKffvpJp52BjIiofjGUERkxudGx5557Dk8//bQC1RARGTeGMiIjxelKIiLDwlBGZGQ2bNiA7OxsnXYGMiIiZTGUERkRudGxvXtDMHp0h/ovhoiIJBjKiIyAIAiYNm2aTjtHx4iIDAdDGVEjN2fOHNy+fVunnYGMiMiwMJQRNWJy05Vjx46FnZ1d/RdDRERVYigjaoTu3r2LTz/9VKedo2NERIaLoYyokZEbHQMAd3cGMiIiQ8ZQRtSIyAWyyZMnw8zMTIFqiIioJhjKiBqBgoICLFy4UKed05VERA0HQxlRAyc3OqZWqzF69GgFqiEiotpiKCNqwOQCWUxMDFQqlQLVEBHR42AoI2qALly4gMTERJ12TlcSETVcDGVEDYzc6FifPn3w4osvKlANERHpi4nSBSxduhQdOnSApaUlPD09sXfv3ir7r1+/Hl26dIGlpSV69uyJrVu3Sl4XBAExMTFwdHSElZUVfH19cfr0aUmfgIAAtGvXDpaWlnB0dMTw4cNx+fJlSZ8jR47g6aefhqWlJTQaDWbPnl3jWoj0TS6QxcbGMpARETUCioay5ORkREZGIjY2FgcPHkSvXr3g5+eHq1evyvZPT0/HsGHDEBYWhqysLAQGBiIwMBDZ2dlin9mzZ2PRokVISEhAZmYmbGxs4Ofnh+LiYrHPgAEDsG7dOpw6dQobNmzA2bNnMWTIEPH1wsJC/Otf/0L79u1x4MABzJkzB1OnTsUXX3xRo1qI9OXYsWOVBjIiImocVIIgCEod3NPTE3369MGSJUsAAOXl5dBoNBgzZgwmTZqk0z8oKAhFRUXYsmWL2NavXz+4ubkhISEBgiDAyckJ48aNw/jx4wHcXyrAwcEBSUlJGDp0qGwdmzdvRmBgIEpKSmBmZoZly5bh448/hlarhbm5OQBg0qRJSElJwcmTJ6tVS3UUFhbC1tYWBQUFaN68ebW2IeMjF8YOHRqCESO6IyBAgYKIiIxcXf39VmykrLS0FAcOHICvr+/fxZiYwNfXFxkZGbLbZGRkSPoDgJ+fn9g/JycHWq1W0sfW1haenp6V7vOvv/7CqlWr4O3tLS6wmZGRgWeeeUYMZBXHOXXqFG7cuFGtWoj0obLRse++YyAjImpsFAtl169fR1lZGRwcHCTtDg4O0Gq1sttotdoq+1f8rM4+J06cCBsbG7Rq1Qrnz5/Hpk2bHnmcB4/xqFrklJSUoLCwUPIgkpORkcHpSiIiI6P4hf5K+eijj5CVlYXt27fD1NQUwcHBqOuZ3Pj4eNja2ooPjUZTp8ejhikuLg7bt2+XtLm6hjOQERE1cootiWFvbw9TU1Pk5eVJ2vPy8qBWq2W3UavVVfav+JmXlwdHR0dJHzc3N53j29vbo3PnzujatSs0Gg327NkDLy+vSo/z4DEeVYucqKgoREZGis8LCwsZzEgkCAKmTZum084wRkRkHBQbKTM3N4eHhwfS0tLEtvLycqSlpcHLy0t2Gy8vL0l/ANixY4fY39nZGWq1WtKnsLAQmZmZle6z4rjA/enFiuPs3r0bd+/elRzH1dUVLVq0qFYtciwsLNC8eXPJgwgANm3axEBGRGTkFF08NjIyEiEhIejduzf69u2LhQsXoqioCCNGjAAABAcHo23btoiPjwcAjB07Fj4+Ppg3bx78/f2xdu1a7N+/X1yqQqVSISIiAtOnT4eLiwucnZ0xZcoUODk5ITAwEACQmZmJffv2oX///mjRogXOnj2LKVOmoGPHjmKgeuONNxAXF4ewsDBMnDgR2dnZ+Oyzz7BgwQKx9kfVQlRdcteOjR8/HjY2NgpUQ0RESlE0lAUFBeHatWuIiYmBVquFm5sbUlNTxQvoz58/DxOTvwfzvL29sXr1akRHR2Py5MlwcXFBSkoKevToIfaZMGECioqKEB4ejvz8fPTv3x+pqamwtLQEAFhbW2Pjxo2IjY1FUVERHB0dMXDgQERHR8PCwgLA/W9sbt++He+//z48PDxgb2+PmJgYhIeH16gWoqrcu3cPM2bM0Gnn6BgRkXFSdJ0yY8d1yozX559/jmvXrknaHBwc8M477yhUERERVVdd/f3mvS+J6pncdOXHH3+MJk34f0ciImPGvwJE9aSoqAhz587Vaed0JRERAQxlRPVCbnTM3d0dAVyWn4iI/j+GMqI6JhfIYmJioFKpFKiGiIgMFUMZUR25evUqli1bptPO6UoiIpLDUEZUB+RGxwYOHAhPT08FqiEiooaAoYxIz3gjcSIiqg2GMiI9OXfuHJKSknTaGciIiKg6GMqI9EBudCwzcwTeeaedAtUQEVFDxFBG9Jg4XUlERPrAUEZUS4cPH0ZKSopOOwMZERHVBkMZUS3IjY6NGTMGLVu2VKAaIiJqDBjKiGpAEARMmzZNp52jY0RE9LgYyoiq6aeffsIvv/wiabO1tUVERIQyBRERUaPCUEZUDXLTlRMmTICVlZUC1RARUWPEUEZUhbKyMkyfPl2nndOVRESkbwxlRJVYu3YtTp06JWnr2rUrXn/9dYUqIiKixoyhjEiG3HRldHQ0TE1NFaiGiIiMgYnSBRAZkjt37sgGsm3bYhnIiIioTnGkjOj/mz9/Pm7evClpU6t98PXXz2LSJGVqIiIi48FQRgT56cqYmBioVCqMHq1AQUREZHQYysio/fnnn1iyZIlOO79dSURE9Y2hjIyW3OjYyy+/jH/84x8KVENERMaOoYyMklwg4+gYEREpiaGMjMq5c+eQlJSk085ARkRESmMoI6MhNzo2YsQItGvXToFqiIiIpBjKyChwupKIiAwdQxk1aocPH0ZKSopOOwMZEREZGoYyarTkRsfGjBmDli1bKlANERFR1RjKqNERBAHTpk3TaefoGBERGTKGMmpU0tLS8Ouvv0ra7OzsMHbsWIUqIiIiqh6GMmo05KYrJ0yYACsrKwWqISIiqhmGMmrwysrKMH36dJ12TlcSEVFDwlBGDdq2bduwZ88eSZtW2xXLlr2uUEVERES1w1BGDZbcdOX27dGYONFUgWqIiIgeD0MZNTjFxcWYNWuWTntsbCw4Y0lERA0VQxk1GJs3Azt2fAt7+7OSdn9/f/Tu3VuhqoiIiPSDoYwajKysONjbS9tiYmKgUqmUKYiIiEiPGMrI4N26dQvz5s3Taee3K4mIqDFhKCODtm7dOpw4cULS9tZbb6Fjx44KVURERFQ3TJQuYOnSpejQoQMsLS3h6emJvXv3Vtl//fr16NKlCywtLdGzZ09s3bpV8rogCIiJiYGjoyOsrKzg6+uL06dPi6/n5uYiLCwMzs7OsLKyQseOHREbG4vS0lKxz9SpU6FSqXQeNjY2Yp+kpCSd1y0tLfV0Vgi4/+3KhwNZbGwsAxkRETVKioay5ORkREZGIjY2FgcPHkSvXr3g5+eHq1evyvZPT0/HsGHDEBYWhqysLAQGBiIwMBDZ2dlin9mzZ2PRokVISEhAZmYmbGxs4Ofnh+LiYgDAyZMnUV5ejuXLl+PYsWNYsGABEhISMHnyZHEf48ePx5UrVySPbt264bXXXpPU07x5c0mfc+fO1cFZMj5//vmnznIXarWa05VERNSoqQRBEJQ6uKenJ/r06YMlS5YAAMrLy6HRaDBmzBhMmjRJp39QUBCKioqwZcsWsa1fv35wc3NDQkICBEGAk5MTxo0bh/HjxwMACgoK4ODggKSkJAwdOlS2jjlz5mDZsmX4448/ZF8/fPgw3NzcsHv3bjz99NMA7o+URUREID8/v9bvv7CwELa2tigoKEDz5s1rvZ/G5PPPP8e1a9ckbWPGjEHLli0VqoiIiEiqrv5+KzZSVlpaigMHDsDX1/fvYkxM4Ovri4yMDNltMjIyJP0BwM/PT+yfk5MDrVYr6WNrawtPT89K9wncD25V/dFfsWIFOnfuLAayCrdu3UL79u2h0WgwePBgHDt2rPI3DKCkpASFhYWSB/0tLi5OJ5DFxsYykBERkVFQLJRdv34dZWVlcHBwkLQ7ODhAq9XKbqPVaqvsX/GzJvs8c+YMFi9ejNGjR8u+XlxcjFWrViEsLEzS7urqisTERGzatAnffvstysvL4e3tjYsXL1byjoH4+HjY2tqKD41GU2lfY3LhwgWd6cru3btzupKIiIyKUX/78tKlSxg4cCBee+01jBo1SrbPd999h5s3byIkJETS7uXlBS8vL/G5t7c3unbtiuXLl+OTTz6R3VdUVBQiIyPF54WFhUYfzORulTR+/HjJlyqIiIiMgWKhzN7eHqampsjLy5O05+XlQa1Wy26jVqur7F/xMy8vD46OjpI+bm5uku0uX76MAQMGwNvbG1988UWlda5YsQKDBg3SGX17mJmZGdzd3XHmzJlK+1hYWMDCwqLK/RgLQRAwbdo0nXaOjhERkbFSbPrS3NwcHh4eSEtLE9vKy8uRlpYmGYF6kJeXl6Q/AOzYsUPs7+zsDLVaLelTWFiIzMxMyT4vXbqEZ599Fh4eHli5ciVMTORPQ05ODnbu3KkzdSmnrKwMR48elYRBknfy5EmdQNa/f38GMiIiMmqKTl9GRkYiJCQEvXv3Rt++fbFw4UIUFRVhxIgRAIDg4GC0bdsW8fHxAICxY8fCx8cH8+bNg7+/P9auXYv9+/eLI10qlQoRERGYPn06XFxc4OzsjClTpsDJyQmBgYEA/g5k7du3x9y5cyUXlj88QpeYmAhHR0e88MILOrVPmzYN/fr1Q6dOnZCfn485c+bg3LlzGDlyZF2cqkZDbrpy8uTJMDMzU6AaIiIiw6FoKAsKCsK1a9cQExMDrVYLNzc3pKamilOF58+fl4xieXt7Y/Xq1YiOjsbkyZPh4uKClJQU9OjRQ+wzYcIEFBUVITw8HPn5+ejfvz9SU1PFhV137NiBM2fO4MyZM3jiiSck9Ty4Okh5eTmSkpIQGhoKU1NTndpv3LiBUaNGQavVokWLFvDw8EB6ejq6deum13PUWJSXl8tea8fRMSIiovsUXafM2BnLOmV79+7FDz/8IGnz9/dH7969FaqIiIio9urq77dRf/uS6p7cdOWUKVMqvY6PiIjIWDGUUZ0oLS0VrwV8EKcriYiI5DGUkd7t2LED6enpkrahQ4fC1dVVoYqIiIgMH0MZ6ZXcdGVMTAxUKpUC1RARETUcDGWkF7du3cK8efMkbaampoiOjlaoIiIiooaFoYweW3JyMk6ePClpGzlyJNq2batQRURERA0PQxk9FrnpSl7MT0REVHMMZVQrf/75J5YsWSJpc3R0RHh4uEIVERERNWwMZVRjn3/+ueT2VAAwZswYtGzZUqGKiIiIGj6GMqqWzZuBmTMBPz9OVxIREdUFhjKqls8/vwQ/vxWSth49euDVV19VqCIiIqLGhaGMHmn9+vXw8jouaRs/fjxsbGwUqoiIiKjxYSijSgmCgGnTpum0c7qSiIhI/xjKSNalS5ewYoV0uvKVV15Bz549FaqIiIiocWMoIx1z565EUdF5SdvHH3+MJk3460JERFRX+FeWRIIgYPHixSgquiG2NWvWDJGRkQpWRUREZBwYyggA8Ndff2Hx4sWSto4dh+Ott55UqCIiIiLjwlBG+PXXX5GWliY+t7e3x3vvvQeVSqVgVURERMaFocyIlZeXY+bMmbh7967YFhAQAHd3dwWrIiIiMk4MZUZKq9Vi+fLlkrbIyEg0a9ZMoYqIiIiMG0OZEdqxYwfS09PF5+3bt0dISAinK4mIiBTEUGZE7t27hxkzZkjaXn/9dXTt2lWhioiIiKgCQ5mROH/+PFauXClpmzBhAqysrBSqiIiIiB7EUGYENm/ejKysLPF5165d8frrrytYERERET2MoawRKykpwcyZMyVtw4cPx5NPcu0xIiIiQ8NQ1kidOXMGq1atkrRFRUXB3NxcoYqIiIioKgxljVB+fr4kkHl4eGDQoEEKVkRERESPwlDWCD24GGxYWBieeOIJBashIiKi6mAoa4Rat26NXr0+xuzZTeDuDjCTERERGT4TpQugujF7dhNkZAAPXedPREREBoqhrJGaNAnw8rr/k4iIiAwfpy8bqYCA+w8iIiJqGDhSRkRERGQAGMqIiIiIDABDGREREZEBYCgjIiIiMgAMZUREREQGgKGMiIiIyAAwlBEREREZAIYyIiIiIgOgeChbunQpOnToAEtLS3h6emLv3r1V9l+/fj26dOkCS0tL9OzZE1u3bpW8LggCYmJi4OjoCCsrK/j6+uL06dPi67m5uQgLC4OzszOsrKzQsWNHxMbGorS0VNJHpVLpPPbs2VOjWoiIiIiqS9FQlpycjMjISMTGxuLgwYPo1asX/Pz8cPXqVdn+6enpGDZsGMLCwpCVlYXAwEAEBgYiOztb7DN79mwsWrQICQkJyMzMhI2NDfz8/FBcXAwAOHnyJMrLy7F8+XIcO3YMCxYsQEJCAiZPnqxzvB9//BFXrlwRHx4eHjWqhYiIiKi6VIIgCEod3NPTE3369MGSJUsAAOXl5dBoNBgzZgwmydy0MSgoCEVFRdiyZYvY1q9fP7i5uSEhIQGCIMDJyQnjxo3D+PHjAQAFBQVwcHBAUlIShg4dKlvHnDlzsGzZMvzxxx8A7o+UOTs7IysrC25ubrLbPKqW6igsLIStrS0KCgrQvHnzam1DREREyqqrv9+KjZSVlpbiwIED8PX1/bsYExP4+voiIyNDdpuMjAxJfwDw8/MT++fk5ECr1Ur62NrawtPTs9J9AveDW8uWLXXaAwIC0KZNG/Tv3x+bN2+uUS1ySkpKUFhYKHkQERERAQqGsuvXr6OsrAwODg6SdgcHB2i1WtlttFptlf0rftZkn2fOnMHixYsxevRosa1p06aYN28e1q9fj++//x79+/dHYGCgJJg9qhY58fHxsLW1FR8ajabSvkRERGRcmihdgJIuXbqEgQMH4rXXXsOoUaPEdnt7e0RGRorP+/Tpg8uXL2POnDkICAio9fGioqIk+y0oKEC7du04YkZERNSAVPzd1vcVYIqFMnt7e5iamiIvL0/SnpeXB7VaLbuNWq2usn/Fz7y8PDg6Okr6PHxt2OXLlzFgwAB4e3vjiy++eGS9np6e2LFjR7VrkWNhYQELCwvxecV/VI6YERERNTw3b96Era2t3vanWCgzNzeHh4cH0tLSEBgYCOD+hf5paWn44IMPZLfx8vJCWloaIiIixLYdO3bAy8sLAODs7Ay1Wo20tDQxhBUWFiIzMxPvvvuuuM2lS5cwYMAAeHh4YOXKlTAxefQs7qFDhyRB71G1VIeTkxMuXLiAZs2aQaVSVXu7xqqwsBAajQYXLlzgFx/+P54TKZ4PXTwnUjwfunhOpPRxPgRBwM2bN+Hk5KTf4gQFrV27VrCwsBCSkpKE48ePC+Hh4YKdnZ2g1WoFQRCE4cOHC5MmTRL7//bbb0KTJk2EuXPnCidOnBBiY2MFMzMz4ejRo2KfmTNnCnZ2dsKmTZuEI0eOCIMHDxacnZ2FO3fuCIIgCBcvXhQ6deokPP/888LFixeFK1euiI8KSUlJwurVq4UTJ04IJ06cEGbMmCGYmJgIiYmJNaqFaqagoEAAIBQUFChdisHgOZHi+dDFcyLF86GL50TKkM+HoteUBQUF4dq1a4iJiYFWq4WbmxtSU1PFC+jPnz8vGcXy9vbG6tWrER0djcmTJ8PFxQUpKSno0aOH2GfChAkoKipCeHg48vPz0b9/f6SmpsLS0hLA/dGsM2fO4MyZM3jiiSck9QgPzA1/8sknOHfuHJo0aYIuXbogOTkZQ4YMqVEtRERERNWl6DplRA/ium26eE6keD508ZxI8Xzo4jmRMuTzofhtlogqWFhYIDY2VvJlCGPHcyLF86GL50SK50MXz4mUIZ8PjpQRERERGQCOlBEREREZAIYyIiIiIgPAUEZERERkABjKiIiIiAwAQxlV29KlS9GhQwdYWlrC09MTe/furbL/+vXr0aVLF1haWqJnz57YunWr5HVBEBATEwNHR0dYWVnB19cXp0+fFl/Pzc1FWFgYnJ2dYWVlhY4dOyI2NhalpaVin6lTp0KlUuk8bGxsxD5JSUk6r1esW9eQzgcABAQEoF27drC0tISjoyOGDx+Oy5cvS/ocOXIETz/9NCwtLaHRaDB79uwa11JbhnhOdu3ahcGDB8PR0RE2NjZwc3PDqlWrJPswpt+R3Nxc2f/P7Nmzp0a11JYhnhNj+xypUFJSAjc3N6hUKhw6dEjymrF9jlSo7JzU2+eIMmvWUkOzdu1awdzcXEhMTBSOHTsmjBo1SrCzsxPy8vJk+//222+CqampMHv2bOH48eNCdHS07N0XbG1thZSUFOHw4cNCQECA5O4LP/zwgxAaGips27ZNOHv2rLBp0yahTZs2wrhx48R93Lx5U3JXhitXrgjdunUTQkJCxD4rV64UmjdvLulTcdeIhnQ+BEEQ5s+fL2RkZAi5ubnCb7/9Jnh5eQleXl7i6wUFBYKDg4Pw5ptvCtnZ2cKaNWsEKysrYfny5TWqpTGdkxkzZgjR0dHCb7/9Jpw5c0ZYuHChYGJiIvzvf/8T+xjT70hOTo4AQPjxxx8l77e0tLRGtTSmc2JsnyMVPvzwQ+GFF14QAAhZWVliuzF+jjzqnNTX5whDGVVL3759hffff198XlZWJjg5OQnx8fGy/V9//XXB399f0ubp6SmMHj1aEARBKC8vF9RqtTBnzhzx9fz8fMHCwkJYs2ZNpXXMnj1bcHZ2rvT1Q4cOCQCE3bt3i20rV64UbG1tq3x/NWUo52PTpk2CSqUS/6B+/vnnQosWLYSSkhKxz8SJEwVXV9dq11JbhnpO5Lz44ovCiBEjxOfG9DtSEcoe/INT01pqy1DPycOM4XNk69atQpcuXYRjx47p/D4Y6+dIVedETl18jnD6kh6ptLQUBw4cgK+vr9hmYmICX19fZGRkyG6TkZEh6Q8Afn5+Yv+cnBxotVpJH1tbW3h6ela6TwAoKChAy5YtK319xYoV6Ny5M55++mlJ+61bt9C+fXtoNBoMHjwYx44dq/wNP4KhnI+//voLq1atgre3N8zMzMTjPPPMMzA3N5cc59SpU7hx40a1aqkNQz4ncuR+j4zld6RCQEAA2rRpg/79+2Pz5s01qqU2GsI5qdDYP0fy8vIwatQofPPNN7C2tpY9jrF9jjzqnMipi88RhjJ6pOvXr6OsrEy8J2kFBwcHaLVa2W20Wm2V/St+1mSfZ86cweLFizF69GjZ14uLi7Fq1SqEhYVJ2l1dXZGYmIhNmzbh22+/RXl5Oby9vXHx4sVK3nHVlD4fEydOhI2NDVq1aoXz589j06ZNjzzOg8d4VC21Ycjn5GHr1q3Dvn37MGLECLHNmH5HmjZtinnz5mH9+vX4/vvv0b9/fwQGBkqCmTH/jjT2zxFBEBAaGop33nkHvXv3rtFxHjxGY/odqc45eVhdfY4wlFGDcOnSJQwcOBCvvfYaRo0aJdvnu+++w82bNxESEiJp9/LyQnBwMNzc3ODj44ONGzeidevWWL58eX2UrncfffQRsrKysH37dpiamiI4OBiCkd+Yo7rnZOfOnRgxYgS+/PJLdO/eXWw3pt8Re3t7REZGwtPTE3369MHMmTPx1ltvYc6cOQpXXbeq+zvS2D9HFi9ejJs3byIqKkrpUgxGTc9JXX6OMJTRI9nb28PU1BR5eXmS9ry8PKjVatlt1Gp1lf0rflZnn5cvX8aAAQPg7e2NL774otI6V6xYgUGDBun8i+hhZmZmcHd3x5kzZ6rsVxmlz4e9vT06d+6Mf/7zn1i7di22bt0qfnOusuM8eIxH1VIbhnxOKvz888946aWXsGDBAgQHB1f5fhrz74gcT09PyXs11t8RoPF/jvz000/IyMiAhYUFmjRpgk6dOgEAevfuLQZRY/scqc45qVDXnyMMZfRI5ubm8PDwQFpamthWXl6OtLQ0eHl5yW7j5eUl6Q8AO3bsEPs7OztDrVZL+hQWFiIzM1Oyz0uXLuHZZ5+Fh4cHVq5cCRMT+V/ZnJwc7Ny5U2fKQU5ZWRmOHj0KR0fHR/aVo+T5eFh5eTmA+1/jrjjO7t27cffuXclxXF1d0aJFi2rVUhuGfE6A+19n9/f3x6xZsxAeHv7I99OYf0fkHDp0SPJejfF3BDCOz5FFixbh8OHDOHToEA4dOiQuH5GcnIwZM2aIxzGmz5HqnBOgnj5HHutrAmQ01q5dK1hYWAhJSUnC8ePHhfDwcMHOzk78uu/w4cOFSZMmif1/++03oUmTJsLcuXOFEydOCLGxsbJfU7azsxM2bdokHDlyRBg8eLDka8oXL14UOnXqJDz//PPCxYsXJV8zflh0dLTg5OQk3Lt3T+e1uLg4cVmNAwcOCEOHDhUsLS2FY8eONajzsWfPHmHx4sVCVlaWkJubK6SlpQne3t5Cx44dheLiYkEQ7n+ryMHBQRg+fLiQnZ0trF27VrC2ttb5KvujamlM5+Snn34SrK2thaioKMnv0J9//ikex5h+R5KSkoTVq1cLJ06cEE6cOCHMmDFDMDExERITE2tUS2M6JxWM4XPkYXLfxjW2z5HqnJP6+hxhKKNqW7x4sdCuXTvB3Nxc6Nu3r7Bnzx7xNR8fH8maPoIgCOvWrRM6d+4smJubC927dxe+//57yevl5eXClClTBAcHB8HCwkJ4/vnnhVOnTomvr1y5UgAg+3hQWVmZ8MQTTwiTJ0+WrTsiIkKs28HBQXjxxReFgwcPPubZqP/zceTIEWHAgAFCy5YtBQsLC6FDhw7CO++8I1y8eFGyn8OHDwv9+/cXLCwshLZt2wozZ87Uqf1RtdSWIZ6TkJAQ2d8hHx8fsY8x/Y4kJSUJXbt2FaytrYXmzZsLffv2FdavX69TuzH9jgiC8XyOPKyyJVKM6XPkYXLnpL4+R1SCYORXCBMREREZAF5TRkRERGQAGMqIiIiIDABDGREREZEBYCgjIiIiMgAMZUREREQGgKGMiIiIyAAwlBEREREZAIYyIiIiIgPAUEZE9JgyMjJgamoKf39/SXtubi5UKpX4aNmyJXx8fPDLL78oVCkRGTKGMiKix/TVV19hzJgx2L17Ny5fvqzz+o8//ogrV65g9+7dcHJywqBBg5CXl6dApURkyBjKiIgew61bt5CcnIx3330X/v7+SEpK0unTqlUrqNVq9OjRA5MnT0ZhYSEyMzPrv1giMmgMZUREj2HdunXo0qULXF1d8dZbbyExMRGV3VL4zp07+M9//gMAMDc3r88yiagBaKJ0AUREDdlXX32Ft956CwAwcOBAFBQU4Oeff8azzz4r9vH29oaJiQlu374NQRDg4eGB559/XqGKichQcaSMiKiWTp06hb1792LYsGEAgCZNmiAoKAhfffWVpF9ycjKysrKwYcMGdOrUCUlJSTAzM1OiZCIyYBwpIyKqpa+++gr37t2Dk5OT2CYIAiwsLLBkyRKxTaPRwMXFBS4uLrh37x5efvllZGdnw8LCQomyichAcaSMiKgW7t27h//85z+YN28eDh06JD4OHz4MJycnrFmzRna7IUOGoEmTJvj888/ruWIiMnQMZUREtbBlyxbcuHEDYWFh6NGjh+Tx6quv6kxhVlCpVPjwww8xc+ZM3L59u56rJiJDxlBGRFQLX331FXx9fWFra6vz2quvvor9+/ejsLBQdtuQkBDcvXtXMsVJRKQSKvvuNhERERHVG46UERERERkAhjIiIiIiA8BQRkRERGQAGMqIiIiIDABDGREREZEBYCgjIiIiMgAMZUREREQGgKGMiIiIyAAwlBEREREZAIYyIiIiIgPAUEZERERkABjKiIiIiAzA/wN0SLKJUS2jkgAAAABJRU5ErkJggg==", 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K+fPn4efnh/z8fLzyyisYN24c3n//fTRv3hy//PILYmNjUVFRoTEolZWVISIiAhEREfjyyy/RokULFBQUICIiAhUVFTqrk4iIjMPWrVtx7do1xc/PPfecynqJYmNQagQSiURn019i2b9/P3799Ve88847OHHiBGpqarB8+XLF1OhXX32l1N/GxgbV1dVKbRcuXMAff/yBxYsXK9ahOn78eOMcABERGYyysjIsW7ZMqW306NEG+VBxBiVSUV5eDplMhurqahQWFiI9PR2JiYl45ZVXMGzYMJw5cwaVlZVYu3Yt+vXrh8OHD6s8QsbX1xf3799HZmYmAgMD0aRJE7Rq1Qo2NjZYu3Ytxo4dizNnzmDBggUiHSUREYnh7Nmz+PrrrxU/W1hYYNasWQb7MHGzXnCS1EtPT4eXlxd8fX0RGRmJAwcOYM2aNdi1axcsLS0RGBiIFStW4IMPPkCnTp3w5Zdfqlys3qNHD4wdOxbR0dFo0aIFlixZghYtWmDLli3YsWMHOnbsiMWLF6v8j4KIiEyTIAj49NNPlUJSr169MHfuXIMNSYDID8U1ZnwoLv0Vf9+JiNS7d+8eVqxYodQ2btw4uLu7N3ot2j4Ul1NvREREpDenTp1Camqq4mdbW1tMnz5dafkfQ8agRERERDonCAI2btyo9OD6F198ET179hSxKu0xKBEREZFOyeVyrFy5UqltwoQJcHNzE6mi+mNQIiIiIp355JNP8Pvvvyt+btq0Kd555x2jmWr7KwYlIiIiajBBEDB//nyltgsXIrB9e3eRKtINBiUiIiJqkPz8fHz22WdKbcePD8GoUW1Fqkh3GJSIiIio3tavX4/bt28rtc2dO9dop9r+ikGJiIiItFZTU6PydAUPDw+MHTtWpIr0g0GJiIiItHLlyhV8+eWXSm0jRoxAq1atRKpIf0xjXIyMTkxMDKKiohQ/9+7dG1OnTm3QPnWxDyIiqt2KFStUQlJ8fLxJhiSAI0r0FzExMYoL8qytrdGqVSsMGzYMs2bNgpWV/v64fPvtt7C2tq5T359++gnPP/887t69CxcXl3rtg4iItFNdXY2FCxcqtbVq1QojRowQqaLGwaBEKiIjI7F582aUl5djz549mDBhAqytrTFz5kylfhUVFbCxsdHJezZv3twg9kFERKrOnz+Pr776Sqlt1KhR8Pb2FqmixsOpN1Jha2sLT09PtG7dGuPGjUN4eDh2796tmC57//334e3tjfbt2wMArl+/jn//+99wcXFB8+bNMWDAAOTn5yv2V11djbi4OLi4uMDV1RXTp0/HX5/F/Ndps/LycvznP/+Bj48PbG1t0a5dO3zyySfIz8/H888/DwBo1qwZJBIJYmJi1O7j7t27GDZsGJo1a4YmTZqgT58+uHz5suL1LVu2wMXFBRkZGejQoQOaNm2KyMhI3Lp1S9Hnp59+Qrdu3eDg4AAXFxc899xz+O2333R0pomIDN+iRYtUQlJ8fLxZhCSAQYnqwN7eHhUVFQCAzMxMXLx4EXv37kVaWhoqKysREREBR0dHHDp0CIcPH1YEjifbLF++HFu2bMGnn36KX375BXfu3MHOnTtrfc9hw4Zh+/btWLNmDc6fP4+PPvoITZs2hY+PD7755hsAwMWLF3Hr1i2sXr1a7T5iYmJw/Phx7N69G1lZWRAEAS+//DIqKysVfR48eIBly5Zh69atOHjwIAoKCvDuu+8CAKqqqhAVFYVevXrh9OnTyMrKwujRoyGRSBp8TomIDF1VVRUSEhKU/s309/eHVCo1q38HOfVGGgmCgMzMTGRkZGDSpEkoLi6Gg4MDNm3apJhy++KLL1BTU4NNmzYp/uJs3rwZLi4u+Omnn/DSSy9h1apVmDlzJl577TUAQFJSEjIyMjS+76VLl/DVV19h7969CA8PBwC0adNG8fqTKTZ3d3ela5T+7PLly9i9ezcOHz6MHj16AAC+/PJL+Pj4IDU1FW+88QYAoLKyEklJSWjb9vGiaBMnTlSsLCuXy1FaWopXXnlF8XqHDh20P5FEREbm119/xbfffqvUNnbsWHh4eIhUkXg4omTgdu8GevR4/L2xpKWloWnTprCzs0OfPn0QHR2NefPmAQA6d+6sdF3SqVOncOXKFTg6OqJp06Zo2rQpmjdvjkePHuHq1asoLS3FrVu3EBoaqtjGysoKXbp00fj+J0+ehKWlJXr16lXvYzh//jysrKyU3tfV1RXt27fH+fPnFW1NmjRRhCAA8PLyQlFREYDHgSwmJgYRERHo168fVq9erTQtR0RkihISElRCklQqNcuQBHBEyeAtXgxkZT3+3r9/47zn888/jw0bNsDGxgbe3t5Kd7s5ODgo9b1//z5CQkJUbhUFgBYtWtTr/e3t7eu1XX389S45iUSidP3U5s2bMXnyZKSnpyMlJQVz5szB3r170b27cT+7iIjoryoqKpCYmKjU1qlTJwwcOFCkigwDR5QM3IwZQFjY4++NxcHBAe3atUOrVq3+dkmAZ599FpcvX4a7uzvatWun9OXs7AxnZ2d4eXkhOztbsU1VVRVOnDihcZ+dO3dGTU0Nfv75Z7WvPxnRqq6u1riPDh06oKqqSul9//jjD1y8eBEdO3as9Zj+Kjg4GDNnzsSRI0fQqVMnbNu2TavtiYgMXU5OjkpImjBhgtmHJIBByeD17w8cOdJ4o0naeuutt+Dm5oYBAwbg0KFDyMvLw08//YTJkyfj999/BwBMmTIFixcvRmpqKi5cuIDx48ejpKRE4z59fX0xfPhwjBw5EqmpqYp9PrnronXr1pBIJEhLS0NxcTHu37+vsg9/f38MGDAAo0aNwi+//IJTp05hyJAhaNmyJQYMGFCnY8vLy8PMmTORlZWF3377DT/++CMuX77M65SIyKQkJCTgu+++U2qTSqVwc3MTqSLDwqBEDdKkSRMcPHgQrVq1wmuvvYYOHTogNjYWjx49gpOTEwBg2rRpGDp0KIYPH46wsDA4Ojri1VdfrXW/GzZswOuvv47x48cjICAAo0aNQllZGQCgZcuWSEhIwIwZM+Dh4YGJEyeq3cfmzZsREhKCV155BWFhYRAEAXv27KnzopRNmjTBhQsXMHDgQDz99NMYPXo0JkyYgDFjxmhxhoiIDNOjR4+QkJCg1Pbss89CKpWKVJFhkgh/XdCG6kQul8PZ2RmlpaWKQPDEo0ePkJeXBz8/P9jZ2YlUITU2/r4TkbHIzs5Genq6UtvkyZPRrFkzkSpqPLV9fqvDi7mJiIjMyF9HkQBwFKkWDEpERERm4MGDB1i6dKlSW/fu3RERESFSRcaBQYmIiMjEbdu2TekRTgDwzjvv1GnqydwxKBEREZkwTrU1DIOSHvE6efPC328iMiQymQwfffSRUtvvv7dCv34jRKrIODEo6cGT288fPHjQqKtMk7gePHgAQHW1byKixqZuFOnw4bGYONHDYNflM1QMSnpgaWkJFxcXxTPDmjRpYlZPWjY3giDgwYMHKCoqgouLCywtLcUuiYjMGKfadItBSU88PT0BQBGWyPS5uLgoft+JiBrblStX1D53MziYIakhGJT0RCKRwMvLC+7u7qisrBS7HNIza2trjiQRkWjUjSKNGzcO7u7uIlRjWhiU9MzS0pIfoEREpDecatMvBiUiIiIjdPbsWXz99dcq7QxJusWgREREZGTUjSKZy7PaGhuDEhERkRHhVFvjYlAiIiIyAjk5Ofjuu++U2qytrTFr1iyRKjIPDEpEREQGTt0oUlxcHBwdHUWoxrwwKBERERkoQRAwf/58lXZOtTUeBiUiIiIDdPjwYezbt0+prXnz5pg0aZJIFZknBiUiIiIDo26qbfr06Xx+qAgYlIiIiAwEp9oMD4MSERGRAdi3bx8OHz6s1Obj44ORI0eKVBEBDEpERESiUzfVNnPmTNjY2IhQDf0ZgxIREZFIampqsGDBApV2TrUZDguxC1i/fj18fX1hZ2eH0NBQHD16tNb+O3bsQEBAAOzs7NC5c2fs2bNH6fVvv/0WL730ElxdXSGRSHDy5EmVfTx69AgTJkyAq6srmjZtioEDB6KwsFCXh0VERFSrL774QiUkBQQEMCQZGFGDUkpKCuLi4iCVSpGTk4PAwEBERESgqKhIbf8jR45g8ODBiI2NRW5uLqKiohAVFYUzZ84o+pSVlaFnz5744IMPNL7vO++8g++++w47duzAzz//jJs3b+K1117T+fERERGpk5CQgKtXryq1zZ49G9HR0SJVRJpIBEEQxHrz0NBQdO3aFevWrQPweAjSx8cHkyZNwowZM1T6R0dHo6ysDGlpaYq27t27IygoCElJSUp98/Pz4efnh9zcXAQFBSnaS0tL0aJFC2zbtg2vv/46AODChQvo0KEDsrKy0L179zrVLpfL4ezsjNLSUjg5OWl76EREZIYqKyuxaNEilfbgYCn69xehIDOk7ee3aNcoVVRU4MSJE5g5c6aizcLCAuHh4cjKylK7TVZWFuLi4pTaIiIikJqaWuf3PXHiBCorKxEeHq5oCwgIQKtWrbQKSkRERNrYsGGDyoxJ27ZtMWTIEJEqoroQLSjdvn0b1dXV8PDwUGr38PDAhQsX1G4jk8nU9pfJZHV+X5lMBhsbG7i4uGi1n/LycpSXlyt+lsvldX5PIiIyb+ruaps7dy4sLES/VJj+Bn+H6igxMRHOzs6KLx8fH7FLIiIiA1deXq42JEmlUoYkIyHaiJKbmxssLS1V7jYrLCyEp6en2m08PT216q9pHxUVFSgpKVEaVfq7/cycOVNp2k8ulzMsERGRRkuWLMHDhw+V2gIDAxEVFSVOQVQvosVZGxsbhISEIDMzU9FWU1ODzMxMhIWFqd0mLCxMqT8A7N27V2N/dUJCQmBtba20n4sXL6KgoKDW/dja2sLJyUnpi4iISJ2EhASVkBQfH8+QZIREXXAyLi4Ow4cPR5cuXdCtWzesWrUKZWVlGDFiBABg2LBhaNmyJRITEwEAU6ZMQa9evbB8+XL07dsXycnJOH78ODZu3KjY5507d1BQUICbN28CeByCgMcjSZ6ennB2dkZsbCzi4uLQvHlzODk5YdKkSQgLC+OF3ERE1CD379/H8uXLVdq5NpLxEjUoRUdHo7i4GPHx8ZDJZAgKCkJ6errigu2CggKlOdwePXpg27ZtmDNnDmbNmgV/f3+kpqaiU6dOij67d+9WBC0AGDRoEIDHf0jnzZsHAFi5ciUsLCwwcOBAlJeXIyIiAh9++GEjHDEREZkqddcihYWF4aWXXhKhGtIVUddRMmZcR4mIiJ5QF5Li4+MhkUhEqIZqYzTrKBERERm7kpISrF69WqWdU22mg0GJiIioHtSNIr344ovo2bOnCNWQvjAoERERaUnT2khkehiUiIiI6qi4uFjtzT8MSaaLQYmIiKgO1I0ivfLKKwgJCRGhGmosDEpERER/g1Nt5otBiYiISIMbN25g06ZNKu0MSeaDQYmIiEgNdaNIr7/+Op555hkRqiGxMCgRERH9Bafa6AkGJSIiov938eJFJCcnq7QzJJkvBiUiIiKoH0XKzY3GyJEBIlRDhoJBiYiIzB6n2kgTBiUiIjJbJ0+exK5du1TaGZLoCQYlIiIyS+pGkYYPHw5fX9/GL4YMFoMSERGZHU61UV0xKBERkdk4cuQI9u7dq9LOkESaMCgREZFZUDeKNHr0aHh5eYlQDRkLBiUiIjJ5nGqj+mJQIiIik7V7927k5uaqtDMkUV0xKBERkUlSN4o0ceJEuLq6ilANGSsGJSIiMimCIGD+/Pkq7cHBUjAjkbYYlIiIyGR8+eWXuHLliko7p9qovhiUiIjIJKibaps6dSqcnZ1FqIZMBYMSEREZNU1TbRxFIl1gUCIiIqO1YcMGFBUVqbQzJJGuMCgREZFRUjfV9t5776FJkyYiVEOmikGJiIiMSnV1NRYuXKjSzlEk0gcGJSIiMhrqRpEAhiTSHwYlIiIyCupC0owZM2BraytCNWQuGJSIiMigVVRUIDExUaU9OFgKZiTSNwYlIiIyWJxqI7ExKBERkUFSF5Jmz54NKyt+dFHj4Z82IiIyKPfv38fy5ctV2jmKRGJgUCIiIoOhbhTJysoKs2fPFqEaIgYlIiIyEOpCUkbGXBw5YiFCNUSPMSgREZGo7ty5g7Vr16q0Z2RIMWOGCAUR/QmDEhERiUbdKJK9vT2mT58OXpJEhoBBiYiIRKEuJMXHx0MikYhQDZF6DEpERNSobt26hY0bN6q08642MkQMSkRE1GjUjSJ5eXlh9OjRIlRD9PcYlIiIqFGoC0kcRSJDx6BERER6lZeXh88//1ylnSGJjAGDEhER6Y26UaSAgABER0eLUA2R9hiUiIhILzjVRqaAQYmIiHTqzJkz+Oabb1TaGZLIGDEoERGRzqgbRQoNDUVkZKQI1RA1HIMSERHpBKfayBQxKBERUYMcPXoUP/zwg0o7QxKZAgYlIiKqN3WjSOHh4XjuuedEqIZI9yzELmD9+vXw9fWFnZ0dQkNDcfTo0Vr779ixAwEBAbCzs0Pnzp2xZ88epdcFQUB8fDy8vLxgb2+P8PBwXL58WanPpUuXMGDAALi5ucHJyQk9e/bEgQMHdH5sRESmTNNUG0MSmRJRg1JKSgri4uIglUqRk5ODwMBAREREoKioSG3/I0eOYPDgwYiNjUVubi6ioqIQFRWFM2fOKPosWbIEa9asQVJSErKzs+Hg4ICIiAg8evRI0eeVV15BVVUV9u/fjxMnTiAwMBCvvPIKZDKZ3o+ZiMjYZWZm8nokMhsSQRAEsd48NDQUXbt2xbp16wAANTU18PHxwaRJkzBjxgyV/tHR0SgrK0NaWpqirXv37ggKCkJSUhIEQYC3tzemTZuGd999FwBQWloKDw8PbNmyBYMGDcLt27fRokULHDx4EP/4xz8AAPfu3YOTkxP27t2L8PDwOtUul8vh7OyM0tJSODk5NfRUEBEZBXUBacCAAQgKCmr8YojqQdvPb9FGlCoqKnDixAmlYGJhYYHw8HBkZWWp3SYrK0slyERERCj65+XlQSaTKfVxdnZGaGiooo+rqyvat2+Pzz//HGVlZaiqqsJHH30Ed3d3hISEaKy3vLwccrlc6YuIyJxoGkViSCJTJtrF3Ldv30Z1dTU8PDyU2j08PHDhwgW128hkMrX9n0yZPfleWx+JRIJ9+/YhKioKjo6OsLCwgLu7O9LT09GsWTON9SYmJqr9R4KIyNTt3LkTp0+fVmnnVBuZA7O7600QBEyYMAHu7u44dOgQ7O3tsWnTJvTr1w/Hjh2Dl5eX2u1mzpyJuLg4xc9yuRw+Pj6NVTYRkSjU/QfxzTffhL+/vwjVEDU+0YKSm5sbLC0tUVhYqNReWFgIT09Ptdt4enrW2v/J98LCQqXAU1hYqBga3r9/P9LS0nD37l3F3OSHH36IvXv34rPPPlN7bRQA2NrawtbWVvsDJSIyUrxgm0jEa5RsbGwQEhKCzMxMRVtNTQ0yMzMRFhamdpuwsDCl/gCwd+9eRX8/Pz94enoq9ZHL5cjOzlb0efDgAYDH10P9mYWFBWpqahp+YERERu6zzz5jSCL6f6JOvcXFxWH48OHo0qULunXrhlWrVqGsrAwjRowAAAwbNgwtW7ZEYmIiAGDKlCno1asXli9fjr59+yI5ORnHjx/Hxo0bATy+/mjq1KlYuHAh/P394efnh7lz58Lb2xtRUVEAHoetZs2aYfjw4YiPj4e9vT0+/vhj5OXloW/fvqKcByIiQ6EuIMXGxuKpp54SoRoi8YkalKKjo1FcXIz4+HjIZDIEBQUhPT1dcTF2QUGB0shPjx49sG3bNsyZMwezZs2Cv78/UlNT0alTJ0Wf6dOno6ysDKNHj0ZJSQl69uyJ9PR02NnZAXg85Zeeno7Zs2fjhRdeQGVlJZ555hns2rULgYGBjXsCiIgMCEeRiFSJuo6SMeM6SkRkKlavXo2SkhKVdoYkMkXafn5rPaJkaWmJW7duwd3dXan9jz/+gLu7O6qrq7XdJRERiUTdKNL48ePRokULEaohMjxaByVNA1Dl5eWwsbFpcEFERKR/giBg/vz5Ku0cRSJSVuegtGbNGgCPL5jetGkTmjZtqnituroaBw8eREBAgO4rJCIindK0eC5DEpGqOgellStXAnj8v5CkpCRYWloqXrOxsYGvry+SkpJ0XyEREemMupD0zjvv8FpLIg3qHJTy8vIAAM8//zy+/fbbWh/3QUREhqW6uhoLFy5UaecoElHttL5G6cCBA/qog4iI9IRTbUT1p3VQGjlyZK2vf/rpp/UuhoiIdEtdSJo+fTrs7e1FqIbI+GgdlO7evav0c2VlJc6cOYOSkhK88MILOiuMiIjqr6KiQvFUgz/jKBKRdrQOSjt37lRpq6mpwbhx49C2bVudFEVERPXHqTYi3dHZytwXL15E7969cevWLV3szuBxZW4iMkTqQtKsWbNgbW0tQjVEhkfvK3NrcvXqVVRVVelqd0REpIWysjIsW7ZMpZ2jSEQNo3VQiouLU/pZEATcunUL33//PYYPH66zwoiIqG441UakP1oHpdzcXKWfLSws0KJFCyxfvvxv74gjIiLdUheS5s6dCwsLCxGqITI9XEeJiMgIlZSUYPXq1SrtHEUi0q16X6NUVFSEixcvAgDat28Pd3d3nRVFRESaqRtFqqqyxoIFs0Sohsi0aT02K5fLMXToUHh7e6NXr17o1asXWrZsiSFDhqC0tFQfNRIR0f9TF5IyMuLRtStDEpE+aB2URo0ahezsbHz//fcoKSlBSUkJ0tLScPz4cYwZM0YfNRIRmT2ZTKY2JEmlUhw5IkH//iIURWQGtF5HycHBARkZGejZs6dS+6FDhxAZGYmysjKdFmiouI4SETUWdQHJ3d0d48aNE6EaIuOm93WUXF1d4ezsrNLu7OyMZs2aabs7IiKqhaZRJCJqHFpPvc2ZMwdxcXGQyWSKNplMhvfeew9z587VaXFERObq6tWrDElEBkDrqbfg4GBcuXIF5eXlaNWqFQCgoKAAtra28Pf3V+qbk5Oju0oNDKfeiEhf1AWkp59+GoMHDxahGiLToveptwEDBkAikdSrOCIiqp36u9qk4EASkTi0Dkrz5s3TQxlEROYtJycH3333nUp7RoYUM2aIUBARAahHUGrTpg2OHTsGV1dXpfaSkhI8++yzuHbtms6KIyIyB+pGkW7e7IyPPnqNI0lEItP6Yu78/HxUV1ertJeXl+P333/XSVFEROZCXUhKTpaib9/XRKiGiP6qziNKu3fvVvw6IyNDaYmA6upqZGZmws/PT7fVERGZqH379uHw4cMq7VIpr0ciMiR1DkpRUVEAAIlEguHDhyu9Zm1tDV9fXyxfvlynxRERmSJ1o0hubl0xYcLLIlRDRLWpc1CqqakBAPj5+eHYsWNwc3PTW1FERKaKayMRGRetL+bOy8vTRx1ERCZt27ZtuHz5sko7QxKRYdM6KM2fP7/W1+Pj4+tdDBGRKVI3iuTp+U+MGfO8CNUQkTa0Dko7d+5U+rmyshJ5eXmwsrJC27ZtGZSIiP6EU21Exk3roJSbm6vSJpfLERMTg1dffVUnRRERGbulS5fiwYMHKu3BwQxJRMZE62e9afLrr7+iX79+yM/P18XuDB6f9UZEmqgbRfrXv/6FHj16iFANEf2Ztp/fWi84qUlpaSlKS0t1tTsiIqOk6VltDElExknrqbc1a9Yo/SwIAm7duoWtW7eiT58+OiuMiMiYqAtIAJ/VRmTstJ56++vq2xYWFmjRogVeeOEFzJw5E46Ojjot0FBx6o2InlAXkgYOHIhOnTqJUA0R1Ubbz2+uo0RE1AC8q43ItGkdlACgpKQEV65cAQC0a9cOLi4uuqyJiMjgaZpq411tRKZFq6CUn5+PCRMmICMjA09m7CQSCSIjI7Fu3Tr4+vrqo0YiIoOiLiQNGTIEbdu2FaEaItKnOgel69evo3v37rC2tsaCBQvQoUMHAMC5c+ewYcMGhIWF4dixY3jqqaf0ViwRkZgEQVD7dAJOtRGZrjpfzB0bG4srV64gIyMDdnZ2Sq89fPgQkZGR8Pf3x6ZNm/RSqKHhxdxE5kXTVBtDEpFx0dvF3Onp6UhJSVEJSQBgb2+PBQsWYNCgQdpVS0RkBNSFpDFjxsDT01OEaoioMdU5KN2+fbvWa5DatGmDO3fu6KImIiKDUFVVhffff1+lnaNIROajzkHJy8sL586d03gN0pkzZ/i/KyIyGZxqIyJAi6AUFRWFd999F5mZmWjRooXSa0VFRfjPf/6DqKgoXddHRNTo1IWkqVOnwtnZWYRqiEhMdb6Y++7duwgNDYVMJsOQIUMQEBAAQRBw/vx5bNu2DZ6envjvf/+L5s2b67tmg8CLuYlMz8OHD7FkyRKVdo4iEZkOvV3M3axZM2RnZ2PWrFlITk5GSUkJAMDFxQVvvvkmFi1aZDYhiYhMD6faiEgdrZ/1BjxeS6S4uBgA0KJFC0gkEp0XZug4okRkOtSFpP/85z9q7/IlIuOm7ee3RX3eRCKRwN3dHe7u7g0OSevXr4evry/s7OwQGhqKo0eP1tp/x44dCAgIgJ2dHTp37ow9e/YovS4IAuLj4+Hl5QV7e3uEh4fj8uXLKvv5/vvvERoaCnt7ezRr1ozXVxGZodLSUo3PamNIIiKgnkFJV1JSUhAXFwepVIqcnBwEBgYiIiICRUVFavsfOXIEgwcPRmxsLHJzcxEVFYWoqCicOXNG0WfJkiVYs2YNkpKSkJ2dDQcHB0RERODRo0eKPt988w2GDh2KESNG4NSpUzh8+DDefPNNvR8vERmOhIQErFq1SqWdU21E9Gf1mnrTldDQUHTt2hXr1q0DANTU1MDHxweTJk3CjBkzVPpHR0ejrKwMaWlpirbu3bsjKCgISUlJEAQB3t7emDZtGt59910Aj//H6OHhgS1btmDQoEGoqqqCr68vEhISEBsbW+/aOfVGZLzUjSLNnj0bVlb1ek44ERmRRpl604WKigqcOHEC4eHh/yvGwgLh4eHIyspSu01WVpZSfwCIiIhQ9M/Ly4NMJlPq4+zsjNDQUEWfnJwc3LhxAxYWFggODoaXlxf69OmjNCqlTnl5OeRyudIXERkXmUymcaqNIYmI1BEtKN2+fRvV1dXw8PBQavfw8IBMJlO7jUwmq7X/k++19bl27RoAYN68eZgzZw7S0tLQrFkz9O7du9aVxRMTE+Hs7Kz48vHx0eJoiUhsCQkJ+Oijj1TaOdVGRLWp03+h1qxZU+cdTp48ud7FNIaamhoAj4fZBw4cCADYvHkznnrqKezYsQNjxoxRu93MmTMRFxen+FkulzMsERkJdaNI8fHxZnnHLhFpp05BaeXKlXXamUQiqXNQcnNzg6WlJQoLC5XaCwsLNT4KxdPTs9b+T74XFhbCy8tLqU9QUBAAKNo7duyoeN3W1hZt2rRBQUGBxnptbW1ha2tbp2MjIsNw5coVfPnllyrtHEUiorqqU1DKy8vT+Rvb2NggJCQEmZmZilvza2pqkJmZiYkTJ6rdJiwsDJmZmZg6daqibe/evQgLCwMA+Pn5wdPTE5mZmYpgJJfLkZ2djXHjxgEAQkJCYGtri4sXL6Jnz54AgMrKSuTn56N169Y6P04iEgcXkCQiXaj31YsVFRXIy8tD27Zt630RZFxcHIYPH44uXbqgW7duWLVqFcrKyjBixAgAwLBhw9CyZUskJiYCAKZMmYJevXph+fLl6Nu3L5KTk3H8+HFs3LgRwOMRralTp2LhwoXw9/eHn58f5s6dC29vb0UYc3JywtixYyGVSuHj44PWrVtj6dKlAIA33nijvqeDiAyIpgu2iYi0pXXCefDgASZNmoTPPvsMAHDp0iW0adMGkyZNQsuWLdXe1q9JdHQ0iouLER8fD5lMhqCgIKSnpysuxi4oKICFxf+uN+/Rowe2bduGOXPmYNasWfD390dqaio6deqk6DN9+nSUlZVh9OjRKCkpQc+ePZGenq60eNzSpUthZWWFoUOH4uHDhwgNDcX+/fvRrFkzbU8HERmQnJwcfPfddyrtDElEVF9ar6M0ZcoUHD58GKtWrUJkZCROnz6NNm3aYNeuXZg3bx5yc3P1VatB4TpKRIaFU21EVBd6eyjuE6mpqUhJSUH37t2V7hh55plncPXqVW13R0TUYOpCUkaGFEeOiFAMEZkUrYNScXEx3N3dVdrLysp4qy0RNaqff/4ZP/30k0p7RoYUWlwFQESkkdYLTnbp0gXff/+94ucn4WjTpk2Ku8+IiPQtISFBJSRZWVlBKn08ktS/vzh1EZFp0XpEadGiRejTpw/OnTuHqqoqrF69GufOncORI0fw888/66NGIiIlvKuNiBqL1iNKPXv2xMmTJ1FVVYXOnTvjxx9/hLu7O7KyshASEqKPGomIAAA7duxQG5KCgxmSiEg/tL7rjR7jXW9EjUtdQHJ1ddW4QC0RkTp6uetNLpfXuQCGBiLSNU61EZFY6hSUXFxc6nxHW3V1dYMKIiJ64sMPP0RxcbFKO0MSETWWOgWlAwcOKH6dn5+PGTNmICYmRnGXW1ZWFj777DPFo0aIiBpK3ShSQEAAoqOjRaiGiMyV1tcovfjii3j77bcxePBgpfZt27Zh48aNatc0MUW8RolIfzjVRkT6ou3nt9ZBqUmTJjh16hT8/f2V2i9duoSgoCA8ePBAu4qNFIMSke4lJiaioqJCpZ0hiYh0RdvPb62XB/Dx8cHHH3+s0r5p0yb4+PhouzsiIgCPR5H+GpLc3XswJBGRqLRecHLlypUYOHAgfvjhB4SGhgIAjh49isuXL+Obb77ReYFEZPo0rY3E1bWJSGz1Wkfp999/x4cffogLFy4AADp06ICxY8ea1YgSp96IGk5dQAI41UZE+qP3a5ToMQYlooZRF5Jat45CTEygCNUQkbnQy4KTf1VSUoJPPvkE58+fBwA888wzGDlyJJydneuzOyIyM7yrjYiMhdYjSsePH0dERATs7e3RrVs3AMCxY8fw8OFD/Pjjj3j22Wf1Uqih4YgSkfY41UZEYtP71Ns//vEPtGvXDh9//DGsrB4PSFVVVeHtt9/GtWvXcPDgwfpVbmQYlIi0oy4kDR06FG3atBGhGiIyV3oPSvb29sjNzUVAQIBS+7lz59ClSxeuo0RESgRBwPz581XaMzKkOHJEhIKIyKzp/RolJycnFBQUqASl69evw9HRUdvdEZEJ0zTVlpEhxYwZjVwMEVE9aL3gZHR0NGJjY5GSkoLr16/j+vXrSE5OVvtYEyIyX+pC0pEjoxEc/HgkiWskEZEx0HpEadmyZZBIJBg2bBiqqqoAANbW1hg3bhwWL16s8wKJyLhUV1dj4cKFKu28YJuIjFG911F68OABrl69CgBo27YtmjRpotPCDB2vUSJSxbvaiMjQNco6SsDjh+N27ty5vpsTkYlRF5ImT56MZs2aiVANEZFu1DkojRw5sk79Pv3003oXQ0TG59GjR/jggw9U2jmKRESmoM5BacuWLWjdujWCg4PBp54QEcCpNiIyfXUOSuPGjcP27duRl5eHESNGYMiQIWjevLk+ayMiA6YuJE2fPh329vYiVENEpB91Xh5g/fr1uHXrFqZPn47vvvsOPj4++Pe//42MjAyOMBGZEblcrvFZbQxJRGRq6n3X22+//YYtW7bg888/R1VVFc6ePYumTZvquj6DxbveyBxxqo2IjF2j3fVmYWEBiUQCQRBQXV1d390QkZFQF5ICA2cjKqre/4wQERk8rVbmLi8vx/bt2/Gvf/0LTz/9NH799VesW7cOBQUFZjWaRGROCgsLNU61MSQRkamr879y48ePR3JyMnx8fDBy5Ehs374dbm5u+qyNiETGqTYiMnd1vkbJwsICrVq1QnBwMCQSicZ+3377rc6KM2S8RolMnbqQFB8fX+vffyIiQ6e3a5SGDRvGfyCJzMC1a9ewdetWlXaOIhGROdJqwUkiMm2aptqCgxmSiMg88UpMIgKgPiRxFImIzB2DEpGZO336NHbu3KnSzpBERMSgRGTWeFcbEVHtGJSIzBSn2oiI/h6DEpGZOXToEPbv36/SzpBERKSKQYnIjHCqjYhIOwxKRGaCU21ERNpjUCIycbt370Zubq5KO0MSEdHfY1AiMmHqRpHc3d0xbtw4EaohIjI+DEpEJopTbUREDcegRGRitmzZgt9++02lnSGJiEh7DEpEJkTdKFLHjh3xxhtviFANEZHxY1AiMhGcaiMi0j0LsQsAgPXr18PX1xd2dnYIDQ3F0aNHa+2/Y8cOBAQEwM7ODp07d8aePXuUXhcEAfHx8fDy8oK9vT3Cw8Nx+fJltfsqLy9HUFAQJBIJTp48qatDImo0S5cuZUgiItIT0YNSSkoK4uLiIJVKkZOTg8DAQERERKCoqEht/yNHjmDw4MGIjY1Fbm4uoqKiEBUVhTNnzij6LFmyBGvWrEFSUhKys7Ph4OCAiIgIPHr0SGV/06dPh7e3t96Oj0ifEhIS8ODBA6W25557jiGJiEhHJIIgCGIWEBoaiq5du2LdunUAgJqaGvj4+GDSpEmYMWOGSv/o6GiUlZUhLS1N0da9e3cEBQUhKSkJgiDA29sb06ZNw7vvvgsAKC0thYeHB7Zs2YJBgwYptvvhhx8QFxeHb775Bs888wxyc3MRFBRUp7rlcjmcnZ1RWloKJyenBpwBovrhKBIRkfa0/fwWdUSpoqICJ06cQHh4uKLNwsIC4eHhyMrKUrtNVlaWUn8AiIiIUPTPy8uDTCZT6uPs7IzQ0FClfRYWFmLUqFHYunUrmjRp8re1lpeXQy6XK30RiSEhIYEhiYiokYgalG7fvo3q6mp4eHgotXt4eEAmk6ndRiaT1dr/yffa+giCgJiYGIwdOxZdunSpU62JiYlwdnZWfPn4+NRpOyJdUheQzp7ti+BghiQiIn0wy7ve1q5di3v37mHmzJl13mbmzJmIi4tT/CyXyxmWqFFxFImIqPGJGpTc3NxgaWmJwsJCpfbCwkJ4enqq3cbT07PW/k++FxYWwsvLS6nPk+uP9u/fj6ysLNja2irtp0uXLnjrrbfw2Wefqbyvra2tSn+ixqAuIAHgKBIRUSMQderNxsYGISEhyMzMVLTV1NQgMzMTYWFharcJCwtT6g8Ae/fuVfT38/ODp6enUh+5XI7s7GxFnzVr1uDUqVM4efIkTp48qVheICUlBe+//75Oj5GoIdSFpJycQZBKpejfX4SCiIjMjOhTb3FxcRg+fDi6dOmCbt26YdWqVSgrK8OIESMAAMOGDUPLli2RmJgIAJgyZQp69eqF5cuXo2/fvkhOTsbx48exceNGAIBEIsHUqVOxcOFC+Pv7w8/PD3PnzoW3tzeioqIAAK1atVKqoWnTpgCAtm3b4qmnnmqkIyfSTBAEzJ8/X6U9I0MKNTeDEhGRnogelKKjo1FcXIz4+HjIZDIEBQUhPT1dcTF2QUEBLCz+N/DVo0cPbNu2DXPmzMGsWbPg7++P1NRUdOrUSdFn+vTpKCsrw+jRo1FSUoKePXsiPT0ddnZ2jX58RNrSNNUmlUrBS5KIiBqX6OsoGSuuo0T6oC4kjRw5kjcOEBHpiLaf36KPKBERUF1djYULF6q08642IiJxMSgRiay2qTYiIhIXgxKRiNSFpIkTJ8LV1VWEaoiI6K8YlIhEUF5ejsWLF6u0BwdLwYxERGQ4GJSIGlltC0hybSQiIsPCoETUiNSFpPfee69OD2YmIqLGx6BE1Aju3buHFStWqLTzgm0iIsPGoESkZ7yrjYjIeIn6rDciU6cuJO3dO4sPtCUiMhIcUSLSg9u3b2P9+vUq7XwMCRGRcWFQItIxTrUREZkOBiUiHVIXkubOnav0YGciIjIeDEpEOvDbb79hy5YtKu0cRSIiMm4MSkQNpGmqraKCIYmIyNgxKBE1gLqQFB8fD4lEIkI1RESkawxKRPVw6tQppKamqrRzqo2IyLQwKBFpqbZntRERkWnhrThEWlAXkjIypHygLRGRieKIElEdHDx4EAcOHFBp5wKSRESmjUGJ6G9wAUkiIvPFoERUC3UhiQGJiMh8MCgRqbFr1y6cPHlSpZ0hiYjIvDAoEf2FulEkR0dHxMXFiVANERGJiUGJ6E841UZERH/GoEQEYMOGDSgqKlJpZ0giIjJvDEpk9tSNIv3xhx/WrBkmQjVERGRIuOAkmTVNC0iGhzMkERERR5TITNX2GBLOthER0RMMSmR21IWkTp06YeDAgSJUQ0REhoxBicwK72ojIiJtMCiRWahtqo2IiEgTXsxNJk9dSPrll14IDpaif38RCiIiIqPBESUyaZruapsxAwxJRET0txiUyCRpmmqTSnlXGxER1R2DEpkcdSFpwIABCAoKavxiiIjIqDEokckQBAHz589XaeddbUREVF8MSmQSeFcbERHpA4MSGT11IWno0KFo06aNCNUQEZEpYVAio1VdXY2FCxeqtHOqjYiIdIVBiYxSbXe1ERER6QqDEhkddSFp3LhxcHd3F6EaIiIyZQxKZDTKy8uxePFilXaOIhERkb4wKJFR4FQbERGJgUGJDJ66kNSpUxwGDnQUoRoiIjInDEpksORyOVauXKnSzlEkIiJqLAxKZJA41UZERIaAQYkMjrqQNGPGDNja2opQDRERmTMGJTIYhYWFSEpKUmkPDpaCGYmIiMRgIXYBALB+/Xr4+vrCzs4OoaGhOHr0aK39d+zYgYCAANjZ2aFz587Ys2eP0uuCICA+Ph5eXl6wt7dHeHg4Ll++rHg9Pz8fsbGx8PPzg729Pdq2bQupVIqKigq9HB/9vYSEBI0hqX9/EQoiIiKCAQSllJQUxMXFQSqVIicnB4GBgYiIiEBRUZHa/keOHMHgwYMRGxuL3NxcREVFISoqCmfOnFH0WbJkCdasWYOkpCRkZ2fDwcEBERERePToEQDgwoULqKmpwUcffYSzZ89i5cqVSEpKwqxZsxrlmEmZuqm2uXPnQiplSCIiInFJBEEQxCwgNDQUXbt2xbp16wAANTU18PHxwaRJkzBjxgyV/tHR0SgrK0NaWpqirXv37ggKCkJSUhIEQYC3tzemTZuGd999FwBQWloKDw8PbNmyBYMGDVJbx9KlS7FhwwZcu3atTnXL5XI4OzujtLQUTk5O2h42AcjLy8Pnn3+u0s4LtomISF+0/fwWdUSpoqICJ06cQHh4uKLNwsIC4eHhyMrKUrtNVlaWUn8AiIiIUPTPy8uDTCZT6uPs7IzQ0FCN+wQeh6nmzZs35HBICwkJCQxJRERk8ES9mPv27duorq6Gh4eHUruHhwcuXLigdhuZTKa2v0wmU7z+pE1Tn7+6cuUK1q5di2XLlmmstby8HOXl5Yqf5XK5xr5UO3VTbRkZ8ZgxQyJCNURERJqJfo2S2G7cuIHIyEi88cYbGDVqlMZ+iYmJcHZ2Vnz5+Pg0YpWm4fTp02pDklQqxZEjEl6PREREBkfUoOTm5gZLS0sUFhYqtRcWFsLT01PtNp6enrX2f/K9Lvu8efMmnn/+efTo0QMbN26stdaZM2eitLRU8XX9+vW/P0BSSEhIwM6dO1XaOdVGRESGTNSgZGNjg5CQEGRmZiraampqkJmZibCwMLXbhIWFKfUHgL179yr6+/n5wdPTU6mPXC5Hdna20j5v3LiB3r17IyQkBJs3b4aFRe2nwtbWFk5OTkpfVDeaRpEYkoiIyNCJvuBkXFwchg8fji5duqBbt25YtWoVysrKMGLECADAsGHD0LJlSyQmJgIApkyZgl69emH58uXo27cvkpOTcfz4ccWIkEQiwdSpU7Fw4UL4+/vDz88Pc+fOhbe3N6KiogD8LyS1bt0ay5YtQ3FxsaIeTSNZpL2srCz8+OOPKu0MSEREZCxED0rR0dEoLi5GfHw8ZDIZgoKCkJ6errgYu6CgQGm0p0ePHti2bRvmzJmDWbNmwd/fH6mpqejUqZOiz/Tp01FWVobRo0ejpKQEPXv2RHp6Ouzs7AA8HoG6cuUKrly5gqeeekqpHpFXSzAZfFYbERGZAtHXUTJWXEdJM3Uhaft2KTTcyEhERNRotP38Fn1EiUzHgQMHcPDgQZX2jAwpliwRoSAiIqIGYlAinVA3inTnTnO8+OIkcLaNiIiMFYMSNZj6BSSlmDEDXBuJiIiMGoMS1dvOnTtx+vRplfbHt/6LUBAREZGOMShRvagbRbp1qxNefnmgCNUQERHpB4MSaU1dSAoO5igSERGZHgYlqrOtW7fi2rVrKu3BwVJei0RERCaJQYnqRN0o0uXLz+Pf//4nQxIREZksBiX6W5qe1UZERGTqGJRIo9WrV6OkpESlPTiYIYmIiMwDgxKppW4U6ddf+2PYsGBOtRERkdlgUCIVnGojIiJ6jEGJFBYtWoTKykqVdoYkIiIyVwxKBED9KNKbb74Jf39/EaohIiIyDAxKZk4QBMyfP1+lPThYCmYkIiIydwxKZkzdKBLAqTYiIqInGJTMlLqQNGbMGHh6eopQDRERkWFiUDIzmqbaOIpERESkikHJjHCqjYiISDsMSmZCXUj6+ecp2L/fpfGLISIiMhIMSiauuroaCxcuVGlftkyKL78UoSAiIiIjwqBkwhYvXozy8nKV9uTkxyGJjyIhIiKqHYOSiVI31bZmzQyMHWuL8+dFKIiIiMgIMSiZmMrKSixatEilvaJCij/+EKEgIiIiI8agZELUjSLV1FggJGQup9mIiIjqgUHJRKgLSbNnz4aVFX+LiYiI6oufokbu4cOHWLJkiUo710YiIiJqOAYlI6ZuFKlZs2aYPHmyCNUQERGZHgYlI6UuJM2dOxcWFhYiVENERGSaGJSMzP3797F8+XKVdk61ERER6R6DkhFRN4p0544vVq8eLkI1REREpo/zNEZCXUjKyIjHiy8yJBEREekLR5QM3KNHj/DBBx+otEulUnC2jYiISL8YlAxYeno6srOzldp+/PEV9O4dIlJFRERE5oVByUDt3LkTp0+fVmpbtkyKe/dEKoiIiMgMMSgZmKqqKrz//vtKbQ8e2GPNmumIixOpKCIiIjPFoGRgkpL2KP28fv27GDXKAQ8filQQERGRGeNdbwbm4MGWAIDCwvaQSqUoKnLAXwaYiIiIqJEwKBmYIUNCkJEhRWTkILFLISIiMnucejMw/fs//iIiIiLxcUSJiIiISAMGJSIiIiINGJSIiIiINGBQIiIiItKAQYmIiIhIAwYlIiIiIg0YlIiIiIg0YFAiIiIi0oBBiYiIiEgDgwhK69evh6+vL+zs7BAaGoqjR4/W2n/Hjh0ICAiAnZ0dOnfujD17lB8kKwgC4uPj4eXlBXt7e4SHh+Py5ctKfe7cuYO33noLTk5OcHFxQWxsLO7fv6/zYyMiIiLjJXpQSklJQVxcHKRSKXJychAYGIiIiAgUFRWp7X/kyBEMHjwYsbGxyM3NRVRUFKKionDmzBlFnyVLlmDNmjVISkpCdnY2HBwcEBERgUePHin6vPXWWzh79iz27t2LtLQ0HDx4EKNHj9b78RIREZHxkAiCIIhZQGhoKLp27Yp169YBAGpqauDj44NJkyZhxowZKv2jo6NRVlaGtLQ0RVv37t0RFBSEpKQkCIIAb29vTJs2De+++y4AoLS0FB4eHtiyZQsGDRqE8+fPo2PHjjh27Bi6dOkCAEhPT8fLL7+M33//Hd7e3n9bt1wuh7OzM0pLS+Hk5KSLU0FERER6pu3nt6gjShUVFThx4gTCw8MVbRYWFggPD0dWVpbabbKyspT6A0BERISif15eHmQymVIfZ2dnhIaGKvpkZWXBxcVFEZIAIDw8HBYWFsjOzlb7vuXl5ZDL5UpfREREZNqsxHzz27dvo7q6Gh4eHkrtHh4euHDhgtptZDKZ2v4ymUzx+pO22vq4u7srvW5lZYXmzZsr+vxVYmIiEhISVNoZmIiIiIzHk8/tuk6oiRqUjMnMmTMRFxen+PnGjRvo2LEjfHx8RKyKiIiI6uPevXtwdnb+236iBiU3NzdYWlqisLBQqb2wsBCenp5qt/H09Ky1/5PvhYWF8PLyUuoTFBSk6PPXi8Wrqqpw584dje9ra2sLW1tbxc9NmzbFuXPn0LFjR1y/fp3XKWlJLpfDx8eH566eeP7qj+euYXj+6o/nrmF0df4EQcC9e/fqdD0yIHJQsrGxQUhICDIzMxEVFQXg8cXcmZmZmDhxotptwsLCkJmZialTpyra9u7di7CwMACAn58fPD09kZmZqQhGcrkc2dnZGDdunGIfJSUlOHHiBEJCQgAA+/fvR01NDUJDQ+tUu4WFBVq2bAkAcHJy4h/6euK5axiev/rjuWsYnr/647lrGF2cv7qMJD0h+tRbXFwchg8fji5duqBbt25YtWoVysrKMGLECADAsGHD0LJlSyQmJgIApkyZgl69emH58uXo27cvkpOTcfz4cWzcuBEAIJFIMHXqVCxcuBD+/v7w8/PD3Llz4e3trQhjHTp0QGRkJEaNGoWkpCRUVlZi4sSJGDRoUJ0TJhEREZk+0YNSdHQ0iouLER8fD5lMhqCgIKSnpysuxi4oKICFxf9uzuvRowe2bduGOXPmYNasWfD390dqaio6deqk6DN9+nSUlZVh9OjRKCkpQc+ePZGeng47OztFny+//BITJ07Eiy++CAsLCwwcOBBr1qxpvAMnIiIiwydQvT169EiQSqXCo0ePxC7F6PDcNQzPX/3x3DUMz1/98dw1jFjnT/QFJ4mIiIgMleiPMCEiIiIyVAxKRERERBowKBERERFpwKBEREREpIFZB6X169fD19cXdnZ2CA0NxdGjR2vtv2PHDgQEBMDOzg6dO3fGnj17lF4XBAHx8fHw8vKCvb09wsPDcfnyZaU+d+7cwVtvvQUnJye4uLggNjYW9+/f1/mxNYbGPn/5+fmIjY2Fn58f7O3t0bZtW0ilUlRUVOjl+PRJjD97T5SXlyMoKAgSiQQnT57U1SE1KrHO3/fff4/Q0FDY29ujWbNmirXZjIkY5+7SpUsYMGAA3Nzc4OTkhJ49e+LAgQM6P7bGoOvz9+233+Kll16Cq6urxr+Tjx49woQJE+Dq6oqmTZti4MCBKk+oMAaNfe7u3LmDSZMmoX379rC3t0erVq0wefJklJaWald4o95jZ0CSk5MFGxsb4dNPPxXOnj0rjBo1SnBxcREKCwvV9j98+LBgaWkpLFmyRDh37pwwZ84cwdraWvj1118VfRYvXiw4OzsLqampwqlTp4T+/fsLfn5+wsOHDxV9IiMjhcDAQOG///2vcOjQIaFdu3bC4MGD9X68uibG+fvhhx+EmJgYISMjQ7h69aqwa9cuwd3dXZg2bVqjHLOuiPVn74nJkycLffr0EQAIubm5+jpMvRHr/H399ddCs2bNhA0bNggXL14Uzp49K6SkpOj9eHVJrHPn7+8vvPzyy8KpU6eES5cuCePHjxeaNGki3Lp1S+/HrEv6OH+ff/65kJCQIHz88cca/06OHTtW8PHxETIzM4Xjx48L3bt3F3r06KGvw9QLMc7dr7/+Krz22mvC7t27hStXrgiZmZmCv7+/MHDgQK1qN9ug1K1bN2HChAmKn6urqwVvb28hMTFRbf9///vfQt++fZXaQkNDhTFjxgiCIAg1NTWCp6ensHTpUsXrJSUlgq2trbB9+3ZBEATh3LlzAgDh2LFjij4//PCDIJFIhBs3bujs2BqDGOdPnSVLlgh+fn4NOZRGJ+a527NnjxAQECCcPXvWaIOSGOevsrJSaNmypbBp0yZdH06jEuPcFRcXCwCEgwcPKvrI5XIBgLB3716dHVtj0PX5+7O8vDy1fydLSkoEa2trYceOHYq28+fPCwCErKysBhxN4xLj3Knz1VdfCTY2NkJlZWWdazfLqbeKigqcOHEC4eHhijYLCwuEh4cjKytL7TZZWVlK/QEgIiJC0T8vLw8ymUypj7OzM0JDQxV9srKy4OLigi5duij6hIeHw8LCAtnZ2To7Pn0T6/ypU1paiubNmzfkcBqVmOeusLAQo0aNwtatW9GkSRNdHlajEev85eTk4MaNG7CwsEBwcDC8vLzQp08fnDlzRteHqDdinTtXV1e0b98en3/+OcrKylBVVYWPPvoI7u7uimdtGgN9nL+6OHHiBCorK5X2ExAQgFatWmm1HzGJde7UKS0thZOTE6ys6v5gErMMSrdv30Z1dbXiMSlPeHh4QCaTqd1GJpPV2v/J97/r4+7urvS6lZUVmjdvrvF9DZFY5++vrly5grVr12LMmDH1Og4xiHXuBEFATEwMxo4dqxTUjY1Y5+/atWsAgHnz5mHOnDlIS0tDs2bN0Lt3b9y5c6fhB9YIxDp3EokE+/btQ25uLhwdHWFnZ4cVK1YgPT0dzZo108mxNQZ9nL+6kMlksLGxgYuLS4P2Iyaxzp26OhYsWIDRo0drtZ1ZBiUyfjdu3EBkZCTeeOMNjBo1SuxyDN7atWtx7949zJw5U+xSjFJNTQ0AYPbs2Rg4cCBCQkKwefNmSCQS7NixQ+TqDJsgCJgwYQLc3d1x6NAhHD16FFFRUejXrx9u3boldnlkJuRyOfr27YuOHTti3rx5Wm1rlkHJzc0NlpaWKncNFBYWwtPTU+02np6etfZ/8v3v+hQVFSm9XlVVhTt37mh8X0Mk1vl74ubNm3j++efRo0cPbNy4sUHH0tjEOnf79+9HVlYWbG1tYWVlhXbt2gEAunTpguHDhzf8wBqJWOfPy8sLANCxY0fF67a2tmjTpg0KCgoacESNR8w/e2lpaUhOTsZzzz2HZ599Fh9++CHs7e3x2Wef6eTYGoM+zl9deHp6oqKiAiUlJQ3aj5jEOndP3Lt3D5GRkXB0dMTOnTthbW2t1fZmGZRsbGwQEhKCzMxMRVtNTQ0yMzMRFhamdpuwsDCl/gCwd+9eRX8/Pz94enoq9ZHL5cjOzlb0CQsLQ0lJCU6cOKHos3//ftTU1CA0NFRnx6dvYp0/4PFIUu/evRX/o7ewMK4/wmKduzVr1uDUqVM4efIkTp48qbjNNiUlBe+//75Oj1GfxDp/ISEhsLW1xcWLFxV9KisrkZ+fj9atW+vs+PRJrHP34MEDAFD5u2phYaEYqTMG+jh/dRESEgJra2ul/Vy8eBEFBQVa7UdMYp074PGfx5deegk2NjbYvXs37OzstD+AOl/2bWKSk5MFW1tbYcuWLcK5c+eE0aNHCy4uLoJMJhMEQRCGDh0qzJgxQ9H/8OHDgpWVlbBs2TLh/PnzglQqVXubrIuLi7Br1y7h9OnTwoABA9QuDxAcHCxkZ2cLv/zyi+Dv72+0ywM09vn7/fffhXbt2gkvvvii8Pvvvwu3bt1SfBkTsf7s/Zk2d4kYGrHO35QpU4SWLVsKGRkZwoULF4TY2FjB3d1duHPnTuMdfAOJce6Ki4sFV1dX4bXXXhNOnjwpXLx4UXj33XcFa2tr4eTJk417AhpIH+fvjz/+EHJzc4Xvv/9eACAkJycLubm5Sv+ujR07VmjVqpWwf/9+4fjx40JYWJgQFhbWeAeuA2Kcu9LSUiE0NFTo3LmzcOXKFaXPjKqqqjrXbrZBSRAEYe3atUKrVq0EGxsboVu3bsJ///tfxWu9evUShg8frtT/q6++Ep5++mnBxsZGeOaZZ4Tvv/9e6fWamhph7ty5goeHh2Brayu8+OKLwsWLF5X6/PHHH8LgwYOFpk2bCk5OTsKIESOEe/fu6e0Y9amxz9/mzZsFAGq/jI0Yf/b+zJiDkiCIc/4qKiqEadOmCe7u7oKjo6MQHh4unDlzRm/HqC9inLtjx44JL730ktC8eXPB0dFR6N69u7Bnzx69HaM+6fr8afp3TSqVKvo8fPhQGD9+vNCsWTOhSZMmwquvvmp0/0EUhMY/dwcOHND4mZGXl1fnuiWCIAjaj0MRERERmT7jusCDiIiIqBExKBERERFpwKBEREREpAGDEhEREZEGDEpEREREGjAoEREREWnAoERERESkAYMSERERkQYMSkRkkmQyGSZNmoQ2bdrA1tYWPj4+6NevHzIzM3Hnzh1MmjQJ7du3h729PVq1aoXJkyejtLRUsX1+fj4kEglOnjypsu/evXtj6tSpSm3nz59H//794ezsDAcHB3Tt2tVoHphLRJpZiV0AEZGu5efn47nnnoOLiwuWLl2Kzp07o7KyEhkZGZgwYQK+/vpr3Lx5E8uWLUPHjh3x22+/YezYsbh58ya+/vprrd/v6tWr6NmzJ2JjY5GQkAAnJyecPXu2fg/gJCKDwkeYEJHJefnll3H69GlcvHgRDg4OSq+VlJTAxcVFZZsdO3ZgyJAhKCsrg5WVFfLz8+Hn54fc3FwEBQUp9e3duzeCgoKwatUqAMCgQYNgbW2NrVu36umIiEgsnHojIpNy584dpKenY8KECSohCYDakAQApaWlcHJygpWVdgPtNTU1+P777/H0008jIiIC7u7uCA0NRWpqaj2qJyJDw6BERCblypUrEAQBAQEBdd7m9u3bWLBgAUaPHq3yWo8ePdC0aVOlr0OHDileLyoqwv3797F48WJERkbixx9/xKuvvorXXnsNP//8s06OiYjEw2uUiMikaHs1gVwuR9++fdGxY0fMmzdP5fWUlBR06NBBqe2tt95S/LqmpgYAMGDAALzzzjsAgKCgIBw5cgRJSUno1auXlkdARIaEQYmITIq/vz8kEgkuXLjwt33v3buHyMhIODo6YufOnbC2tlbp4+Pjg3bt2im12dvbK37t5uYGKysrdOzYUalPhw4d8Msvv9TzKIjIUHDqjYhMSvPmzREREYH169ejrKxM5fWSkhIAj0eSXnrpJdjY2GD37t31vkPNxsYGXbt2xcWLF5XaL126hNatW9drn0RkOBiUiMjkrF+/HtXV1ejWrRu++eYbXL58GefPn8eaNWsQFhamCEllZWX45JNPIJfLIZPJIJPJUF1drfX7vffee0hJScHHH3+MK1euYN26dfjuu+8wfvx4PRwdETUmTr0Rkclp06YNcnJy8P7772PatGm4desWWrRogZCQEGzYsAE5OTnIzs4GAJVptby8PPj6+mr1fq+++iqSkpKQmJiIyZMno3379vjmm2/Qs2dPXR0SEYmE6ygRERERacCpNyIiIiINGJSIiIiINGBQIiIiItKAQYmIiIhIAwYlIiIiIg0YlIiIiIg0YFAiIiIi0oBBiYiIiEgDBiUiIiIiDRiUiIiIiDRgUCIiIiLSgEGJiIiISIP/A7zniZ1R2S7WAAAAAElFTkSuQmCC", + "image/png": 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RFHbs2IGLFy+qtc2fPx8WFhaYMEGiokRIdhWbpaUlfH19kZaWpmqrqqpCWloa/P39Rbfx9/dX6w8AqampNfavSXFxMQYNGgRLS0vs2bOHC2qrSU5OhqurK9zd3TF48GAcPnwYa9euxe7du2Fubo4ePXpg9erV+Oijj9CtWzd8++23GovQ+/XrhylTpiA4OBitWrXC8uXL0apVK8TFxSEhIQFdunTBsmXLsHLlSomOkoiIGpMgCIiOjtYIRwqFQqvLQbRF0qvY4uPjERISgs8++wx9+vTBmjVrsGPHDmRnZ8PFxQXjxo1DmzZtVB++6enpGDBgAJYtW4ahQ4di+/bt+PDDD3H69Gl069YNwOOV8Hl5ebhx44aqT6dOnSCXyyGXy1Xh6MGDB9i1axeaNm2qqqdVq1YwNzevU+31uYqNTAP/3ImIxN28eRObNm1Sa3v11VfRtWvXRq9F769iAx5ftl9QUICoqCgolUp4e3sjOTlZtRA7Ly9PtcYFeDwqsXXrVkRGRmL+/Pnw9PREYmKiKhwBwJ49ezDhL2N0o0aNAvA4oS5cuBCnT59GZmYmAKBjx45q9eTk5MDd3V1Xh0tERGRyvv76a+Tk5Ki16cNVan9H0hEkQ8YRJKqOf+5ERP8jCAIWLVqk1mZlZSV6n8PGZBAjSERERGR8Lly4gISEBLW206dHYffuThJVVH8MSERERKQ1YvcMPHDgA7z/vmFFDsOqloiIiPRSVVWV6K1bFAqFXtz4sb4YkIiIiOipnDlzBrt371ZrGzp0KHr16iVRRU+PAYmIiIgaTGxKbcGCBWpXoRsiBiQiIiKqt8rKSixZskSjXWGI82kiGJCIiIioXo4fP479+/ertb3yyivo3r27RBVpHwMSSWL8+PEoLCxEYmIiAGDgwIHw9vbGmjVrGrxPbeyDiIhqJzalFhUVBZlMJkE1umPYE4SkdePHj4dMJoNMJoOlpSU6duyIRYsWoaKiQqfvu3Pnzjo/uPaHH36ATCZDYWFhg/dBRET1U15eLhqOFAqF0YUjgCNIJGLw4MHYsmULSktLsW/fPkybNg0WFhaIiIhQ61dWVgZLS0utvKejo6Ne7IOIiDQdPXoUhw4dUmsbNWoUOnUynBs/1hdHkEiDlZUV5HI52rdvj3feeQcBAQHYs2cPxo8fj6CgICxduhStW7dW/cW4du0aXn/9dTg4OMDR0REjRoxAbm6uan+VlZUIDw+Hg4MDnJycMHfuXFR/ws3AgQMxa9Ys1c+lpaV4//334ebmBisrK3Ts2BFffvklcnNz8dxzzwEAWrRoAZlMhvHjx4vu4+7duxg3bhxatGgBW1tbDBkyBFeuXFG9HhcXBwcHB6SkpKBz585o1qwZBg8ejJs3b6r6/PDDD+jTpw+aNm0KBwcH/OMf/8Dvv/+upTNNRKT/oqOjNcJRVFSUUYcjgAGJ6sDGxgZlZWUAgLS0NFy6dAmpqalISkpCeXk5AgMD0bx5cxw9ehTHjh1TBY0n26xatQpxcXHYvHkzfvrpJ9y5cwe7du2q9T3HjRuHbdu2Ye3atbh48SI+++wzNGvWDG5ubvj+++8BAJcuXcLNmzfxySefiO5j/PjxOHnyJPbs2YOMjAwIgoAXX3wR5eXlqj4PHjzAypUr8c033+DIkSPIy8vDnDlzAAAVFRUICgrCgAED8PPPPyMjIwOTJk0yyqFkIqLqysrKTGpKrTpOsVGNBEFAWloaUlJSEBYWhoKCAjRt2hRffPGFamrtP//5D6qqqvDFF1+o/sJs2bIFDg4O+OGHHzBo0CCsWbMGEREReOWVVwAAsbGxSElJqfF9L1++jB07diA1NRUBAQEAgGeeeUb1+pOpNGdnZzg4OIju48qVK9izZw+OHTuGfv36AQC+/fZbuLm5ITExEa+99hqAx3PqsbGx6NChAwBg+vTpqocrFhcXo6ioCC+99JLq9c6dO9f/RBIRGZiDBw/i2LFjam1jx45V/b/QFHAESc/t2QP06/f4e2NJSkpCs2bNYG1tjSFDhiA4OBgLFy4EAHTv3l1t3dHZs2dx9epVNG/eHM2aNUOzZs3g6OiIR48e4ddff0VRURFu3rwJPz8/1TZNmjSp9e6qZ86cgbm5OQYMGNDgY7h48SKaNGmi9r5OTk7o1KkTLl68qGqztbVV+wvv6uqKW7duAXgcxMaPH4/AwEAMGzYMn3zyidr0GxGRMYqOjtYIRwqFwqTCEcARJL23bBmQkfH4+/DhjfOezz33HDZu3AhLS0u0bt0aTZr879ekadOman3v378PX19ffPvttxr7adWqVYPe38bGpkHbNYSFhYXazzKZTG191JYtWzBjxgwkJycjPj4ekZGRSE1NRd++fRutRiKixvDw4UMsX75crc3MzAwLFiyQqCJpcQRJz82bB/j7P/7eWJo2bYqOHTuiXbt2auFITM+ePXHlyhU4OzujY8eOal/29vawt7eHq6srMjMzVdtUVFTg1KlTNe6ze/fuqKqqwo8//ij6+pMRrMrKyhr30blzZ1RUVKi9759//olLly6hS5cutR5TdT4+PoiIiEB6ejq6deuGrVu31mt7IiJ998UXX2iEowkTJphsOAIYkPTe8OFAenrjjR7V15gxY9CyZUuMGDECR48eRU5ODn744QfMmDED169fBwDMnDkTy5YtQ2JiIrKzszF16lSNexj9lbu7O0JCQvDWW28hMTFRtc8dO3YAANq3bw+ZTIakpCQUFBTg/v37Gvvw9PTEiBEjMHHiRPz00084e/Ysxo4dizZt2mDEiBF1OracnBxEREQgIyMDv//+Ow4cOIArV65wHRIRGZXo6Gj88ccfam0KhQLt2rWTqCL9wIBET8XW1hZHjhxBu3bt8Morr6Bz584IDQ3Fo0ePYGdnBwCYPXs23nzzTYSEhMDf3x/NmzfHyy+/XOt+N27ciFdffRVTp06Fl5cXJk6ciJKSEgBAmzZtEB0djXnz5sHFxQXTp08X3ceWLVvg6+uLl156Cf7+/hAEAfv27dOYVqvt2LKzszFy5Eg8++yzmDRpEqZNm4bJkyfX4wwREemnoqIi0avUUlKM41lqT0smVL8hDdVJcXEx7O3tUVRUpAoCTzx69Ag5OTnw8PCAtbW1RBVSY+OfOxEZirVr1+Lu3btqbR07jsOGDR6YN09/Zy20obbP77/iIm0iIiITUtO9jQBgzJjGrkZ/MSARERGZgD///BPr16/XaH8SjkgdAxIREZGRi4mJUT3d4InQ0FC0bdtWoor0HwMSERGREattSo1qxoCkQ1z/blr4501E+kSpVOKzzz7TaGc4qhsGJB14chn5gwcPGvWu0CStBw8eANC8OzcRUWMTGzV655134OzsLEE1hokBSQfMzc3h4OCgeqaXra2tSTz52FQJgoAHDx7g1q1bcHBwgLm5udQlEZEJ45SadjAg6YhcLgcAVUgi4+fg4KD6cyciamzXrl3D5s2b1dpsbW3x3nvvSVSRYWNA0hGZTAZXV1c4OzujvLxc6nJIxywsLDhyRESSERs1CgsLg6OjowTVGAcGJB0zNzfnBycREekMp9R0gwGJiIjIAP3222/45ptv1NpatmyJadOmSVSRcWFAIiIiMjBio0bvvvturc8Wo/phQCIiIjIgnFJrHAxIREREBuDUqVNISkpSa7t7tx3WrJkgUUXGjQGJiIhIz4mNGv3wQzjefbe5BNWYBgYkIiIiPSUIAhYtWqTRrlAowFk13WJAIiIi0kPHjh3DwYMH1docHR0RFhYmUUWmhQGJiIhIz4hNqc2dO5fP92xEDEhERER6orYpNWpcDEhERER64ODBgzh27JhaW7t27TBhAq9SkwIDEhERkcTEptQiIiJgaWkpQTUEMCARERFJpqqqCosXL9Zo55Sa9BiQiIiIJLBnzx5kZWWptXl5eSE4OFiiiuivGJCIiIgamdiU2gcffIAmTfixrC/4J0FERNRIKioqsHTpUo12TqnpHwYkIiKiRrB9+3ZcunRJra1nz54YNmyYRBVRbRiQiIiIdExsSm3BggUwMzOToBqqCwYkIiIiHSkrK0NMTIxGO6fU9J/k0XXDhg1wd3eHtbU1/Pz8cPz48Vr7JyQkwMvLC9bW1ujevTv27dun9vrOnTsxaNAgODk5QSaT4cyZMxr7ePToEaZNmwYnJyc0a9YMI0eORH5+vjYPi4iITNyXX36pEY769+/PcGQgJA1I8fHxCA8Ph0KhwOnTp9GjRw8EBgbi1q1bov3T09MxevRohIaGIisrC0FBQQgKCsK5c+dUfUpKStC/f3989NFHNb7vu+++i//7v/9DQkICfvzxR9y4cQOvvPKK1o+PiIhMU3R0NK5fv67WFhUVhRdeeEGiiqi+ZIIgCFK9uZ+fH3r37o3169cDeHzDLDc3N4SFhWHevHka/YODg1FSUoKkpCRVW9++feHt7Y3Y2Fi1vrm5ufDw8EBWVha8vb1V7UVFRWjVqhW2bt2KV199FQCQnZ2Nzp07IyMjA3379q1T7cXFxbC3t0dRURHs7Ozqe+hERGSEHjx4gBUrVmi0c9RIf9T181uyNUhlZWU4deoUIiIiVG1mZmYICAhARkaG6DYZGRkIDw9XawsMDERiYmKd3/fUqVMoLy9HQECAqs3Lywvt2rWrNSCVlpaitLRU9XNxcXGd35OIiIzf2rVrcffuXbW2f//73+jXr59EFdHTkCwg3b59G5WVlXBxcVFrd3FxQXZ2tug2SqVStL9Sqazz+yqVSlhaWsLBwaFe+4mJiRG9CoGIiEjs8yEqKgoymUyCakgbJF+kbSgiIiJQVFSk+rp27ZrUJRERkcTu3bsnGo4UCgXDkYGTbASpZcuWMDc317h6LD8/H3K5XHQbuVxer/417aOsrAyFhYVqo0h/tx8rKytYWVnV+X2IiMi4ffjhhygvL1drGzZsGHr27ClRRaRNko0gWVpawtfXF2lpaaq2qqoqpKWlwd/fX3Qbf39/tf4AkJqaWmN/Mb6+vrCwsFDbz6VLl5CXl1ev/RARkemKjo7WCEcKhYLhyIhIeqPI8PBwhISEoFevXujTpw/WrFmDkpISTJgwAQAwbtw4tGnTRnUfiZkzZ2LAgAFYtWoVhg4diu3bt+PkyZPYtGmTap937txBXl4ebty4AQCq27rL5XLI5XLY29sjNDQU4eHhcHR0hJ2dHcLCwuDv71/nK9iIiMg03blzB+vWrdNo51VqxkfSgBQcHIyCggJERUVBqVTC29sbycnJqoXYeXl5ardh79evH7Zu3YrIyEjMnz8fnp6eSExMRLdu3VR99uzZowpYADBq1CgAj395Fy5cCAD4+OOPYWZmhpEjR6K0tBSBgYH49NNPG+GIiYjIUImtNXrttdfQpUsXCaohXZP0PkiGjPdBIiIyHTUtxCbDo/f3QSIiItJ3+fn5GjciBhiOTAEDEhERkQixUaMxY8agY8eOElRDjY0BiYiIqBpOqREDEhER0f/322+/4ZtvvtFoZzgyPQxIREREEB81OnlyLCZO7CBBNSQ1BiQiIjJ5nFKj6hiQiIjIZF24cAEJCQka7QxHxIBEREQmSWzUKDQ0FG3btpWgGtI3DEhERGRyOKVGf4cBiYiITEZ6ejpSU1M12hmOqDoGJCIiMglio0bp6ZMwbZqrBNWQvmNAIiIio8cpNaovBiQiIjJaqampSE9P12hnOKK/w4BERERGSWzUaPr06XBycpKgGjI0DEhERGRUBEHAokWLNNo5akT1wYBERERG49tvv8XVq1c12n18GI6ofhiQiIjIKIhNqXXrFo6RI5tLUA0ZOgYkIiIyaJxSI11gQCIiIoO1ceNG3Lp1S6Od4YieFgMSEREZJLEptffeew+2trYSVEPGhgGJiIgMSmVlJZYsWaLRzlEj0iYGJCIiMhgxMTEoKyvTaGc4Im1jQCIiIoMgNqU2b948WFlZSVANGTsGJCIi0mtlZWWIiYnRaOeoEekSAxIREektsVEjc3NzREZGSlANmRIGJCIi0kti4SgyMhLm5uYSVEOmhgGJiIj0ysOHD7F8+XKNdk6pUWNiQCIiIr0hNmrk6OiIsLAwCaohU8aAREREekEsHEVFRUEmk0lQDZk6BiQiIpJUUVER1qxZo9HOKTWSEgMSERFJRmzUyN3dHSEhIRJUQ/Q/DEhERCQJTqmRPmNAIiKiRlVQUIBPP/1Uo93HRwFmI9IXDEhERNRoxEaNOnfujNdff12CaohqxoBERESNQiwccSE26SsGJCIi0qnc3Fx89dVXGu0MR6TPGJCIiEhnxEaNfv+9NzZvflGCaojqzkzqAoiIyDiJhaOUFAWCghiOSP9xBImIiLTq4sWL2LFjh0a7QqEAZ9XIUDAgERGR1oiNGvXr1w///ve/JaiGqOEYkIiISCt4lRoZEwYkIiJ6KhkZGThw4IBGO8MRGTIGJCIiajCxUaMrV57D66//S4JqiLSHAYmIiBpELBz5+HAhNhkHBiQiIqqXAwcOICMjQ6Pdx0eB4cMlKIhIBxiQiIiozsRGjYYNG4aePXtKUA2R7jAgERFRnfAqNTIlkt9Je8OGDXB3d4e1tTX8/Pxw/PjxWvsnJCTAy8sL1tbW6N69O/bt26f2uiAIiIqKgqurK2xsbBAQEIArV66o9bl8+TJGjBiBli1bws7ODv3798fhw4e1fmxERMYgISGB4YhMjqQBKT4+HuHh4VAoFDh9+jR69OiBwMBA3Lp1S7R/eno6Ro8ejdDQUGRlZSEoKAhBQUE4d+6cqs/y5cuxdu1axMbGIjMzE02bNkVgYCAePXqk6vPSSy+hoqIChw4dwqlTp9CjRw+89NJLUCqVOj9mIiJDEh0djQsXLqi1jRo1iuGIjJ5MEARBqjf38/ND7969sX79egBAVVUV3NzcEBYWhnnz5mn0Dw4ORklJCZKSklRtffv2hbe3N2JjYyEIAlq3bo3Zs2djzpw5AICioiK4uLggLi4Oo0aNwu3bt9GqVSscOXIE//znPwEA9+7dg52dHVJTUxEQEFCn2ouLi2Fvb4+ioiLY2dk97akgItI7HDUiY1TXz2/JRpDKyspw6tQptUBiZmaGgIAA0asjgMc3I6seYAIDA1X9c3JyoFQq1frY29vDz89P1cfJyQmdOnXC119/jZKSElRUVOCzzz6Ds7MzfH19tX2YREQGZ926dTVewk9kKiRbpH379m1UVlbCxcVFrd3FxQXZ2dmi2yiVStH+T6bGnnyvrY9MJsPBgwcRFBSE5s2bw8zMDM7OzkhOTkaLFi1qrLe0tBSlpaWqn4uLi+t4pEREhkMsGIWEhMDd3b3xiyGSkOSLtBubIAiYNm0anJ2dcfToURw/fhxBQUEYNmwYbt68WeN2MTExsLe3V325ubk1YtVERLpX05QawxGZIskCUsuWLWFubo78/Hy19vz8fMjlctFt5HJ5rf2ffK+tz6FDh5CUlITt27fjH//4B3r27IlPP/0UNjY2+Oqrr2qsNyIiAkVFRaqva9eu1e+AiYj0VHR0NNcbEVVT74Bkbm4uepXZn3/+CXNz8zrvx9LSEr6+vkhLS1O1VVVVIS0tDf7+/qLb+Pv7q/UHgNTUVFV/Dw8PyOVytT7FxcXIzMxU9Xnw4AGAx+ud/srMzAxVVVU11mtlZQU7Ozu1LyIiQycWjCZPnsxwRCav3muQarrorbS0FJaWlvXaV3h4OEJCQtCrVy/06dMHa9asQUlJCSZMmAAAGDduHNq0aYOYmBgAwMyZMzFgwACsWrUKQ4cOxfbt23Hy5Els2rQJwOP1RbNmzcKSJUvg6ekJDw8PLFiwAK1bt0ZQUBCAxyGrRYsWCAkJQVRUFGxsbPD5558jJycHQ4cOre/pICIySIIgYNGiRRrtDEZEj9U5IK1duxbA4xDyxRdfoFmzZqrXKisrceTIEXh5edXrzYODg1FQUICoqCgolUp4e3sjOTlZtcg6Ly9PbaSnX79+2Lp1KyIjIzF//nx4enoiMTER3bp1U/WZO3cuSkpKMGnSJBQWFqJ///5ITk6GtbU1gMdTe8nJyfjggw/w/PPPo7y8HF27dsXu3bvRo0ePetVPRGSIxEaNAIYjor+q832QPDw8AAC///472rZtqzadZmlpCXd3dyxatAh+fn66qVTP8D5IRGSIxMLRzJkz4eDg0PjFEEmgrp/fdR5BysnJAQA899xz2LlzZ62XxBMRkX6prKzEkiVLNNo5akQkrt5rkPjMMiIiw8IpNaL6q3dAeuutt2p9ffPmzQ0uhoiItEssHL333nuwtbWVoBoiw1HvgHT37l21n8vLy3Hu3DkUFhbi+eef11phRETUcKWlpVi2bJlGO0eNiOqm3gFp165dGm1VVVV455130KFDB60URUREDccpNaKnV+er2P7OpUuXMHDgwFof12FMeBUbEekjsXA0f/58WFhYSFANkf7R+lVsf+fXX39FRUWFtnZHRET1cO/ePaxevVqjnaNGRA1T74AUHh6u9rMgCLh58yb27t2LkJAQrRVGRER1U9OUmo8PwxFRQ9U7IGVlZan9bGZmhlatWmHVqlV/e4UbERFpl1g4ioyMrNezMYlIE++DRERkgG7fvo0NGzZotHNKjUg7GrwG6datW7h06RIAoFOnTnB2dtZaUUREVDNepUake/UOSMXFxZg2bRq2bduGqqoqAIC5uTmCg4OxYcMG2Nvba71IIiJ6TCwcRUVFQSaTSVANkfEyq+8GEydORGZmJvbu3YvCwkIUFhYiKSkJJ0+exOTJk3VRIxGRybt27ZpoOFIoFAxHRDpQ7/sgNW3aFCkpKejfv79a+9GjRzF48GCUlJRotUB9xfsgEVFj4ZQakfbo7D5ITk5OotNo9vb2aNGiRX13R0REtahp1IiIdKveU2yRkZEIDw+HUqlUtSmVSrz33ntYsGCBVosjIjJV2dnZDEdEEqr3FJuPjw+uXr2K0tJStGvXDgCQl5cHKysreHp6qvU9ffq09irVM5xiIyJdEQtGDg4OmDlzpgTVEBkXnU2xjRgxggsCiYh0hKNGRPpBaw+rNTUcQSIibTp58iT27t2r0c5wRKRdOhtBeuaZZ3DixAk4OTmptRcWFqJnz5747bff6l8tEZEJExs16tChA8aOHStBNUQENCAg5ebmorKyUqO9tLQU169f10pRRESmglNqRPqpzgFpz549qv9OSUlRu9S/srISaWlp8PDw0G51RERG6vDhwzhy5IhGO8MRkX6oc0AKCgoCAMhkMoSEhKi9ZmFhAXd3d6xatUqrxRERGSOxUaOePXti2LBhElRDRGLqHJCePHfNw8MDJ06cQMuWLXVWFBGRseKUGpFhqPcapJycHF3UQURk1Hbt2oWff/5Zo53hiEg/1TsgLVq0qNbXo6KiGlwMEZExEhs1eu655/Cvf/1LgmqIqC7qHZB27dql9nN5eTlycnLQpEkTdOjQgQGJiOgvOKVGZJjqHZCysrI02oqLizF+/Hi8/PLLWimKiMjQbdmyBXl5eRrtDEdEhkFrd9L+5ZdfMGzYMOTm5mpjd3qPd9ImopqIjRoNHz4cPj4+ElRDRH+lsztp16SoqAhFRUXa2h0RkUHilBqRcah3QFq7dq3az4Ig4ObNm/jmm28wZMgQrRVGRGRIVq9ejXv37mm0MxwRGaZ6B6SPP/5Y7WczMzO0atUKISEhiIiI0FphRESGQmzU6I033oCnp6cE1RCRNvA+SERET4FTakTGqUFrkAoLC3H16lUAQMeOHeHg4KDNmoiI9J5YMAIYjoiMRb0CUm5uLqZNm4aUlBQ8ufhNJpNh8ODBWL9+Pdzd3XVRIxGRXhELR2+//TbatGkjQTVEpAt1DkjXrl1D3759YWFhgcWLF6Nz584AgAsXLmDjxo3w9/fHiRMn0LZtW50VS0QkJUEQRJ8mwFEjIuNT5/sghYaG4urVq0hJSYG1tbXaaw8fPsTgwYPh6emJL774QieF6hveB4nItHBKjcg4aP0+SMnJyYiPj9cIRwBgY2ODxYsXY9SoUQ2rlohIj4mFo+nTp8PJyUmCaoioMdQ5IN2+fbvWNUbPPPMM7ty5o42aiIj0QmVlJZYsWaLRzlEjIuNX54Dk6uqKCxcu1LjG6Ny5c5DL5VorjIhISpxSIzJtdQ5IQUFBmDNnDtLS0tCqVSu1127duoX3338fQUFB2q6PiKjRiYWj2bNno1mzZhJUQ0RSqPMi7bt378LPzw9KpRJjx46Fl5cXBEHAxYsXsXXrVsjlcvz3v/+Fo6OjrmvWC1ykTWR8SktLsWzZMo12jhoRGQ+tL9Ju0aIFMjMzMX/+fGzfvh2FhYUAAAcHB7zxxhv48MMPTSYcEZHx4ZQaEf1VnUeQ/koQBBQUFAAAWrVqBZlMpvXC9B1HkIiMh1g4mjdvHqysrCSohoh0SesjSH8lk8ng7Ozc4OKIiPTB/fv3sWrVKo12jhoRUYMCEhGRoeOUGhHVhgGJiEyOWDiKjIyEubm5BNUQkT4yk7oAIqLG8ueff4qGo5QUBcMREamRPCBt2LAB7u7usLa2hp+fH44fP15r/4SEBHh5ecHa2hrdu3fHvn371F4XBAFRUVFwdXWFjY0NAgICcOXKFY397N27F35+frCxsUGLFi14DyciIxcdHY3169drtKekKDBvngQFEZFeq9MU29q1a+u8wxkzZtS5b3x8PMLDwxEbGws/Pz+sWbMGgYGBuHTpkugi8PT0dIwePRoxMTF46aWXsHXrVgQFBeH06dPo1q0bAGD58uVYu3YtvvrqK3h4eGDBggUIDAzEhQsXVM+R+/777zFx4kR8+OGHeP7551FRUYFz587VuW4iMixio0ZRUVGQyWTgkiMiElOny/w9PDzqtjOZDL/99lud39zPzw+9e/dW/auuqqoKbm5uCAsLwzyRf9IFBwejpKQESUlJqra+ffvC29sbsbGxEAQBrVu3xuzZszFnzhwAQFFREVxcXBAXF4dRo0ahoqIC7u7uiI6ORmhoaJ1rrY6X+RPpv+vXr+PLL7/UaOdCbCLTpdXL/HNycrRW2BNlZWU4deoUIiIiVG1mZmYICAhARkaG6DYZGRkIDw9XawsMDERiYqKqTqVSiYCAANXr9vb28PPzQ0ZGBkaNGoXTp0/jjz/+gJmZGXx8fKBUKuHt7Y0VK1aoRqHElJaWorS0VPVzcXFxQw6biBoJr1IjoqfR4DVIZWVluHTpEioqKhq0/e3bt1FZWQkXFxe1dhcXFyiVStFtlEplrf2ffK+tz5MRroULFyIyMhJJSUlo0aIFBg4ciDt37tRYb0xMDOzt7VVfbm5u9ThaImpMYuFIoVAwHBFRndU7ID148AChoaGwtbVF165dkZeXBwAICwsTfYaRvqmqqgIAfPDBBxg5ciR8fX2xZcsWyGQyJCQk1LhdREQEioqKVF/Xrl1rrJKJqI4uXrxYYzgiIqqPegekiIgInD17Fj/88INq0TMABAQEID4+vs77admyJczNzZGfn6/Wnp+fD7lcLrqNXC6vtf+T77X1cXV1BQB06dJF9bqVlRWeeeYZVdgTY2VlBTs7O7UvItIf0dHR2LFjh1pbRYUFfHwYjoio/uodkBITE7F+/Xr0799f7RlsXbt2xa+//lrn/VhaWsLX1xdpaWmqtqqqKqSlpcHf3190G39/f7X+AJCamqrq7+HhAblcrtanuLgYmZmZqj6+vr6wsrLCpUuXVH3Ky8uRm5uL9u3b17l+ItIfNY0aLV48H8OHS1AQERm8et9Ju6CgQPQS/JKSkno/tDY8PBwhISHo1asX+vTpgzVr1qCkpAQTJkwAAIwbNw5t2rRBTEwMAGDmzJkYMGAAVq1ahaFDh2L79u04efIkNm3aBODxVXSzZs3CkiVL4OnpqbrMv3Xr1qr7HNnZ2WHKlClQKBRwc3ND+/btsWLFCgDAa6+9Vt/TQUQSSk9PR2pqqkY7R42I6GnVOyD16tULe/fuRVhYGACoQtEXX3xR48hPTYKDg1FQUICoqCjV1WTJycmqRdZ5eXkwM/vfIFe/fv2wdetWREZGYv78+fD09ERiYqLa1Wdz585FSUkJJk2ahMLCQvTv3x/Jyclq04ErVqxAkyZN8Oabb+Lhw4fw8/PDoUOH0KJFi/qeDiKSiNiokYODA2bOnClBNURkbOp0H6S/+umnnzBkyBCMHTsWcXFxmDx5Mi5cuID09HT8+OOP8PX11VWteoX3QSKSDhdiE1FD1fXzu95rkPr3748zZ86goqIC3bt3x4EDB+Ds7IyMjAyTCUdEJI09e/YwHBFRo6j3CBI9xhEkosYlFoxu3+6AdevGSlANERkqrd5Juz53jWZYICJtEwtHfMgsEelSnQKSg4NDna9Qq6ysfKqCiIieiIuLw++//67R/viu2BIUREQmo04B6fDhw6r/zs3Nxbx58zB+/HjVVWsZGRn46quvVJfjExE9LbFRI29vb4wYMUKCaojI1NR7DdILL7yAt99+G6NHj1Zr37p1KzZt2oQffvhBm/XpLa5BItIdLsQmIl2p6+d3vQOSra0tzp49C09PT7X2y5cvw9vbGw8ePGhYxQaGAYlI+1auXImSkhKNdoYjItIWnV3m7+bmhs8//1yj/YsvvuAT7omowaKjozXC0ZUrAxmOiEgS9b6T9scff4yRI0di//798PPzAwAcP34cV65cwffff6/1AonI+PEqNSLSNw26D9L169fx6aefIjs7GwDQuXNnTJkyxaRGkDjFRvT0xIIRwCk1ItIdna1BoscYkIiejlg4GjFiBLy9vRu/GCIyGVq9UWR1hYWF+PLLL3Hx4kUAQNeuXfHWW2/B3t6+YdUSkUnhVWpEpO/qPYJ08uRJBAYGwsbGBn369AEAnDhxAg8fPsSBAwfQs2dPnRSqbziCRFR/nFIjIqnpbIrtn//8Jzp27IjPP/8cTZo8HoCqqKjA22+/jd9++w1Hjhx5usoNBAMSUf2IhaP4+LFYtqwDhg+XoCAiMkk6C0g2NjbIysqCl5eXWvuFCxfQq1cv3geJiNQIgoBFixZptD+5So3hiIgak87WINnZ2SEvL08jIF27dg3Nmzevf6VEZLRqm1LjrBoR6bN6B6Tg4GCEhoZi5cqV6NevHwDg2LFjeO+99zQeP0JEpkssHIWGhqJt27YSVENEVD/1DkgrV66ETCbDuHHjUFFRAQCwsLDAO++8g2XLlmm9QCIyLBUVFVi6dKlGu4+PAsxGRGQoGnwfpAcPHuDXX38FAHTo0AG2trZaLUzfcQ0SkSZepUZE+k6n90ECHj+0tnv37g3dnIiMjFg4mj59OpycnCSohojo6dQ5IL311lt16rd58+YGF0NEhufhw4dYvny5RjtHjYjIkNU5IMXFxaF9+/bw8fEBn05CREDNU2o+PgxHRGTY6hyQ3nnnHWzbtg05OTmYMGECxo4dC0dHR13WRkR6TCwcHTr0HmbPtuW9jYjI4NVrkXZpaSl27tyJzZs3Iz09HUOHDkVoaCgGDRoEmUymyzr1Dhdpk6kqLCzEJ598otHOKTUiMgQ6u5P2E7///jvi4uLw9ddfo6KiAufPn0ezZs0aXLChYUAiU8Sr1IjI0On8KjYzMzPIZDIIgoDKysqG7oaIDIRYOJo/fz4sLCwkqIaISLfM6tO5tLQU27Ztw7///W88++yz+OWXX7B+/Xrk5eWZ1OgRkSm5ceOGaDhSKBQMR0RktOo8gjR16lRs374dbm5ueOutt7Bt2za0bNlSl7URkcQ4pUZEpqrOa5DMzMzQrl07+Pj41Loge+fOnVorTp9xDRIZO7FwtGDBApiZ1WvgmYhIr2h9DdK4ceNM7ko1IlN0+fJlbNu2TaOdo0ZEZErqdaNIIjJunFIjInqswVexEZFxqWkhNhGRKWJAIjJx//3vf5GSkqLRzseFEJEpY0AiMmG1PUuNjwshIlPGgERkojilRkRUMwYkIhOzb98+nDhxQqOd4YiI6H8YkIhMCK9SIyKqG97xjchEiIWjlBQFF2MTEYngCBKRkfv666+Rk5Oj0a5QKMCBIyIicQxIREaMU2pERA3DgERkpHiVGhFRwzEgERmZpUuXoqKiQqOda42IiOqOAYnIiIiNGjk6OiIsLEyCaoiIDBcDEpGR4JQaEZH2MCARGbjaHhdCREQNw4BEZMDEwpGXlxeCg4MlqIaIyHgwIBEZKE6pERHpjl7cSXvDhg1wd3eHtbU1/Pz8cPz48Vr7JyQkwMvLC9bW1ujevTv27dun9rogCIiKioKrqytsbGwQEBCAK1euiO6rtLQU3t7ekMlkOHPmjLYOiUhnoqOjGY6IiHRM8oAUHx+P8PBwKBQKnD59Gj169EBgYCBu3bol2j89PR2jR49GaGgosrKyEBQUhKCgIJw7d07VZ/ny5Vi7di1iY2ORmZmJpk2bIjAwEI8ePdLY39y5c9G6dWudHR+RNokFo/79+zMcERFpmUwQBEHKAvz8/NC7d2+sX78eAFBVVQU3NzeEhYVh3rx5Gv2Dg4NRUlKCpKQkVVvfvn3h7e2N2NhYCIKA1q1bY/bs2ZgzZw4AoKioCC4uLoiLi8OoUaNU2+3fvx/h4eH4/vvv0bVrV2RlZcHb27tOdRcXF8Pe3h5FRUWws7N7ijNAVDdi4cjHR4HhwyUohojIQNX181vSNUhlZWU4deoUIiIiVG1mZmYICAhARkaG6DYZGRkIDw9XawsMDERiYiIAICcnB0qlEgEBAarX7e3t4efnh4yMDFVAys/Px8SJE5GYmAhbW9u/rbW0tBSlpaWqn4uLi+t8nERPg48LISJqfJJOsd2+fRuVlZVwcXFRa3dxcYFSqRTdRqlU1tr/yffa+giCgPHjx2PKlCno1atXnWqNiYmBvb296svNza1O2xE9DbFwlJw8nJfwExHpmORrkKSwbt063Lt3T23k6u9ERESgqKhI9XXt2jUdVkimThAE0XCUkqJARIQPp9WIiHRM0im2li1bwtzcHPn5+Wrt+fn5kMvlotvI5fJa+z/5np+fD1dXV7U+T9YXHTp0CBkZGbCyslLbT69evTBmzBh89dVXGu9rZWWl0Z9IF2qbUuOsGhFR45B0BMnS0hK+vr5IS0tTtVVVVSEtLQ3+/v6i2/j7+6v1B4DU1FRVfw8PD8jlcrU+xcXFyMzMVPVZu3Ytzp49izNnzuDMmTOq2wTEx8dj6dKlWj1GovoQC0dvvPEG1xsRETUyyW8UGR4ejpCQEPTq1Qt9+vTBmjVrUFJSggkTJgAAxo0bhzZt2iAmJgYAMHPmTAwYMACrVq3C0KFDsX37dpw8eRKbNm0CAMhkMsyaNQtLliyBp6cnPDw8sGDBArRu3RpBQUEAgHbt2qnV0KxZMwBAhw4d0LZt20Y6cqL/qaioEA3nDEZERNKQPCAFBwejoKAAUVFRUCqV8Pb2RnJysmqRdV5eHszM/jfQ1a9fP2zduhWRkZGYP38+PD09kZiYiG7duqn6zJ07FyUlJZg0aRIKCwvRv39/JCcnw9rautGPj+jv8FlqRET6R/L7IBkq3geJtEEsHHXqNBGjRvHmpUREumAQ90EiMlUPHz7E8uXLNdo5pUZEpB8YkIgaGW/8SESk/0zyPkhEUhELRz/+OIvrjYiI9AxHkIgawZ9//ql63uBfcdSIiEg/MSAR6Rin1IiIDA8DEpEOiYWjefPm8a7sRER6jgGJSAfy8vKwZcsWjXaOGhERGQYGJCIt440fiYgMH69iI9IisXB04MAC+PgoMHy4BAUREVGDcASJSAt++eUX7Ny5U6NdoVCAs2pERIaHAYnoKXFKjYjI+DAgET0FsXAUFRUFmUwmQTVERKQtDEhEDXDkyBEcPnxYo51XqRERGQcGJKJ64o0fiYiMHwMSUT2IhSMGIyIi48OARFQHu3fvxpkzZzTaGY6IiIwT74NE9Deio6NFw1FKCsMREZGx4ggSUS3EptR8fBRYtgyYN0+CgoiIqFEwIBGJ2LRpE27evKnR/mRKjXfFJiIybgxIRNXwKjUiImJAIvoLXqVGREQAAxIRAI4aERGROgYkMnli4cjCwgLz58+XoBoiItIHDEhk0jilRkREYhiQyCTVNKXm48NwREREDEhkgsTCUYcOHTB27FgJqiEiIn3EgEQmhVNqRERUFwxIZBJ4lRoREdUHAxIZPbFw1KqVH6ZOHSxBNUREZAgYkMiocUqNiIgaggGJjBKn1IiI6GkwIJHREQtHQ4YMQZ8+fSSohoiIDBEDEhkNQRCwaNEijXaOGhERUX0xIJFR4I0fiYhIm8ykLoDoaYmFo1OnxsDHR4HhwyUoiIiIDB5HkMhgVVZWYsmSJRrtnFIjIqKnxYBEBolXqRERkS4xIJHBEQtHkydPhlwul6AaIiIyRgxIZDAePXqEjz76SKOdo0ZERKRtDEhkEDilRkREjYlXsZHeEwtHP/zwLi/hJyIineEIEumtoqIirFmzRqNdoVCAA0dERKRLDEiklzilRkREUmJAIr0jFo4OHpyHo0etJKiGiIhMEQMS6Y2bN29i06ZNGu0pKQrMmydBQUREZLIYkEgv1Dalxlk1IiJqbAxIJDmxcHTgwAIcO8aLLImISBp68Qm0YcMGuLu7w9raGn5+fjh+/Hit/RMSEuDl5QVra2t0794d+/btU3tdEARERUXB1dUVNjY2CAgIwJUrV1Sv5+bmIjQ0FB4eHrCxsUGHDh2gUChQVlamk+MjcVeuXBENRykpCrz/vl78ahIRkYmS/FMoPj4e4eHhUCgUOH36NHr06IHAwEDcunVLtH96ejpGjx6N0NBQZGVlISgoCEFBQTh37pyqz/Lly7F27VrExsYiMzMTTZs2RWBgIB49egQAyM7ORlVVFT777DOcP38eH3/8MWJjYzF//vxGOWZ6PGq0detWjXaFQoH0dGD4cAmKIiIi+v9kgiAIUhbg5+eH3r17Y/369QCAqqoquLm5ISwsDPNEVuYGBwejpKQESUlJqra+ffvC29sbsbGxEAQBrVu3xuzZszFnzhwAj++n4+Ligri4OIwaNUq0jhUrVmDjxo347bff6lR3cXEx7O3tUVRUBDs7u/oetkkTGzWKioqCTCaToBoiIjIldf38lnQEqaysDKdOnUJAQICqzczMDAEBAcjIyBDdJiMjQ60/AAQGBqr65+TkQKlUqvWxt7eHn59fjfsEHocoR0fHGl8vLS1FcXGx2hfVz7lz50TDkY+PguGIiIj0iqSLtG/fvo3Kykq4uLiotbu4uCA7O1t0G6VSKdpfqVSqXn/SVlOf6q5evYp169Zh5cqVNdYaExNT45VW9PdqOnc+PgpOpxERkd4x+avY/vjjDwwePBivvfYaJk6cWGO/iIgIhIeHq34uLi6Gm5tbY5Ro8MTCEe+ITURE+kzSgNSyZUuYm5sjPz9frT0/Px9yuVx0G7lcXmv/J9/z8/Ph6uqq1sfb21ttuxs3buC5555Dv379RG9Q+FdWVlawsuKdnOsjMzMTycnJGu18yCwREek7SdcgWVpawtfXF2lpaaq2qqoqpKWlwd/fX3Qbf39/tf4AkJqaqurv4eEBuVyu1qe4uBiZmZlq+/zjjz8wcOBA+Pr6YsuWLTAzk/yCPqMSHR0tGo4UCk6pERGR/pN8ii08PBwhISHo1asX+vTpgzVr1qCkpAQTJkwAAIwbNw5t2rRBTEwMAGDmzJkYMGAAVq1ahaFDh2L79u04efKkagRIJpNh1qxZWLJkCTw9PeHh4YEFCxagdevWCAoKAvC/cNS+fXusXLkSBQUFqnpqGrmiuuOUGhERGTrJA1JwcDAKCgoQFRUFpVIJb29vJCcnqxZZ5+XlqY3u9OvXD1u3bkVkZCTmz58PT09PJCYmolu3bqo+c+fORUlJCSZNmoTCwkL0798fycnJsLa2BvB4xOnq1au4evUq2rZtq1aPxHc9MGhHjhzB4cOHNdoZjoiIyNBIfh8kQ8X7IKkTGzUqLrbHgAGzOKVGRER6o66f35KPIJHhEwtHn3+uwKef8o7YRERkmBiQqMH2798v+tw8hUIBzqoREZEhY0CiBhEbNerRo4dqITwREZEhY0CieqvpcSGcTiMiImPBgER1tmvXLvz8888a7bxKjYiIjA0DEtWJ2KjRxYuD8cYbfhJUQ0REpFsMSPS3xMJRSooC6ekSFENERNQIGJCoRlu3bsWVK1c02lNSFJg3T4KCiIiIGgkDEokSGzV69dVX0bVrV17CT0RERo8BiTTUdJVa164SFENERCQBBiRS2bx5M65du6bRzqvUiIjI1DAgEQDxUaMJEyagXbt2ElRDREQkLQYkEycIAhYtWqTRXlamALMRERGZKgYkE7Z69Wrcu3dPo72sTIGlSyUoiIiISE8wIJkosSm16dOnw8nJSYJqiIiI9AsDkompaUqNC7GJiIj+hwHJhCxfvhwPHz7UaPfxYTgiIiL6KwYkEyE2pTZ79mw0a9ZMgmqIiIj0GwOSkausrMSSJUs02jmlRkREVDMGJCMmNmoEcEqNiIjo7zAgGSmxcDRv3jxYWVlJUA0REZFhYUAyMuXl5fjwww812n18FGA2IiIiqhsGJCOybNkylJaWarT7+CgwfLgEBRERERkoBiQjITal9vHHH+Drr5swHBEREdUTA5KBKy0txbJlyzTaU1IU+PprMBwRERE1AAOSAYuLi8Pvv/+u1qZUdsGQIa+BV/ETERE1HAOSgRKbUouKioJMJpOgGiIiIuPCgGRgHj16hI8++kij3cdHAWYjIiIi7WBAMiA7duzAxYsX1dpeeOEF9O/fX6KKiIiIjBMDkoEQm1JbuTIKCgWHjYiIiLSNAUnP1TSltnKlAjNmSFAQERGRCWBA0mNHjhzB4cOH1dpef/11dO7cmVepERER6RADkp5KTU1Fenq6WpuPjwKdO0tUEBERkQlhQNIzFRUVWLp0qVpbSYktfvrpPY4aERERNRIGJD0TG7tP7efvv5+L8nIbiCxDIiIiIh1hQNIzR460QdeuWcjP74RPPx3FUSMiIiIJmEldAKkbO9YXKSkKDB48SupSiIiITBZHkPTM8OF8wCwREZHUOIJEREREVA0DEhEREVE1DEhERERE1TAgEREREVXDgERERERUDQMSERERUTUMSERERETVMCARERERVcOARERERFSNXgSkDRs2wN3dHdbW1vDz88Px48dr7Z+QkAAvLy9YW1uje/fu2LdP/QGvgiAgKioKrq6usLGxQUBAAK5cuaLW586dOxgzZgzs7Ozg4OCA0NBQ3L9/X+vHRkRERIZH8oAUHx+P8PBwKBQKnD59Gj169EBgYCBu3bol2j89PR2jR49GaGgosrKyEBQUhKCgIJw7d07VZ/ny5Vi7di1iY2ORmZmJpk2bIjAwEI8ePVL1GTNmDM6fP4/U1FQkJSXhyJEjmDRpks6Pl4iIiPSfTBAEQcoC/Pz80Lt3b6xfvx4AUFVVBTc3N4SFhWHevHka/YODg1FSUoKkpCRVW9++feHt7Y3Y2FgIgoDWrVtj9uzZmDNnDgCgqKgILi4uiIuLw6hRo3Dx4kV06dIFJ06cQK9evQAAycnJePHFF3H9+nW0bt36b+suLi6Gvb09ioqKYGdnp41TQURERDpW189vSUeQysrKcOrUKQQEBKjazMzMEBAQgIyMDNFtMjIy1PoDQGBgoKp/Tk4OlEqlWh97e3v4+fmp+mRkZMDBwUEVjgAgICAAZmZmyMzM1NrxERERkWFqIuWb3759G5WVlXBxcVFrd3FxQXZ2tug2SqVStL9SqVS9/qSttj7Ozs5qrzdp0gSOjo6qPtWVlpaitLRU9XNRURGAx0mUiIiIDMOTz+2/m0CTNCAZkpiYGERHR2u0u7m5SVANERERPY179+7B3t6+xtclDUgtW7aEubk58vPz1drz8/Mhl8tFt5HL5bX2f/I9Pz8frq6uan28vb1VfaovAq+oqMCdO3dqfN+IiAiEh4erfi4sLET79u2Rl5dX6wkmdcXFxXBzc8O1a9e4dqseeN4ahuet4XjuGobnrWEa87wJgoB79+797XpjSQOSpaUlfH19kZaWhqCgIACPF2mnpaVh+vTpotv4+/sjLS0Ns2bNUrWlpqbC398fAODh4QG5XI60tDRVICouLkZmZibeeecd1T4KCwtx6tQp+Pr6AgAOHTqEqqoq+Pn5ib6vlZUVrKysNNrt7e35l6AB7OzseN4agOetYXjeGo7nrmF43hqmsc5bXQY2JJ9iCw8PR0hICHr16oU+ffpgzZo1KCkpwYQJEwAA48aNQ5s2bRATEwMAmDlzJgYMGIBVq1Zh6NCh2L59O06ePIlNmzYBAGQyGWbNmoUlS5bA09MTHh4eWLBgAVq3bq0KYZ07d8bgwYMxceJExMbGory8HNOnT8eoUaPqdAUbERERGTfJA1JwcDAKCgoQFRUFpVIJb29vJCcnqxZZ5+Xlwczsfxfb9evXD1u3bkVkZCTmz58PT09PJCYmolu3bqo+c+fORUlJCSZNmoTCwkL0798fycnJsLa2VvX59ttvMX36dLzwwgswMzPDyJEjsXbt2sY7cCIiItJfAjXIo0ePBIVCITx69EjqUgwKz1vD8Lw1DM9bw/HcNQzPW8Po43mT/EaRRERERPpG8keNEBEREekbBiQiIiKiahiQiIiIiKphQCIiIiKqxmQD0oYNG+Du7g5ra2v4+fnh+PHjtfZPSEiAl5cXrK2t0b17d+zbt0/tdUEQEBUVBVdXV9jY2CAgIABXrlxR63Pnzh2MGTMGdnZ2cHBwQGhoKO7fv6/1Y9Olxj5vubm5CA0NhYeHB2xsbNChQwcoFAqUlZXp5Ph0RYrftydKS0vh7e0NmUyGM2fOaOuQGo1U527v3r3w8/ODjY0NWrRoobqPmqGQ4rxdvnwZI0aMQMuWLWFnZ4f+/fvj8OHDWj82XdL2edu5cycGDRoEJyenGv8OPnr0CNOmTYOTkxOaNWuGkSNHajwxQt819nm7c+cOwsLC0KlTJ9jY2KBdu3aYMWOG6jmpWiHhFXSS2b59u2BpaSls3rxZOH/+vDBx4kTBwcFByM/PF+1/7NgxwdzcXFi+fLlw4cIFITIyUrCwsBB++eUXVZ9ly5YJ9vb2QmJionD27Flh+PDhgoeHh/Dw4UNVn8GDBws9evQQ/vvf/wpHjx4VOnbsKIwePVrnx6stUpy3/fv3C+PHjxdSUlKEX3/9Vdi9e7fg7OwszJ49u1GOWRuk+n17YsaMGcKQIUMEAEJWVpauDlMnpDp33333ndCiRQth48aNwqVLl4Tz588L8fHxOj9ebZHqvHl6egovvviicPbsWeHy5cvC1KlTBVtbW+HmzZs6P2Zt0MV5+/rrr4Xo6Gjh888/r/Hv4JQpUwQ3NzchLS1NOHnypNC3b1+hX79+ujpMrZPivP3yyy/CK6+8IuzZs0e4evWqkJaWJnh6egojR47U2nGZZEDq06ePMG3aNNXPlZWVQuvWrYWYmBjR/q+//rowdOhQtTY/Pz9h8uTJgiAIQlVVlSCXy4UVK1aoXi8sLBSsrKyEbdu2CYIgCBcuXBAACCdOnFD12b9/vyCTyYQ//vhDa8emS1KcNzHLly8XPDw8nuZQGpWU523fvn2Cl5eXcP78eYMMSFKcu/LycqFNmzbCF198oe3DaTRSnLeCggIBgHDkyBFVn+LiYgGAkJqaqrVj0yVtn7e/ysnJEf07WFhYKFhYWAgJCQmqtosXLwoAhIyMjKc4msYjxXkTs2PHDsHS0lIoLy+v3wHUwOSm2MrKynDq1CkEBASo2szMzBAQEICMjAzRbTIyMtT6A0BgYKCqf05ODpRKpVofe3t7+Pn5qfpkZGTAwcEBvXr1UvUJCAiAmZkZMjMztXZ8uiLVeRNTVFQER0fHpzmcRiPlecvPz8fEiRPxzTffwNbWVpuH1SikOnenT5/GH3/8ATMzM/j4+MDV1RVDhgzBuXPntH2IOiHVeXNyckKnTp3w9ddfo6SkBBUVFfjss8/g7OyseualPtPFeauLU6dOoby8XG0/Xl5eaNeuXb32IxWpzpuYoqIi2NnZoUkT7TwkxOQC0u3bt1FZWal6lMkTLi4uUCqVotsolcpa+z/5/nd9nJ2d1V5v0qQJHB0da3xffSLVeavu6tWrWLduHSZPntyg42hsUp03QRAwfvx4TJkyRS2UGxKpzt1vv/0GAFi4cCEiIyORlJSEFi1aYODAgbhz587TH5iOSXXeZDIZDh48iKysLDRv3hzW1tZYvXo1kpOT0aJFC60cmy7p4rzVhVKphKWlJRwcHJ5qP1KR6ryJ1bF48WJMmjSpwfuozuQCEhmuP/74A4MHD8Zrr72GiRMnSl2OXlu3bh3u3buHiIgIqUsxOFVVVQCADz74ACNHjoSvry+2bNkCmUyGhIQEiavTX4IgYNq0aXB2dsbRo0dx/PhxBAUFYdiwYbh586bU5ZERKy4uxtChQ9GlSxcsXLhQa/s1uYDUsmVLmJuba1whkJ+fD7lcLrqNXC6vtf+T73/X59atW2qvV1RU4M6dOzW+rz6R6rw9cePGDTz33HPo168fNm3a9FTH0pikOm+HDh1CRkYGrKys0KRJE3Ts2BEA0KtXL4SEhDz9gTUCqc6dq6srAKBLly6q162srPDMM88gLy/vKY6ocUj5O5eUlITt27fjH//4B3r27IlPP/0UNjY2+Oqrr7RybLqki/NWF3K5HGVlZSgsLHyq/UhFqvP2xL179zB48GA0b94cu3btgoWFRb33UROTC0iWlpbw9fVFWlqaqq2qqgppaWnw9/cX3cbf31+tPwCkpqaq+nt4eEAul6v1KS4uRmZmpqqPv78/CgsLcerUKVWfQ4cOoaqqCn5+flo7Pl2R6rwBj0eOBg4cqPqXvJmZ4fzaSnXe1q5di7Nnz+LMmTM4c+aM6hLa+Ph4LF26VKvHqCtSnTtfX19YWVnh0qVLqj7l5eXIzc1F+/bttXZ8uiLVeXvw4AEAaPz9NDMzU43K6TNdnLe68PX1hYWFhdp+Ll26hLy8vHrtRypSnTfg8e/goEGDYGlpiT179sDa2rr+B1AbrSz1NjDbt28XrKyshLi4OOHChQvCpEmTBAcHB0GpVAqCIAhvvvmmMG/ePFX/Y8eOCU2aNBFWrlwpXLx4UVAoFKKXwDo4OAi7d+8Wfv75Z2HEiBGil/n7+PgImZmZwk8//SR4enoa3GX+jX3erl+/LnTs2FF44YUXhOvXrws3b95UfRkKqX7f/qo+V4LoE6nO3cyZM4U2bdoIKSkpQnZ2thAaGio4OzsLd+7cabyDfwpSnLeCggLByclJeOWVV4QzZ84Ily5dEubMmSNYWFgIZ86cadwT0EC6OG9//vmnkJWVJezdu1cAIGzfvl3IyspS+3/YlClThHbt2gmHDh0STp48Kfj7+wv+/v6Nd+BPSYrzVlRUJPj5+Qndu3cXrl69qvbZUFFRoZXjMsmAJAiCsG7dOqFdu3aCpaWl0KdPH+G///2v6rUBAwYIISEhav137NghPPvss4KlpaXQtWtXYe/evWqvV1VVCQsWLBBcXFwEKysr4YUXXhAuXbqk1ufPP/8URo8eLTRr1kyws7MTJkyYINy7d09nx6gLjX3etmzZIgAQ/TIkUvy+/ZWhBiRBkObclZWVCbNnzxacnZ2F5s2bCwEBAcK5c+d0doy6IMV5O3HihDBo0CDB0dFRaN68udC3b19h3759OjtGXdD2eavp/2EKhULV5+HDh8LUqVOFFi1aCLa2tsLLL79sUP8IFITGP2+HDx+u8bMhJydHK8ckEwRB0O6YFBEREZFhM5zFHERERESNhAGJiIiIqBoGJCIiIqJqGJCIiIiIqmFAIiIiIqqGAYmIiIioGgYkIiIiomoYkIiIiIiqYUAiIqOkVCoRFhaGZ555BlZWVnBzc8OwYcOQlpaGO3fuICwsDJ06dYKNjQ3atWuHGTNmoKioSLV9bm4uZDIZzpw5o7HvgQMHYtasWWptFy9exPDhw2Fvb4+mTZuid+/eBvFwWyIS10TqAoiItC03Nxf/+Mc/4ODggBUrVqB79+4oLy9HSkoKpk2bhu+++w43btzAypUr0aVLF/z++++YMmUKbty4ge+++67e7/frr7+if//+CA0NRXR0NOzs7HD+/HntPzyTiBoNHzVCREbnxRdfxM8//4xLly6hadOmaq8VFhbCwcFBY5uEhASMHTsWJSUlaNKkCXJzc+Hh4YGsrCx4e3ur9R04cCC8vb2xZs0aAMCoUaNgYWGBb775RkdHRESNjVNsRGRU7ty5g+TkZEybNk0jHAEQDUcAUFRUBDs7OzRpUr+B9aqqKuzduxfPPvssAgMD4ezsDD8/PyQmJjageiLSFwxIRGRUrl69CkEQ4OXlVedtbt++jcWLF2PSpEkar/Xr1w/NmjVT+zp69Kjq9Vu3buH+/ftYtmwZBg8ejAMHDuDll1/GK6+8gh9//FErx0REjY9rkIjIqNR31UBxcTGGDh2KLl26YOHChRqvx8fHo3PnzmptY8aMUf13VVUVAGDEiBF49913AQDe3t5IT09HbGwsBgwYUM8jICJ9wIBEREbF09MTMpkM2dnZf9v33r17GDx4MJo3b45du3bBwsJCo4+bmxs6duyo1mZjY6P675YtW6JJkybo0qWLWp/OnTvjp59+auBREJHUOMVGREbF0dERgYGB2LBhA0pKSjReLywsBPB45GjQoEGwtLTEnj17GnzFmaWlJXr37o1Lly6ptV++fBnt27dv0D6JSHoMSERkdDZs2IDKykr06dMH33//Pa5cuYKLFy9i7dq18Pf3V4WjkpISfPnllyguLoZSqYRSqURlZWW93++9995DfHw8Pv/8c1y9ehXr16/H//3f/2Hq1Kk6ODoiagycYiMio/PMM8/g9OnTWLp0KWbPno2bN2+iVatW8PX1xcaNG3H69GlkZmYCgMb0WU5ODtzd3ev1fi+//DJiY2MRExODGTNmoFOnTvj+++/Rv39/bR0SETUy3geJiIiIqBpOsRERERFVw4BEREREVA0DEhEREVE1DEhERERE1TAgEREREVXDgERERERUDQMSERERUTUMSERERETVMCARERERVcOARERERFQNAxIRERFRNQxIRERERNX8P7ECrup6bXl9AAAAAElFTkSuQmCC", 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", + "image/png": 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", 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", 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", + "image/png": 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gc5fkM0ArVqzA+PHjMWbMGHTt2hUZGRmws7PDxo0bRY/v168fnn32WXTp0gV+fn5488030aNHD/zwww9qx9nY2MDNzU311a5dO30Mh4iIyKhVV1drhB9bW1uTCj+AxAGotrYWhw8fRnh4uKrNwsIC4eHhyM/Pv+/rBUFAbm4uzp49iyeeeELtuby8PLi4uCAgIAATJkzA9evXG+ynpqYGFRUVal/UdLGxsRg+fLjqcb9+/TBt2rRm9amLPoiI6N6+/PJLLFu2TK3t1VdfxaxZsySqqOVIugR27do11NfXw9XVVa3d1dUVZ86cafB15eXl8PT0RE1NDeRyOd577z08/fTTqucjIyMxYsQI+Pj4oKCgALNnz8bAgQORn58PuVyu0V9ycrLoVJ+piY2NxaZNmwAAVlZW6NixI6KjozF79mxYWrbcR2H79u2wsrJq1LF5eXno378/bty4AUdHxyb1QURE2jP1Ja9/k3wPUFO0adMGR48eVd2RPC4uDr6+vujXrx8AYNSoUapju3fvjh49esDPzw95eXl46qmnNPpLSEhAXFyc6nFFRQW8vLxafBxSuLs3qqamBrt27cKkSZNgZWWFhIQEteNqa2thbW2tk/ds3769QfRBRESabt68iRUrVqi1tWvXDlOnTpWoIv2QdAnMyckJcrkcJSUlau0lJSVwc3Nr8HUWFhbw9/dHUFAQpk+fjpEjR2rcsfyffH194eTkhPPnz4s+b2Njg7Zt26p9maq7e6M6deqECRMmIDw8HF999ZVq2erdd9+Fh4cHAgICAAAXL17ECy+8AEdHR7Rv3x7Dhg1DUVGRqr/6+nrExcXB0dERHTp0wMyZM/Hv++v+e/mqpqYGs2bNgpeXF2xsbODv74/3338fRUVF6N+/P4A7//hkMhliY2NF+7hx4waio6PRrl072NnZYeDAgTh37pzq+czMTDg6OiInJwddunRB69atERkZiStXrqiOycvLQ1hYGOzt7eHo6IhHH30Uf/zxh46+00REhm/btm0a4WfChAkmH34AiQOQtbU1QkJCkJubq2pTKpXIzc1F7969G92PUqlETU1Ng89funQJ169fh7u7e7PqNUWtWrVCbW0tAKj2U3377bfYuXMn6urqEBERgTZt2mD//v04cOCAKkjcfc3y5cuRmZmJjRs34ocffsBff/2FL7744p7vGR0dja1bt2L16tU4ffo0/vOf/6B169bw8vLC559/DuDODfauXLmCVatWifYRGxuLn3/+GV999RXy8/MhCAKeeeYZ1NXVqY6prq5GamoqNm/ejH379uHChQuYMWMGAOD27dsYPnw4+vbti+PHjyM/Px+vvfaa0V7Qi4hIW/Pnz9fYbpKUlCR6BrYpknwJLC4uDjExMQgNDUVYWBjS0tJQVVWFMWPGALjzy9LT01M1w5OcnIzQ0FD4+fmplnE2b96MdevWAQAqKysxf/58PPfcc3Bzc0NBQQFmzpwJf39/tdPkzd3dDeQ5OTmYMmUKSktLYW9vjw0bNqiWvj766CMolUps2LBBFQw++OADODo6Ii8vDwMGDEBaWhoSEhIwYsQIAEBGRgZycnIafN/ffvsNn3zyCb799lvV5ndfX1/V83eXulxcXNT2AP3TuXPn8NVXX+HAgQPo06cPAODjjz+Gl5cXduzYgeeffx7AnfvVZGRkwM/PDwAwefJkLFiwAMCdZc7y8nIMHjxY9XyXLl20/0YSERmZsrIyjf+59PDwwPjx4yWqSBqSB6CoqCiUlpYiMTERxcXFCAoKQnZ2tmpj9IULF2Bh8b+JqqqqKkycOBGXLl1Cq1atEBgYiI8++ghRUVEAALlcjuPHj2PTpk0oKyuDh4cHBgwYgIULFxrctYC++gpISQHi44GhQ/Xznjt37kTr1q1RV1cHpVKJF198EfPmzcOkSZPQvXt3tX0/x44dw/nz59GmTRu1Pm7duoWCggKUl5fjypUrUCgUqucsLS0RGhqqsQx219GjRyGXy9G3b98mj+H06dOwtLRUe98OHTogICAAp0+fVrXZ2dmpwg0AuLu74+rVqwDuBK3Y2FhERETg6aefRnh4OF544QXOEhKRScvMzNRY6p88eTI6dOggUUXSkTwAAXe++ZMnTxZ9Li8vT+3xokWLsGjRogb7atWq1T1nIAxJSgqQn3/nv/oKQP3798e6detgbW0NDw8PtbO/7O3t1Y6trKxESEgIPv74Y41+nJ2dm/T+rVq1atLrmuLfZ43JZDK1YPbBBx9g6tSpyM7OxrZt2zB37lx8++23eOSRR/RWIxGRvpjbWV73I/mFEM1ZfDzQu/ed/+qLvb09/P390bFjx/ue+v7www/j3LlzcHFxgb+/v9qXg4MDHBwc4O7ujoMHD6pec/v2bRw+fLjBPrt37w6lUonvv/9e9Pm7M1D19fUN9tGlSxfcvn1b7X2vX7+Os2fPomvXrvcc078FBwcjISEBP/74Ix566CFs2bJFq9cTERm6P//8UyP8+Pn5mXX4ARiAJDV0KPDjj/qb/dHWSy+9BCcnJwwbNgz79+9HYWEh8vLyMHXqVFy6dAkA8OabbyIlJQU7duzAmTNnMHHiRJSVlTXYp7e3N2JiYjB27Fjs2LFD1ecnn3wCAOjUqRNkMhl27tyJ0tJSVFZWavTRuXNnDBs2DOPHj8cPP/yAY8eO4eWXX4anpyeGDRvWqLEVFhYiISEB+fn5+OOPP7B7926cO3eO+4CIyKTMnz8fGzZsUGubNm0aXn75ZYkqMhwMQNQgOzs77Nu3Dx07dsSIESPQpUsXjBs3Drdu3VJdKmD69Ol45ZVXEBMTg969e6NNmzZ49tln79nvunXrMHLkSEycOBGBgYEYP348qqqqAACenp6YP38+4uPj4erq2uDS6AcffICQkBAMHjwYvXv3hiAI2LVrV6MvlmhnZ4czZ87gueeew4MPPojXXnsNkyZNwuuvv67Fd4iIyHCJLXnl5CTBwcFBgmoMj0xoaLeqGauoqICDgwPKy8s1rgl069YtFBYWwsfHB7a2thJVSPrGv3ciMhZFRUWqq/7fZWXlgJ07p+n1pBsp3Ov3978ZxCZoIiIiaj6xWZ8333wTjo6OmD1bgoIMGAMQERGRCeBZXtphACIiIjJiv/32G7Zu3arWZo4XNtQWAxAREZGREpv1mT59Olq3bi1BNcaFAaiJuHfcvPDvm4gMDZe8mocBSEt3T7Ourq7W61WNSVrV1dUANK8uTUSkbydOnFDdOPouPz8/XttHSwxAWpLL5XB0dFTdU8rOzo53EDdhgiCguroaV69ehaOjI+RyudQlEZEZE5v1mTlzJv+HvAkYgJrAzc0NAFQhiEyfo6Oj6u+diEjfBEHAggULNNq55NV0DEBNIJPJ4O7uDhcXF9TV1UldDrUwKysrzvwQkWQOHz6MnTt3qrV1794dI0aMkKgi08AA1AxyuZy/GImIqMWILXklJCSobhxNTccAREREZGC45NXyGICIiIgMyP79+/Hdd9+ptfXq1QvPPPOMRBWZJgYgIiIiAyG25DVnzhxYWvLXta7xO0pERCSx+vp6LFq0SKOdS14thwGIiIhIQp9//jlOnDih1vbnnz0wePCzElVkHhiAiIiIJCK25DV37lyeYawHDEBERER6VldXh8WLF2u0c8lLfxiAiIiI9OjDDz9EYWGhWtsjjzyCiIgIiSoyTwxAREREeiK25JWYmMh7SkqAAYiIiKiF3bp1C0uWLNFo55KXdBiAiIiIWtC6des0bp7dv39/PPHEExJVRAADEBERUYvhkpfhYgAiIiLSscrKSixfvlyjnUtehoMBiIiISIeWLVuG6upqtbaBAwciLCxMoopIDAMQERGRjogteXHWxzAxABERETVTWVkZVq1apdHO8GO4GICIiIiaQWzW59lnn0WPHj0kqIYaiwGIiIioibjkZbwYgIiIiLR07do1pKena7Qz/BgPBiAiIiItiM36jBo1CgEBARJUQ03FAERERNRIXPIyHQxARERE93H58mWsX79eo53hx3gxABEREd2D2KxPTEwMvL299V8M6YyF1AUAQHp6Ory9vWFrawuFQoFDhw41eOz27dsRGhoKR0dH2NvbIygoCJs3b1Y7RhAEJCYmwt3dHa1atUJ4eDjOnTvX0sMgIiIT09CSF8OP8ZM8AG3btg1xcXFISkrCL7/8gp49eyIiIkLjzrl3tW/fHnPmzEF+fj6OHz+OMWPGYMyYMcjJyVEds3TpUqxevRoZGRk4ePAg7O3tERERgVu3bulrWEREZMSKioq438fEyQRBEKQsQKFQoFevXli7di0AQKlUwsvLC1OmTEF8fHyj+nj44YcxaNAgLFy4EIIgwMPDA9OnT8eMGTMAAOXl5XB1dUVmZiZGjRp13/4qKirg4OCA8vJytG3btumDIyIioyMWfMaPHw8PDw8JqiFtaPP7W9IZoNraWhw+fBjh4eGqNgsLC4SHhyM/P/++rxcEAbm5uTh79iyeeOIJAEBhYSGKi4vV+nRwcIBCoWiwz5qaGlRUVKh9ERGR+Wlo1ofhx/RIugn62rVrqK+vh6urq1q7q6srzpw50+DrysvL4enpiZqaGsjlcrz33nt4+umnAQDFxcWqPv7d593n/i05OVn0Q09ERObht99+w9atWzXaueRluozyLLA2bdrg6NGjqKysRG5uLuLi4uDr64t+/fo1qb+EhATExcWpHldUVMDLy0tH1RIRkSET+x/gSZMmwcnJSYJqSF8kDUBOTk6Qy+UoKSlRay8pKYGbm1uDr7OwsIC/vz8AICgoCKdPn0ZycjL69eunel1JSQnc3d3V+gwKChLtz8bGBjY2Ns0cDRERGRtudDZfku4Bsra2RkhICHJzc1VtSqUSubm56N27d6P7USqVqKmpAQD4+PjAzc1Nrc+KigocPHhQqz6JiMh0HTx4kOHHzEm+BBYXF4eYmBiEhoYiLCwMaWlpqKqqwpgxYwAA0dHR8PT0RHJyMoA7+3VCQ0Ph5+eHmpoa7Nq1C5s3b8a6desAADKZDNOmTcOiRYvQuXNn+Pj44J133oGHhweGDx8u1TCJiMhAiAWfN998E46OjvovhiQjeQCKiopCaWkpEhMTUVxcjKCgIGRnZ6s2MV+4cAEWFv+bqKqqqsLEiRNx6dIltGrVCoGBgfjoo48QFRWlOmbmzJmoqqrCa6+9hrKyMjz22GPIzs6Gra2t3sdHRESGQyz85OQkgRM/5kfy6wAZIl4HiIjItOzduxf79u3TaM/JSUJ8PDB0qARFkc5p8/tb8hkgIiKiliQ26/PWW2+hbdu2nPkxYwxARERkkgRBwIIFCzTaudGZAAYgIiIyQTt37sThw4fV2mQyGRITEyWqiAwNAxAREZkUsSWvt99+G3Z2dhJUQ4aKAYiIiEyCUqnEwoULNdq55EViGICIiMjobdiwAX/++adGe3Awww+JYwAiIiKjJrbkFR8fz1sc0T0xABERkVG6ffs23n33XY12LnlRYzAAERGR0VmxYgVu3ryp1mZvb48ZM2ZIVBEZGwYgIiIyKmJLXnPmzIGlJX+lUePx00JEREbh1q1bWLJkiUZ7cHASmH1IW/zIEBGRwROb9bG1dcGsWRMkqIZMAQMQEREZNLHw884778DCwkKCashUMAAREZFBqqysxPLlyzXaeZYX6QIDEBERGRyxWR93d3e89tprElRDpogBiIiIDIpY+ElMTIRMJpOgGjJVDEBERGQQ/vrrL6xZs0ajnUte1BIYgIiISHJisz4BAQEYNWqUBNWQOWAAIiIiSYmFH876UEtjACIiIkkUFxfjP//5j0Y7ww/pAwMQERHpndisT2hoKAYNGiRBNWSOGICIiEivuORFhoABiIiI9OKPP/5AZmamRjvDD0mBAYiIiFqc2KxP37590a9fP/0XQwQGICIiamFc8iJDxABEREQt4tSpU/j000812hl+yBAwABERkc6JzfqcPh2BrKxHJKiGSJOF1AUQEZFpEQs/OTlJePFFhh8yHJwBIiIinfj555/x9ddfa7QnJSWBq15kaBiAiIio2cRmfUaMGIHu3btLUA3R/TEAERFRs/AsLzJGDEBERNQku3fvRn5+vkY7ww8ZAwYgIiLSmtisz5EjURg7NlCCaoi0xwBERERa4ZIXmQIGICIiapRt27bhzJkzGu0MP2SMGICIiOi+xGZ9YmNj0alTJwmqIWo+g7gQYnp6Ory9vWFrawuFQoFDhw41eOz69evx+OOPo127dmjXrh3Cw8M1jo+NjYVMJlP7ioyMbOlhEBGZpIaWvBh+yJhJPgO0bds2xMXFISMjAwqFAmlpaYiIiMDZs2fh4uKicXxeXh5Gjx6NPn36wNbWFkuWLMGAAQNw8uRJeHp6qo6LjIzEBx98oHpsY2Ojl/EQEZmKNWvW4K+//tJoDw7mkhcZP5kgCIKUBSgUCvTq1Qtr164FACiVSnh5eWHKlCmIj4+/7+vr6+vRrl07rF27FtHR0QDuzACVlZVhx44dTaqpoqICDg4OKC8vR9u2bZvUBxGRMROb9QkIeA2jRrlLUA1R42jz+1vSJbDa2locPnwY4eHhqjYLCwuEh4eLXltCTHV1Nerq6tC+fXu19ry8PLi4uCAgIAATJkzA9evXdVo7EZEpEgRBNPwEBycx/JBJkXQJ7Nq1a6ivr4erq6tau6urq+iZBmJmzZoFDw8PtRAVGRmJESNGwMfHBwUFBZg9ezYGDhyI/Px8yOVyjT5qampQU1OjelxRUdHEERERGS+x4APwLC8yTZLvAWqOlJQUZGVlIS8vD7a2tqr2UaNGqf7cvXt39OjRA35+fsjLy8NTTz2l0U9ycnKD//CJiMyB2M/AKVOmaMyuE5kKSZfAnJycIJfLUVJSotZeUlICNze3e742NTUVKSkp2L17N3r06HHPY319feHk5ITz58+LPp+QkIDy8nLV18WLF7UbCBGRkVIqlQ2e5cXwQ6ZM0hkga2trhISEIDc3F8OHDwdw5x9jbm4uJk+e3ODrli5dinfffRc5OTkIDQ297/tcunQJ169fh7u7+Pq1jY0NzxIjIrPDJS8yZ5IvgcXFxSEmJgahoaEICwtDWloaqqqqMGbMGABAdHQ0PD09kZycDABYsmQJEhMTsWXLFnh7e6O4uBgA0Lp1a7Ru3RqVlZWYP38+nnvuObi5uaGgoAAzZ86Ev78/IiIiJBsnEZEhEQs/06dPR+vWrSWohkj/JA9AUVFRKC0tRWJiIoqLixEUFITs7GzVxugLFy7AwuJ/K3Xr1q1DbW0tRo4cqdZPUlIS5s2bB7lcjuPHj2PTpk0oKyuDh4cHBgwYgIULF3KWh4jMXl1dHRYvXqzRHhycBGYfMieSXwfIEPE6QERkirjkRaZOm9/fks8AERFRyxMLP/Hx8ZwZJ7PFAEREZMKqq6uxbNkyjXbO+pC50zoAyeVyXLlyReM+XdevX4eLiwvq6+t1VhwRETUdl7yIGqZ1AGpoy1BNTQ2sra2bXRARETWfWPiZM2cOLC058U8EaBGAVq9eDQCQyWTYsGGD2qmS9fX12LdvHwIDA3VfIRERNVpZWRlWrVql0c5ZHyJ1jQ5AK1euBHBnBigjI0PtnlrW1tbw9vZGRkaG7iskIqJG4ZIXUeM1OgAVFhYCAPr374/t27ejXbt2LVYUERFpRyz8JCYmQiaTSVANkeHTejF47969LVEHERE1QUlJiejsO2d9iO5N6wA0duzYez6/cePGJhdDRESNxyUvoqbTOgDduHFD7XFdXR1OnDiBsrIyPPnkkzorjIiIGtbQHdyJqHG0DkBffPGFRptSqcSECRPg5+enk6KIiEhcUVERNm3apNHO8EOkHZ3dC+zs2bPo168frly5oovuJMV7gRGRIRKb9XF0dMSbb74pQTVEhkeSe4EVFBTg9u3buuqOiIj+gUteRLqldQCKi4tTeywIAq5cuYKvv/4aMTExOiuMiIiAY8eOYceOHRrtwcEMP0TNoXUAOnLkiNpjCwsLODs7Y/ny5fc9Q4yIiBpPbNbHw8MD48ePl6AaItPC6wARERkgLnkRtawm7wG6evUqzp49CwAICAjQuDs8ERFp78CBA9izZ49GO8MPkW5pHYAqKiowadIkbN26FUqlEgAgl8sRFRWF9PR0ODg46LxIIiJzIDbr061bN4wcOVKCaohMm4W2Lxg/fjwOHjyIr7/+GmVlZSgrK8POnTvx888/4/XXX2+JGomITF5DS14MP0QtQ+vrANnb2yMnJwePPfaYWvv+/fsRGRmJqqoqnRYoBV4HiIj0ZdeuXfjpp5802rnkRaS9Fr0OUIcOHUSXuRwcHHiHeCIiLYjN+vTu3RsDBgyQoBoi86L1EtjcuXMRFxeH4uJiVVtxcTHefvttvPPOOzotjojIVDW05MXwQ6QfWi+BBQcH4/z586ipqUHHjh0BABcuXICNjQ06d+6sduwvv/yiu0r1iEtgRNRStmzZgnPnzmm0c8mLqPladAls2LBhkMlkTS6OiMhcic36REZGQqFQSFANkXnT2c1QTQlngIhI13hhQ6KW16IzQL6+vvjpp5/QoUMHtfaysjI8/PDD+P3337XtkojIZKWnp+PatWsa7Qw/RNLSOgAVFRWhvr5eo72mpgaXLl3SSVFERKZAbNbn+eefR9euXSWohoj+qdEB6KuvvlL9OScnR+1U+Pr6euTm5sLHx0e31RERGSkueREZtkYHoOHDhwMAZDIZYmJi1J6zsrKCt7c3li9frtPiiIiMzYIFCyC2tZLhh8iwNDoA3b3vl4+PD3766Sc4OTm1WFFERMZIbNYnJiYG3t7e+i+GiO5J6z1AhYWFLVEHEZHREgQBCxYs0GjnrA+R4dI6AIn9I/+nxMTEJhdDRGRsxGZ9AIYfIkOndQD64osv1B7X1dWhsLAQlpaW8PPzYwAiIrMhFn7eeOMNuLq6SlANEWlD6wB05MgRjbaKigrExsbi2Wef1UlRRESGTKlUYuHChRrtnPUhMh46uxL0r7/+iiFDhqCoqEgX3UmKV4ImooZwyYvIcLXolaAbUl5ejvLycl11R0RkcMTCz7Rp09Sui0ZExkHrALR69Wq1x4Ig4MqVK9i8eTMGDhyos8KIiAxFXV0dFi9erNHOWR8i46V1AFq5cqXaYwsLCzg7OyMmJgYJCQlNKiI9PR3Lli1DcXExevbsiTVr1iAsLEz02PXr1+PDDz/EiRMnAAAhISFYvHix2vGCICApKQnr169HWVkZHn30Uaxbtw6dO3duUn1EZL645EVkmiS/DtC2bdsQFxeHjIwMKBQKpKWlISIiAmfPnoWLi4vG8Xl5eRg9ejT69OkDW1tbLFmyBAMGDMDJkyfh6ekJAFi6dClWr16NTZs2wcfHB++88w4iIiJw6tQp2Nra6rR+IjJdYuFn5syZaNWqlQTVEJEuNWkTdFlZGc6fPw8A8Pf3h6OjY5MLUCgU6NWrF9auXQvgztkVXl5emDJlCuLj4+/7+vr6erRr1w5r165FdHQ0BEGAh4cHpk+fjhkzZgC4sz/J1dUVmZmZGDVq1H375CZoIvNWXV2NZcuWabRz1ofIsGnz+9tCm46LioowaNAgODk5QaFQQKFQwMnJCYMHD27S2V+1tbU4fPgwwsPD/1eQhQXCw8ORn5/fqD6qq6tRV1eH9u3bA7gzQ1VcXKzWp4ODAxQKRaP7JCLzNX/+fIYfIjPQ6CWwixcv4pFHHoGVlRUWLlyILl26AABOnTqFdevWoXfv3vjpp5/wwAMPNPrNr127hvr6eo2Lhrm6uuLMmTON6mPWrFnw8PBQBZ7i4mJVH//u8+5z/1ZTU4OamhrV44qKikaPgYhMh9iS1+zZs2FlZSVBNUTUkhodgObNm4eAgADk5OSo7aMZPnw43nrrLURGRmLevHnYsGFDixQqJiUlBVlZWcjLy2vW3p7k5OQGNzoSkekrKyvDqlWrNNo560NkuhodgLKzs7Ft2zbRoNGqVSssXLiwUftr/snJyQlyuRwlJSVq7SUlJXBzc7vna1NTU5GSkoI9e/agR48eqva7ryspKYG7u7tan0FBQaJ9JSQkIC4uTvW4oqICXl5eWo2FiIwTz/IiMk+N3gN07do1eHt7N/i8r68v/vrrL63e3NraGiEhIcjNzVW1KZVK5Obmonfv3g2+bunSpVi4cCGys7MRGhqq9pyPjw/c3NzU+qyoqMDBgwcb7NPGxgZt27ZV+yIi0ycWft555x2GHyIz0OgZIHd3d5w6darBPT4nTpy476yNmLi4OMTExCA0NBRhYWFIS0tDVVUVxowZAwCIjo6Gp6cnkpOTAQBLlixBYmIitmzZAm9vb9W+ntatW6N169aQyWSYNm0aFi1ahM6dO6tOg/fw8MDw4cO1ro+ITE9xcTH+85//aLQz+BCZj0YHoOHDh2PGjBnIzc2Fs7Oz2nNXr17FrFmzmhQwoqKiUFpaisTERBQXFyMoKAjZ2dmqTcwXLlyAhcX/JqrWrVuH2tpajBw5Uq2fpKQkzJs3D8Cd63RUVVXhtddeQ1lZGR577DFkZ2fzGkBExCUvIgKgxXWAbty4AYVCgeLiYrz88ssIDAyEIAg4ffo0tmzZAjc3N/z3v/9VnY5uzHgdICLTJBZ+EhMTIZPJJKiGiHStRW6G2q5dOxw8eBCzZ89GVlYWysrKAACOjo548cUXsXjxYpMIP0RkegoLC/Hhhx9qtHPWh8h8NelK0IIgoLS0FADg7Oxscv/3xBkgItMhNutjaWmJOXPmSFANEbWkFpkB+ieZTCZ6ny4iIkMiFn4460NEQBMDEBGRITtx4gQ+//xzjXaGHyK6iwGIiEyK2KyPq6sr3njjDQmqISJDxQBERCZDLPzk5CThxx8lKIaIDBoDEBEZvfz8fOzevVujPScnCfHxEhRERAavUQFo9erVje5w6tSpTS6GiEhbYrM+gYGBiIqKArf8EFFDGnUavI+PT+M6k8nw+++/N7soqfE0eCLjwLO8iOifdH4afGFhoU4KIyLShezsbBw8eFCjneGHiBqryXuAamtrUVhYCD8/P1hacisREemH2KyPQqFAZGSkBNUQkbGyuP8h6qqrqzFu3DjY2dmhW7duuHDhAgBgypQpSElJ0XmBRER3NbTkxfBDRNrSOgAlJCTg2LFjyMvLU7u7enh4OLZt26bT4oiIAGDDhg2i4Sc4mEteRNQ0Wq9d7dixA9u2bcMjjzyidg+wbt26oaCgQKfFERGJBZ/ffnsSH3/8uATVEJGp0HoGqLS0VPQ+YFVVVSZ3U1QiklZDFzaMimL4IaLm0XoGKDQ0FF9//TWmTJkCAKrQs2HDBvTu3Vu31RGRWUpJSUFNTY1Ge1JSEq/tQ0Q6oXUAWrx4MQYOHIhTp07h9u3bWLVqFU6dOoUff/wR33//fUvUSERmRGzWZ/DgwQgJCZGgGiIyVVovgT322GM4evQobt++je7du2P37t1wcXFBfn4+f0ARUbM0dJYXf7YQka416krQ5oZXgibSL7HgA/DChkSkHZ1fCbqioqLRb87AQETaEAs/R48+jzFjukpQDRGZi0YFIEdHx0af4VVfX9+sgojIPAiCgAULFmi0c9aHiPShUQFo7969qj8XFRUhPj4esbGxqrO+8vPzsWnTJiQnJ7dMlURkUrjkRURS03oP0FNPPYVXX30Vo0ePVmvfsmUL/u///g95eXm6rE8S3ANE1HLEws/YsWPh5eUlQTVEZEq0+f2t9Vlg+fn5CA0N1WgPDQ3FoUOHtO2OiMyEUqls8Cwvhh8i0jetrwPk5eWF9evXY+nSpWrtGzZs4A8xIhLFJS8iMjRaB6CVK1fiueeewzfffAOFQgEAOHToEM6dO4fPP/9c5wUSkXETCz/790/C1KlOElRDRHRHk64DdOnSJbz33ns4c+YMAKBLly544403TGYGiHuAiJqvrq4Oixcv1mjnrA8RtRRtfn/zQogiGICImodLXkQkBZ1fCPHfysrK8P777+P06dMAgG7dumHs2LFwcHBoSndEZELEws/06dPRunVrCaohIhKn9VlgP//8M/z8/LBy5Ur89ddf+Ouvv7BixQr4+fnhl19+aYkaicgIVFZWioafnJwkhh8iMjhaL4E9/vjj8Pf3x/r162FpeWcC6fbt23j11Vfx+++/Y9++fS1SqD5xCYxIOw0teWVlJWHJEmDoUD0XRERmqUX3ALVq1QpHjhxBYGCgWvupU6cQGhqK6upq7Ss2MAxARI0nFn727InH22/bMPgQkV616IUQ27ZtiwsXLmi0X7x4EW3atNG2OyIyUqWlpQ1e2HD/foYfIjJsWm+CjoqKwrhx45Camoo+ffoAAA4cOIC3335b4/YYRGSaeJYXERk7rQNQamoqZDIZoqOjcfv2bQCAlZUVJkyYgJSUFJ0XSESGRSz8BAXNxbBhcgmqISJqmiZfB6i6uhoFBQUAAD8/P9jZ2em0MClxDxCRpsLCQnz44Yca7Zz1ISJD0eLXAQIAOzs7dO/evakvJyIjwiUvIjI1jQ5AY8eObdRxGzdu1KqA9PR0LFu2DMXFxejZsyfWrFmDsLAw0WNPnjyJxMREHD58GH/88QdWrlyJadOmqR0zb948jR/WAQEBqtt2EJF2xMJPYmIiZDKZBNUQEelGowNQZmYmOnXqhODgYOjq7hnbtm1DXFwcMjIyoFAokJaWhoiICJw9exYuLi4ax1dXV8PX1xfPP/883nrrrQb77datG/bs2aN6fPd6RUTUeMePH8cXX3yh0R4cnARmHyIydo1OBhMmTMDWrVtRWFiIMWPG4OWXX0b79u2b9eYrVqzA+PHjMWbMGABARkYGvv76a2zcuBHx8fEax/fq1Qu9evUCANHn77K0tISbm1uzaiMyZw0teQUHJ/H0diIyCY2+DlB6ejquXLmCmTNn4v/9v/8HLy8vvPDCC8jJyWnSjFBtbS0OHz6M8PDw/xVjYYHw8HDk5+dr3d8/nTt3Dh4eHvD19cVLL70ket2if6qpqUFFRYXaF5G5aujaPklJDD9EZDq0uhCijY0NRo8ejW+//RanTp1Ct27dMHHiRHh7e6OyslKrN7527Rrq6+vh6uqq1u7q6ori4mKt+vonhUKBzMxMZGdnY926dSgsLMTjjz+OmzdvNvia5ORkODg4qL68vLya/P5Exmrfvn0Nhh8iIlPT5M0xFhYWkMlkEAQB9fX1uqypWQYOHKj6c48ePaBQKNCpUyd88sknGDdunOhrEhISEBcXp3pcUVHBEERmhWd5EZG50SoA1dTUYPv27di4cSN++OEHDB48GGvXrkVkZCQsLLS7q4aTkxPkcjlKSkrU2ktKSnS6f8fR0REPPvggzp8/3+AxNjY2sLGx0dl7EhkTzvoQkTlqdGqZOHEi3N3dkZKSgsGDB+PixYv49NNP8cwzz2gdfgDA2toaISEhyM3NVbUplUrk5uaid+/eWvfXkMrKShQUFMDd3V1nfRKZgh07djD8EJHZavQMUEZGBjp27AhfX198//33+P7770WP2759e6PfPC4uDjExMQgNDUVYWBjS0tJQVVWlOissOjoanp6eSE5OBnBn4/SpU6dUf/7zzz9x9OhRtG7dGv7+/gCAGTNmYMiQIejUqRMuX76MpKQkyOVy3qeM6B+45EVE5q7RASg6OlrnFz6LiopCaWkpEhMTUVxcjKCgIGRnZ6s2Rl+4cEFtduny5csIDg5WPU5NTUVqair69u2LvLw8AMClS5cwevRoXL9+Hc7Oznjsscfw3//+F87OzjqtnchYcdaHiKgZ9wIzZbwXGJmi9evX4/LlyxrtDD9EZCr0ci8wIjIeYrM+7du3x5QpUySohohIegxARCZOLPzwis5EZO4YgIhMFG9nQUTUMAYgIhMkFn78/Pzw8ssvS1ANEZHhYQAiMjE8y4uI6P4YgIhMBK/tQ0TUeAxARCZALPwoFApERkZKUA0RkeFjACIyclzyIiLSHgMQkZHikhcRUdMxABEZIbHwc/r0QGRlhUlQDRGR8dH+Nu5EJCmx8JOTk4QXX2T4ISJqLM4AERmJey15cdWLiEg7DEBERkAs/Lzwwgvo0qWLBNUQERk/BiAiA6ZUKrFw4UKN9uDgJDD7EBE1HQMQkYHiWV5ERC2HAYjIAImFn3HjxuGBBx6QoBoiItPDAERkQGpra5GcnKzRzlkfIiLdYgAiMhBc8iIi0h8GICIDIBZ+unadiuefbydBNUREpo8BiEhCN2/exIoVKzTaOetDRNSyGICIJNLQkldwMMMPEVFL460wiCQgFn6++24mgoOTMHSoBAUREZkZzgAR6VFJSQkyMjI02nk7CyIi/WIAItITnuVFRGQ4GICI9EAs/MyZMweWlvwnSEQkBf70JWpBZ8+eRVZWlkZ7cHASmH2IiKTDH8FELeReZ3lxozMRkbQYgIhagFj4SUxMhEwmk6AaIiL6NwYgIh06dOgQvvnmG412bnQmIjIsDEBEOsILGxIRGQ8GICIdEAs/nPUhIjJcDEBEzfDNN9/g0KFDGu0MP0REho0BiKiJeGFDIiLjxQBE1ARi4ScnJwk//ihBMUREpDUGICItbNq0CUVFRRrtOTlJiI/Xfz1ERNQ0DEBEjXSvJS+uehERGRcGIKJG4JIXEZFpsZC6gPT0dHh7e8PW1hYKhUL0jJq7Tp48ieeeew7e3t6QyWRIS0trdp9E95KSktJg+OGSFxGR8ZI0AG3btg1xcXFISkrCL7/8gp49eyIiIgJXr14VPb66uhq+vr5ISUmBm5ubTvokasj8+fNRU1Oj0Z6UdGfmh/fzIiIyXjJBEASp3lyhUKBXr15Yu3YtAECpVMLLywtTpkxB/H3+99rb2xvTpk3DtGnTdNbnXRUVFXBwcEB5eTnatm2r/cDI6PHChkRExkeb39+S7QGqra3F4cOHkZCQoGqzsLBAeHg48vPz9dpnTU2N2v/pV1RUNOn9yfjx2j5EROZBsiWwa9euob6+Hq6urmrtrq6uKC4u1mufycnJcHBwUH15eXk16f3JuImFn7Zt2zL8EBGZIJ4FBiAhIQFxcXGqxxUVFQxBZoZLXkRE5kWyAOTk5AS5XI6SkhK19pKSkgY3OLdUnzY2NrCxsWnSe5Jxa2jJKyeH1/YhIjJlki2BWVtbIyQkBLm5uao2pVKJ3Nxc9O7d22D6JNMlFn4cHbvxFHciIjMg6RJYXFwcYmJiEBoairCwMKSlpaGqqgpjxowBAERHR8PT0xPJyckA7mxyPnXqlOrPf/75J44ePYrWrVvD39+/UX0SAfde8nrzTX1XQ0RE+iZpAIqKikJpaSkSExNRXFyMoKAgZGdnqzYxX7hwARYW/5ukunz5MoKDg1WPU1NTkZqair59+yIvL69RfZJ541leREQESHwdIEPF6wCZJrHw07dvX/Tr10//xRARkc4ZxXWAiPSJZ3kREdE/MQCRSeOSFxERiWEAIpMlFn6GDx+Onj17SlANEREZEgYgMjmCIGDBggUa7cHBSWD2ISIigAGITAyXvIiIqDEYgMhkiIWfMWPGoGPHjhJUQ0REhowBiIxeXV0dFi9erNHOWR8iImoIAxAZtYaWvIKDGX6IiKhhDEBktMTCz+TJk9GhQwcJqiEiImPCAERGp7KyEsuXL9do55IXERE1FgMQGRWe5UVERLrAAERGQyz8zJgxA/b29hJUQ0RExowBiAzetWvXkJ6ertEeHJwEZh8iImoKBiAyaPc6y2voUD0XQ0REJoMBiAyWWPiZM2cOLC35sSUioubhbxIyOH/88QcyMzM12rnRmYiIdIUBiAwKz/IiIiJ9YAAigyEWfhITEyGTySSohoiITBkDEEnu119/xfbt2zXaOetDREQthQGIJMV7eRERkRQYgEgyYuGHsz5ERKQPDECkd/v378d3332n0c7wQ0RE+sIARHrFs7yIiMgQWEhdAJkPsfCTk5PE/T5ERKR3nAGiFvf555/jxIkTGu1JSUngxA8REUmBAYhaFJe8iIjIEHEJjFoMl7yIiMhQcQaIdC4zMxN//PGHRntwMJe8iIjIMDAAkU6JzfrIZDIkJiZKUA0REZE4BiDSGbHwExychKFDJSiGiIjoHhiAqNk2bNiAP//8U6O9tpbhh4iIDBMDEDWL2KxPly5d8MILL0hQDRERUeMwAFGTccmLiIiMFQMQaS0lJQU1NTUa7by2DxERGQsGINKK2KxP37590a9fP/0XQ0RE1ES8ECI1WkMXNmT4ISIiY8MZILqvhm5nkZWVhCVL9FwMERGRDhjEDFB6ejq8vb1ha2sLhUKBQ4cO3fP4Tz/9FIGBgbC1tUX37t2xa9cutedjY2Mhk8nUviIjI1tyCCZLLPz897/DERychNOnwQ3PRERklCQPQNu2bUNcXBySkpLwyy+/oGfPnoiIiMDVq1dFj//xxx8xevRojBs3DkeOHMHw4cMxfPhwjbuNR0ZG4sqVK6qvrVu36mM4JkUs/CQlJeGbb3oy+BARkVGTCYIgSFmAQqFAr169sHbtWgCAUqmEl5cXpkyZgvj4eI3jo6KiUFVVhZ07d6raHnnkEQQFBSEjIwPAnRmgsrIy7Nixo0k1VVRUwMHBAeXl5Wjbtm2T+jBmvIM7EREZI21+f0s6A1RbW4vDhw8jPDxc1WZhYYHw8HDk5+eLviY/P1/teACIiIjQOD4vLw8uLi4ICAjAhAkTcP369QbrqKmpQUVFhdqXuRILPwcPjuEd3ImIyKRIugn62rVrqK+vh6urq1q7q6srzpw5I/qa4uJi0eOLi4tVjyMjIzFixAj4+PigoKAAs2fPxsCBA5Gfnw+5XK7RZ3JycoOzHuZCEAQsWLBAo52zPkREZIpM8iywUaNGqf7cvXt39OjRA35+fsjLy8NTTz2lcXxCQgLi4uJUjysqKuDl5aWXWg1BQ+GPsz5ERGSqJA1ATk5OkMvlKCkpUWsvKSmBm5ub6Gvc3Ny0Oh4AfH194eTkhPPnz4sGIBsbG9jY2DRhBMZP/F5ek/HCCx0kqIaIiEg/JN0DZG1tjZCQEOTm5qralEolcnNz0bt3b9HX9O7dW+14APj2228bPB4ALl26hOvXr8Pd3V03hZuA+vr6Bs/yYvghIiJTJ/kSWFxcHGJiYhAaGoqwsDCkpaWhqqoKY8aMAQBER0fD09MTycnJAIA333wTffv2xfLlyzFo0CBkZWXh559/xv/93/8BACorKzF//nw899xzcHNzQ0FBAWbOnAl/f39ERERINk5DwrO8iIjI3EkegKKiolBaWorExEQUFxcjKCgI2dnZqo3OFy5cgIXF/yaq+vTpgy1btmDu3LmYPXs2OnfujB07duChhx4CAMjlchw/fhybNm1CWVkZPDw8MGDAACxcuNBsl7n+SSz8PPTQdDz3XGsJqiEiIpKG5NcBMkSmeB2guro6LF68WKOdsz5ERGQqtPn9LfkMELU8LnkRERGpYwAycWLhZ/nyBHz0kbUE1RARERkGBiAT9ffff2Pp0qUa7VlZSfjoI97ElIiIzBsDkAm615IXV72IiIgYgEyOWPiZO3eu6C1AiIiIzBUDkIm4efMmVqxYodEeHJwEZh8iIiJ1DEAmQGzWp02bNmr3NyMiIqL/YQAycmLhJycnET/+KJOgGiIiIuPAAGSkrl+/jrVr12q05+QkIT5egoKIiIiMCAOQERKb9blx4wGkpY3jWV5ERESNIOnd4El74kteSXjyyXESVENERGScOANkJK5du4b09HSN9uBgXtuHiIhIWwxARkBs1ufo0VCMGTOIV3QmIiJqAgYgA9fQkld8PG9nQURE1FQMQAaKS15EREQthwHIAInN+vz66zB89lmQ/oshIiIyQTwLzMA0tOQVHR2k/2KIiIhMFGeADERDS168gzsREZHuMQAZgEWLFqG+vl6tbdu2aJw65SNRRURERKaNS2ASmz9/vkb4SU1NwrPPMvwQERG1FM4ASeTWrVtYsmSJRjuXvIiIiFoeA5AEfv/9d2zevFmtLTBwAqKiXCSqiIiIyLwwAOnZZ599hpMnT6q1paYm4eZNiQoiIiIyQwxAenT+/Hm18FNXNxarVnlh6lQJiyIiIjJDDEB6ZGdnh9u3rWFpWYtvv52NH36wwqJFUldFRERkfhiA9MjDwwO9eiUgJQWIj5e6GiIiIvPFAKRnQ4fyJqZERERS43WAiIiIyOwwABEREZHZYQAiIiIis8MARERERGaHAYiIiIjMDgMQERERmR0GICIiIjI7DEBERERkdhiAiIiIyOwwABEREZHZYQAiIiIis8MARERERGaHAYiIiIjMDu8GL0IQBABARUWFxJUQERFRY939vX339/i9MACJuHnzJgDAy8tL4kqIiIhIWzdv3oSDg8M9j5EJjYlJZkapVOLy5cto06YNZDKZ1OXoXUVFBby8vHDx4kW0bdtW6nL0juM37/ED/B5w/By/sY5fEATcvHkTHh4esLC49y4fzgCJsLCwwAMPPCB1GZJr27at0X34dYnjN+/xA/wecPwcvzGO/34zP3dxEzQRERGZHQYgIiIiMjsMQKTBxsYGSUlJsLGxkboUSXD85j1+gN8Djp/jN4fxcxM0ERERmR3OABEREZHZYQAiIiIis8MARERERGaHAYiIiIjMDgOQGUhPT4e3tzdsbW2hUChw6NChex7/6aefIjAwELa2tujevTt27dql9nxsbCxkMpnaV2RkZEsOodm0+R6cPHkSzz33HLy9vSGTyZCWltbsPqWm6/HPmzdP4zMQGBjYgiNoHm3Gv379ejz++ONo164d2rVrh/DwcI3jBUFAYmIi3N3d0apVK4SHh+PcuXMtPYwm0/X4Tf1nwPbt2xEaGgpHR0fY29sjKCgImzdvVjvGlD8DjRm/MX4GNAhk0rKysgRra2th48aNwsmTJ4Xx48cLjo6OQklJiejxBw4cEORyubB06VLh1KlTwty5cwUrKyvh119/VR0TExMjREZGCleuXFF9/fXXX/oakta0/R4cOnRImDFjhrB161bBzc1NWLlyZbP7lFJLjD8pKUno1q2b2megtLS0hUfSNNqO/8UXXxTS09OFI0eOCKdPnxZiY2MFBwcH4dKlS6pjUlJSBAcHB2HHjh3CsWPHhKFDhwo+Pj7C33//ra9hNVpLjN/Ufwbs3btX2L59u3Dq1Cnh/PnzQlpamiCXy4Xs7GzVMab8GWjM+I3tMyCGAcjEhYWFCZMmTVI9rq+vFzw8PITk5GTR41944QVh0KBBam0KhUJ4/fXXVY9jYmKEYcOGtUi9LUHb78E/derUSTQANKdPfWuJ8SclJQk9e/bUYZUtp7l/V7dv3xbatGkjbNq0SRAEQVAqlYKbm5uwbNky1TFlZWWCjY2NsHXrVt0WrwO6Hr8gmNfPgLuCg4OFuXPnCoJgfp8BQVAfvyAY32dADJfATFhtbS0OHz6M8PBwVZuFhQXCw8ORn58v+pr8/Hy14wEgIiJC4/i8vDy4uLggICAAEyZMwPXr13U/AB1oyvdAij5bSkvWeu7cOXh4eMDX1xcvvfQSLly40NxydU4X46+urkZdXR3at28PACgsLERxcbFanw4ODlAoFCb59//v8d9lLj8DBEFAbm4uzp49iyeeeAKAeX0GxMZ/l7F8BhrCm6GasGvXrqG+vh6urq5q7a6urjhz5ozoa4qLi0WPLy4uVj2OjIzEiBEj4OPjg4KCAsyePRsDBw5Efn4+5HK57gfSDE35HkjRZ0tpqVoVCgUyMzMREBCAK1euYP78+Xj88cdx4sQJtGnTprll64wuxj9r1ix4eHiofoHc/bdwv38nhqAlxg+Yx8+A8vJyeHp6oqamBnK5HO+99x6efvppAObxGbjX+AHj+gw0hAGItDZq1CjVn7t3744ePXrAz88PeXl5eOqppySsjPRl4MCBqj/36NEDCoUCnTp1wieffIJx48ZJWJlupaSkICsrC3l5ebC1tZW6HL1raPzm8DOgTZs2OHr0KCorK5Gbm4u4uDj4+vqiX79+UpemF/c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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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VblJTU5WVlVVl2tq1a5WammpRRb7l+PHjyszM1J133qnhw4dr6dKl1eZp3bq1YmNjlZSUpBkzZqi4uFj/+Mc/vF8sAMBn5RaVaPrKL1VhzvxcYaQZK3f6TQuOpeHm+PHjys7OVnZ2tqQzt3pnZ2crJ+dMOpw+fbrGjRvnnP+OO+7QgQMH9NBDD2nPnj165ZVX9Je//EX333+/FeXXydvNeX/5y190wQUXqHv37rrlllv0+uuvyxhT47wlJSX685//LEmMPgwAqOJg4QlnsKlUbowOFZ60piAXWXor+LZt2zR06FDnz1OnTpUkjR8/XkuXLlVubq4z6EhSx44d9eGHH+r+++/X73//e51//vl67bXX3HobuLtkbs1xpt4gh5RxXbJG9Wvv0c9cvHixbrnlFknSVVddpaKiIv3v//6vhgwZ4pxnwIABCgoK0smTJ2WMUZ8+fXTFFVd4tC4AgH/pGB2pIIeqBJxgh0OJ0f4xDpzD1HZqb1PFxcWKiopSUVFRtf43P//8sw4ePKiOHTsqPDy8wZ+RW1SigfPWV/tSbJw2VHFRNfcNaqy9e/cqKSlJ3333ndq2bStJmjJlioqKivTmm2/q0KFD6tixo1atWqULLrhAO3fu1EMPPaT3339fSUlJtb6vu7YJAMC/ZG7N0YyVO1VujIIdDs29LsnjJ+nncq7j9y/xbCkPOFdznqfCzeLFi3X69Gm1a9fOOc0Yo7CwML388svOaQkJCeratau6du2q06dP69prr9XOnTsVFhbmkboAAP5pVL/2uqxbGx0qPKnE6KYeO355gl91KPYXlc15Z/Nkc97p06f15z//Wc8//7yzD1N2drb++c9/ql27dlq2bFmNy91www0KCQnRK6+84pG6AAD+LS4qQqmdW/tVsJEINx4RFxWhjOuSFfz/H11Q2ZznqS/HBx98oKNHj+rWW29VUlJSldf111+vxYsX17icw+HQPffco3nz5unkSf/oJAYAQF0INx4yql97bZw2VMtuu0Qbpw316HXKxYsXKy0tTVFRUdV+d/3112vbtm21PnZi/PjxOnXqVJVLVwAA+DP63HhQXFSEV5ry/va3v9X6u/79+ztvB6+p73jTpk31448/eqw2AAC8jZYbAABgK4QbAABgK4QbAABgK4QbAABgK4SbGgTYoM3nxLYAAPgbws1ZmjRpIkmM+XKWsrIySVJwcLDFlQAAUD/cCn6W4OBgtWjRQt9//72kM7dJOxyOOpayr4qKChUUFKhp06YKCeGrAgDwDxyxfiE2NlaSnAEn0AUFBal9+/YBHfIAAP6FcPMLDodDcXFxatu2rU6dOmV1OZYLDQ1VUBBXLwEA/oNwU4vg4GD6mQAA4Ic4JQcAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZiebhZsGCBEhMTFR4erpSUFG3ZsuWc88+fP1/du3dXRESEEhISdP/99+vnn3/2UrUAAMDXWRpuMjMzNXXqVM2ePVs7duxQr169lJ6eru+//77G+d955x1NmzZNs2fP1u7du7V48WJlZmZqxowZXq4cAAD4KkvDzQsvvKDbbrtNEydO1IUXXqiFCxeqadOmev3112ucf9OmTRo4cKDGjBmjxMRE/frXv9bo0aPrbO0BAACBw7JwU1ZWpu3btystLe3fxQQFKS0tTZs3b65xmQEDBmj79u3OMHPgwAGtXr1aV199da2fU1paquLi4iovAABgXyFWfXBhYaHKy8sVExNTZXpMTIz27NlT4zJjxoxRYWGhBg0aJGOMTp8+rTvuuOOcl6UyMjI0Z84ct9YOAAB8l+Udil2xYcMGzZ07V6+88op27NihlStX6sMPP9QTTzxR6zLTp09XUVGR83X48GEvVgwAALzNspab6OhoBQcHKz8/v8r0/Px8xcbG1rjMrFmzNHbsWE2aNEmSlJycrBMnTuj222/XI488oqCg6lktLCxMYWFh7l8BAADgkyxruQkNDVWfPn2UlZXlnFZRUaGsrCylpqbWuMzJkyerBZjg4GBJkjHGc8UCAAC/YVnLjSRNnTpV48ePV9++fdW/f3/Nnz9fJ06c0MSJEyVJ48aNU3x8vDIyMiRJI0aM0AsvvKCLLrpIKSkp2rdvn2bNmqURI0Y4Qw4AAAhsloabUaNGqaCgQI8++qjy8vLUu3dvrVmzxtnJOCcnp0pLzcyZM+VwODRz5kx99913atOmjUaMGKGnnnrKqlUAAAA+xmEC7HpOcXGxoqKiVFRUpObNm1tdDgAAqAdXjt9+dbcUAABAXQg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AAAEsNyiEm3aX6jcohKrS3GbEKsLAAAA1sjcmqPpK79UhZGCHFLGdcka1a+91WU1Gi03AAAEoNyiEmewkaQKI81YudMWLTiEGwAAAtDBwhPOYFOp3BgdKjxpTUFuRLgBACAAdYyOVJCj6rRgh0OJ0U2tKciNCDcAAASguKgIZVyXrGDHmYQT7HBo7nVJiouKsLiyxqNDMQAAAWpUv/a6rFsbHSo8qcToprYINhLhBgCAgBYXFWGbUFOJy1IAAMBWLA83CxYsUGJiosLDw5WSkqItW7acc/6ffvpJkydPVlxcnMLCwtStWzetXr3aS9UCAABfZ+llqczMTE2dOlULFy5USkqK5s+fr/T0dO3du1dt27atNn9ZWZmuvPJKtW3bVitWrFB8fLy++eYbtWjRwvvFAwAAn+Qwxpi6Z/OMlJQU9evXTy+//LIkqaKiQgkJCbr77rs1bdq0avMvXLhQzz77rPbs2aMmTZo06DOLi4sVFRWloqIiNW/evFH1AwAA73Dl+G3ZZamysjJt375daWlp/y4mKEhpaWnavHlzjcusWrVKqampmjx5smJiYpSUlKS5c+eqvLy81s8pLS1VcXFxlRcAALAvy8JNYWGhysvLFRMTU2V6TEyM8vLyalzmwIEDWrFihcrLy7V69WrNmjVLzz//vJ588slaPycjI0NRUVHOV0JCglvXAwAA+BbLOxS7oqKiQm3bttWf/vQn9enTR6NGjdIjjzyihQsX1rrM9OnTVVRU5HwdPnzYixUDAABvs6xDcXR0tIKDg5Wfn19len5+vmJjY2tcJi4uTk2aNFFwcLBzWo8ePZSXl6eysjKFhoZWWyYsLExhYWHuLR4AAPgsy1puQkND1adPH2VlZTmnVVRUKCsrS6mpqTUuM3DgQO3bt08VFRXOaV9//bXi4uJqDDYAACDwWHpZaurUqVq0aJHeeOMN7d69W3feeadOnDihiRMnSpLGjRun6dOnO+e/88479eOPP+ree+/V119/rQ8//FBz587V5MmTrVoFAADgYywd52bUqFEqKCjQo48+qry8PPXu3Vtr1qxxdjLOyclRUNC/81dCQoI++ugj3X///erZs6fi4+N177336uGHH7ZqFQAAgI+xdJwbKzDODQAA/scvxrkBAADwBMINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAAABILeoRJv2Fyq3qMTqUjzObeHm559/1nPPPeeutwMAAG6SuTVHA+et15hF/9DAeeuVuTXH6pI8yqVwU1BQoA8++EAff/yxysvLJUmnTp3S73//eyUmJmrevHkeKdLOAilJAwC8L7eoRNNXfqkKc+bnCiPNWLnT1sedkPrOuHHjRl1zzTUqLi6Ww+FQ3759tWTJEo0cOVIhISF67LHHNH78eE/WajuZW3OcX7ggh5RxXbJG9WtvdVkAABs5WHjCGWwqlRujQ4UnFRcVYU1RHlbvlpuZM2fq6quv1hdffKGpU6dq69atuvbaazV37lx99dVXuuOOOxQRYc+N5AmBmKQBAN7XMTpSQY6q04IdDiVGN7WmIC+od7j58ssvNXPmTCUlJenxxx+Xw+HQM888oxtuuMGT9dnWuZI0AADuEhcVoYzrkhXsOJNwgh0Ozb0uybatNpILl6WOHj2q6OhoSVJERISaNm2qpKQkjxVmd5VJ+uyAY/ckDQCwxqh+7XVZtzY6VHhSidFNbR1sJBfCjSR99dVXysvLkyQZY7R3716dOHGiyjw9e/Z0X3U2VpmkZ6zcqXJjAiJJAwCsExcVETDHGIcxxtQ9mxQUFCSHw6Fzze5wOJx3Ufmq4uJiRUVFqaioSM2bN7e6HOUWlQRMkgYAoKFcOX7Xu+Xm4MGDdc5z7Nix+r4d/r9AStIAAHhDvcNNhw4dapx+7NgxLVu2TIsXL9a2bdt8vuUGAADYW4NHKP700081fvx4xcXF6bnnntPQoUP197//3Z21AQAAuMylDsV5eXlaunSpFi9erOLiYt14440qLS3VX//6V1144YWeqhEAAKDe6t1yM2LECHXv3l1ffPGF5s+fryNHjuill17yZG0AAAAuq3fLzf/8z//onnvu0Z133qmuXbt6siYAAIAGq3fLzcaNG3Xs2DH16dNHKSkpevnll1VYWOjJ2gAAAFxW73BzySWXaNGiRcrNzdV//dd/afny5WrXrp0qKiq0du1abgMHAAA+od6D+NVk7969Wrx4sd5880399NNPuvLKK7Vq1Sp31ud2vjaIHwAAqJsrx+8G3wouSd27d9czzzyjb7/9VsuWLWvMWwEAALhFo1pu/BEtNwAA+B+vtdwAAAD4GsINAACwFcKNF+QWlWjT/kLlFpVYXQoAALbn0uMX4LrMrTmavvJLVRgpyCFlXJesUf3aW10WAAC2RcuNB+UWlTiDjSRVGGnGyp204AAA4EGEGw86WHjCGWwqlRujQ4UnrSkIAIAAQLjxoI7RkQpyVJ0W7HAoMbqpNQUBABAACDceFBcVoYzrkhXsOJNwgh0Ozb0uSXFRERZXBgCAfdGh2MNG9Wuvy7q10aHCk0qMbkqwAQDAwwg3XhAXFUGoAQDAS7gsBQAAbIVwAwAAbIVwAwAAbIVwA8Bn8KgSAO5Ah2IAPoFHlQBwF1puAFiOR5UAcCefCDcLFixQYmKiwsPDlZKSoi1bttRrueXLl8vhcGjkyJGeLRCAR/GoEgDuZHm4yczM1NSpUzV79mzt2LFDvXr1Unp6ur7//vtzLnfo0CE9+OCDuvTSS71UKQBP4VElANzJ8nDzwgsv6LbbbtPEiRN14YUXauHChWratKlef/31WpcpLy/XzTffrDlz5qhTp05erBaAJ/CoEgDuZGmH4rKyMm3fvl3Tp093TgsKClJaWpo2b95c63KPP/642rZtq1tvvVX/93//d87PKC0tVWlpqfPn4uLixhcOwO14VAkAd7E03BQWFqq8vFwxMTFVpsfExGjPnj01LrNx40YtXrxY2dnZ9fqMjIwMzZkzp7GlAvACHlUCwB0svyzlimPHjmns2LFatGiRoqOj67XM9OnTVVRU5HwdPnzYw1UCAAArWdpyEx0dreDgYOXn51eZnp+fr9jY2Grz79+/X4cOHdKIESOc0yoqKiRJISEh2rt3rzp37lxlmbCwMIWFhXmgegAA4IssbbkJDQ1Vnz59lJWV5ZxWUVGhrKwspaamVpv/ggsu0Jdffqns7Gzn6ze/+Y2GDh2q7OxsJSQkeLN8AADggywfoXjq1KkaP368+vbtq/79+2v+/Pk6ceKEJk6cKEkaN26c4uPjlZGRofDwcCUlJVVZvkWLFpJUbToAAAhMloebUaNGqaCgQI8++qjy8vLUu3dvrVmzxtnJOCcnR0FBftU1CAAAWMhhjDF1z2YfxcXFioqKUlFRkZo3b251OQAAoB5cOX7TJAIAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMEmNyiEm3aX6jcohKrSwEAj7D8wZkAvCdza46mr/xSFUYKckgZ1yVrVL/2VpcFAG5Fyw0QIHKLSpzBRpIqjDRj5U5acADYDuEGCBAHC084g02lcmN0qPCkNQUBgIcQboAA0TE6UkGOqtOCHQ4lRje1piAA8BDCDRAg4qIilHFdsoIdZxJOsMOhudclKS4qwuLKAMC96FAMBJBR/drrsm5tdKjwpBKjm9oi2Pzz8FFtOfSj+ie2Uq+EllaXA8AHEG6AABMXFWGLUCNJD/wlW+/t+M758/UXx+v5G3s7f84tKtHBwhPqGB1pm3UGUDfCDQC/9M/DR6sEG0l6b8d3GpfaQb0SWnLbO1BPdjwJINwA8EtbDv1Y4/Rth46qbfPwGm97v6xbG9vsvAF3sOtJAB2KAfil/omtapzeN7GlR257Z2Rn2I2dx76i5QaAX+qV0FLXXxxfrc9Nr4SWyi0qUZBDVQJOY257t+vZLQLbuU4C/L2Fk3CDBrHjNVr4n+dv7K1xqR207dBR9U1s6bxbqvK29xkrd6rcmEbd9l7b2S2XuODvKse+ctdJgC8h3MBlnMXCl/RKaFnjLeDuuu3dzme3CGzuPAnwNYQbuISzWPgTd9z2buezW8COY19JdCiGi3g+EQINIzvD7uKiIpTaubWtvtO03MAlnMUiENn17BawK1pu4BLOYhGofOHsltvRgfqh5QYu4ywW8D468gP1R8sNGsQXzmKBQGHnwdYATyDcAD6OSxG+wcq/Ax35AddwWQrwYVyK8A1W/x3oyA+4hpYbwEdxKcI3+MLfgY78gGtoufFDPPogMDAyrm/wlb8DHfmB+iPc+Bmrm8fhPVyK8A2+9Hdwx4jLQCDgspQf8YXmcXgPlyJ8A38HwP/QcuNHfKV5HN7DpQjfwN8B8C+EGz/iS83j8B4uRfgGf/070EcPgYjLUn6E5nEArsjcmqOB89ZrzKJ/aOC89crcmmN1SYBXOIwxpu7Z7KO4uFhRUVEqKipS8+bNrS6nQXKLSmgeB3BOuUUlGjhvfbWW3o3ThrLfgF9y5fjNZSk/5K/N4wC8hz56CGRclgIAG6rso3c2u/fR41ElqES4AeDXOKDVLND66NG/CGejzw0Av8WglnULhD569C8KDK4cv2m5AeCXGNSyfuKiIpTaubWtD/I8NR2/RLgB4Jc4oKFSIPYvwrkRbgD4JQ5oqBRo/YtQN24FB+Dzahplt/KANmPlTpUbwwEtwPGIDJyNcAPAp52r0zAHNJyNMcBQictSAHxWfToNB0KHWQCuIdwAqJNVY8nQaRhAQ3BZCsA5WTmWTGWn4V+OX0KnYQDnQssNgFpZPZYMd8EAaAhabgDUyhcevkinYc+o6Q40wC4IN2gQdoyBwVcuC3EXjHvx2ArYHZel4DIeUBc4uCxkP1ZfakT98VDYhqPlBi6pbcd4Wbc2HPBsistC9uILlxpRN1rXGscnWm4WLFigxMREhYeHKyUlRVu2bKl13kWLFunSSy9Vy5Yt1bJlS6WlpZ1zfrgXt+YGJsaSsQ8eW+H7aF1rPMvDTWZmpqZOnarZs2drx44d6tWrl9LT0/X999/XOP+GDRs0evRoffLJJ9q8ebMSEhL061//Wt99952XKw9M7BgB/8alRt/HSWTjOYwxpu7ZPCclJUX9+vXTyy+/LEmqqKhQQkKC7r77bk2bNq3O5cvLy9WyZUu9/PLLGjduXJ3zFxcXKyoqSkVFRWrevHmj6w9ED/wlW+/t+HeYvP7ieD1/Y2/rCgLgstyiEi41+qjcohINnLe+Wkf+jdOGBvTfypXjt6UtN2VlZdq+fbvS0tKc04KCgpSWlqbNmzfX6z1OnjypU6dOqVWrVjX+vrS0VMXFxVVeaLjcohK9/3nVVrK/fn6E5lJUQ2dI38alRt9F61rjWdqhuLCwUOXl5YqJiakyPSYmRnv27KnXezz88MNq165dlYB0toyMDM2ZM6fRteIMOiOiPugMCTQOHfkbx/I+N40xb948LV++XO+//77Cw8NrnGf69OkqKipyvg4fPuzlKu2FPjeoC50hPYfWsMBC61rDWdpyEx0dreDgYOXn51eZnp+fr9jY2HMu+9xzz2nevHlat26devbsWet8YWFhCgsLc0u9+Hdz6YyVO1VuDM2lqIbWPc+gNQyoP0vDTWhoqPr06aOsrCyNHDlS0pkOxVlZWZoyZUqtyz3zzDN66qmn9NFHH6lv375eqhaVaC7FufjKqMZ2wvhSgGssvyw1depULVq0SG+88YZ2796tO++8UydOnNDEiRMlSePGjdP06dOd8z/99NOaNWuWXn/9dSUmJiovL095eXk6fvy4VasQkGguRW3oDOl+3BoMuMbyEYpHjRqlgoICPfroo8rLy1Pv3r21Zs0aZyfjnJwcBQX9O4O9+uqrKisr0w033FDlfWbPnq3HHnvMm6UDqAWte+5Faxh8ja8/X9DycW68jXFuAPijzK051fq60ecGVrCq/5crx2/CDQD4CQbeQyWrWk6sHGDQleO35ZelAp2vN+0B8B1xURHsJ2DpnXP+cjck4cZC3NoJAHCF1XfO+Uv/L8vvlgpUDHQGAHCV1XfO+cvdkLTcWMRfmvYAAL7DF1pO/OFuSFpuLMJjDAAArvKVlhNfH+uMlhuL8BgDAEBD+EPLidUINxbiCwoAga2hd8xy59y5EW4sxhcUAAITd8x6Dn1uAADwMu6Y9SzCDQAAXmb1Ld12R7hBrXKLSrRpfyFnEgDgZtwx61mEG9Qoc2uOBs5brzGL/qGB89Yrc2uO1SUBgG34yi3ddsWDM1FNblGJBmSs19lfDIdD2jTtco/8x+P5WvbE3xWoGw9DrT8enGkDVh4Ytn9zVL9MvMZIO745quE93VsLdwvYE39XoH64Y9YzuCzlg6y+JFRbY5672/i4W8Ce+LsCsBrhxsf4woGhb2Ir/aKfmxyS+iS2dOvncLeAPfF39Q/cMOBdbG/v4rKUj/GFB2rGRUVo3vXJmv7el6rQmQSccX2y2z/fFx4AVxf6jbjOH/6ugY7Lht7F9vY+wo2P8ZUDgzceDeHrz9dih9Qwvv53DXS1tQ5f1q0NfyMPYHtbg3DjY3zpwOCNjm6++nwtdkiN46t/V/hG63AgYXtbg3DjgwLtwOCLdwuwQ2o8X/y7NoZdLlHWp3XYLuvqC3ylNT7QEG58lN0ODP6GHRLO5s+XKH8ZVOpqHfbndfVFvtQaH0gYxA+1CvSzt8ytOdV2SOzkA09uUYkGzltfLehunDbU5/9fnCuo1DR4nD+sq7/ulxisr/EYxA+Nxtlb4F0eRM389RJlXf3Gamod9vV19ef9Eq3x3sU4N6jGF8ba8RVxURFK7dyanVIA89cHHDZkvCFfXlf2S3AF4QbVMAgb8G/++oDDhgQVX15X9ktwBZelUA2daYGq/PESZUM7svrqurJfgivoUIwa0ZkWsAc7dWRlvxTYXDl+E25QKzvtFNE4/nqHCuyH/VLg4m4puAW9+yH59x0qsB/2S6gPOhT7KJ4gC1/AHSoA/BEtNz6IM2WczcpLQr4+7gkA1IRw42N4YCPOZnXQ7RgdKYeks/ONQ+IOFQC18oU+eoQbH+MvZ8q+8OW1O58Nuo66ZwEQmKw+IatEnxuL/bJvjS+PEFopc2uOBs5brzGL/qGB89Yrc2uO1SXZki8MWnaw8IR+eTulMWLgNHgc/Q79jy/10aPlxkK1JVxffoKsz7Ym2JAvDFrmCzXAd3irxdZXzv7hGl+68kC4sci5QoKvjhAq+daX1xusvPzW0BFm7VYDfIO3AgcnUP7Ll06GCDcWqSsk+OpYDr705fU0Xzh79IWg6ws1wFreDByBdgJlJ750MkS4sYi/hgRf+vJ6ki+dPfpC0PWFGryFzvLVeTNw+Ou+EWf4yskQ4cYi/hwSfOHL25ADkCvLcPYYmHyhtc4XeTNw+PO+EWf4wskQ4cZCvhASGsrKL29DDkB1LfPL4MPZY+DxpdY6X+PtwOHP+0b4BsKNxXwh4fqThhyA6lrGH+9ag/vRWndu3g4c7BvRGIQb+JWGHIDqGi/GH+9ag/vRWlc3Agf8BYP4wa80ZJDDcy1TV/CJi4pQaufWPrtDZ6Az96m89BLsOPNlobUO8F+03HhBIN194el1bci1/7qW8dezdTq/uh+tdf4tkPa1ODeHMeaXo6vbWnFxsaKiolRUVKTmzZu79b1r+o8VSAcgb65rblGJyweg2pbJ3JpTLfjUp24rd6S5RSUaOG99tVC2cdpQj9TCQQO+LpD2tYHKleM3LTduUtN/rMu6tQmYuy+8fadJQ67917ZMQ87Wrd6RerPzq9XrCtSFO93wS/S5cYPa/mNtO/Sj5Q8+9BZfeMhjY7jSt8YXHg7nrQes+sK6AnXx9/0P3I9w4wa1/ccKcjh8/gnf7uLtp5lb2ZG2MTtSd9Xtrc6vHDTgD7y9/4Hv47KUG9R2C+nFHVoGzFgp3hzky+rLJA29ZdjddXuj8yu3R6O+fOEhs2f//7Lrvhb1Q7hxg3Md2APp7gtvrGt9rq374h1bnuoT4OlxRxgKH/Vh9QlHpcrbYwLrNhnUhHDjJuc6sAfSwFeeXte6OtKeayfrztDjapDz59FvAymgw3W+0Jm3sobK/2JGdCgOdIQbNwqkEGOVc10mOddO9tOvC9x+ZunK39vfL+/w3UZtfCG4+0IN8C10KIZfOVdH2tp2cNsPHbX8jh9Gv4Ud1NQh3hc680aGBtc4vWkoh7hARcsN/E5tl0lqax3RL6ZJ1pzVcXkH/qy2S76+0C/r8NGaT1S+PVqiXgktvVYHfAfhBn6ppsskte1k+3Ro6TOXhLi8A39UV78abwb3mvrO1TbQPh2LAxfhBrZS207W6jNL+BYeJ+HaNqhPnxZvBPfaWo/6JraSQ9LZJTok9Umk1SZQ+cQFyQULFigxMVHh4eFKSUnRli1bzjn/u+++qwsuuEDh4eFKTk7W6tWrvVQp/EFNow2P6tdeG6cN1bLbLtHGaUN5fEAAy9yao4Hz1mvMon9o4Lz1ytyaY3VJXufqNvCFfjXnGi07LipC865Pdh7QgiTNuz45YIMrfCDcZGZmaurUqZo9e7Z27NihXr16KT09Xd9//32N82/atEmjR4/Wrbfeqs8//1wjR47UyJEjtXPnTi9XDn/jyiMWKlk5EjLcj8dJNGwb+EKH+LpGyx7Vr70+m365lt12iT6bfnmVExj+Hwcey58KnpKSon79+unll1+WJFVUVCghIUF33323pk2bVm3+UaNG6cSJE/rggw+c0y655BL17t1bCxcurPPzPPlUcNiLrwxMBvfZtL9QYxb9o9r0ZbddotTOrS2oyPsasw1yi0os6xCfW1SigfPWV+s7t3Ha0HPWwv9j+3Dl+G1py01ZWZm2b9+utLQ057SgoCClpaVp8+bNNS6zefPmKvNLUnp6eq3zl5aWqri4uMoLqAtn+PbkC5dXrNaYbdCQ1k93aUjrEf+PA5el4aawsFDl5eWKiYmpMj0mJkZ5eXk1LpOXl+fS/BkZGYqKinK+EhIS3FM8bI0HRtqTL1xesZo/bwNX+87x/zhw2f5uqenTp2vq1KnOn4uLiwk4qJO/jyiM2jHekH9vg0AaGRwNZ2nLTXR0tIKDg5Wfn19len5+vmJjY2tcJjY21qX5w8LC1Lx58yovoC7+fHaLull5ecVXBMI24P9x4LK05SY0NFR9+vRRVlaWRo4cKelMh+KsrCxNmTKlxmVSU1OVlZWl++67zzlt7dq1Sk1N9ULFCCT+fHYL4Az+Hwcmyy9LTZ06VePHj1ffvn3Vv39/zZ8/XydOnNDEiRMlSePGjVN8fLwyMjIkSffee68GDx6s559/XsOHD9fy5cu1bds2/elPf7JyNWBTjCgM+D/+Hwcey8PNqFGjVFBQoEcffVR5eXnq3bu31qxZ4+w0nJOTo6Cgf189GzBggN555x3NnDlTM2bMUNeuXfXXv/5VSUlJVq0CAADwIZaPc+NtjHMDAID/8ZtxbgAAANyNcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGzF8scveFvlgMzFxcUWVwIAAOqr8rhdnwcrBFy4OXbsmCQpISHB4koAAICrjh07pqioqHPOE3DPlqqoqNCRI0fUrFkzORwOt753cXGxEhISdPjw4YB8blWgr7/ENmD9A3v9JbZBoK+/5LltYIzRsWPH1K5duyoP1K5JwLXcBAUF6fzzz/foZzRv3jxgv9QS6y+xDVj/wF5/iW0Q6OsveWYb1NViU4kOxQAAwFYINwAAwFYIN24UFham2bNnKywszOpSLBHo6y+xDVj/wF5/iW0Q6Osv+cY2CLgOxQAAwN5ouQEAALZCuAEAALZCuAEAALZCuAEAALZCuHHRggULlJiYqPDwcKWkpGjLli21zrtr1y5df/31SkxMlMPh0Pz5871XqIe4sv6LFi3SpZdeqpYtW6ply5ZKS0s75/z+wpVtsHLlSvXt21ctWrRQZGSkevfurTfffNOL1bqfK+t/tuXLl8vhcGjkyJGeLdDDXFn/pUuXyuFwVHmFh4d7sVrPcPU78NNPP2ny5MmKi4tTWFiYunXrptWrV3upWvdzZf2HDBlS7TvgcDg0fPhwL1bsfq5+B+bPn6/u3bsrIiJCCQkJuv/++/Xzzz97rkCDelu+fLkJDQ01r7/+utm1a5e57bbbTIsWLUx+fn6N82/ZssU8+OCDZtmyZSY2Nta8+OKL3i3YzVxd/zFjxpgFCxaYzz//3OzevdtMmDDBREVFmW+//dbLlbuPq9vgk08+MStXrjRfffWV2bdvn5k/f74JDg42a9as8XLl7uHq+lc6ePCgiY+PN5deeqn57W9/651iPcDV9V+yZIlp3ry5yc3Ndb7y8vK8XLV7uboNSktLTd++fc3VV19tNm7caA4ePGg2bNhgsrOzvVy5e7i6/j/88EOVv//OnTtNcHCwWbJkiXcLdyNXt8Hbb79twsLCzNtvv20OHjxoPvroIxMXF2fuv/9+j9VIuHFB//79zeTJk50/l5eXm3bt2pmMjIw6l+3QoYPfh5vGrL8xxpw+fdo0a9bMvPHGG54q0eMauw2MMeaiiy4yM2fO9ER5HteQ9T99+rQZMGCAee2118z48eP9Oty4uv5LliwxUVFRXqrOO1zdBq+++qrp1KmTKSsr81aJHtXYfcCLL75omjVrZo4fP+6pEj3O1W0wefJkc/nll1eZNnXqVDNw4ECP1chlqXoqKyvT9u3blZaW5pwWFBSktLQ0bd682cLKvMMd63/y5EmdOnVKrVq18lSZHtXYbWCMUVZWlvbu3avLLrvMk6V6REPX//HHH1fbtm116623eqNMj2no+h8/flwdOnRQQkKCfvvb32rXrl3eKNcjGrINVq1apdTUVE2ePFkxMTFKSkrS3LlzVV5e7q2y3cYd+8HFixfrpptuUmRkpKfK9KiGbIMBAwZo+/btzktXBw4c0OrVq3X11Vd7rM6Ae3BmQxUWFqq8vFwxMTFVpsfExGjPnj0WVeU97lj/hx9+WO3atavyn8KfNHQbFBUVKT4+XqWlpQoODtYrr7yiK6+80tPlul1D1n/jxo1avHixsrOzvVChZzVk/bt3767XX39dPXv2VFFRkZ577jkNGDBAu3bt8vgDfD2hIdvgwIEDWr9+vW6++WatXr1a+/bt01133aVTp05p9uzZ3ijbbRq7H9yyZYt27typxYsXe6pEj2vINhgzZowKCws1aNAgGWN0+vRp3XHHHZoxY4bH6iTcwCvmzZun5cuXa8OGDbboUOmKZs2aKTs7W8ePH1dWVpamTp2qTp06aciQIVaX5lHHjh3T2LFjtWjRIkVHR1tdjiVSU1OVmprq/HnAgAHq0aOH/vjHP+qJJ56wsDLvqaioUNu2bfWnP/1JwcHB6tOnj7777js9++yzfhduGmvx4sVKTk5W//79rS7FqzZs2KC5c+fqlVdeUUpKivbt26d7771XTzzxhGbNmuWRzyTc1FN0dLSCg4OVn59fZXp+fr5iY2Mtqsp7GrP+zz33nObNm6d169apZ8+enizToxq6DYKCgtSlSxdJUu/evbV7925lZGT4Xbhxdf3379+vQ4cOacSIEc5pFRUVkqSQkBDt3btXnTt39mzRbuSOfUCTJk100UUXad++fZ4o0eMasg3i4uLUpEkTBQcHO6f16NFDeXl5KisrU2hoqEdrdqfGfAdOnDih5cuX6/HHH/dkiR7XkG0wa9YsjR07VpMmTZIkJScn68SJE7r99tv1yCOPKCjI/T1k6HNTT6GhoerTp4+ysrKc0yoqKpSVlVXlzMyuGrr+zzzzjJ544gmtWbNGffv29UapHuOu70BFRYVKS0s9UaJHubr+F1xwgb788ktlZ2c7X7/5zW80dOhQZWdnKyEhwZvlN5o7/v7l5eX68ssvFRcX56kyPaoh22DgwIHat2+fM9hK0tdff624uDi/CjZS474D7777rkpLS3XLLbd4ukyPasg2OHnyZLUAUxl2jaceb+mxrso2tHz5chMWFmaWLl1qvvrqK3P77bebFi1aOG/tHDt2rJk2bZpz/tLSUvP555+bzz//3MTFxZkHH3zQfP755+Zf//qXVavQKK6u/7x580xoaKhZsWJFlVshjx07ZtUqNJqr22Du3Lnm448/Nvv37zdfffWVee6550xISIhZtGiRVavQKK6u/y/5+91Srq7/nDlzzEcffWT2799vtm/fbm666SYTHh5udu3aZdUqNJqr2yAnJ8c0a9bMTJkyxezdu9d88MEHpm3btubJJ5+0ahUapaH/BwYNGmRGjRrl7XI9wtVtMHv2bNOsWTOzbNkyc+DAAfPxxx+bzp07mxtvvNFjNRJuXPTSSy+Z9u3bm9DQUNO/f3/z97//3fm7wYMHm/Hjxzt/PnjwoJFU7TV48GDvF+4mrqx/hw4dalz/2bNne79wN3JlGzzyyCOmS5cuJjw83LRs2dKkpqaa5cuXW1C1+7iy/r/k7+HGGNfW/7777nPOGxMTY66++mqzY8cOC6p2L1e/A5s2bTIpKSkmLCzMdOrUyTz11FPm9OnTXq7afVxd/z179hhJ5uOPP/ZypZ7jyjY4deqUeeyxx0znzp1NeHi4SUhIMHfddZc5evSox+pzGOOpNiEAAADvo88NAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAHjQhAkTNHLkSKvLAAIK4QYIUBMmTJDD4XC+WrdurauuukpffPGF1aW5xdnrVvkaNGiQxz7v0KFDcjgcys7OrjL997//vZYuXeqxzwVQHeEGCGBXXXWVcnNzlZubq6ysLIWEhOiaa66xuiy3WbJkiXP9cnNztWrVqhrnO3XqlMdqiIqKUosWLTz2/gCqI9wAASwsLEyxsbGKjY1V7969NW3aNB0+fFgFBQW6/PLLNWXKlCrzFxQUKDQ01PlE4MTERD3xxBMaPXq0IiMjFR8frwULFlRZ5oUXXlBycrIiIyOVkJCgu+66S8ePH3f+/ptvvtGIESPUsmVLRUZG6le/+pVWr14tSTp69KhuvvlmtWnTRhEREeratauWLFlS7/Vr0aKFc/1iY2PVqlUrZwtLZmamBg8erPDwcL399tv64YcfNHr0aMXHx6tp06ZKTk7WsmXLqrxfRUWFnnnmGXXp0kVhYWFq3769nnrqKUlSx44dJUkXXXSRHA6HhgwZIqn6ZanS0lLdc889atu2rcLDwzVo0CBt3brV+fsNGzbI4XAoKytLffv2VdOmTTVgwADt3bu33usNBDrCDQBJ0vHjx/XWW2+pS5cuat26tSZNmqR33nlHpaWlznneeustxcfH6/LLL3dOe/bZZ9WrVy99/vnnmjZtmu69916tXbvW+fugoCD94Q9/0K5du/TGG29o/fr1euihh5y/nzx5skpLS/Xpp5/qyy+/1NNPP63zzjtPkjRr1ix99dVX+p//+R/t3r1br776qqKjo92yvpW17t69W+np6fr555/Vp08fffjhh9q5c6duv/12jR07Vlu2bHEuM336dM2bN89Z1zvvvKOYmBhJcs63bt065ebmauXKlTV+7kMPPaT33ntPb7zxhnbs2KEuXbooPT1dP/74Y5X5HnnkET3//PPatm2bQkJC9J//+Z9uWW8gIHjskZwAfNr48eNNcHCwiYyMNJGRkUaSiYuLM9u3bzfGGFNSUmJatmxpMjMzncv07NnTPPbYY86fO3ToYK666qoq7ztq1CgzbNiwWj/33XffNa1bt3b+nJycXOU9zzZixAgzceLEBq2fJBMeHu5cv8jISPP++++bgwcPGklm/vz5db7H8OHDzQMPPGCMMaa4uNiEhYWZRYsW1Thv5ft+/vnnVaaf/ST048ePmyZNmpi3337b+fuysjLTrl0788wzzxhjjPnkk0+MJLNu3TrnPB9++KGRZEpKSlzZBEDAouUGCGBDhw5Vdna2srOztWXLFqWnp2vYsGH65ptvFB4errFjx+r111+XJO3YsUM7d+7UhAkTqrxHampqtZ93797t/HndunW64oorFB8fr2bNmmns2LH64YcfdPLkSUnSPffcoyeffFIDBw7U7Nmzq3RovvPOO7V8+XL17t1bDz30kDZt2uTS+r344ovO9cvOztaVV17p/F3fvn2rzFteXq4nnnhCycnJatWqlc477zx99NFHysnJkSTt3r1bpaWluuKKK1yq4Wz79+/XqVOnNHDgQOe0Jk2aqH///lW2mST17NnT+e+4uDhJ0vfff9/gzwYCCeEGCGCRkZHq0qWLunTpon79+um1117TiRMntGjRIknSpEmTtHbtWn377bdasmSJLr/8cnXo0KHe73/o0CFdc8016tmzp9577z1t377d2SenrKzM+RkHDhzQ2LFj9eWXX6pv37566aWXJMkZtO6//34dOXJEV1xxhR588MF6f35sbKxz/bp06aLIyMgq6362Z599Vr///e/18MMP65NPPlF2drbS09OddUZERNT7c92hSZMmzn87HA5JZ/r8AKgb4QaAk8PhUFBQkEpKSiRJycnJ6tu3rxYtWqR33nmnxn4ff//736v93KNHD0nS9u3bVVFRoeeff16XXHKJunXrpiNHjlR7j4SEBN1xxx1auXKlHnjgAWe4kqQ2bdpo/PjxeuuttzR//nz96U9/cucqO3322Wf67W9/q1tuuUW9evVSp06d9PXXXzt/37VrV0VERDg7U/9SaGiopDMtQLXp3LmzQkND9dlnnzmnnTp1Slu3btWFF17opjUBEGJ1AQCsU1paqry8PEln7kx6+eWXdfz4cY0YMcI5z6RJkzRlyhRFRkbq2muvrfYen332mZ555hmNHDlSa9eu1bvvvqsPP/xQktSlSxedOnVKL730kkaMGKHPPvtMCxcurLL8fffdp2HDhqlbt246evSoPvnkE2c4evTRR9WnTx/96le/UmlpqT744APn79yta9euWrFihTZt2qSWLVvqhRdeUH5+vjN0hIeH6+GHH9ZDDz2k0NBQDRw4UAUFBdq1a5duvfVWtW3bVhEREVqzZo3OP/98hYeHKyoqqspnREZG6s4779Tvfvc7tWrVSu3bt9czzzyjkydP6tZbb/XIegGBiJYbIICtWbNGcXFxiouLU0pKirZu3ap3333XeRuzJI0ePVohISEaPXq0wsPDq73HAw88oG3btumiiy7Sk08+qRdeeEHp6emSpF69eumFF17Q008/raSkJL399tvKyMiosnx5ebkmT56sHj166KqrrlK3bt30yiuvSDrTGjJ9+nT17NlTl112mYKDg7V8+XKPbIuZM2fq4osvVnp6uoYMGaLY2NhqIwvPmjVLDzzwgB599FH16NFDo0aNcvaDCQkJ0R/+8Af98Y9/VLt27fTb3/62xs+ZN2+err/+eo0dO1YXX3yx9u3bp48++kgtW7b0yHoBgchhjDFWFwHAdx06dEidO3fW1q1bdfHFF1f5XWJiou677z7dd9991hQHADXgshSAGp06dUo//PCDZs6cqUsuuaRasAEAX8VlKQA1+uyzzxQXF6etW7dW6ydjtblz5+q8886r8TVs2DCrywNgMS5LAfA7P/74Y7URfStFREQoPj7eyxUB8CWEGwAAYCtclgIAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALby/wCefz+H3x/XagAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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UQgjMnDkTO3bsQHp6Ovz8/Jp8TXh4ONLS0jS27du3D+Hh4QAAPz8/eHp6apRRq9U4cuSIVOZeSqVSWvOnqbV/anN+2rZt22RdSfdqh76qq6uNXBMiIjJnRu0BmjFjBrZs2YJvvvkG7du3l3J0HB0dYW9vDwCYOHEiOnXqhISEBADAa6+9hsGDB2PVqlUYMWIEtm7diuPHj+Pjjz8GcHfxo1mzZmHZsmXo3r07/Pz8sGDBAnh7e2P06NE6qzuHX4yD152IiHTBqAHQ2rVrAQBDhgzR2L5x40ZMmjQJAJCfn68x6yciIgJbtmzBW2+9hfnz56N79+7YuXOnRuL0G2+8gfLycrz44osoLS3FwIEDkZqaCjs7O723iYiIiEyfSa0DZCoaW0fg1q1byM3NhZ+fHwMqI+D1JyKihmizDhAXVJGZwsJCvPLKK+jatSuUSiV8fHwwcuTIOnlVQgjExMRAoVBIaywBQF5eHhQKBbKysuoce8iQIZg1a5b0fPv27Xj00Ufh4uLS4Gtu3bqFGTNmwMXFBe3atcPTTz9dJ8mdiIhMU4GqAodzSlCgqjB2VbTGAEhG8vLyEBwcjPT0dLz77rv45ZdfkJqaiqFDh2LGjBkaZRMTE1udb1NeXo6BAwdi5cqVDZaZPXs2/v3vf2Pbtm348ccfceXKFTz11FOtOi8REelfyrF8RK5Ix/h1RxC5Ih0px/KNXSWtmMQ0eDKM6dOnQ6FQ4OjRo3BwcJC29+nTBy+88IL0PCsrC6tWrcLx48c1lhLQ1oQJEwDcDbzqo1KpsH79emzZsgXDhg0DcDf/q3fv3vjpp58wYMCAFp+biIj0p0BVgfjtv6Dm/5JoagQwf/tpDOrhBi9He+NWrpnYA2REhuw6/OOPP5CamooZM2ZoBD+1OnbsCAC4efMmxo8fj6SkJGlNJX3JzMzE7du3NW5b0qtXL3Tu3BkZGRl6PTcREbVcbkm5FPzUqhYCeSU3jVOhFmAPkJGkHMuXomcrBZDwVADGPNhZb+fLzs6GEAK9evVqtNzs2bMRERGBUaNGNVouIiKizj25KioqGlxssj6FhYWwtbWVgq9ajd22hIiIjM/P1QFWCmgEQdYKBXxdzWeNPAZARmCMrsPmTPbbtWsX0tPTcfLkySbLpqSkoHfv3hrbnnvuuRbXj4iIzIeXoz0SngrA/O2nUS0ErBUKLH+qr9kMfwEMgIyisa5Dff3xdO/eHQqFAufOnWuwTHp6OnJycur0yDz99NN46KGHsH//fmmbj48P/P39NcrVLl7ZXJ6enqiqqkJpaanGOf98axMiIjJNYx7sjEE93JBXchO+rm3NKvgBmANkFLVdh3+m765DZ2dnREdHIykpCeXl5XX2l5aWYt68eTh16hSysrKkBwCsWbMGGzdu1HmdgoOD0aZNG40p+OfPn0d+fn6Dty0hIiLT4eVoj/BuLmYX/ADsATIKY3UdJiUlITIyEqGhoXj77bfRr18/3LlzB/v27cPatWtx9uzZenteOnfu3Kz7tN3rjz/+QH5+Pq5cuQLgbnAD3O358fT0hKOjI6ZMmYK4uDg4OzujQ4cOeOWVVxAeHs4ZYEREpFcMgIzEGF2HXbt2xYkTJ/D3v/8dc+bMQUFBAdzc3BAcHCzdlkSXdu3ahcmTJ0vPx44dCwBYtGgRFi9eDOBu75KVlRWefvppVFZWIjo6Gh9++KHO60JERPRnvBVGPXgrDNPF609ERA3hrTCIiIiIGsEAiIiIiGSHARARERHJDgMgIiIikh0GQC3E3HHj4HUnIiJdYACkpTZt2gC4e9NQMryqqioAgLW1tZFrQkRE5ozrAGnJ2toaHTt2xNWrVwEAbdu2hUKhaOJVpAs1NTUoLi5G27ZtYWPDP10iImo5fou0QO1qybVBEBmOlZUVOnfuzKCTiIhahQFQCygUCnh5ecHd3R23b982dnVkxdbWFlZWHLklIqLWYQDUCtbW1sxFISIiMkP8KU1ERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHaMGQAcOHMDIkSPh7e0NhUKBnTt3Nlp+0qRJUCgUdR59+vSRyixevLjO/l69eum5JURERGROjBoAlZeXIzAwEElJSc0q//7776OgoEB6XLx4Ec7OznjmmWc0yvXp00ej3MGDB/VRfSIiIjJTNsY8eUxMDGJiYppd3tHREY6OjtLznTt34vr165g8ebJGORsbG3h6euqsnkRERGRZzDoHaP369YiKikKXLl00tl+4cAHe3t7o2rUrnnvuOeTn5zd6nMrKSqjVao0HERERWS6zDYCuXLmC//znP5g6darG9rCwMGzatAmpqalYu3YtcnNz8dBDD+HGjRsNHishIUHqXXJ0dISPj4++q09ERERGpBBCCGNXAgAUCgV27NiB0aNHN6t8QkICVq1ahStXrsDW1rbBcqWlpejSpQtWr16NKVOm1FumsrISlZWV0nO1Wg0fHx+oVCp06NBBq3YQERGRcajVajg6Ojbr+9uoOUAtJYTAhg0bMGHChEaDHwDo2LEjevTogezs7AbLKJVKKJVKXVeTiIiITJRZDoH9+OOPyM7ObrBH58/KysqQk5MDLy8vA9SMiIiIzIFRA6CysjJkZWUhKysLAJCbm4usrCwpaTk+Ph4TJ06s87r169cjLCwMffv2rbNv7ty5+PHHH5GXl4fDhw/jySefhLW1NcaNG6fXthAREZH5MOoQ2PHjxzF06FDpeVxcHAAgNjYWmzZtQkFBQZ0ZXCqVCl9//TXef//9eo956dIljBs3DteuXYObmxsGDhyIn376CW5ubvprCBEREZkVk0mCNiXaJFERERGRadDm+9ssc4CIiIiIWoMBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESyY9QA6MCBAxg5ciS8vb2hUCiwc+fORsvv378fCoWizqOwsFCjXFJSEnx9fWFnZ4ewsDAcPXpUj60gIiIic2PUAKi8vByBgYFISkrS6nXnz59HQUGB9HB3d5f2paSkIC4uDosWLcKJEycQGBiI6OhoXL16VdfVJyIiIjNlY8yTx8TEICYmRuvXubu7o2PHjvXuW716NaZNm4bJkycDAJKTk/Htt99iw4YNmDdvXmuqS0RERBbCLHOAgoKC4OXlhUceeQSHDh2StldVVSEzMxNRUVHSNisrK0RFRSEjI6PB41VWVkKtVms8iIiIyHKZVQDk5eWF5ORkfP311/j666/h4+ODIUOG4MSJEwCAkpISVFdXw8PDQ+N1Hh4edfKE/iwhIQGOjo7Sw8fHR6/tICIiIuMy6hCYtnr27ImePXtKzyMiIpCTk4M1a9bgs88+a/Fx4+PjERcXJz1Xq9UMgoiIiCyYWQVA9QkNDcXBgwcBAK6urrC2tkZRUZFGmaKiInh6ejZ4DKVSCaVSqdd6EhERkekwqyGw+mRlZcHLywsAYGtri+DgYKSlpUn7a2pqkJaWhvDwcGNVkYiIiEyMUXuAysrKkJ2dLT3Pzc1FVlYWnJ2d0blzZ8THx+Py5cv49NNPAQCJiYnw8/NDnz59cOvWLXzyySdIT0/H3r17pWPExcUhNjYWISEhCA0NRWJiIsrLy6VZYURERERGDYCOHz+OoUOHSs9r83BiY2OxadMmFBQUID8/X9pfVVWFOXPm4PLly2jbti369euH77//XuMYY8aMQXFxMRYuXIjCwkIEBQUhNTW1TmI0ERERyZdCCCGMXQlTo1ar4ejoCJVKhQ4dOhi7OkRERNQM2nx/m30OEBEREZG2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDsMgIiIiEh2GAARERGR7DAAIiIiItlhAERERESywwCIiIiIZIcBEBEREckOAyAiIiKSHQZAREREJDtGDYAOHDiAkSNHwtvbGwqFAjt37my0/Pbt2/HII4/Azc0NHTp0QHh4OL777juNMosXL4ZCodB49OrVS4+tICIiInNj1ACovLwcgYGBSEpKalb5AwcO4JFHHsGePXuQmZmJoUOHYuTIkTh58qRGuT59+qCgoEB6HDx4UB/VJyIiIjNlY8yTx8TEICYmptnlExMTNZ4vX74c33zzDf7973+jf//+0nYbGxt4enrqqppERERkYcw6B6impgY3btyAs7OzxvYLFy7A29sbXbt2xXPPPYf8/PxGj1NZWQm1Wq3xICIiIstl1gHQe++9h7KyMjz77LPStrCwMGzatAmpqalYu3YtcnNz8dBDD+HGjRsNHichIQGOjo7Sw8fHxxDVJyIiIiNRCCGEsSsBAAqFAjt27MDo0aObVX7Lli2YNm0avvnmG0RFRTVYrrS0FF26dMHq1asxZcqUestUVlaisrJSeq5Wq+Hj4wOVSoUOHTpo1Q4iIiIyDrVaDUdHx2Z9fxs1B6iltm7diqlTp2Lbtm2NBj8A0LFjR/To0QPZ2dkNllEqlVAqlbqups4UqCqQW1IOP1cHeDnaG7s6REREZs/sAqAvv/wSL7zwArZu3YoRI0Y0Wb6srAw5OTmYMGGCAWqneynH8hG//RfUCMBKASQ8FYAxD3Y2drWIiIjMmlFzgMrKypCVlYWsrCwAQG5uLrKysqSk5fj4eEycOFEqv2XLFkycOBGrVq1CWFgYCgsLUVhYCJVKJZWZO3cufvzxR+Tl5eHw4cN48sknYW1tjXHjxhm0bbpQoKqQgh8AqBHA/O2nUaCqMG7FiIiIzJxRA6Djx4+jf//+0hT2uLg49O/fHwsXLgQAFBQUaMzg+vjjj3Hnzh3MmDEDXl5e0uO1116Tyly6dAnjxo1Dz5498eyzz8LFxQU//fQT3NzcDNs4HcgtKZeCn1rVQiCv5KZxKkRERGQhTCYJ2pRok0SlTwWqCkSuSNcIgqwVChycN5S5QERERPfQ5vvbrKfBWzovR3skPBUAa4UCwN3gZ/lTfRn8EBERtZLZJUHLzZgHO2NQDzfkldyEr2tbBj9EREQ6wADIDHg52jPwISIi0iEOgREREZHsMAAiIiIi2WEARERERLLDAIiIiIhkhwEQERERyQ4DICIiIpIdBkBEREQkOwyAiIiISHYYABEREZHsMAAiIiIi2WEARERERLLDAIiIiIhkhwEQERERyQ4DICIiIpIdBkBEREQkOwyAiIiISHYYABEREZHs2LTkRUePHkVGRgYKCwsBAJ6enggPD0doaKhOK0dERESkD1oFQFevXsXTTz+NQ4cOoXPnzvDw8AAAFBUVYfbs2YiMjMTXX38Nd3d3vVSWiIiISBe0GgKbPn06qqurcfbsWeTl5eHIkSM4cuQI8vLycPbsWdTU1GDGjBn6qisRERGRTiiEEKK5hdu3b48DBw6gf//+9e7PzMzEkCFDcOPGDZ1V0BjUajUcHR2hUqnQoUMHY1eHiIiImkGb72+teoCUSiXUanWD+2/cuAGlUqnNIYmIiMgEFKgqcDinBAWqCmNXxSC0CoDGjBmD2NhY7NixQyMQUqvV2LFjByZPnoxx48bpvJJERESkPynH8hG5Ih3j1x1B5Ip0pBzLN3aV9E6rJOjVq1ejpqYGY8eOxZ07d2BrawsAqKqqgo2NDaZMmYL33ntPLxUlIiIi3StQVSB++y+o+b+EmBoBzN9+GoN6uMHL0d64ldMjrQIgpVKJtWvXYuXKlcjMzNSYBh8cHMx8GSIiIjOTW1IuBT+1qoVAXslNBkD36tChA4YOHarruhAREZGB+bk6wEoBjSDIWqGAr2tb41XKAHS6EnRRURHefvttXR6SiIiI9MjL0R4JTwXAWqEAcDf4Wf5UX4vu/QG0nAbflJ9//hkPPPAAqqurdXVIo+A0eCIikpsCVQXySm7C17Wt2QY/2nx/azUEdurUqUb3nz9/XpvDERERkYnwcrQ328CnJbQaAgsKCkL//v0RFBRU59G/f3+MHTtWq5MfOHAAI0eOhLe3NxQKBXbu3Nnka/bv348HHngASqUS/v7+2LRpU50ySUlJ8PX1hZ2dHcLCwnD06FGt6kVERESWTasAyNnZGevWrUNubm6dx2+//Ybdu3drdfLy8nIEBgYiKSmpWeVzc3MxYsQIDB06FFlZWZg1axamTp2K7777TiqTkpKCuLg4LFq0CCdOnEBgYCCio6Nx9epVrepGRERElkurIbDg4GBcuXIFXbp0qXd/aWkptEkpiomJQUxMTLPLJycnw8/PD6tWrQIA9O7dGwcPHsSaNWsQHR0N4O5aRdOmTcPkyZOl13z77bfYsGED5s2b1+xzERERkeXSqgfopZdegq+vb4P7O3fujI0bN7a2Tg3KyMhAVFSUxrbo6GhkZGQAuLsgY2ZmpkYZKysrREVFSWXqU1lZCbVarfEgIiIiy6VVD9CTTz7Z6H4nJyfExsa2qkKNKSwshIeHh8Y2Dw8PqNVqVFRU4Pr166iurq63zLlz5xo8bkJCApYsWaKXOhMREZHp0ek6QOYqPj4eKpVKely8eNHYVSIiIiI90qoHKC4urlnlVq9e3aLKNMXT0xNFRUUa24qKitChQwfY29vD2toa1tbW9Zbx9PRs8LhKpZJ3sSciIpIRrQKgkydPajw/ePAggoODYW//v3UDFP+3kqQ+hIeHY8+ePRrb9u3bh/DwcACAra0tgoODkZaWhtGjRwMAampqkJaWhpkzZ+qtXrpQoKpAbkk5/FwdZLUOAxERkTFoFQD98MMPGs/bt2+PLVu2oGvXri06eVlZGbKzs6Xnubm5yMrKgrOzMzp37oz4+HhcvnwZn376KYC7Sdj//Oc/8cYbb+CFF15Aeno6/vWvf+Hbb7+VjhEXF4fY2FiEhIQgNDQUiYmJKC8vl2aFmaKUY/nSnXitFEDCUwEY82BnY1eLiIjIYrXoZqi6cvz4cY2bqtYOscXGxmLTpk0oKChAfn6+tN/Pzw/ffvstZs+ejffffx/33XcfPvnkE2kKPACMGTMGxcXFWLhwIQoLCxEUFITU1NQ6idGmokBVIQU/wN2b0c3ffhqDerixJ4iIiEhPWnUvsPbt2+Pnn39ucQ+QqTLkvcAO55Rg/LojdbZ/OW0Awru56PXcRERElkSb72/OAjMyP1cHWN2TNmWtUMDXta1xKkRERCQDrboZqhAC586dQ1lZmcb2fv36tb5mMuHlaI+EpwIwf/tpVAsBa4UCy5/qy+EvogZwwgAR6YJWQ2BWVlZQKBT13u6idrtCoUB1dbVOK2lohhwCq1WgqkBeyU34urblhzpRAzhhgIgao833t1Y9QLm5ua2qGDXMy9GegQ9RIzhhgIh0SasAqKGboBIR6VtuSbkU/NSqFgJ5JTcZABGR1nSSBD1s2DD8/vvvujgUEVG9OGGAyHgKVBU4nFOCAlWFsauiM1r1AO3atave7QcOHMDu3bvh4+MDAHjiiSdaXzMioj/hhAEi47DU3DudJUFLB2QSNBHpEScMEBlOgaoCkSvSNYafrRUKHJw31CTff3pbByg6OhoxMTEoLCxETU2N9LC2tsbp06dRU1Nj9sEPEZk2L0d7hHdzMckPXyJL01junbnTKgD6z3/+g4cffhghISHYvXu3vupEREREJsCSc++0ToKePXs2du3ahTfffBN//etfcfOm+UeBREREVFdt7p214m4UZEm5dy26GWpQUBCOHz+O2bNnIygoqNGcICIiIjJfYx7sjEE93Cwu967Fd4O3t7dHcnIydu3ahR9++AGurq66rBcRERGZCEtcrFerIbD09HTcf//9UKvV0rYnnngCa9asgVKpRJ8+ffD//t//03kliYiIiHRJqwAoMTER06ZNq3dqmaOjI/76179i9erVOqscERERkT5oFQD9/PPPGD58eIP7H330UWRmZra6UkRERET6pFUAVFRUhDZt2jS438bGBsXFxa2uFBEREZE+aRUAderUCadPn25w/6lTp+Dl5dXqSpH5s8T7xhARkeXQahbYY489hgULFmD48OGws7PT2FdRUYFFixbh8ccf12kFyfxY6n1jTFmBqgK5JeXwc3WwuJkaRET6oNW9wIqKivDAAw/A2toaM2fORM+ePQEA586dQ1JSEqqrq3HixAl4eHjorcKGwHuBtZy53TfGEjDgJCK6S5vvb616gDw8PHD48GG8/PLLiI+PlxZAVCgUiI6ORlJSktkHP9Q6jd03hgGQ7hWoKqTgBwBqBDB/+2kM6uHG601E1AitF0Ls0qUL9uzZg+vXryM7OxtCCHTv3h1OTk76qB+Zmdr7xtzbA2QJ940xRQw4m6ehIUIOHRLJV4tXgnZycsKDDz6oy7qQBai9b8z87adRLYRF3TfGFDHgbFpDQ4QcOiSSN61ygOSCOUCtV6CqsLj7xpiqlGP5dQJOfpHf1VBO2vbp4Xjyw8PMVSOyMHrLASJqLku8b4ypstQbFepCQ0OEx/Kuc+iQSOYYABFZAAac9WtoiPBBXycOHRLJnFYLIZL54EKERP/LSbNWKABAGiIM9HGqdzuDSCL5YA5QPcw9B4jJnUSaGspJY64akWVhDpCMcV0YoroaGiLk0CGRfHEIzMI0ti4MUXNw+JSI5IA9QBaG68JQa3D4lIjkgj1AFqahpE9281NTGho+ZU8QEVkikwiAkpKS4OvrCzs7O4SFheHo0aMNlh0yZAgUCkWdx4gRI6QykyZNqrN/+PDhhmiKSRjzYGccnDcUX04bgIPzhvIXPDULh0+JSE6MPgSWkpKCuLg4JCcnIywsDImJiYiOjsb58+fh7u5ep/z27dtRVVUlPb927RoCAwPxzDPPaJQbPnw4Nm7cKD1XKpX6a4QJYnInaYvDp0QkJ0bvAVq9ejWmTZuGyZMn4/7770dycjLatm2LDRs21Fve2dkZnp6e0mPfvn1o27ZtnQBIqVRqlOPNWokax+FTIpITo/YAVVVVITMzE/Hx8dI2KysrREVFISMjo1nHWL9+PcaOHQsHBweN7fv374e7uzucnJwwbNgwLFu2DC4uLvUeo7KyEpWVldJztVrdgtYQmT/eVoOI5MKoPUAlJSWorq6Gh4eHxnYPDw8UFhY2+fqjR4/i9OnTmDp1qsb24cOH49NPP0VaWhpWrlyJH3/8ETExMaiurq73OAkJCXB0dJQePj4+LW8UkZnzcrRHeDcXiwp+OLWfiO5l9Byg1li/fj0CAgIQGhqqsX3s2LHS/wcEBKBfv37o1q0b9u/fj4cffrjOceLj4xEXFyc9V6vVDIKILASn9hNRfYzaA+Tq6gpra2sUFRVpbC8qKoKnp2ejry0vL8fWrVsxZcqUJs/TtWtXuLq6Ijs7u979SqUSHTp00HgQkfkz9NR+9jQRmQ+jBkC2trYIDg5GWlqatK2mpgZpaWkIDw9v9LXbtm1DZWUlnn/++SbPc+nSJVy7dg1eXl6trjMRmQ9DTu1POZaPyBXpGL/uCCJXpCPlWL7Oz0FEumP0WWBxcXFYt24dNm/ejLNnz+Lll19GeXk5Jk+eDACYOHGiRpJ0rfXr12P06NF1EpvLysrw+uuv46effkJeXh7S0tIwatQo+Pv7Izo62iBtIiLTUDu1/8/0MbWfi0gSmR+j5wCNGTMGxcXFWLhwIQoLCxEUFITU1FQpMTo/Px9WVppx2vnz53Hw4EHs3bu3zvGsra1x6tQpbN68GaWlpfD29sajjz6KpUuXym4tICK5q53aP3/7aVQLobep/Y31NFlSMjmRJVEIIUTTxeRFrVbD0dERKpWK+UBEFqBAVaHXqf0FqgpErkivs4jkwXlDGQARGZA2399GHwIjItI3fU/t5yKSRObH6ENgRESWgItIEpkXBkBERDrCe/ARmQ8OgRER6QjXASIyH+wBIiLSAa44TWRe2ANERNRKXAeIyPwwACIiaiVDrjhNRLrBAIiIqJUMteI0EekOAyAiolbiOkBE5odJ0EREOmDO6wAVqCqQW1IOP1cHs6o3UWswACIi0hFzXAeIs9dIrjgERkQkU5y9RnLGAIiISKY4e43kjAEQEZFMcfYayRkDICIimeLsNZIzJkETEcmYOc9eI2oNBkBERDJnjrPXiFqLQ2BEREQkOwyAiIiISHYYABGRxStQVeBwTgnXtyEiCXOAiMgiNHQ7B650TET1YQBERGavoSCnoZWOB/VwY9IvkcxxCIyIzFpjt3PgSsdE1BAGQERk1hoLcrjSMRE1hAEQEZm1xoIcrnRMRA1hDhARmbXaIGf+9tOoFqJOkMOVjomoPgyAiMjsNRXkcKVjIroXAyAiMpqfL17H0bw/EOrrjEAfp1Ydi0EOEWmDARARGcWcf2Xh6xOXpedPP9AJq54NMl6FiEhWmARNRAb388XrGsEPAHx94jJ+vnjdSDUiIrlhAEREBnc07496tx/PYwBERIbBAIj0gvdeosaE+jrXuz3Et3V5QEREzWUSAVBSUhJ8fX1hZ2eHsLAwHD16tMGymzZtgkKh0HjY2dlplBFCYOHChfDy8oK9vT2ioqJw4cIFfTeD/k/KsXxErkjH+HVHELkiHSnH8o1dJTIxgT5OePqBThrbnn6gU6sToYmImsvoAVBKSgri4uKwaNEinDhxAoGBgYiOjsbVq1cbfE2HDh1QUFAgPX7//XeN/e+88w4++OADJCcn48iRI3BwcEB0dDRu3bql7+bIXmO3JSD6s1XPBuGbGRFYMKI3vpkRwQRoE8SeXLJkRp8Ftnr1akybNg2TJ08GACQnJ+Pbb7/Fhg0bMG/evHpfo1Ao4OnpWe8+IQQSExPx1ltvYdSoUQCATz/9FB4eHti5cyfGjh2rn4YQgMZvS8ApynSvQB8n9vqYqIZuMEtkKYzaA1RVVYXMzExERUVJ26ysrBAVFYWMjIwGX1dWVoYuXbrAx8cHo0aNwpkzZ6R9ubm5KCws1Dimo6MjwsLCGj0m6QbvvURk/tiTS3Jg1ACopKQE1dXV8PDw0Nju4eGBwsLCel/Ts2dPbNiwAd988w0+//xz1NTUICIiApcuXQIA6XXaHLOyshJqtVrjQS3Dey8Rmb/GenLJtBhqmNISh0ONPgSmrfDwcISHh0vPIyIi0Lt3b3z00UdYunRpi46ZkJCAJUuW6KqKssd7LxGZt9qe3D8HQezJNT2GGqa01OFQo/YAubq6wtraGkVFRRrbi4qKGszxuVebNm3Qv39/ZGdnA4D0Om2OGR8fD5VKJT0uXryobVOoHgKi6UJEZHLYk2v6DDVMacnDoUbtAbK1tUVwcDDS0tIwevRoAEBNTQ3S0tIwc+bMZh2juroav/zyCx577DEAgJ+fHzw9PZGWloagoCAAgFqtxpEjR/Dyyy/XewylUgmlUtnq9tBdlvprgUhO2JNr2gw14cSSJ7YYfQgsLi4OsbGxCAkJQWhoKBITE1FeXi7NCps4cSI6deqEhIQEAMDbb7+NAQMGwN/fH6WlpXj33Xfx+++/Y+rUqQDuzhCbNWsWli1bhu7du8PPzw8LFiyAt7e3FGSR/jT0a2FQDzezf7MQmYMCVQVyS8rh5+rQ6vccbzBrugw1TGnJw6FGD4DGjBmD4uJiLFy4EIWFhQgKCkJqaqqUxJyfnw8rq/+N1F2/fh3Tpk1DYWEhnJycEBwcjMOHD+P++++XyrzxxhsoLy/Hiy++iNLSUgwcOBCpqal1Fkw0d7r8oNMVS/61QGTq2Ptq3rT5TK8dppy//TSqhdDbMKWhzmMMCiEEEzXuoVar4ejoCJVKhQ4dOhi7OvUy1Q+6AlUFIlek1/m1cHDeUIt4wxCZKr73zFtLP9MLVBUGGaY01HlaS5vvb6OvBE3aM+WkNCZPEhkHp66br9Z8pns52iO8m0udz1hdT1tv6DwtYSpT6o0+BEbaM/VhJiZPEhmeJedqWDpdf6ab6ggBYFp1Yw+QGTKH1ZZ1+WuBiJrG3lfzpcvPdFMeITC1urEHyAxZclIaEbUce1/Nky4/0015hMDU6sYAyEzxg46I6sOp6+ZJV5/ppjwUamp14xCYGWgoYYzDTGRKTCWxkchc6eIz3ZSHQk2tbpwGXw9TmgZvSgljRA3h3ymRaTHlaev6rJs2398MgOphKgEQ1/Ugc8C/U9NiigukEhmKNt/fzAEyYaaWMEb1k/sXDv9OTQd74oiajwGQCTO1hDGqi184d/9OFQD+HAMpFODfqYHxPnxE2mEStAkztYQx0mRqa1qYFA6sGxxXgibSDnuATBynu5suDv3clVtSXifeEYDsroOxsceYSDvsATIDnO5umkxpRW5jTkE3pesgZ+wxJtIOe4CIWshUVuQ2dh6SqVwHYo8xkTY4Db4epjINnsyDMdfbMKUp6Ka87ggRyQOnwRMZkDFvPWBKeUi8BQMRmRPmAFGr8PYHxsX8GyKilmEARC2WciwfkSvSMX7dEUSuSEfKsXxjV0l2mPhKRNQyzAGqB3OAmmZKuSfE/BsiIoA5QGQAppR7Qsy/IbJkcr/djr4wAKIW4aJrRET6Z+xlLlrD1AM35gBRizD3hIhIv8z5djvmkCPKHiBqMS66RkSkP+aaamAuN+ZlAEStwtwTIiL9MNdUA3MJ3DgERkRE9eI6X8ZlrqkG5rI+GXuAiIhkrr5kVXNOvrUk5phqYC73B+Q6QPXgOkBEJBf1BTqDerhxnS9qNWOsT6bN9zeHwIiIZKqhZNXM3683mMNB1FxejvYI7+ZiskEzAyAiIplqKFkV/9cb9GemmMNB1BoMgIiIZKqhZNVgXyezTL4l0gaToKlVTH2lTyJqWGPJquaYfEukDQZA1GKGnCXCQItIPxoLdLjOF1kyBkDUIoZc6ZPTcYn0i4EOyZFJ5AAlJSXB19cXdnZ2CAsLw9GjRxssu27dOjz00ENwcnKCk5MToqKi6pSfNGkSFAqFxmP48OH6boasNLbSpy6Z871wiIjIdBk9AEpJSUFcXBwWLVqEEydOIDAwENHR0bh69Wq95ffv349x48bhhx9+QEZGBnx8fPDoo4/i8uXLGuWGDx+OgoIC6fHll18aojmyYaiVPg0VaBERkbwYPQBavXo1pk2bhsmTJ+P+++9HcnIy2rZtiw0bNtRb/osvvsD06dMRFBSEXr164ZNPPkFNTQ3S0tI0yimVSnh6ekoPJycnQzRHNgy1RLu5LKlORETmxag5QFVVVcjMzER8fLy0zcrKClFRUcjIyGjWMW7evInbt2/D2dlZY/v+/fvh7u4OJycnDBs2DMuWLYOLi0u9x6isrERlZaX0XK1Wt6A1zWNJybyGmCViLkuqExGReTFqAFRSUoLq6mp4eHhobPfw8MC5c+eadYw333wT3t7eiIqKkrYNHz4cTz31FPz8/JCTk4P58+cjJiYGGRkZsLa2rnOMhIQELFmypHWNaQZLTeYV0O/dVDgdl8yFJf3AsUT896E/M+tZYCtWrMDWrVuxf/9+2NnZSdvHjh0r/X9AQAD69euHbt26Yf/+/Xj44YfrHCc+Ph5xcXHSc7VaDR8fH53W1ZCzpgzFkAEdZ6mQqbPUHziWgv8+dC+j5gC5urrC2toaRUVFGtuLiorg6enZ6Gvfe+89rFixAnv37kW/fv0aLdu1a1e4uroiOzu73v1KpRIdOnTQeOiapSXzcnYW0f/w/WDa+O9D9TFqAGRra4vg4GCNBObahObw8PAGX/fOO+9g6dKlSE1NRUhISJPnuXTpEq5duwYvLy+d1LslLC2Z19ICOqLW4PvhfwpUFTicU2JSwQX/fag+Rp8FFhcXh3Xr1mHz5s04e/YsXn75ZZSXl2Py5MkAgIkTJ2okSa9cuRILFizAhg0b4Ovri8LCQhQWFqKsrAwAUFZWhtdffx0//fQT8vLykJaWhlGjRsHf3x/R0dFGaSNguFlThtJUQGeKH4JE+mJpP3BaKuVYPiJXpGP8uiOIXJGOlGP5xq4SAP77UP2MngM0ZswYFBcXY+HChSgsLERQUBBSU1OlxOj8/HxYWf0vTlu7di2qqqrwl7/8ReM4ixYtwuLFi2FtbY1Tp05h8+bNKC0thbe3Nx599FEsXboUSqXSoG27lyUl8zY2O4tj7SQ3nK1o2nmOXo72eLJ/J3x94n/rxY3u7230epFxKYQQ+p3CY4bUajUcHR2hUqn0kg9kSQpUFRoBXYGqApEr0jW6m60VChycN5QfNmTx7n0/yMnhnBKMX3ekzvYvpw1AeLf6lyAxlOZ8LnGGmGXQ5vvb6D1AZN7unZ3V2Fg7P1TI0sl5tmLtMNO9QYYpDDM19bnEXmt5MnoOEFkWOY61M9+JyLTzHBv7XOIMMfliDxDplNxyIfjLkeh/TDXPsbHPpcM5Jey1likGQGbA3MamTfVDUNdMOemT9Mfc3o+GZqrDgA19Lpny0B3pFwMgE2euPQym+iGoS8x3kh9zfT/SXfV9Lsmt15r+hwGQCWMPg+Fp8+uevxzlhe/H5jHHHjK59FqTJgZAJow9DIbV1K/7ez/Y+ctRXvh+bJo595DJodeaNDEAMmHsYTCcpn7dN/TBzl+O8sH3Y+PYQ0bmhtPgTZgpTyu1NI39um9qmqyXoz3Cu7nw38XC8f3YON5vi8wNe4BMHHsYDKOxX/cc+qBafD82zBx6yMwxP4n0hwGQiWjsjcmxaf1rKp/H1D/YyXD4fqyfqefEmXN+EukH7wVWD0PfC4xvTNPR0L2cUo7l1/lg578RUV2meD+0AlUFIhLS8ecvO4UCODxvmMnUkXSD9wIzI0wcNC0N/brn0AdR85hiD1nm79dx7y99IYATv1/HiH6mVVcyHAZARtaa/BKOZxuWKX6wE1HTGhro4PiHvDEAMrKWJg5y2IyIqHlCfJ2hADSHwAAE+zoZqUZkCjgN3shaMrWWdy8mU1SgqsDhnBL+HZLJ8XK0x4qnA6QvPCsAK54OYI+uzLEHyARom1/CadmmRddDkeY4tMkeSTJ1zOOjezEAMhHa5JeYw3obcqHrL35zDCSYyE/mgnl89GccAjNDXJFWf7QZxtH1UKS5Dm1yBWBqLg6TkilhD5CZYneu7mnb+6LroUhzHdpkjyQ1hzn2bpJlYw+QGeM9qHSnJb0vtV/8f9aaL35dH89Q2CMpT8bsLSXSBfYAEaFlvS+6Xvrf1G8l0Bj2SMqLsXtLiXSBARARWj6M09QXv7Yzusw5kGCCqTy0JOnd0MOkDb3vzHGGJekPAyAitK73paEv/pbmPOgykOAHPumaKfSWNqah9x1zkOhevBlqPQx9M1TSj5Z8+evqRo4FqgpErkiv84v34LyhBgtE+IFP+tCav2193yi1obptnx6OJz88bNT3IxmGNt/fTIImi5RyLB+RK9Ixft0RRK5IR8qx/Ga9TleJ5caeGs6kU9KX1iS963viRkPvu2N517lUA9XBITCyOM3JUdD30JCxp4Yz6ZT0yVRz1Rp63z3o68SlGqgO9gCRwel7MbSmel9a2jukDWNPDTfXKfVkPkxxGY6G3neBPk5cqoHqYA5QPZgDpD+GyEtpLEcBgEFzc/Sd89CYlGP5dZJOmQNEcvDzxes4lncdD/o6IdDnf3d8N+b7kQxDm+9vDoGRwRjqnlGNzTg5nFNi0KEhY04NN9VhCiJ9auxHFpdqoD9jAEQGY8i8lIa+/I2dm2No/MAnOeGNeUkbzAEigzF0Xkp9OQrGzs0hMifmdvNSY8++JPNiEgFQUlISfH19YWdnh7CwMBw9erTR8tu2bUOvXr1gZ2eHgIAA7NmzR2O/EAILFy6El5cX7O3tERUVhQsXLuizCdQMphJ8jHmwMw7OG4ovpw3AwXlDmRejI+b2ZdkUS2uPtgwxWaA16vv3YfI/acPoSdApKSmYOHEikpOTERYWhsTERGzbtg3nz5+Hu7t7nfKHDx/GoEGDkJCQgMcffxxbtmzBypUrceLECfTt2xcAsHLlSiQkJGDz5s3w8/PDggUL8Msvv+DXX3+FnZ1dk3ViErR+GSoRkasgG46lLbpoae3Rliks5NmYxv59mPwvb9p8fxs9AAoLC8ODDz6If/7znwCAmpoa+Pj44JVXXsG8efPqlB8zZgzKy8uxe/duaduAAQMQFBSE5ORkCCHg7e2NOXPmYO7cuQAAlUoFDw8PbNq0CWPHjm2yTgyAzJ/cv8AMydS/LLVlae1picM5JRi/7kid7V9OG4Dwbi5GqNH/NOffh7O95MtsVoKuqqpCZmYmoqKipG1WVlaIiopCRkZGva/JyMjQKA8A0dHRUvnc3FwUFhZqlHF0dERYWFiDx6ysrIRardZ4kPniKsiGZWl5F5bWnpYw5aGk5vz7mOIaRWR6jBoAlZSUoLq6Gh4eHhrbPTw8UFhYWO9rCgsLGy1f+19tjpmQkABHR0fp4ePj06L2kGngF5hhmfKXZUtYWntawlTy9erDfx/SFZNIgja2+Ph4qFQq6XHx4kVjV4lagR+QhmXKX5YtYWntaSlTnSzAfx/SFaOuA+Tq6gpra2sUFRVpbC8qKoKnp2e9r/H09Gy0fO1/i4qK4OXlpVEmKCio3mMqlUoolcqWNoNMTGMLIZJ+WNqii5bWnpYy1XWk+O9DumDUHiBbW1sEBwcjLS1N2lZTU4O0tDSEh4fX+5rw8HCN8gCwb98+qbyfnx88PT01yqjVahw5cqTBY5LlMdVfr5bM0vIuLK09lob/PtRaRl8JOi4uDrGxsQgJCUFoaCgSExNRXl6OyZMnAwAmTpyITp06ISEhAQDw2muvYfDgwVi1ahVGjBiBrVu34vjx4/j4448BAAqFArNmzcKyZcvQvXt3aRq8t7c3Ro8ebaxmkhGY6q9XIiIyPqMHQGPGjEFxcTEWLlyIwsJCBAUFITU1VUpizs/Ph5XV/zqqIiIisGXLFrz11luYP38+unfvjp07d0prAAHAG2+8gfLycrz44osoLS3FwIEDkZqa2qw1gIiIiMjyGX0dIFPEdYCIiIjMj9msA0RERERkDAyAiIiISHYYABEREZHsMAAiIiIi2WEARERERLLDAIiIiIhkhwEQERERyQ4DICIiIpIdBkBEREQkO0a/FYYpql0cW61WG7kmRERE1Fy139vNuckFA6B63LhxAwDg4+Nj5JoQERGRtm7cuAFHR8dGy/BeYPWoqanBlStX0L59eygUCp0eW61Ww8fHBxcvXpTlfcbYfnm3H+A1kHv7AV4Dtl9/7RdC4MaNG/D29ta4kXp92ANUDysrK9x33316PUeHDh1k+Ydfi+2Xd/sBXgO5tx/gNWD79dP+pnp+ajEJmoiIiGSHARARERHJDgMgA1MqlVi0aBGUSqWxq2IUbL+82w/wGsi9/QCvAdtvGu1nEjQRERHJDnuAiIiISHYYABEREZHsMAAiIiIi2WEARERERLLDAEjHkpKS4OvrCzs7O4SFheHo0aMNlj1z5gyefvpp+Pr6QqFQIDEx0XAV1SNtrsG6devw0EMPwcnJCU5OToiKimq0vDnQpv3bt29HSEgIOnbsCAcHBwQFBeGzzz4zYG31Q5tr8Gdbt26FQqHA6NGj9VtBPdOm/Zs2bYJCodB42NnZGbC2+qHt30BpaSlmzJgBLy8vKJVK9OjRA3v27DFQbXVPm/YPGTKkzt+AQqHAiBEjDFhj3dL23z8xMRE9e/aEvb09fHx8MHv2bNy6dUu/lRSkM1u3bhW2trZiw4YN4syZM2LatGmiY8eOoqioqN7yR48eFXPnzhVffvml8PT0FGvWrDFshfVA22swfvx4kZSUJE6ePCnOnj0rJk2aJBwdHcWlS5cMXHPd0Lb9P/zwg9i+fbv49ddfRXZ2tkhMTBTW1tYiNTXVwDXXHW2vQa3c3FzRqVMn8dBDD4lRo0YZprJ6oG37N27cKDp06CAKCgqkR2FhoYFrrVvaXoPKykoREhIiHnvsMXHw4EGRm5sr9u/fL7Kysgxcc93Qtv3Xrl3T+Pc/ffq0sLa2Fhs3bjRsxXVE2/Z/8cUXQqlUii+++ELk5uaK7777Tnh5eYnZs2frtZ4MgHQoNDRUzJgxQ3peXV0tvL29RUJCQpOv7dKli0UEQK25BkIIcefOHdG+fXuxefNmfVVRr1rbfiGE6N+/v3jrrbf0UT2DaMk1uHPnjoiIiBCffPKJiI2NNesASNv2b9y4UTg6Ohqodoah7TVYu3at6Nq1q6iqqjJUFfWqtZ8Da9asEe3btxdlZWX6qqJeadv+GTNmiGHDhmlsi4uLE5GRkXqtJ4fAdKSqqgqZmZmIioqStllZWSEqKgoZGRlGrJnh6OIa3Lx5E7dv34azs7O+qqk3rW2/EAJpaWk4f/48Bg0apM+q6k1Lr8Hbb78Nd3d3TJkyxRDV1JuWtr+srAxdunSBj48PRo0ahTNnzhiiunrRkmuwa9cuhIeHY8aMGfDw8EDfvn2xfPlyVFdXG6raOqOLz8H169dj7NixcHBw0Fc19aYl7Y+IiEBmZqY0TPbbb79hz549eOyxx/RaV94MVUdKSkpQXV0NDw8Pje0eHh44d+6ckWplWLq4Bm+++Sa8vb013jzmoqXtV6lU6NSpEyorK2FtbY0PP/wQjzzyiL6rqxctuQYHDx7E+vXrkZWVZYAa6ldL2t+zZ09s2LAB/fr1g0qlwnvvvYeIiAicOXNG7zdl1oeWXIPffvsN6enpeO6557Bnzx5kZ2dj+vTpuH37NhYtWmSIautMaz8Hjx49itOnT2P9+vX6qqJetaT948ePR0lJCQYOHAghBO7cuYOXXnoJ8+fP12tdGQCRyVixYgW2bt2K/fv3W0QSaHO1b98eWVlZKCsrQ1paGuLi4tC1a1cMGTLE2FXTuxs3bmDChAlYt24dXF1djV0dowgPD0d4eLj0PCIiAr1798ZHH32EpUuXGrFmhlNTUwN3d3d8/PHHsLa2RnBwMC5fvox3333X7AKg1lq/fj0CAgIQGhpq7KoYzP79+7F8+XJ8+OGHCAsLQ3Z2Nl577TUsXboUCxYs0Nt5GQDpiKurK6ytrVFUVKSxvaioCJ6enkaqlWG15hq89957WLFiBb7//nv069dPn9XUm5a238rKCv7+/gCAoKAgnD17FgkJCWYZAGl7DXJycpCXl4eRI0dK22pqagAANjY2OH/+PLp166bfSuuQLj4H2rRpg/79+yM7O1sfVdS7llwDLy8vtGnTBtbW1tK23r17o7CwEFVVVbC1tdVrnXWpNX8D5eXl2Lp1K95++219VlGvWtL+BQsWYMKECZg6dSoAICAgAOXl5XjxxRfxt7/9DVZW+snWYQ6Qjtja2iI4OBhpaWnStpqaGqSlpWn8urNkLb0G77zzDpYuXYrU1FSEhIQYoqp6oau/gZqaGlRWVuqjinqn7TXo1asXfvnlF2RlZUmPJ554AkOHDkVWVhZ8fHwMWf1W08XfQHV1NX755Rd4eXnpq5p61ZJrEBkZiezsbCn4BYD//ve/8PLyMqvgB2jd38C2bdtQWVmJ559/Xt/V1JuWtP/mzZt1gpzaYFjo83alek2xlpmtW7cKpVIpNm3aJH799Vfx4osvio4dO0pTWidMmCDmzZsnla+srBQnT54UJ0+eFF5eXmLu3Lni5MmT4sKFC8ZqQqtpew1WrFghbG1txVdffaUxDfTGjRvGakKraNv+5cuXi71794qcnBzx66+/ivfee0/Y2NiIdevWGasJrabtNbiXuc8C07b9S5YsEd99953IyckRmZmZYuzYscLOzk6cOXPGWE1oNW2vQX5+vmjfvr2YOXOmOH/+vNi9e7dwd3cXy5YtM1YTWqWl74GBAweKMWPGGLq6Oqdt+xctWiTat28vvvzyS/Hbb7+JvXv3im7duolnn31Wr/VkAKRj//jHP0Tnzp2Fra2tCA0NFT/99JO0b/DgwSI2NlZ6npubKwDUeQwePNjwFdchba5Bly5d6r0GixYtMnzFdUSb9v/tb38T/v7+ws7OTjg5OYnw8HCxdetWI9Rat7S5Bvcy9wBICO3aP2vWLKmsh4eHeOyxx8SJEyeMUGvd0vZv4PDhwyIsLEwolUrRtWtX8fe//13cuXPHwLXWHW3bf+7cOQFA7N2718A11Q9t2n/79m2xePFi0a1bN2FnZyd8fHzE9OnTxfXr1/VaR4UQ+uxfIiIiIjI9zAEiIiIi2WEARERERLLDAIiIiIhkhwEQERERyQ4DICIiIpIdBkBEREQkOwyAiIiISHYYABERGdGkSZMwevRoY1eDSHYYABFRvSZNmgSFQiE9XFxcMHz4cJw6dcrYVdOJP7et9jFw4EC9nS8vLw8KhQJZWVka299//31s2rRJb+clovoxACKiBg0fPhwFBQUoKChAWloabGxs8Pjjjxu7WjqzceNGqX0FBQXYtWtXveVu376ttzo4OjqiY8eOejs+EdWPARARNUipVMLT0xOenp4ICgrCvHnzcPHiRRQXF2PYsGGYOXOmRvni4mLY2tpKd4L29fXF0qVLMW7cODg4OKBTp05ISkrSeM3q1asREBAABwcH+Pj4YPr06SgrK5P2//777xg5ciScnJzg4OCAPn36YM+ePQCA69ev47nnnoObmxvs7e3RvXt3bNy4sdnt69ixo9Q+T09PODs7Sz01KSkpGDx4MOzs7PDFF1/g2rVrGDduHDp16oS2bdsiICAAX375pcbxampq8M4778Df3x9KpRKdO3fG3//+dwCAn58fAKB///5QKBQYMmQIgLpDYJWVlXj11Vfh7u4OOzs7DBw4EMeOHZP279+/HwqFAmlpaQgJCUHbtm0RERGB8+fPN7vdRMQAiIiaqaysDJ9//jn8/f3h4uKCqVOnYsuWLaisrJTKfP755+jUqROGDRsmbXv33XcRGBiIkydPYt68eXjttdewb98+ab+VlRU++OADnDlzBps3b0Z6ejreeOMNaf+MGTNQWVmJAwcO4JdffsHKlSvRrl07AMCCBQvw66+/4j//+Q/Onj2LtWvXwtXVVSftra3r2bNnER0djVu3biE4OBjffvstTp8+jRdffBETJkzA0aNHpdfEx8djxYoVUr22bNkCDw8PAJDKff/99ygoKMD27dvrPe8bb7yBr7/+Gps3b8aJEyfg7++P6Oho/PHHHxrl/va3v2HVqlU4fvw4bGxs8MILL+ik3USyoddbrRKR2YqNjRXW1tbCwcFBODg4CADCy8tLZGZmCiGEqKioEE5OTiIlJUV6Tb9+/cTixYul5126dBHDhw/XOO6YMWNETExMg+fdtm2bcHFxkZ4HBARoHPPPRo4cKSZPntyi9gEQdnZ2UvscHBzEjh07RG5urgAgEhMTmzzGiBEjxJw5c4QQQqjVaqFUKsW6devqLVt73JMnT2psj42NFaNGjRJCCFFWVibatGkjvvjiC2l/VVWV8Pb2Fu+8844QQogffvhBABDff/+9VObbb78VAERFRYU2l4BI1tgDREQNGjp0KLKyspCVlYWjR48iOjoaMTEx+P3332FnZ4cJEyZgw4YNAIATJ07g9OnTmDRpksYxwsPD6zw/e/as9Pz777/Hww8/jE6dOqF9+/aYMGECrl27hps3bwIAXn31VSxbtgyRkZFYtGiRRhL2yy+/jK1btyIoKAhvvPEGDh8+rFX71qxZI7UvKysLjzzyiLQvJCREo2x1dTWWLl2KgIAAODs7o127dvjuu++Qn58PADh79iwqKyvx8MMPa1WHP8vJycHt27cRGRkpbWvTpg1CQ0M1rhkA9OvXT/p/Ly8vAMDVq1dbfG4iuWEAREQNcnBwgL+/P/z9/fHggw/ik08+QXl5OdatWwcAmDp1Kvbt24dLly5h48aNGDZsGLp06dLs4+fl5eHxxx9Hv3798PXXXyMzM1PKEaqqqpLO8dtvv2HChAn45ZdfEBISgn/84x8AIAVjs2fPxpUrV/Dwww9j7ty5zT6/p6en1D5/f384ODhotP3P3n33Xbz//vt488038cMPPyArKwvR0dFSPe3t7Zt9Xl1o06aN9P8KhQLA3RwkImoeBkBE1GwKhQJWVlaoqKgAAAQEBCAkJATr1q3Dli1b6s1D+emnn+o87927NwAgMzMTNTU1WLVqFQYMGIAePXrgypUrdY7h4+ODl156Cdu3b8ecOXOkAAwA3NzcEBsbi88//xyJiYn4+OOPddlkyaFDhzBq1Cg8//zzCAwMRNeuXfHf//5X2t+9e3fY29tLCeD3srW1BXC3J6kh3bp1g62tLQ4dOiRtu337No4dO4b7779fRy0hIgCwMXYFiMh0VVZWorCwEMDdGVf//Oc/UVZWhpEjR0plpk6dipkzZ8LBwQFPPvlknWMcOnQI77zzDkaPHo19+/Zh27Zt+PbbbwEA/v7+uH37Nv7xj39g5MiROHToEJKTkzVeP2vWLMTExKBHjx64fv06fvjhBymAWrhwIYKDg9GnTx9UVlZi9+7d0j5d6969O7766iscPnwYTk5OWL16NYqKiqTAxM7ODm+++SbeeOMN2NraIjIyEsXFxThz5gymTJkCd3d32NvbIzU1Fffddx/s7Ozg6OiocQ4HBwe8/PLLeP311+Hs7IzOnTvjnXfewc2bNzFlyhS9tItIrtgDREQNSk1NhZeXF7y8vBAWFoZjx45h27Zt0hRuABg3bhxsbGwwbtw42NnZ1TnGnDlzcPz4cfTv3x/Lli3D6tWrER0dDQAIDAzE6tWrsXLlSvTt2xdffPEFEhISNF5fXV2NGTNmoHfv3hg+fDh69OiBDz/8EMDdXpX4+Hj069cPgwYNgrW1NbZu3aqXa/HWW2/hgQceQHR0NIYMGQJPT886KzgvWLAAc+bMwcKFC9G7d2+MGTNGysuxsbHBBx98gI8++gje3t4YNWpUvedZsWIFnn76aUyYMAEPPPAAsrOz8d1338HJyUkv7SKSK4UQQhi7EkRkvvLy8tCtWzccO3YMDzzwgMY+X19fzJo1C7NmzTJO5YiIGsAhMCJqkdu3b+PatWt46623MGDAgDrBDxGRKeMQGBG1yKFDh+Dl5YVjx47VydsxtuXLl6Ndu3b1PmJiYoxdPSIyARwCIyKL88cff9RZObmWvb09OnXqZOAaEZGpYQBEREREssMhMCIiIpIdBkBEREQkOwyAiIiISHYYABEREZHsMAAiIiIi2WEARERERLLDAIiIiIhkhwEQERERyc7/B8bU0O0L0FXBAAAAAElFTkSuQmCC", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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mCq1WKz799FNx5MgRMXnyZOHl5SUKCwtrjd+9e7dwdnYWixcvFkePHhVz584VTZo0EYcPH5ZiFi1aJHQ6nfj666/FL7/8Ih5++GEREhIiysvLpZj58+eLd999V8yYMUPodLoa1/nkk0/Es88+K3bt2iVOnTolPvvsM+Hu7i6WLVtm1v0ZDAYBQBgMBpPt5eXl4ujRoyZlooZj/RERkTXV9f19K40Qyk2Rio6ORq9evfD+++8DuLl+UFBQEJ555hkkJyfXiB85ciTKysqQlpYmbevduzfCw8OxcuVKCCEQEBCAmTNnYtasWQBurk/k6+uLNWvWYNSoUSbnW7NmDaZPn46SkpLblnXq1KnIy8tDRkZGg++vtLQUOp0OBoMBnp6e0vZr164hPz8fISEhcHNza/D56CbWHxERWVNd39+3Uqw7r7KyEjk5OYiLi/u/wjg5IS4uDpmZmbUek5mZaRIPAPHx8VJ8fn4+9Hq9SYxOp0N0dHSd52yovy4WWZeKigqUlpaavIiIiMgxKZZEFRUVoaqqCr6+vibbfX19a8zYqqbX6+uNr/7XnHM2xJ49e7Bhw4Yas8xulZKSAp1OJ72CgoJkX5OIiIjUTfGB5WqXm5uL4cOHY8GCBXjggQfqjZ09ezYMBoP0Onv2rI1KSURERLamWBLl7e0NZ2dnFBYWmmwvLCys8Ty1an5+fvXGV/9rzjnrc/ToUQwaNAhTpkzB3Llzbxvv6uoKT09Pk1d9FByOZtdYb0REpAaKJVFarRYRERFIT0+XthmNRqSnpyMmJqbWY2JiYkziAWDHjh1SfEhICPz8/ExiSktLkZWVVec563LkyBEMHDgQSUlJeP3118069naaNGkCALh61fYPS3QElZWVAABnZ2eFS0JERI2Zoo99mTFjBpKSkhAZGYmoqCgsWbIEZWVlmDBhAgBg3LhxCAwMREpKCgDgueeeQ2xsLN555x0MHToUqamp2L9/Pz766CMAgEajwfTp07Fw4UJ07NgRISEhmDdvHgICApCQkCBd98yZMyguLsaZM2dQVVWFQ4cOAQA6dOiAZs2aITc3F/fddx/i4+MxY8YMaTyVs7MzfHx87vi+nZ2d4eXlhQsXLgAAmjZtCo1Gc5ujCLiZaF+8eBFNmzaFiwufWkRERMpR9Fto5MiRuHjxIubPnw+9Xo/w8HBs27ZNGhh+5swZODn9X2NZnz598Pnnn2Pu3LmYM2cOOnbsiK+//hrdunWTYl544QWUlZVhypQpKCkpQb9+/bBt2zaTqfDz58/H2rVrpb979uwJ4OZK2wMGDMCXX36JixcvYt26dVi3bp0U17ZtW5w+fdoi917dvVidSFHDOTk5oU2bNkw8iYhIUYquE+XoGrLORFVVFa5fv27jktk3rVZrklwTERFZUkPXiWJ/iMKcnZ05toeIiMgO8ec8ERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZFA8iVq+fDmCg4Ph5uaG6Oho7Nu3r974TZs2oVOnTnBzc0NYWBi2bt1qsl8Igfnz58Pf3x/u7u6Ii4vDiRMnTGJef/119OnTB02bNoWXl1et1zlz5gyGDh2Kpk2bonXr1vjHP/6BGzdu3NG9EhERkeNQNInasGEDZsyYgQULFuDAgQPo0aMH4uPjceHChVrj9+zZg8TEREycOBEHDx5EQkICEhISkJubK8UsXrwYS5cuxcqVK5GVlQUPDw/Ex8fj2rVrUkxlZSX+53/+B0899VSt16mqqsLQoUNRWVmJPXv2YO3atVizZg3mz59v2QogIiIi+yUUFBUVJaZOnSr9XVVVJQICAkRKSkqt8Y899pgYOnSoybbo6GjxxBNPCCGEMBqNws/PT7z11lvS/pKSEuHq6iq++OKLGudbvXq10Ol0NbZv3bpVODk5Cb1eL21bsWKF8PT0FBUVFQ2+P4PBIAAIg8HQ4GOIiIhIWQ39/lasJaqyshI5OTmIi4uTtjk5OSEuLg6ZmZm1HpOZmWkSDwDx8fFSfH5+PvR6vUmMTqdDdHR0nees6zphYWHw9fU1uU5paSmOHDlS53EVFRUoLS01eREREZFjUiyJKioqQlVVlUmiAgC+vr7Q6/W1HqPX6+uNr/7XnHOac52/XqM2KSkp0Ol00isoKKjB1yQiIiL7ovjAckcye/ZsGAwG6XX27Fmli0RERERWolgS5e3tDWdnZxQWFppsLywshJ+fX63H+Pn51Rtf/a855zTnOn+9Rm1cXV3h6elp8iIiIiLHpFgSpdVqERERgfT0dGmb0WhEeno6YmJiaj0mJibGJB4AduzYIcWHhITAz8/PJKa0tBRZWVl1nrOu6xw+fNhkluCOHTvg6emJLl26NPg8RERE5LhclLz4jBkzkJSUhMjISERFRWHJkiUoKyvDhAkTAADjxo1DYGAgUlJSAADPPfccYmNj8c4772Do0KFITU3F/v378dFHHwEANBoNpk+fjoULF6Jjx44ICQnBvHnzEBAQgISEBOm6Z86cQXFxMc6cOYOqqiocOnQIANChQwc0a9YMDzzwALp06YKxY8di8eLF0Ov1mDt3LqZOnQpXV1eb1hERERGplI1mC9Zp2bJlok2bNkKr1YqoqCixd+9eaV9sbKxISkoyid+4caMIDQ0VWq1WdO3aVXz77bcm+41Go5g3b57w9fUVrq6uYtCgQeL48eMmMUlJSQJAjdfOnTulmNOnT4sHH3xQuLu7C29vbzFz5kxx/fp1s+6NSxwQERHZn4Z+f2uEEELBHM6hlZaWQqfTwWAwcHwUERGRnWjo9zdn5xERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMiieRC1fvhzBwcFwc3NDdHQ09u3bV2/8pk2b0KlTJ7i5uSEsLAxbt2412S+EwPz58+Hv7w93d3fExcXhxIkTJjHFxcUYM2YMPD094eXlhYkTJ+LKlSsmMdu3b0fv3r3RvHlz+Pj44NFHH8Xp06ctcs9ERERk/xRNojZs2IAZM2ZgwYIFOHDgAHr06IH4+HhcuHCh1vg9e/YgMTEREydOxMGDB5GQkICEhATk5uZKMYsXL8bSpUuxcuVKZGVlwcPDA/Hx8bh27ZoUM2bMGBw5cgQ7duxAWloafvrpJ0yZMkXan5+fj+HDh+O+++7DoUOHsH37dhQVFWHEiBHWqwwiIiKyL0JBUVFRYurUqdLfVVVVIiAgQKSkpNQa/9hjj4mhQ4eabIuOjhZPPPGEEEIIo9Eo/Pz8xFtvvSXtLykpEa6uruKLL74QQghx9OhRAUBkZ2dLMd99953QaDTijz/+EEIIsWnTJuHi4iKqqqqkmC1btgiNRiMqKysbfH8Gg0EAEAaDocHHEBERkbIa+v2tWEtUZWUlcnJyEBcXJ21zcnJCXFwcMjMzaz0mMzPTJB4A4uPjpfj8/Hzo9XqTGJ1Oh+joaCkmMzMTXl5eiIyMlGLi4uLg5OSErKwsAEBERAScnJywevVqVFVVwWAw4LPPPkNcXByaNGlS5z1VVFSgtLTU5EVERESOSbEkqqioCFVVVfD19TXZ7uvrC71eX+sxer2+3vjqf28X07p1a5P9Li4uaNmypRQTEhKC77//HnPmzIGrqyu8vLxw7tw5bNy4sd57SklJgU6nk15BQUH1xhMREZE8BYZy7DlVhAJDuWJlUHxguRrp9XpMnjwZSUlJyM7Oxo8//gitVou//e1vEELUedzs2bNhMBik19mzZ21YaiIiosZhQ/YZ9F2UgdGrstB3UQY2ZJ9RpBwuilwVgLe3N5ydnVFYWGiyvbCwEH5+frUe4+fnV2989b+FhYXw9/c3iQkPD5dibh24fuPGDRQXF0vHL1++HDqdDosXL5Zi1q1bh6CgIGRlZaF37961ls/V1RWurq63u3UiIiKSqcBQjtmbD8P4v20aRgHM2ZyL/qE+8Ne527QsirVEabVaREREID09XdpmNBqRnp6OmJiYWo+JiYkxiQeAHTt2SPEhISHw8/MziSktLUVWVpYUExMTg5KSEuTk5EgxGRkZMBqNiI6OBgBcvXoVTk6mVePs7CyVkYiIiJSRX1QmJVDVqoTA6aKrNi+Lot15M2bMwKpVq7B27Vrk5eXhqaeeQllZGSZMmAAAGDduHGbPni3FP/fcc9i2bRveeecdHDt2DC+//DL279+PadOmAQA0Gg2mT5+OhQsXYsuWLTh8+DDGjRuHgIAAJCQkAAA6d+6MwYMHY/Lkydi3bx92796NadOmYdSoUQgICAAADB06FNnZ2Xj11Vdx4sQJHDhwABMmTEDbtm3Rs2dP21YSERERSUK8PeCkMd3mrNEg2Lup7Qtjm8mCdVu2bJlo06aN0Gq1IioqSuzdu1faFxsbK5KSkkziN27cKEJDQ4VWqxVdu3YV3377rcl+o9Eo5s2bJ3x9fYWrq6sYNGiQOH78uEnMpUuXRGJiomjWrJnw9PQUEyZMEJcvXzaJ+eKLL0TPnj2Fh4eH8PHxEQ8//LDIy8sz6964xAEREZHlpe77XbRL/la0fTFNtEv+VqTu+92i52/o97dGiHpGStMdKS0thU6ng8FggKenp9LFISIichgFhnKcLrqKYO+mFh8L1dDvb8UGlhMRERHJ5a9zt/lA8ltxiQMiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQwucg8sKSnByZMnAQAdOnSAl5eXpcpEREREpHpmt0SdPn0aQ4cOhbe3N6KjoxEdHQ1vb2889NBDOH36tBWKSERERKQ+ZrVEnT17Fr1790aTJk3w2muvoXPnzgCAo0ePYsWKFYiJiUF2djbuuusuqxSWiIiIHE+BoRz5RWUI8faAv85d6eI0mEYIIRoaPHHiRJw8eRLbt2+Hm5ubyb7y8nIMHjwYHTt2xMcff2zxgtqj0tJS6HQ6GAwGeHp6Kl0cIiIi1dmQfQazNx+GUQBOGiBlRBhG9mqjaJka+v1tVnfetm3b8Prrr9dIoADA3d0dr732GrZu3Wp+aYmIiKjRKTCUSwkUABgFMGdzLgoM5coWrIHMSqKKiooQHBxc5/527dqhuLj4TstEREREjUB+UZmUQFWrEgKni64qUyAzmZVE+fv74+jRo3Xuz83NhZ+f3x0XioiIiBxfiLcHnDSm25w1GgR7N1WmQGYyK4lKSEjArFmzcPHixRr7Lly4gBdffBEJCQmWKhsRERE5MH+dO1JGhMFZczOTctZo8MaIbnYzuNysgeV//vknoqOjodfr8fjjj6NTp04QQiAvLw+ff/45/Pz8sHfvXrRs2dKaZbYbHFhORER0ewWGcpwuuopg76aqSKAa+v1t1hIHLVq0QFZWFubMmYPU1FSUlJQAALy8vDB69Gi88cYbTKCIiIjILP46d1UkT+YyqyXqr4QQUreej48PNBrNbY5ofNgSRUREZH+s0hL1VxqNBq1bt5Z7OBERWYm9LlxIZG/MTqK2bt2KzZs3o2XLlpgwYYK0ajlwc8zUo48+ioyMDIsWkoiIGsbWCxcyYaPGzKzZeZ9//jkefvhh6PV6ZGZm4p577sH69eul/ZWVlfjxxx/NKsDy5csRHBwMNzc3REdHY9++ffXGb9q0CZ06dYKbmxvCwsJqLO4phMD8+fPh7+8Pd3d3xMXF4cSJEyYxxcXFGDNmDDw9PeHl5YWJEyfiypUrNc7z9ttvIzQ0FK6urggMDMTrr79u1r0REdmSrRcu3JB9Bn0XZWD0qiz0XZSBDdlnrHIdIrUyK4l666238O677yItLQ3/7//9P6xduxZPPPEEPvnkE1kX37BhA2bMmIEFCxbgwIED6NGjB+Lj43HhwoVa4/fs2YPExERMnDgRBw8eREJCAhISEpCbmyvFLF68GEuXLsXKlSuRlZUFDw8PxMfH49q1a1LMmDFjcOTIEezYsQNpaWn46aefMGXKFJNrPffcc/j444/x9ttv49ixY9iyZQuioqJk3ScRkS3YcuFCe19pmsgSzBpY3qxZMxw+fBghISHStp07d+Lhhx/GW2+9hUceeQQBAQGoqqpq0Pmio6PRq1cvvP/++wAAo9GIoKAgPPPMM0hOTq4RP3LkSJSVlSEtLU3a1rt3b4SHh2PlypUQQiAgIAAzZ87ErFmzAAAGgwG+vr5Ys2YNRo0ahby8PHTp0gXZ2dmIjIwEcPNxNkOGDMG5c+cQEBCAvLw8dO/eHbm5ubj77rsbWj01cGA5EdlSgaEcfRdlmCRSzhoNfk4eaPGutj2nijB6VVaN7V9M7o2Y9q0sei0iW7PKs/M8PT1RWFhosm3gwIFIS0vDP/7xDyxbtqzB56qsrEROTg7i4uL+rzBOToiLi0NmZmatx2RmZprEA0B8fLwUn5+fD71ebxKj0+kQHR0txWRmZsLLy0tKoAAgLi4OTk5OyMq6+YHwzTffoF27dkhLS0NISAiCg4MxadKk2z7SpqKiAqWlpSYvIiJbseXChfa+0jSRJZiVREVFReG7776rsT02NhbffPMNlixZ0uBzFRUVoaqqCr6+vibbfX19odfraz1Gr9fXG1/97+1ibp1V6OLigpYtW0oxv/32G37//Xds2rQJ//rXv7BmzRrk5OTgb3/7W733lJKSAp1OJ72CgoLqjScisrSRvdrg5+SB+GJyb/ycPNBqg8rtfaVpIkswa3be888/jz179tS6b8CAAfjmm2/wr3/9yyIFU5LRaERFRQX+9a9/ITQ0FADwySefICIiAsePH6+zi2/27NmYMWOG9HdpaSkTKSKyOVstXDiyVxv0D/VR1UrTRLZkVhLVs2dP9OzZs85uqoiICERERDToXN7e3nB2dq7RPVhYWFjnQ4z9/Pzqja/+t7CwEP7+/iYx4eHhUsytA9dv3LiB4uJi6Xh/f3+4uLhICRQAaSmHM2fO1JlEubq6wtXVtd77JiJyJPa60jSRJZjVnefl5YUWLVrc9tUQWq0WERERSE9Pl7YZjUakp6cjJiam1mNiYmJM4gFgx44dUnxISAj8/PxMYkpLS5GVlSXFxMTEoKSkBDk5OVJMRkYGjEYjoqOjAQB9+/bFjRs3cOrUKSnmv//9LwCgbdu2Dbo/IiIicmxmtUTt3LlT+t9CCAwZMgQff/wxAgMDZV18xowZSEpKQmRkJKKiorBkyRKUlZVhwoQJAIBx48YhMDAQKSkpAG4uOxAbG4t33nkHQ4cORWpqKvbv34+PPvoIwM1V1KdPn46FCxeiY8eOCAkJwbx58xAQEICEhAQAN1uUBg8ejMmTJ2PlypW4fv06pk2bhlGjRiEgIADAzYHm99xzD/7+979jyZIlMBqNmDp1Ku6//36T1ikiIiJqxMQdaNasmTh16tSdnEIsW7ZMtGnTRmi1WhEVFSX27t0r7YuNjRVJSUkm8Rs3bhShoaFCq9WKrl27im+//dZkv9FoFPPmzRO+vr7C1dVVDBo0SBw/ftwk5tKlSyIxMVE0a9ZMeHp6igkTJojLly+bxPzxxx9ixIgRolmzZsLX11eMHz9eXLp0yax7MxgMAoAwGAxmHUdERKS08yVXxe6TF8X5kqtKF8XmGvr9LfsBxADQvHlz/PLLL2jXrp3lsjoHwnWiiIjIHtn68UFqY5V1ooiIiMixcTX6hrvjJEqj0dw+iIiIiOyCLR8fZO/MGlg+YsQIk7+vXbuGJ598Eh4eHibbN2/efOclIyIiIpurXo3+1scHcTX6msxKonQ6ncnfjz/+uEULQ8ooMJQjv6gMId4eXO+FiKiRq16Nfs7mXFQJwdXo63FHA8upfvYwsLyxDx4kIqLaFRjKG+1q9BxYTrfFwYNERFQXf507Ytq3anQJlDmYRDmYAkM59pwqalAixMGDRERE8pk1JorUzdyuOQ4eJCJ7wbGbpEZsiXIQcrrmqgcPOv/vMhUcPEhEarQh+wz6LsrA6FVZ6LsoAxuyzyhdJCIAbIlyGPV1zdWXFI3s1Qb9Q30sOniQvxiJyFLq+oHYP9SHny+kOCZRDuJOuub8de4W+zDibD8isiS5PxCJbIHdeQ5CDV1znO1HRJZW/QPxrzh2k9SCLVEOxBpdc+bgL0YisjQu/EhqxiTKwViya85cnO1HRNag9A9EorqwO48sRg1dikTkmLjwI6kRW6LIoviLkYiIGgsmUWRxSnYpEhER2Qq784iIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiJSsQJDOfacKuIzKIlUiOtEERGp1IbsM9JDvZ00QMqIMIzs1UbpYhHR/2JLFPGXLpEKFRjKpQQKuPlMyjmbc/nfKZGKsCWqkeMvXSJ1yi8qM3mYNwBUCYHTRVf5RAAilWBLVCPGX7pE6hXi7QEnjek2Z40Gwd5NlSkQEdXAJKoRq++XLhEpy1/njpQRYXDW3MyknDUavDGiG1uhiFSE3XmNWPUv3b8mUvylS6QeI3u1Qf9QH5wuuopg76ZMoIhUhi1RjRh/6RKpn7/OHTHtW/G/SyIVYktUI8dfukTqVmAoR35RGUK8PfjfJ5HKMIki+Ovc+eFMpEKcPUukbuzOIyJSIc6eJVI/JlFERHVQciFazp4lUj925xER1ULprjTOniVSP7ZEERHdQg1daZw9S6R+bIkiIrqFWh65wtmzROrGJIqI6BZq6krj7Fki9WJ3HhHRLdiVRkQNwZYoIqJasCuNiG6HSRQRUR3YlWYdXIWdHAWTKCIishmll44gsiRVjIlavnw5goOD4ebmhujoaOzbt6/e+E2bNqFTp05wc3NDWFgYtm7darJfCIH58+fD398f7u7uiIuLw4kTJ0xiiouLMWbMGHh6esLLywsTJ07ElStXar3eyZMn0bx5c3h5ed3RfVqKkgsAEhHJpYalI4gsSfEkasOGDZgxYwYWLFiAAwcOoEePHoiPj8eFCxdqjd+zZw8SExMxceJEHDx4EAkJCUhISEBubq4Us3jxYixduhQrV65EVlYWPDw8EB8fj2vXrkkxY8aMwZEjR7Bjxw6kpaXhp59+wpQpU2pc7/r160hMTMS9995r+ZuXYUP2GfRdlIHRq7LQd1EGNmSfUbpINTDJI6LacBV2cjQaIYS4fZj1REdHo1evXnj//fcBAEajEUFBQXjmmWeQnJxcI37kyJEoKytDWlqatK13794IDw/HypUrIYRAQEAAZs6ciVmzZgEADAYDfH19sWbNGowaNQp5eXno0qULsrOzERkZCQDYtm0bhgwZgnPnziEgIEA694svvojz589j0KBBmD59OkpKShp8b6WlpdDpdDAYDPD09JRTPSYKDOXouyijxrTrn5MHqmZcAZvqiagu9vAZRgQ0/Ptb0ZaoyspK5OTkIC4uTtrm5OSEuLg4ZGZm1npMZmamSTwAxMfHS/H5+fnQ6/UmMTqdDtHR0VJMZmYmvLy8pAQKAOLi4uDk5ISsrCxpW0ZGBjZt2oTly5c36H4qKipQWlpq8rIktf+KY1M9EdWHS0eQo1F0YHlRURGqqqrg6+trst3X1xfHjh2r9Ri9Xl9rvF6vl/ZXb6svpnXr1ib7XVxc0LJlSynm0qVLGD9+PNatW9fgVqSUlBS88sorDYqVQ00LANZGLas8E5F6cekIciSKj4lSq8mTJ2P06NHo379/g4+ZPXs2DAaD9Dp79qxFy6T2X3HVSd5fqSnJIyJ18Ne5I6Z9K9V8dhHJpWhLlLe3N5ydnVFYWGiyvbCwEH5+frUe4+fnV2989b+FhYXw9/c3iQkPD5dibh24fuPGDRQXF0vHZ2RkYMuWLXj77bcB3JzxZzQa4eLigo8++gh///vfa5TN1dUVrq6uDb19WdT8K646yZuzORdVQqguySMiIrIkRVuitFotIiIikJ6eLm0zGo1IT09HTExMrcfExMSYxAPAjh07pPiQkBD4+fmZxJSWliIrK0uKiYmJQUlJCXJycqSYjIwMGI1GREdHA7g5burQoUPS69VXX0Xz5s1x6NAhPPLII5apAJnU/CtuZK82+Dl5IL6Y3Bs/Jw/koHIiInJYii+2OWPGDCQlJSEyMhJRUVFYsmQJysrKMGHCBADAuHHjEBgYiJSUFADAc889h9jYWLzzzjsYOnQoUlNTsX//fnz00UcAAI1Gg+nTp2PhwoXo2LEjQkJCMG/ePAQEBCAhIQEA0LlzZwwePBiTJ0/GypUrcf36dUybNg2jRo2SZuZ17tzZpJz79++Hk5MTunXrZqOasV9c5ZmI5OJq5tRQanivKJ5EjRw5EhcvXsT8+fOh1+sRHh6Obdu2SQPDz5w5Ayen/2sw69OnDz7//HPMnTsXc+bMQceOHfH111+bJDcvvPACysrKMGXKFJSUlKBfv37Ytm0b3NzcpJj169dj2rRpGDRoEJycnPDoo49i6dKltrtxIlLFhyCpB5dIoYZSy3tF8XWiHJml14kiciRq+RAkdeAaUtRQtniv2MU6UUTUOHFNMeWo9YkCal8Hj9RDTe8VxbvziKjxUdOaYo2pS1HNrX9qXweP1ENN7xW2RBGRzallTTF7eBalpai99U/t6+CReqjpvcKWKCKyOTWsKVZXUtE/1Mchv7jV1PpXFzWvg0fqopb3CpMoIlKE0h+C9pBUWJKaukDqwyVSqKHU8F5hdx4RKUbJhWPV0qVoK2rqAiFyFGyJIqJGSQ1diramdOsfkaNhEkVEjVZjTCps1QXSmGY9UuPFJIqIGjU1jKtwNNZYSoFJGakRkygiIrKYhsx6NDchUvP6VtS4MYkiIiKLud2sR3MTosa2FIWasPXv9phEERGRxdS3lIKchKixLUWhFmz9axgucUBERBZT31IKcp551tiWolADta9uryZsiSJZ2MxLRHWpa9ajnAU/G+NSFEpj61/DMYkis7GZl4hup7ZZj3ITosa4FIWS7GV1ezXQCCHE7cNIjtLSUuh0OhgMBnh6eipdHIsoMJSj76KMGv9x/Zw8kB9sRNQgBYZyJkQqtyH7TI1ktzH9WG7o9zdbosgsbOYlojvFtbnUj61/DcMkiszCZl4iosaBye7tcXYemYUPMSUiIrqJLVFkNjbzEhERMYkimdjMS0REjR2784iICMDNWXN7ThWpclFFNZeNGi+2RBERkarXf1Nz2ahxY0sUEVEjp+bHfKi5bERMooiIGjk5z7SzFTWXjYhJFBFRI6fmh/yquWxETKKIiBo5Na//puayEfHZeVbkiM/OIyLHpeZn2qm5bOR4+Oy8RqrAUI78ojKEeHvwg4aIzKLm9d/UXDZqvJhEORBOAyZLYTJORHR7TKIcRF3TgPuH+vBLkMzCZJyIqGE4sNxBcBowWQLX5CEiajgmUQ6C04DJEpiMExE1HJMoB8FpwGQJTMaJiBqOY6IcyMhebdA/1IfTgB2ULQZ7VyfjczbnokoIJuNERPVgEuVg6poGzNlW9s2Wg72ZjBMRNQyTqEaAs63smxIzL7kmDxHR7XFMlIPjbCv7x8HeRETqxCTKwfEL2P5xsDcRkToxiXJw/AK2f5x5SUSkThwT5eA428oxcLA3EZH6MIlqBPgF7Bg42JuISF2YRDUS/AImIiKyLFWMiVq+fDmCg4Ph5uaG6Oho7Nu3r974TZs2oVOnTnBzc0NYWBi2bt1qsl8Igfnz58Pf3x/u7u6Ii4vDiRMnTGKKi4sxZswYeHp6wsvLCxMnTsSVK1ek/bt27cLw4cPh7+8PDw8PhIeHY/369Za7aSIiIrJriidRGzZswIwZM7BgwQIcOHAAPXr0QHx8PC5cuFBr/J49e5CYmIiJEyfi4MGDSEhIQEJCAnJzc6WYxYsXY+nSpVi5ciWysrLg4eGB+Ph4XLt2TYoZM2YMjhw5gh07diAtLQ0//fQTpkyZYnKd7t2746uvvsKvv/6KCRMmYNy4cUhLS7NeZRAREZHd0AghxO3DrCc6Ohq9evXC+++/DwAwGo0ICgrCM888g+Tk5BrxI0eORFlZmUky07t3b4SHh2PlypUQQiAgIAAzZ87ErFmzAAAGgwG+vr5Ys2YNRo0ahby8PHTp0gXZ2dmIjIwEAGzbtg1DhgzBuXPnEBAQUGtZhw4dCl9fX3z66acNurfS0lLodDoYDAZ4enqaVS9ERESkjIZ+fyvaElVZWYmcnBzExcVJ25ycnBAXF4fMzMxaj8nMzDSJB4D4+HgpPj8/H3q93iRGp9MhOjpaisnMzISXl5eUQAFAXFwcnJyckJWVVWd5DQYDWrZsaf6NEhERkcNRdGB5UVERqqqq4Ovra7Ld19cXx44dq/UYvV5fa7xer5f2V2+rL6Z169Ym+11cXNCyZUsp5lYbN25EdnY2Pvzwwzrvp6KiAhUVFdLfpaWldcYSERGRfVN8TJQ92LlzJyZMmIBVq1aha9eudcalpKRAp9NJr6CgIBuWkoiIiGxJ0STK29sbzs7OKCwsNNleWFgIPz+/Wo/x8/OrN77639vF3Dpw/caNGyguLq5x3R9//BHDhg3De++9h3HjxtV7P7Nnz4bBYJBeZ8+erTeeiIiI7JeiSZRWq0VERATS09OlbUajEenp6YiJian1mJiYGJN4ANixY4cUHxISAj8/P5OY0tJSZGVlSTExMTEoKSlBTk6OFJORkQGj0Yjo6Ghp265duzB06FC8+eabJjP36uLq6gpPT0+TFxERETkoobDU1FTh6uoq1qxZI44ePSqmTJkivLy8hF6vF0IIMXbsWJGcnCzF7969W7i4uIi3335b5OXliQULFogmTZqIw4cPSzGLFi0SXl5e4j//+Y/49ddfxfDhw0VISIgoLy+XYgYPHix69uwpsrKyxM8//yw6duwoEhMTpf0ZGRmiadOmYvbs2aKgoEB6Xbp0qcH3ZjAYBABhMBjupIqIrOJ8yVWx++RFcb7kqtJFISJSlYZ+fyueRAkhxLJly0SbNm2EVqsVUVFRYu/evdK+2NhYkZSUZBK/ceNGERoaKrRarejatav49ttvTfYbjUYxb9484evrK1xdXcWgQYPE8ePHTWIuXbokEhMTRbNmzYSnp6eYMGGCuHz5srQ/KSlJAKjxio2NbfB9MYkitUrd97sISU4TbV9MEyHJaSJ13+9KF4mIVKYx/9Bq6Pe34utEOTKuE0VqVGAoR99FGTD+5b98Z40GPycP5KOBiAgAsCH7DGZvPgyjAJw0QMqIMIzs1UbpYtmMXawTRUS2l19UZpJAAUCVEDhddFWZAhGRqhQYyqUECgCMApizORcFhnJlC6ZCTKKIGpkQbw84aUy3OWs0CPZuqkyByOIKDOXYc6qIX3okC39oNRyTKKJGxl/njpQRYXDW3MyknDUavDGiG7vyHMSG7DPouygDo1dloe+iDGzIPqN0kcjO8IdWw3FMlBVxTBSpWYGhHKeLriLYuykTKAfB8W5kKRuyz2DO5lxUCSH90OKYqJoUfewLUbUCQznyi8oQ4u1hkQ97S5/PEfnr3Fk3Dqa+bhj+f03mGNmrDfqH+vCH1m0wiSLFWXoWSGOfVUKNV3U3zK0tUeyGITn4Q+v2OCaKFGXpWSCcVUKNGce7EdkWW6JIUZbufmB3BjV27IYhsh0mUaQoS3c/sDuDiN0wRLbC7jxSlKW7H9idQUrjGk1EjQeXOLAiLnHQcJaebs/p+6SExjipgTNh5WPdqReXOCC7YunuB3ZnkK3VNamhf6iPw74XG2PSaCmsO8fA7jwiIgtobI/K4ExY+dRWd+yClo8tUUREFtCQSQ2O1H3DmbDyqanu2CJ2Z9gSRURkAbeb1OBoz7Tj89XkU0vdqa1FzB4xiSLVY1MzKcXc997IXm3wc/JAfDG5N35OHij9onfELyvOhJVPLXXX2LqgrYHdeaRqcpuaHanbhJQh971X26QGNXXfWBIX9pRPDXXHdfXuHFuiSLXk/nq3524Ttrqpg6VbjtTSfWMN/jp3xLRvxQRKBqXrTi0tYvaMLVGkWnJ+vdvzNHNHHeBpj62Clm45qv6ymrM5F1VC8MuKVEMNLWL2jEkUqZacpmZ77Tax5+SvPvaaGFqjm4NfVqRWXFdPPnbnkc2Y21Ulp6nZXrtNHHGApz0PprZWN4fS3TdEZFlsiSKbkNsiYe6vd3vtNnHEAZ722ipYjS1HRHQ7TKLI6u60q8rcpmZ7/PKz1+SvPo6QGKqhm6O+MWVqGG+mhjIQKYVJFFmdEi0S9X35/XL2T+w7XYyo4JboEdTCKteXwx6Tv/o4YmJoa/W14KphvJkaynA7TPLImphEkdWpqUVi5sZD+OrAH9Lfj94TiHceC7d5OeqihpYPS3K0xNCW6mvBBaD4RAR7mAxhD0ke2TcOLCerU8taJL+c/dMkgQKArw78gV/O/mnTcjQ2HEwtT30tuGqYiKCGMtTHnic2kP1gSxTZxMhebdDJrzmyT/+JXsEtLNKNZm4z/b7TxbVu33/6z9uWx167BNRQbjWUwR7drgVX6dZdNbUw18beJzaQfWASRTZh6WZ1OeeLCm5Z6/bI4PoTKHvtElBDudVQBnt1uzFlSo83U/uYN7UneeQYNEIIcfswkqO0tBQ6nQ4GgwGenp5KF0cxBYZy9F2UUePD7OfkgbI+cO/kfOaOibJ02W1FDeVWQxkcQYGhvM4xZfXtU0P5lLYh+0yNJI9JPDVEQ7+/2RJFVmfpZvU7Od87j4VjXExb7D/9JyIb0K3YkGupsbtKDV0ZaiiDI6hvsoEaJiKooQx1cdSJDWr8zGkIey13fZhEkdVZuln9Ts/XI6jhY7Judy21dlepoStDDWVQG0f8ElE7NSd5ctT3maPm95daPyvvFGfnkdVZenaeLWf71XctNc/+UcOMSDWUQU02ZJ9B30UZGL0qC30XZWBD9hmli0R2pr7PHDW/v9T8WXmn2BJFNmHpZnVbNtPXda076a6y9C/G2s6nhq4MNZRBDexhTSVSv7o+c3JO/6nq95cjd+0ziSKbsXSzui2b6Wu7ltzuKlvOVFRDV4aly6DmLou6OPKXCNlOXZ85uGUboK73lyN37bM7j0gmOd1Vlm7WduRm8tqoucuiPtVfIn/lKF8iZDt1feZEtG2h6veXI3ftsyWK6A6Y211l6S7AxtTCYc9dYmpfU4nsR12fOWp/fzlq1z6TKKI7ZE53VYi3BzQA/pr3aDSQ3QXoyM3kt7L3hNFRv0RIGQKm/zHYw/tLDcMLLI1JFJHSbrPc7e1aYB7pGWiygGhCzwCrfVApOR5JbQmjnLpwxC8Rsi21j4FsbDgmisiG8ovKauRMAqj3oa31tcAUGMrx74OmD1X++uB5q4yJUno8kprGVShdF9Q4NbYxkPaALVFENiSnNaW+Y2zVxaWW8Uhq6LJQS11Q42PvXdqOiC1RRFZSYCjHnlNFJr8S5bSm1HeMrWZ91ffhbWv+OnfEtG+l2JeGmuqCGhfO8lQftkQRWUF94xbktKbUdYytZn2pbTySklgXpBTO8lQfjRDiNsNaSa6GPgWaHEuBoRx9F2XU+JL9OXmgVQd8W7uLa0P2mRof3o7w7Cs5WBekpF/O/ons03+iVwMeok7yNPT7my1RRBamxLgFW8zKUcN4JLVgXZBSHPVBvvZKFWOili9fjuDgYLi5uSE6Ohr79u2rN37Tpk3o1KkT3NzcEBYWhq1bt5rsF0Jg/vz58Pf3h7u7O+Li4nDixAmTmOLiYowZMwaenp7w8vLCxIkTceXKFZOYX3/9Fffeey/c3NwQFBSExYsXW+aGyaE1ZNxCbeOl7IHS45HUhHVBtsbZeeqjeBK1YcMGzJgxAwsWLMCBAwfQo0cPxMfH48KFC7XG79mzB4mJiZg4cSIOHjyIhIQEJCQkIDc3V4pZvHgxli5dipUrVyIrKwseHh6Ij4/HtWvXpJgxY8bgyJEj2LFjB9LS0vDTTz9hypQp0v7S0lI88MADaNu2LXJycvDWW2/h5ZdfxkcffWS9yiBFWSqxud3gcU6PJyI5bjepwV5/nNkzxcdERUdHo1evXnj//fcBAEajEUFBQXjmmWeQnJxcI37kyJEoKytDWlqatK13794IDw/HypUrIYRAQEAAZs6ciVmzZgEADAYDfH19sWbNGowaNQp5eXno0qULsrOzERkZCQDYtm0bhgwZgnPnziEgIAArVqzASy+9BL1eD61WCwBITk7G119/jWPHjjXo3jgmyn5Yo4m8tnFKSoyXIiLH8MvZPzF8+Z4a2/8ztQ+O6S+zm8+CGvr9rWhLVGVlJXJychAXFydtc3JyQlxcHDIzM2s9JjMz0yQeAOLj46X4/Px86PV6kxidTofo6GgpJjMzE15eXlICBQBxcXFwcnJCVlaWFNO/f38pgaq+zvHjx/Hnn3/WWraKigqUlpaavEj9rNVEXlt3D6fHE5FcZZVVtW4/W8xuPqUomkQVFRWhqqoKvr6+Jtt9fX2h1+trPUav19cbX/3v7WJat25tst/FxQUtW7Y0iantHH+9xq1SUlKg0+mkV1BQUO03Tqpiy8SG67wQkVx1fX7gliU3AP44sxXFx0Q5ktmzZ8NgMEivs2fPKl0kagBbJjZqenQJEdmXuj4/Itq24I8zhSi6xIG3tzecnZ1RWFhosr2wsBB+fn61HuPn51dvfPW/hYWF8Pf3N4kJDw+XYm4duH7jxg0UFxebnKe26/z1GrdydXWFq6trnfdL6mTrBew4PZ6I5Krr84OLcCpD0SRKq9UiIiIC6enpSEhIAHBzYHl6ejqmTZtW6zExMTFIT0/H9OnTpW07duxATEwMACAkJAR+fn5IT0+XkqbS0lJkZWXhqaeeks5RUlKCnJwcREREAAAyMjJgNBoRHR0txbz00ku4fv06mjRpIl3n7rvvRosWXNzM0dg6seHT1olIrto+P/jjTCFCYampqcLV1VWsWbNGHD16VEyZMkV4eXkJvV4vhBBi7NixIjk5WYrfvXu3cHFxEW+//bbIy8sTCxYsEE2aNBGHDx+WYhYtWiS8vLzEf/7zH/Hrr7+K4cOHi5CQEFFeXi7FDB48WPTs2VNkZWWJn3/+WXTs2FEkJiZK+0tKSoSvr68YO3asyM3NFampqaJp06biww8/bPC9GQwGAUAYDIY7qSIiIiKyoYZ+fyueRAkhxLJly0SbNm2EVqsVUVFRYu/evdK+2NhYkZSUZBK/ceNGERoaKrRarejatav49ttvTfYbjUYxb9484evrK1xdXcWgQYPE8ePHTWIuXbokEhMTRbNmzYSnp6eYMGGCuHz5sknML7/8Ivr16ydcXV1FYGCgWLRokVn3xSSKiIjI/jT0+1vxdaIcGdeJIiIisj92sU4UERERkb1iEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZFD0AcSOrnox+NLSUoVLQkRERA1V/b19u4e6MImyosuXLwMAgoKCFC4JERERmevy5cvQ6XR17uez86zIaDTi/PnzaN68OTQajVnHlpaWIigoCGfPnm3Uz91jPbAOqrEeWAcA66Aa68G6dSCEwOXLlxEQEAAnp7pHPrElyoqcnJxw11133dE5PD09G+1/IH/FemAdVGM9sA4A1kE11oP16qC+FqhqHFhOREREJAOTKCIiIiIZmESplKurKxYsWABXV1eli6Io1gProBrrgXUAsA6qsR7UUQccWE5EREQkA1uiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCRKQcuXL0dwcDDc3NwQHR2Nffv21Rl75MgRPProowgODoZGo8GSJUtsV1ArM6ceVq1ahXvvvRctWrRAixYtEBcXV2+8vTCnDjZv3ozIyEh4eXnBw8MD4eHh+Oyzz2xYWuswpw7+KjU1FRqNBgkJCdYtoI2YUw9r1qyBRqMxebm5udmwtNZh7nuhpKQEU6dOhb+/P1xdXREaGoqtW7faqLTWY049DBgwoMZ7QaPRYOjQoTYsseWZ+15YsmQJ7r77bri7uyMoKAjPP/88rl27Zr0CClJEamqq0Gq14tNPPxVHjhwRkydPFl5eXqKwsLDW+H379olZs2aJL774Qvj5+Yn33nvPtgW2EnPrYfTo0WL58uXi4MGDIi8vT4wfP17odDpx7tw5G5fccsytg507d4rNmzeLo0ePipMnT4olS5YIZ2dnsW3bNhuX3HLMrYNq+fn5IjAwUNx7771i+PDhtimsFZlbD6tXrxaenp6ioKBAeun1ehuX2rLMrYOKigoRGRkphgwZIn7++WeRn58vdu3aJQ4dOmTjkluWufVw6dIlk/dBbm6ucHZ2FqtXr7ZtwS3I3DpYv369cHV1FevXrxf5+fli+/btwt/fXzz//PNWKyOTKIVERUWJqVOnSn9XVVWJgIAAkZKScttj27Zt6zBJ1J3UgxBC3LhxQzRv3lysXbvWWkW0ujutAyGE6Nmzp5g7d641imcTcurgxo0bok+fPuLjjz8WSUlJDpFEmVsPq1evFjqdzkalsw1z62DFihWiXbt2orKy0lZFtIk7/Vx47733RPPmzcWVK1esVUSrM7cOpk6dKu677z6TbTNmzBB9+/a1WhnZnaeAyspK5OTkIC4uTtrm5OSEuLg4ZGZmKlgy27JEPVy9ehXXr19Hy5YtrVVMq7rTOhBCID09HcePH0f//v2tWVSrkVsHr776Klq3bo2JEyfaophWJ7cerly5grZt2yIoKAjDhw/HkSNHbFFcq5BTB1u2bEFMTAymTp0KX19fdOvWDW+88QaqqqpsVWyLs8Rn4yeffIJRo0bBw8PDWsW0Kjl10KdPH+Tk5Ehdfr/99hu2bt2KIUOGWK2cfACxAoqKilBVVQVfX1+T7b6+vjh27JhCpbI9S9TDiy++iICAAJP/0OyJ3DowGAwIDAxERUUFnJ2d8cEHH+D++++3dnGtQk4d/Pzzz/jkk09w6NAhG5TQNuTUw913341PP/0U3bt3h8FgwNtvv40+ffrgyJEjd/zwcyXIqYPffvsNGRkZGDNmDLZu3YqTJ0/i6aefxvXr17FgwQJbFNvi7vSzcd++fcjNzcUnn3xirSJanZw6GD16NIqKitCvXz8IIXDjxg08+eSTmDNnjtXKySSK7NaiRYuQmpqKXbt2OcRgWnM0b94chw4dwpUrV5Ceno4ZM2agXbt2GDBggNJFs7rLly9j7NixWLVqFby9vZUujqJiYmIQExMj/d2nTx907twZH374IV577TUFS2Y7RqMRrVu3xkcffQRnZ2dERETgjz/+wFtvvWW3SdSd+uSTTxAWFoaoqCili2JTu3btwhtvvIEPPvgA0dHROHnyJJ577jm89tprmDdvnlWuySRKAd7e3nB2dkZhYaHJ9sLCQvj5+SlUKtu7k3p4++23sWjRIvzwww/o3r27NYtpVXLrwMnJCR06dAAAhIeHIy8vDykpKXaZRJlbB6dOncLp06cxbNgwaZvRaAQAuLi44Pjx42jfvr11C20FlvhcaNKkCXr27ImTJ09ao4hWJ6cO/P390aRJEzg7O0vbOnfuDL1ej8rKSmi1WquW2Rru5L1QVlaG1NRUvPrqq9YsotXJqYN58+Zh7NixmDRpEgAgLCwMZWVlmDJlCl566SU4OVl+BBPHRClAq9UiIiIC6enp0jaj0Yj09HSTX5WOTm49LF68GK+99hq2bduGyMhIWxTVaiz1XjAajaioqLBGEa3O3Dro1KkTDh8+jEOHDkmvhx9+GAMHDsShQ4cQFBRky+JbjCXeC1VVVTh8+DD8/f2tVUyrklMHffv2xcmTJ6VEGgD++9//wt/f3y4TKODO3gubNm1CRUUFHn/8cWsX06rk1MHVq1drJErVybWw1mOCrTZkneqVmpoqXF1dxZo1a8TRo0fFlClThJeXlzQ9eezYsSI5OVmKr6ioEAcPHhQHDx4U/v7+YtasWeLgwYPixIkTSt2CRZhbD4sWLRJarVZ8+eWXJtN5L1++rNQt3DFz6+CNN94Q33//vTh16pQ4evSoePvtt4WLi4tYtWqVUrdwx8ytg1s5yuw8c+vhlVdeEdu3bxenTp0SOTk5YtSoUcLNzU0cOXJEqVu4Y+bWwZkzZ0Tz5s3FtGnTxPHjx0VaWppo3bq1WLhwoVK3YBFy/5vo16+fGDlypK2LaxXm1sGCBQtE8+bNxRdffCF+++038f3334v27duLxx57zGplZBKloGXLlok2bdoIrVYroqKixN69e6V9sbGxIikpSfo7Pz9fAKjxio2NtX3BLcycemjbtm2t9bBgwQLbF9yCzKmDl156SXTo0EG4ubmJFi1aiJiYGJGamqpAqS3LnDq4laMkUUKYVw/Tp0+XYn19fcWQIUPEgQMHFCi1ZZn7XtizZ4+Ijo4Wrq6uol27duL1118XN27csHGpLc/cejh27JgAIL7//nsbl9R6zKmD69evi5dfflm0b99euLm5iaCgIPH000+LP//802rl0whhrTYuIiIiIsfFMVFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiMgBjB8/HgkJCUoXg6hRYRJFRFY1fvx4aDQa6dWqVSsMHjwYv/76q9JFs4i/3lv1q1+/fla73unTp6HRaHDo0CGT7f/85z+xZs0aq12XiGpiEkVEVjd48GAUFBSgoKAA6enpcHFxwUMPPaR0sSxm9erV0v0VFBRgy5YttcZdv37damXQ6XTw8vKy2vmJqCYmUURkda6urvDz84Ofnx/Cw8ORnJyMs2fP4uLFi7jvvvswbdo0k/iLFy9Cq9VKT3APDg7Ga6+9hsTERHh4eCAwMBDLly83Oebdd99FWFgYPDw8EBQUhKeffhpXrlyR9v/+++8YNmwYWrRoAQ8PD3Tt2hVbt24FAPz5558YM2YMfHx84O7ujo4dO2L16tUNvj8vLy/p/vz8/NCyZUupxWjDhg2IjY2Fm5sb1q9fj0uXLiExMRGBgYFo2rQpwsLC8MUXX5icz2g0YvHixejQoQNcXV3Rpk0bvP766wCAkJAQAEDPnj2h0WgwYMAAADW78yoqKvDss8+idevWcHNzQ79+/ZCdnS3t37VrFzQaDdLT0xEZGYmmTZuiT58+OH78eIPvm6ixYxJFRDZ15coVrFu3Dh06dECrVq0wadIkfP7556ioqJBi1q1bh8DAQNx3333Strfeegs9evTAwYMHkZycjOeeew47duyQ9js5OWHp0qU4cuQI1q5di4yMDLzwwgvS/qlTp6KiogI//fQTDh8+jDfffBPNmjUDAMybNw9Hjx7Fd999h7y8PKxYsQLe3t4Wud/qsubl5SE+Ph7Xrl1DREQEvv32W+Tm5mLKlCkYO3Ys9u3bJx0ze/ZsLFq0SCrX559/Dl9fXwCQ4n744QcUFBRg8+bNtV73hRdewFdffYW1a9fiwIED6NChA+Lj41FcXGwS99JLL+Gdd97B/v374eLigr///e8WuW+iRsFqjzYmIhJCJCUlCWdnZ+Hh4SE8PDwEAOHv7y9ycnKEEEKUl5eLFi1aiA0bNkjHdO/eXbz88svS323bthWDBw82Oe/IkSPFgw8+WOd1N23aJFq1aiX9HRYWZnLOvxo2bJiYMGGCrPsDINzc3KT78/DwEP/+979Ffn6+ACCWLFly23MMHTpUzJw5UwghRGlpqXB1dRWrVq2qNbb6vAcPHjTZnpSUJIYPHy6EEOLKlSuiSZMmYv369dL+yspKERAQIBYvXiyEEGLnzp0CgPjhhx+kmG+//VYAEOXl5eZUAVGjxZYoIrK6gQMH4tChQzh06BD27duH+Ph4PPjgg/j999/h5uaGsWPH4tNPPwUAHDhwALm5uRg/frzJOWJiYmr8nZeXJ/39ww8/YNCgQQgMDETz5s0xduxYXLp0CVevXgUAPPvss1i4cCH69u2LBQsWmAxsf+qpp5Camorw8HC88MIL2LNnj1n3995770n3d+jQIdx///3SvsjISJPYqqoqvPbaawgLC0PLli3RrFkzbN++HWfOnAEA5OXloaKiAoMGDTKrDH916tQpXL9+HX379pW2NWnSBFFRUSZ1BgDdu3eX/re/vz8A4MKFC7KvTdSYMIkiIqvz8PBAhw4d0KFDB/Tq1Qsff/wxysrKsGrVKgDApEmTsGPHDpw7dw6rV6/Gfffdh7Zt2zb4/KdPn8ZDDz2E7t2746uvvkJOTo40ZqqyslK6xm+//YaxY8fi8OHDiIyMxLJlywBASuief/55nD9/HoMGDcKsWbMafH0/Pz/p/jp06AAPDw+Te/+rt956C//85z/x4osvYufOnTh06BDi4+Olcrq7uzf4upbQpEkT6X9rNBoAN8dkEdHtMYkiIpvTaDRwcnJCeXk5ACAsLAyRkZFYtWoVPv/881rH5ezdu7fG3507dwYA5OTkwGg04p133kHv3r0RGhqK8+fP1zhHUFAQnnzySWzevBkzZ86UkjgA8PHxQVJSEtatW4clS5bgo48+suQtS3bv3o3hw4fj8ccfR48ePdCuXTv897//lfZ37NgR7u7u0qD6W2m1WgA3W7Tq0r59e2i1WuzevVvadv36dWRnZ6NLly4WuhMiclG6AETk+CoqKqDX6wHcnAn3/vvv48qVKxg2bJgUM2nSJEybNg0eHh545JFHapxj9+7dWLx4MRISErBjxw5s2rQJ3377LQCgQ4cOuH79OpYtW4Zhw4Zh9+7dWLlypcnx06dPx4MPPojQ0FD8+eef2Llzp5SEzZ8/HxEREejatSsqKiqQlpYm7bO0jh074ssvv8SePXvQokULvPvuuygsLJSSGzc3N7z44ot44YUXoNVq0bdvX1y8eBFHjhzBxIkT0bp1a7i7u2Pbtm2466674ObmBp1OZ3INDw8PPPXUU/jHP/6Bli1bok2bNli8eDGuXr2KiRMnWuW+iBojtkQRkdVt27YN/v7+8Pf3R3R0NLKzs7Fp0yZpej4AJCYmwsXFBYmJiXBzc6txjpkzZ2L//v3o2bMnFi5ciHfffRfx8fEAgB49euDdd9/Fm2++iW7dumH9+vVISUkxOb6qqgpTp05F586dMXjwYISGhuKDDz4AcLN1Z/bs2ejevTv69+8PZ2dnpKamWqUu5s6di3vuuQfx8fEYMGAA/Pz8aqw0Pm/ePMycORPz589H586dMXLkSGmckouLC5YuXYoPP/wQAQEBGD58eK3XWbRoER599FGMHTsW99xzD06ePInt27ejRYsWVrkvosZII4QQSheCiOj06dNo3749srOzcc8995jsCw4OxvTp0zF9+nRlCkdEVAt25xGRoq5fv45Lly5h7ty56N27d40EiohIrdidR0SK2r17N/z9/ZGdnV1jHJPS3njjDTRr1qzW14MPPqh08YhIYezOIyKqQ3FxcY0Vvqu5u7sjMDDQxiUiIjVhEkVEREQkA7vziIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEM/x8pab2+L5F6TwAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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wcGzfvl0aGF5YWAil8v/neX379sWaNWswe/ZszJo1Cx07dsSmTZukOaIA4NVXX0V5eTkSEhJQWlqK/v37Y/v27dIcUQAwd+5crFq1Svr7vvvuAwDs2rULAwcOxBdffIGLFy9i9erVWL16tRTXtm1bnD171lq7w2lkZWXh3nvvxWuvvQZ/f39s3boVY8eOhZeXFx577DEAfyRQffr0gUqlwooVK9CtWzecPXsWs2fPRu/evZGdnY127drZuCVERNQY0nMLMXPjERgEoFQAKcPCMKJ3G1tX67ZsOk+Us6trnonKykoUFBQgJCTEKLlzBOPHj0dpaak0rUSN3bt3Y9CgQfj9999NzgIfGxsLf39/fPLJJwCA559/Hp999hlOnz5tNF6toqICHTt2RFhYGL7++muTdXDk/UdERMaK9BXot3AnDLdkIy4KBfYmDUKAl7tN6mTuPFG8Os8JOEIX6K3zbBkMBqxbtw6jR4+uNeDf3d0dL7zwAr755htcvnzZFlUlIqJGVFBSbpRAAUC1EDhbcs02FbKATU/n0Z2zRRfo1q1b0aJFC6Oy+uZsWr9+PXJzc7Fy5UoAwMWLF1FaWoouXbqYjO/SpQuEEDh9+jQiIiIaruJERGR3Qnw8oFSgVk9UsI/9z6XInigHVqSvkBIo4I834KyNR63eIzVo0CBpMH7N46OPPjIZu2vXLkyYMAEffvghunXrZrSMZ5KJiCjAyx0pw8Lg8r85AF0UCrw1rLvNTuVZgj1RDqy+LlBrvvk8PDzQoUMHo7Lz58/Xivvuu+/w+OOP47333sPYsWOlcl9fX3h7eyM/P9/k9vPz86FQKGo9BxEROacRvdtgQKgvzpZcQ7BPc4dIoAD2RDm0mi7QW9lLF+ju3bsRGxuLt99+u9btcpRKJYYPH441a9bUuh1PRUUF/vnPf0Kr1d72XoVEROQ8ArzcEdX+bodJoAAmUQ7NXrtAd+3ahdjYWLz00kt46qmnpHm2bh0o/tZbb0Gj0eChhx7C119/jXPnzmHPnj3QarW4ceMGUlNTbdgCIiKi2+PpPAdnj12gq1atwrVr15CSkoKUlBSpPDo6Grt37wYA3H333fj++++xYMECTJ48GTqdDq1atcIjjzyC1atXo00b+58fhIiImjbOE2VFzjhPlD3g/iMiImviPFFEREREVsQkioiIiEgGJlFEREREMjCJIiIiIpKBSZQNcUy/PNxvRERkD5hE2UCzZs0AANeu2f/NFe1RVVUVAMDFxcXGNSEioqaM80TZgIuLC7y9vXHhwgUAQPPmzaFQKG6zFgGAwWDAxYsX0bx5c7i68u1LRES2w6OQjWg0GgCQEikyn1KpRJs2bZh4EhGRTTGJshGFQoGAgAD4+fnhxo0btq6OQ1GpVFAqeSaaiIhsi0mUjbm4uHBsDxERkQPiz3kiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDLYPIlKTU1FcHAw1Go1IiMjsX///nrjN2zYgM6dO0OtViMsLAwZGRlGy4UQmDt3LgICAuDu7o6YmBicOnXKKObNN99E37590bx5c3h7e5t8nsLCQsTGxqJ58+bw8/PDK6+8gps3b95RW4mIiMh52DSJSk9PR2JiIpKTk3Hw4EH06NEDWq0WFy5cMBmflZWF+Ph4TJw4EYcOHUJcXBzi4uJw9OhRKWbRokVYunQpVqxYgZycHHh4eECr1aKyslKKqaqqwjPPPIPnn3/e5PNUV1cjNjYWVVVVyMrKwqpVq5CWloa5c+c27A4gIiIixyVsKCIiQkyZMkX6u7q6WgQGBoqUlBST8cOHDxexsbFGZZGRkWLy5MlCCCEMBoPQaDTinXfekZaXlpYKNzc3sXbt2lrb+/TTT4WXl1et8oyMDKFUKoVOp5PKli9fLjw9PcX169fNbp9erxcAhF6vN3sdIiIisi1zj98264mqqqpCXl4eYmJipDKlUomYmBhkZ2ebXCc7O9soHgC0Wq0UX1BQAJ1OZxTj5eWFyMjIOrdZ1/OEhYXB39/f6HnKyspw7NixOte7fv06ysrKjB5ERETknGyWRJWUlKC6utooUQEAf39/6HQ6k+vodLp642v+tWSbljzPrc9hSkpKCry8vKRHUFCQ2c9JREREjsXmA8udycyZM6HX66XHuXPnbF0lIiIishKbJVE+Pj5wcXFBcXGxUXlxcTE0Go3JdTQaTb3xNf9ask1LnufW5zDFzc0Nnp6eRg8iIiJyTjZLolQqFXr27InMzEypzGAwIDMzE1FRUSbXiYqKMooHgB07dkjxISEh0Gg0RjFlZWXIycmpc5t1Pc+RI0eMrhLcsWMHPD090bVrV7O3Q0RERM7L1ZZPnpiYiHHjxqFXr16IiIjAkiVLUF5ejgkTJgAAxo4di9atWyMlJQUAMG3aNERHR+Pdd99FbGws1q1bhwMHDuBf//oXAEChUGD69Ol444030LFjR4SEhGDOnDkIDAxEXFyc9LyFhYW4fPkyCgsLUV1djcOHDwMAOnTogBYtWuDhhx9G165dMWbMGCxatAg6nQ6zZ8/GlClT4Obm1qj7iIiIiOxUI10tWKdly5aJNm3aCJVKJSIiIsT3338vLYuOjhbjxo0zil+/fr0IDQ0VKpVKdOvWTWzbts1oucFgEHPmzBH+/v7Czc1NDB48WJw8edIoZty4cQJArceuXbukmLNnz4pHHnlEuLu7Cx8fHzFjxgxx48YNi9rGKQ6IiIgcj7nHb4UQQtgwh3NqZWVl8PLygl6v5/goIiIiB2Hu8ZtX5xERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkg82TqNTUVAQHB0OtViMyMhL79++vN37Dhg3o3Lkz1Go1wsLCkJGRYbRcCIG5c+ciICAA7u7uiImJwalTp4xiLl++jNGjR8PT0xPe3t6YOHEirl69ahTzzTffoE+fPmjZsiV8fX3x1FNP4ezZsw3SZiIiInJ8Nk2i0tPTkZiYiOTkZBw8eBA9evSAVqvFhQsXTMZnZWUhPj4eEydOxKFDhxAXF4e4uDgcPXpUilm0aBGWLl2KFStWICcnBx4eHtBqtaisrJRiRo8ejWPHjmHHjh3YunUr9uzZg4SEBGl5QUEBhg4digcffBCHDx/GN998g5KSEgwbNsx6O4OIiIgci7ChiIgIMWXKFOnv6upqERgYKFJSUkzGDx8+XMTGxhqVRUZGismTJwshhDAYDEKj0Yh33nlHWl5aWirc3NzE2rVrhRBCHD9+XAAQubm5UszXX38tFAqF+PXXX4UQQmzYsEG4urqK6upqKWbLli1CoVCIqqoqs9un1+sFAKHX681eh4iIiGzL3OO3zXqiqqqqkJeXh5iYGKlMqVQiJiYG2dnZJtfJzs42igcArVYrxRcUFECn0xnFeHl5ITIyUorJzs6Gt7c3evXqJcXExMRAqVQiJycHANCzZ08olUp8+umnqK6uhl6vx2effYaYmBg0a9aszjZdv34dZWVlRg8iIiJyTjZLokpKSlBdXQ1/f3+jcn9/f+h0OpPr6HS6euNr/r1djJ+fn9FyV1dXtGrVSooJCQnBt99+i1mzZsHNzQ3e3t44f/481q9fX2+bUlJS4OXlJT2CgoLqjSciIiLHZfOB5fZIp9Ph2Wefxbhx45Cbm4vvvvsOKpUKTz/9NIQQda43c+ZM6PV66XHu3LlGrDURERE1JldbPbGPjw9cXFxQXFxsVF5cXAyNRmNyHY1GU298zb/FxcUICAgwigkPD5di/jxw/ebNm7h8+bK0fmpqKry8vLBo0SIpZvXq1QgKCkJOTg769Oljsn5ubm5wc3O7XdOJiIjICdisJ0qlUqFnz57IzMyUygwGAzIzMxEVFWVynaioKKN4ANixY4cUHxISAo1GYxRTVlaGnJwcKSYqKgqlpaXIy8uTYnbu3AmDwYDIyEgAwLVr16BUGu8aFxcXqY5ERERENr06b926dcLNzU2kpaWJ48ePi4SEBOHt7S10Op0QQogxY8aIpKQkKX7fvn3C1dVVLF68WOTn54vk5GTRrFkzceTIESlm4cKFwtvbW2zevFn8+OOPYujQoSIkJERUVFRIMUOGDBH33XefyMnJEXv37hUdO3YU8fHx0vLMzEyhUCjE/PnzxU8//STy8vKEVqsVbdu2FdeuXTO7fbw6j4iIyPGYe/y2aRIlhBDLli0Tbdq0ESqVSkRERIjvv/9eWhYdHS3GjRtnFL9+/XoRGhoqVCqV6Natm9i2bZvRcoPBIObMmSP8/f2Fm5ubGDx4sDh58qRRzKVLl0R8fLxo0aKF8PT0FBMmTBBXrlwxilm7dq247777hIeHh/D19RVPPPGEyM/Pt6htTKKIiIgcj7nHb4UQ9YyUpjtSVlYGLy8v6PV6eHp62ro6DqlIX4GCknKE+HggwMvd1tUhIqImwNzjt80GlhPdTnpuIWZuPAKDAJQKIGVYGEb0bmPrahEREQHgFAdkp4r0FVICBQAGAczaeBRF+grbVoyIiOh/mESRXSooKZcSqBrVQuBsyTXbVIiIiOhPmESRXQrx8YBSYVzmolAg2Ke5bSpERET0J0yiyC4FeLkjZVgYXBR/ZFIuCgXeGtadg8uJiMhucGA52a0RvdtgQKgvzpZcQ7BPcyZQRERkV5hEkV0L8HJn8kRERHaJp/OIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRURERCQDkygiIiIiGZhEEREREcnAJIqIiIhIBiZRRERERDIwiSIiIiKSgUkUERERkQxMooiIiIhkYBJFREREJIOr3BVLS0tx+vRpAECHDh3g7e3dUHUiIiIisnsW90SdPXsWsbGx8PHxQWRkJCIjI+Hj44PHHnsMZ8+etUIViYiIiOyPRT1R586dQ58+fdCsWTO8/vrr6NKlCwDg+PHjWL58OaKiopCbm4t77rnHKpUlIiIishcKIYQwN3jixIk4ffo0vvnmG6jVaqNlFRUVGDJkCDp27IiPPvqowSvqiMrKyuDl5QW9Xg9PT09bV4eIiIjMYO7x26KeqO3btyM9Pb1WAgUA7u7ueP311zFy5EjLa0tERETkYCwaE1VSUoLg4OA6l7dr1w6XL1+2qAKpqakIDg6GWq1GZGQk9u/fX2/8hg0b0LlzZ6jVaoSFhSEjI8NouRACc+fORUBAANzd3RETE4NTp04ZxVy+fBmjR4+Gp6cnvL29MXHiRFy9erXWdhYvXozQ0FC4ubmhdevWePPNNy1qGxERETkvi5KogIAAHD9+vM7lR48ehUajMXt76enpSExMRHJyMg4ePIgePXpAq9XiwoULJuOzsrIQHx+PiRMn4tChQ4iLi0NcXByOHj0qxSxatAhLly7FihUrkJOTAw8PD2i1WlRWVkoxo0ePxrFjx7Bjxw5s3boVe/bsQUJCgtFzTZs2DR999BEWL16MEydOYMuWLYiIiDC7bUREROTkhAWmTZsmwsLCxIULF2otKy4uFvfee6+YNm2a2duLiIgQU6ZMkf6urq4WgYGBIiUlxWT88OHDRWxsrFFZZGSkmDx5shBCCIPBIDQajXjnnXek5aWlpcLNzU2sXbtWCCHE8ePHBQCRm5srxXz99ddCoVCIX3/9VYpxdXUVJ06cMLstpuj1egFA6PX6O9oOERERNR5zj98W9UQlJyejsrIS7du3xwsvvIClS5fi/fffx3PPPYcOHTqgoqICc+fONWtbVVVVyMvLQ0xMjFSmVCoRExOD7Oxsk+tkZ2cbxQOAVquV4gsKCqDT6YxivLy8EBkZKcVkZ2fD29sbvXr1kmJiYmKgVCqRk5MDAPjqq6/Qrl07bN26FSEhIQgODsakSZNue6ry+vXrKCsrM3oQERGRc7JoYPldd92FnJwczJo1C+vWrUNpaSkAwNvbG6NGjcJbb72FVq1ambWtkpISVFdXw9/f36jc398fJ06cMLmOTqczGa/T6aTlNWX1xfj5+Rktd3V1RatWraSYn3/+Gb/88gs2bNiAf//736iursbf/vY3PP3009i5c2edbUpJScH8+fNv13QiIiJyAhbPWH7XXXdh+fLl+Oc//4mLFy8CAHx9faFQKBq8crZiMBhw/fp1/Pvf/0ZoaCgA4OOPP0bPnj1x8uRJdOrUyeR6M2fORGJiovR3WVkZgoKCGqXORERE1Lhk3ztPoVDAz88Pfn5+shIoHx8fuLi4oLi42Ki8uLi4zsHpGo2m3viaf28X8+eB6zdv3sTly5elmICAALi6ukoJFABpYtHCwsI62+Tm5gZPT0+jBxERETkni5OojIwMTJo0Ca+++iry8/ONlv3+++948MEHzdqOSqVCz549kZmZKZUZDAZkZmYiKirK5DpRUVFG8QCwY8cOKT4kJAQajcYopqysDDk5OVJMVFQUSktLkZeXJ8Xs3LkTBoMBkZGRAIB+/frh5s2bOHPmjBTz008/AQDatm1rVvuIiIjIyVkyWv3zzz8XLi4uIjY2VvTv31+o1WqxevVqablOpxNKpdLs7a1bt064ubmJtLQ0cfz4cZGQkCC8vb2FTqcTQggxZswYkZSUJMXv27dPuLq6isWLF4v8/HyRnJwsmjVrJo4cOSLFLFy4UHh7e4vNmzeLH3/8UQwdOlSEhISIiooKKWbIkCHivvvuEzk5OWLv3r2iY8eOIj4+XlpeXV0t7r//fjFgwABx8OBBceDAAREZGSkeeughS3YXr84jIiJyQOYevy1KosLDw8X7778v/Z2eni48PDzERx99JISwPIkSQohly5aJNm3aCJVKJSIiIsT3338vLYuOjhbjxo0zil+/fr0IDQ0VKpVKdOvWTWzbts1oucFgEHPmzBH+/v7Czc1NDB48WJw8edIo5tKlSyI+Pl60aNFCeHp6igkTJogrV64Yxfz6669i2LBhokWLFsLf31+MHz9eXLp0yaK2MYkiIiJyPOYevy26d16LFi1w5MgRhISESGW7du3CE088gXfeeQdPPvkkAgMDUV1d3eA9Zo6I984jIiJyPFa5d56npyeKi4uNkqhBgwZh69ateOyxx3D+/Hn5NSYiIiJyIBYNLI+IiMDXX39dqzw6OhpfffUVlixZ0lD1IiIiIrJrFiVRf/vb36BWq00uGzhwIL766iuMHTu2QSpGRER0p4r0Fcg6U4IifYWtq0JOyKIxUebexoTjf/7AMVFERLaTnluImRuPwCAApQJIGRaGEb3b2Lpa5ACsMibK29vbrIk1ObCciIhsqUhfISVQAGAQwKyNRzEg1BcBXu62rRw5DYuSqF27dkn/F0Lg0UcfxUcffYTWrVs3eMWIiIjkKigplxKoGtVC4GzJNSZR1GAsSqKio6ON/nZxcUGfPn3Qrl27Bq0UERHRnQjx8YBSAaNEykWhQLBPc9tVipyO7HvnERER2asAL3ekDAuDy/+GoLgoFHhrWHf2QlGDsqgnioiIyFGM6N0GA0J9cbbkGoJ9mjOBogZ3x0mUOQPNiYiIbCHAy53JE1mNRUnUsGHDjP6urKzEc889Bw8PD6PyjRs33nnNiIiIGlmRvgIFJeUI8fFg8kW3ZVES5eXlZfT3X/7ylwatDBERka1wXimylEWTbZJlONkmEZFjKNJXoN/CnbWu5tubNIg9Uk2QucdvXp1HRERNXn3zShHVhUkUERE1eTXzSt2K80rR7TCJagJ4A04iovpxXimSg/NEOTkOlCQiMg/nlSJLsSfKidV1A072SBERmRbg5Y6o9nczgSKzMIlyYhwoSUREZD1MopwYB0oSERFZD5MoJ8aBkkRERNbDgeVOjgMliYiIrINJVBPAG3ASERE1PJ7OIyIiIpKBSRQRERGRDEyiiIiIiGRgEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZGASRXatSF+BrDMlKNJX2LoqRERERngDYrJb6bmFmLnxCAwCUCqAlGFhGNG7ja2rRUREBIA9UWSnivQVUgIFAAYBzNp4lD1SRERkN5hEkV0qKCmXEqga1ULgbMk121SIiIjoT5hEkV0K8fGAUmFc5qJQINinuW0qRERE9CdMosguBXi5I2VYGFwUf2RSLgoF3hrWHQFe7jauGRER0R84sJzs1ojebTAg1BdnS64h2Kc5EygiIrIrTKLIrgV4uTN5IiIiu8TTeUREREQyMIkiIiIikoFJFBEREZEMTKKIiIiIZLCLJCo1NRXBwcFQq9WIjIzE/v37643fsGEDOnfuDLVajbCwMGRkZBgtF0Jg7ty5CAgIgLu7O2JiYnDq1CmjmMuXL2P06NHw9PSEt7c3Jk6ciKtXr5p8vtOnT6Nly5bw9va+o3YSNTTeW5CIyHZsnkSlp6cjMTERycnJOHjwIHr06AGtVosLFy6YjM/KykJ8fDwmTpyIQ4cOIS4uDnFxcTh69KgUs2jRIixduhQrVqxATk4OPDw8oNVqUVlZKcWMHj0ax44dw44dO7B161bs2bMHCQkJtZ7vxo0biI+PxwMPPNDwjSe6A+m5hei3cCdGfZiDfgt3Ij230NZVIiJqUhRCCHH7MOuJjIxE79698cEHHwAADAYDgoKC8OKLLyIpKalW/IgRI1BeXo6tW7dKZX369EF4eDhWrFgBIQQCAwMxY8YMvPzyywAAvV4Pf39/pKWlYeTIkcjPz0fXrl2Rm5uLXr16AQC2b9+ORx99FOfPn0dgYKC07ddeew2//fYbBg8ejOnTp6O0tNTstpWVlcHLywt6vR6enp5ydg+RSUX6CvRbuNPo1jguCgX2Jg3ilBBERHfI3OO3TXuiqqqqkJeXh5iYGKlMqVQiJiYG2dnZJtfJzs42igcArVYrxRcUFECn0xnFeHl5ITIyUorJzs6Gt7e3lEABQExMDJRKJXJycqSynTt3YsOGDUhNTb3zxhI1IN5bkIjI9mw62WZJSQmqq6vh7+9vVO7v748TJ06YXEen05mM1+l00vKasvpi/Pz8jJa7urqiVatWUsylS5cwfvx4rF692uxepOvXr+P69evS32VlZWatR2SpmnsL/rknivcWJCJqPDYfE2Wvnn32WYwaNQoDBgwwe52UlBR4eXlJj6CgICvWkJoy3luQiMj2bNoT5ePjAxcXFxQXFxuVFxcXQ6PRmFxHo9HUG1/zb3FxMQICAoxiwsPDpZg/D1y/efMmLl++LK2/c+dObNmyBYsXLwbwxxV/BoMBrq6u+Ne//oW//vWvteo2c+ZMJCYmSn+XlZUxkSKr4b0FiYhsy6Y9USqVCj179kRmZqZUZjAYkJmZiaioKJPrREVFGcUDwI4dO6T4kJAQaDQao5iysjLk5ORIMVFRUSgtLUVeXp4Us3PnThgMBkRGRgL4Y9zU4cOHpceCBQvQsmVLHD58GE8++aTJurm5ucHT09PoQWRNAV7uiGp/NxMoIiIbsPkNiBMTEzFu3Dj06tULERERWLJkCcrLyzFhwgQAwNixY9G6dWukpKQAAKZNm4bo6Gi8++67iI2Nxbp163DgwAH861//AgAoFApMnz4db7zxBjp27IiQkBDMmTMHgYGBiIuLAwB06dIFQ4YMwbPPPosVK1bgxo0bmDp1KkaOHCldmdelSxejeh44cABKpRLdu3dvpD1DRERE9szmSdSIESNw8eJFzJ07FzqdDuHh4di+fbs0MLywsBBK5f/vMOvbty/WrFmD2bNnY9asWejYsSM2bdpklNy8+uqrKC8vR0JCAkpLS9G/f39s374darVaivn8888xdepUDB48GEqlEk899RSWLl3aeA0nIiIih2bzeaKcGeeJIiIicjwOMU8UUUPjbVCIyJnwO82+2fx0HlFDSc8txMyNR2AQgFIBpAwLw4jebWxdLSIiWfidZv/YE0VOoUhfIX3ZAH9MQjlr41H+eiMih8TvNMfAJIqcAm+DQkTOhN9pjoFJFDmFmtug3Iq3QSEiR+UM32lNYTwXkyhyCrwNChE5E0f/TkvPLUS/hTsx6sMc9Fu4E+m5hbauklVwigMr4hQHja9IX8HboBCR03DE77QifQX6LdxZ6wbpe5MGOUwbzD1+8+o8cioBXu4O8yElIrodR/xOq288V0O2pUhfgYKScoT4eNhsHzGJIiIiogZTM57rzz1RDTmey16mf+CYKGrSmsLARyKixmTt8Vz2NP0De6KoybKXXzJERM5mRO82GBDqa5XxXI11utAc7IlqAtjbUps9/ZIhInJGAV7uiGp/d4MnNvY0/QOTKCfXVC4ztRQnsiMickz2NP0DT+c5sbp6WwaE+jrc1R4NrTEGPhIRkXVY83ShJdgT5cTY21I3e/olQ0RElrPW6UJLsCfKiXmoXEyWN1cxdwbs55cMERE5JiZRTqy8qtpk+bUqQyPXxH454kR2RERkH9gl4cTs6QoGIiIiZ8Mkyolx3A8RkWU4JQxZgqfznBzH/RARmYcT8JKl2BPVBNjDFQxERPaME/CSHEyiiIioyeOUMCQHkygiImryeCGO47GH8WtMooiIqMnjhTgNz5pJjr3c0kwhhBC3DyM5ysrK4OXlBb1eD09PT1tXh4iIbqNIX8ELcRqANQfpF+kr0G/hzlq37dqbNKjBXjNzj9/siSIiIvofXohz56w9SN+exq8xiSKnYg/nyImImjJrJzn2NH6NSRQ5DXs5R05E1JRZO8mxp/FrHBNlRRwT1Xga4xw5ERGZJz23ELM2HkW1EFKS09ATl1pz/Jq5x2/OWE5Oob7uY0dKoor0FSgoKUeIj4dD1ZuI6FaNcbcMe7iBPJMocgohPh5QALg1j1Io4FBzvPCWE0TU2Kz5w80ekhxrYxJFzsuBTlTXdTXLgFBfp/8SIiLb4A+3O8eB5eQUCkrKa+VMAnCYWzbY0yW7ROT8eK/AhsEkipyCPV3yKoej15+IHAt/uDUMJlHkFOzpklc5HL3+RORY+MOtYXCKAyviFAeNz9Fv2eDo9Scix9EY0xA4KnOP30yirIhJFBER2TP+cDON984jIiKi2xKOdCmzneEUB+RUOFklEZF5OMXBnWMSRU5DzhcCky4iaoo4N13DYBJFTkHOFwJ/hRFRU+Ust8qyNY6JIqdg6ZwnnGjO8RTpK5B1poSvEVED4BQHDYNJFDkFS78QONGcY0nPLUS/hTsx6sMc9Fu4E+m5hbauEpFD49x0DYOn88gp1Hwh/HnOk7q+EGqSrlsTKf4Ks08cu0FkHSN6t8GAUF9OcXAHmESR07DkC8HSpItsh2M3iKwnwMudn6M7wCSKnIolXwj8FeYY2GtITRmvILZvTKKoSeOvMPvXFHsNeeAkgFcQOwK7GFiempqK4OBgqNVqREZGYv/+/fXGb9iwAZ07d4ZarUZYWBgyMjKMlgshMHfuXAQEBMDd3R0xMTE4deqUUczly5cxevRoeHp6wtvbGxMnTsTVq1el5bt378bQoUMREBAADw8PhIeH4/PPP2+4RhOR2Ub0boO9SYOw9tk+2Js0yKkPJBxETwCvIHYUNk+i0tPTkZiYiOTkZBw8eBA9evSAVqvFhQsXTMZnZWUhPj4eEydOxKFDhxAXF4e4uDgcPXpUilm0aBGWLl2KFStWICcnBx4eHtBqtaisrJRiRo8ejWPHjmHHjh3YunUr9uzZg4SEBKPnuffee/Hll1/ixx9/xIQJEzB27Fhs3brVejuDiOoU4OWOqPZ3O3XPDA+cVINXEDsGm9+AODIyEr1798YHH3wAADAYDAgKCsKLL76IpKSkWvEjRoxAeXm5UTLTp08fhIeHY8WKFRBCIDAwEDNmzMDLL78MANDr9fD390daWhpGjhyJ/Px8dO3aFbm5uejVqxcAYPv27Xj00Udx/vx5BAYGmqxrbGws/P398cknn5jVNt6AmIgskXWmBKM+zKlVvvbZPohqf7cNakS2UqSvQL+FO2uNBdybNMipf0jYC4e4AXFVVRXy8vIQExMjlSmVSsTExCA7O9vkOtnZ2UbxAKDVaqX4goIC6HQ6oxgvLy9ERkZKMdnZ2fD29pYSKACIiYmBUqlETk7tL7Aaer0erVq1qnP59evXUVZWZvSgpo0TRJIlOAEi1eA8To7BpgPLS0pKUF1dDX9/f6Nyf39/nDhxwuQ6Op3OZLxOp5OW15TVF+Pn52e03NXVFa1atZJi/mz9+vXIzc3FypUr62xPSkoK5s+fX+dyalo4KJQs1RQH0VPd7O0KYl7wUBuvzjPDrl27MGHCBHz44Yfo1q1bnXEzZ85EYmKi9HdZWRmCgoIao4pkZzhBJN3KkoOPvR04ybbs5Qpi/ig0zaZJlI+PD1xcXFBcXGxUXlxcDI1GY3IdjUZTb3zNv8XFxQgICDCKCQ8Pl2L+PHD95s2buHz5cq3n/e677/D444/jvffew9ixY+ttj5ubG9zc3OqNoaaBE0RSDTkHH3s5cBIB/FFYH5uOiVKpVOjZsycyMzOlMoPBgMzMTERFRZlcJyoqyigeAHbs2CHFh4SEQKPRGMWUlZUhJydHiomKikJpaSny8vKkmJ07d8JgMCAyMlIq2717N2JjY/H2228bXblHdDsc20IAr7Yj58ArBetm8ykOEhMT8eGHH2LVqlXIz8/H888/j/LyckyYMAEAMHbsWMycOVOKnzZtGrZv3453330XJ06cwLx583DgwAFMnToVAKBQKDB9+nS88cYb2LJlC44cOYKxY8ciMDAQcXFxAIAuXbpgyJAhePbZZ7F//37s27cPU6dOxciRI6Ur83bt2oXY2Fi89NJLeOqpp6DT6aDT6XD58uXG3UHkkDgolAAefMg58Edh3Ww+JmrEiBG4ePEi5s6dC51Oh/DwcGzfvl0aGF5YWAil8v/nen379sWaNWswe/ZszJo1Cx07dsSmTZvQvXt3KebVV19FeXk5EhISUFpaiv79+2P79u1Qq9VSzOeff46pU6di8ODBUCqVeOqpp7B06VJp+apVq3Dt2jWkpKQgJSVFKo+Ojsbu3butuEfIWXBsC/GWNeQMeMFD3Ww+T5Qz4zxRRJSeW1jr4MMBueSIivQVTeZHobnHb5v3RBERObPG6JHkpefUGHjBQ21Mosiu8eBAzsCaBx9eek5kO0yiyG7x4EBUP156bnv8ode0MYkiu+QsBwd+wTove3htOR+ZbfGHHjGJIrvkDAcHfsE6L3t5bXn1n+04yw89ujM2nyeKyBRHn5eEkyw6L3t6bTkfme1wDjAC2BNFdsrR5yVxhp40Ms3eXlvOR2Yb7AUkgEkU2TFHPjjwC9Z5eahcTJY3V9muY5+Xnjc+R/+hRw2DSRTZNUc9OPAL1nmVV1WbLL9WZWjkmpCtOfIPPWoYTKKIrIRfsLZlravn2MtIt3LUH3rUMJhEEVmRpV+w9nDZvDOw5tVz9tjLyPcNkW3w3nlWxHvnkSXs5bJ5R1ekr0C/hTtr9RTtTRrUoAmGvdxHjO8booZn7vGbUxwQ2QF7umze0TXWpecBXu6Ian+3zXug+L4hsh0mUUR2gHPONBxHn2PMEnzfENkWkygiO9CUDvzW1pQmoOT7hsi2OLCcyA7Y42BlRzaidxt01rRE7tnf0Tv4LvQIusvWVbIKvm+IbIsDy62IA8vJ0qum7GWwsqOzt8HW1r56ju8booZl7vGbPVFEViLnQG5PUyI46mXz9nZj2MZI6DhXEZFtMIkisoLGOJBb8+Bsbz05lrCne9vZW0JHRA2LA8upSSvSVyDrTEmDXxJu7aumrHlpu6NfNt9Yg63Nee/w6jki58aeKHIqlpyCsmZvi7VvDWLN3hZ76smRozEGW5v73mmqt4hx1FPBRJZiEkVOw5KkyNqnWax9ILfmwdkZDvzWvG+hJe+dpnj1nCOfCiayFJMocgqWJkWN0dtizcvsrXlwdqYDv0DDX3xs6XunKd2ImmPAqKlhEkVOwdIDW2P0tlj7F7k1D86OfuC3t1O1TeXqOUc/FUxkKQ4sJ6dg6WBia89q3ViDs615/zZ7uDecHNbe901pRnRLcQZ1amrYE0VOQc4pKGv2tvAXue3I3feWDIZ29J46a3GmU8FE5mASRU5DzoHNWqdZnGFwtqOSs+8bY2LUpoIJJjUlPJ1HTsVeTkHxlI/tWLrv5Z7+s9YcY3LYU10A+/kcElkbe6KIrIS/yG3Hkn0v5/SfPV3Gb091IWpq2BNFTZq1f8Fb+xe5vfVA2BNz972lg6GL9BVI+tK45ypp4xGbvAaOPrs8kaNjEkV3zFEP5Om5hei3cCdGfZiDfgt3Ij230NZVsoij199S1nqfWXr6L++X32vNPiUEcPCX3xu0XubgbWXozxz1+xhwzLrzdB7dEUc9leDokwI6ev0tZU9zbl0uv15HeVWD1cdcvICh4TnyLWsc9fsYcNy6syfKAdlLtu7IpxIc/Re8o9ffEvY251YrD7c6ylUNWh9z2OsFDJZ+R9nLd5oj9+468vexI9edPVEOxp6ydUeeC6mxfsFb61dtU+qBsLf3Wc+2d0EBGJ3SUyiA+9s23G19LNEYFzBY88be9vKd5ui9u/b2ObGEI9edPVEOxN6ydUeenbgxfsFb81etvfZAWIO9vc8CvNyx8KkwqU5KBbBwWJhN9701L2Cw5H1s6XeUPX2nOXrvrr19TizhyHVnT5QDsbds3dFnJ7bmL/jG+FXbVKZQsMf3WVPZ99a+sbc9fac5eu+uPX5OzOXIdWcS5UDs8UPu6AcTa8063ZgHB1HrWjHnI+d91hgDhJ1931v7xt4eKheT5c1VjX+SxJEP5DUc+fvYUevOJMqB2OuHnLe/qK0xEl57GUvSWCx5n1l73zSVfW/p+9jS76jyqmqT5deqDHdcdzkc9UB+K0f+PnbEuiuEEM79U8qGysrK4OXlBb1eD09PzwbbbpG+wm4GkVLd0nMLax1MGupAW6SvQL+FO2sd3PYmDWryr5m1902RvgJ9U3bWGlielfRgg+17e/oMynkfm/sdxfcx2Stzj9/siXJA1szWm8ov7MZgzV+19jSWxN5Ye9/UN9lm7L13vn17+wxa88be9tq7TmQuJlEkcfRLfO2RtRJeexwfZy+svW/q6rxviD59e/0MWvOHmzOcQqOmi1MckMTRL/FtSprSFAeWsva+6RXcCn+6GhsKAD2D73yeqKb6GbT2PSaJrIU9USRh74Zj4S/4ullz39TMEzXzyyMw4I9foilPNcw8UfwMEjkWDiy3ImsNLLcmaw6GptuzpwHFVD9rXeDBzyCR7Zl7/GYSZUWOmEQB1r/6j0yztwHF9qYpJZj8DBLZFq/OI9kcca4OR2evA4rthT0mmD+c+x37z15GRHAr9Ahq2Pvm8TNI5BjsYmB5amoqgoODoVarERkZif3799cbv2HDBnTu3BlqtRphYWHIyMgwWi6EwNy5cxEQEAB3d3fExMTg1KlTRjGXL1/G6NGj4enpCW9vb0ycOBFXr141ivnxxx/xwAMPQK1WIygoCIsWLWqYBt8he7njOTWcpjqg2Bz2dH+1GjPWH8bQ1Cy8ue0EhqZmYcb6w/XG8zNL5JxsnkSlp6cjMTERycnJOHjwIHr06AGtVosLFy6YjM/KykJ8fDwmTpyIQ4cOIS4uDnFxcTh69KgUs2jRIixduhQrVqxATk4OPDw8oNVqUVlZKcWMHj0ax44dw44dO7B161bs2bMHCQkJ0vKysjI8/PDDaNu2LfLy8vDOO+9g3rx5+Ne//mW9nWEGa97UlmzHkW/AaW32lmD+cO53fHnwV6OyLw/+ih/O/W4ynp9ZIudl8zFRkZGR6N27Nz744AMAgMFgQFBQEF588UUkJSXVih8xYgTKy8uxdetWqaxPnz4IDw/HihUrIIRAYGAgZsyYgZdffhkAoNfr4e/vj7S0NIwcORL5+fno2rUrcnNz0atXLwDA9u3b8eijj+L8+fMIDAzE8uXL8fe//x06nQ4qlQoAkJSUhE2bNuHEiRNmta2hx0Rxdl/nxgHFptnb+/7D/57Bm9tqfwfMie2CiQ+0Myqzt7oTkXnMPX7btCeqqqoKeXl5iImJkcqUSiViYmKQnZ1tcp3s7GyjeADQarVSfEFBAXQ6nVGMl5cXIiMjpZjs7Gx4e3tLCRQAxMTEQKlUIicnR4oZMGCAlEDVPM/Jkyfx+++mf3Fev34dZWVlRo+GZG+/yKlhjejdBnuTBmHts32wN2kQE6j/sbc5sSKCW5ks72Vinih+Zomcm00HlpeUlKC6uhr+/v5G5f7+/nX29uh0OpPxOp1OWl5TVl+Mn5+f0XJXV1e0atXKKCYkJKTWNmqW3XVX7S/MlJQUzJ8/v+4G3yHOIeP8OKDYNHuaE6tH0F146v7WRqf0nrq/tcnB5fzMEjk3m4+JciYzZ86EXq+XHufOnWvQ7dvbL3KixmRPs1q/Ozwcm6f0xZzYLtg8pS/eHR5uMo6fWSLnZtOeKB8fH7i4uKC4uNiovLi4GBqNxuQ6Go2m3viaf4uLixEQEGAUEx4eLsX8eeD6zZs3cfnyZaPtmHqeW5/jz9zc3ODm5lZnexuCPf0iJ2rKegTdZdbUBvzMEjkvm/ZEqVQq9OzZE5mZmVKZwWBAZmYmoqKiTK4TFRVlFA8AO3bskOJDQkKg0WiMYsrKypCTkyPFREVFobS0FHl5eVLMzp07YTAYEBkZKcXs2bMHN27cMHqeTp06mTyV15js6Rc5Ed0eP7NETkrY2Lp164Sbm5tIS0sTx48fFwkJCcLb21vodDohhBBjxowRSUlJUvy+ffuEq6urWLx4scjPzxfJycmiWbNm4siRI1LMwoULhbe3t9i8ebP48ccfxdChQ0VISIioqKiQYoYMGSLuu+8+kZOTI/bu3Ss6duwo4uPjpeWlpaXC399fjBkzRhw9elSsW7dONG/eXKxcudLstun1egFA6PX6O9lFRERE1IjMPX7bPIkSQohly5aJNm3aCJVKJSIiIsT3338vLYuOjhbjxo0zil+/fr0IDQ0VKpVKdOvWTWzbts1oucFgEHPmzBH+/v7Czc1NDB48WJw8edIo5tKlSyI+Pl60aNFCeHp6igkTJogrV64Yxfzwww+if//+ws3NTbRu3VosXLjQonYxiSIiInI85h6/bT5PlDNz1HvnERERNWUOMU8UERERkaNiEkVEREQkA5MoIiIiIhmYRBERERHJwCSKiIiISAYmUUREREQyMIkiIiIikoFJFBEREZEMNr0BsbOrmce0rKzMxjUhIiIic9Uct283HzmTKCu6cuUKACAoKMjGNSEiIiJLXblyBV5eXnUu521frMhgMOC3335Dy5YtoVAoGmy7ZWVlCAoKwrlz55zydjLO3j7A+dvo7O0DnL+NbJ/jc/Y2WrN9QghcuXIFgYGBUCrrHvnEnigrUiqVuOeee6y2fU9PT6f8YNRw9vYBzt9GZ28f4PxtZPscn7O30Vrtq68HqgYHlhMRERHJwCSKiIiISAYmUQ7Izc0NycnJcHNzs3VVrMLZ2wc4fxudvX2A87eR7XN8zt5Ge2gfB5YTERERycCeKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJshOpqakIDg6GWq1GZGQk9u/fX2/8kiVL0KlTJ7i7uyMoKAh/+9vfUFlZeUfbtKaGbt+8efOgUCiMHp07d7Z2M+pkSftu3LiBBQsWoH379lCr1ejRowe2b99+R9tsDA3dRnt6Dffs2YPHH38cgYGBUCgU2LRp023X2b17N+6//364ubmhQ4cOSEtLqxVjL6+hNdrnyK9fUVERRo0ahdDQUCiVSkyfPt1k3IYNG9C5c2eo1WqEhYUhIyOj4StvJmu0MS0trdZrqFarrdOA27C0fRs3bsRDDz0EX19feHp6IioqCt98802tOGt/BplE2YH09HQkJiYiOTkZBw8eRI8ePaDVanHhwgWT8WvWrEFSUhKSk5ORn5+Pjz/+GOnp6Zg1a5bsbVqTNdoHAN26dUNRUZH02Lt3b2M0pxZL2zd79mysXLkSy5Ytw/Hjx/Hcc8/hySefxKFDh2Rv09qs0UbAfl7D8vJy9OjRA6mpqWbFFxQUIDY2FoMGDcLhw4cxffp0TJo0yehL3J5eQ2u0D3Dc1+/69evw9fXF7Nmz0aNHD5MxWVlZiI+Px8SJE3Ho0CHExcUhLi4OR48ebciqm80abQT+mO371tfwl19+aagqW8TS9u3ZswcPPfQQMjIykJeXh0GDBuHxxx9v/O9RQTYXEREhpkyZIv1dXV0tAgMDRUpKisn4KVOmiAcffNCoLDExUfTr10/2Nq3JGu1LTk4WPXr0sEp9LWVp+wICAsQHH3xgVDZs2DAxevRo2du0Nmu00Z5ew1sBEP/5z3/qjXn11VdFt27djMpGjBghtFqt9Le9vYY1Gqp9jvz63So6OlpMmzatVvnw4cNFbGysUVlkZKSYPHnyHdbwzjVUGz/99FPh5eXVYPVqKJa2r0bXrl3F/Pnzpb8b4zPInigbq6qqQl5eHmJiYqQypVKJmJgYZGdnm1ynb9++yMvLk7olf/75Z2RkZODRRx+VvU1rsUb7apw6dQqBgYFo164dRo8ejcLCQus1pA5y2nf9+vVaXebu7u7Sr3h7ev3k1ud2baxhD6+hHNnZ2Ub7AwC0Wq20P+ztNbTU7dpXw1FfP3OYuw8c3dWrV9G2bVsEBQVh6NChOHbsmK2rJIvBYMCVK1fQqlUrAI33GWQSZWMlJSWorq6Gv7+/Ubm/vz90Op3JdUaNGoUFCxagf//+aNasGdq3b4+BAwdKp7vkbNNarNE+AIiMjERaWhq2b9+O5cuXo6CgAA888ACuXLli1fb8mZz2abVa/OMf/8CpU6dgMBiwY8cObNy4EUVFRbK3aU3WaCNgP6+hHDqdzuT+KCsrQ0VFhd29hpa6XfsAx379zFHXPnCE189cnTp1wieffILNmzdj9erVMBgM6Nu3L86fP2/rqlls8eLFuHr1KoYPHw6g8b5HmUQ5oN27d+Ott97CP//5Txw8eBAbN27Etm3b8Prrr9u6ag3CnPY98sgjeOaZZ3DvvfdCq9UiIyMDpaWlWL9+vQ1rbp73338fHTt2ROfOnaFSqTB16lRMmDABSqXzfBzNaaMjv4bE188ZREVFYezYsQgPD0d0dDQ2btwIX19frFy50tZVs8iaNWswf/58rF+/Hn5+fo363K6N+mxUi4+PD1xcXFBcXGxUXlxcDI1GY3KdOXPmYMyYMZg0aRIAICwsDOXl5UhISMDf//53Wdu0Fmu0z1Sy4e3tjdDQUJw+fbrhG1EPOe3z9fXFpk2bUFlZiUuXLiEwMBBJSUlo166d7G1akzXaaIqtXkM5NBqNyf3h6ekJd3d3uLi42NVraKnbtc8UR3r9zFHXPnCE10+uZs2a4b777nOo13DdunWYNGkSNmzYYHTqrrG+R53np6+DUqlU6NmzJzIzM6Uyg8GAzMxMREVFmVzn2rVrtRIJFxcXAIAQQtY2rcUa7TPl6tWrOHPmDAICAhqo5ua5k32tVqvRunVr3Lx5E19++SWGDh16x9u0Bmu00RRbvYZyREVFGe0PANixY4e0P+ztNbTU7dpniiO9fuaQsw8cXXV1NY4cOeIwr+HatWsxYcIErF27FrGxsUbLGu0z2GBD1Em2devWCTc3N5GWliaOHz8uEhIShLe3t9DpdEIIIcaMGSOSkpKk+OTkZNGyZUuxdu1a8fPPP4tvv/1WtG/fXgwfPtzsbTp6+2bMmCF2794tCgoKxL59+0RMTIzw8fERFy5csPv2ff/99+LLL78UZ86cEXv27BEPPvigCAkJEb///rvZ22xs1mijPb2GV65cEYcOHRKHDh0SAMQ//vEPcejQIfHLL78IIYRISkoSY8aMkeJ//vln0bx5c/HKK6+I/Px8kZqaKlxcXMT27dulGHt6Da3RPkd+/YQQUnzPnj3FqFGjxKFDh8SxY8ek5fv27ROurq5i8eLFIj8/XyQnJ4tmzZqJI0eONGrbalijjfPnzxfffPONOHPmjMjLyxMjR44UarXaKKaxWNq+zz//XLi6uorU1FRRVFQkPUpLS6WYxvgMMomyE8uWLRNt2rQRKpVKREREiO+//15aFh0dLcaNGyf9fePGDTFv3jzRvn17oVarRVBQkHjhhReMDlC322Zja+j2jRgxQgQEBAiVSiVat24tRowYIU6fPt2ILTJmSft2794tunTpItzc3MTdd98txowZI3799VeLtmkLDd1Ge3oNd+3aJQDUetS0ady4cSI6OrrWOuHh4UKlUol27dqJTz/9tNZ27eU1tEb7HP31MxXftm1bo5j169eL0NBQoVKpRLdu3cS2bdsap0EmWKON06dPl96f/v7+4tFHHxUHDx5svEbdwtL2RUdH1xtfw9qfQYUQdZwfISIiIqI6cUwUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMjCJIiJqQnbv3g2FQoHS0lJbV4XI4TGJIiKrGD9+PBQKBRYuXGhUvmnTJigUCulvIQQ+/PBDREVFwdPTEy1atEC3bt0wbdo0s2+Eeu3aNcycORPt27eHWq2Gr68voqOjsXnzZikmODgYS5YsaZC2WVvNvlMoFGjWrBlCQkLw6quvorKy0qLtDBw4ENOnTzcq69u3L4qKiuDl5dWANSZqmphEEZHVqNVqvP322/j9999NLhdCYNSoUXjppZfw6KOP4ttvv8Xx48fx8ccfQ61W44033jDreZ577jls3LgRy5Ytw4kTJ7B9+3Y8/fTTuHTpUkM2p1ENGTIERUVF+Pnnn/Hee+9h5cqVSE5OvuPtqlQqaDQao0SWiGRq0JvIEBH9z7hx48Rjjz0mOnfuLF555RWp/D//+Y+o+epZu3atACA2b95schsGg8Gs5/Ly8hJpaWl1Ljd1n60a//3vf0X//v2FWq0W99xzj3jxxRfF1atXpeX//ve/Rc+ePUWLFi2Ev7+/iI+PF8XFxdLymnt+bd++XYSHhwu1Wi0GDRokiouLRUZGhujcubNo2bKliI+PF+Xl5Wa1Z9y4cWLo0KFGZcOGDRP33Xef9HdJSYkYOXKkCAwMFO7u7qJ79+5izZo1Rtv4c5sLCgqk+t56L8ovvvhCdO3aVahUKtG2bVuxePFis+pJ1NSxJ4qIrMbFxQVvvfUWli1bhvPnz9davnbtWnTq1AlPPPGEyfXN7S3RaDTIyMjAlStXTC7fuHEj7rnnHixYsABFRUUoKioCAJw5cwZDhgzBU089hR9//BHp6enYu3cvpk6dKq1748YNvP766/jhhx+wadMmnD17FuPHj6/1HPPmzcMHH3yArKwsnDt3DsOHD8eSJUuwZs0abNu2Dd9++y2WLVtmVnv+7OjRo8jKyoJKpZLKKisr0bNnT2zbtg1Hjx5FQkICxowZg/379wMA3n//fURFReHZZ5+V2hwUFFRr23l5eRg+fDhGjhyJI0eOYN68eZgzZw7S0tJk1ZWoSbF1FkdEzunW3pQ+ffqIv/71r0II456ozp07iyeeeMJovWnTpgkPDw/h4eEhWrdubdZzfffdd+Kee+4RzZo1E7169RLTp08Xe/fuNYpp27ateO+994zKJk6cKBISEozK/vvf/wqlUikqKipMPldubq4AIK5cuSKE+P89Uf/3f/8nxaSkpAgA4syZM1LZ5MmThVarNas948aNEy4uLsLDw0O4ubkJAEKpVIovvvii3vViY2PFjBkzpL+jo6PFtGnTjGL+3BM1atQo8dBDDxnFvPLKK6Jr165m1ZWoKWNPFBFZ3dtvv41Vq1YhPz//trF///vfcfjwYcydOxdXr141a/sDBgzAzz//jMzMTDz99NM4duwYHnjgAbz++uv1rvfDDz8gLS0NLVq0kB5arRYGgwEFBQUA/uipefzxx9GmTRu0bNkS0dHRAIDCwkKjbd17773S//39/dG8eXO0a9fOqOzChQtmtQcABg0ahMOHDyMnJwfjxo3DhAkT8NRTT0nLq6ur8frrryMsLAytWrVCixYt8M0339Sq1+3k5+ejX79+RmX9+vXDqVOnUF1dbdG2iJoaJlFEZHUDBgyAVqvFzJkzjco7duyIkydPGpX5+vqiQ4cO8PPzs+g5mjVrhgceeACvvfYavv32WyxYsACvv/46qqqq6lzn6tWrmDx5Mg4fPiw9fvjhB5w6dQrt27dHeXk5tFotPD098fnnnyM3Nxf/+c9/AKDWdps1ayb9v+aqulspFAoYDAaz2+Ph4YEOHTqgR48e+OSTT5CTk4OPP/5YWv7OO+/g/fffx2uvvYZdu3bh8OHD0Gq19baXiBqWq60rQERNw8KFCxEeHo5OnTpJZfHx8Rg1ahQ2b96MoUOHNujzde3aFTdv3kRlZSVUKhVUKlWtnpX7778fx48fR4cOHUxu48iRI7h06RIWLlwojSc6cOBAg9bTHEqlErNmzUJiYiJGjRoFd3d37Nu3D0OHDsVf/vIXAIDBYMBPP/2Erl27SuuZavOfdenSBfv27TMq27dvH0JDQ+Hi4tLwjSFyIuyJIqJGERYWhtGjR2Pp0qVS2ciRI/H0009j5MiRWLBgAXJycnD27Fl89913SE9PN/sgPnDgQKxcuRJ5eXk4e/YsMjIyMGvWLAwaNAienp4A/pgnas+ePfj1119RUlICAHjttdeQlZWFqVOn4vDhwzh16hQ2b94sDSxv06YNVCoVli1bhp9//hlbtmy57SlCa3nmmWfg4uKC1NRUAH/04u3YsQNZWVnIz8/H5MmTUVxcbLROcHCwtE9LSkpM9oTNmDEDmZmZeP311/HTTz9h1apV+OCDD/Dyyy83SruIHBmTKCJqNAsWLDA6kCsUCqSnp2PJkiXIyMjA4MGD0alTJ/z1r39FUFAQ9u7da9Z2tVotVq1ahYcffhhdunTBiy++CK1Wi/Xr1xs999mzZ9G+fXv4+voC+GMc03fffYeffvoJDzzwAO677z7MnTsXgYGBAP44tZiWloYNGzaga9euWLhwIRYvXtyAe8R8rq6umDp1KhYtWoTy8nLMnj0b999/P7RaLQYOHAiNRoO4uDijdV5++WW4uLiga9eu8PX1NTle6v7778f69euxbt06dO/eHXPnzsWCBQtMXoFIRMYUQghh60oQERERORr2RBERERHJwCSKiOzerVMQ/Pnx3//+19bVs0hhYWG97bF0igIish2eziMiu1ffjYhbt24Nd3f3RqzNnbl58ybOnj1b5/Lg4GC4uvLCaSJHwCSKiIiISAaeziMiIiKSgUkUERERkQxMooiIiIhkYBJFREREJAOTKCIiIiIZmEQRERERycAkioiIiEgGJlFEREREMvw/G2qoRBvoqD0AAAAASUVORK5CYII=", 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", 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", 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nT5vEVFZWIiEhAT4+PvDz80NSUhKuXr0qLV+5ciUUCkWjl7e3t3wNJyIiIqdi88QqKysLKSkpSEtLw6FDhzBkyBDodLomB9gVFBQgPj4eSUlJOHz4MGJjYxEbG4tjx45JMWvXrsWGDRuQkZGBoqIieHt7Q6fToa6uTopJSEjA8ePHkZubi927d2Pv3r1ITk6Wli9evBhlZWUmr4EDB+K3v/2t9XYGEREROTZhY1FRUeLxxx+Xfq6vrxfBwcEiPT3dbPz06dPF5MmTTco0Go147LHHhBBCGI1GoVarxYsvvigtr6qqEp6enmLr1q1CCCFOnDghAIji4mIp5p///KdQKBTihx9+MPu+R44cEQDE3r17W902g8EgAAiDwdDqdYiIiMi22nP+tmmP1Y0bN1BSUgKtViuVKZVKaLVaFBYWml2nsLDQJB4AdDqdFF9aWgq9Xm8S4+vrC41GI8UUFhbCz88Pw4cPl2K0Wi2USiWKiorMvu+bb76Jvn37YsyYMU225/r166iurjZ5ERERkeuwaWJVUVGB+vp6BAYGmpQHBgZCr9ebXUev1zcb3/BvSzHdu3c3We7u7o5u3bqZfd+6ujps3rwZSUlJzbYnPT0dvr6+0iskJKTZeCIiInIuNh9j5Qg++OADXLlyBXPmzGk2bsmSJTAYDNLr/PnzHVRDIiIisgc2Taz8/f3h5uaG8vJyk/Ly8vImZ0NVq9XNxjf821LM7YPjb926hcrKSrPv++abb+KBBx5o1At2O09PT/j4+Ji8iIiIyHXYNLHy8PDAsGHDkJeXJ5UZjUbk5eUhOjra7DrR0dEm8QCQm5srxYeFhUGtVpvEVFdXo6ioSIqJjo5GVVUVSkpKpJj8/HwYjUZoNBqTbZeWluKzzz5r8TIgERERkc1nXk9JScGcOXMwfPhwREVFYf369aipqUFiYiIAYPbs2ejRowfS09MBAAsWLEBMTAxeeuklTJ48Gdu2bcPBgwfx+uuvA/h52v6FCxfi+eefR3h4OMLCwrB8+XIEBwcjNjYWADBgwABMnDgR8+bNQ0ZGBm7evIn58+djxowZCA4ONqnfW2+9haCgINx///0dt1OIiIjIIdk8sYqLi8OlS5ewYsUK6PV6REZGIicnR7rsdu7cOSiV/+1YGzlyJLZs2YJly5Zh6dKlCA8Px65duzB48GAp5umnn0ZNTQ2Sk5NRVVWF0aNHIycnByqVSorZvHkz5s+fjwkTJkCpVGLatGnYsGGDSd2MRiMyMzMxd+5cPgKAiIiIWqQQQghbV8JZVVdXw9fXFwaDgeOtiIiIHER7zt+8K5CIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGTCxIqIiIhIJkysiIiIiGRi88Rq06ZNCA0NhUqlgkajwYEDB5qN37FjB/r37w+VSoWIiAhkZ2ebLBdCYMWKFQgKCoKXlxe0Wi1Onz5tElNZWYmEhAT4+PjAz88PSUlJuHr1aqPtrFu3Dn379oWnpyd69OiBP//5z/I0moiIiJySTROrrKwspKSkIC0tDYcOHcKQIUOg0+lw8eJFs/EFBQWIj49HUlISDh8+jNjYWMTGxuLYsWNSzNq1a7FhwwZkZGSgqKgI3t7e0Ol0qKurk2ISEhJw/Phx5ObmYvfu3di7dy+Sk5NN3mvBggV48803sW7dOnz99df46KOPEBUVZZ0dQURERM5B2FBUVJR4/PHHpZ/r6+tFcHCwSE9PNxs/ffp0MXnyZJMyjUYjHnvsMSGEEEajUajVavHiiy9Ky6uqqoSnp6fYunWrEEKIEydOCACiuLhYivnnP/8pFAqF+OGHH6QYd3d38fXXX7erfQaDQQAQBoOhXdshIiKijtOe87fNeqxu3LiBkpISaLVaqUypVEKr1aKwsNDsOoWFhSbxAKDT6aT40tJS6PV6kxhfX19oNBopprCwEH5+fhg+fLgUo9VqoVQqUVRUBAD4+OOP0bt3b+zevRthYWEIDQ3Fo48+isrKymbbdP36dVRXV5u8iIiIyHXYLLGqqKhAfX09AgMDTcoDAwOh1+vNrqPX65uNb/i3pZju3bubLHd3d0e3bt2kmO+++w7ff/89duzYgb///e/IzMxESUkJHnnkkWbblJ6eDl9fX+kVEhLSbDwRERE5F5sPXrdHRqMR169fx9///neMGTMG48aNw9/+9jd89tlnOHXqVJPrLVmyBAaDQXqdP3++A2tNREREtmazxMrf3x9ubm4oLy83KS8vL4darTa7jlqtbja+4d+WYm4fHH/r1i1UVlZKMUFBQXB3d0ffvn2lmAEDBgAAzp0712SbPD094ePjY/IiIiIi12GzxMrDwwPDhg1DXl6eVGY0GpGXl4fo6Giz60RHR5vEA0Bubq4UHxYWBrVabRJTXV2NoqIiKSY6OhpVVVUoKSmRYvLz82E0GqHRaAAAo0aNwq1bt3DmzBkp5ptvvgEA9OrVqz3NJiIiImdmhcH0rbZt2zbh6ekpMjMzxYkTJ0RycrLw8/MTer1eCCHErFmzRGpqqhS/f/9+4e7uLtatWydOnjwp0tLSRKdOncTRo0elmNWrVws/Pz/x4Ycfiq+++kpMmTJFhIWFidraWilm4sSJYujQoaKoqEjs27dPhIeHi/j4eGl5fX29uOeee8TYsWPFoUOHxMGDB4VGoxH33XefRe3jXYFERESOpz3nb5smVkIIsXHjRtGzZ0/h4eEhoqKixBdffCEti4mJEXPmzDGJ3759u+jbt6/w8PAQgwYNEp988onJcqPRKJYvXy4CAwOFp6enmDBhgjh16pRJzOXLl0V8fLzo0qWL8PHxEYmJieLKlSsmMT/88IOYOnWq6NKliwgMDBRz584Vly9ftqhtTKyIiIgcT3vO3wohhLBtn5nzqq6uhq+vLwwGA8dbEREROYj2nL95VyARERGRTJhYEREREcmEiZULKDPUouBMBcoMtbauChERkVNzt3UFyLqyis9hyc6jMApAqQDSp0YgbkRPW1eLiIjIKbHHyomVGWqlpAoAjAJYuvMYe66IiIishImVEyutqJGSqgb1QuBsxTXbVIiIiMjJMbFyYmH+3lAqTMvcFAqE+ne2TYWIiIicHBMrJxbk64X0qRFwU/ycXbkpFHhh6mAE+XrZuGZERETOiYPXnVzciJ4Y2zcAZyuuIdS/M5MqIiIiK2Ji5QKCfL2YUBEREXUAXgokIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKyIiIiKZ2EVitWnTJoSGhkKlUkGj0eDAgQPNxu/YsQP9+/eHSqVCREQEsrOzTZYLIbBixQoEBQXBy8sLWq0Wp0+fNomprKxEQkICfHx84Ofnh6SkJFy9elVafvbsWSgUikavL774Qr6GExERkVOxeWKVlZWFlJQUpKWl4dChQxgyZAh0Oh0uXrxoNr6goADx8fFISkrC4cOHERsbi9jYWBw7dkyKWbt2LTZs2ICMjAwUFRXB29sbOp0OdXV1UkxCQgKOHz+O3Nxc7N69G3v37kVycnKj9/v3v/+NsrIy6TVs2DD5dwIRERE5B2FjUVFR4vHHH5d+rq+vF8HBwSI9Pd1s/PTp08XkyZNNyjQajXjssceEEEIYjUahVqvFiy++KC2vqqoSnp6eYuvWrUIIIU6cOCEAiOLiYinmn//8p1AoFOKHH34QQghRWloqAIjDhw+3uW0Gg0EAEAaDoc3bICIioo7VnvO3TXusbty4gZKSEmi1WqlMqVRCq9WisLDQ7DqFhYUm8QCg0+mk+NLSUuj1epMYX19faDQaKaawsBB+fn4YPny4FKPVaqFUKlFUVGSy7Yceegjdu3fH6NGj8dFHHzXbnuvXr6O6utrkRURERK7DpolVRUUF6uvrERgYaFIeGBgIvV5vdh29Xt9sfMO/LcV0797dZLm7uzu6desmxXTp0gUvvfQSduzYgU8++QSjR49GbGxss8lVeno6fH19pVdISEhLu4CIiIiciLutK2Cv/P39kZKSIv08YsQI/Pjjj3jxxRfx0EMPmV1nyZIlJutUV1czuSIiInIhNu2x8vf3h5ubG8rLy03Ky8vLoVarza6jVqubjW/4t6WY2wfH37p1C5WVlU2+LwBoNBp8++23TS739PSEj4+PyYuIiIhch00TKw8PDwwbNgx5eXlSmdFoRF5eHqKjo82uEx0dbRIPALm5uVJ8WFgY1Gq1SUx1dTWKioqkmOjoaFRVVaGkpESKyc/Ph9FohEajabK+R44cQVBQkOUNJSIiIpdg80uBKSkpmDNnDoYPH46oqCisX78eNTU1SExMBADMnj0bPXr0QHp6OgBgwYIFiImJwUsvvYTJkydj27ZtOHjwIF5//XUAgEKhwMKFC/H8888jPDwcYWFhWL58OYKDgxEbGwsAGDBgACZOnIh58+YhIyMDN2/exPz58zFjxgwEBwcDAN555x14eHhg6NChAICdO3firbfewptvvtnBe4iIiIgchc0Tq7i4OFy6dAkrVqyAXq9HZGQkcnJypMHn586dg1L53461kSNHYsuWLVi2bBmWLl2K8PBw7Nq1C4MHD5Zinn76adTU1CA5ORlVVVUYPXo0cnJyoFKppJjNmzdj/vz5mDBhApRKJaZNm4YNGzaY1O25557D999/D3d3d/Tv3x9ZWVl45JFHrLxHiIiIyFEphBDC1pVwVtXV1fD19YXBYOB4KyIiIgfRnvO3zWdeJyIiInIWTKyIiIiIZMLEioiIiEgmTKyIiIiIZMLEioiIiEgmTKyIiIiIZMLEimRRZqhFwZkKlBlqbV0VIiIim7H5BKHk+LKKz2HJzqMwCkCpANKnRiBuRE9bV4uIiKjDsceK2qXMUCslVQBgFMDSncfYc0VERC6JiRW1S2lFjZRUNagXAmcrrtmmQkRERDbExIraJczfG0qFaZmbQoFQ/862qRAREZENMbGidgny9UL61Ai4KX7OrtwUCrwwdTCCfL1sXDMiIqKOx8Hr1G5xI3pibN8AnK24hlD/zkyqiIjIZTGxIlkE+XoxoSIiIpfHS4FEREREMmFiRURERCQTJlZEREREMmFiRURERCQTJlZEREREMmFiRURERCQTJlZEREREMmFiRURERCQTJlZEREREMmFiRURERCQTJlZEREREMmFiRURERCQTJlZEREREMmFiRURERCQTJlYkizJDLQrOVKDMUGvrqhAREdmMu60rQI4vq/gcluw8CqMAlAogfWoE4kb0tHW1iIiIOhx7rKhdygy1UlIFAEYBLN15jD1XRETkkphYUbuUVtRISVWDeiFwtuKabSpERERkQ0ysqF3C/L2hVJiWuSkUCPXvbJsK2TGOQyMicn5MrKhdgny9kD41Am6Kn7MrN4UCL0wdjCBfLxvXzL5kFZ/DqNX5mPlGEUatzkdW8TlbV4mIiKzALhKrTZs2ITQ0FCqVChqNBgcOHGg2fseOHejfvz9UKhUiIiKQnZ1tslwIgRUrViAoKAheXl7QarU4ffq0SUxlZSUSEhLg4+MDPz8/JCUl4erVq2bf79tvv0XXrl3h5+fXrnY6q7gRPbEvdTy2zrsX+1LHc+D6bTgOjYjIddg8scrKykJKSgrS0tJw6NAhDBkyBDqdDhcvXjQbX1BQgPj4eCQlJeHw4cOIjY1FbGwsjh07JsWsXbsWGzZsQEZGBoqKiuDt7Q2dToe6ujopJiEhAcePH0dubi52796NvXv3Ijk5udH73bx5E/Hx8RgzZoz8jXciQb5eiO5zB3uqzHCmcWi8nElE1DyFEEK0HGY9Go0GI0aMwCuvvAIAMBqNCAkJwRNPPIHU1NRG8XFxcaipqcHu3bulsnvvvReRkZHIyMiAEALBwcFYtGgRFi9eDAAwGAwIDAxEZmYmZsyYgZMnT2LgwIEoLi7G8OHDAQA5OTmYNGkSLly4gODgYGnbzzzzDH788UdMmDABCxcuRFVVVavbVl1dDV9fXxgMBvj4+LRl95ATKDPUYtTqfJPkyk2hwL7U8Q6ViHJaDSJyFe05f9u0x+rGjRsoKSmBVquVypRKJbRaLQoLC82uU1hYaBIPADqdToovLS2FXq83ifH19YVGo5FiCgsL4efnJyVVAKDVaqFUKlFUVCSV5efnY8eOHdi0aVOr2nP9+nVUV1ebvIicYRwaL2cSEbWOTScIraioQH19PQIDA03KAwMD8fXXX5tdR6/Xm43X6/XS8oay5mK6d+9ustzd3R3dunWTYi5fvoy5c+fi3XffbXW2mp6ejmeffbZVseRa4kb0xNi+AThbcQ2h/p0dKqkCmr+c6WhtISKyJpuPsbJX8+bNw8yZMzF27NhWr7NkyRIYDAbpdf78eSvWkByNI49D47QaREStY9PEyt/fH25ubigvLzcpLy8vh1qtNruOWq1uNr7h35Zibh8cf+vWLVRWVkox+fn5WLduHdzd3eHu7o6kpCQYDAa4u7vjrbfeMls3T09P+Pj4mLyInIEzXM4kIuoINk2sPDw8MGzYMOTl5UllRqMReXl5iI6ONrtOdHS0STwA5ObmSvFhYWFQq9UmMdXV1SgqKpJioqOjUVVVhZKSEikmPz8fRqMRGo0GwM/jsI4cOSK9Vq1aha5du+LIkSN4+OGH5dkBRA6E02oQEbXM5g9hTklJwZw5czB8+HBERUVh/fr1qKmpQWJiIgBg9uzZ6NGjB9LT0wEACxYsQExMDF566SVMnjwZ27Ztw8GDB/H6668DABQKBRYuXIjnn38e4eHhCAsLw/LlyxEcHIzY2FgAwIABAzBx4kTMmzcPGRkZuHnzJubPn48ZM2ZIdwQOGDDApJ4HDx6EUqnE4MGDO2jPENmfIF8v9lIRETXD4sQqOzsbO3fuRLdu3fC73/0O/fv3l5b99NNPmDZtGvLz81u9vbi4OFy6dAkrVqyAXq9HZGQkcnJypMHn586dg1L53461kSNHYsuWLVi2bBmWLl2K8PBw7Nq1yyThefrpp1FTU4Pk5GRUVVVh9OjRyMnJgUqlkmI2b96M+fPnY8KECVAqlZg2bRo2bNhg6e4gIiIiklg0j9WWLVswe/ZsTJw4EQaDAQcPHsSbb76JhIQEAD+PYwoODkZ9fb3VKuxIOI8VERGR42nP+duiHqsXX3wRf/nLX/Dkk08CALZv347f/e53qKurQ1JSkkVvTERERORsLEqsTp8+jQcffFD6efr06QgICMBDDz2EmzdvclA3ERERuTSLEisfHx+Ul5cjLCxMKhs/fjx2796NBx54ABcuXJC9gkRERESOwqLpFqKiovDPf/6zUXlMTAw+/vhjrF+/Xq56ERERETkcixKrP/7xjyZ31v3SuHHj8PHHH2P27NmyVIyIiIioKWWGWhScqbC7Z5ZadFcgWYZ3BRIREckvq/ic9GB4pQJInxoh66TF7Tl/W9RjpVQq4ebm1uzL3d3mc44SERGRkyoz1EpJFQAYBbB05zG76bmyKAv64IMPmlxWWFiIDRs2wGg0trtSREREROaUVtRISVWDeiFwtuKaXTwZwqLEasqUKY3KTp06hdTUVHz88cdISEjAqlWrZKscERER0S+F+XtDqYBJcuWmUCDUv7PtKvULbX4I848//oh58+YhIiICt27dwpEjR/DOO++gV69ectaPiIiISBLk64X0qRFwUygA/JxUvTB1sF30VgFteFagwWDACy+8gI0bNyIyMhJ5eXkYM2aMNepGRERELqLMUIvSihqE+Xu3mCTFjeiJsX0DcLbiGkL9O9tNUgVYmFitXbsWa9asgVqtxtatW81eGiRqDUt+gYiIyLm15S6/IF8vuzx/WDTdglKphJeXF7RaLdzc3JqM27lzpyyVc3ScbsE8a98mS0REjqPMUItRq/MbjZnalzreZolThz2Eefbs2VD83zVNorZo6jbZsX0D7PIvDyIisq623uVnr1c+LEqsMjMzrVQNcnSt/YDb+22yRETUsdpyl589X/lo812BRA2yis9h1Op8zHyjCKNW5yOr+FyTsQ2/QL9kT7fJEhFRx7L0Lj+nmiCU6HaWXtoL8vXCw0N74P1DP0hlsUODHbK3yl67oYmIHE3ciJ7or+6K4rM/YUTorzAk5FdNxtr7lQ8mVtQuln7Aywy1+ODwDyZluw7/iMW6fnbxC9Fa9twNTUTkaCz5TnXaCUKJAMsv7TWXiDkKe++GJiJyJJZ+pzrdBKFEv9TwAV+68xjqhWjxA27vf2m0hr13QxMROZK2fKc6zQShROZY8gG3NBGzR86QHBIR2Yu2fqc6xQShZBlOENq0MkOtXf6l0VpZxecaJYccY0VE1Db29p3anvM3EysrYmLl3Bw9OSQisif29J3aYTOvE9F/2Vs3NKd/ICJHZm/fqW3FxIrICXD6ByIi+8DpFogcHKd/ICKyH0ysiBycM8wNRkTkLJhYETk4Pn+RiMh+MLEicnD2PgsxEZEr4eB1Iidgz7MQExG5EiZWRE7CWW5VJiJyZLwUSERERCQTJlZEREQupsxQi4IzFZyWxQp4KZCIiMiFdNSEwq76NAi76LHatGkTQkNDoVKpoNFocODAgWbjd+zYgf79+0OlUiEiIgLZ2dkmy4UQWLFiBYKCguDl5QWtVovTp0+bxFRWViIhIQE+Pj7w8/NDUlISrl69Ki0/deoUxo8fj8DAQKhUKvTu3RvLli3DzZs35Ws4ERFRB+qoCYWzis9h1Op8zHyjCKNW5yOr+Jys27dnNk+ssrKykJKSgrS0NBw6dAhDhgyBTqfDxYsXzcYXFBQgPj4eSUlJOHz4MGJjYxEbG4tjx45JMWvXrsWGDRuQkZGBoqIieHt7Q6fToa6uTopJSEjA8ePHkZubi927d2Pv3r1ITk6Wlnfq1AmzZ8/Gp59+ilOnTmH9+vV44403kJaWZr2dQUREZEUdMaGwqz8NQiGEEC2HWY9Go8GIESPwyiuvAACMRiNCQkLwxBNPIDU1tVF8XFwcampqsHv3bqns3nvvRWRkJDIyMiCEQHBwMBYtWoTFixcDAAwGAwIDA5GZmYkZM2bg5MmTGDhwIIqLizF8+HAAQE5ODiZNmoQLFy4gODjYbF1TUlJQXFyM//znP61qW3ueju1oXLXLl4jIkZQZajFqdb5JcuWmUGBf6njZvrsLzlRg5htFjcq3zrsX0X3ukOU9rK0952+b9ljduHEDJSUl0Gq1UplSqYRWq0VhYaHZdQoLC03iAUCn00nxpaWl0Ov1JjG+vr7QaDRSTGFhIfz8/KSkCgC0Wi2USiWKihp/GADg22+/RU5ODmJiYppsz/Xr11FdXW3ycgWu3OVLRORIOmJCYVd/GoRNE6uKigrU19cjMDDQpDwwMBB6vd7sOnq9vtn4hn9biunevbvJcnd3d3Tr1q3R+44cORIqlQrh4eEYM2YMVq1a1WR70tPT4evrK71CQkKajHUWrt7lS0TkaOJG9MS+1PHYOu9e7EsdL/vAdVd/GgTvCmxBVlYWrly5gi+//BJPPfUU1q1bh6efftps7JIlS5CSkiL9XF1d7fTJVXPX613ll4iIyNFYOqGwpcM9XPlpEDZNrPz9/eHm5oby8nKT8vLycqjVarPrqNXqZuMb/i0vL0dQUJBJTGRkpBRz++D4W7duobKystH7NiRGAwcORH19PZKTk7Fo0SK4ubk1qpunpyc8PT1barZTaejyvf16vat0+RIRObu2Ts/gqk+DsOmlQA8PDwwbNgx5eXlSmdFoRF5eHqKjo82uEx0dbRIPALm5uVJ8WFgY1Gq1SUx1dTWKioqkmOjoaFRVVaGkpESKyc/Ph9FohEajabK+RqMRN2/ehNFotLyxTsrVu3yJiJwZh3tYzuaXAlNSUjBnzhwMHz4cUVFRWL9+PWpqapCYmAgAmD17Nnr06IH09HQAwIIFCxATE4OXXnoJkydPxrZt23Dw4EG8/vrrAACFQoGFCxfi+eefR3h4OMLCwrB8+XIEBwcjNjYWADBgwABMnDgR8+bNQ0ZGBm7evIn58+djxowZ0h2BmzdvRqdOnRAREQFPT08cPHgQS5YsQVxcHDp16tTxO8qOuXKXLxGRM+NwD8vZPLGKi4vDpUuXsGLFCuj1ekRGRiInJ0cafH7u3Dkolf/tWBs5ciS2bNmCZcuWYenSpQgPD8euXbswePBgKebpp59GTU0NkpOTUVVVhdGjRyMnJwcqlUqK2bx5M+bPn48JEyZAqVRi2rRp2LBhg7Tc3d0da9aswTfffAMhBHr16oX58+fjj3/8YwfsFcfjql2+RETOjMM9LGfzeaycmSvNY0VERM4pq/gclu48hnohpOEe1ngEjj1pz/nb5j1WREREZL843MMyTKyIiIioWRzu0Xo2f1YgkaMqM9Si4EwF744hIiIJe6yI2qCt87oQETkiPg+29ZhYEVmoqXldxvYN4BcOETkd/iFpGV4KJLJQc/O6EBE5E04QajkmVkQWcvUntxOR6+AfkpZjYkVkIT7Gh4hcBf+QtBzHWDkgDiK0Pc7rQkSuoOEPydsnCOV3XtOYWDkYDiK0H5zXhYhcAf+QtAwvBToQDiIkIiJbCPL1QnSfO5hUtQITKwfCQYRERET2jYmVA+EgQiL7xxn5yRb4ubMfHGPlQDiIkMi+cQwk2QI/d/ZFIYQQLYdRW1RXV8PX1xcGgwE+Pj6ybbfMUMtBhER2psxQi1Gr800u17spFNiXOp6/p2Q1/NxZR3vO3+yxckC8G43I/jQ3BpK/r2Qt/NzZH46xIiKSAcdAki14e7iZLe/swdO7rXDPExHJgDPyky3U3Kg3W37thrGDa0INeCmQiEgmnEiROlpDT+ntY6zYU2o77LEiIpIRJ1KkjsSeUvvDHisiIiIHxp5S+8LEioiIyMHxbnH7wUuB1EhbZvDlrL9ERPLhd6rjYo8VmWjLDL6c9ZeISD78TnVs7LEiSZmhVvplBn6+y2TpzmPN/sXUlnWIiMg8fqc6PiZWJGluBl851yEicjWtvbTH71THx0uBJGnLfCicQ4WIqHmWXNrjd6rjY48VSdoyHwrnUCEiapqll/b4ner42GNFJtoyHwrnUCEiMq8tD0nmd6pjY2JFjbRlPhTOoUJE1FhbL+3xO9Vx8VIgUQfhvDREroeX9lwPe6yIOgDnpSFyXby051rYY0VkZZyXhoj4cG7XYReJ1aZNmxAaGgqVSgWNRoMDBw40G79jxw70798fKpUKERERyM7ONlkuhMCKFSsQFBQELy8vaLVanD592iSmsrISCQkJ8PHxgZ+fH5KSknD16lVp+Z49ezBlyhQEBQXB29sbkZGR2Lx5s3yNJpfBeWmIiFyHzROrrKwspKSkIC0tDYcOHcKQIUOg0+lw8eJFs/EFBQWIj49HUlISDh8+jNjYWMTGxuLYsWNSzNq1a7FhwwZkZGSgqKgI3t7e0Ol0qKurk2ISEhJw/Phx5ObmYvfu3di7dy+Sk5NN3ufuu+/G+++/j6+++gqJiYmYPXs2du/ebb2dQU6pYfDqL3FeGiIi56QQQoiWw6xHo9FgxIgReOWVVwAARqMRISEheOKJJ5CamtooPi4uDjU1NSYJzr333ovIyEhkZGRACIHg4GAsWrQIixcvBgAYDAYEBgYiMzMTM2bMwMmTJzFw4EAUFxdj+PDhAICcnBxMmjQJFy5cQHBwsNm6Tp48GYGBgXjrrbda1bbq6mr4+vrCYDDAx8fHov1CziWr+ByW7jyGeiGkwascY0VEZJ/ac/62aY/VjRs3UFJSAq1WK5UplUpotVoUFhaaXaewsNAkHgB0Op0UX1paCr1ebxLj6+sLjUYjxRQWFsLPz09KqgBAq9VCqVSiqKioyfoaDAZ069bN8oaSy4sb0RP7Usdj67x7sS91PJMqIiInZdO7AisqKlBfX4/AwECT8sDAQHz99ddm19Hr9Wbj9Xq9tLyhrLmY7t27myx3d3dHt27dpJjbbd++HcXFxXjttdeabM/169dx/fp16efq6uomY8n1cF4aIiLnZ/MxVo7gs88+Q2JiIt544w0MGjSoybj09HT4+vpKr5CQkA6sJREREdmaTRMrf39/uLm5oby83KS8vLwcarXa7DpqtbrZ+IZ/W4q5fXD8rVu3UFlZ2eh9P//8czz44IN4+eWXMXv27Gbbs2TJEhgMBul1/vz5ZuOJiIjIudg0sfLw8MCwYcOQl5cnlRmNRuTl5SE6OtrsOtHR0SbxAJCbmyvFh4WFQa1Wm8RUV1ejqKhIiomOjkZVVRVKSkqkmPz8fBiNRmg0Gqlsz549mDx5MtasWWNyx2BTPD094ePjY/IiIiIiFyJsbNu2bcLT01NkZmaKEydOiOTkZOHn5yf0er0QQohZs2aJ1NRUKX7//v3C3d1drFu3Tpw8eVKkpaWJTp06iaNHj0oxq1evFn5+fuLDDz8UX331lZgyZYoICwsTtbW1UszEiRPF0KFDRVFRkdi3b58IDw8X8fHx0vL8/HzRuXNnsWTJElFWVia9Ll++3Oq2GQwGAUAYDIb27KIO92PVNbH/20vix6prtq4KERFRh2vP+dvmiZUQQmzcuFH07NlTeHh4iKioKPHFF19Iy2JiYsScOXNM4rdv3y769u0rPDw8xKBBg8Qnn3xistxoNIrly5eLwMBA4enpKSZMmCBOnTplEnP58mURHx8vunTpInx8fERiYqK4cuWKtHzOnDkCQKNXTExMq9vliInVtgPfi7DU3aLXM7tFWOpuse3A97auEhERUYdqz/nb5vNYOTNHm8eqzFCLUavzGz2FfV/qeN7NRkROq8xQi9KKGoT5e/O7jgC07/zNhzCTpLlHr/DLhoicER+QTnLjdAsk4aNXyNGUGWpRcKaCD7SmNuED0skamFiRJMjXC+lTI+Cm+Dm7anj0CnuryB5lFZ/DqNX5mPlGEUatzkdW8TlbV4kcDB+QTtbAS4FkIm5ET4ztG4CzFdcQ6t+ZSRXZpaZ6Gsb2DXD6zyzHA8mnoZf+9nGl7KWn9mBiRY3w0Stk71x1PCDHA8mroZf+9gekO/NniKyPiRURORxX7Glw5V46a2IvPcmNY6yIyOE09DQ0fIEpAafvaeB4IOsJ8vVCdJ87nPrzQx2HiRU5BN79RWYpbvvXifGuXSLHwMSK7B7v/qLbueJt8rxrl8gxcIwV2TWOKyFzXHXwOscDEdk/JlZk11z1BErNc8XB6w141y6RfeOlQLJrHFdC5vCyGBHZK/ZYkV3jPDPUFF4WIyJ7xMSK7J6rnkA5w3bLeFnMMfGzTc6MiRU5BFc7gXKGbevgCd32+NkmZ8cxVkR2xhWnEugInLbD9vjZJlfAxIrIznCGbfnxhG4f+Nl2La46sTMvBRLZGVeeSsBaOG2HfXCWzzYvKbfMlS/5sseKyM5wKgH5cdoO++AMn21eUm6Zq/cQs8eKyA656p2Q1sJpO+yHI3+2+SSI1nH1HmImVkR2ytXuhLQ2Rz6hOyMB0XKQnXH1hKG1nOWSb1sxsSIil8Fk1fYceexNRyYMjjyOy9V7iBVCCMf7s8FBVFdXw9fXFwaDAT4+PrauDhGRTZUZajFqdX6jxGRf6niHOelmFZ9rlDDInRg6cvL5S2WGWoftIW7P+Zs9VkRE1CGc4VKatS8pO9M4LlftIWZiRUREHcJZxt5YM2FwhuTT1XG6BaI2ctXJ74jayhmmW7A2Tg3i+NhjRdQGzjIGgqij8e7M5rn6wG9nwMHrVsTB687JGQbg2itHvhOKSE6OPPDbGXDwOlEH4hgI62AvINF/uerAb2fAMVZEFuIYCPm5+iMwXA3HJ5IzY2JFZCEOwJVfc72A5Fz4rD1ydrwUSNQGHIArL2e5DZ+a50xzNBE1hT1WRG0U5OuF6D538IQgA/YCugb2TJIrYI8VEdkF9gI6P/ZMkiuweY/Vpk2bEBoaCpVKBY1GgwMHDjQbv2PHDvTv3x8qlQoRERHIzs42WS6EwIoVKxAUFAQvLy9otVqcPn3aJKayshIJCQnw8fGBn58fkpKScPXqVWl5XV0d5s6di4iICLi7uyM2Nla29hJR09gL6NzYM0muwKaJVVZWFlJSUpCWloZDhw5hyJAh0Ol0uHjxotn4goICxMfHIykpCYcPH0ZsbCxiY2Nx7NgxKWbt2rXYsGEDMjIyUFRUBG9vb+h0OtTV1UkxCQkJOH78OHJzc7F7927s3bsXycnJ0vL6+np4eXnhySefhFartd4OICJyMXEjemJf6nhsnXcv9qWO55Qa5HRsOkGoRqPBiBEj8MorrwAAjEYjQkJC8MQTTyA1NbVRfFxcHGpqarB7926p7N5770VkZCQyMjIghEBwcDAWLVqExYsXAwAMBgMCAwORmZmJGTNm4OTJkxg4cCCKi4sxfPhwAEBOTg4mTZqECxcuIDg42OQ9586di6qqKuzatcvi9nGCUCJyBZzYlZxNe87fNuuxunHjBkpKSkx6hJRKJbRaLQoLC82uU1hY2KgHSafTSfGlpaXQ6/UmMb6+vtBoNFJMYWEh/Pz8pKQKALRaLZRKJYqKimRrHxGRK+D0CUSmbJZYVVRUoL6+HoGBgSblgYGB0Ov1ZtfR6/XNxjf821JM9+7dTZa7u7ujW7duTb5va12/fh3V1dUmLyIiZ8WJXYkas/ngdWeSnp4OX19f6RUSEmLrKhERWQ2nTyBqzGaJlb+/P9zc3FBeXm5SXl5eDrVabXYdtVrdbHzDvy3F3D44/tatW6isrGzyfVtryZIlMBgM0uv8+fPt2h4RkT3j452IGrNZYuXh4YFhw4YhLy9PKjMajcjLy0N0dLTZdaKjo03iASA3N1eKDwsLg1qtNomprq5GUVGRFBMdHY2qqiqUlJRIMfn5+TAajdBoNO1qk6enJ3x8fExeRETOitMnEDVm0wlCU1JSMGfOHAwfPhxRUVFYv349ampqkJiYCACYPXs2evTogfT0dADAggULEBMTg5deegmTJ0/Gtm3bcPDgQbz++usAAIVCgYULF+L5559HeHg4wsLCsHz5cgQHB0tzUQ0YMAATJ07EvHnzkJGRgZs3b2L+/PmYMWOGyR2BJ06cwI0bN1BZWYkrV67gyJEjAIDIyMgO2z9ERB3N0jv8OLErkSmbJlZxcXG4dOkSVqxYAb1ej8jISOTk5EiDz8+dOwel8r+daiNHjsSWLVuwbNkyLF26FOHh4di1axcGDx4sxTz99NOoqalBcnIyqqqqMHr0aOTk5EClUkkxmzdvxvz58zFhwgQolUpMmzYNGzZsMKnbpEmT8P3330s/Dx06FMDPE5ASuSreVu/csorPSYPRlQogfWpEq+aZCvL14ueB6P/YdB4rZ8d5rBwHE4aWtfWkS46hzFCLUavzGz1uZl/qeP5OkMtpz/mbzwokl8eEoWVN3VY/tm8AT7pOork7/HiMiVqP0y2QS+M8PK3D2+qdn7eHm9nyzh48TRBZgr8x5NKYMLQOb6tvvTJDLQrOVDhccl5zo95s+bUbxg6uCZFjY2JFLo0JQ+vwtvrWceTHu/B3gUgeHLxuRRy87hiyis9h6c5jqBdCShg4xsq8MkMtb6tvgjMM/ubvAtHPOHidqB04D0/r8bb6pjnD4G/+LhC1HxMrIjBhcFT2NE1Gw6W023usHO1SGn8XiNqHY6yIyCHZ23gmjkMjIoBjrKyKY6yIrMOexzNxHBqR4+MYKyJyKfY8nomX0ohcGy8FEpHD4dQA1tOWebgcde6ujsR95DrYY0VEdqO1g9EbxjPdPjUAe4rapy2Pd+IjoVqWVXwOqe8fhQCgALB6muPtI3u6UcTecYyVFXGMFVHrteUEzfFM8mnLuLWOGuvmyCf1MkMtRqbn45cnWgWAgiW/dpi2uGLy3J7zNy8FEpHNtfWZjUG+Xojuc4fDnKDsWVse79QRj4Syt7s/LXXwbCVu770QAErO/mSL6liMz1O1HBMrIrK5tp6gXXHcirXa3JZxa9Ye6+YMJ3WFQtFEeQdXpI34PFXLMbEiIptrywna0Xsy2sKabW7LPFzWnrvLGU7qw3r9CrfnUAoFcE+vX9mkPpbijSKW4+B1IrI5SwejN9WTMbZvgNNeFuyINrflkTbWfAyOM8xmH+TrhdXTIhqNUXKUzylvFLEcEysisguWnKDteR4ra+moNrdlHi5rzd3lLCd1R38Go6PXv6MxsSIiu9HaE7Qz9GRYytvDzWx5Zw/nHtHhLCd1R5841tHr35Gc+zeSiJySKz6Xr+ZGvdnyazeMHVyTjse7P8mRsMeKiBySs/RktJYr9tIROSL2WBHZKVecSsBSrtST4Yq9dESOiD1WRHaorbOQO+rs1NQ6rtZLR+SImFgR2Zm23Fbvio+ccFUcRExk33gpkMjOWDopojPMTk3Ww0vKRB2LPVZEdsbSQcquOKcTtU5H9WTyMjTRf7HHisjOWDpImY+ccC2t7YHqqJ5MV3y0EFFz2GNFZIcsGaTsLLNTU8ss6YHqiJ5MV3y0EFFLmFgR2SlLBim35W4xXr6xji/P/4QDZysRFdoNQ0Lke9CupUlMR8x7xcvQRI0xsSJyEpYkYryL0DoWbT+C9w/9IP087Z4eeGl6pCzbtjSJCfL1wsNDe5jUJ3ZocIufEUsSbk5aStQYx1gRuRjeRWgdX57/ySSJAYD3D/2AL8//JMv2LR1LV2aoxQeHTeuz6/CPzR5nS8dLcdJSosaYWLkA3m7tGlp7nC2dzqEj6uQMDpytNFt+8GzziVVr95GlSUxHTdsRN6In9qWOx9Z592Jf6nj2fJLL46VAJ8dLPq7BkuPcUZdvXO2z19vf22x5c/s1q/gcUt8/CgFAAWD1tOb3kSVj6Tpy2g5OWkr0X+yxcmK85OMaLD3OHXH5xhU/e14e5v9O7ezRyWx5maFWSqoAQABIff9oq3quWvN8RE7bQWQbdpFYbdq0CaGhoVCpVNBoNDhw4ECz8Tt27ED//v2hUqkQERGB7Oxsk+VCCKxYsQJBQUHw8vKCVqvF6dOnTWIqKyuRkJAAHx8f+Pn5ISkpCVevXjWJ+eqrrzBmzBioVCqEhIRg7dq18jS4g3TkJR+ynbYcZ2tfvnHFz56licnBs5W4bRdBACiR6dIhYNlx5ngpInnYPLHKyspCSkoK0tLScOjQIQwZMgQ6nQ4XL140G19QUID4+HgkJSXh8OHDiI2NRWxsLI4dOybFrF27Fhs2bEBGRgaKiorg7e0NnU6Huro6KSYhIQHHjx9Hbm4udu/ejb179yI5OVlaXl1djd/85jfo1asXSkpK8OKLL2LlypV4/fXXrbczZMa/QF1DW49za3s+OrJOjszSxEShUDRR3vR7tGUyTkuOM8dLEbWfQghx+x9NHUqj0WDEiBF45ZVXAABGoxEhISF44oknkJqa2ig+Li4ONTU12L17t1R27733IjIyEhkZGRBCIDg4GIsWLcLixYsBAAaDAYGBgcjMzMSMGTNw8uRJDBw4EMXFxRg+fDgAICcnB5MmTcKFCxcQHByMV199FX/605+g1+vh4eEBAEhNTcWuXbvw9ddft6pt1dXV8PX1hcFggI+PT7v2U1tlFZ9rNHEkvyydjz0eZ3usU0coM9S2agxUmaEWI9PzTXqtFAqgIPXXZtcrM9Ri1Or8RmOm9qWOZ68Skczac/626eD1GzduoKSkBEuWLJHKlEoltFotCgsLza5TWFiIlJQUkzKdToddu3YBAEpLS6HX66HVaqXlvr6+0Gg0KCwsxIwZM1BYWAg/Pz8pqQIArVYLpVKJoqIiPPzwwygsLMTYsWOlpKrhfdasWYOffvoJv/pV44n/rl+/juvXr0s/V1dXW7ZDrKAtE0eS47HH42yPdeoIrR3IHeTrhdXTIhoN8G/LXX6usm+JHIFNE6uKigrU19cjMDDQpDwwMLDJXiG9Xm82Xq/XS8sbypqL6d69u8lyd3d3dOvWzSQmLCys0TYalplLrNLT0/Hss8823WAb4R07rsEej7M91smeWPMuPyKyDZuPsXImS5YsgcFgkF7nz5+3dZWIyM5Z6y4/IrINm/ZY+fv7w83NDeXl5Sbl5eXlUKvVZtdRq9XNxjf8W15ejqCgIJOYyMhIKeb2wfG3bt1CZWWlyXbMvc8v3+N2np6e8PT0bLK9RETt4aqXV4kciU17rDw8PDBs2DDk5eVJZUajEXl5eYiOjja7TnR0tEk8AOTm5krxYWFhUKvVJjHV1dUoKiqSYqKjo1FVVYWSkhIpJj8/H0ajERqNRorZu3cvbt68afI+/fr1M3sZkIioI1jzbk4ikoGwsW3btglPT0+RmZkpTpw4IZKTk4Wfn5/Q6/VCCCFmzZolUlNTpfj9+/cLd3d3sW7dOnHy5EmRlpYmOnXqJI4ePSrFrF69Wvj5+YkPP/xQfPXVV2LKlCkiLCxM1NbWSjETJ04UQ4cOFUVFRWLfvn0iPDxcxMfHS8urqqpEYGCgmDVrljh27JjYtm2b6Ny5s3jttdda3TaDwSAACIPB0J5dRERERB2oPedvmydWQgixceNG0bNnT+Hh4SGioqLEF198IS2LiYkRc+bMMYnfvn276Nu3r/Dw8BCDBg0Sn3zyiclyo9Eoli9fLgIDA4Wnp6eYMGGCOHXqlEnM5cuXRXx8vOjSpYvw8fERiYmJ4sqVKyYxX375pRg9erTw9PQUPXr0EKtXr7aoXUysiIiIHE97zt82n8fKmdnDPFZERERkmfacv3lXIBEREZFMmFgRERERyYSJFREREZFMmFgRERERyYSJFREREZFMmFgRERERyYSJFREREZFMmFgRERERycSmD2F2dg1zr1ZXV9u4JkRERNRaDefttsyhzsTKiq5cuQIACAkJsXFNiIiIyFJXrlyBr6+vRevwkTZWZDQa8eOPP6Jr165QKBSybbe6uhohISE4f/68Uz8qh+10Lmync3GFdrpCGwG20xwhBK5cuYLg4GAolZaNmmKPlRUplUrceeedVtu+j4+PU/8SNGA7nQvb6VxcoZ2u0EaA7bydpT1VDTh4nYiIiEgmTKyIiIiIZMLEygF5enoiLS0Nnp6etq6KVbGdzoXtdC6u0E5XaCPAdsqNg9eJiIiIZMIeKyIiIiKZMLEiIiIikgkTKyIiIiKZMLEiIiIikgkTKzuxadMmhIaGQqVSQaPR4MCBA83Gr1+/Hv369YOXlxdCQkLwxz/+EXV1de3aprXJ3caVK1dCoVCYvPr372/tZrTIknbevHkTq1atQp8+faBSqTBkyBDk5OS0a5sdRe522uPx3Lt3Lx588EEEBwdDoVBg165dLa6zZ88e3HPPPfD09MRdd92FzMzMRjH2djyt0U5nOJ5lZWWYOXMm+vbtC6VSiYULF5qN27FjB/r37w+VSoWIiAhkZ2fLX/lWskYbMzMzGx1LlUplnQa0kqXt3LlzJ+677z4EBATAx8cH0dHR+Ne//tUoTo7fTSZWdiArKwspKSlIS0vDoUOHMGTIEOh0Oly8eNFs/JYtW5Camoq0tDScPHkSf/vb35CVlYWlS5e2eZvWZo02AsCgQYNQVlYmvfbt29cRzWmSpe1ctmwZXnvtNWzcuBEnTpzA73//ezz88MM4fPhwm7fZEazRTsD+jmdNTQ2GDBmCTZs2tSq+tLQUkydPxvjx43HkyBEsXLgQjz76qMkXuD0eT2u0E3D843n9+nUEBARg2bJlGDJkiNmYgoICxMfHIykpCYcPH0ZsbCxiY2Nx7NgxOaveatZoI/DzbOW/PJbff/+9XFVuE0vbuXfvXtx3333Izs5GSUkJxo8fjwcffNA637WCbC4qKko8/vjj0s/19fUiODhYpKenm41//PHHxa9//WuTspSUFDFq1Kg2b9ParNHGtLQ0MWTIEKvUt60sbWdQUJB45ZVXTMqmTp0qEhIS2rzNjmCNdtrj8fwlAOKDDz5oNubpp58WgwYNMimLi4sTOp1O+tkej+cvydVOZzievxQTEyMWLFjQqHz69Oli8uTJJmUajUY89thj7axh+8nVxrffflv4+vrKVi+5WdrOBgMHDhTPPvus9LNcv5vssbKxGzduoKSkBFqtVipTKpXQarUoLCw0u87IkSNRUlIidVF+9913yM7OxqRJk9q8TWuyRhsbnD59GsHBwejduzcSEhJw7tw56zWkBW1p5/Xr1xt1qXt5eUl/2dvbsWxrnVpqZwN7Op5tUVhYaLJfAECn00n7xR6PZ1u01M4Gjn48W6O1+8LRXb16Fb169UJISAimTJmC48eP27pK7WI0GnHlyhV069YNgLy/m0ysbKyiogL19fUIDAw0KQ8MDIRerze7zsyZM7Fq1SqMHj0anTp1Qp8+fTBu3DjpMllbtmlN1mgjAGg0GmRmZiInJwevvvoqSktLMWbMGFy5csWq7WlKW9qp0+nwl7/8BadPn4bRaERubi527tyJsrKyNm/T2qzRTsD+jmdb6PV6s/uluroatbW1dnk826KldgLOcTxbo6l94UjHsyX9+vXDW2+9hQ8//BDvvvsujEYjRo4ciQsXLti6am22bt06XL16FdOnTwcg73ctEysHtGfPHrzwwgv43//9Xxw6dAg7d+7EJ598gueee87WVZNNa9p4//3347e//S3uvvtu6HQ6ZGdno6qqCtu3b7dhzS3z17/+FeHh4ejfvz88PDwwf/58JCYmQql0rl/N1rTTGY4n/RePp/OIjo7G7NmzERkZiZiYGOzcuRMBAQF47bXXbF21NtmyZQueffZZbN++Hd27d5d9++6yb5Es4u/vDzc3N5SXl5uUl5eXQ61Wm11n+fLlmDVrFh599FEAQEREBGpqapCcnIw//elPbdqmNVmjjeYSDz8/P/Tt2xfffvut/I1ohba0MyAgALt27UJdXR0uX76M4OBgpKamonfv3m3eprVZo53m2Pp4toVarTa7X3x8fODl5QU3Nze7O55t0VI7zXHE49kaTe0LRzqelurUqROGDh3qkMdy27ZtePTRR7Fjxw6Ty35yftc615/FDsjDwwPDhg1DXl6eVGY0GpGXl4fo6Giz61y7dq1RYuHm5gYAEEK0aZvWZI02mnP16lWcOXMGQUFBMtXcMu3Z7yqVCj169MCtW7fw/vvvY8qUKe3eprVYo53m2Pp4tkV0dLTJfgGA3Nxcab/Y4/Fsi5baaY4jHs/WaMu+cHT19fU4evSowx3LrVu3IjExEVu3bsXkyZNNlsn6u2nxMHqS3bZt24Snp6fIzMwUJ06cEMnJycLPz0/o9XohhBCzZs0SqampUnxaWpro2rWr2Lp1q/juu+/Ep59+Kvr06SOmT5/e6m12NGu0cdGiRWLPnj2itLRU7N+/X2i1WuHv7y8uXrzY4e1rYGk7v/jiC/H++++LM2fOiL1794pf//rXIiwsTPz000+t3qYtWKOd9ng8r1y5Ig4fPiwOHz4sAIi//OUv4vDhw+L7778XQgiRmpoqZs2aJcV/9913onPnzuKpp54SJ0+eFJs2bRJubm4iJydHirHH42mNdjrD8RRCSPHDhg0TM2fOFIcPHxbHjx+Xlu/fv1+4u7uLdevWiZMnT4q0tDTRqVMncfTo0Q5tWwNrtPHZZ58V//rXv8SZM2dESUmJmDFjhlCpVCYxHc3Sdm7evFm4u7uLTZs2ibKyMulVVVUlxcj1u8nEyk5s3LhR9OzZU3h4eIioqCjxxRdfSMtiYmLEnDlzpJ9v3rwpVq5cKfr06SNUKpUICQkRf/jDH0xOUi1t0xbkbmNcXJwICgoSHh4eokePHiIuLk58++23Hdgi8yxp5549e8SAAQOEp6enuOOOO8SsWbPEDz/8YNE2bUXudtrj8fzss88EgEavhrbNmTNHxMTENFonMjJSeHh4iN69e4u333670Xbt7Xhao53OcjzNxffq1cskZvv27aJv377Cw8NDDBo0SHzyyScd0yAzrNHGhQsXSp/XwMBAMWnSJHHo0KGOa5QZlrYzJiam2fgGcvxuKoRo4roKEREREVmEY6yIiIiIZMLEioiIiEgmTKyIiIiIZMLEioiIiEgmTKyIiIiIZMLEioiIiEgmTKyIiIiIZMLEiojIhezZswcKhQJVVVW2rgqRU2JiRURWMXfuXCgUCqxevdqkfNeuXVAoFNLPQgi88cYbiI6Oho+PD7p06YJBgwZhwYIFrX7I67Vr17BkyRL06dMHKpUKAQEBiImJwYcffijFhIaGYv369bK0zdoa9p1CoUCnTp0QFhaGp59+GnV1dRZtZ9y4cVi4cKFJ2ciRI1FWVgZfX18Za0xEDZhYEZHVqFQqrFmzBj/99JPZ5UIIzJw5E08++SQmTZqETz/9FCdOnMDf/vY3qFQqPP/88616n9///vfYuXMnNm7ciK+//ho5OTl45JFHcPnyZTmb06EmTpyIsrIyfPfdd3j55Zfx2muvIS0trd3b9fDwgFqtNkluiUhGbXxMDxFRs+bMmSMeeOAB0b9/f/HUU09J5R988IFo+OrZunWrACA+/PBDs9swGo2tei9fX1+RmZnZ5HJzzwlr8J///EeMHj1aqFQqceedd4onnnhCXL16VVr+97//XQwbNkx06dJFBAYGivj4eFFeXi4tb3hmWU5OjoiMjBQqlUqMHz9elJeXi+zsbNG/f3/RtWtXER8fL2pqalrVnjlz5ogpU6aYlE2dOlUMHTpU+rmiokLMmDFDBAcHCy8vLzF48GCxZcsWk23c3ubS0lKpvr987uZ7770nBg4cKDw8PESvXr3EunXrWlVPImqMPVZEZDVubm544YUXsHHjRly4cKHR8q1bt6Jfv3546KGHzK7f2l4VtVqN7OxsXLlyxezynTt34s4778SqVatQVlaGsrIyAMCZM2cwceJETJs2DV999RWysrKwb98+zJ8/X1r35s2beO655/Dll19i165dOHv2LObOndvoPVauXIlXXnkFBQUFOH/+PKZPn47169djy5Yt+OSTT/Dpp59i48aNrWrP7Y4dO4aCggJ4eHhIZXV1dRg2bBg++eQTHDt2DMnJyZg1axYOHDgAAPjrX/+K6OhozJs3T2pzSEhIo22XlJRg+vTpmDFjBo4ePYqVK1di+fLlyMzMbFNdiVyerTM7InJOv+x1uffee8Xvfvc7IYRpj1X//v3FQw89ZLLeggULhLe3t/D29hY9evRo1Xt9/vnn4s477xSdOnUSw4cPFwsXLhT79u0zienVq5d4+eWXTcqSkpJEcnKySdl//vMfoVQqRW1trdn3Ki4uFgDElStXhBD/7bH697//LcWkp6cLAOLMmTNS2WOPPSZ0Ol2r2jNnzhzh5uYmvL29haenpwAglEqleO+995pdb/LkyWLRokXSzzExMWLBggUmMbf3WM2cOVPcd999JjFPPfWUGDhwYKvqSkSm2GNFRFa3Zs0avPPOOzh58mSLsX/6059w5MgRrFixAlevXm3V9seOHYvvvvsOeXl5eOSRR3D8+HGMGTMGzz33XLPrffnll8jMzESXLl2kl06ng9FoRGlpKYCfe3QefPBB9OzZE127dkVMTAwA4Ny5cybbuvvuu6X/BwYGonPnzujdu7dJ2cWLF1vVHgAYP348jhw5gqKiIsyZMweJiYmYNm2atLy+vh7PPfccIiIi0K1bN3Tp0gX/+te/GtWrJSdPnsSoUaNMykaNGoXTp0+jvr7eom0REQevE1EHGDt2LHQ6HZYsWWJSHh4ejlOnTpmUBQQE4K677kL37t0teo9OnTphzJgxeOaZZ/Dpp59i1apVeO6553Djxo0m17l69Soee+wxHDlyRHp9+eWXOH36NPr06YOamhrodDr4+Phg8+bNKC4uxgcffAAAjbbbqVMn6f8Nd/P9kkKhgNFobHV7vL29cdddd2HIkCF46623UFRUhL/97W/S8hdffBF//etf8cwzz+Czzz7DkSNHoNPpmm0vEVmfu60rQESuYfXq1YiMjES/fv2ksvj4eMycORMffvghpkyZIuv7DRw4ELdu3UJdXR08PDzg4eHRqAfmnnvuwYkTJ3DXXXeZ3cbRo0dx+fJlrF69WhqfdPDgQVnr2RpKpRJLly5FSkoKZs6cCS8vL+zfvx9TpkzB//zP/wAAjEYjvvnmGwwcOFBaz1ybbzdgwADs37/fpGz//v3o27cv3Nzc5G8MkZNjjxURdYiIiAgkJCRgw4YNUtmMGTPwyCOPYMaMGVi1ahWKiopw9uxZfP7558jKymr1iX3cuHF47bXXUFJSgrNnzyI7OxtLly7F+PHj4ePjA+Dneaz27t2LH374ARUVFQCAZ555BgUFBZg/fz6OHDmC06dP48MPP5QGr/fs2RMeHh7YuHEjvvvuO3z00UctXl60lt/+9rdwc3PDpk2bAPzc25ebm4uCggKcPHkSjz32GMrLy03WCQ0NlfZpRUWF2R6zRYsWIS8vD8899xy++eYbvPPOO3jllVewePHiDmkXkbNhYkVEHWbVqlUmJ3eFQoGsrCysX78e2dnZmDBhAvr164ff/e53CAkJwb59+1q1XZ1Oh3feeQe/+c1vMGDAADzxxBPQ6XTYvn27yXufPXsWffr0QUBAAICfx0V9/vnn+OabbzBmzBgMHToUK1asQHBwMICfL0tmZmZix44dGDhwIFa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", 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", 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]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1421,913 +1192,688 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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FmUwmjXCVe+WVV6Q/P/7441i1ahWefPJJ3L59Gy1btqyX+6gOR6YaEZWqEKGh+yCT6QFAmjPFR3xERNRQCSGkx3Zff/01nnnmGbi4uMDOzg5jxoxBXl4e7ty5U+050tPTERoaivbt28POzg6BgYEAgMuXL5u8/TXBkalGxts7Ex4eF5Cfr4Zanc8gRUREDdq5c+fg7u6OS5cuYejQoXj99dfx7rvvQq1W47vvvsOECRNQUlJS5crvRUVFCA4ORnBwMLZu3Yq2bdvi8uXLCA4ObjAfvGKYaoRUqkKGKCIiavC++eYbnD59Gv/4xz+Qnp4OvV6P5cuXS0slbN++3aBeLpejrKzMYNtPP/2EvLw8LFmyBK6urgCAkydP1s8N1BAf8zUCcrm8TuuIiIjqWnFxMbRaLf773/8iIyMD7733HoYPH46hQ4di7Nix6NixI0pLS7F69Wr88ssv2LJlC+Lj4w3O4ebmhtu3byMlJQU3b97EnTt30L59e8jlcum4PXv2YNGiRWa6y8o1iDC1du1auLm5QalUws/PD8ePH6+2fseOHejSpQuUSiV69OiB/fv3G+wXQmD+/PnQaDSwtrZGUFAQzp8/X+m5iouL4eXlBZlMhqysLGn7kSNHMHz4cGg0Gtja2sLLywtbt241+l4fhb29PSIjIxEREVHliwt2EhGROSUnJ0Oj0cDNzQ2DBw/G4cOHsWrVKnzxxRewtLREr169sGLFCixduhTdu3fH1q1bERMTY3COgIAATJo0CSNHjkTbtm0RGxuLtm3bIiEhATt27MATTzyBJUuWYNmyZWa6y8rJhBDCnA3Ytm0bxo4di/j4ePj5+SEuLg47duxAdnZ2petHHD16FAMGDEBMTAyGDh2KxMRELF26FBkZGejevTsAYOnSpYiJicHmzZvh7u6OefPm4fTp0zh79iyUSqXB+aZMmYLz58/jq6++QmZmJry8vAAA7733Hv744w8MGTIEjo6O2LdvH6Kjo/HFF19g6NChNbq3goICqFQq6HQ6tGrVyriOIiKiJu3u3bvIycmBu7t7hd9VD9MQ1plqbKrr79r+/jZ7mPLz88OTTz4p/SXQ6/VwdXXFG2+8gVmzZlWoHzlyJIqKirBv3z5pW9++feHl5YX4+HgIIeDs7IypU6dK61PodDo4OjoiISEBo0aNko776quvEB0djZ07d6Jbt24GYaoyISEhcHR0xMaNG2t0bwxTRERUU8aEKcD8K6A3NnUZpsw6Ab2kpATp6emYPXu2tM3CwgJBQUFITU2t9JjU1FRER0cbbAsODsbu3bsBADk5OdBqtQgKCpL2q1Qq+Pn5ITU1VQpTubm5mDhxInbv3l3lJwgepNPp0LVr1yr3FxcXo7i4WHpfUFBQo/MSEREZi0HJfMw6Z+rmzZsoKyuDo6OjwXZHR0dotdpKj9FqtdXWl/+srkYIgXHjxmHSpEnw9fWtUVu3b9+OEydOSEvdVyYmJgYqlUp6lX/qgIiIiJquBjEBvb6tXr0ahYWFBiNi1Tl8+DDGjx+P9evXo1u3blXWzZ49GzqdTnpduXKlrppMREREDZRZw5SDgwMsLS2Rm5trsD03N7fCUvLlnJycqq0v/1ldzTfffIPU1FQoFAq0aNECHTt2BAD4+voiPDzc4Lhvv/0WoaGhWLlyJcaOHVvt/SgUCrRq1crgRURERE2bWcOUXC6Hj48PUlJSpG16vR4pKSnw9/ev9Bh/f3+DegA4dOiQVO/u7g4nJyeDmoKCAqSlpUk1q1atwg8//ICsrCxkZWVJSyts27YN7777rnTckSNHEBISgqVLlyIiIqJubpqIiIiaFLOvgB4dHY3w8HD4+vqiT58+iIuLQ1FRkTQ3aezYsXBxcZHWopgyZQoCAwOxfPlyhISEICkpCSdPnsS6desAADKZDFFRUVi8eDE8PT2lpRGcnZ0RFhYGAGjfvr1BG8q/JNHDwwOPPfYYgHuP9oYOHYopU6ZgxIgR0nwruVwOtVpt8n4hIiKixsHsYWrkyJG4ceMG5s+fD61WCy8vLyQnJ0sTyC9fviwtOw/cW9ArMTERc+fOxZw5c+Dp6Yndu3dLa0wBwIwZM1BUVISIiAjcunUL/fr1Q3Jycq0+arp582bcuXMHMTExBouKBQYG4siRI8bfOBERETUJZl9nqinjOlNERFRTxq4zRbVTl+tMNctP8xEREVHjceTIEchkMty6davGx7i5uSEuLs5kbbofwxQREREZZdy4cZDJZJg0aVKFfZMnT4ZMJsO4cePqv2H1hGGKiIiIjObq6oqkpCT88ccf0ra7d+8iMTGxwge/mhqGKSIiIjKat7c3XF1dsWvXLmnbrl270L59e/Tu3VvaVlxcjDfffBPt2rWDUqlEv379cOLECYNz7d+/H506dYK1tTUGDhyIS5cuVbjed999h/79+8Pa2hqurq548803UVRUZLL7qw7DFNVaXl4erl27VuUrLy/P3E0kImrWfvsNOHz43s/69Morr2DTpk3S+40bN1b4GrYZM2Zg586d2Lx5MzIyMtCxY0cEBwcjPz8fAHDlyhU899xzCA0NRVZWFl599VXMmjXL4BwXL17E4MGDMWLECJw6dQrbtm3Dd999h8jISNPfZCXMvjQCNS55eXlYs2aN9F6ns0N+vj3U6jyoVIXS9sjISH7pJhGRGWzYAEREAHo9YGEBrFsHTJhQP9d++eWXMXv2bPz6668AgO+//x5JSUnSkkJFRUX46KOPkJCQgCFDhgAA1q9fj0OHDmHDhg2YPn06PvroI3h4eGD58uUAgM6dO+P06dNYunSpdJ2YmBi89NJLiIqKAgB4enpi1apVCAwMxEcffVTvn4ZkmKJaKSkpkf6ckdEbe/cOhRAWkMn0CA3dB2/vzAp1RERUP3777X9BCrj387XXgOBg4P+vSW1Sbdu2RUhICBISEiCEQEhICBwcHKT9Fy9eRGlpKZ566ilpm5WVFfr06YNz584BAM6dOwc/Pz+D8z74rSg//PADTp06ha1bt0rbhBDQ6/XIyclB165dTXF7VWKYokei09lJQQoAhLDA3r1D4eFxwWCEioiI6s/58/8LUuXKyoALF+onTAH3HvWVP25bu3atSa5x+/ZtvPbaa3jzzTcr7DPHZHeGKXok+fn2UpAqJ4QF8vPVDFNERGbi6Xnv0d79gcrSEujYsf7aMHjwYJSUlEAmkyE4ONhgn4eHB+RyOb7//nt06NABAFBaWooTJ05Ij+y6du2KPXv2GBx37Ngxg/fe3t44e/YsOtbnjVWDE9DpkajVeZDJDP/zRybTQ63ON1OLiIjoscfuzZGytLz33tIS+Pjj+huVundNS5w7dw5nz56FZXlD/j9bW1u8/vrrmD59OpKTk3H27FlMnDgRd+7cwYT/P7Fr0qRJOH/+PKZPn47s7GwkJiYiISHB4DwzZ87E0aNHERkZiaysLJw/fx5ffPGF2SagM0zRI1GpChEauk8KVOVzpjgqRURkXhMmAJcu3fs036VL9Tf5/H6tWrWq8mtYlixZghEjRmDMmDHw9vbGhQsXcODAAbRp0wbAvcd0O3fuxO7du9GrVy/Ex8fjvffeMzhHz5498e233+Lnn39G//790bt3b8yfPx/Ozs4mv7fK8Lv5TKgpfjfftWvXsG7dOun9vU/zqaFW5xsEqYiICGg0GnM0kYioUeJ389WvuvxuPs6ZIqOoVIUcjSIiomaNj/moVuRyeZ3WERERNXYcmaJasbe3R2RkZLXrSMnlci7YSUREzQbDFNUagxIREdH/8DEfERFRA8LPhdWPuuxnhikiIqIGwMrKCgBw584dM7ekeSjv5/J+NwYf81GDkJeXx3lYRNSsWVpaonXr1rh+/ToAwMbGBjKZzMytanqEELhz5w6uX7+O1q1bV1hY9FEwTJHZ5eXlYc2aNQ+ti4yMZKAioibNyckJAKRARabTunVrqb+NxTBFZvfgiNS9hUDtoVbnGaxhVd3IFRFRUyCTyaDRaNCuXTuUlpaauzlNlpWVVZ2MSJVjmKIGJSOjN/buHQohLKSvqPH2zjR3s4iI6pWlpWWd/rIn0+IEdGowdDo7KUgBgBAW2Lt3KHQ6OzO3jIiIqGoMU9Rg5OfbS0GqnBAWyM9Xm6lFRERED8cwRQ2GWp0HmUxvsE0m00OtzjdTi4iIiB6OYYoaDJWqEKGh+6RAVT5nil+kTEREDRknoFOD4u2dCQ+PC8jPV0OtzmeQIiKiBo9himqtrhfYlMvlBu9VqsJKQ9SDdURERA0BwxTViikW2LS3t0dkZCRXQCciokaJYYpqpaYLZ9Z2gU0GJSIiaqw4AZ2MotPZISfHjWtBERFRs8WRKXpkXK2ciIiIYYoeUVWrlXt4XOAn8Mysrj8gQERE1WOYokdS3WrlDFPmY4oPCBARUfU4Z4oeCVcrb5hM9QEBIiKqWoMIU2vXroWbmxuUSiX8/Pxw/Pjxaut37NiBLl26QKlUokePHti/f7/BfiEE5s+fD41GA2trawQFBeH8+fOVnqu4uBheXl6QyWTIysoy2Hfq1Cn0798fSqUSrq6uiI2NNeo+mxKuVt441McHBPLy8nDt2rUqX3l5eSa7NhFRQ2D2x3zbtm1DdHQ04uPj4efnh7i4OAQHByM7Oxvt2rWrUH/06FGMHj0aMTExGDp0KBITExEWFoaMjAx0794dABAbG4tVq1Zh8+bNcHd3x7x58xAcHIyzZ89CqVQanG/GjBlwdnbGDz/8YLC9oKAAf/nLXxAUFIT4+HicPn0ar7zyClq3bo2IiAjTdUgDd//CmdWtVs4FNs2vPj4gwMeKRESATAghzNkAPz8/PPnkk9I/yHq9Hq6urnjjjTcwa9asCvUjR45EUVER9u3bJ23r27cvvLy8EB8fDyEEnJ2dMXXqVEybNg0AoNPp4OjoiISEBIwaNUo67quvvkJ0dDR27tyJbt26ITMzE15eXgCAjz76CG+99Ra0Wq0UDGbNmoXdu3fjp59+qtG9FRQUQKVSQafToVWrVo/UPw0RJzg3XNeuXcO6deug09khLi7KYF6bTKZHVFQcVKpCREREQKPR1Nn1HqaurkdEVB9q+/vbrI/5SkpKkJ6ejqCgIGmbhYUFgoKCkJqaWukxqampBvUAEBwcLNXn5ORAq9Ua1KhUKvj5+RmcMzc3FxMnTsSWLVtgY2NT6XUGDBhgMMJSPmL2+++/V9q24uJiFBQUGLyaInt7e2g0mipfDFLmV90HBEyJ644RUXNk1sd8N2/eRFlZGRwdHQ22Ozo6Vjn6o9VqK63XarXS/vJtVdUIITBu3DhMmjQJvr6+uHTpUqXXcXd3r3CO8n1t2rSpcExMTAzefvvtqm6XqN6Uf0DgwZEpU35AgOuOEVFz1SAmoNe31atXo7CwELNnz67T886ePRs6nU56XblypU7PT1RT9f0BgarWHeMIFRE1B2YdmXJwcIClpSVyc3MNtufm5sLJyanSY5ycnKqtL/+Zm5trMEcjNzdXmg/1zTffIDU1FQqFwuA8vr6+eOmll7B58+Yqr3P/NR6kUCgqnJNMh3O3KjLXBwS47hgRNWdmDVNyuRw+Pj5ISUlBWFgYgHsT0FNSUhAZGVnpMf7+/khJSUFUVJS07dChQ/D39wcAuLu7w8nJCSkpKVJ4KigoQFpaGl5//XUAwKpVq7B48WLp+KtXryI4OBjbtm2Dn5+fdJ233noLpaWlsLKykq7TuXPnSh/xUf3ip8gqZ29vj8jIyHoPmeZ4rEhE1FCYfWmE6OhohIeHw9fXF3369EFcXByKioowfvx4AMDYsWPh4uKCmJgYAMCUKVMQGBiI5cuXIyQkBElJSTh58qT0iSKZTIaoqCgsXrwYnp6e0tIIzs7OUmBr3769QRtatmwJAPDw8MBjjz0GAHjxxRfx9ttvY8KECZg5cybOnDmDDz74ACtXrqyPbqGHeDAs6HR2yM+3h1qdZzAS0hwXpzRHeCx/rPjgnCmOShFRc2D2MDVy5EjcuHED8+fPh1arhZeXF5KTk6XJ3pcvX4aFxf/+azcgIACJiYmYO3cu5syZA09PT+zevVtaYwq4t3ZUUVERIiIicOvWLfTr1w/JyckV1piqjkqlwsGDBzF58mT4+PjAwcEB8+fPb9ZrTDVUnPhsPlx3jIioAawz1ZQ11XWmGoL6Xk+Jqsa5a0TU1NT297fZR6aIjMGJz+bHoEREzV2zXBqBmg5+4TIREZkbwxQ1avzCZSIiMjc+5qNGr7qJz0RERKbGMEWN0oOfDlOpCisNUfwUGRERmRrDFDVK5lqckoiI6EEMU9RoMSgREVFDwAnoREREREZgmCIiIiIyAsMUERERkREYpoiIiIiMwDBFREREZASGKSIiIiIjMEwRERERGYFhioiIiMgIDFNERERERmCYIiIiIjICwxQRERGRERimiIiIiIzAMEVERERkBIYpIiIiIiMwTBEREREZgWGKiIiIyAgMU0RERERGYJgiIiIiMgLDFBEREZERGKaIiIiIjNDC3A0gIqoLeXl5KCkpqXK/XC6Hvb19PbaIiJoLhikiavTy8vKwZs2ah9ZFRkYyUBFRneNjPiJq9B4ckdLp7JCT4wadzq7aOiKiusCRKSKqd4/6SK6q427evCn9OSOjN/buHQohLCCT6REaug/e3pl103AiokowTBFRvXrwkZxOZ4f8fHuo1XlQqQql7Q8+kqvJozydzk4KUgAghAX27h0KD48LBucmIqpLDFNEVK/uH1mqbhTpwRGoyh7lPRjC8vPtpSBVTggL5OerG0yY4kR5oqaHYYqIzMKYUaSqQphanQeZTG8QqGQyPdTqfJPeS01xojxR02T2Cehr166Fm5sblEol/Pz8cPz48Wrrd+zYgS5dukCpVKJHjx7Yv3+/wX4hBObPnw+NRgNra2sEBQXh/PnzBjXDhg1D+/btoVQqodFoMGbMGFy9etWg5sCBA+jbty/s7OzQtm1bjBgxApcuXaqTeyai6keRqlNVCNPp7KBSFSI0dB9kMj0ASEGroYxK1XQCPCfKEzUuZg1T27ZtQ3R0NBYsWICMjAz06tULwcHBuH79eqX1R48exejRozFhwgRkZmYiLCwMYWFhOHPmjFQTGxuLVatWIT4+HmlpabC1tUVwcDDu3r0r1QwcOBDbt29HdnY2du7ciYsXL+L555+X9ufk5GD48OEYNGgQsrKycODAAdy8eRPPPfec6TqDqJkpH0W6X01GkR4Wwry9MxEVFYfw8ARERcU16MnnVX3qkIgaF7OGqRUrVmDixIkYP348nnjiCcTHx8PGxgYbN26stP6DDz7A4MGDMX36dHTt2hWLFi2Ct7e3NGwuhEBcXBzmzp2L4cOHo2fPnvjkk09w9epV7N69WzrPP/7xD/Tt2xcdOnRAQEAAZs2ahWPHjqG0tBQAkJ6ejrKyMixevBgeHh7w9vbGtGnTkJWVJdUQkXEedRSpJiFMpSqEu/uvFc4ll8vrqPXGy8jojbi4KGzeHI64uChkZPQ2d5OI6BGZbc5USUkJ0tPTMXv2bGmbhYUFgoKCkJqaWukxqampiI6ONtgWHBwsBaWcnBxotVoEBQVJ+1UqFfz8/JCamopRo0ZVOGd+fj62bt2KgIAAWFlZAQB8fHxgYWGBTZs2Ydy4cbh9+za2bNmCoKAgqaYyxcXFKC4ult4XFBQ8vCOImjFv70x4eFxAfr4aanV+jR7HlYewB+dMlR/73HPPwcHBocJxDWliNz91SNS0mC1M3bx5E2VlZXB0dDTY7ujoiJ9++qnSY7RabaX1Wq1W2l++raqacjNnzsSaNWtw584d9O3bF/v27ZP2ubu74+DBg3jhhRfw2muvoaysDP7+/hXmZz0oJiYGb7/9drU1RGRIpSqsdYCoLoQ5ODhAo9HUdTPrVGP41CER1ZzZJ6Cby/Tp05GZmYmDBw/C0tISY8eOhRACwL1QNnHiRISHh+PEiRP49ttvIZfL8fzzz0s1lZk9ezZ0Op30unLlSn3dDlGjUdNHbQ/WPfi+MTzKq8qjzhcjoobJbCNTDg4OsLS0RG5ursH23NxcODk5VXqMk5NTtfXlP3Nzcw3+yzQ3NxdeXl4Vru/g4IBOnTqha9eucHV1xbFjx+Dv74+1a9dCpVIhNjZWqv/000/h6uqKtLQ09O3bt9L2KRQKKBSKmnUAUTNlb2+PyMjIWq+19KjHNUQPe1RJRI2L2cKUXC6Hj48PUlJSEBYWBgDQ6/VISUlBZGRkpcf4+/sjJSUFUVFR0rZDhw7B398fwL3Hc05OTkhJSZHCU0FBAdLS0vD6669X2Ra9/t5/IZbPd7pz5w4sLAwH7SwtLQ1qiejRVRd4yhe1vHbtWqX7G0tgqsz9o2bVPapsDKNrRPQ/Zl20Mzo6GuHh4fD19UWfPn0QFxeHoqIijB8/HgAwduxYuLi4ICYmBgAwZcoUBAYGYvny5QgJCUFSUhJOnjyJdevWAQBkMhmioqKwePFieHp6wt3dHfPmzYOzs7MU2NLS0nDixAn069cPbdq0wcWLFzFv3jx4eHhIoSwkJAQrV67EO++8g9GjR6OwsBBz5sxBhw4d0Ls3P3FDZCqP+lUzjUVTGl0jov8xa5gaOXIkbty4gfnz50Or1cLLywvJycnSBPLLly8bjBAFBAQgMTERc+fOxZw5c+Dp6Yndu3eje/fuUs2MGTNQVFSEiIgI3Lp1C/369UNycjKUSiUAwMbGBrt27cKCBQtQVFQEjUaDwYMHY+7cudIjukGDBiExMRGxsbGIjY2FjY0N/P39kZycDGtr63rsIaLm5VG/aqYxYVAianpkoroZ1WSUgoICqFQq6HQ6tGrVytzNIWrwrl27hnXr1kGns0NcXFSFr4WJioqDSlWIiIiIBv+JPSJqvGr7+5vfzUdEDU5jXzqAX2ZM1LwwTBFRg9PQv7C4OvwyY6Lmp9muM0VEDVdD/8Li6jw4IlXV9+815nlfRGSII1NE1CA9ylfNNDTVTaInoqaDI1NE1GBVtcp5Y1DV9+89OEJFRI0fwxQRNRiP+lUzDVF1k+iJqGnhYz4iajCa0qKWjXkSPRHVDsMUETUojSEo1QS/f4+o+WCYIiIykaYwiZ6IHo5hioioDj04n0ulKqw0RDWGeV9EVDMMU0REdagpzfsiopphmCIiqmMMSkTNC5dGICIiIjICwxQRERGRERimiIiIiIzAMEVERERkBIYpIiIiIiMwTBEREREZgWGKiIiIyAgMU0RERERGYJgiIiIiMgLDFBEREZERGKaIiIiIjMAwRURERGQEhikiIiIiIzBMERERERmBYYqIiIjICAxTREREREZgmCIiIiIyAsMUERERkREYpoiIiIiMwDBFREREZASGKSIiIiIjMEwRERERGcHsYWrt2rVwc3ODUqmEn58fjh8/Xm39jh070KVLFyiVSvTo0QP79+832C+EwPz586HRaGBtbY2goCCcP3/eoGbYsGFo3749lEolNBoNxowZg6tXr1Y4z7Jly9CpUycoFAq4uLjg3XffrZubJiIioibDrGFq27ZtiI6OxoIFC5CRkYFevXohODgY169fr7T+6NGjGD16NCZMmIDMzEyEhYUhLCwMZ86ckWpiY2OxatUqxMfHIy0tDba2tggODsbdu3elmoEDB2L79u3Izs7Gzp07cfHiRTz//PMG15oyZQr++c9/YtmyZfjpp5+wZ88e9OnTxzQdQURERI2WTAghzHVxPz8/PPnkk1izZg0AQK/Xw9XVFW+88QZmzZpVoX7kyJEoKirCvn37pG19+/aFl5cX4uPjIYSAs7Mzpk6dimnTpgEAdDodHB0dkZCQgFGjRlXajj179iAsLAzFxcWwsrLCuXPn0LNnT5w5cwadO3d+5PsrKCiASqWCTqdDq1atHvk8REREVH9q+/vbbCNTJSUlSE9PR1BQ0P8aY2GBoKAgpKamVnpMamqqQT0ABAcHS/U5OTnQarUGNSqVCn5+flWeMz8/H1u3bkVAQACsrKwAAHv37sXjjz+Offv2wd3dHW5ubnj11VeRn59f7T0VFxejoKDA4EVERERNm9nC1M2bN1FWVgZHR0eD7Y6OjtBqtZUeo9Vqq60v/1mTc86cORO2trawt7fH5cuX8cUXX0j7fvnlF/z666/YsWMHPvnkEyQkJCA9Pb3Co8AHxcTEQKVSSS9XV9dq64mIiKjxM/sEdHOZPn06MjMzcfDgQVhaWmLs2LEof+Kp1+tRXFyMTz75BP3798fTTz+NDRs24PDhw8jOzq7ynLNnz4ZOp5NeV65cqa/bISIiIjNpYa4LOzg4wNLSErm5uQbbc3Nz4eTkVOkxTk5O1daX/8zNzYVGozGo8fLyqnB9BwcHdOrUCV27doWrqyuOHTsGf39/aDQatGjRAp06dZLqu3btCgC4fPlylfOoFAoFFApFDe6eiIiImgqzjUzJ5XL4+PggJSVF2qbX65GSkgJ/f/9Kj/H39zeoB4BDhw5J9e7u7nBycjKoKSgoQFpaWpXnLL8ucG/OEwA89dRT+PPPP3Hx4kWp5ueffwYAdOjQoTa3SURERE2c2UamACA6Ohrh4eHw9fVFnz59EBcXh6KiIowfPx4AMHbsWLi4uCAmJgbAveUKAgMDsXz5coSEhCApKQknT57EunXrAAAymQxRUVFYvHgxPD094e7ujnnz5sHZ2RlhYWEAgLS0NJw4cQL9+vVDmzZtcPHiRcybNw8eHh5S4AoKCoK3tzdeeeUVxMXFQa/XY/LkyXj22WcNRquIiIiIzBqmRo4ciRs3bmD+/PnQarXw8vJCcnKyNIH88uXLsLD43+BZQEAAEhMTMXfuXMyZMweenp7YvXs3unfvLtXMmDEDRUVFiIiIwK1bt9CvXz8kJydDqVQCAGxsbLBr1y4sWLAARUVF0Gg0GDx4MObOnSs9orOwsMDevXvxxhtvYMCAAbC1tcWQIUOwfPnyeuwdIiIiagzMus5UU8d1poiIiBqfRrPOFBEREVFTwDBFREREZASGKSIiIiIjMEwRERERGYFhioiIiMgIDFNERERERmCYIiIiIjICwxQRERGREeosTN29exfLli2rq9MRERERNQq1ClM3btzAvn37cPDgQZSVlQEASktL8cEHH8DNzQ1LliwxSSOJiIiIGqoafzffd999h6FDh6KgoAAymQy+vr7YtGkTwsLC0KJFCyxcuBDh4eGmbCsRERFRg1Pjkam5c+fir3/9K06dOoXo6GicOHECf/vb3/Dee+/h7NmzmDRpEqytrU3ZViIiIqIGp8ZfdGxvb4///Oc/eOKJJ/DHH3+gZcuW2LVrF4YPH27qNjZa/KJjIiKixsdkX3T8+++/w8HBAQBgbW0NGxsbdO/e/dFbSkRERNQE1HjOFACcPXsWWq0WACCEQHZ2NoqKigxqevbsWXetIyIiImrgavyYz8LCAjKZDNWVy2Qy6VN+xMd8REREjVFtf3/XeGQqJyfnoTWFhYU1PR0RERFRk1DjMNWhQ4dKtxcWFuKzzz7Dhg0bcPLkSY5MERERUbPyyCug//vf/0Z4eDg0Gg2WLVuGgQMH4tixY3XZNiIiIqIGr1YT0LVaLRISErBhwwYUFBTghRdeQHFxMXbv3o0nnnjCVG0kIiIiarBqPDIVGhqKzp0749SpU4iLi8PVq1exevVqU7aNiIiIqMGr8cjUV199hTfffBOvv/46PD09TdkmIiIiokajVt/Nt2HDBvj4+KBr164YM2YMRo0aZcq2ERERVSkvLw8lJSVV7pfL5bC3t6/HFlFzVeN1psoVFRVh27Zt2LhxI44fP46ysjKsWLECr7zyCuzs7EzVzkaJ60wREZlGXl4e1qxZ89C6yMhIBiqqNZN9nUw5W1tbvPLKK/juu+9w+vRpTJ06FUuWLEG7du0wbNiwR2o0ERFRbTw4IqXT2SEnxw06nV21dUSm8MhLIwBA586dERsbi99++w2fffZZXbWJiIioxjIyeiMuLgqbN4cjLi4KGRm9zd0kamaMClPlLC0tERYWhj179tTF6YiIiGpEp7PD3r1DIcS9X2dCWGDv3qEVRqiITKlOwhQREZE55OfbS0GqnBAWyM9Xm6lF1BwxTBERUaOlVudBJtMbbJPJ9FCr883UImqOGKaIiKjRUqkKERq6TwpUMpkeoaH7oFIVmrll1JzU6utkiIiIGhpv70x4eFxAfr4aanU+gxTVO4YpIiJqdORyucF7laqw0hD1YB2RKTBMERFRo2Nvb4/IyEiugE4NAsMUERE1SgxK1FA0iAnoa9euhZubG5RKJfz8/HD8+PFq63fs2IEuXbpAqVSiR48e2L9/v8F+IQTmz58PjUYDa2trBAUF4fz58wY1w4YNQ/v27aFUKqHRaDBmzBhcvXq10utduHABdnZ2aN26tVH3SURERE2P2cPUtm3bEB0djQULFiAjIwO9evVCcHAwrl+/Xmn90aNHMXr0aEyYMAGZmZkICwtDWFgYzpw5I9XExsZi1apViI+PR1paGmxtbREcHIy7d+9KNQMHDsT27duRnZ2NnTt34uLFi3j++ecrXK+0tBSjR49G//796/7miYiIqNGr9Rcd1zU/Pz88+eST0hdW6vV6uLq64o033sCsWbMq1I8cORJFRUXYt2+ftK1v377w8vJCfHw8hBBwdnbG1KlTMW3aNACATqeDo6MjEhISMGrUqErbsWfPHoSFhaG4uBhWVlbS9pkzZ+Lq1at45plnEBUVhVu3btX43vhFx0RERI2Pyb/ouC6VlJQgPT0dQUFB0jYLCwsEBQUhNTW10mNSU1MN6gEgODhYqs/JyYFWqzWoUalU8PPzq/Kc+fn52Lp1KwICAgyC1DfffIMdO3Zg7dq1Nbqf4uJiFBQUGLyIiIioaTNrmLp58ybKysrg6OhosN3R0RFarbbSY7RabbX15T9rcs6ZM2fC1tYW9vb2uHz5Mr744gtpX15eHsaNG4eEhIQajyrFxMRApVJJL1dX1xodR0RERI2X2edMmdP06dORmZmJgwcPwtLSEmPHjkX5U8+JEyfixRdfxIABA2p8vtmzZ0On00mvK1eumKrpRERE1ECYdWkEBwcHWFpaIjc312B7bm4unJycKj3Gycmp2vryn7m5udBoNAY1Xl5eFa7v4OCATp06oWvXrnB1dcWxY8fg7++Pb775Bnv27MGyZcsA3PuEoF6vR4sWLbBu3Tq88sorFdqmUCigUChq1wlERETUqJl1ZEoul8PHxwcpKSnSNr1ej5SUFPj7+1d6jL+/v0E9ABw6dEiqd3d3h5OTk0FNQUEB0tLSqjxn+XWBe/OegHtzs7KysqTXO++8Azs7O2RlZeFvf/vbo90wERERNTlmX7QzOjoa4eHh8PX1RZ8+fRAXF4eioiKMHz8eADB27Fi4uLggJiYGADBlyhQEBgZi+fLlCAkJQVJSEk6ePIl169YBAGQyGaKiorB48WJ4enrC3d0d8+bNg7OzM8LCwgAAaWlpOHHiBPr164c2bdrg4sWLmDdvHjw8PKTA1bVrV4N2njx5EhYWFujevXs99QwRERE1BmYPUyNHjsSNGzcwf/58aLVaeHl5ITk5WZpAfvnyZVhY/G8ALSAgAImJiZg7dy7mzJkDT09P7N692yDkzJgxA0VFRYiIiMCtW7fQr18/JCcnQ6lUAgBsbGywa9cuLFiwAEVFRdBoNBg8eDDmzp3Lx3RERERUK2ZfZ6op4zpTREREjU+jWmeKiIiIqLFjmCIiIiIyAsMUERERkREYpoiIiIiMwDBFREREZASGKSIiIiIjMEwRERERGYFhioiIiMgIDFNERERERmCYIiIiIjICwxQRERGRERimiIiIiIzAMEVERERkBIYpIiIiIiMwTBEREREZgWGKiIiIyAgMU0RERERGYJgiIiIiMgLDFBEREZERGKaIiIiIjMAwRURERGQEhikiIiIiIzBMERERERmhhbkbQERE1Fzl5eWhpKSkyv1yuRz29vb12CJ6FAxTREREZpCXl4c1a9Y8tC4yMpKBqoHjYz4iIiIzeHBESqezQ06OG3Q6u2rrqOHhyBQREZGZZWT0xt69QyGEBWQyPUJD98HbO9PczaIa4sgUERGRGel0dlKQAgAhLLB379AKI1TUcDFMERERmVF+vr0UpMoJYYH8fLWZWkS1xTBFRERkRmp1HmQyvcE2mUwPtTrfTC2i2mKYIiIiMiOVqhChofukQFU+Z0qlKjRzy6imOAGdiIjIzLy9M+HhcQH5+Wqo1fkMUo0MwxQREZEZyOVyg/cqVWGlIerBOmp4GKaIiIjMwN7eHpGRkVwBvQlgmCIiIjITBqWmoUFMQF+7di3c3NygVCrh5+eH48ePV1u/Y8cOdOnSBUqlEj169MD+/fsN9gshMH/+fGg0GlhbWyMoKAjnz583qBk2bBjat28PpVIJjUaDMWPG4OrVq9L+I0eOYPjw4dBoNLC1tYWXlxe2bt1adzdNRERETYLZw9S2bdsQHR2NBQsWICMjA7169UJwcDCuX79eaf3Ro0cxevRoTJgwAZmZmQgLC0NYWBjOnDkj1cTGxmLVqlWIj49HWloabG1tERwcjLt370o1AwcOxPbt25GdnY2dO3fi4sWLeP755w2u07NnT+zcuROnTp3C+PHjMXbsWOzbt890nUFERESNjkwIIczZAD8/Pzz55JPSlz3q9Xq4urrijTfewKxZsyrUjxw5EkVFRQahpm/fvvDy8kJ8fDyEEHB2dsbUqVMxbdo0AIBOp4OjoyMSEhIwatSoStuxZ88ehIWFobi4GFZWVpXWhISEwNHRERs3bqzRvRUUFEClUkGn06FVq1Y1OoaIiIjMq7a/v806MlVSUoL09HQEBQVJ2ywsLBAUFITU1NRKj0lNTTWoB4Dg4GCpPicnB1qt1qBGpVLBz8+vynPm5+dj69atCAgIqDJIAfdCmVpd9Yq0xcXFKCgoMHgRERFR02bWMHXz5k2UlZXB0dHRYLujoyO0Wm2lx2i12mrry3/W5JwzZ86Era0t7O3tcfnyZXzxxRdVtnX79u04ceIExo8fX2VNTEwMVCqV9HJ1da2yloiIiJoGs8+ZMqfp06cjMzMTBw8ehKWlJcaOHYvKnnoePnwY48ePx/r169GtW7cqzzd79mzodDrpdeXKFVM2n4iIiBoAsy6N4ODgAEtLS+Tm5hpsz83NhZOTU6XHODk5VVtf/jM3NxcajcagxsvLq8L1HRwc0KlTJ3Tt2hWurq44duwY/P39pZpvv/0WoaGhWLlyJcaOHVvt/SgUCigUiupvmoiIiJoUs45MyeVy+Pj4ICUlRdqm1+uRkpJiEGju5+/vb1APAIcOHZLq3d3d4eTkZFBTUFCAtLS0Ks9Zfl3g3rynckeOHEFISAiWLl2KiIiI2t8gERERNXlmX7QzOjoa4eHh8PX1RZ8+fRAXF4eioiJpbtLYsWPh4uKCmJgYAMCUKVMQGBiI5cuXIyQkBElJSTh58iTWrVsHAJDJZIiKisLixYvh6ekJd3d3zJs3D87OzggLCwMApKWl4cSJE+jXrx/atGmDixcvYt68efDw8JAC1+HDhzF06FBMmTIFI0aMkOZbyeXyaiehExERUTMjGoDVq1eL9u3bC7lcLvr06SOOHTsm7QsMDBTh4eEG9du3bxedOnUScrlcdOvWTXz55ZcG+/V6vZg3b55wdHQUCoVCPPPMMyI7O1vaf+rUKTFw4EChVquFQqEQbm5uYtKkSeK3336TasLDwwWACq/AwMAa35dOpxMAhE6nq12HEBERkdnU9ve32deZasq4zhQREVHj06jWmSIiIiJq7BimiIiIiIzAMEVERERkBIYpIiIiIiOYfWkEIiIiqp28vDyUlJRUuV8ul8Pe3r4eW9S8MUwRERE1Inl5eVizZs1D6yIjIxmo6gkf8xERETUiD45I6XR2yMlxg05nV20dmQ5HpoiIiBqpjIze2Lt3KISwgEymR2joPnh7Z5q7Wc0OR6aIiIgaIZ3OTgpSACCEBfbuHVphhIpMj2GKiIioEcrPt5eCVDkhLJCfz++PrW8MU0RERI2QWp0HmUxvsE0m00OtzjdTi5ovhikiIqJGSKUqRGjoPilQlc+ZUqkKzdyy5ocT0ImIiBopb+9MeHhcQH6+Gmp1PoOUmTBMERERNSJyudzgvUpVWGmIerCOTIdhioiIqBGxt7dHZGQkV0BvQBimiIiIGhkGpYaFE9CJiIiIjMAwRURERGQEhikiIiIiIzBMERERERmBE9CJiIiagby8PH4C0EQYpoiIiJq4vLw8rFmz5qF1kZGRDFSPgI/5iIiImrjqRqQepY4MMUwRERE1MzqdHXJy3KDT2Zm7KU0CH/MRERE1IxkZvbF371AIYSF9ObK3d6a5m9WocWSKiIiomdDp7KQgBQBCWGDv3qEcoTISwxQREVEzkZ9vLwWpckJYID9fbaYWNQ0MU0RERM2EWp0HmUxvsE0m00OtzjdTi5oGhikiIqJmQqUqRGjoPilQlc+ZUqkKzdyyxo0T0ImIiJo4uVwu/dnbOxMeHheQn6+GWp1vEKTur6OaY5giIiJq4uzt7REZGckV0E2EYYqIiKgZYFAyHc6ZIiIiIjICwxQRERGRERpEmFq7di3c3NygVCrh5+eH48ePV1u/Y8cOdOnSBUqlEj169MD+/fsN9gshMH/+fGg0GlhbWyMoKAjnz583qBk2bBjat28PpVIJjUaDMWPG4OrVqwY1p06dQv/+/aFUKuHq6orY2Ni6uWEiIiJqMsweprZt24bo6GgsWLAAGRkZ6NWrF4KDg3H9+vVK648ePYrRo0djwoQJyMzMRFhYGMLCwnDmzBmpJjY2FqtWrUJ8fDzS0tJga2uL4OBg3L17V6oZOHAgtm/fjuzsbOzcuRMXL17E888/L+0vKCjAX/7yF3To0AHp6el4//33sXDhQqxbt850nUFERESNjzCzPn36iMmTJ0vvy8rKhLOzs4iJiam0/oUXXhAhISEG2/z8/MRrr70mhBBCr9cLJycn8f7770v7b926JRQKhfjss8+qbMcXX3whZDKZKCkpEUII8eGHH4o2bdqI4uJiqWbmzJmic+fONb43nU4nAAidTlfjY4iIiMi8avv726wjUyUlJUhPT0dQUJC0zcLCAkFBQUhNTa30mNTUVIN6AAgODpbqc3JyoNVqDWpUKhX8/PyqPGd+fj62bt2KgIAAWFlZSdcZMGCAwZobwcHByM7Oxu+//17peYqLi1FQUGDwIiIioqbNrGHq5s2bKCsrg6Ojo8F2R0dHaLXaSo/RarXV1pf/rMk5Z86cCVtbW9jb2+Py5cv44osvHnqd+6/xoJiYGKhUKunl6upaaR0RERE1HWafM2VO06dPR2ZmJg4ePAhLS0uMHTsWQohHPt/s2bOh0+mk15UrV+qwtURERNQQmXXRTgcHB1haWiI3N9dge25uLpycnCo9xsnJqdr68p+5ubnQaDQGNV5eXhWu7+DggE6dOqFr165wdXXFsWPH4O/vX+V17r/GgxQKBRQKxUPumoiIiJoSs45MyeVy+Pj4ICUlRdqm1+uRkpICf3//So/x9/c3qAeAQ4cOSfXu7u5wcnIyqCkoKEBaWlqV5yy/LnBv3lP5df7973+jtLTU4DqdO3dGmzZtanmnRERE1GSZdj78wyUlJQmFQiESEhLE2bNnRUREhGjdurXQarVCCCHGjBkjZs2aJdV///33okWLFmLZsmXi3LlzYsGCBcLKykqcPn1aqlmyZIlo3bq1+OKLL8SpU6fE8OHDhbu7u/jjjz+EEEIcO3ZMrF69WmRmZopLly6JlJQUERAQIDw8PMTdu3eFEPc+Aejo6CjGjBkjzpw5I5KSkoSNjY34+OOPa3xv/DQfERFR41Pb399mD1NCCLF69WrRvn17IZfLRZ8+fcSxY8ekfYGBgSI8PNygfvv27aJTp05CLpeLbt26iS+//NJgv16vF/PmzROOjo5CoVCIZ555RmRnZ0v7T506JQYOHCjUarVQKBTCzc1NTJo0Sfz2228G5/nhhx9Ev379hEKhEC4uLmLJkiW1ui+GKSIiosantr+/ZUIYMeOaqlVQUACVSgWdTodWrVqZuzlERERUA7X9/d2sP81HREREZCyGKSIiIiIjMEwRERERGcGs60wRERFRw5aXl4eSkpIq98vlctjb29djixoehikiIiKqVF5eHtasWfPQusjIyGYdqBimiIiIqFLVjUjd7+rVq5XWNpdRK4YpIiIiqhGdzg75+fZQq/OgUhVK23ft2lXlMc1h1IphioiIiB4qI6M39u4dCiEsIJPpERq6D97emdL+qoJWTUe3GjOGKSIiIqqWTmcnBSkAEMICe/cOhYfHBahUhQ8NWk0dl0YgIiKiauXn20tBqpwQFsjPV1cZtHQ6O3M01SwYpoiIiKhaanUeZDK9wTaZTA+1Or/aoNVcMEwRERFRtVSqQoSG7pMCVfmjPJWqsNqg1VxwzhQRERFVSi6XS3/29s6Eh8cF5OeroVbnS5PMy4PWg3Om7p+E3tQxTBEREVGl7O3tERkZWekn8m7evCktiVBV0GouGKaIiIioSlWtEXX/qBVwb4SqshD1YF1TxDBFREREtVbdqFU5roBOREREVI3mEJRqgp/mIyIiIjICwxQRERGRERimiIiIiIzAMEVERERkBIYpIiIiIiMwTBEREREZgWGKiIiIyAgMU0RERERGYJgiIiIiMgLDFBEREZERGKaIiIiIjMAwRURERGQEhikiIiIiIzBMERERERmBYYqIiIjICAxTREREREZgmCIiIiIygtnD1Nq1a+Hm5galUgk/Pz8cP3682vodO3agS5cuUCqV6NGjB/bv32+wXwiB+fPnQ6PRwNraGkFBQTh//ry0/9KlS5gwYQLc3d1hbW0NDw8PLFiwACUlJQbnOXDgAPr27Qs7Ozu0bdsWI0aMwKVLl+rsvomIiKhpMGuY2rZtG6Kjo7FgwQJkZGSgV69eCA4OxvXr1yutP3r0KEaPHo0JEyYgMzMTYWFhCAsLw5kzZ6Sa2NhYrFq1CvHx8UhLS4OtrS2Cg4Nx9+5dAMBPP/0EvV6Pjz/+GD/++CNWrlyJ+Ph4zJkzRzpHTk4Ohg8fjkGDBiErKwsHDhzAzZs38dxzz5m2Q4iIiKjxEWbUp08fMXnyZOl9WVmZcHZ2FjExMZXWv/DCCyIkJMRgm5+fn3jttdeEEELo9Xrh5OQk3n//fWn/rVu3hEKhEJ999lmV7YiNjRXu7u7S+x07dogWLVqIsrIyaduePXuETCYTJSUlNb4/nU4nAAidTlfjY4iIiMi8avv722wjUyUlJUhPT0dQUJC0zcLCAkFBQUhNTa30mNTUVIN6AAgODpbqc3JyoNVqDWpUKhX8/PyqPCcA6HQ6qNVq6b2Pjw8sLCywadMmlJWVQafTYcuWLQgKCoKVlVWV5ykuLkZBQYHBi4iIiJo2s4WpmzdvoqysDI6OjgbbHR0dodVqKz1Gq9VWW1/+szbnvHDhAlavXo3XXntN2ubu7o6DBw9izpw5UCgUaN26NX777Tds37692nuKiYmBSqWSXq6urtXWExERUeNn9gno5vTf//4XgwcPxv/93/9h4sSJ0natVouJEyciPDwcJ06cwLfffgu5XI7nn38eQogqzzd79mzodDrpdeXKlfq4DSIiIjKjFua6sIODAywtLZGbm2uwPTc3F05OTpUe4+TkVG19+c/c3FxoNBqDGi8vL4Pjrl69ioEDByIgIADr1q0z2Ld27VqoVCrExsZK2z799FO4uroiLS0Nffv2rbR9CoUCCoWimrsmIiKipsZsI1NyuRw+Pj5ISUmRtun1eqSkpMDf37/SY/z9/Q3qAeDQoUNSvbu7O5ycnAxqCgoKkJaWZnDO//73v3j66afh4+ODTZs2wcLCsBvu3LlTYZulpaXURiIiIqJyZhuZAoDo6GiEh4fD19cXffr0QVxcHIqKijB+/HgAwNixY+Hi4oKYmBgAwJQpUxAYGIjly5cjJCQESUlJOHnypDSyJJPJEBUVhcWLF8PT0xPu7u6YN28enJ2dERYWBuB/QapDhw5YtmwZbty4IbWnfGQrJCQEK1euxDvvvIPRo0ejsLAQc+bMQYcOHdC7d+967CEiIiIql5eXV2FdyPvJ5XLY29vXY4vuMWuYGjlyJG7cuIH58+dDq9XCy8sLycnJ0gTyy5cvG4wQBQQEIDExEXPnzsWcOXPg6emJ3bt3o3v37lLNjBkzUFRUhIiICNy6dQv9+vVDcnIylEolgHsjWRcuXMCFCxfw2GOPGbSnfD7UoEGDkJiYiNjYWMTGxsLGxgb+/v5ITk6GtbW1qbuFiIiIHpCXl4c1a9ZI73U6O+Tn20OtzoNKVShtj4yMrPdAJRPVzagmoxQUFEClUkGn06FVq1bmbg4REVGjde3aNelJVEZGb+zdOxRCWEAm0yM0dB+8vTMBABEREQbzph9FbX9/N+tP8xEREVHjotPZSUEKAISwwN69Q6HT2ZmtTQxTRERE1Gjk59vjwTXHhbBAfr66iiNMj2GKiIiIGg21Og8ymeEn62UyPdTqfDO1iGGKiIiIGhGVqhChofukQFU+Z+r+Sej1zayf5iMiIiKqLW/vTHh4XEB+vhpqdb5ZgxTAMEVERESNkEpVaPYQVY6P+YiIiKjBk8vldVpXlzgyRURERA2evb09IiMjuQI6ERER0aMyR1CqCT7mIyIiIjICwxQRERGRERimiIiIiIzAMEVERERkBIYpIiIiIiMwTBEREREZgWGKiIiIyAgMU0RERERGYJgiIiIiMgJXQDchIQQAoKCgwMwtISIiopoq/71d/nv8YRimTKiw8N63Wbu6upq5JURERFRbhYWFUKlUD62TiZrGLqo1vV6Pq1evws7ODjKZrML+goICuLq64sqVK2jVqpUZWtjwsE8MsT8MsT8qYp8YYn8YYn9UVJM+EUKgsLAQzs7OsLB4+IwojkyZkIWFBR577LGH1rVq1Yp/yR/APjHE/jDE/qiIfWKI/WGI/VHRw/qkJiNS5TgBnYiIiMgIDFNERERERmCYMiOFQoEFCxZAoVCYuykNBvvEEPvDEPujIvaJIfaHIfZHRaboE05AJyIiIjICR6aIiIiIjMAwRURERGQEhikiIiIiIzBMERERERmBYcrE1q5dCzc3NyiVSvj5+eH48eNV1v74448YMWIE3NzcIJPJEBcXV38NrUe16ZP169ejf//+aNOmDdq0aYOgoKBq6xuj2vTHrl274Ovri9atW8PW1hZeXl7YsmVLPbbW9GrTH/dLSkqCTCZDWFiYaRtoBrXpk4SEBMhkMoOXUqmsx9aaXm3/jty6dQuTJ0+GRqOBQqFAp06dsH///npqrenVpj+efvrpCn8/ZDIZQkJC6rHFplfbvyNxcXHo3LkzrK2t4erqin/84x+4e/duzS8oyGSSkpKEXC4XGzduFD/++KOYOHGiaN26tcjNza20/vjx42LatGnis88+E05OTmLlypX12+B6UNs+efHFF8XatWtFZmamOHfunBg3bpxQqVTit99+q+eWm0Zt++Pw4cNi165d4uzZs+LChQsiLi5OWFpaiuTk5HpuuWnUtj/K5eTkCBcXF9G/f38xfPjw+mlsPaltn2zatEm0atVKXLt2TXpptdp6brXp1LY/iouLha+vr/jrX/8qvvvuO5GTkyOOHDkisrKy6rnlplHb/sjLyzP4u3HmzBlhaWkpNm3aVL8NN6Ha9snWrVuFQqEQW7duFTk5OeLAgQNCo9GIf/zjHzW+JsOUCfXp00dMnjxZel9WViacnZ1FTEzMQ4/t0KFDkwxTxvSJEEL8+eefws7OTmzevNlUTaxXxvaHEEL07t1bzJ071xTNq3eP0h9//vmnCAgIEP/85z9FeHh4kwtTte2TTZs2CZVKVU+tq3+17Y+PPvpIPP7446KkpKS+mlivjP03ZOXKlcLOzk7cvn3bVE2sd7Xtk8mTJ4tBgwYZbIuOjhZPPfVUja/Jx3wmUlJSgvT0dAQFBUnbLCwsEBQUhNTUVDO2zHzqok/u3LmD0tJSqNVqUzWz3hjbH0IIpKSkIDs7GwMGDDBlU+vFo/bHO++8g3bt2mHChAn10cx69ah9cvv2bXTo0AGurq4YPnw4fvzxx/porsk9Sn/s2bMH/v7+mDx5MhwdHdG9e3e89957KCsrq69mm0xd/Ju6YcMGjBo1Cra2tqZqZr16lD4JCAhAenq69Cjwl19+wf79+/HXv/61xtflFx2byM2bN1FWVgZHR0eD7Y6Ojvjpp5/M1Crzqos+mTlzJpydnQ3+j9JYPWp/6HQ6uLi4oLi4GJaWlvjwww/x7LPPmrq5Jvco/fHdd99hw4YNyMrKqocW1r9H6ZPOnTtj48aN6NmzJ3Q6HZYtW4aAgAD8+OOPNfri9YbsUfrjl19+wTfffIOXXnoJ+/fvx4ULF/D3v/8dpaWlWLBgQX0022SM/Tf1+PHjOHPmDDZs2GCqJta7R+mTF198ETdv3kS/fv0ghMCff/6JSZMmYc6cOTW+LsMUNRpLlixBUlISjhw50uQm1NaGnZ0dsrKycPv2baSkpCA6OhqPP/44nn76aXM3rV4VFhZizJgxWL9+PRwcHMzdnAbD398f/v7+0vuAgAB07doVH3/8MRYtWmTGlpmHXq9Hu3btsG7dOlhaWsLHxwf//e9/8f777zf6MGWsDRs2oEePHujTp4+5m2JWR44cwXvvvYcPP/wQfn5+uHDhAqZMmYJFixZh3rx5NToHw5SJODg4wNLSErm5uQbbc3Nz4eTkZKZWmZcxfbJs2TIsWbIEX3/9NXr27GnKZtabR+0PCwsLdOzYEQDg5eWFc+fOISYmptGHqdr2x8WLF3Hp0iWEhoZK2/R6PQCgRYsWyM7OhoeHh2kbbWJ18e+IlZUVevfujQsXLpiiifXqUfpDo9HAysoKlpaW0rauXbtCq9WipKQEcrncpG02JWP+fhQVFSEpKQnvvPOOKZtY7x6lT+bNm4cxY8bg1VdfBQD06NEDRUVFiIiIwFtvvQULi4fPiOKcKRORy+Xw8fFBSkqKtE2v1yMlJcXgvxqbk0ftk9jYWCxatAjJycnw9fWtj6bWi7r6O6LX61FcXGyKJtar2vZHly5dcPr0aWRlZUmvYcOGYeDAgcjKyoKrq2t9Nt8k6uLvSFlZGU6fPg2NRmOqZtabR+mPp556ChcuXJCCNgD8/PPP0Gg0jTpIAcb9/dixYweKi4vx8ssvm7qZ9epR+uTOnTsVAlN5+BY1/friR5goTzWUlJQkFAqFSEhIEGfPnhURERGidevW0seUx4wZI2bNmiXVFxcXi8zMTJGZmSk0Go2YNm2ayMzMFOfPnzfXLdS52vbJkiVLhFwuF59//rnBx3kLCwvNdQt1qrb98d5774mDBw+KixcvirNnz4ply5aJFi1aiPXr15vrFupUbfvjQU3x03y17ZO3335bHDhwQFy8eFGkp6eLUaNGCaVSKX788Udz3UKdqm1/XL58WdjZ2YnIyEiRnZ0t9u3bJ9q1aycWL15srluoU4/6/5l+/fqJkSNH1ndz60Vt+2TBggXCzs5OfPbZZ+KXX34RBw8eFB4eHuKFF16o8TUZpkxs9erVon379kIul4s+ffqIY8eOSfsCAwNFeHi49D4nJ0cAqPAKDAys/4abUG36pEOHDpX2yYIFC+q/4SZSm/546623RMeOHYVSqRRt2rQR/v7+IikpyQytNp3a9MeDmmKYEqJ2fRIVFSXVOjo6ir/+9a8iIyPDDK02ndr+HTl69Kjw8/MTCoVCPP744+Ldd98Vf/75Zz232nRq2x8//fSTACAOHjxYzy2tP7Xpk9LSUrFw4ULh4eEhlEqlcHV1FX//+9/F77//XuPryYSo6RgWERERET2Ic6aIiIiIjMAwRURERGQEhikiIiIiIzBMERERERmBYYqIiIjICAxTREREREZgmCIiIiIyAsMUEVETMG7cOISFhZm7GUTNEsMUEZnUuHHjIJPJpJe9vT0GDx6MU6dOmbtpdeL+eyt/9evXz2TXu3TpEmQyGbKysgy2f/DBB0hISDDZdYmoagxTRGRygwcPxrVr13Dt2jWkpKSgRYsWGDp0qLmbVWc2bdok3d+1a9ewZ8+eSutKS0tN1gaVSoXWrVub7PxEVDWGKSIyOYVCAScnJzg5OcHLywuzZs3ClStXcOPGDQwaNAiRkZEG9Tdu3IBcLpe++d3NzQ2LFi3C6NGjYWtrCxcXF6xdu9bgmBUrVqBHjx6wtbWFq6sr/v73v+P27dvS/l9//RWhoaFo06YNbG1t0a1bN+zfvx8A8Pvvv+Oll15C27ZtYW1tDU9PT2zatKnG99e6dWvp/pycnKBWq6URpG3btiEwMBBKpRJbt25FXl4eRo8eDRcXF9jY2KBHjx747LPPDM6n1+sRGxuLjh07QqFQoH379nj33XcBAO7u7gCA3r17QyaT4emnnwZQ8TFfcXEx3nzzTbRr1w5KpRL9+vXDiRMnpP1HjhyBTCZDSkoKfH19YWNjg4CAAGRnZ9f4vonoHoYpIqpXt2/fxqeffoqOHTvC3t4er776KhITE1FcXCzVfPrpp3BxccGgQYOkbe+//z569eqFzMxMzJo1C1OmTMGhQ4ek/RYWFli1ahV+/PFHbN68Gd988w1mzJgh7Z88eTKKi4vx73//G6dPn8bSpUvRsmVLAMC8efNw9uxZfPXVVzh37hw++ugjODg41Mn9lrf13LlzCA4Oxt27d+Hj44Mvv/wSZ86cQUREBMaMGYPjx49Lx8yePRtLliyR2pWYmAhHR0cAkOq+/vprXLt2Dbt27ar0ujNmzMDOnTuxefNmZGRkoGPHjggODkZ+fr5B3VtvvYXly5fj5MmTaNGiBV555ZU6uW+iZqXOvqKZiKgS4eHhwtLSUtja2gpbW1sBQGg0GpGeni6EEOKPP/4Qbdq0Edu2bZOO6dmzp1i4cKH0vkOHDmLw4MEG5x05cqQYMmRIldfdsWOHsLe3l9736NHD4Jz3Cw0NFePHj3+k+wMglEqldH+2trbiX//6l8jJyREARFxc3EPPERISIqZOnSqEEKKgoEAoFAqxfv36SmvLz5uZmWmwPTw8XAwfPlwIIcTt27eFlZWV2Lp1q7S/pKREODs7i9jYWCGEEIcPHxYAxNdffy3VfPnllwKA+OOPP2rTBUTNHkemiMjkBg4ciKysLGRlZeH48eMIDg7GkCFD8Ouvv0KpVGLMmDHYuHEjACAjIwNnzpzBuHHjDM7h7+9f4f25c+ek919//TWeeeYZuLi4wM7ODmPGjEFeXh7u3LkDAHjzzTexePFiPPXUU1iwYIHBBPjXX38dSUlJ8PLywowZM3D06NFa3d/KlSul+8vKysKzzz4r7fP19TWoLSsrw6JFi9CjRw+o1Wq0bNkSBw4cwOXLlwEA586dQ3FxMZ555plateF+Fy9eRGlpKZ566ilpm5WVFfr06WPQZwDQs2dP6c8ajQYAcP369Ue+NlFzxDBFRCZna2uLjh07omPHjnjyySfxz3/+E0VFRVi/fj0A4NVXX8WhQ4fw22+/YdOmTRg0aBA6dOhQ4/NfunQJQ4cORc+ePbFz506kp6dLc6pKSkqka/zyyy8YM2YMTp8+DV9fX6xevRoApGD3j3/8A1evXsUzzzyDadOm1fj6Tk5O0v117NgRtra2Bvd+v/fffx8ffPABZs6cicOHDyMrKwvBwcFSO62trWt83bpgZWUl/VkmkwG4N2eLiGqOYYqI6p1MJoOFhQX++OMPAECPHj3g6+uL9evXIzExsdJ5O8eOHavwvmvXrgCA9PR06PV6LF++HH379kWnTp1w9erVCudwdXXFpEmTsGvXLkydOlUKcwDQtm1bhIeH49NPP0VcXBzWrVtXl7cs+f777zF8+HC8/PLL6NWrFx5//HH8/PPP0n5PT09YW1tLk+8fJJfLAdwb4aqKh4cH5HI5vv/+e2lbaWkpTpw4gSeeeKKO7oSIyrUwdwOIqOkrLi6GVqsFcO+Tc2vWrMHt27cRGhoq1bz66quIjIyEra0t/va3v1U4x/fff4/Y2FiEhYXh0KFD2LFjB7788ksAQMeOHVFaWorVq1cjNDQU33//PeLj4w2Oj4qKwpAhQ9CpUyf8/vvvOHz4sBTG5s+fDx8fH3Tr1g3FxcXYt2+ftK+ueXp64vPPP8fRo0fRpk0brFixArm5uVLIUSqVmDlzJmbMmAG5XI6nnnoKN27cwI8//ogJEyagXbt2sLa2RnJyMh577DEolUqoVCqDa9ja2uL111/H9OnToVar0b59e8TGxuLOnTuYMGGCSe6LqDnjyBQRmVxycjI0Gg00Gg38/Pxw4sQJ7NixQ/pYPwCMHj0aLVq0wOjRo6FUKiucY+rUqTh58iR69+6NxYsXY8WKFQgODgYA9OrVCytWrMDSpUvRvXt3bN26FTExMQbHl5WVYfLkyejatSsGDx6MTp064cMPPwRwb7Rn9uzZ6NmzJwYMGABLS0skJSWZpC/mzp0Lb29vBAcH4+mnn4aTk1OFlcvnzZuHqVOnYv78+ejatStGjhwpzWNq0aIFVq1ahY8//hjOzs4YPnx4pddZsmQJRowYgTFjxsDb2xsXLlzAgQMH0KZNG5PcF1FzJhNCCHM3gojo0qVL8PDwwIkTJ+Dt7W2wz83NDVFRUYiKijJP44iIqsHHfERkVqWlpcjLy8PcuXPRt2/fCkGKiKih42M+IjKr77//HhqNBidOnKgwz8nc3nvvPbRs2bLS15AhQ8zdPCJqIPiYj4ioCvn5+RVWDC9nbW0NFxeXem4RETVEDFNERERERuBjPiIiIiIjMEwRERERGYFhioiIiMgIDFNERERERmCYIiIiIjICwxQRERGRERimiIiIiIzAMEVERERkhP8HMCl3Yz14rfEAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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p0iV3l2FZ/v7+GV6SBwBAdhBqrmKM0dGjR/Xnn3+6uxRL8/HxUcWKFeXv7+/uUgAAFkKoucrlQFOyZEkVLFiQG/Tlgss3QDxy5IjKly9PGwMAcgyh5n9SU1OdgaZ48eLuLsfSQkND9fvvv+vvv/+Wn5+fu8sBAFgEJzb8z+VzaAoWLOjmSqzv8mGn1NRUN1cCALASQs01OByS+2hjAEBuINQAAABLINQAAABLINRYQM+ePWWz2WSz2eTn56dSpUrp3nvv1Zw5c2S32zO9nNjYWBUtWjT3CgUAWFZiorRmjePdXbj6KYecOnVKFy9evO54f3//XL2qqnXr1po7d65SU1N17NgxrVixQoMHD9ann36qzz77TAUK8KMGAOSOmBipb1/Jbpd8fKTZs6XevfO+DvZ0OeDUqVOaMWPGDacbOHBgrgWbgIAAlS5dWpJUrlw51a1bV3feeafuuecexcbG6sknn9SUKVM0d+5c7d+/X8WKFVO7du302muvqXDhwlq7dq169eol6cqJvCNHjtSoUaP03nvv6Y033tDu3btVqFAhtWzZUlOnTlXJkiVzZV0AAN4jMfFKoJEc7/36SdHRUlhY3tbC4acckFEPTXamyyktW7ZUrVq1tGjRIkmOO/lOmzZNv/76q+bNm6f//ve/ev755yVJjRs31tSpUxUcHKwjR47oyJEjevbZZyU5LncfO3asfvrpJy1ZskQJCQnq2bNnnq4LAMAzxcdfCTSXpaZKe/fmfS301FhctWrV9PPPP0uShgwZ4hxeoUIFvfLKK+rfv7/efPNN+fv7KyQkRDabzdnjc9kTTzzh/HelSpU0bdo03XHHHTp79qwKFy6cJ+sBAPBMlSs7DjldHWx8faWoqLyvhZ4aizPGOA8nff3117rnnntUrlw5FSlSRN27d9epU6d0/vz5DJexZcsWtWvXTuXLl1eRIkXUrFkzSdLBgwdzvX4AgGcLC3OcQ+Pr6/js6yvNmpX3h54kQo3lxcXFqWLFikpISNADDzyg22+/XQsXLtSWLVs0c+ZMSRkfFjt37pyio6MVHBysDz74QJs3b9bixYtvOB8AIP/o3VtKSHBc/ZSQ4J6ThCUOP1naf//7X/3yyy/617/+pS1btshut+s///mPfHwcWfbjjz92md7f3z/Nowt27dqlU6dOacKECQoPD5ck/fjjj3mzAgAArxEW5p7emavRU2MRKSkpOnr0qA4fPqytW7dq3Lhx6tChgx544AE9/vjjioqK0qVLlzR9+nTt379f7733nt5++22XZVSoUEFnz57V6tWrdfLkSZ0/f17ly5eXv7+/c77PPvtMY8eOddNaArAyT7jPCbwbocYiVqxYoTJlyqhChQpq3bq11qxZo2nTpmnp0qXy9fVVrVq1NGXKFE2cOFE1a9bUBx98oPHjx7sso3Hjxurfv786d+6s0NBQvfbaawoNDVVsbKw++eQTVa9eXRMmTNDkyZPdtJYArComRoqIkFq2dLzHxLi7IngjmzHGuLuIvJKcnKyQkBAlJSUpODjYZdyFCxd04MABVaxYUYGBgVlarifcp8ab3ExbA7CexERHkLn26pmEBPcfzoBnyGj/fTXOqckBxYsX18CBA916R2EA8FYZ3eeEUIOsINTkEAILAGSPJ93nBN6Nc2oAAG7lSfc5gXejpwYA4Ha9ezueFbR3r6OHhkCD7CDUAAA8gifc5wTejcNPAADAEgg1AADAEgg1AADAEgg1AADAEgg1uKG1a9fKZrPpzz//zPQ8FSpU0NSpU3OtJgAArkWosYCePXvKZrOpf//+acYNGDBANptNPXv2zPvCAADIQ4QaiwgPD9dHH32kv/76yznswoULmj9/vsqXL+/GygAAyBteFWoOHz6sxx57TMWLF1dQUJBuu+02/fjjj+4uyyPUrVtX4eHhWrRokXPYokWLVL58edWpU8c5LCUlRYMGDVLJkiUVGBiou+++W5s3b3ZZ1vLly1WlShUFBQWpRYsWSkhISPN969evV5MmTRQUFKTw8HANGjRI586dy7X1AwDgRrwm1Jw+fVp33XWX/Pz89OWXX2rnzp36z3/+o1tuucXdpaWRmCitWeN4z0tPPPGE5s6d6/w8Z84c9erVy2Wa559/XgsXLtS8efO0detWRUVFKTo6Wn/88Yck6dChQ3rwwQfVrl07bd++XU8++aReeOEFl2Xs27dPrVu31kMPPaSff/5ZCxYs0Pr16zVw4MDcX0kAAK7HeIlhw4aZu++++6aWkZSUZCSZpKSkNOP++usvs3PnTvPXX3/d1Hf83/8Z4+NjjOR4/7//u6nFZUqPHj1Mhw4dzPHjx01AQIBJSEgwCQkJJjAw0Jw4ccJ06NDB9OjRw5w9e9b4+fmZDz74wDnvxYsXTdmyZc1rr71mjDFm+PDhpnr16i7LHzZsmJFkTp8+bYwxpnfv3qZv374u06xbt874+Pg42y8iIsK8/vrr6dabU20NAMgfMtp/X81remo+++wz1a9fX4888ohKliypOnXq6J133slwnpSUFCUnJ7u8clNiotS375UnzdrtUr9+eddjExoaqrZt2yo2NlZz585V27ZtVaJECef4ffv26dKlS7rrrrucw/z8/NSgQQPFxcVJkuLi4tSwYUOX5TZq1Mjl808//aTY2FgVLlzY+YqOjpbdbteBAwdycQ0BALg+r3n20/79+/XWW29p6NChevHFF7V582YNGjRI/v7+6tGjR7rzjB8/XqNHj86zGuPjrwSay1JTHQ9oy6vnmTzxxBPOw0AzZ87Mle84e/as+vXrp0GDBqUZx0nJAAB38ZpQY7fbVb9+fY0bN06SVKdOHe3YsUNvv/32dUPN8OHDNXToUOfn5ORkhYeH51qNlStLPj6uwcbX1/HE2bzSunVrXbx4UTabTdHR0S7jIiMj5e/vrw0bNigiIkKSdOnSJW3evFlDhgyRJN1666367LPPXOb77rvvXD7XrVtXO3fuVFRerhgAADfgNYefypQpo+rVq7sMu/XWW3Xw4MHrzhMQEKDg4GCXV24KC5Nmz3YEGcnxPmtW3j511tfXV3Fxcdq5c6d8LxfyP4UKFdJTTz2l5557TitWrNDOnTvVp08fnT9/Xr1795Yk9e/fX/Hx8Xruuee0e/duzZ8/X7GxsS7LGTZsmDZu3KiBAwdq+/btio+P19KlSzlRGADgVl7TU3PXXXdp9+7dLsP27Nnj7HHwFL17S9HRjkNOUVF5G2guyyi8TZgwQXa7Xd27d9eZM2dUv359ffXVV86ryMqXL6+FCxfqX//6l6ZPn64GDRpo3LhxeuKJJ5zLuP322/XNN99oxIgRatKkiYwxioyMVOfOnXN93QAAuB6bMca4u4jM2Lx5sxo3bqzRo0erU6dO+uGHH9SnTx/Nnj1b3bp1y9QykpOTFRISoqSkpDQ7/gsXLujAgQOqWLGiAgMDc2MV8D+0NQAgKzLaf1/Naw4/3XHHHVq8eLE+/PBD1axZU2PHjtXUqVMzHWgAAIC1ec3hJ0l64IEH9MADD7i7DAAA4IG8pqcGAAAgI4QaAABgCYSaa3jJedNejTYGAOQGQs3/+Pn5SZLOnz/v5kqs7+LFi5KU5j46AADcDK86UTg3+fr6qmjRojp+/LgkqWDBgrLZbG6uynrsdrtOnDihggULqkABNj8AQM5hr3KV0qVLS5Iz2CB3+Pj4qHz58oRGAECOItRcxWazqUyZMipZsqQuXbrk7nIsy9/fXz4+HPkEAOQsQk06fH19Od8DAAAvw5/LAADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AAB4sMREac0axzsyRqgBAMBDxcRIERFSy5aO95gYd1fk2Qg1AAB4oMREqW9fyW53fLbbpX796LHJCKEGAAAPFB9/JdBclpoq7d3rnnq8AaEGAAAPVLmydO1j8nx9pago99TjDQg1AAB4oLAwafZsR5CRHO+zZjmGI3080BIAAA/Vu7cUHe045BQVRaC5EUINAAAeLCyMMJNZHH4CAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAACWQKgBAMCCEhOlNWsc7/kFoQYAAIuJiZEiIqSWLR3vMTHurihvEGoAALCQxESpb1/Jbnd8ttulfv3yR4+N14aaCRMmyGazaciQIe4uBQAAjxEffyXQXJaaKu3d65568pJXhprNmzdr1qxZuv32291dCgAAHqVyZcnnmr27r68UFeWeevKS14Was2fPqlu3bnrnnXd0yy23uLscAAA8SliYNHu2I8hIjvdZsxzDrc7rQs2AAQPUtm1btWrV6obTpqSkKDk52eUFAIDV9e4tJSQ4rn5KSHB8zg8KuLuArPjoo4+0detWbd68OVPTjx8/XqNHj87lqgAA8DxhYfmjd+ZqXtNTc+jQIQ0ePFgffPCBAgMDMzXP8OHDlZSU5HwdOnQol6sEAADuYjPGGHcXkRlLlizRP/7xD/lePkgoKTU1VTabTT4+PkpJSXEZl57k5GSFhIQoKSlJwcHBuV0yAADIAZndf3vN4ad77rlHv/zyi8uwXr16qVq1aho2bNgNAw0AALA2rwk1RYoUUc2aNV2GFSpUSMWLF08zHAAA5D9ec04NAABARrympyY9a9eudXcJAADAQ9BTAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALIFQAwAALMFrQs348eN1xx13qEiRIipZsqQ6duyo3bt3u7ssAADgIbwm1HzzzTcaMGCAvvvuO61atUqXLl3Sfffdp3Pnzrm7NAAA4AFsxhjj7iKy48SJEypZsqS++eYbNW3aNFPzJCcnKyQkRElJSQoODs7lCgEAQE7I7P67QB7WlKOSkpIkScWKFbvuNCkpKUpJSXF+Tk5OzvW6AACAe3jN4aer2e12DRkyRHfddZdq1qx53enGjx+vkJAQ5ys8PDwPqwQAAHnJKw8/PfXUU/ryyy+1fv16hYWFXXe69HpqwsPDOfwEAIAXsezhp4EDB+qLL77Qt99+m2GgkaSAgAAFBATkUWUAAMCdvCbUGGP09NNPa/HixVq7dq0qVqzo7pIAAIAH8ZpQM2DAAM2fP19Lly5VkSJFdPToUUlSSEiIgoKC3FwdAABwN685p8Zms6U7fO7cuerZs2emlsEl3QAAeB/LnVPjJdkLAAC4iVde0g0AAHAtQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg2QDyQmSmvWON7hirYBrINQA1hcTIwUESG1bOl4j4lxd0Weg7YBrMVmjDHuLiKvJCcnKyQkRElJSQoODnZ3OUCuS0x07Kzt9ivDfH2lhAQpLMxtZXkE2gbwHpndf9NTA1hYfLzrTluSUlOlvXvdU48noW0A6yHUABZWubLkc81vua+vFBXlnno8CW0DWA+hBrCwsDBp9mzHzlpyvM+axeEVibYBrIhzaoB8IDHRcVglKoqd9rVoG8DzZXb/XSA7C09MTFTRokVVuHBhl+GXLl3Spk2b1LRp0+wsFkAuCQtjh309tA1gHVk6/HTkyBE1aNBAERERKlq0qB5//HGdPXvWOf6PP/5QixYtcrxIAACAG8lSqHnhhRfk4+Oj77//XitWrNDOnTvVokULnT592jlNPjqaBQAAPEiWQs3XX3+tadOmqX79+mrVqpU2bNigMmXKqGXLlvrjjz8kSTabLVcKBQAAyEiWQk1SUpJuueUW5+eAgAAtWrRIFSpUUIsWLXT8+PEcLxAAACAzshRqKlWqpJ9//tllWIECBfTJJ5+oUqVKeuCBB3K0OAAAgMzKUqhp06aNZs+enWb45WBTu3btnKoLAAAgS7J0n5q///5b58+fd14jfvLkSUlSiRIlnOMPHz6siIiIXCj15nGfGuS0xETH7fYrV+ayYADILbny7KcCBQrIbrdrwIABKlGihEqVKqVSpUqpRIkSGjhwoM6ePeuxgQbIaTzhGQA8S5Z6av744w81atRIhw8fVrdu3XTrrbdKknbu3Kn58+crPDxcGzdudDmZ2JPQU4OcwhOeASDv5ModhceMGSN/f3/t27dPpUqVSjPuvvvu05gxY/T6669nr2rAS2T0hGdCDQC4R5YOPy1ZskSTJ09OE2gkqXTp0nrttde0ePHiHCsO8FQ84RkAPE+WH5NQo0aN646vWbOmjh49etNFAZ6OJzwDgOfJ0uGnEiVKKCEhQWHX+Z/7wIEDKlasWI4UBni63r2l6Gie8AwAniJLPTXR0dEaMWKELl68mGZcSkqKXn75ZbVu3TrHigM8XViY1Lw5gQYAPEGWrn5KTExU/fr1FRAQoAEDBqhatWoyxiguLk5vvvmmUlJS9OOPPyo8PDw3a842rn4CAMD75MrVT2FhYdq0aZP++c9/avjw4c4ncttsNt17772aMWOGxwYaAABgbVkKNZJUsWJFffnllzp9+rTi4+MlSVFRUZxLAwAA3CrLoeayW265RQ0aNMjJWrzKqVOn0j236DJ/f38VL148zfB9+/bp/Pnz152vYMGCioyMzJEab1Z219Fb5suuvP6+7LqZOq2+jt6yfnnNHe3iLT+LvK4zu/sKb6kzt2Q71LjLzJkzNWnSJB09elS1atXS9OnT8zxcnTp1SjNmzLjhdAMHDnTZePbt26f333//hvM99thjbg822V1Hb5kvu/L6+7LrZuq0+jp6y/rlNXe0i7f8LPK6zuzuK7ylztyUpauf3G3BggUaOnSoRo4cqa1bt6pWrVqKjo7W8ePH87SOjFJwRtNllGazM11uyu46est82ZXX35ddN1On1dfRW9Yvr7mjXbzlZ5HXdWZ3X+EtdeYmrwo1U6ZMUZ8+fdSrVy9Vr15db7/9tgoWLKg5c+a4uzQAAOBmXhNqLl68qC1btqhVq1bOYT4+PmrVqpU2bdqU7jwpKSlKTk52eQEAAGvymlBz8uRJpaampnnuVKlSpa77aIbx48crJCTE+eJycwAArMtrQk12DB8+XElJSc7XoUOH3F0SAADIJV5z9VOJEiXk6+urY8eOuQw/duyYSpcune48AQEBCggIyIvyAACAm3lNT42/v7/q1aun1atXO4fZ7XatXr1ajRo1cmNlAADAE3hNT40kDR06VD169FD9+vXVoEEDTZ06VefOnVOvXr3ytA5/f/9sTVewYMFMzZfZ6XJTdtfRW+bLrrz+vuy6mTqtvo7esn55zR3t4i0/i7yuM7v7Cm+pMzdl6YGWnmDGjBnOm+/Vrl1b06ZNU8OGDTM1b04+0JI7CnvOnYG5o3D6uKOw52wz3oI7Cl+ft9yp11vqzKrM7r+9LtTcDJ7SDQCA98ns/ttrzqkBAADICKEGAABYAqEGAABYAqEGAJBGYqK0Zo3jHfAWhBoAgIuYGCkiQmrZ0vEeE+PuioDMIdQAAJwSE6W+fSW73fHZbpf69aPHBt6BUAMAcIqPvxJoLktNlfbudU89QFYQagAATpUrSz7X7Bl8faWoKPfUA2QFoQYA4BQWJs2e7QgykuN91izHcMDTedWznwAAua93byk62nHIKSqKQAPvQagBAKQRFkaYgffh8BMAALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AALAEQg0AAB4sMVFas8bxjowRauCR+CUGACkmRoqIkFq2dLzHxLi7Is9GqIHH4ZcYABx/1PXtK9ntjs92u9SvH3/sZYRQA4/CLzEAOMTHX/m/8LLUVMfT05E+Qg08Cr/EAOBQubLkc81e2tdXiopyTz3egFADj8IvMQA4hIVJs2c7/g+UHO+zZjmGI32EGngUfokB4IrevaWEBMeFEwkJjs+4Ppsxxri7iLySnJyskJAQJSUlKTg42N3lIAOJiY5DTlFRBBoA8AaJiY5TCCpXzvn/tzO7/6anBh4pLExq3pxAAwDewFOuWiXUAACAbPOkq1YJNQAAINs86apVQg0AAFnAHc9dedJVq4QaAAAyyVPOHfEknnTVKlc/AbCE3LzyApAc21hEhOuhFl9fx6XWbHO5e9UqVz8ByDf46xl5wZPOHfFEnnDVKqEGgFfzpCsvYG2edO4I0keoAeDV+OsZecWTzh1B+gq4uwAAuBmX/3q+9jwH/npGbujdW4qO5o7nnoqeGgBejb+ekdc84dwRpI+eGgBej7+eAUiEGgAWERZGmAHyOw4/AQAASyDUAAAAS/CKUJOQkKDevXurYsWKCgoKUmRkpEaOHKmLFy+6uzQAAOAhvOKcml27dslut2vWrFmKiorSjh071KdPH507d06TJ092d3kAAMADeO2znyZNmqS33npL+/fvz/Q8PPsJAADvk9n9t1f01KQnKSlJxYoVy3CalJQUpaSkOD8nJyfndlkAAMBNvOKcmmvt3btX06dPV79+/TKcbvz48QoJCXG+wsPD86hCAACQ19waal544QXZbLYMX7t27XKZ5/Dhw2rdurUeeeQR9enTJ8PlDx8+XElJSc7XoUOHcnN1AACAG7n1nJoTJ07o1KlTGU5TqVIl+fv7S5J+//13NW/eXHfeeadiY2Plc+3jUm+Ac2oAAPA+XnFOTWhoqEJDQzM17eHDh9WiRQvVq1dPc+fOzXKgAQAA1uYVJwofPnxYzZs3V0REhCZPnqwTJ044x5UuXdqNlQEAAE/hFaFm1apV2rt3r/bu3auwax7u4qVXpAMAgBzmFcdwevbsKWNMui8AAADJS0INAADAjRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqAACAJRBqkKsSE6U1axzvAADkJkINck1MjBQRIbVs6XiPiXF3RQAAKyPUIFckJkp9+0p2u+Oz3S7160ePjTeitw2AtyDUIFfEx18JNJelpkp797qnHmQPvW0AvAmhBrmicmXJ55qty9dXiopyTz3IOnrbAHgbQg1yRViYNHu2I8hIjvdZsxzD4R3obQPgbQq4uwBYV+/eUnS0YycYFUWg8TaXe9uuDjb0tgHwZPTUIFeFhUnNmxNovBG9bQC8DT01AK6L3jYA3oRQAyBDYWGEGQDegcNPAADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1AADAEgg1bpSYKK1Z43gHAAA3h1DjJjExUkSE1LKl4z0mxt0VAQDg3Qg1bpCYKPXtK9ntjs92u9SvHz02AADcDEKNG8THXwk0l6WmSnv3uqceAACsgFDjBpUrSz7XtLyvrxQV5Z56AACwAq8LNSkpKapdu7ZsNpu2b9/u7nKyJSxM6t7dddhjjzmGAwCA7PG6UPP888+rbNmy7i7jpiQmSu+95zrs/fc5pwYAgJvhVaHmyy+/1MqVKzV58mR3l3JTOKcGAICcV8DdBWTWsWPH1KdPHy1ZskQFCxbM1DwpKSlKSUlxfk5OTs6t8rLk8jk1VwcbTz+nJjHREcYqV+YwGQDAM3lFT40xRj179lT//v1Vv379TM83fvx4hYSEOF/h4eG5WGXmhYVJs2c7gozkeJ81y3PDAvfUAQB4A5sxxrjry1944QVNnDgxw2ni4uK0cuVKffzxx/rmm2/k6+urhIQEVaxYUdu2bVPt2rWvO296PTXh4eFKSkpScHBwTq1GtiUmOg45RUV5bqBJTHQEmWt7lRISPLdmAIC1JCcnKyQk5Ib7b7eGmhMnTujUqVMZTlOpUiV16tRJn3/+uWw2m3N4amqqfH191a1bN82bNy9T35fZRsEVa9Y4emjSG968eZ6XAwDIh7wi1GTWwYMHXc6H+f333xUdHa1PP/1UDRs2VFgmuwwINVlHTw0AwN0yu//2ihOFy5cv7/K5cOHCkqTIyMhMBxpkz+Xzf/r1c1yh5enn/wAA8i+vCDVwr969pehozz//BwCQv3llqKlQoYK84KiZpYSFEWYAAJ7NKy7pBgAAuBFCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCDQAAsARCTQ5ITJTWrHG8w734WQBA/kWouUkxMVJEhNSypeM9JsbdFeVf/CwAIH+zGWOMu4vIK8nJyQoJCVFSUpKCg4NvenmJiY6dp91+ZZivr5SQIIWF3fTikQX8LADAujK7/6an5ibEx7vuRCUpNVXau9c99eRn/CwAAISam1C5suRzTQv6+kpRUe6pJz/jZwEAINTchLAwafZsx85TcrzPmsXhDnfgZwEA4JyaHJCY6DjMERXFTtTd+FkAgPVkdv9dIA9rsqywMHagnoKfBQDkX151+GnZsmVq2LChgoKCdMstt6hjx47uLgkAAHgIr+mpWbhwofr06aNx48apZcuW+vvvv7Vjxw53lwUAADyEV4Sav//+W4MHD9akSZPUu3dv5/Dq1au7sSoAAOBJvOLw09atW3X48GH5+PioTp06KlOmjNq0aXPDnpqUlBQlJye7vAAAgDV5RajZv3+/JGnUqFF66aWX9MUXX+iWW25R8+bN9ccff1x3vvHjxyskJMT5Cg8Pz6uSAQBAHnNrqHnhhRdks9kyfO3atUv2/90qdsSIEXrooYdUr149zZ07VzabTZ988sl1lz98+HAlJSU5X4cOHcqrVQMAAHnMrefUPPPMM+rZs2eG01SqVElHjhyR5HoOTUBAgCpVqqSDBw9ed96AgAAFBATkSK0AAMCzuTXUhIaGKjQ09IbT1atXTwEBAdq9e7fuvvtuSdKlS5eUkJCgiIiI3C4TAAB4Aa+4+ik4OFj9+/fXyJEjFR4eroiICE2aNEmS9Mgjj7i5OgAA4Am8ItRI0qRJk1SgQAF1795df/31lxo2bKj//ve/uuWWW9xdGgAA8AA8+wkAAHg0nv2Ujsv5jfvVAADgPS7vt2/UD5OvQs2ZM2ckifvVAADghc6cOaOQkJDrjs9Xh5/sdrt+//13FSlSRDabzd3l5Ink5GSFh4fr0KFD+f6QG21xBW1xBW1xBW1xBW1xhSe0hTFGZ86cUdmyZeXjc/1b7OWrnhofHx+FhYW5uwy3CA4Ozve/mJfRFlfQFlfQFlfQFlfQFle4uy0y6qG5zCsekwAAAHAjhBoAAGAJhBqLCwgI0MiRI3lchGiLq9EWV9AWV9AWV9AWV3hTW+SrE4UBAIB10VMDAAAsgVADAAAsgVADAAAsgVADAAAsgVDjhWbOnKkKFSooMDBQDRs21A8//JDh9FOnTlXVqlUVFBSk8PBw/etf/9KFCxec40eNGiWbzebyqlatWm6vRo7ISltcunRJY8aMUWRkpAIDA1WrVi2tWLHippbpKXK6Hbx1m/j222/Vrl07lS1bVjabTUuWLLnhPGvXrlXdunUVEBCgqKgoxcbGppnGG7eJ3GiL/LJdHDlyRF27dlWVKlXk4+OjIUOGpDvdJ598omrVqikwMFC33Xabli9fnvPF57DcaIvY2Ng020VgYGDurMANEGq8zIIFCzR06FCNHDlSW7duVa1atRQdHa3jx4+nO/38+fP1wgsvaOTIkYqLi1NMTIwWLFigF1980WW6GjVq6MiRI87X+vXr82J1bkpW2+Kll17SrFmzNH36dO3cuVP9+/fXP/7xD23bti3by/QEudEOknduE+fOnVOtWrU0c+bMTE1/4MABtW3bVi1atND27ds1ZMgQPfnkk/rqq6+c03jjNiHlTltI+WO7SElJUWhoqF566SXVqlUr3Wk2btyoRx99VL1799a2bdvUsWNHdezYUTt27MjJ0nNcbrSF5Ljb8NXbxW+//ZZTJWeNgVdp0KCBGTBggPNzamqqKVu2rBk/fny60w8YMMC0bNnSZdjQoUPNXXfd5fw8cuRIU6tWrVypNzdltS3KlCljZsyY4TLswQcfNN26dcv2Mj1BbrSDt24TV5NkFi9enOE0zz//vKlRo4bLsM6dO5vo6GjnZ2/cJq6VU22RX7aLqzVr1swMHjw4zfBOnTqZtm3bugxr2LCh6dev301WmHdyqi3mzp1rQkJCcqyum0FPjRe5ePGitmzZolatWjmH+fj4qFWrVtq0aVO68zRu3Fhbtmxxdpfv379fy5cv1/333+8yXXx8vMqWLatKlSqpW7duOnjwYO6tSA7ITlukpKSk6RINCgpy/qWZnWW6W260w2Xetk1kx6ZNm1zaTpKio6OdbeeN20R23agtLssP20VmZLa98ouzZ88qIiJC4eHh6tChg3799Ve31EGo8SInT55UamqqSpUq5TK8VKlSOnr0aLrzdO3aVWPGjNHdd98tPz8/RUZGqnnz5i6Hnxo2bKjY2FitWLFCb731lg4cOKAmTZrozJkzubo+NyM7bREdHa0pU6YoPj5edrtdq1at0qJFi3TkyJFsL9PdcqMdJO/cJrLj6NGj6bZdcnKy/vrrL6/cJrLrRm0h5Z/tIjOu115W2y4yo2rVqpozZ46WLl2q999/X3a7XY0bN1ZiYmKe10Kosbi1a9dq3LhxevPNN7V161YtWrRIy5Yt09ixY53TtGnTRo888ohuv/12RUdHa/ny5frzzz/18ccfu7HynPfGG2+ocuXKqlatmvz9/TVw4ED16tUrw8fYW1Fm2iG/bBPIGrYLpKdRo0Z6/PHHVbt2bTVr1kyLFi1SaGioZs2alee15K//zb1ciRIl5Ovrq2PHjrkMP3bsmEqXLp3uPC+//LK6d++uJ598Urfddpv+8Y9/aNy4cRo/frzsdnu68xQtWlRVqlTR3r17c3wdckp22iI0NFRLlizRuXPn9Ntvv2nXrl0qXLiwKlWqlO1lultutEN6vGGbyI7SpUun23bBwcEKCgryym0iu27UFumx6naRGddrL6ttF9nh5+enOnXquGW7INR4EX9/f9WrV0+rV692DrPb7Vq9erUaNWqU7jznz59P0xPh6+srSTLXeezX2bNntW/fPpUpUyaHKs952WmLywIDA1WuXDn9/fffWrhwoTp06HDTy3SX3GiH9HjDNpEdjRo1cmk7SVq1apWz7bxxm8iuG7VFeqy6XWRGdtorv0hNTdUvv/zinu3C3WcqI2s++ugjExAQYGJjY83OnTtN3759TdGiRc3Ro0eNMcZ0797dvPDCC87pR44caYoUKWI+/PBDs3//frNy5UoTGRlpOnXq5JzmmWeeMWvXrjUHDhwwGzZsMK1atTIlSpQwx48fz/P1y4qstsV3331nFi5caPbt22e+/fZb07JlS1OxYkVz+vTpTC/TE+VGO3jrNnHmzBmzbds2s23bNiPJTJkyxWzbts389ttvxhhjXnjhBdO9e3fn9Pv37zcFCxY0zz33nImLizMzZ840vr6+ZsWKFc5pvHGbMCZ32iK/bBfGGOf09erVM127djXbtm0zv/76q3P8hg0bTIECBczkyZNNXFycGTlypPHz8zO//PJLnq5bVuVGW4wePdp89dVXZt++fWbLli2mS5cuJjAw0GWavEKo8ULTp0835cuXN/7+/qZBgwbmu+++c45r1qyZ6dGjh/PzpUuXzKhRo0xkZKQJDAw04eHh5p///KfLDqxz586mTJkyxt/f35QrV8507tzZ7N27Nw/XKPuy0hZr1641t956qwkICDDFixc33bt3N4cPH87SMj1VTreDt24Ta9asMZLSvC6vf48ePUyzZs3SzFO7dm3j7+9vKlWqZObOnZtmud64TeRGW+Sn7SK96SMiIlym+fjjj02VKlWMv7+/qVGjhlm2bFnerNBNyI22GDJkiPP3o1SpUub+++83W7duzbuVuortfwUDAAB4Nc6pAQAAlkCoAQAAlkCoAQAAlkCoAQAAlkCoAQAAlkCoAQAAlkCoAQAAlkCoAQA3Wrt2rWw2m/788093lwJ4PUINkE/07NlTNptNEyZMcBm+ZMkS2Ww252djjN555x01atRIwcHBKly4sGrUqKHBgwdn+gF158+f1/DhwxUZGanAwECFhoaqWbNmWrp0qXOaChUqaOrUqTmybrntctvZbDb5+fmpYsWKev7553XhwoUsLad58+YaMmSIy7DGjRvryJEjCgkJycGKgfyJUAPkI4GBgZo4caJOnz6d7nhjjLp27apBgwbp/vvv18qVK7Vz507FxMQoMDBQr7zySqa+p3///lq0aJGmT5+uXbt2acWKFXr44Yd16tSpnFydPNW6dWsdOXJE+/fv1+uvv65Zs2Zp5MiRN71cf39/lS5d2iVYAsgmtzycAUCe69Gjh3nggQdMtWrVzHPPPeccvnjxYnP5v4IPP/zQSDJLly5Ndxl2uz1T3xUSEmJiY2OvO75Zs2ZpniVz2bp168zdd99tAgMDTVhYmHn66afN2bNnnePfffddU69ePVO4cGFTqlQp8+ijj5pjx445x19+ts2KFStM7dq1TWBgoGnRooU5duyYWb58ualWrZopUqSIefTRR825c+cytT49evQwHTp0cBn24IMPmjp16jg/nzx50nTp0sWULVvWBAUFmZo1a5r58+e7LOPadT5w4ICz3qufx/bpp5+a6tWrG39/fxMREWEmT56cqTqB/I6eGiAf8fX11bhx4zR9+nQlJiamGf/hhx+qatWqat++fbrzZ7Y3oXTp0lq+fLnOnDmT7vhFixYpLCxMY8aM0ZEjR3TkyBFJ0r59+9S6dWs99NBD+vnnn7VgwQKtX79eAwcOdM576dIljR07Vj/99JOWLFmihIQE9ezZM813jBo1SjNmzNDGjRt16NAhderUSVOnTtX8+fO1bNkyrVy5UtOnT8/U+lxrx44d2rhxo/z9/Z3DLly4oHr16mnZsmXasWOH+vbtq+7du+uHH36QJL3xxhtq1KiR+vTp41zn8PDwNMvesmWLOnXqpC5duuiXX37RqFGj9PLLLys2NjZbtQL5irtTFYC8cXVvw5133mmeeOIJY4xrT021atVM+/btXeYbPHiwKVSokClUqJApV65cpr7rm2++MWFhYcbPz8/Ur1/fDBkyxKxfv95lmoiICPP666+7DOvdu7fp27evy7B169YZHx8f89dff6X7XZs3bzaSzJkzZ4wxV3pqvv76a+c048ePN5LMvn37nMP69etnoqOjM7U+PXr0ML6+vqZQoUImICDASDI+Pj7m008/zXC+tm3bmmeeecb5uVmzZmbw4MEu01zbU9O1a1dz7733ukzz3HPPmerVq2eqViA/o6cGyIcmTpyoefPmKS4u7obTjhgxQtu3b9e///1vnT17NlPLb9q0qfbv36/Vq1fr4Ycf1q+//qomTZpo7NixGc73008/KTY2VoULF3a+oqOjZbfbdeDAAUmOnox27dqpfPnyKlKkiJo1ayZJOnjwoMuybr/9due/S5UqpYIFC6pSpUouw44fP56p9ZGkFi1aaPv27fr+++/Vo0cP9erVSw899JBzfGpqqsaOHavbbrtNxYoVU+HChfXVV1+lqetG4uLidNddd7kMu+uuuxQfH6/U1NQsLQvIbwg1QD7UtGlTRUdHa/jw4S7DK1eurN27d7sMCw0NVVRUlEqWLJml7/Dz81OTJk00bNgwrVy5UmPGjNHYsWN18eLF685z9uxZ9evXT9u3b3e+fvrpJ8XHxysyMlLnzp1TdHS0goOD9cEHH2jz5s1avHixJKVZrp+fn/Pfl69auprNZpPdbs/0+hQqVEhRUVGqVauW5syZo++//14xMTHO8ZMmTdIbb7yhYcOGac2aNdq+fbuio6MzXF8AOauAuwsA4B4TJkxQ7dq1VbVqVeewRx99VF27dtXSpUvVoUOHHP2+6tWr6++//9aFCxfk7+8vf3//ND0PdevW1c6dOxUVFZXuMn755RedOnVKEyZMcJ6P8uOPP+ZonZnh4+OjF198UUOHDlXXrl0VFBSkDRs2qEOHDnrsscckSXa7XXv27FH16tWd86W3zte69dZbtWHDBpdhGzZsUJUqVeTr65vzKwNYCD01QD512223qVu3bpo2bZpzWJcuXfTwww+rS5cuGjNmjL7//nslJCTom2++0YIFCzK9U23evLlmzZqlLVu2KCEhQcuXL9eLL76oFi1aKDg4WJLjPjXffvutDh8+rJMnT0qShg0bpo0bN2rgwIHavn274uPjtXTpUueJwuXLl5e/v7+mT5+u/fv367PPPrvhIa3c8sgjj8jX11czZ86U5OjlWrVqlTZu3Ki4uDj169dPx44dc5mnQoUKzjY9efJkuj1FzzzzjFavXq2xY8dqz549mjdvnmbMmKFnn302T9YL8GaEGiAfGzNmjMuO1WazacGCBZo6daqWL1+ue+65R1WrVtUTTzyh8PBwrV+/PlPLjY6O1rx583Tffffp1ltv1dNPP63o6Gh9/PHHLt+dkJCgyMhIhYaGSnKcB/PNN99oz549atKkierUqaN///vfKlu2rCTHobDY2Fh98sknql69uiZMmKDJkyfnYItkXoECBTRw4EC99tprOnfunF566SXVrVtX0dHRat68uUqXLq2OHTu6zPPss8/K19dX1atXV2hoaLrn29StW1cff/yxPvroI9WsWVP//ve/NWbMmHSv8ALgymaMMe4uAgAA4GbRUwMAACyBUAMgy66+5Pra17p169xdXpYcPHgww/XJ6iXZANyHw08AsiyjB1uWK1dOQUFBeVjNzfn777+VkJBw3fEVKlRQgQJcKAp4A0INAACwBA4/AQAASyDUAAAASyDUAAAASyDUAAAASyDUAAAASyDUAAAASyDUAAAASyDUAAAAS/h/MUTqvl5DAOcAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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pYcOGqVixgvtP4cMPP5Sfn1+ett2wYYPatGmj8+fPq0yZMvnaBwDg1p08eVLz5s2zqT3//PMqWbKkSR25FgHIy7Rr104LFy5UVlaWPv74Yw0YMEB+fn5KSkqy2S47O9tlC1iVLVu2SOwDAJA3H330kbZt22Z9XLNmTfXo0cPEjlyPu8F7mYCAAFWqVEnh4eF6+umnFRMTo9WrVys+Pl5xcXEaN26cQkNDVbduXUlXb2j30EMPqUyZMipbtqw6d+6slJQU6/5ycnKUkJCgMmXKqFy5cnrhhRf05/vrtm7dWoMHD7Y+zsrK0osvvqiwsDAFBASoVq1a+te//qWUlBS1adNGknTbbbfJYrEoPj7e4T7Onz+vnj176rbbblNQUJDat2+vQ4cOWZ9ftGiRypQpo3Xr1ql+/foqWbKk2rVrp9TUVOs2GzZsUMuWLVWiRAmVKVNGf/nLX3Ts2DEX/aQBwP1kZ2dr1KhRNuGne/fuHhd+JAKQ1ytevLiys7MlScnJyTpw4IA+++wzrVmzRpcvX1ZsbKxKlSqlTZs26auvvrIGiWuvmTx5shYtWqQFCxZo8+bNOnfunFasWHHD9+zZs6eWLFmiGTNmaN++fXrzzTdVsmRJhYWF6YMPPpAkHThwQKmpqZo+fbrDfcTHx2vbtm1avXq1tm7dKsMwdN999+ny5cvWbS5duqRJkybpnXfe0caNG3X8+HENGTJEknTlyhXFxcUpOjpau3fv1tatW/XEE0+49QV9AHArDh8+rAkTJtjUEhMTVadOHZM6KlhMgXkpwzCUnJysdevWadCgQTpz5oxKlCih+fPnW6e+3n33XeXm5mr+/PnWYLBw4UKVKVNGGzZs0L333qtp06YpKSlJ//jHPyRJc+fO1bp16677vgcPHtT777+vzz77TDExMZKkGjVqWJ+/NtVVoUIFm2uA/ujQoUNavXq1vvrqK7Vq1UqStHjxYoWFhWnlypV68MEHJUmXL1/W3LlzrTfiGzhwoEaPHi1JysjI0IULF3T//fdbn69fv77zP0gA8ABLlizRwYMHrY+bNm2qTp06mdhRwWMEyGSrV0utWl39vTCsWbNGJUuWVGBgoNq3b69u3brplVdekSQ1atTI5rqf7777TocPH1apUqVUsmRJlSxZUmXLltXvv/+uH3/8URcuXFBqaqoiIyOtrylWrJiaN29+3ffftWuXfH19FR0dne9j2Ldvn4oVK2bzvuXKlVPdunW1b98+ay0oKMjmLsSVK1fW6dOnJV0NWvHx8YqNjVXHjh01ffp0m+kxAPAGmZmZGjVqlE346dOnj8eHH4kRINNNnCht3Xr198L4761NmzaaM2eO/P39FRoaavPtrxIlSthse/HiRTVr1kyLFy+2209ISEi+3r948eL5el1+/PlbYxaLxeb6pIULF+qZZ57R2rVrtWzZMg0fPlyfffaZ7rrrrkLrEQDMsnv3brtLFl566aUC/VZwUcIIkMkSE6WoqKu/F4YSJUqoVq1aqlat2k3/I7/zzjt16NAhVahQQbVq1bL5FRwcrODgYFWuXFn//e9/ra+5cuWKtm/fft19NmrUSLm5ufryyy8dPn9tBConJ+e6+6hfv76uXLli876//PKLDhw4oAYNGtzwmP6sadOmSkpK0pYtW3T77bfrvffec+r1AOBuDMPQnDlzbMJPdHS0Ro4c6TXhRyIAma5TJ2nLlsIZ/XHWo48+qvLly6tz587atGmTjh49qg0bNuiZZ57RTz/9JEl69tlnNXHiRK1cuVL79+9X//79lZ6eft19RkREqFevXurdu7dWrlxp3ef7778vSQoPD5fFYtGaNWt05swZXbx40W4ftWvXVufOndWvXz9t3rxZ3333nXr06KEqVaqoc+fOeTq2o0ePKikpSVu3btWxY8f06aef6tChQ1wHBMCjnT9/XqNHj7ZeDiBJAwYMUOvWrc1ryiQEIFxXUFCQNm7cqGrVqukf//iH6tevrz59+uj3339X6dKlJV1dFOuxxx5Tr169FBUVpVKlSqlLly433O+cOXPUtWtX9e/fX/Xq1VO/fv2UmZkpSapSpYpGjRqlxMREVaxYUQMHDnS4j4ULF6pZs2a6//77FRUVJcMw9PHHH+d5scSgoCDt379fDzzwgOrUqaMnnnhCAwYM0JNPPunETwgA3MeWLVs0Y8YM6+Pg4GCNGDFC5cuXN7Er81iMPy/aAmVkZCg4OFgXLlywftBf8/vvv+vo0aOqXr26AgMDTeoQhY3zDsBd5ebm6tVXX7UuXyJJHTp0uOEXVtzVjT6//8x7JvsAAPAyaWlpevPNN21qzz333E3DgTcgAAEA4IHWrVunr7/+2vo4PDxcvXr1YsHX/0cAAgDAg1y+fFnjx4+3qT300EN8yeNPCEAAAHiIo0eP6u2337apvfDCC4W6Bpu7IADlE9eOexfON4Cibvny5dq7d6/18R133HHTb+V6MwKQk659zfrSpUskai9y7dsTvr6+JncCALYuXbqk119/3aYWHx+v8PBwkzpyDwQgJ/n6+qpMmTLWRaSCgoK4oMzD5ebm6syZMwoKCvKqVVIBFH0//PCD/v3vf9vUvOl2FreCn1A+VKpUSZJsVtKEZ/Px8VG1atUIuwCKBMMwNH/+fJ08edJa+8tf/qKYmBgTu3IvBKB8sFgsqly5sipUqKDLly+b3Q4Kgb+/v3x8WDgdgPkuXLigadOm2dSeeuopVaxY0ZyG3JTpAWj27Nl6/fXXlZaWpsaNG2vmzJlq2bKlw21/+OEHjRgxQtu3b9exY8c0depUDR482GabjRs36vXXX9f27duVmpqqFStWKC4urkB69/X15ZoQAECh+e9//6u1a9daHwcFBen555/nH2j5YOpPbNmyZUpISNDIkSO1Y8cONW7cWLGxsdedWrp06ZJq1KihiRMnWqeh/iwzM1ONGzfW7NmzC7J1AAAKTW5url5//XWb8NOuXTsNHTqU8JNPpt4LLDIyUi1atNCsWbMkXT3BYWFhGjRokBITE2/42oiICA0ePNhuBOiPLBZLvkaAnLmXCAAABen06dOaM2eOTW3w4MEKDg42qaOiyy3uBZadna3t27crKSnJWvPx8VFMTIy2bt1aqL1kZWUpKyvL+jgjI6NQ3x8AAEeSk5O1efNm6+PQ0FD17duXL2S4gGkB6OzZs8rJybG7aKtixYrav39/ofYyYcIEjRo1qlDfEwCA67ly5YrGjRtnU+vatasaNmxoUkeeh4lDSUlJSbpw4YL114kTJ8xuCQDgpY4dO2YXfoYOHUr4cTHTRoDKly8vX19fnTp1yqZ+6tSp617gXFACAgIUEBBQqO8JAMCfrVixQrt377Y+btCggR588EETO/Jcpo0A+fv7q1mzZkpOTrbWcnNzlZycrKioKLPaAgCg0F26dEmjRo2yCT89e/Yk/BQgU9cBSkhIUK9evdS8eXO1bNlS06ZNU2Zmph5//HFJV09+lSpVNGHCBElXL5y+dqO37Oxs/fzzz9q1a5dKliypWrVqSZIuXryow4cPW9/j6NGj2rVrl8qWLatq1aoV8hECAHBjmzdvthkMkKRhw4ZZ7z2JgmHq1+AladasWdaFEJs0aaIZM2YoMjJSktS6dWtFRERo0aJFkqSUlBRVr17dbh/R0dHasGGDJGnDhg1q06aN3Ta9evWy7udm+Bo8AKCgGYah0aNH29RCQiLVv387kzpyf858fpsegIoiAhAAoCAdPXpUb7/9tk3t3XcfUUhIbW3ZYlJTHsAt1gECAMAbOVp2Zdmy4QoJ8dVN1gCGCxGAAAAoBDk5ORo7dqxdfd26kZo4UerUyYSmvBgBCACAArZjxw795z//sak9+OCDatCggUaONKkpL0cAAgCgADma8hoxYgS3szAZAQgAgALw+++/69VXX7WplSxZUs8//7xJHeGPCEAAALjYp59+andj7759+6pKlSomdYQ/IwABAOBCjqa8RnKhT5FDAAIAwAXOnz+vGTNm2NTq1q2rhx9+2KSOcCMEIAAAbtHbb7+to0eP2tQGDx6s4OBgkzrCzRCAAAC4BUx5uScCEAAA+XD8+HEtXLjQpvbXv/5Vf/vb30zqCM4gAAEA4KQxY8YoNzfXppaYmKiAgACTOoKzCEAAAORRbm6uxowZY1dnysv9EIAAAMiDL7/8Uhs2bLCp7d4dpw8+aGxOQ7glBCAAAG7C0YXOn346XC++6GtCN3AFAhAAANfx22+/6bXXXrOrjxw5kpuYujkCEAAADixZskQHDx60qXXt2lUNGzY0qSO4EgEIAIA/YW0fz0cAAgDg/509e1azZ8+2qxN+PA8BCAAASRMmTFB2drZNrV+/fgoNDTWpIxQkAhAAwOsx5eV9CEAAAK91+PBhLV682KZWpUoV9e3b16SOUFgIQAAAr+Ro1CchIUGlSpUyoRsUNgIQAMCrcDsLSAQgAIAX+eSTT/TNN9/Y1Fq2bKn27dub1BHMQgACAHgFR1Nen332kkaO5KPQG3HWAQAe7ffff9err75qV1+3bqQSE01oCEUCAQgA4LHmzJmj06dP29Rat26t6Oho7uXl5QhAAACP5GjKa8SIEbJYLCZ0g6KGAAQA8ChnzpzRG2+8YVfnW174IwIQAMBjOBr16d69u+rUqWNCNyjKCEAAAI/A7SzgDAIQAMCt7d+/X8uWLbOrE35wIwQgAIDbcjTqM2DAAJUvX96EbuBOCEAAALdjGIZGjx5tV2fUB3lFAAIAuJUvvvhCmzZtsqmFhoaqX79+JnUEd0QAAgC4DUdTXi+++KICAwNN6AbujAAEACjysrOzNWHCBLs6U17ILwIQAKBIW7RokY4dO2ZTi4qK0r333mtSR/AEBCAAQJHF7SxQUAhAAIAih9tZoKARgAAARYqjUZ+9e+/To4+2MKEbeCoCEACgyHAUftatG6mtW6UTJ6ROnUxoCh6JAAQAMN3evXu1fPlyu/rIkSPVtKk0caKUmGhCY/BYBCAAgKkcjfr07t1bYWFhkq6O+jDyA1cjAAEATMHtLGAmAhAAoNCtW7dOX3/9tV2d8IPCQgACABQqR1Nezz//vEqWLGlCN/BWBCAAQKG4fPmyxo8fb1dn1AdmIAABAArc7NmzdfbsWZta9erV1bNnT5M6grcjAAEACpSjKa/hw4fL19fXhG6AqwhAAIACcf78ec2YMcOuzpQXigICEADA5RyN+rRt21Z33323Cd0A9ghAAACXchR+GPVBUUMAAgC4xOHDh7V48WK7OuEHRREBCABwyxyN+vTo0UM1a9Y0oRvg5ghAAIBbwpQX3BEBCACQLx9//LG+/fZbuzrhB+6AAAQAcJqjUZ9nn31WZcqUKfxmgHwgAAEA8uzKlSsaN26cXb1p05Ei+8CdEIAAAHkyZcoU/frrrza1nJxiGjPmJUVFSZ06mdQYkA8EIADATTma8ho2bJg++cRPUVFSYqIJTQG3gAAEALiuCxcuaNq0aXb1axc6d+rEyA/cEwEIAOCQo1GfO+64Q126dDGhG8C1CEAAADuOws+IESNksVhM6AZwPQIQAMCK21nAWxCAAACSHI/6dO7cWU2aNCn8ZoACRgACAHA7C3gdAhAAeLGNGzdq/fr1dnXCDzwdAQgAvJSjUZ8nn3xSlSpVMqEboHD5mN2AJM2ePVsREREKDAxUZGSkvvnmm+tu+8MPP+iBBx5QRESELBaLw/UpnN0nAHiT3Nzc6055EX7gLUwPQMuWLVNCQoJGjhypHTt2qHHjxoqNjdXp06cdbn/p0iXVqFFDEydOvO5fVGf3CQDeYtmyZRozZoxN7fffS6ppU6a84F0shmEYZjYQGRmpFi1aaNasWZKu/sskLCxMgwYNUuJN1laPiIjQ4MGDNXjwYJftU5IyMjIUHBysCxcuqHTp0vk7MAAoYhyN+iQmJiogIMCEbgDXc+bz2+kRIF9fX4cjKb/88ot8fX2d2ld2dra2b9+umJiY/zXk46OYmBht3brV2dbyvc+srCxlZGTY/AIAT3Hp0qXrTnkRfuCtnL4I+noDRllZWfL393dqX2fPnlVOTo4qVqxoU69YsaL279/vbGv53ueECRMc/s8BANzd6NGj7f6/feedd6pjx44mdQQUDXkOQDNmzJAkWSwWzZ8/XyVLlrQ+l5OTo40bN6pevXqu77AQJCUlKSEhwfo4IyNDYWFhJnYEALfO0T/smjQZoY4duZ0FkOcANHXqVElXR4Dmzp1rM93l7++viIgIzZ0716k3L1++vHx9fXXq1Cmb+qlTp/L9TYT87DMgIIBhYAAeIzU1Vf/85z/t6q+8MlJRUVLnziY0BRQxeQ5AR48elSS1adNGH374oW677bZbfnN/f381a9ZMycnJiouLk3T1guXk5GQNHDiwyOwTANzF9W5ncfx4E0VFSXn4HgjgFZy+BsjRiqG3IiEhQb169VLz5s3VsmVLTZs2TZmZmXr88cclST179lSVKlU0YcIESVcvct67d6/1zz///LN27dqlkiVLqlatWnnaJwB4ohvdzqJJE6lTp0JuCCjCnA5AvXv3vuHzCxYscGp/3bp105kzZzRixAilpaWpSZMmWrt2rfUi5uPHj8vH539fVjt58qSaNm1qfTxp0iRNmjRJ0dHR2rBhQ572CQCeZNeuXVq1apVdndtZANfn9DpAXbp0sXl8+fJl7dmzR+np6Wrbtq0+/PBDlzZoBtYBAuAuHI36PPHEE6pcubIJ3QDmcubz2+kRoBUrVtjVcnNz9fTTT6tmzZrO7g4AkA+GYWj06NF2dUZ9gLxx2UrQBw4cUOvWrZWamuqK3ZmKESAARdnixYt1+PBhm5rFYtGIESNM6ggoGgp0BOh6fvzxR125csVVuwMAOOBoymvo0KEKCgoyoRvAfTkdgP64YKB0dRg2NTVVH330kXr16uWyxgAA/5OVlaWJEyfa1Zs2HSmyD+A8pwPQzp07bR77+PgoJCREkydPvuk3xAAAzps8ebIuXrxoU7twobKmTHnCpI4A92f6OkAAgOtzNOW1bt3LSkx0+l7WAP4g39cAnT59WgcOHJAk1a1bVxUqVHBZUwDg7c6ePavZs2fb1UeOHCm+6AXcOqcDUEZGhgYMGKAlS5YoNzdXkuTr66tu3bpp9uzZCg4OdnmTAOBNHI36tG/fXi1btjShG8AzOT2G2q9fP/33v//VRx99pPT0dKWnp2vNmjXatm2bnnzyyYLoEQC8xvVuZ0H4AVzL6XWASpQooXXr1umvf/2rTX3Tpk1q166dMjMzXdqgGVgHCEBh27dvn95//327OgsbAnlXoOsAlStXzuE0V3BwsEvuEA8A3sbRqM/jjz+uatWqmdAN4B2cngIbPny4EhISlJaWZq2lpaVp6NChevnll13aHAB4MsMwrjvlRfgBCpbTU2BNmzbV4cOHlZWVZf0Levz4cQUEBKh27do22+7YscN1nRYipsAAFLTPPvtMW7Zssasz5QXkX4FOgXXu3FkWiyXfzQGAt3M06nP77Ql64IFSJnQDeCeX3QzVkzACBKAgXLlyRePGjbOrM+oDuEaBjgDVqFFD3377rcqVK2dTT09P15133qkjR444u0sA8Hhz5szR6dOnbWrh4eGKj483pyHAyzkdgFJSUpSTk2NXz8rK0k8//eSSpgDAkzia8ho+fLh8fX1N6AaA5EQAWr16tfXP69ats/kqfE5OjpKTk1W9enXXdgcAbiw9PV3Tp0+3qzPlBZgvzwEoLi5OkmSxWNSrVy+b5/z8/BQREaHJkye7tDkAcFeORn3atGmje+65x4RuAPxZngPQtft+Va9eXd9++63Kly9fYE0BgDu73to+AIoOp68BOnr0aEH0AQBu78cff9S7775rVyf8AEWP0wFo9OjRN3x+xIgR+W4GANyVo1GfRx99VLVq1TKhGwA343QAWrFihc3jy5cv6+jRoypWrJhq1qxJAALgdZjyAtyP0wFo586ddrWMjAzFx8erS5cuLmkKANzBpk2b9MUXX9jVCT9A0eeylaC///57dezYUSkpKa7YnalYCRrAzTga9XnmmWd02223mdANAKmAV4K+ngsXLujChQuu2h0AFEk5OTkaO3asXZ1RH8C9OB2AZsyYYfPYMAylpqbqnXfeUfv27V3WGAAUNW+//bbdN2FDQkLUv39/kzoCkF9OB6CpU6faPPbx8VFISIh69eqlpKQklzUGAEWJoymvYcOGyc/Pz4RuANwq1gECgBu4ePGiw1XumfIC3Fu+rgFKT0/X4cOHJUm1atVSmTJlXNkTABQJjkZ9oqKidO+995rQDQBXcioApaSkaMCAAVq3bp2ufXnMYrGoXbt2mjVrliIiIgqiRwAodI7Cz4gRI2SxWEzoBoCr5TkAnThxQnfddZf8/Pw0ZswY1a9fX5K0d+9ezZkzR1FRUfr2229VtWrVAmsWAAraiRMntGDBArs6U16AZ8nzOkB9+vTR4cOHtW7dOgUGBto899tvv6ldu3aqXbu25s+fXyCNFibWAQK8z+rV0s6d9qM+Dz74oBo0aGBCRwCcVSDrAK1du1bLli2zCz+SVLx4cY0ZM0YPP/yw890CQBHgKPww6gN4rjwHoLNnz97wGp8aNWro3LlzrugJAArNN998o08++cSuTvgBPFueA1DlypW1d+/e617js2fPHlWqVMlljQFAQXN0oXP//v0VEhJiQjcACpNPXjeMi4vTkCFDdObMGbvnTp8+rRdffFFxcXGu7A0ACkRubu517+BO+AG8Q54vgj5//rwiIyOVlpamHj16qF69ejIMQ/v27dN7772nSpUq6euvv1bZsmULuucCx0XQgOdavny59u7da1MrUaKEhgwZYlJHAFylQC6Cvu222/Tf//5Xw4YN09KlS5Weni5JKlOmjB555BGNHz/eI8IPAM/laNQnMTFRAQEBJnQDwEx5HgH6I8MwrFNhISEhHrcwGCNAgGf57bff9Nprr9nVudAZ8CwFMgL0RxaLRRUqVMhXcwBQmMaOHaucnBybWpMmTdS5c2eTOgJQFOQrAAGAO+B2FgCuhwAEwOOkpaXpzTfftKsz5QXgGgIQAI/iaNSnU6dOatq0qQndACiqCEAAPMb11vYBgD/LUwCaMWNGnnf4zDPP5LsZAMiP3bt3a8WKFXZ1wg+A68nT1+CrV6+et51ZLDpy5MgtN2U2vgYPuA9Hoz79+vVTaGioCd0AMJPLvwZ/9OhRlzQGAK5iGIZGjx5tV2fUB0Be5PsaoOzsbB09elQ1a9ZUsWJcSgSg8KxZs0bbt2+3qxN+AOSV08nl0qVLGjRokN566y1J0sGDB1WjRg0NGjRIVapUUWJiosubBIBrHE15DR06VEFBQSZ0A8Bd5flu8NckJSXpu+++04YNGxQYGGitx8TEaNmyZS5tDgCuyc7Ovu63vAg/AJzl9AjQypUrtWzZMt111102q6k2bNhQP/74o0ubAwBJmjJlin799VebWt26dfXwww+b1BEAd+d0ADpz5ozD+4BlZmayvDwAl3M06vPyyy/Lx8fpAWwAsHL6/yDNmzfXRx99ZH18LfTMnz9fUVFRrusMgFf75ZdfrjvlRfgBcKucHgEaP3682rdvr7179+rKlSuaPn269u7dqy1btujLL78siB4BeBlHwSc2NlZ33XWXCd0A8ERO/zPqr3/9q3bt2qUrV66oUaNG+vTTT1WhQgVt3bpVzZo1K4geAXiR6436EH4AuFKeVoL2NqwEDRS+/fv3O/wmKWv7AMgrl68EnZGRkec3JzAAcJajUZ/4+HiFh4eb0A0Ab5CnAFSmTJk8f8MrJyfnlhoC4F24gzsAM+QpAK1fv97655SUFCUmJio+Pt76ra+tW7fqrbfe0oQJEwqmSwAe5/PPP9dXX31lVyf8ACgMTl8D9Le//U19+/ZV9+7dbervvfee/vnPf2rDhg2u7M8UXAMEFCxHoz7PPfccf98A3BJnPr+d/hbY1q1b1bx5c7t68+bN9c033zi7OwBe5MqVK9ed8iL8AChMTq8DFBYWpnnz5um1116zqc+fP19hYWEuawyAZ3nzzTeVlpZmUwsLC1Pv3r1N6giAN3M6AE2dOlUPPPCAPvnkE0VGRkqSvvnmGx06dEgffPCByxsE4P4cjfoMHz5cvr6+JnQDAPmYArvvvvt06NAhdezYUefOndO5c+fUsWNHHTx4UPfdd19B9AjATV24cOG6U16EHwBmYiFEB7gIGrh1joJPdHS0WrduXfjNAPAKLl8I8c/S09P1r3/9S/v27ZMkNWzYUL1791ZwcHB+dgfAw7C2D4CizukpsG3btqlmzZqaOnWqdQpsypQpqlmzpnbs2FEQPQJwE0eOHHEYfpo2JfwAKFqcngK7++67VatWLc2bN0/Fil0dQLpy5Yr69u2rI0eOaOPGjQXSaGFiCgxwnqPg88gjj6h27domdAPAGznz+e10ACpevLh27typevXq2dT37t2r5s2b69KlS853XMQQgADnMOUFoCgo0IUQS5curePHj9vVT5w4oVKlSjm7O0nS7NmzFRERocDAQEVGRt50QcXly5erXr16CgwMVKNGjfTxxx/bPH/q1CnFx8crNDRUQUFBateunQ4dOpSv3gBc3+bNmwk/ANyS0wGoW7du6tOnj5YtW6YTJ07oxIkTWrp0qcPbY+TFsmXLlJCQoJEjR2rHjh1q3LixYmNjdfr0aYfbb9myRd27d1efPn20c+dOxcXFKS4uTnv27JEkGYahuLg4HTlyRKtWrdLOnTsVHh6umJgYZWZmOt0fAMdGjRql5ORkm9ozzzxD+AHgFpyeAsvOztbQoUM1d+5cXblyRZLk5+enp59+WhMnTlRAQIBTDURGRqpFixaaNWuWJCk3N1dhYWEaNGiQEhMT7bbv1q2bMjMztWbNGmvtrrvuUpMmTTR37lwdPHhQdevW1Z49e9SwYUPrPitVqqTx48erb9++N+2JKTDg+nJycjR27Fi7OsEHgNkKdArM399f06dP1/nz57Vr1y7t2rVL586d09SpU50OP9nZ2dq+fbtiYmL+15CPj2JiYrR161aHr9m6davN9pIUGxtr3T4rK0uSFBgYaLPPgIAAbd682eE+s7KylJGRYfMLgL133nnHLvyUK1eO8APA7eRrHSBJCgoKUqNGjW7pzc+ePaucnBxVrFjRpl6xYkXt37/f4WvS0tIcbn/tHkP16tVTtWrVlJSUpDfffFMlSpTQ1KlT9dNPPyk1NdXhPidMmODwOgYA/+Po78iwYcPk5+dnQjcAcGvyHIDyesPCBQsW5LsZV/Dz89OHH36oPn36qGzZsvL19VVMTIzat2+v6832JSUlKSEhwfo4IyODG7sC/+/ixYuaPHmyXZ1RHwDuLM8BaNGiRQoPD1fTpk2vGyScVb58efn6+urUqVM29VOnTqlSpUoOX1OpUqWbbt+sWTPt2rVLFy5cUHZ2tkJCQhQZGanmzZs73GdAQIDT03eAN3A06hMZGal27dqZ0A0AuE6eA9DTTz+tJUuW6OjRo3r88cfVo0cPlS1b9pbe3N/fX82aNVNycrLi4uIkXb1gOTk5WQMHDnT4mqioKCUnJ2vw4MHW2meffaaoqCi7ba/dmuPQoUPatm2bxowZc0v9At7EUfgZMWKELBaLCd0AgGvl+SLo2bNnKzU1VS+88IL+85//KCwsTA899JDWrVt3SyNCCQkJmjdvnt566y3t27dPTz/9tDIzM/X4449Lknr27KmkpCTr9s8++6zWrl2ryZMna//+/XrllVe0bds2m8C0fPlybdiwwfpV+L///e+Ki4vTvffem+8+AW/x008/XXdtH8IPAE/h1EXQAQEB6t69u7p3765jx45p0aJF6t+/v65cuaIffvhBJUuWdLqBbt266cyZMxoxYoTS0tLUpEkTrV271nqh8/Hjx+Xj87+c1qpVK7333nsaPny4hg0bptq1a2vlypW6/fbbrdukpqYqISFBp06dUuXKldWzZ0+9/PLLTvcGeBtHwadr167WJSUAwFM4vQ7QNSdOnNDChQu1aNEiZWdna//+/fkKQEUR6wDBG7GiMwB3V2DrAGVlZWnJkiX6+9//rjp16uj777/XrFmzdPz4cY8JP4C32bZtG+EHgNfJ8xRY//79tXTpUoWFhal3795asmSJypcvX5C9AShgjoLP008/rQoVKpjQDQAUnjxPgfn4+KhatWpq2rTpDS+E/PDDD13WnFmYAoOnMwxDo0ePtqsz6gPAnTnz+Z3nEaCePXvyDRDAA/z73//WDz/8YFMLDAzUiy++aFJHAFD4nFoIEYB7czTl9eKLL9rcOw8AvEG+7wUGwH389ttveu211+zqTHkB8FYEIMDDjR8/XpcvX7ap3XHHHerSpYtJHQGA+QhAgAfjdhYA4BgBCPBAp06d0ty5c+3qTHkBwFUEIMDDOBr16dixo+68804TugGAookABHgQVnQGgLwhAAEeYPfu3VqxYoVdnfADAI4RgAA352jUp2/fvqpSpYoJ3QCAeyAAAW6K21kAQP4RgAA39NFHH2nbtm12dcIPAOQNAQhwM46mvIYMGaISJUqY0A0AuCcCEOAmsrOzNWHCBLs6oz4A4DwCEOAGpk+frvT0dJtanTp11L17d3MaAgA3RwACijhHU14vv/yyfHx8TOgGADwDAQgootLT0zV9+nS7OlNeAHDrCEBAEeRo1Ofvf/+7WrVqZUI3AOB5GEMHihhH4WfdupGEHwBwIUaAgCIiJSVFb731ll193bqRSkw0oSEA8GAEIKAIcDTq069fP4WGhopLfgDA9QhAgMm4gzsAFD4CEGCSb775Rp988olNrUSJEhoyZIhJHQGA9yAAASZwNOrz/PPPq2TJkiZ0AwDehwAEFKKcnByNHTvWrs6UFwAULgIQUEg+/PBDff/99za1O+64Q126dDGpIwDwXgQgoBA4mvIaPny4fH19TegGAEAAAgrQxYsXNXnyZLs6U14AYC4CEFBAXn/9dV26dMmmdt9996lFixYmdQQAuIYABBQA1vYBgKKNAAS40M8//6z58+fb1Qk/AFC0EIAAF3E06hMfH6/w8HATugEA3AgBCHABprwAwL0QgIBbsGvXLq1atcqmZrFYNGLECJM6AgDkBQEIyCdHoz6DBw9WcHCwCd0AAJxBAAKclJubqzFjxtjVmfICAPdBAAKc8NFHH2nbtm02tdq1a+uRRx4xqSMAQH4QgIA8cjTlNWzYMPn5+ZnQDQDgVhCAgJv47bff9Nprr9nVmfICAPdFAAJuYNasWfrll19sam3bttXdd99tUkcAAFcgAAHX4WjKa8SIEbJYLCZ0AwBwJQIQ8CenT5/WnDlz7OpMeQGA5yAAAX/gaNTnkUceUe3atU3oBgBQUAhAwP/jdhYA4D0IQPB6e/fu1fLly+3qhB8A8FwEIHg1R6M+gwYNUtmyZU3oBgBQWAhA8EqGYWj06NF2dUZ9AMA7EIDgdZKTk7V582abWtWqVdWnTx+TOgIAFDYCELyKoymvxMREBQQEmNANAMAsBCB4haysLE2cONGuzpQXAHgnAhA83oIFC3TixAmb2l/+8hfFxMSY1BEAwGwEIHg0bmcBAHCEAASPdO7cOc2cOdOuzpQXAEAiAMEDORr1efDBB9WgQQMTugEAFEUEIHgUbmcBAMgLAhA8wuHDh7V48WK7OuEHAOAIAQhuz9Goz1NPPaWKFSua0A0AwB0QgOC2uJ0FACC/CEBwS5s3b1ZycrJNrVy5cho4cKBJHQEA3AkBCG7H0ZTXCy+8oOLFi5vQDQDAHRGA4DYuX76s8ePH29WZ8gIAOIsABLewZMkSHTx40KbWrFkz3X///SZ1BABwZwQgFHmOprxefvll+fj4mNANAMATEIBQZF24cEHTpk2zqzPlBQC4VQQgFEnz5s3TyZMnbWqdO3dWkyZNzGkIAOBRCEAocridBQCgoBWJiyhmz56tiIgIBQYGKjIyUt98880Nt1++fLnq1aunwMBANWrUSB9//LHN8xcvXtTAgQNVtWpVFS9eXA0aNNDcuXML8hDgAmlpaQ7DT9OmhB8AgGuZPgK0bNkyJSQkaO7cuYqMjNS0adMUGxurAwcOqEKFCnbbb9myRd27d9eECRN0//3367333lNcXJx27Nih22+/XZKUkJCgL774Qu+++64iIiL06aefqn///goNDVWnTp0K+xCRB46CT//+/RUSEmJCNwAAT2cxDMMws4HIyEi1aNFCs2bNkiTl5uYqLCxMgwYNUmJiot323bp1U2ZmptasWWOt3XXXXWrSpIl1lOf2229Xt27d9PLLL1u3adasmdq3b6+xY8fetKeMjAwFBwfrwoULKl269K0eIm6CKS8AgCs48/lt6hRYdna2tm/frpiYGGvNx8dHMTEx2rp1q8PXbN261WZ7SYqNjbXZvlWrVlq9erV+/vlnGYah9evX6+DBg7r33nsL5kCQL3v37rULP7Vq1SL8AAAKnKlTYGfPnlVOTo7dXbsrVqyo/fv3O3xNWlqaw+3T0tKsj2fOnKknnnhCVatWVbFixeTj46N58+bpnnvucbjPrKwsZWVlWR9nZGTk95CQR9zOAgBgJtOvASoIM2fO1Ndff63Vq1crPDxcGzdu1IABAxQaGmo3eiRJEyZMcPiBDNfLzc3VmDFj7OqM+gAACpOpAah8+fLy9fXVqVOnbOqnTp1SpUqVHL6mUqVKN9z+t99+07Bhw7RixQp16NBBknTHHXdo165dmjRpksMAlJSUpISEBOvjjIwMhYWF3dKxwd6GDRv05Zdf2tTuuecetWnTxqSOAADeytRrgPz9/dWsWTMlJydba7m5uUpOTlZUVJTD10RFRdlsL0mfffaZdfvLly/r8uXLdrdJ8PX1VW5ursN9BgQEqHTp0ja/4FqjRo2yCz/Dhw8n/AAATGH6FFhCQoJ69eql5s2bq2XLlpo2bZoyMzP1+OOPS5J69uypKlWqaMKECZKkZ599VtHR0Zo8ebI6dOigpUuXatu2bfrnP/8pSSpdurSio6M1dOhQFS9eXOHh4fryyy/19ttva8qUKaYdp7f67bff9Nprr9nVmfICAJjJ9ADUrVs3nTlzRiNGjFBaWpqaNGmitWvXWi90Pn78uM1oTqtWrfTee+9p+PDhGjZsmGrXrq2VK1da1wCSpKVLlyopKUmPPvqozp07p/DwcI0bN05PPfVUoR+fN3N0B/euXbuqYcOGJnUEAMBVpq8DVBSxDtCtY20fAEBhc+bz2/QRIHiW8+fPa8aMGXZ1wg8AoCghAMFl5s6da/cNvS1bntCAAZVN6ggAAMcIQHAJprwAAO6EAIRbcuzYMS1atMim1qBBAz344IPmNAQAQB4QgJBvjkZ9hg4dqqCgIBO6AQAg7whAcJphGBo9erRdnSkvAIC7IADBKTt37tTq1attam3bttXdd99tUkcAADiPAIQ8czTlNXz4cPn6+prQDQAA+UcAwk1lZ2dbb0XyR0x5AQDcFQEIN7Ru3Tp9/fXXNrWHHnpI9evXN6kjAABuHQEI1+VoymvEiBGyWCwmdAMAgOsQgGAnIyNDU6dOtan5+/srKSnJpI4AAHAtAhBsLFq0SMeOHbOpPfHEE6pcmdtZAAA8BwEIVtzOAgDgLQhA0M8//6z58+fb1GrWrKkePXqY1BEAAAWLAOTlRo8eLcMwbGoJCQkqVaqUSR0BAFDwCEBeittZAAC8GQHIC+3Zs0cffPCBTe2vf/2r/va3v5nUEQAAhYsA5GUcXeg8bNgw+fn5mdANAADmIAB5iStXrmjcuHF2daa8AADeiADkBdavX6+NGzfa1Lp06aI77rjDpI4AADAXAcjDcTsLAADsEYA81MWLFzV58mS7OlNeAAAQgDzSkiVLdPDgQZta7969FRYWZlJHAAAULQQgD8PtLAAAuDkCkIdIT0/X9OnTbWpVq1ZVnz59TOoIAICiiwDkAVatWqVdu3bZ1Bo2HKyuXYPNaQgAgCKOAOTGrnc7i1deGamoKKlrVxOaAgDADRCA3NTJkyc1b948m1qnTp104kRTRUVJiYkmNQYAgBsgALmhefPm6eTJkza1a7ezaNpU6tTJpMYAAHATBCA3kpubqzFjxtjUSpYsqeeff96kjgAAcE8EIDdx4MABLV261Kb22GOPqUaNGiZ1BACA+yIAuYHRo0fLMAybGrezAAAg/whARdiKFdnavXuCTa1mzZrq0aOHSR0BAOAZCEBFVEpKinbvfsum9vTTT6tChQomdQQAgOcgABVB//nPf7Rjxw6bGrezAADAdQhARUhWVpYmTpxoU4uNjdVdd91lUkcAAHgmAlARcejQIb333ns2taSkJPn7+5vUEQAAnosAVAQsXrxYhw8ftj5u3ry5OnToYGJHAAB4NgKQiS5evKjJkyfb1Pr27asqVaqY1BEAAN6BAGSSXbt2adWqVdbHFotFL730knx9fU3sCgAA70AAKmSGYeiNN97Q2bNnrbU2bdronnvuMbErAAC8CwGoEOXk5Gjs2LE2tYEDB6pcuXImdQQAgHciABWiEydOWP9ctmxZDRw4kNtZAABgAh+zG/AmYWFhqlatk7799jGFhw8i/AAAYBJGgAqRr6+v5s1rqq1bpXPnpE6dzO4IAADvxAhQIUtMlKKirv4OAADMwQhQIevUiZEfAADMxggQAADwOgQgAADgdQhAAADA6xCAAACA1yEAAQAAr0MAAgAAXocABAAAvA4BCAAAeB0CEAAA8DoEIAAA4HUIQAAAwOsQgAAAgNchAAEAAK/D3eAdMAxDkpSRkWFyJwAAIK+ufW5f+xy/EQKQA7/++qskKSwszOROAACAs3799VcFBwffcBuLkZeY5GVyc3N18uRJlSpVShaLxex2PFpGRobCwsJ04sQJlS5d2ux2kEecN/fDOXNPnDfnGIahX3/9VaGhofLxufFVPowAOeDj46OqVaua3YZXKV26NH+53RDnzf1wztwT5y3vbjbycw0XQQMAAK9DAAIAAF6HAARTBQQEaOTIkQoICDC7FTiB8+Z+OGfuifNWcLgIGgAAeB1GgAAAgNchAAEAAK9DAAIAAF6HAAQAALwOAQguNXv2bEVERCgwMFCRkZH65ptvbrj98uXLVa9ePQUGBqpRo0b6+OOPbZ6/ePGiBg4cqKpVq6p48eJq0KCB5s6dW5CH4JVcfd5OnTql+Ph4hYaGKigoSO3atdOhQ4cK8hC8kjPn7YcfftADDzygiIgIWSwWTZs27Zb3ifxx9XnbuHGjOnbsqNDQUFksFq1cubLgmvcgBCC4zLJly5SQkKCRI0dqx44daty4sWJjY3X69GmH22/ZskXdu3dXnz59tHPnTsXFxSkuLk579uyxbpOQkKC1a9fq3Xff1b59+zR48GANHDhQq1evLqzD8niuPm+GYSguLk5HjhzRqlWrtHPnToWHhysmJkaZmZmFeWgezdnzdunSJdWoUUMTJ05UpUqVXLJPOK8gzltmZqYaN26s2bNnF2TrnscAXKRly5bGgAEDrI9zcnKM0NBQY8KECQ63f+ihh4wOHTrY1CIjI40nn3zS+rhhw4bG6NGjbba58847jZdeesmFnXs3V5+3AwcOGJKMPXv22OwzJCTEmDdvXgEcgXdy9rz9UXh4uDF16lSX7hN5UxDn7Y8kGStWrLjFLr0DI0BwiezsbG3fvl0xMTHWmo+Pj2JiYrR161aHr9m6davN9pIUGxtrs32rVq20evVq/fzzzzIMQ+vXr9fBgwd17733FsyBeJmCOG9ZWVmSpMDAQJt9BgQEaPPmza4+BK+Un/Nmxj5hi59x0UIAgkucPXtWOTk5qlixok29YsWKSktLc/iatLS0m24/c+ZMNWjQQFWrVpW/v7/atWun2bNn65577nH9QXihgjhv9erVU7Vq1ZSUlKTz588rOztbr776qn766SelpqYWzIF4mfycNzP2CVv8jIsWAhCKtJkzZ+rrr7/W6tWrtX37dk2ePFkDBgzQ559/bnZruA4/Pz99+OGHOnjwoMqWLaugoCCtX79e7du3l48P/8sBUDQUM7sBeIby5cvL19dXp06dsqmfOnXquhfuVapU6Ybb//bbbxo2bJhWrFihDh06SJLuuOMO7dq1S5MmTbKbhoHzCuK8SVKzZs20a9cuXbhwQdnZ2QoJCVFkZKSaN2/u+oPwQvk5b2bsE7b4GRct/HMMLuHv769mzZopOTnZWsvNzVVycrKioqIcviYqKspme0n67LPPrNtfvnxZly9fths18PX1VW5urouPwDsVxHn7o+DgYIWEhOjQoUPatm2bOnfu7NoD8FL5OW9m7BO2+BkXMWZfhQ3PsXTpUiMgIMBYtGiRsXfvXuOJJ54wypQpY6SlpRmGYRiPPfaYkZiYaN3+q6++MooVK2ZMmjTJ2LdvnzFy5EjDz8/P+P77763bREdHGw0bNjTWr19vHDlyxFi4cKERGBhovPHGG4V+fJ6qIM7b+++/b6xfv9748ccfjZUrVxrh4eHGP/7xj0I/Nk/m7HnLysoydu7caezcudOoXLmyMWTIEGPnzp3GoUOH8rxP3LqCOG+//vqrdRtJxpQpU4ydO3cax44dK/TjcycEILjUzJkzjWrVqhn+/v5Gy5Ytja+//tr6XHR0tNGrVy+b7d9//32jTp06hr+/v9GwYUPjo48+snk+NTXViI+PN0JDQ43AwECjbt26xuTJk43c3NzCOByv4erzNn36dKNq1aqGn5+fUa1aNWP48OFGVlZWYRyKV3HmvB09etSQZPcrOjo6z/uEa7j6vK1fv97hNn/+ewtbFsMwjMIfdwIAADAP1wABAACvQwACAABehwAEAAC8DgEIAAB4HQIQAADwOgQgAADgdQhAAADA6xCAAACA1yEAAfBIaWlpGjRokGrUqKGAgACFhYWpY8eONvdh2rJli+677z7ddtttCgwMVKNGjTRlyhTl5OSY2DmAwkAAAuBxUlJS1KxZM33xxRd6/fXX9f3332vt2rVq06aNBgwYIElasWKFoqOjVbVqVa1fv1779+/Xs88+q7Fjx+rhhx8Wi+QDno1bYQDwOPfdd592796tAwcOqESJEjbPpaeny8/PT+Hh4YqOjtYHH3xg8/x//vMfderUSUuXLlW3bt0Ks20AhYgRIAAe5dy5c1q7dq0GDBhgF34kqUyZMvr000/1yy+/aMiQIXbPd+zYUXXq1NGSJUsKo10AJiEAAfAohw8flmEYqlev3nW3OXjwoCSpfv36Dp+vV6+edRsAnokABMCjODOrzxUAgPciAAHwKLVr15bFYtH+/fuvu02dOnUkSfv27XP4/L59+6zbAPBMBCAAHqVs2bKKjY3V7NmzlZmZafd8enq67r33XpUtW1aTJ0+2e3716tU6dOiQunfvXhjtAjAJAQiAx5k9e7ZycnLUsmVLffDBBzp06JD27dunGTNmKCoqSiVKlNCbb76pVatW6YknntDu3buVkpKif/3rX4qPj1fXrl310EMPmX0YAAoQX4MH4JFSU1M1btw4rVmzRqmpqQoJCVGzZs303HPPqXXr1pKkTZs2ady4cdq6dat+//131a5dW48//rgGDx4sX19fcw8AQIEiAAEAAK/DFBgAAPA6BCAAAOB1CEAAAMDrEIAAAIDXIQABAACvQwACAABehwAEAAC8DgEIAAB4HQIQAADwOgQgAADgdQhAAADA6xCAAACA1/k/PHzDjHhZMqYAAAAASUVORK5CYII=", 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tDeB/BVWLFi2wcuVK5ObmivmUXTXZsWMHjIyM0KVLFwDA/v37sW3bNmzZsqXKzoWhoSFCQkKqbPvP27c2XLx4EU5OTsjIyMDrr7+OiRMnYsmSJWjcuDF+/vlnBAYGori4uNwnyRcWFsLb2xve3t7YuXMnmjRpghs3bsDb21vyHDEiIqpbCgoKsHr1aklswoQJKuOs5SZrUTV06FDk5uYiNDQUWVlZ6Ny5Mw4dOiSepBs3bkiuInl6emLXrl1YsGAB5s2bhzZt2iA2NlZ8RpW+vj7Onz+PHTt2IC8vD/b29ujfvz8WL14sds0lJiYiPT0d6enpaNasmSQfQRDEPy9evBjXr1+HgYEBXFxcEBMTg7fffrvKzoVCodBKF5xcfvjhB/z222+YOXMmUlNToVQqERUVJf797d27V9LeyMhI5eaBS5cu4e7du4iMjBTHo50+fbp6DoCIiGqkTZs2SYb+1KtXD7Nnz65QL1N1Uwj/rCRIawoKCmBubo78/HyYmZlJlj1+/BjXrl2Dk5MTTExMZMqwcgICApCdnY3o6GiUlpYiOzsbhw4dQkREBPr06YPY2FikpaWhc+fOWLNmDQYPHoxffvkFISEhuHXrFu7fvw8LCwskJyfj5ZdfxuHDh9GpUyfUr18fhYWFaNasGaZPn44JEyYgLS0NwcHBuHLlCs6ePYvOnTvLffgvrDb/3RMRVafS0lJ89NFHktilS/2xe3f5Q3604Vm/389T88o8qvEOHToEOzs7ODo6wsfHBz/++CPWrVuHb775Bvr6+ujUqRNWrVqFZcuWoUOHDti5c6fKIHxPT09MmDABQ4cORZMmTbB8+XI0adIE27dvx759+/DSSy8hMjJS5Sn3RESk+y5evKhSUB0/PgbDh1dtQfWieKWqiujqlSp6Mfy7JyJ6tkWLFuHfpUloaGi1TUj/IleqdO45VURERFT7FBcXq/RqtGrVCu+9955MGWmORRURERHJ6uTJk/juu+8ksYkTJ8La2lqmjCqHRRURERHJJjw8XCUWFhYmQyYvjkUVERERVbuHDx8iKipKEuvevTsGDhwoU0YvjkUVERERVauEhAQcP35cEps5c6bGA8NrGhZVREREVG10qbvv31hUERERUZW7c+cONmzYIIn179//mfP31jYsqoiIiKhKxcTE4NKlS5LY3LlzxSnkdAWLKiIiIqoSgiBg0aJFKnFd6e77NxZVVCMFBAQgLy8PsbGxAIA+ffqI8wlWlja2QUREFXPjxg1ER0dLYm+//Tbat28vU0ZVj0UVaSQgIAA7duwAABgaGqJ58+bw9/fHvHnzYGBQdR+n/fv3w9DQsEJtjxw5gr59+4qTN1dmG0REVHkbNmzAnTt3JLEFCxZAX19fpoyqB4sq0piPjw+io6NRVFSEgwcPYvLkyTA0NERISIikXXFxMYyMjLSyz8aNG9eIbRARUflKS0tVJkJu3Lgxpk6dKlNG1UtP7gSo9jE2NoatrS1atGiBiRMnwsvLC3FxcQgICICfnx+WLFkCe3t7ODs7AwD++usvvPPOO7CwsEDjxo3xxhtvICMjQ9xeaWkpgoKCYGFhAUtLS3zwwQcqk2n26dMHM2bMEN8XFRVhzpw5cHBwgLGxMVq3bo2tW7ciIyMDffv2BQA0atQICoUCAQEBardx//59+Pv7o1GjRqhfvz4GDBiAP/74Q1y+fft2WFhYICEhAe3atUODBg3g4+ODzMxMsc2RI0fQo0cPmJqawsLCAi+//DKuX7+upTNNRFR7XLhwQaWgCggIqDMFFcCiirSgXr16KC4uBgAkJSXh8uXLSExMRHx8PJ48eQJvb280bNgQx44dwy+//CIWJ2XrREVFYfv27di2bRt+/vln3Lt3DwcOHHjmPv39/bF7926sW7cOFy9exKeffooGDRrAwcEBX3/9NQDg8uXLyMzMxNq1a9VuIyAgAKdPn0ZcXBxSUlIgCAIGDhyIJ0+eiG0ePXqElStX4osvvsDRo0dx48YNzJ49GwBQUlICPz8/9O7dG+fPn0dKSgref//9aptJnYiopggPD8e+ffsksdDQULRo0UKmjOTB7j+qNEEQkJSUhISEBEydOhW5ubkwNTXFli1bxG6/L7/8EkqlElu2bBGLjejoaFhYWODIkSPo378/1qxZg5CQEPznP/8BAGzatAkJCQnl7vfKlSvYu3cvEhMT4eXlBQBo2bKluLysm8/a2loypuqf/vjjD8TFxeGXX36Bp6cnAGDnzp1wcHBAbGwshgwZAgB48uQJNm3ahFatWgEApkyZIt7JUlBQgPz8fLz++uvi8nbt2ml+IomIaqmioiJERkZKYm3btsXw4cNlykhevFKlA+LiAE/Pp/+tDvHx8WjQoAFMTEwwYMAADB06FAsXLgQAuLq6SsZR/frrr0hPT0fDhg3RoEEDNGjQAI0bN8bjx49x9epV5OfnIzMzE+7u7uI6BgYG6NatW7n7P3fuHPT19dG7d+9KH8PFixdhYGAg2a+lpSWcnZ1x8eJFMVa/fn2xYAIAOzs75OTkAHhavAUEBMDb2xuDBw/G2rVrJV2DRES6LCUlRaWgmjRpUp0tqABeqdIJkZFASsrT//r6Vv3++vbti40bN8LIyAj29vaSu/5MTU0lbR8+fAg3Nzfs3LlTZTtNmjSp1P7r1atXqfUq4993CyoUCsl4r+joaEybNg2HDh1CTEwMFixYgMTERPTs2bPaciQiqm66PNXMi+CVKh0wdy7g4fH0v9XB1NQUrVu3RvPmzZ/7GIWuXbvijz/+gLW1NVq3bi15mZubw9zcHHZ2djhx4oS4TklJCVJTU8vdpqurK5RKJX766Se1y8uulJWWlpa7jXbt2qGkpESy37t37+Ly5ct46aWXnnlM/9alSxeEhIQgOTkZHTp0wK5duzRan4iotnjw4IFKQdWzZ08WVP8fiyod4OsLJCdXz1UqTY0YMQJWVlZ44403cOzYMVy7dg1HjhzBtGnTcPPmTQDA9OnTERkZidjYWFy6dAmTJk1CXl5eudt0dHTEqFGjMGbMGMTGxorb3Lt3LwCgRYsWUCgUiI+PR25uLh4+fKiyjTZt2uCNN97AuHHj8PPPP+PXX3/Fe++9h6ZNm+KNN96o0LFdu3YNISEhSElJwfXr1/H999/jjz/+4LgqItJJ3333HVatWiWJBQUFwdvbW6aMah4WVVSl6tevj6NHj6J58+b4z3/+g3bt2iEwMBCPHz+GmZkZAGDWrFkYOXIkRo0aBQ8PDzRs2BBvvvnmM7e7ceNGvP3225g0aRJcXFwwbtw4FBYWAgCaNm2K8PBwzJ07FzY2NpgyZYrabURHR8PNzQ2vv/46PDw8IAgCDh48WOEHhNavXx+XLl3CW2+9hbZt2+L999/H5MmTMX78eA3OEBFRzRceHo6TJ09KYmFhYWjYsKFMGdVMCuHfDwQirSgoKIC5uTny8/PF4qHM48ePce3aNTg5OcHExESmDEkO/LsnotokJycHGzdulMR8fHwkN/nommf9fj8PB6oTERGRil27dkkeiAwAHTvOhbu7sUwZ1XwsqoiIiEgkCIL4PL5/4mD052NRRURERACAjIwM7NixQxJ75513eANOBbGoIiIiIqxbtw7379+XxBYsWAB9fX2ZMqp9WFTJiPcI1D38Oyeimqa0tFRlIuQmTZpg0qRJMmVUe7GokkHZLfuPHj2q1qeDk/wePXoEQPVJ7UREckhLSxMnoS8zZswYODg4yJRR7caiSgb6+vqwsLAQ55CrX7++ONkw6SZBEPDo0SPk5OTAwsKCl9OJSHbqppoJDQ3l79ELYFElE1tbWwAQCyuqGywsLMS/eyIiOTx+/BjLli2TxNq1a4d33nlHpox0B4sqmSgUCtjZ2cHa2hpPnjyROx2qBoaGhrxCRUSy+uWXX3D48GFJbMqUKbC0tJQpI93Cokpm+vr6/KElIqIqp667j8+e0i4WVURERDqsoKAAq1evlsRefvlleHl5yZSR7mJRRUREpKO+/fZbnD59WhKbNWsWGjRoIFNGuo1FFRERkQ5id1/1Y1FFRESkQ7Kzs7Fp0yZJbODAgejevbtMGdUdenInsGHDBjg6OsLExATu7u44efLkM9vv27cPLi4uMDExgaurKw4ePChZHhAQAIVCIXn5+PhI2ty7dw8jRoyAmZkZLCwsEBgYiIcPH0ranD9/Hr169YKJiQkcHBywfPly7RwwERFRFfniiy9UCqqQkBAWVNVE1qIqJiYGQUFBCAsLw5kzZ9CpUyd4e3uX++ym5ORkDB8+HIGBgTh79iz8/Pzg5+eHtLQ0STsfHx9kZmaKr927d0uWjxgxAr///jsSExMRHx+Po0eP4v333xeXFxQUoH///mjRogVSU1OxYsUKLFy4EJs3b9b+SSAiInpBgiAgPDwcf/75pxjT19dHWFgYjIyMZMysblEIMk5G5u7uju7du2P9+vUAAKVSCQcHB0ydOhVz585VaT906FAUFhYiPj5ejPXs2ROdO3cWK/OAgADk5eUhNjZW7T4vXryIl156CadOnUK3bt0AAIcOHcLAgQNx8+ZN2NvbY+PGjZg/fz6ysrLED+PcuXMRGxuLS5cuVejYCgoKYG5ujvz8fJiZmVX4nBAREWnizz//xBdffCGJDRs2DM7OzjJlVLu9yO+3bFeqiouLkZqaKrmlU09PD15eXkhJSVG7TkpKisotoN7e3irtjxw5Amtrazg7O2PixIm4e/euZBsWFhZiQQUAXl5e0NPTw4kTJ8Q2r776qqS69/b2xuXLl1Vm8C5TVFSEgoICyYuIiKgqrVq1SqWg+vDDD1lQyUS2ourOnTsoLS2FjY2NJG5jY4OsrCy162RlZT23vY+PDz7//HMkJSVh2bJl+OmnnzBgwACUlpaK27C2tpZsw8DAAI0bNxa3U95+ypapExERAXNzc/HFySiJiKiqFBcXIzw8HA8ePBBjtra2CAsLg56e7MOl6yydu/tv2LBh4p9dXV3RsWNHtGrVCkeOHEG/fv2qbL8hISEICgoS3xcUFLCwIiIirUtMTERycrIkFhgYiGbNmsmUEZWRrZy1srKCvr4+srOzJfHs7OxyJ5y1tbXVqD0AtGzZElZWVkhPTxe38e+B8CUlJbh37564nfL2U7ZMHWNjY5iZmUleRERE2hQeHq5SUCUkhLKgqiFkK6qMjIzg5uaGpKQkMaZUKpGUlAQPDw+163h4eEjaA08r9vLaA8DNmzdx9+5d2NnZidvIy8tDamqq2OaHH36AUqmEu7u72Obo0aOSiY4TExPh7OyMRo0aaX6wREREL+DBgwcqD/NUKPSQkBCGuXMVMmVF/ybr3X8xMTEYNWoUPv30U/To0QNr1qzB3r17cenSJdjY2MDf3x9NmzZFREQEgKePVOjduzciIyMxaNAg7NmzB0uXLsWZM2fQoUMHPHz4EOHh4Xjrrbdga2uLq1ev4oMPPsCDBw/w22+/wdjYGAAwYMAA8eFoT548wejRo9GtWzfs2rULAJCfnw9nZ2f0798fc+bMQVpaGsaMGYPVq1dLHr3wLLz7j4iItGHnzp1ib0uZsWPHomnTpjJlpNte5Pdb1jFVQ4cORW5uLkJDQ5GVlYXOnTvj0KFD4qDwGzduSAbceXp6YteuXViwYAHmzZuHNm3aIDY2Fh06dADw9Jkc58+fx44dO5CXlwd7e3v0798fixcvFgsq4OkHdMqUKejXrx/09PTw1ltvYd26deJyc3NzfP/995g8eTLc3NxgZWWF0NDQChdURERE2sCpZmoXWa9U6TJeqSIiospSN9WMg4MDxowZI1NGdUetvVJFREREUitXrkRhYaEkNn36dFhYWMiTEFUYiyoiIqIagt19tRuLKiIiIpmlp6dj586dkpibmxtef/11mTKiymBRRUREJCN1V6fmzJkDExMTGbKhF8GiioiISAZKpRKLFy9WibO7r/ZiUUVERFTNTp06hYMHD0pi3t7e6Nmzp0wZkTawqCIiIqpG6rr7FixYAH19fRmyIW1iUUVERFQNiouLxRlC/ondfbqDRRUREVEVO3ToEE6cOCGJDRs2DM7OzjJlRFWBRRUREVEVUtfdFxoaCoWCEyHrGhZVREREVaCgoACrV6+WxIyMjBASEiJTRlTVWFQRERFp2eeff45r165JYu+//z7s7OxkyoiqA4sqIiIiLeJUM3UXiyoiIiItyMzMxObNmyWxBg0cMWvWKJkyourGooqIiOgFRUZGoqioSBJbtWom2rc3w6xZMiVF1Y5FFRERUSUJgoBFixapxLt0CUP79sDcuTIkRbJhUUVERFQJly9fxp49eySxJk16YNKkAQAAX185siI5sagiIiLSkLrB6EuXzoWbmzEmTZIhIaoRWFQRERFVkFKpxOLFi1XiXbqEwc2N3X11HYsqIiKiCjhx4gQOHTokiQ0cOBDdu3cHwO4+YlFFRET0XOq6+z788EPo6enJkA3VVCyqiIiIylFUVITIyEiVOB/mSeqwqCIiIlLj22+/xenTpyWxd999F23atJEpI6rpWFQRERH9C6eaocpgUUVERPT/3bt3Dx9//LEkZmpqitmzZ8uUEdUmLKqIiIgArFq1Cg8ePJDEJkyYABsbG5kyotqGty0QEVGdFx4erlJQJSSEsaAijfBKFRER1VnXr1/H9u3bJTETEyt8881kPsiTNMaiioiI6iR1g9FnzJgBc3NzzJkjQ0JU67GoIiKiOkUQBCxatEglzrv76EWxqCIiojrj119/RWxsrCTWsWNHvPnmm/IkRDqFRRUREdUJ6rr75s6dC2NjYxmyIV3EooqIiHRaaWkpPvroI5U4u/tI21hUERGRzjp8+DB++eUXSczLywsvv/yyTBmRLmNRRUREOkldd9+HH34IPT0+opGqBosqIiLSKY8ePcKKFStU4uzuo6ome7m+YcMGODo6wsTEBO7u7jh58uQz2+/btw8uLi4wMTGBq6srDh48WG7bCRMmQKFQYM2aNWLsyJEjUCgUal+nTp0CAGRkZKhdfvz4ca0cMxERVY2dO3eqFFRDhw5lQUXVQtaiKiYmBkFBQQgLC8OZM2fQqVMneHt7IycnR2375ORkDB8+HIGBgTh79iz8/Pzg5+eHtLQ0lbYHDhzA8ePHYW9vL4l7enoiMzNT8ho7diycnJzQrVs3SdvDhw9L2rm5uWnv4ImISKvCw8ORnp4uiYWFhcHFxUWmjKiukbWoWrVqFcaNG4fRo0fjpZdewqZNm1C/fn1s27ZNbfu1a9fCx8cHwcHBaNeuHRYvXoyuXbti/fr1kna3bt3C1KlTsXPnThgaGkqWGRkZwdbWVnxZWlrim2++wejRo6FQKCRtLS0tJW3/vS0iIpLfnTt31I6f4tUpqm6yFVXFxcVITU2Fl5fX/5LR04OXlxdSUlLUrpOSkiJpDwDe3t6S9kqlEiNHjkRwcDDat2//3Dzi4uJw9+5djB49WmWZr68vrK2t8corryAuLq6ih0ZERNVk+fLl2LBhgyT2/vvvs6AiWcg2UP3OnTsoLS1VmQHcxsYGly5dUrtOVlaW2vZZWVni+2XLlsHAwADTpk2rUB5bt26Ft7c3mjVrJsYaNGiAqKgovPzyy9DT08PXX38NPz8/xMbGwtfXV+12ioqKUFRUJL4vKCio0P6JiKhyeHWKahqduvsvNTUVa9euxZkzZ1S68tS5efMmEhISsHfvXkncysoKQUFB4vvu3bvj9u3bWLFiRblFVUREhNp/4EREpF3Xrl3D559/LollZtri/PnxYE1FcpKt+8/Kygr6+vrIzs6WxLOzs2Fra6t2HVtb22e2P3bsGHJyctC8eXMYGBjAwMAA169fx6xZs+Do6KiyvejoaFhaWpZbKP2Tu7u7ygDIfwoJCUF+fr74+uuvv567TSIi0kx4eLhKQdW+/UycPz8ec+fKlBTR/ydbUWVkZAQ3NzckJSWJMaVSiaSkJHh4eKhdx8PDQ9IeABITE8X2I0eOxPnz53Hu3DnxZW9vj+DgYCQkJEjWEwQB0dHR8Pf3r9AA9HPnzsHOzq7c5cbGxjAzM5O8iIhIOwRBKLe77+23zZCcDFTg/4+JqpSs3X9BQUEYNWoUunXrhh49emDNmjUoLCwUB437+/ujadOmiIiIAABMnz4dvXv3RlRUFAYNGoQ9e/bg9OnT2Lx5M4Cnd+tZWlpK9mFoaAhbW1s4OztL4j/88AOuXbuGsWPHquS1Y8cOGBkZoUuXLgCA/fv3Y9u2bdiyZYvWzwERET3bmTNn8N///lcS69KlS4V6GYiqk6xF1dChQ5Gbm4vQ0FBkZWWhc+fOOHTokDgY/caNG5LpBDw9PbFr1y4sWLAA8+bNQ5s2bRAbG4sOHTpovO+tW7fC09Oz3OeXLF68GNevX4eBgQFcXFwQExODt99+u3IHSkRElaLu6lRISAiMjIxkyIbo2RSCIAhyJ6GLCgoKYG5ujvz8fHYFEhFpqKSkBEuWLFGJ8+4+qmov8vutU3f/ERFR7ZeQkKAyLZi3tzd69uwpU0ZEFcOiioiIagx13X2hoaEVekwOkdxYVBERkewKCwuxcuVKlTi7+6g2YVFFRESy2rFjBzIyMiSx4cOHo23btvIkRFRJLKqIiEg2nGqGdAmLKiIiqnY5OTnYuHGjSpwFFdVmLKqIiKhaLVmyBCUlJZLY+PHjy52ijKi2YFFFRETVht19pMtYVBERUZVLT0/Hzp07JbFmzZohMDBQpoyItI9FFRERVSl1V6dmzZqFBg0ayJANUdVhUUVERFVCEAQsWrRIJc7uPtJVLKqIiEjrTp06hYMHD0pi3bt3x8CBA2XKiKjqsagiIiKtUtfdN2/ePBgaGsqQDVH1YVFFRERa8eTJEyxdulQlzu4+qitYVBER0Qv79ttvcfr0aUls4MCB6N69u0wZEVU/FlVERPRC1HX3hYaGQqFQyJANkXxYVBERUaU8fPgQUVFRKnF291FdxaKKiIg0tnXrVty8eVMSe++999CqVSuZMiKSH4sqIiLSCKeaIVKPRRUREVVIdnY2Nm3aJIkZGBhg/vz5MmVEVLOwqCIioudSd3Vq0qRJaNKkiQzZENVMepquoK+vj5ycHJX43bt3oa+vr5WkiIio5iivu48FFZGUxleqBEFQGy8qKoKRkdELJ0RERDXDlStXsHv3bknM0dERo0aNkikjopqtwkXVunXrAAAKhQJbtmyRzC5eWlqKo0ePwsXFRfsZEhFRtVN3dWr27NkwNTWVIRui2qHCRdXq1asBPL1StWnTJklXn5GRERwdHVUGMBIRUe0iCAIWLVqkEufdfUTPV+Gi6tq1awCAvn37Yv/+/WjUqFGVJUVERNXv+PHjSEhIkMR69uwJb29vmTIiql00HlP1448/VkUeREQkI3XdffPnz4eBAW8SJ6oojf+1jBkz5pnLt23bVulkiIioehUXFyMiIkIlzu4+Is1pXFTdv39f8v7JkydIS0tDXl4eXnvtNa0lRkREVSsuLg5nz56VxAYPHoyuXbvKlBFR7aZxUXXgwAGVmFKpxMSJEznnExFRLaGuuy80NBQKhUKGbIh0g8YP/1S7ET09BAUFiXcIEhFRzVRQUFDuwzxZUBG9GK2NQLx69SpKSkq0tTkiItKyTz/9FFlZWZKYv78/nJycZMqISLdoXFQFBQVJ3guCgMzMTHz77bd8yi4RUQ1V3tUpItIejYuqfw9q1NPTQ5MmTRAVFfXcOwOJiKh67dmTicuXN0tiJiYmmDNnjkwZEekuPqeKiEhHqbs6NXnyZFhZWcmQDZHuq/SYqpycHFy+fBkA4OzsDGtra60lRUREL4bdfUTVT+OiqqCgAJMnT8bu3buhVCoBAPr6+hg6dCg2bNgAc3NzrSdJREQVc/HiRezdu1cSa926NUaMGCFTRkR1h8aPVBg3bhxOnDiBb7/9Fnl5ecjLy0N8fDxOnz6N8ePHa5zAhg0b4OjoCBMTE7i7u+PkyZPPbL9v3z64uLjAxMQErq6uOHjwYLltJ0yYAIVCgTVr1kjijo6OUCgUkldkZKSkzfnz59GrVy+YmJjAwcEBy5cv1/jYiIiqU3h4uEpBFRwczIKKqJpoXFTFx8dj27Zt8Pb2hpmZGczMzODt7Y3PPvsM//3vfzXaVkxMDIKCghAWFoYzZ86gU6dO8Pb2Rk5Ojtr2ycnJGD58OAIDA3H27Fn4+fnBz88PaWlpKm0PHDiA48ePw97eXu22Fi1ahMzMTPE1depUcVlBQQH69++PFi1aIDU1FStWrMDChQuxefNmtdsiIpKTUqkst7uvfv36MmREVDdpXFRZWlqq7eIzNzdHo0aNNNrWqlWrMG7cOIwePRovvfQSNm3ahPr165c7f+DatWvh4+OD4OBgtGvXDosXL0bXrl2xfv16Sbtbt25h6tSp2LlzJwwNDdVuq2HDhrC1tRVfpqam4rKdO3eiuLgY27ZtQ/v27TFs2DBMmzYNq1at0uj4iIiqUlwc8O67v2Dx4sWS+Msvv8zxU0Qy0LioWrBgAYKCgiQPkMvKykJwcDA+/PDDCm+nuLgYqamp8PLy+l8yenrw8vJCSkqK2nVSUlIk7QHA29tb0l6pVGLkyJEIDg5G+/bty91/ZGQkLC0t0aVLF6xYsULy4NKUlBS8+uqrMDIykuzn8uXLKnMflikqKkJBQYHkRURUlc6eDYez82FJbMGCBSrfk0RUPTQeqL5x40akp6ejefPmaN68OQDgxo0bMDY2Rm5uLj799FOx7ZkzZ8rdzp07d1BaWgobGxtJ3MbGBpcuXVK7TlZWltr2/yzwli1bBgMDA0ybNq3cfU+bNg1du3ZF48aNkZycjJCQEGRmZopXorKyslSeMFy236ysLLVX5CIiItRefici0raioiKVcaAA7+4jkpvGRdUbb7xRY+eHSk1Nxdq1a3HmzJln5vjPp8J37NgRRkZGGD9+PCIiImBsbFypfYeEhEi2W1BQAAcHh0pti4ioPAcOHMD58+clMT8/P3Tq1EmmjIiojMZF1cKFC7WyYysrK+jr6yM7O1sSz87Ohq2trdp1bG1tn9n+2LFjyMnJEa+gAUBpaSlmzZqFNWvWICMjQ+123d3dUVJSgoyMDDg7O5e7n7Ic1DE2Nq50QUZEVBHqroaHhobW2P/RJaprNB5T1bJlS9y9e1clnpeXh5YtW1Z4O0ZGRnBzc0NSUpIYUyqVSEpKgoeHh9p1PDw8JO0BIDExUWw/cuRInD9/HufOnRNf9vb2CA4ORkJCQrm5nDt3Dnp6euIDTD08PHD06FE8efJEsh9nZ2eNB+MTEb2or77KL/fuPhZURDWHxleqMjIyUFpaqhIvKirCzZs3NdpWUFAQRo0ahW7duqFHjx5Ys2YNCgsLMXr0aABPZ09v2rQpIiIiAADTp09H7969ERUVhUGDBmHPnj04ffq0+KgDS0tLWFpaSvZhaGgIW1tbODs7A3g6CP3EiRPo27cvGjZsiJSUFMycORPvvfeeWDC9++67CA8PR2BgIObMmYO0tDSsXbsWq1ev1uxkERG9oOjoaNy4cUMSCwgIQIsWLWTKiIjKU+GiKi4uTvxzQkKC5LEKpaWlSEpKUhnc/TxDhw5Fbm4uQkNDkZWVhc6dO+PQoUPioPAbN25AT+9/F9M8PT2xa9cuLFiwAPPmzUObNm0QGxuLDh06VHifxsbG2LNnDxYuXIiioiI4OTlh5syZkvFQ5ubm+P777zF58mS4ubnBysoKoaGheP/99zU6PiKiF8GpZohqF4UgCEJFGpYVNwqFAv9exdDQEI6OjoiKisLrr7+u/SxroYKCApibmyM/Px9mZmZyp0NEtcidO3ewYcMGSezx44aIiAgqZw0i0pYX+f2u8JWqsnn+nJyccOrUKc5yTkRUBdR19/34YxCCghrKlBERVVSFr1SRZniliog0xe4+IvlVy5WqMosWLXrm8tDQUE03SURUp12/fh3bt2+XxPr06YPevXvLkxARVYrGRdWBAwck7588eYJr167BwMAArVq1YlFFRKSBiIgIFBcXS2IhISGSabKIqHbQuKg6e/asSqygoAABAQF48803tZIUEZGuUyqVKhMhA+zuI6rNtDam6rfffsPgwYPLfWp5XcMxVURUnlOnTuHgwYOS2JtvvomOHTvKlBERlanWMVXlyc/PR35+vrY2R0Skk9QNRv/++w8RFqbxBBdEVMNoXFStW7dO8l4QBGRmZuKLL77AgAEDtJYYEZEuKSoqQmRkpEo8ISEMc+fKkBARaZ3G3X//fmq6np4emjRpgtdeew0hISFo2JDPUgHY/UdE//Pdd9/h5MmTktiwYcPE6bOIqOao1u6/a9euaboKEVGdpa67LzQ0lBMhE+mgSo2pysvLQ3p6OgCgdevWsLCw0GZORES1XkFBgcok7MbGxpjLvj4inaVRUZWRkYHJkycjISFBnP9PoVDAx8cH69evh6OjY1XkSERUq+zYsUPlTuj3338fdnZ28iRERNWiwkXVX3/9hZ49e8LQ0BCLFy9Gu3btAAAXLlzAxo0b4eHhgVOnTqFZs2ZVliwRUU3HqWaI6q4KD1QPDAxEeno6EhISYGJiIln2999/w8fHB23atMGWLVuqJNHahgPVieqWzMxMbN68WRJzcnKCv7+/TBkRUWVUy0D1Q4cOISYmRqWgAoB69eph8eLFGDZsmEY7JyLSBeqmmpk5cyb/h4qojqlwUXXnzp1njplq2bIl7t27p42ciIhqBUEQ1E4yz+4+orqpwkWVnZ0dLly4UO6YqbS0NNja2motMSKimuzy5cvYs2ePJObu7g4fHx+ZMiIiuVW4qPLz88Ps2bORlJSEJk2aSJbl5ORgzpw58PPz03Z+REQ1jrrB6CEhITAyMpIhGyKqKSo8UP3+/ftwd3dHVlYW3nvvPbi4uEAQBFy8eBG7du2Cra0tjh8/jsaNG1d1zrUCB6oT6Z7S0lJ89NFHKnF29xHpjmoZqN6oUSOcOHEC8+bNw549e5CXlwcAsLCwwLvvvoulS5eyoCIinXX8+HEkJCRIYgMHDkT37t1lyoiIahqN5/4Dng7OzM3NBQA0adKE0y2owStVRLpDXXffhx9+CD09PRmyIaKqVK1z/wFPn6JubW1dmVWJiGqNx48fY9myZSpxdvcRkTqVKqqIiHRdfHw8UlNTJbERI0agdevWMmVERDUdiyoion/hVDNEVBksqoiI/r99++7jwoV1kpipqSlmz54tU0ZEVJuwqCIigvqrUxMmTICNjY0M2RBRbVShomrdunXPb/T/TZs2rdLJEBHJgd19RKQNFXqkgpOTU8U2plDgzz//fOGkdAEfqUBU86Wnp2Pnzp2SmLGxMebOnStTRkQktyp/pMK1a9cqlRgRUU2l7urUtGnT0KhRIxmyISJdUOkn1xUXF+Py5csoKSnRZj5ERFVKEIRyu/tYUBHRi9C4qHr06BECAwNRv359tG/fHjdu3AAATJ06FZGRkVpPkIhIW06cOIFFixZJYq1bt+b4KSLSCo2LqpCQEPz66684cuQITExMxLiXlxdiYmK0mhwRkbaEh4fj0KFDktjcuXMxYsQImTIiIl2j8SMVYmNjERMTg549e0rm/Gvfvj2uXr2q1eSIiF5USUkJlixZohLn1Ski0jaNi6rc3Fy18/4VFhZyYmUiqlHi4uJw9uxZSaxXr1547bXXZMqIiHSZxkVVt27d8O2332Lq1KkAIBZSW7ZsgYeHh3azIyKqJHWD0T/88EPo6VX6/hwiomfSuKhaunQpBgwYgAsXLqCkpARr167FhQsXkJycjJ9++qkqciQiqrDCwkKsXLlSJc7uPiKqahoXVa+88grOnTuHyMhIuLq64vvvv0fXrl2RkpICV1fXqsiRiKhCNm7ciJycHEnszTffRMeOHWXKiIjqkkpdB2/VqhU+++wznDx5EhcuXMCXX35Z6YJqw4YNcHR0hImJCdzd3XHy5Mlntt+3bx9cXFxgYmICV1dXHDx4sNy2EyZMgEKhwJo1a8RYRkYGAgMD4eTkhHr16qFVq1YICwtDcXGxpI1CoVB5HT9+vFLHSERVLzw8XKWgCgsLY0FFRNWmQleqCgoKKrxBTR7pHhMTg6CgIGzatAnu7u5Ys2YNvL29cfnyZbWD4ZOTkzF8+HBERETg9ddfx65du+Dn54czZ86gQ4cOkrYHDhzA8ePHYW9vL4lfunQJSqUSn376KVq3bo20tDSMGzdObZfB4cOH0b59e/G9paVlhY+NiKpHdnY2Nm3apBJndx8RVbcKzf2np6dX4Tv7SktLK7xzd3d3dO/eHevXrwcAKJVKODg4YOrUqWrn3ho6dCgKCwsRHx8vxnr27InOnTtLvlRv3boFd3d3JCQkYNCgQZgxYwZmzJhRbh4rVqzAxo0bxXkLMzIy4OTkhLNnz6Jz584VPp5/4tx/RFVP3WD0wMBANGvWTIZsiEgXVPncfz/++KP454yMDMydOxcBAQHi3X4pKSnYsWMHIiIiKrzj4uJipKamIiQkRIzp6enBy8sLKSkpatdJSUlBUFCQJObt7Y3Y2FjxvVKpxMiRIxEcHCy5yvQs+fn5aNy4sUrc19cXjx8/Rtu2bfHBBx/A19e33G0UFRWhqKhIfK/J1T0i0lx5U80QEcmlQkVV7969xT8vWrQIq1atwvDhw8WYr68vXF1dsXnzZowaNapCO75z5w5KS0thY2MjidvY2ODSpUtq18nKylLbPisrS3y/bNkyGBgYYNq0aRXKIz09HR9//LGk669BgwaIiorCyy+/DD09PXz99dfw8/NDbGxsuYVVRESE2i95ItKuK1euYPfu3ZKYqakpZs+eLVNGRERPaXz3X0pKitrxC926dcPYsWO1klRlpaamYu3atThz5kyFuitv3boFHx8fDBkyBOPGjRPjVlZWkiti3bt3x+3bt7FixYpyi6qQkBDJOgUFBXBwcHiBoyGif1P3Py4zZsyAubm5DNkQEUlpfPefg4MDPvvsM5X4li1bNCoirKysoK+vj+zsbEk8Ozsbtra2atextbV9Zvtjx44hJycHzZs3h4GBAQwMDHD9+nXMmjULjo6OkvVu376Nvn37wtPTE5s3b35uvu7u7khPTy93ubGxMczMzCQvItIOQRDK7e5jQUVENYXGV6pWr16Nt956C9999x3c3d0BACdPnsQff/yBr7/+usLbMTIygpubG5KSkuDn5wfg6XiopKQkTJkyRe06Hh4eSEpKkgw6T0xMFMd2jRw5El5eXpJ1vL29MXLkSIwePVqM3bp1C3379oWbmxuio6Mr9ITlc+fOwc7OrsLHR0TakZycjMTEREnM2dkZw4YNkykjIiL1NC6qBg4ciD/++AOffPKJOPZp8ODBmDBhgsbdXUFBQRg1ahS6deuGHj16YM2aNSgsLBQLIH9/fzRt2lQcAD99+nT07t0bUVFRGDRoEPbs2YPTp0+LV5osLS1VHntgaGgIW1tbODs7A3haUPXp0wctWrTAypUrkZubK7Ytu+K1Y8cOGBkZoUuXLgCA/fv3Y9u2bdiyZYump4uIXoC6q1MhISEwMjKSIRsiomfTuKgCgGbNmmHp0qUvvPOhQ4ciNzcXoaGhyMrKQufOnXHo0CFxMPqNGzckV5E8PT2xa9cuLFiwAPPmzUObNm0QGxur8oyqZ0lMTER6ejrS09NVbrv+59MlFi9ejOvXr8PAwAAuLi6IiYnB22+//YJHTEQV8eTJE7XfMby7j4hqsgo9p+rf8vLysHXrVly8eBEA0L59e4wZM4ZjG/6Bz6kiqpwDBw7g/PnzklifPn0kdyETEVWVF/n91rioOn36NLy9vVGvXj306NEDAHDq1Cn8/fff4jyAxKKKqDLUdfd17hyKN96o2MOHiYheVLUWVb169ULr1q3x2WefwcDgae9hSUkJxo4diz///BNHjx7VKAFdxaKKqOIePHiAVatWqcTZ3UdE1a1ai6p69erh7NmzcHFxkcQvXLiAbt264dGjRxoloKtYVBFVzMcff4x79+5JYm+//XaFZ0QgItKmKp+m5p/MzMxw48YNlaLqr7/+QsOGDTXdHBHVYZxqhoh0icZF1dChQxEYGIiVK1fC09MTAPDLL78gODhYMnUNEVF5MjMz1T50lwUVEdVmGhdVK1euhEKhgL+/P0pKSgA8fRbUxIkTERkZqfUEiUi3qLs6NW7cONjb28uQDRGR9lTqkQoA8OjRI1y9ehUA0KpVK9SvX1+ridV2HFNFpIrdfURU01XrmKoy9evXh6ura2VXJ6I65OLFi9i7d68kZmFhgenTp8uUERGR9lW4qBozZkyF2m3btq3SyRCR7lF3dWrmzJm8gktEOqfCRdX27dvRokULdOnSBZXsMSSiOkQQBCxatEglzu4+ItJVFS6qJk6ciN27d+PatWsYPXo03nvvPTRu3LgqcyOiWurYsWP44YcfJLH27dtz/kwi0mkaDVQvKirC/v37sW3bNiQnJ2PQoEEIDAxE//79oVBwGol/4kB1qqvUdffNmzcPhoaGMmRDRKSZan2iepnr169j+/bt+Pzzz1FSUoLff/8dDRo0qMymdBKLKqpriouLERERoRJndx8R1Say3P2np6cHhUIBQRBQWlpa2c0QkQ7Yt28fLly4IIn169cPr7zyikwZERFVPz1NGhcVFWH37t34v//7P7Rt2xa//fYb1q9fjxs3bvAqFVEdFR4erlJQhYaGsqAiojqnwleqJk2ahD179sDBwQFjxozB7t27YWVlVZW5EVENVlBQgNWrV6vE2d1HRHVVhcdU6enpoXnz5ujSpcszB6Xv379fa8nVZhxTRbps9erVKCgokMSGDh2qMtE6EVFtUy1jqvz9/XmHHxFxqhkionJo9PBPIqq7bt26hS1btqjEWVARET1V6bv/iKjuUHd1avz48bC1tZUhGyKimolFFRE9E7v7iIgqhkUVEan1+++/46uvvpLErKysMHnyZJkyIiKq2VhUEZEKdVenZs2axefRERE9A4sqIhIplUosXrxYJc7uPiKi52NRRUQAgCNHjuCnn36SxDp16gQ/Pz95EiIiqmVYVBGR2u6++fPnw8CAXxFERBXFb0yiOuzJkydYunSpSpzdfUREmmNRRVRHJSUl4eeff5bE+vfvDw8PD5kyIiKq3VhUEdVB6rr7OncOhYcHp6IiIqosFlVEdcjDhw8RFRWlEmd3HxHRi2NRRVRH7NmzB5cvX5bEAgMD0axZM5kyIiLSLSyqiOoATjVDRFT1WFQR6bCcnBxs3LhREmvatCnGjh0rU0ZERLqLRRWRjlq9ejUKCgoksWnTpqFRo0YyZUREpNtYVBHpiLg4IDISmDsXOHuW3X1ERNWNRRWRjoiMBLKzr+Ls2S8l8S5dusDX11emrIiI6g49uRPYsGEDHB0dYWJiAnd3d5w8efKZ7fft2wcXFxeYmJjA1dUVBw8eLLfthAkToFAosGbNGkn83r17GDFiBMzMzGBhYYHAwEA8fPhQ0ub8+fPo1asXTExM4ODggOXLl1f6GImqw8CBy+HvLy2o5syZw4KKiKiayFpUxcTEICgoCGFhYThz5gw6deoEb29v5OTkqG2fnJyM4cOHIzAwEGfPnoWfnx/8/PyQlpam0vbAgQM4fvw47O3tVZaNGDECv//+OxITExEfH4+jR4/i/fffF5cXFBSgf//+aNGiBVJTU7FixQosXLgQmzdv1t7BE2mJIAgIDw9HaenfknhYWBhMTExkyoqIqO5RCIIgyLVzd3d3dO/eHevXrwcAKJVKODg4YOrUqZg7d65K+6FDh6KwsBDx8fFirGfPnujcuTM2bdokxm7dugV3d3ckJCRg0KBBmDFjBmbMmAEAuHjxIl566SWcOnUK3bp1AwAcOnQIAwcOxM2bN2Fvb4+NGzdi/vz5yMrKgpGREQBg7ty5iI2NxaVLlyp0bAUFBTA3N0d+fj7MzMwqdX6InufPP//EF198IYlxqhkiosp7kd9v2a5UFRcXIzU1FV5eXv9LRk8PXl5eSElJUbtOSkqKpD0AeHt7S9orlUqMHDkSwcHBaN++vdptWFhYiAUVAHh5eUFPTw8nTpwQ27z66qtiQVW2n8uXL+P+/fuVO2AiLYuKilIpqD788EMWVEREMpFtoPqdO3dQWloKGxsbSdzGxqbcq0FZWVlq22dlZYnvly1bBgMDA0ybNq3cbVhbW0tiBgYGaNy4sbidrKwsODk5qeynbJm6W9KLiopQVFQkvv/3rexE2hAXByxfXoL/+78lkridnZ2kC5uIiKqfTt39l5qairVr1+LMmTNQKKp3YtiIiAi1T60m0qbPP/8V//d/sZLY2LFj0bRpU3kSIiIikWzdf1ZWVtDX10d2drYknp2dDVtbW7Xr2NraPrP9sWPHkJOTg+bNm8PAwAAGBga4fv06Zs2aBUdHR3Eb/x4IX1JSgnv37onbKW8/ZcvUCQkJQX5+vvj666+/KnAWiCouPDwcrq6xklhoaCgLKiKiGkK2osrIyAhubm5ISkoSY0qlEklJSeWOCfHw8JC0B4DExESx/ciRI3H+/HmcO3dOfNnb2yM4OBgJCQniNvLy8pCamipu44cffoBSqYS7u7vY5ujRo3jy5IlkP87OzuU+jdrY2BhmZmaSF5E2/P333ypXQV1dXREWFlbtV2SJiKh8snb/BQUFYdSoUejWrRt69OiBNWvWoLCwEKNHjwYA+Pv7o2nTpoiIiAAATJ8+Hb1790ZUVBQGDRqEPXv24PTp0+KjDiwtLWFpaSnZh6GhIWxtbeHs7AwAaNeuHXx8fDBu3Dhs2rQJT548wZQpUzBs2DDx8QvvvvsuwsPDERgYiDlz5iAtLQ1r167F6tWrq+vUEAEAjh49ih9//FESmzp1Kho3bixTRkREVB5Zi6qhQ4ciNzcXoaGhyMrKQufOnXHo0CFxUPiNGzegp/e/i2menp7YtWsXFixYgHnz5qFNmzaIjY1Fhw4dNNrvzp07MWXKFPTr1w96enp46623sG7dOnG5ubk5vv/+e0yePBlubm6wsrJCaGgoBwJTtVI3Ro9TzRAR1VyyPqdKl/E5VVRZeXl5WLt2rST26quvom/fvjJlRERUd7zI77dO3f1HVNvFxcXh7NmzklhwcDDq168vU0ZERFRRLKqIagh29xER1W4sqohklpmZqTKv5ODBg9G1a1eZMiIiospgUUUko+joaNy4cUMSmzdvHgwNDWXKiIiIKotFFZEMlEolFi9eLIkZGxurnUiciIhqBxZVRNUsPT0dO3fulMTeffddtGnTRqaMiIhIG1hUEVWj5cuX4++//5bEPvzwQ8nz2IiIqHZiUUVUDZ48eYKlS5dKYg4ODhgzZoxMGRERkbaxqCKqYmfPnkVcXJwkNm7cOHFaJCIi0g0sqoiqkLpnT4WGhnIiZCIiHcSiiqgKPHr0CCtWrJDEOnfujDfeeEOmjIiIqKqxqCLSsiNHjuCnn36SxKZNm4ZGjRrJlBEREVUHFlVEWsSpZoiI6i4WVURacP/+faxbt04S69OnD3r37i1TRkREVN1YVBG9oAMHDuD8+fOS2AcffIB69erJlBEREcmBRRVRJQmCgEWLFqnE2d1HRFQ3sagiqoTbt2/js88+k8TeeOMNdO7cWZ6EiIhIdiyqiDS0ZcsW3Lp1SxKbP38+DAz4z4mIqC7jrwBRBSmVSixevFgSMzU1xezZs2XKiIiIahIWVUQVcOXKFezevVsSe++999CqVSuZMiIiopqGRRXRc0RERKC4uFgS41QzRET0byyqiMrx5MkTLF26VBJr0aIFAgIC5EmIiIhqNBZVRGqkpqYiPj5eEhs/fjxsbW1lyoiIiGo6FlVE/8KpZoiIqDJYVBH9f4WFhVi5cqUk1rVrVwwePFimjIiIqDZhUUUEICkpCT///LMkNmPGDJibm8uUERER1TYsqqjOY3cfERFpA4sqqrPu3buHjz/+WBJ77bXX0KtXL5kyIiKi2oxFFdVJX331FX7//XdJbM6cOTAxMZEpIyIiqu1YVFGdIggCFi1apBJndx8REb0oFlVUZ9y8eRNbt26VxN5880107NhRpoyIiEiXsKiiOuHTTz9FVlaWJDZ//nwYGPCfABERaQd/UUinKZVKLF68WBIzMzPDzJkzZcqIiIh0lZ7cCRBpQ1wc4On59L9lLl++rFJQ+fv7s6AiIqIqwStVpBMiI4GUlKf/9fUFPvroI5SWlkrahIaGQqFQyJQhERHpOl6pIp0wdy7g4QEEBxcjPDxcUlC1bNkSYWFhLKiIiKhK8UoV6QRfX8DO7hQOHjwoiU+cOBHW1tYyZUVERHWJ7FeqNmzYAEdHR5iYmMDd3R0nT558Zvt9+/bBxcUFJiYmcHV1VfkRXbhwIVxcXGBqaopGjRrBy8sLJ06cEJcfOXIECoVC7evUqVMAgIyMDLXLjx8/rv0TQFoRHh6u8lkICwtjQUVERNVG1qIqJiYGQUFBCAsLw5kzZ9CpUyd4e3sjJydHbfvk5GQMHz4cgYGBOHv2LPz8/ODn54e0tDSxTdu2bbF+/Xr89ttv+Pnnn+Ho6Ij+/fsjNzcXAODp6YnMzEzJa+zYsXByckK3bt0k+zt8+LCknZubW9WdDKqU4uJilbn7unXrxod5EhFRtVMIgiDItXN3d3d0794d69evB/D09ncHBwdMnToVc+fOVWk/dOhQFBYWIj4+Xoz17NkTnTt3xqZNm9Tuo6CgAObm5jh8+DD69eunsvzJkydo2rQppk6dig8//BDA0ytVTk5OOHv2LDp37lypYyvbb35+PszMzCq1DXq2q1ev4ssvv5TEZs6cyfNNRESV9iK/37JdqSouLkZqaiq8vLz+l4yeHry8vJCSkqJ2nZSUFEl7APD29i63fXFxMTZv3gxzc3N06tRJbZu4uDjcvXsXo0ePVlnm6+sLa2trvPLKK4j75736ahQVFaGgoEDyoqoTExMjKag6d+6MsLAwFlRERCQb2Qaq37lzB6WlpbCxsZHEbWxscOnSJbXrZGVlqW3/7ydlx8fHY9iwYXj06BHs7OyQmJgIKysrtdvcunUrvL290axZMzHWoEEDREVF4eWXX4aenh6+/vpr+Pn5ITY2Fr6+vmq3ExERodINRdr36NEjrFixQhIbM2YMHBwcZMqIiIjoKZ28+69v3744d+4c7ty5g88++wzvvPMOTpw4oTJo+ebNm0hISMDevXslcSsrKwQFBYnvu3fvjtu3b2PFihXlFlUhISGSdQoKCvhDr2W///47vvrqK0mMU80QEVFNIduvkZWVFfT19ZGdnS2JZ2dnw9bWVu06tra2FWpvamqK1q1bo3Xr1ujZsyfatGmDrVu3IiQkRNIuOjoalpaW5RZK/+Tu7o7ExMRylxsbG8PY2Pi52yHNCYKArVu34tatW2KsV69eeO2112TMioiISEq2MVVGRkZwc3NDUlKSGFMqlUhKSoKHh4fadTw8PCTtASAxMbHc9v/cblFRkSQmCAKio6Ph7+8PQ0PD5+Z77tw52NnZPbcdaVd+fj4WLVokKagmTpzIgoqIiGocWftNgoKCMGrUKHTr1g09evTAmjVrUFhYKA4a9/f3R9OmTREREQEAmD59Onr37o2oqCgMGjQIe/bswenTp7F582YAQGFhIZYsWQJfX1/Y2dnhzp072LBhA27duoUhQ4ZI9v3DDz/g2rVrGDt2rEpeO3bsgJGREbp06QIA2L9/P7Zt24YtW7ZU5emgfzl1SvowT1NTUwQFBUFPT/bHqxEREamQtagaOnQocnNzERoaiqysLHTu3BmHDh0SB6PfuHFD8gPq6emJXbt2YcGCBZg3bx7atGmD2NhYdOjQAQCgr6+PS5cuYceOHbhz5w4sLS3RvXt3HDt2DO3bt5fse+vWrfD09ISLi4va3BYvXozr16/DwMAALi4uiImJwdtvv11FZ4L+SalUYs2aNXjw4IEY8/b2Rs+ePWXMioiI6NlkfU6VLuNzqionNzcXn3zyiSQ2ffp0WFhYyJMQERHVKS/y+83bpqjGOHLkCH766SfxvZ2dHcaNG8eJkImIqFZgUUWyKykpwZIlSySx//znP3B1dZUpIyIiIs2xqCJZ3bx5E1u3bpXEZs+eDVNTU5kyIiIiqhwWVSSb+Ph4pKamiu/btm2L4cOHy5gRERFR5bGoompXVFSEyMhISWzEiBFo3bq1TBkRERG9OBZVVK3S09Oxc+dOSWzu3Ll8Gj0REdV6LKqo2uzevRtXrlwR33ft2hWDBw+WMSMiIiLtYVFFVa6wsBArV66UxAIDA9GsWTOZMiIiItI+FlVUpX777Tfs379fEps/fz4MDPjRIyIi3cJfNqoSgiBg8+bNyMrKEmOvvvoq+vbtK2NWREREVYdFFWldXl4e1q5dK4lNmjQJTZo0kSkjIiKiqseiirTqxIkTOHTokPi+YcOGmDFjhmRibCIiIl3Eooq0QqlUIioqCo8ePRJjAwYMQI8ePWTMioiIqPqwqKIXlpOTg40bN0piM2bMgLm5uUwZERERVT8WVfRCfvjhBxw7dkx8b29vj7Fjx0KhUMiYFRERUfVjUUWVUlJSgiVLlkhib731Fjp06CBTRkRERPJiUUUa++uvv7Bt2zZJLDg4GPXr15cpIyIiIvmxqCKNxMXF4ezZs+J7FxcXDB06VMaMiIiIagYWVVQhRUVFiIyMlMTee+89tGrVSqaMiIiIahYWVfRcf/zxB3bt2iWJhYSEwMjISKaMiIiIah4WVfRMO3fuRHp6uvi+W7duGDRokIwZERER1UwsqkitwsJCrFy5UhIbO3YsmjZtKlNGRERENRuLKlJx/vx5HDhwQHyvUCgwf/586Ovry5gVERFRzcaiikSCIODTTz9Fdna2GOvTpw969+4tY1ZERES1A4sqAgDk5eVh7dq1ktjkyZNhZWUlU0ZERES1C4sqwvHjx5GQkCC+NzMzw4wZMzjVDBERkQZYVNVhSqUSK1euxN9//y3GBg0ahG7dusmYFRERUe3EoqqOys7OxqZNmySxmTNnwszMTKaMiIiIajcWVXXQ4cOH8csvv4jvmzVrhjFjxrC7j4iI6AWwqKpDSkpKsGTJEklsyJAheOmll2TKiIiISHewqKojbty4gejoaEnsgw8+QL169WTKiIiISLewqKoDvvnmG5w7d058/9JLL2HIkCHyJURERKSDWFTpsMePH2PZsmWS2MiRI9GyZUuZMiIiItJdLKp01JUrV7B7925JLCQkBEZGRjJlREREpNtYVOmgL7/8ElevXhXf9+jRAwMGDJAxIyIiIt3HokqHPHz4EFFRUZLYuHHjYG9vL1NGREREdQeLKh1x7tw5fPPNN+J7fX19hISEQF9fX8asiIiI6g49uRPYsGEDHB0dYWJiAnd3d5w8efKZ7fft2wcXFxeYmJjA1dUVBw8elCxfuHAhXFxcYGpqikaNGsHLywsnTpyQtHF0dIRCoZC8IiMjJW3Onz+PXr16wcTEBA4ODli+fLl2DljLBEHAJ598IimoXnvtNSxYsIAFFRERUTWStaiKiYlBUFAQwsLCcObMGXTq1Ane3t7IyclR2z45ORnDhw9HYGAgzp49Cz8/P/j5+SEtLU1s07ZtW6xfvx6//fYbfv75Zzg6OqJ///7Izc2VbGvRokXIzMwUX1OnThWXFRQUoH///mjRogVSU1OxYsUKLFy4EJs3b66aE1FJ9+/fx6JFiyTHNmXKFPTq1UvGrIiIiOomhSAIglw7d3d3R/fu3bF+/XoATyf4dXBwwNSpUzF37lyV9kOHDkVhYSHi4+PFWM+ePdG5c2eVeezKFBQUwNzcHIcPH0a/fv0APL1SNWPGDMyYMUPtOhs3bsT8+fORlZUl3i03d+5cxMbG4tKlSxU6trL95ufnV8l8esnJyUhMTBTfN2rUCFOnTuVUM0RERC/gRX6/ZbtSVVxcjNTUVHh5ef0vGT09eHl5ISUlRe06KSkpkvYA4O3tXW774uJibN68Gebm5ujUqZNkWWRkJCwtLdGlSxesWLECJSUlkv28+uqrkscPeHt74/Lly7h//77afRUVFaGgoEDyqipbtmyRFFSvv/46pk2bxoKKiIhIRrINVL9z5w5KS0thY2MjidvY2JR7NSgrK0tt+6ysLEksPj4ew4YNw6NHj2BnZ4fExERYWVmJy6dNm4auXbuicePGSE5ORkhICDIzM7Fq1SpxP05OTir7KVvWqFEjldwiIiIQHh5ewaOvvNLSUty6dUt8P3PmzCq5EkZERESa0cm7//r27Ytz587hzp07+Oyzz/DOO+/gxIkTsLa2BgAEBQWJbTt27AgjIyOMHz8eERERMDY2rtQ+Q0JCJNstKCiAg4PDix2IGvr6+hgyZAgePnyI7t278+oUERFRDSFb95+VlRX09fWRnZ0tiWdnZ8PW1lbtOra2thVqb2pqitatW6Nnz57YunUrDAwMsHXr1nJzcXd3R0lJCTIyMp65n7Jl6hgbG8PMzEzyqirp6S9hxowe+O9/WVARERHVFLIVVUZGRnBzc0NSUpIYUyqVSEpKgoeHh9p1PDw8JO0BIDExsdz2/9xuUVFRucvPnTsHPT098UqWh4cHjh49iidPnkj24+zsrLbrr7pFRgIpKU//S0RERDWDrI9UCAoKwmeffYYdO3bg4sWLmDhxIgoLCzF69GgAgL+/P0JCQsT206dPx6FDhxAVFYVLly5h4cKFOH36NKZMmQIAKCwsxLx583D8+HFcv34dqampGDNmDG7duoUhQ4YAeDoIfc2aNfj111/x559/YufOnZg5cybee+89sWB69913YWRkhMDAQPz++++IiYnB2rVrJd17cpo7F/DwePpfIiIiqiEEmX388cdC8+bNBSMjI6FHjx7C8ePHxWW9e/cWRo0aJWm/d+9eoW3btoKRkZHQvn174dtvvxWX/f3338Kbb74p2NvbC0ZGRoKdnZ3g6+srnDx5UmyTmpoquLu7C+bm5oKJiYnQrl07YenSpcLjx48l+/n111+FV155RTA2NhaaNm0qREZGanRc+fn5AgAhPz9fo/WIiIhIPi/y+y3rc6p0WVU/p4qIiIi0r1Y+p4qIiIhIl7CoIiIiItICFlVEREREWsCiioiIiEgLWFQRERERaQGLKiIiIiItYFFFREREpAUsqoiIiIi0gEUVERERkRawqCIiIiLSAhZVRERERFrAooqIiIhICwzkTkBXlc1TXVBQIHMmREREVFFlv9tlv+OaYFFVRR48eAAAcHBwkDkTIiIi0tSDBw9gbm6u0ToKoTKlGD2XUqnE7du30bBhQygUCq1vv6CgAA4ODvjrr79gZmam9e3XdHX9+AGeA4DnoK4fP8BzAPAcANo9B4Ig4MGDB7C3t4eenmajpHilqoro6emhWbNmVb4fMzOzOvuPCODxAzwHAM9BXT9+gOcA4DkAtHcONL1CVYYD1YmIiIi0gEUVERERkRawqKqljI2NERYWBmNjY7lTkUVdP36A5wDgOajrxw/wHAA8B0DNOQccqE5ERESkBbxSRURERKQFLKqIiIiItIBFFREREZEWsKgiIiIi0gIWVdVgw4YNcHR0hImJCdzd3XHy5Mlntt+3bx9cXFxgYmICV1dXHDx4ULJ84cKFcHFxgampKRo1agQvLy+cOHFC0sbR0REKhULyioyMlLQ5f/48evXqBRMTEzg4OGD58uXaOWA1qvscHDlyROX4y16nTp0CAGRkZKhdfvz4ce2fAGj/HPzThAkToFAosGbNGkn83r17GDFiBMzMzGBhYYHAwEA8fPhQ0qY2fw7+Sd05yMjIQGBgIJycnFCvXj20atUKYWFhKC4ulrSprs+BHJ8BXf8u+Cd156AufBcEBASo5O7j4yNpU5O+C6r7+Kv1e0CgKrVnzx7ByMhI2LZtm/D7778L48aNEywsLITs7Gy17X/55RdBX19fWL58uXDhwgVhwYIFgqGhofDbb7+JbXbu3CkkJiYKV69eFdLS0oTAwEDBzMxMyMnJEdu0aNFCWLRokZCZmSm+Hj58KC7Pz88XbGxshBEjRghpaWnC7t27hXr16gmffvqpTpyDoqIiybFnZmYKY8eOFZycnASlUikIgiBcu3ZNACAcPnxY0q64uLhWnIMy+/fvFzp16iTY29sLq1evlizz8fEROnXqJBw/flw4duyY0Lp1a2H48OHi8tr+OShT3jn47rvvhICAACEhIUG4evWq8M033wjW1tbCrFmzxDbV9TmQ6zOg698FZco7B3Xhu2DUqFGCj4+PJPd79+5JtlNTvgvkOP7q/B5gUVXFevToIUyePFl8X1paKtjb2wsRERFq27/zzjvCoEGDJDF3d3dh/Pjx5e4jPz9f/DCUadGihcqX6z998sknQqNGjYSioiIxNmfOHMHZ2fl5h6Qxuc7BPxUXFwtNmjQRFi1aJMbK/hGdPXtWg6OpnKo6Bzdv3hSaNm0qpKWlqfydX7hwQQAgnDp1Sox99913gkKhEG7duiUIgm58Dp51DtRZvny54OTkJL6vrs+BXMdfF74LNPkM6OJ3wahRo4Q33nij3H3WpO8COY5fnar6HmD3XxUqLi5GamoqvLy8xJienh68vLyQkpKidp2UlBRJewDw9vYut31xcTE2b94Mc3NzdOrUSbIsMjISlpaW6NKlC1asWIGSkhLJfl599VUYGRlJ9nP58mXcv39f42Mtj9znoExcXBzu3r2L0aNHqyzz9fWFtbU1XnnlFcTFxVX00Cqsqs6BUqnEyJEjERwcjPbt26vdhoWFBbp16ybGvLy8oKenJ3aV1vbPwfPOgTr5+flo3LixSrwqPwdyH78ufxdo+hnQxe8C4Gk3p7W1NZydnTFx4kTcvXtXso2a8F0g1/GrU1XfAyyqqtCdO3dQWloKGxsbSdzGxgZZWVlq18nKyqpQ+/j4eDRo0AAmJiZYvXo1EhMTYWVlJS6fNm0a9uzZgx9//BHjx4/H0qVL8cEHHzx3P2XLtEXOc/BPW7duhbe3t2SS6wYNGiAqKgr79u3Dt99+i1deeQV+fn5a/zKtqnOwbNkyGBgYYNq0aeVuw9raWhIzMDBA48aNxe3U9s/B887Bv6Wnp+Pjjz/G+PHjxVh1fA7kPH5d/y7Q9DOgi98FPj4++Pzzz5GUlIRly5bhp59+woABA1BaWipuoyZ8F8h1/P9Wld8DBhq1phqjb9++OHfuHO7cuYPPPvsM77zzDk6cOCH+wwkKChLbduzYEUZGRhg/fjwiIiJkf4y/tjzvHJS5efMmEhISsHfvXkncyspKcp66d++O27dvY8WKFfD19a2WY6is1NRUrF27FmfOnIFCoZA7HVloeg5u3boFHx8fDBkyBOPGjRPjtfVzUNHj1+XvAk0/A7r4XQAAw4YNE//s6uqKjh07olWrVjhy5Aj69esnY2bVQ5Pjr+rvAV6pqkJWVlbQ19dHdna2JJ6dnQ1bW1u169ja2laovampKVq3bo2ePXti69atMDAwwNatW8vNxd3dHSUlJcjIyHjmfsqWaUtNOAfR0dGwtLSs0D8Md3d3pKenP7edJqriHBw7dgw5OTlo3rw5DAwMYGBggOvXr2PWrFlwdHQUt5GTkyPZRklJCe7duydupzZ/DipyDsrcvn0bffv2haenJzZv3vzcfLX9OZD7+P9Jl74LND0HuvhdoE7Lli1hZWUl5l9TvgvkOv4y1fE9wKKqChkZGcHNzQ1JSUliTKlUIikpCR4eHmrX8fDwkLQHgMTExHLb/3O7RUVF5S4/d+4c9PT0xKs4Hh4eOHr0KJ48eSLZj7OzMxo1avTcY6souc+BIAiIjo6Gv78/DA0Nn5vvuXPnYGdn99x2mqiKczBy5EicP38e586dE1/29vYIDg5GQkKCuI28vDykpqaK2/jhhx+gVCrh7u4utqmtn4OKnAPg6f+Z9unTB25uboiOjoae3vO/9rT9OZDz+P9Nl74LNDkHuvpdoM7Nmzdx9+5dMf+a8l0g1/ED1fg98ELD3Om59uzZIxgbGwvbt28XLly4ILz//vuChYWFkJWVJQiCIIwcOVKYO3eu2P6XX34RDAwMhJUrVwoXL14UwsLCJLePPnz4UAgJCRFSUlKEjIwM4fTp08Lo0aMFY2NjIS0tTRAEQUhOThZWr14tnDt3Trh69arw5ZdfCk2aNBH8/f3F/eTl5Qk2NjbCyJEjhbS0NGHPnj1C/fr1q+w26uo+B2UOHz4sABAuXryoktf27duFXbt2CRcvXhQuXrwoLFmyRNDT0xO2bdtW48+BOuruevLx8RG6dOkinDhxQvj555+FNm3aSG6jrs2fA3X+fQ5u3rwptG7dWujXr59w8+ZNya3SZarrcyDH8ev6d0FFzkEZXf0uePDggTB79mwhJSVFuHbtmnD48GGha9euQps2bYTHjx+L26kp3wVyHH91fg+wqKoGH3/8sdC8eXPByMhI6NGjh3D8+HFxWe/evYVRo0ZJ2u/du1do27atYGRkJLRv31749ttvxWV///238Oabbwr29vaCkZGRYGdnJ/j6+gonT54U26Smpgru7u6Cubm5YGJiIrRr105YunSp5B+YIAjCr7/+KrzyyiuCsbGx0LRpUyEyMrJqToBQ/eegzPDhwwVPT0+1OW3fvl1o166dUL9+fcHMzEzo0aOHsG/fPu0csBraPAfqqPsxuXv3rjB8+HChQYMGgpmZmTB69GjhwYMHkja19XOgzr/PQXR0tABA7atMdX4Oqvv4df27QJ3yiipd/S549OiR0L9/f6FJkyaCoaGh0KJFC2HcuHFikVKmJn0XVPfxV+f3gEIQBEGza1tERERE9G8cU0VERESkBSyqiIiIiLSARRURERGRFrCoIiIiItICFlVEREREWsCiioiIiEgLWFQRERERaQGLKiIiIiItYFFFRAQgKysLU6dORcuWLWFsbAwHBwcMHjxYMu9YcnIyBg4ciEaNGsHExASurq5YtWoVSktLxTYZGRkIDAyEk5MT6tWrh1atWiEsLAzFxcVyHBYRVSMDuRMgIpJbRkYGXn75ZVhYWGDFihVwdXXFkydPkJCQgMmTJ+PSpUs4cOAA3nnnHYwePRo//vgjLCwscPjwYXzwwQdISUnB3r17oVAocOnSJSiVSnz66ado3bo10tLSMG7cOBQWFmLlypVyHyoRVSFOU0NEdd7AgQNx/vx5XL58GaamppJleXl5MDQ0RIsWLdC7d298/fXXkuX//e9/4evriz179mDo0KFqt79ixQps3LgRf/75Z5UdAxHJj91/RFSn3bt3D4cOHcLkyZNVCioAsLCwwPfff4+7d+9i9uzZKssHDx6Mtm3bYvfu3eXuIz8/H40bN9Zq3kRU87CoIqI6LT09HYIgwMXFpdw2V65cAQC0a9dO7XIXFxexjbrtf/zxxxg/fvyLJ0tENRqLKiKq0zQZAaHpaIlbt27Bx8cHQ4YMwbhx4zRNjYhqGRZVRFSntWnTRhxgXp62bdsCAC5evKh2+cWLF8U2ZW7fvo2+ffvC09MTmzdv1l7CRFRjsagiojqtcePG8Pb2xoYNG1BYWKiyPC8vD/3790fjxo0RFRWlsjwuLg5//PEHhg8fLsZu3bqFPn36wM3NDdHR0dDT41ctUV3Au/+IqM77888/8fLLL6Nx48ZYtGgROnbsiJKSEiQmJmLjxo24ePEivvrqKwwbNgxjxozBlClTYGZmhqSkJAQHB6Nfv37iIxXKCqoWLVpgx44d0NfXF/dja2sr41ESUVVjUUVEBCAzMxNLlixBfHw8MjMz0aRJE7i5uWHmzJno06cPAODYsWNYsmQJUlJS8PjxY7Rp0wajR4/GjBkzxOJp+/btGD16tNp98OuWSLexqCIiIiLSAnb0ExEREWkBiyoiIiIiLWBRRURERKQFLKqIiIiItIBFFREREZEWsKgiIiIi0gIWVURERERawKKKiIiISAtYVBERERFpAYsqIiIiIi1gUUVERESkBSyqiIiIiLTg/wF6I533cRIP+wAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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/Pz388MPKzc3Vxx9/rMjISNWsWVPPPfdcqe5Q8vPzU2hoqN3rWo4ePaoHHnhAAQEBql69uh555BFlZmZKkrKzs+Xp6anvvvtO0pW5b2rVqqXbbrvNtv2iRYsUERFR4tpuBCEHKEPpWRdtAeeqQsPQkaxc9xQEmERZdgEHBAQoICBA//rXv5Sfn1/k8x07dkiS5s+fr4yMDNv7b7/9Vv3799fzzz+vH3/8UXPnztWCBQs0ceJE27YeHh565513tHfvXn388cfasGGDXn75Zbv95+bm6p133tGSJUu0Zs0abdq0SQ8++KBWr16t1atX65NPPtHcuXO1bNkyp5+71WrVAw88oLNnz+rrr7/WunXrdPjwYSUkJEiSAgMD1apVK23atEmStHv3blksFu3atcsW1r7++mt17drV6bUVh5ADlKH6Qf7y+MMdkp4WiyKDmCcEcFTyjqPqNHmDHp33/6nT5A1K3nHUpcerUqWKFixYoI8//lg1atRQp06dNHr0aP3www+SrkxoJ0k1atRQaGio7f348eM1cuRIDRgwQA0aNFD37t01YcIEzZ0717bvYcOGqVu3boqMjNQdd9yhN998U59//rnd8S9duqTZs2erdevW6tKlix5++GFt3rxZH374oZo1a6b77rtP3bp108aNG0t8Tu+9954tvAUEBOiFF14odr2UlBTt3r1bn376qdq0aaOYmBgtXLhQX3/9tS3MxcbG2kLOpk2b1L17dzVt2lSbN2+2LSPkACYUFuirxN7R8vzfuSA8LRZN6t1cYYG+bq4MqJjc1QX80EMP6cSJE1qxYoXuvvtubdq0SbfeeqsWLFhwzW2+//57vfHGG3ZhYsiQIcrIyFBu7pWruevXr9edd96p8PBwVatWTf369dOZM2dsn0tXupYaNmxoex8SEqLIyEgFBATYLTt16lSJz6dv375KS0uzvUaNGlXsevv27VNERIRdd1OzZs1Uo0YN7du3T5LUtWtXbd68WYWFhfr6668VGxtrCz4nTpzQwYMHFRsbW+LabkTFH1UEVDAJ7eqqS+PaOpKVq8ggPwIOcAOu1wXs6u8tHx8fde/eXd27d9eYMWM0ePBgjRs3TgMHDix2/QsXLmj8+PHq3bt3sfs6cuSI7rvvPj311FOaOHGiatWqpc2bN+vxxx9XQUGBbWboPz7uwGKxFLvMarWW+FwCAwMVFRVV4vWvp0uXLjp//rx27typb775RpMmTVJoaKgmT56sli1bqk6dOmrUqJFTjvVnCDmAG4QF+hJuACe42gX8+6Djri7gZs2a2W4br1q1apGBv7feeqsOHDhwzTCRmpoqq9WqadOmycPjSkfLH7uq3K1p06Y6duyYjh07Zrua8+OPP+rcuXNq1qyZpCvddC1atNDMmTNVtWpVNWnSRMHBwUpISNDKlSvLrKtKorsKAFCBuaML+MyZM7rjjju0aNEi/fDDD0pPT9fSpUs1depUPfDAA5KuTKCXkpKikydP6tdff5UkjR07VgsXLtT48eO1d+9e7du3T0uWLNFrr70mSYqKitKlS5f07rvv6vDhw/rkk080Z84cl52HI+Li4hQdHa2+fftq586d2r59u/r376+uXbuqbdu2tvViY2O1ePFiW6CpVauWmjZtquTk5MoTcr755hv17NlTderUKfHESVf7Pb29vRUVFXXd/k8ArsFkhihPEtrV1eaR3fTZkNu0eWQ3l887FRAQoJiYGM2YMUNdunRR8+bNNWbMGA0ZMkQzZ86UJE2bNk3r1q1TRESEWrduLUmKj4/XypUr9dVXX6ldu3a67bbbNGPGDNWrV0+S1LJlS02fPl1TpkxR8+bNtXjxYiUmJrr0XErLYrHo3//+t2rWrKkuXbooLi5ODRo0UHJyst16Xbt2VWFhod3Ym9jY2CLLXF6vYRjGn6/mGv/5z3+0ZcsWtWnTRr1799YXX3yhXr16XXP99PR0NW/eXE8++aQGDx6slJQUDRs2TKtWrVJ8fHyJjpmTk6PAwEBlZ2erevXqTjoToPJgMkM4y2+//ab09HTVr19fPj4+7i4H5cD1viYc+f3t1jE5PXr0UI8ePUq8/pw5c1S/fn1NmzZNkmy3pM2YMaPEIQeA4651J0uXxrUZYwSg3KlQY3K2bdumuLg4u2Xx8fHatm3bNbfJz89XTk6O3QuAY5jMEKhYvv32W7tb1v/4MrsKdXfVyZMnFRISYrcsJCREOTk5ysvLk69v0b8kExMTNX78+LIqETC18nQnC4A/17ZtW6Wlpbm7DLepUCHHEaNGjdKIESNs73NycsrsmRmA2Vy9k2X08j0qNAwmMwTKOV9fX6fNf1MRVaiQExoaansI2FWZmZmqXr16sVdxJMnb21ve3t5lUR5QKTCZIZzNjfe/oJxx9tdChQo5HTp00OrVq+2WrVu3Th06dHBTRUDlxGSGcIars/Tm5uZe8w9VVC4FBQWSJE9PT6fsz60h58KFCzp48KDtfXp6utLS0lSrVi3VrVtXo0aN0vHjx7Vw4UJJ0pNPPqmZM2fq5Zdf1t///ndt2LBBn3/+uVatWuWuUwAAOMjT01M1atSwPWPJz89PFovlT7aCWVmtVp0+fVp+fn6qUsU58cStIee7775Tt27dbO+vjp0ZMGCAFixYoIyMDB09+n9Pk61fv75WrVql4cOH6x//+IduvvlmffDBB9w+DgAVVGhoqCSV6mGSMC8PDw/VrVvXaWHXrZMBugOTAQJA+VNYWKhLly65uwy4mZeXl+25XX9U4SYDBABAutJ15axxGMBVFWoyQAAAgJIi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMqFyFn1qxZioyMlI+Pj2JiYrR9+/brrp+UlKRbbrlFvr6+ioiI0PDhw/Xbb7+VUbUAAKAicHvISU5O1ogRIzRu3Djt3LlTLVu2VHx8vE6dOlXs+p9++qlGjhypcePGad++ffrwww+VnJys0aNHl3HlAACgPHN7yJk+fbqGDBmiQYMGqVmzZpozZ478/Pz00UcfFbv+1q1b1alTJz366KOKjIzUXXfdpT59+vzp1R8AAFC5uDXkFBQUKDU1VXFxcbZlHh4eiouL07Zt24rdpmPHjkpNTbWFmsOHD2v16tW65557il0/Pz9fOTk5di8AAGB+Vdx58KysLBUWFiokJMRueUhIiPbv31/sNo8++qiysrLUuXNnGYahy5cv68knn7xmd1ViYqLGjx/v9NoBAED55vbuqtLatGmTJk2apPfee087d+7U8uXLtWrVKk2YMKHY9UeNGqXs7Gzb69ixY2VcMQAAcAe3XskJCgqSp6enMjMz7ZZnZmYqNDS02G3GjBmjfv36afDgwZKk6OhoXbx4UU888YReffVVeXjY5zZvb295e3u75gQAAEC55dYrOV5eXmrTpo1SUlJsy6xWq1JSUtShQ4dit8nNzS0SZDw9PSVJhmG4rlgAAFChuPVKjiSNGDFCAwYMUNu2bdW+fXslJSXp4sWLGjRokCSpf//+Cg8PV2JioiSpZ8+emj59ulq3bq2YmBgdPHhQY8aMUc+ePW1hBwAAwO0hJyEhQadPn9bYsWN18uRJtWrVSmvWrLENRj569KjdlZvXXntNFotFr732mo4fP67atWurZ8+emjhxortOAQAAlEMWo5L18eTk5CgwMFDZ2dmqXr26u8sBAAAl4Mjv7wp3dxUAAEBJEHIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApEXIAAIApVXFko7p16yo2NlZdu3ZVbGysGjZs6Oy6AAAAbohDV3ImTZokHx8fTZkyRY0aNVJERIQee+wxzZs3Tz/99JOzawQAACg1i2EYxo3sICMjQ19//bVWrlyp5ORkWa1WFRYWOqs+p8vJyVFgYKCys7NVvXp1d5cDAABKwJHf3w51V0lSbm6uNm/erE2bNmnjxo3atWuXmjdvrtjYWEd3CQAA4DQOhZyOHTtq165datq0qWJjYzVy5Eh16dJFNWvWdHZ9AAAADnFoTM7+/fvl7++vJk2aqEmTJmratCkBBwAAlCsOhZwzZ85ow4YNuu2227R27Vp16tRJ4eHhevTRRzVv3jxn1wgAAFBqNzzw2DAMpaamaubMmVq8eDEDjwEAgNOV2cDjnTt3atOmTdq0aZM2b96s8+fPKzo6Ws8++6y6du3qyC4BAACcyqGQ0759e7Vu3Vpdu3bVkCFD1KVLFwUGBjq7NgAAAIc5NCbn7Nmz2rFjh95++2317NnzhgPOrFmzFBkZKR8fH8XExGj79u3XXf/cuXMaOnSowsLC5O3trcaNG2v16tU3VAMAADAXh67kXO0LS01N1b59+yRJzZo106233lrqfSUnJ2vEiBGaM2eOYmJilJSUpPj4eB04cEDBwcFF1i8oKFD37t0VHBysZcuWKTw8XD///LNq1KjhyKkAAACTcmjg8alTp5SQkKCvv/7aFi7OnTunbt26acmSJapdu3aJ9xUTE6N27dpp5syZkiSr1aqIiAg9++yzGjlyZJH158yZo7feekv79+9X1apVS1s6A48BAKiAHPn97VB31bPPPqsLFy5o7969Onv2rM6ePas9e/YoJydHzz33XIn3U1BQoNTUVMXFxf1fQR4eiouL07Zt24rdZsWKFerQoYOGDh2qkJAQNW/eXJMmTbrmHV35+fnKycmxewEAAPNzKOSsWbNG7733npo2bWpb1qxZM82aNUv/+c9/SryfrKwsFRYWKiQkxG55SEiITp48Wew2hw8f1rJly1RYWKjVq1drzJgxmjZtmt58881i109MTFRgYKDtFRERUeL6AJhXRnaeth7KUkZ2nrtLAeAiDo3JsVqtxXYVVa1aVVar9YaL+rNjBwcH6/3335enp6fatGmj48eP66233tK4ceOKrD9q1CiNGDHC9j4nJ6dSBJ2M7DylZ11U/SB/hQX6urscoFxJ3nFUo5bvltWQPCxSYu9oJbSr6+6yADiZQyHnjjvu0PPPP6/PPvtMderUkSQdP35cw4cP15133lni/QQFBcnT01OZmZl2yzMzMxUaGlrsNmFhYapatao8PT1ty5o2baqTJ0+qoKBAXl5edut7e3vL29u7xDWZAT/AgWvLyM6zfX9IktWQRi/foy6Na/MHAWAyDnVXzZw5Uzk5OYqMjFTDhg3VsGFD1a9fXzk5OXr33XdLvB8vLy+1adNGKSkptmVWq1UpKSnq0KFDsdt06tRJBw8etLti9N///ldhYWFFAk5ldK0f4FySB65Iz7po+/64qtAwdCQr1z0FAXAZh67kREREaOfOnVq/fr32798v6crVlN8PIC6pESNGaMCAAWrbtq3at2+vpKQkXbx4UYMGDZIk9e/fX+Hh4UpMTJQkPfXUU5o5c6aef/55Pfvss/rpp580adKkUg14NrPr/QDnr1RAqh/kLw+L7L5PPC0WRQb5ua8oAC7hUMiRJIvFou7du6t79+43VEBCQoJOnz6tsWPH6uTJk2rVqpXWrFljG4x89OhReXj83wWniIgIrV27VsOHD1eLFi0UHh6u559/Xq+88soN1WEW/AAHri8s0FeJvaM1evkeFRqGPC0WTerdnD8CABMq8Tw577zzTol3Wp6vqlSGeXKSdxwt8gOcMTmAvYzsPB3JylVkkB8BB6gAHPn9XeKQU79+/ZLt0GLR4cOHS7SuO1SGkCPxAxwAYC4ufQp5enq6w4Wh7IUF+hJuAACVWqnurnL1HDgAAADOUqqQU7VqVZ06dcr2/qWXXtLZs2edXhQAAMCNKlXI+ePwnblz5+rcuXPOrAcAAMApHJoM8CoHHmAOAABQJm4o5AAAAJRXpZ4McOzYsfLzuzKxXEFBgSZOnKjAwEC7daZPn+6c6gAAABxUqpDTpUsXHThwwPa+Y8eORebEsVgszqkMAADgBpQq5GzatMlFZQAAADiXS8fkVK9evVzPfgwAAMzLpSGHu68AAIC7cHcVAAAwJUIOAAAwJUIOAAAwJZeGHG4nBwAA7sLAYwAAYEouDTn/+c9/FB4e7spDAAAAFKvUj3WQrlyhWbZsmTZu3KhTp07JarXafb58+XJJUufOnW+8QgAAAAc4FHKGDRumuXPnqlu3bgoJCWHsDQAAKHccCjmffPKJli9frnvuucfZ9QAAADiFQ2NyAgMD1aBBA2fXAgAA4DQOhZzXX39d48ePV15enrPrAQAAcAqHuqseeeQRffbZZwoODlZkZKSqVq1q9/nOnTudUhwAAPhzGdl5Ss+6qPpB/goL9HV3OeWGQyFnwIABSk1N1WOPPcbAYwAA3Ch5x1GNWr5bVkPysEiJvaOV0K6uu8sqFxwKOatWrdLatWu5RRwAADfKyM6zBRxJshrS6OV71KVxba7oyMExOREREapevbqzawEAAKWQnnXRFnCuKjQMHcnKdU9B5YxDIWfatGl6+eWXdeTIESeXAwAASqp+kL88/jBixNNiUWSQn3sKKmcc6q567LHHlJubq4YNG8rPz6/IwOOzZ886pTgAAHBtYYG+SuwdrdHL96jQMORpsWhS7+Z0Vf0vh0JOUlKSk8sAAACOSGhXV10a19aRrFxFBvkRcH7HYlSyR4Xn5OQoMDBQ2dnZjCsCAKCCcOT3t0NXcn7vt99+U0FBgd0ywgMAAHA3hwYeX7x4Uc8884yCg4Pl7++vmjVr2r0AAADczaGQ8/LLL2vDhg2aPXu2vL299cEHH2j8+PGqU6eOFi5c6OwaAQAASs2h7qovv/xSCxcuVGxsrAYNGqTbb79dUVFRqlevnhYvXqy+ffs6u04AAIBScehKztmzZ21PIa9evbrtlvHOnTvrm2++cV51AAAADnIo5DRo0EDp6emSpCZNmujzzz+XdOUKT40aNZxWHAAAgKMcCjmDBg3S999/L0kaOXKkZs2aJR8fHw0fPlwvvfSSUwsEAABwhFPmyfn555+VmpqqqKgotWjRwhl1uQzz5AAAUPG4bZ6cevXqqV69eje6KwAAAKdxqLuqsLBQEyZMUHh4uAICAnT48GFJ0pgxY/Thhx86tUAAAABHOBRyJk6cqAULFmjq1Kny8vKyLW/evLk++OADpxUHAADgKIdCzsKFC/X++++rb9++8vT0tC1v2bKl9u/f77TiAAAAHOVQyDl+/LiioqKKLLdarbp06dINFwUAAHCjHAo5zZo107fffltk+bJly9S6desbLgoAAOBGOXR31dixYzVgwAAdP35cVqtVy5cv14EDB7Rw4UKtXLnS2TUCAACUmkNXch544AF9+eWXWr9+vfz9/TV27Fjt27dPX375pbp37+7sGgEAAErNKZMBViRMBggAQMXjyO9vh59ddebMmSLLz507Z3twJwAAgDs5FHKOHDmiwsLCIsvz8/N1/PjxGy4KAADgRpVq4PGKFSts/167dq0CAwNt7wsLC5WSkqLIyEinFQcAAOCoUoWcXr16SZIsFosGDBhg91nVqlUVGRmpadOmOa04AAAAR5Uq5FitVklS/fr1tWPHDgUFBbmkKAAAgBtVqjE527Zt08qVK5Wenm4LOAsXLlT9+vUVHBysJ554Qvn5+S4pFAAAoDRKFXLGjx+vvXv32t7v3r1bjz/+uOLi4jRy5Eh9+eWXSkxMdHqRAAAApVWqkPP999/rzjvvtL1fsmSJYmJiNG/ePI0YMULvvPOOPv/8c6cXCQAAUFqlCjm//vqrQkJCbO+//vpr9ejRw/a+Xbt2OnbsWKmLmDVrliIjI+Xj46OYmBht3769RNstWbJEFovFNiAaAADgqlKFnJCQEKWnp0uSCgoKtHPnTt122222z8+fP6+qVauWqoDk5GSNGDFC48aN086dO9WyZUvFx8fr1KlT193uyJEjevHFF3X77beX6ngAAKByKFXIueeeezRy5Eh9++23GjVqlPz8/OxCxg8//KCGDRuWqoDp06dryJAhGjRokJo1a6Y5c+bIz89PH3300TW3KSwsVN++fTV+/HhmWAYAAMUqVciZMGGCqlSpoq5du2revHmaN2+evLy8bJ9/9NFHuuuuu0q8v4KCAqWmpiouLu7/CvLwUFxcnLZt23bN7d544w0FBwfr8ccf/9Nj5OfnKycnx+4FAADMr1Tz5AQFBembb75Rdna2AgIC5Onpaff50qVLFRAQUOL9ZWVlqbCw0G6cj3SlW2z//v3FbrN582Z9+OGHSktLK9ExEhMTNX78+BLXBAAAzMGhZ1cFBgYWCTiSVKtWLbsrO852/vx59evXT/PmzSvxRISjRo1Sdna27eXIwGgAAFDxlOpKjrMFBQXJ09NTmZmZdsszMzMVGhpaZP1Dhw7pyJEj6tmzp23Z1VmYq1SpogMHDhQZE+Tt7S1vb28XVA8AAMozh67kOIuXl5fatGmjlJQU2zKr1aqUlBR16NChyPpNmjTR7t27lZaWZnvdf//96tatm9LS0hQREVGW5QMAgHLMrVdyJGnEiBEaMGCA2rZtq/bt2yspKUkXL17UoEGDJEn9+/dXeHi4EhMT5ePjo+bNm9ttX6NGDUkqshwAAFRubg85CQkJOn36tMaOHauTJ0+qVatWWrNmjW0w8tGjR+Xh4dYLTgAAoAKyGIZhuLuIspSTk6PAwEBlZ2erevXq7i4HAACUgCO/v7lEAgAATImQAwAATImQAwAAnCYjO09bD2UpIzvP3aW4f+AxAAAwh+QdRzVq+W5ZDcnDIiX2jlZCu7puq4crOQAA4IZlZOfZAo4kWQ1p9PI9br2iQ8gBAAA3LD3roi3gXFVoGDqSleuegkTIAQAATlA/yF8eFvtlnhaLIoP83FOQCDkAAMAJwgJ9ldg7Wp6WK0nH02LRpN7NFRbo67aaGHgMAACcIqFdXXVpXFtHsnIVGeTn1oAjEXIAAIAThQX6uj3cXEV3FQAAMCVCDlyqPE0KBQCoXOiugsuUt0mhAACVC1dy4BLlcVIoAEDlQsiBS5THSaEAAJULIQcuUR4nhQIAVC6EHLhEeZwUCgBQuTDwGC5T3iaFAgBULoQcuFR5mhQKAFC50F0FAABMiZADAABMiZADAABMiZADAABMiZADoFg8dwxARcfdVQCKqOzPHcvIzlN61kXVD/Ln7kCgAiPkALBzreeOdWlcu1L8wq/sAQ8wE7qrANipzM8d48GygLkQcgDYqczPHavMAQ8wI0IOADuV+bljlTngAWbEmBwARVTW545dDXijl+9RoWFUqoAHmBEhB0CxKutzxyprwAPMiJADAH9QWQMeYDaMyQEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAoJzIyM7T1kNZysjOc3cpplDF3QUAAAApecdRjVq+W1ZD8rBIib2jldCurrvLqtC4kgMAgJtlZOfZAo4kWQ1p9PI9XNG5QYQcAADcLD3roi3gXFVoGDqSleuegkyCkAMAgJvVD/KXh8V+mafFosggP/cUZBKEHAAA3Cws0FeJvaPlabmSdDwtFk3q3Vxhgb5urqxiY+AxAADlQEK7uurSuLaOZOUqMsiPgOMEhBwAAMqJsEBfwo0T0V0FAABMiZADAABMqVyEnFmzZikyMlI+Pj6KiYnR9u3br7nuvHnzdPvtt6tmzZqqWbOm4uLirrs+AAConNwecpKTkzVixAiNGzdOO3fuVMuWLRUfH69Tp04Vu/6mTZvUp08fbdy4Udu2bVNERITuuusuHT9+vIwrBwAA5ZnFMAzjz1dznZiYGLVr104zZ86UJFmtVkVEROjZZ5/VyJEj/3T7wsJC1axZUzNnzlT//v3/dP2cnBwFBgYqOztb1atXv+H6AQCA6zny+9utV3IKCgqUmpqquLg42zIPDw/FxcVp27ZtJdpHbm6uLl26pFq1ahX7eX5+vnJycuxeAADA/NwacrKyslRYWKiQkBC75SEhITp58mSJ9vHKK6+oTp06dkHp9xITExUYGGh7RURE3HDdAK6NpygDKC8q9Dw5kydP1pIlS7Rp0yb5+PgUu86oUaM0YsQI2/ucnByCDuAiPEUZQHni1pATFBQkT09PZWZm2i3PzMxUaGjodbd9++23NXnyZK1fv14tWrS45nre3t7y9vZ2Sr0Aru1aT1Hu0rg2k5sBcAu3dld5eXmpTZs2SklJsS2zWq1KSUlRhw4drrnd1KlTNWHCBK1Zs0Zt27Yti1IB/AmeogygvHF7d9WIESM0YMAAtW3bVu3bt1dSUpIuXryoQYMGSZL69++v8PBwJSYmSpKmTJmisWPH6tNPP1VkZKRt7E5AQIACAgLcdh5AZXf1Kcq/Dzo8RRmAO7l9npyEhAS9/fbbGjt2rFq1aqW0tDStWbPGNhj56NGjysjIsK0/e/ZsFRQU6OGHH1ZYWJjt9fbbb7vrFACIpygDKH/cPk9OWWOeHMC1MrLzeIoyAKdz5Pe327urAJgLT1EGUF64vbsKAADAFQg5AAA4GZNilg90VwEA4ERMill+cCUHAAAnudakmFzRcQ9CDgAATsKkmOULIQcAACe5Oinm7zEppvsQcgAAcBImxSxfGHgMAIATJbSrqy6NazMpZjlAyAEAwMmYFLN8oLsKAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHMLmM7DxtPZSljOw8d5cCAGWKB3QCJpa846hGLd8tqyF5WKTE3tFKaFfX3WUBQJngSg5gUhnZebaAI0lWQxq9fA9XdABUGoQcwKTSsy7aAs5VhYahI1m57ikIAMoYIQcwqfpB/vKw2C/ztFgUGeTnnoIAoIwRcgCTCgv0VWLvaHlariQdT4tFk3o3V1igr5srA4CywcBjk8rIzlN61kXVD/Lnl1olltCurro0rq0jWbmKDPLjawFljp9F7leZ/w8IOSbEHTX4vbBA30r3gw3lAz+L3K+y/x/QXWUyrrqjhrlWAJQGd/e5H/8HXMkxnevdUePoX/OV/S8BAKXnip9FKB3+D7iSYzrOvqOGvwQAOIK7+9yP/wNCjuk4+44a5loB4Agz3d1XUbvrzfR/4Ci6q0zImXfUXP1L4PdBp7L9JQDAMWa4u6+id9eb4f/gRlgMwzD+fDXzyMnJUWBgoLKzs1W9enV3l1MhJO84qtHL96jQMGx/CVSkb3IAcERGdp46Td5Q5I+8zSO7VbqwUB448vubKzn4U5X9LwEAlRMDdys+Qg5KhLlWAFQ2dNdXfAw8BgCgGAzcrfi4kgMAwDXQXV+xEXIAALgOuusrLrqrAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFynCgjO09bD2UpIzvP3aUAAFDpMRmgkyTvOKpRy3fLakgeFimxdzRP6gYAwI24kuMEGdl5toAjXXmY2+jle7iiAwCAGxFynCA966LdU2olqdAwdCQr1z0FAeUUXboAyhLdVU5QP8hfHhbZBR1Pi0WRQX7uKwooZ+jSBVDWuJLjBGGBvkrsHS1Pi0XSlYAzqXdzHugG/C+6dAG4A1dynCShXV11aVxbR7JyFRnkR8ABfud6Xbp8rwBwFUKOE4UF+vIDGygGXboA3IHuKgAuR5cuAHcoFyFn1qxZioyMlI+Pj2JiYrR9+/brrr906VI1adJEPj4+io6O1urVq8uoUgCOSmhXV5tHdtNnQ27T5pHdGHQMwOXcHnKSk5M1YsQIjRs3Tjt37lTLli0VHx+vU6dOFbv+1q1b1adPHz3++OPatWuXevXqpV69emnPnj1lXDmA0goL9FWHhjdxBQdAmbAYhmH8+WquExMTo3bt2mnmzJmSJKvVqoiICD377LMaOXJkkfUTEhJ08eJFrVy50rbstttuU6tWrTRnzpw/PV5OTo4CAwOVnZ2t6tWrO+9EAACAyzjy+9utV3IKCgqUmpqquLg42zIPDw/FxcVp27ZtxW6zbds2u/UlKT4+/prr5+fnKycnx+4FAADMz60hJysrS4WFhQoJCbFbHhISopMnTxa7zcmTJ0u1fmJiogIDA22viIgI5xQPAADKNbePyXG1UaNGKTs72/Y6duyYu0sCAABlwK3z5AQFBcnT01OZmZl2yzMzMxUaGlrsNqGhoaVa39vbW97e3s4pGAAAVBhuvZLj5eWlNm3aKCUlxbbMarUqJSVFHTp0KHabDh062K0vSevWrbvm+gAAoHJy+4zHI0aM0IABA9S2bVu1b99eSUlJunjxogYNGiRJ6t+/v8LDw5WYmChJev7559W1a1dNmzZN9957r5YsWaLvvvtO77//vjtPAwAAlDNuDzkJCQk6ffq0xo4dq5MnT6pVq1Zas2aNbXDx0aNH5eHxfxecOnbsqE8//VSvvfaaRo8erUaNGulf//qXmjdv7q5TAAAA5ZDb58kpa8yTAwBAxVPh5skBAABwFUIOAAAwJbePySlrV3vnmPkYAICK4+rv7dKMsql0Ief8+fOSxMzHAABUQOfPn1dgYGCJ1q10A4+tVqtOnDihatWqyWKxFLtOTk6OIiIidOzYMQYni/YoDm1ij/YoijaxR3vYoz2K+rM2MQxD58+fV506dezuur6eSnclx8PDQzfffHOJ1q1evTpffL9DexRFm9ijPYqiTezRHvZoj6Ku1yYlvYJzFQOPAQCAKRFyAACAKRFyiuHt7a1x48bxYM//RXsURZvYoz2Kok3s0R72aI+iXNEmlW7gMQAAqBy4kgMAAEyJkAMAAEyJkAMAAEyJkAMAAEyp0oacWbNmKTIyUj4+PoqJidH27duvue7evXv10EMPKTIyUhaLRUlJSWVXaBkpTXvMmzdPt99+u2rWrKmaNWsqLi7uuutXVKVpk+XLl6tt27aqUaOG/P391apVK33yySdlWK3rlaY9fm/JkiWyWCzq1auXawt0g9K0yYIFC2SxWOxePj4+ZVit65X2a+TcuXMaOnSowsLC5O3trcaNG2v16tVlVK3rlaY9YmNji3x9WCwW3XvvvWVYseuV9mskKSlJt9xyi3x9fRUREaHhw4frt99+K/kBjUpoyZIlhpeXl/HRRx8Ze/fuNYYMGWLUqFHDyMzMLHb97du3Gy+++KLx2WefGaGhocaMGTPKtmAXK217PProo8asWbOMXbt2Gfv27TMGDhxoBAYGGr/88ksZV+46pW2TjRs3GsuXLzd+/PFH4+DBg0ZSUpLh6elprFmzpowrd43StsdV6enpRnh4uHH77bcbDzzwQNkUW0ZK2ybz5883qlevbmRkZNheJ0+eLOOqXae07ZGfn2+0bdvWuOeee4zNmzcb6enpxqZNm4y0tLQyrtw1StseZ86csfva2LNnj+Hp6WnMnz+/bAt3odK2yeLFiw1vb29j8eLFRnp6urF27VojLCzMGD58eImPWSlDTvv27Y2hQ4fa3hcWFhp16tQxEhMT/3TbevXqmS7k3Eh7GIZhXL582ahWrZrx8ccfu6rEMnejbWIYhtG6dWvjtddec0V5Zc6R9rh8+bLRsWNH44MPPjAGDBhgupBT2jaZP3++ERgYWEbVlb3Stsfs2bONBg0aGAUFBWVVYpm60Z8hM2bMMKpVq2ZcuHDBVSWWudK2ydChQ4077rjDbtmIESOMTp06lfiYla67qqCgQKmpqYqLi7Mt8/DwUFxcnLZt2+bGytzDGe2Rm5urS5cuqVatWq4qs0zdaJsYhqGUlBQdOHBAXbp0cWWpZcLR9njjjTcUHBysxx9/vCzKLFOOtsmFCxdUr149RURE6IEHHtDevXvLolyXc6Q9VqxYoQ4dOmjo0KEKCQlR8+bNNWnSJBUWFpZV2S7jjJ+rH374of72t7/J39/fVWWWKUfapGPHjkpNTbV1aR0+fFirV6/WPffcU+LjVroHdGZlZamwsFAhISF2y0NCQrR//343VeU+zmiPV155RXXq1LH74q3IHG2T7OxshYeHKz8/X56ennrvvffUvXt3V5frco60x+bNm/Xhhx8qLS2tDCose460yS233KKPPvpILVq0UHZ2tt5++2117NhRe/fuLfFDg8srR9rj8OHD2rBhg/r27avVq1fr4MGDevrpp3Xp0iWNGzeuLMp2mRv9ubp9+3bt2bNHH374oatKLHOOtMmjjz6qrKwsde7cWYZh6PLly3ryySc1evToEh+30oUcONfkyZO1ZMkSbdq0yXSDKEurWrVqSktL04ULF5SSkqIRI0aoQYMGio2NdXdpZer8+fPq16+f5s2bp6CgIHeXU2506NBBHTp0sL3v2LGjmjZtqrlz52rChAlurMw9rFargoOD9f7778vT01Nt2rTR8ePH9dZbb1X4kHOjPvzwQ0VHR6t9+/buLsWtNm3apEmTJum9995TTEyMDh48qOeff14TJkzQmDFjSrSPShdygoKC5OnpqczMTLvlmZmZCg0NdVNV7nMj7fH2229r8uTJWr9+vVq0aOHKMsuUo23i4eGhqKgoSVKrVq20b98+JSYmVviQU9r2OHTokI4cOaKePXvallmtVklSlSpVdODAATVs2NC1RbuYM36OVK1aVa1bt9bBgwddUWKZcqQ9wsLCVLVqVXl6etqWNW3aVCdPnlRBQYG8vLxcWrMr3cjXx8WLF7VkyRK98cYbriyxzDnSJmPGjFG/fv00ePBgSVJ0dLQuXryoJ554Qq+++qo8PP58xE2lG5Pj5eWlNm3aKCUlxbbMarUqJSXF7q+sysLR9pg6daomTJigNWvWqG3btmVRaplx1teI1WpVfn6+K0osU6VtjyZNmmj37t1KS0uzve6//35169ZNaWlpioiIKMvyXcIZXyOFhYXavXu3wsLCXFVmmXGkPTp16qSDBw/aArAk/fe//1VYWFiFDjjSjX19LF26VPn5+XrsscdcXWaZcqRNcnNziwSZq6HYKOljNx0YIF3hLVmyxPD29jYWLFhg/Pjjj8YTTzxh1KhRw3Y7Z79+/YyRI0fa1s/Pzzd27dpl7Nq1ywgLCzNefPFFY9euXcZPP/3krlNwqtK2x+TJkw0vLy9j2bJldrc8nj9/3l2n4HSlbZNJkyYZX331lXHo0CHjxx9/NN5++22jSpUqxrx589x1Ck5V2vb4IzPeXVXaNhk/fryxdu1a49ChQ0Zqaqrxt7/9zfDx8TH27t3rrlNwqtK2x9GjR41q1aoZzzzzjHHgwAFj5cqVRnBwsPHmm2+66xScytHvmc6dOxsJCQllXW6ZKG2bjBs3zqhWrZrx2WefGYcPHza++uoro2HDhsYjjzxS4mNWypBjGIbx7rvvGnXr1jW8vLyM9u3bG//zP/9j+6xr167GgAEDbO/T09MNSUVeXbt2LfvCXaQ07VGvXr1i22PcuHFlX7gLlaZNXn31VSMqKsrw8fExatasaXTo0MFYsmSJG6p2ndK0xx+ZMeQYRunaZNiwYbZ1Q0JCjHvuucfYuXOnG6p2ndJ+jWzdutWIiYkxvL29jQYNGhgTJ040Ll++XMZVu05p22P//v2GJOOrr74q40rLTmna5NKlS8brr79uNGzY0PDx8TEiIiKMp59+2vj1119LfDyLYZT0mg8AAEDFUenG5AAAgMqBkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMALjRw4ED16tXL3WUAlRIhB6ikBg4cKIvFYnvddNNNuvvuu/XDDz+4uzSn+P25XX117tzZZcc7cuSILBaL0tLS7Jb/4x//0IIFC1x2XADXRsgBKrG7775bGRkZysjIUEpKiqpUqaL77rvP3WU5zfz5823nl5GRoRUrVhS73qVLl1xWQ2BgoGrUqOGy/QO4NkIOUIl5e3srNDRUoaGhatWqlUaOHKljx47p9OnTuuOOO/TMM8/YrX/69Gl5eXnZniQcGRmpCRMmqE+fPvL391d4eLhmzZplt8306dMVHR0tf39/RURE6Omnn9aFCxdsn//888/q2bOnatasKX9/f/3lL3/R6tWrJUm//vqr+vbtq9q1a8vX11eNGjXS/PnzS3x+NWrUsJ1faGioatWqZbvikpycrK5du8rHx0eLFy/WmTNn1KdPH4WHh8vPz0/R0dH67LPP7PZntVo1depURUVFydvbW3Xr1tXEiRMlSfXr15cktW7dWhaLRbGxsZKKdlfl5+frueeeU3BwsHx8fNS5c2ft2LHD9vmmTZtksViUkpKitm3bys/PTx07dtSBAwdKfN4AriDkAJAkXbhwQYsWLVJUVJRuuukmDR48WJ9++qny8/Nt6yxatEjh4eG64447bMveeusttWzZUrt27dLIkSP1/PPPa926dbbPPTw89M4772jv3r36+OOPtWHDBr388su2z4cOHar8/Hx988032r17t6ZMmaKAgABJ0pgxY/Tjjz/qP//5j/bt26fZs2crKCjIKed7tdZ9+/YpPj5ev/32m9q0aaNVq1Zpz549euKJJ9SvXz9t377dts2oUaM0efJkW12ffvqpQkJCJMm23vr165WRkaHly5cXe9yXX35Z//znP/Xxxx9r586dioqKUnx8vM6ePWu33quvvqpp06bpu+++U5UqVfT3v//dKecNVCpOe7QogAplwIABhqenp+Hv72/4+/sbkoywsDAjNTXVMAzDyMvLM2rWrGkkJyfbtmnRooXx+uuv297Xq1fPuPvuu+32m5CQYPTo0eOax126dKlx00032d5HR0fb7fP3evbsaQwaNMih85Nk+Pj42M7P39/f+OKLL4z09HRDkpGUlPSn+7j33nuNF154wTAMw8jJyTG8vb2NefPmFbvu1f3u2rXLbvnvn8B+4cIFo2rVqsbixYttnxcUFBh16tQxpk6dahiGYWzcuNGQZKxfv962zqpVqwxJRl5eXmmaAKj0uJIDVGLdunVTWlqa0tLStH37dsXHx6tHjx76+eef5ePjo379+umjjz6SJO3cuVN79uzRwIED7fbRoUOHIu/37dtne79+/XrdeeedCg8PV7Vq1dSvXz+dOXNGubm5kqTnnntOb775pjp16qRx48bZDXx+6qmntGTJErVq1Uovv/yytm7dWqrzmzFjhu380tLS1L17d9tnbdu2tVu3sLBQEyZMUHR0tGrVqqWAgACtXbtWR48elSTt27dP+fn5uvPOO0tVw+8dOnRIly5dUqdOnWzLqlatqvbt29u1mSS1aNHC9u+wsDBJ0qlTpxw+NlAZEXKASszf319RUVGKiopSu3bt9MEHH+jixYuaN2+eJGnw4MFat26dfvnlF82fP1933HGH6tWrV+L9HzlyRPfdd59atGihf/7zn0pNTbWN2SkoKLAd4/Dhw+rXr592796ttm3b6t1335UkW+AaPny4Tpw4oTvvvFMvvvhiiY8fGhpqO7+oqCj5+/vbnfvvvfXWW/rHP/6hV155RRs3blRaWpri4+Ntdfr6+pb4uM5QtWpV278tFoukK2OCAJQcIQeAjcVikYeHh/Ly8iRJ0dHRatu2rebNm6dPP/202HEh//M//1PkfdOmTSVJqampslqtmjZtmm677TY1btxYJ06cKLKPiIgIPfnkk1q+fLleeOEFW8iSpNq1a2vAgAFatGiRkpKS9P777zvzlG22bNmiBx54QI899phatmypBg0a6L///a/t80aNGsnX19c26PqPvLy8JF25InQtDRs2lJeXl7Zs2WJbdunSJe3YsUPNmjVz0pkAuKqKuwsA4D75+fk6efKkpCt3Ms2cOVMXLlxQz549besMHjxYzzzzjPz9/fXggw8W2ceWLVs0depU9erVS+vWrdPSpUu1atUqSVJUVJQuXbqkd999Vz179tSWLVs0Z84cu+2HDRumHj16qHHjxvr111+1ceNGW0gaO3as2rRpo7/85S/Kz8/XypUrbZ85W6NGjbRs2TJt3bpVNWvW1PTp05WZmWkLHz4+PnrllVf08ssvy8vLS506ddLp06e1d+9ePf744woODpavr6/WrFmjm2++WT4+PgoMDLQ7hr+/v5566im99NJLqlWrlurWraupU6cqNzdXjz/+uEvOC6jMuJIDVGJr1qxRWFiYwsLCFBMTox07dmjp0qW2258lqU+fPqpSpYr69OkjHx+fIvt44YUX9N1336l169Z68803NX36dMXHx0uSWrZsqenTp2vKlClq3ry5Fi9erMTERLvtCwsLNXToUDVt2lR33323GjdurPfee0/Slasjo0aNUosWLdSlSxd5enpqyZIlLmmL1157Tbfeeqvi4+MVGxur0NDQIjMVjxkzRi+88ILGjh2rpk2bKiEhwTZOpkqVKnrnnXc0d+5c1alTRw888ECxx5k8ebIeeugh9evXT7feeqsOHjyotWvXqmbNmi45L6AysxiGYbi7CADl15EjR9SwYUPt2LFDt956q91nkZGRGjZsmIYNG+ae4gDgOuiuAlCsS5cu6cyZM3rttdd02223FQk4AFDe0V0FoFhbtmxRWFiYduzYUWQcjbtNmjRJAQEBxb569Ojh7vIAlBN0VwGocM6ePVtkhuCrfH19FR4eXsYVASiPCDkAAMCU6K4CAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACm9P8DCuaGqQBAYMsAAAAASUVORK5CYII=", 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jjz+K7OxscfXq1Tv2S2pqqlCpVNWumzhxovDw8BA3b94UQggREhIiFi1aZNAmKipKPt7b3+HU1FShVqvF5cuXhRBCPPTQQ2LcuHEG23Xs2FHMmDGjxrrM/V0jIrK2fdlXRMiU76pMGdlFZtn/nX6//8qmHoR4e7xFs2bNAABHjx5FRUUF+vfvL7eJiIhAixYtsH//fnTv3r3KPnJzc1FQUGCwjUqlQrdu3bB//3489dRTVbbR6XTQ6XTyvFarNdsx3cnGw/lyEnaQgJShkRjWpYVFP/O7775DkyZNcPPmTeh0Ojg4OGDJkiXQ6XSYN28eduzYgZiYGABAeHg40tPT8fHHH6N3797yaxlUKhX8/f0BAB999BGCg4OxZMkSSJKEiIgIXLp0CVOmTMGMGTPg4HDrJGPr1q2xYMECuQ61Wg0AmDt3LmJjYwEAo0ePRnJyMnJychAeHg4AePzxx7Fr1y5MmTKlVjXeNnv2bDz00EN17q+IiAhcu3YNV69eha+v713b+/j4APjjNRa3jRkzBi+88AIWLlwIpVKJzMxMHD9+HF9//XWdayQiaijCvN3hIEE+AwQACklCqHf9P1vPZgZB6/V6TJo0CbGxsejQoQOAW6+ccHZ2rjLWxM/PDwUFBdXu5/ZyPz+/Wm+TkpIClUolT8HBwXU8mruz1mnAvn37IisrCwcPHkRiYiJGjRqFxx57DNnZ2SgrK8NDDz2EJk2ayNNnn32GnJycGvd36tQpxMTEGDyzJjY2FiUlJbh48aK8LDo6utrtO3bsKP/bz88Pbm5ucvi5vez2KyGMqbFz587GdUwNhLj1P1Bdn8kTHx8PhUKBLVu2ALg18Lpv377yJTMiInsQoHJFytBIKP73N1UhSZg3tAMCVK71XovNnAEaP348Tpw4UavxGuaWnJyMpKQkeV6r1Vo8BOUWlRokYACoFAJ5RWUW/SK4u7ujVatWAICVK1ciKioKn376qRw6v//+ewQFBRlso1QqzfK51XFycpL/ffvutD+TJEkeI1NSUlLrGmv6PGOdOnUKnp6e8mtPHBwc5FB0W22e1Ozs7IwRI0YgNTUVQ4cOxbp16/D++++bpUYiooZkWJcW6NXGB3lFZQj1drNK+AFsJABNmDAB3333Hfbu3Yt77rlHXu7v748bN26guLjY4CxQYWGhweWFP7u9vLCw0OAlkoWFhejUqVO12yiVSrP8yBvDFk4DOjg4YNq0aUhKSsIvv/wCpVKJ/Px8g0tJd9OuXTt8+eWXEELIZ0n27dsHDw8Pg/8tzaF9+/Ym1Wiqy5cvY926dYiPj5cv5fn4+MiX74BbYTk3N9dgOycnJ1RWVlbZ35gxY9ChQwd89NFHuHnz5l0H8xMRNVYBKlerBZ/brHoJTAiBCRMmYMuWLdi5cyfCwsIM1kdHR8PJyQlpaWnysjNnziA/P18eA/JXYWFh8Pf3N9hGq9Xi4MGDNW5jDbZyGvCJJ56AQqHAxx9/jMmTJ+OVV17B6tWrkZOTg8zMTHz44YdYvXp1jduPGzcOFy5cwEsvvYTTp0/j66+/xsyZM5GUlCSHBnPx8PAwqcbaEEKgoKAAarUap06dwsqVK9GjRw+oVCrMnz9fbvfggw/i888/x3/+8x8cP34ciYmJVV5UGhoairS0NBQUFOD333+Xl7dr1w7du3fHlClTkJCQIL/wlIiI6p9VzwCNHz8e69atw9dffw0PDw95jI5KpYKrqytUKhVGjx6NpKQkNGvWDJ6ennjppZcQExNjMAA6IiICKSkp8q3HkyZNwty5c9G6dWuEhYVh+vTpCAwMtLknF9vCaUBHR0dMmDABCxYsQG5uLnx8fJCSkoJz587By8sL999/P6ZNm1bj9kFBQdi6dStee+01REVFoVmzZhg9ejTefPNNi9Q7Z84co2usDa1Wi4CAAEiSBE9PT7Rt2xaJiYmYOHEiPD095XbJycnIzc3F3//+d6hUKsyZM6fKGaD33nsPSUlJWLFiBYKCgpCXlyevu317/XPPPVeneomIqI7Mct+ZiQBUO6WmpsptysvLxbhx40TTpk2Fm5ubGDJkiFCr1VX28+dt9Hq9mD59uvDz8xNKpVL069dPnDlzptZ11edt8GRfZs+eLSIjI2vVlt81IiLjGHMbvCTEX0Z0ErRaLVQqFTQajcF//QO3nkydm5uLsLAwuLi4WKlCamhKSkqQl5eHfv36Ye7cuRg7duxdt+F3jYjIOHf6/f4rm7kNnshS7r33XoPb5v88rV27tl5qmDBhAqKjo9GnTx9e/iIisgE2cRcYkSVt3bq1xlvV//q8KEtZtWpVo3l5LBFRY8AARI1eSEiItUsgIiIbw0tgJuLQKbI0fseIiCyHAchIt59U/Oc3lBNZwu3v2F+fjk1ERHXHS2BGUigU8PLykt9P5ebmVuf3RBH9mRACZWVluHz5Mry8vKo8aJGIiOqOAcgEt1+3cTsEEVnCX98oT0RE5sMAZAJJkhAQEABfX99avQiTyFhOTk4880NEZEEMQHWgUCj4I0VERNQAcRA0ERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu2PVALR3714MGjQIgYGBkCQJX331lcF6SZKqnd59990a9zlr1qwq7SMiIix8JERERNSQWDUAlZaWIioqCkuXLq12vVqtNphWrlwJSZLw2GOP3XG/9957r8F26enpliifiIiIGihHa374wIEDMXDgwBrX+/v7G8x//fXX6Nu3L8LDw++4X0dHxyrbEhEREd3WYMYAFRYW4vvvv8fo0aPv2vbs2bMIDAxEeHg4nnnmGeTn59+xvU6ng1arNZiIiIio8WowAWj16tXw8PDA0KFD79iuW7duWLVqFbZt24Zly5YhNzcXDzzwAK5du1bjNikpKVCpVPIUHBxs7vKJiIjIhkhCCGHtIoBbA563bNmC+Pj4atdHRETgoYcewocffmjUfouLixESEoKFCxfWePZIp9NBp9PJ81qtFsHBwdBoNPD09DTq84iIiMg6tFotVCpVrX6/rToGqLb+85//4MyZM9i4caPR23p5eaFNmzbIzs6usY1SqYRSqaxLiURERNSANIhLYJ9++imio6MRFRVl9LYlJSXIyclBQECABSojIiKihsiqAaikpARZWVnIysoCAOTm5iIrK8tg0LJWq8WmTZswZsyYavfRr18/LFmyRJ6fPHky9uzZg7y8PGRkZGDIkCFQKBRISEiw6LEQERFRw2HVS2BHjhxB37595fmkpCQAQGJiIlatWgUA2LBhA4QQNQaYnJwcFBUVyfMXL15EQkICrl69Ch8fH/Ts2RMHDhyAj4+P5Q6EiIiIGhSbGQRtS4wZREVERES2wZjf7wYxBoiIiIjInBiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERGRnVBrypGRUwS1ptzapVidVQPQ3r17MWjQIAQGBkKSJHz11VcG60eOHAlJkgymAQMG3HW/S5cuRWhoKFxcXNCtWzccOnTIQkdARETUMGw8nI/Y+Tvx9IqDiJ2/ExsP51u7JKuyagAqLS1FVFQUli5dWmObAQMGQK1Wy9P69evvuM+NGzciKSkJM2fORGZmJqKiohAXF4fLly+bu3wiIqIGQa0pR/Lm49CLW/N6AUzbfMKuzwQ5WvPDBw4ciIEDB96xjVKphL+/f633uXDhQowdOxajRo0CACxfvhzff/89Vq5cialTp9apXiIiooYot6hUDj+3VQqBvKIyBKhcrVOUldn8GKDdu3fD19cXbdu2xYsvvoirV6/W2PbGjRs4evQo+vfvLy9zcHBA//79sX///hq30+l00Gq1BhMREVFjEebtDgfJcJlCkhDq7WadgmyATQegAQMG4LPPPkNaWhreeecd7NmzBwMHDkRlZWW17YuKilBZWQk/Pz+D5X5+figoKKjxc1JSUqBSqeQpODjYrMdBRERkTQEqV6QMjYRCupWCFJKEeUM72O3ZH8DKl8Du5qmnnpL/HRkZiY4dO6Jly5bYvXs3+vXrZ7bPSU5ORlJSkjyv1WoZgoiIqFEZ1qUFerXxQV5RGUK93ew6/AA2HoD+Kjw8HN7e3sjOzq42AHl7e0OhUKCwsNBgeWFh4R3HESmVSiiVSrPXS0REZEsCVK52H3xus+lLYH918eJFXL16FQEBAdWud3Z2RnR0NNLS0uRler0eaWlpiImJqa8yiYiIyMZZNQCVlJQgKysLWVlZAIDc3FxkZWUhPz8fJSUleO2113DgwAHk5eUhLS0NgwcPRqtWrRAXFyfvo1+/fliyZIk8n5SUhBUrVmD16tU4deoUXnzxRZSWlsp3hRERERFZ9RLYkSNH0LdvX3n+9jicxMRELFu2DD/99BNWr16N4uJiBAYG4uGHH8acOXMMLlfl5OSgqKhInh82bBiuXLmCGTNmoKCgAJ06dcK2bduqDIwmIiIi+yUJIcTdm9kXrVYLlUoFjUYDT09Pa5dDREREtWDM73eDGgNEREREZA4MQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3TA5AoaGhmD17NvLz881ZDxEREZHFmRyAJk2ahM2bNyM8PBwPPfQQNmzYAJ1OZ87aiIiIiCyiTgEoKysLhw4dQrt27fDSSy8hICAAEyZMQGZmpjlrJCIiIjIrSQghzLGjiooKfPTRR5gyZQoqKioQGRmJl19+GaNGjYIkSeb4iHqj1WqhUqmg0Wjg6elp7XKIiIioFoz5/Xas64dVVFRgy5YtSE1Nxfbt29G9e3eMHj0aFy9exLRp07Bjxw6sW7eurh9DREREZDYmXwLLzMw0uOx177334sSJE0hPT8eoUaMwffp07NixA1u2bKlxH3v37sWgQYMQGBgISZLw1VdfyesqKiowZcoUREZGwt3dHYGBgRgxYgQuXbp0x7pmzZoFSZIMpoiICFMPk4iIiBohkwNQly5dcPbsWSxbtgy//vor/vGPf1QJGmFhYXjqqadq3EdpaSmioqKwdOnSKuvKysqQmZmJ6dOnIzMzE5s3b8aZM2fw6KOP3rW2e++9F2q1Wp7S09ONP0AiIiJqtEy+BHbu3DmEhITcsY27uztSU1NrXD9w4EAMHDiw2nUqlQrbt283WLZkyRJ07doV+fn5aNGiRY37dXR0hL+//x1rIyIiIvtl8hmgvn374urVq1WWFxcXIzw8vE5F1USj0UCSJHh5ed2x3dmzZxEYGIjw8HA888wzd31WkU6ng1arNZiIiIio8TI5AOXl5aGysrLKcp1Oh19//bVORVXn+vXrmDJlChISEu44srtbt25YtWoVtm3bhmXLliE3NxcPPPAArl27VuM2KSkpUKlU8hQcHGz2+omIiMh2GH0J7JtvvpH//cMPP0ClUsnzlZWVSEtLQ2hoqFmKu62iogJPPvkkhBBYtmzZHdv++ZJax44d0a1bN4SEhOBf//oXRo8eXe02ycnJSEpKkue1Wi1DEBERUSNmdACKj48HAEiShMTERIN1Tk5OCA0NxXvvvWeW4oA/ws/58+exc+dOo5/L4+XlhTZt2iA7O7vGNkqlEkqlsq6lEhERUQNhdADS6/UAbt3hdfjwYXh7e5u9qNtuh5+zZ89i165daN68udH7KCkpQU5ODoYPH26BComIiKghMnkMUG5ubp3DT0lJCbKyspCVlSXvMysrC/n5+aioqMDjjz+OI0eOYO3ataisrERBQQEKCgpw48YNeR/9+vXDkiVL5PnJkydjz549yMvLQ0ZGBoYMGQKFQoGEhIQ61UpERESNh8m3wc+ePfuO62fMmHHXfRw5cgR9+/aV52+Pw0lMTMSsWbPk8UadOnUy2G7Xrl3o06cPACAnJwdFRUXyuosXLyIhIQFXr16Fj48PevbsiQMHDsDHx6c2h0VERER2wOR3gd13330G8xUVFcjNzYWjoyNatmzZoF+IyneBERERNTz18i6wY8eOVfvBI0eOxJAhQ0zdLREREZHFmTwGqDqenp546623MH36dHPuloiIiMiszBqAgFtPa9ZoNObeLREREZHZmHwJ7IMPPjCYF0JArVbj888/r/H9XkRERES2wOQAtGjRIoN5BwcH+Pj4IDExEcnJyXUujIiIiMhSTA5Aubm55qyDiIiIqN7UaQyQEAJFRUXVvhWeiIiIyFaZFIAKCgowYsQING3aFH5+fvD19UXTpk3x3HPPobCw0Nw1EhEREZmV0ZfAtFotevTogZKSEowaNQoREREQQuDnn3/G+vXrkZ6ejszMTDRp0sQS9RIRERHVmdEB6P3334dCocDJkyervF7izTffRGxsLD744ANMmzbNbEUSERERmZPRl8C+//57TJs2rdp3a/n6+iI5ORnffvutWYojIiIisgSjA9Avv/yCHj161Li+R48eOHPmTJ2KIiIiIrIkowOQVquFl5dXjeu9vLyg1WrrUhMRERGRRRkdgIQQcHCoeTNJkmDiC+aJiIiI6oXRg6CFEGjTpg0kSapxPREREZEtMzoApaamWqIOIiIionpjdABKTEw0qv369evx6KOPwt3d3diPIiIiIrKIOr0Kozb+7//+j0+HJiIiIpti8QDEMUFERERkaywegIiIiIhsDQMQEdWKWlOOjJwiqDXl1i6FiKjOjB4ETUT2Z+PhfCRvPg69ABwkIGVoJIZ1aWHtsoiITMYzQER0R2pNuRx+AEAvgGmbT/BMEBE1aCYFoMrKSuzduxfFxcV3bRsSEgInJydTPoaIbEBuUakcfm6rFAJ5RWXWKYiIyAxMCkAKhQIPP/wwfv/997u2PXHiBIKDg035GCKyAWHe7nD4y4PfFZKEUG836xRERGQGJl8C69ChA86dO2fOWojIBgWoXJEyNBKK/73+RiFJmDe0AwJUrlaujIjIdJIw8UE927ZtQ3JyMubMmYPo6OgqT3r29PQ0S4HWoNVqoVKpoNFoGvRxEJmTWlOOvKIyhHq7MfwQkU0y5vfb5AD05zfC//nFqEIISJKEyspKU3ZrExiAiIiIGh5jfr9Nvg1+165dpm5KREREZFUmB6DevXubsw4iIiKielOn5wD95z//wbPPPosePXrg119/BQB8/vnnSE9Pr9X2e/fuxaBBgxAYGAhJkvDVV18ZrBdCYMaMGQgICICrqyv69++Ps2fP3nW/S5cuRWhoKFxcXNCtWzccOnTI6GMjIiKixsvkAPTll18iLi4Orq6uyMzMhE6nAwBoNBrMmzevVvsoLS1FVFQUli5dWu36BQsW4IMPPsDy5ctx8OBBuLu7Iy4uDtevX69xnxs3bkRSUhJmzpyJzMxMREVFIS4uDpcvXzb+IImIiKhxEibq1KmTWL16tRBCiCZNmoicnBwhhBCZmZnCz8/P6P0BEFu2bJHn9Xq98Pf3F++++668rLi4WCiVSrF+/foa99O1a1cxfvx4eb6yslIEBgaKlJSUWtei0WgEAKHRaIw7CCIiIrIaY36/TT4DdObMGfTq1avKcpVKVasnRN9Nbm4uCgoK0L9/f4N9d+vWDfv37692mxs3buDo0aMG2zg4OKB///41bgMAOp0OWq3WYCIiIqLGy+QA5O/vj+zs7CrL09PTER4eXqeiAKCgoAAA4OfnZ7Dcz89PXvdXRUVFqKysNGobAEhJSYFKpZInPrmaiIiocTM5AI0dOxYTJ07EwYMHIUkSLl26hLVr12Ly5Ml48cUXzVmjxSUnJ0Oj0cjThQsXrF0SERGR2ak15cjIKeLLjFGH2+CnTp0KvV6Pfv36oaysDL169YJSqcTkyZPx0ksv1bkwf39/AEBhYSECAgLk5YWFhejUqVO123h7e0OhUKCwsNBgeWFhoby/6iiVSiiVyjrXTEREZKs2Hs5H8ubj0AvAQQJShkZiWJcW1i7Lakw+AyRJEt544w389ttvOHHiBA4cOIArV65gzpw5ZiksLCwM/v7+SEtLk5dptVocPHgQMTEx1W7j7OyM6Ohog230ej3S0tJq3IaIiKixU2vK5fADAHoBTNt8wq7PBJl8Bug2Z2dntG/f3qRtS0pKDMYR5ebmIisrC82aNUOLFi0wadIkzJ07F61bt0ZYWBimT5+OwMBAxMfHy9v069cPQ4YMwYQJEwAASUlJSExMROfOndG1a1csXrwYpaWlGDVqVJ2Ok4iIqKHKLSqVw89tlUIgr6jMbt/tZ3IAun79Oj788EPs2rULly9fhl6vN1ifmZl5130cOXIEffv2leeTkpIAAImJiVi1ahVef/11lJaW4vnnn0dxcTF69uyJbdu2wcXFRd4mJycHRUVF8vywYcNw5coVzJgxAwUFBejUqRO2bdtWZWA0ERGRvQjzdoeDBIMQpJAkhHq7Wa8oKzP5ZajPPPMMfvzxRzz++OPw8/MzeCEqAMycOdMsBVoDX4ZKjYFaU47colKEebvb7X/hEdEfNh7Ox7TNJ1ApBBSShHlDOzS6MUD18jZ4lUqFrVu3IjY21qQibRkDEDV0HOxIRNVRa8qRV1SGUG+3RvkfRsb8fps8CDooKAgeHh6mbk5EFsLBjkRUkwCVK2JaNm+U4cdYJgeg9957D1OmTMH58+fNWQ8R1dGdBjsSEdEtJg+C7ty5M65fv47w8HC4ubnBycnJYP1vv/1W5+KIyHgc7EhEdHcmB6CEhAT8+uuvmDdvXrWDoInIOgJUrkgZGlllsCNPeRMR/cHkQdBubm7Yv38/oqKizF2T1XEQNDUGjX2wIxHRXxnz+23yGaCIiAiUl3NQJZGtClC5MvgQEdXA5EHQ8+fPx6uvvordu3fj6tWr0Gq1BhMRERGRrTL5EpiDw63s9NexP0IISJKEysrKuldnJbwERkRE1PDUyyWwXbt2mbopERERkVWZFIAqKiowe/ZsLF++HK1btzZ3TUREREQWZdIYICcnJ/z000/mroWIiIioXpg8CPrZZ5/Fp59+as5aiIiIiOqFyWOAbt68iZUrV2LHjh2Ijo6Gu7u7wfqFCxfWuTgiIiIiSzA5AJ04cQL3338/AOCXX34xWMenQhMREZEt411gREREZHdMHgP0ZxcvXsTFixfNsatGT60pR0ZOEdQaPkWbiIjIWkwOQHq9HrNnz4ZKpUJISAhCQkLg5eWFOXPmQK/Xm7PGRmPj4XzEzt+Jp1ccROz8ndh4ON/aJREREdklky+BvfHGG/j0008xf/58xMbGAgDS09Mxa9YsXL9+HW+//bbZimwM1JpyJG8+Dv3/nrutF8C0zSfQq40P39dERERUz0wOQKtXr8Y///lPPProo/Kyjh07IigoCOPGjWMA+ovcolI5/NxWKQTyisoYgIiIiOqZyZfAfvvtN0RERFRZHhERgd9++61ORTVGYd7ucPjLzXEKSUKot5t1CiIiIrJjJgegqKgoLFmypMryJUuWICoqqk5FNUYBKlekDI2E4n+PCFBIEuYN7cCzP0RERFZg8iWwBQsW4JFHHsGOHTsQExMDANi/fz8uXLiArVu3mq3AxmRYlxbo1cYHeUVlCPV2Y/ghIiKyEkkIIe7erHqXLl3C0qVLcfr0aQBAu3btMG7cOAQGBpqtQGvQarVQqVTQaDTw9PS0djlERERUC8b8fht1Bmjo0KFYtWoVPD098dlnn2HYsGEc7ExEREQNjlFjgL777juUlpYCAEaNGgWNRmORooiIiIgsyagzQBEREUhOTkbfvn0hhMC//vWvGk8xjRgxwiwFEhEREZmbUWOAMjIykJSUhJycHPz222/w8PCo9sWnkiQ16FvhOQaIiIio4THm99vkQdAODg4oKCiAr6+vSUXaMgYgIiKihseY32+TnwOUm5sLHx8fUzcnIiIishqTA1BISAjS09Px7LPPIiYmBr/++isA4PPPP0d6errZCiQiIiIyN5MD0Jdffom4uDi4urri2LFj0Ol0AACNRoN58+aZrcDQ0FBIklRlGj9+fLXtV61aVaWti4uL2eohIiKihs/kADR37lwsX74cK1asgJOTk7w8NjYWmZmZZikOAA4fPgy1Wi1P27dvBwA88cQTNW7j6elpsM358+fNVg8RERE1fCa/CuPMmTPo1atXleUqlQrFxcV1qcnAX8cZzZ8/Hy1btkTv3r1r3EaSJPj7+5utBiJrUmvKkVtUijBvd74+hYjITEw+A+Tv74/s7Owqy9PT0xEeHl6nompy48YNrFmzBs8991y1t9/fVlJSgpCQEAQHB2Pw4ME4efLkHfer0+mg1WoNJiJbsPFwPmLn78TTKw4idv5ObDycb+2SiIgaBZMD0NixYzFx4kQcPHgQkiTh0qVLWLt2LV599VW8+OKL5qxR9tVXX6G4uBgjR46ssU3btm2xcuVKfP3111izZg30ej169OiBixcv1rhNSkoKVCqVPAUHB1ugeiLjqDXlSN58HPr/PahCL4Bpm09ArSm3bmFERI2Ayc8BEkJg3rx5SElJQVlZGQBAqVTitddeQ3JyMlxdzX+qPi4uDs7Ozvj2229rvU1FRQXatWuHhIQEzJkzp9o2Op1OHsQN3HqOQHBwMJ8DRFaVkVOEp1ccrLJ8/djuiGnZ3AoVERHZtnp5DpAkSXjjjTfw22+/4cSJEzhw4ACuXLkClUqFsLAwU3dbo/Pnz2PHjh0YM2aMUds5OTnhvvvuq/Zy3W1KpRKenp4GE5G1hXm7w+EvV3oVkoRQbzfrFERE1IgYHYB0Oh2Sk5PRuXNnxMbGYuvWrWjfvj1OnjyJtm3b4v3338crr7xi9kJTU1Ph6+uLRx55xKjtKisrcfz4cQQEBJi9JiJLClC5ImVoJBT/G++mkCTMG9qBA6GJiMzA6LvAZsyYgY8//hj9+/dHRkYGnnjiCYwaNQoHDhzAe++9hyeeeAIKhcKsRer1eqSmpiIxMRGOjoYljxgxAkFBQUhJSQEAzJ49G927d0erVq1QXFyMd999F+fPnzf6zBGRLRjWpQV6tfFBXlEZQr3dGH6IiMzE6AC0adMmfPbZZ3j00Udx4sQJdOzYETdv3sR///vfO96ZVRc7duxAfn4+nnvuuSrr8vPz4eDwx4ms33//HWPHjkVBQQGaNm2K6OhoZGRkoH379hapjcjSAlSuDD5ERGZm9CBoZ2dn5ObmIigoCADg6uqKQ4cOITIy0iIFWgNfhkpERNTwWHQQdGVlJZydneV5R0dHNGnSxPgqiYiIiKzE6EtgQgiMHDkSSqUSAHD9+nW88MILcHd3N2i3efNm81RIREREZGZGB6DExESD+WeffdZsxRARERHVB6MDUGpqqiXqICIiIqo3Jj8IkYiIiKihYgAiIiIiu8MAREREAG69gDcjp4gv3CW7YPQYICIianw2Hs5H8ubj0AvAQQJShkZiWJcW1i6LyGJ4BoiIyM6pNeVy+AEAvQCmbT7BM0HUqDEAERHZudyiUjn83FYpBPKKyqxTEFE9YAAiIrJzYd7ucPjLqxwVkoRQbzfrFERUDxiAiIjsXIDKFSlDI6H43wutFZKEeUM78CW81KhxEDQREWFYlxbo1cYHeUVlCPV2Y/ihRo8BiIiIANw6E8TgQ8ZQa8qRW1SKMG/3BvfdYQAiIiIiozX0RydwDBAREREZpTE8OoEBiIiIiIzSGB6dwABERERERmkMj05gACIiIiKjNIZHJ3AQNBERERmtoT86gQGIiIiITNKQH53AS2BERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO7YfACaNWsWJEkymCIiIu64zaZNmxAREQEXFxdERkZi69at9VQtERERNQQ2H4AA4N5774VarZan9PT0GttmZGQgISEBo0ePxrFjxxAfH4/4+HicOHGiHismIiIiW9YgApCjoyP8/f3lydvbu8a277//PgYMGIDXXnsN7dq1w5w5c3D//fdjyZIl9VgxERER2bIGEYDOnj2LwMBAhIeH45lnnkF+fn6Nbffv34/+/fsbLIuLi8P+/ftr3Ean00Gr1RpMRERE1HjZfADq1q0bVq1ahW3btmHZsmXIzc3FAw88gGvXrlXbvqCgAH5+fgbL/Pz8UFBQUONnpKSkQKVSyVNwcLBZj4GIiIhsi80HoIEDB+KJJ55Ax44dERcXh61bt6K4uBj/+te/zPYZycnJ0Gg08nThwgWz7ZuIiIhsj6O1CzCWl5cX2rRpg+zs7GrX+/v7o7Cw0GBZYWEh/P39a9ynUqmEUqk0a51ERERku2z+DNBflZSUICcnBwEBAdWuj4mJQVpamsGy7du3IyYmpj7KIyKi/1FrypGRUwS1ptzapRBVYfNngCZPnoxBgwYhJCQEly5dwsyZM6FQKJCQkAAAGDFiBIKCgpCSkgIAmDhxInr37o333nsPjzzyCDZs2IAjR47gk08+seZhEBHZlY2H85G8+Tj0AnCQgJShkRjWpYW1yyKS2fwZoIsXLyIhIQFt27bFk08+iebNm+PAgQPw8fEBAOTn50OtVsvte/TogXXr1uGTTz5BVFQUvvjiC3z11Vfo0KGDtQ6BiMiuqDXlcvgBAL0Apm0+wTNBZFMkIYSwdhG2RqvVQqVSQaPRwNPT09rlEBE1KBk5RXh6xcEqy9eP7Y6Yls2tUBHZC2N+v23+DBARUUPCcS9AmLc7HCTDZQpJQqi3m3UKIqoGAxARkZlsPJyP2Pk78fSKg4idvxMbD9f80NbGLEDlipShkVBIt1KQQpIwb2gHBKhcrVwZ0R94CawavARGRMZSa8oRO3+nPO4FuPXDnz61r93+8Ks15cgrKkOot5vd9gHVL2N+v23+LjAiooYgt6jUIPwAQKUQyCsqs9sf/wCVq90eO9k+XgIjIjIDjnshalgYgIiIzIDjXogaFl4CIyIyk2FdWqBXGx+OeyFqABiAiIjMiONeiBoGXgIjIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAii1JrypGRUwS1ptzapRAREcn4JGiymI2H85G8+Tj0AnCQgJShkRjWpYW1yyIiIuIZILIMtaZcDj8AoBfAtM0neCaIiIhsAgMQWURuUakcfm6rFAJ5RWXWKYiIiOhPGIDIIsK83eEgGS5TSBJCvd2sUxAREdGfMACRRQSoXJEyNBIK6VYKUkgS5g3twLdkExGRTeAgaLKYYV1aoFcbH+QVlSHU243hh4iIbAYDEFlUgMqVwYeIiGwOL4ER2Rk+m4mIiGeAiOwKn81ERHQLzwAR2Qk+m4mI6A8MQER2gs9mIiL6AwMQkZ3gs5mIiP7AAERkJ/hsJiKiP3AQNJEd4bOZiIhusfkzQCkpKejSpQs8PDzg6+uL+Ph4nDlz5o7brFq1CpIkGUwuLi71VDGRbQtQuSKmZXOGHyKyazYfgPbs2YPx48fjwIED2L59OyoqKvDwww+jtLT0jtt5enpCrVbL0/nz5+upYiIiIrJ1Nn8JbNu2bQbzq1atgq+vL44ePYpevXrVuJ0kSfD397d0eURERNQA2fwZoL/SaDQAgGbNmt2xXUlJCUJCQhAcHIzBgwfj5MmTNbbV6XTQarUGExERETVeDSoA6fV6TJo0CbGxsejQoUON7dq2bYuVK1fi66+/xpo1a6DX69GjRw9cvHix2vYpKSlQqVTyFBwcbKlDICIiIhsgCSHE3ZvZhhdffBH//ve/kZ6ejnvuuafW21VUVKBdu3ZISEjAnDlzqqzX6XTQ6XTyvFarRXBwMDQaDTw9Pc1SOxEREVmWVquFSqWq1e+3zY8Bum3ChAn47rvvsHfvXqPCDwA4OTnhvvvuQ3Z2drXrlUollEqlOcokIiKiBsDmL4EJITBhwgRs2bIFO3fuRFhYmNH7qKysxPHjxxEQEGCBComIiKihsfkzQOPHj8e6devw9ddfw8PDAwUFBQAAlUoFV9dbzzEZMWIEgoKCkJKSAgCYPXs2unfvjlatWqG4uBjvvvsuzp8/jzFjxljtOIgsQa0pR25RKcK83flcHyIiI9h8AFq2bBkAoE+fPgbLU1NTMXLkSABAfn4+HBz+OJn1+++/Y+zYsSgoKEDTpk0RHR2NjIwMtG/fvr7KJrK4jYfz5be7O0hAytBIDOvSwtplERE1CA1qEHR9MWYQFZE1qDXliJ2/0+Dt7gpJQvrUvjwTRER2y5jfb5sfA0REVeUWlRqEHwCoFAJ5RWXWKYiIqIFhACJqgMK83eEgGS5TSBJCvd2sUxARUQPDAETUAAWoXJEyNBIK6VYKUkgS5g3twMtfRES1ZPODoKlh411KljOsSwv0auODvKIyhHq7sX+JiIzAAEQWw7uULC9A5crgQ0RkAl4CI4tQa8rl8AMAegFM23wCak25dQsjIiICAxBZCO9SIiIiW8YARBbBu5SIiMiWMQCRRfAuJSIismUcBE0Ww7uUiIjIVjEAkUXxLiUiIrJFvARGREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERHRXak15cjIKeL7/KjR4HOAiIjojjYezpdfbuwgASlDIzGsSwtrl0VUJzwDRERENVJryuXwAwB6AUzbfIJngqjBYwAiIqIa5RaVyuHntkohkFdUZp2CiMyEAYiIiGoU5u0OB8lwmUKSEOrtZp2CiMyEAYiIiGoUoHJFytBIKKRbKUghSZg3tAPf8UcNHgdBExHRHQ3r0gK92vggr6gMod5uDD/UKDAAERHRXQWoXK0efNSacuQWlSLM293qtVDDxwDUwPEPAhHZA96KT+bGANSA8Q8CNVYM9vRnNd2K36uND78fZDIGoAaKfxCosWKwp7+60634/HtHpuJdYA0Un81BjREfukfV4a34ZAkMQA0U/yBQY8RgT9XhrfhkCQ0iAC1duhShoaFwcXFBt27dcOjQoTu237RpEyIiIuDi4oLIyEhs3bq1niqtP/yDQI0Rgz3VZFiXFkif2hfrx3ZH+tS+vCxKdWbzAWjjxo1ISkrCzJkzkZmZiaioKMTFxeHy5cvVts/IyEBCQgJGjx6NY8eOIT4+HvHx8Thx4kQ9V255/INgPXwztmUw2NOdBKhcEdOyOb8PZBaSEELcvZn1dOvWDV26dMGSJUsAAHq9HsHBwXjppZcwderUKu2HDRuG0tJSfPfdd/Ky7t27o1OnTli+fHmtPlOr1UKlUkGj0cDT09M8B0KNBgfpWp5aU86H7hGR0Yz5/bbpM0A3btzA0aNH0b9/f3mZg4MD+vfvj/3791e7zf79+w3aA0BcXFyN7YmMwUG69YP/pU9ElmbTt8EXFRWhsrISfn5+Bsv9/Pxw+vTparcpKCiotn1BQUGNn6PT6aDT6eR5rVZbh6qpMePtuEREjYNNnwGqLykpKVCpVPIUHBxs7ZLIRnGQLhFR42DTAcjb2xsKhQKFhYUGywsLC+Hv71/tNv7+/ka1B4Dk5GRoNBp5unDhQt2Lp0aJg3SJiBoHm74E5uzsjOjoaKSlpSE+Ph7ArUHQaWlpmDBhQrXbxMTEIC0tDZMmTZKXbd++HTExMTV+jlKphFKpNGfp1IjxzdhERA2fTQcgAEhKSkJiYiI6d+6Mrl27YvHixSgtLcWoUaMAACNGjEBQUBBSUlIAABMnTkTv3r3x3nvv4ZFHHsGGDRtw5MgRfPLJJ9Y8DGpkbOHN2EREZDqbD0DDhg3DlStXMGPGDBQUFKBTp07Ytm2bPNA5Pz8fDg5/XMnr0aMH1q1bhzfffBPTpk1D69at8dVXX6FDhw7WOgQiIiKyMTb/HCBr4HOAiIiIGp5G8xwgIiIiIktgACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd2x+VdhWMPth2NrtVorV0JERES1dft3uzYvuWAAqsa1a9cAAMHBwVauhIiIiIx17do1qFSqO7bhu8CqodfrcenSJXh4eECSpGrbaLVaBAcH48KFC3b/vjD2xS3sh1vYD39gX9zCfriF/fAHS/WFEALXrl1DYGCgwYvSq8MzQNVwcHDAPffcU6u2np6edv9Fvo19cQv74Rb2wx/YF7ewH25hP/zBEn1xtzM/t3EQNBEREdkdBiAiIiKyOwxAJlIqlZg5cyaUSqW1S7E69sUt7Idb2A9/YF/cwn64hf3wB1voCw6CJiIiIrvDM0BERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MAdAdLly5FaGgoXFxc0K1bNxw6dKjGtidPnsRjjz2G0NBQSJKExYsX11+h9cCYvlixYgUeeOABNG3aFE2bNkX//v3v2L4hMaYfNm/ejM6dO8PLywvu7u7o1KkTPv/883qs1nKM6Yc/27BhAyRJQnx8vGULrEfG9MWqVasgSZLB5OLiUo/VWo6x34ni4mKMHz8eAQEBUCqVaNOmDbZu3VpP1VqOMf3Qp0+fKt8HSZLwyCOP1GPFlmHs92Hx4sVo27YtXF1dERwcjFdeeQXXr1+3bJGCqrVhwwbh7OwsVq5cKU6ePCnGjh0rvLy8RGFhYbXtDx06JCZPnizWr18v/P39xaJFi+q3YAsyti+efvppsXTpUnHs2DFx6tQpMXLkSKFSqcTFixfruXLzMrYfdu3aJTZv3ix+/vlnkZ2dLRYvXiwUCoXYtm1bPVduXsb2w225ubkiKChIPPDAA2Lw4MH1U6yFGdsXqampwtPTU6jVankqKCio56rNz9h+0Ol0onPnzuJvf/ubSE9PF7m5uWL37t0iKyurnis3L2P74erVqwbfhRMnTgiFQiFSU1Prt3AzM7Yf1q5dK5RKpVi7dq3Izc0VP/zwgwgICBCvvPKKRetkAKpB165dxfjx4+X5yspKERgYKFJSUu66bUhISKMKQHXpCyGEuHnzpvDw8BCrV6+2VIn1oq79IIQQ9913n3jzzTctUV69MaUfbt68KXr06CH++c9/isTExEYTgIzti9TUVKFSqeqpuvpjbD8sW7ZMhIeHixs3btRXifWirn8jFi1aJDw8PERJSYmlSqwXxvbD+PHjxYMPPmiwLCkpScTGxlq0Tl4Cq8aNGzdw9OhR9O/fX17m4OCA/v37Y//+/VasrP6Zoy/KyspQUVGBZs2aWapMi6trPwghkJaWhjNnzqBXr16WLNWiTO2H2bNnw9fXF6NHj66PMuuFqX1RUlKCkJAQBAcHY/DgwTh58mR9lGsxpvTDN998g5iYGIwfPx5+fn7o0KED5s2bh8rKyvoq2+zM8bfy008/xVNPPQV3d3dLlWlxpvRDjx49cPToUfky2blz57B161b87W9/s2itfBlqNYqKilBZWQk/Pz+D5X5+fjh9+rSVqrIOc/TFlClTEBgYaPB/iIbG1H7QaDQICgqCTqeDQqHARx99hIceesjS5VqMKf2Qnp6OTz/9FFlZWfVQYf0xpS/atm2LlStXomPHjtBoNPjHP/6BHj164OTJk7V+AbOtMaUfzp07h507d+KZZ57B1q1bkZ2djXHjxqGiogIzZ86sj7LNrq5/Kw8dOoQTJ07g008/tVSJ9cKUfnj66adRVFSEnj17QgiBmzdv4oUXXsC0adMsWisDEFnU/PnzsWHDBuzevbvRDPY0hoeHB7KyslBSUoK0tDQkJSUhPDwcffr0sXZp9eLatWsYPnw4VqxYAW9vb2uXY3UxMTGIiYmR53v06IF27drh448/xpw5c6xYWf3S6/Xw9fXFJ598AoVCgejoaPz666949913G2wAqqtPP/0UkZGR6Nq1q7VLqXe7d+/GvHnz8NFHH6Fbt27Izs7GxIkTMWfOHEyfPt1in8sAVA1vb28oFAoUFhYaLC8sLIS/v7+VqrKOuvTFP/7xD8yfPx87duxAx44dLVmmxZnaDw4ODmjVqhUAoFOnTjh16hRSUlIabAAyth9ycnKQl5eHQYMGycv0ej0AwNHREWfOnEHLli0tW7SFmOPvhJOTE+677z5kZ2dbosR6YUo/BAQEwMnJCQqFQl7Wrl07FBQU4MaNG3B2drZozZZQl+9DaWkpNmzYgNmzZ1uyxHphSj9Mnz4dw4cPx5gxYwAAkZGRKC0txfPPP4833ngDDg6WGa3DMUDVcHZ2RnR0NNLS0uRler0eaWlpBv/1Zg9M7YsFCxZgzpw52LZtGzp37lwfpVqUub4Ter0eOp3OEiXWC2P7ISIiAsePH0dWVpY8Pfroo+jbty+ysrIQHBxcn+WblTm+E5WVlTh+/DgCAgIsVabFmdIPsbGxyM7OlsMwAPzyyy8ICAhokOEHqNv3YdOmTdDpdHj22WctXabFmdIPZWVlVULO7XAsLPm6UosOsW7ANmzYIJRKpVi1apX4+eefxfPPPy+8vLzkW1aHDx8upk6dKrfX6XTi2LFj4tixYyIgIEBMnjxZHDt2TJw9e9Zah2A2xvbF/PnzhbOzs/jiiy8MbvG8du2atQ7BLIzth3nz5okff/xR5OTkiJ9//ln84x//EI6OjmLFihXWOgSzMLYf/qox3QVmbF+89dZb4ocffhA5OTni6NGj4qmnnhIuLi7i5MmT1joEszC2H/Lz84WHh4eYMGGCOHPmjPjuu++Er6+vmDt3rrUOwSxM/f9Gz549xbBhw+q7XIsxth9mzpwpPDw8xPr168W5c+fEjz/+KFq2bCmefPJJi9bJAHQHH374oWjRooVwdnYWXbt2FQcOHJDX9e7dWyQmJsrzubm5AkCVqXfv3vVfuAUY0xchISHV9sXMmTPrv3AzM6Yf3njjDdGqVSvh4uIimjZtKmJiYsSGDRusULX5GdMPf9WYApAQxvXFpEmT5LZ+fn7ib3/7m8jMzLRC1eZn7HciIyNDdOvWTSiVShEeHi7efvttcfPmzXqu2vyM7YfTp08LAOLHH3+s50oty5h+qKioELNmzRItW7YULi4uIjg4WIwbN078/vvvFq1REsKS55eIiIiIbA/HABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIisaOTIkYiPj7d2GUR2hwGIiKo1cuRISJIkT82bN8eAAQPw008/Wbs0s/jzsd2eevbsabHPy8vLgyRJyMrKMlj+/vvvY9WqVRb7XCKqHgMQEdVowIABUKvVUKvVSEtLg6OjI/7+979buyyzSU1NlY9PrVbjm2++qbZdRUWFxWpQqVTw8vKy2P6JqHoMQERUI6VSCX9/f/j7+6NTp06YOnUqLly4gCtXruDBBx/EhAkTDNpfuXIFzs7O8pugQ0NDMWfOHCQkJMDd3R1BQUFYunSpwTYLFy5EZGQk3N3dERwcjHHjxqGkpERef/78eQwaNAhNmzaFu7s77r33XmzduhUA8Pvvv+OZZ56Bj48PXF1d0bp1a6Smptb6+Ly8vOTj8/f3R7NmzeQzNRs3bkTv3r3h4uKCtWvX4urVq0hISEBQUBDc3NwQGRmJ9evXG+xPr9djwYIFaNWqFZRKJVq0aIG3334bABAWFgYAuO+++yBJEvr06QOg6iUwnU6Hl19+Gb6+vnBxcUHPnj1x+PBhef3u3bshSRLS0tLQuXNnuLm5oUePHjhz5kytj5uIGICIqJZKSkqwZs0atGrVCs2bN8eYMWOwbt066HQ6uc2aNWsQFBSEBx98UF727rvvIioqCseOHcPUqVMxceJEbN++XV7v4OCADz74ACdPnsTq1auxc+dOvP766/L68ePHQ6fTYe/evTh+/DjeeecdNGnSBAAwffp0/Pzzz/j3v/+NU6dOYdmyZfD29jbL8d6u9dSpU4iLi8P169cRHR2N77//HidOnMDzzz+P4cOH49ChQ/I2ycnJmD9/vlzXunXr4OfnBwByux07dkCtVmPz5s3Vfu7rr7+OL7/8EqtXr0ZmZiZatWqFuLg4/Pbbbwbt3njjDbz33ns4cuQIHB0d8dxzz5nluInshkVftUpEDVZiYqJQKBTC3d1duLu7CwAiICBAHD16VAghRHl5uWjatKnYuHGjvE3Hjh3FrFmz5PmQkBAxYMAAg/0OGzZMDBw4sMbP3bRpk2jevLk8HxkZabDPPxs0aJAYNWqUSccHQLi4uMjH5+7uLrZs2SJyc3MFALF48eK77uORRx4Rr776qhBCCK1WK5RKpVixYkW1bW/v99ixYwbLExMTxeDBg4UQQpSUlAgnJyexdu1aef2NGzdEYGCgWLBggRBCiF27dgkAYseOHXKb77//XgAQ5eXlxnQBkV3jGSAiqlHfvn2RlZWFrKwsHDp0CHFxcRg4cCDOnz8PFxcXDB8+HCtXrgQAZGZm4sSJExg5cqTBPmJiYqrMnzp1Sp7fsWMH+vXrh6CgIHh4eGD48OG4evUqysrKAAAvv/wy5s6di9jYWMycOdNgEPaLL76IDRs2oFOnTnj99deRkZFh1PEtWrRIPr6srCw89NBD8rrOnTsbtK2srMScOXMQGRmJZs2aoUmTJvjhhx+Qn58PADh16hR0Oh369etnVA1/lpOTg4qKCsTGxsrLnJyc0LVrV4M+A4COHTvK/w4ICAAAXL582eTPJrI3DEBEVCN3d3e0atUKrVq1QpcuXfDPf/4TpaWlWLFiBQBgzJgx2L59Oy5evIjU1FQ8+OCDCAkJqfX+8/Ly8Pe//x0dO3bEl19+iaNHj8pjhG7cuCF/xrlz5zB8+HAcP34cnTt3xocffggAchh75ZVXcOnSJfTr1w+TJ0+u9ef7+/vLx9eqVSu4u7sbHPufvfvuu3j//fcxZcoU7Nq1C1lZWYiLi5PrdHV1rfXnmoOTk5P8b0mSANwag0REtcMARES1JkkSHBwcUF5eDgCIjIxE586dsWLFCqxbt67acSgHDhyoMt+uXTsAwNGjR6HX6/Hee++he/fuaNOmDS5dulRlH8HBwXjhhRewefNmvPrqq3IAAwAfHx8kJiZizZo1WLx4MT755BNzHrJs3759GDx4MJ599llERUUhPDwcv/zyi7y+devWcHV1lQeA/5WzszOAW2eSatKyZUs4Oztj37598rKKigocPnwY7du3N9OREBEAOFq7ACKyXTqdDgUFBQBu3XG1ZMkSlJSUYNCgQXKbMWPGYMKECXB3d8eQIUOq7GPfvn1YsGAB4uPjsX37dmzatAnff/89AKBVq1aoqKjAhx9+iEGDBmHfvn1Yvny5wfaTJk3CwIED0aZNG/z+++/YtWuXHKBmzJiB6Oho3HvvvdDpdPjuu+/kdebWunVrfPHFF8jIyEDTpk2xcOFCFBYWysHExcUFU6ZMweuvvw5nZ2fExsbiypUrOHnyJEaPHg1fX1+4urpi27ZtuOeee+Di4gKVSmXwGe7u7njxxRfx2muvoVmzZmjRogUWLFiAsrIyjB492iLHRWSveAaIiGq0bds2BAQEICAgAN26dcPhw4exadMm+RZuAEhISICjoyMSEhLg4uJSZR+vvvoqjhw5gvvuuw9z587FwoULERcXBwCIiorCwoUL8c4776BDhw5Yu3YtUlJSDLavrKzE+PHj0a5dOwwYMABt2rTBRx99BODWWZXk5GR07NgRvXr1gkKhwIYNGyzSF2+++Sbuv/9+xMXFoU+fPvD396/yBOfp06fj1VdfxYwZM9CuXTsMGzZMHpfj6OiIDz74AB9//DECAwMxePDgaj9n/vz5eOyxxzB8+HDcf//9yM7Oxg8//ICmTZta5LiI7JUkhBDWLoKIGq68vDy0bNkShw8fxv3332+wLjQ0FJMmTcKkSZOsUxwRUQ14CYyITFJRUYGrV6/izTffRPfu3auEHyIiW8ZLYERkkn379iEgIACHDx+uMm7H2ubNm4cmTZpUOw0cONDa5RGRDeAlMCJqdH777bcqT06+zdXVFUFBQfVcERHZGgYgIiIisju8BEZERER2hwGIiIiI7A4DEBEREdkdBiAiIiKyOwxAREREZHcYgIiIiMjuMAARERGR3WEAIiIiIrvz/54a+Tn3rnT2AAAAAElFTkSuQmCC", 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", 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", + "image/png": 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", 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", 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", 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", 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WpXH2faUhPbNqj67pFKzwAF+31gYAaBweeughWSwWbdu2TX5+frblV1xxhe655x7b+8zMTL322mvasWOHwsPDa72/0aNHSzobrqpTWFiod955Rx999JGuvfZaSdKSJUvUpUsX/fOf/9RVV11V633XlFtDztChQzV06NAat1+3bp3+8Y9/6PDhwwoMDJQkRUdHO6m6mssqKLEFnHMqDEPZBacIOQDQSOUWliqroERtg/yc/l3w888/a926dXrppZfsAs45LVq0kCSdOnVKo0aNUlpamsLCwpxaU0ZGhk6fPq34+Hjbss6dOysqKkpbt241f8hx1Jo1a9S7d2+9/PLLev/99+Xn56dbb71VM2fOlK9v9T9AZWVlKisrs70vKiqq87raBvnJwyK7oONpsSg6iPssAEBjtHx7jm2E38MipQyP0Yg+UU7b38GDB2UYhjp37nzBdpMnT1b//v112223XbBd//79qzxDqrS0VD179qxxTXl5efL29rYFrHNCQ0OVl5dX4+1cigYVcg4fPqyvv/5aPj4+Wr16tQoKCvTQQw/pp59+0pIlS6pdJyUlRTNmzHBqXeEBvkoZHqNnVu1RhWHI02LRrOHdGMUBgEbIHVMYDMO4aJs1a9Zo48aN2rVr10XbLl++XF26dLFbdvfdd9e6PndpUCGnsrJSFotFH374oQICAiRJc+fO1R133KH58+dXO5qTnJyspKQk2/uioiJFRkbWeW0j+kTpmk7Byi44peigpgQcAGik3DGFoWPHjrJYLNq/f/9522zcuFGHDh2qMrJy++236+qrr9amTZtsyyIjI9WhQwe7duc7Y3I+YWFhKi8v14kTJ+z2mZ+f7/RTZec0qEvIw8PDFRERYQs4ktSlSxcZhqEffvih2nWsVqv8/f3tXk6rL8BXce1bEXAAoBE7N4Xht5w9hSEwMFAJCQlKS0tTSUlJlc9PnDihKVOm6JtvvlFmZqbtJUnz5s0779mQSxEbG6smTZrYXb5+4MAB5eTkKC4urs73V50GNZIzYMAArVixQsXFxWrWrJkk6dtvv5WHh4cuu+wyN1cHAID7pjCkpaVpwIAB6tu3r1544QV1795dZ86c0YYNG7RgwQLt27ev2hGUqKgotW3b1uH9/fzzz8rJydGxY8cknQ0w0tkRnLCwMAUEBOjee+9VUlKSAgMD5e/vr4cfflhxcXEumXQsuTnkFBcX6+DBg7b3WVlZyszMVGBgoKKiopScnKyjR4/qvffekySNGjVKM2fO1Pjx4zVjxgwVFBToySef1D333OPwMBoAAM7ijikM7dq1086dO/XSSy/p8ccfV25uroKDgxUbG6sFCxbU+f7WrFmj8ePH297fddddkqTp06fr+eefl3R2lMjDw0O33367ysrKlJCQoPnz59d5LedjMWoyW8lJNm3apCFDhlRZPnbsWC1dulTjxo1Tdna23XnC/fv36+GHH9bmzZvVqlUr3XnnnXrxxRdrHHKKiooUEBCgwsJCp566AgA0PL/++quysrLUtm1b+fj4uLucRudC/V+b72+3juQMHjz4gjPCly5dWmVZ586dtWHDBidWBQAAzKBBTTwGAACoKUIOAAAwJUIOAAAwJUIOAAD/w43X5DRqdd3vhBwAAP6rSZMmks4+yBKuV15eLkny9PSsk+01qJsBAgDgTJ6enmrRooV+/PFHSVLTpk1lsVgushbqQmVlpY4fP66mTZvKy6tu4gkhBwCA3zh3V+BzQQeu4+HhoaioqDoLloQcAAB+w2KxKDw8XCEhITp9+rS7y2lUvL295eFRdzNpCDkAAFTD09OzzuaGwD2YeAwAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEzJrSHnq6++UmJiolq3bi2LxaJPPvmkxutu3rxZXl5e6tmzp9PqAwAADZdbQ05JSYl69OihtLQ0h9Y7ceKExowZo+uuu85JlQEAgIbOy507Hzp0qIYOHerweg888IBGjRolT09Ph0Z/AABA49Hg5uQsWbJEhw8f1vTp02vUvqysTEVFRXYvAABgfg0q5Hz33XeaMmWKPvjgA3l51WwQKiUlRQEBAbZXZGSkk6sEAAD1QYMJORUVFRo1apRmzJihTp061Xi95ORkFRYW2l5HjhxxYpUAAKC+cOucHEecPHlSO3bs0K5duzRp0iRJUmVlpQzDkJeXlz7//HNde+21VdazWq2yWq2uLhcAALhZgwk5/v7+2r17t92y+fPna+PGjVq5cqXatm3rpsoAAEB95NaQU1xcrIMHD9reZ2VlKTMzU4GBgYqKilJycrKOHj2q9957Tx4eHurWrZvd+iEhIfLx8amyHAAAwK0hZ8eOHRoyZIjtfVJSkiRp7NixWrp0qXJzc5WTk+Ou8gAAQANmMQzDcHcRrlRUVKSAgAAVFhbK39/f3eUAAIAaqM33d4O5ugoAAMARhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKbg05X331lRITE9W6dWtZLBZ98sknF2y/atUqXX/99QoODpa/v7/i4uK0fv161xQLAAAaFLeGnJKSEvXo0UNpaWk1av/VV1/p+uuv19q1a5WRkaEhQ4YoMTFRu3btcnKlAACgobEYhmG4uwhJslgsWr16tYYNG+bQeldccYVGjBihadOm1ah9UVGRAgICVFhYKH9//1pUCgAAXK02399eTq7JqSorK3Xy5EkFBgaet01ZWZnKysps74uKilxRGgAAcLMGPfH41VdfVXFxse68887ztklJSVFAQIDtFRkZ6cIKAQCAuzTYkPPRRx9pxowZ+stf/qKQkJDztktOTlZhYaHtdeTIERdWCQAA3KVBnq5atmyZJkyYoBUrVig+Pv6Cba1Wq6xWq4sqAwAA9UWDG8n5+OOPNX78eH388ce6+eab3V0OAACop9w6klNcXKyDBw/a3mdlZSkzM1OBgYGKiopScnKyjh49qvfee0/S2VNUY8eO1euvv65+/fopLy9PkuTr66uAgAC3HAMAAKif3DqSs2PHDvXq1Uu9evWSJCUlJalXr162y8Fzc3OVk5Nja//WW2/pzJkzmjhxosLDw22vRx991C31AwCA+qve3CfHVbhPDgAADU9tvr8b3JwcAACAmiDkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAHBYbmGpthwqUG5hqbtLAYDzcmvI+eqrr5SYmKjWrVvLYrHok08+ueg6mzZt0pVXXimr1aoOHTpo6dKlTq8TwP9Zvj1HA2Zv1KhF/9KA2Ru1fHuOu0sCgGq5NeSUlJSoR48eSktLq1H7rKws3XzzzRoyZIgyMzP12GOPacKECVq/fr2TKwUgnR3BSV61W5XG2feVhvTMqj2M6ACol7zcufOhQ4dq6NChNW6/cOFCtW3bVq+99pokqUuXLvr66681b948JSQkOKtMAP+VVVBiCzjnVBiGsgtOKTzA1z1FAcB5NKg5OVu3blV8fLzdsoSEBG3duvW865SVlamoqMjuBaB22gb5ycNiv8zTYlF0UFP3FAQAF9CgQk5eXp5CQ0PtloWGhqqoqEilpdUPl6ekpCggIMD2ioyMdEWpgCmFB/gqZXiMPC1nk46nxaJZw7sxigOgXnLr6SpXSE5OVlJSku19UVERQQe4BCP6ROmaTsHKLjil6KCmBBwA9VaDCjlhYWHKz8+3W5afny9/f3/5+lb/D63VapXVanVFeUCjER7gS7gB6pHcwlJlFZSobZAfv5u/0aBCTlxcnNauXWu3bMOGDYqLi3NTRQAAuNfy7Tm2qx49LFLK8BiN6BPl7rLqBbfOySkuLlZmZqYyMzMlnb1EPDMzUzk5Z++7kZycrDFjxtjaP/DAAzp8+LCeeuop7d+/X/Pnz9df/vIXTZ482R3lAwDgVtzW4cLcGnJ27NihXr16qVevXpKkpKQk9erVS9OmTZMk5ebm2gKPJLVt21afffaZNmzYoB49eui1117T22+/zeXjAIBG6UK3dYBkMQzDuHgz8ygqKlJAQIAKCwvl7+/v7nIAAKi13MJSDZi90S7oeFos+nrKENPNzanN93eDuoQcAFyBZ3OhoeC2DhfWoCYeA4CzMYkTDQ23dTg/RnIA4L+YxImGKjzAV3HtWxFw/gchBwD+i0mcgLkQcgDgv3g2F2AuhBwA+C8mcQLmwsRjAPgNJnEC5kHIAYD/wbO5AHPgdBUAADAlQg4AADAlQg4AADAlQg4AADClWk083rZtm7Zu3aq8vDxJUlhYmOLi4tS3b986LQ4AAKC2HAo5P/74o26//XZt3rxZUVFRCg0NlSTl5+dr8uTJGjBggP76178qJCTEKcUCAADUlEOnqx566CFVVFRo3759ys7O1r/+9S/961//UnZ2tvbt26fKykpNnDjRWbUCAADUmMUwDOPizc5q3ry5vvrqK/Xq1avazzMyMjR48GCdPHmyzgqsa0VFRQoICFBhYaH8/f3dXQ4AAKiB2nx/OzSSY7VaVVRUdN7PT548KavV6sgmAQAAnMKhkDNixAiNHTtWq1evtgs7RUVFWr16tcaPH6+RI0fWeZEAAACOcmji8dy5c1VZWam77rpLZ86ckbe3tySpvLxcXl5euvfee/Xqq686pVAAAABHODQn55yioiJlZGTYXUIeGxvbIOa4MCcHAICGpzbf37W6T46/v7+GDBlSm1UBAABcok7veJyfn68XXnihLjcJAABQK3UacvLy8jRjxoy63CQAAECtOHS66ptvvrng5wcOHLikYgAAAOqKQyGnZ8+eslgsqm6u8rnlFoulzooDAACoLYdCTmBgoF5++WVdd9111X6+d+9eJSYm1klhAAAAl8KhkBMbG6tjx46pTZs21X5+4sSJakd5AAAAXM2hkPPAAw+opKTkvJ9HRUVpyZIll1wUAADAparVzQAbMm4GCABAw+P0B3QCAAA0FA6drkpKSqpRu7lz59aqGAAAgLriUMjZtWuX3fuvv/5asbGx8vX1tS2rzSXkaWlpeuWVV5SXl6cePXrojTfeUN++fc/bPjU1VQsWLFBOTo6CgoJ0xx13KCUlRT4+Pg7vGwAAmJNDIefLL7+0e9+8eXN99NFHateuXa0LWL58uZKSkrRw4UL169dPqampSkhI0IEDBxQSElKl/UcffaQpU6Zo8eLF6t+/v7799luNGzdOFouFESQAAGDj9jk5c+fO1X333afx48era9euWrhwoZo2barFixdX237Lli0aMGCARo0apejoaN1www0aOXKktm3b5uLKAQBAfebWkFNeXq6MjAzFx8fblnl4eCg+Pl5bt26tdp3+/fsrIyPDFmoOHz6stWvX6qabbqq2fVlZmYqKiuxeAADA/Bw6XVXXCgoKVFFRodDQULvloaGh2r9/f7XrjBo1SgUFBRo4cKAMw9CZM2f0wAMP6Jlnnqm2fUpKCg8NBQCgEbqkB3QahqH9+/eruLjYbnn37t0vvbLz2LRpk2bNmqX58+erX79+OnjwoB599FHNnDlTU6dOrdI+OTnZ7qqwoqIiRUZGOq0+AABQP1zyAzpvueUWSfYP6KyoqKjR9oKCguTp6an8/Hy75fn5+QoLC6t2nalTp2r06NGaMGGCJCkmJkYlJSW6//779eyzz8rDw/4MnNVqldVqrfExmkVuYamyCkrUNshP4QG+F18BAACTcSjkZGVl1enOvb29FRsbq/T0dA0bNkySVFlZqfT0dE2aNKnadU6dOlUlyHh6ekoSz836r+Xbc5S8arcqDcnDIqUMj9GIPlHuLgsAAJdyKOSc78GclyIpKUljx45V79691bdvX6WmpqqkpETjx4+XJI0ZM0YRERFKSUmRJCUmJmru3Lnq1auX7XTV1KlTlZiYaAs7jVluYakt4EhSpSE9s2qPrukUzIgOAKBRqZOJx9dee62WLFlSqxA0YsQIHT9+XNOmTVNeXp569uypdevW2SYj5+Tk2I3cPPfcc7JYLHruued09OhRBQcHKzExUS+99FJdHEqDl1VQYgs451QYhrILThFyAACNikMP6FyzZk21y4cPH67XX3/dNqH31ltvrZvqnMDsD+jMLSzVgNkb7YKOp8Wir6cMIeQAABqs2nx/OxRyPDw8qkw8rrJBByYeu4PZQ450dk7OM6v2qMIw5GmxaNbwbszJAQA0aLX5/nbodFVCQoI8PT21ePFiu0cuNGnSRP/+97/VtWtXxyqGU4zoE6VrOgUru+CUooOaMoIDAGiUHLrj8d///nddd9116t27tz799FNn1YQ6EB7gq7j2rQg4AIBGy+HHOkyePFlr1qzR008/rT/+8Y86deqUM+oCAAC4JLV6dlXPnj21Y8cOWSwW9ezZk/vTAACAeqfWl5D7+vpq4cKFWrNmjb788ksFBQXVZV0AAACXxKGRnI0bN6pr1652T/K+9dZbNW/ePFmtVl1xxRX6//6//6/OiwQAAHCUQyEnNTVV9913X7WXbgUEBOiPf/yj5s6dW2fFAQAA1JZDIeff//63brzxxvN+fsMNNygjI+OSiwIAALhUDoWc/Px8NWnS5Lyfe3l56fjx45dcFAAAwKVyKORERERoz5495/38m2++UXh4+CUXBQAAcKkcCjk33XSTpk6dql9//bXKZ6WlpZo+fbpuueWWOisOAABcmtzCUm05VKDcwlJ3l+JyDj27Kj8/X1deeaU8PT01adIkXX755ZKk/fv3Ky0tTRUVFdq5c6ftCeL1UWN4dhUAANLZZxkmr9qtSkPysEgpw2Ma7LMMnf6ATkn6/vvv9eCDD2r9+vW2mwBaLBYlJCQoLS1Nbdu2dbxyFyLkAAAag9zCUg2YvVGVv/mW97RY9PWUIQ3ykT9Of0CnJLVp00Zr167VL7/8ooMHD8owDHXs2FEtW7Z0uGAAAOAcWQUldgFHkioMQ9kFpxpkyKmNWt/xuGXLlurTp09d1gIAAOpI2yA/eVhUZSQnOqip+4pysVo9uwoAANRv4QG+ShkeI0+LRdLZgDNreLdGM4ojXcJIDgAAqN9G9InSNZ2ClV1wStFBTRtVwJEIOQAAmFp4gG+jCzfncLoKAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYUr0IOWlpaYqOjpaPj4/69eunbdu2XbD9iRMnNHHiRIWHh8tqtapTp05au3ati6oFAAANgdufQr58+XIlJSVp4cKF6tevn1JTU5WQkKADBw4oJCSkSvvy8nJdf/31CgkJ0cqVKxUREaHvv/9eLVq0cH3xAACg3rIYhmG4s4B+/fqpT58+evPNNyVJlZWVioyM1MMPP6wpU6ZUab9w4UK98sor2r9/v5o0aeLw/oqKihQQEKDCwkL5+/tfcv0AAMD5avP97dbTVeXl5crIyFB8fLxtmYeHh+Lj47V169Zq11mzZo3i4uI0ceJEhYaGqlu3bpo1a5YqKiqqbV9WVqaioiK7FwAAMD+3hpyCggJVVFQoNDTUbnloaKjy8vKqXefw4cNauXKlKioqtHbtWk2dOlWvvfaaXnzxxWrbp6SkKCAgwPaKjIys8+MAAAD1T72YeOyIyspKhYSE6K233lJsbKxGjBihZ599VgsXLqy2fXJysgoLC22vI0eOuLhiAADgDm6deBwUFCRPT0/l5+fbLc/Pz1dYWFi164SHh6tJkyby9PS0LevSpYvy8vJUXl4ub29vu/ZWq1VWq7XuiwcAAPWaW0dyvL29FRsbq/T0dNuyyspKpaenKy4urtp1BgwYoIMHD6qystK27Ntvv1V4eHiVgAMAABovt5+uSkpK0qJFi/Tuu+9q3759evDBB1VSUqLx48dLksaMGaPk5GRb+wcffFA///yzHn30UX377bf67LPPNGvWLE2cONFdhwAAAOoht98nZ8SIETp+/LimTZumvLw89ezZU+vWrbNNRs7JyZGHx/9lscjISK1fv16TJ09W9+7dFRERoUcffVRPP/20uw4BAADUQ26/T46rcZ8cAAAangZ3nxwAAABnIeQAAABTIuQAqFZuYam2HCpQbmGpu0sBgFpx+8RjAPXP8u05Sl61W5WG5GGRUobHaESfKHeXBQAOYSQHMDlHR2RyC0ttAUeSKg3pmVV7GNEB0OAwkgOYWG1GZLIKSmwB55wKw1B2wSmFB/g6sVoAqFuM5AAmVdsRmbZBfvKw2C/ztFgUHdTUSZUCgHMQcgCTutCIzIWEB/gqZXiMPC1nk46nxaJZw7sxigOgweF0FWBS50Zkfht0ajoiM6JPlK7pFKzsglOKDmpKwAHQIDGSA5jUpY7IhAf4Kq59KwIOgAaLkRzAxBiRAdCYEXIAkwsP8CXcAGiUOF0FAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMqV6EnLS0NEVHR8vHx0f9+vXTtm3barTesmXLZLFYNGzYMOcWCAAAGhy3h5zly5crKSlJ06dP186dO9WjRw8lJCToxx9/vOB62dnZeuKJJ3T11Ve7qFIAANCQuD3kzJ07V/fdd5/Gjx+vrl27auHChWratKkWL1583nUqKip09913a8aMGWrXrp0Lq4WjcgtLteVQgXILS91dCgCgkXFryCkvL1dGRobi4+Ntyzw8PBQfH6+tW7eed70XXnhBISEhuvfeey+6j7KyMhUVFdm94BrLt+dowOyNGrXoXxowe6OWb89xd0kAgEbErSGnoKBAFRUVCg0NtVseGhqqvLy8atf5+uuv9c4772jRokU12kdKSooCAgJsr8jIyEuuGxeXW1iq5FW7VWmcfV9pSM+s2sOIDgDAZdx+usoRJ0+e1OjRo7Vo0SIFBQXVaJ3k5GQVFhbaXkeOHHFylZCkrIISW8A5p8IwlF1wyj0FAQAaHS937jwoKEienp7Kz8+3W56fn6+wsLAq7Q8dOqTs7GwlJiballVWVkqSvLy8dODAAbVv395uHavVKqvV6oTqcSFtg/zkYZFd0PG0WBQd1NR9RQEAGhW3juR4e3srNjZW6enptmWVlZVKT09XXFxclfadO3fW7t27lZmZaXvdeuutGjJkiDIzMzkVVY+EB/gqZXiMPC0WSWcDzqzh3RQe4OvmygAAjYVbR3IkKSkpSWPHjlXv3r3Vt29fpaamqqSkROPHj5ckjRkzRhEREUpJSZGPj4+6detmt36LFi0kqcpyuN+IPlG6plOwsgtOKTqoKQEHAOBSbg85I0aM0PHjxzVt2jTl5eWpZ8+eWrdunW0yck5Ojjw8GtTUIfxGeIAv4aaRyS0sVVZBidoG+fF3D8CtLIZhGBdvZh5FRUUKCAhQYWGh/P393V0OYCrLt+fYrqrzsEgpw2M0ok+Uu8sCYAK1+f5miARAneC2AQDqG0IOgDrBbQMA1DeEHAB14txtA36L2wYAcCdCDoA6wW0DANQ3br+6CoB5cNsAAPUJIQdAneK2AQDqC05XAQAAUyLkAAAAUyLkAG6QW1iqLYcKuIcMADgRc3IAF+OuwADgGozkAC7EXYEBwHUIOYALcVdgAHAdQg7gQtwVGABch5ADuBB3BQYA12HiMeBi3BUYAFyDkAO4AXcFBgDn43QVAAAwJUIOAAAwJUIOAAAwJUKOSfHYAABAY8fEYxPisQEAADCSYzo8NgAAgLMIOSbDYwMAADiLkGMyPDYAAICzCDkmw2MDAAA4i4nHJsRjAwAAIOSYFo8NAAA0dpyuAgAApkTIAQCgnuBGrnWL01UAANQD3Mi17jGSAwCAm3EjV+eoFyEnLS1N0dHR8vHxUb9+/bRt27bztl20aJGuvvpqtWzZUi1btlR8fPwF2wMAUN9xI1fncHvIWb58uZKSkjR9+nTt3LlTPXr0UEJCgn788cdq22/atEkjR47Ul19+qa1btyoyMlI33HCDjh496uLKAQCNgSvmyXAjV+ewGIZhXLyZ8/Tr1099+vTRm2++KUmqrKxUZGSkHn74YU2ZMuWi61dUVKhly5Z68803NWbMmIu2LyoqUkBAgAoLC+Xv73/J9QMAzMuV82SWb8/RM6v2qMIwbDdyZU7O/6nN97dbJx6Xl5crIyNDycnJtmUeHh6Kj4/X1q1ba7SNU6dO6fTp0woMDKz287KyMpWVldneFxUVXVrRAEwht7BUWQUlahvkxz2lUK3zzZO5plOwU35muJFr3XNryCkoKFBFRYVCQ0PtloeGhmr//v012sbTTz+t1q1bKz4+vtrPU1JSNGPGjEuuFYB5cBULauJC82ScFUC4kWvdcvucnEsxe/ZsLVu2TKtXr5aPj0+1bZKTk1VYWGh7HTlyxMVVAqhPuIoFNcU8mYbPrSEnKChInp6eys/Pt1uen5+vsLCwC6776quvavbs2fr888/VvXv387azWq3y9/e3ewFovLiKBTXFA48bPreervL29lZsbKzS09M1bNgwSWcnHqenp2vSpEnnXe/ll1/WSy+9pPXr16t3794uqhaAGZz73/lvgw7/O8f5ME+mYXP76aqkpCQtWrRI7777rvbt26cHH3xQJSUlGj9+vCRpzJgxdhOT58yZo6lTp2rx4sWKjo5WXl6e8vLyVFxc7K5DANCA8L9zOCo8wFdx7VvxM9IAuf2xDiNGjNDx48c1bdo05eXlqWfPnlq3bp1tMnJOTo48PP4viy1YsEDl5eW644477LYzffp0Pf/8864sHUADxf/OgcbB7ffJcTXukwMAQMNTm+9vt5+uAgAAcAZCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgDAaXILS7XlUIFyC0vdXQpcpD79nXu5uwAAgDkt356j5FW7VWlIHhYpZXiMRvSJcndZcKL69nfOSA4AoM7lFpbavuwkqdKQnlm1p1787x7OUR//zgk5AIA6l1VQYvuyO6fCMJRdcMo9BcHp6uPfOSEHAFDn2gb5ycNiv8zTYlF0UFP3FASnq49/54QcAECdCw/wVcrwGHlazn7reVosmjW8m8IDfN1cGZylPv6dWwzDMC7ezDyKiooUEBCgwsJC+fv7u7scADC13MJSZRecUnRQUwJOI+Gsv/PafH9zdRUAwGnCA3wJN41Mffo753QVAAAwJUIOAAAwJUIOAAAwJUIOAAAwJUIOAAAwJUIOAJepTw/uA2B+XEIOwCXq24P7AJgfIzkAnK4+PrgPgPkRcgA4XX18cB8A8yPkAHC6+vjgPsCZmH9WPxByUCP8wuJS1McH9wHOsnx7jgbM3qhRi/6lAbM3avn2HHeX1GjVi5CTlpam6Oho+fj4qF+/ftq2bdsF269YsUKdO3eWj4+PYmJitHbtWhdV2jjxC4u6MKJPlL6eMkQf33eVvp4yhEnHMCXmn9Uvbg85y5cvV1JSkqZPn66dO3eqR48eSkhI0I8//lht+y1btmjkyJG69957tWvXLg0bNkzDhg3Tnj17XFx548AvLOpSeICv4tq3YgQHpsX8s/rF7SFn7ty5uu+++zR+/Hh17dpVCxcuVNOmTbV48eJq27/++uu68cYb9eSTT6pLly6aOXOmrrzySr355psurrxx4BcWAGqO+Wf1i1tDTnl5uTIyMhQfH29b5uHhofj4eG3durXadbZu3WrXXpISEhLO276srExFRUV2L9Qcv7AAUHPMP6tf3HozwIKCAlVUVCg0NNRueWhoqPbv31/tOnl5edW2z8vLq7Z9SkqKZsyYUTcFN0LnfmGfWbVHFYbBLywAXMSIPlG6plOwsgtOKTqoKf9eupHp73icnJyspKQk2/uioiJFRka6saKGh19YAHBMeIAv/1bWA24NOUFBQfL09FR+fr7d8vz8fIWFhVW7TlhYmEPtrVarrFZr3RTciPELCwBoaNw6J8fb21uxsbFKT0+3LausrFR6erri4uKqXScuLs6uvSRt2LDhvO0BAEDj5PbTVUlJSRo7dqx69+6tvn37KjU1VSUlJRo/frwkacyYMYqIiFBKSook6dFHH9WgQYP02muv6eabb9ayZcu0Y8cOvfXWW+48DAAAUM+4PeSMGDFCx48f17Rp05SXl6eePXtq3bp1tsnFOTk58vD4vwGn/v3766OPPtJzzz2nZ555Rh07dtQnn3yibt26uesQAABAPWQxDMO4eDPzKCoqUkBAgAoLC+Xv7+/ucgAAQA3U5vvb7TcDBAAAcAZCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCVCDgAAMCW33wzQ1c7dFqioqMjNlQAAgJo6973tyO39Gl3IOXnypCTxJHIAABqgkydPKiAgoEZtG90djysrK3Xs2DE1b95cFoul2jZFRUWKjIzUkSNHuCuy6I/q0Cf26I+q6BN79Ic9+qOqi/WJYRg6efKkWrdubfe4pwtpdCM5Hh4euuyyy2rU1t/fnx++36A/qqJP7NEfVdEn9ugPe/RHVRfqk5qO4JzDxGMAAGBKhBwAAGBKhJxqWK1WTZ8+XVar1d2l1Av0R1X0iT36oyr6xB79YY/+qMoZfdLoJh4DAIDGgZEcAABgSoQcAABgSoQcAABgSoQcAABgSo025KSlpSk6Olo+Pj7q16+ftm3bdt62e/fu1e23367o6GhZLBalpqa6rlAXcaQ/Fi1apKuvvlotW7ZUy5YtFR8ff8H2DZUjfbJq1Sr17t1bLVq0kJ+fn3r27Kn333/fhdU6nyP98VvLli2TxWLRsGHDnFugGzjSJ0uXLpXFYrF7+fj4uLBa53P0Z+TEiROaOHGiwsPDZbVa1alTJ61du9ZF1TqfI/0xePDgKj8fFotFN998swsrdj5Hf0ZSU1N1+eWXy9fXV5GRkZo8ebJ+/fXXmu/QaISWLVtmeHt7G4sXLzb27t1r3HfffUaLFi2M/Pz8attv27bNeOKJJ4yPP/7YCAsLM+bNm+fagp3M0f4YNWqUkZaWZuzatcvYt2+fMW7cOCMgIMD44YcfXFy58zjaJ19++aWxatUq4z//+Y9x8OBBIzU11fD09DTWrVvn4sqdw9H+OCcrK8uIiIgwrr76auO2225zTbEu4mifLFmyxPD39zdyc3Ntr7y8PBdX7TyO9kdZWZnRu3dv46abbjK+/vprIysry9i0aZORmZnp4sqdw9H++Omnn+x+Nvbs2WN4enoaS5YscW3hTuRon3z44YeG1Wo1PvzwQyMrK8tYv369ER4ebkyePLnG+2yUIadv377GxIkTbe8rKiqM1q1bGykpKRddt02bNqYLOZfSH4ZhGGfOnDGaN29uvPvuu84q0eUutU8MwzB69eplPPfcc84oz+Vq0x9nzpwx+vfvb7z99tvG2LFjTRdyHO2TJUuWGAEBAS6qzvUc7Y8FCxYY7dq1M8rLy11Voktd6r8h8+bNM5o3b24UFxc7q0SXc7RPJk6caFx77bV2y5KSkowBAwbUeJ+N7nRVeXm5MjIyFB8fb1vm4eGh+Ph4bd261Y2VuUdd9MepU6d0+vRpBQYGOqtMl7rUPjEMQ+np6Tpw4ICuueYaZ5bqErXtjxdeeEEhISG69957XVGmS9W2T4qLi9WmTRtFRkbqtttu0969e11RrtPVpj/WrFmjuLg4TZw4UaGhoerWrZtmzZqliooKV5XtNHXx7+o777yju+66S35+fs4q06Vq0yf9+/dXRkaG7ZTW4cOHtXbtWt1000013m+je0BnQUGBKioqFBoaarc8NDRU+/fvd1NV7lMX/fH000+rdevWdj+8DVlt+6SwsFAREREqKyuTp6en5s+fr+uvv97Z5Tpdbfrj66+/1jvvvKPMzEwXVOh6temTyy+/XIsXL1b37t1VWFioV199Vf3799fevXtr/NDg+qo2/XH48GFt3LhRd999t9auXauDBw/qoYce0unTpzV9+nRXlO00l/rv6rZt27Rnzx698847zirR5WrTJ6NGjVJBQYEGDhwowzB05swZPfDAA3rmmWdqvN9GF3JQt2bPnq1ly5Zp06ZNpptE6ajmzZsrMzNTxcXFSk9PV1JSktq1a6fBgwe7uzSXOnnypEaPHq1FixYpKCjI3eXUG3FxcYqLi7O979+/v7p06aI///nPmjlzphsrc4/KykqFhITorbfekqenp2JjY3X06FG98sorDT7kXKp33nlHMTEx6tu3r7tLcatNmzZp1qxZmj9/vvr166eDBw/q0Ucf1cyZMzV16tQabaPRhZygoCB5enoqPz/fbnl+fr7CwsLcVJX7XEp/vPrqq5o9e7a++OILde/e3ZllulRt+8TDw0MdOnSQJPXs2VP79u1TSkpKgw85jvbHoUOHlJ2drcTERNuyyspKSZKXl5cOHDig9u3bO7doJ6uLf0eaNGmiXr166eDBg84o0aVq0x/h4eFq0qSJPD09bcu6dOmivLw8lZeXy9vb26k1O9Ol/HyUlJRo2bJleuGFF5xZosvVpk+mTp2q0aNHa8KECZKkmJgYlZSU6P7779ezzz4rD4+Lz7hpdHNyvL29FRsbq/T0dNuyyspKpaen2/0vq7GobX+8/PLLmjlzptatW6fevXu7olSXqaufkcrKSpWVlTmjRJdytD86d+6s3bt3KzMz0/a69dZbNWTIEGVmZioyMtKV5TtFXfyMVFRUaPfu3QoPD3dWmS5Tm/4YMGCADh48aAvAkvTtt98qPDy8QQcc6dJ+PlasWKGysjL94Q9/cHaZLlWbPjl16lSVIHMuFBs1fexmLSZIN3jLli0zrFarsXTpUuM///mPcf/99xstWrSwXc45evRoY8qUKbb2ZWVlxq5du4xdu3YZ4eHhxhNPPGHs2rXL+O6779x1CHXK0f6YPXu24e3tbaxcudLukseTJ0+66xDqnKN9MmvWLOPzzz83Dh06ZPznP/8xXn31VcPLy8tYtGiRuw6hTjnaH//LjFdXOdonM2bMMNavX28cOnTIyMjIMO666y7Dx8fH2Lt3r7sOoU452h85OTlG8+bNjUmTJhkHDhwwPv30UyMkJMR48cUX3XUIdaq2vzMDBw40RowY4epyXcLRPpk+fbrRvHlz4+OPPzYOHz5sfP7550b79u2NO++8s8b7bJQhxzAM44033jCioqIMb29vo2/fvsY///lP22eDBg0yxo4da3uflZVlSKryGjRokOsLdxJH+qNNmzbV9sf06dNdX7gTOdInzz77rNGhQwfDx8fHaNmypREXF2csW7bMDVU7jyP98b/MGHIMw7E+eeyxx2xtQ0NDjZtuusnYuXOnG6p2Hkd/RrZs2WL069fPsFqtRrt27YyXXnrJOHPmjIurdh5H+2P//v2GJOPzzz93caWu40ifnD592nj++eeN9u3bGz4+PkZkZKTx0EMPGb/88kuN92cxjJqO+QAAADQcjW5ODgAAaBwIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQDgROPGjdOwYcPcXQbQKBFygEZq3LhxslgstlerVq1044036ptvvnF3aXXit8d27jVw4ECn7S87O1sWi0WZmZl2y19//XUtXbrUafsFcH6EHKARu/HGG5Wbm6vc3Fylp6fLy8tLt9xyi7vLqjNLliyxHV9ubq7WrFlTbbvTp087rYaAgAC1aNHCadsHcH6EHKARs1qtCgsLU1hYmHr27KkpU6boyJEjOn78uK699lpNmjTJrv3x48fl7e1te5JwdHS0Zs6cqZEjR8rPz08RERFKS0uzW2fu3LmKiYmRn5+fIiMj9dBDD6m4uNj2+ffff6/ExES1bNlSfn5+uuKKK7R27VpJ0i+//KK7775bwcHB8vX1VceOHbVkyZIaH1+LFi1sxxcWFqbAwEDbiMvy5cs1aNAg+fj46MMPP9RPP/2kkSNHKiIiQk2bNlVMTIw+/vhju+1VVlbq5ZdfVocOHWS1WhUVFaWXXnpJktS2bVtJUq9evWSxWDR48GBJVU9XlZWV6ZFHHlFISIh8fHw0cOBAbd++3fb5pk2bZLFYlJ6ert69e6tp06bq37+/Dhw4UOPjBnAWIQeAJKm4uFgffPCBOnTooFatWmnChAn66KOPVFZWZmvzwQcfKCIiQtdee61t2SuvvKIePXpo165dmjJlih599FFt2LDB9rmHh4f+9Kc/ae/evXr33Xe1ceNGPfXUU7bPJ06cqLKyMn311VfavXu35syZo2bNmkmSpk6dqv/85z/6+9//rn379mnBggUKCgqqk+M9V+u+ffuUkJCgX3/9VbGxsfrss8+0Z88e3X///Ro9erS2bdtmWyc5OVmzZ8+21fXRRx8pNDRUkmztvvjiC+Xm5mrVqlXV7vepp57SX//6V7377rvauXOnOnTooISEBP3888927Z599lm99tpr2rFjh7y8vHTPPffUyXEDjUqdPVoUQIMyduxYw9PT0/Dz8zP8/PwMSUZ4eLiRkZFhGIZhlJaWGi1btjSWL19uW6d79+7G888/b3vfpk0b48Ybb7Tb7ogRI4yhQ4eed78rVqwwWrVqZXsfExNjt83fSkxMNMaPH1+r45Nk+Pj42I7Pz8/PWL16tZGVlWVIMlJTUy+6jZtvvtl4/PHHDcMwjKKiIsNqtRqLFi2qtu257e7atctu+W+fwF5cXGw0adLE+PDDD22fl5eXG61btzZefvllwzAM48svvzQkGV988YWtzWeffWZIMkpLSx3pAqDRYyQHaMSGDBmizMxMZWZmatu2bUpISNDQoUP1/fffy8fHR6NHj9bixYslSTt37tSePXs0btw4u23ExcVVeb9v3z7b+y+++ELXXXedIiIi1Lx5c40ePVo//fSTTp06JUl65JFH9OKLL2rAgAGaPn263cTnBx98UMuWLVPPnj311FNPacuWLQ4d37x582zHl5mZqeuvv972We/eve3aVlRUaObMmYqJiVFgYKCaNWum9evXKycnR5K0b98+lZWV6brrrnOoht86dOiQTp8+rQEDBtiWNWnSRH379rXrM0nq3r277c/h4eGSpB9//LHW+wYaI0IO0Ij5+fmpQ4cO6tChg/r06aO3335bJSUlWrRokSRpwoQJ2rBhg3744QctWbJE1157rdq0aVPj7WdnZ+uWW25R9+7d9de//lUZGRm2OTvl5eW2fRw+fFijR4/W7t271bt3b73xxhuSZAtckydP1rFjx3TdddfpiSeeqPH+w8LCbMfXoUMH+fn52R37b73yyit6/fXX9fTTT+vLL79UZmamEhISbHX6+vrWeL91oUmTJrY/WywWSWfnBAGoOUIOABuLxSIPDw+VlpZKkmJiYtS7d28tWrRIH330UbXzQv75z39Wed+lSxdJUkZGhiorK/Xaa6/pqquuUqdOnXTs2LEq24iMjNQDDzygVatW6fHHH7eFLEkKDg7W2LFj9cEHHyg1NVVvvfVWXR6yzebNm3XbbbfpD3/4g3r06KF27drp22+/tX3esWNH+fr62iZd/y9vb29JZ0eEzqd9+/by9vbW5s2bbctOnz6t7du3q2vXrnV0JADO8XJ3AQDcp6ysTHl5eZLOXsn05ptvqri4WImJibY2EyZM0KRJk+Tn56ff/e53VbaxefNmvfzyyxo2bJg2bNigFStW6LPPPpMkdejQQadPn9Ybb7yhxMREbd68WQsXLrRb/7HHHtPQoUPVqVMn/fLLL/ryyy9tIWnatGmKjY3VFVdcobKyMn366ae2z+pax44dtXLlSm3ZskUtW7bU3LlzlZ+fbwsfPj4+evrpp/XUU0/J29tbAwYM0PHjx7V3717de++9CgkJka+vr9atW6fLLrtMPj4+CggIsNuHn5+fHnzwQT355JMKDAxUVFSUXn75ZZ06dUr33nuvU44LaMwYyQEasXXr1ik8PFzh4eHq16+ftm/frhUrVtguf5akkSNHysvLSyNHjpSPj0+VbTz++OPasWOHevXqpRdffFFz585VQkKCJKlHjx6aO3eu5syZo27duunDDz9USkqK3foVFRWaOHGiunTpohtvvFGdOnXS/PnzJZ0dHUlOTlb37t11zTXXyNPTU8uWLXNKXzz33HO68sorlZCQoMGDByssLKzKnYqnTp2qxx9/XNOmTVOXLl00YsQI2zwZLy8v/elPf9Kf//xntW7dWrfddlu1+5k9e7Zuv/12jR49WldeeaUOHjyo9evXq2XLlk45LqAxsxiGYbi7CAD1V3Z2ttq3b6/t27fryiuvtPssOjpajz32mB577DH3FAcAF8DpKgDVOn36tH766Sc999xzuuqqq6oEHACo7zhdBaBamzdvVnh4uLZv315lHo27zZo1S82aNav2NXToUHeXB6Ce4HQVgAbn559/rnKH4HN8fX0VERHh4ooA1EeEHAAAYEqcrgIAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKb0/wNPaVpMVv3QpAAAAABJRU5ErkJggg==", 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FBWw2W5XbrVYrDh06xEnYREREdI042Pr160Wr1cqqVavkyJEjkpCQIAaDQcxms4iIjB07VmbMmKHU79mzR1xcXGTRokVy9OhRSUxMFFdXVzl06JBSM3/+fDEYDPL111/LwYMHZeTIkRIYGChlZWVKzdChQ6VXr16Snp4uu3fvlqCgIImJiVG2r169WpKTk+XIkSNy4sQJSU5OFj8/P4mNjVVqXnvtNdm2bZucOHFCMjMzZcyYMaLT6SQ7O/uOXrvFYhEAYrFYKm0rKyuTI0eO2PXckOTl5cnkyZMlMDBQ3Nzc5N5775VHH31UvvvuOxERadu2rSxevLjSfomJidKzZ88qj7lu3ToBICNHjrzluRv62BERNXS/F5XKnuPn5PeiUke3oopbvX/fyOH30Ro9ejTOnTuHOXPmwGw2IyQkBFu3blUms586dQpOTv+98NavXz+sXbsWs2bNwquvvoqgoCB89dVXyj20AODll1/GpUuXkJCQgKKiIvTv3x9bt261u5nomjVrMHnyZAwZMgROTk54/PHHsXTpUmW7i4sL3nrrLfznP/+BiKBt27aYPHkyXnjhBaXmwoULeOqpp2A2m9GyZUv07t0be/fuRdeuXdUcsnrv5MmTeOCBB2AwGLBw4UIEBwejoqIC27Ztw6RJk/Dzzz/X6JgvvvgiBgwYoELHRERUW5IzTmHmxkOwCeCkAZJGBWN03zaObsthHH4frabsVvfhuHz5MnJzcxEYGKjK3ebVNGzYMBw8eBA5OTnw8PCw21ZUVASDwYCAgABMnToVU6dOtds+d+5cfPXVVzhw4ICyzmq1YuDAgfif//kf/Pvf/0ZRUZFyO4+qNOSxIyJqyPIsZXhg/g7Y/pAsnDUa7J4xGCa9u+Maq2XVuY8Wv3XYRORZyrD3RAHyLGWqnqewsBBbt27FpEmTKoUs4Npd26vr9ddfh4+PD+Lj42uhQyIiUktuwSW7kAUAVhGcLCh1TEP1gMM/OiT11eVl3OPHj0NE0Llz59vWvvLKK5g1a5bduitXrth99Lp79258/PHHdle4iIiofgr09oCTBpWuaAV4N917QvKKViOXZylTQhZw7S//qxsPq3ZlqzqfRL/00ks4cOCA3fLMM88o2y9evIixY8dixYoV8Pb2VqNdIiKqRSa9O5JGBcP5/7+fobNGg3mjujeqjw2ri1e0GrlbXcZV4y9+UFAQNBrNHU149/b2RocOHezWeXl5KX8+ceIETp48iREjRijrrt9aw8XFBTk5OWjfvn0tdU5ERLVhdN82GNixFU4WlCLAu1mTDlkAr2g1etcv4/6Rmpdxvby8EBUVhWXLluHSpUuVtlfnAdydO3fGoUOH7K54/elPf8LgwYNx4MCBWz7CiIiIHMekd0d4+3uafMgCGLQaPUdcxl22bBmsVitCQ0Px5Zdf4tixYzh69CiWLl160xvRVkWn06F79+52i8FgQIsWLdC9e3e4ubmp9hqIiIhqAz86bALq+jJuu3btkJWVhb///e+YPn068vLy0KpVK/Tu3RsffPCBqucmIiKqT3gfLQdqrPfRcjSOHRERqYn30SIiIiKqBxi0iIiIiFTCoEVERESkEgYtIiIiIpUwaNVz/K5C9XHMiIiovmDQqqdcXV0BAKWlTfdBnDV1fcyujyEREZGj8D5a9ZSzszMMBgPOnj0LAGjWrBk0Gs1t9mraRASlpaU4e/YsDAYDnJ2dHd0SERE1cQxa9ZjRaAQAJWzRnTEYDMrYERERORKDVj2m0WhgMpng4+ODiooKR7fTILi6uvJKFhER1RsMWg2As7MzwwMREVEDxMnwRERERCph0CIiIiJSCYMWERERkUoYtIiIiIhUwqBFREREjVKepQx7TxQgz1LmsB74rUMiIiJqdJIzTmHmxkOwCeCkAZJGBWN03zZ13gevaBEREVGjkmcpU0IWANgEeHXjYYdc2WLQIiIiokYlt+CSErKus4rgZEHdPz+YQYuIiIgalUBvDzjd8HhgZ40GAd7N6rwXBi0iIiJqVEx6dySNCoaz5lractZoMG9Ud5j07nXeCyfDExERUaMzum8bDOzYCicLShHg3cwhIQtg0CIiIqJGyqR3d1jAuo4fHRIRERGphEGLiIiISCUMWkREREQqYdAiIiIiUgmDFhEREZFKGLSIiIiIVMKgRURERKQSBi0iIiIildSLoLVs2TIEBARAp9MhLCwM+/btu2X9hg0b0LlzZ+h0OgQHB2PLli1220UEc+bMgclkgru7OyIjI3Hs2DG7msLCQsTGxsLT0xMGgwHx8fEoKSlRtufk5GDw4MHw9fWFTqdDu3btMGvWLFRUVFSrFyIiImq6HB60kpOTMW3aNCQmJiIrKws9e/ZEVFQUzp49W2X93r17ERMTg/j4eOzfvx/R0dGIjo7G4cOHlZoFCxZg6dKlWL58OdLT0+Hh4YGoqChcvnxZqYmNjUV2djZSUlKwadMm7Nq1CwkJCcp2V1dXjBs3Dtu3b0dOTg6WLFmCFStWIDExsVq9EBERURMmDhYaGiqTJk1SfrdareLn5ydJSUlV1j/xxBMyfPhwu3VhYWHy9NNPi4iIzWYTo9EoCxcuVLYXFRWJVquVdevWiYjIkSNHBIBkZGQoNd9++61oNBr57bffbtrrCy+8IP3797/jXm7HYrEIALFYLHdUT0RERI5Xnfdvh17RunLlCjIzMxEZGamsc3JyQmRkJNLS0qrcJy0tza4eAKKiopT63NxcmM1muxq9Xo+wsDClJi0tDQaDAX369FFqIiMj4eTkhPT09CrPe/z4cWzduhURERF33AsRERE1bQ4NWgUFBbBarfD19bVb7+vrC7PZXOU+ZrP5lvXXf96uxsfHx267i4sLvLy8Kp23X79+0Ol0CAoKwoABA/D666/fcS83Ki8vR3Fxsd1CREREjZfD52jVd8nJycjKysLatWuxefNmLFq0qMbHSkpKgl6vVxZ/f/9a7JSIiIjqG4cGLW9vbzg7OyM/P99ufX5+PoxGY5X7GI3GW9Zf/3m7mhsn21+9ehWFhYWVzuvv74+uXbsiJiYG8+fPx9y5c2G1Wu+olxvNnDkTFotFWU6fPl1lHRERETUODg1abm5u6N27N1JTU5V1NpsNqampCA8Pr3Kf8PBwu3oASElJUeoDAwNhNBrtaoqLi5Genq7UhIeHo6ioCJmZmUrNjh07YLPZEBYWdtN+bTYbKioqYLPZ7qiXG2m1Wnh6etotRERE1IjVweT8W1q/fr1otVpZtWqVHDlyRBISEsRgMIjZbBYRkbFjx8qMGTOU+j179oiLi4ssWrRIjh49KomJieLq6iqHDh1SaubPny8Gg0G+/vprOXjwoIwcOVICAwOlrKxMqRk6dKj06tVL0tPTZffu3RIUFCQxMTHK9tWrV0tycrIcOXJETpw4IcnJyeLn5yexsbHV6uVW+K1DIiKihqc6798OD1oiIu+++660adNG3NzcJDQ0VH744QdlW0REhIwfP96u/vPPP5eOHTuKm5ubdOvWTTZv3my33WazyezZs8XX11e0Wq0MGTJEcnJy7GrOnz8vMTEx0rx5c/H09JS4uDi5ePGisn39+vVy3333SfPmzcXDw0O6du0q8+bNswtrd9LLrTBoERERNTzVef/WiIg49ppa01VcXAy9Xg+LxcKPEYmIiBqI6rx/81uHRERERCph0CIiIiJSCYMWERERkUoYtIiIiIhUwqBFREREpBIGLSIiIiKVMGgRERERqYRBi4iIiEglDFpEREREKmHQIiIiIlIJgxYRERGRShi0iIiIiFTCoEVERESkEgYtIiIiIpUwaBERERGphEGLiIiISCUMWkREREQqYdAiIiIiUgmDFhEREZFKGLSIiIiIVMKgRURERKQSBi0iIiKqVXmWMuw9UYA8S5mjW3E4F0c3QERERI1HcsYpzNx4CDYBnDRA0qhgjO7bxtFtOQyvaBEREVGtyLOUKSELAGwCvLrxcJO+ssWgRURERLUit+CSErKus4rgZEGpYxqqBxi0iIiIqFYEenvASWO/zlmjQYB3M8c0VA8waBEREVGtMOndkTQqGM6aa2nLWaPBvFHdYdK7O7gzx+FkeCIiIqo1o/u2wcCOrXCyoBQB3s2adMgCGLSIiIiolpn07k0+YF3Hjw6JiIiIVMKgRURERKQSBi0iIiIilTBoEREREamEQYuIiIhIJQxaRERERCph0CIiIiJSCYMWERERkUoYtIiIiIhUwqBFREREpJJ6EbSWLVuGgIAA6HQ6hIWFYd++fbes37BhAzp37gydTofg4GBs2bLFbruIYM6cOTCZTHB3d0dkZCSOHTtmV1NYWIjY2Fh4enrCYDAgPj4eJSUlyvadO3di5MiRMJlM8PDwQEhICNasWWN3jFWrVkGj0dgtOp3uLkeDiIiIGguHB63k5GRMmzYNiYmJyMrKQs+ePREVFYWzZ89WWb93717ExMQgPj4e+/fvR3R0NKKjo3H48GGlZsGCBVi6dCmWL1+O9PR0eHh4ICoqCpcvX1ZqYmNjkZ2djZSUFGzatAm7du1CQkKC3Xl69OiBL7/8EgcPHkRcXBzGjRuHTZs22fXj6emJvLw8Zfn1119reYSIiIiowRIHCw0NlUmTJim/W61W8fPzk6SkpCrrn3jiCRk+fLjdurCwMHn66adFRMRms4nRaJSFCxcq24uKikSr1cq6detEROTIkSMCQDIyMpSab7/9VjQajfz222837XXYsGESFxen/L5y5UrR6/V3/mJvYLFYBIBYLJYaH4OIiIjqVnXevx16RevKlSvIzMxEZGSkss7JyQmRkZFIS0urcp+0tDS7egCIiopS6nNzc2E2m+1q9Ho9wsLClJq0tDQYDAb06dNHqYmMjISTkxPS09Nv2q/FYoGXl5fdupKSErRt2xb+/v4YOXIksrOzb7p/eXk5iouL7RYiIiJqvBwatAoKCmC1WuHr62u33tfXF2azucp9zGbzLeuv/7xdjY+Pj912FxcXeHl53fS8n3/+OTIyMhAXF6es69SpEz755BN8/fXXWL16NWw2G/r164czZ85UeYykpCTo9Xpl8ff3r7KOiIiIGgeHz9FqCL7//nvExcVhxYoV6Natm7I+PDwc48aNQ0hICCIiIrBx40a0atUKH374YZXHmTlzJiwWi7KcPn26rl4CEREROYBDg5a3tzecnZ2Rn59vtz4/Px9Go7HKfYxG4y3rr/+8Xc2Nk+2vXr2KwsLCSuf917/+hREjRmDx4sUYN27cLV+Pq6srevXqhePHj1e5XavVwtPT024hIiKixsuhQcvNzQ29e/dGamqqss5msyE1NRXh4eFV7hMeHm5XDwApKSlKfWBgIIxGo11NcXEx0tPTlZrw8HAUFRUhMzNTqdmxYwdsNhvCwsKUdTt37sTw4cPx1ltv2X0j8WasVisOHToEk8l0B6+eiIiIGr06mJx/S+vXrxetViurVq2SI0eOSEJCghgMBjGbzSIiMnbsWJkxY4ZSv2fPHnFxcZFFixbJ0aNHJTExUVxdXeXQoUNKzfz588VgMMjXX38tBw8elJEjR0pgYKCUlZUpNUOHDpVevXpJenq67N69W4KCgiQmJkbZvmPHDmnWrJnMnDlT8vLylOX8+fNKzWuvvSbbtm2TEydOSGZmpowZM0Z0Op1kZ2ff0Wvntw6JiIganuq8fzs8aImIvPvuu9KmTRtxc3OT0NBQ+eGHH5RtERERMn78eLv6zz//XDp27Chubm7SrVs32bx5s912m80ms2fPFl9fX9FqtTJkyBDJycmxqzl//rzExMRI8+bNxdPTU+Li4uTixYvK9vHjxwuASktERIRSM3XqVKVvX19fGTZsmGRlZd3x62bQIiIianiq8/6tERFx2OW0Jq64uBh6vR4Wi4XztYiIiBqI6rx/81uHRERERCph0CIiIiJSCYMWERERkUoYtIiIiIhUwqBFREREpBIGLSK6K3mWMuw9UYA8S5mjWyEiqndcHN0AETVcyRmnMHPjIdgEcNIASaOCMbpvG0e3RURUb/CKFhHVSJ6lTAlZAGAT4NWNh3lli4joDxi0iKhGcgsuKSHrOqsIThaUOqYhIqJ6iEGLiGok0NsDThr7dc4aDQK8mzmmISKieohBi4hqxKR3R9KoYDhrrqUtZ40G80Z1h0nv7uDOiIjqD06GJ6IaG923DQZ2bIWTBaUI8G7GkEVEdAMGLSK6Kya9OwMWEdFN8KNDIiIiIpUwaBERERGphEGLiIiISCUMWkREREQqYdAiIiIiUgmDFhEB4MOhiYjUwNs7EBEfDk1EpBJe0SJq4vhwaCIi9TBoETVxfDg0EZF6GLSImjg+HJqISD0MWkRNHB8OTUSkHk6GJyI+HJqISCUMWkQEgA+HJiJSAz86JCIiIlIJgxYRERGRShi0iIiIiFTCoEVERESkEgYtIiIiIpUwaBERERGphEGLiIiI6rU8Sxn2nihokM9g5X20iIiIqN5KzjilPPjeSQMkjQrG6L5tHN3WHeMVLSIiIqqX8ixlSsgCAJsAr2483KCubDFoERERUb2UW3BJCVnXWUVwsqDUMQ3VAIMWERER1UuB3h5w0tivc9ZoEODdzDEN1QCDFhEREdVLJr07kkYFw1lzLW05azSYN6p7g3ouKyfDExERUb01um8bDOzYCicLShHg3axBhSygnlzRWrZsGQICAqDT6RAWFoZ9+/bdsn7Dhg3o3LkzdDodgoODsWXLFrvtIoI5c+bAZDLB3d0dkZGROHbsmF1NYWEhYmNj4enpCYPBgPj4eJSUlCjbd+7ciZEjR8JkMsHDwwMhISFYs2ZNtXshIiKiu2PSuyO8/T0NLmQB9SBoJScnY9q0aUhMTERWVhZ69uyJqKgonD17tsr6vXv3IiYmBvHx8di/fz+io6MRHR2Nw4cPKzULFizA0qVLsXz5cqSnp8PDwwNRUVG4fPmyUhMbG4vs7GykpKRg06ZN2LVrFxISEuzO06NHD3z55Zc4ePAg4uLiMG7cOGzatKlavRARqaEh31eIqCnRiIjcvkw9YWFh6Nu3L9577z0AgM1mg7+/P/76179ixowZlepHjx6NS5cu2QWe+++/HyEhIVi+fDlEBH5+fpg+fTpefPFFAIDFYoGvry9WrVqFMWPG4OjRo+jatSsyMjLQp08fAMDWrVsxbNgwnDlzBn5+flX2Onz4cPj6+uKTTz65o15up7i4GHq9HhaLBZ6ennc4YkTU1DX0+woRNXTVef926BWtK1euIDMzE5GRkco6JycnREZGIi0trcp90tLS7OoBICoqSqnPzc2F2Wy2q9Hr9QgLC1Nq0tLSYDAYlJAFAJGRkXByckJ6evpN+7VYLPDy8rrjXm5UXl6O4uJiu4WIqDoaw32FiJoShwatgoICWK1W+Pr62q339fWF2Wyuch+z2XzL+us/b1fj4+Njt93FxQVeXl43Pe/nn3+OjIwMxMXF3XEvN0pKSoJer1cWf3//KuuIiG6mMdxXiKgpcfgcrYbg+++/R1xcHFasWIFu3brV+DgzZ86ExWJRltOnT9dil/Y4f4OocWoM9xUiakocGrS8vb3h7OyM/Px8u/X5+fkwGo1V7mM0Gm9Zf/3n7WpunGx/9epVFBYWVjrvv/71L4wYMQKLFy/GuHHjqtXLjbRaLTw9Pe0WNSRnnMID83fgyRXpeGD+DiRnnFLlPERU9xrDfYWImhKHBi03Nzf07t0bqampyjqbzYbU1FSEh4dXuU94eLhdPQCkpKQo9YGBgTAajXY1xcXFSE9PV2rCw8NRVFSEzMxMpWbHjh2w2WwICwtT1u3cuRPDhw/HW2+9ZfeNxDvtxRE4f4Oo8Rvdtw12zxiMdU/dj90zBnMiPFE95vAblk6bNg3jx49Hnz59EBoaiiVLluDSpUvKXKhx48ahdevWSEpKAgA8//zziIiIwNtvv43hw4dj/fr1+PHHH/HRRx8BADQaDaZOnYo333wTQUFBCAwMxOzZs+Hn54fo6GgAQJcuXTB06FA89dRTWL58OSoqKjB58mSMGTNG+cbh999/j0cffRTPP/88Hn/8cWXelZubmzIh/na9OMKt5m/w/3iJGg+T3p3/TRM1BFIPvPvuu9KmTRtxc3OT0NBQ+eGHH5RtERERMn78eLv6zz//XDp27Chubm7SrVs32bx5s912m80ms2fPFl9fX9FqtTJkyBDJycmxqzl//rzExMRI8+bNxdPTU+Li4uTixYvK9vHjxwuASktERES1erkVi8UiAMRisdzxPrfze1GpBM7YJG1f+e/SbsZm+b2otNbOQURE1JRV5/3b4ffRasrUuo9WcsYpvLrxMKwiyvwNfrRARERUO6rz/u3wjw6p9jX050IRERE1FgxajVRtz9/Is5Qht+ASAr09GNyIiIjuEIMW3RYf90FERFQzvGEp3RJvF0FERFRzDFp0S3zcBxERUc0xaNEt8XEfRERENcegRbfEx30QERHVXK1Mhs/NzYW/vz9cXDi3vjHi7SKIiIhqplauaHXq1AnHjh2rjUNRPWXSuyO8/T0MWURERNVQrUtQo0aNqnK91WrFlClT0KJFCwDAxo0b774zIiIiogauWle0vvrqKxQWFkKv19stANC8eXO734mIiIiaumo963D9+vV46aWX8PrrryMuLk5Z7+rqip9++gldu3ZVpcnGSq1nHRIREZF6qvP+Xa0rWmPGjMG///1vfPzxx3j88cdx4cKFu2qUiIiIqDGr9mT4gIAA7Nq1C927d0fPnj2xbds2aDSa2+9IRERE1MTU6H4MTk5OeO211/DQQw9h3LhxsFqttd0XERERUYN3Vze+6t+/Pw4ePIgTJ06gffv2tdUTERERUaNw13cYbd68OXr27FkbvRARERE1KtUKWr169bqj+VhZWVk1boiIiIiosahW0IqOjlb+LCJISkrCM888Ay8vr9rui4iIiKjBq9Z9tG7UokUL/PTTT2jXrl1t9tRk8D5aREREDY9q99EiIiIiojvHoEVERESkEgYtIiIiIpVUazL80qVL7X6/evUqVq1aBW9vb7v1U6ZMufvOiIiIiBq4ak2GDwwMvP0BNRr88ssvd9VUU8HJ8ERERA1Pdd6/q3VFKzc3964aIyIiImpKqjVHa8eOHejatSuKi4srbbNYLOjWrRv+/e9/11pzRNT05FnKsPdEAfIsZY5uhYjorlXritaSJUvw1FNPVXmZTK/X4+mnn8Y777yDAQMG1FqDRNR0JGecwsyNh2ATwEkDJI0Kxui+bRzdFhFRjVXritZPP/2EoUOH3nT7ww8/jMzMzLtuioianjxLmRKyAMAmwKsbD/PKFhE1aNUKWvn5+XB1db3pdhcXF5w7d+6umyKipie34JISsq6ziuBkQaljGiIiqgXVClqtW7fG4cOHb7r94MGDMJlMd90UETU9gd4ecLrhmfXOGg0CvJs5piEiolpQraA1bNgwzJ49G5cvX660raysDImJiXj00UdrrTkiajpMenckjQqGs+Za2nLWaDBvVHeY9O4O7oyIqOaqdR+t/Px83HfffXB2dsbkyZPRqVMnAMDPP/+MZcuWwWq1IisrC76+vqo13JjwPlpEleVZynCyoBQB3s0YsoioXqrO+3e1ghYA/Prrr3j22Wexbds2XN9Vo9EgKioKy5Ytu6ObmtI1DFpEREQNj2o3LAWAtm3bYsuWLbhw4QKOHz8OEUFQUBBatmxZ44aJiIiIGqNqB63rWrZsib59+9ZmL0RERESNSrUmwxMRNSa8Cz0Rqa3GV7SIiBoy3oWeiOoCr2gRUZPDu9ATUV1xeNBatmwZAgICoNPpEBYWhn379t2yfsOGDejcuTN0Oh2Cg4OxZcsWu+0igjlz5sBkMsHd3R2RkZE4duyYXU1hYSFiY2Ph6ekJg8GA+Ph4lJSUKNsvX76MCRMmIDg4GC4uLoiOjq7Ux86dO6HRaCotZrO55oNBRHWCd6Enorri0KCVnJyMadOmITExEVlZWejZsyeioqJw9uzZKuv37t2LmJgYxMfHY//+/YiOjkZ0dLTd3eoXLFiApUuXYvny5UhPT4eHhweioqLsbrIaGxuL7OxspKSkYNOmTdi1axcSEhKU7VarFe7u7pgyZQoiIyNv+RpycnKQl5enLD4+Pnc5KkSkNt6FnojqSrXvo1WbwsLC0LdvX7z33nsAAJvNBn9/f/z1r3/FjBkzKtWPHj0aly5dwqZNm5R1999/P0JCQrB8+XKICPz8/DB9+nS8+OKLAACLxQJfX1+sWrUKY8aMwdGjR9G1a1dkZGSgT58+AICtW7di2LBhOHPmDPz8/OzOOWHCBBQVFeGrr76yW79z504MHjwYFy5cgMFgqNHr5320iBwnOeMUXt14GFYR5S70nKNFRHeiOu/fDruideXKFWRmZtpdMXJyckJkZCTS0tKq3CctLa3SFaaoqCilPjc3F2az2a5Gr9cjLCxMqUlLS4PBYFBCFgBERkbCyckJ6enp1X4dISEhMJlMeOihh7Bnz55b1paXl6O4uNhuISLHGN23DXbPGIx1T92P3TMGM2QRkSocFrQKCgpgtVorPa7H19f3pvOczGbzLeuv/7xdzY0f77m4uMDLy6ta86tMJhOWL1+OL7/8El9++SX8/f0xaNAgZGVl3XSfpKQk6PV6ZfH397/j8xFR7TPp3RHe/h4+6oeIVMPbO9RQp06dlGc9AkC/fv1w4sQJLF68GJ999lmV+8ycORPTpk1Tfi8uLmbYIiIiasQcdkXL29sbzs7OyM/Pt1ufn58Po9FY5T5Go/GW9dd/3q7mxsn2V69eRWFh4U3Pe6dCQ0Nx/Pjxm27XarXw9PS0W4iIiKjxcljQcnNzQ+/evZGamqqss9lsSE1NRXh4eJX7hIeH29UDQEpKilIfGBgIo9FoV1NcXIz09HSlJjw8HEVFRcjMzFRqduzYAZvNhrCwsLt6TQcOHIDJZLqrYxAREVHj4dCPDqdNm4bx48ejT58+CA0NxZIlS3Dp0iXExcUBAMaNG4fWrVsjKSkJAPD8888jIiICb7/9NoYPH47169fjxx9/xEcffQQA0Gg0mDp1Kt58800EBQUhMDAQs2fPhp+fn3IvrC5dumDo0KF46qmnsHz5clRUVGDy5MkYM2aM3TcOjxw5gitXrqCwsBAXL17EgQMHAFyb/A4AS5YsQWBgILp164bLly/jn//8J3bs2IHt27fXzeARERFRvefQoDV69GicO3cOc+bMgdlsRkhICLZu3apMZj916hScnP570a1fv35Yu3YtZs2ahVdffRVBQUH46quv0L17d6Xm5ZdfxqVLl5CQkICioiL0798fW7duhU6nU2rWrFmDyZMnY8iQIXBycsLjjz+OpUuX2vU2bNgw/Prrr8rvvXr1AnDthqjAtW9NTp8+Hb/99huaNWuGHj164LvvvsPgwYNrf6CIiIioQXLofbSaOt5Hi4iIqOFpEPfRIiIiImrsGLSIiIiIVMKgRURERKQSBi0iIiIilTBoEREREamEQYuIiIhIJQxaRERERCph0CIionojz1KGvScKkGcpc3QrRLXCoXeGJyIiui454xRmbjwEmwBOGiBpVDBG923j6LaI7gqvaBERkcPlWcqUkAUANgFe3XiYV7aowWPQIiIih8stuKSErOusIjhZUOqYhohqCYMWERE5XKC3B5w09uucNRoEeDdzTENEtYRBi4iIHM6kd0fSqGA4a66lLWeNBvNGdYdJ7+7gzojuDifDExFRvTC6bxsM7NgKJwtKEeDdjCGLGgUGLSIiqjdMencGLGpU+NEhERERkUoYtIiIiIhUwqBFREREpBIGLSIiIiKVMGgRERERqYRBi4iIiEglDFpEREREKmHQIiIiIlIJgxYRERGRShi0iIiIiFTCoEVERESkEgYtIiIiIpUwaBERERGphEGLiIiISCUMWkREREQqYdAiIiIiUgmDFhEREZFKGLSIiIiIVMKgRURERKQSBi0iIiIilTBoEREREamEQYuIiIhIJQxaRERERCpxeNBatmwZAgICoNPpEBYWhn379t2yfsOGDejcuTN0Oh2Cg4OxZcsWu+0igjlz5sBkMsHd3R2RkZE4duyYXU1hYSFiY2Ph6ekJg8GA+Ph4lJSUKNsvX76MCRMmIDg4GC4uLoiOjq6yl507d+K+++6DVqtFhw4dsGrVqhqNARERETVODg1aycnJmDZtGhITE5GVlYWePXsiKioKZ8+erbJ+7969iImJQXx8PPbv34/o6GhER0fj8OHDSs2CBQuwdOlSLF++HOnp6fDw8EBUVBQuX76s1MTGxiI7OxspKSnYtGkTdu3ahYSEBGW71WqFu7s7pkyZgsjIyCp7yc3NxfDhwzF48GAcOHAAU6dOxcSJE7Ft27ZaGh0iIiJq8MSBQkNDZdKkScrvVqtV/Pz8JCkpqcr6J554QoYPH263LiwsTJ5++mkREbHZbGI0GmXhwoXK9qKiItFqtbJu3ToRETly5IgAkIyMDKXm22+/FY1GI7/99lulc44fP15GjhxZaf3LL78s3bp1s1s3evRoiYqKus2r/i+LxSIAxGKx3PE+RERE5FjVef922BWtK1euIDMz0+6KkZOTEyIjI5GWllblPmlpaZWuMEVFRSn1ubm5MJvNdjV6vR5hYWFKTVpaGgwGA/r06aPUREZGwsnJCenp6Xfc/+16qUp5eTmKi4vtFiIiImq8HBa0CgoKYLVa4evra7fe19cXZrO5yn3MZvMt66//vF2Nj4+P3XYXFxd4eXnd9LzV6aW4uBhlZWVV7pOUlAS9Xq8s/v7+d3w+IiIiangcPhm+KZk5cyYsFouynD592tEtERERkYocFrS8vb3h7OyM/Px8u/X5+fkwGo1V7mM0Gm9Zf/3n7WpunGx/9epVFBYW3vS81enF09MT7u7uVe6j1Wrh6elptxAREVHj5bCg5ebmht69eyM1NVVZZ7PZkJqaivDw8Cr3CQ8Pt6sHgJSUFKU+MDAQRqPRrqa4uBjp6elKTXh4OIqKipCZmanU7NixAzabDWFhYXfc/+16ISIiInLotw7Xr18vWq1WVq1aJUeOHJGEhAQxGAxiNptFRGTs2LEyY8YMpX7Pnj3i4uIiixYtkqNHj0piYqK4urrKoUOHlJr58+eLwWCQr7/+Wg4ePCgjR46UwMBAKSsrU2qGDh0qvXr1kvT0dNm9e7cEBQVJTEyMXW/Z2dmyf/9+GTFihAwaNEj2798v+/fvV7b/8ssv0qxZM3nppZfk6NGjsmzZMnF2dpatW7fe8evntw6JiIganuq8fzs0aImIvPvuu9KmTRtxc3OT0NBQ+eGHH5RtERERMn78eLv6zz//XDp27Chubm7SrVs32bx5s912m80ms2fPFl9fX9FqtTJkyBDJycmxqzl//rzExMRI8+bNxdPTU+Li4uTixYt2NW3bthUAlZY/+v777yUkJETc3NykXbt2snLlymq9dgYtIiKihqc6798aERGHXU5r4oqLi6HX62GxWDhfi4iIqIGozvs3v3VIREREpBIGLSIiIiKVMGgR0W3lWcqw90QB8ixV34yXiIiq5uLoBoiofkvOOIWZGw/BJoCTBkgaFYzRfds4ui0iogaBV7SI6KbyLGVKyAIAmwCvbjzMK1tERHeIQYuIbiq34JISsq6ziuBkQaljGiIiamAYtIjopgK9PeCksV/nrNEgwLuZYxoiImpgGLSoUeBkbXWY9O5IGhUMZ821tOWs0WDeqO4w6at+nicREdnjZHhq8DhZW12j+7bBwI6tcLKgFAHezRiyiIiqgVe0qEHjZO26YdK7I7z9PQxZRETVxKBFDRonaxMRUX3GoEUNGidrExFRfcagRQ0aJ2sTEVF9xsnw1OBxsjYREdVXDFrUKJj07gxYRERU7/CjQyIiIiKVMGgRERERqYRBi4iIiEglDFrUKPARPEREVB9xMjw1eHwEDxER1Ve8okUNGh/BQ0RE9RmDFjVofAQPERHVZwxa1KDxETxERFSfMWhRg8ZH8BARUX3GyfDU4PERPEREVF8xaFGjwEfwEBFRfcSPDomIiIhUwqBFREREpBIGLSIiIiKVMGgRERERqYRBi4iIiEglDFpEREREKmHQIiIiIlIJgxYRERGRShi0iIiIiFTCoEVERESkEgYtIiIiIpUwaBERERGphEGLiIiISCX1ImgtW7YMAQEB0Ol0CAsLw759+25Zv2HDBnTu3Bk6nQ7BwcHYsmWL3XYRwZw5c2AymeDu7o7IyEgcO3bMrqawsBCxsbHw9PSEwWBAfHw8SkpK7GoOHjyIAQMGQKfTwd/fHwsWLLDbvmrVKmg0GrtFp9PdxUgQERFRY+LwoJWcnIxp06YhMTERWVlZ6NmzJ6KionD27Nkq6/fu3YuYmBjEx8dj//79iI6ORnR0NA4fPqzULFiwAEuXLsXy5cuRnp4ODw8PREVF4fLly0pNbGwssrOzkZKSgk2bNmHXrl1ISEhQthcXF+Phhx9G27ZtkZmZiYULF2Lu3Ln46KOP7Prx9PREXl6esvz666+1PEJERETUYImDhYaGyqRJk5TfrVar+Pn5SVJSUpX1TzzxhAwfPtxuXVhYmDz99NMiImKz2cRoNMrChQuV7UVFRaLVamXdunUiInLkyBEBIBkZGUrNt99+KxqNRn777TcREXn//felZcuWUl5ertS88sor0qlTJ+X3lStXil6vr+ErF7FYLAJALBZLjY9BREREdas6798OvaJ15coVZGZmIjIyUlnn5OSEyMhIpKWlVblPWlqaXT0AREVFKfW5ubkwm812NXq9HmFhYUpNWloaDAYD+vTpo9RERkbCyckJ6enpSs3AgQPh5uZmd56cnBxcuHBBWVdSUoK2bdvC398fI0eORHZ29k1fb3l5OYqLi+0WIiIiarwcGrQKCgpgtVrh6+trt97X1xdms7nKfcxm8y3rr/+8XY2Pj4/ddhcXF3h5ednVVHWMP56jU6dO+OSTT/D1119j9erVsNls6NevH86cOVNl70lJSdDr9cri7+9fZR0RERE1Dg6fo9WQhYeHY9y4cQgJCUFERAQ2btyIVq1a4cMPP6yyfubMmbBYLMpy+vTpOu6YGpo8Sxn2nihAnqXM0a0QEVENuDjy5N7e3nB2dkZ+fr7d+vz8fBiNxir3MRqNt6y//jM/Px8mk8muJiQkRKm5cbL91atXUVhYaHecqs7zx3PcyNXVFb169cLx48er3K7VaqHVaqvcRnSj5IxTmLnxEGwCOGmApFHBGN23jaPbIiKianDoFS03Nzf07t0bqampyjqbzYbU1FSEh4dXuU94eLhdPQCkpKQo9YGBgTAajXY1xcXFSE9PV2rCw8NRVFSEzMxMpWbHjh2w2WwICwtTanbt2oWKigq783Tq1AktW7assjer1YpDhw7ZBTyimsizlCkhCwBsAry68TCvbBERNTAO/+hw2rRpWLFiBT799FMcPXoUzz77LC5duoS4uDgAwLhx4zBz5kyl/vnnn8fWrVvx9ttv4+eff8bcuXPx448/YvLkyQAAjUaDqVOn4s0338Q333yDQ4cOYdy4cfDz80N0dDQAoEuXLhg6dCieeuop7Nu3D3v27MHkyZMxZswY+Pn5AQCefPJJuLm5IT4+HtnZ2UhOTsY//vEPTJs2Tenl9ddfx/bt2/HLL78gKysLf/nLX/Drr79i4sSJdTR61FjlFlxSQtZ1VhGcLCh1TENERFQjDv3oEABGjx6Nc+fOYc6cOTCbzQgJCcHWrVuVieenTp2Ck9N/82C/fv2wdu1azJo1C6+++iqCgoLw1VdfoXv37krNyy+/jEuXLiEhIQFFRUXo378/tm7dancz0TVr1mDy5MkYMmQInJyc8Pjjj2Pp0qXKdr1ej+3bt2PSpEno3bs3vL29MWfOHLt7bV24cAFPPfUUzGYzWrZsid69e2Pv3r3o2rWrmkNGTUCgtwecNLALW84aDQK8mzmuKaJ6Ks9ShtyCSwj09oBJ7+7odojsaEREbl9GaiguLoZer4fFYoGnp6ej26F6JjnjFF7deBhWEThrNJg3qjvnaBHdgHMZyRGq8/7NoOVADFp0O3mWMpwsKEWAdzP+nzrRDfIsZXhg/o5KV353zxjM/15IVdV5/3b4R4dEdHMmvTvfMIhu4lZzGfnfDdUXDp8MT0REVBPX5zL+EecyUn3DoEVERA2SSe+OpFHBcNZcS1vX5zLyahbVJ/zokIiIGqzRfdtgYMdWnMtI9RaDFhER1anavh0D5zJSfcagRUREdYa3Y6CmhnO0iIioTvDRUtQUMWgREVGd4KOlqCli0CIiojrB2zFQU8SgRUREdYK3Y6CmiJPhiYiozvB2DNTUMGgREVGd4u0YqCnhR4dEREREKmHQIiIiIlIJgxYRERGRShi0iIiIiFTCoEVERESkEgYtIiIiIpUwaBERERGphEGLiIiISCUMWkREREQqYdAiIiIiUgmDFhEREZFKGLSIiIiIVMKgRURERKQSBi0iIiIilTBoEREREamEQYuIiIhIJQxaRERERCph0CIiIvqDPEsZ9p4oQJ6lzNGtUCPg4ugGiIiI6ovkjFOYufEQbAI4aYCkUcEY3beNo9uiBoxXtIiIiHDtStb1kAUANgFe3XiYV7borjBoERERAcgtuKSErOusIjhZUOqYhqhRYNAiIiICEOjtASeN/TpnjQYB3s0c0xA1CgxaRKQKTiimhsakd0fSqGA4a66lLWeNBvNGdYdJ7+7gzqgh42R4Iqp1nFBMDdXovm0wsGMrnCwoRYB3M4Ysumu8okVEtYoTiqmhM+ndEd7+HoYsqhUMWkRUqzihmIjov+pF0Fq2bBkCAgKg0+kQFhaGffv23bJ+w4YN6Ny5M3Q6HYKDg7Flyxa77SKCOXPmwGQywd3dHZGRkTh27JhdTWFhIWJjY+Hp6QmDwYD4+HiUlJTY1Rw8eBADBgyATqeDv78/FixYUO1eiJoaTigmIvovhwet5ORkTJs2DYmJicjKykLPnj0RFRWFs2fPVlm/d+9exMTEID4+Hvv370d0dDSio6Nx+PBhpWbBggVYunQpli9fjvT0dHh4eCAqKgqXL19WamJjY5GdnY2UlBRs2rQJu3btQkJCgrK9uLgYDz/8MNq2bYvMzEwsXLgQc+fOxUcffVStXoiaGk4oJiL6A3Gw0NBQmTRpkvK71WoVPz8/SUpKqrL+iSeekOHDh9utCwsLk6efflpERGw2mxiNRlm4cKGyvaioSLRaraxbt05ERI4cOSIAJCMjQ6n59ttvRaPRyG+//SYiIu+//760bNlSysvLlZpXXnlFOnXqdMe93I7FYhEAYrFY7qieqCH5vahU9h4vkN+LSh3dChFRrarO+7dDr2hduXIFmZmZiIyMVNY5OTkhMjISaWlpVe6TlpZmVw8AUVFRSn1ubi7MZrNdjV6vR1hYmFKTlpYGg8GAPn36KDWRkZFwcnJCenq6UjNw4EC4ubnZnScnJwcXLly4o15uVF5ejuLiYruFqLHihGIiIgd/dFhQUACr1QpfX1+79b6+vjCbzVXuYzabb1l//eftanx8fOy2u7i4wMvLy66mqmP88Ry36+VGSUlJ0Ov1yuLv719lHRERETUODp+j1ZTMnDkTFotFWU6fPu3oloiIiEhFDg1a3t7ecHZ2Rn5+vt36/Px8GI3GKvcxGo23rL/+83Y1N062v3r1KgoLC+1qqjrGH89xu15upNVq4enpabcQERFR4+XQoOXm5obevXsjNTVVWWez2ZCamorw8PAq9wkPD7erB4CUlBSlPjAwEEaj0a6muLgY6enpSk14eDiKioqQmZmp1OzYsQM2mw1hYWFKza5du1BRUWF3nk6dOqFly5Z31AsRERE1cXUwOf+W1q9fL1qtVlatWiVHjhyRhIQEMRgMYjabRURk7NixMmPGDKV+z5494uLiIosWLZKjR49KYmKiuLq6yqFDh5Sa+fPni8FgkK+//loOHjwoI0eOlMDAQCkrK1Nqhg4dKr169ZL09HTZvXu3BAUFSUxMjLK9qKhIfH19ZezYsXL48GFZv369NGvWTD788MNq9XIr/NYhERFRw1Od92+HBy0RkXfffVfatGkjbm5uEhoaKj/88IOyLSIiQsaPH29X//nnn0vHjh3Fzc1NunXrJps3b7bbbrPZZPbs2eLr6ytarVaGDBkiOTk5djXnz5+XmJgYad68uXh6ekpcXJxcvHjRruann36S/v37i1arldatW8v8+fMr9X67Xm6FQYuIiKjhqc77t0ZE5NbXvEgtxcXF0Ov1sFgsnK9FRETUQFTn/ZvfOiQiIiJSCYMWERERkUoYtIiIiIhUwqBFREREpBIXRzfQlF3/HgKfeUhERNRwXH/fvpPvEzJoOdDFixcBgM88JCIiaoAuXrwIvV5/yxre3sGBbDYbfv/9d7Ro0QIajabKmuLiYvj7++P06dNN/hYQHItrOA7XcBz+i2NxDcfhGo7Df6k1FiKCixcvws/PD05Ot56FxStaDuTk5IR77733jmr5bMT/4lhcw3G4huPwXxyLazgO13Ac/kuNsbjdlazrOBmeiIiISCUMWkREREQqYdCq57RaLRITE6HVah3disNxLK7hOFzDcfgvjsU1HIdrOA7/VR/GgpPhiYiIiFTCK1pEREREKmHQIiIiIlIJgxYRERGRShi0iIiIiFTCoFUPLFu2DAEBAdDpdAgLC8O+fftuWpudnY3HH38cAQEB0Gg0WLJkSd01WgeqMxYrVqzAgAED0LJlS7Rs2RKRkZG3rG9IqjMOGzduRJ8+fWAwGODh4YGQkBB89tlndditeqozDn+0fv16aDQaREdHq9tgHarOWKxatQoajcZu0el0dditeqr7d6KoqAiTJk2CyWSCVqtFx44dsWXLljrqVj3VGYdBgwZV+vug0WgwfPjwOuxYPdX9O7FkyRJ06tQJ7u7u8Pf3xwsvvIDLly+r16CQQ61fv17c3Nzkk08+kezsbHnqqafEYDBIfn5+lfX79u2TF198UdatWydGo1EWL15ctw2rqLpj8eSTT8qyZctk//79cvToUZkwYYLo9Xo5c+ZMHXdeu6o7Dt9//71s3LhRjhw5IsePH5clS5aIs7OzbN26tY47r13VHYfrcnNzpXXr1jJgwAAZOXJk3TSrsuqOxcqVK8XT01Py8vKUxWw213HXta+641BeXi59+vSRYcOGye7duyU3N1d27twpBw4cqOPOa1d1x+H8+fN2fxcOHz4szs7OsnLlyrptXAXVHYs1a9aIVquVNWvWSG5urmzbtk1MJpO88MILqvXIoOVgoaGhMmnSJOV3q9Uqfn5+kpSUdNt927Zt26iC1t2MhYjI1atXpUWLFvLpp5+q1WKduNtxEBHp1auXzJo1S4326kxNxuHq1avSr18/+ec//ynjx49vNEGrumOxcuVK0ev1ddRd3anuOHzwwQfSrl07uXLlSl21WCfu9t+IxYsXS4sWLaSkpEStFutMdcdi0qRJ8uCDD9qtmzZtmjzwwAOq9ciPDh3oypUryMzMRGRkpLLOyckJkZGRSEtLc2Bnda82xqK0tBQVFRXw8vJSq03V3e04iAhSU1ORk5ODgQMHqtmqqmo6Dq+//jp8fHwQHx9fF23WiZqORUlJCdq2bQt/f3+MHDkS2dnZddGuamoyDt988w3Cw8MxadIk+Pr6onv37pg3bx6sVmtdtV3rauPfyo8//hhjxoyBh4eHWm3WiZqMRb9+/ZCZmal8vPjLL79gy5YtGDZsmGp98qHSDlRQUACr1QpfX1+79b6+vvj5558d1JVj1MZYvPLKK/Dz87P7j66hqek4WCwWtG7dGuXl5XB2dsb777+Phx56SO12VVOTcdi9ezc+/vhjHDhwoA46rDs1GYtOnTrhk08+QY8ePWCxWLBo0SL069cP2dnZd/wg+/qmJuPwyy+/YMeOHYiNjcWWLVtw/PhxPPfcc6ioqEBiYmJdtF3r7vbfyn379uHw4cP4+OOP1WqxztRkLJ588kkUFBSgf//+EBFcvXoVzzzzDF599VXV+mTQokZh/vz5WL9+PXbu3NloJv1WR4sWLXDgwAGUlJQgNTUV06ZNQ7t27TBo0CBHt1YnLl68iLFjx2LFihXw9vZ2dDsOFx4ejvDwcOX3fv36oUuXLvjwww/xxhtvOLCzumWz2eDj44OPPvoIzs7O6N27N3777TcsXLiwwQatu/Xxxx8jODgYoaGhjm7FIXbu3Il58+bh/fffR1hYGI4fP47nn38eb7zxBmbPnq3KORm0HMjb2xvOzs7Iz8+3W5+fnw+j0eigrhzjbsZi0aJFmD9/Pr777jv06NFDzTZVV9NxcHJyQocOHQAAISEhOHr0KJKSkhps0KruOJw4cQInT57EiBEjlHU2mw0A4OLigpycHLRv317dplVSG/9OuLq6olevXjh+/LgaLdaJmoyDyWSCq6srnJ2dlXVdunSB2WzGlStX4ObmpmrParibvw+XLl3C+vXr8frrr6vZYp2pyVjMnj0bY8eOxcSJEwEAwcHBuHTpEhISEvC3v/0NTk61P6OKc7QcyM3NDb1790ZqaqqyzmazITU11e7/RpuCmo7FggUL8MYbb2Dr1q3o06dPXbSqqtr6O2Gz2VBeXq5Gi3WiuuPQuXNnHDp0CAcOHFCWP/3pTxg8eDAOHDgAf3//umy/VtXG3wmr1YpDhw7BZDKp1abqajIODzzwAI4fP66EbgD4z3/+A5PJ1CBDFnB3fx82bNiA8vJy/OUvf1G7zTpRk7EoLS2tFKauB3FR69HPqk2zpzuyfv160Wq1smrVKjly5IgkJCSIwWBQvoo9duxYmTFjhlJfXl4u+/fvl/3794vJZJIXX3xR9u/fL8eOHXPUS6g11R2L+fPni5ubm3zxxRd2X12+ePGio15CrajuOMybN0+2b98uJ06ckCNHjsiiRYvExcVFVqxY4aiXUCuqOw43akzfOqzuWLz22muybds2OXHihGRmZsqYMWNEp9NJdna2o15CrajuOJw6dUpatGghkydPlpycHNm0aZP4+PjIm2++6aiXUCtq+t9G//79ZfTo0XXdrqqqOxaJiYnSokULWbdunfzyyy+yfft2ad++vTzxxBOq9cigVQ+8++670qZNG3Fzc5PQ0FD54YcflG0REREyfvx45ffc3FwBUGmJiIio+8ZVUJ2xaNu2bZVjkZiYWPeN17LqjMPf/vY36dChg+h0OmnZsqWEh4fL+vXrHdB17avOONyoMQUtkeqNxdSpU5VaX19fGTZsmGRlZTmg69pX3b8Te/fulbCwMNFqtdKuXTv5+9//LlevXq3jrmtfdcfh559/FgCyffv2Ou5UfdUZi4qKCpk7d660b99edDqd+Pv7y3PPPScXLlxQrT+NiFrXyoiIiIiaNs7RIiIiIlIJgxYRERGRShi0iIiIiFTCoEVERESkEgYtIiIiIpUwaBERERGphEGLiIiISCUMWkRETcSECRMQHR3t6DaImhQGLSJyuAkTJkCj0SjLPffcg6FDh+LgwYOObq1W/PG1XV/69++v2vlOnjwJjUaDAwcO2K3/xz/+gVWrVql2XiKqjEGLiOqFoUOHIi8vD3l5eUhNTYWLiwseffRRR7dVa1auXKm8vry8PHzzzTdV1lVUVKjWg16vh8FgUO34RFQZgxYR1QtarRZGoxFGoxEhISGYMWMGTp8+jXPnzuHBBx/E5MmT7erPnTsHNzc3pKamAgACAgLwxhtvICYmBh4eHmjdujWWLVtmt88777yD4OBgeHh4wN/fH8899xxKSkqU7b/++itGjBiBli1bwsPDA926dcOWLVsAABcuXEBsbCxatWoFd3d3BAUFYeXKlXf8+gwGg/L6jEYjvLy8lCtPycnJiIiIgE6nw5o1a3D+/HnExMSgdevWaNasGYKDg7Fu3Tq749lsNixYsAAdOnSAVqtFmzZt8Pe//x0AEBgYCADo1asXNBoNBg0aBKDyR4fl5eWYMmUKfHx8oNPp0L9/f2RkZCjbd+7cCY1Gg9TUVPTp0wfNmjVDv379kJOTc8evm6ipY9AionqnpKQEq1evRocOHXDPPfdg4sSJWLt2LcrLy5Wa1atXo3Xr1njwwQeVdQsXLkTPnj2xf/9+zJgxA88//zxSUlKU7U5OTli6dCmys7Px6aefYseOHXj55ZeV7ZMmTUJ5eTl27dqFQ4cO4a233kLz5s0BALNnz8aRI0fw7bff4ujRo/jggw/g7e1dK6/3eq9Hjx5FVFQULl++jN69e2Pz5s04fPgwEhISMHbsWOzbt0/ZZ+bMmZg/f77S19q1a+Hr6wsASt13332HvLw8bNy4scrzvvzyy/jyyy/x6aefIisrCx06dEBUVBQKCwvt6v72t7/h7bffxo8//ggXFxf8z//8T628bqImQbXHVRMR3aHx48eLs7OzeHh4iIeHhwAQk8kkmZmZIiJSVlYmLVu2lOTkZGWfHj16yNy5c5Xf27ZtK0OHDrU77ujRo+WRRx656Xk3bNgg99xzj/J7cHCw3TH/aMSIERIXF1ej1wdAdDqd8vo8PDzkf//3fyU3N1cAyJIlS257jOHDh8v06dNFRKS4uFi0Wq2sWLGiytrrx92/f7/d+vHjx8vIkSNFRKSkpERcXV1lzZo1yvYrV66In5+fLFiwQEREvv/+ewEg3333nVKzefNmASBlZWXVGQKiJotXtIioXhg8eDAOHDiAAwcOYN++fYiKisIjjzyCX3/9FTqdDmPHjsUnn3wCAMjKysLhw4cxYcIEu2OEh4dX+v3o0aPK79999x2GDBmC1q1bo0WLFhg7dizOnz+P0tJSAMCUKVPw5ptv4oEHHkBiYqLdZPxnn30W69evR0hICF5++WXs3bu3Wq9v8eLFyus7cOAAHnroIWVbnz597GqtViveeOMNBAcHw8vLC82bN8e2bdtw6tQpAMDRo0dRXl6OIUOGVKuHPzpx4gQqKirwwAMPKOtcXV0RGhpqN2YA0KNHD+XPJpMJAHD27Nkan5uoKWHQIqJ6wcPDAx06dECHDh3Qt29f/POf/8SlS5ewYsUKAMDEiRORkpKCM2fOYOXKlXjwwQfRtm3bOz7+yZMn8eijj6JHjx748ssvkZmZqczhunLlinKOX375BWPHjsWhQ4fQp08fvPvuuwCghL4XXngBv//+O4YMGYIXX3zxjs9vNBqV19ehQwd4eHjYvfY/WrhwIf7xj3/glVdewffff48DBw4gKipK6dPd3f2Oz1sbXF1dlT9rNBoA1+aIEdHtMWgRUb2k0Wjg5OSEsrIyAEBwcDD69OmDFStWYO3atVXOE/rhhx8q/d6lSxcAQGZmJmw2G95++23cf//96NixI37//fdKx/D398czzzyDjRs3Yvr06UrQA4BWrVph/PjxWL16NZYsWYKPPvqoNl+yYs+ePRg5ciT+8pe/oGfPnmjXrh3+85//KNuDgoLg7u6ufBHgRm5ubgCuXRm7mfbt28PNzQ179uxR1lVUVCAjIwNdu3atpVdCRC6OboCICLj2DTiz2Qzg2jf83nvvPZSUlGDEiBFKzcSJEzF58mR4eHjgscceq3SMPXv2YMGCBYiOjkZKSgo2bNiAzZs3AwA6dOiAiooKvPvuuxgxYgT27NmD5cuX2+0/depUPPLII+jYsSMuXLiA77//Xglqc+bMQe/evdGtWzeUl5dj06ZNyrbaFhQUhC+++AJ79+5Fy5Yt8c477yA/P18JQDqdDq+88gpefvlluLm54YEHHsC5c+eQnZ2N+Ph4+Pj4wN3dHVu3bsW9994LnU4HvV5vdw4PDw88++yzeOmll+Dl5YU2bdpgwYIFKC0tRXx8vCqvi6gp4hUtIqoXtm7dCpPJBJPJhLCwMGRkZGDDhg3KrQkAICYmBi4uLoiJiYFOp6t0jOnTp+PHH39Er1698Oabb+Kdd95BVFQUAKBnz55455138NZbb6F79+5Ys2YNkpKS7Pa3Wq2YNGkSunTpgqFDh6Jjx454//33AVy7SjRz5kz06NEDAwcOhLOzM9avX6/KWMyaNQv33XcfoqKiMGjQIBiNxkp3dJ89ezamT5+OOXPmoEuXLhg9erQyb8rFxQVLly7Fhx9+CD8/P4wcObLK88yfPx+PP/44xo4di/vuuw/Hjx/Htm3b0LJlS1VeF1FTpBERcXQTRER34uTJk2jfvj0yMjJw33332W0LCAjA1KlTMXXqVMc0R0RUBX50SET1XkVFBc6fP49Zs2bh/vvvrxSyiIjqK350SET13p49e2AymZCRkVFpXpWjzZs3D82bN69yeeSRRxzdHhE5GD86JCK6C4WFhZXupH6du7s7WrduXccdEVF9wqBFREREpBJ+dEhERESkEgYtIiIiIpUwaBERERGphEGLiIiISCUMWkREREQqYdAiIiIiUgmDFhEREZFKGLSIiIiIVPL/AXOlUhCO1mKmAAAAAElFTkSuQmCC", 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", 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", 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g/vF4mFq+fLkmT56s2bNna9u2beratauSk5N17NixSttv2rRJI0aM0Lhx47R9+3alpKQoJSVFu3btstrMnTtX8+fPV3p6urKyshQYGKjk5GSdPXvWajNy5Ejt3r1b69at0+rVq/XZZ59p/PjxFc5XVlamESNGqG/fvtV/8QAA1ACeNauoOvvE40/zJSQkqEePHnrllVckSU6nU1FRUbrvvvs0bdq0Cu1TU1NVXFys1atXW9uuv/56xcfHKz09XcYYRUZGasqUKdbLDwsLCxUWFqYlS5Zo+PDh1qqoW7ZsUffu3SVJa9eu1aBBg/Tdd98pMjLSOvbDDz+s77//XgMGDNCkSZNUUFBw2dfG03wAAE8qLy/XV199pdDQUDVv3tzT5dQqJ06c0LFjxxQbGytvb2+XfVX9/e3RdabOnTun7OxsTZ8+3drm5eWlpKQkZWZmVvqZzMxMTZ482WVbcnKy3n//fUnS4cOHlZubq6SkJGt/UFCQEhISlJmZqeHDhyszM1PBwcFWkJKkpKQkeXl5KSsrS7fddpskaf369VqxYoV27NihlStX/uL1lJaWqrS01Pq+qKjolzsBAAA38fb2VnBwsHW3p2HDhjW29lJtZYxRSUmJjh07puDg4ApB6kp4NEzl5+ervLxcYWFhLtvDwsIu+j6e3NzcStvn5uZa+y9su1Sb0NBQl/0+Pj5q1qyZ1ebEiRMaM2aM3nzzzcseVUpLS9Pjjz9+WW0BAKgJ4eHhknTR6TNXq+DgYKtv7GIF9Iu45557dOedd+qGG2647M9Mnz7dZdSsqKhIUVFR7igPAIDL4nA4FBERodDQUJWVlXm6nFqhQYMG1TIidYFHw1RISIi8vb2Vl5fnsj0vL++iaTE8PPyS7S/8mZeXp4iICJc28fHxVpufJ/Tz58/r5MmT1ufXr1+vVatW6fnnn5f047Cg0+mUj4+PXnvtNf3nf/5nhdr8/Pzk5+d3uZcPAECN8fb2rtYAgf/j0af5fH191a1bN2VkZFjbnE6nMjIylJiYWOlnEhMTXdpL0rp166z2bdq0UXh4uEuboqIiZWVlWW0SExNVUFCg7Oxsq8369evldDqVkJAg6ce5WTt27LC+nnjiCTVu3Fg7duyw5lQBAAB4/Dbf5MmTNXr0aHXv3l09e/bUvHnzVFxcrLFjx0qS7rrrLrVs2VJpaWmSpAceeED9+vXTCy+8oMGDB2vZsmXaunWrXnvtNUk/DmdOmjRJTz31lGJiYtSmTRvNnDlTkZGRSklJkSR17NhRAwcO1D333KP09HSVlZVp4sSJGj58uPUkX8eOHV3q3Lp1q7y8vNS5c+ca6hkAAFAXeDxMpaam6vjx45o1a5Zyc3MVHx+vtWvXWhPIv/32W3l5/d8AWq9evfTWW29pxowZeuSRRxQTE6P333/fJeQ89NBDKi4u1vjx41VQUKA+ffpo7dq1Li8yXLp0qSZOnKgBAwbIy8tLt99+u+bPn19zFw4AAOoFj68zVZ+xzhQAAHVPVX9/e3wFdAAAgLqMMAUAAGADYQoAAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAAABsIEwBAADYQJgCAACwgTAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgAAwAbCFAAAgA2EKQAAABsIUwAAADYQpgAAAGwgTAEAANhAmAIAALCBMAUAAGADYQoAAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAAABsIEwBAADYQJgCAACwgTAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgAAwAbCFAAAgA2EKQAAABsIUwAAADYQpgAAAGwgTAEAANhAmAIAALCBMAUAAGADYQoAAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAAABsIEwBAADYQJgCAACwgTAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgAAwAbCFAAAgA2EKQAAABsIUwAAADbUijC1YMECRUdHy9/fXwkJCdq8efMl269YsUIdOnSQv7+/4uLitGbNGpf9xhjNmjVLERERCggIUFJSkg4cOODS5uTJkxo5cqSaNGmi4OBgjRs3TqdPn7b279+/XzfeeKPCwsLk7++vtm3basaMGSorK6u+CwcAAHWex8PU8uXLNXnyZM2ePVvbtm1T165dlZycrGPHjlXaftOmTRoxYoTGjRun7du3KyUlRSkpKdq1a5fVZu7cuZo/f77S09OVlZWlwMBAJScn6+zZs1abkSNHavfu3Vq3bp1Wr16tzz77TOPHj7f2N2jQQHfddZc+/vhj7d+/X/PmzdOiRYs0e/Zs93UGAACocxzGGOPJAhISEtSjRw+98sorkiSn06moqCjdd999mjZtWoX2qampKi4u1urVq61t119/veLj45Weni5jjCIjIzVlyhRNnTpVklRYWKiwsDAtWbJEw4cP1969e9WpUydt2bJF3bt3lyStXbtWgwYN0nfffafIyMhKa508ebK2bNmi//f//t9lXVtRUZGCgoJUWFioJk2aVKlfAACAZ1T197dHR6bOnTun7OxsJSUlWdu8vLyUlJSkzMzMSj+TmZnp0l6SkpOTrfaHDx9Wbm6uS5ugoCAlJCRYbTIzMxUcHGwFKUlKSkqSl5eXsrKyKj3vwYMHtXbtWvXr1++i11NaWqqioiKXLwAAUL95NEzl5+ervLxcYWFhLtvDwsKUm5tb6Wdyc3Mv2f7Cn7/UJjQ01GW/j4+PmjVrVuG8vXr1kr+/v2JiYtS3b1898cQTF72etLQ0BQUFWV9RUVEXbQsAAOoHj8+Zqu2WL1+ubdu26a233tKHH36o559//qJtp0+frsLCQuvr6NGjNVgpAADwBB9PnjwkJETe3t7Ky8tz2Z6Xl6fw8PBKPxMeHn7J9hf+zMvLU0REhEub+Ph4q83PJ7ifP39eJ0+erHDeC6NLnTp1Unl5ucaPH68pU6bI29u7Qm1+fn7y8/P7pcsGAAD1iEdHpnx9fdWtWzdlZGRY25xOpzIyMpSYmFjpZxITE13aS9K6deus9m3atFF4eLhLm6KiImVlZVltEhMTVVBQoOzsbKvN+vXr5XQ6lZCQcNF6nU6nysrK5HQ6q36xAACgXvLoyJT04xNyo0ePVvfu3dWzZ0/NmzdPxcXFGjt2rCTprrvuUsuWLZWWliZJeuCBB9SvXz+98MILGjx4sJYtW6atW7fqtddekyQ5HA5NmjRJTz31lGJiYtSmTRvNnDlTkZGRSklJkSR17NhRAwcO1D333KP09HSVlZVp4sSJGj58uPUk39KlS9WgQQPFxcXJz89PW7du1fTp05WamqoGDRrUfEcBAIBayeNhKjU1VcePH9esWbOUm5ur+Ph4rV271ppA/u2338rL6/8G0Hr16qW33npLM2bM0COPPKKYmBi9//776ty5s9XmoYceUnFxscaPH6+CggL16dNHa9eulb+/v9Vm6dKlmjhxogYMGCAvLy/dfvvtmj9/vrXfx8dHzz77rL766isZY9S6dWtNnDhRDz74YA30CgAAqCs8vs5UfcY6UwAA1D11ap0pAACAuo4wBQAAYANhCgAAwAbCFAAAgA2EKQAAABsIUwAAADYQpgAAAGwgTAEAANhAmAIAALCBMAUAAGADYQoAAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAAABsIEwBAADYQJgCAACwgTAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgAAwAbCFAAAgA2EKQAAABsIUwAAADYQpgAAAGwgTAEAANhAmAIAALCBMAUAAGADYQoAAMAGwhRsySk8o02H8pVTeMbTpQAA4BE+ni4AddfyLd9q+sov5TSSl0NKGxan1B6tPF0WAAA1ipEpXJGcwjNWkJIkp5EeWbmLESoAwFWHMIUrcji/2ApSF5QboyP5JZ4pCAAADyFM4Yq0CQmUl8N1m7fDoeiQhp4pCAAADyFM4YpEBAUobVicvB0/Jipvh0PPDOusiKAAD1cGAEDNYgI6rlhqj1a6IbaFjuSXKDqkIUEKAHBVIkzBloigAEIUAOCqxm0+AAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgA8JKfwjDYdyuedlkAdxzpTAOABy7d8a70s3MshpQ2LU2qPVp4uC8AVYGQKAGpYTuEZK0hJktNIj6zcxQgVUEcRpgCghh3OL7aC1AXlxuhIfolnCgJgC2EKAGpYm5BAeTlct3k7HIoOaeiZggDYQpgCgBoWERSgtGFx8nb8mKi8HQ49M6wz77kE6igmoAOAB6T2aKUbYlvoSH6JokMaEqSAOowwBQAeEhEUQIgC6oErus23YsUKDRs2TJ07d1bnzp01bNgwvfvuu9VdGwAAQK1XpTDldDqVmpqq1NRU7dmzR+3bt1f79u21e/dupaamavjw4TLG/PKBAAAA6okq3eb785//rE8++USrVq3Sb3/7W5d9q1at0tixY/XnP/9ZkyZNqs4aAQAAaq0qjUwtXrxYzz33XIUgJUm33nqr5s6dqzfeeKPaigMAAKjtqhSmDhw4oKSkpIvuT0pK0oEDB2wXBQAAUFdUKUwFBASooKDgovuLiork7+9vtyYAAIA6o0phKjExUQsXLrzo/gULFigxMdF2UQAAAHVFlSagP/roo+rfv79OnDihqVOnqkOHDjLGaO/evXrhhRf0wQcfaMOGDe6qFQAAoNapUpjq1auXli9frvHjx+u9995z2de0aVO9/fbb6t27d7UWCAAAUJs5zBUsDFVSUqKPPvrImmweGxurm2++WQ0b8pLOnyoqKlJQUJAKCwvVpEkTT5cDAAAuQ1V/f1dpZGr9+vWaOHGivvjiC912220u+woLC3XttdcqPT1dffv2rVrVAAAAdVSVJqDPmzdP99xzT6UpLSgoSH/4wx/04osvVltxAAAAtV2VwtS//vUvDRw48KL7b775ZmVnZ9suCgAAoK6oUpjKy8tTgwYNLrrfx8dHx48fr3IRCxYsUHR0tPz9/ZWQkKDNmzdfsv2KFSvUoUMH+fv7Ky4uTmvWrHHZb4zRrFmzFBERoYCAgEoXEz158qRGjhypJk2aKDg4WOPGjdPp06et/Z9++qmGDh2qiIgIBQYGKj4+XkuXLq3ytQEAgPqtSmGqZcuW2rVr10X379y5UxEREVUqYPny5Zo8ebJmz56tbdu2qWvXrkpOTtaxY8cqbb9p0yaNGDFC48aN0/bt25WSkqKUlBSXuubOnav58+crPT1dWVlZCgwMVHJyss6ePWu1GTlypHbv3q1169Zp9erV+uyzzzR+/HiX83Tp0kXvvfeedu7cqbFjx+quu+7S6tWrq3R9AACgnjNVMHHiRNO5c2dz5syZCvtKSkpM586dzX333VeVQ5qePXuaCRMmWN+Xl5ebyMhIk5aWVmn7O+64wwwePNhlW0JCgvnDH/5gjDHG6XSa8PBw89xzz1n7CwoKjJ+fn3n77beNMcbs2bPHSDJbtmyx2vz3f/+3cTgc5t///vdFax00aJAZO3bsZV9bYWGhkWQKCwsv+zMAAMCzqvr7u0ojUzNmzNDJkycVGxuruXPn6oMPPtAHH3ygZ599Vtdcc41OnjypRx999LKPd+7cOWVnZ7u878/Ly0tJSUnKzMys9DOZmZkV3g+YnJxstT98+LByc3Nd2gQFBSkhIcFqk5mZqeDgYHXv3t1qk5SUJC8vL2VlZV203sLCQjVr1uyi+0tLS1VUVOTyBQAA6rcqLY0QFhamTZs26U9/+pOmT58u879LVDkcDiUnJ2vBggUKCwu77OPl5+ervLy8wmfCwsK0b9++Sj+Tm5tbafvc3Fxr/4Vtl2oTGhrqst/Hx0fNmjWz2vzcO++8oy1btujVV1+96PWkpaXp8ccfv+h+AABQ/1QpTElS69attWbNGv3www86ePCgjDGKiYlR06ZN3VFfrbBhwwaNHTtWixYt0rXXXnvRdtOnT9fkyZOt74uKihQVFVUTJQIAAA+pcpi6oGnTpurRo4etk4eEhMjb21t5eXku2/Py8hQeHl7pZ8LDwy/Z/sKfeXl5LpPh8/LyFB8fb7X5+QT38+fP6+TJkxXO+z//8z8aMmSIXnrpJd11112XvB4/Pz/5+fldsg0AAKhfqjRnqrr5+vqqW7duysjIsLY5nU5lZGQoMTGx0s8kJia6tJekdevWWe3btGmj8PBwlzZFRUXKysqy2iQmJqqgoMBlTaz169fL6XQqISHB2vbpp59q8ODBevbZZ12e9AMAALC4dTr8ZVi2bJnx8/MzS5YsMXv27DHjx483wcHBJjc31xhjzKhRo8y0adOs9hs3bjQ+Pj7m+eefN3v37jWzZ882DRo0MF9++aXVZs6cOSY4ONh88MEHZufOnWbo0KGmTZs2Lk8hDhw40Fx33XUmKyvLfP755yYmJsaMGDHC2r9+/XrTsGFDM336dJOTk2N9nThx4rKvjaf5AACoe6r6+9vjYcoYY15++WXTqlUr4+vra3r27Gm++OILa1+/fv3M6NGjXdq/8847JjY21vj6+pprr73WfPjhhy77nU6nmTlzpgkLCzN+fn5mwIABZv/+/S5tTpw4YUaMGGEaNWpkmjRpYsaOHWtOnTpl7R89erSRVOGrX79+l31dhCkAAOqeqv7+dhjzv4/kodpV9a3TAADA86r6+9ujc6YAAADqOsIUAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQCAy5JTeEabDuUrp/CMp0upVa74dTIAAODqsXzLt5q+8ks5jeTlkNKGxSm1RytPl1UrMDIFAAAuKafwjBWkJMlppEdW7mKE6n8RpgAAwCUdzi+2gtQF5cboSH6JZwqqZQhTAADgktqEBMrL4brN2+FQdEhDzxRUyxCmANRaTHYFaoeIoAClDYuTt+PHROXtcOiZYZ0VERTg4cpqByagA6iVmOwK1C6pPVrphtgWOpJfouiQhgSpn2BkCkCtw2RXoHaKCApQYrvmBKmfIUwBqHWY7AqgLiFMAah1mOwKoC4hTKFeYKJy/cJkVwB1CRPQUecxUbl+YrIrgLqCkSnUaUxUrt+Y7AqgLiBMoU5jojIAwNMIU6jTmKgMAPA0whTqNCYqAwA8jQnoqPOYqAwA8CTCFOqFiKAAQhQAwCO4zQcAAGADYQoAAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAAABsIEwBAADbcgrPaNOh/Kvy3aisMwUAAGxZvuVb66XzXg4pbVicUnu08nRZNYaRKQAAcMVyCs9YQUqSnEZ6ZOWuq2qEijAFAACu2OH8YitIXVBujI7kl3imIA8gTAEAgCvWJiRQXg7Xbd4Oh6JDGnqmIA8gTAEAgCsWERSgtGFx8nb8mKi8HQ49M6zzVfW+VCagAwAAW1J7tNINsS10JL9E0SENr6ogJRGmAABANYgICrjqQtQF3OYDAACwgTAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBcCjcgrPaNOhfOUUnvF0KQBwRXidDACPWb7lW01f+aWcRvJySGnD4pTao5WnywKAKmFkCoBH5BSesYKUJDmN9MjKXYxQAahzCFMAPOJwfrEVpC4oN0ZH8ks8UxCAOqU2TRHgNh8Aj2gTEigvh1wClbfDoeiQhp4rCkCdUNumCDAyBcAjIoIClDYsTt4Oh6Qfg9QzwzorIijAw5UBqM1q4xQBRqYAeExqj1a6IbaFjuSXKDqkIUEKwC+61BQBT/0dQpgC4FERQQGEKACXrTZOEeA2HwAAqDNq4xQBRqYAAECdUtumCBCmAABAnVObpghwmw8AAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAVJva9K4sAKgpPM0HoFrUtndlAUBNYWQKgG218V1ZAFBTCFMAbLvUu7IAoL4jTAGw7cK7sn7K0+/KAoCaQpgCYFttfFcWANQUJqADqBa17V1ZAFBTCFMAqk1telcWANQUj9/mW7BggaKjo+Xv76+EhARt3rz5ku1XrFihDh06yN/fX3FxcVqzZo3LfmOMZs2apYiICAUEBCgpKUkHDhxwaXPy5EmNHDlSTZo0UXBwsMaNG6fTp09b+8+ePasxY8YoLi5OPj4+SklJqbbrBQAA9YtHw9Ty5cs1efJkzZ49W9u2bVPXrl2VnJysY8eOVdp+06ZNGjFihMaNG6ft27crJSVFKSkp2rVrl9Vm7ty5mj9/vtLT05WVlaXAwEAlJyfr7NmzVpuRI0dq9+7dWrdunVavXq3PPvtM48ePt/aXl5crICBA999/v5KSktzXAQAAoM5zGGPMLzdzj4SEBPXo0UOvvPKKJMnpdCoqKkr33Xefpk2bVqF9amqqiouLtXr1amvb9ddfr/j4eKWnp8sYo8jISE2ZMkVTp06VJBUWFiosLExLlizR8OHDtXfvXnXq1ElbtmxR9+7dJUlr167VoEGD9N133ykyMtLlnGPGjFFBQYHef//9Kl9fUVGRgoKCVFhYqCZNmlT58wAAoOZV9fe3x0amzp07p+zsbJeRHy8vLyUlJSkzM7PSz2RmZlYYKUpOTrbaHz58WLm5uS5tgoKClJCQYLXJzMxUcHCwFaQkKSkpSV5eXsrKyqq26wMAAFcHj01Az8/PV3l5ucLCwly2h4WFad++fZV+Jjc3t9L2ubm51v4L2y7VJjQ01GW/j4+PmjVrZrW5UqWlpSotLbW+LyoqsnU8ABeXU3hGh/OL1SYkkEnvADyKp/mqUVpamh5//HFPlwHUe7wHEEBt4rHbfCEhIfL29lZeXp7L9ry8PIWHh1f6mfDw8Eu2v/DnL7X5+QT38+fP6+TJkxc97+WaPn26CgsLra+jR4/aOh6AingPIIDaxmNhytfXV926dVNGRoa1zel0KiMjQ4mJiZV+JjEx0aW9JK1bt85q36ZNG4WHh7u0KSoqUlZWltUmMTFRBQUFys7OttqsX79eTqdTCQkJtq7Jz89PTZo0cfkCUL14DyCA2sajt/kmT56s0aNHq3v37urZs6fmzZun4uJijR07VpJ01113qWXLlkpLS5MkPfDAA+rXr59eeOEFDR48WMuWLdPWrVv12muvSZIcDocmTZqkp556SjExMWrTpo1mzpypyMhIa62ojh07auDAgbrnnnuUnp6usrIyTZw4UcOHD3d5km/Pnj06d+6cTp48qVOnTmnHjh2SpPj4+BrrHwAVXXgP4E8DFe8BBOBJHg1TqampOn78uGbNmqXc3FzFx8dr7dq11gTyb7/9Vl5e/zd41qtXL7311luaMWOGHnnkEcXExOj9999X586drTYPPfSQiouLNX78eBUUFKhPnz5au3at/P39rTZLly7VxIkTNWDAAHl5een222/X/PnzXWobNGiQvvnmG+v76667TtKPi4IC8JwL7wF8ZOUulRvDewABeJxH15mq71hnCnCfnMIzvAcQgFtU9fc3T/MBqJN4DyCA2sLj7+YDAACoywhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgAAwAbCFAAAgA2EKQAAABsIUwAAADYQpgAAAGwgTAEAANhAmAIAALCBMAUAAGADYQoAAMAGwhQAAIANhCkAAAAbCFMAAAA2EKYAAABsIEwBAADYQJgCAKAWyCk8o02H8pVTeMbTpaCKfDxdAAAAV7vlW77V9JVfymkkL4eUNixOqT1aebosXCZGpgAA8KCcwjNWkJIkp5EeWbmLEao6hDAFAIAHHc4vtoLUBeXG6Eh+iWcKQpURpgAA8KA2IYHycrhu83Y4FB3S0DMFocoIUwAAeFBEUIDShsXJ2/FjovJ2OPTMsM6KCArwcGW4XExABwDAw1J7tNINsS10JL9E0SENCVJ1DGEKANwop/CMDucXq01IIL8gcUkRQQH8jNRRhCkAcBMedweuDsyZAuoxFgH0HB53B64ejEwB9RSjIp51qcfduZUD1C+MTAH1EKMinsfj7sDVgzAF1EMsAuh5PO4OXD24zQfUQxdGRX4aqBgVqXk87g5cHRiZAuohRkVqj4igACW2a07fA/UYI1NAPcWoCADUDMIUUI+xCCCAyrCYbPUiTAEAcBVh2ZTqx5wpAACuEiyb4h6EKQAArhIsm+IehCkAAK4SLCbrHoQpAACuEiyb4h5MQIctPBECAHULy6ZUP8IUrhhPhABA3cSyKdWL23y4IjwRAqA2ySk8o02H8vk7CB7ByBSuyKWeCOFfOwBqEqPk8DRGpnBFeCIEQG3AKDlqA8IUrghPhACoDVg3CbUBt/lwxXgiBICnXRgl/2mgYpQcNY2RKdgSERSgxHbNCVIAPIJRctQGjEwBAOo0RsnhaYQpAECdx7pJ8CRu8wEAANhAmAIAoA5jwVLP4zYfAAB1FAuW1g6MTAEAUAexYGntQZgCAKAOYsHS2oMwBQBAHcRrvWoPwhQAAHUQC5bWHkxABwCgjmLB0tqBMAUAQB3GgqWex20+APUO6+4AqEmMTAGoV1h3B0BNY2QKQL3BujsAPIEwBaDeYN0dAJ5AmAJQb7DuDgBPIEwBqDdYdweAJzABHUC9wro7AGoaYQq1Tk7hGR3OL1abkEB+EeKKsO4OgJpUK27zLViwQNHR0fL391dCQoI2b958yfYrVqxQhw4d5O/vr7i4OK1Zs8ZlvzFGs2bNUkREhAICApSUlKQDBw64tDl58qRGjhypJk2aKDg4WOPGjdPp06dd2uzcuVN9+/aVv7+/oqKiNHfu3Oq5YFzU8i3fqvec9bpzUZZ6z1mv5Vu+9XRJAABcksfD1PLlyzV58mTNnj1b27ZtU9euXZWcnKxjx45V2n7Tpk0aMWKExo0bp+3btyslJUUpKSnatWuX1Wbu3LmaP3++0tPTlZWVpcDAQCUnJ+vs2bNWm5EjR2r37t1at26dVq9erc8++0zjx4+39hcVFenmm29W69atlZ2dreeee06PPfaYXnvtNfd1xlWOx9oBAHWRwxhjfrmZ+yQkJKhHjx565ZVXJElOp1NRUVG67777NG3atArtU1NTVVxcrNWrV1vbrr/+esXHxys9PV3GGEVGRmrKlCmaOnWqJKmwsFBhYWFasmSJhg8frr1796pTp07asmWLunfvLklau3atBg0apO+++06RkZFauHChHn30UeXm5srX11eSNG3aNL3//vvat2/fZV1bUVGRgoKCVFhYqCZNmtjqp6vBpkP5unNRVoXtb99zvRLbNfdARQCAq1FVf397dGTq3Llzys7OVlJSkrXNy8tLSUlJyszMrPQzmZmZLu0lKTk52Wp/+PBh5ebmurQJCgpSQkKC1SYzM1PBwcFWkJKkpKQkeXl5KSsry2pzww03WEHqwnn279+vH374odLaSktLVVRU5PKFy8dj7QCAusijYSo/P1/l5eUKCwtz2R4WFqbc3NxKP5Obm3vJ9hf+/KU2oaGhLvt9fHzUrFkzlzaVHeOn5/i5tLQ0BQUFWV9RUVGVXzgqxWPtAIC6iKf5qtH06dM1efJk6/uioiICVRXxWDsAoK7xaJgKCQmRt7e38vLyXLbn5eUpPDy80s+Eh4dfsv2FP/Py8hQREeHSJj4+3mrz8wnu58+f18mTJ12OU9l5fnqOn/Pz85Ofn99FrxeXh8faAQB1iUdv8/n6+qpbt27KyMiwtjmdTmVkZCgxMbHSzyQmJrq0l6R169ZZ7du0aaPw8HCXNkVFRcrKyrLaJCYmqqCgQNnZ2Vab9evXy+l0KiEhwWrz2WefqayszOU811xzjZo2bWrzygEAQL1hPGzZsmXGz8/PLFmyxOzZs8eMHz/eBAcHm9zcXGOMMaNGjTLTpk2z2m/cuNH4+PiY559/3uzdu9fMnj3bNGjQwHz55ZdWmzlz5pjg4GDzwQcfmJ07d5qhQ4eaNm3amDNnzlhtBg4caK677jqTlZVlPv/8cxMTE2NGjBhh7S8oKDBhYWFm1KhRZteuXWbZsmWmYcOG5tVXX73sayssLDSSTGFhoZ0uAgAANaiqv789HqaMMebll182rVq1Mr6+vqZnz57miy++sPb169fPjB492qX9O++8Y2JjY42vr6+59tprzYcffuiy3+l0mpkzZ5qwsDDj5+dnBgwYYPbv3+/S5sSJE2bEiBGmUaNGpkmTJmbs2LHm1KlTLm3+9a9/mT59+hg/Pz/TsmVLM2fOnCpdF2EKAIC6p6q/vz2+zlR9xjpTAADUPXVqnSkAAIC6jjAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANhCmAAAAbOBFx250YQmvoqIiD1cCAAAu14Xf25e7FCdhyo1OnTolSYqKivJwJQAAoKpOnTqloKCgX2zHCuhu5HQ69f3336tx48ZyOBwV9hcVFSkqKkpHjx5lhfT/RZ+4oj9c0R8V0Seu6A9X9EdFl9MnxhidOnVKkZGR8vL65RlRjEy5kZeXl371q1/9YrsmTZrwQ/4z9Ikr+sMV/VERfeKK/nBFf1T0S31yOSNSFzABHQAAwAbCFAAAgA2EKQ/y8/PT7Nmz5efn5+lSag36xBX94Yr+qIg+cUV/uKI/KnJHnzABHQAAwAZGpgAAAGwgTAEAANhAmAIAALCBMAUAAGADYcrNFixYoOjoaPn7+yshIUGbN2++aNvdu3fr9ttvV3R0tBwOh+bNm1dzhdagqvTJokWL1LdvXzVt2lRNmzZVUlLSJdvXRVXpj5UrV6p79+4KDg5WYGCg4uPj9fe//70Gq3W/qvTHTy1btkwOh0MpKSnuLdADqtInS5YskcPhcPny9/evwWrdr6o/IwUFBZowYYIiIiLk5+en2NhYrVmzpoaqdb+q9Ef//v0r/Hw4HA4NHjy4Bit2v6r+jMybN0/XXHONAgICFBUVpQcffFBnz569/BMauM2yZcuMr6+veeONN8zu3bvNPffcY4KDg01eXl6l7Tdv3mymTp1q3n77bRMeHm5eeumlmi24BlS1T+68806zYMECs337drN3714zZswYExQUZL777rsartw9qtofGzZsMCtXrjR79uwxBw8eNPPmzTPe3t5m7dq1NVy5e1S1Py44fPiwadmypenbt68ZOnRozRRbQ6raJ4sXLzZNmjQxOTk51ldubm4NV+0+Ve2P0tJS0717dzNo0CDz+eefm8OHD5tPP/3U7Nixo4Yrd4+q9seJEydcfjZ27dplvL29zeLFi2u2cDeqap8sXbrU+Pn5maVLl5rDhw+bjz76yERERJgHH3zwss9JmHKjnj17mgkTJljfl5eXm8jISJOWlvaLn23dunW9DFN2+sQYY86fP28aN25s/va3v7mrxBpltz+MMea6664zM2bMcEd5Ne5K+uP8+fOmV69e5q9//asZPXp0vQtTVe2TxYsXm6CgoBqqruZVtT8WLlxo2rZta86dO1dTJdYou3+HvPTSS6Zx48bm9OnT7iqxxlW1TyZMmGBuuukml22TJ082vXv3vuxzcpvPTc6dO6fs7GwlJSVZ27y8vJSUlKTMzEwPVuY51dEnJSUlKisrU7NmzdxVZo2x2x/GGGVkZGj//v264YYb3FlqjbjS/njiiScUGhqqcePG1USZNepK++T06dNq3bq1oqKiNHToUO3evbsmynW7K+mPVatWKTExURMmTFBYWJg6d+6sZ555RuXl5TVVtttUx9+pr7/+uoYPH67AwEB3lVmjrqRPevXqpezsbOtW4Ndff601a9Zo0KBBl31eXnTsJvn5+SovL1dYWJjL9rCwMO3bt89DVXlWdfTJww8/rMjISJf/o9RVV9ofhYWFatmypUpLS+Xt7a2//OUv+s1vfuPuct3uSvrj888/1+uvv64dO3bUQIU170r65JprrtEbb7yhLl26qLCwUM8//7x69eql3bt3X9aL12uzK+mPr7/+WuvXr9fIkSO1Zs0aHTx4UPfee6/Kyso0e/bsmijbbez+nbp582bt2rVLr7/+urtKrHFX0id33nmn8vPz1adPHxljdP78ef3xj3/UI488ctnnJUyhzpgzZ46WLVumTz/9tN5NqK2Kxo0ba8eOHTp9+rQyMjI0efJktW3bVv379/d0aTXq1KlTGjVqlBYtWqSQkBBPl1NrJCYmKjEx0fq+V69e6tixo1599VU9+eSTHqzMM5xOp0JDQ/Xaa6/J29tb3bp107///W8999xzdT5M2fX6668rLi5OPXv29HQpHvXpp5/qmWee0V/+8hclJCTo4MGDeuCBB/Tkk09q5syZl3UMwpSbhISEyNvbW3l5eS7b8/LyFB4e7qGqPMtOnzz//POaM2eOPvnkE3Xp0sWdZdaYK+0PLy8vtW/fXpIUHx+vvXv3Ki0trc6Hqar2x6FDh3TkyBENGTLE2uZ0OiVJPj4+2r9/v9q1a+feot2sOv4eadCgga677jodPHjQHSXWqCvpj4iICDVo0EDe3t7Wto4dOyo3N1fnzp2Tr6+vW2t2Jzs/H8XFxVq2bJmeeOIJd5ZY466kT2bOnKlRo0bp7rvvliTFxcWpuLhY48eP16OPPiovr1+eEcWcKTfx9fVVt27dlJGRYW1zOp3KyMhw+Vfj1eRK+2Tu3Ll68skntXbtWnXv3r0mSq0R1fUz4nQ6VVpa6o4Sa1RV+6NDhw768ssvtWPHDuvr1ltv1Y033qgdO3YoKiqqJst3i+r4GSkvL9eXX36piIgId5VZY66kP3r37q2DBw9aQVuSvvrqK0VERNTpICXZ+/lYsWKFSktL9fvf/97dZdaoK+mTkpKSCoHpQvg2l/v64iuYKI/LtGzZMuPn52eWLFli9uzZY8aPH2+Cg4Otx5RHjRplpk2bZrUvLS0127dvN9u3bzcRERFm6tSpZvv27ebAgQOeuoRqV9U+mTNnjvH19TXvvvuuy+O8p06d8tQlVKuq9sczzzxjPv74Y3Po0CGzZ88e8/zzzxsfHx+zaNEiT11Ctapqf/xcfXyar6p98vjjj5uPPvrIHDp0yGRnZ5vhw4cbf39/s3v3bk9dQrWqan98++23pnHjxmbixIlm//79ZvXq1SY0NNQ89dRTnrqEanWl/5/p06ePSU1Nrelya0RV+2T27NmmcePG5u233zZff/21+fjjj027du3MHXfccdnnJEy52csvv2xatWplfH19Tc+ePc0XX3xh7evXr58ZPXq09f3hw4eNpApf/fr1q/nC3agqfdK6detK+2T27Nk1X7ibVKU/Hn30UdO+fXvj7+9vmjZtahITE82yZcs8ULX7VKU/fq4+hiljqtYnkyZNstqGhYWZQYMGmW3btnmgavep6s/Ipk2bTEJCgvHz8zNt27Y1Tz/9tDl//nwNV+0+Ve2Pffv2GUnm448/ruFKa05V+qSsrMw89thjpl27dsbf399ERUWZe++91/zwww+XfT6HMZc7hgUAAICfY84UAACADYQpAAAAGwhTAAAANhCmAAAAbCBMAQAA2ECYAgAAsIEwBQAAYANhCgDqgTFjxiglJcXTZQBXJcIUALcaM2aMHA6H9dW8eXMNHDhQO3fu9HRp1eKn13bhq0+fPm4735EjR+RwOLRjxw6X7X/+85+1ZMkSt50XwMURpgC43cCBA5WTk6OcnBxlZGTIx8dHv/3tbz1dVrVZvHixdX05OTlatWpVpe3KysrcVkNQUJCCg4PddnwAF0eYAuB2fn5+Cg8PV3h4uOLj4zVt2jQdPXpUx48f10033aSJEye6tD9+/Lh8fX2tN79HR0frySef1IgRIxQYGKiWLVtqwYIFLp958cUXFRcXp8DAQEVFRenee+/V6dOnrf3ffPONhgwZoqZNmyowMFDXXnut1qxZI0n64YcfNHLkSLVo0UIBAQGKiYnR4sWLL/v6goODresLDw9Xs2bNrBGk5cuXq1+/fvL399fSpUt14sQJjRgxQi1btlTDhg0VFxent99+2+V4TqdTc+fOVfv27eXn56dWrVrp6aefliS1adNGknTdddfJ4XCof//+kire5istLdX999+v0NBQ+fv7q0+fPtqyZYu1/9NPP5XD4VBGRoa6d++uhg0bqlevXtq/f/9lXzeAHxGmANSo06dP680331T79u3VvHlz3X333XrrrbdUWlpqtXnzzTfVsmVL3XTTTda25557Tl27dtX27ds1bdo0PfDAA1q3bp2138vLS/Pnz9fu3bv1t7/9TevXr9dDDz1k7Z8wYYJKS0v12Wef6csvv9Szzz6rRo0aSZJmzpypPXv26L//+7+1d+9eLVy4UCEhIdVyvRdq3bt3r5KTk3X27Fl169ZNH374oXbt2qXx48dr1KhR2rx5s/WZ6dOna86cOVZdb731lsLCwiTJavfJJ58oJydHK1eurPS8Dz30kN577z397W9/07Zt29S+fXslJyfr5MmTLu0effRRvfDCC9q6dat8fHz0n//5n9Vy3cBVpdpe0QwAlRg9erTx9vY2gYGBJjAw0EgyERERJjs72xhjzJkzZ0zTpk3N8uXLrc906dLFPPbYY9b3rVu3NgMHDnQ5bmpqqrnlllsuet4VK1aY5s2bW9/HxcW5HPOnhgwZYsaOHXtF1yfJ+Pv7W9cXGBho/vGPf5jDhw8bSWbevHm/eIzBgwebKVOmGGOMKSoqMn5+fmbRokWVtr1w3O3bt7tsHz16tBk6dKgxxpjTp0+bBg0amKVLl1r7z507ZyIjI83cuXONMcZs2LDBSDKffPKJ1ebDDz80ksyZM2eq0gXAVY+RKQBud+ONN2rHjh3asWOHNm/erOTkZN1yyy365ptv5O/vr1GjRumNN96QJG3btk27du3SmDFjXI6RmJhY4fu9e/da33/yyScaMGCAWrZsqcaNG2vUqFE6ceKESkpKJEn333+/nnrqKfXu3VuzZ892mQD/pz/9ScuWLVN8fLweeughbdq0qUrX99JLL1nXt2PHDv3mN7+x9nXv3t2lbXl5uZ588knFxcWpWbNmatSokT766CN9++23kqS9e/eqtLRUAwYMqFINP3Xo0CGVlZWpd+/e1rYGDRqoZ8+eLn0mSV26dLH+OyIiQpJ07NixKz43cDUiTAFwu8DAQLVv317t27dXjx499Ne//lXFxcVatGiRJOnuu+/WunXr9N1332nx4sW66aab1Lp168s+/pEjR/Tb3/5WXbp00Xvvvafs7GxrTtW5c+esc3z99dcaNWqUvvzyS3Xv3l0vv/yyJFnB7sEHH9T333+vAQMGaOrUqZd9/vDwcOv62rdvr8DAQJdr/6nnnntOf/7zn/Xwww9rw4YN2rFjh5KTk606AwICLvu81aFBgwbWfzscDkk/ztkCcPkIUwBqnMPhkJeXl86cOSNJiouLU/fu3bVo0SK99dZblc7b+eKLLyp837FjR0lSdna2nE6nXnjhBV1//fWKjY3V999/X+EYUVFR+uMf/6iVK1dqypQpVpiTpBYtWmj06NF68803NW/ePL322mvVecmWjRs3aujQofr973+vrl27qm3btvrqq6+s/TExMQoICLAm3/+cr6+vpB9HuC6mXbt28vX11caNG61tZWVl2rJlizp16lRNVwLgAh9PFwCg/istLVVubq6kH5+ce+WVV3T69GkNGTLEanP33Xdr4sSJCgwM1G233VbhGBs3btTcuXOVkpKidevWacWKFfrwww8lSe3bt1dZWZlefvllDRkyRBs3blR6errL5ydNmqRbbrlFsbGx+uGHH7RhwwYrjM2aNUvdunXTtddeq9LSUq1evdraV91iYmL07rvvatOmTWratKlefPFF5eXlWSHH399fDz/8sB566CH5+vqqd+/eOn78uHbv3q1x48YpNDRUAQEBWrt2rX71q1/J399fQUFBLucIDAzUn/70J/3Xf/2XmjVrplatWmnu3LkqKSnRuHHj3HJdwNWMkSkAbrd27VpFREQoIiJCCQkJ2rJli1asWGE91i9JI0aMkI+Pj0aMGCF/f/8Kx5gyZYq2bt2q6667Tk899ZRefPFFJScnS5K6du2qF198Uc8++6w6d+6spUuXKi0tzeXz5eXlmjBhgjp27KiBAwcqNjZWf/nLXyT9ONozffp0denSRTfccIO8vb21bNkyt/TFjBkz9Otf/1rJycnq37+/wsPDK6xcPnPmTE2ZMkWzZs1Sx44dlZqaas1j8vHx0fz58/Xqq68qMjJSQ4cOrfQ8c+bM0e23365Ro0bp17/+tQ4ePKiPPvpITZs2dct1AVczhzHGeLoIADhy5IjatWunLVu26Ne//rXLvujoaE2aNEmTJk3yTHEAcAnc5gPgUWVlZTpx4oRmzJih66+/vkKQAoDajtt8ADxq48aNioiI0JYtWyrMc/K0Z555Ro0aNar065ZbbvF0eQBqCW7zAcBFnDx5ssKK4RcEBASoZcuWNVwRgNqIMAUAAGADt/kAAABsIEwBAADYQJgCAACwgTAFAABgA2EKAADABsIUAACADYQpAAAAGwhTAAAANvx/SFJcLmWoc+IAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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GAFyAU+9z8+2336pVq1Zq1aqVJCkxMVGtWrWyXtadnZ2trKwsa/9//etfOn/+vJ5++mmFhYVZX0OGDHFoXZyKwqU4HgDAtTh15CY2NlZXu83OvHnzbN6npaWVb0EAAMDlOXXkBrBXRESEpk6d6uwyAAAVEOHGjRw9elRPPfWU6tatKx8fH4WGhio+Pl4bN26UdOH0ytKlS51bZCnFxsbKZDJd9jp//ryzSwMAp8jOK9Km/bnKzitydikVnstdLYUre+ihh3T27FnNnz9fDRo0UE5OjlJTU3Xs2DFnl2aXwYMHa+zYsTZtVapwyAKofFK2ZClpyU5ZDMnDJCX3jFJCu7rOLqvCYuTGTZw4cULr16/XxIkT1a1bN9WrV0/t27dXUlKS7r//fkVEREiSHnzwQZlMJut7SVq2bJlat24tX19fNWjQQGPGjLEZIZk8ebKioqJUtWpVhYeH6+9//7tOnTpl/XzevHmqXr26Pv/8czVu3Fj+/v56+OGHVVhYqPnz5ysiIkI1atTQc889p+Li4lJvk7+/v0JDQ21eV5KVlaUHHnhAAQEBCgwMVK9evZSTkyNJysvLk6enp7799ltJFx7bUbNmTd12223W5T/44IOr3twRAJwlO6/IGmwkyWJIw5d8zwjOVRBuytGNHEIMCAhQQECAli5davMsrYu2bNkiSZo7d66ys7Ot79evX6/+/ftryJAh+uGHHzR79mzNmzdPr7/+unVZDw8Pvf3229q1a5fmz5+vr776Si+99JLN+gsLC/X2229r4cKFWrVqldLS0vTggw9q5cqVWrlypd5//33Nnj1bixcvdvi2WywWPfDAAzp+/LjWrl2rNWvW6MCBA0pISJAkmc1mtWzZ0johfefOnTKZTNq2bZs1pK1du1YxMTEOrw0ArldGboE12FxUbBjKzC10TkEugHBTTlK2ZKnzhK/UZ87/1HnCV0rZknXtha5DlSpVNG/ePM2fP1/Vq1dX586dNXz4cH333XeSpODgYEm/P0rg4vsxY8Zo2LBhGjBggBo0aKC77rpL48aN0+zZs63rHjp0qLp166aIiAjdcccdeu2117Ro0SKb7z937pxmzpypVq1aqWvXrnr44Ye1YcMGvfvuu2rWrJn+/Oc/q1u3bvr6669LvU3vvPOONbQFBAToH//4R4n9UlNTtXPnTn300Udq06aNOnTooAULFmjt2rXWEBcbG2sNN2lpabrrrrvUtGlTbdiwwdpGuAFQEdUPqiqPP9yRwtNkUkQQ92S7EsJNOXDWEOJDDz2kX3/9VcuXL9ef/vQnpaWlqXXr1pddUn+pHTt2aOzYsTYhYvDgwcrOzlZh4YV/FXz55Ze68847VadOHVWrVk39+vXTsWPHrJ9LF04hRUZGWt+HhIQoIiJCAQEBNm0XH2VQGn379tX27dutr6SkpBL77d69W+Hh4TanlZo1a6bq1atr9+7dkqSYmBht2LBBxcXFWrt2rWJjY62B59dff9W+ffsUGxtb6toA4EYJM/spuWeUPP/vnlueJpPG92yuMLOfkyuruJidWQ6uNoRY3gejr6+v7rrrLt1111169dVXNWjQII0aNUqPPvpoif1PnTqlMWPGqGfPniWuKzMzU3/+85/11FNP6fXXX1fNmjW1YcMGPfbYYzp79qz17r1eXl42y5pMphLbLBZLqbfFbDarYcOGpe5/NV27dtXJkye1detWrVu3TuPHj1doaKgmTJig6Oho1a5dW7fccotDvgsAHC2hXV11bRSszNxCRQT5E2yugXBTDi4OIV4acJw1hNisWTPr5d9eXl6XTeht3bq19u7de8UQkZ6eLovFokmTJsnD48JA3x9PSTlb06ZNdfDgQR08eNA6evPDDz/oxIkTatasmaQLp+NatGih6dOny8vLS02aNFGtWrWUkJCgzz//nFNSACq8MLMfoaaUOC1VDpwxhHjs2DHdcccd+uCDD/Tdd98pIyNDn3zyid544w098MADki7c+C41NVWHDx/Wb7/9JkkaOXKkFixYoDFjxmjXrl3avXu3Fi5cqBEjRkiSGjZsqHPnzmnatGk6cOCA3n//fc2aNavctsMecXFxioqKUt++fbV161Z988036t+/v2JiYtS2bVtrv9jYWH344YfWIFOzZk01bdpUKSkphBsAcCOEm3KS0K6uNgzrpo8H36YNw7qV+/0IAgIC1KFDB02ZMkVdu3ZV8+bN9eqrr2rw4MGaPn26JGnSpElas2aNwsPDrc/zio+P1+eff64vvvhC7dq102233aYpU6aoXr16kqTo6GhNnjxZEydOVPPmzfXhhx8qOTm5XLelrEwmk5YtW6YaNWqoa9euiouLU4MGDZSSkmLTLyYmRsXFxTZza2JjYy9rAwC4NpNxtYc7uaH8/HyZzWbl5eUpMDDQ5rPTp08rIyND9evXl6+vr5MqREXDcQEAzne13+8/YuQGAAC4FcINbrj169fbXHr+xxcAANeDq6Vww7Vt21bbt293dhkAADdFuMEN5+fn57D71wAA8EeclipBJZtjjWvgeAAA10K4ucTFO+pe+lgB4OLx8Mc7LgMAKiZOS13C09NT1atXtz7/yN/fXyaT6RpLwV0ZhqHCwkIdOXJE1atXl6enp7NLAgCUAuHmD0JDQyWpTA94hHu7+CR1AIBrINz8gclkUlhYmGrVqqVz5845uxw4mZeXFyM2AOBiCDdX4OnpyY8aAAAuiAnFAADArRBuAACAWyHcAAAAt0K4AQAAboVwAwAA3ArhBgAAuBXCDQAAcCuEGwAA4FYINwAAwK0QbgAAgFsh3AAAALdCuAEAAG6FcAMAANwK4QYAALgVwg0AAHArhBsAAOBWCDcAAMCtEG4AAIBbIdwAAAC3QrgBAABuhXADAADcCuEGAAC4FcINAABwK4QbAADgVgg3AADArTg13Kxbt07du3dX7dq1ZTKZtHTp0msuk5aWptatW8vHx0cNGzbUvHnzyr1OAADgOpwabgoKChQdHa0ZM2aUqn9GRobuu+8+devWTdu3b9fQoUM1aNAgrV69upwrBQAArqKKM7/8nnvu0T333FPq/rNmzVL9+vU1adIkSVLTpk21YcMGTZkyRfHx8eVVJgAAcCEuNedm8+bNiouLs2mLj4/X5s2br7jMmTNnlJ+fb/MCAADuy6XCzeHDhxUSEmLTFhISovz8fBUVFZW4THJyssxms/UVHh5+I0oFAABO4lLhxh5JSUnKy8uzvg4ePOjskgAAQDly6pybsgoNDVVOTo5NW05OjgIDA+Xn51fiMj4+PvLx8bkR5QEAgArApUZuOnbsqNTUVJu2NWvWqGPHjk6qCAAAVDRODTenTp3S9u3btX37dkkXLvXevn27srKyJF04pdS/f39r/yeffFIHDhzQSy+9pD179uidd97RokWL9PzzzzujfAAAUAE5Ndx8++23atWqlVq1aiVJSkxMVKtWrTRy5EhJUnZ2tjXoSFL9+vW1YsUKrVmzRtHR0Zo0aZL+/e9/cxk4AACwMhmGYTi7iBspPz9fZrNZeXl5CgwMdHY5AACgFMry++1Sc24AAACuhXADAADcCuEGAAC4FcINAABwK4QbAADgVgg3AADArRBuAACAWyHcAAAAt0K4AQAAboVwAwAA3ArhBgAAuBXCDQAAcCuEGwAA4FYINwAAwK0QbgAAgFsh3AAAALdCuAEAAG6FcAMAANwK4QYAALgVwg0AAHArhBsAAOBWCDcAAMCtEG4AAIBbIdwAAAC3QrgBAABuhXADAADcCuEGAAC4FcINAABwK4QbAADgVgg3AADArRBuAACAWyHcAAAAt0K4AQAAboVwA8BtZOcVadP+XGXnFTm7FABOVMXZBQCAI6RsyVLSkp2yGJKHSUruGaWEdnWdXRYAJ2DkBoDLy84rsgYbSbIY0vAl3zOCA1RShBsALi8jt8AabC4qNgxl5hY6pyAATkW4AeDy6gdVlYfJts3TZFJEkL9zCgLgVIQbAC4vzOyn5J5R8jRdSDieJpPG92yuMLOfkysD4AxMKAbgFhLa1VXXRsHKzC1URJA/wQaoxAg3ANxGmNmPUAOA01IAAMC9EG4AAIBbIdwAAAC3QrgBAABuhXADAADcCuEGAAC4lQoRbmbMmKGIiAj5+vqqQ4cO+uabb67af+rUqWrcuLH8/PwUHh6u559/XqdPn75B1QIAgIrM6eEmJSVFiYmJGjVqlLZu3aro6GjFx8fryJEjJfb/6KOPNGzYMI0aNUq7d+/Wu+++q5SUFA0fPvwGVw4AACoip4ebyZMna/DgwRo4cKCaNWumWbNmyd/fX++9916J/Tdt2qTOnTurT58+ioiI0N13363evXtfc7QHAABUDk4NN2fPnlV6erri4uKsbR4eHoqLi9PmzZtLXKZTp05KT0+3hpkDBw5o5cqVuvfee0vsf+bMGeXn59u8AACA+3Lq4xdyc3NVXFyskJAQm/aQkBDt2bOnxGX69Omj3Nxc3X777TIMQ+fPn9eTTz55xdNSycnJGjNmjMNrBwAAFZPTT0uVVVpamsaPH6933nlHW7du1ZIlS7RixQqNGzeuxP5JSUnKy8uzvg4ePHiDKwYAADeSU0dugoKC5OnpqZycHJv2nJwchYaGlrjMq6++qn79+mnQoEGSpKioKBUUFOjxxx/XK6+8Ig8P27zm4+MjHx+f8tkAAABQ4Th15Mbb21tt2rRRamqqtc1isSg1NVUdO3YscZnCwsLLAoynp6ckyTCM8isWAAC4BKeO3EhSYmKiBgwYoLZt26p9+/aaOnWqCgoKNHDgQElS//79VadOHSUnJ0uSunfvrsmTJ6tVq1bq0KGD9u3bp1dffVXdu3e3hhwAAFB5OT3cJCQk6OjRoxo5cqQOHz6sli1batWqVdZJxllZWTYjNSNGjJDJZNKIESN06NAhBQcHq3v37nr99dedtQkAAKACMRmV7FxOfn6+zGaz8vLyFBgY6OxyAABAKZTl99vlrpYCAAC4GsINAABwK4QbAADgVgg3AADArRBuAACAWyHcAAAAt0K4AQAAboVwAwAA3ArhBgAAuBXCDQAAcCuEGwAA3Fh2XpE27c9Vdl6Rs0u5YZz+4EwAAFA+UrZkKWnJTlkMycMkJfeMUkK7us4uq9wxcgMAgBvKziuyBhtJshjS8CXfV4oRHMINAABuKCO3wBpsLio2DGXmFjqnoBuIcAMAgBuqH1RVHibbNk+TSRFB/s4p6AYi3AAA4IbCzH5K7hklT9OFhONpMml8z+YKM/s5ubLyx4RiAADcVEK7uuraKFiZuYWKCPKvFMFGsjPc1K1bV7GxsYqJiVFsbKwiIyMdXRcAAHCAMLNfpQk1F9l1Wmr8+PHy9fXVxIkTdcsttyg8PFx//etfNWfOHP3000+OrhEAAKDUTIZhGNfudmXZ2dlau3atPv/8c6WkpMhisai4uNhR9Tlcfn6+zGaz8vLyFBgY6OxyAABAKZTl99vuOTeFhYXasGGD0tLS9PXXX2vbtm1q3ry5YmNj7V0lAADAdbMr3HTq1Enbtm1T06ZNFRsbq2HDhqlr166qUaOGo+sDAAAoE7vm3OzZs0dVq1ZVkyZN1KRJEzVt2pRgAwAAKgS7ws2xY8f01Vdf6bbbbtPq1avVuXNn1alTR3369NGcOXMcXSMAAECpXfeEYsMwlJ6erunTp+vDDz9kQjFQgWXnFSkjt0D1g6pWuktDAbi2cp9QvHXrVqWlpSktLU0bNmzQyZMnFRUVpWeffVYxMTF2FQ2gfFXWpwMDqHzsGrmpUqWKWrVqpZiYGMXExKhr164ym83lUZ/DMXKDyig7r0idJ3xl8xA9T5NJG4Z1YwQHgEso95Gb48ePEwwAF3K1pwMTbgC4G7vCzcVgk56ert27d0uSmjVrptatWzuuMgAOc/HpwH8cuakMTwcGUPnYFW6OHDmihIQErV27VtWrV5cknThxQt26ddPChQsVHBzsyBoBXKeLTwcevuR7FRtGpXo6MIDKx65w8+yzz+rUqVPatWuXmjZtKkn64YcfNGDAAD333HP6+OOPHVokgOtXWZ8ODKDysWtCsdls1pdffql27drZtH/zzTe6++67deLECUfV53BMKAYAwPWU5ffbrpv4WSwWeXl5Xdbu5eUli8VizyoBAAAcwq5wc8cdd2jIkCH69ddfrW2HDh3S888/rzvvvNNhxQEAAJSVXeFm+vTpys/PV0REhCIjIxUZGan69esrPz9f06ZNc3SNAAAnyc4r0qb9ucrOK3J2KUCp2TWhODw8XFu3btWXX36pPXv2SJKaNm2quLg4hxYHAHAe7moNV3Xdz5ZyNUwoBoBr467WqGjK5Q7Fb7/9dqkLeO6550rdFwBQ8XBXa7iyUoebKVOmlKqfyWQi3ACAi+Ou1nBlpQ43GRkZ5VkHAKAC4a7WcGVlmlBssVjk4WHXBVYAABfDXa3hqsqUVLy8vHTkyBHr+xdffFHHjx93eFEAgIohzOynjpE3EWzgUsoUbv54YdXs2bMr9KMWAABA5XNd55gq2VXkAADABTCBBgAAuJUy36F45MiR8ve/cCng2bNn9frrr8tsNtv0mTx5smOqAwAAKKMyhZuuXbtq79691vedOnXSgQMHbPqYTCbHVAYAAGCHMoWbtLS0cilixowZ+uc//6nDhw8rOjpa06ZNU/v27a/Y/8SJE3rllVe0ZMkSHT9+XPXq1dPUqVN17733lkt9AADAdZTrnJvAwMDLRnb+KCUlRYmJiRo1apS2bt2q6OhoxcfH21xyfqmzZ8/qrrvuUmZmphYvXqy9e/dqzpw5qlOnTnlsAgAAcDF2PRW8tEpzNdXkyZM1ePBgDRw4UJI0a9YsrVixQu+9956GDRt2Wf/33ntPx48f16ZNm+Tl5SVJioiIcGjdAADAdTn1aqmzZ88qPT1dcXFx1jYPDw/FxcVp8+bNJS6zfPlydezYUU8//bRCQkLUvHlzjR8/XsXFxSX2P3PmjPLz821eAADAfTk13OTm5qq4uFghISE27SEhITp8+HCJyxw4cECLFy9WcXGxVq5cqVdffVWTJk3Sa6+9VmL/5ORkmc1m6ys8PNzh2wEAACoOl7vPjcViUa1atfSvf/1Lbdq0UUJCgl555RXNmjWrxP5JSUnKy8uzvg4ePHiDKwYAADdSuc65udZl4UFBQfL09FROTo5Ne05OjkJDQ0tcJiwsTF5eXvL09LS2NW3aVIcPH9bZs2fl7e1t09/Hx0c+Pj52bgEAAHA15Tpyc60Jxd7e3mrTpo1SU1OtbRaLRampqerYsWOJy3Tu3Fn79u2TxWKxtv34448KCwu7LNgAAIDKp1zDzf/7f//vmpdoJyYmas6cOZo/f752796tp556SgUFBdarp/r376+kpCRr/6eeekrHjx/XkCFD9OOPP2rFihUaP368nn766fLclHKVnVekTftzlZ1X5OxSAABweXadljIMQ4sXL9bXX3+tI0eO2IyiSNKSJUskSbfffvs115WQkKCjR49q5MiROnz4sFq2bKlVq1ZZJxlnZWXJw+P3DBYeHq7Vq1fr+eefV4sWLVSnTh0NGTJEL7/8sj2b4nQpW7KUtGSnLIbkYZKSe0YpoV1dZ5cFAIDLMhl2PNp7yJAhmj17trp166aQkJDL5tbMnTvXYQU6Wn5+vsxms/Ly8hQYGOjUWrLzitR5wleyXPJfwNNk0oZh3RRm9nNeYQAAVDBl+f22a+Tm/fff15IlS3jcwXXKyC2wCTaSVGwYyswtJNwAAGAnu+bcmM1mNWjQwNG1VDr1g6rK4w8XlHmaTIoI8ndOQQAAuAG7ws3o0aM1ZswYFRUxAfZ6hJn9lNwzSp7/d1rP02TS+J7NGbUBAOA62DXnpqioSA8++KA2btyoiIgI6zOeLtq6davDCnS0ijTn5qLsvCJl5hYqIsifYAMAQAnKfc7NgAEDlJ6err/+9a8lTihG2YSZ/Qg1AAA4iF3hZsWKFVq9enWpLvUGAAC4keyacxMeHl5hTungxuFmgwAAV2BXuJk0aZJeeuklZWZmOrgcVFQpW7LUecJX6jPnf+o84SulbMlydkkAAJTIrgnFNWrUUGFhoc6fPy9/f//LJhQfP37cYQU6WkWcUFzRcbNBAICzlfuE4qlTp9qzGFwUNxsEALgSu6+WQuVx8WaDfxy54WaDAICK6LqfCn769Gnl5+fbvOBeuNkgAMCV2DVyU1BQoJdfflmLFi3SsWPHLvu8uLj4ugtDxZLQrq66NgrmZoMAgArPrpGbl156SV999ZVmzpwpHx8f/fvf/9aYMWNUu3ZtLViwwNE1ooIIM/upY+RNBBsAQIVm18jNZ599pgULFig2NlYDBw5Uly5d1LBhQ9WrV08ffvih+vbt6+g6AQAASsWukZvjx49bnwoeGBhovfT79ttv17p16xxXHQAAQBnZFW4aNGigjIwMSVKTJk20aNEiSRdGdKpXr+6w4gAAAMrKrnAzcOBA7dixQ5I0bNgwzZgxQ76+vnr++ef14osvOrRAAACAsrDrDsV/9PPPPys9PV0NGzZUixYtHFFXueEOxQAAuJ5yv0PxpU6fPq169eqpXr1617sqAHCa7LwiZeQWqH5QVa4IBFycXaeliouLNW7cONWpU0cBAQE6cOCAJOnVV1/Vu+++69ACAaC88WBYwL3YFW5ef/11zZs3T2+88Ya8vb2t7c2bN9e///1vhxUHAOUtO69ISUt2Wh8vYjGk4Uu+V3ZekXMLA2A3u8LNggUL9K9//Ut9+/aVp6entT06Olp79uxxWHEAUN6u9mBYAK7JrnBz6NAhNWzY8LJ2i8Wic+fOXXdRAHCjXHww7KV4MCzg2uwKN82aNdP69esva1+8eLFatWp13UUBwI3Cg2EB92PX1VIjR47UgAEDdOjQIVksFi1ZskR79+7VggUL9Pnnnzu6RgAoVzwYFnAvdt/nZv369Ro7dqx27NihU6dOqXXr1ho5cqTuvvtuR9foUNznBgAA11OW32+H3MTPlRBuAABwPWX5/bb72VLHjh27rP3EiRPWB2oCAAA4g13hJjMzU8XFxZe1nzlzRocOHbruogAAAOxVpgnFy5cvt/559erVMpvN1vfFxcVKTU1VRESEw4oDAAAoqzKFmx49ekiSTCaTBgwYYPOZl5eXIiIiNGnSJIcVBwAAUFZlCjcWi0WSVL9+fW3ZskVBQUHlUhQAAIC9yjTnZvPmzfr888+VkZFhDTYLFixQ/fr1VatWLT3++OM6c+ZMuRQKAABQGmUKN2PGjNGuXbus73fu3KnHHntMcXFxGjZsmD777DMlJyc7vEgAAIDSKlO42bFjh+68807r+4ULF6pDhw6aM2eOEhMT9fbbb2vRokUOLxIAAKC0yhRufvvtN4WEhFjfr127Vvfcc4/1fbt27XTw4EHHVQcAAFBGZQo3ISEhysjIkCSdPXtWW7du1W233Wb9/OTJk/Ly8nJshQAAAGVQpnBz7733atiwYVq/fr2SkpLk7++vLl26WD//7rvvFBkZ6fAiAQAASqtMl4KPGzdOPXv2VExMjAICAjR//nx5e3tbP3/vvfcq/IMzAQCAe7PrwZl5eXkKCAiQp6enTfvx48cVEBBgE3gqGh6cCQCA6ynL73eZRm4uuvSxC5eqWbOmPasDAABwGLsenAkAQHnIzivSpv25ys4rcnYpcGF2jdwAAOBoKVuylLRkpyyG5GGSkntGKaFdXWeXBRfEyA0AwOmy84qswUaSLIY0fMn3jODALoQbAIDTZeQWWIPNRcWGoczcQucUBJdGuAEAOF39oKryMNm2eZpMigjyd05BcGkVItzMmDFDERER8vX1VYcOHfTNN9+UarmFCxfKZDKpR48e5VsgAKBchZn9lNwzSp6mCwnH02TS+J7NFWb2c3JlcEVOn1CckpKixMREzZo1Sx06dNDUqVMVHx+vvXv3qlatWldcLjMzUy+88ILNHZIBAK4roV1ddW0UrMzcQkUE+RNsYDenj9xMnjxZgwcP1sCBA9WsWTPNmjVL/v7+eu+99664THFxsfr27asxY8aoQYMGN7BaAEB5CjP7qWPkTQQbXBenhpuzZ88qPT1dcXFx1jYPDw/FxcVp8+bNV1xu7NixqlWrlh577LEbUSYAAHAhTj0tlZubq+LiYoWEhNi0h4SEaM+ePSUus2HDBr377rvavn17qb7jzJkzOnPmjPV9fn6+3fUCAICKz+mnpcri5MmT6tevn+bMmaOgoKBSLZOcnCyz2Wx9hYeHl3OVAADAmZw6chMUFCRPT0/l5OTYtOfk5Cg0NPSy/vv371dmZqa6d+9ubbNYLJKkKlWqaO/evYqMjLRZJikpSYmJidb3+fn5BBwAANyYU8ONt7e32rRpo9TUVOvl3BaLRampqXrmmWcu69+kSRPt3LnTpm3EiBE6efKk3nrrrRJDi4+Pj3x8fMqlfgAAUPE4/VLwxMREDRgwQG3btlX79u01depUFRQUaODAgZKk/v37q06dOkpOTpavr6+aN29us3z16tUl6bJ2AABQOTk93CQkJOjo0aMaOXKkDh8+rJYtW2rVqlXWScZZWVny8HCpqUEAAMCJTIZhGNfu5j7y8/NlNpuVl5enwMBAZ5cDAABKoSy/3wyJAAAAt0K4AQAAboVwAwAA3ArhBgAAuBXCDQAAcCuEGwAA4FYINwAAwK0QbgAAKKPsvCJt2p+r7LwiZ5eCEjj9DsUAALiSlC1ZSlqyUxZD8jBJyT2jlNCurrPLwiUYuQEAoJSy84qswUaSLIY0fMn3jOBUMIQbAABKKSO3wBpsLio2DGXmFjqnIJSIcAMAQCnVD6oqD5Ntm6fJpIggf+cUhBIRbgAAKKUws5+Se0bJ03Qh4XiaTBrfs7nCzH5OrqziqAiTrZlQDABAGSS0q6uujYKVmVuoiCB/gs0lKspka0ZuAAAoozCznzpG3kSwuURFmmxNuAEAANetIk22JtwAAIDrVpEmWxNuAADAdatIk62ZUAwAAByioky2JtwAAACHCTP7OX2iNaelAACAWyHcAAAAt0K4AQAAboVwAwAA3ArhBgAAuBXCDQAAcCuEGwAA4FYINwAAwK0QbgAAgFsh3AAAALdCuAEAAG6FcAMAANwK4QYAALgVwg0AAHArhBsAAFxAdl6RNu3PVXZekbNLqfCqOLsAAABwdSlbspS0ZKcshuRhkpJ7RimhXV1nl1VhMXIDAEAFlp1XZA02kmQxpOFLvmcE5yoINwAAVGAZuQXWYHNRsWEoM7fQOQW5AMINAAAVWP2gqvIw2bZ5mkyKCPJ3TkEugHADAEAFFmb2U3LPKHmaLiQcT5NJ43s2V5jZz8mVVVxMKAYAoIJLaFdXXRsFKzO3UBFB/gSbayDcAADgAsLMfoSaUuK0FAAAcCuEGwAA4FYINwAAwK0QbgAAgFsh3AAAALdCuAEAAG6lQoSbGTNmKCIiQr6+vurQoYO++eabK/adM2eOunTpoho1aqhGjRqKi4u7an8AAFC5OD3cpKSkKDExUaNGjdLWrVsVHR2t+Ph4HTlypMT+aWlp6t27t77++mtt3rxZ4eHhuvvuu3Xo0KEbXDkAAKiITIZhGNfuVn46dOigdu3aafr06ZIki8Wi8PBwPfvssxo2bNg1ly8uLlaNGjU0ffp09e/f/5r98/PzZTablZeXp8DAwOuuHwAAlL+y/H47deTm7NmzSk9PV1xcnLXNw8NDcXFx2rx5c6nWUVhYqHPnzqlmzZolfn7mzBnl5+fbvAAAgPtyarjJzc1VcXGxQkJCbNpDQkJ0+PDhUq3j5ZdfVu3atW0C0qWSk5NlNputr/Dw8OuuGwAAlCw7r0ib9ucqO6/IaTW49LOlJkyYoIULFyotLU2+vr4l9klKSlJiYqL1fX5+PgEHAIBykLIlS0lLdspiSB4mKblnlBLa1b3hdTg13AQFBcnT01M5OTk27Tk5OQoNDb3qsm+++aYmTJigL7/8Ui1atLhiPx8fH/n4+DikXgAAULLsvCJrsJEkiyENX/K9ujYKvuEP/HTqaSlvb2+1adNGqamp1jaLxaLU1FR17Njxisu98cYbGjdunFatWqW2bdveiFIBAMBVZOQWWIPNRcWGoczcwhtei9NPSyUmJmrAgAFq27at2rdvr6lTp6qgoEADBw6UJPXv31916tRRcnKyJGnixIkaOXKkPvroI0VERFjn5gQEBCggIMBp2wEAQGVWP6iqPEyyCTieJpMigvxveC1Ov89NQkKC3nzzTY0cOVItW7bU9u3btWrVKusk46ysLGVnZ1v7z5w5U2fPntXDDz+ssLAw6+vNN9901iYAAFDphZn9lNwzSp4mk6QLwWZ8z+Y3/JSUVAHuc3OjcZ8bAADKT3ZekTJzCxUR5O/QYFOW32+nn5YCAADuI8zs55TRmks5/bQUAACAIxFuAFxTRbgpFwCUFqelAAfIzitSRm6B6gdVdfpwrKNVlJtyAUBpEW6A6+TOP/4V6aZcAFBanJYCrsOVfvzd5fRNRbopFwCUFuEGuA7u/uN/8aZcl3LWTbkAoLQIN8B1cPcf/4p0Uy4AKC3m3ADX4eKP//Al36vYMNzyxz+hXV11bRRcLjflAoDyQLgBrlNl+PGvCDflAoDSItwADsCPPwBUHMy5AQAAboVwAwAA3ArhBgAAuBXCDQAAcCuEGwAA4FYINwAAwK0QbgAAgFsh3AAAALdCuAEAAG6FcAMAANwK4QYAALgVwg0AAHArhBsAAOBWCDcAAMCtEG4AAIBbIdwAAAC3QrhxoOy8Im3an6vsvCJnlwIAQKVVxdkFuIuULVlKWrJTFkPyMEnJPaOU0K6us8sCAKDSYeTGAbLziqzBRpIshjR8yfelHsFhxAcAAMdh5MYBMnILrMHmomLDUGZuocLMflddlhEfAAAci5EbB6gfVFUeJts2T5NJEUH+V13uekd8AADA5Qg3DhBm9lNyzyh5mi4kHE+TSeN7Nr/mqM3VRnwAAIB9OC3lIAnt6qpro2Bl5hYqIsj/msFG+n3E59KAU5oRHwAAcGWM3DhQmNlPHSNvKlWwudjfnhEfAABwZYzcOJk9Iz6VSXZekTJyC1Q/qCr7BgBQKoSbCiDM7McPdwm4kgwAYA9OS6FC4koyAIC9CDeokLiSDABgL8INKiR77x0EAADhBhUSV5IBAOzFhGJUWFxJBgCwB+EG5e56LufmSjIAQFkRblCuuJwbAHCjMecG5YbLuQEAzkC4Qbnhcm4AgDMQblBunHk5d3ZekTbtz2WUCAAqoQoRbmbMmKGIiAj5+vqqQ4cO+uabb67a/5NPPlGTJk3k6+urqKgorVy58gZVirJw1uXcKVuy1HnCV+oz53/qPOErpWzJKtfvAwBULE4PNykpKUpMTNSoUaO0detWRUdHKz4+XkeOHCmx/6ZNm9S7d2899thj2rZtm3r06KEePXro+++/v8GVozQS2tXVhmHd9PHg27RhWLdyn0zMPB8AgMkwDOPa3cpPhw4d1K5dO02fPl2SZLFYFB4ermeffVbDhg27rH9CQoIKCgr0+eefW9tuu+02tWzZUrNmzbrm9+Xn58tsNisvL0+BgYGO2xBUCJv256rPnP9d1v7x4NvUMfImJ1QEAHCEsvx+O3Xk5uzZs0pPT1dcXJy1zcPDQ3Fxcdq8eXOJy2zevNmmvyTFx8dfsf+ZM2eUn59v84L74rENAACnhpvc3FwVFxcrJCTEpj0kJESHDx8ucZnDhw+XqX9ycrLMZrP1FR4e7pjiUSHx2AYAgNvfxC8pKUmJiYnW9/n5+QQcN8djGwCgcnNquAkKCpKnp6dycnJs2nNychQaGlriMqGhoWXq7+PjIx8fH8cUDJfBYxsAoPJy6mkpb29vtWnTRqmpqdY2i8Wi1NRUdezYscRlOnbsaNNfktasWXPF/gAAoHJx+mmpxMREDRgwQG3btlX79u01depUFRQUaODAgZKk/v37q06dOkpOTpYkDRkyRDExMZo0aZLuu+8+LVy4UN9++63+9a9/OXMzAABABeH0cJOQkKCjR49q5MiROnz4sFq2bKlVq1ZZJw1nZWXJw+P3AaZOnTrpo48+0ogRIzR8+HDdcsstWrp0qZo3b+6sTQAAABWI0+9zc6NxnxsAAFyPy9znBgAAwNEINwAAwK0QbgAAgFsh3AAAALdCuAEAAG6FcAMAANwK4QYAALgVp9/E70a7eFuf/Px8J1cCAABK6+Lvdmluz1fpws3JkycliSeDAwDggk6ePCmz2XzVPpXuDsUWi0W//vqrqlWrJpPJ5Oxybpj8/HyFh4fr4MGDlf7OzOyL37Evfse+uID98Dv2xe8qwr4wDEMnT55U7dq1bR7LVJJKN3Lj4eGhm2++2dllOE1gYGCl/5/0IvbF79gXv2NfXMB++B374nfO3hfXGrG5iAnFAADArRBuAACAWyHcVBI+Pj4aNWqUfHx8nF2K07Evfse++B374gL2w+/YF79ztX1R6SYUAwAA98bIDQAAcCuEGwAA4FYINwAAwK0QbgAAgFsh3LioGTNmKCIiQr6+vurQoYO++eabq/afOnWqGjduLD8/P4WHh+v555/X6dOnrZ+PHj1aJpPJ5tWkSZPy3gyHKMu+OHfunMaOHavIyEj5+voqOjpaq1atuq51ViSO3heuelysW7dO3bt3V+3atWUymbR06dJrLpOWlqbWrVvLx8dHDRs21Lx58y7r44rHRXnsC1c8Lsq6H7Kzs9WnTx81atRIHh4eGjp0aIn9PvnkEzVp0kS+vr6KiorSypUrHV+8g5XHvpg3b95lx4Svr2/5bEApEG5cUEpKihITEzVq1Cht3bpV0dHRio+P15EjR0rs/9FHH2nYsGEaNWqUdu/erXfffVcpKSkaPny4Tb9bb71V2dnZ1teGDRtuxOZcl7LuixEjRmj27NmaNm2afvjhBz355JN68MEHtW3bNrvXWVGUx76QXPO4KCgoUHR0tGbMmFGq/hkZGbrvvvvUrVs3bd++XUOHDtWgQYO0evVqax9XPS7KY19IrndclHU/nDlzRsHBwRoxYoSio6NL7LNp0yb17t1bjz32mLZt26YePXqoR48e+v777x1ZusOVx76QLty9+NJj4ueff3ZUyWVnwOW0b9/eePrpp63vi4uLjdq1axvJyckl9n/66aeNO+64w6YtMTHR6Ny5s/X9qFGjjOjo6HKptzyVdV+EhYUZ06dPt2nr2bOn0bdvX7vXWVGUx75w1ePiUpKMTz/99Kp9XnrpJePWW2+1aUtISDDi4+Ot7131uLiUo/aFqx8XpdkPl4qJiTGGDBlyWXuvXr2M++67z6atQ4cOxhNPPHGdFd44jtoXc+fONcxms8Pqul6M3LiYs2fPKj09XXFxcdY2Dw8PxcXFafPmzSUu06lTJ6Wnp1uH0A8cOKCVK1fq3nvvten3008/qXbt2mrQoIH69u2rrKys8tsQB7BnX5w5c+ayoVI/Pz/rvzrtWWdFUB774iJXOy7ssXnzZpt9J0nx8fHWfeeqx4U9rrUvLqoMx8W1lHZfVRanTp1SvXr1FB4ergceeEC7du1yWi2EGxeTm5ur4uJihYSE2LSHhITo8OHDJS7Tp08fjR07Vrfffru8vLwUGRmp2NhYm9NSHTp00Lx587Rq1SrNnDlTGRkZ6tKli06ePFmu23M97NkX8fHxmjx5sn766SdZLBatWbNGS5YsUXZ2tt3rrAjKY19Irnlc2OPw4cMl7rv8/HwVFRW57HFhj2vtC6nyHBfXcqV95W7HRGk0btxY7733npYtW6YPPvhAFotFnTp10i+//OKUegg3lUBaWprGjx+vd955R1u3btWSJUu0YsUKjRs3ztrnnnvu0V/+8he1aNFC8fHxWrlypU6cOKFFixY5sXLHe+utt3TLLbeoSZMm8vb21jPPPKOBAwfKw6Py/a9Qmn1RWY4LlA3HBf6oY8eO6t+/v1q2bKmYmBgtWbJEwcHBmj17tlPqqXx/o7u4oKAgeXp6Kicnx6Y9JydHoaGhJS7z6quvql+/fho0aJCioqL04IMPavz48UpOTpbFYilxmerVq6tRo0bat2+fw7fBUezZF8HBwVq6dKkKCgr0888/a8+ePQoICFCDBg3sXmdFUB77oiSucFzYIzQ0tMR9FxgYKD8/P5c9LuxxrX1REnc9Lq7lSvvK3Y4Je3h5ealVq1ZOOyYINy7G29tbbdq0UWpqqrXNYrEoNTVVHTt2LHGZwsLCy0YmPD09JUnGFR4tdurUKe3fv19hYWEOqtzx7NkXF/n6+qpOnTo6f/68/vOf/+iBBx647nU6U3nsi5K4wnFhj44dO9rsO0las2aNdd+56nFhj2vti5K463FxLfbsq8qiuLhYO3fudN4x4ewZzSi7hQsXGj4+Psa8efOMH374wXj88ceN6tWrG4cPHzYMwzD69etnDBs2zNp/1KhRRrVq1YyPP/7YOHDggPHFF18YkZGRRq9evax9/vGPfxhpaWlGRkaGsXHjRiMuLs4ICgoyjhw5csO3ryzKui/++9//Gv/5z3+M/fv3G+vWrTPuuOMOo379+sZvv/1W6nVWVOWxL1z1uDh58qSxbds2Y9u2bYYkY/Lkyca2bduMn3/+2TAMwxg2bJjRr18/a/8DBw4Y/v7+xosvvmjs3r3bmDFjhuHp6WmsWrXK2sdVj4vy2BeueFyUdT8YhmHt36ZNG6NPnz7Gtm3bjF27dlk/37hxo1GlShXjzTffNHbv3m2MGjXK8PLyMnbu3HlDt62symNfjBkzxli9erWxf/9+Iz093XjkkUcMX19fmz43EuHGRU2bNs2oW7eu4e3tbbRv397473//a/0sJibGGDBggPX9uXPnjNGjRxuRkZGGr6+vER4ebvz973+3+RFLSEgwwsLCDG9vb6NOnTpGQkKCsW/fvhu4RfYry75IS0szmjZtavj4+Bg33XST0a9fP+PQoUNlWmdF5uh94arHxddff21Iuux1cfsHDBhgxMTEXLZMy5YtDW9vb6NBgwbG3LlzL1uvKx4X5bEvXPG4sGc/lNS/Xr16Nn0WLVpkNGrUyPD29jZuvfVWY8WKFTdmg65DeeyLoUOHWv/fCAkJMe69915j69atN26j/sBkGFc4LwEAAOCCmHMDAADcCuEGAAC4FcINAABwK4QbAADgVgg3AADArRBuAACAWyHcAAAAt0K4AYAKIC0tTSaTSSdOnHB2KYDLI9wAlcyjjz4qk8mkCRMm2LQvXbpUJpPJ+t4wDM2ZM0cdO3ZUYGCgAgICdOutt2rIkCGlfhheYWGhkpKSFBkZKV9fXwUHBysmJkbLli2z9omIiNDUqVMdsm3l7eK+M5lM8vLyUv369fXSSy/p9OnTZVpPbGyshg4datPWqVMnZWdny2w2O7BioHIi3ACVkK+vryZOnKjffvutxM8Nw1CfPn303HPP6d5779UXX3yhH374Qe+++658fX312muvlep7nnzySS1ZskTTpk3Tnj17tGrVKj388MM6duyYIzfnhvrTn/6k7OxsHThwQFOmTNHs2bM1atSo616vt7e3QkNDbQImADs57cEPAJxiwIABxp///GejSZMmxosvvmht//TTT42LfyV8/PHHhiRj2bJlJa7DYrGU6rvMZrMxb968K34eExNz2fNqLlq/fr1x++23G76+vsbNN99sPPvss8apU6esny9YsMBo06aNERAQYISEhBi9e/c2cnJyrJ9ffH7OqlWrjJYtWxq+vr5Gt27djJycHGPlypVGkyZNjGrVqhm9e/c2CgoKSrU9AwYMMB544AGbtp49exqtWrWyvs/NzTUeeeQRo3bt2oafn5/RvHlz46OPPrJZxx+3OSMjw1rvpc98W7x4sdGsWTPD29vbqFevnvHmm2+Wqk6gsmPkBqiEPD09NX78eE2bNk2//PLLZZ9//PHHaty4se6///4Sly/t6EJoaKhWrlypkydPlvj5kiVLdPPNN2vs2LHKzs5Wdna2JGn//v3605/+pIceekjfffedUlJStGHDBj3zzDPWZc+dO6dx48Zpx44dWrp0qTIzM/Xoo49e9h2jR4/W9OnTtWnTJh08eFC9evXS1KlT9dFHH2nFihX64osvNG3atFJtzx99//332rRpk7y9va1tp0+fVps2bbRixQp9//33evzxx9WvXz998803kqS33npLHTt21ODBg63bHB4eftm609PT1atXLz3yyCPauXOnRo8erVdffVXz5s2zq1agUnF2ugJwY106+nDbbbcZf/vb3wzDsB25adKkiXH//ffbLDdkyBCjatWqRtWqVY06deqU6rvWrl1r3HzzzYaXl5fRtm1bY+jQocaGDRts+tSrV8+YMmWKTdtjjz1mPP744zZt69evNzw8PIyioqISv2vLli2GJOPkyZOGYfw+cvPll19a+yQnJxuSjP3791vbnnjiCSM+Pr5U2zNgwADD09PTqFq1quHj42NIMjw8PIzFixdfdbn77rvP+Mc//mF9HxMTYwwZMsSmzx9Hbvr06WPcddddNn1efPFFo1mzZqWqFajMGLkBKrGJEydq/vz52r179zX7vvLKK9q+fbtGjhypU6dOlWr9Xbt21YEDB5SamqqHH35Yu3btUpcuXTRu3LirLrdjxw7NmzdPAQEB1ld8fLwsFosyMjIkXRjZ6N69u+rWratq1aopJiZGkpSVlWWzrhYtWlj/HBISIn9/fzVo0MCm7ciRI6XaHknq1q2btm/frv/9738aMGCABg4cqIceesj6eXFxscaNG6eoqCjVrFlTAQEBWr169WV1Xcvu3bvVuXNnm7bOnTvrp59+UnFxcZnWBVQ2hBugEuvatavi4+OVlJRk037LLbdo7969Nm3BwcFq2LChatWqVabv8PLyUpcuXfTyyy/riy++0NixYzVu3DidPXv2isucOnVKTzzxhLZv32597dixQz/99JMiIyNVUFCg+Ph4BQYG6sMPP9SWLVv06aefStJl6/Xy8rL++eJVTpcymUyyWCyl3p6qVauqYcOGio6O1nvvvaf//e9/evfdd62f//Of/9Rbb72ll19+WV9//bW2b9+u+Pj4q24vAMeq4uwCADjXhAkT1LJlSzVu3Nja1rt3b/Xp00fLli3TAw884NDva9asmc6fP6/Tp0/L29tb3t7el41EtG7dWj/88IMaNmxY4jp27typY8eOacKECdb5Kt9++61D6ywNDw8PDR8+XImJierTp4/8/Py0ceNGPfDAA/rrX/8qSbJYLPrxxx/VrFkz63IlbfMfNW3aVBs3brRp27hxoxo1aiRPT0/HbwzgRhi5ASq5qKgo9e3bV2+//ba17ZFHHtHDDz+sRx55RGPHjtX//vc/ZWZmau3atUpJSSn1j2tsbKxmz56t9PR0ZWZmauXKlRo+fLi6deumwMBASRfuc7Nu3TodOnRIubm5kqSXX35ZmzZt0jPPPKPt27frp59+0rJly6wTiuvWrStvb29NmzZNBw4c0PLly695qqu8/OUvf5Gnp6dmzJgh6cKo15o1a7Rp0ybt3r1bTzzxhHJycmyWiYiIsO7T3NzcEkeO/vGPfyg1NVXjxo3Tjz/+qPnz52v69Ol64YUXbsh2Aa6McANAY8eOtfmBNZlMSklJ0dSpU7Vy5Urdeeedaty4sf72t78pPDxcGzZsKNV64+PjNX/+fN19991q2rSpnn32WcXHx2vRokU2352ZmanIyEgFBwdLujBPZu3atfrxxx/VpUsXtWrVSiNHjlTt2rUlXThFNm/ePH3yySdq1qyZJkyYoDfffNOBe6T0qlSpomeeeUZvvPGGCgoKNGLECLVu3Vrx8fGKjY1VaGioevToYbPMCy+8IE9PTzVr1kzBwcElzsdp3bq1Fi1apIULF6p58+YaOXKkxo4dW+IVYQBsmQzDMJxdBAAAgKMwcgMAANwK4QaA3S69VPuPr/Xr1zu7vDLJysq66vaU9VJuAM7DaSkAdrvaAzTr1KkjPz+/G1jN9Tl//rwyMzOv+HlERISqVOECU8AVEG4AAIBb4bQUAABwK4QbAADgVgg3AADArRBuAACAWyHcAAAAt0K4AQAAboVwAwAA3ArhBgAAuJX/DzA2GRmUts3nAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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ZUlOnTtXAgQNlt9t15MgR3XDDDRo2bJheeeUV/fTTT7r77rsVHBzs/IGXpIULF+r+++/Xhg0bfv/emZmSpClTpujFF19U9erVNWDAAA0YMEBBQUFatGiRcnJy1K9fP82ePVvjxo27ZI1JSUnOzxs/frz+9a9/qUmTJqpdu7Zb+yciIkK33nqrXn75ZeXn55dorpdNmzYpIiJCaWlpuv766+Xv76/w8HD17NlTaWlp6tixo7NvWlqahg0bJj8/n/3vAwDwqIys084gc0G+MdqflVvhv4EyXrR69WojqdArJSXF2eell14yzZo1M8HBwSYhIcEsW7asVJ/hcDiMJONwOAotO3PmjPnxxx/NmTNnyvpVzJtfHzBx4983MePeN3Hj3zdvfn2gzNu8mJSUFOPv729CQ0NNUFCQkWT8/PzMO++8Y86ePWuqV69uNm7c6LLOnXfeaQYPHux8b7fbTVpamvP9xIkTTcuWLU1BQYGzbc6cOaZGjRomPz/fGGNMUlKSufzyy122e+F/x08//dTZlpqaaiSZvXv3Otvuvfdek5ycbIwxJarxwnZL8795WlqasdvtRS6bO3eukWSOHz9ujDEmJibGzJw506VPQkKCmTx5svO9JLN06VKXPosXLza1a9c2Z8+eNcYYs3nzZmOz2UxGRkaxdXnyWAMAX3D0ZK7zd+/Cq8n4D8zRk7ke2f7Ffr//zKtnZrp37y5jzEX73HHHHbrjjjsqqCL3FHeqrVuL8HJNpz169NDcuXN1+vRpzZw5U9WqVdPf//53/fDDD8rNzdV1113n0v+3337T5ZdfXuz2duzYoS5durjMiZKYmKicnBwdPnzYOetyhw4dilz/sssuc/67fv36ql69upo0aeLS9vXXX0uS9uzZU+Ia/3gGpCwuHGtlnfOlb9++GjFihJYuXapBgwYpPT1dPXr0cF6WAoCqIMoeotT+7TRxyXblGyN/m03T+7et+LMy4tlMHuGtU22hoaFq1qyZJOnll19WQkKCXnrpJbVt21aS9MEHH6hhw4Yu6wQFBXnkc4sSEBDg/PeFu6z+yGazOceU5OTklLjG4j6vtHbs2KGwsDDnoyv8/PwKhemSzNAbGBio22+/XWlpaerfv78WLVqk559/3iM1AoCVDOzUWN1ahGt/Vq5i61X3SpCRCDMeEVcvVH42uQQaf5tNsfUq7jEJfn5+mjhxosaOHatdu3YpKChIBw8edBl7cimtWrXSu+++K2OM8+zFhg0bVLNmTTVq1Mij9bZu3dqtGt114sQJLVq0SH379nWOawkPD3eO95Gk7OxsZWRkuKwXEBCg/Pz8Qtu766671LZtW/3nP//R+fPn1b9///L9AgDgo6LsIV4LMRcwWtEDLpxq8/9/AcBbp9puueUW+fv7a/78+XrkkUf00EMPaeHChdq7d6+2bNmi2bNna+HChcWu/8ADD+jQoUN68MEH9dNPP2n58uWaPHmyxo4d6/GBrTVr1nSrxpIwxujYsWPKzMzUjh079PLLL6tr166y2+2aMWOGs98111yjV199VZ9//rm+//57paSkFBoYHBsbq1WrVunYsWP63//+52xv1aqV/vKXv2jcuHEaPHiw82GSAICKx5kZD/GFU23VqlXTyJEj9eyzzyojI0Ph4eFKTU3Vvn37VKtWLV1xxRWaOHFises3bNhQK1as0KOPPqqEhATVqVNHd955px5//PFyqXfatGmlrrEksrOzFRUVJZvNprCwMLVs2VIpKSkaPXq0y636EyZMUEZGhv72t7/Jbrdr2rRphc7M/Pvf/9bYsWO1YMECNWzYUPv373cuu3DLt6+P6QKAys5mLjUC1+Kys7Nlt9vlcDgKzTlz9uxZZWRkKC4uzmNP4kbVMW3aNL399tv67rvvLtmXYw0ASudiv99/xmUmoJRycnK0fft2vfjii3rwwQe9XQ4AVHmEGVhKmzZtVKNGjSJfr7/+eoXUMHLkSHXo0EHdu3fnEhMA+ADGzMBSVqxYUezt0/Xr16+QGtLT0yvNgzkBoDIgzMBSLvZcLgBA1cRlJumSsxADZcUxBgDlp0qHmQsz1P7xSc5AebhwjP15VmQAQNlV6ctM/v7+qlWrlk6cOCFJql69epmf2wP8kTFGubm5OnHihGrVqlWip3UDAEqnSocZSYqMjJQkZ6ABykOtWrWcxxoAwLOqfJix2WyKiopSREREiR4yCJRWQEAAZ2QAoBxV+TBzgb+/Pz84AABYUJUeAAwAAKyPMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACzNq2Fm3bp1uvHGG9WgQQPZbDYtW7as2L733XefbDabZs2aVWH1AQAA3+fVMHP69GklJCRozpw5F+23dOlSffnll2rQoEEFVQYAAKyimjc/vFevXurVq9dF+xw5ckQPPvigPv74Y/Xu3buCKgMAAFbh1TBzKQUFBRo6dKgeffRRtWnTpkTr5OXlKS8vz/k+Ozu7vMoDAAA+wKcHAD/zzDOqVq2aRo0aVeJ1UlNTZbfbna/o6OhyrBAAAHibz4aZzZs36/nnn1d6erpsNluJ15swYYIcDofzdejQoXKsEgAAeJvPhpnPP/9cJ06cUOPGjVWtWjVVq1ZNBw4c0MMPP6zY2Nhi1wsKClJYWJjLCwAAVF4+O2Zm6NCh6tmzp0tbcnKyhg4dquHDh3upKgAA4Gu8GmZycnK0Z88e5/uMjAxt27ZNderUUePGjVW3bl2X/gEBAYqMjFTLli0rulQAAOCjvBpmvvnmG/Xo0cP5fuzYsZKklJQUpaene6kqAABgJV4NM927d5cxpsT99+/fX37FAAAAS/LZAcAAAAAlQZgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACW5tUws27dOt14441q0KCBbDabli1b5lx27tw5jRs3Tu3atVNoaKgaNGig22+/XUePHvVewQAAwOd4NcycPn1aCQkJmjNnTqFlubm52rJliyZNmqQtW7ZoyZIl2rlzp2666SYvVAoAAHyVzRhjvF2EJNlsNi1dulR9+/Ytts+mTZvUuXNnHThwQI0bNy7RdrOzs2W32+VwOBQWFuahagEAQHkqze93tQqqySMcDodsNptq1apVbJ+8vDzl5eU532dnZ1dAZQAAwFssMwD47NmzGjdunAYPHnzRhJaamiq73e58RUdHV2CVAACgolkizJw7d04DBgyQMUZz5869aN8JEybI4XA4X4cOHaqgKgEAgDf4/GWmC0HmwIED+uyzzy553SwoKEhBQUEVVB0AAPA2nw4zF4LM7t27tXr1atWtW9fbJQEAAB/j1TCTk5OjPXv2ON9nZGRo27ZtqlOnjqKionTzzTdry5Ytev/995Wfn69jx45JkurUqaPAwEBvlQ0AAHyIV2/NXrNmjXr06FGoPSUlRVOmTFFcXFyR661evVrdu3cv0WdwazYAANZjmVuzu3fvrotlKR+ZAgcAAPgwS9zNBAAAUBzCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCDAAAsDTCTBlkOs5o494sZTrOeLsUAACqrGreLsCqFm86qAlLvleBkfxsUmr/dhrYqbG3ywIAoMrhzIwbMh1nnEFGkgqMNHHJds7QAADgBW6HmdjYWE2dOlUHDx70ZD2WkJF12hlkLsg3Rvuzcr1TEAAAVZjbYWbMmDFasmSJmjRpouuuu05vvvmm8vLyPFmbz4qrFyo/m2ubv82m2HrVvVMQAABVWJnCzLZt2/T111+rVatWevDBBxUVFaWRI0dqy5YtnqzR50TZQ5Tav538bb8nGn+bTdP7t1WUPcTLlQEAUPXYjDHm0t0u7dy5c/rPf/6jcePG6dy5c2rXrp1GjRql4cOHy2azXXoD5SQ7O1t2u10Oh0NhYWEe3Xam44z2Z+Uqtl51ggwAAB5Umt/vMt/NdO7cOS1dulRpaWn65JNP9Je//EV33nmnDh8+rIkTJ+rTTz/VokWLyvoxPinKHkKIAQDAy9wOM1u2bFFaWpreeOMN+fn56fbbb9fMmTMVHx/v7NOvXz916tTJI4UCAAAUxe0w06lTJ1133XWaO3eu+vbtq4CAgEJ94uLiNGjQoDIVCAAAcDFuh5l9+/YpJibmon1CQ0OVlpbm7kfAR2Q6zigj67Ti6oVyWQ0A4HPcvpupR48e+uWXXwq1nzx5Uk2aNClTUfAdizcdVOKMzzRkwVdKnPGZFm+qevMKAQB8m9thZv/+/crPzy/UnpeXpyNHjpSpKPgGZjoGAFhBqS8zvffee85/f/zxx7Lb7c73+fn5WrVqlWJjY0u0rXXr1umf//ynNm/erMzMTC1dulR9+/Z1LjfGaPLkyVqwYIFOnjypxMREzZ07V82bNy9t2XDDxWY65nITAMBXlDrMXAgbNptNKSkpLssCAgIUGxurf//73yXa1unTp5WQkKA77rhD/fv3L7T82Wef1QsvvKCFCxcqLi5OkyZNUnJysn788UcFBweXtnSUUly9UNkk/THP2GxipmMAgE8pdZgpKCiQ9PudSps2bVK9evXc/vBevXqpV69eRS4zxmjWrFl6/PHH1adPH0nSK6+8ovr162vZsmXcJeUtHpliEQAAz3F7zExGRkaZgkxJtn/s2DH17NnT2Wa323XllVfqiy++KHa9vLw8ZWdnu7zgnoys04Wyi5F4oCYAwKe4fWv21KlTL7r8iSeecHfTkqRjx45JkurXr+/SXr9+feeyoqSmpurJJ58s02fjdxceqPnHcTM8UBMA4GvcDjNLly51eX/u3DllZGSoWrVqatq0aZnDjLsmTJigsWPHOt9nZ2crOjraK7VY3YUHak5csl35xvBATQCAT3I7zGzdurVQW3Z2toYNG6Z+/fqVqShJioyMlCQdP35cUVFRzvbjx4+rffv2xa4XFBSkoKCgMn8+fjewU2N1axHOAzUBAD7L7TEzRQkLC9OTTz6pSZMmlXlbcXFxioyM1KpVq5xt2dnZ+uqrr9SlS5cybx8lF2UPUZemdQkyAACfVOanZv+Zw+GQw+EoUd+cnBzt2bPH+T4jI0Pbtm1TnTp11LhxY40ZM0ZPPfWUmjdv7rw1u0GDBi5z0QAAgKrN7TDzwgsvuLw3xigzM1Ovvvpqsbdb/9k333yjHj16ON9fGOuSkpKi9PR0PfbYYzp9+rTuuecenTx5UldddZU++ugj5pgBAABONmOMWzOHxMXFubz38/NTeHi4rrnmGk2YMEE1a9b0SIFllZ2dLbvdLofDobCwMG+XAwAASqA0v99un5nJyMhwd1UAAACPKdMAYGOMsrKyinx6NgAAQEVwK8wcO3ZMt99+u2rXrq369esrIiJCtWvX1h133KHjx497ukYAAIBilfoyU3Z2trp27aqcnBwNHz5c8fHxMsboxx9/1BtvvKH169dry5YtqlGjRnnUCwAA4KLUYeb555+Xv7+/fvjhB4WHh7sse/zxx5WYmKgXXnhBEydO9FiRAAAAxSn1ZaYPPvhAEydOLBRkJCkiIkITJkzQf//7X48UBwAAcCmlDjO7du1S165di13etWtX7dy5s0xFAQAAlFSpw0x2drZq1apV7PJatWopOzu7LDUBAHxcpuOMNu7NUqbjjLdLAUo/ZsYYIz+/4jOQzWaTm/PwAQAsYPGmg5qw5HsVGMnPJqX2b6eBnRp7uyxUYW6FmRYtWshmsxW7HABQOWU6zjiDjCQVGGniku3q1iKch9HCa0odZtLS0sqjDgCABWRknXYGmQvyjdH+rFzCDLym1GEmJSWlVP3feOMN3XTTTQoNDS3tRwEAfExcvVD52eQSaPxtNsXWq+69olDllelxBiVx7733VtpZgRkAB6CqibKHKLV/O/n/v6EG/jabpvdvy1kZeJXbD5osqco6hoYBcACqqoGdGqtbi3Dtz8pVbL3qBBl4XbmfmamMihsAxxkaAFVFlD1EXZrWJcjAJxBm3HCxAXAAAKBiEWbccGEA3B8xAA4AAO8gzLiBAXAAAPgOtwYA5+fna8OGDbrssssu+mgDSYqJiVFAQIA7H+PTGAAHAIBvsBk3bzcKDg7Wjh07FBcX5+maPCo7O1t2u10Oh0NhYWHeLgcAAJRAaX6/3b7M1LZtW+3bt8/d1QEAADzC7TDz1FNP6ZFHHtH777+vzMxMZWdnu7wAAAAqgtuXmf745Ow/PnTSGCObzab8/PyyV+cBXGYCAMB6SvP77fYMwKtXr3Z3VQAAAI9xO8wkJSV5sg4AAAC3lGmemc8//1y33XabunbtqiNHjkiSXn31Va1fv94jxQEAAFyK22Hm3XffVXJyskJCQrRlyxbl5eVJkhwOh6ZPn+6xAgEAAC6mTHczzZs3TwsWLHCZFC8xMVFbtmzxSHEAAACX4naY2blzp7p161ao3W636+TJk2WpCQAAoMTcDjORkZHas2dPofb169erSZMmZSoKAACgpNwOM3fffbdGjx6tr776SjabTUePHtXrr7+uRx55RPfff78nawQAACiW27dmjx8/XgUFBbr22muVm5urbt26KSgoSI888ogefPBBT9YIAABQLLdnAL7gt99+0549e5STk6PWrVurRo0anqrNI5gBGAAA66mQGYAvCAwMVOvWrcu6GQAAALe4HWbOnj2r2bNna/Xq1Tpx4oQKCgpclnN7NgAAqAhuh5k777xTK1eu1M0336zOnTu7PGzSk/Lz8zVlyhS99tprOnbsmBo0aKBhw4bp8ccfL7fPBAAA1uF2mHn//fe1YsUKJSYmerKeQp555hnNnTtXCxcuVJs2bfTNN99o+PDhstvtGjVqVLl+NgAA8H1uh5mGDRuqZs2anqylSBs3blSfPn3Uu3dvSVJsbKzeeOMNff311+X+2QAAwPe5Pc/Mv//9b40bN04HDhzwZD2FdO3aVatWrdKuXbskSd9++63Wr1+vXr16Fdk/Ly9P2dnZLi8AAFB5uX1mpmPHjjp79qyaNGmi6tWruzyfSZJ+/fXXMhcn/T6fTXZ2tuLj4+Xv76/8/Hw9/fTTuvXWW4vsn5qaqieffNIjnw0AAHyf22Fm8ODBOnLkiKZPn6769euX22Dct956S6+//roWLVqkNm3aaNu2bRozZowaNGiglJSUQv0nTJigsWPHOt9nZ2crOjq6XGoDAADe5/akedWrV9cXX3yhhIQET9fkIjo6WuPHj9eIESOcbU899ZRee+01/fTTT5dcn0nzAACwntL8frs9ZiY+Pl5nzpxxd/USy83NlZ+fa5n+/v6F5rUBAABVk9uXmWbMmKGHH35YTz/9tNq1a1dozIynzoLceOONevrpp9W4cWO1adNGW7du1XPPPac77rjDI9sHAADW5vZlpgtnS/48VsYYI5vNpvz8/LJXJ+nUqVOaNGmSli5dqhMnTqhBgwYaPHiwnnjiCQUGBl5yfS4zAQBgPaX5/XY7zKxdu/aiy5OSktzZrMcRZgAAsJ5yf9DkuXPnNHXqVM2bN0/Nmzd3q0gAAABPcGsAcEBAgL777jtP1wIAAFBqbt/NdNttt+mll17yZC0AAACl5vbdTOfPn9fLL7+sTz/9VB06dFBoaKjL8ueee67MxQEAAFyK22Fm+/btuuKKKyTJ+dykC8prNmAAAIA/czvMrF692pN1AAAAuMXtMTN/dPjwYR0+fNgTmwIAACgVt8NMQUGBpk6dKrvdrpiYGMXExKhWrVqaNm0ajxoAAAAVxu3LTP/4xz/00ksvacaMGUpMTJQkrV+/XlOmTNHZs2f19NNPe6xIAACA4rg9A3CDBg00b9483XTTTS7ty5cv1wMPPKAjR454pMCyYgZgAACsp0Kemv3rr78qPj6+UHt8fLx+/fVXdzcLAABQKm6HmYSEBL344ouF2l988UUlJCSUqSgAAICScnvMzLPPPqvevXvr008/VZcuXSRJX3zxhQ4dOqQVK1Z4rEAAAICLcfvMTFJSknbt2qV+/frp5MmTOnnypPr376+dO3fq6quv9mSNAAAAxSrVmZn+/fsrPT1dYWFheuWVVzRw4EDuWgIAAF5VqjMz77//vk6fPi1JGj58uBwOR7kUBQAAUFKlOjMTHx+vCRMmqEePHjLG6K233ir2dqnbb7/dIwUCAABcTKnmmdm4caPGjh2rvXv36tdff1XNmjWLfKikzWbzmduzmWcGAADrKc3vt9uT5vn5+enYsWOKiIhwq8iKQpgBAMB6KmTSvIyMDIWHh7u7OgAAgEe4HWZiYmK0fv163XbbberSpYvz8QWvvvqq1q9f77ECAQAALsbtMPPuu+8qOTlZISEh2rp1q/Ly8iRJDodD06dP91iBAAAAF+N2mHnqqac0b948LViwQAEBAc72xMREbdmyxSPFAQAAXIrbYWbnzp3q1q1boXa73a6TJ0+WpSYAAIASczvMREZGas+ePYXa169fryZNmpSpKAAAgJJyO8zcfffdGj16tL766ivZbDYdPXpUr7/+uh5++GHdf//9nqwRAACgWG4/NXv8+PEqKCjQtddeq9zcXHXr1k1BQUF69NFHddddd3myRgAAgGK5fWbGZrPpH//4h3799Vdt375dX375pX7++WfZ7XbFxcV5skYAAIBilTrM5OXlacKECerYsaMSExO1YsUKtW7dWj/88INatmyp559/Xg899FB51AoAAFBIqS8zPfHEE5o/f7569uypjRs36pZbbtHw4cP15Zdf6t///rduueUW+fv7l0etAAAAhZQ6zLz99tt65ZVXdNNNN2n79u267LLLdP78eX377bdFPnQSAACgPJX6MtPhw4fVoUMHSVLbtm0VFBSkhx56iCADAAC8otRhJj8/X4GBgc731apVU40aNTxaFAAAQEmV+jKTMUbDhg1TUFCQJOns2bO67777FBoa6tJvyZIlnqkQAADgIkodZlJSUlze33bbbR4rBgAAoLRKHWbS0tLKo46LOnLkiMaNG6cPP/xQubm5atasmdLS0tSxY8cKrwUAAPgWt2cArij/+9//lJiYqB49eujDDz9UeHi4du/erdq1a3u7NAAA4AN8Psw888wzio6OdjkjxAzDAADgArcfZ1BR3nvvPXXs2FG33HKLIiIidPnll2vBggXF9s/Ly1N2drbLCwAAVF4+H2b27dunuXPnqnnz5vr44491//33a9SoUVq4cGGR/VNTU2W3252v6OjoCq4YACpWpuOMNu7NUqbjjLdLAbzCZowx3i7iYgIDA9WxY0dt3LjR2TZq1Cht2rRJX3zxRaH+eXl5ysvLc77Pzs5WdHS0HA6HwsLCKqRmeFem44wysk4rrl6oouwh3i4HKFeLNx3UhCXfq8BIfjYptX87DezU2NtloQopr7+52dnZstvtJfr99vkxM1FRUWrdurVLW6tWrfTuu+8W2T8oKMg5Bw6qHv6woyrJdJxxHu+SVGCkiUu2q1uLcII8KoSv/M31+ctMiYmJ2rlzp0vbrl27FBMT46WK4KuK+8POqXdUVhlZp53H+wX5xmh/Vq53CkKV4kt/c30+zDz00EP68ssvNX36dO3Zs0eLFi3S//k//0cjRozwdmnwMfxhR1UTVy9Ufn96LJ6/zabYetW9UxCqFF/6m+vzYaZTp05aunSp3njjDbVt21bTpk3TrFmzdOutt3q7NPgY/rCjqomyhyi1fzv5/78H/frbbJrevy2XmFAhfOlvrs8PAC6r0gwggvUt3nRQE5dsV74xzj/sjJlBZZfpOKP9WbmKrVedIIMKVZ5/c0vz+02YQaXDH3YAqDjl9Te3Ut3NBJRWlD2EEAMAFcQX/ub6/JgZAACAiyHMAAAASyPMAAAASyPMAAAAt/nCs8EYAAwAANzC4wwAAIBl8TgDAABgaTzOAAAAWJovPc6AMAMAAErNl54NxgBgAADgloGdGqtbi3CvP0KGMAMAANzG4wwAAADKiDADAAAsjTADwKf5wuyiAHwbY2YA+CxfmV0UgG/jzAwAn+RLs4sC8G2EGQA+yZdmFwXg2wgzAHySL80uCsC3EWYA+CRfml0UgG9jADAAn+Urs4sC8G2EGQA+zRdmFwXg27jMBAAALI0wAwAALI0wAwBAJVFVZ8xmzAwAAJVAVZ4xmzMzAABYXFWfMZswAwCAxVX1GbMJMwAAWFxVnzGbMAMAgMVV9RmzGQAMAEAlUJVnzCbMAABQSVTVGbO5zAQAACzNUmFmxowZstlsGjNmjLdLAQAAPsIyYWbTpk2aP3++LrvsMm+XAgAAfIglwkxOTo5uvfVWLViwQLVr1/Z2OQAAwIdYIsyMGDFCvXv3Vs+ePS/ZNy8vT9nZ2S4vAABQefn83UxvvvmmtmzZok2bNpWof2pqqp588slyrgoAAPgKnz4zc+jQIY0ePVqvv/66goODS7TOhAkT5HA4nK9Dhw6Vc5UAAMCbbMYYc+lu3rFs2TL169dP/v7+zrb8/HzZbDb5+fkpLy/PZVlRsrOzZbfb5XA4FBYWVt4lAwAADyjN77dPX2a69tpr9f3337u0DR8+XPHx8Ro3btwlgwwA35DpOKOMrNOKqxdaJSf0AlC+fDrM1KxZU23btnVpCw0NVd26dQu1Ayh/7oSSxZsOasKS71VgJD+blNq/nQZ2alzOlQKoSnw6zADwHe6EkkzHGec6klRgpIlLtqtbi3DO0ADwGMuFmTVr1ni7BKDKcTeUZGSddq5zQb4x2p+VS5gB4DE+fTcTAN9wsVByMXH1QuVnc23zt9kUW6+6hysEUJURZgBckruhJMoeotT+7eRvsznXmd6/LWdlAHiU5S4zAah4F0LJxCXblW9MqULJwE6N1a1FuPZn5Sq2XnWCDACPI8wAKJGyhJIoewghBkC5IcwAKDFCCQBfxJgZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZAABgaYQZALC4TMcZbdybpUzHGW+XAngFk+YBgIUt3nTQ+URzP5uU2r+dBnZq7O2ygArFmRkAsKhMxxlnkJGkAiNNXLKdMzSocggzAGBRGVmnnUHmgnxjtD8r1zsFAV5CmAEAi4qrFyo/m2ubv82m2HrVvVMQ4CWEGQDljgGq5SPKHqLU/u3kb/s90fjbbJrevy0PA0WVwwBgAOWKAarla2CnxurWIlz7s3IVW686QQZVEmdmAJQbBqhWjCh7iLo0rUuQQZVFmAFQbhigCqAiEGYAlBsGqAKoCIQZAOWGAaoAKgIDgAGUKwaoAihvhBkA5S7KHkKIAVBuuMwEAAAsjTADAAAsjTADAAAsjTADeBHT/ANA2TEAGPASpvkHAM/gzAzgBUzzDwCeQ5gBvIBp/gHAcwgzgBcwzT8AeA5hBvACpvkHAM9hADDgJUzzXzllOs4oI+u04uqF8r8pUEF8/sxMamqqOnXqpJo1ayoiIkJ9+/bVzp07vV0W4BFR9hB1aVqXH71KYvGmg0qc8ZmGLPhKiTM+0+JNB71dElAl+HyYWbt2rUaMGKEvv/xSn3zyic6dO6e//vWvOn36tLdLAwAn7lADvMfnLzN99NFHLu/T09MVERGhzZs3q1u3bl6qCgBcXewONc68AeXL58PMnzkcDklSnTp1ilyel5envLw85/vs7OwKqQtA1XbhDrU/BhruUAMqhs9fZvqjgoICjRkzRomJiWrbtm2RfVJTU2W3252v6OjoCq4SQFXEHWqA99iMMebS3XzD/fffrw8//FDr169Xo0aNiuxT1JmZ6OhoORwOhYWFVVSpAKqoTMcZ7lADPCA7O1t2u71Ev9+Wucw0cuRIvf/++1q3bl2xQUaSgoKCFBQUVIGVAcD/L8oeQogBKpjPX2YyxmjkyJFaunSpPvvsM8XFxXm7JADwOJ6gDrjP58/MjBgxQosWLdLy5ctVs2ZNHTt2TJJkt9sVEsJ//QCwPp6gDpSNz4+ZsdlsRbanpaVp2LBhl1y/NNfcAKCiZTrOKHHGZ4Xuglo/vgeXq1ClVaoxMz6etQCgTJifBig7nx8zAwCVGU9QB8qOMAMAXsT8NEDZ+fxlJgCo7HiCOlA2hBkA8AHMTwO4j8tMAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAIBKjSeSV37MMwMAqLR4InnVwJkZAECllOk44wwyklRgpIlLtnOGphIizAAAKqWLPZEclQthBgBQKfFE8qqDMAMAqJR4InnVwQBgAEClxRPJqwbCDACgUuOJ5JUfl5kAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClVfpnMxljJEnZ2dlergQAAJTUhd/tC7/jF1Ppw8ypU6ckSdHR0V6uBAAAlNapU6dkt9sv2sdmShJ5LKygoEBHjx5VzZo1ZbPZyrSt7OxsRUdH69ChQwoLC/NQhdbGPimMfVIY+6Qw9knR2C+FVdV9YozRqVOn1KBBA/n5XXxUTKU/M+Pn56dGjRp5dJthYWFV6oAqCfZJYeyTwtgnhbFPisZ+Kawq7pNLnZG5gAHAAADA0ggzAADA0ggzpRAUFKTJkycrKCjI26X4DPZJYeyTwtgnhbFPisZ+KYx9cmmVfgAwAACo3DgzAwAALI0wAwAALI0wAwAALI0wAwAALK3Kh5k5c+YoNjZWwcHBuvLKK/X1119ftP+sWbPUsmVLhYSEKDo6Wg899JDOnj3rXD5lyhTZbDaXV3x8fHl/DY8qzT45d+6cpk6dqqZNmyo4OFgJCQn66KOPyrRNX+TpfWL142TdunW68cYb1aBBA9lsNi1btuyS66xZs0ZXXHGFgoKC1KxZM6WnpxfqY+XjpDz2SVU7TjIzMzVkyBC1aNFCfn5+GjNmTJH93n77bcXHxys4OFjt2rXTihUrPF98OSmPfZKenl7oOAkODi6fL+CjqnSYWbx4scaOHavJkydry5YtSkhIUHJysk6cOFFk/0WLFmn8+PGaPHmyduzYoZdeekmLFy/WxIkTXfq1adNGmZmZztf69esr4ut4RGn3yeOPP6758+dr9uzZ+vHHH3XfffepX79+2rp1q9vb9DXlsU8kax8np0+fVkJCgubMmVOi/hkZGerdu7d69Oihbdu2acyYMbrrrrv08ccfO/tY/Tgpj30iVa3jJC8vT+Hh4Xr88ceVkJBQZJ+NGzdq8ODBuvPOO7V161b17dtXffv21fbt2z1Zerkpj30i/T478B+PkwMHDniqZGswVVjnzp3NiBEjnO/z8/NNgwYNTGpqapH9R4wYYa655hqXtrFjx5rExETn+8mTJ5uEhIRyqbcilHafREVFmRdffNGlrX///ubWW291e5u+pjz2idWPkz+SZJYuXXrRPo899php06aNS9vAgQNNcnKy873Vj5M/8tQ+qWrHyR8lJSWZ0aNHF2ofMGCA6d27t0vblVdeae69994yVljxPLVP0tLSjN1u91hdVlRlz8z89ttv2rx5s3r27Ols8/PzU8+ePfXFF18UuU7Xrl21efNm56nvffv2acWKFbrhhhtc+u3evVsNGjRQkyZNdOutt+rgwYPl90U8yJ19kpeXV+h0ZkhIiPO/Ht3Zpi8pj31ygVWPE3d88cUXLvtQkpKTk5370OrHiTsutU8uqErHSUmUdL9VNTk5OYqJiVF0dLT69OmjH374wdslVagqG2aysrKUn5+v+vXru7TXr19fx44dK3KdIUOGaOrUqbrqqqsUEBCgpk2bqnv37i6Xma688kqlp6fro48+0ty5c5WRkaGrr75ap06dKtfv4wnu7JPk5GQ999xz2r17twoKCvTJJ59oyZIlyszMdHubvqQ89olk7ePEHceOHStyH2ZnZ+vMmTOWP07ccal9IlW946QkittvlfU4KYmWLVvq5Zdf1vLly/Xaa6+poKBAXbt21eHDh71dWoWpsmHGHWvWrNH06dP1n//8R1u2bNGSJUv0wQcfaNq0ac4+vXr10i233KLLLrtMycnJWrFihU6ePKm33nrLi5WXn+eff17NmzdXfHy8AgMDNXLkSA0fPvySj2uvzEqyT6racQL3cJygJLp06aLbb79d7du3V1JSkpYsWaLw8HDNnz/f26VVmCr7i1OvXj35+/vr+PHjLu3Hjx9XZGRkketMmjRJQ4cO1V133aV27dqpX79+mj59ulJTU1VQUFDkOrVq1VKLFi20Z88ej38HT3Nnn4SHh2vZsmU6ffq0Dhw4oJ9++kk1atRQkyZN3N6mLymPfVIUKx0n7oiMjCxyH4aFhSkkJMTyx4k7LrVPilLZj5OSKG6/VdbjxB0BAQG6/PLLq9RxUmXDTGBgoDp06KBVq1Y52woKCrRq1Sp16dKlyHVyc3MLnXHw9/eXJJliHnGVk5OjvXv3KioqykOVlx939skFwcHBatiwoc6fP693331Xffr0KfM2fUF57JOiWOk4cUeXLl1c9qEkffLJJ859aPXjxB2X2idFqezHSUm4s9+qmvz8fH3//fdV6zjx9ghkb3rzzTdNUFCQSU9PNz/++KO55557TK1atcyxY8eMMcYMHTrUjB8/3tl/8uTJpmbNmuaNN94w+/btMytXrjRNmzY1AwYMcPZ5+OGHzZo1a0xGRobZsGGD6dmzp6lXr545ceJEhX8/d5R2n3z55Zfm3XffNXv37jXr1q0z11xzjYmLizP/+9//SrxNX1ce+8Tqx8mpU6fM1q1bzdatW40k89xzz5mtW7eaAwcOGGOMGT9+vBk6dKiz/759+0z16tXNo48+anbs2GHmzJlj/P39zUcffeTsY/XjpDz2SVU7Towxzv4dOnQwQ4YMMVu3bjU//PCDc/mGDRtMtWrVzL/+9S+zY8cOM3nyZBMQEGC+//77Cv1u7iqPffLkk0+ajz/+2Ozdu9ds3rzZDBo0yAQHB7v0qeyqdJgxxpjZs2ebxo0bm8DAQNO5c2fz5ZdfOpclJSWZlJQU5/tz586ZKVOmmKZNm5rg4GATHR1tHnjgAZcfqYEDB5qoqCgTGBhoGjZsaAYOHGj27NlTgd+o7EqzT9asWWNatWplgoKCTN26dc3QoUPNkSNHSrVNK/D0PrH6cbJ69WojqdDrwn5ISUkxSUlJhdZp3769CQwMNE2aNDFpaWmFtmvl46Q89klVPE6K6h8TE+PS56233jItWrQwgYGBpk2bNuaDDz6omC/kAeWxT8aMGeP8/039+vXNDTfcYLZs2VJxX8oH2Iwp5voIAACABVTZMTMAAKByIMwAAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABLI8wAgBesWbNGNptNJ0+e9HYpgOURZoBKbtiwYbLZbJoxY4ZL+7Jly2Sz2ZzvjTFasGCBunTporCwMNWoUUNt2rTR6NGjS/zAutzcXE2YMEFNmzZVcHCwwsPDlZSUpOXLlzv7xMbGatasWR75buXtwr6z2WwKCAhQXFycHnvsMZ09e7ZU2+nevbvGjBnj0ta1a1dlZmbKbrd7sGKgaiLMAFVAcHCwnnnmGf3vf/8rcrkxRkOGDNGoUaN0ww03aOXKlfrxxx/10ksvKTg4WE899VSJPue+++7TkiVLNHv2bP3000/66KOPdPPNN+uXX37x5NepUNdff70yMzO1b98+zZw5U/Pnz9fkyZPLvN3AwEBFRka6BEoAbvLu0xQAlLeUlBTzt7/9zcTHx5tHH33U2b506VJz4U/AG2+8YSSZ5cuXF7mNgoKCEn2W3W436enpxS5PSkoq9IyZCz7//HNz1VVXmeDgYNOoUSPz4IMPmpycHOfyV155xXTo0MHUqFHD1K9f3wwePNgcP37cufzCM28++ugj0759exMcHGx69Ohhjh8/blasWGHi4+NNzZo1zeDBg83p06dL9H1SUlJMnz59XNr69+9vLr/8cuf7rKwsM2jQINOgQQMTEhJi2rZtaxYtWuSyjT9/54yMDGe9f3y22zvvvGNat25tAgMDTUxMjPnXv/5VojqBqo4zM0AV4O/vr+nTp2v27Nk6fPhwoeVvvPGGWrZsqZtuuqnI9Ut69iAyMlIrVqzQqVOnily+ZMkSNWrUSFOnTlVmZqYyMzMlSXv37tX111+vv//97/ruu++0ePFirV+/XiNHjnSue+7cOU2bNk3ffvutli1bpv3792vYsGGFPmPKlCl68cUXtXHjRh06dEgDBgzQrFmztGjRIn3wwQdauXKlZs+eXaLv82fbt2/Xxo0bFRgY6Gw7e/asOnTooA8++EDbt2/XPffco6FDh+rrr7+WJD3//PPq0qWL7r77bud3jo6OLrTtzZs3a8CAARo0aJC+//57TZkyRZMmTVJ6erpbtQJVirfTFIDy9cezC3/5y1/MHXfcYYxxPTMTHx9vbrrpJpf1Ro8ebUJDQ01oaKhp2LBhiT5r7dq1plGjRiYgIMB07NjRjBkzxqxfv96lT0xMjJk5c6ZL25133mnuuecel7bPP//c+Pn5mTNnzhT5WZs2bTKSzKlTp4wx//+ZmU8//dTZJzU11Ugye/fudbbde++9Jjk5uUTfJyUlxfj7+5vQ0FATFBRkJBk/Pz/zzjvvXHS93r17m4cfftj5PikpyYwePdqlz5/PzAwZMsRcd911Ln0effRR07p16xLVClRlnJkBqpBnnnlGCxcu1I4dOy7Z9x//+Ie2bdumJ554Qjk5OSXafrdu3bRv3z6tWrVKN998s3744QddffXVmjZt2kXX+/bbb5Wenq4aNWo4X8nJySooKFBGRoak389c3HjjjWrcuLFq1qyppKQkSdLBgwddtnXZZZc5/12/fn1Vr15dTZo0cWk7ceJEib6PJPXo0UPbtm3TV199pZSUFA0fPlx///vfncvz8/M1bdo0tWvXTnXq1FGNGjX08ccfF6rrUnbs2KHExESXtsTERO3evVv5+fml2hZQ1RBmgCqkW7duSk5O1oQJE1zamzdvrp07d7q0hYeHq1mzZoqIiCjVZwQEBOjqq6/WuHHjtHLlSk2dOlXTpk3Tb7/9Vuw6OTk5uvfee7Vt2zbn69tvv9Xu3bvVtGlTnT59WsnJyQoLC9Prr7+uTZs2aenSpZJUaLsBAQHOf1+4C+mPbDabCgoKSvx9QkND1axZMyUkJOjll1/WV199pZdeesm5/J///Keef/55jRs3TqtXr9a2bduUnJx80e8LwLOqebsAABVrxowZat++vVq2bOlsGzx4sIYMGaLly5erT58+Hv281q1b6/z58zp79qwCAwMVGBhY6EzDFVdcoR9//FHNmjUrchvff/+9fvnlF82YMcM53uSbb77xaJ0l4efnp4kTJ2rs2LEaMmSIQkJCtGHDBvXp00e33XabJKmgoEC7du1S69atnesV9Z3/rFWrVtqwYYNL24YNG9SiRQv5+/t7/ssAlQhnZoAqpl27drr11lv1wgsvONsGDRqkm2++WYMGDdLUqVP11Vdfaf/+/Vq7dq0WL15c4h/T7t27a/78+dq8ebP279+vFStWaOLEierRo4fCwsIk/T7PzLp163TkyBFlZWVJksaNG6eNGzdq5MiR2rZtm3bv3q3ly5c7BwA3btxYgYGBmj17tvbt26f33nvvkpeuysstt9wif39/zZkzR9LvZ7U++eQTbdy4UTt27NC9996r48ePu6wTGxvr3KdZWVlFnhl6+OGHtWrVKk2bNk27du3SwoUL9eKLL+qRRx6pkO8FWBlhBqiCpk6d6vKDarPZtHjxYs2aNUsrVqzQtddeq5YtW+qOO+5QdHS01q9fX6LtJicna+HChfrrX/+qVq1a6cEHH1RycrLeeustl8/ev3+/mjZtqvDwcEm/j3NZu3atdu3apauvvlqXX365nnjiCTVo0EDS75e80tPT9fbbb6t169aaMWOG/vWvf3lwj5RctWrVNHLkSD377LM6ffq0Hn/8cV1xxRVKTk5W9+7dFRkZqb59+7qs88gjj8jf31+tW7dWeHh4keNprrjiCr311lt688031bZtWz3xxBOaOnVqkXdsAXBlM8YYbxcBAADgLs7MAAAASyPMACixP946/efX559/7u3ySuXgwYMX/T6lvbUagPdwmQlAiV3sgZMNGzZUSEhIBVZTNufPn9f+/fuLXR4bG6tq1bjhE7ACwgwAALA0LjMBAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABLI8wAAABL+/8AJ8iRgin2DTMAAAAASUVORK5CYII=", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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h6tWrsNlsDvutVisOHDjARdhERER0nTjZ6tWrxcPDQ9LS0uTw4cOSnJwsBoNBzGaziIiMGzdOpk+frsTv2rVL3NzcZOHChXLkyBFJSUkRd3d3OXDggBIzf/58MRgMsmHDBtm/f7+MGDFCgoODpbKyUokZOnSo9OrVS3JycmTnzp0SEhIi8fHxSv+KFSskPT1dDh8+LAUFBZKeni4BAQGSkJCgxLz66qvyxRdfSEFBgeTl5cmYMWNEp9PJoUOH6nTtFotFAIjFYqnRV1lZKYcPH7bLuSkpKiqSKVOmSHBwsGi1Wrn33nvlkUcekf/85z8iItKpUyd5++23a+yXkpIi4eHhDo+5atUqASAjRoyo9dxNfeyIiBqzH8ouya7jP8oPZZecnYrT1Pb5fSOn30dr9OjR+PHHHzFnzhyYzWZERERgy5YtymL206dPw8Xl54m3fv36YeXKlZg1axZmzpyJkJAQrF+/XrmHFgC8/PLLuHjxIpKTk1FWVob+/ftjy5YtdjcT/fjjjzFlyhQMGTIELi4ueOKJJ7B48WKl383NDW+++Sb++9//QkTQqVMnTJkyBX/+85+VmJ9++gkTJ06E2WxG27Zt0bt3b2RlZSE0NFTNIWv0Tp48iYceeggGgwFvvfUWwsLCcPXqVXzxxReYPHkyvvvuu9s65osvvogBAwaokDEREdVFeu5pzFh3ADYBXDRA6sgwjO7b0dlpNWpOv49WS1bbfTiqqqpQWFiI4OBgVe42r6Zhw4Zh//79OHr0KLy8vOz6ysrKYDAYEBQUhKlTp2Lq1Kl2/XPnzsX69euxd+9epc1qtWLgwIH4wx/+gK+//hplZWXK7TwcacpjR0TUWBVZKvHQ/G2w/aJqcNVosHP6YPjrPZ2XmBPU5z5a/NVhC1FkqURWQQmKLJWqnqe0tBRbtmzB5MmTaxRZwPW7ttfXvHnz0L59eyQlJTVAhkREdDsKSy7aFVkAYBXByZJLzkmoiXD6V4ekvrs51Xv8+HGICLp3737L2FdeeQWzZs2ya7ty5YrdV687d+7E+++/bzfDRUREd1+wjxdcNKgxoxXkw/s91oYzWs1ckaVSKbKA6/+CzFx3ULWZrfp8E/3SSy9h7969dtszzzyj9F+4cAHjxo3De++9Bx8fHzXSJSKiOvLXeyJ1ZBhc//97FbpqNHhjZM8W97VhfXFGq5mrbapXjX85QkJCoNFo6rTg3cfHB/fdd59dW7t27ZR/LigowMmTJ/Hoo48qbdW31nBzc8PRo0fRpUuXBsqciIhuZXTfjhjY1RcnSy4hyKcVi6w64IxWM1c91ftLak71tmvXDiaTCUuXLsXFixdr9NfnAdzdu3fHgQMH7Ga8HnvsMQwePBh79+6t9RFGRESkDn+9J6K73MMiq45YaDVzzpjqXbp0KaxWKyIjI/Hpp5/i2LFjOHLkCBYvXnzTG9E6otPp0LNnT7vNYDCgTZs26NmzJ7RarWrXQERE1BD41WELcLenejt37oz8/Hz85S9/wQsvvICioiL4+vqid+/eePfdd1U9NxERUWPC+2g5UXO9j5azceyIiEhNvI8WERERUSPAQouIiIhIJSy0iIiIiFTCQouIiIhIJSy0Gjn+VqH+OGZERNRYsNBqpNzd3QEAly7xYZ31VT1m1WNIRETkLLyPViPl6uoKg8GAc+fOAQBatWoFjUZzi71aNhHBpUuXcO7cORgMBri6ujo7JSIiauFYaDViRqMRAJRii+rGYDAoY0dERORMLLQaMY1GA39/f7Rv3x5Xr151djpNgru7O2eyiIio0WCh1QS4urqyeCAiImqCuBieiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiImqUiSyWyCkpQZKl0Wg781SERERE1O+m5pzFj3QHYBHDRAKkjwzC6b8e7ngdntIiIiKhZKbJUKkUWANgEmLnuoFNmtlhoERERUbNSWHJRKbKqWUVwsuTuPz+YhRYRERE1K8E+XnC54fHArhoNgnxa3fVcWGgRERFRs+Kv90TqyDC4aq5XW64aDd4Y2RP+es+7ngsXwxMREVGzM7pvRwzs6ouTJZcQ5NPKKUUWwEKLiIiImil/vafTCqxq/OqQiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhU0igKraVLlyIoKAg6nQ5RUVHYvXt3rfFr165F9+7dodPpEBYWhs2bN9v1iwjmzJkDf39/eHp6IjY2FseOHbOLKS0tRUJCAry9vWEwGJCUlISKigql/+jRoxg8eDD8/Pyg0+nQuXNnzJo1C1evXq1XLkRERNRyOb3QSk9Px7Rp05CSkoL8/HyEh4fDZDLh3LlzDuOzsrIQHx+PpKQk7NmzB3FxcYiLi8PBgweVmAULFmDx4sVYtmwZcnJy4OXlBZPJhKqqKiUmISEBhw4dQkZGBjZu3IgdO3YgOTlZ6Xd3d8f48eOxdetWHD16FIsWLcJ7772HlJSUeuVCRERELZg4WWRkpEyePFl5bbVaJSAgQFJTUx3Gjxo1SoYPH27XFhUVJZMmTRIREZvNJkajUd566y2lv6ysTDw8PGTVqlUiInL48GEBILm5uUrM559/LhqNRr7//vub5vrnP/9Z+vfvX+dcbsVisQgAsVgsdYonIiIi56vP57dTZ7SuXLmCvLw8xMbGKm0uLi6IjY1Fdna2w32ys7Pt4gHAZDIp8YWFhTCbzXYxer0eUVFRSkx2djYMBgP69OmjxMTGxsLFxQU5OTkOz3v8+HFs2bIFMTExdc6FiIiIWjanFlolJSWwWq3w8/Oza/fz84PZbHa4j9lsrjW++u+tYtq3b2/X7+bmhnbt2tU4b79+/aDT6RASEoIBAwZg3rx5dc7lRpcvX0Z5ebndRkRERM2X09doNXbp6enIz8/HypUrsWnTJixcuPC2j5Wamgq9Xq9sgYGBDZgpERERNTZOLbR8fHzg6uqK4uJiu/bi4mIYjUaH+xiNxlrjq//eKubGxfbXrl1DaWlpjfMGBgYiNDQU8fHxmD9/PubOnQur1VqnXG40Y8YMWCwWZTtz5ozDOCIiImoenFpoabVa9O7dG5mZmUqbzWZDZmYmoqOjHe4THR1tFw8AGRkZSnxwcDCMRqNdTHl5OXJycpSY6OholJWVIS8vT4nZtm0bbDYboqKibpqvzWbD1atXYbPZ6pTLjTw8PODt7W23ERERUTN2Fxbn12r16tXi4eEhaWlpcvjwYUlOThaDwSBms1lERMaNGyfTp09X4nft2iVubm6ycOFCOXLkiKSkpIi7u7scOHBAiZk/f74YDAbZsGGD7N+/X0aMGCHBwcFSWVmpxAwdOlR69eolOTk5snPnTgkJCZH4+Hilf8WKFZKeni6HDx+WgoICSU9Pl4CAAElISKhXLrXhrw6JiIianvp8fju90BIRWbJkiXTs2FG0Wq1ERkbKN998o/TFxMTIhAkT7OLXrFkjXbt2Fa1WKz169JBNmzbZ9dtsNpk9e7b4+fmJh4eHDBkyRI4ePWoXc/78eYmPj5fWrVuLt7e3JCYmyoULF5T+1atXy4MPPiitW7cWLy8vCQ0NlTfeeMOuWKtLLrVhoUVERNT01OfzWyMi4tw5tZarvLwcer0eFouFXyMSERE1EfX5/OavDomIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIiISCUstIiIiIhUwkKLiIioCSuyVCKroARFlkpnp0IOuDk7ASIiIro96bmnMWPdAdgEcNEAqSPDMLpvR2enRb/AGS0iIqImqMhSqRRZAGATYOa6g5zZamRYaBERETVBhSUXlSKrmlUEJ0suOSchcoiFFhERURMU7OMFF419m6tGgyCfVs5JiBxioUVERNQE+es9kToyDK6a69WWq0aDN0b2hL/e08mZ0S9xMTwREVETNbpvRwzs6ouTJZcQ5NOKRVYjxEKLiIioCfPXe7LAasT41SERERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamkURRaS5cuRVBQEHQ6HaKiorB79+5a49euXYvu3btDp9MhLCwMmzdvtusXEcyZMwf+/v7w9PREbGwsjh07ZhdTWlqKhIQEeHt7w2AwICkpCRUVFUr/9u3bMWLECPj7+8PLywsRERH4+OOP7Y6RlpYGjUZjt+l0ujscDSIiImounF5opaenY9q0aUhJSUF+fj7Cw8NhMplw7tw5h/FZWVmIj49HUlIS9uzZg7i4OMTFxeHgwYNKzIIFC7B48WIsW7YMOTk58PLygslkQlVVlRKTkJCAQ4cOISMjAxs3bsSOHTuQnJxsd54HHngAn376Kfbv34/ExESMHz8eGzdutMvH29sbRUVFynbq1KkGHiEiIiJqssTJIiMjZfLkycprq9UqAQEBkpqa6jB+1KhRMnz4cLu2qKgomTRpkoiI2Gw2MRqN8tZbbyn9ZWVl4uHhIatWrRIRkcOHDwsAyc3NVWI+//xz0Wg08v33398012HDhkliYqLy+sMPPxS9Xl/3i72BxWIRAGKxWG77GERERHR31efz26kzWleuXEFeXh5iY2OVNhcXF8TGxiI7O9vhPtnZ2XbxAGAymZT4wsJCmM1muxi9Xo+oqCglJjs7GwaDAX369FFiYmNj4eLigpycnJvma7FY0K5dO7u2iooKdOrUCYGBgRgxYgQOHTp00/0vX76M8vJyu42IiIiaL6cWWiUlJbBarfDz87Nr9/Pzg9lsdriP2WyuNb76761i2rdvb9fv5uaGdu3a3fS8a9asQW5uLhITE5W2bt264YMPPsCGDRuwYsUK2Gw29OvXD2fPnnV4jNTUVOj1emULDAx0GEdERETNg9PXaDUFX375JRITE/Hee++hR48eSnt0dDTGjx+PiIgIxMTEYN26dfD19cXy5csdHmfGjBmwWCzKdubMmbt1CUREROQETi20fHx84OrqiuLiYrv24uJiGI1Gh/sYjcZa46v/3irmxsX2165dQ2lpaY3zfvXVV3j00Ufx9ttvY/z48bVej7u7O3r16oXjx4877Pfw8IC3t7fdRkRERM2XUwstrVaL3r17IzMzU2mz2WzIzMxEdHS0w32io6Pt4gEgIyNDiQ8ODobRaLSLKS8vR05OjhITHR2NsrIy5OXlKTHbtm2DzWZDVFSU0rZ9+3YMHz4cb775pt0vEm/GarXiwIED8Pf3r8PVExERUbN3Fxbn12r16tXi4eEhaWlpcvjwYUlOThaDwSBms1lERMaNGyfTp09X4nft2iVubm6ycOFCOXLkiKSkpIi7u7scOHBAiZk/f74YDAbZsGGD7N+/X0aMGCHBwcFSWVmpxAwdOlR69eolOTk5snPnTgkJCZH4+Hilf9u2bdKqVSuZMWOGFBUVKdv58+eVmFdffVW++OILKSgokLy8PBkzZozodDo5dOhQna6dvzokIiJqeurz+e30QktEZMmSJdKxY0fRarUSGRkp33zzjdIXExMjEyZMsItfs2aNdO3aVbRarfTo0UM2bdpk12+z2WT27Nni5+cnHh4eMmTIEDl69KhdzPnz5yU+Pl5at24t3t7ekpiYKBcuXFD6J0yYIABqbDExMUrM1KlTlbz9/Pxk2LBhkp+fX+frZqFFRETU9NTn81sjIuK06bQWrry8HHq9HhaLheu1iIiImoj6fH7zV4dEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRXaKLJXIKihBkaXS2akQERE1eW7OToAaj/Tc05ix7gBsArhogNSRYRjdt6Oz0yIiImqyOKNFAK7PZFUXWQBgE2DmuoOc2SIiIroDLLQIAFBYclEpsqpZRXCy5JJzEiIiImoGWGgRACDYxwsuGvs2V40GQT6tnJMQERFRM8BCiwAA/npPpI4Mg6vmerXlqtHgjZE94a/3dHJmRERETRcXw5NidN+OGNjVFydLLiHIpxWLLCIiojvEQovs+Os9WWARERE1EH51SERERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpkhw+VJiIiaji8vQMp+FBpIiKihsUZLQLAh0oTERGpgYUWAeBDpYmIiNTAQosA8KHSREREamChRQD4UGkiIiI1cDE8KfhQaSIioobFQovs8KHSREREDYdfHRIRERGphIUWERERkUpYaBERERGphIUWERERkUpYaBERERGphIUWERERkUpYaBGpqMhSiayCEj4zkoioheJ9tIhUkp57WnlQt4sGSB0ZhtF9Ozo7LSIiuos4o0WkgiJLpVJkAYBNgJnrDnJmi4iohWGhRaSCwpKLSpFVzSqCkyWXnJMQERE5BQstIhUE+3jBRWPf5qrRIMinlXMSIiIip2ChRaQCf70nUkeGwVVzvdpy1WjwxsiefI4kEVELw8XwRCoZ3bcjBnb1xcmSSwjyacUii4ioBWoUM1pLly5FUFAQdDodoqKisHv37lrj165di+7du0On0yEsLAybN2+26xcRzJkzB/7+/vD09ERsbCyOHTtmF1NaWoqEhAR4e3vDYDAgKSkJFRUVSv/27dsxYsQI+Pv7w8vLCxEREfj444/rnQu1bP56T0R3uYdFFhFRC+X0Qis9PR3Tpk1DSkoK8vPzER4eDpPJhHPnzjmMz8rKQnx8PJKSkrBnzx7ExcUhLi4OBw8eVGIWLFiAxYsXY9myZcjJyYGXlxdMJhOqqqqUmISEBBw6dAgZGRnYuHEjduzYgeTkZLvzPPDAA/j000+xf/9+JCYmYvz48di4cWO9ciF18P5URETUFGhERG4dpp6oqCj07dsX77zzDgDAZrMhMDAQzz77LKZPn14jfvTo0bh48aJdwfOrX/0KERERWLZsGUQEAQEBeOGFF/Diiy8CACwWC/z8/JCWloYxY8bgyJEjCA0NRW5uLvr06QMA2LJlC4YNG4azZ88iICDAYa7Dhw+Hn58fPvjggzrlcivl5eXQ6/WwWCzw9vau44gR709FRETOVJ/Pb6fOaF25cgV5eXmIjY1V2lxcXBAbG4vs7GyH+2RnZ9vFA4DJZFLiCwsLYTab7WL0ej2ioqKUmOzsbBgMBqXIAoDY2Fi4uLggJyfnpvlaLBa0a9euzrnc6PLlyygvL7fbqH54fyoiImpKnFpolZSUwGq1ws/Pz67dz88PZrPZ4T5ms7nW+Oq/t4pp3769Xb+bmxvatWt30/OuWbMGubm5SExMrHMuN0pNTYVer1e2wMBAh3F0c7w/FRERNSVOX6PVFHz55ZdITEzEe++9hx49etz2cWbMmAGLxaJsZ86cacAsWwben4qI7hauBaWG4NRCy8fHB66uriguLrZrLy4uhtFodLiP0WisNb76761iblxsf+3aNZSWltY471dffYVHH30Ub7/9NsaPH1+vXG7k4eEBb29vu43qh/enIqK7IT33NB6avw1j38vBQ/O3IT33tLNToibKqYWWVqtF7969kZmZqbTZbDZkZmYiOjra4T7R0dF28QCQkZGhxAcHB8NoNNrFlJeXIycnR4mJjo5GWVkZ8vLylJht27bBZrMhKipKadu+fTuGDx+ON9980+4XiXXNhdQxum9H7Jw+GKsm/go7pw/mQngialBcC0oNyek3LJ02bRomTJiAPn36IDIyEosWLcLFixeVtVDjx49Hhw4dkJqaCgB4/vnnERMTg7/+9a8YPnw4Vq9ejW+//Rb//Oc/AQAajQZTp07F66+/jpCQEAQHB2P27NkICAhAXFwcAOD+++/H0KFDMXHiRCxbtgxXr17FlClTMGbMGOUXh19++SUeeeQRPP/883jiiSeUdVdarVZZEH+rXEg9/npPzmIRkSpqWwvK/+5QvUkjsGTJEunYsaNotVqJjIyUb775RumLiYmRCRMm2MWvWbNGunbtKlqtVnr06CGbNm2y67fZbDJ79mzx8/MTDw8PGTJkiBw9etQu5vz58xIfHy+tW7cWb29vSUxMlAsXLij9EyZMEAA1tpiYmHrlUhuLxSIAxGKx1HkfIiJS1w9llyR4+kbp9MrPW+fpm+SHskvOTo0aifp8fjv9PlotGe+jRUTUOKXnnsbMdQdhFVHWgnKZAlWrz+e30786JCIiamz4rFJqKCy0iJqZIkslCksuItjHix8ORHeAa0GpIbDQImpG+HgiIqLGhTcsJWom+JN0IqLGh4UWUTPBxxMRETU+LLSImgk+noiIqPFhoUXUTPDxREREjU+DLIYvLCxEYGAg3Ny4tp7ImfiTdCKixqVBZrS6deuGY8eONcShiOgO+es9Ed3lHhZZRESNQL2moEaOHOmw3Wq14rnnnkObNm0AAOvWrbvzzIiIiIiauHrNaK1fvx6lpaXQ6/V2GwC0bt3a7jURERFRS1evZx2uXr0aL730EubNm4fExESl3d3dHfv27UNoaKgqSTZXfNYhERFR01Ofz+96zWiNGTMGX3/9Nd5//3088cQT+Omnn+4oUSIiIqLmrN6L4YOCgrBjxw707NkT4eHh+OKLL6DRaG69IxEREVELc1v3Y3BxccGrr76K3/zmNxg/fjysVmtD50VERETU5N3Rja/69++P/fv3o6CgAF26dGmonIiIiIiahTu+w2jr1q0RHh7eELkQERERNSv1KrR69epVp/VY+fn5t50QERERUXNRr0IrLi5O+WcRQWpqKp555hm0a9euofMiIiIiavLqdR+tG7Vp0wb79u1D586dGzKnFoP30SIiImp6VLuPFhERERHVHQstIiIiIpWw0CIiIiJSSb0Wwy9evNju9bVr15CWlgYfHx+79ueee+7OMyMiIiJq4uq1GD44OPjWB9RocOLEiTtKqqXgYngiIqKmpz6f3/Wa0SosLLyjxIiIiIhaknqt0dq2bRtCQ0NRXl5eo89isaBHjx74+uuvGyw5ooZWZKlEVkEJiiyVzk6FiIhagHrNaC1atAgTJ050OE2m1+sxadIk/O1vf8OAAQMaLEGihpKeexoz1h2ATQAXDZA6Mgyj+3Z0dlpERNSM1WtGa9++fRg6dOhN+x9++GHk5eXdcVJEDa3IUqkUWQBgE2DmuoOc2SIiIlXVq9AqLi6Gu7v7Tfvd3Nzw448/3nFSRA2tsOSiUmRVs4rgZMkl5yREREQtQr0KrQ4dOuDgwYM37d+/fz/8/f3vOCmihhbs4wWXG56H7qrRIMinlXMSIiKiFqFehdawYcMwe/ZsVFVV1eirrKxESkoKHnnkkQZLjqih+Os9kToyDK6a69WWq0aDN0b2hL/e08mZERFRc1av+2gVFxfjwQcfhKurK6ZMmYJu3boBAL777jssXboUVqsV+fn58PPzUy3h5oT30br7iiyVOFlyCUE+rVhkERHRbanP53e9Ci0AOHXqFP74xz/iiy++QPWuGo0GJpMJS5curdNNTek6FlpERERNj2o3LAWATp06YfPmzfjpp59w/PhxiAhCQkLQtm3b206YiIiIqDmqd6FVrW3btujbt29D5kJERETUrNRrMTwRERHR7WqJT+e47RktIiIiorpqqU/n4IwWERERqaolP53D6YXW0qVLERQUBJ1Oh6ioKOzevbvW+LVr16J79+7Q6XQICwvD5s2b7fpFBHPmzIG/vz88PT0RGxuLY8eO2cWUlpYiISEB3t7eMBgMSEpKQkVFhdJfVVWFp556CmFhYXBzc0NcXFyNPLZv3w6NRlNjM5vNtz8YREREzVBLfjqHUwut9PR0TJs2DSkpKcjPz0d4eDhMJhPOnTvnMD4rKwvx8fFISkrCnj17EBcXh7i4OLu71S9YsACLFy/GsmXLkJOTAy8vL5hMJrubrCYkJODQoUPIyMjAxo0bsWPHDiQnJyv9VqsVnp6eeO655xAbG1vrNRw9ehRFRUXK1r59+zscFSIioualJT+do9730WpIUVFR6Nu3L9555x0AgM1mQ2BgIJ599llMnz69Rvzo0aNx8eJFbNy4UWn71a9+hYiICCxbtgwigoCAALzwwgt48cUXAQAWiwV+fn5IS0vDmDFjcOTIEYSGhiI3Nxd9+vQBAGzZsgXDhg3D2bNnERAQYHfOp556CmVlZVi/fr1d+/bt2zF48GD89NNPMBgMt3X9vI8WERG1FOm5pzFz3UFYRZSnczTVNVr1+fx22ozWlStXkJeXZzdj5OLigtjYWGRnZzvcJzs7u8YMk8lkUuILCwthNpvtYvR6PaKiopSY7OxsGAwGpcgCgNjYWLi4uCAnJ6fe1xEREQF/f3/85je/wa5du2qNvXz5MsrLy+02IiKilmB0347YOX0wVk38FXZOH9xki6z6clqhVVJSAqvVWuNxPX5+fjdd52Q2m2uNr/57q5gbv95zc3NDu3bt6rW+yt/fH8uWLcOnn36KTz/9FIGBgRg0aBDy8/Nvuk9qair0er2yBQYG1vl8RERETZ2/3hPRXe5pUY9A4+0dblO3bt2UZz0CQL9+/VBQUIC3334b//rXvxzuM2PGDEybNk15XV5ezmKLiIioGXPajJaPjw9cXV1RXFxs115cXAyj0ehwH6PRWGt89d9bxdy42P7atWsoLS296XnrKjIyEsePH79pv4eHB7y9ve02IiIiar6cVmhptVr07t0bmZmZSpvNZkNmZiaio6Md7hMdHW0XDwAZGRlKfHBwMIxGo11MeXk5cnJylJjo6GiUlZUhLy9Pidm2bRtsNhuioqLu6Jr27t0Lf3//OzoGERERNR9O/epw2rRpmDBhAvr06YPIyEgsWrQIFy9eRGJiIgBg/Pjx6NChA1JTUwEAzz//PGJiYvDXv/4Vw4cPx+rVq/Htt9/in//8JwBAo9Fg6tSpeP311xESEoLg4GDMnj0bAQEByr2w7r//fgwdOhQTJ07EsmXLcPXqVUyZMgVjxoyx+8Xh4cOHceXKFZSWluLChQvYu3cvgOuL3wFg0aJFCA4ORo8ePVBVVYX/+Z//wbZt27B169a7M3hERETU6Dm10Bo9ejR+/PFHzJkzB2azGREREdiyZYuymP306dNwcfl50q1fv35YuXIlZs2ahZkzZyIkJATr169Hz549lZiXX34ZFy9eRHJyMsrKytC/f39s2bIFOp1Oifn4448xZcoUDBkyBC4uLnjiiSewePFiu9yGDRuGU6dOKa979eoF4PoNUYHrv5p84YUX8P3336NVq1Z44IEH8J///AeDBw9u+IEiIiKiJsmp99Fq6XgfLSIioqanSdxHi4iIiKi5Y6FFREREpBIWWkREREQqYaFFREREpBIWWkREREQqYaFFREREpBIWWkREREQqYaHVTBVZKpFVUIIiS6WzUyEiImqxnHpneFJHeu5pzFh3ADYBXDRA6sgwjO7b0dlpERERtTic0WpmiiyVSpEFADYBZq47yJktIiIiJ2Ch1cwUllxUiqxqVhGcLLnknISIiIhaMBZazUywjxdcNPZtrhoNgnxaOSchIiKiFoyFVjPjr/dE6sgwuGquV1uuGg3eGNkT/npPJ2dGRETU8nAxfDM0um9HDOzqi5MllxDk04pFFhERkZOw0Gqm/PWeLLCIiIicjF8dEhEREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREamEhRYRERGRSlhoEREREanE6YXW0qVLERQUBJ1Oh6ioKOzevbvW+LVr16J79+7Q6XQICwvD5s2b7fpFBHPmzIG/vz88PT0RGxuLY8eO2cWUlpYiISEB3t7eMBgMSEpKQkVFhdJfVVWFp556CmFhYXBzc0NcXJzDXLZv344HH3wQHh4euO+++5CWlnZbY0BERETNk1MLrfT0dEybNg0pKSnIz89HeHg4TCYTzp075zA+KysL8fHxSEpKwp49exAXF4e4uDgcPHhQiVmwYAEWL16MZcuWIScnB15eXjCZTKiqqlJiEhIScOjQIWRkZGDjxo3YsWMHkpOTlX6r1QpPT08899xziI2NdZhLYWEhhg8fjsGDB2Pv3r2YOnUqnn76aXzxxRcNNDpERETU5IkTRUZGyuTJk5XXVqtVAgICJDU11WH8qFGjZPjw4XZtUVFRMmnSJBERsdlsYjQa5a233lL6y8rKxMPDQ1atWiUiIocPHxYAkpubq8R8/vnnotFo5Pvvv69xzgkTJsiIESNqtL/88svSo0cPu7bRo0eLyWS6xVX/zGKxCACxWCx13oeIiIicqz6f306b0bpy5Qry8vLsZoxcXFwQGxuL7Oxsh/tkZ2fXmGEymUxKfGFhIcxms12MXq9HVFSUEpOdnQ2DwYA+ffooMbGxsXBxcUFOTk6d879VLo5cvnwZ5eXldhsRERE1X04rtEpKSmC1WuHn52fX7ufnB7PZ7HAfs9lca3z131vFtG/f3q7fzc0N7dq1u+l565NLeXk5KisrHe6TmpoKvV6vbIGBgXU+HxERETU9Tl8M35LMmDEDFotF2c6cOePslIiIiEhFTiu0fHx84OrqiuLiYrv24uJiGI1Gh/sYjcZa46v/3irmxsX2165dQ2lp6U3PW59cvL294enp6XAfDw8PeHt7221ERETUfDmt0NJqtejduzcyMzOVNpvNhszMTERHRzvcJzo62i4eADIyMpT44OBgGI1Gu5jy8nLk5OQoMdHR0SgrK0NeXp4Ss23bNthsNkRFRdU5/1vlQkREROTUXx2uXr1aPDw8JC0tTQ4fPizJycliMBjEbDaLiMi4ceNk+vTpSvyuXbvEzc1NFi5cKEeOHJGUlBRxd3eXAwcOKDHz588Xg8EgGzZskP3798uIESMkODhYKisrlZihQ4dKr169JCcnR3bu3CkhISESHx9vl9uhQ4dkz5498uijj8qgQYNkz549smfPHqX/xIkT0qpVK3nppZfkyJEjsnTpUnF1dZUtW7bU+fr5q0MiIqKmpz6f304ttERElixZIh07dhStViuRkZHyzTffKH0xMTEyYcIEu/g1a9ZI165dRavVSo8ePWTTpk12/TabTWbPni1+fn7i4eEhQ4YMkaNHj9rFnD9/XuLj46V169bi7e0tiYmJcuHCBbuYTp06CYAa2y99+eWXEhERIVqtVjp37iwffvhhva6dhRYREVHTU5/Pb42IiNOm01q48vJy6PV6WCwWrtciIiJqIurz+c1fHRIRERGphIUWERERkUpYaBEREVGdFVkqkVVQgiKL45tzkz03ZydARERETUN67mnMWHcANgFcNEDqyDCM7tvR2Wk1apzRIiIiolsqslQqRRYA2ASYue4gZ7ZugYUWERER3VJhyUWlyKpmFcHJkkvOSaiJYKFFREREtxTs4wUXjX2bq0aDIJ9WzkmoiWChRVQHXPxJRC2dv94TqSPD4Kq5Xm25ajR4Y2RP+OsdP9+XruNieKJb4OJPIqLrRvftiIFdfXGy5BKCfFqxyKoDzmgR1YKLP4mI7PnrPRHd5R4WWXXEQouoFlz8SUREd4KFFlEtuPiTiIjuBAstolpw8ScREd0JLoYnugUu/iQiotvFQouoDvz1niywiIio3vjVIREREZFKWGgRERERqYSFFhEREZFKWGgRkVPwsUZE1BJwMTwR3XV8rBERtRSc0SKiu4qPNSKiloSFFhHdVXysERG1JCy0iOiu4mONiKglYaFFRHcVH2tERC0JF8MT0V3HxxoRUUvBQouInIKPNSKiloBfHRIRERGphIUWERERkUpYaBERERGphIUWERERkUpYaBERERGphIUWERERkUpYaBERERGphIUWERERkUpYaBERERGphIUWERERkUpYaBERERGphIUWERERkUpYaBERERGppFEUWkuXLkVQUBB0Oh2ioqKwe/fuWuPXrl2L7t27Q6fTISwsDJs3b7brFxHMmTMH/v7+8PT0RGxsLI4dO2YXU1paioSEBHh7e8NgMCApKQkVFRV2Mfv378eAAQOg0+kQGBiIBQsW2PWnpaVBo9HYbTqd7g5GgoiIiJoTpxda6enpmDZtGlJSUpCfn4/w8HCYTCacO3fOYXxWVhbi4+ORlJSEPXv2IC4uDnFxcTh48KASs2DBAixevBjLli1DTk4OvLy8YDKZUFVVpcQkJCTg0KFDyMjIwMaNG7Fjxw4kJycr/eXl5Xj44YfRqVMn5OXl4a233sLcuXPxz3/+0y4fb29vFBUVKdupU6caeISIiIioyRIni4yMlMmTJyuvrVarBAQESGpqqsP4UaNGyfDhw+3aoqKiZNKkSSIiYrPZxGg0yltvvaX0l5WViYeHh6xatUpERA4fPiwAJDc3V4n5/PPPRaPRyPfffy8iIv/4xz+kbdu2cvnyZSXmlVdekW7duimvP/zwQ9Hr9bd55SIWi0UAiMViue1jEBER0d1Vn89vp85oXblyBXl5eYiNjVXaXFxcEBsbi+zsbIf7ZGdn28UDgMlkUuILCwthNpvtYvR6PaKiopSY7OxsGAwG9OnTR4mJjY2Fi4sLcnJylJiBAwdCq9Xanefo0aP46aeflLaKigp06tQJgYGBGDFiBA4dOnTT6718+TLKy8vtNiIiImq+nFpolZSUwGq1ws/Pz67dz88PZrPZ4T5ms7nW+Oq/t4pp3769Xb+bmxvatWtnF+PoGL88R7du3fDBBx9gw4YNWLFiBWw2G/r164ezZ886zD01NRV6vV7ZAgMDHcYRERFR8+D0NVpNWXR0NMaPH4+IiAjExMRg3bp18PX1xfLlyx3Gz5gxAxaLRdnOnDlzlzMmIiJqOYoslcgqKEGRpdJpObg57cwAfHx84OrqiuLiYrv24uJiGI1Gh/sYjcZa46v/FhcXw9/f3y4mIiJCiblxsf21a9dQWlpqdxxH5/nlOW7k7u6OXr164fjx4w77PTw84OHh4bCPiIiIGk567mnMWHcANgFcNEDqyDCM7tvxrufh1BktrVaL3r17IzMzU2mz2WzIzMxEdHS0w32io6Pt4gEgIyNDiQ8ODobRaLSLKS8vR05OjhITHR2NsrIy5OXlKTHbtm2DzWZDVFSUErNjxw5cvXrV7jzdunVD27ZtHeZmtVpx4MABuwKPiIiI7q4iS6VSZAGATYCZ6w46ZWbL6V8dTps2De+99x4++ugjHDlyBH/84x9x8eJFJCYmAgDGjx+PGTNmKPHPP/88tmzZgr/+9a/47rvvMHfuXHz77beYMmUKAECj0WDq1Kl4/fXX8dlnn+HAgQMYP348AgICEBcXBwC4//77MXToUEycOBG7d+/Grl27MGXKFIwZMwYBAQEAgLFjx0Kr1SIpKQmHDh1Ceno6/v73v2PatGlKLvPmzcPWrVtx4sQJ5Ofn4/e//z1OnTqFp59++i6NHhEREd2osOSiUmRVs4rgZMmlu56LU786BIDRo0fjxx9/xJw5c2A2mxEREYEtW7YoC89Pnz4NF5ef68F+/fph5cqVmDVrFmbOnImQkBCsX78ePXv2VGJefvllXLx4EcnJySgrK0P//v2xZcsWu5uJfvzxx5gyZQqGDBkCFxcXPPHEE1i8eLHSr9frsXXrVkyePBm9e/eGj48P5syZY3evrZ9++gkTJ06E2WxG27Zt0bt3b2RlZSE0NFTNISMiapGKLJUoLLmIYB8v+Os9nZ0ONWLBPl5w0cCu2HLVaBDk0+qu56IREbl1GKmhvLwcer0eFosF3t7ezk6HiKjRaizrbajpSM89jZnrDsIqAleNBm+M7Nlg75n6fH6z0HIiFlpERLdWZKnEQ/O31Zid2Dl9MGe2qFZFlkqcLLmEIJ9WDfpeqc/nt9O/OiQiIqpNbettWGhRbfz1nk5/jzh9MTwREVFtqtfb/JKz1tsQ1RcLLSIiatT89Z5IHRkGV831aqt6vY2zZyqI6oJfHRIRUaM3um9HDOzqq8p6GyI1sdAiIlIBb0XQ8BrDehui+mKhRUTUwHgrAiKqxjVaREQNqDE9+oOInI+FFhFRA2pMj/4gIudjoUVE1IB4KwIi+iUWWkREDYi3IiCiX+JieCKiBsZbERBRNRZaREQq4K0IiAjgV4dEREREqmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURERKQSFlpEREREKmGhRURNSpGlElkFJSiyVDo7FSKiW3JzdgJERHWVnnsaM9YdgE0AFw2QOjIMo/t2dHZaREQ3xRktImoSiiyVSpEFADYBZq47yJktImrUWGgRUZNQWHJRKbKqWUVwsuSScxIiIqoDFlpE1CQE+3jBRWPf5qrRIMinlXMSIiKqAxZaRNQk+Os9kToyDK6a69WWq0aDN0b2hL/e08mZNSwu9idqXrgYnoiajNF9O2JgV1+cLLmEIJ9Wza7I4mJ/ouaHM1pE1KT46z0R3eWeZldkcbE/UfPEQouIqBHgYn+i5qlRFFpLly5FUFAQdDodoqKisHv37lrj165di+7du0On0yEsLAybN2+26xcRzJkzB/7+/vD09ERsbCyOHTtmF1NaWoqEhAR4e3vDYDAgKSkJFRUVdjH79+/HgAEDoNPpEBgYiAULFtQ7FyKiuuBif6LmyemFVnp6OqZNm4aUlBTk5+cjPDwcJpMJ586dcxiflZWF+Ph4JCUlYc+ePYiLi0NcXBwOHjyoxCxYsACLFy/GsmXLkJOTAy8vL5hMJlRVVSkxCQkJOHToEDIyMrBx40bs2LEDycnJSn95eTkefvhhdOrUCXl5eXjrrbcwd+5c/POf/6xXLkREddFSFvsTtTjiZJGRkTJ58mTltdVqlYCAAElNTXUYP2rUKBk+fLhdW1RUlEyaNElERGw2mxiNRnnrrbeU/rKyMvHw8JBVq1aJiMjhw4cFgOTm5ioxn3/+uWg0Gvn+++9FROQf//iHtG3bVi5fvqzEvPLKK9KtW7c653IrFotFAIjFYqlTPBE1fz+UXZKs4yXyQ9klZ6dCRDdRn89vp85oXblyBXl5eYiNjVXaXFxcEBsbi+zsbIf7ZGdn28UDgMlkUuILCwthNpvtYvR6PaKiopSY7OxsGAwG9OnTR4mJjY2Fi4sLcnJylJiBAwdCq9Xanefo0aP46aef6pTLjS5fvozy8nK7jYjol5rrYn+ilsqphVZJSQmsViv8/Pzs2v38/GA2mx3uYzaba42v/nurmPbt29v1u7m5oV27dnYxjo7xy3PcKpcbpaamQq/XK1tgYKDDOCIiImoenL5GqyWZMWMGLBaLsp05c8bZKREREZGKnFpo+fj4wNXVFcXFxXbtxcXFMBqNDvcxGo21xlf/vVXMjYvtr127htLSUrsYR8f45TlulcuNPDw84O3tbbcRERFR8+XUQkur1aJ3797IzMxU2mw2GzIzMxEdHe1wn+joaLt4AMjIyFDig4ODYTQa7WLKy8uRk5OjxERHR6OsrAx5eXlKzLZt22Cz2RAVFaXE7NixA1evXrU7T7du3dC2bds65UJEREQt3F1YnF+r1atXi4eHh6Slpcnhw4clOTlZDAaDmM1mEREZN26cTJ8+XYnftWuXuLm5ycKFC+XIkSOSkpIi7u7ucuDAASVm/vz5YjAYZMOGDbJ//34ZMWKEBAcHS2VlpRIzdOhQ6dWrl+Tk5MjOnTslJCRE4uPjlf6ysjLx8/OTcePGycGDB2X16tXSqlUrWb58eb1yqQ1/dUhERNT01Ofz2+mFlojIkiVLpGPHjqLVaiUyMlK++eYbpS8mJkYmTJhgF79mzRrp2rWraLVa6dGjh2zatMmu32azyezZs8XPz088PDxkyJAhcvToUbuY8+fPS3x8vLRu3Vq8vb0lMTFRLly4YBezb98+6d+/v3h4eEiHDh1k/vz5NXK/VS61YaFFRETU9NTn81sjIlL7nBeppby8HHq9HhaLheu1iIiImoj6fH7zV4dEREREKmGhRURERKQSFlpEREREKmGhRURERKQSN2cn0JJV/w6BzzwkIiJqOqo/t+vye0IWWk504cIFAOAzD4mIiJqgCxcuQK/X1xrD2zs4kc1mww8//IA2bdpAo9E4O527pry8HIGBgThz5kyLv60Fx+I6jsPPOBY/41j8jGPxs8YwFiKCCxcuICAgAC4uta/C4oyWE7m4uODee+91dhpOw+c9/oxjcR3H4Wcci59xLH7GsfiZs8fiVjNZ1bgYnoiIiEglLLSIiIiIVMJCi+46Dw8PpKSkwMPDw9mpOB3H4jqOw884Fj/jWPyMY/GzpjYWXAxPREREpBLOaBERERGphIUWERERkUpYaBERERGphIUWERERkUpYaNEdW7p0KYKCgqDT6RAVFYXdu3fXGr9o0SJ069YNnp6eCAwMxJ///GdUVVUp/XPnzoVGo7HbunfvrvZlNIj6jMXVq1cxb948dOnSBTqdDuHh4diyZcsdHbMxaeixaKrvix07duDRRx9FQEAANBoN1q9ff8t9tm/fjgcffBAeHh647777kJaWViOmqb0v1BiHlvKeKCoqwtixY9G1a1e4uLhg6tSpDuPWrl2L7t27Q6fTISwsDJs3b2745BuYGmORlpZW432h0+nUuYA6YKFFdyQ9PR3Tpk1DSkoK8vPzER4eDpPJhHPnzjmMX7lyJaZPn46UlBQcOXIE77//PtLT0zFz5ky7uB49eqCoqEjZdu7ceTcu547UdyxmzZqF5cuXY8mSJTh8+DCeeeYZPP7449izZ89tH7OxUGMsgKb5vrh48SLCw8OxdOnSOsUXFhZi+PDhGDx4MPbu3YupU6fi6aefxhdffKHENMX3hRrjALSM98Tly5fh6+uLWbNmITw83GFMVlYW4uPjkZSUhD179iAuLg5xcXE4ePBgQ6be4NQYC+D6XeN/+b44depUQ6Vcf0J0ByIjI2Xy5MnKa6vVKgEBAZKamuowfvLkyfLrX//arm3atGny0EMPKa9TUlIkPDxclXzVVN+x8Pf3l3feeceubeTIkZKQkHDbx2ws1BiLpvq++CUA8u9//7vWmJdffll69Ohh1zZ69GgxmUzK66b6vqjWUOPQUt4TvxQTEyPPP/98jfZRo0bJ8OHD7dqioqJk0qRJd5jh3dNQY/Hhhx+KXq9vsLzuFGe06LZduXIFeXl5iI2NVdpcXFwQGxuL7Oxsh/v069cPeXl5ytccJ06cwObNmzFs2DC7uGPHjiEgIACdO3dGQkICTp8+rd6FNIDbGYvLly/XmM729PRU/o/8do7ZGKgxFtWa2vvidmRnZ9uNHQCYTCZl7Jrq+6K+bjUO1VrCe6Iu6jpeLUVFRQU6deqEwMBAjBgxAocOHXJaLiy06LaVlJTAarXCz8/Prt3Pzw9ms9nhPmPHjsW8efPQv39/uLu7o0uXLhg0aJDdV4dRUVFIS0vDli1b8O6776KwsBADBgzAhQsXVL2eO3E7Y2EymfC3v/0Nx44dg81mQ0ZGBtatW4eioqLbPmZjoMZYAE3zfXE7zGazw7ErLy9HZWVlk31f1NetxgFoOe+JurjZeDWn90RddevWDR988AE2bNiAFStWwGazoV+/fjh79qxT8mGhRXfV9u3b8cYbb+Af//gH8vPzsW7dOmzatAmvvfaaEvPb3/4Wv/vd7/DAAw/AZDJh8+bNKCsrw5o1a5yYecP7+9//jpCQEHTv3h1arRZTpkxBYmIiXFxa3r+WdRmLlvK+oLrje4IciY6Oxvjx4xEREYGYmBisW7cOvr6+WL58uVPyaXn/RacG4+PjA1dXVxQXF9u1FxcXw2g0Otxn9uzZGDduHJ5++mmEhYXh8ccfxxtvvIHU1FTYbDaH+xgMBnTt2hXHjx9v8GtoKLczFr6+vli/fj0uXryIU6dO4bvvvkPr1q3RuXPn2z5mY6DGWDjSFN4Xt8NoNDocO29vb3h6ejbZ90V93WocHGmu74m6uNl4Naf3xO1yd3dHr169nPa+YKFFt02r1aJ3797IzMxU2mw2GzIzMxEdHe1wn0uXLtWYsXF1dQUAyE0eu1lRUYGCggL4+/s3UOYN73bGoppOp0OHDh1w7do1fPrppxgxYsQdH9OZ1BgLR5rC++J2REdH240dAGRkZChj11TfF/V1q3FwpLm+J+ridsarpbBarThw4IDz3hfOXo1PTdvq1avFw8ND0tLS5PDhw5KcnCwGg0HMZrOIiIwbN06mT5+uxKekpEibNm1k1apVcuLECdm6dat06dJFRo0apcS88MILsn37diksLJRdu3ZJbGys+Pj4yLlz5+769dVHfcfim2++kU8//VQKCgpkx44d8utf/1qCg4Plp59+qvMxGys1xqKpvi8uXLgge/bskT179ggA+dvf/iZ79uyRU6dOiYjI9OnTZdy4cUr8iRMnpFWrVvLSSy/JkSNHZOnSpeLq6ipbtmxRYpri+0KNcWgp7wkRUeJ79+4tY8eOlT179sihQ4eU/l27dombm5ssXLhQjhw5IikpKeLu7i4HDhy4q9dWX2qMxauvvipffPGFFBQUSF5enowZM0Z0Op1dzN3EQovu2JIlS6Rjx46i1WolMjJSvvnmG6UvJiZGJkyYoLy+evWqzJ07V7p06SI6nU4CAwPlT3/6k90H6ujRo8Xf31+0Wq106NBBRo8eLcePH7+LV3T76jMW27dvl/vvv188PDzknnvukXHjxsn3339fr2M2Zg09Fk31ffHll18KgBpb9fVPmDBBYmJiauwTEREhWq1WOnfuLB9++GGN4za194Ua49CS3hOO4jt16mQXs2bNGunatatotVrp0aOHbNq06e5c0B1QYyymTp2q/Lvh5+cnw4YNk/z8/Lt3UTfQiNzk+xoiIiIiuiNco0VERESkEhZaRERERCphoUVERESkEhZaRERERCphoUVERESkEhZaRERERCphoUVERESkEhZaRERkZ/v27dBoNCgrK3N2KkRNHgstInKap556ChqNBvPnz7drX79+PTQajfJaRPDee+8hOjoa3t7eaN26NXr06IHnn3++zg+KvXTpEmbMmIEuXbpAp9PB19cXMTEx2LBhgxITFBSERYsWNci1qa167DQaDdzd3REcHIyXX34ZVVVV9TrOoEGDMHXqVLu2fv36oaioCHq9vgEzJmqZWGgRkVPpdDq8+eab+Omnnxz2iwjGjh2L5557DsOGDcPWrVtx+PBhvP/++9DpdHj99dfrdJ5nnnkG69atw5IlS/Ddd99hy5YtePLJJ3H+/PmGvJy7aujQoSgqKsKJEyfw9ttvY/ny5UhJSbnj42q1WhiNRrtil4huk9Me/kNELd6ECRPkkUceke7du8tLL72ktP/73/+W6v88rVq1SgDIhg0bHB7DZrPV6Vx6vV7S0tJu2h8TE1Pj+WnVvv76a+nfv7/odDq599575dlnn5WKigql/3//93+ld+/e0rp1a/Hz85P4+HgpLi5W+quf57ZlyxaJiIgQnU4ngwcPluLiYtm8ebN0795d2rRpI/Hx8XLx4sU6Xc+ECRNkxIgRdm0jR46UXr16Ka9LSkpkzJgxEhAQIJ6entKzZ09ZuXKl3TFuvObCwkIl318+g/STTz6R0NBQ0Wq10qlTJ1m4cGGd8iRq6TijRURO5erqijfeeANLlizB2bNna/SvWrUK3bp1w2OPPeZw/7rOuhiNRmzevBkXLlxw2L9u3Trce++9mDdvHoqKilBUVAQAKCgowNChQ/HEE09g//79SE9Px86dOzFlyhRl36tXr+K1117Dvn37sH79epw8eRJPPfVUjXPMnTsX77zzDrKysnDmzBmMGjUKixYtwsqVK7Fp0yZs3boVS5YsqdP13OjgwYPIysqCVqtV2qqqqtC7d29s2rQJBw8eRHJyMsaNG4fdu3cDAP7+978jOjoaEydOVK45MDCwxrHz8vIwatQojBkzBgcOHMDcuXMxe/ZspKWl3VauRC2Ksys9Imq5fjkr86tf/Ur+8Ic/iIj9jFb37t3lscces9vv+eefFy8vL/Hy8pIOHTrU6VxfffWV3HvvveLu7i59+vSRqVOnys6dO+1iOnXqJG+//bZdW1JSkiQnJ9u1ff311+Li4iKVlZUOz5WbmysA5MKFCyLy84zWf/7zHyUmNTVVAEhBQYHSNmnSJDGZTHW6ngkTJoirq6t4eXmJh4eHABAXFxf55JNPat1v+PDh8sILLyivY2Ji5Pnnn7eLuXFGa+zYsfKb3/zGLuall16S0NDQOuVK1JJxRouIGoU333wTH330EY4cOXLL2P/3//4f9u7dizlz5qCioqJOxx84cCBOnDiBzMxMPPnkkzh06BAGDBiA1157rdb99u3bh7S0NLRu3VrZTCYTbDYbCgsLAVyf8Xn00UfRsWNHtGnTBjExMQCA06dP2x3rgQceUP7Zz88PrVq1QufOne3azp07V6frAYDBgwdj7969yMnJwYQJE5CYmIgnnnhC6bdarXjttdcQFhaGdu3aoXXr1vjiiy9q5HUrR44cwUMPPWTX9tBDD+HYsWOwWq31OhZRS8NCi4gahYEDB8JkMmHGjBl27SEhITh69Khdm6+vL+677z60b9++Xudwd3fHgAED8Morr2Dr1q2YN28eXnvtNVy5cuWm+1RUVGDSpEnYu3evsu3btw/Hjh1Dly5dcPHiRZhMJnh7e+Pjjz9Gbm4u/v3vfwNAjeO6u7sr/1z9a8Ff0mg0sNlsdb4eLy8v3HfffQgPD8cHH3yAnJwcvP/++0r/W2+9hb///e945ZVX8OWXX2Lv3r0wmUy1Xi8RNSw3ZydARFRt/vz5iIiIQLdu3ZS2+Ph4jB07Fhs2bMCIESMa9HyhoaG4du0aqqqqoNVqodVqa8zQPPjggzh8+DDuu+8+h8c4cOAAzp8/j/nz5yvrm7799tsGzbMuXFxcMHPmTEybNg1jx46Fp6cndu3ahREjRuD3v/89AMBms+G///0vQkNDlf0cXfON7r//fuzatcuubdeuXejatStcXV0b/mKImhHOaBFRoxEWFoaEhAQsXrxYaRszZgyefPJJjBkzBvPmzUNOTg5OnjyJr776Cunp6XX+oB80aBCWL1+OvLw8nDx5Eps3b8bMmTMxePBgeHt7A7h+H60dO3bg+++/R0lJCQDglVdeQVZWFqZMmYK9e/fi2LFj2LBhg7IYvmPHjtBqtViyZAlOnDiBzz777JZfR6rld7/7HVxdXbF06VIA12cDMzIykJWVhSNHjmDSpEkoLi622ycoKEgZ05KSEoczai+88AIyMzPx2muv4b///S8++ugjvPPOO3jxxRfvynURNWUstIioUZk3b57dh71Go0F6ejoWLVqEzZs3Y8iQIejWrRv+8Ic/IDAwEDt37qzTcU0mEz766CM8/PDDuP/++/Hss8/CZDJhzZo1duc+efIkunTpAl9fXwDX11V99dVX+O9//4sBAwagV69emDNnDgICAgBc/xozLS0Na9euRWhoKObPn4+FCxc24IjUnZubG6ZMmYIFCxbg4sWLmDVrFh588EGYTCYMGjQIRqMRcXFxdvu8+OKLcHV1RWhoKHx9fR2u33rwwQexZs0arF69Gj179sScOXMwb948h7+sJCJ7GhERZydBRERE1BxxRouIiIhIJSy0iKhZ+OXtF27cvv76a2enVy+nT5+u9Xrqe3sGInIefnVIRM1CbQ+X7tChAzw9Pe9iNnfm2rVrOHny5E37g4KC4ObGH40TNQUstIiIiIhUwq8OiYiIiFTCQouIiIhIJSy0iIiIiFTCQouIiIhIJSy0iIiIiFTCQouIiIhIJSy0iIiIiFTCQouIiIhIJf8fKyZbvO/7wm0AAAAASUVORK5CYII=", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -2369,7 +1915,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:23:37 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" + "2025-03-17 17:38:21 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n" ] }, { @@ -2526,19 +2072,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Steam flowrate = 0.6059308497978191\n", - "Reformer duty = 21066.948701158035\n", - "Mole Fraction Ar = 0.0036793744229938544\n", - "Mole Fraction C2H6 = 0.004187279676589231\n", - "Mole Fraction C3H8 = 0.000523414598169937\n", - "Mole Fraction C4H10 = 0.0009159732540583096\n", - "Mole Fraction CH4 = 0.12786005023329045\n", - "Mole Fraction CO = 0.09697157382062967\n", - "Mole Fraction CO2 = 0.046010703278916036\n", - "Mole Fraction H2 = 0.2938730753304199\n", - "Mole Fraction H2O = 0.11952683194799225\n", - "Mole Fraction N2 = 0.30644275497350865\n", - "Mole Fraction O2 = -5.551115123125783e-17\n" + "Steam flowrate = 0.6059308499318983\n", + "Reformer duty = 21072.561832018007\n", + "Mole Fraction Ar = 0.0036781507731262764\n", + "Mole Fraction C2H6 = 0.004188774062981508\n", + "Mole Fraction C3H8 = 0.0005235984950613115\n", + "Mole Fraction C4H10 = 0.0009162955373055101\n", + "Mole Fraction CH4 = 0.12790313523629443\n", + "Mole Fraction CO = 0.09689201209690862\n", + "Mole Fraction CO2 = 0.04604873637953224\n", + "Mole Fraction H2 = 0.2938240725288012\n", + "Mole Fraction H2O = 0.1196592238027376\n", + "Mole Fraction N2 = 0.3063429562761972\n", + "Mole Fraction O2 = 2.7755575615628914e-17\n" ] } ], @@ -2616,29 +2162,29 @@ " inequality constraints with only upper bounds: 1\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 -2.9387308e-01 2.91e-10 2.31e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 -2.9586866e-01 1.29e+00 1.91e-03 -1.7 4.88e+02 - 1.00e+00 1.00e+00f 1\n", - " 2 -3.1973235e-01 1.17e+02 9.90e-03 -2.5 6.00e+03 - 8.80e-01 1.00e+00h 1\n", - " 3 -3.2537868e-01 1.23e+02 3.60e-03 -2.5 4.80e+03 - 1.00e+00 1.00e+00h 1\n", - " 4 -3.2610698e-01 2.48e+01 3.26e-04 -2.5 1.90e+03 - 1.00e+00 1.00e+00h 1\n", - " 5 -3.2615002e-01 1.98e-02 2.27e-06 -2.5 9.78e+01 - 1.00e+00 1.00e+00h 1\n", - " 6 -3.3107576e-01 8.06e+01 5.01e-03 -3.8 3.89e+03 - 9.20e-01 1.00e+00h 1\n", - " 7 -3.3136311e-01 1.95e+00 8.59e-04 -3.8 6.41e+02 - 1.00e+00 1.00e+00h 1\n", - " 8 -3.3128690e-01 2.96e-01 1.24e-05 -3.8 2.18e+02 - 1.00e+00 1.00e+00h 1\n", - " 9 -3.3157177e-01 2.17e-01 1.01e-04 -5.7 1.63e+02 - 9.95e-01 1.00e+00h 1\n", + " 0 -2.9382407e-01 1.16e-09 2.26e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -2.9582077e-01 1.21e+00 1.83e-03 -1.7 4.88e+02 - 1.00e+00 1.00e+00f 1\n", + " 2 -3.1968300e-01 1.09e+02 9.85e-03 -2.5 5.99e+03 - 8.81e-01 1.00e+00h 1\n", + " 3 -3.2538469e-01 1.26e+02 3.59e-03 -2.5 4.76e+03 - 1.00e+00 1.00e+00h 1\n", + " 4 -3.2610509e-01 2.51e+01 2.09e-04 -2.5 1.95e+03 - 1.00e+00 1.00e+00h 1\n", + " 5 -3.2613184e-01 2.39e-02 6.69e-07 -2.5 8.59e+01 - 1.00e+00 1.00e+00h 1\n", + " 6 -3.3098523e-01 6.27e+01 5.34e-03 -3.8 3.83e+03 - 9.23e-01 1.00e+00h 1\n", + " 7 -3.3145573e-01 1.00e+00 6.67e-05 -3.8 9.27e+02 - 1.00e+00 1.00e+00h 1\n", + " 8 -3.3135505e-01 2.55e-01 8.48e-07 -3.8 2.33e+02 - 1.00e+00 1.00e+00h 1\n", + " 9 -3.3164790e-01 2.07e-01 4.73e-04 -5.7 1.93e+02 - 1.00e+00 9.80e-01h 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 -3.3157586e-01 4.17e-04 1.45e-08 -5.7 9.24e+00 - 1.00e+00 1.00e+00h 1\n", - " 11 -3.3157954e-01 4.46e-05 7.07e-09 -8.6 1.84e+00 - 1.00e+00 1.00e+00h 1\n", - " 12 -3.3157954e-01 1.34e-09 4.26e-14 -8.6 1.29e-03 - 1.00e+00 1.00e+00h 1\n", + " 10 -3.3165534e-01 4.99e-04 9.50e-09 -5.7 1.17e+01 - 1.00e+00 1.00e+00h 1\n", + " 11 -3.3165902e-01 4.56e-05 9.88e-10 -8.6 1.91e+00 - 1.00e+00 1.00e+00h 1\n", + " 12 -3.3165902e-01 2.10e-09 2.51e-14 -8.6 2.53e-03 - 1.00e+00 1.00e+00h 1\n", "\n", "Number of Iterations....: 12\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: -3.3157953843145921e-01 -3.3157953843145921e-01\n", - "Dual infeasibility......: 4.2581604889876294e-14 4.2581604889876294e-14\n", - "Constraint violation....: 3.5549293562270731e-12 1.3387762010097504e-09\n", - "Complementarity.........: 2.5059038878794521e-09 2.5059038878794521e-09\n", - "Overall NLP error.......: 2.5059038878794521e-09 2.5059038878794521e-09\n", + "Objective...............: -3.3165902458760038e-01 -3.3165902458760038e-01\n", + "Dual infeasibility......: 2.5091040356528538e-14 2.5091040356528538e-14\n", + "Constraint violation....: 5.5610359819253185e-12 2.0954757928848267e-09\n", + "Complementarity.........: 2.5059038350654027e-09 2.5059038350654027e-09\n", + "Overall NLP error.......: 2.5059038350654027e-09 2.5059038350654027e-09\n", "\n", "\n", "Number of objective function evaluations = 13\n", @@ -2648,8 +2194,8 @@ "Number of equality constraint Jacobian evaluations = 13\n", "Number of inequality constraint Jacobian evaluations = 13\n", "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", - "Total CPU secs in NLP function evaluations = 0.001\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" @@ -2659,7 +2205,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[+ 0.07] solve\n", + "[+ 0.01] solve\n", "Model status: \n", "Problem: \n", "- Lower bound: -inf\n", @@ -2674,27 +2220,27 @@ " Termination condition: optimal\n", " Id: 0\n", " Error rc: 0\n", - " Time: 0.058537960052490234\n", + " Time: 0.009012460708618164\n", "Solution: \n", "- number of solutions: 0\n", " number of solutions displayed: 0\n", "\n", - "Solve time: 0.074246599979233\n", - "fs.bypass_frac : 0.10000006743045317\n", - "fs.ng_steam_ratio : 1.1111587695778178\n", - "fs.steam_flowrate : 1.211913588633006\n", - "fs.reformer_duty : 38820.801141863\n", - "fs.AR : 0.004107083638191985\n", - "fs.C2H6 : 0.0005392232921631147\n", - "fs.C4H10 : 0.00011795111658708467\n", - "fs.C3H8 : 6.741873287192471e-05\n", - "fs.CH4 : 0.016806518961534973\n", - "fs.CO : 0.10494647112247085\n", - "fs.CO2 : 0.05346576317928386\n", - "fs.H2 : 0.3315795384314592\n", - "fs.H2O : 0.14839740391436385\n", - "fs.N2 : 0.3400000043352795\n", - "fs.O2 : 8.2852873934491285e-16\n" + "Solve time: 0.012646308001421858\n", + "fs.bypass_frac : 0.10000006056376089\n", + "fs.ng_steam_ratio : 1.1232321922655462\n", + "fs.steam_flowrate : 1.2250817840789683\n", + "fs.reformer_duty : 39050.36780003482\n", + "fs.AR : 0.0041071693974047445\n", + "fs.C2H6 : 0.0005239281969648362\n", + "fs.C4H10 : 0.00011460579359301395\n", + "fs.C3H8 : 6.54784416506138e-05\n", + "fs.CH4 : 0.01634185961879082\n", + "fs.CO : 0.10487575125628976\n", + "fs.CO2 : 0.053535619369001815\n", + "fs.H2 : 0.3316590245876004\n", + "fs.H2O : 0.14885681247114485\n", + "fs.N2 : 0.3400000042903256\n", + "fs.O2 : -5.134104614949322e-16\n" ] } ], @@ -2745,7 +2291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_test.ipynb index 94803a2c..1c3f7c84 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_usr.ipynb index 94803a2c..1c3f7c84 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/pysmo_flowsheet_optimization_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_cardinal_sine_function.py b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_cardinal_sine_function.py index 3c02d5d3..cfd886f1 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_cardinal_sine_function.py +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_cardinal_sine_function.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_griewank_function.py b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_griewank_function.py index edfe08aa..00f4dfd8 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_griewank_function.py +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_griewank_function.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ 2d-Griewank function, see Griewank (1981) paper diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_six_hump_function.py b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_six_hump_function.py index 6c62bd7c..3c875938 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_six_hump_function.py +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_six_hump_function.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# from idaes.surrogate.pysmo.radial_basis_function import * from idaes.surrogate.pysmo import sampling as sp diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_three_hump_function.py b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_three_hump_function.py index 787c38e9..722771ab 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_three_hump_function.py +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/rbf_three_hump_function.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# from idaes.surrogate.pysmo.radial_basis_function import * from idaes.surrogate.pysmo import sampling as sp diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/sampling_examples.py b/idaes_examples/notebooks/docs/surrogates/pysmo/sampling_examples.py index 5cc13b71..231d01c6 100644 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/sampling_examples.py +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/sampling_examples.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# import pandas as pd diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb index 66cd6539..a561f827 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "11cae4c1", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -586,8 +613,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb index 72d78965..167f60a8 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -63,7 +89,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -91,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -123,9 +149,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ], "source": [ "# Import training data\n", "np.set_printoptions(precision=7, suppress=True)\n", @@ -163,117 +198,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ***************************************************************************\n", - " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", - "\n", - " If you use this software, please cite:\n", - " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", - " Automatic Learning of Algebraic Models for Optimization,\n", - " AIChE Journal, 60, 2211-2227, 2014.\n", - "\n", - " ALAMO is powered by the BARON software from http://www.minlp.com/\n", - " ***************************************************************************\n", - " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", - " ***************************************************************************\n", - " Reading input data\n", - " Checking input consistency and initializing data structures\n", - " \n", - " Step 0: Initializing data set\n", - " User provided an initial data set of 400 data points\n", - " We will sample no more data points at this stage\n", - " ***************************************************************************\n", - " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", - " \n", - " Step 1: Model building using BIC\n", - " \n", - " Model building for variable CO2SM_CO2_Enthalpy\n", - " ----\n", - " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", - " ----\n", - " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", - " ----\n", - " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", - " ----\n", - " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", - " ----\n", - " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", - " ----\n", - " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", - " \n", - " Model building for variable CO2SM_CO2_Entropy\n", - " ----\n", - " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", - " ----\n", - " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", - " ----\n", - " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", - " ----\n", - " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", - " ----\n", - " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", - " ----\n", - " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", - " ----\n", - " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", - " ----\n", - " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", - " ----\n", - " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", - " ----\n", - " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", - " \n", - " Calculating quality metrics on observed data set.\n", - " \n", - " Quality metrics for output CO2SM_CO2_Enthalpy\n", - " ---------------------------------------------\n", - " SSE OLR: 0.515E+08\n", - " SSE: 0.659E+08\n", - " RMSE: 406.\n", - " R2: 0.999\n", - " R2 adjusted: 0.999\n", - " Model size: 6\n", - " BIC: 0.484E+04\n", - " Cp: 0.659E+08\n", - " AICc: 0.482E+04\n", - " HQC: 0.483E+04\n", - " MSE: 0.168E+06\n", - " SSEp: 0.659E+08\n", - " RIC: 0.659E+08\n", - " MADp: 0.594\n", - " \n", - " Quality metrics for output CO2SM_CO2_Entropy\n", - " --------------------------------------------\n", - " SSE OLR: 541.\n", - " SSE: 558.\n", - " RMSE: 1.18\n", - " R2: 0.997\n", - " R2 adjusted: 0.997\n", - " Model size: 10\n", - " BIC: 193.\n", - " Cp: 178.\n", - " AICc: 154.\n", - " HQC: 169.\n", - " MSE: 1.43\n", - " SSEp: 558.\n", - " RIC: 606.\n", - " MADp: 0.130E+04\n", - " \n", - " Total execution time 0.52 s\n", - " Times breakdown\n", - " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", - " MINLP time: 0.0 s in 0 optimization problem(s)\n", - " Simulation time: 0.0 s to simulate 0 point(s)\n", - " All other time: 0.22 s in 1 iteration(s)\n", - " \n", - " Normal termination\n", - " ***************************************************************************\n" + "Alamo not found.\n" ] } ], @@ -330,108 +262,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", 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", 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", 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", 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MhIWFBbZt24ZevXoBACIjI+Hg4PDOOT55JSUlwcTEBImJiZIQlJqaivv376NGjRrQ1dVV3BujMo+fPRFRwQICgMWLs9Gp01JkZaWK7S4uLqV+U1Fh3995qcwcH3d3d/z55584cOAAjIyMxHk7JiYm0NPTg4mJCYYNG4bJkyfD3NwcxsbGGDduHBwdHXlHFxERUQlo0SIWHTpsQO7pnxMmTCjT2xupTPBZt24dAKBt27aSdj8/P3GxvJUrV0JDQwO9evVCWloaXFxc8PPPP5dypUREROXf4cOH8e+//4rHNjY2GDJkiLhESlmlMsGnKCNyurq6WLt2LdauXVsKFREREamf9PT0fHNAe/fujXr16impouJRmeBDREREynXnzh1s3bpV0ubh4aFS8x8ZfIiIiOi9tm7dijt37ojHT558il9+6a7Eij6Myt3OTkRERIqVs2v6zJn5d09PTk6Gj4+PJPScPz8UXbqoXugB2ONDRESk9nJWYf73XyA7W37crRtw+fJl/PXXX+J1MpkMM2fOhKamphKr/TgMPkRERGrO0xPo3x9ITpYvRujhIeDHH1eJ+2ICgLOzM1q3bq3EKhWDQ11ERERqrls3YPx4eegZP/45QkPnSkLPmTNjkZCg+qEHYPAhBXJ1dUWPHj3E47Zt22LixIkf9ZqKeA0iInq/4GCgRYsT0NZ+u/6dhYUFgoJm4/jxivD1VWJxCsShLjXg6uoKf39/AICWlhaqV6+OQYMGYcaMGahQoeT+E9i7dy+0tLSKdO3Jkyfh7OyM+Ph4yYqfxXkNIiL6MBkZGXBxWShp++abb/DJJ5+gWrW3u6+XBww+aqJjx47w8/NDWloaDh06BHd3d2hpacHLy0tyXXp6OrS1tRXyM83NzcvEaxARkVRAAODhAQgC8MMPUbhzx19yfurUqTAwMAAgHwbr1k0ZVZYMDnWpCR0dHVhZWcHW1hajR49G+/btERAQIA5PLViwAFWrVoW9vT0A4NGjR+jduzdMTU1hbm6O7t27IyoqSny9rKwsTJ48GaampqhYsSKmT5+eb3XtvMNUaWlp8PDwgI2NDXR0dFC7dm1s3LgRUVFRcHZ2BgCYmZlBJpOJ25DkfY34+HgMGjQIZmZm0NfXR6dOnXD79m3x/ObNm2FqaoqgoCDUq1cPhoaG6NixI2JiYsRrTp48iRYtWsDAwACmpqZo1aoVHjx4oKA/aSKiss/XF4iIABo12i0JPfXq1YO3t7cYesojBh81paenh/T0dADA8ePHERkZiaNHjyIwMPD/uzxdYGRkhDNnzuDs2bNigMh5zvLly7F582Zs2rQJ//zzD+Li4rBv3753/sxBgwZh27ZtWL16NcLDw7FhwwYYGhrCxsYGe/bsAQBERkYiJiYGq1atKvA1XF1dcfHiRQQEBCAkJASCIODrr79GRkaGeM3r16+xbNkybNmyBadPn8bDhw8xdepUAEBmZiZ69OiBNm3a4Nq1awgJCcGIESPK/N4yREQfImd9ntzr8gDA1KmvMWeODxo2vCG21a49CL179y7lCksfh7rUjCAIOH78OIKCgjBu3Dg8f/4cBgYG+O2338Qhrj/++APZ2dn47bffxEDg5+cHU1NTnDx5Eh06dMCPP/4ILy8v9OzZEwCwfv16BAUFFfpzb926hZ07d+Lo0aNo3749AKBmzZri+ZwhLUtLy0J39b19+zYCAgJw9uxZODk5AZCvJGpjY4P9+/fju+++AyAfq16/fj1q1aoFABg7dizmzp0LAEhKSkJiYiK6dOkinleV/WWIiIorZ32enHV5AOD69eu4fn2v5Lr582egeXMt9O+vhCJLGXt8lKSwFF5SAgMDYWhoCF1dXXTq1Al9+vTBnDlzAACNGjWSzOu5evUq7ty5AyMjIxgaGsLQ0BDm5uZITU3F3bt3kZiYiJiYGLRs2VJ8ToUKFdCsWbNCf35oaCg0NTXRpk2bD34P4eHhqFChguTnVqxYEfb29ggPDxfb9PX1xVADAFWqVMGzZ88AyAOWq6srXFxc0LVrV6xatUoyDEZEVJ54egKOjvJfBUHA+vXrsXfv29DTqlUrNGnijebNtcrN5OX3YfBRktwpvDQ4OzsjNDQUt2/fxps3b+Dv7y+O4eYdy01OTkbTpk0RGhoqedy6dQv9+vX7oJ+vp6f30e+hqPLeBSaTySTzj/z8/BASEgInJyfs2LEDdevWxfnz50utPiKikpbzj2sAOHcO+OKLOMydOxdPnz4Vr8mZ76luGHyUJHcKLw0GBgaoXbs2qlev/t5b2D/77DPcvn0blpaWqF27tuRhYmICExMTVKlSBf/++6/4nMzMTFy6dKnQ12zUqBGys7Nx6tSpAs/n9DhlZWUV+hr16tVDZmam5Oe+fPkSkZGRqF+//jvfU15NmjSBl5cXzp07h4YNG+LPP/8s1vOJiMqy3P+4/ueff7BmzRrxnLGxMX744QdYWlrmu1YdMPgoSbdu8hReFm8R7N+/PypVqoTu3bvjzJkzuH//Pk6ePInx48fj8ePHAIAJEybA19cX+/fvR0REBMaMGYOEhIRCX9POzg6DBw/G0KFDsX//fvE1d+7cCQCwtbWFTCZDYGAgnj9/juTk5HyvUadOHXTv3h1ubm74559/cPXqVQwYMADW1tbo3r1om+Xdv38fXl5eCAkJwYMHD3DkyBHcvn2b83yIqFzx9ATq1cvCV1/Nw/Hjx8X2Ll26YNKkSdDQ0JBcW5r/EFc2Bh/KR19fH6dPn0b16tXRs2dP1KtXD8OGDUNqaiqMjY0BAFOmTMHAgQMxePBgODo6wsjICN988807X3fdunX49ttvMWbMGDg4OMDNzQ0pKSkAAGtra/j4+MDT0xOVK1fG2LFjC3wNPz8/NG3aFF26dIGjoyMEQcChQ4eKvMihvr4+IiIi0KtXL9StWxcjRoyAu7s7Ro4cWYw/ISKisi0l5TH69JkPDY1ssW3y5Mlo2rRpvjmmZfkf4iVBJuRdfEXNJSUlwcTEBImJieKXPACkpqbi/v37qFGjBnR1dZVYIZU2fvZEVNYFBOReXTkAV65cEc8ZGdXE5MkDxWMnJ/nQlqOjPPCUF4V9f+fF29mJiIhUnK8vcPlyKq5cWSxpr1mzHwYOrCNp8/QsX1tQFBeDDxERURmXu0enoCGpUaMicP/+Dkmbp6cndHR08l1b3ragKC4GHyIiojKuoIUIAeDAAQHBwZthZvZQbGvevDm+/vprJVSpGji5mYiIqIwr6M6rxMREhIbOlYQeNzc3ZGZ+DQcHoF690lskV5Uw+BQT54KrH37mRKQseRciBOTHGzf+hx9//FG8LjVVB0eOzELVqlXh6wtERso3IVWXtXmKg8GniHJul379+rWSK6HSlvOZF/WWeSIiRcm7uODixdn48sslePz4b/Eaa+sOOHXKEx4emgDkvUL29oCDg/pOYH4XzvEpIk1NTZiamop7Punr63NH73JOEAS8fv0az549g6mpKTQ1NZVdEhGpmdx3YMXGxqJDhw2S8xMmTICpqSmGD3/bpu6Tl9+H6/jk8a51AARBQGxs7DtXKKbyx9TUFFZWVgy6RFTiCrt7KygoSLKnYEKCNVasGMa/l3Ip6jo+DD55FOUPLisrCxkZGaVcGSmDlpYWe3qIqNTkLC7o4ACYmQHTpqXj2rVFkmtCQ7/DkCH12auTBxcwLEGampr8MiQiIoXz9AQ8PICoKCAj4y6uXftDct7ffzp+/FGPoecjMPgQEREpUc7wlrMzEBwMCALwzTfbYG9/S7wmNLQx9u/vASD/Wj5UPAw+RERESjR9uvz284sXAW3tZEybtlxyfuPGIXj0qDoAwNqad2p9LAYfIiIiJcqZn9y48RV06SJdcXDevJnIypJ/Vc+YASxYUNrVlT8MPkRERErk6yvg3Lk10NePF9tu326L3r3boGVL+cKFTk4MPYrC4ENERKQkL168QGjoWujrv23bsGEsYmIq4tIlIDxcebWVVww+REREShAcHIzTp0+Lx4JQCcuXj0Fysuz/j5VVWfnG4ENERFSKMjMzsSDPuNW+fT1w925jJCcDurqAnR2weLFy6ivvGHyIiIhKyZ9/PsDt25slbUuWTMXr1waYMUN+O3veVZtJsRh8iIiISlhAAHDw4F5UrXpdbAsPd8COHX0AABUqcPJyaWHwISIiKiEBAcDy5W/Qrt0SVK36tn3LloG4e7emeFy5shKKU1MMPkRERCVk8+YwtGu3R9J29OgM3L2rBUC+IGH16lyUsDQx+BARESnYgQMCgoN/QePGsWLb/ftO6NnzK2zfLg88RkbyCcycz1O6GHyIiIgUKD4+HqGhq2Fm9rZt48ZRyM6ujH//BSIiAEdH+cKEVPoYfIiIiBTk7NmzOHbsmHiclGSElSsnQhA0AAD29vLQw6Et5WHwISIi+khZWVnw9fVFZmam2BYY2BkXLzaDhoZ8MUJdXWDJEg5tKRuDDxER0Ud48uQJfvvtN0nb8uWT8OqVMQCgSpW3E5gZepSPwYeIiOgDBQYG4tKlS+LxvXs1EBg4ECkpMjg5yXt6GHjKFgYfIiKiYkpLS4Ovr6+k7c8/++L27bqoWhWIi5OHHk5gLnsYfIiIiIphy5ZI3Lu3XdK2e7cnoqN1IAiAoSEnMJdlDD5ERERFIAgC/P398eDBA7EtK6sZVq7sjPHjgZYtAV9fDm2VdQw+RERE75GUlISVK1dK2oKC3BAXVxXJyfLNRRcsYOBRBQw+RERE73DhwgUcOnRIPNbS0sK6dR54/FgT5uYc1lI1DD5EREQFyM7OxooVK5CSkiK2ffXVV3jxwglPnsiP37zhBGZVw+BDRESUx9OnT7F+/XpJ2y+/TECTJqbw9ZXfsaWhAUyapKQC6YMx+BAREf2/gABg794jqFEjRGx7+bIqfvttON68kcHDQ76xKCcxqy4GHyIiIgDp6em4cmURatR42/bXX99CR6cBUlPlx4IgDzsMPKqLwYeIiNRaQADwyy/30Lz5Fkn7hg3TEROjJx5raMj32iLVxuBDRERqKSBAPmTVoMF2NG8eKbZHR3+CX375BjLZ22s1NDi0VV5oKLsAIiIiZVixIgUuLj6oVu1t6BkyZAgqVfpGXH1ZVxewtgb27ZOv00Oqjz0+RESkVgICgN9/D4Wz8wFJ+8yZM1GhQgUEBwPJycDLl0CTJuzpKW/Y40NERGpDEAT8888aNGr0NvRYWbXBr796w8ioAmbOlAcdR0dAJgNCQuTDYVR+MPgQEVG5FRAAODnJf33x4gXmzp0LA4M48Xy9eu5YubItnjwBUlOB1avlvTvnzslvW+eqzOUPh7qIiKjc8vWV99rs2nUSV66cEttTUsyxbNlYGBjIYGIib5PJgPHj3z6Xt62XTww+RERULgUEAAkJmZgzRzoruXv37nj48FOsWyefy2Nt/bZnh0Gn/ONQFxERlQu5h7UAYP36h+jTRxp6Gjacik8//RTdugFbt8oDz5Il8qEthh71wB4fIiJSeQEBQP/+8h4cX18gK2sfWra8Jp6/c6cu/vijr7gez4IFHMpSV+Wyx2ft2rWws7ODrq4uWrZsif/++0/ZJRERkYLl7uHx9ZWHnkqV3sDFxQfXrr0NPTt2DMCePX0BANnZwIoVyqqYyoJyF3x27NiByZMnw9vbG5cvX0bjxo3h4uKCZ8+eKbs0IiJSoJyJyzkbhvbocQNjx0r3lFi1ygvh4bXw5s3btooVS7lQKlPKXfBZsWIF3NzcMGTIENSvXx/r16+Hvr4+Nm3apOzSiIhIQQICgLg4wMEB8PAQEBv7Cz79dLd43tLSEcuWeSM+Xluy9YS1NfDzz0oomMqMcjXHJz09HZcuXYKXl5fYpqGhgfbt2yMkJKTA56SlpSEtLU08TkpKKvE6iYjo4/j6ApGRgLNzAkJDV0nOjRo1Cm3aVEZysnzLicmTgeBg3rVFcuUq+Lx48QJZWVmoXLmypL1y5cqIiIgo8DmLFi2Cj49PaZRHREQfKWc+j7MzYGt7Dg4OR8VzBgYGmDx5MjQ0NMReHjs77rFFUuVuqKu4vLy8kJiYKD4ePXqk7JKIiKgQvr7Av/9mQUNjoST0XLz4NX77bSoCA+VfazmrLi9erKxKqawqVz0+lSpVgqamJp4+fSppf/r0KaysrAp8jo6ODnR0dEqjPCIi+kjjx0cjMvJXSdvJk5Nw8qQxAHkwyrlNncNaVJBy1eOjra2Npk2b4vjx42JbdnY2jh8/DkdHRyVWRkREHyL3LesHDx6UhB5DQzsEBc2Gk5MxHBzkE5fj498uYEhUkHIVfABg8uTJ+PXXX+Hv74/w8HCMHj0aKSkpGDJkiLJLIyKid8i78jIg78G5dCkNV6744OLFi2J7Rsb3mDVrMEJCZNizBwgPB6pXByIiuJs6vVu5GuoCgD59+uD58+eYPXs2YmNj8emnn+Lw4cP5JjwTEVHZkntdnpxhqtGjb+HevW2S6xYt8oSWlg5SU+XHOROZPT3frulDVBiZIAiCsosoS5KSkmBiYoLExEQYGxsruxwiIrWRc8eWpyfQtauALVu24P79++L5ixebIjCwCwwN5buo79kjDz2LF3M+DxX9+5vBJw8GHyIi5Xr16hVW5NlXYvPm4YiKsoaGBrBvH4MO5VfU7+9yN9RFRESq6+LFizh48KB4nJFRAYsWeaJKFU2xp4ehhz4Ggw8REZWq3ENaOSEmOzsbK1euRHJysnjd8ePtceZMK8hkwODBXIiQFINDXXlwqIuIqGQ5OcknMTs4AGZmwIQJzxARsU5yzY8/jkdCghk0NOQ7qjs6AufOKalgUgkc6iIiojIp5+6ruDjAwOAYIiLOiudiYqywadMIaGrKYG0t7+nJ2WeLSBEYfIiIqEQUNKQFyH/fqVMGFi5cKLl+165eiIhoCA8PDmtRyeFQVx4c6iIiUgwHB/kO6vb28oUFc9y/fx+///675NrFi6fhzRt9DmnRB+NQFxERKVXOwoI5vwLAjh07EJErBd282RBnz/aCuTlgaMghLSp5DD5ERKRwAQGAIMh7fRYvBlJSUrBs2TLJNVu3umLZMlvs2KGkIkktMfgQEZHC+frKh7kMDYELF67iypX9kvPz589E7doVuCYPlToGHyIiUpicCc3OzsD16wIGD/4ZFSq8EM+fPNkaZ886o0IFoGdPJRZKaovBh4iIFCZno1F9/ZeYOvUnybmffnJHYmIlZGQAGRnA3r1vb1Vnzw+VFgYfIiJSGE9PwN//ND75JFhs09Y2w19/jcPGjfJZzh4e8vk/gpB/N3aiksbgQ0RECrF/fyauXl2ATz5523b9ejfs3t0EXl5v23JCTu51fohKC4MPERF9tFmzHkFLa5OkbfXqKfDzMyz0Od26saeHSh+DDxERfZQ1a/ZDS+uqePzwYR2Eh/eDnx+DDZU9DD5ERPRB9u59g+vXl0jaDh0agJkza2HjRiUVRfQeDD5ERFRkOfNy2rW7CS2tXZJzO3d64cYNbSVVRlQ0DD5ERFSggABg+nT5lhOLF8uHrXx9Bbi4zJVcFxLyOU6ccMHu3UoqlKgYGHyIiKhAOasv5/y+RYtYuLhskFyTkTESSUlW2L2b83lINTD4EBFRgTw93/b49OmzGxs23JCc/+GHH6ChoaGk6og+DIMPERHlkzOXx9c3C1evzkdCwttzn3zyCb755hul1Ub0MRh8iIgoH19f4MWLW7h6dZukffTo0bC0tFRSVUQfj8GHiIhEOT09HTqshkwWLzk3e/ZsyGQyJVVGpBgMPkREJAaelJTX6NlzqeRc1ar/g5vbF0qqjEixGHyIiAi+vkB29r/o2fOwpH3q1KkwMDBQUlVEisfgQ0SkxnJ6elxcfCTtBgYGmDp1qpKqIio5DD5ERGps3ryX6NLlJ0nblSu9sX9/PSVVRFSyuAADEZGamDkTMDICvvsOqFYN+Oabv/KFnqNHZ2DoUIYeKr9kgiAIyi6iLElKSoKJiQkSExNhbGys7HKIiBQiIADo0QMQBEBDIxuzZ8+TnDc2rotJk/oqpzgiBSjq9zeHuoiIyrmAAKB/f3nosbOLgqurv+S8m5sbqlatqqTqiEoXgw8RUTmVM3E5Ph5ITgZGjPgNVas+kVwTFDQb3t5cm4fUB+f4EBGVAwEBgJOT/Nccvr5ASAjw6FEa5szxkYSeypW/QFCQNzw9GXpIvXCOTx6c40NEqsjJSR5yHBwAMzPA2RnYuxcwMrqCzp0DJNdOnDgRJiYmSqqUqGRwjg8RkRrx9JT38MTFyQPQ9evA1Kk+ea6Swdt7tlLqIyorGHyIiMqRRo2A+PhEjBnzo6S9R48eaNy4sXKKIipDij3HR1NTE8+ePcvX/vLlS2hqaiqkKCIiKp6c+TyJicfyhZ5PPvFk6CH6f8Xu8SlsSlBaWhq0tbU/uiAiIio+Dw8BoaFzJW0GBjaYOnWokioiKpuKHHxWr14NAJDJZPjtt99gaGgonsvKysLp06fh4OCg+AqJiOidnjx5gtDQ3yRtrq6usLW1VVJFRGVXkYPPypUrAch7fNavXy8Z1tLW1oadnR3Wr1+v+AqJiEiUszaPp6f8+MiRrbCwuCO55ocffoCGBlcrISpIkYPP/fv3AQDOzs7Yu3cvzMzMSqwoIiJ6K3fYmT4diIwE+vfPwNSpC2Fh8fa6Zs2aoXPnzsorlEgFcB2fPLiODxGVJQEBQJ8+QGoqoKsLVKwImJjcQO/euyXXjR07FhUrVlRSlUTKV2Lr+Awd+u6Jcps2bSruSxIRUQFy9thKTZUfp6YCgwYtgY7OG8l13t7eSqiOSDUVO/jEx8dLjjMyMhAWFoaEhAS0a9dOYYUREak7X1/5Hlu6ukC1askYMGC55HynTp3QokULJVVHpJqKHXz27duXry07OxujR49GrVq1FFIUEZG6yj2fx9lZvgLzxIlnUKHCCcl106dPh56enpKqJFJdCpvjExkZibZt2yImJkYRL6c0nONDRMqQE3ji4uSTl3V1gawsATNnStfmqVixIsaOHaukKonKrlLfq+vu3bvIzMxU1MsREamNnLk8ycmAnh6gqQkYGz/FmDHSJUL69euHOnXqKKlKovKh2MFn8uTJkmNBEBATE4ODBw9i8ODBCiuMiEhd5MzlkcmAN2+Anj334pNPrkuumTVrFrcFIlKAYgefK1euSI41NDRgYWGB5cuXv/eOLyIiyi9nLo+ubhbGjp0vOdewYUP06tVLSZURlT/FDj7BwcElUQcRkdoKDgasrG5jwIA/Je2jRo1C5cqVlVQVUfn0wXN8nj17hsjISACAvb09LC0tFVYUEVF5lvvOrW7dgA4dfoJM9lJyzezZsyGTyZRUIVH5Vezgk5SUBHd3d2zbtg3Z2dkAAE1NTfTp0wdr166FiYmJwoskIiovck9kXr78Da5cWYLc+ebWrXbo0+dLMPMQlYxi72Ln5uaGf//9FwcPHkRCQgISEhIQGBiIixcvYuTIkSVRIxGRygoIAJyc5L8Cbycyf/nlf2jXbonk2oYNp2Dr1i/RrZsSCiVSE8Vex8fAwABBQUH44osvJO1nzpxBx44dkZKSotACSxvX8SEiRXJyAkJCAENDYOtWeduVKz6Sa9LT9dCy5XQGHqKPUGLr+FSsWLHA4SwTExPu2E5ElEtAgHxBQj09eS+Pp+dL9Onzk+Sa7777DvXr11dShUTqp9hDXbNmzcLkyZMRGxsrtsXGxmLatGn44YcfFFocEZGqyT205esrX4W5enVg6NCD+ULPjBkzGHqISlmxh7qaNGmCO3fuIC0tDdWrVwcAPHz4EDo6OvlWFL18+bLiKi0lHOoioo+RM7Tl6Ci/a8vXNxsuLvMk12Rn14aPT38lVUhUPpXYUFf37t15iyURUSE8PYHp04H4eCA5+QFcXDZLzg8fPhzW1tbKKY6IFLdJaXnBHh8iKq686/LUqwe0arURNjaPJddxbR6iklPU7+9iz/GpWbMmXr58ma89ISEBNWvWLO7LERGpPF9f+fCWry+QlpaG77/3kYSee/daoUkTb4YeojKg2ENdUVFRyMrKyteelpaGx48fF/AMIqLyTT6XB3Bzuwpf3/2Sc6dOTcCJE6ZKqYuI8ity8AnIWX0LQFBQkOSW9qysLBw/fhw1atRQbHVERGVYQADg4QEIAvD99z54+FB6PijIG56eyqmNiApW5Dk+GhryUTGZTIa8T9HS0oKdnR2WL1+OLl26KL7KUsQ5PkT0Lrnn8/j6AjduJGLy5B8l11y/3g27dzdRToFEakrhc3yys7ORnZ2N6tWr49mzZ+JxdnY20tLSEBkZWWKhJyoqCsOGDUONGjWgp6eHWrVqwdvbG+np6ZLrrl27hi+//BK6urqwsbHBkiVLCnlFIqLiyVmfx8Pj7XyeQYOO5ws9u3Z5YtAghh6isqrYc3zu379fEnW8U0REBLKzs7FhwwbUrl0bYWFhcHNzQ0pKCpYtWwZAnvQ6dOiA9u3bY/369bh+/TqGDh0KU1NTjBgxotRrJqLyJWcCs7094OgowMVlLp4+fXv+8WNr3LgxHGFhyquRiN6v2MFn7ty57zw/e/bsDy6mMB07dkTHjh3F45o1ayIyMhLr1q0Tg8/WrVuRnp6OTZs2QVtbGw0aNEBoaChWrFjB4ENEH83ZGbh+Hfj222hoaf0qOVe79mAEBdlxPg+RCih28Nm3b5/kOCMjA/fv30eFChVQq1atEgk+BUlMTIS5ubl4HBISgtatW0NbW1tsc3FxweLFixEfH899xIioWPKuzRMcDHTr9ie0tG5Lrjty5Ad4e2ugPxdiJlIJxQ4+V65cydeWlJQEV1dXfPPNNwop6n3u3LmDNWvWiL09gHy/sLx3lVWuXFk8V1jwSUtLQ1pamniclJRUAhUTkarJvTZPp04ZcHFZKDmfldUUx451YS8PkYop9gKGBTE2NoaPj0+xNyn19PSETCZ75yMiIkLynCdPnqBjx4747rvv4Obm9tG1L1q0CCYmJuLDxsbmo1+TiFRPzuTlmTPlvzo7y/fbGjUqHAsXSkPPmjVjcexYF5w7J+8NIiLVUewen8IkJiYiMTGxWM+ZMmUKXF1d33lN7tWgo6Oj4ezsDCcnJ/zyyy+S66ysrPA090xDQDy2srIq9PW9vLwwefJk8TgpKYnhh0gN5fTwXL8OJCfLf/X0XIb791Mk123b5g0LC7Cnh0hFFTv4rF69WnIsCAJiYmKwZcsWdOrUqVivZWFhAQsLiyJd++TJEzg7O6Np06bw8/MT1xXK4ejoiJkzZyIjIwNaWloAgKNHj8Le3v6d83t0dHSgo6NTrLqJqPxxdgZCQwFtbaBixRSMG7cMmZlvzx8/3hFTp7aEt7fSSiQiBSj2JqV559FoaGjAwsIC7dq1g5eXF4yMjBRaICAPPW3btoWtrS38/f2hqakpnsvpzUlMTIS9vT06dOgADw8PhIWFYejQoVi5cmWx7uriAoZE6iPvYoQhIUCrVv/gq6+OS647cWIapkzR57AWURlW1O9vldidffPmzRgyZEiB53KXf+3aNbi7u+PChQuoVKkSxo0bBw8Pj2L9LAYfIvXh5CQPO46OgIeHgNBQ6XIdZmZmGD9+vJKqI6LiKNHgk5CQgDt37gAAateuDVNT0w8utKxh8CFSHzk9PhMmPENExDrJuZo1+2LgwLpKqoyIiquo39/FmuMTFRUFd3d3BAUFiT0tMpkMHTt2xE8//QQ7O7uPKpqIqDR16wZkZe3DtWvXJO3z5s1CixaaGDhQSYURUYkpcvB59OgRPv/8c2hpaWHevHmoV68eAODmzZtYt24dHB0dceHCBVSrVq3EiiUi+lABAcD06YBMBixeDHTunIX58+dLrrl7twG2b/8WVla8a4uovCryUNewYcNw584dBAUFQVdXV3LuzZs36NixI+rUqYPffvutRAotLRzqIiqfcubzAECXLnfQrNlWyfm//x6Ff/+VL3rq6AicO1faFRLRx1D47uyHDx/GggUL8oUeANDT08O8efNw6NChD6uWiKgE5CxKGBAg78GxtwcmTfo5X+iZM2c2EhMrw8FBfg17e4jKryIPdb148eKdc3hq1qyJuLg4RdRERKQQ06cDkZHyX69ceYO+fZdIzt++7Qxb29ZwdHy7JxcRlW9FDj5VqlTBzZs3C53DExYW9s4VkomISltysvxXG5sLWLJE2iMdHDwFp04ZcliLSM0UOfj06NEDU6dOxfHjx/Ottvzs2TN4eHigR48eiq6PiOiDGRoCc+b4SNp0dXXh4eGBJk2A9HQOaxGpmyJPbo6Pj0fLli0RGxuLAQMGwMHBAYIgIDw8HH/++SesrKxw/vx5mJubl3TNJYqTm4nKh7i4OKxZs0bS9u2336JBgwZKqoiISpLC1/ExMzPDv//+ixkzZmD79u1ISEgAAJiamqJfv35YuHChyoceIlJtOQsSDhjwN54//09ybsaMGeI+fkSkvj5o5WZBEPD8+XMA8o1GZTKZwgtTFvb4EKkuJ6dsuLjMk7TVrFkTA7kSIVG5p/Db2XOTyWSwtLSEpaVluQo9RKQ6ct+qDgAPHz7MF3rOnx/G0ENEEsXasoKIqKzI2U3d1xd4+dIPDx8+lJwPCpoNT0/+w4yIpD6ox4eISNk8PYEvvkiHi4uPJPRYWjoiKMgbnp4yrstDRPkw+BCRSrKzu4b27RdJ2iZMmIAtWzqIPUFERHlxqIuIVM78+fORlZUlafP29kZAABAfz20niKhwRQo+q1evLvILjh8//oOLISJ6l6SkJKxcuVLS1rVrV3z22WcA5L08ERHyTUY5zEVEBSnS7ew1atQo2ovJZLh3795HF6VMvJ2dqGw6ceIEzpw5I2nz9PSEjo6OeJyzjg/33SJSPwpdwPD+/fsKK4yIqDgEQcDcuXMlbbGxVdCp0wjkyjwA5GGHgYeI3uWDJzenp6cjMjISmZmZiqyHiEgUExOTL/RcuDAInTqNYMAhog9S7MnNr1+/xrhx4+Dv7w8AuHXrFmrWrIlx48bB2toanpxRSEQKsH37dkRGRkrafvjhB2ho8GZUIvpwxf4bxMvLC1evXsXJkyehq6srtrdv3x47duxQaHFEpH4yMzPh4+MjCT1NmjSBt7e3GHryrtpMRFRUxe7x2b9/P3bs2IHPP/9csl1FgwYNcPfuXYUWR0TqJSIiIt8/oP75xx1NmlQSjwMCgP79geRk+URmDnkRUXEUO/g8f/4clpaW+dpTUlK4bxcRfbAVK1bg1atXkragIG+EhACPHslDjrMzsHq1PPQYGnKtHiIqvmIHn2bNmuHgwYMYN24cAIhh57fffoOjo6NiqyOici8lJQXLli2TtHXo0AGOjo5o0kQeeB4+BCIjgStXgNRUeejZupW9PURUfMUOPgsXLkSnTp1w8+ZNZGZmYtWqVbh58ybOnTuHU6dOlUSNRFRO/frrOURHH5W0TZs2Dfr6+gDe3p7u4CA/V7EiUL061+khog9X7MnNX3zxBUJDQ5GZmYlGjRrhyJEjsLS0REhICJo2bVoSNRJROXPggAAfHx9J6DExMYG3t7cYenJbskS+GvPPPwPnzjH0ENGH+6C9umrVqoVff/1V0bUQkRp4/vw5QkN/lrTVrPk9Bg60z3dt7pWYz50rrQqJqDwrUvBJSkoq8gtymwciKkhAABAYuB/W1lcl7TNnzkSFCgX/VeTrC3GndfbyEJEiFCn4mJqaFvmOrbw7JhMRZWVl4cqV+bC2fttWv359fPfdd+98nqfn2x4fIiJFKFLwCQ4OFn8fFRUFT09PuLq6indxhYSEwN/fH4sWLSqZKolIZT19+hTr16+XtI0cORJWVlaStoI2GOXeW0SkaEXanT23//3vfxg+fDj69u0raf/zzz/xyy+/4OTJk4qsr9Rxd3YixTly5AhCQkIkbbNnz87Xg5x7UUJHR87nIaLiU+ju7LmFhITk+9cbIF/fZ/jw4cV9OSIqh/btS8e1a9Ie4Jy1eQri68tFCYmodBT7dnYbG5sC7+j67bffYGNjo5CiiEh1/fHHvXyhZ/r06e9c4NTTU97Tw0UJiaikFbvHZ+XKlejVqxf+/vtvtGzZEgDw33//4fbt29izZ4/CCyQi1RAQAAQFbYOl5S2xzczsE4wf/817n8u5PERUWord4/P111/j9u3b6Nq1K+Li4hAXF4euXbvi1q1b+Prrr0uiRiIq45KTk3Hlio8k9NSpM6TQ0MPd1YlIWYo9ubm84+RmouIJDQ3FgQMHJG3vWpsHkIeekBBOZCYixSmxyc0AkJCQgI0bNyI8PBwA0KBBAwwdOhQmJiYfVi0RqRxBEPDTTz8hLi5ObGvbti3atGmT79q8t6pzfR4iUpZi9/hcvHgRLi4u0NPTQ4sWLQAAFy5cwJs3b3DkyBF89tlnJVJoaWGPD9H77dz5AuHhayVt7u7uqFSpUoHX16sHRETINxv9/38vEREpVFG/v4sdfL788kvUrl0bv/76q9iVnZmZieHDh+PevXs4ffr0x1WuZAw+RO+2YcNJxMaeEo9fvKiIS5fcce7c27V5cnp4nJ2B4GDg4UPgyRPA3l4egIiIFK3EhrouXrwoCT0AUKFCBUyfPh3NmjX7sGqJqMzLzMzEggULJG2PH3fHjRuf5huyytljKzQUePMGsLaWz+fh0BYRKVuxg4+xsTEePnwIBwcHSfujR49gZGSksMKIqOx4+PAh/Pz8JG1Ll07FJ58YFDg52dkZuH4d0NaWBx9DQ05iJqKyodjBp0+fPhg2bBiWLVsGJycnAMDZs2cxbdq0fNtYEJHq27t3L65fvy4e29vbQ1//e3zyScE9ODNnynt8srPlc3rs7dnTQ0RlR7GDz7JlyyCTyTBo0CBkZmYCALS0tDB69Gj4+voqvEAiUo43b95gyZIlkraBAweiZs2a71x/Z/VqeejR0AAWL+bChERUtnzwOj6vX7/G3bt3AQC1atWCvr6+QgtTFk5uJgJu3LiB3bt3S9qOHp2Bf/7RAvDudXhmzpSHn/HjgTxTgoiISkyJ3dVV3jH4kDoTBAG//vorYmJixDZLS0esWtUBMtnbHpy8d23lrM9DRKQsCg8+Q4cOLdIP3rRpU9EqLKMYfEhdxcfHY/Xq1ZK2UaNGoXLlyoX28HAFZiIqKxR+O/vmzZtha2uLJk2agJ1EROXLuXPncPToUfG4QgVDeHlNgoaGfDu/wlZa5grMRKRqitzj4+7ujm3btsHW1hZDhgzBgAEDYG5uXtL1lTr2+JA6ycrKgq+vr3ijAgAEBn4NLa3m7MEhIpVS1O/vIu/OvnbtWsTExGD69On466+/YGNjg969eyMoKIg9QEQqKDo6GvPnz5eEnuXLJyEiojl7cIio3Prgyc0PHjzA5s2b8fvvvyMzMxM3btyAoaGhousrdezxIXUQGBiIS5cuicd2dnYwNR2ExYtlnKhMRCqpRHdnBwANDQ3IZDIIgoCsrKwPfRkiKkVpaWn51tuqWfN7rFtnD09PTlAmovKvyENdgPwvzW3btuGrr75C3bp1cf36dfz00094+PBhuejtISrPbt26lS/0eHp6Yt06e4SEyCcpExGVd0Xu8RkzZgy2b98OGxsbDB06FNu2bUOlSpVKsjYiUgBBELBlyxbcv39fbGvatCm6dOkCQHpnVs76PBzuIqLyqshzfDQ0NFC9enU0adIEMpms0Ov27t2rsOKUgXN8qDxJSkrCypUrJW3Dhw+HtbV1gddzXR4iUlUKn+MzaNCgdwYeIipbLly4gEOHDonHWlpa8PDwgKamZqHP4bo8RFTeccuKPNjjQ6ouOzsbK1euRHJystj21VdfwcnJSYlVERGVLIWv40NEZd/Tp08xb948SegJCBiPYcOcMHOmfCjrXTurExGVdww+ROXE0aNHsX79evG4SpUq2L59Ni5fNkNEhHzH9Lx3bwUEAA4OQL16DEREpB4+eB0fIiobMjIysHDhQknbt99+i7t3GyAqSn6sqwuMH/92J/Ucvr5AZOTb3/NOLiIq7xh8iFTYvXv3sGXLFknb9OnToaenBzc3IDUVMDQEtm4tONR4egLTpwMyGSc0E5F6YPAhUlE7duxARESEeNyoUSP07NlTPM59h1bu0JN3rR728hCROuFdXXnwri4q61JSUrBs2TJJm6urK2xtbYv0fK7VQ0TlUYnv1UVEpS80NBQHDhyQtM2cORMVKrz/f+Wcnh5nZ/kxh7aISB0x+BCpAEEQsHbtWrx8+VJss7JqA3//tmjSpPDhqtzDWr6+8p4egD09RKS+VO529rS0NHz66aeQyWQIDQ2VnLt27Rq+/PJL6OrqwsbGBkuWLFFOkUQK9PLlS8ydO1cSetzd3eHv3/a9m4vmhJ2c8OPoyJ4eIlJvKhd8pk+fjqpVq+ZrT0pKQocOHWBra4tLly5h6dKlmDNnDn755RclVEmkGKdOncJPP/0kHpubm2P27NmoVKlSkYJM7mu6dZP39HAyMxGpM5Ua6vr7779x5MgR7NmzB3///bfk3NatW5Geno5NmzZBW1sbDRo0QGhoKFasWIERI0YoqWKiD5OZmYkFCxZI2rp164YmTZrkOn5/iOFdW0REUioTfJ4+fQo3Nzfs378f+vr6+c6HhISgdevW0NbWFttcXFywePFixMfHw8zMrMDXTUtLQ1pamniclJSk+OKJiuHRo0fYtGmTpG3KlCkwNDRUUkVEROWHSgx1CYIAV1dXjBo1Cs2aNSvwmtjYWFSuXFnSlnMcGxtb6GsvWrQIJiYm4sPGxkZxhRMV0/79+yWhp27duvD29mboISJSEKUGH09PT8hksnc+IiIisGbNGrx69QpeXl4Kr8HLywuJiYni49GjRwr/GUTv8+bNG/j4+ODq1ati24ABA9C3b1/JdQEB3GiUiOhjKHWoa8qUKXB1dX3nNTVr1sSJEycQEhICHR0dyblmzZqhf//+8Pf3h5WVFZ4+fSo5n3NsZWVV6Ovr6Ojke12i0nTz5k3s2rVL0ubl5SUZts2R+y4tzt0hIio+pQYfCwsLWFhYvPe61atXY/78+eJxdHQ0XFxcsGPHDrRs2RIA4OjoiJkzZyIjIwNaWloA5LtV29vbFzq/h0iZBEHAxo0b8eTJE7Ht888/h4uLS75rufggEZFiqMTk5urVq0uOc+Y71KpVC9WqVQMA9OvXDz4+Phg2bBg8PDwQFhaGVatWYeXKlaVeL9H7JCQkYNWqVZK2kSNHFto7ycUHiYgUQyWCT1GYmJjgyJEjcHd3R9OmTVGpUiXMnj2bt7JTmRMSEoIjR46Ix/r6+pgyZQo0NPJPuWNPDxGRYqlk8LGzs0NBe6t+8sknOHPmjBIqInq/rKwsLFmyBOnp6WJbtWqdsHFjC9jbFzxnhz09RESKpRK3sxOpuujoaMyfP18SeiZNmoSNG1uIk5UDAgAHB6Bevbd3bXGbCSIixZIJBXWdqLGibmtPVFSHDh3ChQsXxGNbW1sMHjwYMpms0E1EHR3Zw0NEVBxF/f5WyaEuIlWQlpYG3zw7iPbp0wcODg7icd4tJaZPB2Qy9vAQEZUUDnURlYBbt27lCz0eHh64dcsh33AW8HYS85IlQHg41+ghIiopHOrKg0Nd9LG2bNmCe/fuicefffYZunbtCkC+6nLOcJaDA2BmJh3m4hAXEdGH4VAXUSl79eoVVqxYIWkbPnw4rK2txWNPz7fDWYLwdhXmnPDDIS4iopLF4EOkAJcuXUJgYKB4rKmpCS8vL2hqakquyz2nZ+ZMYPVq+Ro9eef6EBFRyWDwIfoI2dnZ+PHHH/Hq1SuxrX379mjVqpV4nPvOrdzhJjgYSE6W/0pERKWDk5uJPtCzZ88wb948SegZP368JPQAb+fv9O8vndDMNXqIiEofJzfnwcnNVBTHjh3D2bNnxWMrKyuMGDECMpks37UBAfLQk5zMyctERCWlqN/f7PEhKoaMjAz4+PhIQk+vXr1QpcpItGolk/To5OjWDdi6lb07RERlAXt88mCPDxXm/v37+P333yVt06ZNg76+vnibek6PTmHzeoiIqGTwdnYiBdq5cyfCw8PF44YNG6JXr17icd7b0XPm9fj6MvgQEZUlDD5E75CSkoJly5ZJ2lxdXWFraytpy3s7OtflISIqmzjHh6gQ165dyxd6Zs6ciatXbeHkhALn8+To1k0+5MXeHiKisoVzfPLgHB8SBAE///wzXrx4IbZ9+eWXaNeuHYC3207Y2wPm5pzHQ0RUFvCuLqIPEBcXh7lz50pCz5gxY8TQA7xdf0cmezuPh4iIVAPn+BD9v9OnTyM41zLKpqamGD9+PP76S5Zv4nJBvyciorKPQ115cKhL/WRmZmLBggWStq5du+Kzzz4DAMmt6gB3USciKot4OztRETx69AibNm2StE2ZMgWGhobicUG3qrOXh4hINbHHJw/2+KiPAwcOIDQ0VDyuU6cO+vXrp7yCiIjog7HHh6gQqampWLx4saStf//+qF27tpIqIiKi0sLgQ2olPDwcO3fulLR5eXlBW1tbSRUREVFp4u3sVGYFBOC9CwUWlSAI2LhxoyT0tGzZEk2aeKNtW22F/AwiIir7OMcnD87xKTvybvz5oRITE/Hjjz9K2kaOHAkrKyuF/QwiIlIuLmBIKi9nocCPuYPq/PnzktCjp6eHH374AVZWVgr7GUREpDrY45MHe3xUV0DA21vNBSEbFy4sgZZWmng+PLwj+vVrKV7DbSaIiMqPon5/M/jkweCjunKGrTp0iIGT0y+ScwcOTMSbNyYAgIgIDm0REZU3HOoitePpCQwZ8rck9BgYVMfs2bOhq2uCiAhAEDi0RUSkzng7O5UL6enpuHJlEWxt37b16dMHDg4OAKSrL3OIi4hIfTH4kErKPZ+nXr3b+PPPPyXnPTw8oKurKx5368bAQ0REDD6konx95fN5jh79A1eu3BXbHz9ugq5duyFX5iEiIhIx+JBKmjLlFcLCVkjazp8fhsOHq+HGDfbuEBFRwRh8SOVcunQJYWGB4rGGhgZmzJiBgwc1kZjIictERFQ4Bh9SGYIgYNWqVUhMTBTb2rVrhy+//BKAdB5P7jlA7P0hIqIcvJ2dikyRe2cV1/PnzzF37lxJ6Bk3bpwYevLKmQPk61taFRIRkSpg8KEiU1aYOHHiBH7++WfxuHLlypg9ezbMzc0LfQ63oiAiooJwqIuKLPdaOKUhIyMDCxculLT17NkTjRo1eu9zefs6EREVhMGHiqw0w0RUVBT8/f0lbdOmTYO+vn7pFEBEROUSh7pIIRQ5/2f37t2S0NOgQQN4e3sz9BAR0Udjjw8pRO75Px/aK/T69WssXbpU0jZ48GDY2dl9fIFERERg8CEF+dj5P9evX8fevXslbTNmzICWlpYCqiMiIpJj8KGP9jFr5giCgPXr1+PZs2di2xdffIH//e9/Cq6SiIiIwYcU4EOHueLi4rBmzRpJ25gxY2BhYaHgComIiOQYfOijfcgw15kzZ3DixAnx2MTEBBMmTIBMJiuBComIiOQYfOijFec296ysLCxYsACCIIhtXbt2xWeffVZC1REREb3F29mp1Dx+/Bjz58+XhJ7JkyeLoUeZW2IQEZF6YPChUhEQEICNGzeKx3fu1EJQkDeMjIzENu6vRUREJY1DXVSiUlNTsXjxYknb33/3R0JCbSxZIr22tLfEICIi9cPgQwqV+9b2unUjsGPHDsn5hQu90LSpNiIi8j+X+2sREVFJY/AhhZIPVwk4ccIPV648EttbtGiBjIxOaNqUPTpERKQ8DD4k8TGLEQLA5MmJuHHjR0nbiBEjUKVKFQDs0SEiIuXi5GYVU9J3Pn3MBON///1XEnp0dXXxww8/iKGHiIhI2Rh8VExJ3/nk6Qk4Or5/OCp3AMvOzsaSJUtw+PBh8byLiws8PDygocH/xIiIqOyQCbkXVSEkJSXBxMQEiYmJMDY2VnY5+XzsUJSiODnJA1iHDrFwctogOTdhwgSYmpoqpzAiIlJLRf3+5hwfFVMW7nwKCADi4oC+fQ/D3v5fsd3GxgZDhgzhthNERFRmMfhQsS1dmo6+fRdJ2nr37o169eopqSIiIqKiYfChYrlz5w7at98qafPw8ICurq6SKiIiIio6zjylItu6dSu2bn0bej799FN4e3u/M/Rw/y0iIipLGHxUgLLDQ3JyMnx8fHDnzh2xbejQoejevft7n8v9t4iIqCxh8FEBygwPV65cwfLly8VjmUyGWbNmwcbGpkjPL+rt8URERKWBc3xUgDI27xQEAatXr0ZCQoLY5uzsjNatWxfrdcrCXWhEREQ52OOjArp1A86d+7gAUZzhsufPn2Pu3LmS0DN27Nhihx4iIqKyhj0+aiL3cNm7AlRwcDBOnz4tHltYWGD06NFcm4eIiMoFlerxOXjwIFq2bAk9PT2YmZmhR48ekvMPHz5E586doa+vD0tLS0ybNg2ZmZnKKbaMed9cm8zMTPj4+EhCzzfffIMxY8Yw9BARUbmhMj0+e/bsgZubGxYuXIh27dohMzMTYWFh4vmsrCx07twZVlZWOHfuHGJiYjBo0CBoaWlh4cKFSqy8bHjXXJuoqCj4+/tL2qZOnQoDA4NSqIyIiKj0qMReXZmZmbCzs4OPjw+GDRtW4DV///03unTpgujoaFSuXBkAsH79enh4eOD58+fQ1tYu0s8q63t1Kdru3btx48YN8bhevXro3bu3EisiIiIqvqJ+f6vEUNfly5fx5MkTaGhooEmTJqhSpQo6deok6fEJCQlBo0aNxNADyHcIT0pKknyx55WWloakpCTJQx28fv0aPj4+kj+bQYMGMfQQEVG5phLB5969ewCAOXPmYNasWQgMDISZmRnatm2LuLg4AEBsbKwk9AAQj2NjYwt97UWLFsHExER8FHV9GlUWFhaGpUuXStpmzJiBGjVqKKkiIiKi0qHU4OPp6QmZTPbOR0REBLKzswEAM2fORK9evdC0aVP4+flBJpNh165dH1WDl5cXEhMTxcejR48U8dbKpAMHBEyZsh579uwR21q1agVvb29oaWkpsTIiIqLSodTJzVOmTIGrq+s7r6lZsyZiYmIAAPXr1xfbdXR0ULNmTTx8+BAAYGVlhf/++0/y3KdPn4rnCqOjowMdHZ0PKV+lxMXFITR0DXIPe44ePRqWlpbKK4qIiKiUKTX4WFhYwMLC4r3XNW3aFDo6OoiMjMQXX3wBAMjIyEBUVBRsbW0BAI6OjliwYAGePXsmfpkfPXoUxsbGksCkjv755x8cP35cPNbSMoan5wRoaKjESCcREZHCqMTt7MbGxhg1ahS8vb1hY2MDW1tbcY7Kd999BwDo0KED6tevj4EDB2LJkiWIjY3FrFmz4O7urhY9OgEBb7e1yLltPSsrC4sWLUJWVpZ4XZcuXdC0aVMlVUlERKRcKvNP/qVLl+L777/HwIED0bx5czx48AAnTpyAmZkZAEBTUxOBgYHQ1NSEo6MjBgwYgEGDBmHu3LlKrrxwitx1Pe9Gpk+ePMH8+fMloWfy5MkMPUREpNZUYh2f0lSa6/g4OcnDiqOjfC+udymoR6ew80AArly5Ip6rWbMmBg4cqNDaiYiIypKifn8z+ORRmsHnfWEmt6KEpNTUVCxevFjS1q9fP9SpU0dBFRMREZVNRf3+Vok5PuXVu7aRyMvTM3ePTn6RkZHYvn17nud4qsX8JiIioqJi8FERhYUkQRDg7++PBw8eiG3NmzfH119/XYrVERERqQYGHxUVEACsXJmEtm1XStrd3NxQtWpVJVVFRERUtjH4qKitW/9D27Z/i8c6OjqYNm0aNDU1lVgVERFR2aYyt7Ors9y3vWdnZ2Pp0qWoX/9t6OnQoQM8PT0ZeoiIiN6DPT4qIGeNnrVrY3HlygbJuQkTJsDU1FQ5hREREakYBh8V4OkJ7N17BDVqhIht1tbWGDZsGGQymRIrIyIiUi0MPmVceno6rlxZhBo13rZ99913ar//GBER0Ydg8CnD7t69iz/++EPSNn36dOjp6SmpIiIiItXG4FNGbdu2Dbdu3RKPGzdujB49eiivICIionKAwaeMSU5OxvLlyyVtQ4YMQfXq1ZVUERERUfnB4FOGhIaG4sCBA5K2mTNnokIFfkxERESKwG/UMkAQBKxZswbx8fFiW9u2bdGmTRslVkVERFT+MPgo2YsXL7B27VpJ29ixY1GxYkUlVURERFR+Mfgo0cmTJ3Hq1CnxuFKlShgzZgzX5iEiIiohDD5KkJmZiQULFkjaevTogcaNGyupIiIiIvXA4FPKHjx4gM2bN0vapk6dCgMDA+UUREREpEYYfErR3r17cf36dfHYwcEBffr0UWJFRERE6oW7s5eSX375RRJ6Bg4cKIae3LuvExERUclh8CklL1++EX8/Y8YM1KxZUzzO2X3d11cZlREREakPDnWVkiNHhuDmzVTUqmUJb2/pOU9Peejx9FRObUREROqCPT6lZNIkY9SqZVlguOnWDTh3Tv4rERERlRz2+JSSbt0YbIiIiJSNPT5ERESkNhh8Sgnv3CIiIlI+Bp9Swju3iIiIlI/Bp5R4egKOjrxzi4iISJk4ubmUcHIzERGR8rHHh4iIiNQGgw8RERGpDQYfIiIiUhsMPkRERKQ2GHyIiIhIbTD4EBERkdpg8CEiIiK1weBDREREaoPBh4iIiNQGgw8RERGpDQYfIiIiUhsMPkRERKQ2GHyIiIhIbXB39jwEQQAAJCUlKbkSIiIiKqqc7+2c7/HCMPjk8erVKwCAjY2NkishIiKi4nr16hVMTEwKPS8T3heN1Ex2djaio6NhZGQEmUym7HJKVFJSEmxsbPDo0SMYGxsruxzKhZ9N2cTPpWzi51J2leZnIwgCXr16hapVq0JDo/CZPOzxyUNDQwPVqlVTdhmlytjYmH9ZlFH8bMomfi5lEz+Xsqu0Ppt39fTk4ORmIiIiUhsMPkRERKQ2GHzUmI6ODry9vaGjo6PsUigPfjZlEz+XsomfS9lVFj8bTm4mIiIitcEeHyIiIlIbDD5ERESkNhh8iIiISG0w+BAREZHaYPBRYwcPHkTLli2hp6cHMzMz9OjRQ3L+4cOH6Ny5M/T19WFpaYlp06YhMzNTOcWqobS0NHz66aeQyWQIDQ2VnLt27Rq+/PJL6OrqwsbGBkuWLFFOkWoiKioKw4YNQ40aNaCnp4datWrB29sb6enpkuv4uSjH2rVrYWdnB11dXbRs2RL//fefsktSK4sWLULz5s1hZGQES0tL9OjRA5GRkZJrUlNT4e7ujooVK8LQ0BC9evXC06dPlVIvg4+a2rNnDwYOHIghQ4bg6tWrOHv2LPr16yeez8rKQufOnZGeno5z587B398fmzdvxuzZs5VYtXqZPn06qlatmq89KSkJHTp0gK2tLS5duoSlS5dizpw5+OWXX5RQpXqIiIhAdnY2NmzYgBs3bmDlypVYv349ZsyYIV7Dz0U5duzYgcmTJ8Pb2xuXL19G48aN4eLigmfPnim7NLVx6tQpuLu74/z58zh69CgyMjLQoUMHpKSkiNdMmjQJf/31F3bt2oVTp04hOjoaPXv2VE7BAqmdjIwMwdraWvjtt98KvebQoUOChoaGEBsbK7atW7dOMDY2FtLS0kqjTLV26NAhwcHBQbhx44YAQLhy5Yp47ueffxbMzMwkn4OHh4dgb2+vhErV15IlS4QaNWqIx/xclKNFixaCu7u7eJyVlSVUrVpVWLRokRKrUm/Pnj0TAAinTp0SBEEQEhISBC0tLWHXrl3iNeHh4QIAISQkpNTrY4+PGrp8+TKePHkCDQ0NNGnSBFWqVEGnTp0QFhYmXhMSEoJGjRqhcuXKYpuLiwuSkpJw48YNZZStNp4+fQo3Nzds2bIF+vr6+c6HhISgdevW0NbWFttcXFwQGRmJ+Pj40ixVrSUmJsLc3Fw85udS+tLT03Hp0iW0b99ebNPQ0ED79u0REhKixMrUW2JiIgCI/39cunQJGRkZks/JwcEB1atXV8rnxOCjhu7duwcAmDNnDmbNmoXAwECYmZmhbdu2iIuLAwDExsZKQg8A8Tg2NrZ0C1YjgiDA1dUVo0aNQrNmzQq8hp+N8t25cwdr1qzByJEjxTZ+LqXvxYsXyMrKKvDPnX/mypGdnY2JEyeiVatWaNiwIQD5f//a2towNTWVXKusz4nBpxzx9PSETCZ75yNnrgIAzJw5E7169ULTpk3h5+cHmUyGXbt2KfldlE9F/WzWrFmDV69ewcvLS9klq4Wifi65PXnyBB07dsR3330HNzc3JVVOVDa5u7sjLCwM27dvV3Yphaqg7AJIcaZMmQJXV9d3XlOzZk3ExMQAAOrXry+26+jooGbNmnj48CEAwMrKKt+dETkz8K2srBRYtXoo6mdz4sQJhISE5NvXplmzZujfvz/8/f1hZWWV724IfjYfpqifS47o6Gg4OzvDyckp36Rlfi6lr1KlStDU1Czwz51/5qVv7NixCAwMxOnTp1GtWjWx3crKCunp6UhISJD0+ijtcyr1WUWkdImJiYKOjo5kcnN6erpgaWkpbNiwQRCEt5Obnz59Kl6zYcMGwdjYWEhNTS31mtXFgwcPhOvXr4uPoKAgAYCwe/du4dGjR4IgvJ1Em56eLj7Py8uLk2hL2OPHj4U6deoI33//vZCZmZnvPD8X5WjRooUwduxY8TgrK0uwtrbm5OZSlJ2dLbi7uwtVq1YVbt26le98zuTm3bt3i20RERFKm9zM4KOmJkyYIFhbWwtBQUFCRESEMGzYMMHS0lKIi4sTBEEQMjMzhYYNGwodOnQQQkNDhcOHDwsWFhaCl5eXkitXL/fv3893V1dCQoJQuXJlYeDAgUJYWJiwfft2QV9fXwytpHiPHz8WateuLfzvf/8THj9+LMTExIiPHPxclGP79u2Cjo6OsHnzZuHmzZvCiBEjBFNTU8kdqVSyRo8eLZiYmAgnT56U/L/x+vVr8ZpRo0YJ1atXF06cOCFcvHhRcHR0FBwdHZVSL4OPmkpPTxemTJkiWFpaCkZGRkL79u2FsLAwyTVRUVFCp06dBD09PaFSpUrClClThIyMDCVVrJ4KCj6CIAhXr14VvvjiC0FHR0ewtrYWfH19lVOgmvDz8xMAFPjIjZ+LcqxZs0aoXr26oK2tLbRo0UI4f/68sktSK4X9v+Hn5yde8+bNG2HMmDGCmZmZoK+vL3zzzTeSfziUJtn/F01ERERU7vGuLiIiIlIbDD5ERESkNhh8iIiISG0w+BAREZHaYPAhIiIitcHgQ0RERGqDwYeIiIjUBoMPERERqQ0GH6JyJjY2FuPGjUPNmjWho6MDGxsbdO3aFcePHxevOXfuHL7++muYmZlBV1cXjRo1wooVK5CVlSVeExUVhWHDhqFGjRrQ09NDrVq14O3tjfT0dMnP+/XXX9G4cWMYGhrC1NQUTZo0waJFi8Tzc+bMgUwmQ8eOHfPVunTpUshkMrRt27bI7y8pKQkzZ86Eg4MDdHV1YWVlhfbt22Pv3r3IvR7rjRs30Lt3b1hYWEBHRwd169bF7Nmz8fr1a/GauLg4jBs3Dvb29tDT00P16tUxfvx4JCYmFqmWqKioQnd1P3/+fJHfU9u2bTFx4sQiX09EH467sxOVI1FRUWjVqhVMTU2xdOlSNGrUCBkZGQgKCoK7uzsiIiKwb98+9O7dG0OGDEFwcDBMTU1x7NgxTJ8+HSEhIdi5cydkMhkiIiKQnZ2NDRs2oHbt2ggLC4ObmxtSUlKwbNkyAMCmTZswceJErF69Gm3atEFaWhquXbuGsLAwSV1VqlRBcHAwHj9+LNm1edOmTahevXqR319CQgK++OILJCYmYv78+WjevDkqVKiAU6dOYfr06WjXrh1MTU1x/vx5tG/fHu3bt8fBgwdRuXJl/Pfff5gyZQqOHz+O4OBgaGtrIzo6GtHR0Vi2bBnq16+PBw8eYNSoUYiOjsbu3buLXNexY8fQoEEDSVvFihWL/PyiEAQBWVlZqFCBf20TfRSlbJRBRCWiU6dOgrW1tZCcnJzvXHx8vJCcnCxUrFhR6NmzZ77zAQEBAgBh+/bthb7+kiVLhBo1aojH3bt3F1xdXd9Zk7e3t9C4cWOhS5cuwvz588X2s2fPCpUqVRJGjx4ttGnTpgjvTr4ZooGBgfDkyZN85169eiVkZGQI2dnZQv369YVmzZoJWVlZkmtCQ0MFmUz2zj20du7cKWhraxdpX7rC9lLLLef9//7774Ktra1gbGws9OnTR0hKShIEQRAGDx6cb4+j+/fvC8HBwQIA4dChQ8Jnn30maGlpCcHBwUJqaqowbtw4wcLCQtDR0RFatWol/Pfff+LPy3leYGCg0KhRI0FHR0do2bKlcP36dUEQBCE5OVkwMjISdu3aJalz3759gr6+vlgXUXnFoS6iciIuLg6HDx+Gu7s7DAwM8p03NTXFkSNH8PLlS0ydOjXf+a5du6Ju3brYtm1boT8jMTER5ubm4rGVlRXOnz+PBw8evLe+oUOHYvPmzeLxpk2b0L9/f2hra7/3uQCQnZ2N7du3o3///qhatWq+84aGhqhQoQJCQ0Nx8+ZNTJ48GRoa0r/iGjdujPbt27/3PRobGyu0Z+Xu3bvYv38/AgMDERgYiFOnTsHX1xcAsGrVKjg6OsLNzQ0xMTGIiYmBjY2N+FxPT0/4+voiPDwcn3zyCaZPn449e/bA398fly9fRu3ateHi4oK4uDjJz5w2bRqWL1+OCxcuwMLCAl27dkVGRgYMDAzw/fffw8/PT3K9n58fvv32WxgZGSnsfROVRQw+ROXEnTt3IAgCHBwcCr3m1q1bAIB69eoVeN7BwUG8pqDXX7NmDUaOHCm2eXt7w9TUFHZ2drC3t4erqyt27tyJ7OzsfM/v0qULkpKScPr0aaSkpGDnzp0YOnRokd/fixcvEB8f/873B7z/PdarV6/Q9/jixQvMmzcPI0aMKHJdAODk5ARDQ0PJI7fs7Gxs3rwZDRs2xJdffomBAweKc65MTEygra0NfX19WFlZwcrKCpqamuJz586di6+++gq1atWCjo4O1q1bh6VLl6JTp06oX78+fv31V+jp6WHjxo2Sn+nt7Y2vvvoKjRo1gr+/P54+fYp9+/YBAIYPH46goCDExMQAAJ49e4ZDhw4V6/MgUlUMPkTlhJBrYq8irwWAJ0+eoGPHjvjuu+/g5uYmtlepUgUhISG4fv06JkyYgMzMTAwePBgdO3bMF360tLQwYMAA+Pn5YdeuXahbty4++eSTEqu5uNcnJSWhc+fOqF+/PubMmVOs5+7YsQOhoaGSR252dnaSnpQqVarg2bNnRXrtZs2aib+/e/cuMjIy0KpVK7FNS0sLLVq0QHh4uOR5jo6O4u/Nzc1hb28vXtOiRQs0aNAA/v7+AIA//vgDtra2aN26ddHeMJEKY/AhKifq1KkjTkouTN26dQEg35dkjvDwcPGaHNHR0XB2doaTkxN++eWXAp/XsGFDjBkzBn/88QeOHj2Ko0eP4tSpU/muGzp0KHbt2oW1a9cWu3fBwsICpqam73x/wIe9x1evXqFjx44wMjLCvn37oKWlVazabGxsULt2bckjt7yvJ5PJCuwVK0hBw5aKMHz4cHHo0c/PD0OGDIFMJiuRn0VUljD4EJUT5ubmcHFxwdq1a5GSkpLvfEJCAjp06ABzc3MsX7483/mAgADcvn0bffv2FduePHmCtm3bomnTpvDz88s3Z6Yg9evXB4ACa2jQoAEaNGiAsLAw9OvXrzhvDxoaGvj++++xdetWREdH5zufnJyMzMxMfPrpp3BwcMDKlSvzhYurV6/i2LFjkveYlJSEDh06QFtbGwEBAdDV1S1WXYqgra0tWUqgMLVq1YK2tjbOnj0rtmVkZODChQvin3uO3LfTx8fH49atW5LhvwEDBuDBgwdYvXo1bt68icGDByvgnRCVfQw+ROXI2rVrkZWVhRYtWmDPnj24ffs2wsPDsXr1ajg6OsLAwAAbNmzAgQMHMGLECFy7dg1RUVHYuHEjXF1d8e2336J3794A3oae6tWrY9myZXj+/DliY2MRGxsr/rzRo0dj3rx5OHv2LB48eIDz589j0KBBsLCwkAy15HbixAnExMTA1NS02O9vwYIFsLGxQcuWLfH777/j5s2buH37NjZt2oQmTZogOTkZMpkMGzduxM2bN9GrVy/8999/ePjwIXbt2oWuXbvC0dFRXDMnJ/SkpKRg48aNSEpKEt9jUYJIjpcvX4rPy3mkpqYW+fl2dnb4999/ERUVhRcvXhTaG2RgYIDRo0dj2rRpOHz4MG7evAk3Nze8fv0aw4YNk1w7d+5cHD9+HGFhYXB1dUWlSpXQo0cP8byZmRl69uyJadOmoUOHDpJlBojKNaXeU0ZEChcdHS24u7sLtra2gra2tmBtbS1069ZNCA4OFq85ffq04OLiIhgbGwva2tpCgwYNhGXLlgmZmZniNX5+fvlus8555Ni9e7fw9ddfC1WqVBG0tbWFqlWrCr169RKuXbsmXpNzO3dhJkyYUOTb2QVBEBISEgRPT0+hTp06gra2tlC5cmWhffv2wr59+4Ts7GzxumvXrgm9evUSzM3NBS0tLaFWrVrCrFmzhJSUFPGanFu/C3rcv3//vbXk3M5e0GPbtm2Fvv+VK1cKtra24nFkZKTw+eefC3p6evluZ4+Pj5c8982bN8K4ceOESpUqvfN29r/++kto0KCBoK2tLbRo0UK4evVqvvqPHz8uABB27tz53vdKVF7IBKGYMwCJiKjMOnnyJJydnREfH//eXrUtW7Zg0qRJiI6OLvKyAkSqjkuAEhGpmdevXyMmJga+vr4YOXIkQw+pFc7xIaIyI+9aOLkfZ86cKfV6Ro0aVWg9o0aNKvV6FGXJkiVwcHCAlZUVvLy8lF0OUaniUBcRlRl37twp9Jy1tTX09PRKsRr5wn5JSUkFnjM2NoalpWWp1kNEH4/Bh4iIiNQGh7qIiIhIbTD4EBERkdpg8CEiIiK1weBDREREaoPBh4iIiNQGgw8RERGpDQYfIiIiUhsMPkRERKQ2/g/cF/AoD44LLwAAAABJRU5ErkJggg==", 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", 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'alm_surr' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# visualize with IDAES surrogate plotting tools\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m surrogate_scatter2D(\u001b[43malm_surr\u001b[49m, data_training)\n\u001b[32m 3\u001b[39m surrogate_parity(alm_surr, data_training)\n\u001b[32m 4\u001b[39m surrogate_residual(alm_surr, data_training)\n", + "\u001b[31mNameError\u001b[39m: name 'alm_surr' is not defined" + ] } ], "source": [ @@ -565,7 +408,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_doc.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_doc.md) file." ] } ], @@ -585,10 +428,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb index ed7713b4..6678e644 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb @@ -1,594 +1,619 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Introduction\n", - "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", - "\n", - "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", - "\n", - "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", - "\n", - "### 1.1 Need for ML Surrogate\n", - "\n", - "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", - "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", - "\n", - "### 1.2 Supercritical CO2 cycle process\n", - "\n", - "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", - "\n", - "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=7, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "\n", - "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", - "cols = csv_data.columns\n", - "cols = [item.replace(\".\", \"_\") for item in cols]\n", - "csv_data.columns = cols\n", - "\n", - "data = csv_data.sample(n=500, random_state=0)\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogate with ALAMO\n", - "\n", - "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", - "\n", - "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ***************************************************************************\n", - " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", - "\n", - " If you use this software, please cite:\n", - " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", - " Automatic Learning of Algebraic Models for Optimization,\n", - " AIChE Journal, 60, 2211-2227, 2014.\n", - "\n", - " ALAMO is powered by the BARON software from http://www.minlp.com/\n", - " ***************************************************************************\n", - " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", - " ***************************************************************************\n", - " Reading input data\n", - " Checking input consistency and initializing data structures\n", - " \n", - " Step 0: Initializing data set\n", - " User provided an initial data set of 400 data points\n", - " We will sample no more data points at this stage\n", - " ***************************************************************************\n", - " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", - " \n", - " Step 1: Model building using BIC\n", - " \n", - " Model building for variable CO2SM_CO2_Enthalpy\n", - " ----\n", - " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", - " ----\n", - " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", - " ----\n", - " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", - " ----\n", - " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", - " ----\n", - " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", - " ----\n", - " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", - " \n", - " Model building for variable CO2SM_CO2_Entropy\n", - " ----\n", - " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", - " ----\n", - " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", - " ----\n", - " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", - " ----\n", - " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", - " ----\n", - " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", - " ----\n", - " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", - " ----\n", - " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", - " ----\n", - " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", - " ----\n", - " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", - " ----\n", - " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", - " \n", - " Calculating quality metrics on observed data set.\n", - " \n", - " Quality metrics for output CO2SM_CO2_Enthalpy\n", - " ---------------------------------------------\n", - " SSE OLR: 0.515E+08\n", - " SSE: 0.659E+08\n", - " RMSE: 406.\n", - " R2: 0.999\n", - " R2 adjusted: 0.999\n", - " Model size: 6\n", - " BIC: 0.484E+04\n", - " Cp: 0.659E+08\n", - " AICc: 0.482E+04\n", - " HQC: 0.483E+04\n", - " MSE: 0.168E+06\n", - " SSEp: 0.659E+08\n", - " RIC: 0.659E+08\n", - " MADp: 0.594\n", - " \n", - " Quality metrics for output CO2SM_CO2_Entropy\n", - " --------------------------------------------\n", - " SSE OLR: 541.\n", - " SSE: 558.\n", - " RMSE: 1.18\n", - " R2: 0.997\n", - " R2 adjusted: 0.997\n", - " Model size: 10\n", - " BIC: 193.\n", - " Cp: 178.\n", - " AICc: 154.\n", - " HQC: 169.\n", - " MSE: 1.43\n", - " SSEp: 558.\n", - " RIC: 606.\n", - " MADp: 0.130E+04\n", - " \n", - " Total execution time 0.52 s\n", - " Times breakdown\n", - " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", - " MINLP time: 0.0 s in 0 optimization problem(s)\n", - " Simulation time: 0.0 s to simulate 0 point(s)\n", - " All other time: 0.22 s in 1 iteration(s)\n", - " \n", - " Normal termination\n", - " ***************************************************************************\n" - ] - } - ], - "source": [ - "# Create ALAMO trainer object\n", - "has_alamo = alamo.available()\n", - "if has_alamo:\n", - " trainer = AlamoTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - " )\n", - "\n", - " # Set ALAMO options\n", - " trainer.config.constant = True\n", - " trainer.config.linfcns = True\n", - " trainer.config.multi2power = [1, 2]\n", - " trainer.config.monomialpower = [2, 3]\n", - " trainer.config.ratiopower = [1]\n", - " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", - " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", - " trainer.config.overwrite_files = True\n", - "\n", - " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", - " success, alm_surr, msg = trainer.train_surrogate()\n", - "\n", - " # save model to JSON\n", - " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", - "\n", - " # create callable surrogate object\n", - " surrogate_expressions = trainer._results[\"Model\"]\n", - " input_labels = trainer._input_labels\n", - " output_labels = trainer._output_labels\n", - " xmin, xmax = [7, 306], [40, 1000]\n", - " input_bounds = {\n", - " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", - " }\n", - "\n", - " alm_surr = AlamoSurrogate(\n", - " surrogate_expressions, input_labels, output_labels, input_bounds\n", - " )\n", - "else:\n", - " print(\"Alamo not found.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Visualizing Surrogates\n", - "\n", - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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PD8cBFf9//LHaJRJPfn4+N3PmTG7MmAWcTlfGARz/T6cr48aMWcDNnDmTy8/PV7uohIzk5+dzubm5XG5uLvfLL3ncli353C+/5PGf0fPXHqZnOn/+Nc7Hp/yffqqcmz//Gj1TAZw/z3F791b8T7iG0PFbM5Ywc65evYoNGzagbdu28Pf3BwB88803qFu3LrZv346EhATEx8djyJAhuHr1Kv93mZmZSEpKsrhWcnIyMjMzAQAlJSU4fPiwxTk+Pj5ISkrizzl8+DBKS0stzmncuDFq167Nn5OZmYkWLVqgZs2aFt9TVFSE//3vf3bv686dOygqKrL4xxoFBQU4fPgShg3jUF5e8Vl5OfDKKxwOH76kKctRSUkJAODq1QhYvwoc54OrV8MtziM8j4KCAixbtgwffvghhg8/hIcfjsLzz0fg4YejMHz4IXz44YdYtmyZptq1t2N6pu+99xkmTAhFebkOAFBersOECaF4773P6Jk6YNUqoE4d/OMVqPidkA9NibBJkyYhODgYEREROHfuHL7++mv+2P/93//h7Nmz2LJlCz755BOsXbsWhw8ftnBZ5uXlWQgjAKhZsyaKiopw69Yt5Ofno6yszOY5eXl5/DUCAgIQFhbm8Bxb1zAds8ecOXOg1+v5f3FxcQJrRhlMndv77+/kOzYTZWU6vP/+d5rs3MLDC6DTlVt8ptOVIzz8qp2/YBuTi/XixYs4fPgSvviiQjibPtPa85ETk8A2GkPwzTfdeDHOcT745ptuMBpDLM4j2EdrkytW3KVamGCzUldSoqoIe+ONN6DT6Rz+O3nyJH/+hAkT8Ouvv2LXrl3w9fXFyy+/DI7jAADl5eW4c+cOPvnkEzz22GPo0KEDVq1ahX379uHUqVNq3aIoJk+eDKPRyP87f/682kWywNRpORMtrHRuQtHrr6N79+38Pel05ejefTv0+usql0w8ZNlxDWcDNqE9WJ5csRaLqoUJtqda6PzU/PJx48ZhwIABDs+pW7cu/3NkZCQiIyPRsGFDNGnSBHFxcTh48CAMBgNiYmLg5+eHhg0b8uc3adIEQMVKxUaNGiE6OrrSKsZLly4hNDQUVatWha+vL3x9fW2eEx0dDQCIjo5GSUkJCgsLLaxh1udYr6g0XdN0ji0CAwMRGBjosD5YwCRaTJYDpUTLhQvAX38BDRoAtWpJe+2WLX9FvXqncfVqOMLDr2pSgAHOLTv16p2GXn9dc0JZbkwDtrkQY2XAJlxDrX7KGSbBYzSGYPHi0eA4S3fp33+vhl5/HWlpaYiIiFCkTNYTbHvvgVr9xoULwLBhsLLQAcnJ0o8FSqOqCIuKikJUVJRLf1v+z9O4c+cOAKBdu3a4e/cusrOzUa9ePQDAn3/+CQCoU6cOAMBgMODbb7+1uE5GRgYMBgMAICAgAK1atcKePXvQs2dP/nv27NmDtLQ0AECrVq3g7++PPXv2oFevXgCAU6dO4dy5c/x1DAYD3nnnHVy+fBk1atTgvyc0NBRNmzZ16X5ZQynRUlBQgJKSEmzcWBUTJ+pRXq6Djw+HefOM6Nv3FgICAiTrqPT666p20KZ7BYDcXB/k5PghIeEuYmMr2rqYe3Vk2VF7EJIb83q0ha16ZHXA9iZceW7OYHFyJcRdqtZEicX3oKCgAAcPAuXlls++rAzIyipA1apQTKzKgaoiTChZWVk4dOgQHn30UVSvXh3Z2dmYNm0a6tWrxwufpKQktGzZEoMGDcLixYtRXl6OkSNH4sknn+StY8OHD8eyZcswceJEDBo0CHv37sXmzZuxY8cO/rvGjh2L/v37o3Xr1njkkUewePFi3Lx5EwMHDgQA6PV6DB48GGPHjkV4eDhCQ0MxatQoGAwGtGnTBgDQuXNnNG3aFC+99BLmzZuHvLw8TJ06FSNHjtSEpUsocosWoTPGfv368cJbq5juFQCOHHmoUifYsuWvACB4duytlh3zegQqLIJXr0YgPLzAoq3aqkcWB2xvwZ3n5gy1J1f2YPUdZek9MB8DdLrRlepq//51OH5cWauh1GhChAUFBSE9PR0zZszAzZs3ERMTgy5dumDq1Km8qPHx8cE333yDUaNGoX379ggODsZTTz2FBQsW8NdJSEjAjh07MGbMGCxZsgS1atXCxx9/jOTkZP6c1NRUXLlyBdOnT0deXh4efPBB7Ny50yLQftGiRfDx8UGvXr1w584dJCcn44MPPuCP+/r6Yvv27RgxYgQMBgOCg4PRv39/zJ49W4Ha8hyczRizshLRufNufPrpp5p8Cc1n/vn5+QCkcyOyOKNVAvP6cSRm7dUjqwO2p+Puc9MiLL+jrLwHpuftrK603C40IcJatGiBvXv3Oj0vNjYWW7dudXhOhw4d8Ouvvzo8Jy0tjXc/2qJKlSpYvnw5li9fbvecOnXqVHJ9Eq5ha8YIAAcOGJCYmOWy6T4gIEDS88Rge+Yfj5s3gyRzI7I0o1UaZ2KWYBNveG6FhYX8z/XqnUavXlsBcIiLu+Ax9ygHntqfaUKEEd6NXn8dBkMmDhxoZ3XEvRiniIgIpKWlSR6HIgR7M3+gHAAH4N4KJaEuioKCAt6iZoKVGa3SCImJKygogNFoFHQ9OYQ4URkpYhnVnFw5o6CgAJs3bwbg2OKndeRaROWJ/RmJMEIwanZuiYlZOHDAAPOsKlLET6jtwrSe+VfcXzl0Ok6Ui0JoTI234Czexmg0YtOmTU6v07t3b9SoUUP1duItSBEnpebkyhlCVy9rEbkXUXlqn0YijBCMmp2bXn8dPXqwGT/hDrZm/oAPevXajODgYsFmd6ExNSY83bLjLIaktLTU4nx7HXxYWJiig7UcKwS1hFRxUqzXEWurl92dYMuddsOTrYYkwghRqNm5eWJMgL2Zv734kPz8fIcDsbMZdkpKCmJjY5kfpKRAaHthpYO3tmbaQ41FKEqKQ098z60RYvFTcqLk7gRbrrQbhYWFHmk1NIdEGKEpPC0mwNnM39pCk56eDsD+QOysE4yMjPQKAWbCWXthqYO3HqDsWeeUXgkmtzj0xlhGZ+99amqq4u+pFN8nZdoNU/zc1avxTFkNpYZEGMEsrLjM5LYC2Jv5u7JUn9XcQ3JjekZCA+39/Cq6PtbcQiZYss7l5uZafCalOBQby8hKnyAFjix+er1exZK5jpRpN4Rm8Qe03S5IhBHMEhERgX79+uHTTz91eq5cL6GcSSTNsZ75u2qhYTn3kFwItdT07t0bYWFhCAgIENXBKw0r1jlb9Sq1OBQay+ipbnRPtPhJ7U5m0WooJSTCCKapV6+eqiud5Eoi6Uw0umOh8YaYGnOE1n1YWBhiYmIAABcvXgTApmhlxTpnXa8XLsTIJg6dCU9vc6NrHanFpSdaDU2QCCNkQ6q9EFnofKW2TtgLhM3Pz0d6errbFhpPnGELRexSdtZEKyvWOXPX7pEjD2Hbtm4wTxEDSCcOWRGecsNyDjPW8dQ+jUQYIQtS74WoNnIMEo7uW6yFhjr3CoS4y2wlabXXwatRXyxY5woKCvg8aqYJiLUAA6QTh6wIT7lhOYcZoQ4kwghZEJqUUCt7fqkxSIix0FDnLsxaKSZ+TM0krWpb58zbke1cdpBUHLIgPJXCE99BmgS6DokwQlY8xc2g1iAhxgTviZ27GIS0NTHxY0rXp/UAxYp1Ljc3BtZbaQHlGDz4Y9SqdVGy71FbeGoJ1pL60iTQdUiEEbLiSW4GJQYJoQNscXExH1xu7zre1uG50tZY2gqFxYHMaAzB7t1JsBRgHJ58cnclASaFOPTUuB8pUWrFtlik/i5vsa6RCCNkxdPcDHIPEkIG4uLiYkFpO7QSbycVYtsaK7m4zGHtedl2ReoQG3svd5g76SO0NNCyYn2Sa8W2O0i1CMscFiclckAijJAdLbsZlBgkxHbu1hYwVjKrq4V53Ttqa+b5wVjJxcU6QqyL7uTv0spAy+KWUqy0YTkXYan93JWARBghCdZCwlO2IZF7kHC3c2fRmqM0Yp6RScB6Sqyi3DizLvbu3dvtgVILG6SzuKUUK23Y0xZhKQ2JMMJthAoJrSLnICG0Y7J1HiszYRYQ+4w8KVZRbhxZF8PCwtQrmEiksmaxMvFhrQ2zIgq1RuV1xwQhEluzxJyceBiNIU7/loVYD5YQU3eOOj3CMSYLj05XEbOi9VhFqbG1UjMh4Wyl+tHC+1tQUICLFy/a3APT1rvmaGJkb+Ij5H2VGtbasEkUmkMTG+eQJYyQFGf7v0VGRvLnshDrwRJiZ9iszYRdRcmAZzHxY3LCSpC3PbQSq+UMe9YvV61ZrFl7WIi3LSwsBOB5i7CUgkQYIRlC9n8z7d1HWOKKa9ETOj2lA55ZEBcsBnnbgnWBJQRX3Pjm8azWbYHFiY+a8bYFBQXYvHkz/3u9eqfRq9dWABzi4i5oqi9SCxJhhGS4M0tk3TIgN2LqzjTzBNiYCdtC6PN0JybOVdRuR2rcM1GB0RiC//2vmcN3LT093eJYWloa/zMLEx+W0noITZdB2IdEGCEZrs4StWIZkBOhdWc98wTYyaxuQszzJNhKGOvJmIsE6x0AHPVT1mJY7YkPC9Zca2iRkOuQCCMkw9VZIovLv5VGaN0JrYPU1FTVBKu7Kz69SZCQ9UAZrEVChQCrEGKmegeAnJx4m22PtS2lWJuMCrHka2ERhxqQCCMkxd1ZorcNSu4GitsTLXq9Xr5Ci0SosPK2Z0/WA+Wwl/k/OXknmjb9A9nZ9bF48Wi7bY9F6xNLOLPkqzUp1EKYC4kwwm2kmiV646DkTueuBdEitIze+OxZW2nnydgTCU2b/gEAgtqe2oM1yziz5KsxKWR1j01rSIQRbiPVLNFbByVXOgAtiBYxZVTj2as9S2ZxpZ2n4kgk5OTEe2W/IzVqx8pZw+Iem7YgEUZIghSDFQ1KwtGCYBVTRqWfPQuLQVhYaScHaotb6+8yYU8kUL9jiTvPj8Xt6VifsJIII5jBUwclOdDCwCGkjKZB0tmzlzqoV800EawkjJUDFsStOY6s9Pn5+UhPT6d+xwyxz4+ldBn2YH3CSiKMYArWTNqswsLAYW/GbEp2KaSM1oPk9OlXcOaMH+Lj7yI29mEAD/MduGnzbVuwEGArFDmDvNW2Qqklbl25b08Ww64idqW6VG1ZznbL+oSVRBghGLleFNaWf7MMKwOH0BmzkDKat5mYGKBVK9e+yx3ritKpMeQQQqxZoZTC1fumFY+OcRRHZb6rAOBePcndblmYsDqCRBghCDlfFOoM7WNL+Pbu3Rt3794FAPj7+1daeeRuhyhlpnvr/UJdKaPc1hV3VpmqbXkyh8VM/EqIW3fu2xv7FCE4i6Oy3lUAcF0kKZEnkmUPC4kwQhByvyjUGVZGacuGHJnuWd8v1J2gXW+1PAlFrRQq3pbwVw5ciaOSQtzL2WZYXDQAkAgjXEAL+ak8ATksG44sN9YuBjHfp9WBz52gXRYtT+ao+UzUWpFGfZM0OIqjkqtdSd1mtLBoACARRoiE9eW+LOOu68rdzk+o5UYsSg98Ug4CrAftuooSz8RWezYJeTVWpFHfJB324qiys+vL1q6kbjNaCXMhEUaIwtmLkp+fz0TDZg13XVdSDKpiLTJCxI7SA5+QYGEh7U+O1BisWAOVeCbO2rMa4pb1VARawzqOCgC/tRPgXrsyF/Cm91aONqOFcYhEGCEKZy+KKWDTW+Ng7OHuptZKz/CFij6hA58UAexigoWdtT+hqTGEtmGW3GBKiBFn7VnpvG+A51o1lcTRSnWpdhawJ+BZX8UoFyTCCFFYvyhAOQyGzErnqb0VhBqIibcSYzWRa1C1VwYxok/IwCdVALuYehDS/pylxhAKa24wVsRIy5a/Ii2tAYqKargsbsWg1iDO0gpZd7HlwjMltZWqXTla5MXyKka5IBFGiMb0omRlJSIz04ADB9ohM9Pg1UGwYuKtxFpN5BhUHZVBiNgR485zN4Dd9F1qiAshwpoVN5hauw84onnzMMTEyCtA1Myd54krZB1tSSS1yLXXD3lTnkgSYYQgbL0AmZkGZmb/aiNUaLhiNZG683NWBiFiR4w7z1GmeyGYf5den4tZs2L5ekhK2o2rVysGDanbndABlhXLk9QuVjGoGQ+nZgA26ytkpUAukeusHzLPMagla6JYSIQRgjB1dLm5uUhPT2dm9q81xNSbXJ2fszIItaS46s5zZcA2fdewYRdhNC7G1avhyM2Nxe7dSbLFYQkdOFmyPEnlYhUDC/FwnjpAs4BcItdZP8R6jkGpIBFGCMY8Uzors38lkSLmS0y9ydX5OStDSkoKhg2LlMWSIsWAbarPTz55mQlLrJz1xTqsxcOpjRoWQSVi0uRow944htiCRBjhEs5m/2LSBWgBd2K+kpJ2Izb2It8xi3EtSll3QmOGYmNjERERIbklRcoBmyVLrGnGrpTliSVYeg6uIpWIUcMiqOWYNG9dDWkNiTDCZRy5yMSkC9AC7sR8ZWQ8CUBn0TGrsfG2EjFDSiXwVGMWzUoeMBYQuliC9WBqqUSMWhZBrcekeeNqSGtIhBGicJRHxh6sdgByYEtoADoAlTvmgQOftNjcWgmroZwxQ0ok8FRrBSALcU8sYS7o77uvCJMm6VFWpoOvL4e5c4vQt+8LmrCCSyViWLEIsjJRcGRdNBqNFr/bG0NYF/BSQSKMEIV555uTk4OMjAz+mL0OoLCw0OMCLMXEfJnjyYGnSiTwVGMFIMU92cZUx+PGAampwOnTQP36OtSqFQYgTM2iKQ4L8U1KThSciaxNmzY5vUbv3r0RFhZm85gWBLxUkAgjRGN6OcyD0R11AHfv3lWlnNYoEftROZktB5MlDPDOwFNzpEjgqfQKQFasHCxTq1bFP60j1pLESm42JScKUu1BGxYW5lGTUFchEUa4jdEYgm3busOe240FlIr9SElJQUoKUK+e/TQKrNSJWiiRwFMKPCXuSSo8KTO8LVyxJKmZm80cJScKcuxB682QCCPc5vz5OJhbe4CKDuD8+VrQ60/I/v1CBgelYj8iIyMREBDAxzkkJJxF8+bHFQ/CZwGtd7724p58fDhMn56LlJQn4efnh5KSEj4hrRpCRAlxpOVVeEJwx5KkRm42a9R0hzp6zymW0jkkwghNI3RwSE1NtfjdVYHgSjZ5W2jdauAMT+l8zeOeOne+hgULvv7nWV+H2QJgC5QUIkqJI62vwnOG1l3OaqV7cPSeUyylMEiEEW4TF3ceQDkA806sHHFxF2T/bqGdfmlpKf+zKwJBbOyHJwssZ3hq5xsZeRsJCWednqekEPF0cSQXJuuhKa6VhcB6V1Bz30xn77nWha1SkAgjXMbf3x9AhTDp0WM7tm3rhgohVo4ePe4JE9N5SuDMwuWqQJAq9sPT42oA7VsVCOdo2dVsy3qodmC9q6hpdXf2nmtV2CoNiTDCZfR6Pf+zo1mY+XlCcUWsCLFwuSMQ3I398PS4Ggpk9w607mq2169Y92EVefzY33pKrbI5e8+ldJF68uSVRBjhMkITt4oddMWIFRNCLVxqzs7kch2x0kHJkcCTlXuzBYvWILnL5KmuZhPmfZjYPH4st1U5ECKypHCRevrklUQY4TJymcKtr2dvYDE/T6iFi6X9yuzdl/Vm4CZs1SVrHZSUCTxZuzdzWLQGKVEmcjXbhqW2KrcYFBqHJlUyVk+PeyQRRriF1PsMAsKTwJrjzMLl53evqau1d6M5ju4r3d6yO1TuxFnuoNxN4MnqvbFoDVKqTJ4Y5yOF9VBoG8zNza10rpQWMiXEoNqrv1m0QLsDiTBCFYR0FmIGFmcWrrCwMGbSRrgzYGp1tudJsGgNkrtMrGSGlxqlLZr2JlhSWciUmrio5fZj0QLtLiTCCFUQ4nIUMrCIWaLNSryAnAOmp80SWYLFhQdKlYmVzPBSIqf1UOx7SJMr57BogZYCEmGE6tib3XhqYlQh9+WKmPLEWaI5agtMqRceSBG7I8diCEffZUKtzPBSInQyJOQ5mcPSe6j2OyMlLFqgpYBEGKEqzmY3zoLotbgiydl9udKJe+os0YSjOhGzkMFdpFp4IGXsjpSLIbwBMdZDoc+pd+/eAJy/h0qKIpbEoBR4YjwiQCKMUBlnsxtHLkaj0YhNmzY5/Q5Wli4LcZ26KqY8dZYIOK8TMQsZpMSdhQdyxe64uxjCGxBjPTTtCeqMu3fvAnD8HmZn11dMFKk5KZNrYszSynYpIRFGqIqQ2Y29/GMcx1n8LiSVhZo4cp3m5+cjPT3dqZgyt/qYizpPnSUC7glMVp69FGjR6ssqclkP7b2H/v4liooipSZl1m1SjomxmlszKQGJMEJVnM1uOnbsiOrVq/Pn+/v7Q6/Xo7i4GMXFxfznWjG9O+t4nIkpa6tPv379ALC9as1V8SDUbeQNsJSHytMQYz20N9Ez38LN1ntYWhqoqKVaiXdG6Ap3dyfGWoz7FQOJMEJ1HM1u9u3bV+n8fv364dNPP+V/94R4KKEpAKwJCgpyumqtuLgYJSUldl0rcnZg7ogHR26jadNyAWjj2QrBWayQ3KkHyMrmHEcTPb1eb/M9DA29jAMHfoXRGKLIRELJVCLO2pqUE2NPbnskwgjJEdKhC93yyBbmFjDAM+KhrGd7aWmnsGFDFi9KHQ3SjlatFRQU4MMPP3T6/XJZUNwVD/bcRr6+vjC/LS2vAlPbiktWNucImejZeg8LCoADB5SzVKuZSsT8HQSg+YmxUmhGhPXo0QNHjx7F5cuXUb16dSQlJWHu3LmIjY0FAMycOROzZs2q9HdBQUG4efMm//uWLVswbdo0nDlzBg0aNMDcuXPx9NNP88c5jsOMGTPw0UcfobCwEO3atcOKFSvQoEED/pyrV69i1KhR+Oabb+Dj44NevXphyZIlqFatGn/OsWPHMHLkSBw6dAhRUVEYNWoUJk6cKEfVMIWYDt3axGyKizIhdGD197/jEe4q644xIeEsAPcGaVazzruCudvI3KintohxB1etuFKKTk9qI3Lh6kRPDVGkRioR63fQYMjU/MRYKTQjwjp27Ig333wTMTEx+PvvvzF+/Hg899xzOHDgAABg/PjxGD58uMXfPPHEE3j44Yf53w8cOIAXXngBc+bMQbdu3bBx40b07NkTR44cQfPmzQEA8+bNw9KlS7Fu3TokJCRg2rRpSE5Oxh9//IEqVaoAAF588UVcvHgRGRkZKC0txcCBAzFs2DBs3LgRAFBUVITOnTsjKSkJK1euxO+//45BgwYhLCwMw4YNU6K6VEPMvo+ONscVOrCanwdwAHQOXXhac7so5WpVMs2DlGjdFe3K4K5l0alV3Imx8rT8atbYegcPHDB4xMRYCTQjwsaMGcP/XKdOHbzxxhvo2bMnSktL4e/vj2rVqllYon777Tf88ccfWLlyJf/ZkiVL0KVLF0yYMAEA8NZbbyEjIwPLli3DypUrwXEcFi9ejKlTp+KZZ54BAHzyySeoWbMmvvrqK/Tp0wcnTpzAzp07cejQIbRu3RoA8P777+Ppp5/G/PnzERsbiw0bNqCkpASrV69GQEAAmjVrhqNHj2LhwoUeL8LMEZvbyfSZ0IHV+jxAB6Acgwd/jFq1Ksc/adHtIrWr1Z4oVirNg9RuQ626ol3NdO/s3cjPz3dbOGvZtSs1nrpdk5TYegcBHxgM+5GZafCodBJyoBkRZs7Vq1exYcMGtG3bll+VYs3HH3+Mhg0b4rHHHuM/y8zMxNixYy3OS05OxldffQUAyMnJQV5eHpKSkvjjer0eiYmJyMzMRJ8+fZCZmYmwsDBegAFAUlISfHx8kJWVhWeffRaZmZlo3769xYuZnJyMuXPn4tq1axar/cy5c+cO7ty5w/9eVFQkvFIYw53cTkIHVnsvf2mpZYdoeg5adLtIucrJVQuKVPUh9PvFxBSytIWQGOwtOvDx4TB9ei5SUp6Ev78/v6DCNEFx9m6Y3itXhTNZ2SzxxO2apMbeO5iYmIXExCybC66Ie2hKhE2aNAnLli1DcXEx2rRpg+3bt9s87/bt29iwYQPeeOMNi8/z8vJQs2ZNi89q1qyJvLw8/rjpM0fn1KhRw+K4n58fwsPDLc5JSEiodA3TMXsibM6cOTbj2rSIUCFla9YtVHg4Oy8lJQWxsbF2O0gtzPilSlCotttO6Pe7ElMo93Y9cmG+6KBz52tYsODrf9ruddibowh9N1wRzmq3EVbxdHeiuzjro2y1HVYnR2qgqgh74403MHfuXIfnnDhxAo0bNwYATJgwAYMHD8bZs2cxa9YsvPzyy9i+fTt0Op3F33z55Ze4fv06+vfvL1vZ5WDy5MkWlrqioiLExcWpWCLXcZS0MCcnHuHhBXYzSAsVHs7Oi4yMtDsIsz7jlzpBoRBRLKcoFSrKxVgrTTGFnrBdT2TkbX4hhiPkzBquVdcuoQ5C+yhrUlNTmZ4cKY2qImzcuHEYMGCAw3Pq1q3L/xwZGYnIyEg0bNgQTZo0QVxcHA4ePAiDwWDxNx9//DG6detWyaIVHR2NS5cuWXx26dIlREdH88dNn5kHjV+6dAkPPvggf87ly5ctrnH37l1cvXrV4jq2vsf8O2wRGBiIwMBAu8e1hK3B4v77j2HVqiH/dPTlqIjhqhDQ1rNuRy91UFAQ/7MrAkULM36pExQ6s6DIJUqVcht6y3Y9HTt2BLCPb/P+/iUoLQ2E0RjidtulpLiEGKz7KKGr2/V6veJlZRlVRVhUVBSioqJc+tvy8nIAsIihAiriuvbt24dt27ZV+huDwYA9e/Zg9OjR/GcZGRm8iEtISEB0dDT27NnDi66ioiJkZWVhxIgR/DUKCwtx+PBhtPrHLr13716Ul5cjMTGRP2fKlCn8ogHT9zRq1MiuK9ITMRdI/v4lZgIMAKxjuSxn3SkpKYiMjKx0jkl4uCNQtDLjl2K2KCSwWE5RKmafPltowWWsJKb+Q6+/LtlehO4Gn2ttxTEhHVr1NLCEJmLCsrKycOjQITz66KOoXr06srOzMW3aNNSrV6+SFWz16tWIiYnBU089Vek6r7/+Oh5//HEsWLAAXbt2xeeff45ffvmFT2ap0+kwevRovP3222jQoAGfoiI2NhY9e/YEADRp0gRdunTB0KFDsXLlSpSWliItLQ19+vThc5b17dsXs2bNwuDBgzFp0iQcP34cS5YswaJFi+StKAawl4Q1JyfeRhC9Jeaz7sjISIcpLNzp1L1pxu8osDggIAHp6b/KLkpd3aePlY5cDZHhTHxKKZzdCT7X4opjQl604GlgCU2IsKCgIKSnp2PGjBm4efMmYmJi0KVLF0ydOtXCfVdeXo61a9diwIAB8PX1rXSdtm3bYuPGjZg6dSrefPNNNGjQAF999RWfIwwAJk6ciJs3b2LYsGEoLCzEo48+ip07d/I5wgBgw4YNSEtLwxNPPMEna126dCl/XK/XY9euXRg5ciRatWqFyMhITJ8+XRPpKcwHnNxcH+Tk+CEh4S5iYyssj84GHHsmalvCByiHTgfFlzDLGVfDIvYCiy9erHimSopSoW5DVjpyNUSGEPEptXB2NfhciyuOTZAFTx604mlgBU2IsBYtWmDv3r1Oz/Px8cH58+cdnvP888/j+eeft3tcp9Nh9uzZmD17tt1zwsPD+cSs9rj//vvx008/OS4wY5gPOI4GAmcDjq1j9oSP0GBOKZA62F3rsJwDiZWOXGmRIVR8ihHOJDYqQxY8+WDR08DyO6AJEUYog6mROhsIXB1w7AkfpZYwSx3srnVYzoHEYkeuBM7EpynGVKhwVlpsiInhU3Ng1LIFj3VY8zSwLrhJhBGVkNIKIXSj7t69eyMsLIz/G7leBm8RWEJhLQcSy9Y5ORG6ijQqKkqUcFZSbIiJ4WN9YCTEw6qnQcxWempAIoyohJRWCLI+EWJwZJ0LDm6I0tIE+Pn58ZnkzVGiHcm1WtPVVaQsCGdAfAwfa5Yoe8+1sLDQ4d9R33UPJft6V62orCz2MYdEGFEJqc3J1EkRYrBlnauwnGxy+rdyWk7k7sBdXUUqBrlEJCsxfK7g6Llu3rzZ6d8raa1jObYJUKavd9WKyspiH2tIhBE2EZMB2RNhvbPzNlyxnEj5DJXuwOVIPiuniHRmPTftfWmvztXKByfFc1XKWkcu3ApcdS+yOlEgEUbYxV78lqdDnZ32kfoZytmBKyH45RKRQmP4zDOpp6WlWVxDTReRmOeqduJg1ly4riB1WxfTdlhd7EMijJAN6xeusLAQd+/e5X/39/e32MKCFesS64GchPMBUaoBS+5tl5QS/M7EhslSZY2YvID5+fmoV2+xU+u5eZ2r7SISOjCzGEuktigUi9RtXWzbYW3VpgkSYQSP0IFEyHnWL5x1h2GvA2HNusRi5+vtKPlM3N12yRlKWTeciQ1zS5U1YvICirWeq+UiEmrBA9QXiraQ+x2Qwzor9eTWlbbDYpgNiTCCR8rVLebXsO4w7r//GI4du99mB8KSdYnFztfbcfWZuGM1UCJgXi7EiA17uPpOCqlztVxEQnLkGY1GbNq0iblYIrn7JSWss1KISKFtR2iaJLXS3pAIIyyQYw886w7jt98eAKDjf2dV2LDW+RKuPRMprQZyBMybI7WLScjeoXLgrM5ZyAcnNEee0kLRXhjHtWvXAMjfL8kdjiGViBQ6sWA9TZJLImzfvn3o2LGj1GUhGMDdvSOtsdVhmASYCbmFjaumdVYDOb0Zsc9ES9ZMuVxMzvYONSGVABRS5yzv1mCNkrFEQqxQSvZLcrRJKUWkUPciC+3IHi6JsC5duqBWrVoYOHAg+vfvj7i4OKnLRaiAVHtHmmN7424O5kLMvAMxGo2IiYmR5H4A57FpJmzdE6uBnN6Iq5YTrVgz1RaLUg62Quuctd0arFEjA7wQ65JS/ZJcbdJdEcm6e1EsLomwv//+G+vXr8e6deswa9YsdOrUCYMHD0bPnj01c+NEZeTYO9JWh2EeEwaUIylpN/8Sbdq0SdLgfEexaUJi0VgM5PRGXLWcaMWaqaZYlHqw1UqdO4MFN5a9BU316p3G6NG2V6IKHYPteQhMK2XlapPuikgWnouUuCTCIiMjMWbMGIwZMwZHjhzBmjVr8Oqrr+LVV19F3759MXjwYDzwwANSl5VQCKlfPltCpkaNy9i9Owkc54Pdu5NQteptWYPzxQw0njbT8hTEWE5YiDkSg5rCRar3XYo6Zy1JspoDuZgFTSkpKYiMjBRcP2q7Pd2d3GpFYAnB7cD8li1bIjo6GhEREXj33XexevVqfPDBBzAYDFi5ciWaNWsmRTkJBZHj5TMXMkZjCC/AAGVcL2IGGk+baXkjWok5kkMsihUyUr3v7ta50FV5vXv3RlhYmM1jLDxTKRC7oCkyMlJUGIeQ4Hup3Z40ubWNyyKstLQUX3/9NVavXo2MjAy0bt0ay5YtwwsvvIArV65g6tSpeP755/HHH39IWV5CAaR4+Ry9SGq4XsQONJ7QkXs7rMccAdKLRTHpBeQQgO7UuVALuLP9HFnLNWgPW2LZkStQrgVNjsI0pAzHoMmtbVwSYaNGjcJnn30GjuPw0ksvYd68eWjevDl/PDg4GPPnz0dsbKxkBSWURQpzsfULl5OTg4yMDFVcLxRkT7CKlGJRTPLXmJgYpq2Frq7WZCnXoD2ciWWxC5pcRUiYhpQWK28TWEJwSYT98ccfeP/995GSkoLAwECb50RGRmLfvn1uFY5QF3f3jrT3wrkjiNyJGWExyJ61GBjCs3AmZFi1FgpZram1bXvMcSYUnS1okmoS6cwrYYo1s4b6JelwSYTt2bPH+YX9/PD444+7cnnCC3BFELmTbsKEu8JSSmijcLbRukDW6pZbQqwzWr03e9jqy2z1kZ067ZU0TYYzr4TYWDNCPC7HhJ06dQrvv/8+Tpw4AQBo0qQJRo0ahUaNGklWOEJZpNw7UghiBZEr6SaUvicx0EbhbFJQUIDLly87jT0C2BXIaucdcwdn1hln92a+GTnrQhlw3JcNHPgk/Pz8cPfuXf58f39/6PV6/nd37lGMV0LrkxJWcUmEbd26FX369EHr1q1hMBgAAAcPHkTz5s3x+eefo1evXpIWklAGuQMnpRJEQjph82z/qampKC0thZ+fn81VVSx0Hp42s9cqQq2TJlgVyFpJUmsLZ9YZZ/dmvRk5q0IZcN6XibFEuSqShHgl3LXak4Czj0sibOLEiZg8eTJmz55t8fmMGTMwceJEEmEaRs4XQSqR56wTnjo1B99808xM0GS5lO1fKbRstfA07LVNrcUfaTlhqjPrjNh7Y1UoA9KJZbEiSWy6CHes9lKEkXgyLomwixcv4uWXX670eb9+/fDee++5XSjCc5HiJXPUCUuZ7V8ptGy18HSMxhBkZSXiwAEDAPtWStZm+lpcDSx0myB79wYAOTnxmhHKgHRiWaxIcmdCLNZq7+6uJZ6OSyKsQ4cO+Omnn1C/fn2Lz3/++Wc89thjkhSMIOzhaIDJyYnXnKDRstXCkzEfMEzYslKytMBCjf0OpcKZMDAajdi0aROAyveWnV0fixePZtadby3STXFrcohloSLJlbbojtWeLP62cUmE9ejRA5MmTcLhw4fRpk0bABUxYVu2bMGsWbOwbds2i3MJQmrsDTBaFDRatFp4OtYDhjnWol5Mbi650XpCTEflss5rlp+fj/T0dGYHd5PwKiwsdLjIQ0qxLHdduGO1J4u/bVwSYa+++ioA4IMPPsAHH3xg8xgA6HQ6lJWVuVE8grCPrTgGtXKQuQuLOcyEIFedqe3es52xvAJnol7t+DEt1rdQbJWBxcFd7AIPvf46Bg580iInlyt1LndduDPJ1eIEWQlcEmHl5eVSl4MgnCJ0VqhWDjKxaH0vNbnccHJe15nQMGE7YzkAOBb1WlzlypI71RVYHNwdxWgBsNm3SJGTS+66cGeSSxZ/27i9gTdBKIUjV4vJNWFCiRxk7qJ115Fcbjg5ritmc2jA9oBhMGQiMTGrkrvIVA5W3WLO0Hq+OtYHd8vYQu6ff/KIdCXqwh2rvVYt/nIiWIQtXbpU8EVfe+01lwpDEM6QW5AoPZCyKrBcQS43nBTXFSo0dLp7e/M5GjBSU1MRFRWFiIgIXLx4EQCbbjGxaMmSp4VFCJVjC3Uw7f8oV98ih9CR0mrP0q4lLCBYhC1atEjQeTqdjkQYoThSJYL1hIFUDRwN3uYZzK1xZukTel0xFkNH19Tr9S5bJ1l0i4lBSBJkliyzWrAkO4otBKTrW+QObXCnrlnetYQFBIuwnJwcOctBMMyFC8BffwENGgC1aqldGttI1SFrfSBVA2eDt3UGc2vsxRqJva6QmCUhlk4ltoBhEaGZ6FmKDWOlHPawH1tYgVR9ixKC1NW/1YJYVhOKCSNsYgpi3rixKiZO1KO8XAcfHw7z5hnRt+8tJl8aKcqj9YFUDYRaD8XGGtm77vnztXD16i2XYpbksHRqwS0mBKETEFZjw1jEVn/CcYB5TJhUfQtr/bE5LJdNbVwWYRcuXMC2bdtw7ty5Si/lwoUL3S4YoR6mIGajMeSfBIgVMQzl5TpMmBCKv/9eDb3+OlMzYimh4FFxCBm8XYk1sm1FKMfWrc+5HLPkrKyuuNw8ZaZvLRiAisUI1hQWFrq9io8FlErLYd2fANCkSFcKraRLkQqXRNiePXvQo0cP1K1bFydPnkTz5s1x5swZcByHli1bSl1GQmFML4Azq4Enz4i9PXhUTDoHZ9ZDVxc72BIFgI6fFLgS2OysrK663JydW1BQwAfw24KVgcUkGLKyEpGZacCBA+2QmWmwELubN2/W/ARM7rQczmK0TD+npqZCr9cz8/zVxhv3mXRJhE2ePBnjx4/HrFmzEBISgq1bt6JGjRp48cUX0aVLF6nLSKiEN8VHeXPwqLXgMt8epuJ32x1hv379+J8dWQ/FugDtufdu3gzGF188L/g69hBi6ZRygsF6Hi5bbToz0+BQNGt9AiZ3Wg5PsY4qjTfuM+mSCDtx4gQ+++yzigv4+eHWrVuoVq0aZs+ejWeeeQYjRoyQtJCEOrAcH+XOYgFz0ZGb64OcHD8kJNxF7969cffuXfj7+0Ov11f6O0/sNO3PPEOg11932BEGBQU5HGhMudvEinnrAcx8expXJwX2LBNGY4jsmz6ztK2RLUz1nZubi/T0dLuiOSsrEZ0771aljHIiV1oOsX2Ft7nhHKHVnHuu4JIICw4O5htLTEwMsrOz0axZMwCOl6MT2oOl+CgpFguYi47KnW8WWrb8FUZjCO6/vxdatKiC2NiK3SE8tQN0NPNMStqN3buT3F5J6IqYt3VddyYF1kLD1v0qlRNL7W2NbBEREcG3BXsr+g4cMFRKVqt1WBnsWbeWKo27C2jsTbRZ7M9dEmFt2rTBzz//jCZNmuDpp5/GuHHj8PvvvyM9PZ3f0JtgF7FWJBbio6RaLOAsu/mtW1V44WE9MHtyB2irPjIykgBIs5JQKjHvznXMhYZag68WkqHq9ddhMGTiwIF2Vkc8L18eK3kBhbpHc3NzbVrLWBIVUuBOKIzjiTZ7/blLImzhwoW4ceMGAGDWrFm4ceMGNm3ahAYNGtDKSMZZtQoYNgwoLwd8fIAPPwQGD1a7VM6RerGAves4svyYru2JbgPbSSV93IoJlCqBpByJKNUYfFmxutjDvP4SE7Nw4IAB5iLcE+NBWYx7dSQcTG55Tw9Wd8fqLXSixUpcmUsirG7duvzPwcHBWLlypWQFIuShoKAAZ87cxbBhNVBebrIiAa+8wuHBBy8jPt5PEy+wVJ2mvfQHzgZmT129Y69ezV2SYmMCpQpOliPIWYnB1yTWTSEarFhd7BEREYHU1FRs2rQJev119OjBZjyolLAW9+pMOHhLsDrgvvWc9ffNhFvJWktKSnD58mWUl5dbfF67dm23CkVIi0k45OTEo7y8v8WxsjId3n33RwQH38K4cT3QokV1plcKStVp2rqOdQwUUHlg9tTVO/bqtWXLX9G8+XGXO0KphKjUglbOwbegoACXL1/G5s2bLT5n0epijfmCFJbiQeWEpft0JBwAMG1JlQN3QmG08L4BLoqwP//8E4MHD8aBAwcsPuc4DjqdDmVlZZIUjpAGxwG395Jfrl/P/eOeZHt5tZzxRVWr3hY0MLPuWnIFe/XKQkygVMid3d5egLXJYuqOZVEJ5N6DkBVYvU9HwkEty46S4RdSGgBYs3LawyURNnDgQPj5+WH79u2IiYmBTqeTulyEDDhLfllersMrrwDJyUCtWrZfKvOgfjWRShhYX0eowNOKqVssYupVjgFK7g5f7vxNtgKss7IS+fgqk8U1NjaXyYzp3pLfitX7dCYclLbsKB1+IfVzYcnKaQ+XRNjRo0dx+PBhNG7cWOryEDLjLPllWRmQlVWAqlUru4C0GtQvFiFCRCumbmcIHfh79+6NsLAwi7+TeoBSqsNXamA1d1ebMC3+GD16MfT660hJSUFkZCRTwoaVcsgNq/fpyCKttGVHjfALOUIPWBRfJlwSYU2bNqV8YBrGPFGlLSGxf/86HD9+L92DpwT1A9JZG8R0iCyvpmTJIuBJ8XbW7mpzzC2mkZGRHrEPI+EeQt2jall2PDH8ghUEi7CioiL+57lz52LixIn417/+hRYtWsDf39/i3NDQUOlKSMiGMyFRUlLiNKj//fe/Q0LCWdlXA0oVK2BLdFhv0+Po2uZ/J6RD1MJqStYEtCd0+LZTflSgRYspIS+OJkOmHSNMqGHZ0VL4BcsLy2whWISFhYVZxH5xHIcnnnjC4hwKzNcezoSEo6B+88FEbuuElBYb63NiYmIEX9t6E2ZnHaInWXeUQksdvj3sZZ0HXHchsWxRlQpvuEd72LsvFkSFlsIvWLLuC0GwCNu3b5+c5SBUROjMymDI5Df2VWOliZwvjdBru9oheoJ1Rym01OHbw5aV2WDIdHnbH2/Y1kYLVmOhCBWTQs9TW1RoZaWhCdbbhzmCRdjjjz/O/3zu3DnExcVVWhXJcRzOnz8vXekISXB3hmQZYFyOtm33e9weckJxtUP0BOuOUmitw7eHEHe10HdT6LY2WraoeorVWKhg7tevHz799FP+d9ZFpxZWGmoRlwLzExIScPHiRdSoUcPi86tXryIhIYHckYxhLRysYwzsYTQacf48ZxVg7IPMzIqNfL0VVzpET7DuSIm1BaCwsBBXrlzhf9dqhy80wDo1NRVRUVEutSUt7D/pDlq3GgsVzMXFxfzPWhGdrK801CIuiTBT7Jc1N27cQJUqVdwuFCE9rnT2mzZtQk5OPDiuicXnallwxG48zhKeYt1xhhD3CgAHbifL3xMSzspeZimR23WkdYEiBE+yGgsRzEKfqRrxcizEo3k6okTY2LFjAQA6nQ7Tpk1DUFAQf6ysrAxZWVl48MEHJS0goS5qW3BMHc/GjVUxcaIe5eU6+PhwmDfPiL59bzEVYOkMrVp3hCI0pqd37978z9aD1P33H8OxY/c7HLRY7/DlbI+eJFDsoXafIxVCxZWQZ6pWvBwL8WiejigR9uuvFZ0hx3H4/fffLTrDgIAAPPDAAxg/fry0JSQkR8wgpqYFx9TxGI0hWLx4tEVm/wkTQvH336uh119nJmZCCNbmfFNnevx4If+ZVjs1oTE9d+/eBWB7kPrttwcA6PjfzQetlJQUxMbGarJupMJTBIojPMVqLFQwC3mmnhIvJxfmVsLcXB/k5PghIeEuYmMr9rVmuU8VJcJMKyQHDhyIJUuWUD4wjSJkdlNYWMhvQCxlgLEYTOVz1pnZug9WXJeO6sW8M/3kE8vOVEvC0hp3LAAmAWbCOrGpVutEKpwJFHtJtFkehGzhCVZjoYJZjOhU2h2thRWr5mV0JFBZ7VNdiglbs2aN1OUgFEZsY7QXkKmEdULs7J+l7ZXsLYpw1plqeUbrjgUA4GAuxDzNyiMFjgSKqW3ZcwPLvfWUlGg9CFyMuGJ1z1otWOBM363VPtUlEXbz5k28++672LNnDy5fvozy8nKL4//3f/8nSeEI9lHCOiG0M1NjeyUhFjdb3+nJsT3uWABsxYRpvT6kQOiqS3sDpdEYgrlz/6uYBcOVIHJPDAIXY9Fjec9aLSwI0Wqf6pIIGzJkCH788Ue89NJLiImJsblSkiCkxFlnpuT2So4WC3TocAfnz1dB69Z6h25QT47tcdcC0KnTXk27oeRAyLY29gbKW7eqYPfuJMUsGK4mlvWUIHChgtl8YZuz65nqRK14OS0IHK32qS6JsO+++w47duxAu3btpC4PQdjF0UxRqe2VHC0WGD8+FDpdRefkbAWnpwQf28MdC4DW3VBy4Ux82BsoTQLM9LvcFgx3EstKLbDUSOsgRky6sl2aGvFyWhA4Wu1TXRJh1atXR3h4uNRlIRhCq64BuV9ER4sFAB9wXMVPQlZwekLwsSPsiSl/f3+XrsdaW2MN2zF2lfevVNKCoWZiWTW3ehJ6PVe/V+mJilYEjhb7VJdE2FtvvYXp06dj3bp1gk2qhLbQsmtAiRfR/gbN93C2ghPwLKuPUJEUFRVVqW0VFhbyqSuACqGm1+strs1iW2MJWwNlUtJuC0sYoJwFQ+04Ik/a6omFSbHYflWttBFa61NdEmELFixAdnY2atasifj4+Eoz2yNHjkhSOEJd1B70hHQoRmMIsrKCEBd32+JzuV9E6wEPKEfFqj7vXdnnjnCPiYmRs2heg62BsmrV25JZMITuiACwFUek9a2eWJkUC+1XtZ42QklcEmE9e/aUuBgEURlnHc/GjVUxe7Yeixbp4OMTim7dHlK0Y7Ue8LKz6zsd7FiY0cqJt3eoUuFOLJP1QCmVZVhozqjU1FQA7MQRqW2Rkwo13i1X+ysl00ZovU91SYTNmDFD6nIQhE1sdTymVBSmlYlARQyWGh2r+YAnZLBjZUZLsIvYBJlCBhcpLMNCc0aVlpby38lCHBFLFjmt4W5/JXXd23Nx9u7dG3fv3q0UxiCkjGojSoT997//RatWreDr62vz+J07d/D1119b7A1HEFLiKBUFCx2rkMGO1c6AYAOxCTJtDZRGoxGbNm1y+l2uWAfEWJZYCJRmxSInN3LFYLnTX0lZ945dnFn8e5GamoqbN6tb3H9JSQkKCgqY7HvtRxXbwGAwoKCggP89NDTUIjFrYWEhXnjhBelKZ0aPHj1Qu3ZtVKlSBTExMXjppZeQm5trcc7333+PNm3aICQkBFFRUejVqxfOnDljcc4PP/yAli1bIjAwEPXr18fatWsrfdfy5csRHx+PKlWqIDExEf/9738tjt++fRsjR45EREQEqlWrhl69euHSpUsW55w7dw5du3ZFUFAQatSogQkTJlgEHhOuYZ2KwhwhLzerJmmCsMae2DEaQyqdGxERgZiYGP5f48aNkZaWhmHDhtn952o8jiPrBgD4+VnO7fX660hIOOuya95dTBY5U39ha6unixcvWoxtWsMkUD788EMMH34IDz8cheefj8DDD0dh+PBD+PDDD7Fs2TLF79FZ3YvBmYvT9F5MmvQnM/cvBFGWMM60/t7O7/Y+k4KOHTvizTffRExMDP7++2+MHz8ezz33HA4cOAAAyMnJwTPPPIOxY8diw4YNMBqNGDNmDFJSUviFAjk5OejatSuGDx+ODRs2YM+ePRgyZAhiYmKQnJwMANi0aRPGjh2LlStXIjExEYsXL0ZycjJOnTqFGjVqAADGjBmDHTt2YMuWLdDr9UhLS0NKSgr2798PACgrK0PXrl0RHR2NAwcO4OLFi3j55Zfh7++Pf/3rX7LUj7fhzNWRkpKCyMhIi7+RwiRNIk5e1MjrxCruunLkqidn1o2wsDDmXO7WFjkAyMmJR3h4AdLT0/nztBoozvLWPVJbQ51NAli7f2e4FBPmCLmy548ZM4b/uU6dOnjjjTfQs2dPlJaWwt/fH4cPH0ZZWRnefvtt+PhUPIDx48fjmWee4c9ZuXIlEhISsGDBAgBAkyZN8PPPP2PRokW8CFu4cCGGDh2KgQMHAgBWrlyJHTt2YPXq1XjjjTdgNBqxatUqbNy4EZ06dQJQsZdmkyZNcPDgQbRp0wa7du3CH3/8gd27d6NmzZp48MEH8dZbb2HSpEmYOXOmRw/kSm6c7ejljoyMlGXFnbXrR063j7ehhc2C5aagoIDfhJtVN5qQWC8Wno+9zPWu7oGoVsoFMbAa/yblanVH7wWr9+8IyUWYEly9ehUbNmxA27Zt+fQYrVq1go+PD9asWYMBAwbgxo0bWL9+PZKSkvhzMjMzkZSUZHGt5ORkjB49GkDFC3j48GFMnjyZP+7j44OkpCRkZmYCAA4fPozS0lKL6zRu3Bi1a9dGZmYm2rRpg8zMTLRo0QI1a9a0+J4RI0bgf//7Hx566CGb93Xnzh3cuXOH/72oqMiNWlIeNTbOViMnjHlHGxMTU2nWb91Bs9A5awG1NgtmxfpmK7mowZCJAwcMANhKkMlCrJczzCdMzrZ2cragRyspF1gV7lLibBKgtfsXLcL++OMP5OXlAahwPZ48eRI3btwAAH4GJxeTJk3CsmXLUFxcjDZt2mD79u38sYSEBOzatQu9e/fGK6+8grKyMhgMBnz77bf8OXl5eRbCCABq1qyJoqIi3Lp1C9euXUNZWZnNc06ePMlfIyAgAGFhYZXOMdWLve8xHbPHnDlzMGvWLIG1wQ5qbJzNEhEREQ73k+zb95ZqQaFyzN7ltggomVKAJeubIxFqMOxHYmIWU2JHC0kxrZ+Zq5YSlt195ri6IlXqd1rutBH2JgGsrMgVg2gR9sQTT1jEfXXr1g1AhRuS4zhR7sg33ngDc+fOdXjOiRMn0LhxYwDAhAkTMHjwYJw9exazZs3Cyy+/jO3bt0On0yEvLw9Dhw5F//798cILL+D69euYPn06nnvuOWRkZGhik/HJkydj7Nix/O9FRUWIi4tTsUTOUXLjbFZxtJ+ks62LlCgXIN3sXQmLgJIuBbWsb46wNdBnZhqQmJilWBlsofV8TID7liItuLtcyWwv9TutRCoee5MALVhpzRElwnJyciT98nHjxmHAgAEOz6lbty7/c2RkJCIjI9GwYUM0adIEcXFxOHjwIAwGA5YvXw69Xo958+bx53/66aeIi4tDVlYW2rRpg+jo6EqrGC9duoTQ0FBUrVoVvr6+8PX1tXlOdHQ0ACA6OholJSUoLCy0sIZZn2O9otJ0TdM5tggMDERgYKDD+mANpTbOZhlH+0kK2bpI7nJJOXtXwiKghkuFpYSeQgd6pcWOJ+S4c9dSohV3nxgrpVzvtBLtwJ7lWgtWWhOiRFidOnVEXfzVV1/F7NmzK61SMxEVFYWoqChR1zRRXl5hJjXFUBUXF/MB+SZM+cxM51q7JwEgIyMDBoMBQEUH0qpVK+zZs4ffFaC8vBx79uxBWloagIrYM39/f+zZswe9evUCAJw6dQrnzp3jr2MwGPDOO+/g8uXL/IrKjIwMhIaGomnTpi7dL+soZQZmeTbOagctx+xdTouAu23JFdcKSxYOZ+0oJSUFsbGxqogdlgWWUNyxlGjR3SUUlt4BW1j36VrfisqErIH5n376KcaPH29XhAklKysLhw4dwqOPPorq1asjOzsb06ZNQ7169Xjh07VrVyxatAizZ8/m3ZFvvvkm6tSpwwfCDx8+HMuWLcPEiRMxaNAg7N27F5s3b8aOHTv47xo7diz69++P1q1b45FHHsHixYtx8+ZNfrWkXq/H4MGDMXbsWISHhyM0NBSjRo2CwWBAmzZtAACdO3dG06ZN8dJLL2HevHnIy8vD1KlTMXLkSM1ZusSghBnY3dm4nKs3We2g5RCHcgtOV9uSI9eKwZDJx1WlpqZaTABZEtDO2lFkZKRHiCE1ccdS4qht2ouLlttCKMXklKV3wBYRERHo3bs3Nm/e7LLlmkVXuawiTKqcYUFBQUhPT8eMGTNw8+ZNxMTEoEuXLpg6dSovajp16oSNGzdi3rx5mDdvHoKCgmAwGLBz505UrVoVQEXw/o4dOzBmzBgsWbIEtWrVwscff8ynpwAqsu1euXIF06dPR15eHh588EHs3LnTItB+0aJF8PHxQa9evXDnzh0kJyfjgw8+4I/7+vpi+/btGDFiBAwGA4KDg9G/f3/Mnj1bkvpgGSXMwK52Zkqs3mQxHkEOcaiE4HSlLTlyrRw40A4HDhjQo8d2ABVpRUy7e7AmoFlsR1pGagu6vbZpWoGp9OIOKVzFrL0DtjCFADmz2smVJ1IONJGiokWLFti7d6/T8/r06YM+ffo4PKdDhw749VfHJsu0tDTe/WiLKlWqYPny5Vi+fLndc+rUqVPJ9Umog9KrN5WKRxBj1RMzqDty55nP9KUWClIOlLY66QosZ8zmu1iwJny0FNfCOkrFs6m5uEOKPoy1d8Aezqx2cuWJlANNiDCCcBVPW73pLBWGo4FEyKAudKWUmGsKRcqB0lYnbcJRnIuawkfJeEd3cqOxkldNLO6USUids7S4wx2kfAfkSmejBaudUEiEER6NJ63eVCIVhtCVUnIh1eBt3UmbY/7cTYmcnaFELIlS1hp3cqOxlFdNSRw9G1MiWNYD25VG7nQ2WrHaOYNEGOEWLK9WNEfOmZNSdSA2FYY75XJ3QFH7eQP3OumsrES7WedNe7+yYtlR4nvcyY3GYl41pXD2bFgPbLeHXP2XEulsPMFlL6sI69evH0JDQ+X8CkJltJQ7SK6Zk9g6cHeFptDO3p1nIyRNgr1Vz6w8b6Cik+7ceTcSE7PsPndWyqo07lg7PcX1JiVadZHJ3YeThdAxLomw8vLySjm5TJ9fuHABtWvXBgCsWLHCvdIRmkBLg5hcMydndVBQUICLFy+6FMtljZjO3tVnIyRNglYCXwHPmDFLjTuDIw2sttGqi0zOPlxKC6FWPC9iECXCioqKMGTIEHzzzTcIDQ3FK6+8ghkzZvBJUa9cuYKEhASUlZXJUliC0CJyxHIp0dlrcUBhsfNlNZDdncFRq643JSDBb4mUFkIteV6EIkqETZs2Db/99hvWr1+PwsJCvP322zhy5AjS09P5zk+q3GAE4SnIta2REp291gYUUyd9+fJlbN682en5cos2lgPZ3Rkctep6k4Pi4mL+Z3vPF1BngsDKBEDKCZ3Q8sqZmFtKRImwr776CuvWrUOHDh0AAD179kTXrl3RvXt3bNu2DQA0sVE2QagBWQ+UISIigpkZM+uB7O4Mjlq0lEpNQUEBPv30UwCOn2+/fv0UF9msTQCUmNC5k8JHLUSJsCtXrljsHxkZGYndu3cjOTkZTz/9ND7++GPJC0gQ7sBSDIEWrAcs1Ze7sNTZshzI7s7gqDVLqdQIXQEYFBSkWtkANicAUqNECh85ECXCateujRMnTiAhIYH/LCQkBLt27ULnzp3x7LPPSl5AgnAHViwiJtyxHsgpkMzdFu3b98X//Z8vate+g+joiozy/v7+0Ov1/PVZ6sS0AEuB7O60I08S6VLC0vO1Rq0JgNJtRa6wD7kRJcI6d+6MNWvW4Omnn7b4vFq1avj+++/x5JNPSlo4gpAC1gSDq9YDuQSl3EkVCbZc0e60I7UmNazENtmDpedrjVoC0dW2olQKH1YQJcJmzZqF3Nxcm8dCQkKQkZGBI0eOSFIwgiAqI8dAo0RSRW+HNVe0O+3I22ObbMHa8zVHTVEi9HlIGcvF8rOwhSgRVr16dVSvXt3u8ZCQEDz++ONuF4ogCOVh2aViD7n2ppMDCmR3Da3ENrH6fFkXJVpN4SMVopO13r17F4sWLcJnn32GP//8EwDQsGFD9O3bF6+//rrgvdgIwlvQShyN1sz4WnSjensguzsoEdvkrqhn9fmyLEq0nMJHCkSJsFu3buHJJ59EZmYmkpKS0L59ewDAiRMnMGnSJGzbtg27du1ClSpVZCksQWgR1hYH2KPyptflSErazXdkRqORqSz5rLlRbcUtFRYWCvpbtQW4FpDbUqtFUS8G1kWJ1iaBUiFKhL377rs4f/48fv31V9x///0Wx3777Tf06NED7777LmbOnCllGQlC82il027Z8lfculUFu3cngeN8sHt3EqpWvY2WLX/Fpk2bmBqATALH2eBcWFgou3h0HLd077zU1FR+lakJFgS4FpB7kHZF1LNs5RbynUZjCHbtKkXz5pdUd+Er4TY1GkPw7be38OCD6t+vCVEi7PPPP8fChQsrCTAAeOCBBzB//nxMmTKFRBhBaBSjMYQXYADbwfl375rSZ9wBwAEwTxTNwd+/xOI8OREat6TX65myJmoJpWKbxFjcWLZy2ypbYWEhv5MEi9Y+OVP4sHi/gEgRdvbsWTzyyCN2j7dp0wbnzp1zu1AEoSVY2R5DiiB1qV0+SgTOl5YGwlKAAYAOpaXKWx9YTsrqCSgR2yTW4sayFdNe2Vhx4dtCyhQ++fn5SE9PZ/p+RYmw0NBQXL58GXFxcTaP5+XlISQkRJKCEQTLsLY9hjvxLOYzSCldPkrF2LAUS6LFFaZSoVQuL7ljm1hfTSgFntpO7bUvlu9XlAjr2LEj/vWvf2Hr1q02j7/77rvo2LGjJAUjvBtWrEu2YHF7DHeC1CMiItC7d29s3rxZ0gFIqcB5lgZNlgShksiZy0uNuCuWVxOa46rw1VI7NbWl48cL+c/ECnqW71eUCJsxYwYSExPRpk0bjB07Fo0bNwbHcThx4gQWLVqEP/74AwcPHpSrrISHw5p1yR4sb4/h6owvLCyM/1nqAUiJWSgrgyZLglBJ5MzlpVbcFeurCd0Rviy1U0fi2bwtffKJ6xZ0lu7XGlEirGnTpsjIyMDgwYPRp08f6HQVFgCO49C4cWPs2rULzZo1k6WghGfDonXJGSzOrqQqk5QDkFz1ZJ2T0F6Zlc5dyIogVAO5YuJYeedZwl3hy0o7tRbZcsVxsXK/1ohO1tqmTRv873//w9GjRy2StT744INSl43wItS0LrkaPM7i7MqbymSd6sHd86SEdSuKXLAce+MMltNNOMKZWDl+vBD79wcgIeEuAgLyLf6WlXZqq3+Voy2xcr/miBZhRUVFqFatGh588EEL4VVeXo4bN24gNDRUyvIRXobS1iV3g8dZnF25Uia5ByA56omlQZOlsqiJs/c3P/+eCGAlvMCEWLcnK5uKOxIr2dn1MWtWI7M+LQstW7r3fUptFcaip0EORImwL7/8EpMmTcLRo0cRFBRkcezWrVt4+OGHMX/+fHTv3l3SQhLeg9KWHCmCx1mcXYktkxJxN1LXk3WZjUYjSktLLc7x8/NDSUkJLl68KOugyHK+KCWx9/4CQE5OPNasyWBm021biNlwmpVNxe2JFX//Epddw/YmC0ruKiDFWKCFyZEoEbZixQpMnDixkgADgODgYEyaNAnLli0jEUa4hRrWJTlM3+azfhMsD8SslssR5laJTZs28Z+rMShaX9faUmISgyZYbgvuYP3+ZmfX/yfOk71Nt12FpU3F7YmV0tJAh31aSkoKIiMjK13PUbuUY8Wz9Xti3m+6OxZoYXIkSoQdP34cH3zwgd3j7du3x9SpU90uFEEItZpIZRp3Zvp2FFdhjzVrMhQTAizO+JQsE0uDIlDZUmIP1ixBUmF6f+VMXsuCO5CV5Ly2xIrRGOKwT4uMjHR59wapJq32LYoh/HXctaCz/n6JEmHXrl1zuAVIaWkprl275nahCEIIUprGHZm+jxx5SHRchdJCgMUZnxplYmVQtL5ne5Y5rVqCrLEnpOUK1GdF5LK0EMFarMgZ2iFVvJbQyZMnI0qExcfH45dffkHjxo1tHv/ll19Qp04dSQpGEM6Q2jRubzbp6NrmJn2hS6ulggVLgDOU/n6WBkUT3jC42EszIFdwNSsiV83gcSEWZLlCO6QWeK70mZ6yyEWUCEtJScGUKVPw5JNPombNmhbH8vLyMHXqVPTr10/SAhLegTuuKykHXuvZpLNr2zLpKyEEhAYGp6am2k3RwIJIkxrWVlSxYplTArXSuKgpctVMCWPP0mwSwOZllKM8rgg86/CRo0dv8X2XmPg1T+q7RImwN954A19//TUaNGiAfv36oVGjRgCAkydPYsOGDYiLi8Mbb7whS0EJz8Yd15WcA68r1xayTN/dTkSoGd8UrK7m6i0lYS1PGouWOaWRc6ENCyJXyvsTa91W+90VI/Bsh4/UhE43GklJuyWNX1MqjYYUiBJhISEh2L9/PyZPnoxNmzbx8V9hYWHo168f3nnnHdrAm3AZV18KdwZeZxY4V65t/TdAOQyGTP64aZYqhQASMgixEKiuJCzlbmPNMqcWclljWBG5UtyfVGkvhHoViouLXS+sCzgKH9m9OwlJSbuxe3eS25MnJdNoSIHoZK16vR4ffPABli9fjvz8fHAch6ioKH4LI3P279+P1q1bIzAwUJLCEoQ9XB14hZj0Xbm26W+yshKRmWnAgQPtkJlpkFwAORuEWLAUqAErudvcmSBoIebPHkqtjFVL5Mpxf1Kt8I2IiEC/fv3w6aef8p/ZEnSffvqpaCEixX3b67NiY3MxevRitydPcqTRkBPRIsyETqdDVFSUw3OeeuopHD16FHXr1nX1awhCMK4OvEI6IVevnZlpkFUAORuEWLEUyA2LKTpMuBo7w8LqP1dRamWsWu5nOe9PiomTeS5PKS3hUty3oz5LysmTVvo+l0WYEDiOk/PyBMEU1gO8Ep2As0HIW9xhrKXosG4L9gYXe6KQldV/7qBUXavlfpbr/qTsN+SwhKshnF2ZPGml75NVhBGEqwhxxchp/XDl2iYhkJubK+vyfGtMg9D587UA6BAXd54/xlqgupywZBGSUhR6Q4oLsbgrcm3BivtXyn6DVWuQI+Es1UpIrfR9JMII5hAToCq19cO8I05NTbW5F2FYWJjda0dERPB/r2QnkJ1d3+5A7ajD09rWSlpCijr01pg+Z0ht+WRpL0gp+w2WrUH2hLM7mfytcdb3sbBykkQYwRxiAlSlelkB6eJwCgsL+Z/lcpWYz/CFDNT2Ojwlt1ZSG3ctHWpYSli1ZLCAlHXN2rZXUvUb7go6VqyD7mCv75s6NQfffNNM9ZWTsoowWysmCXZg/QVT2gogRRxOQUEBNm/e7PQa7hIREYHU1FRs2rTJ5YGahcFGKdwV2GoFyrNsyfBEWLI8ShWk7qqgk9o6yNLiGZZWTlJgvpfCkvndHmpaAVyNwxGzF5q7nY0pE76zgVrNrZVYwV2BrVagvFbiWjwFNfscOUWKK4JOauugUotnhNQPSxZmWUXY9evUUbAKa+Z3W6hlBZBCoDi7RmpqKiIiIiSxRjobqNXaWolV3A10VzpQnqXks56OmpZHKUWKlIJOygmbEhN6R/Uo956mriBKhHXq1EnQeXv37nWpMITysGwRcSYuzIPKpXSdSiFQnF1Dr9dL6uISO1Cz1AkpibvtXan3RerVf6yHHrCC2pZHqZ6BlIJOixM2dyeuSiJKhP3www+oU6cOunbtCn9/f7nKRCgI6y+YLXFhcgWtWZMhi+tUCoEi5BpSu7jEuBxY6oSUxN32rtT7IuUgqvXEr0rjKZZHqZ6lp07YWHnOokTY3LlzsWbNGmzZsgUvvvgiBg0ahObNm8tVNkIBtPCCmYsLJVynUggUsddwxcXlrsuBlU5ISdxt70q+L1INokLfC09bjOEOrGx7xQKePGFj4TmLEmETJkzAhAkTkJmZidWrV6Ndu3Zo1KgRBg0ahL59+yI0NFSuchIyweILZk80KOk6lUKgCL2Gq/clhbWEhU5ISdxt7yy+L2KRa8WulmFp5R6LeOOETSlcCsw3GAwwGAxYsmQJtmzZguXLl2P8+PHIzc0lIaZBWHvBrMWFKZhSbleQHFm4hYgcd+5LrLWEBhv327s7f692bBZl37cNa9tesYgnTNhY7P/cWh155MgR/Pjjjzhx4gSaN29OcWIahrUXzFZnJ7crSIqOWOjLW1xcjOLiYgDKu7i8cbBxV2BLIdDVjs1iaRGO2mLUFp7W5t2FRcHiLiz2f6JFWG5uLtauXYu1a9eiqKgI/fr1Q1ZWFpo2bSpH+QiZ0OILpoXsz0Je8uLiYnz66af870q7uLxxsHG385Wi81Z7U25WFuGoLUYJYbAmWKTqv1lrU6JE2NNPP419+/ahc+fOeO+999C1a1f4+dHOR1qEtRdMKM5cQfZe1MLCQotM9vaQouN39vcXL16s9BlrLmFPRO7nKgY13IKsLMKhhQLagZX+XwvJxV1FlILauXMnYmJicO7cOcyaNQuzZs2yed6RI0ckKRwhL1prrCbsuYKECi1HqNHxm3coCQlnKx1nyRpJuI9abkFWFxXQQgHvRah1SwvJxV1FlAibMWOGXOUgCLsIFSFi9iplpeN31KGkpKQgNjZWs2KZsI3SbkHz98eRxVUNsU8LBbwXMdYt83NYiWuUChJhBPMIdZ0KjblhpeN31qFERkaSAPNAlHYLshp64IkDKiEcodat3Nxc/jxW4hqlRJKArh9//BE3b96EwWBA9erVpbgkQVggZIAwj7Wy91Kz1PF7YodCOEcNt6D5+2PLBVRSUsK/P0oJMmr/BGBfjNeokYfS0kCLnVFYiWuUEtEZ82/cuIG33noLAMBxHJ566ins2rULAFCjRg3s2bMHzZo1k76kBCEQR0KLpY7fEzsUQhhqLcRgaWUitX8CsC/GP/54CADLSTSrcY3u4OP8lHts2rTJYpuiL774Av/5z3/w008/IT8/H61bt7YbrE8QSuFIaJk6fnPU6vhNHYqpPJ7QoRD2sZVrLCHhbKXnLWdsli2XfU5OPIzGEIfnyQG1fwKAzT4Z4GCSJ6ZJtKmNtmz5K0aPXoz+/ddi9OjFmo8hFGUJy8nJwf3338///u233+K5555Du3btAABTp07F888/L20JCY9H6vxdjmbYLMykWA6U9kRYSQzKWmyWWrGR1P4Jc2z1yc68FawlF3cHUSLs7t27CAwM5H/PzMzE6NGj+d9jY2ORn58vWeEIz0cO94gzoaV2x8/aYCwVSoodod/FkvsNYCctjJqxkZ7a/gnXMe+T/f1LsGrVEJfc1FoU7qJEWL169fCf//wHdevWxblz5/Dnn3+iffv2/PELFy7Qi0OIQsrEjUJn2KmpqdDr9Tb/Xqn262nviZJiR8x3UWJQ26gdG+lp7Z+wj70Jk7XBxty65WgSnZKSgsjIyErX06pwFyXCRo4cibS0NPz00084ePAgDAaDxXZFe/fuxUMPPSR5IQlCCDTDVg8lxQ4JK/ehoHhCCYROmKxxNImOjIxETEyMlMVUFVEibOjQofD19cU333yD9u3bV8oblpubi0GDBklaQMK7cDeJKgks27ASF6U2rCTpVRsWYiO1iNLvkdbfW6G5G1NSUgAA6enp/GdC4r60Xj+AC3nCBg0aZFdoffDBB24XiFAWlhoxK0lUPQ014qKUFDtCv4valyW0X6k4lH6PWItndBdH719kZKTgeC7TeZ6ynyTtvu3FsPSSs5RE1dNQ2n0nldgREksi9LuofVVgK02GrfvXYoCz3Cj9Hgm1ImnB7S7k/RMbTuIp+0mKEmGlpaWYMmUK0tPTER4ejuHDh1tYxS5duoTY2FiUlZVJXlBCeoQ2ztzcXLvnSmUpUztQmJAGqcSOkAmCmO+i9lUBxU1Kh5LWXikmNmp6PYS+f658v9YnWKJE2DvvvINPPvkE48ePR2FhIcaOHYusrCz8+9//5s/hOE7yQhLqYu6nt4UUljJPDRRmyd1rQs7BQyqxI2SCIOa7PLV9uQIJLPdR0rUthchQ2+sh5/un9QmWqIz5GzZswMcff4zx48fj7bffxi+//IK9e/di4MCBvPjS6XSyFLRHjx6oXbs2qlSpgpiYGLz00ksWG3sCwObNm/Hggw8iKCgIderUwXvvvVfpOj/88ANatmyJwMBA1K9fH2vXrq10zvLlyxEfH48qVaogMTER//3vfy2O3759GyNHjkRERASqVauGXr164dKlSxbnnDt3Dl27dkVQUBBq1KiBCRMm4O7du+5XBIO4Y+41uT2cZc/WonvE1PF9+OGHdv8tW7YMBQUFipXpyJGHsHjxaKxb1x+LF4/GkSPSrmZWckcCId/lye2LUAd7osh61wGpcCQyhHL58mVB58nlupNzdwSWdkFxBVGWsL///tti26L69evjhx9+QKdOnfDSSy9h3rx5khfQRMeOHfHmm28iJiYGf//9N8aPH4/nnnsOBw4cAAB89913ePHFF/H++++jc+fOOHHiBIYOHYqqVasiLS0NQEXG/65du2L48OHYsGED9uzZgyFDhiAmJgbJyckAKrZmGjt2LFauXInExEQsXrwYycnJOHXqFGrUqAEAGDNmDHbs2IEtW7ZAr9cjLS0NKSkp2L9/PwCgrKwMXbt2RXR0NA4cOICLFy/i5Zdfhr+/P/71r3/JVkfuosbKMWv3yPTpV3DmjB/i4+8iNvZhAA9r1j3CWioFOc321mLH3qo7KcWOkO/y5PZFqIPSlhd3rUgFBQXYvHmzxWdq9PVyLQTR+kpfUSIsOjoa2dnZiI+P5z+77777sG/fPnTs2BEDBgyQuHj3GDNmDP9znTp18MYbb6Bnz54oLS2Fv78/1q9fj549e2L48OEAgLp162Ly5MmYO3cuRo4cCZ1Oh5UrVyIhIQELFiwAADRp0gQ///wzFi1axIuwhQsXYujQoRg4cCAAYOXKldixYwdWr16NN954A0ajEatWrcLGjRvRqVMnAMCaNWvQpEkTHDx4EG3atMGuXbvwxx9/YPfu3ahZsyYefPBBvPXWW5g0aRJmzpzJ5KxbzZVj5gNgTAzQqpUiX+t1yDl4yC127A0ab7+d4PS7WGtfLLqovQUp6l5p17a7IsP6fvfvN2D37iRF+nqlFoJoeaWvKBHWqVMnbNy4EU888YTF57Gxsdi7dy86dOggZdnscvXqVWzYsAFt27aFv78/AODOnTsICgqyOK9q1aq4cOECzp49i/j4eGRmZiIpKcninOTkZH7rpZKSEhw+fBiTJ0/mj/v4+CApKQmZmZkAgMOHD6O0tNTiOo0bN0bt2rWRmZmJNm3aIDMzEy1atEDNmjUtvmfEiBH43//+Zzeh7Z07d3Dnzh3+96KiIhdqRzxaD2wkhOHO4CF28JJS7Dhb2h4TU1N1YSUUtWNzvBmp6l4Ny4tUImP/fgMyMp4EUBE2JHdfr+RCEK3uJylKhE2bNg0nT560eey+++7Djz/+iIyMDEkKZotJkyZh2bJlKC4uRps2bbB9+3b+WHJyMsaMGYMBAwagY8eOOH36NG/xunjxIuLj45GXl2chjACgZs2aKCoqwq1bt3Dt2jWUlZXZPMd033l5eQgICEBYWFilc/Ly8vhzbF3DdMwec+bMwaxZs0TUiDQItZDYs0bk5+fT7J1h3HUVqikcPG2CwJqL2ptwt+6FbotmNBol6Q+ltiIZjSHIyEiCSYCZkDuIXa5xQWxeMVYRJcLq1KmDOnXq2D0eGxuL/v37C77eG2+8gblz5zo858SJE2jcuDEAYMKECRg8eDDOnj2LWbNm4eWXX8b27duh0+kwdOhQZGdno1u3bigtLUVoaChef/11zJw5Ez4+otYfqMbkyZMxduxY/veioiLExcXJ9n2mxinEQuLIGmFaPUmzd8eola3dXVehmsJB6yufCM/B9B5dvnwZmzdvtiuKNm3aBMD9/lBqK9LVqxGwtRZPS0Hs5nhKuhWXkrVu2bIFn332Gf78808AQMOGDdG3b18899xzoq4zbtw4p3FkdevW5X+OjIxEZGQkGjZsiCZNmiAuLo7fw1Kn02Hu3Ln417/+hby8PERFRWHPnj0W14iOjq60ivHSpUsIDQ1F1apV4evrC19fX5vnREdH89coKSlBYWGhhTXM+hzrFZWma5rOsUVgYCACAwMd1oeUmDfi++4rwqRJepSV6eDry2Hu3CJ07vwk0tPTBVsjaPZuH7WztbMWF+UMoRME1me5LEIxaRW4MimKiIhQdFIi5XOw9S4BHJKSdmt2QuMJ7VSUCCsvL8cLL7yALVu2oGHDhryF6n//+x9SU1Px/PPP47PPPhOcpiIqKgpRUVHiS/1PWQBYxFABgK+vL+677z4AwGeffQaDwcB/h8FgwLfffmtxfkZGBgwGA4CKzqdVq1bYs2cPevbsyX/Pnj17+BWWrVq1gr+/P/bs2YNevXoBAE6dOoVz587x1zEYDHjnnXdw+fJlfkVlRkYGQkNDLTY8ZwFTIx43DkhNBU6fBurX16FWrTAUFFQk3SVrhHt4mktNCZxNEPr2fcEjxILS1lE1XMssij61J0XWWNeR0WhEaWkp/7ufn5/FpN+VOrMORwDK8eSTu9GuXabFdQllESXClixZgt27d2Pbtm3o1q2bxbFt27Zh4MCBWLJkCR/oLhVZWVk4dOgQHn30UVSvXh3Z2dmYNm0a6tWrxwuf/Px8fPHFF+jQoQNu376NNWvWYMuWLfjxxx/56wwfPhzLli3DxIkTMWjQIOzduxebN2/Gjh07+HPGjh2L/v37o3Xr1njkkUewePFi3Lx5k18tqdfrMXjwYIwdOxbh4eEIDQ3FqFGjYDAY0KZNGwBA586d0bRpUz5tR15eHqZOnYqRI0cqaukSS61aFf9MmAbCI0cu45NPKNGlWEwdmjMRq7WOTynh4GiCAITJ9r1CkEJYqCEElHYts7gQQepJkbvvg9A6ssaVOnMUy5aamqr5SY0WESXC1qxZg/fee6+SAAMqkqnOmzdPFhEWFBSE9PR0zJgxAzdv3kRMTAy6dOmCqVOnWoiadevWYfz48eA4DgaDAT/88AMeeeQR/nhCQgJ27NiBMWPGYMmSJahVqxY+/vhjPj0FUNEQr1y5gunTpyMvLw8PPvggdu7caRFov2jRIvj4+KBXr164c+cOkpOTLTYv9/X1xfbt2zFixAgYDAYEBwejf//+mD17tqT1ogQRERFo3rxEk3lY1J6Bm0TsmTN3sX49h/LyexZiX18Oo0Y9hfh4P011fGpZEKwnCGoihbDwFusoiwsRpLTsS/E+uHrvQv9OaIC/q14pwj1EibC//vqrUooHc5KSkni3nZS0aNECe/fudXhOZGQkn0bCER06dMCvvzp+SdLS0hzeR5UqVbB8+XIsX77c7jl16tSp5PrUMlrLw8LKDDwiIgIREcCHHwKvvAKUlQG+vsC//61Dq1Y1nV/ADKlFpdjreYtwcIYUwoIVF79ai0XURKo8X3K9D1I/E08JYBeL2pNwoYgSYVWrVkVhYSFq165t83hRURGqVKkiScEI9tBSHhbWZuCDBwPJySaXmnOrjnUHUlhYWCnrtS2EikoxItUEK8JBy7C04EANq6Zaoq+goACFhYUApNvVQY73wdYzqZj8uldnLIgNqXEksoxGI79K1REsrOgXJcIMBgNWrFiBFStW2Dy+fPlyPkaL8Fy8cfYsBUJdaq7GiADSi8+SkhKmhAOLiHkfWFlwoIZVUy1Xtq33yZZlPzU1FVFRUYLrXurM+baeybZt3aDTgZkFBKzgTh9pDgsr+kWJsClTpqBDhw4oKCjA+PHj0bhxY3AchxMnTmDBggX4+uuvsW/fPrnKSkiEWDOt+eDqqCP11kFYaljoGMxhRTiwiCvCgoUFB0pbNdV0Zdt7n6wt+3q9XlAblmufVFvPBPABx1X85K3uf1uI7SNZNhyIEmFt27bFpk2bMGzYMGzdutXiWPXq1fHZZ5+hXbt2khaQkBZXYqXMA8xnz64Bjru35cWOHd0xfXqi5gLMCXGwIBxYw5mwyM/Pr/Q31mJVrQUHSu9/6EmubLn2SbWdx8sSrdaZ3DgSWaylI7FGdLLWZ599FsnJyfj+++/x119/AahI1tq5c+dKezcS7OGquyoiIgLHjgH/pGfjKSvT4fr1mmBJfxUUFNgcAAlpYGmlopo4ExamnSSsUTMORS4rjjOUFn2OkMIqIkfyY1t5vCq2GLq3qppSA1XGkcjSwmIiUSJs7969SEtLw8GDB/Hss89aHDMajWjWrBlWrlyJxx57TNJCEmzQoAHg42MpxHx9KwLN3UHKVSxSxQqwjtTmdZbN9aziqrBQ090slxXHHmqJPnvIbRVxpS9ztCdldnZ91euMZZyJLC1YYEWJsMWLF2Po0KEIDQ2tdEyv1+OVV17BwoULSYR5KLVq2Uq14J5VROpUEvZXy3iOyJB6IGHdXM8aQoUFq0hlxREqOJQUfY6Q2yrial9mK4WEecb8tLRTyM0NQvPmVRSvM9ZxJrJYssDaQ5QI++233xxuuN25c2fMnz/f7UIR7OIo1YIrs0AlUklofTGBuYAEIHm2b9bN9axhT1iEhl7GgQPeIV5dFRxy71tqqw8yhSbIbRVxpy+z7hdjYmL4n1u0cK9cnowzkaWFiZIoEXbp0iX4+/vbv5ifH65cueJ2oQi2sRUTxEpyVGvsiYy0tAZo2bIG87NJawFpMGRKsgWSJ26ppGRyRlvC4uLFEhw4cO8cT7K+WsNaHj7AeR+kBauIGmglqakthIgsR4nGWejfRImw++67D8ePH0d9O0FAx44ds1DwhPcgtLPNzc21OFfuAHp7IqOoiF0BZuoYbAnIzEwDKgJ2792TK1sgedqWSqxNAsjFqzzO+iCl49K0IMJZe2+E4iiOzryue/fubbHxufU1WLgnUSLs6aefxrRp09ClS5dKmfFv3bqFGTNm2NxXkiBM2FsxJhf2Zr/x8XcVLYcYTAJp3z5g0aLKAnL48Bv46KPgf3J1ubYFkul7pNpSSW1Yssx4o4tXC4IDqBiw09IaoKiohqxxaVoR4Sy9N2LwpK2YRImwqVOnIj09HQ0bNkRaWhoaNWoEADh58iSWL1+OsrIyTJkyRZaCEtIg1l2ldezNfis6X3aJiIhAmza2V6NOmVINU6YI3wLJGWK3VCIco4UVWVLCquCwJwybNw9DTIx8g7M3inA10ILAEoIoEVazZk0cOHAAI0aMwOTJk8H9k8pXp9MhOTkZy5cvR82a2ppBexueNIMQim1zNdsiDHC+GlVKsUS5v9zHG7d3YlVwqCkMtSzCtWLR9CREJ2utU6cOvv32W1y7dg2nT58Gx3Fo0KABqlevLkf5CBkwF1gXLgB//VWRA8wTBmHrAc68U0lIOGv3PFZhwUqlxcBdNQYTb9zeiUXBobYw1OoCAFYtmp6OaBFmonr16nj4YfatCYR9Vq0Chg2rcHf5+FRYXQYPVrtUthEqmswHwo0bq2L2bD3Ky3Xw8eEwb54Rffve0txAqKaVSouBu2oOJt62vROLgkMtYchaYloxqC1cvRmXRRihbS5cuCfAgIr/X3mlwuqi1oCfkpKCkpIayMnxQ0LCXcTGVhROrGiKiIjAhQvAxInm96fDpElhSE0NY2qLJdbRWuAuS4OJJ7t4WRYcaglDlhLTioVFi6a3QCLMS/nrL1v7QFbM3F0ZOKTobHftisPEiWGSWOakvj+10aJLUA1oMFEGFgUHC8JQycS0UsKiRdNbIBHmpUi9D6SjgP/8/HynqSmMxpB/XIcVv7trmZNrn0s10KJLUGm8MShebWHOmuBgURiyjljhqnab80RIhHkpcuwDaS/gPzbW+cB39WqERcJQwD3LlRz3pxZacwmqgbcFxZMwtw1rwpB1xAhXanPyQCLMi5Fr5V3lgH/naTHy86tg/XppLVcsrCz0NFhewu5NQfEkzAmpECpcqc3JA4kwL0fq4GH7Af8R/PfYSosREyOP5cqTg6OVRktL2Om5EwShBUiEES5jKz7g4MEAlJdbmqLN3YqO0mKQ5YpdWFp1SFTGnoXSfG9WT3LHEoSnQCKMcAl78QFGYwh0utEWgdEmt6KQtBhkwWALU0Cus1WHnhTwrjUcWSitF8SkpqZCr9fbvA6JNGFQcHoFLIcmaAkSYYRL2OuErFfZ+Ppy+Pe/dahVC9i3z7PSRngDpsDdM2fuYv16zmLxhK8vh1GjnkJ8vJ9XDDosItZCuWnTJofXYyWomlWhQ8HpFWgpNIF1SIQRkmO+V+OoUU+hVauK/UQ9KW2Emig9A42IiEBEhK2YPR3/bAl1kDovGgtB1SwLHQpOp9AEqSERRsiCXn/9HzfVZVy8WKG6fH2BhQtDMG5cNc2njVASc1efoxmo3C5BitljD09MsklCh20oIbK0kAgjZMVWktZff30NBQXVaSAXiLlLcPbsGuC4Cpcgx/lgx47umD49UTGXIMXssYGzJJsAkJMTb9daSvE8hFi8MSGyEpAIIxQnMvI2WrRQuxTaIiIiAseO2Yqp0+H69Zq0H6aXYZ1kMy3tFDZsyEJ4+FVkZ9fH4sWj7cbrCInnYSkmi2XByHLZpMbbEiIrBYkwghCBmoOT1DF1LA20hHisn01Cwlmn8TpC4nlYisliOQCc5bLJhTclRFYKEmEEIRC1Bycpt2JS+14IeXAWryMknoeVmCwlA8DFTkg8ITjd3UkYhSZIA4kwwiW80e/PwuAkVXC8u/dCVjS2EBqvo6VAfqUCwMVMSJQum1zQJIwdSIQRLmEdkwJUZOe2FYhvDxrIXUPtGSh14OzhLF7n6aefwubNm+0G8puEQ0BAQKV3Uq24J6UEo5gJiacEp7MwoSQqIBFGuIw7AywN5NrDJJrNt8JxBHXgyuIsXsd80jR9+hWcOeOH+Pi7iI19GMDD/KTn4sWL/DXViHtytvLTXDAqDQWnE1JDIoxQBU+eiZlEiid1xkJFsxKQBdU5tqyl5nUSEwO0auX4GmrFPVlb2R0JRjlwZvmj4HRCSkiEEZJw4QLwyy9BMBpDnHbQtlwenoS5S1YrljxnAw8rz4ssqMqhZtyTWMEoFWItf2qHBkiFN6XaYA0SYYTbrFpl2phbDx+fMZg3z4i+fW/ZPNeWy8OTYUW8OMJdl5OSHbgnW1BZg+UgfjmsoZ6w4tEVvDHVBkuQCCPc4sIFkwCr+L28XIdJk8KQmhrmETNER3jC7NHdgYc6cM+D5ZgsQD5rqNZXPLqCtwpPliARRrjFX3/ZyuJeESfhaSJM6B6OWsB0L84GHnsDrdEYgvPn41zqwKW0YniCEGYNtWOynCGXNZRly59ceKPwZA0SYYRbOMrifuFChUhr0MAzBJmjPRy1Nns0v5f16zmUl+v4Y76+HEaNesrufpTmAtQaZx24lFYMrQthllErJssV3BXirFv+5MBTUm14AiTCCLewl8X9++/vuSl9fCrOGTxY7dK6j709HLU4e4yIiEBEhK3np0OrVjVt/o21+8IaZx24UOtEbm4uX0Yh5dCiECbcRwohzrrlTw4o1QY7kAgj3MY6izsA1KljHidWMcgnJ9+ziAmdYbE4E6uw/llaj3S6cty8GSxodShriMnCb8t9YUKnK8eMGbkYNkx4B27PimFaYWrPIkZuFO/AluvalAJGSiGuJcufVFCqDTYgEUZIgvlS7X37nMeJ2cq4bw2rM7FatYB584yYMCGUn4FzHPDFF89r1i3mbKm9I/cFUI7nnvsCcXEXkJbWX/AzE2LFsNc+vDF+x9tw5romIS4dWki14an5AUmEEZLjKE7MHPMXRmvxYwMGlOHvvxfj/Pla+OKL5wDYno2zaMlzBefui8dEB9TbsmLUqJGHWrXspy/xxvgdb8WZ65qEuPfgyfkBSYQRkmMvTsyeuLqXZ0w78WMRERGYMqU/9u0Dvvii8my8Xbv+6NDBva2dWENK94U9K8bHHw9Bjx72LYneGL9D2MaZECc8B0/OD0gijJAFoXFGlfOMVY4fY5WIiAi0aWPb6peYWBH07qm4676w7dYEAOdxPd4Yv0PYjh9s2fJX1Kt3GlevhiM8/KpFmyFrKKEFSIQRsiFkoNZ6njGxVj+iAmsrhjneFNfjqXEuUuMoflCvv46BA59EZGQkfz7VG6EVSIQRqiI0foxlxKwu9HbMrRMtW/6KGjXy8PHHQ2CKqQO8J67HOs7F3kpRLca5SImt+MFt2yytpZGRkYiJiVGzmAThErbXmhOEQpgsSb6+Fb9r1ZJUqxbQoYP2yq00ppiulJQUAECtWhfRo8d26HQVKtyb4nrMLWBHjjyExYtHY926/li8eDSOHHnI5nneiO20KD7IykpUpTyE+hiNIcjJiYfRGKJ2UdyGLGGE6pAlybuwtup4e1wPJZ51THh4AYByWNsMMjMNSEzMojryMjxtpwwSYQQTaCFPDXEP81im3Fwf5OT4ISHhLmJjKyxazmJytJwnTmoo35VtzNORtG2biQMH2lkcpzryPjxxwkIijCAIUZjHMjmalTqLZfIGgeWIwsJCAJTvyh4RERHo3bs3Nm/ejMTELGRmGqiOvBSTIHc2YdGi5ZxiwgiCcMiFCxW7IFy4UPG7yXplb1ZqitPw9lgmRxQUFGDz5s0AgOzs+uC4e8e8KS7OGWFhYQDurab1xthB4p7lfNSoLvDx4SyO+fpyGDXqKc0uYCFLGEF4KO7sQmByN27cWBUTJ+pRXq6Djw+HefOM6Ny5Yu8+cqO5jrWQNZ8PcxxQr95plUrGFtarab05dlDruJuOJSKiIvdi5ZRAOrRqVVOOIisCiTCC8EDc2YXA5G40GkOwePFocFzFRuXl5TpMmBCKv//OgF5v343m71+CnJx45Ob6gLIGOMbeyj8SshVQ7KBnIOW2Q562kItEGEF4EAUFBThz5i6GDauB8nKTeAJeeYXDgw9eRny8n9NOzjTgObN02do25v77j2HVqiHgOB+sX89pYgsqNRESD+btVh4SWNpH6m2HPGkhF4kwgvAQTLPNnJx4lJf3tzhWVqbD++9/h4SEs4JjJ4QIBHMXkb9/CS/AgArLmRjx54042/8wNTWV6o0gPBgSYQRhB3diqtTANIt0Jp6EzjaFbpBssorl5MRXspy5Iv68DUexTnq9XsWSEQQhNyTCCMIG7sRUqY1Q8SQERwLBGqnEnzdiErIEQXgXJMIIwooLF+4JMMAUU1URDKoFixggTjw5Q6hAkFL8EQRBuIK7qzCVhkQYQVjx11+WG4oDFcuhT5/WjggD5LeupKSkIDIyEvn5+UhPTwcgrfjzZIQG23t6UD6LAyaLZfI07G1W7y5SrsJUChJhBGFFgwYVLkhzIebrW7EcmrhHZGQkYmzkoCDXmnO0lHpBLlHC4oDJYpk8DTn3fpR6FaYSkAgjCDMKCgrg61uCefOqYtIkPcrKdPD15TB3rhG+vrdQUMDGwCgGsbNOstIogxbakbUosdeWXBEl1gOhvWsrOWBqcRDXAqa+wtnej97Yp5AII4h/sB5wXnsthHer3bhxHR9+WPG5q7NgNVZb2pp1OsOelcZ8o25KOeEdmLcBRxYMd0WJnNYRQn1Mfcq+fcCiRZVzD7Zr1x8dOmhjYiI1tHckQfyD9UCi119HQsLZStYjVwacVauAOnWATp0q/l+1yq2i2sR6Fmlv1pmfX8XptSIiIhATE8P/+/bbGDzySE08/3wEHnmkJr76yvs6S2/G2T6hrF7bXYzGEOTkxDNRFq0TERGBNm0i4GOlOnx9gcTECK8UYIAGRdidO3fw4IMPQqfT4ejRoxbHjh07hsceewxVqlRBXFwc5s2bV+nvt2zZgsaNG6NKlSpo0aIFvv32W4vjHMdh+vTpiImJQdWqVZGUlIS//vrL4pyrV6/ixRdfRGhoKMLCwjB48GDcuHFDdFkI78DeakvThthSYZptDhs2DMOGDUPbtv1tZrwvKKgu6rrOyk/uS2Ww3khdSRztnsDytd3hyJGHsHjxaKxb1x+LF4/GkSMPqVoeT6BWrYp0P76+Fb9X7P2orQVPUqM5d+TEiRMRGxuL3377zeLzoqIidO7cGUlJSVi5ciV+//13DBo0CGFhYRg2bBgA4MCBA3jhhRcwZ84cdOvWDRs3bkTPnj1x5MgRNG/eHAAwb948LF26FOvWrUNCQgKmTZuG5ORk/PHHH6hSpcKC8OKLL+LixYvIyMhAaWkpBg4ciGHDhmHjxo2Cy0J4D0qutjSfTbZpI80CA2fl11KQudZwtJF63763FKtXIbsnsHhtV3EWu0S4jqft/egumhJh3333HXbt2oWtW7fiu+++szi2YcMGlJSUYPXq1QgICECzZs1w9OhRLFy4kBc+S5YsQZcuXTBhwgQAwFtvvYWMjAwsW7YMK1euBMdxWLx4MaZOnYpnnnkGAPDJJ5+gZs2a+Oqrr9CnTx+cOHECO3fuxKFDh9C6dWsAwPvvv4+nn34a8+fPR2xsrKCySEV5eTkFiUpEaWkpgoODK31eXFwMjuNcvq5aqy1Ns85XXqkQTa7OOoWUnwSWOITEBzrfSH019PrriqzUkzMHHIv55Zztm0q4h1J7P8qVCkNKNCPCLl26hKFDh+Krr75CUFBQpeOZmZlo3769hcsjOTkZc+fOxbVr11C9enVkZmZi7NixFn+XnJyMr776CgCQk5ODvLw8JCUl8cf1ej0SExORmZmJPn36IDMzE2FhYbwAA4CkpCT4+PggKysLzz77rKCy2OLOnTu4c+cO/3tRUZHDOikpKUFOTg7Krc0UhEuUlZWhXbt2lT6/desWDh06hNu3b7t0XanEkCtIMeuUq/xa2xZKKoTuxiB0I3VXJmGu1L2cOeBYyy/HonWOcI75mOtosQdLoRGaEGEcx2HAgAEYPnw4WrdujTNnzlQ6Jy8vDwkJCRaf1axZkz9WvXp15OXl8Z+Zn5OXl8efZ/539s6pUaOGxXE/Pz+Eh4dbnOOsLLaYM2cOZs2aZbsSrOA4DhcvXoSvry/i4uLgYx3tSIimpKQEhYWFFp9xHIdr166hUaNGlVzgYlDTBC/FrFPq8mt5Wyh3cGU3BqkEgRSuTSlzwFkPhPaureSAafouZ9Y5lgZx4h6m0IgzZ+5i9uwavPWY43ywY0d3TJ+eyNzKblVF2BtvvIG5c+c6POfEiRPYtWsXrl+/jsmTJytUMnWYPHmyhaWuqKgIcXFxNs+9e/cuiouLERsba9MySIjHx8cHfn6VXwm9Xo+oqCgEBAS45fpVygQvF1KV3xO2hXKFgoICHDwIlJdbDgBlZUBWVgGqVrXt1pXCXeeqa1PORRcsxhJal2n69Cs4c8YP8fF3ERv7MICHKb6RcSIiInDsmK04Vh2uX68J1h6dqiJs3LhxGDBggMNz6tati7179yIzMxOBgYEWx1q3bo0XX3wR69atQ3R0NC5dumRx3PR7dHQ0/7+tc8yPmz4zzwR+6dIlPPjgg/w5ly9ftrjG3bt3cfXqVaffY/4dtggMDKx0j/YoKysDQDMyKbFnTfTx8YGPjw/8/f1RUlKiiTqXKsu5HNnSPWVbKDGYiyCdbnQlq9b+/etw/Lj9+C533XWuujblFkosihnzMsXEAK1aqVgYwiW0tOuJqiIsKioKUVFRTs9bunQp3n77bf733NxcJCcnY9OmTUhMTAQAGAwGTJkyBaWlpfD39wcAZGRkoFGjRrz7z2AwYM+ePRg9ejR/rYyMDBgMBgBAQkICoqOjsWfPHl50FRUVISsrCyNGjOCvUVhYiMOHD6PVP2/n3r17UV5eLqosUqHT6SS9njfj5+eHGjVqVIqxu337Nq5fv47evXsjODiYyYHDHKm2XpFrCxctdZBSYRIxzqxajsSOFK5AV1ybrLd3grBGzThcsWgiJqx27doWv1erVg0AUK9ePdT6p1b79u2LWbNmYfDgwZg0aRKOHz+OJUuWYNGiRfzfvf7663j88cexYMECdO3aFZ9//jl++eUXfPhPKnSdTofRo0fj7bffRoMGDfgUFbGxsejZsycAoEmTJujSpQuGDh2KlStXorS0FGlpaejTpw9iY2MFl4VgE1vuyPLycvj6+qJGjRp8mhIlcDVwXaqtV+TawkVLHaQcqBmEzuJKRIKQA62kwtCECBOCXq/Hrl27MHLkSLRq1QqRkZGYPn26RUqItm3bYuPGjZg6dSrefPNNNGjQAF999RWfIwyoyEN28+ZNDBs2DIWFhXj00Uexc+dOi8F3w4YNSEtLwxNPPAEfHx/06tULS5cuFVUWb2XAgAFYt24dgHsLGu6//3688MILGDBggOAFBmvXrsXo0aMrBdJ7Cp4euM5yB6nEqk01NzlnbSUiQciFFuJwNSnC4uPjbeZtuv/++/HTTz85/Nvnn38ezz//vN3jOp0Os2fPxuzZs+2eEx4ezidmtYeQsngrXbp0wZo1a1BWVoZLly5h586deP311/HFF19g27ZtNq1RWsWVmCpvCVxnsYNkSfzKGRSvpggkCOIenjPaEaKQI+BaKIGBgfwChfvuuw8tW7ZEmzZt8MQTT2Dt2rUYMmQIFi5ciDVr1uD//u//EB4eju7du2PevHmoVq0afvjhBwwcOBDAvZi4GTNmYObMmVi/fj2WLFmCU6dOITg4GJ06dcLixYsrpRVRAldjqpQIXDcaQ7B/fwDatGFPCKkFa+KXxdWDBEFIC4kwL0SugGt36NSpEx544AGkp6djyJAh8PHxwdKlS5GQkID/+7//w6uvvoqJEyfigw8+QNu2bbF48WJMnz4dp06dAnAvTrC0tBRvvfUWGjVqhMuXL2Ps2LEYMGBApT1ClcDVmCq5A9dNSQwXLfJR3Nqjpvh3BourNklgEYRnQyLMC5Er4NpdGjdujGPHjgGAxQrW+Ph4vP322xg+fDg++OADBAQEQK/XQ6fTVUr5MWjQIP7nunXrYunSpXj44Ydx48YNXqixjpyB69Z74ilp7WFR/Jvjyas2WdhknWUBThBqQSKMYAaO43j34u7duzFnzhycPHkSRUVFuHv3Lm7fvo3i4mKHyWkPHz6MmTNn4rfffsO1a9f4dBPnzp1D06ZNFbkPKZArcN1WniilrD2sin8TcopftUWQ2q5NawFub08/tQQ4QagFiTCCGU6cOIGEhAScOXMG3bp1w4gRI/DOO+8gPDwcP//8MwYPHoySkhK7IuzmzZtITk5GcnIyNmzYgKioKJw7dw7Jycma3OTc1cB1RwO5rTxR9qw9agsHJTFZaZ5+GsjK8jHLkl6OixfdFyhqiyBTGdTC/L4d7emnxfeUINyBRBjBBHv37sXvv/+OMWPG4PDhwygvL8eCBQv4lBWbN2+2OD8gIIDfNcDEyZMnUVBQgHfffZff7umXX35R5gYYwtmAf999RZg0SY+yMp1Daw8LwkEJ7LlJjx+3/N1dK43S9cTiBunW7nCO88E333RDvXqnabUm4ZWQCCMU586dO8jLy7NIUTFnzhx069YNL7/8Mo4fP47S0lK8//776N69O/bv34+VK1daXCM+Ph43btzAnj178MADDyAoKAi1a9dGQEAA3n//fQwfPhzHjx/HW2+9pdJdqoujAX/cOCA1VZirU+sCSwisu0ldgaVUG+Y42zaJILwNYZkxCUJCdu7ciZiYGMTHx6NLly7Yt28fli5diq+//hq+vr544IEHsHDhQsydOxfNmzfHhg0bMGfOHItrtG3bFsOHD0dqaiqioqIwb948REVFYe3atdiyZQuaNm2Kd999F/Pnz1fpLtmmVi2gQwd2LCQmTKkzLlxQuyTSceECsG8fFLmngoICHD58CcOGcVapNjgcPnwJBQUF8hfCASZ3uDnOtk0iCE+GLGGEoqxduxZr1651et6YMWMwZswYi89eeukli99XrFiBFStWWHz2wgsv4IUXXrD4zFZiXyXwppgqKVAzdYZcKGmRMrlVc3LiUV7e3+JYWZkO77//HRISzqoa/O5s26TCwkLExMSoUjaCUAMSYV4IiQNl8JaYKilQM3WGXCid/NXUzpxt0q22W9W0bdL587UA6BAXd54/tnnzZlohSXgVJMK8EBIHykF1eA9Hol7N1BlyoVbyV6U36RaS/8ua7Oz6tEKSIEAizGshcUAojSPxn5vrg/XrOZSX6/jPtJwotaCgAKGhd+HjU8PqnjiEhFxGQYGfrO+gUpt0C83/1a9fP4tzaIUkQVRAIowgCMWwJzxiYuRLlKo05sKkWzfLnFhdu27H9u0VFh+53W5KbNItNP9XUFAQUlNTsWnTJlohKRDaYcA7IBFGEAzhzR2vXLsEOEPqGEnz5+fIIuVJbjch1i29Xg/Aecwawf4WX4R0kAgjCEaQu+PVgsBzdZcAd5A7RlIJi5TaiLFuKR2zpkU8MXcdYRsSYQTBCHJ2vDSzdow33rOUiLVuKRWz5unk5+fb/JyFCRUhDBJhBOEF0Mza81Ez9Ywr1i25LYRasPy6S3p6ut1j3jqh0hokwgiCUBVvGCyVQO3UM86sW0qKRLL80oRKK5AIIzyKH374AR07dsS1a9cQFhYm6G/i4+MxevRojB49WtayEZWhwVJa1K4jR9YtJUUiWX4JrUB7RxKKMmDAAOh0OgwfPrzSsZEjR0Kn02HAgAHKF4xQBRostY1Y61ZERARiYmLs/lNbRBKE0pAljFCcuLg4fP7551i0aBGqVq0KALh9+zY2btyI2rVrq1w6gnAPb9oWTG0XKEFoHRJhhOK0bNkS2dnZSE9Px4svvgigIsC0du3aSEhI4M+7c+cOJkyYgM8//xxFRUVo3bo1Fi1ahIcffpg/59tvv8Xo0aNx/vx5tGnTBv3796/0fT///DMmT56MX375BZGRkXj22WcxZ84cBAcHy3+zhNfhbcJEC/dhL5M/q1gLdK2VnxAOiTBCFQYNGoQ1a9bwImz16tUYOHAgfvjhB/6ciRMnYuvWrVi3bh3q1KmDefPmITk5GadPn0Z4eDjOnz+PlJQUjBw5EsOGDcMvv/yCcePGWXxPdnY2unTpgrfffhurV6/GlStXkJaWhrS0NKxZs0bJW3aKN1lQPB0tCBNvwVEmf1YxF/IbN1bF7Nl6lJfr4OPDYfr0vwGsUruIhESQCCNw4ULFZsMNGiiXKLNfv36YPHkyzp49CwDYv38/Pv/8c16E3bx5EytWrMDatWvx1FNPAQA++ugjZGRkYNWqVZgwYQJWrFiBevXqYcGCBQCARo0a4ffff8fcuXP575kzZw5efPFFPui+QYMGWLp0KR5//HGsWLECVapUUeaGBSCnBYUEHuGNaHmfyoiICFy4AEyceG8j+PJyHd566z689loI8+UnhEEizMtZtQoYNqziJffxqdi/b/Bg+b83KioKXbt2xdq1a8FxHLp27YrIyEj+eHZ2NkpLS9GuXTv+M39/fzzyyCM4ceIEAODEiRNITEy0uK7BYLD4/bfffsOxY8ewYcMG/jOO41BeXo6cnBw0adJEjttzGbksKN7mIiMIQFwmfxb56697AsxEWZlOUPlpQqUNSIR5MRcu3BNgQMX/r7xSsX+fEhaxQYMGIS0tDQCwfPlyWb7jxo0beOWVV/Daa69VOuZtiwBIYBHegkmAOMvkz7pQadCgYnJsLsR8fYFx455BZORtAEBurg9ycvyQkHAXsbEVJ9KESjuQCPNibM+yKjZQVkKEdenSBSUlJdDpdEhOTrY4Vq9ePQQEBGD//v2oU6cOAKC0tBSHDh3iXYtNmjTBtm3bLP7u4MGDFr+3bNkSf/zxB+rXry/fjRAuQ25SQg7MLb/33VeESZP0KCvTwdeXw9y5Rejb9wVNCJVatSq8E6+8UtE3+/oC//430KJFdQDqeTII6SAR5sXYm2UppVd8fX1516Kvr6/FseDgYIwYMQITJkxAeHg4ateujXnz5qG4uBiD/+llhg8fjgULFmDChAkYMmQIDh8+jLVr11pcZ9KkSWjTpg3S0tIwZMgQBAcH448//kBGRoagJKGEvJCblJALU5sZNw5ITa2YXNavr0OtWmEAwtQsmigGD67wTlSU/94EWW1PBiENJMK8GHuzLCVf4NDQULvH3n33XZSXl+Oll17C9evX0bp1a3z//feoXr1iFli7dm1s3boVY8aMwfvvv49HHnkE//rXvzBo0CD+Gvfffz9+/PFHTJkyBY899hg4jkO9evWQmpoq+70RwiCBRchNrVraFia2yq+2J4OQBh3HcZzahSBsU1RUBL1eD6PRWEms3L59Gzk5OUhISHB7hd+FC5VnWcQ9pKxrgiAIKbhwAahTp7In48wZ6sdZwNH4bQ5tW0SgVi2gQwd6cQmCILSCyZNhiuRQw5NBuA+5IwmCIAhCg9iLFyO0A4kwgiAIgtAoWo9383bIHUkQBEEQBKECJMIIgiAIgiBUgESYxqHFrfJDdUwQBEHIAYkwjWJKbuooySUhDaY6tk4oSxAEQRDuQIH5GsXPzw9BQUG4cuUK/P394eNDeloOysvLceXKFQQFBcHPj14XgiAIQjpoVNEoOp0OMTExyMnJwdmzZ9Uujkfj4+OD2rVrQ6fTqV0UgiAIwoMgEaZhAgIC0KBBA3JJykxAQABZGgmCIAjJIRGmcXx8fGgrHYIgCILQIDS9JwiCIAiCUAESYQRBEARBECpAIowgCIIgCEIFKCaMYUxJQouKilQuCUEQBEEQQjGN286SfZMIY5jr168DAOLi4lQuCUEQBEEQYrl+/Tr0er3d4zqO9mRhlvLycuTm5iIkJMQrc1QVFRUhLi4O58+fR2hoqNrF0SxUj9JA9eg+VIfSQPUoDXLWI8dxuH79OmJjYx2mOCJLGMP4+PigVq1aahdDdUJDQ6mjkQCqR2mgenQfqkNpoHqUBrnq0ZEFzAQF5hMEQRAEQagAiTCCIAiCIAgVIBFGMEtgYCBmzJiBwMBAtYuiaagepYHq0X2oDqWB6lEaWKhHCswnCIIgCIJQAbKEEQRBEARBqACJMIIgCIIgCBUgEUYQBEEQBKECJMIIgiAIgiBUgEQYoTr/+c9/0L17d8TGxkKn0+Grr76yOM5xHKZPn46YmBhUrVoVSUlJ+Ouvv9QpLKM4q8MBAwZAp9NZ/OvSpYs6hWWYOXPm4OGHH0ZISAhq1KiBnj174tSpUxbn3L59GyNHjkRERASqVauGXr164dKlSyqVmE2E1GOHDh0qtcnhw4erVGI2WbFiBe6//34+majBYMB3333HH6e2KAxn9ahmWyQRRqjOzZs38cADD2D58uU2j8+bNw9Lly7FypUrkZWVheDgYCQnJ+P27dsKl5RdnNUhAHTp0gUXL17k/3322WcKllAb/Pjjjxg5ciQOHjyIjIwMlJaWonPnzrh58yZ/zpgxY/DNN99gy5Yt+PHHH5Gbm4uUlBQVS80eQuoRAIYOHWrRJufNm6dSidmkVq1aePfdd3H48GH88ssv6NSpE5555hn873//A0BtUSjO6hFQsS1yBMEQALgvv/yS/728vJyLjo7m3nvvPf6zwsJCLjAwkPvss89UKCH7WNchx3Fc//79uWeeeUaV8miZy5cvcwC4H3/8keO4irbn7+/PbdmyhT/nxIkTHAAuMzNTrWIyj3U9chzHPf7449zrr7+uXqE0SvXq1bmPP/6Y2qKbmOqR49Rti2QJI5gmJycHeXl5SEpK4j/T6/VITExEZmamiiXTHj/88ANq1KiBRo0aYcSIESgoKFC7SMxjNBoBAOHh4QCAw4cPo7S01KI9Nm7cGLVr16b26ADrejSxYcMGREZGonnz5pg8eTKKi4vVKJ4mKCsrw+eff46bN2/CYDBQW3QR63o0oVZbpA28CabJy8sDANSsWdPi85o1a/LHCOd06dIFKSkpSEhIQHZ2Nt5880089dRTyMzMhK+vr9rFY5Ly8nKMHj0a7dq1Q/PmzQFUtMeAgACEhYVZnEvt0T626hEA+vbtizp16iA2NhbHjh3DpEmTcOrUKaSnp6tYWvb4/fffYTAYcPv2bVSrVg1ffvklmjZtiqNHj1JbFIG9egTUbYskwgjCC+jTpw//c4sWLXD//fejXr16+OGHH/DEE0+oWDJ2GTlyJI4fP46ff/5Z7aJoGnv1OGzYMP7nFi1aICYmBk888QSys7NRr149pYvJLI0aNcLRo0dhNBrxxRdfoH///vjxxx/VLpbmsFePTZs2VbUtkjuSYJro6GgAqLTi59KlS/wxQjx169ZFZGQkTp8+rXZRmCQtLQ3bt2/Hvn37UKtWLf7z6OholJSUoLCw0OJ8ao+2sVePtkhMTAQAapNWBAQEoH79+mjVqhXmzJmDBx54AEuWLKG2KBJ79WgLJdsiiTCCaRISEhAdHY09e/bwnxUVFSErK8vCn0+I48KFCygoKEBMTIzaRWEKjuOQlpaGL7/8Env37kVCQoLF8VatWsHf39+iPZ46dQrnzp2j9miGs3q0xdGjRwGA2qQTysvLcefOHWqLbmKqR1so2RbJHUmozo0bNyxmHDk5OTh69CjCw8NRu3ZtjB49Gm+//TYaNGiAhIQETJs2DbGxsejZs6d6hWYMR3UYHh6OWbNmoVevXoiOjkZ2djYmTpyI+vXrIzk5WcVSs8fIkSOxceNGfP311wgJCeFja/R6PapWrQq9Xo/Bgwdj7NixCA8PR2hoKEaNGgWDwYA2bdqoXHp2cFaP2dnZ2LhxI55++mlERETg2LFjGDNmDNq3b4/7779f5dKzw+TJk/HUU0+hdu3auH79OjZu3IgffvgB33//PbVFETiqR9XboiprMgnCjH379nEAKv3r378/x3EVaSqmTZvG1axZkwsMDOSeeOIJ7tSpU+oWmjEc1WFxcTHXuXNnLioqivP39+fq1KnDDR06lMvLy1O72Mxhqw4BcGvWrOHPuXXrFvfqq69y1atX54KCgrhnn32Wu3jxonqFZhBn9Xju3Dmuffv2XHh4OBcYGMjVr1+fmzBhAmc0GtUtOGMMGjSIq1OnDhcQEMBFRUVxTzzxBLdr1y7+OLVFYTiqR7Xboo7jOE5+qUcQBEEQBEGYQzFhBEEQBEEQKkAijCAIgiAIQgVIhBEEQRAEQagAiTCCIAiCIAgVIBFGEARBEAShAiTCCIIgCIIgVIBEGEEQBEEQhAqQCCMIgiAIglABEmEEQTBLXl4eRo0ahbp16yIwMBBxcXHo3r27xX55Bw4cwNNPP43q1aujSpUqaNGiBRYuXIiysjL+nDNnzmDw4MFISEhA1apVUa9ePcyYMQMlJSUW3/fRRx/hgQceQLVq1RAWFoaHHnoIc+bM4Y/PnDkTOp0OXbp0qVTW9957DzqdDh06dBB0b6Zr6XQ6+Pn5IT4+HmPGjMGNGzdE1hJBEFqF9o4kCIJJzpw5g3bt2iEsLAzvvfceWrRogdLSUnz//fcYOXIkTp48iS+//BK9e/fGwIEDsW/fPoSFhWH37t2YOHEiMjMzsXnzZuh0Opw8eRLl5eX497//jfr16+P48eMYOnQobt68ifnz5wMAVq9ejdGjR2Pp0qV4/PHHcefOHRw7dgzHjx+3KFdMTAz27duHCxcuoFatWvznq1evRu3atUXdY7NmzbB7927cvXsX+/fvx6BBg1BcXIx///vflc4tKSlBQECACzUpHyyWiSA0hSKbIxEEQYjkqaee4u677z7uxo0blY5du3aNu3HjBhcREcGlpKRUOr5t2zYOAPf555/bvf68efO4hIQE/vdnnnmGGzBggMMyzZgxg3vggQe4bt26cW+//Tb/+f79+7nIyEhuxIgR3OOPPy7g7u5dy5yhQ4dy0dHRFsc/+ugjLj4+ntPpdBzHVdz74MGDucjISC4kJITr2LEjd/ToUf4aR48e5Tp06MBVq1aNCwkJ4Vq2bMkdOnSI4ziOO3PmDNetWzcuLCyMCwoK4po2bcrt2LGD4ziOW7NmDafX6y3K8+WXX3Lmw4SrZSIIwjbkjiQIgjmuXr2KnTt3YuTIkQgODq50PCwsDLt27UJBQQHGjx9f6Xj37t3RsGFDfPbZZ3a/w2g0Ijw8nP89OjoaBw8exNmzZ52Wb9CgQVi7di3/++rVq/Hiiy+6bRWqWrWqhYv09OnT2Lp1K9LT03H06FEAwPPPP4/Lly/ju+++w+HDh9GyZUs88cQTuHr1KgDgxRdfRK1atXDo0CEcPnwYb7zxBvz9/QEAI0eOxJ07d/Cf//wHv//+O+bOnYtq1aqJKqMrZSIIwjbkjiQIgjlOnz4NjuPQuHFju+f8+eefAIAmTZrYPN64cWP+HFvXf//993lXJADMmDEDKSkpiI+PR8OGDWEwGPD000/jueeeg4+P5Xy1W7duGD58OP7zn/+gVatW2Lx5M37++WesXr1a7K3yHD58GBs3bkSnTp34z0pKSvDJJ58gKioKAPDzzz/jv//9Ly5fvozAwEAAwPz58/HVV1/hiy++wLBhw3Du3DlMmDCBr7sGDRrw1zt37hx69eqFFi1aAADq1q0rupyulIkgCNuQCCMIgjk4jpPlXAD4+++/0aVLFzz//PMYOnQo/3lMTAwyMzNx/Phx/Oc//8GBAwfQv39/fPzxx9i5c6eFEPP390e/fv2wZs0a/N///R8aNmyI+++/X1Q5AOD3339HtWrVUFZWhpKSEnTt2hXLli3jj9epU4cXOwDw22+/4caNG4iIiLC4zq1bt5CdnQ0AGDt2LIYMGYL169cjKSkJzz//POrVqwcAeO211zBixAjs2rULSUlJ6NWrl+hyu1ImgiBsQyKMIAjmaNCgAR9Qb4+GDRsCAE6cOIG2bdtWOn7ixAk0bdrU4rPc3Fx07NgRbdu2xYcffmjzus2bN0fz5s3x6quvYvjw4Xjsscfw448/omPHjhbnDRo0CImJiTh+/DgGDRok9hYBAI0aNcK2bdvg5+eH2NjYSu5Ma1fsjRs3EBMTgx9++KHStcLCwgBUrLrs27cvduzYge+++w4zZszA559/jmeffRZDhgxBcnIyduzYgV27dmHOnDlYsGABRo0aBR8fn0qCtrS0tNL3uFImgiBsQzFhBEEwR3h4OJKTk7F8+XLcvHmz0vHCwkJ07twZ4eHhWLBgQaXj27Ztw19//YUXXniB/+zvv/9Ghw4d0KpVK6xZs6aSi9EWJhFnqwzNmjVDs2bNcPz4cfTt21fM7fEEBASgfv36iI+PFxRP1rJlS+Tl5cHPzw/169e3+BcZGcmf17BhQ4wZMwa7du1CSkoK1qxZwx+Li4vD8OHDkZ6ejnHjxuGjjz4CAERFReH69esW92qK+ZKiTARBVIZEGEEQTLJ8+XKUlZXhkUcewdatW/HXX3/hxIkTWLp0KQwGA4KDg/Hvf/8bX3/9NYYNG4Zjx47hzJkzWLVqFQYMGIDnnnsOvXv3BnBPgNWuXRvz58/HlStXkJeXh7y8PP77RowYgbfeegv79+/H2bNncfDgQbz88suIioqCwWCwWca9e/fi4sWLill8kpKSYDAY0LNnT+zatQtnzpzBgQMHMGXKFPzyyy+4desW0tLS8MMPP+Ds2bPYv38/Dh06xMfNjR49Gt9//z1ycnJw5MgR7Nu3jz+WmJiIoKAgvPnmm8jOzsbGjRstFh+4WiaCIOxD7kiCIJikbt26OHLkCN555x2MGzcOFy9eRFRUFFq1aoUVK1YAAJ577jns27cP77zzDh577DHcvn0bDRo0wJQpUzB69GjodDoAQEZGBk6fPo3Tp09b5PYC7sWUJSUlYfXq1VixYgUKCgoQGRkJg8GAPXv2VIp3MmFr5aac6HQ6fPvtt5gyZQoGDhyIK1euIDo6Gu3bt0fNmjXh6+uLgoICvPzyy7h06RIiIyORkpKCWbNmAQDKysowcuRIXLhwAaGhoejSpQsWLVoEoML6+Omnn2LChAn46KOP8MQTT2DmzJlOA+udlYkgCPvoOLFRrQRBEARBEITbkDuSIAiCIAhCBUiEEQRByEC1atXs/vvpp5/ULh5BEAxA7kiCIAgZOH36tN1j9913H6pWrapgaQiCYBESYQRBEARBECpA7kiCIAiCIAgVIBFGEARBEAShAiTCCIIgCIIgVIBEGEEQBEEQhAqQCCMIgiAIglABEmEEQRAEQRAqQCKMIAiCIAhCBUiEEQRBEARBqMD/A73T4mqlkO6gAAAAAElFTkSuQmCC", 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Introduction\n", + "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogate\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(alm_surr, data_training)\n", - "surrogate_parity(alm_surr, data_training)\n", - "surrogate_residual(alm_surr, data_training)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=7, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "\n", + "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", + "cols = csv_data.columns\n", + "cols = [item.replace(\".\", \"_\") for item in cols]\n", + "csv_data.columns = cols\n", + "\n", + "data = csv_data.sample(n=500, random_state=0)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with ALAMO\n", + "\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ***************************************************************************\n", + " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", + "\n", + " If you use this software, please cite:\n", + " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", + " Automatic Learning of Algebraic Models for Optimization,\n", + " AIChE Journal, 60, 2211-2227, 2014.\n", + "\n", + " ALAMO is powered by the BARON software from http://www.minlp.com/\n", + " ***************************************************************************\n", + " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", + " ***************************************************************************\n", + " Reading input data\n", + " Checking input consistency and initializing data structures\n", + " \n", + " Step 0: Initializing data set\n", + " User provided an initial data set of 400 data points\n", + " We will sample no more data points at this stage\n", + " ***************************************************************************\n", + " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", + " \n", + " Step 1: Model building using BIC\n", + " \n", + " Model building for variable CO2SM_CO2_Enthalpy\n", + " ----\n", + " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", + " ----\n", + " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", + " ----\n", + " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", + " ----\n", + " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", + " \n", + " Model building for variable CO2SM_CO2_Entropy\n", + " ----\n", + " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", + " ----\n", + " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", + " ----\n", + " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", + " ----\n", + " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", + " \n", + " Calculating quality metrics on observed data set.\n", + " \n", + " Quality metrics for output CO2SM_CO2_Enthalpy\n", + " ---------------------------------------------\n", + " SSE OLR: 0.515E+08\n", + " SSE: 0.659E+08\n", + " RMSE: 406.\n", + " R2: 0.999\n", + " R2 adjusted: 0.999\n", + " Model size: 6\n", + " BIC: 0.484E+04\n", + " Cp: 0.659E+08\n", + " AICc: 0.482E+04\n", + " HQC: 0.483E+04\n", + " MSE: 0.168E+06\n", + " SSEp: 0.659E+08\n", + " RIC: 0.659E+08\n", + " MADp: 0.594\n", + " \n", + " Quality metrics for output CO2SM_CO2_Entropy\n", + " --------------------------------------------\n", + " SSE OLR: 541.\n", + " SSE: 558.\n", + " RMSE: 1.18\n", + " R2: 0.997\n", + " R2 adjusted: 0.997\n", + " Model size: 10\n", + " BIC: 193.\n", + " Cp: 178.\n", + " AICc: 154.\n", + " HQC: 169.\n", + " MSE: 1.43\n", + " SSEp: 558.\n", + " RIC: 606.\n", + " MADp: 0.130E+04\n", + " \n", + " Total execution time 0.52 s\n", + " Times breakdown\n", + " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", + " MINLP time: 0.0 s in 0 optimization problem(s)\n", + " Simulation time: 0.0 s to simulate 0 point(s)\n", + " All other time: 0.22 s in 1 iteration(s)\n", + " \n", + " Normal termination\n", + " ***************************************************************************\n" + ] + } + ], + "source": [ + "# Create ALAMO trainer object\n", + "has_alamo = alamo.available()\n", + "if has_alamo:\n", + " trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + " )\n", + "\n", + " # Set ALAMO options\n", + " trainer.config.constant = True\n", + " trainer.config.linfcns = True\n", + " trainer.config.multi2power = [1, 2]\n", + " trainer.config.monomialpower = [2, 3]\n", + " trainer.config.ratiopower = [1]\n", + " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + " trainer.config.overwrite_files = True\n", + "\n", + " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [7, 306], [40, 1000]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n", + "else:\n", + " print(\"Alamo not found.\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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EdHS0/H3v3r3FtGnT5O+Li4uFv7+/eO2114QQQuTl5Ql7e3uxbt06uc2JEycEAJGcnCyEEOKHH34QKpVK5OTkyG0+/vhj4erqKm7evFnue9TpdAKA0Ol0Fuf+/vtvcfz4cfH333+X+3pGV65cEfPnzy/z48qVKxW+dlkmTpwoHnjgAYvjO3bsEADEZ599JoQQ4u233xahoaHC2dlZBAQEiCeffFIUFBQIIYTYtWuXAKD4mDdvnhBCiC+++EL07NlTNG/eXPj6+opx48aJixcv3lafb+dZExFR7Vq0qEBIUrEAhAD0/342/SgWw4d/L06cOFFlv/dK+/1tqt6MhJnKzc3F119/jYiICLlu06ZNm9CmTRts3rwZQUFBaN26NaZMmYLc3Fz5dcnJyYiMjFRcKzo6GsnJyQAMFehTU1MVbVQqFSIjI+U2qampKCoqUrTp0KEDWrZsKbdJTk5Gly5d4Ovrq3if/Px8HDt2zOZ93bx5E/n5+YqP6lDebY5qcjuke++9F926dcPGjRsBGJ77+++/j2PHjmHlypXYuXMnZs2aBQCIiIjAokWL4OrqKo/cPffccwCAoqIivPzyyzh8+DC+++47nD17FpMmTaqx+yAiorpBq9UiNfUiZsxohpJwx9pqdxU2bRqKJUt+wOLFi2t0VKxeBWGzZ89Gs2bN4OnpifPnz+P777+Xz/355584d+4c1q1bhy+++AIrVqxAamqqYsoyJydHERgBgK+vL/Lz8/H333/jypUrKC4uttomJydHvoaDg4NFPpJ5G2vXMJ6z5bXXXoNarZY/AgMDy/lkGoYOHTrg7NmzAID4+HgMGDAArVu3xr333otXXnkFa9euBWCo26VWqyFJElq0aIEWLVqgefPmAIDJkydj8ODBaNOmDfr06YP3338fP/74I65du1Zbt0VERDUsPT0dixcvxuuv74FeX3aZISFUyM31AFCzAxC1GoQ9//zzcpK2rY+TJ0/K7WfOnImDBw9i+/btsLOzwyOPPAIhBADD9jI3b97EF198gX79+uGee+7BsmXLsGvXLpw6daq2brFC5syZA51OJ39kZmbWdpdqlBBCrsmVlJSE++67D3fccQdcXFzw8MMPQ6vV4saNG6VeIzU1FcOGDUPLli3h4uKCu+++G4BhoQQRETV8Wq0WX331FQ4c6IH160eV6zWSpIeHR27ZDatYrW5b9Oyzz5Y5VdSmTRv5ay8vL3h5eaFdu3bo2LEjAgMD8csvv0Cj0cDPzw9NmjRBu3bt5PYdO3YEYPgF3L59e7Ro0cJiFePFixfh6uoKJycn2NnZwc7OzmqbFi1aAABatGiBwsJC5OXlKUbDzNuYr6g0XtPYxhpHR0c4OjqW+jwashMnTiAoKAhnz57F0KFD8eSTT+LVV1+Fh4cHfv75Z8TGxqKwsNDmNk3Xr19HdHQ0oqOj8fXXX8Pb2xvnz59HdHR0jf5lQ0REtSMrC/jlFyAryw8JCUNRvrEmPYYN2wy1uqC6u2ehVoMwb29veHt7V+q1xnIGN2/eBAD07dsXt27dQnp6OoKDgwEAf/zxBwCgVatWAACNRoMffvhBcZ3ExERoNBoAhmmunj17YseOHRgxYoT8Pjt27EBcXBwAoGfPnrC3t8eOHTswapQhwj516hTOnz8vX0ej0eDVV1/FpUuX4OPjI7+Pq6srOnXqVKn7beh27tyJo0ePYvr06UhNTYVer8fbb78tb0xunIo0cnBwsNhG6OTJk9BqtXj99dflqdzff/+9Zm6AiIhqhVarxaVLl/DZZ3osWtQJQngCeAzW879MCURE7Ed4eEqtBGBAPdnAOyUlBb/99hvuuusuuLu7Iz09HS+99BKCg4PlwCcyMhJhYWGYPHkyFi1aBL1ej2nTpiEqKkoeHXviiSewePFizJo1C5MnT8bOnTuxdu1abNmyRX6vGTNmYOLEiejVqxd69+6NRYsW4fr163j00UcBAGq1GrGxsZgxYwY8PDzg6uqKp556ChqNBn369AEADBw4EJ06dcLDDz+MhQsXIicnBy+++CKmTZvWqEe6jG7evImcnBwUFxfj4sWL2Lp1K1577TUMHToUjzzyCNLS0lBUVIQPPvgAw4YNw759+/DJJ58ortG6dWtcu3YNO3bsQLdu3eDs7IyWLVvCwcEBH3zwAZ544gmkpaXh5ZdfrqW7JCKi6qTVanH58mWsWbMGWVl+WLZsCoQwBl4SDIvnrQdikqRHbOxSBARcqKnuWlUvEvOdnZ2xceNG3HfffWjfvj1iY2PRtWtX7N69Ww5qVCoVNm3aBC8vL/Tv3x9DhgxBx44d8c0338jXCQoKwpYtW5CYmIhu3brh7bffxtKlSxEdHS23GTNmDN566y3MnTsX3bt3x6FDh7B161ZFov27776LoUOHYtSoUejfvz9atGghr+oDADs7O2zevBl2dnbQaDSYMGECHnnkEfz3v/+tgadV923duhV+fn5o3bo1Bg0ahF27duH999/H999/Dzs7O3Tr1g3vvPMO3njjDYSGhuLrr7/Ga6+9prhGREQEnnjiCYwZMwbe3t5YuHAhvL29sWLFCqxbtw6dOnXC66+/jrfeequW7pKIiKqLMfF+zZo1OHCgB5YunQLLgg/GQMzI8LUkGaYfazsAAwBJGDPbqc7Jz8+HWq2GTqeDq6ur4tw///yDjIwMBAUFoWnTphW6rlarxeLFi8tsFxcXB09PzwpduyG6nWdNRERVy/R3mE7ngkWL4q0EYKYE7r03CW3aZKCoyAEeHrmlTj9OnToVfn5+t9XH0n5/m6oX05FUtTw9PREXF1dqsrqDgwMDMCIiqjOMuV+XL1+Wj+3Z06+MAAwAJNjbF5d75MvBweE2elkxDMIaKQZYRERUX5jP4GRl+WHPnv7444/25Xi1QGBg6WWKBg4ciNatW9f4AASDMCIiIqqztFotsrOz5e+//fYBHD7cDWWvfgQAgW7dDpc5CtauXbtaGZxgEEZERER1jlarRXp6On788Uf5WFaWX7kCMEnSo3//3QgJOV1mADZhwoRamx1iEEZERER1ivn0o07ngtxcT5w82Q62AzBDSQrj6sewsIM2rz9y5Eh4eXnVev4zgzAiIiKqdcbE+6tXryr2WT5woAc2bRr6bwK+7YIOXbocRljYoTJXPwKAv79/nciNZhBGREREtcr6yFdr2NvfRELCMJSMftkaBdMjMnKn1eArODgY3t7ecHd3R2BgYK2PfpliEEZERES1yrRkknLkS4/y5H+Vtvfjfffdd9t1v6oLgzAiIiKqUVqtVg68Tp68hsOHb0CncwEAkwAMKH1jHz1Gj16PwMCsUqcfa7LuV0UxCKMG5aeffsKAAQNw9epVuLm5les1rVu3Rnx8POLj46u1b0REpJx6NB31kqQu0GiSy1F8FQAEoqKSEBp6wurZupJ4X5Z6sXckNRyTJk2CJEl44oknLM5NmzYNkiRh0qRJNd8xIiKqEcYRMJ3ORTHqJYQK+/drUFryvYEeUVGJ6Ns32WYLf39/+Pn51ekADOBIGNWCwMBAfPPNN3j33Xfh5OQEwLA/46pVq9CyZcta7h0REVWnvLw8AEBurqeVUa/Spx/vv38L2rc/bXP6ceTIkXVm5WN5cCSMalxYWBgCAwOxceNG+djGjRvRsmVL9OjRQz528+ZNPP300/Dx8UHTpk1x11134bffflNc64cffkC7du3g5OSEAQMG4OzZsxbv9/PPP6Nfv35wcnJCYGAgnn76aVy/fr3a7o+IiEpotVpcuHABFy5cwIkTJ7B27VoAgIeHFpKkL+dVDNOPvXsfKDX/qz4FYABHwghAVhZw+jQQEgIEBNTMe06ePBnLly/H+PHjAQCff/45Hn30Ufz0009ym1mzZmHDhg1YuXIlWrVqhYULFyI6OhpnzpyBh4cHMjMzMXLkSEybNg1Tp07F77//jmeffVbxPunp6Rg0aBBeeeUVfP7557h8+TLi4uIQFxeH5cuX18zNEhE1UualJ4x0OhekpIRDlDXzCMPqx8jIJKvTj4MHD0ZgYCAA1Pn8L2sYhDVyy5YBU6cCej2gUgFLlgCxsdX/vhMmTMCcOXNw7tw5AMC+ffvwzTffyEHY9evX8fHHH2PFihUYPHgwAOCzzz5DYmIili1bhpkzZ+Ljjz9GcHAw3n77bQBA+/btcfToUbzxxhvy+7z22msYP368nHQfEhKC999/H3fffTc+/vhjNG3atPpvloioETFd+XjlyhXFOWPwZcj9Kn0yTpL0GDXK+urHmJgY+Pj41LugyxyDsEYsK6skAAMMnx9/HIiOrv4RMW9vbwwZMgQrVqyAEAJDhgyBl5eXfD49PR1FRUXo27evfMze3h69e/fGiROG1TAnTpxAeHi44roajUbx/eHDh3HkyBF8/fXX8jEhBPR6PTIyMtCxY8fquD0iokbFGHjpdDqsWbPGahtl/a+yGGp/GVc/9urVCx4eHnB3d4e3t3e9D76MGIQ1YqdPlwRgRsXFwJkzNTMtOXnyZMTFxQEAPvzww2p5j2vXruHxxx/H008/bXGOiwCIiG6frSlHU+YrIUsjSXrExi5VbLwdFhZWZwuu3g4GYY1YSIhhCtI0ELOzA9q2rZn3HzRoEAoLCyFJEqKjoxXngoOD4eDggH379qFVq1YAgKKiIvz222/y1GLHjh2RkJCgeN0vv/yi+D4sLAzHjx9H25q6KSKiRsa02j1Qstm2h4cWgGEV5OXL1lZCmlJuvm0agAF1u+Dq7WAQ1ogFBBhywB5/3DACZmcHfPppzSXn29nZyVOLdnZ2inPNmjXDk08+iZkzZ8LDwwMtW7bEwoULcePGDcT+m7T2xBNP4O2338bMmTMxZcoUpKamYsWKFYrrzJ49G3369EFcXBymTJmCZs2a4fjx40hMTCzzLzciIiqdVqtVrEq3vuWQhNJqf3XufBQaTTKKihwsNt8eM2ZMg5p+NMcgrJGLjTXkgJ05YxgBq6kAzMjV1dXmuddffx16vR4PP/wwCgoK0KtXL2zbtg3u7u4ADNOJGzZswPTp0/HBBx+gd+/e+N///ofJkyfL1+jatSt2796NF154Af369YMQAsHBwRgzZky13xsRUUNmbdNt21sOWd//UZL0GDgw0SLxvr7V+6osSYjyLBCl2pCfnw+1Wg2dTmcRrPzzzz/IyMhAUFAQV/hVMz5rImrsTBPvr169iqKiIhQUFOD333+X22RktMbKlRPLfU3j1GNY2EGLc3FxcfU6ACvt97cpjoQRERGRTeVJvAfwbw6YHmXXgbe98XZjGQEzYhBGRERENpWWeG8aRKWlhcLWtKN54r2tjbcbUwAGMAgjIiKicjJPvO/ZMxVBQWdx8aIP9u7tD9tBmITo6K3o1Om4InAbMGAA3N3dYW9v36AT8G1hEEZERERlspZ4n5p6J1JT74RxpMsWSdJbBGAA0Llz50YXeJliEFbPcV1F9eMzJqLGxHTbIaBk66Hc3NJqfZUegA0btlkOwEaOHAkvL696uddjVWMQVk8Z62oVFhbCycmplnvTsBn/Z2Rey4yIqKEpLQm//In3RtYT8Btb3ldpGITVU02aNIGzszMuX74Me3t7qFTl/UdBFaHX63H58mU4OzujSRP+cyGihu3SpUs2z6nVBYiISMb+/X1ttDCdktRj+HDLBPwJEyYwADPB3yr1lCRJ8PPzQ0ZGBs6dO1fb3WnQVCoVWrZsCUmyPdxORFTfaLVaXL58Wa75lZ+fj7/++stqW+OKyE6djiE5WWNlWlKPqKgkqNV5AGAx+hUTEwMfHx8GYGYYhNVjDg4OCAkJsVg+TFXLwcGBI41EVO+Z5nrpdDqsWbOmzNfodC5ISQmXAy9J0qNr1yM4cqSr/H1YWCr6999rsd2QWq0GAOZ+lYJBWD2nUqlYxZ2IiEpV3oKrpvbt0yAxMQqmSfdCqHDkSFfExi61utdjVFQU2rdvz6CrnBiEERERNXDlLbhqtGPHPTbrfgmhQlGRA4KCLFNhgoKCGIBVAIMwIiKiBkyr1cplJgBlwVVj+Yjg4DPIzfWEvf1NpKT0xtGj3VDaptseHrlWzzk4OFTHLTRYDMKIiIgaqPT0dHz11Vfy9+YFV4VQISFh6L9nVShP0VVrNb8A5n5VBoMwIiKiBsI0+T4rKws//PCD4rz1gqum35e2ClyP2NilCAi4IB/x8vKCn5/f7XW6EWMQRkRE1ACUJ/new0MLSdKXUvneFkPdL9MADOD04+1iEEZERFTPabVaZGdnK46ZJ9/rdC7IzAxEWFgqUlN7onyV7wW6dDmMyMid8hRkVFQUgoKCOP1YBRiEERER1WPWRsDMk++7dj2Cw4dNk+3LsyeuoQBr377JiqMsQVF1GIQRERHVU7ZGwMyT75UBGFBW7lfPnsoCrBEREQgMDIS3tzcDsCrEIIyIiKgespUDZj35vjzbrukREZGM8PAUqNUFctV7TjtWHwZhRERE9YDpykcAitpfpuztbwLQo3w5XwCgx/33b0H79qdx333t4ePTD23atGHgVQMYhBEREdVRxsArLy8Pa9euLbWtcZ/H/fs1KE/NL6OIiGT07n0AABAWFsaSEzWIQRgREVEdVJH9Hk0T8UuUbwoyPDylUv2j21fRQiFERERUzawl3NtinohfNj0AQ/X74cM3K/aOZN2vmsWRMCIiojrE1giYad0vAPLX1hPxbdFjypSlKCpywMCBbTBgwJ0A7gTAbYdqA4MwIiKiOsJWyYmUlHAkJ2v+DbbEvx+GGmCRkUnlrIKvrHofHT2MQVctYxBGRERUB9gqumrYYNs818uQ7yWEComJkaVeV5L0GDVqPQIDszB16v1Qq+/kqFcdwSCMiIiolpiWncjIyFCcM+Z6lZ2+bfu8JOkxbNhmzJ3bEf7+9zHwqmMYhBEREdWCslY/ZmYGVmKjbSNl4VUvrzsZgNVBDMKIiIhqgWnhVXPGkhOVY0i+N+Z+AVz1WFcxCCMiIqpBxilI84r3xtWP9vY3K1hyAjAWZjVOPxoDsJiYGPj4+HAUrI5iEEZERFTNjIGXTqfDmjVrLM6bFlu1vdLRVgV8gX799qBNmwx4eOTi0Uej4OXF5Pv6gEEYERFRNUpPT8dXX31l87x5sdWSMhTmAZdkctz4WY+oqCT07Zsst/L392fwVU8wCCMiIqoGWq0Wly5dstjz0bzo6rFjna2MfNnackhCdPRWBAaeR1GRAzw8chUV7ydMmMAArB5hEEZERFRFKjLtaNg+yFjzq3ybbUuSHp06HYdaXYCRI0fCy8tLPsfpx/qHQRgREVEVKKvkhOUej+YFWMsKxAxJ98aRL0471n8MwoiIiKqAeckJ02lHtbqgHHs82g7AJEmP2FhD2YkxY8bA29ubAVgDwCCMiIioCuh0Ovlr89WOkZFJyM11R3mnHU1Jkh4zZ57BpEn9WG6igWEQRkREVAnG/K+8vDzk5eVh+/btAKyvdkxMjEJFgy9Aj/vv34L27U9j1qyJDL4aIAZhREREFVRa/pf1aceKj34NG7YZYWEHMWbMGAZgDRSDMCIiogrQarXIzs5WHDPN//Lw0JZScLUsJaNfxgR8b2/vKug11UUMwoiIiGww1vq6desWCgoKkJ+fj5SUFEUb8/yvYcM2Y9iwzUhIGArDCsjy5oHpMXy4YfQL4JZDjQGDMCIiIivKKjkBWM//2rRpKO66ay9KAi9rle5N6RERkYzw8BSo1QVc/diIMAgjIiL6lzHZHoDFBtvWZGYGWkw7CqHC3r39oQy2DFsM9eu3Fz//3E8u1tqzZyr699/L4KuRqsyEda0YPnw4WrZsiaZNm8LPzw8PP/ywYk5+/vz5kCTJ4qNZs2aK66xbtw4dOnRA06ZN0aVLF/zwww+K80IIzJ07F35+fnByckJkZCROnz6taJObm4vx48fD1dUVbm5uiI2NxbVr1xRtjhw5gn79+qFp06YIDAzEwoULq/iJEBFRVTKOfC1ZsgRLlizBxo0bFed1OhekpXVCWlon6HQuOHCgB9avH2XjatamH1Vo0yYD8fGLsGzZn/j998vYtCkAM2eOQ1xcHDp06MAArJGpNyNhAwYMwH/+8x/4+fnhr7/+wnPPPYfRo0dj//79AIDnnnsOTzzxhOI19913H+688075+/3792PcuHF47bXXMHToUKxatQojRozAgQMHEBoaCgBYuHAh3n//faxcuRJBQUF46aWXEB0djePHj6Np06YAgPHjx+PChQtITExEUVERHn30UUydOhWrVq0CAOTn52PgwIGIjIzEJ598gqNHj2Ly5Mlwc3PD1KlTa+JxERFRBV26dEnxvWmyfXp6WyQkDENJcKX/93NFxjL08l6Pgwc7wc/Ptwp6TfWZJIQQtd2JykhISMCIESNw8+ZN2NvbW5w/fPgwunfvjj179qBfv34AgDFjxuD69evYvHmz3K5Pnz7o3r07PvnkEwgh4O/vj2effRbPPfccAEPxPV9fX6xYsQJjx47FiRMn0KlTJ/z222/o1asXAGDr1q24//77kZWVBX9/f3z88cd44YUXkJOTAwcHBwDA888/j++++w4nT54s9z3m5+dDrVZDp9PB1dW10s+KiIhs02q1uHz5smKvR/Nke8NvytuZPFIm3cfFxXHUqwEr7+/vejMdaSo3Nxdff/01IiIirAZgALB06VK0a9dODsAAIDk5GZGRkYp20dHRSE5OBgBkZGQgJydH0UatViM8PFxuk5ycDDc3NzkAA4DIyEioVCp5xUxycjL69+8vB2DG9zl16hSuXr1q875u3ryJ/Px8xQcREVUPrVaLkydPYvHixYoAzFqyfeV/XQp06XII06cvUqx6ZABGQD0LwmbPno1mzZrB09MT58+fx/fff2+13T///IOvv/4asbGxiuM5OTnw9VUO//r6+iInJ0c+bzxWWhsfHx/F+SZNmsDDw0PRxto1TN/Dmtdeew1qtVr+CAwMtNmWiIgqz5j/ZRp8AYYA7NixzpWs8WVOj6ioRIwa9b1c8wuAxe8QarxqNQh7/vnnrSbTm36YTt/NnDkTBw8exPbt22FnZ4dHHnkE1mZTv/32WxQUFGDixIk1eTu3bc6cOdDpdPJHZmZmbXeJiKhBsrbZ9vbtkVi0KB7bt0fDUErClLByzDpJ0qNnz98wffoi9O2bLB+PiYnhNCQp1Gpi/rPPPotJkyaV2qZNmzby115eXvDy8kK7du3QsWNHBAYG4pdffoFGo1G8ZunSpRg6dKjFaFSLFi1w8eJFxbGLFy+iRYsW8nnjMT8/P0Wb7t27y23Mkzdv3bqF3NxcxXWsvY/pe1jj6OgIR0dHm+eJiKjqHTjQw6SwqpH56kaBoKA/kZERbOWckR6jR69HYGAWRozoBXf3KNjb20OtVsPBwYHBF1mo1SDM29u70tsx6PWGlSk3b95UHM/IyMCuXbuQkJBg8RqNRoMdO3YgPj5ePpaYmCgHcUFBQWjRogV27NghB13G6shPPvmkfI28vDykpqaiZ8+eAICdO3dCr9cjPDxcbvPCCy+gqKhIzllLTExE+/bt4e7uXqn7JSKiqqPT6f79bMj/KntiSIWMjLYoGREzBmKGr42V8kNDTwAAOnfuzKCLylQvSlSkpKTgt99+w1133QV3d3ekp6fjpZdeQnBwsMUo2Oeffw4/Pz8MHjzY4jrPPPMM7r77brz99tsYMmQIvvnmG/z+++9YsmQJAECSJMTHx+OVV15BSEiIXKLC398fI0aMAAB07NgRgwYNwmOPPYZPPvkERUVFiIuLw9ixY+Hv7w8AeOihh7BgwQLExsZi9uzZSEtLw3vvvYd33323eh8UERHZZCzEqtPp5Fww65ttl8ZQ9f7eexPh4XEVERH+UKv90aaNHu3b3wngTo56UbnViyDM2dkZGzduxLx583D9+nX4+flh0KBBePHFFxXTd3q9HitWrMCkSZNgZ2dncZ2IiAisWrUKL774Iv7zn/8gJCQE3333nVwjDABmzZqF69evY+rUqcjLy8Ndd92FrVu3yjXCAODrr79GXFwc7rvvPqhUKowaNQrvv/++fF6tVmP79u2YNm0aevbsCS8vL8ydO5c1woiIaoBp1fvsbBUyMprA0zMXe/assmhbuc22JdjbFyM09AQiIzuiS5c2Zb+EyIp6WyesMWCdMCKiijHd79HaxtrGMhGmTNuVj8CUKZ8hIOACRo4ciS5dulThHVBD0KDrhBEREVljHAGztbG2Tudi8Zrg4DMYNWoD7r9/E2yvgBTy527dDiMg4AIAQ4kiosriTw8RETU41nK9hFAhN9dDUbNr3z4NkpIi5dGyTp2O4/jxTjBdASlJeowduxq5uZ4IDDwvB2AAa37R7WEQRkREDYJWq8WVK1cAWM/1kiTD3o2AYaRsz55+SE3tBWPAJYQKJ050RL9+e7B3bz8AKqhUAs89dwYPPBAEd3d3uLl1k6/HBHy6XQzCiIio3jPNBQMAtboAw4ZttsgJU6sLsG+fBomJUbBW70sIFdq0yUCvXqno23ciwsM9ERDQDkC7mrsZajQYhBERUb1huvIRMNT7Kioqwl9//WXRNizsIIKDzyA310MeAduw4QEcPdoNpRVc9fDIhVpdgIiIQpjU7SaqcgzCiIioXjAf7SoPtbqgzNEvUz17pso5Yw4ODpXtKlG5MAgjIqI6SavV4tKlS7h16xYA4OrVq4rzOp0LcnM94eGhVSTbm5/7/fee2Lu3P8oKwACB/v33AgCioqKY70XVjkEYERHVOeajXjqdCzIzAwF0QmBgJtLT29qsAabcC9JYWsJWAKbcdsgYzHl4eFTTnRGVYBBGRER1jmnelyGoGoaSQEr/79clqxo3bRoKH58c5OW5m23GXdrol0Dv3r+gY8dTch6YUWX3NSaqCAZhRERUZ5VssG0aTFnWGRdChaVLH0PZU47yKxAVlYi+fZMBAPfffz8CAgIAsPQE1ZxKBWG7du3CgAEDqrovRETUyJiudjx58hr+/NMOLVvehKPjZQAV2WDbMK1YnnZduhxGZOROeeRrzJgx6NChQ+VugOg2VCoIGzRoEAICAvDoo49i4sSJCAwMrOp+ERFRA5eeno6vvvoKgOU+j5GRmfD3bw17+5s2NtjWQ5Lw7/GyAjDjeT2iopLk0S8jTj1SbalUEPbXX3/hyy+/xMqVK7FgwQLce++9iI2NxYgRI7ikl4iIyqTVauUATKdzUeRxCaGSy0lIkh5dux7BkSNdTQIxQzBlb1+IH34YgrICsH799qBNmwyLvK/BgwcjODiYU49UayQhhK3dSsvlwIEDWL58OVavXg0AeOihhxAbG4tu3bqV8UoqS3l3YSciqm8uXLiAJUuWAAC2b4/E/v19bbaVJD1iY5ciL88NAKDTucn7PZbOcuRr5MiR8PLyYt4XVavy/v6+7cT8sLAwtGjRAp6ennj99dfx+eef46OPPoJGo8Enn3yCzp073+5bEBFRA6XTuSA5WVNqGyFUKCpyQGBgFjIzA8sRgAlEROxHeHiKRf0wf39/Bl9UZ1Q6CCsqKsL333+Pzz//HImJiejVqxcWL16McePG4fLly3jxxRfx4IMP4vjx41XZXyIiakDKk3gvSXpkZ/vjiy8eKbNtu3YnMWTID4rgi6NfVFdVKgh76qmnsHr1aggh8PDDD2PhwoUIDQ2Vzzdr1gxvvfUW/P39q6yjRETU8Hh4aK0k3ot/PwxJ+mFhqeWafpQkvUUABnD0i+quSgVhx48fxwcffICRI0fC0dHRahsvLy/s2rXrtjpHRET1k1arxeXLl1FUVGT1fEGBIVBSqwswbNhms5WRSVCrdTh1KgRHj3ZFauqdpbyT9Yr3HP2i+uC2E/Op+jAxn4jqI2tbDtna41HZxgPZ2f4mo16llZ7QY/To9XBzy0NRkYPFyse4uDgGX1Rrqj0x/9SpU/jggw9w4sQJAEDHjh3x1FNPoX379pW9JBERNQDmWw4ZR7ls1ekCIAdQK1dOREngZT0AM456hYYafv8MGDAA7u490aRJE7i5uXH0i+qNSgVhGzZswNixY9GrVy9oNIZVLb/88gtCQ0PxzTffYNSoUVXaSSIiqttMK9+npeUhI8NQaLUkAAMAY/0vWA3EDBt0l1bzyzD6FRiYpRj1CgkJgZ+fXxXdCVHNqVQQNmvWLMyZMwf//e9/FcfnzZuHWbNmMQgjImpETKcfS0a+OtqodC8hKSkSarUOgYGZKChojvPnW6Fly3NlvItAVFSSPPplikXCqb6qVBB24cIFPPLIIxbHJ0yYgDfffPO2O0VERPWHcQTMuNm2MfCyldclhArr1z8IQP/vOQmAQKdOx/89pgzcjMn6pqNnMTExnHqkeq9SQdg999yDvXv3om3btorjP//8M/r161clHSMiovrFes0vQ4BlfZpRpWh3/Hgn9Ou3B3v39vv3nB4REckWRVcnTJiA4ODgqu4+UY2rVBA2fPhwzJ49G6mpqejTpw8AQ07YunXrsGDBAiQkJCjaEhFRw2et5pck6XHnnSn49dc+KD3fCwAkODv/jenTFyE318NixWNMTAx8fHw48kUNRqVKVKhUZe3X9e/FJQnFxcUV7hQZsEQFEdUVxsR7nU6Hq1evKup/Xb58GWlpaQAsV0NGRCTD3z/73+nHsghMmfIZAgIuYMCAAQgJCZHPcNqR6pNqLVGh1+sr3TEiIqpfKlL3KyzsIIKDzyAlJRzJyZp/N+bWo/SaXwAg0K3bYQQEXAAAuLu7c8UjNXi3vYE3ERE1bLbqfhnrdYWFHZTP63QuyMwMRHKyRlGaomQrImOOGOSv/f2zcf/9W+QAjKixKHcQ9v7775f7ok8//XSlOkNERHWXtdWPCQlDcfGiF7p0ScOlSy3M6oKZMgRc99+/Ce3bn0ZBQXNkZrZEYOB5q8FXkyYcI6CGr9w/5e+++2652kmSxCCMiKieM9378erVqwBsrX5UISUlAikpGhg33bZNQnGxPdTqAqjVBaWOfPn4+NzuLRDVeeUOwjIyMqqzH0REVAuysoDTp4GQECAgwHDMPAfMyNrqxxLGel+lEQgMPA8A6NWrF1q2bKk4y22HqLHheC8RUSNjXOm4apUTZs1SQ6+XoFIJLFyow0MP/Y28vDxFe2OeFwBERiYhMTESpY94GSmLsZom3rds2RJdunSpwrsiqn8qHYRlZWUhISEB58+fVyRtAsA777xz2x0jIqKqZxzl0ulcsGhRPIQwjF7p9RJmznTFX399rljxeOBADyQkDEVJ0CXQu3cKfv01HJYjX8bk+5KkfR+fHKu5X8z5IqpkELZjxw4MHz4cbdq0wcmTJxEaGoqzZ89CCIGwsLCq7iMREVUR4x/N1vK7hFAhN9dDDsJ0OhezAAwAJJMAzLTshGGk6957d1oUWrWW+8WcL6JKBmFz5szBc889hwULFsDFxQUbNmyAj48Pxo8fj0GDBlV1H4mIqIpZz+/S4/r1ZtDpXKBWFyA31xPWpx0lk896hIf/gi5d0uRgy7x2GFCy1yPAwqtERpUKwk6cOIHVq1cbLtCkCf7++280b94c//3vf/HAAw/gySefrNJOEhFR5Wm1Wpw9ewsZGU1QWHgNGRmt4eGhxbBhmxXV7QEJ69c/CEnSQ6NJRqdOx1B2kVUVOnT4w2K0a8yYMVCr1QAYdBHZUqkgrFmzZvKQtp+fH9LT09G5c2cAwJUrV6qud0REVGnp6em4ePEi3njjism0ogeAEDlny1qivRAq7N/fF/v3a8rxLnp4eOQCAEaOHAkvLy8GXUTlVKkgrE+fPvj555/RsWNH3H///Xj22Wdx9OhRbNy4Ud7Qm4iIaoa1MhPp6en46quv/s3rikdJkGUY1TIWWpUkwPZKx7JWQApERSXJ049eXl7caoioAioVhL3zzju4du0aAGDBggW4du0a1qxZg5CQEK6MJCKqQcuWAVOnAno9oFIB77xzDTExBfjrr78A4N/SEraDLCFsnCqDJOkRGZmEvn2T5WMODg6VuxhRI1WpIKxNmzby182aNcMnn3xSZR0iIqKyGfO8pk71gV5vLDMBTJ/ujHPnlkCtLoBO54JLl7xLuYoekgQbxVdtvyYiIhnh4SmKBPyYmBhOQRJV0G0VaiksLMSlS5eg1+sVx82rIBMRUdXIygJ+/12HPXtWIjfXE3r9RMV5Y5mJ9PS2Jkn31pLr9Rg+fDMAlNGuRHj4fkRE/GJ19aNx5SMRlV+lgrA//vgDsbGx2L9/v+K4EAKSJKG4uLhKOkdERCVKph7VkKR4REYmWZSZkCQ97O0LzTbSLqnpJUl6dOx4DBERyfKKxuDgMzh2rBO2b7ddYkiS9DYDMIBTkUSVUakg7NFHH0WTJk2wefNm+Pn5QZLK2i+MiIhux2+/AY89BjmHSwgVkpIiERmZhKSkSAhRUqW+qMjRyhSjhHbtTuKPP9rh+PEuOH68s2JasXPn40hMHGj2upLAbdiwzXIA1rFjRwQEBMDd3Z17PRLdhkoFYYcOHUJqaio6dOhQ1f0hIiIz7713DdOnN5O3GDISQgV//2zExy9SVKnPyvKzutH2H3+0R8l0o6EMRXKyBsOGbUZY2EFF3TBj4r2/f7ai+j1gCMK47yPR7atUENapUyfWAyMiqmZarRZHjuRi+vRgiwAMMEwRGgMkY5B04ECPUnK8LK8hhAqbNg1FcPAZhIUdRHDwGYtth8zZ29tXwd0RUbmDsPz8fPnrN954A7NmzcL//vc/dOnSxeIfpKura9X1kIioETJutJ2R0RpChFhpYahqb0qnc7GSC1Y20z0jTQM6W7y9S1txSUTlVe4gzM3NTZH7JYTAfffdp2jDxHwioqph3JXE+h6PhsQw8+lEa5tyW7JcAWkcUSuNcRsi5n8RVZ1yB2G7du2qzn4QETVq1qreA4bNsM1ztQzJ+YZgy3Q60cNDC8MekMpNuY21wIx5XtevN0NyskaRzG8c/TJuPWSKgRdR9Sh3EHb33XfLX58/fx6BgYEWqyKFEMjMzKy63hERNQLmVe+XLAHuv7/kvGmu1vXrzbB+/YOK1wuhwrFjnf4dBTP9/7KhFpi1PK/w8BSruV/ceoio5lQqMT8oKAgXLlyAj4+P4nhubi6CgoI4HUlEVA62qt4//rjA+vV/Kdoac7V0Oher05OGGl+WifjBwWes5nmVJ/eLiKpXRfaqkBlzv8xdu3YNTZs2ve1OERE1dMbE+9df3yMHYEbFxRK++y5N/l6nc0FaWiekpXUCAAwbthmSZNypxDTwMv//soTMzABUBIuuEtWcCo2EzZgxAwAgSRJeeuklODs7y+eKi4uRkpKC7t27V2kHiYgaGq1Wi+zsbLmchDnTRPl9+zRITIxEyd/MAsOHb0J8/CKkpvbAnj0Dyv2+EyZMUPx/2xxzv4hqVoWCsIMHDwIwjIQdPXpU8ReTg4MDunXrhueee65qe0hE1IAYR8AM5STiraxmLEmUNwRgUTCfYty0aSgiI5OwZ8/dKJ0egYFZAICoqCgEBwdX4Z0Q0e2qUBBmXCH56KOP4r333mM9MCKiCsjKAn75xTC9aKucRM+eqQgLOwidzgVJSZGwVWBVOTpmjUBUVJKc9+Xi4lI1N0FEVaZSifnLly+v6n4QETVoJSsgPW1uvg0Aqak90b//3jJqfglYD8AM5SmMpSj69k220oaI6opKBWHXr1/H66+/jh07duDSpUvQ6/WK83/++WeVdI6IqL6ztgLSuPl2WFgqUlPvNHuFCpmZAcjICIK1wqrWjxncf/8WeHtrrW45xK2GiOqeSgVhU6ZMwe7du/Hwww/Dz8/P6kpJIqLGLj09HR9++D22bx8Ivd5XcU4IFYKCMpCa2hPmxVU3bBhtZRRMICgoHRkZba2+lyTp0b79aZtlJ7jVEFHdU6kg7Mcff8SWLVvQt2/fqu4PEVG9p9VqcenSJTz//GkbyfeGoCkwMAvDh2822e9RD0Cyulk3IKFnzwM4e7ZNqcn8ANCrVy+0bNkSgGEEzNvbm6seieqgSgVh7u7u8PDwqOq+EBHVe2WvfjTQaJKhVhcoquGnpXW2Mj1pYAzazLcw0miSER6eohgBa9myJbp06VIt90dEVadSQdjLL7+MuXPnYuXKlaXWnCEiamyMG2+XnlivR3h4ivydWl2AgoLmSE3tZbO9caTLNGizlvsFAE2aVOp/7URUwyr1L/Xtt99Geno6fH190bp1a4uEzwMHDlRJ54iI6ipbG24beXhora5+NO7naBo8HTjQAwkJQ2Et4b5z56MYODBR0b6sLYfc3NwqeDdEVBsqFYSNGDGiirtBRFR/WNtwOzZW2UatLrA5dQgAGRmt4eGhBYB/AzDrJSfMA7Dy4NZDRPVDpYKwefPmVXU/iIjqvNI23O7e/RJat1b+L9V06tDevhBFRY5ISwtFUlKkHJiFhaXCVtHViIhkqwFYVFSURfFVe3t7qNVqbj1EVI9UKAj79ddf0bNnT9jZ2Vk9f/PmTXz//feIiYmpks4REdUVxoT7jIzW0OsnKs4VF0v44IMfERR0DmPGjFGcU6sLkJ7e1mQFZEmdLyFU/+aBGYqsKinzxgBgzJgxXOlI1ICUtueFBY1GA61WK3/v6uqqKMyal5eHcePGVV3vTAwfPhwtW7ZE06ZN4efnh4cffhjZ2dmKNtu2bUOfPn3g4uICb29vjBo1CmfPnlW0+emnnxAWFgZHR0e0bdsWK1assHivDz/8EK1bt0bTpk0RHh6OX3/9VXH+n3/+wbRp0+Dp6YnmzZtj1KhRuHjxoqLN+fPnMWTIEDg7O8PHxwczZ87ErVu3quRZEFHNMybcG3O9TJluuJ2bm6s4Z1glOdQkN8w878v4fck1Jckyb2zChAno0KEDAzCiBqRCQZgQotTvbR2rCgMGDMDatWtx6tQpbNiwAenp6Rg9erR8PiMjAw888ADuvfdeHDp0CNu2bcOVK1cwcuRIRZshQ4ZgwIABOHToEOLj4zFlyhRs27ZNbrNmzRrMmDED8+bNw4EDB9CtWzdER0fj0qVLcpvp06dj06ZNWLduHXbv3o3s7GzF+xQXF2PIkCEoLCzE/v37sXLlSqxYsQJz586tlmdDRDXHmOtlDMQkSVmjKzExUW6r07ng2LHOpaySNFJh9Oj1GD16LUaPXov4+EUICzsonx0zZgw33yZqgCRRgahJpVIhJycHPj4+AAwbwh4+fBht2rQBAFy8eBH+/v4oLi6unt6aSEhIwIgRI3Dz5k3Y29tj/fr1GDduHG7evAmVyvA/vE2bNuGBBx6Q28yePRtbtmxBWlqafJ2xY8ciLy8PW7duBQCEh4fjzjvvxOLFiwEAer0egYGBeOqpp/D8889Dp9PB29sbq1atkoPAkydPomPHjkhOTkafPn3w448/YujQocjOzoavr6FK9ieffILZs2fj8uXL5U6azc/Ph1qthk6n42bpRNWorJWOAHDhwgUsWbJE/l6nc0FmZgAACYGBmRa5W/v2aeTcr9K2GgIMgVx8/CKbCfhTp06Fn59fBe+KiGpLeX9/V2gkrK7Izc3F119/jYiICLk8Rs+ePaFSqbB8+XIUFxdDp9Phyy+/RGRkpNwmOTkZkZGRimtFR0cjOdmwyW1hYSFSU1MVbVQqFSIjI+U2qampKCoqUrTp0KEDWrZsKbdJTk5Gly5d5ADM+D75+fk4duyYzfu6efMm8vPzFR9EVL2WLQNatQLuvdfwedkyw/GsLGDXLsNnwJBuYSo9vS02bBiN9esfxLvvxmP79kjodIZk+X37NEhMjDKbgrT1965AZGRShVdAElH9V+HVkcePH0dOTg4Aw9TjyZMnce3aNQDAlStXqrZ3ZmbPno3Fixfjxo0b6NOnDzZv3iyfCwoKwvbt2xETE4PHH38cxcXF0Gg0+OGHH+Q2OTk5isAIAHx9fZGfn4+///4bV69eRXFxsdU2J0+elK/h4OBgUYfH19dXfi623sd4zpbXXnsNCxYsKOfTIKLbYWul49SpAgcPnsPHH7eCXi9BkgRGjsyAu/teFBUZykoUFDQ3Kyuhwv79fZGcrMFdd+3F3r39YC33q3Pnozh2LBSmeWBRUUno2ze51L6y5ARRw1ThIOy+++5T5H0NHToUACBJEoQQFdrM+/nnn8cbb7xRapsTJ06gQ4cOAICZM2ciNjYW586dw4IFC/DII49g8+bNkCQJOTk5eOyxxzBx4kSMGzcOBQUFmDt3LkaPHo3ExMR6scn4nDlzMGPGDPn7/Px8BAYG1mKPiBqm0lY66vUSPvqopbx/oxASNmxoA+AxlIxoCVibSBBChb17+8Pa1KMkGWp+DRyY+O80JhAYmGUxAjZy5Eh4eXnJ37PkBFHDVaEgLCMjo0rf/Nlnn8WkSZNKbWPMNwMALy8veHl5oV27dujYsSMCAwPxyy+/QKPR4MMPP4RarcbChQvl9l999RUCAwORkpKCPn36oEWLFharGC9evAhXV1c4OTnBzs4OdnZ2Vtu0aNECANCiRQsUFhYiLy9PMRpm3sZ8RaXxmsY21jg6OsLR0bHU50FEt898paMycd5alXugJLCSUFp+l/VzyilHtfqEzVd7eXkx/4uokahQTlirVq3K9WH0//7f/yt1itLb2xsdOnQo9cPWMLxeb1iZdPPmTQDAjRs35IR8I2M9M2NbjUaDHTt2KNokJiZCo9EAMPzF2bNnT0UbvV6PHTt2yG169uwJe3t7RZtTp07h/PnzchuNRoOjR48qVlQmJibC1dUVnTp1svk8iKhmWVvpGBWVZFGC4vboERWVWOaUoxGnHokajwqtjqwoV1dXHDp0SDGaVRkpKSn47bffcNddd8Hd3R3p6el46aWXcPHiRRw7dgyOjo7YuXMnIiMjMX/+fHk68j//+Q9OnjyJEydOwMnJCRkZGQgNDcW0adMwefJk7Ny5E08//TS2bNmC6OhoAIYSFRMnTsSnn36K3r17Y9GiRVi7di1Onjwp53U9+eST+OGHH7BixQq4urriqaeeAgDs378fgKFERffu3eHv74+FCxciJycHDz/8MKZMmYL//e9/5b5vro4kqlrGVZCurhexefMn8nGdzkWxIbYxsb70ES8jY8BmfduhKVOWIiDgQrn6N2HCBJaiIGoAyvv7u1LbFpVXVcV3zs7O2LhxI+bNm4fr16/Dz88PgwYNwosvvihP3917771YtWoVFi5ciIULF8LZ2RkajQZbt26Fk5MTAEPy/pYtWzB9+nS89957CAgIwNKlS+UADDDU47l8+TLmzp2LnJwcdO/eHVu3blUk2r/77rtQqVQYNWoUbt68iejoaHz00UfyeTs7O2zevBlPPvkkNBoNmjVrhokTJ+K///1vlTwPIqo45X6PPhg6tIdci8t0Q+wDB3ogMTESlgGYscxESU6YsUaYg8NNrF//oMV7RkQkWw3ABg4ciObNm8vf29vbsxI+USNUrSNh5nXEqGI4EkZ0+4yrIHv3LlkFaWAYpSoqcoSHhxZqdQF0OhcsWhRvs7iqYXVjZxgDsMhIw8pGa6+zVftrzJgx8mIjImqY6sRIGBFRbSptFSSgwtKlU2A6ouXufrWU6vZ6OQADDCshk5IiERqaJueWGbcnMq+ib8rb27sqb5GI6jEGYUTUYJmugrS+SXZJQLVp01CMHbvaympJw6hWmzbpSE8PURwXQoXcXA+o1QUICzuI4OAzitwyUyNHjoS/vz+nHIlIVi8r5hMRVYRaXYCIiNJXJwqhwurV40y2GQIAgeDgM4iNXYo//7SWMF+ycbfxfYKCzlkdAWMARkTmqjUImzBhAnOZiKjGGbccys4u+V9ceHhKGaUnTAuwltQE+/PPNsjLc7c6TRkRkVyu7YZiYmIYgBGRhUoFYca6W9aOnz9/Xv7+448/VlR+JiKqLlqtFhcuXMDbb+ehVSuBe+8Fevf2wYEDPQBYrwlmLC9h+Np6OQrjyJh5ACdJeoSHp5Srbz4+PpW6JyJq2CqUE5afn48pU6Zg06ZNcHV1xeOPP4558+bJRVEvX76MoKAgFBcXV0tniYisMSbgl6xSNO4FKWHTpqEIDj5jNW8LAHJzPWBvX4hly6ZYHe2SJD0CA7PKTLw3327IiNsOEZEtFQrCXnrpJRw+fBhffvkl8vLy8Morr+DAgQPYuHGjXOW5GiteEBFZZUzAz831tAikhFAhMzMAubl/y6UojMGTTucC4wiYRpOM/fs1MEwQGGqCmQZbZSXeM+eLiCqqQkHYd999h5UrV+Kee+4BAIwYMQJDhgzBsGHDkJCQAAD1YqNsImqYrO0FKUl6rF8/GqalKMLCDuLAgR7yyJZp0KXR7EOnTsdQVORgEWyZBnCmmPNFRJVRoZywy5cvK/aG9PLyQlJSEgoKCnD//ffjxo0bVd5BIqLyspb3ZRicV5aiyMryMwnAAONomBAqJCdr4OJyzeYqR2vc3Nyq9kaIqFGo0EhYy5YtceLECQQFBcnHXFxcsH37dgwcOBD/93//V+UdJCIypdVq5enH7GwVMjKaQK3Ok8+bThtev97MYjshw/RkS5tFWU1rf5UXN90mosqoUBA2cOBALF++HPfff7/iePPmzbFt2zZERUVVaeeIiEwZE/ABKKYTJckdw4ZZ7gWp07lYnZ60XbzVeN6QtD9gwAA0bdoUTk5OaNKkidURLybeE1FlVSgIW7BgAbKzs62ec3FxQWJiIg4cOFAlHSMiMmccAdPpXBTTicZpRuMqSGOb3FxPREYmISkpUs79EkKFb74Zh44dT+DEiU5QlqYQiIxMkq8REhICPz+/GrxDImpMKhSEubu7w93d3eZ5FxcX3H333bfdKSIic1qtFleuXAEAZGYGWl0FaZxGVI6S6XHXXXuxd28/mOaGnTzZEf367cHPP/f791p6REUZNuQmIqoJFd478tatW3j33XexevVq/PHHHwCAdu3a4aGHHsIzzzwDe3v7Ku8kETVu5tOQCQlDLdoYpxGzsvz+PV8ScJkGYEZCqNCmTQZ69Uq1WXaCiKg6VSgI+/vvvxEVFYXk5GRERkaif//+AIATJ05g9uzZSEhIwPbt29G0adNq6SwRNWzmSfdHj/4DN7fLsLe/CJ3OBZmZgdi0qSTAMjKWnkhPb2v1vLE8hWVuWK7NshNERNWtQkHY66+/jszMTBw8eBBdu3ZVnDt8+DCGDx+O119/HfPnz6/KPhJRI2A76T4QXbsewZEjg22uaBw1aj3c3PKwdOkU2Eq2N80Ns1bxnoioplUoCPvmm2/wzjvvWARgANCtWze89dZbeOGFFxiEEVGFlZZ0f/hwN9ja21GS9NDp3LBhg6Egq6WSAq2hoWkVmnpk6Qkiqk4VCsLOnTuH3r172zzfp08fxQbeRETmsrKA06cBL6+r8PL6B3l5ecjKAo4dK0RWlh/On29lZcTL1k4cerPVj+YExo1bjfbtzwCwXfHe2r6PLD1BRNWtQkGYq6srLl26hMDAQKvnc3Jy4OLiUiUdI6KGw5jrtWqVE2bNUkOvlyBJagwbthcATEa+usIQcAmYl46wFoiNHr0ezZr9bXOaEpDg4FBUZv+8vLxYioKIalyFgrABAwbgf//7HzZs2GD1/Ouvv44BAwZUSceIqP4yjnaFhABOToZcL53OBYsWxUOIki2CDMEXUDKNKJl8LtnPsW3b0zh9uh2UgZgegAR7+5sWSfdGpoVXiYjqmgoFYfPmzUN4eDj69OmDGTNmoEOHDhBC4MSJE3j33Xdx/Phx/PLLL9XVVyKqw6yNdqlUAnPn/g0AyM31tFrbyzYJ0dFboderkJQUCeUImSEAW7/+QUiS/t/E/a4Wm3GXN/meuV9EVBsqFIR16tQJiYmJiI2NxdixYyFJxr9oBTp06IDt27ejc+fO1dJRIqq7jCsbzUe79HoJCxb4Iz7eBR4eWqtlIpQjYVCcCww8j2XLpphttC0U7YVQ4ciRroiNXYqiIgfY2xeiqMjBIvl+wIABCAkJsXgf5n4RUW2pcLHWPn364NixYzh06JCiWGv37t2rum9EVE9cvnwZgO3RrtxcDwQFncOwYZsVleyHDdsMwDQnTDmKVVTkWK4kfSFUKCpyQFDQOZt99Pb2Zt4XEdUpFQ7C8vPz0bx5c3Tv3l0ReOn1ely7dg2urq5V2T8iquO0Wi3WrFkDAMjO9oN5Er1pXlZY2EEEB5+xKBNhPGY+irVjxz0W17OmPLlf1jbfJiKqTaUlZFj49ttv0atXL/zzzz8W5/7++2/ceeed2LRpU5V1jojqPtP6XiW5W0bKDbEBQ5mIoKBzFsc8PHJRVOQIe/tC5OZ6YseOe7B3b3+L65krb+4X876IqK6p0EjYxx9/jFmzZsHZ2dniXLNmzTB79mwsXrwYw4YNq7IOElHdY7q9kHFTbWtTkYAEf/9sAIYgLTfXEx4eWouAybRCfsnIl7URMGNSvmHD7YiIZISHp1jkfrm7uwMAmjRpAjc3N+Z9EVGdVKEgLC0tDR999JHN8/3798eLL754250iorrDNOA6deo6jh79BxkZ2wEYAi97+5soKmpttVSEcZpQuQ1RSQV7wLJCvrJMhTk9pkxZajXx3igkJIS5X0RUL1QoCLt69Spu3bpl83xRURGuXr16250iorrB1n6OQCcYgiRlPS/TUhHWE+9L6oMFB5+BWl1gYwTNGoGoqCQEBFyohjslIqp5FQrCWrdujd9//x0dOnSwev73339Hq1atqqRjRFT7bO3nqEwnLSm+aloqwjhSlZHR2uqKyczMAOTm/l1qsdUSekRFJaFv3+Qy+8zcLyKqLyoUhI0cORIvvPACoqKi4OvrqziXk5ODF198ERMmTKjSDhJR7SvvaJW1UhH29jdhbRui9euNG27r0anTCZw40dHme4wevR6hoScUx5j7RUT1XYWCsOeffx7ff/89QkJCMGHCBLRv3x4AcPLkSXz99dcIDAzE888/Xy0dJaKapdVq5aR7a4VWrbFWKqKoyBHWE+yNx1Q4frwT+vXbA1/fi9iwYbRFXllgYJbFezH3i4jquwoFYS4uLti3bx/mzJmDNWvWyPlfbm5umDBhAl599VVu4E3UAJjmghlpNMnYv18Dy8o2pW8TVL4ATsLevf0wffoiqwVdrSXgc9qRiOq7ChdrVavV+Oijj/Dhhx/iypUrEELA29tb3sLI1L59+9CrVy84OjpWSWeJqGYYc8EAa+UjzEno338XevY8qAiWQkNDkZaWBrW6QBFY2S6+asgTs1XQ1dSECRM47UhE9V6FgzAjSZLg7e1dapvBgwfj0KFDaNOmTWXfhohqke3yEUo+PpctgqW0tDT567Cwg7h6VW2l+Kp1anWB1eBr5MiR8Pf3ZwBGRA1CpYOw8hDC2l/NRFQXlb8Aqzk93NzykJHR2mohVsAQzO3d2w+lBWC2cr9MMQAjooakWoMwIqp9psGVNcbcKtMcMEN1e9sFWA1/XxlWNnbrdgTLlk2xWog1IiIC+/fvR26uJ6zvkmY9n2zAgAEICQmx6CcDMCJqSBiEETVg5gn2trYOioiIkL82r25vrQCr6YbbxgAMsCzE+uOPR20Gc4BAv3570KZNhkXul7u7O1c+ElGDxyCMqAGzlWBvPmK1f/9+AEBWlp9FdXtrBVgBlFqINSUlHF5eWpvBXEWKrxIRNVTVGoRZWzFJRFWnrKnGvLw8AJYJ9kKokJAwFA4ONxEYmAm1ugAHDvRAQsJQmE8bWivAamSr/MT+/RpIEsoVzFnTpAn/PiSiho+J+UT1VHmnGgFbCfYqrF//ICRJj8jIJCQlRcJ63pYe9vbWAz21uuDf+mF9La5t/s+/tGDOnI+PT5ltiIjqu2oNwgoKbP+lS0S3p7xTjUDpBVOFUCEpKbKUVZAqLF36GIYP3yRfc8CAAdi1axcAIDw8pdQirkbWqukbDR48GIGBgQCYgE9EjUeFgrB77723XO127txZqc4QUcVZm2o0TY4HShuxgvwaQA/rI2EAICmuaVojUK0uQESEtWtLcuBnvvpx5MiR8PLyAsCgi4garwoFYT/99BNatWqFIUOGwN7evrr6REQVYG2qUQgVcnM9FNOSnTodszFiBcWUpK0RMdNrurm5YcyYMVizZg0Aw2hYcrLGopSFrRwwLy8vrn4kokavQkHYG2+8geXLl2PdunUYP348Jk+ejNDQ0OrqGxGVg/WpRoHsbH85/8o4XWkIwIzThMoaXWFhBxEamobMzACsXz8a5sGa+XSiWq02+brA6p6PAQEXqumuiYjqvwoFYTNnzsTMmTORnJyMzz//HH379kX79u0xefJkPPTQQ3B1da2ufhKRDWp1ASIjk5CYGIWSHCwJSUmRCA01bB1kvvWQJOkxduxqODgUWZSdALL+nV4sGTWztpG2+Qba5dnzkYiISlQqMV+j0UCj0eC9997DunXr8OGHH+K5555DdnY2AzGiWuDvfwHmWwIZpw8Byep0pYNDkcVKRfME/7Cw3xAUlIHAwCyLoMrT0xMxMTFYu3atfMzWno/mzAM4IqLG6LZWRx44cAC7d+/GiRMnEBoayjwxohpgrA1m3N8RsD4laTp9WNo5I2sJ/gcO9ET//nstAitjEFXeUhIxMTFwc3OTX8tEfCKiSgRh2dnZWLFiBVasWIH8/HxMmDABKSkp6NSpU3X0j6hRMy/GmpeXpxh5MrKVk2UMnszPRUYm/bufI+Q2ZSX49+rVC+7u7vD19ZWDKE9PT8TFxZW5NyWDLiIiSxUKwu6//37s2rULAwcOxJtvvokhQ4awsjVRFTMGXrYCLltKy8kyPZed7S+vgjRNyrc1mnb9ejPodC74/fff5eNxcXGKQIyIiCpOEhUoa69SqeDn5wcfH59StyQ6cOBAlXSuscvPz4darYZOp2OuXSNhXgW/LKVVybfVftGieItAa9SoDQgMzER6elvFiJnh/w6WBWCnTp3KEhNERDaU9/d3hYax5s2bd9sdIyLbSpvWMw+4yqqSb2rgwIHYvn27zSlH4/ZFw4ZtRmzsUvzxRzvs2dMfxtWR1grAEhHR7WEQRlSHGQOv7Gw/xRSieWFV8yDJvCK9Mbgra/uihIShio23zc+bF4AlIqLKq5KErt27d+P69evQaDRwd3eviksSNXqmI12m+zDa2uvRVpBkupLSPIHfkuXG20al7f1IREQVV+GK+deuXcPLL78MABBCYPDgwdi+fTsAw3L1HTt2oHPnzlXfU6JGxLxchLUaYOZ7PZoGSRs3brR5bWOSvq3K+NZYK9ZKRES3p+z/+5pYs2aNYpui9evXY8+ePdi7dy+uXLmCXr16YcGCBVXeSaLGxlrulilJ0iMqKgmSpJe/Nw2SdDoXZGS0hk7nYvX1anUBQkNPICoqCYZRNlv0GD16LeLjF9nMNyMiosqp0EhYRkYGunbtKn//ww8/YPTo0ejbty8A4MUXX8SDDz5YtT0kqsfM63yZs1VDy9Z+kNb2ejQvSVFawv7IkSPRpEkTufSFtUr7RsbXhoaeqNS9ExFR6SoUhN26dQuOjo7y98nJyYiPj5e/9/f3V1TxJmrMyltuwlhzS6vV2szdMibj+/tnW+z1OG5cPzRt2hQbN260WvXeNGHfy8sLfn5+GDNmDNasWWMj2NNj9Oj1VrcqMuK2Q0REt69CQVhwcDD27NmDNm3a4Pz58/jjjz/Qv39/+XxWVhYLNxL9q7QRMPN21gK28m6IHRAQIH9dVtX7K1euwMHBAWq1GoDtSvvWRr+MKy5ZAZ+IqGpUKAibNm0a4uLisHfvXvzyyy/QaDSK7Yp27tyJHj16VHkniRo6WwFbeTfENiprD0ljwn5MTIx8vrzBnr+/P4MvIqIqVKEg7LHHHoOdnR02bdqE/v37W9QNy87OxuTJk6u0g0QNha3q9uWdwrf1+oyMDNy6dQuAtRIUemg0yRbXMrY3shXscfSLiKj6VGjbIqpZ3Laobqlokv2FCxewZMkSAKUny1tjGnABQEpKOJKTNRV6fUpKOPbv18CwCFqP4cNLXnP//ffjhx9+KPOeTfeIJCKi8qmWbYuIGquKJtmbKitZ3pyySKsehtWLJSsYS3u9MXizt79pEoABgKEavvE1TZs2RVxcXKVWbhIRUdWoUBBWVFSEF154ARs3boSHhweeeOIJxfTjxYsX4e/vj+Li4irvKFFtMI5+lXfK0FpQU1ayvCnLIq3Wa4WZv97ayJfla1XIzAyAWm1IumeARURUuyoUhL366qv44osv8NxzzyEvLw8zZsxASkoKPv30U7kNZzepoSjv6Jcp02DN+HVpyfLmeV5lFWk1fz1gPnJmVKE6zEREVAsq9H/qr7/+GkuXLsVzzz2HV155Bb///jt27tyJRx99VA6+JMl64cfbNXz4cLRs2RJNmzaFn58fHn74YWRnZyvarF27Ft27d4ezszNatWqFN9980+I6P/30E8LCwuDo6Ii2bdtixYoVFm0+/PBDtG7dGk2bNkV4eDh+/fVXxfl//vkH06ZNg6enJ5o3b45Ro0bh4sWLijbnz5/HkCFD4OzsDB8fH8ycOdMiGZrqtvKWmDC1ceNGLFmyBEuWLJFXIhqT5c2r26ent8WiRfFYuXIiFi2Kx4EDPeSArTSm1fEtR85Kf11gYFaF74mIiKpHhYKwv/76S7FtUdu2bfHTTz9h//79ePjhh6t1GnLAgAFYu3YtTp06hQ0bNiA9PR2jR4+Wz//4448YP348nnjiCaSlpeGjjz7Cu+++qxjJyMjIwJAhQzBgwAAcOnQI8fHxmDJlCrZt2ya3WbNmDWbMmIF58+bhwIED6NatG6Kjo3Hp0iW5zfTp07Fp0yasW7cOu3fvRnZ2NkaOHCmfLy4uxpAhQ1BYWIj9+/dj5cqVWLFiBebOnVttz4dqVlnbApkLCzuIbdtOYdmyPxEfvwjBwWes5okBUARsgF7xdUTEPsUWQpmZgaUEYHqb2xoREVHtq9DqyDZt2uCzzz7DfffdpzienZ2NAQMGoFWrVtixY0eN5IQlJCRgxIgRuHnzJuzt7fHQQw+hqKgI69atk9t88MEHWLhwIc6fPw9JkjB79mxs2bIFaWlpcpuxY8ciLy8PW7duBQCEh4fjzjvvlIM3vV6PwMBAPPXUU3j++eeh0+ng7e2NVatWyUHgyZMn0bFjRyQnJ6NPnz748ccfMXToUGRnZ8PX1xcA8Mknn2D27Nm4fPlyuauNc3Vk7TJd3WiqoisdjaZOnQqdToc1a9YgI6M1Vq6caNFm4sQVCAo6B53OBd7eGoSEGEaWT58WuHw5uZQcMHOG1ZC26n/FxMSgY8eO5XsQRERUIeX9/V2hkbB7770Xq1atsjju7++PnTt3IiMjo+I9rYTc3Fx8/fXXiIiIgL29PQDg5s2baNq0qaKdk5MTsrKycO7cOQCGbZYiIyMVbaKjo5GcbKijVFhYiNTUVEUblUqFyMhIuU1qaiqKiooUbTp06ICWLVvKbZKTk9GlSxc5ADO+T35+Po4dO2bzvm7evIn8/HzFB9UttlY6lmdEzBiAAbAx7SiQne0PwDCFWVi4HceObcOxY9tQWLhdsTfku+/GY//+vjD/JyxJhtGy6dMNo2VqdQGCgs5ZjIC5ublV/OaJiKhKVSgIe+mllxSVtk3dcccd2L17Nz7//PMq6Zg1s2fPRrNmzeDp6Ynz58/j+++/l89FR0dj48aN2LFjB/R6Pf744w+8/fbbAAwjGgCQk5OjCIwAwNfXF/n5+fj7779x5coVFBcXW22Tk5MjX8PBwcHil5h5G2vXMJ6z5bXXXoNarZY/AgMDy/toqIK0Wi0uXLhg80Or1Vq8RqdzwbFjnW2udCxLbm6u/LVaXYDIyCQYNuU2kpCUFFlqQGcMAm390x01aj0GDkwqc9qRez8SEdW+Cq2ObNWqFVq1amXzvL+/PyZOtJxiseX555/HG2+8UWqbEydOoEOHDgCAmTNnIjY2FufOncOCBQvwyCOPYPPmzZAkCY899hjS09MxdOhQFBUVwdXVFc888wzmz58Plap+rBSbM2cOZsyYIX+fn5/PQKwalHfV45gxY+SvlSsQBUzrdpmuVARsV7Y3X5jh739BcR3AdukKo9JWT5om3vfq1QseHh5o3rw5mjRpovijgfW/iIjqhkoVa123bh1Wr16NP/74AwDQrl07PPTQQ4pE+fJ49tlnMWnSpFLbtGnTRv7ay8sLXl5eaNeuHTp27IjAwEB5D0tJkvDGG2/gf//7H3JycuDt7Y0dO3YortGiRQuLVYwXL16Eq6srnJycYGdnBzs7O6ttWrRoIV+jsLAQeXl5il9s5m3MV1Qar2lsY42joyMcHR1LfR50+8q76rGoqAiAtdpdEoyBmCTp8dRTaYqpQlv5Yjdu3FBcv6x9Hq0Fc9ZeY3ydaeJ9WFgY/Pz8yv1MiIio5lUoCNPr9Rg3bhzWrVuHdu3aySNUx44dw5gxY/Dggw9i9erV5S5T4e3tDW9v74r3+t++AIY8KlN2dna44447AACrV6+GRqOR30Oj0Vhs1ZKYmAiNRgPAMELQs2dP7NixAyNGjJDfZ8eOHYiLiwMA9OzZE/b29tixYwdGjRoFADh16hTOnz8vX0ej0eDVV1/FpUuX4OPjI7+Pq6urYsNzqtuaNDH887A++iQhOnorOnU6jgcf1GD79rIr46ekpFi8h0aTLCfXmwZStoI5a3tDRkQkIzw8RTF6xulGIqK6r0JB2HvvvYekpCQkJCRg6NChinMJCQl49NFH8d577yE+Pr4q+4iUlBT89ttvuOuuu+Du7o709HS89NJLCA4OlgOfK1euYP369bjnnnvwzz//YPny5XIJCaMnnngCixcvxqxZszB58mTs3LkTa9euxZYtW+Q2M2bMwMSJE9GrVy/07t0bixYtwvXr1/Hoo48CANRqNWJjYzFjxgx4eHjA1dUVTz31FDQaDfr06QMAGDhwIDp16oSHH34YCxcuRE5ODl588UVMmzaNI131iJubG+Li4nD27C18+aWAXl/yx4WdncCrr/ZA69Z3yuVLKlIZ3zzI0mj2yYGUTueChISSvC/zYC4s7KDNVY8jR46Ev78/pxuJiOqBCiVLLV++HG+++aZFAAYYiqkuXLiwWhLznZ2dsXHjRtx3331o3749YmNj0bVrV+zevVsR1KxcuRK9evVC3759cezYMfz000/o3bu3fD4oKAhbtmxBYmIiunXrhrfffhtLly5FdHS03GbMmDF46623MHfuXHTv3h2HDh3C1q1bFYn27777LoYOHYpRo0ahf//+aNGihVyYEzCMxm3evBl2dnbQaDSYMGECHnnkEfz3v/+t8mdD1cvT0xM9e/piyRIJdnaGY3Z2wKefSujZ0xeenp7ytLRhs23zFY/KfDHA+ohZcrJGPp+SEg7zf5rmyf+2Vj0yACMiqj8qVCfMyckJp06dQsuWLa2eP3fuHDp06IC///67yjrYmLFOWPU4ceIE1q5dW2a7qKgouLu7ywn1OTlNkJnpiDZt9GjfvhkAw7RfYWEhlixZAp3OBe++Ox3mSfvx8YsUwVJpNcLs7QuxdOljME/YB/SYPt1wnZEjR8LLy8vi9Uy4JyKqG8r7+7tC05FOTk7Iy8uzGYTl5+db1OoiqmvKu31UYmKi1eOZmYDJLLdctiU31xPlWe2Yne0Hayss//wzCHv39rO4BgBERJQUauVoFxFRw1Ch6UiNRoOPP/7Y5vkPP/xQztEiqi8qugWROeNCFOsFWPWwty9ZjanTuSApKRLKQEvA2/sS9u7tD2v/JCVJj/BwQ1J/TEwMAzAiogaiQiNhL7zwAu655x5otVo899xz6NChA4QQOHHiBN5++218//332LVrV3X1lQiAoc5XaWUmypqWM+6yAFR+CyJTarUacXFxyM7ORnr6ZrN6YiosWzZFvq6tlZaXLvnC2giYeekJ42pbIiKq/yoUhEVERGDNmjWYOnUqNmzYoDjn7u6O1atXo2/fvlXaQSJT5S20GhcXZzMQU6vVAMouKVFeV65ckXO0wsIOwscnB8uWTbF63ZLkfctAzJIesbFLERBwAQMGDEDnzp05CkZE1IBUuFjr//3f/yE6Ohrbtm3D6dOnARiKtQ4cOBDOzs5V3kEiU+UttJqdnW2zrU6nA1CxkhKlMV0ZCwBFRY42r+vhkYuePVORmtoL5lOSloGYBBeXawAMf+QwACMialgqFITt3LkTcXFx+OWXX/B///d/inM6nQ6dO3fGJ598gn79+lVpJ4kqyjwwsqasivWAsmo9AKvbEZX3utnZ/vjii0fkIqv/noH1AMxwzhgQGgvHEhFRw1Gh/7MvWrQIjz32mNXllmq1Go8//jjeeecdBmFUL5hXnzfPv7LcL9KQ41VW7pi160ZGJiEpKdIkMDMEYv3778aePfdYvY5pQGi+YTwREdV/FQrCDh8+XOqG2wMHDsRbb711250iskar1eLKlStVfl1jpTzjZ53OBZmZgYqq9YaRKunfdmXnjplXtbeekK+Cj8+lcu0FSUREDU+FgrCLFy8qVpZZXKxJE1y+fPm2O0VkrrwJ+eUVExODGzc88N//+qBkKlCFhIShkCRYCZiUhFAhMzMAubl/25yeVKsLFMetTVEGBmZxL0giokaqQkHYHXfcgbS0NLRt29bq+SNHjsDPz69KOkZkqqqDezc3N+Tk+EJvXtYLKpRvDwk9NmwYXWZpC9OcMltTn6XtBRkTEwMfHx8m5RMRNUAVCsLuv/9+vPTSSxg0aJBFZfy///4b8+bNs7qvJNHt0Gq1WLNmTZVfNyQEUKlgJRCzZBjFAoy5XIAEIUqmJxMShsLB4SYCAzOt5pQZg674+EXIzfXA+PHhCAgIwa1bQbC3t5fLZpjiNkRERA1bhYKwF198ERs3bkS7du0QFxeH9u3bAwBOnjyJDz/8EMXFxXjhhReqpaPUeJW3LEVFBQQAS5YAjz8OFBcDxuDKfDuhUaPWIzAwCwCQm+uB69ebYf36B82upvr3mB5RUUkIDU2zWoMsPn4RgoLOITQ0mqPGRESNXIWCMF9fX+zfvx9PPvkk5syZA+Pe35IkITo6Gh9++CF8fX2rpaNE1SE2FoiOBlJStNi3byXS09tajF6Fhp6Q26vVBdDpXKwm0xuokJgYhZwc3yqpQUZERA1XhYsPtWrVCj/88AOuXr2KM2fOQAiBkJAQuLu7V0f/iEplmnNlLbixdd5YsNXBwQEBAZ6wsytEWlpJflZmZgAACYGBmYrrjRkzBmq1Gmp1NhYs8LcRiEk4erSr5VGzGmRERNS4VboCpLu7O+68886q7AtRhZS172Np501zzOLi4hTXtTYaZnydWq2Gn58fpk69AJ1uETIzA7B+/WiUtQ2ReckJrnYkIiKW4aZ6ydq+jwkJQ3HXXd1w772GRSP//a+PInneVm0v05yz8u4n6eDg8G8JihPQ6ZKQmBgF61XvDUaNWo/Q0BMYOXIk/P39mXBPREQWf74T1Qu2ip/OmtUKvXv7YOnSZtDrlUGRMSerote19jpPT09MmDABANC3bzKiohIhScZllsoaF8Z6YADg5eXFAIyIiABwJIzqKWv7Mxrp9RI+/dQZhtWOtveFNLpy5Yq8N2NZ+0nm5eXJx52dnRETE4Nbt25h8OC/ERpqKD+Rne0vb1HEaUgiIrKFQRjVebYCF40mGfv3a2BtQFcIFSIi9iE5WWM1GDJlutm3+b6PKpXAggUXUVxseN3atWtt9nPatAlwdnYGAGRnX8bZs03QuvUt+PvfCeBO1v0iIiIFSYjy1Qenmpefnw+1Wg2dTmd10/TGRKvVyrlbq1Y5YdYsNfR6CZKkR1hYKg4c6GkxehUfvwgA5Er0hq9tr6Q0ZVhV6YGnnhoMf389lixZUmYfp06dytpfRERU7t/fHAmjesE4gpSVBcycabrptgoHDvREZGSSzSlAtbqgzJWU5oz7Pvr7l6OcPhERUSUwCKNqYzp6ZU1lpufee68kADMSQgV//2x5SyDz/RfLs+KxrHpjREREVY1BGFULrVaLxYsXl9kuLi6u3IFYVhbwzjvWzujlwMtaAFXaiseyRsny8vLg5uameC0DNiIiqgoMwqhalHe/x4rsC3n6tPXNtiMikm0GQzExMcjKAr74wvqKx7JGydauXYuYmBj5dRWd1iQiIrKFdcKo3ggJAVRmP7GSpEd4eAp0OhdkZLSGTueiOO/m5obQUDcMG7ZZruNlmjNWnrpgt27dAmB7WtP8PYmIiMqDI2FUpYx5YFeuXKnyawcEAEuWAI8/DhQXlwRTpW0z5ODggMLCQnlPSPOcsbLqgpkqa1qTiIioIhiEUZUpbx7Y7YiNBaKjgTNnAE9PHQoLw9G7t3J7oi1bhmHu3HC0bt0Enp6euHDhAoCSFY86nQvS0joBAAIDMxV1wazVE7O3twdQdsDGQqxERFQRDMKoylQkv+t2BAQYPgB37NplmSdWXCwhK6sp/P1v4MKFC4pRuQMHeiAhYRhK9nnUIyIiGbGxS5GX5wZAQmBgpuJ6arUacXFxKCwsxB135GP2bDWKiyXY2Qm88UY+HnpoHAuxEhFRhTEIo1plbdrSOKJUnvIWxjwx00DMzk5g9+5lOHRIOUVozOlSbrStwv79fbF/fwQMez5aT7g3BljPPguMGWMYiWvbVkJAgBsAtwrdMxEREcAgjGqZ6ZZBFRUXF4eAAE9FnpidHfDGGzpcu1a+UhUlJBiDM2t1xEyVjMQRERFVHldHUr11+fJlAIY8sbNngV27DJ8feuhvq+2NOV3lYb5CkoiIqKoxCKPbptVqLXKvasKaNWug1WoBGEam7rnH+giVsXwFAEWpitIw4Z6IiKobpyPpttTEisjSlLUYwFpx1fj4RcjMDEBGRpC88bdh1SNgzAn7738vIjaWCfdERFR9GITRbampFZG25OXlwc/Pz+o5W8VVR43agMDATISGnkD//nvl2mEAkJvrgUmT7sI997StsXsgIqLGidORVCNsVbS/3deuXbtWnpI0Z6u46vr1D2LRonjs26f5dw/Ikn0ng4LOoX37ZhXuIxERUUVxJIyq3e3st2j6WkCPqKgk9O2brGhTWFgoV+oHSspeWCuuaiSEComJUQAk7gFJRES1gkEYVauyNsjW6Vz+HY3SWpSDMH8tYAycoAjE8vLysHbtWov3VqsLFNXwLZWvJAUREVF1YBBG1aq0/RZL2/MRADIzA60ETxKSkiIRGpomB0zGDbatMe4ZmZkZgPXrR8PWDDz3gCQioprGnDCqNjqdC65fdwagLAkhSXrY2xdaHSEz5n0dONADGzaMsnpdIVRISQkv9X1Nc8jU6gKEhp7A8OGm5SmERZ9YkoKIiGoSR8KoWihzuQQMgVjJiFdRkaPNETIApUwhGuzfr0F4eIrFyFVp+WevvBKE+Ph0ZGTYIS3NCe+91wJ6vQSVSmDhQu4BSURENYtBGN0Wa6NGlrlcEiRJYNSotQgMzAJgmGo0T5o3jkZZn4Y0VzJ9ePXqVavva57r5eXlhS5d/DBggOEKM2ZwD0giIqo9DMLotnh6eiIuLk5RL2zfPge8+67lKFezZjcUeWCAXg7EjKNWxvOWBEw33jadPty1axeA0vPPuAckERHVNQzC6LaZTt9lZQF6PaBSGT4bWcsDA1QQQo/Ro0tGyBYtircyCqZHt25HcORIV0XAZh5YWStJYRqsERER1SUMwqjKLFsGTJ1qCL4kqSQQs7MD3n77Bu64YxSWLjUPsAwjZGp1AbZvj7Q6DTl69HqEhp7A9Ola7NqVKRdXNWdeksJWsEZERFQXMAijKpGVVRKAAYAQhkBs7VpAowECApojK6u5xQiZnZ2Ah0cudDoX7N+vsbiuJOnlUbKzZ39GUFDJOWs1xowlKYxbEZkGYFz1SEREdQmDMKoSp08rgyvA8L23d0neVUAAsGQJ8PjjQHGxYYTsjTd0uHatABkZrWGtYopGk2x1JKu0VZDGLYhMxcTEcNUjERHVKawTRlUiJMQw/WjKzg5oa7YPdmwscPYssGuX4fNDD/0NoCSfS0mPTp2OKY7odC5IS+uEhATbNcascXNzq/hNERERVSMGYVQljKNcdnaG7+3sgE8/tb76MCAAuOce5TljPpeymKoKy5ZNwYEDPQAYRr8WLYrH+vUPwvxH17gK8nY2CiciIqpJnI6kKhMbC0RHG2tvla/8g2meVljYQfj45GDZsikWo1w+PjmlFnCVJD2ys/3xxRePVGqjcCIioprGIIyqlHntLa1WK9cQy85WISOjCYKCbsHf3zDi5eDggLi4OGRnZ2Pjxo02K+lnZrYsNQCLjExCUlKkzUKtREREdQ2DMKo2Wq0WixcvBlB6In1MTAxycpogI6M17O1vWq31FRh43urxUaPWIzAwq8xCrVwZSUREdQ2DMKo2hYWF0OlckJkZiISEoTDmcZmPUj3//Ol/A7SOkCQ9una1LMwaEHDBag2w0NATAIDBgwfjyy8F9PqSqvp2dgJPPTUYrVs34cpIIiKqcxiEUZU7evQqTp3SY/9+YaMCvu3NuoVQ4ciRroiNXYqiIgdFra/SaoCFhflgyRJJUf7i008l9OzpWwN3TEREVHEMwqhKvffeNUyfrv43qFLu92jKuJ2QrWnEoiIHBAWds3idsQZYTEyMXHbCwcEBnp6elVoYQEREVFsYhFGV0Gq1OHv2FmbM8IEQxsDLdgBmup1QZfZ7dHNzg5+fn8VxbspNRET1BYMwum3GBPyMjNbQ6yeW0lLg/vs3o33703IAxv0eiYiosWIQRuWWlWXYnigkRDnaZCxBYax6b6uUBCDB21trEWCVlutFRETUULFiPpXLsmVAq1bAvfcaPi9bZtnGetX7EqVNM6rVBQgKOscAjIiIGg2OhFGZsrKAqVNLNujW6w2bcEdHW+ZfmY5qZWf7ywVUOc1IRESkxCCMynT6dEkAZlRcbFiFaC0J3riCMSjoHEJD06xOM7Zt2xZnzpwp8711Ohfk5nrCw8NyGpOIiKg+YxBGZQoJAVQqZSBmZ2coA1EWY0BmrjwBWGlV9omIiOo75oRRqbRaLezsLmDhwjzY2RlyvFQqgTlzdLCzuwCtVlvua+l0LsjIaA2dzkXxta225kVcN20aKrfnNkRERFTfcSSMbDp69CrefnuzPBV4770aJCZGQq9X4ZVXXJCSkoK+fZMxZsyYMq9lOqplSNgXAGyPcNkq4tq370Tccw+4DREREdV7DMLIqmXLgKlT3aDXT4Qk6REZmYSkpEiUDJ6qkJgYBQCIiRE2rwNYjmoZirgaCrkKoUJCQsk+kkbWyl3Y2QHh4Z5g/EVERA1BvZuOvHnzJrp37w5JknDo0CHFuSNHjqBfv35o2rQpAgMDsXDhQovXr1u3Dh06dEDTpk3RpUsX/PDDD4rzQgjMnTsXfn5+cHJyQmRkJE6fPq1ok5ubi/Hjx8PV1RVubm6IjY3FtWvXKtyXuqpkNWRJoGRc5agkISkpEjdueCAuLg5Tp06VR8VMpxutjWopqZCSEq44Yl7uws5O4NNPWQ2fiIgajnoXhM2aNQv+/v4Wx/Pz8zFw4EC0atUKqampePPNNzF//nwsWbJEbrN//36MGzcOsbGxOHjwIEaMGIERI0YgLS1NbrNw4UK8//77+OSTT5CSkoJmzZohOjoa//zzj9xm/PjxOHbsGBITE7F582bs2bMHU6dOrVBf6jJrqyENQZTeoq0QKpw92wSenp5wcHCAWq2Gnd1UvPfedKxcORGLFsUjO9vPpHaYdcnJGov8sLCwg4iPX4SJE1cgJeUSYmNv986IiIjqEFGP/PDDD6JDhw7i2LFjAoA4ePCgfO6jjz4S7u7u4ubNm/Kx2bNni/bt28vfx8TEiCFDhiiuGR4eLh5//HEhhBB6vV60aNFCvPnmm/L5vLw84ejoKFavXi2EEOL48eMCgPjtt9/kNj/++KOQJEn89ddf5e5Leeh0OgFA6HS6Cr3udmVmCqFSCQGUfEhSsejX7ycB6C2O//57jjhyJFdMnLhCTJnyqZCkYos2UVHbTI4rr2H8mDhxuZg+/W0xceIKMX3622L+/PnyR3Z2do0+AyIiosoq7+/vejMSdvHiRTz22GP48ssv4ezsbHE+OTkZ/fv3V6yai46OxqlTp3D16lW5TWRkpOJ10dHRSE5OBgBkZGQgJydH0UatViM8PFxuk5ycDDc3N/Tq1UtuExkZCZVKhZSUlHL3xZqbN28iPz9f8VEbAgKAJUsgr4Y05oS1aXMW/frtkUe1jEn1P/3kiO7d3bBy5UQsXfqY1YR6f/9seVRrypTPLEbGJEmP7Gx/LFoUL4+gHTjQQz7P1ZBERNTQ1IvEfCEEJk2ahCeeeAK9evXC2bNnLdrk5OQgKChIcczX11c+5+7ujpycHPmYaZucnBy5nenrbLXx8fFRnG/SpAk8PDwUbcrqizWvvfYaFixYYP0h1LDYWKB790v44IMfLSrfR0Ymwd8/W96CaNasYXL+mDHh3pRxuyLTmmHmm3YbE/9NS1Js2TIMc+eGo3XrJlwNSUREDU6tjoQ9//zzkCSp1I+TJ0/igw8+QEFBAebMmVOb3a12c+bMgU6nkz8yMzNrtT/+/obgyTw4SkqKlIOq3FxPkwDMkq3tikzzvb799iDGjGlrMYJWXCyhoMCXARgRETVItToS9uyzz2LSpEmltmnTpg127tyJ5ORkODo6Ks716tUL48ePx8qVK9GiRQtcvHhRcd74fYsWLeTP1tqYnjce8/PzU7Tp3r273ObSpUuKa9y6dQu5ubllvo/pe1jj6OhocY+1ycHBwWa9rtxcD6jVBVZLSZgaNWo9QkNPyN+PHDkSXl5eivfw9PREVlblq/ITERHVR7U6Eubt7Y0OHTqU+uHg4ID3338fhw8fxqFDh3Do0CG5rMSaNWvw6quvAgA0Gg327NmDoqIi+fqJiYlo3769PP2n0WiwY8cORR8SExOh0WgAAEFBQWjRooWiTX5+PlJSUuQ2Go0GeXl5SE1Nldvs3LkTer0e4eHh5e5LfeDp6YkpU+62mr9lnIo0LyVh3i4wMEtxzMvLC35+fvKHcZSrJA/N0M7ODixJQUREDVq9SMxv2bIlQkND5Y927doBAIKDgxHw72/phx56CA4ODoiNjcWxY8ewZs0avPfee5gxY4Z8nWeeeQZbt27F22+/jZMnT2L+/Pn4/fffERcXBwCQJAnx8fF45ZVXkJCQgKNHj+KRRx6Bv78/RowYAQDo2LEjBg0ahMceewy//vor9u3bh7i4OIwdO1YunVGevtQFWVnArl2Gz7YEBzsqgixr04vGqcWIiH0wlrGwNQ1ZmthY4OxZQ5/OngVLUhARUYNWLxLzy0OtVmP79u2YNm0aevbsCS8vL8ydO1dRvysiIgKrVq3Ciy++iP/85z8ICQnBd999h9DQULnNrFmzcP36dUydOhV5eXm46667sHXrVjRt2lRu8/XXXyMuLg733XcfVCoVRo0ahffff79Cfalthor4huk/lcowCmUr6AkOPoNRozYAEAgMzLIaWKnVBRg4MAnh4SnIzfWQc8YqKiCAo19ERNQ4SEKI0vecoVqTn58PtVoNnU4HV1fXKrtuVhbQqpVl/tXZs4CTkxaFhYXy8SVLirFggb+8itHaPo8VERcXx0R7IiJq0Mr7+7vBjIRR+VmriF9cDKSm6nDo0GL5mE7ngkWL4hUrIzdtstznsTxGjhwJf39/BmBERET/qhc5YVS1QkIAyayqhCQBAQH/KI6VtjKyory8vBiAERERmWAQRgAsgzIAcvkJZbuSlZEVwYr3RERESpyObIROnzbs1mhKrwfOnlX+OBjLT5hWtjeueNTpXJCb6wkPD22pU5NjxoyBt7c3R8GIiIjMMAhrhEJCrBdGbd36FtLSlG3Dwg4iOPiMYsXjgQM9LAIzY7K+aTFWYyFWIiIissTpyEbIVmFUf3/LgqslI1658giYMQADSpL1dToXAMpirAzAiIiIbONIWCMVGwtERwNnzhi2BgoIAC5cULaxNuLl7n611G2MiIiIqHw4EtYIGSvlA8A99yiLo+p0LsjIaI2sLD+rI1729jerLFmfiIioMeNIWCNTWqX8tWtL6oJZ25RbCBWKihxsJusTERFR+TEIa0SyskoCMMDw+fHHDdOSADBjRnN51aQhABMASmpXGEe8goLOWSTrExERUcUwCGtEbFXKP3PGULLC/JwhABPy565dj8gBl1pdwOCLiIjoNjAnrBExlqYwZWcHeHpehSSdscj1Uo6ESThypKu8CtIWFmUlIiIqH46ENSLG0hSPP24YAbOzA95++xo2bnwfADBsWA+TZHzlVCRguQrStCYYwLpgREREFcEgrJEZMUKL7t1v4ezZJmjd+hYcHC5h40bDubCwg/DxycGyZVMskvIBy1WQ3JCbiIio8hiENSJarRaLFy+Wvzevjg8ARUWOVgMwQLkKMioqigEYERHRbWAQ1ohcvny5zDbGTbtNAzFJ0iM2dikCAkqqubq4lJ4bRkRERKVjYn4jUlRUVGYb46bdxiR9Yx0w0wAMAJo0YfxORER0O/ibtJHQarW4evVqudpa27TbnI+PT1V3kYiIqFFhENYImOeClYetOmAjR45kQj4REVEV4HRkI1BYWFhl12IARkREVDUYhFG5xcTEMAAjIiKqIgzCGjmdzgUZGa0VlfCtHQOYB0ZERFSVmBPWiB04UFIh37gKEoDi2Lx52Zg61Y7V8ImIiKoYg7BGSqdzMdmiyLAlUULCUEgSFMdefvkOxMZKYPxFRERUtTgd2Ujl5npaqYyvsjhWXCzhzJma6xcREVFjwSCskTJWxlfSWxyzswPatq25fhERETUWDMIaAQcHB4tj5pXxAQHDj4MESRIADAHYp58CAQE11lUiIqJGQxJCiNruBFmXn58PtVoNnU4HV1fX27qWVqu1Wi/s4MEmGDrUC0JI8jGVCvjmG0CjYQBGRERUUeX9/c3E/EbC1srGkycB8zBcrwe8vRmAERERVSdORzZyISGGkS9TzAMjIiKqfgzCGrmAAGDJEkPgBTAPjIiIqKZwOpIQGwtERwNnzhhGwBiAERERVT8GYQTAEHgx+CIiIqo5nI4kIiIiqgUMwoiIiIhqAYMwIiIiolrAIIyIiIioFjAIIyIiIqoFDMKIiIiIagGDMCIiIqJawCCMiIiIqBYwCCMiIiKqBQzCiIiIiGoBgzAiIiKiWsC9I+swIQQAID8/v5Z7QkREROVl/L1t/D1uC4OwOqygoAAAEBgYWMs9ISIioooqKCiAWq22eV4SZYVpVGv0ej2ys7Ph4uICSZIqfZ38/HwEBgYiMzMTrq6uVdjD+oPPgM8A4DMA+AwAPgOAzwCo3mcghEBBQQH8/f2hUtnO/OJIWB2mUqkQEBBQZddzdXVttP/YjPgM+AwAPgOAzwDgMwD4DIDqewaljYAZMTGfiIiIqBYwCCMiIiKqBQzCGgFHR0fMmzcPjo6Otd2VWsNnwGcA8BkAfAYAnwHAZwDUjWfAxHwiIiKiWsCRMCIiIqJawCCMiIiIqBYwCCMiIiKqBQzCiIiIiGoBg7B66uOPP0bXrl3lInMajQY//vijfP6ff/7BtGnT4OnpiebNm2PUqFG4ePGi4hrnz5/HkCFD4OzsDB8fH8ycORO3bt2q6VupMq+//jokSUJ8fLx8rKE/h/nz50OSJMVHhw4d5PMN/f6N/vrrL0yYMAGenp5wcnJCly5d8Pvvv8vnhRCYO3cu/Pz84OTkhMjISJw+fVpxjdzcXIwfPx6urq5wc3NDbGwsrl27VtO3UimtW7e2+DmQJAnTpk0D0Dh+DoqLi/HSSy8hKCgITk5OCA4Oxssvv6zYu6+h/xwAhm1y4uPj0apVKzg5OSEiIgK//fabfL6hPYM9e/Zg2LBh8Pf3hyRJ+O677xTnq+p+jxw5gn79+qFp06YIDAzEwoULq+YGBNVLCQkJYsuWLeKPP/4Qp06dEv/5z3+Evb29SEtLE0II8cQTT4jAwECxY8cO8fvvv4s+ffqIiIgI+fW3bt0SoaGhIjIyUhw8eFD88MMPwsvLS8yZM6e2bum2/Prrr6J169aia9eu4plnnpGPN/TnMG/ePNG5c2dx4cIF+ePy5cvy+YZ+/0IIkZubK1q1aiUmTZokUlJSxJ9//im2bdsmzpw5I7d5/fXXhVqtFt999504fPiwGD58uAgKChJ///233GbQoEGiW7du4pdffhF79+4Vbdu2FePGjauNW6qwS5cuKX4GEhMTBQCxa9cuIUTj+Dl49dVXhaenp9i8ebPIyMgQ69atE82bNxfvvfee3Kah/xwIIURMTIzo1KmT2L17tzh9+rSYN2+ecHV1FVlZWUKIhvcMfvjhB/HCCy+IjRs3CgDi22+/VZyvivvV6XTC19dXjB8/XqSlpYnVq1cLJycn8emnn952/xmENSDu7u5i6dKlIi8vT9jb24t169bJ506cOCEAiOTkZCGE4QdXpVKJnJwcuc3HH38sXF1dxc2bN2u877ejoKBAhISEiMTERHH33XfLQVhjeA7z5s0T3bp1s3quMdy/EELMnj1b3HXXXTbP6/V60aJFC/Hmm2/Kx/Ly8oSjo6NYvXq1EEKI48ePCwDit99+k9v8+OOPQpIk8ddff1Vf56vJM888I4KDg4Ver280PwdDhgwRkydPVhwbOXKkGD9+vBCicfwc3LhxQ9jZ2YnNmzcrjoeFhYkXXnihwT8D8yCsqu73o48+Eu7u7op/C7Nnzxbt27e/7T5zOrIBKC4uxjfffIPr169Do9EgNTUVRUVFiIyMlNt06NABLVu2RHJyMgAgOTkZXbp0ga+vr9wmOjoa+fn5OHbsWI3fw+2YNm0ahgwZorhfAI3mOZw+fRr+/v5o06YNxo8fj/PnzwNoPPefkJCAXr164cEHH4SPjw969OiBzz77TD6fkZGBnJwcxXNQq9UIDw9XPAc3Nzf06tVLbhMZGQmVSoWUlJSau5kqUFhYiK+++gqTJ0+GJEmN5ucgIiICO3bswB9//AEAOHz4MH7++WcMHjwYQOP4Obh16xaKi4vRtGlTxXEnJyf8/PPPjeIZmKqq+01OTkb//v3h4OAgt4mOjsapU6dw9erV2+ojN/Cux44ePQqNRoN//vkHzZs3x7fffotOnTrh0KFDcHBwgJubm6K9r68vcnJyAAA5OTmK/+EazxvP1RfffPMNDhw4oMh5MMrJyWnwzyE8PBwrVqxA+/btceHCBSxYsAD9+vVDWlpao7h/APjzzz/x8ccfY8aMGfjPf/6D3377DU8//TQcHBwwceJE+T6s3afpc/Dx8VGcb9KkCTw8POrNczD67rvvkJeXh0mTJgFoHP8OAOD5559Hfn4+OnToADs7OxQXF+PVV1/F+PHjAaBR/By4uLhAo9Hg5ZdfRseOHeHr64vVq1cjOTkZbdu2bRTPwFRV3W9OTg6CgoIsrmE85+7uXuk+Mgirx9q3b49Dhw5Bp9Nh/fr1mDhxInbv3l3b3aoxmZmZeOaZZ5CYmGjxl19jYfwrHwC6du2K8PBwtGrVCmvXroWTk1Mt9qzm6PV69OrVC//73/8AAD169EBaWho++eQTTJw4sZZ7V/OWLVuGwYMHw9/fv7a7UqPWrl2Lr7/+GqtWrULnzp1x6NAhxMfHw9/fv1H9HHz55ZeYPHky7rjjDtjZ2SEsLAzjxo1DampqbXeNrOB0ZD3m4OCAtm3bomfPnnjttdfQrVs3vPfee2jRogUKCwuRl5enaH/x4kW0aNECANCiRQuL1VHG741t6rrU1FRcunQJYWFhaNKkCZo0aYLdu3fj/fffR5MmTeDr69sonoMpNzc3tGvXDmfOnGk0Pwd+fn7o1KmT4ljHjh3laVnjfVi7T9PncOnSJcX5W7duITc3t948BwA4d+4ckpKSMGXKFPlYY/k5mDlzJp5//nmMHTsWXbp0wcMPP4zp06fjtddeA9B4fg6Cg4Oxe/duXLt2DZmZmfj1119RVFSENm3aNJpnYFRV91ud/z4YhDUger0eN2/eRM+ePWFvb48dO3bI506dOoXz589Do9EAADQaDY4ePar44UtMTISrq6vFL7S66r777sPRo0dx6NAh+aNXr14YP368/HVjeA6mrl27hvT0dPj5+TWan4O+ffvi1KlTimN//PEHWrVqBQAICgpCixYtFM8hPz8fKSkpiueQl5enGC3YuXMn9Ho9wsPDa+Auqsby5cvh4+ODIUOGyMcay8/BjRs3oFIpf6XZ2dlBr9cDaFw/BwDQrFkz+Pn54erVq9i2bRseeOCBRvcMqup+NRoN9uzZg6KiIrlNYmIi2rdvf1tTkQBYoqK+ev7558Xu3btFRkaGOHLkiHj++eeFJEli+/btQgjDkvSWLVuKnTt3it9//11oNBqh0Wjk1xuXpA8cOFAcOnRIbN26VXh7e9erJenWmK6OFKLhP4dnn31W/PTTTyIjI0Ps27dPREZGCi8vL3Hp0iUhRMO/fyEM5UmaNGkiXn31VXH69Gnx9ddfC2dnZ/HVV1/JbV5//XXh5uYmvv/+e3HkyBHxwAMPWF2m3qNHD5GSkiJ+/vlnERISUmeX5VtTXFwsWrZsKWbPnm1xrjH8HEycOFHccccdcomKjRs3Ci8vLzFr1iy5TWP4Odi6dav48ccfxZ9//im2b98uunXrJsLDw0VhYaEQouE9g4KCAnHw4EFx8OBBAUC888474uDBg+LcuXNCiKq537y8POHr6ysefvhhkZaWJr755hvh7OzMEhWN2eTJk0WrVq2Eg4OD8Pb2Fvfdd58cgAkhxN9//y3+3//7f8Ld3V04OzuL//u//xMXLlxQXOPs2bNi8ODBwsnJSXh5eYlnn31WFBUV1fStVCnzIKyhP4cxY8YIPz8/4eDgIO644w4xZswYRX2shn7/Rps2bRKhoaHC0dFRdOjQQSxZskRxXq/Xi5deekn4+voKR0dHcd9994lTp04p2mi1WjFu3DjRvHlz4erqKh599FFRUFBQk7dxW7Zt2yYAWNyXEI3j5yA/P18888wzomXLlqJp06aiTZs24oUXXlCUFWgMPwdr1qwRbdq0EQ4ODqJFixZi2rRpIi8vTz7f0J7Brl27BACLj4kTJwohqu5+Dx8+LO666y7h6Ogo7rjjDvH6669XSf8lIUzKCRMRERFRjWBOGBEREVEtYBBGREREVAsYhBERERHVAgZhRERERLWAQRgRERFRLWAQRkRERFQLGIQRERER1QIGYURERES1gEEYEVW7nJwcPPXUU2jTpg0cHR0RGBiIYcOGKfZ0279/P+6//364u7ujadOm6NKlC9555x0UFxfLbc6ePYvY2FgEBQXByckJwcHBmDdvHgoLCxXv99lnn6Fbt25o3rw53Nzc0KNHD3kjZwCYP38+JEnCoEGDLPr65ptvQpIk3HPPPWXeV+vWrSFJks2PSZMmVfxh1XH33HMP4uPja7sbRA1Ck9ruABE1bGfPnkXfvn3h5uaGN998E126dEFRURG2bduGadOm4eTJk/j2228RExODRx99FLt27YKbmxuSkpIwa9YsJCcnY+3atZAkCSdPnoRer8enn36Ktm3bIi0tDY899hiuX7+Ot956CwDw+eefIz4+Hu+//z7uvvtu3Lx5E0eOHEFaWpqiX35+fti1axeysrIQEBAgH//888/RsmXLct3bb7/9JgeJ+/fvx6hRo3Dq1Cm4uroCAJycnKriEdaIoqIi2Nvb19j7FRYWwsHBocbej6hOqpLNj4iIbBg8eLC44447xLVr1yzOXb16VVy7dk14enqKkSNHWpxPSEgQAMQ333xj8/oLFy4UQUFB8vcPPPCAmDRpUql9mjdvnujWrZsYOnSoeOWVV+Tj+/btE15eXuLJJ58Ud999dznuroRxD7urV6/Kx7777jvRo0cP4ejoKIKCgsT8+fMV+zECEJ988okYMmSIcHJyEh06dBD79+8Xp0+fFnfffbdwdnYWGo1GsR+ose+ffPKJCAgIEE5OTuLBBx9U7A8ohBCfffaZ6NChg3B0dBTt27cXH374oXwuIyNDfq79+/cXjo6OYvny5eLKlSti7Nixwt/fXzg5OYnQ0FCxatUq+XUTJ0602KMvIyNDLF++XKjVasX7f/vtt8L0V4yx35999plo3bq1kCRJCGH4GYiNjRVeXl7CxcVFDBgwQBw6dKhCz56ovuJ0JBFVm9zcXGzduhXTpk1Ds2bNLM67ublh+/bt0Gq1eO655yzODxs2DO3atcPq1attvodOp4OHh4f8fYsWLfDLL7/g3Llz/7+9ew2J6nnjAP51zRCvv4zNa2qpa3l7sVa6hummqVSUmYqoKVlaiexGFnQx0bwQZakvTBRLo8KSkqi8kGxl4i2sNKTFvFCSWgYVpuV9/i/C8/udzMp/yVY8H1hwZp6dmXP2hQ8zc3a/O7+oqCgUFRVx5XPnziEsLOyXrNDU1NQgIiICcrkcT58+RV5eHoqKipCWlsaLS0lJQUREBJqbm7Fs2TKEhoZi165dOHToEJqamsAYQ1xcHO89HR0dKCkpwc2bN1FZWYnHjx8jNjaWa7906RISExORlpYGpVKJ9PR0HD16FOfPn+f1c/DgQcjlciiVSvj6+mJ4eBjOzs4oKytDa2srYmJisG3bNjx48AAAkJ2dDYlEgujoaPT19aGvrw+LFy/+4XvS0dGBa9euobS0FM3NzQCAoKAg9Pf3o6KiAg8fPoRYLIaXlxfevn07m9tNyJ9J1VkgIeTv1djYyACw0tLSGWOOHz8+bQXpvzZt2sSWL1/+1bb29namp6fH8vPzubre3l7m6urKADCRSMQiIyPZlStX2MTEBBcztSozOjrKFi1axKqrq9ng4CDT1dVlLS0tTC6X//RKmJeXF0tPT+fFXLhwgRkbG3NlACwhIYEr19fXMwDs7NmzXF1xcTHT1NTkzV1dXZ29fPmSq6uoqGACgYD19fUxxhizsrLirWAxxlhKSgqTSCSMsX9XwrKysr57XRs2bGDx8fFc2cPDg8nlcl7Mj66EaWhosP7+fq6upqaG6enpseHhYd57raysWF5e3nfnRsifjs6EEULmDGNsTmIBoKenB35+fggKCkJ0dDRXb2xsjPr6erS2tuL+/fuoq6tDZGQkCgoKUFlZCYHg3w0ADQ0NhIeHo7CwEF1dXRCJRHBycprVPGbS0tKC2tpa3srXxMQEhoeH8fHjR2hpaQEAbzxDQ0MAgKOjI69ueHgYAwMD3Fkzc3NzmJqacjESiQSTk5Noa2uDrq4uOjs7sWPHDt59GR8fh76+Pm+OK1as4JUnJiaQnp6OkpIS9PT0YHR0FCMjI9xcf5aFhQWEQiFXbmlpweDgIBYuXMiL+/TpEzo7O3/JmIT8zigJI4TMGRsbG+5A/UxEIhEAQKlUws3NbVq7UqmEnZ0dr663txdSqRRubm7Iz8//ar8ODg5wcHBAbGwsdu/eDXd3d1RXV0MqlfLioqKi4OLigtbWVkRFRc32Emc0ODiI5ORkBAQETGvT1NTk/v7vYXg1NbUZ6yYnJ394XODzE6IuLi68NnV1dV75yy3ikydPIjs7G1lZWXB0dIS2tjb27t077enTLwkEgmlJ9NjY2LS4L8cbHByEsbEx7t27Ny32n3/++eaYhPwNKAkjhMwZAwMD+Pr6IicnBzKZbNo/4ffv38PHxwcGBgY4derUtCTsxo0baG9vR0pKClfX09MDqVQKZ2dnFBYW8la2ZjKVxA0NDU1rs7e3h729PZ48eYLQ0ND/5zK/SiwWo62tDdbW1r+szynd3d3o7e2FiYkJAKChoQECgQC2trYwNDSEiYkJurq6EBYWNqt+a2trsXnzZoSHhwP4nPg9e/aMlwTPnz+f97UhACAUCvHhwwcMDQ1xn/HUma9vEYvFePXqFebNmwdLS8tZzZWQvwElYYSQOZWTk4PVq1dj1apVOHbsGJycnDA+Po6qqirk5uZCqVQiLy8PISEhiImJQVxcHPT09KBQKHDgwAEEBgYiODgYwOcEzNPTExYWFsjIyMCbN2+4cYyMjAAAe/bsgYmJCdauXQszMzP09fUhNTUVQqEQEonkq3O8c+cOxsbGfunqS2JiIjZu3Ahzc3MEBgZCIBCgpaUFra2tSE1N/am+NTU1ERkZiYyMDAwMDEAmkyE4OJi7B8nJyZDJZNDX14efnx9GRkbQ1NSEd+/eYd++fTP2a2Njg6tXr6Kurg4LFizA6dOn8fr1a14SZmlpicbGRjx//hw6OjowMDCAi4sLtLS0cPjwYchkMjQ2NvIeeJiJt7c3JBIJ/P39ceLECYhEIvT29qKsrAxbtmyZtl1KyN+Gno4khMyppUuX4tGjR5BKpYiPj4eDgwPWrVsHhUKB3NxcAEBgYCDu3r2L7u5uuLu7w9bWFpmZmThy5AguX77MbclVVVWho6MDCoUCZmZmMDY25l5TvL290dDQgKCgIIhEImzduhWamppQKBTTzh5N0dbW/uXbX76+vrh16xZu376NlStXwtXVFZmZmbCwsPjpvq2trREQEID169fDx8cHTk5OOHPmDNe+c+dOFBQUoLCwEI6OjvDw8EBRURGWLFnyzX4TEhIgFovh6+sLT09PGBkZwd/fnxezf/9+qKurw87ODkKhEN3d3TAwMMDFixdRXl4OR0dHFBcXIykp6bvXoaamhvLycqxZswbbt2+HSCRCSEgIXrx4wZ2PI+RvpsZmexqWEEKIyiQlJeH69es/tN1HCPm90UoYIYQQQogKUBJGCCHfoKOjM+OrpqZG1dMjhPzBaDuSEEK+oaOjY8Y2U1PTP+r3IQkhvxdKwgghhBBCVIC2IwkhhBBCVICSMEIIIYQQFaAkjBBCCCFEBSgJI4QQQghRAUrCCCGEEEJUgJIwQgghhBAVoCSMEEIIIUQFKAkjhBBCCFGB/wH9UcF9FZ+yuQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_training)\n", + "surrogate_parity(alm_surr, data_training)\n", + "surrogate_residual(alm_surr, data_training)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UtYmPj0dOTo5amwYNGqB27dqqNnFxcWjatKna9GRQUBAyMzPxxx9/lPi8srKykJmZqfZFRERUHsocMGtra5F7YhyU17kyuXd6FYRNmTIFNjY2cHR0xO3bt/Htt9+qjv3999+4desW9u7di6+++gpbt25FfHw8+vbtq2qTkpKiFhgBgIuLCzIzM/Hs2TM8fPgQeXl5xbZJSUlRnaNatWqwt7cvtU1x51AeK8n8+fMhl8tVX9yyiIiINMUpyKqhjessahA2depUyGSyUr+uXr2qaj9p0iT89ttvOHr0KExNTTF06FAIggAgf7PrrKwsfPXVV2jbti06dOiATZs24cSJE7h27ZpYT1Ej06ZNQ0ZGhurrn3/+EbtLREREpCOiJuZPmDChyMrEwurUqaP6u5OTE5ycnFCvXj00bNgQnp6eOHv2LAICAuDm5gYzMzPUq1dP1b5hw4YA8lcq1q9fH66urrh3757a+e/duwc7OztYWVnB1NQUpqamxbZxdXUFALi6uiI7Oxvp6elqo2GF2/zyyy9FzqE8VhILCwtYWFiUej2IiIjIMIg6Eubs7IwGDRqU+lWtWrVif1ahUADIz6MCgNdeew25ublITExUtfnrr78AAF5eXgCAgIAAxMbGqp0nJiYGAQEBAIBq1arB399frY1CoUBsbKyqjb+/P8zNzdXaXLt2Dbdv31a1CQgIwOXLl1UrJ5S/x87ODo0aNarAlSIiMhypqalITk5GcnIy4uPv4euvUxEff0/1WGpqqthdpCo2fPhw1QyYubk5XFxc8MYbb2Dz5s2q+315bN26tUi6kJTpRYmKc+fO4fz583j99ddRo0YNJCYmYubMmfD19VUFPoGBgWjRogVGjhyJFStWQKFQICwsDG+88YZqdGzMmDFYs2YNJk+ejJEjR+L48ePYs2cPDh06pPpdkZGRGDZsGF555RW8+uqrWLFiBZ48eYIRI0YAAORyOUJCQhAZGQkHBwfY2dlh3LhxCAgIQOvWrQEAXbp0QaNGjTBkyBAsWrQIKSkpmDFjBsLCwjjSRURGLTU1FWvWrAEAXLjwMr77rjsEwQQymQI9ehxEixa/AQDCw8Ph6OgoZleNTmpqKrKzs0s8Xq1aNZ3+mwQHB2PLli3Iy8vDvXv3cPjwYXz44Yf4+uuvceDAAYOss6kXz8ja2hrR0dGYPXs2njx5Ajc3NwQHB2PGjBmqoMbExATfffcdxo0bh3bt2sHGxgZdu3bF0qVLVefx8fHBoUOHMH78eKxcuRIeHh748ssvVTXCAKBfv3548OABZs2ahZSUFDRv3hyHDx9WS7Rfvnw5TExM0KdPH7VirUqmpqY4ePAgxo4di4CAANjY2GDYsGH4+OOPq+BqERFJl/Imn5FhqwrAAEAQTPDdd93h63sDcvmjUoMB0r6CwXFpdBkcW1hYqFJ2atWqhRYtWqB169bo3Lkztm7dilGjRmHZsmXYsmUL/v77bzg4OKBHjx5YtGgRqlevjpMnT6oGTJRJ87Nnz8acOXOwfft2rFy5EteuXYONjQ06deqEFStWqOp/ikUvgrCmTZvi+PHjZbZzd3fHvn37Sm3ToUMH/Pbbb6W2CQ8PR3h4eInHLS0tsXbtWqxdu7bENl5eXvj+++9L7zARkZFKS3NE4SpJgmCCtDQHyOWPROqV8Spv0FvVwXGnTp3QrFkzREdHY9SoUTAxMcGqVavg4+ODv//+G++//z4mT56MdevWoU2bNlixYgVmzZqlWpBXvXp1APllJD755BPUr18f9+/fR2RkJIYPHy76fVovgjAiIjIsDg6pkMkUaoGYTKaAg0NaKT9FxqhBgwa4dOkSACAiIkL1uLe3Nz799FOMGTMG69atQ7Vq1SCXyyGTyYosghs5cqTq73Xq1MGqVavQsmVLPH78WBWoiUGv6oQREZFhkMsfoUePg5DJ8pOulTlhHAWjwgRBUE0vHjt2DJ07d0atWrVga2uLIUOGIDU1FU+fPi31HPHx8ejRowdq164NW1tbtG/fHkB+9QQxcSSMiIhE0aLFb/D1vYG0NAc4OKQxAKNiJSQkwMfHBzdv3kT37t0xduxYzJs3Dw4ODvj5558REhKC7OzsEncKePLkCYKCghAUFIQdO3bA2dkZt2/fRlBQkOi5hwzCiIhINHL5IwZfVKLjx4/j8uXLGD9+POLj46FQKLB06VKYmORP5O3Zs0etfbVq1ZCXl6f22NWrV5GamooFCxaodqL59ddfq+YJlIHTkURERCS6rKwspKSk4N9//8WFCxfw2Wef4a233kL37t0xdOhQ1K1bFzk5OVi9ejX+/vtvbN++HRs2bFA7h7e3Nx4/fozY2Fg8fPgQT58+Re3atVGtWjXVzx04cACffPKJSM9SHYMwIjJoBQuDFvfFwqBVq6QC3BVtR4bj8OHDcHNzg7e3N4KDg3HixAmsWrUK3377LUxNTdGsWTMsW7YMCxcuRJMmTbBjxw7Mnz9f7Rxt2rTBmDFj0K9fPzg7O2PRokVwdnbG1q1bsXfvXjRq1AgLFizAkiVLRHqW6mSCcvNFkpzMzEzI5XJkZGTAzs5O7O4Q6R0p1D6iosQuCmqonj9/jqSkJPj4+MDS0lKjn+X/Fc2Vdr3Le/9mThgRSVZlb9aFfzYjwxZpaY5wcEhVy0MSOznX2PAmLj2Ojo4IDw9ncFzFGIQR6TFDHlHQ9ifz0rbIISIGx2JgEEakpwx9+kCbo1hlbZFDhsWQP5yQYWEQRqSnjGmqrbKjWNwix3gY+ocTMiwMwogMgCFPtWljFItb5BgPY/pwQvqPQRiRnjP0qTZtjGIpt8gpHKgawvWhkhnyhxMyDAzCiPScvky1VTRPR1ujWNwix7jo+sMJ885IGxiEEek5fZhqq0yeTmVGsQoX/CxpixwWBjU8uvxwwrwz0hYGYUR6Th+m2sqbf1NSu4qOYrH2kfHS5YcT5p2RtjAIIzIA+jbVVtJNqyBtjWIxwDJOVfXhpKy8M05bSsPJkyfRsWNH/Pfff7C3ty/Xz3h7eyMiIgIRERE66xeDMCI9pa9TbeVNluYoFlWWrj+clJV3dufOHXz//fdlnofTlsDw4cOxbds2vPfee0U25Q4LC8O6deswbNgwbN26VZwO6giDMCI9pY9BiqbJ0lLqO+mHqvxwUlbeWeEAjNOWpfP09MSuXbuwfPlyWFlZAcjfnzEqKgq1a9cWuXe6wSCMSI/pW5CiLys5SX9V5YcTTfLOTp8OQExMIACWyyhJixYtkJiYiOjoaAwaNAgAEB0djdq1a8PHx0fVLisrC5MmTcKuXbuQmZmJV155BcuXL0fLli1Vbb7//ntERETgn3/+QevWrTFs2LAiv+/nn3/GtGnT8Ouvv8LJyQlvv/025s+fDxsbG90/2f9nUnYTIiLtUN60Cip403r48CGSk5ORnJyM1NRUMbpIBsDR0RFubm4lfmnrw4sy70z5mi4p7yw/AHsDyluucgQ4I8NWK/3QhTt3gBMn8v+sSiNHjsSWLVtU32/evBkjRoxQazN58mTs27cP27Ztw4ULF1C3bl0EBQUhLS3/feSff/5B79690aNHD1y8eBGjRo3C1KlT1c6RmJiI4OBg9OnTB5cuXcLu3bvx888/Izw8XPdPsgCOhBFRlSkrWTo6OlqtPXNlSOrKyjvLyLDFsWOBAGRqj0t5BHjTJiA0FFAoABMTYONGICSkan734MGDMW3aNNy6dQsAcPr0aezatQsnT54EADx58gTr16/H1q1b0bVrVwDAF198gZiYGGzatAmTJk3C+vXr4evri6VLlwIA6tevj8uXL2PhwoWq3zN//nwMGjRIlXTv5+eHVatWoX379li/fj0sLS2r5PkyCCMinSuYf6NJsjRzZUiKypt3BhQ/BZ9PWrX8lO7ceRGAAfl/vvceEBQEeHjo/vc7OzujW7du2Lp1KwRBQLdu3eDk5KQ6npiYiJycHLz22muqx8zNzfHqq68iISEBAJCQkIBWrVqpnTcgIEDt+99//x2XLl3Cjh07VI8JggCFQoGkpCQ0bNhQF0+vCAZhRJXA5eflUzhP5+HDh0VGvYj0RXnyztLT07Fnz55i88YAAW+8cUySo2DXr78IwJTy8oAbN6omCAPypySV04Jr164ts31ubi4UCgUUCgWys7PV/g4AJiZFg+DHjx/jvffewwcffFDkWFUuAmAQRnpHKoEPq2ZrprRrUJ66YURSUt7/08VNwQcGHsNrr8XpuIcV4+eXPwVZMBAzNQXq1q26PgQHByM7OxsymQxBQUFqx3x9fVGtWjWcPn0aXl5eyM3Nxb///otffvkFo0ePxr17aahVywexsYfx8OFD1c+dOXNG7TwtWrTAn3/+ibpV+cSKwSCM9IqUAp/KVoGnfNxkmQxdWVPwUqrl5+GRnwP23nv5I2CmpsDnn1fdKBgAmJqaqqYWTU1N1Y7Z2Nhg7NixmDRpEhwcHODm5oZ58+bh+fPneOutkbh3zwVvvhmJL79cj1mz5mPo0Hdw+fJlfPXVV2rnmTJlClq3bo3w8HCMGjUKNjY2+PPPPxETE1Oue4y2MAgjvSLl7UI4mqM5XW+yTCWTyoiyoSpv3tjgwYMld51DQvJzwG7cyB8Bq8oATMnOzq7EYwsWLIBCocCQIUPw6NEjvPTSS9i+fScEwQuADK6utbFw4T4sXz4e//vfF2jevDk+/vhjhIaGqs7x0ksv4dSpU5g+fTratm0LQRDg6+uLfv36VcGze4FBGOktKY2gSKkv+oR1w8QhpRFlQ6WPxZQL8vCo2uCrrEr4+/fvV/3d0tISq1atwqpVq5CdnY2HDx8iK6saUlNfrEBt27Y72rbtDkfHh7CwyIaTkxNGjx6tds6WLVvi6NGjJf7OmzdvVuSpaIRBGOklKY2gSKkv+kaXmyxTyTiVXjWkGmAZIjOzXAAC1EuBCP//uHSxWCvppdJGUIy5L/pCOVVTVrFLKeXKGLKMDFskJXlLungoUWlMTRWwt89AfiAGAALs7TNgaqoo7cdEx5Ew0ktSGkGRUl/0ReGpmlmzHuDmTTN4e+fC3b0lgJaSnqoxJJxKJ0Nhbf0UFhbPkZtrBjOzXMkHYACDMNJTZVVeN9a+6JOCAZabG+DvL2JnjBSn0snQmJoqYGqqP9PoDMJIb2lSeV0XylsFnlNqJFVcGGGYBEEou5GBKK4Qa2XaaUIb15lBGOmV8i77rorAR99XPxFxKt2wmJubAwCePn0KKysrkXtTNczMzFCzZk0oCpf5L8DExARmZtoPd54+fQrgxXWvCAZhpFekFvgwwCJ9xql0w2Jqagp7e3vcv38fAGBtbQ2ZTFbGTxk+hUKB3FztrZIUBAFPnz7F/fv3YW9vX6SgrCYYhJHeYeBDVDmcSjdcrq6uAKAKxEh37O3tVde7omSCMU0e65nMzEzI5XJkZGSUWj2YiEhTrJhv2PLy8pCTkyN2NwyWubl5qSNg5b1/cySMiMgIMcAybKamppWaJqOqwSCMiKgSOKJERBXFIIyIqIK4ByMRVQaDMCPBT+tE2sc9GImoMhiEGQF+WiciIpIebuBtBAp/Ci9ps15+WiciIqo6HAkzMtysl0h3MjJskZbmCAeHVBY8JaIyMQgzIlLYrJe5aWSo+AGHKoPvjcaJQZgREXuzXuamkaGSwgcc0l98bzRezAkzIsrNeguqys16mZtGhqq0DzhEZeEqW+PFkTAjIqXNejl1Q4ZAubei8gNOwUCs4Acc7sFIRMVhEGZkStust6pw6kY/MWelKEdHR4SHhyM7Oxu1amViyhQ58vJkMDUVsHBhJgYOHGCU14Uqhws8jAeDMCNQ+FO4XP6o2P/YVfVpXezcNNIcc1ZKpny+EyYA/foBN24AdevK4OFhD8BezK6RHuIsgXFhEGYECn5aL0lVflova+qGpIc5K+Xj4ZH/RVQRnCUwPgzCjISURieklJtGRCQVnCUwPgzCSBRSyE2jimPOCpH2cZbA+DAIoyojtdw0qhjmrBBpl/I9r6xZAr43Gh4GYVRlpJabRppjzgqR9hV+b5w16wFu3jSDt3cu3N1bAmjJ90YDxSCMqhTfRPQbc1aIdKPge6ObG+DvX3w7looxLAzCiKjcmLNCJB6WijE83LaIiMpUOGdFuf0Vc1aIqg5LxRgejoQRUZmYs0IkPVylrP8YhBFRuZQ3Z4WIdI+rlA0DgzAiIiI9UtlVykzulw4GYURERHqkMquUmdwvLUzMJyIi0iPKVcoFlXeVMpP7pYVBGBERkR4pa5WyJjIybJGU5I2MDFttd5PKgdORREREeqBgCZjS9t8tb6kYJveLj0EYERGRHtDm1m9lJfc/fPiwUuen8mEQRkSkJ7iqjbT171tWcn90dHSJP8ukfe3Rm5ywnj17onbt2rC0tISbmxuGDBmCu3fvqo7PmTMHMpmsyJeNjY3aefbu3YsGDRrA0tISTZs2xffff692XBAEzJo1C25ubrCyskJgYCCuX7+u1iYtLQ2DBg2CnZ0d7O3tERISgsePH6u1uXTpEtq2bQtLS0t4enpi0aJFWr4iRGRMlKvaNm7cWOLXmjVrkJqaKnZXSQ9URXI/lU1vgrCOHTtiz549uHbtGvbt24fExET07dtXdXzixIlITk5W+2rUqBHeeecdVZszZ85gwIABCAkJwW+//YZevXqhV69euHLliqrNokWLsGrVKmzYsAHnzp2DjY0NgoKC8Pz5c1WbQYMG4Y8//kBMTAwOHjyIH3/8EaGhoarjmZmZ6NKlC7y8vBAfH4/Fixdjzpw52Lhxo46vEhEZKq5qI20qT3I/k/Z1TyYIgiB2JyriwIED6NWrF7KysmBubl7k+O+//47mzZvjxx9/RNu2bQEA/fr1w5MnT3Dw4EFVu9atW6N58+bYsGEDBEGAu7s7JkyYgIkTJwIAMjIy4OLigq1bt6J///5ISEhAo0aNcP78ebzyyisAgMOHD+PNN9/EnTt34O7ujvXr12P69OlISUlRJUhOnToV+/fvx9WrV8v9HDMzMyGXy5GRkQE7O7sKXysi0n/Jycnl+iAXGhoKNze3KugR6aPCdcLytz4qmtxfWtI+X2NlK+/9Wy9zwtLS0rBjxw60adOm2AAMAL788kvUq1dPFYABQFxcHCIjI9XaBQUFYf/+/QCApKQkpKSkIDAwUHVcLpejVatWiIuLQ//+/REXFwd7e3tVAAYAgYGBMDExwblz5/D2228jLi4O7dq1U1uhEhQUhIULF+K///5DjRo1tHEZiIiINFJacv/Dhw8RHR1d6Yr8VH56FYRNmTIFa9aswdOnT9G6dWu1Ea2Cnj9/jh07dmDq1Klqj6ekpMDFxUXtMRcXF6SkpKiOKx8rrU3NmjXVjpuZmcHBwUGtjY+PT5FzKI+VFIRlZWUhKytL9X1mZmax7YiIuHkzVVRZSfWVqchPmhE1J2zq1KnFJtMX/Co4fTdp0iT89ttvOHr0KExNTTF06FAUN5v6zTff4NGjRxg2bFhVPp1Kmz9/PuRyuerL09NT7C4RkQRduPAyVqyIwLZtw7BiRQQuXHhZ7C6RAalM0j5pRtSRsAkTJmD48OGltqlTp47q705OTnByckK9evXQsGFDeHp64uzZswgICFD7mS+//BLdu3cvMqLl6uqKe/fuqT127949uLq6qo4rHys4333v3j00b95c1eb+/ftq58jNzUVaWpraeYr7PQV/R3GmTZumNl2amZnJQIzIiBUsSaGs28SpItI1ZdJ+4Zwwvr60T9QgzNnZGc7OzhX6WYUiP0ovOH0H5Od1nThxAgcOHCjyMwEBAYiNjUVERITqsZiYGFUQ5+PjA1dXV8TGxqqCrszMTJw7dw5jx45VnSM9PR3x8fHw9/cHABw/fhwKhQKtWrVStZk+fTpycnJUOWsxMTGoX79+qflgFhYWsLCwqMDVICJDU9JGy5wqIl3RdkV+Kpte5ISdO3cO58+fx+uvv44aNWogMTERM2fOhK+vb5FRsM2bN8PNzQ1du3Ytcp4PP/wQ7du3x9KlS9GtWzfs2rULv/76q2rFkUwmQ0REBD799FP4+fnBx8cHM2fOhLu7O3r16gUAaNiwIYKDgzF69Ghs2LABOTk5CA8PR//+/eHu7g4AGDhwIObOnYuQkBBMmTIFV65cwcqVK7F8+XLdXigiMhgllZpQThUVDMQKThXxBkkVpc2K/FQ+ehGEWVtbIzo6GrNnz8aTJ0/g5uaG4OBgzJgxQ23kSKFQYOvWrRg+fDhMTU2LnKdNmzaIiorCjBkz8NFHH8HPzw/79+9HkyZNVG0mT56MJ0+eIDQ0FOnp6Xj99ddx+PBhWFpaqtrs2LED4eHh6Ny5M0xMTNCnTx+sWrVKdVwul+Po0aMICwuDv78/nJycMGvWLLVaYkREFVF4qsjERMCiRZkYOHAAb5BUaXz9VC29rRNmDFgnjMh4lVUXTFnfady4rvD3dymxnVi4xRIZM4OuE0ZEZGwKl6RQfrm7K8r+4SpWUj5bYdyDkIwdgzAiIokrrXq5FHGLJaLy0Zu9I4mIjFFJJSm4nx+R/mMQRkQkYaWVpCAi/cbpSCIiCVKWmpBiSYqyku4zMjIKfc8tloiKwyCMiEiCCtZsqlUrE1OmyJGXJ4OpqYCFC8UrSVHepHslfctnI6pKDMKIiCRKGWBNmAD06wfcuAHUrSuDh4c9AHtR+qRJMj23WCIqHYMwIiI94OGR/6VPuMUSUekYhBFJEAtdkr4oLd9LivlsRFLCIIyMkpSDHBa6JH1RWr7Xu+++C3t7e0nlsxFJDYMwMjpSD3JY6JL0QVn5Xvb29nBzc5NUPhuR1DAII6NTOHgpaTpFKkEOl/eTFGmS76WP+WxEVYFBGBk1qS+fl3r/yHiVle9F0iHl9AtjxyCMjJbUl89LvX9knJTJ9HL5I/TocbDIhwTla5NJ99Ig9fQLY1ehIOzEiRPo2LGjtvtCVKWkvnxe6v0j41SwiCwAzJr1ADdvmsHbOxfu7i0BtOTIioQwx1TaKhSEBQcHw8PDAyNGjMCwYcPg6emp7X4R6ZzUp1Ok3j8yXgUDLDc3wN9fxM6QRphjKi0V2sD733//RXh4OL7++mvUqVMHQUFB2LNnDyNp0ivK6RSZTAEARaZTxCb1/hGRfrlw4WWsWBGBbduGYcWKCFy48LLYXTJ6FRoJc3Jywvjx4zF+/HhcuHABW7Zswfvvv4/3338fAwcOREhICJo1a6btvhJpXYsWv8HX9wbS0hzg4JAmiQCnYC5Naf1jzg0RlRdzTKWp0on5LVq0gKurKxwdHbFgwQJs3rwZ69atQ0BAADZs2IDGjRtro59EWlM4eJHLHxX7JiRWkFM456Y4zLkhQ8cVfdrFHFNpqnAQlpOTg2+//RabN29GTEwMXnnlFaxZswYDBgzAgwcPMGPGDLzzzjv4888/tdlfokrThyCHNxcyZlzRp33MMZWmCgVh48aNw86dOyEIAoYMGYJFixahSZMmquM2NjZYsmQJ3N3dtdZRIm3iGzeRdHFFn/aVVVKExFGhIOzPP//E6tWr0bt3b1hYWBTbxsnJCSdOnKhU54iIqHicrqPyYI6ptFUoCIuNjS37xGZmaN++fUVOT0REpTC26TqWVag4fUi/MGYVzgm7du0aVq9ejYSEBABAw4YNMW7cONSvX19rnSMioqKMabqOW3dVHgMs6apQnbB9+/ahSZMmiI+PR7NmzdCsWTNcuHABTZo0wb59+7TdRyIiKkVGhi2SkryRkWErdle0qqSyCob2PMl4VWgkbPLkyZg2bRo+/vhjtcdnz56NyZMno0+fPlrpHBGRVEklJ8uQR4pYVoEMXYWCsOTkZAwdOrTI44MHD8bixYsr3SkiIimTSk6WoRfgZFkFMnQVmo7s0KEDfvrppyKP//zzz2jbtm2lO0VEJGVSyckqbaRInylX6pW1dRdX9JG+q9BIWM+ePTFlyhTEx8ejdevWAICzZ89i7969mDt3Lg4cOKDWlojIkIm1es9QR4oKr+ibNesBbt40g7d3LtzdWwJoqTbdK5WpYSJNVSgIe//99wEA69atw7p164o9BgAymQx5eXmV6B4RkbSJmZNlyAU4CwZNbm6Av3/x7aQyNUxUERUKwhQKhbb7QUSkd8TKyWIBzhekMjUsBo4A6r9Kb+BNRGSsxFq9xwKcJTOWwq6GMALIIFKDIGzVqlXlPukHH3xQoc4QEekTMXOyDP3mVBGGXK6jsMLBS0nBp1RHAAsHkSX1X8pBpDaUOwhbvnx5udrJZDIGYURkFAw5J0vfGHq5jtLoY/BZMDgsrf9SDSK1pdxBWFJSki77QUSkN5iTJT3GWthV34NPfe9/ZTEnjIhIQ8zJkh5DLddRFn0PPvW9/5VV4SDszp07OHDgAG7fvl3kjWjZsmWV7hiR2Aomjd69a4KkJDP4+OTC3T1/dTBvssaN//bSYqxTw/oefOp7/yurQkFYbGwsevbsiTp16uDq1ato0qQJbt68CUEQ0KJFC233kajKFUwaLS1fwdCTRvUdV18ZPmOfGtb34FPf+19ZFQrCpk2bhokTJ2Lu3LmwtbXFvn37ULNmTQwaNAjBwcHa7iNRlVPeuMvKVzD0pFF9ZghL+KlsnBouPfjUB/re/8qoUBCWkJCAnTt35p/AzAzPnj1D9erV8fHHH+Ott97C2LFjtdpJIrEYe76CPtP3JfxUfoYcYJWk8MieXP6o2PckfRkBLKn/hq5CQZiNjY3qjcvNzQ2JiYlo3LgxAODhw4fa6x2RyIw9X8FQ6OMSfqLS6PsIYHmDQ30JIiuqQkFY69at8fPPP6Nhw4Z48803MWHCBFy+fBnR0dGqDb2JDIGx5ysYAmNfAk+GS6oBVnnoexCpLRUKwpYtW4bHjx8DAObOnYvHjx9j9+7d8PPz48pIMjjGnK9gCDilTCRNhh5glUeFgrA6deqo/m5jY4MNGzZorUNEUmSs+QqGgFPKRCRVlSrWmp2djfv370OhUKg9Xrt27Up1isRz5w5w/Trg5wd4eIjdG6LK45QyGQOWY9FPFQrC/vrrL4SEhODMmTNqjwuCAJlMhry8PK10jqrWpk1AaCigUAAmJsDGjUBIiNi9EgeTRg2LMU4p86ZsPFiORX9VKAgbMWIEzMzMcPDgQbi5uUEmk2m7X1SFUlNTcfNmLkJDa0KhyP+3VCiA994T0Lz5fXh7mxndf1wmjeo/Q1vCrwnelI0Ly7HorwoFYRcvXkR8fDwaNGig7f5QFVO+WScleUOhGKZ2LC9PhtWrf4CPzy2jfLM2tudraIw5kOZN2XixHIt+qVAQ1qhRI9YDMxDKN+Gykpf5Zk36yBADLE3xpmw8WI5F/5iU3SRfZmam6mvhwoWYPHkyTp48idTUVLVjmZmZuuwv6YgyeVkmy19kweRlIv1X0k05I8NW5J6RLpRWjoWkqdwjYfb29mq5X4IgoHPnzmptmJiv34wxeZnIkLFGmnExxHIsBReY3L1rgqQkM/j45MLdPX/AQN9TCsodhJ04cUKX/SCJYD0sIsNhiDdlKpmhlWMpuMCktGl1fc5ZLncQ1r59e9Xfb9++DU9PzyKrIgVBwD///KO93hERUYUZ2k2ZymZIMxrKEbCyct30OWe5Qon5Pj4+SE5ORs2aNdUeT0tLg4+PD6cjiYgkwpBuylQ8Qy/HYsjT6hUKwpS5X4U9fvwYlpaWle4UEek3FgoVl6HflEmdoZdjMeRpdY2CsMjISACATCbDzJkzYW1trTqWl5eHc+fOoXnz5lrtIOkWK8OTthUuFFpSjSp9zuOQOkO/KVNRhvxvacjT6hoFYb/9lp8EJwgCLl++rHZjrlatGpo1a4aJEydqt4ekU3yzJm0r+FoqLZlWn/M49AH/z5IhMdRpdY2CMOUKyREjRmDlypWws7PTSaeoavHNmnSBhSOJSJsMcfV+uYu1FrRlyxYGYERUKhaOJCIqXYUS8588eYIFCxYgNjYW9+/fh0KhUDv+999/a6VzRKS/DDmZloh0zxhylisUhI0aNQqnTp3CkCFD4ObmVuxKSdI/d+4A168Dfn6Ah4fYvSF9Z8jJtESke8aQs1yhIOyHH37AoUOH8Nprr2m7PySSTZuA0FBAoQBMTICNG4GQELF7RfrOUJNpiahq6HOAVR4VygmrUaMGHByY12Eo7tx5EYAB+X++917+40SVJZc/go/PLQZgRESFVCgI++STTzBr1iw8ffpU2/2hKpaamoqzZ1NRKK0PeXnAuXOpSE1NFadjpLeMIY+DiEgbKjQduXTpUiQmJsLFxQXe3t4wNzdXO37hwgWtdI50S1lUMyPDFjJZRJEE6tOnt+HKlUcsqvn/mDNXPsaQx0FEpA0VCsJ69eql5W6QGJQ3ybISqI25qKZy+52oKCtMniyHQiGDiYmARYsyMHDgM4MOJgpuPXT3rgmSkszg45MLd/f8YdPSnruhXhMiIm2qUBA2e/ZsbfeDRMYE6qIKjhSuWBEBQchfBaxQyDBpkh3+/Xcz5HLDHCksuPVQaVXvDfG5K3H/SyLSNY2CsF9++QX+/v4wNTUt9nhWVha+/fZbvPvuu1rpHFUtQ6xGXBnKG3BpRUfl8kcGOVKofE5lVb03xOcOcP9LIqoaGiXmBwQEqCVq29nZqRVmTU9Px4ABA7TXuwJ69uyJ2rVrw9LSEm5ubhgyZAju3r2r1ubIkSNo3bo1bG1t4ezsjD59+uDmzZtqbU6ePIkWLVrAwsICdevWxdatW4v8rrVr18Lb2xuWlpZo1aoVfvnlF7Xjz58/R1hYGBwdHVG9enX06dMH9+7dU2tz+/ZtdOvWDdbW1qhZsyYmTZqE3NxcrVwLqlrKoqMFGUvRUWOtel94/8sVKyKwbdswrFgRgQsXXi62HRGRpjQKwgRBKPX7kh7Tho4dO2LPnj24du0a9u3bh8TERPTt21d1PCkpCW+99RY6deqEixcv4siRI3j48CF69+6t1qZbt27o2LEjLl68iIiICIwaNQpHjhxRtdm9ezciIyMxe/ZsXLhwAc2aNUNQUBDu37+vajN+/Hh899132Lt3L06dOoW7d++q/Z68vDx069YN2dnZOHPmDLZt24atW7di1qxZOrk2pFvKnDllIGZMRUeNOQAFSh4JzMiwFblnRGQIKpQTVhpdVc8fP3686u9eXl6YOnUqevXqhZycHJibmyM+Ph55eXn49NNPYWKS/4Y5ceJEvPXWW6o2GzZsgI+PD5YuXQoAaNiwIX7++WcsX74cQUFBAIBly5Zh9OjRGDFiBABgw4YNOHToEDZv3oypU6ciIyMDmzZtQlRUFDp16gQgfy/Nhg0b4uzZs2jdujWOHj2KP//8E8eOHYOLiwuaN2+OTz75BFOmTMGcOXO4NL+cpLQa0Vhz5oy96n1ZU9FERJVRoTphYktLS8OOHTvQpk0bVXkMf39/mJiYYMuWLcjLy0NGRga2b9+OwMBAVZu4uDgEBgaqnSsoKAhxcXEA8qcW4uPj1dqYmJggMDBQ1SY+Ph45OTlqbRo0aIDatWur2sTFxaFp06ZwcXFR+z2ZmZn4448/SnxeWVlZyMzMVPsyVps2AV5eQKdO+X9u2iR2j4y36GiLFr8hImIFhg3bioiIFaqkfGNg7COBRKRbGgdhf/75Jy5duoRLly5BEARcvXpV9X1pAYY2TJkyBTY2NnB0dMTt27fx7bffqo75+Pjg6NGj+Oijj2BhYQF7e3vcuXMHe/bsUbVJSUlRC4wAwMXFBZmZmXj27BkePnyIvLy8YtukpKSozlGtWjXY29uX2qa4cyiPlWT+/PmQy+WqL09Pz3JemYqRYlHN1NRUxMffQ2ioUKiCv4D4+HssHisSYw1AjXkqmoh0T+PpyM6dO6vlfXXv3h1A/jSkIAgaTUdOnToVCxcuLLVNQkICGjRoAACYNGkSQkJCcOvWLcydOxdDhw7FwYMHIZPJkJKSgtGjR2PYsGEYMGAAHj16hFmzZqFv376IiYnRi03Gp02bhsjISNX3mZmZOg3EpFZUU7kiLSnJGwrFMLVjeXkyrF79A3x8bnFFGlUpY52KZokOIt3TKAhLSkrS6i+fMGEChg8fXmqbOnXqqP7u5OQEJycn1KtXDw0bNoSnpyfOnj2LgIAArF27FnK5HIsWLVK1/9///gdPT0+cO3cOrVu3hqura5FVjPfu3YOdnR2srKxgamoKU1PTYtu4uroCAFxdXZGdnY309HS10bDCbQqvqFSeU9mmOBYWFrCwsCj1emiblN5ElW/4yimgwhX8lVNAVbUiTYojhVXFmJ97cYytfAtLdBBVDY2CMC8vL41O/v777+Pjjz+Gk5NTscednZ3h7Oys0TmVFP8/V5WVlQUAePr0qSohX0lZz0zZNiAgAN9//71am5iYGAQEBADIv6H4+/sjNjZWtSuAQqFAbGwswsPDAeTnnpmbmyM2NhZ9+vQBAFy7dg23b99WnScgIADz5s3D/fv3UbNmTdXvsbOzQ6NGjSr0fI2JVJLBpTZSWJWM+bmnpqYiPT0dQMnBh5KhBqGFS3SUVKyXJTqIKkfrqyML+t///oeJEyeWGISV17lz53D+/Hm8/vrrqFGjBhITEzFz5kz4+vqqAp9u3bph+fLl+Pjjj1XTkR999BG8vLzw8sv5dX3GjBmDNWvWYPLkyRg5ciSOHz+OPXv24NChQ6rfFRkZiWHDhuGVV17Bq6++ihUrVuDJkyeq1ZJyuRwhISGIjIyEg4MD7OzsMG7cOAQEBKB169YAgC5duqBRo0YYMmQIFi1ahJSUFMyYMQNhYWFVPtKlr6QyBWSIQUZ5GeNzL+9OAf369YOzs7PBX6OyivUSUeXodHWktmqGWVtbIzo6Gp07d0b9+vUREhKCl156CadOnVIFNZ06dUJUVBT279+Pl19+GcHBwbCwsMDhw4dhZWUFID95/9ChQ4iJiUGzZs2wdOlSfPnll6ryFED+m+uSJUswa9YsNG/eHBcvXsThw4fVEu2XL1+O7t27o0+fPmjXrh1cXV0RHR2tOm5qaoqDBw/C1NQUAQEBGDx4MIYOHYqPP/5YK9fDWBhrMjiJp6ydApT1weRyucEHYIDxFuslqio6HQnTlqZNm+L48eNltuvfvz/69+9fapsOHTrgt99KX2IfHh6umn4sjqWlJdauXYu1a9eW2MbLy6vI1CcR6QfWB8tXVn4mEVWOXtYJIyqvO3eAEyfy/yQqL9YHy8cSHUS6pRcjYUQVsWkTEBqaX2fMxATYuBEICRG7V6QPpLI4RAqkkp9JZIgYhJFkaKssQmpqKm7ezEVoaE0oFPn14ZQFX5s3vw9vbzOjyOehymHw8YKxleggqio6DcIGDx4MOzs7Xf4KMiDaKIvAgq+kTcYafLBOHFHVqFAQplAoitTkUj5+584d1K5dGwCwfv36yvWOjE5lAyOpFXwl0kfGXCeOqCpplJifmZmJd999FzY2NnBxccGsWbOQl5enOv7gwQP4+PhovZNEmmJCMVUER4BecHR0hJubW4lfDMCIKk+jkbCZM2fi999/x/bt25Geno5PP/0UFy5cQHR0tOpNSVu1wYgqizk9pCmOABFRVdIoCNu/fz+2bduGDh06AAB69eqFbt26oUePHjhw4AAA6MVG2WQ8jDWnhyqOAZZ+K7jx+N27JkhKMoOPTy7c3fNHxRlEk5RoFIQ9ePBAbf9IJycnHDt2DEFBQXjzzTfx5Zdfar2DRERE5VHebae4MIekQqOcsNq1ayMhIUHtMVtbWxw9ehTPnj3D22+/rdXOERERlVd5t53iwhySCo2CsC5dumDLli1FHq9evTqOHDkCS0tLrXWMiIioIrjnJekLjaYj586di7t37xZ7zNbWFjExMbhw4YJWOkZUEVzdRlWtYA5ScZiDVPW45yXpC42CsBo1aqBGjRolHre1tUX79u0r3SmiiuLqNqpKBXOQgPxpsLQ0Rzg4pKotCHn33Xdhb29f7Dn4etQ+bjtVMi5ckBaNi7Xm5uZi+fLl2LlzJ/766y8AQL169TBw4EB8+OGHMDc313oniTTBNxCqKgWD/dISwffs2QOg5CCNieLaxxI1RenbwoU7d4Dr1wE/P8DDQ+ze6IZGQdizZ8/wxhtvIC4uDoGBgWjXrh0AICEhAVOmTMGBAwdw9OhR5oYRkVEpKRHc1/eG6uZf2k2PieK6wRI16spauKB8vYr5elSO1EVFWWHyZDkUChlMTAQsWpSBgQOfGdxInUZB2IIFC/DPP//gt99+w0svvaR27Pfff0fPnj2xYMECzJkzR5t9JBKFMXwKI+0oLRFcLn9UriBNF5ivRsUp6/UqFuVIXUaGLVasiIAg5NcdVShkmDTJDv/+uxly+SPJjNRpg0ZB2K5du7Bs2bIiARgANGvWDEuWLMH06dMZhJHeMrZPYaQdZSWCi3HTK2++miHd0Lgwp3ykunBB+YGhrP8vhjRyrFEQduvWLbz66qslHm/dujVu375d6U4RicEYP4WRdpSVCC7GTa+8+WqGdEPjwpzykfrCBakGibqgURBmZ2eH+/fvw9PTs9jjKSkpsLW11UrHiKqaMX4KI+0pLRFczJueWFOhYjH2AKu8pLxwQepBojZpFIR17NgRn332Gfbt21fs8QULFqBjx45a6RiRWIzpUxhpV2mJ4GLd9KSa/0Pik/LCBSkHidqkURA2e/ZstGrVCq1bt0ZkZCQaNGgAQRCQkJCA5cuX488//8TZs2d11VeiKmFMn8KocjTNLRLjpscPFaSvpBwkaotGQVijRo0QExODkJAQ9O/fHzJZfs6MIAho0KABjh49isaNG+uko0RVyVg+hVHllJWDlJGRgd27d5d5Hl0miuvDhwqu4qwaXLggPRoXa23dujX++OMPXLx4Ua1Ya/PmzbXdNyJRGcOnMKq80oIDNzc3SSSKS/lDhTGu4hQLFy5Ij8ZBWGZmJqpXr47mzZurBV4KhQKPHz+GnZ2dNvtHRKTXpHJDk+qHCmNcxSkmqbwei2OMI3UaBWHffPMNpkyZgosXL8La2lrt2LNnz9CyZUssWbIEPXr00GoniYhIM/p2QzO2VZxUlDGO1GkUhK1fvx6TJ08uEoABgI2NDaZMmYI1a9YwCCO9pG83LalgPo806dsNjas4CZD2SJ0uaBSEXblyBevWrSvxeLt27TBjxoxKd4pIDPp205IC5vNImz5dc67iJGOkURD233//ITc3t8TjOTk5+O+//yrdKSKx6NNNSwqYz0Paog+rOIm0TaMgzNvbG7/++isaNGhQ7PFff/0VXl5eWukYEekP5vOQNkh5FSeRLpiU3eSF3r17Y/r06bh3716RYykpKZgxYwb69Omjtc4RkX4oLZ+HSBNy+SP4+NxiAEZGQaORsKlTp+Lbb7+Fn58fBg8ejPr16wMArl69ih07dsDT0xNTp07VSUeJSLqYz0MVxQUxZMw0CsJsbW1x+vRpTJs2Dbt371blf9nb22Pw4MGYN28eN/AmMkLM59EeY1ttygUxZMw0LtYql8uxbt06rF27Fg8fPoQgCHB2dlZtYVTQ6dOn8corr8DCwkIrnSUi6WI+T+UZ62pTQ3ouRJrQOAhTkslkcHZ2LrVN165dcfHiRdSpU6eiv4aI9IhUq7LrC642JTIuGiXma0oQBF2enohExnwe3ShptWlGBtM9iAxJhUfCiIiYz6MbrB5PZBwYhBFRpTDA0j6uNiUyDgzCiIgkhqtNydAZ2yrgkug0CCtuxSQREZWttNWmDx8+VGtrLDcsMgzGugq4ODoNwpiYT0RUcSWtNo2Oji7ymDHcsMgwcBXwCzoNwh494tC5MeHwMlHllGcVaUmjBsZwwzJUxvreyT1nNQzCOnXqVK52x48fr1BnSH9xeJmo8opbbfrw4UPVyFdpowaknwq/d5bEEN87uQpYwyDs5MmT8PLyQrdu3WBubq6rPpEe4vAykXaUdKPlqIFhKvyeaEwjnVwFrGEQtnDhQmzZsgV79+7FoEGDMHLkSDRp0kRXfSM9xBsFGSqxp4w4amD4jG2kk6uANQzCJk2ahEmTJiEuLg6bN2/Ga6+9hvr162PkyJEYOHAg7OzsdNVPqqSquoHwRkGGSApTRhw1MGzG+gHW2PecrVBifkBAAAICArBy5Urs3bsXa9euxcSJE3H37l0GYhJUlflavFGQIZLClBFHDQybMX+ANeY9Zyu1OvLChQs4deoUEhIS0KRJE+aJSVRV5mvxRkGGTswpI2MfNTBk+vwBVtOZFu45+4LGQdjdu3exdetWbN26FZmZmRg8eDDOnTuHRo0a6aJ/pEVVNdzNGwUZKjGmjArfiEoaNTCGG5Yh09cPsBWZaeGesy9oFIS9+eabOHHiBLp06YLFixejW7duMDPjzkf6oiqHu415eJkMlxhTRrxhGQ99/ABb0ZkWvl7zaRRBHT58GG5ubrh9+zbmzp2LuXPnFtvuwoULWukcaZcuh7s5vEz6rrQpFeU2QWJNGfGGZbgMZaTTWBcWVJZGQdjs2bN11Q+qAroc7uanddJn5V39qK9TRiRdhvLeacwLCyqDQZiR0eVwt9TfJIhKosmiFH2cMiJpM4T3Tn1eWCAmrSR0nTp1Ck+ePEFAQABq1KihjVOSDjFfi6h0JSUXK+nrlJGuiF3IlsTHUeKK0bhi/uPHj/HJJ58AAARBQNeuXXH06FEAQM2aNREbG4vGjRtrv6dUYczXIiq/0pKLe/fuDScnp2J/zlgDDSkUsqUXxAyIOUqsOY2CsN27d2PKlCmq77/++mv8+OOP+Omnn9CwYUMMHToUc+fOxZ49e7TeUao4Q8k5INK1spKLnZyc4ObmJnIvpaW8U7mGuPeh1EghIOZMi2Y0CsKSkpLw0ksvqb7//vvv0bdvX7z22msAgBkzZuCdd97Rbg9JKxhgEZWNycWVV9ZULumOGAExZ1oqR6MgLDc3FxYWFqrv4+LiEBERofre3d1dtZSbiEjfMLm4coxtA2qpq4qAmDMtlaNREObr64sff/wRderUwe3bt/HXX3+hXbt2quN37tzhhSYivcXk4opjnShpqcqAmPf9itMoCAsLC0N4eDh++uknnD17FgEBAWrbFR0/fhwvv/yy1jtJRKRLBadKSksu5pRKyXQ5lcvVl5phQKw/NArCRo8eDVNTU3z33Xdo165dkbphd+/exciRI7XaQSIiXeOUSuXpaipXCsnm+oa5jfpD4zphI0eOLDHQWrduXaU7REQkBt7AK0dXU7lcfak55jbqD+6+TUREFVbVU7lcfVk25jbqD42CsJycHEyfPh3R0dFwcHDAmDFj1EbF7t27B3d3d+Tl5Wm9o0REJD1VOZXL1ZelY26j/tEoCJs3bx6++uorTJw4Eenp6YiMjMS5c+fw+eefq9oIgqD1ThIRkXRVxVQuk83LxtxG/aNRELZjxw58+eWX6N69OwBg+PDh6Nq1K0aMGIHNmzcDAGQymfZ7SVWGq5CISIqYbF4+fH/WLxoFYf/++y+aNGmi+r5u3bo4efIkOnXqhCFDhmDRokVa7yBVHa5CIiKpYrI5GSKTspu84OrqisTERLXHatWqhRMnTuD8+fMYPny4NvtGVYyrkIhIqpTJ5jKZAgCYbE4GQaMgrFOnToiKiiryuLu7O44fP46kpCStdaywnj17onbt2rC0tISbmxuGDBmCu3fvqrXZs2cPmjdvDmtra3h5eWHx4sVFznPy5Em0aNECFhYWqFu3LrZu3Vqkzdq1a+Ht7Q1LS0u0atUKv/zyi9rx58+fIywsDI6OjqhevTr69OmDe/fuqbW5ffs2unXrBmtra9SsWROTJk1Cbm5u5S8EEZERKZxsHhGxAsOGbUVExAq1pHxtJ5unpqYiOTm5xK/U1FSt/j4yThpNR86cORNXr14t9litWrVw6tQpxMTEaKVjhXXs2BEfffQR3Nzc8O+//2LixIno27cvzpw5AwD44YcfMGjQIKxevRpdunRBQkICRo8eDSsrK4SHhwPI34C8W7duGDNmDHbs2IHY2FiMGjUKbm5uCAoKAgDs3r0bkZGR2LBhA1q1aoUVK1YgKCgI165dQ82aNQEA48ePx6FDh7B3717I5XKEh4ejd+/eOH36NAAgLy8P3bp1g6urK86cOYPk5GQMHToU5ubm+Oyzz3RyfaSGuWVEpA1iJJszNYOqikzQ0+WMBw4cQK9evZCVlQVzc3MMHDgQOTk52Lt3r6rN6tWrsWjRIty+fRsymQxTpkzBoUOHcOXKFVWb/v37Iz09HYcPHwYAtGrVCi1btlT9B1QoFPD09MS4ceMwdepUZGRkwNnZGVFRUejbty8A4OrVq2jYsCHi4uLQunVr/PDDD+jevTvu3r0LFxcXAMCGDRswZcoUPHjwoNyf2DIzMyGXy5GRkQE7OzutXLfSJCcnY+PGjWW2Cw0NhZubW4nH+QZGRPpMW++Fho4ftktW3vt3hYq17t27Fzt37sRff/0FAKhXrx4GDhyoCkp0LS0tDTt27ECbNm1gbm4OAMjKyoK1tbVaOysrK9y5cwe3bt2Ct7c34uLiEBgYqNYmKCgIERERAPJzneLj4zFt2jTVcRMTEwQGBiIuLg4AEB8fj5ycHLXzNGjQALVr11YFYXFxcWjatKkqAFP+nrFjx+KPP/7Qm/01K1oUkbllRGRIWCC2KH7Y1g6NgjCFQoEBAwZg7969qFevHho0aAAA+OOPP9CvXz+888472Llzp87KVEyZMgVr1qzB06dP0bp1axw8eFB1LCgoCOPHj8fw4cPRsWNH3LhxA0uXLgWQ/6nG29sbKSkpaoERALi4uCAzMxPPnj3Df//9h7y8vGLbKKdhU1JSUK1aNdjb2xdpk5KSompT3DmUx0qSlZWFrKws1feZmZnluSw6waKIRC/wE7/x4nth8fhhWzs0CsJWrlyJY8eO4cCBA6paYUoHDhzAiBEjsHLlStXIUlmmTp2KhQsXltomISFBFexNmjQJISEhuHXrFubOnYuhQ4fi4MGDkMlkGD16NBITE9G9e3fk5OTAzs4OH374IebMmQMTE43WH4hm/vz5mDt3rtjdYFFEogL4id948b2w/DhaWDEaBWFbtmzB4sWLiwRgQP7qxUWLFmkUhE2YMKHMshZ16tRR/d3JyQlOTk6oV68eGjZsCE9PT5w9exYBAQGQyWRYuHAhPvvsM6SkpMDZ2RmxsbFq53B1dS2yivHevXuws7ODlZUVTE1NYWpqWmwbV1dX1Tmys7ORnp6uNhpWuE3hFZXKcyrbFGfatGmIjIxUfZ+ZmQlPT89Sr482KXPVyiqKyC0vyJjwE7/xYoHY8uFoYcVpFIRdv369SE5VQYGBgaqViOXh7OwMZ2dnTbqgolDk14opOH0HAKampqhVqxYAYOfOnQgICFD9joCAAHz//fdq7WNiYhAQEAAgPwjx9/dHbGwsevXqpfo9sbGxqufl7+8Pc3NzxMbGok+fPgCAa9eu4fbt26rzBAQEYN68ebh//75qRWVMTAzs7OzQqFGjEp+ThYUFLCwsKnQ9tEG5CunmzVxs3y5AoXgxrWxqKmDcuK7w9jbT+NM+PyERkT5igdiycbSwcjQKwqysrJCeno7atWsXezwzMxOWlpZa6VhB586dw/nz5/H666+jRo0aSExMxMyZM+Hr66sKfB4+fIivv/4aHTp0wPPnz7Flyxbs3bsXp06dUp1nzJgxWLNmDSZPnoyRI0fi+PHj2LNnDw4dOqRqExkZiWHDhuGVV17Bq6++ihUrVuDJkycYMWIEAEAulyMkJASRkZFwcHCAnZ0dxo0bh4CAALRu3RoA0KVLFzRq1Ei1i0BKSgpmzJiBsLAwUYOs4hSX6+LuDixalIEpU+TIy5PB1BT4/HMZ/P1dSjhLyfgJiQwNP1QYD2WB2MLvYfx3f4GjhZWjURAWEBCA9evXY/369cUeX7t2rSoo0iZra2tER0dj9uzZePLkCdzc3BAcHIwZM2aoBTXbtm3DxIkTIQgCAgICcPLkSbz66quq4z4+Pjh06BDGjx+PlStXwsPDA19++aWqRhgA9OvXDw8ePMCsWbOQkpKC5s2b4/Dhw2qJ9suXL4eJiQn69OmDrKwsBAUFYd26darjpqamOHjwIMaOHYuAgADY2Nhg2LBh+Pjjj7V+bSqjrFyXDz6wRVqaAyZMeAtNm9bQ+Pz8hESGRmofKrhgQDcKF4j19b2BtDQHODikqb13MTWDo4WVpVEQNn36dHTo0AGpqamYOHEiGjRoAEEQkJCQgKVLl+Lbb7/FiRMntN7Jpk2b4vjx46W2cXJyUpWRKE2HDh3w22+lv2mGh4eXOq1qaWmJtWvXYu3atSW28fLyKjL1KTVl5bDI5Y8glz+Ck9Nzjc7L3DIyRFL7UMEFA7ojRoFYfcXRwsrRKAhr06YNdu/ejdDQUOzbt0/tWI0aNbBz50689tprWu0g6R9d5ZYRiUlq0y5cMKBbfH8qHUcLtUPjYq1vv/02goKCcOTIEVy/fh1AfrHWLl26FCmWSsbL0dERjo7Axo3Ae+8BeXmoVG4Zkdg47UL0AkcLtUOjIOz48eMIDw/H2bNn8fbbb6sdy8jIQOPGjbFhwwa0bdtWq50k/RUSAgQFATduAHXrAh4eYveISDPKT/JlTbvwEz8ZGwZYladRELZixQqMHj262H2Q5HI53nvvPSxbtoxBGKnx8GDwRfqr8Cf+WbMe4OZNM3h758LdvSWAlpL4xM9Vm0T6R6Mg7Pfffy+1wn2XLl2wZMmSSneKiEhKCgZYbm6Av7+InSmG1FZtGhquQiVd0SgIu3fvnmrD7GJPZmaGBw8eVLpTRERUPlJbtWlouAqVdEmjTRVr1aqFK1eulHj80qVLcHNzq3SnqGqUN4eFuS5E0lXaqk2qPK5CJV3SaCTszTffxMyZMxEcHFykMv6zZ88we/bsYveVJGkqnOty964JkpLM4OOTC3f3/G2hOMxOJE3KD0dlrdrkhygi6dIoCJsxYwaio6NRr149hIeHo379+gCAq1evYu3atcjLy8P06dN10lHSDWWAtWkTEBoKKBSAiUl+aYmQEJE7R0QlKvghqlatzALbjAlYuDATAwcO4IcoHeACCNImmSAIgiY/cOvWLYwdOxZHjhyB8kdlMhmCgoKwdu1a+Pj46KSjxigzMxNyuRwZGRnFrkjVljt3AC+v/ABMydQUuHmTqxqJ9MWdOywFowvJycnYuHEjgNIXQISGhjIdh1TKe//WuFircjue//77Dzdu3IAgCPDz80ONGprvLUjScP26egAG5BdXvXGDb+ZE+oKlYHSLCyBIFzQOwpRq1KiBli1barMvJBI/v/wpyMIjYXXritcnIiJNaLOMRMFzPXz4EID2tq1iuQsqqMJBGBkOD4/ithcy3E/VfBMkMizaLCNR0rm0sW0Vy11QYQzCCIDxbC/EN0Eiw6PNMhIltdHGtlUsd0GFMQgjFWPIKeGbIBFVVIsWv8HX9wbS0hwwaFArNGkinW2rSD8xCCMiIionufwR5PJHaNIkqNKrIVnughiEERGRQdFmcKOrQIn7fRLAIIyIiAyINoMbXQVKLHdBSgzCiIioSuh6ZbI2gxtdBkraKndB+o9BGBER6VxVrEzWZnCjy0BJG+UuyDCYlN2EiIiocnS5MrnwZuYFabqZuTbPVdK5leUulOevSLkLMgwcCSOjUt43N74JEukPbW5mrsuN0QueGwBmzXqAmzfN4O2dC3d3lrswRhpv4E1Vp6o28DY2BfNS7t41QVKSGXx8cuHunv+plG+CRNpXcCNsoORVh6VthF3enDJtbmZuDBujcxcR7dPZBt5E+k75ZrJpExAamr9npolJ/tZNISEid47ICFRk1aEmOWUeHo5aC5gMvYg1dxERF3PCyCjdufMiAAPy/3zvvfzHiUh3Slp1mJFhW+rPcbcL3eB1FReDMDJK16+/CMCU8vLypx2ISHdKW3VIZGwYhJFR8vPLn4IsyNQ0P++DiHSnrFWH5ZWRYYukJO8yR9CIpIw5YWSUPDzyc8Deey9/BMzUFPj8c8PO/SASU+HyDIVzwjQpz8Atf3SH+1lWLQZhZLRCQoCgIMNf+UQkBdoqz8Atf3SHwW3VYxBGRs3QVz4RSUnBAMvNDfD31/wc3PJHNxjcioM5YUREpDe0lVNG6rhgQhwMwoiISPK45Y9u6HKbJiobK+ZLGCvmExG9UHi3ixc5ZdztojKU1zUqyqrQNk0ZGDjwGa9rBZT3/s0gTMIYhBGRMeI2OuIxhm2aqgK3LSIiIr3DbXTExcVKVYtBGJEe4MgAGYvybo9z//59/p8gvccgjEjiODJAVNSePXvKbMP/EyR1XB1JJHHcYJeMWWW2J+L/CZI6joQREZEksYI7GTqOhBERkeSUVMG9uBExbuZN+oojYUR6hhvskjEo7/ZEHC0jfcYgjEiP8IZDxkJZwb1gIFZ4eyLud6hbXJWtewzCiPQEbzhkDApvT1T4Q0fB1zo389YdrsquGgzCiPQEbzhkDBwdHREeHq4agZk160GB7YlaAmiJjIwM7N69u1yjZVQxXJVdNRiEEUlc4Q12S7rhcINdMhQFR1bc3AB/f/Xj5R0t4/8JkjoGYUQSV3BkoFatzEIb7GZi4MABzM0go1Ke0TL+nyB9wCCMSA8obyYTJgD9+ik32JXBw8MegL2YXSMSRVmjZaRdXJWtGwzCiPQMN9gloqrEVdm6wyCMDBaXVxMRVQ5XZesWgzAySFxeTURUeVyVrVsMwsggcXk1EVHFGeqqbKnNkDAIIyIiIjWGuCpbijMkDMLIKHBlDxGRZgxtVbYUZ0gYhJHB48oeIqLK4aps3TApuwmR/ippZU9Ghq3IPSMiImPHIIwMWmkre4iIiMTEIIwMmnJlT0Hc4JeIiKSAQRgZpMIb/CoDMW7wS0REQH66SlKSt6jpKUzMJ4PEDX6JpEFqdZmIAOks2GIQRgaLG/wSiUuKdZnIeClnPsraiqkqZ0gYhBERkU4UHgErqV4fd66gqqCcITlxAli+vOiCrddeG4YOHcCK+USkW3fuANevA35+rP1DVUMq0z9k3BwdHdG6NWBiAigKrNkyNQVatXJEVQ/IMjGfyMhs2gR4eQGdOuX/uWmT2D0iQ8d6fSQlHh7Axo35gReQ/+fnn4vzgZRBGJERuXMHCA198QlQoQDeey//cSJdYb0+kpqQEODmTeDEifw/Q0LE6QenI4mMyPXr6kPwAJCXl78nHKclSVeU9foKBmKs10dik8JWTAzCqMKYV6R//PyKz4WoW1e8PpHhU9brK5wTVjA531iwZAcVxCCMKmTTphfTWiYm+fPrYg3nUvkpcyHeey9/BEzMXAgyLi1a/AZf3xtIS3OAg0Oa0QZgLNlBBTEII42VlFcUFMSbuT4ICcn/t7pxI38EjP9mpCuF6y3J5Y+KDb6MZeeK8pbiYMkO46F3iflZWVlo3rw5ZDIZLl68qHbs0qVLaNu2LSwtLeHp6YlFixYV+fm9e/eiQYMGsLS0RNOmTfH999+rHRcEAbNmzYKbmxusrKwQGBiI69evq7VJS0vDoEGDYGdnB3t7e4SEhODx48ca90VflZZXRNKVmpqK5ORkJCcnw9Q0GfXr5/+pfCw1NVXsLpKBUdZlCg0NLfGLoz5kzPRuJGzy5Mlwd3fH77//rvZ4ZmYmunTpgsDAQGzYsAGXL1/GyJEjYW9vj9DQUADAmTNnMGDAAMyfPx/du3dHVFQUevXqhQsXLqBJkyYAgEWLFmHVqlXYtm0bfHx8MHPmTAQFBeHPP/+EpaUlAGDQoEFITk5GTEwMcnJyMGLECISGhiIqKqrcfdFnzCvSP5wGIbHw9VSykorXkvHQqyDshx9+wNGjR7Fv3z788MMPasd27NiB7OxsbN68GdWqVUPjxo1x8eJFLFu2TBX4rFy5EsHBwZg0aRIA4JNPPkFMTAzWrFmDDRs2QBAErFixAjNmzMBbb70FAPjqq6/g4uKC/fv3o3///khISMDhw4dx/vx5vPLKKwCA1atX480338SSJUvg7u5err7oM+YV6R9OgxBJC4vXEqBH05H37t3D6NGjsX37dlhbWxc5HhcXh3bt2qnlFgQFBeHatWv477//VG0CAwPVfi4oKAhxcXEAgKSkJKSkpKi1kcvlaNWqlapNXFwc7O3tVQEYAAQGBsLExATnzp0rd1+Kk5WVhczMTLUvqZJKjRUiIn3D4rWkpBdBmCAIGD58OMaMGaMW/BSUkpICFxcXtceU36ekpJTapuDxgj9XUpuaNWuqHTczM4ODg0OZv6fg7yjO/PnzIZfLVV+enp4ltpUCDw+gQweOgBERaYLFa0lJ1CBs6tSpkMlkpX5dvXoVq1evxqNHjzBt2jQxu6tz06ZNQ0ZGhurrn3/+EbtLRESkZcritQWxeK1xEjUnbMKECRg+fHipberUqYPjx48jLi4OFhYWasdeeeUVDBo0CNu2bYOrqyvu3bundlz5vaurq+rP4toUPK58zM3NTa1N8+bNVW3u37+vdo7c3FykpaWV+XsK/o7iWFhYFHmORERkGJQpKmUVrzWWkh0kchDm7OwMZ2fnMtutWrUKn376qer7u3fvIigoCLt370arVq0AAAEBAZg+fTpycnJgbm4OAIiJiUH9+vVRo0YNVZvY2FhERESozhUTE4OAgAAAgI+PD1xdXREbG6sKujIzM3Hu3DmMHTtWdY709HTEx8fD398fAHD8+HEoFAqN+kJERMZFWbJDuQBm1qwHuHnTDN7euXB3bwmgJSvmGxm9WB1Zu3Ztte+rV68OAPD19YXH/yckDRw4EHPnzkVISAimTJmCK1euYOXKlVi+fLnq5z788EO0b98eS5cuRbdu3bBr1y78+uuv2LhxIwBAJpMhIiICn376Kfz8/FQlKtzd3dGrVy8AQMOGDREcHIzRo0djw4YNyMnJQXh4OPr37w93d/dy94WIiIxPwQDLzQ34/8/yZKT0IggrD7lcjqNHjyIsLAz+/v5wcnLCrFmz1EpCtGnTBlFRUZgxYwY++ugj+Pn5Yf/+/aoaYUB+HbInT54gNDQU6enpeP3113H48GFVjTAgvxxGeHg4OnfuDBMTE/Tp0werVq3SqC9EVam80xucBiEiqjoyQRAEsTtBxcvMzIRcLkdGRgbs7OzE7g7pOW4cTERUNcp7/zaYkTAiKh0DLCIiaWEQRkREREZBajMCDMKIiIi0SGo3esonxT10GYQRERFpiRRv9JRPinvo6sW2RURERPpAijd6ki4GYUREREQiYBBGRFSMO3eAEyfy/yQi0gUGYUREhWzaBHh5AZ065f+5aZPYPSIiQ8TEfNIIV/2QobtzBwgNBRSK/O8VCuC994CgIOD/d0kjKreMDFukpTnCwSFVtUE3kRKDMCo3rvohY3D9+osATCkvD7hxg0EYaebChZfx3XfdIQgmkMkU6NHjIFq0+E3sbpGEcDqSyo2rfsgY+PkBJoXeGU1Ngbp1xekP6aeMDFtVAAYAgmCC777rjowMW5F7ZrykuIcuR8KIiArw8AA2bsyfgszLyw/APv+co2BUPsobeFqaoyoAUxIEE6SlOUAuf1SlN3rK5+joiPDwcEml1DAIIyIqJCQkPwfsxo38ETAGYFReyhv9zZu52L5dgEIhUx0zNRUwblxXeHubMWVDJFK77gzCiIj+X8GFJ6amQP36+Y8nJ+f/yYUnVB6Ojo5wdCxuRFUGf38XsbtH/+/OnfwcUD8/8T5oMQgjIgIXnpD2cURVujZterEK2sQkP2AOCan6fjAxn4gIXHhCuuHhAXTowABMSkoqQyNGYWaOhBERkdpU7N27JkhKMoOPTy7c3fPvVJyKJUMhpTI0DMKo3KS4vJeIKq/gVGxpta04FUuGQFmGpmAgJlYZGgZhVG5SXN5LRJWn/D9dUm0rX98bkMsfcSqWDIKUytAwCCONMMAiMlxl1bYiMhRSWTTBIIyIiAAADg6pkMkUaoGYTKaAg0OaiL0i0g0PD/EXTHB1JBERAQDk8kfo0eMgZLL8ZBllThhHwYh0gyNhRETgwhOlFi1+g6/vDaSlOcDBIY0BGJEOMQgjIgIXnhQklz9i8EVUBRiEERH9P2MIsIhIOpgTRkRk5DgVSyQOjoQRERk5TsUSiYNBGBERMcAiEgGnI4mIiIhEwCCMiIiISAQMwoiIiIhEwCCMiIiISAQMwoiIiIhEwCCMiIiISAQMwoiIiIhEwCCMiIiISAQMwoiIiIhEwIr5EiYIAgAgMzNT5J4QERFReSnv28r7eEkYhEnYo0ePAACenp4i94SIiIg09ejRI8jl8hKPy4SywjQSjUKhwN27d2FrawuZTCZ2d6pMZmYmPD098c8//8DOzk7s7ug1Xkvt4HXUHl5L7eB11B5dXEtBEPDo0SO4u7vDxKTkzC+OhEmYiYkJPDw8xO6GaOzs7PjmoiW8ltrB66g9vJbaweuoPdq+lqWNgCkxMZ+IiIhIBAzCiIiIiETAIIwkx8LCArNnz4aFhYXYXdF7vJbaweuoPbyW2sHrqD1iXksm5hMRERGJgCNhRERERCJgEEZEREQkAgZhRERERCJgEEZEREQkAgZhJJoff/wRPXr0gLu7O2QyGfbv3692XBAEzJo1C25ubrCyskJgYCCuX78uTmclrqxrOXz4cMhkMrWv4OBgcTorYfPnz0fLli1ha2uLmjVrolevXrh27Zpam+fPnyMsLAyOjo6oXr06+vTpg3v37onUY2kqz3Xs0KFDkdfkmDFjROqxdK1fvx4vvfSSqpBoQEAAfvjhB9Vxvh7Lp6zrKNbrkUEYiebJkydo1qwZ1q5dW+zxRYsWYdWqVdiwYQPOnTsHGxsbBAUF4fnz51XcU+kr61oCQHBwMJKTk1VfO3furMIe6odTp04hLCwMZ8+eRUxMDHJyctClSxc8efJE1Wb8+PH47rvvsHfvXpw6dQp3795F7969Rey19JTnOgLA6NGj1V6TixYtEqnH0uXh4YEFCxYgPj4ev/76Kzp16oS33noLf/zxBwC+HsurrOsIiPR6FIgkAIDwzTffqL5XKBSCq6ursHjxYtVj6enpgoWFhbBz504Reqg/Cl9LQRCEYcOGCW+99ZYo/dFn9+/fFwAIp06dEgQh/zVobm4u7N27V9UmISFBACDExcWJ1U3JK3wdBUEQ2rdvL3z44YfidUqP1ahRQ/jyyy/5eqwk5XUUBPFejxwJI0lKSkpCSkoKAgMDVY/J5XK0atUKcXFxIvZMf508eRI1a9ZE/fr1MXbsWKSmpordJcnLyMgAADg4OAAA4uPjkZOTo/a6bNCgAWrXrs3XZSkKX0elHTt2wMnJCU2aNMG0adPw9OlTMbqnN/Ly8rBr1y48efIEAQEBfD1WUOHrqCTG65EbeJMkpaSkAABcXFzUHndxcVEdo/ILDg5G79694ePjg8TERHz00Ufo2rUr4uLiYGpqKnb3JEmhUCAiIgKvvfYamjRpAiD/dVmtWjXY29urteXrsmTFXUcAGDhwILy8vODu7o5Lly5hypQpuHbtGqKjo0XsrTRdvnwZAQEBeP78OapXr45vvvkGjRo1wsWLF/l61EBJ1xEQ7/XIIIzICPTv31/196ZNm+Kll16Cr68vTp48ic6dO4vYM+kKCwvDlStX8PPPP4vdFb1W0nUMDQ1V/b1p06Zwc3ND586dkZiYCF9f36rupqTVr18fFy9eREZGBr7++msMGzYMp06dErtbeqek69ioUSPRXo+cjiRJcnV1BYAiq3zu3bunOkYVV6dOHTg5OeHGjRtid0WSwsPDcfDgQZw4cQIeHh6qx11dXZGdnY309HS19nxdFq+k61icVq1aAQBfk8WoVq0a6tatC39/f8yfPx/NmjXDypUr+XrUUEnXsThV9XpkEEaS5OPjA1dXV8TGxqoey8zMxLlz59Tm8Kli7ty5g9TUVLi5uYndFUkRBAHh4eH45ptvcPz4cfj4+Kgd9/f3h7m5udrr8tq1a7h9+zZflwWUdR2Lc/HiRQDga7IcFAoFsrKy+HqsJOV1LE5VvR45HUmiefz4sdqnjKSkJFy8eBEODg6oXbs2IiIi8Omnn8LPzw8+Pj6YOXMm3N3d0atXL/E6LVGlXUsHBwfMnTsXffr0gaurKxITEzF58mTUrVsXQUFBIvZaesLCwhAVFYVvv/0Wtra2qrwauVwOKysryOVyhISEIDIyEg4ODrCzs8O4ceMQEBCA1q1bi9x76SjrOiYmJiIqKgpvvvkmHB0dcenSJYwfPx7t2rXDSy+9JHLvpWXatGno2rUrateujUePHiEqKgonT57EkSNH+HrUQGnXUdTXY5WvxyT6fydOnBAAFPkaNmyYIAj5ZSpmzpwpuLi4CBYWFkLnzp2Fa9euidtpiSrtWj59+lTo0qWL4OzsLJibmwteXl7C6NGjhZSUFLG7LTnFXUMAwpYtW1Rtnj17Jrz//vtCjRo1BGtra+Htt98WkpOTxeu0BJV1HW/fvi20a9dOcHBwECwsLIS6desKkyZNEjIyMsTtuASNHDlS8PLyEqpVqyY4OzsLnTt3Fo4ePao6ztdj+ZR2HcV8PcoEQRB0G+YRERERUWHMCSMiIiISAYMwIiIiIhEwCCMiIiISAYMwIiIiIhEwCCMiIiISAYMwIiIiIhEwCCMiIiISAYMwIiIiIhEwCCMiyUpJScG4ceNQp04dWFhYwNPTEz169FDbK+/MmTN48803UaNGDVhaWqJp06ZYtmwZ8vLyVG1u3ryJkJAQ+Pj4wMrKCr6+vpg9ezays7PVft8XX3yBZs2aoXr16rC3t8fLL7+M+fPnq47PmTMHMpkMwcHBRfq6ePFiyGQydOjQoVzPTXkumUwGMzMzeHt7Y/z48Xj8+LGGV4mI9BX3jiQiSbp58yZee+012NvbY/HixWjatClycnJw5MgRhIWF4erVq/jmm2/w7rvvYsSIEThx4gTs7e1x7NgxTJ48GXFxcdizZw9kMhmuXr0KhUKBzz//HHXr1sWVK1cwevRoPHnyBEuWLAEAbN68GREREVi1ahXat2+PrKwsXLp0CVeuXFHrl5ubG06cOIE7d+7Aw8ND9fjmzZtRu3ZtjZ5j48aNcezYMeTm5uL06dMYOXIknj59is8//7xI2+zsbFSrVq0CV1J3pNgnIr2i842RiIgqoGvXrkKtWrWEx48fFzn233//CY8fPxYcHR2F3r17Fzl+4MABAYCwa9euEs+/aNEiwcfHR/X9W2+9JQwfPrzUPs2ePVto1qyZ0L17d+HTTz9VPX769GnByclJGDt2rNC+fftyPLsX5ypo9OjRgqurq9rxL774QvD29hZkMpkgCPnPPSQkRHBychJsbW2Fjh07ChcvXlSd4+LFi0KHDh2E6tWrC7a2tkKLFi2E8+fPC4IgCDdv3hS6d+8u2NvbC9bW1kKjRo2EQ4cOCYIgCFu2bBHkcrlaf7755huh4G2ion0iouJxOpKIJCctLQ2HDx9GWFgYbGxsihy3t7fH0aNHkZqaiokTJxY53qNHD9SrVw87d+4s8XdkZGTAwcFB9b2rqyvOnj2LW7duldm/kSNHYuvWrarvN2/ejEGDBlV6VMjKykptivTGjRvYt28foqOjcfHiRQDAO++8g/v37+OHH35AfHw8WrRogc6dOyMtLQ0AMGjQIHh4eOD8+fOIj4/H1KlTYW5uDgAICwtDVlYWfvzxR1y+fBkLFy5E9erVNepjRfpERMXjdCQRSc6NGzcgCAIaNGhQYpu//voLANCwYcNijzdo0EDVprjzr169WjUVCQCzZ89G79694e3tjXr16iEgIABvvvkm+vbtCxMT9c+r3bt3x5gxY/Djjz/C398fe/bswc8//4zNmzdr+lRV4uPjERUVhU6dOqkey87OxldffQVnZ2cAwM8//4xffvkF9+/fh4WFBQBgyZIl2L9/P77++muEhobi9u3bmDRpkura+fn5qc53+/Zt9OnTB02bNgUA1KlTR+N+VqRPRFQ8BmFEJDmCIOikLQD8+++/CA4OxjvvvIPRo0erHndzc0NcXByuXLmCH3/8EWfOnMGwYcPw5Zdf4vDhw2qBmLm5OQYPHowtW7bg77//Rr169fDSSy9p1A8AuHz5MqpXr468vDxkZ2ejW7duWLNmjeq4l5eXKtgBgN9//x2PHz+Go6Oj2nmePXuGxMREAEBkZCRGjRqF7du3IzAwEO+88w58fX0BAB988AHGjh2Lo0ePIjAwEH369NG43xXpExEVj0EYEUmOn5+fKqG+JPXq1QMAJCQkoE2bNkWOJyQkoFGjRmqP3b17Fx07dkSbNm2wcePGYs/bpEkTNGnSBO+//z7GjBmDtm3b4tSpU+jYsaNau5EjR6JVq1a4cuUKRo4cqelTBADUr18fBw4cgJmZGdzd3YtMZxaein38+DHc3Nxw8uTJIueyt7cHkL/qcuDAgTh06BB++OEHzJ49G7t27cLbb7+NUaNGISgoCIcOHcLRo0cxf/58LF26FOPGjYOJiUmRgDYnJ6fI76lIn4ioeMwJIyLJcXBwQFBQENauXYsnT54UOZ6eno4uXbrAwcEBS5cuLXL8wIEDuH79OgYMGKB67N9//0WHDh3g7++PLVu2FJliLI4yiCuuD40bN0bjxo1x5coVDBw4UJOnp1KtWjXUrVsX3t7e5cona9GiBVJSUmBmZoa6deuqfTk5Oana1atXD+PHj8fRo0fRu3dvbNmyRXXM09MTY8aMQXR0NCZMmIAvvvgCAODs7IxHjx6pPVdlzpc2+kRERTEIIyJJWrt2LfLy8vDqq69i3759uH79OhISErBq1SoEBATAxsYGn3/+Ob799luEhobi0qVLuHnzJjZt2oThw4ejb9++ePfddwG8CMBq166NJUuW4MGDB0hJSUFKSorq940dOxaffPIJTp8+jVu3buHs2bMYOnQonJ2dERAQUGwfjx8/juTk5Cob8QkMDERAQAB69eqFo0eP4ubNmzhz5gymT5+OX3/9Fc+ePUN4eDhOnjyJW7du4fTp0zh//rwqby4iIgJHjhxBUlISLly4gBMnTqiOtWrVCtbW1vjoo4+QmJiIqKgotcUHFe0TEZWM05FEJEl16tTBhQsXMG/ePEyYMAHJyclwdnaGv78/1q9fDwDo27cvTpw4gXnz5qFt27Z4/vw5/Pz8MH36dEREREAmkwEAYmJicOPGDdy4cUOtthfwIqcsMDAQmzdvxvr165GamgonJycEBAQgNja2SL6TUnErN3VJJpPh+++/x/Tp0zFixAg8ePAArq6uaNeuHVxcXGBqaorU1FQMHToU9+7dg5OTE3r37o25c+cCAPLy8hAWFoY7d+7Azs4OwcHBWL58OYD80cf//e9/mDRpEr744gt07twZc+bMKTOxvqw+EVHJZIKmWa1EREREVGmcjiQiIiISAYMwIiIdqF69eolfP/30k9jdIyIJ4HQkEZEO3Lhxo8RjtWrVgpWVVRX2hoikiEEYERERkQg4HUlEREQkAgZhRERERCJgEEZEREQkAgZhRERERCJgEEZEREQkAgZhRERERCJgEEZEREQkAgZhRERERCL4PwM6cqQOq5s2AAAAAElFTkSuQmCC", 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", 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", 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", 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QoUOiXbt2Qi6Xi8aNG4t169apvN/SpUuFk5OTkMvlokOHDuLYsWNK2x8/fixGjx4tateuLUxNTcU777wjbt68qVQnJSVFdOvWTZiYmAhra2sxYcIEkZeXV67jLmlK9OPHj8X58+fF48ePy7W/6mDo0KGid+/eKuUxMTECgPj++++FEEIsWrRItG7dWpiamooGDRqIUaNGiQcPHgghis4hAKVHaGioEEKI9evXC3d3d+nfwMCBA8WtW7eq6vAqnTafeyKi6mTnTiEUiqI/K5qmljSRCfF/Mw2o2snKyoKlpSUyMzNhYWEhlT958gTJyclwdnaGsbExgKLFaTW1zIWhoWGZZ2X6+/sjIyMDO3bsUNnWrl07ODg4YM+ePfj666/Rtm1bODs74/Llyxg9ejTefPNNrFixArm5uVi5ciWmT58uTYIxNzeHubk51q5dC3t7ezRv3hy3b99GUFAQrKyssGfPnoo8ZI1Rd+6JiKj8vL3v4cQJA7RsaYmn7mNQIUr6/q5sWnNHCSpdXl4e5s6dq5H3DgkJKfHax/Jo0aIFzpw5AwAYP368VN6oUSN8+eWXCAwMxIoVKyCXy2FpaQmZTKYy8WTYsGHS3xs3bowlS5bg1VdfxcOHD2Fubv7SbSQiIu1WWFiIefPmoWPHXHh4mKBDh0kAdGO5KK24po5qBiGE1ON34MABvPXWW6hfvz5q1aqFwYMH4969e3j06FGp+zh58iR69eoFJycn1KpVC506dQJQNAOaiIhqtlu3bmHWrFnIzc0FAFhYCPTurRuBDmBPnc4wNDRESEiIxt67IiQkJMDZ2RkpKSno2bMnRo0ahdmzZ6NOnTr466+/EBAQgNzc3BLvpJGdnQ0fHx/4+Phgw4YNsLGxwdWrV+Hj4yP9ABMRUc0UExODv/76S3ru5OT03GXVtA1DnY6QyWQVMgSqKQcPHsS///6Lzz77DCdPnkRhYSEWLVoEPb2izuTIyEil+nK5HAUFBUpliYmJuHfvHsLDw6W7cZw4caJqDoCIiKql/Px8zJ49W6nsvffe08klxhjqqMrl5OQgLS0NBQUFuHXrFvbu3Yu5c+eiZ8+eGDJkCM6ePYu8vDwsXboUvXr1wpEjR7Bq1SqlfTRq1AgPHz5ETEwM2rZtC1NTUzg5OUEul2Pp0qUIDAzE2bNnMWvWLA0dJRERadr169exZs0apbJJkybp7L3TeU0dVbm9e/fC3t4ejRo1gq+vLw4dOoQlS5Zg586d0NfXR9u2bfHVV19h3rx5aN26NTZs2KAyCcTLywuBgYHo378/bGxsMH/+fNjY2CAiIgJbt25Fy5YtER4ejoULF2roKImISJN2796tFOiaN2+O0NBQnQ10AMAlTaqx8ixpQjUDzz0RUelyc3NVOgIGDRqEpk2bVlkbuKQJERER0UtITk7G+vXrlcqCg4NrzN2cGOqIiIhI661bt05p+ap27dqhd+/eGmxR1WOoIyIiIq2VkZGBb775RqnM398fDRs21FCLNIehjoiIiLTSr7/+in/++UepbP/+zxEaWjHrp2obhjoiIiLSOmFhYSpl0dGhCA7WQGOqCYY6IiIi0hrnzp3Dtm3bninrichId4SGaqhR1QRDHREREWkFdb1zMTFTMHEil3gCGOqIiIiomhNCYObMmSrlbm6hNb537mkMdURERFRtRUdH49ixY0plWVl26NRpJPz8NNSoaoqhjnSSv78/MjIysGPHDgBA586d0a5dO3z99dcvvM+K2AcREZWduuHWmrSYcHnx3q9Upfz9/SGTySCTySCXy9G0aVPMnDkT+fn5lfq+v/zyC2bNmlWmuocPH4ZMJkNGRsYL74OIiF5cXl6e2kAXGhrKQFcK9tRRlfP19cW6deuQk5ODPXv2YMyYMTA0NERISIhSvdzcXMjl8gp5zzp16lSLfRARUelmzZqFwsJCpbIrV1pj7dq+GmqR9mBPHVU5IyMj2NnZoWHDhhg1ahS8vb0RFRUFf39/9OnTB7Nnz4aDgwOaN28OALh27Rref/99WFlZoU6dOujduzdSUlKk/RUUFCAoKAhWVlaoW7cuJk+eDCGE0nt27twZ48ePl57n5ORgypQpcHR0hJGREZo2bYo1a9YgJSUFXbp0AQDUrl0bMpkM/v7+avdx//59DBkyBLVr14apqSm6deuGixcvStsjIiJgZWWF6OhouLq6wtzcHL6+vrh586ZU5/Dhw+jQoQPMzMxgZWWF1157DVeuXKmgT5qISLuEhYWpBLpZs6bh6FEGurJgqCONMzExQW5uLgAgJiYGSUlJ2L9/P3bt2oW8vDz4+PigVq1a+PPPP3HkyBEpHBW/ZtGiRYiIiMDatWvx119/IT09Hdu3by/1PYcMGYJNmzZhyZIlSEhIwLfffgtzc3M4Ojri559/BgAkJSXh5s2bKrefKebv748TJ04gKioKsbGxEEKge/fuyMvLk+o8evQICxcuxI8//og//vgDV69excSJEwEA+fn56NOnDzp16oQzZ84gNjYWH3/8MWQy2Ut/pkRE2iQzM1PtcKubWyg6dNDH/PkaaJQW4vAraYwQAjExMYiOjsa4ceNw584dmJmZYfXq1dKw608//YTCwkKsXr1aCjvr1q2DlZUVDh8+jK5du+Lrr79GSEgI3n33XQDAqlWrEB0dXeL7XrhwAZGRkdi/fz+8vb0BAI0bN5a2Fw+z1qtXD1ZWVmr3cfHiRURFReHIkSPw8vICAGzYsAGOjo7YsWMH3nvvPQBF14WsWrUKTZo0AQCMHTtWmpaflZWFzMxM9OzZU9ru6upa/g+SiEiLqQtzt2+7YPnygQDAGa7lwJ46QlQU4OVV9GdV2LVrF8zNzWFsbIxu3bqhf//+mDFjBgCgTZs2StfRnT59Gv/99x9q1aoFc3NzmJubo06dOnjy5AkuXbqEzMxM3Lx5Ex4eHtJrDAwM0L59+xLf/9SpU9DX10enTp1e+BgSEhJgYGCg9L5169ZF8+bNkZCQIJWZmppKgQ0A7O3tcfv2bQBF4dHf3x8+Pj7o1asXvvnmG6WhWSIiXacu0C1cOB0+PgM10Brtx546Qng4EBtb9GdV/I+oS5cuWLlyJeRyORwcHGBg8L9/hmZmZkp1Hz58CHd3d2zYsEFlPzY2Ni/0/iYmJi/0uhdhaKh8U2mZTKZ0vd+6devwySefYO/evdiyZQumTZuG/fv3w9PTs8raSERU1W7cuIHVq1erlEdHh2LDBvbOvSj21BGCgwGFAlV2E2QzMzM0bdoUTk5OSoFOnVdeeQUXL15EvXr10LRpU6WHpaUlLC0tYW9vj7i4OOk1+fn5OHnyZIn7bNOmDQoLC/H777+r3V7cU1hQUFDiPlxdXZGfn6/0vvfu3UNSUhJatmxZ6jE9y83NDSEhITh69Chat26NjRs3luv1RETaJCwsTCXQtW3bFqGhoTh6lIHuZTDUEfz8UG1/kAYNGgRra2v07t0bf/75J5KTk3H48GF88sknuH79OgDg008/RXh4OHbs2IHExESMHj1aZY25pzVq1AhDhw7FsGHDsGPHDmmfkZGRAICGDRtCJpNh165duHPnDh4+fKiyj2bNmqF3794YMWIE/vrrL5w+fRoffvgh6tevj969e5fp2JKTkxESEoLY2FhcuXIF+/btw8WLF3ldHRHprJLWnuvTp0/VN0YHMdRRtWZqaoo//vgDTk5OePfdd+Hq6oqAgAA8efIEFhYWAIAJEyZg8ODBGDp0KBQKBWrVqoV33nmn1P2uXLkS/fr1w+jRo9GiRQuMGDEC2dnZAID69esjLCwMwcHBsLW1xdixY9XuY926dXB3d0fPnj2hUCgghMCePXtUhlxLO7bExET07dsXLi4u+PjjjzFmzBiMHDmyHJ8QEVH1t27dqRJnt1LFkYlnF/SiaiMrKwuWlpbIzMyUAgwAPHnyBMnJyXB2doaxsbEGW0hVjeeeiLSNujCXlPQ2Nm3ygkJRNFKka0r6/q5snChBRERElaKk3jk3NyAlpequ5a4pGOqIiIioQm3evBlJSUkq5TNmhEq9c9XxOm5tx1BHREREFUZd79y///ZB8+Ztq3SlhZqIoY6IiIgqREnDraGcD1ElGOq0GOe41Dw850RUHYWHhyMnJ0elvHi4lUOtVYOhTgsVL5nx6NGjKr07Amneo0ePAKjeqYKISFPU9c75+/vj9OmGHG6tYgx1WkhfXx9WVlbSPURNTU2lm92TbhJC4NGjR7h9+zasrKygr6+v6SYRUQ1XWFiIWbNmqZSH/t9Ya8OG7KGragx1WsrOzg4ApGBHNYOVlZV07omINEVd7xzwv0BHmsFQp6VkMhns7e1Rr1495OXlabo5VAUMDQ3ZQ0dEGqcu0H366aewsrKq+saQEoY6Laevr88veiIiqnRPnjzBvHnzVMrZO1d9MNQRERFRqTjcqh0Y6oiIiKhE6gJdSEgI5HK5BlpDpWGoIyIiIhX37t3DsmXLVMrZO1d9MdQRERGREg63aieGOiIiIpKoC3TTp0/neqhagKGOiIiIkJSUhM2bN6uUs3dOezDUERER1XDqeufkcjlCQkI00Bp6UXqabgARERFpjrpA5+YWil9/DUFUlAYaRC+MPXVEREQ10IEDB3DkyBGVcje3UISHA7GxQHg479+qTRjqiIiIahh1vXPOzs4YMmSI9Dw8HAgOrspW0ctiqCMiIqpB1AW6ZydD+Pmxh04bMdQRERHVAF999RUePHigUs7ZrbqDoY6IiEjHqeudS0z0xMCBPhpoDVUWhjoiIiIdpi7QzZgRCoWCQ6y6hqGOiIhIB5V0qy83t6JAx0kQuoehjoiISMeoC3R9+/ZF69atAbCHTldx8WEiIiIdUVhYqDbQRUeH4vLl1hpoEVUl9tQRERHpgJKGW6OjQ7mQcA3BUEdERKTl1AW6kSNHws7ODm5uXEi4pmCoIyIi0lKPHj3CggULVMqfXnuOCwnXHAx1REREWqik4VYuJlxzMdQRERFpGXWBbvLkyTAxMdFAa6i6YKgjIiLSEjdv3sR3332nUu7mFgrmOWKoIyIi0gIlDbfy7hBUjKGOiIiomlMX6KZPn45ff5Xx7hAkYagjIiKqpk6cOIHdu3erlBdPhuDMVnoaQx0REVE1xNmtVF4MdURERNWMukDn5hbKXjkqFUMdERFRNbFt2zacO3dOpZyTIags9DTdgLLy8/ODk5MTjI2NYW9vj8GDByM1NVXaPmPGDMhkMpWHmZmZ0n62bt2KFi1awNjYGG3atMGePXuUtgshMH36dNjb28PExATe3t64ePGiUp309HQMGjQIFhYWsLKyQkBAAB4+fKhU58yZM+jYsSOMjY3h6OiI+fPnV/AnQkREuiQsLEwl0Onr68PNLZSTIahMtCbUdenSBZGRkUhKSsLPP/+MS5cuoV+/ftL2iRMn4ubNm0qPli1b4r333pPqHD16FAMHDkRAQADi4+PRp08f9OnTB2fPnpXqzJ8/H0uWLMGqVasQFxcHMzMz+Pj44MmTJ1KdQYMG4dy5c9i/fz927dqFP/74Ax9//LG0PSsrC127dkXDhg1x8uRJLFiwADNmzFC7thAREZG64dbQ0FBMmzYNfn7A0aPspaPnkwkhhKYb8SKioqLQp08f5OTkwNDQUGX76dOn0a5dO/zxxx/o2LEjAKB///7Izs7Grl27pHqenp5o164dVq1aBSEEHBwcMGHCBEycOBEAkJmZCVtbW0RERGDAgAFISEhAy5Ytcfz4cbRv3x4AsHfvXnTv3h3Xr1+Hg4MDVq5cialTpyItLQ1yuRwAEBwcjB07diAxMbHMx5iVlQVLS0tkZmbCwsLihT8rIiKqnjgZQjdp6vtba3rqnpaeno4NGzbAy8tLbaADgNWrV8PFxUUKdAAQGxsLb29vpXo+Pj6IjY0FACQnJyMtLU2pjqWlJTw8PKQ6sbGxsLKykgIdAHh7e0NPTw9xcXFSnTfeeEMKdMXvk5SUhPv377/k0RMRkS5QF+hatWrFQEcvTKtC3ZQpU2BmZoa6devi6tWr2Llzp9p6T548wYYNGxAQEKBUnpaWBltbW6UyW1tbpKWlSduLy0qrU69ePaXtBgYGqFOnjlIddft4+j3UycnJQVZWltKDiIh0j7pAFx0dqnRZEVF5aTTUBQcHq53c8PTj6eHKSZMmIT4+Hvv27YO+vj6GDBkCdaPH27dvx4MHDzB06NCqPJyXNnfuXFhaWkoPR0dHTTeJiIgqUFhYWImBjhMh6GVpdEmTCRMmwN/fv9Q6jRs3lv5ubW0Na2truLi4wNXVFY6Ojjh27BgUCoXSa1avXo2ePXuq9JbZ2dnh1q1bSmW3bt2CnZ2dtL24zN7eXqlOu3btpDq3b99W2kd+fj7S09OV9qPufZ5+D3VCQkIQFBQkPc/KymKwIyLSEerCXIMGvlizxgPBwZwIQS9Po6HOxsYGNjY2L/TawsJCAEVDlk9LTk7GoUOHEBUVpfIahUKBmJgYjB8/Xirbv3+/FAqdnZ1hZ2eHmJgYKcRlZWUhLi4Oo0aNkvaRkZGBkydPwt3dHQBw8OBBFBYWwsPDQ6ozdepU5OXlSdf87d+/H82bN0ft2rVLPCYjIyMYGRm9wKdBRETVlRACM2fOVCkPDQ2FlxcQGwuEhzPU0cvTisWH4+LicPz4cbz++uuoXbs2Ll26hC+++AJNmjRR6aVbu3Yt7O3t0a1bN5X9fPrpp+jUqRMWLVqEHj16YPPmzThx4oS01IhMJsP48ePx5ZdfolmzZnB2dsYXX3wBBwcH9OnTBwDg6uoKX19fjBgxAqtWrUJeXh7Gjh2LAQMGwMHBAQDwwQcfICwsDAEBAZgyZQrOnj2Lb775BosXL67cD4qIiKqV581uDQ4uCnQceqWKoBVLmvz777/49NNPcfr0aWRnZ8Pe3h6+vr6YNm0a6tevL9UrLCxEw4YNMWTIEMyePVvtvrZu3Ypp06YhJSUFzZo1w/z589G9e3dpuxACoaGh+O6775CRkYHXX38dK1asgIuLi1QnPT0dY8eOxa+//go9PT307dsXS5Ysgbm5uVTnzJkzGDNmDI4fPw5ra2uMGzcOU6ZMKddxc0kTIiLtpS7QBQQEoEGDBhpoDVUlTX1/a0Woq6kY6oiItM+jR4+wYMEClXIuVVJzaOr7WyuGX4mIiLQBFxMmTWKoIyIiqgDqAt3EiRNV7kFOVFm0avFhIiKi6ubGjRtqA52bWyjeftsMahZjIKoU7KkjIiJ6QaUNt3K5EqpqDHVEREQvQF2g++KLL6CnVzQIxuVKqKox1BEREZVDXFwc9u7dq1Lu5hYKvacuavLzYw8dVS2GOiIiojIqabh1xoxQKBQMcaRZDHVERERloC7QhYaGIioKUCg4zEqax1BHRERUisjISCQkJKiUF689x2FWqi4Y6oiIiErAxYRJm3CdOiIiIjXUBbro6FC4uTHQUfXEnjoiIqKnlNQ7Fx0dynXnqFpjqCMiIvo/6gJdkyZN8OGHH8LNjevOUfXGUEdERISSh1uPHi36OydEUHXHUEdERDVaacOt7JUjbcJQR0RENZa6QHfhwlvo3/91cIIraRuGOiIiqnGEEJg5c6ZKOZcqIW3GUEdERDUK154jXcV16oiIqMZQF+h++GEIoqMZ6Ej7MdQREZHOe/TokdpA5+YWCjs7Z06IIJ3A4VciItJpzxtu5TIlpCsY6oiISGepC3RBQUGoVauWBlpDVLkY6oiISOdcu3YNa9euVSnnZAjSZQx1RESkUzi7lWoqhjoiItIZ6gLdvn1f4MgRzgsk3cdQR0REWu/PP//EwYMHVcp5qy+qSRjqiIhIq5U23MoRV6pJ2B9NRERaS12gmzEjlIsJU43EnjoiItI633//PVJTU1XK3dxCoVCAQ65UIzHUERGRVuFiwkTqMdQREZHWUBfouFQJURGGOiIiqvZK6p2LjuZkCKJiDHVERFStqQt0BgZm2L17Iq+dI3oKQx0REVVbpQ23Tp1a1a0hqt4Y6oiIqNrhcCtR+THUERFRtaIu0CUleSAlxZfDrUSlYKgjIqJqQQiBmTNnqpQX3+qLS5UQlY6hjoiINI63+iJ6eQx1RESkEVFRQHg44OOjGuj69++PFi1aaKBVRNqLoY6IiDRi0aJH8PFZoFLOxYSJXgxDHRERVbmwsDC8+aZqOQMd0YtjqCMioiql7vq5Tz75BLVr19ZAa4h0B0MdERFViStXriAiIkKlnL1zRBWj3KFOX18fN2/eRL169ZTK7927h3r16qGgoKDCGkdERLqhpNmtbm4MdEQVpdyhTgihtjwnJwdyufylG0RERLpFXaCbNm0a9PX1NdAaIt1V5lC3ZMkSAIBMJsPq1athbm4ubSsoKMAff/zB6edERCQ5fPgwfv/9d5VyDrcSVY4yh7rFixcDKOqpW7VqldL/sORyORo1aoRVq1ZVfAuJiKjaK15zrvjOD6UtJkxElaPMoS45ORkA0KVLF/zyyy+cpURERJLwcCA2FpgyBYiPVw10DHNEla/c19QdOnSoMtpBRERaLDgYOHLkG5iaZqhsY6AjqhrlDnXDhg0rdfvatWtfuDFERKSd4uPDYGqqWs5AR1R1yh3q7t+/r/Q8Ly8PZ8+eRUZGBt5Utzw4ERHpNHXXzzHMEVW9coe67du3q5QVFhZi1KhRaNKkSYU0ioiIqj+uPUdUvchESQvPlVNSUhI6d+6MmzdvVsTuCEBWVhYsLS2RmZkJCwsLTTeHiEia5erjw9mtRCXR1Pd3hd0m7NKlS8jPz6+o3RERUTVUUqBjmCPSvHKHuqCgIKXnQgjcvHkTu3fvxtChQyusYUREVL2EhYXBx0e1nIGOqHood6iLj49Xeq6npwcbGxssWrTouTNjiYhIO6m7fq5p06YYNGiQBlpDROpwnToiIiqREAIzZ85UKWfvHFH188LX1N2+fRtJSUkAgObNm6NevXoV1igiItI83uqLSLuUO9RlZWVhzJgx2LRpEwoLCwEA+vr66N+/P5YvXw5LS8sKbyQREVUtdYHOz88Pbm5uGmgNEZWFXnlfMGLECMTFxWH37t3IyMhARkYGdu3ahRMnTmDkyJGV0UYiIqoi2dnZJS4mzEBHVL2Ve506MzMzREdH4/XXX1cq//PPP+Hr64vs7OwKbWBNxnXqiKgqcbiVqGJozTp1devWVTvEamlpidq1a1dIo4iIqGqpC3SjR4+GjY2NBlpDRC+i3MOv06ZNQ1BQENLS0qSytLQ0TJo0CV988UWFNo6IiCpWVBTg5VX0JwAkJyeXONzKQEekXco9/Orm5ob//vsPOTk5cHJyAgBcvXoVRkZGaNasmVLdf/75p+JaWgNx+JWIKpqXFxAbCygUvNUXUWXRmuHX3r17QyaTVUZbiIiokgUHl3yrr6lTp8LAoMLuHklEVazcPXVUddhTR0QVISqqKMgFBwOmpgdw5MgRlTrsnSOqOJr6/i73NXWNGzfGvXv3VMozMjLQuHHjCmkUERFVnPDwoiHX+PgwBjoiHVbufvaUlBQUFBSolOfk5OD69esV0igiIqoYUVFAejowY4b6yRBEpDvK3FMXFRWFqP+bLhUdHS09j4qKwvbt2zFr1iw4OztXWkP9/Pzg5OQEY2Nj2NvbY/DgwUhNTVWqEx0dDU9PT9SqVQs2Njbo27cvUlJSlOocPnwYr7zyCoyMjNC0aVNERESovNfy5cvRqFEjGBsbw8PDA3///bfS9idPnmDMmDGoW7cuzM3N0bdvX9y6dUupztWrV9GjRw+YmpqiXr16mDRpEvLz8yvksyAiKouoKCA2dh4GDmSgI6oJynxNnZ5eUf6TyWR49iWGhoZo1KgRFi1ahJ49e1Z8KwEsXrwYCoUC9vb2uHHjBiZOnAgAOHr0KICiafmurq4ICgpCQEAAMjMz8dlnn+HBgwfSLNzk5GS0bt0agYGBGD58OGJiYjB+/Hjs3r0bPj4+AIAtW7ZgyJAhWLVqFTw8PPD1119j69atSEpKku5vO2rUKOzevRsRERGwtLTE2LFjoaenJw1rFBQUoF27drCzs8OCBQtw8+ZNDBkyBCNGjMCcOXPKfMy8po6IXgYXEybSDE19f5d7ooSzszOOHz8Oa2vrympTmURFRaFPnz7IycmBoaEhtm3bhoEDByInJ0cKoL/++it69+4t1ZkyZQp2796Ns2fPSvsZMGAAMjIysHfvXgCAh4cHXn31VSxbtgwAUFhYCEdHR4wbNw7BwcHIzMyEjY0NNm7ciH79+gEAEhMT4erqitjYWHh6euK3335Dz549kZqaCltbWwDAqlWrMGXKFNy5cwdyubxMx8hQR0QvqqS154io8mnNRInk5GSNB7r09HRs2LABXl5eMDQ0BAC4u7tDT08P69atQ0FBATIzM/Hjjz/C29tbqhMbGwtvb2+lffn4+CA2NhYAkJubi5MnTyrV0dPTg7e3t1Tn5MmTyMvLU6rTokULODk5SXViY2PRpk0bKdAVv09WVhbOnTtX4nHl5OQgKytL6UFEVB5hYWEMdEQ1VLknSsycObPU7dOnT3/hxjzPlClTsGzZMjx69Aienp7YtWuXtM3Z2Rn79u3D+++/j5EjR6KgoAAKhQJ79uyR6qSlpSkFLQCwtbVFVlYWHj9+jPv376OgoEBtncTERGkfcrkcVlZWKnWK77JR0vsUbyvJ3LlzSxwuISJ61tNLlfj5cbiVqKYrd0/d9u3blR6RkZGYN28eFi1ahB07dpRrX8HBwZDJZKU+isMUAEyaNAnx8fHYt28f9PX1MWTIEOn6vrS0NIwYMQJDhw7F8ePH8fvvv0Mul6Nfv34q1wBWVyEhIcjMzJQe165d03STiKgaK16qJDy85OFWBjqimqPcPXXx8fEqZVlZWfD398c777xTrn1NmDAB/v7+pdZ5eu07a2trWFtbw8XFBa6urnB0dMSxY8egUCiwfPlyWFpaYv78+VL9n376CY6OjoiLi4Onpyfs7OxUZqneunULFhYWMDExgb6+PvT19dXWsbOzAwDY2dkhNzcXGRkZSr11z9Z5dsZs8T6L66hjZGQEIyOjUj8PIqJiwcFFa8+pwzBHVPOUu6dOHQsLC4SFheGLL74o1+tsbGzQokWLUh8lTSooLCwEUHQdGgA8evRImiBRTF9fX6muQqFATEyMUp39+/dDoVAAAORyOdzd3ZXqFBYWIiYmRqrj7u4OQ0NDpTpJSUm4evWqVEehUODff//F7du3ld7HwsICLVu2LNdnRESkTlSU+kDn6OjIQEdUQ1XYTf6KhwwrQ1xcHI4fP47XX38dtWvXxqVLl/DFF1+gSZMmUpDq0aMHFi9ejJkzZ2LgwIF48OABPv/8czRs2BBubm4AgMDAQCxbtgyTJ0/GsGHDcPDgQURGRmL37t3SewUFBWHo0KFo3749OnTogK+//hrZ2dn46KOPAACWlpYICAhAUFAQ6tSpAwsLC4wbNw4KhQKenp4AgK5du6Jly5YYPHgw5s+fj7S0NEybNg1jxoxhTxwRvTQhBOLjVa9vZpgjqtnKHeqWLFmi9FwIgZs3b+LHH39Et27dKqxhTzM1NcUvv/yC0NBQZGdnw97eHr6+vpg2bZoUkt58801s3LgR8+fPx/z582FqagqFQoG9e/fCxMQEQNFkit27d+Ozzz7DN998gwYNGmD16tXSGnUA0L9/f9y5cwfTp09HWloa2rVrh7179ypNfFi8eDH09PTQt29f5OTkwMfHBytWrJC26+vrY9euXRg1ahQUCgXMzMwwdOjQ504yISIqTUm9cwADHRG94Dp1T9PT04ONjQ3efPNNhISEoFatWhXawJqM69QR0dPUTYbo1q0bOnTooIHWEFFJNPX9Xe6euuTk5MpoBxERleDRo0dYsGCBSjl754joaS90TV1GRgb+++8/AEDTpk1V1mwjIqKKwbXniKisyjX7NSUlBT169IC1tTU8PDzg4eEBa2tr9OzZEykpKZXURCKimiEqCvDyKvoTUB/oAgMDGeiISK0yX1N37do1vPrqqzA0NMTo0aPh6uoKADh//jxWrlyJ/Px8HD9+HA0aNKjUBtckvKaOqGbx8ipaTLh79xR06PCDynaGOSLtoKnv7zKHuoCAAPz333+Ijo6GsbGx0rbHjx/D19cXzZo1w+rVqyuloTURQx1RzcLZrUS6odpPlNi7dy+2bNmiEugAwMTEBLNmzcKAAQMqtHFERDWJukD3+eefw9DQUAOtISJtU+ZQd/fuXTRq1KjE7Y0bN0Z6enpFtImIqEaJiYnBX3/9pVLO3jkiKo8yhzp7e3ucP3++xGvmzp49W+p9TYmISBVntxJRRSlzqOvTpw8mTpyImJgY2NjYKG27ffs2pkyZgj59+lR0+4iIdJa6QMcwR0QvqswTJe7fvw8PDw+kpaXhww8/RIsWLSCEQEJCAjZu3Ag7OzscO3YMderUqew21xicKEGkm2bPXoj8/GyVcgY6It1Q7SdK1K5dG3Fxcfj888+xefNmZGRkAACsrKzwwQcfYM6cOQx0RETPweFWIqos5b73KwAIIXDnzh0AgI2NDWQyWYU3jNhTR6RrONxKVDNU+566p8lkMtSrV6+i20JEpJPYO0dEVaFctwkjIqLyYaAjoqryQj11RERUsqgoIDwc8PHhcCsRVR321BERVZCoqKL7t8bHhzHQEVGVY08dEVEFKal3zszMERMnDtNAi4ioJilTqFuyZEmZd/jJJ5+8cGOIiLRN8VDrlCkCPj4zVbazd46IqkqZljRxdnYu285kMly+fPmlG0VFuKQJUfXn5aW+dw5goCOqqar1kibJycmV3Q4iIq2kLtD5+vrCw8NDA60hoprsha+py83NRXJyMpo0aQIDA16aR0Q1y5MnTzBv3jyVcvbOEZGmlHv266NHjxAQEABTU1O0atUKV69eBQCMGzcO4eHhFd5AIqLqJiwsjIGOiKqdcoe6kJAQnD59GocPH4axsbFU7u3tjS1btlRo44iIqht1iwkHBgYy0BGRxpV73HTHjh3YsmULPD09le752qpVK1y6dKlCG0dEVF3cuHEDq1evVilnmCOi6qLcoe7OnTtq7/uanZ2tFPKIiHQFb/VFRNqg3KGuffv22L17N8aNGwcAUpBbvXo1FApFxbaOiEjD1AW6zz//HIaGhhpoDRFRycod6ubMmYNu3brh/PnzyM/PxzfffIPz58/j6NGj+P333yujjUREVS4uLg579+5VKWfvHBFVV+WeKPH666/j1KlTyM/PR5s2bbBv3z7Uq1cPsbGxcHd3r4w2EhFVqbCwMAY6ItI6ZbqjBGkG7yhBVLWiooD4eNXhVoY5IiqPan1HiaysrDLvkOGDiLTRmjVrcP36dZVyBjoi0hZlCnVWVlZlntlaUFDwUg0iIqpqnN1KRLqgTKHu0KFD0t9TUlIQHBwMf39/abZrbGwsfvjhB8ydO7dyWklEVEnUBTqGOSLSRuW+pu6tt97C8OHDMXDgQKXyjRs34rvvvsPhw4crsn01Gq+pI6o87J0josqiqe/vcs9+jY2NRfv27VXK27dvj7///rtCGkVEVJkY6IhIF5V7nTpHR0d8//33mD9/vlL56tWr4ejoWGENIyKqDBxuJSJdVe5Qt3jxYvTt2xe//fYbPDw8AAB///03Ll68iJ9//rnCG0hEVBHYO0dEuq7cw6/du3fHxYsX0atXL6SnpyM9PR29evXChQsX0L1798poIxHRS1EX6MzNnRnoiEincPHhaowTJYhejhACM2fOVClnmCOiylStFx9+VkZGBtasWYOEhAQAQKtWrTBs2DBYWlpWaOOIiF4Uh1uJqKYpd0/diRMn4OPjAxMTE3To0AEAcPz4cTx+/Bj79u3DK6+8UikNrYnYU0f0YtQFum7dukm/s4iIKpOmvr/LHeo6duyIpk2b4vvvv4eBQVFHX35+PoYPH47Lly/jjz/+qJSG1kQMdUTls317Ds6cCVcpZ+8cEVUlrQl1JiYmiI+PR4sWLZTKz58/j/bt2+PRo0cV2sCajKGOqOw43EpE1YXWXFNnYWGBq1evqoS6a9euoVatWhXWMCKislIX6D7++GPY29troDVERJpR7lDXv39/BAQEYOHChfDy8gIAHDlyBJMmTVK5dRgRUWWaNu0mDA2/Uyln7xwR1UTlDnULFy6ETCbDkCFDkJ+fDwAwNDTEqFGjEB6uei0LEVFlCAsLg6GharmbGwMdEdVML7xO3aNHj3Dp0iUAQJMmTWBqalqhDSNeU0dUEnXDrSEhIZDL5RpoDRGRMq25pq6Yqakp2rRpU5FtISIq1YkTJ7B7926Vcg63EhGVI9QNGzasTPXWrl37wo0hIioJZ7cSEZWuzKEuIiICDRs2hJubG3hnMSKqSuoCHcMcEZGyMoe6UaNGYdOmTUhOTsZHH32EDz/8EHXq1KnMthFRDRcZGSndjvBpubkMdEREzyrXRImcnBz88ssvWLt2LY4ePYoePXogICAAXbt2hUwmq8x21kicKEE1GYdbiUhbac0dJYpduXIFERERWL9+PfLz83Hu3DmYm5tXdPtqNIY6qqnUBTo3t1D4+WmgMURE5aR1s1/19PQgk8kghEBBQUFFtomIaij2zhERvTi98lTOycnBpk2b8Pbbb8PFxQX//vsvli1bhqtXr7KXjoheCgMdEdHLKXNP3ejRo7F582Y4Ojpi2LBh2LRpE6ytrSuzbURUQ3B2KxHRyyvzNXV6enpwcnKCm5tbqZMifvnllwprXE3Ha+pI15XUOxcdHYqjR6u4MUREFaTaX1M3ZMgQznAlogqjLtBZWbXEli3vIThYAw0iItJyLzz7lSofe+pIV3G4lYh0WbXvqSMielmlDbcy0xERvRyGOiKqEuoCXX5+H8TEtOVwKxFRBWCoI6JKlZeXhzlz5qiUFw+3zppV1S0iItJNDHVEVGm49hwRUdVhqCOiSqEu0AUGBsLW1lYDrSEi0n0MdURUoe7du4dly5aplLu5hYJ5joio8jDUEVGFKWm4dcaMUCgUgJ9fFTeIiKgGYagjogqhLtCFhIRg7145FApwhisRUSVjqCOil3Lu3Dls27ZNpbx4MoSfH3voiIiqgp6mG1BWfn5+cHJygrGxMezt7TF48GCkpqYq1YmMjES7du1gamqKhg0bYsGCBSr7OXz4MF555RUYGRmhadOmiIiIUKmzfPlyNGrUCMbGxvDw8MDff/+ttP3JkycYM2YM6tatC3Nzc/Tt2xe3bt1SqnP16lX06NEDpqamqFevHiZNmoT8/PyX/yCIqpGwsLBSAx0REVUdrQl1Xbp0QWRkJJKSkvDzzz/j0qVL6Nevn7T9t99+w6BBgxAYGIizZ89ixYoVWLx4sdIF28nJyejRowe6dOmCU6dOYfz48Rg+fDiio6OlOlu2bEFQUBBCQ0Pxzz//oG3btvDx8cHt27elOp999hl+/fVXbN26Fb///jtSU1Px7rvvStsLCgrQo0cP5Obm4ujRo/jhhx8QERGB6dOnV/KnRFR11A23Tp8+nYGOiEhDtPber1FRUejTpw9ycnJgaGiIDz74AHl5edi6datUZ+nSpZg/fz6uXr0KmUyGKVOmYPfu3Th79qxUZ8CAAcjIyMDevXsBAB4eHnj11VelMFhYWAhHR0eMGzcOwcHByMzMhI2NDTZu3CiFysTERLi6uiI2Nhaenp747bff0LNnT6SmpkrLN6xatQpTpkzBnTt3IJfLy3SMvPcrVUe//fabSu81wN45IqJimvr+1pqeuqelp6djw4YN8PLygqGhIQAgJycHxsbGSvVMTExw/fp1XLlyBQAQGxsLb29vpTo+Pj6IjY0FAOTm5uLkyZNKdfT09ODt7S3VOXnyJPLy8pTqtGjRAk5OTlKd2NhYtGnTRmk9Lh8fH2RlZeHcuXMlHldOTg6ysrKUHkTVSVhYGAMdEVE1pVWhbsqUKTAzM0PdunVx9epV7Ny5U9rm4+ODX375BTExMSgsLMSFCxewaNEiAMDNmzcBAGlpaSoLn9ra2iIrKwuPHz/G3bt3UVBQoLZOWlqatA+5XA4rK6tS66jbR/G2ksydOxeWlpbSw9HRsawfDVGlUzfcGhoaykBHRFRNaDTUBQcHQyaTlfpITEyU6k+aNAnx8fHYt28f9PX1MWTIEBSPHo8YMQJjx45Fz549IZfL4enpiQEDBgAo6m3TBiEhIcjMzJQe165d03STiPDtt9+WGOiiogAvLyAqSgMNIyIiJRpd0mTChAnw9/cvtU7jxo2lv1tbW8Pa2houLi5wdXWFo6Mjjh07BoVCAZlMhnnz5mHOnDlIS0uDjY0NYmJilPZhZ2enMkv11q1bsLCwgImJCfT19aGvr6+2jp2dnbSP3NxcZGRkKPXWPVvn2SGq4n0W11HHyMgIRkZGpX4eRFVJXZjLzjbFX39NQmgoEB4OxMYW/cllS4iINEujoc7GxgY2NjYv9NrCwkIARdehPU1fXx/169cHAGzatAkKhUJ6D4VCgT179ijV379/PxQKBQBALpfD3d0dMTEx6NOnj/Q+MTExGDt2LADA3d0dhoaGiImJQd++fQEASUlJuHr1qrQfhUKB2bNn4/bt26hXr570PhYWFmjZsuULHS9RVVMX6DZvDkXt2v9bSDg4uCjQcWFhIiLN04rFh+Pi4nD8+HG8/vrrqF27Ni5duoQvvvgCTZo0kYLU3bt3sW3bNnTu3BlPnjzBunXrpCVHigUGBmLZsmWYPHkyhg0bhoMHDyIyMhK7d++W6gQFBWHo0KFo3749OnTogK+//hrZ2dn46KOPAACWlpYICAhAUFAQ6tSpAwsLC4wbNw4KhQKenp4AgK5du6Jly5YYPHgw5s+fj7S0NEybNg1jxoxhTxxVeyXd6is6OhTz5in3yHFhYSKi6kMrQp2pqSl++eUXhIaGIjs7G/b29vD19cW0adOUQtIPP/yAiRMnQggBhUKBw4cPo0OHDtJ2Z2dn7N69G5999hm++eYbNGjQAKtXr4aPj49Up3///rhz5w6mT5+OtLQ0tGvXDnv37lWa+LB48WLo6emhb9++yMnJgY+PD1asWCFt19fXx65duzBq1CgoFAqYmZlh6NChmDlzZiV/UkQvR12g69ChA7p16wY3t6JeOYBBjoioOtLadepqAq5TR1VJXaCLjg5FcHBRiPPyKrp+TqEAjh7VQAOJiLSEpr6/taKnjogqT2nDrU9PguD1c0RE1RtDHVENpi7Q9e/fHy1atJCGW4tDHK+fIyKq3hjqiGqg/Px8zJ49W6X86YWEGeKIiLQLQx1RDVPScCvvDEFEpN0Y6ohqEHWBbuzYsahbt64GWkNERBVJO+6fRUQvJSsrq8TZrUeOMNAREekC9tQR6biyzm4lIiLtxlBHpMPUBbqQkBDI5XKV2a1ERKTdGOqIdNCVK1cQERGhUs7ZrUREuovX1BHpmLCwsOcGuqdFRRXdLSIqqpIbRkRElYqhjkiHqBtunTFjOqKjS16uJDwc0rV1RESkvTj8SqQDjh07hujoaJVyN7dQKBSlXzfH238REekGmRBCaLoRpJ6mbghM2oWLCRMRVS+a+v5mTx2RFlMX6BjmiIhqJoY6Ii20efNmJCUlqZQz0BER1VwMdURaRl3vnIODA0aMGKGB1hARUXXBUEekRTjcSkREJWGoI9ICERERuHLlikp5bi4DHRERFWGoI6rm1PXO7dnTC3///QqMjYHZszXQKCIiqna4+DBRNVbScOuNG68AAOrWreoWERFRdcWeOqJq6Hlrz61YwQWDiYhIGUMdUTWjLtB99NFHcHJykp77+RU9iIiIijHUEVUThYWFmDVrlko5Z7cSEVFZMNQRVQPludVXVNT/hl7ZW0dERMU4UYJIw9QFusOHP4Obm/oeuvBwIDa26E8iIqJi7Kkj0pBHjx5hwYIFKuXR0aGIjQVyctT3xAUHc5IEERGpYqgj0oDShlvd3EoPbZwkQURE6jDUEVUxdYHu888/h6GhIQCGNiIiejG8po6oiqSlpakNdNHRofjtN0MNtIiIiHQJe+qIqkBJw60LF4bi4cOi4Vb2zhER0ctgqCOqZOoC3fTp0/HaazI8fAiYm3PSAxERvTwOvxJVkrNnz5Z471aZTIbgYEChADZsYC8dERG9PPbUEVUCdWHOyMgIwU91yXFCBBERVSSGOqIKVlLvHBERUWViqCOqIPv27UNsbKxKOQMdERFVBYY6ogqgrneuZcuWeO+99zTQGiIiqokY6oheEodbiYioOmCoI3pB69evR3Jysko5Ax0REWkCQx3RC1DXO9e1a1coFAoNtIaIiIihjqjc1AU6N7dQMM8REZEmMdQRldHy5ctx9+5dlfIZM4oCHdecIyIiTWKoIyoDdb1zsbEj4O7uAIWCt/kiIiLNY6gjKkVhYSFmzZqlUr55cygSE4ErV4CEBA00jIiI6BkMdUQlUNc7BxTNbt20qejvQlRhg4iIiErBUEekhrpAFxQUhFq1agEA5s8HwsM57EpERNUHQx3RU3JychAeHq5S/uzac35+nBhBRETVC0Md0f8pbbiViIioumOoI4L6QDd16lQYGPBHhIiItIOephtApEkZGRklLib8xhsGiIrSQKOIiIheALshqMZSF+Zq1aqFoKAgeHkBsbFFkyF47RwREWkDhjqqkdQFuunTp0MmkwEomtXK2a1ERKRNGOqoRklNTcX333+vUs7ZrUREpO0Y6qjGUNc717JlS7z33nsaaA0REVHFYqijGkFdoONSJUREpEsY6kinJSQkIDIyUqU8OjoUbm4cYiUiIt3BUEc6S13vnK+vLz77zIMzW4mISOcw1JFOKm24lTNbiYhIFzHUkU75888/cfDgQZXyp6+f48xWIiLSRQx1pDPU9c4NGjQITZs21UBriIiIqhZvE0Y6oaTh1uJAFxUFeHmBt/0iIiKdxZ460mrbtm3DuXPnVMqfXa4kPJy3/SIiIt3GUEdaS13v3KhRo1CvXj2Vck6OICIiXcdQR1pHCIGZM2eqlJe2mDAnRxARka7jNXVULZT1mreIiIhyBzoiIqKagD11VC2U5Zo3dcOtrVtPRN++ZpXcOiIiouqPoY6qhdKuecvLy8OcOXNUytk7R0RE9D8MdVQtlHTN2+zZs5Gfn69UZmZmhokTJ1ZRy4iIiLQDr6mjUmlyfbewsDCVQDd16lQGOiIiIjW0LtTl5OSgXbt2kMlkOHXqlNK2M2fOoGPHjjA2NoajoyPmz5+v8vqtW7eiRYsWMDY2Rps2bbBnzx6l7UIITJ8+Hfb29jAxMYG3tzcuXryoVCc9PR2DBg2ChYUFrKysEBAQgIcPH5a7Ldrg6WvdqsqjR49KXEzYwICdy0REROpoXaibPHkyHBwcVMqzsrLQtWtXNGzYECdPnsSCBQswY8YMfPfdd1Kdo0ePYuDAgQgICEB8fDz69OmDPn364OzZs1Kd+fPnY8mSJVi1ahXi4uJgZmYGHx8fPHnyRKozaNAgnDt3Dvv378euXbvwxx9/4OOPPy5XW7RFcDCgUFTd+m5hYWFYsGCBUlnTpk15/RwREdFzyIQQQtONKKvffvsNQUFB+Pnnn9GqVSvEx8ejXbt2AICVK1di6tSpSEtLg1wuBwAEBwdjx44dSExMBAD0798f2dnZ2LVrl7RPT09PtGvXDqtWrYIQAg4ODpgwYYI0xJeZmQlbW1tERERgwIABSEhIQMuWLXH8+HG0b98eALB37150794d169fh4ODQ5naUhZZWVmwtLREZmYmLCwsXvrzq+7U9c5Nnz4dMplMA60hIiJ6MZr6/taanrpbt25hxIgR+PHHH2FqaqqyPTY2Fm+88YYUogDAx8cHSUlJuH//vlTH29tb6XU+Pj6IjY0FACQnJyMtLU2pjqWlJTw8PKQ6sbGxsLKykgIdAHh7e0NPTw9xcXFlbos6OTk5yMrKUnrUBBkZGWoDXXR0KH79lYGOiIioLLQi1Akh4O/vj8DAQKUw9bS0tDTY2toqlRU/T0tLK7XO09uffl1JdZ69DZWBgQHq1Knz3Pd5+j3UmTt3LiwtLaWHo6NjiXV1RVhYGL755hulMm9vb0RHh1b5tXxERETaTKOhLjg4GDKZrNRHYmIili5digcPHiAkJESTza10ISEhyMzMlB7Xrl3TdJMqVUmTIV577bUqv5aPiIhI22l0KuGECRPg7+9fap3GjRvj4MGDiI2NhZGRkdK29u3bY9CgQfjhhx9gZ2eHW7duKW0vfm5nZyf9qa7O09uLy+zt7ZXqFF+7Z2dnh9u3byvtIz8/H+np6c99n6ffQx0jIyOVY9RFqamp+P7771XKn54MwXu1EhERlY9GQ52NjQ1sbGyeW2/JkiX48ssvpeepqanw8fHBli1b4OHhAQBQKBSYOnUq8vLyYGhoCADYv38/mjdvjtq1a0t1YmJiMH78eGlf+/fvh0KhAAA4OzvDzs4OMTExUojLyspCXFwcRo0aJe0jIyMDJ0+ehLu7OwDg4MGDKCwsLFdbaip1vXMffvghmjRpooHWEBER6Q6tmv1aLCUlBc7OzkqzXzMzM9G8eXN07doVU6ZMwdmzZzFs2DAsXrxYWm7k6NGj6NSpE8LDw9GjRw9s3rwZc+bMwT///IPWrVsDAObNm4fw8HD88MMPcHZ2xhdffIEzZ87g/PnzMDY2BgB069YNt27dwqpVq5CXl4ePPvoI7du3x8aNG8vclrLQtdmvJQ23EhER6RJNfX/rzEqulpaW2LdvH8aMGQN3d3dYW1tj+vTpSiHKy8sLGzduxLRp0/D555+jWbNm2LFjhxTogKJ18LKzs/Hxxx8jIyMDr7/+Ovbu3SsFOgDYsGEDxo4di7feegt6enro27cvlixZUq621CQXL16UAu/TGOiIiIgqjlb21NUUutBTp653buTIkaVeW0hERKTN2FNHOofDrURERFWHoY4q3IkTJ7B7926VcgY6IiKiysNQRxVKXe/cZ599prXDx0RERNqCoY4qhBACM2fOVCln7xwREVHVYKijl3bgwAEcOXJEqaxevXrS2n5ERERU+Rjq6KWoG26dMmWK0hIwREREVPkY6uiFFBYWYtasWSrlHG4lIiLSDIY6Krfjx49jz549SmWvvfYavL29NdQiIiIiYqgjREUB4eFAcDDg51d6XXXDrdOmTYO+vn4ltY6IiIjKQk/TDSDNCw8HYmOL/ixJXl5eiYsJM9ARERFpHkMdITgYUCiK/lTn0KFDmDNnjlJZ//794eYWCi+vop4+IiIi0ize+7Uaqw73flXXOzd9+nTIZDJ4eRX18CkUwNGjGmgcERFRNaSp72/21JFaubm5JQ63ymQyAM/v4SMiIqKqw4kSpELd7NaPPvoITk5OSmV+fs+fWEFERERVg6GOlJTUO0dERETVG4dfCQDw6NEjlUD36quvlinQRUWBEyaIiIg0jD11hL/++gsxMTFKZePHj4elpWWZXv/0kigcjiUiItIM9tTVcNu3b1cJdKGhoWUOdAAnTBAREVUH7KmrgaKigIULn+Ctt+YplXfu3BmdOnUq9/44YYKIiEjzGOpqoNWrL+KttzYqlX3++ecwNDTUUIuIiIjoZTHU1UDu7v8LdB4eHvD19dVga4iIiKgiMNTVQG+88Qbi4uIwZMgQODg4aLo5REREVAF4m7BqrDrcJoyIiIjKh7cJIyIiIqIXxlBHREREpAMY6oiIiIh0AEMdERERkQ5gqCMiIiLSAQx1RERERDqAoY6IiIhIBzDUEREREekAhjoiIiIiHcBQR0RERKQDGOqIiIiIdABDHREREZEOYKgjIiIi0gEMdUREREQ6wEDTDaCSCSEAAFlZWRpuCREREZVV8fd28fd4VWGoq8YePHgAAHB0dNRwS4iIiKi8Hjx4AEtLyyp7P5mo6hhJZVZYWIjU1FTUqlULMplM082pdFlZWXB0dMS1a9dgYWGh6eZQCXieqj+eI+3A86QdXuQ8CSHw4MEDODg4QE+v6q50Y09dNaanp4cGDRpouhlVzsLCgr/gtADPU/XHc6QdeJ60Q3nPU1X20BXjRAkiIiIiHcBQR0RERKQDGOqo2jAyMkJoaCiMjIw03RQqBc9T9cdzpB14nrSDNp0nTpQgIiIi0gHsqSMiIiLSAQx1RERERDqAoY6IiIhIBzDUEREREekAhjqqEDk5OWjXrh1kMhlOnTqltO3MmTPo2LEjjI2N4ejoiPnz56u8fuvWrWjRogWMjY3Rpk0b7NmzR2m7EALTp0+Hvb09TExM4O3tjYsXLyrVSU9Px6BBg2BhYQErKysEBATg4cOH5W6LLvLz84OTkxOMjY1hb2+PwYMHIzU1ValOZGQk2rVrB1NTUzRs2BALFixQ2c/hw4fxyiuvwMjICE2bNkVERIRKneXLl6NRo0YwNjaGh4cH/v77b6XtT548wZgxY1C3bl2Ym5ujb9++uHXrllKdq1evokePHjA1NUW9evUwadIk5Ofnv/wHUc2V5TxFR0fD09MTtWrVgo2NDfr27YuUlBSlOjxPlet552nGjBmQyWQqDzMzM6X98Pde5SrLz5MQAgsXLoSLiwuMjIxQv359zJ49W6mOVv08CaIK8Mknn4hu3boJACI+Pl4qz8zMFLa2tmLQoEHi7NmzYtOmTcLExER8++23Up0jR44IfX19MX/+fHH+/Hkxbdo0YWhoKP7991+pTnh4uLC0tBQ7duwQp0+fFn5+fsLZ2Vk8fvxYquPr6yvatm0rjh07Jv7880/RtGlTMXDgwHK1RVd99dVXIjY2VqSkpIgjR44IhUIhFAqFtH3Pnj3CwMBArFy5Uly6dEns2rVL2Nvbi6VLl0p1Ll++LExNTUVQUJA4f/68WLp0qdDX1xd79+6V6mzevFnI5XKxdu1ace7cOTFixAhhZWUlbt26JdUJDAwUjo6OIiYmRpw4cUJ4enoKLy8vaXt+fr5o3bq18Pb2FvHx8WLPnj3C2tpahISEVPKnpHnPO0+XL18WRkZGIiQkRPz333/i5MmT4o033hBubm5KdXieKtfzztODBw/EzZs3lR4tW7YUQ4cOlerw917le955EkKIcePGiebNm4udO3eKy5cvixMnToh9+/ZJ27Xt54mhjl7anj17RIsWLcS5c+dUQt2KFStE7dq1RU5OjlQ2ZcoU0bx5c+n5+++/L3r06KG0Tw8PDzFy5EghhBCFhYXCzs5OLFiwQNqekZEhjIyMxKZNm4QQQpw/f14AEMePH5fq/Pbbb0Imk4kbN26UuS01xc6dO4VMJhO5ublCCCEGDhwo+vXrp1RnyZIlokGDBqKwsFAIIcTkyZNFq1atlOr0799f+Pj4SM87dOggxowZIz0vKCgQDg4OYu7cuUKIovNmaGgotm7dKtVJSEgQAERsbKwQoujfk56enkhLS5PqrFy5UlhYWCidu5rg2fO0detWYWBgIAoKCqQ6UVFRSnV4nqres+fpWadOnRIAxB9//CGV8fde1Xv2PJ0/f14YGBiIxMTEEl+jbT9PHH6ll3Lr1i2MGDECP/74I0xNTVW2x8bG4o033oBcLpfKfHx8kJSUhPv370t1vL29lV7n4+OD2NhYAEBycjLS0tKU6lhaWsLDw0OqExsbCysrK7Rv316q4+3tDT09PcTFxZW5LTVBeno6NmzYAC8vLxgaGgIoGj43NjZWqmdiYoLr16/jypUrAJ5/nnJzc3Hy5EmlOnp6evD29pbqnDx5Enl5eUp1WrRoAScnJ6Vz2aZNG9ja2iq9T1ZWFs6dO1dRH0O1p+48ubu7Q09PD+vWrUNBQQEyMzPx448/wtvbW6rD81S11J2nZ61evRouLi7o2LGjVMbfe1VL3Xn69ddf0bhxY+zatQvOzs5o1KgRhg8fjvT0dOl12vbzxFBHL0wIAX9/fwQGBir9UnlaWlqa0j9SANLztLS0Uus8vf3p15VUp169ekrbDQwMUKdOnee+z9PvocumTJkCMzMz1K1bF1evXsXOnTulbT4+Pvjll18QExODwsJCXLhwAYsWLQIA3Lx5E0DJn19WVhYeP36Mu3fvoqCg4LnnSS6Xw8rKqtQ6PE/qz5OzszP27duHzz//HEZGRrCyssL169cRGRkp1eF5qhqlnaenPXnyBBs2bEBAQIBSOX/vVY3SztPly5dx5coVbN26FevXr0dERAROnjyJfv36SXW07eeJoY5UBAcHq73I9+lHYmIili5digcPHiAkJETTTa6Rynqeik2aNAnx8fHYt28f9PX1MWTIEIj/u6HMiBEjMHbsWPTs2RNyuRyenp4YMGAAgKL/ddKLq8jzlJaWhhEjRmDo0KE4fvw4fv/9d8jlcvTr10+qQy+mIs/T07Zv344HDx5g6NChVXk4Oqsiz1NhYSFycnKwfv16dOzYEZ07d8aaNWtw6NAhJCUlaeoQX4qBphtA1c+ECRPg7+9fap3GjRvj4MGDiI2NVbkfXvv27TFo0CD88MMPsLOzU5nhU/zczs5O+lNdnae3F5fZ29sr1WnXrp1U5/bt20r7yM/PR3p6+nPf5+n30CZlPU/FrK2tYW1tDRcXF7i6usLR0RHHjh2DQqGATCbDvHnzMGfOHKSlpcHGxgYxMTFK+yjp87OwsICJiQn09fWhr6//3HOZm5uLjIwMpf+1Plvn2ZljPE9F52n58uWwtLRUmr34008/wdHREXFxcfD09OR5ekEVeZ6etnr1avTs2VOlF4a/915MRZ4ne3t7GBgYwMXFRarv6uoKoGgmavPmzbXv56nMV98RPePKlSvi33//lR7R0dECgNi2bZu4du2aEOJ/F+k+fQFxSEiIykSJnj17Ku1boVCoXDC8cOFCaXtmZqbaC4ZPnDgh1YmOjlZ7wXBpbakprly5IgCIQ4cOlVhn8ODBSjPFJk+eLFq3bq1UZ+DAgSoXDI8dO1Z6XlBQIOrXr69ywfC2bdukOomJiWovGH565ti3334rLCwsxJMnT17sgLXUs+cpKChIdOjQQalOamqqACCOHDkihOB50oSSfp4uX74sZDKZ+PXXX1Vew997Ve/Z81T8nfXff/9JdYontSQlJQkhtO/niaGOKkxycrLK7NeMjAxha2srBg8eLM6ePSs2b94sTE1NVZY0MTAwEAsXLhQJCQkiNDRU7dR+KysrsXPnTnHmzBnRu3dvtVP73dzcRFxcnPjrr79Es2bNlKb2l6UtuujYsWNi6dKlIj4+XqSkpIiYmBjh5eUlmjRpIv2yuHPnjli5cqVISEgQ8fHx4pNPPhHGxsYiLi5O2k/x1P5JkyaJhIQEsXz5crVT+42MjERERIQ4f/68+Pjjj4WVlZXSjK7AwEDh5OQkDh48KE6cOKGyzEDx1P6uXbuKU6dOib179wobGxudXyqjLOcpJiZGyGQyERYWJi5cuCBOnjwpfHx8RMOGDcWjR4+EEDxPla0s56nYtGnThIODg8jPz1fZD3/vVa6ynKeCggLxyiuviDfeeEP8888/4sSJE8LDw0O8/fbb0n607eeJoY4qjLpQJ4QQp0+fFq+//rowMjIS9evXF+Hh4SqvjYyMFC4uLkIul4tWrVqJ3bt3K20vLCwUX3zxhbC1tRVGRkbirbfekv4nVezevXti4MCBwtzcXFhYWIiPPvpIPHjwoNxt0TVnzpwRXbp0EXXq1BFGRkaiUaNGIjAwUFy/fl2qc+fOHeHp6SnMzMyEqampeOutt8SxY8dU9nXo0CHRrl07IZfLRePGjcW6detU6ixdulQ4OTkJuVwuOnTooLKfx48fi9GjR4vatWsLU1NT8c4774ibN28q1UlJSRHdunUTJiYmwtraWkyYMEHk5eVVzAdSTZXlPAkhxKZNm4Sbm5swMzMTNjY2ws/PTyQkJCjV4XmqPGU9TwUFBaJBgwbi888/L3Ff/L1Xecp6nm7cuCHeffddYW5uLmxtbYW/v7+4d++eUh1t+nmSCcGra4mIiIi0Hae1EREREekAhjoiIiIiHcBQR0RERKQDGOqIiIiIdABDHREREZEOYKgjIiIi0gEMdUREREQ6gKGOiIhUHD58GDKZDBkZGS+1n0aNGuHrr7+ukDYRUekY6oioQqWlpWHcuHFo3LgxjIyM4OjoiF69eiEmJkaqc/ToUXTv3h21a9eGsbEx2rRpg6+++goFBQVSnZSUFAQEBMDZ2RkmJiZo0qQJQkNDkZubq/R+33//Pdq2bQtzc3NYWVnBzc0Nc+fOlbbPmDEDMpkMvr6+Km1dsGABZDIZOnfuXObjy8rKwtSpU9GiRQsYGxvDzs4O3t7e+OWXX/D0Wu7nzp3D+++/DxsbGxgZGcHFxQXTp0/Ho0ePpDrp6ekYN24cmjdvDhMTEzg5OeGTTz5BZmZmmdqSkpICmUym9nHs2LEyH1Pnzp0xfvz4MtcnourJQNMNICLdkZKSgtdeew1WVlZYsGAB2rRpg7y8PERHR2PMmDFITEzE9u3b8f777+Ojjz7CoUOHYGVlhQMHDmDy5MmIjY1FZGQkZDIZEhMTUVhYiG+//RZNmzbF2bNnMWLECGRnZ2PhwoUAgLVr12L8+PFYsmQJOnXqhJycHJw5cwZnz55Vape9vT0OHTqE69evo0GDBlL52rVr4eTkVObjy8jIwOuvv47MzEx8+eWXePXVV2FgYIDff/8dkydPxptvvgkrKyscO3YM3t7e8Pb2xu7du2Fra4u///4bEyZMQExMDA4dOgS5XI7U1FSkpqZi4cKFaNmyJa5cuYLAwECkpqZi27ZtZW7XgQMH0KpVK6WyunXrlvn1RKQjynVTMSKiUnTr1k3Ur19fPHz4UGXb/fv3xcOHD0XdunXFu+++q7I9KipKABCbN28ucf/z588Xzs7O0vPevXsLf3//UtsUGhoq2rZtK3r27Cm+/PJLqfzIkSPC2tpajBo1SnTq1KkMRyfEqFGjhJmZmbhx44bKtgcPHoi8vDxRWFgoWrZsKdq3by8KCgqU6pw6dUrIZLJS770ZGRkp5HJ5me75WNL9lp9WfPzr168XDRs2FBYWFqJ///4iKytLCCHE0KFDBQClR3Jysjh06JAAIA4cOCDc3d2FiYmJUCgUIjExUdr3f//9J/z8/ES9evWEmZmZaN++vdi/f7/S+zds2FAsXrxYeg5ArFixQvj6+gpjY2Ph7Owstm7dKm3v0qWLGDNmjNI+bt++LQwNDcWBAwee+5kQ1WQcfiWiCpGeno69e/dizJgxMDMzU9luZWWFffv24d69e5g4caLK9l69esHFxQWbNm0q8T0yMzNRp04d6bmdnR2OHTuGK1euPLd9w4YNQ0REhPR87dq1GDRoEORy+XNfCwCFhYXYvHkzBg0aBAcHB5Xt5ubmMDAwwKlTp3D+/HkEBQVBT0/5V2zbtm3h7e393GO0sLCAgUHFDaRcunQJO3bswK5du7Br1y78/vvvCA8PBwB88803UCgUGDFiBG7evImbN2/C0dFReu3UqVOxaNEinDhxAgYGBhg2bJi07eHDh+jevTtiYmIQHx8PX19f9OrVC1evXi21PV988QX69u2L06dPY9CgQRgwYAASEhIAAMOHD8fGjRuRk5Mj1f/pp59Qv359vPnmmxX2mRDpIoY6IqoQ//33H4QQaNGiRYl1Lly4AABwdXVVu71FixZSHXX7X7p0KUaOHCmVhYaGwsrKCo0aNULz5s3h7++PyMhIFBYWqry+Z8+eyMrKwh9//IHs7GxERkYqBZTnuXv3Lu7fv1/q8QHPP0ZXV9cSj/Hu3buYNWsWPv744zK3CwC8vLxgbm6u9HhaYWEhIiIi0Lp1a3Ts2BGDBw+WrnG0tLSEXC6Hqakp7OzsYGdnB319fem1s2fPRqdOndCyZUsEBwfj6NGjePLkCYCikDpy5Ei0bt0azZo1w6xZs9CkSRNERUWV2t733nsPw4cPh4uLC2bNmoX27dtj6dKlAIB3330XALBz506pfkREBPz9/SGTycr1uRDVNAx1RFQhxFOTBCqyLgDcuHEDvr6+eO+99zBixAip3N7eHrGxsfj333/x6aefIj8/H0OHDoWvr69KsDM0NMSHH36IdevWYevWrXBxccH/+3//r9LaXN76WVlZ6NGjB1q2bIkZM2aU67VbtmzBqVOnlB5Pa9SoEWrVqiU9t7e3x+3bt8u076c/I3t7ewCQXvvw4UNMnDgRrq6usLKygrm5ORISEp7bU6dQKFSeF/fUGRsbY/DgwVi7di0A4J9//sHZs2fh7+9fpvYS1WScKEFEFaJZs2bSBIeSuLi4AAASEhLg5eWlsj0hIQEtW7ZUKktNTUWXLl3g5eWF7777Tu1+W7dujdatW2P06NEIDAxEx44d8fvvv6NLly5K9YYNGwYPDw+cPXu2XL10AGBjYwMrK6tSjw9QPkY3NzeV7QkJCVKdYg8ePICvry9q1aqF7du3w9DQsFxtc3R0RNOmTUvc/uz+ZDKZ2t7M5722uKes+LUTJ07E/v37sXDhQjRt2hQmJibo16+fygzl8ho+fDjatWuH69evY926dXjzzTfRsGHDl9onUU3AnjoiqhB16tSBj48Pli9fjuzsbJXtGRkZ6Nq1K+rUqYNFixapbI+KisLFixcxcOBAqezGjRvo3Lkz3N3dsW7dOpVr1NQpDoXq2tCqVSu0atUKZ8+exQcffFCew4Oenh4GDBiADRs2IDU1VWX7w4cPkZ+fj3bt2qFFixZYvHixSnA6ffo0Dhw4oHSMWVlZ6Nq1K+RyOaKiomBsbFyudlUEuVyutJxMWR05cgT+/v5455130KZNG9jZ2SElJeW5r3t2uZVjx44pDVe3adMG7du3x/fff4+NGzeWO4AT1VQMdURUYZYvX46CggJ06NABP//8My5evIiEhAQsWbIECoUCZmZm+Pbbb7Fz5058/PHHOHPmDFJSUrBmzRr4+/ujX79+eP/99wH8L9A5OTlh4cKFuHPnDtLS0pCWlia936hRozBr1iwcOXIEV65cwbFjxzBkyBDY2NioDPEVO3jwIG7evAkrK6tyH9/s2bPh6OgIDw8PrF+/HufPn8fFixexdu1auLm54eHDh5DJZFizZg3Onz+Pvn374u+//8bVq1exdetW9OrVCwqFQloTrjjQZWdnY82aNcjKypKOsTwh6969e9Lrih/F172VRaNGjRAXF4eUlBTcvXu3zL14zZo1wy+//IJTp07h9OnT+OCDD8r02q1bt2Lt2rW4cOECQkND8ffff2Ps2LFKdYYPH47w8HAIIfDOO++U+ViIajRNTr0lIt2TmpoqxowZIxo2bCjkcrmoX7++8PPzE4cOHZLq/PHHH8LHx0dYWFgIuVwuWrVqJRYuXCjy8/OlOuvWrVNZaqP4UWzbtm2ie/fuwt7eXsjlcuHg4CD69u0rzpw5I9UpXtKjJJ9++mmZlzQRQoiMjAwRHBwsmjVrJuRyubC1tRXe3t5i+/btorCwUKp35swZ0bdvX1GnTh1haGgomjRpIqZNmyays7OlOsXLhqh7JCcnP7ctxUuaqHts2rSpxONfvHixaNiwofQ8KSlJeHp6ChMTE5UlTe7fvy/Vi4+PV2pbcnKy6NKlizAxMRGOjo5i2bJlolOnTuLTTz+VXqNuSZPly5eLt99+WxgZGYlGjRqJLVu2qBzbgwcPhKmpqRg9evRzPwciKiITopxX8xIREb0gmUyG7du3o0+fPqXWS0lJQZMmTXD8+HG88sorVdM4Ii3HiRJERFRt5OXl4d69e5g2bRo8PT0Z6IjKgdfUERH9n2fXenv68eeff1Z5ewIDA0tsT2BgYJW3pyocOXIE9vb2OH78OFatWqXp5hBpFQ6/EhH9n//++6/EbfXr14eJiUkVtqZoPbisrCy12ywsLFCvXr0qbQ8RVW8MdUREREQ6gMOvRERERDqAoY6IiIhIBzDUEREREekAhjoiIiIiHcBQR0RERKQDGOqIiIiIdABDHREREZEOYKgjIiIi0gH/H1vGzWntUJnfAAAAAElFTkSuQmCC", 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", 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Fs88+Kzw8PIS9vb3o1KmT2Ldvn9L2/f39xcqVK6XnAMTnn38uoqKiRP369UVAQIDYsmWLVN67d28RExOjtI7bt28LKysrsX//fpX7WNVnRt3zt0G7zBo3boylS5ciKSkJp06dQp8+ffDcc8/h/PnzAIAZM2bgl19+wZYtW5CQkICMjAxER0dLry8tLcWAAQNQXFyMo0eP4l//+he++eYbvPXWW1KdtLQ0DBgwAL1790ZycjKmT5+OyZMnY8+ePXrfX11Iu5OPMqG8rFQIXLtToPoFRGRW9Nmdfu/ePezevRsxMTGwt7evUO7i4oK9e/fi7t27mDVrVoXyQYMGITAwEN9//32l25DL5XB1dZWee3l54dixY7h+/Xq18U2cOBHffPON9HzdunUYPXo0rK2tq30tAJSVleGHH37A6NGj4ePjU6HcwcEB9erVQ3JyMi5cuICZM2fCwkL5NNuuXTuEh4dXu49OTk6oV097nTipqan46aefsHPnTuzcuRMJCQlYunQpAODjjz9GaGgopkyZgszMTGRmZsLX11d67RtvvIEPP/wQp06dQr169TBx4kSpLC8vD/3798eBAwfwxx9/ICoqCoMGDUJ6enqV8SxYsABDhw7FmTNnMHr0aIwYMQIXL14EAEyePBkbN25EUVGRVH/Dhg1o1KgR+vTpo7VjUkGV6ZIBNGjQQHz11VciNzdXWFlZKWWNFy9eFABEYmKiEEKIX3/9VVhYWCi1Gq1evVo4OTmJoqIiIYQQc+bMEW3atFHaxvDhw0VkZKRGcbGFiIj0RVstRD+cuC59PwTM2yl+OHFdSxGqdvz4cQFAbNu2rdI6S5cuFQDE33//rbL82WefFa1bt1ZZduXKFeHk5CS++OILaVlGRobo1q2bACACAwPFuHHjxKZNm5RaZspbSIqLi4WHh4dISEgQeXl5wtHRUZw5c0a89tprarUQZWdnCwBixYoVVdb74Ycfqmy1efXVV4Wtra3KspycHOHn5ydef/31auMR4n8tRLa2tsLe3l7pUW7hwoXCzs5OahESQojZs2eLrl27Ss979eolXnvtNaV1P95CVG7Xrl0CQJXvzTZt2ohPP/1Ueq6qhWjq1KlKr+natauYNm2aEOLR+79BgwZi06ZNUnnbtm3FokWLKt2mybcQPa60tBQ//PAD8vPzERoaiqSkJJSUlCA8PFyq06pVK/j5+SExMREAkJiYiJCQEHh6ekp1IiMjoVAopFamxMREpXWU1ylfR2WKioqgUCiUHsbI29kWcdEhsPzvHAyWMhneiw6Gt7OtgSMjIkPKlBdKYwsBoEwAr29L0WlLkRCi+ko1qAsAt27dQlRUFJ5//nlMmTJFWu7t7Y3ExEScO3cOr732Gh4+fIhx48YhKioKZWVlSuuwsrLCmDFjsH79emzZsgWBgYFo27atzmLWtL5CocCAAQMQFBSERYsWafTaTZs2ITk5WenxuCZNmsDR0VF67u3tLd3uojqPH6PyW2OUvzYvLw+zZs1C69at4eLiAgcHB1y8eLHaFqLQ0NAKz8tbiOrXr48XX3wR69atAwCcPn0aKSkpGD9+vFrx1pTBB1WfO3cOoaGhePDgARwcHLB9+3YEBQUhOTkZ1tbWcHFxUarv6emJrKwsAI+uZHg8GSovLy+rqo5CoUBhYaE05feT4uLisHjxYm3sos4N7+yHnoHuuHanAE3c7JgMEVGV3em6+o5o0aKFNBi6MoGBgQCAixcv4umnn65QfvHiRQQFBSkty8jIQO/evfH000/jiy++ULne4OBgBAcH4+WXX8bUqVPRo0cPJCQkoHfv3kr1Jk6ciK5duyIlJUWp60cd7u7ucHFxqXL/AOV9fOqppyqUX7x4UapT7v79+4iKioKjoyO2b98OKysrjWLz9fVF8+bNKy1/cn0ymaxCwqjOa8snQCx/7axZs7Bv3z4sX74czZs3h62tLYYNG1bhSkBNTZ48Ge3bt8fNmzexfv169OnTB/7+/rVaZ3UM3kLUsmVLJCcn4/jx45g2bRrGjRuHCxcuGDoszJ8/H3K5XHrcuHHD0CFVydvZFqHNGjIZIiIAQICbPSyemLzXUiZDEzfd3Q7E1dUVkZGRWLVqFfLz8yuU5+bmIiIiAq6urvjwww8rlO/YsQNXrlzByJEjpWW3bt1CWFgYOnbsiPXr11cYk6NKeUKlKoY2bdqgTZs2SElJwahRozTZPVhYWGDEiBH47rvvkJGRUaE8Ly8PDx8+RPv27dGqVSusXLmyQtJx5swZ7N+/X2kfFQoFIiIiYG1tjR07dqB+/foaxaUN1tbWSlMeqOvIkSMYP348hgwZgpCQEHh5eeHatWvVvu7JKQGOHTuG1q1bS89DQkLQqVMnfPnll9i4caPGyWtNGDwhsra2RvPmzdGxY0fExcWhXbt2+Pjjj+Hl5YXi4uIKE0JlZ2fDy8sLwKPBdNnZ2RXKy8uqquPk5FRp6xAA2NjYwMnJSelBRGQqDNWdvmrVKpSWlqJLly748ccfceXKFVy8eBGffPIJQkNDYW9vj7Vr1+Lnn3/GSy+9hLNnz+LatWv4+uuvMX78eAwbNgwvvPACgP8lQ35+fli+fDlycnKQlZUl9QAAwLRp0/DOO+/gyJEjuH79Oo4dO4axY8fC3d29QrdMuYMHDyIzM7NCD4Q63n33Xfj6+qJr167497//jQsXLuDKlStYt24dnnrqKeTl5UEmk+Hrr7/GhQsXMHToUJw4cQLp6enYsmULBg0ahNDQUGnOn/JkKD8/H19//TUUCoW0j5okKHfv3pVeV/548OCB2q9v0qQJjh8/jmvXruHOnTtqtx61aNEC27ZtQ3JyMs6cOYNRo0ap9dotW7Zg3bp1+PPPP7Fw4UKcOHECsbGxSnUmT56MpUuXQgiBIUOGqL0vNVblCCMD6N27txg3bpw0qHrr1q1S2aVLl1QOqs7OzpbqrF27Vjg5OYkHDx4IIR4Nqg4ODlbaxsiRI+vMoGoiqnu0edl9Rm6BOHr1jl4vtMjIyBAxMTHC399fWFtbi0aNGolnn31WxMfHS3UOHz4sIiMjhZOTk7C2thZt2rQRy5cvFw8fPpTqrF+/vsLl4OWPclu3bhX9+/cX3t7ewtraWvj4+IihQ4eKs2fPSnXKB1VXRt1B1eVyc3PFvHnzRIsWLYS1tbXw9PQU4eHhYvv27aKsrEyqd/bsWTF06FDh6uoqrKysRLNmzcSbb74p8vPzpTrlA5dVPdLS0qqNpXxQtarH999/X+n+r1y5Uvj7+0vPL1++LLp16yZsbW0rXHb/+AD4P/74Qym2tLQ00bt3b2Frayt8fX3FZ599VmGAtqpB1atWrRL/+Mc/hI2NjWjSpInSAOpy9+/fF3Z2duLll1+u9jhoY1C1QROiefPmiYSEBJGWlibOnj0r5s2bJ2Qymdi7d68QQoipU6cKPz8/cfDgQXHq1CkRGhoqQkNDpdc/fPhQBAcHi4iICJGcnCx2794t3N3dxfz586U6f/31l7CzsxOzZ88WFy9eFKtWrRKWlpZi9+7dGsXKhIiI9MWU5yEiqg4AsX379mrrpaWlCQsLC5GUlFRtXW0kRAYdVH379m2MHTsWmZmZcHZ2Rtu2bbFnzx784x//AACsXLkSFhYWGDp0KIqKihAZGYnPP/9cer2lpSV27tyJadOmSU2x48aNw9tvvy3VCQgIwK5duzBjxgx8/PHHaNy4Mb766itERkbqfX+JiIioaiUlJbh79y7efPNNdOvWDR06dNDLdmX/zdaoGgqFAs7OztKEWUREuvLgwQOkpaUhICDAIANszZmDg0OlZb/99ht69Oihx2iAqVOnYsOGDSrLxowZgzVr1ug1Hm2QyWTYvn07Bg8erLL80KFD6N27NwIDA7F161aEhIRUu86qPjPqnr8Nftk9ERGRsXhy/p7HNWrUSH+B/Nfbb7+tclZvACb747y6dpiwsDCN53DSBiZERERE/1XVXD6G4OHhAQ8PD0OHYRYMftk9ERERkaExISIiMlLqzgVDZO608VlhlxkRkZGxtraGhYUFMjIy4O7uDmtra+mWCUT0P0IIFBcXIycnBxYWFrC2tq7xupgQEREZGQsLCwQEBCAzM1PlLSKISJmdnR38/PzUurVLZZgQEREZIWtra/j5+eHhw4c1uscUkbmwtLREvXr1at2KyoSIiMhIyWQyWFlZaXzncyLSHAdVExERkdljQkRERERmjwkRERERmT0mRERERGT2mBARERGR2WNCRERERGaPCRERERGZPSZEREREZPaYEBEREZHZY0JEREREZo8JEREREZk9JkRERERk9pgQERERkdljQkRERERmjwkRERERmT0mRERERGT2mBARERGR2WNCRERERGaPCRERERGZPSZEREREZPaYEBEREZHZY0JEREREZo8JEREREZk9JkRERERk9pgQERERkdljQkRERERmjwkRERERmT0mRERERGT2mBARERGR2WNCRERERGaPCRERERGZPSZEREREZPaYEBEREZHZY0JEREREZo8JEREREZk9JkRERERk9gyaEMXFxaFz585wdHSEh4cHBg8ejMuXLyvVCQsLg0wmU3pMnTpVqU56ejoGDBgAOzs7eHh4YPbs2Xj48KFSnUOHDqFDhw6wsbFB8+bN8c033+h694iIiMhEGDQhSkhIQExMDI4dO4Z9+/ahpKQEERERyM/PV6o3ZcoUZGZmSo8PPvhAKistLcWAAQNQXFyMo0eP4l//+he++eYbvPXWW1KdtLQ0DBgwAL1790ZycjKmT5+OyZMnY8+ePXrbVyIiIjJeMiGEMHQQ5XJycuDh4YGEhAT07NkTwKMWovbt2+Ojjz5S+ZrffvsNAwcOREZGBjw9PQEAa9aswdy5c5GTkwNra2vMnTsXu3btQkpKivS6ESNGIDc3F7t371YrNoVCAWdnZ8jlcjg5OdVuR4mIiEgv1D1/G9UYIrlcDgBwdXVVWv7dd9/Bzc0NwcHBmD9/PgoKCqSyxMREhISESMkQAERGRkKhUOD8+fNSnfDwcKV1RkZGIjExUVe7QkRERCaknqEDKFdWVobp06eje/fuCA4OlpaPGjUK/v7+8PHxwdmzZzF37lxcvnwZ27ZtAwBkZWUpJUMApOdZWVlV1lEoFCgsLIStrW2FeIqKilBUVCQ9VygU2tlRIiIiMjpGkxDFxMQgJSUFv//+u9Lyl156Sfo7JCQE3t7e6Nu3L1JTU9GsWTOdxRMXF4fFixfrbP1ERERkPIyiyyw2NhY7d+5EfHw8GjduXGXdrl27AgCuXr0KAPDy8kJ2drZSnfLnXl5eVdZxcnJS2ToEAPPnz4dcLpceN27c0HzHiIiIyCQYNCESQiA2Nhbbt2/HwYMHERAQUO1rkpOTAQDe3t4AgNDQUJw7dw63b9+W6uzbtw9OTk4ICgqS6hw4cEBpPfv27UNoaGil27GxsYGTk5PSg4iIiOomgyZEMTEx2LBhAzZu3AhHR0dkZWUhKysLhYWFAIDU1FS88847SEpKwrVr17Bjxw6MHTsWPXv2RNu2bQEAERERCAoKwosvvogzZ85gz549ePPNNxETEwMbGxsAwNSpU/HXX39hzpw5uHTpEj7//HNs3rwZM2bMMNi+ExERkfEw6GX3MplM5fL169dj/PjxuHHjBsaMGYOUlBTk5+fD19cXQ4YMwZtvvqnUYnP9+nVMmzYNhw4dgr29PcaNG4elS5eiXr3/DZE6dOgQZsyYgQsXLqBx48ZYsGABxo8fr3asvOyeiIjI9Kh7/jaqeYiMGRMiIiIi02OS8xARERERGQITIiIiIjJ7TIiIiIjI7DEhIiIiIrPHhIiIiIjMHhMiIiIiMntMiIiIiMjsMSEiIiIis8eEiIiIiMweEyIiIiIye0yIiIiIyOwxISIiIiKzx4SIiIiIzB4TIiIiIjJ7TIiIiIjI7DEhIiIiIrPHhIiIiIjMHhMiIiIiMntMiIiIiMjsMSEiIiIis8eEiIiIiMweEyIiIiIye0yIiIiIyOwxISIiIiKzx4SIiIiIzB4TIiIiIjJ7TIiIiIjI7DEhIiIiIrNXo4QoPj5e23EQERERGUyNEqKoqCg0a9YMS5YswY0bN7QdExEREZFe1SghunXrFmJjY7F161Y0bdoUkZGR2Lx5M4qLi7UdHxEREZHO1SghcnNzw4wZM5CcnIzjx48jMDAQL7/8Mnx8fPDqq6/izJkz2o6TiIiISGdqPai6Q4cOmD9/PmJjY5GXl4d169ahY8eO6NGjB86fP6+NGImIiIh0qsYJUUlJCbZu3Yr+/fvD398fe/bswWeffYbs7GxcvXoV/v7+eP7557UZKxEREZFOyIQQQtMXvfLKK/j+++8hhMCLL76IyZMnIzg4WKlOVlYWfHx8UFZWprVgDUmhUMDZ2RlyuRxOTk6GDoeIiIjUoO75u15NVn7hwgV8+umniI6Oho2Njco6bm5uvDyfiIiITEKNWojMEVuIiIiITI9OW4gA4PLly/j0009x8eJFAEDr1q3xyiuvoGXLljVdJREREZFB1GhQ9Y8//ojg4GAkJSWhXbt2aNeuHU6fPo3g4GD8+OOP2o6RiIiISKdq1GXWrFkzjB49Gm+//bbS8oULF2LDhg1ITU3VWoDGgl1mREREpkfd83eNWogyMzMxduzYCsvHjBmDzMzMmqySiIiIyGBqlBCFhYXhP//5T4Xlv//+O3r06FHroIiIiIj0qUaDqp999lnMnTsXSUlJ6NatGwDg2LFj2LJlCxYvXowdO3Yo1SUiIiIyZjUaQ2RhoV7DkkwmQ2lpqcZBGSOOISIiIjI9Or3svq7MPk1EREQEaOHmrrURFxeHzp07w9HRER4eHhg8eDAuX76sVOfBgweIiYlBw4YN4eDggKFDhyI7O1upTnp6OgYMGAA7Ozt4eHhg9uzZePjwoVKdQ4cOoUOHDrCxsUHz5s3xzTff6Hr3iIiIyESo3UL0ySefqL3SV199Va16CQkJiImJQefOnfHw4UO8/vrriIiIwIULF2Bvbw8AmDFjBnbt2oUtW7bA2dkZsbGxiI6OxpEjRwAApaWlGDBgALy8vHD06FHpCjgrKyu89957AIC0tDQMGDAAU6dOxXfffYcDBw5g8uTJ8Pb2RmRkpNr7RURERHWT2mOIAgIC1FuhTIa//vqrRsHk5OTAw8MDCQkJ6NmzJ+RyOdzd3bFx40YMGzYMAHDp0iW0bt0aiYmJ6NatG3777TcMHDgQGRkZ8PT0BACsWbMGc+fORU5ODqytrTF37lzs2rULKSkp0rZGjBiB3Nxc7N69W63YOIaIiIjI9Gh9DFFaWppWAquKXC4HALi6ugIAkpKSUFJSgvDwcKlOq1at4OfnJyVEiYmJCAkJkZIhAIiMjMS0adNw/vx5PPXUU0hMTFRaR3md6dOnVxpLUVERioqKpOcKhUIbu0hERERGyKBjiB5XVlaG6dOno3v37ggODgYAZGVlwdraGi4uLkp1PT09kZWVJdV5PBkqLy8vq6qOQqFAYWGhynji4uLg7OwsPXx9fWu9j0RERGScanxz15s3b2LHjh1IT09HcXGxUtmKFSs0Xl9MTAxSUlLw+++/1zQkrZo/fz5mzpwpPVcoFEyKiIiI6qgaJUQHDhzAs88+i6ZNm+LSpUsIDg7GtWvXIIRAhw4dNF5fbGwsdu7cicOHD6Nx48bSci8vLxQXFyM3N1eplSg7OxteXl5SnRMnTiitr/wqtMfrPHllWnZ2NpycnGBra6syJhsbG9jY2Gi8L0RERGR6atRlNn/+fMyaNQvnzp1D/fr18eOPP+LGjRvo1asXnn/+ebXXI4RAbGwstm/fjoMHD1YYuN2xY0dYWVnhwIED0rLLly8jPT0doaGhAIDQ0FCcO3cOt2/flurs27cPTk5OCAoKkuo8vo7yOuXrICIiIjMnasDBwUFcvXpVCCGEi4uLSElJEUIIkZycLPz9/dVez7Rp04Szs7M4dOiQyMzMlB4FBQVSnalTpwo/Pz9x8OBBcerUKREaGipCQ0Ol8ocPH4rg4GAREREhkpOTxe7du4W7u7uYP3++VOevv/4SdnZ2Yvbs2eLixYti1apVwtLSUuzevVvtWOVyuQAg5HK52q8hIiIiw1L3/F2jFiJ7e3tp3JC3tzdSU1Olsjt37qi9ntWrV0MulyMsLAze3t7SY9OmTVKdlStXYuDAgRg6dCh69uwJLy8vbNu2TSq3tLTEzp07YWlpidDQUIwZMwZjx47F22+/LdUJCAjArl27sG/fPrRr1w4ffvghvvrqK85BRERERABqeC+zwYMHY8CAAZgyZQpmzZqFn3/+GePHj8e2bdvQoEED7N+/XxexGhTnISIiIjI9Or2X2YoVK5CXlwcAWLx4MfLy8rBp0ya0aNGiRleYERERERlSjVqIzBFbiIiIiEyPTluIyhUXF+P27dsoKytTWu7n51eb1RIRERHpVY0Soj///BOTJk3C0aNHlZYLISCTyVBaWqqV4IiIiIj0oUYJ0YQJE1CvXj3s3LkT3t7ekMlk2o6LiIiISG9qlBAlJycjKSkJrVq10nY8RERERHpXo3mIgoKCNJpviIiIiMiYqZ0QKRQK6fH+++9jzpw5OHToEO7evatUplAodBkvERERkdap3WXm4uKiNFZICIG+ffsq1eGgaiIiIjJFaidE8fHxuoyDiIiIyGDUToh69eol/Z2eng5fX98KV5cJIXDjxg3tRUdERESkBzUaVB0QEICcnJwKy+/du4eAgIBaB0VERESkTzVKiMrHCj0pLy8P9evXr3VQRERERPqk0TxEM2fOBADIZDIsWLAAdnZ2UllpaSmOHz+O9u3bazVAIiIiIl3TKCH6448/ADxqITp37hysra2lMmtra7Rr1w6zZs3SboREREREOqZRQlR+pdmECRPw8ccf867vREREVCfU6NYd69ev13YcRERERAZTo4QoPz8fS5cuxYEDB3D79m2UlZUplf/1119aCY6IiIhIH2qUEE2ePBkJCQl48cUXebd7IiIiMnk1Soh+++037Nq1C927d9d2PERERER6V6N5iBo0aABXV1dtx0JERERkEDVKiN555x289dZbKCgo0HY8RERERHpXoy6zDz/8EKmpqfD09ESTJk1gZWWlVH769GmtBEdERESkDzVKiAYPHqzlMIiIiIgMRyaEEIYOwhQoFAo4OztDLpdzQkoiIiIToe75W6MxRCdOnEBpaWml5UVFRdi8ebMmqyQiIiIyOI0SotDQUNy9e1d67uTkpDQJY25uLkaOHKm96IiIiIj0QKOE6MneNVW9beyBIyIiIlNTo8vuq8JZq4mIiMjUaD0hIiIiIjI1Gl92f+HCBWRlZQF41D126dIl5OXlAQDu3Lmj3eiIiIiI9ECjy+4tLCwgk8lUjhMqXy6Tyaq8Es1U8bJ7IiIi06Pu+VujFqK0tLRaB0ZERERkbDRKiPz9/TVa+csvv4y3334bbm5uGr2OiIiISJ90Oqh6w4YNUCgUutwEERERUa3pNCHinERERERkCnjZPREREZk9JkRERERk9pgQERERkdljQkRERERmT6cJ0ZgxYziJIRERERm9GiVEZWVllS5PT0+Xnq9evZpzEBEREZHR0yghUigUeOGFF2Bvbw9PT0+89dZbSrfpyMnJQUBAgNaDJCIiItIljWaqXrBgAc6cOYNvv/0Wubm5WLJkCU6fPo1t27bB2toaAOceIiIiItOjUQvRTz/9hLVr12LYsGGYPHkyTp06hZycHAwaNAhFRUUAHt3kVV2HDx/GoEGD4OPjA5lMhp9++kmpfPz48ZDJZEqPqKgopTr37t3D6NGj4eTkBBcXF0yaNAl5eXlKdc6ePYsePXqgfv368PX1xQcffKDJbhMREVEdp1FClJOTo3Q/Mzc3N+zfvx/3799H//79UVBQoNHG8/Pz0a5dO6xatarSOlFRUcjMzJQe33//vVL56NGjcf78eezbtw87d+7E4cOH8dJLL0nlCoUCERER8Pf3R1JSEpYtW4ZFixbhiy++0ChWIiIiqrs06jLz8/PDxYsXlcYJOTo6Yu/evYiIiMCQIUM02ni/fv3Qr1+/KuvY2NjAy8tLZdnFixexe/dunDx5Ep06dQIAfPrpp+jfvz+WL18OHx8ffPfddyguLsa6detgbW2NNm3aIDk5GStWrFBKnIiIiMh8adRCFBERgfXr11dY7uDggD179qB+/fpaC6zcoUOH4OHhgZYtW2LatGm4e/euVJaYmAgXFxcpGQKA8PBwWFhY4Pjx41Kdnj17SmOcACAyMhKXL1/G33//rfV4iYiIyPRo1EK0ePFiZGRkqCxzdHTEvn37cPr0aa0EBjzqLouOjkZAQABSU1Px+uuvo1+/fkhMTISlpSWysrLg4eGh9Jp69erB1dUVWVlZAICsrKwKV755enpKZQ0aNFC57aKiImlcFPCo642IiIjqJo0SogYNGlSaQACPkqJevXrVOqhyI0aMkP4OCQlB27Zt0axZMxw6dAh9+/bV2nZUiYuLw+LFi3W6DSIiIjIOGk/M+PDhQyxbtgwdOnSAg4MDHBwc0KFDByxfvhwlJSW6iFHStGlTuLm54erVqwAALy8v3L59u0J89+7dk8YdeXl5ITs7W6lO+fPKxiYBwPz58yGXy6XHjRs3tLkrREREZEQ0SogKCwsRFhaGefPmwd3dHZMnT8bkyZPh7u6OuXPnom/fvnjw4IGuYsXNmzdx9+5deHt7AwBCQ0ORm5uLpKQkqc7BgwdRVlaGrl27SnUOHz6slKzt27cPLVu2rLK1y8bGBk5OTkoPIiIiqps06jJbunQpbty4gT/++ANt27ZVKjtz5gyeffZZLF26FIsWLVJrfXl5eVJrDwCkpaUhOTkZrq6ucHV1xeLFizF06FB4eXkhNTUVc+bMQfPmzREZGQkAaN26NaKiojBlyhSsWbMGJSUliI2NxYgRI+Dj4wMAGDVqFBYvXoxJkyZh7ty5SElJwccff4yVK1dqsutERERUlwkNBAYGiq1bt1ZavnnzZtGiRQu11xcfHy8AVHiMGzdOFBQUiIiICOHu7i6srKyEv7+/mDJlisjKylJax927d8XIkSOFg4ODcHJyEhMmTBD3799XqnPmzBnxzDPPCBsbG9GoUSOxdOlSTXZbCCGEXC4XAIRcLtf4tURERGQY6p6/ZUKof6+N+vXr48qVK/D19VVZfuPGDbRo0UKn3WaGolAo4OzsDLlczu4zIiIiE6Hu+VujMUROTk4VBjE/LisrC46OjpqskoiIiMjgNEqIevfujffee6/S8qVLl6J37961DoqIiIhInzQaVL1w4UJ07doV3bp1w8yZM9GqVSsIIXDx4kWsXLkSFy5cwLFjx3QVKxEREZFOaJQQBQUFYd++fZg0aRJGjBgh3dleCIFWrVph7969aNOmjU4CJSIiItIVjRIiAOjWrRvOnz+P5ORk/PnnnwCAwMBAtG/fXtuxEREREemFxgmRQqGAg4MD2rdvr5QElZWVIS8vj1dgERERkcnRaFD19u3b0alTJ5WX1RcWFqJz58745ZdftBYcERERkT5olBCtXr0ac+bMgZ2dXYUye3t7zJ07F5999pnWgiMiIiLSB40SopSUFISFhVVa3rNnT5w7d662MRERERHplUYJ0d9//42HDx9WWl5SUoK///671kERERER6ZNGCVGTJk1w6tSpSstPnToFf3//WgdFREREpE8aJUTR0dF44403kJ2dXaEsKysLb775JoYOHaq14IiIiIj0QaObu96/fx+hoaFIT0/HmDFj0LJlSwDApUuX8N1338HX1xfHjh2rk/cz481diYiITI+652+N5iFydHTEkSNHMH/+fGzatEkaL+Ti4oIxY8bg3XffrZPJEBEREdVtGrUQPU4IgTt37kAIAXd3d+k2Ho87cuQIOnXqBBsbm1oHamhsISIiIjI96p6/NRpD9DiZTAZ3d3d4eHioTIYAoF+/frh161ZNN0FERERmIFNeiKOpd5ApLzRYDBrfukMTNWx8IiIiIjOx6WQ65m87hzIBWMiAuOgQDO/sp/c4atxCRERERFQbmfJCKRkCgDIBvL4txSAtRUyIiIiIyCDS7uRLyVC5UiFw7U6B3mNhQkREREQGEeBmD4snhiFbymRo4lbxnqm6ptOEqLLB1kRERETezraIiw6B5X/zBUuZDO9FB8Pb2VbvsXBQNRERERnM8M5+6Bnojmt3CtDEzc4gyRCg44To/v37ulw9ERER1QHezrYGS4TKaZQQ9enTR616Bw8erFEwRERERIagUUJ06NAh+Pv7Y8CAAbCystJVTERERER6pVFC9P7772P9+vXYsmULRo8ejYkTJyI4OFhXsRERERHphUZXmc2ePRsXLlzATz/9hPv376N79+7o0qUL1qxZA4VCoasYiYiIiHSqxjd3BYCCggJs2bIFq1atwoULF5CRkVFnb3zKm7sSERGZHp3f3BUATp8+jYSEBFy8eBHBwcEcV0REREQmSeOEKCMjA++99x4CAwMxbNgwuLq64vjx4zh27BhsbQ17yRwRERFRTWg0qLp///6Ij49HREQEli1bhgEDBqBePZ1OZURERESkcxqNIbKwsIC3tzc8PDyqvC3H6dOntRKcMeEYIiIiItOj7vlbo+adhQsX1jowIiIiImNTq6vMzAlbiIgMJ1NeiLQ7+Qhwszf49P5EZFp00kJUmYSEBOTn5yM0NBQNGjTQxiqJiAAAm06mY/62cygTgIUMiIsOwfDOfoYOi4jqGI2uMnv//fexYMEC6bkQAlFRUejduzcGDhyI1q1b4/z581oPkojMU6a8UEqGAKBMAK9vS0GmvNCwgRFRnaNRQrRp0yalW3Vs3boVhw8fxn/+8x/cuXMHnTp1wuLFi7UeJFFdlikvxNHUOzzJq5B2J19KhsqVCoFrdwoMExAR1VkadZmlpaWhbdu20vNff/0Vw4YNQ/fu3QEAb775Jp5//nntRkhUh7E7qGoBbvawkEEpKbKUydDEzc5wQRFRnaRRC9HDhw9hY2MjPU9MTMTTTz8tPffx8cGdO3e0Fx1RHcbuoOp5O9siLjoElv+d5sNSJsN70cEcWE1EWqdRC1GzZs1w+PBhNG3aFOnp6fjzzz/Rs2dPqfzmzZto2LCh1oMkqouq6g7iCf9/hnf2Q89Ad1y7U4AmbnY8NkSkExolRDExMYiNjcV//vMfHDt2DKGhoQgKCpLKDx48iKeeekrrQRLVRewOUp+3sy0TISLSKY26zKZMmYJPPvkE9+7dQ8+ePfHjjz8qlWdkZGDixIlaDZCormJ3EBGR8eDEjGrixIykK5nyQnYHERHpiF4nZiSimmN3EBGR4WnUZVZSUoI5c+agefPm6NKlC9atW6dUnp2dDUtLS60GSERERKRrGiVE7777Lv79739j6tSpiIiIwMyZM/HPf/5TqY4mPXCHDx/GoEGD4OPjA5lMhp9++qnCut566y14e3vD1tYW4eHhuHLlilKde/fuYfTo0XBycoKLiwsmTZqEvLw8pTpnz55Fjx49UL9+ffj6+uKDDz7QZLeJiIiojtMoIfruu+/w1VdfYdasWViyZAlOnTqFgwcPYsKECVIiJPvvAFF15Ofno127dli1apXK8g8++ACffPIJ1qxZg+PHj8Pe3h6RkZF48OCBVGf06NE4f/489u3bh507d+Lw4cN46aWXpHKFQoGIiAj4+/sjKSkJy5Ytw6JFi/DFF19osutERERUlwkN2NrairS0NKVlN2/eFIGBgWL06NHi1q1bwsLCQpNVSgCI7du3S8/LysqEl5eXWLZsmbQsNzdX2NjYiO+//14IIcSFCxcEAHHy5Empzm+//SZkMpm4deuWEEKIzz//XDRo0EAUFRVJdebOnStatmypUXxyuVwAEHK5vCa7R0RERAag7vlboxYiLy8vpKamKi1r1KgR4uPjcfLkSYwfP15beRrS0tKQlZWF8PBwaZmzszO6du2KxMREAI9mynZxcUGnTp2kOuHh4bCwsMDx48elOj179oS1tbVUJzIyEpcvX8bff/9d6faLioqgUCiUHkRERFQ3aZQQ9enTBxs3bqyw3MfHBwcPHkRaWprWAsvKygIAeHp6Ki339PSUyrKysuDh4aFUXq9ePbi6uirVUbWOx7ehSlxcHJydnaWHr69v7XaIiIiIjJZGCdGCBQvwwgsvqCxr1KgREhISKlx5Zqrmz58PuVwuPW7cuGHokIiIiEhHNJqHyN/fH/7+/pWW+/j4YNy4cbUOCnjUPQc8upTf29tbWp6dnY327dtLdW7fvq30uocPH+LevXvS6728vJCdna1Up/x5eR1VbGxslG5kS0RERHWXRi1E5bZs2YLo6GgEBwcjODgY0dHR2Lp1q1YDCwgIgJeXFw4cOCAtUygUOH78OEJDQwEAoaGhyM3NRVJSklTn4MGDKCsrQ9euXaU6hw8fRklJiVRn3759aNmyJRo0aKDVmImIiMg0aZQQlZWVYfjw4Rg+fDguXLiA5s2bo3nz5jh//jyGDx+OESNGaDQPUV5eHpKTk5GcnAzg0UDq5ORkpKenQyaTYfr06ViyZAl27NiBc+fOYezYsfDx8cHgwYMBAK1bt0ZUVBSmTJmCEydO4MiRI4iNjcWIESPg4+MDABg1ahSsra0xadIknD9/Hps2bcLHH3+MmTNnarLrepUpL8TR1DvIlBcaOhS9Mtf9JiIiI6DJpWsrVqwQrq6u4pdffqlQ9vPPPwtXV1excuVKtdcXHx8vAFR4jBs3Tgjx6NL7BQsWCE9PT2FjYyP69u0rLl++rLSOu3fvipEjRwoHBwfh5OQkJkyYIO7fv69U58yZM+KZZ54RNjY2olGjRmLp0qWa7LYQQn+X3f9w4roImLdT+M/dKQLm7RQ/nLiu0+0ZC3PdbyIi0i11z98a3dy1bdu2mD59eqV3tP/666/x8ccf4+zZs7XP1IyMPm7umikvRPelB1H22H/EUibD7/N61+l7XZnrfhMRke6pe/7WqMvsypUrSvMCPUnVrTVIfWl38pWSAgAoFQLX7hQYJiA9Mdf9JiIi46FRQmRra4vc3NxKyxUKBerXr1/bmMxWgJs9LJ6484mlTIYmbnaGCUhPzHW/iYjIeGiUEIWGhmL16tWVlq9atUq6Aow05+1si7joEFj+935wljIZ3osOrvPdRua630REZDw0mofojTfeQFhYGO7evYtZs2ahVatWEELg4sWL+PDDD/Hzzz8jPj5eV7GaheGd/dAz0B3X7hSgiZud2SQF5rrfRERkHDQaVA0A27dvx0svvYR79+4pLW/QoAHWrl2LoUOHajVAY6GPQdVERESkXeqevzVOiACgoKAAe/bskQZQBwYGIiIiAnZ2dXfMBxMiIiIi06Pu+VujLrODBw8iNjYWx44dw5AhQ5TK5HI52rRpgzVr1qBHjx41i5qIiIjIADQaVP3RRx9hypQpKjMsZ2dn/POf/8SKFSu0FhwRERGRPmiUEJ05cwZRUVGVlkdERCjdV4yIiIjIFGiUEGVnZ8PKyqrS8nr16iEnJ6fWQRERERHpk0YJUaNGjZCSklJp+dmzZ+Ht7V3roIiIiIj0SaOEqH///liwYAEePHhQoaywsBALFy7EwIEDtRYcERERkT5odNl9dnY2OnToAEtLS8TGxqJly5YAgEuXLmHVqlUoLS3F6dOn4enpqbOADYWX3RMREZkenVx27+npiaNHj2LatGmYP38+ynMpmUyGyMhIrFq1qk4mQ0RERFS3aZQQAYC/vz9+/fVX/P3337h69SqEEGjRogUaNGigi/iIiIiIdE7jhKhcgwYN0LlzZ23GQkRERGQQGg2qJiIiIqqLmBARERGR2WNCRERERGaPCRERERGZPSZEREREZPaYEBEREZHZY0JEREREZo8JEREREZk9JkRERERk9pgQERERkdljQkRERERmjwkRERERmT0mRERERGT2mBARERGR2WNCRERERGaPCREREREZVKa8EEdT7yBTXmiwGOoZbMtUpUx5IdLu5CPAzR7ezraGDoeIiEgnNp1Mx/xt51AmAAsZEBcdguGd/fQeBxMiI2Qsbw4iIiJdypQXSuc7ACgTwOvbUtAz0F3vjQHsMjMylb05DNmMSEREpAtpd/Kl8125UiFw7U6B3mNhQmRkdP3mMIZ+WiIiIgAIcLOHhUx5maVMhiZudnqPhQmRkdHlm2PTyXR0X3oQo748ju5LD2LTyfRar5OIiKimvJ1tERcdAkvZoxOfpUyG96KDDTJ2ViaEENVXI4VCAWdnZ8jlcjg5Oel0W5tOpuP1bSkoFUJ6c9R2DFGmvBDdlx5Uan2ylMnw+7zeHLRNREQGlSkvxLU7BWjiZqf1c5K6528OqjZCwzv7oWegu1bfHFV1xTEhIiIiQ/J2tjX4uYgJkZHS9pujvCvuyRYiQ/TTEhERGRuOITITxtRPS0REZGzYQmRGdNEVR0REVBcwITJiupit2hj6aYmIiIwNEyIj9eRs1ZOeCcDEZwKYzBAREemA0Y8hWrRoEWQymdKjVatWUvmDBw8QExODhg0bwsHBAUOHDkV2drbSOtLT0zFgwADY2dnBw8MDs2fPxsOHD/W9K2pTNVv1l/9Jw9Nxhps7iBM6EhFRXWYSLURt2rTB/v37pef16v0v7BkzZmDXrl3YsmULnJ2dERsbi+joaBw5cgQAUFpaigEDBsDLywtHjx5FZmYmxo4dCysrK7z33nt63xd1qLpEHgAEDHOPF95bjYiI6jqjbyECHiVAXl5e0sPNzQ0AIJfL8fXXX2PFihXo06cPOnbsiPXr1+Po0aM4duwYAGDv3r24cOECNmzYgPbt26Nfv3545513sGrVKhQXFxtytyqlarbqcvq+xwvvrUZERObAJBKiK1euwMfHB02bNsXo0aORnv6o2ygpKQklJSUIDw+X6rZq1Qp+fn5ITEwEACQmJiIkJASenp5SncjISCgUCpw/f16/O6Km8kvkVSVF+p47yJhuvGeM2JVIRFQ3GH2XWdeuXfHNN9+gZcuWyMzMxOLFi9GjRw+kpKQgKysL1tbWcHFxUXqNp6cnsrKyAABZWVlKyVB5eXlZZYqKilBUVCQ9VygUWtoj9ZRfIr/+SBq+OpyGMhhm7iBO6Fg5diUSEdUdRp8Q9evXT/q7bdu26Nq1K/z9/bF582bY2uouMYiLi8PixYt1tn51eDvb4vX+QZjQPcBgcweVt1Y9eW81c7/arbKuRH2P7yIiIu0w+oToSS4uLggMDMTVq1fxj3/8A8XFxcjNzVVqJcrOzoaXlxcAwMvLCydOnFBaR/lVaOV1VJk/fz5mzpwpPVcoFPD19dXinqjP0HMHcULHinhvOCKiusUkxhA9Li8vD6mpqfD29kbHjh1hZWWFAwcOSOWXL19Geno6QkNDAQChoaE4d+4cbt++LdXZt28fnJycEBQUVOl2bGxs4OTkpPQwZ97Otght1rBWJ/u6NN5G1cB3diUSEZkuo28hmjVrFgYNGgR/f39kZGRg4cKFsLS0xMiRI+Hs7IxJkyZh5syZcHV1hZOTE1555RWEhoaiW7duAICIiAgEBQXhxRdfxAcffICsrCy8+eabiImJgY2NjYH3znzUtfE27EokIqpbjD4hunnzJkaOHIm7d+/C3d0dzzzzDI4dOwZ3d3cAwMqVK2FhYYGhQ4eiqKgIkZGR+Pzzz6XXW1paYufOnZg2bRpCQ0Nhb2+PcePG4e233zbULpmdujrehl2JRER1h0wIoWIKQHqSQqGAs7Mz5HK52Xefaepo6h2M+vJ4heXfT+mG0GYNDRAREZkjXdwfkoyfuudvo28hItPHS/eJyNDqWrc9aZ/JDaom01M+3sZS9mgUMsfbEJE+1bUZ9+vSBSrGhC1EpBccb0NEhlKXpslgS5fusIWI9EYbl+6X4y8kIlJXXZkmo661dBkbJkRkcjadTEf3pQcx6svj6L70IDadTDd0SERkxOpKtz3vLalb7DIjk1JXL+EnIt2qC932vEBFt9hCRCaFv5CIqKa02W1vCHWlpctYsYWITEL5/CH21pb8hURq4ZwzVBfVhZYuY8WEiIzek1dVDHmqEX76I4O3zKBK6ftKHCZfpE+GvuF3XcWEiIyaqjFDP/2RgW0vh6KguIy/kKgCfY8z42XQpCtMtPWLCREZtcrGDBUUl/G2H6SSPuecqQuD/HnSNU5MtPWPCRHpnSZfwLyqonrmfEJTte/6eM+Ub/duXpHK5Cvp2t9wdTD+/4m2Trrm/B7UhbqQaJsiJkSkV5p+AZdfVfH6thSOGVLBnH9FVrbvun7PPLldGYDHcyIZgFd/+MPo/yfaOulW9x40hWTJ2GKsSzNrmxImRKQ3Nf0C5lUVqpnzr8jq9l1X7xlV25XJAAsBlOHRPCYCMIn/iTZOutX9H7SdsOsicTHGHxVsGTcMzkNEelObOYRMff4QXTDnOZnU2XddvGdUbVcI4NNRT+H7Kd3wyain8ESx0f5PtHE7i6r+D9q+zYQuZqhXFeP8becMfisMY5xvyBxul8QWItIb/urRLnM+noba98q228G/AbydbZEpL9R6XNpuFXl8fbXtWqzq/6DNbh9dtYaqirFMAOuPpOH1/kE1Xq82GFPLuDG2oukCW4hIb4zxV48pM+fjaah9r2672o5L260iT64PAH6f1xvfT+mG3+f11vgkV9X+avOGqrpqDVUVIwB8dTjNKFpCjKFl3JxuKCsTQjzZwksqKBQKODs7Qy6Xw8nJydDhmLRMeaFR/OqpK8z5eBpq36vbrjbiypQXovvSgxVaX36f17vSdVbVmlST9am7/sf3F4BU5/CfORVaoGp6FduTsVsA+GTUU+j439a5mnp31wV8+Z+0Csu/n9KNU3sAOJp6B6O+PF5huSkdH3XP3+wyI73jLKvaZc7H01D7Xt12axNXdZf0V9blVF23Rm27sKpaf/n+qqrz+7zetU4On7xysPzKvtiNf9S6C2fiMwH46j9pSmO/zKXrWR3m1DXPLjMiIiPxeJfWaz8k48nenMpOROp0a6jThVXZwFl11l9ZHQBa6fYZ3tkPv8/rjc9GPgWZ7H9THdS2C8fb2RZLh5pn17M6zKlrni1ERCbG2OZMIe2o7pL+qk5E6rT+VDc/U1UtQOqsXx9z53g728LVQfvbMaYBzMZI1fGpi99DTIiITIi5XO1hjiq7pP+zUU/B1d6myhO1ut0alZ34q7uKS53166trRVfbMXTXs7EnGI8fn7r6PcQuMzI55jAfhirmdLWHOaqsS6uDf4Nqu5w06dZQdeVSdVdxqbN+fXWt1MUuHF3MsaQrdfl7iC1EZFLq6i8TdehrOn9j/6VaV9X2liO16fZRp9VFnfXrq+tJn11cuv48mNqM83X5tiJMiMhkmNoXh7bpo0vCnBNOY1DbE31Nu33UTcbUWb++up70sR1NPg81TZxMLcGoy1edMSEik2FqXxzaVtsWhOqYe8JpLAw1loUDi5Vp8nmozQ8JU0swNPkeMrXWZiZEZDJM7YtDF3R50jL3hJMMP7DYmKj7eajtDwld/9DRBXW+h0yxtZkJEZkMU/zi0AVdnbSMPeE0tV+bZNrU/Txo44eEsbTOafIZq+p7yFRbm5kQkUkxli8OU6BpAmHMCWdNfm0ygdINQx9XfW1f3c+Dtn5IPD6f0+PP9UWbLTqm2trMe5mpifcyI1NS2ZebOicTY7s3Wk3uwaXOl7uuTqyGThh0qTYnTW0cF0N0w6jzedh0Mr3W92wzZBeTNu5zp8v11RbvZUZkpiprrs4tKMH7uy9V+4VrbONIKvu1efr63xjQVvVAzuqa63V18jHFcRPqqk03iDaOi6G6YdT5PNS25drQXUzabtEx5tbmqnBiRjNgrhMZmqvKvtyW/nbJJCdTUzVhIfDoxp6qJrCrbpJBXU0sp8l6a/qZNORnubrjWhltHe+abl9fVE14qS519k2X/3t17nOnqfJ7z30/pRt+n9fbJH4YsIWojqvLv1hJNVVjGp58DphGnz7wv1+bj59UgUc391T1K7q6MR26Gt+g7npr+pk09Ge5pmNltHW8jX3Qf21Ut2+6/t/rqkXH2Fqbq8MWojqsLk+xXhVzbxFTdWuDuf1aVfoL0BSO1/DOfvh4RPsKyytrIZj8TID05fbkl7sufg2ru96afiYN9Vl+/L2h6n01p19LpN3JrzIObR3vunjLjnJV7Zu+/vePt+hsezkUvq52Rv2doAtsIarDTHWkf20Y+le0sVA1psHF1qrCL8DDf+aYzPHq1MS12haCJ///Lz3TFBOeaaLynlu6+DVc3Xpr+pk0xGe5ss9S+fvq7M1cvP+bemPStHW86/JVppXtmz7/997Otib1naBtTIjqsLrcxKyKoQcmGpsnm6uf/MIFoHQliLEfr+pOrKr+/1//noYJzzSpsC5dnVirW29NP5P6/iyr81ka/dUxtd872jzeptYNowlV+1aT/31Nr+gz9+9QdpnVYXW5iVkVXQ+6NIWupeo8PvDT2AepqlLVQE1N96c2g2CrUtV6a/qZ1PdnubpjWZNBwLo63sautt8bmv7vN51MR/elBzHqy+N4eulBrD2cqva2TPE7QZvYQlTH1eUm5ifp8ld0XeyKM5UWxCd/7VbWQmAq+1PTz2RNXlfTloLqjqWhBwGbCm0dB3X/90+28AgBxP16CRDAP3s1q3Y7pvIZ0hW2EBmYPlod1PllVldaP3TxK7quDk43hRbEx3/tdl96UOVl9uV0vT/a/IzUtLVEk9dpcuxUbaeqY2kMg4D1oTb/c3WOgybrV+d/r6qFBwDe/+2S2tsw9u8EXWILkQEZy68oY4lDG3TRIlaXB6cbcwtiTcYz6Gp/TO0zoo2xINUdS2MYBKxLtf2fV3ccdPGeCnCzh0z2qGXocWWA2sffmL8TdI0tRAZiLL+ijCUObdL2WAVdXaZtLIx1bEdNxzNoe39M8TOirbEg1R1LVeV14fOijf95VcdBV+8pb2dbzOvXqsJyTY+/sX4n6BoTIgMxlsFr2oyjLnS7qWLuzciGYiwnVmP5rGrCkMeuLnxetPE/r+o46PI99c+ezTC/X6tK5+GiyrHLzEAC3Owhw6PZdsvJAL1/2WtrEJ2pdSloypybkVXRxw1MdTVfkKZMcaCpOsdOl/9DU/+8aOt/Xtlx0PV76p+9muHZ9j4me/wNhXe7V5O273afKS/E03EHlRMiGXB0Xh+9v3lre6dmbd7ZuC7fKbyu0Hfyq87dxnVNG3czN4TKjp2hfsDo4/OtrW3o+n9uqu8pU8S73Ru5tDv5eDITFUL9gW/aVNtfc9oaRFnXW5nqAkNM3FbdRHz6OMlW9hkx9gRe1bEz1OR7+vh8a3Mbum7lMvVWtLrIrMYQrVq1Ck2aNEH9+vXRtWtXnDhxwmCxGMv4iHK1GUSnjX0xxYGr5sjYxpzV5tJyTT35Ganptg091s4QY6L08fnWZBvq/g+0ObhY1TbNdfCysTKbhGjTpk2YOXMmFi5ciNOnT6Ndu3aIjIzE7du3DRJPXRh4WE4b+2KKA1crY+gTni5pK5Ffm5CKp+Nql8gYMomu6bb1mcBVxhA/xvTx+VZ3G7WZyVldT34HGMP/napnNl1mK1aswJQpUzBhwgQAwJo1a7Br1y6sW7cO8+bNM0hMdanJtLb7YooDV1Wp691+2hjovPZwKuJ+uyQ9r2mXjSHnu6nJto3lPlGGGKyuj8+3Otuo7UzO6njyO2Buv1bSTXAB87s/mCkxi4SouLgYSUlJmD9/vrTMwsIC4eHhSExMVPmaoqIiFBUVSc8VCoVOYqtufIQpqc2+GMsVRbVhLCc8XatN8pspL8TSx5KhcjVJZAyZRNdk28Y0YaG+f4zp4/Otzjaqmsn52fY+OpnV/vFkqJwpTlRpDswiIbpz5w5KS0vh6emptNzT0xOXLlX8cgaAuLg4LF68WB/h0X+ZeouZMZ3wdK2myW/anfwKs+gCj/ruNU1kDJlE12TbxtYKqu8fY/r4fFe3DW3M5FwVVd8BZQIVplgxxdZvc2AWCVFNzJ8/HzNnzpSeKxQK+Pr6GjAi82DKLWbGdsIzRqqOEfCoW6Em/3dDJtGabrsutILWlj4+31Vto3wm57hflX8Ia+tzWtl3wJyolvhg92Wz/b+bCrNIiNzc3GBpaYns7Gyl5dnZ2fDy8lL5GhsbG9jY2OgjPKojeMKr3pPHqHyMxT971nz8hiGTaE23beqtoHXBP3s2A8q7sqDdC1oq+w4Y3tmPEyWaALOZmLFr167o0qULPv30UwBAWVkZ/Pz8EBsbq9agam1PzEh1lzFMJGjseIzI0HT5HuT727hwYsYnzJw5E+PGjUOnTp3QpUsXfPTRR8jPz5euOiPSFlPu9tMXHiMyNF2+B/n+Nk1mkxANHz4cOTk5eOutt5CVlYX27dtj9+7dFQZaExERkfkxmy6z2mKXGRERkelR9/xtNjNVExEREVWGCRERERGZPSZEREREZPaYEBEREZHZY0JEREREZo8JEREREZk9JkRERERk9pgQERERkdljQkRERERmz2xu3VFb5RN6KxQKA0dCRERE6io/b1d3Yw4mRGq6f/8+AMDX19fAkRAREZGm7t+/D2dn50rLeS8zNZWVlSEjIwOOjo6QyWSGDkdvFAoFfH19cePGDd7DrZZ4LLWDx1F7eCy1g8dRe3RxLIUQuH//Pnx8fGBhUflIIbYQqcnCwgKNGzc2dBgG4+TkxA+6lvBYagePo/bwWGoHj6P2aPtYVtUyVI6DqomIiMjsMSEiIiIis8eEiKpkY2ODhQsXwsbGxtChmDweS+3gcdQeHkvt4HHUHkMeSw6qJiIiIrPHFiIiIiIye0yIiIiIyOwxISIiIiKzx4SIiIiIzB4TIgIAHD58GIMGDYKPjw9kMhl++uknpXIhBN566y14e3vD1tYW4eHhuHLlimGCNXLVHcvx48dDJpMpPaKiogwTrBGLi4tD586d4ejoCA8PDwwePBiXL19WqvPgwQPExMSgYcOGcHBwwNChQ5GdnW2giI2TOscxLCyswnty6tSpBorYeK1evRpt27aVJg0MDQ3Fb7/9JpXz/aie6o6jod6PTIgIAJCfn4927dph1apVKss/+OADfPLJJ1izZg2OHz8Oe3t7REZG4sGDB3qO1PhVdywBICoqCpmZmdLj+++/12OEpiEhIQExMTE4duwY9u3bh5KSEkRERCA/P1+qM2PGDPzyyy/YsmULEhISkJGRgejoaANGbXzUOY4AMGXKFKX35AcffGCgiI1X48aNsXTpUiQlJeHUqVPo06cPnnvuOZw/fx4A34/qqu44AgZ6PwqiJwAQ27dvl56XlZUJLy8vsWzZMmlZbm6usLGxEd9//70BIjQdTx5LIYQYN26ceO655wwSjym7ffu2ACASEhKEEI/eg1ZWVmLLli1SnYsXLwoAIjEx0VBhGr0nj6MQQvTq1Uu89tprhgvKhDVo0EB89dVXfD/WUvlxFMJw70e2EFG10tLSkJWVhfDwcGmZs7MzunbtisTERANGZroOHToEDw8PtGzZEtOmTcPdu3cNHZLRk8vlAABXV1cAQFJSEkpKSpTel61atYKfnx/fl1V48jiW++677+Dm5obg4GDMnz8fBQUFhgjPZJSWluKHH35Afn4+QkND+X6soSePYzlDvB95c1eqVlZWFgDA09NTabmnp6dURuqLiopCdHQ0AgICkJqaitdffx39+vVDYmIiLC0tDR2eUSorK8P06dPRvXt3BAcHA3j0vrS2toaLi4tSXb4vK6fqOALAqFGj4O/vDx8fH5w9exZz587F5cuXsW3bNgNGa5zOnTuH0NBQPHjwAA4ODti+fTuCgoKQnJzM96MGKjuOgOHej0yIiPRsxIgR0t8hISFo27YtmjVrhkOHDqFv374GjMx4xcTEICUlBb///ruhQzFplR3Hl156Sfo7JCQE3t7e6Nu3L1JTU9GsWTN9h2nUWrZsieTkZMjlcmzduhXjxo1DQkKCocMyOZUdx6CgIIO9H9llRtXy8vICgApXS2RnZ0tlVHNNmzaFm5sbrl69auhQjFJsbCx27tyJ+Ph4NG7cWFru5eWF4uJi5ObmKtXn+1K1yo6jKl27dgUAvidVsLa2RvPmzdGxY0fExcWhXbt2+Pjjj/l+1FBlx1EVfb0fmRBRtQICAuDl5YUDBw5IyxQKBY4fP67U50s1c/PmTdy9exfe3t6GDsWoCCEQGxuL7du34+DBgwgICFAq79ixI6ysrJTel5cvX0Z6ejrfl4+p7jiqkpycDAB8T6qhrKwMRUVFfD/WUvlxVEVf70d2mREAIC8vTyn7TktLQ3JyMlxdXeHn54fp06djyZIlaNGiBQICArBgwQL4+Phg8ODBhgvaSFV1LF1dXbF48WIMHToUXl5eSE1NxZw5c9C8eXNERkYaMGrjExMTg40bN+Lnn3+Go6OjNA7D2dkZtra2cHZ2xqRJkzBz5ky4urrCyckJr7zyCkJDQ9GtWzcDR288qjuOqamp2LhxI/r374+GDRvi7NmzmDFjBnr27Im2bdsaOHrjMn/+fPTr1w9+fn64f/8+Nm7ciEOHDmHPnj18P2qgquNo0Pej3q9rI6MUHx8vAFR4jBs3Tgjx6NL7BQsWCE9PT2FjYyP69u0rLl++bNigjVRVx7KgoEBEREQId3d3YWVlJfz9/cWUKVNEVlaWocM2OqqOIQCxfv16qU5hYaF4+eWXRYMGDYSdnZ0YMmSIyMzMNFzQRqi645ieni569uwpXF1dhY2NjWjevLmYPXu2kMvlhg3cCE2cOFH4+/sLa2tr4e7uLvr27Sv27t0rlfP9qJ6qjqMh348yIYTQbcpFREREZNw4hoiIiIjMHhMiIiIiMntMiIiIiMjsMSEiIiIis8eEiIiIiMweEyIiIiIye0yIiIiIyOwxISIiIiKzx4SIiNSSlZWFV155BU2bNoWNjQ18fX0xaNAgpXs3HT16FP3790eDBg1Qv359hISEYMWKFSgtLZXqXLt2DZMmTUJAQABsbW3RrFkzLFy4EMXFxUrb+/LLL9GuXTs4ODjAxcUFTz31FOLi4qTyRYsWQSaTISoqqkKsy5Ytg0wmQ1hYmFr7Vr4umUyGevXqoUmTJpgxYwby8vI0PEpEZKp4LzMiqta1a9fQvXt3uLi4YNmyZQgJCUFJSQn27NmDmJgYXLp0Cdu3b8cLL7yACRMmID4+Hi4uLti/fz/mzJmDxMREbN68GTKZDJcuXUJZWRnWrl2L5s2bIyUlBVOmTEF+fj6WL18OAFi3bh2mT5+OTz75BL169UJRURHOnj2LlJQUpbi8vb0RHx+PmzdvKt3Bfd26dfDz89NoH9u0aYP9+/fj4cOHOHLkCCZOnIiCggKsXbu2Qt3i4mJYW1vX4EjqjjHGRGRSdH5zECIyef369RONGjUSeXl5Fcr+/vtvkZeXJxo2bCiio6MrlO/YsUMAED/88EOl6//ggw9EQECA9Py5554T48ePrzKmhQsXinbt2omBAweKJUuWSMuPHDki3NzcxLRp00SvXr3U2Lv/retxU6ZMEV5eXkrlX375pWjSpImQyWRCiEf7PmnSJOHm5iYcHR1F7969RXJysrSO5ORkERYWJhwcHISjo6Po0KGDOHnypBBCiGvXromBAwcKFxcXYWdnJ4KCgsSuXbuEEEKsX79eODs7K8Wzfft28fhXdk1jIiLV2GVGRFW6d+8edu/ejZiYGNjb21cod3Fxwd69e3H37l3MmjWrQvmgQYMQGBiI77//vtJtyOVyuLq6Ss+9vLxw7NgxXL9+vdr4Jk6ciG+++UZ6vm7dOowePbrWrSW2trZK3XhXr17Fjz/+iG3btiE5ORkA8Pzzz+P27dv47bffkJSUhA4dOqBv3764d+8eAGD06NFo3LgxTp48iaSkJMybNw9WVlYAHt2FvqioCIcPH8a5c+fw/vvvw8HBQaMYaxITEanGLjMiqtLVq1chhECrVq0qrfPnn38CAFq3bq2yvFWrVlIdVev/9NNPpe4yAFi4cCGio6PRpEkTBAYGIjQ0FP3798ewYcNgYaH8O27gwIGYOnUqDh8+jI4dO2Lz5s34/fffsW7dOk13VZKUlISNGzeiT58+0rLi4mL8+9//hru7OwDg999/x4kTJ3D79m3Y2NgAAJYvX46ffvoJW7duxUsvvYT09HTMnj1bOnYtWrSQ1peeno6hQ4ciJCQEANC0aVON46xJTESkGhMiIqqSEEIndQHg1q1biIqKwvPPP48pU6ZIy729vZGYmIiUlBQcPnwYR48exbhx4/DVV19h9+7dSkmRlZUVxowZg/Xr1+Ovv/5CYGAg2rZtq1EcAHDu3Dk4ODigtLQUxcXFGDBgAD777DOp3N/fX0o8AODMmTPIy8tDw4YNldZTWFiI1NRUAMDMmTMxefJkfPvttwgPD8fzzz+PZs2aAQBeffVVTJs2DXv37kV4eDiGDh2qcdw1iYmIVGNCRERVatGihTQYujKBgYEAgIsXL+Lpp5+uUH7x4kUEBQUpLcvIyEDv3r3x9NNP44svvlC53uDgYAQHB+Pll1/G1KlT0aNHDyQkJKB3795K9SZOnIiuXbsiJSUFEydO1HQXAQAtW7bEjh07UK9ePfj4+FTocnuyuzAvLw/e3t44dOhQhXW5uLgAeHT12qhRo7Br1y789ttvWLhwIX744QcMGTIEkydPRmRkJHbt2oW9e/ciLi4OH374IV555RVYWFhUSC5LSkoqbKcmMRGRahxDRERVcnV1RWRkJFatWoX8/PwK5bm5uYiIiICrqys+/PDDCuU7duzAlStXMHLkSGnZrVu3EBYWho4dO2L9+vUVusFUKU+oVMXQpk0btGnTBikpKRg1apQmuyextrZG8+bN0aRJE7XGH3Xo0AFZWVmoV68emjdvrvRwc3OT6gUGBmLGjBnYu3cvoqOjsX79eqnM19cXU6dOxbZt2/B///d/+PLLLwEA7u7uuH//vtK+lo8R0kZMRFQREyIiqtaqVatQWlqKLl264Mcff8SVK1dw8eJFfPLJJwgNDYW9vT3Wrl2Ln3/+GS+99BLOnj2La9eu4euvv8b48eMxbNgwvPDCCwD+lwz5+flh+fLlyMnJQVZWFrKysqTtTZs2De+88w6OHDmC69ev49ixYxg7dizc3d0RGhqqMsaDBw8iMzNTby0h4eHhCA0NxeDBg7F3715cu3YNR48exRtvvIFTp06hsLAQsbGxOHToEK5fv44jR47g5MmT0jir6dOnY8+ePUhLS8Pp06cRHx8vlXXt2hV2dnZ4/fXXkZqaio0bNyoNHK9pTERUOXaZEVG1mjZtitOnT+Pdd9/F//3f/yEzMxPu7u7o2LEjVq9eDQAYNmwY4uPj8e6776JHjx548OABWrRogTfeeAPTp0+HTCYDAOzbtw9Xr17F1atXleYOAv43Bik8PBzr1q3D6tWrcffuXbi5uSE0NBQHDhyoMD6mnKor4HRJJpPh119/xRtvvIEJEyYgJycHXl5e6NmzJzw9PWFpaYm7d+9i7NixyM7OhpubG6Kjo7F48WIAQGlpKWJiYnDz5k04OTkhKioKK1euBPCoVW7Dhg2YPXs2vvzyS/Tt2xeLFi2qdlB0dTERUeVkQtNRkERERER1DLvMiIiIyOwxISKiOs/BwaHSx3/+8x9Dh0dERoBdZkRU5129erXSskaNGsHW1laP0RCRMWJCRERERGaPXWZERERk9pgQERERkdljQkRERERmjwkRERERmT0mRERERGT2mBARERGR2WNCRERERGaPCRERERGZvf8HoD+tO7SHDZIAAAAASUVORK5CYII=", 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GfvnlFyQkJMDJyanKfHd3d+zcuRPXr1/Hm2++WWV+7969ER4ejnXr1unchlKphKenp/q7r68vDh06hIsXL9YY38iRI/Hll1+qv69evRpDhw6FnZ1djb8FgPLycqxfvx5Dhw6Fv79/lfnOzs6oX78+0tLScPr0aUyZMgU2NpqXzxYtWiA+Pr7GfXR1dZW05iMjIwNbt27Fjz/+iB9//BF79+7FggULAACffPIJYmNjMWbMGOTk5CAnJwcBAQHq386YMQMLFixAeno6mjdvjmnTpuG7777DV199hdTUVISFhaFr1664ceOGxjanTp2Kjz76CEeOHIG3tzd69+6N0tJSODk5YfDgwUhKStJYPikpCQMGDICLi4tk+10ZExkzCfFygs0Dvc7qKRQI9uIYE0RUvQ1HLiFuwW4MWXUYcQt2Y8ORSybd3vnz5yGEQEREhM5l/vnnHwBAs2bNtM6PiIhQL6Nt/Z999hleffVV9bTZs2fD3d0dwcHBaNq0KYYPH46NGzeivLy8yu979eoFlUqFffv2obCwEBs3bsTIkSP13r/8/HzcvHmz2v0Dat7HZs2a6dzH/Px8vP/++xg7dqzecQFAu3bt4OzsrPGprLy8HF9++SWio6Px9NNP4+WXX1a3WXJzc4OdnR0cHR3h6+sLX19fjUHn3nvvPTz77LMIDQ2Fvb09li9fjkWLFqF79+6IjIzEqlWr4ODggC+++EJjm7Nnz8azzz6LmJgYfPXVV7h69SqSk5MBAKNHj8aOHTuQk5MD4P5gd9u3bzfo/8NQTGTMxM/NAYn9YlDv//ehr6dQ4IN+0fBzczBzZERkyXKUxeq2dQBQLoC3tpwyac2MEKLmhYxYFgCuXLmCbt264YUXXsCYMWPU0/38/HDw4EGcPHkSr7/+Ou7du4dhw4ahW7duVZIZW1tbvPTSS0hKSsKmTZsQHh6O5s2bmyxmQ5dXqVTo2bMnIiMjMWfOHIN+u2HDBqSlpWl8KgsODtao6fDz81MP+1+TVq1aqf+dkZGB0tJSxMXFqafZ2tqiTZs2SE9P1/hdbGys+t+enp5o2rSpepk2bdogKioKX331FQBgzZo1CAoKQvv27fXbYSPItGWPdRjUOhDtw71xIb8IwV6OTGKIqEbVPZY21TmkSZMm6ka6uoSHhwMA0tPT0a5duyrz09PTERkZqTEtOzsbnTp1Qrt27fCf//xH63qjo6MRHR2N1157DePGjcPTTz+NvXv3olOnThrLjRw5Em3btsWpU6cMvvv39vaGu7t7tfsHaO7jY489VmV+enq6epkKt2/fRrdu3eDi4oLk5GTY2toaFFtAQADCwsJ0zn9wfQqFQmutlTbaHhNKYfTo0Vi2bBlmzJiBpKQkjBgxwqQDQbJGxsz83BwQG9qQSQwR6cUcj6U9PT3RtWtXLFu2DIWFhVXm37p1C126dIGnpyc++uijKvO3bduGc+fO4cUXX1RPu3LlCjp27IgnnngCSUlJVdqcaFORCGmLISoqClFRUTh16hSGDBliyO7BxsYGgwcPxrfffovs7Owq8wsKCnDv3j20bNkSERER+Pjjj6skC8ePH8dvv/2msY8qlQpdunSBnZ0dtm3bhgYNGhgUlxTs7Ow0ur7rEhoaCjs7O+zfv189rbS0FEeOHKmSgFbu/n3z5k38888/Go/bXnrpJVy8eBGffvopTp8+jWHDhkmwJ7oxkSEikhFzPZZetmwZysrK0KZNG3z33Xc4d+4c0tPT8emnnyI2NhZOTk5YuXIlvv/+e4wdOxYnTpzAhQsX8MUXX2D48OEYMGAABg4cCOB/SUxgYCAWL16MvLw85ObmIjc3V7298ePH4/3338f+/ftx8eJFHDp0CK+88gq8vb01Hm1Utnv3buTk5MDd3d3g/Zs/fz4CAgLQtm1bfP311zh9+jTOnTuH1atX47HHHkNBQQEUCgW++OILnD59Gv3798eff/6JS5cuYdOmTejduzdiY2PVY7ZUJDGFhYX44osvoFKp1PuoT2JR4fr16+rfVXzu3Lmj9++Dg4Nx+PBhXLhwAfn5+Tpra5ycnDB+/HhMnToVv/zyC06fPo0xY8agqKgIo0aN0lj2vffew65du3Dq1CkMHz4cXl5e6NOnj3q+h4cH+vXrh6lTp6JLly4a3eJNoto+TVbAUrtfE9HDReru19m3isSB8/l1OmRDdna2SEhIEEFBQcLOzk40btxYPPfccyIlJUW9zL59+0TXrl2Fq6ursLOzE1FRUWLx4sXi3r176mWSkpKqdAuu+FTYvHmz6NGjh/Dz8xN2dnbC399f9O/fX5w4cUK9TEX3Y11ef/11vbtfCyHErVu3xIwZM0STJk2EnZ2daNSokYiPjxfJycmivLxcvdyJEydE//79haenp7C1tRWhoaHinXfeEYWFheplKroqa/tkZmbWGEtF92ttn3Xr1unc/48//lgEBQWpv589e1Y8+eSTwsHBoUr365s3b2r8tri4WEycOFF4eXlV2/36hx9+EFFRUcLOzk60adNGHD9+vEr8u3btEgDExo0bq91PKbpfK4QwsNWSzKhUKri5uam7vRERmcOdO3eQmZmJkJAQszxiIKqtPXv2oFOnTrh582aNtV7ffPMN3njjDWRnZ1fbDb66vwt9r99s7EtERESSKCoqQk5ODhYsWIBXX31V77F8aoNtZIiIyOo9OBZL5c/vv/9e5/GMGzdOZzzjxo2r83ik8uGHHyIiIgK+vr6YOXNmnWyTj5aIiOoAHy2Z1/nz53XOa9y4MRwc6rbn6LVr16BSqbTOc3V1hY+PT53GYy58tERERKSH6sZiMQcfH5+HJlkxNT5aIiIiItliIkNEVIf0HXWV6GEgxd8DHy0REdUBOzs72NjYIDs7G97e3rCzszPpsO1ElkwIgbt37yIvLw82Nja16t3ERIaIqA7Y2NggJCQEOTk5WofBJ3oYOTo6IjAwUK9XVOjCRIaIqI7Y2dkhMDAQ9+7dM2iYeiJrVK9ePdSvX7/WNZNMZIiI6pBCoYCtra3Bb0EmIu3Y2JeIiIhki4kMERERyRYTGSIiIpItJjJEREQkW0xkiIiISLaYyBAREZFsMZEhIiIi2WIiQ0RERLLFRIaIiIhki4kMERERyZZZE5l9+/ahd+/e8Pf3h0KhwNatW9XzSktLMX36dMTExMDJyQn+/v545ZVX+LI1IiIiUjNrIlNYWIgWLVpg2bJlVeYVFRUhNTUVs2bNQmpqKrZs2YKzZ8/iueeeM0OkREREZIkUQghh7iCA+y9SS05ORp8+fXQuc+TIEbRp0wYXL15EYGCgXutVqVRwc3ODUqmEq6urRNESERGRKel7/ZbV26+VSiUUCgXc3d11LlNSUoKSkhL1d5VKVQeRERERkTnIprHvnTt3MH36dLz44ovVZmaJiYlwc3NTfwICAuowSiIiIqpLskhkSktLMXDgQAghsHz58mqXnTlzJpRKpfqTlZVVR1ESERFRXbP4R0sVSczFixexe/fuGtu52Nvbw97evo6iIyIiInOy6ESmIok5d+4cUlJS0LBhQ3OHRERERBbErIlMQUEBzp8/r/6emZmJtLQ0eHp6ws/PDwMGDEBqaip+/PFHlJWVITc3FwDg6ekJOzs7c4VNREREFsKs3a/37NmDTp06VZk+bNgwzJkzByEhIVp/l5KSgo4dO+q1DXa/JiIikh9ZdL/u2LEjqsujLGSIGyIiIrJQsui1RERERKQNExkiIiKSLSYyREREJFtMZIiIiEi2mMgQERGRbDGRISIiItliIkNERESyxUSGiIiIZIuJDBEREckWExkiIiKSLSYyREREJFtMZIiIiEi2mMgQERGRbDGRISIiItliIkNERESyxUSGiIiIZIuJDBEREckWExkiIiKSLSYyREREJFtMZIiIiEi2mMgQERGRbDGRISIiItliIkNERESyxUSGiIiIZIuJDBEREckWExkiIiKSLSYyREREJFtMZIiIiEi2mMgQERGRbDGRISIiItliIkNERESyxUSGiIiIZIuJDBEREckWExkiIiKSLSYyREREJFtMZIiIiEi2mMgQERGRbDGRISIiItliIkNERESyZdZEZt++fejduzf8/f2hUCiwdetWjflCCLz77rvw8/ODg4MD4uPjce7cOfMES0RERBbHrIlMYWEhWrRogWXLlmmd/+GHH+LTTz/FihUrcPjwYTg5OaFr1664c+dOHUdKRERElqi+OTfevXt3dO/eXes8IQSWLl2Kd955B88//zwA4Ouvv0ajRo2wdetWDB48uC5DJSIiIgtksW1kMjMzkZubi/j4ePU0Nzc3tG3bFgcPHjRjZERERGQpzFojU53c3FwAQKNGjTSmN2rUSD1Pm5KSEpSUlKi/q1Qq0wRIREREZmexNTLGSkxMhJubm/oTEBBg7pCIiIjIRCw2kfH19QUAXL16VWP61atX1fO0mTlzJpRKpfqTlZVl0jiJiIjIfCw2kQkJCYGvry927dqlnqZSqXD48GHExsbq/J29vT1cXV01PkRERGSdzNpGpqCgAOfPn1d/z8zMRFpaGjw9PREYGIjJkydj3rx5aNKkCUJCQjBr1iz4+/ujT58+5guaiIiILIZZE5mjR4+iU6dO6u9TpkwBAAwbNgxffvklpk2bhsLCQowdOxa3bt3CU089hV9++QUNGjQwV8hERERkQRRCCGHuIExJpVLBzc0NSqWSj5mIiIhkQt/rt8W2kSEiIiKqCRMZIiIiki0mMkRERCRbTGSIiIhItpjIEBERkWwxkSEiIiLZYiJDREREsmVUIlNYWCh1HEREREQGMyqRadSoEUaOHIk//vhD6niIiIiI9GZUIrNmzRrcuHEDzzzzDMLDw7FgwQJkZ2dLHRsRERFRtYxKZPr06YOtW7fiypUrGDduHNauXYugoCD06tULW7Zswb1796SOk4iIiKgKyd619Nlnn2Hq1Km4e/cuvLy8MG7cOMyYMQOOjo5SrN5ofNcSERGR/Oh7/a7V26+vXr2Kr776Cl9++SUuXryIAQMGYNSoUbh8+TIWLlyIQ4cOYefOnbXZBBEREZFORiUyW7ZsQVJSEnbs2IHIyEi89tpreOmll+Du7q5epl27dmjWrJlUcRIRERFVYVQiM2LECAwePBj79+9H69attS7j7++Pt99+u1bBEREREVXHqDYyRUVFZm/7oi+2kSEiIpIfk7aRcXR0RFlZGZKTk5Geng4AaNasGfr06YP69WvV7IaIiIhIb0ZlHX///Td69+6Nq1evomnTpgCAhQsXwtvbGz/88AOio6MlDZKIiIhIG6PGkRk9ejSio6Nx+fJlpKamIjU1FVlZWWjevDnGjh0rdYxEREREWhlVI5OWloajR4/Cw8NDPc3DwwPz58/X2fiXiIiISGpG1ciEh4fj6tWrVaZfu3YNYWFhtQ6KiIiISB9GJTKJiYmYNGkSNm/ejMuXL+Py5cvYvHkzJk+ejIULF0KlUqk/RERERKZiVPdrG5v/5T8KhQIAULGayt8VCgXKysqkiNNo7H5NREQkPybtfp2SkmJ0YERERERSMSqR6dChg9RxEBERERnM6NHrbt26hS+++EI9IF5UVBRGjhwJNzc3yYIjIiIiqo5RjX2PHj2K0NBQfPzxx7hx4wZu3LiBJUuWIDQ0FKmpqVLHSERERKSVUY19n376aYSFhWHVqlXqVxLcu3cPo0ePxn//+1/s27dP8kCNxca+RERE8qPv9duoRMbBwQF//fUXIiIiNKafPn0arVq1QlFRkeERmwgTGSIiIvnR9/pt1KMlV1dXXLp0qcr0rKwsuLi4GLNKIiIiIoMZlcgMGjQIo0aNwoYNG5CVlYWsrCysX78eo0ePxosvvih1jERERERaGdVrafHixVAoFHjllVdw7949AICtrS3Gjx+PBQsWSBogERERkS4Gt5EpKyvD/v37ERMTA3t7e2RkZAAAQkND4ejoaJIga4NtZIiIiOTHZCP71qtXD126dEF6ejpCQkIQExNTq0CJiIiIjGVUG5no6Gj897//lToWIiIiIoMYlcjMmzcPb775Jn788Ufk5ORovO2ab7wmIiKiuiLZ268By3njdWVsI0NERCQ/fPs1ERERWT2jEpmQkBAEBARo1MYA92tksrKyJAmMiIiIqCZGtZEJCQlBXl5elek3btxASEhIrYMiIiIi0odRiUxFW5gHFRQUoEGDBrUOqkJZWRlmzZqFkJAQODg4IDQ0FO+//z6MaNZDREREVsigR0tTpkwBcL+B76xZszQGwCsrK8Phw4fRsmVLyYJbuHAhli9fjq+++gpRUVE4evQoRowYATc3N0yaNEmy7RAREZE8GZTI/PXXXwDu18icPHkSdnZ26nl2dnZo0aIF3nzzTcmCO3DgAJ5//nn07NkTABAcHIx169bhzz//lGwbREREJF8GJTIVvZVGjBiBTz75xOTdmdu1a4f//Oc/+OeffxAeHo7jx4/jjz/+wJIlS3T+pqSkBCUlJervHNeGiIjIehnVaykpKUnqOLSaMWMGVCoVIiIiUK9ePZSVlWH+/PkYOnSozt8kJiZi7ty5dRIfERERmZdRiUxhYSEWLFiAXbt24dq1aygvL9eYL9XrCzZu3Ihvv/0Wa9euRVRUFNLS0jB58mT4+/tj2LBhWn8zc+ZMdVse4H6NTEBAgCTxEBERkWUxKpEZPXo09u7di5dffhl+fn5aezBJYerUqZgxYwYGDx4MAIiJicHFixeRmJioM5Gxt7eHvb29SeIhIiIiy2JUIvPzzz/jp59+QlxcnNTxaCgqKtJ4HQJw/+3bD9YAERER0cPJqETGw8MDnp6eUsdSRe/evTF//nwEBgYiKioKf/31F5YsWYKRI0eafNtERERk+Yx6aeSaNWvw/fff46uvvtIYS0Zqt2/fxqxZs5CcnIxr167B398fL774It59912Nrt/V4UsjiYiI5Eff67dRicxjjz2GjIwMCCEQHBwMW1tbjfmpqamGR2wiTGSIiIjkx6Rvv+7Tp4+xcRERERFJxqgaGTlhjQwREZH86Hv9NuilkX/++SfKysp0zi8pKcHGjRsNWSURERGR0QxKZGJjY3H9+nX1d1dXV43B727duoUXX3xRuuiIiIiIqmFQIvPgUyhtT6Ws/EkVERERWRCDEhl9mGqUXyIiIqIHSZ7IEBEREdUVg7tfnz59Grm5uQDuP0Y6c+YMCgoKAAD5+fnSRkdERERUDYO6X9vY2EChUGhtB1MxXaFQVNuzqa6x+zUREZH8mGRAvMzMzFoHRkRERCQVgxKZoKAgg1b+2muv4b333oOXl5dBvyMiIiLSh0kb+65ZswYqlcqUmyAiIqKHmEkTGY4pQ0RERKbE7tdEREQkW0xkiIiISLaYyBAREZFsMZEhIiIi2TJpIvPSSy9xEDoiIiIyGaMSmfLycp3TL126pP6+fPlyjiFDREREJmNQIqNSqTBw4EA4OTmhUaNGePfddzVeR5CXl4eQkBDJgyQiIiLSxqCRfWfNmoXjx4/jm2++wa1btzBv3jykpqZiy5YtsLOzA8CxY4iIiKjuGFQjs3XrVqxcuRIDBgzA6NGjcfToUeTl5aF3794oKSkBcP/lkURERER1waBEJi8vT+N9S15eXvjtt99w+/Zt9OjRA0VFRZIHSERERKSLQYlMYGAg0tPTNaa5uLhg586dKC4uRt++fSUNjoiIiKg6BiUyXbp0QVJSUpXpzs7O2LFjBxo0aCBZYEREREQ1Maix79y5c5Gdna11nouLC3799VekpqZKEhgRERFRTQxKZDw8PODh4aFzvouLCzp06FDroIiIiIj0YfCAePfu3cOiRYvw+OOPw9nZGc7Oznj88cexePFilJaWmiJGIiIiIq0MqpEpLi7Gs88+i4MHDyI+Ph7t27cHAKSnp2P69OnYtm0bdu7cybYyREREVCcMSmQWLFiArKws/PXXX2jevLnGvOPHj+O5557DggULMGfOHCljJCIiItLKoEdL69evx5IlS6okMQDQokULLF68GGvXrpUsOCIiIqLqGJTIXLx4EW3atNE5/8knn9R4aSQRERGRKRmUyLi6uuLatWs65+fm5sLFxaXWQRERERHpw6BEplOnTvjggw90zl+wYAE6depU66CIiIiI9GFQY9/Zs2ejbdu2ePLJJzFlyhRERERACIH09HR8/PHHOH36NA4dOmSqWImIiIg0GJTIREZG4tdff8WoUaMwePBg9ZuuhRCIiIjAzp07ERUVZZJAiYiIiB5kUCID3G/Q+/fffyMtLQ3//PMPACA8PBwtW7aUOjYiIiKiahmcyKhUKjg7O6Nly5YayUt5eTkKCgrg6uoqZXxEREREOhnU2Dc5ORmtWrXCnTt3qswrLi5G69at8cMPP0gWHBEREVF1DEpkli9fjmnTpsHR0bHKPCcnJ0yfPh3//ve/JQuOiIiIqDoGJTKnTp1Cx44ddc5v3749Tp48WduYNFy5cgUvvfQSGjZsCAcHB8TExODo0aOSboOIiIjkyaA2Mjdv3sS9e/d0zi8tLcXNmzdrHVTl7cXFxaFTp074+eef4e3tjXPnzsHDw0OybRAREZF8GZTIBAcH4+jRo4iIiNA6/+jRowgKCpIkMABYuHAhAgICkJSUpJ4WEhIi2fqJiIhI3gx6tNSvXz+8/fbbuHr1apV5ubm5eOedd9C/f3/Jgtu2bRtatWqFF154AT4+PnjsscewatWqan9TUlIClUql8SEiIiLrpBBCCH0Xvn37NmJjY3Hp0iW89NJLaNq0KQDgzJkz+PbbbxEQEIBDhw5J9r6lBg0aAACmTJmCF154AUeOHMHrr7+OFStWYNiwYVp/M2fOHMydO7fKdKVSya7hREREMqFSqeDm5lbj9dugRAa4nxDMnDkTGzZsULeHcXd3x+DBgzF//nxJ26/Y2dmhVatWOHDggHrapEmTcOTIERw8eFDrb0pKSlBSUqL+rlKpEBAQwESGiIhIRvRNZAweEM/NzQ3/93//h2XLliE/Px9CCHh7e6tfV1DZ/v370apVK9jb2xu6GQCAn58fIiMjNaY1a9YM3333nc7f2NvbG709IiIikheD2shUplAo4O3tDR8fH61JDAB0794dV65cMTq4uLg4nD17VmPaP//8I2mDYiIiIpIvoxMZfRj41KqKN954A4cOHcIHH3yA8+fPY+3atfjPf/6DhIQEiSIkIiIiOTNpIlNbrVu3RnJyMtatW4fo6Gi8//77WLp0KYYOHWru0IiIiMgCGNxGpq716tULvXr1MncYREREZIEsukaGiIiIqDomTWR0NQImIiIikoJFN/YlIiIiqo5J28jcvn3blKsnIiKih5xBicwzzzyj13K7d+82KhgiIiIiQxiUyOzZswdBQUHo2bMnbG1tTRUTERERkV4MSmQWLlyIpKQkbNq0CUOHDsXIkSMRHR1tqtiIiIiIqmVQY9+pU6fi9OnT2Lp1K27fvo24uDi0adMGK1asgEqlMlWMRERERFoZ/PbryoqKirBp0yYsW7YMp0+fRnZ2tsW9YVrft2cSERGR5dD3+l2r7tepqanYu3cv0tPTER0dzXYzREREVKcMTmSys7PxwQcfIDw8HAMGDICnpycOHz6MQ4cOwcHBwRQxEhEREWllUGPfHj16ICUlBV26dMGiRYvQs2dP1K9v8a9rIiIiIitlUBsZGxsb+Pn5wcfHp9rXD6SmpkoSnBTYRoaIiEh+9L1+G1SdMnv27FoHRkRERCSVWvVakgPWyBAREcmPSWpkdNm7dy8KCwsRGxsLDw8PKVZJREREVCODR/YtKCjA+++/D+D+2627d++OnTt3AgB8fHywa9cuREVFSR8pERER0QMM6n69YcMGjVcSbN68Gfv27cPvv/+O/Px8tGrVCnPnzpU8SCIiIiJtDEpkMjMz0bx5c/X37du3Y8CAAYiLi4OnpyfeeecdHDx4UPIgiYiIiLQxKJG5d+8e7O3t1d8PHjyIdu3aqb/7+/sjPz9fuuiIiIiIqmFQIhMaGop9+/YBAC5duoR//vkH7du3V8+/fPkyGjZsKG2ERERERDoY1Ng3ISEBEyZMwO+//45Dhw4hNjYWkZGR6vm7d+/GY489JnmQRERERNoYlMiMGTMG9erVww8//ID27dtXGSAvOzsbI0eOlDRAIiIiIl04IB4RERFZHH2v3wa//ZqIiIjIUhiUyJSWlmLatGkICwtDmzZtsHr1ao35V69eRb169SQNkIiIiEgXgxKZ+fPn4+uvv8a4cePQpUsXTJkyBa+++qrGMlb+pIqIiIgsiEGNfb/99lt8/vnn6NWrFwBg+PDh6N69O0aMGKGunVEoFNJHSURERKSFQTUyV65c0XhFQVhYGPbs2YMDBw7g5ZdfRllZmeQBEhEREeliUCLj6+uLjIwMjWmNGzdGSkoKjhw5guHDh0sZGxEREVG1DEpknnnmGaxdu7bKdH9/f+zevRuZmZmSBUZERERUE4PayMyaNQtnzpzROq9x48bYu3cvfv31V0kCIyIiIqoJB8QjIiIii2PSAfE2bdqEfv36ITo6GtHR0ejXrx82b95sdLBERERExjAokSkvL8egQYMwaNAgnD59GmFhYQgLC8Pff/+NQYMGYfDgwRxHhoiIiOqMQW1kPvnkE/z222/Ytm2beiyZCtu2bcOIESPwySefYPLkyVLGSERERKSVQTUySUlJWLRoUZUkBgCee+45fPjhh1VeW0BERERkKgYlMufOnUN8fLzO+fHx8Th37lytgyIiIiLSh0GJjIODA27duqVzvkqlQoMGDWobExEREZFeDEpkYmNjsXz5cp3zly1bhtjY2FoHRURERKQPgxKZt99+G1988QUGDhyIP//8EyqVCkqlEocOHcILL7yA1atX4+233zZVrFiwYAEUCgUbExMREREAA3sttWvXDhs2bMDYsWPx3Xffaczz8PDAunXrEBcXJ2mAFY4cOYKVK1eiefPmJlk/ERERyY9BiQwA9O3bF127dsWOHTvUDXvDw8PRpUsXODo6Sh4gABQUFGDo0KFYtWoV5s2bZ5JtEBERkfwY9Ghp9+7diIyMxL1799C3b19MmzYN06ZNQ58+fVBaWoqoqCj8/vvvkgeZkJCAnj17VttjqkJJSQlUKpXGh4iIiKyTQYnM0qVLMWbMGK3vPHBzc8Orr76KJUuWSBYcAKxfvx6pqalITEzUa/nExES4ubmpPwEBAZLGQ0RERJbDoETm+PHj6Natm875Xbp0wbFjx2odVIWsrCy8/vrr+Pbbb/Xu1j1z5kwolUr1JysrS7J4iIiIyLIY1Ebm6tWrsLW11b2y+vWRl5dX66AqHDt2DNeuXcPjjz+unlZWVoZ9+/bh3//+N0pKSlCvXj2N39jb28Pe3l6yGIiIiMhyGZTING7cGKdOnUJYWJjW+SdOnICfn58kgQFA586dcfLkSY1pI0aMQEREBKZPn14liSEiIqKHi0GJTI8ePTBr1ix069atyqOe4uJizJ49W+t7mIzl4uKC6OhojWlOTk5o2LBhlelERET08DEokXnnnXewZcsWhIeHY8KECWjatCkA4MyZM1i2bBnKyspMOiAeERERUWUKIYQw5AcXL17E+PHjsWPHDlT8VKFQoGvXrli2bBlCQkJMEqixVCoV3NzcoFQqtfa2IiIiIsuj7/Xb4AHxgoKCsH37dty8eRPnz5+HEAJNmjSBh4dHrQImIiIiMpTBiUwFDw8PtG7dWspYiIiIiAxi0DgyRERERJaEiQwRERHJFhMZIiIiMkqOshgHMvKRoyw2WwxGt5EhIiKih9eGI5cwc8tJlAvARgEk9ovBoNaBdR4Ha2SIiIjIIDnKYnUSAwDlAnhryymz1MwwkSEiIiKDZOYXqpOYCmVC4EJ+UZ3HwkSGiIiIDBLi5QQbhea0egoFgr0c6zwWJjJERERkED83ByT2i0E9xf1spp5CgQ/6RcPPzaHOY2FjXyIiIjLYoNaBaB/ujQv5RQj2cjRLEgMwkSEiIiIj+bk5mC2BqcBHS0RERCRbTGSIiIhItpjIEBERkWwxkSEiIiLZYiJDREREssVEhohMxhJeKEdE1o3dr4nIJCzlhXJEZN1YI0NEkrOkF8oRkXVjIkNEkrOkF8oRkXVjIkNEkrOkF8oRkXVjIkNEkrOkF8oRkXVjY18iMglLeaEcEVk3JjJERspRFiMzvxAhXk68SOtgCS+UIyLrxkSGyAjsWkxEZBnYRobIQOxaTERkOZjIEBmIXYuJiCwHExkiA7FrMRGR5WAiQ2Qgdi0mIrIcbOxLZAR2LSYisgxMZIiMxK7FRETmx0dLVGs5ymIcyMhnrx0iIqpzrJGhWuF4KkREZE6skSGjcTwVIqKHmyXUyLNGhoxW3XgqbDtCRGTdLKVGnjUyRrKELNTcOJ4KEdHDyZJq5JnIGGHDkUuIW7AbQ1YdRtyC3dhw5JK5QzILjqdCRPRwsqQRzvloyUC6stD24d4P5QWc46kQET18KmrkKycz5qqRt+gamcTERLRu3RouLi7w8fFBnz59cPbsWbPGZElZqKXwc3NAbGhDJjFERA8JS6qRt+gamb179yIhIQGtW7fGvXv38NZbb6FLly44ffo0nJyczBKTJWWhRERE5mIpNfIKIYSoeTHLkJeXBx8fH+zduxft27fX6zcqlQpubm5QKpVwdXWVJI4NRy7hrS2nUCaEOgvl2ClERETS0ff6bdE1Mg9SKpUAAE9PT7PGYSlZKBkvR1mMzPxChHg58f+PiEjGZJPIlJeXY/LkyYiLi0N0dLTO5UpKSlBSUqL+rlKpTBIP37MjX5Yy9gEREdWeRTf2rSwhIQGnTp3C+vXrq10uMTERbm5u6k9AQEAdRUhyYEljHxARUe3JIpGZMGECfvzxR6SkpOCRRx6pdtmZM2dCqVSqP1lZWXUUJckBe50REVkXi360JITAxIkTkZycjD179iAkJKTG39jb28Pe3r4OoiM5Yq8zIiLrYtE1MgkJCVizZg3Wrl0LFxcX5ObmIjc3F8XFfAxAxrGksQ+IiKj2LLr7tUKh0Do9KSkJw4cP12sdpuh+TfKXoyxmrzMzYq8xIqqJVXS/tuAcSzZ4wdCOvc7Mh73GiEhKFp3IUO3wgkGWhu8qIyKpWXQbGTIeuxmTJWKvMSKSGhMZK8ULBlmiil5jlbHXGBHVBhMZKyXlBSNHWYwDGfmszaFaY68xIpIa28hYqYoLxoMvtzT0gsF2NiQ1S35XGRvHE8mPRXe/lsLD3v26Nt2Mc5TFiFuwu8rgcX/M6MSTPFkdJu1ElkXf6zcfLZmAJT2K8XNzQGxoQ6MSD7azoYcFG8cTyRcfLUnMmu7qOJw/PSyqS9pZ+0hk2VgjIyFru6tjw0x6WLA3FRnLkmrgpSSn/WKNjISs8a7OkhtmEklFqsbx5sJGyuZhTTXwlcltv5jISOjkZWWVadZwV8fh/OlhIMekPUdZjNV/ZOKLPzJlc9GxFtY6SrUc94uJjERylMVY+MuZKtOndW9qsf/5RKRJTkn7hiOXMOO7k6hcCSyHi461sMYaeECe+8U2MhLR9p8PAM0bu9d5LJZMTs9diWrLVMd7xV2ztrEz2LOwblhruyo57hdrZCTCHj7aVX52v++fPFk9dyWqDVO2M9B14wTwvFNX5N6uShc57hcHxJPQhiOXqvznP8wX6son8ooEv/LBxsH1yFqZejBJbesHdCdMuhoD69tImI2JdavNoKOWzBL2S9/rN2tkJCR1Y0E5nzwebDBWXRW43PaNqCambmfw4F2zDYDR7UMwIi6kyvp11QzpW2Mktx4sdU1O7aoMIaf9YiIjMan+8+V+8qiu6rsCq8DJWtXFo2Z9bpx09UCJ8HXRq2eKHHuwWDI535xaMjb2tUDWMLCetgZjCkA9TQ7PXYmMVVeDSdb0ChJdNUNHLtzUOv2nEzka5xm+pkQ6G45cQtyC3Riy6jDiFuzGhiOX6mS7D0MHC9bIWCA5dn97kK4GY3Ibp4PIWJYwLo2umqHWwR5VpgPAvJ/S8cH2dHUNsFw7MVhazYe5arbkXrOvLyYytVDbBnS6yPXk8SBdJ3JLOLEQ1QVztzPQdUPRIsBDY3pllS+yADDqqRD1gHtyqEm1xIu3OW5OH6bHgkxkjFTbBnTVkWP3N13MfSK3ZJZ21yg1a98/udB1Q1Ex/acTOZj3U7rGb8qEQNL+THz+e6a61+FYHY2JLYmlXrzNcXNqDTX7+mIiY4TaNqDThyVUS5PpWOJdo5Rqu391mQRZY8L14D7puqHwc3NAz+Z++GB7usZFz0YBrNqXqe5tKAB88fsFjIgLqZP4jSXlxVvK48IcN6fWUrOvDyYyRjC0AZ2xGTBrM6yTpd41SqW2+2fKJO/Bi5M1JpSG7pO2i+zIp4Kx6vdMjeXkcDcv1cXbFMeFqW9OtSWv1lKzXxMmMkYwpAGdlBmwNd45Poysvcq3NvtnyiTvwYvT9O4RWPjzGatKKI0tvwcvsgDU7WIq1OZcVlfnLiku3lIcg7r2V9+bU0PLS1fi9bDU7DORMYK+DeikzICt4c6Ridh91l7lW5v9M1WSp+3iVDmJkXJb1cVg6uO/NuX34EVWqnNZXZ+7anvxru0xWNv9NfT3NSVeD0PNPhMZI9XUgE7KDLimA1UOCYI1JGJSsfYq39rsn6mSPG0Xp4pGrA++NsMUCWVdHf9Slp8U5zJzPUatzcW7NmVY2/015vfWXsOrDyYytVBdAzopD6DqDlQ5vIjR2tuEGMPaq3yN3T9TJXm6Lk7TujXFh7+cNWlCWZfHv9TlV9tzmRwvstrKcFr3psjML1TP10XX/qZevAkPp5pvNo0pL2uv4dUHExkZ0HWgOtrZyCJBkOPJrC5Ye5WvPvunrTbRFEmergv8oNaBeK6lv0kTyro+/i0pSZbrRbZyGZ64fEv9GLKml3I62dWrsr8KBTBh7V8QqPlm05jysvYaXn0wkZEBXQdq4d0yWSQIcj2ZkWlV97jFFEledQM0Wls3WEtJkuviIns86yb+vHADbYI90SLAQ7L1VsQ49PND1d4sPngc932sMbb+lX3/ZZ4KQIj/Pb6s6WbT2PKypOTVHJjIyIS2AzVHWSyLBEFOdwxyaG9kDeTYdgIw7viQ0/GvS23+LnSduwxZn67l/7UxDd+lXlF/7/94Y3w0sKVB8VVeNwCN7dRUm6btON76Vza2vBaLorvlyC+4g4nr0nT+XpvaPJaV0zElJSYyMvLggSqnE6Qc7hjYIFk/UiR7cnzcWJvjQ6qGs+ZIsqUarVxXDUZN69O1/PGsmxpJDAB8l3oFr8QGoUWAh17lVXndFe+4rfwIqH24d7U3i7qO46K75YgNbWj0zebDnJQYg4mMzMkhQahgyX+cdVFDYA21PVIle3J73CjF8VHT8V/d8WGuJFvqvwtD11fd8n9euKF1G0cv3MSZ3Ns1lteD666cj1Rs548Znaq9WazpOLbEm01rOA89iImMidXFQWPJCYJcmLqGwBpqe6S8qFniCb465jw+zNnrT+r9NnR91S3fJthT6zaCvRwx5utjNZaXtnVr2051N4v6HMeWdLNpDechbZjImJC1HjTWyJQ1BJbU/bw2ibXUFzVto8keyMi3yDtFfY4PY8u2puPDnI/hpP67qG592sqvuuX93BzQ//HGVdrIONjV16u8tK27sgdrVmrTpsUSbjYt6TwkNRtzB2CtdB00Ocpi8wb2kMpRFuNARr7O8q+4s6qnuP+kXMoaguouRFKrbj83HLmEuAW7MWTVYcQt2I0NRy4ZtO6KE39l+lzUqovJz80BsaENse+fPL1jq+n/0lg1xVnd8VGbsq3p+NBW7jYAHO1Md/quKAsAkv5d6CpHXf//NZX7RwNb4vuEdpjVsxm+T2iHjwa21Ps4fXDdCtzvKm3MflYcx5acENTleaiuKYQQ1VSuyZ9KpYKbmxuUSiVcXV3rbLsHMvIxZNXhKtPXjXkSsaEN6ywOMqxmLEdZLHkVcI6yGHELdle5q/xjRidJT3w1PZ6QIoYNRy5pHYtF35hGPRWCkU+FaGxTW2w2CiD5tXZVutOaqpZTn/XmKItx9MIN2CgUeDzIQ70PtS1bfX5fudwrmKqWV1tZmGK08so1cTXtv6F/l4Ycpw/GYgmPgEyhrs5DUtL3+s0aGRMx9u6VpGVozZgp7qykqu2prsagpv2U6m5sUOtA/DGjE9aNeRJ/zOhk8DtgVv2eiXaJmjUWul4f0GfZAY3lTFXLqc96K2pcJq5Lw8R1f2HfP3nVxm9I2epzfAxqHYgtr8Wqawx0xVlbusoCgKR/F5X/zvQpP0P/Lg05TiuvWw41K8YyZa2zubGNjInIrTGjtbKUbr61bfBXU41BTfspVVsHQ9qB6GpMKaD5bF5XW4UHl5Py/7LyfhgzVkhN8RtatvqMtVJ4twwP1p9LfSyb4+/FmPLT5zi0hHYplsaSGh5LiYmMCcnloNH34iTHbnuW1M3X2BOrPo306qIbqKGPdaprTFn54lgRW+V91LacVP+XD+7H9G4RRo0V8mD8tb1pqWmslZrGNJGCuUYhNqT8Vu7NwIKfz+g15D9VZY0JHhMZE7P0g0bfi1NNy1lqkmMNNWP63CWbuhuoMT0eqktQHrw4DmodiAhfF/RZdkDn26il+L/Uth8f/nIW07tH4MOftb88Up+Lu5Q3LbrKuqYxTQzdhra/V1P/vejarr7lt3JfBhJ/PqP+bk09b8h4skhkli1bhkWLFiE3NxctWrTAZ599hjZt2pg7LNnT9+JU03JSNsA0RUIkl5oxXap7aWjl7sqm7AZq7COHipiS9mfi832ZKIfuZ/MtAjywoL9px+TQtR/NG7vjjxmdjB4rpGI5U/dyk+JYrunv1VR/LzVtt6byy1EWY0GlJKaCpY8IbYjanv8s9YbS1Cw+kdmwYQOmTJmCFStWoG3btli6dCm6du2Ks2fPwsfHx9zhyZq+F6eaGuNJNTaBKcfdsfSascoePBlpu5D2ecwfff/vQJWyMtV+1uaRg5+bA97qEYkRcSE1XhxNkYxVLs+axiWpTVxS0ecxobHbP551EzO+O1njSwylPo6kGMMkM7+wShsh4H6Plbp+VGyKhKG257+Hedwyi++1tGTJEowZMwYjRoxAZGQkVqxYAUdHR6xevdrcocmevj2rqltOqt4wHHfnPl3jkVTuhbHltVgk/3WlTstKih4P+vYIkbLnyIPlue+fPKP3o656tJiqd8mGI5fQ5/80H90BdTOWiBTnCW3nIQCY3j2iTm9Sajsekza1Pf897OdPi66RuXv3Lo4dO4aZM2eqp9nY2CA+Ph4HDx7U+puSkhKUlJSov6tUKpPHKVeGVJlXt5wUjQMtpXeROdV011rxOZCRb5ayktsjuuramuh6jGQppC7rirLQVqNRF43fpWhE/OB5yEZxP4l5tX2oCSLWzlSj49b2/Pewnz8tOpHJz89HWVkZGjVqpDG9UaNGOHOm6rNSAEhMTMTcuXPrIjyroO8JU9dyUjUOtKTeReai78nInGUlp0d01ZWnHMYKkbKsdXWFtwHqpPG7VOcJcyfTpkoYavs3/bCfPy06kTHGzJkzMWXKFPV3lUqFgIAAM0Zk+fQ9YepaToqTizX0LqotfU9GLCv9POwn98q0lYWu0ZNNRaokxJzJtKmOqdr+TT/s5wSLfkXB3bt34ejoiM2bN6NPnz7q6cOGDcOtW7fw/fff17gOc72igIxjilcEyImxQ6s/jGWlD0NfqWDNWBbSMGU51vZv2trOCfpevy06kQGAtm3bok2bNvjss88AAOXl5QgMDMSECRMwY8aMGn/PRIbkxtpORubG8vwfloU0WI51Q9/rt8U/WpoyZQqGDRuGVq1aoU2bNli6dCkKCwsxYsQIc4dGZBJyaociByzP/2FZSIPlaFksPpEZNGgQ8vLy8O677yI3NxctW7bEL7/8UqUBMBERET18LP7RUm3x0RIREZH86Hv9tvgB8YiIiIh0YSJDREREssVEhoiIiGSLiQwRERHJFhMZIiIiki0mMkRERCRbTGSIiIhItpjIEBERkWwxkSEiIiLZsvhXFNRWxcDFKpXKzJEQERGRviqu2zW9gMDqE5nbt28DAAICAswcCRERERnq9u3bcHNz0znf6t+1VF5ejuzsbLi4uEChUJg7nDqlUqkQEBCArKwsvmeqFliO0mFZSoPlKB2WpTRMUY5CCNy+fRv+/v6wsdHdEsbqa2RsbGzwyCOPmDsMs3J1deUfqARYjtJhWUqD5SgdlqU0pC7H6mpiKrCxLxEREckWExkiIiKSLSYyVsze3h6zZ8+Gvb29uUORNZajdFiW0mA5SodlKQ1zlqPVN/YlIiIi68UaGSIiIpItJjJEREQkW0xkiIiISLaYyBAREZFsMZGRuX379qF3797w9/eHQqHA1q1bNeYLIfDuu+/Cz88PDg4OiI+Px7lz58wTrIWrqSyHDx8OhUKh8enWrZt5grVgiYmJaN26NVxcXODj44M+ffrg7NmzGsvcuXMHCQkJaNiwIZydndG/f39cvXrVTBFbJn3KsWPHjlWOyXHjxpkpYsu1fPlyNG/eXD1YW2xsLH7++Wf1fB6P+qmpHM11PDKRkbnCwkK0aNECy5Yt0zr/ww8/xKeffooVK1bg8OHDcHJyQteuXXHnzp06jtTy1VSWANCtWzfk5OSoP+vWravDCOVh7969SEhIwKFDh/Drr7+itLQUXbp0QWFhoXqZN954Az/88AM2bdqEvXv3Ijs7G/369TNj1JZHn3IEgDFjxmgckx9++KGZIrZcjzzyCBYsWIBjx47h6NGjeOaZZ/D888/j77//BsDjUV81lSNgpuNRkNUAIJKTk9Xfy8vLha+vr1i0aJF62q1bt4S9vb1Yt26dGSKUjwfLUgghhg0bJp5//nmzxCNn165dEwDE3r17hRD3j0FbW1uxadMm9TLp6ekCgDh48KC5wrR4D5ajEEJ06NBBvP766+YLSsY8PDzE559/zuOxlirKUQjzHY+skbFimZmZyM3NRXx8vHqam5sb2rZti4MHD5oxMvnas2cPfHx80LRpU4wfPx7Xr183d0gWT6lUAgA8PT0BAMeOHUNpaanGcRkREYHAwEAel9V4sBwrfPvtt/Dy8kJ0dDRmzpyJoqIic4QnG2VlZVi/fj0KCwsRGxvL49FID5ZjBXMcj1b/0siHWW5uLgCgUaNGGtMbNWqknkf669atG/r164eQkBBkZGTgrbfeQvfu3XHw4EHUq1fP3OFZpPLyckyePBlxcXGIjo4GcP+4tLOzg7u7u8ayPC5101aOADBkyBAEBQXB398fJ06cwPTp03H27Fls2bLFjNFappMnTyI2NhZ37tyBs7MzkpOTERkZibS0NB6PBtBVjoD5jkcmMkR6Gjx4sPrfMTExaN68OUJDQ7Fnzx507tzZjJFZroSEBJw6dQp//PGHuUORNV3lOHbsWPW/Y2Ji4Ofnh86dOyMjIwOhoaF1HaZFa9q0KdLS0qBUKrF582YMGzYMe/fuNXdYsqOrHCMjI812PPLRkhXz9fUFgCqt769evaqeR8Z79NFH4eXlhfPnz5s7FIs0YcIE/Pjjj0hJScEjjzyinu7r64u7d+/i1q1bGsvzuNROVzlq07ZtWwDgMamFnZ0dwsLC8MQTTyAxMREtWrTAJ598wuPRQLrKUZu6Oh6ZyFixkJAQ+Pr6YteuXeppKpUKhw8f1nimSca5fPkyrl+/Dj8/P3OHYlGEEJgwYQKSk5Oxe/duhISEaMx/4oknYGtrq3Fcnj17FpcuXeJxWUlN5ahNWloaAPCY1EN5eTlKSkp4PNZSRTlqU1fHIx8tyVxBQYFGtpuZmYm0tDR4enoiMDAQkydPxrx589CkSROEhIRg1qxZ8Pf3R58+fcwXtIWqriw9PT0xd+5c9O/fH76+vsjIyMC0adMQFhaGrl27mjFqy5OQkIC1a9fi+++/h4uLi7qdgZubGxwcHODm5oZRo0ZhypQp8PT0hKurKyZOnIjY2Fg8+eSTZo7ectRUjhkZGVi7di169OiBhg0b4sSJE3jjjTfQvn17NG/e3MzRW5aZM2eie/fuCAwMxO3bt7F27Vrs2bMHO3bs4PFogOrK0azHY533kyJJpaSkCABVPsOGDRNC3O+CPWvWLNGoUSNhb28vOnfuLM6ePWveoC1UdWVZVFQkunTpIry9vYWtra0ICgoSY8aMEbm5ueYO2+JoK0MAIikpSb1McXGxeO2114SHh4dwdHQUffv2FTk5OeYL2gLVVI6XLl0S7du3F56ensLe3l6EhYWJqVOnCqVSad7ALdDIkSNFUFCQsLOzE97e3qJz585i586d6vk8HvVTXTma83hUCCGEaVMlIiIiItNgGxkiIiKSLSYyREREJFtMZIiIiEi2mMgQERGRbDGRISIiItliIkNERESyxUSGiIiIZIuJDBEREckWExmih0Bubi4mTpyIRx99FPb29ggICEDv3r013i9z4MAB9OjRAx4eHmjQoAFiYmKwZMkSlJWVqZe5cOECRo0ahZCQEDg4OCA0NBSzZ8/G3bt3Nba3atUqtGjRAs7OznB3d8djjz2GxMRE9fw5c+ZAoVCgW7duVWJdtGgRFAoFOnbsqNe+VaxLoVCgfv36CA4OxhtvvIGCggIDS4mI5IjvWiKychcuXEBcXBzc3d2xaNEixMTEoLS0FDt27EBCQgLOnDmD5ORkDBw4ECNGjEBKSgrc3d3x22+/Ydq0aTh48CA2btwIhUKBM2fOoLy8HCtXrkRYWBhOnTqFMWPGoLCwEIsXLwYArF69GpMnT8ann36KDh06oKSkBCdOnMCpU6c04vLz80NKSgouX76s8Vbn1atXIzAw0KB9jIqKwm+//YZ79+5h//79GDlyJIqKirBy5coqy969exd2dnZGlKTpWGJMRLJh8pcgEJFZde/eXTRu3FgUFBRUmXfz5k1RUFAgGjZsKPr161dl/rZt2wQAsX79ep3r//DDD0VISIj6+/PPPy+GDx9ebUyzZ88WLVq0EL169RLz5s1TT9+/f7/w8vIS48ePFx06dNBj7/63rsrGjBkjfH19NeavWrVKBAcHC4VCIYS4v++jRo0SXl5ewsXFRXTq1EmkpaWp15GWliY6duwonJ2dhYuLi3j88cfFkSNHhBBCXLhwQfTq1Uu4u7sLR0dHERkZKX766SchhBBJSUnCzc1NI57k5GRR+XRrbExEVBUfLRFZsRs3buCXX35BQkICnJycqsx3d3fHzp07cf36dbz55ptV5vfu3Rvh4eFYt26dzm0olUp4enqqv/v6+uLQoUO4ePFijfGNHDkSX375pfr76tWrMXTo0FrXTjg4OGg87jp//jy+++47bNmyBWlpaQCAF154AdeuXcPPP/+MY8eO4fHHH0fnzp1x48YNAMDQoUPxyCOP4MiRIzh27BhmzJgBW1tbAPffTF1SUoJ9+/bh5MmTWLhwIZydnQ2K0ZiYiKgqPloismLnz5+HEAIRERE6l/nnn38AAM2aNdM6PyIiQr2MtvV/9tln6sdKADB79mz069cPwcHBCA8PR2xsLHr06IEBAwbAxkbz3qlXr14YN24c9u3bhyeeeAIbN27EH3/8gdWrVxu6q2rHjh3D2rVr8cwzz6in3b17F19//TW8vb0BAH/88Qf+/PNPXLt2Dfb29gCAxYsXY+vWrdi8eTPGjh2LS5cuYerUqeqya9KkiXp9ly5dQv/+/RETEwMAePTRRw2O05iYiKgqJjJEVkwY8HJ7Q5YFgCtXrqBbt2544YUXMGbMGPV0Pz8/HDx4EKdOncK+fftw4MABDBs2DJ9//jl++eUXjWTG1tYWL730EpKSkvDf//4X4eHhaN68uUFxAMDJkyfh7OyMsrIy3L17Fz179sS///1v9fygoCB1wgAAx48fR0FBARo2bKixnuLiYmRkZAAApkyZgtGjR+Obb75BfHw8XnjhBYSGhgIAJk2ahPHjx2Pnzp2Ij49H//79DY7bmJiIqComMkRWrEmTJupGurqEh4cDANLT09GuXbsq89PT0xEZGakxLTs7G506dUK7du3wn//8R+t6o6OjER0djddeew3jxo3D008/jb1796JTp04ay40cORJt27bFqVOnMHLkSEN3EQDQtGlTbNu2DfXr14e/v3+VR1MPPlYrKCiAn58f9uzZU2Vd7u7uAO73hhoyZAh++ukn/Pzzz5g9ezbWr1+Pvn37YvTo0ejatSt++ukn7Ny5E4mJifjoo48wceJE2NjYVEkKS0tLq2zHmJiIqCq2kSGyYp6enujatSuWLVuGwsLCKvNv3bqFLl26wNPTEx999FGV+du2bcO5c+fw4osvqqdduXIFHTt2xBNPPIGkpKQqj4u0qUiEtMUQFRWFqKgonDp1CkOGDDFk99Ts7OwQFhaG4OBgvdrXPP7448jNzUX9+vURFham8fHy8lIvFx4ejjfeeAM7d+5Ev379kJSUpJ4XEBCAcePGYcuWLfjXv/6FVatWAQC8vb1x+/ZtjX2taAMjRUxEpImJDJGVW7ZsGcrKytCmTRt89913OHfuHNLT0/Hpp58iNjYWTk5OWLlyJb7//nuMHTsWJ06cwIULF/DFF19g+PDhGDBgAAYOHAjgf0lMYGAgFi9ejLy8POTm5iI3N1e9vfHjx+P999/H/v37cfHiRRw6dAivvPIKvL29ERsbqzXG3bt3Iycnp85qHuLj4xEbG4s+ffpg586duHDhAg4cOIC3334bR48eRXFxMSZMmIA9e/bg4sWL2L9/P44cOaJuRzR58mTs2LEDmZmZSE1NRUpKinpe27Zt4ejoiLfeegsZGRlYu3atRoNmY2MiIu34aInIyj366KNITU3F/Pnz8a9//Qs5OTnw9vbGE088geXLlwMABgwYgJSUFMyfPx9PP/007ty5gyZNmuDtt9/G5MmToVAoAAC//vorzp8/j/Pnz2uM/QL8r41NfHw8Vq9ejeXLl+P69evw8vJCbGwsdu3aVaX9RwVtPapMSaFQYPv27Xj77bcxYsQI5OXlwdfXF+3bt0ejRo1Qr149XL9+Ha+88gquXr0KLy8v9OvXD3PnzgUAlJWVISEhAZcvX4arqyu6deuGjz/+GMD9WrA1a9Zg6tSpWLVqFTp37ow5c+bU2Fi3ppiISDuFMLSFHxEREZGF4KMlIiIiki0mMkRk0ZydnXV+fv/9d3OHR0RmxkdLRGTRzp8/r3Ne48aN4eDgUIfREJGlYSJDREREssVHS0RERCRbTGSIiIhItpjIEBERkWwxkSEiIiLZYiJDREREssVEhoiIiGSLiQwRERHJFhMZIiIikq3/B80dznzoItdLAAAAAElFTkSuQmCC", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_validation)\n", + "surrogate_parity(alm_surr, data_validation)\n", + "surrogate_residual(alm_surr, data_validation)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_test.ipynb) file." ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(alm_surr, data_validation)\n", - "surrogate_parity(alm_surr, data_validation)\n", - "surrogate_residual(alm_surr, data_validation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_test.ipynb) file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb index 03f3dc97..4aec9441 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb @@ -1,594 +1,619 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Introduction\n", - "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", - "\n", - "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", - "\n", - "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", - "\n", - "### 1.1 Need for ML Surrogate\n", - "\n", - "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", - "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", - "\n", - "### 1.2 Supercritical CO2 cycle process\n", - "\n", - "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", - "\n", - "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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sekxsQoLEJSdLXX4lhYdl5m2R/6zLkueWLZfjzhsoa9atk0eeeEIaN25s9wAAoHoprm98mL4R8BTzPl27Xp7N/EOOO/dc8z599KmneJ8CqHL0c23Mww/K+jVrZODxx8iq156STR+8KluW/273QLQQrHpMVnq6LBw82CwBr1m8ebO8um69vLpmrZxx6aWydsMGGf3QQ9KgQQO7BwAA1Yt/33g6fSPgSeZ9utb3Pj3tkktk3caNcs8jj/A+BVDl6efcg/eMlg3r1splZ54iGyZNkJz0CZKX+ZvdA5EiWPWYvIwMWTNxolkCXrFgU65MWLNO3t2YI3+9+hpZnZUlt6WlSWxsrN0DAIDqxb9vHGj7xtvpGwFPMe/T1WvlnQ3Zct6VV5r36R333MP7FEC1o597d99+m2StWSXXXnCebP74Ddn4zvOyefGvdg+Ei2DVYxJSUsw4qzocALC/zc3JkedXrZaPtm6TwSNHyrLVq2XkLbfYrQAAVD+ub5y8ZYtc/ve/m77x7/SNgKeY9+nK1fJh3hYZVPgd9o81a+TG22+3WwGgevvHyBGy+o9l8o8rB8ufX78vWW+Mk9yFP9utCBXBqsdosNpuzBhpmppqW4CKl7ExW55ZvlK+zN8tN9x1tyxatkyG3nCD3QoAQPWjfeO4Fatk2s5dcn1h37j4j+Vy7fXX260AvMD3Pl0pU//Ml+vuvFOWLF8uw0aMsFsBAP6GXXuNLFv0m4waOVxiZn4h6/79hOTM+8luRbAIVj1me2amGQYgPyfHtgAVZ9aGjfLkHytkZs1YueOhh2Te4sUy+Mor7VYAAKqfmRs27Okbby/sG39dskQup28EPEXfp08sW174Pq0ptz3woMz//XcZcvXVdisAoDRDBg+WRb/OlUfvuk3q/fq9rJn4mOT8/IPdirIQrHqMjq/6Y/fusmLsWNsClL/v12fJY0uXybz69eWBp56S2fPny4WXXGK3AgBQ/XyftUEez1wm8xvEyf2ub7z4YrsVgBfod9jH9TtsvQZy/xNPFL5PF8hFl15qtwIAQnHBBRfIvNk/yjMPPyDxS3+WVS88JJtmf2e3oiQEq0A1Nr3wpPHhJUtlaeMm8vSECTJjzs9yzrnn2q0AAFQ/0zdslIeW/C5LExvLUy9NkO8z5si59I2Ap3yzPsv3HbbwffrEiy/KDz//LOf99a92KwAgEgMGDJCMGd/JS08/IU3WLJLl4++V3J++sVsRKGb18hUFS5f/IX369LFNAMIVExMjz/TuZe9511eFJ43XLl5i1vM//1xOOOEEs14dfVn430z1KygwSwBAdFWWvvHrjRtl28XPm/Xr+hZU674R1U9leZ9+mbVBBv2+VOoXfm/LT0+XE/7yF7sFiL7Ue6aYZfrd/c0SqI50mMrpiYmyvVYtuaDJAdKkT39p2DPFbvWu2XdcIQUVdI5PxSpQTeQXfqhM3bBRRs1fIJsPOti2CieOAIBqy/SNG7Mlbf7Cwr6xjW2lbwS8RN+nU9ZvMN9h8w4+WOo3aGDa+/XrZ5YAgPIXV/jZ+97bb8rB27NlyT9vk83ffyEFu/Lt1uqNYNVjdGxVHWNVx1oFomH7rl3y2YaNcve8X2VX+w4y+fMv5MMpvl9fAQCojrRv/CI7x9c3tmsvH37+uXzwxRd2KwAvMN9hs/Q77PzC77Dt7XfYqRIbG2v3AABUpGOOOUa+mPyBfP7Rh9Kh5g5Z9PitkvftJ7J7x3a7R/VEsOoxWmadl5Eh2zMzbQsQnrz8fPl4w0a5a+6vUrdLV/nq2+/kvx99JL16ef8yLwAAyoP2jZ9m55i+sfZhXegbAQ8y32GzNsqdv8yTOocdVvg+/VYmffIJ71MA8Aj9PJ486V359qsvpWvDWrLgsZsl7+vJkr81z+5RvRCsekzT1FRpN2aMJKR4f8wKeFPOn3/Khxuy5c45v0jjI3vITxkZ8np6uhxxxBF2DwAAqhftGydv9PWNCd2P9PWNkybRNwIeou/TD9ZvMO/TxO7dZfacOfLGe+/xPgUAj9LP5/++/prM+ekn6dGskcx/5B+yedp7sjM32+5RPRCsekxccrK0HjGCYBUhy9qxQ97bkC13zJ4jB/3f/8lvixbJxDfflEMPPdTuAQBA9eLrGzeavrG16xvfeIO+EfAQ8z7N2rDX+/Tlt9/mfQoAlYR+Xr8+8SVZ9NtvckzbljL34X9I3pRJsmPjertH1Uaw6jE6DICOr6pLIBhrtm2T/xaeNN4/b74c2r+/rFq1Sp59+WVJSkqyewAAUL1o3zhpY7bcN/dXOfSEE3x940T6RsBLzHfY9VnmfdrxeN932OdffZX3KQBUUvr5PeG58ebz/IQu7WXRU6Nk82dvy/b1a+weVRPBqsdkpafLwsGDzRIozcqtW+Wtwi+jYxb/Lt1PPU1WrV4tTzz3nLRo0cLuAQBA9bJC+8asDfLP3xZL99NOl9Vr1sgTz9I3Al5i3qfrsuSfiwrfp6efYd6nT/7rX7xPAaCK0M/z8U+MldWrVsmZRyVL5gsPyeaP35Btq5fbPaoWglWPqZuUZIYDiE1IsC3A3jLztsjr67NkfOYfcuy558nqtWvl0aeekiZNmtg9AACoXnx94wZ51vaNa9avl0eeeIK+EfAQ8z5dt968T/uec46sKVznOywAVF36+T7m4Ydk3erVcl7K/8mq156STR+8KltXLLV7VA0Eqx7TfNAg6TF7thlnFfC3ePNmeXXtOnl59Ro59eJLZO2GDXLvI49IXFyc3QMAgOrF9I3r1ssra9YW9o0X+/rGhx+mbwQ8xLxP16yTVwq/w55y4UXmfXr/Y4/xPgWAakI/7x+8Z7RkrV0jl515iqx/9wXJSZ8geZmL7B6VG8Eq4HELNuXKhMITxnc2ZMt5V11tvozeMXq01KpVy+4BAED14usb1+3pG9dkZdE3Ah5j3qer18jbWRvl3CuvlDWF32HvvO8+3qcAUE3p5//dt98mG9aukWvOP0c2f/S6ZL/7L9m85Fe7R+VEsOoxmWlpMj0xUVaMHWtbUF3NzcmRf61aI5O3bJVBI0bKH2vWyI233Wa3IhKzP/9MXhhxg9zW7xj55uCDzE3XtU23AQC8yfSNq7Vv3CKDR9q+8dZb7VYAXmDepytXy+S8LXLZDSNk+dq1ctMdd9itAACI3Pz3v8vq5cvkxiGXyY5p70nWG+Mkd+HPdmvlQrDqQfmFX0b0huopY2O2jFuxUqbt3CXDCr+ELv7jDxnG0BBRsX75chlz2aXy/r33yO45s+XoBg2kd7fDzU3Xd83+Ud4bnSaPX3Sh2RcA4A3aN45fscr0jdfdeVdh37hcht5wg90KwAvMd9jlK2Tan/ky9LbbZHHhd6nhhSfOAACUZPjQa+WPJYtk1MjhIjO/kHX/fkJy5v1kt1YOBKseo2Osdps61SxRvczasFGeWLZcfqhZU2574EH5dckSueLqq+1WROq7Sf+VtDNOk7iNG6Rf40Q5NCFBEurUkVqF/9560/VOjRtLStMmErchS9JOP1Wmv/WmfTQAYH/QvvHJP1bIzJqxcttDD5m+8fIrr7RbAXiB731a+B22Rk259f4H5Nfff5crhw61WwEAKNuQwYNl8a9z5dG7bpO6876TtS8/Ljk//2C3ehvBqsfUTUqShJQUs0T18P36LHl86TKZV7+BPPDkk5Ixf4FcdOmldiuiYfq778h7jz4iKS2aS8fYmra1ZJ3q1ZWUli3k/ccfk+nvvG1bAQAV5fusDfLPzD9kXoPCvvHpp2X2/Ply4cUX260AvMB8h/09U+bVqy/3jRkjGQsWyMUUhwAAInDBBRfIrxk/ydMP3S/xv8+RVS88JLkZ39mt3kSw6jFZ6emyZORIyZk2zbagqppe+GX04SVLJbNxE3nqpZdkxpw5cu7AgXYroiUvO1vevO9e6dEoXg6sX9+2lk337ZHQSN68/z5zDABA+ft240Z5+Hdf3/ik9o0Zc+Scc86xWwF4wTfr1stDi3+XpQmJ8sQLL8iMn3+W8/52vt0KAEDkBgwYIBk/fC8vPf2ENF69SJaPv1c2//SN3eotBKsek5eRYSauIlitur7K2iD3/bZY1jZvIS++/rp8NXOmnH7GGXYrou31u++STi1byQF169qW4Gm42qFpU/nPHUwaBgDl6auNG+W+RYtlzYHN5cX/FPaNP/wgp59+ut0KwAu+XLfe9x22WXN5qfA77Nc//ihnnn223QoAQPTp98Efvv5SXp/wgjTPWSnLnh4lm2d5Ky8jWPWYuORkaZqaylAAVcyuggKZmrVB0hb8JrkHHSxv/u9/8vn06XLiiSfaPRCqhYMHy/bMTHuveAu+/14WFZ6cH1Y71raE7vC4BuYYeiwAQPRo3zgtO8fXN7Yu7BvTC/vGb76hbwQ8RN+nX6xbL6PmL5Tcg9uY77BffPednHjSSXYPAADKn34//PrzTyX9zdfl4O3Z8vuY22XLD1OkYNcuu8f+Q7DqMRqqdpk0icmrqogdu3bLZ1kb5K5582Vnu/bywaefyuSpU+XYY4+1eyBcWtU9o21bmTdgQIkB689ffC5JjRMkxt4Phz724Lg4mf3hB74GAEBEtG/8IjtH7v51gew8pJ2vb5wyhb4R8BDzHXZ9ltxd+B12d4eO8uFnn8lHhd+9eJ8CAPYn7Ye+mPyBfPrh+9IuZrssevwW2fLdZ7L7zx12j4pHsOox+Tk5JiTSJSqvLfn58vGGjXLX3HlS+7Au8uX06ZL+ySdy1FFH2T0QLToucUkB69yvvpQDdxfYe+FrUaeOzPt2ur0HAAiH9o2fZueYvrFW58Nk2jffyKSPPqJvBDzEfIddn7XnO+y0wu+wk/gOCwDwGO2XJk96V6Z/OU0Oa1BDFjx6s2z55iPJ35pn96g4MauXryhYuvwP6dOnj23C/pSZlibLRo+WNqNGSVLhOiqXmJgYOeWg1jJl9Rq55Pzz5R933CGdOnWyW73ly8LXqvT/a5XRyieeKPYHCK36bjdmjBlOY0SvHnJq8wOlXmwtuzU8W/N3yieF/03HzpptWwAAwfLvGy+7+GK58ZZbPNs3pt4zxSzT7+5vlkB1oe/Tk1u1lGlr18nFHv8OOz0x0XwH7JudLbEJCbYViD76BMBX/Kefu/p5q5+7XrVgwQJ5+LHH5d+vvCzNjzlZVkz9UAoKIi+yCgbBqscQrFZu+qV02NVXm5PGtm3b2lZvcl9KqyoNV9PGPS0XdzksoqEA1O7C22vzfpUXflvsawAABK0y9Y2cRKO60vfpNZdfLjffeWel+Q5LsIryRp8AVJ5g1Vm6dKk8+vg/ZfwzTxOsVmcu7OKLAspTXkaGuYy+siqpYjUhJcWEqjoRnFasnta8mdSNDX/yKrUtf6d8TMUqAFR5nEQD3kewiopCnwBUvmB1fyBYBVAp6biq/mOq+geqzt2nnCRHFC4PqF/P1xCm9Vu3SUZ+vtz3xVTbAgCoijiJBryPYBUVhT4BIFgNBpNXeYxWEOokPGsmTrQtAEqjgWqP2bOl29Spe4Wqqsuxx8m6mEgHAhBZvWO7dOnzf/YeAAAAAAAAwarnuMuzA2c3B7C30gJVp9uJJ0lmdrZEMrKKPvaPzVuk+5ln+RoAAAAAAAAK1fDFBvAKDYt04ipdAijZoRMmlBioOp2OPlo6HtVb5m7fYVtC90vuZmnfs6c5FgAAAAAAgEPFqsdooJqUlkawCkTJ+ffcK4vWrpV1W7faluDpY37bsEEueuhh2wIAAAAAAOBDsOoxDAUARFdcYqL89Y475afcvJDCVd33p+xNcv6dd5ljAAAAAAAA+KshEvnELogeJq8Coq/vuefJWTfeKFNXrZYF27fb1pLNz8uTqStXyek33CB9zxtoWwEAAAAAAIpQseoxsQkJ5gYguv7vvIEyevLHsqXJATJtfZYs2LhRcnbskJ27dpmbri8sbJu6Zq1sOeBAGf3RJ3LsRRfbRwMAAAAAAOyNYNVjWo8YIX2zs804qwCi64CDDpK///s1OTtttNTo1l2+25wnX/8yV2Zk/GzWYw4/Qv5y731y43/eMPsCAAAAAACUhGAVQLXT/cST5Monn5KHvp4u5yQdIqc2aWrWr3x6nNkGAAAAAABQFoJVj1kxdqzMaNvWLAGUr5xp08zNTRoHAAAAAAAQLIJVj8nPyZHtmZlmCaB8LRs92q7tvQ4AAAAAAFAWglWPaZqaKl0mTZLmgwbZFgDlwVWrOlStAgAAAACAUNQQKbCr8IK45GQTrtZNSrItAMpDcRWqVK0CAAAAAIBgUbHqMVpBp+OravUcgPIRWK3qULUKAAAAAACCRbDqMRr2LBk5knAHKEelVaZStQoAAAAAAIJBsOoxOgRAQkoKQwEA5USrUnWCOH2PFXfTieOKq2YFAAAAAADwR7DqMTppVbepU5m8CignOo5x76VL99zajRljbv5t+uMGAAAAAABAaQhWPUar5fQGoGLMGzDA3AAAAAAAAEJBsOoxOnHV9MREyUxLsy0AylNsQoK5AQAAAAAAhIJgFUC11jc729wAAAAAAABCQbDqMa1HjJAes2czxioAAAAAAADgYQSrHqOXJOvkOjo7OYDyN6NtW3MDAAAAAAAIBcGqx2Slp8vCwYPNEkD5256ZaW4AAAAAAAChIFj1mLyMDFkzcaJZAih/XSZNMjcAAAAAAIBQEKx6TEJKihlnVYcDAFD+mqammhsAAAAAAEAoCFY9RoPVdmPGEPQAFWTF2LHmBgAAAAAAEAqCVY/RsR51GID8nBzbAqA8LRk50twAAAAAAABCQbDqMTq+6o/du1NBB1QQrRLXGwAAAAAAQCgIVgFUa92mTjU3AAAAAACAUBCsekxSWpr0KygwSwDlT4fdYOgNAAAAAAAQKoJVANXa9MREcwMAAAAAAAgFwarH6NiqOsaqjrUKAAAAAAAAwJsIVj1GL0nOy8iQ7ZmZtgVAeeoxe7a5AQAAAAAAhIJg1WOapqZKuzFjmKUcqCBxycnmBgAAAAAAEAqCVY/RgKf1iBEEq0AFWTh4sLkBAAAAAACEgmDVY3QYAB1fVZcAyp++3xjTGAAAAAAAhIpg1WOy0tNN9ZwuAZQ/rRDXGwAAAAAAQCgIVj2mblKSGQ4gNiHBtgAoTzqmsd4AAAAAAABCQbDqMc0HDTIzlFNBB1QMHXaDoTcAAAAAAECoCFYBVGs/du9ubgAAAAAAAKEgWPWYzLQ0mZ6YKCvGjrUtAAAAAAAAALyGYNWD8nNyzA1A+etXUGBuAAAAAAAAoSBY9RgdY7Xb1KlmCQAAAAAo4ib5pRAFAOAFBKseUzcpSRJSUswSQPljjFX8P3vvASBZVab9P7dy7K4OkzMzwOAwzICkIQtIRkBRZEGFVTEtK6j/NbAK+n2gu58Kyuq6uizoigooQaJIlCxpCMPk2JM7d+V4/+c5996ZmpoKPT2pe+b9Dafr3nNPrjqHuk+99z2CIAiCIAiCIAiCMBREWB1mdN1/P5Zfey36nnnGjhEEYXeSmD9fB0EQBEEQBEEQBEEQhB1BhNVhBgUeblwlwqog7Bmm33yzDoIgCIIgCIIgCIIgCDuCCKvDjMjcuWi/8EJxBSAIe4iJ11yjgyAIgiAIgiAIgiAIwo4gwuowg6LqrPvuk82rBGEPsfGOO3QQBEEQBEEQBEEQBEHYEURYHWZwd8vMqlWyy6Ug7CEWX3mlDoIgCIIgCIIgCIIgCDuCCKvDDPpXfWXaNP0qCMLuh+43GARBEARBEARBEARBEHYEEVYFQdivef+bb+ogCIIgCIIgCIIgCIKwI4iwOsyYesMNOL63VzbTEQRBEARBEARBEARBEIRhjAirwxBPLKaDIAi7nxdaWnQQBEEQBEEQBEEQBEHYEURYHWZ03X8/Flx0kexSLgh7CG4UJ5vFCYIgCIIgCIIgCIKwo4iwOsxIzJ+vxdXMqlV2jCAIu5M5Tz+tgyAIgiAIgiAIgiAIwo4gwuowI3bKKZhy/fX6VRCE3Q/nmsw3QRAEYSRjGIaEfSQIwkii2md4XwoPXH+aDtWu7UtBEISdw9jQ0WGu7OjAvHnz7ChBEIT9h+XXXqtfp998s34VBEEQ9l8u/N5T+vX+75yqX0cKvDHeGBd/4SOdsdFemKZpnwm1eGXaNP103zErVyIwdaodK+wNZO0Z+ci6IzSCbvO4Jwn3AeJG68L2iMXqMENcAQjCnmXtLbfoIAiCIAiCIAiCIAiCsCOIsDrMkM2rBGHP0n7hhToIgiAIgiAIww9aS731gQ9sCc6mo7xncuL6nnlGxwmCIAjCnkaE1WEGzasZBEHYM8y67z4dBEEQBEEQhOGHc39E8ZTBEVb5pB/P+aRfZO5cHScIgiAIexoRVocZE6+5RvutmHrDDXaMIAi7E34ZF9cbgiAIgiAIw5d6vvAnfPnLYpgiCIIg7DVEWBUEYb+GGyAwCIIgCIIgCMMTblJVzXUT48decYV9JgiCIAh7HhFWhxncRIcij2ymIwiCIAiCIAiCYFHNalWsVQVBEIS9jQirwwz6DOJjyY7vIEEQdi90vcEgCIIgCIIgDF8qrVbFWlUQBEEYDoiwOszglwVupCNfEgRhz+BsiCAIgiAIgiAMb8qtVsVaVRAEQRgOiLA6zOCOlhRX+QusIAi7nwUXXaSDIAiCIAiCMLxxrFbFWlUQBEEYLoiwOszoe+YZ7V81MX++HSMIwu6k6/77dRAEQRAEQRCGP7RaFWtVQRAEYbggwuowg8Lq8muvFaFHEPYQU66/XgdBEARBEARhePL7Nzbjkl+/hyk3vIS2n3XgiOUH6eNLfr1AXxMEQRCEvYUIq8MMPtYSO+UUcQUgCHuIqTfcoIMgCIIg7Ku8+FzBPqpNozS//FnWPqpOf7+Jd98u2mfVefShvH1UnUZtGEwdjcq4686cfVSdXVHHruiHYPFaRxzH/Ph1fPfBZViydBMONNP4QKSAo9uD+njJ0k59jWmYVhheDGa+dawp2WfV2RPrRqM27OyawPJ3to5GY8n8jepo1E5BEIaGCKvDDPoKmvP00+IzSBD2EOIKQBAEQdjX+dXPM3XFCV5jmno89nCu7k07b9jrlUFx40ffT9tn1WEd9W78B1PHlz+ftM+q89hDO9cP8p1vpOyj6tx1Z7auCDKYOgTge4+twnE/eRO+VBozjQym+ExE3IDHMHXg8RRfSV/zqjRMe/0jK+3cwnCg0Xy767e5hnP6O1+vP9+Yf2fmG+s4/bgB+6w6V16aqNuPRm149ME8fnhT7fWPbfjIOfV/GGg0lrzOemoxmHVeEIShIcLqMKPQ16eDIAh7Btm8ShAEQdgXqHfDTUsm3nTXYsHbhbqCJvMvUKHeTfvdd2ZVHbWvs3yWU68elk9RshaDqYPjUGssKF5QXLjrt0Ovg31gqCeiLHiH/axdxkvP5+vWIQA3/XUN/t9THTg5UkBbqfZYO7SrNEz7o2fW6rzCnqPefOOcrCco8scUiqu14LUBVU6t+cY6WH+9+cY5XW/dceqv1Q/Od7ah1vrHNlh9rd0G9rPenG/UBsZz7ao/lvWFU647HAe2VRCEXYsIq8MMblz1QksLVsmjyYKwR+DGB7L5gSAIgjDSqSUW8mb87PO9dYUF3pCfdZ63pnjBvJdc7qt50+4ICyyjljBAYfeSy3w1hVO287gTPTXbOZg6KKDoOmqMBcWLq77kV/2ofp3ls+xG/fjaNwM1RRSnHxRXa8E+1uvrvkoyM7j+8pH+Gx5bhWNDeYR34G6VaeepPDf8ZZW4BdhJBvtekVpiH+fbZ7/or/k55xxrbjbqzjeKgbf8IlxzvrHss8711pxvzrrBNZBzsxoUPb/6zWDNdYNz/nv/FtLpqsE2HHqYu2YbKMw6/aw1Fk4baq2xFHWtdad6ftYxabJLt6PaWHIMmJfruPyoIwi7HhFWBUHYrzm+t1cHQRAEQRiOvLuqF0/N32Cf1aaWWEhhYt4J3ppCnnND/tkv1hYLedPP65OmuKretDvCwlnn1RY1KTpQnKglDNBqlvnZzqHU4YiirKPeWLAftYQc1sH6WUctgYMi9Ge/FKgporAfznhXE3IYx/H+2GX+uta5+yIU6y783lO47fGldkx1rvr9Ihzd4tohUdWBeY5sMlQZi+0YYSis3BjHZf/+Nzz4cocdU5tac4Hz7ZLL/TXXHoqFs2Zbc7qaOLtFFFXztVYd/DHFWTeq/TBEcfe4E9ScPpeC4vbXuQ5Q9LR+OKo+H50fnjpWV7eG121Q5ddqA/vONUGvXVXmfHkbalnFcg12xrJaG9g3rimso1oZztpG8bbaOAiCsHOIsDrMmHjNNXj/m2+Kj1VBEIR9gHVXh5F+8177TBAEYcehGPXTPy/UIkc9gXUwYmG1m3rnhryWxRWFDT4GSzHwEpWu2k17ubBQTZxwrDgpHtQUBh620rCd1UTNRnU4/axlAeeINOzHcSd6q/ajXIR2Hs0txxGhnX5UE1GcfrCcagIGhVeON9NUE5v2ByjW1RJYucN/IpnTj/YPldHIqzKyuixh6HDt4XvUSAyv9lnm/HPmW821xxYLawmnjihKqs03Z05z7eIPJtXmW7m4W60OR9yttW6Ur11f+1Zwu3XDaQPT1BIt2QZazNbqZ3kbWE5lGxyLV2csq4nQW9dPb1Vf1s76yXJYHtssCMKuQ4TVYQYfSY7MnYvA1Kl2jCAIu5NXpk3TQRB2F31/uBrJ535lnwmCIAyNRgJrNbGQYke5EFhNyHNuyAlfK8ULiht81Jbwpv3u320rkPAGnUIA87KeauKEY8VJqgkD2wmWFaLmYOqgcODUUWssHJGmlvig09SpwxGhCUWKSgvf8n5QRKk33k5fK+vYn6gmsP7h9Y2IlrYXvXeUJjOvytpknwk7Sz0xnHOhUjjl/HPmWzVBsVwsJJwLlfPFEV5JNYv68jnNcirnkiN6ltdRub6V11HtRx3nxxZSbf0rbwP7U9kG9pOUt6HSkr2yDZXrI+tw2lBrLJ11h6Fy7SpfPwndpYg7AEHYtRgbOjrMlR0dmDdvnh0l7E24O3n3Aw+g7YIL0H7hhXasIAi7i2cNQ7+ebMovt8Kuhxar7R/7Ifqe+ClCR1+K6Lnfsa8IgjAcoXBAPn3Ggfp1OLByc6KqkBoOeHQ7T507Dob6f9nitTEcPatfvzrwBn3WYR59M064Yz6trpybfN6QU2C8/fcRfc4bfoqHP/lFWJ8T7obNx+udPEepOu59NLrlnHkoPnzvByF9TuGiv88EfZk6nH78AP70SFTf9PMmf0fbOdg6nnihSR9Xq4P9oDjjiAuV/WIdFGedsWjUD+fcqZNU6wetfJ06q413eb8+9+M/6te9CT9Xu5N6vjvPP3YSbnihEwcjo3f83xkSRXVfZXjx4wtn2DEW9ep3SGQai06p3PaWh9UYTH17Mg1JDqJ/LKteeXyvuP5w7dkYb9luLnz4nLheR5z59Z1vpLQ46MwNzpWmZteW+VW59lAI/M7XU1vmCqmso3IOs45Zsz1aOCS//FkWA/0lvZYQlsl6nToq5yPXjdOPG8CrC5r1OWlUZ+W6UtmGyjWBIilF6FptIFxjy9swmLEsr6Ny7apcZ9jPa9Ta5NQ5NtoLU+6DhDpwc3XuA0QjQHGhVx0RVocZ3LRq9Xe/iynXX4+psoGVIOx2+GMGkR8yhN0BhdWxX7gHxWQv+p68Ff5px6D5kp/YVwVBGG44wupIYnQsgDu+fiY6Or3b3YBXCoG8wab1qCM0VN6Qk3IhgTfgFA7uVWU4UKwgzk175U1+pahZTTioFE4r20lhoGN1cUs7G9VRKRyQ8rGo1o9K8aFyLAbTD7bLcU9Aqo13uVjUqI7hIKzuTQ6d2oL/XjCAI0MleIydE3oKprHTZQjVofjOH3TOP3oSxrQEtbBaLihWmyuVgmKlQEnK155KUZSU11FtTjOuXDBsVAfnY7m4S8rXpmrrSvm6MZg2cB3isbMmkMG0wflBZjBjWbnuVK4rbGO5+EvKfyATYVVohAirjRFhdZjR98wz2mK1+eSTRegRBEEY4TjCqqZUQN+T/wEjMgqtn77TihMEYVjBDVueenujfTY82NyXwSuLOu2zrVDcOO/oiTht7vgt4kb5TX+1G/LKG+5KIZaUi4WVAiepFA4qb+pJuaBRTbwtL3cw7RxqHc5YVBNIWEe5dVq1OspFlmp1lAuntfqxI3UMB4FjsFaPQ2FzXxrX/vJV+2wrFFQ/fcYMTBsbRej/+xtOixbhxs4Lq5uLBi4/cowdYzEYi9yQr7G5bCSw9XNQj8HUtyfTkPAg2s618Pt3v2OfWbB8R1DlDzrEsVjl55+P0XMuVP744sDPPwXFanOFlP+AUm1tKl97qgmvxJlj9AvNuVUuepJycbaa8Fo+p6sJkuVzmmnLf6hyoGj5xItNerOrWv0cbBuqrTvEGctqaxtx1pUmtdawjspxKH+PRFgVGiHCamNEWBUEYb9m7S236FduHCcIu5pthFWb/r/9N0r5NNquugeGf9sv24IgCJVQVC0XOChuOIJqpbjBm/6PnBPXN9y1bsidG26KjtVu+iuFBW7Iws1hynGEA1pOlT8+71AualYTE9lORzht1E6KMOVWnw6DqcMZi2oCCXGstmqNRXkd1YQe4jy2O9TxLheZ93WBgz8SXPXTF+2zbQVVh0nXv4SDkd4lrgAWI4CO7x5nxwg7wrurevGvv3lTH1cTVB2ctYc4Yl+tueIIivy8V5srjjjLuVptrhBnrlNgrRQkiVMH51s14dURZ7muNaqjmiBJnDlNVwXV1keuBZOmuHe6n4MZywXvWP5XK+tw1hXWX0385Tg4/RNhVWiECKuN2XaGCnudzKpVSMyfrz+8giDsfpZfe60OgrCnaD7pM/A0jUbXzaeh2L3KjhUEQagPxY1LTpqKm686GpeecsB2AgehsEhBgb4EyzelKsfZ2IliJW/IK+ENOgVT3ngzVIoGhDtcc/MTZ4f7Srh5CoUDCggUBMoFT8JzZ4OVF58vVK3DaSfr4IYulQymDo4Fr5dv3FIOxQZnLBr1g+VVihuE5dYbb7b9rt9mG9Sx7YY4+zoUVG++6ij8308evo2oSg6fEEF3cdv3cih0F126LGHocM05ZuYo/T7Rn2q1Naccfv4p+NWaKx/7B79aW/J6zlebK1wHuDFTrbWJMB83t+Ocq1aHszN/+YZQ5bBter7WmI+EdVDwrLYuEa5NnNO11sfPfilgt6H6mlDez3ptoEDbcCzVWl0pqhJnXeEPX2xvJXocVpf0WAiCsPOIsDrM2HjHHXj98MO3WNEJgrB7iZ1yig6CsCeJHnUJ/FOPROctZyC3+nU7VhAEYXsGI6iWQysobTFVRWwkvOGmaEBxgwJpNRzxwtntupKt4kV14UCLAVNcWpSsLRx46woHTjt3pg6OBYWeavkJ66AA06gO7gReS+ihcEoLtlrjzXIpcNQSkJmH7dgfBA5+lmsJqg6XHjkG/Ub1sd4R+g2PKmusfSbsKKNjQf0+ffNjs2u+V5VwvnHdqDUf+fmnEMjPfLW5QjhfaG1Za23i2sM5fda51a+z3Ho/hBCnjprrwrk+/ag8X6vB+frD72dqro9OG2qtCWQwbeC60Wgsa+VnvWwnxd9aaZwflgRB2HlEWBUEYb9mztNP6yAIe5rwYecicsRF6L71HGTe+4sdKwiCsC207huMoOpwyeU+fcNe64acN9y80a4nPFAstATJ+uJFPeGAm6/QqrWmcKBu+tnOataohOXOOsy9U3VwLCg+1BJFWS6tthrVQZGlngjNOuqNN/vK11rj7Vjn7utQWG0k0l16xGhEI35sKFV/TwfDuqIbTaoMliUMDa43gxVUHfj55g8R1X5AcOA8qjVXCMVZ5q81V5x5VM0a1YFl1JrzxHl8v9ac55ymFWitdYX56B+1URsG0896beBY1moD4VjWEn8J19Za4i/R61Ksev2CIOwYIqwOM6becANONk39KgjC7oduN8T1hrC3CB58MppP/RJ6fvlxJF/+rR0rCIIwdHijzhv2euIGb7hnza59nTfzzN/opr6ecMCbduavJRwwvp54QShq7mwd7Ee1R2UdKJA0qoP56wk9LKPeeFM4rSUgExE4tuW2f5iJN+NAcghGvMzzVsLAf6syhD0PN1GqNVcIBcV6c555KXzWgz5J69XBH1Rq/RBCOGcb1UH/rfVgPxq1YWf7ybGstbaRRmPJa/XEX2ftEgRh55HNqwRB2K951rC+sPAHDUHY1VTbvKoa+U2L0ffETxH5wNWInP4VO1YQBGFwlG8gQ2hNWk/o4+Oh3DW7njDQqAw+us4dp+vd+A+mjHptYDvJztTBMurlH8xY0LK2ngAxmH40qoPIJjJb+b+Pr8b3VTguUkC4/rBtgaLqi0kPvnH6FHz7zCl2rLA7qVx7hJGHrDtCI2TzqsYM8n9Twp6CvlXpY5W+VgVBEIT9A++YgxE75zqkXvo1Bu7/lh0rCIIwNOoJjYRCYyORr1EZzF9PsCSDKaMeLH9n62iUfzBj0ciqazD9aJRG2JZ/PWMKvnzyBDwb9+hH+xuxNu/Saf/5pAkiqgqCIAh7FPk//DCDvwYk5s9HZpXsFC0Ie4L3v/mmDoKwt/G2jEfLudcht/Rv6Lvz83asIAiCIOyf3HT+dLx4zeEwo2G8lfNiVdaFRFHdL5mGDjxelTX0NTRHdNrvqzyCIAiCsCcRYXWY0X7hhZh+882yS7kg7CEic+fqIAjDAVcggpZzv4Vibwe6/+tiQB7NEgRBEPZjjpwUxWtfOxI3ffhgTJ3WisUI4Mm4WwceT53Wrq8xDdMKgiAIwp5GfKwKgrBfs/jKK/Xrwbffrl8FYVcyWB+r1Yi/+Bvk45vR+tm74Y6027GCIAjbI34O9w3E16Ew0pC1Z+Qj647QCPGx2hixWB1m0A0A/avyVRCE3Q/nm/g0FoYj0eM+CW/7NHTf8kEUNi2xYwVBEARBEARBEIThggirw4yu++/XFnR8FQRh9zPxmmt0EIThSPT9H0Hw4JPQ9ZMzkFv+oh0rCIKwY7z4XME+qk2jNL/8WdY+qg53vucO/fV49KG8fVSdRm0YTB2NyuAO//XYFXXsin4Iwr7AYOZCx5qSfVadPbFuNGrDzq4Jg+lno+uN1i62sVEZjdopCMLQEGF1mBGYOlX7e6SZtSAIux/6NGYQhOFKaNaZiB5zGTp/ejbS8+VHN0EQdpxf/TxTV5zgNaapx2MP5+retPOGvV4ZFBZ+9P20fVYd1lHvxn8wdXz580n7rDqPPbRz/SDf+UbKPqrOXXdm64ogg6lDEPYF+DmvN9/u+m1Ohdo/2nBOf+fr9ecb69jZdePDZ8fts+pceWlCp6sF1656a+xg+tmoDY3WLl5/9MHabaDwyrVJEIRdjwirw4yxV1yhdygXCzpB2DPQ7Ya43hCGO4EDj0fLOd9A72+vQuqF2+xYQRCErdS74eYNNW+6a7Hg7UJdYYL5F6hQ76b9bnXD/lgdYYHls5x69bD8ejf+g6mD41BrLCheUPyoJ3A0qoN9sASKOuP5DvtZu4yXns/XrUMQRhL15hvnZL35xrnwq5/Xvs78A6oczrlaWPNx59YN1lGrHyyf1+qVwbWr3ho72H42Gst6azDXHdZTC7ZP1h1B2D2IsCoIwn7N64cfroMgDHf8k+ag7fzvIP6Xf8PAo9+3YwVBECxqiRcUEs8+36tvymvx2MN5nHWet6ZYyLyXXO6rafXFm34GllFLGOBN/SWX+WoKIGzncSd6arZzMHVQQNF11BgLWo1d9SV/TYGD5bPsRv342jcDqp3VBQqnHxQ5asE+1uurIIwkaol9W9eN2vNtUHO6ztrTaH3bkTpq9YNz/ie/COvXalB4PfQw925tgzOWtaz+HeGZr6yrGs46zzETBGHXIsLqMGPVDTfoHdfW3nKLHSMIgiAIFp5RB6Dl3OuQefNP6P/jV+1YQRAEPg5bXbygBdO8E7w1hTzeiE+a7MJnv1hbLORjrrw+aYqrqjDAciksnHVebVGTQuP3/i1UU3yg1Szzs51DqYNiAvOxjnpjwX7UEjhYB+tnHbWEHIoTn/1SoKZwyn44411NwGAcx/tjl/nrWtkJwkihlthHsfCsc33159sJHjVXvFUFRUeQ/Oo3gzXXjfL1bajrhlPH3b+rvXbVKp9QcOV8rteGwfaz1rrDsfzYP/jrjiXHgT8sVbNKddZ5jkMtgVgQhKEjwuowpNDXp4MgCLufk01TB0EYjvTFk3j21Xfw6/ufwKp1m3ScOzoKLed+E7k1b6Dv11fqOEEQhMGIhdWEPEcUoPhQTSzkDT8fUeVN+SUqXTVhwBFQWE81UdOx4mxuNmqKDxQsnXZWExca1eH0k3VUGwtHvGA/agkc5SINrVsrccQJpx/VLHydfrCcagIGhVdHhGGbBWGkw89yrfnGa7XmG+c0r3G+VpvznB9cl+qtG+XrW7UyOAedOV1r3XDq6O/b/lF8xxLUWjfqz/m6a5e6xn5WE6HL21DtxytnLJmm1ljyxy9a7vJHn2rrjrPOn63aUM+aXhCEoSHC6jCDPlbnPP20fhUEQRD2bz593c047Ypv6NcZZ/yjFlmJyxtE61n/H8xUD7p/9iGY+eoWDoIg7D9Uu+HmDXu5EMjzShxRgFQTDigwnnWuVx9TGKi06uINP4UA5mU91URNx4qTUGD44U3bigvbCZYVouZg6qB44dRRayxoNUbqCRz16nDECUKRt9LCt7wfFDDqjbfT18o6BGGkwTldbb5RCCScS5WCoyMWOnOhmqBYPqdZR6XFaaN1g1BE5FysNaedH2wIrdmr9cNpAy1G6815tqEyv9PPegJxeRuq/XjVaCxZnvPjF+vgOess58XnrbWN8LXa2iQIwtARYXWYEZg6FbFTTtGvgiDsfip9rG684w4dHKvxxPz5+pyvhPFOGoeu++/fJk1m1Sp9zngHJ09luX3PPKPPiZPGgdd4XllueRqnbl4j9cqVPu35Pk38jxQ8s84dcnjgyZfw5U9egD/99F8Ri4bxm/uf0HU4NJ38ObiDzei65XQU+9bZsYIg7I9UEwspAjhCYLWb+nJRgFBAqBQOaMV5yeVby6i06uINunPDTqqJmpXibeWjquWCJeuoFEAGUwfTU0Ah1caC4gXzkWpj4Tyi74zFYPpRaflV3g/CNOUCRuV417JwE4SRBOdb5Q8u5aIoP++Vwmm5WEiqCYrlc5pzqdLitNG64VjKO1TO6XJxl9CHKde7chxLUML21pvz1daVyn5WitCVbWAfWGc55cJrtbFkec6PX4Rlla+xXHeYj4HwfdmX3ZCsuzqM9Jv32meCsGcQYXWYwRv85ddeu81NvCAIu4/E/PlbRDSy+MordWA82fTrX+vzdT/5iT5nWieNk4/XeO4IdMzLc85lh9Xf/a6Oc+Y20/Kc8aS8XEdQdOrmK2G8k8ahstxqdTt5pE97r087w7e/eBkuOG0ePnXR6VpopXuAcqLHXgrf+Fno+skZyK+zLFoFQdj/4E3zLHUDX37DXS4Ekkqrr0ohsPIxUeemn2KgQ6VVl/P4vEOlqFkpJvK1UgCpbCcFkPJ2NqqjUkCpHItK8YJUChzOI/oOjfpBWF65cFp1vMsEjGrCazUrO0EYSXBONKlQPqd57IiipFI4LRcLSaWgWG1OD2bdqJzT9daNStGTdXCec60grMuxBHVgfWybQ7klKKlcY6v1s1yErtaGjtWlumtX5ViW//hFuEZzrXHgMdvlwLLK1619kb4/XI3kc7+yzwRh9yPC6jAjoW70uXGVc1MvCMLu5eDbb0f7hRfaZ5Y7DgZPLKbPw3Pm6HO+EsY7aRzaLrhAn0fmztXntDjneXm5Yz71KR3nlMu0PG8++WR9TnjO4MBrPHfqdsotT+PU7Vi5O+XS8t3BySN92vN9WvtPIRQWPDykcNuNluDbN5DQr6vWbcaUCWO05WolkcMvQOjQs9B1yxnILpb/fwjC/kr5DXctIbDc6ouiQPlNPSm/6abVk/P4vEOlVRfTlt/0sz6eO8JApZhIyoXTau2k+LBNOxvUUSmgkPKxqBQviFXHVmvRSpGGdZQLOdX6UW75NRThtbIOQRiplM+3SlGU8HNeLpzyM185F8oFxWpzulw4rbVulAun1eZb+Q8ulT/YkPLNn1hXuSUoYXpHtGQbWGZ5G1ifs3ZREK3Wz3Kr/1ptaLR2OWuwI7yWi788dsonleIv28Dz8jT7Gq3n/SsST/wY8Ye/Z8cIwu7F2NDRYa7s6MC8efPsKGFvQusoWj05AoAgCIIwcuHjSGO/cI99tmPQMrX92I/p4zkzD8Bbi1bgO1/8B3znS5fpuGpkVryMvsdvRusnf4XgkR+3YwVB2NcxDAMb4y36Bvsj58TxxAtN2o/prMM821iNkS9/PomvfSuob+4pQtz++4h9xYKiCIWDn/wijCsvTeidqitv7I+a1Y8nXmzSN/20yKosg35aWf5VX/Lj9OMH8KdHotuID2zn0aqMxWtjDdtJ8cJpTzmDqcMZC/aDVlzl4gJhP+59NFpzLMrr+LAqi20oFzAIy3h1QfOQx5t1dKwu6jRjo70wZUNNYQRRvvZcoz7r/HzXmgvOfOOcpjD4vR+E7CsWzNfU7Nol68Zg5jTr4PpQTnk/uG58799C2815J98vf5ZFc8zQQmg55WtXtX4yHxlMG77zjZQWXuuNJUVo1lcO882a7VHBXXOdd/Lta+uO8927mOxF35O3wj/tGDRfYj3RJgwNPrH3QkuLNjw5vrfXjhXKEYvVYQYtp2bdd5+IqoIgCPs5tEx98o4faFF19bpN2h3AP39yq3VtNQIHHIvWD30Hffd8DYln/sOOFQRhf6HcGqrSWsvBsfqiWFlpKUV4A0/BlDf3DJWiKqHPQVp18ca80oqT0KKK1qC86a+0KiM8dyw1q1nNEqedrKP8MVaHwdTBseB11lNtLCgqOGPRqB8sr1JgIY7lV63xdh4Nrl/HVutcQRiJcH44c63WXHAsMatZaRLuaM9r9ea0Y3FaaYXpUL6+1ZpvtJytZlVLWAfLd9a/anOeYiXXSJbTqA3V+ulY/Q+mDayn3lhqVwPV1sdzrc31mJ/trYRl7uvrjjvcgrbzvoXCxoXoua22UYIg7ApEWB1m8NcA+udzfAIKgiAI+y8nHzUbr//pVnS9fPeWDawaQX+rred/G8ln/xPxhyzfsIIg7D/QMpPWXNWECUJhgRZMFCacTVkq4U03fX9WugFwoBUrRYNaAgrr5QYrFCWriRuE4gPbqdNWES+cdu5MHRwLWm5Vy08ckaVRHfSJWE0gIRQ1aKFWa7xZLgWMWgIy87AdFFIEYSRDsa/e2uMIp7XEQuahqMr5VmtO0+WAs25Uq4NziWtCvTlNlwO1RE/CfLQYrbX+cc5zXWFZtdYutoH+qistTYnTz1oCM2EbvvN1Wp26q/aTY8m1q9aPX8zP+llHuf9VB5a5X6w7Lg9iH7wGyKfRdevZMLOWey1B2NWIsDrMoH/VV6ZN06+CIAiCMBQ8rZPQeu51yCx4DL1/uNqOFQRhf4DWUHzctZYwwRtq3nTXEgWIIxxQ/KyGIwzUElAIBRDto7WKuEF4U892VrO2IiyX1mk7UwfHgiJOLfGC5VJkaVQHH92tJ0Kzjnrjzb7ytdZ4OxZugjCSodjXaO1ptG5wnukfdXZi3eCPIfXqsDZ3qr1u8DqtSWvVwXxcN6pZghKnDbXKJxSh6/WTP1416ifbUEv8Jbqda6z1rRr707rTfNJn4Gkaja6bT0Ox29p8VhB2JSKsCoIgCMI+iCsUQ8u530Rp8xL0/PeldqwgCPs6vOGmBVM1KyYH3qzXEgUIb8iZv54wQAGkloBCKIAwfy1xg/G05qpXB0XNna2D/ahmNeZAgaNRHcxfS5xgHSyj3nhTwKglkBAtvMaq90EQRgrOfNuZdYOCYqM5zbm4M+sGf3CpVwfL55yvNaeZj3O2miWoA9tQ6wcdQhF6MGtXo7Gs9eMXYf2Vm2+Vs7+tO9GjLoF/6pHovOUM5Fa/bscKwq5BNq8ahjhuAJxdqQVBEISRyc5sXrUr6X/uNpTSCbR97h4YwW03SRAEYeTjbCDjQKuwekIfHx8dqOE/0KFRGbSEalI3/7WEATKYMuq1ge0kO1MHy6iXfzBjQcsxiqe1GEw/GtVBZPMqYaRRufbUeszfYVfM6T21vtXLvyfWrkZjORjYjnptIPvq5lW1SC9+FvEX7kDLP/4GgfedaccK9ZDNqxojwqogCIIg7CaGi7BK4q/ehey699B21V3wtB9gxwqCsC9QKW4IIxMRVoWRhqw9I5/9TVgl2VWvoe/xm9H88VsRPvZyO1aohQirjan/s6mwx+m6/34suOgibLzjDjtGEARBEHYePgIVmn4sum45E7lVr9qxgiAIgiAIgrD/QJcArRd8B/EHv43EEz+2YwVh6IiwOsxIzJ+vxdXMKnGqLAiCIOxaQrPPRuT9H0bXrecg8+5jdqwgCIIgCIIg7D94xxyM2DnXIfXSrzFw/7fsWEEYGiKsDjNip5yCKddfr18FQRAEYVcTPPhkxE7/Z/TcdhlSL//GjhUEQRAEQRCE/Qdvy3i0nHsdckv/hr47P2/HCsKOI8LqMIOC6tQbbhBhVRAEQdht+Ke8H60f+jb6H7wBib/+yI4VBEEQBEEQhP0HVyCClnO/hWJvB7r/62JA/FwLQ0CE1WGGuAIQBEEQ9gTeMQeh9bzrkHzlf+URKEEQBEEQBGG/JXbql+B2e9B5y2koJrrsWEEYHCKsDjNk8ypBEARhT+FpHofWc69Ddtnz6PvtVXasIAiCIAw/Fr38Mu7+/k34zhmn4dqj3o/PHDRDv/Kc8bwuCIIwVKLHfRLe9mnovuWDKGxaYscKQmNEWB1meGIxHQRBEARhT+Dyh9F6zjdQ6l+Prl98GGapaF8RBEEQhL1PorcX//O1r+J//vmfsOnRR3CY4cIZY0fjslnvw5nqleeM/5+r/wn/fe01Or0wPHjxuYJ9VJ3+fhMda0r2WXUefShvH1VnMHW8+3b97zaN2tAo/67oZ6Prd92Zs4+qwzY2KqNROwUg+v6PIHjwSej6yRnILX/RjhWE+oiwOsyYeM01OF59GaCfVUEQBEHYUzR/4Itwe3z6V/pSotOOFQRhX2AwN9ON0vzyZ1n7qDoUDhqVsScEkkZ1NBInBlNHo3YOph+N0ggWz9/1B1x32geQXvAuzpkwHoe1tWJUKIiQx6tvZIPqleeMP2fieGTeW4BvnXoKnlP5hL3Pr36eqSv23fXbnAq11xbOle98PWWfVYd11JtPvMY0tWAdHz47bp9V58pLEzpdLR57OFd37RlMPxu14bGHcnXHktcffbB2G7iu3XVn/XVcsAjNOhPRYy5D50/PRnr+/XasINRGhFVBEARBEDRNfARq9HR03nwaChsX2bGCIIx0KCrUExQpCNQTHsjdv8vWvamneFHvpn2wAkm9dg5GIGlUB9vYqB8N6/hGqq7IMph+iMDRmJfuvw8P33IzThjVjkMDfhh2fC14fXYoiBNHj8Kjt9yi8wu7n1rziXNEf9brCIovPc+1p/Z15h9Q5dT7saORYHi3uvZYHdHTqaNWP1g+r9Urg4Imhc1aDLafjcaynnC64J2irqcWbF+9PgjbEjjweLSc8w30/vYqpF64zY4VhOqIsDrMWKu+BLwybZp+FQRBEIQ9TeSIixCaeSo6bzlD+17dUbgJoyAIe55awgNvyHmz/uJztW+oKTxQXK0lFmphYXWprkDCMnjjXwtHOKglOA62nYOpo9ZYMH6BCj+8KW3HbM9g6mA5tQQKpx/1RJZGdQhAZ0cH/vfb/4ojmqIYHQrZsYOD6Q9viuj8LEfYvdQS+/gZv+RyX01BkXOF4azzvDUFRc4Vq4zqP3Zw3Tr7fG/N+bQjddTqB+fyT34RrjmnuR4ceph7t7bBGcsffb/62uWseXxlXdV47OG8bkMjq/5K9ufvdf5Jc9B2/ncQ/8u/YeDR79uxgrA9IqwOMwp9fcisWqVfBUEQBGFvEJp1BqLzLkf3recg89bgHoHqe+YZvfkigyAIe55awgNvyM8616utmarBm3De7H/tmwH9uGo1KCh8799CDQUSChy1hFMKByyjlnDKdh53oqduOwdTx1e/Gaw5FuwHrzcSQBrVcfvvIzVFFme8OaYsq5LB1FGL/Ung+PXXvoq5kybtsKjqwHxzJkzAr7/6FTumOhxT2TR456gl9nGunHWur6agqOf8CR41771VBUVnrtSbs7TQnHeCV68dteqg6HnWeb6qPwyV10Gr/GpwTapVPuFa8LHL/HXbMNh+1lq7OJYf+wd/3bHkOFxymU+1Z/s6KLhOmuzS41Br7aqE3+ve+sAH9vvvdZ5RB6Dl3OuQefNP6P/jV+1YQdgWEVaHGe0XXohZ992HsVdcYccIgiAIwtD46f8+gFnnfQ6tx3wU533+eixauda+0pjggSeg5Zxvofe3n0fyuV/asdvjCKr88t11v/ihEoS9RS1BkjfkvKHnTX81IU/f9Ktrn/1SQPsJrIZj6VTrpp6CLIUD3vhXE04d4YA3/fXaSRGmVjsHW8dVX/LXrIPxFDQZqlltOQJIvTrY/7PVOFCoqDYW5eNdTeBw6vjsF9V474DAsT/9cPXmE39Fcv06HOz32TFDY2YwgOS6tbq8ShLz52P5tdfq/3et+8lP7FhhKFQTFJ35yGu1BEVnrnBdqSYocq5QFG1uNqrWQZz1i4JhtTI4xxzhtdoPQ+V19PdZ87scxxKUoiTLqLY2cX2s1wa9tqlr7Gc1Ebq8DZOmuGqOJdPUGkuu3VzX9DpeZV1xxF+uXbXWRwdHUGXg8f7GM39/Gz/8nz/hzgefRi5vCfru6Ci0nPtN5Na8gb5fX6njBKEcEVaHGZG5c7W4Gpg61Y4RBEEQhB2HoupXfvBLLF+zAbNmTMFjz72Gi7703S1fEgeDb9JhaD3/24j/9UeIP3KTHWshgqogDC94Y18pFpaLG7SGqnbD7QiavKnn4/7VhAWKCrxOcaDaY/S0Grvkcr++8a920+6IiaSaOFHezlqCY6M6HOGVVBuLcoGE/a01Fg37ocombGelwDHY8aY4wnZwrJm+FvvrOvv3e+/FeL/fPts5xvt9eFWV58AnAx1Bla7X+JSgPCm4c3BdqJwLjlhIqgmn5XOllqDIuUJRlLCOSovT8rWJ5VSzuNc/pqj6rfVr+x+GnPWPVJvT7IfThmo/uFS2odaawLFw0tRrwyWX+Xd4LFkeXaA47ai2rrz4/Na1i68ss5L9XVAlX/23X+L0K7+Jb/zof/Cpb/wQcy74whajBJc3iNaz/j+YqR50/+xDMPPbi+jC/ouxoaPDXNnRgXnz5tlRwt6Ei1hi/nzETjlFi6yCIAjCyGXd1WGM/cI99tmehZaqFFVXPnkHxo1q1b++84vizvDFc4/AjV/7kbbuqXeT74nF7KO9w96uf2cZqe0fyeM+0j8zx6r5+JtHouAGLvQF6EBxccHbBXztW0F9fvrxA/iTSsebb8Kbb+52fa+KIxQ8abVFq08HCqmzDvNocYLpTz9uAK8uaLavWjf1LOOJF5r0OTd2mjXbo61THXidbgB4488yrvl8Uj9O79ConYOp48PnxHXfWQdFg8qxKO8HYR1OeaRyLGr1g+ILhYnK9KSyH0fN6se9j0Z1m0ijOsZGe2Gapr4fqLbO0vDimJUr7bN9l2+efAKODYcR2wXial82i5eTSVz/69/qMeVj/5VCqjOuzjXnPoz3ZHwveJ2GL2TVDTfoVz5dyHhed9IwjvlXf/e7Os2U66/XawsF3Ozq1Wi74AJdNst1rGQPvv12/Uqxl3knfPnLum6+95t+/WuE58zBVLtOx2J5+s036/rYlv5nn8WYT31K181yWQ6Z8/TT+pV5KCazLewD+8i6WYdT9+uHH67r5lOTjGe5rJvpWRfzMw3hOLFPLJf9vkXlu3FtDB9R8698PvFz7lhIkvL5SThX+GPJ935guXqotvaUz1HOnaPVfFqs6nKonNNfVusK595g66icjzyvXJsq2125buyONlSuKzx31h1S2aZf/iyLgf7SlnWncl2h+EtLWadfbBN/9HHWx7PUunOH+lzy/ayGMz/42eJn2XnClp8L7g1Dju/t3fK54GeXnzd+bnnMOF5jGsI8zMsyWBbLZNmcG/zc8rP4QkuLTvv+N9/Un8nFV16pP7sTr7lGfyb5WXc+kyerNZPwnPHtJ/ow8xc7vnEdLVUpqrY0RXDnD7+Otxev1N+bzzrxSDz0C2tOO8Rf/j3y3SvR+tm74I5NsGP3XZz3pPx9FLZFhNVhBv9Hxv8ZO4uRIAiCMHLZm8IqH/+npepzd/5Qnz/+whs456pv6+Oh8vmTJ+Hrh03HpgdX6S/FgiAMD85TYVm8Zbubft6QO4ImqbzhrhQCeVNfKZBUipwUDmhV5dzkV97UUwTlzvzOTXw1oYCC4xMvNm0pk9dp4elYZVW2s1odFDQcYaBSeCWVY1HZj8HUMZh+lAunlePNMokjolQb73IhhwLHby+8cFj/cFXO7mrLHQEfPnLgDHjd1udhZ8gXi3j3tTdwVDqjxYFqOMKRI8xQuKGAQyGHgg6FHQo8hOICy6EARCFopIhNu6tP9E77A7X2NBIgKwVFzpVysbByflUKkqRStKyc05V1VIqerKNcnK1WR/naVG1d4bpBC9ZywZjz12lD5Ryv1s/yNbZWG5x1pdq6U20sy9edyrWL4zBpinvLWscyy38gu1itOz9Uc6DW97qRJqz+hzr+o44ZGheePg9//Mm/6uMTL/saFixbjZ5Xtv8un3jzAWSWPY/Wz/wB3gmz7dh9E+c9KX8fhW0RYXWYwQWDvxA6vzwKgiAII5e9KazSpyof///BV/8Rhx08DZd97d/QO6BuEG7/Pk45+jA71eAxC1n0P/kfcLVMRMsVv9ZflvlFuPKLOL+AOzdrewt+ARzJjNT2j+RxH+mfmVHqxnVjvGUbsbDaDTnjyoW8ypt+QrGC13mTzpt+PqbqpCeMK7d2qhRUSLngUSlYkh1tZ606HHGiUR2VFluEcXyk1ulHpThByvtRKZiQcoGjVj/K46rVUS7kNBI49hf+cPCBuHjmQfAYW8dpqFBYvXflKnzr05/VlprVxtYRjijwVFqW8r7M3dy8xeCl0mKV/y9kOoo/FIm4lvB+jlAEIrQEdNKwXLaBcRQpmIewHObldZbLNMzDNIzjNceikOeMZxoGHrNsUp6G8DrzMg3LJSyXOHmYhjANA9MzECePc87rDoxraWnRP+qUz4VqYmHlXCifvw7lgmKlKErK66g2p1lHuXBaPn8dyteSSpGUlIuQ1daV8vWvURt4XPlDFSnvZ602NDW76o6lsz5WjqtD+fhWWz/LRWrHUr7e9zrOj5GCYWx9v4fCCe+fhWd+8+/Y0NmDaaddgemTx2HBQ/9lX92W1MInkXj5d2j9zO/hP9iac/sinOsirNZHhFVBEARB2E3sTWGVPqHoU3Xp6vV2jPoi/ckL8KOvX2WfDZ5Cogt9T9yKwIEnovmjP7ZjLSq/iI+0L+CCsC/AG0kKq+WWnNVEAeLc1Dc1G1VvyMvFi2riBnFu2qsJC6Rc1KwmJpYLA/XaScGRfl8b1VFNOCgfi0b9qGaZRir7UW5VS9gPx/JrqONdLqIMRuDY2z9cleOIbbua6y+7FMc3N6N5JzevIo4rgO8/+7w+p3haKbDK/7eGjrP2lM/pWvPNmQtcNyrFQlI+F6qJoqzDES1r1eEIhlzDqq0b5etbufjoUN6PamsXcfJxzjfHDL0+lOO0oVY/mY8Mpg3VhFdSPpaVP/gQZ+2aNdtddRzKfyhy1h2Hkf69bqjfvbkHAX2q8nvzvLmH4NV3lqBQLOLH37gK//yJC+xU25NZ8TL6Hr8ZrZ/8FYJHftyO3bcQYbUxO/8zoLBL4Yd2d31JEQRBEPYfZk6biLce+E/8+gdf01artFQdkqjatRK9D92I4NyLthNVCa1t+IWbj3Txy7cgCHsPCgDOpkvcJZobMVVCC1VujkI/pM5mT+WUb47i7HZdCeMcyy1nY5dynI2bKFgyVAoTFEsoCPAahYdq7aRYwV31G9XBclheZR3bjkXtflBo4XicdW7tOih0MJSLqoT1Mq5ePyhcsPzyzbXK0bt4q/eqnHrrqmNVOBwC27Y7wgGHzcGm3PY7uA+FTek0pr5vln1mWZFybOlflHUJu4byOV1rvnFOcy5wrlSb05wLvMZyOH/LRVXC81n2fCvfjKkcZ9d8zlv6eK2E6xvnG9evavlZB8t35nzlukIoVnL9ZDmN2lCtn5dc7tP9HEwb9DpdZyz1xlfnbf8DBDfD4kZbzM/2VsIyf/Xz6nNsf/1e5/N6cN/Prtc+Vfn4Py1VG4mqJHDAsWj90HfQd8/XkHiGjgiE/RERVocZ/BWVvwY4j5kIgiAIwlDhl8TLzv8AvvaPHxnS4/+5jrfR8+D/QfSDX0XTOd+yY6vjfBF3NsMQBGHvwBtmWnM5u0RXwpt6CgK8Ia8mBPKmnmIhrapYVqW4QZxd9fWO2+dvLxwwD8UR7uJdTbAkFHh5nQJCtXZqwXEQddz9u607h1fC9tNyq5pIQ9gG1lFLhHbq+NXPMlVFUUJRg0J0rX5QyGEbawmvrINWucxfyf4qcBz9kY9gfXb7Hd6HwoZcXpV3sX22FT7KLwLrroViH9eemvPNFk5riYXlc7qaKEro35l1MG21OpwfhmqJu858qyV6EuajxWi9Oc91hWXVmvNsg1671HElTj9rCcxEr11fT2lRtFo/nR9kqv3gQ5if9ddbd9jOauuOw/74vY5GCdyoij5V+fh/I1HVwTd+FlrP/zaSz/4n4g9tu9GVsH8gwqogCIIgCNuRXvo8eh+5CS2X/yfCJw7e0tXx6yYIwt6BVpY//H6mpqDpCAu1rLGIIxzUu+mnOEJqlUGRhRZR1W7qCQXeRu2k+EDq1UGrrWrCK+FYUCCuJdKwXNbBuobaD4oTrKNeP+gGoJbwShyr1lrsbwLH4ad/EKFx47EoY1lOD5VFqTRC4yfo8mpBgZUb5gg7D8U+WoDXmm/OnOY84HE1OJe1dXcVUZRwvrGOalaaRM/lKa66dVg/qFQXXom26lfXa9XBfFxDq1mCEqcNtconXFfq9ZNrV6N+sg21xF+i21ln3XEsaxsh3+sGh6d1ElrPvQ6ZBY+h9w9X27HC/kL1WSbsNfh4Cn0nycZVgiAIwt4iteBxxF/6LdqufgSBORfZsYIgjAT0Tb26ka4lBBIKefVuyClesIxq1lYOFEBqCSiEIgtv7Gvd1A+2nY3qoLVWvTp4vZ7AwX7UEpAJ63CEmmqwDook9fpBK7tawivheNNXYyP2J4Hjih/9GG+tXYfNqZQds2Mw39vr1+OKH99sxwi7m8HOt3pzmoIi87OsajjrRr06ON/q1cEfderVwfK59lWzBCXMxznbaM43Wlca9XMwY0lxtBasf1esO8LgcYViaDn3myhtXoKe/77UjhX2B2TzKkEQBEHYTezNzauGSuKN+5BZ+QraPns3PGNn2rGCIAxnnA1kHGo9autAa9VargIcaFlWS1ggtISiJWYtYYDQ6queOFvPmoqwnaReHYNpZ6M6Go0FLccontZiV9RBKjeR2d956f778MD3b8L7Y80YHdp2A6B6UFR9va8fF3zzW5h3ofw4uDsZytpDdmZON7q+q9a3evn3xNrVaCwHA9tRrw1kX1t3hst37/7nbkMpnUDb5+6BEdx2g7KRhmxe1RgRVocZ3IWv+4EH0HbBBfqxH0EQBGHkMtKE1YEXf4PiwGa0XnUXXJFRdqwgCMOdSnFDGJmIsLo9f/vdnfjj//t3HDh6FGaHQqgnEXHk3k2msKSzExd/7f/DSZddbl0Qdhuy9ox8RFjdfcRfvQvZde+hTX2v9rQfYMeOPERYbUz9n02FPU5i/nxsvOMO/SoIgiAIe4r+p3+OYiGHtmv+KqKqIAiCMCw46R8uw01PPYPg+w7FI+vW4+3uHnSm0kgX8uC2Oyn1ynPGP7J2PYKHztbpRVQVBGFvEz3qEoSmH4uuW85EbtWrdqywLyLC6jCDvpPoZzUyd64dIwiCIAi7j1I2iZ5HfgBX83i0f/5eGK7aj6UJgiAIwp4m0tKCz9x8C/7xp/+BMWefg7fNEl5fuhx/e+VVPL5xsz5n/D/e+h/49I9v1ukFQRCGA6HZZyPy/g+j69ZzkHn3MTtW2NcQVwCCIAiCsJsY7q4ACv0b0PfkrQgeeg6aLrzJjhUEYaQhj+PuG4grgMHz1gc+oB9P5aa/wt5D1p6Rj7gC2DNkV7+OvsdvQeySmxE69pN27MhAXAE0RixWhxmZVau0GwB+eAVBEARhd5HftAQ9D92I8DGfEFFVEARBGDH0PfOMDrxn4v4UgiAIwx3/lPej9UPfRv+DNyDx1x/ZscK+ggirwwz6V3398MOx9pZb7BhBEARB2LXwV/OeP/8fNJ9/AyIf/KodKwiCIAjDn9Xf/a59tO2xIAjCcMY75iC0nncdkq/8Lwbu/5YdK+wLiLAqCIIgCPsR6cXPou+Jn6L103eOuEeRBEEQhP0bx1rVQaxWBUEYSXiax6H13OuQXfY8+n57lR0rjHREWB1mTL3hBpxsmvpVEARBEHYlqXceReL1P6H96kcQOPQsO1YQBEEQRgbVLFTFalUQhJGEyx9G6znfQKl/Pbp+8WGYpaJ9RRipiLAqCIIgCPsB8VfvQnrFK2i/5nH4ph5lxwqCIAjCyKDSWtVBrFYFQRiJNH/gi3B7fOi+5YMoJTrtWGEkIsLqMIO+Veljlb5WBUEQBGFX0P/cbSj2bcKoa56Ap/0AO1YQBEEQRg71LFPFalUQhJFI03GfhHf0dHTefBoKGxfZscJIQ4TVYUahr0//6ppZtcqOEQRBEIShYZYK6HviJ4Dbh7YvPwYj2GRfEQRBEISRg3N/FJg6VQcH55z3UNWsWQVBEIY7kSMuQmjmqei85Qzte1UYeRgbOjrMlR0dmDdvnh0l7E34pYFfCiJz5yJ2yil2rCAIgjASWXd1GGO/cI99NjT64km8tWgFVq3bhJOPmo2pE8bYV+pTSqmbzCdvhWfKkWj5+K12rCAI+yKGYdhHwkjHNE37SKjHs/ZnnntTCHsPWXv2DfaldWdXfPfeW6SXPo+Bp/4Drf/4GwTmXGjHNoYaEvWj3QV/uHqhpQWeWAzH9/basUI5IqwKgiAIwm5iV3y5+8g//1888ORL9hnw5B0/0AJrPQo9Heh78qcIHfExRM+/3o4VBEEQhH0D5+m+cutVQRCE4SCspjM5BAM++2zHyHW8jb4nbkHTh76H8IlX2bHVoUHeup/8RAurx6xcacfuekRYbYwIq8MMTgrnFwcGfoidLw7OrxC8TpxzXmc6ftCdLxe10vA609UrtzLNYMqtl6ZWuaQyzY6U66QZTLmVaQZTbr00tcollWl2pFwnzWDKrUwzmHLrpalVLqlMsyPlOmkGU25lmsGUWy9NrXJJZZodKddJM5hyK9MMptx6aWqVSyrT7Ei5TprBlFuZZjDl1ktTq1xSmWZHynXSDKbcyjSDKbdeGue8/7bjMfE/Uvp4Z/jyJy/ASUfOxqevuxkXnDYPt914rX1le3LrF6Dvrz9B9OxvIHLKP9mxgiAIgiAIgrBvszeF1Y1dvbj+1v/FHff9FYdMn4wrLzpDf4ffUQpdK9H7xE8RPvZTiJ7zLTt2K46g6mzax/sREVb3MhRWX3zxRVMYHqy8/nrzGcBcds01+rz36af1+fOxmD4nr82dq+M23H67Pu+4+WZ9zngHnjPE33xTn7M8ni+64gp9zngnTb63V8fNP+UUfc7ySOd99+nzl6dO1eeE7WAcrxGnve9eeKE+Z1k8Z0ivXKnjeI3n0ifpE5E+SZ94zrCv94n51v5TiM9W7XToevlus7DgYVN9OTNj0fCW88qQePDb5tqrI2bq1d/rdgiCIAjCvgj/3+78/10QBMGB372rfUfeE+HTF59pGoZhXn35h8xTj52rv8MvevRXVdM2CplXfm1uvPFIs+/ua+2eWfdIzn1TeSi/b9kdOPc65fdmwrbI5lXDjLFXXKF9q7qbm/U5fxWgBZRjBUUqz/kLBc8dyylSmcY/ZYo+Z3mkXrmVaeqVO5i6w3Pm6HPpk/SJSJ+kT5Vp9tU+OdfVl6MhB8cytW8goV9XrduMKRPGIBYN6/NyUgufxMCzv0L7lx5E8MiP27GCIAiCsO+x8Y47dBAE8vzzz4P+XmuFnb1eLfz7v/+7DtWulYcZM2bg4x//uK5D2Hfh4/+0VP2ny87Hzd/8HB6/7UYE/D7MPPuz8Mw6d4dD4JhPYex1r+EbP/sjOn98PhZcdBHe+sAHtliplsOn6WhRurvCK9Om2TUJtRBXAIIgCIKwm9jZx5G4cVX7sR/Tx3NmHqA3sfrOF/8B3/nSZTrOIfHmA8gsfR6tn/0DvBPq+18VBEHYE5x11ln4y1/+Yp9ty7/927/hX/7lX+wzCwoUX//61+2zwWGaphYuGnHmmWfi1FNPxac//Wm0tbXZscJIZvm11g+P02++Wb8KArnuuutw00032Wfqc7J8OQ444AD7DPjDH/6ASy+91D4Dfv/732vRk3At+cUvfoGLL75YrxOVaxLXG0KB9Fr1+fvoRz+6ZR076qij8Nprr+ljrjePPfaYPmZ9X/ziF9FrPz5dXp+we9ibrgDmXvQljG6N4U8//Vfc/ejf8Lnrf2pfGTqfP3kSvnvOLKz8z0VbXJrtLWgEePDtt9tnQjlisSoIgiAIwxRapnKzKoqqq9dt0v5V//mT2+4SGn/5d9qvavs1j4uoKgjCsIHCQldXl31m8YUvfEGLE5WiqsORRx6J+fPn6zSOiOFAMZZxLJPHDox7+OGH7TOL5557TsdTVLnkkku0wEuBhGJvd3e3nUoYyVBQFVFVqKTZfvrJoVxUJRMnTrSPLMrPuT597nOfa/jjywknnICbKz57tfJQRP35z39un0GLrLIG7bvQp+pTL8/HmOM/rkXVpkgIiTfuq/pUWqOQeeXX2HjjkfjBly7G6H95VPtQnXXffds8MefAOPo+3d1BRNXaiLAqCIIgCMOYk4+ajdf/dCu6Xr5b/wJe7gag/9n/QjE9gPZrnoA7NsGOFQRBGB5Uig1Tq9wQlnPXXXdhzpw59ll1WCaFWVqFOTQ1NdlH20JRhRZj06dP1+e0KLvtttv0sTCy4eYtDIKwqygXQBtBcbXWD0SVlIu3tFxduHChfSbsa3Cjqr/f/RN8+uIzceu/fhHr/3andgewo+jNqx66EcG5F6H5oz+2Y4H2Cy+sKbDSndnuDkJtRFgVBEEQhBFGKZ9Gz2P/D65QK9q+9GcY3oB9RRAEYWRCkaLSuqwezqO2g4E+Dh2eeuop+0gYydDXIIMgjDSi0ah9JOyLHDFrhhZVv3DpuUMSVXMdb6Pnwf+D6Ae/iqZzvmXHbks9gVXYO4iwKgiCIAgjiGK8E70PfR++yUcg9il5JEcQBGFHiInVjSAIe5C1a9faR5b/1UZW+cL+S3rp8+h95Ca0XP6fCJ94lR1bG0dglUf09z4irAqCIAjCCKHQuQK9D9+IwBEfQfPFP7JjBUEQhHosW7bMPgL+6Z/+yT4SRjInm6YOglCP8t35GU488UT7yu6HvlSdzasIfbjeeeed+lgQKkkteBzxl36LtqsfQWDORXbs4Iidcop9JOwthp2wyv89amf1KvBfSZ87F6wTJ15fs18ZrBPL2b2d1PlTESx4VFJ/Syjqv4IgCIIwXMl2vIXuB7+H6Jn/gqazv2nHCoIgCLV466239OYx3MSKfla5yRV9IwqCsH+gdYGywI3tdjfcLI8ibnt7Oy699FLtV5Wb8tGHa6ONsYT9E25wlVr0FEZd8zj8M+T/USOR4Wuxapa4EvIAMKwoLYOq+FKJMqiJggrd+SIW9aexNJFBf0ldM5w0TGELplxIVW7+dRZVnqlU+p8+t6sSBEEQhOFGZukL6H3kB2i5/JcIHf8ZO1YQBEGoBq3SKGzMnTtXb4h1ySWXaKvVc845x04hjHReP/xwHQRhuMHH/fljTktLix0DfOYz8t1NqM7Ai79BfvNyjLr2SXjGzrRjhZHGsBNWDVsMLRpsmgGXaemqpvpjqiNqnyV1KaEOFicLeGxVL+55Zz3ue289/rahD6tzJWTh1umLFFkNlZCZdAFWmYxgHSzNreLc8MBgoYIgCIIwzEgt+Avir9yJUf/8KIJzL7RjBUEQhFrQKu1b39q66QfF1UceecQ+E/YFEvPn6yAIwxFuxPfb3/7WPgNee+01XHfddfaZIFj0P/1zFAs5tF3zV7gio+xYYSQy7NREiqclw7AEUfucUqu2VjXUDabhwuqsicfXZ3D7u914byCHww8ah5lj2vDumj488N56vNQTx6YCpVOPyqfKozrLkhyBVXXbUP9cJXVVW7OaKKo0JbtOQRAEQRgOxF//E9KL/4b2Lz8O3/Tj7FhBEIR9D8cP4a7ixhtvxJFHHmmfAZdffjlWrFhhnwkjHW7WIhu2CMMZWsiX/8Bz0003yQ88gqaUTaLnkR/A1Twe7Z+/F4bLbV8RRirDTlilHalRMuApqSMzD9MsqLgSMjCxoVDCC105/G5BAvcsyWJhxg9/LIqZLT6cP6UZH507GS0BL15YvBaPLdmE9/py6C+6kDfduoySUUDRKKKgQlHVZRoUWS0R1zRUXTpWEARBEPY+cT4a1LVS/4rtGXOQHSsIgrDv4WzysquhparzOC79HNIlgLBvMPaKK3QQhOGM/MAjVFLo34Ceh2/UvlRjl//SjhVGOsNMWDVVgwrqtaT+8nF+ugQoordg4p3ePO5ZMoD/XRjHC3EXeoIR5D1RvLcujzc2JJFWuWaEPPjYIeNxwSHTUMyU8MiCDfjLij6sTOSQNFWphirfLMAoFVAyi6oOAwVax9J6Vf+j0CoIgiAIe5e+p36GYrGAUdc8CXek3Y4VBEHYN7nttttw9NFH22e7Dnkcd99l4x136CAI5fT399tHFpUi5tq1a+0ji8rzclatWmUfWfAHoFqUX6tMV+0HHm6sJ+x/5DctQc9DNyJ8zCfQdOFNdqywLzDMhFXrMX1CdwADph8LUy48vroPjy7ajPldeXR6QsiF/IC7BJfLhXTRh66UgWTRRMk04S+ZmN0awIePmIgjD2jDqr5u/HnRajy/fgDrMgZyhg9uww2PSucyi9palfW6Sh4YpphgC4IgCHuPUiaB3odvgrtlEto+90f1vyf5wU8QhJFLpcDwxhtv2EcWFD3+/d//HV//+tftmK1UCiKVgkk5AwMD9pFF+Xm1x3FZpzCyWXzllToIAnn++ef1hnWc3+VMnz5dxzvXuUt/OTx3rjtwfWDcf/7nf9oxFtzl/6yzzrLPLJy0/NHGgceMc9aZaj/wcGO98jqFfZ/s6tfR8+f/g+bzb0Dkg1+1Y4V9BWNDR4e5sqMD8+bNs6P2LJasuRXu9J9RkZ2ZAhZsTuG9DQPwevM4eFI7liXdeGpdEQkjBI9Le0ZFKJ/G+RO8+Mi0MJpdJRRNywKV//F1Y76I1zu6sHBjH4KhIGZPaMXBLSGMdpfgM0souSw/rC7TbolaBC0qWyYIgiAIO8a6q8MY+4V77LP65HvXY+CpWxGYfa78ii0IwoiHAsRf/vIX+6wxX/jCF/Dzn/9cH1OUqAU3pjrhhBPss/ppTf393uKoo47aRvzgzt2PPfaYfSaMNF4//HD9+v4339SvgiAIZEe+e+8p0oufxcDz/4PWK/8XgUO3FeeFfYNdL6za31/0JvzWEQzQXyq/9NhffIySTlCCS6dx8br6l1Ghs1jE0t4k3l3bh2yqgEPGNWPW+AhChqni8/jL2hzeS3qQd/tVMRkcEMziogOiOLYtCF9JlavQm/+z3pKqz+XS5a5J5vDK6o1Y2Z/G2JZmHK7KnR7xolldt2xki6qNqiWGW31BU8f0t6otWN2qJNVKVT/buKUPgiAIgtCAwX65y29ajL4nforIB65G5PSv2LGCIAiCIAiCIAyW4Saspt55FEkVWj/ze/imHmXHCvsau94VgH60niIlxUnTElQtpVNB4bMI/njMI74yeVGd9ZTyeHsgg4eXdOP5pZvREvLhQ3Mm4IRxfjSl4gjkizhiTATnT/PhpNYk5oT7cHwsgzNHGZjuyQO5jK7BNOgpVQUtkqqyVSVuFaaHfbjofZNx1sxJyKezeHzBejy6Jo6F2SIGVBuLWkC1G8W8pkfFUfJlrAosjME+FQRBEIRdQXbVa+h54HuInv9/RFQVBEEQBEEQhH2A+Kt3Ib3iFbRf87iIqvs4u9xiVVt30gJVC5C07mTgi4mSUYKp4rhFFY+p6xaKwJpMAa9u7MWKTQMYFQrjqKltmB5WBSTiupxIJAyvz4uCOu5PpZE1XMipvB51MVQqopRIwOfxIRRpgtvv1YIuoc9VQltTvd+/bpQLcVX1K+sG8GTHAAy/C8dNjOKIWBBjvS742VzTpdMbKr1Lt1P1ynSrnHZfSNmhIAiCIFSj0a/mfDQo/sIdaPnH3yDwvjPtWEEQBEEQ6vGs7QLiZPt+TxAEgQwXi9X+526DmUmi9aq7YQSb7FhhX2WXW6xaIqYbJRW0+KgCDVZN/T8/SpMGr6BolrAhk8Wz6+O4d8EmLO5KY+bkMTj7fWNxkCcNd38PwoEQYq3t8Hh8qgATmXwBpYKJVpcHowsFtKm4ADei8oVQNHwYSGVQKNLG1KqYOiotZtlJj4p1qXO2rFVFjIkFYQajeLvfh98vTuC3S3rxSm8OnUUDeZVdN9coqdSqTgrFFFi1MMt464UwxgmCIAiCMFiSbz+MxBv3oe3qR0RUFQRBEARBEIQRjlkqoO+JnwBuH9q+/JiIqvsJu1xYtSRNS2qkdWpJHXJDqaJ6NUzas5rYXCzh1e4cHlrYhTfWdGF0SwinHTIJs2IRIJFSeV1oam2DPxhQxbAsNtMFM5uF3zDg8XmRR1FbpPp9QXXNrT63LhjuEhKJBIqqMmajM3urPSq3+uNWR3QLkEQBCzozWDVQRDHYjE7vWPytx4tfL+zFfSv78HYii17TdgJgutXkcNP81WpLmahKnNOKaEEQBEGoCR8NoguAUXw0aMr77VhBEARBEAbDnKef1kEQBGG4UEr1offh78M1+iDtU1XYf9gNwio9k+bADaD0dk+mCXcpj5JZQm/Jhbf783hg6Wb8Zfl6BENufPjwyfjglBaMLRXgTWcRcBvwB4MwXT6VhxtcqQ+oy0CeYmmhAJ+7BI8q26XKo9Dp9qg6VCgVsgj7PSqugGQyri5RwqVTAhOlUglFlTefy6CQyyKVyiCRzqFouqCqg9ul0vhj2IBmvLQxgYcXd+Dpjl6sTJnImlqOVeXS1tVxMrAtIqoKgiAIg6X/b/+NwsBmtF/7JNxtU+1YQRAEQRAGS+yUU3QQBEHY1Tz76jv43s/uxK/vf8KOaUyhpwM9D9+IwPvOQsvHb7Vjhf0F99e+8pUb+gYGMGnSJDtq56HQSFEUhgumYSJl0o9qES9t6MeLa/rQVzQwc9IoHD2xBeO8Kl0ug2Ihh4DLQNTngc/n1u5ZuXUU5UyXKieTyaBUKiIQDMLlciNHtwBuN9xeH7zqNZfN65qDgSCSyRSyuaxOl83mkMmmVfosMuk88gUTBZVycyaHNakS0iU3PKYJV7GIFncBp04L4eCWIJati2NJZxpp1Y6g342AV9WlLWAtadWyhbVx1NayKEEQBEGIP3oTIkd9zDrRjwb9FEawGW2fvxcG3dwIgiAIgrDDLL/2WvT+5S9oPessfb7xjjvQ98wzcAUC8I0di8yqVTpu4OWX0XTssduk4XVPLKaPu+6/H7mNGxGaOVOnWXvLLToPz1mWk6ak7kUDU6fWLbey7tSiRYjMnavT1Cq3Wt2NypU+SZ+kT7X7VFx579bv3kPgI//8f/Gtm+/AW4tW4O5H/4bv/fx3mDPzAMw8oLZellu/AL2P/jsip1+L6Jn/YscK+xXcvOrFF180d5SSDvxbtM8siuqQoVQqmslSwVyaLZgPrOs3f/DaKvNf/77avHFJn3nj0pR53dtJ87tv95v/s6zffHZT3OxI58yMylgoFlReK5ilnCoxp0ovmn39fWb/wICuSV01e+Nxs3cgbuZyeTOXzZudnV3mmjVrzN6ebrNz82Zz2bLlZkfHOp0nlUmZ2XzWLBWLZjqTMzv7+81FPf3mrxYPmJ99rtv8xLNd5j8+t8H80bubzaWJrJlXdazIlMzfLVxv3vDce+bP3lprvtiZMjfnS6o17DF7bvVe/3GCIAiCIJSx9p9CZmHBw2b27781N33/GLPvD/9sXxEEQRAEYag8A+iQXrlSn7974YX6fNk11+jz3qef1ufPx2L6nLw2d66O23D77fq84+ab9TnjHXjOEH/zTX3O8ni+6Ior9DnjnTT53l4dN/+UU/Q5yyOd992nz1+eOlWfE7aDcbxGVl5/vT5nuwnL4jmD9En6RKRPQ+sTv3tDHe9MOPyQ6WbXy3ebr//pVnPqhDFmLBrW3+erhcSD3zbXXh0xU6/+XrdF2D8ZksUqXY1yQye9y7/6Z5RKepMoZ/f9ggH0F0t4pz+FZ1duxqquBMaOaUGwJYZFXXkVSujI+LEy7cbSngx6MlmMiXoxLuSFlyWo8ujbtGC4kVchkckhncvB7fbq/aMK+SwS8Tgy6RRMlbZQyMFwqbqLJlxuN1pamhEKR2CqvKbXj4zHj4TpQrZQQDqVRMDlxoRYBJNjAUwMG5gSKuLYcSEc2eJFq6rA7fEg4gHGhj0Y1RxGdzKHt9b2ojNThMvnRtALeFQfOQ5sq9V36A2vrI2zLDcF2q6VpzwkPBYEQRD2G2ixGjjgGPQ9+u8IHXUJmi680b4iCIIgCMJQoRsAb0uLtnKjtRyt1vzjxiE8Z84Wqzbut9F88slbXAbQSi58yCE6D63lmMcdDOo8Tppif7++znOWyzwsN3rMMbpc5uG+H04anpPgtGn6nOUyD8tlnvK6o4cfrstgGsL2s32M43WnXOmT9En6NPQ+mRsewY//zqeZh86Rsw/EFRd9EGPbW7RbgNXrNuFfPvNR++pWUgufRPyFX6Pt839C4LDz7Fhhf8SgxerKjg7MmzfPjhoEWjQs6Mf1TZN+TRlpoOgyMVAysTKRx1vre7CxdwCjAkHMmTgaaZXsoZVxLBjwo+iPwHS5UNJCZAmeTC+OjxVw6YwWTA+6UcwVUDDd6FPlrkwWsLanF1FXCQfHmtDiMeFR9eRyebhUGU1NTfB4VOGKdDql4tUED4dVnB+dmTxe25TC/L4ccqqm2U0eHDcmhLFBF8xSUftxhaHqU3nZhWwuh3gyg5LLq7tomEWEIgEkC0Us7VJt70wiqdp28JgYDh3TjHEBN0JqgaH/WCq+brgopepjXaJ2hyBqqiAIwv7KuqvDcIViaDrvBoRP/KwdKwiCIAiCIAjCrobfvcd+4R77bMfoiyfRfuzHMHXCGHzygtOwev1m7Wf1UxeejttuvNZOZZF48wFklj6P1s/+Ad4Js+1YYX9laBartlZomC6YcKHgMpBECR3pIv6+oR+vru5CNpvH0ZNH4fSDxmBUwINX1w7glR4g640ALpXPxZxE5Te8yOfzGO0pod1tIpdKI10q4O+b4rh3eQIv9QIbMiWMaw5gxqgmhP1+vSEVxU+fz6derQ2qXIaBdDqNoior4Pdgjcrz4NoUXugpYU3KQLGQx4xYAKODPuQp6pY88JhFuI08skWgP6Py593IFF3IlrgFlxvpTE5b0R6o6j1wbIvqr4GFG+JY1p1GTpXh87vh9RjwajGVPaKVarmYqo7LTwVBEIT9BlqstnziVwgdc7kdIwiCIAiCIAjC7mCb/Q12kIDfhwtOm4dNXb346f8+oOM+97Gz8Z0vXaavOcRf/h3ym5ei7Ut/hmf0DDtW2J8ZgsWqdgKAkukC/3EjqE35IhZ2JfDeul7kzSIOm9SGGWE/2j1AcyiInmIRdy8fwEMbPMh5w4DbhOky4CmoEkygYJqIlAZw/rgSzhrtR6yUw4DbjXs6Mnh0kxtZXwyBXBxnjcnhokkBxAygP57QYmwkEtFCJq1GXapNyWQauWwO0aYIFmeAe9bnsLAQUc124UBvHJcfFMYx7U263UaJj/QXkYeJ3pSBnrQBU5XlUnF0clAy3KpcEz7VyLagGzG/qkflW6vK/3tHN1ZuHkB7OIrZE1sxPeZDzO2Cl8arHCZ2zLFaFXFVEARBEARBEARBEARht7EzFquDof/Z/4JZMtF61d0wvJbrAkGwjEZ3CAM0FeWO/S51uCqZxUNLNuG1lZvRFvTi7EMm4sQxUYwppeEr5S0B0+XSPksDBh+6pwsAPn7vQlHVbhq0AS3Ab5QQdBvwqEINlxt+txttIS9ingIC+QG0efMYF/EhFgohFA5rQTUajerQ3NyMWHMMTbEmjBk3GrG2dphuP8aFAzgs6sZEVxpjjBQObfFicjSgO+2BCS+FT1V7RjUrUSih4Cqh5FYtduXgduVV/wpQTQLUNbongJpAPtXy6X4vLpwxDmcfOkWP4GOL1+KZNV3oLxRV21V6BX3P8p91xL+CIAiCIAiCIAiCIAjCSKKUT6Pnsf8HV6hVW6qKqCqUMwRhFdpC1MXH79Xx0u4BdKbyOPF9k3H++8ZinJlDumcA9Djq9/vhUemaVfpJYRdafFmYxQyNXnXevGEiR7G1mMVYfwnjte/TPFIlE/SaeniLBx8YBRwVjeOsScC8MSGE3G4qtXCrVwb6WWWwzl3wetV1Vw4D8W7EfCWcM7MNnzwoiEunenDmxAjG++k6gLXTmpQbT1EvNbTVKuPYL9N0oajaz1RsKa8WiwyqrbRAVRcCKhzWFMAls8fj0LERdPT0oTOR1jlUEdRgLViN/U8YOdC9BIMgCIIgCIIgCIIgCPsnxXgneh/6PnyTj0DsU7fbsYKwlSEJq7TypPRp2Z+aGB3yYnLAi5jLjaDHh0Qig2yhBMPrQ6mQR36gHxO9Jby/zY1xrgQC+SR8+Sy8xQwChRQmepI4epQLU0MGAoYBvyrDKBoIJxM4PmriUwe34INjQmhV9dFylIKX3iSqArNoIplIal+q0ZAPftW70R4XThoVwUltPoxGXiXiQ/4u0JVByaUSuAzdB4+pDimkMd7woWj4UaJdK3VU1a+MupZIZZBJZ7QLgmK+oPqWQ7Mqc0rIj5A3iLztfJZ/KclZwqzD9u0Vhj+irQqCIAiCIAiCIAjC/kehcwV6H74RgSM+guaLf2THCsK2DMHHKqEAaehd/f+6uhMr+ws4c/poTAu7kckV0N3VqWVEPpqfzWVhFExEmpvQa7jx7No+vLE5i96iV7sBoJXqYa0+HB5zY4zPQC6bh5kvIeDzIZNPw+Nxw+fz602k8kVTux8I+Lwo5IsomkWEwyH41fVCsYhUIqlfo81RLehmkmkEwhH4Az4kU2nkc/TJGoLH7dF+MaiDFs08EukMBjJAGj4U3G4VT72ZVrkluFR5HtXOoBcI07q2VEQun4fH5QZUWq/Pg7e6MninJ4Pjp8RwWCyoFdWSy4Sh/b5afltZl6uGuEqhmNawjoWkY4FbDq+VivRtu61c2xirTtUEy9JYtZvHW2Gd1ds1WLhxmLbmtTG0RbHq7bYVNaSynF2BY9VcD9aZU5+NbDarRfNcLodMJqviS/D7fQgGA3qTNJ/63NEKu/K9EQRBEARBEARBEARh77IrfaxmO95C319vRuyimxA6/jN2rCBszxCEVQpxlhhXUOGJNZuxoj+PM6aPwYyQB4lEUotTmUwaqVQKrW1tiDU3WyKbaaIrmUJXvoSU26MtOkeHfNqqNBcfQC5fRCQa1ZajnZ2d6jyHUe3tCIcjlvanqqU4ms0XkEqkkM2lEQoG4fUGkM5k4FHlxJqb4PGqsksm4qpM1tvU1KTFslQqjUDAj4DfjwKF12wW6WwO0CKpF/GCAVWKqoiuAOjMoAR3MY+w14WWSABhVT7hhlalUhFZisAuF17ZlMDC3gxOntqG2bGAymnCoOqs8psqC21iqwmYFBIp+vX29mLpshVIxJN6jCZMGI/pMw7QorIDhb7ly1dg0+bNtmipytRvhSqT/gzKUWUwxqX6rsdd/ceyQqEgmumLVo0HxUKKhlZ6FVQ6S9g1VZvYzlpipPOIvNUXlr9u3VqsWLFKjznF31hLDAceOF37waXbBd3WGvA6y6Pg29GxDqtWrkZRjQv98tIuWrdtCOgy3QamTZuGiRPHq3Mdq+PZdkP1sVgooKenB2vWWO1ft24d0hTgbXGVwirFVAr7waAfEyaOw4wZMzBlyhS0trboNlvl8X1k+fxjjYsgCIIgCIIgCIIgCHuOXSWsZpa+gL6n/gOtV/4awbkX2rGCUJ1dIqyuGsjjg9PHYnrAwObOLmSzOTQ1RbUlIIVDCmwej0cLrflsSluNur0hXVpJ/ytQCUMqzsfsCzpvOp3R6b1enxYFA4GALsNwW+JVvpBX6Sni5pDMpLW+2BSOwq/S06KQghjFMVohhkIhbXHY3z+gLVV9Xo8WfllWKByBz+9XZWQxkM7rx/kLWqx0aUtVVymPoNeF5nAAXgqwrJ1iZRkvbOzHW5sTOHFyK2bHgpTvGgqrliBnlbV48WL8/vd3Y8WqNTBU/KmnnoKPfvQjWgh16O3tw91334OXXn5ZW02yLBqvaj2voj120VvgZbfH0AJhOBxGW1sLZsyYjlmzZmHc+AlaaLbao9qt0lr927bMrVCctI6YjuN7//3344knnlTvtWVJPGbsaFxyyUcxZ85sK2EdLOHVqu+vf30S9/7pAf3eutV7rSuq7MwgKRYL+jNw8Ucv1uNJKGQTfibj8QTmvzUfL7/0Mlav6UAuk9cDpVvCDdTsY6sJliBLr8IU5inWnnDCcTj00EMRDKr3W/eBJVv9EARBEARBEARBEARhz7IrhNXUgr8g+eYDaP307+CbfpwdKwi10brcjmOJcA604KSVYW9/HxLJhBZS9U79sZgWpBKJhBZZaQloer0oen06vckn600DRdWMEtyIhqMI+Pzo7u5BJpNBS0uLCjFdRzwe10JrIZ+DoTK6Vb5sno/lmxjXPgYHTJyIpgjF2pIWTfv7++1HujMYGBjQFp+0WO0fiCNfKCLa1IyWWKt2OZDLZpFNpxH2uhE0ivDkk2jxuzA6GkR7JAhPMY+8uk7xzJLNKLJtK/rVMcosG6nq0KLTq8YlaFvTetyVj+szjQGvh4+iB+HzMfi1xSlFYYrGFKCdYFlZMqhjVS5FStNwI5PNoau7G4sWL8GDDz2K/77tDjzxxFPo7umx+qYrpeVqReXbYIuOdgNXrlyJJYuXwu3yqrYHtWuGvp5eLFZ1ZDM5nWawUDj3+dlH9svqg1cHnrNf7J8TpwKPy4L1uL4VPDqdJbITiqpsM0XV/r4B/OWxx3H33X/CkmXLdX99auz9QT+C6jMUaYpqcZ+WvZFIWIunFPb9voD63AILFy1Wef+I5557Xn++LItchnrjJgiCIAiCIAiCIAjCcCX++p+QXvw3tH/5cRFVhUHj/tpXvnJD38AAJk2aZEcNBkqFFEMNrOhPojOZx1i/F8F8SltEtrZymylbDPR6tVUjhVG3y4VgKAS32wNXiT5MaRVIRc+lSuM/QwtoyVRSi6iOpaljrZrNZLVYSvkqp4774wmEI2E0RSNa3HV7fNqak+lZL0mn09qtQDKZ0gIar0ebouq6R5dDwZfCr9/nhV/F5VR6n9uNSMiHoNcNr8cNs1jQlrEejxd8/JviLR+/t9puYG08g03JHCY3hTCGzlgVhhYn1TjxxfpjpbdxhEm+9vT04r2Fi7RFLWOnTp2CQw6ZuaUPJJ3OYtHCJVi/YT2rRDAYwvjx4zF69Gi0tMb0o+kUovna1taq3wMet6rjpuZmhNT7QjHW8jdqaMGRfmeXLV2GuKp37LhxWkx0cNpXSbmlLa2LX3zxJbzz7ntwqfeU0P0ALU75mP2ECRPQ1s7PAvNUL8/Qbgx4zcCqVau0SFssqfdStS+m+jN6zBjEYs1otgPdDFCwZ6Do7hxbgWI+QxOam5v0uBx88EEYp/pm1UUL2xyeePIJPPXUU+q9N9WYWJ+tUaPaVdqDcdjsQ3WgNe/MQw7GAdOnY9z4cepzG9S+dfn54+eXIv2GDRt0vZMmTdTlC4IgCIIgCIIgCIKwd4g/ehMiR33MPtsx4i/+Bvm+dWj70oPwtO6IPibs7wzNFQCfQYeBguHCE6s3Y2lPGqdOG43JnjxchluLq+VQWKUIxceox4wZrXLSryZlRhfVLhalSqW4ZupH9Sl00hKQ4hrFVQqlFMWo6ZuTmTQAAExdSURBVA3Ek+jq3azOS/AEoohGo4gF/XpTJ4P+UVUi+j+1/LxmtBBIMc2y5LTcAdBHKS0rKQJmVRqXx60tbFPJFLiJUcDerIjiKykVixgY6NfiIa0Y2X9rEymXFiqfX9+Lt7qSOGkyfazy8X3V0DJXAEyn/YXqPm6FbWO/li5dhnv++CesXt2h23/SSSfiwx++QFtKOvT19uPee+/H3197DfT9ecghh+AjF12ICRPGIV+obxlKK8tMOoPevj50qPf6vfcWahGT40O/qKbq31FHHYXzP3QuRo1qs3NVp1xYXbJkGf74x3uxavVqy6LTr96HUkmL2RyXM888E2effQY8arydvm6P9VlieObpZ/HAAw9q6+JAKIhzzz0XJ554nBa1We12uasVZzXPuqYyWe+jV9fPNi1Y8J52qbBx4yb9mfCrdh9++FyceNIJmDRxot4crRpZ9blYsmSpauMzWLJ0qa6H4v7sObPx0Y9+WH2ux9gpBUEQBEEQBEEQBEHY0wzVFUDfUz8DvEG0XaXyVtUtBKE2WvYbCvbD8BoKVxQO6R/US9+YFVDUokDKR6bTqSxKFEANN0wXc7EUyqpFFUrI5ij2lbQlIIXFZDKp/a3SjyXFUm6O1NzUinyoFe/EC5jfn8OmAlAy3SgWijrtwIDlNoCiGi0329raQD+tfMS8paVZC79+fwAJVfZAIq6tVukuIJlOo6DqtjaV2jqZXG7u/u/X9XOTLLoSoNCWzmSRUCGdK9jCX/UJuEumJQuxC+J4Uhz2B/nIu1uPU70QCQfR3t6CA2dMw6kfOAlXXPEJnH76qXqjL/2+qfflzTfn45VX/q6tUBtBgbSgxmDRwsXYsGGjKsPQAvdRRx2JyZMn6/GjZeeSJYuxft36LXkaovtopeMLN4yiJXFEvV/RSFg/lr9NUPHbhfJrkYj+DDiiKlm9eg26u3rgcXthqnbOnXMYLrjgPEyZZImqemMyHdQ4qz/sC/OzHbMPfR8+9KFzMXXKJPWZKWihvWNNBzo61uqyBUEQBEEQBEEQBEEYGZQyCfQ+fBPcLZPQ9rk/btEjBGFHGLKwSjHN+chRfMpkcjDVh5C+LcuhKEXLSAqrseZmLXymUxn7Iv9Q2mMzrJDPWsIeH89mHoqg6XRKW5omEgNg8Ua0CW+k3fjTeuA3S5J4aEUP1vQOYCA+gGzO2qyKYirzsn6Kqpwf2SwFWlWLFtlMLTqOHTcebe1tWiikGEghl4/mJxNJ1c6Utr5MqNciSkhns9i8ebN2a0ArWAqtdG/g83ng0n42dyeWAL01qL/sjMIS/7YNKpYpdDBN1fpS0bqm0kbCIZzxwdNxyiknqbEKaBGZfZ//5ltYsWIli6yJI5BSUFy0aLHKl2UNmDJ1Ck479QOYefBB+r3jGFNwXErrThu212lzNcov8Zj9ss+GFNhfWuQ6babgvmnTJv1es+ympghmzToE0UhEp2VgSloya8mf7h7svE5bpkyZgtmzZ+s+koT6nHRu7tLHgiAIgiAIgiAIgiAMf/K969H78I3wHXgSYpf9wo4VhB1nSGogZStLWqUIZT1m3t+fQCZbQK5YQl4FLUSZlqjK9B6vXz92HQz5kcqmkM6mVf4i5SsUVQLarBZo+Vkq2kKlyqTyBwN8HN5AZ2cXn8BHJBLFplQOL2/KYLkZxSpXDK90ZrG0s1+nD4eC2rqQJoe0PFR/QR01EAyqNvGxeNW+Qh6JgQF43R7dJvr09LgNxKJR7QOWj9on4wls3LgRGzZtQE93NzKpjHZlELA3jNKbczU3waPqGigZSJVcug/EGp/dixYo7YrYP1qdlgfrrWV71PtUfk0lZl6KqUcfdRRmzXqfGhc15qpfGzZuwOIlS/QYEUcI5b9yKFa+t3Ax1q5bp8o00KTG7X0zZ6KtrQUHHDAV48aO0e8FhWlulLVp82Y7J9tpjVEtrNqc+ramrSfI1oJ1lVen3UOoz6olRJv6feR7Sqw6rTpYFfNSLHfay1enDRMnTtA+ibVfV26upq4VCuWWvlvLEgRBEARBEARBEARh+JDftBh9j9yI0LxPoenCm+xYQRgaQxJWy9UqCnSFPB+LdsPj9iART2JgIKktWPlYeTaThtttwOP3omiWtDVp0B9EOplGLldQZXETLMu+ktamLNrv82tdihsgxeMDur72UaNB/60U60pFE0HDg3CpgGAhh9ZACBPGjtUWqAPxuLZspXjqSFv0h6p3x3e5VR059PX1I5vPaZExlU6jp6cP/b2WFSr3UuJj5KNGj8akyZMxfsJEtLa1IeALwqvaUVL54n10NZDR/VmWzOLVzhR68gYMNQa7X1Dj2NuCn974iWx9P6pDgXHbNBQJm5qbcOihs9DU0oyCGsu8Gm/6eaWIbaey69g279qOddoNQDqb0Y/MTz9gGg46aLq+xo2cDjxwhrbk5b9VK1di2fIV+hrZXiB1BGAbXV1ley1x2Eq3Y4HlOGXRZ69laUpXCh69odnmzk5dJT9blvjMtKYWVx0qx27K1Kn46EcvxhVXfgKfuuITeP/7j9AWultx6hcEQRAEQRAEQRAEYbiQXfUaeh74HqLn/x9ETv+KHSsIQ2cIwioFI5d+7J9iKDehoqDq9boRDnjQ3hxBwO9BKptBZ3cnUqkEPEYJrhJ9q5ZQVOkprtIvazyZRiZX0JsrZfMmMgUTpscHw+NFoVBCPJ7QO7FHomE0N0fhcruwYXMnPJl+HD/KjaPCBRzmSePYVgMToh69c3usqRkFVWBfPx/nH0A+V0QuW1R1JZEr5bC5tw8ru1NYXQxiUa6EDarOvNuLSNsoxNpaEY2G0ByLwuPzaBEuoNoTUe1tagoh2hRFU3OrtZlVTze6Eim8uSmBhT15FFz0M8sHyEmleLir2bnyy4XCiRMnYcK48do/Ld/b7u5u9Pb0WhfLKK9x0eLFWLN2jbbo5NjMnDkTo0eP1te4idUhh9B6tY3ypH78fvGiRer96LfrrRQcq/fFshq1T3YSR8yl79X2Ue2WT2BVdiKZwt///jreevsdLbA7LgLYzmp1sxxeD6s+T506GQcffCAOOnCG6nu7/oFBEARBEARBEARBEIThSXrxs+h/6mdoveoPCB97uR0rCDvHkCxWjZIKpvXgOzf44WP2wUgAmQI3dsogHPKjrTkKn5v+Vj3IZotI9A+gyMewtYBnIhiOoOjyoiueRncih854BvECkFXpk/kC+pIJmKqCaDSi8qjz3l79yHosFkXM78JR7V5ceWirCi04ssUDd44WsFn9SLbLo+p1G+jq7MS6teuRUHUUVJv7VHdXGhH83RyF+zt9+N2qHP64JoNXBoCkz6eF34IqgxsT0UkBH3nXfgrUK6U50+VCULUn1h7TIh39yQ7kTKRKPrgMrx6TcrY9G1444iofZ6d1rlv7RTWQVOPe39+vr+k0uhNbe7Ju3Xq899572vcs4ydPsQRG4giYU6dOxfQZ022B0sDy5SuwcuUqfW17wZJ5ykZKXXeETbpkIHyEfyiBm5I5QimhUD5t2lTEWmMo0v2B16fatRq/v/MPuPvuP+Lll/+ud/7fvLkTiURCu7Hg58kpg1apjmUq+2r5cKXvWn6mBUEQBEEQBEEQBEEYjiTffhiJN+5D29WPIPC+M+1YQdh53F/7yldu6BsY0D4jdwRKYQXDwIq+FOL5Eg4aE0MAJSRyJkxvAKlsHm4YaGlugS8Y1o/jU6ji4/bcIijPx/pLLqTyQLZoIgcXCipHrlhUabLwutzwe73IpFNaMKUlZDgcQYg+MU03MokMgvkUWnwuZNMZDPT1wWOa2oqSomgwFEBrSww+T1D7Zs16PZjfX8TjG4p4KxtCR8GLTaqtq5IlrOlPw+MxMSrshq+QV8duuGjVqP65VDBdKlDw4x9VmKlamkpmVR+L2JA3sSyRR3PAg7ltfoz20x2AndZQY8EX9c/Ced0KBbuenh68995CvUEX4QZJtPqkZaUDx27hwkVYt36DFnzHjBm9ZeMli+3LHgwUGzds3Ihly1ZoAZGWq5MmTdCP81tlWuKmI06+/sYbePXVV0E3D3xPTjh+HubOnaOvOWn8fp9+zyimZjJZpDNpRNV7N336dNUna9OnrfCTxHwGVq1ajcWLl+p2uNT7zx39E8mkdk/QsXat3gyrY439Wi2oNHRTwJ3/16tx8vv9aGqK6locotEmLZquVWkp3Hrc6rOkxpabcb3z7rt4990FWLhoIZYsXYrNmzajT32uEomULaRaYavAagnC7LbVd6v/giAIgiAIgiAIgiDsWeKP3oTIUR+zz7YSf/Uu5Na+jfYv/Rnece+zYwVh1zA0YVULhiUUaQHal0J/toRpbREYbi/e6MriVRU25kw0h7xoCdF3pWFtFBT0w6BVZLaAgXQB6ZJbW62WXG5VngrqGkVXa/f6Ik0WEfB5EQoFtS9PinS5TA65nGX1msskUSgWEI40wa/qptAXaYogqOqiP1VaYHJzKopn73X24y9r0liSjyIfCMPwuOBy+1D0BZEwXehPZtHmMzAp7EPA49HXWYdRMlBQ7aH1Y0G1O5NJIZOnC4MiSgV1XaXrzOagWo/D2kPbCKvaMQBFty2C2/bC294WVsm6dRv0Dv+01KUAPmP6dBx88EH6mqWVqn4aLmzcuAnPPPOstlqlsfMB06bhpJNORHNzk06jU7LLKpPfH9DiJkVbWjXzfZo4YTza29u1OMk0VlrmYyWWsMp2WNeBtWs78NZbb2HBggV49933VHh3i/i5fXhXp2OYr/IsXboMk/QmUxN1uwjLpVg7duw4bYna092lLVtZt8tNi12Xdj3R29uHDRs2YPmy5XjnnXe1he6CBQuxbNkydHf3aJcBbo9bvz/022pBK9uhvweCIAiCIAiCIAiCIAydasJq/9/+G8V0P9r/6SG4opYLQ0HYlQzJFQAVMe6BT0mJWhJFs/5cHm/3pvDI2jj+uCaN+1an8FZ/HimKdfks8pksCpk83IYbQX9AP65fNFwosQAGtkSdmy6Piveoc68Wu3KqXG4URb+mFLECoTDaYk0Y296MCWNHIxr0wY88/D4PsoWiFsaowlLiokhL68d8MYd4yY1+dxgljweqBrhVH0xVH8UwlzeAzQUflg8UkTbdemOjUjGPfC6rfW/GEwm9KVYql4HhcSMUjqCttQWeUhFjkMXclgBa3YYWEPnPERlHCvRdS1FRC4Oq6Rwzip4OjmC4ZOkSrFrFR/oNBIIBzJo1S4uXFlYaOyna29vwvvfNRCQSUWW7sXHDJu2blYLmVlHVSluOJUJbF5jPoz4nbjeDRwePx6fjtg++inRe9XFyRM+tUCBvbY3hggs+hI9+7GIcNudQjBrVrjc+c6s8zOv1+uDzBdSrX7chnkhiTUcH3nzzLTz40MO47X/uwG9+cydeevFlvRGabnW1zgiCIAiCIAiCIAiCsOcpFdD311sAbxDtVz8Kw+8YpQnCrmWHhVUtG5ZrSKoEw2UiVyhiXV8GG3IeZEMx9Jb8WN2TRWdfEslkEvF4CgPxNPr7E0imkqBvSlp0sigt0ppaBrXKd3lQKJooqkDLx2AgAJ/Pr60NoeqyNsFSKb0qLtSEdC6PfDGv49OZjCpAlasb6UYynUY+n0PB40XScCPncqNgqPLVMd2nuvjYuao7p5J3pjLoTeaRyeQxMDCg/Y0W1WRkG2LNMbQ2NyHkD6KYLSKTSsHn98Cv2hN1FRDxubUlaTWqxw4fKFprK2GOm2H5Et1WJ3Tp3fMXvPceEomkFkXHjxuHAw+cri2QKZbSipMbjlmvBZ1r2rRp2mKUZRfUe0D/pXwEn7B81rc9fNzecj/Q3NyM8ePHY5yqa9y4sXYYUyfw+jidZ8yYMfpzU85WS1l+rnx6N/9PfeoT+MdPX4kLL7xAW9/OmTMHkydPRmtrqxbQvbbAyuDx+uBxe7Xl9ML3FuHue/6I++//s7bk3Rb2a7i/64IgCIIgCIIgCIKw71FM9qL7oZvgGXsIWj99px0rCLsHY0NHh7myowPz5s2zoxpDq0wDRdCL6l87urGyK41jJrVjY6aAP3dksSlrYLQnh3Mmh/GBiVFEjJJ+pN4wXaAxaipfQFcqi0TRgGn44GI8ZVHDVMEFd7GEEAoIuUvwq0C/psUCNwoytbDKR/v5aDp1OVoUUojLZtLIqnJNjx/haBj+gF/7cy2kMmiO+PFaErh9WQ4r80EYXo+WvVRRKrA3BYQLSRzljeO8cSEc2BqF21eEV2/oRJ+glH6BQjaLZDKlex8KqfIzWQwUing9UcLqRBGnTIxhdsyvR8goUXG2hGJthVnFotER+vjY+j1//JP2JcpOUeD78Icv0FaUDvT1ee+99+Pvr76mxcvZsw/Fxz52McaNHaOuWrUMFkfQdKwsH3/8CTz40COq3LwWVc879xycfbblzJlJmeyll17B/fc9gHg8zkHHzJmH4Kgj3w+/36s3gtqK1Ra+L8VCAa+9/gYWLHhP1+nzeXH+eefgtNNO1ems/jMP/xh4+uln8MCfH9bvGy1iz/jg6Zh37NHIq3btCGyzSxUcDofVGG4rrjYiny8ioTfwGkB3V5cWlHt7etDT06sD+5/NZnTaQrGo+l7CB045Beeffw7CodB2YysIgiAIgiAIgiAIwu5n3dVhtH/sh+j7608QOu5TiJ71TfuKIOw+huRj1ZKMaDnqwvL+NAZyJRw2pgnTYwH4TROjPHkcM96PI0eH0Gr7KqV0SrGL1qOpVAq5Eu1L6ZmU1ylcUmQ1tOWql4/ql/LqmK4DTHi8HgQCIQR18MPr88Ljtnax11avqkFsEzfMWt6XwZt9OSweyKA3nkHE7UbU70JelbkxmUG3CqAwa7rgof9UlTGn6prkzuOEUW7MUW1uivi1eEu/ryXT0P5ds+m0tmDlZk/Rpmakcxlk8nkYoWasThbRl8nhgFgQYwLW5ky0mNVGszxm62oIbRTgdszH6notMFs+Vt+3Uz5WWTfLfXP+W1i5coUWBcOhIA4/fO6WzwObTVH3b88+hxXLV6mx92vXAYyz/Jm+jbfffndLeEuFd95+B2/Nfxvvquv0Scp6nLoo3E6ZPAmRSFTFWZapVtvpY3UVFi1agkK+oEXYI46Yq61iKVjuUAiHEFKv3CjLEm8tK1WOm2OZ6wigzivTELfqGy1dYzFay47DgTOmaxH7sMNmY+bMg9HW1qY/vwPxAZXHpa1zU6k0JkwYj9GjR6k4XcyW8gRBEARBEARBEARB2P3Qx2pm5d8RPftbiJz2ZTtWEHYvO+wKYCuUCw0ticKgeFVELJ/ESTHgEzPbccb4GMZ7DbiLBUtkVCmz+SwSiT54VPr2SBgRj1v7KaX1KzcxcpdK6ryAkMoX9ntUOgO0Vs1m04jH+5FIxvVmQ5SsKHBSAKNVYliVFWtpQdv4cVjrDuCRDQX8cVUWz27OocugR1UTY9wlHNfuwSGRHAKFpKonD6+ZR8DMYIzZj7nNRRzY5IYrn0E+m0MmV0IubyKbKyLeH0c6mdCbaEWbmlR7ssjkC8h7vEiVaO9qjQSHohy2c7jLaxR1N27YqN0umOp9ampq1o/Bl7N06QosW75SC6p8S0pFNTZ0v6DeC4qghZwKfFWhqI6LauwK+aIaRyuOG2IR+shdvXo1li9foc+3//hRgOUwWsInfb1W4oiitYKDc+wInCxr7dp1mD//bR244RX77viWrZa/PI4bo1E8PeWUk3DxxR/B1KnTtKhK8bu/vw+bNm3W6Yb/Oy4IgiAIgiAIgiAI+yaxj9+K8Imftc8EYfczJGGVUpNlgwp4SurIdCNbdCOdKyBomGh2A/5SgU/CqzQe9ceFTDaDRHxAWxFGmyIIqdfWoB/NATd8RgE+M4tAKYeo10BLyIu2aAjRcEhX5lL5g6GQtmLUj9/3x5EYiCOTTqOQz28R4FJFExsKHnR6m9EXGouOQhDdBROBYBgt4QiOGteCc6a24OjmIg7x9OMgbxzHRnL46AQ3jo+VEKAVaq6EZLaIZCKFns2dWL9uPQYScbj9HrCWgb4BHegRtuD2IENRzkW3BBRXa7BVrxsmUMC0WtvRsQ4bN22G22NZd1Kgbo4162tkQI3ze+++h96eXv14P62FW9tatXUmd/hn4OZPo/hqn7e1t22Jb29vRay5Sb/v3Ek/EU9oq1TuvG+JkGWjpg+3tq3auPFaveBQfkz4GXn99ddx++2343/+53/wm9/8r3ZRUE61/NbLtiLrlCmTtbUwxX3GcUOshPqM0EUDqaxbEARBEARBEARBEITdy4Rbkwge/mH7TBD2DEO0WKUFKu1MiQmXYSKVzaLg8sITCmvRsWTyUXqPfhw+m0whnUggEAgiEmkGH6E2TBNBL9DsB6LuIsLIIWimEXYVEeAu9apkbjDEDYy4U3sqmYRZKuld5puiUS1q8RFsPj6fTCT1Dv5eo4R2VWaLmUWkmEC7r4AWPwVgE9lsHvmBJGb4DJwzJYyPHhjBp2c147Pvi+H8qaPQptq9puDGQgSwwetH3udSdRTR3hbB6DGj4PUHkM3lVX39qsuq9y43svT7yk2wthhWbiuoWQK0Yi/rbI4oWC4OEoqbFBf5WL9b9YfC9bSpUzB61Gg7BbB8+XIsXbFMvY90/WBi5iGH4LJLL8UVn7wcn/zEP+CTn7wMnygLn/zU1sDzT33qk7j8sssw65CZVDf1WCxbvgwrVjhWq2Xopm1t49aW7jy0LI2qz42L7h3UBzcRT2Lzpk5teUvKhmULlkDqhK3jSGgt7fV4t7Z1VzZWEARBEARBEARBEARBGPbsuLBqgk/+642f9KkqIV/IoJjLwO8G6FKVYpOhXwtIJQeQTcURCYcQpj9Qww392LzLEquMUhF+PpIP9ari6FPVpPLFy6zL5UYkGkVzc1SLp3zsmpsG0YcmH1lvaorqcvrjcZSSSRwe8+OUVuCEUApnTwpgRpNXi2i0lPSqRuddHqzsziOVKWJiyMBYtwl6Ml2WAe5bV8R/vduP3767GcsSJTS3j0KsOYag14eQL6D9rtKac/TYUdqPp1eVpa1yVVuHu5GiIwBSLGRbc7kCXv3763hvwUItqnLjKu6kf+BBB2o/oySpxpPCa1dXtz6PxZrw/iPm4uCZMzB12hRMn36ACtPqhmkq3cyZB+HwIw633ivTQE9vHxYuWqw3ArM+glsHj83cavFpf8h2EfSbGggGrfJVWL1mNdatW6ev0V+vM0ZWvU7YFual9Wtff/+WTbXoTiASCcPjURNAEARBEARBEARBEARB2C+wFLQdgaqqfije2naKCmo8lUE6m4ZZzCGXzsAsFFHM5zEQjyOTyyHa3IRAKIAShStbqzJt4YrCKnfmd7tc8Pi8KBQLyOWyVhqDtqbqVV33eb1oiTXrXd65idRA3BK2PCq+qakJrS2tiASDmBgATmgBzh7nw/sCBZT6e8BNiygK0tp17UAazy7djHfW9mMgU1ClGxgoFbCwN4MlmTA2esZhadyDTWkTRbeP3l+11S2tY+lTNBC0Nm/iRlw+uOEtAm7dLas/e47yR9+3CpPVYDqKf4SPrdPK929/ew7PPPMsUhQ3VXZadM6dOwfTDzhApyPLl6/EkiXL9PixjAMPmoFpB0y1rzqwz/w8VA/cLIoccMA0HDBdlU1NU/1j2atWr9HXtowbr/E9d0639G+oWG1z2jBu3FiMHTtaf8ZobUp/ry+//Ire7Z9Y9ZW/h1vrLx/DpUuXYuHChdrHKj+bFIzp+oBsFWcFQRAEQRAEQRAEQRCEfZkdFlYpG/GxcAqsOnPJhVC4GbHWNsDjRzJXQG/fANZv2IxEKodAuAkGBUruyG7lZi4Nj7KmgSw8KLo9MPlotYs78VtpuKEVxS3tdoDimAFVVwSRlhbkVO098QRS6bQqm5tfGQj4/Ii6XWguJBHK9MOTy6uSvXCpuvP5AjL5HJo8JRw+JohD28KIebiDvypXtcHrMuB35+D2pOAPFLSbAm7MRWktk8nrEPAH4fP7YRZLMHM5lPJZuFS7nEHc2rNqVLm6RTgsu1a/ENUeCqrWbvSJhOp/KoV4PF430E9qT28v1qzpwN///ir+8Ie78dCDD1suADxu7WJh9uxZOOboo7QvVMId/BctXISuzi4tIjc3N+GQmTMRa6b/VTbSaSj7wBGoFSxL0FgspvIfgqao+jyo8jZv7sTSpcuQ135J7XGwi3SsR+l2obu7R28MtWnTJmzcWB42lx1vrAhW/IYNG7WITGGYjB8/ATNVG7gRFWMY/9JLL+P++/+MZcuWq/FMatcAhUIJRfUeU4QuqvZxrLPZnC7rjTfeUmP3CFavWq034zLNorbKnTRpoq6DaHFVBFZBEARBEARBEARBEIR9GmNDR4e5sqMD8+bNs6MaQ2+b3Om/aBj46+pOrIjnccb0sZgRdCGXz6N/YEBvKkUhEhRUTRNev1dbRXo8BrgZFQXUzkIR73WnkMmbmBIxMDHsA/JFGKUSmiIhbbFK0ZO6GMVcSlX9JWBNMoe+dAF+dT7KXUSrt4SA24NiAXpDK8MoobklBr8/oHenp1iWzmQQ7x+AmxanoQi31EJL0AufakeqCLy0eQAvbUqjp2hiesyDcye2YGrYi0yxiIGBFPxuN6JNIapxyCdVWemkKicEdziC59cOYFlvCqdMbsFhMdUH1VKjpPqohWGKs5bVqz7WBxSL9YuWFGkBec8992LN6rV6rE466UR8+MMXIBgKMJWmt68P9933AP7+99e0oEe3CmNGj1Jj7FPjY70fDpYcrMrmuKny6Bs1XyhoNwq0zqTIzDIYaIU58+ADcf5552DS5Ek6PYXP995biHvuvhfr168H3TYcccRcfOTDF2rLTN32waKHQJWpyqBAyn6+8+4CXQ+tWD/20Y+oV8sK9qmnn8EDf35QvWdsnwdR1cdQOGyVsbV7W1Hx1khWtojCLF1U5HHkUUfgtNNO1T5RyaZNnbj/gQfx1vy3tAsEPT7qPW5uacbUqVMxZcoUtLS06Mf6KShTfGV7urq6sHzFcqxevUZb+brVZ9lU+caOacOHLjwfc+YerltB21gX54ducLVGC4IgCIIgCIIgCIIgCPsCQxBWLYHTElaBx9dsxoqBHM44YCymeYropy9Tn0+LYhToSoUScvkcsrksigU+el9S193ocwfw5Losnl2X09aBx45y4UMzWtFsZrWQFYs2AW5VARUyCmWqrL6Sib9t6Mdf1yTQmTXQHvDgpLEhzPFn4Uv26jr9wbAWvUKhgCVtqewsgpabuWwOfn8QGdWWgWQ/ItEgmkJhZFMFxHMF5ClS+v1wFYsY41Nl+DzoTcS1+NYcDWsRMplIa5E2EvKpOsKqBjee29iLtzcncOKkdsxuoYBnVhFWLZFNR9kHtMylyLxkyVLcc/cftUUp0594MoXVixAKBvU5+09h9U9/uh+vvvoqfD4/6OeTflFL9Eera9C9VcH5a8G2O9CFAcVU57F2bgI2Z+4cnHLySRg1ul1brhoqnuP05wcewlNPPa3riURDuODCC3DiiSeo8igaqjJ1f7bWWR1LVCe0siVPPPEkHnr4YaTSGXg8Xpx37tk4++wzVSkGnnjqKVXvg9pilO2j4FnizmD22PGN3NobhYov05O3gfXms1mc+oGTcPHFH9E+eWldahhuPc4PPfQoFi1aZJWpCi2oz2ZR9ZViKut20V+qyxJWdTvUNdbuiNGUT8e0j8GZZ56OI48+Ah7byrak2sPx4ZZp9cdGEARBEARBEARBEARBGMlYatdOYGmHBjLZHBLJJAJ+P6KRqL5GMcpwG/AH/Fv8oDZFYvB4QljXn8Mb3TmsNaLY4I7gnVQJa9I5ld4S1NLqOJ8vIpPPIKsCha/VfUm8sCGFRbkIugOjsSgfwrPqvN/wYfLkCQgFA+jv7dOWqWZRNUy1i3JYItGPYjGL5tYoos1hjGqJIdYcRU6VuWFzJwYSAwiaRYxCAdNDPrQZQC6bRV8qBRRMNAeCcKuSEgP9up8h1ZcAhTrWodAPvG/R0KqLaVtlNubZ6veTaKGTflvVWHG8eO5g1UBswc9FYc/QFpV8pJ3iLkXDYCioLVytwOOg3qgpFA4h0hRBc3Ozfoyf1piTJ0/GccfNwz9c9g+46MMXaFHVEg4t6Pt0ydKlWnCk0Dpt2jQceOAM+yoj7deGQaHSsuSSGl9y0EEH6sfm3aqPxUIey5Yv1Y/vE4qrVv8oALu0QO9X76lf9ZPWx/pVB3/Fq3PshIAeG/rDpZW0A4VRtmPy5Em45JKP4PTTT0X7qFHaty/ropsHt8er+2wJ1xRUVT9UW91et7rOND71GYpi7uGzcMmlH8KRxxyuPwBF9dkxDO2RV/1znEgIgiAIgiAIgiAIgiAI+yrG+o4Oc9VQLFbVP0plf+3oxKLONE4Y34zDWv3wefmAvqGS0XqPaS2JiekpwlKLLBkG3u1N4q6Vcbyb8MNdMnFgNI+LDohgVqCEZM8AvN4QfEEfcmYKrgItKQN4oz+HP64HVqAZLo8L+byJiUYK/zjdg5PHRrSW193Zpf2Ocvd+iorZbFZbLoYjYXj9Pp2G1pOJVAb9ff3w+1Sbw0HE40k+O46IykODxHhyQFuUtre1w+/1oTcxgGIxj1ikCR51ToFO2yWqvry0sR9vdSZwwsQ2HNbC/m9vsUpxjnVTZuQBLS15xaX+dW7uwltvvat9nnK8Dph2AGYfNgs+n0dn4filVHvfeftdrF27VltN6qIULNt5Ryzs+nik2k/Bln0MBkOIRi2BtbWtBS0tTToNYT+dPGzmkiXL8S4f12dT1flBB8/ArFmHaGtOllku/A4WJx/dELz99jtYsXyljg+psT/88DkYP348lixdgQW2mwBuZrZ1szOnvm37ufW4OvSResD0qTh09vv05mdWeVY7GNi/9es3YfHixVizZg26uruRTCT15lZM6vzqQDcGHMOWlhjGT5iAgw6cgenTp8AXoHVyyRo/9V5bMj4leLfOu+OjJAiCIAiCIAiCIAiCIIwUdl5YXbMZS3uz+OD0cZgZ8ehHt3mNT34znanFJvW3aCKfK2i3APlCFvES8HbSjbd7SvAWi5jb6sX7xzehLehGOp6Cy+VFIOxXufNwqbyFogtv92Vw/8o03k57UfC64M6XcGjEhUsPDGBWs09bGOYyGT7Brd0OJPrj4KPfLW2tCIT8KPJRcNUml4rr74ujv6cXbe0t8DdFEI+n4TMMeD0mksk4NqzfAL8viDETxiOl2m0YHu0OwOdRHStRVDVAxwYUAF/c2I/5nUmcOKkVc2J+3d+awioVO3VI0ZnQn6y+7nLr8y2YLL0C1W6deadRg69VU4Zy7PdW12NtYmXB9JbLAQur7YNri5NWQTWZWbbpB6+rvtIy1MU6HTlzV6HK1W1nPWVtsRqi/rP6WSqqzyQ3Q0ul9MZdFGXZJgqwfr/6LIaC2hKbriasNnJMnPJYljWefLfpHsJxfSAIgiAIgiAIgiAIgiDsmwxdWDUtH6sUVpf35/DBA8bioDB3mGcaWlGWwMfduYlVNqcC/asabni5C30hj6ILKIabkIYbAdOEP5eGV+UJhyOID8RVOSYiTWHkCjlkUxkUVGX5YAjvxgt4fl0cXekCWtwGjp/cjHnjQ4ipvJlsEfFEBqGQX+/y39vVpeUufyQCXzAIn9cNj2Egm8lr8ZbuAYJNQf0YeLIvqTc4CkYC6E8MIJMuIpfMIJlJwB8KojXaAj9dAngM7U+T5remy0RKtf/Jjn4s60ngg1PaMTtGv6gldZmPg1tiniWs6kMrqGM9TOovUxXSCWR7NsLMpqDNZdV4aMmOeTTqjFaR6oj67Jay9CvT28fEOdZCri7Fto7lgfPHCU4m50gnUKgW2xXpWnU5qk0sqBzn1MlWjfI0LEed66K3KYuCJL2S8p+VxsEeiRo4hZHyfHrEVXVsszVu25XijI96I9hXuldwaf+zajy3lEOYk61Qn+hSSYWi/mzqcVcJ9RX1ufbH2lUYpeIo1LItVn8EQRAEQRAEQRAEQRCEfZMhCKvEkqkKMPDEms1Y3JXGB6ePxfua/dpilZsA5fM57ae0VCzA7fHBGwqh6HJhcyKLzqy67nYDPhfafG5M8HtgpBMwKKaGo0gmE8hmMtqvJcvz+vza16bH7UFa1btZ5e/PqNoLOYwJqDLCfmRNA13pPOKZHKIqX7BURFSVHfT7EE8kkc4XEPKH4FFtoFga8ge1xW28WNSP9hfTWRXnB3d0z+Vy8KnrA919gLuAllEtKGYLyKp4w3TD51b9CRooqvJf7crjviX9iKi+XDqzFbOi9OlpamHVGicGQ/1HCY6ndB/AWPVPtZE6as/aFVj14l+Q27RG+/is1C91RgqAPNCPnBMnEV8rM1Sia7OPnbR2u3RgmdZ1CpGMKk9FKDjqVNqK047UOCmqY/VlawYeGVrwZF0sV8Uw0i5mm7LV8dZ216I8QyXMy/aqtuv6nLEj1hH/6hrUH123CnoMVMSW94Gn+iLzO4k4HkxbRN7lw6T3n4QpKsBFlwN0XcFU/AwIgiAIgiAIgiAIgiAI+yJDFFYpSQJ5uPFURzfmb0jhmPExHN7qhVHMaWGSvkP9fp/eQMil0iULRSzrS+K1zgIWJYDufEE/Dj824MGx48OYHSmizWUi6AsiGY8jEeeu/VG0tLSCmwlpUUurYwb0s/6KVDaNXCYNVyiCd/pL+FtHDxL5PA5uCeH4CU2YFlJ1a+tCE6VCAQMDSfSqsoPNITQ3NWMgXUJvDqodblVkEV7Vr4BZQMBtiWdukxalBfgCftWPMErqeiFTRCaTV33PYVMJuL8jj2c3AYeO8uOKGUHMjvp0W3U7bYGOopzlFoCnWnGzj0vqUgmF5ACy3etgZhPq3LlYhl0MRVWNFgjL01nHlgipjvV/Zde3tKNS6FNpdJGW/ayFnVbHq3brLE59tYRCO48tPjp5iW6T05SyQwdaqloHzGRd3ZqcGXRhNuqKTmbH8bJ1VB19UWdQf3li/dVo1VQFZ7x1/UzBV5XO7osVy96rI36W2F59jXnVPKCvXcMLT+t4+FrGqWOPTmsVa5ctCIIgCIIgCIIgCIIg7HMMyRUAxSRKcQXDg6fX9eHR5UlMCntwSruJyTEvosEg/G6fFkQLpRLSqRTWZYp4YnMRf+sy0OcKwXR5Ve2qrEIO44wsTmotYl6rC2P8LnjchraMDKpyuLu7Fr22CGCWrEtdzK3KTqbiWJ0u4c9r83i2B8ireg/yZvGJgyI4dpS1cz+zcmOh/ngSqXQWbq8XedX2lOlF1qAQCrhUn+g11meWEFBpmyM+RAI+JAf6QHebkeYmuG2RkfIcW7OwP4PfLMvg1V5gbruBT80IYXbEr9tOQdJli6t0GUCqWTBqK0qnb/sEVl/Z212FUyIZriNF1xeWOGttaCYIgiAIgiAIgiAIgiDs22yv9DXEsK3yPDpzyWWg0yyhx+2CNxBEyBOCzx1QaVza52lfbwpplXZ1ycD8uIkeVxQFfxAFr4GSx0BRHa9HCK92F7A+byDa0oxYczO8Xi8KBe7Obqoqy4QqderWgYKkC0FvGLk8MFDIweVzw+XxIquuZQt5LcIZLkrAJhLxFMxCCaNGtcEfiSJTcqOoeuAyTB1oBWu63MiqMgteNwyPtZmU10+Zle4BMvqcW0oVqZ+pOsaG/Jja5EXYyMIwWR8vKLY01zrX7dgauQ2OCJdIJjEwEEdJW0Vui9brKuC4cId9btjFcWLedJqbLpV0nG6iHbhrPdOzHB5b+ayx1Qa9LNBOTb+4Oqg+8zWdSWNz52b09/epcy1p69Rb0249d+L4KDxfWQc3gcpmc+jrG0Amk7VSqqRWUOm3lGeVUA7z9vR0Y/26tXozsQ0bNqK3r09vgKZ9nW6p26LynDAunkjoUPX6Ni2wYDIrKf+UBwern7TM7urqVG3qATdn46ZfIqoKgiAIgiAIgiAIgiDsH+ywsEp5qUBhSf3jNj1GyYC3ZGJU0I0xzWGU8kUkk2mkcnn0JxPwBTwIhoPoTBTQk3Xp3f49RVbs1sFtulBy+zHgDiDn9sCtH1c3UCpaguF26MslmAzqn9vrwahIGAeGfZhmpDChNICZTT6MC/pglpjGg1QqjWw+g1DUB7fbhUy+hLzhRsnlVjXR/lSVZKg61XnR40ZG5ctk8yioPnBTIwqraVVGQeWjz9eCWdRWrM0eF44eF8AhbWoMTIqr24pv7IfltqCx2LZxw0asXbsW+XxeC4oU7bg7vTMGFEuz2eyWOKZh+vXr16s4Cnxd2jft5s2bsHr1KmQz9G9rtZeiLjde4nhk0hmsWbMGnZ1dWgSkn1f6s02ns7oOq60qmCqXei82bdyMBe++p8rv1X2mSJpOp7U4y3QsN8fNyVS81VZDddml0pq6no0bNyIeH1DHq9DT06P7R/+7jlBZUOWwPPaN7SuHdaxYsQrvvbdI9XUd1nasxaKFi7BmdYctQBtb2sPxsgRRU7eH48RrjIv3D6C/r0+XyTZSgGZ9bIs1RkV9zDgGiraWPuq8d4bqjxo7PU7qc6DGiWPDMXznnXexYf0mXe9WeFx+LgiCIAiCIAiCIAiCIOxruL/6la/c0DcwgEmTJtlRg8EEn26nMLcplca6eArRoA/jWgLwukro7+9HIZtBS7Mf4ZAfhWIJ724u4L2kF0WPT2uNJW0QasBNLc0w4SlmcEgUODDmh+Wl0rKudLtccHOjKxtLvyqhxDJUOgqfnlIB4WJe5Q3imMnNOGZsEG3FlPa/WiyUkM+mEQwF9a7/qilI5V3IqNeSar8lrCooMqoj/qVbAHc+i3wyiVQqoQW1lOpjMZ8HN7fSwqdqSE+ugM2FAjqTOTSpfLNaw2j1edQRhVpqwLpkdtNmy8F29PX1q7+8bmD9+o1atKOYSCvPcDiMzZs7sXLlKv2qBcGSieXLV+jzUCikxkjL3Fi3bj3WrOlAIBDUAiJFxnA4pMvvV+8zX5cvX66vRaMRLXYuW7YCGzdu0HGsi9bCfG+Tqv/Lli3T8WPHjlVjkVJ5V2oxl8ehUBi9vf1a/Ozs7FTvhUuV2aTb0d3dg4ULF+v6fD6/LiMep/DbqfL06TZRrFy5ciVWrVqjhWHmj0Qium7CfvKzNGHCRMyadYh6Ha/bxvHxeDw6/9Kly/U4JRJJPQ4crxUrVupx6OvrU3Xbrh5cbp2HYu+qVat1uyj4UhBlHevWrVNt26yFYNbBdjhQSOV1jvf69Rv0mHK8Oc4DakzHjh2DWCym278ttd9vQRAEQRAEQRAEQRAEYWQzRGEVWljlhkzBkA8BvxfruuNY3tmPJEraSrPJ70MsGKJMiZzpwqpkCYsTJWRcHphul964iiKllqKKRcSQw6yIC+O8JRiFvBa8KGhSVKXQZVkEbhWqLNHS0AJrIZ9GMZNGS9SPWDRoPe7u8yGZyyPV3Y1IwItIU4tqsFu1B8gWisgUaM1Kq0y7VL6qfIZZQkC1rSUUQEtTRIuHUfpX9XjgUiEYjaDg9mNNysTza3vw+qpNaFXxJ0wZhSlNAXh1eaoPqiL6VLU2f7LrsP9Wo6enV79StKWI19bWipaWGHp7e7WQRwGUwl17e5seFwqGtL6kH9q2tjZ0q35yszAKzRQmx40bp4VFCrCxWLMWOilsRiJhna+1tUXVVdCiZEtLC5qamrFp0yadl+eEYmR//4Cug/XxOts0YcIELULSypVWsr29PRg/fpxuB8VLRxil6BiJRFVdrVp4ZTkTJ07Q7aI1KgVWCpwTJ05S59Zj9RR2mY5Q0GQa9ot9IBQ0E4m4tizlmPGR/IkTJ2phltakFIPj8TjGjBmj+86mWBa2dEfQp9s0ZcpkXQ7b5PX6dDytmqdNm6byp7RIy36yL2Tdug3o6FinBdTRo0frcWDb/D4/XOrzOW7cWC3qOv223ufa77UgCIIgCIIgCIIgCIIw8qk0sRsUfGCcWz3RfLTN5cIpY6K4eNZETIlGsHBtHK91F7EwaWDVQAG9yTzSqSQmN5Uwo6kIbyGjN3ei61ODj3MXs4iU4pgRKWJKzA2/G/qx7kQioa0iKZRRSKQYqP1q8nFxipamAT7Iz0fR4+p1oy+Ip7qK+K93unHrO124fUkCL/X7EA+2IGt4kOSj/EXrsfVSLgs/8vCaBccEVouqtFT1qeBV9aiCtdWp2+OG2+WBLxxGxuPFykwJz3dn8JdlXehOFnHCgZNx8ZzJmN0SgJYDKc5qXc0S1lg6LVgHCwVkCpETJozD6NGjtNDoUW2gRWShkNeWrHzEnZt6UfRtbm7SlqeE6Rjf3NyMpqYmLYw6Wh/HibA85mlujuq4nBoLS7QuIRj06zTWuVVeMMjyoqALhUDAj/Hjx6K9vVULs9lsRr8nFHEpVlIQdfJSaGRdTU0RXS7bOm7cGC0MUyTle8r3mLAdHjW2Pl9AHW87VrzmtJ2wTsePLC1aCV/9fr8W4B1htqurW4u+FJopSFPIpdUsx3bMmNFaCG5vb9fXWO+YMWO1MMxxpihaKGytM6U+v6FQAJMmTcTYsaMRU+NXVO+Fz+fRY8Px3yqqCoIgCIIgCIIgCIIgCPsDQxJWXdrWk2KkCy7ThL9UwvQAcO6B7Tj3sCkIe114Zc1GPLhsA94ayFBlw6HtUXxwXACzfGk0F/oRLGQQUCFWSmB2NI+TR7swNWAiHPBrYXDUqFEIqnwUzWhxSCtEbkCUTGeQz+VRKOZVK0rI5opYEy/i6Y0FPLy2hJf7Q3g7HcYL3Sb+vCaNFxJu9HgjGEglsXHTBgwMJBHx+9AaDsDvtqxmKQbSUpXbWYV8bu3WIJNLoy+ZQMEE6BF0wHDjvXgejyxch/mrNmJyewgXzpmIE8ZGMUrl9aqEHAtVmJZRy2W2baXC2jiipOX6wPLraYmCfPS/pK0waTHJsaAlK0VDPvrubAplYcVZvkJLWywwafFKwZq+QSky0kKTQmQoFNHCIIMlhlqWl05b9GZUqhyKqpZVaZcWRGk1Ggh4bStZ9Ymw028VGHlOq+OsrpfCrDMqFE99Kh9F0FAwhOamGCLhqH78nkJuOSyXAjsf16dVLh+/Z3v4+aCYagnFzbrtfn9Ajxnjpk6drPM6rhPYforOtMDlRl+04KU1LPvndluWy1Z9/Mtzqz+EY8K+08KVFq8J9bnwB1S/1ftEYblc+BUEQRAEQRAEQRAEQRD2D3bYFQD1J0siU0c80c+8qwOKi0YRo3weTB1FkSyEDYkcVvZnkSy5EPJ70O4zMDGqXgNAqyuPaYESjhntxwljwpiEHEKlEuiPEy63LpN72/t8XsSiTTqeG0wVCwVksymkcznk8iV0ZXJ4fmMKL/d40euJweTj2S4DpteNpGppVyKPZlcJE4MmzGIOfr8XTYGALtft52ZaJvyq3JDHQDjgQpNqZ1il8Xq8SGby6CuUsCZTxItrOrF4Qx9GR8P4wIHjcOyYCNrctNvlY/90TGA9gs+hsMRFfaBfiB6vOlD8Y5soAFJYpdBJwZKP64dCQW2xS2Evk0lrIZEWlyyegilfmYaCIoVOCpBsA4XKvr5eLSDy0Xdaa9JalWIrhcq2tnb9KPyGDfRH2qPFSQq3FFwJy6UIyzQUMsnGjZuxadNmbRU6adIU/bi816vGTNVdDuundSnrp+UsXRBY7aNAm0dI5Wd7aJG8YcMG3a+W1hbthmCrOEuR2PLNSjGXfeXYTJkyRbWzXaVzqbhu7UKAedhGtp3jZPlX9erH9NlWCrYUVim00pqV/ae1LttA4dQRadk2j4e+YqO6b4TxdNHAdrJsXps8eYquk4Ixx7ncD7AgCIIgCIIgCIIgCIKw72Os7+gwV3V0YN68eXbUTmDyv6L2n1qEB91F4K31fXhnQ48WRWeOasJhY6NoDvjAh7gpRVGOo6Xn+o2b4XaZGDe6TT/izwf903nu1J5ByOcHfXxysyltUmgW9CZU8YEk3u4ewJ97PFiQi8H0BnTOkotyZ1E/3m9kMjg5lsMnD4piQtCL1EAPipkcAtEYPEFubMTyaINL61Va4jKvC7QBXZ3I4M113Vij6mgOh3HE5FE4pDUIvT1TiRtoqUYYFFVVoIGjowcOAVpXllt9OsdsHwVES6i0rFApAlJ05TGFQF5zxFDmowhLGEf3ATSodDZxYrG00mQaioF83J8WpU65Vho1FrYVqtMM1sHhz+XU2BeLWsClgM2y2UYrWOkcmJ+Wnmy/075ymJb1WhatlkBbjtVWq1ynHbQuJTxnVfl8Ufdxq+Ws5T7AKtOt+u1RcVbb6UZh9eoO3WemoYA6depU7TaB507bWafV363vA485/jznOFltZ7pt0wuCIAiCIAiCIAiCIAj7B7tWWCVmSQXamtK/p4GcCquyBby2thcdmwfQHvLj0IktmBoLotVtwqtSZktubB7IIl0ooLXJi2avoeI9yOYKiKfTCAb82kVAqVTQG2ZRyiwVTWSyebyt8v1uvYEFqRACtjCXM9zaPyof70c2i2ObsvjU9CAOjPq1IpdIJZHK5FSZAYRUoA1uyVBtVnkKpgud+QLe60ni3Y092mJ0zrhROGJMDG2egiqTga1WdVFUs+vRkpoxJM8KW6BAR/GOUAgtF+qcY+txfktIdQS9Wmx/3VRjqHrr2hqnkqg09sl2UFS0LtJylWNBK1BaoFJUzOUyqr1We1gXhU1ah9LSs7Luam1t1H5S2T7mcWDddDXAx/E5HrRItTbPshMoyutgVrpFoLUq20pfr7T0rdZWsjXftueVNLouCIIgCIIgCIIgCIIg7HvssCuARtAylRafNGSkhakBE60eYEZrBK2xCNb1p/HO+l5szhTg9vsQ8HqRyZtI5lwoFF3IF62NjPwuNzwqf7GYBy1PfV66CGBptHA1kEomtauAjDeIBf0FrFf5+fh/idqWysc0tIQ1SnlMDpRwWLMbrW4KgIYWB91uE6lMGqVCER5adqr6aGG7qDeJF1dsxPLOfkwc3YJTD5yIObEgmgzatJagWo2iSktB1apHvahDHttHQ4aWoIsWLcLatWv1I/G0jKRm5/g55WPxvEYrVT6O7sBrFPf4Wihwk69trS9ZLgPho+u8zjIY54isTMe8TjonP32WEj5uv3HjRgQCQd0upl26dClWrlypH9PnI/J8rJ/uBChwMh/LckRHUlm+1TYrHXHawFcGtlPFbmmLdU5h2KX97q5YsUKNRwd6enr1Tv2M52ZZzvvg5OeLVS436Apo61aOJceCfWE6XuerZfW6tX6rbVZ5TtustvOc42pZBzOf005BEARBEATh/2/vzH4bubI7/GNVcWlSOyVKYsvuRUonSJxBMvEYM0AeggxmnvOeP3Ke8pDHIC9OAjiBHRh2DzojtZqSWiu1keJWlfudIt20ppexp+NG2ueTuNStuufeW9LTh1PnOo7jOI7jvP+89YxV9GP+gwQtiEfkYyxUOBpFkdrh25fHHX22faLuYKRHd+f14eKMyuHaeDRSPEy1UI60VC2qGEsXF6FHGml2bkGjgrlVZb0bXV5fKrlT1U25pt88OdU/HQx1XlxQIUoUUfM0DBkNe1rILvXL9Vi/apS0EI7tsfRSpFKxoKGiEKenm0JRx4VEXxycqnVyoeb8jD6+t6rNmZLusJY0De8RNtCEMauhbAAvU8eIttCGVvtj1Br1P588eWLScHNz03baJysTaYm85HF5Nq5C4iHC8535e1ZPNN/Iinqk4b4kie1yTxvHZLkSk3qi7ITP9YhIJCF1SZeW6nYd4/NYPVK30VgJ/QZ2HRmhjI9o3NraskfnGQsJjER9+PChCUjmRwYpgpV5sZs+9WCp20obtVmJubS0YLvvkz16cXEe+hVUq82M+3QsW5c1MB79mc/l5YVdTz+yYvf29vT06VO7D2zq9exZy8ZDOHMNn9RkZZ4hjM2ff8Pl5bqN8fjx17ZG5s5mVmdnpzbm+vp6+EsUbJMqRCrrmZubtYxYNgzLr1mzDF7qzQ4G+XnmMCml4DiO4ziO4ziO4ziO47z/vPWM1fyx+Cx84YH9cdYothEvqZEqStWslvWwMW8Zfl/tn+p3Z9casMHRnZKWKAPQ74bLByqWirYrfzYKbXGiOIkIo+51xzJGKzM13YkiVRLprHOj9s3AJGyUFpSkqeaGV/rZ4ki/vDenD2fuaJgU9TzNdD0cajQY6mqQ6Eglffb8Qp9tH4X5SZ88XNff3l/Rh6VYRcbG0IZ55ht25TI1tpzcsK5winNvQ6ySucmO92yUhExkwyY+9/cPTCIi7xCLCEIkJps9scHVRJJ2wvqRp9VqTcfHpyZTyUolJvITOcq1SEUEKoIQWUi8bvcmjPPcsi+Rg2Sm0sbO+dfXVzYOm0SRoTkRiMwhz1LtmLhst89tHcxxe/t3yuu3JrZxFsKUmMwJKYpgpe3w8LkJTUQtbfRdXFywzbeOj4+/WStCuNXas8xUxOikDAI7/NPGXMmUXVxcsvtFbGQs4pVxWTPzQTQjXxG2CFfkK8KXa9iAiu9IVsZF1LJOxtre3gn9uiZs2fiKNbfbF3Zv2PgKCYsMvr2Bl+M4juM4juM4juM4jvP+8vZLAZhajCyzFLHKj+V4FjgTXmlqj8/PxAXdn6+ouTKn9s1Ijw/aOu8iu0qqlhMNB30lyFQV1e8NxGPbSZG6qz11en2VS5XwKoVYqRYqJdVrJZWzvkqDjuZ0o/XijX6+WtSv789rq0oc6Yujjv55+0S9pKS5uXl9ddbTv24f6LLf11/fW9ffba7q0UxJMynTzdjTKswYQRwIb9RhDQvIRbG95Vmsdjh+fV+QiUhQZCiZktQBJSJSEEm4ufnA5B/ylJ39OUc2K1IRqbdcXzahiHBEECJTySBFZG5tbY775rIUkfno0Z9YJuns7Lxt6nR+3rZ2ZCJxJnVIHzy4Z1mcUZRYtilysVJBrKbWj/GQvsCYkxqw9+/f08pK3eaCgCXDE/nJC6mJMCV7mKzcepg7sZrNZvg/3AjryePPzuabSrHmfN3zlnGLyKWdsgSIZGK3Ws9sfsTnHnI9cyGrl5qwzIM/GWvkXhAfiYsk7Xa6JmWpG0tc1o903dx8aPcaIY3QZU3MnfvOPcizYZlHZXxfqNfrOI7jOI7jOI7jOI7j/Bh4+xmrY8VYYMd8ZVZnFQFpOZ/hO5mmJl+zWEmWWobqg3pN9fkZHbav9d+tY7XTSElcsozRarmoESbNxGqsi86V0ijWbLVm2bEUHYjDdSuVRI/qVf3Z0h19tFTSzzeq+lmjrLViGDZl3Ei/Pe3o0/2OWn1p+7St/bNzba3O6VePmlZHdSHMmQxb5si0EcFUi83zUclZpY5mbGux3FVbW7gu9LFv4+PvCtmkT58+M0nJY+YIRcQjO+oj7hCVCD6yPxGjPL6P2EPEMuTa6qqJSbIt19ebGg2Zcz4Xsn7r9SWThZeX1yYU6YOMRUCSgYmEJLsUgUvWJXKVTMx+v2eZnkjGgwOyYrth7GU7j/zlUXtEJcJxZqZmfRCVnENaAnPON70qmxglFlmgEwHK5lHIX9bFHNhQijkhdrkX4ZZoZ2fHrr97txniFG3+rda+Dg8PtbbWCO3rti7kKfcGobq7+8zWzZj8+zBPxCuSF7ivcVxUlvK3LWi1sRr+54phHbN276jHSn/+DqyTtSFPJ+UFWAeClvns7++bhK7X6xbbcRzHcRzHcRzHcRzHef9562IVn5f7Rd5QVEjW8aEd5YIVNQkIrFL4XK0k2mrMqVYq6cnzth6f3Kg9GCquxOpEiU6HmXppatKwVi6rXIxVyEZikyrbsCrEu5NlWilFWg99lpKCKqENv8uD+1dhuKNw7eEg1fVNV/cXy/r1n67rF415rSBtEWxMKcrnaxsRTb04ttfYuoZ3wz7H13wXkKhATIQoNUQRh83mumWAsskSmZHlclE89r+zs2vlARCdZE8iCqn5iQjd+GBD152Orq4u1ev3TWSm6TAsJ9Pc7JzFGwwHJgTr9QWThTs72zo6OlSSUK/1Q2vjkXjEIWKULFVEJ4/EU2qA+SFBEauIUSDLFaF7cnJmj+AzP9aDtEU6IhuRnYuL82EdpRB/P8SnpMDA5s8tQ9IidMk6ZSzi8UJ8Nptrdg3jLy8vaW1t1e4bYxSLicnX3d2WiU1qxFKmYGOjGdov1G6fWfYrGa2t1q5lnXJPuF8rKw1dhGuurq+0OK5Te3B4ENraqtaqNlfGRfJOMoDpNykdwDnkLeUKkM/cD7J/WbPjOI7jOI7jOI7jOI7z4+Ctb171h4NYzGUm2Z8Yylw1SnujTJ/unenr1r4Ul3QTzavbT1UvDfWLu7P6ZHVW1QLykE6JhjG74qcqjfK6rrhPXmkUqZdl2r8Z6PPDc311eKFqMdHHG8v6qF7TfLgm/CpF1o2FnVmzH4BpsYqkQySS/YiM5BTnyehEbiI6Ly6uTELyuDm1QhGsk8fu6UfGJrVBqYOKAORu8h0ByYv4xCEesRGNfJKFmY+RmpxkzPn5WRuLeSBL2aAJ0ZskRRs3nyPz65v85TqOOYdk5DwxgXiUcaANEcqcySKdZMzymD7zQ5BSS5b19Xo3YjMrSgMcHuY1UtnMCxnLOoB4yGDuC8IYqTk7W7P5PnnyPzYPSiAgSRGn1EEFYrA2sm/zfrM2d0QsWbVz82GMEJcsYmKwNtY5WRtzBsYnJrGJSUat4ziO4ziO4ziO4ziO8+Ph3YnV3CuOP3n8nh33OaCOqdRTolZ3oE932/q35yPtDYsaDHv66Uqsf3y0oEfVWIOUR/PJfu2FnuEzLYVQIU4s9UOMs/5QX51d6b9aJ+r1R/qLjYZ+2lzQ3ShcPaJMAf1HoWcYuUAVVsTsDwMyb4IJ3QBtk3baJu3TcJ7m/DLO/36cvO1F3zwm8SbfX1w7GXN6vLyNa6Zjvug7YfrcNJN4Eyb9ptsQpNPHSE4yU5GvloW7cddqwpJFy+P9PGY/HXfy/fY4ZM4iPJGyZOoyDhJ0mum223ObPp4eDyZznm5zHMdxHMdxHMdxHMdxfpz84GJ17K2Mb/RUaCTDNP+aSz00J4/l9woFfXkx1L+0rvSfx10VleofHs7q7+/OqBQ62ab9Go5lV6JhOD7LUj25uNbnz451fN7VZmNRn9xr6INyrGKaKs2GGkXI1ERJFtlmWhk1A5Bm+cg/CNPibiL03kR+OW/5fbKjb+5d/vmH8G1pSMfp78TMj6dj3haN+fcX8/i+TOJOx77Nq+/PpD2fCxti5TAvzr06JjDmdOzpNU7PZ/qaSfvtOb1u/o7jOI7jOI7jOI7jOM77xbsTqwX7xX/lYEg5yNgqihfwbWiZpSdZrM+Puvps91Sj4UA/aS7oo8aMGsVYxdCXbNdO6PS0M9DnByfaOTxXo1bTJ/fXtDVfVpVYGXVHCxpZhddUkW1WhUiNTawyH6sB+46Ylnq/z/j+vJRJn1f1fRNviv26uK/rC2/q/8fwsrFvj/Xdxn7938BxHMdxHMdxHMdxHMdxct5JKQDk6rfcFW5sIlbzA3uNwvso/NCSKBbqk/qr/7F7rsd7Z5qpxPrJxqI2F6vqptIXR+f6eu9YlSjWxx+s6i8bM5oLoZCuhI+szEBoyNj4iq/U62QUxovzc47jOI7jOI7jOI7jOI7jOG/gHYhVNCnclpg8up1vTDQ5h/xEwlJ/FelJHusofPbCuaedof5950h7Z5eaq1XVH2W66Q/0581F/U1zQasJZQLSsTbNlGTkoo7raka5rOWxf/O5ogwAn7zdnpfjOI7jOI7jOI7jOI7jOM63eQelAHKxyuPW469TiaJZ+EGuRipQdJXz4aIMHxoOLYc1pT3SKC6Ifd5/277WlzsnqpSK+qv7y3pwp6hylpqkJQQ61WKFXxsoxMxswyqCxyEiJxiRIgGQvzuO4ziO4ziO4ziO4ziO47yKd1IK4PtjpvVFFqvYz18a2DfKBUhRlloWaiGaCNLwSbdX+NLXnHIcx3Ecx3Ecx3Ecx3Ecx3kp726npu8FWa55+mr+CD9Zppkq4VvJWnkL72PpOm755uNlvOaU4ziO4ziO4ziO4ziO4zjOS/l/JlbHznSMZaaGTwoI8M7j/FFo8V3dHcdxHMdxHMdxHMdxHMf5v0P6X8UIufTATV4kAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=7, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "\n", - "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", - "cols = csv_data.columns\n", - "cols = [item.replace(\".\", \"_\") for item in cols]\n", - "csv_data.columns = cols\n", - "\n", - "data = csv_data.sample(n=500, random_state=0)\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogate with ALAMO\n", - "\n", - "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", - "\n", - "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ***************************************************************************\n", - " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", - "\n", - " If you use this software, please cite:\n", - " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", - " Automatic Learning of Algebraic Models for Optimization,\n", - " AIChE Journal, 60, 2211-2227, 2014.\n", - "\n", - " ALAMO is powered by the BARON software from http://www.minlp.com/\n", - " ***************************************************************************\n", - " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", - " ***************************************************************************\n", - " Reading input data\n", - " Checking input consistency and initializing data structures\n", - " \n", - " Step 0: Initializing data set\n", - " User provided an initial data set of 400 data points\n", - " We will sample no more data points at this stage\n", - " ***************************************************************************\n", - " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", - " \n", - " Step 1: Model building using BIC\n", - " \n", - " Model building for variable CO2SM_CO2_Enthalpy\n", - " ----\n", - " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", - " ----\n", - " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", - " ----\n", - " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", - " ----\n", - " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", - " ----\n", - " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", - " ----\n", - " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", - " \n", - " Model building for variable CO2SM_CO2_Entropy\n", - " ----\n", - " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", - " ----\n", - " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", - " ----\n", - " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", - " ----\n", - " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", - " ----\n", - " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", - " ----\n", - " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", - " ----\n", - " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", - " ----\n", - " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", - " ----\n", - " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", - " ----\n", - " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", - " \n", - " Calculating quality metrics on observed data set.\n", - " \n", - " Quality metrics for output CO2SM_CO2_Enthalpy\n", - " ---------------------------------------------\n", - " SSE OLR: 0.515E+08\n", - " SSE: 0.659E+08\n", - " RMSE: 406.\n", - " R2: 0.999\n", - " R2 adjusted: 0.999\n", - " Model size: 6\n", - " BIC: 0.484E+04\n", - " Cp: 0.659E+08\n", - " AICc: 0.482E+04\n", - " HQC: 0.483E+04\n", - " MSE: 0.168E+06\n", - " SSEp: 0.659E+08\n", - " RIC: 0.659E+08\n", - " MADp: 0.594\n", - " \n", - " Quality metrics for output CO2SM_CO2_Entropy\n", - " --------------------------------------------\n", - " SSE OLR: 541.\n", - " SSE: 558.\n", - " RMSE: 1.18\n", - " R2: 0.997\n", - " R2 adjusted: 0.997\n", - " Model size: 10\n", - " BIC: 193.\n", - " Cp: 178.\n", - " AICc: 154.\n", - " HQC: 169.\n", - " MSE: 1.43\n", - " SSEp: 558.\n", - " RIC: 606.\n", - " MADp: 0.130E+04\n", - " \n", - " Total execution time 0.52 s\n", - " Times breakdown\n", - " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", - " MINLP time: 0.0 s in 0 optimization problem(s)\n", - " Simulation time: 0.0 s to simulate 0 point(s)\n", - " All other time: 0.22 s in 1 iteration(s)\n", - " \n", - " Normal termination\n", - " ***************************************************************************\n" - ] - } - ], - "source": [ - "# Create ALAMO trainer object\n", - "has_alamo = alamo.available()\n", - "if has_alamo:\n", - " trainer = AlamoTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - " )\n", - "\n", - " # Set ALAMO options\n", - " trainer.config.constant = True\n", - " trainer.config.linfcns = True\n", - " trainer.config.multi2power = [1, 2]\n", - " trainer.config.monomialpower = [2, 3]\n", - " trainer.config.ratiopower = [1]\n", - " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", - " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", - " trainer.config.overwrite_files = True\n", - "\n", - " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", - " success, alm_surr, msg = trainer.train_surrogate()\n", - "\n", - " # save model to JSON\n", - " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", - "\n", - " # create callable surrogate object\n", - " surrogate_expressions = trainer._results[\"Model\"]\n", - " input_labels = trainer._input_labels\n", - " output_labels = trainer._output_labels\n", - " xmin, xmax = [7, 306], [40, 1000]\n", - " input_bounds = {\n", - " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", - " }\n", - "\n", - " alm_surr = AlamoSurrogate(\n", - " surrogate_expressions, input_labels, output_labels, input_bounds\n", - " )\n", - "else:\n", - " print(\"Alamo not found.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Visualizing Surrogates\n", - "\n", - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Introduction\n", + "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogate\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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BoaysTOukKJaRkSE0atRIKC4utnvv4sWLQnFxsVC3bl1hwIABdu8vX75cACAsXLhQ8vvfeOMNISEhwfz/e+65Rxg2bJjDNE2bNk1IS0sT7rrrLuHVV181v75p0yYhKipKGDt2rNC1a1cZRycIY8eOFUJCQoSTJ0/avXf58mXh2rVrQmVlpdCyZUuhXbt2QkVFhdU2u3fvFgwGg5CVlSW5jy+//FIICAgQrl275jQ9ubm5AgBh165dktuYjv9///ufEBcXJ9SpU0cYNGiQUFRUJAjCjfsnAKu/3NxcYe3atQIA4fvvvxduueUWoWbNmsLatWuFK1euCE888YQQHR0tBAYGCp07dxZ+++038/5Mn/vuu++E1NRUITAwUOjYsaOwb98+QRAEobi4WAgNDRW++uorq3QuXbpUCA4ONqfLkqNrQsn9W9Omnw8++AD33Xcfxo0bhxYtWuCZZ57B448/jldeeUXLZJHO5RaWoFKwfq1CEJBXWCr+ASIf481mzQsXLuDHH3/E+PHjERISYvd+eHg4Vq1ahfPnz+OZZ56xe79fv35ISkrCggULJPdhNBoRGRlp/n+DBg2wdetWHDt2zGn6RowYgU8++cT8/3nz5uGhhx6SPQy8srISCxcuxEMPPYSGDRvavV+7dm3UqFEDu3fvxoEDB/D000/b1fSnpaWhR48eTo+xTp06qtZc5OTkYNmyZfjuu+/w3XffYf369cjKygIAvPfee0hPT8fo0aNRUFCAgoICxMbGmj87ZcoUZGVl4eDBg2jdujUmT56Mr7/+Gp9++il27tyJpk2bolevXrhw4YLVPp999lm89dZb2LZtG6Kjo9GvXz9cu3YNISEhGDx4MLKzs622z87Oxn333YfQ0FDVjtuWpoFKaGgo3n33XRw7dgxlZWXIycnBq6++ynkIyKGEqBD42Yx68zcYEB/FSbbI9y3adhyds9ZgyNxf0TlrDRZtO+7R/R09ehSCICA5OVlyG9MUEi1atBB9Pzk52byN2Pd/8MEHePzxx82vTZs2DeHh4YiPj0fz5s0xbNgwfPnll6LN/nfddReKioqwYcMGlJSU4Msvv8SIESNkH19hYSEuXrzo8PgA58fYokULyWMsLCzEK6+8gscee0x2uoAbU3TUrl3b6s9SZWUlPvnkE6SkpOC2227DI488Yu4zFBYWhoCAAAQHB6NBgwZo0KCB1Vwl//73v3HnnXciMTERgYGBmD17NmbOnImMjAy0bNkSc+fORVBQED7++GOrfU6bNg133nknUlNT8emnn+LMmTNYunQpAGDUqFFYuXIlCgoKANyYzO37779X9Hu4gnM8k8+JCQtC5oBU+P81Rt/fYMDrA1IQExakccqI3FNgLDP3vQKASgF4fsl+j9asCILgfCMXtgWAkydPonfv3rj//vsxevRo8+sxMTHYsmUL9u3bhyeffBLXr1/H0KFD0bt3b7tgpWbNmnj44YeRnZ2Nr776CklJSWjdurXH0qx0+6KiIvTt2xctW7bE9OnTFX120aJF2L17t9Wfpfj4eKuaipiYGPO09M60a9fO/O+cnBxcu3YNnTt3Nr9Ws2ZNdOjQAQcPHrT6XHp6uvnfkZGRaN68uXmbDh06oFWrVuaRuZ9//jni4uLQpUsXeQfsIh/uXUPV2aD2jdElKRp5haWIjwpmkEJVgqNmTU/l8WbNmpk7wUpJSkoCABw8eBCdOnWye//gwYNo2bKl1WunTp1C9+7d0alTJ/z3v/8V/d6UlBSkpKRg3LhxGDNmDG677TasX78e3bt3t9puxIgR6NixI/bv36/46T06Ohrh4eEOjw+wPsY2bdrYvX/w4EHzNiaXL19G7969ERoaiqVLl6JmzZqK0hYbG4umTZtKvm/7fQaDQfZgE7FmPDWMGjUKs2bNwpQpU5CdnY3hw4d7fJJD1qiQz4oJC0J6Yl0GKVRlaNGsGRkZiV69emHWrFkoKSmxe//SpUvo2bMnIiMj8dZbb9m9v3z5chw5cgQPPvig+bWTJ0+iW7duaNu2LbKzs2Ut0GgKdMTS0KpVK7Rq1Qr79+/HkCFDlBwe/Pz8MHjwYHzxxRc4deqU3fvFxcW4fv06br75ZiQnJ+Odd96xCwb27NmD1atXWx1jUVERevbsiYCAACxfvhy1atVSlC41BAQEWA0Nl5KYmIiAgABs2rTJ/Nq1a9ewbds2uwDTcnj0xYsX8ccff1g1hz388MM4duwY3n//fRw4cABDhw5V4UgcY6BCRKQTWjVrzpo1CxUVFejQoQO+/vprHDlyBAcPHsT777+P9PR0hISEYM6cOfjmm2/w2GOPYe/evcjLy8PHH3+MYcOG4b777sMDDzwA4O8gpXHjxnjzzTdx7tw5nD59GqdPnzbvb+zYsXjllVewadMmHDt2DFu3bsWjjz6K6Ohoq6YHS2vWrEFBQQHCw8MVH99rr72G2NhYdOzYEf/73/9w4MABHDlyBPPmzUObNm1QXFwMg8GAjz/+GAcOHMDAgQPx22+/4fjx4/jqq6/Qr18/pKenm+csMQUpJSUl+Pjjj1FUVGQ+RjmBg8n58+fNnzP9XblyRfbn4+Pj8euvvyIvLw+FhYWStS0hISEYO3Ysnn32Wfz44484cOAARo8ejdLSUowcOdJq23//+9/4+eefsX//fgwbNgxRUVHo37+/+f2IiAgMGDAAzz77LHr27Gk1bNxjnI4L0jEOTyYivVBzePKpS6XC5qOFXh1yf+rUKWH8+PFCXFycEBAQIDRq1Ei4++67hbVr15q32bBhg9CrVy+hTp06QkBAgNCqVSvhzTffFK5fv27eJjs7227YrOnPZPHixUKfPn2EmJgYISAgQGjYsKEwcOBAYe/eveZtTMNzpTz55JOyhycLgiBcunRJmDJlitCsWTMhICBAqF+/vtCjRw9h6dKlQmVlpXm7vXv3CgMHDhQiIyOFmjVrComJicK//vUvoaSkxLyNaSiv2F9ubq7TtJiGJ4v9LViwQPL433nnHSEuLs78/8OHDwv/+Mc/hKCgILvhyRcvXrT6bFlZmfDEE08IUVFRDocnf/vtt0KrVq2EgIAAoUOHDsKePXvs0v/zzz8LAIQvv/zS4XGqNTzZIAgKew7pSFFREcLCwszDwoiItHLlyhXk5uYiISFBk2YAInesW7cO3bt3x8WLF53WWn322Wd46qmncOrUKYejdB1dE0ru3+xMS0RERE6VlpaioKAAWVlZePzxx702lQj7qBARkU+znYvE8u+XX37xenrGjBkjmZ4xY8Z4PT1qeeONN5CcnIwGDRpg6tSpXtsvm36IiFTAph/tHD16VPK9Ro0aISjIuyMDz549i6KiItH36tSpg3r16nk1PVph0w8RERHgcC4SLdSrV6/aBCPewKYfIiIi0i0GKkREKvLh1nQiVcmdRdcZNv0QEamgZs2aMBgMOHfuHKKjoz0+rTiRXgmCgKtXr+LcuXPw8/Nze3QQAxUiIhX4+/vjpptuwokTJ5CXl6d1cog0FxwcjMaNG8taQsERBipERCqpXbs2mjVrhmvXrmmdFCJN+fv7o0aNGqrULDJQISJSkb+/P/z9/bVOBlGVwc60REREpFsMVIiIiEi3GKgQERGRbjFQISIiIt1ioEJERES6xUCFiIiIdIuBChEREekWAxUiIiLSLQYqREREpFsMVIiIiEi3GKgQERGRbjFQISIiIt1ioEJERES6xUCFiIiIdIuBChEREemWpoFKfHw8DAaD3d/48eO1TBYRERHpRA0td75t2zZUVFSY/79//37ceeeduP/++zVMFREREemFpoFKdHS01f+zsrKQmJiIrl27apQiIiIi0hPd9FG5evUqPv/8c4wYMQIGg0Hr5BAREZEOaFqjYmnZsmW4dOkShg0bJrlNeXk5ysvLzf8vKiryQsqIiIhIK7qpUfn444+RkZGBhg0bSm6TmZmJsLAw819sbKwXU0hERETeZhAEQdA6EceOHUOTJk2wZMkS3HPPPZLbidWoxMbGwmg0ok6dOt5IKhEREbmpqKgIYWFhsu7fumj6yc7ORr169dC3b1+H2wUGBiIwMNBLqSIiIiKtad70U1lZiezsbAwdOhQ1augibiIiIiKd0DxQWb16NY4fP44RI0ZonRQiIiLSGc2rMHr27AkddJMhIiIiHdK8RoWIiIhICgMVIiIi0i0GKkRERKRbDFSIiIhItxioEBERkW4xUCEiIiLdYqBCREREusVAhYiIiHSLgQoRERHpFgMVIiIi0i0GKkRERKRbDFSIRBQYy7A5pxAFxjKtk0JEVK1pvighkd4s2nYcU5fsQ6UA+BmAzAGpGNS+sdbJIiKqllijQmShwFhmDlIAoFIAnl+ynzUrREQaYaBCZCG3sMQcpJhUCALyCku1SRARUTXHQIXIQkJUCPwM1q/5GwyIjwrWJkFERNUcAxUiCzFhQcgckAp/w41oxd9gwOsDUhATFqRxyoiIqid2piWyMah9Y3RJikZeYSnio4IZpBARaYiBCpGImLAgBihERDrAph8iIiLSLQYqREREpFsMVIiIiEi3GKiQbnDaeiIissXOtKQLnLaeiIjEsEaFNMdp64mISAoDFdIcp60nIiIpDFRIc5y2noiIpDBQIc1x2noiIpLCzrSkC5y2noiIxDBQId3gtPVERGSLTT9ERESkWwxUiIiISLc0D1ROnjyJhx9+GHXr1kVQUBBSU1Oxfft2rZNFREREOqBpH5WLFy+ic+fO6N69O3744QdER0fjyJEjiIiI0DJZREREpBOaBiozZsxAbGwssrOzza8lJCRomCIiIiLSE02bfpYvX4527drh/vvvR7169dCmTRvMnTtXcvvy8nIUFRVZ/REREVHVpWmg8ueff2L27Nlo1qwZVq5cibFjx2LixIn49NNPRbfPzMxEWFiY+S82NtbLKSYiIiJvMgiCIDjfzDMCAgLQrl07bN682fzaxIkTsW3bNmzZssVu+/LycpSXl5v/X1RUhNjYWBiNRtSpU8craSYiIiL3FBUVISwsTNb9W9MalZiYGLRs2dLqtRYtWuD48eOi2wcGBqJOnTpWf0RERFR1aRqodO7cGYcPH7Z67Y8//kBcXJxGKSIiIiI90TRQeeqpp7B161a8/vrrOHr0KObPn4///ve/GD9+vJbJIiIiIp3QNFBp3749li5digULFiAlJQWvvPIK3n33XTz00ENaJouIiIh0QtPOtO5S0hmHiIiI9MFnOtMSEREROcJAhYiIiHSLgQoRERHpFgMVIiIi0i0GKkRERKRbDFSIiIhItxioEBERkW4xUCEiIiLdYqBCREREuuVSoFJSUqJ2OoiIiIjsuBSo1K9fHyNGjMDGjRvVTg8RERGRmUuByueff44LFy7g9ttvR1JSErKysnDq1Cm100ZERETVnEuBSv/+/bFs2TKcPHkSY8aMwfz58xEXF4e77roLS5YswfXr19VOJxEREVVDqq2e/MEHH+DZZ5/F1atXERUVhTFjxmDKlCkIDg5W4+tFcfVkIiIi36Pk/l3DnR2dOXMGn376KT755BMcO3YM9913H0aOHIkTJ05gxowZ2Lp1K1atWuXOLoiIiKgacylQWbJkCbKzs7Fy5Uq0bNkS48aNw8MPP4zw8HDzNp06dUKLFi3USicRERFVQy4FKsOHD8fgwYOxadMmtG/fXnSbhg0b4oUXXnArcURERFS9udRHpbS01KN9T+RiHxUiIiLf4/E+KsHBwaioqMDSpUtx8OBBAECLFi3Qv39/1KjhVrcXIiIiIjOXoorff/8d/fr1w5kzZ9C8eXMAwIwZMxAdHY1vv/0WKSkpqiaSiIiIqieX5lEZNWoUUlJScOLECezcuRM7d+5Efn4+Wrdujccee0ztNBIREVE15VKNyu7du7F9+3ZERESYX4uIiMBrr70m2bmWiIiISCmXalSSkpJw5swZu9fPnj2Lpk2bup0oIiIiIsDFQCUzMxMTJ07E4sWLceLECZw4cQKLFy/GpEmTMGPGDBQVFZn/iIiIiFzl0vBkP7+/4xuDwQAAMH2N5f8NBgMqKirUSKcoDk8mIiLyPR4fnrx27VqXEkZERESkhEuBSteuXdVOBxEREZEdl2dnu3TpEj7++GPzhG+tWrXCiBEjEBYWplriiIiIqHpzqTPt9u3bkZiYiHfeeQcXLlzAhQsX8PbbbyMxMRE7d+5UO41ERERUTbnUmfa2225D06ZNMXfuXPOU+devX8eoUaPw559/YsOGDaonVAw70xIREfkeJfdvlwKVoKAg7Nq1C8nJyVavHzhwAO3atUNpaanSr3QJAxUiIiLfo+T+7VLTT506dXD8+HG71/Pz8xEaGurKVxIRERHZcSlQGTRoEEaOHIlFixYhPz8f+fn5WLhwIUaNGoUHH3xQ9vdMnz4dBoPB6s+2loaIiIiqL5dG/bz55pswGAx49NFHcf36dQBAzZo1MXbsWGRlZSn6rlatWmH16tV/J6iGywORiIiIqIpRHBVUVFRg69atmD59OjIzM5GTkwMASExMRHBwsPIE1KiBBg0aKP4cERERVX2Km378/f3Rs2dPXLp0CcHBwUhNTUVqaqpLQQoAHDlyBA0bNkSTJk3w0EMPifZ9MSkvL7daR4hrCREREVVtLvVRSUlJwZ9//un2zjt27IhPPvkEP/74I2bPno3c3FzcdtttuHz5suj2mZmZCAsLM//Fxsa6nQYiIiLSL5eGJ//444+YOnUqXnnlFbRt2xYhISFW77s6VPjSpUuIi4vD22+/jZEjR9q9X15ejvLycvP/i4qKEBsby+HJREREPsTjixL26dMHAHD33XebV0sG3F8xOTw8HElJSTh69Kjo+4GBgQgMDHTpu4mIiMj36Gr15OLiYuTk5OCRRx7xyPcTERGRb3EpUElISEBsbKxVbQpwo0YlPz9f9vc888wz6NevH+Li4nDq1ClMmzYN/v7+iuZiISIioqrL5UCloKAA9erVs3r9woULSEhIkN30c+LECTz44IM4f/48oqOjceutt2Lr1q2Ijo52JVlERERUxbgUqJj6otgqLi5GrVq1ZH/PwoULXdk9ERERVROKApWnn34aAGAwGPDiiy9azZ1SUVGBX3/9FTfffLOqCSSivxUYy5BbWIKEqBDEhAVpnRwiIo9TFKjs2rULwI0alX379iEgIMD8XkBAANLS0vDMM8+om0IiAgAs2nYcU5fsQ6UA+BmAzAGpGNS+sdbJIiLyKEWBimm0z/Dhw/Hee+9x7hIiLykwlpmDFACoFIDnl+xHl6Ro1qwQUZXm0sy02dnZDFKIvCi3sMQcpJhUCALyCku1SRARkZe41Jm2pKQEWVlZ+Pnnn3H27FlUVlZava/G9PpE9LeEqBD4GWAVrPgbDIiPcm2NLSIiX+FSoDJq1CisX78ejzzyCGJiYkRHABGRemLCgpA5IBXPL9mPCkGAv8GA1weksNmHiKo8l9b6CQ8Px4oVK9C5c2dPpEk2JWsFEFUFBcYy5BWWIj4qmEEKEfksj6/1ExERgcjISJcSR0SuiwkLYoBCRNWKS51pX3nlFbz00ksoLWVHPiIiIvIcl2pU3nrrLeTk5KB+/fqIj49HzZo1rd7fuXOnKokjIiKi6s2lQKV///4qJ4OIiIjInkudafWCnWmJiIh8j5L7t6I+Kr/99pvDlZHLy8vx5ZdfKvlKIiIiIkmKApX09HScP3/e/P86depYTe526dIlPPjgg+qljoiIiKo1RYGKbSuRWKuRD7ckERERkc64NDzZEc5SS0RERGpRPVAhIiIiUovi4ckHDhzA6dOnAdxo5jl06BCKi4sBAIWFheqmjoiIiKo1RcOT/fz8YDAYRPuhmF43GAwORwapicOTiYiIfI/H1vrJzc11K2FERERESigKVOLi4hR9+bhx4/Dvf/8bUVFRij5HREREBHi4M+3nn3+OoqIiT+6CiIiIqjCPBiqcU4WIiIjcweHJREREpFsMVIiIiEi3GKgQERGRbjFQISIiIt3yaKDy8MMPcyI2IiIicplLgUplZaXk68ePHzf/f/bs2ZxDhYiIiFymKFApKirCAw88gJCQENSvXx8vvfSS1XT5586dQ0JCguqJJCIioupJ0cy0L774Ivbs2YPPPvsMly5dwquvvoqdO3diyZIlCAgIAMC5U4iIiEg9impUli1bhjlz5uC+++7DqFGjsH37dpw7dw79+vVDeXk5gBuLE7oiKysLBoMBkyZNcunzpI0CYxk25xSiwFimdVKIiKgKUhSonDt3zmq9n6ioKKxevRqXL19Gnz59UFpa6lIitm3bhjlz5qB169YufZ60sWjbcXTOWoMhc39F56w1WLTtuPMPERERKaAoUGncuDEOHjxo9VpoaChWrVqFsrIy3HvvvYoTUFxcjIceeghz585FRESE4s+TNgqMZZi6ZB8q/2rpqxSA55fs99maFdYMERHpk6JApWfPnsjOzrZ7vXbt2li5ciVq1aqlOAHjx49H37590aNHD8WfJe3kFpaYgxSTCkFAXqFrtWpaYs0QEZF+KepM+/LLL+PUqVOi74WGhuKnn37Czp07ZX/fwoULsXPnTmzbtk3W9uXl5ea+MAC4MrOGEqJC4GeAVbDibzAgPipYu0S5QKpmqEtSNGLCgrRNHBERKatRiYiIQKtWrSTfDw0NRdeuXWV9V35+Pp588kl88cUXsmtiMjMzERYWZv6LjY2V9TlSX0xYEDIHpML/r87T/gYDXh+Q4nM396pUM0REVBUZBIXjia9fv4533nkHCxYswB9//AEASEpKwpAhQ/Dkk0+iZs2asr5n2bJluPfee+Hv729+raKiAgaDAX5+figvL7d6DxCvUYmNjYXRaOQMuBopMJYhr7AU8VHBPhekADfS3zlrjV3N0MYp3X3yeIiIfEFRURHCwsJk3b8VBSplZWW48847sWXLFvTo0QMtWrQAABw8eBCrV69G586dsWrVKlk1JJcvX8axY8esXhs+fDiSk5Px3HPPISUlxel3KDlQIimLth3H80v2o0IQzDVDg9o31jpZRERVlpL7t6I+KllZWcjPz8euXbvshhLv2bMHd999N7KysjB9+nSn3xUaGmoXjISEhKBu3bqyghQitQxq3xhdkqJ9umaIiKiqUtRHZeHChXj77bdF5ztJS0vDm2++ifnz56uWOCJviQkLQnpiXQYpREQ6o6hG5dixY+jQoYPk+//4xz+sFiVUat26dS5/loiIiKoeRTUqderUwdmzZyXfP336NEJDQ91OFBERERGgMFDp3r07Xn/9dcn3s7Ky0L17d7cTRURERAQobPqZNm0aOnbsiH/84x94+umnkZycDEEQcPDgQbzzzjs4cOAAtm7d6qm0EhERUTWjKFBp2bIlfvrpJ4wcORKDBw82r5QsCAKSk5OxatUqhxPCERERESmhKFABbnSY/f3337F7926rCd9uvvlmtdNGRERE1ZziQKWoqAi1a9fGzTffbBWcVFZWori4mBOvERERkWoUdaZdunQp2rVrhytXrti9V1ZWhvbt2+Pbb79VLXFERERUvSkKVGbPno3JkycjONh+hdyQkBA899xz+M9//qNa4oiIiKh6UxSo7N+/H926dZN8v0uXLti3b5+7aSIiIiICoDBQuXjxIq5fvy75/rVr13Dx4kW3E0VEREQEKAxU4uPjsX37dsn3t2/fjri4OLcTRURERAQoDFQGDBiAF154AWfOnLF77/Tp0/jXv/6FgQMHqpY4IiIiqt4MgiAIcje+fPky0tPTcfz4cTz88MNo3rw5AODQoUP44osvEBsbi61bt3ptvZ+ioiKEhYXBaDRyWDQREZGPUHL/VjSPSmhoKDZt2oSpU6di0aJF5v4o4eHhePjhh/Haa69xUUIiIiJSjaIaFUuCIKCwsBCCICA6Oto8nb6lTZs2oV27dggMDHQ7oWJYo0JEROR7lNy/FfVRsWQwGBAdHY169eqJBikAkJGRgZMnT7q6CyIiIqrmXA5U5HCxsoaIABQYy7A5pxAFxjKtk0JEpBnFa/0Qkect2nYcU5fsQ6UA+BmAzAGpGNS+sdbJIiLyOo/WqBCRcgXGMnOQAgCVAvD8kv2sWSGiaomBCpHO5BaWmIMUkwpBQF5hqTYJIiLSkEcDFalOtkQkLSEqBH42l46/wYD4KPvFQImIqjp2piXSmZiwIGQOSIX/X4G+v8GA1wekICYsSOOUERF5n0c7016+fNmTX0+kKwXGMuQWliAhKsTtoGJQ+8bokhSNvMJSxEcFM0ghompLUaBy++23y9puzZo1LiWGyFd5YpROTFgQAxQiqvYUBSrr1q1DXFwc+vbti5o1a3oqTUQ+RWqUTpekaAYaRERuUhSozJgxA9nZ2fjqq6/w0EMPYcSIEUhJSfFU2oh8gqNROgxUiIjco6gz7bPPPosDBw5g2bJluHz5Mjp37owOHTrgww8/RFFRkafSSKRrHKVDROQ5Lo36SU9Px9y5c1FQUIDx48dj3rx5aNiwIYMVqpY4SoeIyHPcGvWzc+dOrF+/HgcPHkRKSgr7rVC1xVE6RESeoThQOXXqFD755BN88sknKCoqwsMPP4xff/0VLVu29ET6iHwGR+kQEalPUaDSp08frF27Fj179sTMmTPRt29f1KjBdQ2JiIjIMwyCgulj/fz8EBMTg3r16jmcHn/nzp2qJM6ZoqIihIWFwWg0ok6dOl7ZJxEREblHyf1bUXXItGnT3EqYrdmzZ2P27NnIy8sDALRq1QovvfQSMjIyVN0PERER+SZFNSpq+/bbb+Hv749mzZpBEAR8+umnmDlzJnbt2oVWrVo5/TxrVIiIiHyPkvu3KoHK+vXrUVJSgvT0dERERLj1XZGRkZg5cyZGjhzpdFsGKkRERL7HY00/M2bMQHFxMV555RUAN1ZHzsjIwKpVqwAA9erVw88//yyrNsRWRUUFvvrqK3PAI6a8vBzl5eXm/3PeFiIioqpN0YRvixYtspoyf/HixdiwYQN++eUXFBYWol27dnj55ZcVJWDfvn2oXbs2AgMDMWbMGCxdulRyqHNmZibCwsLMf7GxsYr2RURERL5FUdNPREQENm/ejBYtWgAAhg8fjoqKCvzvf/8DAGzduhX3338/8vPzZSfg6tWrOH78OIxGIxYvXoyPPvoI69evFw1WxGpUYmNj2fRDRETkQzzW9HP9+nUEBgaa/79lyxZMmjTJ/P+GDRuisLBQUWIDAgLQtGlTAEDbtm2xbds2vPfee5gzZ47dtoGBgVb7JyIioqpNUdNPYmIiNmzYAAA4fvw4/vjjD3Tp0sX8/okTJ1C3bl23ElRZWWlVa0JERETVl6IalfHjx2PChAn45ZdfsHXrVqSnp1s10axZswZt2rSR/X1Tp05FRkYGGjdujMuXL2P+/PlYt24dVq5cqSRZREREVEUpClRGjx4Nf39/fPvtt+jSpYvdBHCnTp3CiBEjZH/f2bNn8eijj6KgoABhYWFo3bo1Vq5ciTvvvFNJsoiIiKiK0nTCN3dxHhUiIiLfo+T+raiPChEREZE3KQpUrl27hsmTJ6Np06bo0KED5s2bZ/X+mTNn4O/vr2oCiYiIqPpSFKi89tpr+N///ocxY8agZ8+eePrpp/H4449bbePDLUlERESkM4o6037xxRf46KOPcNdddwEAhg0bhoyMDAwfPtxcu2IwGNRPJREREVVLimpUTp48aTWFftOmTbFu3Tps3rwZjzzyCCoqKlRPIBEREVVfigKVBg0aICcnx+q1Ro0aYe3atdi2bRuGDRumZtqIiIiomlMUqNx+++2YP3++3esNGzbEmjVrkJubq1rCiFxVYCzD5pxCFBjLquX+iYiqEkV9VF588UUcOnRI9L1GjRph/fr1+Omnn1RJGJErFm07jqlL9qFSAPwMQOaAVAxq37ja7J+IqKrhhG9UZRQYy9A5aw0qLXK0v8GAjVO6IyYsqMrvn4jIV3h8wrevvvoKAwYMQEpKClJSUjBgwAAsXrzYpcQSqSW3sMQqSACACkFAXmFptdg/EVFVpChQqaysxKBBgzBo0CAcOHAATZs2RdOmTfH7779j0KBBGDx4MOdRIc0kRIXAz2Z0vL/BgPio4GqxfyKiqkhRoPLee+9h9erVWL58OQ4dOoRly5Zh2bJlOHz4MJYuXYqffvoJ7733nqfSSuRQTFgQMgekwv+vuXz8DQa8PiDFa80uWu/fW9hZmIi8SVEfldatW2PSpEmSKyR//PHHeO+997B3717VEugI+6iQmAJjGfIKSxEfFaxJkKD1/j2JnYWJSA1K7t+KApWgoCAcPnwYjRuLF0zHjh1DcnIyysq886TlS4FKgbEMuYUlSIgKqXI3L6oe2FmYiNSi5P6taHhyUFAQLl26JBmoFBUVoVatWkq+slrgUyhVBY46CzNQISJPUdRHJT09HbNnz5Z8f9asWUhPT3c7UVVJgbHMHKQAQKUAPL9kP9v3yeewszARaUFRoPLCCy/g448/xgMPPIDffvsNRUVFMBqN2Lp1K+6//37MmzcPL7zwgqfS6pM4ZJWqiurSWZiI9EVR00+nTp2waNEiPPbYY/j666+t3ouIiMCCBQvQuXNnVRPo60xPobbt+nwKJS252mdqUPvG6JIUXWU7CxNVN77Qf9KlmWlLS0uxcuVKHDlyBACQlJSEnj17IjjYuzdfX+lMu2jbcTy/ZD8qBMH8FMo+KqQV9pkiIkDbssBjo37WrFmDCRMmYOvWrXZfbDQa0alTJ3z44Ye47bbbXEu5Qr4SqADiQ1Z9IZKlqkWPI3d4HRB5n9ZlgcdG/bz77rsYPXq06JeGhYXh8ccfx9tvv+21QMWXxIQFWf34fKolLeht5A6vAyJt6K0scERRZ9o9e/agd+/eku/37NkTO3bscDtRVR1HApFW9DRyh9cBkXb0VBY4oyhQOXPmDGrWrCn5fo0aNXDu3Dm3E1XVcSSQ79mTfxFzf8nBnvyLWifFLXoaucPrgEg7eioLnFHU9NOoUSPs378fTZs2FX1/7969iImJUSVhVRlHAqnL030c/vnlbny986T5/wNvaYS3HrhZ9f14i15G7vA6INKWXsoCZxTVqPTp0wcvvvgirly5YvdeWVkZpk2bhrvuuku1xFVVvhTJ6t2ibcfROWsNhsz9FZ2z1mDRtuOqfv+e/ItWQQoAfL3zZJWoWUlPrKtpnuN1QKQ9PZQFziga9XPmzBnccsst8Pf3x4QJE9C8eXMAwKFDhzBr1ixUVFRg586dqF+/vscSbMmXRv2IqcqL13mDs17ratS0zP0lB6+tOGT3+ot9W2DkbU0UpZUjW8TxOiCqfjw26qd+/frYvHkzxo4di6lTp8IU4xgMBvTq1QuzZs3yWpBSFdiOBCJlHPVx2PDHOVVGk3SIjxR9vV18hOzv4MgWx3gdEJEjigIVAIiLi8P333+Pixcv4ujRoxAEAc2aNUNEhPyCm6oHT9ciSPVxCA7wEx1N0iUpWnE60mIjMPCWRnZ9VNJi5eV3qZEtrqSFiKg6UhyomERERKB9+/ZqpoWqEG/UIpj6ONjO+ltytULV+QHeeuBmPJoeh+15F9EuPkJ2kAL41lwFRER65HKgQp7ly30avFmLINZrvcBYpvpokrRYZQGKCUe2EBG5R9GoH/IOT49k8TRvz49h22tdT6NJ9JQWIiJfxBoVnakKfRr0UIugp/kB9JQWIiJfo2mNSmZmJtq3b4/Q0FDUq1cP/fv3x+HDh7VMkua0mK2zwFiGzTmFqk1drpdaBD3ND6CntBAR+RJNa1TWr1+P8ePHo3379rh+/Tqef/559OzZEwcOHEBISIiWSdOMt2sjPNXptbrXIvhyHyMiIj1RNOGbp507dw716tXD+vXr0aVLF6fbV4UJ38RuZou2HbcbyeKJeTe0Xua7qpqzIQdZPxyCwHlTiIhEeWzCN08zGo0AgMhI8Um2ysvLUV5ebv5/UVGRV9LlCY5qMrxVG8Ghs+qbsz4HmT/8PZOtL/Yx8ibWPBGRM7oZ9VNZWYlJkyahc+fOSElJEd0mMzMTYWFh5r/Y2Fgvp1Idcpa3l+rToGZ/El9a5tsXFBjLkPWD/XT7XBFYnK+PbiMi79BNoDJ+/Hjs378fCxculNxm6tSpMBqN5r/8/HwvplA9rnaYVbtg10un16oit7AEYu2ofgZoFvyp3VFaLXKCdSIiQCdNPxMmTMB3332HDRs24KabbpLcLjAwEIGBgV5MmWe40mHWU8OWq3unVzWJ/a4A8FxGsibnVc9rDLHZkYjk0rRGRRAETJgwAUuXLsWaNWuQkJCgZXK8xpWaDE8OW+bQWXXY/q5+AKZmJOPxLoleT4veayzY7EhEcmlaozJ+/HjMnz8f33zzDUJDQ3H69GkAQFhYGIKCqvZNU2lNxr4TRrvXWLDrj15qqPReYyG1TpMe0kZE+qJpoDJ79mwAQLdu3axez87OxrBhw7yfIC+Tu7x9gbEMM36076Q5OaO51wt2jtJwTu7v6kl6mB3YRCrPeDuoY94l8k2aBio6msLFIa0LOLGnYwBo3Sjcq+nQc58HsqaXGgtnecZbQR3zLpHv0kVnWj1Tu4BzJejRw9NxVViDqLrRuhlKL3lGL+kgItfoZniyHqndIdHV4cV6GEasxRpE5D4tO0rrJc9IpWPF3gLddC4mImmsUXFAzQ6J7j7Vaf107G6tjtbNZ+R9eqgJlEoHALy64iBe//4gm4GIdI41Kg6oOYRSjadLTz4dO5sYzJ1aHc5AWj3poSZQLB2W9DZsWyt6nRiQCGCNikNqdkjUy9OlGLn9cFyp1WH/gOpN65pA23Ss2FuAV1cctHpPT8O2tcCOxlVTVarFZqDihFoFrV5GYdhSGkgoHaWh9/k8qhK9Fkx6GK5tSkff1jF4/fuDTh8Y9Hou1cYHiaqpqgWfDFRkUKug1cvTpSVPBxJ6rkmqSqpaweQpch4YqtO55INE1VMVg08GKl6ml6dLE08HEnqtSapKqmLB5EmOHhiq27nkg0TVUxWDTwYq1Zw3Agk91iT5CjlNEFWxYPI0qQeG6nYu+SBR9VTF4JOBCnklkNBbTZIvkNsEURULJq1Ux3PJB4mqxZ3gU699swyCr8xjL6KoqAhhYWEwGo2oU6eO1snxKrkZSq8ZjxwrMJahc9YauxvmxindRX/HRduO2xVMVbVfhafxXFJVUGAsUxR8ertvlpL7N2tUfJDcDFWdOgVWNUqbIPhUrB6ey6qluj6sKanF1nvfLAYqPkZuhtJ7xtMLvRZirjRBVMXmNa1+n6p4LqsjX31Y83a+13vfLAYqPkZuhnI142l94/bm/h0VYlqfB3Zy9N2bDOmDrz6saZHv9d43i4GKj5GboVzJeFrfGLy5f0eF2IY/zuniBqnHJghvBXC+dJPROqglcXqvJRCjVb7X+4MRAxUfIzdDKc14Wt8YvL1/qUJs57GLXkmH3JubZROE1jdEbwaSvnKT0Tq4J2l6ryUQo2W+1+ODkQkDFR/kbMIq081MScbT+sbg7f1LFWKVguDxdLhyc3P3huhukOPtQNIXbjJaB/fkmN5rCcRone/12jeLgYqPEstQUjczsYxne+PS+gLx9v6lCrF28ZEeTYcrNzd3b4hqPPV7O5D01k3GnQBO6+CenNNzLYEYvQRXWtfe2mKgUkUouZlJ3bi0vEBcaapy90KSKsQ8eR5cubm5c0NU66lfi0DW0zcZdwM4rYP76sSd612vtQRStA6u9NicyUClipB7M3N049L6ApG7fzUvJLFCzJPnwZWbmzs3RLWe+rV60vPUTUaNAE4vT79VnbdunHqqRXAn37tzHHptzmSgUkXIvZk5u3Fp/fThbP/eupA8dR5cubm5c0NUGuQ4KuS0DmTVpFYAV5XOiR5563rXYy2CK9w9Dr02ZzJQqSLk3sx8vbparxeSEq7c3Fy9ISoJcuQUcloHsmpR8zqoKufEW5Q88at9vYvt21kwpKeaFkfUCOr0en9goFKFyLmZ+Xp1tV4vJKVcubm5ekOUky/0WuXrKVpcB75yw/OkRduOY8rX+yAAMADIGuj4iV/N610qEHcUDOllTiU51Ajq9Hp/YKDiIXqe+tuXq6v1eiHpnbN8URVqqpTy5nWgdtOCLwY9BcYyc5ACAAKAKV/vcxgMq3W9OwrE95002m3vbzAgOMDPp4J3tYI6Pd4fGKioxLLg8IUo3Jerq/V4Ifm6qlJTpZQ3rgO1a6t8tT/F9rwLsImFIQDYkXcRd6VJnwe517uj4E0qEF/w63H8Z+1Ru++a3Ls5Sq5WSAbvpu/UU6Co5kOc3u4PDFRUYFtwCALMF6Teo3BfpbcLydexpkqau7UXatZW+UoTndg5MxgMottKvGzF2fXuLHgTC8QB4P019kEKALS+KRzxUcGiwfvek5fw0EdbdRkoVtWHOAYqbhIrOGxVCILTpwZv8MXqYhNfTruvqKqFnDvUqL1Qs7ZKT010Utek1DlrGxcBA2BVq2IwALfERbidDmfBm20g7ojptxEL3if3bo4ZPxzSdaBYFR/iGKi4SazgEDNx4S6UXL2uWeTtq9XFgG+n3ddUxULOVWrVXqhZWyUV9AQH+GFzTqHXAnmpa9LZOcsamGr3OXfTKzd4MwXiK/YW4NUVB0W/y/a3sQ3elQaKfMBSBwMVN4kVHIa/Hhss87OWkbevVBeL8eW0k3u0LuTVrL1Qq7ZKLOjp36Yh7v2/zV4L5B1dk47OGQDERgZj6bhOKL1a6XatnSl/hAT4y66xigkLQt/WMXj9+4NW2/sB+Hf/VggLqol28ZF2n7FMp9x98QFLPQxU3CT1tBQSWAMT5u+y2larKlo9VRcD2s6jQL5BD4W82h2M1aqtsgx6ggP8zEEK4J1A3tE1KXXO9p6w79eRnljX5TTY5o972zTCsl2nZNVYSQV7L33zu9P8Jrd2zNU1vVj7Io6BigpsC46SqxWKony1SGV0LUd02KZJ6Q3I02ln4aA/eqlF03MHY1PQszmn0GOBvCvliWi/jgx1+3WI5Y9lu05hybh02TU17gR7cmrHlD5gOSsXbX+L6lZuaRqobNiwATNnzsSOHTtQUFCApUuXon///lomSZKzjBETFmQ3LNk2yh/TtQm+23sKHeIjkRbrXgcyW44yulYFrm2anuudjBk/KiuwPJl2PTy1a02PBZ6eatH03sHYU4G8O+WJu/06nJH6vtKrlYpqadwJ9pzVjin5XZwF5mK1R0t3naxW5ZamgUpJSQnS0tIwYsQIDBgwQMukOCTnhuYsyv9sax5mrcsxbz/wlkZ464GbVUmf2L6nLtmH5Aah5oDI2wWuWJpm/HAIlTbbySmwPJF2vTy1a0mvgZre5nTRcwdjTwTycq4NZ9ekq/065FA7f7j6fY6CfCW/i7N+Pba/xdc7T5q3qy7llqaBSkZGBjIyMrRMglNyb2hSmS3/QhliI4Pw/b7TVu99vfMkHk2PU6VmRWzflQJwz6zNmNonGY93SQSgXoEr5ylcNE0i28ktYNS+WejpqV0Leg7U9NzkokdiQYM7NWVyrw2516S7v6ftscj5PiXH70r65AT5ch+wHAVKckaVVodyy6f6qJSXl6O8vNz8/6KiIo/vU+5FKzWh0MSFu5CR0kD0u7fnXVQlUJHaNwBkfn8IEIDHuya6vR9A/lO4ozRZmpzRXJMLTG9P7WqRW0DrPVDTe5OL3lgGDe7UlBUYy3C+uFz1a8PV31PqWBx9nyvHryR9SoJ8ucHcqFsT8NEvuaiE/RBpZ+Wou7U/vsBP6wQokZmZibCwMPNfbGysx/dpuqFZEssYpqjc9oRWCsAP+09DTLt49fqpjLw1wS6dJjN+OIQCY5nb+5C6QMW+23Q+/P+adlIqo7VuFO52ulwRExaE53onm9NVFZ7aF207js5ZazBk7q/onLUGi7YdN79XYCzD5pxC828lN1+7y3a/SsSEBSE9sa5P/ybeJtoM/PU+fLf3lNPfwJR/nliwG4Lw94yxal0bjn5PsXwi1aS9J/+i5PcpKaOUpM+Ss6YaJUzn/L+/5AIG4LHbmmDjlO52/YFM5aiYyb0dP+w5Khd8hU8FKlOnToXRaDT/5efne3yfthnF0UU7qH1jvD+kjd3rlQLQpVmU1WsDb2mkSm2KKRPO/SVXMuquBFy6iGwpvUAHtW+MjVO6Y8Hof2Dp+E6Kbozu3ODkWLTt+I2OvbhRIE/OaK6L/hmuclRAixVUSvK1qzxRQHo6X/g6qSbXCfN3OfwNbPOPAMAgALOGtLG6cXqCVD6RatLuP2uz5HGoGURIkQryTZPuyc2bYtfsxxtz7bYzlaP/6ttC9Hta3xSuaB9yAzc98ammn8DAQAQGBnp9v0qqBdvGRYhWm864rzXOFl3B9ryLaBcfoUqQYpsJAdhNUW3avxpPyq40l5iqPguMZQ6rNy15upOnXaEsAG/8cBh3pzXU/Ond1SpaqQJ657GLktXUnmxe8UQfGL12/tUTR02ujn4DqQAnMiTQo9eEo3widSwCpI/DG026UvOwmIY4GwBMyUh22tzurPnVtizo2zoGr604aFW+Ozs2vTfxyuVTgYqW1Og4FhMWpOqwZLFMKAAY0jEWC3/NdxoQKOVqpzjbG8xjtzbB8FvjJauAPd3JU68Xrys3Ymezc1YKgsNj9dSIFrXP8Z78i5jy9T4u9umE7TVqS+o3UDqcVq3+Do7ySXpiXWQOSLV7GHN0HN7qiO1oHhYBQOYPhwADzAMZxEgFYntPXMLxCyV2ZYEtA2A+Nj3OoaUmTQOV4uJiHD369+qVubm52L17NyIjI9G4sW88KYllEG91BJTKhE/c3gxP3N7MI/tXemxS1ZvDb40X3d4bQYSeLl7LQENpgCZnds528ZGaHKua53jRtuOYsmSfXU2hHoJLtakRBJiu0R15FzFx4S5Zv4HcG7zatVrO8smg9o2R3CAU/Wdtll2T4K3y1xTki83DAtzoG+ioljYmLAjPZSTfGPBg8zlYnBNTPyMY7Bd07JIUrcs5tNSmaaCyfft2dO/e3fz/p59+GgAwdOhQfPLJJxqlSj5nGcTTmcFZJlRr/2LDA+V+t9LAQ+kNzpWCXS8Xr2X+MRhuNEFZcrbYmdzZOb15rJa/hxr7NR2n2IK3aucLb3CULneDANvvvistCCVXr0v+BrbbO7vBe6K2U861mBYbgayByvKSJ8pfR7UWYk3ulcLffQOlfvPCy+WwVQn7LxN9TYDDpl1vPzh7kqaBSrdu3SA4WXJbr/QyD4WnM6GjVVLl3AiUBh5Kggh3CnatL16xfjK2HJ0nJbNzeutYxX6PjVO6u7VfqXkkDABGSNTKOcoXWgYwztLlTnnibBjvzmMXUSkI5gX3pLZ3dIN31A+qb2vxz8g533Lyp9bXq7OH0ikZyTeaeyxIrXFk+ZuLdZ414MaDi+W59vvrDaVNuybeeHD2JPZRcZGe+jl4KhNKFZ6Xyq6Z1+5wFry4UnshtnZSgbFM1jBEJYGilhev1A3YD5DVt0irPgVSCoxldn1IpizZh81Tbndr8TnR1clx4+Fy7i+5+Hhjruwbvu0SF97sjOssv7pTnjj7btvjdmUpC0C6T8WE+btQXH7d7lwqeZCQcy1qdb3KKWse75oIGGAuF+WscSRVBozukoDE6Np2ZSYAu9fUatrVaw2kCQMVF+mpn4OnSBWeWT8cMtcAmIOX0mvmws8AYPRtCRh+a4KsKmUxYgWsZUGnp0DRFVL5x9R0IxWgmcgJAAuMZZi38cbN3NM35x3HLtpVfQt/VU1LPW3LYXucfjZNZHJv+At+PY7/rD2qSQ1ogbEM3+095TC/ulOeKJ2C3dWlLEy/hW3nVgH251IvNc5qkFvWPN4lEXenNXS6xtGKvQXo2zpG9Df3MwDDO98oN8XKTLHX3G1i9YXRdAxUXKSXfg6eJHohwf6Jyhy8/PV/AcB/f8nFRxZPu0qfhpwVdFoEimo+dUjln7TYCNkFh7PZOS1rOADHNwt3j02qCVeNll3L4ywsvoInFuy2et/ZDR8A3l9zFLa8Edha/pa2LPOrO+WJ1HUqdbM0zR0kuHDtDGrfGMEB/g5/A8D3HyQsKSlrbMs5sbz46oqDeP37g8gckOq0j6HtuRJ7zZ1mMV8JKBmoyCBViGvdbuppYoXn5N7NraqNAekpnk2ZPrlBKEquVii6CTor6OTWKIj9bo5uylLveeKpQ2qNFiUFh1jBZe6AKrJPsZuF3EU3HQUy7eIj7ToUGgC0VWn2ZdNxFhjLHN40bPOFI94IbB0FKbb5VU554qh51TIwFQBs+OMcuiRFi56vyRnN8cYPh116yJLT3KBljbPazRjuBJGWc0dZMl3XS8al493BafAzGHBLXIRbnZLV6gOmx4CSgYoTzgpxX++k5IxY4RkeXNM6eLFpi7VUIQjmoYVK5gaRs96IK+t9OPo9HXUcVvOpQ2wUlYkaBYejhcxsz6GcY5MTyMSEBSFrYCqmfr0PlbjxRJ85MFX1a0POTcOUL1bsLcCrKw5Kngd3akCd3QylmnsA4MW+LdCndQwAYHNOIRKiQgD8PTJEqk+Po9+hS1K0VS2JqTlm45TuoudrUPvGVs0USs6DnN9AqxpnTzVjKH0otU1H35QYrNhXYLVNhSCg//9thuDhJhfLKRBsHxh9pQuDQfDVYTe4sShhWFgYjEYj6tSpo/r3FxjL0Dlrjd2PuHFK9yodnMhRYCyzumgXbTtuvkk54uz8WQ3ZBW7MHSDAqoCVkzax323JuHSriZks0wNA8rfOLSzBkLm/2u1nweh/KO4o6qwgVSPPiX0HIL6/zTmFDo9NaXps84Xte2o96Traj+U2tmn3A/DBkDZuPb06+w2dNfdsnNLdqv+VaTZ2R8G8s99Bzu+ods2v3N/AUzXOtvlJL+X1zwdPY9SnO6xqF019qxzdbD2RVrG8KPaAJhbIepqS+zdrVBzwlWoxLdjWBJieOLI35uGjjX+aC3El509svRE/AfiPwhuL1O+2Le+iZHoESA/zU+upQ07thRpPojFhNxZcNHWa9AMwqkuCuZOeJWfHpvQakKphVPtJ17IvhOX/bW9eYueyb+uGivcnd2I+29FPlixHb9jmc5NK4cbkXra1da50zLRtEvNE7Zaz7/RUjbNYfoqNDNa8vP7nl7vx9c6Tdq9XCsBjXRLw8S95NzqFAy51aFZCqunRtkm+S1K029MIeBoDFQc8VS3mreGi3h5uFhMWhOf7tsDwW+NFp5YGlM8N4sp6I1K/W/t48XWYTOmRek+tamy5N313+z7ZLrj4XEay5FTezo5NjWvAE01nYqOZAIgGQ+50NMwtLMG+k0Zz06bYxF6Wv6HY6CfgxueWjEtHWmyE5EymJpUAsjfm4XmLReikOgnL7ZhZlUjlpyXj0jVtxtiTf1E0SDGlY3jnGw8LrpSNrnDUBOytZie1MFBxwBPtrN4YCqb1cDPLpyhH5882mHJ3bhBns6KmxUY4TM9zGclW8yBYvqdGx2l3Rg/I5cqCi46OTY1rwN05Qix/Z6nRTKbZawWL1yyDIaUBSvbGXMz9Jdd+yLXI9pa/oeToJwClV288QztaONDko41/Wq2H5aiTsOlYN07prsqTsd7n1HA02aGWwdpveRdEXzcYYDeaB/D8jNH7Thgdvm87xYTeRvpYYqDihJoje6SeBFwZFaN0H1plQqnzJxVMubreCGD/NC1WaDtKj+VT8+TezUUnsXP3HMpdQdpVrgYFjo7Ncu0YGG6sEK6Eq7Uytr+zKZAUu79LdeRWWpXuqH+JJdPx2P6GpplfxbZXMjKpUoBkTZtYJ2HLhfz0/iDlLkf5KT2xrmYjMTtI/PYfPdoWd7RoYPe6p1cvn/HjIdH3vNHspDYGKjKo1c4qdRORUwUn9ylHj/1qbM+fo2DK9uIF/h4ZYeoDYPtZqafpjVO6i3Z2dZYeAcAbPx62mmnS1WG7JkpWkHaHp5or3ZnV1ZVaGbHfWWpkGSDeWdHdJioplhPzWd5gTHlhap9kZH3/d0Bl+Ot8STXvOWoGEAuU+7aOwevfH/RIk7SeHnKkOMtPrtSgqVGDlBYbgYG3NLJq/hl4SyPRIMXyWDxxbqWafV7s2wLt4iM83uykNgYqKpGT2aWqfJ1VwSl5yvGF4WZy5kgxVfPL6TDn7tO0khl4lQ7bBZSvIO0OTzRXqnEDU/r0KNpfyUGQYqpVU7uJypZlM6Il27wwJSMZjcKDYDBAsiO4syZSqeDQE7+x1PFbXkd6ahJytTZCrClRzYUg33rgZjyaHofteRfRLj7CLp8o4c75lroP9Gkd4zD/6Ok3tsRARQVyM7ttBpFTBefKBGByCzF3M6Wrn5cTTEnVnCwd10l02ml3nqaVzMBrWWjL/V28XculRpWy5W+rVvqVPD3K6cchNppJyXHL6SNl6TGJkVNieeGNHw9bDTW13Bdgv5qu7W92tuiKef4h03da5i9PNBs4ui712CSktDZCqinR1QBc6pykxToOUOSUm+6eb2f3AbH8o8ff2ISBiptEb6giwwtN5Fb5msi9SVhmfjmFmLuZ0p3PywmmpJ6oV+wrEP0s4PrTtFh6xGbgdXXYrha1XO5UKdsV6L2TZQWWnpwN1JafAVg6rpPdDUHuccvpI2Xr41/yMLxzgt3rzvKC3dxAEJ8zxbImcYrIzMK2+UvtZgOp6/Js0RW7BSf12CTkiNymRLUWgpQidxZoNZrg5NwHhL9+Vb03+zFQcZPUkFrb4YWWnFX5WmYMOTc5R0u2i3E3U3qjKUDq6fajDbnYNPV20Y6y7jxhypmB19Vhu56qqvcEqdqB5zKSJadcn7M+x7zWkydmAxXrPFop/D2KRik5faQcdVhVEoiK9X+yPAapxfzE+th6ownX9jrY8Me5G33obLZTq0bQW00NUg8+tsPNnZ1jU3ovlFxVHOTILTfVrIGVCmZt7xmD28fqrm+jJQYqEuReQAlRIaJzK9gOL5Ti7Ibt7CbnLPOLHcf2vAtuZUpPNAWIdRoceWsC5v6Sa/WZSkBydIO7T5i2n3c2bPfeNtYd5/q3cW0IsJ5I/batG4WLBodzNuQg84e/RxeY8p9aI9liwhx3HnXlRuds8jSpffoBOF9SbreitaNr1NmcKXIW8zPt21vBrek6cBQ0+QEIDvBzeR+OVvb2RPAiFUxO7t0cb/wob80j25oxpUGO3HLT0zWwYveM+b/l222np76NDFREKOkkmVtYggc7xNr90JWC/fBCKbY3SNsL1dFNzlHmF+uMB9zo62FLLFNKFRhy+5g4K2xM22w6Woj/W5tj90Q+4q+hvK72PXFEbmEoFvwUGMuw49hFLLGZ3GnZrlN4pldzh7+lXgMUE0e/rVg+zfrBfgik2pNJSQUCtvl75K0JGHGrfR8SOccIWE+eZtsMZLopTZi/S/SYpK5RZ/1e5CzmJ9XM5WmOOhdXArj3/za7NBJOci6cr/dhz4lLWPBrvuq1c1J5aFD7xrj7ZudrHonVjBkgPUxdjNwAxFHgq0YQJ6fTuDcDYzkYqNiQWz1nG8woja6lOGrGEcs0IQH+ovsODvAT7TsDkUJTLFM6Ctac1SbICfSk5quwPN8AMPo2ZfOOeLqjmqN5NioEATvyLuKuNGWjgrzN0TlS0kyVW1gi+rQNqD+ZlNiwdct1XSoFYO4vufjol1xkDXR8nmPCrJcYsCTWDLQj7yImLtwlq6lTsKlbtT2fBsBu/So5tTPeDlIA50GW2Hlwluf35F8U7X8D3Ah+5v/69wNfpXDjoUqtfhJSwaScBwixm7sA4IPBbVC3dqCsWlIl15YnO7vK6aj+wZA2Li014SkMVGzIqZ4TC2YMhhvr0rgzkZfSCeFMGdc2SHl9QApKrlaI9p0RKyFsM6Wc5qSlu8RrEwA4DfSczVdRIQh2awbJmXfE0x3V5MyzMXHhLpRcvY4uSdG67Jwm5xzJbaaSU+AB4tePK0+FljcUqSYVAc7Ps+USA87SGxMWhMjazssER+dVLMhydG7Fzr9WS2LIGaW489hFRIQ4XwdJrCbFmUoByN6Ui+f7tFTtmFw5f1K1IW3jlS1uqaQJ2LZpXK3yxPZ3teVvMOAWhZM6ehoDFRtyqudEo2vhxuJ5kSGBLhcuSiaEE7tp+hn+Xk+kwFgm6yYilimdBWuO3ne0uJ+zdngTA2AOUgB58454o6OanCpT037fe/Bm3XVOU1LYySnQ7W5kBmBct0T837ocyetHzurRcvuGSeVvJQtfipHTHGO5jdzFJm1rThwdr+X2WtbMORulaDDcaA4T/vq37X3P9FsAsHuokuujDbmiw8K9SUltiJzvUvo5tac4sPxd9568JNlRXi8YqNiQ0z4YEuAvWnBZTuzkSuGiZEI4qV7sppEQolXOsK9QmZzR3C5TOiuYnb3vLNBz9iQu1ufH2UXpqY5qljcSsc+KdaSu+GtSF3fWLVJKzncoLezkfKfYE2JsZLDk9ePsiVvuNWPK32JBh9KFL23ZXhPOblKu3kS8OVTVHZY3VtvA1HL+IkcjlaTOuwHAgx1jsfC3fIf9YVxZF0ptanWIdyWdSvsFAvZz9dgy/a7piXVxd5rzfjpaYqAiQk774L1tGmHZrlMujcSRIreqNa+wVFbGNR1H9sY8zP3lT9GnmdaNwp2mQ2yKakfvO3vykKp6NE3CdHdaQyzcli87mADU6ahmS+xGkjkg1a76WqyPUNt4xwsgOtqH3KdlsdV9HX2HkiBNKl1ihaztE6JUge7ohg44bzK0Zc7fm3Lx0QZ5/ZjkNFeJXROOblKujNIoMJbZzU0yRaQ/ht6WxLA8D4XFV/DEgt1225jKLdvfQmxSxaXjb3QSTrsp3GFThNJ1oTxV6+Rq05GJq+mUKreAG82gtit8A+Jz9QDigZK7x+VpBkFquU8fUFRUhLCwMBiNRtSpU8dj+ykwlll13AOk1/sAbmScIXN/tfueBaP/Ibr2jNj+HE0It2RcOkquVmDfSaNdlZ3YE5lt2i2/y3L2TKl0SEXZjt539lnb47Q9j4u2HRftoe+Iks/IOTap31ys+tsgWBfOpv06O0di+3D0m5g+J7W6r7PvsD1Hk3s3R+pNYVZPYSEB/qL5znISPFenG5c63tzCElWuGTlPhJbnwJac8+/sO+Xk1+/2nsKE+bvsXp8l0l/MlTziiJInekfbOrpG8i+UmRewlHtNm37DvScu2Q0ZdnQuXTlHWvT5UeO3tMznlqPeHLHch546+Cu5f7NGRQZHy4qLFaLujoOXqmr1NxjQv01D803ENGto65vCJQtoyXkZDM6Hn1k+CVv+Xyydjo5BznHacqWaVeozrjxBSP3m2/IuyuqfJOcYXXlalrO6r+2cIJZs26bFnsKkmrOyLFYudqUJwlltllrXjInUzcjqHIjcFN1t85eTX6WeD21fVrNvBKDsid523pDRtyVguMXwb6m0HTp9WXQfcuaMMjdFyBgybKL0OtLqZi2VTsuRgs6YzpHcBTRN+3C11lIvGKjIoDTwULNwcdSZrVKwX1NETtr9IG9eBq2jb1eqI20/4+oxSP3m7eMjnPZPkmJ745QaWu6ov4zcwsl2ThBLpnQ+9NFW83dZfqXY14s1l7jSBGE55Nf0xG1Kk7OJDZU8ATv73V29KTqiJL+2i4+0++0NANrG21+TavaNkHujEps35L+/5OKjjblORzTZDhu33Ifcc6TkXCopn5WeAzl5zt0O4KaRgkomu5PT18rEUT8hy1FbeluI0BIDFRlcCTzUXhguPbGu6JBMZzcLqbQ7WzRre94FXUbfprQZDAaramWpbV09BkfnzZUgVKyP09JdJ0WHliupgXHE0fHKmvTpr0LVNMW20n5DUqRWBZa6ZpQGm64s5OntPB0TFoSsgamY+vU+VOLGw0PmwFS3aiidUVLzIJU/nI1ocqWMksPduX+UTn0vN8+51AH8r9/cxHROL5Vek9206qivlaO5euwGA1iM2nLUoVvrFZUZqMjkSuDhTuEidgF0SYp2qXpcSdqdTWim5fBa23kYDIDDyb3c7Ygodd6U5gWxG+fXNrPa+hmA/z56C4ICathN0W4iVTj54UYnZH8/g9P1aRyNXLNk6muwaFs+5v9248+Av4egqj1XkNQTtyvBphodUL1ROHt7WQUlNQ9SS4MAjs+l1D6CA/ywOafQpfPp7tw/Sqe+N09K5yTPuZI3B7VvjJDAGnb9k5Q2rYoFZ5N7Nzd3AwDs5+qx/YztqC2xfWpdq27CQEUBbz15SV0AG6d0t4rIlUxzLCftzpoWtFz7wXaUBHDjAnM0c6Uaa2ZInTcleUHu/CujPt0h+mRjedO0LWgGt2+MJ+5oam63lloTBxCv1TGNXDP8VXoL+DsI2Zp7wWqYuIAbkxr+Z0gbWU1dcs+FoxufK0GHu7+7ksLZ3YBGST5SY1/PZSSb+yW5Gmw6G2Zvu4Clbb86pSPb3Jn7R+nU91KT0onlOVcD4rZx9s3HrjStyunzY6tLUjTeHZwGP4MBlYJgN2rLcp96GBpvwkBFh5wN4zQ/EhhsP6n+fk08NRGQkjZZsaRVCtJzLJirWi2epkY6mDTOU+TO4Cr2ZCPWTDK5d3Nk/XWzWbjtONJiw8zLLDiaA8i20Fm265TVyDUAVn0NOmWtsUtjJYDIkECX84HSIMKVoMOVploTJYWzN5821djXom3HrTpPT+7dXDQYjgkLkrzepB6ObNP3XO9kNAoPwsXSq3jpm99d7oTtqANqZG3nc4bsOCbS+R3iU9+bF2EUSYdYnnM1IBatDclobv5tTPwAWd8l91q0+40ykh2mX09D4xmo6IizCeXE1u9RM8KV6nj7gRtP0I4oKXylqqL9DM4vZlMVrgDxDoGeJlYw9W/T0FybIfU0tSPvov16TX9VSUsV/FJPWdl/rVJru49teRetRgdZ9jUQG5gip/BUei7EbnxStUhygw5Xm1XkFs7efNpUY19iNQtv/HgYd9/c0C4Yfq53MhpFBNldb1KLI4qlL+uHQzBIBOdKbnZSkyya1l5yNGeIqXbEltTU90pHSLoTEIvlz/Cgmla1OQJu9OdSo5wS+43e+OEwnuudLDnqzdOrOCvBQEUn5EwoJ7Z+j5oRrtSF54nFqVzp8Jg10HqyNcNfBZPUsUs9IVUK4pNrKUm70ip4sYLpmV7NkVdYitKr1zDy0x12n7lYat/xT07BL9bPY+4vuaLpkhodJFUL9FxGstt5zVkQIRbAbpzSXXHQ4UpTrdzC2ZtPm85qWF0dJSIVDGf+tSK2ZX8OR53wpRbsk5qhS+mwc9tJMAXAKuAysSxDAPEp+x1Ny+DKCEl3+hnZ5s8uSdFWyxAIcC0gFcsPUr9/65vCJa8tdwIxtTFQ0QE51fKm6ns1I1yxTO2tDn6uFPSWQ1sNBjit5XHUlCUIwM5jF9G3tfod+2zZjt4yMRVUm3MKRT8XERwg2pZtWaMCOM8DUtX4JmJBoljHu+cykvF4l0SHxyqXVBDhqH+Ws4nf1OgAK6dwLjCW4bu9BXafNf0OanfE3XfCKLqvvScumYeYuzJKxN9gEF1N3cRUS/HB4DYOF9+T27Rp2qfSm51lmXS+pFx0ojwTR2uOAcD7g9vgrjTxBy+p397ZNA6O8rIaa705C37lzFDtKAB3FNB7u8O3FF0EKrNmzcLMmTNx+vRppKWl4YMPPkCHDh20TpbXOJpczLZaXq0I19EN1/R9UhO9iVF6Ubo67XhuYYnsFUudFaBK52R2pQpeTmDjaGVWqWmzLQMIsfWanH2/LbEC0RuFlFjfCFcKazX7izgbQSK1AvDk3s0lh167qsBYhhk/HrJ7fWy3JuahrIBro0ReH5Ai2rHTUqUA1K39d58kqckTxWo9bJuN3ncS8Dhiupk6W2zVzwAUFl9B48hgyWvKESWTRjqi1lpvzspEqVGatvnBnXuHKzWTatM8UFm0aBGefvppfPjhh+jYsSPeffdd9OrVC4cPH0a9evW0Tp5X7Dtp/8QEiFfLqzU/i1qLwwGuXZRKLxx39mE7bwEgPrmWs8JITsc+V/oxODoXUr/3pdJr5g61M344hPCgmk7nb3B0I5EqED1ZSNn+pmO7JcLP4Hz4qC1P9BcRO25HnS0BoFF4kLnvhFrpkKoVDA8OkN2XxpSnpfKS2LpbJo5GjTkqlzb8cc4uP0vVZChhm5dtCQLwxILdTtdjc7YP2+DUk/P4SB2bszQ7G6Vpmx/0UjviCs0DlbfffhujR4/G8OHDAQAffvghVqxYgXnz5mHKlCkap87zCoxlmPGD/ROTiVS1vDuZTM3F4dy5Sci9cNTYh+XCjGKTa7la82HZsc/2c0pqBxydC7E+JzN+VDadvZwbiTcLLrHfdNbaHLvt5KTNW/1FnI2KE2tGcTcdSmdItgzopPK0o7y39+Qlu/XDTDUZzq5By3zqyZvioPaNkdwgFP3/b7NdrajlNSHWfK6Ut+fxUXLenE17ILUoqy8FKCaaBipXr17Fjh07MHXqVPNrfn5+6NGjB7Zs2WK3fXl5OcrLy83/Lyoq8ko6PUnOHBtqF7qOqhi9Mc+FJTkXjhr7eL5vCwy/NV60AHC15sO2Y5/t51xZesGTbdneupHIISffv9q/Fe5oUd9p2rw1OkFywr2/OmiKNaO4mw5H/SacLTngyuy86Yl1cXea/ZICruQ5T94US65WOG26dbQem1xazOMj97w5atLVsuOrJ2gaqBQWFqKiogL169e3er1+/fo4dMi+liEzMxMvv/yyt5LnFXL6D6hd6DqrYvT0PBdKqbUPqQLA1ZoPsY59lp9Ts0+RJU+fD2+Qk+/Lr1XKDkS9MTpBLFAd1SUBwzv/vVCfJ9LhygzJ7gT3YvlCT0NVpdJjS430eXseHyXE9mM5O21VCVIAHTT9KDF16lQ8/fTT5v8XFRUhNjZWwxS5TyyzWc6x4alMLlXIKb3IvHFRenofrtZ8yBmF5YmaC28VhJ7krK8BALRz0vHRkrdqiJztx1PpkAoqpV5XO7DQW57zVrnp6nHrJT9WFQZBar1xL7h69SqCg4OxePFi9O/f3/z60KFDcenSJXzzzTcOP19UVISwsDAYjUbUqVPHw6n1rAJjmVVms/2/1ulRe3tvpEmJRduO2xVGckZruPo5NWidR9RgOobPtuTh+/2nza8PvKUR3nrgZu0SVgV4Im/qLc95q9zU23FXBUru35oGKgDQsWNHdOjQAR988AEAoLKyEo0bN8aECROcdqatSoEKac/VwoiFmDr25F/E9ryLaBcf4XTuCpKHeZP0Ssn9W/Omn6effhpDhw5Fu3bt0KFDB7z77rsoKSkxjwIi8hZX+2z4ak96vUmLZYCiNuZNqgo0D1QGDRqEc+fO4aWXXsLp06dx880348cff7TrYEtERETVj+ZNP+5g0w8REZHvUXL/9vNSmoiIiIgUY6BCREREusVAhYiIiHSLgQoRERHpFgMVIiIi0i0GKkRERKRbDFSIiIhItxioEBERkW4xUCEiIiLd0nwKfXeYJtUtKirSOCVEREQkl+m+LWdyfJ8OVC5fvgwAiI2N1TglREREpNTly5cRFhbmcBufXuunsrISp06dQmhoKAwGg9bJ8bqioiLExsYiPz+fax25gedRHTyP6uB5VAfPozo8dR4FQcDly5fRsGFD+Pk57oXi0zUqfn5+uOmmm7ROhubq1KnDC1EFPI/q4HlUB8+jOnge1eGJ8+isJsWEnWmJiIhItxioEBERkW4xUPFhgYGBmDZtGgIDA7VOik/jeVQHz6M6eB7VwfOoDj2cR5/uTEtERERVG2tUiIiISLcYqBAREZFuMVAhIiIi3WKgQkRERLrFQMUHbNiwAf369UPDhg1hMBiwbNkyq/cFQcBLL72EmJgYBAUFoUePHjhy5Ig2idUxZ+dx2LBhMBgMVn+9e/fWJrE6lZmZifbt2yM0NBT16tVD//79cfjwYattrly5gvHjx6Nu3bqoXbs2Bg4ciDNnzmiUYn2Scx67detmlx/HjBmjUYr1afbs2WjdurV5MrL09HT88MMP5veZF+Vxdh61zosMVHxASUkJ0tLSMGvWLNH333jjDbz//vv48MMP8euvvyIkJAS9evXClStXvJxSfXN2HgGgd+/eKCgoMP8tWLDAiynUv/Xr12P8+PHYunUrfvrpJ1y7dg09e/ZESUmJeZunnnoK3377Lb766iusX78ep06dwoABAzRMtf7IOY8AMHr0aKv8+MYbb2iUYn266aabkJWVhR07dmD79u24/fbbcc899+D3338HwLwol7PzCGicFwXyKQCEpUuXmv9fWVkpNGjQQJg5c6b5tUuXLgmBgYHCggULNEihb7A9j4IgCEOHDhXuueceTdLjq86ePSsAENavXy8Iwo28V7NmTeGrr74yb3Pw4EEBgLBlyxatkql7tudREASha9euwpNPPqldonxURESE8NFHHzEvusl0HgVB+7zIGhUfl5ubi9OnT6NHjx7m18LCwtCxY0ds2bJFw5T5pnXr1qFevXpo3rw5xo4di/Pnz2udJF0zGo0AgMjISADAjh07cO3aNav8mJycjMaNGzM/OmB7Hk2++OILREVFISUlBVOnTkVpaakWyfMJFRUVWLhwIUpKSpCens686CLb82iiZV706UUJCTh9+jQAoH79+lav169f3/weydO7d28MGDAACQkJyMnJwfPPP4+MjAxs2bIF/v7+WidPdyorKzFp0iR07twZKSkpAG7kx4CAAISHh1tty/woTew8AsCQIUMQFxeHhg0bYu/evXjuuedw+PBhLFmyRMPU6s++ffuQnp6OK1euoHbt2li6dClatmyJ3bt3My8qIHUeAe3zIgMVor8MHjzY/O/U1FS0bt0aiYmJWLduHe644w4NU6ZP48ePx/79+7Fx40atk+LTpM7jY489Zv53amoqYmJicMcddyAnJweJiYneTqZuNW/eHLt374bRaMTixYsxdOhQrF+/Xutk+Ryp89iyZUvN8yKbfnxcgwYNAMCuJ/uZM2fM75FrmjRpgqioKBw9elTrpOjOhAkT8N1332Ht2rW46aabzK83aNAAV69exaVLl6y2Z34UJ3UexXTs2BEAmB9tBAQEoGnTpmjbti0yMzORlpaG9957j3lRIanzKMbbeZGBio9LSEhAgwYN8PPPP5tfKyoqwq+//mrVvkjKnThxAufPn0dMTIzWSdENQRAwYcIELF26FGvWrEFCQoLV+23btkXNmjWt8uPhw4dx/Phx5kcLzs6jmN27dwMA86MTlZWVKC8vZ150k+k8ivF2XmTTjw8oLi62ilxzc3Oxe/duREZGonHjxpg0aRJeffVVNGvWDAkJCXjxxRfRsGFD9O/fX7tE65Cj8xgZGYmXX34ZAwcORIMGDZCTk4PJkyejadOm6NWrl4ap1pfx48dj/vz5+OabbxAaGmpu6w8LC0NQUBDCwsIwcuRIPP3004iMjESdOnXwxBNPID09Hf/4xz80Tr1+ODuPOTk5mD9/Pvr06YO6deti7969eOqpp9ClSxe0bt1a49Trx9SpU5GRkYHGjRvj8uXLmD9/PtatW4eVK1cyLyrg6DzqIi9qNt6IZFu7dq0AwO5v6NChgiDcGKL84osvCvXr1xcCAwOFO+64Qzh8+LC2idYhR+extLRU6NmzpxAdHS3UrFlTiIuLE0aPHi2cPn1a62Tritj5AyBkZ2ebtykrKxPGjRsnRERECMHBwcK9994rFBQUaJdoHXJ2Ho8fPy506dJFiIyMFAIDA4WmTZsKzz77rGA0GrVNuM6MGDFCiIuLEwICAoTo6GjhjjvuEFatWmV+n3lRHkfnUQ950SAIguCdkIiIiIhIGfZRISIiIt1ioEJERES6xUCFiIiIdIuBChEREekWAxUiIiLSLQYqREREpFsMVIiIiEi3GKgQERGRbjFQIfJxp0+fxhNPPIEmTZogMDAQsbGx6Nevn9UaJ5s3b0afPn0QERGBWrVqITU1FW+//TYqKirM2+Tl5WHkyJFISEhAUFAQEhMTMW3aNFy9etVqf3PnzkVaWhpq166N8PBwtGnTBpmZmeb3p0+fDoPBgN69e9uldebMmTAYDOjWrZusYzN9l8FgQI0aNRAfH4+nnnoKxcXFCs8SEfkqrvVD5MPy8vLQuXNnhIeHY+bMmUhNTcW1a9ewcuVKjB8/HocOHcLSpUvxwAMPYPjw4Vi7di3Cw8OxevVqTJ48GVu2bMGXX34Jg8GAQ4cOobKyEnPmzEHTpk2xf/9+jB49GiUlJXjzzTcBAPPmzcOkSZPw/vvvo2vXrigvL8fevXuxf/9+q3TFxMRg7dq1OHHihNWqwPPmzUPjxo0VHWOrVq2wevVqXL9+HZs2bcKIESNQWlqKOXPm2G179epVBAQEuHAmPUePaSLyKV6brJ+IVJeRkSE0atRIKC4utnvv4sWLQnFxsVC3bl1hwIABdu8vX75cACAsXLhQ8vvfeOMNISEhwfz/e+65Rxg2bJjDNE2bNk1IS0sT7rrrLuHVV181v75p0yYhKipKGDt2rNC1a1cZR/f3d1kaPXq00KBBA6v3586dK8THxwsGg0EQhBvHPnLkSCEqKkoIDQ0VunfvLuzevdv8Hbt37xa6desm1K5dWwgNDRVuueUWYdu2bYIgCEJeXp5w1113CeHh4UJwcLDQsmVLYcWKFYIgCEJ2drYQFhZmlZ6lS5cKlkWpq2kiInFs+iHyURcuXMCPP/6I8ePHIyQkxO798PBwrFq1CufPn8czzzxj936/fv2QlJSEBQsWSO7DaDQiMjLS/P8GDRpg69atOHbsmNP0jRgxAp988on5//PmzcNDDz3kdu1CUFCQVXPU0aNH8fXXX2PJkiXm5efvv/9+nD17Fj/88AN27NiBW265BXfccQcuXLgAAHjooYdw0003Ydu2bdixYwemTJmCmjVrArixsnF5eTk2bNiAffv2YcaMGahdu7aiNLqSJiISx6YfIh919OhRCIKA5ORkyW3++OMPAECLFi1E309OTjZvI/b9H3zwgbnZBwCmTZuGAQMGID4+HklJSUhPT0efPn1w3333wc/P+rnnrrvuwpgxY7Bhwwa0bdsWX375JTZu3Ih58+YpPVSzHTt2YP78+bj99tvNr129ehX/+9//EB0dDQDYuHEjfvvtN5w9exaBgYEAgDfffBPLli3D4sWL8dhjj+H48eN49tlnzeeuWbNm5u87fvw4Bg4ciNTUVABAkyZNFKfTlTQRkTgGKkQ+SlCw8LmSbQHg5MmT6N27N+6//36MHj3a/HpMTAy2bNmC/fv3Y8OGDdi8eTOGDh2Kjz76CD/++KNVsFKzZk08/PDDyM7Oxp9//omkpCS0bt1aUToAYN++fahduzYqKipw9epV9O3bF//5z3/M78fFxZkDAgDYs2cPiouLUbduXavvKSsrQ05ODgDg6aefxqhRo/DZZ5+hR48euP/++5GYmAgAmDhxIsaOHYtVq1ahR48eGDhwoOJ0u5ImIhLHQIXIRzVr1szcCVZKUlISAODgwYPo1KmT3fsHDx5Ey5YtrV47deoUunfvjk6dOuG///2v6PempKQgJSUF48aNw5gxY3Dbbbdh/fr16N69u9V2I0aMQMeOHbF//36MGDFC6SECAJo3b47ly5ejRo0aaNiwoV3TkW2zV3FxMWJiYrBu3Tq77woPDwdwYzTRkCFDsGLFCvzwww+YNm0aFi5ciHvvvRejRo1Cr169sGLFCqxatQqZmZl466238MQTT8DPz88u6Lt27ZrdflxJExGJYx8VIh8VGRmJXr16YdasWSgpKbF7/9KlS+jZsyciIyPx1ltv2b2/fPlyHDlyBA8++KD5tZMnT6Jbt25o27YtsrOz7ZpzxJgCHbE0tGrVCq1atcL+/fsxZMgQJYdnFhAQgKZNmyI+Pl5W/5ZbbrkFp0+fRo0aNdC0aVOrv6ioKPN2SUlJeOqpp7Bq1SoMGDAA2dnZ5vdiY2MxZswYLFmyBP/85z8xd+5cAEB0dDQuX75sdaymPihqpImI7DFQIfJhs2bNQkVFBTp06ICvv/4aR44cwcGDB/H+++8jPT0dISEhmDNnDr755hs89thj2Lt3L/Ly8vDxxx9j2LBhuO+++/DAAw8A+DtIady4Md58802cO3cOp0+fxunTp837Gzt2LF555RVs2rQJx44dw9atW/Hoo48iOjoa6enpomlcs2YNCgoKvFZz0KNHD6Snp6N///5YtWoV8vLysHnzZrzwwgvYvn07ysrKMGHCBKxbtw7Hjh3Dpk2bsG3bNnM/nkmTJmHlypXIzc3Fzp07sXbtWvN7HTt2RHBwMJ5//nnk5ORg/vz5Vh2GXU0TEUlj0w+RD2vSpAl27tyJ1157Df/85z9RUFCA6OhotG3bFrNnzwYA3HfffVi7di1ee+013Hbbbbhy5QqaNWuGF154AZMmTYLBYAAA/PTTTzh69CiOHj1qNfcJ8Hcflx49emDevHmYPXs2zp8/j6ioKKSnp+Pnn3+2639hIjYiyZMMBgO+//57vPDCCxg+fDjOnTuHBg0aoEuXLqhfvz78/f1x/vx5PProozhz5gyioqIwYMAAvPzyywCAiooKjB8/HidOnECdOnXQu3dvvPPOOwBu1GJ9/vnnePbZZzF37lzccccdmD59utPOsM7SRETSDILSXnZEREREXsKmHyIiItItBipEpJnatWtL/v3yyy9aJ4+IdIBNP0SkmaNHj0q+16hRIwQFBXkxNUSkRwxUiIiISLfY9ENERES6xUCFiIiIdIuBChEREekWAxUiIiLSLQYqREREpFsMVIiIiEi3GKgQERGRbjFQISIiIt36f4Yf/NhtfB6xAAAAAElFTkSuQmCC", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(alm_surr, data_training)\n", - "surrogate_parity(alm_surr, data_training)\n", - "surrogate_residual(alm_surr, data_training)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Model Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=7, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "\n", + "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", + "cols = csv_data.columns\n", + "cols = [item.replace(\".\", \"_\") for item in cols]\n", + "csv_data.columns = cols\n", + "\n", + "data = csv_data.sample(n=500, random_state=0)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with ALAMO\n", + "\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ***************************************************************************\n", + " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", + "\n", + " If you use this software, please cite:\n", + " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", + " Automatic Learning of Algebraic Models for Optimization,\n", + " AIChE Journal, 60, 2211-2227, 2014.\n", + "\n", + " ALAMO is powered by the BARON software from http://www.minlp.com/\n", + " ***************************************************************************\n", + " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", + " ***************************************************************************\n", + " Reading input data\n", + " Checking input consistency and initializing data structures\n", + " \n", + " Step 0: Initializing data set\n", + " User provided an initial data set of 400 data points\n", + " We will sample no more data points at this stage\n", + " ***************************************************************************\n", + " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", + " \n", + " Step 1: Model building using BIC\n", + " \n", + " Model building for variable CO2SM_CO2_Enthalpy\n", + " ----\n", + " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", + " ----\n", + " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", + " ----\n", + " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", + " ----\n", + " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", + " \n", + " Model building for variable CO2SM_CO2_Entropy\n", + " ----\n", + " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", + " ----\n", + " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", + " ----\n", + " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", + " ----\n", + " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", + " \n", + " Calculating quality metrics on observed data set.\n", + " \n", + " Quality metrics for output CO2SM_CO2_Enthalpy\n", + " ---------------------------------------------\n", + " SSE OLR: 0.515E+08\n", + " SSE: 0.659E+08\n", + " RMSE: 406.\n", + " R2: 0.999\n", + " R2 adjusted: 0.999\n", + " Model size: 6\n", + " BIC: 0.484E+04\n", + " Cp: 0.659E+08\n", + " AICc: 0.482E+04\n", + " HQC: 0.483E+04\n", + " MSE: 0.168E+06\n", + " SSEp: 0.659E+08\n", + " RIC: 0.659E+08\n", + " MADp: 0.594\n", + " \n", + " Quality metrics for output CO2SM_CO2_Entropy\n", + " --------------------------------------------\n", + " SSE OLR: 541.\n", + " SSE: 558.\n", + " RMSE: 1.18\n", + " R2: 0.997\n", + " R2 adjusted: 0.997\n", + " Model size: 10\n", + " BIC: 193.\n", + " Cp: 178.\n", + " AICc: 154.\n", + " HQC: 169.\n", + " MSE: 1.43\n", + " SSEp: 558.\n", + " RIC: 606.\n", + " MADp: 0.130E+04\n", + " \n", + " Total execution time 0.52 s\n", + " Times breakdown\n", + " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", + " MINLP time: 0.0 s in 0 optimization problem(s)\n", + " Simulation time: 0.0 s to simulate 0 point(s)\n", + " All other time: 0.22 s in 1 iteration(s)\n", + " \n", + " Normal termination\n", + " ***************************************************************************\n" + ] + } + ], + "source": [ + "# Create ALAMO trainer object\n", + "has_alamo = alamo.available()\n", + "if has_alamo:\n", + " trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + " )\n", + "\n", + " # Set ALAMO options\n", + " trainer.config.constant = True\n", + " trainer.config.linfcns = True\n", + " trainer.config.multi2power = [1, 2]\n", + " trainer.config.monomialpower = [2, 3]\n", + " trainer.config.ratiopower = [1]\n", + " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + " trainer.config.overwrite_files = True\n", + "\n", + " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [7, 306], [40, 1000]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n", + "else:\n", + " print(\"Alamo not found.\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_training)\n", + "surrogate_parity(alm_surr, data_training)\n", + "surrogate_residual(alm_surr, data_training)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UtYmPj0dOTo5amwYNGqB27dqqNnFxcWjatKna9GRQUBAyMzPxxx9/lPi8srKykJmZqfZFRERUHsocMGtra5F7YhyU17kyuXd6FYRNmTIFNjY2cHR0xO3bt/Htt9+qjv3999+4desW9u7di6+++gpbt25FfHw8+vbtq2qTkpKiFhgBgIuLCzIzM/Hs2TM8fPgQeXl5xbZJSUlRnaNatWqwt7cvtU1x51AeK8n8+fMhl8tVX9yyiIiINMUpyKqhjessahA2depUyGSyUr+uXr2qaj9p0iT89ttvOHr0KExNTTF06FAIggAgf7PrrKwsfPXVV2jbti06dOiATZs24cSJE7h27ZpYT1Ej06ZNQ0ZGhurrn3/+EbtLREREpCOiJuZPmDChyMrEwurUqaP6u5OTE5ycnFCvXj00bNgQnp6eOHv2LAICAuDm5gYzMzPUq1dP1b5hw4YA8lcq1q9fH66urrh3757a+e/duwc7OztYWVnB1NQUpqamxbZxdXUFALi6uiI7Oxvp6elqo2GF2/zyyy9FzqE8VhILCwtYWFiUej2IiIjIMIg6Eubs7IwGDRqU+lWtWrVif1ahUADIz6MCgNdeew25ublITExUtfnrr78AAF5eXgCAgIAAxMbGqp0nJiYGAQEBAIBq1arB399frY1CoUBsbKyqjb+/P8zNzdXaXLt2Dbdv31a1CQgIwOXLl1UrJ5S/x87ODo0aNarAlSIiMhypqalITk5GcnIy4uPv4euvUxEff0/1WGpqqthdpCo2fPhw1QyYubk5XFxc8MYbb2Dz5s2q+315bN26tUi6kJTpRYmKc+fO4fz583j99ddRo0YNJCYmYubMmfD19VUFPoGBgWjRogVGjhyJFStWQKFQICwsDG+88YZqdGzMmDFYs2YNJk+ejJEjR+L48ePYs2cPDh06pPpdkZGRGDZsGF555RW8+uqrWLFiBZ48eYIRI0YAAORyOUJCQhAZGQkHBwfY2dlh3LhxCAgIQOvWrQEAXbp0QaNGjTBkyBAsWrQIKSkpmDFjBsLCwjjSRURGLTU1FWvWrAEAXLjwMr77rjsEwQQymQI9ehxEixa/AQDCw8Ph6OgoZleNTmpqKrKzs0s8Xq1aNZ3+mwQHB2PLli3Iy8vDvXv3cPjwYXz44Yf4+uuvceDAAYOss6kXz8ja2hrR0dGYPXs2njx5Ajc3NwQHB2PGjBmqoMbExATfffcdxo0bh3bt2sHGxgZdu3bF0qVLVefx8fHBoUOHMH78eKxcuRIeHh748ssvVTXCAKBfv3548OABZs2ahZSUFDRv3hyHDx9WS7Rfvnw5TExM0KdPH7VirUqmpqY4ePAgxo4di4CAANjY2GDYsGH4+OOPq+BqERFJl/Imn5FhqwrAAEAQTPDdd93h63sDcvmjUoMB0r6CwXFpdBkcW1hYqFJ2atWqhRYtWqB169bo3Lkztm7dilGjRmHZsmXYsmUL/v77bzg4OKBHjx5YtGgRqlevjpMnT6oGTJRJ87Nnz8acOXOwfft2rFy5EteuXYONjQ06deqEFStWqOp/ikUvgrCmTZvi+PHjZbZzd3fHvn37Sm3ToUMH/Pbbb6W2CQ8PR3h4eInHLS0tsXbtWqxdu7bENl5eXvj+++9L7zARkZFKS3NE4SpJgmCCtDQHyOWPROqV8Spv0FvVwXGnTp3QrFkzREdHY9SoUTAxMcGqVavg4+ODv//+G++//z4mT56MdevWoU2bNlixYgVmzZqlWpBXvXp1APllJD755BPUr18f9+/fR2RkJIYPHy76fVovgjAiIjIsDg6pkMkUaoGYTKaAg0NaKT9FxqhBgwa4dOkSACAiIkL1uLe3Nz799FOMGTMG69atQ7Vq1SCXyyGTyYosghs5cqTq73Xq1MGqVavQsmVLPH78WBWoiUGv6oQREZFhkMsfoUePg5DJ8pOulTlhHAWjwgRBUE0vHjt2DJ07d0atWrVga2uLIUOGIDU1FU+fPi31HPHx8ejRowdq164NW1tbtG/fHkB+9QQxcSSMiIhE0aLFb/D1vYG0NAc4OKQxAKNiJSQkwMfHBzdv3kT37t0xduxYzJs3Dw4ODvj5558REhKC7OzsEncKePLkCYKCghAUFIQdO3bA2dkZt2/fRlBQkOi5hwzCiIhINHL5IwZfVKLjx4/j8uXLGD9+POLj46FQKLB06VKYmORP5O3Zs0etfbVq1ZCXl6f22NWrV5GamooFCxaodqL59ddfq+YJlIHTkURERCS6rKwspKSk4N9//8WFCxfw2Wef4a233kL37t0xdOhQ1K1bFzk5OVi9ejX+/vtvbN++HRs2bFA7h7e3Nx4/fozY2Fg8fPgQT58+Re3atVGtWjXVzx04cACffPKJSM9SHYMwIjJoBQuDFvfFwqBVq6QC3BVtR4bj8OHDcHNzg7e3N4KDg3HixAmsWrUK3377LUxNTdGsWTMsW7YMCxcuRJMmTbBjxw7Mnz9f7Rxt2rTBmDFj0K9fPzg7O2PRokVwdnbG1q1bsXfvXjRq1AgLFizAkiVLRHqW6mSCcvNFkpzMzEzI5XJkZGTAzs5O7O4Q6R0p1D6iosQuCmqonj9/jqSkJPj4+MDS0lKjn+X/Fc2Vdr3Le/9mThgRSVZlb9aFfzYjwxZpaY5wcEhVy0MSOznX2PAmLj2Ojo4IDw9ncFzFGIQR6TFDHlHQ9ifz0rbIISIGx2JgEEakpwx9+kCbo1hlbZFDhsWQP5yQYWEQRqSnjGmqrbKjWNwix3gY+ocTMiwMwogMgCFPtWljFItb5BgPY/pwQvqPQRiRnjP0qTZtjGIpt8gpHKgawvWhkhnyhxMyDAzCiPScvky1VTRPR1ujWNwix7jo+sMJ885IGxiEEek5fZhqq0yeTmVGsQoX/CxpixwWBjU8uvxwwrwz0hYGYUR6Th+m2sqbf1NSu4qOYrH2kfHS5YcT5p2RtjAIIzIA+jbVVtJNqyBtjWIxwDJOVfXhpKy8M05bSsPJkyfRsWNH/Pfff7C3ty/Xz3h7eyMiIgIRERE66xeDMCI9pa9TbeVNluYoFlWWrj+clJV3dufOHXz//fdlnofTlsDw4cOxbds2vPfee0U25Q4LC8O6deswbNgwbN26VZwO6giDMCI9pY9BiqbJ0lLqO+mHqvxwUlbeWeEAjNOWpfP09MSuXbuwfPlyWFlZAcjfnzEqKgq1a9cWuXe6wSCMSI/pW5CiLys5SX9V5YcTTfLOTp8OQExMIACWyyhJixYtkJiYiOjoaAwaNAgAEB0djdq1a8PHx0fVLisrC5MmTcKuXbuQmZmJV155BcuXL0fLli1Vbb7//ntERETgn3/+QevWrTFs2LAiv+/nn3/GtGnT8Ouvv8LJyQlvv/025s+fDxsbG90/2f9nUnYTIiLtUN60Cip403r48CGSk5ORnJyM1NRUMbpIBsDR0RFubm4lfmnrw4sy70z5mi4p7yw/AHsDyluucgQ4I8NWK/3QhTt3gBMn8v+sSiNHjsSWLVtU32/evBkjRoxQazN58mTs27cP27Ztw4ULF1C3bl0EBQUhLS3/feSff/5B79690aNHD1y8eBGjRo3C1KlT1c6RmJiI4OBg9OnTB5cuXcLu3bvx888/Izw8XPdPsgCOhBFRlSkrWTo6OlqtPXNlSOrKyjvLyLDFsWOBAGRqj0t5BHjTJiA0FFAoABMTYONGICSkan734MGDMW3aNNy6dQsAcPr0aezatQsnT54EADx58gTr16/H1q1b0bVrVwDAF198gZiYGGzatAmTJk3C+vXr4evri6VLlwIA6tevj8uXL2PhwoWq3zN//nwMGjRIlXTv5+eHVatWoX379li/fj0sLS2r5PkyCCMinSuYf6NJsjRzZUiKypt3BhQ/BZ9PWrX8lO7ceRGAAfl/vvceEBQEeHjo/vc7OzujW7du2Lp1KwRBQLdu3eDk5KQ6npiYiJycHLz22muqx8zNzfHqq68iISEBAJCQkIBWrVqpnTcgIEDt+99//x2XLl3Cjh07VI8JggCFQoGkpCQ0bNhQF0+vCAZhRJXA5eflUzhP5+HDh0VGvYj0RXnyztLT07Fnz55i88YAAW+8cUySo2DXr78IwJTy8oAbN6omCAPypySV04Jr164ts31ubi4UCgUUCgWys7PV/g4AJiZFg+DHjx/jvffewwcffFDkWFUuAmAQRnpHKoEPq2ZrprRrUJ66YURSUt7/08VNwQcGHsNrr8XpuIcV4+eXPwVZMBAzNQXq1q26PgQHByM7OxsymQxBQUFqx3x9fVGtWjWcPn0aXl5eyM3Nxb///otffvkFo0ePxr17aahVywexsYfx8OFD1c+dOXNG7TwtWrTAn3/+ibpV+cSKwSCM9IqUAp/KVoGnfNxkmQxdWVPwUqrl5+GRnwP23nv5I2CmpsDnn1fdKBgAmJqaqqYWTU1N1Y7Z2Nhg7NixmDRpEhwcHODm5oZ58+bh+fPneOutkbh3zwVvvhmJL79cj1mz5mPo0Hdw+fJlfPXVV2rnmTJlClq3bo3w8HCMGjUKNjY2+PPPPxETE1Oue4y2MAgjvSLl7UI4mqM5XW+yTCWTyoiyoSpv3tjgwYMld51DQvJzwG7cyB8Bq8oATMnOzq7EYwsWLIBCocCQIUPw6NEjvPTSS9i+fScEwQuADK6utbFw4T4sXz4e//vfF2jevDk+/vhjhIaGqs7x0ksv4dSpU5g+fTratm0LQRDg6+uLfv36VcGze4FBGOktKY2gSKkv+oR1w8QhpRFlQ6WPxZQL8vCo2uCrrEr4+/fvV/3d0tISq1atwqpVq5CdnY2HDx8iK6saUlNfrEBt27Y72rbtDkfHh7CwyIaTkxNGjx6tds6WLVvi6NGjJf7OmzdvVuSpaIRBGOklKY2gSKkv+kaXmyxTyTiVXjWkGmAZIjOzXAAC1EuBCP//uHSxWCvppdJGUIy5L/pCOVVTVrFLKeXKGLKMDFskJXlLungoUWlMTRWwt89AfiAGAALs7TNgaqoo7cdEx5Ew0ktSGkGRUl/0ReGpmlmzHuDmTTN4e+fC3b0lgJaSnqoxJJxKJ0Nhbf0UFhbPkZtrBjOzXMkHYACDMNJTZVVeN9a+6JOCAZabG+DvL2JnjBSn0snQmJoqYGqqP9PoDMJIb2lSeV0XylsFnlNqJFVcGGGYBEEou5GBKK4Qa2XaaUIb15lBGOmV8i77rorAR99XPxFxKt2wmJubAwCePn0KKysrkXtTNczMzFCzZk0oCpf5L8DExARmZtoPd54+fQrgxXWvCAZhpFekFvgwwCJ9xql0w2Jqagp7e3vcv38fAGBtbQ2ZTFbGTxk+hUKB3FztrZIUBAFPnz7F/fv3YW9vX6SgrCYYhJHeYeBDVDmcSjdcrq6uAKAKxEh37O3tVde7omSCMU0e65nMzEzI5XJkZGSUWj2YiEhTrJhv2PLy8pCTkyN2NwyWubl5qSNg5b1/cySMiMgIMcAybKamppWaJqOqwSCMiKgSOKJERBXFIIyIqIK4ByMRVQaDMCPBT+tE2sc9GImoMhiEGQF+WiciIpIebuBtBAp/Ci9ps15+WiciIqo6HAkzMtysl0h3MjJskZbmCAeHVBY8JaIyMQgzIlLYrJe5aWSo+AGHKoPvjcaJQZgREXuzXuamkaGSwgcc0l98bzRezAkzIsrNeguqys16mZtGhqq0DzhEZeEqW+PFkTAjIqXNejl1Q4ZAubei8gNOwUCs4Acc7sFIRMVhEGZkStust6pw6kY/MWelKEdHR4SHhyM7Oxu1amViyhQ58vJkMDUVsHBhJgYOHGCU14Uqhws8jAeDMCNQ+FO4XP6o2P/YVfVpXezcNNIcc1ZKpny+EyYA/foBN24AdevK4OFhD8BezK6RHuIsgXFhEGYECn5aL0lVflova+qGpIc5K+Xj4ZH/RVQRnCUwPgzCjISURieklJtGRCQVnCUwPgzCSBRSyE2jimPOCpH2cZbA+DAIoyojtdw0qhjmrBBpl/I9r6xZAr43Gh4GYVRlpJabRppjzgqR9hV+b5w16wFu3jSDt3cu3N1bAmjJ90YDxSCMqhTfRPQbc1aIdKPge6ObG+DvX3w7looxLAzCiKjcmLNCJB6WijE83LaIiMpUOGdFuf0Vc1aIqg5LxRgejoQRUZmYs0IkPVylrP8YhBFRuZQ3Z4WIdI+rlA0DgzAiIiI9UtlVykzulw4GYURERHqkMquUmdwvLUzMJyIi0iPKVcoFlXeVMpP7pYVBGBERkR4pa5WyJjIybJGU5I2MDFttd5PKgdORREREeqBgCZjS9t8tb6kYJveLj0EYERGRHtDm1m9lJfc/fPiwUuen8mEQRkSkJ7iqjbT171tWcn90dHSJP8ukfe3Rm5ywnj17onbt2rC0tISbmxuGDBmCu3fvqo7PmTMHMpmsyJeNjY3aefbu3YsGDRrA0tISTZs2xffff692XBAEzJo1C25ubrCyskJgYCCuX7+u1iYtLQ2DBg2CnZ0d7O3tERISgsePH6u1uXTpEtq2bQtLS0t4enpi0aJFWr4iRGRMlKvaNm7cWOLXmjVrkJqaKnZXSQ9URXI/lU1vgrCOHTtiz549uHbtGvbt24fExET07dtXdXzixIlITk5W+2rUqBHeeecdVZszZ85gwIABCAkJwW+//YZevXqhV69euHLliqrNokWLsGrVKmzYsAHnzp2DjY0NgoKC8Pz5c1WbQYMG4Y8//kBMTAwOHjyIH3/8EaGhoarjmZmZ6NKlC7y8vBAfH4/Fixdjzpw52Lhxo46vEhEZKq5qI20qT3I/k/Z1TyYIgiB2JyriwIED6NWrF7KysmBubl7k+O+//47mzZvjxx9/RNu2bQEA/fr1w5MnT3Dw4EFVu9atW6N58+bYsGEDBEGAu7s7JkyYgIkTJwIAMjIy4OLigq1bt6J///5ISEhAo0aNcP78ebzyyisAgMOHD+PNN9/EnTt34O7ujvXr12P69OlISUlRJUhOnToV+/fvx9WrV8v9HDMzMyGXy5GRkQE7O7sKXysi0n/Jycnl+iAXGhoKNze3KugR6aPCdcLytz4qmtxfWtI+X2NlK+/9Wy9zwtLS0rBjxw60adOm2AAMAL788kvUq1dPFYABQFxcHCIjI9XaBQUFYf/+/QCApKQkpKSkIDAwUHVcLpejVatWiIuLQ//+/REXFwd7e3tVAAYAgYGBMDExwblz5/D2228jLi4O7dq1U1uhEhQUhIULF+K///5DjRo1tHEZiIiINFJacv/Dhw8RHR1d6Yr8VH56FYRNmTIFa9aswdOnT9G6dWu1Ea2Cnj9/jh07dmDq1Klqj6ekpMDFxUXtMRcXF6SkpKiOKx8rrU3NmjXVjpuZmcHBwUGtjY+PT5FzKI+VFIRlZWUhKytL9X1mZmax7YiIuHkzVVRZSfWVqchPmhE1J2zq1KnFJtMX/Co4fTdp0iT89ttvOHr0KExNTTF06FAUN5v6zTff4NGjRxg2bFhVPp1Kmz9/PuRyuerL09NT7C4RkQRduPAyVqyIwLZtw7BiRQQuXHhZ7C6RAalM0j5pRtSRsAkTJmD48OGltqlTp47q705OTnByckK9evXQsGFDeHp64uzZswgICFD7mS+//BLdu3cvMqLl6uqKe/fuqT127949uLq6qo4rHys4333v3j00b95c1eb+/ftq58jNzUVaWpraeYr7PQV/R3GmTZumNl2amZnJQIzIiBUsSaGs28SpItI1ZdJ+4Zwwvr60T9QgzNnZGc7OzhX6WYUiP0ovOH0H5Od1nThxAgcOHCjyMwEBAYiNjUVERITqsZiYGFUQ5+PjA1dXV8TGxqqCrszMTJw7dw5jx45VnSM9PR3x8fHw9/cHABw/fhwKhQKtWrVStZk+fTpycnJUOWsxMTGoX79+qflgFhYWsLCwqMDVICJDU9JGy5wqIl3RdkV+Kpte5ISdO3cO58+fx+uvv44aNWogMTERM2fOhK+vb5FRsM2bN8PNzQ1du3Ytcp4PP/wQ7du3x9KlS9GtWzfs2rULv/76q2rFkUwmQ0REBD799FP4+fnBx8cHM2fOhLu7O3r16gUAaNiwIYKDgzF69Ghs2LABOTk5CA8PR//+/eHu7g4AGDhwIObOnYuQkBBMmTIFV65cwcqVK7F8+XLdXigiMhgllZpQThUVDMQKThXxBkkVpc2K/FQ+ehGEWVtbIzo6GrNnz8aTJ0/g5uaG4OBgzJgxQ23kSKFQYOvWrRg+fDhMTU2LnKdNmzaIiorCjBkz8NFHH8HPzw/79+9HkyZNVG0mT56MJ0+eIDQ0FOnp6Xj99ddx+PBhWFpaqtrs2LED4eHh6Ny5M0xMTNCnTx+sWrVKdVwul+Po0aMICwuDv78/nJycMGvWLLVaYkREFVF4qsjERMCiRZkYOHAAb5BUaXz9VC29rRNmDFgnjMh4lVUXTFnfady4rvD3dymxnVi4xRIZM4OuE0ZEZGwKl6RQfrm7K8r+4SpWUj5bYdyDkIwdgzAiIokrrXq5FHGLJaLy0Zu9I4mIjFFJJSm4nx+R/mMQRkQkYaWVpCAi/cbpSCIiCVKWmpBiSYqyku4zMjIKfc8tloiKwyCMiEiCCtZsqlUrE1OmyJGXJ4OpqYCFC8UrSVHepHslfctnI6pKDMKIiCRKGWBNmAD06wfcuAHUrSuDh4c9AHtR+qRJMj23WCIqHYMwIiI94OGR/6VPuMUSUekYhBFJEAtdkr4oLd9LivlsRFLCIIyMkpSDHBa6JH1RWr7Xu+++C3t7e0nlsxFJDYMwMjpSD3JY6JL0QVn5Xvb29nBzc5NUPhuR1DAII6NTOHgpaTpFKkEOl/eTFGmS76WP+WxEVYFBGBk1qS+fl3r/yHiVle9F0iHl9AtjxyCMjJbUl89LvX9knJTJ9HL5I/TocbDIhwTla5NJ99Ig9fQLY1ehIOzEiRPo2LGjtvtCVKWkvnxe6v0j41SwiCwAzJr1ADdvmsHbOxfu7i0BtOTIioQwx1TaKhSEBQcHw8PDAyNGjMCwYcPg6emp7X4R6ZzUp1Ok3j8yXgUDLDc3wN9fxM6QRphjKi0V2sD733//RXh4OL7++mvUqVMHQUFB2LNnDyNp0ivK6RSZTAEARaZTxCb1/hGRfrlw4WWsWBGBbduGYcWKCFy48LLYXTJ6FRoJc3Jywvjx4zF+/HhcuHABW7Zswfvvv4/3338fAwcOREhICJo1a6btvhJpXYsWv8HX9wbS0hzg4JAmiQCnYC5Naf1jzg0RlRdzTKWp0on5LVq0gKurKxwdHbFgwQJs3rwZ69atQ0BAADZs2IDGjRtro59EWlM4eJHLHxX7JiRWkFM456Y4zLkhQ8cVfdrFHFNpqnAQlpOTg2+//RabN29GTEwMXnnlFaxZswYDBgzAgwcPMGPGDLzzzjv4888/tdlfokrThyCHNxcyZlzRp33MMZWmCgVh48aNw86dOyEIAoYMGYJFixahSZMmquM2NjZYsmQJ3N3dtdZRIm3iGzeRdHFFn/aVVVKExFGhIOzPP//E6tWr0bt3b1hYWBTbxsnJCSdOnKhU54iIqHicrqPyYI6ptFUoCIuNjS37xGZmaN++fUVOT0REpTC26TqWVag4fUi/MGYVzgm7du0aVq9ejYSEBABAw4YNMW7cONSvX19rnSMioqKMabqOW3dVHgMs6apQnbB9+/ahSZMmiI+PR7NmzdCsWTNcuHABTZo0wb59+7TdRyIiKkVGhi2SkryRkWErdle0qqSyCob2PMl4VWgkbPLkyZg2bRo+/vhjtcdnz56NyZMno0+fPlrpHBGRVEklJ8uQR4pYVoEMXYWCsOTkZAwdOrTI44MHD8bixYsr3SkiIimTSk6WoRfgZFkFMnQVmo7s0KEDfvrppyKP//zzz2jbtm2lO0VEJGVSyckqbaRInylX6pW1dRdX9JG+q9BIWM+ePTFlyhTEx8ejdevWAICzZ89i7969mDt3Lg4cOKDWlojIkIm1es9QR4oKr+ibNesBbt40g7d3LtzdWwJoqTbdK5WpYSJNVSgIe//99wEA69atw7p164o9BgAymQx5eXmV6B4RkbSJmZNlyAU4CwZNbm6Av3/x7aQyNUxUERUKwhQKhbb7QUSkd8TKyWIBzhekMjUsBo4A6r9Kb+BNRGSsxFq9xwKcJTOWwq6GMALIIFKDIGzVqlXlPukHH3xQoc4QEekTMXOyDP3mVBGGXK6jsMLBS0nBp1RHAAsHkSX1X8pBpDaUOwhbvnx5udrJZDIGYURkFAw5J0vfGHq5jtLoY/BZMDgsrf9SDSK1pdxBWFJSki77QUSkN5iTJT3GWthV34NPfe9/ZTEnjIhIQ8zJkh5DLddRFn0PPvW9/5VV4SDszp07OHDgAG7fvl3kjWjZsmWV7hiR2Aomjd69a4KkJDP4+OTC3T1/dTBvssaN//bSYqxTw/oefOp7/yurQkFYbGwsevbsiTp16uDq1ato0qQJbt68CUEQ0KJFC233kajKFUwaLS1fwdCTRvUdV18ZPmOfGtb34FPf+19ZFQrCpk2bhokTJ2Lu3LmwtbXFvn37ULNmTQwaNAjBwcHa7iNRlVPeuMvKVzD0pFF9ZghL+KlsnBouPfjUB/re/8qoUBCWkJCAnTt35p/AzAzPnj1D9erV8fHHH+Ott97C2LFjtdpJIrEYe76CPtP3JfxUfoYcYJWk8MieXP6o2PckfRkBLKn/hq5CQZiNjY3qjcvNzQ2JiYlo3LgxAODhw4fa6x2RyIw9X8FQ6OMSfqLS6PsIYHmDQ30JIiuqQkFY69at8fPPP6Nhw4Z48803MWHCBFy+fBnR0dGqDb2JDIGx5ysYAmNfAk+GS6oBVnnoexCpLRUKwpYtW4bHjx8DAObOnYvHjx9j9+7d8PPz48pIMjjGnK9gCDilTCRNhh5glUeFgrA6deqo/m5jY4MNGzZorUNEUmSs+QqGgFPKRCRVlSrWmp2djfv370OhUKg9Xrt27Up1isRz5w5w/Trg5wd4eIjdG6LK45QyGQOWY9FPFQrC/vrrL4SEhODMmTNqjwuCAJlMhry8PK10jqrWpk1AaCigUAAmJsDGjUBIiNi9EgeTRg2LMU4p86ZsPFiORX9VKAgbMWIEzMzMcPDgQbi5uUEmk2m7X1SFUlNTcfNmLkJDa0KhyP+3VCiA994T0Lz5fXh7mxndf1wmjeo/Q1vCrwnelI0Ly7HorwoFYRcvXkR8fDwaNGig7f5QFVO+WScleUOhGKZ2LC9PhtWrf4CPzy2jfLM2tudraIw5kOZN2XixHIt+qVAQ1qhRI9YDMxDKN+Gykpf5Zk36yBADLE3xpmw8WI5F/5iU3SRfZmam6mvhwoWYPHkyTp48idTUVLVjmZmZuuwv6YgyeVkmy19kweRlIv1X0k05I8NW5J6RLpRWjoWkqdwjYfb29mq5X4IgoHPnzmptmJiv34wxeZnIkLFGmnExxHIsBReY3L1rgqQkM/j45MLdPX/AQN9TCsodhJ04cUKX/SCJYD0sIsNhiDdlKpmhlWMpuMCktGl1fc5ZLncQ1r59e9Xfb9++DU9PzyKrIgVBwD///KO93hERUYUZ2k2ZymZIMxrKEbCyct30OWe5Qon5Pj4+SE5ORs2aNdUeT0tLg4+PD6cjiYgkwpBuylQ8Qy/HYsjT6hUKwpS5X4U9fvwYlpaWle4UEek3FgoVl6HflEmdoZdjMeRpdY2CsMjISACATCbDzJkzYW1trTqWl5eHc+fOoXnz5lrtIOkWK8OTthUuFFpSjSp9zuOQOkO/KVNRhvxvacjT6hoFYb/9lp8EJwgCLl++rHZjrlatGpo1a4aJEydqt4ekU3yzJm0r+FoqLZlWn/M49AH/z5IhMdRpdY2CMOUKyREjRmDlypWws7PTSaeoavHNmnSBhSOJSJsMcfV+uYu1FrRlyxYGYERUKhaOJCIqXYUS8588eYIFCxYgNjYW9+/fh0KhUDv+999/a6VzRKS/DDmZloh0zxhylisUhI0aNQqnTp3CkCFD4ObmVuxKSdI/d+4A168Dfn6Ah4fYvSF9Z8jJtESke8aQs1yhIOyHH37AoUOH8Nprr2m7PySSTZuA0FBAoQBMTICNG4GQELF7RfrOUJNpiahq6HOAVR4VygmrUaMGHByY12Eo7tx5EYAB+X++917+40SVJZc/go/PLQZgRESFVCgI++STTzBr1iw8ffpU2/2hKpaamoqzZ1NRKK0PeXnAuXOpSE1NFadjpLeMIY+DiEgbKjQduXTpUiQmJsLFxQXe3t4wNzdXO37hwgWtdI50S1lUMyPDFjJZRJEE6tOnt+HKlUcsqvn/mDNXPsaQx0FEpA0VCsJ69eql5W6QGJQ3ybISqI25qKZy+52oKCtMniyHQiGDiYmARYsyMHDgM4MOJgpuPXT3rgmSkszg45MLd/f8YdPSnruhXhMiIm2qUBA2e/ZsbfeDRMYE6qIKjhSuWBEBQchfBaxQyDBpkh3+/Xcz5HLDHCksuPVQaVXvDfG5K3H/SyLSNY2CsF9++QX+/v4wNTUt9nhWVha+/fZbvPvuu1rpHFUtQ6xGXBnKG3BpRUfl8kcGOVKofE5lVb03xOcOcP9LIqoaGiXmBwQEqCVq29nZqRVmTU9Px4ABA7TXuwJ69uyJ2rVrw9LSEm5ubhgyZAju3r2r1ubIkSNo3bo1bG1t4ezsjD59+uDmzZtqbU6ePIkWLVrAwsICdevWxdatW4v8rrVr18Lb2xuWlpZo1aoVfvnlF7Xjz58/R1hYGBwdHVG9enX06dMH9+7dU2tz+/ZtdOvWDdbW1qhZsyYmTZqE3NxcrVwLqlrKoqMFGUvRUWOtel94/8sVKyKwbdswrFgRgQsXXi62HRGRpjQKwgRBKPX7kh7Tho4dO2LPnj24du0a9u3bh8TERPTt21d1PCkpCW+99RY6deqEixcv4siRI3j48CF69+6t1qZbt27o2LEjLl68iIiICIwaNQpHjhxRtdm9ezciIyMxe/ZsXLhwAc2aNUNQUBDu37+vajN+/Hh899132Lt3L06dOoW7d++q/Z68vDx069YN2dnZOHPmDLZt24atW7di1qxZOrk2pFvKnDllIGZMRUeNOQAFSh4JzMiwFblnRGQIKpQTVhpdVc8fP3686u9eXl6YOnUqevXqhZycHJibmyM+Ph55eXn49NNPYWKS/4Y5ceJEvPXWW6o2GzZsgI+PD5YuXQoAaNiwIX7++WcsX74cQUFBAIBly5Zh9OjRGDFiBABgw4YNOHToEDZv3oypU6ciIyMDmzZtQlRUFDp16gQgfy/Nhg0b4uzZs2jdujWOHj2KP//8E8eOHYOLiwuaN2+OTz75BFOmTMGcOXO4NL+cpLQa0Vhz5oy96n1ZU9FERJVRoTphYktLS8OOHTvQpk0bVXkMf39/mJiYYMuWLcjLy0NGRga2b9+OwMBAVZu4uDgEBgaqnSsoKAhxcXEA8qcW4uPj1dqYmJggMDBQ1SY+Ph45OTlqbRo0aIDatWur2sTFxaFp06ZwcXFR+z2ZmZn4448/SnxeWVlZyMzMVPsyVps2AV5eQKdO+X9u2iR2j4y36GiLFr8hImIFhg3bioiIFaqkfGNg7COBRKRbGgdhf/75Jy5duoRLly5BEARcvXpV9X1pAYY2TJkyBTY2NnB0dMTt27fx7bffqo75+Pjg6NGj+Oijj2BhYQF7e3vcuXMHe/bsUbVJSUlRC4wAwMXFBZmZmXj27BkePnyIvLy8YtukpKSozlGtWjXY29uX2qa4cyiPlWT+/PmQy+WqL09Pz3JemYqRYlHN1NRUxMffQ2ioUKiCv4D4+HssHisSYw1AjXkqmoh0T+PpyM6dO6vlfXXv3h1A/jSkIAgaTUdOnToVCxcuLLVNQkICGjRoAACYNGkSQkJCcOvWLcydOxdDhw7FwYMHIZPJkJKSgtGjR2PYsGEYMGAAHj16hFmzZqFv376IiYnRi03Gp02bhsjISNX3mZmZOg3EpFZUU7kiLSnJGwrFMLVjeXkyrF79A3x8bnFFGlUpY52KZokOIt3TKAhLSkrS6i+fMGEChg8fXmqbOnXqqP7u5OQEJycn1KtXDw0bNoSnpyfOnj2LgIAArF27FnK5HIsWLVK1/9///gdPT0+cO3cOrVu3hqura5FVjPfu3YOdnR2srKxgamoKU1PTYtu4uroCAFxdXZGdnY309HS10bDCbQqvqFSeU9mmOBYWFrCwsCj1emiblN5ElW/4yimgwhX8lVNAVbUiTYojhVXFmJ97cYytfAtLdBBVDY2CMC8vL41O/v777+Pjjz+Gk5NTscednZ3h7Oys0TmVFP8/V5WVlQUAePr0qSohX0lZz0zZNiAgAN9//71am5iYGAQEBADIv6H4+/sjNjZWtSuAQqFAbGwswsPDAeTnnpmbmyM2NhZ9+vQBAFy7dg23b99WnScgIADz5s3D/fv3UbNmTdXvsbOzQ6NGjSr0fI2JVJLBpTZSWJWM+bmnpqYiPT0dQMnBh5KhBqGFS3SUVKyXJTqIKkfrqyML+t///oeJEyeWGISV17lz53D+/Hm8/vrrqFGjBhITEzFz5kz4+vqqAp9u3bph+fLl+Pjjj1XTkR999BG8vLzw8sv5dX3GjBmDNWvWYPLkyRg5ciSOHz+OPXv24NChQ6rfFRkZiWHDhuGVV17Bq6++ihUrVuDJkyeq1ZJyuRwhISGIjIyEg4MD7OzsMG7cOAQEBKB169YAgC5duqBRo0YYMmQIFi1ahJSUFMyYMQNhYWFVPtKlr6QyBWSIQUZ5GeNzL+9OAf369YOzs7PBX6OyivUSUeXodHWktmqGWVtbIzo6Gp07d0b9+vUREhKCl156CadOnVIFNZ06dUJUVBT279+Pl19+GcHBwbCwsMDhw4dhZWUFID95/9ChQ4iJiUGzZs2wdOlSfPnll6ryFED+m+uSJUswa9YsNG/eHBcvXsThw4fVEu2XL1+O7t27o0+fPmjXrh1cXV0RHR2tOm5qaoqDBw/C1NQUAQEBGDx4MIYOHYqPP/5YK9fDWBhrMjiJp6ydApT1weRyucEHYIDxFuslqio6HQnTlqZNm+L48eNltuvfvz/69+9fapsOHTrgt99KX2IfHh6umn4sjqWlJdauXYu1a9eW2MbLy6vI1CcR6QfWB8tXVn4mEVWOXtYJIyqvO3eAEyfy/yQqL9YHy8cSHUS6pRcjYUQVsWkTEBqaX2fMxATYuBEICRG7V6QPpLI4RAqkkp9JZIgYhJFkaKssQmpqKm7ezEVoaE0oFPn14ZQFX5s3vw9vbzOjyOehymHw8YKxleggqio6DcIGDx4MOzs7Xf4KMiDaKIvAgq+kTcYafLBOHFHVqFAQplAoitTkUj5+584d1K5dGwCwfv36yvWOjE5lAyOpFXwl0kfGXCeOqCpplJifmZmJd999FzY2NnBxccGsWbOQl5enOv7gwQP4+PhovZNEmmJCMVUER4BecHR0hJubW4lfDMCIKk+jkbCZM2fi999/x/bt25Geno5PP/0UFy5cQHR0tOpNSVu1wYgqizk9pCmOABFRVdIoCNu/fz+2bduGDh06AAB69eqFbt26oUePHjhw4AAA6MVG2WQ8jDWnhyqOAZZ+K7jx+N27JkhKMoOPTy7c3fNHxRlEk5RoFIQ9ePBAbf9IJycnHDt2DEFBQXjzzTfx5Zdfar2DRERE5VHebae4MIekQqOcsNq1ayMhIUHtMVtbWxw9ehTPnj3D22+/rdXOERERlVd5t53iwhySCo2CsC5dumDLli1FHq9evTqOHDkCS0tLrXWMiIioIrjnJekLjaYj586di7t37xZ7zNbWFjExMbhw4YJWOkZUEVzdRlWtYA5ScZiDVPW45yXpC42CsBo1aqBGjRolHre1tUX79u0r3SmiiuLqNqpKBXOQgPxpsLQ0Rzg4pKotCHn33Xdhb29f7Dn4etQ+bjtVMi5ckBaNi7Xm5uZi+fLl2LlzJ/766y8AQL169TBw4EB8+OGHMDc313oniTTBNxCqKgWD/dISwffs2QOg5CCNieLaxxI1RenbwoU7d4Dr1wE/P8DDQ+ze6IZGQdizZ8/wxhtvIC4uDoGBgWjXrh0AICEhAVOmTMGBAwdw9OhR5oYRkVEpKRHc1/eG6uZf2k2PieK6wRI16spauKB8vYr5elSO1EVFWWHyZDkUChlMTAQsWpSBgQOfGdxInUZB2IIFC/DPP//gt99+w0svvaR27Pfff0fPnj2xYMECzJkzR5t9JBKFMXwKI+0oLRFcLn9UriBNF5ivRsUp6/UqFuVIXUaGLVasiIAg5NcdVShkmDTJDv/+uxly+SPJjNRpg0ZB2K5du7Bs2bIiARgANGvWDEuWLMH06dMZhJHeMrZPYaQdZSWCi3HTK2++miHd0Lgwp3ykunBB+YGhrP8vhjRyrFEQduvWLbz66qslHm/dujVu375d6U4RicEYP4WRdpSVCC7GTa+8+WqGdEPjwpzykfrCBakGibqgURBmZ2eH+/fvw9PTs9jjKSkpsLW11UrHiKqaMX4KI+0pLRFczJueWFOhYjH2AKu8pLxwQepBojZpFIR17NgRn332Gfbt21fs8QULFqBjx45a6RiRWIzpUxhpV2mJ4GLd9KSa/0Pik/LCBSkHidqkURA2e/ZstGrVCq1bt0ZkZCQaNGgAQRCQkJCA5cuX488//8TZs2d11VeiKmFMn8KocjTNLRLjpscPFaSvpBwkaotGQVijRo0QExODkJAQ9O/fHzJZfs6MIAho0KABjh49isaNG+uko0RVyVg+hVHllJWDlJGRgd27d5d5Hl0miuvDhwqu4qwaXLggPRoXa23dujX++OMPXLx4Ua1Ya/PmzbXdNyJRGcOnMKq80oIDNzc3SSSKS/lDhTGu4hQLFy5Ij8ZBWGZmJqpXr47mzZurBV4KhQKPHz+GnZ2dNvtHRKTXpHJDk+qHCmNcxSkmqbwei2OMI3UaBWHffPMNpkyZgosXL8La2lrt2LNnz9CyZUssWbIEPXr00GoniYhIM/p2QzO2VZxUlDGO1GkUhK1fvx6TJ08uEoABgI2NDaZMmYI1a9YwCCO9pG83LalgPo806dsNjas4CZD2SJ0uaBSEXblyBevWrSvxeLt27TBjxoxKd4pIDPp205IC5vNImz5dc67iJGOkURD233//ITc3t8TjOTk5+O+//yrdKSKx6NNNSwqYz0Paog+rOIm0TaMgzNvbG7/++isaNGhQ7PFff/0VXl5eWukYEekP5vOQNkh5FSeRLpiU3eSF3r17Y/r06bh3716RYykpKZgxYwb69Omjtc4RkX4oLZ+HSBNy+SP4+NxiAEZGQaORsKlTp+Lbb7+Fn58fBg8ejPr16wMArl69ih07dsDT0xNTp07VSUeJSLqYz0MVxQUxZMw0CsJsbW1x+vRpTJs2Dbt371blf9nb22Pw4MGYN28eN/AmMkLM59EeY1ttygUxZMw0LtYql8uxbt06rF27Fg8fPoQgCHB2dlZtYVTQ6dOn8corr8DCwkIrnSUi6WI+T+UZ62pTQ3ouRJrQOAhTkslkcHZ2LrVN165dcfHiRdSpU6eiv4aI9IhUq7LrC642JTIuGiXma0oQBF2enohExnwe3ShptWlGBtM9iAxJhUfCiIiYz6MbrB5PZBwYhBFRpTDA0j6uNiUyDgzCiIgkhqtNydAZ2yrgkug0CCtuxSQREZWttNWmDx8+VGtrLDcsMgzGugq4ODoNwpiYT0RUcSWtNo2Oji7ymDHcsMgwcBXwCzoNwh494tC5MeHwMlHllGcVaUmjBsZwwzJUxvreyT1nNQzCOnXqVK52x48fr1BnSH9xeJmo8opbbfrw4UPVyFdpowaknwq/d5bEEN87uQpYwyDs5MmT8PLyQrdu3WBubq6rPpEe4vAykXaUdKPlqIFhKvyeaEwjnVwFrGEQtnDhQmzZsgV79+7FoEGDMHLkSDRp0kRXfSM9xBsFGSqxp4w4amD4jG2kk6uANQzCJk2ahEmTJiEuLg6bN2/Ga6+9hvr162PkyJEYOHAg7OzsdNVPqqSquoHwRkGGSApTRhw1MGzG+gHW2PecrVBifkBAAAICArBy5Urs3bsXa9euxcSJE3H37l0GYhJUlflavFGQIZLClBFHDQybMX+ANeY9Zyu1OvLChQs4deoUEhIS0KRJE+aJSVRV5mvxRkGGTswpI2MfNTBk+vwBVtOZFu45+4LGQdjdu3exdetWbN26FZmZmRg8eDDOnTuHRo0a6aJ/pEVVNdzNGwUZKjGmjArfiEoaNTCGG5Yh09cPsBWZaeGesy9oFIS9+eabOHHiBLp06YLFixejW7duMDPjzkf6oiqHu415eJkMlxhTRrxhGQ99/ABb0ZkWvl7zaRRBHT58GG5ubrh9+zbmzp2LuXPnFtvuwoULWukcaZcuh7s5vEz6rrQpFeU2QWJNGfGGZbgMZaTTWBcWVJZGQdjs2bN11Q+qAroc7uanddJn5V39qK9TRiRdhvLeacwLCyqDQZiR0eVwt9TfJIhKosmiFH2cMiJpM4T3Tn1eWCAmrSR0nTp1Ck+ePEFAQABq1KihjVOSDjFfi6h0JSUXK+nrlJGuiF3IlsTHUeKK0bhi/uPHj/HJJ58AAARBQNeuXXH06FEAQM2aNREbG4vGjRtrv6dUYczXIiq/0pKLe/fuDScnp2J/zlgDDSkUsqUXxAyIOUqsOY2CsN27d2PKlCmq77/++mv8+OOP+Omnn9CwYUMMHToUc+fOxZ49e7TeUao4Q8k5INK1spKLnZyc4ObmJnIvpaW8U7mGuPeh1EghIOZMi2Y0CsKSkpLw0ksvqb7//vvv0bdvX7z22msAgBkzZuCdd97Rbg9JKxhgEZWNycWVV9ZULumOGAExZ1oqR6MgLDc3FxYWFqrv4+LiEBERofre3d1dtZSbiEjfMLm4coxtA2qpq4qAmDMtlaNREObr64sff/wRderUwe3bt/HXX3+hXbt2quN37tzhhSYivcXk4opjnShpqcqAmPf9itMoCAsLC0N4eDh++uknnD17FgEBAWrbFR0/fhwvv/yy1jtJRKRLBadKSksu5pRKyXQ5lcvVl5phQKw/NArCRo8eDVNTU3z33Xdo165dkbphd+/exciRI7XaQSIiXeOUSuXpaipXCsnm+oa5jfpD4zphI0eOLDHQWrduXaU7REQkBt7AK0dXU7lcfak55jbqD+6+TUREFVbVU7lcfVk25jbqD42CsJycHEyfPh3R0dFwcHDAmDFj1EbF7t27B3d3d+Tl5Wm9o0REJD1VOZXL1ZelY26j/tEoCJs3bx6++uorTJw4Eenp6YiMjMS5c+fw+eefq9oIgqD1ThIRkXRVxVQuk83LxtxG/aNRELZjxw58+eWX6N69OwBg+PDh6Nq1K0aMGIHNmzcDAGQymfZ7SVWGq5CISIqYbF4+fH/WLxoFYf/++y+aNGmi+r5u3bo4efIkOnXqhCFDhmDRokVa7yBVHa5CIiKpYrI5GSKTspu84OrqisTERLXHatWqhRMnTuD8+fMYPny4NvtGVYyrkIhIqpTJ5jKZAgCYbE4GQaMgrFOnToiKiiryuLu7O44fP46kpCStdaywnj17onbt2rC0tISbmxuGDBmCu3fvqrXZs2cPmjdvDmtra3h5eWHx4sVFznPy5Em0aNECFhYWqFu3LrZu3Vqkzdq1a+Ht7Q1LS0u0atUKv/zyi9rx58+fIywsDI6OjqhevTr69OmDe/fuqbW5ffs2unXrBmtra9SsWROTJk1Cbm5u5S8EEZERKZxsHhGxAsOGbUVExAq1pHxtJ5unpqYiOTm5xK/U1FSt/j4yThpNR86cORNXr14t9litWrVw6tQpxMTEaKVjhXXs2BEfffQR3Nzc8O+//2LixIno27cvzpw5AwD44YcfMGjQIKxevRpdunRBQkICRo8eDSsrK4SHhwPI34C8W7duGDNmDHbs2IHY2FiMGjUKbm5uCAoKAgDs3r0bkZGR2LBhA1q1aoUVK1YgKCgI165dQ82aNQEA48ePx6FDh7B3717I5XKEh4ejd+/eOH36NAAgLy8P3bp1g6urK86cOYPk5GQMHToU5ubm+Oyzz3RyfaSGuWVEpA1iJJszNYOqikzQ0+WMBw4cQK9evZCVlQVzc3MMHDgQOTk52Lt3r6rN6tWrsWjRIty+fRsymQxTpkzBoUOHcOXKFVWb/v37Iz09HYcPHwYAtGrVCi1btlT9B1QoFPD09MS4ceMwdepUZGRkwNnZGVFRUejbty8A4OrVq2jYsCHi4uLQunVr/PDDD+jevTvu3r0LFxcXAMCGDRswZcoUPHjwoNyf2DIzMyGXy5GRkQE7OzutXLfSJCcnY+PGjWW2Cw0NhZubW4nH+QZGRPpMW++Fho4ftktW3vt3hYq17t27Fzt37sRff/0FAKhXrx4GDhyoCkp0LS0tDTt27ECbNm1gbm4OAMjKyoK1tbVaOysrK9y5cwe3bt2Ct7c34uLiEBgYqNYmKCgIERERAPJzneLj4zFt2jTVcRMTEwQGBiIuLg4AEB8fj5ycHLXzNGjQALVr11YFYXFxcWjatKkqAFP+nrFjx+KPP/7Qm/01K1oUkbllRGRIWCC2KH7Y1g6NgjCFQoEBAwZg7969qFevHho0aAAA+OOPP9CvXz+888472Llzp87KVEyZMgVr1qzB06dP0bp1axw8eFB1LCgoCOPHj8fw4cPRsWNH3LhxA0uXLgWQ/6nG29sbKSkpaoERALi4uCAzMxPPnj3Df//9h7y8vGLbKKdhU1JSUK1aNdjb2xdpk5KSompT3DmUx0qSlZWFrKws1feZmZnluSw6waKIRC/wE7/x4nth8fhhWzs0CsJWrlyJY8eO4cCBA6paYUoHDhzAiBEjsHLlStXIUlmmTp2KhQsXltomISFBFexNmjQJISEhuHXrFubOnYuhQ4fi4MGDkMlkGD16NBITE9G9e3fk5OTAzs4OH374IebMmQMTE43WH4hm/vz5mDt3rtjdYFFEogL4id948b2w/DhaWDEaBWFbtmzB4sWLiwRgQP7qxUWLFmkUhE2YMKHMshZ16tRR/d3JyQlOTk6oV68eGjZsCE9PT5w9exYBAQGQyWRYuHAhPvvsM6SkpMDZ2RmxsbFq53B1dS2yivHevXuws7ODlZUVTE1NYWpqWmwbV1dX1Tmys7ORnp6uNhpWuE3hFZXKcyrbFGfatGmIjIxUfZ+ZmQlPT89Sr482KXPVyiqKyC0vyJjwE7/xYoHY8uFoYcVpFIRdv369SE5VQYGBgaqViOXh7OwMZ2dnTbqgolDk14opOH0HAKampqhVqxYAYOfOnQgICFD9joCAAHz//fdq7WNiYhAQEAAgPwjx9/dHbGwsevXqpfo9sbGxqufl7+8Pc3NzxMbGok+fPgCAa9eu4fbt26rzBAQEYN68ebh//75qRWVMTAzs7OzQqFGjEp+ThYUFLCwsKnQ9tEG5CunmzVxs3y5AoXgxrWxqKmDcuK7w9jbT+NM+PyERkT5igdiycbSwcjQKwqysrJCeno7atWsXezwzMxOWlpZa6VhB586dw/nz5/H666+jRo0aSExMxMyZM+Hr66sKfB4+fIivv/4aHTp0wPPnz7Flyxbs3bsXp06dUp1nzJgxWLNmDSZPnoyRI0fi+PHj2LNnDw4dOqRqExkZiWHDhuGVV17Bq6++ihUrVuDJkycYMWIEAEAulyMkJASRkZFwcHCAnZ0dxo0bh4CAALRu3RoA0KVLFzRq1Ei1i0BKSgpmzJiBsLAwUYOs4hSX6+LuDixalIEpU+TIy5PB1BT4/HMZ/P1dSjhLyfgJiQwNP1QYD2WB2MLvYfx3f4GjhZWjURAWEBCA9evXY/369cUeX7t2rSoo0iZra2tER0dj9uzZePLkCdzc3BAcHIwZM2aoBTXbtm3DxIkTIQgCAgICcPLkSbz66quq4z4+Pjh06BDGjx+PlStXwsPDA19++aWqRhgA9OvXDw8ePMCsWbOQkpKC5s2b4/Dhw2qJ9suXL4eJiQn69OmDrKwsBAUFYd26darjpqamOHjwIMaOHYuAgADY2Nhg2LBh+Pjjj7V+bSqjrFyXDz6wRVqaAyZMeAtNm9bQ+Pz8hESGRmofKrhgQDcKF4j19b2BtDQHODikqb13MTWDo4WVpVEQNn36dHTo0AGpqamYOHEiGjRoAEEQkJCQgKVLl+Lbb7/FiRMntN7Jpk2b4vjx46W2cXJyUpWRKE2HDh3w22+lv2mGh4eXOq1qaWmJtWvXYu3atSW28fLyKjL1KTVl5bDI5Y8glz+Ck9Nzjc7L3DIyRFL7UMEFA7ojRoFYfcXRwsrRKAhr06YNdu/ejdDQUOzbt0/tWI0aNbBz50689tprWu0g6R9d5ZYRiUlq0y5cMKBbfH8qHUcLtUPjYq1vv/02goKCcOTIEVy/fh1AfrHWLl26FCmWSsbL0dERjo7Axo3Ae+8BeXmoVG4Zkdg47UL0AkcLtUOjIOz48eMIDw/H2bNn8fbbb6sdy8jIQOPGjbFhwwa0bdtWq50k/RUSAgQFATduAHXrAh4eYveISDPKT/JlTbvwEz8ZGwZYladRELZixQqMHj262H2Q5HI53nvvPSxbtoxBGKnx8GDwRfqr8Cf+WbMe4OZNM3h758LdvSWAlpL4xM9Vm0T6R6Mg7Pfffy+1wn2XLl2wZMmSSneKiEhKCgZYbm6Av7+InSmG1FZtGhquQiVd0SgIu3fvnmrD7GJPZmaGBw8eVLpTRERUPlJbtWlouAqVdEmjTRVr1aqFK1eulHj80qVLcHNzq3SnqGqUN4eFuS5E0lXaqk2qPK5CJV3SaCTszTffxMyZMxEcHFykMv6zZ88we/bsYveVJGkqnOty964JkpLM4OOTC3f3/G2hOMxOJE3KD0dlrdrkhygi6dIoCJsxYwaio6NRr149hIeHo379+gCAq1evYu3atcjLy8P06dN10lHSDWWAtWkTEBoKKBSAiUl+aYmQEJE7R0QlKvghqlatzALbjAlYuDATAwcO4IcoHeACCNImmSAIgiY/cOvWLYwdOxZHjhyB8kdlMhmCgoKwdu1a+Pj46KSjxigzMxNyuRwZGRnFrkjVljt3AC+v/ABMydQUuHmTqxqJ9MWdOywFowvJycnYuHEjgNIXQISGhjIdh1TKe//WuFircjue//77Dzdu3IAgCPDz80ONGprvLUjScP26egAG5BdXvXGDb+ZE+oKlYHSLCyBIFzQOwpRq1KiBli1barMvJBI/v/wpyMIjYXXritcnIiJNaLOMRMFzPXz4EID2tq1iuQsqqMJBGBkOD4/ithcy3E/VfBMkMizaLCNR0rm0sW0Vy11QYQzCCIDxbC/EN0Eiw6PNMhIltdHGtlUsd0GFMQgjFWPIKeGbIBFVVIsWv8HX9wbS0hwwaFArNGkinW2rSD8xCCMiIionufwR5PJHaNIkqNKrIVnughiEERGRQdFmcKOrQIn7fRLAIIyIiAyINoMbXQVKLHdBSgzCiIioSuh6ZbI2gxtdBkraKndB+o9BGBER6VxVrEzWZnCjy0BJG+UuyDCYlN2EiIiocnS5MrnwZuYFabqZuTbPVdK5leUulOevSLkLMgwcCSOjUt43N74JEukPbW5mrsuN0QueGwBmzXqAmzfN4O2dC3d3lrswRhpv4E1Vp6o28DY2BfNS7t41QVKSGXx8cuHunv+plG+CRNpXcCNsoORVh6VthF3enDJtbmZuDBujcxcR7dPZBt5E+k75ZrJpExAamr9npolJ/tZNISEid47ICFRk1aEmOWUeHo5aC5gMvYg1dxERF3PCyCjdufMiAAPy/3zvvfzHiUh3Slp1mJFhW+rPcbcL3eB1FReDMDJK16+/CMCU8vLypx2ISHdKW3VIZGwYhJFR8vPLn4IsyNQ0P++DiHSnrFWH5ZWRYYukJO8yR9CIpIw5YWSUPDzyc8Deey9/BMzUFPj8c8PO/SASU+HyDIVzwjQpz8Atf3SH+1lWLQZhZLRCQoCgIMNf+UQkBdoqz8Atf3SHwW3VYxBGRs3QVz4RSUnBAMvNDfD31/wc3PJHNxjcioM5YUREpDe0lVNG6rhgQhwMwoiISPK45Y9u6HKbJiobK+ZLGCvmExG9UHi3ixc5ZdztojKU1zUqyqrQNk0ZGDjwGa9rBZT3/s0gTMIYhBGRMeI2OuIxhm2aqgK3LSIiIr3DbXTExcVKVYtBGJEe4MgAGYvybo9z//59/p8gvccgjEjiODJAVNSePXvKbMP/EyR1XB1JJHHcYJeMWWW2J+L/CZI6joQREZEksYI7GTqOhBERkeSUVMG9uBExbuZN+oojYUR6hhvskjEo7/ZEHC0jfcYgjEiP8IZDxkJZwb1gIFZ4eyLud6hbXJWtewzCiPQEbzhkDApvT1T4Q0fB1zo389YdrsquGgzCiPQEbzhkDBwdHREeHq4agZk160GB7YlaAmiJjIwM7N69u1yjZVQxXJVdNRiEEUlc4Q12S7rhcINdMhQFR1bc3AB/f/Xj5R0t4/8JkjoGYUQSV3BkoFatzEIb7GZi4MABzM0go1Ke0TL+nyB9wCCMSA8obyYTJgD9+ik32JXBw8MegL2YXSMSRVmjZaRdXJWtGwzCiPQMN9gloqrEVdm6wyCMDBaXVxMRVQ5XZesWgzAySFxeTURUeVyVrVsMwsggcXk1EVHFGeqqbKnNkDAIIyIiIjWGuCpbijMkDMLIKHBlDxGRZgxtVbYUZ0gYhJHB48oeIqLK4aps3TApuwmR/ippZU9Ghq3IPSMiImPHIIwMWmkre4iIiMTEIIwMmnJlT0Hc4JeIiKSAQRgZpMIb/CoDMW7wS0REQH66SlKSt6jpKUzMJ4PEDX6JpEFqdZmIAOks2GIQRgaLG/wSiUuKdZnIeClnPsraiqkqZ0gYhBERkU4UHgErqV4fd66gqqCcITlxAli+vOiCrddeG4YOHcCK+USkW3fuANevA35+rP1DVUMq0z9k3BwdHdG6NWBiAigKrNkyNQVatXJEVQ/IMjGfyMhs2gR4eQGdOuX/uWmT2D0iQ8d6fSQlHh7Axo35gReQ/+fnn4vzgZRBGJERuXMHCA198QlQoQDeey//cSJdYb0+kpqQEODmTeDEifw/Q0LE6QenI4mMyPXr6kPwAJCXl78nHKclSVeU9foKBmKs10dik8JWTAzCqMKYV6R//PyKz4WoW1e8PpHhU9brK5wTVjA531iwZAcVxCCMKmTTphfTWiYm+fPrYg3nUvkpcyHeey9/BEzMXAgyLi1a/AZf3xtIS3OAg0Oa0QZgLNlBBTEII42VlFcUFMSbuT4ICcn/t7pxI38EjP9mpCuF6y3J5Y+KDb6MZeeK8pbiYMkO46F3iflZWVlo3rw5ZDIZLl68qHbs0qVLaNu2LSwtLeHp6YlFixYV+fm9e/eiQYMGsLS0RNOmTfH999+rHRcEAbNmzYKbmxusrKwQGBiI69evq7VJS0vDoEGDYGdnB3t7e4SEhODx48ca90VflZZXRNKVmpqK5ORkJCcnw9Q0GfXr5/+pfCw1NVXsLpKBUdZlCg0NLfGLoz5kzPRuJGzy5Mlwd3fH77//rvZ4ZmYmunTpgsDAQGzYsAGXL1/GyJEjYW9vj9DQUADAmTNnMGDAAMyfPx/du3dHVFQUevXqhQsXLqBJkyYAgEWLFmHVqlXYtm0bfHx8MHPmTAQFBeHPP/+EpaUlAGDQoEFITk5GTEwMcnJyMGLECISGhiIqKqrcfdFnzCvSP5wGIbHw9VSykorXkvHQqyDshx9+wNGjR7Fv3z788MMPasd27NiB7OxsbN68GdWqVUPjxo1x8eJFLFu2TBX4rFy5EsHBwZg0aRIA4JNPPkFMTAzWrFmDDRs2QBAErFixAjNmzMBbb70FAPjqq6/g4uKC/fv3o3///khISMDhw4dx/vx5vPLKKwCA1atX480338SSJUvg7u5err7oM+YV6R9OgxBJC4vXEqBH05H37t3D6NGjsX37dlhbWxc5HhcXh3bt2qnlFgQFBeHatWv477//VG0CAwPVfi4oKAhxcXEAgKSkJKSkpKi1kcvlaNWqlapNXFwc7O3tVQEYAAQGBsLExATnzp0rd1+Kk5WVhczMTLUvqZJKjRUiIn3D4rWkpBdBmCAIGD58OMaMGaMW/BSUkpICFxcXtceU36ekpJTapuDxgj9XUpuaNWuqHTczM4ODg0OZv6fg7yjO/PnzIZfLVV+enp4ltpUCDw+gQweOgBERaYLFa0lJ1CBs6tSpkMlkpX5dvXoVq1evxqNHjzBt2jQxu6tz06ZNQ0ZGhurrn3/+EbtLRESkZcritQWxeK1xEjUnbMKECRg+fHipberUqYPjx48jLi4OFhYWasdeeeUVDBo0CNu2bYOrqyvu3bundlz5vaurq+rP4toUPK58zM3NTa1N8+bNVW3u37+vdo7c3FykpaWV+XsK/o7iWFhYFHmORERkGJQpKmUVrzWWkh0kchDm7OwMZ2fnMtutWrUKn376qer7u3fvIigoCLt370arVq0AAAEBAZg+fTpycnJgbm4OAIiJiUH9+vVRo0YNVZvY2FhERESozhUTE4OAgAAAgI+PD1xdXREbG6sKujIzM3Hu3DmMHTtWdY709HTEx8fD398fAHD8+HEoFAqN+kJERMZFWbJDuQBm1qwHuHnTDN7euXB3bwmgJSvmGxm9WB1Zu3Ztte+rV68OAPD19YXH/yckDRw4EHPnzkVISAimTJmCK1euYOXKlVi+fLnq5z788EO0b98eS5cuRbdu3bBr1y78+uuv2LhxIwBAJpMhIiICn376Kfz8/FQlKtzd3dGrVy8AQMOGDREcHIzRo0djw4YNyMnJQXh4OPr37w93d/dy94WIiIxPwQDLzQ34/8/yZKT0IggrD7lcjqNHjyIsLAz+/v5wcnLCrFmz1EpCtGnTBlFRUZgxYwY++ugj+Pn5Yf/+/aoaYUB+HbInT54gNDQU6enpeP3113H48GFVjTAgvxxGeHg4OnfuDBMTE/Tp0werVq3SqC9EVam80xucBiEiqjoyQRAEsTtBxcvMzIRcLkdGRgbs7OzE7g7pOW4cTERUNcp7/zaYkTAiKh0DLCIiaWEQRkREREZBajMCDMKIiIi0SGo3esonxT10GYQRERFpiRRv9JRPinvo6sW2RURERPpAijd6ki4GYUREREQiYBBGRFSMO3eAEyfy/yQi0gUGYUREhWzaBHh5AZ065f+5aZPYPSIiQ8TEfNIIV/2QobtzBwgNBRSK/O8VCuC994CgIOD/d0kjKreMDFukpTnCwSFVtUE3kRKDMCo3rvohY3D9+osATCkvD7hxg0EYaebChZfx3XfdIQgmkMkU6NHjIFq0+E3sbpGEcDqSyo2rfsgY+PkBJoXeGU1Ngbp1xekP6aeMDFtVAAYAgmCC777rjowMW5F7ZrykuIcuR8KIiArw8AA2bsyfgszLyw/APv+co2BUPsobeFqaoyoAUxIEE6SlOUAuf1SlN3rK5+joiPDwcEml1DAIIyIqJCQkPwfsxo38ETAGYFReyhv9zZu52L5dgEIhUx0zNRUwblxXeHubMWVDJFK77gzCiIj+X8GFJ6amQP36+Y8nJ+f/yYUnVB6Ojo5wdCxuRFUGf38XsbtH/+/OnfwcUD8/8T5oMQgjIgIXnpD2cURVujZterEK2sQkP2AOCan6fjAxn4gIXHhCuuHhAXTowABMSkoqQyNGYWaOhBERkdpU7N27JkhKMoOPTy7c3fPvVJyKJUMhpTI0DMKo3KS4vJeIKq/gVGxpta04FUuGQFmGpmAgJlYZGgZhVG5SXN5LRJWn/D9dUm0rX98bkMsfcSqWDIKUytAwCCONMMAiMlxl1bYiMhRSWTTBIIyIiAAADg6pkMkUaoGYTKaAg0OaiL0i0g0PD/EXTHB1JBERAQDk8kfo0eMgZLL8ZBllThhHwYh0gyNhRETgwhOlFi1+g6/vDaSlOcDBIY0BGJEOMQgjIgIXnhQklz9i8EVUBRiEERH9P2MIsIhIOpgTRkRk5DgVSyQOjoQRERk5TsUSiYNBGBERMcAiEgGnI4mIiIhEwCCMiIiISAQMwoiIiIhEwCCMiIiISAQMwoiIiIhEwCCMiIiISAQMwoiIiIhEwCCMiIiISAQMwoiIiIhEwIr5EiYIAgAgMzNT5J4QERFReSnv28r7eEkYhEnYo0ePAACenp4i94SIiIg09ejRI8jl8hKPy4SywjQSjUKhwN27d2FrawuZTCZ2d6pMZmYmPD098c8//8DOzk7s7ug1Xkvt4HXUHl5L7eB11B5dXEtBEPDo0SO4u7vDxKTkzC+OhEmYiYkJPDw8xO6GaOzs7PjmoiW8ltrB66g9vJbaweuoPdq+lqWNgCkxMZ+IiIhIBAzCiIiIiETAIIwkx8LCArNnz4aFhYXYXdF7vJbaweuoPbyW2sHrqD1iXksm5hMRERGJgCNhRERERCJgEEZEREQkAgZhRERERCJgEEZEREQkAgZhJJoff/wRPXr0gLu7O2QyGfbv3692XBAEzJo1C25ubrCyskJgYCCuX78uTmclrqxrOXz4cMhkMrWv4OBgcTorYfPnz0fLli1ha2uLmjVrolevXrh27Zpam+fPnyMsLAyOjo6oXr06+vTpg3v37onUY2kqz3Xs0KFDkdfkmDFjROqxdK1fvx4vvfSSqpBoQEAAfvjhB9Vxvh7Lp6zrKNbrkUEYiebJkydo1qwZ1q5dW+zxRYsWYdWqVdiwYQPOnTsHGxsbBAUF4fnz51XcU+kr61oCQHBwMJKTk1VfO3furMIe6odTp04hLCwMZ8+eRUxMDHJyctClSxc8efJE1Wb8+PH47rvvsHfvXpw6dQp3795F7969Rey19JTnOgLA6NGj1V6TixYtEqnH0uXh4YEFCxYgPj4ev/76Kzp16oS33noLf/zxBwC+HsurrOsIiPR6FIgkAIDwzTffqL5XKBSCq6ursHjxYtVj6enpgoWFhbBz504Reqg/Cl9LQRCEYcOGCW+99ZYo/dFn9+/fFwAIp06dEgQh/zVobm4u7N27V9UmISFBACDExcWJ1U3JK3wdBUEQ2rdvL3z44YfidUqP1ahRQ/jyyy/5eqwk5XUUBPFejxwJI0lKSkpCSkoKAgMDVY/J5XK0atUKcXFxIvZMf508eRI1a9ZE/fr1MXbsWKSmpordJcnLyMgAADg4OAAA4uPjkZOTo/a6bNCgAWrXrs3XZSkKX0elHTt2wMnJCU2aNMG0adPw9OlTMbqnN/Ly8rBr1y48efIEAQEBfD1WUOHrqCTG65EbeJMkpaSkAABcXFzUHndxcVEdo/ILDg5G79694ePjg8TERHz00Ufo2rUr4uLiYGpqKnb3JEmhUCAiIgKvvfYamjRpAiD/dVmtWjXY29urteXrsmTFXUcAGDhwILy8vODu7o5Lly5hypQpuHbtGqKjo0XsrTRdvnwZAQEBeP78OapXr45vvvkGjRo1wsWLF/l61EBJ1xEQ7/XIIIzICPTv31/196ZNm+Kll16Cr68vTp48ic6dO4vYM+kKCwvDlStX8PPPP4vdFb1W0nUMDQ1V/b1p06Zwc3ND586dkZiYCF9f36rupqTVr18fFy9eREZGBr7++msMGzYMp06dErtbeqek69ioUSPRXo+cjiRJcnV1BYAiq3zu3bunOkYVV6dOHTg5OeHGjRtid0WSwsPDcfDgQZw4cQIeHh6qx11dXZGdnY309HS19nxdFq+k61icVq1aAQBfk8WoVq0a6tatC39/f8yfPx/NmjXDypUr+XrUUEnXsThV9XpkEEaS5OPjA1dXV8TGxqoey8zMxLlz59Tm8Kli7ty5g9TUVLi5uYndFUkRBAHh4eH45ptvcPz4cfj4+Kgd9/f3h7m5udrr8tq1a7h9+zZflwWUdR2Lc/HiRQDga7IcFAoFsrKy+HqsJOV1LE5VvR45HUmiefz4sdqnjKSkJFy8eBEODg6oXbs2IiIi8Omnn8LPzw8+Pj6YOXMm3N3d0atXL/E6LVGlXUsHBwfMnTsXffr0gaurKxITEzF58mTUrVsXQUFBIvZaesLCwhAVFYVvv/0Wtra2qrwauVwOKysryOVyhISEIDIyEg4ODrCzs8O4ceMQEBCA1q1bi9x76SjrOiYmJiIqKgpvvvkmHB0dcenSJYwfPx7t2rXDSy+9JHLvpWXatGno2rUrateujUePHiEqKgonT57EkSNH+HrUQGnXUdTXY5WvxyT6fydOnBAAFPkaNmyYIAj5ZSpmzpwpuLi4CBYWFkLnzp2Fa9euidtpiSrtWj59+lTo0qWL4OzsLJibmwteXl7C6NGjhZSUFLG7LTnFXUMAwpYtW1Rtnj17Jrz//vtCjRo1BGtra+Htt98WkpOTxeu0BJV1HW/fvi20a9dOcHBwECwsLIS6desKkyZNEjIyMsTtuASNHDlS8PLyEqpVqyY4OzsLnTt3Fo4ePao6ztdj+ZR2HcV8PcoEQRB0G+YRERERUWHMCSMiIiISAYMwIiIiIhEwCCMiIiISAYMwIiIiIhEwCCMiIiISAYMwIiIiIhEwCCMiIiISAYMwIiIiIhEwCCMiyUpJScG4ceNQp04dWFhYwNPTEz169FDbK+/MmTN48803UaNGDVhaWqJp06ZYtmwZ8vLyVG1u3ryJkJAQ+Pj4wMrKCr6+vpg9ezays7PVft8XX3yBZs2aoXr16rC3t8fLL7+M+fPnq47PmTMHMpkMwcHBRfq6ePFiyGQydOjQoVzPTXkumUwGMzMzeHt7Y/z48Xj8+LGGV4mI9BX3jiQiSbp58yZee+012NvbY/HixWjatClycnJw5MgRhIWF4erVq/jmm2/w7rvvYsSIEThx4gTs7e1x7NgxTJ48GXFxcdizZw9kMhmuXr0KhUKBzz//HHXr1sWVK1cwevRoPHnyBEuWLAEAbN68GREREVi1ahXat2+PrKwsXLp0CVeuXFHrl5ubG06cOIE7d+7Aw8ND9fjmzZtRu3ZtjZ5j48aNcezYMeTm5uL06dMYOXIknj59is8//7xI2+zsbFSrVq0CV1J3pNgnIr2i842RiIgqoGvXrkKtWrWEx48fFzn233//CY8fPxYcHR2F3r17Fzl+4MABAYCwa9euEs+/aNEiwcfHR/X9W2+9JQwfPrzUPs2ePVto1qyZ0L17d+HTTz9VPX769GnByclJGDt2rNC+fftyPLsX5ypo9OjRgqurq9rxL774QvD29hZkMpkgCPnPPSQkRHBychJsbW2Fjh07ChcvXlSd4+LFi0KHDh2E6tWrC7a2tkKLFi2E8+fPC4IgCDdv3hS6d+8u2NvbC9bW1kKjRo2EQ4cOCYIgCFu2bBHkcrlaf7755huh4G2ion0iouJxOpKIJCctLQ2HDx9GWFgYbGxsihy3t7fH0aNHkZqaiokTJxY53qNHD9SrVw87d+4s8XdkZGTAwcFB9b2rqyvOnj2LW7duldm/kSNHYuvWrarvN2/ejEGDBlV6VMjKykptivTGjRvYt28foqOjcfHiRQDAO++8g/v37+OHH35AfHw8WrRogc6dOyMtLQ0AMGjQIHh4eOD8+fOIj4/H1KlTYW5uDgAICwtDVlYWfvzxR1y+fBkLFy5E9erVNepjRfpERMXjdCQRSc6NGzcgCAIaNGhQYpu//voLANCwYcNijzdo0EDVprjzr169WjUVCQCzZ89G79694e3tjXr16iEgIABvvvkm+vbtCxMT9c+r3bt3x5gxY/Djjz/C398fe/bswc8//4zNmzdr+lRV4uPjERUVhU6dOqkey87OxldffQVnZ2cAwM8//4xffvkF9+/fh4WFBQBgyZIl2L9/P77++muEhobi9u3bmDRpkura+fn5qc53+/Zt9OnTB02bNgUA1KlTR+N+VqRPRFQ8BmFEJDmCIOikLQD8+++/CA4OxjvvvIPRo0erHndzc0NcXByuXLmCH3/8EWfOnMGwYcPw5Zdf4vDhw2qBmLm5OQYPHowtW7bg77//Rr169fDSSy9p1A8AuHz5MqpXr468vDxkZ2ejW7duWLNmjeq4l5eXKtgBgN9//x2PHz+Go6Oj2nmePXuGxMREAEBkZCRGjRqF7du3IzAwEO+88w58fX0BAB988AHGjh2Lo0ePIjAwEH369NG43xXpExEVj0EYEUmOn5+fKqG+JPXq1QMAJCQkoE2bNkWOJyQkoFGjRmqP3b17Fx07dkSbNm2wcePGYs/bpEkTNGnSBO+//z7GjBmDtm3b4tSpU+jYsaNau5EjR6JVq1a4cuUKRo4cqelTBADUr18fBw4cgJmZGdzd3YtMZxaein38+DHc3Nxw8uTJIueyt7cHkL/qcuDAgTh06BB++OEHzJ49G7t27cLbb7+NUaNGISgoCIcOHcLRo0cxf/58LF26FOPGjYOJiUmRgDYnJ6fI76lIn4ioeMwJIyLJcXBwQFBQENauXYsnT54UOZ6eno4uXbrAwcEBS5cuLXL8wIEDuH79OgYMGKB67N9//0WHDh3g7++PLVu2FJliLI4yiCuuD40bN0bjxo1x5coVDBw4UJOnp1KtWjXUrVsX3t7e5cona9GiBVJSUmBmZoa6deuqfTk5Oana1atXD+PHj8fRo0fRu3dvbNmyRXXM09MTY8aMQXR0NCZMmIAvvvgCAODs7IxHjx6pPVdlzpc2+kRERTEIIyJJWrt2LfLy8vDqq69i3759uH79OhISErBq1SoEBATAxsYGn3/+Ob799luEhobi0qVLuHnzJjZt2oThw4ejb9++ePfddwG8CMBq166NJUuW4MGDB0hJSUFKSorq940dOxaffPIJTp8+jVu3buHs2bMYOnQonJ2dERAQUGwfjx8/juTk5Cob8QkMDERAQAB69eqFo0eP4ubNmzhz5gymT5+OX3/9Fc+ePUN4eDhOnjyJW7du4fTp0zh//rwqby4iIgJHjhxBUlISLly4gBMnTqiOtWrVCtbW1vjoo4+QmJiIqKgotcUHFe0TEZWM05FEJEl16tTBhQsXMG/ePEyYMAHJyclwdnaGv78/1q9fDwDo27cvTpw4gXnz5qFt27Z4/vw5/Pz8MH36dEREREAmkwEAYmJicOPGDdy4cUOtthfwIqcsMDAQmzdvxvr165GamgonJycEBAQgNja2SL6TUnErN3VJJpPh+++/x/Tp0zFixAg8ePAArq6uaNeuHVxcXGBqaorU1FQMHToU9+7dg5OTE3r37o25c+cCAPLy8hAWFoY7d+7Azs4OwcHBWL58OYD80cf//e9/mDRpEr744gt07twZc+bMKTOxvqw+EVHJZIKmWa1EREREVGmcjiQiIiISAYMwIiIdqF69eolfP/30k9jdIyIJ4HQkEZEO3Lhxo8RjtWrVgpWVVRX2hoikiEEYERERkQg4HUlEREQkAgZhRERERCJgEEZEREQkAgZhRERERCJgEEZEREQkAgZhRERERCJgEEZEREQkAgZhRERERCL4PwM6cqQOq5s2AAAAAElFTkSuQmCC", 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", 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/XadOHaxatQrPPPMM7t+/jxo1atjlfZSFPT5UYXl5nvqLPQAIggv27u0hm54f3V1XeZ8Dh/yIyNEJgqAfujp48CA6deqEWrVqwdPTE2+++SbUajUePnxo8hinT59Gz549Ubt2bXh6eqJdu3YAgGvXrtm8/aYw8KEKy83101/sdQTBBbm5vnZrgxRml0nhcyAisqULFy4gMjISGRkZ6NGjB5o0aYKdO3fi9OnTWLt2LQDTN3kPHjxAfHw8vLy8sG3bNpw6dQq7d+8u9/fsgUNdNuLm5mbV/aTA11cNhUJjcNFXKDTw9c21y+tLIREQEP9zICKypUOHDuGXX37BhAkTcPr0aWg0Grz//vtwcdGe8z799FOD/d3c3FBUVGTw3MWLF6FWq7Fw4UKEhYUBAH788Uf7vIFyMPCxET8/P4wZM0YSlZutNV1SpbqHnj33lcptUanuWbO5RpV8D3l5nsjN9YOvr9qgDba+mxD7cyAispaCggJkZ2cbTGdfsGABevTogYEDB+LcuXMoLCzE6tWr0bNnTxw7dgzr1683OEZERATu37+Pb7/9Fk2bNkW1atVQu3ZtuLm5YfXq1Rg5ciTOnTuHefPmifQuDTHwsaHiwURmJnD5MhATA4SG2q8N1uglKd4r1aLFT4iKSkNuri98fXMNLvb27L0SO8na1Ofg6MSuK0JE9vPNN98gODgYVapUgY+PD5o2bYpVq1Zh0KBBcHFxQdOmTbFs2TIsWrQI06dPR9u2bbFgwQIMHDhQf4zWrVtj5MiR6Nu3L9RqtX46++bNm/HPf/4Tq1atQosWLbB06VL06tVLxHerxcDHDjZuBBITAY0GcHEBNmwAEhLs89rWmC4ppd4rwHhycVRUml0DEJXqnlMFPEDpQNlYr1p5w4nFg6cbN1yQnl4FkZGPERKirQbL4IlI/JSIzZs3Y/PmzeXuN2HCBEyYMMHguTfffNPg8bp167Bu3TqD5/r164d+/foZPCcIgmWNtSIGPjaWmfkk6AG0/44YAcTH27fnp7KkdJEylVzsbIFIZZnbe1N8X1O9aqaOyWn/RBUjtZtKuWDgY2OXLz8JenSKioC0NMcKfKRErORise/OzFWZYc7K9KqVN+1fdwyxZ3bImVhD71Qagxr7Y+BjYzEx2uGt4sGPUglER4vTHmNDF45ErORiR7s7q0wyuDV61dgzJ01iDr0TSQEDHxsLDdWeWEaM0Pb0KJXAhx+Kc5cldkKwNYmVXCyVoMZc5v6/t0avGqf9S4+zDL0TVQYLGNpBQgKQkQEcPqz9V4y7K2eoulxyCEmluofIyKulgh6pDDVJhSX/73W9agqF9gppSa+aNY5B1qNWq3HihLrMofeTJ9VcZ45kgz0+dhIaKu4dlTMMOzjaUJNUlPf//vbt2/rn8/Ly9P9tjV41Z57270h0+V55eZ5QKMaX6oU7dmwLzp27x4RzkgUGPk5O1/tR3rCDo/SS8KRsvvL+3+tWYC6LNabsO+O0f0eju1koLz+OCeckBwx8nFzxXpJatfIxdaoKRUUKKJUCFi3KR//+/dhL4uRskQzuKIEylcZeOJI7Bj4yoAtqJk0C+vbVTqWPjlYgNNQbgLeYTSM7KetiZ2yW12uvvQZvb2+jxyovUHa0af9yxF44kjOHCny+//57LFmyBKdPn0ZWVhZ2796N3r1767cLgoDZs2fjo48+wt27d9GmTRusW7cOMTEx4jVaYsTONSL7KSsZXHexMzXLy9vbG8HBwRa/LnOxiAgAjhw5gg4dOuDOnTsmb6aKi4iIwPjx4zF+/HibtcuhZnU9ePAATZs2xdq1a8vcvnjxYqxatQrr16/HyZMnUb16dcTHx+Ovv/6yc0uJxKcLQBITE5GYmIg+ffoAsM8MPz8/PwQHBxv9YdBDJL7BgwdDoVBg5MiRpbaNHj0aCoUCgwcPtn/DbMyheny6deuGbt26lblNEASsWLECb7/9Nl566SUAwL///W8EBgbiiy++wOuvv27PphJJQlkBhjPM8CMi6wgLC8Mnn3yC5cuXw8PDAwDw119/Yfv27ahdu7bIrbMNh+rxMSU9PR3Z2dno3Lmz/jmVSoWWLVsiJSXF6O8VFBQgPz/f4IfImelmeRXHwoJE8tSiRQuEhYUZzO7ctWsXateujebNm+ufKygowLhx41CzZk1UrVoVzz33HE6dOmVwrK+++gp169aFh4cHOnTogIyMjFKv98MPP+D555+Hh4cHwsLCMG7cODx48MBm768sThP4ZGdnAwACAwMNng8MDNRvK8uCBQugUqn0P2FhYTZtJ5HYWFhQfphw7hgyM7WFbjMz7fu6Q4cORVJSkv7xpk2bMGTIEIN9pkyZgp07d2LLli04c+YMoqOjER8fj9xc7Q3T9evX0adPH/Ts2ROpqakYNmwYpk2bZnCMK1euoGvXrnj55Zdx9uxZ7NixAz/88APGjBlj+zdZjEMNddnC9OnTMXHiRP3j/Px8Bj/klIpf1ExNaebFz/nYI+FcrVbrj3/jhgvS06sgMvIxQkI0Vjm+sxNzDbUBAwZg+vTpuHr1KgDg2LFj+OSTT3DkyBEA2vzadevWYfPmzfp0k48++ggHDhzAxo0bMXnyZKxbtw5RUVF4//33AQD16tXDL7/8gkWLFulfZ8GCBXjjjTf0icsxMTFYtWoV2rVrh3Xr1qFq1ap2eb9OE/gEBQUBAHJycgxmpOTk5KBZs2ZGf8/d3R3u7u62bh6R6DjbSt5s+f9VVxkaMD1jkJWhyyb2GmoBAQHo3r07Nm/eDEEQ0L17d/j7++u3X7lyBYWFhWjTpo3+OVdXVzz77LO4cOECAODChQto2bKlwXHj4uIMHv/88884e/Ystm3bpn9OEARoNBqkp6fjqaeessXbK8VpAp/IyEgEBQXh22+/1Qc6+fn5OHnyJEaNGiVu44gkghcdsgVdMG1sxmBUVBpUqnusDG3E5csocw21tDT7lR8ZOnSofsjJ2Mzpyrp//z5GjBiBcePGldpmz0Rqhwp87t+/j7S0NP3j9PR0pKamwtfXF7Vr18b48ePx7rvvIiYmBpGRkZg5cyZCQkIMav0QEZFtcMagZWJitMNbxYMfpRKIjrZfG7p27YpHjx5BoVAgPj7eYFtUVBTc3Nxw7NgxhIeHAwAKCwtx6tQp/bDVU089hT179hj83okTJwwet2jRAr/++iui7fnGyuBQgc+PP/6IDh066B/rcnMGDRqEzZs3Y8qUKXjw4AESExNx9+5dPPfcc/jmm2/sNm5IZG3F8ybKwqEpx+Ls/z/LWxeuspw1jyg0VJvTM2KEtqdHqQQ+/NC+xWaVSqV+2EqpVBpsq169OkaNGoXJkyfrOxoWL16Mhw8fIuH/E5FGjhyJ999/H5MnT8awYcNw+vRpbN682eA4U6dORatWrTBmzBgMGzYM1atXx6+//ooDBw7oh0rtwaECn/bt20MQBKPbFQoF3nnnHbzzzjt2bBWRbRTPmzCFeROOQQ7/P22xLpyOs+cRJSRoc3q0SwqJU2Hfy8vL6LaFCxdCo9HgzTffxL179/D0009j//798PHxAaAdqtq5cycmTJiA1atX49lnn8V7772HoUOH6o/RpEkTfPfdd5gxYwaef/55CIKAqKgo9O3b1+bvrTiHCnyI5KSi+RDMm3AMcvn/aatFUM3JI3LUnjV7LylUskempC+++EL/31WrVsWqVauwatUqo/v36NEDPXr0MHiu5LT4Z555BsnJyUaPUVbtH2tj4ENEDsdRL2zFGVsk1hnYchHU8vKIMjMz8dVXX5V7HEftGaLKY+BDRA7FGYaMTA3VkGnl5REVD3ry8jxx/bq2LltY2HWDYMzRe9bIcgx8iMihOPqQUXlDNY7InpWhK5pHdOZMc+zZ0xOA4v+f0aBXLwaYxMCHyGE489CInDjjlG97F8csL49IF1w+CXoAwAV79jh2gEnWwcCHyAFwaMR52HrKt1jsPaxoKo+orOBSy3YBpqkZx2Q91vicnWaRUiJnZWxoJC/PU+SWSUNenifS0yMc5vPgIrG2pwsuS7N+gOnq6goAePjwoVWPS2XTfc66z90S7PEhkihdPkR5QyNyXlTUkXrCuEhs5ZjzuZTMA9LS5vhYO8BUKpXw9vbGzZs3AQDVqlWDQqEo57fIXIIg4OHDh7h58ya8vb1LFVk0BwMfIonS5U1kZDzG1q0CNJonJ1OlUsDYsd0QEVFFsjOXbM3RkoS5SGzlVOTzu3v3Lj799FMAT4LL69e1hXHCwjJt9r3QLZKtC37Idry9vfWft6UY+BBJmJ+fH/z8yipnr0BsbKDYzROVIyYJM6ipHHM/P20e0IUyt1mzZ02hUCA4OBg1a9ZEYWGh1Y5LhlxdXSvV06PDwIfIAUihnL1U6C5Y5SUJc8hIfir6/3zAgAE2CUKVSqVVLsxkWwqBqegG8vPzoVKpkJeXZ3LdEiISj65y8/btHpg6VYWiIgWUSgGLFuWhf/8/OWQkY85Q1dsRSPFzruj1m4FPCQx8bEeKfyjk+DIz2RNGZE9SrZ5e0es3h7rILqT6h0KOz94LOxLJnaNXT2cdH7KLkn8AxmqvSPUPhYiInAN7fMjuHKn2ChERORf2+JBdsQoxERGJiT0+ZFeOWHuFiJwTJ1zIEwMfsiupLtDIEyCRvHDChfXk5XkiN9cPvr5qh7iBZeBDdlVyDR0pLNDIEyCR/Dj6zCSpcMScTQY+ZHemFmgUA0+AREQVp6uQXd56eVKtns7Ah+yi5B+Adg2d0gGPFP5QHK3blmyLw6DywL/7itMtGHv4MLB8eemczTZtBqF9e+muTcfAh+zCUVamdsRuW7IdDoPKA//uzefn54dWrQAXF0CjefK8Ugm0bKldXFmqGPiQ3Uj9wlBety3JD4dBnR//7i0XGgps2ACMGAEUFWmDng8/lH4ldQY+EpCZCVy+DMTESP8L48w41Z5Ifiryd8/hTuMSEoD4eMdaL4+Bj8g2bgQSE7VdhS4u2ug5IUHsVsmTVKfak30Vv8jdvn1b5NaQrZX3d5+Xl4cdO3aUexw5D3c62np5DHxEolarkZHxGImJNaHRKABog58RIwQ0a3YTERFVZPtHJBYpTrUn+yovp4cJsM5DN5GivL97QRAqdDwOdzoOBj4i0J1c09MjoNEMMthWVKTA6tVfIzLyqqzvIOyp+EwyU1PtpTDjjGzL1MWLCbDOpeSEi1mzbiEjowoiIh4jJOQZAM/Azc2NAY0TYuAjAt0fUnldrPyDsw9HmXFG4mECrHMq/jcdHAzExpbeJysry+Axe/0cHwMfEXFoRToY1JApTHwnoHK9fkyQlg6nDHzWrl2LJUuWIDs7G02bNsXq1avx7LPPit2sMkmtijERPaG7u3d1LTDZO8thUOdXmV4/1oOSFqcLfHbs2IGJEydi/fr1aNmyJVasWIH4+HhcunQJNWvWFLt5ZTJWxZiIxFPy7r5Jk7M4e7YJBMEFLi4CFi/OR//+/XinLhOV6fVjPShpcbrAZ9myZRg+fDiGDBkCAFi/fj2+/PJLbNq0CdOmTRO5dWQO1jcisZR1d3/2bBMkJPwLhYVuGDu2G2JjAwF4i9rOkjicYjssd+E8nCrwefToEU6fPo3p06frn3NxcUHnzp2RkpIiYsuoonQn7u3bPTBligoajeL/767z0L//nzxxk10Yu7svLHRDZORVhIRojPymeDicYhsVnfZuznCnsQRpY3WjeN6zLqcKfG7fvo2ioiIEBgYaPB8YGIiLFy+W+TsFBQUoKCjQP87Pz7dpG8k43Yk7L88TK1aMhyDo6hspMHmyF/74YxNUqns8cZPN6C5e5d3di5HTU15vTl5eXoWOw+EU81R02ntFz0mmEqR37dpl9Pd43rMepwp8LLFgwQLMnTvXrq9Z/KRpamqk3BImdSeW8sbSeeImWyl+katVKx9Tp6pQVKSAUilg0SLxcnoq2ptDtlGRae8VUZkEaZ73rMepAh9/f38olUrk5OQYPJ+Tk4OgoKAyf2f69OmYOHGi/nF+fj7CwsJs2k7dyXXzZiXeeaf0cA4g765NjqWTmHR/d5MmAX376tYgUiA01Bti5fRYctFjvRnpYVkEaXCqwMfNzQ2xsbH49ttv0bt3bwCARqPBt99+izFjxpT5O+7u7nB3d7djK7X+/NMPU6Zol6kAtMM5U6d6o29fb9kn8rK+EUmFo61BpMMq09JUkZs6Bqy251SBDwBMnDgRgwYNwtNPP41nn30WK1aswIMHD/SzvKTi8uUnQY9OUZH27tIRT7TWxvpGRMaZujiyyrT0VDRBmgGrfThd4NO3b1/cunULs2bNQnZ2Npo1a4ZvvvmmVMKzmNRqNby8HsPF5ckCpQCgVArw9LwJtZoLlAKsb0RUlvIujhxOkR5TCdJubpHYtesnBqx25HSBD6DNfjc2tCW24kmKPXoYnsC6d9+Hffu0JzBm8BNRSRW5ODJHTpqMJUhnZWm7/hmw2o9TBj5SVjxJ0dRwDjP4iaikilwcrVlvhuyHAav9MPARGYdznqjoCZknbpKr8i6Or732Gry9vQFUvt4M2YctCiSSaQx8SDJKjoOXhSdueRN7SQaxXr+iF8eaNWvqX78y9WbIfqxdIJHKx8CHJIV/3GSM2EsyiPn6vDg6N2sVSKSKYeBDRA5B7BWuxX59XhyJrIOBDxE5JLELvYn9+rYg9lAikT0w8CEihyN2oTexX98WxB5KJLIXBj52xplLRJUjdqE3sV/fVkr29Bjr0WKpjYph75l0MfCxM85cImdn6xO+2IXexH59e3DGHi17Yu+ZtDHwEYFUv+iZmdo1xGJiuF4YWcYeJ3yxC72J/fq25qw9WvbE3jNpcyl/F5KDjRuB8HCgY0ftvxs3it0ickRlnfDT0yOQl+dpcj9z6GrZKBTaUv8la9nYmtivb2umerTIfGfONMeKFeOxZcsgrFgxHmfONBe7SbLHHh9CZiaQmPhktXiNBhgxAoiPZ88PWc7awyXF895MLfdiq/w4sV/fXpy9R8ue2HsmTQx8CJcvPwl6dIqKgLQ08QMfDr85Jluc8MXOjxP79e2lvOrQVHFyyAdzRAx8CDExgIuLYfCjVALR0eK1CdAOt+l6olxcgA0bgIQEcdtEFWOrE77YQYXYr28vpnq0AM5Yqij2nkkTAx+ZU6vVUCofYfFiD0ydqkJRkQJKpYBFi/KgVP4Jtdr+JzC1Wo2MjMdITKwJjUYBQDf8JqBZs5uIiKjCk6rE8YTveEoO0RlbQPnhw4fYsGGD/rGxxF3OWGLvmVQx8JGxkjNwxo3z1N/h3b9/D7pzmz1PYLo2padHQKMZZLCtqEiB1au/RmTkVZ5UJY4nfMdT0aG84ttN5XFxxpJWeb1nZH8MfGSs5InJ2B2ePU9gutcqr8eAJ1Xp4wnf8VTkZiIrKwsAE3dNqWjvmaMnwjsqBj4kSewxMJ8U8i54wpcPJu4aJ5dEeEfFwIckiz0GFVdy2FKsvAue8OWDeVym8TsuXQx8SNKM9RiQISnlXfCELw9y7ZUt3rN644YL0tOrIDLyMUJCtNNiGdhLHwMfIifCvAvxSGGo0d7k1itbvGfV1A0GJ19IGwMfsjoWHRQP8y7EIZWhRjHIqVdWF9iWd4PByRfSxsCHrIpFB8XFvAtxSGmo0dYqmpjuzAnsvMFwbAx8ZMzaJzBrrPnFk2rlyDXvQirkMNTIBHbHvsGQ45BsSQx8ZMzaJzBrrPkl95OqNRIn5ZZ3ISVy6Qlw1r+/inLUG4ySQ7LGOOOQbHEMfGTOWl9utVoNL6/HcHF5sswEACiVAjw9b0KtrvgyE878B2eKNRMn5ZR3ISWO3BNA5nHEG4ySN5TGctGcYUjWFAY+VGnFL9g9ehhesLt334d9+zjToSIqkzjJIUJpcNSeALKMI99gmLq5cnYWBT4PHjxA9erVrd0WclDFL8Sm7oKc/S7CWiwZLpH7EKGUOGJPAMmLHHLRTLEo8AkMDMRrr72GoUOH4rnnnrN2m8jBOfJdkBRYOlzCoEY6+DfgnEz1mBYfNpJ6z6pcctGMsSjw+c9//oPNmzejY8eOiIiIwNChQzFw4ECEhIRYu31EssPhEsfDoUZ5MNazun27B955RwWNRgEXFwENGyokXcZD7rloFgU+vXv3Ru/evXHr1i1s3boVmzdvxsyZMxEfH4+hQ4eiV69eqFKF6UNEluJwiWPhUKN8FP9/qFarkZHxGFOmqPSTOjQaBUaMENCs2U1ERFR8Uoc9yf3mqlLRSUBAACZOnIiJEydi9erVmDx5Mr766iv4+/tj5MiRmDZtGqpVq2aVhs6fPx9ffvklUlNT4ebmhrt375ba59q1axg1ahQOHz6MGjVqYNCgQViwYAGDMHJIHC5xLFK8wJHt6CZ1pKdHQKMZZLCtqEiB1au/RmTkVclO6pDzzVWlIoKcnBxs2bIFmzdvxtWrV/HKK68gISEBmZmZWLRoEU6cOIHk5GSrNPTRo0d49dVXERcXh40bN5baXlRUhO7duyMoKAjHjx9HVlYWBg4cCFdXV7z33ntWaQMR2RaLq5Gj0H1Pyxs2ktKkjpJDrcZurpx9SNaiwGfXrl1ISkrC/v370aBBA/z973/HgAED4O3trd+ndevWeOqpp6zVTsydOxcAsHnz5jK3Jycn49dff8XBgwcRGBiIZs2aYd68eZg6dSrmzJnj9P8jyfHJPU9EzutdkeNypGEjDslqWRT4DBkyBK+//jqOHTuGZ555psx9QkJCMGPGjEo1zhwpKSlo3LgxAgMD9c/Fx8dj1KhROH/+PJo3b17m7xUUFKCgoED/OD8/3+ZtdTZyv2Bbi9xPSnJa74qciyMNGznr+cMcFgU+WVlZ5ebueHh4YPbs2RY1yhLZ2dkGQQ8A/ePs7Gyjv7dgwQJ9bxJZRu4XbGviZ8QaI+SYmJPnOCwKfKpVq4aioiLs3r0bFy5cAAA89dRT6N27t1mJxNOmTcOiRYtM7nPhwgXUr1/fkmZWyPTp0zFx4kT94/z8fISFhdns9ZwVL9hkLXKvMUJEtmVR4HP+/Hn07NkTOTk5qFevHgBg0aJFCAgIwN69e9GoUaMKHWfSpEkYPHiwyX3q1KlToWMFBQXhf//7n8FzOTk5+m3GuLu7w93dvUKvQUS2J/caI0RkWxYFPsOGDUOjRo1w+vRp+Pj4AADu3LmDwYMHIzExEcePH6/QcQICAhAQEGBJE0qJi4vD/PnzcfPmTdSsWRMAcODAAXh5eaFBgwZWeQ2yvcxM7SrvMTEVX9GdnIsjJYsSOaPisytv3HBBenoVREY+RkiIBoDjpy5YFPikpqbixx9/1Ac9AODj44P58+cbTXaurGvXriE3NxfXrl1DUVERUlNTAQDR0dGoUaMGunTpggYNGuDNN9/E4sWLkZ2djbfffhujR49mj46D2LgRSEwENBrAxQXYsAGSrn5K1qNWq3H79m39Y0dKFiV5ctZJHcVnV5qaZODIsystCnzq1q2LnJwcNGzY0OD5mzdvIjo62ioNK2nWrFnYsmWL/rFultbhw4fRvn17KJVK7Nu3D6NGjUJcXByqV6+OQYMG4Z133rFJe8i6MjOfBD2A9t8RI4D4ePb8ODtT09gjI6+K2DIi45x1Uofu/ZQ3ycCRZ1daFPgsWLAA48aNw5w5c9CqVSsAwIkTJ/DOO+9g0aJFBlPCvby8rNLQzZs3G63hoxMeHo6vvvrKKq9H9qNWq3HiBKDRGJ4gioqAkyfV8PBg8rQzq+g0dh1Hu4Mm5+XM5yVnnmRgUeDTo0cPAMBrr70GhUK7PokgCACAnj176h8rFAoUFRVZo53kpHR3+3l5nlAoxpdKaD12bAvOnbvn0N2qVDHl3WH26dMHISEh/B4Q2YEzTzKwKPA5fPiwtdtBMqW72y8vodWRu1WpYsq7w/T395dN0MMkfxKbM08ysCjwadeunbXbQcSEVplz5jvMitDNpNm+3UO/2reLi4DFi/PQv/+fDpkvQo7NWc/JFi9SevfuXWzcuFFfwLBhw4YYOnQoVCqV1RpH8sPqp/LlzHeY5Sk+5LtixXgIgjaFQKNRYPJkL/zxxyaoVBzyJftzxnOyRYHPjz/+iPj4eHh4eODZZ58FACxbtgzz589HcnIyWrRoYdVGEpE8OOsdZnl0Q7nlDfdxyJeo8iwKfCZMmIBevXrho48+0i9R8fjxYwwbNgzjx4/H999/b9VGEpF8OOMdZkXJfbiPxOes9YmKs7jHp3jQAwBVqlTBlClT8PTTT1utcUTk/ORwoq0oOQ/3kTQ4a32i4iwKfLy8vHDt2rVSi4dev34dnp6eVmkYEcmDHE605pDrcB9Jh7P/rVkU+PTt2xcJCQlYunQpWrduDQA4duwYJk+ejH79+lm1geTceLdPgPOfaM0l5+E+IluzKPBZunQpFAoFBg4ciMePHwMAXF1dMWrUKCxcuNCqDSTnxrt9IiKyJ7MDn6KiIpw4cQJz5szBggULcOXKFQBAVFQUqlWrZvUGOgsWJDOOQQ0REdmLS/m7GFIqlejSpQvu3r2LatWqoXHjxmjcuDGDHhM2bgTCw4GOHbX/btxom9fJzAQOH9b+S0SOg0O+RPZj0VBXo0aN8PvvvyMyMtLa7XEqarUaGRmPkZhYExqNriAZMGKEgGbNbiIioorVejs2bnyyurmLC7BhA5CQYJVDE0mOrsqxMY42PMohXyL7UQi61UXN8M0332D69OmYN28eYmNjUb16dYPt1lqRXQz5+flQqVTIy8ur1PvQVWJNT4/Ali2DSm0fNGgzIiOvWqUSa2amtidJo3nynFIJZGRwWI2cj+5vSycvzxO5uX7w9VUbJASzyjGRvFT0+m1Rj8+LL74IAOjVq5d+dXaAK7IXp7tzK68gWWUrsarVapw4AWg0hif4oiLg5Ek1PDyYQ0POpfjfzJkzzUvVvGnR4icAwI0bN4z+fbH3xHaYz0hSx9XZbcyWBcmKr++jUIwvFVwdO7YF585xfR9yTnl5nvq/K0C7tMPevT0QFZUGleoedu3apd+PPUL2wSF3cgQWBT6RkZEICwsz6O0BtD0+169ft0rDnImtCpLp7mbLC664vg85o/LWtQJM9wjx78K6MjOfBD2ALp8RiI9nzw9Ji8WBT1ZWFmrWrGnwfG5uLiIjIznUVQZbFyRjtVeSm/KGkcvrESLr4ZA7ORKLAh9dLk9J9+/fR9WqVSvdKLIMq73Kh7PNarJEeT2dFekRosrjkDs5GrMCn4kTJwIAFAoFZs6caVC7p6ioCCdPnkSzZs2s2kAiMlRyVpMxcrjQmOrpFGul8+JB6Y0bLkhPr4LIyMcICdGOATlbUMohd3I0ZgU+P/2kHRsXBAG//PKLQTEtNzc3NG3aFG+99ZZ1W0hEBkpeQIwl78rlQmOsp1OMlc6LB6Wm8oucNSjlkDs5ArMCH91sriFDhmDlypUOXa/H1liJlezB1MXVWZnzN2PvC7Eu2Cwvv8iZg1IOuZPUWZTjk5SUZO12OB1WYiVbk2vybnl/W7dv39ZPZQfEuRAzv4hIuiwKfB48eICFCxfi22+/xc2bN6EpXjIYwO+//26Vxjk6Wwc17FWSNzlfXE39bUnh70Ks/CKSPhZ4FJ9Fgc+wYcPw3Xff4c0330RwcHCZM7zI9tirJG+8uJZNCn8XYuQXkfSxwKM0WBT4fP311/jyyy/Rpk0ba7eHzMSgRr54cTVOCn8XTPSl4ljgUTosCnx8fHzg6+tr7bYQkZl4cZU2OST6SmFo0RFcvmy4kDSgLfCYlsbAx94sCnzmzZuHWbNmYcuWLQa1fIjI9kpeQIxdXOV+oaGKq0zeiRSGFqVOrVbDy+sxXFxqQqN5khqiVArw9LwJtbqKrD8fe7Mo8Hn//fdx5coVBAYGIiIiAq6urgbbz5w5Y5XGEVFpvNBIlyP1fugKLW7f7oEpU1TQaBRwcRGweHEe+vf/06zvEL9rxhWv7dSjh2H5ie7d92HfPueu7SRFFgU+vXv3tnIziMgcPEFKk6MEpcWXmVixYjwEQdsLodEoMHmyF/74YxNUKi4zYQ3FvwumhqalUttJDrPOLAp8Zs+ebe12mJSRkYF58+bh0KFDyM7ORkhICAYMGIAZM2YY3DmdPXsWo0ePxqlTpxAQEICxY8diypQpdm0rEcmbIwQKuotseSURpHIxdiZSzvuSy6wzl/J3eeJ///ufyZXXCwoK8Omnn1a6USVdvHgRGo0GH374Ic6fP4/ly5dj/fr1+Oc//6nfJz8/H126dEF4eDhOnz6NJUuWYM6cOdiwYYPV20NE5Ax0JRGKY0kE+VGr1Th9OgeJiUKJWWcCTp/OgVqtFreBVmZW4BMXF2fwAXh5eRkUK7x79y769etnvdb9v65duyIpKQldunRBnTp10KtXL7z11lsG1Vm3bduGR48eYdOmTWjYsCFef/11jBs3DsuWLbN6e4iInIGuJIIu+GFJBPnRDXuuXv2NQeI1ABQVKbB69ddYs2aNUwU/Zg11CYJg8rGx52whLy/PYEp9SkoK2rZtazD0FR8fj0WLFuHOnTvw8fEp8zgFBQUoKCjQP87Pz7ddo4lIkoqvqF4WKeTl2ApLIsib7ntfXkFUZxr2tCjHxxR7VHFOS0vD6tWrsXTpUv1z2dnZiIyMNNgvMDBQv81Y4LNgwQLMnTvXdo0lIkkrPusGML7avTMn+ko574TsQ04FUa0e+Jhj2rRpWLRokcl9Lly4gPr16+sf//HHH+jatSteffVVDB8+vNJtmD59OiZOnKh/nJ+fj7CwsEofl4gcQ/E7WVOr3TvTHS9RWeTS+2d24PPrr78iOzsbgHZY6+LFi7h//z4A7arI5pg0aRIGDx5scp86dero//vGjRvo0KEDWrduXSppOSgoCDk5OQbP6R4HBQUZPb67uzvc3d3NajcROR+5rnZPtuVItZ0AefT+mR34dOrUySCPp0ePHgC0Q1yCIJg11BUQEICAgIAK7fvHH3+gQ4cOiI2NRVJSElxcDPOy4+LiMGPGDBQWFuoLKh44cAD16tUzOsxFRKQjp9XuHe1i7MgcpbaTnJgV+KSnp9uqHSb98ccfaN++PcLDw7F06VLcunVLv03Xm9O/f3/MnTsXCQkJmDp1Ks6dO4eVK1di+fLlorS5ouScVEkkJXJa7Z4XY/vi5ygtZgU+4eHhZh3873//O9555x34+/ub9XslHThwAGlpaUhLS0NoiVKSut4nlUqF5ORkjB49GrGxsfD398esWbOQmJhYqde2pZJJlcY4c1IlkVTIKbkT4MWY5Mumyc3/+c9/8NZbb1U68Bk8eHC5uUAA0KRJExw9erRSr2VPFU2WZFIlkX2YSu4smcPIHhFyBnIc9rRp4GOvmj7Owtg0WiKyH2PJncULpuqwN5YcnRyHPUWdzk5PmJpGSyRX9siBq8ydLHtjyRk4U1BTEQx8JIDTaIlKs1cOXFl3vLdv3zbo4WFvLJHzYOAjAXKaRktUUfbMgTMVOLE3lsi5mLVIKdkGV0gmkiZjvbF5eZ4it4yILGXTHp8BAwbAy8vLli/hFKw1jZY1gYisi72xRM7HosBHo9GUqpysez4zMxO1a9cGAKxbt65yrXNyxZMqTU2jrUjyJWsCkbMTI89GTkUNieTCrMAnPz8fw4YNw969e+Hl5YURI0Zg9uzZUCqVAIBbt24hMjISRUVFNmmss7HmNELWBCJHZKqXsnjdHLHybORW1JBIDswKfGbOnImff/4ZW7duxd27d/Huu+/izJkz2LVrl75XgrV7zMPeF5KrivZSijHr0Zq9sSR9mZnA5ctATAxQYnEAckJmBT5ffPEFtmzZgvbt2wMAevfuje7du6Nnz57Ys2cPAJi1SCkRyVdFex/FyLORY1E3udq4EUhMBDQawMUF2LABSEgQu1VkS2YFPrdu3TJYr8vf3x8HDx5EfHw8XnzxRfzrX/+yegOdjb0SkFl3hJxFeXk2tup1YVDj3NRqNTIyHiMxsSY0Gu0Nu0YDjBghoFmzm4iIqOK03wG593CZFfjUrl0bFy5cQGRkpP45T09PJCcno0uXLvjb3/5m9QY6E3slILPuCDmTIUNeQK1a+Zg6VYWiIgWUSgGLFuWjf/9+7HUhi+jOxenpEdBoBhlsKypSYPXqrxEZedWpJoPobrq3b/fAlCkqaDQKuLgIWLw4D/37/ymrvyWzAp8uXbogKSkJL774osHzNWrUwP79+/HCCy9YtXHOxh4JyKwCTY7KWC+lv78/Jk3yRt++QFoaEB2tQGioNwBvsZpKDk53ji2vN9FZJoPoAr28PE+sWDEegqDr4VJg8mQv/PHHJqhU95wq0DPFrMBn7ty5uHHjRpnbPD09ceDAAZw5c8YqDSPLsO4IOaKK9FKGhsqzW55sRy6z9nQBXHnXB2cJ9MpjVuDj4+MDHx8fo9s9PT3Rrl27SjeKzKfLcxArH4LIUuylJDGZmrXnbFiXSsvsAoaPHz/G8uXL8fHHH+O3334DANStWxf9+/fHP/7xD7i6ulq9kVS+4rNQmA9BjoS9lCQ2leqeLL5rcunhKo9Zgc+ff/6JF154ASkpKejcuTPatm0LALhw4QKmTp2KPXv2IDk5GVWrVrVJY8k0XVAzaRKYD0GSx15KIvuTUw+XMWYFPgsXLsT169fx008/oUmTJgbbfv75Z/Tq1QsLFy7EnDlzrNlGp2XLKefMhyCpYy9l5XBtPrKUXHq4jDEr8Pnkk0+wbNmyUkEPADRt2hRLly7FjBkzGPhUAKecE7GX0lJcm4905F6TxxKlVxo14erVq3j22WeNbm/VqhWuXbtW6UY5K12XvbFkzrw8T4P9iOQkNBRo354n74oo2dOTl+eJ9PQI/TnE2H6kVdFzrFTPxWq1GllZWXj//bsIDxfQsSMQHi7g/ffvIisrC2q1WuwmSppZPT5eXl64efMmwsLCytyenZ0NT0/PMrfRk679w4eB5ctLJ3O2aTMI7duzYiwRVRx7j83nyEuSWFKTx9EDPWszK/Dp0KED3nvvPezcubPM7QsXLkSHDh2s0jBn5efnh1attGvCaDRPnlcqgZYt/SDBvzMikiiWArCcFIOairCkJo8jB3q2YFbgM3v2bLRs2RKtWrXCxIkTUb9+fQiCgAsXLmD58uX49ddfceLECVu11WmEhmoXwhsxAigq0gY9H37ILn4iMg9LAciXuTV55BLUVIRZgU+DBg1w4MABJCQk4PXXX9evxC4IAurXr4/k5GQ0bNjQJg11NgkJQHy8LpmTQQ8RmY8F6eSLNXksZ3YBw1atWuH8+fNITU01KGDYrFkza7fN6XHKORFVhi0vfpwubxl7zrJiTR7LmB345Ofno0aNGmjWrJlBsKPRaHD//n14eXlZs31ERGSCLS5+nC5vHjFXPpd7TR5LmDWdfffu3Xj66afx119/ldr2559/4plnnsHevXut1jgiIiqt5OwbleoeIiOvlroAWjpLp6LT4Dld/kmQuGTJx5g82QsajeEsqyVLPsaaNWs4xVxCzOrxWbduHaZMmYJq1aqV2la9enVMnToVa9asQc+ePa3WQCIiMmTvWTq2rDLv6LjyueMxK/A5d+4cPvjgA6Pb27Zti7fffrvSjSIiItPsNcTEOkEVY69Ec9bkqTyzAp87d+7g8ePHRrcXFhbizp07lW4UERGJj3WCKs5es6xYk6fyzAp8IiIi8OOPP6J+/fplbv/xxx8RHh5ulYaV1KtXL6SmpuLmzZvw8fFB586dsWjRIoSEhOj3OXv2LEaPHo1Tp04hICAAY8eOxZQpU2zSHiIiZ8c6Qeax1ywrBjWVY1Zyc58+fTBjxgzk5OSU2padnY23334bL7/8stUaV1yHDh3w6aef4tKlS9i5cyeuXLmCV155Rb89Pz8fXbp0QXh4OE6fPo0lS5Zgzpw52LBhg03aQ0Tk7HTDN8WxTpBpxhLNSTrM6vGZNm0a/vvf/yImJgYDBgxAvXr1AAAXL17Etm3bEBYWhmnTptmkoRMmTND/d3h4OKZNm4bevXujsLAQrq6u2LZtGx49eoRNmzbBzc0NDRs2RGpqKpYtW4bExESbtImIbId1ZMTHInnkjMwKfDw9PXHs2DFMnz4dO3bs0OfzeHt7Y8CAAZg/f75dFinNzc3Ftm3b0Lp1a7i6ugIAUlJS0LZtW4OErvj4eCxatAh37tyBj4+PzdtFRNbBOjLiKn4eNTV8wwRackRmFzBUqVT44IMPsHbtWty+fRuCICAgIEC/fEVxx44dw9NPPw13d3erNFY3Xf7hw4do1aoV9u3bp9+WnZ2NyMhIg/0DAwP124wFPgUFBSgoKNA/zs/Pt0pbichyrCMjLibQVhxnWTkeswMfHYVCgYCAAJP7dOvWDampqahTp06Z26dNm4ZFixaZPMaFCxf0ydSTJ09GQkICrl69irlz52LgwIHYt29fmUFXRS1YsABz5861+PeJiJwRg5qKYZDoeCwOfCpCEAST2ydNmoTBgweb3Kd40OTv7w9/f3/UrVsXTz31FMLCwnDixAnExcUhKCioVNK17nFQUJDR40+fPh0TJ07UP87Pz0dYWJjJNhERkfU5al6XFNtExtk08ClPQEBAub1Gxmg02pkGumGquLg4zJgxQ5/sDAAHDhxAvXr1TOb3uLu7W20ojoiILMO8LrIXs6azi+XkyZNYs2YNUlNTcfXqVRw6dAj9+vVDVFQU4uLiAAD9+/eHm5sbEhIScP78eezYsQMrV6406M0hIiJpKtnTk5fnifT0COTleZrcj8hcovb4VFS1atWwa9cuzJ49Gw8ePEBwcDC6du2Kt99+W99bo1KpkJycjNGjRyM2Nhb+/v6YNWsWp7ITETkYLpNBtmTTwKcyScfFNW7cGIcOHSp3vyZNmuDo0aNWeU0iIrI/LpNBtiZqcjPJh6MmLZI4Sk79NbY6uBhThPldti0uk0G2ZtPA5949fkmJSYtkvuJThLdv98A776ig0Sjg4iJg8eI89O//pygBBr/LtmevVc5JvswKfDp27Fih/SoyLEXywWJ0ZAk/Pz9kZgJTpgD/P4kTGo0CU6d6o29fb4gRV/C7bHtcJsM09jhWnlmBz5EjRxAeHo7u3bvrp4wTEdnK5ctPgh6doiIgLQ0IDRWnTWR79lrl3NGwx9E6zAp8Fi1ahKSkJHz22Wd44403MHToUDRq1MhWbSMimYuJAVxcDIMfpRKIjhavTWQbJfO1VKp7ZQY8cl76oawp/2XlvrHH0TSzAp/Jkydj8uTJSElJwaZNm9CmTRvUq1cPQ4cORf/+/eHl5WWrdhKRDIWGAhs2ACNGaHt6lErgww/Z2+OMuPSDeTjl33IWJTfHxcUhLi4OK1euxGeffYa1a9firbfewo0bNxj8EJFVJSQA8fHa4a3oaAY9zoxBTcVwyn/lVGpW15kzZ/Ddd9/hwoULaNSoEfN+iMgmQkMZ8BDpcMp/5Zi9ZMWNGzfw3nvvoW7dunjllVfg6+uLkydP4sSJE/Dw8LBFG4mIJMnYsgpEtqSb8l8cp/xXnFk9Pi+++CIOHz6MLl26YMmSJejevTuqVHGIVS9IRBVNRpRz0iI5huLfUVM5Fvwuky1xyn/lKAQzyiu7uLggODgYNWvWNLkcxZkzZ6zSODHk5+dDpVIhLy+P+UpWxNoT5CzUajUyMh7j2WdrQqN5ch5UKgWcPHkTERFV+F0mm8jKysKGDRv0j7WzukpP+U9MTERwcLAYTRRVRa/fZnXXzJ49u9INI3nihYCchZ+fH86eLau+kAL37gWKUliR5IFT/q3DrB4fOWCPj+1lZmoL08XEMGGVHFNmJhAeXrq+UEYGv9PWwB5i4/jZGGeTHh9jvvvuOzx48ABxcXHw8fGxxiHJSW3cCCQmai8YLi7aGi0JCWK3isg8rC9kO6xObJoc37O1mV25+f79+5g3bx4A7err3bp1Q3JyMgCgZs2a+Pbbb9GwYUPrt5QcXmbmk6AH0P47YoS2RgsvGORoWF/INrgeGtmaWdPZd+zYYbBExeeff47vv/8eR48exe3bt/H0009j7ty5Vm8kOQdT6y4ROaLQUKB9ewY9RI7ErMAnPT0dTZo00T/+6quv8Morr6BNmzbw9fXF22+/jZSUFKs3kpyDbt2l4rjuEhGZwlpJZG1mDXU9fvwY7u7u+scpKSkYP368/nFISAhu375ttcaRc2FeBBGZg+tRkS2Y1eMTFRWF77//HgBw7do1/Pbbb2jbtq1+e2ZmJhOvyKSEBO3Ml8OHtf8ysZmIymJsPSr2/FBlmdXjM3r0aIwZMwZHjx7FiRMnEBcXhwYNGui3Hzp0CM2bN7d6I8m5cN0lIioP16MiWzEr8Bk+fDiUSiX27t2Ltm3blipoeOPGDQwdOtSqDSQiImmyZU0Z3XpUxYMfrkdF1mB2HZ+hQ4caDW4++OCDSjeIiIikz9r1dnRBVF5eHoDy16NidWKyFFcYJSIis1mz3o6xIKpFi58QFZWmX49q+PBu8PZ+xuyeJFY7puLMCnwKCwsxY8YM7Nq1C76+vhg5cqRB709OTg5CQkJQVFRk9YYSEZFzMhWUFF+Pytvb2+zFN1kJmkoya1bX/Pnz8e9//xsjR45Ely5dMHHiRIwYMcJgHy79RUREUsFK0FSSWT0+27Ztw7/+9S/06NEDADB48GB069YNQ4YMwaZNmwAACoXC+q0kIiIisgKzenz++OMPgyUroqOjceTIERw/fhxvvvkmh7iIiGTKmhWWbVmtmZWgyawen6CgIFy5cgURERH652rVqoXDhw+jQ4cOGDx4sJWbR0RElrJXUq81KyzbslozK0ETYGbg07FjR2zfvh2dOnUyeD4kJASHDh1C+/btrdk2IiKykL2Seo1VWI6KSjO70KA1j2XPY5NjMSvwmTlzJi5evFjmtlq1auG7777DgQMHrNIwIiKynK2TenV1dMqrsGxOvR1bVmtmJWjSMSvwCQ8PR3h4uNHtISEhGDRoUKUbRURE0ubn54cxY8YgI+Mxtm4VoNE8mdiiVAoYO7YbIiKqVKg3SRcclVetuTJFC1kJmnTMSm7W+eyzz9CnTx80atQIjRo1Qp8+ffD5559bu21lKigoQLNmzaBQKJCammqw7ezZs3j++edRtWpVhIWFYfHixXZpExGRHPn5+SE2NhAbNiigVGqfUyqBDz9UIDY2sMJDaLogavLkfliyJB9KpfD/xxKwZEk+Jk/uZ/GQnC5Y0lWCVig0AMBK0DJmVo+PRqNBv3798Nlnn6Fu3bqoX78+AOD8+fPo27cvXn31VXz88cc2ndI+ZcoUhISE4OeffzZ4Pj8/H126dEHnzp2xfv16/PLLLxg6dCi8vb2RmJhos/YQETmziiRIJyT4IT4eSEsDoqMtW4RYF9RMmgT07as7lgKhod4AvC1qu+64Y8aM0b+HWbNuISOjCiIiHiMk5BkA5leCJsdmVuCzcuVKHDx4EHv27NHX8tHZs2cPhgwZgpUrV2L8+PHWbKPe119/jeTkZOzcuRNff/21wbZt27bh0aNH2LRpE9zc3NCwYUOkpqZi2bJlDHyIiCxgToJ0aKifRQFPWUJDLQuejCke1AQHA7Gx1js2OR6zhrqSkpKwZMmSUkEPAPTq1QuLFy/WFzK0tpycHAwfPhxbt25FtWrVSm1PSUlB27ZtDbor4+PjcenSJdy5c8focQsKCpCfn2/wQ0TkbCypX8Oqx+SMzAp8Ll++jM6dOxvd3rlzZ1y+fLnSjSpJEAQMHjwYI0eOxNNPP13mPtnZ2QgMDDR4Tvc4Ozvb6LEXLFgAlUql/wkLC7New4mIJODMmeZYsWI8tmwZhBUrxuPMmeZiN4lINGYFPh4eHrh7967R7fn5+ahatWqFjzdt2jQoFAqTPxcvXsTq1atx7949TJ8+3ZzmVsj06dORl5en/7l+/brVX4OIyN50vd/G6tfoen6Y1EtyY1aOT1xcHNatW4d169aVuX3t2rWIi4ur8PEmTZpUbrXnOnXq4NChQ0hJSYG7u7vBtqeffhpvvPEGtmzZgqCgIOTk5Bhs1z0OCgoyenx3d/dSxyUC7Ff1lsgWdEm9hw8Dy5eXrl/Tps0gtG8PfodJdswKfGbMmIH27dtDrVbjrbfeQv369SEIAi5cuID3338f//3vf3H48OEKHy8gIAABAQHl7rdq1Sq8++67+sc3btxAfHw8duzYgZYtWwLQBmUzZsxAYWEhXF1dAQAHDhxAvXr14OPjY87bJLJb1VsiW/Lz80OrVoCLC6DRPHleqQRatvQDv7ri4Y2VeMwKfFq3bo0dO3YgMTERO3fuNNjm4+ODjz/+GG3atLFqAwGgdu3aBo9r1KgBAIiKikLo/6f+9+/fH3PnzkVCQgKmTp2Kc+fOYeXKlVi+fLnV20POj0md5CxCQ4ENG4ARI4CiIl2dHctmTeXleSI31w++vmpWO64E3liJy6zABwD+9re/IT4+Hvv379cnMtetWxddunQpc7aVvahUKiQnJ2P06NGIjY2Fv78/Zs2axansRCR7CQmodJ0dLvBpPbyxEpdZgc+hQ4cwZswYnDhxAn/7298MtuXl5aFhw4ZYv349nn/+eas2sqSIiAgIglDq+SZNmuDo0aM2fW0iIkdkSW2c8hKkdQt8MkGaHIlZs7pWrFiB4cOHw8vLq9Q2lUqFESNGYNmyZVZrHBERiUeXIN269aAyF/hs02YQh2PI4ZgV+Pz888/o2rWr0e1dunTB6dOnK90oIiKSBm2CtB9cSlwtniRIM+ipLEuKS5LlzBrqysnJ0c+YKvNgVarg1q1blW4UERFJhzUTpMkQc6fsz6wen1q1auHcuXNGt589exbBwcGVbhQREUlLQgKQkQEcPqz9NyFB7BY5vvKKS5JtmBX4vPjii5g5cyb++uuvUtv+/PNPzJ49u8x1vIgcTUWTNZnUSXISGgq0b8+eHmvJzfUrM3cqN9dXpBbJg1lDXW+//TZ27dqFunXrYsyYMahXrx4A4OLFi1i7di2KioowY8YMmzSUyJ50SZ266aQ3brggPb0KIiMfIyREWwmOBcbImbCgnv3obph8fdVQKDQGwY9CoYGvb67BfmRdCqGseeEmXL16FaNGjcL+/fv1U8oVCgXi4+Oxdu1aREZG2qSh9pKfnw+VSoW8vLwyZ6+R/GzcCCQmaivfurhocx3YzU/OhAX17E8XaG7f7oGpU1UoKlJAqRSwaFEe+vf/k4GmBSp6/TY78NG5c+cO0tLSIAgCYmJinGZZCAY+VFxmJhAeXrrcf0YGu/vJeWRlZWHDhg3l7peYmMg8ThvIzKxccUnSquj12+zKzTo+Pj545plnLP11Iodw+bJh0ANoZ7WkpfEERfJz9+5dk9vZS2EZS4pLkuUsDnyIyuJseQIxMWUv8BgdLV6biMTy6aeflrsPh8NI6hj4kNU4Y54A65cQmYfrS5HUMfAhqyl5wjO2krOjnRitscAjERFJAwMfsglnq0bKMXiSE2M3LUTOgIEPWV15KzkTkXQ5200LUUlmVW4mqghWIyVyLLpCeeYsocCFNclRsceHrK68aqREJC26SuWHDwPLl5e+aWnTZhAaNryFHTt2AGCvEDk29viQ1alU99Cz5z4oFNo54LoTI4e5iKTLz88PrVr5waXEVUGpBFq29INKpQLAhTXJ8bHHh2yiRYufEBWVhtxcX/j65jLoIXIApso3qNXa4TBTQ9kq1T2uL0WSx8DHwWRmaqsJx8RIb5ZRyROeSnWvzICHJ0Yi6TJWvkE3HJaR8RhbtwrQaBT631EqBYwd2w0REVUcpkYXyZfFa3U5Kymv1eUIi2U6W+VmIipt48bSvUJSOxeR/Nh8kVJnJdXAh4tlEpGUcGFN2+DNo+Vsvkgp2RcXyyQiKWFRT+tzxmV/pIizuhyEbrHM4rhYJhGR86jocj6OtuyP1DDwcRC62RZKpfYxF8skIiIyH4e6HAgXyyQiIqocBj4OhuPqREREluNQFxERkQRxPTTbYI8PERGRxHA9NNthjw8REZGEcD0022LgQ0REJAG65XxMrYdWfD+yjMMEPhEREVAoFAY/CxcuNNjn7NmzeP7551G1alWEhYVh8eLFIrWWiIjIPLr10MaO7QoXF8NFFXTrobF4YeU5VI7PO++8g+HDh+sfe3o+6fbLz89Hly5d0LlzZ6xfvx6//PILhg4dCm9vbyQmJorRXCIiIrP4+fnBz09bt81wPTQFYmMDxW6eU3CowMfT0xNBQUFlbtu2bRsePXqETZs2wc3NDQ0bNkRqaiqWLVvGwIeIiBwK67bZjsMMdQHAwoUL4efnh+bNm2PJkiV4/PixfltKSgratm1rMPYZHx+PS5cu4c6dO0aPWVBQgPz8fIMfIiIisYWGAu3bM+ixNofp8Rk3bhxatGgBX19fHD9+HNOnT0dWVhaWLVsGAMjOzkZkZKTB7wQGBuq3+fj4lHncBQsWYO7cubZtPBEREUmCqIHPtGnTsGjRIpP7XLhwAfXr18fEiRP1zzVp0gRubm4YMWIEFixYAHd3d4vbMH36dINj5+fnIywszOLjERER0RNqtdrkwqpubm52TdgWNfCZNGkSBg8ebHKfOnXqlPl8y5Yt8fjxY2RkZKBevXoICgpCTk6OwT66x8byggDA3d29UoETERERlU2tVmPNmjXl7mfP2WqiBj4BAQEICAiw6HdTU1Ph4uKCmjVrAgDi4uIwY8YMFBYWwtXVFQBw4MAB1KtXz+gwF5E9ZWYCly8DMTEcsycieTDV02PJftbgEMnNKSkpWLFiBX7++Wf8/vvv2LZtGyZMmIABAwbog5r+/fvDzc0NCQkJOH/+PHbs2IGVK1caDGMRiWXjRiA8HOjYUfvvxo1it4iISJ4cIrnZ3d0dn3zyCebMmYOCggJERkZiwoQJBkGNSqVCcnIyRo8ejdjYWPj7+2PWrFmcyk6iy8wEEhMBjUb7WKPR1ueIj2fPDxGRvTlE4NOiRQucOHGi3P2aNGmCo0eP2qFFRBV3+fKToEenqEhbn4OBD9mC1JJJiaTEIQIfIkcWEwO4uBgGP0qltigZkbVJMZmUSEocIseHyJGFhmrLzyuV2sfa8vPs7SHbkGIyKZGUsMeHyA5Yfp7EkpfnidxcP/j6qqFS3RO7OUSiY+BDZCehoQx4yL7OnGmOvXt7QBBcoFBo0LPnPrRo8ZPYzSIZKb6MlDX2swYGPkRETigvz1Mf9ACAILhg794eiIpKY88P2Y2fnx/GjBkjqWR7Bj5ENsTZNSSW3Fw/fdCjIwguyM31ZeBDdiW1cxwDHyIb4ewaEpOvrxoKhcYg+FEoNPD1zRWxVSR3Uqhgz1ldRDbC2TUkJpXqHnr23AeFQltHQZfjw94eEotUKtizx4eIyIkUTxJt0eInREWlITfXF76+uQZBjz2TSYmkVMGegQ8RkRORYjIpkZQq2DPwISJyMgxqSGqkVMGegQ8RETktzqyUBl0F+xEjtD09YlawZ+BDREROiTMrpUUqFewZ+BARkVPizErpkUIFe05nJ7IRKZZqJyKSO/b4ENkIZ9cQSQsXbCWAgQ+RTTGoIZIGLthKOhzqIiIip2Zswda8PE+RW0ZiYOBDREROzdSCrSQ/DHyIiMip6RZsLY4LtsoXAx8iInJKuhmT5S3YypmV8qIQBEEQuxFSkp+fD5VKhby8PHh5eYndHCIiqoTilZtv3HBBRkYVREQ8RkiINgjizErnUdHrN2d1ERGR0yoe1AQHA7GxIjaGJIFDXURERCQbDHyIiIhINhj4EBERkWww8CEiIiLZYOBDREREssHAh4iIiGSDgQ8RERHJhkMFPl9++SVatmwJDw8P+Pj4oHfv3gbbr127hu7du6NatWqoWbMmJk+ejMePH4vTWCtSq9XIysoy+qNWq8VuIhERkUNwmAKGO3fuxPDhw/Hee++hY8eOePz4Mc6dO6ffXlRUhO7duyMoKAjHjx9HVlYWBg4cCFdXV7z33nsitrxy1Go11qxZU+5+Y8aMYfVRIiKicjhE4PP48WP84x//wJIlS5CQkKB/vkGDBvr/Tk5Oxq+//oqDBw8iMDAQzZo1w7x58zB16lTMmTPHYddi0ZVat9Z+REREcuYQQ11nzpzBH3/8ARcXFzRv3hzBwcHo1q2bQY9PSkoKGjdujMDAQP1z8fHxyM/Px/nz58VoNhEREUmMQwQ+v//+OwBgzpw5ePvtt7Fv3z74+Pigffv2yM3NBQBkZ2cbBD0A9I+zs7ONHrugoAD5+fkGP0REROScRA18pk2bBoVCYfLn4sWL0Gi0q+jOmDEDL7/8MmJjY5GUlASFQoHPPvusUm1YsGABVCqV/icsLMwab42IiIgkSNQcn0mTJmHw4MEm96lTpw6ysrIAGOb0uLu7o06dOrh27RoAICgoCP/73/8MfjcnJ0e/zZjp06dj4sSJ+sf5+fkMfoiIHIxarTaZ6+jm5sYJIARA5MAnICAAAQEB5e4XGxsLd3d3XLp0Cc899xwAoLCwEBkZGQgPDwcAxMXFYf78+bh58yZq1qwJADhw4AC8vLwMAqaS3N3d4e7uboV3Q0SVlZkJXL4MxMQAoaFit4YcBWe/kjkcYlaXl5cXRo4cidmzZyMsLAzh4eFYsmQJAODVV18FAHTp0gUNGjTAm2++icWLFyM7Oxtvv/02Ro8ezcCGyAFs3AgkJgIaDeDiAmzYABSbxElkFGe/SpvUeuMcIvABgCVLlqBKlSp488038eeff6Jly5Y4dOgQfHx8AABKpRL79u3DqFGjEBcXh+rVq2PQoEF45513RG555VR0Gr6jTtcnArQ9PbqgB9D+O2IEEB/Pnh8iRybF3jiHCXxcXV2xdOlSLF261Og+4eHh+Oqrr+zYKtvz8/PDmDFjJBUtE1nb5ctPgh6doiIgLY2BD5kvL88Tubl+8PVVQ6W6J3ZzZE2KvXEOE/jIGYMacnYxMdrhreLBj1IJREeL1yZyTGfONMfevT0gCC5QKDTo2XMfWrT4SexmkYQ4RB0fInJuoaHanB6lUvtYqQQ+/JC9PWSevDxPfdADAILggr17eyAvz1PklpGUsMeHiCQhIUGb05OWpu3pYdBD5srN9dMHPTqC4ILcXF8OeZEeAx8ikozQUAY8ZDlfXzUUCo1B8KNQaODrmytiq0hqONRFREQOTTerVaW6h54990Gh0CaL6XJ8dL09nP1KAHt8iIjIwZWc/Tpr1i1kZFRBRMRjhIQ8A+AZzn4lPQY+RCQqqRU3I8dU/DsSHAzExorYGNKTYi06Bj5EJBopFjcjIuuRYi06Bj5EJBopFjcjIuuS2k0Lk5uJiIhINhj4EBERkWww8CEikoDMTODwYe2/RGQ7DHyIiES2cSMQHg507Kj9d+NGsVtE5LwY+BARiSgzE0hMfLJAq0YDjBjBnh9yTlLo2WTgQ0QkErVajRMn1Aar0gNAURFw8qQaarVanIYR2YBUejYZ+BCRaKRY3MxedDWMjh/fol9iQUeh0ODYsS1Ys2YNgx9yClLq2WQdHyISjRSLm9mL7j3r1pfau7cHBMGl1PpSrGFEzuDyZZTZs5mWZv+FiRn4EJGonDGoMVeLFj8hKioNubm+8PXN1Qc9RM4iJgZwcTEMfpRKIDra/m3hUBcRkQSoVPcQGXmVQQ85pdBQYMMGbbADaP/98EP79/YA7PEhIiIiO0hIAOLjtcNb0dHiBD0AAx8iIiKyk9BQ8QIeHQ51ERERkWww8CEiIiLZYOBDRCQCOdcwIhITc3yIiEQg5xpGRGJi4ENEJBIGNUT2x6EuIiIikg0GPkRERCQbDHyIiIhINhj4EBERkWww8CEiIiLZcIjA58iRI1AoFGX+nDp1Sr/f2bNn8fzzz6Nq1aoICwvD4sWLRWw1ERERSY1DTGdv3bo1srKyDJ6bOXMmvv32Wzz99NMAgPz8fHTp0gWdO3fG+vXr8csvv2Do0KHw9vZGYmKiGM0mIiIiiXGIwMfNzQ1BQUH6x4WFhfjvf/+LsWPHQqFQAAC2bduGR48eYdOmTXBzc0PDhg2RmpqKZcuWMfAhIiIiAA4y1FXSnj17oFarMWTIEP1zKSkpaNu2rUF59/j4eFy6dAl37twxeqyCggLk5+cb/BAREZFzcogen5I2btyI+Ph4hBZb2z47OxuRkZEG+wUGBuq3+fj4lHmsBQsWYO7cuaWeZwBERETkOHTXbUEQTO4nauAzbdo0LFq0yOQ+Fy5cQP369fWPMzMzsX//fnz66adWacP06dMxceJE/eM//vgDDRo0QFhYmFWOT0RERPZz7949qFQqo9tFDXwmTZqEwYMHm9ynTp06Bo+TkpLg5+eHXr16GTwfFBSEnJwcg+d0j4vnB5Xk7u4Od3d3/eMaNWrg+vXr8PT01OcPyUF+fj7CwsJw/fp1eHl5id0ch8bP0jr4OVoPP0vr4OdoPbb4LAVBwL179xASEmJyP1EDn4CAAAQEBFR4f0EQkJSUhIEDB8LV1dVgW1xcHGbMmIHCwkL9tgMHDqBevXpGh7nK4uLiYjCEJjdeXl78g7YSfpbWwc/RevhZWgc/R+ux9mdpqqdHx6GSmw8dOoT09HQMGzas1Lb+/fvDzc0NCQkJOH/+PHbs2IGVK1caDGMRERGRvDlUcvPGjRvRunVrg5wfHZVKheTkZIwePRqxsbHw9/fHrFmzOJWdiIiI9Bwq8Nm+fbvJ7U2aNMHRo0ft1Brn4u7ujtmzZxvkO5Fl+FlaBz9H6+FnaR38HK1HzM9SIZQ374uIiIjISThUjg8RERFRZTDwISIiItlg4ENERESywcCHiIiIZIOBj8x8//336NmzJ0JCQqBQKPDFF18YbBcEAbNmzUJwcDA8PDzQuXNnXL58WZzGSlx5n+XgwYOhUCgMfrp27SpOYyVswYIFeOaZZ+Dp6YmaNWuid+/euHTpksE+f/31F0aPHg0/Pz/UqFEDL7/8cqlK7XJXkc+xffv2pb6TI0eOFKnF0rVu3To0adJEX1wvLi4OX3/9tX47v48VU97nKNb3kYGPzDx48ABNmzbF2rVry9y+ePFirFq1CuvXr8fJkydRvXp1xMfH46+//rJzS6WvvM8SALp27YqsrCz9z8cff2zHFjqG7777DqNHj8aJEydw4MABFBYWokuXLnjw4IF+nwkTJmDv3r347LPP8N133+HGjRvo06ePiK2Wnop8jgAwfPhwg+/k4sWLRWqxdIWGhmLhwoU4ffo0fvzxR3Ts2BEvvfQSzp8/D4Dfx4oq73MERPo+CiRbAITdu3frH2s0GiEoKEhYsmSJ/rm7d+8K7u7uwscffyxCCx1Hyc9SEARh0KBBwksvvSRKexzZzZs3BQDCd999JwiC9jvo6uoqfPbZZ/p9Lly4IAAQUlJSxGqm5JX8HAVBENq1ayf84x//EK9RDszHx0f417/+xe9jJek+R0EQ7/vIHh/SS09PR3Z2Njp37qx/TqVSoWXLlkhJSRGxZY7ryJEjqFmzJurVq4dRo0ZBrVaL3STJy8vLAwD4+voCAE6fPo3CwkKD72X9+vVRu3Ztfi9NKPk56mzbtg3+/v5o1KgRpk+fjocPH4rRPIdRVFSETz75BA8ePEBcXBy/jxYq+TnqiPF9dKjKzWRb2dnZAIDAwECD5wMDA/XbqOK6du2KPn36IDIyEleuXME///lPdOvWDSkpKVAqlWI3T5I0Gg3Gjx+PNm3aoFGjRgC030s3Nzd4e3sb7MvvpXFlfY6Adk3D8PBwhISE4OzZs5g6dSouXbqEXbt2idhaafrll18QFxeHv/76CzVq1MDu3bvRoEEDpKam8vtoBmOfIyDe95GBD5GNvP766/r/bty4MZo0aYKoqCgcOXIEnTp1ErFl0jV69GicO3cOP/zwg9hNcWjGPsfiaxc2btwYwcHB6NSpE65cuYKoqCh7N1PS6tWrh9TUVOTl5eHzzz/HoEGD8N1334ndLIdj7HNs0KCBaN9HDnWRXlBQEACUmp2Qk5Oj30aWq1OnDvz9/ZGWliZ2UyRpzJgx2LdvHw4fPozQ0FD980FBQXj06BHu3r1rsD+/l2Uz9jmWpWXLlgDA72QZ3NzcEB0djdjYWCxYsABNmzbFypUr+X00k7HPsSz2+j4y8CG9yMhIBAUF4dtvv9U/l5+fj5MnTxqMyZJlMjMzoVarERwcLHZTJEUQBIwZMwa7d+/GoUOHEBkZabA9NjYWrq6uBt/LS5cu4dq1a/xeFlPe51iW1NRUAOB3sgI0Gg0KCgr4fawk3edYFnt9HznUJTP37983iKbT09ORmpoKX19f1K5dG+PHj8e7776LmJgYREZGYubMmQgJCUHv3r3Fa7REmfosfX19MXfuXLz88ssICgrClStXMGXKFERHRyM+Pl7EVkvP6NGjsX37dvz3v/+Fp6enPk9CpVLBw8MDKpUKCQkJmDhxInx9feHl5YWxY8ciLi4OrVq1Ern10lHe53jlyhVs374dL774Ivz8/HD27FlMmDABbdu2RZMmTURuvbRMnz4d3bp1Q+3atXHv3j1s374dR44cwf79+/l9NIOpz1HU76Pd55GRqA4fPiwAKPUzaNAgQRC0U9pnzpwpBAYGCu7u7kKnTp2ES5cuidtoiTL1WT58+FDo0qWLEBAQILi6ugrh4eHC8OHDhezsbLGbLTllfYYAhKSkJP0+f/75p/D3v/9d8PHxEapVqyb87W9/E7KyssRrtASV9zleu3ZNaNu2reDr6yu4u7sL0dHRwuTJk4W8vDxxGy5BQ4cOFcLDwwU3NzchICBA6NSpk5CcnKzfzu9jxZj6HMX8PioEQRBsG1oRERERSQNzfIiIiEg2GPgQERGRbDDwISIiItlg4ENERESywcCHiIiIZIOBDxEREckGAx8iIiKSDQY+REREJBsMfIjIQHZ2NsaOHYs6derA3d0dYWFh6Nmzp8HaRMePH8eLL74IHx8fVK1aFY0bN8ayZctQVFSk3ycjIwMJCQmIjIyEh4cHoqKiMHv2bDx69Mjg9T766CM0bdoUNWrUgLe3N5o3b44FCxbot8+ZMwcKhQJdu3Yt1dYlS5ZAoVCgffv2FXpvumMpFApUqVIFERERmDBhAu7fv2/mp0REjoprdRGRXkZGBtq0aQNvb28sWbIEjRs3RmFhIfbv34/Ro0fj4sWL2L17N1577TUMGTIEhw8fhre3Nw4ePIgpU6YgJSUFn376KRQKBS5evAiNRoMPP/wQ0dHROHfuHIYPH44HDx5g6dKlAIBNmzZh/PjxWLVqFdq1a4eCggKcPXsW586dM2hXcHAwDh8+jMzMTIMVxzdt2oTatWub9R4bNmyIgwcP4vHjxzh27BiGDh2Khw8f4sMPPyy176NHj+Dm5mbBJ2k7UmwTkUOx+aIYROQwunXrJtSqVUu4f/9+qW137twR7t+/L/j5+Ql9+vQptX3Pnj0CAOGTTz4xevzFixcLkZGR+scvvfSSMHjwYJNtmj17ttC0aVOhR48ewrvvvqt//tixY4K/v78watQooV27dhV4d0+OVdzw4cOFoKAgg+0fffSREBERISgUCkEQtO89ISFB8Pf3Fzw9PYUOHToIqamp+mOkpqYK7du3F2rUqCF4enoKLVq0EE6dOiUIgiBkZGQIPXr0ELy9vYVq1aoJDRo0EL788ktBEAQhKSlJUKlUBu3ZvXu3UPzUbGmbiKhsHOoiIgBAbm4uvvnmG4wePRrVq1cvtd3b2xvJyclQq9V46623Sm3v2bMn6tati48//tjoa+Tl5cHX11f/OCgoCCdOnMDVq1fLbd/QoUOxefNm/eNNmzbhjTfeqHTvh4eHh8HwW1paGnbu3Ildu3YhNTUVAPDqq6/i5s2b+Prrr3H69Gm0aNECnTp1Qm5uLgDgjTfeQGhoKE6dOoXTp09j2rRpcHV1BaBdNb2goADff/89fvnlFyxatAg1atQwq42WtImIysahLiICoL24CoKA+vXrG93nt99+AwA89dRTZW6vX7++fp+yjr969Wr9MBcAzJ49G3369EFERATq1q2LuLg4vPjii3jllVfg4mJ4X9ajRw+MHDkS33//PWJjY/Hpp5/ihx9+wKZNm8x9q3qnT5/G9u3b0bFjR/1zjx49wr///W8EBAQAAH744Qf873//w82bN+Hu7g4AWLp0Kb744gt8/vnnSExMxLVr1zB58mT9ZxcTE6M/3rVr1/Dyyy+jcePGAIA6deqY3U5L2kREZWPgQ0QAAEEQbLIvAPzxxx/o2rUrXn31VQwfPlz/fHBwMFJSUnDu3Dl8//33OH78OAYNGoR//etf+OabbwyCH1dXVwwYMABJSUn4/fffUbduXTRp0sSsdgDAL7/8gho1aqCoqAiPHj1C9+7dsWbNGv328PBwfYABAD///DPu378PPz8/g+P8+eefuHLlCgBg4sSJGDZsGLZu3YrOnTvj1VdfRVRUFABg3LhxGDVqFJKTk9G5c2e8/PLLZrfbkjYRUdkY+BARAG0vhS4p2Zi6desCAC5cuIDWrVuX2n7hwgU0aNDA4LkbN26gQ4cOaN26NTZs2FDmcRs1aoRGjRrh73//O0aOHInnn38e3333HTp06GCw39ChQ9GyZUucO3cOQ4cONfctAgDq1auHPXv2oEqVKggJCSk1VFZymO/+/fsIDg7GkSNHSh3L29sbgHa2WP/+/fHll1/i66+/xuzZs/HJJ5/gb3/7G4YNG4b4+Hh8+eWXSE5OxoIFC/D+++9j7NixcHFxKRVEFhYWlnodS9pERGVjjg8RAQB8fX0RHx+PtWvX4sGDB6W23717F126dIGvry/ef//9Utv37NmDy5cvo1+/fvrn/vjjD7Rv3x6xsbFISkoqNXxVFl3gVFYbGjZsiIYNG+LcuXPo37+/OW9Pz83NDdHR0YiIiKhQflCLFi2QnZ2NKlWqIDo62uDH399fv1/dunUxYcIEJCcno0+fPkhKStJvCwsLw8iRI7Fr1y5MmjQJH330EQAgICAA9+7dM3ivuhwea7SJiEpj4ENEemvXrkVRURGeffZZ7Ny5E5cvX8aFCxewatUqxMXFoXr16vjwww/x3//+F4mJiTh79iwyMjKwceNGDB48GK+88gpee+01AE+Cntq1a2Pp0qW4desWsrOzkZ2drX+9UaNGYd68eTh27BiuXr2KEydOYODAgQgICEBcXFyZbTx06BCysrLs1rPRuXNnxMXFoXfv3khOTkZGRgaOHz+OGTNm4Mcff8Sff/6JMWPG4MiRI7h69SqOHTuGU6dO6fOgxo8fj/379yM9PR1nzpzB4cOH9dtatmyJatWq4Z///CeuXLmC7du3GyRwW9omIjKOQ11EpFenTh2cOXMG8+fPx6RJk5CVlYWAgADExsZi3bp1AIBXXnkFhw8fxvz58/H888/jr7/+QkxMDGbMmIHx48dDoVAAAA4cOIC0tDSkpaUZ1N4BnuQIde7cGZs2bcK6deugVqvh7++PuLg4fPvtt6XyV3TKmnFmSwqFAl999RVmzJiBIUOG4NatWwgKCkLbtm0RGBgIpVIJtVqNgQMHIicnB/7+/ujTpw/mzp0LACgqKsLo0aORmZkJLy8vdO3aFcuXLweg7WX7z3/+g8mTJ+Ojjz5Cp06dMGfOnHKTk8trExEZpxDMzVIkIiIiclAc6iIiIiLZYOBDRE6jRo0aRn+OHj0qdvOISAI41EVETiMtLc3otlq1asHDw8OOrSEiKWLgQ0RERLLBoS4iIiKSDQY+REREJBsMfIiIiEg2GPgQERGRbDDwISIiItlg4ENERESywcCHiIiIZIOBDxEREcnG/wGBBQm9y8tLyAAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_validation)\n", + "surrogate_parity(alm_surr, data_validation)\n", + "surrogate_residual(alm_surr, data_validation)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_usr.ipynb) file." ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(alm_surr, data_validation)\n", - "surrogate_parity(alm_surr, data_validation)\n", - "surrogate_residual(alm_surr, data_validation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_usr.ipynb) file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb index 2783470f..97485dd5 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "53c44468", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -710,8 +737,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py index df312dc4..5936a332 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py @@ -1,15 +1,16 @@ -############################################################################### +################################################################################# # The Institute for the Design of Advanced Energy Systems Integrated Platform # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. -############################################################################### +# +################################################################################# """ Maintainer: Javal Vyas diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb index b4786423..a53ec77c 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -47,7 +73,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -75,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -128,36 +154,162 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2025-03-17 17:38:27 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:27 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "--------------------------------------------------------------------\n", "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", + "--------------------------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", @@ -217,12 +369,25 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.001\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "====================================================================================\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Unit : fs.boiler Time: 0.0\n", "------------------------------------------------------------------------------------\n", " Unit Performance\n", @@ -358,7 +523,13 @@ " Unit Performance\n", "\n", " Variables: \n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Key : Value : Units : Fixed : Bounds\n", " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", "\n", @@ -709,10 +880,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb index b4786423..1edd7306 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb @@ -1,718 +1,743 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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sekxsQoLEJSdLXX4lhYdl5m2R/6zLkueWLZfjzhsoa9atk0eeeEIaN25s9wAAoHoprm98mL4R8BTzPl27Xp7N/EOOO/dc8z599KmneJ8CqHL0c23Mww/K+jVrZODxx8iq156STR+8KluW/273QLQQrHpMVnq6LBw82CwBr1m8ebO8um69vLpmrZxx6aWydsMGGf3QQ9KgQQO7BwAA1Yt/33g6fSPgSeZ9utb3Pj3tkktk3caNcs8jj/A+BVDl6efcg/eMlg3r1splZ54iGyZNkJz0CZKX+ZvdA5EiWPWYvIwMWTNxolkCXrFgU65MWLNO3t2YI3+9+hpZnZUlt6WlSWxsrN0DAIDqxb9vHGj7xtvpGwFPMe/T1WvlnQ3Zct6VV5r36R333MP7FEC1o597d99+m2StWSXXXnCebP74Ddn4zvOyefGvdg+Ei2DVYxJSUsw4qzocALC/zc3JkedXrZaPtm6TwSNHyrLVq2XkLbfYrQAAVD+ub5y8ZYtc/ve/m77x7/SNgKeY9+nK1fJh3hYZVPgd9o81a+TG22+3WwGgevvHyBGy+o9l8o8rB8ufX78vWW+Mk9yFP9utCBXBqsdosNpuzBhpmppqW4CKl7ExW55ZvlK+zN8tN9x1tyxatkyG3nCD3QoAQPWjfeO4Fatk2s5dcn1h37j4j+Vy7fXX260AvMD3Pl0pU//Ml+vuvFOWLF8uw0aMsFsBAP6GXXuNLFv0m4waOVxiZn4h6/79hOTM+8luRbAIVj1me2amGQYgPyfHtgAVZ9aGjfLkHytkZs1YueOhh2Te4sUy+Mor7VYAAKqfmRs27Okbby/sG39dskQup28EPEXfp08sW174Pq0ptz3woMz//XcZcvXVdisAoDRDBg+WRb/OlUfvuk3q/fq9rJn4mOT8/IPdirIQrHqMjq/6Y/fusmLsWNsClL/v12fJY0uXybz69eWBp56S2fPny4WXXGK3AgBQ/XyftUEez1wm8xvEyf2ub7z4YrsVgBfod9jH9TtsvQZy/xNPFL5PF8hFl15qtwIAQnHBBRfIvNk/yjMPPyDxS3+WVS88JJtmf2e3oiQEq0A1Nr3wpPHhJUtlaeMm8vSECTJjzs9yzrnn2q0AAFQ/0zdslIeW/C5LExvLUy9NkO8z5si59I2Ap3yzPsv3HbbwffrEiy/KDz//LOf99a92KwAgEgMGDJCMGd/JS08/IU3WLJLl4++V3J++sVsRKGb18hUFS5f/IX369LFNAMIVExMjz/TuZe9511eFJ43XLl5i1vM//1xOOOEEs14dfVn430z1KygwSwBAdFWWvvHrjRtl28XPm/Xr+hZU674R1U9leZ9+mbVBBv2+VOoXfm/LT0+XE/7yF7sFiL7Ue6aYZfrd/c0SqI50mMrpiYmyvVYtuaDJAdKkT39p2DPFbvWu2XdcIQUVdI5PxSpQTeQXfqhM3bBRRs1fIJsPOti2CieOAIBqy/SNG7Mlbf7Cwr6xjW2lbwS8RN+nU9ZvMN9h8w4+WOo3aGDa+/XrZ5YAgPIXV/jZ+97bb8rB27NlyT9vk83ffyEFu/Lt1uqNYNVjdGxVHWNVx1oFomH7rl3y2YaNcve8X2VX+w4y+fMv5MMpvl9fAQCojrRv/CI7x9c3tmsvH37+uXzwxRd2KwAvMN9hs/Q77PzC77Dt7XfYqRIbG2v3AABUpGOOOUa+mPyBfP7Rh9Kh5g5Z9PitkvftJ7J7x3a7R/VEsOoxWmadl5Eh2zMzbQsQnrz8fPl4w0a5a+6vUrdLV/nq2+/kvx99JL16ef8yLwAAyoP2jZ9m55i+sfZhXegbAQ8y32GzNsqdv8yTOocdVvg+/VYmffIJ71MA8Aj9PJ486V359qsvpWvDWrLgsZsl7+vJkr81z+5RvRCsekzT1FRpN2aMJKR4f8wKeFPOn3/Khxuy5c45v0jjI3vITxkZ8np6uhxxxBF2DwAAqhftGydv9PWNCd2P9PWNkybRNwIeou/TD9ZvMO/TxO7dZfacOfLGe+/xPgUAj9LP5/++/prM+ekn6dGskcx/5B+yedp7sjM32+5RPRCsekxccrK0HjGCYBUhy9qxQ97bkC13zJ4jB/3f/8lvixbJxDfflEMPPdTuAQBA9eLrGzeavrG16xvfeIO+EfAQ8z7N2rDX+/Tlt9/mfQoAlYR+Xr8+8SVZ9NtvckzbljL34X9I3pRJsmPjertH1Uaw6jE6DICOr6pLIBhrtm2T/xaeNN4/b74c2r+/rFq1Sp59+WVJSkqyewAAUL1o3zhpY7bcN/dXOfSEE3x940T6RsBLzHfY9VnmfdrxeN932OdffZX3KQBUUvr5PeG58ebz/IQu7WXRU6Nk82dvy/b1a+weVRPBqsdkpafLwsGDzRIozcqtW+Wtwi+jYxb/Lt1PPU1WrV4tTzz3nLRo0cLuAQBA9bJC+8asDfLP3xZL99NOl9Vr1sgTz9I3Al5i3qfrsuSfiwrfp6efYd6nT/7rX7xPAaCK0M/z8U+MldWrVsmZRyVL5gsPyeaP35Btq5fbPaoWglWPqZuUZIYDiE1IsC3A3jLztsjr67NkfOYfcuy558nqtWvl0aeekiZNmtg9AACoXnx94wZ51vaNa9avl0eeeIK+EfAQ8z5dt968T/uec46sKVznOywAVF36+T7m4Ydk3erVcl7K/8mq156STR+8KltXLLV7VA0Eqx7TfNAg6TF7thlnFfC3ePNmeXXtOnl59Ro59eJLZO2GDXLvI49IXFyc3QMAgOrF9I3r1ssra9YW9o0X+/rGhx+mbwQ8xLxP16yTVwq/w55y4UXmfXr/Y4/xPgWAakI/7x+8Z7RkrV0jl515iqx/9wXJSZ8geZmL7B6VG8Eq4HELNuXKhMITxnc2ZMt5V11tvozeMXq01KpVy+4BAED14usb1+3pG9dkZdE3Ah5j3qer18jbWRvl3CuvlDWF32HvvO8+3qcAUE3p5//dt98mG9aukWvOP0c2f/S6ZL/7L9m85Fe7R+VEsOoxmWlpMj0xUVaMHWtbUF3NzcmRf61aI5O3bJVBI0bKH2vWyI233Wa3IhKzP/9MXhhxg9zW7xj55uCDzE3XtU23AQC8yfSNq7Vv3CKDR9q+8dZb7VYAXmDepytXy+S8LXLZDSNk+dq1ctMdd9itAACI3Pz3v8vq5cvkxiGXyY5p70nWG+Mkd+HPdmvlQrDqQfmFX0b0huopY2O2jFuxUqbt3CXDCr+ELv7jDxnG0BBRsX75chlz2aXy/r33yO45s+XoBg2kd7fDzU3Xd83+Ud4bnSaPX3Sh2RcA4A3aN45fscr0jdfdeVdh37hcht5wg90KwAvMd9jlK2Tan/ky9LbbZHHhd6nhhSfOAACUZPjQa+WPJYtk1MjhIjO/kHX/fkJy5v1kt1YOBKseo2Osdps61SxRvczasFGeWLZcfqhZU2574EH5dckSueLqq+1WROq7Sf+VtDNOk7iNG6Rf40Q5NCFBEurUkVqF/9560/VOjRtLStMmErchS9JOP1Wmv/WmfTQAYH/QvvHJP1bIzJqxcttDD5m+8fIrr7RbAXiB731a+B22Rk259f4H5Nfff5crhw61WwEAKNuQwYNl8a9z5dG7bpO6876TtS8/Ljk//2C3ehvBqsfUTUqShJQUs0T18P36LHl86TKZV7+BPPDkk5Ixf4FcdOmldiuiYfq778h7jz4iKS2aS8fYmra1ZJ3q1ZWUli3k/ccfk+nvvG1bAQAV5fusDfLPzD9kXoPCvvHpp2X2/Ply4cUX260AvMB8h/09U+bVqy/3jRkjGQsWyMUUhwAAInDBBRfIrxk/ydMP3S/xv8+RVS88JLkZ39mt3kSw6jFZ6emyZORIyZk2zbagqppe+GX04SVLJbNxE3nqpZdkxpw5cu7AgXYroiUvO1vevO9e6dEoXg6sX9+2lk337ZHQSN68/z5zDABA+ft240Z5+Hdf3/ik9o0Zc+Scc86xWwF4wTfr1stDi3+XpQmJ8sQLL8iMn3+W8/52vt0KAEDkBgwYIBk/fC8vPf2ENF69SJaPv1c2//SN3eotBKsek5eRYSauIlitur7K2iD3/bZY1jZvIS++/rp8NXOmnH7GGXYrou31u++STi1byQF169qW4Gm42qFpU/nPHUwaBgDl6auNG+W+RYtlzYHN5cX/FPaNP/wgp59+ut0KwAu+XLfe9x22WXN5qfA77Nc//ihnnn223QoAQPTp98Efvv5SXp/wgjTPWSnLnh4lm2d5Ky8jWPWYuORkaZqaylAAVcyuggKZmrVB0hb8JrkHHSxv/u9/8vn06XLiiSfaPRCqhYMHy/bMTHuveAu+/14WFZ6cH1Y71raE7vC4BuYYeiwAQPRo3zgtO8fXN7Yu7BvTC/vGb76hbwQ8RN+nX6xbL6PmL5Tcg9uY77BffPednHjSSXYPAADKn34//PrzTyX9zdfl4O3Z8vuY22XLD1OkYNcuu8f+Q7DqMRqqdpk0icmrqogdu3bLZ1kb5K5582Vnu/bywaefyuSpU+XYY4+1eyBcWtU9o21bmTdgQIkB689ffC5JjRMkxt4Phz724Lg4mf3hB74GAEBEtG/8IjtH7v51gew8pJ2vb5wyhb4R8BDzHXZ9ltxd+B12d4eO8uFnn8lHhd+9eJ8CAPYn7Ye+mPyBfPrh+9IuZrssevwW2fLdZ7L7zx12j4pHsOox+Tk5JiTSJSqvLfn58vGGjXLX3HlS+7Au8uX06ZL+ySdy1FFH2T0QLToucUkB69yvvpQDdxfYe+FrUaeOzPt2ur0HAAiH9o2fZueYvrFW58Nk2jffyKSPPqJvBDzEfIddn7XnO+y0wu+wk/gOCwDwGO2XJk96V6Z/OU0Oa1BDFjx6s2z55iPJ35pn96g4MauXryhYuvwP6dOnj23C/pSZlibLRo+WNqNGSVLhOiqXmJgYOeWg1jJl9Rq55Pzz5R933CGdOnWyW73ly8LXqvT/a5XRyieeKPYHCK36bjdmjBlOY0SvHnJq8wOlXmwtuzU8W/N3yieF/03HzpptWwAAwfLvGy+7+GK58ZZbPNs3pt4zxSzT7+5vlkB1oe/Tk1u1lGlr18nFHv8OOz0x0XwH7JudLbEJCbYViD76BMBX/Kefu/p5q5+7XrVgwQJ5+LHH5d+vvCzNjzlZVkz9UAoKIi+yCgbBqscQrFZu+qV02NVXm5PGtm3b2lZvcl9KqyoNV9PGPS0XdzksoqEA1O7C22vzfpUXflvsawAABK0y9Y2cRKO60vfpNZdfLjffeWel+Q5LsIryRp8AVJ5g1Vm6dKk8+vg/ZfwzTxOsVmcu7OKLAspTXkaGuYy+siqpYjUhJcWEqjoRnFasnta8mdSNDX/yKrUtf6d8TMUqAFR5nEQD3kewiopCnwBUvmB1fyBYBVAp6biq/mOq+geqzt2nnCRHFC4PqF/P1xCm9Vu3SUZ+vtz3xVTbAgCoijiJBryPYBUVhT4BIFgNBpNXeYxWEOokPGsmTrQtAEqjgWqP2bOl29Spe4Wqqsuxx8m6mEgHAhBZvWO7dOnzf/YeAAAAAAAAwarnuMuzA2c3B7C30gJVp9uJJ0lmdrZEMrKKPvaPzVuk+5ln+RoAAAAAAAAK1fDFBvAKDYt04ipdAijZoRMmlBioOp2OPlo6HtVb5m7fYVtC90vuZmnfs6c5FgAAAAAAgEPFqsdooJqUlkawCkTJ+ffcK4vWrpV1W7faluDpY37bsEEueuhh2wIAAAAAAOBDsOoxDAUARFdcYqL89Y475afcvJDCVd33p+xNcv6dd5ljAAAAAAAA+KshEvnELogeJq8Coq/vuefJWTfeKFNXrZYF27fb1pLNz8uTqStXyek33CB9zxtoWwEAAAAAAIpQseoxsQkJ5gYguv7vvIEyevLHsqXJATJtfZYs2LhRcnbskJ27dpmbri8sbJu6Zq1sOeBAGf3RJ3LsRRfbRwMAAAAAAOyNYNVjWo8YIX2zs804qwCi64CDDpK///s1OTtttNTo1l2+25wnX/8yV2Zk/GzWYw4/Qv5y731y43/eMPsCAAAAAACUhGAVQLXT/cST5Monn5KHvp4u5yQdIqc2aWrWr3x6nNkGAAAAAABQFoJVj1kxdqzMaNvWLAGUr5xp08zNTRoHAAAAAAAQLIJVj8nPyZHtmZlmCaB8LRs92q7tvQ4AAAAAAFAWglWPaZqaKl0mTZLmgwbZFgDlwVWrOlStAgAAAACAUNQQKbCr8IK45GQTrtZNSrItAMpDcRWqVK0CAAAAAIBgUbHqMVpBp+OravUcgPIRWK3qULUKAAAAAACCRbDqMRr2LBk5knAHKEelVaZStQoAAAAAAIJBsOoxOgRAQkoKQwEA5USrUnWCOH2PFXfTieOKq2YFAAAAAADwR7DqMTppVbepU5m8CignOo5x76VL99zajRljbv5t+uMGAAAAAABAaQhWPUar5fQGoGLMGzDA3AAAAAAAAEJBsOoxOnHV9MREyUxLsy0AylNsQoK5AQAAAAAAhIJgFUC11jc729wAAAAAAABCQbDqMa1HjJAes2czxioAAAAAAADgYQSrHqOXJOvkOjo7OYDyN6NtW3MDAAAAAAAIBcGqx2Slp8vCwYPNEkD5256ZaW4AAAAAAAChIFj1mLyMDFkzcaJZAih/XSZNMjcAAAAAAIBQEKx6TEJKihlnVYcDAFD+mqammhsAAAAAAEAoCFY9RoPVdmPGEPQAFWTF2LHmBgAAAAAAEAqCVY/RsR51GID8nBzbAqA8LRk50twAAAAAAABCQbDqMTq+6o/du1NBB1QQrRLXGwAAAAAAQCgIVgFUa92mTjU3AAAAAACAUBCsekxSWpr0KygwSwDlT4fdYOgNAAAAAAAQKoJVANXa9MREcwMAAAAAAAgFwarH6NiqOsaqjrUKAAAAAAAAwJsIVj1GL0nOy8iQ7ZmZtgVAeeoxe7a5AQAAAAAAhIJg1WOapqZKuzFjmKUcqCBxycnmBgAAAAAAEAqCVY/RgKf1iBEEq0AFWTh4sLkBAAAAAACEgmDVY3QYAB1fVZcAyp++3xjTGAAAAAAAhIpg1WOy0tNN9ZwuAZQ/rRDXGwAAAAAAQCgIVj2mblKSGQ4gNiHBtgAoTzqmsd4AAAAAAABCQbDqMc0HDTIzlFNBB1QMHXaDoTcAAAAAAECoCFYBVGs/du9ubgAAAAAAAKEgWPWYzLQ0mZ6YKCvGjrUtAAAAAAAAALyGYNWD8nNyzA1A+etXUGBuAAAAAAAAoSBY9RgdY7Xb1KlmCQAAAAAo4ib5pRAFAOAFBKseUzcpSRJSUswSQPljjFX8P3vvASBZVab9P7dy7K4OkzMzwOAwzICkIQtIRkBRZEGFVTEtK6j/NbAK+n2gu58Kyuq6uizoigooQaJIlCxpCMPk2JM7d+V4/+c5996ZmpoKPT2pe+b9Dafr3nNPrjqHuk+99z2CIAiCIAiCIAiCMBREWB1mdN1/P5Zfey36nnnGjhEEYXeSmD9fB0EQBEEQBEEQBEEQhB1BhNVhBgUeblwlwqog7Bmm33yzDoIgCIIgCIIgCIIgCDuCCKvDjMjcuWi/8EJxBSAIe4iJ11yjgyAIgiAIgiAIgiAIwo4gwuowg6LqrPvuk82rBGEPsfGOO3QQBEEQBEEQBEEQBEHYEURYHWZwd8vMqlWyy6Ug7CEWX3mlDoIgCIIgCIIgCIIgCDuCCKvDDPpXfWXaNP0qCMLuh+43GARBEARBEARBEARBEHYEEVYFQdivef+bb+ogCIIgCIIgCIIgCIKwI4iwOsyYesMNOL63VzbTEQRBEARBEARBEARBEIRhjAirwxBPLKaDIAi7nxdaWnQQBEEQBEEQBEEQBEHYEURYHWZ03X8/Flx0kexSLgh7CG4UJ5vFCYIgCIIgCIIgCIKwo4iwOsxIzJ+vxdXMqlV2jCAIu5M5Tz+tgyAIgiAIgiAIgiAIwo4gwuowI3bKKZhy/fX6VRCE3Q/nmsw3QRAEYSRjGIaEfSQIwkii2md4XwoPXH+aDtWu7UtBEISdw9jQ0WGu7OjAvHnz7ChBEIT9h+XXXqtfp998s34VBEEQ9l8u/N5T+vX+75yqX0cKvDHeGBd/4SOdsdFemKZpnwm1eGXaNP103zErVyIwdaodK+wNZO0Z+ci6IzSCbvO4Jwn3AeJG68L2iMXqMENcAQjCnmXtLbfoIAiCIAiCIAiCIAiCsCOIsDrMkM2rBGHP0n7hhToIgiAIgiAIww9aS731gQ9sCc6mo7xncuL6nnlGxwmCIAjCnkaE1WEGzasZBEHYM8y67z4dBEEQBEEQhOGHc39E8ZTBEVb5pB/P+aRfZO5cHScIgiAIexoRVocZE6+5RvutmHrDDXaMIAi7E34ZF9cbgiAIgiAIw5d6vvAnfPnLYpgiCIIg7DVEWBUEYb+GGyAwCIIgCIIgCMMTblJVzXUT48decYV9JgiCIAh7HhFWhxncRIcij2ymIwiCIAiCIAiCYFHNalWsVQVBEIS9jQirwwz6DOJjyY7vIEEQdi90vcEgCIIgCIIgDF8qrVbFWlUQBEEYDoiwOszglwVupCNfEgRhz+BsiCAIgiAIgiAMb8qtVsVaVRAEQRgOiLA6zOCOlhRX+QusIAi7nwUXXaSDIAiCIAiCMLxxrFbFWlUQBEEYLoiwOszoe+YZ7V81MX++HSMIwu6k6/77dRAEQRAEQRCGP7RaFWtVQRAEYbggwuowg8Lq8muvFaFHEPYQU66/XgdBEARBEARhePL7Nzbjkl+/hyk3vIS2n3XgiOUH6eNLfr1AXxMEQRCEvYUIq8MMPtYSO+UUcQUgCHuIqTfcoIMgCIIg7Ku8+FzBPqpNozS//FnWPqpOf7+Jd98u2mfVefShvH1UnUZtGEwdjcq4686cfVSdXVHHruiHYPFaRxzH/Ph1fPfBZViydBMONNP4QKSAo9uD+njJ0k59jWmYVhheDGa+dawp2WfV2RPrRqM27OyawPJ3to5GY8n8jepo1E5BEIaGCKvDDPoKmvP00+IzSBD2EOIKQBAEQdjX+dXPM3XFCV5jmno89nCu7k07b9jrlUFx40ffT9tn1WEd9W78B1PHlz+ftM+q89hDO9cP8p1vpOyj6tx1Z7auCDKYOgTge4+twnE/eRO+VBozjQym+ExE3IDHMHXg8RRfSV/zqjRMe/0jK+3cwnCg0Xy767e5hnP6O1+vP9+Yf2fmG+s4/bgB+6w6V16aqNuPRm149ME8fnhT7fWPbfjIOfV/GGg0lrzOemoxmHVeEIShIcLqMKPQ16eDIAh7Btm8ShAEQdgXqHfDTUsm3nTXYsHbhbqCJvMvUKHeTfvdd2ZVHbWvs3yWU68elk9RshaDqYPjUGssKF5QXLjrt0Ovg31gqCeiLHiH/axdxkvP5+vWIQA3/XUN/t9THTg5UkBbqfZYO7SrNEz7o2fW6rzCnqPefOOcrCco8scUiqu14LUBVU6t+cY6WH+9+cY5XW/dceqv1Q/Od7ah1vrHNlh9rd0G9rPenG/UBsZz7ao/lvWFU647HAe2VRCEXYsIq8MMblz1QksLVsmjyYKwR+DGB7L5gSAIgjDSqSUW8mb87PO9dYUF3pCfdZ63pnjBvJdc7qt50+4ICyyjljBAYfeSy3w1hVO287gTPTXbOZg6KKDoOmqMBcWLq77kV/2ofp3ls+xG/fjaNwM1RRSnHxRXa8E+1uvrvkoyM7j+8pH+Gx5bhWNDeYR34G6VaeepPDf8ZZW4BdhJBvtekVpiH+fbZ7/or/k55xxrbjbqzjeKgbf8IlxzvrHss8711pxvzrrBNZBzsxoUPb/6zWDNdYNz/nv/FtLpqsE2HHqYu2YbKMw6/aw1Fk4baq2xFHWtdad6ftYxabJLt6PaWHIMmJfruPyoIwi7HhFWBUHYrzm+t1cHQRAEQRiOvLuqF0/N32Cf1aaWWEhhYt4J3ppCnnND/tkv1hYLedPP65OmuKretDvCwlnn1RY1KTpQnKglDNBqlvnZzqHU4YiirKPeWLAftYQc1sH6WUctgYMi9Ge/FKgporAfznhXE3IYx/H+2GX+uta5+yIU6y783lO47fGldkx1rvr9Ihzd4tohUdWBeY5sMlQZi+0YYSis3BjHZf/+Nzz4cocdU5tac4Hz7ZLL/TXXHoqFs2Zbc7qaOLtFFFXztVYd/DHFWTeq/TBEcfe4E9ScPpeC4vbXuQ5Q9LR+OKo+H50fnjpWV7eG121Q5ddqA/vONUGvXVXmfHkbalnFcg12xrJaG9g3rimso1oZztpG8bbaOAiCsHOIsDrMmHjNNXj/m2+Kj1VBEIR9gHVXh5F+8177TBAEYcehGPXTPy/UIkc9gXUwYmG1m3rnhryWxRWFDT4GSzHwEpWu2k17ubBQTZxwrDgpHtQUBh620rCd1UTNRnU4/axlAeeINOzHcSd6q/ajXIR2Hs0txxGhnX5UE1GcfrCcagIGhVeON9NUE5v2ByjW1RJYucN/IpnTj/YPldHIqzKyuixh6HDt4XvUSAyv9lnm/HPmW821xxYLawmnjihKqs03Z05z7eIPJtXmW7m4W60OR9yttW6Ur11f+1Zwu3XDaQPT1BIt2QZazNbqZ3kbWE5lGxyLV2csq4nQW9dPb1Vf1s76yXJYHtssCMKuQ4TVYQYfSY7MnYvA1Kl2jCAIu5NXpk3TQRB2F31/uBrJ535lnwmCIAyNRgJrNbGQYke5EFhNyHNuyAlfK8ULiht81Jbwpv3u320rkPAGnUIA87KeauKEY8VJqgkD2wmWFaLmYOqgcODUUWssHJGmlvig09SpwxGhCUWKSgvf8n5QRKk33k5fK+vYn6gmsP7h9Y2IlrYXvXeUJjOvytpknwk7Sz0xnHOhUjjl/HPmWzVBsVwsJJwLlfPFEV5JNYv68jnNcirnkiN6ltdRub6V11HtRx3nxxZSbf0rbwP7U9kG9pOUt6HSkr2yDZXrI+tw2lBrLJ11h6Fy7SpfPwndpYg7AEHYtRgbOjrMlR0dmDdvnh0l7E24O3n3Aw+g7YIL0H7hhXasIAi7i2cNQ7+ebMovt8Kuhxar7R/7Ifqe+ClCR1+K6Lnfsa8IgjAcoXBAPn3Ggfp1OLByc6KqkBoOeHQ7T507Dob6f9nitTEcPatfvzrwBn3WYR59M064Yz6trpybfN6QU2C8/fcRfc4bfoqHP/lFWJ8T7obNx+udPEepOu59NLrlnHkoPnzvByF9TuGiv88EfZk6nH78AP70SFTf9PMmf0fbOdg6nnihSR9Xq4P9oDjjiAuV/WIdFGedsWjUD+fcqZNU6wetfJ06q413eb8+9+M/6te9CT9Xu5N6vjvPP3YSbnihEwcjo3f83xkSRXVfZXjx4wtn2DEW9ep3SGQai06p3PaWh9UYTH17Mg1JDqJ/LKteeXyvuP5w7dkYb9luLnz4nLheR5z59Z1vpLQ46MwNzpWmZteW+VW59lAI/M7XU1vmCqmso3IOs45Zsz1aOCS//FkWA/0lvZYQlsl6nToq5yPXjdOPG8CrC5r1OWlUZ+W6UtmGyjWBIilF6FptIFxjy9swmLEsr6Ny7apcZ9jPa9Ta5NQ5NtoLU+6DhDpwc3XuA0QjQHGhVx0RVocZ3LRq9Xe/iynXX4+psoGVIOx2+GMGkR8yhN0BhdWxX7gHxWQv+p68Ff5px6D5kp/YVwVBGG44wupIYnQsgDu+fiY6Or3b3YBXCoG8wab1qCM0VN6Qk3IhgTfgFA7uVWU4UKwgzk175U1+pahZTTioFE4r20lhoGN1cUs7G9VRKRyQ8rGo1o9K8aFyLAbTD7bLcU9Aqo13uVjUqI7hIKzuTQ6d2oL/XjCAI0MleIydE3oKprHTZQjVofjOH3TOP3oSxrQEtbBaLihWmyuVgmKlQEnK155KUZSU11FtTjOuXDBsVAfnY7m4S8rXpmrrSvm6MZg2cB3isbMmkMG0wflBZjBjWbnuVK4rbGO5+EvKfyATYVVohAirjRFhdZjR98wz2mK1+eSTRegRBEEY4TjCqqZUQN+T/wEjMgqtn77TihMEYVjBDVueenujfTY82NyXwSuLOu2zrVDcOO/oiTht7vgt4kb5TX+1G/LKG+5KIZaUi4WVAiepFA4qb+pJuaBRTbwtL3cw7RxqHc5YVBNIWEe5dVq1OspFlmp1lAuntfqxI3UMB4FjsFaPQ2FzXxrX/vJV+2wrFFQ/fcYMTBsbRej/+xtOixbhxs4Lq5uLBi4/cowdYzEYi9yQr7G5bCSw9XNQj8HUtyfTkPAg2s618Pt3v2OfWbB8R1DlDzrEsVjl55+P0XMuVP744sDPPwXFanOFlP+AUm1tKl97qgmvxJlj9AvNuVUuepJycbaa8Fo+p6sJkuVzmmnLf6hyoGj5xItNerOrWv0cbBuqrTvEGctqaxtx1pUmtdawjspxKH+PRFgVGiHCamNEWBUEYb9m7S236FduHCcIu5pthFWb/r/9N0r5NNquugeGf9sv24IgCJVQVC0XOChuOIJqpbjBm/6PnBPXN9y1bsidG26KjtVu+iuFBW7Iws1hynGEA1pOlT8+71AualYTE9lORzht1E6KMOVWnw6DqcMZi2oCCXGstmqNRXkd1YQe4jy2O9TxLheZ93WBgz8SXPXTF+2zbQVVh0nXv4SDkd4lrgAWI4CO7x5nxwg7wrurevGvv3lTH1cTVB2ctYc4Yl+tueIIivy8V5srjjjLuVptrhBnrlNgrRQkiVMH51s14dURZ7muNaqjmiBJnDlNVwXV1keuBZOmuHe6n4MZywXvWP5XK+tw1hXWX0385Tg4/RNhVWiECKuN2XaGCnudzKpVSMyfrz+8giDsfpZfe60OgrCnaD7pM/A0jUbXzaeh2L3KjhUEQagPxY1LTpqKm686GpeecsB2AgehsEhBgb4EyzelKsfZ2IliJW/IK+ENOgVT3ngzVIoGhDtcc/MTZ4f7Srh5CoUDCggUBMoFT8JzZ4OVF58vVK3DaSfr4IYulQymDo4Fr5dv3FIOxQZnLBr1g+VVihuE5dYbb7b9rt9mG9Sx7YY4+zoUVG++6ij8308evo2oSg6fEEF3cdv3cih0F126LGHocM05ZuYo/T7Rn2q1Naccfv4p+NWaKx/7B79aW/J6zlebK1wHuDFTrbWJMB83t+Ocq1aHszN/+YZQ5bBter7WmI+EdVDwrLYuEa5NnNO11sfPfilgt6H6mlDez3ptoEDbcCzVWl0pqhJnXeEPX2xvJXocVpf0WAiCsPOIsDrM2HjHHXj98MO3WNEJgrB7iZ1yig6CsCeJHnUJ/FOPROctZyC3+nU7VhAEYXsGI6iWQysobTFVRWwkvOGmaEBxgwJpNRzxwtntupKt4kV14UCLAVNcWpSsLRx46woHTjt3pg6OBYWeavkJ66AA06gO7gReS+ihcEoLtlrjzXIpcNQSkJmH7dgfBA5+lmsJqg6XHjkG/Ub1sd4R+g2PKmusfSbsKKNjQf0+ffNjs2u+V5VwvnHdqDUf+fmnEMjPfLW5QjhfaG1Za23i2sM5fda51a+z3Ho/hBCnjprrwrk+/ag8X6vB+frD72dqro9OG2qtCWQwbeC60Wgsa+VnvWwnxd9aaZwflgRB2HlEWBUEYb9mztNP6yAIe5rwYecicsRF6L71HGTe+4sdKwiCsC207huMoOpwyeU+fcNe64acN9y80a4nPFAstATJ+uJFPeGAm6/QqrWmcKBu+tnOataohOXOOsy9U3VwLCg+1BJFWS6tthrVQZGlngjNOuqNN/vK11rj7Vjn7utQWG0k0l16xGhEI35sKFV/TwfDuqIbTaoMliUMDa43gxVUHfj55g8R1X5AcOA8qjVXCMVZ5q81V5x5VM0a1YFl1JrzxHl8v9ac55ymFWitdYX56B+1URsG0896beBY1moD4VjWEn8J19Za4i/R61Ksev2CIOwYIqwOM6becANONk39KgjC7oduN8T1hrC3CB58MppP/RJ6fvlxJF/+rR0rCIIwdHijzhv2euIGb7hnza59nTfzzN/opr6ecMCbduavJRwwvp54QShq7mwd7Ee1R2UdKJA0qoP56wk9LKPeeFM4rSUgExE4tuW2f5iJN+NAcghGvMzzVsLAf6syhD0PN1GqNVcIBcV6c555KXzWgz5J69XBH1Rq/RBCOGcb1UH/rfVgPxq1YWf7ybGstbaRRmPJa/XEX2ftEgRh55HNqwRB2K951rC+sPAHDUHY1VTbvKoa+U2L0ffETxH5wNWInP4VO1YQBGFwlG8gQ2hNWk/o4+Oh3DW7njDQqAw+us4dp+vd+A+mjHptYDvJztTBMurlH8xY0LK2ngAxmH40qoPIJjJb+b+Pr8b3VTguUkC4/rBtgaLqi0kPvnH6FHz7zCl2rLA7qVx7hJGHrDtCI2TzqsYM8n9Twp6CvlXpY5W+VgVBEIT9A++YgxE75zqkXvo1Bu7/lh0rCIIwNOoJjYRCYyORr1EZzF9PsCSDKaMeLH9n62iUfzBj0ciqazD9aJRG2JZ/PWMKvnzyBDwb9+hH+xuxNu/Saf/5pAkiqgqCIAh7FPk//DCDvwYk5s9HZpXsFC0Ie4L3v/mmDoKwt/G2jEfLudcht/Rv6Lvz83asIAiCIOyf3HT+dLx4zeEwo2G8lfNiVdaFRFHdL5mGDjxelTX0NTRHdNrvqzyCIAiCsCcRYXWY0X7hhZh+882yS7kg7CEic+fqIAjDAVcggpZzv4Vibwe6/+tiQB7NEgRBEPZjjpwUxWtfOxI3ffhgTJ3WisUI4Mm4WwceT53Wrq8xDdMKgiAIwp5GfKwKgrBfs/jKK/Xrwbffrl8FYVcyWB+r1Yi/+Bvk45vR+tm74Y6027GCIAjbI34O9w3E16Ew0pC1Z+Qj647QCPGx2hixWB1m0A0A/avyVRCE3Q/nm/g0FoYj0eM+CW/7NHTf8kEUNi2xYwVBEARBEARBEIThggirw4yu++/XFnR8FQRh9zPxmmt0EIThSPT9H0Hw4JPQ9ZMzkFv+oh0rCIKwY7z4XME+qk2jNL/8WdY+qg53vucO/fV49KG8fVSdRm0YTB2NyuAO//XYFXXsin4Iwr7AYOZCx5qSfVadPbFuNGrDzq4Jg+lno+uN1i62sVEZjdopCMLQEGF1mBGYOlX7e6SZtSAIux/6NGYQhOFKaNaZiB5zGTp/ejbS8+VHN0EQdpxf/TxTV5zgNaapx2MP5+retPOGvV4ZFBZ+9P20fVYd1lHvxn8wdXz580n7rDqPPbRz/SDf+UbKPqrOXXdm64ogg6lDEPYF+DmvN9/u+m1Ohdo/2nBOf+fr9ecb69jZdePDZ8fts+pceWlCp6sF1656a+xg+tmoDY3WLl5/9MHabaDwyrVJEIRdjwirw4yxV1yhdygXCzpB2DPQ7Ya43hCGO4EDj0fLOd9A72+vQuqF2+xYQRCErdS74eYNNW+6a7Hg7UJdYYL5F6hQ76b9bnXD/lgdYYHls5x69bD8ejf+g6mD41BrLCheUPyoJ3A0qoN9sASKOuP5DvtZu4yXns/XrUMQRhL15hvnZL35xrnwq5/Xvs78A6oczrlaWPNx59YN1lGrHyyf1+qVwbWr3ho72H42Gst6azDXHdZTC7ZP1h1B2D2IsCoIwn7N64cfroMgDHf8k+ag7fzvIP6Xf8PAo9+3YwVBECxqiRcUEs8+36tvymvx2MN5nHWet6ZYyLyXXO6rafXFm34GllFLGOBN/SWX+WoKIGzncSd6arZzMHVQQNF11BgLWo1d9SV/TYGD5bPsRv342jcDqp3VBQqnHxQ5asE+1uurIIwkaol9W9eN2vNtUHO6ztrTaH3bkTpq9YNz/ie/COvXalB4PfQw925tgzOWtaz+HeGZr6yrGs46zzETBGHXIsLqMGPVDTfoHdfW3nKLHSMIgiAIFp5RB6Dl3OuQefNP6P/jV+1YQRAEPg5bXbygBdO8E7w1hTzeiE+a7MJnv1hbLORjrrw+aYqrqjDAciksnHVebVGTQuP3/i1UU3yg1Szzs51DqYNiAvOxjnpjwX7UEjhYB+tnHbWEHIoTn/1SoKZwyn44411NwGAcx/tjl/nrWtkJwkihlthHsfCsc33159sJHjVXvFUFRUeQ/Oo3gzXXjfL1bajrhlPH3b+rvXbVKp9QcOV8rteGwfaz1rrDsfzYP/jrjiXHgT8sVbNKddZ5jkMtgVgQhKEjwuowpNDXp4MgCLufk01TB0EYjvTFk3j21Xfw6/ufwKp1m3ScOzoKLed+E7k1b6Dv11fqOEEQhMGIhdWEPEcUoPhQTSzkDT8fUeVN+SUqXTVhwBFQWE81UdOx4mxuNmqKDxQsnXZWExca1eH0k3VUGwtHvGA/agkc5SINrVsrccQJpx/VLHydfrCcagIGhVdHhGGbBWGkw89yrfnGa7XmG+c0r3G+VpvznB9cl+qtG+XrW7UyOAedOV1r3XDq6O/b/lF8xxLUWjfqz/m6a5e6xn5WE6HL21DtxytnLJmm1ljyxy9a7vJHn2rrjrPOn63aUM+aXhCEoSHC6jCDPlbnPP20fhUEQRD2bz593c047Ypv6NcZZ/yjFlmJyxtE61n/H8xUD7p/9iGY+eoWDoIg7D9Uu+HmDXu5EMjzShxRgFQTDigwnnWuVx9TGKi06uINP4UA5mU91URNx4qTUGD44U3bigvbCZYVouZg6qB44dRRayxoNUbqCRz16nDECUKRt9LCt7wfFDDqjbfT18o6BGGkwTldbb5RCCScS5WCoyMWOnOhmqBYPqdZR6XFaaN1g1BE5FysNaedH2wIrdmr9cNpAy1G6815tqEyv9PPegJxeRuq/XjVaCxZnvPjF+vgOess58XnrbWN8LXa2iQIwtARYXWYEZg6FbFTTtGvgiDsfip9rG684w4dHKvxxPz5+pyvhPFOGoeu++/fJk1m1Sp9zngHJ09luX3PPKPPiZPGgdd4XllueRqnbl4j9cqVPu35Pk38jxQ8s84dcnjgyZfw5U9egD/99F8Ri4bxm/uf0HU4NJ38ObiDzei65XQU+9bZsYIg7I9UEwspAjhCYLWb+nJRgFBAqBQOaMV5yeVby6i06uINunPDTqqJmpXibeWjquWCJeuoFEAGUwfTU0Ah1caC4gXzkWpj4Tyi74zFYPpRaflV3g/CNOUCRuV417JwE4SRBOdb5Q8u5aIoP++Vwmm5WEiqCYrlc5pzqdLitNG64VjKO1TO6XJxl9CHKde7chxLUML21pvz1daVyn5WitCVbWAfWGc55cJrtbFkec6PX4Rlla+xXHeYj4HwfdmX3ZCsuzqM9Jv32meCsGcQYXWYwRv85ddeu81NvCAIu4/E/PlbRDSy+MordWA82fTrX+vzdT/5iT5nWieNk4/XeO4IdMzLc85lh9Xf/a6Oc+Y20/Kc8aS8XEdQdOrmK2G8k8ahstxqdTt5pE97r087w7e/eBkuOG0ePnXR6VpopXuAcqLHXgrf+Fno+skZyK+zLFoFQdj/4E3zLHUDX37DXS4Ekkqrr0ohsPIxUeemn2KgQ6VVl/P4vEOlqFkpJvK1UgCpbCcFkPJ2NqqjUkCpHItK8YJUChzOI/oOjfpBWF65cFp1vMsEjGrCazUrO0EYSXBONKlQPqd57IiipFI4LRcLSaWgWG1OD2bdqJzT9daNStGTdXCec60grMuxBHVgfWybQ7klKKlcY6v1s1yErtaGjtWlumtX5ViW//hFuEZzrXHgMdvlwLLK1619kb4/XI3kc7+yzwRh9yPC6jAjoW70uXGVc1MvCMLu5eDbb0f7hRfaZ5Y7DgZPLKbPw3Pm6HO+EsY7aRzaLrhAn0fmztXntDjneXm5Yz71KR3nlMu0PG8++WR9TnjO4MBrPHfqdsotT+PU7Vi5O+XS8t3BySN92vN9WvtPIRQWPDykcNuNluDbN5DQr6vWbcaUCWO05WolkcMvQOjQs9B1yxnILpb/fwjC/kr5DXctIbDc6ouiQPlNPSm/6abVk/P4vEOlVRfTlt/0sz6eO8JApZhIyoXTau2k+LBNOxvUUSmgkPKxqBQviFXHVmvRSpGGdZQLOdX6UW75NRThtbIOQRiplM+3SlGU8HNeLpzyM185F8oFxWpzulw4rbVulAun1eZb+Q8ulT/YkPLNn1hXuSUoYXpHtGQbWGZ5G1ifs3ZREK3Wz3Kr/1ptaLR2OWuwI7yWi788dsonleIv28Dz8jT7Gq3n/SsST/wY8Ye/Z8cIwu7F2NDRYa7s6MC8efPsKGFvQusoWj05AoAgCIIwcuHjSGO/cI99tmPQMrX92I/p4zkzD8Bbi1bgO1/8B3znS5fpuGpkVryMvsdvRusnf4XgkR+3YwVB2NcxDAMb4y36Bvsj58TxxAtN2o/prMM821iNkS9/PomvfSuob+4pQtz++4h9xYKiCIWDn/wijCsvTeidqitv7I+a1Y8nXmzSN/20yKosg35aWf5VX/Lj9OMH8KdHotuID2zn0aqMxWtjDdtJ8cJpTzmDqcMZC/aDVlzl4gJhP+59NFpzLMrr+LAqi20oFzAIy3h1QfOQx5t1dKwu6jRjo70wZUNNYQRRvvZcoz7r/HzXmgvOfOOcpjD4vR+E7CsWzNfU7Nol68Zg5jTr4PpQTnk/uG58799C2815J98vf5ZFc8zQQmg55WtXtX4yHxlMG77zjZQWXuuNJUVo1lcO882a7VHBXXOdd/Lta+uO8927mOxF35O3wj/tGDRfYj3RJgwNPrH3QkuLNjw5vrfXjhXKEYvVYQYtp2bdd5+IqoIgCPs5tEx98o4faFF19bpN2h3AP39yq3VtNQIHHIvWD30Hffd8DYln/sOOFQRhf6HcGqrSWsvBsfqiWFlpKUV4A0/BlDf3DJWiKqHPQVp18ca80oqT0KKK1qC86a+0KiM8dyw1q1nNEqedrKP8MVaHwdTBseB11lNtLCgqOGPRqB8sr1JgIY7lV63xdh4Nrl/HVutcQRiJcH44c63WXHAsMatZaRLuaM9r9ea0Y3FaaYXpUL6+1ZpvtJytZlVLWAfLd9a/anOeYiXXSJbTqA3V+ulY/Q+mDayn3lhqVwPV1sdzrc31mJ/trYRl7uvrjjvcgrbzvoXCxoXoua22UYIg7ApEWB1m8NcA+udzfAIKgiAI+y8nHzUbr//pVnS9fPeWDawaQX+rred/G8ln/xPxhyzfsIIg7D/QMpPWXNWECUJhgRZMFCacTVkq4U03fX9WugFwoBUrRYNaAgrr5QYrFCWriRuE4gPbqdNWES+cdu5MHRwLWm5Vy08ckaVRHfSJWE0gIRQ1aKFWa7xZLgWMWgIy87AdFFIEYSRDsa/e2uMIp7XEQuahqMr5VmtO0+WAs25Uq4NziWtCvTlNlwO1RE/CfLQYrbX+cc5zXWFZtdYutoH+qistTYnTz1oCM2EbvvN1Wp26q/aTY8m1q9aPX8zP+llHuf9VB5a5X6w7Lg9iH7wGyKfRdevZMLOWey1B2NWIsDrMoH/VV6ZN06+CIAiCMBQ8rZPQeu51yCx4DL1/uNqOFQRhf4DWUHzctZYwwRtq3nTXEgWIIxxQ/KyGIwzUElAIBRDto7WKuEF4U892VrO2IiyX1mk7UwfHgiJOLfGC5VJkaVQHH92tJ0Kzjnrjzb7ytdZ4OxZugjCSodjXaO1ptG5wnukfdXZi3eCPIfXqsDZ3qr1u8DqtSWvVwXxcN6pZghKnDbXKJxSh6/WTP1416ifbUEv8Jbqda6z1rRr707rTfNJn4Gkaja6bT0Ox29p8VhB2JSKsCoIgCMI+iCsUQ8u530Rp8xL0/PeldqwgCPs6vOGmBVM1KyYH3qzXEgUIb8iZv54wQAGkloBCKIAwfy1xg/G05qpXB0XNna2D/ahmNeZAgaNRHcxfS5xgHSyj3nhTwKglkBAtvMaq90EQRgrOfNuZdYOCYqM5zbm4M+sGf3CpVwfL55yvNaeZj3O2miWoA9tQ6wcdQhF6MGtXo7Gs9eMXYf2Vm2+Vs7+tO9GjLoF/6pHovOUM5Fa/bscKwq5BNq8ahjhuAJxdqQVBEISRyc5sXrUr6X/uNpTSCbR97h4YwW03SRAEYeTjbCDjQKuwekIfHx8dqOE/0KFRGbSEalI3/7WEATKYMuq1ge0kO1MHy6iXfzBjQcsxiqe1GEw/GtVBZPMqYaRRufbUeszfYVfM6T21vtXLvyfWrkZjORjYjnptIPvq5lW1SC9+FvEX7kDLP/4GgfedaccK9ZDNqxojwqogCIIg7CaGi7BK4q/ehey699B21V3wtB9gxwqCsC9QKW4IIxMRVoWRhqw9I5/9TVgl2VWvoe/xm9H88VsRPvZyO1aohQirjan/s6mwx+m6/34suOgibLzjDjtGEARBEHYePgIVmn4sum45E7lVr9qxgiAIgiAIgrD/QJcArRd8B/EHv43EEz+2YwVh6IiwOsxIzJ+vxdXMKnGqLAiCIOxaQrPPRuT9H0bXrecg8+5jdqwgCIIgCIIg7D94xxyM2DnXIfXSrzFw/7fsWEEYGiKsDjNip5yCKddfr18FQRAEYVcTPPhkxE7/Z/TcdhlSL//GjhUEQRAEQRCE/Qdvy3i0nHsdckv/hr47P2/HCsKOI8LqMIOC6tQbbhBhVRAEQdht+Ke8H60f+jb6H7wBib/+yI4VBEEQBEEQhP0HVyCClnO/hWJvB7r/62JA/FwLQ0CE1WGGuAIQBEEQ9gTeMQeh9bzrkHzlf+URKEEQBEEQBGG/JXbql+B2e9B5y2koJrrsWEEYHCKsDjNk8ypBEARhT+FpHofWc69Ddtnz6PvtVXasIAiCIAw/Fr38Mu7+/k34zhmn4dqj3o/PHDRDv/Kc8bwuCIIwVKLHfRLe9mnovuWDKGxaYscKQmNEWB1meGIxHQRBEARhT+Dyh9F6zjdQ6l+Prl98GGapaF8RBEEQhL1PorcX//O1r+J//vmfsOnRR3CY4cIZY0fjslnvw5nqleeM/5+r/wn/fe01Or0wPHjxuYJ9VJ3+fhMda0r2WXUefShvH1VnMHW8+3b97zaN2tAo/67oZ6Prd92Zs4+qwzY2KqNROwUg+v6PIHjwSej6yRnILX/RjhWE+oiwOsyYeM01OF59GaCfVUEQBEHYUzR/4Itwe3z6V/pSotOOFQRhX2AwN9ON0vzyZ1n7qDoUDhqVsScEkkZ1NBInBlNHo3YOph+N0ggWz9/1B1x32geQXvAuzpkwHoe1tWJUKIiQx6tvZIPqleeMP2fieGTeW4BvnXoKnlP5hL3Pr36eqSv23fXbnAq11xbOle98PWWfVYd11JtPvMY0tWAdHz47bp9V58pLEzpdLR57OFd37RlMPxu14bGHcnXHktcffbB2G7iu3XVn/XVcsAjNOhPRYy5D50/PRnr+/XasINRGhFVBEARBEDRNfARq9HR03nwaChsX2bGCIIx0KCrUExQpCNQTHsjdv8vWvamneFHvpn2wAkm9dg5GIGlUB9vYqB8N6/hGqq7IMph+iMDRmJfuvw8P33IzThjVjkMDfhh2fC14fXYoiBNHj8Kjt9yi8wu7n1rziXNEf9brCIovPc+1p/Z15h9Q5dT7saORYHi3uvZYHdHTqaNWP1g+r9Urg4Imhc1aDLafjcaynnC64J2irqcWbF+9PgjbEjjweLSc8w30/vYqpF64zY4VhOqIsDrMWKu+BLwybZp+FQRBEIQ9TeSIixCaeSo6bzlD+17dUbgJoyAIe55awgNvyHmz/uJztW+oKTxQXK0lFmphYXWprkDCMnjjXwtHOKglOA62nYOpo9ZYMH6BCj+8KW3HbM9g6mA5tQQKpx/1RJZGdQhAZ0cH/vfb/4ojmqIYHQrZsYOD6Q9viuj8LEfYvdQS+/gZv+RyX01BkXOF4azzvDUFRc4Vq4zqP3Zw3Tr7fG/N+bQjddTqB+fyT34RrjmnuR4ceph7t7bBGcsffb/62uWseXxlXdV47OG8bkMjq/5K9ufvdf5Jc9B2/ncQ/8u/YeDR79uxgrA9IqwOMwp9fcisWqVfBUEQBGFvEJp1BqLzLkf3recg89bgHoHqe+YZvfkigyAIe55awgNvyM8616utmarBm3De7H/tmwH9uGo1KCh8799CDQUSChy1hFMKByyjlnDKdh53oqduOwdTx1e/Gaw5FuwHrzcSQBrVcfvvIzVFFme8OaYsq5LB1FGL/Ung+PXXvoq5kybtsKjqwHxzJkzAr7/6FTumOhxT2TR456gl9nGunHWur6agqOf8CR41771VBUVnrtSbs7TQnHeCV68dteqg6HnWeb6qPwyV10Gr/GpwTapVPuFa8LHL/HXbMNh+1lq7OJYf+wd/3bHkOFxymU+1Z/s6KLhOmuzS41Br7aqE3+ve+sAH9vvvdZ5RB6Dl3OuQefNP6P/jV+1YQdgWEVaHGe0XXohZ992HsVdcYccIgiAIwtD46f8+gFnnfQ6tx3wU533+eixauda+0pjggSeg5Zxvofe3n0fyuV/asdvjCKr88t11v/ihEoS9RS1BkjfkvKHnTX81IU/f9Ktrn/1SQPsJrIZj6VTrpp6CLIUD3vhXE04d4YA3/fXaSRGmVjsHW8dVX/LXrIPxFDQZqlltOQJIvTrY/7PVOFCoqDYW5eNdTeBw6vjsF9V474DAsT/9cPXmE39Fcv06HOz32TFDY2YwgOS6tbq8ShLz52P5tdfq/3et+8lP7FhhKFQTFJ35yGu1BEVnrnBdqSYocq5QFG1uNqrWQZz1i4JhtTI4xxzhtdoPQ+V19PdZ87scxxKUoiTLqLY2cX2s1wa9tqlr7Gc1Ebq8DZOmuGqOJdPUGkuu3VzX9DpeZV1xxF+uXbXWRwdHUGXg8f7GM39/Gz/8nz/hzgefRi5vCfru6Ci0nPtN5Na8gb5fX6njBKEcEVaHGZG5c7W4Gpg61Y4RBEEQhB2HoupXfvBLLF+zAbNmTMFjz72Gi7703S1fEgeDb9JhaD3/24j/9UeIP3KTHWshgqogDC94Y18pFpaLG7SGqnbD7QiavKnn4/7VhAWKCrxOcaDaY/S0Grvkcr++8a920+6IiaSaOFHezlqCY6M6HOGVVBuLcoGE/a01Fg37ocombGelwDHY8aY4wnZwrJm+FvvrOvv3e+/FeL/fPts5xvt9eFWV58AnAx1Bla7X+JSgPCm4c3BdqJwLjlhIqgmn5XOllqDIuUJRlLCOSovT8rWJ5VSzuNc/pqj6rfVr+x+GnPWPVJvT7IfThmo/uFS2odaawLFw0tRrwyWX+Xd4LFkeXaA47ai2rrz4/Na1i68ss5L9XVAlX/23X+L0K7+Jb/zof/Cpb/wQcy74whajBJc3iNaz/j+YqR50/+xDMPPbi+jC/ouxoaPDXNnRgXnz5tlRwt6Ei1hi/nzETjlFi6yCIAjCyGXd1WGM/cI99tmehZaqFFVXPnkHxo1q1b++84vizvDFc4/AjV/7kbbuqXeT74nF7KO9w96uf2cZqe0fyeM+0j8zx6r5+JtHouAGLvQF6EBxccHbBXztW0F9fvrxA/iTSsebb8Kbb+52fa+KIxQ8abVFq08HCqmzDvNocYLpTz9uAK8uaLavWjf1LOOJF5r0OTd2mjXbo61THXidbgB4488yrvl8Uj9O79ConYOp48PnxHXfWQdFg8qxKO8HYR1OeaRyLGr1g+ILhYnK9KSyH0fN6se9j0Z1m0ijOsZGe2Gapr4fqLbO0vDimJUr7bN9l2+efAKODYcR2wXial82i5eTSVz/69/qMeVj/5VCqjOuzjXnPoz3ZHwveJ2GL2TVDTfoVz5dyHhed9IwjvlXf/e7Os2U66/XawsF3Ozq1Wi74AJdNst1rGQPvv12/Uqxl3knfPnLum6+95t+/WuE58zBVLtOx2J5+s036/rYlv5nn8WYT31K181yWQ6Z8/TT+pV5KCazLewD+8i6WYdT9+uHH67r5lOTjGe5rJvpWRfzMw3hOLFPLJf9vkXlu3FtDB9R8698PvFz7lhIkvL5SThX+GPJ935guXqotvaUz1HOnaPVfFqs6nKonNNfVusK595g66icjzyvXJsq2125buyONlSuKzx31h1S2aZf/iyLgf7SlnWncl2h+EtLWadfbBN/9HHWx7PUunOH+lzy/ayGMz/42eJn2XnClp8L7g1Dju/t3fK54GeXnzd+bnnMOF5jGsI8zMsyWBbLZNmcG/zc8rP4QkuLTvv+N9/Un8nFV16pP7sTr7lGfyb5WXc+kyerNZPwnPHtJ/ow8xc7vnEdLVUpqrY0RXDnD7+Otxev1N+bzzrxSDz0C2tOO8Rf/j3y3SvR+tm74I5NsGP3XZz3pPx9FLZFhNVhBv9Hxv8ZO4uRIAiCMHLZm8IqH/+npepzd/5Qnz/+whs456pv6+Oh8vmTJ+Hrh03HpgdX6S/FgiAMD85TYVm8Zbubft6QO4ImqbzhrhQCeVNfKZBUipwUDmhV5dzkV97UUwTlzvzOTXw1oYCC4xMvNm0pk9dp4elYZVW2s1odFDQcYaBSeCWVY1HZj8HUMZh+lAunlePNMokjolQb73IhhwLHby+8cFj/cFXO7mrLHQEfPnLgDHjd1udhZ8gXi3j3tTdwVDqjxYFqOMKRI8xQuKGAQyGHgg6FHQo8hOICy6EARCFopIhNu6tP9E77A7X2NBIgKwVFzpVysbByflUKkqRStKyc05V1VIqerKNcnK1WR/naVG1d4bpBC9ZywZjz12lD5Ryv1s/yNbZWG5x1pdq6U20sy9edyrWL4zBpinvLWscyy38gu1itOz9Uc6DW97qRJqz+hzr+o44ZGheePg9//Mm/6uMTL/saFixbjZ5Xtv8un3jzAWSWPY/Wz/wB3gmz7dh9E+c9KX8fhW0RYXWYwQWDvxA6vzwKgiAII5e9KazSpyof///BV/8Rhx08DZd97d/QO6BuEG7/Pk45+jA71eAxC1n0P/kfcLVMRMsVv9ZflvlFuPKLOL+AOzdrewt+ARzJjNT2j+RxH+mfmVHqxnVjvGUbsbDaDTnjyoW8ypt+QrGC13mTzpt+PqbqpCeMK7d2qhRUSLngUSlYkh1tZ606HHGiUR2VFluEcXyk1ulHpThByvtRKZiQcoGjVj/K46rVUS7kNBI49hf+cPCBuHjmQfAYW8dpqFBYvXflKnzr05/VlprVxtYRjijwVFqW8r7M3dy8xeCl0mKV/y9kOoo/FIm4lvB+jlAEIrQEdNKwXLaBcRQpmIewHObldZbLNMzDNIzjNceikOeMZxoGHrNsUp6G8DrzMg3LJSyXOHmYhjANA9MzECePc87rDoxraWnRP+qUz4VqYmHlXCifvw7lgmKlKErK66g2p1lHuXBaPn8dyteSSpGUlIuQ1daV8vWvURt4XPlDFSnvZ602NDW76o6lsz5WjqtD+fhWWz/LRWrHUr7e9zrOj5GCYWx9v4fCCe+fhWd+8+/Y0NmDaaddgemTx2HBQ/9lX92W1MInkXj5d2j9zO/hP9iac/sinOsirNZHhFVBEARB2E3sTWGVPqHoU3Xp6vV2jPoi/ckL8KOvX2WfDZ5Cogt9T9yKwIEnovmjP7ZjLSq/iI+0L+CCsC/AG0kKq+WWnNVEAeLc1Dc1G1VvyMvFi2riBnFu2qsJC6Rc1KwmJpYLA/XaScGRfl8b1VFNOCgfi0b9qGaZRir7UW5VS9gPx/JrqONdLqIMRuDY2z9cleOIbbua6y+7FMc3N6N5JzevIo4rgO8/+7w+p3haKbDK/7eGjrP2lM/pWvPNmQtcNyrFQlI+F6qJoqzDES1r1eEIhlzDqq0b5etbufjoUN6PamsXcfJxzjfHDL0+lOO0oVY/mY8Mpg3VhFdSPpaVP/gQZ+2aNdtddRzKfyhy1h2Hkf69bqjfvbkHAX2q8nvzvLmH4NV3lqBQLOLH37gK//yJC+xU25NZ8TL6Hr8ZrZ/8FYJHftyO3bcQYbUxO/8zoLBL4Yd2d31JEQRBEPYfZk6biLce+E/8+gdf01artFQdkqjatRK9D92I4NyLthNVCa1t+IWbj3Txy7cgCHsPCgDOpkvcJZobMVVCC1VujkI/pM5mT+WUb47i7HZdCeMcyy1nY5dynI2bKFgyVAoTFEsoCPAahYdq7aRYwV31G9XBclheZR3bjkXtflBo4XicdW7tOih0MJSLqoT1Mq5ePyhcsPzyzbXK0bt4q/eqnHrrqmNVOBwC27Y7wgGHzcGm3PY7uA+FTek0pr5vln1mWZFybOlflHUJu4byOV1rvnFOcy5wrlSb05wLvMZyOH/LRVXC81n2fCvfjKkcZ9d8zlv6eK2E6xvnG9evavlZB8t35nzlukIoVnL9ZDmN2lCtn5dc7tP9HEwb9DpdZyz1xlfnbf8DBDfD4kZbzM/2VsIyf/Xz6nNsf/1e5/N6cN/Prtc+Vfn4Py1VG4mqJHDAsWj90HfQd8/XkHiGjgiE/RERVocZ/BWVvwY4j5kIgiAIwlDhl8TLzv8AvvaPHxnS4/+5jrfR8+D/QfSDX0XTOd+yY6vjfBF3NsMQBGHvwBtmWnM5u0RXwpt6CgK8Ia8mBPKmnmIhrapYVqW4QZxd9fWO2+dvLxwwD8UR7uJdTbAkFHh5nQJCtXZqwXEQddz9u607h1fC9tNyq5pIQ9gG1lFLhHbq+NXPMlVFUUJRg0J0rX5QyGEbawmvrINWucxfyf4qcBz9kY9gfXb7Hd6HwoZcXpV3sX22FT7KLwLrroViH9eemvPNFk5riYXlc7qaKEro35l1MG21OpwfhmqJu858qyV6EuajxWi9Oc91hWXVmvNsg1671HElTj9rCcxEr11fT2lRtFo/nR9kqv3gQ5if9ddbd9jOauuOw/74vY5GCdyoij5V+fh/I1HVwTd+FlrP/zaSz/4n4g9tu9GVsH8gwqogCIIgCNuRXvo8eh+5CS2X/yfCJw7e0tXx6yYIwt6BVpY//H6mpqDpCAu1rLGIIxzUu+mnOEJqlUGRhRZR1W7qCQXeRu2k+EDq1UGrrWrCK+FYUCCuJdKwXNbBuobaD4oTrKNeP+gGoJbwShyr1lrsbwLH4ad/EKFx47EoY1lOD5VFqTRC4yfo8mpBgZUb5gg7D8U+WoDXmm/OnOY84HE1OJe1dXcVUZRwvrGOalaaRM/lKa66dVg/qFQXXom26lfXa9XBfFxDq1mCEqcNtconXFfq9ZNrV6N+sg21xF+i21ln3XEsaxsh3+sGh6d1ElrPvQ6ZBY+h9w9X27HC/kL1WSbsNfh4Cn0nycZVgiAIwt4iteBxxF/6LdqufgSBORfZsYIgjAT0Tb26ka4lBBIKefVuyClesIxq1lYOFEBqCSiEIgtv7Gvd1A+2nY3qoLVWvTp4vZ7AwX7UEpAJ63CEmmqwDook9fpBK7tawivheNNXYyP2J4Hjih/9GG+tXYfNqZQds2Mw39vr1+OKH99sxwi7m8HOt3pzmoIi87OsajjrRr06ON/q1cEfderVwfK59lWzBCXMxznbaM43Wlca9XMwY0lxtBasf1esO8LgcYViaDn3myhtXoKe/77UjhX2B2TzKkEQBEHYTezNzauGSuKN+5BZ+QraPns3PGNn2rGCIAxnnA1kHGo9autAa9VargIcaFlWS1ggtISiJWYtYYDQ6queOFvPmoqwnaReHYNpZ6M6Go0FLccontZiV9RBKjeR2d956f778MD3b8L7Y80YHdp2A6B6UFR9va8fF3zzW5h3ofw4uDsZytpDdmZON7q+q9a3evn3xNrVaCwHA9tRrw1kX1t3hst37/7nbkMpnUDb5+6BEdx2g7KRhmxe1RgRVocZ3IWv+4EH0HbBBfqxH0EQBGHkMtKE1YEXf4PiwGa0XnUXXJFRdqwgCMOdSnFDGJmIsLo9f/vdnfjj//t3HDh6FGaHQqgnEXHk3k2msKSzExd/7f/DSZddbl0Qdhuy9ox8RFjdfcRfvQvZde+hTX2v9rQfYMeOPERYbUz9n02FPU5i/nxsvOMO/SoIgiAIe4r+p3+OYiGHtmv+KqKqIAiCMCw46R8uw01PPYPg+w7FI+vW4+3uHnSm0kgX8uC2Oyn1ynPGP7J2PYKHztbpRVQVBGFvEz3qEoSmH4uuW85EbtWrdqywLyLC6jCDvpPoZzUyd64dIwiCIAi7j1I2iZ5HfgBX83i0f/5eGK7aj6UJgiAIwp4m0tKCz9x8C/7xp/+BMWefg7fNEl5fuhx/e+VVPL5xsz5n/D/e+h/49I9v1ukFQRCGA6HZZyPy/g+j69ZzkHn3MTtW2NcQVwCCIAiCsJsY7q4ACv0b0PfkrQgeeg6aLrzJjhUEYaQhj+PuG4grgMHz1gc+oB9P5aa/wt5D1p6Rj7gC2DNkV7+OvsdvQeySmxE69pN27MhAXAE0RixWhxmZVau0GwB+eAVBEARhd5HftAQ9D92I8DGfEFFVEARBGDH0PfOMDrxn4v4UgiAIwx3/lPej9UPfRv+DNyDx1x/ZscK+ggirwwz6V3398MOx9pZb7BhBEARB2LXwV/OeP/8fNJ9/AyIf/KodKwiCIAjDn9Xf/a59tO2xIAjCcMY75iC0nncdkq/8Lwbu/5YdK+wLiLAqCIIgCPsR6cXPou+Jn6L103eOuEeRBEEQhP0bx1rVQaxWBUEYSXiax6H13OuQXfY8+n57lR0rjHREWB1mTL3hBpxsmvpVEARBEHYlqXceReL1P6H96kcQOPQsO1YQBEEQRgbVLFTFalUQhJGEyx9G6znfQKl/Pbp+8WGYpaJ9RRipiLAqCIIgCPsB8VfvQnrFK2i/5nH4ph5lxwqCIAjCyKDSWtVBrFYFQRiJNH/gi3B7fOi+5YMoJTrtWGEkIsLqMIO+Veljlb5WBUEQBGFX0P/cbSj2bcKoa56Ap/0AO1YQBEEQRg71LFPFalUQhJFI03GfhHf0dHTefBoKGxfZscJIQ4TVYUahr0//6ppZtcqOEQRBEIShYZYK6HviJ4Dbh7YvPwYj2GRfEQRBEISRg3N/FJg6VQcH55z3UNWsWQVBEIY7kSMuQmjmqei85Qzte1UYeRgbOjrMlR0dmDdvnh0l7E34pYFfCiJz5yJ2yil2rCAIgjASWXd1GGO/cI99NjT64km8tWgFVq3bhJOPmo2pE8bYV+pTSqmbzCdvhWfKkWj5+K12rCAI+yKGYdhHwkjHNE37SKjHs/ZnnntTCHsPWXv2DfaldWdXfPfeW6SXPo+Bp/4Drf/4GwTmXGjHNoYaEvWj3QV/uHqhpQWeWAzH9/basUI5IqwKgiAIwm5iV3y5+8g//1888ORL9hnw5B0/0AJrPQo9Heh78qcIHfExRM+/3o4VBEEQhH0D5+m+cutVQRCE4SCspjM5BAM++2zHyHW8jb4nbkHTh76H8IlX2bHVoUHeup/8RAurx6xcacfuekRYbYwIq8MMTgrnFwcGfoidLw7OrxC8TpxzXmc6ftCdLxe10vA609UrtzLNYMqtl6ZWuaQyzY6U66QZTLmVaQZTbr00tcollWl2pFwnzWDKrUwzmHLrpalVLqlMsyPlOmkGU25lmsGUWy9NrXJJZZodKddJM5hyK9MMptx6aWqVSyrT7Ei5TprBlFuZZjDl1ktTq1xSmWZHynXSDKbcyjSDKbdeGue8/7bjMfE/Uvp4Z/jyJy/ASUfOxqevuxkXnDYPt914rX1le3LrF6Dvrz9B9OxvIHLKP9mxgiAIgiAIgrBvszeF1Y1dvbj+1v/FHff9FYdMn4wrLzpDf4ffUQpdK9H7xE8RPvZTiJ7zLTt2K46g6mzax/sREVb3MhRWX3zxRVMYHqy8/nrzGcBcds01+rz36af1+fOxmD4nr82dq+M23H67Pu+4+WZ9zngHnjPE33xTn7M8ni+64gp9zngnTb63V8fNP+UUfc7ySOd99+nzl6dO1eeE7WAcrxGnve9eeKE+Z1k8Z0ivXKnjeI3n0ifpE5E+SZ94zrCv94n51v5TiM9W7XToevlus7DgYVN9OTNj0fCW88qQePDb5tqrI2bq1d/rdgiCIAjCvgj/3+78/10QBMGB372rfUfeE+HTF59pGoZhXn35h8xTj52rv8MvevRXVdM2CplXfm1uvPFIs+/ua+2eWfdIzn1TeSi/b9kdOPc65fdmwrbI5lXDjLFXXKF9q7qbm/U5fxWgBZRjBUUqz/kLBc8dyylSmcY/ZYo+Z3mkXrmVaeqVO5i6w3Pm6HPpk/SJSJ+kT5Vp9tU+OdfVl6MhB8cytW8goV9XrduMKRPGIBYN6/NyUgufxMCzv0L7lx5E8MiP27GCIAiCsO+x8Y47dBAE8vzzz4P+XmuFnb1eLfz7v/+7DtWulYcZM2bg4x//uK5D2Hfh4/+0VP2ny87Hzd/8HB6/7UYE/D7MPPuz8Mw6d4dD4JhPYex1r+EbP/sjOn98PhZcdBHe+sAHtliplsOn6WhRurvCK9Om2TUJtRBXAIIgCIKwm9jZx5G4cVX7sR/Tx3NmHqA3sfrOF/8B3/nSZTrOIfHmA8gsfR6tn/0DvBPq+18VBEHYE5x11ln4y1/+Yp9ty7/927/hX/7lX+wzCwoUX//61+2zwWGaphYuGnHmmWfi1FNPxac//Wm0tbXZscJIZvm11g+P02++Wb8KArnuuutw00032Wfqc7J8OQ444AD7DPjDH/6ASy+91D4Dfv/732vRk3At+cUvfoGLL75YrxOVaxLXG0KB9Fr1+fvoRz+6ZR076qij8Nprr+ljrjePPfaYPmZ9X/ziF9FrPz5dXp+we9ibrgDmXvQljG6N4U8//Vfc/ejf8Lnrf2pfGTqfP3kSvnvOLKz8z0VbXJrtLWgEePDtt9tnQjlisSoIgiAIwxRapnKzKoqqq9dt0v5V//mT2+4SGn/5d9qvavs1j4uoKgjCsIHCQldXl31m8YUvfEGLE5WiqsORRx6J+fPn6zSOiOFAMZZxLJPHDox7+OGH7TOL5557TsdTVLnkkku0wEuBhGJvd3e3nUoYyVBQFVFVqKTZfvrJoVxUJRMnTrSPLMrPuT597nOfa/jjywknnICbKz57tfJQRP35z39un0GLrLIG7bvQp+pTL8/HmOM/rkXVpkgIiTfuq/pUWqOQeeXX2HjjkfjBly7G6H95VPtQnXXffds8MefAOPo+3d1BRNXaiLAqCIIgCMOYk4+ajdf/dCu6Xr5b/wJe7gag/9n/QjE9gPZrnoA7NsGOFQRBGB5Uig1Tq9wQlnPXXXdhzpw59ll1WCaFWVqFOTQ1NdlH20JRhRZj06dP1+e0KLvtttv0sTCy4eYtDIKwqygXQBtBcbXWD0SVlIu3tFxduHChfSbsa3Cjqr/f/RN8+uIzceu/fhHr/3andgewo+jNqx66EcG5F6H5oz+2Y4H2Cy+sKbDSndnuDkJtRFgVBEEQhBFGKZ9Gz2P/D65QK9q+9GcY3oB9RRAEYWRCkaLSuqwezqO2g4E+Dh2eeuop+0gYydDXIIMgjDSi0ah9JOyLHDFrhhZVv3DpuUMSVXMdb6Pnwf+D6Ae/iqZzvmXHbks9gVXYO4iwKgiCIAgjiGK8E70PfR++yUcg9il5JEcQBGFHiInVjSAIe5C1a9faR5b/1UZW+cL+S3rp8+h95Ca0XP6fCJ94lR1bG0dglUf09z4irAqCIAjCCKHQuQK9D9+IwBEfQfPFP7JjBUEQhHosW7bMPgL+6Z/+yT4SRjInm6YOglCP8t35GU488UT7yu6HvlSdzasIfbjeeeed+lgQKkkteBzxl36LtqsfQWDORXbs4Iidcop9JOwthp2wyv89amf1KvBfSZ87F6wTJ15fs18ZrBPL2b2d1PlTESx4VFJ/Syjqv4IgCIIwXMl2vIXuB7+H6Jn/gqazv2nHCoIgCLV466239OYx3MSKfla5yRV9IwqCsH+gdYGywI3tdjfcLI8ibnt7Oy699FLtV5Wb8tGHa6ONsYT9E25wlVr0FEZd8zj8M+T/USOR4Wuxapa4EvIAMKwoLYOq+FKJMqiJggrd+SIW9aexNJFBf0ldM5w0TGELplxIVW7+dRZVnqlU+p8+t6sSBEEQhOFGZukL6H3kB2i5/JcIHf8ZO1YQBEGoBq3SKGzMnTtXb4h1ySWXaKvVc845x04hjHReP/xwHQRhuMHH/fljTktLix0DfOYz8t1NqM7Ai79BfvNyjLr2SXjGzrRjhZHGsBNWDVsMLRpsmgGXaemqpvpjqiNqnyV1KaEOFicLeGxVL+55Zz3ue289/rahD6tzJWTh1umLFFkNlZCZdAFWmYxgHSzNreLc8MBgoYIgCIIwzEgt+Avir9yJUf/8KIJzL7RjBUEQhFrQKu1b39q66QfF1UceecQ+E/YFEvPn6yAIwxFuxPfb3/7WPgNee+01XHfddfaZIFj0P/1zFAs5tF3zV7gio+xYYSQy7NREiqclw7AEUfucUqu2VjXUDabhwuqsicfXZ3D7u914byCHww8ah5lj2vDumj488N56vNQTx6YCpVOPyqfKozrLkhyBVXXbUP9cJXVVW7OaKKo0JbtOQRAEQRgOxF//E9KL/4b2Lz8O3/Tj7FhBEIR9D8cP4a7ixhtvxJFHHmmfAZdffjlWrFhhnwkjHW7WIhu2CMMZWsiX/8Bz0003yQ88gqaUTaLnkR/A1Twe7Z+/F4bLbV8RRirDTlilHalRMuApqSMzD9MsqLgSMjCxoVDCC105/G5BAvcsyWJhxg9/LIqZLT6cP6UZH507GS0BL15YvBaPLdmE9/py6C+6kDfduoySUUDRKKKgQlHVZRoUWS0R1zRUXTpWEARBEPY+cT4a1LVS/4rtGXOQHSsIgrDv4WzysquhparzOC79HNIlgLBvMPaKK3QQhOGM/MAjVFLo34Ceh2/UvlRjl//SjhVGOsNMWDVVgwrqtaT+8nF+ugQoordg4p3ePO5ZMoD/XRjHC3EXeoIR5D1RvLcujzc2JJFWuWaEPPjYIeNxwSHTUMyU8MiCDfjLij6sTOSQNFWphirfLMAoFVAyi6oOAwVax9J6Vf+j0CoIgiAIe5e+p36GYrGAUdc8CXek3Y4VBEHYN7nttttw9NFH22e7Dnkcd99l4x136CAI5fT399tHFpUi5tq1a+0ji8rzclatWmUfWfAHoFqUX6tMV+0HHm6sJ+x/5DctQc9DNyJ8zCfQdOFNdqywLzDMhFXrMX1CdwADph8LUy48vroPjy7ajPldeXR6QsiF/IC7BJfLhXTRh66UgWTRRMk04S+ZmN0awIePmIgjD2jDqr5u/HnRajy/fgDrMgZyhg9uww2PSucyi9palfW6Sh4YpphgC4IgCHuPUiaB3odvgrtlEto+90f1vyf5wU8QhJFLpcDwxhtv2EcWFD3+/d//HV//+tftmK1UCiKVgkk5AwMD9pFF+Xm1x3FZpzCyWXzllToIAnn++ef1hnWc3+VMnz5dxzvXuUt/OTx3rjtwfWDcf/7nf9oxFtzl/6yzzrLPLJy0/NHGgceMc9aZaj/wcGO98jqFfZ/s6tfR8+f/g+bzb0Dkg1+1Y4V9BWNDR4e5sqMD8+bNs6P2LJasuRXu9J9RkZ2ZAhZsTuG9DQPwevM4eFI7liXdeGpdEQkjBI9Le0ZFKJ/G+RO8+Mi0MJpdJRRNywKV//F1Y76I1zu6sHBjH4KhIGZPaMXBLSGMdpfgM0souSw/rC7TbolaBC0qWyYIgiAIO8a6q8MY+4V77LP65HvXY+CpWxGYfa78ii0IwoiHAsRf/vIX+6wxX/jCF/Dzn/9cH1OUqAU3pjrhhBPss/ppTf393uKoo47aRvzgzt2PPfaYfSaMNF4//HD9+v4339SvgiAIZEe+e+8p0oufxcDz/4PWK/8XgUO3FeeFfYNdL6za31/0JvzWEQzQXyq/9NhffIySTlCCS6dx8br6l1Ghs1jE0t4k3l3bh2yqgEPGNWPW+AhChqni8/jL2hzeS3qQd/tVMRkcEMziogOiOLYtCF9JlavQm/+z3pKqz+XS5a5J5vDK6o1Y2Z/G2JZmHK7KnR7xolldt2xki6qNqiWGW31BU8f0t6otWN2qJNVKVT/buKUPgiAIgtCAwX65y29ajL4nforIB65G5PSv2LGCIAiCIAiCIAyW4Saspt55FEkVWj/ze/imHmXHCvsau94VgH60niIlxUnTElQtpVNB4bMI/njMI74yeVGd9ZTyeHsgg4eXdOP5pZvREvLhQ3Mm4IRxfjSl4gjkizhiTATnT/PhpNYk5oT7cHwsgzNHGZjuyQO5jK7BNOgpVQUtkqqyVSVuFaaHfbjofZNx1sxJyKezeHzBejy6Jo6F2SIGVBuLWkC1G8W8pkfFUfJlrAosjME+FQRBEIRdQXbVa+h54HuInv9/RFQVBEEQBEEQhH2A+Kt3Ib3iFbRf87iIqvs4u9xiVVt30gJVC5C07mTgi4mSUYKp4rhFFY+p6xaKwJpMAa9u7MWKTQMYFQrjqKltmB5WBSTiupxIJAyvz4uCOu5PpZE1XMipvB51MVQqopRIwOfxIRRpgtvv1YIuoc9VQltTvd+/bpQLcVX1K+sG8GTHAAy/C8dNjOKIWBBjvS742VzTpdMbKr1Lt1P1ynSrnHZfSNmhIAiCIFSj0a/mfDQo/sIdaPnH3yDwvjPtWEEQBEEQ6vGs7QLiZPt+TxAEgQwXi9X+526DmUmi9aq7YQSb7FhhX2WXW6xaIqYbJRW0+KgCDVZN/T8/SpMGr6BolrAhk8Wz6+O4d8EmLO5KY+bkMTj7fWNxkCcNd38PwoEQYq3t8Hh8qgATmXwBpYKJVpcHowsFtKm4ADei8oVQNHwYSGVQKNLG1KqYOiotZtlJj4p1qXO2rFVFjIkFYQajeLvfh98vTuC3S3rxSm8OnUUDeZVdN9coqdSqTgrFFFi1MMt464UwxgmCIAiCMFiSbz+MxBv3oe3qR0RUFQRBEARBEIQRjlkqoO+JnwBuH9q+/JiIqvsJu1xYtSRNS2qkdWpJHXJDqaJ6NUzas5rYXCzh1e4cHlrYhTfWdGF0SwinHTIJs2IRIJFSeV1oam2DPxhQxbAsNtMFM5uF3zDg8XmRR1FbpPp9QXXNrT63LhjuEhKJBIqqMmajM3urPSq3+uNWR3QLkEQBCzozWDVQRDHYjE7vWPytx4tfL+zFfSv78HYii17TdgJgutXkcNP81WpLmahKnNOKaEEQBEGoCR8NoguAUXw0aMr77VhBEARBEAbDnKef1kEQBGG4UEr1offh78M1+iDtU1XYf9gNwio9k+bADaD0dk+mCXcpj5JZQm/Jhbf783hg6Wb8Zfl6BENufPjwyfjglBaMLRXgTWcRcBvwB4MwXT6VhxtcqQ+oy0CeYmmhAJ+7BI8q26XKo9Dp9qg6VCgVsgj7PSqugGQyri5RwqVTAhOlUglFlTefy6CQyyKVyiCRzqFouqCqg9ul0vhj2IBmvLQxgYcXd+Dpjl6sTJnImlqOVeXS1tVxMrAtIqoKgiAIg6X/b/+NwsBmtF/7JNxtU+1YQRAEQRAGS+yUU3QQBEHY1Tz76jv43s/uxK/vf8KOaUyhpwM9D9+IwPvOQsvHb7Vjhf0F99e+8pUb+gYGMGnSJDtq56HQSFEUhgumYSJl0o9qES9t6MeLa/rQVzQwc9IoHD2xBeO8Kl0ug2Ihh4DLQNTngc/n1u5ZuXUU5UyXKieTyaBUKiIQDMLlciNHtwBuN9xeH7zqNZfN65qDgSCSyRSyuaxOl83mkMmmVfosMuk88gUTBZVycyaHNakS0iU3PKYJV7GIFncBp04L4eCWIJati2NJZxpp1Y6g342AV9WlLWAtadWyhbVx1NayKEEQBEGIP3oTIkd9zDrRjwb9FEawGW2fvxcG3dwIgiAIgrDDLL/2WvT+5S9oPessfb7xjjvQ98wzcAUC8I0di8yqVTpu4OWX0XTssduk4XVPLKaPu+6/H7mNGxGaOVOnWXvLLToPz1mWk6ak7kUDU6fWLbey7tSiRYjMnavT1Cq3Wt2NypU+SZ+kT7X7VFx579bv3kPgI//8f/Gtm+/AW4tW4O5H/4bv/fx3mDPzAMw8oLZellu/AL2P/jsip1+L6Jn/YscK+xXcvOrFF180d5SSDvxbtM8siuqQoVQqmslSwVyaLZgPrOs3f/DaKvNf/77avHFJn3nj0pR53dtJ87tv95v/s6zffHZT3OxI58yMylgoFlReK5ilnCoxp0ovmn39fWb/wICuSV01e+Nxs3cgbuZyeTOXzZudnV3mmjVrzN6ebrNz82Zz2bLlZkfHOp0nlUmZ2XzWLBWLZjqTMzv7+81FPf3mrxYPmJ99rtv8xLNd5j8+t8H80bubzaWJrJlXdazIlMzfLVxv3vDce+bP3lprvtiZMjfnS6o17DF7bvVe/3GCIAiCIJSx9p9CZmHBw2b27781N33/GLPvD/9sXxEEQRAEYag8A+iQXrlSn7974YX6fNk11+jz3qef1ufPx2L6nLw2d66O23D77fq84+ab9TnjHXjOEH/zTX3O8ni+6Ior9DnjnTT53l4dN/+UU/Q5yyOd992nz1+eOlWfE7aDcbxGVl5/vT5nuwnL4jmD9En6RKRPQ+sTv3tDHe9MOPyQ6WbXy3ebr//pVnPqhDFmLBrW3+erhcSD3zbXXh0xU6/+XrdF2D8ZksUqXY1yQye9y7/6Z5RKepMoZ/f9ggH0F0t4pz+FZ1duxqquBMaOaUGwJYZFXXkVSujI+LEy7cbSngx6MlmMiXoxLuSFlyWo8ujbtGC4kVchkckhncvB7fbq/aMK+SwS8Tgy6RRMlbZQyMFwqbqLJlxuN1pamhEKR2CqvKbXj4zHj4TpQrZQQDqVRMDlxoRYBJNjAUwMG5gSKuLYcSEc2eJFq6rA7fEg4gHGhj0Y1RxGdzKHt9b2ojNThMvnRtALeFQfOQ5sq9V36A2vrI2zLDcF2q6VpzwkPBYEQRD2G2ixGjjgGPQ9+u8IHXUJmi680b4iCIIgCMJQoRsAb0uLtnKjtRyt1vzjxiE8Z84Wqzbut9F88slbXAbQSi58yCE6D63lmMcdDOo8Tppif7++znOWyzwsN3rMMbpc5uG+H04anpPgtGn6nOUyD8tlnvK6o4cfrstgGsL2s32M43WnXOmT9En6NPQ+mRsewY//zqeZh86Rsw/EFRd9EGPbW7RbgNXrNuFfPvNR++pWUgufRPyFX6Pt839C4LDz7Fhhf8SgxerKjg7MmzfPjhoEWjQs6Mf1TZN+TRlpoOgyMVAysTKRx1vre7CxdwCjAkHMmTgaaZXsoZVxLBjwo+iPwHS5UNJCZAmeTC+OjxVw6YwWTA+6UcwVUDDd6FPlrkwWsLanF1FXCQfHmtDiMeFR9eRyebhUGU1NTfB4VOGKdDql4tUED4dVnB+dmTxe25TC/L4ccqqm2U0eHDcmhLFBF8xSUftxhaHqU3nZhWwuh3gyg5LLq7tomEWEIgEkC0Us7VJt70wiqdp28JgYDh3TjHEBN0JqgaH/WCq+brgopepjXaJ2hyBqqiAIwv7KuqvDcIViaDrvBoRP/KwdKwiCIAiCIAjCrobfvcd+4R77bMfoiyfRfuzHMHXCGHzygtOwev1m7Wf1UxeejttuvNZOZZF48wFklj6P1s/+Ad4Js+1YYX9laBartlZomC6YcKHgMpBECR3pIv6+oR+vru5CNpvH0ZNH4fSDxmBUwINX1w7glR4g640ALpXPxZxE5Te8yOfzGO0pod1tIpdKI10q4O+b4rh3eQIv9QIbMiWMaw5gxqgmhP1+vSEVxU+fz6derQ2qXIaBdDqNoior4Pdgjcrz4NoUXugpYU3KQLGQx4xYAKODPuQp6pY88JhFuI08skWgP6Py593IFF3IlrgFlxvpTE5b0R6o6j1wbIvqr4GFG+JY1p1GTpXh87vh9RjwajGVPaKVarmYqo7LTwVBEIT9BlqstnziVwgdc7kdIwiCIAiCIAjC7mCb/Q12kIDfhwtOm4dNXb346f8+oOM+97Gz8Z0vXaavOcRf/h3ym5ei7Ut/hmf0DDtW2J8ZgsWqdgKAkukC/3EjqE35IhZ2JfDeul7kzSIOm9SGGWE/2j1AcyiInmIRdy8fwEMbPMh5w4DbhOky4CmoEkygYJqIlAZw/rgSzhrtR6yUw4DbjXs6Mnh0kxtZXwyBXBxnjcnhokkBxAygP57QYmwkEtFCJq1GXapNyWQauWwO0aYIFmeAe9bnsLAQUc124UBvHJcfFMYx7U263UaJj/QXkYeJ3pSBnrQBU5XlUnF0clAy3KpcEz7VyLagGzG/qkflW6vK/3tHN1ZuHkB7OIrZE1sxPeZDzO2Cl8arHCZ2zLFaFXFVEARBEARBEARBEARht7EzFquDof/Z/4JZMtF61d0wvJbrAkGwjEZ3CAM0FeWO/S51uCqZxUNLNuG1lZvRFvTi7EMm4sQxUYwppeEr5S0B0+XSPksDBh+6pwsAPn7vQlHVbhq0AS3Ab5QQdBvwqEINlxt+txttIS9ingIC+QG0efMYF/EhFgohFA5rQTUajerQ3NyMWHMMTbEmjBk3GrG2dphuP8aFAzgs6sZEVxpjjBQObfFicjSgO+2BCS+FT1V7RjUrUSih4Cqh5FYtduXgduVV/wpQTQLUNbongJpAPtXy6X4vLpwxDmcfOkWP4GOL1+KZNV3oLxRV21V6BX3P8p91xL+CIAiCIAiCIAiCIAjCSKKUT6Pnsf8HV6hVW6qKqCqUMwRhFdpC1MXH79Xx0u4BdKbyOPF9k3H++8ZinJlDumcA9Djq9/vhUemaVfpJYRdafFmYxQyNXnXevGEiR7G1mMVYfwnjte/TPFIlE/SaeniLBx8YBRwVjeOsScC8MSGE3G4qtXCrVwb6WWWwzl3wetV1Vw4D8W7EfCWcM7MNnzwoiEunenDmxAjG++k6gLXTmpQbT1EvNbTVKuPYL9N0oajaz1RsKa8WiwyqrbRAVRcCKhzWFMAls8fj0LERdPT0oTOR1jlUEdRgLViN/U8YOdC9BIMgCIIgCIIgCIIgCPsnxXgneh/6PnyTj0DsU7fbsYKwlSEJq7TypPRp2Z+aGB3yYnLAi5jLjaDHh0Qig2yhBMPrQ6mQR36gHxO9Jby/zY1xrgQC+SR8+Sy8xQwChRQmepI4epQLU0MGAoYBvyrDKBoIJxM4PmriUwe34INjQmhV9dFylIKX3iSqArNoIplIal+q0ZAPftW70R4XThoVwUltPoxGXiXiQ/4u0JVByaUSuAzdB4+pDimkMd7woWj4UaJdK3VU1a+MupZIZZBJZ7QLgmK+oPqWQ7Mqc0rIj5A3iLztfJZ/KclZwqzD9u0Vhj+irQqCIAiCIAiCIAjC/kehcwV6H74RgSM+guaLf2THCsK2DMHHKqEAaehd/f+6uhMr+ws4c/poTAu7kckV0N3VqWVEPpqfzWVhFExEmpvQa7jx7No+vLE5i96iV7sBoJXqYa0+HB5zY4zPQC6bh5kvIeDzIZNPw+Nxw+fz602k8kVTux8I+Lwo5IsomkWEwyH41fVCsYhUIqlfo81RLehmkmkEwhH4Az4kU2nkc/TJGoLH7dF+MaiDFs08EukMBjJAGj4U3G4VT72ZVrkluFR5HtXOoBcI07q2VEQun4fH5QZUWq/Pg7e6MninJ4Pjp8RwWCyoFdWSy4Sh/b5afltZl6uGuEqhmNawjoWkY4FbDq+VivRtu61c2xirTtUEy9JYtZvHW2Gd1ds1WLhxmLbmtTG0RbHq7bYVNaSynF2BY9VcD9aZU5+NbDarRfNcLodMJqviS/D7fQgGA3qTNJ/63NEKu/K9EQRBEARBEARBEARh77IrfaxmO95C319vRuyimxA6/jN2rCBszxCEVQpxlhhXUOGJNZuxoj+PM6aPwYyQB4lEUotTmUwaqVQKrW1tiDU3WyKbaaIrmUJXvoSU26MtOkeHfNqqNBcfQC5fRCQa1ZajnZ2d6jyHUe3tCIcjlvanqqU4ms0XkEqkkM2lEQoG4fUGkM5k4FHlxJqb4PGqsksm4qpM1tvU1KTFslQqjUDAj4DfjwKF12wW6WwO0CKpF/GCAVWKqoiuAOjMoAR3MY+w14WWSABhVT7hhlalUhFZisAuF17ZlMDC3gxOntqG2bGAymnCoOqs8psqC21iqwmYFBIp+vX29mLpshVIxJN6jCZMGI/pMw7QorIDhb7ly1dg0+bNtmipytRvhSqT/gzKUWUwxqX6rsdd/ceyQqEgmumLVo0HxUKKhlZ6FVQ6S9g1VZvYzlpipPOIvNUXlr9u3VqsWLFKjznF31hLDAceOF37waXbBd3WGvA6y6Pg29GxDqtWrkZRjQv98tIuWrdtCOgy3QamTZuGiRPHq3Mdq+PZdkP1sVgooKenB2vWWO1ft24d0hTgbXGVwirFVAr7waAfEyaOw4wZMzBlyhS0trboNlvl8X1k+fxjjYsgCIIgCIIgCIIgCHuOXSWsZpa+gL6n/gOtV/4awbkX2rGCUJ1dIqyuGsjjg9PHYnrAwObOLmSzOTQ1RbUlIIVDCmwej0cLrflsSluNur0hXVpJ/ytQCUMqzsfsCzpvOp3R6b1enxYFA4GALsNwW+JVvpBX6Sni5pDMpLW+2BSOwq/S06KQghjFMVohhkIhbXHY3z+gLVV9Xo8WfllWKByBz+9XZWQxkM7rx/kLWqx0aUtVVymPoNeF5nAAXgqwrJ1iZRkvbOzHW5sTOHFyK2bHgpTvGgqrliBnlbV48WL8/vd3Y8WqNTBU/KmnnoKPfvQjWgh16O3tw91334OXXn5ZW02yLBqvaj2voj120VvgZbfH0AJhOBxGW1sLZsyYjlmzZmHc+AlaaLbao9qt0lr927bMrVCctI6YjuN7//3344knnlTvtWVJPGbsaFxyyUcxZ85sK2EdLOHVqu+vf30S9/7pAf3eutV7rSuq7MwgKRYL+jNw8Ucv1uNJKGQTfibj8QTmvzUfL7/0Mlav6UAuk9cDpVvCDdTsY6sJliBLr8IU5inWnnDCcTj00EMRDKr3W/eBJVv9EARBEARBEARBEARhz7IrhNXUgr8g+eYDaP307+CbfpwdKwi10brcjmOJcA604KSVYW9/HxLJhBZS9U79sZgWpBKJhBZZaQloer0oen06vckn600DRdWMEtyIhqMI+Pzo7u5BJpNBS0uLCjFdRzwe10JrIZ+DoTK6Vb5sno/lmxjXPgYHTJyIpgjF2pIWTfv7++1HujMYGBjQFp+0WO0fiCNfKCLa1IyWWKt2OZDLZpFNpxH2uhE0ivDkk2jxuzA6GkR7JAhPMY+8uk7xzJLNKLJtK/rVMcosG6nq0KLTq8YlaFvTetyVj+szjQGvh4+iB+HzMfi1xSlFYYrGFKCdYFlZMqhjVS5FStNwI5PNoau7G4sWL8GDDz2K/77tDjzxxFPo7umx+qYrpeVqReXbYIuOdgNXrlyJJYuXwu3yqrYHtWuGvp5eLFZ1ZDM5nWawUDj3+dlH9svqg1cHnrNf7J8TpwKPy4L1uL4VPDqdJbITiqpsM0XV/r4B/OWxx3H33X/CkmXLdX99auz9QT+C6jMUaYpqcZ+WvZFIWIunFPb9voD63AILFy1Wef+I5557Xn++LItchnrjJgiCIAiCIAiCIAjCcCX++p+QXvw3tH/5cRFVhUHj/tpXvnJD38AAJk2aZEcNBkqFFEMNrOhPojOZx1i/F8F8SltEtrZymylbDPR6tVUjhVG3y4VgKAS32wNXiT5MaRVIRc+lSuM/QwtoyVRSi6iOpaljrZrNZLVYSvkqp4774wmEI2E0RSNa3HV7fNqak+lZL0mn09qtQDKZ0gIar0ebouq6R5dDwZfCr9/nhV/F5VR6n9uNSMiHoNcNr8cNs1jQlrEejxd8/JviLR+/t9puYG08g03JHCY3hTCGzlgVhhYn1TjxxfpjpbdxhEm+9vT04r2Fi7RFLWOnTp2CQw6ZuaUPJJ3OYtHCJVi/YT2rRDAYwvjx4zF69Gi0tMb0o+kUovna1taq3wMet6rjpuZmhNT7QjHW8jdqaMGRfmeXLV2GuKp37LhxWkx0cNpXSbmlLa2LX3zxJbzz7ntwqfeU0P0ALU75mP2ECRPQ1s7PAvNUL8/Qbgx4zcCqVau0SFssqfdStS+m+jN6zBjEYs1otgPdDFCwZ6Do7hxbgWI+QxOam5v0uBx88EEYp/pm1UUL2xyeePIJPPXUU+q9N9WYWJ+tUaPaVdqDcdjsQ3WgNe/MQw7GAdOnY9z4cepzG9S+dfn54+eXIv2GDRt0vZMmTdTlC4IgCIIgCIIgCIKwd4g/ehMiR33MPtsx4i/+Bvm+dWj70oPwtO6IPibs7wzNFQCfQYeBguHCE6s3Y2lPGqdOG43JnjxchluLq+VQWKUIxceox4wZrXLSryZlRhfVLhalSqW4ZupH9Sl00hKQ4hrFVQqlFMWo6ZuTmTQAAExdSURBVA3Ek+jq3azOS/AEoohGo4gF/XpTJ4P+UVUi+j+1/LxmtBBIMc2y5LTcAdBHKS0rKQJmVRqXx60tbFPJFLiJUcDerIjiKykVixgY6NfiIa0Y2X9rEymXFiqfX9+Lt7qSOGkyfazy8X3V0DJXAEyn/YXqPm6FbWO/li5dhnv++CesXt2h23/SSSfiwx++QFtKOvT19uPee+/H3197DfT9ecghh+AjF12ICRPGIV+obxlKK8tMOoPevj50qPf6vfcWahGT40O/qKbq31FHHYXzP3QuRo1qs3NVp1xYXbJkGf74x3uxavVqy6LTr96HUkmL2RyXM888E2effQY8arydvm6P9VlieObpZ/HAAw9q6+JAKIhzzz0XJ554nBa1We12uasVZzXPuqYyWe+jV9fPNi1Y8J52qbBx4yb9mfCrdh9++FyceNIJmDRxot4crRpZ9blYsmSpauMzWLJ0qa6H4v7sObPx0Y9+WH2ux9gpBUEQBEEQBEEQBEHY0wzVFUDfUz8DvEG0XaXyVtUtBKE2WvYbCvbD8BoKVxQO6R/US9+YFVDUokDKR6bTqSxKFEANN0wXc7EUyqpFFUrI5ij2lbQlIIXFZDKp/a3SjyXFUm6O1NzUinyoFe/EC5jfn8OmAlAy3SgWijrtwIDlNoCiGi0329raQD+tfMS8paVZC79+fwAJVfZAIq6tVukuIJlOo6DqtjaV2jqZXG7u/u/X9XOTLLoSoNCWzmSRUCGdK9jCX/UJuEumJQuxC+J4Uhz2B/nIu1uPU70QCQfR3t6CA2dMw6kfOAlXXPEJnH76qXqjL/2+qfflzTfn45VX/q6tUBtBgbSgxmDRwsXYsGGjKsPQAvdRRx2JyZMn6/GjZeeSJYuxft36LXkaovtopeMLN4yiJXFEvV/RSFg/lr9NUPHbhfJrkYj+DDiiKlm9eg26u3rgcXthqnbOnXMYLrjgPEyZZImqemMyHdQ4qz/sC/OzHbMPfR8+9KFzMXXKJPWZKWihvWNNBzo61uqyBUEQBEEQBEEQBEEYGZQyCfQ+fBPcLZPQ9rk/btEjBGFHGLKwSjHN+chRfMpkcjDVh5C+LcuhKEXLSAqrseZmLXymUxn7Iv9Q2mMzrJDPWsIeH89mHoqg6XRKW5omEgNg8Ua0CW+k3fjTeuA3S5J4aEUP1vQOYCA+gGzO2qyKYirzsn6Kqpwf2SwFWlWLFtlMLTqOHTcebe1tWiikGEghl4/mJxNJ1c6Utr5MqNciSkhns9i8ebN2a0ArWAqtdG/g83ng0n42dyeWAL01qL/sjMIS/7YNKpYpdDBN1fpS0bqm0kbCIZzxwdNxyiknqbEKaBGZfZ//5ltYsWIli6yJI5BSUFy0aLHKl2UNmDJ1Ck479QOYefBB+r3jGFNwXErrThu212lzNcov8Zj9ss+GFNhfWuQ6babgvmnTJv1es+ympghmzToE0UhEp2VgSloya8mf7h7svE5bpkyZgtmzZ+s+koT6nHRu7tLHgiAIgiAIgiAIgiAMf/K969H78I3wHXgSYpf9wo4VhB1nSGogZStLWqUIZT1m3t+fQCZbQK5YQl4FLUSZlqjK9B6vXz92HQz5kcqmkM6mVf4i5SsUVQLarBZo+Vkq2kKlyqTyBwN8HN5AZ2cXn8BHJBLFplQOL2/KYLkZxSpXDK90ZrG0s1+nD4eC2rqQJoe0PFR/QR01EAyqNvGxeNW+Qh6JgQF43R7dJvr09LgNxKJR7QOWj9on4wls3LgRGzZtQE93NzKpjHZlELA3jNKbczU3waPqGigZSJVcug/EGp/dixYo7YrYP1qdlgfrrWV71PtUfk0lZl6KqUcfdRRmzXqfGhc15qpfGzZuwOIlS/QYEUcI5b9yKFa+t3Ax1q5bp8o00KTG7X0zZ6KtrQUHHDAV48aO0e8FhWlulLVp82Y7J9tpjVEtrNqc+ramrSfI1oJ1lVen3UOoz6olRJv6feR7Sqw6rTpYFfNSLHfay1enDRMnTtA+ibVfV26upq4VCuWWvlvLEgRBEARBEARBEARh+JDftBh9j9yI0LxPoenCm+xYQRgaQxJWy9UqCnSFPB+LdsPj9iART2JgIKktWPlYeTaThtttwOP3omiWtDVp0B9EOplGLldQZXETLMu+ktamLNrv82tdihsgxeMDur72UaNB/60U60pFE0HDg3CpgGAhh9ZACBPGjtUWqAPxuLZspXjqSFv0h6p3x3e5VR059PX1I5vPaZExlU6jp6cP/b2WFSr3UuJj5KNGj8akyZMxfsJEtLa1IeALwqvaUVL54n10NZDR/VmWzOLVzhR68gYMNQa7X1Dj2NuCn974iWx9P6pDgXHbNBQJm5qbcOihs9DU0oyCGsu8Gm/6eaWIbaey69g279qOddoNQDqb0Y/MTz9gGg46aLq+xo2cDjxwhrbk5b9VK1di2fIV+hrZXiB1BGAbXV1ley1x2Eq3Y4HlOGXRZ69laUpXCh69odnmzk5dJT9blvjMtKYWVx0qx27K1Kn46EcvxhVXfgKfuuITeP/7j9AWultx6hcEQRAEQRAEQRAEYbiQXfUaeh74HqLn/x9ETv+KHSsIQ2cIwioFI5d+7J9iKDehoqDq9boRDnjQ3hxBwO9BKptBZ3cnUqkEPEYJrhJ9q5ZQVOkprtIvazyZRiZX0JsrZfMmMgUTpscHw+NFoVBCPJ7QO7FHomE0N0fhcruwYXMnPJl+HD/KjaPCBRzmSePYVgMToh69c3usqRkFVWBfPx/nH0A+V0QuW1R1JZEr5bC5tw8ru1NYXQxiUa6EDarOvNuLSNsoxNpaEY2G0ByLwuPzaBEuoNoTUe1tagoh2hRFU3OrtZlVTze6Eim8uSmBhT15FFz0M8sHyEmleLir2bnyy4XCiRMnYcK48do/Ld/b7u5u9Pb0WhfLKK9x0eLFWLN2jbbo5NjMnDkTo0eP1te4idUhh9B6tY3ypH78fvGiRer96LfrrRQcq/fFshq1T3YSR8yl79X2Ue2WT2BVdiKZwt///jreevsdLbA7LgLYzmp1sxxeD6s+T506GQcffCAOOnCG6nu7/oFBEARBEARBEARBEIThSXrxs+h/6mdoveoPCB97uR0rCDvHkCxWjZIKpvXgOzf44WP2wUgAmQI3dsogHPKjrTkKn5v+Vj3IZotI9A+gyMewtYBnIhiOoOjyoiueRncih854BvECkFXpk/kC+pIJmKqCaDSi8qjz3l79yHosFkXM78JR7V5ceWirCi04ssUDd44WsFn9SLbLo+p1G+jq7MS6teuRUHUUVJv7VHdXGhH83RyF+zt9+N2qHP64JoNXBoCkz6eF34IqgxsT0UkBH3nXfgrUK6U50+VCULUn1h7TIh39yQ7kTKRKPrgMrx6TcrY9G1444iofZ6d1rlv7RTWQVOPe39+vr+k0uhNbe7Ju3Xq899572vcs4ydPsQRG4giYU6dOxfQZ022B0sDy5SuwcuUqfW17wZJ5ykZKXXeETbpkIHyEfyiBm5I5QimhUD5t2lTEWmMo0v2B16fatRq/v/MPuPvuP+Lll/+ud/7fvLkTiURCu7Hg58kpg1apjmUq+2r5cKXvWn6mBUEQBEEQBEEQBEEYjiTffhiJN+5D29WPIPC+M+1YQdh53F/7yldu6BsY0D4jdwRKYQXDwIq+FOL5Eg4aE0MAJSRyJkxvAKlsHm4YaGlugS8Y1o/jU6ji4/bcIijPx/pLLqTyQLZoIgcXCipHrlhUabLwutzwe73IpFNaMKUlZDgcQYg+MU03MokMgvkUWnwuZNMZDPT1wWOa2oqSomgwFEBrSww+T1D7Zs16PZjfX8TjG4p4KxtCR8GLTaqtq5IlrOlPw+MxMSrshq+QV8duuGjVqP65VDBdKlDw4x9VmKlamkpmVR+L2JA3sSyRR3PAg7ltfoz20x2AndZQY8EX9c/Ced0KBbuenh68995CvUEX4QZJtPqkZaUDx27hwkVYt36DFnzHjBm9ZeMli+3LHgwUGzds3Ihly1ZoAZGWq5MmTdCP81tlWuKmI06+/sYbePXVV0E3D3xPTjh+HubOnaOvOWn8fp9+zyimZjJZpDNpRNV7N336dNUna9OnrfCTxHwGVq1ajcWLl+p2uNT7zx39E8mkdk/QsXat3gyrY439Wi2oNHRTwJ3/16tx8vv9aGqK6locotEmLZquVWkp3Hrc6rOkxpabcb3z7rt4990FWLhoIZYsXYrNmzajT32uEomULaRaYavAagnC7LbVd6v/giAIgiAIgiAIgiDsWeKP3oTIUR+zz7YSf/Uu5Na+jfYv/Rnece+zYwVh1zA0YVULhiUUaQHal0J/toRpbREYbi/e6MriVRU25kw0h7xoCdF3pWFtFBT0w6BVZLaAgXQB6ZJbW62WXG5VngrqGkVXa/f6Ik0WEfB5EQoFtS9PinS5TA65nGX1msskUSgWEI40wa/qptAXaYogqOqiP1VaYHJzKopn73X24y9r0liSjyIfCMPwuOBy+1D0BZEwXehPZtHmMzAp7EPA49HXWYdRMlBQ7aH1Y0G1O5NJIZOnC4MiSgV1XaXrzOagWo/D2kPbCKvaMQBFty2C2/bC294WVsm6dRv0Dv+01KUAPmP6dBx88EH6mqWVqn4aLmzcuAnPPPOstlqlsfMB06bhpJNORHNzk06jU7LLKpPfH9DiJkVbWjXzfZo4YTza29u1OMk0VlrmYyWWsMp2WNeBtWs78NZbb2HBggV49933VHh3i/i5fXhXp2OYr/IsXboMk/QmUxN1uwjLpVg7duw4bYna092lLVtZt8tNi12Xdj3R29uHDRs2YPmy5XjnnXe1he6CBQuxbNkydHf3aJcBbo9bvz/022pBK9uhvweCIAiCIAiCIAiCIAydasJq/9/+G8V0P9r/6SG4opYLQ0HYlQzJFQAVMe6BT0mJWhJFs/5cHm/3pvDI2jj+uCaN+1an8FZ/HimKdfks8pksCpk83IYbQX9AP65fNFwosQAGtkSdmy6Piveoc68Wu3KqXG4URb+mFLECoTDaYk0Y296MCWNHIxr0wY88/D4PsoWiFsaowlLiokhL68d8MYd4yY1+dxgljweqBrhVH0xVH8UwlzeAzQUflg8UkTbdemOjUjGPfC6rfW/GEwm9KVYql4HhcSMUjqCttQWeUhFjkMXclgBa3YYWEPnPERlHCvRdS1FRC4Oq6Rwzip4OjmC4ZOkSrFrFR/oNBIIBzJo1S4uXFlYaOyna29vwvvfNRCQSUWW7sXHDJu2blYLmVlHVSluOJUJbF5jPoz4nbjeDRwePx6fjtg++inRe9XFyRM+tUCBvbY3hggs+hI9+7GIcNudQjBrVrjc+c6s8zOv1+uDzBdSrX7chnkhiTUcH3nzzLTz40MO47X/uwG9+cydeevFlvRGabnW1zgiCIAiCIAiCIAiCsOcpFdD311sAbxDtVz8Kw+8YpQnCrmWHhVUtG5ZrSKoEw2UiVyhiXV8GG3IeZEMx9Jb8WN2TRWdfEslkEvF4CgPxNPr7E0imkqBvSlp0sigt0ppaBrXKd3lQKJooqkDLx2AgAJ/Pr60NoeqyNsFSKb0qLtSEdC6PfDGv49OZjCpAlasb6UYynUY+n0PB40XScCPncqNgqPLVMd2nuvjYuao7p5J3pjLoTeaRyeQxMDCg/Y0W1WRkG2LNMbQ2NyHkD6KYLSKTSsHn98Cv2hN1FRDxubUlaTWqxw4fKFprK2GOm2H5Et1WJ3Tp3fMXvPceEomkFkXHjxuHAw+cri2QKZbSipMbjlmvBZ1r2rRp2mKUZRfUe0D/pXwEn7B81rc9fNzecj/Q3NyM8ePHY5yqa9y4sXYYUyfw+jidZ8yYMfpzU85WS1l+rnx6N/9PfeoT+MdPX4kLL7xAW9/OmTMHkydPRmtrqxbQvbbAyuDx+uBxe7Xl9ML3FuHue/6I++//s7bk3Rb2a7i/64IgCIIgCIIgCIKw71FM9qL7oZvgGXsIWj99px0rCLsHY0NHh7myowPz5s2zoxpDq0wDRdCL6l87urGyK41jJrVjY6aAP3dksSlrYLQnh3Mmh/GBiVFEjJJ+pN4wXaAxaipfQFcqi0TRgGn44GI8ZVHDVMEFd7GEEAoIuUvwq0C/psUCNwoytbDKR/v5aDp1OVoUUojLZtLIqnJNjx/haBj+gF/7cy2kMmiO+PFaErh9WQ4r80EYXo+WvVRRKrA3BYQLSRzljeO8cSEc2BqF21eEV2/oRJ+glH6BQjaLZDKlex8KqfIzWQwUing9UcLqRBGnTIxhdsyvR8goUXG2hGJthVnFotER+vjY+j1//JP2JcpOUeD78Icv0FaUDvT1ee+99+Pvr76mxcvZsw/Fxz52McaNHaOuWrUMFkfQdKwsH3/8CTz40COq3LwWVc879xycfbblzJlJmeyll17B/fc9gHg8zkHHzJmH4Kgj3w+/36s3gtqK1Ra+L8VCAa+9/gYWLHhP1+nzeXH+eefgtNNO1ems/jMP/xh4+uln8MCfH9bvGy1iz/jg6Zh37NHIq3btCGyzSxUcDofVGG4rrjYiny8ioTfwGkB3V5cWlHt7etDT06sD+5/NZnTaQrGo+l7CB045Beeffw7CodB2YysIgiAIgiAIgiAIwu5n3dVhtH/sh+j7608QOu5TiJ71TfuKIOw+huRj1ZKMaDnqwvL+NAZyJRw2pgnTYwH4TROjPHkcM96PI0eH0Gr7KqV0SrGL1qOpVAq5Eu1L6ZmU1ylcUmQ1tOWql4/ql/LqmK4DTHi8HgQCIQR18MPr88Ljtnax11avqkFsEzfMWt6XwZt9OSweyKA3nkHE7UbU70JelbkxmUG3CqAwa7rgof9UlTGn6prkzuOEUW7MUW1uivi1eEu/ryXT0P5ds+m0tmDlZk/Rpmakcxlk8nkYoWasThbRl8nhgFgQYwLW5ky0mNVGszxm62oIbRTgdszH6notMFs+Vt+3Uz5WWTfLfXP+W1i5coUWBcOhIA4/fO6WzwObTVH3b88+hxXLV6mx92vXAYyz/Jm+jbfffndLeEuFd95+B2/Nfxvvquv0Scp6nLoo3E6ZPAmRSFTFWZapVtvpY3UVFi1agkK+oEXYI46Yq61iKVjuUAiHEFKv3CjLEm8tK1WOm2OZ6wigzivTELfqGy1dYzFay47DgTOmaxH7sMNmY+bMg9HW1qY/vwPxAZXHpa1zU6k0JkwYj9GjR6k4XcyW8gRBEARBEARBEARB2P3Qx2pm5d8RPftbiJz2ZTtWEHYvO+wKYCuUCw0ticKgeFVELJ/ESTHgEzPbccb4GMZ7DbiLBUtkVCmz+SwSiT54VPr2SBgRj1v7KaX1KzcxcpdK6ryAkMoX9ntUOgO0Vs1m04jH+5FIxvVmQ5SsKHBSAKNVYliVFWtpQdv4cVjrDuCRDQX8cVUWz27OocugR1UTY9wlHNfuwSGRHAKFpKonD6+ZR8DMYIzZj7nNRRzY5IYrn0E+m0MmV0IubyKbKyLeH0c6mdCbaEWbmlR7ssjkC8h7vEiVaO9qjQSHohy2c7jLaxR1N27YqN0umOp9ampq1o/Bl7N06QosW75SC6p8S0pFNTZ0v6DeC4qghZwKfFWhqI6LauwK+aIaRyuOG2IR+shdvXo1li9foc+3//hRgOUwWsInfb1W4oiitYKDc+wInCxr7dp1mD//bR244RX77viWrZa/PI4bo1E8PeWUk3DxxR/B1KnTtKhK8bu/vw+bNm3W6Yb/Oy4IgiAIgiAIgiAI+yaxj9+K8Imftc8EYfczJGGVUpNlgwp4SurIdCNbdCOdKyBomGh2A/5SgU/CqzQe9ceFTDaDRHxAWxFGmyIIqdfWoB/NATd8RgE+M4tAKYeo10BLyIu2aAjRcEhX5lL5g6GQtmLUj9/3x5EYiCOTTqOQz28R4FJFExsKHnR6m9EXGouOQhDdBROBYBgt4QiOGteCc6a24OjmIg7x9OMgbxzHRnL46AQ3jo+VEKAVaq6EZLaIZCKFns2dWL9uPQYScbj9HrCWgb4BHegRtuD2IENRzkW3BBRXa7BVrxsmUMC0WtvRsQ4bN22G22NZd1Kgbo4162tkQI3ze+++h96eXv14P62FW9tatXUmd/hn4OZPo/hqn7e1t22Jb29vRay5Sb/v3Ek/EU9oq1TuvG+JkGWjpg+3tq3auPFaveBQfkz4GXn99ddx++2343/+53/wm9/8r3ZRUE61/NbLtiLrlCmTtbUwxX3GcUOshPqM0EUDqaxbEARBEARBEARBEITdy4Rbkwge/mH7TBD2DEO0WKUFKu1MiQmXYSKVzaLg8sITCmvRsWTyUXqPfhw+m0whnUggEAgiEmkGH6E2TBNBL9DsB6LuIsLIIWimEXYVEeAu9apkbjDEDYy4U3sqmYRZKuld5puiUS1q8RFsPj6fTCT1Dv5eo4R2VWaLmUWkmEC7r4AWPwVgE9lsHvmBJGb4DJwzJYyPHhjBp2c147Pvi+H8qaPQptq9puDGQgSwwetH3udSdRTR3hbB6DGj4PUHkM3lVX39qsuq9y43svT7yk2wthhWbiuoWQK0Yi/rbI4oWC4OEoqbFBf5WL9b9YfC9bSpUzB61Gg7BbB8+XIsXbFMvY90/WBi5iGH4LJLL8UVn7wcn/zEP+CTn7wMnygLn/zU1sDzT33qk7j8sssw65CZVDf1WCxbvgwrVjhWq2Xopm1t49aW7jy0LI2qz42L7h3UBzcRT2Lzpk5teUvKhmULlkDqhK3jSGgt7fV4t7Z1VzZWEARBEARBEARBEARBGPbsuLBqgk/+642f9KkqIV/IoJjLwO8G6FKVYpOhXwtIJQeQTcURCYcQpj9Qww392LzLEquMUhF+PpIP9ari6FPVpPLFy6zL5UYkGkVzc1SLp3zsmpsG0YcmH1lvaorqcvrjcZSSSRwe8+OUVuCEUApnTwpgRpNXi2i0lPSqRuddHqzsziOVKWJiyMBYtwl6Ml2WAe5bV8R/vduP3767GcsSJTS3j0KsOYag14eQL6D9rtKac/TYUdqPp1eVpa1yVVuHu5GiIwBSLGRbc7kCXv3763hvwUItqnLjKu6kf+BBB2o/oySpxpPCa1dXtz6PxZrw/iPm4uCZMzB12hRMn36ACtPqhmkq3cyZB+HwIw633ivTQE9vHxYuWqw3ArM+glsHj83cavFpf8h2EfSbGggGrfJVWL1mNdatW6ev0V+vM0ZWvU7YFual9Wtff/+WTbXoTiASCcPjURNAEARBEARBEARBEARB2C+wFLQdgaqqfije2naKCmo8lUE6m4ZZzCGXzsAsFFHM5zEQjyOTyyHa3IRAKIAShStbqzJt4YrCKnfmd7tc8Pi8KBQLyOWyVhqDtqbqVV33eb1oiTXrXd65idRA3BK2PCq+qakJrS2tiASDmBgATmgBzh7nw/sCBZT6e8BNiygK0tp17UAazy7djHfW9mMgU1ClGxgoFbCwN4MlmTA2esZhadyDTWkTRbeP3l+11S2tY+lTNBC0Nm/iRlw+uOEtAm7dLas/e47yR9+3CpPVYDqKf4SPrdPK929/ew7PPPMsUhQ3VXZadM6dOwfTDzhApyPLl6/EkiXL9PixjAMPmoFpB0y1rzqwz/w8VA/cLIoccMA0HDBdlU1NU/1j2atWr9HXtowbr/E9d0639G+oWG1z2jBu3FiMHTtaf8ZobUp/ry+//Ire7Z9Y9ZW/h1vrLx/DpUuXYuHChdrHKj+bFIzp+oBsFWcFQRAEQRAEQRAEQRCEfZkdFlYpG/GxcAqsOnPJhVC4GbHWNsDjRzJXQG/fANZv2IxEKodAuAkGBUruyG7lZi4Nj7KmgSw8KLo9MPlotYs78VtpuKEVxS3tdoDimAFVVwSRlhbkVO098QRS6bQqm5tfGQj4/Ii6XWguJBHK9MOTy6uSvXCpuvP5AjL5HJo8JRw+JohD28KIebiDvypXtcHrMuB35+D2pOAPFLSbAm7MRWktk8nrEPAH4fP7YRZLMHM5lPJZuFS7nEHc2rNqVLm6RTgsu1a/ENUeCqrWbvSJhOp/KoV4PF430E9qT28v1qzpwN///ir+8Ie78dCDD1suADxu7WJh9uxZOOboo7QvVMId/BctXISuzi4tIjc3N+GQmTMRa6b/VTbSaSj7wBGoFSxL0FgspvIfgqao+jyo8jZv7sTSpcuQ135J7XGwi3SsR+l2obu7R28MtWnTJmzcWB42lx1vrAhW/IYNG7WITGGYjB8/ATNVG7gRFWMY/9JLL+P++/+MZcuWq/FMatcAhUIJRfUeU4QuqvZxrLPZnC7rjTfeUmP3CFavWq034zLNorbKnTRpoq6DaHFVBFZBEARBEARBEARBEIR9GmNDR4e5sqMD8+bNs6MaQ2+b3Om/aBj46+pOrIjnccb0sZgRdCGXz6N/YEBvKkUhEhRUTRNev1dbRXo8BrgZFQXUzkIR73WnkMmbmBIxMDHsA/JFGKUSmiIhbbFK0ZO6GMVcSlX9JWBNMoe+dAF+dT7KXUSrt4SA24NiAXpDK8MoobklBr8/oHenp1iWzmQQ7x+AmxanoQi31EJL0AufakeqCLy0eQAvbUqjp2hiesyDcye2YGrYi0yxiIGBFPxuN6JNIapxyCdVWemkKicEdziC59cOYFlvCqdMbsFhMdUH1VKjpPqohWGKs5bVqz7WBxSL9YuWFGkBec8992LN6rV6rE466UR8+MMXIBgKMJWmt68P9933AP7+99e0oEe3CmNGj1Jj7FPjY70fDpYcrMrmuKny6Bs1XyhoNwq0zqTIzDIYaIU58+ADcf5552DS5Ek6PYXP995biHvuvhfr168H3TYcccRcfOTDF2rLTN32waKHQJWpyqBAyn6+8+4CXQ+tWD/20Y+oV8sK9qmnn8EDf35QvWdsnwdR1cdQOGyVsbV7W1Hx1khWtojCLF1U5HHkUUfgtNNO1T5RyaZNnbj/gQfx1vy3tAsEPT7qPW5uacbUqVMxZcoUtLS06Mf6KShTfGV7urq6sHzFcqxevUZb+brVZ9lU+caOacOHLjwfc+YerltB21gX54ducLVGC4IgCIIgCIIgCIIgCPsCQxBWLYHTElaBx9dsxoqBHM44YCymeYropy9Tn0+LYhToSoUScvkcsrksigU+el9S193ocwfw5Losnl2X09aBx45y4UMzWtFsZrWQFYs2AW5VARUyCmWqrL6Sib9t6Mdf1yTQmTXQHvDgpLEhzPFn4Uv26jr9wbAWvUKhgCVtqewsgpabuWwOfn8QGdWWgWQ/ItEgmkJhZFMFxHMF5ClS+v1wFYsY41Nl+DzoTcS1+NYcDWsRMplIa5E2EvKpOsKqBjee29iLtzcncOKkdsxuoYBnVhFWLZFNR9kHtMylyLxkyVLcc/cftUUp0594MoXVixAKBvU5+09h9U9/uh+vvvoqfD4/6OeTflFL9Eera9C9VcH5a8G2O9CFAcVU57F2bgI2Z+4cnHLySRg1ul1brhoqnuP05wcewlNPPa3riURDuODCC3DiiSeo8igaqjJ1f7bWWR1LVCe0siVPPPEkHnr4YaTSGXg8Xpx37tk4++wzVSkGnnjqKVXvg9pilO2j4FnizmD22PGN3NobhYov05O3gfXms1mc+oGTcPHFH9E+eWldahhuPc4PPfQoFi1aZJWpCi2oz2ZR9ZViKut20V+qyxJWdTvUNdbuiNGUT8e0j8GZZ56OI48+Ah7byrak2sPx4ZZp9cdGEARBEARBEARBEARBGMlYatdOYGmHBjLZHBLJJAJ+P6KRqL5GMcpwG/AH/Fv8oDZFYvB4QljXn8Mb3TmsNaLY4I7gnVQJa9I5ld4S1NLqOJ8vIpPPIKsCha/VfUm8sCGFRbkIugOjsSgfwrPqvN/wYfLkCQgFA+jv7dOWqWZRNUy1i3JYItGPYjGL5tYoos1hjGqJIdYcRU6VuWFzJwYSAwiaRYxCAdNDPrQZQC6bRV8qBRRMNAeCcKuSEgP9up8h1ZcAhTrWodAPvG/R0KqLaVtlNubZ6veTaKGTflvVWHG8eO5g1UBswc9FYc/QFpV8pJ3iLkXDYCioLVytwOOg3qgpFA4h0hRBc3Ozfoyf1piTJ0/GccfNwz9c9g+46MMXaFHVEg4t6Pt0ydKlWnCk0Dpt2jQceOAM+yoj7deGQaHSsuSSGl9y0EEH6sfm3aqPxUIey5Yv1Y/vE4qrVv8oALu0QO9X76lf9ZPWx/pVB3/Fq3PshIAeG/rDpZW0A4VRtmPy5Em45JKP4PTTT0X7qFHaty/ropsHt8er+2wJ1xRUVT9UW91et7rOND71GYpi7uGzcMmlH8KRxxyuPwBF9dkxDO2RV/1znEgIgiAIgiAIgiAIgiAI+yrG+o4Oc9VQLFbVP0plf+3oxKLONE4Y34zDWv3wefmAvqGS0XqPaS2JiekpwlKLLBkG3u1N4q6Vcbyb8MNdMnFgNI+LDohgVqCEZM8AvN4QfEEfcmYKrgItKQN4oz+HP64HVqAZLo8L+byJiUYK/zjdg5PHRrSW193Zpf2Ocvd+iorZbFZbLoYjYXj9Pp2G1pOJVAb9ff3w+1Sbw0HE40k+O46IykODxHhyQFuUtre1w+/1oTcxgGIxj1ikCR51ToFO2yWqvry0sR9vdSZwwsQ2HNbC/m9vsUpxjnVTZuQBLS15xaX+dW7uwltvvat9nnK8Dph2AGYfNgs+n0dn4filVHvfeftdrF27VltN6qIULNt5Ryzs+nik2k/Bln0MBkOIRi2BtbWtBS0tTToNYT+dPGzmkiXL8S4f12dT1flBB8/ArFmHaGtOllku/A4WJx/dELz99jtYsXyljg+psT/88DkYP348lixdgQW2mwBuZrZ1szOnvm37ufW4OvSResD0qTh09vv05mdWeVY7GNi/9es3YfHixVizZg26uruRTCT15lZM6vzqQDcGHMOWlhjGT5iAgw6cgenTp8AXoHVyyRo/9V5bMj4leLfOu+OjJAiCIAiCIAiCIAiCIIwUdl5YXbMZS3uz+OD0cZgZ8ehHt3mNT34znanFJvW3aCKfK2i3APlCFvES8HbSjbd7SvAWi5jb6sX7xzehLehGOp6Cy+VFIOxXufNwqbyFogtv92Vw/8o03k57UfC64M6XcGjEhUsPDGBWs09bGOYyGT7Brd0OJPrj4KPfLW2tCIT8KPJRcNUml4rr74ujv6cXbe0t8DdFEI+n4TMMeD0mksk4NqzfAL8viDETxiOl2m0YHu0OwOdRHStRVDVAxwYUAF/c2I/5nUmcOKkVc2J+3d+awioVO3VI0ZnQn6y+7nLr8y2YLL0C1W6deadRg69VU4Zy7PdW12NtYmXB9JbLAQur7YNri5NWQTWZWbbpB6+rvtIy1MU6HTlzV6HK1W1nPWVtsRqi/rP6WSqqzyQ3Q0ul9MZdFGXZJgqwfr/6LIaC2hKbriasNnJMnPJYljWefLfpHsJxfSAIgiAIgiAIgiAIgiDsmwxdWDUtH6sUVpf35/DBA8bioDB3mGcaWlGWwMfduYlVNqcC/asabni5C30hj6ILKIabkIYbAdOEP5eGV+UJhyOID8RVOSYiTWHkCjlkUxkUVGX5YAjvxgt4fl0cXekCWtwGjp/cjHnjQ4ipvJlsEfFEBqGQX+/y39vVpeUufyQCXzAIn9cNj2Egm8lr8ZbuAYJNQf0YeLIvqTc4CkYC6E8MIJMuIpfMIJlJwB8KojXaAj9dAngM7U+T5remy0RKtf/Jjn4s60ngg1PaMTtGv6gldZmPg1tiniWs6kMrqGM9TOovUxXSCWR7NsLMpqDNZdV4aMmOeTTqjFaR6oj67Jay9CvT28fEOdZCri7Fto7lgfPHCU4m50gnUKgW2xXpWnU5qk0sqBzn1MlWjfI0LEed66K3KYuCJL2S8p+VxsEeiRo4hZHyfHrEVXVsszVu25XijI96I9hXuldwaf+zajy3lEOYk61Qn+hSSYWi/mzqcVcJ9RX1ufbH2lUYpeIo1LItVn8EQRAEQRAEQRAEQRCEfZMhCKvEkqkKMPDEms1Y3JXGB6ePxfua/dpilZsA5fM57ae0VCzA7fHBGwqh6HJhcyKLzqy67nYDPhfafG5M8HtgpBMwKKaGo0gmE8hmMtqvJcvz+vza16bH7UFa1btZ5e/PqNoLOYwJqDLCfmRNA13pPOKZHKIqX7BURFSVHfT7EE8kkc4XEPKH4FFtoFga8ge1xW28WNSP9hfTWRXnB3d0z+Vy8KnrA919gLuAllEtKGYLyKp4w3TD51b9CRooqvJf7crjviX9iKi+XDqzFbOi9OlpamHVGicGQ/1HCY6ndB/AWPVPtZE6as/aFVj14l+Q27RG+/is1C91RgqAPNCPnBMnEV8rM1Sia7OPnbR2u3RgmdZ1CpGMKk9FKDjqVNqK047UOCmqY/VlawYeGVrwZF0sV8Uw0i5mm7LV8dZ216I8QyXMy/aqtuv6nLEj1hH/6hrUH123CnoMVMSW94Gn+iLzO4k4HkxbRN7lw6T3n4QpKsBFlwN0XcFU/AwIgiAIgiAIgiAIgiAI+yJDFFYpSQJ5uPFURzfmb0jhmPExHN7qhVHMaWGSvkP9fp/eQMil0iULRSzrS+K1zgIWJYDufEE/Dj824MGx48OYHSmizWUi6AsiGY8jEeeu/VG0tLSCmwlpUUurYwb0s/6KVDaNXCYNVyiCd/pL+FtHDxL5PA5uCeH4CU2YFlJ1a+tCE6VCAQMDSfSqsoPNITQ3NWMgXUJvDqodblVkEV7Vr4BZQMBtiWdukxalBfgCftWPMErqeiFTRCaTV33PYVMJuL8jj2c3AYeO8uOKGUHMjvp0W3U7bYGOopzlFoCnWnGzj0vqUgmF5ACy3etgZhPq3LlYhl0MRVWNFgjL01nHlgipjvV/Zde3tKNS6FNpdJGW/ayFnVbHq3brLE59tYRCO48tPjp5iW6T05SyQwdaqloHzGRd3ZqcGXRhNuqKTmbH8bJ1VB19UWdQf3li/dVo1VQFZ7x1/UzBV5XO7osVy96rI36W2F59jXnVPKCvXcMLT+t4+FrGqWOPTmsVa5ctCIIgCIIgCIIgCIIg7HMMyRUAxSRKcQXDg6fX9eHR5UlMCntwSruJyTEvosEg/G6fFkQLpRLSqRTWZYp4YnMRf+sy0OcKwXR5Ve2qrEIO44wsTmotYl6rC2P8LnjchraMDKpyuLu7Fr22CGCWrEtdzK3KTqbiWJ0u4c9r83i2B8ireg/yZvGJgyI4dpS1cz+zcmOh/ngSqXQWbq8XedX2lOlF1qAQCrhUn+g11meWEFBpmyM+RAI+JAf6QHebkeYmuG2RkfIcW7OwP4PfLMvg1V5gbruBT80IYXbEr9tOQdJli6t0GUCqWTBqK0qnb/sEVl/Z212FUyIZriNF1xeWOGttaCYIgiAIgiAIgiAIgiDs22yv9DXEsK3yPDpzyWWg0yyhx+2CNxBEyBOCzx1QaVza52lfbwpplXZ1ycD8uIkeVxQFfxAFr4GSx0BRHa9HCK92F7A+byDa0oxYczO8Xi8KBe7Obqoqy4QqderWgYKkC0FvGLk8MFDIweVzw+XxIquuZQt5LcIZLkrAJhLxFMxCCaNGtcEfiSJTcqOoeuAyTB1oBWu63MiqMgteNwyPtZmU10+Zle4BMvqcW0oVqZ+pOsaG/Jja5EXYyMIwWR8vKLY01zrX7dgauQ2OCJdIJjEwEEdJW0Vui9brKuC4cId9btjFcWLedJqbLpV0nG6iHbhrPdOzHB5b+ayx1Qa9LNBOTb+4Oqg+8zWdSWNz52b09/epcy1p69Rb0249d+L4KDxfWQc3gcpmc+jrG0Amk7VSqqRWUOm3lGeVUA7z9vR0Y/26tXozsQ0bNqK3r09vgKZ9nW6p26LynDAunkjoUPX6Ni2wYDIrKf+UBwern7TM7urqVG3qATdn46ZfIqoKgiAIgiAIgiAIgiDsH+ywsEp5qUBhSf3jNj1GyYC3ZGJU0I0xzWGU8kUkk2mkcnn0JxPwBTwIhoPoTBTQk3Xp3f49RVbs1sFtulBy+zHgDiDn9sCtH1c3UCpaguF26MslmAzqn9vrwahIGAeGfZhmpDChNICZTT6MC/pglpjGg1QqjWw+g1DUB7fbhUy+hLzhRsnlVjXR/lSVZKg61XnR40ZG5ctk8yioPnBTIwqraVVGQeWjz9eCWdRWrM0eF44eF8AhbWoMTIqr24pv7IfltqCx2LZxw0asXbsW+XxeC4oU7bg7vTMGFEuz2eyWOKZh+vXr16s4Cnxd2jft5s2bsHr1KmQz9G9rtZeiLjde4nhk0hmsWbMGnZ1dWgSkn1f6s02ns7oOq60qmCqXei82bdyMBe++p8rv1X2mSJpOp7U4y3QsN8fNyVS81VZDddml0pq6no0bNyIeH1DHq9DT06P7R/+7jlBZUOWwPPaN7SuHdaxYsQrvvbdI9XUd1nasxaKFi7BmdYctQBtb2sPxsgRRU7eH48RrjIv3D6C/r0+XyTZSgGZ9bIs1RkV9zDgGiraWPuq8d4bqjxo7PU7qc6DGiWPDMXznnXexYf0mXe9WeFx+LgiCIAiCIAiCIAiCIOxruL/6la/c0DcwgEmTJtlRg8EEn26nMLcplca6eArRoA/jWgLwukro7+9HIZtBS7Mf4ZAfhWIJ724u4L2kF0WPT2uNJW0QasBNLc0w4SlmcEgUODDmh+Wl0rKudLtccHOjKxtLvyqhxDJUOgqfnlIB4WJe5Q3imMnNOGZsEG3FlPa/WiyUkM+mEQwF9a7/qilI5V3IqNeSar8lrCooMqoj/qVbAHc+i3wyiVQqoQW1lOpjMZ8HN7fSwqdqSE+ugM2FAjqTOTSpfLNaw2j1edQRhVpqwLpkdtNmy8F29PX1q7+8bmD9+o1atKOYSCvPcDiMzZs7sXLlKv2qBcGSieXLV+jzUCikxkjL3Fi3bj3WrOlAIBDUAiJFxnA4pMvvV+8zX5cvX66vRaMRLXYuW7YCGzdu0HGsi9bCfG+Tqv/Lli3T8WPHjlVjkVJ5V2oxl8ehUBi9vf1a/Ozs7FTvhUuV2aTb0d3dg4ULF+v6fD6/LiMep/DbqfL06TZRrFy5ciVWrVqjhWHmj0Qium7CfvKzNGHCRMyadYh6Ha/bxvHxeDw6/9Kly/U4JRJJPQ4crxUrVupx6OvrU3Xbrh5cbp2HYu+qVat1uyj4UhBlHevWrVNt26yFYNbBdjhQSOV1jvf69Rv0mHK8Oc4DakzHjh2DWCym278ttd9vQRAEQRAEQRAEQRAEYWQzRGEVWljlhkzBkA8BvxfruuNY3tmPJEraSrPJ70MsGKJMiZzpwqpkCYsTJWRcHphul964iiKllqKKRcSQw6yIC+O8JRiFvBa8KGhSVKXQZVkEbhWqLNHS0AJrIZ9GMZNGS9SPWDRoPe7u8yGZyyPV3Y1IwItIU4tqsFu1B8gWisgUaM1Kq0y7VL6qfIZZQkC1rSUUQEtTRIuHUfpX9XjgUiEYjaDg9mNNysTza3vw+qpNaFXxJ0wZhSlNAXh1eaoPqiL6VLU2f7LrsP9Wo6enV79StKWI19bWipaWGHp7e7WQRwGUwl17e5seFwqGtL6kH9q2tjZ0q35yszAKzRQmx40bp4VFCrCxWLMWOilsRiJhna+1tUXVVdCiZEtLC5qamrFp0yadl+eEYmR//4Cug/XxOts0YcIELULSypVWsr29PRg/fpxuB8VLRxil6BiJRFVdrVp4ZTkTJ07Q7aI1KgVWCpwTJ05S59Zj9RR2mY5Q0GQa9ot9IBQ0E4m4tizlmPGR/IkTJ2phltakFIPj8TjGjBmj+86mWBa2dEfQp9s0ZcpkXQ7b5PX6dDytmqdNm6byp7RIy36yL2Tdug3o6FinBdTRo0frcWDb/D4/XOrzOW7cWC3qOv223ufa77UgCIIgCIIgCIIgCIIw8qk0sRsUfGCcWz3RfLTN5cIpY6K4eNZETIlGsHBtHK91F7EwaWDVQAG9yTzSqSQmN5Uwo6kIbyGjN3ei61ODj3MXs4iU4pgRKWJKzA2/G/qx7kQioa0iKZRRSKQYqP1q8nFxipamAT7Iz0fR4+p1oy+Ip7qK+K93unHrO124fUkCL/X7EA+2IGt4kOSj/EXrsfVSLgs/8vCaBccEVouqtFT1qeBV9aiCtdWp2+OG2+WBLxxGxuPFykwJz3dn8JdlXehOFnHCgZNx8ZzJmN0SgJYDKc5qXc0S1lg6LVgHCwVkCpETJozD6NGjtNDoUW2gRWShkNeWrHzEnZt6UfRtbm7SlqeE6Rjf3NyMpqYmLYw6Wh/HibA85mlujuq4nBoLS7QuIRj06zTWuVVeMMjyoqALhUDAj/Hjx6K9vVULs9lsRr8nFHEpVlIQdfJSaGRdTU0RXS7bOm7cGC0MUyTle8r3mLAdHjW2Pl9AHW87VrzmtJ2wTsePLC1aCV/9fr8W4B1htqurW4u+FJopSFPIpdUsx3bMmNFaCG5vb9fXWO+YMWO1MMxxpihaKGytM6U+v6FQAJMmTcTYsaMRU+NXVO+Fz+fRY8Px3yqqCoIgCIIgCIIgCIIgCPsDQxJWXdrWk2KkCy7ThL9UwvQAcO6B7Tj3sCkIe114Zc1GPLhsA94ayFBlw6HtUXxwXACzfGk0F/oRLGQQUCFWSmB2NI+TR7swNWAiHPBrYXDUqFEIqnwUzWhxSCtEbkCUTGeQz+VRKOZVK0rI5opYEy/i6Y0FPLy2hJf7Q3g7HcYL3Sb+vCaNFxJu9HgjGEglsXHTBgwMJBHx+9AaDsDvtqxmKQbSUpXbWYV8bu3WIJNLoy+ZQMEE6BF0wHDjvXgejyxch/mrNmJyewgXzpmIE8ZGMUrl9aqEHAtVmJZRy2W2baXC2jiipOX6wPLraYmCfPS/pK0waTHJsaAlK0VDPvrubAplYcVZvkJLWywwafFKwZq+QSky0kKTQmQoFNHCIIMlhlqWl05b9GZUqhyKqpZVaZcWRGk1Ggh4bStZ9Ymw028VGHlOq+OsrpfCrDMqFE99Kh9F0FAwhOamGCLhqH78nkJuOSyXAjsf16dVLh+/Z3v4+aCYagnFzbrtfn9Ajxnjpk6drPM6rhPYforOtMDlRl+04KU1LPvndluWy1Z9/Mtzqz+EY8K+08KVFq8J9bnwB1S/1ftEYblc+BUEQRAEQRAEQRAEQRD2D3bYFQD1J0siU0c80c+8qwOKi0YRo3weTB1FkSyEDYkcVvZnkSy5EPJ70O4zMDGqXgNAqyuPaYESjhntxwljwpiEHEKlEuiPEy63LpN72/t8XsSiTTqeG0wVCwVksymkcznk8iV0ZXJ4fmMKL/d40euJweTj2S4DpteNpGppVyKPZlcJE4MmzGIOfr8XTYGALtft52ZaJvyq3JDHQDjgQpNqZ1il8Xq8SGby6CuUsCZTxItrOrF4Qx9GR8P4wIHjcOyYCNrctNvlY/90TGA9gs+hsMRFfaBfiB6vOlD8Y5soAFJYpdBJwZKP64dCQW2xS2Evk0lrIZEWlyyegilfmYaCIoVOCpBsA4XKvr5eLSDy0Xdaa9JalWIrhcq2tnb9KPyGDfRH2qPFSQq3FFwJy6UIyzQUMsnGjZuxadNmbRU6adIU/bi816vGTNVdDuundSnrp+UsXRBY7aNAm0dI5Wd7aJG8YcMG3a+W1hbthmCrOEuR2PLNSjGXfeXYTJkyRbWzXaVzqbhu7UKAedhGtp3jZPlX9erH9NlWCrYUVim00pqV/ae1LttA4dQRadk2j4e+YqO6b4TxdNHAdrJsXps8eYquk4Ixx7ncD7AgCIIgCIIgCIIgCIKw72Os7+gwV3V0YN68eXbUTmDyv6L2n1qEB91F4K31fXhnQ48WRWeOasJhY6NoDvjAh7gpRVGOo6Xn+o2b4XaZGDe6TT/izwf903nu1J5ByOcHfXxysyltUmgW9CZU8YEk3u4ewJ97PFiQi8H0BnTOkotyZ1E/3m9kMjg5lsMnD4piQtCL1EAPipkcAtEYPEFubMTyaINL61Va4jKvC7QBXZ3I4M113Vij6mgOh3HE5FE4pDUIvT1TiRtoqUYYFFVVoIGjowcOAVpXllt9OsdsHwVES6i0rFApAlJ05TGFQF5zxFDmowhLGEf3ATSodDZxYrG00mQaioF83J8WpU65Vho1FrYVqtMM1sHhz+XU2BeLWsClgM2y2UYrWOkcmJ+Wnmy/075ymJb1WhatlkBbjtVWq1ynHbQuJTxnVfl8Ufdxq+Ws5T7AKtOt+u1RcVbb6UZh9eoO3WemoYA6depU7TaB507bWafV363vA485/jznOFltZ7pt0wuCIAiCIAiCIAiCIAj7B7tWWCVmSQXamtK/p4GcCquyBby2thcdmwfQHvLj0IktmBoLotVtwqtSZktubB7IIl0ooLXJi2avoeI9yOYKiKfTCAb82kVAqVTQG2ZRyiwVTWSyebyt8v1uvYEFqRACtjCXM9zaPyof70c2i2ObsvjU9CAOjPq1IpdIJZHK5FSZAYRUoA1uyVBtVnkKpgud+QLe60ni3Y092mJ0zrhROGJMDG2egiqTga1WdVFUs+vRkpoxJM8KW6BAR/GOUAgtF+qcY+txfktIdQS9Wmx/3VRjqHrr2hqnkqg09sl2UFS0LtJylWNBK1BaoFJUzOUyqr1We1gXhU1ah9LSs7Luam1t1H5S2T7mcWDddDXAx/E5HrRItTbPshMoyutgVrpFoLUq20pfr7T0rdZWsjXftueVNLouCIIgCIIgCIIgCIIg7HvssCuARtAylRafNGSkhakBE60eYEZrBK2xCNb1p/HO+l5szhTg9vsQ8HqRyZtI5lwoFF3IF62NjPwuNzwqf7GYBy1PfV66CGBptHA1kEomtauAjDeIBf0FrFf5+fh/idqWysc0tIQ1SnlMDpRwWLMbrW4KgIYWB91uE6lMGqVCER5adqr6aGG7qDeJF1dsxPLOfkwc3YJTD5yIObEgmgzatJagWo2iSktB1apHvahDHttHQ4aWoIsWLcLatWv1I/G0jKRm5/g55WPxvEYrVT6O7sBrFPf4Wihwk69trS9ZLgPho+u8zjIY54isTMe8TjonP32WEj5uv3HjRgQCQd0upl26dClWrlypH9PnI/J8rJ/uBChwMh/LckRHUlm+1TYrHXHawFcGtlPFbmmLdU5h2KX97q5YsUKNRwd6enr1Tv2M52ZZzvvg5OeLVS436Apo61aOJceCfWE6XuerZfW6tX6rbVZ5TtustvOc42pZBzOf005BEARBEATh/2/vzH4bubI7/GNVcWlSOyVKYsvuRUonSJxBMvEYM0AeggxmnvOeP3Ke8pDHIC9OAjiBHRh2DzojtZqSWiu1keJWlfudIt20ppexp+NG2ueTuNStuufeW9LTh1PnOo7jOI7jvP+89YxV9GP+gwQtiEfkYyxUOBpFkdrh25fHHX22faLuYKRHd+f14eKMyuHaeDRSPEy1UI60VC2qGEsXF6FHGml2bkGjgrlVZb0bXV5fKrlT1U25pt88OdU/HQx1XlxQIUoUUfM0DBkNe1rILvXL9Vi/apS0EI7tsfRSpFKxoKGiEKenm0JRx4VEXxycqnVyoeb8jD6+t6rNmZLusJY0De8RNtCEMauhbAAvU8eIttCGVvtj1Br1P588eWLScHNz03baJysTaYm85HF5Nq5C4iHC8535e1ZPNN/Iinqk4b4kie1yTxvHZLkSk3qi7ITP9YhIJCF1SZeW6nYd4/NYPVK30VgJ/QZ2HRmhjI9o3NraskfnGQsJjER9+PChCUjmRwYpgpV5sZs+9WCp20obtVmJubS0YLvvkz16cXEe+hVUq82M+3QsW5c1MB79mc/l5YVdTz+yYvf29vT06VO7D2zq9exZy8ZDOHMNn9RkZZ4hjM2ff8Pl5bqN8fjx17ZG5s5mVmdnpzbm+vp6+EsUbJMqRCrrmZubtYxYNgzLr1mzDF7qzQ4G+XnmMCml4DiO4ziO4ziO4ziO47z/vPWM1fyx+Cx84YH9cdYothEvqZEqStWslvWwMW8Zfl/tn+p3Z9casMHRnZKWKAPQ74bLByqWirYrfzYKbXGiOIkIo+51xzJGKzM13YkiVRLprHOj9s3AJGyUFpSkqeaGV/rZ4ki/vDenD2fuaJgU9TzNdD0cajQY6mqQ6Eglffb8Qp9tH4X5SZ88XNff3l/Rh6VYRcbG0IZ55ht25TI1tpzcsK5winNvQ6ySucmO92yUhExkwyY+9/cPTCIi7xCLCEIkJps9scHVRJJ2wvqRp9VqTcfHpyZTyUolJvITOcq1SEUEKoIQWUi8bvcmjPPcsi+Rg2Sm0sbO+dfXVzYOm0SRoTkRiMwhz1LtmLhst89tHcxxe/t3yuu3JrZxFsKUmMwJKYpgpe3w8LkJTUQtbfRdXFywzbeOj4+/WStCuNXas8xUxOikDAI7/NPGXMmUXVxcsvtFbGQs4pVxWTPzQTQjXxG2CFfkK8KXa9iAiu9IVsZF1LJOxtre3gn9uiZs2fiKNbfbF3Zv2PgKCYsMvr2Bl+M4juM4juM4juM4jvP+8vZLAZhajCyzFLHKj+V4FjgTXmlqj8/PxAXdn6+ouTKn9s1Ijw/aOu8iu0qqlhMNB30lyFQV1e8NxGPbSZG6qz11en2VS5XwKoVYqRYqJdVrJZWzvkqDjuZ0o/XijX6+WtSv789rq0oc6Yujjv55+0S9pKS5uXl9ddbTv24f6LLf11/fW9ffba7q0UxJMynTzdjTKswYQRwIb9RhDQvIRbG95Vmsdjh+fV+QiUhQZCiZktQBJSJSEEm4ufnA5B/ylJ39OUc2K1IRqbdcXzahiHBEECJTySBFZG5tbY775rIUkfno0Z9YJuns7Lxt6nR+3rZ2ZCJxJnVIHzy4Z1mcUZRYtilysVJBrKbWj/GQvsCYkxqw9+/f08pK3eaCgCXDE/nJC6mJMCV7mKzcepg7sZrNZvg/3AjryePPzuabSrHmfN3zlnGLyKWdsgSIZGK3Ws9sfsTnHnI9cyGrl5qwzIM/GWvkXhAfiYsk7Xa6JmWpG0tc1o903dx8aPcaIY3QZU3MnfvOPcizYZlHZXxfqNfrOI7jOI7jOI7jOI7j/Bh4+xmrY8VYYMd8ZVZnFQFpOZ/hO5mmJl+zWEmWWobqg3pN9fkZHbav9d+tY7XTSElcsozRarmoESbNxGqsi86V0ijWbLVm2bEUHYjDdSuVRI/qVf3Z0h19tFTSzzeq+lmjrLViGDZl3Ei/Pe3o0/2OWn1p+7St/bNzba3O6VePmlZHdSHMmQxb5si0EcFUi83zUclZpY5mbGux3FVbW7gu9LFv4+PvCtmkT58+M0nJY+YIRcQjO+oj7hCVCD6yPxGjPL6P2EPEMuTa6qqJSbIt19ebGg2Zcz4Xsn7r9SWThZeX1yYU6YOMRUCSgYmEJLsUgUvWJXKVTMx+v2eZnkjGgwOyYrth7GU7j/zlUXtEJcJxZqZmfRCVnENaAnPON70qmxglFlmgEwHK5lHIX9bFHNhQijkhdrkX4ZZoZ2fHrr97txniFG3+rda+Dg8PtbbWCO3rti7kKfcGobq7+8zWzZj8+zBPxCuSF7ivcVxUlvK3LWi1sRr+54phHbN276jHSn/+DqyTtSFPJ+UFWAeClvns7++bhK7X6xbbcRzHcRzHcRzHcRzHef9562IVn5f7Rd5QVEjW8aEd5YIVNQkIrFL4XK0k2mrMqVYq6cnzth6f3Kg9GCquxOpEiU6HmXppatKwVi6rXIxVyEZikyrbsCrEu5NlWilFWg99lpKCKqENv8uD+1dhuKNw7eEg1fVNV/cXy/r1n67rF415rSBtEWxMKcrnaxsRTb04ttfYuoZ3wz7H13wXkKhATIQoNUQRh83mumWAsskSmZHlclE89r+zs2vlARCdZE8iCqn5iQjd+GBD152Orq4u1ev3TWSm6TAsJ9Pc7JzFGwwHJgTr9QWThTs72zo6OlSSUK/1Q2vjkXjEIWKULFVEJ4/EU2qA+SFBEauIUSDLFaF7cnJmj+AzP9aDtEU6IhuRnYuL82EdpRB/P8SnpMDA5s8tQ9IidMk6ZSzi8UJ8Nptrdg3jLy8vaW1t1e4bYxSLicnX3d2WiU1qxFKmYGOjGdov1G6fWfYrGa2t1q5lnXJPuF8rKw1dhGuurq+0OK5Te3B4ENraqtaqNlfGRfJOMoDpNykdwDnkLeUKkM/cD7J/WbPjOI7jOI7jOI7jOI7z4+Ctb171h4NYzGUm2Z8Yylw1SnujTJ/unenr1r4Ul3QTzavbT1UvDfWLu7P6ZHVW1QLykE6JhjG74qcqjfK6rrhPXmkUqZdl2r8Z6PPDc311eKFqMdHHG8v6qF7TfLgm/CpF1o2FnVmzH4BpsYqkQySS/YiM5BTnyehEbiI6Ly6uTELyuDm1QhGsk8fu6UfGJrVBqYOKAORu8h0ByYv4xCEesRGNfJKFmY+RmpxkzPn5WRuLeSBL2aAJ0ZskRRs3nyPz65v85TqOOYdk5DwxgXiUcaANEcqcySKdZMzymD7zQ5BSS5b19Xo3YjMrSgMcHuY1UtnMCxnLOoB4yGDuC8IYqTk7W7P5PnnyPzYPSiAgSRGn1EEFYrA2sm/zfrM2d0QsWbVz82GMEJcsYmKwNtY5WRtzBsYnJrGJSUat4ziO4ziO4ziO4ziO8+Ph3YnV3CuOP3n8nh33OaCOqdRTolZ3oE932/q35yPtDYsaDHv66Uqsf3y0oEfVWIOUR/PJfu2FnuEzLYVQIU4s9UOMs/5QX51d6b9aJ+r1R/qLjYZ+2lzQ3ShcPaJMAf1HoWcYuUAVVsTsDwMyb4IJ3QBtk3baJu3TcJ7m/DLO/36cvO1F3zwm8SbfX1w7GXN6vLyNa6Zjvug7YfrcNJN4Eyb9ptsQpNPHSE4yU5GvloW7cddqwpJFy+P9PGY/HXfy/fY4ZM4iPJGyZOoyDhJ0mum223ObPp4eDyZznm5zHMdxHMdxHMdxHMdxfpz84GJ17K2Mb/RUaCTDNP+aSz00J4/l9woFfXkx1L+0rvSfx10VleofHs7q7+/OqBQ62ab9Go5lV6JhOD7LUj25uNbnz451fN7VZmNRn9xr6INyrGKaKs2GGkXI1ERJFtlmWhk1A5Bm+cg/CNPibiL03kR+OW/5fbKjb+5d/vmH8G1pSMfp78TMj6dj3haN+fcX8/i+TOJOx77Nq+/PpD2fCxti5TAvzr06JjDmdOzpNU7PZ/qaSfvtOb1u/o7jOI7jOI7jOI7jOM77xbsTqwX7xX/lYEg5yNgqihfwbWiZpSdZrM+Puvps91Sj4UA/aS7oo8aMGsVYxdCXbNdO6PS0M9DnByfaOTxXo1bTJ/fXtDVfVpVYGXVHCxpZhddUkW1WhUiNTawyH6sB+46Ylnq/z/j+vJRJn1f1fRNviv26uK/rC2/q/8fwsrFvj/Xdxn7938BxHMdxHMdxHMdxHMdxct5JKQDk6rfcFW5sIlbzA3uNwvso/NCSKBbqk/qr/7F7rsd7Z5qpxPrJxqI2F6vqptIXR+f6eu9YlSjWxx+s6i8bM5oLoZCuhI+szEBoyNj4iq/U62QUxovzc47jOI7jOI7jOI7jOI7jOG/gHYhVNCnclpg8up1vTDQ5h/xEwlJ/FelJHusofPbCuaedof5950h7Z5eaq1XVH2W66Q/0581F/U1zQasJZQLSsTbNlGTkoo7raka5rOWxf/O5ogwAn7zdnpfjOI7jOI7jOI7jOI7jOM63eQelAHKxyuPW469TiaJZ+EGuRipQdJXz4aIMHxoOLYc1pT3SKC6Ifd5/277WlzsnqpSK+qv7y3pwp6hylpqkJQQ61WKFXxsoxMxswyqCxyEiJxiRIgGQvzuO4ziO4ziO4ziO4ziO47yKd1IK4PtjpvVFFqvYz18a2DfKBUhRlloWaiGaCNLwSbdX+NLXnHIcx3Ecx3Ecx3Ecx3Ecx3kp726npu8FWa55+mr+CD9Zppkq4VvJWnkL72PpOm755uNlvOaU4ziO4ziO4ziO4ziO4zjOS/l/JlbHznSMZaaGTwoI8M7j/FFo8V3dHcdxHMdxHMdxHMdxHMf5v0P6X8UIufTATV4kAAAAAElFTkSuQmCC", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Importing libraries\n", - "\n", - "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " Block,\n", - " Var,\n", - " Param,\n", - " Constraint,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " TerminationCondition,\n", - " value,\n", - " Expression,\n", - " minimize,\n", - " units,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", - "from idaes.models.unit_models import (\n", - " Mixer,\n", - " MomentumMixingType,\n", - " PressureChanger,\n", - " Heater,\n", - " Separator,\n", - " HeatExchanger,\n", - ")\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from properties import SCO2ParameterBlock\n", - "\n", - "import idaes.logger as idaeslog\n", - "\n", - "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Constructing the flowsheet\n", - "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", - "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", - "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 452\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 118\n", - "\n", - "Total number of variables............................: 178\n", - " variables with only lower bounds: 32\n", - " variables with lower and upper bounds: 59\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 178\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", - " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\n", - "====================================================================================\n", - "Unit : fs.boiler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 685.15 893.15\n", - " pressure pascal 3.4510e+07 3.4300e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.turbine Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", - " Pressure Change : -24.979 : pascal : False : (None, None)\n", - " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 692.18\n", - " pressure pascal 3.4300e+07 9.3207e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 692.18 489.15\n", - " pressure pascal 9.3207e+06 9.2507e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 560.75 747.89\n", - " pressure pascal 3.4560e+07 3.4490e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 416.53 598.89\n", - " pressure pascal 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_1 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825.\n", - " temperature kelvin 354.15 354.15 354.15\n", - " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.co2_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807e+06 9.1107e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.main_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 416.53\n", - " pressure pascal 9.1107e+06 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.bypass_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 473.64\n", - " pressure pascal 9.1807e+06 3.4886e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_2 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR \n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 416.53 416.53 416.53\n", - " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.FG_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 21707. : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 416.53 483.15\n", - " pressure pascal 3.4620e+07 3.4560e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.mixer Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 598.89 473.64 560.75\n", - " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", - "====================================================================================\n", - "659.042605510511 kW\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " TerminationCondition,\n", + " value,\n", + " Expression,\n", + " minimize,\n", + " units,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (\n", + " Mixer,\n", + " MomentumMixingType,\n", + " PressureChanger,\n", + " Heater,\n", + " Separator,\n", + " HeatExchanger,\n", + ")\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from properties import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 692.18\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 692.18 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 560.75 747.89\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 21707. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "659.042605510511 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory(\"ipopt\")\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.turbine = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.splitter_1 = Separator(\n", + " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", + " )\n", + "\n", + " m.fs.co2_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.main_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.splitter_2 = Separator(\n", + " property_package=m.fs.properties,\n", + " ideal_separation=False,\n", + " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", + " )\n", + "\n", + " m.fs.FG_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.mixer = Mixer(\n", + " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", + " )\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(\n", + " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", + " )\n", + " m.fs.s04 = Arc(\n", + " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", + " )\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(\n", + " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", + " )\n", + " m.fs.s07 = Arc(\n", + " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", + " )\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(\n", + " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", + " )\n", + " m.fs.s10 = Arc(\n", + " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", + " )\n", + " m.fs.s11 = Arc(\n", + " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", + " )\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline\n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1 / 3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(\n", + " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(\n", + " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import (\n", + " convert_quantity_to_reporting_units,\n", + " report_quantity,\n", + " )\n", + "\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(\n", + " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", + " units.kW,\n", + " )\n", + " return m\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "def main():\n", - " # Setup solver and options\n", - " solver = SolverFactory(\"ipopt\")\n", - " outlvl = 0\n", - " tee = True\n", - "\n", - " # Set up concrete model\n", - " m = ConcreteModel()\n", - "\n", - " # Create a flowsheet block\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Create the properties param block\n", - " m.fs.properties = SCO2ParameterBlock()\n", - "\n", - " # Add unit models to the flowsheet\n", - " m.fs.boiler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.turbine = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=False,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.splitter_1 = Separator(\n", - " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", - " )\n", - "\n", - " m.fs.co2_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.main_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.bypass_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.splitter_2 = Separator(\n", - " property_package=m.fs.properties,\n", - " ideal_separation=False,\n", - " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", - " )\n", - "\n", - " m.fs.FG_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.mixer = Mixer(\n", - " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", - " )\n", - "\n", - " # # Connect the flowsheet\n", - " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", - " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", - " m.fs.s03 = Arc(\n", - " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", - " )\n", - " m.fs.s04 = Arc(\n", - " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", - " )\n", - " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", - " m.fs.s06 = Arc(\n", - " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", - " )\n", - " m.fs.s07 = Arc(\n", - " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", - " )\n", - " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", - " m.fs.s09 = Arc(\n", - " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", - " )\n", - " m.fs.s10 = Arc(\n", - " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", - " )\n", - " m.fs.s11 = Arc(\n", - " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", - " )\n", - " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", - " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", - " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", - " # NETL Baseline\n", - " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", - " m.fs.boiler.inlet.temperature.fix(685.15)\n", - " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", - " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", - " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s01)\n", - "\n", - " m.fs.turbine.ratioP.fix(1 / 3.68)\n", - " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", - " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s02)\n", - " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", - " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s03)\n", - "\n", - " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", - " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", - " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s04)\n", - " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", - "\n", - " m.fs.splitter_1.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s05)\n", - " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", - " m.fs.co2_cooler.deltaP.fix(-0.07)\n", - " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s06)\n", - " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.bypass_compressor.ratioP.fix(3.8)\n", - " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s07)\n", - " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.main_compressor.ratioP.fix(3.8)\n", - " m.fs.main_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s09)\n", - "\n", - " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", - " m.fs.splitter_2.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s10)\n", - " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", - " m.fs.FG_cooler.deltaP.fix(-0.06)\n", - " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s11)\n", - "\n", - " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " # Add constraint heats of the LTR_pseudo shell and tube\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c1 = Constraint(\n", - " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " propagate_state(m.fs.s08)\n", - " propagate_state(m.fs.s12)\n", - " propagate_state(m.fs.s13)\n", - "\n", - " m.fs.mixer.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s14)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", - " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c2 = Constraint(\n", - " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", - "\n", - " print(\"--------------------------------------------------------------------\")\n", - " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", - " print(\"--------------------------------------------------------------------\")\n", - "\n", - " solver.solve(m, tee=tee)\n", - "\n", - " #\n", - " from idaes.core.util.units_of_measurement import (\n", - " convert_quantity_to_reporting_units,\n", - " report_quantity,\n", - " )\n", - "\n", - " # Print reports\n", - " for i in m.fs.component_objects(Block):\n", - " if isinstance(i, UnitModelBlockData):\n", - " i.report()\n", - "\n", - " # Converting units for readability\n", - " print(\n", - " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", - " units.kW,\n", - " )\n", - " return m\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " m = main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb index b4786423..1edd7306 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb @@ -1,718 +1,743 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Importing libraries\n", - "\n", - "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " Block,\n", - " Var,\n", - " Param,\n", - " Constraint,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " TerminationCondition,\n", - " value,\n", - " Expression,\n", - " minimize,\n", - " units,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", - "from idaes.models.unit_models import (\n", - " Mixer,\n", - " MomentumMixingType,\n", - " PressureChanger,\n", - " Heater,\n", - " Separator,\n", - " HeatExchanger,\n", - ")\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from properties import SCO2ParameterBlock\n", - "\n", - "import idaes.logger as idaeslog\n", - "\n", - "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Constructing the flowsheet\n", - "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", - "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", - "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 452\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 118\n", - "\n", - "Total number of variables............................: 178\n", - " variables with only lower bounds: 32\n", - " variables with lower and upper bounds: 59\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 178\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", - " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\n", - "====================================================================================\n", - "Unit : fs.boiler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 685.15 893.15\n", - " pressure pascal 3.4510e+07 3.4300e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.turbine Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", - " Pressure Change : -24.979 : pascal : False : (None, None)\n", - " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 692.18\n", - " pressure pascal 3.4300e+07 9.3207e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 692.18 489.15\n", - " pressure pascal 9.3207e+06 9.2507e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 560.75 747.89\n", - " pressure pascal 3.4560e+07 3.4490e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 416.53 598.89\n", - " pressure pascal 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_1 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825.\n", - " temperature kelvin 354.15 354.15 354.15\n", - " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.co2_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807e+06 9.1107e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.main_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 416.53\n", - " pressure pascal 9.1107e+06 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.bypass_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 473.64\n", - " pressure pascal 9.1807e+06 3.4886e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_2 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR \n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 416.53 416.53 416.53\n", - " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.FG_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 21707. : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 416.53 483.15\n", - " pressure pascal 3.4620e+07 3.4560e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.mixer Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 598.89 473.64 560.75\n", - " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", - "====================================================================================\n", - "659.042605510511 kW\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " TerminationCondition,\n", + " value,\n", + " Expression,\n", + " minimize,\n", + " units,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (\n", + " Mixer,\n", + " MomentumMixingType,\n", + " PressureChanger,\n", + " Heater,\n", + " Separator,\n", + " HeatExchanger,\n", + ")\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from properties import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 692.18\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 692.18 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 560.75 747.89\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 21707. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "659.042605510511 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory(\"ipopt\")\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.turbine = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.splitter_1 = Separator(\n", + " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", + " )\n", + "\n", + " m.fs.co2_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.main_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.splitter_2 = Separator(\n", + " property_package=m.fs.properties,\n", + " ideal_separation=False,\n", + " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", + " )\n", + "\n", + " m.fs.FG_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.mixer = Mixer(\n", + " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", + " )\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(\n", + " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", + " )\n", + " m.fs.s04 = Arc(\n", + " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", + " )\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(\n", + " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", + " )\n", + " m.fs.s07 = Arc(\n", + " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", + " )\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(\n", + " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", + " )\n", + " m.fs.s10 = Arc(\n", + " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", + " )\n", + " m.fs.s11 = Arc(\n", + " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", + " )\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline\n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1 / 3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(\n", + " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(\n", + " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import (\n", + " convert_quantity_to_reporting_units,\n", + " report_quantity,\n", + " )\n", + "\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(\n", + " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", + " units.kW,\n", + " )\n", + " return m\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "def main():\n", - " # Setup solver and options\n", - " solver = SolverFactory(\"ipopt\")\n", - " outlvl = 0\n", - " tee = True\n", - "\n", - " # Set up concrete model\n", - " m = ConcreteModel()\n", - "\n", - " # Create a flowsheet block\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Create the properties param block\n", - " m.fs.properties = SCO2ParameterBlock()\n", - "\n", - " # Add unit models to the flowsheet\n", - " m.fs.boiler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.turbine = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=False,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.splitter_1 = Separator(\n", - " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", - " )\n", - "\n", - " m.fs.co2_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.main_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.bypass_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.splitter_2 = Separator(\n", - " property_package=m.fs.properties,\n", - " ideal_separation=False,\n", - " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", - " )\n", - "\n", - " m.fs.FG_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.mixer = Mixer(\n", - " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", - " )\n", - "\n", - " # # Connect the flowsheet\n", - " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", - " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", - " m.fs.s03 = Arc(\n", - " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", - " )\n", - " m.fs.s04 = Arc(\n", - " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", - " )\n", - " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", - " m.fs.s06 = Arc(\n", - " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", - " )\n", - " m.fs.s07 = Arc(\n", - " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", - " )\n", - " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", - " m.fs.s09 = Arc(\n", - " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", - " )\n", - " m.fs.s10 = Arc(\n", - " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", - " )\n", - " m.fs.s11 = Arc(\n", - " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", - " )\n", - " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", - " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", - " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", - " # NETL Baseline\n", - " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", - " m.fs.boiler.inlet.temperature.fix(685.15)\n", - " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", - " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", - " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s01)\n", - "\n", - " m.fs.turbine.ratioP.fix(1 / 3.68)\n", - " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", - " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s02)\n", - " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", - " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s03)\n", - "\n", - " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", - " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", - " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s04)\n", - " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", - "\n", - " m.fs.splitter_1.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s05)\n", - " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", - " m.fs.co2_cooler.deltaP.fix(-0.07)\n", - " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s06)\n", - " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.bypass_compressor.ratioP.fix(3.8)\n", - " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s07)\n", - " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.main_compressor.ratioP.fix(3.8)\n", - " m.fs.main_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s09)\n", - "\n", - " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", - " m.fs.splitter_2.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s10)\n", - " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", - " m.fs.FG_cooler.deltaP.fix(-0.06)\n", - " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s11)\n", - "\n", - " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " # Add constraint heats of the LTR_pseudo shell and tube\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c1 = Constraint(\n", - " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " propagate_state(m.fs.s08)\n", - " propagate_state(m.fs.s12)\n", - " propagate_state(m.fs.s13)\n", - "\n", - " m.fs.mixer.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s14)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", - " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c2 = Constraint(\n", - " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", - "\n", - " print(\"--------------------------------------------------------------------\")\n", - " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", - " print(\"--------------------------------------------------------------------\")\n", - "\n", - " solver.solve(m, tee=tee)\n", - "\n", - " #\n", - " from idaes.core.util.units_of_measurement import (\n", - " convert_quantity_to_reporting_units,\n", - " report_quantity,\n", - " )\n", - "\n", - " # Print reports\n", - " for i in m.fs.component_objects(Block):\n", - " if isinstance(i, UnitModelBlockData):\n", - " i.report()\n", - "\n", - " # Converting units for readability\n", - " print(\n", - " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", - " units.kW,\n", - " )\n", - " return m\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " m = main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py index 74e18ab0..ce6248dc 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py @@ -1,15 +1,16 @@ -############################################################################## -# Institute for the Design of Advanced Energy Systems Process Systems -# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the -# software owners: The Regents of the University of California, through -# Lawrence Berkeley National Laboratory, National Technology & Engineering -# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia -# University Research Corporation, et al. All rights reserved. +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). # -# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and -# license information, respectively. Both files are also available online -# at the URL "https://github.com/IDAES/idaes-pse". -############################################################################## +# Copyright (c) 2018-2025 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +# +################################################################################# """ Surrogate property package for SCO2 cycle. diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb index 5eab16fe..4d1c92d0 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "183293d6", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -490,8 +517,7 @@ "metadata": { "language_info": { "name": "python" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb index 40a5d434..b44b725c 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -483,16 +509,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_doc.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_doc.md). " ] } ], "metadata": { "language_info": { - "name": "python" - }, - "orig_nbformat": 4 + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb index fb1cfe4b..1c925335 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb @@ -1,498 +1,523 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################################################################\n", - "# Institute for the Design of Advanced Energy Systems Process Systems\n", - "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", - "# software owners: The Regents of the University of California, through\n", - "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", - "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", - "# University Research Corporation, et al. All rights reserved.\n", - "#\n", - "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", - "# license information, respectively. Both files are also available online\n", - "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", - "##############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Constraint,\n", + " Param,\n", + " Reals,\n", + " Set,\n", + " value,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + ")\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component,\n", + ")\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(\n", + " {\n", + " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", + " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", + " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", + " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", + " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", + " }\n", + " )\n", + "\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.s,\n", + " \"length\": units.m,\n", + " \"mass\": units.kg,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_test.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " # Create state variables\n", + "\n", + " self.flow_mol = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol / units.s,\n", + " doc=\"Total molar flowrate [kmol/s]\",\n", + " )\n", + "\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc=\"State pressure [MPa]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760 + 273.15),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " self.entr_mol = Var(\n", + " domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ / units.kmol / units.K,\n", + " doc=\"Entropy [kJ/ kmol / K]\",\n", + " )\n", + "\n", + " self.enth_mol = Var(\n", + " domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ / units.kmol,\n", + " doc=\"Enthalpy [kJ/ kmol]\",\n", + " )\n", + "\n", + " inputs = [self.pressure, self.temperature]\n", + " outputs = [self.enth_mol, self.entr_mol]\n", + " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.alamo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol * self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would in turn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def initialize(\n", + " blk,\n", + " state_args=None,\n", + " hold_state=False,\n", + " outlvl=1,\n", + " state_vars_fixed=False,\n", + " solver=\"ipopt\",\n", + " optarg={\"tol\": 1e-8},\n", + " ):\n", + " \"\"\"\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output information (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating which solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states variables are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " release_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " \"\"\"\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\n", + " \"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\"\n", + " )\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " \"\"\"\n", + " Method to release state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " \"\"\"\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags[\"Fcflag\"][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags[\"Pflag\"][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags[\"Tflag\"][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info(\"{} State Released.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Integration of Surrogate into Custom Property Package\n", - "\n", - "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", - "\n", - "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", - "\n", - "### 1.1 Steps in Creating a Property Package\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **phases and components** of interest.\n", - "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", - "5. Declaring the **state variables** to be used for the property package.\n", - "6. Creating **variables and constraints** to describe the properties of interest.\n", - "7. Creating an **initialization routine** for the property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Importing libraries for making Property Package\n", - "\n", - "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Constraint,\n", - " Param,\n", - " Reals,\n", - " Set,\n", - " value,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - ")\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " LiquidPhase,\n", - " Component,\n", - ")\n", - "from idaes.core.util.initialization import solve_indexed_blocks\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.misc import extract_data\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", - "\n", - "from pyomo.util.model_size import build_model_size_report\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Defining Classes\n", - "\n", - "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", - "\n", - "## 3.1 Physical Parameter Block\n", - "\n", - "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What properties are supported and how they are implemented\n", - "* What components and phases are included in the packages\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To assemble the above mentioned things in a class we need to follow the following steps:\n", - "\n", - "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", - "* Declaring any necessary configuration arguments\n", - "* Writing the build method for our class\n", - "* Creating a define_metadata method for the class.\n", - "\n", - "The code below follows the above mentioned steps. \n", - "\n", - "*NOTE*: The SCO2StateBlock will be discussed in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2ParameterBlock\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " supercritical CO2.\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(PhysicalParameterData, self).build()\n", - "\n", - " self._state_block_class = SCO2StateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Liq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.CO2 = Component()\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(\n", - " {\n", - " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", - " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", - " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", - " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", - " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", - " }\n", - " )\n", - "\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.s,\n", - " \"length\": units.m,\n", - " \"mass\": units.kg,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 State Block\n", - "\n", - "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", - "\n", - "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", - "\n", - "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", - "\n", - "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_test.ipynb file) using the Alamopy Surrogate API of IDAES package\n", - "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", - "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", - "class SCO2StateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super(SCO2StateBlockData, self).build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " # Create state variables\n", - "\n", - " self.flow_mol = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1.0,\n", - " units=units.kmol / units.s,\n", - " doc=\"Total molar flowrate [kmol/s]\",\n", - " )\n", - "\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=8,\n", - " bounds=(7.38, 40),\n", - " units=units.MPa,\n", - " doc=\"State pressure [MPa]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=350,\n", - " bounds=(304.2, 760 + 273.15),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " self.entr_mol = Var(\n", - " domain=Reals,\n", - " initialize=10,\n", - " units=units.kJ / units.kmol / units.K,\n", - " doc=\"Entropy [kJ/ kmol / K]\",\n", - " )\n", - "\n", - " self.enth_mol = Var(\n", - " domain=Reals,\n", - " initialize=1,\n", - " units=units.kJ / units.kmol,\n", - " doc=\"Enthalpy [kJ/ kmol]\",\n", - " )\n", - "\n", - " inputs = [self.pressure, self.temperature]\n", - " outputs = [self.enth_mol, self.entr_mol]\n", - " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " self.surrogate_enth = SurrogateBlock()\n", - " self.surrogate_enth.build_model(\n", - " self.alamo_surrogate,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.flow_mol\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.flow_mol * self.enth_mol\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_mol\": self.flow_mol,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def model_check(blk):\n", - " \"\"\"\n", - " Model checks for property block\n", - " \"\"\"\n", - " # Check temperature bounds\n", - " if value(blk.temperature) < blk.temperature.lb:\n", - " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", - " if value(blk.temperature) > blk.temperature.ub:\n", - " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", - "\n", - " # Check pressure bounds\n", - " if value(blk.pressure) < blk.pressure.lb:\n", - " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", - " if value(blk.pressure) > blk.pressure.ub:\n", - " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Define Initialization Routine\n", - "\n", - "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", - "\n", - "Any initialization routine can be written by following a 3 step process:\n", - "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", - "\n", - "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", - "\n", - "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", - "\n", - "\n", - "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would in turn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _StateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def initialize(\n", - " blk,\n", - " state_args=None,\n", - " hold_state=False,\n", - " outlvl=1,\n", - " state_vars_fixed=False,\n", - " solver=\"ipopt\",\n", - " optarg={\"tol\": 1e-8},\n", - " ):\n", - " \"\"\"\n", - " Initialisation routine for property package.\n", - "\n", - " Keyword Arguments:\n", - " flow_mol : value at which to initialize component flows\n", - " (default=None)\n", - " pressure : value at which to initialize pressure (default=None)\n", - " temperature : value at which to initialize temperature\n", - " (default=None)\n", - " outlvl : sets output level of initialisation routine\n", - "\n", - " * 0 = no output (default)\n", - " * 1 = return solver state for each step in routine\n", - " * 2 = include solver output information (tee=True)\n", - " state_vars_fixed: Flag to denote if state vars have already been\n", - " fixed.\n", - " - True - states have already been fixed by the\n", - " control volume 1D. Control volume 0D\n", - " does not fix the state vars, so will\n", - " be False if this state block is used\n", - " with 0D blocks.\n", - " - False - states have not been fixed. The state\n", - " block will deal with fixing/unfixing.\n", - " optarg : solver options dictionary object (default=None)\n", - " solver : str indicating which solver to use during\n", - " initialization (default = 'ipopt')\n", - " hold_state : flag indicating whether the initialization routine\n", - " should unfix any state variables fixed during\n", - " initialization (default=False).\n", - " - True - states variables are not unfixed, and\n", - " a dict of returned containing flags for\n", - " which states were fixed during\n", - " initialization.\n", - " - False - state variables are unfixed after\n", - " initialization by calling the\n", - " release_state method\n", - "\n", - " Returns:\n", - " If hold_states is True, returns a dict containing flags for\n", - " which states were fixed during initialization.\n", - " \"\"\"\n", - " if state_vars_fixed is False:\n", - " # Fix state variables if not already fixed\n", - " Fcflag = {}\n", - " Pflag = {}\n", - " Tflag = {}\n", - "\n", - " for k in blk.keys():\n", - " if blk[k].flow_mol.fixed is True:\n", - " Fcflag[k] = True\n", - " else:\n", - " Fcflag[k] = False\n", - " if state_args is None:\n", - " blk[k].flow_mol.fix()\n", - " else:\n", - " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", - "\n", - " if blk[k].pressure.fixed is True:\n", - " Pflag[k] = True\n", - " else:\n", - " Pflag[k] = False\n", - " if state_args is None:\n", - " blk[k].pressure.fix()\n", - " else:\n", - " blk[k].pressure.fix(state_args[\"pressure\"])\n", - "\n", - " if blk[k].temperature.fixed is True:\n", - " Tflag[k] = True\n", - " else:\n", - " Tflag[k] = False\n", - " if state_args is None:\n", - " blk[k].temperature.fix()\n", - " else:\n", - " blk[k].temperature.fix(state_args[\"temperature\"])\n", - "\n", - " # If input block, return flags, else release state\n", - " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", - "\n", - " else:\n", - " # Check when the state vars are fixed already result in dof 0\n", - " for k in blk.keys():\n", - " if degrees_of_freedom(blk[k]) != 0:\n", - " raise Exception(\n", - " \"State vars fixed but degrees of freedom \"\n", - " \"for state block is not zero during \"\n", - " \"initialization.\"\n", - " )\n", - "\n", - " if state_vars_fixed is False:\n", - " if hold_state is True:\n", - " return flags\n", - " else:\n", - " blk.release_state(flags)\n", - "\n", - " def release_state(blk, flags, outlvl=0):\n", - " \"\"\"\n", - " Method to release state variables fixed during initialisation.\n", - "\n", - " Keyword Arguments:\n", - " flags : dict containing information of which state variables\n", - " were fixed during initialization, and should now be\n", - " unfixed. This dict is returned by initialize if\n", - " hold_state=True.\n", - " outlvl : sets output level of of logging\n", - " \"\"\"\n", - " if flags is None:\n", - " return\n", - "\n", - " # Unfix state variables\n", - " for k in blk.keys():\n", - " if flags[\"Fcflag\"][k] is False:\n", - " blk[k].flow_mol.unfix()\n", - " if flags[\"Pflag\"][k] is False:\n", - " blk[k].pressure.unfix()\n", - " if flags[\"Tflag\"][k] is False:\n", - " blk[k].temperature.unfix()\n", - "\n", - " if outlvl > 0:\n", - " if outlvl > 0:\n", - " _log.info(\"{} State Released.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb index efd3caff..edd5a83d 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb @@ -1,501 +1,523 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################################################################\n", - "# Institute for the Design of Advanced Energy Systems Process Systems\n", - "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", - "# software owners: The Regents of the University of California, through\n", - "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", - "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", - "# University Research Corporation, et al. All rights reserved.\n", - "#\n", - "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", - "# license information, respectively. Both files are also available online\n", - "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", - "##############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Constraint,\n", + " Param,\n", + " Reals,\n", + " Set,\n", + " value,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + ")\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component,\n", + ")\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(\n", + " {\n", + " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", + " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", + " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", + " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", + " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", + " }\n", + " )\n", + "\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.s,\n", + " \"length\": units.m,\n", + " \"mass\": units.kg,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_usr.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " # Create state variables\n", + "\n", + " self.flow_mol = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol / units.s,\n", + " doc=\"Total molar flowrate [kmol/s]\",\n", + " )\n", + "\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc=\"State pressure [MPa]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760 + 273.15),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " self.entr_mol = Var(\n", + " domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ / units.kmol / units.K,\n", + " doc=\"Entropy [kJ/ kmol / K]\",\n", + " )\n", + "\n", + " self.enth_mol = Var(\n", + " domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ / units.kmol,\n", + " doc=\"Enthalpy [kJ/ kmol]\",\n", + " )\n", + "\n", + " inputs = [self.pressure, self.temperature]\n", + " outputs = [self.enth_mol, self.entr_mol]\n", + " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.alamo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol * self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would in turn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def initialize(\n", + " blk,\n", + " state_args=None,\n", + " hold_state=False,\n", + " outlvl=1,\n", + " state_vars_fixed=False,\n", + " solver=\"ipopt\",\n", + " optarg={\"tol\": 1e-8},\n", + " ):\n", + " \"\"\"\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output information (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating which solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states variables are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " release_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " \"\"\"\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\n", + " \"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\"\n", + " )\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " \"\"\"\n", + " Method to release state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " \"\"\"\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags[\"Fcflag\"][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags[\"Pflag\"][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags[\"Tflag\"][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info(\"{} State Released.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Integration of Surrogate into Custom Property Package\n", - "\n", - "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", - "\n", - "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", - "\n", - "### 1.1 Steps in Creating a Property Package\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **phases and components** of interest.\n", - "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", - "5. Declaring the **state variables** to be used for the property package.\n", - "6. Creating **variables and constraints** to describe the properties of interest.\n", - "7. Creating an **initialization routine** for the property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Importing libraries for making Property Package\n", - "\n", - "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", - "# Import Python libraries\n", - "import logging\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Constraint,\n", - " Param,\n", - " Reals,\n", - " Set,\n", - " value,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - ")\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " LiquidPhase,\n", - " Component,\n", - ")\n", - "from idaes.core.util.initialization import solve_indexed_blocks\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.misc import extract_data\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", - "\n", - "from pyomo.util.model_size import build_model_size_report\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Defining Classes\n", - "\n", - "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", - "\n", - "## 3.1 Physical Parameter Block\n", - "\n", - "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What properties are supported and how they are implemented\n", - "* What components and phases are included in the packages\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To assemble the above mentioned things in a class we need to follow the following steps:\n", - "\n", - "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", - "* Declaring any necessary configuration arguments\n", - "* Writing the build method for our class\n", - "* Creating a define_metadata method for the class.\n", - "\n", - "The code below follows the above mentioned steps. \n", - "\n", - "*NOTE*: The SCO2StateBlock will be discussed in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2ParameterBlock\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " supercritical CO2.\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(PhysicalParameterData, self).build()\n", - "\n", - " self._state_block_class = SCO2StateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Liq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.CO2 = Component()\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(\n", - " {\n", - " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", - " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", - " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", - " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", - " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", - " }\n", - " )\n", - "\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.s,\n", - " \"length\": units.m,\n", - " \"mass\": units.kg,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 State Block\n", - "\n", - "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", - "\n", - "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", - "\n", - "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", - "\n", - "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_usr.ipynb file) using the Alamopy Surrogate API of IDAES package\n", - "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", - "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", - "class SCO2StateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super(SCO2StateBlockData, self).build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " # Create state variables\n", - "\n", - " self.flow_mol = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1.0,\n", - " units=units.kmol / units.s,\n", - " doc=\"Total molar flowrate [kmol/s]\",\n", - " )\n", - "\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=8,\n", - " bounds=(7.38, 40),\n", - " units=units.MPa,\n", - " doc=\"State pressure [MPa]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=350,\n", - " bounds=(304.2, 760 + 273.15),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " self.entr_mol = Var(\n", - " domain=Reals,\n", - " initialize=10,\n", - " units=units.kJ / units.kmol / units.K,\n", - " doc=\"Entropy [kJ/ kmol / K]\",\n", - " )\n", - "\n", - " self.enth_mol = Var(\n", - " domain=Reals,\n", - " initialize=1,\n", - " units=units.kJ / units.kmol,\n", - " doc=\"Enthalpy [kJ/ kmol]\",\n", - " )\n", - "\n", - " inputs = [self.pressure, self.temperature]\n", - " outputs = [self.enth_mol, self.entr_mol]\n", - " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", - " self.surrogate_enth = SurrogateBlock()\n", - " self.surrogate_enth.build_model(\n", - " self.alamo_surrogate,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.flow_mol\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.flow_mol * self.enth_mol\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_mol\": self.flow_mol,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def model_check(blk):\n", - " \"\"\"\n", - " Model checks for property block\n", - " \"\"\"\n", - " # Check temperature bounds\n", - " if value(blk.temperature) < blk.temperature.lb:\n", - " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", - " if value(blk.temperature) > blk.temperature.ub:\n", - " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", - "\n", - " # Check pressure bounds\n", - " if value(blk.pressure) < blk.pressure.lb:\n", - " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", - " if value(blk.pressure) > blk.pressure.ub:\n", - " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Define Initialization Routine\n", - "\n", - "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", - "\n", - "Any initialization routine can be written by following a 3 step process:\n", - "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", - "\n", - "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", - "\n", - "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", - "\n", - "\n", - "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would in turn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _StateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def initialize(\n", - " blk,\n", - " state_args=None,\n", - " hold_state=False,\n", - " outlvl=1,\n", - " state_vars_fixed=False,\n", - " solver=\"ipopt\",\n", - " optarg={\"tol\": 1e-8},\n", - " ):\n", - " \"\"\"\n", - " Initialisation routine for property package.\n", - "\n", - " Keyword Arguments:\n", - " flow_mol : value at which to initialize component flows\n", - " (default=None)\n", - " pressure : value at which to initialize pressure (default=None)\n", - " temperature : value at which to initialize temperature\n", - " (default=None)\n", - " outlvl : sets output level of initialisation routine\n", - "\n", - " * 0 = no output (default)\n", - " * 1 = return solver state for each step in routine\n", - " * 2 = include solver output information (tee=True)\n", - " state_vars_fixed: Flag to denote if state vars have already been\n", - " fixed.\n", - " - True - states have already been fixed by the\n", - " control volume 1D. Control volume 0D\n", - " does not fix the state vars, so will\n", - " be False if this state block is used\n", - " with 0D blocks.\n", - " - False - states have not been fixed. The state\n", - " block will deal with fixing/unfixing.\n", - " optarg : solver options dictionary object (default=None)\n", - " solver : str indicating which solver to use during\n", - " initialization (default = 'ipopt')\n", - " hold_state : flag indicating whether the initialization routine\n", - " should unfix any state variables fixed during\n", - " initialization (default=False).\n", - " - True - states variables are not unfixed, and\n", - " a dict of returned containing flags for\n", - " which states were fixed during\n", - " initialization.\n", - " - False - state variables are unfixed after\n", - " initialization by calling the\n", - " release_state method\n", - "\n", - " Returns:\n", - " If hold_states is True, returns a dict containing flags for\n", - " which states were fixed during initialization.\n", - " \"\"\"\n", - " if state_vars_fixed is False:\n", - " # Fix state variables if not already fixed\n", - " Fcflag = {}\n", - " Pflag = {}\n", - " Tflag = {}\n", - "\n", - " for k in blk.keys():\n", - " if blk[k].flow_mol.fixed is True:\n", - " Fcflag[k] = True\n", - " else:\n", - " Fcflag[k] = False\n", - " if state_args is None:\n", - " blk[k].flow_mol.fix()\n", - " else:\n", - " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", - "\n", - " if blk[k].pressure.fixed is True:\n", - " Pflag[k] = True\n", - " else:\n", - " Pflag[k] = False\n", - " if state_args is None:\n", - " blk[k].pressure.fix()\n", - " else:\n", - " blk[k].pressure.fix(state_args[\"pressure\"])\n", - "\n", - " if blk[k].temperature.fixed is True:\n", - " Tflag[k] = True\n", - " else:\n", - " Tflag[k] = False\n", - " if state_args is None:\n", - " blk[k].temperature.fix()\n", - " else:\n", - " blk[k].temperature.fix(state_args[\"temperature\"])\n", - "\n", - " # If input block, return flags, else release state\n", - " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", - "\n", - " else:\n", - " # Check when the state vars are fixed already result in dof 0\n", - " for k in blk.keys():\n", - " if degrees_of_freedom(blk[k]) != 0:\n", - " raise Exception(\n", - " \"State vars fixed but degrees of freedom \"\n", - " \"for state block is not zero during \"\n", - " \"initialization.\"\n", - " )\n", - "\n", - " if state_vars_fixed is False:\n", - " if hold_state is True:\n", - " return flags\n", - " else:\n", - " blk.release_state(flags)\n", - "\n", - " def release_state(blk, flags, outlvl=0):\n", - " \"\"\"\n", - " Method to release state variables fixed during initialisation.\n", - "\n", - " Keyword Arguments:\n", - " flags : dict containing information of which state variables\n", - " were fixed during initialization, and should now be\n", - " unfixed. This dict is returned by initialize if\n", - " hold_state=True.\n", - " outlvl : sets output level of of logging\n", - " \"\"\"\n", - " if flags is None:\n", - " return\n", - "\n", - " # Unfix state variables\n", - " for k in blk.keys():\n", - " if flags[\"Fcflag\"][k] is False:\n", - " blk[k].flow_mol.unfix()\n", - " if flags[\"Pflag\"][k] is False:\n", - " blk[k].pressure.unfix()\n", - " if flags[\"Tflag\"][k] is False:\n", - " blk[k].temperature.unfix()\n", - "\n", - " if outlvl > 0:\n", - " if outlvl > 0:\n", - " _log.info(\"{} State Released.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb index 44ad9dba..c5327c41 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "fc859e61", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py index 286827cc..2c25c88d 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py @@ -1,15 +1,16 @@ -############################################################################### +################################################################################# # The Institute for the Design of Advanced Energy Systems Integrated Platform # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. -############################################################################### +# +################################################################################# """ Maintainer: Javal Vyas diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb index fa30ded7..b57af297 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb @@ -3,6 +3,32 @@ { "cell_type": "code", "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -47,7 +73,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -75,16 +101,26 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", - "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", - ":241)\n" + "2025-03-17 17:38:32.532072: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-17 17:38:32.532801: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:38:32.535873: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:38:32.542717: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742258312.554978 362716 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742258312.558363 362716 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742258312.567611 362716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258312.567624 362716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258312.567625 362716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258312.567626 362716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-17 17:38:32.571151: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } ], @@ -138,36 +174,189 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:34.163864: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:36 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:37 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", + "--------------------------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: \n", "\n", "******************************************************************************\n", @@ -206,16 +395,16 @@ "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + " 1 0.0000000e+00 7.45e-09 2.74e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 1\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Constraint violation....: 1.8833292723041867e-12 7.4505805969238281e-09\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Overall NLP error.......: 1.8833292723041867e-12 7.4505805969238281e-09\n", "\n", "\n", "Number of objective function evaluations = 2\n", @@ -225,10 +414,17 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.362\n", - "Total CPU secs in NLP function evaluations = 0.008\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.065\n", + "Total CPU secs in NLP function evaluations = 0.001\n", "\n", "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "====================================================================================\n", "Unit : fs.boiler Time: 0.0\n", @@ -238,7 +434,7 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + " Heat Duty : 1.3471e+06 : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", @@ -257,7 +453,7 @@ "\n", " Key : Value : Units : Fixed : Bounds\n", " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Mechanical Work : -9.3116e+05 : watt : False : (None, None)\n", " Pressure Change : -24.979 : pascal : False : (None, None)\n", " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", "\n", @@ -265,7 +461,7 @@ " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 719.28\n", + " temperature kelvin 893.15 723.73\n", " pressure pascal 3.4300e+07 9.3207e+06\n", "====================================================================================\n", "\n", @@ -277,13 +473,13 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + " Heat Duty : -1.5011e+06 : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 719.28 489.15\n", + " temperature kelvin 723.73 489.15\n", " pressure pascal 9.3207e+06 9.2507e+06\n", "====================================================================================\n", "\n", @@ -295,13 +491,13 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + " Heat Duty : 1.5011e+06 : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 543.23 750.68\n", + " temperature kelvin 507.38 713.84\n", " pressure pascal 3.4560e+07 3.4490e+07\n", "====================================================================================\n", "\n", @@ -313,7 +509,7 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + " Heat Duty : -1.0563e+06 : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", @@ -331,13 +527,13 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + " Heat Duty : 1.0563e+06 : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 396.40 579.39\n", + " temperature kelvin 364.57 531.48\n", " pressure pascal 3.4620e+07 3.4620e+07\n", "====================================================================================\n", "\n", @@ -368,7 +564,7 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + " Heat Duty : -3.2521e+05 : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", @@ -385,17 +581,17 @@ "\n", " Variables: \n", "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : -12681. : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 396.40\n", + " temperature kelvin 308.15 364.57\n", " pressure pascal 9.1107e+06 3.4620e+07\n", "====================================================================================\n", "\n", @@ -406,17 +602,17 @@ "\n", " Variables: \n", "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 80458. : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 452.96\n", + " temperature kelvin 354.15 445.82\n", " pressure pascal 9.1807e+06 3.4886e+07\n", "====================================================================================\n", "\n", @@ -435,7 +631,7 @@ " Stream Table\n", " Units Inlet to_FG_cooler to_LTR \n", " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 396.40 396.40 396.40\n", + " temperature kelvin 364.57 364.57 364.57\n", " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", "====================================================================================\n", "\n", @@ -447,13 +643,13 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 25836. : watt : False : (None, None)\n", + " Heat Duty : 37619. : watt : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", " Units Inlet Outlet \n", " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 396.40 483.15\n", + " temperature kelvin 364.57 483.15\n", " pressure pascal 3.4620e+07 3.4560e+07\n", "====================================================================================\n", "\n", @@ -463,10 +659,10 @@ " Stream Table\n", " Units FG_out LTR_out bypass Outlet \n", " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 579.39 452.96 543.23\n", + " temperature kelvin 483.15 531.48 445.82 507.38\n", " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", "====================================================================================\n", - "641.5293430698576 kW\n" + "863.3861430194434 kW\n" ] } ], @@ -724,7 +920,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb index fa30ded7..3d458b82 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb @@ -1,732 +1,758 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Importing libraries\n", - "\n", - "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", - "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", - ":241)\n" - ] - } - ], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " Block,\n", - " Var,\n", - " Param,\n", - " Constraint,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " TerminationCondition,\n", - " value,\n", - " Expression,\n", - " minimize,\n", - " units,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", - "from idaes.models.unit_models import (\n", - " Mixer,\n", - " MomentumMixingType,\n", - " PressureChanger,\n", - " Heater,\n", - " Separator,\n", - " HeatExchanger,\n", - ")\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from properties import SCO2ParameterBlock\n", - "\n", - "import idaes.logger as idaeslog\n", - "\n", - "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Constructing the flowsheet\n", - "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", - "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", - "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 51411\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2674\n", - "\n", - "Total number of variables............................: 5920\n", - " variables with only lower bounds: 32\n", - " variables with lower and upper bounds: 5669\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 5920\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 1\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 2\n", - "Number of objective gradient evaluations = 2\n", - "Number of equality constraint evaluations = 2\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 2\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.362\n", - "Total CPU secs in NLP function evaluations = 0.008\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\n", - "====================================================================================\n", - "Unit : fs.boiler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 685.15 893.15\n", - " pressure pascal 3.4510e+07 3.4300e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.turbine Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", - " Pressure Change : -24.979 : pascal : False : (None, None)\n", - " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 719.28\n", - " pressure pascal 3.4300e+07 9.3207e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 719.28 489.15\n", - " pressure pascal 9.3207e+06 9.2507e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 543.23 750.68\n", - " pressure pascal 3.4560e+07 3.4490e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 396.40 579.39\n", - " pressure pascal 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_1 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825.\n", - " temperature kelvin 354.15 354.15 354.15\n", - " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.co2_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807e+06 9.1107e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.main_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 396.40\n", - " pressure pascal 9.1107e+06 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.bypass_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 452.96\n", - " pressure pascal 9.1807e+06 3.4886e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_2 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR \n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 396.40 396.40 396.40\n", - " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.FG_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 25836. : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 396.40 483.15\n", - " pressure pascal 3.4620e+07 3.4560e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.mixer Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 579.39 452.96 543.23\n", - " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", - "====================================================================================\n", - "641.5293430698576 kW\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", + "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", + ":241)\n" + ] + } + ], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " TerminationCondition,\n", + " value,\n", + " Expression,\n", + " minimize,\n", + " units,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (\n", + " Mixer,\n", + " MomentumMixingType,\n", + " PressureChanger,\n", + " Heater,\n", + " Separator,\n", + " HeatExchanger,\n", + ")\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from properties import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 51411\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2674\n", + "\n", + "Total number of variables............................: 5920\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 5669\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 5920\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.362\n", + "Total CPU secs in NLP function evaluations = 0.008\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 719.28\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 719.28 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 543.23 750.68\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 396.40 579.39\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 396.40\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 452.96\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 396.40 396.40 396.40\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 25836. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 396.40 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 579.39 452.96 543.23\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "641.5293430698576 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory(\"ipopt\")\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.turbine = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.splitter_1 = Separator(\n", + " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", + " )\n", + "\n", + " m.fs.co2_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.main_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.splitter_2 = Separator(\n", + " property_package=m.fs.properties,\n", + " ideal_separation=False,\n", + " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", + " )\n", + "\n", + " m.fs.FG_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.mixer = Mixer(\n", + " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", + " )\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(\n", + " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", + " )\n", + " m.fs.s04 = Arc(\n", + " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", + " )\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(\n", + " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", + " )\n", + " m.fs.s07 = Arc(\n", + " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", + " )\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(\n", + " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", + " )\n", + " m.fs.s10 = Arc(\n", + " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", + " )\n", + " m.fs.s11 = Arc(\n", + " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", + " )\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # initialize twice if needed\n", + " def init_once_or_twice(blk, outlvl=0):\n", + " try:\n", + " blk.initialize(outlvl=outlvl)\n", + " except:\n", + " blk.initialize(outlvl=outlvl)\n", + "\n", + " # NETL Baseline\n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " init_once_or_twice(m.fs.boiler)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1 / 3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(\n", + " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(\n", + " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import (\n", + " convert_quantity_to_reporting_units,\n", + " report_quantity,\n", + " )\n", + "\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(\n", + " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", + " units.kW,\n", + " )\n", + " return m\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "def main():\n", - " # Setup solver and options\n", - " solver = SolverFactory(\"ipopt\")\n", - " outlvl = 0\n", - " tee = True\n", - "\n", - " # Set up concrete model\n", - " m = ConcreteModel()\n", - "\n", - " # Create a flowsheet block\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Create the properties param block\n", - " m.fs.properties = SCO2ParameterBlock()\n", - "\n", - " # Add unit models to the flowsheet\n", - " m.fs.boiler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.turbine = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=False,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.splitter_1 = Separator(\n", - " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", - " )\n", - "\n", - " m.fs.co2_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.main_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.bypass_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.splitter_2 = Separator(\n", - " property_package=m.fs.properties,\n", - " ideal_separation=False,\n", - " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", - " )\n", - "\n", - " m.fs.FG_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.mixer = Mixer(\n", - " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", - " )\n", - "\n", - " # # Connect the flowsheet\n", - " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", - " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", - " m.fs.s03 = Arc(\n", - " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", - " )\n", - " m.fs.s04 = Arc(\n", - " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", - " )\n", - " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", - " m.fs.s06 = Arc(\n", - " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", - " )\n", - " m.fs.s07 = Arc(\n", - " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", - " )\n", - " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", - " m.fs.s09 = Arc(\n", - " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", - " )\n", - " m.fs.s10 = Arc(\n", - " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", - " )\n", - " m.fs.s11 = Arc(\n", - " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", - " )\n", - " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", - " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", - " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", - " # initialize twice if needed\n", - " def init_once_or_twice(blk, outlvl=0):\n", - " try:\n", - " blk.initialize(outlvl=outlvl)\n", - " except:\n", - " blk.initialize(outlvl=outlvl)\n", - "\n", - " # NETL Baseline\n", - " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", - " m.fs.boiler.inlet.temperature.fix(685.15)\n", - " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", - " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", - " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " init_once_or_twice(m.fs.boiler)\n", - "\n", - " propagate_state(m.fs.s01)\n", - "\n", - " m.fs.turbine.ratioP.fix(1 / 3.68)\n", - " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", - " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s02)\n", - " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", - " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", - "\n", - " propagate_state(m.fs.s03)\n", - "\n", - " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", - " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", - " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s04)\n", - " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", - "\n", - " m.fs.splitter_1.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s05)\n", - " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", - " m.fs.co2_cooler.deltaP.fix(-0.07)\n", - " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s06)\n", - " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.bypass_compressor.ratioP.fix(3.8)\n", - " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s07)\n", - " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.main_compressor.ratioP.fix(3.8)\n", - " m.fs.main_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s09)\n", - "\n", - " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", - " m.fs.splitter_2.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s10)\n", - " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", - " m.fs.FG_cooler.deltaP.fix(-0.06)\n", - " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s11)\n", - "\n", - " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " # Add constraint heats of the LTR_pseudo shell and tube\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c1 = Constraint(\n", - " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " propagate_state(m.fs.s08)\n", - " propagate_state(m.fs.s12)\n", - " propagate_state(m.fs.s13)\n", - "\n", - " m.fs.mixer.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s14)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", - " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c2 = Constraint(\n", - " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", - "\n", - " print(\"--------------------------------------------------------------------\")\n", - " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", - " print(\"--------------------------------------------------------------------\")\n", - "\n", - " solver.solve(m, tee=tee)\n", - "\n", - " #\n", - " from idaes.core.util.units_of_measurement import (\n", - " convert_quantity_to_reporting_units,\n", - " report_quantity,\n", - " )\n", - "\n", - " # Print reports\n", - " for i in m.fs.component_objects(Block):\n", - " if isinstance(i, UnitModelBlockData):\n", - " i.report()\n", - "\n", - " # Converting units for readability\n", - " print(\n", - " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", - " units.kW,\n", - " )\n", - " return m\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " m = main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb index fa30ded7..3d458b82 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb @@ -1,732 +1,758 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Importing libraries\n", - "\n", - "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", - "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", - ":241)\n" - ] - } - ], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " Block,\n", - " Var,\n", - " Param,\n", - " Constraint,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " TerminationCondition,\n", - " value,\n", - " Expression,\n", - " minimize,\n", - " units,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", - "from idaes.models.unit_models import (\n", - " Mixer,\n", - " MomentumMixingType,\n", - " PressureChanger,\n", - " Heater,\n", - " Separator,\n", - " HeatExchanger,\n", - ")\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from properties import SCO2ParameterBlock\n", - "\n", - "import idaes.logger as idaeslog\n", - "\n", - "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Constructing the flowsheet\n", - "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", - "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", - "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 51411\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2674\n", - "\n", - "Total number of variables............................: 5920\n", - " variables with only lower bounds: 32\n", - " variables with lower and upper bounds: 5669\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 5920\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 1\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", - "\n", - "\n", - "Number of objective function evaluations = 2\n", - "Number of objective gradient evaluations = 2\n", - "Number of equality constraint evaluations = 2\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 2\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.362\n", - "Total CPU secs in NLP function evaluations = 0.008\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\n", - "====================================================================================\n", - "Unit : fs.boiler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 685.15 893.15\n", - " pressure pascal 3.4510e+07 3.4300e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.turbine Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", - " Pressure Change : -24.979 : pascal : False : (None, None)\n", - " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 719.28\n", - " pressure pascal 3.4300e+07 9.3207e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 719.28 489.15\n", - " pressure pascal 9.3207e+06 9.2507e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 543.23 750.68\n", - " pressure pascal 3.4560e+07 3.4490e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 396.40 579.39\n", - " pressure pascal 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_1 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825.\n", - " temperature kelvin 354.15 354.15 354.15\n", - " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.co2_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807e+06 9.1107e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.main_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 396.40\n", - " pressure pascal 9.1107e+06 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.bypass_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 452.96\n", - " pressure pascal 9.1807e+06 3.4886e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_2 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR \n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 396.40 396.40 396.40\n", - " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.FG_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 25836. : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 396.40 483.15\n", - " pressure pascal 3.4620e+07 3.4560e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.mixer Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 579.39 452.96 543.23\n", - " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", - "====================================================================================\n", - "641.5293430698576 kW\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", + "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", + ":241)\n" + ] + } + ], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " TerminationCondition,\n", + " value,\n", + " Expression,\n", + " minimize,\n", + " units,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (\n", + " Mixer,\n", + " MomentumMixingType,\n", + " PressureChanger,\n", + " Heater,\n", + " Separator,\n", + " HeatExchanger,\n", + ")\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from properties import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 51411\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2674\n", + "\n", + "Total number of variables............................: 5920\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 5669\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 5920\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.362\n", + "Total CPU secs in NLP function evaluations = 0.008\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 719.28\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 719.28 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 543.23 750.68\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 396.40 579.39\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 396.40\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 452.96\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 396.40 396.40 396.40\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 25836. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 396.40 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 579.39 452.96 543.23\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "641.5293430698576 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory(\"ipopt\")\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.turbine = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.splitter_1 = Separator(\n", + " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", + " )\n", + "\n", + " m.fs.co2_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.main_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.splitter_2 = Separator(\n", + " property_package=m.fs.properties,\n", + " ideal_separation=False,\n", + " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", + " )\n", + "\n", + " m.fs.FG_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.mixer = Mixer(\n", + " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", + " )\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(\n", + " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", + " )\n", + " m.fs.s04 = Arc(\n", + " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", + " )\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(\n", + " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", + " )\n", + " m.fs.s07 = Arc(\n", + " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", + " )\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(\n", + " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", + " )\n", + " m.fs.s10 = Arc(\n", + " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", + " )\n", + " m.fs.s11 = Arc(\n", + " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", + " )\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # initialize twice if needed\n", + " def init_once_or_twice(blk, outlvl=0):\n", + " try:\n", + " blk.initialize(outlvl=outlvl)\n", + " except:\n", + " blk.initialize(outlvl=outlvl)\n", + "\n", + " # NETL Baseline\n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " init_once_or_twice(m.fs.boiler)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1 / 3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(\n", + " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(\n", + " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import (\n", + " convert_quantity_to_reporting_units,\n", + " report_quantity,\n", + " )\n", + "\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(\n", + " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", + " units.kW,\n", + " )\n", + " return m\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "def main():\n", - " # Setup solver and options\n", - " solver = SolverFactory(\"ipopt\")\n", - " outlvl = 0\n", - " tee = True\n", - "\n", - " # Set up concrete model\n", - " m = ConcreteModel()\n", - "\n", - " # Create a flowsheet block\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Create the properties param block\n", - " m.fs.properties = SCO2ParameterBlock()\n", - "\n", - " # Add unit models to the flowsheet\n", - " m.fs.boiler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.turbine = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=False,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.splitter_1 = Separator(\n", - " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", - " )\n", - "\n", - " m.fs.co2_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.main_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.bypass_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.splitter_2 = Separator(\n", - " property_package=m.fs.properties,\n", - " ideal_separation=False,\n", - " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", - " )\n", - "\n", - " m.fs.FG_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.mixer = Mixer(\n", - " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", - " )\n", - "\n", - " # # Connect the flowsheet\n", - " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", - " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", - " m.fs.s03 = Arc(\n", - " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", - " )\n", - " m.fs.s04 = Arc(\n", - " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", - " )\n", - " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", - " m.fs.s06 = Arc(\n", - " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", - " )\n", - " m.fs.s07 = Arc(\n", - " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", - " )\n", - " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", - " m.fs.s09 = Arc(\n", - " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", - " )\n", - " m.fs.s10 = Arc(\n", - " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", - " )\n", - " m.fs.s11 = Arc(\n", - " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", - " )\n", - " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", - " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", - " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", - " # initialize twice if needed\n", - " def init_once_or_twice(blk, outlvl=0):\n", - " try:\n", - " blk.initialize(outlvl=outlvl)\n", - " except:\n", - " blk.initialize(outlvl=outlvl)\n", - "\n", - " # NETL Baseline\n", - " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", - " m.fs.boiler.inlet.temperature.fix(685.15)\n", - " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", - " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", - " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " init_once_or_twice(m.fs.boiler)\n", - "\n", - " propagate_state(m.fs.s01)\n", - "\n", - " m.fs.turbine.ratioP.fix(1 / 3.68)\n", - " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", - " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s02)\n", - " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", - " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", - "\n", - " propagate_state(m.fs.s03)\n", - "\n", - " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", - " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", - " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s04)\n", - " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", - "\n", - " m.fs.splitter_1.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s05)\n", - " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", - " m.fs.co2_cooler.deltaP.fix(-0.07)\n", - " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s06)\n", - " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.bypass_compressor.ratioP.fix(3.8)\n", - " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s07)\n", - " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.main_compressor.ratioP.fix(3.8)\n", - " m.fs.main_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s09)\n", - "\n", - " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", - " m.fs.splitter_2.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s10)\n", - " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", - " m.fs.FG_cooler.deltaP.fix(-0.06)\n", - " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s11)\n", - "\n", - " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " # Add constraint heats of the LTR_pseudo shell and tube\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c1 = Constraint(\n", - " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " propagate_state(m.fs.s08)\n", - " propagate_state(m.fs.s12)\n", - " propagate_state(m.fs.s13)\n", - "\n", - " m.fs.mixer.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s14)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", - " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c2 = Constraint(\n", - " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", - "\n", - " print(\"--------------------------------------------------------------------\")\n", - " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", - " print(\"--------------------------------------------------------------------\")\n", - "\n", - " solver.solve(m, tee=tee)\n", - "\n", - " #\n", - " from idaes.core.util.units_of_measurement import (\n", - " convert_quantity_to_reporting_units,\n", - " report_quantity,\n", - " )\n", - "\n", - " # Print reports\n", - " for i in m.fs.component_objects(Block):\n", - " if isinstance(i, UnitModelBlockData):\n", - " i.report()\n", - "\n", - " # Converting units for readability\n", - " print(\n", - " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", - " units.kW,\n", - " )\n", - " return m\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " m = main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index d958de18..4aaa8c04 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "69350c71", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -1115,8 +1142,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb index c873f19b..8a8e7d59 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb @@ -3,6 +3,32 @@ { "cell_type": "code", "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -53,662 +79,3684 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-17 17:38:42.032800: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-17 17:38:42.033487: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:38:42.036537: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:38:42.043175: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742258322.054590 362852 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742258322.057876 362852 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742258322.067226 362852 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258322.067243 362852 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258322.067244 362852 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258322.067245 362852 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-17 17:38:42.070698: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] = [\n", + " \"pressure\",\n", + " \"temperature\",\n", + " \"enth_mol\",\n", + " \"entr_mol\",\n", + " \"CO2_enthalpy\",\n", + " \"CO2_entropy\",\n", + "]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "# Creating input_data and output_data from data\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n", + "2025-03-17 17:38:44.131094: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 1s - 84ms/step - loss: 0.0188 - mae: 0.0963 - mse: 0.0188 - val_loss: 0.0043 - val_mae: 0.0456 - val_mse: 0.0043\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 0s - 6ms/step - loss: 0.0034 - mae: 0.0438 - mse: 0.0034 - val_loss: 0.0031 - val_mae: 0.0438 - val_mse: 0.0031\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 3/250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 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val_mae: 0.0085 - val_mse: 1.6977e-04\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", - "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", - ":241)\n" + "Epoch 246/250\n" ] - } - ], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import random as rn\n", - "import tensorflow as tf\n", - "import tensorflow.keras as keras\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "\n", - "# fix environment variables to ensure consist neural network training\n", - "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", - "np.random.seed(46)\n", - "rn.seed(1342)\n", - "tf.random.set_seed(62)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "csv_data.columns.values[0:6] = [\n", - " \"pressure\",\n", - " \"temperature\",\n", - " \"enth_mol\",\n", - " \"entr_mol\",\n", - " \"CO2_enthalpy\",\n", - " \"CO2_entropy\",\n", - "]\n", - "data = csv_data.sample(n=500)\n", - "\n", - "# Creating input_data and output_data from data\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogate with TensorFlow Keras\n", - "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", - "\n", - "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", - "\n", - "* Activation function: sigmoid, **tanh**\n", - "* Optimizer: **Adam**\n", - "* Number of hidden layers: 3, **4**, 5, 6\n", - "* Number of neurons per layer: **20**, 40, 60\n", - "\n", - "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", - "\n", - "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", - "\n", - "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 0s - 8ms/step - loss: 1.6944e-04 - mae: 0.0096 - mse: 1.6944e-04 - val_loss: 1.6748e-04 - val_mae: 0.0084 - val_mse: 1.6748e-04\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 247/250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 0s - 6ms/step - loss: 1.6734e-04 - mae: 0.0095 - mse: 1.6734e-04 - val_loss: 1.6522e-04 - val_mae: 0.0084 - val_mse: 1.6522e-04\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 248/250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 0s - 8ms/step - loss: 1.6525e-04 - mae: 0.0095 - mse: 1.6525e-04 - val_loss: 1.6299e-04 - val_mae: 0.0083 - val_mse: 1.6299e-04\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 249/250\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/250\n", - "13/13 - 3s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 3s/epoch - 249ms/step\n", - "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 237ms/epoch - 18ms/step\n", - "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 262ms/epoch - 20ms/step\n", - "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 283ms/epoch - 22ms/step\n", - "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 351ms/epoch - 27ms/step\n", - "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 364ms/epoch - 28ms/step\n", - "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 245ms/epoch - 19ms/step\n", - "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 203ms/epoch - 16ms/step\n", - "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 300ms/epoch - 23ms/step\n", - "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 255ms/epoch - 20ms/step\n", - "Epoch 11/250\n", - "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 247ms/epoch - 19ms/step\n", - "Epoch 12/250\n", - "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 321ms/epoch - 25ms/step\n", - "Epoch 13/250\n", - "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 219ms/epoch - 17ms/step\n", - "Epoch 14/250\n", - "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 272ms/epoch - 21ms/step\n", - "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 299ms/epoch - 23ms/step\n", - "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 271ms/epoch - 21ms/step\n", - "Epoch 17/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 248ms/epoch - 19ms/step\n", - "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 256ms/epoch - 20ms/step\n", - "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 153ms/epoch - 12ms/step\n", - "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 239ms/epoch - 18ms/step\n", - "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 263ms/epoch - 20ms/step\n", - "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 243ms/epoch - 19ms/step\n", - "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", - "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 189ms/epoch - 15ms/step\n", - "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 280ms/epoch - 22ms/step\n", - "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 222ms/epoch - 17ms/step\n", - "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 286ms/epoch - 22ms/step\n", - "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 116ms/epoch - 9ms/step\n", - "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 157ms/epoch - 12ms/step\n", - "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 267ms/epoch - 21ms/step\n", - "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 264ms/epoch - 20ms/step\n", - "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 269ms/epoch - 21ms/step\n", - "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 353ms/epoch - 27ms/step\n", - "Epoch 34/250\n", - "13/13 - 1s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 537ms/epoch - 41ms/step\n", - "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 330ms/epoch - 25ms/step\n", - "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 289ms/epoch - 22ms/step\n", - "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 155ms/epoch - 12ms/step\n", - "Epoch 38/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 289ms/epoch - 22ms/step\n", - "Epoch 39/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 118ms/epoch - 9ms/step\n", - "Epoch 40/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 299ms/epoch - 23ms/step\n", - "Epoch 41/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 42/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 192ms/epoch - 15ms/step\n", - "Epoch 43/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 237ms/epoch - 18ms/step\n", - "Epoch 44/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 154ms/epoch - 12ms/step\n", - "Epoch 45/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 46/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 47/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 116ms/epoch - 9ms/step\n", - "Epoch 48/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 288ms/epoch - 22ms/step\n", - "Epoch 49/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 140ms/epoch - 11ms/step\n", - "Epoch 50/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 51/250\n", - "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 52/250\n", - "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 265ms/epoch - 20ms/step\n", - "Epoch 53/250\n", - "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 179ms/epoch - 14ms/step\n", - "Epoch 54/250\n", - "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 287ms/epoch - 22ms/step\n", - "Epoch 55/250\n", - "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 284ms/epoch - 22ms/step\n", - "Epoch 56/250\n", - "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 297ms/epoch - 23ms/step\n", - "Epoch 57/250\n", - "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 206ms/epoch - 16ms/step\n", - "Epoch 58/250\n", - "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 59/250\n", - "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 274ms/epoch - 21ms/step\n", - "Epoch 60/250\n", - "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 61/250\n", - "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 62/250\n", - "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 63/250\n", - "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 154ms/epoch - 12ms/step\n", - "Epoch 64/250\n", - "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 284ms/epoch - 22ms/step\n", - "Epoch 65/250\n", - "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 168ms/epoch - 13ms/step\n", - "Epoch 66/250\n", - "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 113ms/epoch - 9ms/step\n", - "Epoch 67/250\n", - "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 264ms/epoch - 20ms/step\n", - "Epoch 68/250\n", - "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 189ms/epoch - 15ms/step\n", - "Epoch 69/250\n", - "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 175ms/epoch - 13ms/step\n", - "Epoch 70/250\n", - "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 322ms/epoch - 25ms/step\n", - "Epoch 71/250\n", - "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 207ms/epoch - 16ms/step\n", - "Epoch 72/250\n", - "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 257ms/epoch - 20ms/step\n", - "Epoch 73/250\n", - "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 233ms/epoch - 18ms/step\n", - "Epoch 74/250\n", - "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 250ms/epoch - 19ms/step\n", - "Epoch 75/250\n", - "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 76/250\n", - "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 77/250\n", - "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 126ms/epoch - 10ms/step\n", - "Epoch 78/250\n", - "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 242ms/epoch - 19ms/step\n", - "Epoch 79/250\n", - "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 80/250\n", - "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 81/250\n", - "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 166ms/epoch - 13ms/step\n", - "Epoch 82/250\n", - "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 83/250\n", - "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 84/250\n", - "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 148ms/epoch - 11ms/step\n", - "Epoch 85/250\n", - "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 282ms/epoch - 22ms/step\n", - "Epoch 86/250\n", - "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 246ms/epoch - 19ms/step\n", - "Epoch 87/250\n", - "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", - "Epoch 88/250\n", - "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 208ms/epoch - 16ms/step\n", - "Epoch 89/250\n", - "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 193ms/epoch - 15ms/step\n", - "Epoch 90/250\n", - "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 168ms/epoch - 13ms/step\n", - "Epoch 91/250\n", - "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 156ms/epoch - 12ms/step\n", - "Epoch 92/250\n", - "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 93/250\n", - "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 94/250\n", - "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 303ms/epoch - 23ms/step\n", - "Epoch 95/250\n", - "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 96/250\n", - "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 97/250\n", - "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 98/250\n", - "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 99/250\n", - "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 130ms/epoch - 10ms/step\n", - "Epoch 100/250\n", - "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 101/250\n", - "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 102/250\n", - "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 260ms/epoch - 20ms/step\n", - "Epoch 103/250\n", - "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 275ms/epoch - 21ms/step\n", - "Epoch 104/250\n", - "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 105/250\n", - "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 275ms/epoch - 21ms/step\n", - "Epoch 106/250\n", - "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 157ms/epoch - 12ms/step\n", - "Epoch 107/250\n", - "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 183ms/epoch - 14ms/step\n", - "Epoch 108/250\n", - "13/13 - 0s - loss: 6.7955e-04 - mae: 0.0198 - mse: 6.7955e-04 - val_loss: 7.6778e-04 - val_mae: 0.0204 - val_mse: 7.6778e-04 - 192ms/epoch - 15ms/step\n", - "Epoch 109/250\n", - "13/13 - 1s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 516ms/epoch - 40ms/step\n", - "Epoch 110/250\n", - "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 111/250\n", - "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 208ms/epoch - 16ms/step\n", - "Epoch 112/250\n", - "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 215ms/epoch - 17ms/step\n", - "Epoch 113/250\n", - "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 114/250\n", - "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 115/250\n", - "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 166ms/epoch - 13ms/step\n", - "Epoch 116/250\n", - "13/13 - 0s - loss: 6.5036e-04 - mae: 0.0194 - mse: 6.5036e-04 - val_loss: 6.5858e-04 - val_mae: 0.0191 - val_mse: 6.5858e-04 - 107ms/epoch - 8ms/step\n", - "Epoch 117/250\n", - "13/13 - 0s - loss: 6.4983e-04 - mae: 0.0194 - mse: 6.4983e-04 - val_loss: 7.0443e-04 - val_mae: 0.0198 - val_mse: 7.0443e-04 - 109ms/epoch - 8ms/step\n", - "Epoch 118/250\n", - "13/13 - 0s - loss: 6.4994e-04 - mae: 0.0195 - mse: 6.4994e-04 - val_loss: 6.3181e-04 - val_mae: 0.0193 - val_mse: 6.3181e-04 - 296ms/epoch - 23ms/step\n", - "Epoch 119/250\n", - "13/13 - 0s - loss: 6.6252e-04 - mae: 0.0199 - mse: 6.6252e-04 - val_loss: 6.3527e-04 - val_mae: 0.0191 - val_mse: 6.3527e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 120/250\n", - "13/13 - 0s - loss: 6.4578e-04 - mae: 0.0193 - mse: 6.4578e-04 - val_loss: 6.3127e-04 - val_mae: 0.0189 - val_mse: 6.3127e-04 - 190ms/epoch - 15ms/step\n", - "Epoch 121/250\n", - "13/13 - 0s - loss: 6.1375e-04 - mae: 0.0191 - mse: 6.1375e-04 - val_loss: 6.5351e-04 - val_mae: 0.0192 - val_mse: 6.5351e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 122/250\n", - "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 142ms/epoch - 11ms/step\n", - "Epoch 123/250\n", - "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 278ms/epoch - 21ms/step\n", - "Epoch 124/250\n", - "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 246ms/epoch - 19ms/step\n", - "Epoch 125/250\n", - "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 135ms/epoch - 10ms/step\n", - "Epoch 126/250\n", - "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 127/250\n", - "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 142ms/epoch - 11ms/step\n", - "Epoch 128/250\n", - "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 249ms/epoch - 19ms/step\n", - "Epoch 129/250\n", - "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 279ms/epoch - 21ms/step\n", - "Epoch 130/250\n", - "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 356ms/epoch - 27ms/step\n", - "Epoch 131/250\n", - "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 132/250\n", - "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 102ms/epoch - 8ms/step\n", - "Epoch 133/250\n", - "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 239ms/epoch - 18ms/step\n", - "Epoch 134/250\n", - "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 285ms/epoch - 22ms/step\n", - "Epoch 135/250\n", - "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 178ms/epoch - 14ms/step\n", - "Epoch 136/250\n", - "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 113ms/epoch - 9ms/step\n", - "Epoch 137/250\n", - "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 138ms/epoch - 11ms/step\n", - "Epoch 138/250\n", - "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 144ms/epoch - 11ms/step\n", - "Epoch 139/250\n", - "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 279ms/epoch - 21ms/step\n", - "Epoch 140/250\n", - "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 141/250\n", - "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 118ms/epoch - 9ms/step\n", - "Epoch 142/250\n", - "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 120ms/epoch - 9ms/step\n", - "Epoch 143/250\n", - "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 304ms/epoch - 23ms/step\n", - "Epoch 144/250\n", - "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 178ms/epoch - 14ms/step\n", - "Epoch 145/250\n", - "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 146/250\n", - "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 396ms/epoch - 30ms/step\n", - "Epoch 147/250\n", - "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 148/250\n", - "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 155ms/epoch - 12ms/step\n", - "Epoch 149/250\n", - "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 120ms/epoch - 9ms/step\n", - "Epoch 150/250\n", - "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 151/250\n", - "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 315ms/epoch - 24ms/step\n", - "Epoch 152/250\n", - "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 198ms/epoch - 15ms/step\n", - "Epoch 153/250\n", - "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 154/250\n", - "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 155/250\n", - "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 267ms/epoch - 21ms/step\n", - "Epoch 156/250\n", - "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 182ms/epoch - 14ms/step\n", - "Epoch 157/250\n", - "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 158/250\n", - "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 159/250\n", - "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 172ms/epoch - 13ms/step\n", - "Epoch 160/250\n", - "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 164ms/epoch - 13ms/step\n", - "Epoch 161/250\n", - "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 169ms/epoch - 13ms/step\n", - "Epoch 162/250\n", - "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 188ms/epoch - 14ms/step\n", - "Epoch 163/250\n", - "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 258ms/epoch - 20ms/step\n", - "Epoch 164/250\n", - "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 165/250\n", - "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 166/250\n", - "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 265ms/epoch - 20ms/step\n", - "Epoch 167/250\n", - "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 168/250\n", - "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 169/250\n", - "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 234ms/epoch - 18ms/step\n", - "Epoch 170/250\n", - "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 171/250\n", - "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 243ms/epoch - 19ms/step\n", - "Epoch 172/250\n", - "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 148ms/epoch - 11ms/step\n", - "Epoch 173/250\n", - "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 174/250\n", - "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 175/250\n", - "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 176/250\n", - "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 177/250\n", - "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 316ms/epoch - 24ms/step\n", - "Epoch 178/250\n", - "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 214ms/epoch - 16ms/step\n", - "Epoch 179/250\n", - "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 181ms/epoch - 14ms/step\n", - "Epoch 180/250\n", - "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 144ms/epoch - 11ms/step\n", - "Epoch 181/250\n", - "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 302ms/epoch - 23ms/step\n", - "Epoch 182/250\n", - "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 216ms/epoch - 17ms/step\n", - "Epoch 183/250\n", - "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 296ms/epoch - 23ms/step\n", - "Epoch 184/250\n", - "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 195ms/epoch - 15ms/step\n", - "Epoch 185/250\n", - "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 186/250\n", - "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 187/250\n", - "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 188/250\n", - "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 451ms/epoch - 35ms/step\n", - "Epoch 189/250\n", - "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 110ms/epoch - 8ms/step\n", - "Epoch 190/250\n", - "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 121ms/epoch - 9ms/step\n", - "Epoch 191/250\n", - "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 173ms/epoch - 13ms/step\n", - "Epoch 192/250\n", - "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 193/250\n", - "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 243ms/epoch - 19ms/step\n", - "Epoch 194/250\n", - "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 272ms/epoch - 21ms/step\n", - "Epoch 195/250\n", - "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 196/250\n", - "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 197/250\n", - "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 198/250\n", - "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 114ms/epoch - 9ms/step\n", - "Epoch 199/250\n", - "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 209ms/epoch - 16ms/step\n", - "Epoch 200/250\n", - "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 198ms/epoch - 15ms/step\n", - "Epoch 201/250\n", - "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 202/250\n", - "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 203/250\n", - "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 204/250\n", - "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 205/250\n", - "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 121ms/epoch - 9ms/step\n", - "Epoch 206/250\n", - "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 294ms/epoch - 23ms/step\n", - "Epoch 207/250\n", - "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 208/250\n", - "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 209/250\n", - "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 210/250\n", - "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 211/250\n", - "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 212/250\n", - "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 309ms/epoch - 24ms/step\n", - "Epoch 213/250\n", - "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 214/250\n", - "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 215/250\n", - "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 177ms/epoch - 14ms/step\n", - "Epoch 216/250\n", - "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 253ms/epoch - 19ms/step\n", - "Epoch 217/250\n", - "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 218/250\n", - "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 254ms/epoch - 20ms/step\n", - "Epoch 219/250\n", - "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 220/250\n", - "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 157ms/epoch - 12ms/step\n", - "Epoch 221/250\n", - "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 222/250\n", - "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 223/250\n", - "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 176ms/epoch - 14ms/step\n", - "Epoch 224/250\n", - "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 421ms/epoch - 32ms/step\n", - "Epoch 225/250\n", - "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 159ms/epoch - 12ms/step\n", - "Epoch 226/250\n", - "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 307ms/epoch - 24ms/step\n", - "Epoch 227/250\n", - "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 193ms/epoch - 15ms/step\n", - "Epoch 228/250\n", - "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 133ms/epoch - 10ms/step\n", - "Epoch 229/250\n", - "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 107ms/epoch - 8ms/step\n", - "Epoch 230/250\n", - "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 231/250\n", - "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 276ms/epoch - 21ms/step\n", - "Epoch 232/250\n", - "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 134ms/epoch - 10ms/step\n", - "Epoch 233/250\n", - "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 234/250\n", - "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 235/250\n", - "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 153ms/epoch - 12ms/step\n", - "Epoch 236/250\n", - "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 187ms/epoch - 14ms/step\n", - "Epoch 237/250\n", - "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 209ms/epoch - 16ms/step\n", - "Epoch 238/250\n", - "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 175ms/epoch - 13ms/step\n", - "Epoch 239/250\n", - "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 234ms/epoch - 18ms/step\n", - "Epoch 240/250\n", - "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 130ms/epoch - 10ms/step\n", - "Epoch 241/250\n", - "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 122ms/epoch - 9ms/step\n", - "Epoch 242/250\n", - "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 243/250\n", - "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 321ms/epoch - 25ms/step\n", - "Epoch 244/250\n", - "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 245/250\n", - "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 246/250\n", - "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 191ms/epoch - 15ms/step\n", - "Epoch 247/250\n", - "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 248/250\n", - "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 122ms/epoch - 9ms/step\n", - "Epoch 249/250\n", - "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 250/250\n", - "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 127ms/epoch - 10ms/step\n" + "13/13 - 0s - 7ms/step - loss: 1.6316e-04 - mae: 0.0094 - mse: 1.6316e-04 - val_loss: 1.6079e-04 - val_mae: 0.0082 - val_mse: 1.6079e-04\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 250/250\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 - 0s - 6ms/step - loss: 1.6107e-04 - mae: 0.0094 - mse: 1.6107e-04 - val_loss: 1.5862e-04 - val_mae: 0.0082 - val_mse: 1.5862e-04\n" ] }, { "data": { - "image/png": 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Obdq0QWFhoUnHCAsLg42NDT788ENs2bIF4eHhBjeXK++z8PvvvyM1NbXK7yc4OBiNGjXCBx98YLC/hIQEo7oajcaoh2T9+vVG405sbW0BwGAqf0UGDx4MnU4nTaMvtWjRIqhUqocar2WqN998E3FxcZg+fXqFdTIyMnDy5Emj8qKiIiQnJ0OtVj/UZVdquNhzQ1RGr1694OTkhKioKOlW/V9++WWtml46d+5c/PTTT+jduzcmT54sfWl16dLloaYoq9VqozvJluedd97BgQMHEB4eLt299+DBg/jiiy/g7OxsNDg2JycHX331Vbn7MuXmfhERERgzZgw+/PBDhISEwNHR0WB9p06d0K9fP/j5+cHZ2Rl//PEHvvnmG0ybNq3SfQP6MBQWFiaNuyl7SQoAnnjiCSQlJeGpp57CkCFDkJaWhmXLlqFTp07Iz8836RilXF1dMXv2bMTHx+OJJ57A4MGDcejQIWzZssWgN6b0uPPnz0d0dDR69eqFY8eO4euvvzbo8QGA1q1bw9HREcuWLUPjxo1ha2uLgIAAtGzZ0uj4Q4cORf/+/fHqq6/i/Pnz8PX1xU8//YRNmzZh5syZBoOH5da3b1/07dv3gXUuXbqEnj174vHHH8eAAQPg7u6OrKwsrF69GkeOHMHMmTONztOuXbuM7qIN6Ac/V+fu0lQ/MNwQldGkSRP88MMPmDVrFl577TU4OTlhzJgxGDBgAEJCQpRuHgDAz88PW7ZswezZs/H666/Dy8sL8+fPx6lTp0yazfWwXnnlFaxatQq//PILvv76a9y6dQseHh4YOXIkXn/9daMv1UuXLmHs2LHl7suUcPPkk0/C2toaeXl5BrOkSr3wwgv47rvv8NNPP6GwsBDe3t5488038eKLL5r8niIjI7Fq1Sp4eHjg8ccfN1g3fvx4ZGRk4OOPP8a2bdvQqVMnfPXVV1i/fv1DPZfszTffhJWVFZYtW4adO3ciICAAP/30E4YMGWJQ75VXXkFBQQFWrVqFtWvX4tFHH8WPP/5odJ+lRo0a4fPPP8ecOXPw/PPPo7i4GCtXriw33KjVanz33XeIjY3F2rVrsXLlSvj4+GDBggWYNWtWld+L3Nq3b4+EhARs3rwZH374ITIzM2FlZYUuXbpg+fLlmDhxotE277//frn7iouLY7ghqERt+pOUiB5aWFjYQ0+NJiKqTzjmhqgOuv9p3mfOnMHmzZvRr18/ZRpERFSLsOeGqA7y8PDA+PHj0apVK/z111/46KOPUFhYiEOHDhndS4WIqKHhmBuiOig0NBSrV69GRkYGtFotAgMD8Z///IfBhogI7LkhIiKieoZjboiIiKheYbghIiKieqXBjbkpKSnBlStX0Lhx4yrdvpyIiIiUI4RAXl4emjVrBrX6wX0zDS7cXLlyBV5eXko3g4iIiB7CxYsX4enp+cA6DS7clD407uLFi7C3t1e4NURERGSK3NxceHl5GTz8tSINLtyUXoqyt7dnuCEiIqpjTBlSwgHFREREVK8w3BAREVG9wnBDRERE9UqDG3NDRETVV1JSgqKiIqWbQfWMpaVlpdO8TcFwQ0REVVJUVIS0tDSUlJQo3RSqZ9RqNVq2bAlLS8tq7YfhhoiITCaEQHp6OjQaDby8vGT5K5sI+Psmu+np6WjRokW1brTLcENERCYrLi7GrVu30KxZM9jY2CjdHKpnXF1dceXKFRQXF6NRo0YPvZ9aEbmXLl0KHx8fWFlZISAgAPv27auwbmJiIlQqlcFiZWVlxtYSETVcOp0OAKp92YCoPKWfq9LP2cNSPNysXbsWMTExiIuLw8GDB+Hr64uQkBBkZWVVuI29vT3S09Ol5a+//jJji4mIiM/mo5og1+dK8XDz3nvv4dlnn0V0dDQ6deqEZcuWwcbGBitWrKhwG5VKBXd3d2lxc3MzY4vLp9MBKSnA6tX6f6sZOomIiOghKRpuioqKcODAAQQHB0tlarUawcHBSE1NrXC7/Px8eHt7w8vLC8OGDcOJEycqrFtYWIjc3FyDRW5JSYCPD9C/PzB6tP5fHx99ORER1U8+Pj5ISEgwuX5KSgpUKhVu3rxZY20iPUXDTXZ2NnQ6nVHPi5ubGzIyMsrdpn379lixYgU2bdqEr776CiUlJejVqxcuXbpUbv34+Hg4ODhIi9xPBE9KAkaMAO4//OXL+nIGHCIiY+bs7b5/nOb9y9y5cx9qv/v378ekSZNMrt+rVy+kp6fDwcHhoY5nqtIQ5eTkhDt37his279/v/S+y1q+fDl8fX1hZ2cHR0dHPPLII4iPj5fWz507t9xz16FDhxp9Lw+rzs2WCgwMRGBgoPS6V69e6NixIz7++GO88cYbRvXnzJmDmJgY6XXpU0XloNMBM2YAQhivEwJQqYCZM4FhwwCNRpZDEhHVeUlJ+t+dZf8o9PQEFi8GwsPlP156err089q1axEbG4vTp09LZXZ2dtLPQgjodDpYWFT+9ejq6lqldlhaWsLd3b1K21RH48aNsWHDBowaNUoq++yzz9CiRQtcuHBBKluxYgVmzpyJ999/H3379kVhYSGOHj2K48ePG+yvc+fO2LFjh0GZKedJCYr23Li4uECj0SAzM9OgPDMz0+QPQKNGjfDII4/g7Nmz5a7XarXSE8DlfhL4rl3GPTZlCQFcvKivR0REyvR2lx2j6eDgYDBu888//0Tjxo2xZcsW+Pn5QavV4rfffsO5c+cwbNgwuLm5wc7ODj169DD6Yr//spRKpcKnn36Kp556CjY2Nmjbti2+++47af39l6USExPh6OiIbdu2oWPHjrCzs0NoaKhBGCsuLsYLL7wAR0dHNGnSBC+99BKioqIQFhZW6fuOiooyGL96+/ZtrFmzBlFRUQb1vvvuOzzzzDOYOHEi2rRpg86dO2PUqFF46623DOpZWFgYnEt3d3e4uLhU2g4lKBpuLC0t4efnh+TkZKmspKQEycnJBr0zD6LT6XDs2DF4eHjUVDMrVObzJ0s9IqK6RgigoMC0JTcXeOGFinu7AX2PTm6uafsrbz8P6+WXX8bbb7+NU6dOoVu3bsjPz8fgwYORnJyMQ4cOITQ0FEOHDjXo8SjPvHnz8Mwzz+Do0aMYPHgwIiMjcf369Qrr37p1CwsXLsSXX36JX3/9FRcuXMDs2bOl9e+88w6+/vprrFy5Ert370Zubi42btxo0nsaO3Ysdu3aJbX522+/hY+PDx599FGDeu7u7ti7d2/9mnksFLZmzRqh1WpFYmKiOHnypJg0aZJwdHQUGRkZQgghxo4dK15++WWp/rx588S2bdvEuXPnxIEDB8TIkSOFlZWVOHHihEnHy8nJEQBETk5Otdu+c6cQ+v+9Hrzs3FntQxER1Qq3b98WJ0+eFLdv3xZCCJGfb9rvwZpY8vOr3v6VK1cKBwcH6fXOnTsFALFx48ZKt+3cubP44IMPpNfe3t5i0aJF0msA4rXXXpNe5+fnCwBiy5YtBse6ceOG1BYA4uzZs9I2S5cuFW5ubtJrNzc3sWDBAul1cXGxaNGihRg2bFiF7Sx7nLCwMDFv3jwhhBD9+/cXixcvFhs2bBBlv/6vXLkiHnvsMQFAtGvXTkRFRYm1a9cKnU4n1YmLixNqtVrY2toaLM8991yl560q7v98lVWV72/FL5ZFRETg6tWriI2NRUZGBrp3746tW7dKg4wvXLhgcHvvGzdu4Nlnn0VGRgacnJzg5+eHPXv2oFOnTmZve1CQ/jrx5cvl/wWhUunXBwWZvWlERFQF/v7+Bq/z8/Mxd+5c/Pjjj0hPT0dxcTFu375dac9Nt27dpJ9tbW1hb2//wPu22djYoHXr1tJrDw8PqX5OTg4yMzPRs2dPab1Go4Gfn5/Jz/WaMGECZsyYgTFjxiA1NRXr16/HrvvGSnh4eCA1NRXHjx/Hr7/+ij179iAqKgqffvoptm7dKn0Ht2/f3uAyGwBZh3rISfFwAwDTpk3DtGnTyl2XkpJi8HrRokVYtGiRGVpVOY1GPwBuxAjjdaUD0RMSOJiYiOovGxsgP9+0ur/+CgweXHm9zZuBPn1MO7ZcbG1tDV7Pnj0b27dvx8KFC9GmTRtYW1tjxIgRlT4J/f5HBqhUqgcGkfLqCxmvtw0aNAiTJk3CxIkTMXToUDRp0qTCul26dEGXLl0wZcoUPP/88wgKCsIvv/yC/v37A9APJWnTpo1sbatJit/Er64LDwe++Qa4/z6Cnp768poY+U9EVFuoVICtrWnLwIH6340V3YRWpQK8vPT1TNlfTd4keffu3Rg/fjyeeuopdO3aFe7u7jh//nzNHbAcDg4OcHNzw/79+6UynU6HgwcPmrwPCwsLjBs3DikpKZgwYYLJ25VeDSkoKDC9wbVIrei5qevCw4GWLYFHHwXs7YFNm/SXothjQ0T0t7K93SqV4eX82tbb3bZtWyQlJWHo0KFQqVR4/fXXTb4UJKfp06cjPj4ebdq0QYcOHfDBBx/gxo0bVXpMwRtvvIEXX3yxwl6byZMno1mzZnj88cfh6emJ9PR0vPnmm3B1dTWY3FNcXGx0DzqVSlUrnhJwP/bcyKR0qr9WC/TrVzv+5yQiqm1Ke7ubNzcsr2293e+99x6cnJzQq1cvDB06FCEhIUazjMzhpZdewqhRozBu3DgEBgbCzs4OISEhVXpgtKWlJVxcXCoMRMHBwdi7dy+efvpptGvXDsOHD4eVlRWSk5MNAtGJEyfg4eFhsHh7e1f7PdYElZDz4l4dkJubCwcHB+Tk5Mg6EOrECaBLF8DFBbh6VbbdEhHVKnfu3EFaWhpatmxZpS/Y++l0+nuApacDHh7s7TZVSUkJOnbsiGeeeabcG9fWdQ/6fFXl+5uXpWRSOqFLgV5LIqI6R6PR93LTg/3111/46aefpDsHL1myBGlpaRg9erTSTavVeFlKJgw3REQkN7VajcTERPTo0QO9e/fGsWPHsGPHDnTs2FHpptVq7LmRCcMNERHJzcvLC7t371a6GXUOe25kUjpOi+GGiIhIWQw3MintuWlYw7OJiIhqH4YbmfCyFBERUe3AcCMThhsiIqLageFGJgw3REREtQPDjUwYboiIiGoHhhuZcLYUEVH91q9fP8ycOVN67ePjg4SEhAduo1KpsHHjxmofW679NBQMNzLhbCkioirQ6YCUFGD1av2/Ol2NHWro0KEIDQ0td92uXbugUqlw9OjRKu93//79mDRpUnWbZ2Du3Lno3r27UXl6ejoGDRok67Hul5iYCJVKVe4NAtevXw+VSgUfHx+pTKfT4e2330aHDh1gbW0NZ2dnBAQE4NNPP5XqjB8/HiqVymip6L+HXHgTP5moy8REIf7uySEiovskJQEzZgCXLv1d5umpf2R4DTw5c+LEiRg+fDguXboET09Pg3UrV66Ev78/unXrVuX9urq6ytXESrm7u5vlOLa2tsjKykJqaqrBE8E/++wztGjRwqDuvHnz8PHHH2PJkiXw9/dHbm4u/vjjD9y4ccOgXmhoKFauXGlQptVqa+5NgD03sikbbnhpioioAklJwIgRhsEGAC5f1pcnJcl+yCeeeAKurq5ITEw0KM/Pz8f69esxceJEXLt2DaNGjULz5s1hY2ODrl27YvXq1Q/c7/2Xpc6cOYM+ffrAysoKnTp1wvbt2422eemll9CuXTvY2NigVatWeP3113H37l0A+p6TefPm4ciRI1IPR2mb778sdezYMTz++OOwtrZGkyZNMGnSJOTn50vrx48fj7CwMCxcuBAeHh5o0qQJpk6dKh2rIhYWFhg9ejRWrFghlV26dAkpKSlGz7P67rvvMGXKFDz99NNo2bIlfH19MXHiRMyePdugnlarhbu7u8Hi5OT0wHZUF8ONTBhuiKhBEgIoKDBtyc0FXnih/Ov3pWUzZujrmbI/E8cBWFhYYNy4cUhMTIQos8369euh0+kwatQo3LlzB35+fvjxxx9x/PhxTJo0CWPHjsW+fftMOkZJSQnCw8NhaWmJ33//HcuWLcNLL71kVK9x48ZITEzEyZMnsXjxYixfvhyLFi0CAERERGDWrFno3Lkz0tPTkZ6ejoiICKN9FBQUICQkBE5OTti/fz/Wr1+PHTt2YNq0aQb1du7ciXPnzmHnzp34/PPPkZiYaBTwyjNhwgSsW7cOt27dAqAPXaGhoXBzczOo5+7ujp9//hlXr1416RyZlWhgcnJyBACRk5Mj635v3hRC/3+aEHfuyLprIqJa4/bt2+LkyZPi9u3b+oL8/L9/+Zl7yc83ud2nTp0SAMTOnTulsqCgIDFmzJgKtxkyZIiYNWuW9Lpv375ixowZ0mtvb2+xaNEiIYQQ27ZtExYWFuLy5cvS+i1btggAYsOGDRUeY8GCBcLPz096HRcXJ3x9fY3qld3PJ598IpycnER+mff/448/CrVaLTIyMoQQQkRFRQlvb29RXFws1Xn66adFREREhW1ZuXKlcHBwEEII0b17d/H555+LkpIS0bp1a7Fp0yaxaNEi4e3tLdU/ceKE6Nixo1Cr1aJr167iueeeE5s3bzbYZ1RUlNBoNMLW1tZgeeutt8ptg9Hnq4yqfH+z50YmZcfYsOeGiKh26dChA3r16iVdbjl79ix27dqFiRMnAtAPjn3jjTfQtWtXODs7w87ODtu2bcOFCxdM2v+pU6fg5eWFZs2aSWVlx6yUWrt2LXr37g13d3fY2dnhtddeM/kYZY/l6+sLW1tbqax3794oKSnB6dOnpbLOnTtDo9FIrz08PJCVlWXSMSZMmICVK1fil19+QUFBAQYPHmxUp1OnTjh+/Dj27t2LCRMmICsrC0OHDsW//vUvg3r9+/fH4cOHDZbnn3++Su+5qhhuZHL/gGIiogbBxgbIzzdt2bzZtH1u3mza/mxsqtTUiRMn4ttvv0VeXh5WrlyJ1q1bo2/fvgCABQsWYPHixXjppZewc+dOHD58GCEhISgqKqrqGalQamoqIiMjMXjwYPzwww84dOgQXn31VVmPUVajRo0MXqtUKpSY+Nd3ZGQk9u7di7lz52Ls2LGwsCh//pFarUaPHj0wc+ZMJCUlITExEZ999hnS0tKkOra2tmjTpo3B4uzs/PBvzAScLSUTjrkhogZJpQLK9CA80MCB+llRly+X/1egSqVfP3AgUKbHQS7PPPMMZsyYgVWrVuGLL77A5MmTobrX7b57924MGzYMY8aMAaAfQ/Pf//4XnTp1MmnfHTt2xMWLF5Geng4PDw8AwN69ew3q7NmzB97e3nj11Velsr/++sugjqWlJXSVTIvv2LEjEhMTUVBQIPXe7N69G2q1Gu3btzepvZVxdnbGk08+iXXr1mHZsmUmb1d6vgoKCmRpx8Niz41MGG6IiCqh0einewPG98sofZ2QUCPBBgDs7OwQERGBOXPmID09HePHj5fWtW3bFtu3b8eePXtw6tQpPPfcc8jMzDR538HBwWjXrh2ioqJw5MgR7Nq1yyDElB7jwoULWLNmDc6dO4f3338fGzZsMKjj4+ODtLQ0HD58GNnZ2SgsLDQ6VmRkJKysrBAVFYXjx49j586dmD59OsaOHWs06Lc6EhMTkZ2djQ4dOpS7fsSIEVi0aBF+//13/PXXX0hJScHUqVPRrl07g20KCwuRkZFhsGRnZ8vWzvIw3MiE4YaIyATh4cA33wDNmxuWe3rqy2vgPjdlTZw4ETdu3EBISIjB+JjXXnsNjz76KEJCQtCvXz+4u7sjLCzM5P2q1Wps2LABt2/fRs+ePfGvf/0Lb731lkGdJ598Ev/+978xbdo0dO/eHXv27MHrr79uUGf48OEIDQ1F//794erqWu50dBsbG2zbtg3Xr19Hjx49MGLECAwYMABLliyp2smoROk084qEhITg+++/x9ChQ6Vg16FDB/z0008Gl7G2bt0KDw8Pg+Uf//iHrG29n0qIhjVCJDc3Fw4ODsjJyYG9vb1s+y0uBkovb167BtTw5UQiIkXcuXMHaWlpaNmyJaysrB5+RzodsGsXkJ4OeHgAQUE11mNDdceDPl9V+f7mmBuZlO1hbVhxkYjoIWg0QL9+SreC6ilelpIJL0sRERHVDgw3MuF9boiIiGoHhhsZlfbeMNwQEREph+FGRgw3RNRQNLC5KGQmcn2uGG5kxHBDRPVd6e38a+quutSwlX6uNNWcOcfZUjIqHXfDP2iIqL6ysLCAjY0Nrl69ikaNGkGt5t/IJI+SkhJcvXoVNjY2FT7uwVQMNzJizw0R1XcqlQoeHh5IS0szenQAUXWp1Wq0aNFCeizGw2K4kRHDDRE1BJaWlmjbti0vTZHsLC0tZekNZLiREcMNETUUarW6encoJqpBvFgqI4YbIiIi5THcyIjhhoiISHkMNzLibCkiIiLlMdzIiD03REREymO4kRHDDRERkfIYbmTEcENERKQ8hhsZMdwQEREpj+FGRhxQTEREpDyGGxmx54aIiEh5DDcyYrghIiJSHsONjBhuiIiIlMdwIyOGGyIiIuUx3MiI4YaIiEh5DDcy4mwpIiIi5THcyIg9N0RERMpjuJERww0REZHyGG5kxHBDRESkPIYbGTHcEBERKY/hRkYMN0RERMpjuJERZ0sREREpj+FGRuy5ISIiUh7DjYwYboiIiJRXK8LN0qVL4ePjAysrKwQEBGDfvn0mbbdmzRqoVCqEhYXVbANNxHBDRESkPMXDzdq1axETE4O4uDgcPHgQvr6+CAkJQVZW1gO3O3/+PGbPno2goCAztbRyDDdERETKUzzcvPfee3j22WcRHR2NTp06YdmyZbCxscGKFSsq3Ean0yEyMhLz5s1Dq1atzNjaB2O4ISIiUp6i4aaoqAgHDhxAcHCwVKZWqxEcHIzU1NQKt5s/fz6aNm2KiRMnVnqMwsJC5ObmGiw1hbOliIiIlKdouMnOzoZOp4Obm5tBuZubGzIyMsrd5rfffsNnn32G5cuXm3SM+Ph4ODg4SIuXl1e1210R9twQEREpT/HLUlWRl5eHsWPHYvny5XBxcTFpmzlz5iAnJ0daLl68WGPtY7ghIiJSnoWSB3dxcYFGo0FmZqZBeWZmJtzd3Y3qnzt3DufPn8fQoUOlspJ7ScLCwgKnT59G69atDbbRarXQarU10HpjDDdERETKU7TnxtLSEn5+fkhOTpbKSkpKkJycjMDAQKP6HTp0wLFjx3D48GFpefLJJ9G/f38cPny4Ri85mYLhhoiISHmK9twAQExMDKKiouDv74+ePXsiISEBBQUFiI6OBgCMGzcOzZs3R3x8PKysrNClSxeD7R0dHQHAqFwJpeGGA4qJiIiUo3i4iYiIwNWrVxEbG4uMjAx0794dW7dulQYZX7hwAWp13RgaVDpbij03REREylEJ0bD6GXJzc+Hg4ICcnBzY29vLuu/Bg4EtW4CVK4Hx42XdNRERUYNWle/vutElUkdwzA0REZHyGG5kxHBDRESkPIYbGTHcEBERKY/hRkZ8/AIREZHyGG5kxJ4bIiIi5THcyIjhhoiISHkMNzJiuCEiIlIew42MGG6IiIiUx3AjI4YbIiIi5THcyIizpYiIiJTHcCMj9twQEREpj+FGRgw3REREymO4kRHDDRERkfIYbmTEcENERKQ8hhsZlYYbDigmIiJSDsONjEpnS7HnhoiISDkMNzLiZSkiIiLlMdzIiOGGiIhIeQw3MmK4ISIiUh7DjYwYboiIiJTHcCMjzpYiIiJSHsONjDhbioiISHkMNzLiZSkiIiLlMdzIiOGGiIhIeQw3MmK4ISIiUh7DjYwYboiIiJTHcCMjzpYiIiJSHsONjDhbioiISHkMNzLiZSkiIiLlMdzIiOGGiIhIeQw3MmK4ISIiUh7DjYwYboiIiJTHcCMjzpYiIiJSHsONjDhbioiISHkMNzLiZSkiIiLlMdzIiOGGiIhIeQw3MmK4ISIiUh7DjYw4oJiIiEh5DDcy4oBiIiIi5THcyIiXpYiIiJTHcCMjhhsiIiLlMdzIiOGGiIhIeQw3MmK4ISIiUh7DjYw4W4qIiEh5DDcy4mwpIiIi5THcyIiXpYiIiJTHcCMjhhsiIiLlMdzIiOGGiIhIeQw3MmK4ISIiUh7DjYw4W4qIiEh5DDcy4mwpIiIi5THcyIiXpYiIiJTHcCMjhhsiIiLlMdzIiOGGiIhIeQw3MuKAYiIiIuUx3MiIPTdERETKY7iREWdLERERKa9WhJulS5fCx8cHVlZWCAgIwL59+yqsm5SUBH9/fzg6OsLW1hbdu3fHl19+acbWVow9N0RERMpTPNysXbsWMTExiIuLw8GDB+Hr64uQkBBkZWWVW9/Z2RmvvvoqUlNTcfToUURHRyM6Ohrbtm0zc8uNMdwQEREpTyWEssNfAwIC0KNHDyxZsgQAUFJSAi8vL0yfPh0vv/yySft49NFHMWTIELzxxhuV1s3NzYWDgwNycnJgb29frbbfb+tWYNAg4JFHgIMHZd01ERFRg1aV729Fe26Kiopw4MABBAcHS2VqtRrBwcFITU2tdHshBJKTk3H69Gn06dOn3DqFhYXIzc01WGoKZ0sREREpT9Fwk52dDZ1OBzc3N4NyNzc3ZGRkVLhdTk4O7OzsYGlpiSFDhuCDDz7AP//5z3LrxsfHw8HBQVq8vLxkfQ9l8bIUERGR8hQfc/MwGjdujMOHD2P//v146623EBMTg5SUlHLrzpkzBzk5OdJy8eLFGmsXZ0sREREpz0LJg7u4uECj0SAzM9OgPDMzE+7u7hVup1ar0aZNGwBA9+7dcerUKcTHx6Nfv35GdbVaLbRaraztrrhd+n8ZboiIiJSjaM+NpaUl/Pz8kJycLJWVlJQgOTkZgYGBJu+npKQEhYWFNdHEKmG4ISIiUp6iPTcAEBMTg6ioKPj7+6Nnz55ISEhAQUEBoqOjAQDjxo1D8+bNER8fD0A/hsbf3x+tW7dGYWEhNm/ejC+//BIfffSRkm8DAMMNERFRbaB4uImIiMDVq1cRGxuLjIwMdO/eHVu3bpUGGV+4cAFq9d8dTAUFBZgyZQouXboEa2trdOjQAV999RUiIiKUegsSzpYiIiJSnuL3uTG3mrzPTWoq0KsX0Lo1cPasrLsmIiJq0OrMfW7qG86WIiIiUh7DjYw45oaIiEh5DDcyYrghIiJSHsONjBhuiIiIlMdwIyPOliIiIlIew42MOKCYiIhIeQw3MuJlKSIiIuUx3MiI4YaIiEh5DDcyYrghIiJSHsONjDigmIiISHkMNzJizw0REZHyGG5kxNlSREREymO4kRF7boiIiJTHcCMjhhsiIiLlMdzIiOGGiIhIeQw3MuJsKSIiIuUx3MiIPTdERETKq1K4effdd3H79m3p9e7du1FYWCi9zsvLw5QpU+RrXR3D2VJERETKUwlh+kUUjUaD9PR0NG3aFABgb2+Pw4cPo1WrVgCAzMxMNGvWDDqdrmZaK4Pc3Fw4ODggJycH9vb2su47MxNwd9f/zEtTRERE8qnK93eVem7uz0FVyEUNgrrM2eSpISIiUgbH3MiobLjhpSkiIiJlMNzIiD03REREyrOo6gaffvop7OzsAADFxcVITEyEi4sLAP2A4oaMPTdERETKq9KAYh8fH6hKpwQ9QFpaWrUaVZNqckBxbi7g4KD/+fZtwMpK1t0TERE1WFX5/q5Sz8358+er0656jz03REREyuOYGxkx3BARESmvSuEmNTUVP/zwg0HZF198gZYtW6Jp06aYNGmSwU39GhoOKCYiIlJelcLN/PnzceLECen1sWPHMHHiRAQHB+Pll1/G999/j/j4eNkbWVew54aIiEh5VQo3hw8fxoABA6TXa9asQUBAAJYvX46YmBi8//77WLduneyNrCsYboiIiJRXpXBz48YNuLm5Sa9/+eUXDBo0SHrdo0cPXLx4Ub7W1TFlJ5Ix3BARESmjSuHGzc1NmuZdVFSEgwcP4rHHHpPW5+XloVGjRvK2sA5hzw0REZHyqhRuBg8ejJdffhm7du3CnDlzYGNjg6CgIGn90aNH0bp1a9kbWVew54aIiEh5VbrPzRtvvIHw8HD07dsXdnZ2SExMhKWlpbR+xYoVGDhwoOyNrEvUan2w4WwpIiIiZVQp3Li4uODXX39FTk4O7OzsoNFoDNavX78ejRs3lrWBdU1puGHPDRERkTKqFG4mTJhgUr0VK1Y8VGPqg9JxNww3REREyqhSuElMTIS3tzceeeQRVOGRVA1K6bgbhhsiIiJlVCncTJ48GatXr0ZaWhqio6MxZswYODs711Tb6iT23BARESmrSrOlli5divT0dPzf//0fvv/+e3h5eeGZZ57Btm3b2JNzD8MNERGRsqr84EytVotRo0Zh+/btOHnyJDp37owpU6bAx8cH+fn5NdHGOqU03DDrERERKaNaTwVXq9VQqVQQQkCn08nVpjqNPTdERETKqnK4KSwsxOrVq/HPf/4T7dq1w7Fjx7BkyRJcuHABdnZ2NdHGOoUDiomIiJRVpQHFU6ZMwZo1a+Dl5YUJEyZg9erVcHFxqam21UnsuSEiIlKWSlRhJLBarUaLFi3wyCOPQFX2WQP3SUpKkqVxNSE3NxcODg7IycmBvb297Pt3dQWys4Hjx4HOnWXfPRERUYNUle/vKvXcjBs37oGhhjigmIiISGlVvokfPRgvSxERESmrWrOlyBjDDRERkbIYbmTG2VJERETKYriRGXtuiIiIlMVwIzOGGyIiImUx3MiMs6WIiIiUxXAjM/bcEBERKYvhRmYMN0RERMpiuJEZZ0sREREpi+FGZuy5ISIiUhbDjcwYboiIiJTFcCMzzpYiIiJSVq0IN0uXLoWPjw+srKwQEBCAffv2VVh3+fLlCAoKgpOTE5ycnBAcHPzA+ubGnhsiIiJlKR5u1q5di5iYGMTFxeHgwYPw9fVFSEgIsrKyyq2fkpKCUaNGYefOnUhNTYWXlxcGDhyIy5cvm7nl5WO4ISIiUpZKCGUvoAQEBKBHjx5YsmQJAKCkpAReXl6YPn06Xn755Uq31+l0cHJywpIlSzBu3LhK6+fm5sLBwQE5OTmwt7evdvvv9+ijwKFDwJYtQGio7LsnIiJqkKry/a1oz01RUREOHDiA4OBgqUytViM4OBipqakm7ePWrVu4e/cunJ2da6qZVcKeGyIiImVZKHnw7Oxs6HQ6uLm5GZS7ubnhzz//NGkfL730Epo1a2YQkMoqLCxEYWGh9Do3N/fhG2wChhsiIiJlKT7mpjrefvttrFmzBhs2bICVlVW5deLj4+Hg4CAtXl5eNdomzpYiIiJSlqLhxsXFBRqNBpmZmQblmZmZcHd3f+C2CxcuxNtvv42ffvoJ3bp1q7DenDlzkJOTIy0XL16Upe0VYc8NERGRshQNN5aWlvDz80NycrJUVlJSguTkZAQGBla43bvvvos33ngDW7duhb+//wOPodVqYW9vb7DUJIYbIiIiZSk65gYAYmJiEBUVBX9/f/Ts2RMJCQkoKChAdHQ0AGDcuHFo3rw54uPjAQDvvPMOYmNjsWrVKvj4+CAjIwMAYGdnBzs7O8XeRyk+W4qIiEhZioebiIgIXL16FbGxscjIyED37t2xdetWaZDxhQsXoFb/3cH00UcfoaioCCNGjDDYT1xcHObOnWvOppeLPTdERETKUjzcAMC0adMwbdq0ctelpKQYvD5//nzNN6gaOKCYiIhIWXV6tlRtxJ4bIiIiZTHcyIzhhoiISFkMNzJjuCEiIlIWw43MOFuKiIhIWQw3MmPPDRERkbIYbmTG2VJERETKYriRGXtuiIiIlMVwIzOGGyIiImUx3MiMA4qJiIiUxXAjM/bcEBERKatWPH6hXtDpgF27EHQpHdfgAVEcBECjdKuIiIgaHIYbOSQlATNmAJcuYSaAmQDyYj2B5ouB8HBl20ZERNTA8LJUdSUlASNGAJcuGRTb3bysL09KUqhhREREDRPDTXXodPoem3JuaqPCvbKZM/X1iIiIyCwYbqpj1y6jHhsDQgAXL+rrERERkVkw3FRHerq89YiIiKjaGG6qw8ND3npERERUbQw31REUBHh6/n3nvvupVICXl74eERERmQXDTXVoNMDixfqf7ws4AvdeJyTo6xEREZFZMNxUV3g48M03Rpeech089eW8zw0REZFZMdzIITwcOH5cejkQ2/DBv9MYbIiIiBTAcCMXW1vpx33oCR0fvUBERKQIhhu5NGokjbuxwp3y7utHREREZsBwIxeVCrCyAqAPN3wqOBERkTIYbuR0L9xoUchwQ0REpBCGGzmx54aIiEhxDDdy0moBMNwQEREpieFGTuy5ISIiUhzDjZzKhBvOliIiIlIGw42c2HNDRESkOIYbOXG2FBERkeIYbuTEAcVERESKY7iREy9LERERKY7hRk4MN0RERIpjuJETZ0sREREpjuFGThxQTEREpDiGGznxshQREZHiGG7kxNlSREREimO4kRN7boiIiBTHcCMnDigmIiJSHMONnNhzQ0REpDiGGzlxthQREZHiGG7kVGZA8f/+B6SkADqdsk0iIiJqaBhuZLTv2N+XpfbsAfr3B3x8gKQkZdtFRETUkDDcyCQpCXgn4e9wU+ryZWDECAYcIiIic2G4kYFOB8yYAdyGcbgpnTU1cyYvUREREZkDw40Mdu0CLl0C7pQTbgB9wLl4UV+PiIiIahbDjQzS0/X/loYbLQofWI+IiIhqDsONDDw89P8W4u/ZUg+qR0RERDWH4UYGQUGApydQWMFlKZUK8PLS1yMiIqKaxXAjA40GWLy4/DE3KpX+34QEfT0iIiKqWQw3MgkPB97/xDjceHoC33yjX09EREQ1j+FGRoPD9eGmEYph1UiHnTuBtDQGGyIiInNiuJHTvccvAIDqbiGCgngpioiIyNwYbuR078GZgP7SVEGBgm0hIiJqoBhu5GRhAXGvq8YKd5Cfr3B7iIiIGiCGG5mprP4eVMxwQ0REZH6Kh5ulS5fCx8cHVlZWCAgIwL59+yqse+LECQwfPhw+Pj5QqVRISEgwX0NNVSbc5OUp3BYiIqIGSNFws3btWsTExCAuLg4HDx6Er68vQkJCkJWVVW79W7duoVWrVnj77bfh7u5u5taayOrvRzCw54aIiMj8FA037733Hp599llER0ejU6dOWLZsGWxsbLBixYpy6/fo0QMLFizAyJEjoS0zM6lW0f79CAb23BAREZmfYuGmqKgIBw4cQHBw8N+NUasRHByM1NRU2Y5TWFiI3Nxcg6VGccwNERGRohQLN9nZ2dDpdHBzczMod3NzQ0ZGhmzHiY+Ph4ODg7R4eXnJtu9yMdwQEREpSvEBxTVtzpw5yMnJkZaLFy/W7AE5oJiIiEhRFkod2MXFBRqNBpmZmQblmZmZsg4W1mq15h2fw54bIiIiRSnWc2NpaQk/Pz8kJydLZSUlJUhOTkZgYKBSzaq+e0FKi0L23BARESlAsZ4bAIiJiUFUVBT8/f3Rs2dPJCQkoKCgANHR0QCAcePGoXnz5oiPjwegH4R88uRJ6efLly/j8OHDsLOzQ5s2bRR7HwbYc0NERKQoRcNNREQErl69itjYWGRkZKB79+7YunWrNMj4woULUKv/7ly6cuUKHnnkEen1woULsXDhQvTt2xcpKSnmbn75yoSb6ww3REREZqdouAGAadOmYdq0aeWuuz+w+Pj4QAhhhlZVAwcUExERKarez5YyO16WIiIiUhTDjdwsLQEAj+AQ2l5OAXQ6ZdtDRETUwDDcyCkpCbj36Ihh+A4fnuoP+Pjoy4mIiMgsGG7kkpQEjBgBo4E2ly/ryxlwiIiIzILhRg46HTBjBlDeYOfSspkzeYmKiIjIDBhu5LBrF3DpUsXrhQAuXtTXIyIiohrFcCOH9HR56xEREdFDY7iRg4eHvPWIiIjooTHcyCEoCPD0BFSq8terVICXl74eERER1SiGGzloNMDixeWuEqWBJyFBX4+IiIhqFMONXMLDgW++Ae49F6tUUVNPfXl4uEINIyIialgYbuQUHg7s2wcA0EGFfvgZS2elQTeMwYaIiMhcGG5ktum3JgAADQT+QA/M+j8Nb1JMRERkRgw3MkpKAp6KtEEx9GNrHJADgDcpJiIiMieGG5lINymGCjlwAPB3uOFNiomIiMyH4UYmZW9SfH+4AXiTYiIiInNhuJFJ2ZsPl4YbR9x8YD0iIiKSH8ONTMrefPgmHAEY9tyUV4+IiIjkx3Ajk7I3KS7vshRvUkxERGQeDDcyKXuT4vvDDW9STEREZD4MNzIqvUmxzs4w3HjyJsVERERmY6F0A+qb8HCg5IAD8B99uGnaFEhLY48NERGRubDnpgaonR0B6GdL3brFYENERGRODDc1weHvy1L5+UBRkcLtISIiakAYbmqCg+GYm+vXlWwMERFRw8JwUxPuhRtnjT7cXLumZGOIiIgaFoabmnAv3Diq2HNDRERkbgw3NcHREQBgL9hzQ0REZG4MNzXhXs+NnS4HKpSw54aIiMiMGG5qwr1wo4aAHfLZc0NERGRGDDc1wcoKaNQIgH7GFHtuiIiIzIfhpiaoVAbTwdlzQ0REZD4MNzVBpwMsLQEA/bATN6/pFG4QERFRw8FwI7ekJMDHB7hyBQCwBC9gyY8++nIiIiKqcQw3ckpKAkaMAC5dMih2KbysL2fAISIiqnEMN3LR6YAZMwAhjFapca9s5kx9PSIiIqoxDDdy2bXLqMfGgBDAxYv6ekRERFRjGG7kkp5uUrV9m0yrR0RERA+H4UYuHh4mVXspwYNDb4iIiGoQw41cgoIAT08Ilarc1SVQ4QK8sAtBHHpDRERUgxhu5KLRAIsXA0IfZMoquffvTCRABw2H3hAREdUghhs5hYfjt5nf4DKaGxTfgi3iMBebMEwqM3GIDhEREVURw43MdMPC4YPzeB3zcAtWAAA7FOANxOE8fPAU9ANuTByiQ0RERFXEcCOzoCBgQpNNmIe5sMYdg3XNcRnfYASebZKEoCCFGkhERFTPMdzITAMdFmMGAIH7hxaX3swvATOhAUcUExER1QSGG7nt2gWba5cqPLFqCNhc44hiIiKimsJwIzfezI+IiEhRDDdy4838iIiIFMVwI7dKb+YHXIAnb+ZHRERUQxhu5PaAm/kB+hNug9t4Ept4Mz8iIqIawHBTE+7dzO86nMtd7Yzr+AYj8BSSeDM/IiIimTHc1BDdE8NwG9b3Jn8bKjsl/OxpXpciIiKSE8NNDQnCLnjhUjkXpvTUEGiBi0ietwvr15u1aURERPUaw00N0WSZdr1pCpZgdIQOa9fWcIOIiIgaCAulG1BvmTgl/Bl8i8HCHstH/gv/fvsp+M0MQvYNDVxdgebN9ZOvNJoabisREVE9ohJClDcspN7Kzc2Fg4MDcnJyYG9vX3MH0ukAHx+ISxVfmirPNThhI4ZhJx6HC67htnUT9O10FR7aaxAAVE2c0cijKXRZ16Bp2kT69276VeBa9evIvb9qHbO5OzI0zVHo1wvaA3vgVnwZxVcy9XVUamR36YeiwH7IvqFBkybAtWtAkybAVf1uAADOzkDTpn+vu3YNBsER0M9YS0/X51GzhEmdToGDEhHVbVX5/ma4qUlJSRDDh1cp3JAxHVTQlDs0G8iFLdbjaSkMZqMJXHEVTaAPSdfhjKtoKq0rW8dNrU9AmSV/17lt3QR9u1xDi0dqJui5/Hc32qZth1VRnvQe7tg44WznYbj5yOOKhMs6F3p5LngueC5q/bmw8HCFbbvm6DolCBpLef54q9L3t6gFlixZIry9vYVWqxU9e/YUv//++wPrr1u3TrRv315otVrRpUsX8eOPP5p8rJycHAFA5OTkVLfZJtHNmCkEwIULFy5cuDS45bLGU6S++K0s36dV+f5WfEDx2rVrERMTg7i4OBw8eBC+vr4ICQlBVlZWufX37NmDUaNGYeLEiTh06BDCwsIQFhaG48ePm7nlplGHDVO6CURERIpw111CzwUjsPf/zPu8IcUvSwUEBKBHjx5YsmQJAKCkpAReXl6YPn06Xn75ZaP6ERERKCgowA8//CCVPfbYY+jevTuWLVtW6fHMelkKeOixN0RERPVBCVRI13jC/VZatS5RVeX7W9Gem6KiIhw4cADBwcFSmVqtRnBwMFJTU8vdJjU11aA+AISEhFRYv7CwELm5uQaLWd17HIMKqGDUCBERUf2lhkBz3UUc+9B8zxtSNNxkZ2dDp9PBzc3NoNzNzQ0ZGRnlbpORkVGl+vHx8XBwcJAWLy8veRpfFeHhwLffQuVc/uMYiIiI6rtb58z3vCHFx9zUtDlz5iAnJ0daLl68qExDwsOBrCwgIoI9OERE1ODYtDbt/m9yUDTcuLi4QKPRIDMz06A8MzMT7u7u5W7j7u5epfparRb29vYGi2I0GmDNGqjWr4dwcVWuHURERGZSAhUua7zQdUqQ2Y6paLixtLSEn58fkpOTpbKSkhIkJycjMDCw3G0CAwMN6gPA9u3bK6xfK40YAVVGOrBzJ0q++ArpodEotOUlKyIiql9K7v17MSZBtvvdmELxxy/ExMQgKioK/v7+6NmzJxISElBQUIDo6GgAwLhx49C8eXPEx8cDAGbMmIG+ffvi//2//4chQ4ZgzZo1+OOPP/DJJ58o+TaqTqMB+vWDGoDH2Mi/71p7+TJw9SpKnJrgf/uvIbO4CYqu1O+bQpVX17XoMtqd+h7aguvSKbtj44QznYYCeflGN8Kri0qgkp4QT0RUH6VrvHAxJgGPvRtu1uMqHm4iIiJw9epVxMbGIiMjA927d8fWrVulQcMXLlyAWv13B1OvXr2watUqvPbaa3jllVfQtm1bbNy4EV26dFHqLcjjXtgppQbQJgpoo1iDaoH7HlNgFRSErqWPKbgvDFbl+QslTk1wZs9VpJ+4BiGAZl2d0TqgKc4f0IfJ4swaDnr3Hh1xNyBIeqxESebVWn/H0YZwzNraLp4LnovacsyHuUNxczP22JRS/D435mb2+9wQERFRtdWZ+9wQERERyY3hhoiIiOoVhhsiIiKqVxhuiIiIqF5huCEiIqJ6heGGiIiI6hWGGyIiIqpXGG6IiIioXmG4ISIionpF8ccvmFvpDZlzc3MVbgkRERGZqvR725QHKzS4cJOXp3/YopeXl8ItISIioqrKy8uDg4PDA+s0uGdLlZSU4MqVK2jcuDFUKpUs+8zNzYWXlxcuXrzI51XVMJ5r8+B5Nh+ea/PgeTafmjrXQgjk5eWhWbNmBg/ULk+D67lRq9Xw9PSskX3b29vzfxoz4bk2D55n8+G5Ng+eZ/OpiXNdWY9NKQ4oJiIionqF4YaIiIjqFYYbGWi1WsTFxUGr1SrdlHqP59o8eJ7Nh+faPHiezac2nOsGN6CYiIiI6jf23BAREVG9wnBDRERE9QrDDREREdUrDDdERERUrzDcyGDp0qXw8fGBlZUVAgICsG/fPqWbVKfNnTsXKpXKYOnQoYO0/s6dO5g6dSqaNGkCOzs7DB8+HJmZmQq2uG749ddfMXToUDRr1gwqlQobN240WC+EQGxsLDw8PGBtbY3g4GCcOXPGoM7169cRGRkJe3t7ODo6YuLEicjPzzfju6gbKjvX48ePN/qMh4aGGtThua5cfHw8evTogcaNG6Np06YICwvD6dOnDeqY8vviwoULGDJkCGxsbNC0aVO8+OKLKC4uNudbqfVMOdf9+vUz+lw///zzBnXMda4Zbqpp7dq1iImJQVxcHA4ePAhfX1+EhIQgKytL6abVaZ07d0Z6erq0/Pbbb9K6f//73/j++++xfv16/PLLL7hy5QrCw8MVbG3dUFBQAF9fXyxdurTc9e+++y7ef/99LFu2DL///jtsbW0REhKCO3fuSHUiIyNx4sQJbN++HT/88AN+/fVXTJo0yVxvoc6o7FwDQGhoqMFnfPXq1Qbrea4r98svv2Dq1KnYu3cvtm/fjrt372LgwIEoKCiQ6lT2+0Kn02HIkCEoKirCnj178PnnnyMxMRGxsbFKvKVay5RzDQDPPvuswef63XffldaZ9VwLqpaePXuKqVOnSq91Op1o1qyZiI+PV7BVdVtcXJzw9fUtd93NmzdFo0aNxPr166WyU6dOCQAiNTXVTC2s+wCIDRs2SK9LSkqEu7u7WLBggVR28+ZNodVqxerVq4UQQpw8eVIAEPv375fqbNmyRahUKnH58mWztb2uuf9cCyFEVFSUGDZsWIXb8Fw/nKysLAFA/PLLL0II035fbN68WajVapGRkSHV+eijj4S9vb0oLCw07xuoQ+4/10II0bdvXzFjxowKtzHnuWbPTTUUFRXhwIEDCA4OlsrUajWCg4ORmpqqYMvqvjNnzqBZs2Zo1aoVIiMjceHCBQDAgQMHcPfuXYNz3qFDB7Ro0YLnvBrS0tKQkZFhcF4dHBwQEBAgndfU1FQ4OjrC399fqhMcHAy1Wo3ff//d7G2u61JSUtC0aVO0b98ekydPxrVr16R1PNcPJycnBwDg7OwMwLTfF6mpqejatSvc3NykOiEhIcjNzcWJEyfM2Pq65f5zXerrr7+Gi4sLunTpgjlz5uDWrVvSOnOe6wb34Ew5ZWdnQ6fTGfyHAgA3Nzf8+eefCrWq7gsICEBiYiLat2+P9PR0zJs3D0FBQTh+/DgyMjJgaWkJR0dHg23c3NyQkZGhTIPrgdJzV95nuXRdRkYGmjZtarDewsICzs7OPPdVFBoaivDwcLRs2RLnzp3DK6+8gkGDBiE1NRUajYbn+iGUlJRg5syZ6N27N7p06QIAJv2+yMjIKPdzX7qOjJV3rgFg9OjR8Pb2RrNmzXD06FG89NJLOH36NJKSkgCY91wz3FCtM2jQIOnnbt26ISAgAN7e3li3bh2sra0VbBmRPEaOHCn93LVrV3Tr1g2tW7dGSkoKBgwYoGDL6q6pU6fi+PHjBuPzqGZUdK7Ljgnr2rUrPDw8MGDAAJw7dw6tW7c2axt5WaoaXFxcoNFojEbeZ2Zmwt3dXaFW1T+Ojo5o164dzp49C3d3dxQVFeHmzZsGdXjOq6f03D3os+zu7m40UL64uBjXr1/nua+mVq1awcXFBWfPngXAc11V06ZNww8//ICdO3fC09NTKjfl94W7u3u5n/vSdWSoonNdnoCAAAAw+Fyb61wz3FSDpaUl/Pz8kJycLJWVlJQgOTkZgYGBCrasfsnPz8e5c+fg4eEBPz8/NGrUyOCcnz59GhcuXOA5r4aWLVvC3d3d4Lzm5ubi999/l85rYGAgbt68iQMHDkh1fv75Z5SUlEi/xOjhXLp0CdeuXYOHhwcAnmtTCSEwbdo0bNiwAT///DNatmxpsN6U3xeBgYE4duyYQZjcvn077O3t0alTJ/O8kTqgsnNdnsOHDwOAwefabOda1uHJDdCaNWuEVqsViYmJ4uTJk2LSpEnC0dHRYDQ4Vc2sWbNESkqKSEtLE7t37xbBwcHCxcVFZGVlCSGEeP7550WLFi3Ezz//LP744w8RGBgoAgMDFW517ZeXlycOHTokDh06JACI9957Txw6dEj89ddfQggh3n77beHo6Cg2bdokjh49KoYNGyZatmwpbt++Le0jNDRUPPLII+L3338Xv/32m2jbtq0YNWqUUm+p1nrQuc7LyxOzZ88WqampIi0tTezYsUM8+uijom3btuLOnTvSPniuKzd58mTh4OAgUlJSRHp6urTcunVLqlPZ74vi4mLRpUsXMXDgQHH48GGxdetW4erqKubMmaPEW6q1KjvXZ8+eFfPnzxd//PGHSEtLE5s2bRKtWrUSffr0kfZhznPNcCODDz74QLRo0UJYWlqKnj17ir179yrdpDotIiJCeHh4CEtLS9G8eXMREREhzp49K62/ffu2mDJlinBychI2NjbiqaeeEunp6Qq2uG7YuXOnAGC0REVFCSH008Fff/114ebmJrRarRgwYIA4ffq0wT6uXbsmRo0aJezs7IS9vb2Ijo4WeXl5Cryb2u1B5/rWrVti4MCBwtXVVTRq1Eh4e3uLZ5991ugPIp7rypV3jgGIlStXSnVM+X1x/vx5MWjQIGFtbS1cXFzErFmzxN27d838bmq3ys71hQsXRJ8+fYSzs7PQarWiTZs24sUXXxQ5OTkG+zHXuVbdazQRERFRvcAxN0RERFSvMNwQERFRvcJwQ0RERPUKww0RERHVKww3REREVK8w3BAREVG9wnBDRERE9QrDDRE1SCqVChs3blS6GURUAxhuiMjsxo8fD5VKZbSEhoYq3TQiqgcslG4AETVMoaGhWLlypUGZVqtVqDVEVJ+w54aIFKHVauHu7m6wODk5AdBfMvroo48waNAgWFtbo1WrVvjmm28Mtj927Bgef/xxWFtbo0mTJpg0aRLy8/MN6qxYsQKdO3eGVquFh4cHpk2bZrA+OzsbTz31FGxsbNC2bVt899130robN24gMjISrq6usLa2Rtu2bY3CGBHVTgw3RFQrvf766xg+fDiOHDmCyMhIjBw5EqdOnQIAFBQUICQkBE5OTti/fz/Wr1+PHTt2GISXjz76CFOnTsWkSZNw7NgxfPfdd2jTpo3BMebNm4dnnnkGR48exeDBgxEZGYnr169Lxz958iS2bNmCU6dO4aOPPoKLi4v5TgARPTzZH8VJRFSJqKgoodFohK2trcHy1ltvCSH0TyB+/vnnDbYJCAgQkydPFkII8cknnwgnJyeRn58vrf/xxx+FWq2Wnq7drFkz8eqrr1bYBgDitddek17n5+cLAGLLli1CCCGGDh0qoqOj5XnDRGRWHHNDRIro378/PvroI4MyZ2dn6efAwECDdYGBgTh8+DAA4NSpU/D19YWtra20vnfv3igpKcHp06ehUqlw5coVDBgw4IFt6Natm/Szra0t7O3tkZWVBQCYPHkyhg8fjoMHD2LgwIEICwtDr169Huq9EpF5MdwQkSJsbW2NLhPJxdra2qR6jRo1MnitUqlQUlICABg0aBD++usvbN68Gdu3b8eAAQMwdepULFy4UPb2EpG8OOaGiGqlvXv3Gr3u2LEjAKBjx444cuQICgoKpPW7d++GWq1G+/bt0bhxY/j4+CA5OblabXB1dUVUVBS++uorJCQk4JNPPqnW/ojIPNhzQ0SKKCwsREZGhkGZhYWFNGh3/fr18Pf3xz/+8Q98/fXX2LdvHz777DMAQGRkJOLi4hAVFYW5c+fi6tWrmD59OsaOHQs3NzcAwNy5c/H888+jadOmGDRoEPLy8rB7925Mnz7dpPbFxsbCz88PnTt3RmFhIX744QcpXBFR7cZwQ0SK2Lp1Kzw8PAzK2rdvjz///BOAfibTmjVrMGXKFHh4eGD16tXo1KkTAMDGxgbbtm3DjBkz0KNHD9jY2GD48OF47733pH1FRUXhzp07WLRoEWbPng0XFxeMGDHC5PZZWlpizpw5OH/+PKytrREUFIQ1a9bI8M6JqKaphBBC6UYQEZWlUqmwYcMGhIWFKd0UIqqDOOaGiIiI6hWGGyIiIqpXOOaGiGodXi0noupgzw0RERHVKww3REREVK8w3BAREVG9wnBDRERE9QrDDREREdUrDDdERERUrzDcEBERUb3CcENERET1CsMNERER1Sv/H/3nbZyJcUQcAAAAAElFTkSuQmCC", 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", 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" ] @@ -776,17 +3824,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" - ] - } - ], + "outputs": [], "source": [ "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", "xmin, xmax = [7, 306], [40, 1000]\n", @@ -816,19 +3856,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 1s 3ms/step\n" + "\r", + "\u001b[1m 1/13\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m13/13\u001b[0m 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", + "image/png": 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", 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K4WdNRFR3qdVqFBUVIS8vD+vXry93/LRp0+Dt7V2l1yzr729DdWYmzFBubi7WrFmD6OhouVB6y5YtaNWqFbZu3YrQ0FCEhIRg8uTJyM3NlZ+XmpqKmJgYo3PFxsYiNTUVgK7lw4EDB4zGKBQKxMTEyGMOHDiA4uJiozHt27dHixYt5DGpqano3LmzHMD0r5Ofn49jx45ZfV+FhYXIz883+qpvJk6cCEmSIEkSHB0d4e/vjwEDBuCTTz6pUKuNVatWwdPTs+YulIiI6jy1Wo1ly5bho48+simAAcDly5dr+KpK1akQNnv2bLi5ucHb2xvnz5/Hd999Jx/73//+h3PnzuHrr7/GZ599hlWrVuHAgQNGS5Y5OTlGwQgA/P39kZ+fj5s3b+LKlSsoKSmxOCYnJ0c+h5OTk1kAMB1j6Rz6Y9YsWLAASqVS/goODrbxk6k4e07JDho0CNnZ2Th79iy2b9+O/v3748knn8TQoUNrpG6OiIgaJsNSIlsVFxfXwJVYZtcQNmfOHHlWxNrXyZMn5fEzZ87EoUOHsHPnTjg4OODBBx+EfjVVq9WisLAQn332Gfr06YN77rkHK1euxO7du3Hq1Cl7vcUKeeaZZ6DRaOSvCxcu1MjrGP7LwNrXsmXLaiyIOTs7IyAgAM2aNUNERASeffZZfPfdd9i+fbt8o8Tbb7+Nzp07w83NDcHBwfjvf/+La9euAdDdXPHQQw9Bo9HIPycvvPACAODzzz9Hjx494O7ujoCAAPznP/+p1P+ERERUt6nVaptnv+zFrn3CnnrqKUycOLHMMa1atZJ/7+PjAx8fH7Rt2xYdOnRAcHAwfvvtN6hUKgQGBqJRo0Zo27atPL5Dhw4AdHcqtmvXDgEBAWZ3MV68eBEeHh5wcXGBg4MDHBwcLI4JCAgAAAQEBMhry4azYaZjTO+o1J9TP8YSZ2dnODs7l/l5VAdbtzm6k9sh3XvvvejatSs2bdqEyZMnQ6FQ4J133kFoaCj+97//4b///S9mzZqF999/H9HR0ViyZAnmz58vB+wmTZoA0P0L5uWXX0a7du1w6dIlzJgxAxMnTsT3339/x94LERHZX13Y0s+uIczX1xe+vr6Veq6+fqiwsBAA0KtXL9y+fRvp6ekICwsDAPz1118AgJYtWwIAVCqV2V/GSUlJUKlUAHT9oCIjI7Fr1y6MGDFCfp1du3Zh2rRpAIDIyEg4Ojpi165diIuLAwCcOnUK58+fl8+jUqnw6quv4tKlS/Dz85Nfx8PDA+Hh4ZV6vw1B+/btceTIEQBAYmKi/HhISAheeeUVPProo3j//ffh5OQEpVIJSZLMQu2kSZPk37dq1QrvvPMO7rrrLly7dk0OakREVD+lp6fj4sWLuH37ttXaLo3GHbm53vDyUkOpLLjDV2isTnTMT0tLw759+9C7d280bdoU6enpmDdvHsLCwuTgExMTg4iICEyaNAlLliyBVqvF1KlTMWDAAHl27NFHH8WyZcswa9YsTJo0CT/++CPWr1+Pbdu2ya81Y8YMTJgwAT169MDdd9+NJUuW4Pr163jooYcAAEqlEgkJCZgxYwa8vLzg4eGBxx9/HCqVCj179gQADBw4EOHh4XjggQewaNEi5OTk4LnnnsPUqVPvyExXXSWEkHtyJScnY8GCBTh58iTy8/Nx+/Zt3Lp1Czdu3Chzh4ADBw7ghRdewB9//IGrV6/KYf38+fMMwERE9ZD+7scLFy5g+/btZY49eLA7tmwZCiEUkCQthg3bioiIQ3foSs3ViRDm6uqKTZs24fnnn8f169cRGBiIQYMG4bnnnpNDjUKhwJYtW/D444+jb9++cHNzw+DBg/HWW2/J5wkNDcW2bdswffp0LF26FM2bN8eKFSsQGxsrj4mPj8fly5cxf/585OTkoFu3btixY4dRof3ixYuhUCgQFxeHwsJCxMbG4v3335ePOzg4YOvWrXjsscegUqng5uaGCRMm4KWXXroDn1bddeLECYSGhuLs2bMYOnQoHnvsMbz66qvw8vLCnj17kJCQgKKiIqsh7Pr164iNjUVsbCzWrFkDX19fnD9/HrGxsXViWpqIiComPT0dX3zxRbnjNBp3XLgQjM2bh0JfDi+EAps3D4WfXw6aN8+u4Su1rE6EsM6dO+PHH38sd1xQUBA2btxY5ph77rkHhw6VnXqnTZsmLz9a0rhxY7z33nt47733rI5p2bIl65Aq4Mcff8Sff/6J6dOn48CBA9BqtXjrrbfkjclNiyudnJzMthE6efIk1Go1Fi5cKN9Zun///jvzBoiI6I5Sq9U2BTDD2S9zCqxcOdloRqxRozsXjepUiwqqHwoLC5GTk4O///4bBw8exGuvvYb7778fQ4cOxYMPPojWrVujuLgY7777Lv73v//h888/x/Lly43OERISgmvXrmHXrl24cuUKbty4gRYtWsDJyUl+3ubNm/Hyyy/b6V0SEVFNUKvVOHnyZJl9N/UyMwOxebO1AKYjhAJbtgyFRuMOAHIt953AEEZ33I4dOxAYGIiQkBAMGjQIu3fvxjvvvIPvvvsODg4O6Nq1K95++228/vrr6NSpE9asWYMFCxYYnSM6OhqPPvoo4uPj4evri0WLFsHX1xerVq3C119/jfDwcCxcuBBvvvmmnd4lERFVN32LpXXr1mH37t1ljj14sDtWrpwMW6KOEAp06jSiWrrlV0Sd3baoIaipbYv0P8TludM/jLUVty0iIrI/tVqNrKwsbNq0yeoY/Z2Pjo6FWLHCWgDTxx7J6LGdOzUYMMCzWq7V1m2L6kRNGFUvb29vTJs2rcxidScnJwYwIiKyK/2djxqNBuvWrbM6TqNxR1paFFJSVNAFLy2sz4BJFh9zdPSs8vVWFENYA8WARUREtVl5qzb6Wa+srEAkJ8eY1H1VrNrKwQFo3bqSF1oFDGFERERUa6jValy+fBlnz561Osb4jkcBy7Nb5dE9z8EB+PBDoHnzyl1vVTCEERERUa1gS98vjcbdpOWELQHMUlCTsHgxMGqUfQIYwLsjiYiIqBawpe+XRuOOY8c6ltlywjIJkkkGc3CwbwADOBNGREREdqIvvM/Ly8O5c+fKHFuVJUgHB4GFCyXMmQOUlMCuS5CGGMKIiIjojrO1XRKgb7o6DKXBq2IB7MMPJSQkAGPGAGfO6Irw7R3AAIYwIiIisoPLly/bNG7vXhWSkmJQ0eJ7SdLi44+vITbWQw5czZvXjvClxxBGREREd9zVq1etHtNvuH3qVBv8+WdXVLT4XpIEFi++gYQE641SawOGMKpXfvrpJ/Tv3x9Xr16Fp6enTc8JCQlBYmIiEhMTa/TaiIgaMsP6r7y8POzcuVM+pu/55eWlRnp6a5Olx7Log5cWffr8Cn//ixg8eDAGDnRH8+ZNauidVB+GMLqjJk6ciNWrV+ORRx4x25R76tSpeP/99zFhwgSsWrXKPhdIRETVzlL9l362KyMjBAcPRhoU3NtWdN+37260bXsaxcVOmDixN9q1awsnp07w9navkfdQExjC6I4LDg7GV199hcWLF8PFxQWAbn/GtWvXokWLFna+OiIiqm6m2+QdPNjdymyXZOExS7SIjDyEUaN6ol27dnV2Fxj2CaM7LiIiAsHBwUabsG7atAktWrRA9+7d5ccKCwvxxBNPwM/PD40bN0bv3r2xb98+o3N9//33aNu2LVxcXNC/f3+LHZb37NmDPn36wMXFBcHBwXjiiSdw/fr1Gnt/RERUKj09HadPn5a/12jcK7DcaIkWw4dvhVJZUKcDGMAQRgAyM4Hdu3W/3imTJk3Cp59+Kn//ySef4KGHHjIaM2vWLGzcuBGrV6/GwYMH0bp1a8TGxiI3NxcAcOHCBYwcORLDhg3D4cOHMXnyZMyZM8foHOnp6Rg0aBDi4uJw5MgRrFu3Dnv27MG0adNq/k0SETUgarUa2dnZRl+7d+/GF198gd27dwPQtZrYteteVC6AafHSSyexf/9lLF9+F6ZNm1anAxjA5cgGb+VKYMoUQKsFFArgo4+AhISaf93x48fjmWeekZvz7d27F1999RV++uknAMD169fxwQcfYNWqVRg8eDAA4OOPP0ZSUhJWrlyJmTNn4oMPPkBYWBjeeustAEC7du3w559/4vXXX5dfZ8GCBRg3bpxcdN+mTRu888476NevHz744AM0bty45t8sEVE9Z0vPr/XrR+H48XBULIDp6sMkSYthw7biv//tVeeDlyGGsAYsM7M0gAG6Xx95BIiNrfk+Kr6+vhgyZAhWrVoFIQSGDBkCHx8f+Xh6ejqKi4vRq1cv+TFHR0fcfffdOHHiBADgxIkTiIqKMjqvSqUy+v6PP/7AkSNHsGbNGvkxIQS0Wi0yMjLQoUOHmnh7REQNimnNF1BaeA8A5883LyeAWSrG12LAgGTcf38zhIc7oUuX+hXAAIawBu306dIApldSousmfCea2U2aNEleFnzvvfdq5DWuXbuGRx55BE888YTZMd4EQERUdWq1GkePHjV6zLzwvrw7Hs2PjRq1AS+91Lle/2OZIawBa9NGtwRpGMQcHHTbOdwJgwYNQlFRESRJQmxsrNGxsLAwODk5Ye/evWjZsiUAoLi4GPv27ZOXFjt06IDNmzcbPe+3334z+j4iIgLHjx9H6zv1poiIGgi1Wo3Lly9j3bp1Ro/rthgaCuNgVVYA0/8lVFqmLklaBAdnws/vvuq63FqJIawBa95cVwP2yCP22dDUwcFBXlp0cHAwOubm5obHHnsMM2fOhJeXF1q0aIFFixbhxo0bSPinaO3RRx/FW2+9hZkzZ2Ly5Mk4cOCAWX+x2bNno2fPnpg2bRomT54MNzc3HD9+HElJSTbvWUZERMbS09PxxRdfyN/rm61mZQUiKWkAyq/7Mq71AiBvzi1JWrz0Ug4ee2xCvVt+NMUQ1sAlJOhqwOy1oamHh/UtJRYuXAitVosHHngABQUF6NGjB3744Qc0bdoUgG45cePGjZg+fTreffdd3H333XjttdcwadIk+RxdunTBzz//jLlz56JPnz4QQiAsLAzx8fE1/t6IiOoTw47369evB6ALX2lpUUhJUUE3k1XWsmNpd/u7705Dhw6n4OWVC6WyAAAQFnYGubleGDOmBwYN6lTzb6gWkIQQwt4XQZbl5+dDqVRCo9GYhZVbt24hIyMDoaGhvMOvhvGzJqKGzNqy48GD3eXZq/LpiuyDgrKMgpdejx490KJFC7i6uiIsLKwar94+yvr72xBnwoiIiMgia60nNBr3CgQwgbFjv0S7dmesjmjfvn29CF8VxRBGREREFhm2ntDXfTk6FuL8+ZY2BjAAkODkVGz16PDhwxtkAAMYwoiIiMiEvv7rypUrAEyXHvW1XbZttC1JWnh55Ro9Nnr0aHh6esLJyaneF9+XhSGMiIiIZIZLkLqGq+EmS4+Sya+WGN/9aFgDNnr06Hrd+6siGMLqON5XUfP4GRNRQ6FWq3Hq1CkAFS281xOIjNyP7t0PobjYyWIRvp+fXzVecd3GEFZH6ftqFRUVwcXFxc5XU7/payJMe5kREdUH+qVHjUYj3wGp0bibdLwvnyRpEROTjF69UuXHOnXqhMDAQHh5eUGpVDb45UdTDGF1VKNGjeDq6orLly/D0dERCkVF/qVCttJqtbh8+TJcXV3RqBH/dyGiuk+tVuPSpUu4ffs2CgoKkJSUZDbml1/6wNYAJklaxMVtQHBwptmsV3R0NAIDA6vjsusl/q1SR0mShMDAQGRkZODcuXP2vpx6TaFQoEWLFpAk2/9FSERUG1lrOaGn33T7wIEeNp1PX/PVqdMJi8ednJwqdZ0NBUNYHebk5IQ2bdpY3L2eqo+TkxNnGomoXrDWcqK42BlZWYFITo4ppwastOt9dHQqoqLSzGa/AGDkyJEICgri0mM5GMLqOIVCwS7uRERkleHy49WrVwFUtuWEQJ8+v6BVqwyLBfeGGMBswxBGRERUT1laftQV3Q+Fbq9HoPyWE1pERh5A376/Wgxe/fv3h6+vLzw9PQGAxfcVwBBGRERUj+jvdgQgN1vVy8wMxI4dg1AawMqixahRlgvuDbVp04bF95XEEEZERFRPlFV4v27dKJw4EY6KNFm1VnBviMX3lccQRkREVA+o1WpkZWWZPa7RuCM5+T4bAhgQGbkfnTodK7fmKyoqCi1btoSfnx+XHquAIYyIiKiOszYDtnevCklJA2DrHo/W6r5M3XXXXQxf1YAhjIiIqI4xrPsCjGu/9L2+Tp1qiz//7ILK7vE4cOBANGnSBI6OjlAqlfLjLLyvPgxhREREdYi1WS+Nxh1paVFISVHB1sL7Pn1+NWs5MXDgQLRt25ZB6w5gCCMiIqpDLl++bPR9ZmYgfv65L06fbgfbthoS6Nz5D8TE/Ghx6TEkJIQB7A5hCCMiIqrlrG2yvWnTv3DuXAhsDV+RkfttrvuimscQRkREVIulp6fjiy++MHpMV3AfA9uWHQFAiwEDktGrV2q1Xx9VHkMYERFRLaVWq40CmL7dRPkF93oC0dEpVvd4tIR9v+4chjAiIqJayvAOSNvbTehqvtq1+6vcbvcAMGDAAISGhgLgnY93GkMYERFRLaKv/8rLy8OxYxpkZITgxIl2+P33KNgSwAYMSKrQsmO7du0YvOyEIYyIiKiWMGw/cfBgd2zZMhRC9IS+n5d1Zd/xCOi63Ddr1gwA5N5fnPmyL4YwIiKiWuLs2dvIyAhBUVEjbN48DKXBy1oAKz986XXt2pUbbdcyDGFERES1wMqVwJQpftBqJ6D8mS+AdzzWfQxhREREdpaZCTz8sIAQ5c18ARWZ/TLEux5rH4YwIiIiO8nMBE6f1s2ClQawslR89is+Ph6+vr6s/aqFGMKIiIjuAMNNt7OyFFixwg0ffuj2T/gqv/C+Mt3ux48fj7CwsCpdN9UchjAiIqIaZnjXo+V+X2UHsIq2nQAYwOoChjAiIqIadunSJWg07vjllz44cKAHbCm6/7//2wZX15s2NVw1xOXHuoMhjIiIqBqZLjv++ectrF17AcnJiRDClr0eBQYMSMbddx+06fVGjhwJHx8fAOx4X9cwhBEREVUTy81WFQBaoLwlR93xihfeBwUFMXjVUQxhRERE1UQ/A6bRuBsEMKCsZquRkfvRvfshFBc7wcsr16alx9GjR8PT05MzX3UcQxgREVE10mjcsXPnQBuWHivXbJUF9/UHQxgREVE1efttCYsXJwIoO4B17PgnBg5MqlDBPaArumcAqz8YwoiIiKogMxPYv1+DH34owvLl/rDlzsfKBDAA8PX1rdQ1Uu3EEEZERFQB+/YBO3ZcQ2TkTaSmOuGVVzwAKFF2w1XdMUnSYtiwrRUOYCNHjmQBfj3EEEZERGSjiROB1asFgCYA3P55tOz9HiVJi5iYZAQFZdlceG+KAax+YggjIiIqQ2Ym8NNPf+Pq1SKsXh2C8kJXqcptNQQAPXr0QNOmTeHl5cXGq/UYQxgREZEVK1cCU6YIaLXNUP7+jqX0s18VvfNRr2fPngxeDQBDGBERkQm1Wo20NC0eftjnnw22AdsCmBajRm2o0FZDhh3vAXa9b0hs2T+hVhg+fDhatGiBxo0bIzAwEA888ACysrLk4y+88AIkSTL7cnNzMzrP119/jfbt26Nx48bo3Lkzvv/+e6PjQgjMnz8fgYGBcHFxQUxMDE6fPm00Jjc3F+PGjYOHhwc8PT2RkJCAa9euGY05cuQI+vTpg8aNGyM4OBiLFi2q5k+EiIiqU2YmsHs3kJSUh+HDT2LIEG+DAGYLLYYP34pOnU5UaPnRx8cHgYGB8hcDWMNRZ0JY//79sX79epw6dQobN25Eeno6Ro0aJR9/+umnkZ2dbfQVHh6Of//73/KYlJQUjB07FgkJCTh06BBGjBiBESNG4OjRo/KYRYsW4Z133sHy5cuRlpYGNzc3xMbG4tatW/KYcePG4dixY0hKSsLWrVvxyy+/YMqUKfLx/Px8DBw4EC1btsSBAwfwxhtv4IUXXsBHH31Uw58SERFVxsqVQMuWwL33AgMHKpGS0gu2/xWpRWTkPkyfvgQREYcq/NpOTk4Vfg7VD5IQQtj7Iipj8+bNGDFiBAoLC+Ho6Gh2/I8//kC3bt3wyy+/oE+fPgB0Te6uX7+OrVu3yuN69uyJbt26Yfny5RBCICgoCE899RSefvppAIBGo4G/vz9WrVqFMWPG4MSJEwgPD8e+ffvQo0cPAMCOHTvwf//3f8jMzERQUBA++OADzJ07Fzk5OfL/XHPmzMG3336LkydP2vwe8/PzoVQqodFo4OHhUenPioiIrMvMBFq2FNBqKzLrBURFpSA4OLNCS4+m2P2+frL17+86WROWm5uLNWvWIDo62mIAA4AVK1agbdu2cgADgNTUVMyYMcNoXGxsLL799lsAQEZGBnJychATEyMfVyqViIqKQmpqKsaMGYPU1FR4enrKAQwAYmJioFAokJaWhn/9619ITU1F3759jf51Exsbi9dffx1Xr15F06ZNLV5zYWEhCgsL5e/z8/Nt/1CIiKjC1Go1kpOvQattWaHnSZIW0dG/VSh8DRgwAF5eXlAqlQBY+0V1aDkSAGbPng03Nzd4e3vj/Pnz+O677yyOu3XrFtasWYOEhASjx3NycuDv72/0mL+/P3JycuTj+sfKGuPn52d0vFGjRvDy8jIaY+kchq9hyYIFC6BUKuWv4OBgq2OJiKhq1Go1li1bhr17fwCgtfl5lWm4On78eERHR6N9+/as/SKZXUPYnDlzLBbTG34ZLt/NnDkThw4dws6dO+Hg4IAHH3wQllZTv/nmGxQUFGDChAl38u1U2TPPPAONRiN/Xbhwwd6XRERUbxUVFeHgwe5YuXIydH8dlledo8WoUeuRmGhb7dfIkSMxZcoUTJs2jUuOZJFdlyOfeuopTJw4scwxrVq1kn/v4+MDHx8ftG3bFh06dEBwcDB+++03qFQqo+esWLECQ4cONZuNCggIwMWLF40eu3jxIgICAuTj+scCAwONxnTr1k0ec+nSJaNz3L59G7m5uUbnsfQ6hq9hibOzM5ydna0eJyKiisvMBE6fBtq0AZo3L3380KFG2LJlKITQz0dYrwnTz3516nTC5tdll3sqj11DmK+vb6U3I9VqdVPHhjVUgK6ua/fu3di8ebPZc1QqFXbt2oXExET5saSkJDnEhYaGIiAgALt27ZJDV35+PtLS0vDYY4/J58jLy8OBAwcQGRkJAPjxxx+h1WoRFRUlj5k7dy6Ki4vlmrWkpCS0a9fOaj0YERFVP12zVUCrBRQK4KOPgIQE4M03gZkzfWDLZtsV7fs1cOBAtG3blgGMylUn7o5MS0vDvn370Lt3bzRt2hTp6emYN28eLl68iGPHjhnNHs2bNw+ffPIJzp8/DwcHB6PzpKSkoF+/fli4cCGGDBmCr776Cq+99hoOHjyITp06AQBef/11LFy4EKtXr0ZoaCjmzZuHI0eO4Pjx42jcuDEAYPDgwbh48SKWL1+O4uJiPPTQQ+jRowfWrl0LQHdHZbt27TBw4EDMnj0bR48exaRJk7B48WKjVhbl4d2RRESVo1arcfbsbdx9t5/RXY8KhUC/fnnYvdsTlgOYFpIECKGQZ78q2nZi2rRpDGANXL26O9LV1RWbNm3C888/j+vXryMwMBCDBg3Cc889ZxTAtFotVq1ahYkTJ5oFMACIjo7G2rVr8dxzz+HZZ59FmzZt8O2338oBDABmzZqF69evY8qUKcjLy0Pv3r2xY8cOOYABwJo1azBt2jTcd999UCgUiIuLwzvvvCMfVyqV2LlzJ6ZOnYrIyEj4+Phg/vz5FQpgRERUOfqC+4yMEGi1xrXBWq2E3bstr0joQ1dY2Bnk5nrZvNl2//790aZNGwC845Eqpk7MhDVUnAkjIqq47OxsfPTRR8jMDMTKlZMNar7KosXkySvQvHl2hV8vPj4e7du3r/iFUr1Vr2bCiIiIKuLgwe7YvHkoSu96LKv2S2DAgORKBTAAct8vooqqU33CiIiIDOn3e8zMLH0sK0thEMCA8gJYnz6/oFev1EpfA7cdosriTBgREdVJlu58HDFCjYULG8G2OQYtBgxItjmARUVFoVmzZnB0dGTXe6oWDGFERFQnZGYCKSm634eGlgYwQPfrlCkCmzal4Pvvh5RxFi0iIw8gNDSjwns+3nXXXQxcVK0YwoiIqNZbuRJ4+GGg9FYy8zovrVbC998PK+MsFSu+HzlyJHx8fABwxotqBkMYERHVapmZpgEMKL/Jqikthg/fWqHiex8fH6PdU4iqG0MYERHVGoZbDAG6369efRNCuFTyjLrlx759f63Q0iPRncAQRkREtYJhob30z0SXbvarcVlPs0iStIiLq9h2Q0R3GkMYERHZXWamcaF91ZYeBWJikiu02bYlbD1BNY0hjIiI7G7p0tIAVjUVazthaPDgwQgODgbAQny6MxjCiIjIrv788yreessT5TVVLfu4FtHRqYiKSqvU8uPo0aPRoUOHCj+PqCoYwoiIyG7S09Px1lt7IMQEC0f1wcv011KSpIVKVfnwpefn51fp5xJVFkMYERHZhVqtxhdffAEvL3dIktZko21Lwcvw+6rNfOmNHj0afn5+XHoku2AIIyIiuygqKgIAKJUFGDZsK7ZsGfpPEDMNXoYkxMbuQHj48UqHrwEDBiA0NJR1X2R33MCbiIjuCEubbeuFhZ1BXNxGREWloKzaL0nSVimAAUC7du0QGBjIAEZ2x5kwIiKqcaabbS9cCISGOkGjccfRo52QlBQD3byAKOMsutYTFQlgAwcOREhIiPw9Z7+oNmEIIyKiGmXaA0yrBWbNAgBvAInQzXxZqvsyVLnWE23btmXoolqLIYyIiGrU6dNl9QCzVBWjK7zXHatcAf7IkSMRFBTEAEa1GkMYERHVGLVaDQ+P21Ao/KDV2tr5XovJk1eguNgJXl65lar/8vHxYQCjWo8hjIiIaoRarcayZcsAAEOHdje4+7EsAgMGJKN58+wqvTa3HKK6gCGMiIiqXWYm8NtvQGZmILKygnDtWhPcc8+P2L07xupzJEmLmJjKbTmkFx8fD19fX86CUZ3AEEZERBWWmamr9WrTBmje3PhY6Z2Q3gAeRmmRvUBprZehqjdeZdNVqosYwoiIqEJM20189BGQkKA7ZnonpPFdjvrf64JYVbccGj16NDw9Pdl2guoshjAiIrKZpXYTU6YARUVA795XsWiRE7RatzLOIKFv358QGnqu0kX3ADB+/HiEhYVV6rlEtQVDGBER2cxSuwmtFvjvfwFAibK63esItG17utKF92w9QfUJty0iIiKb6NpNXIRCYa2rvQJlhzCBrl3/qNKdjwxgVJ9wJoyIiMpVuXYTAKBF//67UFLSqFIzYCNHjoSPjw8AbjlE9Q9DGBFRA1fWnY56RUVF0GjckZvrjbCwMxgz5kt8+eV/UN7yY2TkAfTrl1Lpa/Px8UFgYGCln09UmzGEERE1YGXd6Who7VoXLFmS+M/sl0Dp/o6W9nnU06Jv31+rdH1sukr1GWvCiIgaKEt3Oj7yiO5x03GzZikNlh8llP71odvnsXPnw9AFMj0thg/fWum7HwHdHZBcfqT6jDNhREQNlKU7HUtKgDNnSpcl1Wo1fvtN33jVGgUiIg4jJuZHXLige2JwcGa5AaxDhw4ICAgAADRq1Aju7u5o1KgRe39Rg8EQRkTUQO3fb/6YQgG0bq2b/dq/X4NfflmNo0c7ARiAspYd9T2/lMoTNr9+hw4d0Llz58pcOlG9wBBGRNRAGBbgX716FXPmeMI0WGm1Ao8+ehPbt7tAq1UCSPxnjLUApttwuyrLjkQNFUMYEVEDYFyAL9Cz53Fotb0sjJSwbZsLSkNXWaXDAn36/FLpDbddXV0r9Tyi+oIhjIionsvMBB5+GBD/1M1rtRJSUqJheTNtoPyu9wCgxYAByRUKYAMGDIC7uzsAXQDjtkPU0DGEERHVA9Z6fWVm6tpOCLMm9xIiI/fjwIFIVORG+cpuuj148GDcfffdNo8naggYwoiI6jhrvb4MH7ckNDQDffv+il9+6YMDB3qg/BkwLRISVlRq26Hg4OAKP4eovmOfMCKiOsxar699+8oOYIBWbiMxbNj3GDAgCbrlScskSdf3q7L7PrLpKpE5zoQREdVh1np97dlTdgCLjDyAU6fawtX1JoKDL6BXr1S0bHkWK1dONtoTUpK0iIvbYFPfL2tGjx7Nnl9EFjCEERHVIaa1X23a6JYgDQOXg4NAu3ZXoFD4QKstXWKUJC06dTqCP//sigMH7jI4q26WKyLiEIYN2ypvzi1JWgwbthWdOtne+8sSPz+/Kj2fqL6ShDAv16TaIT8/H0qlEhqNBh4eHva+HCKyszfeAGbP1hXZKxTA229fw+jRBVi71gWzZytRUiJBoRAYOnQLwsLOIC0tCqmpKoP9Hsva61GL6dOXQKks+Gejbi+5AautBgwYgNDQUKPH2PmeGiJb//7mTBgRUR3w5pvArFml32u1wPTprjh37iMolQV44onS4JSe3tpgs20tjFtRWCu+VyA31+ufrvcFlVp6DA0NRWBgYIWfR9RQsTCfiKiWy8zUzYCZEkIh79WoVBYgNPQcAMjLiToK2PZHvW7roapg8T1RxXAmjIiolrNUfK+3ceMoFBXp6rkAIDfX26iw3jLTJUldTVhFZr969OgBLy8vNGnSBI0aNYKfnx+XHYkqiCGMiKiW8/G5CoXC06jIXk8IBbZsGYqwsDNQKgvg5aWG9U74OpGR+xEamoEbN1z+uTuy4nc+RkREcOmRqIoYwoiIaglLXe/T09OxadMXGDq0u8kyYyn9smRu7k1kZQWirKarkqRF376/VnnDbS49ElUdQxgRUS1gqev9iBFqfPHFFwCAiIhDCAs7gwsXmmPDhlEwnOmSJK3BY9bufoTccqKyAWzkyJHw8fHhHY9E1YQhjIjIzqx1ve/W7bbRON1diydQVGTcy0vXaKi8ux8rv+WQXlBQEMMXUTViCCMisrOUFMtd78+eLf0jWte7yxteXmp5Viw31wvnzzfH7t0x5bxCxbYc0s94GeLsF1H1YwgjIrIj/TKkKQcHICTkNo4eBQ4e7G7WxT4i4hB+/PFe/PFHV6vnliQtVKpUREWlVWgJ0sfHh0X3RHcAQxgRkZ3oliGF2V2PCoXA669rkJubi6NHw40K8vV3Q0rS7X8CWM0tPxJRzWIIIyKyk/37NdBqlWaPd+++H7/8ko2ZM4dCiA5mx4VQ4Lvv/oXyCvArG8B45yPRncEQRkR0h5i2oAgOvgXAHaY9vQ4ejMSBAzB7vJS1OyAF/u//tqJdu9M2LT/26tUL/v7+8veOjo7w9fVl7RfRHcIQRkR0B5i2oHj77Wu4995LiI4+g5SUXkZjy+54b60Rq0DXrn/g7rsPlnstI0eO5J2ORLUA944kIqphllpQTJ/uik8/TUJUVBokyfjWSN335vsUSZIWY8d+aTYe0GLs2LX417++s+l6fHx8GMCIagGGMCKiKsrMBHbv1v1qiaUWFEIokJvrBaWyAMOGbZWDlSRpEROTjI4dj5udRwgFnJyKzcYPH74V7dqdqdb3REQ1z+blyPz8fJtP6uHhUamLISKqayx1uk9IKD2+dOk1TJ/uBvMaLgFHxyIAMOr7lZUVhOTkmH+WJI1rvyRJCy+vXISGnpPHe3nlVnkLIiKyD5tDmKenJyTJ+n5kACCEgCRJKCkpqfKFERHVdtY63cfG6grv//zzKqZPV0IIS392SiguLr0LUaksQEFBE4MAphujD2KmWw7puudXLnzx7kei2sHmELZ79+6avA4iojrn9GnLne7PnNGFsIMHCyBEU4vP1c9q6R082B2bNw+FeZWIhNjYHQgPP17p0BUfHw+lUtcKg53viWoPm0NYv379avI6iIjqnDZtdEuQhkHMwQFo3RpQq9Vwc8uCJDU3u9vRdFZLo3HHli2WAphubGUCWP/+/dGmTRuGLqJarNItKvLy8rBy5UqcOHECANCxY0dMmjRJ/tcWEVF9plarcfHibUyZ4oYPP3STlxy1WoFVq7JRUvIxAGDYsEsGHe+1iI7WbSNUUNAEP/7YD02aXIOr602LbSlMw1pF+Pn5cesholpOEkKIij5p//79iI2NhYuLC+6++24AwL59+3Dz5k3s3LkTERER1X6hDVF+fj6USiU0Gg1vdiCqRdRqNSZN2msQrswL6BMTlxjNdBkW0X/zzf0mWw7pp9IMg5gWkydXftuhKVOmMIQR2Ymtf39XaiZs+vTpGD58OD7++GM0aqQ7xe3btzF58mQkJibil19+qdxVExHVAWfP3jbaz9H0zkd9+wkAyM31hpeXGqGh5wAAmZmBFvZ81M2S6RuxVnXbIYDF90R1QaVC2P79+40CGAA0atQIs2bNQo8ePart4oiIaqOMjEZldrWXJC2ysoLw2WcPQojSUBURcQgpKdGwvOWQAqNGrYeb240qtZ2Ij4/n1kNEdUSlQpiHhwfOnz+P9u3bGz1+4cIFuLu7V8uFERHVRmq1GkrlJUhSU5MgVtpKIiYm2ajVhBAKbNkyFH5+OThxItzKmbUIDs4sM3wNHDgQTZo0QaNGjeDp6Wl2nEX4RHVLpUJYfHw8EhIS8OabbyI6OhoAsHfvXsycORNjx46t1gskIqot1Go1li1bBgAYNqy7vCSpD15BQVnw8spFbq632UyZEAqcP9/Cygyarut9ebNfISEhrPMiqkcqtW3Rm2++iZEjR+LBBx9ESEgIQkJCMHHiRIwaNQqvv/56dV8jAGD48OFo0aIFGjdujMDAQDzwwAPIysoyGvPDDz+gZ8+ecHd3h6+vL+Li4nD27FmjMT/99BMiIiLg7OyM1q1bY9WqVWav9d577yEkJASNGzdGVFQUfv/9d6Pjt27dwtSpU+Ht7Y0mTZogLi4OFy9eNBpz/vx5DBkyBK6urvDz88PMmTNx+/btavksiKj6lbf1EAAUFRXJv4+IOITExCUYNWo94uI2olOnowgNPQelsgBeXmoL+zsK3LzpYnHfx8mTVyAi4lC518g6L6L6pVIhzMnJCUuXLsXVq1dx+PBhHD58GLm5uVi8eDGcnZ2r+xoB6HrerF+/HqdOncLGjRuRnp6OUaNGycczMjJw//33495778Xhw4fxww8/4MqVKxg5cqTRmCFDhqB///44fPgwEhMTMXnyZPzwww/ymHXr1mHGjBl4/vnncfDgQXTt2hWxsbG4dOmSPGb69OnYsmULvv76a/z888/Iysoyep2SkhIMGTIERUVFSElJwerVq7Fq1SrMnz+/Rj4bIqqalSuBli2Be+/V/bpype7x8oJZenprbNw4Chs2/BuLFydi584YaDS6kowWLc5Bt0SpJ2HPnj6IiUk22/exvAL8kSNHYtq0aVxqJKpnKtWiojbYvHkzRowYgcLCQjg6OmLDhg0YO3YsCgsLoVDosuWWLVtw//33y2Nmz56Nbdu24ejRo/J5xowZg7y8POzYsQMAEBUVhbvuuktectBqtQgODsbjjz+OOXPmQKPRwNfXF2vXrpVD4MmTJ9GhQwekpqaiZ8+e2L59O4YOHYqsrCz4+/sDAJYvX47Zs2fj8uXLNv9rli0qiGpeZqYueJk2XF2wAJgzx3hPyG7dLuLdd3fAy0uNgoImWLFiMsz/LSv++bL8b9wJE1b9s2Rp+76PDGBEdUuNtqi4desW3n33XezevRuXLl2C1mTfjoMHD1bmtDbLzc3FmjVrEB0dDUdHRwBAZGQkFAoFPv30U0ycOBHXrl3D559/jpiYGHlMamoqYmJijM4VGxuLxMREALqlhgMHDuCZZ56RjysUCsTExCA1NRUAcODAARQXFxudp3379mjRooUcwlJTU9G5c2c5gOlf57HHHsOxY8fQvXt3i++rsLAQhYWF8vcV2TSdiCrH2tZDs2cD+n+iarXAww8DkuQHrXYCyg5aEizf/QgAWjl4lRW+Ro4cCR8fHwAstieqzyoVwhISErBz506MGjUKd999d7kbe1eX2bNnY9myZbhx4wZ69uyJrVu3ysdCQ0Oxc+dOjB49Go888ghKSkqgUqnw/fffy2NycnKMghEA+Pv7Iz8/Hzdv3sTVq1dRUlJicczJkyflczg5OZndmeTv74+cnJwyX0d/zJoFCxbgxRdftPHTIKKqUqvV8PC4DYXCD1pt6Z9jCoUw+h7QBbLSjbjLClrWCAwYkGzTzJePjw8L8IkagEqFsK1bt+L7779Hr169qvTic+bMKbeQ/8SJE3IrjJkzZyIhIQHnzp3Diy++iAcffBBbt26FJEnIycnBww8/jAkTJmDs2LEoKCjA/PnzMWrUKCQlJd2xoFgVzzzzDGbMmCF/n5+fj+DgYDteEVH9ZXin49Chxnc63nefcYuJqtLfPdmrV2q1nI+I6odKhbBmzZpVSz+wp556ChMnTixzTKtWreTf+/j4wMfHB23btkWHDh0QHByM3377DSqVCu+99x6USiUWLVokj//iiy8QHByMtLQ09OzZEwEBAWZ3MV68eBEeHh5wcXGBg4MDHBwcLI4JCAgAAAQEBKCoqAh5eXlGs2GmY0zvqNSfUz/GEmdn5xq7sYGIjJne6RgWdsaoTsvF5ZbRno/lz34JC8cFIiP3o2/fXyvdfJWI6q9K/TPvrbfewuzZs3Hu3Lkqvbivry/at29f5pe1InZ9HZq+hurGjRtyQb6eg4OD0ViVSoVdu3YZjUlKSoJKpQKgq72IjIw0GqPVarFr1y55TGRkJBwdHY3GnDp1CufPn5fHqFQq/Pnnn0Z3VCYlJcHDwwPh4dYaNRLRnaC/4zEry/jPC6WyQG4xAQBhYWcQEXEApbVfwqC9hKX7mSR07Pgn9PtASpIWAwYkYdiw7yscwNiKgqhhqNRMWI8ePXDr1i20atUKrq6ucuG7Xm5ubrVcnF5aWhr27duH3r17o2nTpkhPT8e8efMQFhYmB58hQ4Zg8eLFeOmll+TlyGeffRYtW7aUC+EfffRRLFu2DLNmzcKkSZPw448/Yv369di2bZv8WjNmzMCECRPQo0cP3H333ViyZAmuX7+Ohx56CACgVCqRkJCAGTNmwMvLCx4eHnj88cehUqnQs2dPALqu1uHh4XjggQewaNEi5OTk4LnnnsPUqVM500VkRytXAlOm6O949MPQod0t9uc6eLA7Nm8eCuN/pyogROn+jpY27R44MAkDByZV6M5HU+PHj2chPlEDUakQNnbsWPz999947bXX4O/vX+P1Vq6urti0aROef/55XL9+HYGBgRg0aBCee+45OdTce++9WLt2LRYtWoRFixbB1dUVKpUKO3bsgIuLCwBd8f62bdswffp0LF26FM2bN8eKFSsQGxsrv1Z8fDwuX76M+fPnIycnB926dcOOHTuMCu0XL14MhUKBuLg4FBYWIjY2Fu+//7583MHBAVu3bsVjjz0GlUoFNzc3TJgwAS+99FKNfk5EZCwzU3f3Y5s2uu/1AQwAtFoJmzcPhZNTIYKDL8iBSaNxx5YtpgFMz/AxCYZbFQ0bVtrx3pbwNXr0aLMbfHgnJFHDUqk+Ya6urkhNTUXXrl1r4proH+wTRlR5xrNeAlOmXMfy5U0sjjXcYDsjIwSrV0+w+XViY3cgPPx4hWa9xo8fj7CwMJvHE1HdUqN9wtq3b4+bN29W+uKIiGpSZqb5rNeHH7qidCnRmH6D7bCwM3B0LISlIntJ0v7TN0xh9FhFAtjIkSMRFBTE2S4iAlDJwvyFCxfiqaeewk8//QS1Wo38/HyjLyIie1Gr1fjtN7VZA1YhFIiOTrWwd2Pp8bS0qH+64BsGMC2io/ciMXEJhg/farTlkOESpC18fHwYwIhIVqmZsEGDBgEA7rvvPqPHhRCQJAklJSVVvzIiogrS9/7SaNwhSYlGfb4kSYuoqDRERaXhwoXm2LhxlNnxlBQVLP3bNCoqDUplgcVWFhXBux6JyFClQtju3bur+zqIiKpM3/tLqSzAsGFbjRqwGhfOn0BRkfFxlSoVKSmWGlArkJvrZVR0X5m7HkePHs1ZMCIyUqkQ1q9fP5vG/fe//8VLL70k74FGRHSnlDdrZXq8oKCJxZkwSdLt91hVfn5+VT4HEdUvlQphtvriiy/w9NNPM4QRkV2UN2ulVBagoKAJdu4ciGPHwmHa/6sidV+Gm26bYusJIrKkRkNYJbpfEBHdERqNOzZvHob09NYwLsTX9f0aPHgb2rU7bfPSI+96JKKKqtEQRkRUG1nuiF9KCAV8fdVmAWzgwIEICQkxG8+ZLiKqDIYwImpQyu6Ir2OtDiwkJASBgYE1eHVE1JAwhBFRnaNWq+U7IbOyFMjIaITQ0NtwcrpS5vM0GnccO9bRqDWFOet1YGwxQUTViSGMiOoUfS8wQLesaNyGIg0REZafZzjWUkd8QCAs7AyGD99iFMD69+8PX19f+Pn5ccmRiKpVjYaw8ePHc89DIqpW+hkw/bKiflbLcOshw824c3O9UVTUyKQGrHTzbUCLjh2PQaVKRfPm2Wav16ZNGy5BElGNqHQIy8vLw++//45Lly5Ba7I/yIMPPggA+OCDD6p2dUREVuTmepstKwpR2li1/JkvqVKbbxMRVZdKhbAtW7Zg3LhxuHbtGjw8PCBJpX+4SZIkhzAiopri5aX+Z1Nt462HHB2LcPRouIWZL2MV3XybiKi6VSqEPfXUU5g0aRJee+01uLq6Vvc1EVEDk5kJnD4NtGkDNG9e+rhhAb5eRkYGAPOtiQBACGDFiodhKXQZq/jm20RE1a1SIezvv//GE088wQBGRJWmD1hr17pg1iwltFoJCoXAiy/m4P77r+DmzZv46qs9yM31hpeXGgDk3yuVunOEhZ2BcU/osu561JEkLRISVlis/yIiupMqFcJiY2Oxf/9+tGrVqrqvh4gaAP0djhqNO5YsSYQQupkrrVbC/Pn+yM39EunprbFlS6JBTZeALmRpER2diqioNFy4EAxbgpe+Jky/DVFFAhjbUhBRTbE5hG3evFn+/ZAhQzBz5kwcP34cnTt3hqOjo9HY4cOHV98VElG9o19itFZcf+FCc6NlRt3yon6JUYGUlF5ISYmGLlxZJ0laxMVtgKdnHoqLnSxu5A1Y3/eRnfCJqCbZHMJGjBhh9thLL71k9pgkSSgpKanSRRFR/WNY33X0aB4yMkLg6FhoVlwPaAFI5TRUBYyDWelzJQkGfcO2olOnE+Vem4+PD9tQENEdZ3MIM21DQURkK8sNVjtAkrTo0uUI/vijCwzvZNRolBbCWflGjdqA4OBM5OZ6WZ31IiKqLSr2J9w/PvvsMxQWFpo9XlRUhM8++6zKF0VEdVtmJrB7t+5XoOwGq3/80QWS0YSWhOTkGMTEJEOSdP/40/1a3j8EBTw986BUFiA09FyFAhjrvojIHioVwh566CFoNBqzxwsKCvDQQw9V+aKIqO5Rq9XIzs7GW2/loWVLgXvvBVq2FHjrrTxcuaLb09FSDRigsFgXFhSUhcTEJZgwYdU/gUw+auUKJBQXVyxMjRw5EtOmTWPdFxHZRaXujhRCGDVo1cvMzIRSf+84ETUYZd3tOHOmB/7+OwlKpeUGq4Z1XHqSpIWXVy4A4Pp1NyQlxcC48ap+Vszyc2wVFBTEAEZEdlOhENa9e3dIkgRJknDfffehUaPSp5eUlCAjIwODBg2q9oskotrt0qVLAMrfSsi0waq+eB6A2WO6FhVDrdSFKRAdvRepqSqj5+iXIAcMGICmTZvi9u3bAABHR0ezfyDyzkcisrcKhTD9HZKHDx9GbGwsmjRpIh9zcnJCSEgI4uLiqvUCiah2U6vVWL9+PQAgKysQpvs0ms5QRUQcQljYGbPiecPHAPwzo2a5YkKStIiKSkNUVJrFIvzQ0FDe7UhEtV6FQtjzzz8PAAgJCUF8fDwaN25cIxdFRHWHYdF9cnIMjNtGCMTEJJsVyetnxaw9dvRoeBl3Rhqfk3dAElFdVamasAkTJgDQ/eF76dIls/YVLVq0qPqVEVGtpO/3pdFoUFxcjKtXrwKwVnQvQanU9QTTbTdUfmDSt7CwTkJQUFbl3wARUS1RqRB2+vRpTJo0CSkpKUaP6wv22ayVqH7RB6+8vDx8/PF2s/0cHR0Dcf26K3QF88ZF9xs3jjKq24qIOGT1dUxbWFhSmQJ8IqLaqFIhbOLEiWjUqBG2bt2KwMBAi3dKElH9YN5o1dJ+jvo6MIHSIKbvfK/780EIBbZsGYqwsDNWZ8Qsz6aVMi3At4Z9v4ioLqhUCDt8+DAOHDiA9u3bV/f1EFEtY63RqvG2QaW/SpJAXNx6ABI2bPi30bmEUODYsXB07HgcgH4WrRDFxc7w8lJbbGEhSVoMHrwNrq43ERycaRTALO35yLseiaiuqFQICw8Pl5svElHDUN4slZ4QCri53YCXV66FnmACO3cOws6dsTCdRdPPcllqYWFtCZN7PhJRXVapEPb6669j1qxZeO2119C5c2c4OjoaHffw8KiWiyMi+9HXgen/wWW50ao5fc2WaU8w49YV5rNo+uXKxMQlSExcYtP+j1x2JKK6TBJCWNsDxCqFwnCpoLQejIX51Ss/Px9KpRIajYbBlu4owzowQLcUmZvrjaysQCQnx1gIVcazWYYzVxqNO44dC8fOnbY1cp4wYRVCQ8+VOy4+Pp4lEURUK9n693elZsJ2795d6QsjotpPXwcGlLaM0C8Ptm59GqdPt4VxQ1aBuLivjWq29MHNy0uNjh2PIylpoM2zaLbw9fWt+BsjIqpFKhXC+vXrh19//RUffvgh0tPTsWHDBjRr1gyff/45QkNDq/saichOTIvxhVCYBTD9425uN+QAZhrchg3bipiYZCQlDTB7rsFZLDZ2NaQvxGfxPRHVB+VX2VqwceNGxMbGwsXFBYcOHUJhYSEAQKPR4LXXXqvWCySiO0OtViM7OxvZ2dlyHZi1BqymDGewLAW3LVuGwtGxyOJzdbQYMCAJvXqllnmNQUFBCAwMZAAjonqhUjNhr7zyCpYvX44HH3wQX331lfx4r1698Morr1TbxRHRnWFaA6ZnuRjfeG9I/QwWAGRkhOD6dVeLm3ir1V4WX7tv392IjDxkNgNm2n6Cs19EVN9UKoSdOnUKffv2NXtcqVQiLy+vqtdERDVAf7ejKY1Gg0uXLpk8VlrPZdoyokuXIzhypMs/QUuLAQOS4eJyS95wW5K0MO+cD6SlqWBpc29LAQxg+wkiqv8qFcICAgJw5swZhISEGD2+Z88etGrVqjqui4iqkbWZLkss1XOZtoy4994f5e8ByAEMgEk3fcMZMwmAFpIkjM7NDbiJqKGqVAh7+OGH8eSTT+KTTz6BJEnIyspCamoqnn76acybN6+6r5GIrDCc3crLy8Pt27eNjjs6OkKpVNrcXNlaPVdi4hKjthFKZYEcnjIyQmyqG9NRIC5uvdzMlT3AiKghq1QImzNnDrRaLe677z7cuHEDffv2hbOzM55++mk8/vjj1X2NRGRBRWa3TBkuNxoGoQsXgi3Wc+XmelkNTNbrxvQd8UtJktZs6yFLxo8fz/ovIqr3KhXCJEnC3LlzMXPmTJw5cwbXrl1DeHg4mjRpUt3XR0RWmNZ3GQYrABZDFmC+3BgTk4xevVJx8GB3bN481Ox1TO98ND2vUlmA3r1/xa+/9oX5XpKltWHlLT/Gx8dDqVSyAJ+IGoxKhTA9JycnhIeHV9e1EFElGQYrw1ko0w72lpYbk5IG4NYtZ+zZ0wfmXWu06N37V+TmeuPo0U5yt3zT87q43IL5EqSEvn1/gp/fZQAoc/NtBi8iaoiqFMKIyP5Mg5Xhvoz6mq6wsDNQKgus9v369VdLAQwAFPj117749dd+MCy0Nz1vixbnYKl1Rdu2p9G8ebbF6+bdj0TU0FWqWSsR1R6Wg1UpfU0XUFq/ZU5h5XHAfInR/LzNm2eja9c/oAtiACDQtesfVgMYERFxJoyozrNcGF9KX9Olr+cyr9+CXBtWujl3+Uz3efzXv77DXXf9jgsXWiA4+DwDGBFRORjCiOo4pbLArKGqEIBhTVh6emuj4+Hhx3H8eAejMbqlRQ1u3GiM7duHWAlj+iVHy0X2zZtn2xy+2IKCiBo6hjCieiAi4hDCws4YNVAtq5nqiRMdMHnyChQXO8HLKxfp6a2NOt536HACJ050MCj01wUv/eyZZK0NmBX6Ox/1WIhPRMQQRlSrWdpqSKPRoLi4GAUFxrNQhg1U9d8DlpupCqFAcbETQkPPWbxj8vjxcOiDV+fORxAcfOGf2THLhfmm+zwaYuAiIrKMIYyoFlKr1bh06RLWr19fqedHR0cjJSUFgOWaMcN6Lmt3TOoo8Oef3fDnn11geh+PYRNX3ulIRFRxDGFEtUxVOuHrBQQEyL+3VDNmWM9VXmG/juGypI5pYT4REVUMQxhRLWO6/FgdTGvGlMoCo+73hiHNvN+XniSHNW6+TURUdQxhRHWYtT0gLTGsGTPusK9FdHQqEhJW4PjxjkhJibb4fEnSIiGhtJifAYyIqGoYwojqCNPAZboHpOE2QlevXrX6XAAmHfYVSEnphZQU1T/fG3e918+ADRu21Wr7CbabICKqOIYwolpOo3FHWlrUPyFJYbGxqundirt375afbxrWVKpUK/Vflh6TEBu7A+Hhx7nvIxFRNWMII6rFjJcNdXSbbsegrLsV9Sy1n0hJUdlQiK+jb+xquvTIuyGJiKqOIYyoBlnq82WorFkk8425DSnKbDuhZ7n9hAIREftw4EAkyto+lsX3REQ1iyGMqIbY2mpi2rRpFoNYWRtzmy5JWgtM1tpP6AKY5bb3kqRFXNwGBAdnWg1grAEjIqo6hjCiGmJrqwnTcfqAY71/V2kRfqdOR5Gb64URIzrhjz8OmZ3btEdYKevhbtiwrejU6YTZMX0dGGvAiIiqR/lFIUR0R3l7e2P8+PFygJIkLQBdQIqO3ovp05fId0EqlQUIDT2HsDBns/NoNO7IyAhBWNgZxMVttOGVdS0o9Oc2FRQUhMDAQAYwIqJqwpkwIju7cuWK0fdOTk5wdXUFYLnJqi1M74iMiUm2MKumhSTBaDnTtAUFZ7+IiGoOQxiRjapSZF+WTZs2mT02evRo+femG3NbYtgXzNIdkcnJMejd+1fs2dPHKHSVF/CCgoIYvoiIaghDGJENqlpkD1Ssu/3t27crdH2GfcHS0qLM6siEUODXX/tgwIBkBAVlwcsrF//+twpNmoSanatRo0bw9PTk7BcRUQ1jCCOyQWWL7PXK6m5fEeUFOY3G3aDzvSndjFhi4hIolQUICQlhry8iIjtiCCMqg34J0rRuqyIsLQ8adre39RxpaVFITVWVGeRyc71R1v02lhq6EhGRfdSZuyOHDx+OFi1aoHHjxggMDMQDDzyArKwsozHr169Ht27d4OrqipYtW+KNN94wO89PP/2EiIgIODs7o3Xr1li1apXZmPfeew8hISFo3LgxoqKi8Pvvvxsdv3XrFqZOnQpvb280adIEcXFxuHjxotGY8+fPY8iQIXB1dYWfnx9mzpxZ4SUmsi/9EuRHH31ksW7LkitXriA7OxvZ2dnQaDQALPf70ochWxw82B2LFyciJaWXWZDTaNwBlN4J6ehYKN9NaYmlhq5ERGQfdWYmrH///nj22WcRGBiIv//+G08//TRGjRqFlJQUAMD27dsxbtw4vPvuuxg4cCBOnDiBhx9+GC4uLpg2bRoAICMjA0OGDMGjjz6KNWvWYNeuXZg8eTICAwMRGxsLAFi3bh1mzJiB5cuXIyoqCkuWLEFsbCxOnToFPz8/AMD06dOxbds2fP3111AqlZg2bRpGjhyJvXv3AgBKSkowZMgQBAQEICUlBdnZ2XjwwQfh6OiI1157zQ6fHlXGpUuXKvwcS2HNUr8vW8OQfhbN0r+X9EEuPb210VJnly5HcORIF/l7IQB9h312wCciqj0kIXR/RNc1mzdvxogRI1BYWAhHR0f85z//QXFxMb7++mt5zLvvvotFixbh/PnzkCQJs2fPxrZt23D06FF5zJgxY5CXl4cdO3YAAKKionDXXXfJRdharRbBwcF4/PHHMWfOHGg0Gvj6+mLt2rUYNWoUAODkyZPo0KEDUlNT0bNnT2zfvh1Dhw5FVlYW/P39AQDLly/H7NmzcfnyZZu7jefn50OpVEKj0cDDw6NaPjeyja2F+LY6eLA7tm0bhpISyWgp0VqNV3x8PNatW4eMjBCsXj3B4jklSdfXa+XKyWYBLyFhBYqLneSgZ+kOyClTprAmjIioBtj693edWY40lJubizVr1iA6OhqOjo4AgMLCQjRu3NhonIuLCzIzM3Hu3DkAQGpqKmJiYozGxMbGIjU1FYCuqPrAgQNGYxQKBWJiYuQxBw4cQHFxsdGY9u3bo0WLFvKY1NRUdO7cWQ5g+tfJz8/HsWPHqutjoBpUXiG+fvlPvxxYnoiIQ0hLu4QNG9RITNQ1Wz14sDuWLEnE6tUTsGRJIg4e7C6PLy4uxujRozF0aDuLy4v6IHf8eEeLS53FxU4IDT0nt7fQ/94Qtx4iIrKvOrMcCQCzZ8/GsmXLcOPGDfTs2RNbt26Vj8XGxmL69OmYOHEi+vfvjzNnzuCtt94CAGRnZyMkJAQ5OTlGwQgA/P39kZ+fj5s3b+Lq1asoKSmxOObkyZMAgJycHDg5OcHT09NsTE5OjjzG0jn0x6wpLCxEYWGh/H1+fr4tHwvdYZW909HVNRfdujnh6NGCcov1DZc1hw27ZDBWi+joVISHH0NWVpDFOyENlzr1zVZNsf0EEZH92XUmbM6cOZAkqcwvffgBgJkzZ+LQoUPYuXMnHBwc8OCDD0K/mvrwww9j2rRpGDp0KJycnNCzZ0+MGTMGgG42qy5YsGABlEql/BUcHGzvS6q31Gq1XEBv6SsvL8/i86yFJ1tmxNavX4/i4mIAFSvWj4g4hMTEJZgwYRUmT14BAFixYjK+/95yrZhKlSrPeum3GjL9YgAjIrI/u86EPfXUU5g4cWKZY1q1aiX/3sfHBz4+Pmjbti06dOiA4OBg/Pbbb1CpVJAkCa+//jpee+015OTkwNfXF7t27TI6R0BAgNldjBcvXoSHhwdcXFzg4OAABwcHi2MCAgLkcxQVFSEvL89oNsx0jOkdlfpz6sdY8swzz2DGjBny9/n5+QxiNaCy9V4ajTuOHbO8/Gdr2wd9sX9Fi/WVygKkp7fG5s2Wg1cpLaKi0gDo6soYtoiIai+7hjBfX1/4+vpW6rlara5OxnD5DgAcHBzQrFkzAMCXX34JlUolv4ZKpcL3339vND4pKQkqlW5Jx8nJCZGRkdi1axdGjBghv86uXbvkOywjIyPh6OiIXbt2IS4uDgBw6tQpnD9/Xj6PSqXCq6++ikuXLsl3VCYlJcHDwwPh4eFW35OzszOcnc03YqbqZWvjVUOGS5CAACDJxxQKYXPbB31ne/3m3KbLmtaCXFl3SZbSYvjw0nMolUqbromIiOyjTtSEpaWlYd++fejduzeaNm2K9PR0zJs3D2FhYXLwuXLlCjZs2IB77rkHt27dwqeffoqvv/4aP//8s3yeRx99FMuWLcOsWbMwadIk/Pjjj1i/fj22bdsmj5kxYwYmTJiAHj164O6778aSJUtw/fp1PPTQQwB0f7ElJCRgxowZ8PLygoeHBx5//HGoVCr07NkTADBw4ECEh4fjgQcewKJFi5CTk4PnnnsOU6dOZci6wyzt91jRxqumS5C6AKYLYpKkxYsv5qCkpMBovC3bE9myObf+XNevu5rNwBnS3xFpuAE3C++JiGq3OhHCXF1dsWnTJjz//PO4fv06AgMDMWjQIDz33HNGoWb16tV4+umnIYSASqXCTz/9hLvvvls+Hhoaim3btmH69OlYunQpmjdvjhUrVsg9wgDdEs7ly5cxf/585OTkoFu3btixY4dRof3ixYuhUCgQFxeHwsJCxMbG4v3335ePOzg4YOvWrXjsscegUqng5uaGCRMm4KWXXqrhT4oMVVebCUv1W4CE2NgdmDOnNTp3bgr9y1S0aL+szblNzwVoYWkmTP86+gAWHx8PX19fLkUSEdVydbZPWEPAPmFVk52djY8++qjK59m7V4WkpAEwXIKUJC0SE5dg7twJ8Pb2xsmTJ/HRR99jyZJEszov/V6NllibNcvMDLTY/0vfeBXQIjLyAEJDMxAcnCk/Nz4+Hu3bt6/yeyYiosqz9e/vOjETRmQvERHD8dJL3WAYwACBxMQcTJnyf/IjSqWyzDseLYUwa7NmBw92t1iAL4QCo0ath5vbDavLl5WtsSQiojuPIYyoDBs3HoFW293kUQm5uTuxbp2uCbD+po2K3PFordWFm1uB1TsgJUkrz3rFx8ebFd6z9xcRUd3CEEZUBluCVVFREZycnMzueAQAIYD09NZmdWEXLgRbnDX78sv/wHjWrfQ1hw3bioceGoCgoCCGLSKieoAhjOocS3c8GqrojFBZdzPa2krC29sb06ZNw9mzt7Fli2GIMu6ED8BgudGUceuLUqV3PgYF9WIAIyKqJxjCqE6x9Y5H/RJheWy5m7G8VhKGLS8uXHCFEMZByrAuzHq/L8t3PgJAdHQqmjfPxujRoxnAiIjqEYYwqlNsbbR6+fJleYsga8rbv9FQWa0kDPd51GjcoVBMh1ZrfCelfvnScrsLwPIMmO65+g74pvuVEhFR3VY3NlUkqqB169YZhSNLKrJ/o62UygLMmpX+T18vXYiKiUlGbq43NBp3ucbMmBbWliHL6qJPRER1G2fCqMGq6P6NtmrceA0SE92Rm+uFrKwgJCfHyMudMTHJUKlSkZKiAlD6mH6M4XWYdsAnIqL6hSGM6gy1Wl3hLYcMmRbgm9/NqAtEFW2saon++GefPWi03Klv+ipJWqhUexEVlQalsgAuLrfMatMYwIiI6jeGMKoTqroFkaUC/Ndfb4spU5To0qUAr77qASEUSE6OgYvLLbPi/IpuRwRY3+4I0AWy1FSVXO9lyz6S3AuSiKh+YQijOuHSpUuVfq61Avz58y+jSRN/vPYaoN+8S3/Mzy8HxcXO8PJSA4DNBfyGLC13GjLtpm+t+J97QRIR1U8MYVTrqdVqrF+/vtLPt1aAf/ZsI+TnA1qTOnkhFFixYjL0NVsqVWqFtiPSS09vjbJ2ZpUkLf71r85o2zbK6p2P7IJPRFR/MYRRrWdrWwprrBXgh4Tchr8/oFCYBjEB/Y3D+mVD0z5ekqSFo2MRMjJCLNaIWe8HVvr8YcO2ondvNl8lImqo2KKC6j19Ab5h24hhw7YiKEiL5s2Bjz4CHByEfMy0XYQQCkRHpxo9v0uXI1i5cjJWr56AJUsScfCgbn9JjcYdGRkhOHWqrdVlSECLL75IxyefMIARETVknAmjBsFS4btG0xZOTk5ISPBGt26X8O672+HoWISVKyebzZpFRaUhKioNXbvGwdGxCOPHdzGrEbt5s7FBqwnL65D6ANi//10MYEREDRxDGNVptrSNMBwTGnpOfnzdunUAdFscBQVp5WNl7RU5cKAT9u5tYrFGLCkpBqWTy6bNV7WIjk6VW1I4OfWq8nsnIqK6jSGM6ixb2kbYMsa05qy8dhGhobct3PVYWkdmyahRG9Cp0wmMHDkSQUFBnAUjIiLWhFHdZK3tRGZmIDIyQqDRuFsdo9G42/gq5lsJ5eXlIShIa1Rjpgtglvd+BHRLkMHBmQAAHx8fBjAiIgLAmTCqo6y1ndDXc1W0tYRhI9SyZs/Wr1+P+Ph4REQcgp9fjtzKwhrT5UwiIiI9hjCq9Sx1irfcCFUYzXqlpKhs3hvS29sb8fHx+Oij78ttzKpvHFtc7IyyWlDExW1AcHCmUQBj13siItJjCKNaz9vbG9OmTTOq3dq79xzS0lKRmqqS9300D0QKqFR75THlzUoplUqrM2yGs2e7d+8GYL0jvv51OnU6AUDX8V6pVLLxKhERGWEIozrBMLysXAlMmRIArVaC7q7DvQgPP1Zma4my9mQ0ZK2xq+HsmeHdlsZ3Ugo8+OAVjBt3Bc2bt4Gn510MXkREZBVDGNUpmZnAlCn4J4ABgK6jfXj4MahUqVZnvWytydI3drXWosJSvVhi4hLk5nrh8ccHIzLSH4BvDbxzIiKqbxjCqMao1eoytxyqzCzR0qVl7/WonxnT9+MyZK2nWEZGBq5cuYKbN28CKG1RceFCcwASgoMvyM+3VC+WmLgEoaHnEBRkcmFERERlYAijGqFWq7Fs2bJyx02bNs3mIJaZCbz1lqUjhj26dDNjUVFpAIDRo0fD09MT7713E0uWhFi84zEpKcnsjOnprc1mvJo2vVqpjbyJiIgsYZ8wqhG2brpdkc25T58GhMXdgMz3eszN9QIAeHp6oqQkEAsWhNrcL8zajJejY6FBb7B/XtnK3ZZERETlYQijOqNNG0Bh9hOrLTcYnT5tWEOmYxjUTFm7Q7K42MniRuCcBSMiosrgciRVK30d2JUrV6r93M2bAx99BDzyCFBSUhqCAGDz5qHQ14QZBiMnJ6d/wpswCmJlzWCVdYdkaOi5Mrc0IiIishVDGFUbW+vAqiIhAYiNBc6cAby9NfDxuQtr17pg61YJWi2gUEjo27cv/vMf4/YQ8+f/jRdfDCqzX5i11hMKhcDQocZ3WloKX2zESkREFcEQRtWmIvVdVdG8ue4LaIrMTGDWrNI7JrVaCbNne2LgQAEfn1vIzs4GAPTv/z9oNOvlGayCgiZISekJb+8rcHK6jaysQCQnx1htPRES0gtFRXdZvSb2AyMioopiCCO7srRsqZ9RsqW9ha7ey/hYSQnw1lvfITT0nNHjSqVuFuubb+7HH390ha6gX5j8arn1BAMWERFVN4YwsqtNmzZV+rnTpk1DmzbeUCiMg5iDg7Ba75WZGWgQwGDhVx22niAioprGuyOpzioqKpKL9R0cdI85OACvv66xGp7On28J08BlCVtPEBFRTWMIoypTq9XIzs6ukTsibZGQAJw9C+zerfv1//4v2+rYFi3OQbf0aInucdPCfRbcExFRTeByJFXJnbgj0hb6Yv309HSsX7/e6JjhXY/u7tcQFnYG6emtYTgjJklaxMQkIygoC15euXj44cHcgJuIiGoUQxhVyZ26I9KSK1euGIUktVqNL774wmiM4YbbgBa64KUvxC8lBNCp01EolQWIj49H+/bt78h7ICKihovLkXRHaDTuyMgIsbpVUGVs2rQJy5Ytg1qtBmAeCE23H9L9uFsuxAdKO+grlcpqu0YiIiJrOBNGNc5wNsp082xbGC4nWiq4LyoqglqtNqtJ++WXPmbbD1nDQnwiIrrTGMKoRlnbDDss7IxN7R9sCXB5eXlmdWB796pw4ECPcs6u6w3GPSCJiMgeGMKoRlnbDFvfg6usWS5LAW7z5qHw88tB8+ald0Devn3b6HmZmYFIShoAy60oSoOXYSE+AxgREd1pDGFUYzQad1y/7gpdQbz5Ztims1y6UJQtBzJLAQ5QYOXKyVaXNA8e7P7PZt6WApgWkyevQHGxU5nBiy0piIjoTmAIoxphfFeigD6I6Zf+AJjNculnr/RjwsLOQJK0FmfSLC1p6mfOLN9vIjBgQLI8gzZy5Ej4+PiYjWJLCiIiulMYwqhKLM0amd+VKEGSBOLi1iM4OBMAcOxYRwuzXOZ7Nw4btvWfmS3rS5p6lmfOAECgT59f0KtXqvxIUFAQwxYREdkVQxhVibe3N6ZNm2bUHmLvXicsXmwemtzcbiA9vbXJDJnlLYSEUOD48XCEhx/H5MkrsHLlZKOAZXg3Y0GBLohlZQVaOaeEVq0y5O/Gjx/PAEZERHbHPmFUZd7e3ggMDERgYCBKSgKh1XpDMslBkqSFo2OR2QxZadNU062EBH74YRCWLEnEpUsBGDZsKyRJK5/L8G7GpKQkaDTuSE6OgaVQZxjY4uPjERYWVi3vm4iIqCo4E0bVZuVKYMoUQKsFJEn3JYRuU+233rqBZs3isGKF+RJkbOwOaLUKJCfHmAQ046XJxMQlaNnyPoSHOyEgIBRAKAoKCpCUlGR1KdI0sLERKxER1RYMYVQtMjNLAxhQGr6+/BJQqYDmzZsgM7MJFIrSMQDg4CAQHn4cAJCUFGPx3Pr6r9DQc8jL+xYpKeZjvLzUFor4tUhIWGHUzoKIiKi24HIkVYvTp43DFQCUlAC+vrqNtQHdrx99pAtngO7X11/XyO0orP04Gi4nWtv+SKksMFuyHD58KwMYERHVWpwJo2rRpg0szHIBrVsbj0tIAGJjgTNndMccHG7io4+szWQBQOlyYnnd8yMiDiEs7Axyc73YgJWIiGo9zoRRtbA0y/Xhh6WzYKZj77nH+JjpTJa+UF9f4G9t+yNLM2KhoefYiJWIiGo9zoRRtTGd5bIUwEwZhqKIiEPw88sxakehD1txcRvL3P6oPCNHjmRvMCIiqlUYwqhaNW9uW/jS0/cZy8rKwqZNm1Bc7GwxbAHCbLnSsFasPD4+PgxgRERUq3A5kmpcZiawe7fuV0NqtRrZ2dkoKipCTk4jZGSEwNGx0GBJUkf3vYSYmGSrvcLKw2VIIiKqbTgTRtUuM1N3t2SbNsAPP5S2rlAodHVjCQm6ALZs2TIAhvtMdoAkadGlyxEcOdJFLsAXAtiw4d8Gm3xnmRXeW9sLEuB+kEREVDsxhFG1Mm7YqiuuF0JXXa/VAo88ItCt2yW4uV0FYLng/siRLkhIWIG8PE9s2DAK+glbIXQNXRMTl5jNgLHei4iI6hqGMKo25g1bzbcQKimR8O672xEaeg6A5U23hVCguNgJbm43YWnj7k6dRkClKoSnpycAznQREVHdxJowspm12i5At7z4229qs4atpkyL6fX9wSyNsXbs6NFvsX79ejg5OSEwMJABjIiI6iSGMLLJypVAy5bAvffqfl25svSYvr4rJWW1WWgypVKlGi0lpqe3hjDau1sLlSoVgOUu+IbF+EVFRdXy3oiIiOyBy5FULtNlRl1tl64nWPPmpWFIH5pKa7wE9Btx//NMREWlyd/p68GM/y0gISWlF1JTVXJHfHbBJyKi+oghjMplbV/IM2d0v//tNydoNO5QKguMQlNWVhCSk2OMthnShyiNxh3HjnW0sE2RLrTpm7SGhZ2BUlnA8EVERPUOQxiVy9q+kPv3A/fdB2i13pCkRHnmSh+aQkPPoVOno0azWBqNO9LSopCSooJuBsx0tqxURTriExER1TUMYVQu/b6QjzyimwFzcAAWLABmzza8E9J45krPcBbLcAPuUhJKg5hxIKtIR3wiIqK6hoX5DVRZdzpaGpOQAJw9C6xfD6xdCzRpYr5EqZ+5ssS0H5gxXQDr0+eXSnfEJyIiqms4E9YAGTZUNexiX94YwLhA35ThzJVG447cXG94eamhVBZY7Adm8my0apWBHj0OsAifiIgaBIawBqa8Ox2tjZkyRfzze8v1W4YzV4bLjvrHw8LOmG3Abfp8ffDifpBERNQQMIQ1MGXd6agPYZbGWAtfeoMHb0PTpleRmRlotg3Rli1DkZi4xKR9hSGBmJjkMsOX6d6Q7JJPRER1XZ2rCSssLES3bt0gSRIOHz5sdOzIkSPo06cPGjdujODgYCxatMjs+V9//TXat2+Pxo0bo3Pnzvj++++NjgshMH/+fAQGBsLFxQUxMTE4ffq00Zjc3FyMGzcOHh4e8PT0REJCAq5du1bha7EH/Z2OhhwcgNatyx4DaK02YpUkgR07hmL16glYufJhi9sQ5eZ6ISLiEBITlyA6ei8A/bm0GDAgCb16pZZ53T4+PggMDJS/GMCIiKiuq3MhbNasWQgKCjJ7PD8/HwMHDkTLli1x4MABvPHGG3jhhRfwkb6YCUBKSgrGjh2LhIQEHDp0CCNGjMCIESNw9OhRecyiRYvwzjvvYPny5UhLS4ObmxtiY2Nx69Ytecy4ceNw7NgxJCUlYevWrfjll18wZcqUCl2LvejvdHRw0H3v4AB8+GHpLJjxGN0SpCRpMWBA8j+d7PXhSd/mXgshSmfKLO0XaVgrplQWYODAZEyfvgQTJqzC9OlLyg1gRERE9ZEkhPGmMbXZ9u3bMWPGDGzcuBEdO3bEoUOH0K1bNwDABx98gLlz5yInJ0euFZozZw6+/fZbnDx5EgAQHx+P69evY+vWrfI5e/bsiW7dumH58uUQQiAoKAhPPfUUnn76aQCARqOBv78/Vq1ahTFjxuDEiRMIDw/Hvn370KNHDwDAjh078H//93/IzMxEUFCQTddii/z8fCiVSmg0Gnh4eFT58zOUmalbgmzd2jiAGTpw4CLefXe7WdNVlSoV4eHHUFzshOvX3bBhw7+tvo5CITB06BZERByq0vVOmTIFgYGBVToHERHRnWDr3991Zibs4sWLePjhh/H555/D1dXV7Hhqair69u1rVKwdGxuLU6dO4erVq/KYmJgYo+fFxsYiNVU3E5ORkYGcnByjMUqlElFRUfKY1NRUeHp6ygEMAGJiYqBQKJCWlmbztdhb8+bAPfdYD2AAEBSkm8HSBzBAt7SYmqqCu/s1hIaeQ3DwhTL3ixw58usqBzCARfhERFT/1InCfCEEJk6ciEcffRQ9evTA2bNnzcbk5OQgNDTU6DF/f3/5WNOmTZGTkyM/ZjgmJydHHmf4PGtj/Pz8jI43atQIXl5eRmPKuxZLCgsLUVhYKH+fn59vcdydZKm1hGEne/P9IktJkhbBwWU0IrOCRfhERNQQ2HUmbM6cOZAkqcyvkydP4t1330VBQQGeeeYZe15ujVuwYAGUSqX8FRwcXKOvZ0vDVi8vtdlMl2kne0sF91VpthoUFMQifCIiqvfsOhP21FNPYeLEiWWOadWqFX788UekpqbC2dnZ6FiPHj0wbtw4rF69GgEBAbh48aLRcf33AQEB8q+Wxhge1z9mWH908eJFufYsICAAly5dMjrH7du3kZubW+7rGL6GJc888wxmzJghf5+fn19jQcyWhq2ArpA+JibZ6kbchuMGDkxGVFRalZqtjh8/nqGLiIgaBLuGMF9fX/j6+pY77p133sErr7wif5+VlYXY2FisW7cOUVFRAACVSoW5c+eiuLgYjo6OAICkpCS0a9dOXv5TqVTYtWsXEhMT5XMlJSVBpVIBAEJDQxEQEIBdu3bJoSs/Px9paWl47LHH5HPk5eXhwIEDiIyMBAD8+OOP0Gq1FboWS5ydnc2CZk2wpWEroFsGPHiwu0FNmBYxMckWa7wMO+SHhp6z+tr9+/c3+wwcHR2hVCq57EhERA1KnagJa9GihdH3TZo0AQCEhYWh+T+p4T//+Q9efPFFJCQkYPbs2Th69CiWLl2KxYsXy8978skn0a9fP7z11lsYMmQIvvrqK+zfv19uHSFJEhITE/HKK6+gTZs2CA0Nxbx58xAUFIQRI0YAADp06IBBgwbh4YcfxvLly1FcXIxp06ZhzJgxcusMW67Fnmxp2AoAN296Y+vWYQZtJxRITo5Bp05HjWa5LHXIt1aM36ZNG97lSEREhDoSwmyhVCqxc+dOTJ06FZGRkfDx8cH8+fON+ndFR0dj7dq1eO655/Dss8+iTZs2+Pbbb9GpUyd5zKxZs3D9+nVMmTIFeXl56N27N3bs2IHGjRvLY9asWYNp06bhvvvug0KhQFxcHN55550KXYs9tWkDSBJg2JxEoTBu2Arow5px3y/DonyNxh0XLgRj8+ah0JcX6jvkh4Wd4d6PREREZahTfcIamprqE5aZCbRoYRzCJAk4f954JiwzE2jZUhgFMUnSIjFxCdLTW1vZgkhnwoRVFpcl2e+LiIjqu3rXJ4yqz+nTxgEM0H1/5gygVquRnZ2N7OxsODhkY/78v+W7I/VLjQDKDGCmd08aYr8vIiIinXqzHEm20+8NaVgX5uAAFBfn4dFHfwQABAdfkJcTExPdje54zMgIKTOAWbp7cuTIkQgKCmLhPRER0T8Ywhog/d6QjzyiK8h3cADGjwdiY5UQQr8FkcDw4brthvRNWfX0vcMMg5gkaREXtwHBwZkWa8F8fHwYwIiIiAxwObIByswEWrUCUlN1zVpTU4HPPjPdfFvCli1DodG4mz1f3yXfdJmyU6cTVovxuQxJRERkjDNhDYylJq2tWpnXiAG6Ox0vXGgOpfKE2bGIiEMICztTbmPWgQMHom3btpwFIyIiMsGZsAbEWpPWJk10d0dasmHDKBw82N3iMaWyAKGh58psRRESEsIARkREZAFDWANirUnr9evAxx8DkmSpW4nC6rIkERERVR5DWAOivyvSkIMD4OamW5LcuvUK+vbdbfY8fYNWIiIiqj4MYQ2Ii4saixblwcFBN+Pl4CAwcuQN9OwpcO+9wLBhPnB2LpYL7vXK6vtFRERElcPC/AZCrVZj2bJlAIAnntD1/XJ0LMLKlZPluyK1WgnJyTGIiUmWN+221veLiIiIqoYhrIEoKiqSf6/v+2Wp6aoQCowZ0xqdOi0p985HIiIiqjyGsAYsKysQgABQemukg4NA165uuHChoFrCF/uDERERWcYQ1kBpNO5ITo6BYQADBJ54IhtOTuoqnTs+Ph5KpRJOTk5sT0FERGQFQ1gDlZvrbWH/Rwm5uTuxadM5m84xcuRI+Pj4GD3G4EVERGQbhrAGytr+jxW5C5IbchMREVUeQ1gDpd//ccuWoRW6C1I/+8UZLyIioqphCGvAbN3/0ZCPjw8CAwPvwNURERHVbwxhDYS1uxT17SqIiIjozmLH/AbC29sbAwcOtPdlEBER0T8YwhoItVqN4uJie18GERER/YPLkQ2A4ZZFREREVDtwJqwBMNyyqKrYAZ+IiKh6cCaMbBIfHw9fX1+2pSAiIqomDGFkVf/+/eHr6ws/Pz+GLyIiomrGEEZWtWnThj3BiIiIaghrwsgq1n8RERHVHIYwsig+Pp5LkERERDWIIYws8vX1tfclEBER1WusCWsAbF1WHD16NDw9Pbk5NxER0R3AENYAeHt7Y9q0aWX2C2PwIiIiurMYwhoIBiwiIqLahTVhRERERHbAEEZERERkBwxhRERERHbAEEZERERkBwxhRERERHbAEEZERERkBwxhRERERHbAEEZERERkBwxhRERERHbAjvm1mBACAJCfn2/nKyEiIiJb6f/e1v89bg1DWC1WUFAAAAgODrbzlRAREVFFFRQUQKlUWj0uifJiGtmNVqtFVlYW3N3dIUlSpc+Tn5+P4OBgXLhwAR4eHtV4hXUHPwMdfg78DAB+BgA/Az1+DjXzGQghUFBQgKCgICgU1iu/OBNWiykUCjRv3rzazufh4dFg/yfT42egw8+BnwHAzwDgZ6DHz6H6P4OyZsD0WJhPREREZAcMYURERER2wBDWADg7O+P555+Hs7OzvS/FbvgZ6PBz4GcA8DMA+Bno8XOw72fAwnwiIiIiO+BMGBEREZEdMIQRERER2QFDGBEREZEdMIQRERER2QFDWB31wQcfoEuXLnJzOZVKhe3bt8vHb926halTp8Lb2xtNmjRBXFwcLl68aHSO8+fPY8iQIXB1dYWfnx9mzpyJ27dv3+m3Um0WLlwISZKQmJgoP9YQPocXXngBkiQZfbVv314+3hA+AwD4+++/MX78eHh7e8PFxQWdO3fG/v375eNCCMyfPx+BgYFwcXFBTEwMTp8+bXSO3NxcjBs3Dh4eHvD09ERCQgKuXbt2p99KpYSEhJj9HEiShKlTpwJoGD8HJSUlmDdvHkJDQ+Hi4oKwsDC8/PLLRvv31fefA0C3VU5iYiJatmwJFxcXREdHY9++ffLx+vgZ/PLLLxg2bBiCgoIgSRK+/fZbo+PV9Z6PHDmCPn36oHHjxggODsaiRYuqduGC6qTNmzeLbdu2ib/++kucOnVKPPvss8LR0VEcPXpUCCHEo48+KoKDg8WuXbvE/v37Rc+ePUV0dLT8/Nu3b4tOnTqJmJgYcejQIfH9998LHx8f8cwzz9jrLVXJ77//LkJCQkSXLl3Ek08+KT/eED6H559/XnTs2FFkZ2fLX5cvX5aPN4TPIDc3V7Rs2VJMnDhRpKWlif/973/ihx9+EGfOnJHHLFy4UCiVSvHtt9+KP/74QwwfPlyEhoaKmzdvymMGDRokunbtKn777Tfx66+/itatW4uxY8fa4y1V2KVLl4x+BpKSkgQAsXv3biFEw/g5ePXVV4W3t7fYunWryMjIEF9//bVo0qSJWLp0qTymvv8cCCHE6NGjRXh4uPj555/F6dOnxfPPPy88PDxEZmamEKJ+fgbff/+9mDt3rti0aZMAIL755huj49XxnjUajfD39xfjxo0TR48eFV9++aVwcXERH374YaWvmyGsHmnatKlYsWKFyMvLE46OjuLrr7+Wj504cUIAEKmpqUII3Q+sQqEQOTk58pgPPvhAeHh4iMLCwjt+7VVRUFAg2rRpI5KSkkS/fv3kENZQPofnn39edO3a1eKxhvIZzJ49W/Tu3dvqca1WKwICAsQbb7whP5aXlyecnZ3Fl19+KYQQ4vjx4wKA2Ldvnzxm+/btQpIk8ffff9fcxdeQJ598UoSFhQmtVttgfg6GDBkiJk2aZPTYyJEjxbhx44QQDePn4MaNG8LBwUFs3brV6PGIiAgxd+7cBvEZmIaw6nrP77//vmjatKnR/w+zZ88W7dq1q/S1cjmyHigpKcFXX32F69evQ6VS4cCBAyguLkZMTIw8pn379mjRogVSU1MBAKmpqejcuTP8/f3lMbGxscjPz8exY8fu+HuoiqlTp2LIkCFG7xdAg/ocTp8+jaCgILRq1Qrjxo3D+fPnATScz2Dz5s3o0aMH/v3vf8PPzw/du3fHxx9/LB/PyMhATk6O0eegVCoRFRVl9Dl4enqiR48e8piYmBgoFAqkpaXduTdTDYqKivDFF19g0qRJkCSpwfwcREdHY9euXfjrr78AAH/88Qf27NmDwYMHA2gYPwe3b99GSUkJGjdubPS4i4sL9uzZ0yA+A1PV9Z5TU1PRt29fODk5yWNiY2Nx6tQpXL16tVLXxg2867A///wTKpUKt27dQpMmTfDNN98gPDwchw8fhpOTEzw9PY3G+/v7IycnBwCQk5Nj9Iet/rj+WF3x1Vdf4eDBg0b1Dno5OTkN4nOIiorCqlWr0K5dO2RnZ+PFF19Enz59cPTo0QbzGfzvf//DBx98gBkzZuDZZ5/Fvn378MQTT8DJyQkTJkyQ34el92n4Ofj5+Rkdb9SoEby8vOrM56D37bffIi8vDxMnTgTQcP5fmDNnDvLz89G+fXs4ODigpKQEr776KsaNGwcADeLnwN3dHSqVCi+//DI6dOgAf39/fPnll0hNTUXr1q0bxGdgqrrec05ODkJDQ83OoT/WtGnTCl8bQ1gd1q5dOxw+fBgajQYbNmzAhAkT8PPPP9v7su6YCxcu4Mknn0RSUpLZv/oaEv2/8gGgS5cuiIqKQsuWLbF+/Xq4uLjY8cruHK1Wix49euC1114DAHTv3h1Hjx7F8uXLMWHCBDtf3Z23cuVKDB48GEFBQfa+lDtq/fr1WLNmDdauXYuOHTvi8OHDSExMRFBQUIP6Ofj8888xadIkNGvWDA4ODoiIiMDYsWNx4MABe18ameByZB3m5OSE1q1bIzIyEgsWLEDXrl2xdOlSBAQEoKioCHl5eUbjL168iICAAABAQECA2Z1R+u/1Y2q7AwcO4NKlS4iIiECjRo3QqFEj/Pzzz3jnnXfQqFEj+Pv7N4jPwZSnpyfatm2LM2fONJifhcDAQISHhxs91qFDB3lZVv8+LL1Pw8/h0qVLRsdv376N3NzcOvM5AMC5c+eQnJyMyZMny481lJ+DmTNnYs6cORgzZgw6d+6MBx54ANOnT8eCBQsANJyfg7CwMPz888+4du0aLly4gN9//x3FxcVo1apVg/kMDFXXe66J/0cYwuoRrVaLwsJCREZGwtHREbt27ZKPnTp1CufPn4dKpQIAqFQq/Pnnn0Y/dElJSfDw8DD7y6y2uu+++/Dnn3/i8OHD8lePHj0wbtw4+fcN4XMwde3aNaSnpyMwMLDB/Cz06tULp06dMnrsr7/+QsuWLQEAoaGhCAgIMPoc8vPzkZaWZvQ55OXlGc0W/Pjjj9BqtYiKiroD76J6fPrpp/Dz88OQIUPkxxrKz8GNGzegUBj/tebg4ACtVgugYf0cAICbmxsCAwNx9epV/PDDD7j//vsb3GcAVN9/d5VKhV9++QXFxcXymKSkJLRr165SS5EA2KKirpozZ474+eefRUZGhjhy5IiYM2eOkCRJ7Ny5Uwihux29RYsW4scffxT79+8XKpVKqFQq+fn629EHDhwoDh8+LHbs2CF8fX3r1O3olhjeHSlEw/gcnnrqKfHTTz+JjIwMsXfvXhETEyN8fHzEpUuXhBAN4zP4/fffRaNGjcSrr74qTp8+LdasWSNcXV3FF198IY9ZuHCh8PT0FN999504cuSIuP/++y3eot69e3eRlpYm9uzZI9q0aVOrb8s3VVJSIlq0aCFmz55tdqwh/BxMmDBBNGvWTG5RsWnTJuHj4yNmzZolj2kIPwc7duwQ27dvF//73//Ezp07RdeuXUVUVJQoKioSQtTPz6CgoEAcOnRIHDp0SAAQb7/9tjh06JA4d+6cEKJ63nNeXp7w9/cXDzzwgDh69Kj46quvhKurK1tUNESTJk0SLVu2FE5OTsLX11fcd999cgATQoibN2+K//73v6Jp06bC1dVV/Otf/xLZ2dlG5zh79qwYPHiwcHFxET4+PuKpp54SxcXFd/qtVCvTENYQPof4+HgRGBgonJycRLNmzUR8fLxRf6yG8BkIIcSWLVtEp06dhLOzs2jfvr346KOPjI5rtVoxb9484e/vL5ydncV9990nTp06ZTRGrVaLsWPHiiZNmggPDw/x0EMPiYKCgjv5Nqrkhx9+EADM3pcQDePnID8/Xzz55JOiRYsWonHjxqJVq1Zi7ty5Ri0FGsLPwbp160SrVq2Ek5OTCAgIEFOnThV5eXny8fr4GezevVsAMPuaMGGCEKL63vMff/whevfuLZydnUWzZs3EwoULq3TdkhAGrYSJiIiI6I5gTRgRERGRHTCEEREREdkBQxgRERGRHTCEEREREdkBQxgRERGRHTCEEREREdkBQxgRERGRHTCEEREREdkBQxgR1Sv33HMPEhMT7X0ZNe6FF15At27d7H0ZRFQFDGFERLVIUVHRHX09IQRu3759R1+TiHQYwoio3pg4cSJ+/vlnLF26FJIkQZIknD17FkePHsXgwYPRpEkT+Pv744EHHsCVK1fk591zzz14/PHHkZiYiKZNm8Lf3x8ff/wxrl+/joceegju7u5o3bo1tm/fLj/np59+giRJ2LZtG7p06YLGjRujZ8+eOHr0qNE17dmzB3369IGLiwuCg4PxxBNP4Pr16/LxkJAQvPzyy3jwwQfh4eGBKVOmAABmz56Ntm3bwtXVFa1atcK8efNQXFwMAFi1ahVefPFF/PHHH/L7XLVqFc6ePQtJknD48GH5/Hl5eZAkCT/99JPRdW/fvh2RkZFwdnbGnj17oNVqsWDBAoSGhsLFxQVdu3bFhg0bqvs/EREZYAgjonpj6dKlUKlUePjhh5GdnY3s7Gy4u7vj3nvvRffu3bF//37s2LEDFy9exOjRo42eu3r1avj4+OD333/H448/jsceewz//ve/ER0djYMHD2LgwIF44IEHcOPGDaPnzZw5E2+99Rb27dsHX19fDBs2TA5L6enpGDRoEOLi4nDkyBGsW7cOe/bswbRp04zO8eabb6Jr1644dOgQ5s2bBwBwd3fHqlWrcPz4cSxduhQff/wxFi9eDACIj4/HU089hY4dO8rvMz4+vkKf1Zw5c7Bw4UKcOHECXbp0wYIFC/DZZ59h+fLlOHbsGKZPn47x48fj559/rtB5iagCqrT9NxFRLdOvXz/x5JNPyt+//PLLYuDAgUZjLly4IACIU6dOyc/p3bu3fPz27dvCzc1NPPDAA/Jj2dnZAoBITU0VQgixe/duAUB89dVX8hi1Wi1cXFzEunXrhBBCJCQkiClTphi99q+//ioUCoW4efOmEEKIli1bihEjRpT7vt544w0RGRkpf//888+Lrl27Go3JyMgQAMShQ4fkx65evSoAiN27dxtd97fffiuPuXXrlnB1dRUpKSlG50tISBBjx44t99qIqHIa2TMAEhHVtD/++AO7d+9GkyZNzI6lp6ejbdu2AIAuXbrIjzs4OMDb2xudO3eWH/P39wcAXLp0yegcKpVK/r2XlxfatWuHEydOyK995MgRrFmzRh4jhIBWq0VGRgY6dOgAAOjRo4fZta1btw7vvPMO0tPTce3aNdy+fRseHh4Vfv/WGL7mmTNncOPGDQwYMMBoTFFREbp3715tr0lExhjCiKheu3btGoYNG4bXX3/d7FhgYKD8e0dHR6NjkiQZPSZJEgBAq9VW6LUfeeQRPPHEE2bHWrRoIf/ezc3N6FhqairGjRuHF198EbGxsVAqlfjqq6/w1ltvlfl6CoWuwkQIIT+mXxo1Zfia165dAwBs27YNzZo1Mxrn7Oxc5msSUeUxhBFRveLk5ISSkhL5+4iICGzcuBEhISFo1Kj6/8j77bff5EB19epV/PXXX/IMV0REBI4fP47WrVtX6JwpKSlo2bIl5s6dKz927tw5ozGm7xMAfH19AQDZ2dnyDJZhkb414eHhcHZ2xvnz59GvX78KXSsRVR4L84moXgkJCUFaWhrOnj2LK1euYOrUqcjNzcXYsWOxb98+pKen44cffsBDDz1kFmIq46WXXsKuXbtw9OhRTJw4ET4+PhgxYgQA3R2OKSkpmDZtGg4fPozTp0/ju+++MyvMN9WmTRucP38eX331FdLT0/HOO+/gm2++MXufGRkZOHz4MK5cuYLCwkK4uLigZ8+ecsH9zz//jOeee67c9+Du7o6nn34a06dPx+rVq5Geno6DBw/i3XffxerVqyv92RBR2RjCiKheefrpp+Hg4IDw8HD4+vqiqKgIe/fuRUlJCQYOHIjOnTsjMTERnp6e8vJdVSxcuBBPPvkkIiMjkZOTgy1btsDJyQmArs7s559/xl9//YU+ffqge/fumD9/PoKCgso85/DhwzF9+nRMmzYN3bp1Q0pKinzXpF5cXBwGDRqE/v37w9fXF19++SUA4JNPPsHt27cRGRmJxMREvPLKKza9j5dffhnz5s3DggUL0KFDBwwaNAjbtm1DaGhoJT4VIrKFJAyLB4iIyCY//fQT+vfvj6tXr8LT09Pel0NEdRBnwoiIiIjsgCGMiIiIyA64HElERERkB5wJIyIiIrIDhjAiIiIiO2AIIyIiIrIDhjAiIiIiO2AIIyIiIrIDhjAiIiIiO2AIIyIiIrIDhjAiIiIiO2AIIyIiIrKD/wcnjf4SfQ+W4gAAAABJRU5ErkJggg==", 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4VCF81kREdZdarUZhYSFycnKwefPmMtvPmDEDHh4ed/We1n5/66szI2H6srOzsX79ekRERMiJ0jt27ECrVq2wc+dOBAcHIygoCFOnTkV2drb8usTERERGRhpcKzo6GomJiQB0JR+OHj1q0EahUCAyMlJuc/ToURQVFRm0ad++PVq0aCG3SUxMRJcuXeQATHqf3Nxc/PXXXxbvq6CgALm5uQYf9c3kyZMhCAIEQYC9vT18fHwQFRWFL774okKlNtasWYOmTZtWX0eJiKjOU6vVWL58OVauXFmuAAwArl69Ws29KlWngrD58+fDxcUFHh4euHTpEv73v//J5/755x9cvHgR33zzDf773/9izZo1OHr0qMGUZWZmpkFgBAA+Pj7Izc3FrVu3cO3aNZSUlJhtk5mZKV/DwcHBJAAwbmPuGtI5SxYvXgylUil/BAYGlvPJVJwth2QHDx6MjIwMXLhwAbt378bAgQMxc+ZMDBs2rFry5oiIqGHSTyUqr6KiomroiXk2DcIWLFggj4pY+jhz5ozcfu7cuTh+/Dj27t0LOzs7PProo5BmU7VaLQoKCvDf//4X/fr1w7333ovVq1dj//79OHv2rK1usUKee+45aDQa+SMtLa1a3kf/LwNLH8uXL6+2QMzR0RG+vr5o3rw5QkND8fzzz+N///sfdu/eLS+UeP/999GlSxe4uLggMDAQ//nPf3Djxg0AusUVjz32GDQajfxz8sorrwAAvvrqK/Ts2ROurq7w9fXFww8/XKl/hEREVLep1epyj37Zik3rhM2ZMweTJ0+22qZVq1by156envD09ETbtm3RoUMHBAYG4tdff4VKpYKfnx8aNWqEtm3byu07dOgAQLdSsV27dvD19TVZxXjlyhW4ubnByckJdnZ2sLOzM9vG19cXAODr6yvPLeuPhhm3MV5RKV1TamOOo6MjHB0drT6PqlDebY5qcjuk++67D926dcO2bdswdepUKBQKfPjhhwgODsY///yD//znP5g3bx4++eQTREREYOnSpVi4cKEcYDdp0gSA7i+Y1157De3atUNWVhZmz56NyZMn4/vvv6+xeyEiIturC1v62TQI8/LygpeXV6VeK+UPFRQUAAD69OmD4uJipKSkICQkBADw999/AwBatmwJAFCpVCa/jOPj46FSqQDo6kGFhYVh3759GDlypPw++/btw4wZMwAAYWFhsLe3x759+zB69GgAwNmzZ3Hp0iX5OiqVCm+88QaysrLg7e0tv4+bmxs6duxYqfttCNq3b4+TJ08CAOLi4uTjQUFBeP311/Hkk0/ik08+gYODA5RKJQRBMAlqp0yZIn/dqlUrfPjhh+jVqxdu3LghB2pERFQ/paSk4MqVKyguLq7R3K7KqhMV85OSknDkyBH07dsXzZo1Q0pKCl566SWEhITIgU9kZCRCQ0MxZcoULF26FFqtFtOnT0dUVJQ8Ovbkk09i+fLlmDdvHqZMmYIff/wRmzdvxq5du+T3mj17NiZNmoSePXvinnvuwdKlS5Gfn4/HHnsMAKBUKhEbG4vZs2fD3d0dbm5uePrpp6FSqdC7d28AwKBBg9CxY0dMnDgRb7/9NjIzM/Hiiy9i+vTpNTLSVVeJoijX5EpISMDixYtx5swZ5Obmori4GLdv38bNmzet7hBw9OhRvPLKK/jjjz9w/fp1OVi/dOkSA2AionpIWv2YlpaG3bt327o7FVIngjBnZ2ds27YNL7/8MvLz8+Hn54fBgwfjxRdflIMahUKBHTt24Omnn0b//v3h4uKCIUOG4L333pOvExwcjF27dmHWrFlYtmwZAgICsGrVKkRHR8ttYmJicPXqVSxcuBCZmZno3r079uzZY5Bo/8EHH0ChUGD06NEoKChAdHQ0PvnkE/m8nZ0ddu7ciaeeegoqlQouLi6YNGkSXn311Rp4WnXX6dOnERwcjAsXLmDYsGF46qmn8MYbb8Dd3R2//PILYmNjUVhYaDEIy8/PR3R0NKKjo7F+/Xp4eXnh0qVLiI6OrhPD0kREVDEpKSlYt26drbtRaXUiCOvSpQt+/PHHMtv5+/tj69atVtvce++9OH78uNU2M2bMkKcfzWncuDE+/vhjfPzxxxbbtGzZknlIFfDjjz/izz//xKxZs3D06FFotVq899578sbkxsmVDg4OJtsInTlzBmq1Gm+99Za8svT333+vmRsgIqIapVar7yoA02hckZ3tAXd3NZTKPPl4o0Y1FxrViSCM6peCggJkZmaipKQEV65cwZ49e7B48WIMGzYMjz76KJKTk1FUVISPPvoIw4cPx6FDh7BixQqDawQFBeHGjRvYt28funXrBmdnZ7Ro0QIODg746KOP8OSTTyI5ORmvvfaaje6SiIiqg1qtxtWrV+9q5fuxYz2wY8cwiKICgqDF8OE7ERqqG6CRcrlrQp2qE0b1w549e+Dn54egoCAMHjwY+/fvx4cffoj//e9/sLOzQ7du3fD+++9jyZIl6Ny5M9avX4/FixcbXCMiIgJPPvkkYmJi4OXlhbfffhteXl5Ys2YNvvnmG3Ts2BFvvfUW3n33XRvdJRERVTWpxNKmTZuwf//+Sl1Do3GVAzAAEEUFduwYhoiIsVVSLb8i6uy2RQ1BdW1bJP0Ql6WmfxhrK25bRERke2q1Gunp6di2bVulXi9NP+bnO2PLlodMzn/3nQYPPqi8224CKP+2RZyObIA8PDwwY8YMq8nqDg4ODMCIiMimpJWPGo0GmzZtqtBr9XO+UlJa641+aQGIAAS91iL8/asmAKsIBmENFAMsIiKqzco7a2OOfs6XLuASUZqBZS4TS0B+fuX6eTcYhBEREVGtISXeX7hwoVKv12hcsX37cJSOdAkwHPUyZWcHtG5dqbe7KwzCiIiIqFaoirpfaWmBKCvo0qdQAJ99BgQE3NXbVgqDMCIiIrK5ytb9knK/7O0LkJ7uj3/+CS73awUB+PVXoFevCr9tlWAQRkRERDYhJd7n5OTg4sWL5X6dFHj9808Qfvmln17ulzQCZpx4b96SJbYLwAAGYURERGQDlU28N02618/9gt7XlgMxhUIXgD37bIXfvkoxCCMiIqIad/Xq1Qq1v3zZD3/+2QVJSb1hPvAyJmDgwALs3+9ocmbjRuAh01JhNY5BGBEREdW469evl6udRuOKbdv+DxcvBqEiCfeAiNmzHXHgAKDVlh61swNUqor0tPpw2yKqV3766ScIgoCcnJxyvyYoKAhLly6ttj4REZFu+jEjIwOnT59GYmIi9u7da7W9RuOKvXsj8cEHs3DxYjAqFoABkyYJGDYMWLlSF3gBus+2WglpDoMwqlGTJ0+GIAh48sknTc5Nnz4dgiBg8uTJNd8xIiKqNlL+18qVK7F58+YyA7Bjx3rggw/icPhwH1Q0+AKATz8F1qzRfR0bC1y4AOzfr/scG1vhy1UbBmFU4wIDA7Fx40bcunVLPnb79m1s2LABLVq0sGHPiIioOljbJk+i0bgiNTUIZ8+2xvbtw1DZEMXODhg2zPBYQABw7721ZwRMwiCMalxoaCgCAwMNNmHdtm0bWrRogR49esjHCgoK8Mwzz8Db2xuNGzdG3759ceTIEYNrff/992jbti2cnJwwcOBAsxWWf/nlF/Tr1w9OTk4IDAzEM888g3xb7E9BRNQApaSk4Ny5c1bbHDvWA0uXxmHt2kn4+uuHcTcBWG2abiwLgzDC5cu6YdrLl2vuPadMmYIvv/xS/v6LL77AY489ZtBm3rx52Lp1K9auXYtjx46hdevWiI6ORnZ2NgAgLS0No0aNwvDhw3HixAlMnToVCxYsMLhGSkoKBg8ejNGjR+PkyZPYtGkTfvnlF8yYMaP6b5KIqAGRcr70P/bv349169Zh//79Fl+n0bjqlZwArE8/ihg8+BYEQTQ4qlAAmzfXvunGsnB1ZAO3ejUwbZpu5YhCoUtgrIkf4EceeQTPPfecXJzv0KFD2LhxI3766ScAQH5+Pj799FOsWbMGQ4YMAQB8/vnniI+Px+rVqzF37lx8+umnCAkJwXvvvQcAaNeuHf78808sWbJEfp/FixdjwoQJiIuLAwC0adMGH374IQYMGIBPP/0UjRs3rv6bJSKq5ypa80sqtururkZ2todeAGaNFnv35iIqqilWrwaeeAIoKSkd/aoNJScqikFYA3b5cmkABug+P/EEEB1d/UO5Xl5eGDp0KNasWQNRFDF06FB4enrK51NSUlBUVIQ+ffrIx+zt7XHPPffg9OnTAIDTp08jPDzc4Loqo3XHf/zxB06ePIn169fLx0RRhFarRWpqKjp06FAdt0dE1KCUJ+dLol9sVRC0CA09irIq3AuCiA8+uImoqKYAdIMF0dHA+fO6jbfryvSjMQZhDdi5c4a1UwDdXxXnz9fMD/SUKVPkacGPP/64Wt7jxo0beOKJJ/DMM8+YnOMiACKiu6dWq5GcnFyutsZTj6KowNGjvaALwkwJgog5cwTMnCkgIKCJwbmAgLobfEkYhDVgbdropiCNi9i1bl0z7z948GAUFhZCEARER0cbnAsJCYGDgwMOHTqEli1bAgCKiopw5MgReWqxQ4cO2L59u8Hrfv31V4PvQ0NDcerUKbSuqZsiImog1Go1rl69ik2bNpX7NWlpgRamHk1Hwfr334/ly7ujS5dmd9HL2o2J+Q1YQIBti9jZ2dnh9OnTOHXqFOykTtzh4uKCp556CnPnzsWePXtw6tQpPP7447h58yZi7yStPfnkkzh37hzmzp2Ls2fPYsOGDVgjFYa5Y/78+Th8+DBmzJiBEydO4Ny5c/jf//7HxHwioruQkpKC5cuXVygAO3RIhS1bRperrSCI9T4AAzgS1uDZel7dzc3N4rm33noLWq0WEydORF5eHnr27IkffvgBzZrp/lG2aNECW7duxaxZs/DRRx/hnnvuwZtvvokpU6bI1+jatSsOHDiAF154Af369YMoiggJCUFMTEy13xsRUX2iVqtRWFiInJwcbN682WpbKfHe3r4ARUWO+OefIBw82B/lK7wqYskSod4HYAAgiKJofiKWbC43NxdKpRIajcYkWLl9+zZSU1MRHBzMFX7VjM+aiBqyikw7ajSuSEoKx+HDKugm26SEe+uJ9xKFQheAPfvsXXbaxqz9/tbHkTAiIiIyq7ylJ0yDL4lg9NkSEZs3C1CphDqfbF8RDMKIiIjIrPKUnjh0SIX4+EhUPM1cNzqmUIhYuVKok3W+7haDMCIiIjIg5X9du3bNajtdABaF8uZ6Se0EQcQLL+SiZ08gLEzZoEa/9DEIIyIiIll5piA1GlekpQXeGQErXwDWr9/P8PG5goiICDz0UAACApRV0t+6jEFYHcd1FdWPz5iIGgq1Wo2zZ89aPK/RuOLnn/vh6NGeKF/wJRHQqlUqgoMv4tFH74eHx113tV5gEFZHSXW1CgsL4eTkZOPe1G9SToRxLTMiovpAmnrUaDRmV0BK5SbS0/3Kkfsl/dFqGKApFCKefnoIgoIawYMRmIxBWB3VqFEjODs74+rVq7C3t4dCwbq71UGr1eLq1atwdnZGo0b850JEdZ9arUZWVhaKi4uRl5eH+Ph4i23193ksb5mJqKh45Oe7yCsldYXABYSF+VTZPdQXrBNWi5VVZ6SwsBCpqanQGm8ASVVKoVAgODgYDg4Otu4KEdFdKW/JCQC4fNkPq1dPtbDNkD7D4EwQtIiLWwoAGDAgtkEm3rNOWAPg4OCANm3aVGj3eqo4BwcHjjQSUb1Q1u+Lik09AuZGx0RRgc6dR+LBB5Xw8GDyvTUMwuo4hULBKu5ERGSR/vTj9evXTc7rB14JCZEVmHo030YQtOjVqxk8POr/tkN3i0EYERFRPVXW9KPlnK/KBWAKhYj337/ZIPZ9rAoMwoiIiOoRabUjAKvFVjUaV2zfPgylU44VKzkBaKE/XalQAL/+KqBXryYV7XKDxSCMiIionihr5EsqsnrzphPOn2+NsstNmA/MBEGLyMgEefpStwIS6NXrrrrf4DAIIyIiqgfUajXS09Mtnj92rAe2bx8O6yNeusBLELQWV0UKghYzZ/6FadMCUVysgVrdDK1bo8GtgKwKDMKIiIjqOGsjYNLoV9kBGBAW9js6d/4L9vaFZspTaDFmzBYEBl7GCy9MYtHVKsAgjIiIqI7Rz/sCLOd+GSbeWycIWvTvfxBKZR4AYMGCVCxZ0gparQA7OxFLluTi4Yf7wcHBgQFYFWEQRkREVIdUZINtw8R7a7QYPnwnlMo8DBo0CG3btoWHhwf+8x/g/HmgdWsBAQFNATS9+xsgGYMwIiKiOuTq1atWz1dk9EtfSMh5AEBQUJA80hUQwFyv6sQgjIiIqJYra5NtiUbjWqkADFAgO9tdnoqkmsEgjIiIqBZLSUnBunXrytU2O9ujEgGYLh/M3T27wq+ju8MgjIiIqJZSq9VWAzAp9wsAmja9jvx8Z6vlJSS6NgCggCCU5oMBuv1yqWYwCCMiIqqlLG24rdG4IikpHIcPR6C07IRUXFXUaykd00IQdJtrS0FXSMh5ZGe7Y+TIzggP7wWgF1c+1jAGYURERLWIlP+Vk5NjkoSv0bji55/74ejRnjCt+WVu30cRY8Z8g8DAywCA7Gx3uLtny6NeSmUe+vUbzsDLRhiEERER1RKWyk+YH/kqDwVcXG5CqcxDeHg4mjdvDgCwt7eHUqnkyJeNMQgjIiKqJYynH0uDLxXKV+/LkH7Cfbdu3eDn51cV3aQqwiCMiIiolkhLS5O/PnRIhfj4KFRs5AvQ3/9RP+Geah8GYURERLWAWq3G7t27AdxdAHbPPUno0OGMQe4XwFWPtRGDMCIiologKysLgG4KMj4+EhUPwABAQIcOZxAcfFE+EhMTAy8vL+Z+1UIMwoiIiGqA8abb+nJycrB582YAuoKrZed/SaUnDBkXXX3kkUcQEhJSyR5TdWMQRkREVM3Ks+m2JD3dD+aDrNJcr65dT+Lkya53irKazwFjAFb7MQgjIiKqZtJUI2BY5T4wMM0gb0ujcUVCgrmpSC2iohLg758u53rdd9+PyM52h719IYqKHOTjnH6sOxiEERERVSHjaUf9qcZjx3pg+/ZhKJ1uFDFixA6EhJxHWlogsrK8zG45NGbMFnTufNrgmFKZB6UyD6NGjYKnpycAsO5XHcMgjIiIqIpYm3bUaFyNAjAAELB9+3DophRLAzP9kTBB0MoV783x9/dn4FVHMQgjIiKqIuaKrWZne8DdXW0l4V6A4fSjtP+j5VpfY8eORdOmTTnyVccxCCMiIqoGx471wI4dw+RNsyMjEwBoUb7K97pNt2NjVyEgIMPgDBPu64+K74FAREREZuXk5ADQjYBJARgAiKICCQmRiIqSArHyUKCoyLDAakxMDAOweoRBGBER0V1Sq9U4c+YMNm/eDI3GFXv3DjJJsBdFBQoKHDB16ir077+/zGsa1/wCAC8vryrtN9kWpyOJiIgqSH8F5OXLl/H9998DkLYbioSlMY6ff74XP/88AFFR8RAErVGgJkJK0DfOBRs1ahQT8OshBmFEREQVYLwCUpd8H4R//gnCwYP9UfZ2QwISEiIRGZmAhIRIOWds+PCdCAk5j+xsd5N9HxmA1U8MwoiIiMqQkpKCmzdvAgD+/fdf+bjhyJf5rYTMEUUF/P3TERe31CToUirz0LNnTzRr1gzu7u4svFqPMQgjIiKyIiUlBevWrZO/v3zZD3//PQD//uuHlJS2KA28LAVgpsGZlO8lFVw11rt3bwZeDQCDMCIiIiP6OV/6I1/ffvsg/vijG8o34iUiLOx3uLtfN8gTM5fvJVW8B1j1viGpM6sjR4wYgRYtWqBx48bw8/PDxIkTkZ6eLp9/5ZVXIAiCyYeLi4vBdb755hu0b98ejRs3RpcuXeRkSokoili4cCH8/Pzg5OSEyMhInDt3zqBNdnY2JkyYADc3NzRt2hSxsbG4ceOGQZuTJ0+iX79+aNy4MQIDA/H2229X8RMhIqLqIOV8rVy5EitXrsRXX53B4cO9cfx4l3IHYIKgRVRUPIYP/x59+iRi1qylGDNmM8aM2Yy4uKUIDT0ut/X09ISfn5/8wQCs4agzI2EDBw7E888/Dz8/P/z777949tlnMWbMGBw+fBgA8Oyzz+LJJ580eM3999+PXr16yd8fPnwY48ePx+LFizFs2DBs2LABI0eOxLFjx9C5c2cAwNtvv40PP/wQa9euRXBwMF566SVER0fj1KlTaNy4MQBgwoQJyMjIQHx8PIqKivDYY49h2rRp2LBhAwAgNzcXgwYNQmRkJFasWIE///wTU6ZMQdOmTTFt2rSaeFxERFQJarVa/gNft83QcKSktIZ+FXvrtBgzZgsCAy8bTDPqph1Pm32Fg4OD2eNU/wmiKIq27kRlbN++HSNHjkRBQQHs7e1Nzv/xxx/o3r07fv75Z/Tr1w+Arshdfn4+du7cKbfr3bs3unfvjhUrVkAURfj7+2POnDl49tlnAQAajQY+Pj5Ys2YNxo0bh9OnT6Njx444cuQIevbsCQDYs2cPHnjgAVy+fBn+/v749NNP8cILLyAzM1P+x7VgwQJ89913OHPmTLnvMTc3F0qlEhqNBm5ubpV+VkREZJ1arcbVq1exadMmaDSuSEoKx+HDKlRswkiLESN2GoxylYXV7+un8v7+rjMjYfqys7Oxfv16REREmA3AAGDVqlVo27atHIABQGJiImbPnm3QLjo6Gt999x0AIDU1FZmZmYiMjJTPK5VKhIeHIzExEePGjUNiYiKaNm0qB2AAEBkZCYVCgaSkJPzf//0fEhMT0b9/f4O/bqKjo7FkyRJcv34dzZo1M9vngoICFBQUyN/n5uaW/6EQEVGFqdVqZGVlyUVWk5IiKxV8hYUdRf/+B80m2UuioqLg7u4OpVIJgLlfVMeCsPnz52P58uW4efMmevfubTCipe/27dtYv349FixYYHA8MzMTPj4+Bsd8fHyQmZkpn5eOWWvj7e1tcL5Ro0Zwd3c3aBMcHGxyDemcpSBs8eLFWLRokfmbJyKiKqVf70t/n8fy6t79d7Ru/Y/J1KM5HPEic2yamL9gwQKzyfT6H/rTd3PnzsXx48exd+9e2NnZ4dFHH4W52dRvv/0WeXl5mDRpUk3ezl177rnnoNFo5I+0tDRbd4mIqN6SVj8a7/NYHoKgxcCBP6Nz59MWA7BRo0Zh2rRpmDFjBgMwMsumI2Fz5szB5MmTrbZp1aqV/LWnpyc8PT3Rtm1bdOjQAYGBgfj111+hUqkMXrNq1SoMGzbMZETL19cXV65cMTh25coV+Pr6yuelY35+fgZtunfvLrfJysoyuEZxcTGys7MNrmPuffTfwxxHR0c4OjpaPE9ERFUvO9ujQgEYIKJr15Nljn6xyj2VxaZBmJeXV6U3I9VqdbvQ6+dQAbq8rv3792P79u0mr1GpVNi3bx/i4uLkY/Hx8XIQFxwcDF9fX+zbt08OunJzc5GUlISnnnpKvkZOTg6OHj2KsLAwAMCPP/4IrVaL8PBwuc0LL7yAoqIiOWctPj4e7dq1szgVSUREtuHurjazj6M1Ak6e7Ir77vvRbCA2aNAgtG3blgEYlalO5IQlJSXhyJEj6Nu3L5o1a4aUlBS89NJLCAkJMRkF++KLL+Dn54chQ4aYXGfmzJkYMGAA3nvvPQwdOhQbN27E77//jpUrVwIABEFAXFwcXn/9dbRp00YuUeHv74+RI0cCADp06IDBgwfj8ccfx4oVK1BUVIQZM2Zg3Lhx8Pf3BwA8/PDDWLRoEWJjYzF//nwkJydj2bJl+OCDD6r3QREREQDDYqv6cnJyUFxcDHt7e6SliUhNDYK7uxrDh+/E9u3DUN4sHVFUIDvb3WwQxgCMyqtOBGHOzs7Ytm0bXn75ZeTn58PPzw+DBw/Giy++aDB9p9VqsWbNGkyePBl2dnYm14mIiMCGDRvw4osv4vnnn0ebNm3w3XffyTXCAGDevHnIz8/HtGnTkJOTg759+2LPnj1yjTAAWL9+PWbMmIH7778fCoUCo0ePxocffiifVyqV2Lt3L6ZPn46wsDB4enpi4cKFrBFGRFQDzG+w7QF3d7UcNB06pLqzeXYHuYL91KmrsHr1VKMRMS0eeGAXvv9+KPQDNGnbIUBXx7JNmzYAuOKRKqbO1glrCFgnjIio4jIyMuQZDv1Vj1KwdetWY8THR0G/8KogaBEXtxQpKa1N2oeGHjd7HakeWExMDNq3b2+LW6Vaql7XCSMiIiqL8apHUVTc+R4wrnwvTS+Ghh5HSMh5ZGe7yxtsA7B4HIBc94uoohiEERFRvZSWFmiSbG85+V4Le3tdDpluiyHTXC9Lx7ntEFUWgzAiIqo31Go1rl27hmPHetxJtDdmbv9HEYACq1dPNZhmNBYeHo7mzZvD3t6eVe+pSjAIIyKiekFKyNdNQ8bBdKWjpQ24dcek6cqQkPNmR7x69erFgIuqFIMwIiKqU/TLT6SnK5Ca2gjBwcUoKEgBYK34qgDLgZiOfumJUaNGwdPTEwBHvKh6MAgjIqI6w9J+j4KgRWTkH/D3D8I//wShrFEvHdHkmH7pCU9PT4PdU4iqGoMwIiKq1aSRr/R0BX788TaSkzvC3r7QoLiqKCr0yk5YH+0qJSAi4hASE1UGpSfK2o6IqKowCCMiolorJSUF69at00u0VwBoCfOBlmD02TpB0CI8PAnh4UlmS08QVTcGYUREVCtJAZhG42pmS6HyBVqlpKBN99l41IulJ8gWGIQREVGto1ar8fHH/0N2dhCSkzuivHs6WiYgOnoPAgMvoajIweyo15AhQxAYGAiAifhUMxiEERGRTemvdjx7Nh8pKQr8/ruIzz6Lu7PKsazd9YynJkVItb8kgqBFx46nLE43jh07Fh06dLiLuyCqOAZhRERkM9KUI6C/qbYUeFnL8TKcXiz9rEVUVAKcnG6b7PVoLd/L29u76m6KqJwYhBERkU2o1WqDAMxwU21rOV9aM+10AdjUqasQEJABABb3etQ3duxYeHt7c+qRbIJBGBER2YQ0BanRuCI+PhLWAy/9ES9L+WEKFBWVJtNb2usxKioKwcHBzPsim2MQRkRENpWd7QHzgZWlKUfz9AutWtOuXTsGX1QrMAgjIiKbcndXQxC0RlsNmQu8rE9Rmsv7GjRoEIKCguTvOfpFtQmDMCIisimlMg/Dh++UE+nLH3jpRr9UqkSEhyeZnXps27Ytgy6qtRiEERFRjbh8GTh3DmjTBggIMDwXGnocISHn8ddfHbF372ArVyldBRkRYTn4GjVqFPz9/RmAUa3GIIyIiKqNVANswwYnzJunhFYrQKEQMXNmJlq0yIJG42pQtb5Fi0uwnPslol+/n9GqVWqZWwx5enoyAKNaj0EYERFVC7VajeXLl0OjccXSpXEQRV1gpdUK+OADXwB+EIQuiIxMQJ8+iQCAoiJHmA/AdPW/pHZl4ZZDVBcwCCMiomohlaDIzvYwSroHpEBLFBWIj4/C7duOaNXqAuztC8wk6RvW/7ImJiYGXl5eHAWjOoFBGBERVSvzqx/1CTh4sD8OHhwAQdCia9eTOHmyq0G1+7ICMBZdpbqIQRgREVUbjcYV2dkeiIxM0NuSyJzSkbGTJ7siNnaVxY22JWPHjkXTpk1ZdoLqLAZhRERUJaQk/JycHFy+DKxe3QSbNsUB0I1o9e17EAcP9oPlivc6oqirfB8cfNFim0ceeQQhISFV2n+imsYgjIiI7pqUhA8Ax4710Kv5pSOKChw82A9RUQkWNunWZ7nyPUtPUH3CIIyIiMpFGukCgPR0BVJTG8HZWYu0NDtcuXIVGo0rAJgEYKUU+OefYMTFLUV2tjsKC+2xceN4k0r5UVEJFqcgGYBRfcIgjIiIymR5pEsazXIH0BYtW160kvcFpKS0Rl5eE3mqUb9SviBoDcpVALqRL09PTwDccojqHwZhRERUJmkETKNxNRrp0p9OVODixaAyriQgLa2FvNpRqpSfne1uNgnf09MTfn5+VXIPRLUNgzAiIio38zW/9Fnf6xEQERh4yeCIUplncfqRRVepPrO+RIWIiEiPVPOr/LTQTVkCgIhu3f4oV9FVQLcCktOPVJ9xJIyIiCySkvGvXbtmoeaX/gpH49WOIvr1O4h27c4iLa0FAgMvGQRgHTp0gK+vLwCgUaNGcHV1RaNGjVj7ixoMBmFERA3c5cvAuXNAmzZAQEDpcdNk/Di9wKt0dCss7Hfk5rri3Ln2RlcW0KpVKgICMsyOfnXo0AFdunSpjlsiqhMYhBERNUDSCNeGDU6YN08JrVaAQiHi7bc1ePjhW3BwcCgjGV8a8VLg6NEwCGZTwSzX+yIiBmFERA2ONMKl0bhi6dI4iKIugtJqBcyd64Z///0CSmUehgwZAqA8yfgKiKLp0YiIRIsJ9wDg7Ox8N7dBVOcxCCMiamCysrIAmA+uRFGB7Gx3AMAnn5yGu7sr0tP9YLm6PQBoIQgwuJYgaBEenmTQKioqCq6uuoKuzs7O3HaIGjwGYUREDYharcbmzZsBwEJwJeLwYRXOn29zJ6jSwnD60Ziuwr2T022DoqvDh+80GAUbMmQI7rnnnmq5J6K6ikEYEVEDceQIsGtXI/z7rx9cXW8gISESpsGVgHPn2kI/58s6Af7+6QgOvmi16GpgYGDV3ARRPcIgjIioHktJScHNmzfx4ovNsX17MwBKAI8jJOS8lTyvsgqu6rUUSpPvWXSVqGIYhBER1VMpKSlYt24dLl/2w/btj6M0uBKQktIauqnGitbsLp2+NDftaM7YsWNZ84vIDAZhRET1gH6tLycnXfmJf//9FxqNK06c6AFz046dOiXj1KlOZoquWvbAAzvh7HwLABAYeLnMAAwAvL29K3w/RA0BgzAiojrKUq2vYcMOITT0uJkCq4YJ+F27nsSgQfHIznbHP/8E4+DBfrA2MiYIWrRrd85i4BUVFYXg4GCDY6x8T2QZgzAiojrIWq2vHTuGwds700yBVdHg88aN4zF8+E6Ehh5HcPBF+PhkYcuWh8y+X3mmHoODg+Hn51eVt0lUrzEIIyKqg6Rq9pZqfaWltTCTeK8fiOnaSQFbQEAGAgPTIAhak3pfo0dvKdfUI5PviSqGQRgRUR0i5X65uekCJXd3tdnAydzxO2cNvhNFBVatmooRI3QjYsOH7zSp99W582mTfvTs2RPu7u5o0qQJGjVqBG9vb047ElUQgzAiolpOyv365BNnvPGGG0RRgELhjWHDepgNnLp2PYmNG8dXIOFeNyIWEnIeoaHHrdb7koSGhnLqkeguMQgjIqrFpDIThw6pEB8fBSmg0moFbN+um0rUD5zs7QuxevVUo1ywsknbFUm1vjj1SFT9GIQREdVSarUa69atg0bjaqG6vQKrV0+Vk+uVyjykpgaVsdm2JaVFVy0ZNWoUPD09ueKRqIpU5l8qERHVAGvJ9xIpuV6j0W2M7e6uhq4IqyXinQ/DY1FRCWWOfvn7+8PPz48BGFEV4UgYEVEtZznJXke3GjIA2dm37mzKrT9iJgVkunyxyEjTzbYjIxPQp0+i/AppxEsfR7+Iqh6DMCKiWkK/6n1AQOlxpTLPIPneONleELTYsmUMdJMbxueAceO+hoNDkUGivbXke09PTybdE9UABmFERDZkqer9229rMGjQNbmdfvJ9ero/EhIi5ZEsUQRKs0tMS1B8/fV4jBixE8HBF+Xj5Um+J6LqxSCMiMhGrFW9nzvXDf/+Gw+lUtdWo3FFdrYH3N3VCA6+iM6dk5Gd7Y78fBeLVe5LlZagKE/gxZWPRDWDQRgRkY1IifdpaYFmq95LJSN0e0AaFlCVVkNevuwHXd6X9XVW+teT9OnTBz4+PvL39vb28PLyYu4XUQ1hEEZEZANqtRrXrl2TAyxjgqCFvX0hkpM7GuwBKa2GDAk5j5SU1ndea36DbuO8MakExahRo+Dv789gi8jGGIQREdUw/WnIHTvizKx61FW9Nyy6WkoUFTh7tg2+/34YSgOt0n0hpar5J092NRg9k0bBPD09GYAR1QIMwoiIakjpvo/FAKzX//rjj66wPMWoxfffD4Vp8VYB/fvvR1iYbqryvvt+LHP7ISKynXIHYbm5ueW+qJubW6U6Q0RU35hf/ajb9zEk5DzM53NZy+/SQhd8md+OyNv7qhxwcQUkUe1W7or5TZs2RbNmzax+SG2IiKh02vGdd77G3Llu0GpLVz9KeWBRUQkwrmAvCFqYq3ovCFo88MAuWN4PUovAwMtl9ourH4lqh3KPhO3fv786+0FEVO9cvXoVgPXVj1Klev26X5GRCUhP98dff3U2eY2z8y0L1fO1GDFip9mRr5iYGCjv1Lpg5Xui2qPcQdiAAQOqsx9ERHWWuUr3arUaWVlZOHasB7ZvN139CIiwt9eVqOjTJ1Gu+6VfiNXcCsfAwMsG1fMFQQuVKhHh4UkGAdjAgQPRpk0bBl1EtVilE/NzcnKwevVqnD59GgDQqVMnTJkyRf5ri4ioPrNW6f6BBzKwefNmefWj+cwPAUVFpdOCSmUe8vKa6AVgujb6Kx6lFY761fMtJd17e3tz6yGiWq5SQdjvv/+O6OhoODk54Z577gEAvP/++3jjjTewd+9ehIaGVmkniYhqk7Ir3X8BpdL66kf9ul0A9EbMjNsLiI7eg44dTxkEW2Ul3fMPYqLar1JB2KxZszBixAh8/vnnaNRId4ni4mJMnToVcXFx+Pnnn6u0k0REtYlU6d5ckKVfmd7dXW02f0vK+8rOLp0mLC26CpO2xgFYeTD5nqj2q/RImH4ABgCNGjXCvHnz0LNnzyrrHBFRbWZvX2AmyBKRnu6P4OCLUCrzDPK3AC0iIhLh4pJvkIivUiWaHTEzLrJaHjExMdx6iKiOqFQQ5ubmhkuXLqF9+/YGx9PS0uDq6lolHSMiqo2MtxsyTaAXkJAQic6dk83mbwG4M4VZug1RYqIKpvXCtIiNXYWAgAz5yKBBg9CkSRM0atQITZs2Nekbk/CJ6pZKBWExMTGIjY3Fu+++i4iICADAoUOHMHfuXIwfP75KO0hEVFtY3m7IsG6XNCUJ6KYs3d3VCA6+CABITQ0yO4UZEXEIiYkqg22G9AMwAAgKCmKyPVE9Uu5irfreffddjBo1Co8++iiCgoIQFBSEyZMnY8yYMViyZElV9xEAMGLECLRo0QKNGzeGn58fJk6ciPT0dIM2P/zwA3r37g1XV1d4eXlh9OjRuHDhgkGbn376CaGhoXB0dETr1q2xZs0ak/f6+OOPERQUhMaNGyM8PBy//fabwfnbt29j+vTp8PDwQJMmTTB69GhcuXLFoM2lS5cwdOhQODs7w9vbG3PnzkVxcXGVPAsisg1ruWD6BEGL9HR/LF0ah7VrJ2Hp0jgcO9YDAJCe7gfj4qyACBeXfMTFLcWkSWsQF7cUoaHHTa7LPC+i+qVSQZiDgwOWLVuG69ev48SJEzhx4gSys7PxwQcfwNHRsar7CEBX82bz5s04e/Ystm7dipSUFIwZM0Y+n5qaigcffBD33XcfTpw4gR9++AHXrl3DqFGjDNoMHToUAwcOxIkTJxAXF4epU6fihx9+kNts2rQJs2fPxssvv4xjx46hW7duiI6ORlZWltxm1qxZ2LFjB7755hscOHAA6enpBu9TUlKCoUOHorCwEIcPH8batWuxZs0aLFy4sFqeDRHVLCnh3pAusJKS7vVLTYiiAjt2DMPly35ISIiEuT0f4+MjAUDOJdM3atQozJgxg1ONRPWMIIqi8Z9kdcL27dsxcuRIFBQUwN7eHlu2bMH48eNRUFAAhUL3P74dO3bgwQcflNvMnz8fu3btQnJysnydcePGIScnB3v27AEAhIeHo1evXli+fDkAQKvVIjAwEE8//TQWLFgAjUYDLy8vbNiwQQ4Cz5w5gw4dOiAxMRG9e/fG7t27MWzYMKSnp8PHxwcAsGLFCsyfPx9Xr14t91+zubm5UCqV0Gg03I+TqBbIyMjAypUrAcAgJ0wQtOjb9yBatUqFu3s2srM9sHbtJJPXDxq0B3v3DrZ4/UmT1sjTlvoYgBHVLeX9/V2pnLDbt2/jo48+wv79+5GVlQWt1vAvwmPHjlXmsuWWnZ2N9evXIyIiAvb29gCAsLAwKBQKfPnll5g8eTJu3LiBr776CpGRkXKbxMREREZGGlwrOjoacXFxAHRTDUePHsVzzz0nn1coFIiMjERiom5rkaNHj6KoqMjgOu3bt0eLFi3kICwxMRFdunSRAzDpfZ566in89ddf6NGjh9n7KigoQEFBgfx9RTZNJ6K7Y67qvTGNRiN/HRp6HLduNUZ8vG7E6+DBfigpaYTw8CQLpSlE3LrlZGHLIQAorRs2atQoeHp6AmCyPVF9VqkgLDY2Fnv37sWYMWNwzz33QBAsbSZbtebPn4/ly5fj5s2b6N27N3bu3CmfCw4Oxt69ezF27Fg88cQTKCkpgUqlwvfffy+3yczMNAiMAMDHxwe5ubm4desWrl+/jpKSErNtzpw5I1/DwcHBZGWSj48PMjMzrb6PdM6SxYsXY9GiReV8GkR0tyxVvV+48F+MGnUdarUTLl50gL9/PlxcriM+Pl5+rUbjemdqUQqoFDh8uA8SE1UYPnwnOnQ4jVOnOkJ/1eQvv/QzmarUMdz30dPTkwn4RA1ApYKwnTt34vvvv0efPn3u6s0XLFhQZiL/6dOn5VIYc+fORWxsLC5evIhFixbh0Ucfxc6dOyEIAjIzM/H4449j0qRJGD9+PPLy8rBw4UKMGTMG8fHxNRYo3o3nnnsOs2fPlr/Pzc1FYGCgDXtEVH9Zq3q/aJE/Dh06ZVDLa/jwnQgJcZVXO5rblBsozf/SXc901aS/fzri4pYiLS0AN286wdn5FgIDL1e4GCsR1X2VCsKaN29eJfXA5syZg8mTJ1tt06pVK/lrT09PeHp6om3btujQoQMCAwPx66+/QqVS4eOPP4ZSqcTbb78tt1+3bh0CAwORlJSE3r17w9fX12QV45UrV+Dm5gYnJyfY2dnBzs7ObBtfX18AgK+vLwoLC5GTk2MwGmbcxnhFpXRNqY05jo6O1bawgYgMlVX1XpcoX5pYv337MAgC9OqCWU6ntbxyUivv9ahUnr77myCiOq1SqyPfe+89zJ8/HxcvmiaQVoSXlxfat29v9cNSEruUhyblUN28eVNOyJfY2dkZtFWpVNi3b59Bm/j4eKhUKgC63IuwsDCDNlqtFvv27ZPbhIWFwd7e3qDN2bNncenSJbmNSqXCn3/+abCiMj4+Hm5ubujYsWMFnxIRVSdzKx113xv/71FhVBesov/7FBEVlVCuES+WoiBqGCoVhPXs2RO3b99Gq1at4OrqCnd3d4OPqpaUlITly5fjxIkTuHjxIn788UeMHz8eISEhcuAzdOhQHDlyBK+++irOnTuHY8eO4bHHHkPLli3lRPgnn3wS//zzD+bNm4czZ87gk08+webNmzFr1iz5vWbPno3PP/8ca9euxenTp/HUU08hPz8fjz32GADdprixsbGYPXs29u/fj6NHj+Kxxx6DSqVC7969AeiqWnfs2BETJ07EH3/8gR9++AEvvvgipk+fzpEuolpG2lpICsSkEhOmJSjKYm2huYioqHj06ZNY5lUeeeQRJuITNRCVmo4cP348/v33X7z55pvw8fGp9nwrZ2dnbNu2DS+//DLy8/Ph5+eHwYMH48UXX5SDmvvuuw8bNmzA22+/jbfffhvOzs5QqVTYs2cPnJycAOiS93ft2oVZs2Zh2bJlCAgIwKpVqxAdHS2/V0xMDK5evYqFCxciMzMT3bt3x549ewwS7T/44AMoFAqMHj0aBQUFiI6OxieffCKft7Ozw86dO/HUU09BpVLBxcUFkyZNwquvvlqtz4mIKsd4ayGlMg9OTrexfbv+ptr6WxOZI1hoo8XUqatMqt+PHTvWZIEPV0ISNSyVqhPm7OyMxMREdOvWrTr6RHewThhR1ZBWQQLA2bP5SElRQKnMwl9//WDxNZcv+2HVqsdhGFRJQZb0v039c+amMIGIiEMYNCjB4NgjjzyCkJCQStwJEdUF1VonrH379rh161alO0dEVFOkVZCAcYHVIAwfnmV2e6Bjx3rcGQUzrWxfGmxpoQvGFGar5AO6qc3w8CT5+1GjRsHf35+jXUQEoJJB2FtvvYU5c+bgjTfeQJcuXeRiqBKO2hCRreiPeqWnK3DiRD40Gt1qbikAA0pXPDo4FCAwME1OmNdtzq0/DalPhH5dMECLMWM2yyUmnJxuG1TRHz58p0EivqenJwMwIpJVKggbPFi37cb9999vcFwURQiCgJKSkrvvGRFRBZkf9fKBIMRBpUo0UzpCgS1bHpIDptDQ41Y25zaX76WAi8tNOdAyl1umj6seiUhfpYKw/fv3V3U/iIjumjQCJo1m6Y96JSaqYClvSyqwGhJyHv/8EwTTgEuL8eO/xsaN402mG6WthiS6GmCmZSjGjh3LUTAiMlCpIGzAgAHlavef//wHr776qrwHGhFRTbBUgDUi4hASE1UWK93//HM/HD3aE8bJ+FFRCWjX7jyGD99pdbrRGm9v77u4IyKqjyoVhJXXunXr8OyzzzIII6IaZW4DbSlJPjw8CWlpAdi6dYzJ+aNHw2AuGd/fPx2A9elG/U23jbH0BBGZU61BWCWqXxAR3TWpAKulUSul8jQKCw3Pq1SJOHzYdD9c4ylHS9ONXPVIRBVVrUEYEZGtlJUkb3wegJmpShGRkaVbDQ0aNAhBQUEm78WRLiKqDAZhRFRvWRq1AnTJ+9nZHigsbIS//uoED49rd0bDVJDKT0RFJRhsNRQUFAQ/P7+a6TwR1XsMwoioztGvBabv2rVr5Xq9ftFWwyr4wp2pyUMID09iiQkiqlYMwoioTtGvBVYZxuUrShPxdZ+lchZSpfuBAwfCy8sL3t7enHIkoipVrUHYI488wur5RFSljEfApGlFd3d1ucpFWC7GWkoUFcjOdodSmYc2bdpwCpKIqkWlg7CcnBz89ttvyMrKglarNTj36KOPAgA+/fTTu+sdEZEVhntBlla9t0SjcUV+vrNJ+Qpj5oqwEhFVtUoFYTt27MCECRNw48YNuLm5QRBK6+oIgiAHYURE1cVcVXyp6r3+PpDSKFlKSmu99lq9QMw0J6wiRViJiCqrUkHYnDlzMGXKFLz55ptwdnau6j4REQEwn4CfmpoKwHJVfGka0TD5XhqtL918WxR1m2/b2xfJgZqDQ5HZchZERNWhUkHYv//+i2eeeYYBGBFVmqUVjhqNBkVFRbh16xZ2795t8fWWquK7u2ebSb43N/Wo23w7OPgigPN3dzNERJVQqSAsOjoav//+O1q1alXV/SGiBuBuVzgCplXxAUAUgeTkzhAEsczk+/LmfbEsBRFVl3IHYdu3b5e/Hjp0KObOnYtTp06hS5cusLe3N2g7YsSIqushEdU75V3hqH8cgEmbkJDzMNwdTYH4+Cjo53iZZ5j3ZWnfR1bCJ6LqVO4gbOTIkSbHXn31VZNjgiCgpKTkrjpFRPWP/vSjflFVSyscTQuqipAq2UdEJN7ZiDsQplON+nW/TAMxQdAiNnYVAgIy5GOenp4sQ0FENa7cQZhxGQoiovKyNP1oaYWjt3emmYKqUjClwOHDfXD4cAR0QZY1Ajp1+hOnTnUyCPL0AzAiIlupVE7Yf//7X8TExMDR0dHgeGFhITZu3MgSFURkwNL0Y36+s9kVjpcutSgzp8swMJNfbXBMELQYNCgegwbFW9zIG2DeFxHZRqWCsMceewyDBw+Gt7e3wfG8vDw89thjDMKIGiBLqx0B69OPuvIR+gGXiFu3nMosqGpOWNjvOHYszGDUSwq6zAVfo0aNgr+/P/O+iMgmKhWEiaJoUKBVcvnyZSiVyrvuFBHVLcbTjdYS7Y2nH3VBmP4IloBffumHyMgEJCREygGVLgHfWlCmRf/+B9G//0Gro176GIARkS1VKAjr0aMHBEGAIAi4//770ahR6ctLSkqQmpqKwYMHV3kniah2y8rKkr+2tpWQ+X0bTQMrUVTA3z8dcXFLkZ3tjvR0/zsBGWBc4V4i/V2oVOZhzJjeaNasGYqLiwEA9vb2Jn8gcuUjEdlahYIwaYXkiRMnEB0djSZNmsjnHBwcEBQUhNGjR1dpB4modlOr1di8eTOAsrcSMldgVbeFEMwWXQWA/HwXxMdHojRYE2A6hWlYLT84OJirHYmo1qtQEPbyyy8DAIKCghATE4PGjRtXS6eIqO7QzwMraysh4wKr0kgZAJNjhns9GlNYrJZPRFRXVConbNKkSQB0//PNysoyKV/RokWLu+8ZEdVKUgK+tL3Q9evX5XPp6X4wt0JRPzgKDT2OkJDzJnlb+scAYOnSOIuJ+YKgNckZ46bbRFTXVCoIO3fuHKZMmYLDhw8bHJcS9lmslah+kQKvnJwceerRmEbjioSESBiWjRARGZlgEhxJo2KWjqWmBllZGam7Zp8+iejcObncSfhERLVNpYKwyZMno1GjRti5cyf8/PzMrpQkovqhvPs8mk+6F6BU5iA1NchkpaQ19vYFMJf3JV3T3z8dgPlgDmDdLyKqGyoVhJ04cQJHjx5F+/btq7o/RFTLWNvnEdAFX/b2BcjPd4Zp4KTF1q1jzK6UtERaXWmpHIX+9Ka5PR+56pGI6opKBWEdO3Y0KL5IRA2D5f0c9ctGSIGYFoAAUdSNlIuiAtu3D4ODQwECA9PMjmAZr640ZTi9yT0fiaguq1QQtmTJEsybNw9vvvkmunTpAnt7e4Pzbm5uVdI5IrIdKQ9M+oPLNEDS3zao9LMgiBg9ejMAAVu2PGR0VQW2bHlITqz3988wmKY0P6Up0SIqSpcLJuG0IxHVZZUKwiIjIwEA9913n0E+GBPzieoHc3lg1gOkUqKogIvLTbi7Z1vcekgUFYiPj4JU8ysiIhHh4Ulm64gJghajR29BYOBlg9GzmJgYTjsSUZ1WqSBs//79Vd0PIqpFzO0Bab7QqikpZ8u4JpiZlnc+K3D4cB8kJqowfPhOs3XEOnc+bfJqLy+vStwZEVHtIYiibiOQijp48CA+++wzpKSkYMuWLWjevDm++uorBAcHo2/fvlXdzwYpNzcXSqUSGo2GU7xUozIyMrBy5Ur5eykZPz3dT67NZVgPTPe1ueR7jcYVaWkBcoK+NYKgRVzcUgAwW3pCSsRn8j0R1Wbl/f1dqZGwrVu3YuLEiZgwYQKOHz+OgoICAIBGo8Gbb76J77//vnK9JiKbkXLAABgsvDHeC7J163M4d64tDAuyihg9+huDKUP9VZSdO59GYaG1UTEdqbp+cPBFs4n73HCbiOqTSgVhr7/+OlasWIFHH30UGzdulI/36dMHr7/+epV1johqhqVaYOb2gjQOwKTjLi435cDJ3CbeISHnUda4u7XyExz9IqL6plJB2NmzZ9G/f3+T40qlEjk5OXfbJyKqBvojXfo0Gg2ysrLMvsZSAVZj+sGTpU28hwz5HpZqf+mw/AQRNSyVCsJ8fX1x/vx5BAUFGRz/5Zdf0KpVq6roFxFVofJWvTdmPhnfcG9IKXgCdNsN5ec7m93E+8YNFyvvZFp+goiovqtUEPb4449j5syZ+OKLLyAIAtLT05GYmIhnn30WL730UlX3kYgs0B/dysnJQXFxscF5e3t7KJXKChdX1s/nMl6t2LXrSZw82fVOoKULnpycbssbbguCFuYq5zdvng5zAdyYMd+YlJ8AWAOMiOq/Sq2OFEURb775JhYvXoybN28CABwdHfHss8/itddeq/JONlRcHUnWVHZ0qyyW8rn0VyvqgjR3eQpSCsBK6VfTLz3WrdsfcgBnbRujRx55BCEhIVV+b0RENaG8v78rXaIC0NUSOn/+PG7cuIGOHTuiSZMmlb0UmcEgjKyxVEZCf09HS5tm67fVP6/RuJoEVFLZCEubb6emBmHt2klmzphuwC0IWsTGrkJRkYNJ+YmYmBgolUom4BNRnVetJSokDg4O6Nix491cgoiqgKU9Hc2NNhmPdEVGluZipaUFms3nys52txiEWS7iar5SflGRA4KDLxqsfmTgRUQN0V0FYURke9b2dJRWJoaEnJenEY1XLuq2DwKcnG5j+/ZhJtcXBC3y812g0bgCMB1hUyrzEBp6FEeP9iqzr/qrKLn6kYgaOgZhRHVcWXs66o9kWSo5ER8fCd02sMbnRIgi7mzEbXmELTj4goUgrHRKUnqNpRE1IqKGhkEYUR1X1p6O+qNP1qYOzWeHlo6qWRthCwxMg/HKRyn/KyenKQCYXQFJRNSQWd/IjYhqPWmjbF1pCOiViDAcfZKS8fv2PQhdwKRPK7++vKQRNqkPI0bsMOjD8OE7ERCQgc6dT6Nz59MsQUFEZIQjYUT1QGjocYMyEoDhBtjGyfgdO57CqVMdoD+1CKDMvR316Y+wmeuDuZWPEibiExExCCOq1cxtNaTRaFBUVIS8PMORJaUyzyDw0d9I2zgZ//TpDpg61bBUhEbjitGjt+LmzcbYvXuomWBMGinTFWnVz+8y3udRHwMuIiLzGIQR1UJqtRpZWVnYvHlzpV4fERGBw4cPAzCfuK9fKgIwLVvRocNpnD7dQf5epUqEi0s+EhIiIYq4k8RfiisdiYgqjkEYUS1TFZXwfX195a/NJeOXteH2qVMdoUuy19UR69w52aCIq3FiPhERVRwT84lqGePpx7tlLnFffyrRUtkKHQXi4yNx9mwbi0VciYiocjgSRtQAmEual1ZL2tsXWC1xASjw/fdDYa4EhX5iPhERVQyDMKI6wtx+j5b2gLx+/brJ6/UT9w23OdIiJCQF//wTorftkVHS151kfEEQDTbflq7HchNERBXHIIyoltNoXJGUFI7Dh1WwVFLCuIL9/v37Ta6hv7m3YSkKBVJS2kAXjJ1HSkqIhZ4oMHr0Zri43MSECeHo3LkXgF5c/UhEVEkMwohqMcMRKx1RVGD79mEQBJQrUd545aNKlWhh6lFxJwAzHgXTEQStXPW+c+doroYkIrpLDMKIqpG5Ol/6rI0imW7Mrc90myH9PSItXUMUFTh8WGUlB8xyAMZ9H4mIqhaDMKJqUt5SEzNmzDAbiFnfmFtrMBIGmE+UN38NBUJDj+Do0TCUtUBaELQYPXqLyb6PzAEjIrp7DMKIqkl5S00Yt5MCHMubbWsxYoRhTphCIWLYMNORKkvX0AVg5ke9JNLoV+fOpwGUVsVnDhgRUdVgEEZUy3h4eOCRRx7BunXrMHz4TpN8rvDwJDnYkspOTJgQjsOHj5tcS6oRZjqtaXkEzNLol7+/P4MvIqIqxCCMyMauXbtm8L2DgwOcnZ0BWN8UGygtO+HrG2ZyXWlFZEjIeYwevRVbtjxUZl84+kVEVHMYhBGV090k2Vuzbds2k2Njx46VvzbemNsc47pgxisiIyMTzExL6ueVaRERYTjKBnD0i4ioOjEIIyqHu02yBywXVjWnuLi4Qv3TrwtmbkVkQkIkIiMT7mzArQvMFixIxcCBhUhLc0RgYAF8fd0ARKFRo0Zo2rQpR7+IiKoZgzCicqhskr3EeGRKv7BqVTO3IlIUFVAqcxAXt1Se2nz66fHw87NUmJWIiKobN/AmskKtViMjI8Mkb6sizI1M7dgxDBqNa6WulZoaZPW16el+0G09ZGjLljFISWmN4OCLrPdFRFQL1JkgbMSIEWjRogUaN24MPz8/TJw4Eenp6QZtNm/ejO7du8PZ2RktW7bEO++8Y3Kdn376CaGhoXB0dETr1q2xZs0akzYff/wxgoKC0LhxY4SHh+O3334zOH/79m1Mnz4dHh4eaNKkCUaPHo0rV64YtLl06RKGDh0KZ2dneHt7Y+7cuRWeYiLbkqYgV65caTZvy5xr164hIyMDGRkZ0Gg0ACyPTGVnu5e7LxqNK/bujcTSpXFYu3YSli6Nw7FjPcy2S0iIhPnyE5UP/oiIqOrVmenIgQMH4vnnn4efnx/+/fdfPPvssxgzZgwOHz4MANi9ezcmTJiAjz76CIMGDcLp06fx+OOPw8nJCTNmzAAApKamYujQoXjyySexfv167Nu3D1OnToWfnx+io6MBAJs2bcLs2bOxYsUKhIeHY+nSpYiOjsbZs2fh7e0NAJg1axZ27dqFb775BkqlEjNmzMCoUaNw6NAhAEBJSQmGDh0KX19fHD58GBkZGXj00Udhb2+PN9980wZPjyojKyurwq8xF6yZq9VlrrCqJceO9cD27cOg/zeTpW2KrBd4NV9Vn4iIbEMQRePNT+qG7du3Y+TIkSgoKIC9vT0efvhhFBUV4ZtvvpHbfPTRR3j77bdx6dIlCIKA+fPnY9euXUhOTpbbjBs3Djk5OdizZw8AIDw8HL169ZKTsLVaLQIDA/H0009jwYIF0Gg08PLywoYNGzBmzBgAwJkzZ9ChQwckJiaid+/e2L17N4YNG4b09HT4+PgAAFasWIH58+fj6tWr5a42npubC6VSCY1GAzc3typ5blQ+ZSXiVyTJHtAFUrt2DUdJiVCunLCYmBhs2rQJGo0rli6NsxhYTZq0BsHBF+X+2NsXYPXqqRbbC4IWcXFLoVTmYdq0adz/kYioGpT393edGQnTl52djfXr1yMiIgL29vYAgIKCArm2ksTJyQmXL1/GxYsXERQUhMTERERGRhq0iY6ORlxcHABdUvXRo0fx3HPPyecVCgUiIyORmJgIADh69CiKiooMrtO+fXu0aNFCDsISExPRpUsXOQCT3uepp57CX3/9hR49TKeRqHaxlohfmST70NDjWLgwHBcuNMKhQ2vlwM1SMFdUVISxY8fihx8KrQZU7u7ZJv3p2vUkTp7sKn+v+zNLYbL/I7ceIiKyrToVhM2fPx/Lly/HzZs30bt3b+zcuVM+Fx0djVmzZmHy5MkYOHAgzp8/j/feew8AkJGRgaCgIGRmZhoERgDg4+OD3Nxc3Lp1C9evX0dJSYnZNmfOnAEAZGZmwsHBAU2bNjVpk5mZKbcxdw3pnCUFBQUoKCiQv8/NzS3PY6EaZCnJ3nha0Bxn52x07+6A5GRdO2vBnDStqdG4QhC6mARiUnsAJv05ebIrYmNXoajIARMmhMPd3R0XLjRCUFAx/P17AejF8hNERLWATRPzFyxYAEEQrH5IwQ8AzJ07F8ePH8fevXthZ2eHRx99FNJs6uOPP44ZM2Zg2LBhcHBwQO/evTFu3DgAutGsumDx4sVQKpXyR2BgoK27VG9Jqx4tfeTk5Ji8RqNxxV9/dap0kv3mzZtRVFQkX6s8KyalbYcEQXvniBYREYcQF7cUISHnsXfvILP9KSpyQHDwRYSGeiMszAejR3sgLMwHfn5+8PPzYwBGRFQL2HQkbM6cOZg8ebLVNq1atZK/9vT0hKenJ9q2bYsOHTogMDAQv/76K1QqFQRBwJIlS/Dmm28iMzMTXl5e2Ldvn8E1fH19TVYxXrlyBW5ubnBycoKdnR3s7OzMtvH19ZWvUVhYiJycHIPRMOM2xisqpWtKbcx57rnnMHv2bPn73NxcBmLVoLyFV/Xpj1rpyj+Urj5UKMRyJ9lLyf7WVkwaj6jpb11kb1+IoiJH/P57GA4e7A9zqyClacqYmBgGW0REtZhNgzAvLy94eXlV6rVarW5kQH/6DgDs7OzQvHlzAMDXX38NlUolv4dKpcL3339v0D4+Ph4qlQqALkcmLCwM+/btw8iRI+X32bdvn7zCMiwsDPb29ti3bx9Gjx4NADh79iwuXbokX0elUuGNN95AVlaWvKIyPj4ebm5u6Nixo8V7cnR0hKOjY6WeB5VfeQuvSoxHrXSBjy4QEwQtFi5MB1C+1YZSZfuKrphUKvOQktLaYiCoT6VKvLPVkbJcfSIiItuoEzlhSUlJOHLkCPr27YtmzZohJSUFL730EkJCQuTA59q1a9iyZQvuvfde3L59G19++SW++eYbHDhwQL7Ok08+ieXLl2PevHmYMmUKfvzxR2zevBm7du2S28yePRuTJk1Cz549cc8992Dp0qXIz8/HY489BgBQKpWIjY3F7Nmz4e7uDjc3Nzz99NNQqVTo3bs3AGDQoEHo2LEjJk6ciLfffhuZmZl48cUXMX36dAZZNczcfo8VLbxqvuyDgOjoPejY8RTGjh2CzZtLz5Rn5aQ0zWicE2apvflA0BwtwsOTADDxnoiotqsTQZizszO2bduGl19+Gfn5+fDz88PgwYPx4osvGgQ1a9euxbPPPgtRFKFSqfDTTz/hnnvukc8HBwdj165dmDVrFpYtW4aAgACsWrVKrhEG6EoDXL16FQsXLkRmZia6d++OPXv2GCTaf/DBB1AoFBg9ejQKCgoQHR2NTz75RD5vZ2eHnTt34qmnnoJKpYKLiwsmTZqEV199tZqfFOmrzLSjOaUV6EsDH0HQomPHU5g27QGD0dyKrJzUn2Z0d8+2mthfVv0vHS1GjNgp94lTkUREtVudrRPWELBO2N3JyMjAypUr7+oa5ut0iYiKikefPonyht1nzpzBypXfm7TVr8tV2fcvu/6XiLCw39G//0FMm/YA2rdvX6n3IiKiqlGv64QR1ZTWrYeYnYocN641IiJayEeUSmWFku0llqYuNRpXJCWFIzFRZbb+V+nInBZRUQno00dXx66yOZZERFTzGIQRWXH+/G4IQjuT0a3k5O+QlqYLmqRFGxVNtrc0dWlpmyL9+l9Dhw6AQuF2p/ZXFwBdWPuLiKiOYRBGDZ61RPryJNAXFhbCwcHBpC0AiCKQktLaJC/MUp0wb+9M7NhhGIBJRFEBlSoSDz6oZLBFRFQPMAijOsfcikd9FRkRKk8ifXkS6D08PDBjxgxcuFCMHTv0Vy6ar6iflhZodury3Lk2Vrcp6tWrGTw8mpXr3oiIqHZjEEZ1SnlXPEpThNZUZAsiXd0t83ld+iUv0tKcIYqG5SOM88JKpxuNaXHgwACz7yEIWrz6aia6dPEv876IiKhuYBBGdUp5C61evXpV3iLIksok0psj7fMI6AI7hWIWtFrDchZSXpgU+JlON2qhS7Q3rv9VuvIxNnZ8uftERES1X93YVJGogjZt2mQQHAG6ACg1NUjen1FKpNdnLZG+PJTKPMyblyJf1ziHzFK9rzZt/ob5AqwCOnf+q9IlLoiIqPbiSBg1CJZyvypStb68Gjdej7g4VzmHDABSU4Pg7q42u4IS0OL8+bZmr3W3QSEREdVeDMKozlCr1RXecgiwnvslJd2npQUAEBAYmFYlfZVyyIyDv8jIBKhUiTh8WAVAd0z3fR8zV6maoJCIiGonBmFUJ9zNFkSWcr+6dx+DqCh7bNjghGXLlNBqBatbDZVnT0jj9sbBX3x8FKSNv1WqQ/I+j1JRVokgaBEbuwoBARnyMe4FSURUvzAIozohKyur0q+1VES1UydHlJT4YN48QHsnNUy/XldRkaMccFVkT0iJpY2/pfdJTFQhPDzJYi0yKQCLiYnhXpBERPUQgzCq9dRqNTZv3lzp11sKcvz9e+HMmdIATCKKCqxaNRXSdGFkZAISEiLLVcpCn/n8L8P3CQmJxn33KTBtWlMsXHgVFy40ulMFvxeAXqyCT0RUjzEIo1qvvGUprDFfcLUX2rQBFArjQEyEtHBYFBUGAZjcohylLJTKPPTtexAHD/aHuZWPgqBF//7+6NBBV3zVzw8IC7u7+yQiorqDJSqoTjMuO2GNUpmH4OCLBoFTQACwciVgZycCwJ3SEqbFVnV1vEoJghb29oVW3/vYsR44eLCfyfUAQKEQ8cEHN9GlC6vfExE1VBwJozqrMnla+jQaDRwcHBAb64Hu3bPw0Ue7YW9fiNWrp5rkj+lPSSoUIrp0OSm3M/felouyAoAWO3ao8cADXndx90REVNcxCKM6qSJbDlla1bhp0yYAui2O/P21CA6+CABm88dCQ4+jc+dk9OkzCU5OWgwb1tXsewO6hPyrV80XZZWuFx5uriQFERE1JAzCqE6yVHYiLS0A2dm3KrSq0TjnzNKG3UplHiIiCnHokIPZ905KCtcrNSGa6bUW69alIDq6D5PtiYiIQRjVTZYqz2/dOsagMGplVjUCljfszsnJQXCwu9mSF1IB1jtHjF6pxYgROzFwYC8GYEREBICJ+VSHqVSJens/6hLqDQujWl7VaMy4EKqlhP/NmzfDxeU6hg/fabA/pK9vBqz9cxozZkuF8tWIiKj+40gY1XrGAZL+FCOgRUTEIfj7p2PLloeMXqkwO2Jlbi9GDw8PxMTEYNOmTWVOYWZlZclTlklJ4Th8WIWMjOYW+y8IWgQGXjZ7L0RE1HAxCKNaz8PDAzNmzEBhYSHS0xV49VVviKI03aerPB8bu8pswKU/JVnWBt1KpbJcCf/79++XX5OYqD8FaUoQtHj11SuIjR3PwqtERGSAQRjVCVLw8v775ivcFxU5WF3VaJxkb4mlhH9zhVnNb0uko1CI+PTTHKhUQJcufhW8WyIiaggYhFGdcfky8N57pselwqnNml1HbOwqFBU5mKxqLM+G24DlfSbNTWFa2pbIzg747DMBsbEsxEpERJYxCKNqo1arrW45VNHpuXPnANFM5YdWrVJMCqdKNb/0WaoXlpqaimvXruHWrVsW95nUb69/Hf22CoWI2bMFzJypq8RPRERkjSCK5n6tUW2Qm5t7J09JAzc3N1t3p0LUajWWL19eZrsZM2aUOxC7fBlo2dJ4OlILQYDJyFVc3FIolXkYO3YsmjZtio8/voU33wwqd3V9XaBlOoVpLmlfqin29NNDEBbmU657ISKi+qu8v79ZooKqRXk33a7I5tyl+zzqvhcELSIiEq2WoWjatClKSvyweHGwSbK9tf0mlco8uLtnIzvbQ25nKWkfAIKDL8LfX2vxekRERMY4HUl1SmwsEB0NJCWpcejQWgDQq1KvY5zDde4coNWabsptLtleYm7Eq1mz6+VO2iciIioLR8KoSqnVamRkZODatWvV9h4BAcC995Ym3A8fvhO6Yq0AYJjD5eDggDZtdKsV9VlKtgcsj3jZ2xfoFYct+zpERETWcCSMqkx588Cqgn7tsA0bnLBzpwCtFlAoBPTv3x8PP9zLIPF/4cJ/sWiRf7nqhVkqU2GpDIZ+wEdERFReDMKoylQkv6sqeHh44PJlYN680mR9rVbA/PlNMWiQCE/P28jIyAAADBz4DzSazeWqF2au9ISdnYinnx4Cf38tFi68igsXGiEoqBj+/r0A9GIhViIiqjAGYWRT5qYtpRGl8pS30OV7GZ4rKQHee+9/JmUqlEpYDb4slZ6wsxPx2WeCvPLRzw8ICyvvHRIREZnHIIxsatu2bZV+7YwZM9CmjQcUCsNAzM5OLDNP6/JlP1y61BIeHtfg4FCM9HQ/k+2N4uKWsvQEERFVGwZhVGcVFhbKZSueeEI3AmZnByxZosGNG5ZHvL799kH88Uc3AAIA0ehzaSJ+XNxSlp4gIqJqw9WRdNdqYkWkNbGxwIULwP79us8PPJBhse3ly356ARjMfNbRrzXGhHsiIqoOHAmju1KTKyKtCQjQfaSkpGDz5s0W21261BLGAZc5CoUuET8oqBET7omIqFowCKO7Ut4VkZb2bbwb165dM1iVqFarsW7dOquvadHiIvSnHg3pjisUIlauFJgHRkRE1YpBGFU7c9Xnre3bWF5SUr+0/6SlgFA/AHR1vYGQkPNISWkN/VwwQdAiMjIB/v7pTMQnIqIawSCMqpWl6vMhIeerbESssLAQarXabE6afgCoq6ovoDT40kKXFqkLwPr0SQQAJuITEVGNYBBG1UajccVff3W66/0Wy5rKzMnJMZsHZhwAGq5DkYIx3fGEhEh07pzMPSCJiKjGMAijamE4AmWYg6W/32JZAVZ5pjKLi4vN9sHc9kOWcCNuIiKqaQzCqMqZjkAZ5l5J+y2WFWDd7VTmP/8EwXISviH9wJAlKYiIqCYwCKMqZ34ESkB09B507HgKSmWe2QBr+/ZhcHAoQGBgGpTKPIsbaaelBUCpPG21D4cOqXDwYH8YBmCi3BdBEO9cT4CdnYglS3Lx8MPjuQckERHVGAZhdFfMjRqZ2wBbELQGAZi5XDFAgS1bHpJHxUJCzptcBwC2bh2DwkLLKywvX/ZDfHwUTEfABIwZsxkuLjcxZ86DaNasGc6fB1q3FhAQ0BRA04rePhERUaUxCKO74uHhgRkzZsjlIdLTFUhNbQQXlzwsXuyGkhJd3a1hw0ynIC1NFepvGzR8+E5s3z4M+kn1xtOSeXmlU5PHjvW4097cFKQWgYGXMX36gwgJaQZAV+CViIjIFhiE0V2Tpu9WrwamTdNtpq1QAEuWAD17Ah4eOdi27bjVXDFjUqJ8aOhxODgUYMuWh8yeVyrzEB8fD6A0h8z8blwioqISMG3aAwgJCamyeyciIqos7h1JVeLy5dIADNB9XrAAaN0a6NKlGWbMmIGIiElmc8UGDNgPQTCszSUIWuTnu0CjcUVgYJrJeYVCxIQJ4YiKipKPWV4NKSIqKh59+iRCqVRWwd0SERHdPY6EUZU4d640AJOUlADnz+um/Dw8PNC7t26ETL+dnZ2I0NDjUCrzDKYpRREG+WHDh+80WEk5bNhOHD5smBPm7q5GaQFWfSI6d06uhrsmIiKqPAZhVCXatDEXYOlGwiQBAcDKlcATT+gCNDs7YMkSDW7cyENo6HHcutUY8fGR0AVRuilK/fywuLilyM52h7t7ttkSFbqtiMxhDTAiIqp9OB1JVUIKsOzsdN/b2QGffWaa+B4bC1y4AOzfr/v88MO3AOjyuRISpADMkH7+V3DwRbPBlLV8MP0aYERERLUFR8KoysTGAtHRuFP2wfLKw4CA0nMZGbrP1qrblyeIsvR6/eKwAAuxEhFR7cEgjKqUfoBVHlJQZK62mI5hEGVpmyPzr9ciNnYVAgIyMGrUKPj7+7MQKxER1RoMwsimpDpj6enpSEnZqZecr0WnTqegUh1GQIBuuMzaNkdKZZ5J8v7w4Tvl13p6ejIAIyKiWoVBGNmMWq2Wi7wCQGjocYSEnEdSUjgSE1X466/OOHWqo1w9v6x9JKXXm0ve5zQkERHVNgzCyCbUajWWL19u9lxiosok2Bo9eqvZfSSlhP1Ro0bB09PT7PW4HyQREdVGDMKoWuiPcklbGQUHF8PfX1fDQqPRmH2dpU27AdHsfpRSwj7zvYiIqK5hEEZV7s8/r+O993bC3V2NlJTWFvO49EkJ9/b2BWaDrcDAyxg+fCd27hwOrVa3H+Xbb+fi4YfHc6SLiIjqJAZhVCWkka8NG5wwb15TaLWToKteL8C48Kp+HhdgmnDftetJnDzZ1SBwUyrzTHK+Jk+eBA8PP5vcLxER0d1iEEbldvmybnuiNm0My1BI+V0ajSuWLo2DKEobclsvvArAZFNvUVTg5MmuiI1dhaIiB3m6MTU1SC5LIb1WP6mfiIiormEQRuWyenXpBt0Kha46fmys7pwUDFkruCoxLrxqKQesqMgBwcEXrZalICIiqsu4bRGV6fLl0gAM0H1+4gndcX1SwVRrVKpEs0VW9UmBmrlRsh07hkGjcb37myIiIrIxBmFUpnPnDDfmBnQbcJ8/b3hMKphaGlSJRlfSIjw8yeQ1kZEJ8mv0c8AsjZJlZ7vf5R0RERHZHqcjGyhL+V3mtGmjm4LUD8Ts7HT7Q16+DPz6qwM0GleT5Pn0dH8kJESaJNjrO3ash9wG0CIyMkGebjS3FRE34yYiovqCQVgDZC2/y5yAAF2bJ57QjYDZ2QGffQb88IN0HQ8IQpycryUlzwcHX0TnzslmK9gDpkn5gALx8ZFQKjUIDEyzuBWR8XWIiIjqIkEUReM5I6olcnNzoVQqodFo4ObmViXXvHwZaNnSdFTrwoWyR8QuX9ZNQbZurfve+DqCoEVc3NJyB0mpqUFYu3aS2XP6Sfi6GmKmgdy0adPg58cSFUREVLuU9/c3R8IaGGv5XcZB2OXLwO+/axAYeBv+/lrY2QGurgr8+msjqNUKaLXNDNobl58wR78o69WrHtDVEjNfykK/ppi5a3I/SCIiqssYhDUw1vK79OmmLEVotUoIgiuGD98JAAZTg8YBVFn5WvrlJnRJ+1I9Mf2vS1nbG5JV8omIqK6rc6sjCwoK0L17dwiCgBMnThicO3nyJPr164fGjRsjMDAQb7/9tsnrv/nmG7Rv3x6NGzdGly5d8P333xucF0URCxcuhJ+fH5ycnBAZGYlz584ZtMnOzsaECRPg5uaGpk2bIjY2Fjdu3KhwX2xByu+ys9N9L+V36Y+ClZakKK10v337MJNyEbofH91stkIh4uWX0/HQQyqz72ua/6UfdAnQBXTmS1UAgKenJ/z8/OQPBmBERFTX1bkgbN68efD39zc5npubi0GDBqFly5Y4evQo3nnnHbzyyitYuXKl3Obw4cMYP348YmNjcfz4cYwcORIjR45EcnKy3Obtt9/Ghx9+iBUrViApKQkuLi6Ijo7G7du35TYTJkzAX3/9hfj4eOzcuRM///wzpk2bVqG+2FJsrC4HbP9+3WfjpHxzU5aAwkIhVl0ANWXK5wBWY+/evQCA/v0fRqdOMxARMRZAeQq5KhARkWi2VAUREVF9VKcS83fv3o3Zs2dj69at6NSpE44fP47u3bsDAD799FO88MILyMzMlHOFFixYgO+++w5nzpwBAMTExCA/Px87d+6Ur9m7d290794dK1asgCiK8Pf3x5w5c/Dss88CADQaDXx8fLBmzRqMGzcOp0+fRseOHXHkyBH07NkTALBnzx488MADuHz5Mvz9/cvVl/KojsT88jCXvA9oIQiwGEhNmrQGwcEXAeimHfU32h42bAdCQs7f2dLI/OulpH4ATMInIqI6rby/v+vMSNiVK1fw+OOP46uvvoKzs7PJ+cTERPTv398gWTs6Ohpnz57F9evX5TaRkZEGr4uOjkZiYiIAIDU1FZmZmQZtlEolwsPD5TaJiYlo2rSpHIABQGRkJBQKBZKSksrdl9qsdMpSF58LghYjRuw0KsRaSn/aUJp2lKYytVoBO3YMAwCLrwdKK+lLpS2MR8CYhE9ERPVNnUjMF0URkydPxpNPPomePXviwoULJm0yMzMRHBxscMzHx0c+16xZM2RmZsrH9NtkZmbK7fRfZ6mNt7e3wflGjRrB3d3doE1ZfTGnoKAABQUF8ve5ublm29WE2Fige/csfPTRboNRqZCQ80hKCsfhwyoApbW7AF3Jifx8Z4tV7kNDj8PbOxOrVk2FYfxvWEmfSfhERNQQ2DQIW7BgAZYsWWK1zenTp7F3717k5eXhueeeq6Ge2cbixYuxaNEiW3dD5u+vlacYpdIS7u5qDBqUgPDwJHnaMCWltTzVWNaqyYCADIwYYb0Aq7+/P4MuIiKq92wahM2ZMweTJ0+22qZVq1b48ccfkZiYCEdHR4NzPXv2xIQJE7B27Vr4+vriypUrBuel7319feXP5tron5eO6ecfXblyRc498/X1RVZWlsE1iouLkZ2dXeb76L+HOc899xxmz54tf5+bm4vAwECL7WuKfmkJ/SKqSmWe2U22dXRlJ8wFWfpbGxnnfj3yyCMMwIiIqEGwaRDm5eUFLy+vMtt9+OGHeP311+Xv09PTER0djU2bNiE8PBwAoFKp8MILL6CoqAj29vYAgPj4eLRr106e/lOpVNi3bx/i4uLka8XHx0Ol0pVVCA4Ohq+vL/bt2ycHXbm5uUhKSsJTTz0lXyMnJwdHjx5FWFgYAODHH3+EVqutUF/McXR0NAk0bcnBwcFskKVfRNXyqkfdqsnY2FUICMiQjw4cONDkGdjb20OpVHLakYiIGpQ6kRPWokULg++bNGkCAAgJCUHAnQJXDz/8MBYtWoTY2FjMnz8fycnJWLZsGT744AP5dTNnzsSAAQPw3nvvYejQodi4cSN+//13uXSEIAiIi4vD66+/jjZt2iA4OBgvvfQS/P39MXLkSABAhw4dMHjwYDz++ONYsWIFioqKMGPGDIwbN04unVGevtQGZW3i7eHhgdDQGIs5XkplntlNtkspUFRkmFDfpk0brnIkIiJCHVodWRalUom9e/ciNTUVYWFhmDNnDhYuXGhQvysiIgIbNmzAypUr0a1bN2zZsgXfffcdOnfuLLeZN28enn76aUybNg29evXCjRs3sGfPHjRu3Fhus379erRv3x73338/HnjgAfTt29egBlh5+mJrq1frylDcd5/u8+rV5tt16NDIZEWjfo6XtMl2WasmiYiIyFCdqhPW0FRXnbCKbOKdkZGBSZP+REJCpElOmD6NxtXsqknjdqz3RURE9R038CaLrG3i7eSkRmFhoXx85coSOQADtIiMTDAJrADdiJjxqkluuk1ERGQZg7AGqE0b3Kl+X3pMoQA8PK5j+fLl8jGNxtWoyr0CCQmR6Nw52eJ2QlLBVWOjRo1i6QkiIiI99SYnjO6OKMJgBAwwv9+jlJRfUZ6engzAiIiI9DAIa4DOnTMcBQN031+4YDgwKq181Gcp2V6jcUVqahA0Glez78lpSCIiIkOcjmyA2rTRTT8aJ+YHBRUjObn0mLTy0VJ1e6mKfnq6n8XE/UGDBqFt27YcBSMiIjLCIKwBkjbofuIJXUK+nR3w2We6bYqMWapur19FX6qOD5gWcw0KCmIARkREZAaDsAYqNhaIjtatiGzdWheYHT2qQGpqENzd1QbJ9cbJ9sZV9KUATKJfzJWIiIjMYxDWgAUElNYFW70amDbNG1rtJABaREUloE+fRLOvs7xVkQ6LtBIREZWNQVgDolbraoClpyuQmtoIwcHF8PfXIj1dcScAk0a0FIiPjwIAs4GY+a2KLG/YTURERKYYhDUQarUay5cvN8jlkgKmZs2u3xkB0ydYrAlmLmE/MjIB/v7pFou0EhERkSEGYQ1EYWGhSS6XlEQfG7sKgBbGFUtEUYG0tAAoladNrmcpYZ+IiIjKh3XCGhBLxVeLihwQFZUA3ZSioS1bxuDYsR5mr6dU5iE4+KLVAIz1wYiIiMzjSFgDYi6XS0qiDw6+CABISIiCKOqvdjQsOVEeMTExUCqVcHBwYHkKIiIiCxiENSBlFV/t0ycRSqUGW7Y8ZPA6SyUnRo0aBU9PT4NjDLyIiIjKh0FYA2Mul0uqfO/urkZgYJrF0TJj3JCbiIio8hiENUD6xVfNrZa0NlomjX5xxIuIiOjuMAhrwCytloyLW4q4uKVmVz56enrCz8/PVl0mIiKqNxiENRDmVilaWi2Zne1e5qpHIiIiujssUdFAeHh4YNCgQQbHpNWS+rjlEBERUc1gENZAqNVqFBUVGRxLSWkNUa80GLccIiIiqjmcjmwApC2L9En5YPpxuCgCISHna7h3REREDRNHwhqAwsJCk2Pm8sEAXT6YNayAT0REVDU4EtZAWaueb05MTAy8vLxYloKIiKiKMAhroMqqng8AAwcOhJeXF7y9vRl8ERERVTEGYQ2Yuer5+tq0acOaYERERNWEQVgDp1893xjzv4iIiKoPE/PJrJiYGE5BEhERVSMGYWSWl5eXrbtARERUr3E6sgEo77Ti2LFj0bRpU27OTUREVAMYhDUAHh4emDFjhtl6YRIGXkRERDWLQVgDwQCLiIiodmFOGBEREZENMAgjIiIisgEGYUREREQ2wCCMiIiIyAYYhBERERHZAIMwIiIiIhtgEEZERERkAwzCiIiIiGyAQRgRERGRDbBifi0miiIAIDc318Y9ISIiovKSfm9Lv8ctYRBWi+Xl5QEAAgMDbdwTIiIiqqi8vDwolUqL5wWxrDCNbEar1SI9PR2urq4QBKHS18nNzUVgYCDS0tLg5uZWhT2sO/gMdPgc+AwAPgOAz0DC51A9z0AUReTl5cHf3x8KheXML46E1WIKhQIBAQFVdj03N7cG+49Mwmegw+fAZwDwGQB8BhI+h6p/BtZGwCRMzCciIiKyAQZhRERERDbAIKwBcHR0xMsvvwxHR0dbd8Vm+Ax0+Bz4DAA+A4DPQMLnYNtnwMR8IiIiIhvgSBgRERGRDTAIIyIiIrIBBmFERERENsAgjIiIiMgGGITVUZ9++im6du0qF5dTqVTYvXu3fP727duYPn06PDw80KRJE4wePRpXrlwxuMalS5cwdOhQODs7w9vbG3PnzkVxcXFN30qVeeuttyAIAuLi4uRjDeE5vPLKKxAEweCjffv28vmG8AwA4N9//8UjjzwCDw8PODk5oUuXLvj999/l86IoYuHChfDz84OTkxMiIyNx7tw5g2tkZ2djwoQJcHNzQ9OmTREbG4sbN27U9K1USlBQkMnPgSAImD59OoCG8XNQUlKCl156CcHBwXByckJISAhee+01g/376vvPAaDbKicuLg4tW7aEk5MTIiIicOTIEfl8fXwGP//8M4YPHw5/f38IgoDvvvvO4HxV3fPJkyfRr18/NG7cGIGBgXj77bfvruMi1Unbt28Xd+3aJf7999/i2bNnxeeff160t7cXk5OTRVEUxSeffFIMDAwU9+3bJ/7+++9i7969xYiICPn1xcXFYufOncXIyEjx+PHj4vfffy96enqKzz33nK1u6a789ttvYlBQkNi1a1dx5syZ8vGG8BxefvllsVOnTmJGRob8cfXqVfl8Q3gG2dnZYsuWLcXJkyeLSUlJ4j///CP+8MMP4vnz5+U2b731lqhUKsXvvvtO/OOPP8QRI0aIwcHB4q1bt+Q2gwcPFrt16yb++uuv4sGDB8XWrVuL48ePt8UtVVhWVpbBz0B8fLwIQNy/f78oig3j5+CNN94QPTw8xJ07d4qpqaniN998IzZp0kRctmyZ3Ka+/xyIoiiOHTtW7Nixo3jgwAHx3Llz4ssvvyy6ubmJly9fFkWxfj6D77//XnzhhRfEbdu2iQDEb7/91uB8VdyzRqMRfXx8xAkTJojJycni119/LTo5OYmfffZZpfvNIKweadasmbhq1SoxJydHtLe3F7/55hv53OnTp0UAYmJioiiKuh9YhUIhZmZmym0+/fRT0c3NTSwoKKjxvt+NvLw8sU2bNmJ8fLw4YMAAOQhrKM/h5ZdfFrt162b2XEN5BvPnzxf79u1r8bxWqxV9fX3Fd955Rz6Wk5MjOjo6il9//bUoiqJ46tQpEYB45MgRuc3u3btFQRDEf//9t/o6X01mzpwphoSEiFqttsH8HAwdOlScMmWKwbFRo0aJEyZMEEWxYfwc3Lx5U7SzsxN37txpcDw0NFR84YUXGsQzMA7CquqeP/nkE7FZs2YG/x7mz58vtmvXrtJ95XRkPVBSUoKNGzciPz8fKpUKR48eRVFRESIjI+U27du3R4sWLZCYmAgASExMRJcuXeDj4yO3iY6ORm5uLv76668av4e7MX36dAwdOtTgfgE0qOdw7tw5+Pv7o1WrVpgwYQIuXboEoOE8g+3bt6Nnz5546KGH4O3tjR49euDzzz+Xz6empiIzM9PgOSiVSoSHhxs8h6ZNm6Jnz55ym8jISCgUCiQlJdXczVSBwsJCrFu3DlOmTIEgCA3m5yAiIgL79u3D33//DQD4448/8Msvv2DIkCEAGsbPQXFxMUpKStC4cWOD405OTvjll18axDMwVlX3nJiYiP79+8PBwUFuEx0djbNnz+L69euV6hs38K7D/vzzT6hUKty+fRtNmjTBt99+i44dO+LEiRNwcHBA06ZNDdr7+PggMzMTAJCZmWnwP1vpvHSurti4cSOOHTtmkO8gyczMbBDPITw8HGvWrEG7du2QkZGBRYsWoV+/fkhOTm4wz+Cff/7Bp59+itmzZ+P555/HkSNH8Mwzz8DBwQGTJk2S78Pcfeo/B29vb4PzjRo1gru7e515DpLvvvsOOTk5mDx5MoCG829hwYIFyM3NRfv27WFnZ4eSkhK88cYbmDBhAgA0iJ8DV1dXqFQqvPbaa+jQoQN8fHzw9ddfIzExEa1bt24Qz8BYVd1zZmYmgoODTa4hnWvWrFmF+8YgrA5r164dTpw4AY1Ggy1btmDSpEk4cOCArbtVY9LS0jBz5kzEx8eb/NXXkEh/5QNA165dER4ejpYtW2Lz5s1wcnKyYc9qjlarRc+ePfHmm28CAHr06IHk5GSsWLECkyZNsnHvat7q1asxZMgQ+Pv727orNWrz5s1Yv349NmzYgE6dOuHEiROIi4uDv79/g/o5+OqrrzBlyhQ0b94cdnZ2CA0Nxfjx43H06FFbd42McDqyDnNwcEDr1q0RFhaGxYsXo1u3bli2bBl8fX1RWFiInJwcg/ZXrlyBr68vAMDX19dkZZT0vdSmtjt69CiysrIQGhqKRo0aoVGjRjhw4AA+/PBDNGrUCD4+Pg3iORhr2rQp2rZti/PnzzeYnwU/Pz907NjR4FiHDh3kaVnpPszdp/5zyMrKMjhfXFyM7OzsOvMcAODixYtISEjA1KlT5WMN5edg7ty5WLBgAcaNG4cuXbpg4sSJmDVrFhYvXgyg4fwchISE4MCBA7hx4wbS0tLw22+/oaioCK1atWowz0BfVd1zdfwbYRBWj2i1WhQUFCAsLAz29vbYt2+ffO7s2bO4dOkSVCoVAEClUuHPP/80+KGLj4+Hm5ubyS+z2ur+++/Hn3/+iRMnTsgfPXv2xIQJE+SvG8JzMHbjxg2kpKTAz8+vwfws9OnTB2fPnjU49vfff6Nly5YAgODgYPj6+ho8h9zcXCQlJRk8h5ycHIPRgh9//BFarRbh4eE1cBdV48svv4S3tzeGDh0qH2soPwc3b96EQmH4a83Ozg5arRZAw/o5AAAXFxf4+fnh+vXr+OGHH/Dggw82uGcAVN1/d5VKhZ9//hlFRUVym/j4eLRr165SU5EAWKKirlqwYIF44MABMTU1VTx58qS4YMECURAEce/evaIo6pajt2jRQvzxxx/F33//XVSpVKJKpZJfLy1HHzRokHjixAlxz549opeXV51ajm6O/upIUWwYz2HOnDniTz/9JKampoqHDh0SIyMjRU9PTzErK0sUxYbxDH777TexUaNG4htvvCGeO3dOXL9+vejs7CyuW7dObvPWW2+JTZs2Ff/3v/+JJ0+eFB988EGzS9R79OghJiUlib/88ovYpk2bWr0s31hJSYnYokULcf78+SbnGsLPwaRJk8TmzZvLJSq2bdsmenp6ivPmzZPbNISfgz179oi7d+8W//nnH3Hv3r1it27dxPDwcLGwsFAUxfr5DPLy8sTjx4+Lx48fFwGI77//vnj8+HHx4sWLoihWzT3n5OSIPj4+4sSJE8Xk5GRx48aNorOzM0tUNERTpkwRW7ZsKTo4OIheXl7i/fffLwdgoiiKt27dEv/zn/+IzZo1E52dncX/+7//EzMyMgyuceHCBXHIkCGik5OT6OnpKc6ZM0csKiqq6VupUsZBWEN4DjExMaKfn5/o4OAgNm/eXIyJiTGoj9UQnoEoiuKOHTvEzp07i46OjmL79u3FlStXGpzXarXiSy+9JPr4+IiOjo7i/fffL549e9agjVqtFsePHy82adJEdHNzEx977DExLy+vJm/jrvzwww8iAJP7EsWG8XOQm5srzpw5U2zRooXYuHFjsVWrVuILL7xgUFKgIfwcbNq0SWzVqpXo4OAg+vr6itOnTxdzcnLk8/XxGezfv18EYPIxadIkURSr7p7/+OMPsW/fvqKjo6PYvHlz8a233rqrfguiqFdKmIiIiIhqBHPCiIiIiGyAQRgRERGRDTAIIyIiIrIBBmFERERENsAgjIiIiMgGGIQRERER2QCDMCIiIiIbYBBGREREZAMMwoioXrn33nsRFxdn625Uu1deeQXdu3e3dTeI6C4wCCMiqkUKCwtr9P1EUURxcXGNvicR6TAII6J6Y/LkyThw4ACWLVsGQRAgCAIuXLiA5ORkDBkyBE2aNIGPjw8mTpyIa9euya+799578fTTTyMuLg7NmjWDj48PPv/8c+Tn5+Oxxx6Dq6srWrdujd27d8uv+emnnyAIAnbt2oWuXbuicePG6N27N5KTkw369Msvv6Bfv35wcnJCYGAgnnnmGeTn58vng4KC8Nprr+HRRx+Fm5sbpk2bBgCYP38+2rZtC2dnZ7Rq1QovvfQSioqKAABr1qzBokWL8Mcff8j3uWbNGly4cAGCIODEiRPy9XNyciAIAn766SeDfu/evRthYWFwdHTEL7/8Aq1Wi8WLFyM4OBhOTk7o1q0btmzZUtX/iYhID4MwIqo3li1bBpVKhccffxwZGRnIyMiAq6sr7rvvPvTo0QO///479uzZgytXrmDs2LEGr127di08PT3x22+/4emnn8ZTTz2Fhx56CBERETh27BgGDRqEiRMn4ubNmwavmzt3Lt577z0cOXIEXl5eGD58uBwspaSkYPDgwRg9ejROnjyJTZs24ZdffsGMGTMMrvHuu++iW7duOH78OF566SUAgKurK9asWYNTp05h2bJl+Pzzz/HBBx8AAGJiYjBnzhx06tRJvs+YmJgKPasFCxbgrbfewunTp9G1a1csXrwY//3vf7FixQr89ddfmDVrFh555BEcOHCgQtclogq4q+2/iYhqmQEDBogzZ86Uv3/ttdfEQYMGGbRJS0sTAYhnz56VX9O3b1/5fHFxseji4iJOnDhRPpaRkSECEBMTE0VRFMX9+/eLAMSNGzfKbdRqtejk5CRu2rRJFEVRjI2NFadNm2bw3gcPHhQVCoV469YtURRFsWXLluLIkSPLvK933nlHDAsLk79/+eWXxW7duhm0SU1NFQGIx48fl49dv35dBCDu37/foN/fffed3Ob27duis7OzePjwYYPrxcbGiuPHjy+zb0RUOY1sGQASEVW3P/74A/v370eTJk1MzqWkpKBt27YAgK5du8rH7ezs4OHhgS5dusjHfHx8AABZWVkG11CpVPLX7u7uaNeuHU6fPi2/98mTJ7F+/Xq5jSiK0Gq1SE1NRYcOHQAAPXv2NOnbpk2b8OGHHyIlJQU3btxAcXEx3NzcKnz/lui/5/nz53Hz5k1ERUUZtCksLESPHj2q7D2JyBCDMCKq127cuIHhw4djyZIlJuf8/Pzkr+3t7Q3OCYJgcEwQBACAVqut0Hs/8cQTeOaZZ0zOtWjRQv7axcXF4FxiYiImTJiARYsWITo6GkqlEhs3bsR7771n9f0UCl2GiSiK8jFpatSY/nveuHEDALBr1y40b97coJ2jo6PV9ySiymMQRkT1ioODA0pKSuTvQ0NDsXXrVgQFBaFRo6r/X96vv/4qB1TXr1/H33//LY9whYaG4tSpU2jdunWFrnn48GG0bNkSL7zwgnzs4sWLBm2M7xMAvLy8AAAZGRnyCJZ+kr4lHTt2hKOjIy5duoQBAwZUqK9EVHlMzCeieiUoKAhJSUm4cOECrl27hunTpyM7Oxvjx4/HkSNHkJKSgh9++AGPPfaYSRBTGa+++ir27duH5ORkTJ48GZ6enhg5ciQA3QrHw4cPY8aMGThx4gTOnTuH//3vfyaJ+cbatGmDS5cuYePGjUhJScGHH36Ib7/91uQ+U1NTceLECVy7dg0FBQVwcnJC79695YT7AwcO4MUXXyzzHlxdXfHss89i1qxZWLt2LVJSUnDs2DF89NFHWLt2baWfDRFZxyCMiOqVZ599FnZ2dujYsSO8vLxQWFiIQ4cOoaSkBIMGDUKXLl0QFxeHpk2bytN3d+Ott97CzJkzERYWhszMTOzYsQMODg4AdHlmBw4cwN9//41+/fqhR48eWLhwIfz9/a1ec8SIEZg1axZmzJiB7t274/Dhw/KqScno0aMxePBgDBw4EF5eXvj6668BAF988QWKi4sRFhaGuLg4vP766+W6j9deew0vvfQSFi9ejA4dOmDw4MHYtWsXgoODK/FUiKg8BFE/eYCIiMrlp59+wsCBA3H9+nU0bdrU1t0hojqII2FERERENsAgjIiIiMgGOB1JREREZAMcCSMiIiKyAQZhRERERDbAIIyIiIjIBhiEEREREdkAgzAiIiIiG2AQRkRERGQDDMKIiIiIbIBBGBEREZENMAgjIiIisoH/B8bICXAr8uw/AAAAAElFTkSuQmCC", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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dO3dCkiSH9wRkQORkBapyZOQUoUBV7uyiEBGRh1i4cCE6d+7s7GK4FJfrZeZJ1uzPw/y1R6EWgJcEpI1IwKjbop1dLCIiIo/DGiInKVCVy8EQAKgF8OzaY6wpIiIis9RqNdLS0hAbGws/Pz906tQJX375JYC/m5i2bduG7t27w9/fH71790Z2djYAYOXKlVi0aBEOHz4MSZIgSRJWrlwpb7uoqAj33Xcf/P39ccstt2D9+vUWlUmz3y1btqBLly7w8/PD7bffjgsXLmDTpk1o3749AgICMHbsWJ1puCoqKvD4448jNDQUDRo0QN++fbF//377nSwLMSByktyiUjkY0qgSAmeKypTfQERELq02UyDS0tLw3//+F8uWLcPx48cxZ84cPPjgg9i1a5e8znPPPYd//vOfOHDgAOrVq4fJkycDAEaNGoWnnnoKt956KwoKClBQUIBRo0bJ71u0aBEeeOABHDlyBHfeeSfGjRuHS5cuWVy2hQsX4t///jcyMjKQn5+PBx54AG+++SZWrVqFDRs24Pvvv8c777wjrz937lx89dVX+Pjjj/HLL7+gTZs2SEpKsmqf9sCAyEligxvCS6+XoLckISaY42EQEbmbNfvz0Cd9O8Yu34s+6duxZn+ew/ZVUVGBxYsX46OPPkJSUhJatWqFiRMn4sEHH9SZ5eHVV1/FgAEDEB8fj3nz5iEjIwPXrl2Dn58fGjVqhHr16iE8PBzh4eE6MzdMnDgRY8aMQZs2bbB48WJcvXrVYFYIU1555RX06dMHXbp0wZQpU7Br1y4sXboUXbp0Qb9+/XD//fdjx44dAIDS0lIsXboUr7/+OpKTkxEfH4/ly5fDz88PH374of1OmgUYEDlJRKAf0kYkwPvm2AnekoTFIzogItD4dCJEROR6ajsF4vTp0ygrK8Mdd9yBRo0ayX///e9/kZOTI6+nGcQY+HuGB80UF6Zov69hw4YICAiw6H1K7w8LC4O/vz9atWql85pmezk5Obh+/Tr69OkjL69fvz569OiBX3/91eJ92gOTqp1o1G3R6N82BGeKyhAT7M9giIjIDZlKgXDEff3q1asAgA0bNqB58+Y6y3x9feWgqH79+vLrmoEL1Wq12e1rv0/zXkvep/R+SZJqvL3awoDIySIC/RgIERG5MU0KhHZQ5MgUiPj4ePj6+iIvLw8DBgwwWK5dS2SMj48PqqqqHFE8q7Ru3Ro+Pj746aef0LJlSwDV02/s378fs2fPrtWyMCAiIiKqAU0KxLNrj6FKCIenQDRu3BhPP/005syZA7Vajb59+0KlUuGnn35CQECAHFiYEhMTg9zcXGRlZaFFixZo3LixUyY0b9iwIaZPn45nnnkGTZs2RXR0NJYsWYKysjJMmTKlVsvCgIiIiKiGajsF4uWXX0ZISAjS0tLw+++/IygoCF27dsWzzz5rUXPUyJEjsXbtWgwaNAjFxcVYsWIFJk6c6NAyG5Oeng61Wo3x48fjypUr6N69O7Zs2YImTZrUajk4l5mFOJcZEVHdwrnM6g7OZUZERERkBwyIiIiIyKxHH31Up5u/9t+jjz7q7OLVGHOIiIiIyKyXXnoJTz/9tOKyupBKwoCIiIiIzAoNDUVoaKizi+EwbDIjIiIij8eAiIiIPJorjppM1rHHZ8gmMyIi8kg+Pj7w8vLCuXPnEBISAh8fH3mKC3IPQghUVlbi4sWL8PLygo+Pj83bYkBEREQeycvLC7GxsSgoKMC5c+ecXRyqAX9/f0RHR8PLy/aGLwZERETksXx8fBAdHY0bN264xNxeZD1vb2/Uq1evxrV7DIiIiMijaWZk15+VnTwLk6qJiIjI4zEgIiIiIo/HgIiIiIg8HgMiIiIi8ngMiIiIiMjjMSAiIiIij8eAiIiIiDweAyIiIiLyeAyIiIiIyOMxICIiIiKPx4CIiIiIPB4DIiIiIvJ4DIiIiIjI4zEgIiIiIo/HgIiIiIg8HgMiIiIi8ngMiIiIiMjjOTUgWrp0KTp27IiAgAAEBAQgMTERmzZtkpdfu3YNM2bMQLNmzdCoUSOMHDkS58+f19lGXl4eUlJS4O/vj9DQUDzzzDO4ceOGzjo7d+5E165d4evrizZt2mDlypW1cXhERETkJpwaELVo0QLp6ek4ePAgDhw4gNtvvx333nsvjh8/DgCYM2cOvv32W3zxxRfYtWsXzp07hxEjRsjvr6qqQkpKCiorK5GRkYGPP/4YK1euxAsvvCCvk5ubi5SUFAwaNAhZWVmYPXs2pk6dii1bttT68RIREZFrkoQQwtmF0Na0aVO8/vrruP/++xESEoJVq1bh/vvvBwCcPHkS7du3R2ZmJnr16oVNmzbhrrvuwrlz5xAWFgYAWLZsGVJTU3Hx4kX4+PggNTUVGzZswLFjx+R9jB49GsXFxdi8ebPF5SopKUFgYCBUKhUCAgLse9BERETkEJY+v10mh6iqqgqrV69GaWkpEhMTcfDgQVy/fh1DhgyR14mLi0N0dDQyMzMBAJmZmUhISJCDIQBISkpCSUmJXMuUmZmpsw3NOpptGFNRUYGSkhKdPyIiIqqbnB4QHT16FI0aNYKvry8effRRrFu3DvHx8SgsLISPjw+CgoJ01g8LC0NhYSEAoLCwUCcY0izXLDO1TklJCcrLy42WKy0tDYGBgfJfVFRUTQ+ViIiIXJTTA6J27dohKysLe/fuxfTp0zFhwgScOHHC2cXC/PnzoVKp5L/8/HxnF4mIiIgcpJ6zC+Dj44M2bdoAALp164b9+/fjrbfewqhRo1BZWYni4mKdWqLz588jPDwcABAeHo59+/bpbE/TC017Hf2eaefPn0dAQAD8/PyMlsvX1xe+vr41Pj4iIiJyfU6vIdKnVqtRUVGBbt26oX79+ti2bZu8LDs7G3l5eUhMTAQAJCYm4ujRo7hw4YK8ztatWxEQEID4+Hh5He1taNbRbIOIiIjIqTVE8+fPR3JyMqKjo3HlyhWsWrUKO3fuxJYtWxAYGIgpU6bgySefRNOmTREQEIBZs2YhMTERvXr1AgAMHToU8fHxGD9+PJYsWYLCwkI8//zzmDFjhly78+ijj+Lf//435s6di8mTJ2P79u34/PPPsWHDBmceOhEREbkQpwZEFy5cwEMPPYSCggIEBgaiY8eO2LJlC+644w4AwBtvvAEvLy+MHDkSFRUVSEpKwnvvvSe/39vbG9999x2mT5+OxMRENGzYEBMmTMBLL70krxMbG4sNGzZgzpw5eOutt9CiRQt88MEHSEpKqvXjJSIiItfkcuMQuSqOQ0REROR+3G4cIiIiIiJnYUBEREREHo8BEREREXk8BkRERETk8RgQERERkcdjQEREREQejwEREREReTwGREREROTxGBARERGRx2NARERERB6PARERERF5PAZERERE5PEYEBEREZHHY0BEREREHo8BEREREXk8BkRERETk8RgQERERkcdjQEREREQejwEREREReTwGREREROTxGBARERGRx2NARERERB6PARERERF5PAZERERE5PEYEBEREZHHY0BEREREHo8BEREREXk8BkRERETk8RgQERERkcdjQEREREQejwEREREReTwGREREROTxGBARERGRx2NARERERB6PARERERF5PAZERERE5PEYELmIAlU5MnKKUKAqd3ZRiIiIPE49ZxeAgDX78zB/7VGoBeAlAWkjEjDqtmhnF4uIiMhjOLWGKC0tDbfddhsaN26M0NBQDB8+HNnZ2TrrDBw4EJIk6fw9+uijOuvk5eUhJSUF/v7+CA0NxTPPPIMbN27orLNz50507doVvr6+aNOmDVauXOnow7NIgapcDoYAQC2AZ9ceY00RERFRLXJqQLRr1y7MmDEDP//8M7Zu3Yrr169j6NChKC0t1Vnv4YcfRkFBgfy3ZMkSeVlVVRVSUlJQWVmJjIwMfPzxx1i5ciVeeOEFeZ3c3FykpKRg0KBByMrKwuzZszF16lRs2bKl1o7VmNyiUjkY0qgSAmeKypxTICIiIg/k1CazzZs36/x75cqVCA0NxcGDB9G/f3/5dX9/f4SHhytu4/vvv8eJEyfwww8/ICwsDJ07d8bLL7+M1NRULFy4ED4+Pli2bBliY2Pxz3/+EwDQvn177NmzB2+88QaSkpIcd4AWiA1uCC8JOkGRtyQhJtjfeYUiIiLyMC6VVK1SqQAATZs21Xn9008/RXBwMDp06ID58+ejrOzv2pPMzEwkJCQgLCxMfi0pKQklJSU4fvy4vM6QIUN0tpmUlITMzExHHYrFIgL9kDYiAd6SBKA6GFo8ogMiAv2cXDIiIiLP4TJJ1Wq1GrNnz0afPn3QoUMH+fWxY8eiZcuWiIyMxJEjR5Camors7GysXbsWAFBYWKgTDAGQ/11YWGhynZKSEpSXl8PPzzD4qKioQEVFhfzvkpIS+xyoglG3RaN/2xCcKSpDTLA/gyEiIqJa5jIB0YwZM3Ds2DHs2bNH5/Vp06bJ/5+QkICIiAgMHjwYOTk5aN26tcPKk5aWhkWLFjls+/oiAv0YCBERETmJSzSZzZw5E9999x127NiBFi1amFy3Z8+eAIDTp08DAMLDw3H+/HmddTT/1uQdGVsnICBAsXYIAObPnw+VSiX/5efnW39gRERE5BacGhAJITBz5kysW7cO27dvR2xsrNn3ZGVlAQAiIiIAAImJiTh69CguXLggr7N161YEBAQgPj5eXmfbtm0629m6dSsSExON7sfX1xcBAQE6f0RERFQ3OTUgmjFjBj755BOsWrUKjRs3RmFhIQoLC1FeXj0GT05ODl5++WUcPHgQZ86cwfr16/HQQw+hf//+6NixIwBg6NChiI+Px/jx43H48GFs2bIFzz//PGbMmAFfX18AwKOPPorff/8dc+fOxcmTJ/Hee+/h888/x5w5c5x27EREROQ6JCGEML+ag3Z+s2eVvhUrVmDixInIz8/Hgw8+iGPHjqG0tBRRUVG477778Pzzz+vU2Jw9exbTp0/Hzp070bBhQ0yYMAHp6emoV+/vFKmdO3dizpw5OHHiBFq0aIEFCxZg4sSJFpe1pKQEgYGBUKlUrC0iIiJyE5Y+v50aELkTBkRERETux9Lnt0skVRMRERE5EwMiIiIi8ngMiIiIiMjjMSAiIiIij8eAiIiIiDweAyIiIiLyeAyIiIiIyOMxICIiIiKPx4CIiIiIPB4DIiIiIvJ4DIiIiIjI4zEgIiIiIo/HgIiIiIg8HgMiIiIi8ngMiIiIiMjjMSAiIiIij8eAiIiIiDweAyIiIiLyeAyIiIiIyOMxIHIRBapyZOQUoUBV7uyiEBEReZx6zi4AAWv252H+2qNQC8BLAtJGJGDUbdHOLhYREZHHYA2RkxWoyuVgCADUAnh27THWFBEREdUiBkROlltUKgdDGlVC4ExRmXMKRERE5IEYEDlZbHBDeEm6r3lLEmKC/Z1TICIiIg/EgMjJIgL9kDYiAd5SdVTkLUlYPKIDIgL9nFwyIiIiz8Gkahcw6rZo9G8bgjNFZYgJ9mcwREREVMsYELmIiEA/BkJEREROwiYzIiIi8ngMiIiIiMjjMSAiIiIij8eAiIiIiDweAyIiIiLyeAyIiIiIyOMxICIiIiKPx4CIiIiIPB4DIiIiIvJ4DIiIiIjI4zEgIiIiIo/HgIiIiIg8nlMDorS0NNx2221o3LgxQkNDMXz4cGRnZ+usc+3aNcyYMQPNmjVDo0aNMHLkSJw/f15nnby8PKSkpMDf3x+hoaF45plncOPGDZ11du7cia5du8LX1xdt2rTBypUrHX14RERE5CacGhDt2rULM2bMwM8//4ytW7fi+vXrGDp0KEpLS+V15syZg2+//RZffPEFdu3ahXPnzmHEiBHy8qqqKqSkpKCyshIZGRn4+OOPsXLlSrzwwgvyOrm5uUhJScGgQYOQlZWF2bNnY+rUqdiyZUutHi8RERG5JkkIIZxdCI2LFy8iNDQUu3btQv/+/aFSqRASEoJVq1bh/vvvBwCcPHkS7du3R2ZmJnr16oVNmzbhrrvuwrlz5xAWFgYAWLZsGVJTU3Hx4kX4+PggNTUVGzZswLFjx+R9jR49GsXFxdi8ebNFZSspKUFgYCBUKhUCAgLsf/BmFKjKkVtUitjghogI9Kv1/RMREbkjS5/fLpVDpFKpAABNmzYFABw8eBDXr1/HkCFD5HXi4uIQHR2NzMxMAEBmZiYSEhLkYAgAkpKSUFJSguPHj8vraG9Ds45mG0oqKipQUlKi8+csa/bnoU/6doxdvhd90rdjzf48p5WFiIioLqpn6YrWBAS21KCo1WrMnj0bffr0QYcOHQAAhYWF8PHxQVBQkM66YWFhKCwslNfRDoY0yzXLTK1TUlKC8vJy+PkZ1rikpaVh0aJFVh+HvRWoyjF/7VGob9bjqQXw7Npj6N82hDVFREREdmJxQBQUFARJkkyuI4SAJEmoqqqyuiAzZszAsWPHsGfPHqvf6wjz58/Hk08+Kf+7pKQEUVFRtV6O3KJSORjSqBICZ4rKGBARERHZicUB0Y4dOxxWiJkzZ+K7777D7t270aJFC/n18PBwVFZWori4WKeW6Pz58wgPD5fX2bdvn872NL3QtNfR75l2/vx5BAQEKNYOAYCvry98fX1rfGw1FRvcEF4SdIIib0lCTLC/8wpFRERUx1gcEA0YMMDuOxdCYNasWVi3bh127tyJ2NhYneXdunVD/fr1sW3bNowcORIAkJ2djby8PCQmJgIAEhMT8eqrr+LChQsIDQ0FAGzduhUBAQGIj4+X19m4caPOtrdu3Spvw5VFBPohbUQCnl17DFVCwFuSsHhEB9YOERER2ZHNvcyKi4vx4Ycf4tdffwUA3HrrrZg8eTICAwMt3sZjjz2GVatW4ZtvvkG7du3k1wMDA+Wam+nTp2Pjxo1YuXIlAgICMGvWLABARkYGgOpu9507d0ZkZCSWLFmCwsJCjB8/HlOnTsXixYsBVHe779ChA2bMmIHJkydj+/btePzxx7FhwwYkJSVZVFZX6GV2pqgMMcH+DIaIiIgsZOnz26aA6MCBA0hKSoKfnx969OgBANi/fz/Ky8vx/fffo2vXrhZtx1hO0ooVKzBx4kQA1QMzPvXUU/jss89QUVGBpKQkvPfee3JzGACcPXsW06dPx86dO9GwYUNMmDAB6enpqFfv7wqwnTt3Ys6cOThx4gRatGiBBQsWyPuwhLMDIiIiIrKeQwOifv36oU2bNli+fLkcdNy4cQNTp07F77//jt27d9techfFgIiIiMj9ODQg8vPzw6FDhxAXF6fz+okTJ9C9e3eUlZVZX2IXx4CIiIjI/Th0YMaAgADk5RkODpifn4/GjRvbskkiIiIip7EpIBo1ahSmTJmCNWvWID8/H/n5+Vi9ejWmTp2KMWPG2LuMRERERA5lcbd7bf/4xz8gSRIeeugheVb5+vXrY/r06UhPT7drAYmIiIgcrUaTu5aVlSEnJwcA0Lp1a/j7193BAplDRERE5H4sfX7bVEOk4e/vj4SEhJpsgoiIiMjpbAqIrl27hnfeeQc7duzAhQsXoFardZb/8ssvdikcERERUW2wKSCaMmUKvv/+e9x///3o0aOH2UlfiYiIiFyZTQHRd999h40bN6JPnz72Lg8RERFRrbOp233z5s053hARERHVGTYFRP/85z+RmpqKs2fP2rs8RERERLXOpiaz7t2749q1a2jVqhX8/f1Rv359neWXLl2yS+GIiIiIaoNNAdGYMWPw559/YvHixQgLC2NSNREREbk1mwKijIwMZGZmolOnTvYuDxEREVGtsymHKC4uDuXl5fYuCxEREZFT2BQQpaen46mnnsLOnTvx119/oaSkROePiIiIyJ3YNJeZl1d1HKWfOySEgCRJqKqqsk/pXIij5jIrUJUjt6gUscENERHoZ7ftEhERkYPnMtuxY4fNBaO/rdmfh/lrj0ItAC8JSBuRgFG3RTu7WERERB7HpoBowIABFq332GOP4aWXXkJwcLAtu6nTClTlcjAEAGoBPLv2GPq3DWFNERERUS2zKYfIUp988glziozILSqVgyGNKiFwpqjMOQUiIiLyYA4NiGxIT/IYscEN4aU3fJO3JCEm2N85BSIiIvJgDg2IyLiIQD+kjUiA983EdG9JwuIRHdhcRkRE5AQ25RCRfYy6LRr924bgTFEZYoL9GQwRERE5CQMiJ4sI9GMgRERE5GRsMiMiIiKP59CA6MEHH7TrIIZEREREjmBzk1lxcTH27duHCxcuQK1W6yx76KGHAABLly6tWemIiIiIaoFNAdG3336LcePG4erVqwgICNCZwkOSJDkgIiIiInIHNjWZPfXUU5g8eTKuXr2K4uJiXL58Wf67dOmSvctIRERE5FA2BUR//vknHn/8cfj7cxBBIiIicn82BURJSUk4cOCAvctCRERE5BQW5xCtX79e/v+UlBQ888wzOHHiBBISElC/fn2dde+55x77lZCIiIjIwSRh4YRjXl6WVSZJkoSqqqoaFcoVlZSUIDAwECqVikMJEBERuQlLn98W1xDpd60nIiIiqitsyiH673//i4qKCoPXKysr8d///rfGhSIiIiKqTRY3mWnz9vZGQUEBQkNDdV7/66+/EBoayiYzIiIicgmWPr9tqiESQugMxqjxxx9/IDAw0JZNEhERETmNVSNVd+nSBZIkQZIkDB48GPXq/f32qqoq5ObmYtiwYXYvJBEREZEjWVVDNHz4cNx7770QQiApKQn33nuv/Dd69Gi8//77+OSTTyze3u7du3H33XcjMjISkiTh66+/1lk+ceJEOQDT/OkHXJcuXcK4ceMQEBCAoKAgTJkyBVevXtVZ58iRI+jXrx8aNGiAqKgoLFmyxJrDJiIiojrOqhqiF198EQAQExODUaNGoUGDBjXaeWlpKTp16oTJkydjxIgRiusMGzYMK1askP/t6+urs3zcuHEoKCjA1q1bcf36dUyaNAnTpk3DqlWrAFS3HQ4dOhRDhgzBsmXLcPToUUyePBlBQUGYNm1ajcpPREREdYNNk7tOmDABQHWvMqXZ7qOjoy3aTnJyMpKTk02u4+vri/DwcMVlv/76KzZv3oz9+/eje/fuAIB33nkHd955J/7xj38gMjISn376KSorK/HRRx/Bx8cHt956K7KysvCvf/2LAREREREBsDGp+tSpU+jXrx/8/PzQsmVLxMbGIjY2FjExMYiNjbVrAXfu3InQ0FC0a9cO06dPx19//SUvy8zMRFBQkBwMAcCQIUPg5eWFvXv3yuv0798fPj4+8jpJSUnIzs7G5cuX7VpWIiIick821RBNnDgR9erVw3fffYeIiAjFHmf2MGzYMIwYMQKxsbHIycnBs88+i+TkZGRmZsLb2xuFhYUGXf/r1auHpk2borCwEABQWFhoEKSFhYXJy5o0aaK474qKCp2xlkpKSux5aERERORCbAqIsrKycPDgQcTFxdm7PDpGjx4t/39CQgI6duyI1q1bY+fOnRg8eLBD952WloZFixY5dB9ERETkGmxqMouPj0dRUZG9y2JWq1atEBwcjNOnTwMAwsPDceHCBZ11bty4gUuXLsl5R+Hh4Th//rzOOpp/G8tNAoD58+dDpVLJf/n5+fY8FCIiInIhNgVEr732GubOnYudO3fir7/+QklJic6fo/zxxx/466+/EBERAQBITExEcXExDh48KK+zfft2qNVq9OzZU15n9+7duH79urzO1q1b0a5dO6PNZUB1MndAQIDOHxEREdVNNk3d4eX1dxylnT+kGcHa0qk7rl69Ktf2dOnSBf/6178waNAgNG3aFE2bNsWiRYswcuRIhIeHIycnB3PnzsWVK1dw9OhRuft9cnIyzp8/j2XLlsnd7rt37y53u1epVGjXrh2GDh2K1NRUHDt2DJMnT8Ybb7xhVS8zTt1BRETkfix9ftsUEO3atcvk8gEDBli0nZ07d2LQoEEGr0+YMAFLly7F8OHDcejQIRQXFyMyMhJDhw7Fyy+/LCdFA9UDM86cORPffvstvLy8MHLkSLz99tto1KiRvM6RI0cwY8YM7N+/H8HBwZg1axZSU1MtPNpqrhgQFajKkVtUitjghogI9HN2cYiIiFyOQwMiAPjxxx/x/vvvIycnB19++SWaN2+O//3vf4iNjUXfvn1tLrircrWAaM3+PMxfexRqAXhJQNqIBIy6zbLxn4iIiDyFQyd3/eqrr5CUlAQ/Pz8cOnRI7p6uUqmwePFi20pMFitQlcvBEACoBfDs2mMoUJU7t2BERERuyqaA6JVXXsGyZcuwfPly1K9fX369T58++OWXX+xWOFKWW1QqB0MaVULgTFGZcwpERETk5mwKiLKzs9G/f3+D1wMDA1FcXFzTMpEZscEN4aU3Fqa3JCEm2N85BSIiInJzNgVE4eHhcu8wbXv27EGrVq1qXCgyLSLQD2kjEuB9s4eftyRh8YgOTKwmIiK3UaAqR0ZOkcuke9g0UvXDDz+MJ554Ah999BEkScK5c+eQmZmJp59+GgsWLLB3GUnBqNui0b9tCM4UlSEm2J/BEBERuQ1X7BhkU0A0b948qNVqDB48GGVlZejfvz98fX3x9NNPY9asWfYuIxkREejHQIiIiNyKsY5B/duGOPWZZlNAJEkSnnvuOTzzzDM4ffo0rl69ivj4eJ2xf4iIiIj0meoY5HYBkYaPjw/i4+PtVRYiIiKq4zQdg7SDIlfoGGRTUjURERGRLVy1Y1CNaojIOThlBxERuTNX7BjEgMjN2CMznwEVERE5m6t1DGJA5EbskZnvil0diYiInI05RG6kplN2cA40IiIiZQyI3EhNp+zgHGhERETKGBC5kZpm5nMONCIiImXMIXIztmbmaxKpU4fFYcnmbFQJ4TJdHYmIiJyNAZEbsjYzXz+ROjU5Dh2bB7lMV0ciIiJnY5OZmzM3W7BSIvWSTdkMhoiIiLSwhsiNWdKF3lXnjCEiInIlrCFyU5Z2oWciNRERkXkMiNyUpV3oXXXOGCIiIlfCJjM3Zc1swa44ZwwREZErYQ2Rm7K25ici0A+JrZsxGCIiIlLAGiIXZckErKz5ISIisg8GRC7ImglYXW22YCIiIlMs+cHvDAyIXIw9ZrQnIiJyRdb84K9tzCFyMZb2HjM3ICMREZErMTVcjCs801hD5GIs6T3myhE2ERGREmM/+Ff8lIsPfsx1+jONNUQuxlzvMUsHZCQiInIlSgMFe0nA8t25LvFMYw2RCzLVe4xTcRARkTvS/OB/du0xVAkBb0nC5L4xWP5jrs56znqmMSByUcZ6j1kzICMREZEr0f/BDwAf7sl1iWcam8zcjKtNxeEKiXBEROQ+tAcKdqVnmiSEEOZXo5KSEgQGBkKlUiEgIMDZxUGBqtzpAzIyuZuIiOzBkc80S5/fbDJzU84ekFEpuXv+V0c5XhIREVlMe5DGxNbNnFoWNpmRTZSSu9UAVuw544ziEBGRizGXUrFmfx76pG/H2OV70Sd9O9bsz6vlEupiQEQ2iQ1uCEnh9Q/2/M58IiIiD2cu2HHFIWQYENVhjkx4jgj0w8P9Yg1eVwsYjKpNRESew5Jgx9JZGWoTAyI3Y2mQUxtVkZP6xhoMssUhAIiIPJslwY7SII3Ofn4wIHIjlgY5tVUVqd9d0gvAlL4xdt0HERG5F0uCHVfqbq/h1IBo9+7duPvuuxEZGQlJkvD111/rLBdC4IUXXkBERAT8/PwwZMgQnDp1SmedS5cuYdy4cQgICEBQUBCmTJmCq1ev6qxz5MgR9OvXDw0aNEBUVBSWLFni6EOzO0uCHE3t0cGzl2utKnLUbdHYM28QpvVrBUjAf37MRe/07Xh/d47d90VERK7P0mBH8/z47OFe2DNvkNOHbXFqt/vS0lJ06tQJkydPxogRIwyWL1myBG+//TY+/vhjxMbGYsGCBUhKSsKJEyfQoEEDAMC4ceNQUFCArVu34vr165g0aRKmTZuGVatWAagef2Do0KEYMmQIli1bhqNHj2Ly5MkICgrCtGnTavV4a8LclB3aYwJJqP7TXt3RVZEf7PldLp8QQNrGkzhbVIpZg29hN3wiIg9jagoqbc4eQkabywzMKEkS1q1bh+HDhwOorh2KjIzEU089haeffhoAoFKpEBYWhpUrV2L06NH49ddfER8fj/3796N79+4AgM2bN+POO+/EH3/8gcjISCxduhTPPfccCgsL4ePjAwCYN28evv76a5w8edLi8jl7YMYCVTn6pG83GN58z7xBAGCwTAIg3ZziQxOdOyr6zsgpwtjlexWXccBGIiJyJkuf3y6bQ5Sbm4vCwkIMGTJEfi0wMBA9e/ZEZmYmACAzMxNBQUFyMAQAQ4YMgZeXF/bu3Suv079/fzkYAoCkpCRkZ2fj8uXLRvdfUVGBkpISnT9nMlUFqVR7JAC8PbpLrVRFxgY3hKTUBx+u0ZWSiIgcqy5M4+SyI1UXFhYCAMLCwnReDwsLk5cVFhYiNDRUZ3m9evXQtGlTnXViY2MNtqFZ1qRJE8X9p6WlYdGiRTU/EDsyVgVpbMLXbjFNaqUqMiLQD/OS45C2UbnGzVkzFxMRkePVlWmcXLaGyNnmz58PlUol/+Xn59d6GZQibu1J8bRfc3a2/iP9W2N+cpziYI3O7kpJRESO4YoDLNrKZWuIwsPDAQDnz59HRESE/Pr58+fRuXNneZ0LFy7ovO/GjRu4dOmS/P7w8HCcP39eZx3NvzXrKPH19YWvr2+Nj8NW1kbcliawOdIjA1rjns6RWLHnjJxkrR+cac9bwxojIiL3ZqzDz4YjBUjpGOFW93mXDYhiY2MRHh6Obdu2yQFQSUkJ9u7di+nTpwMAEhMTUVxcjIMHD6Jbt24AgO3bt0OtVqNnz57yOs899xyuX7+O+vXrAwC2bt2Kdu3aGW0uczZjEbe5iVNdIVs/ItAPk/rGIKFFALwkCV1b/t1sV1eqVYmIqJpSygYAvLLhVyze+Ktb3eed2mR29epVZGVlISsrC0B1InVWVhby8vIgSRJmz56NV155BevXr8fRo0fx0EMPITIyUu6J1r59ewwbNgwPP/ww9u3bh59++gkzZ87E6NGjERkZCQAYO3YsfHx8MGXKFBw/fhxr1qzBW2+9hSeffNJJR22eKw5pbinN4JGzPsvCrM8OYfdvFwHUrWpVIiKqpp+yoc3d7vNOrSE6cOAABg0aJP9bE6RMmDABK1euxNy5c1FaWopp06ahuLgYffv2xebNm+UxiADg008/xcyZMzF48GB4eXlh5MiRePvtt+XlgYGB+P777zFjxgx069YNwcHBeOGFF1x6DCJjSdKunodjqmbLWJD3y9nLSOnoPlWqRESkS5OyseFIAV7Z8KvOMnfqVOMy4xC5utoeh2jN/jw8u/YYqoRw+DhC9mJsPKLPHu6FmGB/g7GSgOrxktJHuk+VKhERKTM1Xp4zAyK3H4fI07nakOaWMDV/jaZaVX+5gPkq1bowvgURUV3nCj2ea8Jlk6rJNZKkraH5MujXbGmOYdRt0fD38casz7J03meqSpWJ2ERE7sMVejzbigER2ZW5L0P3mKYW50cVqMox76uj8pxsagHMW3sU/duGAAC77xMRuSBLfsy74hAsDIjI7kx9GczVImk7ePYy9BPchADe2XYKq/fns9aIiMhBHBmwuGrNPwMiqnWWVqkay/dfte/vUcM1PdniwhujtLLKpX5tEBG5I0cELJoAq6GPt03j7NUGBkTkFKZqkTRfnOim/pAAnVoi/X8D1TlIw9/LgHCxXxtERO7G1oGBTdEOsIzdw12haz57mZHMFXpzaQZ2HLt8L+57LwMjujaXL1IvAPPujDPoqQZUN6UB7jcQGBGRK7H3wMD6AZZSvb+rjLPHGiICULttusbappV+mXx96BzWzeiNskq13LwW5FdfzkHyAqDW276r/NogInI39h4YWCnAAiDvw5W65jMgIodUkRpjKvAy9sukrFKNxNbN5Ne0c5D8fbxw33sZJr+8rtibgYjIFVnT8cUSxgKstY8l6vzQdQUMiMhkFak9L1RzgZc1v0y0c5BMfXn1A7DU5DgkNA9kcEREZIQ9xhIqUJXjwJlLkCQJqcPisGRzts49ulOU602uzoCIam3uNHOBl62/TIx9eZUCsLSNJwEw+ZqIyJSaDAy8Zn+ezhhyEqrzPzs2DzIaYLlCTT4DIrJ7FakxlgRetv4yUfryGmu7Bgxrp1zhy0hE5O70B9QFqhOpX9t0Ej/Nu92lZyRgQEQAame4dUsDL3tNWaIUgGnT1E7t/u2iS3wZiYjcXW5RqWJPMrWAYhpGbeawmsOAiGS1MXdabc5zox+A6fOWJPj7eLnMl5GIyN3FBjdUHGvIS4JiGkZt5bBaggER1branLRWOwA78kexQWLfhiMFLvNlJCJydxGBfkgfmaCbQ3Sz5l3pnlpbOayWYEBEdZ4mAEts3Qz3dI6Ua6cAYN5XRw3WN/ZLhoiIzNP8ED145jIkCejasold5rd0NAZE5FS1ncysXTuVkVOk2NY9tW8r1g4REdVARKAf7upkmC+kdL+vzVQKUxgQkdMo9Szo3zak1gIkpapaLwCT+sY4dL9ERO6oJj9gzfUkq81UCmMkYWxKcdJRUlKCwMBAqFQqBAQEOLs4TmOvGp0CVTn6pG83yN/RBCi11dtrzf48g6pa9jAjItJVk67xSvd7b0nCnnmDaiUIsvT5zRoispg9x4owNkaQsd5etgZi5t5nbVUtxysiIndT0/uWpV3jje3HlXqSmcKAiCxi77EijHXN1FbTcYIsDeAsrao1mAZkWBwSWnAaECJyXfb4IWtJQGNqP67Uk8wUL2cXgNyDqS+ELSIC/fBwv1iT65gaJ6hAVW7yvcYCOFPvK1CVIyOnSHEdxWlANp3E2OV70Sd9O9bszzNZHiKi2mbLfVCJJqDR5iUBRVevoUBVbnY/EYF+SB0WJwccrjTDvTYGRB7OVBCgTekLUdMIf1LfWINtam978YgOKK2ssikQszaAW7M/D33StxsNcCyZBsTamwwRkSNZcx809SzQdI33lqpv2JIECAHM+iwLfdK3Y8WeXJP7WbM/D69tPgn1zffOTW7nkrmabDLzYNZUpTpirAilbc5NbqczAWCBqtymqtaGPt7yl9bc+yxpDrR0GhBX+8VDRJ7L0qYqS54FmnzLX85exsxVh+R0B7UAPvgx1+h+9O+vQgBLNmXjnk6RLne/ZEDkoWzJCXLEWBHmtmlLIKb5cusHQ8beZ0n7uCXTgLhaezgReTZL7p/WPAsiAv3QpKHhXGVqANP6tsKHe3IN9pORU+QWCdUAAyKPZWvWvyPGijC3TWsCMf0vN1DdLrz2sUR0impisG5uUSka+nhb9CtKZxqQP4uxZFO200dWJSIyxdz909pngbFap0l9YzCpb4zBftwloRpgQOSxzF2kjupebut2LQ3ElL7cagBllWqd1/SriO/r0hxfHzpnNsDRmQakU6TTR1YlIjLH1P3T2oDFXK2TPWr5nYUBkYcydZHac7whbea2a2mwZGo9S77cSlXEXx86h7WPJaKsUm1xgOMKI6sSEdWELQGLtekTrjI1hzkcqdpCdXWk6gJVuc5F6qgRRc1t19IgzJL1zI0+nZFThLHL9xps+7OHeyGxdTObj9EUDuhIRK5M/1lQl3CkarKIfi2Ho0YUNdf909JRUM2tV6AqR1RTf5O1Pdb0QNNma1DjqBo3IiJt2nmRpZVVVt2rWOPNgIj0OCoBztR2LQ3CzK2nFHjo1/hY2wNN/33WBjX2HuGbiEiJ9j1Kw9YfYJ5ao82BGUmH/gBc9kqAM7VdSwd9NLWeJSOymuqBZuqGUZPRXo0FcRuOFHAgRyKyC6V7G2DboLHmBqm1pCyWDPbrilhDRAYclQBnbLuWJvWZWs+SsS4s7YGmrybNiMYGdHxlw69YvPFXNp8RUY2ZGklfOzXBXK1PTWu03T09gAGRi3GVqkpz7cn27j5vaRBmbD1LmvpszR2qSTPi7t8uwli3BbWozp2KC2+M0IAGLvG5E5H7MTWSvrck4cgfxRj3wc9mA5Wa/PirC+kBDIhciLtE144qp6VJfUrrmatlsjV3SGNK31h8eHO+Hs37gOoea8aCGM0NwlQ3TrUA7n03AxIAAdf+3InINRkbSV8zHdJrm05aFKhY++NP+4exozrk1CYGRC7CXaJrVymnUg2Vsdoja0av1qcd/EkApvWPxaQ+sdj920V5GAFjQYypamx92vMCueLnTkSuTfv+5+/jJfeytSZQMTa/ZG5RqbxcQ/+HcWpynNuMSG0MAyIX4S7RdU2rVO3RLGSqhkpTe6RJ7DP2y8WS3CGDSQkBfPjjGaQkRFgUFCr92pJuVgWZipNc8XMnItdnrJbdmkBFZ4qiP4rl2iXte63SD+PXNp7E9IGtsWzX7y4/IrUxDIhchD27uzsyD8nWclrbzGbsGCypobLXLxdjwd/+M5ctCgqNNePFhTfG8HczjAZF7variohcly0jUWuWafKOAN17rbEfme/tysG85Dh0bB7klgM8uny3+4ULF0KSJJ2/uLg4efm1a9cwY8YMNGvWDI0aNcLIkSNx/vx5nW3k5eUhJSUF/v7+CA0NxTPPPIMbN27U9qGYZK/u7jXtMumIclrbbd3UMZgb4FFpX0s2ZSN1WJzV59ZYN//bYpqYHCZAu9vpqNuisWfeIHz2cC/smTcIo26LRqeoJkgf+fc5lHCz5gjVX8gpfWNwoeQaMnKKcDj/stt2YSUi16B0HzLH1L1W6d4IVHdYWbIp2y2DIcBNaohuvfVW/PDDD/K/69X7u9hz5szBhg0b8MUXXyAwMBAzZ87EiBEj8NNPPwEAqqqqkJKSgvDwcGRkZKCgoAAPPfQQ6tevj8WLF9f6sZhS0+7utZXfY205jX2xDp65jKaNdGuBzB2DuRoqY/vq2CIIe+YNsurcGvtl1SmqidXzwOnvT/8cAsCKPWfwwZ7f8Z8fc/GfH3N11meyNRHVhLUjURvruXbkz2Iktm6G1OQ4pG88aVDT7c5N/m4RENWrVw/h4eEGr6tUKnz44YdYtWoVbr/9dgDAihUr0L59e/z888/o1asXvv/+e5w4cQI//PADwsLC0LlzZ7z88stITU3FwoUL4ePjU9uHY1JNhk+vzTwka8oZG9xQ7kWlIQF4fPUhg8DB3DGYq/41FTBZe25NTQOiFBRaG5Bql6dAVY4P9vxuNAmbydZE5Gj6qQqpw+KQtumkzjpLNmXjSvl1vLszR7HZ352b/N0iIDp16hQiIyPRoEEDJCYmIi0tDdHR0Th48CCuX7+OIUOGyOvGxcUhOjoamZmZ6NWrFzIzM5GQkICwsDB5naSkJEyfPh3Hjx9Hly5dnHFIDuGoaTdqavdvF3X+ralpVQocLDkGUzVUtrSXK7FkGhB7zgNnSY80d/7lRUSuTemel9Ai0GC9KiHw7x05ittwx0RqbS4fEPXs2RMrV65Eu3btUFBQgEWLFqFfv344duwYCgsL4ePjg6CgIJ33hIWFobCwEABQWFioEwxplmuWGVNRUYGKigr53yUlJXY6IsexVzBgT0pj8UgK1bCah31i62YWj1pt7eCN1pbZ2qbHmgSkpgZW096Wv4+XybGPiIisZeyet/axRLP3JY0FKe1xZ8cIt74vuXxAlJycLP9/x44d0bNnT7Rs2RKff/45/Pwcd+LT0tKwaNEih23fURw17YatDp417JGlGdNH+2XtwMHaY1DqkeaMpseaBKTGBlbT8ALQu00zuXcac4qIqKY0987TF64o3vPKKtVIG5GAeXqD2urzkuD2wRDgBgGRvqCgILRt2xanT5/GHXfcgcrKShQXF+vUEp0/f17OOQoPD8e+fft0tqHphaaUl6Qxf/58PPnkk/K/S0pKEBUVZccjcZyaBAP2tGZ/HuZ9ddTgdW9Jwtxh7bBkc7bRwMHSY3DEqNk1qempSUCqNLCav48XNhwpxPI9v+PHU0XyupppP/x9vNE9pqlLfN5EVLtqMsSK9r1TieaeFxPsb3rgNFQPbVIX7kFuFxBdvXoVOTk5GD9+PLp164b69etj27ZtGDlyJAAgOzsbeXl5SExMBAAkJibi1VdfxYULFxAaGgoA2Lp1KwICAhAfH290P76+vvD19XX8ATmYs+ZGMzZthZcELB7RAaNui8Y9nSNrVJPlqF51ttb0aJ9r/Xwja/atP+7SB3t+V/x1phbArM+yWFtEVEeYu19rL9/920Wbfwwqjd6vzQvQmTjbVDw0Y2BrPNK/tUX7dXUuHxA9/fTTuPvuu9GyZUucO3cOL774Iry9vTFmzBgEBgZiypQpePLJJ9G0aVMEBARg1qxZSExMRK9evQAAQ4cORXx8PMaPH48lS5agsLAQzz//PGbMmOE2AY+tQY0z50YzliT89uguuKtTJICa12Q5sledtTU9jjrXliRbsweaLnv8CLB0G6YGEOVkvWQNc/cQ/eVC2D7lz4qb8zIa887YLkjpWH2fNpbfqBn0tq4EQ4AbBER//PEHxowZg7/++gshISHo27cvfv75Z4SEhAAA3njjDXh5eWHkyJGoqKhAUlIS3nvvPfn93t7e+O677zB9+nQkJiaiYcOGmDBhAl566SVnHZJVbH3Q2qv2xNYbu7Fmp24xpucOs0ZNmrYsOS5LAzZHjv9kSbI1wB5oGvYITC3dxvu7cpC+6aRBTpe7TNJMrsPcPURpuT5rplBarjfOmTZvSULXln/fp/VrzL0ATL05p2Ndu9+4fEC0evVqk8sbNGiAd999F++++67RdVq2bImNGzfau2gOV5MHrT1qT2pyY6+NHm+27sPeDyxH1lQZ3IwkYEyPaKzam2c0Kd1T2SMwtXQb7+/O0RmfRbNeXHhjl5j8mNyLuXuIJTXFlt4DcotKTU4bpHQPdbXOOo7i8gGRJ6vJg7amYxLZ4+FSG18iW3qk2fuB5ejxn5SOsWOLQKOBoKc219gjMLVkGwWqcqTrDVanWc/See5M8dTPz5OZu4cYmyhaEtVziFnzg1NxWwD+PbYLurZsYvQ+4iqddRyJAZELq8mDVqn2ZO6wdsgtKpWXm2KvWo/a+BKZ24f2F9sRtTm1VRumvT1jgaAnN9fYIzC1ZBu5RaWKSe5egDzPna1l8OTPz5OZu4cYu583b+IHCKBbTBOL7zdKtc6pyXFyzhDgudchAyIXVtMHrfZD88ifxXht00mLL3BXHfXaWvpf7NRhts18b07/tiF4c3QneN1sf6+NX1JKPdI8ubnGHoGpJdswlteVmhxncp47ffq/wD398/N05mq7a3I/V9pXcdl1pN/cxmubTiLIrz5G3Rbt0dchAyIXV9NmJ8364z742aoL3BVHvbaW0hd7yeZspCbHYckm42MgWUs/6JrSNxaT+9Z+wqGp2i/NcndphrG12cgeo5Qbm79Ow9gvbE1vG0vKoPQLPKqpf63NRUiuyVxtt633c30FqnK8tvmkTi+1+V8dRVx4Y5RWVnnsdciAyA04q3t6/7YheGtMZ6urZF2FsePu2Nz6me+NUQq6lv+Yiw9+zEX6yJpVM1sbFBir1TvyR7F8A3WH6u+aVtfb+n2xZP46DXNBj6kyWDNNgj17TVLd4KhcOTWA4e9lYJ6DatHdAQMiD2BL81ddaEM2ddy2PjQ1D56GPt4orazCpdJKxd4fAqZ/tZl7gNly/hXzDJLbyVXrgOtXfzurut6W/dpyDRWoyvHdkXOKD7QNRwt0cpMkCQ7tNckgyj0p3de8JKDo6jUUqMotyqds6OOt2OwrHFSL7i4YEHmIqX1j8cGPuRb1SKgrbcj2bvZTGupeguG8bBrGfrWZe4DV5Pzr11w4ckgAR6jN8jo62V6fuakSlu/O1Z0EWVTX0pqidK3MX3sUFTeqcEd8uNGyK+XWJbQIdGhwxADMPudA/74m3Ryk0dyo9fqf+X1dmmPdL39CrbeevWvR3QkDojpO/0swrW8rTOobY/ICt/Th4A43OHt1/Tc21L1AdUCk9GtLqRbOVLADVJ97pVonax7OmpqLAlU5LpVWmpxI19XUVjK/QUCQ7NhmAnNTJQCGQbUawMEzl3FXJ+u+q2oBvPDNCbz4zQm52Va7ZiDvUpnBNagZU8mW2mBLRuuuyTQT7sbY+bDkh5Cl91PNfe2Xs5cxc9UhkyNWF6jKcfDsZcz76qjOel8fOoflE7ph6n8P6tRM1rQW3Z0xIKrDlB6+H+7JxaS+MSbfZ8lDyVWa1Ow54rQppgZGEwDeGd0FR/4sxge7TdfCGQs2V/xUnXekFsq1TtY+nLU/H+3taZfLFQPamtTqWTPdhkGy/aZspA6LMznhcE32bW5gPS8AUAiqH199CKWVNxS/WwWqcvx1tcLoSOYC1YmyxeXXdZpNTTFWG1mgKseBM5cgSRK63RzFOLeoFEf/VCn2drJlmglXvB6tZey+aK7W19Ym8iYNDQdZ1P7xZKpWskoI+PvUR7qbd56xJwZEddjBs7YNEmfuoeSIJjVbboa1GZSZmkJDMyXJXZ0iMalPrMnaKGPt/9rNJfq1TrY8nLU/H3FzW++M7iInx7tKQKvEllo9a47HaLJ9C9uaCSzZt7nrZ/GIDgCqAxjtJgxj3y1jAa8+NVA9vYgFwZCGfs/Eo3+qkL7xpG5znsL+NGVVGq3b2D7q0vhZpu6L5nqA2no/NfXj1VytpGa9xNbNPGIUakswIKqj1uzPw7yvjhq8bmlNg6mHkr3zLWy5GdZ2npN+kKihNOCltcHm5L4xBnMLaWqdmjXyhb+PF0orq8wmTGoYa0Zp1sjXbca7saZWz9rjsWeyvaX7Npbw3rF5kM73q6FvPcxcdUhnH0ojZesHvJKR8kmAVcEQUP0d1O6ZqMTYJquEQNrGX83WRnkB8n3IkmZkTScGV649MnVfNHXN1eR+auy6Mtb0rr1v/YEfXfW81iYGRHWQ5gaj/13wsrDXioaxL4k98zxsfTg7I1lYO0j09/FCWaXapgHS9INNoLopU/98dotpYlPuhbnPx90Src2x9ng0DxFNbYwXrPte2LpvS2q+urU0HOkaAPacuggBYTQBXACY1j8Wy3/MlQMgCcCwDuHYdKxQsezGapXG9IjGa5sta2JT8nPuZbPrCAC7f7uIUbdFK868XiUEVuw5gw/2/G5Qm+qscb7MMRdom6p1r8n9VGfAxj/+vh8p1Rx6ScDbWjXFpMvL2QUg+zOWr/D26C52qYbWfLm9perfpTVpdzZXlWyM5uajrTaShSMC/ZDYuhk6RTVBTLC/Ypf2AlW5xdvRBJ1K5xNQrko3t31zn4/SudPututubL4WJL3/ailQlSMjp8js+bB239qfu7HlqclxBq+/uzMHY5fvRZ/07Tj6p0rx80toHoivH+uNd8d2wb/HdMHXM3pjy3HlYMhbkjDvZiK5NglAu/BGNgdDlhKovpYP51/GfxRmXpcAg2AI+Hucr95p2/H+rhyLPqPaYu57N+q2aOyZNwifPdwLe+YNku/F9rifRgT6Vd+PtAJZzanTfMbekoS0EQm4q1MkgyEjWENUBxn7pdItpond9mGv3lu21jbZu0u9LexZ06I09UdGTpHN2zf1+djabddVmWo20CzXdjj/MuatPSrXpNQkwdXS69CaHLmE5oFGl2kSwKcPaI2lO3OgBox+fkrXDwAsSGmPOztGICLQD0H+9XWagQWAhetPmCyfvVQJgf/s/l1xWUpCBL47WmD0vQKGPeM0uTrObFazdcBOe9xPjdUcaprePT0/yBIMiOqg2goW7NHuXJOy2isos5W9mg6NPYAt3b6xh62pz8eabrvuwFizgX5Ao8mtM9YzB7A+wdXcdWgqwFL67EwlYGvK+t6uHDl3SDtHSLu8R/9UGbzXW5LkYEhT9rjwxhj+bobONWAvmgoopU16ScDGo4Y1WBKAh/vHYuOxAot7xs1bexQQf+dTzUuOwyMDWtte8Bqw9b5Y0/upqR/C7vRddiYGRHWU0k3a1m6tju4OW5PAxpnJgPYIPM3lUJnbfk1651jSbdedaMprbJ4nAIq5dUDNE1yNXYfGPt+48Mb47kiBnDum/dkZS+DXpnlZaWmVEDh45jJeu1mDom3usHa4UHIN3x05hx4xTdEpqglKK6sUt2Msx8gUzQPZSwKm3hzzbPdvFxU7Iyh1JgCqgyH9SXLN0V5FU3t05dp1jOvVEgfPXoYQAt1jmrrdNW0NxZrSYcZrSskQA6I6TPsmbeuDs7a6w7prL4ea1lKZewCb2r4tCen6wW1tDYRYW0ydTwGhWOOg39nAnufDWHnufTdD5zX9z05/ZnPNNApegMHIwvq8JUlxTCMA+Ol0kdzUBAAjuzbH00ntFI95bnI7eb+W8JKAdY/1NpgUV6kzgrHOBF4SMKlPrOL7/rP7d2xQqFEy5t87cvDvHTk6r03rF4tJLpiQbS/61421HT48HZOqPYCxB6e5ZERb3+dpzCXKatNP1rUkKdfY9q1NSF+zPw990rfLyblr9udZldBpaaKxM5k6n4rJ5Kh+iFub4FqTpGtj9D87zef+SP/WcjLu8gndjHax1y7vn5cNy+UlAbtPFem89tUvf+JCyTXFY36kf2usfSzRYH8SgOGdI3Vfu/nADQ1oAKFQr6TdGcFUZ4K0EQkGzb6a9z1/V7zJY7fEf37Mla/9uqzo6jWDDh/zvzqKw/nmewB6MtYQeQBbmwHslTRcF0agtQdjtW22NrsZyzU58mexwSztpmqTLKnlUprqIqF5zee+sve1Ya6ZUWlZpyjdzgbmmputGQrBIIEdxpuhjNVEafZ99A8VXtt80uj7n7i9DUb3rC5Hn/TtBssHxYVi268XDF4/cOYypvRrpXgNKDWniZvnKDU5DgfPXIYkAV1bVg8R0Sd9e42GoDDXLJk+8u9z6QXgzo7h+O6I5bVGgPvmyVnC1MjUmtns01lTZBQDIg9ga7OIPZpT6sIItPZgKiDp3zYEb43pDAhYlQAZEeiH1GFxOk0gQHUvpHv0utYaC25/OXsZTRpWP+j1gyhTZU/baP3cV/rBj6OuDVMPWUsfwMaam/WThJUGEYwNbqjz/5p9bvv1PJ7/+rji/rwkYFSPKBw4c0kn18XchLDa3tlxGpFN/BDV1F9x/YFtQxQDou43e59qjllT+xUb3NDs2DrdYqqP88Q5lcFcWZYGHdY0lyuN4bXxaKHVieDumidnikXz5dXhYNAeGBC5EEfVpNia/FvTpGF3GBG5thidw0xr8DlbgoKEFoZdtJVu9koPNkmC3MPM1L5NzcNl6WeqVMOkNIaTNflPpph6yFrzAFYaFVqf/ueoHTRJAB7uF4tmjXyRrpDkDAAdmwfgyJ8lWLU3D6v25kECkD6yuhu5pcEQ8Pc5nD6wlcEyb0nCkPgwZOUX46tf/pRfH9m1uU4NmXYvPE05jN0DzAVrpoIOW+512u/RDt6tSb7WsFeenCvVfpubL0+jLgaD9sKAyEU4uibF1uTfmiQN17URkWtCcQ4z6A4+Z0vAaGktnn5wa+mEm8b2oa1KCPxw4jxahzZSfDAoBcZKk42aujYc/f0w9mCz5CGj/zlqry4AxYEHNSQAR8+V6LwmUJ3v8fbYLib3rdT8ViUE3t2ZY7Du3GHtEBHoh38+0BkPJbbEgTOX0T2miU4wVKAq16nlEQDmfXUUGfNvN5jjzZLaCGNBhy2fpan3GBtCAqj+bF4afitOnLuC1fvzoBY1G0i2psfhSIo/em7+V/ucuHOnCUdjUrULqK3kZWuSf+3xPmeNJu2KlBJIp/aLtSopWkPTpHE4/zJyi0qROizOoqRo7ZFy3xrd2eRYPKbKrmTBN8d1krW1GZtbTX9rpnJoHPn90E42752+He/v/jugULqGJeiO/ju6R5RNY/d4SdW1R0oVG2oAEDCakG1spGkvKM9d1rFFkPz/naKaYEq/Vga5UwfOXFLMFzp45rLBPcBcoGhsOhRN0GXNZ2nJ5189hISP4YSzAFqHNMbiEQn4ad7tBqNE28oVO5wo3WPSRyYgfaR9ZhXwBKwhcgF1tSbFHuP0OIsjqsKV8h8+UJjDzFTAqNRMoWmC0p8oVOkYtPNE9GsYTO1bceBDhfWUapqM1WLNHdYOSzZnm702HPn9MGgS0+RHCeCRAa2NXsP6XZutpendFhrQwOAa0CzvFtPEYN/6E8JqjzStOaf685BZ+iNEMhLwKr1sqtZQ0/1eP+ACgI/25Fo97pWln7+52lJ7Du1hroens5rRjNXoczZ7yzAgcgF1bSwYbc4eTdoWjqwK178pWxMwGmumUIvqROo98wZZPO7U7t8u6mxDgvkJTjVlT2zdDP4+3ljwjXKCcJUQ2HCkACk3R0Q2FlSMui0a93SONHtt2Pr9sCSoNVbT8dqmk7inc6TBmED+Pl4orawCUD1bu6kZ4Y3R792WNiJBZyoRCUDayASDfSudI6Xl+kGSpT9CurVsYhAka3qQ6dP/TI0dm7YCVTk+3GPYfOgFmPwsbW0WtvTYLf3xo72eYhO4VN3DU3NN1GbTrjalwM+ewWBdJglhRSaaByspKUFgYCBUKhUCAgLsvv01+/MUHxhUuwpU5XLXYQ1vSdIJNhyxT2NdvIG/f23mFpVi7PK9Rrfz2cO9kNi6mdljUFruJQE/zbvdomM0Nv2FPqVpKmwNjK39flga1BaoytE7fbtiM5PmfBrb5tS+sSbzg/TNHNQafdqEKB5/gaocv5y9DCGs62lojK3n2tofA5r9aA+4aGx/3x7+E7M+yzJ4fVr/WDx7Z7zitnOLStHQx1tnVG9zn781x27p8er0NJSqpwYJ8quvE8gChnldttw7jAU9xsrqSondrsrS5zdriFyEO9ak1EXOaL60pIu3lwSkDosz2kyh/YvZ3DEYy+mx5Bg1tVSW/IrSbz6rya9Ua74f1vRujAj0w7zkOHkYAQ39GgilbX7wY67JZHMNL1Q3aZqaWysi0A8pHe3bs9SWc23tfcjS/Wiua33aI1Mrra+fIDytfywm9dEdaVo/ILC0TJZeJ8aaVWcMam2Q1W5tc6Cp49YPevTLOv+ro8i7VFY9ya+LJHa7OwZELoTVms5nrCrc38fx/Q9MdfFWC2DJ5mykJscZTKeg3zRgromhJk20xpqYXr73VlTcUOOVDb/qvG7PYNLS74e1Qe0j/asfbJq8KP3zWaAqx3dHzhkGkQCm9W2FD/fkyoMu4mbvPaV8H3dh7/uQsaZeL8BgZGpT6wsAH/54RieAsrV529hnqnSdGLvm39uRY/aHgbnvlX5tsLEATfFHDIB3taYm8eRhTeyFARGRFqXcCLUA7nsvw+G/vsz13KkSAh2bB8ldoI01U5jLpahJsruxYGpIfBgAYPHGXw2GFvirtELufWNp1X5NmgGsCfg0+7mnc6RBPlOBqhwf7ck1mG9Le5uT+sZgUt8YnUR51vLqMnZdvzO2C1I6Rlq8PqAbsNg6zpmp8ZOUagYvlVYqbkfA9AS4mp52AOSBLk01gU3ta7zXqbmhL/TX57VnGwZERHpG3RaNuPDGGP5uhk0j79rK3E1Pe4Rgc01GUU39sfaxRJRVquVE4AJVufw+W5toIwKrR8c2VpuiNE3FzFWHDJr/TAWXNU1q1w/4vCRgct8Yq/ZjLk9KKcjU3j/9zViAqpSsbWx97ff5+3ghI6cIl0orrW7eNjV+kv5nqt98rf8WCcCYHlH4bF++4nXyztguuFpxQ3E6E0ubYLW/82kjEjD/q6MmJ/etK51xnIUBEZECpTmcaiOXSCeguHkXFvi7CSa3qFReV4n+Q/6+Ls2x7tCfig99a5pG9OfTUqM6uXRucjudYEUTaB08cxmPrz5ktPnPWHBpTV6HUg2S5vX+bUOwZ94grPgpF8t352L5j7n44MdcPHxztnPAsHli/tqjiAtvjNCABibzpBaktMedN3vQkXmmaiSNDQ1hrAfb8C6RuO+9DMVxrDRMNW8bq33S/0yVmq/1gyIBYNW+fPl7qs1bktCiiZ9cVsCyJjDtJlj9AE3zQ+3edzMUj82dhjVxVQyISEeBqhwHzlyCJEno1rLmvV3clbOGQlAaq0hn7B8TtSZKwYT2NA3aD32lbtHa2zE255g2IZTnTYsI9EPTRuab/5SCS0vyf4zV7BhMDzIsDh/8+Pe4NwLVo0Z/sCdXsXlCLYDh72YoDpip4S1JDIZsoFQjacno09pNw/4+XjoBhrHLq6zSeB2Kse+1/meqdB0a258Quh0gNIFJaWWV0Wv56J8qg+0oNcHqX2cnC6/oBGYSgHl3Go5BRrZhQERyEJSZ8xdW7cuXX9fMZeSJvRZqkmdjj33r70d7vBtjtSYHz142m2Ogeegb+1yVggr9gf60GQtsLG3+02cuEDVWgxQX3lh5ehAj58BYDzEB4MM9uYpNJF6S+bGayDjt69qSmkD970FGTpHZ69vcjxZLv9fG5v4zNkiN0FpHU2taoCpXvMZ+On0R75mYXkVTTn2H8y9Xd/PXek2SYPCDhGzHgMhDaTeBpG86qfjrR6C6RkFzk7I20dXdx8dwlaEQLK01mfeVYbdmJQKWdS82FVRoGHsAmWv+038IaV8rph5Yxs7F/jOGwaCmWc/Y1BjT+rbSmYNMXiaqu3d/+OOZ6hwkAFMVunuT7WwZ3sKSINuSgNXS7/WUvrE6CfWWjNinqTXtFdsUpZVVmD6gtcHccu/d7Cavr0UTw3LI9+k/VUjfaHiftnS4DLIMAyIPZG6Wam2aL9zu3y5alejqahMf2soeXZBrGhhaWmuiVKOhnUOkzdLuxaaCCnMPIKXmv4NnLgNS9ajI2jd7/eZA/clEzZ2L22KaKE8PktwOr21UDuqaNfLBusd66yTPa943qU91AOTsYLiuMnVNG/u+yInFRrrwr30s0WRTsDZT32ulscCsUSWEfE0pvd9YkvbMVYdwteKGTmK/rRPokm04uWsdoZnw09zkgpbMUq1NMwaPNRMZuuLEh86iPXGo0sSnltA8CIxN0GgsUfTt0V3wzwc6Y91jvS2aSNVYXsO85L8nj9WQUF3Fby7IjQj8e1LQ3b9dxOOrD2HmqkPonbYdvdOqz0vaxpM618r8mzVdShMKGzsXoQENMLVvrHxD8wIwpW8M7ukUiXUzeiuW7bVNJxEa0MDo5JfaZSf7MvY57v7tosnvy6jbovHW6M4G21PDdO6QpUyNBaZtQUp7fDOjN94d20Ux6BF6/9VmbGJeTc1tgarcovs0m3DtjzVEdYA1tTHmxrrRJt3clqnkQKUvY12drNZato6TosRUNb+xX9vdYqp/LXeKaoL0kabzJgpU5YqTlA69NQyP9G+NXrFNdWpSBKoHitTM92XtuTB1CaoBvLPtNBaPSFBM8tc/F5qHqOb6798mGHtOF8kJ1FP6Go6ErNnPmaIyl2ka9TRKNYjaU8oY+750j2nqsA4PltwftZOwO0U1wdWKGwY94vRpyquptUxoHoiF98TjhW9O6KynuU8KCNPBEIxPoEu2Y0Dk5hSHdF97FP4+3uge09TqZNfqsTWi0adNM7Ro4ofSyio09PG26gZUlyertYa9A0Nj1fyWJIqae+gbexBsOVaIAlW5RcMQmOqhaE0gDgCr9uXhclklNh8r1O1RkxyHhBaBiA1uiMTWzf5ONNW6/nefKpK3oxYmkqTx96Si9mgaJetpn3elpGml74u9OjwoNc0ZS6aWRHUAral51CaPW/ZehtGmZc2YYPq9RfWvS+1r0tRYTMYm0KWaYUDk5ozNSzXrsyzF2iKlm8n0ga3gJUkIDfDF4PZhcpdYTRdXTS7K14fOWXQDcmYPLVdSm4GhsYBHf2oAYaRuJja4oWLQoMbfeT9KE1dqjkV/IEP9HoqWjrSrbdOxQp1/CwBpN2uxNNfk2l/+NDt9giZJevmPuYozypNrsOb7YmutnsF4Wgq16lP7xuKDH3PlACh1WBzu6RyJFXvO4IM9v8s1j9rvKa2sUgyGNKNVd4qqzpnT7y2qP4aRALD7t4sYdVu0wT107rB26NiC3esdyaNmu3/33Xfx+uuvo7CwEJ06dcI777yDHj16WPReR892byulmcu1GZttWTMj9JE/ig1uDP3bhijOlq75lWPpF7Ims5u7O+0b75LN2RbP0m5PpiaK1Z+FfsWeXMWZ2yVU37T1t6F9LAWqcvRO266Y1P3TvNt1esIp9TrzAkz2ZKspzXcAgF1nlCf7075G7P19MTdlx9xh7eR7oXac4iVVT86rqdnRfo/m3qp0H/aSgIX3xOOO+HBEBPohI6cIY5fvNdi30g8N7e166j3UnjjbvZ41a9bgySefxLJly9CzZ0+8+eabSEpKQnZ2NkJDQ51dPJsZG9VVw1gTjebfSuPbvDm6k2LVdVmlGomtm1lVNk/8EhuM5ZPsuIHTTI3YbGqiWE1uhnbvQX2S1vqabXhJwDuju+gEFLlFpYq1NPpdgo0NOhkT7I/1h88ZzDhvD/q1k/acUZ7sz1H5XOaSlKuEkGsfAcPvi34wpHmP5vpWGmZCCOCFb05g4foT8g9NpYmjzW3XE++hzuIxvcz+9a9/4eGHH8akSZMQHx+PZcuWwd/fHx999JGzi1Zjo26Lxp55gxR7PJhqojGW4+IlSQY9IDwxB8gWSjldSzZlOyQYMtWDzZKJYn85e9nkQ+LxwW0Uxz1p1sjXIKlbqaeNlwSDa0a755b2/9/TKdJgG7Z0eda2IKU99swb5JbDPXgyR/TuszaHTZ9SF3z9e6L2fVhT+6l577NrjwGATs86SSEYUtou1R6PCIgqKytx8OBBDBkyRH7Ny8sLQ4YMQWZmpuJ7KioqUFJSovPnyiIC/ZDSMdJoF2IlmjZ7bZpJF0118ybjTCVS25O5oQ2UPltt3pIEtTDek8VbknB7XKhFgXFEoB/SRyboPDA0PRQtvWaUapkEgFeG32o02BrZtbnBcADa5eQUG6Rh7vtgjv7wE8buiRGBfmjS0MdoBwT9oEkfu9I7l0c0mRUVFaGqqgphYWE6r4eFheHkSeVq+rS0NCxatKg2imdX1lQ5m0p+Zldk29RWIrW5HmwGVfgAcLMaX/M5K3VfBnQTQS1Njtee1FWSgK5WzoNn7LwNbh+G+t5ef89cD91Ro59OaledC/dnMZZsyvboJH4yTuleNze5HbwlCa9s+FXxPZrcHu1cpns6R5q9J5q7B1QHTcrNzG+P7oK7OkXW7GDJZh6RVH3u3Dk0b94cGRkZSExMlF+fO3cudu3ahb17DRPdKioqUFFRIf+7pKQEUVFRLpdUbQ9M3LMvRyaGaiglcSol0Gt/tgAMPmftsnpJwNS+rTCpb4zRbTjy+jB13iwpA69jMkf/GjGWDL3usd4IDWhg8/Vk7h5g6feX7MPSpGqPCIgqKyvh7++PL7/8EsOHD5dfnzBhAoqLi/HNN9+Y3Yar9jIj11QbD2d7BV6uFEi4UlnIMzjqB4y5a7k2fjhRNQZEenr27IkePXrgnXfeAQCo1WpER0dj5syZmDdvntn3MyAiV8QAgqjmnPU94ve3drDbvZ4nn3wSEyZMQPfu3dGjRw+8+eabKC0txaRJk5xdNCKbsVsuUc0563vE769r8ZiAaNSoUbh48SJeeOEFFBYWonPnzti8ebNBojURERF5Ho9pMqspNpkRERG5H0uf3x4xDhERERGRKQyIiIiIyOMxICIiIiKPx4CIiIiIPB4DIiIiIvJ4DIiIiIjI4zEgIiIiIo/HgIiIiIg8HgMiIiIi8ngeM3VHTWkG9C4pKXFySYiIiMhSmue2uYk5GBBZ6MqVKwCAqKgoJ5eEiIiIrHXlyhUEBgYaXc65zCykVqtx7tw5NG7cGJIk2bydkpISREVFIT8/32PnROM5qMbzwHMA8BwAPAcaPA+OOQdCCFy5cgWRkZHw8jKeKcQaIgt5eXmhRYsWdtteQECAx17wGjwH1XgeeA4AngOA50CD58H+58BUzZAGk6qJiIjI4zEgIiIiIo/HgKiW+fr64sUXX4Svr6+zi+I0PAfVeB54DgCeA4DnQIPnwbnngEnVRERE5PFYQ0REREQejwEREREReTwGREREROTxGBARERGRx2NAZAdLly5Fx44d5YGkEhMTsWnTJnn5tWvXMGPGDDRr1gyNGjXCyJEjcf78eZ1t5OXlISUlBf7+/ggNDcUzzzyDGzdu1Pah2E16ejokScLs2bPl1+r6eVi4cCEkSdL5i4uLk5fX9ePX9ueff+LBBx9Es2bN4Ofnh4SEBBw4cEBeLoTACy+8gIiICPj5+WHIkCE4deqUzjYuXbqEcePGISAgAEFBQZgyZQquXr1a24dik5iYGINrQZIkzJgxA4BnXAtVVVVYsGABYmNj4efnh9atW+Pll1/WmU+qrl8HQPV0EbNnz0bLli3h5+eH3r17Y//+/fLyungOdu/ejbvvvhuRkZGQJAlff/21znJ7HfORI0fQr18/NGjQAFFRUViyZEnNCi6oxtavXy82bNggfvvtN5GdnS2effZZUb9+fXHs2DEhhBCPPvqoiIqKEtu2bRMHDhwQvXr1Er1795bff+PGDdGhQwcxZMgQcejQIbFx40YRHBws5s+f76xDqpF9+/aJmJgY0bFjR/HEE0/Ir9f18/Diiy+KW2+9VRQUFMh/Fy9elJfX9ePXuHTpkmjZsqWYOHGi2Lt3r/j999/Fli1bxOnTp+V10tPTRWBgoPj666/F4cOHxT333CNiY2NFeXm5vM6wYcNEp06dxM8//yx+/PFH0aZNGzFmzBhnHJLVLly4oHMdbN26VQAQO3bsEEJ4xrXw6quvimbNmonvvvtO5Obmii+++EI0atRIvPXWW/I6df06EEKIBx54QMTHx4tdu3aJU6dOiRdffFEEBASIP/74QwhRN8/Bxo0bxXPPPSfWrl0rAIh169bpLLfHMatUKhEWFibGjRsnjh07Jj777DPh5+cn3n//fZvLzYDIQZo0aSI++OADUVxcLOrXry+++OILedmvv/4qAIjMzEwhRPXF4+XlJQoLC+V1li5dKgICAkRFRUWtl70mrly5Im655RaxdetWMWDAADkg8oTz8OKLL4pOnTopLvOE49dITU0Vffv2NbpcrVaL8PBw8frrr8uvFRcXC19fX/HZZ58JIYQ4ceKEACD2798vr7Np0yYhSZL4888/HVd4B3niiSdE69athVqt9phrISUlRUyePFnntREjRohx48YJITzjOigrKxPe3t7iu+++03m9a9eu4rnnnvOIc6AfENnrmN977z3RpEkTne9DamqqaNeunc1lZZOZnVVVVWH16tUoLS1FYmIiDh48iOvXr2PIkCHyOnFxcYiOjkZmZiYAIDMzEwkJCQgLC5PXSUpKQklJCY4fP17rx1ATM2bMQEpKis7xAvCY83Dq1ClERkaiVatWGDduHPLy8gB4zvEDwPr169G9e3f83//9H0JDQ9GlSxcsX75cXp6bm4vCwkKdcxEYGIiePXvqnIugoCB0795dXmfIkCHw8vLC3r17a+9g7KCyshKffPIJJk+eDEmSPOZa6N27N7Zt24bffvsNAHD48GHs2bMHycnJADzjOrhx4waqqqrQoEEDndf9/PywZ88ejzgH+ux1zJmZmejfvz98fHzkdZKSkpCdnY3Lly/bVDZO7monR48eRWJiIq5du4ZGjRph3bp1iI+PR1ZWFnx8fBAUFKSzflhYGAoLCwEAhYWFOjc+zXLNMnexevVq/PLLLzrt4xqFhYV1/jz07NkTK1euRLt27VBQUIBFixahX79+OHbsmEccv8bvv/+OpUuX4sknn8Szzz6L/fv34/HHH4ePjw8mTJggH4vSsWqfi9DQUJ3l9erVQ9OmTd3qXADA119/jeLiYkycOBGAZ3wXAGDevHkoKSlBXFwcvL29UVVVhVdffRXjxo0DAI+4Dho3bozExES8/PLLaN++PcLCwvDZZ58hMzMTbdq08YhzoM9ex1xYWIjY2FiDbWiWNWnSxOqyMSCyk3bt2iErKwsqlQpffvklJkyYgF27djm7WLUmPz8fTzzxBLZu3Wrwa8hTaH75AkDHjh3Rs2dPtGzZEp9//jn8/PycWLLapVar0b17dyxevBgA0KVLFxw7dgzLli3DhAkTnFy62vfhhx8iOTkZkZGRzi5Krfr888/x6aefYtWqVbj11luRlZWF2bNnIzIy0qOug//973+YPHkymjdvDm9vb3Tt2hVjxozBwYMHnV000sMmMzvx8fFBmzZt0K1bN6SlpaFTp0546623EB4ejsrKShQXF+usf/78eYSHhwMAwsPDDXqYaP6tWcfVHTx4EBcuXEDXrl1Rr1491KtXD7t27cLbb7+NevXqISwszCPOg7agoCC0bdsWp0+f9pjrAAAiIiIQHx+v81r79u3l5kPNsSgdq/a5uHDhgs7yGzdu4NKlS251Ls6ePYsffvgBU6dOlV/zlGvhmWeewbx58zB69GgkJCRg/PjxmDNnDtLS0gB4znXQunVr7Nq1C1evXkV+fj727duH69evo1WrVh5zDrTZ65gd8R1hQOQgarUaFRUV6NatG+rXr49t27bJy7Kzs5GXl4fExEQAQGJiIo4ePapzAWzduhUBAQEGDxZXNXjwYBw9ehRZWVnyX/fu3TFu3Dj5/z3hPGi7evUqcnJyEBER4THXAQD06dMH2dnZOq/99ttvaNmyJQAgNjYW4eHhOueipKQEe/fu1TkXxcXFOr+it2/fDrVajZ49e9bCUdjHihUrEBoaipSUFPk1T7kWysrK4OWl+4jx9vaGWq0G4FnXAQA0bNgQERERuHz5MrZs2YJ7773X484BYL/PPTExEbt378b169fldbZu3Yp27drZ1FwGgN3u7WHevHli165dIjc3Vxw5ckTMmzdPSJIkvv/+eyFEdRfb6OhosX37dnHgwAGRmJgoEhMT5fdrutgOHTpUZGVlic2bN4uQkBC36mKrRLuXmRB1/zw89dRTYufOnSI3N1f89NNPYsiQISI4OFhcuHBBCFH3j19j3759ol69euLVV18Vp06dEp9++qnw9/cXn3zyibxOenq6CAoKEt988404cuSIuPfeexW73Xbp0kXs3btX7NmzR9xyyy0u3dVYX1VVlYiOjhapqakGyzzhWpgwYYJo3ry53O1+7dq1Ijg4WMydO1dexxOug82bN4tNmzaJ33//XXz//feiU6dOomfPnqKyslIIUTfPwZUrV8ShQ4fEoUOHBADxr3/9Sxw6dEicPXtWCGGfYy4uLhZhYWFi/Pjx4tixY2L16tXC39+f3e6dbfLkyaJly5bCx8dHhISEiMGDB8vBkBBClJeXi8cee0w0adJE+Pv7i/vuu08UFBTobOPMmTMiOTlZ+Pn5ieDgYPHUU0+J69ev1/ah2JV+QFTXz8OoUaNERESE8PHxEc2bNxejRo3SGXunrh+/tm+//VZ06NBB+Pr6iri4OPGf//xHZ7larRYLFiwQYWFhwtfXVwwePFhkZ2frrPPXX3+JMWPGiEaNGomAgAAxadIkceXKldo8jBrZsmWLAGBwXEJ4xrVQUlIinnjiCREdHS0aNGggWrVqJZ577jmdbtKecB2sWbNGtGrVSvj4+Ijw8HAxY8YMUVxcLC+vi+dgx44dAoDB34QJE4QQ9jvmw4cPi759+wpfX1/RvHlzkZ6eXqNyS0JoDRtKRERE5IGYQ0REREQejwEREREReTwGREREROTxGBARERGRx2NARERERB6PARERERF5PAZERERE5PEYEBEREZHHY0BERA4zcOBAzJ4929nFcLiFCxeic+fOzi4GEdUAAyIiIiMqKytrdX9CCNy4caNW90lE1RgQEZFDTJw4Ebt27cJbb70FSZIgSRLOnDmDY8eOITk5GY0aNUJYWBjGjx+PoqIi+X0DBw7ErFmzMHv2bDRp0gRhYWFYvnw5SktLMWnSJDRu3Bht2rTBpk2b5Pfs3LkTkiRhw4YN6NixIxo0aIBevXrh2LFjOmXas2cP+vXrBz8/P0RFReHxxx9HaWmpvDwmJgYvv/wyHnroIQQEBGDatGkAgNTUVLRt2xb+/v5o1aoVFixYIM+yvXLlSixatAiHDx+Wj3PlypU4c+YMJElCVlaWvP3i4mJIkoSdO3fqlHvTpk3o1q0bfH19sWfPHqjVaqSlpSE2NhZ+fn7o1KkTvvzyS3t/RESkhQERETnEW2+9hcTERDz88MMoKChAQUEBGjdujNtvvx1dunTBgQMHsHnzZpw/fx4PPPCAzns//vhjBAcHY9++fZg1axamT5+O//u//0Pv3r3xyy+/YOjQoRg/fjzKysp03vfMM8/gn//8J/bv34+QkBDcfffdcuCSk5ODYcOGYeTIkThy5AjWrFmDPXv2YObMmTrb+Mc//oFOnTrh0KFDWLBgAQCgcePGWLlyJU6cOIG33noLy5cvxxtvvAEAGDVqFJ566inceuut8nGOGjXKqnM1b948pKen49dff0XHjh2RlpaG//73v1i2bBmOHz+OOXPm4MEHH8SuXbus2i4RWaFGU8MSEZkwYMAA8cQTT8j/fvnll8XQoUN11snPz9eZFX7AgAGib9++8vIbN26Ihg0bivHjx8uvFRQUCAAiMzNTCPH37NqrV6+W1/nrr7+En5+fWLNmjRBCiClTpohp06bp7PvHH38UXl5eory8XAghRMuWLcXw4cPNHtfrr78uunXrJv/7xRdfFJ06ddJZJzc3VwAQhw4dkl+7fPmyACB27NihU+6vv/5aXufatWvC399fZGRk6GxvypQpYsyYMWbLRkS2qefMYIyIPMvhw4exY8cONGrUyGBZTk4O2rZtCwDo2LGj/Lq3tzeaNWuGhIQE+bWwsDAAwIULF3S2kZiYKP9/06ZN0a5dO/z666/yvo8cOYJPP/1UXkcIAbVajdzcXLRv3x4A0L17d4OyrVmzBm+//TZycnJw9epV3LhxAwEBAVYfvzHa+zx9+jTKyspwxx136KxTWVmJLl262G2fRKSLARER1ZqrV6/i7rvvxmuvvWawLCIiQv7/+vXr6yyTJEnnNUmSAABqtdqqfT/yyCN4/PHHDZZFR0fL/9+wYUOdZZmZmRg3bhwWLVqEpKQkBAYGYvXq1fjnP/9pcn9eXtUZCUII+TVN850+7X1evXoVALBhwwY0b95cZz1fX1+T+yQi2zEgIiKH8fHxQVVVlfzvrl274quvvkJMTAzq1bP/7efnn3+Wg5vLly/jt99+k2t+unbtihMnTqBNmzZWbTMjIwMtW7bEc889J7929uxZnXX0jxMAQkJCAAAFBQVyzY52grUx8fHx8PX1RV5eHgYMGGBVWYnIdkyqJiKHiYmJwd69e3HmzBkUFRVhxowZuHTpEsaMGYP9+/cjJycHW7ZswaRJkwwCClu89NJL2LZtG44dO4aJEyciODgYw4cPB1DdUywjIwMzZ85EVlYWTp06hW+++cYgqVrfLbfcgry8PKxevRo5OTl4++23sW7dOoPjzM3NRVZWFoqKilBRUQE/Pz/06tVLTpbetWsXnn/+ebPH0LhxYzz99NOYM2cOPv74Y+Tk5OCXX37BO++8g48//tjmc0NEpjEgIiKHefrpp+Ht7Y34+HiEhISgsrISP/30E6qqqjB06FAkJCRg9uzZCAoKkpuYaiI9PR1PPPEEunXrhsLCQnz77bfw8fEBUJ2XtGvXLvz222/o168funTpghdeeAGRkZEmt3nPPfdgzpw5mDlzJjp37oyMjAy595nGyJEjMWzYMAwaNAghISH47LPPAAAfffQRbty4gW7dumH27Nl45ZVXLDqOl19+GQsWLEBaWhrat2+PYcOGYcOGDYiNjbXhrBCRJSSh3cBNROSGdu7ciUGDBuHy5csICgpydnGIyA2xhoiIiIg8HgMiIiIi8nhsMiMiIiKPxxoiIiIi8ngMiIiIiMjjMSAiIiIij8eAiIiIiDweAyIiIiLyeAyIiIiIyOMxICIiIiKPx4CIiIiIPB4DIiIiIvJ4/w8sFxzgN1ESVAAAAABJRU5ErkJggg==", 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", + "image/png": 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", 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,\n", + "
]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -958,19 +4038,28 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 5ms/step\n" + "\r", + "\u001b[1m1/4\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n" ] }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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" ] @@ -1012,12 +4101,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 4ms/step\n" + "\r", + "\u001b[1m1/4\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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cm2s0adJEZ86c0dq1a9W2bVsFBgaqUaNG8vX11cyZMzVkyBDt2LFDEyZMMOmnBACY4dSpU3rllVds2h599FE1bNjQpi05WUpNLfrVrODjNnd14fJ89tlnql+/vpo0aaIePXpo/fr1mjFjhj7++GN5e3urbdu2mjZtmiZNmqQ2bdpo4cKFdhO3O3furCFDhui+++5TaGioJk+erNDQUM2fP1/vv/++WrdureTkZE2dOtWknxIA4GqbNm2yCT1+fn4aN26cXeiRinp6oqOLfjWL2+7OXlEutTs7O3R7Hj57ALBXUFCgiRMn2uwA0KNHD3Xq1MmUekq7OztDXQAAoEwOHDigefPm2bQ988wzbrHtE8EHAACU2tKlS/Xjjz9anzdv3lwPP/ywiRWVDcEHAABc0u+//263QPDDDz+s5s2bm1RR+RB8AABAif773/9q//79Nm3PP/+809aUcyWCDwAAcKiwsNBuiRJ/f3+NHj3apIouH8EHAADY2blzpz744AObtvvuu08tW7Y0qSLnYB0fAABgIzEx0S70/Otf/9KePS3VuXPRCszuiuADAAAkSWfOnFFiYqJN21VXXaX4+Hh5eXnZrLzsrhjqAgAAWrlypTZv3mzT9s9//lO1a9e2Po+L+3OvLXdFjw+cJjY2Vn379rU+v+WWW/T0009f1jWdcQ0AQPEMw1BiYqJd6ImPj7cJPVLR/lqbNpm3z5Yz0OPjAWJjY7VgwQJJko+Pjxo1aqT+/fvr+eefV7VqFfefwNKlS0t9q+OGDRvUtWtXnTx5UiEhIeW6BgCgbDZu3KjPP//cpq1nz57q2LGjSRVVPIKPh+jRo4fmzZunnJwcrVy5UkOHDpWPj4/GjBljc15ubq58fX2d8p4X/0vBrGsAAOxdPJdHct+1ecqCoS4P4efnp/DwcDVu3FhPPPGEunXrpuXLl1uHp1566SU1aNBAkZGRkqSDBw/q3nvvVUhIiGrXrq0+ffooPT3der2CggKNGDFCISEhqlOnjkaNGqWL97u9eJgqJydHo0ePVkREhPz8/NSiRQv95z//UXp6urp27SpJqlWrliwWi2JjYx1e4+TJk+rfv79q1aqlwMBA9ezZUz///LP1+Pz58xUSEqKUlBS1atVKNWrUUI8ePXTkyBHrORs2bFDHjh1VvXp1hYSE6IYbbtCvv/7qpD9pAKjcsrKyHIae3Nz4Kh96JIKPxwoICFBubq4kae3atdq9e7fWrFmjFStWKC8vTzExMapZs6a++uorbdy40RogLrzm5Zdf1vz58zV37lx9/fXXOnHihD766KMS37N///5atGiRZsyYoV27dun1119XjRo1FBERoQ8//FCStHv3bh05ckSvvPKKw2vExsbqu+++0/Lly5WamirDMPR///d/ysvLs56TnZ2tqVOn6u2339aXX36pAwcO6Nlnn5Uk5efnq2/fvrr55pv1ww8/KDU1VY899pgsFstl/5kCQGWXmJio6dOn27Slpt6khIR4zZhhUlEuxlCXhzEMQ2vXrlVKSor++c9/6vjx46pevbreeust6xDXO++8o8LCQr311lvWQDBv3jyFhIRow4YN6t69u/79739rzJgxuvPOOyVJc+bMUUpKSrHvu2fPHr333ntas2aNunXrJklq1qyZ9fiFIa2wsDCbOT5/9fPPP2v58uXauHGjOnfuLElauHChIiIitGzZMt1zzz2SpLy8PM2ZM8e6f8ywYcP0wgsvSCr6l05mZqZ69eplPd6qVauy/0ECgJtx1Mszfvx4jRtn0caN0vDhJhRlAnp8TLJ8uVy6CNSKFStUo0YN+fv7q2fPnrrvvvuUkJAgSbrmmmts5vVs375de/fuVc2aNVWjRg3VqFFDtWvX1vnz57Vv3z5lZmbqyJEj6tSpk/U11apVU4cOHYp9/++//17e3t66+eaby/0z7Nq1S9WqVbN53zp16igyMlK7du2ytgUGBtpsmle/fn0dO3ZMUlHAio2NVUxMjG6//Xa98sorNsNgAFDVfP/99w5DT3x8vD75xKL166WFC6WXXjKhOBPQ42OSvy4C5YrbArt27arZs2fL19dXDRo0sLmbq3r16jbnnjlzRu3bt9fChQvtrhMaGlqu9w8ICCjX68rj4jFqi8ViM/9o3rx5Gj58uD777DMtWbJE48aN05o1a3T99de7rEYAcAVHgWfw4MFq0KCBJNd/F1UG9PiYJC5Oio523SJQ1atXV4sWLdSoUaNL3sJ+7bXX6ueff1ZYWJhatGhh8wgODlZwcLDq16+v//3vf9bX5Ofna8uWLcVe85prrlFhYaG++OILh8cv9DgVFBQUe41WrVopPz/f5n3/+OMP7d69W61bty7xZ7pYVFSUxowZo02bNqlNmzZ69913y/R6AKjM8vPzi+3luRB6JNd/F1UGBB+TVOZFoB566CHVrVtXffr00VdffaX9+/drw4YNGj58uH777TdJ0lNPPaXk5GQtW7ZMaWlpevLJJ3Xq1Klir9mkSRMNGDBAAwcO1LJly6zXfO+99yRJjRs3lsVi0YoVK3T8+HGdOXPG7hpXXnml+vTpo8GDB+vrr7/W9u3b9fDDD6thw4bq06dPqX62/fv3a8yYMUpNTdWvv/6q1atX6+eff2aeD4AqY86cOXrponGrBg0aKD4+3u7cyvxdVFEIPrATGBioL7/8Uo0aNdKdd96pVq1aadCgQTp//ryCgoIkSSNHjlS/fv00YMAARUdHq2bNmrrjjjtKvO7s2bN1991368knn1TLli01ePBgnT17VpLUsGFDJSYmKi4uTvXq1dOwYcMcXmPevHlq3769evXqpejoaBmGoZUrV5b6FszAwEClpaXprrvu0lVXXaXHHntMQ4cO1eOPP16GPyEAqJwSExN19OhRm7axY8dq8ODBJlVU+ViMixdf8XBZWVkKDg5WZmam9Uteks6fP6/9+/eradOm8vf3N7FCuBqfPYDKbtu2bVru4G4ZR708VVVx398XY3IzAABuzNFcnr59+6pt27YmVFP5EXwAAHBDhmFY1yj7q8TEeOXmSuQexwg+AAC4GUe9PJKUkFA0tDVjhuesy1NWBB8AANyIo9CTl/eYPvigvurUkc6e9ZxVmMuD4FNGzAX3PHzmACqD9PR0LViwwK49ISFe0dFSWpoJRbkhgk8pXbhdOjs726WrEMN82dnZkuxXhAYAVyluaGvixHi1bOlZCxBeLoJPKXl7eyskJMS651NgYCA7eldxhmEoOztbx44dU0hIiLy9vc0uCYAHchR6kpPH6/x5iyIjpb9sVYhSIPiUQXh4uCRZww88Q0hIiPWzBwBXKa6XJyUlXufPSzVqSJMnu7ioKoDgUwYWi0X169dXWFiY8vLyzC4HLuDj40NPDwCXcxR6Nm9urxMneikurmhT0bg4z9pqwlkIPuXg7e3NlyEAwOmys7M1ZcoUu/ZFi+JVu/afYYfAU34EHwAAKoHihramTo3XwoWEHWch+AAAYDJHoadNm2f18svVCT1ORvABAMAkS5cu1Y8//mjXHhUVr969pbvuMqGoKo7gAwCACUradiI6ml6eikLwAQDAhYrbXDQqKl6jR0uRkSxIWJEIPgAAuEhxvTzx8UWbi9LLU/G8zC4AAABP4Cj0fPjhnUpJiTehGs9Fjw8AABVo9+7dWrx4sV17VFS8UlIY1nI1gg8AABWEoa3Kh+ADAEAFcBR6xo8fzwbXJiP4AADgRJfq5YG5mNwMAICTOAo9hw/XZwJzJUKPDwAAl6m4zUWZwFz5EHwAALgMTGB2LwQfAADKyVHoeeqppxQSEuL6YlAqBB8AAMpo8eLF2r17t107E5grP4IPAABlwF1b7o27ugAAKCVHoSclJV5RUYQed0GPDwAAl1BcL09KSrxSU6XkZCYxuwuCDwAAJXAUej75pJd8fdsrLq4o9HC7uvsg+AAA4MCuXbv03nvv2bX/dW2e3r3p6XE3BB8AAC7C2jxVF8EHAIC/cDyBebw2bWJz0aqA4AMAgEqewMwcnqqD4AMA8HiOQk9QUJCeeeYZsTxP1ULwAQB4rOI2F2UxwqqL4AMA8EiswOyZquTKzbNmzVKTJk3k7++vTp066dtvvzW7JABAJeIo9LRu/U9CjweocsFnyZIlGjFihOLj47V161a1bdtWMTExOnbsmNmlAQBMNnfuXIehJyEhXtOn1zahIrhalQs+06ZN0+DBg/XII4+odevWmjNnjgIDAzV37lyzSwMAmCgxMVEHDx60a09IiFeNGqy+7Cmq1Byf3NxcbdmyRWPGjLG2eXl5qVu3bkpNTXX4mpycHOXk5FifZ2VlVXidAADXKq6XJzJSio7+cxVmVH1VKvj8/vvvKigoUL169Wza69Wrp7S0NIevSUpKKnaCGwDAvRX3//eoqHgCj4eqUsGnPMaMGaMRI0ZYn2dlZSkiIsLEigAAzuAo9Nxyyy26+eabJRF4PFWVCj5169aVt7e3jh49atN+9OhRhYeHO3yNn5+f/Pz8XFEeAMAFfvzxRy1dutSuPSUlXlFRJhSESqVKTW729fVV+/bttXbtWmtbYWGh1q5dq+joaBMrAwC4QmJiYrGhJzVVSk42oShUKlWqx0eSRowYoQEDBqhDhw7q2LGj/v3vf+vs2bN65JFHzC4NAFCBHA1tvfDCv1S/vpdee60o9HDnFqpc8Lnvvvt0/PhxjR8/XhkZGWrXrp0+++wzuwnPAICqobgJzAkJRYsR1qhRNJ+HOT2QqmDwkaRhw4Zp2LBhZpcBAKhgxYWe3Nx4+ftLdepIkye7uChUalUy+AAAqrbiNhdNSYm33qL+0ksmFIZKj+ADAHArxfXy/HUCM8NaKA7BBwDgNhyFnscee0z169dXVBQTmHFpBB8AQKX3n//8R7/99ptd++LFRWvzXJi8TE8PLoXgAwCo1C511xZDWygLgg8AoNJyFHqmTo3XwoVSZKRksTC0hbIh+AAAKp3ienmmTo3X8OEMa6H8CD4AgErFUej57rv2+uOPXjp92oSCUKUQfAAAlcKuXbv03nvv2bVHRcUrJYUhLTgHwQcAYLrihrbi44smMDOsBWch+AAATOUo9EyePE61anlbb1UHnIXgAwAwRXG9PMnJ8Tp/XsrO5lZ1OJ+X2QUAADxPSWvz1Kkj+ftLDRsyrwfOR48PAMBlzp8/r0mTJtm1x8fHa/lyKTpa1k1GgYpA8AEAuERpJjATeFDRCD4AgArnKPQ8+uijatiwoQnVwJMRfAAAFWbu3Lk6ePCgXfuFXh7A1Qg+AIAKcamhLcAM3NUFAHA6R6GnaAXmoknMgFno8QEAOE1xvTwpKUXbTqSmsjYPzEXwAQA4haPQs3VrO61b10cLFxY9T05mbR6Yi+ADALgse/bs0aJFi+zaL2wuunDhnz089PTAbAQfAEC5sbko3A3BBwBQLo5Cz9ixY1WtGl8tqLz4rxMAUCbcpg53xu3sAIBSI/TA3dHjAwC4pJycHCUnJ9u1E3jgbgg+AIASlbQ2D7kH7obgAwAolqPQ06LFAM2a1YT1eOCWCD4AADuLFi3Snj177NovDG099JCrKwKcg+ADALDBBGZUZdzVBQCwchR6UlLiFRVF6EHVQI8PAKDYXp6oKCYwo2oh+ACAh3MUeg4caKW5c+9VZCTbTqBqIfgAgIf6+eef9e6779q1R0XFa/Hiot9bLC4uCqhgBB8A8EClmcCcnCxuWUeVQ/ABAA/jKPS0bfu8Jk/2UVRU0dDWhQdQ1RB8AMBDlLQC8+LFUlpaUS8PgQdVGbezA4AHKCn0pKZKhiFFRzO0haqPHh8AqMJyc3OVlJRk135hLk9U1J9zeejpgScg+ABAFVWaCczM5YGnYagLAKogR6Fny5aHWIEZHo8eHwCoQt599139/PPPdu0X5vL8/js9PPBsBB8AqCJKGtr661wewJMRfACgCnAUepjLA9grc/Dx9vbWkSNHFBYWZtP+xx9/KCwsTAUFBU4rDgBQstJMYAbwpzIHH8MwHLbn5OTI19f3sgsCAJSOo9DTuHFjxcbGur4YwE2UOvjMmDFDkmSxWPTWW2+pRo0a1mMFBQX68ssv1bJlS+dXCACwsW/fPr3zzjt27fTyAJdW6uAzffp0SUU9PnPmzJG3t7f1mK+vr5o0aaI5c+Y4v0IAgFVJKzBf2GcLQPFKHXz2798vSeratauWLl2qWrVqVVhRAAB7jkJPXFycunb1U2oq+2wBpVHmOT7r16+viDoAAMW41ATmuDhuVQdKq8zBZ+DAgSUenzt3brmLAQDYYtsJwLnKHHxOnjxp8zwvL087duzQqVOndOuttzqtMADwZHl5eZo4caJde25uvNavF/N5gHIqc/D56KOP7NoKCwv1xBNPqHnz5k4pCgA81fLl0rZtjnt5EhLiVaOGdOYM83mA8nLKys1eXl4aMWKEbrnlFo0aNcoZlwQAj+Qo9DRpcrdq1bpa0dFS167S+vXM5wHKy2lbVuzbt0/5+fnOuhwAeJT33ntPu3btsmtPSIhXZKSUlkYPD+AMZQ4+I0aMsHluGIaOHDmiTz/9VAMGDHBaYQDgKYqbwLx4cdEEZovFldUAVVuZg8+2bdtsnnt5eSk0NFQvv/zyJe/4AgDYKmlzUXZUB5zPYhS3+ZaHysrKUnBwsDIzMxUUFGR2OQCqKDYXBZyrtN/f5Z7jc+zYMe3evVuSFBkZabdbOwDAXnF3bTVo0ECDBw82oSLAs5Q5+GRlZWno0KFatGiRCgsLJUne3t667777NGvWLAUHBzu9SACoCg4cOKBt2+bZtUdFxTNxGXARr7K+YPDgwfrf//6nTz/9VKdOndKpU6e0YsUKfffdd3r88ccrokYAcHuJiYmaN88+9CQkxCs52YSCAA9V5h6fFStWKCUlRTfeeKO1LSYmRm+++aZ69Ojh1OIAoCpwNJ9n9OjRWr3aX9HRTF4GXKnMwadOnToOh7OCg4PZsR0A/uJSE5jZYwtwvTIPdY0bN04jRoxQRkaGtS0jI0PPPfec/vWvfzm1OABwV8WFnqgo7toCzFTm4DN79mx98803atSokVq0aKEWLVqoUaNG2rRpk15//XVde+211oezpKena9CgQWratKkCAgLUvHlzxcfHKzc31+a8H374QTfddJP8/f0VERGhyZMnO60GACiNgoICh6EnJSWe+TxAJVDmoa4+ffrI4uJlRNPS0lRYWKjXX39dLVq00I4dOzR48GCdPXtWU6dOlVR0t1n37t3VrVs3zZkzRz/++KMGDhyokJAQPfbYYy6tF4BnKmloi8UIgcrBbRcwnDJlimbPnq1ffvlFUlFP1NixY5WRkSFfX19JUlxcnJYtW6a0tLRSX5cFDAGUh6PQc88996h169YmVAN4ntJ+f5d5qKtZs2b6448/7NpPnTqlZs2alfVy5ZaZmanatWtbn6empqpLly7W0CMV3W22e/dunTx5stjr5OTkKCsry+YBAKW1bNmyYredIPQAlU+Zg096eroKCgrs2nNycvTbb785pahL2bt3r2bOnGmzblBGRobq1atnc96F53+diH2xpKQkBQcHWx8REREVUzSAKicxMVHbt2+3a4+KilfnzkWrNAOoXEo9x2f5X/4Gp6Sk2NzSXlBQoLVr16pp06ZlevO4uDhNmjSpxHN27dqlli1bWp8fOnRIPXr00D333OOU5d3HjBljs+N8VlYW4QfAJZW0uWjnzlJqatGcHm5XByqXUgefvn37SpIsFosGDBhgc8zHx0dNmjTRyy+/XKY3HzlypGJjY0s856/DZ4cPH1bXrl3VuXNnvfHGGzbnhYeH6+jRozZtF56Hh4cXe30/Pz/5+fmVqW4Anqs0m4vGxTGRGaisSh18LuzL1bRpU23evFl169a97DcPDQ1VaGhoqc49dOiQunbtqvbt22vevHny8rIdpYuOjtbYsWOVl5cnHx8fSdKaNWsUGRnJwooAnMJR6AkIqK9lyx5TVNSfvTssTAhUXm5xV9ehQ4d0yy23qHHjxlqwYIG8vb2txy705mRmZioyMlLdu3fX6NGjtWPHDg0cOFDTp08v0+3s3NUF4GIZGRl6/fXX7drj4+PVsqW0e7cUGSmV4QZSAE5W2u/vMq/j88ILL5R4fPz48WW95CWtWbNGe/fu1d69e3XFFVfYHLuQ24KDg7V69WoNHTpU7du3V926dTV+/HjW8AFwWS41tHVhWTMXL28GoJzK3OMTFRVl8zwvL0/79+9XtWrV1Lx5c23dutWpBboaPT4ALnAUeiZNGiVv7wAtXFg0nLV8+Z/zeRjeAsxTYT0+27Ztc/hmsbGxuuOOO8p6OQCodEraZ8vbWzpz5s87tpjPA7iXMq/j40hQUJASExPZpBSA2ysp9CQnS8OHS9HR3LEFuKsy9/gUJzMzU5mZmc66HAC4VGFhoSZMmGDXnpISb709PTW1qG3TJhcXB8Bpyhx8ZsyYYfPcMAwdOXJEb7/9tnr27Om0wgDAVYrr5UlJibcuRMjaPEDVUObJzRevzuzl5aXQ0FDdeuutGjNmjGrWrOnUAl2Nyc2AZ3EUeu677z61bNmSicuAGynt97dbrOPjSgQfwDOsX79eX375pV37X1dgBuA+KuyuLqloJ/a9e/dKklq0aKGQkJByFQkAZijNthMAqqYyBZ/09HQNHTpUKSkp1oUDLRaLevTooVdffVVNmjSpiBoBwGlK2lwUQNVX6uBz8OBBXX/99fLx8dGECRPUqlUrSdJPP/2k2bNnKzo6Wps3b7ZbWRkAKgN6eQBIZZjjM2jQIO3du1cpKSny9/e3OXbu3Dn16NFDV155pd56660KKdRVmOMDVD2OQk+jRo30yCOPmFANgIrg9Dk+n332mZYsWWIXeiQpICBAEyZM0P3331++agGgAhw/flyvvfaaXTu9PIDnKnXw+f3330ucw9OsWTOdOHHCGTUBwGVjaAuAI6UOPvXr19dPP/1U7ByeHTt2KDw83GmFAUB5OQo9zz33nAIDA02oBkBlUurg07dvXz377LNau3atQkNDbY4dO3ZMo0ePVt++fZ1dHwCU2gsvvCBH0xbp5QFwQaknN588eVKdOnVSRkaGHn74YbVs2VKGYWjXrl169913FR4erm+++Ua1a9eu6JorFJObAffE0Bbg2Zw+ublWrVr63//+p+eff16LFy/WqVOnJEkhISF68MEHNXHiRLcPPQDcj2EYeuGFF+zaL+ymHhXFdhMA/lSuLSsMw9Dx48clSaGhobJYLE4vzCz0+ADuo6Rens6di3ZTj45mN3XAE5T2+9urPBe3WCwKCwtTWFhYlQo9ANyHo9Dz3nv3KCWlaGgrLq4o9LCbOoC/KtdeXQBgltTUVK1evdquPSoqXikpfwad3r0Z4gJgj+ADwG1cagIzQQfApRB8ALgFNhcF4AwEHwCVGrepA3CmUgWfGTNmlPqCw4cPL3cxAPBXjkJP48aNFRsb6/piAFQJpbqdvWnTpqW7mMWiX3755bKLMhO3swPmO3nypMN/cNHLA6A4Tl3AcP/+/U4rDABKwtAWgIpUrnV8JCk3N1e7d+9Wfn6+M+sB4MEchZ7Jk5+1rs0DAJerzMEnOztbgwYNUmBgoK6++modOHBAkvTPf/5TycnJTi8QQNU3c+ZMh6EnKipebdtWZxFCAE5T5uAzZswYbd++XRs2bJC/v7+1vVu3blqyZIlTiwNQ9SUmJurEiRN27fHx8erdu2i7CdbnAeAsZb6dfdmyZVqyZImuv/56m+0qrr76au3bt8+pxQGouorbXJS5PAAqUpmDz/HjxxUWFmbXfvbsWfbtAlAqTGAGYJYyD3V16NBBn376qfX5hbDz1ltvKTo62nmVAaiSHIWeu+++m9ADwCXK3OMzceJE9ezZUz/99JPy8/P1yiuv6KefftKmTZv0xRdfVESNAKqAbdu2afny5XbtBB4ArlTmHp8bb7xR33//vfLz83XNNddo9erVCgsLU2pqqtq3b18RNQJwc4mJiYQeAJVCqVZu9iSs3Aw4l6OhrXbtxqtPH+YEAnAep67cnJWVVeo3JiwAkIqfwJyQEK/oaKlPHxcXBAAqZfAJCQkp9R1bBQUFl1UQAPfnKPT89lukVq26Xy1bigUJAZimVMFn/fr11t+np6crLi5OsbGx1ru4UlNTtWDBAiUlJVVMlQDcwocfntaOHdPs2lNS4pWaWvT76GgWJARgnjLP8fn73/+uRx99VA888IBN+7vvvqs33nhDGzZscGZ9LsccH6B8SlqbZ/lyadQoyWKRJk0i+ABwvtJ+f5c5+AQGBmr79u268sorbdr37Nmjdu3aKTs7u3wVVxIEH6DsHIWeZ599VtWrVzehGgCeqLTf32W+nT0iIkJvvvmmXftbb72liIiIsl4OgBtbsmSJw9ATHx9P6AFQKZV5AcPp06frrrvu0qpVq9SpUydJ0rfffquff/5ZH374odMLBFA5FTe0lZISr6gohrMAVE7lWsfnt99+02uvvaa0tDRJUqtWrTRkyJAq0ePDUBdQspI2F+3cWUpNLZrAvGmTCcUB8FgVNsenqiP4AMW71Oaiy5dLyclFt6vT4wPAlZy6gOHFTp06pf/85z/atWuXJOnqq6/WwIEDFRwcXL5qAVR6jkLP/fffr8jISOvz3r0JPAAqtzL3+Hz33XeKiYlRQECAOnbsKEnavHmzzp07p9WrV+vaa6+tkEJdhR4fwFZaWpqWLFli184+WwAqkwob6rrpppvUokULvfnmm6pWrajDKD8/X48++qh++eUXffnll5dXuckIPsCfihvaioqKZ0gLQKVSYcEnICBA27ZtU8uWLW3af/rpJ3Xo0IF1fIAqwlHoGT9+vCwWC5OYAVQ6FTbHJygoSAcOHLALPgcPHlTNmjXLXimASuVSE5ilop6eCz0+AOBOyhx87rvvPg0aNEhTp05V586dJUkbN27Uc889Z7eNBQD34ij0hIS00lNP3WvTxiRmAO6qzMFn6tSpslgs6t+/v/Lz8yVJPj4+euKJJ5ScnOz0AgFUvOzsbE2ZMsWuPSEhXtHR0lNPmVAUAFSAcq/jk52drX379kmSmjdvrsDAQKcWZhbm+MDTMIEZQFVQoev4SEWblV5zzTXlfTmASuBSm4sSeABUNaUOPgMHDizVeXPnzi13MQBcY9WqVfr222/t2lmbB0BVV+rgM3/+fDVu3FhRUVFilwvAfZXmri0AqKpKHXyeeOIJLVq0SPv379cjjzyihx9+WLVr167I2gA4UUmbiwKAp/Aq7YmzZs3SkSNHNGrUKH3yySeKiIjQvffeq5SUFHqAgEouMTHRYehJSYnX8uUmFAQAJin3XV2//vqr5s+fr//+97/Kz8/Xzp07VaNGDWfX53Lc1YWqxtHQ1oMPPqgBA65k9WUAVUaF39Xl5eUli8UiwzBUUFBQ3ssAqCDp6elasGCBXfuFoS1WXwbgicoUfHJycrR06VLNnTtXX3/9tXr16qVXX31VPXr0kJdXqUfNAFSw0kxgZvVlAJ6o1MHnySef1OLFixUREaGBAwdq0aJFqlu3bkXWBqAcStpcFAA8Xann+Hh5ealRo0aKiooq8X+gS5cudVpxZmCOD9zViy++6HDYmbu2AHgCp8/x6d+/P/9iBCopR708hw61U69efUyoBgAqrzItYAigcjl//rwmTZpk156SEq/UVGnHDubxAMBflfuuLgDmKmkCc1QUd2wBgCMEH8ANOQo9I0eOtK6lxR1bAOCY292DnpOTo3bt2slisej777+3OfbDDz/opptukr+/vyIiIjR58mRzigQqyIYNGxyGnvj4+CqxgCgAVDS3Cz6jRo1SgwYN7NqzsrLUvXt3NW7cWFu2bNGUKVOUkJCgN954w4QqAedLTEzUF198YdfOthMAUHpuNdS1atUqrV69Wh9++KFWrVplc2zhwoXKzc3V3Llz5evrq6uvvlrff/+9pk2bpscee8ykigHnKK6Xp3NnKTW1aD4PQ1sAcGluE3yOHj2qwYMHa9myZQoMDLQ7npqaqi5dusjX19faFhMTo0mTJunkyZOqVauWw+vm5OQoJyfH+jwrK8v5xQPldKkVmNl2AgDKxi2Cj2EYio2N1ZAhQ9ShQwelp6fbnZORkaGmTZvatNWrV896rLjgk5SUVOyXC2Cm4jYXvfLKK63PmcQMAGVj6hyfuLg4WSyWEh9paWmaOXOmTp8+rTFjxji9hjFjxigzM9P6OHjwoNPfAyiLw4cPFzu09dfQAwAoO1N7fEaOHKnY2NgSz2nWrJnWrVun1NRU+fn52Rzr0KGDHnroIS1YsEDh4eE6evSozfELz8PDw4u9vp+fn911AbOUZnNRAED5mRp8QkNDFRoaesnzZsyYoRdffNH6/PDhw4qJidGSJUvUqVMnSVJ0dLTGjh2rvLw8+fj4SJLWrFmjyMjIYoe5gMqEzUUBoOK5xRyfRo0a2Ty/sF5J8+bNdcUVV0gqmvuQmJioQYMGafTo0dqxY4deeeUVTZ8+3eX1AmXx3//+V/v377drT0kpWoGZOTwA4Dxut45PcYKDg7V69Wrt379f7du318iRIzV+/HhuZUellpiYaBd6OnXqZN1rKznZpMIAoIpyix6fizVp0kSGYdi1/+1vf9NXX31lQkVA2eTl5WnixIl27Rfm8uTmcps6AFQEtww+gDsrzQRmblMHgIpRZYa6AHfgKPS0afOsoqKKVmFm6wkAqFj0+AAu8NNPP+n999+3a7/Qy8PWEwDgGgQfoII56uVp3ry5Hn74Yetztp4AANcg+AAVyFHoiYqKV3KyFBT0Z+8Oc3oAwDUIPkAFmDx5ss6dO2fXzo7qAGAugg/gZI56eR599FE1bNhQEsNaAGAmgg/gJCdOnNDMmTPt2i/eZ4thLQAwD8EHcAI2FwUA90DwAS4Tm4sCgPsg+ADltHLlSm3evNmunV4eAKi8CD5AOTjq5YmJidH1119vQjUAgNIi+ABlkJ+fr5deesmunV4eAHAPBB+glJjADADuj+ADlIKj0DNq1CgFBASYUA0AoLwIPkAJ9u7dq4ULF9q108sDAO6J4AMUw1EvT+vWrXXPPfeYUA0AwBkIPoADjkIPvTwA4P4IPsBfvPHGGzpy5IhdO6EHAKoGgg/w/znq5Rk8eLAaNGhgQjUAgIpA8IHHO336tKZNm2bXTi8PAFQ9XmYXAJgpMTGxXKFn+XKpc+eiXwEA7oMeH3gsR0Nb7dqNV58+l95cNDlZSk0t+rV374qoDgBQEQg+8DjffPONUlJS7NoTEuIVHS316XPpa8TFFYWeuLgKKBAAUGEIPvAojnp57rjjDqWn/03R0aUPMr1709MDAO6I4AOPUFhYqAkTJti1X5jL87e/EWQAwBMQfFDlzZw5UydOnLBr564tAPA8BB9UaY6GtkaPHi1/f38TqgEAmI3ggyrp8OHDevPNN+3a6eUBAM9G8EGV46iX58Ybb9Tf//53E6oBAFQmBB9UKWwuCgAoCcEHVcLKlSu1efNmu3ZCDwDgrwg+cDvLl/+5eGDv3o57eZ588kmFhoaaUB0AoDIj+MDtXNgu4uWXs7Vt2xS74/TyAACKQ/CB24mLk7Zts+/ladiwoR599FETKgIAuAuCD9yOo9Azfvx4WSyX3lwUAODZCD5wG3v27NGiRYvs2hnaAgCUFsEHbsHRBOZ77rlHrVu3NqEaAIC7IvigUrvU5qIAAJQFwQeV1qJFi7Rnzx6btoCAAI0aNcqkigAA7o7gg0rJ0dBWXFyc/Pz8TKgGAFBVEHxQqfz++++aNWuWXTtDWwAAZyD4oNJw1Mvzj3/8Q507dzahGgBAVUTwQaXA5qIAAFcg+MBUX3/9tdauXWvXTugBAFQEgg9M46iXZ9iwYapTp44J1QAAPAHBBy53/vx5TZo0ya6dXh4AQEUj+MClkpKSlJuba9PWqlUr3XvvvSZVBADwJAQfuIyjoa1//etf8vLyMqEaAIAnIvigwv38889699137doZ2gIAuBrBBxXKUS/PQw89pBYtWphQDQDA0xF8UCHYXBQAUBkRfOB0S5YsUVpamk2bn5+f4uLiTKoIAIAiBB84laOhrdGjR8vf39+EagAAsEXwgVP88ccfevXVV+3aGdoCAFQmBB9cNke9PN26ddMNN9xgQjUAABSP4IPLwuaiAAB3QvBBuWzatElr1qyxayf0AAAqM4IPysxRL8/QoUNVt25dE6oBAKD0CD4otZycHCUnJ9u108sDAHAXBB+UyuTJk3Xu3DmbtsjISN1///0mVQQAQNkRfHBJbC4KAKgqCD4o1r59+/TOO+/YtTO0BQBwVwQfOOSol+eBBx7QVVddZUI1AAA4B8EHNgzD0AsvvGDXTi8PAKAqcKtJGp9++qk6deqkgIAA1apVS3379rU5fuDAAd12220KDAxUWFiYnnvuOeXn55tTrBv64IMP7EKPt7c3oQcAUGW4TY/Phx9+qMGDB2vixIm69dZblZ+frx07dliPFxQU6LbbblN4eLg2bdqkI0eOqH///vLx8dHEiRNNrNw9OBraGjVqlAICAkyoBgCAimExDMMwu4hLyc/PV5MmTZSYmKhBgwY5PGfVqlXq1auXDh8+rHr16kmS5syZo9GjR+v48ePy9fUt1XtlZWUpODhYmZmZCgoKctrPUFmdOHFCM2fOtGunlwcA4E5K+/3tFkNdW7du1aFDh+Tl5aWoqCjVr19fPXv2tOnxSU1N1TXXXGMNPZIUExOjrKws7dy5s9hr5+TkKCsry+bhKRITE+1CT9euXQk9AIAqyy2Czy+//CJJSkhI0Lhx47RixQrVqlVLt9xyi06cOCFJysjIsAk9kqzPMzIyir12UlKSgoODrY+IiIgK+ikql+I2F+3SpYsJ1QAA4BqmBp+4uDhZLJYSH2lpaSosLJQkjR07VnfddZfat2+vefPmyWKx6P3337+sGsaMGaPMzEzr4+DBg8740Sqtb775hh3VAQAey9TJzSNHjlRsbGyJ5zRr1kxHjhyRJLVu3dra7ufnp2bNmunAgQOSpPDwcH377bc2rz169Kj1WHH8/Pzk5+dXnvLdjqPA8+STTyo0NNSEagAAcD1Tg09oaGipvnTbt28vPz8/7d69WzfeeKMkKS8vT+np6WrcuLEkKTo6Wi+99JKOHTumsLAwSdKaNWsUFBRkE5g8UV5ensM72+jlAQB4Gre4nT0oKEhDhgxRfHy8IiIi1LhxY02ZMkWSdM8990iSunfvrtatW6tfv36aPHmyMjIyNG7cOA0dOtRjenQceeedd7Rv3z6btpYtW+q+++4zqSIAAMzjFsFHkqZMmaJq1aqpX79+OnfunDp16qR169apVq1akooW2luxYoWeeOIJRUdHq3r16howYIDDVYg9haOhrXbt/qVJk7wUECD17m1CUQAAmMgt1vFxpaqwjs+hQ4f01ltv2bXHx8erc2cpNVWKjpY2bTKhOAAAKkBpv7/dpscHpeOol2fAgAFq0qSJJCkuTkpOLvoVAABPQ/CpIkq7uWjv3gxxAQA8F8GnCti6das++eQTm7YGDRpo8ODBJlUEAEDlRPBxc46GtkaPHi1/f38TqgEAoHJziy0rYO/s2bMOQ09KSrxWryb0AADgCD0+buirr77SunXrbNruv/9+PfJIpFJTiyYvM48HAAB7BB83U9I+W9yxBQBAyQg+buLYsWOaPXu2TVt0dLS6d+9ufc4dWwAAlIzg4wbmzZtn3Yz1glGjRikgIMCkigAAcE8En0qsoKBAL774ol07m4sCAFA+BJ9K6ocfftBHH31k0/bAAw/oqquukiQtX/7nfB6GtwAAKB2CTyXkaALz+PHjZbFYrM+Tk8UdXAAAlBHBpxI5ffq0pk2bZtPWqlUr3XvvvXbncgcXAABlR/CpJJYvX65t27bZtA0fPly1atVyeD53cAEAUHYEH5OVdnNRAABw+Qg+Jjp16pReeeUVm7bbbrtNHTp0MKkiAACqNoKPSTZu3KjPP//cpm3s2LGqVo2PBACAisK3rIvl5+dr4sSJMgzD2tazZ0917NjRxKoAAPAMBB8XOnDggObNm2fTNmLECNWsWdOkigAA8CwEHxf59NNP9d1331mft2jRQg899JCJFQEA4HkIPi7y19DTr18/NWvWzMRqAADwTAQfF4mKitKhQ4f06KOPysfHx+xyAADwSAQfF+nNaoMAAJjOy+wCAAAAXIXgUwksXy517lz0KwAAqDgEn0rgrzutAwCAikPwqQTi4qToaHZaBwCgojG5uRJgp3UAAFyDHh8AAOAxCD4AAMBjEHxchDu3AAAwH8HHRbhzCwAA8xF8XIQ7twAAMB93dbkId24BAGA+enwAAIDHIPgAAACPQfABAAAeg+ADAAA8BsEHAAB4DIIPAADwGAQfAADgMQg+AADAYxB8AACAxyD4AAAAj0HwAQAAHoPgAwAAPAbBBwAAeAx2Z7+IYRiSpKysLJMrAQAApXXhe/vC93hxCD4XOX36tCQpIiLC5EoAAEBZnT59WsHBwcUetxiXikYeprCwUIcPH1bNmjVlsVjMLqfCZWVlKSIiQgcPHlRQUJDZ5cABPqPKj8+o8uMzqvwu9zMyDEOnT59WgwYN5OVV/Eweenwu4uXlpSuuuMLsMlwuKCiI/xlUcnxGlR+fUeXHZ1T5Xc5nVFJPzwVMbgYAAB6D4AMAADwGwcfD+fn5KT4+Xn5+fmaXgmLwGVV+fEaVH59R5eeqz4jJzQAAwGPQ4wMAADwGwQcAAHgMgg8AAPAYBB8AAOAxCD4e7tNPP1WnTp0UEBCgWrVqqW/fvjbHDxw4oNtuu02BgYEKCwvTc889p/z8fHOK9WA5OTlq166dLBaLvv/+e5tjP/zwg2666Sb5+/srIiJCkydPNqdID5Senq5BgwapadOmCggIUPPmzRUfH6/c3Fyb8/iMzDdr1iw1adJE/v7+6tSpk7799luzS/JISUlJuu6661SzZk2FhYWpb9++2r17t80558+f19ChQ1WnTh3VqFFDd911l44ePeq0Ggg+HuzDDz9Uv3799Mgjj2j79u3auHGjHnzwQevxgoIC3XbbbcrNzdWmTZu0YMECzZ8/X+PHjzexas80atQoNWjQwK49KytL3bt3V+PGjbVlyxZNmTJFCQkJeuONN0yo0vOkpaWpsLBQr7/+unbu3Knp06drzpw5ev75563n8BmZb8mSJRoxYoTi4+O1detWtW3bVjExMTp27JjZpXmcL774QkOHDtU333yjNWvWKC8vT927d9fZs2et5zzzzDP65JNP9P777+uLL77Q4cOHdeeddzqvCAMeKS8vz2jYsKHx1ltvFXvOypUrDS8vLyMjI8PaNnv2bCMoKMjIyclxRZkwij6Hli1bGjt37jQkGdu2bbMee+2114xatWrZfB6jR482IiMjTagUhmEYkydPNpo2bWp9zmdkvo4dOxpDhw61Pi8oKDAaNGhgJCUlmVgVDMMwjh07ZkgyvvjiC8MwDOPUqVOGj4+P8f7771vP2bVrlyHJSE1Ndcp70uPjobZu3apDhw7Jy8tLUVFRql+/vnr27KkdO3ZYz0lNTdU111yjevXqWdtiYmKUlZWlnTt3mlG2xzl69KgGDx6st99+W4GBgXbHU1NT1aVLF/n6+lrbYmJitHv3bp08edKVpeL/y8zMVO3ata3P+YzMlZubqy1btqhbt27WNi8vL3Xr1k2pqakmVgap6O+LJOvfmS1btigvL8/m82rZsqUaNWrktM+L4OOhfvnlF0lSQkKCxo0bpxUrVqhWrVq65ZZbdOLECUlSRkaGTeiRZH2ekZHh2oI9kGEYio2N1ZAhQ9ShQweH5/AZVS579+7VzJkz9fjjj1vb+IzM9fvvv6ugoMDhZ8Cfv7kKCwv19NNP64YbblCbNm0kFf2d8PX1VUhIiM25zvy8CD5VTFxcnCwWS4mPC/MSJGns2LG666671L59e82bN08Wi0Xvv/++yT9F1Vbaz2jmzJk6ffq0xowZY3bJHqe0n9FfHTp0SD169NA999yjwYMHm1Q54D6GDh2qHTt2aPHixS5932oufTdUuJEjRyo2NrbEc5o1a6YjR45Iklq3bm1t9/PzU7NmzXTgwAFJUnh4uN2dDxdm1oeHhzuxas9S2s9o3bp1Sk1Ntdu3pkOHDnrooYe0YMEChYeH293twGd0+Ur7GV1w+PBhde3aVZ07d7abtMxnZK66devK29vb4WfAn795hg0bphUrVujLL7/UFVdcYW0PDw9Xbm6uTp06ZdPr49TPyykzheB2MjMzDT8/P5vJzbm5uUZYWJjx+uuvG4bx5+Tmo0ePWs95/fXXjaCgIOP8+fMur9nT/Prrr8aPP/5ofaSkpBiSjA8++MA4ePCgYRh/TpzNzc21vm7MmDFMnHWh3377zbjyyiuN+++/38jPz7c7zmdkvo4dOxrDhg2zPi8oKDAaNmzI5GYTFBYWGkOHDjUaNGhg7Nmzx+74hcnNH3zwgbUtLS3NqZObCT4e7KmnnjIaNmxopKSkGGlpacagQYOMsLAw48SJE4ZhGEZ+fr7Rpk0bo3v37sb3339vfPbZZ0ZoaKgxZswYkyv3TPv377e7q+vUqVNGvXr1jH79+hk7duwwFi9ebAQGBlrDKyrWb7/9ZrRo0cL4+9//bvz222/GkSNHrI8L+IzMt3jxYsPPz8+YP3++8dNPPxmPPfaYERISYnPHKlzjiSeeMIKDg40NGzbY/H3Jzs62njNkyBCjUaNGxrp164zvvvvOiI6ONqKjo51WA8HHg+Xm5hojR440wsLCjJo1axrdunUzduzYYXNOenq60bNnTyMgIMCoW7euMXLkSCMvL8+kij2bo+BjGIaxfft248YbbzT8/PyMhg0bGsnJyeYU6IHmzZtnSHL4+Cs+I/PNnDnTaNSokeHr62t07NjR+Oabb8wuySMV9/dl3rx51nPOnTtnPPnkk0atWrWMwMBA44477rD5x8Tlsvz/QgAAAKo87uoCAAAeg+ADAAA8BsEHAAB4DIIPAADwGAQfAADgMQg+AADAYxB8AACAxyD4AEAF2bBhgywWi06dOmV2KQD+P4IPALeVkJCgdu3amV0GADdC8AFQ5eXl5ZldAoBKguADwDSFhYVKSkpS06ZNFRAQoLZt2+qDDz6Q9Ocw0dq1a9WhQwcFBgaqc+fO2r17tyRp/vz5SkxM1Pbt22WxWGSxWDR//nxJksVi0ezZs9W7d29Vr15dL730Uol1XHivlJQURUVFKSAgQLfeequOHTumVatWqVWrVgoKCtKDDz6o7Oxs6+tycnI0fPhwhYWFyd/fXzfeeKM2b95cMX9YAJzDabt+AUAZvfjii0bLli2Nzz77zNi3b58xb948w8/Pz9iwYYOxfv16Q5LRqVMnY8OGDcbOnTuNm266yejcubNhGIaRnZ1tjBw50rj66qvtdniWZISFhRlz58419u3bZ/z6668l1nHhva6//nrj66+/NrZu3Wq0aNHCuPnmm43u3bsbW7duNb788kujTp06NhuMDh8+3GjQoIGxcuVKY+fOncaAAQOMWrVqGX/88YfNdU+ePFkxf4AAyozgA8AU58+fNwIDA41NmzbZtA8aNMh44IEHrKHh888/tx779NNPDUnGuXPnDMMwjPj4eKNt27Z215ZkPP3006WuxdF7JSUlGZKMffv2Wdsef/xxIyYmxjAMwzhz5ozh4+NjLFy40Ho8NzfXaNCggTF58mSb6xJ8gMqjmlk9TQA82969e5Wdna1//OMfNu25ubmKioqyPv/b3/5m/X39+vUlSceOHVOjRo1KvH6HDh3KXNNf36tevXoKDAxUs2bNbNq+/fZbSdK+ffuUl5enG264wXrcx8dHHTt21K5du8r83gBcg+ADwBRnzpyRJH366adq2LChzTE/Pz/t27dPUlGYuMBisUgqmht0KdWrVy9zTRe/11+fX2grzXsDqLyY3AzAFK1bt5afn58OHDigFi1a2DwiIiJKdQ1fX18VFBRUcKWONW/eXL6+vtq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", 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", 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", 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", 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", 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", 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", + "image/png": 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]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1114,9 +4234,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb index c7a365e5..fac17676 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb @@ -1,1123 +1,1148 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Introduction\n", - "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", - "\n", - "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", - "\n", - "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", - "\n", - "### 1.1 Need for ML Surrogates\n", - "\n", - "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", - "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", - "\n", - "### 1.2 Supercritical CO2 cycle process\n", - "\n", - "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", - "\n", - "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", - "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", - ":241)\n" - ] - } - ], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import random as rn\n", - "import tensorflow as tf\n", - "import tensorflow.keras as keras\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "\n", - "# fix environment variables to ensure consist neural network training\n", - "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", - "np.random.seed(46)\n", - "rn.seed(1342)\n", - "tf.random.set_seed(62)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "csv_data.columns.values[0:6] = [\n", - " \"pressure\",\n", - " \"temperature\",\n", - " \"enth_mol\",\n", - " \"entr_mol\",\n", - " \"CO2_enthalpy\",\n", - " \"CO2_entropy\",\n", - "]\n", - "data = csv_data.sample(n=500)\n", - "\n", - "# Creating input_data and output_data from data\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogate with TensorFlow Keras\n", - "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", - "\n", - "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", - "\n", - "* Activation function: sigmoid, **tanh**\n", - "* Optimizer: **Adam**\n", - "* Number of hidden layers: 3, **4**, 5, 6\n", - "* Number of neurons per layer: **20**, 40, 60\n", - "\n", - "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", - "\n", - "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", - "\n", - "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/250\n", - "13/13 - 3s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 3s/epoch - 249ms/step\n", - "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 237ms/epoch - 18ms/step\n", - "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 262ms/epoch - 20ms/step\n", - "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 283ms/epoch - 22ms/step\n", - "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 351ms/epoch - 27ms/step\n", - "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 364ms/epoch - 28ms/step\n", - "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 245ms/epoch - 19ms/step\n", - "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 203ms/epoch - 16ms/step\n", - "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 300ms/epoch - 23ms/step\n", - "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 255ms/epoch - 20ms/step\n", - "Epoch 11/250\n", - "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 247ms/epoch - 19ms/step\n", - "Epoch 12/250\n", - "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 321ms/epoch - 25ms/step\n", - "Epoch 13/250\n", - "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 219ms/epoch - 17ms/step\n", - "Epoch 14/250\n", - "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 272ms/epoch - 21ms/step\n", - "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 299ms/epoch - 23ms/step\n", - "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 271ms/epoch - 21ms/step\n", - "Epoch 17/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 248ms/epoch - 19ms/step\n", - "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 256ms/epoch - 20ms/step\n", - "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 153ms/epoch - 12ms/step\n", - "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 239ms/epoch - 18ms/step\n", - "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 263ms/epoch - 20ms/step\n", - "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 243ms/epoch - 19ms/step\n", - "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", - "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 189ms/epoch - 15ms/step\n", - "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 280ms/epoch - 22ms/step\n", - "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 222ms/epoch - 17ms/step\n", - "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 286ms/epoch - 22ms/step\n", - "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 116ms/epoch - 9ms/step\n", - "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 157ms/epoch - 12ms/step\n", - "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 267ms/epoch - 21ms/step\n", - "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 264ms/epoch - 20ms/step\n", - "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 269ms/epoch - 21ms/step\n", - "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 353ms/epoch - 27ms/step\n", - "Epoch 34/250\n", - "13/13 - 1s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 537ms/epoch - 41ms/step\n", - "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 330ms/epoch - 25ms/step\n", - "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 289ms/epoch - 22ms/step\n", - "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 155ms/epoch - 12ms/step\n", - "Epoch 38/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 289ms/epoch - 22ms/step\n", - "Epoch 39/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 118ms/epoch - 9ms/step\n", - "Epoch 40/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 299ms/epoch - 23ms/step\n", - "Epoch 41/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 42/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 192ms/epoch - 15ms/step\n", - "Epoch 43/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 237ms/epoch - 18ms/step\n", - "Epoch 44/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 154ms/epoch - 12ms/step\n", - "Epoch 45/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 46/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 47/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 116ms/epoch - 9ms/step\n", - "Epoch 48/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 288ms/epoch - 22ms/step\n", - "Epoch 49/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 140ms/epoch - 11ms/step\n", - "Epoch 50/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 51/250\n", - "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 52/250\n", - "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 265ms/epoch - 20ms/step\n", - "Epoch 53/250\n", - "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 179ms/epoch - 14ms/step\n", - "Epoch 54/250\n", - "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 287ms/epoch - 22ms/step\n", - "Epoch 55/250\n", - "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 284ms/epoch - 22ms/step\n", - "Epoch 56/250\n", - "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 297ms/epoch - 23ms/step\n", - "Epoch 57/250\n", - "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 206ms/epoch - 16ms/step\n", - "Epoch 58/250\n", - "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 59/250\n", - "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 274ms/epoch - 21ms/step\n", - "Epoch 60/250\n", - "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 61/250\n", - "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 62/250\n", - "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 63/250\n", - "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 154ms/epoch - 12ms/step\n", - "Epoch 64/250\n", - "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 284ms/epoch - 22ms/step\n", - "Epoch 65/250\n", - "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 168ms/epoch - 13ms/step\n", - "Epoch 66/250\n", - "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 113ms/epoch - 9ms/step\n", - "Epoch 67/250\n", - "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 264ms/epoch - 20ms/step\n", - "Epoch 68/250\n", - "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 189ms/epoch - 15ms/step\n", - "Epoch 69/250\n", - "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 175ms/epoch - 13ms/step\n", - "Epoch 70/250\n", - "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 322ms/epoch - 25ms/step\n", - "Epoch 71/250\n", - "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 207ms/epoch - 16ms/step\n", - "Epoch 72/250\n", - "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 257ms/epoch - 20ms/step\n", - "Epoch 73/250\n", - "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 233ms/epoch - 18ms/step\n", - "Epoch 74/250\n", - "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 250ms/epoch - 19ms/step\n", - "Epoch 75/250\n", - "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 76/250\n", - "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 77/250\n", - "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 126ms/epoch - 10ms/step\n", - "Epoch 78/250\n", - "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 242ms/epoch - 19ms/step\n", - "Epoch 79/250\n", - "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 80/250\n", - "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 81/250\n", - "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 166ms/epoch - 13ms/step\n", - "Epoch 82/250\n", - "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 83/250\n", - "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 84/250\n", - "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 148ms/epoch - 11ms/step\n", - "Epoch 85/250\n", - "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 282ms/epoch - 22ms/step\n", - "Epoch 86/250\n", - "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 246ms/epoch - 19ms/step\n", - "Epoch 87/250\n", - "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", - "Epoch 88/250\n", - "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 208ms/epoch - 16ms/step\n", - "Epoch 89/250\n", - "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 193ms/epoch - 15ms/step\n", - "Epoch 90/250\n", - "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 168ms/epoch - 13ms/step\n", - "Epoch 91/250\n", - "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 156ms/epoch - 12ms/step\n", - "Epoch 92/250\n", - "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 93/250\n", - "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 94/250\n", - "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 303ms/epoch - 23ms/step\n", - "Epoch 95/250\n", - "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 96/250\n", - "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 97/250\n", - "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 98/250\n", - "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 99/250\n", - "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 130ms/epoch - 10ms/step\n", - "Epoch 100/250\n", - "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 101/250\n", - "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 102/250\n", - "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 260ms/epoch - 20ms/step\n", - "Epoch 103/250\n", - "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 275ms/epoch - 21ms/step\n", - "Epoch 104/250\n", - "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 105/250\n", - "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 275ms/epoch - 21ms/step\n", - "Epoch 106/250\n", - "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 157ms/epoch - 12ms/step\n", - "Epoch 107/250\n", - "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 183ms/epoch - 14ms/step\n", - "Epoch 108/250\n", - "13/13 - 0s - loss: 6.7955e-04 - mae: 0.0198 - mse: 6.7955e-04 - val_loss: 7.6778e-04 - val_mae: 0.0204 - val_mse: 7.6778e-04 - 192ms/epoch - 15ms/step\n", - "Epoch 109/250\n", - "13/13 - 1s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 516ms/epoch - 40ms/step\n", - "Epoch 110/250\n", - "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 111/250\n", - "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 208ms/epoch - 16ms/step\n", - "Epoch 112/250\n", - "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 215ms/epoch - 17ms/step\n", - "Epoch 113/250\n", - "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 114/250\n", - "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 115/250\n", - "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 166ms/epoch - 13ms/step\n", - "Epoch 116/250\n", - "13/13 - 0s - loss: 6.5036e-04 - mae: 0.0194 - mse: 6.5036e-04 - val_loss: 6.5858e-04 - val_mae: 0.0191 - val_mse: 6.5858e-04 - 107ms/epoch - 8ms/step\n", - "Epoch 117/250\n", - "13/13 - 0s - loss: 6.4983e-04 - mae: 0.0194 - mse: 6.4983e-04 - val_loss: 7.0443e-04 - val_mae: 0.0198 - val_mse: 7.0443e-04 - 109ms/epoch - 8ms/step\n", - "Epoch 118/250\n", - "13/13 - 0s - loss: 6.4994e-04 - mae: 0.0195 - mse: 6.4994e-04 - val_loss: 6.3181e-04 - val_mae: 0.0193 - val_mse: 6.3181e-04 - 296ms/epoch - 23ms/step\n", - "Epoch 119/250\n", - "13/13 - 0s - loss: 6.6252e-04 - mae: 0.0199 - mse: 6.6252e-04 - val_loss: 6.3527e-04 - val_mae: 0.0191 - val_mse: 6.3527e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 120/250\n", - "13/13 - 0s - loss: 6.4578e-04 - mae: 0.0193 - mse: 6.4578e-04 - val_loss: 6.3127e-04 - val_mae: 0.0189 - val_mse: 6.3127e-04 - 190ms/epoch - 15ms/step\n", - "Epoch 121/250\n", - "13/13 - 0s - loss: 6.1375e-04 - mae: 0.0191 - mse: 6.1375e-04 - val_loss: 6.5351e-04 - val_mae: 0.0192 - val_mse: 6.5351e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 122/250\n", - "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 142ms/epoch - 11ms/step\n", - "Epoch 123/250\n", - "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 278ms/epoch - 21ms/step\n", - "Epoch 124/250\n", - "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 246ms/epoch - 19ms/step\n", - "Epoch 125/250\n", - "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 135ms/epoch - 10ms/step\n", - "Epoch 126/250\n", - "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 127/250\n", - "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 142ms/epoch - 11ms/step\n", - "Epoch 128/250\n", - "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 249ms/epoch - 19ms/step\n", - "Epoch 129/250\n", - "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 279ms/epoch - 21ms/step\n", - "Epoch 130/250\n", - "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 356ms/epoch - 27ms/step\n", - "Epoch 131/250\n", - "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 132/250\n", - "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 102ms/epoch - 8ms/step\n", - "Epoch 133/250\n", - "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 239ms/epoch - 18ms/step\n", - "Epoch 134/250\n", - "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 285ms/epoch - 22ms/step\n", - "Epoch 135/250\n", - "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 178ms/epoch - 14ms/step\n", - "Epoch 136/250\n", - "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 113ms/epoch - 9ms/step\n", - "Epoch 137/250\n", - "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 138ms/epoch - 11ms/step\n", - "Epoch 138/250\n", - "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 144ms/epoch - 11ms/step\n", - "Epoch 139/250\n", - "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 279ms/epoch - 21ms/step\n", - "Epoch 140/250\n", - "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 141/250\n", - "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 118ms/epoch - 9ms/step\n", - "Epoch 142/250\n", - "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 120ms/epoch - 9ms/step\n", - "Epoch 143/250\n", - "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 304ms/epoch - 23ms/step\n", - "Epoch 144/250\n", - "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 178ms/epoch - 14ms/step\n", - "Epoch 145/250\n", - "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 146/250\n", - "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 396ms/epoch - 30ms/step\n", - "Epoch 147/250\n", - "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 148/250\n", - "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 155ms/epoch - 12ms/step\n", - "Epoch 149/250\n", - "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 120ms/epoch - 9ms/step\n", - "Epoch 150/250\n", - "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 151/250\n", - "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 315ms/epoch - 24ms/step\n", - "Epoch 152/250\n", - "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 198ms/epoch - 15ms/step\n", - "Epoch 153/250\n", - "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 154/250\n", - "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 155/250\n", - "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 267ms/epoch - 21ms/step\n", - "Epoch 156/250\n", - "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 182ms/epoch - 14ms/step\n", - "Epoch 157/250\n", - "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 158/250\n", - "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 159/250\n", - "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 172ms/epoch - 13ms/step\n", - "Epoch 160/250\n", - "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 164ms/epoch - 13ms/step\n", - "Epoch 161/250\n", - "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 169ms/epoch - 13ms/step\n", - "Epoch 162/250\n", - "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 188ms/epoch - 14ms/step\n", - "Epoch 163/250\n", - "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 258ms/epoch - 20ms/step\n", - "Epoch 164/250\n", - "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 165/250\n", - "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 166/250\n", - "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 265ms/epoch - 20ms/step\n", - "Epoch 167/250\n", - "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 168/250\n", - "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 169/250\n", - "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 234ms/epoch - 18ms/step\n", - "Epoch 170/250\n", - "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 171/250\n", - "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 243ms/epoch - 19ms/step\n", - "Epoch 172/250\n", - "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 148ms/epoch - 11ms/step\n", - "Epoch 173/250\n", - "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 174/250\n", - "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 175/250\n", - "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 176/250\n", - "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 177/250\n", - "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 316ms/epoch - 24ms/step\n", - "Epoch 178/250\n", - "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 214ms/epoch - 16ms/step\n", - "Epoch 179/250\n", - "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 181ms/epoch - 14ms/step\n", - "Epoch 180/250\n", - "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 144ms/epoch - 11ms/step\n", - "Epoch 181/250\n", - "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 302ms/epoch - 23ms/step\n", - "Epoch 182/250\n", - "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 216ms/epoch - 17ms/step\n", - "Epoch 183/250\n", - "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 296ms/epoch - 23ms/step\n", - "Epoch 184/250\n", - "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 195ms/epoch - 15ms/step\n", - "Epoch 185/250\n", - "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 186/250\n", - "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 187/250\n", - "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 188/250\n", - "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 451ms/epoch - 35ms/step\n", - "Epoch 189/250\n", - "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 110ms/epoch - 8ms/step\n", - "Epoch 190/250\n", - "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 121ms/epoch - 9ms/step\n", - "Epoch 191/250\n", - "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 173ms/epoch - 13ms/step\n", - "Epoch 192/250\n", - "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 193/250\n", - "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 243ms/epoch - 19ms/step\n", - "Epoch 194/250\n", - "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 272ms/epoch - 21ms/step\n", - "Epoch 195/250\n", - "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 196/250\n", - "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 197/250\n", - "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 198/250\n", - "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 114ms/epoch - 9ms/step\n", - "Epoch 199/250\n", - "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 209ms/epoch - 16ms/step\n", - "Epoch 200/250\n", - "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 198ms/epoch - 15ms/step\n", - "Epoch 201/250\n", - "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 202/250\n", - "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 203/250\n", - "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 204/250\n", - "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 205/250\n", - "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 121ms/epoch - 9ms/step\n", - "Epoch 206/250\n", - "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 294ms/epoch - 23ms/step\n", - "Epoch 207/250\n", - "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 208/250\n", - "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 209/250\n", - "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 210/250\n", - "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 211/250\n", - "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 212/250\n", - "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 309ms/epoch - 24ms/step\n", - "Epoch 213/250\n", - "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 214/250\n", - "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 215/250\n", - "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 177ms/epoch - 14ms/step\n", - "Epoch 216/250\n", - "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 253ms/epoch - 19ms/step\n", - "Epoch 217/250\n", - "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 218/250\n", - "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 254ms/epoch - 20ms/step\n", - "Epoch 219/250\n", - "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 220/250\n", - "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 157ms/epoch - 12ms/step\n", - "Epoch 221/250\n", - "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 222/250\n", - "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 223/250\n", - "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 176ms/epoch - 14ms/step\n", - "Epoch 224/250\n", - "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 421ms/epoch - 32ms/step\n", - "Epoch 225/250\n", - "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 159ms/epoch - 12ms/step\n", - "Epoch 226/250\n", - "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 307ms/epoch - 24ms/step\n", - "Epoch 227/250\n", - "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 193ms/epoch - 15ms/step\n", - "Epoch 228/250\n", - "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 133ms/epoch - 10ms/step\n", - "Epoch 229/250\n", - "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 107ms/epoch - 8ms/step\n", - "Epoch 230/250\n", - "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 231/250\n", - "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 276ms/epoch - 21ms/step\n", - "Epoch 232/250\n", - "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 134ms/epoch - 10ms/step\n", - "Epoch 233/250\n", - "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 234/250\n", - "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 235/250\n", - "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 153ms/epoch - 12ms/step\n", - "Epoch 236/250\n", - "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 187ms/epoch - 14ms/step\n", - "Epoch 237/250\n", - "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 209ms/epoch - 16ms/step\n", - "Epoch 238/250\n", - "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 175ms/epoch - 13ms/step\n", - "Epoch 239/250\n", - "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 234ms/epoch - 18ms/step\n", - "Epoch 240/250\n", - "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 130ms/epoch - 10ms/step\n", - "Epoch 241/250\n", - "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 122ms/epoch - 9ms/step\n", - "Epoch 242/250\n", - "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 243/250\n", - "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 321ms/epoch - 25ms/step\n", - "Epoch 244/250\n", - "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 245/250\n", - "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 246/250\n", - "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 191ms/epoch - 15ms/step\n", - "Epoch 247/250\n", - "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 248/250\n", - "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 122ms/epoch - 9ms/step\n", - "Epoch 249/250\n", - "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 250/250\n", - "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 127ms/epoch - 10ms/step\n" - ] }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# selected settings for regression (best fit from options above)\n", - "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", - "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", - "\n", - "# Create data objects for training using scalar normalization\n", - "n_inputs = len(input_labels)\n", - "n_outputs = len(output_labels)\n", - "x = input_data\n", - "y = output_data\n", - "\n", - "input_scaler = None\n", - "output_scaler = None\n", - "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", - "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", - "x = input_scaler.scale(x)\n", - "y = output_scaler.scale(y)\n", - "x = x.to_numpy()\n", - "y = y.to_numpy()\n", - "\n", - "# Create Keras Sequential object and build neural network\n", - "model = tf.keras.Sequential()\n", - "model.add(\n", - " tf.keras.layers.Dense(\n", - " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", - " )\n", - ")\n", - "for i in range(1, n_hidden_layers):\n", - " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", - "model.add(tf.keras.layers.Dense(units=n_outputs, activation=keras.activations.linear))\n", - "\n", - "# Train surrogate (calls optimizer on neural network and solves for weights)\n", - "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", - "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", - " \".mdl_co2.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", - ")\n", - "history = model.fit(\n", - " x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save]\n", - ")\n", - "\n", - "# Get the training and validation MSE from the history\n", - "train_mse = history.history[\"mse\"]\n", - "val_mse = history.history[\"val_mse\"]\n", - "\n", - "# Generate a plot of training MSE vs validation MSE\n", - "epochs = range(1, len(train_mse) + 1)\n", - "plt.plot(epochs, train_mse, \"bo-\", label=\"Training MSE\")\n", - "plt.plot(epochs, val_mse, \"ro-\", label=\"Validation MSE\")\n", - "plt.title(\"Training MSE vs Validation MSE\")\n", - "plt.xlabel(\"Epochs\")\n", - "plt.ylabel(\"MSE\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" - ] - } - ], - "source": [ - "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", - "xmin, xmax = [7, 306], [40, 1000]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "\n", - "keras_surrogate = KerasSurrogate(\n", - " model,\n", - " input_labels=list(input_labels),\n", - " output_labels=list(output_labels),\n", - " input_bounds=input_bounds,\n", - " input_scaler=input_scaler,\n", - " output_scaler=output_scaler,\n", - ")\n", - "keras_surrogate.save_to_folder(\n", - " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Visualizing Surrogates\n", - "\n", - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13/13 [==============================] - 1s 3ms/step\n" - ] }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", + "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", + ":241)\n" + ] + } + ], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "13/13 [==============================] - 0s 3ms/step\n" - ] - }, - { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] = [\n", + " \"pressure\",\n", + " \"temperature\",\n", + " \"enth_mol\",\n", + " \"entr_mol\",\n", + " \"CO2_enthalpy\",\n", + " \"CO2_entropy\",\n", + "]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "# Creating input_data and output_data from data\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13/13 [==============================] - 0s 4ms/step\n" - ] }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "13/13 - 3s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 3s/epoch - 249ms/step\n", + "Epoch 2/250\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 237ms/epoch - 18ms/step\n", + "Epoch 3/250\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 262ms/epoch - 20ms/step\n", + "Epoch 4/250\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 283ms/epoch - 22ms/step\n", + "Epoch 5/250\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 351ms/epoch - 27ms/step\n", + "Epoch 6/250\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 364ms/epoch - 28ms/step\n", + "Epoch 7/250\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 245ms/epoch - 19ms/step\n", + "Epoch 8/250\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 203ms/epoch - 16ms/step\n", + "Epoch 9/250\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 300ms/epoch - 23ms/step\n", + "Epoch 10/250\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 255ms/epoch - 20ms/step\n", + "Epoch 11/250\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 247ms/epoch - 19ms/step\n", + "Epoch 12/250\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 321ms/epoch - 25ms/step\n", + "Epoch 13/250\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 219ms/epoch - 17ms/step\n", + "Epoch 14/250\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 272ms/epoch - 21ms/step\n", + "Epoch 15/250\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 299ms/epoch - 23ms/step\n", + "Epoch 16/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 271ms/epoch - 21ms/step\n", + "Epoch 17/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 248ms/epoch - 19ms/step\n", + "Epoch 18/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 256ms/epoch - 20ms/step\n", + "Epoch 19/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 153ms/epoch - 12ms/step\n", + "Epoch 20/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 239ms/epoch - 18ms/step\n", + "Epoch 21/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 263ms/epoch - 20ms/step\n", + "Epoch 22/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 243ms/epoch - 19ms/step\n", + "Epoch 23/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", + "Epoch 24/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 189ms/epoch - 15ms/step\n", + "Epoch 25/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 280ms/epoch - 22ms/step\n", + "Epoch 26/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 222ms/epoch - 17ms/step\n", + "Epoch 27/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 286ms/epoch - 22ms/step\n", + "Epoch 28/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 116ms/epoch - 9ms/step\n", + "Epoch 29/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 157ms/epoch - 12ms/step\n", + "Epoch 30/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 267ms/epoch - 21ms/step\n", + "Epoch 31/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 264ms/epoch - 20ms/step\n", + "Epoch 32/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 269ms/epoch - 21ms/step\n", + "Epoch 33/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 353ms/epoch - 27ms/step\n", + "Epoch 34/250\n", + "13/13 - 1s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 537ms/epoch - 41ms/step\n", + "Epoch 35/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 330ms/epoch - 25ms/step\n", + "Epoch 36/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 289ms/epoch - 22ms/step\n", + "Epoch 37/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 155ms/epoch - 12ms/step\n", + "Epoch 38/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 289ms/epoch - 22ms/step\n", + "Epoch 39/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 118ms/epoch - 9ms/step\n", + "Epoch 40/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 299ms/epoch - 23ms/step\n", + "Epoch 41/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 42/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 192ms/epoch - 15ms/step\n", + "Epoch 43/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 44/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 154ms/epoch - 12ms/step\n", + "Epoch 45/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 46/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 47/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 48/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 288ms/epoch - 22ms/step\n", + "Epoch 49/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 140ms/epoch - 11ms/step\n", + "Epoch 50/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 51/250\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 52/250\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 265ms/epoch - 20ms/step\n", + "Epoch 53/250\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 179ms/epoch - 14ms/step\n", + "Epoch 54/250\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 287ms/epoch - 22ms/step\n", + "Epoch 55/250\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 284ms/epoch - 22ms/step\n", + "Epoch 56/250\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 297ms/epoch - 23ms/step\n", + "Epoch 57/250\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 206ms/epoch - 16ms/step\n", + "Epoch 58/250\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 59/250\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 274ms/epoch - 21ms/step\n", + "Epoch 60/250\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 61/250\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 62/250\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 63/250\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 154ms/epoch - 12ms/step\n", + "Epoch 64/250\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 284ms/epoch - 22ms/step\n", + "Epoch 65/250\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 168ms/epoch - 13ms/step\n", + "Epoch 66/250\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 67/250\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 264ms/epoch - 20ms/step\n", + "Epoch 68/250\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 189ms/epoch - 15ms/step\n", + "Epoch 69/250\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 175ms/epoch - 13ms/step\n", + "Epoch 70/250\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 322ms/epoch - 25ms/step\n", + "Epoch 71/250\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 207ms/epoch - 16ms/step\n", + "Epoch 72/250\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 257ms/epoch - 20ms/step\n", + "Epoch 73/250\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 74/250\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 250ms/epoch - 19ms/step\n", + "Epoch 75/250\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 76/250\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 77/250\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 126ms/epoch - 10ms/step\n", + "Epoch 78/250\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 242ms/epoch - 19ms/step\n", + "Epoch 79/250\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 80/250\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 81/250\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 166ms/epoch - 13ms/step\n", + "Epoch 82/250\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 83/250\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 84/250\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 85/250\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 282ms/epoch - 22ms/step\n", + "Epoch 86/250\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 246ms/epoch - 19ms/step\n", + "Epoch 87/250\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 88/250\n", + "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 89/250\n", + "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 193ms/epoch - 15ms/step\n", + "Epoch 90/250\n", + "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 168ms/epoch - 13ms/step\n", + "Epoch 91/250\n", + "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 156ms/epoch - 12ms/step\n", + "Epoch 92/250\n", + "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 93/250\n", + "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 94/250\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 303ms/epoch - 23ms/step\n", + "Epoch 95/250\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 129ms/epoch - 10ms/step\n", + "Epoch 96/250\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 97/250\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 98/250\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 147ms/epoch - 11ms/step\n", + "Epoch 99/250\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 130ms/epoch - 10ms/step\n", + "Epoch 100/250\n", + "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 101/250\n", + "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 102/250\n", + "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 260ms/epoch - 20ms/step\n", + "Epoch 103/250\n", + "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 275ms/epoch - 21ms/step\n", + "Epoch 104/250\n", + "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 105/250\n", + "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 275ms/epoch - 21ms/step\n", + "Epoch 106/250\n", + "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 157ms/epoch - 12ms/step\n", + "Epoch 107/250\n", + "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 183ms/epoch - 14ms/step\n", + "Epoch 108/250\n", + "13/13 - 0s - loss: 6.7955e-04 - mae: 0.0198 - mse: 6.7955e-04 - val_loss: 7.6778e-04 - val_mae: 0.0204 - val_mse: 7.6778e-04 - 192ms/epoch - 15ms/step\n", + "Epoch 109/250\n", + "13/13 - 1s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 516ms/epoch - 40ms/step\n", + "Epoch 110/250\n", + "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 111/250\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 112/250\n", + "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 113/250\n", + "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 114/250\n", + "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 115/250\n", + "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 166ms/epoch - 13ms/step\n", + "Epoch 116/250\n", + "13/13 - 0s - loss: 6.5036e-04 - mae: 0.0194 - mse: 6.5036e-04 - val_loss: 6.5858e-04 - val_mae: 0.0191 - val_mse: 6.5858e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 117/250\n", + "13/13 - 0s - loss: 6.4983e-04 - mae: 0.0194 - mse: 6.4983e-04 - val_loss: 7.0443e-04 - val_mae: 0.0198 - val_mse: 7.0443e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 118/250\n", + "13/13 - 0s - loss: 6.4994e-04 - mae: 0.0195 - mse: 6.4994e-04 - val_loss: 6.3181e-04 - val_mae: 0.0193 - val_mse: 6.3181e-04 - 296ms/epoch - 23ms/step\n", + "Epoch 119/250\n", + "13/13 - 0s - loss: 6.6252e-04 - mae: 0.0199 - mse: 6.6252e-04 - val_loss: 6.3527e-04 - val_mae: 0.0191 - val_mse: 6.3527e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 120/250\n", + "13/13 - 0s - loss: 6.4578e-04 - mae: 0.0193 - mse: 6.4578e-04 - val_loss: 6.3127e-04 - val_mae: 0.0189 - val_mse: 6.3127e-04 - 190ms/epoch - 15ms/step\n", + "Epoch 121/250\n", + "13/13 - 0s - loss: 6.1375e-04 - mae: 0.0191 - mse: 6.1375e-04 - val_loss: 6.5351e-04 - val_mae: 0.0192 - val_mse: 6.5351e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 122/250\n", + "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 142ms/epoch - 11ms/step\n", + "Epoch 123/250\n", + "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 278ms/epoch - 21ms/step\n", + "Epoch 124/250\n", + "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 246ms/epoch - 19ms/step\n", + "Epoch 125/250\n", + "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 135ms/epoch - 10ms/step\n", + "Epoch 126/250\n", + "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 127/250\n", + "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 142ms/epoch - 11ms/step\n", + "Epoch 128/250\n", + "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 249ms/epoch - 19ms/step\n", + "Epoch 129/250\n", + "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 279ms/epoch - 21ms/step\n", + "Epoch 130/250\n", + "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 356ms/epoch - 27ms/step\n", + "Epoch 131/250\n", + "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 132/250\n", + "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 133/250\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 239ms/epoch - 18ms/step\n", + "Epoch 134/250\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 285ms/epoch - 22ms/step\n", + "Epoch 135/250\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 178ms/epoch - 14ms/step\n", + "Epoch 136/250\n", + "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 137/250\n", + "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 138ms/epoch - 11ms/step\n", + "Epoch 138/250\n", + "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 144ms/epoch - 11ms/step\n", + "Epoch 139/250\n", + "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 279ms/epoch - 21ms/step\n", + "Epoch 140/250\n", + "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 141/250\n", + "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 142/250\n", + "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 143/250\n", + "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 304ms/epoch - 23ms/step\n", + "Epoch 144/250\n", + "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 178ms/epoch - 14ms/step\n", + "Epoch 145/250\n", + "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 146/250\n", + "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 396ms/epoch - 30ms/step\n", + "Epoch 147/250\n", + "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 148/250\n", + "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 155ms/epoch - 12ms/step\n", + "Epoch 149/250\n", + "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 150/250\n", + "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 151/250\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 315ms/epoch - 24ms/step\n", + "Epoch 152/250\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 198ms/epoch - 15ms/step\n", + "Epoch 153/250\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 129ms/epoch - 10ms/step\n", + "Epoch 154/250\n", + "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 155/250\n", + "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 267ms/epoch - 21ms/step\n", + "Epoch 156/250\n", + "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 182ms/epoch - 14ms/step\n", + "Epoch 157/250\n", + "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 158/250\n", + "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 159/250\n", + "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 172ms/epoch - 13ms/step\n", + "Epoch 160/250\n", + "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 164ms/epoch - 13ms/step\n", + "Epoch 161/250\n", + "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 169ms/epoch - 13ms/step\n", + "Epoch 162/250\n", + "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 188ms/epoch - 14ms/step\n", + "Epoch 163/250\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 258ms/epoch - 20ms/step\n", + "Epoch 164/250\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 165/250\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 166/250\n", + "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 265ms/epoch - 20ms/step\n", + "Epoch 167/250\n", + "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 168/250\n", + "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 169/250\n", + "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 170/250\n", + "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 171/250\n", + "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 243ms/epoch - 19ms/step\n", + "Epoch 172/250\n", + "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 173/250\n", + "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 174/250\n", + "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 175/250\n", + "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 136ms/epoch - 10ms/step\n", + "Epoch 176/250\n", + "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 177/250\n", + "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 316ms/epoch - 24ms/step\n", + "Epoch 178/250\n", + "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 214ms/epoch - 16ms/step\n", + "Epoch 179/250\n", + "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 181ms/epoch - 14ms/step\n", + "Epoch 180/250\n", + "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 144ms/epoch - 11ms/step\n", + "Epoch 181/250\n", + "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 302ms/epoch - 23ms/step\n", + "Epoch 182/250\n", + "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 183/250\n", + "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 296ms/epoch - 23ms/step\n", + "Epoch 184/250\n", + "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 195ms/epoch - 15ms/step\n", + "Epoch 185/250\n", + "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 186/250\n", + "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 187/250\n", + "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 147ms/epoch - 11ms/step\n", + "Epoch 188/250\n", + "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 451ms/epoch - 35ms/step\n", + "Epoch 189/250\n", + "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 190/250\n", + "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 191/250\n", + "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 173ms/epoch - 13ms/step\n", + "Epoch 192/250\n", + "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 193/250\n", + "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 243ms/epoch - 19ms/step\n", + "Epoch 194/250\n", + "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 272ms/epoch - 21ms/step\n", + "Epoch 195/250\n", + "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 136ms/epoch - 10ms/step\n", + "Epoch 196/250\n", + "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 197/250\n", + "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 198/250\n", + "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 199/250\n", + "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 200/250\n", + "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 198ms/epoch - 15ms/step\n", + "Epoch 201/250\n", + "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 147ms/epoch - 11ms/step\n", + "Epoch 202/250\n", + "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 203/250\n", + "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 204/250\n", + "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 205/250\n", + "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 206/250\n", + "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 294ms/epoch - 23ms/step\n", + "Epoch 207/250\n", + "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 208/250\n", + "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 209/250\n", + "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 210/250\n", + "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 211/250\n", + "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 212/250\n", + "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 309ms/epoch - 24ms/step\n", + "Epoch 213/250\n", + "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 214/250\n", + "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 215/250\n", + "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 177ms/epoch - 14ms/step\n", + "Epoch 216/250\n", + "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 253ms/epoch - 19ms/step\n", + "Epoch 217/250\n", + "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 136ms/epoch - 10ms/step\n", + "Epoch 218/250\n", + "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 254ms/epoch - 20ms/step\n", + "Epoch 219/250\n", + "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 220/250\n", + "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 157ms/epoch - 12ms/step\n", + "Epoch 221/250\n", + "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 222/250\n", + "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 223/250\n", + "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 176ms/epoch - 14ms/step\n", + "Epoch 224/250\n", + "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 421ms/epoch - 32ms/step\n", + "Epoch 225/250\n", + "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 159ms/epoch - 12ms/step\n", + "Epoch 226/250\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 307ms/epoch - 24ms/step\n", + "Epoch 227/250\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 193ms/epoch - 15ms/step\n", + "Epoch 228/250\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 133ms/epoch - 10ms/step\n", + "Epoch 229/250\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 230/250\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 231/250\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 276ms/epoch - 21ms/step\n", + "Epoch 232/250\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 134ms/epoch - 10ms/step\n", + "Epoch 233/250\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 234/250\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 235/250\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 153ms/epoch - 12ms/step\n", + "Epoch 236/250\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 187ms/epoch - 14ms/step\n", + "Epoch 237/250\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 238/250\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 175ms/epoch - 13ms/step\n", + "Epoch 239/250\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 240/250\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 130ms/epoch - 10ms/step\n", + "Epoch 241/250\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 122ms/epoch - 9ms/step\n", + "Epoch 242/250\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 243/250\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 321ms/epoch - 25ms/step\n", + "Epoch 244/250\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 245/250\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 129ms/epoch - 10ms/step\n", + "Epoch 246/250\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 191ms/epoch - 15ms/step\n", + "Epoch 247/250\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 248/250\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 122ms/epoch - 9ms/step\n", + "Epoch 249/250\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 250/250\n", + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 127ms/epoch - 10ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs, activation=keras.activations.linear))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_co2.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(\n", + " x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save]\n", + ")\n", + "\n", + "# Get the training and validation MSE from the history\n", + "train_mse = history.history[\"mse\"]\n", + "val_mse = history.history[\"val_mse\"]\n", + "\n", + "# Generate a plot of training MSE vs validation MSE\n", + "epochs = range(1, len(train_mse) + 1)\n", + "plt.plot(epochs, train_mse, \"bo-\", label=\"Training MSE\")\n", + "plt.plot(epochs, val_mse, \"ro-\", label=\"Validation MSE\")\n", + "plt.title(\"Training MSE vs Validation MSE\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"MSE\")\n", + "plt.legend()\n", + "plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" + ] + } + ], + "source": [ + "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", + "xmin, xmax = [7, 306], [40, 1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\n", + " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "surrogate_scatter2D(keras_surrogate, data_training)\n", - "surrogate_parity(keras_surrogate, data_training)\n", - "surrogate_residual(keras_surrogate, data_training)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Model Validation\n", - "\n", - "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4/4 [==============================] - 0s 5ms/step\n" - ] }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 1s 3ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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ZSU9EBNCtm+KnWCSCmJPpq2D69On4+eefsXfvXnh5eanabWxsYGZmBgB45513cODAAWzZsgXW1tZ49913AQBnz56t9OtkZmbCxsYGGRkZsLa2VrXn5eUhLi4OjRo1gqmpqYbeFWkDfvZERGU7deoUjh07pjquX78+pkyZUu7QVrduQHQ04OMDVOGruVLK+/4uSWuGur7//nsAQO/evdXaQ0NDVWvGrF27FgYGBggICEB+fj78/Pzw3Xff1XKkREREuk0ulyMkJASFhYWqthEjRuDll1+u8HnBwUBIiOKnWLSmx6e2sMeHSuJnT0T0XGpqqqozQmnevHmir9umcz0+REREJK6oqCicPHlSdezh4aHaCFtbMPEhIiKiCslkMqxcuVJtu6TAwEC0bNlSxKheDBMfIiIiKldSUhJ++OEHtbb58+fDwsJCpIiqR2umsxMREVHtOnz4sFrS07hxYyxZskSV9NSF6elVxR4fIiIiUlNUVISVK1eqtY0ePRre3t6IiFDMzPL1BdatA7KyFMf+/iIFW0VMfIiIiEjl4cOH2LRpk1rbggULVGvmhYQo1uK5dk2R9Fhaijs9vao41EVEREQAgIMHD6olPV5eXmjXbglefdVMNZwVHKxYgHDWLMXPbdu0p7cHYI8PaVBQUBDS09OxZ88eAIrFJtu2bYsvv/zyhe+piXsQEVHFCgsL8cknn6i1jRkzBi+99JJqtWXlcJbyoa3Y46MHlGssKHeTb9q0KZYvX46ioqIafd3w8PBKb1h6/PhxSCQSpKenv/A9iIio6hISEkolPQsXLsRLL70E4HkPjzYNZ1WEPT56YsCAAQgNDUV+fj4OHDiAGTNmwNjYGIsWLVK7rqCgACYmJhp5TXt7+zpxDyIiKltERAQuX76sOm7VqhUCAgLUrtH2Hp6S2OOjJ6RSKVxcXNCwYUO888476Nu3LyIiIhAUFIThw4dj5cqVcHV1VW0A++DBA7z22muwtbWFvb09hg0bhvj4eNX9ZDIZ5s6dC1tbWzg4OGDBggUouftJ7969MXv2bNVxfn4+Fi5cCHd3d0ilUjRt2hSbNm1CfHw8fH19AQB2dnaQSCSq/ddK3iMtLQ3jx4+HnZ0dzM3NMXDgQNy5c0d1fsuWLbC1tUVkZCSaN28OS0tLDBgwAElJSaprjh8/js6dO8PCwgK2trbo3r077t+/r6E/aSKiuq+goADLli1TS3qaNBlXKunRRUx89JSZmRkKCgoAAEePHkVsbCwOHz6Mffv2obCwEH5+frCyssKpU6dw5swZVQKhfM4XX3yBLVu2YPPmzTh9+jSePn2K3bt3V/ia48ePxy+//IJ169bh1q1b2LBhAywtLeHu7o5du3YBAGJjY5GUlISvvvqqzHsEBQXhwoULiIiIQHR0NARBwKBBg9Q2ysvJycHnn3+On376CSdPnkRCQgLmz58PQDFFc/jw4ejVqxf++usvREdH4+2339aq5daJiKojLi4Oq1atUmv75JNFWLmyMZo3B7y9tWtdnqriUJeeEQQBR48eRWRkJN599108fvwYFhYW2Lhxo2qI67///S/kcjk2btyoSghCQ0Nha2uL48ePo3///vjyyy+xaNEijBw5EgCwfv16REZGlvu6t2/fxq+//orDhw+jb9++ABQLYSkph7ScnJxga2tb5j3u3LmDiIgInDlzBt26dQMAbNu2De7u7tizZw9GjRoFQFGkt379ejRp0gQAMHPmTCxfvhyAYhO7jIwMDBkyRHW+efPmVf+DJCLSQuHh4bh27Zrq+PLltoiJGYYOHYCEBCAxUdGuTevyVBV7fERS26td7tu3D5aWljA1NcXAgQMxevRoLF26FADQunVrtbqeq1ev4u7du7CysoKlpSUsLS1hb2+PvLw83Lt3DxkZGUhKSkKXLl1UzzEyMkLHjh3Lff0rV67A0NAQvXr1euH3cOvWLRgZGam9roODA7y8vHDr1i1Vm7m5uSqpAYD69esjNTUVgCLBCgoKgp+fH4YOHYqvvvpKbRiMiEgX5efnY9myZWpJz5EjQUhNHYbVqxWFy0+eKNpNTXWnkLksTHxEolwAKiSkdl7P19cXV65cwZ07d5Cbm4utW7eqlhwvud9KVlYWOnTogCtXrqg9bt++jTFjxrzQ6ysXvqoNxsbGascSiUSt/ig0NBTR0dHo1q0bduzYgWbNmuHcuXO1Fh8RUW26e/cuQkp82Xz55Qfo2bOh6jgkBMjLUyxGuGOH7vb2AEx8RFPb0wMtLCzQtGlTeHh4wMio4hHO9u3b486dO3ByckLTpk3VHjY2NrCxsUH9+vXxxx9/qJ5TVFSEixcvlnvP1q1bQy6X48SJE2WeV/Y4yWSycu/RvHlzFBUVqb3ukydPEBsbixYtWlT4nkpq164dFi1ahLNnz6JVq1b4+eefq/R8IiJt8Ouvv2Lbtm2q43r1OiIycgmmTzfGunXP/wGu/E7StsUIXwQTH5H4+wNnz9bN/8DGjh2LevXqYdiwYTh16hTi4uJw/PhxzJo1Cw8fPgQAvPfeewgJCcGePXsQExOD6dOnl1qDpzhPT09MmDABkyZNwp49e1T3/PXXXwEADRs2hEQiwb59+/D48WNkZWWVusdLL72EYcOGYcqUKTh9+jSuXr2KN998E25ubhg2bFil3ltcXBwWLVqE6Oho3L9/H7///jvu3LnDOh8i0inh4blYtmyZWhnA5MmTMWPGYJw9C0RFqW83UZe/kzSNiQ+VYm5ujpMnT8LDwwMjR45E8+bNMXnyZOTl5cHa2hoAMG/ePIwbNw4TJkyAj48PrKysMGLEiArv+/333yMwMBDTp0+Ht7c3pkyZguzsbACAm5sbli1bhuDgYDg7O2PmzJll3iM0NBQdOnTAkCFD4OPjA0EQcODAgVLDWxW9t5iYGAQEBKBZs2Z4++23MWPGDEydOrUKf0JERHVXbGwsrl1brda2ePFiNGjQQHWsTz08JUmEkouv6LnMzEzY2NggIyND9SUPAHl5eYiLi0OjRo1gamoqYoRU2/jZE5G22LZtG+7evas6dnLygZtbf9Vwli4nOeV9f5fE6exERERaLjw8B9eufabW5uU1Ba+/7lpqry19x6EuIiIiLXbz5s1SSc+KFR9i3TpXALq311Z1sceHiIhIS23ZskVtyx1n51fg6toHnTsrEp2ICOjFMFdVMPEhIiLSMllZWfjiiy/U2ry9p2H0aGfVcUgIkJYGxMRwmKs4DnVVEWvB9Q8/cyKqS65du6aW9MhkRli+/EN89ZV60hMdDQgCh7lKYuJTScrp0jk5OSJHQrVN+ZlXdso8EZGmFN/eSBAE/PjjjwgPD1ed9/X1hUy2GCYmhkhIeL4NkrKuZ/Vq/Vmfp7I4nb2EiqbDJSUlIT09HU5OTjA3N+eO3jpOEATk5OQgNTUVtra2qF+/vtghEZGeadBAsXGovX0mZs1aq3auefPpmDvXEY8eKXp2AEWyc/asCIHWAZzOXgNcXFwAQLXhJekHW1tb1WdPRFSbUlOBtm2vYPjwvaq2ggIzrFgxHz16GKh2U5dIgGbNOKRVGUx8qkAikaB+/fpwcnJCYWGh2OFQLTA2NoahoaHYYRCRHhIEAb16XUCPHgdUbceP98OcOd1gYKBIcqZPB54+BebMAVauFDFYLcLE5wUYGhryy5CIiDSq+NTz/v3z8Ntvv6FHj5uq8xs2zET37g5QbrTu78/anRfBxIeIiEgkixcD69YBs2YpNg6Njga++y4RcXFhSE9Ph4GBAW7e7Ivt27sCkCA8HJDLOT29Opj4EBER1bKICGDBAuD2bUVh8rp1wH//K+CXX/6At/dhpKfLYWJii/HjA3HxohuuXAHi44G8vOc7qtOLYeJDRERUyxYsAGJjFb8bGACzZuUiN3cvmjdXNN682RxJSf5o2dIUISHAp58qruUqzNXHxIeIiKiWKVdDMTMDNm16iJSUMMTGZsDQ0BD16/dHZGQnBAdLVAsRhoRwPR5N4QKGREREtezTTwEfHwFffXUGd++GIiMjA3Z2dpg0aRIcHTsDUGRG3GBU89jjQ0REVMv69s1BVtYe3LlzBwBga9sS7u5DERgoxdOnimEw9vLUDCY+RERENaC8ndETEhIQFhaGZ8+eQSYzxIEDA3DxYgc4OEjw5Ang7c1enprExIeIiEiDlAlP8Z4bf3/lXlunkZgYBQMDAQ4ODti2LRAXLypWhn/69HnCw16emsPEh4iISAOUCU9aGhATo95zk52djd27dyMp6R4MDIA7d1rjyJHB6NNHijt3FElPQACwc6fY70L3MfEhIiKqpogIYOxYICsL8PJSJDy+vkB4OLBiRTxOn94FC4ssFBUZYf/+Qbh1qy3y8iSQyYB//hE7ev3CxIeIiKiaQkIUSY+lJbB6tWKoqnlzOZydT6FXrxMwMBDw+HE9XLgwCqamTpg7V7FSM+t4ah8THyIiomoKDgYWLlSswgwAWVlZ8PUNh7NzHADg8uW2OHlyILZsMWH9jsi4jg8REVEVREQA3bopfir5+wN2dopi5i+++BsrVqyHs3McCgqMER4+HHv3DkNGBpOeuoA9PkRERFVQfDVlZSITEQGkp8vh738c7dqdgkQCpKQ4YefOUfjnn3qQSICRI8WNmxTY40NERFQFwcGKAua0tOe9PmvXZqJLl/+gfXtF0nPxYnv8+ONbyMiohw8+UOyozhlbdQMTHyIioirw9wfs7RVT1kNCgLt376Jv3w3w9LwPQTDBsWMjcfjwUCxYYIyCAmDlSrEjpuKY+BAREVWgrJoeX1/A2lqGZs2OYNu2bSgqyoGLiwveffdtnDjRGrm5THjqKtb4EBERlWHxYmDdOsDGBkhMVKzTs22b4lxoaAYCA3fBw+MBAODy5Y4IC/ODkRG/Vus6fkJERERlWLdOsTZPfj5gYKD4PSQEcHSMxbhxe2FunguZTIr9+/0xYEALMOfRDvyYiIiIyjBggGLlZalUkfQYGsrQt+8RGBqeAwCYm7virbcCsXy5nciRUlWwxoeIiPRayRoe5fH164rZWNnZgK1tGiZODFUlPV26dMGcORNhZ8ekR9tIBEG5ziQBQGZmJmxsbJCRkQFra2uxwyEiohrWrZtiXR4fH+Ds2efHbm7Ao0eAl9ctDB++F6am+TA0NEVg4DB4e3uLHTaVUNnvbw51ERGR3oqIUKzH4+X1fN+s4GBFLc/Dh0UYMOAwunT5EwDQoEEDBAQEwNbWVryAqdqY+BARkd4KCVGsx2NqqthrS8nc/Cn8/cPg6JgEAHBy8kFQ0KswNDQUKVLSFA51lcChLiIi3RYRoUh4lD08r78O5OYqfvf2Bry9b6B58whIpQUoKDCDt/dwjBvXTLyAqVI41EVERFSG4nttnT2rWIU5MREwMipEhw6ReOmliwAACwt3zJkTyH8E6xgmPkREpFeUNTy+vopCZgBwcPgHo0eHwckpBQDQo0cP+Pr6wsCAk591DRMfIiLSC8qVmGfNUiQ/Y8cq1ufx8/sLnTrtg5FRIczNzTFixAg0bdpU7HCphjDxISIinRMRASxYoEhsLC2B1aufr8QcEgI0awbk5xdi5MiDePnlywAAT09PjBw5ElZWViJHTzWJiQ8REemckBAgNvb58ejRUG0pIZcDVlaPMWvWTlhZPQYA9OzZE7169eLQlh7gJ0xERDqh+ArMwcGKtXkcHBTn8vIUvT0A0KXLFQwd+iOsrB7DwsIC48aNYz2PHmGPDxERab2IiOfT0hcuBD79VJHoPH36/BoTkwIMGnQAbdtehVwONG7cGCNGjIClpaV4gVOtY+JDRERaLyTk+Vo8gqA4TkxUHEskQNOmKfDzC0O9ev9AIpGgd+/e6NGjB3t59BATHyIi0krFFyL09QWuXFGsybN6teL89OnAkycC5s+/DFPTgygqKoKVlRVGjhwJT09PMUMnETHxISIirVR8IUJA0ePj4QH4+yuO/fzysW/fPly/fh1FRUDTpk0xfPhwWFhYiBc0iY6JDxERaQ3lNHWJBBg5UtEWHAz88Qdw7Zqi5wcAkpKSEBYWhqdPn0IikaBPnz7o3r07JBKJeMFTnaCTg5vffvstPD09YWpqii5duuDPP/8UOyQiItIA5TT1mBggKkqx5YS/v+L3rCwgKkrA+fPnsWnTJjx9+hTW1tYICgpCjx49mPQQAB1MfHbs2IG5c+diyZIluHTpEtq0aQM/Pz+kpqaKHRoREVWTcpq6t/fzLSeU09dfeSUPo0eH4cCBA5DJZGjWrBmmTp0KDw8PscOmOkTndmfv0qULOnXqhG+++QYAIJfL4e7ujnfffRfByq14K8Dd2YmItEO3booaHx8fICzsEcLCwpCWlgYDAwP07dsXXbt2ZS+PHqns97dO9fgUFBTg4sWL6Nu3r6pN+RcgOjq6zOfk5+cjMzNT7UFERHXD4sWAlZXiZ8nj4GDAx0fApEnnsGnTJqSlpcHGxgYTJ06Ej48Pkx4qk04VN//zzz+QyWRwdnZWa3d2dkZMTEyZz1m1ahWWLVtWG+EREVElKaeqX76sWHV5zRpFHY/yeN06IDU1F7m5Ear/v3t7e8Pf3x9mZmYiR091mU71+LyIRYsWISMjQ/V48OCB2CEREek95VR1CwvAwAAwNlYcm5srNh19772H2LBhA2JiYmBoaIgBAwbgtddeY9JD/0qnEp969erB0NAQKSkpau0pKSlwcXEp8zlSqRTW1tZqDyIiqn1l7bWVna3YVLSwUHGNo6OAyMizkEpDkZGRATs7O0yaNAldunTh0BZVik4NdZmYmKBDhw44evQohg8fDkBR3Hz06FHMnDlT3OCIiKhCyl6ehQsBOzvFWj15eYoenlmzgNOnczB8+B4cPnwHANCyZUsMGTIEpqamIkdO2kSnEh8AmDt3LiZMmICOHTuic+fO+PLLL5GdnY2JEyeKHRoRERVTfMuJP/5Q1O+4uQHPninW6XFzU8zYCg4G2rZNQL16u5CZmaka2urQoQN7eajKdC7xGT16NB4/foyPPvoIycnJaNu2LQ4dOlSq4JmIiMShTHgSEhQbiS5YoPiZlwdkZCgSHkDR03PmjIDTp09jy5YoCIIAe3t7jBo1qtzyBaJ/o3Pr+FQX1/EhIqpZ3t6K1ZeNjRW1O25uiqTn6VMgIAAYN06RGM2bl43c3N24d+8eAKB169YYPHgwpFKpyO+A6qLKfn/rXI8PERHVXRERip4eAHByUqzJc/cuUFSkaEtMVGxB8fLL8di1axeysrJgZGSEgQMHol27dhzaompj4kNERLUmJESxi7qBgaJ+59Ch50mPRAIsXCjHiROncOLECQiCgHr16mHUqFFwcnISN3DSGUx8iIioxinrenx9FbuoZ2Upkp6sLMWQl6EhMHduFjIywnHlShwAoE2bNhg0aBBMTExEjp50CRMfIiKqURERwNixiiQHALZte54ERUUpZm21avU3wsPDEReXDWNjYwwaNAht27YVNW7STUx8iIioRhSfvZWVpRjK8vVV1PD4+yuukcvlOHHiBH766SQAwMnJCYGBgXB0dBQxctJlTHyIiEjjIiKAwEDFrC1DQ0WbIABbtwIrVyqOnz17hl27duH+/fsAgPbt22PAgAEwNjYWKWrSB0x8iIhI40JCnm8zYWio2HZCEBRT1gHg7t272L17N3JycmBiYoIhQ4agdevW4gVMeoOJDxERaUzxIuaEBODJE2DuXMW5deuAWbNkOHIkCmfOnAEAODs7Y9SoUXBwcBAxatInTHyIiOiFFE9ylEXKyv22zp0DXF2BHTue1/MsWJCBXbt24cyZBwCAjh07ws/PD0ZG/Cqi2sOVm0vgys1ERJXTrZsiybG0fD5jy8pK8bvym8XHBzh7Frh9+zb27NmD3NxcSKVSDB06FC1bthQveNI5lf3+NqjFmIiISIcEBysSm1mznrc9e6bo6TE1VWxFsWCBDJGRkfjll1+Qm5uL+vXr4+2332bSQ6Jhj08J7PEhIqo6BwdF4bKREbBrl2J4Kz09HWFhYUhMTAQAdO7cGf369ePQFtUI7tVFREQ1LiJCsbu6mZli89FPP1UkPTExMdi7dy/y8vJgamqKYcOGwdvbW+xwiZj4EBFR5SkLmt3cFFtO2NgoNhYFFMNegwYV4eDBw/jzzz8BAG5ubggMDIStra14QRMVw8SHiIgqbcECIDZWsQqzICg2GPXyUhzPmfMUmzeHISkpCQDg4+ODV199FYbKFQyJ6gAWNxMRUZkiIhQztyIinrdJJIqf9vaK2Vxz5wIxMUBY2A3cu/cDkpKSYGZmhjfeeAP9+/dn0kN1DoubS2BxMxGRQvHp6tu2KWp3lENdwcGK46KiIkRGRuLChQsAAHd3dwQEBMDGxkbk6EnfsLiZiIiqpHhSAyhWXpZIFOvyLFjw/NzZs4rzT548wc6dO5GSkgIA6NGjB3r37s1eHqrTmPgQERGA56suh4QojpVFy5aWigRIec7fH7h27Rr27duHgoICmJubY8SIEWjatKl4wRNVEmt8iIgIgKI3x80NuHxZ8dPNTbEQ4axZimnqPj7AggWFiIiIQHh4OAoKCtCwYUNMmzaNSQ9pDdb4lMAaHyLSZ8otJywtgdatFb08ym0nHj9+jLCwMKSmpgIAevbsiV69esHAgP+GJvGxxoeIiKps1izlLupAly7P63quXLmCAwcOoLCwEBYWFhg5ciQaN24sdrhEVcYenxLY40NE9FxBQQEOHDiAq1evAgAaNWqEkSNHwtLSUuTIiNRxk1IiIiqTcn2exYufr9NT1po9qamp+PHHH3H16lVIJBL07t0bb775JpMe0mrs8SmBPT5EpOuKr8+jrOdxc1OsyOzjA5w5I+Dy5cs4ePAgioqKYGVlhZEjR8LT01Ps0InKxRofIiIqU3CwonbH11dRz5OVpZiu7uMDvP9+Pnbv3o9r164BAJo0aYIRI0bAwsJC5KiJNINDXUREekI5nAUokp+oKEURs4+PYrp6eHgyHj78AdeuXYNEIsGrr76KsWPHMukhncIeHyIiPaFcoHDhQuDhQ0VPD6AY2rpw4QI2boyETCaDtbU1AgIC4OHhIW7ARDWAPT5ERDqkrCJlpeBgxU7q8fHPa3vefz8PYWFhOHDgAGQyGZo1a4apU6cy6SGdxR4fIiIdUnLbCWUtT1SUIvGxt1cUMVtaAj/++AgPHoQhLS0NBgYGePXVV+Hj4wOJcgt2Ih3ExIeISIcoC5eVP6OjgWvXFD08z9sFTJr0J+7c+R1yuRw2NjYIDAxEgwYNxA6fqMYx8SEi0gHFd1ZX7p4OKOp5nj1TTFcPDgb69ctFbm4EYmJiAADe3t7w9/eHmZmZSJET1S7W+BARaani9Twlh7giIhRJT3y8Ypd1e3ugffuH2LBhA2JiYmBgYIABAwbgtddeY9JDeoU9PkREWqp4slN8bZ5u3YC0NOB/nTqwtBQwcWI0QkOPQi6Xw87ODoGBgXB1dRX3DRCJgIkPEZGWKl7P4++veChXZTY1VQxv2dvnYOLEvXj06DYAoEWLFhg6dChMTU1Fjp5IHEx8iIi0lDLZiYgAvL0VBcy5uYpVmPPygJdfTkC/fruQmZkJQ0ND+Pn5oWPHjpy1RXqtyomPoaEhkpKS4OTkpNb+5MkTODk5QSaTaSw4IiL6dyEhiinqShKJgD59zqBLl2PIzBRgb2+PUaNGwcXFRbwgieqIKic+5e1pmp+fDxMTk2oHREREVRMcDCxYoOjxMTXNxquv7oGr610AQOvWrTF48GBIpVKRoySqGyqd+Kxbtw4AIJFIsHHjRlhaWqrOyWQynDx5Et7e3pqPkIiIKqQc8oqPj0d4eDiePXsGIyMjDBw4EO3atePQFlExlU581q5dC0DR47N+/XoYGhqqzpmYmMDT0xPr16/XfIRERKQSEQFMnw48fQrMmQOsXAnI5XKcOnUKJ06cgCAIqFevHgIDA+Hs7Cx2uER1jkQob+yqHL6+vggPD4ednV1NxSSqzMxM2NjYICMjA9bW1mKHQ0R6pvhChP7+pdsWLHhez2NpCSQlZSE8PBxxcXEAgDZt2mDQoEEsPSC9U9nv7yonPrqOiQ8RiSUiAhg7VlGr4+PzfAXm5s0Va/IoqwliYhQztz744G/Y2oYjOzsbxsbGGDRoENq2bSta/ERiquz3d5WLmydNmlTh+c2bN1f1lkREBEWvjnLX9ODg5+3Kf54KArB6NRASIseECSeQnHwS2dmAo6MjRo0aBUdHR3ECJ9IiVU580tLS1I4LCwtx/fp1pKeno0+fPhoLjIhI35RckBBQ9AIBit6eTz8FfH2f4enTXbh//z4AoF27dhg4cCCMjY1FippIu1Q58dm9e3epNrlcjnfeeQdNmjTRSFBERPqmvNqe4kNfLVrcxfr1u5GTkwMTExMMGTIErVu3FjdwIi2jsRqf2NhY9O7dG0lJSZq4nWhY40NEYlBuNaGs7Rk1CggLU5yzspJj9epjSEk5AwBwdnbGqFGj4ODgIGLERHVLjdX4lOfevXsoKirS1O2IiPRK8WGuiIjnSY+1dQb+7/92ISXlAQCgY8eO8PPzg5ERdxwiehFV/pszd+5ctWNBEJCUlIT9+/djwoQJGguMiEgX/dt0deVGowDw0ku3MXr0HmRn58LExAT+/v5o2bKleMET6YAXWsenOAMDAzg6OqJPnz6YNGmS1v8rhENdRFSTSg5pFW/z9gbs7ABfXxkSE4+iUaNoAED9+vURGBgIe3t7ESMnqttqbKgrKiqqWoEREemz4kNaJduePgVu3UpHly5haNQoEQDQuXNn9OvXT+v/UUlUV7zw36TU1FTE/m/5UC8vr1K7tRMRUWnKfbWK++MP4No1YNSoGLi67oWxcR5MTU3h7++P5s2bixMokY4yqOoTMjMzMW7cOLi6uqJXr17o1asX3Nzc8OabbyIjI6MmYiQi0mnffluEHj0OoWHDHTA2zkN6uhsaN57KpIeoBlQ58ZkyZQr++OMP7N+/H+np6UhPT8e+fftw4cIFTJ06tSZiJCLSORERitqenTvT8N57m9G16x8AgLg4H3z99USsXWsrboBEOqrKQ1379u1DZGQkevTooWrz8/PDjz/+iAEDBmg0OCIiXRUSAmRk3MTVqxEwNs6HmZkZhg0bhthYL9y+rV4DRESaU+XEx8HBATY2NqXabWxsdHbHdiIiTVBOW1+woAh9+0bC0PACAMDd3R0BAQGwsbGBl1fpGiAi0pwqD3V9+OGHmDt3LpKTk1VtycnJeP/99/F///d/Gg2OiEjbKYe0IiKA6dOB2NgnOHp0kyrp+fvv7pgwYUKZ/6AkIs2r8jo+7dq1w927d5Gfnw8PDw8AQEJCAqRSKV566SW1ay9duqS5SGsJ1/EhouooazFC5bo9+fnX4Oe3D1JpAYyMzPHHHyPw1ltN2cNDpAE1to7PsGHDIJFIqhUcEZEuKGsV5pAQRaITEqI4fvoU8PAoRJMmB9G06WUAgFzeEO++OxKLF/MfV0S1TWOblOoK9vgQUWWVtQpz8WQoJAS4c+cxRo0Kg7NzKgQB6NnzFfTu3RsGBlWuNCCiCtRYj0/jxo1x/vz5UrsCp6eno3379vj777+rHi0RkRYqaxXm4gsUPnlyFXfv7oeJSSGysizQps1I9OnTWJxgiQjACyQ+8fHxkMlkpdrz8/Px8OFDjQRFRKQNylqFGQB27y7Anj0H0bjxFZiYAPfvN0L9+iPx5puWtR8kEampdOITERGh+j0yMlJtBoJMJsPRo0fRqFEjzUZHRFTH/Nvu6l27puLs2Z1o3PgfyOUS/P13L2zd+gqHtojqiErX+Cj/0kokEpR8irGxMTw9PfHFF19gyJAhmo+yFrHGh4gqUv7u6gICAy+jbduDKCoqQm6uJc6eDcD8+Z6ctUVUCzRe4yOXywEAjRo1wvnz51GvXr3qR0lEpCWUvTq+vorj4nU977+fj4iI/fD0vIaiIqBJkyYYMWIELCwsxAmWiMpV5RqfuLi4moiDiKhOU05TB5739ADA9u3JOH8+DJ6eTyCXS3D3bh989FF3LvtBVEdVOfFZvnx5hec/+uijFw6mPPHx8VixYgWOHTuG5ORkuLq64s0338TixYthYmKiuu6vv/7CjBkzcP78eTg6OuLdd9/FggULNB4PEemfkjO4BEHAxYsXcfPmIVhby5CRYY0DBwKwerUHmPMQ1V1VTnx2796tdlxYWIi4uDgYGRmhSZMmNZL4xMTEQC6XY8OGDWjatCmuX7+OKVOmIDs7G59//jkAxdhe//790bdvX6xfvx7Xrl3DpEmTYGtri7ffflvjMRGRfik+gysvLw/79u3DjRs3YGgIJCa+hD/+GI7Vq81Zz0NUx2lkAcPMzEwEBQVhxIgRGDdunCbi+lefffYZvv/+e9W6Qd9//z0WL16M5ORkVS9QcHAw9uzZg5iYmErfl8XNRFSR7dsf4fLlMJibp0EuN0CDBq/irbd8OLRFJLLKfn9rZH6ltbU1li1bVqublGZkZMDe3l51HB0djZ49e6oNffn5+SE2NhZpaWm1FhcR6SZBEPDHH3/g5s3NMDdPQ0aGDTZvnojQ0G5Meoi0iMYWlsjIyEBGRoamblehu3fv4uuvv8bUqVNVbcnJyXB2dla7TnlcfCf5kvLz85GZman2ICL9U3wX9ZJyc3Px66+/4tChQzA0lCEmxgt79kyFu3sDtdldRFT3VbnGZ926dWrHgiAgKSkJP/30EwYOHFilewUHB+PTTz+t8Jpbt27B29tbdZyYmIgBAwZg1KhRmDJlSpVeryyrVq3CsmXLqn0fItJuxTcXLV6nk5iYiLCwMKSnp0MuN0BkZD/cuNEF27ZJWM9DpIWqnPisXbtW7djAwACOjo6YMGECFi1aVKV7zZs3D0FBQRVe07jx831tHj16BF9fX3Tr1g0//PCD2nUuLi5ISUlRa1Meu7i4lHv/RYsWYe7cuarjzMxMuLu7V/YtEJGO8PUFrl17vk6PIAg4d+4cjhw5ArlcDltbWzg5BeLwYTds21b2VhVEVPeJuo6Po6MjHB0dK3VtYmIifH190aFDB4SGhpZa/t3HxweLFy9GYWEhjI2NAQCHDx+Gl5cX7Ozsyr2vVCqFVCp98TdBRDohKgrIylL8zM3NxZ49e3D79m0AgK1tC0ydOhSmpqZ44w2RAyWianmhGp/09HRcuHABFy5cQHp6uoZDKi0xMRG9e/eGh4cHPv/8czx+/BjJyclqtTtjxoyBiYkJJk+ejBs3bmDHjh346quv1HpziIiKK17XExys2IZi5swHWL9+PW7fvg2ZzBD79g3C3LmBWLHCVOxwiUgDqjSdPT4+HjNmzEBkZKRqvy6JRIIBAwbgm2++gaenZ40EuWXLFkycOLHMc8XDL76AYb169fDuu+9i4cKFVXotTmcn0h/F9906c0bAxo1n8PDhMRgYCLC3t4ej4yiMHesCuRywtASePRM7YiIqT2W/vyud+Dx48ACdOnWCsbExpk+fjubNmwMAbt68ie+//x5FRUU4f/48GjRooJl3IBImPkT6Q7n/1rx52cjL24O7d+8CAJKSWuGrr4ZAKpVi8WJg3Tpg1ixg5UqRAyaicmk88Zk8eTLu3r2LyMhImJqqd/nm5uZiwIABeOmll7Bx48bqRS4yJj5EukmZ5AQHqxcm379/H7t27cKzZ88gCEb4/fcB8PVtj5UruTYPkTbReOLj5uaGHTt2oEePHmWeP3nyJF5//XU8evToxSKuI5j4EOmm4sNaZ88Ccrkcp0+fxvHjxyEIAqTSevjuu0A8eOAMLy+gCgu+E1EdoPGVm//5558Ka3gaN26Mp0+fVilIIqLaoixeDg4GsrKysG3bNkRFRUEQBLRp0wb790/BgweKRU+5EDOR7qp04lO/fn3cvHmz3PPXr1+vcL0cIqLaVtZqzM+exWH9+vX4+++/YWxsDA+PYVi9ejh69jSBlxfg7Q38y7qqRKTFKp34DB8+HPPnz8fjx49LnUtNTcXChQsxfPhwTcZGRFQlJROd4qsxh4TIIZVG4c6d/yA7OxuOjo6YMmUKfvyxLaKjFev3rF4NVLDsFxHpgErX+KSlpaFLly5ITk7Gm2++CW9vbwiCgFu3buHnn3+Gi4sLzp07p7ZxqDZijQ+R9ipZx/N8RtYz2NuHIysrHgDQrl07yOUDsXq1MXx9FUlPcPDzREn5fCLSHpX9/q70ys12dnb4448/8MEHH2D79u2qhQttbW0xZswYfPLJJ1qf9BCRdlMmL8qNQ6OiAGfnexCEcGRl5cDY2BhDhgzByy+/rEqSAPUkZ+FC4OlTRa8Rt6Ug0j1VWsBQSRAE1ZCXo6MjJDpUCcgeHyLdIJfL8cMPUUhJOQ0AcHZ2hr39KHz5pYMqMSprenvJXiMi0g4a7/EpTiKRwMnJ6YWDIyKqSZmZmdi1axdSUhIAAPXqdcDkyX7o1ctYVfNz9mzZPTole42ISLe8UOJDRFRX3b59G3v27EFubi6Kikywe7c/rK1bYsaMyiU1/v4c4iLSZS+0SSkRkViKz9wq/rtMJsPvv/+OX375Bbm5uahfvz5efnkqrK1bqhIdf//ye3qISD+8UI2PLmOND1HdVrwGB1D87uubjrFjd+Hhw4cAgM6dO6Nfv34wMiq/U7u8LSyISDtpfOVmIqK6QLkCs68vkJYG9OkTgz59NuDhw4eQSqV47bXXUFg4ED17GqktXFhS8TV+iEh/VKrHZ926dZW+4axZs6oVkNjY40OkHbp3l8Ha+jC6dv0DAODq6orAwEB8/rkdQkIAubzimVns8SHSLRrdpLRRo0aVelGJRIK///678lHWQUx8iOq+tLQ0bNwYhpwcxabIXbt2Rd++fWFoaAgrKyArCzAwAHbvZlJDpC80Op09Li5OY4EREVXHzZs3ERERgfz8fJiammL48OHw8vJSnZ81S7laM5MeIirthYubCwoKEBcXhyZNmlRYQKht2ONDVDcVFRUhMjISFy5cAAA0aNAAgYGBsLGxETkyIqoLaqy4OScnB5MnT4a5uTlatmyJhATFAmHvvvsuQlglSEQ14MmTJ9i0aZMq6enevTuCgoIqTHrK2pmdiKjKic+iRYtw9epVHD9+HKampqr2vn37YseOHRoNjojo+vXr+OGHH5CcnAxzc3OMGTNGVc9TEc7aIqKyVHmMas+ePdixYwe6du2qtkdXy5Ytce/ePY0GR0T6KSICWL26EK+/fghPnlwCAHh4eCAgIKDSQ9DceoKIylLlHp/Hjx+XuU9Xdna2Tm1WSkTi+frrf/DyyxtVSc8rr7yCCRMm4Phx60oPX3GVZiIqS5UTn44dO2L//v2qY2Wys3HjRvgol1IlInpBV69eRc+eP8DZORVGRhZ488030adPHxgYGKiGr8aOZe0OEb2YKic+n3zyCT744AO88847KCoqwldffYX+/fsjNDQUK1eurIkYiUhHFS9ALigowN69e7Fnzx7I5YXw9PTErFlT0aRJE9X1wcGApaVinZ6yandY0ExE/+aFprPfu3cPISEhuHr1KrKystC+fXssXLgQrVu3rokYaxWnsxPVHuW+W337pmLEiDA8fvwYEokEvXr1wiuvvAIDAwPVCsu+vkBU1POfZa24XHwfr/JWbCYi3aTRlZv1CRMfopr3PJkRcPv2FbRufQCCUARLS0sEBATA09NTdd3YsYoeHiVuQ0FEZdHoys2ZmZmVfmEmC0T0b0JCgIsXC9C69T60anUNggA0adIEI0aMgIWFhdp1xZMeA4OKZ2n5+zPhIaKKVSrxsbW1rfSMLZlMVq2AiEj3zZqVjEuXwmBh8QQSiQS+vr7o0aNHqf/PKKeku7kBhw6VvQ0Fe3mIqCoqlfhERUWpfo+Pj0dwcDCCgoJUs7iio6OxdetWrFq1qmaiJCKtp0hQBLz11kU8enQIFhYy5OVZoXXrQLzyikeZz6lMD07xhQqZ+BDRv6lyjc+rr76Kt956C2+88YZa+88//4wffvgBx48f12R8tY41PkQ145VX8uHi8htatboBAHj8+CWEhg5Hmzbm1SpEZo8PEQEarvEpLjo6GuvXry/V3rFjR7z11ltVvR0R6YGkpCQMGrQTBQVpAAzQr9+rePzYB5cuSaq9sjLreoioKqq8jo+7uzt+/PHHUu0bN26Eu7u7RoIiIt0gCAL+/PNPbNq0CQUFabCxscGkSUHo1q0bV3onIlFUucdn7dq1CAgIwMGDB9GlSxcAwJ9//ok7d+5g165dGg+QiLRTXl4eIiIicOvWLQCAl5cXhg0bBjMzMwCszSEicVS5x2fQoEG4c+cOhg4diqdPn+Lp06cYOnQobt++jUGDBtVEjESkZRITE7FhwwbcunULBgYG8PPzw+jRo1VJD6CoyfHx4SaiRFS7uIBhCSxuJnpxgiDg3LlzOHLkCORyOWxtbREYGAg3NzexQyMiHVdjxc0AkJ6ejk2bNqm6sFu2bIlJkybBxsbmxaIlIq0XHp6Lo0f3wMnpNgCgefPm8Pf3h6mpaalrS87E4swsIqotVR7qunDhApo0aYK1a9eqhrrWrFmDJk2a4NKlSzURIxHVcQ8ePMCff66Hk9NtyOWGGDhwIEaNGlVm0gOo1/eUdazETUeJSNOqnPjMmTMH/v7+iI+PR3h4OMLDwxEXF4chQ4Zg9uzZNRAiEdVVgiDgzJkzCA0NhZlZJrKz7dG8+WQkJ3dG9+6SchOWkvU95dX7lJcQERG9qCrX+JiZmeHy5cvw9vZWa7958yY6duyInJwcjQZY21jjQ1Q52dnZ2LNnD+7evQsAaNWqFYYMGQKpVKqxXdI5BEZElVVjNT7W1tZISEgolfg8ePAAVlZWVY+UiLTO/fv3sWvXLjx79gxGRkYYMGAA2rdvr1qbR7nHFhcnJKK6psqJz+jRozF58mR8/vnn6NatGwDgzJkzeP/990ttY0FEukUQBJw6dQrHjx+HIAhwcHDAqFGj4OzsrHYdExYiqquqnPh8/vnnkEgkGD9+PIqKigAAxsbGeOeddxDCgXginZWVlYXdu3fj77//BgAkJr6Ml18eDGdnk1LXcoiKiOqqF17HJycnB/fu3QMANGnSBObm5hoNTCys8SEqLS4uDuHh4cjKyoKRkREuXx6EXbvawsdHojaspUxyNFXjQ0RUWTW6jg8AmJubo3Xr1i/6dCLSAnK5HCdPnsSJEycAAKamjjhyZBQ6d3ZUzcIqufVERASQkACYmQFubookyNcXiIpiDxARia/Sic+kSZMqdd3mzZtfOBgiqjuePXuG8PBwxMfHAwDs7dtix45BOHPGGNnZ6j05xQuZQ0KAxETF74cOAVlZwLVrip/cl4uIxFbpxGfLli1o2LAh2rVrB+5yQaTb7t27h/DwcOTk5KCoyBgREUNgYfGyqofH11fRk6PswSmezAQHAwsWABIJMHKkoqfHzU2RBPn6iveeiIiAKtT4zJgxA7/88gsaNmyIiRMn4s0334S9vX1Nx1frWOND+kwulyMqKgqnT58GADg7O8PePhBfflmvWjU8rPkhoppW2e/vSq/c/O233yIpKQkLFizAb7/9Bnd3d7z22muIjIxkDxCRDsjMzMTWrVtVSU+HDh0wefJkvPZaPZw9q96r4+sLWFpWvgeHO7ETUV1RpS0rpFIp3njjDRw+fBg3b95Ey5YtMX36dHh6eiIrK6umYiSiGnbnzh2sX78eCQkJMDExQUBAAIYMGQJjY+My98uKilLU7ERFlb5XWdf7+6NU8kREJIYq79WleqKBASQSCQRBgEwm02RMRFRLZDIZDh8+jJ9//hm5ubmoX78+pk6dilatWqmuKWu/rIp6cLi/FhHVZVVKfPLz8/HLL7+gX79+aNasGa5du4ZvvvkGCQkJsLS0rKkYiagGpKenY8uWLTj7v6KbTp06YdKkSaVq95RJjrKgOSKi4h4cDmsRUV1W6eLm6dOnY/v27XB3d8ekSZMwduxY1KtXr6bjq3UsbiZ9EBMTg7179yIvLw9SqRT+/v5o0aJFhSsus0CZiOqyyn5/VzrxMTAwgIeHB9q1a6faiLAs4eHhVY+2DmHiQ7pMObT1xx9/AABcXV0RGBgIOzs7ABUnN9yGgojqMo2v3Dx+/PgKEx4iqtvS0tIQFhaGR48eAQC6du2Kvn37wtDQUHWNpnZVJyKqq154ry5dxR4f0jUREUBo6E106BABmSwfpqamGD58OLy8vKp0Hw51EVFdVuN7dRFR3bdnTxG2bPkd7dqdh0wGNGjQAAEBAbC1ta3yvdgbRES64IWnsxNR3fb06VOcOrUJ7dqdBwA4OXVDUFBQqaSn+Lo7Za3Bo8S1eIhIF3CoqwQOdZEuuH79On777TcUFBSgoMAM3t4jMG7cS2VeW3wIC+BwFhFpJw51EemhwsJCHDp0CJcuXQIAeHh4ICAgoML/CZQcwuJwFhHpMiY+RDrin3/+wc6dO5GamgoAeOWVV9C7d28YGDwf0S45Jb2sKeocyiIiXcahrhI41EXa6OrVq9i/fz8KCwthYWGBESNGoEmTJqWuKzkzizO1iEhXaHx3diKqewoKCrB3717s2bMHhYWF8PT0xNSpU1VJT8li5ZLbSXB7CSLSNxzqItJSqampCAsLw+PHjwEAvXr1Qs+ePdWGtopvGOrv/3wYS7mBaPE2IiJ9wB4fIi0jCAIuX76MH3/8EY8fP4alpSXGjx9fqp4HKLtHh7unE5E+Y48PkRYpKCjA/v378ddffwEAGjdujBEjRsDS0rLUteUVMvv6Ks5zeIuI9BETHyItkZKSgp07d+LJkyeQSCTw9fVFjx49yt1Dr+Qwl/IYYCEzEekvDnUR1XGCIODixYv48ccf8eTJE1hZWWHChAlIS3sF3btLylxlGah8IXNFqzUTEekarZvOnp+fjy5duuDq1au4fPky2rZtqzr3119/YcaMGTh//jwcHR3x7rvvYsGCBVW6P6ezU12Sn5+Pffv24fr16wCApk2bYsSIETA3N4e3NxAbC3h5ATExL/4anNJORLpAZ6ezL1iwAK6urqXaMzMz0b9/fzRs2BAXL17EZ599hqVLl+KHH34QIUqi6ktKSsIPP/yA69evQyKRoG/fvhgzZgzMzc0BAMoRroSE6vXWcEo7EekTrarxOXjwIH7//Xfs2rULBw8eVDu3bds2FBQUYPPmzTAxMUHLli1x5coVrFmzBm+//bZIERNVnSAIOH/+PH7//XfIZDLY2NggICAA7u7uatd9+ikwdiyQlfW8judFcEo7EekTrenxSUlJwZQpU/DTTz+p/sVbXHR0NHr27AkTExNVm5+fH2JjY5GWllbuffPz85GZman2IBJLXl4edu7ciYMHD0Imk8HLywtTp04tlfQAimRl2zb21hARVYVW9PgIgoCgoCBMmzYNHTt2RHx8fKlrkpOT0ahRI7U2Z2dn1Tk7O7sy771q1SosW7ZM4zETVVViYiLCwsKQnp4OAwMD9OvXD126dCl31hbA3hoioqoStccnODgYEomkwkdMTAy+/vprPHv2DIsWLdJ4DIsWLUJGRobq8eDBA42/BlFFBEHAuXPnsHnzZqSnp8PW1haTJk1C165dK0x6KoMztoiI1Ina4zNv3jwEBQVVeE3jxo1x7NgxREdHQyqVqp3r2LEjxo4di61bt8LFxQUpKSlq55XHLi4u5d5fKpWWui9RbcnNzcXevXsRGxsLAGjevDn8/f1hamqqkfuXXMuHiEjfiZr4ODo6wtHR8V+vW7duHT7++GPV8aNHj+Dn54cdO3agS5cuAAAfHx8sXrwYhYWFMDY2BgAcPnwYXl5e5Q5zEYnpwYMH2LVrFzIyMmBoaIj+/fujU6dOZfbylFyFubKCg58/j4iItHAdHwCIj49Ho0aN1NbxycjIgJeXF/r374+FCxfi+vXrmDRpEtauXVulWV1cx4dqmiAIOHv2LI4dOwa5XA47OzuMGjUK9evXL/c5XGuHiKhilf3+1ori5sqwsbHB77//jhkzZqBDhw6oV68ePvroI05lpzrlyZMnOHjwIO7duwcAsLVtiZ07h8LTU1phTw57boiINEMrEx9PT0+U1VH18ssv49SpUyJERPTvTp06hWPHjgEADA0NMXDgQLz7bntER0sgk5U9hFV8iIs9PURE1ac16/gQaSu5XI5PPvlElfQAwMSJE9GhQwcEB0sqXIeneHEyERFVHxMfohqUkpKCFStWoLCwUNU2b948uLm5AVD08pw9q/gZEQE0bw54ez+ffh4crNiLKy2NU9KJiDRBK4ubaxKLm0lToqKicPLkSdWxh4cHgoKCyp21pdx+AlAvYmZhMxHRv9O74maiukImk6ktvwAAgYGBaNmyZbnPCQlRJD1mZoCHh/rQFwubiYg0hz0+JbDHh6pDuaN6cfPnz4eFhQUARc/OwoXAs2eKc5aWwOrVit9fZJ0eIiJSqOz3NxOfEpj40Is6fPgwzhYbi2rcuDHGjRundo1y2Ko4DmEREVUfh7qIaklRURFWrlyp1jZ69Gh4e3uXujY4uHSPD4ewiIhqDxMfompITEzExo0b1doWLFgAMzOzMq/nbupEROLidHaiF3Tw4EG1pMfLywtLliwplfRwh3QiorqDiQ9RFRUWFmLZsmX4888/VW1jxozB66+/rjounuxwEUIiorqDQ11EVZCQkIDQ0FC1toULF8LU1FStrXiyw+noRER1B3t8iCopIiJCLelp1aoVlixZAlNT01LDWcHBUG1FUXx1ZiIiEhd7fIj+RUFBAVatWqXWdv78OLRr11h1XLyHR1nAzESHiKjuYeJDVIG4uDj85z//UWs7cmQRTp82wb17wIIFgEQCjBypOMfhLCKiuo2JD1E5wsPDce3aNdVx27ZtMWzYMLRrB8hkwNOnQGys4lxUFBchJCLSBkx8iErIz89HSIkpWEFBQWjYsCEiIp738gQEALt2KX5nTw8RkXZg4kNUzN27d7Ft2za1tg8++ADGxsYAFDU8xXt5YmJqO0IiIqoOJj5E//Prr7/i1q1bquOOHTti8ODBatcEBz/v8WEvDxGR9mHiQ3ovNzcXq5VbpP/P5MmT0aBBAwDPFyFUTk3nbC0iIu3FxIf0WmxsLLZv367WtnjxYhgZPf+rUXKqOhERaS8mPqS3tm3bhrt376qOfXx80L9//1LXceVlIiLdwcSH9E5OTg4+++wztbYpU6bA1dW1zOs5vEVEpDuY+JBeuXnzJnbu3KnW9uGHH8LQ0FCkiIiIqDZxry6qs0ruf1XZ6xcvLv08QRCwZcsWtaTn5MlXEBm5hEkPEZEeYeKjx6qaWNS24kXFVbl+3Tr152VlZWH58uW4f/++6lpv72nIze3Duh0iIj3DxEePVTWxqG3FdzivyvWzZgHe3ootJbZsuYYvvvhCdY1EYoTff/8QZmbO3DGdiEgPSQRBEMQOoi7JzMyEjY0NMjIyYG1tLXY4Nark+jS6pFs3Aa1abYSb2yNVm6+vL4KDeyI6WpEgcW8tIiLdUdnvb/b46DF/f2htr0dFw3SZmZnw81uulvQ0bz4dwcE94etbtV4kIiLSLZzVRVqpvEUFL1++jIhi2ZCZmRnmz5+PHj0MEB2taGNPDxGR/mLiQ1qp5KKCgiDg+++/x+PHj1XX9OvXD926dSvzeiIi0k+s8SlBn2p8dEVGRga+/PJLtbZ3330X9vb24gRERES1jjU+VKdpair9+fPn1ZKe9HQbREZ+xKSHiIjKxKEuEkV1N/4UBAHr1q1Denq6qq1Bg4GIjOzM4SwiIioXe3yoUjS92GFV1+gp7unTp1i+fLla0vPee+9h8uTOWjtLjYiIagdrfEpgjU/ZunVDnVj/5ty5c4iMjFQdOzg4YMaMGZBIJOIFRUREomOND2nUv/XQ1PT2F3K5HJ9//rla0jNkyBDMnDmTSQ8REVUae3xKYI/Pi6nJHqF//vkH3377rVrbnDlz+PkQEZFKZb+/WdxMGlFT6+ScPn0aR48eVR27uLjg7bffZi8PERG9ECY+VG2a2vOr+H0EQY4LFz6FkVGB6vzw4cPRpk0bDURMRET6ikNdJXCoq+o0NcylvE+/fqno3v17tXNz586FlZVVNSMlIiJdxaEuqjWaGuYKDgZ27jyOpk1PqNosLNwxb95EDm0REZFGMPGhavP3r/7aOTKZDFeurETTps87IAMCAtCqVatqRkdERPQcEx8SXXJyMjZs2KDWNn/+fFhYWIgUERER6SomPlTrihcxm5sfwZkzZ1TnGjdujHHjxokYHRER6TImPlSjSs74iogAxo4FcnOLcPnySrVrR48eDW9vb5EiJSIifcDEh2pU8c1IAUXSY2OTiPnzN6pdt2DBApiZmYkQIRER6RNuWUFqqrv1RMnnF9/qIiQE6NHjEKZMeZ70eHl5YcmSJUx6iIioVnAdnxL0fR2f6q7JU97zCwsL8cknn6hdO2bMGLz00kvVjJiIiIiblOqsmt4M9N82I/23GIo/X3ndzz8nlEp6Fi5cyKSHiIhqHXt8SqjrPT41uRmopmPo1g1wcPgNHTteUrW1bNkSgYGBtRAlERHpE67crKNqajNQTcdQUFAAP79Vam3jxo1D48aNazg6IiKi8jHx0TKaWCW5psXFxeE///mPWltwcDCkUqlIERERESkw8aEqKz5FvWQStnv3bvz111+q47Zt22LYsGG1HCEREVHZWNysBWq6oLmqyiqAzs/Px7Jly9SSnqCgICY9RERUp7C4uYS6WNxcFwqaK3L37l1s27ZNre2DDz6AsbGxSBEREZG+YXGzDqkLBc3l+fXXX3Hr1i3VcceOHTF48GARIyIiIiofEx8toImC5pJ7ZlVXbm4uVq9erdY2efJkNGjQoPo3JyIiqiFMfPRERQXJVRUbG4vt27ertS1evBhGRvzPiYiI6jZ+U+kJTQ2X/fzzz7hz547quGvXrvDz86tmdERERLWDiY+eqO5wWU5ODj777DO1tilTpsDV1bWakREREdUeJj70r27evImdO3eqtX344YcwNDQUKSIiIqIXw8SHKrR161bEx8erjnv06IFXX31VvICIiIiqgYkPlSkrKwtffPGFWtu0adPg7OwsUkRERETVx8SHSrl27RrCw8NVx0ZGRggODubQFhERaT0mPqQiCAI2bdqExMREVVvv3r3Rq1cvEaMiIiLSHK3aq2v//v3o0qULzMzMYGdnh+HDh6udT0hIwODBg2Fubg4nJye8//77KCoqEifYSqjKHlw1vV/Xs2fPsHz5crWkZ/r06Ux6iIhIp2hN4rNr1y6MGzcOEydOxNWrV3HmzBmMGTNGdV4mk2Hw4MEoKCjA2bNnsXXrVmzZsgUfffSRiFFXrPiigpq8tqpJ0uXLl7FmzRrVsZmZGf7v//4Pjo6OlbsBERGRltCKTUqLiorg6emJZcuWYfLkyWVec/DgQQwZMgSPHj1SFeCuX78eCxcuxOPHj2FiYlKp16rNTUqrso3E4sXAunXArFnAypUVX6vc1NTLC7C3L//+giBg/fr1SE1NVbX17dsX3bt3f4F3Q0REJJ7Kfn9rRY/PpUuXkJiYCAMDA7Rr1w7169fHwIEDcf36ddU10dHRaN26tdqsIz8/P2RmZuLGjRvl3js/Px+ZmZlqj9ri76/Ybb0yCwtGRQFZWYqf/yY4WLGTu0RSfi9RRkYGli9frpb0zJw5k0kPERHpNK1IfP7++28AwNKlS/Hhhx9i3759sLOzQ+/evfH06VMAQHJycqmp1srj5OTkcu+9atUq2NjYqB7u7u419C6qR5nMVGbLCWVC9emnZT/nwoUL+PLLL1XH1tbW+Oijj+Dg4KDZoImIiOoYUROf4OBgSCSSCh8xMTGQy+UAFBthBgQEoEOHDggNDYVEIim1onBVLVq0CBkZGarHgwcPNPHWNK4qvUPlPUcQBKxbtw779+9XXTNgwADMmTMHEolEwxETERHVPaJOZ583bx6CgoIqvKZx48ZISkoCALRo0ULVLpVK0bhxYyQkJAAAXFxc8Oeff6o9NyUlRXWuPFKpFFKp9EXC1yppaWlYt26dWtusWbNgZ2cnUkRERES1T9TEx9HRsVIzhzp06ACpVIrY2Fj06NEDAFBYWIj4+Hg0bNgQAODj44OVK1ciNTUVTk5OAIDDhw/D2tpaLWHSR+fOnUNkZKTq2MHBATNmzGAvDxER6R2tWMDQ2toa06ZNw5IlS+Du7o6GDRuqdgofNWoUAKB///5o0aIFxo0bh9WrVyM5ORkffvghZsyYofU9OlWZ/VWcXC7H2rVrkZWVpWobMmQIOnToUANREhER1X1akfgAwGeffQYjIyOMGzcOubm56NKlC44dO6YaqjE0NMS+ffvwzjvvwMfHBxYWFpgwYQKWL18ucuTVV3wNn8omPk+ePME333yj1jZ79mzY2NjUQIRERETaQSvW8alNtbmOT2VVtcfn9OnTOHr0qOrYxcUFb7/9Noe2iIhIZ1X2+1trenz0mb9/5RIeuVyO1atXIz8/X9U2bNgwtG3btuaCIyIi0iJasY6PLtPUHlypqalYsWKFWtIzd+5cJj1ERETFsMdHZC9Sv1PSiRMncPz4cdWxu7s7Jk6cyKEtIiKiEpj4iCw4+Hn9TlXJZDJ88sknqgUeASAgIACtWrXSYIRERES6g4mPyCpbv1NScnIyNmzYoNY2f/58WFhYaCgyIiIi3cPERwsdOXIEZ86cUR03atQI48ePFzEiIiIi7cDER4sUFRVh5cqVam2vvfYamjdvLlJERERE2oWJj5ZITEzExo0b1doWLFgAMzMzkSIiIiLSPpzOXkuqM2390KFDaklPs2bNsGTJEiY9REREVcQen1ryItPWCwsL8cknn6i1vfHGG2jWrFkNREhERKT7mPjUkqpOW3/w4AE2b96s1rZw4UKYmprWQHRERET6gYlPLanKtPV9+/bh4sWLquOWLVsiMDCwhiIjIiLSH0x86pCCggKsWrVKre3NN99EkyZNRIqIiIhItzDxqSPi4+OxdetWtbbg4GBIpVKRIiIiItI9THzqgD179uDq1auq4zZt2mD48OHiBURERKSjmPiIKD8/HyEhIWptEyZMgKenpzgBERER6TgmPiK5d+8e/vvf/6q1LVq0CCYmJiJFREREpPuY+Ihg586duHnzpuq4Q4cOGDJkiIgRERER6QcmPrUoLy8Pn376qVrbpEmT4O7uLlJERERE+oWJTy25c+cOfv75Z7W2Dz74AMbGxiJFREREpH+Y+NSS4klP165d4efnJ2I0RERE+omJTy3p1asXzp07h/Hjx8PV1VXscIiIiPSSRBAEQewg6pLMzEzY2NggIyMD1tbWYodDRERElVDZ72+DWoyJiIiISFRMfIiIiEhvMPEhIiIivcHEh4iIiPQGEx8iIiLSG0x8iIiISG8w8SEiIiK9wcSHiIiI9AYTHyIiItIbTHyIiIhIbzDxISIiIr3BxIeIiIj0BhMfIiIi0htMfIiIiEhvGIkdQF0jCAIAxfb2REREpB2U39vK7/HyMPEp4dmzZwAAd3d3kSMhIiKiqnr27BlsbGzKPS8R/i010jNyuRyPHj2ClZUVJBKJ2OHUqMzMTLi7u+PBgwewtrYWOxwqhp9N3cTPpW7i51J31eZnIwgCnj17BldXVxgYlF/Jwx6fEgwMDNCgQQOxw6hV1tbW/J9FHcXPpm7i51I38XOpu2rrs6mop0eJxc1ERESkN5j4EBERkd5g4qPHpFIplixZAqlUKnYoVAI/m7qJn0vdxM+l7qqLnw2Lm4mIiEhvsMeHiIiI9AYTHyIiItIbTHyIiIhIbzDxISIiIr3BxEeP7d+/H126dIGZmRns7OwwfPhwtfMJCQkYPHgwzM3N4eTkhPfffx9FRUXiBKtn8vPz0bZtW0gkEly5ckXt3F9//YVXXnkFpqamcHd3x+rVq8UJUo/Ex8dj8uTJaNSoEczMzNCkSRMsWbIEBQUFatfxsxHHt99+C09PT5iamqJLly74888/xQ5Jr6xatQqdOnWClZUVnJycMHz4cMTGxqpdk5eXhxkzZsDBwQGWlpYICAhASkqKKPEy8dFTu3btwrhx4zBx4kRcvXoVZ86cwZgxY1TnZTIZBg8ejIKCApw9exZbt27Fli1b8NFHH4kYtf5YsGABXF1dS7VnZmaif//+aNiwIS5evIjPPvsMS5cuxQ8//CBClPojJiYGcrkcGzZswI0bN7B27VqsX78eH3zwgeoafjbi2LFjB+bOnYslS5bg0qVLaNOmDfz8/JCamip2aHrjxIkTmDFjBs6dO4fDhw+jsLAQ/fv3R3Z2tuqaOXPm4LfffsPOnTtx4sQJPHr0CCNHjhQnYIH0TmFhoeDm5iZs3Lix3GsOHDggGBgYCMnJyaq277//XrC2thby8/NrI0y9deDAAcHb21u4ceOGAEC4fPmy6tx3330n2NnZqX0GCxcuFLy8vESIVL+tXr1aaNSokeqYn404OnfuLMyYMUN1LJPJBFdXV2HVqlUiRqXfUlNTBQDCiRMnBEEQhPT0dMHY2FjYuXOn6ppbt24JAITo6Ohaj489Pnro0qVLSExMhIGBAdq1a4f69etj4MCBuH79uuqa6OhotG7dGs7Ozqo2Pz8/ZGZm4saNG2KErRdSUlIwZcoU/PTTTzA3Ny91Pjo6Gj179oSJiYmqzc/PD7GxsUhLS6vNUPVeRkYG7O3tVcf8bGpfQUEBLl68iL59+6raDAwM0LdvX0RHR4sYmX7LyMgAANXfj4sXL6KwsFDtc/L29oaHh4conxMTHz30999/AwCWLl2KDz/8EPv27YOdnR169+6Np0+fAgCSk5PVkh4AquPk5OTaDVhPCIKAoKAgTJs2DR07dizzGn4udcPdu3fx9ddfY+rUqao2fja1759//oFMJivzz51/5uKQy+WYPXs2unfvjlatWgFQ/PdvYmICW1tbtWvF+pyY+OiQ4OBgSCSSCh/KWgUAWLx4MQICAtChQweEhoZCIpFg586dIr8L3VPZz+Xrr7/Gs2fPsGjRIrFD1huV/WyKS0xMxIABAzBq1ChMmTJFpMiJ6qYZM2bg+vXr2L59u9ihlMtI7ABIc+bNm4egoKAKr2ncuDGSkpIAAC1atFC1S6VSNG7cGAkJCQAAFxeXUjMjlBX4Li4uGoxa91X2czl27Biio6NL7WnTsWNHjB07Flu3boWLi0upmRD8XF5cZT8bpUePHsHX1xfdunUrVbTMz6b21atXD4aGhmX+ufPPvPbNnDkT+/btw8mTJ9GgQQNVu4uLCwoKCpCenq7W6yPa51TrVUUkuoyMDEEqlaoVNxcUFAhOTk7Chg0bBEF4XtyckpKiumbDhg2CtbW1kJeXV+sx64P79+8L165dUz0iIyMFAEJYWJjw4MEDQRCeF9AWFBSonrdo0SIW0NaChw8fCi+99JLw+uuvC0VFRaXO87MRR+fOnYWZM2eqjmUymeDm5sbi5lokl8uFGTNmCK6ursLt27dLnVcWN4eFhanaYmJiRCtuZuKjp9577z3Bzc1NiIyMFGJiYoTJkycLTk5OwtOnTwVBEISioiKhVatWQv/+/YUrV64Ihw4dEhwdHYVFixaJHLn+iIuLKzWrKz09XXB2dhbGjRsnXL9+Xdi+fbtgbm6uSlipZjx8+FBo2rSp8OqrrwoPHz4UkpKSVA8lfjbi2L59uyCVSoUtW7YIN2/eFN5++23B1tZWbUYq1ax33nlHsLGxEY4fP672dyMnJ0d1zbRp0wQPDw/h2LFjwoULFwQfHx/Bx8dHlHiZ+OipgoICYd68eYKTk5NgZWUl9O3bV7h+/braNfHx8cLAgQMFMzMzoV69esK8efOEwsJCkSLWP2UlPoIgCFevXhV69OghSKVSwc3NTQgJCREnQD0SGhoqACjzURw/G3F8/fXXgoeHh2BiYiJ07txZOHfunNgh6ZXy/m6EhoaqrsnNzRWmT58u2NnZCebm5sKIESPU/uFQmyT/C5qIiIhI53FWFxEREekNJj5ERESkN5j4EBERkd5g4kNERER6g4kPERER6Q0mPkRERKQ3mPgQERGR3mDiQ0RUQ44fPw6JRIL09HSxQyGi/2HiQ0Raa+nSpWjbtq3YYRCRFmHiQ0Q6r7CwUOwQiKiOYOJDRKKRy+VYtWoVGjVqBDMzM7Rp0wZhYWEAng8THT16FB07doS5uTm6deuG2NhYAMCWLVuwbNkyXL16FRKJBBKJBFu2bAEASCQSfP/99/D394eFhQVWrlxZYRzK14qMjES7du1gZmaGPn36IDU1FQcPHkTz5s1hbW2NMWPGICcnR/W8/Px8zJo1C05OTjA1NUWPHj1w/vz5mvnDIiLNEGWHMCIiQRA+/vhjwdvbWzh06JBw7949ITQ0VJBKpcLx48eFqKgoAYDQpUsX4fjx48KNGzeEV155RejWrZsgCIKQk5MjzJs3T2jZsmWp3aABCE5OTsLmzZuFe/fuCffv368wDuVrde3aVTh9+rRw6dIloWnTpkKvXr2E/v37C5cuXRJOnjwpODg4qG08OmvWLMHV1VU4cOCAcOPGDWHChAmCnZ2d8OTJE7X7pqWl1cwfIBFVGRMfIhJFXl6eYG5uLpw9e1atffLkycIbb7yhShqOHDmiOrd//34BgJCbmysIgiAsWbJEaNOmTal7AxBmz55d6VjKeq1Vq1YJAIR79+6p2qZOnSr4+fkJgiAIWVlZgrGxsbBt2zbV+YKCAsHV1VVYvXq12n2Z+BDVHUZi9TQRkX67e/cucnJy0K9fP7X2goICtGvXTnX88ssvq36vX78+ACA1NRUeHh4V3r9jx45Vjqn4azk7O8Pc3ByNGzdWa/vzzz8BAPfu3UNhYSG6d++uOm9sbIzOnTvj1q1bVX5tIqodTHyISBRZWVkAgP3798PNzU3tnFQqxb179wAokgkliUQCQFEb9G8sLCyqHFPJ1yp+rGyrzGsTUd3F4mYiEkWLFi0glUqRkJCApk2bqj3c3d0rdQ8TExPIZLIajrRsTZo0gYmJCc6cOaNqKywsxPnz59GiRQtRYiKif8ceHyIShZWVFebPn485c+ZALpejR48eyMjIwJkzZ2BtbY2GDRv+6z08PT0RFxeHK1euoEGDBrCysoJUKq2F6BU9Su+88w7ef/992Nvbw8PDA6tXr0ZOTg4mT55cKzEQUdUx8SEi0axYsQKOjo5YtWoV/v77b9ja2qJ9+/b44IMPKjWkFBAQgPDwcPj6+iI9PR2hoaEICgqq+cD/JyQkBHK5HOPGjcOzZ8/QsWNHREZGws7OrtZiIKKqkQiCIIgdBBEREVFtYI0PERER6Q0mPkSk86ZNmwZLS8syH9OmTRM7PCKqRRzqIiKdl5qaiszMzDLPWVtbw8nJqZYjIiKxMPEhIiIivcGhLiIiItIbTHyIiIhIbzDxISIiIr3BxIeIiIj0BhMfIiIi0htMfIiIiEhvMPEhIiIivcHEh4iIiPTG/wPG2TcaySCdxgAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surrogate_scatter2D(keras_surrogate, data_training)\n", + "surrogate_parity(keras_surrogate, data_training)\n", + "surrogate_residual(keras_surrogate, data_training)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4/4 [==============================] - 0s 5ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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06dIFderUweXLlzFr1iw0a9ZMCksBAQHo2LEjRo4cia+++golJSUYO3YsXnvtNakXbvTo0Vi+fDmmTZuGkSNH4uDBg9iyZQt2794tvdekSZMwfPhw+Pj4oHPnzvjqq6+Qm5uLESNGAADs7e0REhKCSZMmoW7durCzs8NHH30EuVwOPz8/AEDPnj3RunVrDB06FAsXLkR6ejpmzpyJsWPHwtLSUis/o8LCQsyfP18r536a6dOnVxiwq8LT0xNnz54FAEyYMEEqb9KkCT777DOMHj0a33zzDSwsLGBvbw+ZTKbSgzly5Ejp702bNsXSpUvx4osvIicnB7Vq1XruNhIRkWE6ceIE9uzZIz23t7fHxx9/rMMWPRuDmJxgY2ODn376CT169ECrVq0QEhKCF154AUeOHJGCkImJCXbu3AlHR0d069YNgYGB8PLywqZNm6TzeHh4YPfu3Thw4ADat2+PxYsXY/Xq1VAoFFKdgQMHYtGiRZg9ezY6dOiA06dPY9++fUqTDb788kv06dMH/fv3R7du3eDi4iL1FAGAqakpdu3aBVNTU8jlcrz77rsYNmwY5s6dWw0/LcMlhJD+z+eXX35Bjx490LBhQ9SuXRtDhw7Fv//+iwcPHjzxHAkJCXjjjTfg7u6O2rVr4+WXXwbwaLiciIiMS0lJCSIiIpRC2+uvv46mTSfgpZdkiI7WYeOegUwY2uCuEcnOzoa9vT2ysrKU7nfLy8tDSkoKPDw8YGVlBaA09OhqNX1zc/NKdzUHBwcjMzMT27dvVzn2wgsvwN3dHcuXL4enpyfGjBmDgQMHom7duvj9998REhKCe/fuwcHBAVFRUZgwYYLSvYa5ublo3LgxFAoFRo8eDScnJ6SlpUGhUCAxMREdOnTQzAfWIXXXnoiI1EtKSsLmzZuVyj7++GM4ODjA3x+IjwfkciAuTrPvW9H3tyYYxFApPZ1MJtPIcKWuHDx4EH/++ScmTpyIhIQElJSUYPHixTAxKe0U3rJli1J9CwsLFBcXK5UlJSXh33//RWRkpLSUysmTJ6vnAxARkV6JiIhQKYuJmQ1vbxmCgoCwMCAysvRPQ2IQQ6VUs+Tn5yM9PR3//PMPTp06hXnz5qFv377o06cPhg0bhubNm6OwsBDLli3DlStX8P3332PVqlVK52jSpAlycnIQGxuLO3fu4MGDB3B3d4eFhYX0uujoaHz66ac6+pRERKQLhYWFKqHNyckJMTHhiI+XITKytCwoqLSnLShIB418DgxuVO327dsHV1dXNGnSBL169cKhQ4ewdOlS7NixA6ampmjfvj2WLFmCBQsWoG3btli/fr3KxAt/f3+MHj0aAwcOhJOTExYuXAgnJydERUVh69ataN26NSIjI7Fo0SIdfUoiIqpu0dHRmDdvnlLZ2bNvolGjDxEWVjosamg9bI/jPW56rCr3uJFx4LUnIlKvoqHR+HiZVu5jexLe40ZERESkRl5eHhYsWKBSHh4eDm9vw7yP7UkY3IiIiMggbd68WWlrTADo27evtIpAUJDh3cP2NAxuREREZHDUDY3Onj3b4HZCqCoGNyIiIjIY9+/fx5IlS1TKw8PDddCa6sfgRkRERAYhMjJS2qO8zNtvvw0vLy+VutHRj+5vq0nDpVwOhIiIiPReRESESmjz9g5XG9qA0tAWHw9p3baagsGNiIiI9Na9e/fU3s82Z074E0NZTVm37XEcKiUiIiK9pC6w9e/fH1eutH1qKKuJM0oBBjciIiLSQ+pCW9kEhLZta2YoqwwOlVKNFBwcjH79+knPX3nlFUyYMOG5zqmJcxAR0ZNlZGQ8MbQZO/a4UbUKDg7G2rVrAQDm5uZwd3fHsGHDMGPGDJiZae8/x59++gnm5uaVqnv48GF0794d9+7dg4ODwzOdg4iIqk5dYGvWbAjefbe5DlqjnxjcqNr16tULa9asQX5+Pvbs2YOxY8fC3Nwc06dPV6pXUFAACwsLjbxn3bp19eIcRESkXkUTEORy4N13ddAgPcWhUqp2lpaWcHFxQePGjTFmzBgEBAQgOjpaGt78/PPP0aBBA7Rq1QoAcO3aNbz99ttwcHBA3bp10bdvX6SmpkrnKy4uxqRJk+Dg4IB69eph2rRpEEIovefjw5z5+fkIDQ2Fm5sbLC0t0bx5c3z77bdITU1F9+7dAQB16tSBTCZDcHCw2nPcu3cPw4YNQ506dWBjY4PevXvjr7/+ko5HRUXBwcEBMTEx8PLyQq1atdCrVy/cvHlTqnP48GF07twZtra2cHBwwEsvvYSrV69q6CdNRKT/0tLS1IY2b+/wGjkr9Hmxx410ztraGv/++y8AIDY2FnZ2djhw4AAAoLCwEAqFAnK5HL/99hvMzMzw2WefoVevXjh79iwsLCywePFiREVF4bvvvoOXlxcWL16Mn3/+Ga+++mqF7zls2DDEx8dj6dKlaN++PVJSUnDnzh24ublh27Zt6N+/P5KTk2FnZwdra2u15wgODsZff/2F6Oho2NnZITQ0FK+//jouXLggDak+ePAAixYtwvfffw8TExO8++67mDJlCtavX4+ioiL069cPo0aNwsaNG1FQUIDjx4/X+O1aiIjKqAtsI0aMgLu7OwDjnYDwJAxupDNCCMTGxiImJgYfffQRbt++DVtbW6xevVoaIv3hhx9QUlKC1atXS4FmzZo1cHBwwOHDh9GzZ0989dVXmD59Ot566y0AwKpVqxATE1Ph+166dAlbtmzBgQMHEBAQAABo2rSpdLxsSLR+/fpK97iVVxbY/vjjD/j7+wMA1q9fDzc3N2zfvh3/+c9/AJQGz1WrVqFZs2YAgHHjxmHu3LkAgOzsbGRlZaFPnz7S8YoWkiQiqmk4AeHZcKiUEB0N+PuX/lkddu3ahVq1asHKygq9e/fGwIEDMWfOHABAu3btlO5rO3PmDP7++2/Url0btWrVQq1atVC3bl3k5eXh8uXLyMrKws2bN+Hr6yu9xszMDD4+PhW+/+nTp2FqaoqXX375mT/DxYsXYWZmpvS+9erVQ6tWrXDx4kWpzMbGRgplAODq6oqMjAwApQExODgYCoUCb7zxBr7++mulYVQioprozJkzDG3PgT1upLQtSHV0S3fv3h0rV66EhYUFGjRooDSb1NbWVqluTk4OOnXqhPXr16ucx8nJ6Znev6KhT214fBaqTCZTuv9uzZo1GD9+PPbt24fNmzdj5syZOHDgAPz8/KqtjURE1UVdYGvZciSWLXODtzeHRiuDPW5U7duC2Nraonnz5nB3d3/qEiAdO3bEX3/9hfr166N58+ZKD3t7e9jb28PV1RXHjh2TXlNUVISEhIQKz9muXTuUlJTgyJEjao+X9fgVFxdXeA4vLy8UFRUpve+///6L5ORktG7d+omf6XHe3t6YPn064uLi0LZtW2zYsKFKryciMgQV9bItW+ZWI/cU1RYGN0JQEBAXp5//pzNkyBA4Ojqib9+++O2335CSkoLDhw9j/PjxuH79OgDg448/RmRkJLZv346kpCR8+OGHyMzMrPCcTZo0wfDhwzFy5Ehs375dOueWLVsAAI0bN4ZMJsOuXbtw+/Zt5OTkqJyjRYsW6Nu3L0aNGoXff/8dZ86cwbvvvouGDRuib9++lfpsKSkpmD59OuLj43H16lXs378ff/31F+9zI6IaJT4+Xm1oi4kJR3R0zd1TVFsY3Eiv2djY4Ndff4W7uzveeusteHl5ISQkBHl5ebCzswMATJ48GUOHDsXw4cMhl8tRu3ZtvPnmm08878qVKzFgwAB8+OGH8PT0xKhRo5CbmwsAaNiwISIiIhAWFgZnZ2eMGzdO7TnWrFmDTp06oU+fPpDL5RBCYM+ePZVepNfGxgZJSUno378/WrZsiffffx9jx47FBx98UIWfEBGR/oqIiMD+/fuVykaNGoWYmHClW3T0tfNAH8nE4wtekd7Izs6Gvb09srKypJACAHl5eUhJSYGHhwesrKx02EKqbrz2RGQonjQBITq6NLSFhdXMwFbR97cmcHICERERacyWLVuUZteXKT9rNCioZga26sDgRkRERBqhrpftm2/GoFmz+uBqH5rB4EZERETPraJtq5o148QDTWJwIyIiome2evVq/PPPPyrlMTHh8PYunXhAmsPgZsA4r8T48JoTkT5R18v20UcfoU+futW6sLsx4XIgBqj8BuZkXMqueWWXHCEi0gYhRIWzRuvWrcu12bSIPW4GyNTUFA4ODtKelzY2NtIG7FQzCSHw4MEDZGRkwMHBAaamprpuEhEZiceX7li0aJG07mV5nDVaPRjcDJSLiwsASOGNjIODg4N07YmIqkP5/awTE1V72SZNmoTatWvroGXGicHNQMlkMri6uqJ+/fooLCzUdXOoGpibm7OnjYiqXVgYsGBBMXr2/EzlWDjX+Kh2DG4GztTUlF/mRESkFdHRpb1sPXuqHmNo0w0GNyIiIlJL3dBobGwofv2VW+7pCoMbERERKcnPz0dkZKRKeUxMOGeK6hiDGxEREUnULfMBlO6CwNFR3WNwIyIiIgDqQ9uMGTO4dqQeYXAjIiIyctu25eDcucUq5ZyAoH8Y3IiIiIxYRUOjDG36icGNiIjIyJTthqBQqIa2WbNmwcSEO2LqKwY3IiIiI7N06R0oFCtUytnLpv8Y3IiIiIxIREQEunZVLWdoMwwMbkREREZC3f1ss2fPhkwm00Fr6FkwuBEREdVwKSkpWLdunUq5t3c4mNkMC4MbERFRDcZZozULgxsREVENpS60MbAZNgY3IiKiGub06dPYsWOHSjlDm+FjcCMiIqpB1PWymZqaYubMmTpoDWkagxsREVENoS60eXuHIyhIB40hrWBwIyIiMmDR0cCWLUfQosVhlWMcGq15GNyIiIgMWGJiBFq0UC6rU6cOxo8fr5sGkVYxuBERERmQsn1Gw8JKQ9vj2MtWszG4ERERGZDISKBhwx+RmHhe5RhDW83H4EZERKTnyveyKRSqvWzu7u4YMWKEDlpG1c1E1w0gIiIiZdHRgL9/6Z9AaWiLj694aJShzXjIhBBC140g9bKzs2Fvb4+srCzY2dnpujlERFRN/P1Lg5qnJ1CnDtCz51LIZPdU6nFoVD9p8/ubPW5ERER6JiwMkMsBIUqHRh8PbW3btmVoM1IMbkRERDr0+LAoAAQFAXFxwODB6odG+/fvX40tJH3CyQlERETVrPxkg7L71yIjIe1woG4HBIBDo8TgRkREVO3Kh7WwMCA0FLh7tzTQqZuA4O/vj9dee00HLSV9w6FSIiKialI2LNq9e+k9bGFhpb1sdeoAyckVzxplaKMy7HEjIiKqJmU9bUDpPWxlFIoIKBSq9Tk0So9jcCMiIqomZfe0hYU9KlN3P1uvXr3g6+tbjS0jQ8HgRkREVE2Cgh5NQADUhzb2stGTMLgRERFVM84apWdlMJMTgoKC4O7uDisrK7i6umLo0KG4ceOGdHzOnDmQyWQqD1tbW6XzbN26FZ6enrCyskK7du2wZ88epeNCCMyePRuurq6wtrZGQEAA/vrrL6U6d+/exZAhQ2BnZwcHBweEhIQgJydHqc7Zs2fRtWtXWFlZwc3NDQsXLtTwT4SIiAyRutD2n//8h6GNKsVgglv37t2xZcsWJCcnY9u2bbh8+TIGDBggHZ8yZQpu3ryp9GjdujX+85//SHXi4uIwePBghISEIDExEf369UO/fv1w7tw5qc7ChQuxdOlSrFq1CseOHYOtrS0UCgXy8vKkOkOGDMH58+dx4MAB7Nq1C7/++ivef/996Xh2djZ69uyJxo0bIyEhAV988QXmzJmD//73v1r+KRERkb4SQlQ4NNq6dWsdtIgMkcHuVRodHY1+/fohPz8f5ubmKsfPnDmDDh064Ndff0XXrl0BAAMHDkRubi527dol1fPz80OHDh2watUqCCHQoEEDTJ48GVOmTAEAZGVlwdnZGVFRURg0aBAuXryI1q1b48SJE/Dx8QEA7Nu3D6+//jquX7+OBg0aYOXKlfjkk0+Qnp4OCwsLAEBYWBi2b9+OpKSkSn9G7lVKRFQzcGjUuHCv0sfcvXsX69evh7+/v9rQBgCrV69Gy5YtpdAGAPHx8QgICFCqp1AoEP//52anpKQgPT1dqY69vT18fX2lOvHx8XBwcJBCGwAEBATAxMQEx44dk+p069ZNCm1l75OcnIx791Q3CS6Tn5+P7OxspQcRERk2daFtxIgRDG30TAwquIWGhsLW1hb16tVDWloaduzYobZeXl4e1q9fj5CQEKXy9PR0ODs7K5U5OzsjPT1dOl5W9qQ69evXVzpuZmaGunXrKtVRd47y76HO/PnzYW9vLz3c3NwqrEtERPqtsLCwwqFRd3d3HbSIagKdBrewsDC1EwrKP8oPLU6dOhWJiYnYv38/TE1NMWzYMKgb6f35559x//59DB8+vDo/znObPn06srKypMe1a9d03SQiInoGERERmDdvnko5e9noeel0OZDJkycjODj4iXWaNm0q/d3R0RGOjo5o2bIlvLy84ObmhqNHj0Iulyu9ZvXq1ejTp49Kr5eLiwtu3bqlVHbr1i24uLhIx8vKXF1dlep06NBBqpORkaF0jqKiIty9e1fpPOrep/x7qGNpaQlLS8sKjxMRkf4ov1H809ZmGzt2LBwdHauxdVRT6TS4OTk5wcnJ6ZleW1JSAqD0vrDyUlJScOjQIURHR6u8Ri6XIzY2FhMmTJDKDhw4IAU/Dw8PuLi4IDY2Vgpq2dnZOHbsGMaMGSOdIzMzEwkJCejUqRMA4ODBgygpKZFWuZbL5fjkk09QWFgo3YN34MABtGrVCnXq1Hmmz0tERPqhLLDdvVu6v2hkZGlwy8nJweLFi1Xqs5eNNMkgZpUeO3YMJ06cQJcuXVCnTh1cvnwZs2bNwq1bt3D+/HmlXqpZs2bhu+++Q1paGkxNTZXOExcXh5dffhmRkZEIDAzEpk2bMG/ePJw6dQpt27YFACxYsACRkZFYu3YtPDw8MGvWLJw9exYXLlyAlZUVAKB37964desWVq1ahcLCQowYMQI+Pj7YsGEDgNKZqK1atULPnj0RGhqKc+fOYeTIkfjyyy+Vlg15Gs4qJSLSL9HRwJAhQE4O4OlZujl8WJj6zeEBhjZjpc3vb4PYOcHGxgY//fQTwsPDkZubC1dXV/Tq1QszZ85UCm0lJSWIiopCcHCwSmgDAH9/f2zYsAEzZ87EjBkz0KJFC2zfvl0KbQAwbdo05Obm4v3330dmZia6dOmCffv2SaENANavX49x48ahR48eMDExQf/+/bF06VLpuL29Pfbv34+xY8eiU6dOcHR0xOzZs6sU2oiISP9ERpaGtlq1gAULSnva1A2NTpkyRWUBeCJNMIgeN2PFHjciIv1S/r42ufw2vvnmG5U67GUjruNGRERUzaKjAX//0j/LBAUBcXGlQ6MMbaQLBjFUSkREVN0iI4H4+EeTD8qoGxr95JNPYGbGr1TSPva4ERERqVE6HFr6JwBcuXKlwgV1GdqouvC/NCIiIjWCgh71tHGvUdIX7HEjIiKjVv5eNnX3takLbbNnz2ZoI51gjxsRERm18veyAY/+3qjRKezcuVOlPgMb6RKDGxERGbWwsEdLfAClf1coIqAmszG0kc4xuBERkdF5fJ/R8rNG1e2CwMBG+oLBjYiIjI66pT727t2L48ePq9RlaCN9wuBGRERG5/HhUc4aJUPB4EZEREbnaUt9MLCRvmJwIyIio7R27VqkpqaqlDO0kT5jcCMiIqPDoVEyVAxuRERkVDg0SoaMwY2IiIzCokWLkJubq1LO0EaGhFteERGRwVO3VVV5ERERDG1UI7DHjYiIDF7ZumxDhgDr1ysvqMuhUapJGNyIiMjghYWVhracnEeL6nICAtVEHColIiKD88knQO3apX8CpUFt/XpALi8NcepCm62tLUMbGTyZEELouhGkXnZ2Nuzt7ZGVlQU7OztdN4eISG/Url3au2ZiAvz8M4dGSb9o8/ubPW5ERGRwxo8vDW0lJaVDo0BpYGNoo5qOPW56jD1uREQVi45+tN9oYqJqYGvevDmGDBmig5aRsdPm9zcnJxARkUEq22+UvWxkTBjciIjIIHHWKBkjBjciIjI46kJbly5d0KNHDx20hqj6MLgREZFB4dAoGTMGNyIi0jvlJx6ULfXBoVEiBjciItJDZVtYPWkXhL59+6JDhw7V3zgiHWJwIyIivRMWVhraQkMFIiLmqhxnLxsZKy7AS0REOhUdDfj7l/5ZJigIUCgicPo0QxtReexxIyIinXp8WBRQPzQ6bNgweHh4VHPriPQLe9yIiEinwsIebQ5fWFhY4axRhjYibnml17jlFREZE84apZqCm8wTEZFBevz+tehowNMT8PJSvqdNXWgbM2YMQxvRY9jjpsfY40ZEhs7fv/T+tVq1gPXrH93PBpQOjx44kItFixapvI6BjQwZe9yIiMgghYWVhracnEcL6rZqVdrrplBEMLQRVRFnlRIRkdYEBT3qaSvbBaGiBXUnT56MWrVq6aCVRIaDwY2IiLSqLKwBwJ07d7BixQqVOuxlI6ocBjciIqoWnDVK9PwY3IiISOvUhbYZM2bA3NxcB60hMlxVnpxgamqKjIwMlfJ///0XpqamGmkUERHpP3VbVT3uypUrFS6oy9BGVHVV7nGraPWQ/Px8WFhYPHeDiIjIMKjbqqo8Do0SaV6lg9vSpUsBADKZDKtXr1aa+VNcXIxff/0Vnp6emm8hERHppbCwR7NFH6cutM2aNQsmJlyFiuh5VHoB3rI94q5evYpGjRopDYtaWFigSZMmmDt3Lnx9fbXTUiPEBXiJyNCcOnUKO3fuVClnLxsZE21+f1e6xy0lJQUA0L17d/z000+oU6eORhtCRESGjUOjRNpX5XvcDh06pI12EBGRAatoAgIRaVaVg9vIkSOfePy777575sYQEZFh+eWXX/DHH3+olDO0EWlHlYPbvXv3lJ4XFhbi3LlzyMzMxKuvvqqxhhERkX6IjlbesqoMh0aJql+Vg9vPP/+sUlZSUoIxY8agWbNmGmkUERHpD3XLfnBolEg3Kj2r9GmSk5Pxyiuv4ObNm5o4HYGzSolIP5TvccvP34oLFy6o1GFoI3pEm9/fGltQ5/LlyygqKtLU6YiISIfK74oQFATExQGJiREMbUQ6VuWh0kmTJik9F0Lg5s2b2L17N4YPH66xhhERkW5ERwNDhgA5OY+GRzk0SqQfqhzcEhMTlZ6bmJjAyckJixcvfuqMUyIi0n+RkaWhrVYt4I03liIi4p5KHYY2It3gOm5ERKSkbCsrhSICBQWqxxnaiHSnysGtTEZGBpKTkwEArVq1Qv369TXWKCIi0p2goNL72R7HwEake1UObtnZ2Rg7diw2btyIkpISAICpqSkGDhyIFStWwN7eXuONJCKi6sG12Yj0W5VnlY4aNQrHjh3D7t27kZmZiczMTOzatQsnT57EBx98oI02EhGRFpSfOQqoD202NjYMbUR6pMrruNna2iImJgZdunRRKv/tt9/Qq1cv5ObmarSBxozruBGRNvn7ly6sK5eX3s/2OAY2omejze/vKg+V1qtXT+1wqL29PerUqaORRhERkfaFham/lw1gaCPSV1UeKp05cyYmTZqE9PR0qSw9PR1Tp07FrFmzNNo4IiJ6Po8Ph5anLrS1aNGCoY1Ij1V5qNTb2xt///038vPz4e7uDgBIS0uDpaUlWrRooVT31KlTmmupEeJQKRE9r/LDoXFxj8q5oC6R9ujVUGnfvn0hk8k02ggiItKOsjXZwsJKn3PWKJFh09gm86R57HEjIk1SF9p8fX3Rq1cvHbSGqObSq03mmzZtin///VelPDMzE02bNtVIo4iISLMqGhplaCMyLFUeKk1NTUVxcbFKeX5+Pq5fv66RRhER0bOLjgamTQNkMmDQIA6NEtUklQ5u0eWmJMXExCgtCVJcXIzY2Fh4eHhotnVERFRlkZFAcjIwZ45qaAsMDISPj48OWkVEmlDpodJ+/fqhX79+kMlkGD58uPS8X79+GDRoEA4cOIDFixdrraFBQUFwd3eHlZUVXF1dMXToUNy4cUOpTkxMDPz8/FC7dm04OTmhf//+SE1NVapz+PBhdOzYEZaWlmjevDmioqJU3mvFihVo0qQJrKys4Ovri+PHjysdz8vLw9ixY1GvXj3UqlUL/fv3x61bt5TqpKWlITAwEDY2Nqhfvz6mTp2KoqIijfwsiIieJDRUqA1t4eHhDG1EBq7Swa2kpAQlJSVwd3dHRkaG9LykpAT5+flITk5Gnz59tNbQ7t27Y8uWLUhOTsa2bdtw+fJlDBgwQDqekpKCvn374tVXX8Xp06cRExODO3fu4K233lKqExgYiO7du+P06dOYMGEC3nvvPcTExEh1Nm/ejEmTJiE8PBynTp1C+/btoVAokJGRIdWZOHEidu7cia1bt+LIkSO4ceOG0vsUFxcjMDAQBQUFiIuLw9q1axEVFYXZs2dr7edDRASU3st2+vRclXIOjRLVDAY7qzQ6Ohr9+vVDfn4+zM3N8eOPP2Lw4MHIz8+HiUlpHt25cyf69u0r1QkNDcXu3btx7tw56TyDBg1CZmYm9u3bB6B0htWLL76I5cuXAygNrG5ubvjoo48QFhaGrKwsODk5YcOGDVJwTEpKgpeXF+Lj4+Hn54e9e/eiT58+uHHjBpydnQEAq1atQmhoKG7fvg0LC4tKfUbOKiWiqlA3AWHQoEFo1aqVDlpDZLz0ah23uXNV/0+uvOroVbp79y7Wr18Pf39/mJubAwA6deoEExMTrFmzBsHBwcjJycH333+PgIAAqU58fDwCAgKUzqVQKDBhwgQAQEFBARISEjB9+nTpuImJCQICAhAfHw8ASEhIQGFhodJ5PD094e7uLgW3+Ph4tGvXTgptZe8zZswYnD9/Ht7e3lr5uRBRzRYd/WhNtqCgR+VFRUX4/PPPVeqzl42o5qlycPv555+VnhcWFiIlJQVmZmZo1qyZVoNbaGgoli9fjgcPHsDPzw+7du2Sjnl4eGD//v14++238cEHH6C4uBhyuRx79uyR6qSnpyuFKQBwdnZGdnY2Hj58iHv37qG4uFhtnaSkJOkcFhYWcHBwUKlTtg1YRe9Tdqwi+fn5yM/Pl55nZ2c/7UdCREYkMrJ0F4TIyEfBjQvqEhmXKq/jlpiYqPQ4d+4cbt68iR49emDixIlVOldYWBhkMtkTH2WBCQCmTp2KxMRE7N+/H6amphg2bBjKRnrT09MxatQoDB8+HCdOnMCRI0dgYWGBAQMGwFBGg+fPnw97e3vp4ebmpusmEZEe6d4dqFWr9E9AfWgbNWoUQxtRDVblHjd17OzsEBERgTfeeANDhw6t9OsmT56M4ODgJ9Ypv6ivo6MjHB0d0bJlS3h5ecHNzQ1Hjx6FXC7HihUrYG9vj4ULF0r1f/jhB7i5ueHYsWPw8/ODi4uLyuzPW7duwc7ODtbW1jA1NYWpqanaOi4uLgAAFxcXFBQUIDMzU6nX7fE6j89ELTtnWR11pk+fjkmTJknPs7OzGd6ISHLoEJCTA/z++wNERHyhcpyBjajm00hwA4CsrCxkZWVV6TVOTk5wcnJ6pvcrKSkBAGlo8cGDB9KkhDKmpqZKdR8fOgWAAwcOQC6XAwAsLCzQqVMnxMbGol+/ftJrY2NjMW7cOACl99KZm5sjNjYW/fv3BwAkJycjLS1NOo9cLsfnn3+OjIwM1K9fX3ofOzs7tG7dusLPZGlpCUtLy2f6eRBRzRcWBiQmcmiUyJhVObgtXbpU6bkQAjdv3sT333+P3r17a6xh5R07dgwnTpxAly5dUKdOHVy+fBmzZs1Cs2bNpLAUGBiIL7/8EnPnzsXgwYNx//59zJgxA40bN5YmA4wePRrLly/HtGnTMHLkSBw8eBBbtmzB7t27pfeaNGkShg8fDh8fH3Tu3BlfffUVcnNzMWLECACAvb09QkJCMGnSJNStWxd2dnb46KOPIJfL4efnBwDo2bMnWrdujaFDh2LhwoVIT0/HzJkzMXbsWAYzIqqS8hMS1IW2CRMmKC2ITkQ1W5WXA3l8dwQTExM4OTnh1VdfxfTp01G7dm2NNhAA/vzzT3z88cc4c+YMcnNz4erqil69emHmzJlo2LChVG/Tpk1YuHAhLl26BBsbG8jlcixYsACenp5SncOHD2PixIm4cOECGjVqhFmzZqkM1y5fvhxffPEF0tPT0aFDByxduhS+vr7S8by8PEyePBkbN25Efn4+FAoFvvnmG6Vh0KtXr2LMmDE4fPgwbG1tMXz4cERGRsLMrPJZmcuBEJG/P/DXX3cwbtwKlWPsZSPST9r8/jbYddyMAYMbEXHWKJHh0at13AAgMzMTf//9NwCgefPmKktjEBFR1ahbo01daAsLC+MtF0RGrErLgaSmpiIwMBCOjo7w9fWFr68vHB0d0adPH5U9QYmIqPJCQ0vXaAsNLd2eT11oCw8PZ2gjMnKV7nG7du0a/Pz8YG5ujk8//RReXl4AgAsXLmDlypWQy+U4ceIEGjVqpLXGEhHVVGU3rQwaFIF161SPc2iUiIAq3OMWEhKCv//+GzExMbCyslI69vDhQ/Tq1QstWrTA6tWrtdJQY8R73IiMR3S0+lmjs2bNUlnqiIj0m17c47Zv3z5s3rxZJbQBgLW1NT799FMMGjRIo40jIjIGp0+fRmLiDpVy9rIR0eMqHdzu3LmDJk2aVHi8adOmuHv3ribaRERUoz1tbTaAoY2I1Kt0cHN1dZXWPlPn3LlzT9zOiYiISpVtFq8utDGwEdGTVPrGiX79+mHKlCm4ffu2yrGMjAyEhoZK20QREVHFhg37BXPmMLQRUdVVenLCvXv34Ovri/T0dLz77rvw9PSEEAIXL17Ehg0b4OLigqNHj6Ju3brabrPR4OQEopqHC+oS1Xx6MTmhTp06OHbsGGbMmIFNmzYhMzMTAODg4IB33nkH8+bNY2gjIqpARbNGGdiIqCqeacsrIYQ0ZOrk5ASZTKbxhhF73Ihqiu+++w7Xrl1TKWdoI6qZ9KLHrTyZTIb69etrtCFERDURh0aJSJOeKbgREdHTVbRtFRHRs2JwIyLSgPJrs509+xmKi4tV6jC0EdHzYnAjItKAJ63NBjC0EZFmMLgREWlARbsgMLARkSZVKrgtXbq00iccP378MzeGiEjflR8SDQoqLeMEBCKqLpVaDsTDw6NyJ5PJcOXKleduFJXiciBE+sffv3RIVC4H4uIY2ohIlc6XA0lJSdHomxIRGaqwMCA0FLh7l7NGiaj6PfM9bgUFBUhJSUGzZs1gZsZb5YjIOAQFcQICEelOpTeZL/PgwQOEhITAxsYGbdq0QVpaGgDgo48+QmRkpMYbSESkT9T1sllZWTG0EVG1qHJwmz59Os6cOYPDhw/DyspKKg8ICMDmzZs12jgiIn0RHV3x0GhoaKgOWkRExqjKY5zbt2/H5s2b4efnp7RHaZs2bXD58mWNNo6ISB9wAgIR6YsqB7fbt2+r3ac0NzeXm80TUY0SHa3+frY2bdpgwIABOmgRERm7Kg+V+vj4YPfu3dLzsrC2evVqyOVyzbWMiEjHKlpQl6GNiHSlyj1u8+bNQ+/evXHhwgUUFRXh66+/xoULFxAXF4cjR45oo41ERNWKQ6NEpK+q3OPWpUsXnD59GkVFRWjXrh3279+P+vXrIz4+Hp06ddJGG4mIqo260PbKK68wtBGRXqjUzgmkG9w5gaj6CCEwd+5clXIGNiKqKp3vnJCdnV3pEzJgEJGh4dAoERmKSgU3BweHSs8YLS4ufq4GERFVJ3Wh7c0338QLL7ygg9YQET1ZpYLboUOHpL+npqYiLCwMwcHB0izS+Ph4rF27FvPnz9dOK4mINKy4uBifffaZSrm3dziY2YhIX1X5HrcePXrgvffew+DBg5XKN2zYgP/+9784fPiwJttn1HiPG5F2cGiUiLRJm9/fVZ5VGh8fDx8fH5VyHx8fHD9+XCONIiLSFnWhLT7+PXh7M7QRkf6rcnBzc3PD//73P5Xy1atXw83NTSONIiLStIcPH1a41+i+fQ0RFKSDRhERVVGVF+D98ssv0b9/f+zduxe+vr4AgOPHj+Ovv/7Ctm3bNN5AIqLnxaFRIqopnmkdt+vXr+Obb75BUlISAMDLywujR49mj5uG8R43ouenLrR9/PHHcHBwqP7GEJFR0Ob3Nxfg1WMMbkTP7u7du1i2bJlKOXvZiEjbdL4A7+MyMzPx7bff4uLFiwCANm3aYOTIkbC3t9do44iIngWHRomopqpyj9vJkyehUChgbW2Nzp07AwBOnDiBhw8fYv/+/ejYsaNWGmqM2ONGVHXqQltoaCisrKx00BoiMkZ6NVTatWtXNG/eHP/73/9gZlbaYVdUVIT33nsPV65cwa+//qrRBhozBjeiyrt27Rq+++47lXL2shFRddOr4GZtbY3ExER4enoqlV+4cAE+Pj548OCBRhtozBjciCqHQ6NEpE/06h43Ozs7pKWlqQS3a9euoXbt2hprGBFRZagLbTNnzoSpqakOWkNEpF1VDm4DBw5ESEgIFi1aBH9/fwDAH3/8galTp6psg0VEpC0XLlzA1q1bVcrZy0ZENVmVg9uiRYsgk8kwbNgwFBUVAQDMzc0xZswYREZGaryBRESP49AoERmrZ17H7cGDB7h8+TIAoFmzZrCxsdFow4j3uBGpoy60zZ49GzKZTAetISJSpVf3uJWxsbFBu3btNNkWIqIK/f7774iNjVUpZy8bERmTSge3kSNHVqqeuun4RETPo6KhUW9vhjYiMi6VDm5RUVFo3LgxvL29wV2yiKi6qAtt7GUjImNV6eA2ZswYbNy4ESkpKRgxYgTeffdd1K1bV5ttIyIjtmvXLiQkJKiUM7QRkTEzqWzFFStW4ObNm5g2bRp27twJNzc3vP3224iJiWEPHBFpVEREhNrQFhPD0EZExu2ZZ5VevXoVUVFRWLduHYqKinD+/HnUqlVL0+0zapxVSsZI3dDoxo3hkMmABQuAoCAdNIqIqAr0clapiYkJZDIZhBAoLi7WZJuIyAitWbMGaWlpKuXh4eHg6CgRUalKD5UCQH5+PjZu3IjXXnsNLVu2xJ9//only5cjLS2NvW1E9MwiIiIqDG1ERPRIpXvcPvzwQ2zatAlubm4YOXIkNm7cCEdHR222jYiMAGeNEhFVXqXvcTMxMYG7uzu8vb2fuEL5Tz/9pLHGGTve40Y12bx581BYWKhSztBGRIZOL+5xGzZsGLeUISKNUNfLZmFhgenTp+ugNUREhqNKC/ASET0vDo0SET27Z55VSkRUFRVtW8XQRkRUeQxuRKR16kKbu7s7RowYoYPWEBEZLgY3ItIqDo0SEWkOgxsRaQWHRomINI/BjYg0Tl1o8/HxQWBgoA5aQ0RUczC4EZFGcWiUiEh7GNyISCM4NEpEpH0MbkT0XKKjgcRE1dDWs2dPyOVyHbSIiKjmYnAjomcmhEBi4lyVcvayERFpB4MbET0TDo0SEVU/E103oLKCgoLg7u4OKysruLq6YujQobhx44ZSnS1btqBDhw6wsbFB48aN8cUXX6ic5/Dhw+jYsSMsLS3RvHlztVt5rVixAk2aNIGVlRV8fX1x/PhxpeN5eXkYO3Ys6tWrh1q1aqF///64deuWUp20tDQEBgbCxsYG9evXx9SpU1FUVPT8PwgiPaAutDVtOpihjYhIywwmuHXv3h1btmxBcnIytm3bhsuXL2PAgAHS8b1792LIkCEYPXo0zp07h2+++QZffvklli9fLtVJSUlBYGAgunfvjtOnT2PChAl47733EBMTI9XZvHkzJk2ahPDwcJw6dQrt27eHQqFARkaGVGfixInYuXMntm7diiNHjuDGjRt46623pOPFxcUIDAxEQUEB4uLisHbtWkRFRWH27Nla/ikRaVdJSYna0DZnTjhWrmyJ6GjA37/0vjciItI8mRBC6LoRzyI6Ohr9+vVDfn4+zM3N8c4776CwsBBbt26V6ixbtgwLFy5EWloaZDIZQkNDsXv3bpw7d06qM2jQIGRmZmLfvn0AAF9fX7z44otS4CspKYGbmxs++ugjhIWFISsrC05OTtiwYYMUHJOSkuDl5YX4+Hj4+flh79696NOnD27cuAFnZ2cAwKpVqxAaGorbt2/DwsKiUp8xOzsb9vb2yMrKgp2dnUZ+bkTPqqKhUW/vcERGAmFhQGQkEB8PyOVAXFw1N5CISE9o8/vbYHrcyrt79y7Wr18Pf39/mJubAwDy8/NhZWWlVM/a2hrXr1/H1atXAQDx8fEICAhQqqNQKBAfHw8AKCgoQEJCglIdExMTBAQESHUSEhJQWFioVMfT0xPu7u5Snfj4eLRr104KbWXvk52djfPnz1f4ufLz85Gdna30INIH6kLbBx98gPDwcAQFlYa0oKDS8CaXl/5JRESaZ1DBLTQ0FLa2tqhXrx7S0tKwY8cO6ZhCocBPP/2E2NhYlJSU4NKlS1i8eDEA4ObNmwCA9PR0pTAFAM7OzsjOzsbDhw9x584dFBcXq62Tnp4uncPCwgIODg5PrKPuHGXHKjJ//nzY29tLDzc3t8r+aIi0oqCgoMIFdV1cXFTKy4c4IiLSPJ0Gt7CwMMhksic+kpKSpPpTp05FYmIi9u/fD1NTUwwbNgxlI72jRo3CuHHj0KdPH1hYWMDPzw+DBg0CUNprZgimT5+OrKws6XHt2jVdN4mMWEREBObPn69SzgkIRES6o9PlQCZPnozg4OAn1mnatKn0d0dHRzg6OqJly5bw8vKCm5sbjh49CrlcDplMhgULFmDevHlIT0+Hk5MTYmNjlc7h4uKiMvvz1q1bsLOzg7W1NUxNTWFqaqq2TlnvgouLCwoKCpCZmanU6/Z4ncdnopadU10vRRlLS0tYWlo+8edBVB3U9bJNnDhRulcjOhrSfW3sXSMiqj467YpycnKCp6fnEx8V3chfUlICoPS+sPJMTU3RsGFDWFhYYOPGjZDL5XBycgIAyOVyKcyVOXDggLS6u4WFBTp16qRUp6SkBLGxsVKdTp06wdzcXKlOcnIy0tLSpDpyuRx//vmn0kzUAwcOwM7ODq1bt36mnxVRdcjJyalwaLR8aBsypHQSQmRkdbeQiMi4GcQCvMeOHcOJEyfQpUsX1KlTB5cvX8asWbPQrFkzKSzduXMHP/74I1555RXk5eVhzZo10nIdZUaPHo3ly5dj2rRpGDlyJA4ePIgtW7Zg9+7dUp1JkyZh+PDh8PHxQefOnfHVV18hNzcXI0aMAADY29sjJCQEkyZNQt26dWFnZ4ePPvoIcrkcfn5+AEq3+mndujWGDh2KhQsXIj09HTNnzsTYsWPZo0Z6q7IL6kZGAjk5QK1anIRARFTthAE4e/as6N69u6hbt66wtLQUTZo0EaNHjxbXr1+X6ty+fVv4+fkJW1tbYWNjI3r06CGOHj2qcq5Dhw6JDh06CAsLC9G0aVOxZs0alTrLli0T7u7uwsLCQnTu3FnlPA8fPhQffvihqFOnjrCxsRFvvvmmuHnzplKd1NRU0bt3b2FtbS0cHR3F5MmTRWFhYZU+d1ZWlgAgsrKyqvQ6oqqaM2eOyiMvL09t3R07hJDLS/8kIiJV2vz+Nth13IwB13Ejbbt9+za++eYblXJOQCAienba/P42iKFSItI87jVKRGR4GNyIjJC60DZz5kyYmprqoDVERFRZhrHAGRFpxLVr1yqcNVoW2rjfKBGR/mKPG5GRqGhoNCYmHN7ej9ZjK9tvNDKSa7QREekb9rgRGQF1oW327NnYuDEc8fHAtGmPyrnfKBGR/mKPG1ENdunSJWzcuFGlvGwCgkwGpT+B0l429rQREeknBjeiGqoys0YXLHi0dRUREek/BjeiGqiiCQjlcb9RIiLDw3vciGqQhISESoU2QHkSAhERGQb2uBHVEFVdUDcsjMOkRESGhsGNqAaobC/b48OjHCIlIjIsDG5EBuy3337DwYMHVcor6mXjGm1ERIaNwY3IQKnrZXN0dMTYsWMrfA2HR4mIDBuDG5EBquzQ6OM4PEpEZNgY3IgMyM6dO3Hq1CmV8sqENiIiMnxcDoTIQERERKiEtnbt2iE8PJwbwxMRGQn2uBEZgKcNjXLSARGRcWBwI9Jj69atQ0pKikr540OjnHRARGQcGNyI9JS6XrauXbsiJ+dV+Psrb1XFSQdERMaBwY1IDz1paNTfn8OiRETGisGNSI8sWbIE9+/fVykvPzTKYVEiIuPF4EakJ9T1svXp0wedOnVSKuOwKBGR8WJwI9IDz7qgLhERGRcGNyId+vTTT1FSUqJSztBGRETqMLgR6Yi6XrYhQ4agefPmOmgNEREZAgY3omomhMDcuXNVytnLRkRET8PgRlSNPvvsMxQXF6uUl21bVTZblJMPiIhIHQY3omqibmg0Lu4DxMS4AOC2VURE9HTcZJ5Iy4QQakPbpk3huHrVRdoYPiwMkMu5PhsREVVMJoQQum4EqZednQ17e3tkZWXBzs5O182hZ6AusAGlQ6NlOyDI5UBcXDU3jIiItEab398cKiXSEnWhbcKECbC3twfAHRCIiKjqGNyINKyoqAiff/65Svnjs0a5AwIREVUVgxuRBj1paJSIiOh5MbgRaYi60DZt2jRYW1vroDVERFQTMbgRPaf8/HxERkaqlLOXjYiINI3Bjeg5cGiUiIiqE4Mb0TNSF9pmzJgBc3NzHbSGiIiMAYMbURXl5uZi0aJFKuXsZSMiIm1jcCOqAg6NEhGRLnHLK6JKUhfaZs2aBW/v0l0QyrauIiIi0hYGN6KnyMzMVBvawsPDYWJiorQ5PBERkTZxqJToCdQFNkvLeoiOHgdv79KdD7h1FRERVRduMq/HuMm8bqkLbbNnz8ZLL8m4OTwREVVIm9/fHColekxGRkaFQ6MymQxhYaWhjT1sRERU3ThUSlSOusDm4eGBYcOGSc+5OTwREekKgxvR/1dRLxsREZG+YHAjo3fjxg3873//UylnaCMiIn3D4EZGTV0vW/v27dGvX7/qbwwREdFTMLiR0VIX2ry9w3n/GhER6S3OKqUaLzoaSjsbXLlypcL72RjaiIhIn7HHjWq88jsbJCaqBraAgAC89NJLOmgZERFR1TC4UY1XtrOBQsFZo0REZNgY3KjG8/L6CwrFBpVyb2+GNiIiMiwMblQjRUdX3Mt2+vQAbN/eBnI5F9IlIiLDwuBGNdKThkajo4Fbt7hlFRERGR4GN6pxLl++DIXiB5XysvvZuGUVEREZKgY3qlHULfMxYsQIuLu766A1REREmsXgRjUG9xolIqKajsGNDF5SUhI2b96sUs7QRkRENQ2DGxk0db1sY8eOhaOjow5aQ0REpF0MbmSwODRKRETGhsGNDM6ZM2ewfft2lXKGNiIiqukY3MigqOtlmzBhAuzt7XXQGiIiourF4EYGg0OjRERk7BjcSO8dPXoUMTExSmW1atXC5MmTddQiIiIi3WBwI72mrpdtypQpsLW11UFriIiIdIvBjfSSEAJz585VKefQKBERGTMTXTegqvLz89GhQwfIZDKcPn1a6djZs2fRtWtXWFlZwc3NDQsXLlR5/datW+Hp6QkrKyu0a9cOe/bsUTouhMDs2bPh6uoKa2trBAQE4K+//lKqc/fuXQwZMgR2dnZwcHBASEgIcnJyqtwWUu/w4cMqoc3FxYWhjYiIjJ7BBbdp06ahQYMGKuXZ2dno2bMnGjdujISEBHzxxReYM2cO/vvf/0p14uLiMHjwYISEhCAxMRH9+vVDv379cO7cOanOwoULsXTpUqxatQrHjh2Dra0tFAoF8vLypDpDhgzB+fPnceDAAezatQu//vor3n///Sq1hdSLiIjAkSNHlMpCQ0PxwQcf6KhFRERE+kMmhBC6bkRl7d27F5MmTcK2bdvQpk0bJCYmokOHDgCAlStX4pNPPkF6ejosLCwAAGFhYdi+fTuSkpIAAAMHDkRubi527dolndPPzw8dOnTAqlWrIIRAgwYNMHnyZEyZMgUAkJWVBWdnZ0RFRWHQoEG4ePEiWrdujRMnTsDHxwcAsG/fPrz++uu4fv06GjRoUKm2VEZ2djbs7e2RlZUFOzu75/756bOSkhJ8+umnKuXsZSMiIkOjze9vg+lxu3XrFkaNGoXvv/8eNjY2Ksfj4+PRrVs3KSgBgEKhQHJyMu7duyfVCQgIUHqdQqFAfHw8ACAlJQXp6elKdezt7eHr6yvViY+Ph4ODgxTaACAgIAAmJiY4duxYpduiTn5+PrKzs5UexmDv3r0qoa1Zs2YMbURERI8xiMkJQggEBwdj9OjR8PHxQWpqqkqd9PR0eHh4KJU5OztLx+rUqYP09HSprHyd9PR0qV7511VUp379+krHzczMULduXaU6T2uLOvPnz1c7i7ImU/d5Z8yYAXNzcx20hoiISL/ptMctLCwMMpnsiY+kpCQsW7YM9+/fx/Tp03XZXK2bPn06srKypMe1a9d03SStKS4urnBBXYY2IiIi9XTa4zZ58mQEBwc/sU7Tpk1x8OBBxMfHw9LSUumYj48PhgwZgrVr18LFxQW3bt1SOl723MXFRfpTXZ3yx8vKXF1dleqU3Uvn4uKCjIwMpXMUFRXh7t27T32f8u+hjqWlpcpnrIm2bdumNCEEAF544QW8+eabOmoRERGRYdBpcHNycoKTk9NT6y1duhSfffaZ9PzGjRtQKBTYvHkzfH19AQByuRyffPIJCgsLpR6bAwcOoFWrVtLQpFwuR2xsLCZMmCCd68CBA5DL5QAADw8PuLi4IDY2Vgpq2dnZOHbsGMaMGSOdIzMzEwkJCejUqRMA4ODBgygpKalSW4yVul62mTNnwtTUVAetISIiMiwGMTnB3d0dbdu2lR4tW7YEUHoDe6NGjQAA77zzDiwsLBASEoLz589j8+bN+PrrrzFp0iTpPB9//DH27duHxYsXIykpCXPmzMHJkycxbtw4AIBMJsOECRPw2WefITo6Gn/++SeGDRuGBg0aoF+/fgAALy8v9OrVC6NGjcLx48fxxx9/YNy4cRg0aJC0TEll2mJsCgsLKxwaZWgjIiKqHIOYnFAZ9vb22L9/P8aOHYtOnTrB0dERs2fPVlpfzd/fHxs2bMDMmTMxY8YMtGjRAtu3b0fbtm2lOtOmTUNubi7ef/99ZGZmokuXLti3bx+srKykOuvXr8e4cePQo0cPmJiYoH///li6dGmV2lJTREcDkZFAWBgQFKS+zrp165CSkqJU5ufnB4VCUQ0tJCIiqjkMah03Y2MI67j5+wPx8YBcDsTFqR5X18s2e/ZsyGSyamgdERFR9eM6bqRT0dGlAS06WvVYWFhpaAsLUy7Py8urcGiUoY2IiOjZsMdNj+lLj9vTetUet3LlSpWZt6+88gpefvllLbWQiIhIf2jz+7vG3ONG2hMW9ug+tqfh0CgREZH2cKiUKlQ2RAqU9rRVNPkAAHJycjg0SkREpGXscaMKRUaWDpFGRj45tH3xxRd48OCBUlnv3r3RuXNnLbeQiIjIuDC4UYUqM0RaUS8bERERaR6DG1UoKKjinrb79+9jyZIlKuUMbURERNrD4EZVtmrVKpW9WN9880288MILOmoRERGRceDkBHriOm2Pi4iIUAlt4eHhDG1ERETVgMGNlCYhVOTevXu8n42IiEjHOFRKT52EsGTJEty/f1+pLDg4GI0bN66G1hEREVEZ9rgZoceHRoOCKl6nLSIiQiW0hYeHM7QRERHpAIObEarM0Ghubi6HRomIiPQMh0qN0NOGRg8fPowjR44olY0dOxaOjo7V0DoiIiKqCIObEXrS+mzsZSMiItJfDG4EQP2Cuq+88gpefvllHbWIiIiIHsfgRjhw4ADi4uKUyqZMmQJbW1sdtYiIiIjUYXAzclu3bsWFCxeUyjg0SkREpJ8Y3IxUXl4eFixYoFTWs2dPyOVyHbWIiIiInobBzQhdunQJGzduVCqbMWMGzM3NddQiIiIiqgwGNyNUPrT5+flBoVDosDVERERUWQxuRmzUqFFo0KCBrptBRERElSQTQghdN4LUy87Ohr29PbKysmBnZ6fr5hAREVElaPP7m1teERERERkIBjciIiIiA8HgRkRERGQgGNyIiIiIDASDGxEREZGBYHAjIiIiMhAMbkREREQGgsGNiIiIyEAwuBEREREZCAY3IiIiIgPB4EZERERkIBjciIiIiAwEgxsRERGRgWBwIyIiIjIQZrpuAFVMCAEAyM7O1nFLiIiIqLLKvrfLvsc1icFNj92/fx8A4ObmpuOWEBERUVXdv38f9vb2Gj2nTGgjDpJGlJSU4MaNG6hduzZkMpmum6N12dnZcHNzw7Vr12BnZ6fr5tBjeH30G6+PfuP10W+avj5CCNy/fx8NGjSAiYlm70pjj5seMzExQaNGjXTdjGpnZ2fHX2x6jNdHv/H66DdeH/2myeuj6Z62MpycQERERGQgGNyIiIiIDASDG+kNS0tLhIeHw9LSUtdNITV4ffQbr49+4/XRb4Z0fTg5gYiIiMhAsMeNiIiIyEAwuBEREREZCAY3IiIiIgPB4EZERERkIBjcSCPy8/PRoUMHyGQynD59WunY2bNn0bVrV1hZWcHNzQ0LFy5Uef3WrVvh6ekJKysrtGvXDnv27FE6LoTA7Nmz4erqCmtrawQEBOCvv/5SqnP37l0MGTIEdnZ2cHBwQEhICHJycqrclpokKCgI7u7usLKygqurK4YOHYobN24o1dmyZQs6dOgAGxsbNG7cGF988YXKeQ4fPoyOHTvC0tISzZs3R1RUlEqdFStWoEmTJrCysoKvry+OHz+udDwvLw9jx45FvXr1UKtWLfTv3x+3bt1SqpOWlobAwEDY2Nigfv36mDp1KoqKip7/B6GnKnN9YmJi4Ofnh9q1a8PJyQn9+/dHamqqUh1eH+152jWaM2cOZDKZysPW1lbpPPwdpx2V+TckhMCiRYvQsmVLWFpaomHDhvj888+V6hjUvyFBpAHjx48XvXv3FgBEYmKiVJ6VlSWcnZ3FkCFDxLlz58TGjRuFtbW1+L//+z+pzh9//CFMTU3FwoULxYULF8TMmTOFubm5+PPPP6U6kZGRwt7eXmzfvl2cOXNGBAUFCQ8PD/Hw4UOpTq9evUT79u3F0aNHxW+//SaaN28uBg8eXKW21DRLliwR8fHxIjU1Vfzxxx9CLpcLuVwuHd+zZ48wMzMTK1euFJcvXxa7du0Srq6uYtmyZVKdK1euCBsbGzFp0iRx4cIFsWzZMmFqair27dsn1dm0aZOwsLAQ3333nTh//rwYNWqUcHBwELdu3ZLqjB49Wri5uYnY2Fhx8uRJ4efnJ/z9/aXjRUVFom3btiIgIEAkJiaKPXv2CEdHRzF9+nQt/5R052nX58qVK8LS0lJMnz5d/P333yIhIUF069ZNeHt7K9Xh9dGep12j+/fvi5s3byo9WrduLYYPHy7V4e847Xna9RFCiI8++ki0atVK7NixQ1y5ckWcPHlS7N+/XzpuaP+GGNzoue3Zs0d4enqK8+fPqwS3b775RtSpU0fk5+dLZaGhoaJVq1bS87ffflsEBgYqndPX11d88MEHQgghSkpKhIuLi/jiiy+k45mZmcLS0lJs3LhRCCHEhQsXBABx4sQJqc7evXuFTCYT//zzT6XbUtPt2LFDyGQyUVBQIIQQYvDgwWLAgAFKdZYuXSoaNWokSkpKhBBCTJs2TbRp00apzsCBA4VCoZCed+7cWYwdO1Z6XlxcLBo0aCDmz58vhCi9Xubm5mLr1q1SnYsXLwoAIj4+XghR+t+RiYmJSE9Pl+qsXLlS2NnZKV2zmuzx67N161ZhZmYmiouLpTrR0dFKdXh9qtfj1+hxp0+fFgDEr7/+KpXxd1z1efz6XLhwQZiZmYmkpKQKX2No/4Y4VErP5datWxg1ahS+//572NjYqByPj49Ht27dYGFhIZUpFAokJyfj3r17Up2AgACl1ykUCsTHxwMAUlJSkJ6erlTH3t4evr6+Up34+Hg4ODjAx8dHqhMQEAATExMcO3as0m2pye7evYv169fD398f5ubmAEqHuK2srJTqWVtb4/r167h69SqAp1+fgoICJCQkKNUxMTFBQECAVCchIQGFhYVKdTw9PeHu7q50Ddu1awdnZ2el98nOzsb58+c19WPQW+quT6dOnWBiYoI1a9aguLgYWVlZ+P777xEQECDV4fWpPuqu0eNWr16Nli1bomvXrlIZf8dVD3XXZ+fOnWjatCl27doFDw8PNGnSBO+99x7u3r0rvc7Q/g0xuNEzE0IgODgYo0ePVvplUl56errSf6QApOfp6elPrFP+ePnXVVSnfv36SsfNzMxQt27dp75P+feoiUJDQ2Fra4t69eohLS0NO3bskI4pFAr89NNPiI2NRUlJCS5duoTFixcDAG7evAmg4p9bdnY2Hj58iDt37qC4uPip18fCwgIODg5PrMPro3x9PDw8sH//fsyYMQOWlpZwcHDA9evXsWXLFqkOr4/2PekalZeXl4f169cjJCREqZy/47TrSdfnypUruHr1KrZu3Yp169YhKioKCQkJGDBggFTH0P4NMbiRirCwMLU325Z/JCUlYdmyZbh//z6mT5+u6yYblcpenzJTp05FYmIi9u/fD1NTUwwbNgzi/2+YMmrUKIwbNw59+vSBhYUF/Pz8MGjQIACl/0dJVafJ65Oeno5Ro0Zh+PDhOHHiBI4cOQILCwsMGDBAqkNVp8lrVN7PP/+M+/fvY/jw4dX5cWocTV6fkpIS5OfnY926dejatSteeeUVfPvttzh06BCSk5N19RGfi5muG0D6Z/LkyQgODn5inaZNm+LgwYOIj49X2dvNx8cHQ4YMwdq1a+Hi4qIyq6bsuYuLi/Snujrlj5eVubq6KtXp0KGDVCcjI0PpHEVFRbh79+5T36f8exiCyl6fMo6OjnB0dETLli3h5eUFNzc3HD16FHK5HDKZDAsWLMC8efOQnp4OJycnxMbGKp2jop+bnZ0drK2tYWpqClNT06dew4KCAmRmZir9H+njdR6fpWXs12fFihWwt7dXmhn4ww8/wM3NDceOHYOfnx+vzzPQ5DUqb/Xq1ejTp49Krwp/x1WNJq+Pq6srzMzM0LJlS6m+l5cXgNIZnq1atTK8f0OVvhuO6DFXr14Vf/75p/SIiYkRAMSPP/4orl27JoR4dLNs+Rt5p0+frjI5oU+fPkrnlsvlKjfuLlq0SDqelZWl9sbdkydPSnViYmLU3rj7pLbUdFevXhUAxKFDhyqsM3ToUKVZWdOmTRNt27ZVqjN48GCVG3fHjRsnPS8uLhYNGzZUuXH3xx9/lOokJSWpvXG3/Cyt//u//xN2dnYiLy/v2T6wgXn8+kyaNEl07txZqc6NGzcEAPHHH38IIXh9qltF/4auXLkiZDKZ2Llzp8pr+Duu+jx+fcq+l/7++2+pTtkEkuTkZCGE4f0bYnAjjUlJSVGZVZqZmSmcnZ3F0KFDxblz58SmTZuEjY2NynIgZmZmYtGiReLixYsiPDxc7VR5BwcHsWPHDnH27FnRt29ftVPlvb29xbFjx8Tvv/8uWrRooTRVvjJtqUmOHj0qli1bJhITE0VqaqqIjY0V/v7+olmzZtIvidu3b4uVK1eKixcvisTERDF+/HhhZWUljh07Jp2nbKr81KlTxcWLF8WKFSvUTpW3tLQUUVFR4sKFC+L9998XDg4OSrOnRo8eLdzd3cXBgwfFyZMnVabtl02V79mzpzh9+rTYt2+fcHJyqrHLTVTm+sTGxgqZTCYiIiLEpUuXREJCglAoFKJx48biwYMHQgheH22qzDUqM3PmTNGgQQNRVFSkch7+jtOOylyf4uJi0bFjR9GtWzdx6tQpcfLkSeHr6ytee+016TyG9m+IwY00Rl1wE0KIM2fOiC5dughLS0vRsGFDERkZqfLaLVu2iJYtWwoLCwvRpk0bsXv3bqXjJSUlYtasWcLZ2VlYWlqKHj16SP+3VObff/8VgwcPFrVq1RJ2dnZixIgR4v79+1VuS01x9uxZ0b17d1G3bl1haWkpmjRpIkaPHi2uX78u1bl9+7bw8/MTtra2wsbGRvTo0UMcPXpU5VyHDh0SHTp0EBYWFqJp06ZizZo1KnWWLVsm3N3dhYWFhejcubPKeR4+fCg+/PBDUadOHWFjYyPefPNNcfPmTaU6qamponfv3sLa2lo4OjqKyZMni8LCQs38QPRMZa6PEEJs3LhReHt7C1tbW+Hk5CSCgoLExYsXlerw+mhHZa9RcXGxaNSokZgxY0aF5+LvOM2r7PX5559/xFtvvSVq1aolnJ2dRXBwsPj333+V6hjSvyGZELzDlYiIiMgQcNoYERERkYFgcCMiIiIyEAxuRERERAaCwY2IiIjIQDC4ERERERkIBjciIiIiA8HgRkRERGQgGNyIiLRAJpNh+/btum6GksOHD0MmkyEzM1PXTSGiZ8TgRkT0HObMmSNtBE5EpG0MbkREREQGgsGNiIxaSUkJ5s+fDw8PD1hbW6N9+/b48ccfATwaWoyNjYWPjw9sbGzg7++P5ORkAEBUVBQiIiJw5swZyGQyyGQyREVFSee+c+cO3nzzTdjY2KBFixaIjo6uVJvK3jcmJgbe3t6wtrbGq6++ioyMDOzduxdeXl6ws7PDO++8gwcPHkivy8/Px/jx41G/fn1YWVmhS5cuOHHihOZ+WESkcwxuRGTU5s+fj3Xr1mHVqlU4f/48Jk6ciHfffRdHjhyR6nzyySdYvHgxTp48CTMzM4wcORIAMHDgQEyePBlt2rTBzZs3cfPmTQwcOFB6XUREBN5++22cPXsWr7/+OoYMGYK7d+9Wum1z5szB8uXLERcXh2vXruHtt9/GV199hQ0bNmD37t3Yv38/li1bJtWfNm0atm3bhrVr1+LUqVNo3rw5FApFld6TiPRclbakJyKqQfLy8oSNjY2Ii4tTKg8JCRGDBw8Whw4dEgDEL7/8Ih3bvXu3ACAePnwohBAiPDxctG/fXuXcAMTMmTOl5zk5OQKA2Lt371Pbpe5958+fLwCIy5cvS2UffPCBUCgU0vnNzc3F+vXrpeMFBQWiQYMGYuHChUrnvXfv3lPbQET6yUyHmZGISKf+/vtvPHjwAK+99ppSeUFBAby9vaXnL7zwgvR3V1dXAEBGRgbc3d2feP7yr7O1tYWdnR0yMjIq3b7yr3d2doaNjQ2aNm2qVHb8+HEAwOXLl1FYWIiXXnpJOm5ubo7OnTvj4sWLlX5PItJvDG5EZLRycnIAALt370bDhg2VjllaWuLy5csASgNQGZlMBqD03rinKf+6stdW5nXqXi+TyZ77fERk+HiPGxEZrdatW8PS0hJpaWlo3ry50sPNza1S57CwsEBxcbGWW/p0zZo1g4WFBf744w+prLCwECdOnEDr1q112DIi0iT2uBGR0apduzamTJmCiRMnoqSkBF26dEFWVhb++OMP2NnZoXHjxk89R5MmTZCSkoLTp0+jUaNGqF27NiwtLauh9cpsbW0xZswYTJ06FXXr1oW7uzsWLlyIBw8eICQkpNrbQ0TaweBGREbt008/hZOTE+bPn48rV67AwcEBHTt2xIwZMyo1DNm/f3/89NNP6N69OzIzM7FmzRoEBwdrv+FqREZGoqSkBEOHDsX9+/fh4+ODmJgY1KlTRyftISLNkwkhhK4bQURERERPx3vciIiIiAwEgxsRUTUbPXo0atWqpfYxevRoXTePiPQYh0qJiKpZRkYGsrOz1R6zs7ND/fr1q7lFRGQoGNyIiIiIDASHSomIiIgMBIMbERERkYFgcCMiIiIyEAxuRERERAaCwY2IiIjIQDC4ERERERkIBjciIiIiA8HgRkRERGQg/h8Bu7sJt+ciGQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(keras_surrogate, data_validation)\n", + "surrogate_parity(keras_surrogate, data_validation)\n", + "surrogate_residual(keras_surrogate, data_validation)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_test.ipynb](./surrogate_embedding_test.ipynb) file." ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4/4 [==============================] - 0s 4ms/step\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(keras_surrogate, data_validation)\n", - "surrogate_parity(keras_surrogate, data_validation)\n", - "surrogate_residual(keras_surrogate, data_validation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_test.ipynb](./surrogate_embedding_test.ipynb) file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb index ed23d255..ca8d855c 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb @@ -1,1123 +1,1148 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Introduction\n", - "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", - "\n", - "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", - "\n", - "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", - "\n", - "### 1.1 Need for ML Surrogates\n", - "\n", - "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", - "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", - "\n", - "### 1.2 Supercritical CO2 cycle process\n", - "\n", - "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", - "\n", - "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", - "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", - ":241)\n" - ] - } - ], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import random as rn\n", - "import tensorflow as tf\n", - "import tensorflow.keras as keras\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")\n", - "\n", - "# fix environment variables to ensure consist neural network training\n", - "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", - "np.random.seed(46)\n", - "rn.seed(1342)\n", - "tf.random.set_seed(62)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "csv_data.columns.values[0:6] = [\n", - " \"pressure\",\n", - " \"temperature\",\n", - " \"enth_mol\",\n", - " \"entr_mol\",\n", - " \"CO2_enthalpy\",\n", - " \"CO2_entropy\",\n", - "]\n", - "data = csv_data.sample(n=500)\n", - "\n", - "# Creating input_data and output_data from data\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# Define labels, and split training and validation data\n", - "input_labels = input_data.columns\n", - "output_labels = output_data.columns\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogate with TensorFlow Keras\n", - "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", - "\n", - "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", - "\n", - "* Activation function: sigmoid, **tanh**\n", - "* Optimizer: **Adam**\n", - "* Number of hidden layers: 3, **4**, 5, 6\n", - "* Number of neurons per layer: **20**, 40, 60\n", - "\n", - "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", - "\n", - "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", - "\n", - "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/250\n", - "13/13 - 3s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 3s/epoch - 249ms/step\n", - "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 237ms/epoch - 18ms/step\n", - "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 262ms/epoch - 20ms/step\n", - "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 283ms/epoch - 22ms/step\n", - "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 351ms/epoch - 27ms/step\n", - "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 364ms/epoch - 28ms/step\n", - "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 245ms/epoch - 19ms/step\n", - "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 203ms/epoch - 16ms/step\n", - "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 300ms/epoch - 23ms/step\n", - "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 255ms/epoch - 20ms/step\n", - "Epoch 11/250\n", - "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 247ms/epoch - 19ms/step\n", - "Epoch 12/250\n", - "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 321ms/epoch - 25ms/step\n", - "Epoch 13/250\n", - "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 219ms/epoch - 17ms/step\n", - "Epoch 14/250\n", - "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 272ms/epoch - 21ms/step\n", - "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 299ms/epoch - 23ms/step\n", - "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 271ms/epoch - 21ms/step\n", - "Epoch 17/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 248ms/epoch - 19ms/step\n", - "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 256ms/epoch - 20ms/step\n", - "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 153ms/epoch - 12ms/step\n", - "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 239ms/epoch - 18ms/step\n", - "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 263ms/epoch - 20ms/step\n", - "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 243ms/epoch - 19ms/step\n", - "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", - "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 189ms/epoch - 15ms/step\n", - "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 280ms/epoch - 22ms/step\n", - "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 222ms/epoch - 17ms/step\n", - "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 286ms/epoch - 22ms/step\n", - "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 116ms/epoch - 9ms/step\n", - "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 157ms/epoch - 12ms/step\n", - "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 267ms/epoch - 21ms/step\n", - "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 264ms/epoch - 20ms/step\n", - "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 269ms/epoch - 21ms/step\n", - "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 353ms/epoch - 27ms/step\n", - "Epoch 34/250\n", - "13/13 - 1s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 537ms/epoch - 41ms/step\n", - "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 330ms/epoch - 25ms/step\n", - "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 289ms/epoch - 22ms/step\n", - "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 155ms/epoch - 12ms/step\n", - "Epoch 38/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 289ms/epoch - 22ms/step\n", - "Epoch 39/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 118ms/epoch - 9ms/step\n", - "Epoch 40/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 299ms/epoch - 23ms/step\n", - "Epoch 41/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 42/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 192ms/epoch - 15ms/step\n", - "Epoch 43/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 237ms/epoch - 18ms/step\n", - "Epoch 44/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 154ms/epoch - 12ms/step\n", - "Epoch 45/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 46/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 47/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 116ms/epoch - 9ms/step\n", - "Epoch 48/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 288ms/epoch - 22ms/step\n", - "Epoch 49/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 140ms/epoch - 11ms/step\n", - "Epoch 50/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 51/250\n", - "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 52/250\n", - "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 265ms/epoch - 20ms/step\n", - "Epoch 53/250\n", - "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 179ms/epoch - 14ms/step\n", - "Epoch 54/250\n", - "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 287ms/epoch - 22ms/step\n", - "Epoch 55/250\n", - "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 284ms/epoch - 22ms/step\n", - "Epoch 56/250\n", - "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 297ms/epoch - 23ms/step\n", - "Epoch 57/250\n", - "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 206ms/epoch - 16ms/step\n", - "Epoch 58/250\n", - "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 59/250\n", - "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 274ms/epoch - 21ms/step\n", - "Epoch 60/250\n", - "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 61/250\n", - "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 62/250\n", - "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 63/250\n", - "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 154ms/epoch - 12ms/step\n", - "Epoch 64/250\n", - "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 284ms/epoch - 22ms/step\n", - "Epoch 65/250\n", - "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 168ms/epoch - 13ms/step\n", - "Epoch 66/250\n", - "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 113ms/epoch - 9ms/step\n", - "Epoch 67/250\n", - "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 264ms/epoch - 20ms/step\n", - "Epoch 68/250\n", - "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 189ms/epoch - 15ms/step\n", - "Epoch 69/250\n", - "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 175ms/epoch - 13ms/step\n", - "Epoch 70/250\n", - "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 322ms/epoch - 25ms/step\n", - "Epoch 71/250\n", - "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 207ms/epoch - 16ms/step\n", - "Epoch 72/250\n", - "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 257ms/epoch - 20ms/step\n", - "Epoch 73/250\n", - "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 233ms/epoch - 18ms/step\n", - "Epoch 74/250\n", - "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 250ms/epoch - 19ms/step\n", - "Epoch 75/250\n", - "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 76/250\n", - "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 77/250\n", - "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 126ms/epoch - 10ms/step\n", - "Epoch 78/250\n", - "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 242ms/epoch - 19ms/step\n", - "Epoch 79/250\n", - "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 80/250\n", - "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 81/250\n", - "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 166ms/epoch - 13ms/step\n", - "Epoch 82/250\n", - "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 83/250\n", - "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 84/250\n", - "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 148ms/epoch - 11ms/step\n", - "Epoch 85/250\n", - "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 282ms/epoch - 22ms/step\n", - "Epoch 86/250\n", - "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 246ms/epoch - 19ms/step\n", - "Epoch 87/250\n", - "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", - "Epoch 88/250\n", - "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 208ms/epoch - 16ms/step\n", - "Epoch 89/250\n", - "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 193ms/epoch - 15ms/step\n", - "Epoch 90/250\n", - "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 168ms/epoch - 13ms/step\n", - "Epoch 91/250\n", - "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 156ms/epoch - 12ms/step\n", - "Epoch 92/250\n", - "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 93/250\n", - "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 94/250\n", - "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 303ms/epoch - 23ms/step\n", - "Epoch 95/250\n", - "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 96/250\n", - "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 97/250\n", - "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 98/250\n", - "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 99/250\n", - "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 130ms/epoch - 10ms/step\n", - "Epoch 100/250\n", - "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 101/250\n", - "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 102/250\n", - "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 260ms/epoch - 20ms/step\n", - "Epoch 103/250\n", - "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 275ms/epoch - 21ms/step\n", - "Epoch 104/250\n", - "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 105/250\n", - "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 275ms/epoch - 21ms/step\n", - "Epoch 106/250\n", - "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 157ms/epoch - 12ms/step\n", - "Epoch 107/250\n", - "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 183ms/epoch - 14ms/step\n", - "Epoch 108/250\n", - "13/13 - 0s - loss: 6.7955e-04 - mae: 0.0198 - mse: 6.7955e-04 - val_loss: 7.6778e-04 - val_mae: 0.0204 - val_mse: 7.6778e-04 - 192ms/epoch - 15ms/step\n", - "Epoch 109/250\n", - "13/13 - 1s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 516ms/epoch - 40ms/step\n", - "Epoch 110/250\n", - "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 111/250\n", - "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 208ms/epoch - 16ms/step\n", - "Epoch 112/250\n", - "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 215ms/epoch - 17ms/step\n", - "Epoch 113/250\n", - "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 114/250\n", - "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 115/250\n", - "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 166ms/epoch - 13ms/step\n", - "Epoch 116/250\n", - "13/13 - 0s - loss: 6.5036e-04 - mae: 0.0194 - mse: 6.5036e-04 - val_loss: 6.5858e-04 - val_mae: 0.0191 - val_mse: 6.5858e-04 - 107ms/epoch - 8ms/step\n", - "Epoch 117/250\n", - "13/13 - 0s - loss: 6.4983e-04 - mae: 0.0194 - mse: 6.4983e-04 - val_loss: 7.0443e-04 - val_mae: 0.0198 - val_mse: 7.0443e-04 - 109ms/epoch - 8ms/step\n", - "Epoch 118/250\n", - "13/13 - 0s - loss: 6.4994e-04 - mae: 0.0195 - mse: 6.4994e-04 - val_loss: 6.3181e-04 - val_mae: 0.0193 - val_mse: 6.3181e-04 - 296ms/epoch - 23ms/step\n", - "Epoch 119/250\n", - "13/13 - 0s - loss: 6.6252e-04 - mae: 0.0199 - mse: 6.6252e-04 - val_loss: 6.3527e-04 - val_mae: 0.0191 - val_mse: 6.3527e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 120/250\n", - "13/13 - 0s - loss: 6.4578e-04 - mae: 0.0193 - mse: 6.4578e-04 - val_loss: 6.3127e-04 - val_mae: 0.0189 - val_mse: 6.3127e-04 - 190ms/epoch - 15ms/step\n", - "Epoch 121/250\n", - "13/13 - 0s - loss: 6.1375e-04 - mae: 0.0191 - mse: 6.1375e-04 - val_loss: 6.5351e-04 - val_mae: 0.0192 - val_mse: 6.5351e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 122/250\n", - "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 142ms/epoch - 11ms/step\n", - "Epoch 123/250\n", - "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 278ms/epoch - 21ms/step\n", - "Epoch 124/250\n", - "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 246ms/epoch - 19ms/step\n", - "Epoch 125/250\n", - "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 135ms/epoch - 10ms/step\n", - "Epoch 126/250\n", - "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 127/250\n", - "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 142ms/epoch - 11ms/step\n", - "Epoch 128/250\n", - "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 249ms/epoch - 19ms/step\n", - "Epoch 129/250\n", - "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 279ms/epoch - 21ms/step\n", - "Epoch 130/250\n", - "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 356ms/epoch - 27ms/step\n", - "Epoch 131/250\n", - "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 132/250\n", - "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 102ms/epoch - 8ms/step\n", - "Epoch 133/250\n", - "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 239ms/epoch - 18ms/step\n", - "Epoch 134/250\n", - "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 285ms/epoch - 22ms/step\n", - "Epoch 135/250\n", - "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 178ms/epoch - 14ms/step\n", - "Epoch 136/250\n", - "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 113ms/epoch - 9ms/step\n", - "Epoch 137/250\n", - "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 138ms/epoch - 11ms/step\n", - "Epoch 138/250\n", - "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 144ms/epoch - 11ms/step\n", - "Epoch 139/250\n", - "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 279ms/epoch - 21ms/step\n", - "Epoch 140/250\n", - "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 174ms/epoch - 13ms/step\n", - "Epoch 141/250\n", - "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 118ms/epoch - 9ms/step\n", - "Epoch 142/250\n", - "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 120ms/epoch - 9ms/step\n", - "Epoch 143/250\n", - "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 304ms/epoch - 23ms/step\n", - "Epoch 144/250\n", - "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 178ms/epoch - 14ms/step\n", - "Epoch 145/250\n", - "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 146/250\n", - "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 396ms/epoch - 30ms/step\n", - "Epoch 147/250\n", - "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 148/250\n", - "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 155ms/epoch - 12ms/step\n", - "Epoch 149/250\n", - "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 120ms/epoch - 9ms/step\n", - "Epoch 150/250\n", - "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 151/250\n", - "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 315ms/epoch - 24ms/step\n", - "Epoch 152/250\n", - "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 198ms/epoch - 15ms/step\n", - "Epoch 153/250\n", - "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 154/250\n", - "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 155/250\n", - "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 267ms/epoch - 21ms/step\n", - "Epoch 156/250\n", - "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 182ms/epoch - 14ms/step\n", - "Epoch 157/250\n", - "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 158/250\n", - "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 159/250\n", - "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 172ms/epoch - 13ms/step\n", - "Epoch 160/250\n", - "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 164ms/epoch - 13ms/step\n", - "Epoch 161/250\n", - "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 169ms/epoch - 13ms/step\n", - "Epoch 162/250\n", - "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 188ms/epoch - 14ms/step\n", - "Epoch 163/250\n", - "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 258ms/epoch - 20ms/step\n", - "Epoch 164/250\n", - "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 165/250\n", - "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 166/250\n", - "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 265ms/epoch - 20ms/step\n", - "Epoch 167/250\n", - "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 168/250\n", - "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 169/250\n", - "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 234ms/epoch - 18ms/step\n", - "Epoch 170/250\n", - "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 171/250\n", - "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 243ms/epoch - 19ms/step\n", - "Epoch 172/250\n", - "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 148ms/epoch - 11ms/step\n", - "Epoch 173/250\n", - "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 143ms/epoch - 11ms/step\n", - "Epoch 174/250\n", - "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 131ms/epoch - 10ms/step\n", - "Epoch 175/250\n", - "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 176/250\n", - "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 177/250\n", - "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 316ms/epoch - 24ms/step\n", - "Epoch 178/250\n", - "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 214ms/epoch - 16ms/step\n", - "Epoch 179/250\n", - "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 181ms/epoch - 14ms/step\n", - "Epoch 180/250\n", - "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 144ms/epoch - 11ms/step\n", - "Epoch 181/250\n", - "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 302ms/epoch - 23ms/step\n", - "Epoch 182/250\n", - "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 216ms/epoch - 17ms/step\n", - "Epoch 183/250\n", - "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 296ms/epoch - 23ms/step\n", - "Epoch 184/250\n", - "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 195ms/epoch - 15ms/step\n", - "Epoch 185/250\n", - "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 186/250\n", - "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 187/250\n", - "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 188/250\n", - "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 451ms/epoch - 35ms/step\n", - "Epoch 189/250\n", - "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 110ms/epoch - 8ms/step\n", - "Epoch 190/250\n", - "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 121ms/epoch - 9ms/step\n", - "Epoch 191/250\n", - "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 173ms/epoch - 13ms/step\n", - "Epoch 192/250\n", - "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 149ms/epoch - 11ms/step\n", - "Epoch 193/250\n", - "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 243ms/epoch - 19ms/step\n", - "Epoch 194/250\n", - "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 272ms/epoch - 21ms/step\n", - "Epoch 195/250\n", - "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 196/250\n", - "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 151ms/epoch - 12ms/step\n", - "Epoch 197/250\n", - "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 160ms/epoch - 12ms/step\n", - "Epoch 198/250\n", - "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 114ms/epoch - 9ms/step\n", - "Epoch 199/250\n", - "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 209ms/epoch - 16ms/step\n", - "Epoch 200/250\n", - "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 198ms/epoch - 15ms/step\n", - "Epoch 201/250\n", - "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 147ms/epoch - 11ms/step\n", - "Epoch 202/250\n", - "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 203/250\n", - "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 137ms/epoch - 11ms/step\n", - "Epoch 204/250\n", - "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 205/250\n", - "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 121ms/epoch - 9ms/step\n", - "Epoch 206/250\n", - "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 294ms/epoch - 23ms/step\n", - "Epoch 207/250\n", - "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 208/250\n", - "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 115ms/epoch - 9ms/step\n", - "Epoch 209/250\n", - "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 210/250\n", - "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 211/250\n", - "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 212/250\n", - "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 309ms/epoch - 24ms/step\n", - "Epoch 213/250\n", - "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 158ms/epoch - 12ms/step\n", - "Epoch 214/250\n", - "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 125ms/epoch - 10ms/step\n", - "Epoch 215/250\n", - "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 177ms/epoch - 14ms/step\n", - "Epoch 216/250\n", - "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 253ms/epoch - 19ms/step\n", - "Epoch 217/250\n", - "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 136ms/epoch - 10ms/step\n", - "Epoch 218/250\n", - "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 254ms/epoch - 20ms/step\n", - "Epoch 219/250\n", - "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 220/250\n", - "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 157ms/epoch - 12ms/step\n", - "Epoch 221/250\n", - "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 218ms/epoch - 17ms/step\n", - "Epoch 222/250\n", - "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 141ms/epoch - 11ms/step\n", - "Epoch 223/250\n", - "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 176ms/epoch - 14ms/step\n", - "Epoch 224/250\n", - "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 421ms/epoch - 32ms/step\n", - "Epoch 225/250\n", - "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 159ms/epoch - 12ms/step\n", - "Epoch 226/250\n", - "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 307ms/epoch - 24ms/step\n", - "Epoch 227/250\n", - "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 193ms/epoch - 15ms/step\n", - "Epoch 228/250\n", - "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 133ms/epoch - 10ms/step\n", - "Epoch 229/250\n", - "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 107ms/epoch - 8ms/step\n", - "Epoch 230/250\n", - "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 231/250\n", - "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 276ms/epoch - 21ms/step\n", - "Epoch 232/250\n", - "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 134ms/epoch - 10ms/step\n", - "Epoch 233/250\n", - "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 146ms/epoch - 11ms/step\n", - "Epoch 234/250\n", - "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 165ms/epoch - 13ms/step\n", - "Epoch 235/250\n", - "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 153ms/epoch - 12ms/step\n", - "Epoch 236/250\n", - "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 187ms/epoch - 14ms/step\n", - "Epoch 237/250\n", - "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 209ms/epoch - 16ms/step\n", - "Epoch 238/250\n", - "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 175ms/epoch - 13ms/step\n", - "Epoch 239/250\n", - "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 234ms/epoch - 18ms/step\n", - "Epoch 240/250\n", - "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 130ms/epoch - 10ms/step\n", - "Epoch 241/250\n", - "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 122ms/epoch - 9ms/step\n", - "Epoch 242/250\n", - "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 128ms/epoch - 10ms/step\n", - "Epoch 243/250\n", - "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 321ms/epoch - 25ms/step\n", - "Epoch 244/250\n", - "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 167ms/epoch - 13ms/step\n", - "Epoch 245/250\n", - "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 129ms/epoch - 10ms/step\n", - "Epoch 246/250\n", - "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 191ms/epoch - 15ms/step\n", - "Epoch 247/250\n", - "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 119ms/epoch - 9ms/step\n", - "Epoch 248/250\n", - "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 122ms/epoch - 9ms/step\n", - "Epoch 249/250\n", - "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 150ms/epoch - 12ms/step\n", - "Epoch 250/250\n", - "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 127ms/epoch - 10ms/step\n" - ] }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# selected settings for regression (best fit from options above)\n", - "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", - "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", - "\n", - "# Create data objects for training using scalar normalization\n", - "n_inputs = len(input_labels)\n", - "n_outputs = len(output_labels)\n", - "x = input_data\n", - "y = output_data\n", - "\n", - "input_scaler = None\n", - "output_scaler = None\n", - "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", - "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", - "x = input_scaler.scale(x)\n", - "y = output_scaler.scale(y)\n", - "x = x.to_numpy()\n", - "y = y.to_numpy()\n", - "\n", - "# Create Keras Sequential object and build neural network\n", - "model = tf.keras.Sequential()\n", - "model.add(\n", - " tf.keras.layers.Dense(\n", - " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", - " )\n", - ")\n", - "for i in range(1, n_hidden_layers):\n", - " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", - "model.add(tf.keras.layers.Dense(units=n_outputs, activation=keras.activations.linear))\n", - "\n", - "# Train surrogate (calls optimizer on neural network and solves for weights)\n", - "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", - "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", - " \".mdl_co2.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", - ")\n", - "history = model.fit(\n", - " x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save]\n", - ")\n", - "\n", - "# Get the training and validation MSE from the history\n", - "train_mse = history.history[\"mse\"]\n", - "val_mse = history.history[\"val_mse\"]\n", - "\n", - "# Generate a plot of training MSE vs validation MSE\n", - "epochs = range(1, len(train_mse) + 1)\n", - "plt.plot(epochs, train_mse, \"bo-\", label=\"Training MSE\")\n", - "plt.plot(epochs, val_mse, \"ro-\", label=\"Validation MSE\")\n", - "plt.title(\"Training MSE vs Validation MSE\")\n", - "plt.xlabel(\"Epochs\")\n", - "plt.ylabel(\"MSE\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" - ] - } - ], - "source": [ - "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", - "xmin, xmax = [7, 306], [40, 1000]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "\n", - "keras_surrogate = KerasSurrogate(\n", - " model,\n", - " input_labels=list(input_labels),\n", - " output_labels=list(output_labels),\n", - " input_bounds=input_bounds,\n", - " input_scaler=input_scaler,\n", - " output_scaler=output_scaler,\n", - ")\n", - "keras_surrogate.save_to_folder(\n", - " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Visualizing Surrogates\n", - "\n", - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13/13 [==============================] - 1s 3ms/step\n" - ] }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", + "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", + ":241)\n" + ] + } + ], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "13/13 [==============================] - 0s 3ms/step\n" - ] - }, - { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] = [\n", + " \"pressure\",\n", + " \"temperature\",\n", + " \"enth_mol\",\n", + " \"entr_mol\",\n", + " \"CO2_enthalpy\",\n", + " \"CO2_entropy\",\n", + "]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "# Creating input_data and output_data from data\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "13/13 [==============================] - 0s 4ms/step\n" - ] }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "13/13 - 3s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 3s/epoch - 249ms/step\n", + "Epoch 2/250\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 237ms/epoch - 18ms/step\n", + "Epoch 3/250\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 262ms/epoch - 20ms/step\n", + "Epoch 4/250\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 283ms/epoch - 22ms/step\n", + "Epoch 5/250\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 351ms/epoch - 27ms/step\n", + "Epoch 6/250\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 364ms/epoch - 28ms/step\n", + "Epoch 7/250\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 245ms/epoch - 19ms/step\n", + "Epoch 8/250\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 203ms/epoch - 16ms/step\n", + "Epoch 9/250\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 300ms/epoch - 23ms/step\n", + "Epoch 10/250\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 255ms/epoch - 20ms/step\n", + "Epoch 11/250\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 247ms/epoch - 19ms/step\n", + "Epoch 12/250\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 321ms/epoch - 25ms/step\n", + "Epoch 13/250\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 219ms/epoch - 17ms/step\n", + "Epoch 14/250\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 272ms/epoch - 21ms/step\n", + "Epoch 15/250\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 299ms/epoch - 23ms/step\n", + "Epoch 16/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 271ms/epoch - 21ms/step\n", + "Epoch 17/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 248ms/epoch - 19ms/step\n", + "Epoch 18/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 256ms/epoch - 20ms/step\n", + "Epoch 19/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 153ms/epoch - 12ms/step\n", + "Epoch 20/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 239ms/epoch - 18ms/step\n", + "Epoch 21/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 263ms/epoch - 20ms/step\n", + "Epoch 22/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 243ms/epoch - 19ms/step\n", + "Epoch 23/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", + "Epoch 24/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 189ms/epoch - 15ms/step\n", + "Epoch 25/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 280ms/epoch - 22ms/step\n", + "Epoch 26/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 222ms/epoch - 17ms/step\n", + "Epoch 27/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 286ms/epoch - 22ms/step\n", + "Epoch 28/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 116ms/epoch - 9ms/step\n", + "Epoch 29/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 157ms/epoch - 12ms/step\n", + "Epoch 30/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 267ms/epoch - 21ms/step\n", + "Epoch 31/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 264ms/epoch - 20ms/step\n", + "Epoch 32/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 269ms/epoch - 21ms/step\n", + "Epoch 33/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 353ms/epoch - 27ms/step\n", + "Epoch 34/250\n", + "13/13 - 1s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 537ms/epoch - 41ms/step\n", + "Epoch 35/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 330ms/epoch - 25ms/step\n", + "Epoch 36/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 289ms/epoch - 22ms/step\n", + "Epoch 37/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 155ms/epoch - 12ms/step\n", + "Epoch 38/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 289ms/epoch - 22ms/step\n", + "Epoch 39/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 118ms/epoch - 9ms/step\n", + "Epoch 40/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 299ms/epoch - 23ms/step\n", + "Epoch 41/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 42/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 192ms/epoch - 15ms/step\n", + "Epoch 43/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 44/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 154ms/epoch - 12ms/step\n", + "Epoch 45/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 46/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 47/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 48/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 288ms/epoch - 22ms/step\n", + "Epoch 49/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 140ms/epoch - 11ms/step\n", + "Epoch 50/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 51/250\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 52/250\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 265ms/epoch - 20ms/step\n", + "Epoch 53/250\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 179ms/epoch - 14ms/step\n", + "Epoch 54/250\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 287ms/epoch - 22ms/step\n", + "Epoch 55/250\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 284ms/epoch - 22ms/step\n", + "Epoch 56/250\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 297ms/epoch - 23ms/step\n", + "Epoch 57/250\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 206ms/epoch - 16ms/step\n", + "Epoch 58/250\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 59/250\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 274ms/epoch - 21ms/step\n", + "Epoch 60/250\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 61/250\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 62/250\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 63/250\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 154ms/epoch - 12ms/step\n", + "Epoch 64/250\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 284ms/epoch - 22ms/step\n", + "Epoch 65/250\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 168ms/epoch - 13ms/step\n", + "Epoch 66/250\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 67/250\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 264ms/epoch - 20ms/step\n", + "Epoch 68/250\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 189ms/epoch - 15ms/step\n", + "Epoch 69/250\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 175ms/epoch - 13ms/step\n", + "Epoch 70/250\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 322ms/epoch - 25ms/step\n", + "Epoch 71/250\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 207ms/epoch - 16ms/step\n", + "Epoch 72/250\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 257ms/epoch - 20ms/step\n", + "Epoch 73/250\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 74/250\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 250ms/epoch - 19ms/step\n", + "Epoch 75/250\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 76/250\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 77/250\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 126ms/epoch - 10ms/step\n", + "Epoch 78/250\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 242ms/epoch - 19ms/step\n", + "Epoch 79/250\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 80/250\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 81/250\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 166ms/epoch - 13ms/step\n", + "Epoch 82/250\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 83/250\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 84/250\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 85/250\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 282ms/epoch - 22ms/step\n", + "Epoch 86/250\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 246ms/epoch - 19ms/step\n", + "Epoch 87/250\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 88/250\n", + "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 89/250\n", + "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 193ms/epoch - 15ms/step\n", + "Epoch 90/250\n", + "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 168ms/epoch - 13ms/step\n", + "Epoch 91/250\n", + "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 156ms/epoch - 12ms/step\n", + "Epoch 92/250\n", + "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 93/250\n", + "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 94/250\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 303ms/epoch - 23ms/step\n", + "Epoch 95/250\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 129ms/epoch - 10ms/step\n", + "Epoch 96/250\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 97/250\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 98/250\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 147ms/epoch - 11ms/step\n", + "Epoch 99/250\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 130ms/epoch - 10ms/step\n", + "Epoch 100/250\n", + "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 101/250\n", + "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 102/250\n", + "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 260ms/epoch - 20ms/step\n", + "Epoch 103/250\n", + "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 275ms/epoch - 21ms/step\n", + "Epoch 104/250\n", + "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 105/250\n", + "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 275ms/epoch - 21ms/step\n", + "Epoch 106/250\n", + "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 157ms/epoch - 12ms/step\n", + "Epoch 107/250\n", + "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 183ms/epoch - 14ms/step\n", + "Epoch 108/250\n", + "13/13 - 0s - loss: 6.7955e-04 - mae: 0.0198 - mse: 6.7955e-04 - val_loss: 7.6778e-04 - val_mae: 0.0204 - val_mse: 7.6778e-04 - 192ms/epoch - 15ms/step\n", + "Epoch 109/250\n", + "13/13 - 1s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 516ms/epoch - 40ms/step\n", + "Epoch 110/250\n", + "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 111/250\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 112/250\n", + "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 113/250\n", + "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 114/250\n", + "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 115/250\n", + "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 166ms/epoch - 13ms/step\n", + "Epoch 116/250\n", + "13/13 - 0s - loss: 6.5036e-04 - mae: 0.0194 - mse: 6.5036e-04 - val_loss: 6.5858e-04 - val_mae: 0.0191 - val_mse: 6.5858e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 117/250\n", + "13/13 - 0s - loss: 6.4983e-04 - mae: 0.0194 - mse: 6.4983e-04 - val_loss: 7.0443e-04 - val_mae: 0.0198 - val_mse: 7.0443e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 118/250\n", + "13/13 - 0s - loss: 6.4994e-04 - mae: 0.0195 - mse: 6.4994e-04 - val_loss: 6.3181e-04 - val_mae: 0.0193 - val_mse: 6.3181e-04 - 296ms/epoch - 23ms/step\n", + "Epoch 119/250\n", + "13/13 - 0s - loss: 6.6252e-04 - mae: 0.0199 - mse: 6.6252e-04 - val_loss: 6.3527e-04 - val_mae: 0.0191 - val_mse: 6.3527e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 120/250\n", + "13/13 - 0s - loss: 6.4578e-04 - mae: 0.0193 - mse: 6.4578e-04 - val_loss: 6.3127e-04 - val_mae: 0.0189 - val_mse: 6.3127e-04 - 190ms/epoch - 15ms/step\n", + "Epoch 121/250\n", + "13/13 - 0s - loss: 6.1375e-04 - mae: 0.0191 - mse: 6.1375e-04 - val_loss: 6.5351e-04 - val_mae: 0.0192 - val_mse: 6.5351e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 122/250\n", + "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 142ms/epoch - 11ms/step\n", + "Epoch 123/250\n", + "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 278ms/epoch - 21ms/step\n", + "Epoch 124/250\n", + "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 246ms/epoch - 19ms/step\n", + "Epoch 125/250\n", + "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 135ms/epoch - 10ms/step\n", + "Epoch 126/250\n", + "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 127/250\n", + "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 142ms/epoch - 11ms/step\n", + "Epoch 128/250\n", + "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 249ms/epoch - 19ms/step\n", + "Epoch 129/250\n", + "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 279ms/epoch - 21ms/step\n", + "Epoch 130/250\n", + "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 356ms/epoch - 27ms/step\n", + "Epoch 131/250\n", + "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 132/250\n", + "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 133/250\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 239ms/epoch - 18ms/step\n", + "Epoch 134/250\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 285ms/epoch - 22ms/step\n", + "Epoch 135/250\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 178ms/epoch - 14ms/step\n", + "Epoch 136/250\n", + "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 137/250\n", + "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 138ms/epoch - 11ms/step\n", + "Epoch 138/250\n", + "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 144ms/epoch - 11ms/step\n", + "Epoch 139/250\n", + "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 279ms/epoch - 21ms/step\n", + "Epoch 140/250\n", + "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 141/250\n", + "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 142/250\n", + "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 143/250\n", + "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 304ms/epoch - 23ms/step\n", + "Epoch 144/250\n", + "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 178ms/epoch - 14ms/step\n", + "Epoch 145/250\n", + "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 146/250\n", + "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 396ms/epoch - 30ms/step\n", + "Epoch 147/250\n", + "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 148/250\n", + "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 155ms/epoch - 12ms/step\n", + "Epoch 149/250\n", + "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 150/250\n", + "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 151/250\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 315ms/epoch - 24ms/step\n", + "Epoch 152/250\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 198ms/epoch - 15ms/step\n", + "Epoch 153/250\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 129ms/epoch - 10ms/step\n", + "Epoch 154/250\n", + "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 155/250\n", + "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 267ms/epoch - 21ms/step\n", + "Epoch 156/250\n", + "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 182ms/epoch - 14ms/step\n", + "Epoch 157/250\n", + "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 158/250\n", + "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 159/250\n", + "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 172ms/epoch - 13ms/step\n", + "Epoch 160/250\n", + "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 164ms/epoch - 13ms/step\n", + "Epoch 161/250\n", + "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 169ms/epoch - 13ms/step\n", + "Epoch 162/250\n", + "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 188ms/epoch - 14ms/step\n", + "Epoch 163/250\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 258ms/epoch - 20ms/step\n", + "Epoch 164/250\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 165/250\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 166/250\n", + "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 265ms/epoch - 20ms/step\n", + "Epoch 167/250\n", + "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 168/250\n", + "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 169/250\n", + "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 170/250\n", + "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 171/250\n", + "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 243ms/epoch - 19ms/step\n", + "Epoch 172/250\n", + "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 173/250\n", + "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 174/250\n", + "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 175/250\n", + "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 136ms/epoch - 10ms/step\n", + "Epoch 176/250\n", + "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 177/250\n", + "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 316ms/epoch - 24ms/step\n", + "Epoch 178/250\n", + "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 214ms/epoch - 16ms/step\n", + "Epoch 179/250\n", + "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 181ms/epoch - 14ms/step\n", + "Epoch 180/250\n", + "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 144ms/epoch - 11ms/step\n", + "Epoch 181/250\n", + "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 302ms/epoch - 23ms/step\n", + "Epoch 182/250\n", + "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 183/250\n", + "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 296ms/epoch - 23ms/step\n", + "Epoch 184/250\n", + "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 195ms/epoch - 15ms/step\n", + "Epoch 185/250\n", + "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 186/250\n", + "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 187/250\n", + "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 147ms/epoch - 11ms/step\n", + "Epoch 188/250\n", + "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 451ms/epoch - 35ms/step\n", + "Epoch 189/250\n", + "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 190/250\n", + "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 191/250\n", + "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 173ms/epoch - 13ms/step\n", + "Epoch 192/250\n", + "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 193/250\n", + "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 243ms/epoch - 19ms/step\n", + "Epoch 194/250\n", + "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 272ms/epoch - 21ms/step\n", + "Epoch 195/250\n", + "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 136ms/epoch - 10ms/step\n", + "Epoch 196/250\n", + "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 197/250\n", + "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 160ms/epoch - 12ms/step\n", + "Epoch 198/250\n", + "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 199/250\n", + "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 200/250\n", + "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 198ms/epoch - 15ms/step\n", + "Epoch 201/250\n", + "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 147ms/epoch - 11ms/step\n", + "Epoch 202/250\n", + "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 203/250\n", + "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 204/250\n", + "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 205/250\n", + "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 206/250\n", + "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 294ms/epoch - 23ms/step\n", + "Epoch 207/250\n", + "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 208/250\n", + "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 209/250\n", + "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 210/250\n", + "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 211/250\n", + "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 212/250\n", + "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 309ms/epoch - 24ms/step\n", + "Epoch 213/250\n", + "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 214/250\n", + "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 215/250\n", + "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 177ms/epoch - 14ms/step\n", + "Epoch 216/250\n", + "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 253ms/epoch - 19ms/step\n", + "Epoch 217/250\n", + "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 136ms/epoch - 10ms/step\n", + "Epoch 218/250\n", + "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 254ms/epoch - 20ms/step\n", + "Epoch 219/250\n", + "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 220/250\n", + "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 157ms/epoch - 12ms/step\n", + "Epoch 221/250\n", + "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 222/250\n", + "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 223/250\n", + "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 176ms/epoch - 14ms/step\n", + "Epoch 224/250\n", + "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 421ms/epoch - 32ms/step\n", + "Epoch 225/250\n", + "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 159ms/epoch - 12ms/step\n", + "Epoch 226/250\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 307ms/epoch - 24ms/step\n", + "Epoch 227/250\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 193ms/epoch - 15ms/step\n", + "Epoch 228/250\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 133ms/epoch - 10ms/step\n", + "Epoch 229/250\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 230/250\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 231/250\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 276ms/epoch - 21ms/step\n", + "Epoch 232/250\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 134ms/epoch - 10ms/step\n", + "Epoch 233/250\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 234/250\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 235/250\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 153ms/epoch - 12ms/step\n", + "Epoch 236/250\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 187ms/epoch - 14ms/step\n", + "Epoch 237/250\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 238/250\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 175ms/epoch - 13ms/step\n", + "Epoch 239/250\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 240/250\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 130ms/epoch - 10ms/step\n", + "Epoch 241/250\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 122ms/epoch - 9ms/step\n", + "Epoch 242/250\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 128ms/epoch - 10ms/step\n", + "Epoch 243/250\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 321ms/epoch - 25ms/step\n", + "Epoch 244/250\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 245/250\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 129ms/epoch - 10ms/step\n", + "Epoch 246/250\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 191ms/epoch - 15ms/step\n", + "Epoch 247/250\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 248/250\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 122ms/epoch - 9ms/step\n", + "Epoch 249/250\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 150ms/epoch - 12ms/step\n", + "Epoch 250/250\n", + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 127ms/epoch - 10ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs, activation=keras.activations.linear))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_co2.keras\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(\n", + " x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save]\n", + ")\n", + "\n", + "# Get the training and validation MSE from the history\n", + "train_mse = history.history[\"mse\"]\n", + "val_mse = history.history[\"val_mse\"]\n", + "\n", + "# Generate a plot of training MSE vs validation MSE\n", + "epochs = range(1, len(train_mse) + 1)\n", + "plt.plot(epochs, train_mse, \"bo-\", label=\"Training MSE\")\n", + "plt.plot(epochs, val_mse, \"ro-\", label=\"Validation MSE\")\n", + "plt.title(\"Training MSE vs Validation MSE\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"MSE\")\n", + "plt.legend()\n", + "plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" + ] + } + ], + "source": [ + "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", + "xmin, xmax = [7, 306], [40, 1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\n", + " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "surrogate_scatter2D(keras_surrogate, data_training)\n", - "surrogate_parity(keras_surrogate, data_training)\n", - "surrogate_residual(keras_surrogate, data_training)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Model Validation\n", - "\n", - "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4/4 [==============================] - 0s 5ms/step\n" - ] }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 1s 3ms/step\n" + ] + }, + { + "data": { + "image/png": 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CLRKP1Wp1Xmd/TJgwQTPHRASPnu5pgiDYQOj4rcm1I8+ePYuNGzeiW7duiIyMBABs27YNLVu2RF5eHvr06QOO45CZmYlFixahYcOGAID8/HxkZma67SsrKws5OTkAgMrKSuzfvx8zZ850fm40GpGZmYn8/HwAwP79+1FVVeW2n7Zt26J58+ZOEZafn48OHTo4BRj/O+PGjcMvv/yCm266yetxXblyBVeuXHG+Li8vD+EsSY/eRIu/gVfMdoT20Ns9TRCEttCUCJs+fTpWrlyJixcvomvXrsjLy3N+9r///Q/Hjx/H+++/j7fffhs1NTWYNGkS7r//fnzxxRcAgJKSEjdhBABNmjRBeXk5Ll26hHPnzqGmpsbrNr/99ptzH1FRUYiPj6+1TUlJid/f4T/zxYIFCzBv3rwgzoiykGjRBmTZEQ7d0wThgPoNdVBVhM2YMQMLFy70u83hw4fRtm1bAMDUqVMxcuRIHD9+HPPmzcPQoUORl5cHg8EAu92OK1eu4O2338a1114LAFizZg06d+6MI0eO+HRRssTMmTMxefJk5+vy8nIkJyer2CJCa5BlhyCIYKF+Qz1UFWFTpkzB8OHD/W7TsmVL5/8TEhKQkJCAa6+9Fu3atUNycjL27duHjIwMJCYmIiIiwinAAKBdu3YAHJmKbdq0QdOmTWtlMZ46dQpxcXGIiYmByWSCyWTyuk3Tpk0BAE2bNkVlZSXKysrcrGGe23hmVPL75LfxRnR0NKKjo/2eD4LwB1l2CIIIFuo31ENVEdaoUSM0atRI1HftdjsAOGOounfvjurqahw9ehRpaWkAgP/+978AgBYtWgAAMjIy8Mknn7jtZ+fOncjIyADgMLd27twZu3fvxoABA5y/s3v3bkyYMAEA0LlzZ0RGRmL37t0YNGgQAODIkSP4888/nfvJyMjACy+8gNOnT6Nx48bO34mLi0P79u1FHS+hf8gdIA10HrUJXTd2sNlicfasBQ0bWmE2V6jaFr3fF5qICSsoKMB3332HW2+9FQ0aNMDRo0cxe/ZspKWlOYVPZmYmOnXqhBEjRmDp0qWw2+0YP3487rzzTqd1bOzYsVi5ciWmTZuGESNG4IsvvsDmzZuxfft2529NnjwZw4YNQ5cuXXDLLbdg6dKluHDhAh577DEAgNlsxsiRIzF58mQ0bNgQcXFxmDhxIjIyMtC1a1cAwF133YX27dvj0UcfxaJFi1BSUoJZs2Zh/PjxZOkSgd4fQoDcAVJB51Gb0HVjhx9+uAnbtvUDxxlhMNjRv38eOnX6UZW2hMN9oQkRVrduXWzduhXPPvssLly4gMTERPTp0wezZs1yihqj0Yht27Zh4sSJ6NGjB+rVq4e7774br7zyinM/qamp2L59OyZNmoRly5ahWbNmePPNN5GVleXcJjs7G2fOnMGcOXNQUlKCG2+8ETt27HALtF+yZAmMRiMGDRqEK1euICsrC6+++qrzc5PJhLy8PIwbNw4ZGRmoV68ehg0bhvnz5ytwtvSFnh9CV3FZWloq6DvkDvBPqG4VliwA4QS5w9jAZot1CjAA4Dgjtm3rh7S0P1R5Hjyvt6/nU8v3hSZEWIcOHZwZjv5ISkrCli1b/G7Ts2dP/Pijf1U/YcIEp/vRG3Xq1MGqVauwatUqn9u0aNGiluuTCB45O2eh9dqEbhcMQsUloRwsWQAI/cKyZf/sWYtTgPFwnBFnzzZUfVKi1+dTEyKMYAM1RYscWCwWTJgwwdkhFhUZUVgYgdTUaiQlOWIO5eoQ5Zi5Wa1WwRY1wnG+bDYbgMAWAK3c03pBrxZJ1i37DRtaYTDY3YSYwWBHw4ZnFW+LK6xZ6KSERBghGE/R4g2txWfxbV2zBhgzBrDbAaMReOMNYORIlRsXBL46d70OZqFSVlaGzZs3O1/7sgB07DgI/frV19Q9rXX0avEA2He7ms0V6N8/r9b5F9J3yGnhY9lCFyokwoig0ONgdPLkVQEGOP59/HEgKwto1kzdtgnFW+fnbzALF8uOLxFaXV3ttp0vC8D119dR9J5n2VWlBHq2eHiDlUmSa3/QqdOPSEv7A2fPNkTDhmfd2uWr35DbwseqhU4KSIQRojl5Evj9d6B1a+2IFW/8/vtVAcZTUwP88Qd7x1VaWipoIPY1mE2Y0BqdOjXW9UDOE4xFxZcFICnpZsXay7qrSgn0bPHwhCWLX6heDrkC6MvKygCEZqFjHRJhhCi07r5zpXVrxzG4CjGTCWjVSr02+erEtm7dCiDwQOxrMCsvDw8BFsiicu7cuVrf8W4BUE6Ese6qkgvXWEY9WzxcYdHiJ1W/IJW4tFqtbiEDaWl/YNCgLQA4JCef1IUAA0iEESLQg/vOlWbNHCLy8ccdFjCTCXj99avHorSLSEgnFmgg9jWYpaRU+/mWduGvER9oH8iismfPHq/7MZsrdNO5awFP618gi4de3Oh6tfhJKS5d+7hAfaKW7wsSYUTQKOm+83y4fFmIQn0IR450iMg//nBYwFwFmBIuIv64IiOvSNKJseBekwIhAhhArWsUrEWFldgc1pB7AuJt394skgMHDkRSUpJurLh6tfjJIS4DCbvs7GxN3xckwoigUdJ95xqrkJsbg/nzzbDbDTAaOSxaZMNDD12SzBLVrFltESmXi8hVNLrO8gA7gNA7MZstFg0anMPIkW+iqipKFfdaqAgVwIMHD671XjAxJCzF5riitjBUI0bN9ZhTU487309ISND0QOuJXmOc5BCXgYSd2WwWvW8WIBFGBE0g9x2PVLNoi8WCkyeBadNcXaAGTJ8ej+zseGixb+bF5bFj1Zg/vzE4zvD3J0YAHACDc1t/nRh/jl3rg3kTFa4DmlYQKmw9Mx15/GV58bAYmwOwIQyVjlGTMpaI1QzTULMQWUcOcalXqyEPiTBCFL7cdzxSz6K1lMEoFIvFgoMHax8XYHB2Ov46MW/nmIqOuuMZ4+VpXQo0y1bjfLEqDOW0zEl1zKxnmOqx1qInQiY/waBXqyEPiTBCNN7cdzxSz6JZzGCUAm/HZTDYnW7EyMhKVFVFw2aLrdXpeDt3vkRF9+7D0LOnPuu8eeJLLHiztKSl/eF1lj18+K3o0KG/KueLxaBtuS1zUh2zFjJM9fgMek5WfCW4iJ3USC3sWIJEGKEJhLpAtUbt4+LQt28emjUrFjXweTPdm0wc0tMtmnTbuiLEEuPrnPmytOTkLPU6y27T5mbFB0t+gArkflHaOqeEZU4ul5PacXVywZrLVQkLn14zl0mEEZohkAtUCeTo1F2Py2Ipw9atvkWDq1vRW4fnzXS/cGE5mjWLl6StoSJ28BAiSP2dM3+WFrXrg/G4DmTXXFOO6dPNqKkxwGTisHBhOR566EFVXFVKWObkcDnJab1TUwR5ulx99UlKu1yl/i29rVXsCxJhhKbw5wKVGzk79avH1QBJSROwZw+wZIl/t2JxcbHb53xnnJb2B3JyljpFxUMPPQggXpJ2hoLYeB2hlhh/YsGXpSUyshKFhSm1svHUgj/uKVOA7Gx+wmH4W0THq9ImOQOj5QpUl8N6xwsvz3VHlRZBQutnKelydRWlRUVGFBZGIDW1GklJjjgLMaI0HOLnABJhBOEXvvNXMuDdYrGga1fvMXC+3IosZNMFQmy8TiBLTGRkJAD/YsFsrsD8+SV49tlE2O2OxIcbbjiINWtGMXvO1JxwuCJnYLRcA63U1jtf1qeiokTs2pWpighiJYHD9dz464fEiFKtCywhkAgjCD/wg4QQy5SUBBMDx0pnLDVCY6QaNWok2I13992nsGLFp4iMrHQKMEA/50xKlCqnIMdAG+iecS3p4oovwefL+uRaTsbbPSSn25KVBA7++AL1Q3pbbksqSIQRkuDZ2Xh2cloOkBVjmZICoTFwrHTGwRLonhATIxXIjZeUZEdq6nEUFqZo8pwpiZbdQYGsd1u3bhXlRvQUGq71/AD3e+jEiRP49NNP3b4rpduStfpZWu2H1IZEGBEygWJ9/JmotRJUqVZ2pj+XFKvZdEIQ6j4VEyMlxI3H2gDGKiwKLH8Itd6JjaXyJjRccb2HXAXYN99kSO62ZK1+Fj1T4iARRoSMv07Em4l6+/b+mDMnHSkpEZrq5FnIznSF1Wy6QIh1n0oRI8UP0movFM1aiQG94Gm98xZEf+JEMj7+uB/45cGCcUV7Exq8S9KXCPrmmwzs3Hkn/LktxcJS/SyzuQKZmbtqiU2ygvmHRBghK95mjjU1BlRUNNFk3SpWgqV5WMymC0Qgt4WnK1tKQeI5SM+ZcwbHjkUgJaX678XNb5ZdALFe1V3r+Dpn7rFc7vi6/zzvBW/iPTNzF5KSiryKIJstFjt3ZsKf2zJU1K6fVVZWBsBxfnkBBjjOC0uJLqxCIoyQFe/FQ7Vf6Z5FWBKI3iw9/OAWyG2xdevWWvuTUpC47icxEejcWZLdCsbzvPiKFaJAZumoHcvljmu5krVrd9aK2XIlGOvT2bMW8BY3z98T66ZjqX6W1WrF5s2bvZxfI3btysT11x8iS1gASIQRsuI5c9RLpXs1Yd2VFcjSIyaWRa+CRAulRfSAv1iuQOVKfBVFFiIufLkvMzN3iRYnLCVM8G1gJSj/5EnHOsOtW2tnjCERRkiO58yenzl27z4M6ekWzTwcQlFSFGnBlSVEMHXq9CMmTGiNEyeicejQR86OWstZtMGi19IiLOLL+jpo0AeIjy8LWK5EqFUpOzsbZrMZNpsNmzZtqjXhAOy4885d6N49P6TjYc1NLSQoXy7LHN//5ubGYNo0M+x2A4xGDosW2fDQQ5ec/S+rAo1EGCEpvmb2ZnMFunWrRGKiNL/DijVIaVGkhQWKPfElrK6/Ph7XXw+cOOF4L9ysQqxYD8IBX9bX668/LKhcSSjWJ5aC5+UikHU7Oztblv6Y739ttlgsXZoDjnPE3tntBkydGoe//noLZnMF4uOnYPLk+rDbHaWG3njDkWjFAiTCCMlQambPkjVIbVHEuuVIqLAKR6uQ2in9rExklMKXGBJ6HeRafJrFEjJi8Cc2zWazLL8pxB0KAPPn13PWeLTbgccf53DjjaeZyNAnEUaEDN+JBJrZS9XZqC18WIF1y1EwwiocrUJq1nlidRFoufEmhuS4DkL7ukceeYTp8xusC0+tTE1/QvrsWQvsdvfs1JoaA1as+BSpqcdVv8dJhBEhw5vqjx2rxoYNnNsNbzJxmDjxbiZmHEqglGVKC5ajYISVGlYhFixBarmqWFwEWg6EiiGprwNLwfNiWbMGGDMGTLrwPAkkpP31LWrf4yTCCEmwWBzL99SuKm9A585NZP1tVlxySlqmtGA5CkZYKW0VUtOl7SkMfFkPlHBTySHmXcVtUZERhYURSE2tRlKSwx+kpPjwJ4ZKS0vdyqFIbcVhWWD5w2q14tixaowZ09g5oVbShSc2gN6XkGZtZQFPSIQRkqJ0VXlWXHJKW6bUjicSgpDOT6lFoj1Rs1aXnFaSYK17Uot5V3Hr79mUWtyKEX4s1dtiBf76FRamwG4f5vaZNxee1OcwVOubLyHNcnIEiTBCcpQqGsqSS05pyxTLs7tghJVQQQIAxcXFfrcRO6irIeTlzBTj8RfnxSO1mOevY6BnU0pxK1b46cFlKDX8uQh0X/DbSXUOlbC+qb2ygC9IhBGCYSGGxhWWXHJyWaY8zzm/RAigrOUoGILtmIV00HK5DlkS8qEiJs5LLjGv5LMZivALJ4EVDMHcF6Gew2Ctb3qDRBghCJbKQvCw4JKTc0FoIefcbK7AmDH3uKWAszB7l/L35XQdsiTkpSJYYSmHq0aNZ1OP11JNlHLhBWt9c0UPrmISYYQgWCoLIafwCRY5F4QWKj7MZjMSpaqCyzhSuw5ZEPJSI0aMSO2qUcNdrsdrqTZKuPAcMXwpaNjQGvQ9E0ziBauQCCM0h5zCR2x7ePwtCB2KO1cq8cGaSzkY5HAdshxbJxYpl5AJZSKjdDC0Hq+l3nEE4jeG3T7Meb1ycpYGdc/46q+0knhBIowQhdplIYQKH1YIxZ0rlfhg0aUcDHK5m1jOnBKDEDEiRUC1t4xEs7msVluUPJ96u5Z65uRJPhPSEYjP92s5OUuRmno85P1rJfGCRBgRNP6sMqWlpUzc2KwRSlyTVOJDzbIMUuDLwnPhQj3YbLEoLS0FIKxjZalWlxwIESOhPKO+MxIboH//m1RduYHVLDjiKlarFfv2AXa7+z0odQyfFsYhEmFEUASyyvA+eFatKSwQrGtRjlgXVuqrBYM3Cw/HAR988AAMBjuOHs1Dp07C7j+tzJJDQU4xIjQj0RdaFbeBEFtolBWUcOG5LrptMOSEfQwfiTAiKIRaZVi1psiJv3gr3kojZtCSOtZFy2UZeAvPiRPN8MEH9wMQX4dKKoHFSoV4NWJgAvUHAwcOREJCQq3fl/J8qB37w1//3NwYTJtmht1ugNHIYdEiGx566JKmxLwSkxN+3xTD54BEGBEUvqwykZHhJ7pcERpvJda1KGWsi9ZS+b25Ds+evQRegPGocQxqVYj3hhrWvUBW2oSEBNkzd9W0arpadZYuzQHH8YVGDZg6NQ5//fUWzOYKTXkGlGxnMP2aXq2nJMLCDLHZcb7KQgAcOM6INWtGacKlJRdC462CcS3KFbektVR+10GWTztn5RjUqBDvD6UHelasGWoJHP66BprYKHX9tegO9dWvuVpRtWRNDBYSYWFEKNlx/EBYVFQEYCsaNy7Bm2+Ogi93UDjjzyISbCVqOWb4rAycweB5jKwdg9asi1Kip4xEsSKGhUlBqOsusoYSVlQWIBEWRoSaHWexWJyfVVVFgwV3kJJIFfMVzKAl1+xPDwMnS8fAwiCsJnrISAxFxEg5KQhWCCqx7mIoaNE6pyQkwsKUULPjQh10tFY0VOjiyEItImqURNBjWQZWBn/WLHOEcKQSMVJMCoIVgiysu+hPZHk7nnvukaUZmoVEWBgiRXZcoEHHX80mLRYNFbo4ciBx6i1bjEdu4alEADMrmYJqwJJlTm7UzkiUCqlFjNhJgVghGMq6i1LgTzReLcYKl+MBCgqMvncYhpAIC0NCiV9x7VQ9Bx0AzjXAXNfs8uzAtFw0NJCADSRO1Y5zkFMAKZEpyPrgz4plTm70UmdNbREDSCME1bDE+hJZWVlATIz3Yqw1NcAvv1wRtH/WBbxUkAgLQ0JxJXrLVDObK/wOuv46MK0VDRUiYMPJIuKKEpmCehn8tY7DBWXRTZyPmu5kqYSgkv2Or4r3NTXA7t3HcezYOp/FWA8c+ABmMzB48GDEx8d73X84PcMkwsKQUDscqdY2VLpoqBSuMqECVg/xVt7wdw55F7TcmYIsdc6sW+bkQG9ZeDxiRIyU118KIaiEJTZQxfufftoCsznw8cTHx4dF9mMgSISFKSwU/1QyrV8qV1mgjkXNmC+5EXoOwylTkDXLnJyZaKxn4UlBsCJG6uuvBSt6MBXvtXA8akMiLIxgrfinkoO1lK4yfx2L2jFfciL0HIZLpuBVwWNR1SWnxLI5LGThyYWvmFShSH28WoorFCKypD4evZW8IBEWRrBW/FONwVoq65uWOkqpCee4ONbWCVRq2RwWAtjlIDc35u/zpo2YVBZRsi/UoyucRFiYwVrxT6UHa7HWt3CM/fFFqHFxWoXFdQKVXjZHT1bOkyeBadPMzuuotVU/Tp4Evv++Lmy22KCy2rWKv2xMLVvESIQRkiF00FWzaKjYQYS12B810dNAHAysrRPoipKufT1YOX1l9/myigfbF8ntMrtqETLDaJzktMR6UlRkxIkTdXDpkln6RijM779fFWA8NTXAH3+QCCPCFLHWISGC5uLFi6isrERxcbHkRT/FDiLhILCEEspArPVZOYuJB0oLYy1bOQNl9/HXkS+hEGxfE6zLLNh+tLZFyIDp0+ORnR3vJkb05rpr3dpxHK5CzGQCWrVSr01SQCKMEE0o1iF/nZrVasUbb7wBQL6in1oeRPyhZNCqr3Oo5wxRgF1LoB4sVEogNLsv2BIKYrNHg+lH/dXnKiiwIibGsT8tuO6CmYxZrVaYTJVYtCgG06ebUVNjgMnEYeFCG0ymS7BatduvkAgjQkKOG1+Jop96grVgcT1niPKwKnj0OrmQC6muY6jZo0Kez0AWvG++WY9Dhxwxib//bhHkuhMzaRMqnrKzs2E2e3eDBtMneS5z98QTsc7rdf58Bf6er2syMxcgEUaoiK/Cn1FR0hf91GtgvZLB4no9h2IhwaMPpLiOSmSPCrXgnTlzBnFx1TAar1rkAMBk4hAbexpWq8MiJ9ZdqXR8rOfv+LpeWp2UkwgjVMF/4c8CdOokbeyNXgPrlQwW1+s5JAJDAtxBIMuRUq5qfxa8TZs2AQD69XPvV/v2zUNeniOUY+DAJzBmTAPR7kp6xqWDRBihCkJdjlJ2aHruOJQKFtfzOVQTsbF8SokjEuDCA92VclUHsuD5a8eRI3ZdZhpqERJhhKoEsuCwGnvDGqwGiyuFEgkJcgmeULLYlBRHehZYgQg20J0VV7WvdpjNp2E0NvTrriSUgUQYoSpCLDisdGisE66CValUfKkFTyhrMbqLThow5URIRqLW2Lt3s193pVaD3LUIiTBCVcLdgiMUoZaecBKsaiwoLdX+Qsmm01v9J6UJxqIpNCNx8ODBcjVXNvxN2rQa5K5FSIQRquOrMwi0sK5r8UI9LejKI6T0hNaQ6lppfUFpMdl0SolOvT5PPMFYNIuLiwEEnixWV1cr0napCadJG6uQCCOYwLMzcM2YdBUePHwnqVergNDSE8HOwNUcYKW8VnpZUFqoJVgp0anX58kTMefIn+UoMjJS0D5CmThpcdIlBXrPzCURRsiKmEHfM2PSsSyHGT17XnHO9q1WK/bvP6WoK0pJhJaeMBgM3r5ei6ioKNUGWDktOHpwZwuJ5fMnOgEORUVJSE09Llp0quHa1SK+A93NsidICLHglZWVYfPmzaJ/g0X0nplLIoyQDX+Dvr9Zizfh4Trbf+SRR/DOO+9o1hUVDIEsPUI6/4sXL/49wHKKD7BKWHD0kJAg1C1kNlcgM3MXdu68EwAvwA3YtSsT119/SNRva921ywpKnJtAv8GSNUhKq7ue7zsSYYTkCJ1Ve4qH0tJSbN26NaDwuHjxIoDQXVFaiH0RYukRsg6nkAH20iWL5OdDKbdhOMW2JCUV46oAc8BbR8vKyoJeMkovrl0isNWI72PlJlzc2lJgDLwJG9x7771o3rw56tSpg8TERDz66KMoKipyfj537lwYDIZaf/Xq1XPbz/vvv4+2bduiTp066NChAz755BO3zzmOw5w5c5CYmIiYmBhkZmbi999/d9vm7NmzePjhhxEXF4f4+HiMHDkS58+fd9vm4MGDuO2221CnTh0kJydj0aJFEp8RNuFn1StW7HCrQQM4Bv2XXvoKU6bk4eefz8FisaCmJhG//ZaImppE56LPvPAwGBxFeXy5mIRu5401a4AWLYBevRz/rlkjxdHLQ6dOPyInZymGDVuHnJylzgXMheA5wLriOsCuW2eS9XyEcq0Id7xdS94luXnzZlitVlH7pWukDywWCxITE73+8X2snPiqqXbypOw/rUk0Ywm744478PTTTyMxMRF//fUXnnrqKdx///3Yu3cvAOCpp57C2LFj3b7Tu3dv3Hzzzc7Xe/fuxYMPPogFCxagX79+yM3NxYABA/DDDz/g+uuvBwAsWrQIy5cvx/r165GamorZs2cjKysLv/76K+rUqQMAePjhh1FcXIydO3eiqqoKjz32GMaMGYPc3FwAQHl5Oe666y5kZmZi9erV+PnnnzFixAjEx8djzJgxSpwu1Qg0q/7gg/sBGLFhA4dHHwU2bLg6W1q06GrBHaEupmBdUVqNfQnV0uPPomazxWL+fLNHpyn9+RDjNtSCtVIO+MzgoiIjPA1bgVySZ86cCWvXLuEbJYLcf//9qgDjoWr8vtGMCJs0aZLz/y1atMCMGTMwYMAAVFVVITIyEvXr10f9+vWd2/z000/49ddfsXr1aud7y5YtQ58+fTB16lQAwHPPPYedO3di5cqVWL16NTiOw9KlSzFr1izcd999AIC3334bTZo0wUcffYQhQ4bg8OHD2LFjB7777jt06dIFALBixQrcc889ePnll5GUlISNGzeisrISb731FqKionDdddfhwIEDWLx4se5FGI+3QZ/jAN74arcbsH49B34QsduBadPMePLJWGfHH0ycjJDtwj32xdcAe/asxavVUo7zEYyY1LNLw98g55oZvGEDhzfeAO65x30bfy7JTZs2hXTNwsm1Gwi9ZeYpEeTeurXjeXUVYiYT0KqV6F3qGs2IMFfOnj2LjRs3olu3bj5Tg998801ce+21uO2225zv5efnY/LkyW7bZWVl4aOPPgIAFBYWoqSkBJmZmc7PzWYz0tPTkZ+fjyFDhiA/Px/x8fFOAQYAmZmZMBqNKCgowP/93/8hPz8fPXr0cHsws7KysHDhQpw7dw4NGjTw2uYrV67gypUrztfl5eXCTwqDuA76Fy7UwwcfPOCxhfsgYrcbnFl/ckCxL94HWNbOhxBrpdYHR2+DYVlZGU6eBObPb+tWkuTxxzl8+mmZW9081q6ZXtFjZp6cbbVarTCZKrFoUQymTzejpsYAk4nDwoU2mEyXYLVq61wpgaZE2PTp07Fy5UpcvHgRXbt2RV5entftLl++jI0bN2LGjBlu75eUlKBJkyZu7zVp0gQlJSXOz/n3/G3TuHFjt88jIiLQsGFDt21SU1Nr7YP/zJcIW7BgAebNm+f94DUKP+g7Kk7XTq13FWJyLDjtq02slzVQUmSwdD6CsVZqfXB0bZvVasXmzZv/Pu52btvV1Bgwd+5Z5OfnuF0fVq6Z3mH5HmIJ/tnleeKJWKfV/fz5CrzxhuN9tT0NrIU4qCrCZsyYgYULF/rd5vDhw2jbti0AYOrUqRg5ciSOHz+OefPmYejQocjLy6tVK+nDDz9ERUUFhg0b5m2XzDJz5kw3S115eTmSk5NVbJF0eBvob7jhIA4evEGVQYT12BelZ+CsnI9grJXBZgGyjP86YHbk52c43+M4I7Zt64ecnKXIyVmq+jUjCKC29dWXW1tNKy2LIQ6qirApU6Zg+PDhfrdp2bKl8/8JCQlISEjAtddei3bt2iE5ORn79u1DRkaG23fefPNN9OvXr5ZFq2nTpjh16pTbe6dOnULTpk2dn/PvuXbwp06dwo033ujc5vTp0277qK6uxtmzZ9324+13XH/DG9HR0YiOjvb5udJIPWPwNtD36vVF0INI3bp1BW0XyErEeuyL0rNFOc6HWIseS9Y5V+SeRXs77oyMfOzd291tOz7+KzX1eMjnROuuXYIQgq+szawsdS1iqoqwRo0aoVGjRqK+a//7TLrGUAGOuK49e/bg448/rvWdjIwM7N69Gzk5Oc73du7c6RRxqampaNq0KXbv3u0UXeXl5SgoKMC4ceOc+ygrK8P+/fvRuXNnAMAXX3wBu92O9PR05zbPPPOMM2mA/502bdr4dEWyhlwzBs+B3tfAP3DgQK/p1Lz1R+uuKKVQe+AM5VqxYp3jUWoW7XncANwsYYB41703EUnPExEOsJq1qYmYsIKCAnz33Xe49dZb0aBBAxw9ehSzZ89GWlpaLSvYW2+9hcTERNx999219vPkk0/i9ttvxyuvvIK+ffvivffew/fff483/nZWGwwG5OTk4Pnnn0fr1q2dJSqSkpIwYMAAAEC7du3Qp08fjB49GqtXr0ZVVRUmTJiAIUOGICkpCQDw0EMPYd68eRg5ciSmT5+OQ4cOYdmyZViyZIm8J0oCQi3hINWgn5CQ4NfdRAOCMPwNsEoVbgzlWrFgrVSjrInncUthFfQnItV+nliL0yG0g9B7h9WsTU2IsLp162Lr1q149tlnceHCBSQmJqJPnz6YNWuWm/vObrdj3bp1GD58OEwmU639dOvWDbm5uZg1axaefvpptG7dGh999JGzRhgATJs2DRcuXMCYMWNQVlaGW2+9FTt27HDWCAOAjRs3YsKECejduzeMRiMGDRqE5cuXOz83m834/PPPMX78eHTu3BkJCQmYM2cO8+UppCjh4DnoKzXQE75Re4DVMqyUNQnVKsiqKwZgM05HC5BwDe7eadbM8fnjjzssYCYT8Prr6p87TYiwDh064Isvvgi4ndFoxIkTJ/xu88ADD+CBBzxLJVzFYDBg/vz5mD9/vs9tGjZs6CzM6osbbrgB//nPf/w3mDGkKuHA6qBPsS/uaPl8FBUZ8dtv8g9ALJU1CcYq6HrNrFYr9u0D7Hb357KmBigosCImRr1nlmVxyDIkXIO7d6xWKyorK3HPPUBBgRHHjkUgJaUaSUl2FBer627XhAgjlIWFoGg5Bn6KfXFHq+fjhx9uwvz5jRUdgFh4JnzhGT/pes14S56jRExOLRH5zTfrcehQRUiWPLEWGZbFIcvoRbiGaskTGuPlWTqD55DHevdqlc4gEUZ4RaqgaKFiKjs7G2az2fkduR4GMfvVs9mftUEu0P1is8X+LYRcY7OUGYDkTBQIZdLhL36SF9iBRKRYS54/i4y/50YJcahXWA0wD8ayLoUlT2iMl9B7W63SGSTCCJ9IERStVWsLD5n9lcXf/VJWVoa9e6M9amgpazmRK1HA23FLHU8ptYj0Z5H57DP/z43c4lDPsBpgLrSvv3TJErIlT0+V+UmEEbJjsVhqzYpdX7P6rOjF7K81vHWefEV5PVtOPJ+TpCTpY/akEpH+XInbt5/FP/7RwCOT1Pdzw1opEpZhXXwI+e09e0Kz5GmlMr9QSIQRsuNpTXr0UWDDBratSyzFq+jZHSqUcLCc1La6KmtFFnqfBXIl7tq1G3a7e/JToOeGhVIkrKMX8RGqJU8LlfmDgUQYISverEnr11/9nBXrkusAFBPDTrwKuUNrozfLif86ZNVISZHfupGbG4Np04TdZ4EEcXLyCa+ZpK7PDRE8lZWVbou4a1F8sG7JUwMSYYQTT7eG5wPvazt/eAsi9URJ65K32b6n0Fm0yFFjTm2rC7lDfaOU5UTuMh4s1CGz2WIxf75Z1H3mSxDr1VqpJrm5MVi61H0R906dfgxqH2pa1fViyZMaEmGEE9fAytzcmL87ZgOMRg6LFtnw0EOXgnJ/WK1WxMVVw2i8OsN3wAG4+lop65I3q1JWVm2hM326GU88EQuzuUI1qwtL7tBwRu7EEhbqkJ09a/F4PoO7z7wJYr1ZK9Xm5Elg2jSzMyuYX8Q9Le0PwedWbCarVOjNjSgVxsCbEOGExWJBTU0ipk2Ld3GNGDB9ejxqahKDEmArV65EXt5q9Ou3DQaDQ+UYDHZ07PiT2+vMzF2yz5K9W5U4bN9+zkuQqAFnzzZ0vjabKyRZKFko/Lnbu3e98zzx8IJ15cqVsFqtirQn3LFYLEhMTPT5J4UY5q2urs+FFHXIhFjoeAHoihT3mdLPjZ5xeBTchTK/iLsQfFnVT550iLMWLYBevRz/rlkjdevVhfWi1GQJI2ohRR0aVzHlbVbcuPFp7NyZCY4zYteuTMTEXA7atB4M3o/JgF27dsJguF+SxZGlIhyC0InayGE9ErJ2qBr3GesDI2s4gtk5NyEWTD/lq0/Pz9d/yAPrZZJIhBFu+HIhmkwcYmNPw2oVt1ixq+nZZovFrl2Z4A2xrqZ1OfB1TAaDHcnJJ5mthA6QWyfckCPWTcjzqvR9JmRgLC2tg4MHG4R1VjBwNZh9zpwazJuXJKqf8pWRyHFsFn6VGpZDN0iEEU5cAyf79bvJTZj07ZuHvDyHpSrUuK2zZy21Cm7ypvWysjKf1b/F4O+Y+E6MdaFD6ftkORFLMLE+St9n/voQIVnB4VC6xTOYPScnNuh+yl9GYlraFS8TbvULv/pDb30BiTDCSSAXorftxOAvCHnz5s2SBucLPSYSOmzDukuBReQqbyL34CYkK1hvpVt8CUqhweyueC7e7i8jMS/PMTnNy+sPu10b5SL01heQCCN8IpcwUTPWScpj0spMSy9opVNlATnLm/gaBMvKyrB58+aA3/f33AjJCpZi2RuWkEJQ8ou4e4oPISLOc3KqhXIRLLZJLCTCCFVg3QXIw3dunsgx0+Jnw3FxlLQcLsjhWlGivIm37ycmJoZkoRC6qHdKymOw25u7fVerpVukEsv+FnEXQrDlIsS4gvXmRpQKEmGEamjBBRhq5yYU99lwY/Trd5Os2aJEcMgVfyS1a0WokJGran0oAkhoVvBPP32g+koWUiFFJrrSiLXc8ff6sWPVKCyMQGpqNZKS3A9eS25EqSARRshCuM1mQqH2bNggqBAjnWNlkDv+SMpBJ5jyJixbJgLFb3o7NgAoLEzBsWPV0MI4LiQTnTVCtdx99JFFV7F8UsDeVSZ0gbcZPl+XSEsoMQB5mw1znBHduw9Dt27eLSThOGNUA60sHeXNlR3I5c96gLM/S7nnsR092sq5pM+GDRzzg7vQTPTs7Gw1m1mLUCx3WnmWlIZEGCEbWhIJ3mK/lBiA/M2GU1KqSWypiFaWjvLnyg7k8meh/WLhj81mi3WKGMBhSWZ9cBeatV1VVaVG83ziq96YkJIWWnS9KgGJMMIJy+4JsQhta1JSkuIDklJ12YjgCSa2Ss1rI9aVrSe81R3U2uDuSyxHRAgbopXok/3VGxNS0iIUAadnSIQRTuR2T6gh8lh2uShVl40InmCXjnIN3AeUKSLqy1LHFz4OFxHmre5gqCt8SI3n/bFvXxRsttiA1yg+Pj6k/kuqvjRQvbFAJS1CFXB6hkQY4YacD4JagkjM/tSoxq2FbNFwJFBslas70PC3R5nj5A08DmSpU3PtU6XxJpRZsiR7vz8sMBhy0L9/XsAs6FDaLlVNN6FFY7317aEKOL1DIoxQFC08ZHqrxk2Ejq9Bp6jI6OYO5Lirn8kZeCzUUicGtZYDCsVqw6ol2dNd7Hp/uK6ZK+fkS46absEQioALB0iEEYQLlMEjHrkGb5bXCDxw4EKtYGNXlAjiF1P42JfgUXMCEshSHsh6wx/32bMWt9dq4i0Y3RU1XcdamBCHAyTCCMIFyuARh1yDN+tWyV9++aiWO9AVpYL4fVkXgsn6ZWEC4u/8eFpvPEve/PDDTbUsgmoWPPaV+eyKVK5jlicqQrDZYvHNN1Ho2lWb7Q8FWh+FIP7G0WmegtHIub3vCPI9BavVqlLL2MbX4H3yJJv7lRLeHWgwOBrp+Pfq/5VaE9UX/IoPrn++Aqf37bN6nYAUFFiZufctFovzOFzFpWeZCt7VZ7PFqtJOPg4qL281+vXb5rw/ALvbvRLIdSzERbtmDdCiBdCrl+PfNWukOALl+OGHm7B0aQ4eeMCiyfaHClnCCAJULkIsctXS0kqNLqC2OxAA82uiuqKVchz+8Famgnf12Ww2RZYec8Vf5rOjvbXvD0+rpZCYLBasl6FQu8abttovBSTCiIBo3dQtBDXKRWi9Lptcg3eg/e7YsQXbtkVjypR70aFDA0mPiSfYc+7pDtSC+OLxF+SfkZFfazsW8Vamgnf1bdq0SXUBKeT+ELNOrdbDJ/RQ4y1USIQRfmE9JkculCgXwXINMyEEW0tLiv3ecMNBrFkzSvblaQJdGy0uwSUEfgJSUJCOvXszsHdvd+TnZzjXZmQVqe9BLSBk7Ump+g65Jozea7xdLeAaDgYAEmGET7Ru6tYCrAqsYBGToRfsfiMjK50CDJB/eRqWr43cVtT8/AzwIcN8fNWcOWegsFfPL57HFso9qLXBXunwiVAmjP7uQU/xbDIBr7/uuAbhYgAgEUb4ROumbqkJ5wweIchlPeT3W1iY4tV1wVqMmBLIaUX1FV/1008XkJJiZeY8WywWDB482K1shZh7UIuDvRrhE2Kvu7+CsdXV1Rg4EJg06X+wWhsgJaUaSUl27N9vxJgxVy18ejYAkAgjfEJrfV2FT39fssSomY5aDCxbBHzF/WghcNwVqc6xXMfq7TwDHN577w+cOJHP1HmOj48P6ft6sfazvtqG5/1itVq91nw7dMjxb2FhCuz2YW6f6dUAQCUqCK841voqxqJFZTCZHCUbHGt9lcFkKlY9Zf3kSWDPHmXKFfjK4GGpVIIUsJ7q7q0cRDBxP1LeM2LcfFFRUcyfY8BxnjMzdwFwLdViwK5dmbDZYnUVX+XP2q8mSvZvahDoHuInAq7o1QBAljCiFqyv9SW3+8Bmi8XZsxY0bGiF2VwRFhk8WrEIiI37kfqe8XSx8K4VVyIjI2E2mwE4BNilSxZNnGMASEoqBuBeYFTNkg9yIGdgu1iRDmjTPSo1/IQrL68/7HZ9L/ZNIoyoBctrfcklFvgO0FvV7bS0P7xk8EifgaQmWor/C9b1Itc943rdA4mSq8VQ2at75k0wsF7yIVTkDmz3FOk2mw1VVVVu20RERDjdqXz8npB79eRJYN++KNhssUy7IEPFc8LFggFADkiEhSEsx/0EQi6xYLFYMHDgE5g/Px4c55gV81lhOTlLa6W/8x21zRaLHj1GoksXs+bOJY9Yi4BcGXpS1kVjoegr68VQecFQVFTkLLuhlZIPYu9BJQLbgxHpPIH6t6tWMgsMhhzVl2aSG5YMAHJBIizM0LqpW85kgdLSBrU6QN4F462j1kOwfigWAbky9Dz3K7YmFyviR656alJisVhq/b5cZUekRKp7kIXA9kCToZ9/jsKYMVf7KH6SmJb2h+ptJ8RDIiyM0Ercjy8cyQKVWLQoBtOnm1FTI12sgK8O0HWBXdeOWi/LbYRqEZBLvEixXxbFjxaEjSssiJNA6MEtJWQy5C1jkJ8k+rpGrK62QVyFRFgYoaW4H0/kTBbw1wH6WmBXr8H6Whh0xaC2+PFM9tDjOSbEI2Qy1LChFUYj5zZJNBo5PPxwOpKTuzqTQIqKjCgsjECbNkZYLNIu66XlUBZWIREWRgh15Tke4hTngMECciYLCLUGueJ9uQ19BeuzglSxZ2qIn5MngRdeiMXrr+e4CXsW43jIasIO3u5Vs7kC/fptc5sk9uuXh717r4YMfPSRRbZwEylDWeheuwqJsDBBqCvP8aA1ht0+zOuA4Tqj1ytCBmtvLi45lgsJZ67OurW5xuayZecxaVI9cFx953ssx/F4q0BPsIW/SeKxY9WyhZtIHcriK5ZPr2uy+oNEWBgg1JU3cOATfwd+umcH8gOGZ/mGFi0u4skn1TgiNlBquZBwpPas26KppIeffz6HSZPMzkxbVwLF8UhNMC6kUCvQE/Lja5JYWBghW7iJHKEsrE2a1IIq5ocB3lx5qanHaz3IR47YvWYHdu8+DP36jUVeXn+n+43jjJgypb5uKzoLxde59IXeK2FLga9Zt1bOmdVqxbffnqsVM8jjmuwhN8FW6Jd7YXBW0ONxms2nYTRybu85QiROhbTCiSNp6ZQs+/ZEj9clEGQJI5ykplZ7jRlLT7doOqifFbReHkQJWKjrFQr+ymIAtZdackXq+mgO95TnIsgcbrzxNFJSvMctyrkwOEvo8Tj37t0seeFZuYvaeuLruvDJBqmp1T7vXa1CIoxwEhV1GosWRXuNGUtIqAOjsYEs9bm0wsCBAwFAVMwCq+VBWJp5Sl3XS41j81UWA7CjW7d8pKcXuAmwgQMHIiEhQdIBnz+P3hdBNmDFik+Rmnrc53lkZYCTOxOPleOUEqlDJJQoauuJ53XR++SVRBjhhBcX3mLGbLZYDBs2DOvXN9T9Wl6+SEhIEP1dVi2JLFkEpK7rpfaxCcm0TUhIELUOoz+Bwh+vv6WHXLdjEb0PvJ5IORGQMwtY6QxjVievUkIijKiF54PmGpDvmNHvRXp6gaJreclp1Qhm32IGLjkXCpYCFgW0VHW91D42OQYtoQIlkJhlFb0NvEIsev4mDOGYMcjD6uRVSkiEEX7xrAwPGJGfn4H09AK37eSeVctp1Qhm38XFxUHtW+mYCj1BRU3dERPnpXaR2mDRekygJ8FY9Hwdl56C0INFzmXqWIFEGOEXb5XhlU6x57l0ySJbjIjQjj1Yi5waMRVax7O6PBFcnJcnWhGzrKz1KRVSWfS8TRJdA9Wjok7r0lImdpk6rVX1JxEWBoQykwoUV6IUrMSIhGqR08qAqBaetehYrS4vFUKfTT3EeQWCxbU+xSK1Rc91W8++cNGiaMnazQpil6ljZZwIBhJhYUAg4eAv5oCFuBLWYkSknIXzVp+iIiNExGdrBiGz06Iio5vrm+Xq8qEycOBAJCUlBX0vsfA8KoHW3KiuyGnR89YXTp9uxhNPxCpyjpSyUotZpo61cUIoJMLChFCEg9odol6DM12tPhs2cJqYtYlByOzUarXiwIEL4Lgmbu+r5foWi1DLlhgBxqP286gUWrUay2XR821dMwh6RkJNWvJnpWYhbk2r4wSJMEIQanaIYoIzWY8L8Ex4sNsNmpi1BYPQQPJAlgNP1zcLHb4vlCqL4e95LC2tg8LClIDWCpbPo16QSjALfUays7NhNptrfT/UpKVjx6oxf35j5zJcHGfE9u39MWdOOjPFU7UaxE8ijGC6MxYTnKmFuABvCQ9amLUJJZhAcqGWA7EuPKVRs325uTGYNi0edvswGI0cFi2y4aGHLtXaTmvV4LWMFBNYoc+I2WwWVXfOHxaLBQcPerMyGVBR0QQs3EZig/hZgEQY4XX2HmxtGjmEnJjgTKXjAsRa3LwFWLNSN0wKxAaS+7McJCQkaP68yInNFov5880u974B06eb0bPnFWasFUToKO2OZr3OodggflYgEUYAcJ+9nzwJHDkSBZstcLCnnNYJMcGZSsYFeLO4ZWW5izJf4tTbjFaPdcPEBJJrNRZIbc6etbgNkoCwZYrkhvXQAC2i1DOihTqHYsYJlhAswsrLywXvNC4uTlRjCPW5KiwsMBhyApYIYMk6oeSMzZvFbcwYgOMcf1fdoO5WRlcLY7jUDQuXQHI5EWJpZrF8RTChASytY0o4oDqH8iNYhMXHx8NgMPjdhuM4GAwG1NTUhNwwQnk8hYWWSgQoOWPzlaXkaoFzd4P6/r1wsfqEy3HKhZBlbVgqXyGmur/cSQ1kkQsdpZ5juZeSYwnBImzPnj1ytoNgAG+uPK2UCFBqxuYvS8kTPQXaE+ojRHywYHUMJinDmxCTAyWTdciiFzpyLiXHGoJF2O233y5nOwgG8JbiG6g6PqsdiVwztmPHqp3p/55WB44DAH0G2hPaQW2rI0vV/cVY5EJFqTIleidczo/owPyysjKsWbMGhw8fBgBcd911GDFihNcaJQT7eEvxNRo5zJlThIED70RkZGSta8tCR2KzxeKbb6KQmlot+285ZtONYbcPc7p6cnKWOq0OR4+2YjJwlXXIcqBNArn31HaPhmKRCxWp90fPSG34+y8urrY3Qkvrz4oSYd9//z2ysrIQExODW265BQCwePFivPDCC/j888/RqVMnSRtJyIu/FF+gAnylCtaEBF/BeckSI4xGDv363STbOoNX4+WuFivctq0fcnKWIjX1OAAKXHUlmEGDLAfawWq1orKy8u96ZGbY7Qa3emSe111N9yhLFrlQoWfEHXf3cmO3vl9r68+KEmGTJk3Cvffei3/961+IiHDsorq6GqNGjUJOTg7+/e9/S9pIQl5YTfH1N5B7qzgvVxKBr0B8b/FyaruCWCHYQYP1wYOCut3jIZcuzXFWT7fbDZg6NQ5//fUWzOYKZGdnu31P7WdCbYucVATzjOj5fq2dmX617wfgc/1ZVq2Eoi1hrgIMACIiIjBt2jR06dJFssYR4Y2/gfybb6KwZIm7GVqOJIJgl9QJRDi5FVgXVoEQYvVh4RiVuqf459Dbag+uz15VVVVIvyMHLCQsKIWUSQgs9le+Esi6dx8GjoPXceH220fCYmEzVEqUCIuLi8Off/6Jtm3bur1/4sQJxMbGStIwggC8D+RWqxWpqdUwGjm3emBiRFEghC4XIhRyK2gDoVYfFlz0St9Tgdx7rKK2RU5u5EhCYK2/8lcLMiWlGlFRUV7Xj+zcmU0BBogUYdnZ2Rg5ciRefvlldOvWDQDwzTffYOrUqXjwwQclbSBBuOKvHpgQURTKjE2q2bTagzYRGKFWH1biiZS8p/Tg3nMN3NYDciYhsNJfCa0FOW/eaMydm6iZ9SNFibCXX34ZBoMBQ4cORXW1IystMjIS48aNw0svvSRpA3nuvfdeHDhwAKdPn0aDBg2QmZmJhQsXIikpybnNZ599hmeffRa//PIL6tSpgx49euCVV15BSkqKc5svv/wSkydPxi+//ILk5GTMmjULw4cPd/utVatW4Z///CdKSkrQsWNHrFixwpmAAACXL1/GlClT8N577+HKlSvIysrCq6++iiZNmji3+fPPPzFu3Djs2bMH9evXx7Bhw7BgwQI3F64ekTsWQWg9sIEDByIhIcHtu1LM2PQ+m2YJFuJatGr1kRt/z57QPk4tl7tn4LbZXIQxY4qD6h9YuDdd0VMSgi+E9v01Nf/S1PqRohRBVFQUli1bhgULFuDo0aMAgLS0NNStW1fSxrlyxx134Omnn0ZiYiL++usvPPXUU7j//vuxd+9eAEBhYSHuu+8+TJ48GRs3boTNZsOkSZMwcOBA/PDDD85t+vbti7Fjx2Ljxo3YvXs3Ro0ahcTERGRlZQEANm3ahMmTJ2P16tVIT0/H0qVLkZWVhSNHjqBx48YAHIkJ27dvx/vvvw+z2YwJEyZg4MCB+OabbwAANTU16Nu3L5o2bYq9e/eiuLgYQ4cORWRkJF588UXZzpHaKFkQkceXKEpISEBiYqK8P+4DLaVHswZrcVhqWn1YG+g98fXsxcfHM+XCcsUzoYfjjJg3Lwk229KA7mXW7k1vSHG/sn7f8fibELOSXCaEkMwydevWRYcOHaRqi18mTZrk/H+LFi0wY8YMDBgwAFVVVYiMjMT+/ftRU1OD559/Hkaj4wF76qmncN999zm3Wb16NVJTU/HKK68AANq1a4evv/4aS5YscYqwxYsXY/To0XjssccAAKtXr8b27dvx1ltvYcaMGbDZbFizZg1yc3PRq1cvAMDatWvRrl077Nu3D127dsXnn3+OX3/9Fbt27UKTJk1w44034rnnnsP06dMxd+5cXQRde+JtLcWry/ao2zYl8ZUercdrDkjbYbMQh+Wt9pCSQd1aGOiFwEIbvT1zYt3LLNybQgnlflVjIh0KepjwihJhly9fxooVK7Bnzx6cPn0ado9UBd7yJBdnz57Fxo0b0a1bN0RGRgIAOnfuDKPRiLVr12L48OE4f/48NmzYgMzMTOc2+fn5yMzMdNtXVlYWcnJyADhU8v79+zFz5kzn50ajEZmZmcjPzwcA7N+/H1VVVW77adu2LZo3b478/Hx07doV+fn56NChg5t7MisrC+PGjcMvv/yCm266yetxXblyBVeuXHG+DmbR9FCQIgPGW8ZKOC3bM3DgQFRWNsb8+Y2dHTTHGbF9e388//xtsFgaqNxC6ZG6w1YrDsu78HGvPaSEG1pLA70WcA0q59fXFOte1lqMoJj7VWsTaa3VA/OFKBE2cuRIfP7557j//vtxyy23BFzYWyqmT5+OlStX4uLFi+jatSvy8vKcn6WmpuLzzz/H4MGD8fjjj6OmpgYZGRn45JNPnNuUlJS4CSMAaNKkCcrLy3Hp0iWcO3cONTU1Xrf57bffnPuIiopCfHx8rW1KSkr8/g7/mS8WLFiAefPmCTwb0hFqBoy/jJVwWbYnMjISR45EuB0/4AiK/e9/7UhKsurmHMi9FIyScVj+hI/Si9ezNNDzwhQAioqMKCyMQELCBUHfZcnq63kfhuqu02uMoK9aiDU1QEGBFTExbFg3eby5lZV+XqVClAjLy8vDJ598gu7du4f04zNmzMDChQv9bnP48GFnKYypU6di5MiROH78OObNm4ehQ4ciLy8PBoMBJSUlGD16NIYNG4YHH3wQFRUVmDNnDu6//37s3LlTMaEYCjNnzsTkyZOdr8vLy5GcnKzIb4t9wIRmrGh19i50QNm0aZPPWmLffLMehw7pw4KhxFIwSsZhCRU+SqL2QO/6TLtbGxqgf/+r1sHBgwfXmoxqwV0airtOD5mhngSqhchi/8XS8xoqokTYNddcI0k9sClTptTKTPSkZcuWzv8nJCQgISEB1157Ldq1a4fk5GTs27cPGRkZWLVqFcxmMxYtWuTc/p133kFycjIKCgrQtWtXNG3aFKdOnXLb/6lTpxAXF4eYmBiYTCaYTCav2zRt2hQA0LRpU1RWVqKsrMytA/Lc5ttvv621D/4zX0RHRyM6Otrv+WANoRkrrJjpg0WIlbCsrAybN28O2EFr9Ry4olQWllRxWEJj1sQKHzmsPmoP9Py1C2RtiI+PVy35JVRCcS/rrfCr0FqILPVfak9UpESUCHvllVcwffp0rF69Gi1atBD9440aNUKjRo1EfZePQ+NjqC5evOgMyOcxmUxu23q6JwFg586dyMjIAODoUDt37ozdu3djwIABzu/u3r0bEyZMAOCIPYuMjMTu3bsxaNAgAMCRI0fw559/OveTkZGBF154AadPn3ZmVO7cuRNxcXFo3769qOPVCnLHzoQavyYmkDyY2Z/eOmhfKCEUxN5LvoLbx4y5gFGjLnh1lwY6HrlKnviChftIT9YGqfF3b6qRXShVZXsW7jtfuLZd7YmKlIgSYV26dMHly5fRsmVL1K1b1xn4znP2rLRqtKCgAN999x1uvfVWNGjQAEePHsXs2bORlpbmFD59+/bFkiVLMH/+fKc78umnn0aLFi2cgfBjx47FypUrMW3aNIwYMQJffPEFNm/ejO3btzt/a/LkyRg2bBi6dOmCW265BUuXLsWFCxec2ZJmsxkjR47E5MmT0bBhQ8TFxWHixInIyMhA165dAQB33XUX2rdvj0cffRSLFi1CSUkJZs2ahfHjx2vO0sUaocSvKZX5Ey61xFjssP3FeK1eXR+rV9fFvffmYeHCa2tNAP0djxolT9S+j/RkbVCKN96owfz5nOJZrVJWtlf7vvOFxWLB4MGDsXnzZgDi+h+W4hV5RImwBx98EH/99RdefPFFNGnSRPZ4q7p162Lr1q149tlnceHCBSQmJqJPnz6YNWuWU9T06tULubm5WLRoERYtWoS6desiIyMDO3bsQExMDABH8P727dsxadIkLFu2DM2aNcObb77pLE8BOFYDOHPmDObMmYOSkhLceOON2LFjh1ug/ZIlS2A0GjFo0CC3Yq08JpMJeXl5GDduHDIyMlCvXj0MGzYM8+fPl/U8hQvBdmYnTwJ79wKjRwMc53iP9cwfVgg0q2etw/YX4+WAd6k56kINHjzY7VPWjkdN9GRtUGINRIfwT1Itq5WVeC058YxB9PW8Km25DgVRImzv3r3Iz89Hx44dpW6PVzp06IAvvvgi4HZDhgzBkCFD/G7Ts2dP/Pij/zTWCRMmON2P3qhTpw5WrVqFVatW+dymRYsWtVyfhPI4rF9craxFgN3MH7XxV6vqrrtKZftdKQdKb1YcHleXGr/iB+EdFq2dYgjVUiTknmMhq5VwoGax7mARJcLatm2LS5cuSd0WgpAUR90b7wIMYDfzR00C16raCbNMa+FK7VJxteK4wqpLTQlrDY+3EhSpqdVISrKjtNRdaPuyNpSWltb6Lt8+Fp+lUNrk794MtQYZEd6IEmEvvfQSpkyZghdeeAEdOnSoFRMWFxcnSeMIQiy+6t7wsJz5oyZql2yQcvDmrTgFBenYuzcDQG2Xmmff5QslYkmkFKH+8F2CIriCl7NmFWLbtuu8flePk5pAx6Mn9y2hHKJEWJ8+fQAAvXv3dnuf4zgYDAbU1NSE3jJCEyg5exdKoLo3gwZ9gOTkk5J1jiyeg1AJdVbPyrGazRW4665dSE8v8OpS49d+ZWWdQyV+R2gJCn8E+m64Tmq06L7VY/+lJUSJsD179kjdDkKjKDV7D4ZAdW+uv/6wpL/H4jkIFTElG3hYPFZ/AfestVUpAlk7vV1j3vVG5St8o7XkDj32X1pClAi7/fbbBW33j3/8A/Pnz/fZWRP6gOWHU6mZKcvnQCyslWwgpCWQtdPfNab4J32hlf4rGKudGvXaxCBKhAnlnXfewVNPPUUijFAVrc1MWUJr545cJsIJJYaJ4p+8Y7PF4uxZCxo2tDJ3LrQiSvwh1Gr30UcWRWpCSoGsIozjizIRBEEoAN9Jnz592lnU0R/hLtpCsRSzGv/kL/MTkN61dvHiRQCBkxzUuNf8lZtRooisHARqryMr3iHAAPZrQsoqwgiCIJTGYrFQnEsQhGLtZM1SKjTzU6rsTavVinfeeSdgosIjjzyi+L0WuNyMMkVklcRXVjzLNSFJhBEEyCLCo6dMKdY6W0J+hGZ+SpW9KbSkS926dSX5PTnappdM1kBZ8azWhCQRRoQtfPYXWUTcXTg9ejyE//3PhObNr6BpU0dF+cjISJj/rtJK50vbhCK0tSLSlc7eZDlRgeW2eUNs7FqgrHhWa0KSCCN0h9ABICkpicQElHfhEOoSiqtWLTev6yRByG8qLTxYTlRguW2eOJaZCz2gntV4RW/IKsIeeeQRqp5PKA7FAwWH0i4cQn1CXcJHSVwnCYDvDETX9X7VEB4sD/wstw1wXONjx6oxZkxj5zJzdrtj2bnExFKkpxuDvu9Yi1f0hWgRVlZWhm+//RanT5+GnU9D+JuhQ4cCAF577bXQWkcQIiGBFTxUgFMegrXiEO64njt/llrPc6yG8GB54Ge1bbzILixMgd0+zO0zu92Afv0s6N8/D2+91V2Xz4koEbZt2zY8/PDDOH/+POLi4mAwXF0g2WAwOEUYQRDaQWuxI1ogGCuOHgcYKRGzzFKwwkPp8hbEVfHsrf8Brl7nH344gk6d9DfBFiXCpkyZghEjRuDFF19UJeuDIJRCDwUOhaKl2BEe1q1MYq04UsH6+QkGuS21FBupLnz/8/HH/QDUvs4bNxZg797jujv/okTYX3/9hSeeeIIEGKFL9FjgUCi8C+fEiWYADEhOPuH8rKysjKmlilizMnkTPKWlpc62iV0sO5T2uJ4fX2hlUBNiqQ0le1NMbCTL2aIst80XnTr9iMaNS7BmzSif11lvsamiRFhWVha+//57tGzZUur2EISqhGOBQ0+OHm3l1QqwefNmpo5bbSuTK/4FoTrxdp7H7UukamVQE2KplSIpJ5hrxXISEMtt80ezZsWiLPJaFJ1AECLs448/dv6/b9++mDp1Kn799Vd06NABkZGRbtvee++90rWQIBREywUOpXA9aTFDUg0rkyeBBGFa2h+qxtsFWlJHKwgJtg9VVAQbG8maiHGF5bb5Q0xShVZFp2ARNmDAgFrvzZ8/v9Z7BoMBNTU1ITWKINQmUEdss9mc77MQwBuKa851ZqjFDEmW2uxLEObkLFUt3k4pkapU/JncWX5ajI3UI/x1ttliUViYImhRdNYElhAEizDPMhQEoWcCdcSbNm0CwE4AbyiuOYvFguzsbGzatEmTGZIstdmfIFSrVpMSIlXO+DM13Eys19UKF7z1ZaWlpUxatMQiKibs7bffRnZ2NqKjo93er6ysxHvvvUclKoiQYSErMVBHzKLrTqzVg1+SSItWAJbaHEgQqlGrSQmRGkr8mRALmhpuJlbraukNX+LZd1+2VFdxuaJE2GOPPYY+ffqgcePGbu9XVFTgscceIxFGiILFrER/HTFLbjAp26RFKwArbRYrCOUMFlZapAYTfxaMBY2lzFxCOvhYrqKiImzdutX5vhbjcsUgSoRxHOdWoJXn5MmTzhk1QQSDFrMSWXKDSd0mLVoBWGmzP0HILxrvihKTC6VEarCWWJYyOLWaXecNrdWH89YWFvtXOQhKhN10000wGAwwGAzo3bs3IiKufr2mpgaFhYXo06eP5I0k9I+aWYmuHdahQ2WCg0BZcoOx3Ca5YHnQ9CUIExISFLPoeB63rzZJeX5CscSqncGp1ew6T5Ssnydl2IinRSxc+rKgRBifIXngwAFkZWWhfv36zs+ioqKQkpKCQYMGSdpAIrxQevbjvUp2O8GDACtusFDbFIqgUWvWzdKgyaIgVOP8iH1+WSgzAmgzu84TuevnyRk2YrFY3NrFYv8qNUGJsGeffRYAkJKSguzsbNSpU0eWRhHhi9KzH6FVsl3dSGVlZdi8ebNbm1nrHIJtk9gBW+2q7KwMmiwJQlfUiKEU8/yyGF+pdeQQtnKHjZw8CezbFwWbLdbZRhb7VykRFRM2bJhjpfPKykqcPn26VvmK5s2bh94yImxRY/YTzCBQXV0taJ/8kjWuyDUQS2GJEdMulmJ61MabQHU97srKShQXFztfa8G1JQYxz2+4xP8oiRzCVo6wkdqWNQsMhhzNFhQOFlEi7Pfff8eIESOwd+9et/f5gH0q1kqEitDZj+tAF0rR1ECDwNq1O72KC3/4+o4cViEWLDFqx/SwhNoWQqUJNf5MjAVNa8HnSiOnsJVq374sa2q5o9VAlAgbPnw4IiIikJeXh8TERK+ZkgQhN97jucQVTfU3CPjbt6ubsrS01JlircZahmoOOKzE9LCC0GusFwuhFJOAYCxo4SZyxSBnaIdU+xZqWfOGFrJUhSBKhB04cAD79+9H27ZtpW4PQQhGaDyX0IHO2yAQaN/est30tkyMECimxz++3LR6Qsy9JtaCRm5wYcgZ2hHqvl3jvwJZ1jxLu+jJyilKhLVv395rvAtBiCWUmCYpBYDnICBm3ywtE5Odne2zdp+UHRnF9PgmWDctS+JabqSwoOnJDS7HtZczsD3YffuL//JnWQultAsLq6/4Q5QIW7hwIaZNm4YXX3wRHTp0QGRkpNvncXFxkjSOCB9C6YxZi30I9B0p1j4Tagng17j0hVTumnCp6RMswVpFw9HNFspx6MkNLtW1Z7FcChA4/isnZylycpZKYrVjcfUVX4gSYZmZmQCAXr16ucWDUWA+EQpiH4pQBECgjkjMvj2/A9iRkZHv/JyPG5NqIBViCVDCXRMONX2CJViraLjFkoWKntzgUrlY+QntmTNnUFVV5XUb10LrSiEk/is19XjI101rq6+IuhJ79uyRuh0EERJiBYAvC5xrkL2YffPfKShIR35+Bvbu7Y78/AzJA/SFWALkdNeoUZVdS4RqpfU1EEthTdUDenWDS/HMulrBpaqaL4WVTe5rpubqK2IQJcJuv/12/Oc//8Hrr7+Oo0eP4oMPPsA111yDDRs2IDU1Veo2EoQgxMY+COmExO47Pz9DVldJoI5GbncNC6UxWCYUK62/gVhqa6pW0aMbXIpnVq6q+VI870pdM60IdFEibMuWLXj00Ufx8MMP48cff8SVK1cAADabDS+++CI++eQTSRtJEEojZsbn+R0lXCWBOhol2hDOIsAXrveCP0uqr/tM6EDMymxeTfTmBpfymZVjEibF8x7sNRNjSdeKQBclwp5//nmsXr0aQ4cOxXvvved8v3v37nj++eclaxxB+EPOAFQxMz7PBWiVmIkF6mi0MhvUG6FaDHwNxCdONIPZfFjStmoROdzgrGSlSvnMshwzx//+2bMWt9d33nmnm0ctlPMeSOyxkDkpSoQdOXIEPXr0qPW+2WxGWVlZqG0iCEHI4QoLtSN2XYBWqZkY39GcONEMgAHJySecnwVqg5JLK4UboZxDbwMxAGzZcj8qK6+6k/jrF27XTOpnn6WsVCn7DZYnYb7cpDt37pT0PPsS6G+8UYP58znVMydFibCmTZvijz/+QEpKitv7X3/9NVq2bClFuwhCEFI+LFJ1xK4TEblcJZ4z/KNHW/mM+/DXBj62yBM9xhqFKrCVtJTwA/HHH/cDcHUA9XQnuV4/PV4zf0h5rKxlpUrVb4Qq6OS656UqsC0WR+ZkEhOZk6JE2OjRo/Hkk0/irbfegsFgQFFREfLz8/HUU09h9uzZUreRkAm5HjBWzPrBIkVHbLVasXnzZrf35CiWaLFYMHjwYGzevFlQ3EewbdBbrFGoAlspS4lnLFlU1BV88MEDbtv4cifp7ZqpiRorHMiVaSxW0Hne86FmWLq2W203KUuZk6JE2IwZM2C329G7d29cvHgRPXr0QHR0NJ566ilMnDhR6jYSMiDXoMKSWT9UxHTEwTy8oZZtiI+PBxC4Q/G1viUQHsvpAKELbKUsJZ5xhcnJJ5h1J+kVtSrwy5lpLGYiKHWGpcViQXZ2NjZt2iSrm1RIv8qSm1aUCDMYDHjmmWcwdepU/PHHHzh//jzat2+P+vXrS90+QibkWntNL2u6SdUR+zr+7OxsWCwWSayGgToUX0t+6Gm5Fz0RKK4wM3NXrWBmQhrUrsAv1cRUyqQlKc8Jv4SanPGy/sQsPwllKXMypLK5UVFRaN++vVRtIVRCrsFYq4O8VJ2Ov+M3m82SWQ3FdChqDzZqE6oF0Nf3bTab6DXuXPFV4qKoKAm7dmVq7pnSCmq7yaRCSquaXOdEztIiQo6LldImyq9dQDCFXIOxlgd5KTodIccvpYsr2A5FL4ONGEKdHPj7/qZNmyRxtbsOoq4u5LffHirqmdJqnKbSsOSmChWprqec50TOxcW18PsAibCwR67BWMuDvBSdjhLHH0ogr54Gm2AIdXIgpbgOhOcgKvae0lOcptyw5KZiBTFlbnhI3AeGRFiYI9dgrOVBXoqOWMzxB+siC8XlEK6DTajiWM3JhdhnirXyCywS6goHekdMmRseV3EvZ4FtrUIiLMyRazDW4iAvZUcc7PGLdZEFO8sM98Em1MmB9yKqwr8vxi3IX4tA95Rer5kS0BqogQnkuhOShKX2eWZRBJIII2QLUGQl8FEoUnQQwYgc/neUjJ9TuxNUi1CFjOf33YuoGnD0aKuAolmsW9Dzms2ZcwbHjkUgJaUaSUk3A7g5qGvGQlkSFmPU9HbPh0owQiSYSaSa55nF/o9EWJgiV2FAufYrBUp0/EIfcuBqLIXSLq5wHGyECJmLFy+isrISxcXFXvdx991349NPP0Va2h8wGACO4z8xCBLNobgFXa9ZYiLQubOgXdWChYxlilHTBoH6Mj5hRKlJpGv/XVRkRGFhBFJTq5GUZAcgvP9m7Z4iERamyDUjUHum4UtolZWV1apk7w2pMtv84TkIaTl+Tkv4EzJWqxVvvPGGoP1oNemElYxlilHTDmqWsHDFtc/0N5HQonAnERbGyHWzqvUQCJ1h+0OJjt/zNyjWR32Cue5aFc1aFY8E24TyPAj1TggN3dCicCcRRugGLT6APN7ixwYOHIikpCTNzez0jlSiWenYLDnFI4sxXoQyiE3CCmZtSh49TiRIhBGEyrh2Pqmpx53vJyQk0MDFGNnZ2c6lV0IJkFcyNkvu7MpQY7xYSBTQC2qJYTFJWELXpiwqKnJup1UrtD9IhBFhCSsdPwuB0kRtfN0fZrPZuSyR2AB5pWOzPOM0J036H/73PyOSk6+gadNUAKmIiIhwJiUEO1CHEuNF9790KJ3wIFUSlq/noXHjElRVRWPt2p3O/Wqx9FEgSIQRusXXQMpKx89KoDThjtz3hxouFX7QtVqt+OorRwLCiRO+t1ciwJnuf2lROuFBqiQsX8/DmjWjvD6DWit9FAgSYYQu8TWQstTx6zG+QevIeX/wFoFALhU5EzFYykyk+19elLD2SyHUvRdA5vw+gyys+SgVJMII1ZE6jsHfQMpSx6/H+AatI+f94Wo5uOaackyfbkZNjQEmE4eFC8vx0EMP6iKAPdDgH6wYpaD/4GHF2i8ETxcjYMfVQsgO9CzOSYQRqiJHHIO/gVRNKwQPLUOjLEIGcaWsVPw9PGUKkJ0N/PEH0KqVAc2axQOID2nfLCBk8A9GjFJh1+BhydovFFcXY2RkpdMVySN0cqrFPpNEGKEqUrpHhAykZnMF5s8/hblzm6pmhZB6GZpwRKh1JJhBXGkrVbNmjj+9EMzgL1SMsuQ+1QosWfuDwdXF6G9yOnDgQCQkJNT6vlb7TBJhhG4IZoY9fLi6VgiplqEJR4IRVsEM4nzWo16tVN6QMm5I7OCvNzGqNqyFOfiaMNlsNp/f8Rd8n5CQ4HxW9QCJMEJXCJ1hh1vHr6e4GiWsI+Fwf0gVN8RCwgFxFZbKOAidMA0ePBjV1dXYunWr8z09Bd/7g0RYmKOnwdmTcBhIhUBxNYQnUsYNKZlwoHZ9P5b7S1eR68+SpKQY9jxXvq5ffHy84HbpTcyTCAtjWByc1e5k9QjF1RA8/AAWyHUY7ECnRMKB2hl/LPaXrkhVt0suAl0/1tsvFyTCwhjWBme1O1lC/4S7yOcHumPHqrFhAwe73eD8zGTiMHHi3UhJiQhpoJPDAs1Cxh9r/aU3WBUoQq8fq+2XExJhREBKS0t9fialm0HtTpbQJkKFFYl8BxaLBRYL8MYbwOOPAzU1gMkEvP66AZ07N1G7eV7RasYfS6jpSqXr5xvNiLB7770XBw4cwOnTp9GgQQNkZmZi4cKFSEpKcm6zefNmvPjii/jvf/+LRo0aYcKECZg6darbfr788ktMnjwZv/zyC5KTkzFr1iwMHz7cbZtVq1bhn//8J0pKStCxY0esWLECt9xyi/Pzy5cvY8qUKXjvvfdw5coVZGVl4dVXX0WTJlc7sD///BPjxo3Dnj17UL9+fQwbNgwLFixARIRmTrkT12BJb4RifpfLPcIKLMeQ6AGhwopEfm1GjgSysnjXIZvxk2oG/Xs+u56TUS1ZVY8ePYp33nkn4HZyuVJZy9hkCc0ogjvuuANPP/00EhMT8ddff+Gpp57C/fffj7179wIAPv30Uzz88MNYsWIF7rrrLhw+fBijR49GTEwMJkyYAAAoLCxE3759MXbsWGzcuBG7d+/GqFGjkJiYiKysLADApk2bMHnyZKxevRrp6elYunQpsrKycOTIETRu3BgAMGnSJGzfvh3vv/8+zGYzJkyYgIEDB+Kbb74BANTU1KBv375o2rQp9u7di+LiYgwdOhSRkZF48cUXVTh78uJPZAgRIXK7R9SC9RgSrSNEWOld5IcK68kraq0yEOjZ1ZJV1Wq1ChJggHyuVJYyNllDMyJs0qRJzv+3aNECM2bMwIABA1BVVYXIyEhs2LABAwYMwNixYwEALVu2xMyZM7Fw4UKMHz8eBoMBq1evRmpqKl555RUAQLt27fD1119jyZIlThG2ePFijB49Go899hgAYPXq1di+fTveeustzJgxAzabDWvWrEFubi569eoFAFi7di3atWuHffv2oWvXrvj888/x66+/YteuXWjSpAluvPFGPPfcc5g+fTrmzp0bNh1+MCKkc+cmmnKPCIHVGBItzeC9EYywUiIGipAXNVYZ8PdMas2q6r1GV2h9gBgLv94W3pYKzYgwV86ePYuNGzeiW7duiIyMBABcuXIFdevWddsuJiYGJ0+exPHjx5GSkoL8/HxkZma6bZOVlYWcnBwAjpt1//79mDlzpvNzo9GIzMxM5OfnAwD279+Pqqoqt/20bdsWzZs3R35+Prp27Yr8/Hx06NDBzT2ZlZWFcePG4ZdffsFNN90k6fmQCqkH52BFiBbcI1pHSzN4XwQrrFiLgSIXtXhCtdxJce61Ht8Uah8QzOTa0+Dgq/ZXuBgmvKEpETZ9+nSsXLkSFy9eRNeuXZGXl+f8LCsrC5MmTcLw4cNxxx134I8//nBavIqLi5GSkoKSkhI3YQQATZo0QXl5OS5duoRz586hpqbG6za//fYbAKCkpARRUVGIj4+vtU1JSYlzG2/74D/zxZUrV3DlyhXn6/LyciGnRRJYGZxZd49oEb6DCzSD99cRsiYcxAgrFkQ+uajVI5hz7w8txzfZbLH4+ON+4BfIFmPFE1r7i1+FIhzLTgSDqiJsxowZWLhwod9tDh8+jLZt2wIApk6dipEjR+L48eOYN28ehg4diry8PBgMBowePRpHjx5Fv379UFVVhbi4ODz55JOYO3cujEaj399ghQULFmDevHmK/Z7QwTkQpaWlYf8gsQxvOdqzB1iypPYMvnv3YejZ03d6OMvCIVhhpbbIZ9VFHQ6Ecu49hYa/+CabzcZsf1hQkA5egPGEYsUTumA74RtVRdiUKVNqZSZ60rJlS+f/ExISkJCQgGuvvRbt2rVDcnIy9u3bh4yMDBgMBixcuBAvvvgiSkpK0KhRI+zevdttH02bNsWpU6fc9n/q1CnExcUhJiYGJpMJJpPJ6zZNmzZ17qOyshJlZWVu1jDPbb799tta++A/88XMmTMxefJk5+vy8nIkJyf7PT+hEGhwvv76AThxYoPzPV8zHj57kmbv7GKxWNC1K2A0Anb71fdNJiA93WFV8gXrwkFtYaU1WLNqso4voeErvmnTpk0A2OsPbbZY5OdnePlEnBVPa7FxrKKqCGvUqBEaNWok6rv2v0cSV/cdAJhMJlxzzTUAgHfffRcZGRnO38jIyMAnn3zitv3OnTuRkeG4MaOiotC5c2fs3r0bAwYMcP7O7t27nSbqzp07IzIyErt378agQYMAAEeOHMGff/7p3E9GRgZeeOEFnD592plRuXPnTsTFxaF9+/Y+jyk6OhrR0dGizodY/A3OHTvWw4kTjtdCZjw0e2ebZs28ue9IwIQTLFs1WSSQ0PAnNljrD73FsgFAt275okST1mPjWEETMWEFBQX47rvvcOutt6JBgwY4evQoZs+ejbS0NKfwKS0txQcffICePXvi8uXLWLt2Ld5//3189dVXzv2MHTsWK1euxLRp0zBixAh88cUX2Lx5M7Zv3+7cZvLkyRg2bBi6dOmCW265BUuXLsWFCxec2ZJmsxkjR47E5MmT0bBhQ8TFxWHixInIyMhA165dAQB33XUX2rdvj0cffRSLFi1CSUkJZs2ahfHjxysusoQQE2PFokUmj/RvG+rVOweAZjxSwUJWIgtxUYR6sG7VVBIhz6NSQkMJ66SvWLb09AK33wl1f1qIjWMJTYiwunXrYuvWrXj22Wdx4cIFJCYmok+fPpg1a5abqFm/fj2eeuopcByHjIwMfPnll25FVlNTU7F9+3ZMmjQJy5YtQ7NmzfDmm286y1MAQHZ2Ns6cOYM5c+agpKQEN954I3bs2OEWaL9kyRIYjUYMGjTIrVgrj8lkQl5eHsaNG4eMjAzUq1cPw4YNw/z582U+U8HjOjN+4olYp3n9/PkK/G1Vl6wjYkGEKIlrh+bPkqh0ZhC57/QBuRXFE8iyL7RIrBTIbZ3kjyVQLNsjjzwS1P6p9pc0aEKEdejQAV988YXfbRISEpxlJPzRs2dP/Pij/6y/CRMm+M2QqVOnDlatWoVVq1b53KZFixa1XJ8s4tqJ+zKvi+2I1BYhag9SrqUU5s9vDI5zlFLgOCO2b++POXPSqUZVmCN2YkJuRfEIsez7KxI7e3YRgAq3/YUyuZTbOum5MPacOWdw7FgEUlKqkZR0M4CbRfeFVPsrdDQhwgh1ETvjUVOEsDJIWSwWHDzoHm8HADU1BlRUNPEbEC83aovUcCeUsjDkVhSPUMu+ryKxJpMJb7zh2IaV0j6BcH2OExOBzp3F70srtb+00r+RCCMEIXbGo5YIYWmQat3ae+JDq1a+vyN3BxJKzaRwcytLiRQ124jgsVqtsNlsAMStQ+nqwi8udvwrV6ws68+Xp2XNG0oIHH99ZFlZGTZv3ux87eucsmApJhFGCCZQNpAvxIgQPRFsVqISVrxgRKrabmU9EWrNNqlhfcCXAs/nyZdlf/Tou9G4cWPB516OoH0tWtbUQGgfCfg/pyxYikmEhSGuM4jS0tKgv++r4/Y1CFNphOCyElmy4gEU2yY1odRskxKtDPih4u058WbZj4+/2es97GlxKSsrAyDOouYPykIXjtCq/Vo4pyTCwoxgZhCuZGdnw2w2Izc3BvPnm2G3G2A0cli0yIaHHroU0PxMpRG0nZXIcmwbSwh1I6s1MSF36FWEWPb99ZeeFjVHaZ9yPPTQg6LccVR3Sxz+JhNaOKckwsIMsdYTs9mMmppETJt2dSC22w2YPj0e2dnxggZhpUSI1WoVZeEj/BPubuVABOtGVmNiwpo7lHUC9Ze8Ra1792FIT7egWbN4APGifovqbgVPoMmEFs4piTBCEFFRUT4sIY5BJJQBRMogdLGWPiIw5Fb2jxg3slQTk2Biu1hxh+oFs7kC3bpVIjFRXF8mtI5XOFgngyWQpUsLtcxIhBFeGThwIBISEgBc7TjksIRIHYTOQqClniG3MhuEmiwRzoJarmQEsX2ZnHW89I4QSxfrtcxIhBFu8B1UZWVjJCY2cfssUMctZhaoVBB6sMkE4UYwA5OWY9v0ghTJEnoU1N76INfQBDmTEULpy6Ss4xVOCLV0ic3sVwISYYQT1w5qwwYOb7zh6Khd8dVxs1Ic1RveOt7nn09FUlJS2M4u9Vh2QivFGaVCimQJPQnqQH2QFJly4VDSQ2uEYulioX8jEUYAqN1B2e0GPP64Q3B5dtLeOm6hs8CioiK3beUOoPfV8c6Zc4bZAdmzY5DDiqe3shMsTwLkhJIlrhKoDwoUPxToeQrWiqYVwabFyYvQqv18Vr+vfbBwXCTCCADeOygpgu492bp1q3Q7E4CvjvfYsQhmTf6uMSJiS4II/R29lJ1grbaaUoRzbFeweIsfMpk4TJx4t9uEw1sdxWCtaP4Em7+Jp9LCQKuTF1aq9ksBibAww9dsz3sHpf0Ztb/AzeJidh9gi8WCkycRUkkQIZAlRT6UsoToMbZLDvj4oe3b+/+9GDfw+usGdO58NfbVlygRYkXjBUEgwRZoIqqk4NHy5EULAksIJMLCDH8ziGuuKcf06WaXDkr7HbqvwM1///tH/Pvf/r+r9uzv99/lKQniCllSpEPNODs9xXZJhTcR3KnTj5gzJx0VFU28ClZfYkOIFa3470UlQy0Qqqbg0YoLVU+QCAtDfAmLKVOA7Gxtz6i9DXBiAzfVnv0pZaUKZEnRYsyIGugtzk7L+BPBSUl2JCYGtz9v1fE9rWg8WigQ6o1wWcaKNUiEhTknTzosLq1bX51Nsyi+hFoPPC19RUVGFBZGIDW1GklJDjVjs9mwadMm2doqFUpaqXxdd63GjKiFnuLstIpc6wW6TuYmTry7lgATWnTVtZ2sWJ20sMaiXiERFsasWQOMGeMYMIxGeC1JoSSuBWJdCdbSwm/L2vGJQe14Hy3HjKgFxdmpi5TrBXoKJf6Pn9C5EqjoamlpqjMejDWrkxbWWNQrJMLClJMnrwoUwPGvr5IUQpAiziUhIQE1NYluljmx+Ds+kynkpioKq9ZJllHTykBxdt6R263N90GB3IFC+yoxQklI0VUWrU5adaHqARJhYYrUQd/+Av5LS0sFlabIzY1xZgOGarnyd3xt2ojbp5pQXJZ/WCs+q7YFkzWUcGu79kHuSUYcFi4sx0MPPSj4OZFTKLFoddLCGot6hURYmCKHy8RX5yZk4LPZYv+uh+V4HaplTk8uIYrLCgyLQfFkwbyKUm5t/vq6JxkZ0KxZPIB4wfuRUyixZHVy7Zv9JTDx29FkUHpIhIUpcrtM3AP+AxfWKyioiyVLDG7vhWKZ83d8f2eSawaKyxIGBcUTrogRwVK7NL3tO5DVScmldIIpekqTQXkgERbGyOUy8R4Q7/+h7NJFesuVr+MT2smxsK4YERx6soASyiOlS9PfvoHagfvAzapYkoT+nqdQ8xV3Ge6TwWAhERbmSO0yERLw71kWg2+HHJY5b8cnpIwFmdW1CQXFE6EilUvT374B34H7WoC17E4tQyKMEI23+IB9+6Jgt7uLF1e3or+yEUoGM+upjIWcKLGYuNSEa1B8oHidixcvom7duj4/V3LiwVKNLH9QXF9tWMzu1DIkwghR+IoPsNliYTDkeF2DUoiVTMlOT+oyHXpEqcXEpSbcBk+h8TqBUCKeR+tWlHAPTmcxu1PLkAgjROGrE/K1vEezZsCePfKvhRgMSqzNqAeUWkycEI9UcThyx/MItaKwKnQoOJ2t7E49QCKMkBxfy3uwFjTNWntYhgQrEQq8uzqQFSUqKoppoUOZylRTTGpIhBGywC/vERV1GsXFjtHbZAIWL47FlCn1mQia1koQNwtxWSRYiVBwreO2YQMHu/1qORqTicPEiXc767gVC6who2ehwzr+aooRwUEijJAVb5Xyf/zxCVitDZgImtZCEDcLcVlaEawEu1gsFlgs3u4jQ63FsLWCVhIMpMBzksdPtANtR/iHRBihOAkJl9Ghg9qtuIoWgrhZiMvSgmAlHAQSB4E+lzMmK9j7iFWho/UEg2AJprArIRwSYQQRBGoGDEsdlyXmWLQgWMOdQOIg0OdKxGQJvY+UEjrBPgvhWqaBBJb0kAgjCIGoHTAsZVyW2sdCyEMgcSBEPLASfK6U0AnmWeDRQ5kGVjNQww0SYYQowtHvr/bgJGVcltrHQkiL0OxDIdmJrFxzpYSOmGdB62UaaBLGDiTCCFF4iw8oLS31GojvC5qJBQ/FZRHeEJJ9GB9/HmVl9SXLTpSLsrIyAGwKHRYX4RYDrQPJDiTCCNGEIpBoJhY8vGg1mYA2bRzvuY6XJFrDm8DZh44MxGCzE5UMjLdardi8eTOAwELn4sWLfgWj2OfB3/Gyugh3KIRbggFrkAgjVEHP7rDS0lIA0ooiEq1XIQuqfwJZS4Oxpio9QHteV1/1qO655x688847AfcX7PMg5Hj1sgg3EL4JBixBIoyQhJMnge+/rwubLTbgw8tS3IkcuLpkpRJFehatwUBiVBiBsg+FZCeyMED7skrVqVNH0PeDeR5YOF6l0UOCgdYhEUaEzJo1/ELYZhiNk5wFRL3BWynUjjtRCq2IIlZrMXlCYlQ51B6g/VmlKircf1+K+1ft41UDFuPuwg0SYURInDzJCzDHa9cCohQ0rg0oJoRwhQ8qDzRAyxl8HsgqtXPnTue2Ut2/4ShIaB1I9SERRoREOC3sLHQNRy3AH0ugwU6OgVbKmC4tXwNWcQ0+v+aackyfbkZNjQEmE4eFC8vx0EMPyh53J9QqJYULUS8Zj2KhdSDVhUQYERL+CoiePOkQaa1b60OQuQ5Ob7xRg6VLk1SzHoUqPvhj2bMHWLKk9mDXvfsw9OwpfYVsKWO6yIInH/y5nzIFyM7mg/gNaNYsHkC87L8v1CpVUJAesgtRjxmPgRC6DqTNZkNRkRGFhRFITa1GUpLd+X09nQ81IRFGhISvAqKffXbVTWk0OrYZOVLt1oYOv4bj/PkcOM5Ra0npAF6pxIfFYkHXrt5FdHq6RZb1KIXGahUVFTnb6I1wDKJWCzWWqhLiJrPZYrF3b0at74pxIeop41EI/taBLCsrc5YJmT79vz77mnBPfpEKEmFEyHimvANAixaucWIOkZaVdbUzF2raZ9EF4HDBGtze4zgjfv21Pdq3/1VWESC1+JCyCr+U8Bmmvjr6cAyiDgdcn/dAbrKzZy0AjPAkIyOf7gEBBBJQgfoaNZNf9ORlIRFGSILrbHnPnsBxYv5mYjysmrwdLljOQ4hx+OyzPvj887tkcYsJXZZGjGhVswp/ILeqr/sjHIOowwEh/QJvqfF2DwB2pKcXOF+xOInTCqxNdPhY0tzcGEybZobdboDRyDmz8VkdLwJBIoyQHKELTbs+MFqa2TRrBixefAGTJtX9u5PiAHh3TUo1CAhZloZfdkYMaricXN2qgB3duuUjPb3Abwcf7kHU4YDQezjQPZCdna3JQZkVWJro8LGkNlssli7NcYaC2O0GTJ0ah7/+egtmc4UmXaQkwgjJCdbFdbXOmHbix558sj569TqHrVuNmDvX7PaZXIHtgZel0Q6erg7AiL17uyM/P8OvJTEcg6jDHc9sWn5FCsC/y9Jsdn8uieBgqXwFf/0DWee0WB+QRBghC0JdXLXrjNWOH2OVDh0aoEEDYP585QLbAX0s4u2tMwWExbiFWxB1OCMkm9ZXZh9ZQ0OHtfIVLFnnpIJEGCEbQlxcWq8zplZguxruQynxHs/jIJwC7GkdTP94nhtfMYQDBw5EQkKC83W4nzcp8SVy1YAl65xUkAgjVEVo/BjL6MEypRSeMV0ff9wPnhluWp/ZCsXTyuNLYGgxzkUO/JVmSUhIQGJiosotJJSANetcqJAII1SF1RIJwaJ1y5RS8DFdjjpgW5GW9gcKCtKRn5+hm5mtUFytPP4EhhbjXKSG6sIphxbKB7FknQsVEmGE6pAlKbxwteqYzRW4665dSE8v8DqzDYe4HhIYgWGtXIKe0XL5IC1CIoxgArIkhRfU0V+FBEZg9BiQzTLh8NyxAokwgiD8IlcNt3Dv6MvKygCQwBCCHgOyw5lg+xQtuEjFQiKMIHSKFOJJizXctIDVanWuz3f0aCtw3NXPSGB4R28B2eGKmD5Fz5ZzEmEEoUNCFU9WqxXHjlVjzJjGzur8jhpuHG688bTf6vxaWv1ALfjBhI8Hc80Q5TggLe0PlVrGFp6WDaoJpm1CqQupRYElBBJhBKEzQi2Ay5dOKCxMgd0+zO2zmhoDVqz4FKmpx72WTiDLWXB4L1pL8WA8eraAhBtWqxX79gF2u/u1qqkBCgqsiInRr9DyB4kwgvCBFi06UnR0/IAXKFbJc2DU8uoHaiEkHkwvVh6xz1M4Dsx6w3XtR4Mhp9b9/s0363HokDbXfgyV2uWqCYLAmjVAixZAr16Of9esUbtFgeE7ur1718NgcF+GgO/oVq5cCavVKmh/fDA0vy9/sUoO8Wf1uvpBQYFV8G+GG4HOsdYXobZarSguLsYrr5ShRQvu7+eJwyuvlKG4uJjuizCBn7AFut+lrol38iSwZ4/jX1YhSxhBeKBVi45nR+crkyyYjk5IMDTNckNDr4tQu94XS5fmgOP42EIDpk6Nw19/vQWzme6LcEOpBAuthEaQCCMID7S+niUgrKMT6h4KVJ1aDvEXbuipAjgPf70D1UHjt9Oi+58Qh9z3u5Ym0iTCCMIDPaxnCfjv6OSaJVIZAX0jRigJiXtj1WpBwlB7aC0BgGLCCMIDfj1Lk8nxWqvrWfqiqMjodZYoVdyE2VyB1NTjJMD8oMXik2LjJAPFAcl9P4pF7rhQLcQraQ2p42KVgCxhRMjocbYox3qWap0nmy0WZ89a0LChFWZzBQoLI0S5Wz33Q4hHa6UXQnXv+LOQHjhwwWdChxpWi1Bq5AmFVcuf1tFiaASJMCIk9NyZSLmepVrn6YcfbqrVEfXrV+3X3erN+uJtPyxZabRIsAO5WiJeKveOL/f4L798xExCRyg18oTuX26BRzjQSmgEiTCNY7fbVVP1JSXACy8AyclX33vxRaB3b6BpU1WaFDLnzp1DVVWV87XBYIDJZILB4OgwxVgn1AoS5aux84MbxxmxbVs/zJlzBm+84WhDTU1td6unlaaoyIj58xs7s9s4zojt2/vj0iWDfI0n3FBLxCuR+cqS1UJsjTwhyC3wWEYt97sWEl5IhGmYyspKFBYWwu5py1eIy5eBV1+t/f65c8ClS8q3J1RqampQUVH7gb106RK+++47XL58GQCC7iSVyrb07MB8ZaWdPFknoLvV9fh++81b+w2ayhZljWCsWmpmeinl3mHNaiHHguFyCjzW0Zr7XUk0J8KuXLmC9PR0/PTTT/jxxx9x4403Oj87ePAgxo8fj++++w6NGjXCxIkTMW3aNLfvv//++5g9ezaOHTuG1q1bY+HChbjnnnucn3Mch2effRb/+te/UFZWhu7du+O1115D69atnducPXsWEydOxLZt22A0GjFo0CAsW7YM9evXD6otocBxHIqLi2EymZCcnAyjUfkci8pKh5jwJC0N0KKnqrKyEmVlZW7vcRyHc+fOoU2bNvjpp5+c2wWDUtmW3ixYGzZwTrcHABiNHJo1u4zi4oswmYA2bRzvFxc7/vXWEQZqvxaDzNUkWKuWHCJejGszWKEU7PVmzWohlzCUQ+BpgXAUWELQnAibNm0akpKSnAMiT3l5Oe666y5kZmZi9erV+PnnnzFixAjEx8djzJgxAIC9e/fiwQcfxIIFC9CvXz/k5uZiwIAB+OGHH3D99dcDABYtWoTly5dj/fr1SE1NxezZs5GVlYVff/0VderUAQA8/PDDKC4uxs6dO1FVVYXHHnsMY8aMQW5uruC2hEp1dTUuXryIpKQk1K1bV5J9BkudOo6soePHr77XogUQF6dKc0LGaDQiIqL2I2E2m9GoUSNERUWJmqXy2Za+3H9S4trRJSZ6/i6Hvn23IS/vR7/78LT0BWq/2FmuHhM6AiHGqiW1iA/FtRmMUAp0X5SWlmLr1q1Cm60KcglD1ix/hHpoSoR9+umn+Pzzz7FlyxZ8+umnbp9t3LgRlZWVeOuttxAVFYXrrrsOBw4cwOLFi53CZ9myZejTpw+mTp0KAHjuueewc+dOrFy5EqtXrwbHcVi6dClmzZqF++67DwDw9ttvo0mTJvjoo48wZMgQHD58GDt27MB3332HLl26AABWrFiBe+65By+//DKSkpIEtSVUav42QaltXWjUCDCbgStXgOhobVrAAmE0GmE0GhEZGSnaVSBHtmWwvxsbezqgAAO8W/qCcV8KQc8JHf4I1qpltVphMlVi0aIYTJ9uRk2NASYTh4ULbTCZLsFqDc6Fo7Rrk6wfvmHN8keog2ZE2KlTpzB69Gh89NFHXi0/+fn56NGjh5soycrKwsKFC3Hu3Dk0aNAA+fn5mDx5stv3srKy8NFHHwEACgsLUVJSgszMTOfnZrMZ6enpyM/Px5AhQ5Cfn4/4+HinAAOAzMxMGI1GFBQU4P/+7/8EtUUq+IBxNYmK0qf44pHqHEuZbSnmd4uLQ4sdlKr9WqpmLTXBWLX4QG6eJ56IdVpOzp+vwBtvON4PJkZRD6tBEIQvtBgaoQkRxnEchg8fjrFjx6JLly44duxYrW1KSkqQmprq9l6TJk2cnzVo0AAlJSXO91y3KSkpcW7n+j1f2zRu3Njt84iICDRs2NBtm0Bt8caVK1dw5coV5+vy8nKv2xGElglnIRCMa9rTIunLchKMdVYvq0EQhDe0mACgasX8GTNmwGAw+P377bffsGLFClRUVGDmzJlqNld2FixYALPZ7PxLdq39oBOGDx/uvLaRkZFo0qQJ7rzzTrz11ltBZXmuW7cO8fHx8jWUAfRaUZsXAq6EkxAYORI4dsxxbY8dU84N63BtFmPRojKYTBwA/O3aLIPJVKx4FXEWrRYstokIDovFgsTERJ9/LAkwQGVL2JQpUzB8+HC/27Rs2RJffPEF8vPzER0d7fZZly5d8PDDD2P9+vVo2rQpTp065fY5/7rp30WrfG3j+jn/XmJiots2fBZm06ZNcfr0abd9VFdX4+zZswF/x/U3vDFz5kw3d2l5ebkuhVifPn2wdu1a1NTU4NSpU9ixYweefPJJfPDBB/j444+9BseHG6zETFmtVslnlUomKohBroQB13MpNDNVyt8W49qUU5SwaLWQs00k8AhvqDraNWrUCI0aNQq43fLly/H88887XxcVFSErKwubNm1Ceno6ACAjIwPPPPMMqqqqEBkZCQDYuXMn2rRp43T/ZWRkYPfu3cjJyXHua+fOncjIyAAApKamomnTpti9e7dTdJWXl6OgoADjxo1z7qOsrAz79+9H586dAQBffPEF7HZ7UG3xRnR0dC2hKRdyDK5CiY6OdorRa665Bp06dULXrl3Ru3dvrFu3DqNGjcLixYuxdu1a/O9//0PDhg3Rv39/LFq0CPXr18eXX36Jxx57DMDVeK1nn30Wc+fOxYYNG7Bs2TIcOXIE9erVQ69evbB06dJaLmRvCC3zIXcnKUXMlLfrW1paGlQ7PAduX4gpLqlWokIg5BK/cp5LIYh1bcotlFizSgDytYlF0RmusJSZrQmTQ/Pmzd1e8/W40tLS0OzvM/jQQw9h3rx5GDlyJKZPn45Dhw5h2bJlWLJkifN7Tz75JG6//Xa88sor6Nu3L9577z18//33eOPvaaDBYEBOTg6ef/55tG7d2lmiIikpCQMGDAAAtGvXDn369MHo0aOxevVqVFVVYcKECRgyZAiSkpIEt0VN1B4QvNGrVy907NgRW7duxahRo2A0GrF8+XKkpqbif//7H/7xj39g2rRpePXVV9GtWzcsXboUc+bMwZEjRwBcvSeqqqrw3HPPoU2bNjh9+jQmT56M4cOH45NPPgnYhoiICDRu3LiWW/Ty5csoLy/H4MGDUa9ePdnPSagxU0Kur5B1IIXGGonNGFUrUcEXciYMyH0u5YREgXTQuVQfVrwMPJoQYUIwm834/PPPMX78eHTu3BkJCQmYM2eOW0mIbt26ITc3F7NmzcLTTz+N1q1b46OPPnLWCAMcdcguXLiAMWPGoKysDLfeeit27NjhrBEGOMphTJgwAb1793YWa12+fHlQbVETVgeEtm3b4uDBgwDgZq1MSUnB888/j7Fjx+LVV19FVFQUzGYzDAZDLffuiBEjnP9v2bIlli9fjptvvhnnz593K6brC2+uULvdDpPJhMaNG7vdB3IRavB0oOvmbR3ITp2ulq4IV3dIOCcMEPLAksWFYDMzW5MiLCUlBRzH1Xr/hhtuwH/+8x+/333ggQfwwAMP+PzcYDBg/vz5mD9/vs9tGjZs6CzM6gshbSHc4TjO6V7ctWsXFixYgN9++w3l5eWorq7G5cuXcfHiRb/Faffv34+5c+fip59+wrlz55xWrT///BPt27dX5DhCRc6YKW/rSW7f3h9z5qQjKcke1u4QyhwkpIQ1iwvB5kRL1exIgnDl8OHDSE1NxbFjx9CvXz/ccMMN2LJlC/bv349Vq1YB8G/luXDhArKyshAXF4eNGzfiu+++w4cffhjwe3JitVpRXFzs889XRppcGXTe1pOsqTGgoqIJk5lDSsKLX5PJ8VrthAEK5NYuviwuest01hosZmZr0hJG6I8vvvgCP//8MyZNmoT9+/fDbrfjlVdecQbLb9682W37qKgo56oBPL/99husViteeuklZ1bp999/r8wBeCHU2Ds5Yqa8LR6sdifEEq4JA/XqAefPOwbOUK7DyZPAvn1RsNlig6qQToHc2oVFiwvBZmY2iTBCca5cuYKSkhK3EhX8ep5Dhw7FoUOHUFVVhRUrVqB///745ptvsHr1ard9pKSk4Pz589i9ezc6duyIunXronnz5oiKisKKFSswduxYHDp0CM8995xKR8lm7J3n4sFKd0JqZuUKpVkz4LPPpHElXXVJWWAw5NSKvwuE2ueCEAe5ttmD73vuuQcoKDDi2LEIpKRUIynJjuJi9foeEmGE4uzYsQOJiYmIiIhAgwYN0LFjRyxfvhzDhg2D0WhEx44dsXjxYixcuBAzZ85Ejx49sGDBAgwdOtS5j27dumHs2LHIzs6G1Wp1lqhYt24dnn76aSxfvhydOnXCyy+/jHvvvVfFo2UPfvHg7t2HIT3doqgAE5K12aPHSHTpYlZtdipV8K7nfjjOiG3b+iEt7Q/F1wwk16ZySL3eJxE6vvqeQ4fcXytZEYCHRBihKOvWrcO6desCbjdp0iRMmjTJ7b1HH33U7fVrr72G1157ze29Bx98EA8++KDbe96SOMIds7kC3bpVwqUmcS2kHriFZm0uWWJUNZBZKleSt/1wnBFnzzb0KcLkEkFKuTbDPRtQjvU+idBh0SvBQyIsDKFZsb6R6voqGZPkmbWpdOq4q5s0Ls4Io7Ex7ParC7ebTBxatQpuIXfvLikOEyfejaSk2kt0ye0OkXvQp2xAedb7JPQNibAwhAJ+9Y2U11epe8B71qYygczeXBX9+rnXUuvbNw8xMd0BCD8f3oOADejcuYnER6A+LNZfIggtQCIsTCGBpW+0dn3VzNr0Jlb5uDnelWQ2V6Cy8uag983q8kyBCNatSNmABCEOqhNGiKK6uhqVlZU+/6qrq9VuIqEh+KxNg8ExkrOQOm42VyA19XjIQfTNmgE9e2pHjKxZA7RoAfTq5fh3zZrA32Gx/hJBaAGyhBFBU11djdOnTwfcrnHjxl6XAQoXKPYuONTK2iSuItatyGL9JYLQAuE7QhKi8VzgOtTt9ArF3gWPkKxNQj5CcSuy7HoN96xNgl1IhBGEjJDAugpZBtkn1CKjcqzyECqUtUmw3PeQCCMIQhHIMsg+YtyKLK+CoHTWJsuDfTjDct9DIowgCMXQssAqLS0V3FGzLEwCEYxbMdT1UeVG6axNlgf7cIfVc04ijNAVX375Je644w6cO3cO8fHxgr6TkpKCnJwc5OTkyNo2gk2EWiW2bt0KILCgYF2YCEGoW5HlSuSAOms4snpNCTahEhWEogwfPhwGgwFjx46t9dn48eNhMBgwfPhw5RvGCFarFcXFxT7/rFYrk/vWMrz1YuDAgYK2DyQoWBcm4QTvXjWZHK8pa5NgDbKEEYqTnJyM9957D0uWLEFMTAwA4PLly8jNzUXz5s1Vbp16yGlB0YN1Rk4sFkvYiyItu1D9wXLWJkGQCCOCxuhZlTHI7Tp16oSjR49i69atePjhhwE4XD3NmzdHamqqc7srV65g6tSpeO+991BeXo4uXbpgyZIluPnmq5XLP/nkE+Tk5ODEiRPo2rUrhg0bVuv3vv76a8ycORPff/89EhIS8H//939YsGAB6tWrF8xhy46cFhSyzhD+0LtIZzFrkyAAckcSIoiIiEDjxo2RkJDg8y9QodYRI0Zg7dq1ztdvvfUWHnvsMbdtpk2bhi1btmD9+vX44Ycf0KpVK2RlZeHs2bMAgBMnTmDgwIHo378/Dhw4gFGjRmHGjBlu+zh69Cj69OmDQYMG4eDBg9i0aRO+/vprTJgwQcIzQhDaRo8indzvhBYgSxghqpBhqJXwH3nkEcycORPHjx8HAHzzzTd477338OWXXwIALly4gNdeew3r1q3D3XffDQD417/+hZ07d2LNmjWYOnUqXnvtNaSlpeGVV14BALRp0wY///wzFi5c6PydBQsW4OGHH3YG3bdu3RrLly/H7bffjtdeew116tQJ6TiI0NGrG4xQD71b9gj9QCIszFGrkGGjRo3Qt29frFu3DhzHoW/fvkhISHB+fvToUVRVVaF79+7O9yIjI3HLLbfg8OHDAIDDhw8jPT3dbb8ZGRlur3/66SccPHgQGzdudL7HcRzsdjsKCwvRrl07OQ6PEAgNloQc6NGyR+gTEmFhjNKFDD0ZMWKE0y24atUqWX7j/PnzePzxx/HEE0/U+iyckwBYgQZLbUPFSQkiNEiEhTFKFzL0pE+fPqisrITBYEBWVpbbZ2lpaYiKisI333yDFi1aAACqqqrw3XffOV2L7dq1w8cff+z2vX379rm97tSpE3799Ve0krMwEKELpBIU4SRMqDgpQYQGibAwRo1Chq6YTCana9HEF/L5m3r16mHcuHGYOnUqGjZsiObNm2PRokW4ePEiRv7tLx07dixeeeUVTJ06FaNGjcL+/fuxbt06t/1Mnz4dXbt2xYQJEzBq1CjUq1cPv/76K3bu3CnIDUaED1IJinATJno5DoJQAxJhYYyYdeKkJi4uzudnL730Eux2Ox599FFUVFSgS5cu+Oyzz9CgQQMADnfili1bMGnSJKxYsQK33HILXnzxRYwYMcK5jxtuuAFfffUVnnnmGdx2223gOA5paWnIzs6W/diCRU4LSjhZZ0JBKkFBwoQgCCEYOI7j1G4E4Z3y8nKYzWbYbLZaYuXy5csoLCxEampqyBl+J09SIUN/SHmuAyFnpiCLWYjFxcV44403Am43ZswYJCYmKtCi8ERvCRJ0XxFq42/8doUsYQQVMmQIOQc4LQyehDqEmwuVIFiBRBhBEAShK4FF7ndCK5AIIwhCNWiwJOSALHuEViARRhCEatBgScgF3TOEFiARRhCEqtBgSRBEuEILeGscSm6VHzrHBEEQhByQCNMofHFTWs5Ffvhz7FlQliAIgiBCgdyRGiUiIgJ169bFmTNnEBkZCaOR9LQc2O12nDlzBnXr1kVEBD0uBEEQhHTQqKJRDAYDEhMTUVhYiOPHj6vdHF1jNBrRvHlzGAwGtZtCEARB6AgSYRomKioKrVu3JpekzERFRZGlkSAIgpAcEmEax2g0yr6UDkEQBEEQ0kPTe4IgCIIgCBUgEUYQBEEQBKECJMIIgiAIgiBUgGLCGIYvElpeXq5ySwiCIAiCEAo/bgcq9k0ijGEqKioAAMnJySq3hCAIgiCIYKmoqIDZbPb5uYGjNVmYxW63o6ioCLGxsWFZo6q8vBzJyck4ceIE4uLi1G6OZqHzKA10HkOHzqE00HmUBjnPI8dxqKioQFJSkt8SR2QJYxij0YhmzZqp3QzViYuLo45GAug8SgOdx9ChcygNdB6lQa7z6M8CxkOB+QRBEARBECpAIowgCIIgCEIFSIQRzBIdHY1nn30W0dHRajdF09B5lAY6j6FD51Aa6DxKAwvnkQLzCYIgCIIgVIAsYQRBEARBECpAIowgCIIgCEIFSIQRBEEQBEGoAIkwgiAIgiAIFSARRqjOv//9b/Tv3x9JSUkwGAz46KOP3D7nOA5z5sxBYmIiYmJikJmZid9//12dxjJKoHM4fPhwGAwGt78+ffqo01iGWbBgAW6++WbExsaicePGGDBgAI4cOeK2zeXLlzF+/HhYLBbUr18fgwYNwqlTp1RqMZsIOY89e/asdU+OHTtWpRazyWuvvYYbbrjBWUw0IyMDn376qfNzuheFEeg8qnkvkggjVOfChQvo2LEjVq1a5fXzRYsWYfny5Vi9ejUKCgpQr149ZGVl4fLlywq3lF0CnUMA6NOnD4qLi51/7777roIt1AZfffUVxo8fj3379mHnzp2oqqrCXXfdhQsXLji3mTRpErZt24b3338fX331FYqKijBw4EAVW80eQs4jAIwePdrtnly0aJFKLWaTZs2a4aWXXsL+/fvx/fffo1evXrjvvvvwyy+/AKB7USiBziOg4r3IEQRDAOA+/PBD52u73c41bdqU++c//+l8r6ysjIuOjubeffddFVrIPp7nkOM4btiwYdx9992nSnu0zOnTpzkA3FdffcVxnOPei4yM5N5//33nNocPH+YAcPn5+Wo1k3k8zyPHcdztt9/OPfnkk+o1SqM0aNCAe/PNN+leDBH+PHKcuvciWcIIpiksLERJSQkyMzOd75nNZqSnpyM/P1/FlmmPL7/8Eo0bN0abNm0wbtw4WK1WtZvEPDabDQDQsGFDAMD+/ftRVVXldj+2bdsWzZs3p/vRD57nkWfjxo1ISEjA9ddfj5kzZ+LixYtqNE8T1NTU4L333sOFCxeQkZFB96JIPM8jj1r3Ii3gTTBNSUkJAKBJkyZu7zdp0sT5GRGYPn36YODAgUhNTcXRo0fx9NNP4+6770Z+fj5MJpPazWMSu92OnJwcdO/eHddffz0Ax/0YFRWF+Ph4t23pfvSNt/MIAA899BBatGiBpKQkHDx4ENOnT8eRI0ewdetWFVvLHj///DMyMjJw+fJl1K9fHx9++CHat2+PAwcO0L0YBL7OI6DuvUgijCDCgCFDhjj/36FDB9xwww1IS0vDl19+id69e6vYMnYZP348Dh06hK+//lrtpmgaX+dxzJgxzv936NABiYmJ6N27N44ePYq0tDSlm8ksbdq0wYEDB2Cz2fDBBx9g2LBh+Oqrr9RulubwdR7bt2+v6r1I7kiCaZo2bQoAtTJ+Tp065fyMCJ6WLVsiISEBf/zxh9pNYZIJEyYgLy8Pe/bsQbNmzZzvN23aFJWVlSgrK3Pbnu5H7/g6j95IT08HALonPYiKikKrVq3QuXNnLFiwAB07dsSyZcvoXgwSX+fRG0reiyTCCKZJTU1F06ZNsXv3bud75eXlKCgocPPnE8Fx8uRJWK1WJCYmqt0UpuA4DhMmTMCHH36IL774AqmpqW6fd+7cGZGRkW7345EjR/Dnn3/S/ehCoPPojQMHDgAA3ZMBsNvtuHLlCt2LIcKfR28oeS+SO5JQnfPnz7vNOAoLC3HgwAE0bNgQzZs3R05ODp5//nm0bt0aqampmD17NpKSkjBgwAD1Gs0Y/s5hw4YNMW/ePAwaNAhNmzbF0aNHMW3aNLRq1QpZWVkqtpo9xo8fj9zcXPy///f/EBsb64ytMZvNiImJgdlsxsiRIzF58mQ0bNgQcXFxmDhxIjIyMtC1a1eVW88Ogc7j0aNHkZubi3vuuQcWiwUHDx7EpEmT0KNHD9xwww0qt54dZs6cibvvvhvNmzdHRUUFcnNz8eWXX+Kzzz6jezEI/J1H1e9FVXIyCcKFPXv2cABq/Q0bNozjOEeZitmzZ3NNmjThoqOjud69e3NHjhxRt9GM4e8cXrx4kbvrrru4Ro0acZGRkVyLFi240aNHcyUlJWo3mzm8nUMA3Nq1a53bXLp0ifvHP/7BNWjQgKtbty73f//3f1xxcbF6jWaQQOfxzz//5Hr06ME1bNiQi46O5lq1asVNnTqVs9ls6jacMUaMGMG1aNGCi4qK4ho1asT17t2b+/zzz52f070oDH/nUe170cBxHCe/1CMIgiAIgiBcoZgwgiAIgiAIFSARRhAEQRAEoQIkwgiCIAiCIFSARBhBEARBEIQKkAgjCIIgCIJQARJhBEEQBEEQKkAijCAIgiAIQgVIhBEEQRAEQagAiTCCIAiCIAgVIBFGEAQhgsrKSrWbUAsW20QQhG9IhBEEQQDo2bMnJkyYgAkTJsBsNiMhIQGzZ88Gv7JbSkoKnnvuOQwdOhRxcXEYM2YMAODrr7/GbbfdhpiYGCQnJ+OJJ57AhQsXnPt99dVX0bp1a9SpUwdNmjTB/fff7/zsgw8+QIcOHRATEwOLxYLMzEznd3v27ImcnBy3Ng4YMADDhw93vhbbJoIg2IBEGEEQxN+sX78eERER+Pbbb7Fs2TIsXrwYb775pvPzl19+GR07dsSPP/6I2bNn4+jRo+jTpw8GDRqEgwcPYtOmTfj6668xYcIEAMD333+PJ554AvPnz8eRI0ewY8cO9OjRAwBQXFyMBx98ECNGjMDhw4fx5ZdfYuDAgQh2Od9g20QQBDvQAt4EQRBwWJ5Onz6NX375BQaDAQAwY8YMfPzxx/j111+RkpKCm266CR9++KHzO6NGjYLJZMLrr7/ufO/rr7/G7bffjgsXLuCTTz7BY489hpMnTyI2Ntbt93744Qd07twZx44dQ4sWLby258Ybb8TSpUud7w0YMADx8fFYt24dAIhqU506dUI6TwRBSAdZwgiCIP6ma9euTgEGABkZGfj9999RU1MDAOjSpYvb9j/99BPWrVuH+vXrO/+ysrJgt9tRWFiIO++8Ey1atEDLli3x6KOPYuPGjbh48SIAoGPHjujduzc6dOiABx54AP/6179w7ty5oNscbJsIgmAHEmEEQRACqVevntvr8+fP4/HHH8eBAwecfz/99BN+//13pKWlITY2Fj/88APeffddJCYmYs6cOejYsSPKyspgMpmwc+dOfPrpp2jfvj1WrFiBNm3aOIWS0Wis5ZqsqqoKuU0EQbADiTCCIIi/KSgocHu9b98+tG7dGiaTyev2nTp1wq+//opWrVrV+ouKigIAREREIDMzE4sWLcLBgwdx7NgxfPHFFwAAg8GA7t27Y968efjxxx8RFRXldC02atQIxcXFzt+qqanBoUOHAh6DkDYRBMEGJMIIgiD+5s8//8TkyZNx5MgRvPvuu1ixYgWefPJJn9tPnz4de/fuxYQJE3DgwAH8/vvv+H//7/85g+Dz8vKwfPlyHDhwAMePH8fbb78Nu92ONm3aoKCgAC+++CK+//57/Pnnn9i6dSvOnDmDdu3aAQB69eqF7du3Y/v27fjtt98wbtw4lJWVBTyGQG0iCIIdItRuAEEQBCsMHToUly5dwi233AKTyYQnn3zSWfbBGzfccAO++uorPPPMM7jtttvAcRzS0tKQnZ0NAIiPj8fWrVsxd+5cXL58Ga1bt8a7776L6667DocPH8a///1vLF26FOXl5WjRogVeeeUV3H333QCAESNG4KeffsLQoUMRERGBSZMm4Y477gh4DIHaRBAEO1B2JEEQBLxnIxIEQcgJuSMJgiAIgiBUgEQYQRAEQRCECpA7kiAIgiAIQgXIEkYQBEEQBKECJMIIgiAIgiBUgEQYQRAEQRCECpAIIwiCIAiCUAESYQRBEARBECpAIowgCIIgCEIFSIQRBEEQBEGoAIkwgiAIgiAIFSARRhAEQRAEoQL/H6+AZe06G5giAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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j+sS6OllERERehyVELlKirpaCIQDQCODxDcdZUkRERGZpNBpkZ2cjPj4eAQEBSElJwccffwzgzyqm7du3o3fv3ggMDES/fv1w6tQpAMDatWuxZMkSHDlyBCqVCiqVCmvXrpX2XVZWhrvuuguBgYG44YYb8Pnnn1uUJu33bt26FT169EBAQABuueUWXLhwAZs3b0bXrl0RFBSE8ePHo6qqSvpcTU0NHnzwQYSHh6NZs2YYMGAA8vPzHXeyLMSAyEWKyiqlYEirTgicKauS/wAREbm1hmwCkZ2djX//+99YuXIlTpw4gfnz5+Pee+/F7t27pW2eeOIJ/P3vf8fBgwfRpEkTTJs2DQAwZswYPPLII7jxxhtRUlKCkpISjBkzRvrckiVLcM899+Do0aO49dZbMWHCBFy6dMnitD3zzDP45z//idzcXJw9exb33HMPXnnlFaxbtw6bNm3CV199hddee03a/rHHHsMnn3yCd955B99++y06deqE9PR0q77TERgQuUh8aHP4GPQS9FWpEBfK8TCIiDzN+vxi9M/ZgfGrDqB/zg6szy922nfV1NRg6dKlePvtt5Geno4OHTpgypQpuPfee/Hmm29K273wwgsYNGgQEhMTsXDhQuTm5uLq1asICAhAixYt0KRJE0RGRiIyMlIa3BAApkyZgnHjxqFTp05YunQprly5gm+++cbi9D3//PPo378/evTogenTp2P37t1YsWIFevTogYEDB+Luu+/Gzp07AQCVlZVYsWIFXnrpJWRkZCAxMRGrVq1CQEAA3nrrLcedNAswIHKRqOAAZI9Kgu8fYyf4qlRYOqobooIDzHySiIjcSUM3gTh9+jSqqqrwl7/8BS1atJD+/v3vf6OwsFDaLjk5Wfpv7ZQW2ikuTNH9XPPmzREUFGTR5+Q+HxERgcDAQHTo0EFvmXZ/hYWFuHbtGvr37y+tb9q0KW666SZ8//33Fn+nI7BRtQuN6ROLtM5hOFNWhbjQQAZDREQeyFQTCGfk61euXAEAbNq0CW3bttVb5+/vLwVFTZs2lZZrBy7UaDRm96/7Oe1nLfmc3OdVKpXd+2soDIhcLCo4gIEQEZEH0zaB0A2KnNkEIjExEf7+/iguLsagQYOM1uuWEinx8/NDXV2dM5JnlY4dO8LPzw/79+9H+/btAdRPv5Gfn4958+Y1aFoYEBEREdlB2wTi8Q3HUSeE05tAtGzZEo8++ijmz58PjUaDAQMGQK1WY//+/QgKCpICC1Pi4uJQVFSEgoICtGvXDi1btoS/v79T0mtK8+bNMWvWLCxYsACtW7dGbGwsli1bhqqqKkyfPr1B08KAiIiIyE4N3QTiueeeQ1hYGLKzs/HTTz8hJCQEPXv2xOOPP25RddTo0aOxYcMGDBkyBOXl5VizZg2mTJni1DQrycnJgUajwcSJE/H777+jd+/e2Lp1K1q1atWg6VAJIYT5zaiiogLBwcFQq9UICgpydXKIiMhOV69eRVFREeLj49GsWTNXJ4fsYOq3tPT5zV5mRERE5PUYEBEREZFZM2fO1Ovmr/s3c+ZMVyfPbmxDRERERGY9++yzePTRR2XXNYamJAyIiIiIyKzw8HCEh4e7OhlOwyozIiIi8noMiIiIyKu546jJZB1H/IasMiMiIq/k5+cHHx8fnDt3DmFhYfDz85OmuCDPIIRAbW0tLl68CB8fH/j5+dm8LwZERETklXx8fBAfH4+SkhKcO3fO1ckhOwQGBiI2NhY+PrZXfDEgIiIir+Xn54fY2Fhcv37dLeb2Iuv5+vqiSZMmdpfuMSAiIiKvpp2R3XBWdvIubFRNREREXo8BEREREXk9BkRERETk9RgQERERkddjQERERERejwEREREReT0GREREROT1GBARERGR12NARERERF6PARERERF5PQZERERE5PUYEBEREZHXc2lAtGLFCiQnJyMoKAhBQUFITU3F5s2bpfVXr17F7Nmz0aZNG7Ro0QKjR4/G+fPn9fZRXFyMzMxMBAYGIjw8HAsWLMD169f1ttm1axd69uwJf39/dOrUCWvXrm2IwyMiIiIP4dKAqF27dsjJycGhQ4dw8OBB3HLLLbjzzjtx4sQJAMD8+fPxxRdf4KOPPsLu3btx7tw5jBo1Svp8XV0dMjMzUVtbi9zcXLzzzjtYu3YtnnrqKWmboqIiZGZmYsiQISgoKMC8efMwY8YMbN26tcGPl4iIiNyTSgghXJ0IXa1bt8ZLL72Eu+++G2FhYVi3bh3uvvtuAMDJkyfRtWtX5OXl4eabb8bmzZtx22234dy5c4iIiAAArFy5EllZWbh48SL8/PyQlZWFTZs24fjx49J3jB07FuXl5diyZYvF6aqoqEBwcDDUajWCgoIce9BERETkFJY+v92mDVFdXR0++OADVFZWIjU1FYcOHcK1a9cwbNgwaZuEhATExsYiLy8PAJCXl4ekpCQpGAKA9PR0VFRUSKVMeXl5evvQbqPdh5KamhpUVFTo/REREVHj5PKA6NixY2jRogX8/f0xc+ZMfPrpp0hMTERpaSn8/PwQEhKit31ERARKS0sBAKWlpXrBkHa9dp2pbSoqKlBdXa2YruzsbAQHB0t/MTEx9h4qERERuSmXB0RdunRBQUEBDhw4gFmzZmHy5Mn47rvvXJ0sLFq0CGq1Wvo7e/asq5NERERETtLE1Qnw8/NDp06dAAC9evVCfn4+li9fjjFjxqC2thbl5eV6pUTnz59HZGQkACAyMhLffPON3v60vdB0tzHsmXb+/HkEBQUhICBAMV3+/v7w9/e3+/iIiIjI/bm8hMiQRqNBTU0NevXqhaZNm2L79u3SulOnTqG4uBipqakAgNTUVBw7dgwXLlyQttm2bRuCgoKQmJgobaO7D+022n0QERERubSEaNGiRcjIyEBsbCx+//13rFu3Drt27cLWrVsRHByM6dOn4+GHH0br1q0RFBSEuXPnIjU1FTfffDMAYPjw4UhMTMTEiROxbNkylJaW4sknn8Ts2bOl0p2ZM2fin//8Jx577DFMmzYNO3bswIcffohNmza58tCJiIjIjbg0ILpw4QImTZqEkpISBAcHIzk5GVu3bsVf/vIXAMDLL78MHx8fjB49GjU1NUhPT8cbb7whfd7X1xcbN27ErFmzkJqaiubNm2Py5Ml49tlnpW3i4+OxadMmzJ8/H8uXL0e7du2wevVqpKenN/jxEhERkXtyu3GI3BXHISIiIvI8HjcOEREREZGrMCAiIiIir8eAiIiIiLweAyI3UaKuRm5hGUrUyqNnExERkXO4fGBGAtbnF2PRhmPQCMBHBWSPSsKYPrGuThYREZHXYAmRi5Woq6VgCAA0Anh8w3GWFBERETUgBkQuVlRWKQVDWnVC4ExZlWsSRERE5IUYELlYfGhz+Kj0l/mqVIgLDXRNgoiIiLwQAyIXiwoOQPaoJPiq6qMiX5UKS0d1Q1Sw8sSzRERE5FhsVO0GxvSJRVrnMJwpq0JcaCCDISIiogbGgMhNRAUHMBAiIiJyEVaZERERkddjQERERERejwEREREReT0GREREROT1GBARERGR12NARERERF6PARERERF5PQZERERE5PUYEBEREZHXY0BEREREXo8BEREREXk9BkRERETk9RgQERERkddjQERERERejwEREREReT0GRG6iRF2N3MIylKirXZ0UIiIir9PE1QkgYH1+MRZtOAaNAHxUQPaoJIzpE+vqZBEREXkNlhC5WIm6WgqGAEAjgMc3HGdJERERUQNiQORiRWWVUjCkVScEzpRVuSZBREREXogBkYvFhzaHj0p/ma9KhbjQQNckiIiIyAsxIHKxqOAAZI9Kgq+qPiryVamwdFQ3RAUHuDhlRERE3oONqt3AmD6xSOschjNlVYgLDWQwRERE1MAYELmJqOAABkJEREQuwiozIiIi8noMiIiIiMjrMSAiIiIir8eAiIiIiLweAyIiIiLyei4NiLKzs9GnTx+0bNkS4eHhGDlyJE6dOqW3zeDBg6FSqfT+Zs6cqbdNcXExMjMzERgYiPDwcCxYsADXr1/X22bXrl3o2bMn/P390alTJ6xdu9bZh0dEREQewqUB0e7duzF79mx8/fXX2LZtG65du4bhw4ejsrJSb7v77rsPJSUl0t+yZcukdXV1dcjMzERtbS1yc3PxzjvvYO3atXjqqaekbYqKipCZmYkhQ4agoKAA8+bNw4wZM7B169YGO1YiIiJyXyohhDC/WcO4ePEiwsPDsXv3bqSlpQGoLyHq3r07XnnlFdnPbN68GbfddhvOnTuHiIgIAMDKlSuRlZWFixcvws/PD1lZWdi0aROOHz8ufW7s2LEoLy/Hli1bLEpbRUUFgoODoVarERQUZN+BEhERUYOw9PntVm2I1Go1AKB169Z6y9977z2EhoaiW7duWLRoEaqq/pz4NC8vD0lJSVIwBADp6emoqKjAiRMnpG2GDRumt8/09HTk5eUppqWmpgYVFRV6f0RERNQ4uc1I1RqNBvPmzUP//v3RrVs3afn48ePRvn17REdH4+jRo8jKysKpU6ewYcMGAEBpaaleMARA+ndpaanJbSoqKlBdXY2AAOMRorOzs7FkyRKHHiMRERG5J7cJiGbPno3jx49j3759esvvv/9+6b+TkpIQFRWFoUOHorCwEB07dnRaehYtWoSHH35Y+ndFRQViYmKc9n1ERETkOm5RZTZnzhxs3LgRO3fuRLt27Uxu27dvXwDA6dOnAQCRkZE4f/683jbaf0dGRprcJigoSLZ0CAD8/f0RFBSk90dERESNk0sDIiEE5syZg08//RQ7duxAfHy82c8UFBQAAKKiogAAqampOHbsGC5cuCBts23bNgQFBSExMVHaZvv27Xr72bZtG1JTUx10JEREROTJXBoQzZ49G++++y7WrVuHli1borS0FKWlpaiurgYAFBYW4rnnnsOhQ4dw5swZfP7555g0aRLS0tKQnJwMABg+fDgSExMxceJEHDlyBFu3bsWTTz6J2bNnw9/fHwAwc+ZM/PTTT3jsscdw8uRJvPHGG/jwww8xf/58lx07ERERuQ+XdrtXqVSyy9esWYMpU6bg7NmzuPfee3H8+HFUVlYiJiYGd911F5588km9Kqyff/4Zs2bNwq5du9C8eXNMnjwZOTk5aNLkzyZSu3btwvz58/Hdd9+hXbt2WLx4MaZMmWJxWtntnoiIyPNY+vx2q3GI3BkDIiIiIs/jkeMQEREREbkCAyIiIiLyegyIiIiIyOsxICIiIiKvx4CIiIiIvB4DIhcrUVcjt7AMJepqVyeFiIjIa7nNXGbeaH1+MRZtOAaNAHxUQPaoJIzpE+vqZBEREXkdlhC5SIm6WgqGAEAjgMc3HGdJERERkQswIHKRorJKKRjSqhMCZ8qqXJMgIiIiL8aAyEXiQ5vDx2DmEl+VCnGhga5JEBERkRdjQOQiUcEByB6VBN8/5nPzVamwdFQ3RAUHuDhlRERE3oeNql1oTJ9YpHUOw5myKsSFBjIYIiIichEGRC4WFRzAQIiIiMjFWGVGREREXo8BEREREXk9BkRERETk9RgQERERkddjQERERERejwEREREReT0GREREROT1GBARERGR12NA5CFK1NXILSxDibra1UkhIiJqdDhStQdYn1+MRRuOQSMAHxWQPSoJY/rEujpZREREjQZLiNxcibpaCoYAQCOAxzccZ0kRERGRAzEgcnNFZZVSMKRVJwTOlFW5JkFERESNEAMiNxcf2hw+Kv1lvioV4kIDXZMgIiKiRogBkZuLCg5A9qgk+KrqoyJflQpLR3VDVHCAi1NGRETUeLBRtQcY0ycWaZ3DcKasCnGhgQyGiIiIHIwBkYeICg5gIEREROQkrDIjIiIir8eAiIiIiLweAyIiIiLyegyIiIiIyOsxICIiIiKvx4CIiIiIvB4DIiIiIvJ6DIiIiIjI6zEgIiIiIq/HgIiIiIi8nksDouzsbPTp0wctW7ZEeHg4Ro4ciVOnTultc/XqVcyePRtt2rRBixYtMHr0aJw/f15vm+LiYmRmZiIwMBDh4eFYsGABrl+/rrfNrl270LNnT/j7+6NTp05Yu3atsw+PiIiIPIRLA6Ldu3dj9uzZ+Prrr7Ft2zZcu3YNw4cPR2VlpbTN/Pnz8cUXX+Cjjz7C7t27ce7cOYwaNUpaX1dXh8zMTNTW1iI3NxfvvPMO1q5di6eeekrapqioCJmZmRgyZAgKCgowb948zJgxA1u3bm3Q4yUiIiL3pBJCCFcnQuvixYsIDw/H7t27kZaWBrVajbCwMKxbtw533303AODkyZPo2rUr8vLycPPNN2Pz5s247bbbcO7cOURERAAAVq5ciaysLFy8eBF+fn7IysrCpk2bcPz4cem7xo4di/LycmzZssWitFVUVCA4OBhqtRpBQUGOP3giIiJyOEuf327VhkitVgMAWrduDQA4dOgQrl27hmHDhknbJCQkIDY2Fnl5eQCAvLw8JCUlScEQAKSnp6OiogInTpyQttHdh3Yb7T6IiIjIuzVxdQK0NBoN5s2bh/79+6Nbt24AgNLSUvj5+SEkJERv24iICJSWlkrb6AZD2vXadaa2qaioQHV1NQICAozSU1NTg5qaGunfFRUV9h0gERERuS23KSGaPXs2jh8/jg8++MDVSQFQ3+A7ODhY+ouJiXF1koiIiMhJ3CIgmjNnDjZu3IidO3eiXbt20vLIyEjU1taivLxcb/vz588jMjJS2saw15n23+a2CQoKki0dAoBFixZBrVZLf2fPnrXrGImIiMh9uTQgEkJgzpw5+PTTT7Fjxw7Ex8frre/VqxeaNm2K7du3S8tOnTqF4uJipKamAgBSU1Nx7NgxXLhwQdpm27ZtCAoKQmJiorSN7j6022j3Icff3x9BQUF6f0RERNQ4WdzLzJo2NJYGDw888ADWrVuH//73v+jSpYu0PDg4WCq5mTVrFr788kusXbsWQUFBmDt3LgAgNzcXQH23++7duyM6OhrLli1DaWkpJk6ciBkzZmDp0qUA6rvdd+vWDbNnz8a0adOwY8cOPPjgg9i0aRPS09MtSit7mREREXkeS5/fFgdEPj4+UKlUJrcRQkClUqGurs6iRCrtb82aNZgyZQqA+oEZH3nkEbz//vuoqalBeno63njjDak6DAB+/vlnzJo1C7t27ULz5s0xefJk5OTkoEmTP9uM79q1C/Pnz8d3332Hdu3aYfHixdJ3WIIBERERkedxeEC0e/dui7980KBBFm/rKRgQEREReR5Ln98Wd7tvjEEOEREREWDHOETl5eV466238P333wMAbrzxRkybNg3BwcEOSxwRERFRQ7Cpl9nBgwfRsWNHvPzyy7h06RIuXbqEf/zjH+jYsSO+/fZbR6eRiIiIyKlsmsts4MCB6NSpE1atWiU1XL5+/TpmzJiBn376CXv27HF4Ql2NbYiIiIg8j8MbVesKCAjA4cOHkZCQoLf8u+++Q+/evVFVVWV9it0cAyIiIiLP49TJXYOCglBcXGy0/OzZs2jZsqUtuyQDJepq5BaWoURd7eqkEBERNXo2NaoeM2YMpk+fjr/97W/o168fAGD//v1YsGABxo0b59AEeqP1+cVYtOEYNALwUQHZo5Iwpk+sq5NFRETUaNkUEP3tb3+DSqXCpEmTcP36dQBA06ZNMWvWLOTk5Dg0gd6mRF0tBUMAoBHA4xuOI61zGKKC5eddIyIiIvvYFBD5+flh+fLlyM7ORmFhIQCgY8eOCAwMdGjivFFRWaUUDGnVCYEzZVUMiIiIiJzE5nGIACAwMBBJSUmOSgsBiA9tDh8V9IIiX5UKcaEMNomIiJzFpoDo6tWreO2117Bz505cuHABGo1Gbz3HIrJdVHAAskcl4fENx1EnBHxVKiwd1Y2lQ0RERE5kU0A0ffp0fPXVV7j77rtx0003mZ30lawzpk8s0jqH4UxZFeJCAxkMEREROZlNAdHGjRvx5Zdfon///o5OD/0hKjiAgRAREVEDsWkcorZt23K8ISIiImo0bAqI/v73vyMrKws///yzo9NDFuCgjURERI5lU5VZ7969cfXqVXTo0AGBgYFo2rSp3vpLly45JHFkjIM2EhEROZ5NAdG4cePw66+/YunSpYiIiGCj6gbCQRuJiIicw6aAKDc3F3l5eUhJSXF0esgEDtpIRETkHDa1IUpISEB1NduvNDTtoI26OGgjERGR/WwKiHJycvDII49g165d+O2331BRUaH3R86hHbTR948qSh8VMG1AnGsTRURE1AiohBDC/Gb6fHzq4yjDtkNCCKhUKtTV1TkmdW6koqICwcHBUKvVCAoKcmlaStTVWLO/CKv2FEGAjauJiIiUWPr8tqkN0c6dO21OGFmuRF2NorJKxIc2N2ojtHpvfTAEsHE1ERGRvWwKiAYNGmTRdg888ACeffZZhIaG2vI1Xs1U93o2riYiInIsm9oQWerdd99lmyIbKHWv1w7EyMbVREREjuXUgMiG5kkE0yVAgHHjal+VCktHdWPpEBERkY1sqjIj59KWAOkGRYYlQGP6xCKtcxjOlFUhLjSQwRAREZEdnFpCRLaxtAQoKjgAqR3bMBgiIiKyE0uI3BRLgIiIiBoOAyI3FhUcYBQImeqKT0RERLZxakB07733unwQw8aEM90TERE5h00jVQNAeXk5vvnmG1y4cAEajUZv3aRJkxySOHfi6pGqS9TV6J+zw6ih9b6FQ1hSREREpMCpI1V/8cUXmDBhAq5cuYKgoCC9KTxUKlWjDIhcjYMxEhEROY9NvcweeeQRTJs2DVeuXEF5eTkuX74s/V26dMnRaSRwMEYiIiJnsikg+vXXX/Hggw8iMJAP44ai1BUfAHILy6RRrImIiMh6NlWZpaen4+DBg+jQoYOj00MmGHbF3/PDRaldERtZExER2c7igOjzzz+X/jszMxMLFizAd999h6SkJDRt2lRv2zvuuMNxKSQ92q74SvOdccZ7IiIi61kcEI0cOdJo2bPPPmu0TKVSoa6uzq5EkXlsZE1EROQ4FgdEhl3rybUsme+MiIiILGNTo+p///vfqKmpMVpeW1uLf//733YniszjjPdERESOY9PAjL6+vigpKUF4eLje8t9++w3h4eGNssrM1QMzKilRV3O+MyIiIgWWPr9tKiESQugNxqj1yy+/IDg42OL97NmzB7fffjuio6OhUqnw2Wef6a2fMmUKVCqV3t+IESP0trl06RImTJiAoKAghISEYPr06bhy5YreNkePHsXAgQPRrFkzxMTEYNmyZZYfrJuzZcb7EnU1u+oTERHpsKrbfY8ePaTAZOjQoWjS5M+P19XVoaioyChgMaWyshIpKSmYNm0aRo0aJbvNiBEjsGbNGunf/v7+eusnTJiAkpISbNu2DdeuXcPUqVNx//33Y926dQDqI8Phw4dj2LBhWLlyJY4dO4Zp06YhJCQE999/vzWH3yhwPjQiIiJjVgVE2p5mBQUFSE9PR4sWLaR1fn5+iIuLw+jRoy3eX0ZGBjIyMkxu4+/vj8jISNl133//PbZs2YL8/Hz07t0bAPDaa6/h1ltvxd/+9jdER0fjvffeQ21tLd5++234+fnhxhtvREFBAf7xj394XUDErvpERETyrAqInn76aQBAXFwcxowZg2bNmjklUbp27dqF8PBwtGrVCrfccguef/55tGnTBgCQl5eHkJAQKRgCgGHDhsHHxwcHDhzAXXfdhby8PKSlpcHPz0/aJj09HS+++CIuX76MVq1ayX5vTU2NXsPxiooKJx1hw2FXfSIiInk2jVQ9efJkAPW9yuRmu4+NdUwVzIgRIzBq1CjEx8ejsLAQjz/+ODIyMpCXlwdfX1+UlpYaNexu0qQJWrdujdLSUgBAaWkp4uPj9baJiIiQ1ikFRNnZ2ViyZIlDjsMaJepqFJVVIj60ucODFHbVJyIikmdTQPTjjz9i2rRpyM3N1VuubWztqF5mY8eOlf47KSkJycnJ6NixI3bt2oWhQ4c65DuULFq0CA8//LD074qKCsTExDj1O53dvkfbVf/xDcdRJwS76hMREf3BpoBoypQpaNKkCTZu3IioqCjZHmfO0KFDB4SGhuL06dMYOnQoIiMjceHCBb1trl+/jkuXLkntjiIjI3H+/Hm9bbT/VmqbBNS3XTJswO1MDdW+x3A+NAZDRERENgZEBQUFOHToEBISEhydHpN++eUX/Pbbb4iKigIApKamory8HIcOHUKvXr0AADt27IBGo0Hfvn2lbZ544glcu3ZNmnNt27Zt6NKli2J1mSs0ZPse7XxoREREVM+mcYgSExNRVlZm95dfuXIFBQUFKCgoAAAUFRWhoKAAxcXFuHLlChYsWICvv/4aZ86cwfbt23HnnXeiU6dOSE9PBwB07doVI0aMwH333YdvvvkG+/fvx5w5czB27FhER0cDAMaPHw8/Pz9Mnz4dJ06cwPr167F8+XK96jB3oG3fo8uR7Xs49hAREZEym0aq3rFjB5588kksXbpUdrZ7S0dy3rVrF4YMGWK0fPLkyVixYgVGjhyJw4cPo7y8HNHR0Rg+fDiee+45qVE0UD8w45w5c/DFF1/Ax8cHo0ePxquvvqo3JMDRo0cxe/Zs5OfnIzQ0FHPnzkVWVpZVx9wQI1Wvzy82at/jiDZEHHuIiIi8laXPb5sCIh+fPwuWdNsPObpRtTtpqKk7HD0VR4m6Gv1zdhj1LNu3cAirzYiIqNGz9PltUxuinTt32pwwMs3R7Xs49hAREZF5NrUhGjRoEHx8fLBq1SosXLgQnTp1wqBBg1BcXAxfX19Hp5Hs4Oy2SURERI2BTQHRJ598gvT0dAQEBODw4cPSiM5qtRpLly51aALJPtqxh3z/qNrk2ENERETGbGpD1KNHD8yfPx+TJk1Cy5YtceTIEXTo0AGHDx9GRkaGNEp0Y9JQbYicxdFtk4iIiDyBU9sQnTp1CmlpaUbLg4ODUV5ebssuyck49hAREZEym6rMIiMjcfr0aaPl+/btQ4cOHexOFBEREVFDsikguu+++/DQQw/hwIEDUKlUOHfuHN577z08+uijmDVrlqPTSERERORUNlWZLVy4EBqNBkOHDkVVVRXS0tLg7++PRx99FHPnznV0GomIiIicyqZG1Vq1tbU4ffo0rly5gsTERL3RoRsbT29UTURE5I2c2qhay8/PD4mJifbsgixQoq5GUVkl4kObs2E0ERGRE9gVEJHzcR4yIiIi57OpUTU1jBJ1tRQMAYBGAI9vOO62M9aXqKuRW1jmtukjIiL34W7PDJYQuTFr5yFzRdVaiboaB89cQt5Pl/DBN8UsySIiIrPcsfaDAZEb085DZjhTvdw8ZK64uNbnF2PhJ8dg2CpfW5KV1jmMbZ6IiEiPUu2Hq58ZrDJzY5bOQ+aKqrUSdbVsMKSlLckiIiLSZar2w5VYQuRG5Kq8xvSJRVrnMJPzkFlbteYIRWWVisEQoFySRURE3s2a2o+GxIDITZiq8jI3D5krLq740OZQAbJBkY8KsiVZRERE2tqPxzccR50QirUfDc2ugRm9iTMHZixRV6N/zg6jgGbfwiGKF4hhadL6/GKji8sVbYjuT4vH1P7xLr+wiYjIvZWoq03WfjhKgwzMSI5hbZWXUmmSuao1R9N+56Ezl6FSAT3bt2IgREREFjFX+9HQGBC5AWuqvMy1zm/oiysqOAC3pbjPBU1ERGQL9jJzA5b2JgPct3U+ERGRJ2MJkZuwtMrLXVvnExEReTKWELmRqOAApHZsY7Lay5rSJCIiIrIMS4g8kCsaUFvLFdOIEBER2YoBkYdyt9b5utxxjhoiIiJTWGXWiLliJmFXTCNCRERkL5YQNVKuKqVxxTQiRERE9mIJUSPkylIabS84XewFR0RE7o4BUSPkyrGK2AuOiIg8EavMGiFXjlVUoq5GTOtAbHggFVW1GrftBUdERKSLJUSNkKtKadbnF6N/zg6MX3UAd72Ri+JLlQyGiIjII7CEqBFyRSmNuTnWiIiIlLjD2HUMiBoZud5lqR3bOP172buMiIhs4S5j17HKrBHxhN5lJepqbDx6Dl8c+ZVjExEReTl3GruOJUSNiCtLabTtlh7fcBx1Qsi2W1qfX4yFnxyDNokqADmjOYo1EZG3cqfaBQZEjYgre5cBpudY074F6F73AsCiT46xnRERkZdy9XNLF6vMGhF3GAMoKjgAqR3bGH2n3FsAAGiABhkfiYiI3I87PLe0WELUyJgqpXElubcAoD4i5yjWRETey12eWywhaoSUSmlcSfsWoNJpeK0CkD06ya3SSUREDc8dnlsuDYj27NmD22+/HdHR0VCpVPjss8/01gsh8NRTTyEqKgoBAQEYNmwYfvzxR71tLl26hAkTJiAoKAghISGYPn06rly5orfN0aNHMXDgQDRr1gwxMTFYtmyZsw+NZIzpE4vchbfg9fE98M9xPZC76BY2qCYiIrfg0oCosrISKSkpeP3112XXL1u2DK+++ipWrlyJAwcOoHnz5khPT8fVq1elbSZMmIATJ05g27Zt2LhxI/bs2YP7779fWl9RUYHhw4ejffv2OHToEF566SU888wz+Ne//uX043OVEnU1cgvL3LJbe1RwADKTo3FbSjRLhqjRcud70N3x3JGrqIQQMk1dG55KpcKnn36KkSNHAqgvHYqOjsYjjzyCRx99FACgVqsRERGBtWvXYuzYsfj++++RmJiI/Px89O7dGwCwZcsW3Hrrrfjll18QHR2NFStW4IknnkBpaSn8/PwAAAsXLsRnn32GkydPWpy+iooKBAcHQ61WIygoyLEH70DuMsAVkTeQG12X96DteO7IGSx9frttG6KioiKUlpZi2LBh0rLg4GD07dsXeXl5AIC8vDyEhIRIwRAADBs2DD4+Pjhw4IC0TVpamhQMAUB6ejpOnTqFy5cvK35/TU0NKioq9P7cnTsNcEXU2OnO3dc/ZwfW5xfzHrSSbmkQz13j42mlfW7by6y0tBQAEBERobc8IiJCWldaWorw8HC99U2aNEHr1q31tomPjzfah3Zdq1atZL8/OzsbS5Yssf9AGpA7DXBF5MnMzatUoq7WG2RU+/B+ZWwK70ELGZYGzRgQz3PXiHhiaZ/blhC52qJFi6BWq6W/s2fPujpJZlk6fQYRKZMr+TH09r4iGLY1qBMCPioV70ELyJUGrd5bxHPXSHhqaZ/bBkSRkZEAgPPnz+stP3/+vLQuMjISFy5c0Ft//fp1XLp0SW8buX3ofoccf39/BAUF6f25O3ca4MoenlbMSu7FnuvHkoy8RF2Nt/YVGX3WB0DP9q0a/B70xPtFrjRbA2DGgA4en3+R6doKd+a2VWbx8fGIjIzE9u3b0b17dwD1DaMOHDiAWbNmAQBSU1NRXl6OQ4cOoVevXgCAHTt2QKPRoG/fvtI2TzzxBK5du4amTZsCALZt24YuXbooVpd5Mt0BrgL9fFBZW4cSdbXHZCqeWMxK7sPe68eSamelUddnpMUjKjjA7kHmzFXX6VI6Xmv2Yc/327pfpekapg6Iw9QBcS4foI/s407TcVjDpQHRlStXcPr0aenfRUVFKCgoQOvWrREbG4t58+bh+eefxw033ID4+HgsXrwY0dHRUk+0rl27YsSIEbjvvvuwcuVKXLt2DXPmzMHYsWMRHR0NABg/fjyWLFmC6dOnIysrC8ePH8fy5cvx8ssvu+KQncYwE9vzw0WPCyyU3s451xlZwhHXjyUZudw2Pipgav8/2ypGBQfYdM1aE9ApHW959TW8uPmkTfe+s15I5PZrajJo3u+ezZLJvt2RS7vd79q1C0OGDDFaPnnyZKxduxZCCDz99NP417/+hfLycgwYMABvvPEGOnfuLG176dIlzJkzB1988QV8fHwwevRovPrqq2jRooW0zdGjRzF79mzk5+cjNDQUc+fORVZWllVpdedu94aZTVZGgpQhavmqVNi3cEiDXJC2vmEu3fQd/rXXuCri/ftuRmrHNo5MIjVCuYVlGL/qgNFya6+f9fnFRhm5YVBgyTbWKlFXo3/ODovvW6XjVakAYcO9b+33W8rUfgGwNKgRK1FXu8Xva+nz26UlRIMHD4apeEylUuHZZ5/Fs88+q7hN69atsW7dOpPfk5ycjL1799qcTncm95ZoGAwBDddbw9Y3zBJ1NVbJBEM+Ks51RpZxVDG9JVVezph7ydpeorIlVTCeL9DSe99ZvVRN7dfVUzWQc9laUuoqbtuomiwj2zhR1M8TpssHQKCfc39ue3oWFJVVGvXaAeobWXrSDUWu48hOBZbMq2S4jb2Nm63tJSp3vFkZCTb31HJWL1X2fiVP4baNqskySm/Fj43ogmVbTqHujxI4DYC73sh1alsie94wld52pw6Ic1ojT2p8XDVrtiUlo+auY1vaXcgdb0hgU5vabkQFB+CuHm3xybe/SstG9rB/ih1PbU9C3sdtpu5wd+7ehkiuPcORs5cx8o1cm9oT2MLeNghyxwHA4xqHk3ex5Lq3trG0vQGdLftwVhsie9JE5Age0YaIHEPprbiytg6G4a4z2xLZ+yZoeBwA9DJo9jojd2SuZFSpKjkhsiUqa+uMSowc0e7Cln3YUsJrTemto9uTsOSYHI0BUSMhl9m4YiwIe6ssdI8jt7CMQ/mT2zN3nykFGtrSW3cp+bQ2v3DlmGEcr4ycgY2qGzFXjVxtSYNUSxqgsjEmNTRbGkabu8/krmPgz67x7jKtgTX5hVyp16JPjuHIWeUJsx3FU6eFIPfHEqJGzpoSm4Yqgrb07c5UFRyLy8nR7Cl1MHWfGV7HPqjv5KDLXUo+Lc0vlKbeGPlGLnKcXFrDSazJWRgQeQFL6u4bqgja2tGE5TJoFpc3fg0d8DpilGtT95nhlDp3vZFrVDUV6OeD3MIylwf5luQXctVrQH2pl7Pb+XnqtBDk/lhlRg1aBG3LpH+6VXAsLm/8LJlt3tEO/XzZ6ZNRaq/jlBjjCWBH9ojGXW/kNugx20Nb6iX3AHH2JJ6NZRJrcj8sIaIGLYK29+2OxeWNW0POZ6cthTr2ixo5m08arXdmqcOYPrFIiGyJ/DOXER8aiPv+fcjhx+zsUjbtMRgO7eGjMj8IrL1pa6jxplg1710YEFGDFkHb2zWfxeWNW0MFvLrVrnJ8VHBqqYPu9xvOPQbYf8wNVa2cEtMKOTr3M1B/b5oaBNZRaXP2tBCsmncudww2GRBRg48ka+3bneGNw1FvG6+GCHgNS6HkjL0pxmkPP8PvVxoa9+iv5RZPSqt7jwBwWCmbdr/N/Xxlx0wCdEqKXs+Vpt9R+s6GLAG0h6ek05PoXqN7frjolsEmAyIC0PBTHlj6dqf0luaK6RnI+ewJeC1945QrhTL0wYGzmHvLDU6pptt49JzZ7weAZZtP4Y4U81NnGN4j0wfEO6SUTa4UTenhVVlbZzQXodx3ekqVt6ek01PolYj+scxc8OwKDIhI4m4zE1s7wi81DrYEvNZUbyj1kNKlARq0ms7Wrvhy98hb+4qgAvQCFN1StiNnL+ObM5dwU1xrpMS0smi/WkoPL0tL9jylyttT0ukJjEpEZbZxl2CTvczIbSmO8Pu6db1x7J2FnBqeJYN7alnb89Cwl5LcoIkNWU3nq1Jh1uCOMEyGNg2616/htSw7HpAA7kuLl+2F9ciHBbjz9Vy8sOkk7nw9F498WCCb3jX7ihQDRrleZJb2/PKUHmKekk5PYEmJrLsEmywhIrelONbJH/9vSVErG0Y2frZUbxiWQu354aLN7dIsqapTeigszuyK60Lgxc0njUp0lo7qptfWQreqQXstp3UOky3JmNo/HlP7x+uVsh05e1lvJnsA+OTbXzEptb1eSVGJuhqr9hYpHq/Sw8vSkj1PqfL2lHS6O7l8XIX6zgQa4V7BJgMicltGI/zKBEemHnxsGOkdlKo3zA10qFtFbOvDz7C32MKMBPw1raPFaewd18pokEYfFbDhgVSEBzXTm9xY99LXXsv7Fg4x2eZK9zi+OXNJ9hgOnrmsFxAVlVXKVmto02zq4WVptbs7VM9bEsi6Qzo9le75lbtG3THYZEBEbs2SEX611QqGmRsbRnoHuYbY2oEOrSkZtPbhJ9dbLPvLk4AA/jpIPyhSaixeWVsnW+VVVasxW9WgvZYtDeZuimstu7x3nH47IqU3+n+O74Ge7VtJ+3fHbtOWYsmxefb8vnLnd9/CIUbXqLtdNwyISJG7ZHi6Dyq5h4pSF042jPQepgJnU13A7bm+lQKWFzefxB3djXuHyQUuJepqk9eoqcbfuttZEsylxLTC6J5t9arNBt4QivCgZnrbKQVvPdu3QlFZJQAYVeUtzEgwCgLdFUuOzbMnYFQ6v/sWDrF4GAlXYUBEstz1DUr7UDl05jKgAmJaBZh8+GWNSMCLm09CA/eqqybH0wYFuYVlZksGHXF9x4c2lx1U0VQPNcPAxdwwA7rrVH90HROw/Vr++z3dMSm1PVbt/Qkbj5Zi749l6J+zw+j45dpYaavvfP44Zu1hCwDZm08CKshWF7oblhybZm/A6MnnlwERGXH3Nyi9t1MTo/zu+eEiXtxSHwypVMBjGV3cIqgj5zJXMnjk7GUs3HBMum5svb6jggOwMCOhvppMh7WlkKaqvAzXAdD7b1smgw0PaoYvj5VK/1YazkL7J5cfyHlx80mLxk1yNVPXh7uUiruSvQGNJ5fMMyAiI+4c4Vsyyq+2Qa3hdpYOdEeebc8PF/WuC5XONBzr84ux8JNjZgcRtPTB+Ne0joCA3aWQpqq85EqVLC3hsqZtnXakacP9WdJtGqh/ALpDHmGOUqmcu46e3NDsDWg8eTYBBkRkxJ0jfKXMWTuwnanGqu4S1JHzaANm3Z9eJYC0zmGy67R0r29rq9P+Oqgj7ugeLbVfqqytQ4m62q7rzFRAZmkJrtJxWDuchSUDWQLuk0dYQq7kTbdHnyOmO/HUUiZHBDSeOmQBAyIvYumN6s4RvlKwtuGBVFTVasw2VlXqiu3pmRjVkx2oEPUlFwJCPpjWKUGytbo4KjjAYSUM5gIyUyW42vXN/XxNHoc1w1lEBQdg+oB4k2MTOXsyXGfQLXlTane26WgJMpOjLB4gdM2+IqzaWyRb0uZJHBHQeOKQBQyIvIS1b73uGuErBWuGUxBY0xXbXRuQk/XMlW4arvMB8OkD/aTrx9bqYke1u7NkP0rHePSXckxY/bXU88tUtaClw1loTTMTEC2540apFE7uxcJciZerX0aUSsGe3/Q9ln75vdk8QW5aFndre2ktTwxo7MWAyAuYymQBKGZG7npD2DIirlJX7ITIlm7dgNxb2fqQtKbXllwwbWt1sVIgZU0Jg6n96AZkcsf4WEaX+nZM2jZzMvs2PA5zw1kYtltalJFQ35vMgArA4v+ewFP/PSF9t9ILhwrAfQPjMXVAvFVtoZzN8JzqMpcnmJqWhdX0noUBUSOnNLt2nRB4YdN32HS01COLdy0N1sx1xc4/c5ltjdyMvQ9Ja3ptyb0E2FJdbG8Jg6n9yAVkY/rEIiGyJfLPXEafuFaybeaAP0uKfP7oZan0QI9pHWhU7Wy4TVK7YMwZ0hFv7Co0mkpE9/+B+vQv2nAMoS38jCb2/NfeIqzeV4SsjAS9IK4hX0bkAm7ttbHpaAme3/S93vam8gRTjc49qV0VMSBq1EzNrg0AG48ad731xJIRS0oTlB40feJaGVejqMBMrIEY/naOqnqypteWIVuqi+0pYTC1H6WAzDBofOCPyWENb3WB+sBFI4CcL0+iovoa+ncKlc63YenNwowEo8HzDL8ra0QCktuF4LfKGsxZd1jxWDQCmP7OIcV1OV+eNNvbzxlMBdxRwQHITI7C0i+/t7iUUCkY9oHntasyxx2qN52JAVEjZaoYV4knloxYWppgqu1RVkaCXuYsRH3XbU8pLfNUcr9dTOtAtyixs6W62NYSBqX9fPvzZWiEQG+DKTdK1NV6QwdoBPDPnYWK+9MtwXl9ZyFe31koBTYvbtGvZjMcYFEuQF225RT2LRyCuNBAi3qfmUuXLme/jFgScEcFB+iVXlkyf5thI/UZAzpg6oA4j8pLzbEkr7UnYHKHYIsBUSOlVIw7sW8s/nOgWPYznla8a21pgtyb//r8YqM3VQHPLS1zZ7oZHgDZ327DA6luO+SDJWwpYZBjqsfa2/uKFCdftZRG/Dl2kiHdARZNtWlK7dhGcSRtSxmWajn7ZcSSNlrr84ulYEgF4LER5gd0dddOKI5iSV5rT1W3u7Ql82nwb6QGoS3G1eWrUuHu3u2MlgOWF++WqKuRW1iGEnW14xJrI3Pdj+VEBQcgtWMbveoZuQzc3H7IOuvzi9E/ZwfGrzqA/jk78Pa+ItnfrqpWg+xRSfBV1V+kum/ntl57pj7njOtZW2KgewyPjeiCorJKi75H7uGz6JNj2Hj0HI6cvYy39in39tJlLnOXC4a036e99pXyEW1wN6ZPLPYtHIL377sZuQtvwWez+8mnRQX0l5nHSq6K7/ENx52Wv5g7HqOBX1FfImZJenTzlsbGVKeBEnW1YsBk6/XuzGvAFJYQNVKmqogMi3fH9olFv05t0Kt9K5P7dJcoXsveASRtaQzpDsW6nkYuw3trX5FR6YD2nKd2bCNbkmfLtWfqc868nnVLDI7+Wi6VOFjyPUpjKc1Zd1i2nZAhHxXw6tge6BXXCp8XnJPtGQbUn++ZgzvgdYPqNsNJYy3pgaZb3XT/wHj8y6CLvkYA+wt/k01rQ1aRmjseV47S7855i7lOAzMGxNt83txpZgQGRI2YUjGuXmb9Szle3HIS674pNjsFgLt1T7d3AEnFxpAKg8zJNS5NahfslhmYO5F9wAvg/rR4vLX3jOxvp/uQtfXaMzfchLOv56jgAFyouKpXJWv4PXIPQVMjQ8sFQ6o//nRHar8tJRpA/SjaUEGvNxd0thvTJxZBAU1Ntpextjpo6oB4rJYpATTko4JRTzOgvmRLt8TG0UGCqeNx1Sj97vayachcp4HVe4tsPm/uNDMCA6JGTqlxqHaZdiA3wPRDwZ2ieF321N0bNYYEMCMtHlP7x8t2OzZ8gGrfvN0xA3MnShne1P7159rcb2frtWfqc3KjVpvap1xvOHMP6vX5xfWTyBosrxMC3/58GQVni/DWH4GD7jVk6uGjpT2f2gDG1D3w17SOuCPlz6lFDLvW665X+h2saWQu18hYLjh6dWwP3JYSjZCApnqNxAXq2xEBMAoS0jqH6bVDszVYMpUvOnqUfnPXiju+bMox1WlAA+D+AR3w1r4iq8+bM865rRgQeTFrHjTuFMUbsqVHkJalAZWp6jV3zcDchbkMz5Yxfiy59qwdtVppn4Zv73f1aItPD/9qtrfNog3HZCcfVgGYbdBd3fAa0l6Xh85cxoMfHDZKp9yYQabOo7l7xJ57SI4lI2H3iquvok/rHAaV6s+JmgXq20xB5/fRCGDhhmNSo23dMZAMfwN7S5Xk8gRr96nd/tgvaqknn9K14q4vm3JMdRqYOiAOUwfE2fRy6i6N0hkQebH40OZGbRJUqC+uNswA3CmKdzRLHgbmJrh01wzMXTiyJM/Sa8/c5yzZp9zb+yff/iqtVwqGTQXQSjVJciNS35YSgMra62anqnFHuveVte12NIDRidINLnVXaX+DhMiW2Hi0RLbUzZ60W1udpTT+m9K14s4vm3LsfcExtV9X558MiEiPAPD5kXOyjUDTOofhlbEp8FGp0LN9K5dfvA3JXDWGO2dg7qIhSvKs+Zwl+zQV2GjJBcNyDzlzY/YoXUPu8vZsD2vb7fgAECrIlrDJqRMCd76eq7fMESW31lZnmRv/Te5a8cSXTVuuSXduNK7FgMiLFZVVyr6tZn/5Z68UbQZQXnXNbNFvY2fYc2jZ5lMek4G5I2szSFsDKlOfM7dPcyWDgHIgM31AvFRa4atSYdqAOMUJUs3NFu8Ob8/2sqbdzsge0digUxJnK3tLbq2tzjIXQLsq6HVGMGLNNenujca1GBB5MbkqMzl1QiBns3JPGW+izQRSO7Yx2xCVjFnTtsIdKD2sPzt8TjEYNpwO4/4/GuoDkAIkXeNvisHcoTd49TUk1+bIXL6kbXdkKmD1AfBbZQ1K1NU2nV9rq7NMBdCWjHhtLo22BDauDkY8pdE44AEDMz7zzDNQqVR6fwkJCdL6q1evYvbs2WjTpg1atGiB0aNH4/z583r7KC4uRmZmJgIDAxEeHo4FCxbg+vXrDX0obicqOAD3DYw3u52Pyjho4sCFjXsgNmfQHZwxW2ZST3cY7FOXdtDGtM5h0uCD+xYOwd/v6a73b92Hi9zAfm/tPQPAeMBGHxWw6NYELB2VzGsIf95PShPV6hqaEIbPHuiH9++7GZ8+0E92sFnty96cdYfRP2cH1ufLj9BvLk1KA4Vauv2iWxNkrxVrGQ5uasnxuMOgh7YMoOsqHlFCdOONN+J///uf9O8mTf5M9vz587Fp0yZ89NFHCA4Oxpw5czBq1Cjs378fAFBXV4fMzExERkYiNzcXJSUlmDRpEpo2bYqlS5c2+LG4G7kxQ1QqQCX+HNfksYwusuOYOKLNjCfUK5P9bGlb4Urm3qqV3ubNVbE0hvZAzmZJNeX2kxex4+RF5Iyu/10Mh88Y2zcGH3xzVi8QWLThGAL9fNE7rrVTe0A54ze2tJTFMD91hx5sntRo3CMCoiZNmiAyMtJouVqtxltvvYV169bhlltuAQCsWbMGXbt2xddff42bb74ZX331Fb777jv873//Q0REBLp3747nnnsOWVlZeOaZZ+Dn59fQh+MycsGHUoM+wxs6JKCpwxv9uboolxqOrW0rXEFp6oyEyJYID2pmMoC3JPNvDO2BnEmbJ5mbnFrgz6BAd5gCbZ/8dQfO6m2vEcDc9wtsymus/c0c/RtbOgeb3LhNrg5GPKnRuEcERD/++COio6PRrFkzpKamIjs7G7GxsTh06BCuXbuGYcOGSdsmJCQgNjYWeXl5uPnmm5GXl4ekpCRERERI26Snp2PWrFk4ceIEevTo4YpDanCmgg+lNxpre+RYQ+mhY6r3hiNLklgy1bDsaVvR0JS6gd/5eq5UDaP0UPWkzN+dafObNfuLsGqP8mS2ulUva/YVYdXeImmcIqX2kQ3VhsWReYy5QFupBGnfwiFucT16Ssmo2wdEffv2xdq1a9GlSxeUlJRgyZIlGDhwII4fP47S0lL4+fkhJCRE7zMREREoLS0FAJSWluoFQ9r12nVKampqUFNTI/27oqLCQUfU8CwpbrXkjcaRbz1KD501+87g8cyuesttKUkylRmxZKrhyQUKj2V0QXLbkAbLIC19QFkydYaph6qnZP7uLio4AI/fmoip/eOxZt8ZrNr7k2yAs//0RUxYXaj3e2mDIqXf0dnVRo7OY8wF2qZKkNzlevSEklG3D4gyMjKk/05OTkbfvn3Rvn17fPjhhwgIcN7Jzc7OxpIlS5y2/4bkDvXIhpR6uK3e9xOmDoiT0mVLDwVTmZFsydQG5ZIpR/GGEilzx+jKjNmaB5RUZfPJMcUZ4QHT95AnZP6eIio4AI9ndkVyTDDmGIzwDQCv7yyUDZQEgHE3xRhVnQHOrTaytb2POVaP5aQCAv3q+03xerSM2/cyMxQSEoLOnTvj9OnTiIyMRG1tLcrLy/W2OX/+vNTmKDIy0qjXmfbfcu2StBYtWgS1Wi39nT1rfFN5Cu3NosvVbTaUerhpBPR6H1jbQ8FcrwqliUbX7JcfH8YRbOkd4mksPUZX9MxTCoJN9bQZ0ycWn87uB5VM7yUtV99D3qZX+1ayvclMdUiTC4bMjflkL0vyLFvzBKX7x7B3G1B/nd/1Rq7d+Y22t6W79QJ1Bo8LiK5cuYLCwkJERUWhV69eaNq0KbZv3y6tP3XqFIqLi5GamgoASE1NxbFjx3DhwgVpm23btiEoKAiJiYmK3+Pv74+goCC9P09lbdfRhjJ1QLzZQM3aYM5cZiS3PwBYvafIKTe80sP4iyO/NpoMxhVde63JpG0NglNiWiFH575RAVKA5C73kDcxzMdMxKomvTq2h0OqyJWuQXN5lrPulzF9YrHhgVS982Lvvr3hZU6X21eZPfroo7j99tvRvn17nDt3Dk8//TR8fX0xbtw4BAcHY/r06Xj44YfRunVrBAUFYe7cuUhNTcXNN98MABg+fDgSExMxceJELFu2DKWlpXjyyScxe/Zs+Pv7u/joGo6l1RUNWbVjSQNUaxupmmt8GBUcgOkD4o1GDNYAZqsQbTk3Sg9jW3u7uKOGrpI1VyVq+BsptQlavacIU/vHIypYefJOw/sGgMvbYngz3d8jt/AiXttRaNXndSeVtYepa9Ce9j7WXFNy12xlbZ3imHHWXq+eNKCio7h9QPTLL79g3Lhx+O233xAWFoYBAwbg66+/RlhYGADg5Zdfho+PD0aPHo2amhqkp6fjjTfekD7v6+uLjRs3YtasWUhNTUXz5s0xefJkPPvss646JJcxV4/s6IaAlgQQlgRq1rQ9sSSAmjYgHqv36vdcMVf9Yeu5MdVAt7FkMI4cZ8TcNWMqk97zw0XZ38hcEKz0OS3D+8aTf6vGQPt7BPr5mA2ItKV62ulT7C3VK1FX4+CZS2YDBWvb+1h7vyjlR468F20N3Dy5vaRKCEunz/NuFRUVCA4Ohlqt9ujqMyUl6mr0z9lhdCPtWzjEpova1T25StTVJgOo9fnFRkGTUvrsPTe63yXn/ftuRmrHNpYdmBPZk5FZcz5N7cPcNZNbWIbxqw4Yffb5kTfiqf+eUPyN3txTqDdHn3b9hgdScdcbuQ677qlhPfJhAT7RmfOsZ2wIjpxVmxxTzVZKs9hrWXMf23O/mMuPHHEvWvI9Ssfljj14LX1+u30JETUMR1Z7uENRq6nSsBJ1NWJaB2LDA6moqtWYzSjtPTdpncPwytgUqKuvYfFnJ/RKprQ9QVz9VmVvRmZvDzJLrxmlErcnPzthtE/dtmMvbj5ptP6xEV1kp4lwdQ9Mstzf7+mOSantcfDMZfSOa4WUmFayL0OOHjfNkLWlMPbcLw01Grq1zRXcId+3FwMiAuDYag9XdvM3F1jIPfjNvdXZc24Mv29Uz7bSxKBA/T5Hvp4LwPSAf46gdG4clZHZ07XX0mtG6hJvZhRj4M/fSGmU7OR29WMguXokX7JPSkx9IKTljC7mpkZat7UqztZ0NuRo6NYEV+44vIu1PK6XGTmHI3uiuaqbv7keEbb27rD13Mh932eHz+Ffk3rq9QQRMB7wz5E9tErU1Xhh03eK58YdJl889ovaaJnSNTOmTyyWj+0uux/tdaf7G5m6Ht21Bya5F+24abp8ALw+vofdk7Zaq6GvWUuHynDH4V2sxRIikriqqNURLCnlsKeRoFwVm7nSKKXvKyqrMjl2iiPfqtbnF2PhJ8f0vs/w3Lh68sUSdTVe3CJTpZXRRfEc9I5rLZtmuWpQc9eju4zkS65hSXX1nh8u6v1bBSB7dBIyk6MbIIXG3PGadUW+72gMiEiPK4paHcGSYMeWB79SFZslbW6Uvq9PXCuTs3k7KhjRBolyX6N7bsxlZM5u36RYpdU2RPEzhmn2ATB9QBzCg5pJwWpuYZmUZnPXozOqWcj9WXIfy91HKlV920BXcsdr1h0DNWswICKbWPKQlLthTX3OngevpcHO9AHxeGtfkUXdcJVKnRIiWyour6ytk9KvFGikxLTSW64CABUgdNIEQO+Bbgtz7R50z41uRhbo54PK2jqUqKvNdkl3BFtLqKQJQPedwep9P+Ffe4uwel8R7urRFp8e/lW2+72nZdDkPJa2nVMaS8yT2sY0JE++zxgQkdVs7ZFk6nP29nIyV8qhu38VgPvT4qVB+ZQolTrln7ksu3zkG7kQBulXemMyNeDfnh8uSt1d7QlClHpkKU1dEBUcoBcAaZsDGLZvcnSvEWuK2uWC5tX7ftJ7qOl2w/bEni7kPLrXj6VV6K6uUnYX2nPX3M8XlbV10v974nhDShgQkVVs7ZFk6nMAHNLLSSn4MPxuAeCtvWcwtb/xXGq6rK3yEjrpX7ThGBIiWyIlppXiG5Ph8qjgABw5exkLNxxT3Jc15KqVZpgIBOXOkyFn9RqxpKhdLmiOaR1otreZp/V0IecwvH6yMhIsCnQaQ9sYwP5xxpR6drrTeEP2YkBEVrG1YbKpzwkIh3XXlAs+bE2zpVVePoDRrOja7vQ5oy3PKOQaQNu6Ly17u80acuabsamidrlA8fENx7HhgVST7bEA73ybJ31yL2TLNp9C1ogELNtyymyg4+ltY+wpgTc3BlNjKoVlQERWsbX42NznnFkkbU+Rt1zVVm5hGdI6h2HfwiFSmxvD0Y6B+hIWSzMKUw2grd2XIUvr9OXOk6OnPjBH7i1WKVCsEwJVtRrZaTm0PPVtnhxL6aUouV2IdB9bMi2QJ15H9o4zZsmLUmMpheU4RGTE1Ezito6BYepzzh5Xw979a8fh0Lbt0Y7l8/a+IsSFBkolRoZjcACWj+djTabjLFHBAcgakSBlCr4qFXJGJ2H/wlvw/n03Y9/CIUjrHGbxLPPWkhtHylSgqA1qpw2Id5sxYsg9mRuLypJxdpzJVJ5rL3vHGZM7d4YaSyksS4hIjyVFq7YWH5v6nKOLpA1LGmzZv+4+AON2Tqv2FmH13iKpKishsiVGvp5r1aSxWqYmgbV2X7Zan1+MF7echAb1pUKPZXTRm8HbmfMUKb3FvjI2RbHdgm5QmzPauGrTVWPEkPtx5NASjh6Gwpr7ypbvtrdRuOG5M+SrUuGxEV1QVFYpbe+pOLmrhRr75K6A4yd4dRVHPLgN9zFjQDz+ZaJaxhETKxp+dmSPaGmaDx8AMwbGIzM5yik9O8z99s6+NpQmbX19fA/Mff+w3vf6APh0dj+jRubmJvQlkrtGrMkvHP1SYM19JffdaZ3DLAqQHDHhq/bcBfr5oKpWI/3/0V/L8eLmk04L6ByBk7uS1RrDXDSOmJdLbh+r9xYpluA4amJFuc8+mt5Fb5wdbVDmyBKaEnU1Nh49Z/K3t/TasDXDU3qL7dm+lWLDdkOe2saDGo7hNWJNfuGMyUutua8Mv3vhhmOAsGwOREeUwCuNKzdh9dcWnRNnljA7CgMikjSG8TYcEdTJDsQG4P4BHfTGvNFy5MSKcp+V+05td/xAP1/0jmtt8/eZ6k6re1yWXBv2ZHimqjTSOofhlbEp8PkjQGLQQ45iTX5hri2OI18EDPNcue/WrduxJDhzxguDPQGdO/ZMY6Nqkji7cXNDcMQEg0r7mDogDvsX3oL70+L1Gh478xyZamytEcDc9wtkJ7K1hKnutIbHZe7asHXiXN20aOeL0zbgHtMnFm/uKUS/nB2Y+34B5r5/2GhOKSJ7WJNfKG179Ndyk5NKG9JtQG1pnmtJw+aGnpAZsPz8ucME0pZgCRHp8fTxNhwxiJq5fTx+ayKm9o9vkHNkSWNrW9+2lIKtxZldcWtylNG+TF0b9pTMKc0X9+buQmRv/nPSV3d9qyTPZU1+IbftYyO6SO1nAPPXqFIpqrk812iQ1T+m+rGlA4ecEnU1Dp65BJVKhV5WlMJaev48pfaBAREZcYe2GPY0vnNEUOcuk4Ga6+GhpRR8GPaU0z2nSpmUXDCkmx7tfnT/bWuGZ2q+uBydYMjccRLZypr8wnBba14EzFUbmbumDb97zw8XHTJ6tuE4XyrU99q0tMG2LQGdu9Y+MCAip7M2uHFE4ztHBCzuEBgCxhOv/nK5GnPWHTb7dmg4fxtg3ADT2kxK6bexNcMzNV+cXPjno4LbvVWS57PmXjfc1tIXAUe0b9T9bke8+JWoq40GPRUAFn5yTPpvS/JgWwI6d8hbDTEgIqeyNrjxlMZ39rI2SNTNcFJiWuFKzXW9qUMeG9HFZCNG3QxP95xak0mZ+21syfCsnS8uKyOhUV0H5FkM71trXgQcWW2km47Ujm2kdknWlqgXlVXKvngo5ReN5SVTCQMiMsmeqiu5B6i5iUobQ9d/cxxRAjamTyzKq68h54/2Cy9uOYmQwKYY0ydWsRu9Lt1zamkmZclvY22Gp/RAkZsvLisjAX9N62jxvokcSem+tfRFwFHVRobpuKtHW3x6+Feb8pP40OZQQX4iZ12NLQ9WwoCIFNn74Jbtvi5MT1TqKY3vbGVPCZhhe6AXN580muy0vOpa/WjTZnI4W86pLb+NJQG10gPFE4rYyTtY0v7HkuvT3DVt7n6RS8cn3/4qrbe2NCcqOAA5o5OM2hABjmuw7UkYEJEsR1RdKfWQElDel6c0vrOVtY0wtZnjnh8uGo2cLbefnM0nZd/2VH/8jxDmhwqQy5S1yyydHRywLqBWeqC4exE7eQdHllwrXdPm7hdLSn7l0mUuyNIGaYfOXIZKBfRs38phDbY9DQMikuWoBoDZo5Jkx7qpEwKHzlxG6xbGN2pjLhmwtJTFMHPU7WKrNHK2Uvd8bTd6AGbPqVymDEBvWVZGApLbhpjcj1J1qb0DSRK5grNLrs29gJoaQNWQjwoou3IVJepqoxcp3SDLMFC6LcU78mBTOJeZhbxhLjNdjpy76sjZy0aTnqpQP4GoOw/jbg1r2lqZm1dI7tzLuX9gB7y1r+jPMVEy9MdEAf78zQDzI+nKfa8PAMg8CMxdB0pzkwGN4/cm7+OI+cAMafONS5W1mLPusNH69++7GXGhgYr5geGch6o/GgQJKFd97Vs4RC9QUgFYmJGAvw5yTfu8hpjfjHOZkV0cWXWVEtNKbzZyH9TfpI2lJ5lhqcr0AfGYNiDe6jYzWqZGp9bSjpw9dUCc3n5CApoa/Wam3hLNfa8GMGpxaUlJoakBJT399ybv5OhSE8NhMQwbN2tLoCwZQPXR9C749ufLesNxyGUhdULg258vG/VAzd58ElDB4k4Ljgpi3G1+MwZEpMiRGUBa5zAsH9ddenUxfBvy1F4MckXdq/YWYfXeIsWG44DptjFywYRKBahEfYAiN62GNi3a6S+qajVScb7u26WpYETue5VKiMxVFZgbUNJTf2/ybubatFkaKMgNi6HCn9Xehve4qQFUtd+pEcJsbzEfFXCytEI2wHpx80nckRKtt0+543BUEOOOQ6wwICJFznoLyMpIaNCeZM4sklV6ezPVcNwcpdI5U8Gp0vQXuYVlFrcFU/pe/HEs1pYUagNqwzdXwHt6rZD3sCZQkJ2sFcBrY3ugTQt/vXvcVGm94XcaljLpNk1Q/dEW8bUdhbJp0oj6Nobm2h0ZtQ38xPRQKkrccYgVBkQky5lvAcs2n7Kqt5I97D0Oc8GUqaohW29u3ZKeXy5XQyOE1BDZ0q642ukvLlXWKhbFyx2bUqmgrSWFUcEByEwO0BtI0pt6rZB3sLa0Q6mRdq84+XnE5O5Lue9UqQAfg5JkpZcSQ74qFQL9fEweh1K1+sg3cpFjZd7qjkOsMCAiI44sylR6C0huF4J9C4c4tReDvcdhSTBlqiedNTe3Njg59qtaahit2yjSVMNHpXOsbciu2z7BknZFcoGXvd3fvbXXCnkHa0s7bGmjaXgPypYyCeCf43ugdXP9UqZWzeVHpNbmCz4qYOmobqisrTN5HIpDqSjkraZeKN1xiBUGRGRE6eY+dOayXtdMSzT385WKarW0gYKzx5ixp0jWmmBK+7Bfs78Iq/cUybbzMUWpS60w+G+lho+mxnvS/r+Pqr44vldcfbG2pe2KlJjK6JTWcUwhaoxK1NX47UqN1aUd9r4kKJWw9NSZrV57Lzb385Vtl6g7sGvhhStIiGxp8jikF8BPjtV3uNBhmLda8kLpbi9KDIjIiNID9sEPDqOy9rriOBaGtDeEYTDUUG8B9hTJ2vLG9/itiZjaP96qm9sw8DJHqeGjUialpRFAmxb+iAoOsKpdkRxTGZ279RohciajnmIWDn6qZc9Lgqk2f7mFZXqlzdopPrTd830MXlIB4F97i7BqXxEGdArF/tNlso27gfogJiGyJUa+kau3D93xjwBY/ELpTi9KDIjIiNJbgO5Fba4rt9yD3gfAhgdSrW58Z+9x2FIk29zPV7HtjbnvtObmtqSLvS5TDR9fHd9DdiwToD6j1qa9uZ+v7DaBfj5mv99UyRlgeSZI5OkMZ4oXqG+/88/xPfRKabTb2tKxw9JRprUvYXt+uCg7ZpFGAJ8dPif1QC27chVz3y8w2p8QwN4fywAA96fFY2p/+eFDUmJaIUcnb9UGgnPfLzA5kr679yxlQESyxvSJRXP/JrLd4w+duWz2wafU+K6qVqkMwzlsKZKVSrZ0ljmrZMvU5Ipyy001fNzwQKpiA2/dHVXW1smmxZLfxlTJmYDwyEyQyBZv7ysyuj81AFo399e73s2NU6YU9Fha2qp9CTNX2lwnBKpqNUjt2AYl6mrlvOIPb+09g6n94xXXK/Ui1Qj5kfRd3WDaEuZfCclr9WrfCj4q/WW+KpXRuDTAnw8+LW11leFnXXFDRAUHILVjG5ursHxU9SVbSlU/Jepq5BaWSUXFSsuU0jbuphjZda+N64FFtyZI51EblCk1fKyq1SB7VJLsTS0A6feR+218dEqQTJH9LICq2mtSOwpdnpAJElmrRF2Nt/YVGS33gf59pDROWb/sHVifX4z1+cXon7MD41cdQP+c+mVKn3t8w3GUqKsV8xZzpc1ybYFMBQCGebqcqOAAtGruJxsYzhjQof55gYZtKmEPlhCRIqUqJ22gZCr6t7a6qiGGb7eEbMmWUC49sWTuL1PtaNbnF+OD/LNGy7VdcG8LjsYdKdFG3W2Vzn9qxzay9ftymeFCnfZdQgB7frhotr2P3ICLGgDT3zkEwLZ2FESeRin4mJGmX8VkapyyRZ8c03u51C1pVyqJXbPvDFbv+0k2bzE1BIhSW6C0zmF6+zT8jDUvSYb5kdxI+u6OARGZpFTlZEmwY2l1lTs1xLWmIbbSIGVKmZxcbyy5Im4fwGgkasPeWrrn30cFTBsQJ603rN+X+33SOofpVaMJWN7eR6lRpXY/Su0oiBoL2VHdVTCqYjI5hQ2gOC2O0qjxuoGLYd4i9xL62IguSG6nPBFzVHAAHs/siqkD4mzuJWvu5deT8gAGRGSWXENhS4Mdc42M3W34dmtKtuyd+0vp7fG18T2QmRxtMp26Xf1X7SnCqr1FeGtfkRRMWjJfmuFXWzO0wtnL1UbBkJZcOwqixsTSfMLUOGXaOR3lOm7I7X/6gDj8a69+NZ1h3mJrN3Zbe8lqWfu97lIjYIgBEdnMEd0l7R2+3Rk3lqU3t71zf5kaR8RSq/cW6TVmNHxjtCbtgPHQCrqkwSN/USNn80nFNLHdEHkDS/MJpXHKRvaIxoZvf5W2U0G/ZNhw/wCwel+R2bzF3q78jvhsQ8yF5gwMiMil7BkryJk3liUZg6lxQPSKrTO6oKisUvqcbkZhy7AA2szmtys1NgeTlgytoNTjxRS2GyJvIpdPyAUDhiUwgX4+uOuNXP15x1SQhq9Q2r+rRna25sXTVL7sbjUChrwqIHr99dfx0ksvobS0FCkpKXjttddw0003uTpZXs2eoMAdbixzc38d/aVcdioO3YzCmqJmc5M5AsDRX8uR2rGNRWlXGlpBN6iyZPDI5+68EZ3CW3pM40kiZzD3kqYNcOQGR9WOMWbq/rG1SsyeknRrXjzN5cvuOKGrLq8JiNavX4+HH34YK1euRN++ffHKK68gPT0dp06dQnh4uKuT59Vsucnd6caSe0vU/nvC6q+ldOomV656yxzZyRxltlu2+ZQ0mrXcPnQzRkt6DFrSnXdYYoRbZGhErmLNS5qpknFzwYu11Vr2lKRb++JpLl92xwlddXnNOET/+Mc/cN9992Hq1KlITEzEypUrERgYiLffftvVSSNYN1YQ4F7jHCkxF0hYMs6Huf3J7V5pv3JjnmhL6EyNFyJ3rrVYRUZUz1QwYEjpvtOONG04LpGtTI1n5OhjAszny5bkN67kFSVEtbW1OHToEBYtWiQt8/HxwbBhw5CXlyf7mZqaGtTU1Ej/rqiocHo6yXK2VrU1JFNdbgHrAzilrr5CmJ9ixNSbnrkSOtnuvBldkNxWuTsvkbextvRDrtG0vZMuG7K3JN3aY7IkX3a3CV11eUVAVFZWhrq6OkREROgtj4iIwMmT8r1lsrOzsWTJkoZIHtnInW8swDhzUAGAHYMWWtqIW26/5jJGc8Xw7n6uiVzNlpc03fvO3kmX5dhbRWXLMVmSVziih7IzeEVAZItFixbh4Ycflv5dUVGBmBj5KRbIddz1xtKSewu0J6gw14hbab+OqLt393NN5Gr2vDg4o32NI0rSbTkmT80rvCIgCg0Nha+vL86fP6+3/Pz584iMjJT9jL+/P/z9/RsiedTIGWYO9mYUSo24zb2JunsVI1FjYGsw4Kx71BGlu54a4FjLKwIiPz8/9OrVC9u3b8fIkSMBABqNBtu3b8ecOXNcmziiBsJqLyL35qx71FsCGnt5RUAEAA8//DAmT56M3r1746abbsIrr7yCyspKTJ061dVJI2owzBiJ3BvvUdfxmoBozJgxuHjxIp566imUlpaie/fu2LJli1FDayIiIvI+KiGUpmgkXRUVFQgODoZarUZQUJCrk0NEREQWsPT57TUDMxIREREpYUBEREREXo8BEREREXk9BkRERETk9RgQERERkddjQERERERejwEREREReT0GREREROT1GBARERGR1/OaqTvspR3Qu6KiwsUpISIiIktpn9vmJuZgQGSh33//HQAQExPj4pQQERGRtX7//XcEBwcrrudcZhbSaDQ4d+4cWrZsCZVKZfN+KioqEBMTg7Nnz3rtnGg8B/V4HngOAJ4DgOdAi+fBOedACIHff/8d0dHR8PFRbinEEiIL+fj4oF27dg7bX1BQkNde8Fo8B/V4HngOAJ4DgOdAi+fB8efAVMmQFhtVExERkddjQERERERejwFRA/P398fTTz8Nf39/VyfFZXgO6vE88BwAPAcAz4EWz4NrzwEbVRMREZHXYwkREREReT0GREREROT1GBARERGR12NARERERF6PAZEDrFixAsnJydJAUqmpqdi8ebO0/urVq5g9ezbatGmDFi1aYPTo0Th//rzePoqLi5GZmYnAwECEh4djwYIFuH79ekMfisPk5ORApVJh3rx50rLGfh6eeeYZqFQqvb+EhARpfWM/fl2//vor7r33XrRp0wYBAQFISkrCwYMHpfVCCDz11FOIiopCQEAAhg0bhh9//FFvH5cuXcKECRMQFBSEkJAQTJ8+HVeuXGnoQ7FJXFyc0bWgUqkwe/ZsAN5xLdTV1WHx4sWIj49HQEAAOnbsiOeee05vPqnGfh0A9dNFzJs3D+3bt0dAQAD69euH/Px8aX1jPAd79uzB7bffjujoaKhUKnz22Wd66x11zEePHsXAgQPRrFkzxMTEYNmyZfYlXJDdPv/8c7Fp0ybxww8/iFOnTonHH39cNG3aVBw/flwIIcTMmTNFTEyM2L59uzh48KC4+eabRb9+/aTPX79+XXTr1k0MGzZMHD58WHz55ZciNDRULFq0yFWHZJdvvvlGxMXFieTkZPHQQw9Jyxv7eXj66afFjTfeKEpKSqS/ixcvSusb+/FrXbp0SbRv315MmTJFHDhwQPz0009i69at4vTp09I2OTk5Ijg4WHz22WfiyJEj4o477hDx8fGiurpa2mbEiBEiJSVFfP3112Lv3r2iU6dOYty4ca44JKtduHBB7zrYtm2bACB27twphPCOa+GFF14Qbdq0ERs3bhRFRUXio48+Ei1atBDLly+Xtmns14EQQtxzzz0iMTFR7N69W/z444/i6aefFkFBQeKXX34RQjTOc/Dll1+KJ554QmzYsEEAEJ9++qneekccs1qtFhEREWLChAni+PHj4v333xcBAQHizTfftDndDIicpFWrVmL16tWivLxcNG3aVHz00UfSuu+//14AEHl5eUKI+ovHx8dHlJaWStusWLFCBAUFiZqamgZPuz1+//13ccMNN4ht27aJQYMGSQGRN5yHp59+WqSkpMiu84bj18rKyhIDBgxQXK/RaERkZKR46aWXpGXl5eXC399fvP/++0IIIb777jsBQOTn50vbbN68WahUKvHrr786L/FO8tBDD4mOHTsKjUbjNddCZmammDZtmt6yUaNGiQkTJgghvOM6qKqqEr6+vmLjxo16y3v27CmeeOIJrzgHhgGRo475jTfeEK1atdK7H7KyskSXLl1sTiurzBysrq4OH3zwASorK5GamopDhw7h2rVrGDZsmLRNQkICYmNjkZeXBwDIy8tDUlISIiIipG3S09NRUVGBEydONPgx2GP27NnIzMzUO14AXnMefvzxR0RHR6NDhw6YMGECiouLAXjP8QPA559/jt69e+P//u//EB4ejh49emDVqlXS+qKiIpSWluqdi+DgYPTt21fvXISEhKB3797SNsOGDYOPjw8OHDjQcAfjALW1tXj33Xcxbdo0qFQqr7kW+vXrh+3bt+OHH34AABw5cgT79u1DRkYGAO+4Dq5fv466ujo0a9ZMb3lAQAD27dvnFefAkKOOOS8vD2lpafDz85O2SU9Px6lTp3D58mWb0sbJXR3k2LFjSE1NxdWrV9GiRQt8+umnSExMREFBAfz8/BASEqK3fUREBEpLSwEApaWlehmfdr12naf44IMP8O233+rVj2uVlpY2+vPQt29frF27Fl26dEFJSQmWLFmCgQMH4vjx415x/Fo//fQTVqxYgYcffhiPP/448vPz8eCDD8LPzw+TJ0+WjkXuWHXPRXh4uN76Jk2aoHXr1h51LgDgs88+Q3l5OaZMmQLAO+4FAFi4cCEqKiqQkJAAX19f1NXV4YUXXsCECRMAwCuug5YtWyI1NRXPPfccunbtioiICLz//vvIy8tDp06dvOIcGHLUMZeWliI+Pt5oH9p1rVq1sjptDIgcpEuXLigoKIBarcbHH3+MyZMnY/fu3a5OVoM5e/YsHnroIWzbts3obchbaN98ASA5ORl9+/ZF+/bt8eGHHyIgIMCFKWtYGo0GvXv3xtKlSwEAPXr0wPHjx7Fy5UpMnjzZxalreG+99RYyMjIQHR3t6qQ0qA8//BDvvfce1q1bhxtvvBEFBQWYN28eoqOjveo6+M9//oNp06ahbdu28PX1Rc+ePTFu3DgcOnTI1UkjA6wycxA/Pz906tQJvXr1QnZ2NlJSUrB8+XJERkaitrYW5eXletufP38ekZGRAIDIyEijHibaf2u3cXeHDh3ChQsX0LNnTzRp0gRNmjTB7t278eqrr6JJkyaIiIjwivOgKyQkBJ07d8bp06e95joAgKioKCQmJuot69q1q1R9qD0WuWPVPRcXLlzQW3/9+nVcunTJo87Fzz//jP/973+YMWOGtMxbroUFCxZg4cKFGDt2LJKSkjBx4kTMnz8f2dnZALznOujYsSN2796NK1eu4OzZs/jmm29w7do1dOjQwWvOgS5HHbMz7hEGRE6i0WhQU1ODXr16oWnTpti+fbu07tSpUyguLkZqaioAIDU1FceOHdO7ALZt24agoCCjB4u7Gjp0KI4dO4aCggLpr3fv3pgwYYL0395wHnRduXIFhYWFiIqK8prrAAD69++PU6dO6S374Ycf0L59ewBAfHw8IiMj9c5FRUUFDhw4oHcuysvL9d6id+zYAY1Gg759+zbAUTjGmjVrEB4ejszMTGmZt1wLVVVV8PHRf8T4+vpCo9EA8K7rAACaN2+OqKgoXL58GVu3bsWdd97pdecAcNzvnpqaij179uDatWvSNtu2bUOXLl1sqi4DwG73jrBw4UKxe/duUVRUJI4ePSoWLlwoVCqV+Oqrr4QQ9V1sY2NjxY4dO8TBgwdFamqqSE1NlT6v7WI7fPhwUVBQILZs2SLCwsI8qoutHN1eZkI0/vPwyCOPiF27domioiKxf/9+MWzYMBEaGiouXLgghGj8x6/1zTffiCZNmogXXnhB/Pjjj+K9994TgYGB4t1335W2ycnJESEhIeK///2vOHr0qLjzzjtlu9326NFDHDhwQOzbt0/ccMMNbt3V2FBdXZ2IjY0VWVlZRuu84VqYPHmyaNu2rdTtfsOGDSI0NFQ89thj0jbecB1s2bJFbN68Wfz000/iq6++EikpKaJv376itrZWCNE4z8Hvv/8uDh8+LA4fPiwAiH/84x/i8OHD4ueffxZCOOaYy8vLRUREhJg4caI4fvy4+OCDD0RgYCC73bvatGnTRPv27YWfn58ICwsTQ4cOlYIhIYSorq4WDzzwgGjVqpUIDAwUd911lygpKdHbx5kzZ0RGRoYICAgQoaGh4pFHHhHXrl1r6ENxKMOAqLGfhzFjxoioqCjh5+cn2rZtK8aMGaM39k5jP35dX3zxhejWrZvw9/cXCQkJ4l//+pfeeo1GIxYvXiwiIiKEv7+/GDp0qDh16pTeNr/99psYN26caNGihQgKChJTp04Vv//+e0Mehl22bt0qABgdlxDecS1UVFSIhx56SMTGxopmzZqJDh06iCeeeEKvm7Q3XAfr168XHTp0EH5+fiIyMlLMnj1blJeXS+sb4znYuXOnAGD0N3nyZCGE4475yJEjYsCAAcLf31+0bdtW5OTk2JVulRA6w4YSEREReSG2ISIiIiKvx4CIiIiIvB4DIiIiIvJ6DIiIiIjI6zEgIiIiIq/HgIiIiIi8HgMiIiIi8noMiIiIiMjrMSAiIqcZPHgw5s2b5+pkON0zzzyD7t27uzoZRGQHBkRERApqa2sb9PuEELh+/XqDficR1WNAREROMWXKFOzevRvLly+HSqWCSqXCmTNncPz4cWRkZKBFixaIiIjAxIkTUVZWJn1u8ODBmDt3LubNm4dWrVohIiICq1atQmVlJaZOnYqWLVuiU6dO2Lx5s/SZXbt2QaVSYdOmTUhOTkazZs1w88034/jx43pp2rdvHwYOHIiAgADExMTgwQcfRGVlpbQ+Li4Ozz33HCZNmoSgoCDcf//9AICsrCx07twZgYGB6NChAxYvXizNsr127VosWbIER44ckY5z7dq1OHPmDFQqFQoKCqT9l5eXQ6VSYdeuXXrp3rx5M3r16gV/f3/s27cPGo0G2dnZiI+PR0BAAFJSUvDxxx87+iciIh0MiIjIKZYvX47U1FTcd999KCkpQUlJCVq2bIlbbrkFPXr0wMGDB7FlyxacP38e99xzj95n33nnHYSGhuKbb77B3LlzMWvWLPzf//0f+vXrh2+//RbDhw/HxIkTUVVVpfe5BQsW4O9//zvy8/MRFhaG22+/XQpcCgsLMWLECIwePRpHjx7F+vXrsW/fPsyZM0dvH3/729+QkpKCw4cPY/HixQCAli1bYu3atfjuu++wfPlyrFq1Ci+//DIAYMyYMXjkkUdw4403Ssc5ZswYq87VwoULkZOTg++//x7JycnIzs7Gv//9b6xcuRInTpzA/Pnzce+992L37t1W7ZeIrGDX1LBERCYMGjRIPPTQQ9K/n3vuOTF8+HC9bc6ePas3K/ygQYPEgAEDpPXXr18XzZs3FxMnTpSWlZSUCAAiLy9PCPHn7NoffPCBtM1vv/0mAgICxPr164UQQkyfPl3cf//9et+9d+9e4ePjI6qrq4UQQrRv316MHDnS7HG99NJLolevXtK/n376aZGSkqK3TVFRkQAgDh8+LC27fPmyACB27typl+7PPvtM2ubq1asiMDBQ5Obm6u1v+vTpYty4cWbTRkS2aeLKYIyIvMuRI0ewc+dOtGjRwmhdYWEhOnfuDABITk6Wlvv6+qJNmzZISkqSlkVERAAALly4oLeP1NRU6b9bt26NLl264Pvvv5e+++jRo3jvvfekbYQQ0Gg0KCoqQteuXQEAvXv3Nkrb+vXr8eqrr6KwsBBXrlzB9evXERQUZPXxK9H9ztOnT6Oqqgp/+ctf9Lapra1Fjx49HPadRKSPARERNZgrV67g9ttvx4svvmi0LioqSvrvpk2b6q1TqVR6y1QqFQBAo9FY9d1//etf8eCDDxqti42Nlf67efPmeuvy8vIwYcIELFmyBOnp6QgODsYHH3yAv//97ya/z8envkWCEEJapq2+M6T7nVeuXAEAbNq0CW3bttXbzt/f3+R3EpHtGBARkdP4+fmhrq5O+nfPnj3xySefIC4uDk2aOD77+frrr6Xg5vLly/jhhx+kkp+ePXviu+++Q6dOnazaZ25uLtq3b48nnnhCWvbzzz/rbWN4nAAQFhYGACgpKZFKdnQbWCtJTEyEv78/iouLMWjQIKvSSkS2Y6NqInKauLg4HDhwAGfOnEFZWRlmz56NS5cuYdy4ccjPz0dhYSG2bt2KqVOnGgUUtnj22Wexfft2HD9+HFOmTEFoaChGjhwJoL6nWG5uLubMmYOCggL8+OOP+O9//2vUqNrQDTfcgOLiYnzwwQcoLCzEq6++ik8//dToOIuKilBQUICysjLU1NQgICAAN998s9RYevfu3XjyySfNHkPLli3x6KOPYv78+XjnnXdQWFiIb7/9Fq+99hreeecdm88NEZnGgIiInObRRx+Fr68vEhMTERYWhtraWuzfvx91dXUYPnw4kpKSMG/ePISEhEhVTPbIycnBQw89hF69eqG0tBRffPEF/Pz8ANS3S9q9ezd++OEHDBw4ED169MBTTz2F6Ohok/u84447MH/+fMyZMwfdu3dHbm6u1PtMa/To0RgxYgSGDBmCsLAwvP/++wCAt99+G9evX0evXr0wb948PP/88xYdx3PPPYfFixcjOzsbXbt2xYgRI7Bp0ybEx8fbcFaIyBIqoVvBTUTkgXbt2oUhQ4bg8uXLCAkJcXVyiMgDsYSIiIiIvB4DIiIiIvJ6rDIjIiIir8cSIiIiIvJ6DIiIiIjI6zEgIiIiIq/HgIiIiIi8HgMiIiIi8noMiIiIiMjrMSAiIiIir8eAiIiIiLweAyIiIiLyev8PBcjMfq1Q8SEAAAAASUVORK5CYII=", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surrogate_scatter2D(keras_surrogate, data_training)\n", + "surrogate_parity(keras_surrogate, data_training)\n", + "surrogate_residual(keras_surrogate, data_training)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4/4 [==============================] - 0s 5ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(keras_surrogate, data_validation)\n", + "surrogate_parity(keras_surrogate, data_validation)\n", + "surrogate_residual(keras_surrogate, data_validation)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_usr.ipynb](./surrogate_embedding_usr.ipynb) file." ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4/4 [==============================] - 0s 4ms/step\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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3PmCoT2hoKHJycpCZmYm2bdvCy8sLarXaKvW9XfPmzfHYY4/hmWeeQatWrRASEoLXXnsNZWVlmDFjhk3qUI3BDRERUR1s3bVg5cqV8PX1RUJCAn777Te0aNECffr0wZIlSxqV/nnggQewdetWDBs2DEVFRdi4cSOmTp1q1TrXlJiYCJ1Oh0mTJuHatWuIjIzE7t270bJlS5vVAQBUQgjRcDHlKC4uhkajgVarhbe3t9TVISIiC7t+/TpycnIQFhYGNzc3qatDJqjvvTPl95ujpYiIiEhRGNwQERE5mNmzZxsMN6/5mD17ttTVazL2uSEiInIwK1aswMKFC43uU0KXDQY3REREDsbPzw9+fn5SV8NqmJYiIiIiRWFwQ0REiiTHmXOpfpYawM20FBERKYqrqyucnJxw8eJF+Pr6wtXVVb9MAcmXEAJXrlwxusyDqRjcEBGRojg5OSEsLAz5+fm4ePGi1NUhE6hUKrRt2xbOzs5NOg6DGyIiUhxXV1eEhISgsrJSsrWWyHTNmjVrcmADMLghIiKFqk5vNDXFQfaHHYqJiIhIURjcEBERkaIwuCEiIiJFYXBDREREisLghoiIiBSFwQ0REREpCoMbIiIiUhQGN0RERKQoDG6IiIhIUSQNbtatW4eePXvC29sb3t7eiIqKws6dO+t9zpYtW9C1a1e4ubmhR48e2LFjh41qS0RERPZA0uCmbdu2SExMxNGjR5GRkYG77roL9913H06fPm20fGpqKsaPH48ZM2bg+PHjGDt2LMaOHYusrCwb15yIiIjkSiWEEFJXoqZWrVph1apVmDFjRq19cXFxKC0txbfffqvfNnDgQPTq1Qvr169v1PGLi4uh0Wig1Wrh7e1tsXoTERGR9Zjy+y2bPjdVVVXYvHkzSktLERUVZbRMWloaRowYYbAtNjYWaWlpdR63oqICxcXFBg8iIiJSLsmDm1OnTsHT0xNqtRqzZ8/Gtm3bEB4ebrRsQUEB/P39Dbb5+/ujoKCgzuMnJCRAo9HoH8HBwRatPxEREcmL5MFNly5dkJmZiSNHjuCxxx7DlClT8NNPP1ns+IsXL4ZWq9U/8vLyLHZsIiIikh8XqSvg6uqKjh07AgD69u2L9PR0rF27Fu+8806tsgEBAbh06ZLBtkuXLiEgIKDO46vVaqjVastWmoiIiGRL8pab2+l0OlRUVBjdFxUVhX379hls27t3b519dIiIiMjxSNpys3jxYowePRohISG4du0aPvnkExw8eBC7d+8GAEyePBlBQUFISEgAAMyfPx8xMTFYvXo1xowZg82bNyMjIwPvvvuulKdBREREMiJpcHP58mVMnjwZ+fn50Gg06NmzJ3bv3o27774bAJCbmwsnp78blwYNGoRPPvkEzz//PJYsWYJOnTrhyy+/RPfu3aU6BSIiIpIZ2c1zY22c54aIiMj+2OU8N0RERESWwOCGiIiIFIXBDRERESkKgxsiIiJSFAY3REREpCgMboiIiEhRGNwQERGRojC4ISIiIkVhcENERESKwuDGgvK15UjNLkS+tlzqqhARETksSdeWUpLk9Fws3noKOgE4qYCEcT0Q1y9E6moRERE5HLbcWEC+tlwf2ACATgBLtmaxBYeIiEgCDG4sIKewVB/YVKsSAucLy6SpEBERkQNjcGMBYT7N4aQy3OasUiHUx0OaChERETkwBjcWEKhxR8K4HnBW3YpwnFUqvDKuOwI17hLXjIjkhIMOiGyDHYotJK5fCIZ09sX5wjKE+ngYBDb52nLkFJYizKc5Ax4iB8VBB0S2w+DGggI17rWCF36hEVFdgw6GdPblDQ+RFTAtZUUcRUVEAAcdENkagxsr4hcaEQEcdEBkawxurIhfaEQEcNABka2xz40VVX+hLdmahSoh+IVG5MDqG3RARJbF4MbK+IVGRNWMDTogIstjcGMD/EIjIiKyHfa5ISIiIkVhcENERESKwuCGiIiIFIXBDRERESkKgxsiIiJSFAY3REREpCgMboiIiEhRGNxYSb62HKnZhVwkk4iIyMY4iZ8VJKfn6lcDd1IBCeN6IK5fiNTVIiIicghsubGwfG25PrABAJ0AlmzNYgsOERGRjTC4sbCcwlJ9YFOtSgicLyyTpkJEREQOhsGNhYX5NIeTynCbs0qFUB8PaSpERETkYBjcWFigxh0J43rAWXUrwnFWqfDKuO5cOJOIiMhG2KHYCuL6hWBIZ1+cLyxDqI8HAxsiIiIbYnBjJYEadwY1REREEmBaioiIiBSFwQ0REREpiqTBTUJCAvr16wcvLy/4+flh7NixOHPmTL3PSUpKgkqlMni4ubnZqMZEREQkd5IGN4cOHcKcOXPwww8/YO/evbh58yZGjhyJ0tLSep/n7e2N/Px8/ePChQs2qjERERHJnaQdinft2mXw/6SkJPj5+eHo0aMYMmRInc9TqVQICAiwdvWIiIjIDsmqz41WqwUAtGrVqt5yJSUlaNeuHYKDg3Hffffh9OnTdZatqKhAcXGxwYOIiIiUSzbBjU6nw4IFCxAdHY3u3bvXWa5Lly54//338dVXX2HTpk3Q6XQYNGgQfv/9d6PlExISoNFo9I/g4GBrnQIRERHJgEoIIRouZn2PPfYYdu7ciZSUFLRt27bRz7t58ya6deuG8ePHY+XKlbX2V1RUoKKiQv//4uJiBAcHQ6vVwtvb2yJ1JyIiIusqLi6GRqNp1O+3LCbxmzt3Lr799lscPnzYpMAGAJo1a4bevXvj3LlzRver1Wqo1WpLVJOIiIjsgKRpKSEE5s6di23btmH//v0ICwsz+RhVVVU4deoUAgMDrVBDIiIisjeSttzMmTMHn3zyCb766it4eXmhoKAAAKDRaODufmvpgsmTJyMoKAgJCQkAgBUrVmDgwIHo2LEjioqKsGrVKly4cAEzZ86U7DyIiIhIPiQNbtatWwcAGDp0qMH2jRs3YurUqQCA3NxcODn93cD0119/YdasWSgoKEDLli3Rt29fpKamIjw83FbVJiIiIhmTTYdiWzGlQxIRERHJgym/37IZCk5ERERkCQxuiIiISFEY3BAREZGiMLghIiIiRWFwQ0RERIrC4IaIiIgUhcENERERKQqDGyIiIlIUBjdERESkKAxuyCbyteVIzS5EvrZc6qoQEZHCSbq2FDmG5PRcLN56CjoBOKmAhHE9ENcvROpqERGRQrHlhqwqX1uuD2wAQCeAJVuz2IJDRERWw+CGrCqnsFQf2FSrEgLnC8ukqRARESkegxuyqjCf5nBSGW5zVqkQ6uMhTYWIiEjxGNyQVQVq3JEwrgecVbciHGeVCq+M645AjbvENSMiIqVih2Kyurh+IRjS2RfnC8sQ6uPBwIaIiKyKwQ3ZRKDGnUENERHZBNNSRKRYnF+JyDGx5YaIFInzKxE5LrbcSIh3lUTWwfmViBwbW24kwrtKIuupb34l9v0iUj623EiAd5VE1sX5lYgcG4MbCSh51l5TUm1My5G1cH4lIsfGtJQEqu8qawY4SrirNCXVxrQcWRvnVyJyXGy5kYAS7ypNSbUxLUe2EqhxR1SH1nb9t0VEpmPLjUSUdldpSgdOUzt75mvLkVNYijCf5nZ/nYiIyPoY3EhISbP2mpJqM6Us01dERGQqpqXIIkxJtTW2LNNXRERkDrbckMWYkmprTFnOVUJEROZgcEMWZUqqraGySh1VRkRE1sW0FMmWEkeVERGR9bHlhmRNaaPKiIjI+hjckOwpaVQZERFZH9NSREREpCgMboiIiEhRGNwQUZ24uCkR2SP2uSEiozg7NBHZK7bcEFEtnB2aiOwZgxuSDaZA5KO+2aHJED+3RPLDtBTJgqkpEK4Ubl2cHbpxmLojkidJW24SEhLQr18/eHl5wc/PD2PHjsWZM2cafN6WLVvQtWtXuLm5oUePHtixY4cNakvWYmoKJDk9F9GJ+zFhwxFEJ+5HcnquDWvrGDg7dMOYuiOSL0mDm0OHDmHOnDn44YcfsHfvXty8eRMjR45EaWlpnc9JTU3F+PHjMWPGDBw/fhxjx47F2LFjkZWVZcOakyWZkgLhD4rtxPULQUr8MHw6ayBS4oexReI2TN0RyZekaaldu3YZ/D8pKQl+fn44evQohgwZYvQ5a9euxahRo/DMM88AAFauXIm9e/firbfewvr1661eZ7I8U1IgXCnctjg7dN0cJXXHFDDZI1l1KNZqtQCAVq1a1VkmLS0NI0aMMNgWGxuLtLQ0q9aNrMeUFEj1D0pNSvxBUQKld7R1hNQdU8Bkr2TToVin02HBggWIjo5G9+7d6yxXUFAAf39/g23+/v4oKCgwWr6iogIVFRX6/xcXF1umwmRRjV0gs/oHZcnWLFQJocgfFCVoTEdbJbQIKHlh17pSwEM6+yrqPEmZZBPczJkzB1lZWUhJSbHocRMSErB8+XKLHpOso7EpECX/oChBY34UlTTKSKmpO6aAyZ7JIi01d+5cfPvttzhw4ADatm1bb9mAgABcunTJYNulS5cQEBBgtPzixYuh1Wr1j7y8PIvVm6QTqHFHVIfW/JKVoYY62rJTuH1gCpjsmaTBjRACc+fOxbZt27B//36EhYU1+JyoqCjs27fPYNvevXsRFRVltLxarYa3t7fBg4isp6EfRY4ysg+O0KeIlEvStNScOXPwySef4KuvvoKXl5e+34xGo4G7+60/oMmTJyMoKAgJCQkAgPnz5yMmJgarV6/GmDFjsHnzZmRkZODdd9+V7DyI6G8N9YuS2ygjJfT9AaxzHkwBk71SCSFEw8Ws9OIqldHtGzduxNSpUwEAQ4cORWhoKJKSkvT7t2zZgueffx7nz59Hp06d8Nprr+Ef//hHo16zuLgYGo0GWq2WrThEVpSvLa/zRzE5PbdW8CNFnxul9P1RynkQ1ceU329JgxspMLghkof6gh9bvX504v5aLUgp8cPsqoVCKedB1BBTfr8bnZYyZQg1gwYiaojUo4yUMhpIKedBZEmNDm5atGhRZxqpmhACKpUKVVVVTa4YEZE1ya3vj7mUch5EltTo4ObAgQPWrAcRkU0pZUJIpZwHkSWxzw0ROTSp+/5YilLOg6guVulzc7uioiL897//xc8//wwAuOOOOzB9+nRoNBpzD0lEZHNS9/1pjMYM87aH8yCyFbNabjIyMhAbGwt3d3f0798fAJCeno7y8nLs2bMHffr0sXhFLYUtN0RkTzjMm+gWqw8Fv/POO9GxY0ds2LABLi63Gn8qKysxc+ZM/Pbbbzh8+LB5NbcBBjdEZC84zJvob1ZPS2VkZBgENgDg4uKCZ599FpGRkeYckoiIbsNh3kTmMWttKW9vb+Tm5tbanpeXBy8vryZXioiI6l6ny8PVCanZhVxslKgOZgU3cXFxmDFjBpKTk5GXl4e8vDxs3rwZM2fOxPjx4y1dRyIih2Rs8cqxvdvg/v+kYsKGI4hO3I/k9No3mkSOzqy01L/+9S+oVCpMnjwZlZWVAIBmzZrhscceQ2JiokUrSJallEUCiRxFzcUrPVydcP9/UvWpKp0AlmzNwpDOvvx7JqrBrODG1dUVa9euRUJCArKzswEAHTp0gIcHZ8SUM466ILJP1cO8U7ML2QeHqBHMnucGADw8PNCjRw9L1YWsKF9brg9sAN7xEdkjLrVA1DhmBTfXr1/Hm2++iQMHDuDy5cvQ6XQG+48dO2aRypHlKHnUBVNt5Ci41AJR45gV3MyYMQN79uzBgw8+iP79+ze4oCZJT6l3fDVTbSoAs+4Mw7TBYfyyJ4uRW/Bcsw8Ol1ogMs6sSfw0Gg127NiB6Ohoa9TJqhx5Er/k9Nxad3z23OfG2ARnAPsTkeWwnxqRfFh9Er+goCDOZ2OHlHbHZyzVBrA/EVkG+6kR2S+z5rlZvXo1Fi1ahAsXLli6PmRlgRp3RHVoLasv53xtuVkTkhmb4KxadX8iInPV10+NpGPu9wU5FrNabiIjI3H9+nW0b98eHh4eaNasmcH+q1evWqRypHxNafav7ly5+ItT0N22Twn9iUhaSu2nZs+YJqTGMiu4GT9+PP744w+88sor8Pf3Z4diqqUxnTAt0exfnWrbmHIe76X8Bp0AR5CQRXBkknzka8uRcf4q04TUaGYFN6mpqUhLS0NERISl60MK0Ni7K0sNTw/UuGPJmG6YNjhUMf2JSB6U1k/NHtX8PrmdUqazIMszK7jp2rUrysuZ76TaTGmNsXSzf/UsrkSW5GifKzkNfb/9++R2TBNSXczqUJyYmIinn34aBw8exJ9//oni4mKDBzkuUzphGlsUkM3+RNJJTs9FdOJ+2SzKWdeISIDfF1Q/s1puRo0aBQAYPny4wXYhBFQqFaqqqppeM7JLprbGsNmfSB7kOPTd2PeJE4A3J/RGn3Yt+X1BdTIruDlw4ICl60EKYU4nTEdr9ieSIzku0VLX98mYnm0kqQ/ZD7OCm5iYmEaVe/zxx7FixQr4+PiY8zJkp9gaQ2R/5Dr0nd8nZA6z+tw01qZNm9gHx0HJcbJAIqqbnPvA8fuETGVWy01jmbFsFRERSYStJKQUVg1uiIjIvsipD5ychqWTfWFwQ0REssOlFqgprNrnhoiIyFR1DUvnYpnUWAxuiIisgKtXm48rslNTmRzcVFZWYsWKFfj9998bLPvII4/A29vbrIoREdkruc30a2+qh6XXJIdh6WQ/TA5uXFxcsGrVKlRWVjZYdt26dZzjhogcClMqTSfnYelkH8zqUHzXXXfh0KFDCA0NtXB1yBo44oDIduQ406894rB0agqzgpvRo0cjPj4ep06dQt++fdG8eXOD/ffee69FKkdNxxEHRLYl15l+7ZGchqWTfVEJM2bac3KqO5sl94Uzi4uLodFooNVqFd8fKF9bjujE/bW+ZFPih/ELg8iKktNza62HxJsKoqYx5ffbrJYbnU5nVsXIttg8TiQNplSIpGXWUPAPP/wQFRUVtbbfuHEDH374YZMrRZbBEQdE0uF6SETSMSu4mTZtGrRaba3t165dw7Rp05pcKbIMjjggIiJHZFZaSggBlUpVa/vvv/8OjUbT5EqR5bB5nIiIHI1JwU3v3r2hUqmgUqkwfPhwuLj8/fSqqirk5ORg1KhRjT7e4cOHsWrVKhw9ehT5+fnYtm0bxo4dW2f5gwcPYtiwYbW25+fnIyAgwJRTcSgccUB0C6dFIHIMJgU31YFHZmYmYmNj4enpqd/n6uqK0NBQPPDAA40+XmlpKSIiIjB9+nSMGzeu0c87c+aMQU9pPz+/Rj+XiBwTp0UgchwmBTdLly4FAISGhiIuLg5ubm5NevHRo0dj9OjRJj/Pz88PLVq0aNJrE5HjqGvW4CGdfdmCQ6RAZvW5mTJlCoBbo6MuX75ca2h4SIh174Z69eqFiooKdO/eHcuWLUN0dHSdZSsqKgxGdhUXF1u1bkQkP3KbFoHpMSLrMiu4OXv2LKZPn47U1FSD7dUdja01iV9gYCDWr1+PyMhIVFRU4L333sPQoUNx5MgR9OnTx+hzEhISsHz5cqvUh4isy1JBgJxmDWZ6jMj6zJqhODo6Gi4uLoiPj0dgYGCtkVMRERGmV0SlarBDsTExMTEICQnBRx99ZHS/sZab4OBgh5ihmEhuTAlWLB0EyGHWYM4aTmQ+q89QnJmZiaNHj6Jr165mVdCS+vfvj5SUlDr3q9VqqNVqG9aI5ITN/01nqWtoSrBijT4yNadF8HB1QumNKuRry236uZBbeoxIqcwKbsLDw1FYWGjpupglMzMTgYGBUleDZIjN/01nqWtoarBirSAgUOOOw79ekexzIaf0GJGSmTVD8auvvopnn30WBw8exJ9//oni4mKDR2OVlJQgMzMTmZmZAICcnBxkZmYiNzcXALB48WJMnjxZX37NmjX46quvcO7cOWRlZWHBggXYv38/5syZY85pkILV9WOary2XtmJ2xJLXsL5gxRhrLR0i9eeCs4YT2YZZLTcjRowAANx1110G/W1M7VCckZFhMCnfU089BeDWaKykpCTk5+frAx3g1uisp59+Gn/88Qc8PDzQs2dPfPfdd0Yn9iPHxub/pmvMNWxsysrUFovqIOD2PjJNfe/k8LngrOFE1mdWcHPgwAGLvPjQoUNRX3/mpKQkg/8/++yzePbZZy3y2qRsbP5vuoauoSkpK3OCFWsEAXL5XHDWcCLrMistFRMTAycnJ2zYsAHx8fHo2LEjYmJikJubC2dnZ0vXkchkbP5vuvquoTnpnbh+IUiJH4ZPZw1ESvywRvVzsfTK2vxcEDkGs1puvvjiC0yaNAkTJ07E8ePH9UOttVotXnnlFezYscOilSQyB5v/m66ua2huekcOLRb8XBApn1ktNy+99BLWr1+PDRs2oFmzZvrt0dHROHbsmMUqR9RUlr7zd0TGrqG1OvzaCj8XRMpmVnBz5swZDBkypNZ2jUaDoqKiptaJiGSO6R3K15YjNbuQIxBJlsxKSwUEBODcuXMIDQ012J6SkoL27dtbol5EJHNM7zguziFFcmdWy82sWbMwf/58HDlyBCqVChcvXsTHH3+MhQsX4rHHHrN0HYlIppjecTxSzxVEtmePrXRmtdzEx8dDp9Nh+PDhKCsrw5AhQ6BWq7Fw4UI88cQTlq4jEckEl7OwH9Z6r+QwVxDZjr220pm1cGa1Gzdu4Ny5cygpKUF4eDg8PT0tWTerMGXhLSL6m71+ycmBrYNCa75XXPzTccjtvTbl99ustFQ1V1dXhIeHo3///nYR2BCReZiKMF9yei6iE/djwoYjiE7cj+T03Iaf1ATWfq/YmdxxmLpsipyYlZYiIsfCVIR5TF0w1BItPLZ4r9iZ3DHIZUZvczC4IaIG2fOXnJRMCTQslUqy1XslhwkZybqstcabLTQpLUVEjoGpCPM0drJDS6aS+F6RJZmzbIocsOWGiBqFqQjTNfbO19KpJL5XZEn22ErH4IaIGs0ev+Sk1phAwxqpJL5X5MiYliIisrKGJjtkKonIsthyQ0QkA0pJJXGiR5IDBjdERDJh76kkTvRIcsG0FBHVYo9ryTgiOb1PnOiR5IQtN0RkgHff9kFu7xMneiQ5YcsNEenx7ts+yPF9auycPkS2wOCGiPTseS0ZRyLH94kjvkhOmJYiIj0us2Af5Po+KWXEF9k/ttwQkR7vvu2DnN+nhub0IbIFlRBCNFxMOYqLi6HRaKDVauHt7S11dYhkKV9bzrtvO8D3iRyJKb/fTEsRUS32Pt/K7ZQ6sZzS3iciS2FwQ0SKJrch00RkfexzQ0SKJcch00RkfQxuiEix5Dhkmkjp5DBzNtNSRKRYch0yTaRUckkDs+WGiBRLzkOmiZRGTmlgttwQkaJxYjki25DT+mIMbohI8Thkmsj65JQGZlqKiIiIGq2uDsNySgOz5YaIiIyyp8kP7amu9qyhDsNySQMzuCEiu8MfMuuTy6iXxrCnutqzujoMD+nsa/B3KIc0MNNSDk4O8xEQmSI5PRfRifsxYcMRRCfuR3J6rtRVUhw5jXppiD3V1d7Z07xRbLlxYLzbIXvT2DtHaho5jXppiD3V1d7JqcNwQ9hy46B4t0P2yJ7uHO1Z9Y9YTQ39iEnVCmxOXck8cuow3BC23Dgo3u2QPbKnO0d7Vv0jtmRrFqqEaPBHTMpWYFPrSk0jlw7DDWFw46D4I0H2iD9kttPYHzE5pArt5QdXKeTQYbghkqalDh8+jHvuuQdt2rSBSqXCl19+2eBzDh48iD59+kCtVqNjx45ISkqyej2VyJ6aF4lqiusXgpT4Yfh01kCkxA9jPzErCtS4I6pD63q/F+SSKmxMXclxSNpyU1paioiICEyfPh3jxo1rsHxOTg7GjBmD2bNn4+OPP8a+ffswc+ZMBAYGIjY21gY1Vhbe7ZC9soc7R0fBVmCSI5UQQjRczPpUKhW2bduGsWPH1llm0aJF2L59O7KysvTbHn74YRQVFWHXrl2Nep3i4mJoNBpotVp4e3s3tdpERA4vOT23VqpwSGdfzkVEFmXK77dd9blJS0vDiBEjDLbFxsZiwYIFdT6noqICFRUV+v8XFxdbq3pERA7p9lbgw79eQXTifk4zQZKxq6HgBQUF8Pf3N9jm7++P4uJilJcbH36YkJAAjUajfwQHB9uiqkREDqW6zwsAi0wzwQlGqSnsKrgxx+LFi6HVavWPvLw8qatERKRYluhgzFmoqansKrgJCAjApUuXDLZdunQJ3t7ecHc3ntNVq9Xw9vY2eBCRdHfGvCNXtqZOqscJRskS7KrPTVRUFHbs2GGwbe/evYiKipKoRiRHXFSxYVJNusYlP5SvqXMRcYJRsgRJg5uSkhKcO3dO//+cnBxkZmaiVatWCAkJweLFi/HHH3/gww8/BADMnj0bb731Fp599llMnz4d+/fvx2effYbt27dLdQokM/zxbJhUk67JYbI3so2mTDPBoeVkCZKmpTIyMtC7d2/07t0bAPDUU0+hd+/eePHFFwEA+fn5yM39O9caFhaG7du3Y+/evYiIiMDq1avx3nvvcY4bAsDm7MaSatI1uUz2RrZh7qR6nGCULEHSlpuhQ4eivml2jM0+PHToUBw/ftyKtSJ7xebsxpHqzph35NRYnGCUmsquOhQT1YerAzeOVHfGvCMnU3A5BWoK2cxQbCucoVjZjM2Uyj43xuVryyW5M5bqdYnIOHsZhGHK7zeDG1Ic/ngSETWOPQ3CMOX3m2kpB6fEOUfYnE1E1DAlD8Kwq3luyLLsKWInIiLLUvIgDLbcOCglR+xERNQwJQ/CYHDjoDjnCBGRbcg1/a/kEYxMSzkozjlCRGR9ck//K3VOIbbcOCglR+xERHJgL+l/JQ7CYMuNA1NqxE5EJAdK7rArdwxuHFygxp1/ZEREVsD0v3SYliIiIrICpv+lw5YbIiIiK2H6XxoMboiIyC7Zy5pITP/bHoMbIiKyO3IfYk3SYp8bIiKyK/YyxNoRyHWCQrbcEBGRXeEQa3mQc+sZW26IiMiuKHlNJHsh99YzBjdERGRXOMTaNNZIHcl9fUKmpYiIyO5wiHXjWCt1JPcJCtlyQ0REdkmJayJZkjVTR3JvPWPLDRERkQJZu+O1nFvPGNwQEZFi2MvEfrZgi9SRXCcoZFrKzsh1TgEiIqklp+ciOnE/Jmw4gujE/UhOz5W6SpKSe+rImlRCCNFwMeUoLi6GRqOBVquFt7e31NUxiZznFCAiklK+thzRiftrtVKkxA9ziB/z+uRry2WZOjKVKb/fbLmxE3KfU4CISEpyH5osJUfseM3gxk7wD5eIqG6c2I9qYnBjJ+z9D5d9hYjImhy5fwnVxtFSdqL6D3fJ1ixUCWFXf7jsK6QsHI1CciXnoclkW+xQbGfsrWMYO/kpCwNVsjcMxpXDlN9vttzYGbnOKVAXrt6rHHV1ah/S2ZfvJckSg3HHxT43ZFX23leI/sZO7WRPHGmEKfs01sbghqyKnfyUg4Eq2RNHCcY5caFxTEuR1Smhkx/z9vbdqZ0cj9xXrbYEporrxuCGbMLe+grVDGYO/3pF0Xl7UwI3JQSq5BgcIRhnn8a6Mbghus3tnRCFAKq/P5R2Z2ROh0t7C1TJcSk9GHeE1ilzsc8NUQ3GmnlvnytBKXl7R+pwSY5LyUsPsE9j3dhyQ1SDsWbe2ynlzohN2kT2T+mtU+ZicEMOy1hfE2PNvCoVoBKADsq6M2KTNimJI3f6Z6q4NgY35JDq6mtSVydEe7wzaujL3hE6XJJj4GR9dDtZLL/w9ttvY9WqVSgoKEBERATefPNN9O/f32jZpKQkTJs2zWCbWq3G9evXG/Va9r78AjVdY5aEsLdlLm5nype9vZ8rOTYu8eI4TPn9lrxDcXJyMp566iksXboUx44dQ0REBGJjY3H58uU6n+Pt7Y38/Hz948KFCzasMdm7xkzuZc+dEE3tKGzsXDnjKdkLR5msj0wjeXDz73//G7NmzcK0adMQHh6O9evXw8PDA++//36dz1GpVAgICNA//P39bVhjsndKn2m3qV/2nPGU7InS/56N4c1HwyQNbm7cuIGjR49ixIgR+m1OTk4YMWIE0tLS6nxeSUkJ2rVrh+DgYNx33304ffq0LapLCqH04ZNN+bLn8HCyN0r/e74dbz4aR9IOxYWFhaiqqqrV8uLv749ffvnF6HO6dOmC999/Hz179oRWq8W//vUvDBo0CKdPn0bbtm1rla+oqEBFRYX+/8XFxZY9CbJLSh4+2ZSOwhweTvZIyX/PNXG5hcazu9FSUVFRiIqK0v9/0KBB6NatG9555x2sXLmyVvmEhAQsX77cllUkO6Hk4ZPVX/bHLvwFnRCIDG3VqOdxeDjZKyX/PVfjzUfjSZqW8vHxgbOzMy5dumSw/dKlSwgICGjUMZo1a4bevXvj3LlzRvcvXrwYWq1W/8jLy2tyvYnsweFfr+CJT4/jiU8zG9187WhN/ET2xBH7F5lL0uDG1dUVffv2xb59+/TbdDod9u3bZ9A6U5+qqiqcOnUKgYGBRver1Wp4e3sbPIiUril9Z+L6hSAlfhg+nTUQKfHDOF8IkUzw5qPxJE9LPfXUU5gyZQoiIyPRv39/rFmzBqWlpfq5bCZPnoygoCAkJCQAAFasWIGBAweiY8eOKCoqwqpVq3DhwgXMnDlTytMgkpWmNl87QhM/kdwZm4jTUfoXNZXkwU1cXByuXLmCF198EQUFBejVqxd27dql72Scm5sLJ6e/G5j++usvzJo1CwUFBWjZsiX69u2L1NRUhIeHS3UKRLLDvjNE9q2+iTh589EwWcxQbEucoZgcRXJ6bq0RU0wxEckfZ102zpTfb8lbbojIOth8TWQ/aqagOCqq6RjcECkYm6+J5O/2FNSi0V2ZVm4iyZdfICIiclTGRja+tvMMFo3qylFRTcCWGyIiIonUlYLq2bYFUuKHMa1sJgY3REREEqlvZCPTyuZjWoqIiEginJjPOthyQ0REJCGObLQ8BjdEREQSYwrKspiWIiIiMiJfW47U7MJGrclG8sKWGyIiotvUt/wByR9bbmSAdwdERPJhbO6ZJVuz+B1tR9hyIzHeHRARyQuXP7B/bLmREO8OiIikUV+LefXcMzVx+QP7wuBGQvXdHRARkXUkp+ciOnE/Jmw4gujE/UhOzzXYz7ln7B/TUhKqb2ZKIiKyvLpazId09jUIXjj3jH1jy42EeHdARGRbprSYB2rcEdWhNb+T7RBbbiTGuwMiItthi7ljYMuNDPDuwP5w+D6RfWKLuWNgyw2RiTh8n8i+scVc+dhyQ2QCDt8nUga2mCsbgxsiE3D4PhGR/DG4ITIBJ/ciIpI/BjdEJmBnRCIi+WOHYiITsTMiEZG8MbghMkOgxp1BDRGRTDEtRURERIrC4IaIiIgUhcENERERKQqDGyIiIlIUBjdEREQWxvXnpMXRUkRERBbE9eekx5YbIiOkuOuy5GvyrpFIGlx/Th7YckN0Gynuuiz5mrxrJJJOfevPcW4s22HLDVENUtx1WfI1eddIZD2NaRE1tv4cAJz8o8h6FaNaGNwQ1SDFqt+WfE2uWk5kHcnpuYhO3I8JG44gOnE/3jmcbTTQCdS4Y9GorrWe/9rOM7zJsCGmpYhqqL7rqhkgWHvVb1NfM19bjpzCUoT5NK/VzC1F/YmUzliLaMKOXwAYT/32aKupdQympmyLLTdENUix6rcpr3n73WNyeq7k9SdSOmMtotWMpX6NpaZ4k2FbKiFEHW+ZMhUXF0Oj0UCr1cLb21vq6pBM5WvLbb7qd0Ovma8tR3Ti/lqtMinxw2qVl6L+REpl7G/vdp/OGoioDq31/09Oz8WSrVmoEkJ/k8GO/U1jyu8301JERkix6ndDr2nKKAyuWk5kOdUtotXByu2MtcrE9QvBkM6+vMmQCIMbIjvB/jRE0qkZrJz8vQiv7Tpj0CpTM3ip2S+uZmsO2Q6DGyI7cfvdI/vTENlWdYtoVIfWuLdXG6OtMpxnSh5k0aH47bffRmhoKNzc3DBgwAD8+OOP9ZbfsmULunbtCjc3N/To0QM7duywUU2JpBXXLwQp8cPw6ayBSIkfxi9NIolUBzm3t9hwnil5kDy4SU5OxlNPPYWlS5fi2LFjiIiIQGxsLC5fvmy0fGpqKsaPH48ZM2bg+PHjGDt2LMaOHYusrCwb15xIGsa+VIlIepxnSj4kHy01YMAA9OvXD2+99RYAQKfTITg4GE888QTi4+NrlY+Li0NpaSm+/fZb/baBAweiV69eWL9+fYOvx9FSRERkDaaMaCTTmfL7LWnLzY0bN3D06FGMGDFCv83JyQkjRoxAWlqa0eekpaUZlAeA2NjYOstXVFSguLjY4EFERGRpnGdKPiTtUFxYWIiqqir4+/sbbPf398cvv/xi9DkFBQVGyxcUFBgtn5CQgOXLl1umwkRERPXgEHB5kLzPjbUtXrwYWq1W/8jLy5O6SkREpGDsFyc9SVtufHx84OzsjEuXLhlsv3TpEgICAow+JyAgwKTyarUaarXaMhUmIiIi2ZO05cbV1RV9+/bFvn379Nt0Oh327duHqKgoo8+JiooyKA8Ae/furbM8ERERORbJJ/F76qmnMGXKFERGRqJ///5Ys2YNSktLMW3aNADA5MmTERQUhISEBADA/PnzERMTg9WrV2PMmDHYvHkzMjIy8O6770p5GkRERCQTkgc3cXFxuHLlCl588UUUFBSgV69e2LVrl77TcG5uLpyc/m5gGjRoED755BM8//zzWLJkCTp16oQvv/wS3bt3l+oUiIiISEYkn+fG1jjPDRERkf2xm3luiIiIiCyNwQ0REREpCoMbIiIiUhQGN0RERKQoDG6IiIhIURjcEBERkaJIPs+NrVWPfOfq4ERERPaj+ne7MTPYOFxwc+3aNQBAcHCwxDUhIiIiU127dg0ajabeMg43iZ9Op8PFixfh5eUFlUrV6OcVFxcjODgYeXl5Dj35H68Dr0E1XodbeB14DarxOtxiresghMC1a9fQpk0bg5ULjHG4lhsnJye0bdvW7Od7e3s79Ie2Gq8Dr0E1XodbeB14DarxOtxijevQUItNNXYoJiIiIkVhcENERESKwuCmkdRqNZYuXQq1Wi11VSTF68BrUI3X4RZeB16DarwOt8jhOjhch2IiIiJSNrbcEBERkaIwuCEiIiJFYXBDREREisLghoiIiBTFoYObdevWoWfPnvqJhqKiorBz5079/uvXr2POnDlo3bo1PD098cADD+DSpUsGx8jNzcWYMWPg4eEBPz8/PPPMM6isrLT1qVhMYmIiVCoVFixYoN/mCNdh2bJlUKlUBo+uXbvq9zvCNaj2xx9/4JFHHkHr1q3h7u6OHj16ICMjQ79fCIEXX3wRgYGBcHd3x4gRI3D27FmDY1y9ehUTJ06Et7c3WrRogRkzZqCkpMTWp2K20NDQWp8HlUqFOXPmAHCMz0NVVRVeeOEFhIWFwd3dHR06dMDKlSsN1vVxhM8CcGu6/wULFqBdu3Zwd3fHoEGDkJ6ert+vxOtw+PBh3HPPPWjTpg1UKhW+/PJLg/2WOueTJ0/izjvvhJubG4KDg/Haa69Z5gSEA/v666/F9u3bxa+//irOnDkjlixZIpo1ayaysrKEEELMnj1bBAcHi3379omMjAwxcOBAMWjQIP3zKysrRffu3cWIESPE8ePHxY4dO4SPj49YvHixVKfUJD/++KMIDQ0VPXv2FPPnz9dvd4TrsHTpUnHHHXeI/Px8/ePKlSv6/Y5wDYQQ4urVq6Jdu3Zi6tSp4siRI+K3334Tu3fvFufOndOXSUxMFBqNRnz55ZfixIkT4t577xVhYWGivLxcX2bUqFEiIiJC/PDDD+J///uf6Nixoxg/frwUp2SWy5cvG3wW9u7dKwCIAwcOCCEc4/Pw8ssvi9atW4tvv/1W5OTkiC1btghPT0+xdu1afRlH+CwIIcRDDz0kwsPDxaFDh8TZs2fF0qVLhbe3t/j999+FEMq8Djt27BDPPfec2Lp1qwAgtm3bZrDfEues1WqFv7+/mDhxosjKyhKffvqpcHd3F++8806T6+/QwY0xLVu2FO+9954oKioSzZo1E1u2bNHv+/nnnwUAkZaWJoS49eY7OTmJgoICfZl169YJb29vUVFRYfO6N8W1a9dEp06dxN69e0VMTIw+uHGU67B06VIRERFhdJ+jXAMhhFi0aJEYPHhwnft1Op0ICAgQq1at0m8rKioSarVafPrpp0IIIX766ScBQKSnp+vL7Ny5U6hUKvHHH39Yr/JWNH/+fNGhQweh0+kc5vMwZswYMX36dINt48aNExMnThRCOM5noaysTDg7O4tvv/3WYHufPn3Ec8895xDX4fbgxlLn/J///Ee0bNnS4G9i0aJFokuXLk2us0OnpWqqqqrC5s2bUVpaiqioKBw9ehQ3b97EiBEj9GW6du2KkJAQpKWlAQDS0tLQo0cP+Pv768vExsaiuLgYp0+ftvk5NMWcOXMwZswYg/MF4FDX4ezZs2jTpg3at2+PiRMnIjc3F4BjXYOvv/4akZGR+Oc//wk/Pz/07t0bGzZs0O/PyclBQUGBwbXQaDQYMGCAwbVo0aIFIiMj9WVGjBgBJycnHDlyxHYnYyE3btzApk2bMH36dKhUKof5PAwaNAj79u3Dr7/+CgA4ceIEUlJSMHr0aACO81morKxEVVUV3NzcDLa7u7sjJSXFYa5DTZY657S0NAwZMgSurq76MrGxsThz5gz++uuvJtXR4RbOvN2pU6cQFRWF69evw9PTE9u2bUN4eDgyMzPh6uqKFi1aGJT39/dHQUEBAKCgoMDgy6t6f/U+e7F582YcO3bMIIdcraCgwCGuw4ABA5CUlIQuXbogPz8fy5cvx5133omsrCyHuQYA8Ntvv2HdunV46qmnsGTJEqSnp2PevHlwdXXFlClT9Odi7FxrXgs/Pz+D/S4uLmjVqpVdXYtqX375JYqKijB16lQAjvM3ER8fj+LiYnTt2hXOzs6oqqrCyy+/jIkTJwKAw3wWvLy8EBUVhZUrV6Jbt27w9/fHp59+irS0NHTs2NFhrkNNljrngoIChIWF1TpG9b6WLVuaXUeHD266dOmCzMxMaLVafP7555gyZQoOHTokdbVsJi8vD/Pnz8fevXtr3Zk4kuq7UQDo2bMnBgwYgHbt2uGzzz6Du7u7hDWzLZ1Oh8jISLzyyisAgN69eyMrKwvr16/HlClTJK6dNP773/9i9OjRaNOmjdRVsanPPvsMH3/8MT755BPccccdyMzMxIIFC9CmTRuH+yx89NFHmD59OoKCguDs7Iw+ffpg/PjxOHr0qNRVozo4fFrK1dUVHTt2RN++fZGQkICIiAisXbsWAQEBuHHjBoqKigzKX7p0CQEBAQCAgICAWiMkqv9fXUbujh49isuXL6NPnz5wcXGBi4sLDh06hDfeeAMuLi7w9/d3iOtwuxYtWqBz5844d+6cw3wWACAwMBDh4eEG27p166ZP0VWfi7FzrXktLl++bLC/srISV69etatrAQAXLlzAd999h5kzZ+q3Ocrn4ZlnnkF8fDwefvhh9OjRA5MmTcKTTz6JhIQEAI71WejQoQMOHTqEkpIS5OXl4ccff8TNmzfRvn17h7oO1Sx1ztb8O3H44OZ2Op0OFRUV6Nu3L5o1a4Z9+/bp9505cwa5ubmIiooCAERFReHUqVMGb+DevXvh7e1d6wdCroYPH45Tp04hMzNT/4iMjMTEiRP1/3aE63C7kpISZGdnIzAw0GE+CwAQHR2NM2fOGGz79ddf0a5dOwBAWFgYAgICDK5FcXExjhw5YnAtioqKDO5q9+/fD51OhwEDBtjgLCxn48aN8PPzw5gxY/TbHOXzUFZWBicnw58IZ2dn6HQ6AI73WQCA5s2bIzAwEH/99Rd2796N++67zyGvg6XOOSoqCocPH8bNmzf1Zfbu3YsuXbo0KSUFwLGHgsfHx4tDhw6JnJwccfLkSREfHy9UKpXYs2ePEOLWcM+QkBCxf/9+kZGRIaKiokRUVJT++dXDPUeOHCkyMzPFrl27hK+vr10N9zSm5mgpIRzjOjz99NPi4MGDIicnR3z//fdixIgRwsfHR1y+fFkI4RjXQIhb0wG4uLiIl19+WZw9e1Z8/PHHwsPDQ2zatElfJjExUbRo0UJ89dVX4uTJk+K+++4zOgS0d+/e4siRIyIlJUV06tRJ1sNejamqqhIhISFi0aJFtfY5wudhypQpIigoSD8UfOvWrcLHx0c8++yz+jKO8lnYtWuX2Llzp/jtt9/Enj17REREhBgwYIC4ceOGEEKZ1+HatWvi+PHj4vjx4wKA+Pe//y2OHz8uLly4IISwzDkXFRUJf39/MWnSJJGVlSU2b94sPDw8OBS8qaZPny7atWsnXF1dha+vrxg+fLg+sBFCiPLycvH444+Lli1bCg8PD3H//feL/Px8g2OcP39ejB49Wri7uwsfHx/x9NNPi5s3b9r6VCzq9uDGEa5DXFycCAwMFK6uriIoKEjExcUZzO3iCNeg2jfffCO6d+8u1Gq16Nq1q3j33XcN9ut0OvHCCy8If39/oVarxfDhw8WZM2cMyvz5559i/PjxwtPTU3h7e4tp06aJa9eu2fI0mmz37t0CQK1zE8IxPg/FxcVi/vz5IiQkRLi5uYn27duL5557zmDYrqN8FpKTk0X79u2Fq6urCAgIEHPmzBFFRUX6/Uq8DgcOHBAAaj2mTJkihLDcOZ84cUIMHjxYqNVqERQUJBITEy1Sf5UQNaabJCIiIrJz7HNDREREisLghoiIiBSFwQ0REREpCoMbIiIiUhQGN0RERKQoDG6IiIhIURjcEBERkaIwuCEiIiJFYXBDRI0ydOhQLFiwQOpqWN2yZcvQq1cvqatBRE3A4IaIHMKNGzds+npCCFRWVtr0NYnoFgY3RNSgqVOn4tChQ1i7di1UKhVUKhXOnz+PrKwsjB49Gp6envD398ekSZNQWFiof97QoUPxxBNPYMGCBWjZsiX8/f2xYcMGlJaWYtq0afDy8kLHjh2xc+dO/XMOHjwIlUqF7du3o2fPnnBzc8PAgQORlZVlUKeUlBTceeedcHd3R3BwMObNm4fS0lL9/tDQUKxcuRKTJ0+Gt7c3Hn30UQDAokWL0LlzZ3h4eKB9+/Z44YUX9KsSJyUlYfny5Thx4oT+PJOSknD+/HmoVCpkZmbqj19UVASVSoWDBw8a1Hvnzp3o27cv1Go1UlJSoNPpkJCQgLCwMLi7uyMiIgKff/65pd8iIqqBwQ0RNWjt2rWIiorCrFmzkJ+fj/z8fHh5eeGuu+5C7969kZGRgV27duHSpUt46KGHDJ77wQcfwMfHBz/++COeeOIJPPbYY/jnP/+JQYMG4dixYxg5ciQmTZqEsrIyg+c988wzWL16NdLT0+Hr64t77rlHH4RkZ2dj1KhReOCBB3Dy5EkkJycjJSUFc+fONTjGv/71L0REROD48eN44YUXAABeXl5ISkrCTz/9hLVr12LDhg14/fXXAQBxcXF4+umncccdd+jPMy4uzqRrFR8fj8TERPz888/o2bMnEhIS8OGHH2L9+vU4ffo0nnzySTzyyCM4dOiQScclIhNYZPlNIlK821eLX7lypRg5cqRBmby8PIOVtGNiYsTgwYP1+ysrK0Xz5s3FpEmT9Nvy8/MFAJGWliaE+Hs14s2bN+vL/Pnnn8Ld3V0kJycLIYSYMWOGePTRRw1e+3//+59wcnIS5eXlQggh2rVrJ8aOHdvgea1atUr07dtX//+lS5eKiIgIgzI5OTkCgDh+/Lh+219//SUAiAMHDhjU+8svv9SXuX79uvDw8BCpqakGx5sxY4YYP358g3UjIvO4SBlYEZH9OnHiBA4cOABPT89a+7Kzs9G5c2cAQM+ePfXbnZ2d0bp1a/To0UO/zd/fHwBw+fJlg2NERUXp/92qVSt06dIFP//8s/61T548iY8//lhfRggBnU6HnJwcdOvWDQAQGRlZq27Jycl44403kJ2djZKSElRWVsLb29vk869Lzdc8d+4cysrKcPfddxuUuXHjBnr37m2x1yQiQwxuiMgsJSUluOeee/Dqq6/W2hcYGKj/d7NmzQz2qVQqg20qlQoAoNPpTHrt//u//8O8efNq7QsJCdH/u3nz5gb70tLSMHHiRCxfvhyxsbHQaDTYvHkzVq9eXe/rOTndyuALIfTbqlNkt6v5miUlJQCA7du3IygoyKCcWq2u9zWJyHwMboioUVxdXVFVVaX/f58+ffDFF18gNDQULi6W/yr54Ycf9IHKX3/9hV9//VXfItOnTx/89NNP6Nixo0nHTE1NRbt27fDcc8/pt124cMGgzO3nCQC+vr4AgPz8fH2LS83OxXUJDw+HWq1Gbm4uYmJiTKorEZmPHYqJqFFCQ0Nx5MgRnD9/HoWFhZgzZw6uXr2K8ePHIz09HdnZ2di9ezemTZtWKzgwx4oVK7Bv3z5kZWVh6tSp8PHxwdixYwHcGvGUmpqKuXPnIjMzE2fPnsVXX31Vq0Px7Tp16oTc3Fxs3rwZ2dnZeOONN7Bt27Za55mTk4PMzEwUFhaioqIC7u7uGDhwoL6j8KFDh/D88883eA5eXl5YuHAhnnzySXzwwQfIzs7GsWPH8Oabb+KDDz4w+9oQUf0Y3BBRoyxcuBDOzs4IDw+Hr68vbty4ge+//x5VVVUYOXIkevTogQULFqBFixb6NE5TJCYmYv78+ejbty8KCgrwzTffwNXVFcCtfjyHDh3Cr7/+ijvvvBO9e/fGiy++iDZt2tR7zHvvvRdPPvkk5s6di169eiE1NVU/iqraAw88gFGjRmHYsGHw9fXFp59+CgB4//33UVlZib59+2LBggV46aWXGnUeK1euxAsvvICEhAR069YNo0aNwvbt2xEWFmbGVSGixlCJmklkIiKJHTx4EMOGDcNff/2FFi1aSF0dIrJDbLkhIiIiRWFwQ0RERIrCtBQREREpCltuiIiISFEY3BAREZGiMLghIiIiRWFwQ0RERIrC4IaIiIgUhcENERERKQqDGyIiIlIUBjdERESkKAxuiIiISFH+P6mFqBfr0aimAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(keras_surrogate, data_validation)\n", - "surrogate_parity(keras_surrogate, data_validation)\n", - "surrogate_residual(keras_surrogate, data_validation)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_usr.ipynb](./surrogate_embedding_usr.ipynb) file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py index d5a60c09..c9fbaaf6 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py @@ -1,15 +1,16 @@ -############################################################################## -# Institute for the Design of Advanced Energy Systems Process Systems -# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the -# software owners: The Regents of the University of California, through -# Lawrence Berkeley National Laboratory, National Technology & Engineering -# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia -# University Research Corporation, et al. All rights reserved. +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). # -# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and -# license information, respectively. Both files are also available online -# at the URL "https://github.com/IDAES/idaes-pse". -############################################################################## +# Copyright (c) 2018-2025 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +# +################################################################################# """ Maintainer: Javal Vyas diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/sco2_keras_model.keras b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/sco2_keras_model.keras index c090ddac..8b5979df 100644 Binary files a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/sco2_keras_model.keras and b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/sco2_keras_model.keras differ diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb index 10a46ccf..286e015a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "27ba4108", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -501,8 +528,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.16" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb index 1a39ef6f..35472420 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -62,9 +88,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:13.768395: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-03-17 17:39:13.769168: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:39:13.772133: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2025-03-17 17:39:13.778950: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742258353.790882 379144 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742258353.794461 379144 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1742258353.804206 379144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258353.804224 379144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258353.804225 379144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1742258353.804226 379144 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-03-17 17:39:13.807756: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "# Import Python libraries\n", "import logging\n", @@ -143,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -500,9 +546,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.16" - }, - "orig_nbformat": 4 + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb index 5f09fc29..ba8aab39 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb @@ -1,509 +1,534 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################################################################\n", - "# Institute for the Design of Advanced Energy Systems Process Systems\n", - "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", - "# software owners: The Regents of the University of California, through\n", - "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", - "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", - "# University Research Corporation, et al. All rights reserved.\n", - "#\n", - "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", - "# license information, respectively. Both files are also available online\n", - "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", - "##############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Constraint,\n", + " Param,\n", + " Reals,\n", + " Set,\n", + " value,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + ")\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component,\n", + ")\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(\n", + " {\n", + " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", + " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", + " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", + " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", + " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", + " }\n", + " )\n", + "\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.s,\n", + " \"length\": units.m,\n", + " \"mass\": units.kg,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_test.ipynb file) using the keras Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " self.flow_mol = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol / units.s,\n", + " doc=\"Total molar flowrate [kmol/s]\",\n", + " )\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc=\"State pressure [MPa]\",\n", + " )\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760 + 273.15),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + " self.entr_mol = Var(\n", + " domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ / units.kmol / units.K,\n", + " doc=\"Entropy [kJ/kmol/K]\",\n", + " )\n", + " self.enth_mol = Var(\n", + " domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ / units.kmol,\n", + " doc=\"Enthalpy [kJ/ kmol]\",\n", + " )\n", + "\n", + " inputs = [self.pressure, self.temperature]\n", + " outputs = [self.enth_mol, self.entr_mol]\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", + " )\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol * self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would inturn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def initialize(\n", + " blk,\n", + " state_args=None,\n", + " hold_state=False,\n", + " outlvl=1,\n", + " state_vars_fixed=False,\n", + " solver=\"ipopt\",\n", + " optarg={\"tol\": 1e-8},\n", + " ):\n", + " \"\"\"\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output information (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating which solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states variables are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " release_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " \"\"\"\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\n", + " \"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\"\n", + " )\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " \"\"\"\n", + " Method to release state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " \"\"\"\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags[\"Fcflag\"][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags[\"Pflag\"][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags[\"Tflag\"][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info(\"{} State Released.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages_test.ipynb). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Integration of Surrogate into Custom Property Package\n", - "\n", - "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", - "\n", - "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", - "\n", - "### 1.1 Steps in Creating a Property Package\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **phases and components** of interest.\n", - "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", - "5. Declaring the **state variables** to be used for the property package.\n", - "6. Creating **variables and constraints** to describe the properties of interest.\n", - "7. Creating an **initialization routine** for the property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Importing libraries for making Property Package\n", - "\n", - "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Constraint,\n", - " Param,\n", - " Reals,\n", - " Set,\n", - " value,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - ")\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " LiquidPhase,\n", - " Component,\n", - ")\n", - "from idaes.core.util.initialization import solve_indexed_blocks\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.misc import extract_data\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "\n", - "from pyomo.util.model_size import build_model_size_report\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Defining Classes\n", - "\n", - "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", - "\n", - "## 3.1 Physical Parameter Block\n", - "\n", - "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What properties are supported and how they are implemented\n", - "* What components and phases are included in the packages\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To assemble the above mentioned things in a class we need to follow the following steps:\n", - "\n", - "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", - "* Declaring any necessary configuration arguments\n", - "* Writing the build method for our class\n", - "* Creating a define_metadata method for the class.\n", - "\n", - "The code below follows the above mentioned steps. \n", - "\n", - "*NOTE*: The SCO2StateBlock will be discussed in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2ParameterBlock\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " supercritical CO2.\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(PhysicalParameterData, self).build()\n", - "\n", - " self._state_block_class = SCO2StateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Liq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.CO2 = Component()\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(\n", - " {\n", - " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", - " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", - " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", - " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", - " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", - " }\n", - " )\n", - "\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.s,\n", - " \"length\": units.m,\n", - " \"mass\": units.kg,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 State Block\n", - "\n", - "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", - "\n", - "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", - "\n", - "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", - "\n", - "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_test.ipynb file) using the keras Surrogate API of IDAES package\n", - "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", - "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", - "class SCO2StateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super(SCO2StateBlockData, self).build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " self.flow_mol = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1.0,\n", - " units=units.kmol / units.s,\n", - " doc=\"Total molar flowrate [kmol/s]\",\n", - " )\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=8,\n", - " bounds=(7.38, 40),\n", - " units=units.MPa,\n", - " doc=\"State pressure [MPa]\",\n", - " )\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=350,\n", - " bounds=(304.2, 760 + 273.15),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - " self.entr_mol = Var(\n", - " domain=Reals,\n", - " initialize=10,\n", - " units=units.kJ / units.kmol / units.K,\n", - " doc=\"Entropy [kJ/kmol/K]\",\n", - " )\n", - " self.enth_mol = Var(\n", - " domain=Reals,\n", - " initialize=1,\n", - " units=units.kJ / units.kmol,\n", - " doc=\"Enthalpy [kJ/ kmol]\",\n", - " )\n", - "\n", - " inputs = [self.pressure, self.temperature]\n", - " outputs = [self.enth_mol, self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", - " )\n", - " self.surrogate_enth = SurrogateBlock()\n", - " self.surrogate_enth.build_model(\n", - " self.keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.flow_mol\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.flow_mol * self.enth_mol\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_mol\": self.flow_mol,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def model_check(blk):\n", - " \"\"\"\n", - " Model checks for property block\n", - " \"\"\"\n", - " # Check temperature bounds\n", - " if value(blk.temperature) < blk.temperature.lb:\n", - " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", - " if value(blk.temperature) > blk.temperature.ub:\n", - " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", - "\n", - " # Check pressure bounds\n", - " if value(blk.pressure) < blk.pressure.lb:\n", - " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", - " if value(blk.pressure) > blk.pressure.ub:\n", - " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Define Initialization Routine\n", - "\n", - "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", - "\n", - "Any initialization routine can be written by following a 3 step process:\n", - "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", - "\n", - "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", - "\n", - "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", - "\n", - "\n", - "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would inturn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _StateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def initialize(\n", - " blk,\n", - " state_args=None,\n", - " hold_state=False,\n", - " outlvl=1,\n", - " state_vars_fixed=False,\n", - " solver=\"ipopt\",\n", - " optarg={\"tol\": 1e-8},\n", - " ):\n", - " \"\"\"\n", - " Initialisation routine for property package.\n", - "\n", - " Keyword Arguments:\n", - " flow_mol : value at which to initialize component flows\n", - " (default=None)\n", - " pressure : value at which to initialize pressure (default=None)\n", - " temperature : value at which to initialize temperature\n", - " (default=None)\n", - " outlvl : sets output level of initialisation routine\n", - "\n", - " * 0 = no output (default)\n", - " * 1 = return solver state for each step in routine\n", - " * 2 = include solver output information (tee=True)\n", - " state_vars_fixed: Flag to denote if state vars have already been\n", - " fixed.\n", - " - True - states have already been fixed by the\n", - " control volume 1D. Control volume 0D\n", - " does not fix the state vars, so will\n", - " be False if this state block is used\n", - " with 0D blocks.\n", - " - False - states have not been fixed. The state\n", - " block will deal with fixing/unfixing.\n", - " optarg : solver options dictionary object (default=None)\n", - " solver : str indicating which solver to use during\n", - " initialization (default = 'ipopt')\n", - " hold_state : flag indicating whether the initialization routine\n", - " should unfix any state variables fixed during\n", - " initialization (default=False).\n", - " - True - states variables are not unfixed, and\n", - " a dict of returned containing flags for\n", - " which states were fixed during\n", - " initialization.\n", - " - False - state variables are unfixed after\n", - " initialization by calling the\n", - " release_state method\n", - "\n", - " Returns:\n", - " If hold_states is True, returns a dict containing flags for\n", - " which states were fixed during initialization.\n", - " \"\"\"\n", - " if state_vars_fixed is False:\n", - " # Fix state variables if not already fixed\n", - " Fcflag = {}\n", - " Pflag = {}\n", - " Tflag = {}\n", - "\n", - " for k in blk.keys():\n", - " if blk[k].flow_mol.fixed is True:\n", - " Fcflag[k] = True\n", - " else:\n", - " Fcflag[k] = False\n", - " if state_args is None:\n", - " blk[k].flow_mol.fix()\n", - " else:\n", - " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", - "\n", - " if blk[k].pressure.fixed is True:\n", - " Pflag[k] = True\n", - " else:\n", - " Pflag[k] = False\n", - " if state_args is None:\n", - " blk[k].pressure.fix()\n", - " else:\n", - " blk[k].pressure.fix(state_args[\"pressure\"])\n", - "\n", - " if blk[k].temperature.fixed is True:\n", - " Tflag[k] = True\n", - " else:\n", - " Tflag[k] = False\n", - " if state_args is None:\n", - " blk[k].temperature.fix()\n", - " else:\n", - " blk[k].temperature.fix(state_args[\"temperature\"])\n", - "\n", - " # If input block, return flags, else release state\n", - " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", - "\n", - " else:\n", - " # Check when the state vars are fixed already result in dof 0\n", - " for k in blk.keys():\n", - " if degrees_of_freedom(blk[k]) != 0:\n", - " raise Exception(\n", - " \"State vars fixed but degrees of freedom \"\n", - " \"for state block is not zero during \"\n", - " \"initialization.\"\n", - " )\n", - "\n", - " if state_vars_fixed is False:\n", - " if hold_state is True:\n", - " return flags\n", - " else:\n", - " blk.release_state(flags)\n", - "\n", - " def release_state(blk, flags, outlvl=0):\n", - " \"\"\"\n", - " Method to release state variables fixed during initialisation.\n", - "\n", - " Keyword Arguments:\n", - " flags : dict containing information of which state variables\n", - " were fixed during initialization, and should now be\n", - " unfixed. This dict is returned by initialize if\n", - " hold_state=True.\n", - " outlvl : sets output level of of logging\n", - " \"\"\"\n", - " if flags is None:\n", - " return\n", - "\n", - " # Unfix state variables\n", - " for k in blk.keys():\n", - " if flags[\"Fcflag\"][k] is False:\n", - " blk[k].flow_mol.unfix()\n", - " if flags[\"Pflag\"][k] is False:\n", - " blk[k].pressure.unfix()\n", - " if flags[\"Tflag\"][k] is False:\n", - " blk[k].temperature.unfix()\n", - "\n", - " if outlvl > 0:\n", - " if outlvl > 0:\n", - " _log.info(\"{} State Released.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages_test.ipynb). " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "idaes-pse", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb index 24237ee2..0242e561 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb @@ -1,509 +1,534 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################################################################\n", - "# Institute for the Design of Advanced Energy Systems Process Systems\n", - "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", - "# software owners: The Regents of the University of California, through\n", - "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", - "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", - "# University Research Corporation, et al. All rights reserved.\n", - "#\n", - "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", - "# license information, respectively. Both files are also available online\n", - "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", - "##############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Constraint,\n", + " Param,\n", + " Reals,\n", + " Set,\n", + " value,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + ")\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component,\n", + ")\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(\n", + " {\n", + " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", + " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", + " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", + " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", + " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", + " }\n", + " )\n", + "\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.s,\n", + " \"length\": units.m,\n", + " \"mass\": units.kg,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_usr.ipynb file) using the keras Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " self.flow_mol = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol / units.s,\n", + " doc=\"Total molar flowrate [kmol/s]\",\n", + " )\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc=\"State pressure [MPa]\",\n", + " )\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760 + 273.15),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + " self.entr_mol = Var(\n", + " domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ / units.kmol / units.K,\n", + " doc=\"Entropy [kJ/kmol/K]\",\n", + " )\n", + " self.enth_mol = Var(\n", + " domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ / units.kmol,\n", + " doc=\"Enthalpy [kJ/ kmol]\",\n", + " )\n", + "\n", + " inputs = [self.pressure, self.temperature]\n", + " outputs = [self.enth_mol, self.entr_mol]\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\n", + " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", + " )\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol * self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would inturn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def initialize(\n", + " blk,\n", + " state_args=None,\n", + " hold_state=False,\n", + " outlvl=1,\n", + " state_vars_fixed=False,\n", + " solver=\"ipopt\",\n", + " optarg={\"tol\": 1e-8},\n", + " ):\n", + " \"\"\"\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output information (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating which solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states variables are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " release_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " \"\"\"\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\n", + " \"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\"\n", + " )\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " \"\"\"\n", + " Method to release state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " \"\"\"\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags[\"Fcflag\"][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags[\"Pflag\"][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags[\"Tflag\"][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info(\"{} State Released.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages_usr.ipynb). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "## 1. Integration of Surrogate into Custom Property Package\n", - "\n", - "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", - "\n", - "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", - "\n", - "### 1.1 Steps in Creating a Property Package\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **phases and components** of interest.\n", - "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", - "5. Declaring the **state variables** to be used for the property package.\n", - "6. Creating **variables and constraints** to describe the properties of interest.\n", - "7. Creating an **initialization routine** for the property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Importing libraries for making Property Package\n", - "\n", - "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Constraint,\n", - " Param,\n", - " Reals,\n", - " Set,\n", - " value,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - ")\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " LiquidPhase,\n", - " Component,\n", - ")\n", - "from idaes.core.util.initialization import solve_indexed_blocks\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.misc import extract_data\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", - "\n", - "from pyomo.util.model_size import build_model_size_report\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Defining Classes\n", - "\n", - "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", - "\n", - "## 3.1 Physical Parameter Block\n", - "\n", - "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What properties are supported and how they are implemented\n", - "* What components and phases are included in the packages\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To assemble the above mentioned things in a class we need to follow the following steps:\n", - "\n", - "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", - "* Declaring any necessary configuration arguments\n", - "* Writing the build method for our class\n", - "* Creating a define_metadata method for the class.\n", - "\n", - "The code below follows the above mentioned steps. \n", - "\n", - "*NOTE*: The SCO2StateBlock will be discussed in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2ParameterBlock\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " supercritical CO2.\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(PhysicalParameterData, self).build()\n", - "\n", - " self._state_block_class = SCO2StateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Liq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.CO2 = Component()\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(\n", - " {\n", - " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", - " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", - " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", - " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", - " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", - " }\n", - " )\n", - "\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.s,\n", - " \"length\": units.m,\n", - " \"mass\": units.kg,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 State Block\n", - "\n", - "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", - "\n", - "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", - "\n", - "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", - "\n", - "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_usr.ipynb file) using the keras Surrogate API of IDAES package\n", - "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", - "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", - "class SCO2StateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super(SCO2StateBlockData, self).build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " self.flow_mol = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1.0,\n", - " units=units.kmol / units.s,\n", - " doc=\"Total molar flowrate [kmol/s]\",\n", - " )\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=8,\n", - " bounds=(7.38, 40),\n", - " units=units.MPa,\n", - " doc=\"State pressure [MPa]\",\n", - " )\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=350,\n", - " bounds=(304.2, 760 + 273.15),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - " self.entr_mol = Var(\n", - " domain=Reals,\n", - " initialize=10,\n", - " units=units.kJ / units.kmol / units.K,\n", - " doc=\"Entropy [kJ/kmol/K]\",\n", - " )\n", - " self.enth_mol = Var(\n", - " domain=Reals,\n", - " initialize=1,\n", - " units=units.kJ / units.kmol,\n", - " doc=\"Enthalpy [kJ/ kmol]\",\n", - " )\n", - "\n", - " inputs = [self.pressure, self.temperature]\n", - " outputs = [self.enth_mol, self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\n", - " keras_folder_name=\"sco2_keras_surr\", keras_model_name=\"sco2_keras_model\"\n", - " )\n", - " self.surrogate_enth = SurrogateBlock()\n", - " self.surrogate_enth.build_model(\n", - " self.keras_surrogate,\n", - " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.flow_mol\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.flow_mol * self.enth_mol\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_mol\": self.flow_mol,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def model_check(blk):\n", - " \"\"\"\n", - " Model checks for property block\n", - " \"\"\"\n", - " # Check temperature bounds\n", - " if value(blk.temperature) < blk.temperature.lb:\n", - " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", - " if value(blk.temperature) > blk.temperature.ub:\n", - " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", - "\n", - " # Check pressure bounds\n", - " if value(blk.pressure) < blk.pressure.lb:\n", - " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", - " if value(blk.pressure) > blk.pressure.ub:\n", - " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Define Initialization Routine\n", - "\n", - "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", - "\n", - "Any initialization routine can be written by following a 3 step process:\n", - "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", - "\n", - "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", - "\n", - "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", - "\n", - "\n", - "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would inturn fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _StateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def initialize(\n", - " blk,\n", - " state_args=None,\n", - " hold_state=False,\n", - " outlvl=1,\n", - " state_vars_fixed=False,\n", - " solver=\"ipopt\",\n", - " optarg={\"tol\": 1e-8},\n", - " ):\n", - " \"\"\"\n", - " Initialisation routine for property package.\n", - "\n", - " Keyword Arguments:\n", - " flow_mol : value at which to initialize component flows\n", - " (default=None)\n", - " pressure : value at which to initialize pressure (default=None)\n", - " temperature : value at which to initialize temperature\n", - " (default=None)\n", - " outlvl : sets output level of initialisation routine\n", - "\n", - " * 0 = no output (default)\n", - " * 1 = return solver state for each step in routine\n", - " * 2 = include solver output information (tee=True)\n", - " state_vars_fixed: Flag to denote if state vars have already been\n", - " fixed.\n", - " - True - states have already been fixed by the\n", - " control volume 1D. Control volume 0D\n", - " does not fix the state vars, so will\n", - " be False if this state block is used\n", - " with 0D blocks.\n", - " - False - states have not been fixed. The state\n", - " block will deal with fixing/unfixing.\n", - " optarg : solver options dictionary object (default=None)\n", - " solver : str indicating which solver to use during\n", - " initialization (default = 'ipopt')\n", - " hold_state : flag indicating whether the initialization routine\n", - " should unfix any state variables fixed during\n", - " initialization (default=False).\n", - " - True - states variables are not unfixed, and\n", - " a dict of returned containing flags for\n", - " which states were fixed during\n", - " initialization.\n", - " - False - state variables are unfixed after\n", - " initialization by calling the\n", - " release_state method\n", - "\n", - " Returns:\n", - " If hold_states is True, returns a dict containing flags for\n", - " which states were fixed during initialization.\n", - " \"\"\"\n", - " if state_vars_fixed is False:\n", - " # Fix state variables if not already fixed\n", - " Fcflag = {}\n", - " Pflag = {}\n", - " Tflag = {}\n", - "\n", - " for k in blk.keys():\n", - " if blk[k].flow_mol.fixed is True:\n", - " Fcflag[k] = True\n", - " else:\n", - " Fcflag[k] = False\n", - " if state_args is None:\n", - " blk[k].flow_mol.fix()\n", - " else:\n", - " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", - "\n", - " if blk[k].pressure.fixed is True:\n", - " Pflag[k] = True\n", - " else:\n", - " Pflag[k] = False\n", - " if state_args is None:\n", - " blk[k].pressure.fix()\n", - " else:\n", - " blk[k].pressure.fix(state_args[\"pressure\"])\n", - "\n", - " if blk[k].temperature.fixed is True:\n", - " Tflag[k] = True\n", - " else:\n", - " Tflag[k] = False\n", - " if state_args is None:\n", - " blk[k].temperature.fix()\n", - " else:\n", - " blk[k].temperature.fix(state_args[\"temperature\"])\n", - "\n", - " # If input block, return flags, else release state\n", - " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", - "\n", - " else:\n", - " # Check when the state vars are fixed already result in dof 0\n", - " for k in blk.keys():\n", - " if degrees_of_freedom(blk[k]) != 0:\n", - " raise Exception(\n", - " \"State vars fixed but degrees of freedom \"\n", - " \"for state block is not zero during \"\n", - " \"initialization.\"\n", - " )\n", - "\n", - " if state_vars_fixed is False:\n", - " if hold_state is True:\n", - " return flags\n", - " else:\n", - " blk.release_state(flags)\n", - "\n", - " def release_state(blk, flags, outlvl=0):\n", - " \"\"\"\n", - " Method to release state variables fixed during initialisation.\n", - "\n", - " Keyword Arguments:\n", - " flags : dict containing information of which state variables\n", - " were fixed during initialization, and should now be\n", - " unfixed. This dict is returned by initialize if\n", - " hold_state=True.\n", - " outlvl : sets output level of of logging\n", - " \"\"\"\n", - " if flags is None:\n", - " return\n", - "\n", - " # Unfix state variables\n", - " for k in blk.keys():\n", - " if flags[\"Fcflag\"][k] is False:\n", - " blk[k].flow_mol.unfix()\n", - " if flags[\"Pflag\"][k] is False:\n", - " blk[k].pressure.unfix()\n", - " if flags[\"Tflag\"][k] is False:\n", - " blk[k].temperature.unfix()\n", - "\n", - " if outlvl > 0:\n", - " if outlvl > 0:\n", - " _log.info(\"{} State Released.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages_usr.ipynb). " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "idaes-pse", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb index f4b67424..50789c92 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "feadf612", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1471,8 +1498,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py index df312dc4..5936a332 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py @@ -1,15 +1,16 @@ -############################################################################### +################################################################################# # The Institute for the Design of Advanced Energy Systems Integrated Platform # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. -############################################################################### +# +################################################################################# """ Maintainer: Javal Vyas diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb index 82ab6d1d..79a33904 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb @@ -1,5 +1,31 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -26,12 +52,14 @@ "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", + "\n", "Maintainer: Javal Vyas\n", "\n", "Author: Javal Vyas\n", "\n", "Updated: 2024-01-24\n", "\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -1469,9 +1497,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb index 82ab6d1d..d957e340 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb @@ -1,1477 +1,1504 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Importing libraries\n", - "\n", - "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " Block,\n", - " Var,\n", - " Param,\n", - " Constraint,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " TerminationCondition,\n", - " value,\n", - " Expression,\n", - " minimize,\n", - " units,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", - "from idaes.models.unit_models import (\n", - " Mixer,\n", - " MomentumMixingType,\n", - " PressureChanger,\n", - " Heater,\n", - " Separator,\n", - " HeatExchanger,\n", - ")\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from properties import SCO2ParameterBlock\n", - "\n", - "import idaes.logger as idaeslog\n", - "\n", - "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Constructing the flowsheet\n", - "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", - "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", + "\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", - "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 452\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 118\n", - "\n", - "Total number of variables............................: 178\n", - " variables with only lower bounds: 32\n", - " variables with lower and upper bounds: 59\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 178\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", - " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\n", - "====================================================================================\n", - "Unit : fs.boiler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 685.15 893.15\n", - " pressure pascal 3.4510e+07 3.4300e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.turbine Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", - " Pressure Change : -24.979 : pascal : False : (None, None)\n", - " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 729.38\n", - " pressure pascal 3.4300e+07 9.3207e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 729.38 489.15\n", - " pressure pascal 9.3207e+06 9.2507e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 535.47 736.02\n", - " pressure pascal 3.4560e+07 3.4490e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 378.99 566.32\n", - " pressure pascal 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_1 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825.\n", - " temperature kelvin 354.15 354.15 354.15\n", - " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.co2_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807e+06 9.1107e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.main_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 378.99\n", - " pressure pascal 9.1107e+06 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.bypass_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 460.04\n", - " pressure pascal 9.1807e+06 3.4886e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_2 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR \n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 378.99 378.99 378.99\n", - " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.FG_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 31903. : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 378.99 483.15\n", - " pressure pascal 3.4620e+07 3.4560e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.mixer Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 566.32 460.04 535.47\n", - " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", - "====================================================================================\n", - "667.9424945058901 kW\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " TerminationCondition,\n", + " value,\n", + " Expression,\n", + " minimize,\n", + " units,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (\n", + " Mixer,\n", + " MomentumMixingType,\n", + " PressureChanger,\n", + " Heater,\n", + " Separator,\n", + " HeatExchanger,\n", + ")\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from properties import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 729.38\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 729.38 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 535.47 736.02\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 31903. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "667.9424945058901 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory(\"ipopt\")\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.turbine = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.splitter_1 = Separator(\n", + " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", + " )\n", + "\n", + " m.fs.co2_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.main_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.splitter_2 = Separator(\n", + " property_package=m.fs.properties,\n", + " ideal_separation=False,\n", + " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", + " )\n", + "\n", + " m.fs.FG_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.mixer = Mixer(\n", + " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", + " )\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(\n", + " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", + " )\n", + " m.fs.s04 = Arc(\n", + " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", + " )\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(\n", + " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", + " )\n", + " m.fs.s07 = Arc(\n", + " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", + " )\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(\n", + " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", + " )\n", + " m.fs.s10 = Arc(\n", + " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", + " )\n", + " m.fs.s11 = Arc(\n", + " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", + " )\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline\n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1 / 3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(\n", + " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(\n", + " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import (\n", + " convert_quantity_to_reporting_units,\n", + " report_quantity,\n", + " )\n", + "\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(\n", + " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", + " units.kW,\n", + " )\n", + " return m\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "def main():\n", - " # Setup solver and options\n", - " solver = SolverFactory(\"ipopt\")\n", - " outlvl = 0\n", - " tee = True\n", - "\n", - " # Set up concrete model\n", - " m = ConcreteModel()\n", - "\n", - " # Create a flowsheet block\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Create the properties param block\n", - " m.fs.properties = SCO2ParameterBlock()\n", - "\n", - " # Add unit models to the flowsheet\n", - " m.fs.boiler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.turbine = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=False,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.splitter_1 = Separator(\n", - " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", - " )\n", - "\n", - " m.fs.co2_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.main_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.bypass_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.splitter_2 = Separator(\n", - " property_package=m.fs.properties,\n", - " ideal_separation=False,\n", - " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", - " )\n", - "\n", - " m.fs.FG_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.mixer = Mixer(\n", - " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", - " )\n", - "\n", - " # # Connect the flowsheet\n", - " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", - " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", - " m.fs.s03 = Arc(\n", - " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", - " )\n", - " m.fs.s04 = Arc(\n", - " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", - " )\n", - " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", - " m.fs.s06 = Arc(\n", - " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", - " )\n", - " m.fs.s07 = Arc(\n", - " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", - " )\n", - " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", - " m.fs.s09 = Arc(\n", - " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", - " )\n", - " m.fs.s10 = Arc(\n", - " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", - " )\n", - " m.fs.s11 = Arc(\n", - " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", - " )\n", - " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", - " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", - " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", - " # NETL Baseline\n", - " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", - " m.fs.boiler.inlet.temperature.fix(685.15)\n", - " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", - " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", - " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s01)\n", - "\n", - " m.fs.turbine.ratioP.fix(1 / 3.68)\n", - " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", - " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s02)\n", - " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", - " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s03)\n", - "\n", - " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", - " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", - " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s04)\n", - " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", - "\n", - " m.fs.splitter_1.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s05)\n", - " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", - " m.fs.co2_cooler.deltaP.fix(-0.07)\n", - " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s06)\n", - " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.bypass_compressor.ratioP.fix(3.8)\n", - " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s07)\n", - " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.main_compressor.ratioP.fix(3.8)\n", - " m.fs.main_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s09)\n", - "\n", - " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", - " m.fs.splitter_2.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s10)\n", - " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", - " m.fs.FG_cooler.deltaP.fix(-0.06)\n", - " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s11)\n", - "\n", - " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " # Add constraint heats of the LTR_pseudo shell and tube\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c1 = Constraint(\n", - " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " propagate_state(m.fs.s08)\n", - " propagate_state(m.fs.s12)\n", - " propagate_state(m.fs.s13)\n", - "\n", - " m.fs.mixer.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s14)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", - " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c2 = Constraint(\n", - " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", - "\n", - " print(\"--------------------------------------------------------------------\")\n", - " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", - " print(\"--------------------------------------------------------------------\")\n", - "\n", - " solver.solve(m, tee=tee)\n", - "\n", - " #\n", - " from idaes.core.util.units_of_measurement import (\n", - " convert_quantity_to_reporting_units,\n", - " report_quantity,\n", - " )\n", - "\n", - " # Print reports\n", - " for i in m.fs.component_objects(Block):\n", - " if isinstance(i, UnitModelBlockData):\n", - " i.report()\n", - "\n", - " # Converting units for readability\n", - " print(\n", - " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", - " units.kW,\n", - " )\n", - " return m\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " m = main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb index 82ab6d1d..d957e340 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb @@ -1,1477 +1,1504 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", - "\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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0GTVKkgrXAfjoeT0qP87rKyeC1WqOYNW7CFZRXrb/8qFkvzJEGv9ltMQ2Psi2Vj0EqwSroYh5e55ZZqd2IlitAASrAMJBsAoUT8/r51+2y95DZdT55Zqc11dSDAUAANXIzhVzZOOEyyThpBuqdKgKAAAAAEB5I1gFgGpid16WbHzpYok/bojUbt3NtgIAAAAAgHAQrMKzVv4ZnUtYc3cVmJvXROvvA4KloWrddn2kXsd+tgUAAAAAAISLYBVRp+OCRsPLWdE5zso/RRZss3ci9N+N0Rm3Zv62gsJ/J4JVVBwdU7VG7foS1z3VtgAAAAAAgEgQrCKqtDJ0UnZ0AkMNaKNRaTp/224TZEbDK1nROY6+Ji9W0aJqyn0/TfLXL5ZGx11hWwAAAAAAQKQIVhFVWh0arcBw864Yc7xILdhe+Lp2Rv6a9O/SgDYaf5/+bfq6gPK2ZfpLsnXWm5KQco1tAQAAAAAA0UCwiqjSSsxoXHbvAswf8iIPMTWcjUZA644RjWP9sCU6fxtQmh3zP5dN794sCccPkxr1GtlWVEVbN22Sb955W54ZNlTuPOVk+bZtkrnpurZ98847Zh8AAAAAQPQQrCKqNHTUQDTSiZnc46NRHaphbzRCTD2Oitax9G+Lxt8HFGfn6vmy4aVLJOGkG6TWgYfYVlRF37/3P7nz1JNl4u23yezPPpU1S3+XJbE1zU3XtW3i7beafXRfAAAAAEB0EKwiqtzl7ZFWdbrHR3q5vAa00Qp79/xtEQ4r4HstvvVI/52A4uzevlmyX7pY4o++UOq06WFbUdVoBepLt9wsL9x0o+Ru2GBbS6b76L76GKpXAQAAACByBKuIKlfVGelkUXqpvFlGWB3qH1xGGmJGKwz1fzzDAaA8ZL94kdQ+6Aip1/kE24KqRoPRu888Xb6d9F/bItKyQwf52213yIgXXpSnf8owN13/2223m22OPkYfS7gKAAAAr9u2s0C+zMy39wDvIVhF1PhXYrpgNFwuoI30cnn/gDfSsDdaQwH4Pz6Svw0oTvZr10pMjEjDngNtC6qiNx64X3LWrrX3RE4cNFju+fAjOWnwYOl6XD+pGxdnbrp+0uDLfdsK93H0sXoMAAAAwMt+WLlL0n/9UzZu5dwZ3kSwiqjxn7QqkvDRP6BVkUyGtWB70euIJOx1Qwoo3+sL/1j+rynSoQ5QdW3PzLRrwcv96AHZtfJnaZRyjW1BZZOfk1Pmf3sdJ9W/UvUv198g599+h71Xsr8V7qP7OnoMxlwFAACAV2m16leZO836J4t9S8BrCFYRNf4VoZFUYgZeah9JpekKvwDUVZyGI/A1Bd4PRe4uu1KIoQBQkhlt28qSkSODDli3/vC6bJ0+QeJTrrYtkcvZvMWuRc+L73wig257XM68ZpTc/eQrsnlLBL+cVEEarJb2314v33/rwQfsPTFVqGcNv87eK5vuq9Wtjh6LIQEAAAAqv8Ubdslj31Styh0dAsBVqv6womi9KliZu1te/zmCYAGeQbCKqPGvxFThBqKBAWiklabO5l0xdi10gX9LJGGv/9+nATTDAaAkK8aODSpg/XPR15L92tXSqP9QqRnX1LaG78uZv0iPc6+Tpkf/VWK7nCHnXn+fZK4suuw8XJfc/KhcPepJ+fd7U+Tjr2fJA8+9KcmpQ2XB0hV2Dzgl/bf/6bPP9kxUZcZUDaJSNZBWt7oxV/VYP332qVkHAABA5XR4w+3yzIwdJqz7eFHVqOz0VavuPbZq+vyqE0Tq36Zh8e2fbZNf1vpVX6HSIVhF1PhXYqpwqzHd5fGtavuW4QaPGn66ytL4mjHmOOFewh8YGocb9voPKeD+vkiGOkD1UFrAmr9+iWx46RJJOHmk1G5+qG0N3/+++E5OGHSrzFnwu23xtfU8N/iqyOJoperrH04z6yf3PVLGp10nyZ3bybJV6+Smh/9l2rGvwP/2c6ZOsVtEjj0v/HF0jz3vr3ZNCo851a4BAACgMsovKCoi0sCuKlR2arWqhqv+NICsCn+bVhdrqKr0b9wYQeEW9j+CVUSNq8Q8sZHv/1bhBqIueLysaU2zDDd4dCFq53oxe0LMzzeF95rckALnNPb9beEOK+D+Nn1Nneu5Y/Ehuj/NGzBAvoyJ8dytOIEhW0H+Dql1YHtp+dAyiTvzHlNdGulNq1PVmSlHSdb3b8niT1+Sbp0OMcMC3PPMa2ZbOL6a9YtZaqg6+fl75cqBp8r4UcNNm1avFvdaIrlpGLz46a3m3zJnmi/Q1X83vb9wsO9SeP03dP/eegm+mnP88eZ+ZlqauZ+Vnm7u67+7o+vaptuU7qv39bFKj6X39eaCcH1Ova+vQelrcvs4P3bvvtfz+HP/7eM//FDidvq+hLVo184sw9Gi3SF2TWT14sV2DQAAAJXRoi32hLeQBnWVfTzS4qpVnapQtfrJoqK/rXH9GDmqlS/7QOVEsIqo8K/E7FTXtwx3YiYXWjas6Qscw6009Q8xj4rzhRfhh72+x13a1PeWCXdYAReitqods+ffKZKhDhA5F45VJnkZGSa8y37pYtsSfX897ThJaNhAklo1k7uHXmja5ixcapbh2JCz2SzPObmvWaojD2tv1xCM2IQEqVn4373Wbt/lAe2P7GGW4fB/bM66yId5AAAAwP6jFaundKhl71X+yk7/atV6tfb923TIg8pKq1UXbyy63PeU9rXM34jKK2b5qnUFyzMXS58+fWwTqpP4+HiZNWuWvVe6Q+f4fvWa2TXWXFrv7/NNu2VY5i4TYg5vVsOs6z66byg0wOw/3/frzZTOsXLJknwTkL7aLnZPOBosfQ36uvT1qKfX7jbVtM8khfZrkIahqb/5XtPCbrWk19x8E9Dq69OANBS3Ld8l/924Wy47oIYc1SBmz79Zesd9/53c69fnLE3Pnj0lNzfX3kOoXMVgvwJvffHwr2R0ElJSpN2YMRKXnGzub3juPKlRq47EH32RuR8prUrVcVWVVqpqqKq0UvWecf+Rv5zQR9598k7TFiqdqMqMqdq5nTxz11Dp2bWjnHHN3fL5t7OlxQGNZfm0V+2e0bNm/EBp9VT0J+AqT1rhGli1qoGq/rdvM2qU3Dr4Mtm+xfc3jfv5F6ldt55ZD9X2vDwZfqTv/0d1GzSQp2fPMevVWczb88wyO7WTJNSiaqC8Dfh2uaSvzJWCgV1sCwCUTa8SWTZ6tOkTk+zVJQB85/U/XZgvj03fvidQPap1rFxwRFEla2Whgeq907bvCVY1VD218HbvtG17/rb2jWvKsKPrmPXK5pnvd+wJVlvF15CbjvFVXHV+uSbn9ZUUFauIipKqQ0OtEPU/joaWbliBcC6Xd5Wveiy9qXDGffUfUkC5YQVm5IX+K5n7OzRUdf9O7m8GSqKhWo/Zs6Xb1Kl7QlXVeMhrsnPd77J17ke2JTJaoarhqdIhAZ545X8mVH3y1f+ZtuN6Hm6W4fjHkIHSpuWBkjF/ifS98Eapl3y2CVXVfSMuM0vsTQPVpqmp5r97l0mTzH/7hAN9YbdaNHOmXQvd4p9+tGuF/939jgkAAIDKSasee7UqKtiprLPoB1ar9kvy/U2pnYtCYg0mK2PVqlbb7lWt6leJi8qLYBVR4S5nb1jTN1FUuBMz+V8qrxra/4eGc7l8eYS9KpJA1P9YrupXX084Qx2g6ispUHViYutI4yH/lryM92X7km9ta2RevH+kqVTVyatufPh5U6mqlayXpZ4oN1z6F7tX6Bo2qCcfPn+vnHpsT3N/9+4CU6mqz6fHRpHiAlWnRfui4RNWLV5i10K3eknRY/2PCQAAgMpLQ0gds9OpbGOtahDsP7bqcYV/j7tM/vBmNU2Fp/P6z5WvQumrpUV/m1bd6t+Eyo9gFVHhqkO1ElO5YDTUSlM3+74LZsOtNHXP6wt5fSGmCzJDDURdqNvS/pjkwt5Qx5DV16QhqntNyv19of47oeorLVD1F3tAO2ky+FXJ+ewJ+XPNQtsaPq1a1WEAdFzVfr0ONxWsj99ylQlAI9WpbWv54NnRsmnWu7Lkswnm8n9C1b1pqFpcoOp0sxNkqa/fecuuhe7rd962a3rM/nYNAAAAlZkZj7R9URVkZata/SpzZ7HVqo5/hadWrGoFaGWxb7XqvsMBonIiWEVUBFZ1uuVK+6EYrFz7OeMCWv9K01C4oLeT3/CDbj30kLZoSAH/ZajHcVWpLjRW7u8jWEWgsgJVf7U7HCuJF46TTVOekV15WbY1MncPu0i+mPiQGVM1kkrV4jSoV9cMC4B9abBa2n/7I086WeKbNDHrqxYtkjceuN+sh+LNwsfoY5Ue68iTTjLrVZWOORbMzTnooIOK3c4turcPPvjA/HsXt62kGwAAKJuOrepftVpZZtHXAPiHlUXBo3+1qhNYtVpZ/jblX62qf0f7JlSrVhVMXlXN6YlKpJNXaSiokztpm5usyk1mFepkUf3n7zQhrf/EUG6yKJ3gyYWaZXlqzS4zWZVOEnV7S9/zP7Bql7y83jeZ1XXNg3tN+rz6/Mq9Jv+24ibyKok+t76GcxrXkAcP8j2/e53F/TsxeVXF8OrkVeHY/NH9sv3nD6TxWXfZluqtMk5eFYzv3/ufvHDTjfaeyF+uv0HOGn6dvVe6959+Sv735BP2nsgVjz0uR58d3eDca4Lt50qboBHRF2wf59DXAVBMXgUUT7/vzL+sKJTUSlX/S+XvSqm3V9jqRfp69XUrfa039a27T7CqtFL1sW+KLh+9vEcdz19Sr9WqL/24w94r/B7Uu84+wSqTV1VeVKwiYq6i078SM5yqTg1oXeWrC1WVO24ox3KX6XfyTbBnuCrYUC7hd6/H//J9XXcn3aEMK+CGFOhUt+hvC7f6FShOw9PukNjW3WTTtPG2BVWRBqH/N+Ace09MUKpVqGXR6lb/UFWPUdVDVQAAgOqoslWtarWq/2X9OglXcaGq0opV/yC1MlStfrKoaKxbqlWrHoJVRGzzLt8Hnn81qU5ipbS6M9iJmQJn33fCGQ7Ahb3+VUcuGA0t7N13SAEVzrACxQXQkUyqBRQn8aJnRYtvN89807agKjr/9jskoVnRbP6fTZwgd59xmnw24SWZ+9WXsj0vz9x0/bMJvm2fF+7j6GP1GAAAAKia/Mda1dDSy7Po6yRbbmxVDYQDx1YN5D/WqhlCwFa6epG+Nv9/ex3iAFULwSoi5ioxezUwC8NX4elbD7aq0+0XGKy2sr9UBVtp6gtzfev+x3KvJ5QQs6TX5O4He5ySXlO41a9AaRKH/Ed2rPhFts771LagqqnfqJHc88HkvSpXddzUNx98QMZeMUSGH5lsbrr+5oNFY6oqfYw+Vo8BAACAqmmfqtVfi6omvWTxhl17BaPHJdUqsVrV2bdq1Zt/m4bFX2UW/W1Uq1ZNBKuIWHHVoap3nO//XsFOzBQ4+74T6lAAC7b5lvo4/yEF/MNet09ZihtSQIU6rIALTX2vYe9/p3An1QJKUqNuQ2l8+Wuy+Yc3ZUfmTNuKqkaD0csffsSMk+omtHIuWbTE3PzpPrqvPoZQFQAAoOpL7WxPgAvpjPRerFr1Dx41CD6qVXDBo//fpgGmF6tWdTIuqlWrPoJVRKSkSkzlAlIXmJbFBbSBxwn1cnkX5AYGmKpzvdDC3pJC41CHFShpSAEVavUrEIxaLTpLk8tfkezPn5Sd6363raiKdJzU+z7+VAY98JB0P+lkad72ELtFzLq2DXrgQbMPY6oCAABUH1oh6T+Lvv+EVl6g1ar+Y6vq8AVlVas6JoRtXRRUeq1q1Vetytiq1QHBahWWl5Fh18qPC1UDq0OVCwxdqFiWkgJaDTVdsBlMpemC7b6AMvA4ak/1axBhb2mhsTtOsGGvq2wt7jWFM6kWEIw6nU+ShHMelk1Tn5Hd2zbZVlRFWoF6zHnnybBnxsl9nxQNAaHr2nbMeQOpUgUAAKiG/Mcj1epJ/yBzf/tkUVGVqQbA/kFpMPzHkfVa1eqXmflm/FelYbH/fwdULQSrVdjCwYNlzvHHS1Z6um2JPlexWVx1qKs0dZNblcZVkBZ3qbxylZ7BVJrm2n7CBZb+XFswE2qVNKSA8r1O33owYa8LaAOHFFAuNGYoAJSHBsdcIfV6DpScqeNtCwAAAIDqIrBq1Suz6GvAq8MTOOFcJu/VqtXixlb1/2+AqoX/slVczrRpMm/AgHILWEurDnWVpr7Kz9JDw9IulVfu+CsLP6DKUtKQAsqFvS7oLI0Lcd3wAYFc2BpM2FvSkALK/c3BVr8CoYo/6x6pdWB7yf3qX7YFAAAAQHVxwRG2KqiQVlF6oWr1q6VFwWP7xjVDrlZ1UjvvXbWqlaL7m74GfS3KVKv6Vdai6iFYrSbKK2B11aHFVWIqV9X5+abSA8OSLrl3iipNzaJEGuC6fRoWM3yJCzaDCXtdaOz+hkDutZY1rID/ayopgA6l+hUIR8KlL8muHVtl84/v2hYAAAAA1cG+s+gHUWlUjgKrVU/pEF6oqjS49A9lP1m0f6tWA6tVdTIuraxF1UWw6lEagn4ZEyOZaWnmvoahen96YqK5r2a0bWvaXFC6YuxYc1/D05JEO2B1lZitaxf/fyX/iadKU9Ls+86eELOMy+WLqkyLxmUN5I5VVqVpaUMKqGCHFfAPVYsb5kCFUv0a6P3Nm81/93Bu+v+h7ZmZ9kio6hoPeU22/z5Dti2YYlsA7C9V/QoFrsAAAMBb/Mf41KrV/TkeaWC1aqSTOgVWrX68H8PVwGrV45KoVq3qCFYRNv9KzJIu4W9o/x9W1sRMpV0qr1z1aVmVpqVVhjou7C0rxCxtSAEV7LAC7nlKClWVe45gJtWKJg1VCVarjxpxTaXJkNckd/rL8ufy8p/cDqiKyvoxLViTNkbnONrHhPOjXHE+3+Tr9yKl/0ZcgQEAgLfsW7W6f8LHaFarOoFVq1ox6iaOqkiB1ao6bizVqlUfwarHaBXpkpEjpdUNN0i/ggJJshWrTVNTzf2+2dnmvuq9dKlp022q9YgR5n63qVPN/eIkpKRIl0mTzD7uceHyDzHLqg4trdLUF5b61ksKMf0vly8tyHTBZHHDADjBhL3+oXFJx3J/c1lhb1lDCihX/RpOhc9ZDRua/+6h3vT/C6h+arXuJo0HTZCcz5+U/I3LbSuAYM3Ii0746PqGSGn/E63JD7/IjdZrKvvHSwAAEJx1OdvNLRpSOxedlGoIuD+qVv0v1degN9JqVSewavWHlRX/twVWq/YLY0IuVD4Eqx6Tl5FhLunXS/ajKZqBqhNMJab/UAAlhYalzb7vz00iVdrJmqsyLenyfRVM2Oueo7TQWLljlfaayhpSQLl/Jyp8UBHqHn6mxJ9xp2ya+ozs3rHVtgIoi37Wf5Fr70RIA9poVL9qiBnOj3LFiVZorP9OFX0FBgAAVdWW7Tvlqie/lRc/XRRxwLq/Z9HXatWVuUXfN7SiM1rMJFF+wx1UdNWqPldgtaq+JlR9BKseE5ecbILPuklJtiUy5RGoOsFUYmoo6YLJkipNXShZWqiq3PirpZ2suecoqfJVBRP2BnMcFcxwAGUNKeBPX080TrSBsjRIGS51upwmudPG2xYAZYlWdageR/uN0vqOYGmfWNZwO8HQvtj3miL/+/T7gev7AABAdLz//fKoBKz+M9RXZNWqPld5Vas6/hWi5vkWV1xw/FXmTqpVqymCVY/R8FOD0OaDBtmW8B06YUK5BKqOq2wprRJTufFXSzoZdQFtWcFjUXVo8SdrLqD1DRtQ8rGCCXuDGVJAuWEFSgp73YmqKus1ub8vGifaQDAanfOw1GjUXHK/mWhbAJRGP59L+1EuWO5z3vVbkdA+MVphr4pG1ar+XZt3ldznAQCA8EUasO6vqtUfVpZftaoTWLWqoXFFVK2aycAK/z6HatXqhWDVY/JzcsxkQrqMlFa/lid30hRsIFrSiWgwl8orVx1a0smaC1xLq6B1ygp73bHKek3ubyvpOO5Etax/I+X+vmicaAPBSrz8Ndm1eb3kzf6fbQFQEvcjWqTDtrjP+dKuwAhWtMPeaPy4515TNKpfAQDYXx586xdJvWfKfr+NfH6mfUV78w9YQxU4HqmODVqe9Dm0otMpj2pVRytF/SeMqoiqVX0OqlWrL4JVj9HxVWe0bWuWXqYnhXrSVFZ1qHLhZEmXKroQs6zw0VWalnSyFmzQq8oKe92JZVnH8h9WoDjBHkeVVf0KlJfEIf+WbQunybbfvrYtAIrj+qtIfwBzV2pEWmnq/zoiDUT3hMYRDivgvh+4dQAAKqsZC9bbNe9qUDc2rKpVDf/8q1b9L9EvD1rN6V856l9VGm2matVvuIPyrlrVY+vYsU4k1apfxsR49qYTraN4BKsIiws2g6kOdcFrcSeQehx3MljWZffKPd/nm/Y9ljsp7NXALErlxmst7gTSnQgGExq7sFcVdwLpXlPLIPoNF75G45JOIBQ1E1pJ48tfldyp4+TPlXNtK4BArr9aaSsSwuWu1IiUC3pV5CGt71iRHsf/h0+CVQBAVZB+d//9ehtzVS/7SopooNq70wFy36Xd5ba/Hm5bQxNYtfpxOYWrgdWqGui2ii/fKEorYiuqatW/WlWfs6pWq274H1c4loRg1WOS0tKkb3a2tB4xwrZ4UyiVmC4M1QqWwMpOd9Klx3EBZWlKqxB1J4XBHKd1bd//9Ys7gXTHCSY0VqUNK+COFcy/U1nVr0B5qp10lCRe9qLkTHla8jettq0AHP+QMNLqUNc36Od9JOGj/+uItO9wx9LjRHIs/9cUjUm1AABAkcBAtW3zhnZL6AKrVstrFn0dZsAdV5+zPMZWDVRRVauLN+zaa/Kv45JqhV2tqvoVFHjupnP3oHQEqx4Um5Bgbl7mKjE71S37Q8NX+elbDxyXLpSAVrnL5QNP1vQkMJRjuTC0uBNIN6SACzrL4p6vuBPRUF5TWdWvQHmrd+R50rD/DbJp6jgpyPdLRwDsCUNVJFWd+vnuHz5Gciz/vjCSEDOwz/F/faHyH84m0upXAADgE81A1V9g1eoPK6M71qqvWrXomEe1qlnu1aqOhsb+Vavp86N/fuP/t5lJwQr/PlQ/BKseo+NWzBswQNZM9PYs3aFWdbpL6gNP3twJWDDDACgXUAaerLmTQH09ZV2+r0oLe4tCY7MoUyv7i1TgSa37W33PVfZrUu41cTKK/SXupBulTvvjZNO0Z20LAOU/cWJxP8oFK3CM8HCPo6IX9hYdR0XrWJH8bQAAwOfAhHpRD1QdU9npN95ptKtWtVrVXSbvq1YNYoy8KPKvWtVxUFfm7v2dJxJareo/tqo+VyTVqqi8CFY9Ji8jw4Sr2zMzbYv3bC787AilElPtCUT9KlmUOwEra/Z9x/9yef8TNncSGGyAqUoKe91rctWjZSkp7HXHcdWxwWA4AHhBo7+OEakbJ7nf/du2wOu0z9Af5NzN8W/zcr9SGQT2X4E/ygUrsBo03EpT7Sf8jxXYL4Yi8DWFe5zA16QC+1gAABAarVaNdqDqz39MUA1BozUeaWC1qg4B4F9BWhECq1ajOUnXJ4uK/jatwtVxXVE9Eax6TEJKirQZNcosvcqdNAVbHapccBpYqeOOFWxA63+5vP/Jm5thOdjjqOLCXv+TwmCPVdKwAqEeR5VU/QpUtMaX/0d2ZmXK1p8n2xZ4mQ4fs2z0aFk4eLC5Oe7+kpEjPT/EjNcF/ugWbmDo+pxIr1Bwwa6vL/athxv2uj7H/bgXbh/k+j39N3LHiqT6FQAAlL/AqtVojUcaWK26vyZ1Su1svygVilbVqh5n8caialUNjalWrb4IVj1GA1WdwMrLwao7mQylOtSdYPmHoeFcKq+KmyzKzbAc7OX7yoW9xYWhoYTGvtfvW/c/qXUnpqG8pkhPtIFoiYmtLY2H/Fvyfv5Ati/51rbCqzQ0bXbZZfbevlrdcAPBagT8f3Q7sZGvbwisYA2WC2iHN/N9BQv8US5Y/n2x66/CDXvda9rzg2OYfZD/lRruWOH8bQAAoGJp6LlXZWeEVasazAZWq+6v4FErSf3/ttd/9gslwvTV0qK/rX3jmntNAobqh2DVYyrDUADhVIe6Ch/fyanv8e4EzIWJwSruZM0dy832H4w9lTl+Yag7mQwl6FXFndS61+T+9mD4DwXAySj2t9imh0iTy1+VnM+ekD/XLLCt8KrWI0ZI3aQke6+IBqq6DeHz/9HthHjf53TgFRjBcsfqHVdjT//g/6NjsPz74j2BaBhhr69f9q0PSIws7HXH0dfDFRgAAFQepmo1irPo6yRYXqhWdfyrVrVi1X9s1FAFVque0oFQtbojWPWYyjB51Qp7MhlKJaZyJ34ufHQTgbj2YLlKU3eypie3/ie9ofKdVPpeUzihsXL7u8f7n6iGcqySql+B/aV2+2Ml8aJnZdOUZ2RXXpZthReVVLVKtWrkin4ILLrEPZwwNPBKjeKuwAiW/5UaxV2BESz//lP7q0j6IP8rNdxxuAIDAIDKIbCyM9yqVS9Vqzr6t+k4qE4kY60GVqu2b8LYqtUdwarH6Mmv10+A3clWKJWYyp2MuhNLV1nTq4FZBM1Vh7qTtb0qZEKoNNXX70JPd4xwhhRQ7qTWhc7u30hPLEN5TcrtH84JMlAe6ve+SBr0vUI2TR1feI//X3pZYNUq1arR4QJD7TNc3+f/o1ywAq/UcH1QOJ/3/ldquOOEE4YGXqnhluEMK+B/pQZXYAAAULlEq2r1q8ydnqpWdVI7F/1t4VatUq2K4hCseoyeAPfNzjbjrHqVO0FyJ3LBamj/3+ZOUMO5VF65E1J3suZO/kINMFVg2Ot/ohqKwJPaSF5TqP+uQEVoeNptUvugZMkx4Sq8KrBqlWrV6HA/vrkf3dzndKjhY+CVGoFXYAQr8EqNhrZQQvvEUMPewCs19vRntj1Yvuf2resxtG93/Xs4gS8AAKh4gbPop8+3nXuQNIj9YWVR8OilSZ20stS/ajXUv035V6tqFSzVqlAEqwiLnjSFGhq6kzWtjgk8AQuFnqi5cFVP1tzJn2sLhQt7tXrW/0TVXZ4ZrMCT2nCHFFDuRBvwmkYXaqhaQzb/8IavAZ7kqlapVo2ewB8CA3+UC5a7UqNTXd/j3fFCvVzev//Uvti/X3TbghV4pUbgFRjBKu5KDdeXhvrvBAAA9h//qlWt0AxlFn0dPsBVq2pA65VqVeeCI4pCAxMCrygKSssSWK2qoTGgCFY9ZsXYsTKjbVuz9LJIqkP9Q1X/E7BQuMfoydqMPN8HfTiBpH/Y63+i6k52gxV4UhvukAIqnDAWqCiJQ16TP1fNk23zPrUt8BpXtUq1anT4/+jmPp8Dr8AIlgtoXX/hgkftF/UWrOKuijixke9FhRpiBl6p4f7GUKtMi3tN7lgr7QkWAADwvsCq1WDHI9UA1j+oPC6plmeqVR2tWNVKUyfYcWQ1LPb/d6BaFf4IVj0mPydHtmdmmqWXuZPCUGj46ALLV7LcyWV4H7TuZE2rf8KdBEv5h73FnRSGwv+k1p2o6qzPoXLVr4AX1ajbUBoP+Y/k/fiO7Fj6g22FF8z44H157rrhcssxfWXs22+am64/N+xasw3hKe6HwD19UAiVpv4/KrrH+/8oF0qQWdyVGv5XYAQrcEgBFe6wAsVdqeF+8HTPAQAAKgf/WfSDrVr1n7BKg9mjWnnzxNb/bwu2ajXw34BqVfgjWPWYpqmp0mXSJGk+aJBt8aZwL1d31Tn/3ej7UAonDFXu+T/ftNuc/PlOTkM/ln/YOynbhb1mETJ3UqvHcSeR4YSk/ifagBfFNjtUEi+bKDmfPyU71y+xrdhfMuf+Ig8OSJXPHnlIavwyR/rGx8mZ7duZm67XmDfXbNN9dF+Exv3o1rle0Vcm/x/lglVcQKvceiiVpu4yff++2PWn7oe9YPgHve51+PdBoQSixV2p4Y4Z6lAHAABg/9KKTP+q1dd/Lv1LweINu/YKKHU4Aa9Vqzr6d/lXrabPL71q1VSr+lW2Uq2KQASrHhOXnGzCVf9Znb0o3EA08HHhXCqv3EmtE0kQ6R7rTmrDDY2LTmp9x9H7LrQNVTiVrkBFqtP5RGl07sOyaco42b1tk21FRXtv7Bh56K8Dpfn2rXJskybSMTFRGtWpI7Vq1DA3Xdc23dZs2xaz7/tj/mkfjWAUVx3q/6NcsIFo0TAAe/cLru8ItdJU+feprl90V3EEw732fV+Trw8KJewNHFJAuX8zDaBDCaEBAMD+51/ZWdYs+p8s2rta1T+49CL/v02D09KqVnUyLq1sdU7pUDQGLaBIbzwmZ9o0M75qXkaGbfGmwJOwYLUK+NUq1Nn3ncDAMjCwDUVgSBvusfYNe8N/TS35rEYl0OCYK6Rej4GSM3WcbUFFmvzsePnspRfkzLZJckjdsn+lalevntn308LHfDjuGduKsrhKzMAf3VxoGGw1phuPNbCPcccNNnjUsLOo+rXoWC7s1eMEewl/SZM/uh89gw17/YcU8J/8UV+PO3aoY7YCAID9S8NR/1n0SxprVatV/Sd18nK1qmOGKmhddDl/SVWrGrp+lVm0TR/j/28CKP4f4TEarC4ZOVKy0tNti/dEEmIGPjbwZC4U/sdyMyyHwz/s9Z0Ehncsd1LrROtvA7ws/ux7pNaBHSX3q3/ZFlQEvaT/vSefkFMOOkga1g7+w0b3PeXgg+X9p59iWIAguUrMfQLREIcD2BM8BmTg7rjBBo/FVas6rt/5fFNwr6m4IQWUO3awwwr4Dyng3w+qcKpfAQCAN6R2Lqr4Kalq1b9atX3jmnsFll7m/7eVVLX6ZWb+nmpVDYsZWxXFIVj1GB0CICElxdNDARR3Mhcs/0oWPU4kVZ3+FaKRhJj+j43kOMr/8eEOKaACq18BL0u49EXZtWOr5M1617aU7MV3PpFBtz0uZ14zSu5+8hXZvIUyNn86eWEw/nPbbdK7zcEhhaqOPuaog1rLa7fealsqr/K+usO/EjNwzGz3o5yrRC2LCykDg8dQJ4vyDzEDhR72+vYLPJY7TrDDCpQ0pIAKtfoVAAB4h44l6l+hmT7ffhGxNGj1r1Y9rm3lCR41KC2tatVXrVoUtupkXFSrojj8v8JjdNKqblOnenryqkguU/dVhPrWIwlVlZssSkUS9vqHmJEcR0XrWIHVr4DXNR7yH9m+9AfZOn+KbdnXJTc/KlePelL+/d4U+fjrWfLAc29KcupQWbB0hd0DerXC9MREWTNxom3Zl87wv3tTdlCX/5ekXf36UpCbY45VmekVHnOOP77crvJwgaF+ngd+Jru+LJihAPwD2sC+wb9fdPuUxgWUxfXFrl8MJuwtaUgB5fqgYMPekoYUUO7vDeY4AADAey44oqiDD5xF/6ule1eren1s1UCBVasf+w13oNWq2qZ81aoRBCGo0ghWPSY/J8fcvCzS8NGdwBV3AhYK9zp8J6XhvyZ3AqkiGVJAuZPaSF+T0n8f97oAr6sR10QaD3lN8r57VXYs+8m2FtFK1dc/nGbWT+57pIxPu06SO7eTZavWyU0PM4yAP+0DFg4eXGLA+tP770nz2Mi/2Okxfnq/cgerSofQmTdgQLkErMFWh5ZVIVoUYBb1gf5ObOTrPIK5XL6koQmUaws27FUl9emujw7mNZU0pIBy/07BhMYAAMB7tErTPzB1M+QHVque0qHyXSYfWLXqKlQDq1V1CAAdlxUoDsGqx+jEVXoynZmWZlu8Y3izGuYEqaSTsGC5x0dyqbxyJ2v+wwuEyx0jWmFvpMdRr7SrKTO7MoYLKo9arY+QxMtelE1Tnpb8DX/YVp+vZvnG89RQdfLz98qVA0+V8aOGmzatXi0oKDu8qW5KClgz5/0qB9atY++FT4+ROW+evVf5lUfA6qpDA4cBUP4/ypUVGhZVvhb/tcv9KBfM5fLRDntL6tPdsYIJVt34sMUdy/0bBVv9CgAAvMd/Fn1XtepfrarBqw4bUBkVV7UaWK16VCvOy1EyglUE7brmNeXVdrHFVtuEwgWqJZ3MBcud1EZ6HOWOEemx3IloNF6TOxkFKpO6h58pDc+4SzZNGye7/9xqW0U25Gw2y3NO7muW6sjD2ts1kVpdz5TYLmdE7fZN4W3x01tN2Kb0kvEvY2JMUKl0LFO9rzd3lYAGcnrf/bCl4Zzen9G2rbmvdF3bXHCn++p9fazSY+l9vbnxUvU59b6+BqWvye3j/Ni9u7m/9uWXbUuRwIB186YcqV8r8opVPcbp07/d81oq48399/UXzYDVVYeW9EOg+1GurArR0i6VV67PKOs4LuTU/qG4vtj1i6qssLe00FgFO6yAviYX4pb0/cD9fcGEtKVJvWdKhdyuevJbWZcT5OC5AABUA1qt6V+1+vrPf+49tmolntRJg9N+fq9fK1WpVkUoCFY9pvWIEdJj9mxPj7EaKQ0fSzopDJWe1AbOsBwOPWku6RLNULiT2kiHFAAqs7iUYVK36xmSM+UZ26IhajuzfPaNyTJjzgLZtWu3nHHN3aYNwdOQtfbu3cInTHCCnQysJG7yppJ+LHPtK21FQ0ly7XlHSQGt+1GuLC7oLe2qiGDD3rJC42DD3rKGFFDu76sswwFoqLp0je/HIAAA4ONfteqvMlerOqd02Ltq1b9a1T90BYoTs3zVuoLlmYulT58+tgnVSXx8vMyaNcveqxha2XLpkl2S3jHyD6gHVu0yJ4VufLpw6Ynhrct3mYrcSF2yJF8ua1oj4tcUrJ49e0pubq69Fzyt5tLKLp0sLSElxbZWP1r1pvpxKXrUZb90sUj+Dok/ZrCZ/V8nqtIxVVWNGjGye7fv3/zF+0fKZaknmvVoWjN+oLR6aou9VznocDCustWJTUiQNqNGmR/cdP3WY/pK30ZxEl87suEANu3YIdNz8+Thb6bblsrHfY7508+zJn/5y55/r2D7uUPn+MYL0yFY9Acyra5M/S3frJc0LMvnm3bLsMxd5vP+maSSTyj6z99pQsUpnUu+6qPX3HzTP5a2z1NrdsnTa3fLZQfUkNtbFv982i++vH63Gb5HrzQpift7S3o+fS36mpT7NymOPpc+5zmNa8iDBxX/fO51+/876b+b/vst7BZc9XW4fV2oHnzrF5mxYL3c9tfDpXenA2wrAK/QK0WWjR5t+sUkDw6dhqpFr2JQ6Xf3N0sv0+878y8rqiAtL6///Odek1epYb3rVPpgVekQAJ/4TV6lNHA91S90LU+dX65ZId91QqVXzOnVc/rd+tAJE2wr/FGx6jF62aL+nzZa48N5kZ6cnRBf/AlaqFrVis5QAHopZDSOo/Q40ToWUJklXv5v2ZW3QbZk/E8aNqgnHz5/r5x6bE+zTUPVFgc0LrdQtSrQULDdmDHSe+lSczWD3ldtDjtM1m3bYdYjsX77Dkk6rLO9V/lpoKr/Xl0mTdrr3ytcwVSHukCytKpO/eHOVWqWdNm9cs/z+aaSj+Uuyy/tSg23rbRL+MsaUkDpNhemllZp6oYUaFnKOYfrE8uqfgUAAN7mPx6pqgrVqo5Wpvpf8k+1KoJFsOoxeRkZ5hcBXVZlWtkSDQMaR2dIAT15PDE+Sq8psUZUXhNQFWi4unX+NNm28Evp1La1fPDsaNk0611Z8tkEWT7tVULVYpQUqDpHnn22rM7f+9f0cOgxjjz7L/Ze5RXtQNUpaxgA5cJQre5044wGciGmHscFlcVxl8uXdBzlwt7SjtO6tq8vKy3EDCY0VsEMK+COVdq/UzB/GwAA8L7AWfQr89iqgfRv6+U3SZX+bdoGlIVg1WP0BFFPDOOSk21L1RSt4LG0k8tQBTvGXVmoVgWK1ExoKY2H/Ftyv3pO/lzxi2lrUK+utGl5oFnH3vQSm5ICVaf3mWdJTHyCLNlmp2IPw+K8PHMMPVZlppeCRjtQdVwlZmljZmsf5MJJNzN+IFftWVbfUNZkURpKBnMsF4aWFva60Lisfs89T2mBaDCvSf+dXH8d6QRWAABg/3JVq1WpWtVxVat6o1oVwSJY9RhXedM0NdW2AEDlVjuplyRe8oJkf/G05Oessq0ojoaDwQSEFz30kPzwx3LZ/Gcp12iXQB8zc+Uqc4zKTvvMaAeqTrBVne6HwpICw7Jm33dcMFlSdagLMPX1lPbjZDBhb1FobBYl0uF2VElhbzBDCjjuNTEcAAAAlZu7RN5/wqeqQv+2U9rXMpWrVKsiWASrHqMzGOswADrzMwBUFfWOPE8anjRCNk0dLwX5oYeB2FtS18PlrOHXySd//BFSuKr7flr4mLOGDTfHQPGCrQ5VewJRG1YGcgFtSbPvO/6XyxdXIeoCyWCu+Cgr7A1mSAFVVhgabPisGA4AAICqI/Ww2tIqSkPpeY0OdVBRE1ahaiBY9RgdX/XH7t3NzNAAUJU0PPFGqdOxnwlXEbkzhg6TEwcNlg+WZppL+8uyKHez2feESwfJGcOG21YUx4WqwVRiusBUJ6kqTjBjtSp9Lhd0uuf3t2C77/hlHUe5fdxj/IUSGpcV9rrjBDOUTlnVrwAAAEBlRLAKAKgwjQb+U2LqNZLc7/5tWxCJv9z4D7n1rbdlbYOG8tW6dfJbdrZs2rFDdu7ebW66rm1frVsv6xrGm33/8o+b7aNREleJ6cYrLY0LFYsLQ7ViVAPJYAJaVdpkUbm7fMuyLt9XLuxdUUzY615nWUMKKN/r9q0XN6yAC0mDeU0MBQAAAICqiGDVY5LS0qRfQYFZAkBVlDjkNcnPWiZbfv7QtiASekn/7en/k5Nvu0N2deos3+bmyX+X/G5uur6r02GF2243+3D5f3BcYBhMdairMvVVgu4dGoZyqbxyz1dcdag7lpv1vzR7KlaLCUNDGVJAlTasQLBDCqiyql8BAACAyohgFQBQoWJq1pLGV7wmW3/5SLb99o1tRaR0hv9rnn1eHvpmuoyb+6u56fo1zz5X6Wf/r2iuqjOYSkzlgszAqtVghwFwXKVp4OXyGtj6V5qWxU2UVVzYG8qQAmrP37Zz7+P4ju1bD+ZYGr66ADbw3wkAAACorAhWPUbHVtUxVnWsVQCoqmo2SZLGg1+RTV88IX+unm9bAW8IpRJTuWrMwKrOotn3gzuOe77Ay+X9A8xgKk31OC6ADQwxQxlSQBWNIWsWe7j7+jzBVr+WNtQBAAAAUBnFrFi1ruCPzMXSp08f24T9KTMtTZaNHi1tRo2qkOEA4uPj7Roqs9zcXLsWvDnHHy8506ZJt6lTJSElxbZWP1/G+AIBHYIDFW/rjNck9727pfHZd0vNhgfY1uhYM36gtHpqi72H6kr7uVmzZtl7JTt0zk6znNI5VvrPz9+zHkxo+NSaXfL02t1yYqMa8kySLRct1H/+ThNAaptuK4tWgfaa63vumV1j9wStL6/fLQ+s2rXP8Utz2/Jd8t+Nu+X2ljXlsgOKntu9plfbxQY16ZRWvOq/h74WfU2Oe016DD1WMHR/fZy+Hn0Nn2/aLQu7BTfrbs+ePcPq60L14Fu/yIwF6+W2vx4uvTtF9zMJQOQq+lwJ1VvqPVPMMv3u/mbpZfp9Z/5l9tdTVEqdX65ZId91QqVFfwsHD5bmgwbJoRMm2Fb4o2LVY5qmpkq7MWMqLOjSNy63yn8DKqv6vS+S+scMkU1Tx4sQbsMDXNVpKJWY7lJ4/0rMUC+VV/6Vpv7jo7rL94MZBsBpafNKVzWrQh1SQJU0rECoQwqokqpfAQAAgMqKYNVj4pKTpfWIEdW6ghBA9RJ/2u1Sq00PydFwFdjPioLVEAJDv4mZHBce+sLS4I/l9vUfVsDN7u+CyWC4wNMNa6D8g95gX1NJwwqEOqSAcs/JUAAAAACoKghWPSYvI8OUWusSAKqLhAueKeyRakrujNdtC7B/uImjQqnE1PBRb8oFoi7QdOOKBss9b2ClqQqpOtSGvW4CLRVOaKw61/N9XfQPe93f17p28F8lXUCrAbR/CA0AAABUVoXfhvli6yVZ6elm/ApdAkB1kjjkNdm5eoFsm/eJbQEqngv8QqkOVS40dNWY4QS0yj2vex0aZhZdvh/8sVzYq8dxwWw4QwooV5Xqwt5whhRQ/tWv/kMdAAAAAJUVFaseUzcpyQwHEJuQYFsAoHqoUSdOGg95TfJmvSvbl86wrUDFcoFfyIFowHAALngM5VJ55Z7XvY5wqlUdF2LOyPNVl4YzpIByz+2qVMMZUsBx+1OxCgAAgKqAYNVjdKa1HrNnm3FWq7oNGzbIc889J7169ZKYmBhza9++vQwdOlQmT55s9tFt5cU9p7sB2P9im3WUxEEvy6YvnpKd6xbbVqDiuMDPTdoUrIb2G5WrVHUhpBsiIFiBk0X5h5ihcmGvO0a4IW3gsALhDimgwvk7AAAAAK+KWbFqbcEfmUukT58+tgkof2+88YYJULOzs6Vnz54yevRoOf300802DVVHjRols2bNMvcLymmm8Dlz5khycrK9V37P41Vzjj9ecqZNk25Tp1brydK+tKF6P2ak95Qt01+SvE8elcZn3yU16odXwb9m/EBp9dQWew/VVXx8/J7+pDSHztlp13zhX3rHWHsvOJ9v2i3DMneZIPWLzjWl19x80z6lc2zIAWT/+TtNGPpqu1h5OWu3OfZlB9SQ21uGlvY+tWaXPL12t5zYqIYMb1ZDUn/zvaaF3WqZZSjcv4/+PXrM/24M7zW5fycn2Nei3xVyc3PtvfLz4Fu/yIwF6+W2vx4uvTsdYFsBeEVmWposKzxvaFN4rpBUuA6Up9R7pphl+t39zdLL9PsOKr+K+K4TKp0DSIer1CLAQydMsK3wR8Wqx+iXhemJibJi7FjbUvVoqHrBBReYUPWUU06RmTNn7glVla5//PHH5iSqPHXr1s2uAfCaBn0vl/pHnS85U8bZFqDihFOJ6T8UgBtnVS/FD+dYveN8X8+0MtRVvoZ6+b5y1aH6eiIZUkC5x+lrCndIAeX+nQAAQPRoIMet8t9QORGselB+To65VUW///67CVWdceOKD02aNGkib775pr0HoDpqeOYoqdWik2z68nnbAlSMUCd3Um6yKPVFrgsxw/ua1dIWcepkUdEYCkDDXnf5frjBqjuWbzKt8I8V6tAIAAAAgJcRrHqMllfrpdm6rIoee+wxuyamWvWQQw6x9/al2wKrVvXy/fPPP3/PuKinnnqqfPPNN3ZrER2/VYcaaNy4sdlPx27VStlg6ePvuOOOPY/X59Q2R5/XvQa96WvQ16bPo4/RABlA5BIueUF279whebPesS1A+QunElN1qudb6mXyKpyAVrnAUi+bVxpGhlP56h/2Tsr2HSvUsWMdN4asHseFveG8JhVuuAsAAAB4DcGqx9RNSjLjXeqyKho/frxdE+nfv+yxanSYAEfDyuOPP95UsmZkZEhWVpYsXrxYjj322H1CUw0+9bkefPBBM3aqBrRaKathazD08Q888IB5/JIlS8xzapsTOFTBvHnzpGHDhmZdhzh45x1CICBamlzxH9meOUu2zveNcwWUt3CDv8DHhRvQBl4uH25Aq1zY68LQcF+T+9siqaB1GA4AAAAAVQXBqsdkpafLkpEjzaRCVU1xlaWhuP322/dMdqXjo+pwAVdddZXZpoGpqyh97rnn9kxUct5555nl8OHDzVLD1rJexyOPPLLn8VdffbWpnNXqWm3zD3D1+Z1NmzaZ/Z588klJTEzc87wAIhdTP1EaD3lN8r57RXYs+8m2AuUj3OpQFRhahhs++leaqkhCzMDHhnuswDA0ktfkql8BAACAyo6vth6Tl5FhJq6qisFqJDQ0dWOu+geajgauM2bMMOsvvPCCWari9v3oo4/sWvHefvttu7av9PR0u7a3rl27mqVOvLVx48ZShzgAELparQ6XhEsnSM4XT0r+xmW2FYi+SKpDAwPZcC+7V/6vo1Pd8EPMVrWKHhtJaBwY9kbyt0USygIAgP3Lf0i8wJsWMRXX7m5axFReNDPQIiugohGsekxccrI0TU2tkkMBtGzZ0q6Fbv78+XatZHPnzjVLV21akh9//NGuFc//8a4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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Importing libraries\n", - "\n", - "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " Block,\n", - " Var,\n", - " Param,\n", - " Constraint,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " TerminationCondition,\n", - " value,\n", - " Expression,\n", - " minimize,\n", - " units,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", - "from idaes.models.unit_models import (\n", - " Mixer,\n", - " MomentumMixingType,\n", - " PressureChanger,\n", - " Heater,\n", - " Separator,\n", - " HeatExchanger,\n", - ")\n", - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from properties import SCO2ParameterBlock\n", - "\n", - "import idaes.logger as idaeslog\n", - "\n", - "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Constructing the flowsheet\n", - "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", - "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", + "\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", - "\n", - "The number of cross-validation cases (3) is used.\n", - "The default training/cross-validation split of 0.75 is used.\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "--------------------------------------------------------------------\n", - "The degrees of freedom for the flowsheet is 0\n", - "--------------------------------------------------------------------\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 452\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 118\n", - "\n", - "Total number of variables............................: 178\n", - " variables with only lower bounds: 32\n", - " variables with lower and upper bounds: 59\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 178\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", - " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "\n", - "====================================================================================\n", - "Unit : fs.boiler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 685.15 893.15\n", - " pressure pascal 3.4510e+07 3.4300e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.turbine Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", - " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", - " Pressure Change : -24.979 : pascal : False : (None, None)\n", - " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 893.15 729.38\n", - " pressure pascal 3.4300e+07 9.3207e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 729.38 489.15\n", - " pressure pascal 9.3207e+06 9.2507e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.HTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 535.47 736.02\n", - " pressure pascal 3.4560e+07 3.4490e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_shell Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 1.2110e+05 1.2110e+05\n", - " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.LTR_pseudo_tube Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 378.99 566.32\n", - " pressure pascal 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_1 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825.\n", - " temperature kelvin 354.15 354.15 354.15\n", - " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.co2_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807e+06 9.1107e+06\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.main_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.510 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 378.99\n", - " pressure pascal 9.1107e+06 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.bypass_compressor Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", - " Pressure Change : 25.706 : pascal : False : (None, None)\n", - " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 460.04\n", - " pressure pascal 9.1807e+06 3.4886e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.splitter_2 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", - " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR \n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 378.99 378.99 378.99\n", - " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.FG_cooler Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 31903. : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 378.99 483.15\n", - " pressure pascal 3.4620e+07 3.4560e+07\n", - "====================================================================================\n", - "\n", - "====================================================================================\n", - "Unit : fs.mixer Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 566.32 460.04 535.47\n", - " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", - "====================================================================================\n", - "667.9424945058901 kW\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sekxsQoLEJSdLXX4lhYdl5m2R/6zLkueWLZfjzhsoa9atk0eeeEIaN25s9wAAoHoprm98mL4R8BTzPl27Xp7N/EOOO/dc8z599KmneJ8CqHL0c23Mww/K+jVrZODxx8iq156STR+8KluW/273QLQQrHpMVnq6LBw82CwBr1m8ebO8um69vLpmrZxx6aWydsMGGf3QQ9KgQQO7BwAA1Yt/33g6fSPgSeZ9utb3Pj3tkktk3caNcs8jj/A+BVDl6efcg/eMlg3r1splZ54iGyZNkJz0CZKX+ZvdA5EiWPWYvIwMWTNxolkCXrFgU65MWLNO3t2YI3+9+hpZnZUlt6WlSWxsrN0DAIDqxb9vHGj7xtvpGwFPMe/T1WvlnQ3Zct6VV5r36R333MP7FEC1o597d99+m2StWSXXXnCebP74Ddn4zvOyefGvdg+Ei2DVYxJSUsw4qzocALC/zc3JkedXrZaPtm6TwSNHyrLVq2XkLbfYrQAAVD+ub5y8ZYtc/ve/m77x7/SNgKeY9+nK1fJh3hYZVPgd9o81a+TG22+3WwGgevvHyBGy+o9l8o8rB8ufX78vWW+Mk9yFP9utCBXBqsdosNpuzBhpmppqW4CKl7ExW55ZvlK+zN8tN9x1tyxatkyG3nCD3QoAQPWjfeO4Fatk2s5dcn1h37j4j+Vy7fXX260AvMD3Pl0pU//Ml+vuvFOWLF8uw0aMsFsBAP6GXXuNLFv0m4waOVxiZn4h6/79hOTM+8luRbAIVj1me2amGQYgPyfHtgAVZ9aGjfLkHytkZs1YueOhh2Te4sUy+Mor7VYAAKqfmRs27Okbby/sG39dskQup28EPEXfp08sW174Pq0ptz3woMz//XcZcvXVdisAoDRDBg+WRb/OlUfvuk3q/fq9rJn4mOT8/IPdirIQrHqMjq/6Y/fusmLsWNsClL/v12fJY0uXybz69eWBp56S2fPny4WXXGK3AgBQ/XyftUEez1wm8xvEyf2ub7z4YrsVgBfod9jH9TtsvQZy/xNPFL5PF8hFl15qtwIAQnHBBRfIvNk/yjMPPyDxS3+WVS88JJtmf2e3oiQEq0A1Nr3wpPHhJUtlaeMm8vSECTJjzs9yzrnn2q0AAFQ/0zdslIeW/C5LExvLUy9NkO8z5si59I2Ap3yzPsv3HbbwffrEiy/KDz//LOf99a92KwAgEgMGDJCMGd/JS08/IU3WLJLl4++V3J++sVsRKGb18hUFS5f/IX369LFNAMIVExMjz/TuZe9511eFJ43XLl5i1vM//1xOOOEEs14dfVn430z1KygwSwBAdFWWvvHrjRtl28XPm/Xr+hZU674R1U9leZ9+mbVBBv2+VOoXfm/LT0+XE/7yF7sFiL7Ue6aYZfrd/c0SqI50mMrpiYmyvVYtuaDJAdKkT39p2DPFbvWu2XdcIQUVdI5PxSpQTeQXfqhM3bBRRs1fIJsPOti2CieOAIBqy/SNG7Mlbf7Cwr6xjW2lbwS8RN+nU9ZvMN9h8w4+WOo3aGDa+/XrZ5YAgPIXV/jZ+97bb8rB27NlyT9vk83ffyEFu/Lt1uqNYNVjdGxVHWNVx1oFomH7rl3y2YaNcve8X2VX+w4y+fMv5MMpvl9fAQCojrRv/CI7x9c3tmsvH37+uXzwxRd2KwAvMN9hs/Q77PzC77Dt7XfYqRIbG2v3AABUpGOOOUa+mPyBfP7Rh9Kh5g5Z9PitkvftJ7J7x3a7R/VEsOoxWmadl5Eh2zMzbQsQnrz8fPl4w0a5a+6vUrdLV/nq2+/kvx99JL16ef8yLwAAyoP2jZ9m55i+sfZhXegbAQ8y32GzNsqdv8yTOocdVvg+/VYmffIJ71MA8Aj9PJ486V359qsvpWvDWrLgsZsl7+vJkr81z+5RvRCsekzT1FRpN2aMJKR4f8wKeFPOn3/Khxuy5c45v0jjI3vITxkZ8np6uhxxxBF2DwAAqhftGydv9PWNCd2P9PWNkybRNwIeou/TD9ZvMO/TxO7dZfacOfLGe+/xPgUAj9LP5/++/prM+ekn6dGskcx/5B+yedp7sjM32+5RPRCsekxccrK0HjGCYBUhy9qxQ97bkC13zJ4jB/3f/8lvixbJxDfflEMPPdTuAQBA9eLrGzeavrG16xvfeIO+EfAQ8z7N2rDX+/Tlt9/mfQoAlYR+Xr8+8SVZ9NtvckzbljL34X9I3pRJsmPjertH1Uaw6jE6DICOr6pLIBhrtm2T/xaeNN4/b74c2r+/rFq1Sp59+WVJSkqyewAAUL1o3zhpY7bcN/dXOfSEE3x940T6RsBLzHfY9VnmfdrxeN932OdffZX3KQBUUvr5PeG58ebz/IQu7WXRU6Nk82dvy/b1a+weVRPBqsdkpafLwsGDzRIozcqtW+Wtwi+jYxb/Lt1PPU1WrV4tTzz3nLRo0cLuAQBA9bJC+8asDfLP3xZL99NOl9Vr1sgTz9I3Al5i3qfrsuSfiwrfp6efYd6nT/7rX7xPAaCK0M/z8U+MldWrVsmZRyVL5gsPyeaP35Btq5fbPaoWglWPqZuUZIYDiE1IsC3A3jLztsjr67NkfOYfcuy558nqtWvl0aeekiZNmtg9AACoXnx94wZ51vaNa9avl0eeeIK+EfAQ8z5dt968T/uec46sKVznOywAVF36+T7m4Ydk3erVcl7K/8mq156STR+8KltXLLV7VA0Eqx7TfNAg6TF7thlnFfC3ePNmeXXtOnl59Ro59eJLZO2GDXLvI49IXFyc3QMAgOrF9I3r1ssra9YW9o0X+/rGhx+mbwQ8xLxP16yTVwq/w55y4UXmfXr/Y4/xPgWAakI/7x+8Z7RkrV0jl515iqx/9wXJSZ8geZmL7B6VG8Eq4HELNuXKhMITxnc2ZMt5V11tvozeMXq01KpVy+4BAED14usb1+3pG9dkZdE3Ah5j3qer18jbWRvl3CuvlDWF32HvvO8+3qcAUE3p5//dt98mG9aukWvOP0c2f/S6ZL/7L9m85Fe7R+VEsOoxmWlpMj0xUVaMHWtbUF3NzcmRf61aI5O3bJVBI0bKH2vWyI233Wa3IhKzP/9MXhhxg9zW7xj55uCDzE3XtU23AQC8yfSNq7Vv3CKDR9q+8dZb7VYAXmDepytXy+S8LXLZDSNk+dq1ctMdd9itAACI3Pz3v8vq5cvkxiGXyY5p70nWG+Mkd+HPdmvlQrDqQfmFX0b0huopY2O2jFuxUqbt3CXDCr+ELv7jDxnG0BBRsX75chlz2aXy/r33yO45s+XoBg2kd7fDzU3Xd83+Ud4bnSaPX3Sh2RcA4A3aN45fscr0jdfdeVdh37hcht5wg90KwAvMd9jlK2Tan/ky9LbbZHHhd6nhhSfOAACUZPjQa+WPJYtk1MjhIjO/kHX/fkJy5v1kt1YOBKseo2Osdps61SxRvczasFGeWLZcfqhZU2574EH5dckSueLqq+1WROq7Sf+VtDNOk7iNG6Rf40Q5NCFBEurUkVqF/9560/VOjRtLStMmErchS9JOP1Wmv/WmfTQAYH/QvvHJP1bIzJqxcttDD5m+8fIrr7RbAXiB731a+B22Rk259f4H5Nfff5crhw61WwEAKNuQwYNl8a9z5dG7bpO6876TtS8/Ljk//2C3ehvBqsfUTUqShJQUs0T18P36LHl86TKZV7+BPPDkk5Ixf4FcdOmldiuiYfq778h7jz4iKS2aS8fYmra1ZJ3q1ZWUli3k/ccfk+nvvG1bAQAV5fusDfLPzD9kXoPCvvHpp2X2/Ply4cUX260AvMB8h/09U+bVqy/3jRkjGQsWyMUUhwAAInDBBRfIrxk/ydMP3S/xv8+RVS88JLkZ39mt3kSw6jFZ6emyZORIyZk2zbagqppe+GX04SVLJbNxE3nqpZdkxpw5cu7AgXYroiUvO1vevO9e6dEoXg6sX9+2lk337ZHQSN68/z5zDABA+ft240Z5+Hdf3/ik9o0Zc+Scc86xWwF4wTfr1stDi3+XpQmJ8sQLL8iMn3+W8/52vt0KAEDkBgwYIBk/fC8vPf2ENF69SJaPv1c2//SN3eotBKsek5eRYSauIlitur7K2iD3/bZY1jZvIS++/rp8NXOmnH7GGXYrou31u++STi1byQF169qW4Gm42qFpU/nPHUwaBgDl6auNG+W+RYtlzYHN5cX/FPaNP/wgp59+ut0KwAu+XLfe9x22WXN5qfA77Nc//ihnnn223QoAQPTp98Efvv5SXp/wgjTPWSnLnh4lm2d5Ky8jWPWYuORkaZqaylAAVcyuggKZmrVB0hb8JrkHHSxv/u9/8vn06XLiiSfaPRCqhYMHy/bMTHuveAu+/14WFZ6cH1Y71raE7vC4BuYYeiwAQPRo3zgtO8fXN7Yu7BvTC/vGb76hbwQ8RN+nX6xbL6PmL5Tcg9uY77BffPednHjSSXYPAADKn34//PrzTyX9zdfl4O3Z8vuY22XLD1OkYNcuu8f+Q7DqMRqqdpk0icmrqogdu3bLZ1kb5K5582Vnu/bywaefyuSpU+XYY4+1eyBcWtU9o21bmTdgQIkB689ffC5JjRMkxt4Phz724Lg4mf3hB74GAEBEtG/8IjtH7v51gew8pJ2vb5wyhb4R8BDzHXZ9ltxd+B12d4eO8uFnn8lHhd+9eJ8CAPYn7Ye+mPyBfPrh+9IuZrssevwW2fLdZ7L7zx12j4pHsOox+Tk5JiTSJSqvLfn58vGGjXLX3HlS+7Au8uX06ZL+ySdy1FFH2T0QLToucUkB69yvvpQDdxfYe+FrUaeOzPt2ur0HAAiH9o2fZueYvrFW58Nk2jffyKSPPqJvBDzEfIddn7XnO+y0wu+wk/gOCwDwGO2XJk96V6Z/OU0Oa1BDFjx6s2z55iPJ35pn96g4MauXryhYuvwP6dOnj23C/pSZlibLRo+WNqNGSVLhOiqXmJgYOeWg1jJl9Rq55Pzz5R933CGdOnWyW73ly8LXqvT/a5XRyieeKPYHCK36bjdmjBlOY0SvHnJq8wOlXmwtuzU8W/N3yieF/03HzpptWwAAwfLvGy+7+GK58ZZbPNs3pt4zxSzT7+5vlkB1oe/Tk1u1lGlr18nFHv8OOz0x0XwH7JudLbEJCbYViD76BMBX/Kefu/p5q5+7XrVgwQJ5+LHH5d+vvCzNjzlZVkz9UAoKIi+yCgbBqscQrFZu+qV02NVXm5PGtm3b2lZvcl9KqyoNV9PGPS0XdzksoqEA1O7C22vzfpUXflvsawAABK0y9Y2cRKO60vfpNZdfLjffeWel+Q5LsIryRp8AVJ5g1Vm6dKk8+vg/ZfwzTxOsVmcu7OKLAspTXkaGuYy+siqpYjUhJcWEqjoRnFasnta8mdSNDX/yKrUtf6d8TMUqAFR5nEQD3kewiopCnwBUvmB1fyBYBVAp6biq/mOq+geqzt2nnCRHFC4PqF/P1xCm9Vu3SUZ+vtz3xVTbAgCoijiJBryPYBUVhT4BIFgNBpNXeYxWEOokPGsmTrQtAEqjgWqP2bOl29Spe4Wqqsuxx8m6mEgHAhBZvWO7dOnzf/YeAAAAAAAAwarnuMuzA2c3B7C30gJVp9uJJ0lmdrZEMrKKPvaPzVuk+5ln+RoAAAAAAAAK1fDFBvAKDYt04ipdAijZoRMmlBioOp2OPlo6HtVb5m7fYVtC90vuZmnfs6c5FgAAAAAAgEPFqsdooJqUlkawCkTJ+ffcK4vWrpV1W7faluDpY37bsEEueuhh2wIAAAAAAOBDsOoxDAUARFdcYqL89Y475afcvJDCVd33p+xNcv6dd5ljAAAAAAAA+KshEvnELogeJq8Coq/vuefJWTfeKFNXrZYF27fb1pLNz8uTqStXyek33CB9zxtoWwEAAAAAAIpQseoxsQkJ5gYguv7vvIEyevLHsqXJATJtfZYs2LhRcnbskJ27dpmbri8sbJu6Zq1sOeBAGf3RJ3LsRRfbRwMAAAAAAOyNYNVjWo8YIX2zs804qwCi64CDDpK///s1OTtttNTo1l2+25wnX/8yV2Zk/GzWYw4/Qv5y731y43/eMPsCAAAAAACUhGAVQLXT/cST5Monn5KHvp4u5yQdIqc2aWrWr3x6nNkGAAAAAABQFoJVj1kxdqzMaNvWLAGUr5xp08zNTRoHAAAAAAAQLIJVj8nPyZHtmZlmCaB8LRs92q7tvQ4AAAAAAFAWglWPaZqaKl0mTZLmgwbZFgDlwVWrOlStAgAAAACAUNQQKbCr8IK45GQTrtZNSrItAMpDcRWqVK0CAAAAAIBgUbHqMVpBp+OravUcgPIRWK3qULUKAAAAAACCRbDqMRr2LBk5knAHKEelVaZStQoAAAAAAIJBsOoxOgRAQkoKQwEA5USrUnWCOH2PFXfTieOKq2YFAAAAAADwR7DqMTppVbepU5m8CignOo5x76VL99zajRljbv5t+uMGAAAAAABAaQhWPUar5fQGoGLMGzDA3AAAAAAAAEJBsOoxOnHV9MREyUxLsy0AylNsQoK5AQAAAAAAhIJgFUC11jc729wAAAAAAABCQbDqMa1HjJAes2czxioAAAAAAADgYQSrHqOXJOvkOjo7OYDyN6NtW3MDAAAAAAAIBcGqx2Slp8vCwYPNEkD5256ZaW4AAAAAAAChIFj1mLyMDFkzcaJZAih/XSZNMjcAAAAAAIBQEKx6TEJKihlnVYcDAFD+mqammhsAAAAAAEAoCFY9RoPVdmPGEPQAFWTF2LHmBgAAAAAAEAqCVY/RsR51GID8nBzbAqA8LRk50twAAAAAAABCQbDqMTq+6o/du1NBB1QQrRLXGwAAAAAAQCgIVgFUa92mTjU3AAAAAACAUBCsekxSWpr0KygwSwDlT4fdYOgNAAAAAAAQKoJVANXa9MREcwMAAAAAAAgFwarH6NiqOsaqjrUKAAAAAAAAwJsIVj1GL0nOy8iQ7ZmZtgVAeeoxe7a5AQAAAAAAhIJg1WOapqZKuzFjmKUcqCBxycnmBgAAAAAAEAqCVY/RgKf1iBEEq0AFWTh4sLkBAAAAAACEgmDVY3QYAB1fVZcAyp++3xjTGAAAAAAAhIpg1WOy0tNN9ZwuAZQ/rRDXGwAAAAAAQCgIVj2mblKSGQ4gNiHBtgAoTzqmsd4AAAAAAABCQbDqMc0HDTIzlFNBB1QMHXaDoTcAAAAAAECoCFYBVGs/du9ubgAAAAAAAKEgWPWYzLQ0mZ6YKCvGjrUtAAAAAAAAALyGYNWD8nNyzA1A+etXUGBuAAAAAAAAoSBY9RgdY7Xb1KlmCQAAAAAo4ib5pRAFAOAFBKseUzcpSRJSUswSQPljjFX8P3vvASBZVab9P7dy7K4OkzMzwOAwzICkIQtIRkBRZEGFVTEtK6j/NbAK+n2gu58Kyuq6uizoigooQaJIlCxpCMPk2JM7d+V4/+c5996ZmpoKPT2pe+b9Dafr3nNPrjqHuk+99z2CIAiCIAiCIAiCMBREWB1mdN1/P5Zfey36nnnGjhEEYXeSmD9fB0EQBEEQBEEQBEEQhB1BhNVhBgUeblwlwqog7Bmm33yzDoIgCIIgCIIgCIIgCDuCCKvDjMjcuWi/8EJxBSAIe4iJ11yjgyAIgiAIgiAIgiAIwo4gwuowg6LqrPvuk82rBGEPsfGOO3QQBEEQBEEQBEEQBEHYEURYHWZwd8vMqlWyy6Ug7CEWX3mlDoIgCIIgCIIgCIIgCDuCCKvDDPpXfWXaNP0qCMLuh+43GARBEARBEARBEARBEHYEEVYFQdivef+bb+ogCIIgCIIgCIIgCIKwI4iwOsyYesMNOL63VzbTEQRBEARBEARBEARBEIRhjAirwxBPLKaDIAi7nxdaWnQQBEEQBEEQBEEQBEHYEURYHWZ03X8/Flx0kexSLgh7CG4UJ5vFCYIgCIIgCIIgCIKwo4iwOsxIzJ+vxdXMqlV2jCAIu5M5Tz+tgyAIgiAIgiAIgiAIwo4gwuowI3bKKZhy/fX6VRCE3Q/nmsw3QRAEYSRjGIaEfSQIwkii2md4XwoPXH+aDtWu7UtBEISdw9jQ0WGu7OjAvHnz7ChBEIT9h+XXXqtfp998s34VBEEQ9l8u/N5T+vX+75yqX0cKvDHeGBd/4SOdsdFemKZpnwm1eGXaNP103zErVyIwdaodK+wNZO0Z+ci6IzSCbvO4Jwn3AeJG68L2iMXqMENcAQjCnmXtLbfoIAiCIAiCIAiCIAiCsCOIsDrMkM2rBGHP0n7hhToIgiAIgiAIww9aS731gQ9sCc6mo7xncuL6nnlGxwmCIAjCnkaE1WEGzasZBEHYM8y67z4dBEEQBEEQhOGHc39E8ZTBEVb5pB/P+aRfZO5cHScIgiAIexoRVocZE6+5RvutmHrDDXaMIAi7E34ZF9cbgiAIgiAIw5d6vvAnfPnLYpgiCIIg7DVEWBUEYb+GGyAwCIIgCIIgCMMTblJVzXUT48decYV9JgiCIAh7HhFWhxncRIcij2ymIwiCIAiCIAiCYFHNalWsVQVBEIS9jQirwwz6DOJjyY7vIEEQdi90vcEgCIIgCIIgDF8qrVbFWlUQBEEYDoiwOszglwVupCNfEgRhz+BsiCAIgiAIgiAMb8qtVsVaVRAEQRgOiLA6zOCOlhRX+QusIAi7nwUXXaSDIAiCIAiCMLxxrFbFWlUQBEEYLoiwOszoe+YZ7V81MX++HSMIwu6k6/77dRAEQRAEQRCGP7RaFWtVQRAEYbggwuowg8Lq8muvFaFHEPYQU66/XgdBEARBEARhePL7Nzbjkl+/hyk3vIS2n3XgiOUH6eNLfr1AXxMEQRCEvYUIq8MMPtYSO+UUcQUgCHuIqTfcoIMgCIIg7Ku8+FzBPqpNozS//FnWPqpOf7+Jd98u2mfVefShvH1UnUZtGEwdjcq4686cfVSdXVHHruiHYPFaRxzH/Ph1fPfBZViydBMONNP4QKSAo9uD+njJ0k59jWmYVhheDGa+dawp2WfV2RPrRqM27OyawPJ3to5GY8n8jepo1E5BEIaGCKvDDPoKmvP00+IzSBD2EOIKQBAEQdjX+dXPM3XFCV5jmno89nCu7k07b9jrlUFx40ffT9tn1WEd9W78B1PHlz+ftM+q89hDO9cP8p1vpOyj6tx1Z7auCDKYOgTge4+twnE/eRO+VBozjQym+ExE3IDHMHXg8RRfSV/zqjRMe/0jK+3cwnCg0Xy767e5hnP6O1+vP9+Yf2fmG+s4/bgB+6w6V16aqNuPRm149ME8fnhT7fWPbfjIOfV/GGg0lrzOemoxmHVeEIShIcLqMKPQ16eDIAh7Btm8ShAEQdgXqHfDTUsm3nTXYsHbhbqCJvMvUKHeTfvdd2ZVHbWvs3yWU68elk9RshaDqYPjUGssKF5QXLjrt0Ovg31gqCeiLHiH/axdxkvP5+vWIQA3/XUN/t9THTg5UkBbqfZYO7SrNEz7o2fW6rzCnqPefOOcrCco8scUiqu14LUBVU6t+cY6WH+9+cY5XW/dceqv1Q/Od7ah1vrHNlh9rd0G9rPenG/UBsZz7ao/lvWFU647HAe2VRCEXYsIq8MMblz1QksLVsmjyYKwR+DGB7L5gSAIgjDSqSUW8mb87PO9dYUF3pCfdZ63pnjBvJdc7qt50+4ICyyjljBAYfeSy3w1hVO287gTPTXbOZg6KKDoOmqMBcWLq77kV/2ofp3ls+xG/fjaNwM1RRSnHxRXa8E+1uvrvkoyM7j+8pH+Gx5bhWNDeYR34G6VaeepPDf8ZZW4BdhJBvtekVpiH+fbZ7/or/k55xxrbjbqzjeKgbf8IlxzvrHss8711pxvzrrBNZBzsxoUPb/6zWDNdYNz/nv/FtLpqsE2HHqYu2YbKMw6/aw1Fk4baq2xFHWtdad6ftYxabJLt6PaWHIMmJfruPyoIwi7HhFWBUHYrzm+t1cHQRAEQRiOvLuqF0/N32Cf1aaWWEhhYt4J3ppCnnND/tkv1hYLedPP65OmuKretDvCwlnn1RY1KTpQnKglDNBqlvnZzqHU4YiirKPeWLAftYQc1sH6WUctgYMi9Ge/FKgporAfznhXE3IYx/H+2GX+uta5+yIU6y783lO47fGldkx1rvr9Ihzd4tohUdWBeY5sMlQZi+0YYSis3BjHZf/+Nzz4cocdU5tac4Hz7ZLL/TXXHoqFs2Zbc7qaOLtFFFXztVYd/DHFWTeq/TBEcfe4E9ScPpeC4vbXuQ5Q9LR+OKo+H50fnjpWV7eG121Q5ddqA/vONUGvXVXmfHkbalnFcg12xrJaG9g3rimso1oZztpG8bbaOAiCsHOIsDrMmHjNNXj/m2+Kj1VBEIR9gHVXh5F+8177TBAEYcehGPXTPy/UIkc9gXUwYmG1m3rnhryWxRWFDT4GSzHwEpWu2k17ubBQTZxwrDgpHtQUBh620rCd1UTNRnU4/axlAeeINOzHcSd6q/ajXIR2Hs0txxGhnX5UE1GcfrCcagIGhVeON9NUE5v2ByjW1RJYucN/IpnTj/YPldHIqzKyuixh6HDt4XvUSAyv9lnm/HPmW821xxYLawmnjihKqs03Z05z7eIPJtXmW7m4W60OR9yttW6Ur11f+1Zwu3XDaQPT1BIt2QZazNbqZ3kbWE5lGxyLV2csq4nQW9dPb1Vf1s76yXJYHtssCMKuQ4TVYQYfSY7MnYvA1Kl2jCAIu5NXpk3TQRB2F31/uBrJ535lnwmCIAyNRgJrNbGQYke5EFhNyHNuyAlfK8ULiht81Jbwpv3u320rkPAGnUIA87KeauKEY8VJqgkD2wmWFaLmYOqgcODUUWssHJGmlvig09SpwxGhCUWKSgvf8n5QRKk33k5fK+vYn6gmsP7h9Y2IlrYXvXeUJjOvytpknwk7Sz0xnHOhUjjl/HPmWzVBsVwsJJwLlfPFEV5JNYv68jnNcirnkiN6ltdRub6V11HtRx3nxxZSbf0rbwP7U9kG9pOUt6HSkr2yDZXrI+tw2lBrLJ11h6Fy7SpfPwndpYg7AEHYtRgbOjrMlR0dmDdvnh0l7E24O3n3Aw+g7YIL0H7hhXasIAi7i2cNQ7+ebMovt8Kuhxar7R/7Ifqe+ClCR1+K6Lnfsa8IgjAcoXBAPn3Ggfp1OLByc6KqkBoOeHQ7T507Dob6f9nitTEcPatfvzrwBn3WYR59M064Yz6trpybfN6QU2C8/fcRfc4bfoqHP/lFWJ8T7obNx+udPEepOu59NLrlnHkoPnzvByF9TuGiv88EfZk6nH78AP70SFTf9PMmf0fbOdg6nnihSR9Xq4P9oDjjiAuV/WIdFGedsWjUD+fcqZNU6wetfJ06q413eb8+9+M/6te9CT9Xu5N6vjvPP3YSbnihEwcjo3f83xkSRXVfZXjx4wtn2DEW9ep3SGQai06p3PaWh9UYTH17Mg1JDqJ/LKteeXyvuP5w7dkYb9luLnz4nLheR5z59Z1vpLQ46MwNzpWmZteW+VW59lAI/M7XU1vmCqmso3IOs45Zsz1aOCS//FkWA/0lvZYQlsl6nToq5yPXjdOPG8CrC5r1OWlUZ+W6UtmGyjWBIilF6FptIFxjy9swmLEsr6Ny7apcZ9jPa9Ta5NQ5NtoLU+6DhDpwc3XuA0QjQHGhVx0RVocZ3LRq9Xe/iynXX4+psoGVIOx2+GMGkR8yhN0BhdWxX7gHxWQv+p68Ff5px6D5kp/YVwVBGG44wupIYnQsgDu+fiY6Or3b3YBXCoG8wab1qCM0VN6Qk3IhgTfgFA7uVWU4UKwgzk175U1+pahZTTioFE4r20lhoGN1cUs7G9VRKRyQ8rGo1o9K8aFyLAbTD7bLcU9Aqo13uVjUqI7hIKzuTQ6d2oL/XjCAI0MleIydE3oKprHTZQjVofjOH3TOP3oSxrQEtbBaLihWmyuVgmKlQEnK155KUZSU11FtTjOuXDBsVAfnY7m4S8rXpmrrSvm6MZg2cB3isbMmkMG0wflBZjBjWbnuVK4rbGO5+EvKfyATYVVohAirjRFhdZjR98wz2mK1+eSTRegRBEEY4TjCqqZUQN+T/wEjMgqtn77TihMEYVjBDVueenujfTY82NyXwSuLOu2zrVDcOO/oiTht7vgt4kb5TX+1G/LKG+5KIZaUi4WVAiepFA4qb+pJuaBRTbwtL3cw7RxqHc5YVBNIWEe5dVq1OspFlmp1lAuntfqxI3UMB4FjsFaPQ2FzXxrX/vJV+2wrFFQ/fcYMTBsbRej/+xtOixbhxs4Lq5uLBi4/cowdYzEYi9yQr7G5bCSw9XNQj8HUtyfTkPAg2s618Pt3v2OfWbB8R1DlDzrEsVjl55+P0XMuVP744sDPPwXFanOFlP+AUm1tKl97qgmvxJlj9AvNuVUuepJycbaa8Fo+p6sJkuVzmmnLf6hyoGj5xItNerOrWv0cbBuqrTvEGctqaxtx1pUmtdawjspxKH+PRFgVGiHCamNEWBUEYb9m7S236FduHCcIu5pthFWb/r/9N0r5NNquugeGf9sv24IgCJVQVC0XOChuOIJqpbjBm/6PnBPXN9y1bsidG26KjtVu+iuFBW7Iws1hynGEA1pOlT8+71AualYTE9lORzht1E6KMOVWnw6DqcMZi2oCCXGstmqNRXkd1YQe4jy2O9TxLheZ93WBgz8SXPXTF+2zbQVVh0nXv4SDkd4lrgAWI4CO7x5nxwg7wrurevGvv3lTH1cTVB2ctYc4Yl+tueIIivy8V5srjjjLuVptrhBnrlNgrRQkiVMH51s14dURZ7muNaqjmiBJnDlNVwXV1keuBZOmuHe6n4MZywXvWP5XK+tw1hXWX0385Tg4/RNhVWiECKuN2XaGCnudzKpVSMyfrz+8giDsfpZfe60OgrCnaD7pM/A0jUbXzaeh2L3KjhUEQagPxY1LTpqKm686GpeecsB2AgehsEhBgb4EyzelKsfZ2IliJW/IK+ENOgVT3ngzVIoGhDtcc/MTZ4f7Srh5CoUDCggUBMoFT8JzZ4OVF58vVK3DaSfr4IYulQymDo4Fr5dv3FIOxQZnLBr1g+VVihuE5dYbb7b9rt9mG9Sx7YY4+zoUVG++6ij8308evo2oSg6fEEF3cdv3cih0F126LGHocM05ZuYo/T7Rn2q1Naccfv4p+NWaKx/7B79aW/J6zlebK1wHuDFTrbWJMB83t+Ocq1aHszN/+YZQ5bBter7WmI+EdVDwrLYuEa5NnNO11sfPfilgt6H6mlDez3ptoEDbcCzVWl0pqhJnXeEPX2xvJXocVpf0WAiCsPOIsDrM2HjHHXj98MO3WNEJgrB7iZ1yig6CsCeJHnUJ/FOPROctZyC3+nU7VhAEYXsGI6iWQysobTFVRWwkvOGmaEBxgwJpNRzxwtntupKt4kV14UCLAVNcWpSsLRx46woHTjt3pg6OBYWeavkJ66AA06gO7gReS+ihcEoLtlrjzXIpcNQSkJmH7dgfBA5+lmsJqg6XHjkG/Ub1sd4R+g2PKmusfSbsKKNjQf0+ffNjs2u+V5VwvnHdqDUf+fmnEMjPfLW5QjhfaG1Za23i2sM5fda51a+z3Ho/hBCnjprrwrk+/ag8X6vB+frD72dqro9OG2qtCWQwbeC60Wgsa+VnvWwnxd9aaZwflgRB2HlEWBUEYb9mztNP6yAIe5rwYecicsRF6L71HGTe+4sdKwiCsC207huMoOpwyeU+fcNe64acN9y80a4nPFAstATJ+uJFPeGAm6/QqrWmcKBu+tnOataohOXOOsy9U3VwLCg+1BJFWS6tthrVQZGlngjNOuqNN/vK11rj7Vjn7utQWG0k0l16xGhEI35sKFV/TwfDuqIbTaoMliUMDa43gxVUHfj55g8R1X5AcOA8qjVXCMVZ5q81V5x5VM0a1YFl1JrzxHl8v9ac55ymFWitdYX56B+1URsG0896beBY1moD4VjWEn8J19Za4i/R61Ksev2CIOwYIqwOM6becANONk39KgjC7oduN8T1hrC3CB58MppP/RJ6fvlxJF/+rR0rCIIwdHijzhv2euIGb7hnza59nTfzzN/opr6ecMCbduavJRwwvp54QShq7mwd7Ee1R2UdKJA0qoP56wk9LKPeeFM4rSUgExE4tuW2f5iJN+NAcghGvMzzVsLAf6syhD0PN1GqNVcIBcV6c555KXzWgz5J69XBH1Rq/RBCOGcb1UH/rfVgPxq1YWf7ybGstbaRRmPJa/XEX2ftEgRh55HNqwRB2K951rC+sPAHDUHY1VTbvKoa+U2L0ffETxH5wNWInP4VO1YQBGFwlG8gQ2hNWk/o4+Oh3DW7njDQqAw+us4dp+vd+A+mjHptYDvJztTBMurlH8xY0LK2ngAxmH40qoPIJjJb+b+Pr8b3VTguUkC4/rBtgaLqi0kPvnH6FHz7zCl2rLA7qVx7hJGHrDtCI2TzqsYM8n9Twp6CvlXpY5W+VgVBEIT9A++YgxE75zqkXvo1Bu7/lh0rCIIwNOoJjYRCYyORr1EZzF9PsCSDKaMeLH9n62iUfzBj0ciqazD9aJRG2JZ/PWMKvnzyBDwb9+hH+xuxNu/Saf/5pAkiqgqCIAh7FPk//DCDvwYk5s9HZpXsFC0Ie4L3v/mmDoKwt/G2jEfLudcht/Rv6Lvz83asIAiCIOyf3HT+dLx4zeEwo2G8lfNiVdaFRFHdL5mGDjxelTX0NTRHdNrvqzyCIAiCsCcRYXWY0X7hhZh+882yS7kg7CEic+fqIAjDAVcggpZzv4Vibwe6/+tiQB7NEgRBEPZjjpwUxWtfOxI3ffhgTJ3WisUI4Mm4WwceT53Wrq8xDdMKgiAIwp5GfKwKgrBfs/jKK/Xrwbffrl8FYVcyWB+r1Yi/+Bvk45vR+tm74Y6027GCIAjbI34O9w3E16Ew0pC1Z+Qj647QCPGx2hixWB1m0A0A/avyVRCE3Q/nm/g0FoYj0eM+CW/7NHTf8kEUNi2xYwVBEARBEARBEIThggirw4yu++/XFnR8FQRh9zPxmmt0EIThSPT9H0Hw4JPQ9ZMzkFv+oh0rCIKwY7z4XME+qk2jNL/8WdY+qg53vucO/fV49KG8fVSdRm0YTB2NyuAO//XYFXXsin4Iwr7AYOZCx5qSfVadPbFuNGrDzq4Jg+lno+uN1i62sVEZjdopCMLQEGF1mBGYOlX7e6SZtSAIux/6NGYQhOFKaNaZiB5zGTp/ejbS8+VHN0EQdpxf/TxTV5zgNaapx2MP5+retPOGvV4ZFBZ+9P20fVYd1lHvxn8wdXz580n7rDqPPbRz/SDf+UbKPqrOXXdm64ogg6lDEPYF+DmvN9/u+m1Ohdo/2nBOf+fr9ecb69jZdePDZ8fts+pceWlCp6sF1656a+xg+tmoDY3WLl5/9MHabaDwyrVJEIRdjwirw4yxV1yhdygXCzpB2DPQ7Ya43hCGO4EDj0fLOd9A72+vQuqF2+xYQRCErdS74eYNNW+6a7Hg7UJdYYL5F6hQ76b9bnXD/lgdYYHls5x69bD8ejf+g6mD41BrLCheUPyoJ3A0qoN9sASKOuP5DvtZu4yXns/XrUMQRhL15hvnZL35xrnwq5/Xvs78A6oczrlaWPNx59YN1lGrHyyf1+qVwbWr3ho72H42Gst6azDXHdZTC7ZP1h1B2D2IsCoIwn7N64cfroMgDHf8k+ag7fzvIP6Xf8PAo9+3YwVBECxqiRcUEs8+36tvymvx2MN5nHWet6ZYyLyXXO6rafXFm34GllFLGOBN/SWX+WoKIGzncSd6arZzMHVQQNF11BgLWo1d9SV/TYGD5bPsRv342jcDqp3VBQqnHxQ5asE+1uurIIwkaol9W9eN2vNtUHO6ztrTaH3bkTpq9YNz/ie/COvXalB4PfQw925tgzOWtaz+HeGZr6yrGs46zzETBGHXIsLqMGPVDTfoHdfW3nKLHSMIgiAIFp5RB6Dl3OuQefNP6P/jV+1YQRAEPg5bXbygBdO8E7w1hTzeiE+a7MJnv1hbLORjrrw+aYqrqjDAciksnHVebVGTQuP3/i1UU3yg1Szzs51DqYNiAvOxjnpjwX7UEjhYB+tnHbWEHIoTn/1SoKZwyn44411NwGAcx/tjl/nrWtkJwkihlthHsfCsc33159sJHjVXvFUFRUeQ/Oo3gzXXjfL1bajrhlPH3b+rvXbVKp9QcOV8rteGwfaz1rrDsfzYP/jrjiXHgT8sVbNKddZ5jkMtgVgQhKEjwuowpNDXp4MgCLufk01TB0EYjvTFk3j21Xfw6/ufwKp1m3ScOzoKLed+E7k1b6Dv11fqOEEQhMGIhdWEPEcUoPhQTSzkDT8fUeVN+SUqXTVhwBFQWE81UdOx4mxuNmqKDxQsnXZWExca1eH0k3VUGwtHvGA/agkc5SINrVsrccQJpx/VLHydfrCcagIGhVdHhGGbBWGkw89yrfnGa7XmG+c0r3G+VpvznB9cl+qtG+XrW7UyOAedOV1r3XDq6O/b/lF8xxLUWjfqz/m6a5e6xn5WE6HL21DtxytnLJmm1ljyxy9a7vJHn2rrjrPOn63aUM+aXhCEoSHC6jCDPlbnPP20fhUEQRD2bz593c047Ypv6NcZZ/yjFlmJyxtE61n/H8xUD7p/9iGY+eoWDoIg7D9Uu+HmDXu5EMjzShxRgFQTDigwnnWuVx9TGKi06uINP4UA5mU91URNx4qTUGD44U3bigvbCZYVouZg6qB44dRRayxoNUbqCRz16nDECUKRt9LCt7wfFDDqjbfT18o6BGGkwTldbb5RCCScS5WCoyMWOnOhmqBYPqdZR6XFaaN1g1BE5FysNaedH2wIrdmr9cNpAy1G6815tqEyv9PPegJxeRuq/XjVaCxZnvPjF+vgOess58XnrbWN8LXa2iQIwtARYXWYEZg6FbFTTtGvgiDsfip9rG684w4dHKvxxPz5+pyvhPFOGoeu++/fJk1m1Sp9zngHJ09luX3PPKPPiZPGgdd4XllueRqnbl4j9cqVPu35Pk38jxQ8s84dcnjgyZfw5U9egD/99F8Ri4bxm/uf0HU4NJ38ObiDzei65XQU+9bZsYIg7I9UEwspAjhCYLWb+nJRgFBAqBQOaMV5yeVby6i06uINunPDTqqJmpXibeWjquWCJeuoFEAGUwfTU0Ah1caC4gXzkWpj4Tyi74zFYPpRaflV3g/CNOUCRuV417JwE4SRBOdb5Q8u5aIoP++Vwmm5WEiqCYrlc5pzqdLitNG64VjKO1TO6XJxl9CHKde7chxLUML21pvz1daVyn5WitCVbWAfWGc55cJrtbFkec6PX4Rlla+xXHeYj4HwfdmX3ZCsuzqM9Jv32meCsGcQYXWYwRv85ddeu81NvCAIu4/E/PlbRDSy+MordWA82fTrX+vzdT/5iT5nWieNk4/XeO4IdMzLc85lh9Xf/a6Oc+Y20/Kc8aS8XEdQdOrmK2G8k8ahstxqdTt5pE97r087w7e/eBkuOG0ePnXR6VpopXuAcqLHXgrf+Fno+skZyK+zLFoFQdj/4E3zLHUDX37DXS4Ekkqrr0ohsPIxUeemn2KgQ6VVl/P4vEOlqFkpJvK1UgCpbCcFkPJ2NqqjUkCpHItK8YJUChzOI/oOjfpBWF65cFp1vMsEjGrCazUrO0EYSXBONKlQPqd57IiipFI4LRcLSaWgWG1OD2bdqJzT9daNStGTdXCec60grMuxBHVgfWybQ7klKKlcY6v1s1yErtaGjtWlumtX5ViW//hFuEZzrXHgMdvlwLLK1619kb4/XI3kc7+yzwRh9yPC6jAjoW70uXGVc1MvCMLu5eDbb0f7hRfaZ5Y7DgZPLKbPw3Pm6HO+EsY7aRzaLrhAn0fmztXntDjneXm5Yz71KR3nlMu0PG8++WR9TnjO4MBrPHfqdsotT+PU7Vi5O+XS8t3BySN92vN9WvtPIRQWPDykcNuNluDbN5DQr6vWbcaUCWO05WolkcMvQOjQs9B1yxnILpb/fwjC/kr5DXctIbDc6ouiQPlNPSm/6abVk/P4vEOlVRfTlt/0sz6eO8JApZhIyoXTau2k+LBNOxvUUSmgkPKxqBQviFXHVmvRSpGGdZQLOdX6UW75NRThtbIOQRiplM+3SlGU8HNeLpzyM185F8oFxWpzulw4rbVulAun1eZb+Q8ulT/YkPLNn1hXuSUoYXpHtGQbWGZ5G1ifs3ZREK3Wz3Kr/1ptaLR2OWuwI7yWi788dsonleIv28Dz8jT7Gq3n/SsST/wY8Ye/Z8cIwu7F2NDRYa7s6MC8efPsKGFvQusoWj05AoAgCIIwcuHjSGO/cI99tmPQMrX92I/p4zkzD8Bbi1bgO1/8B3znS5fpuGpkVryMvsdvRusnf4XgkR+3YwVB2NcxDAMb4y36Bvsj58TxxAtN2o/prMM821iNkS9/PomvfSuob+4pQtz++4h9xYKiCIWDn/wijCsvTeidqitv7I+a1Y8nXmzSN/20yKosg35aWf5VX/Lj9OMH8KdHotuID2zn0aqMxWtjDdtJ8cJpTzmDqcMZC/aDVlzl4gJhP+59NFpzLMrr+LAqi20oFzAIy3h1QfOQx5t1dKwu6jRjo70wZUNNYQRRvvZcoz7r/HzXmgvOfOOcpjD4vR+E7CsWzNfU7Nol68Zg5jTr4PpQTnk/uG58799C2815J98vf5ZFc8zQQmg55WtXtX4yHxlMG77zjZQWXuuNJUVo1lcO882a7VHBXXOdd/Lta+uO8927mOxF35O3wj/tGDRfYj3RJgwNPrH3QkuLNjw5vrfXjhXKEYvVYQYtp2bdd5+IqoIgCPs5tEx98o4faFF19bpN2h3AP39yq3VtNQIHHIvWD30Hffd8DYln/sOOFQRhf6HcGqrSWsvBsfqiWFlpKUV4A0/BlDf3DJWiKqHPQVp18ca80oqT0KKK1qC86a+0KiM8dyw1q1nNEqedrKP8MVaHwdTBseB11lNtLCgqOGPRqB8sr1JgIY7lV63xdh4Nrl/HVutcQRiJcH44c63WXHAsMatZaRLuaM9r9ea0Y3FaaYXpUL6+1ZpvtJytZlVLWAfLd9a/anOeYiXXSJbTqA3V+ulY/Q+mDayn3lhqVwPV1sdzrc31mJ/trYRl7uvrjjvcgrbzvoXCxoXoua22UYIg7ApEWB1m8NcA+udzfAIKgiAI+y8nHzUbr//pVnS9fPeWDawaQX+rred/G8ln/xPxhyzfsIIg7D/QMpPWXNWECUJhgRZMFCacTVkq4U03fX9WugFwoBUrRYNaAgrr5QYrFCWriRuE4gPbqdNWES+cdu5MHRwLWm5Vy08ckaVRHfSJWE0gIRQ1aKFWa7xZLgWMWgIy87AdFFIEYSRDsa/e2uMIp7XEQuahqMr5VmtO0+WAs25Uq4NziWtCvTlNlwO1RE/CfLQYrbX+cc5zXWFZtdYutoH+qistTYnTz1oCM2EbvvN1Wp26q/aTY8m1q9aPX8zP+llHuf9VB5a5X6w7Lg9iH7wGyKfRdevZMLOWey1B2NWIsDrMoH/VV6ZN06+CIAiCMBQ8rZPQeu51yCx4DL1/uNqOFQRhf4DWUHzctZYwwRtq3nTXEgWIIxxQ/KyGIwzUElAIBRDto7WKuEF4U892VrO2IiyX1mk7UwfHgiJOLfGC5VJkaVQHH92tJ0Kzjnrjzb7ytdZ4OxZugjCSodjXaO1ptG5wnukfdXZi3eCPIfXqsDZ3qr1u8DqtSWvVwXxcN6pZghKnDbXKJxSh6/WTP1416ifbUEv8Jbqda6z1rRr707rTfNJn4Gkaja6bT0Ox29p8VhB2JSKsCoIgCMI+iCsUQ8u530Rp8xL0/PeldqwgCPs6vOGmBVM1KyYH3qzXEgUIb8iZv54wQAGkloBCKIAwfy1xg/G05qpXB0XNna2D/ahmNeZAgaNRHcxfS5xgHSyj3nhTwKglkBAtvMaq90EQRgrOfNuZdYOCYqM5zbm4M+sGf3CpVwfL55yvNaeZj3O2miWoA9tQ6wcdQhF6MGtXo7Gs9eMXYf2Vm2+Vs7+tO9GjLoF/6pHovOUM5Fa/bscKwq5BNq8ahjhuAJxdqQVBEISRyc5sXrUr6X/uNpTSCbR97h4YwW03SRAEYeTjbCDjQKuwekIfHx8dqOE/0KFRGbSEalI3/7WEATKYMuq1ge0kO1MHy6iXfzBjQcsxiqe1GEw/GtVBZPMqYaRRufbUeszfYVfM6T21vtXLvyfWrkZjORjYjnptIPvq5lW1SC9+FvEX7kDLP/4GgfedaccK9ZDNqxojwqogCIIg7CaGi7BK4q/ehey699B21V3wtB9gxwqCsC9QKW4IIxMRVoWRhqw9I5/9TVgl2VWvoe/xm9H88VsRPvZyO1aohQirjan/s6mwx+m6/34suOgibLzjDjtGEARBEHYePgIVmn4sum45E7lVr9qxgiAIgiAIgrD/QJcArRd8B/EHv43EEz+2YwVh6IiwOsxIzJ+vxdXMKnGqLAiCIOxaQrPPRuT9H0bXrecg8+5jdqwgCIIgCIIg7D94xxyM2DnXIfXSrzFw/7fsWEEYGiKsDjNip5yCKddfr18FQRAEYVcTPPhkxE7/Z/TcdhlSL//GjhUEQRAEQRCE/Qdvy3i0nHsdckv/hr47P2/HCsKOI8LqMIOC6tQbbhBhVRAEQdht+Ke8H60f+jb6H7wBib/+yI4VBEEQBEEQhP0HVyCClnO/hWJvB7r/62JA/FwLQ0CE1WGGuAIQBEEQ9gTeMQeh9bzrkHzlf+URKEEQBEEQBGG/JXbql+B2e9B5y2koJrrsWEEYHCKsDjNk8ypBEARhT+FpHofWc69Ddtnz6PvtVXasIAiCIAw/Fr38Mu7+/k34zhmn4dqj3o/PHDRDv/Kc8bwuCIIwVKLHfRLe9mnovuWDKGxaYscKQmNEWB1meGIxHQRBEARhT+Dyh9F6zjdQ6l+Prl98GGapaF8RBEEQhL1PorcX//O1r+J//vmfsOnRR3CY4cIZY0fjslnvw5nqleeM/5+r/wn/fe01Or0wPHjxuYJ9VJ3+fhMda0r2WXUefShvH1VnMHW8+3b97zaN2tAo/67oZ6Prd92Zs4+qwzY2KqNROwUg+v6PIHjwSej6yRnILX/RjhWE+oiwOsyYeM01OF59GaCfVUEQBEHYUzR/4Itwe3z6V/pSotOOFQRhX2AwN9ON0vzyZ1n7qDoUDhqVsScEkkZ1NBInBlNHo3YOph+N0ggWz9/1B1x32geQXvAuzpkwHoe1tWJUKIiQx6tvZIPqleeMP2fieGTeW4BvnXoKnlP5hL3Pr36eqSv23fXbnAq11xbOle98PWWfVYd11JtPvMY0tWAdHz47bp9V58pLEzpdLR57OFd37RlMPxu14bGHcnXHktcffbB2G7iu3XVn/XVcsAjNOhPRYy5D50/PRnr+/XasINRGhFVBEARBEDRNfARq9HR03nwaChsX2bGCIIx0KCrUExQpCNQTHsjdv8vWvamneFHvpn2wAkm9dg5GIGlUB9vYqB8N6/hGqq7IMph+iMDRmJfuvw8P33IzThjVjkMDfhh2fC14fXYoiBNHj8Kjt9yi8wu7n1rziXNEf9brCIovPc+1p/Z15h9Q5dT7saORYHi3uvZYHdHTqaNWP1g+r9Urg4Imhc1aDLafjcaynnC64J2irqcWbF+9PgjbEjjweLSc8w30/vYqpF64zY4VhOqIsDrMWKu+BLwybZp+FQRBEIQ9TeSIixCaeSo6bzlD+17dUbgJoyAIe55awgNvyHmz/uJztW+oKTxQXK0lFmphYXWprkDCMnjjXwtHOKglOA62nYOpo9ZYMH6BCj+8KW3HbM9g6mA5tQQKpx/1RJZGdQhAZ0cH/vfb/4ojmqIYHQrZsYOD6Q9viuj8LEfYvdQS+/gZv+RyX01BkXOF4azzvDUFRc4Vq4zqP3Zw3Tr7fG/N+bQjddTqB+fyT34RrjmnuR4ceph7t7bBGcsffb/62uWseXxlXdV47OG8bkMjq/5K9ufvdf5Jc9B2/ncQ/8u/YeDR79uxgrA9IqwOMwp9fcisWqVfBUEQBGFvEJp1BqLzLkf3recg89bgHoHqe+YZvfkigyAIe55awgNvyM8616utmarBm3De7H/tmwH9uGo1KCh8799CDQUSChy1hFMKByyjlnDKdh53oqduOwdTx1e/Gaw5FuwHrzcSQBrVcfvvIzVFFme8OaYsq5LB1FGL/Ung+PXXvoq5kybtsKjqwHxzJkzAr7/6FTumOhxT2TR456gl9nGunHWur6agqOf8CR41771VBUVnrtSbs7TQnHeCV68dteqg6HnWeb6qPwyV10Gr/GpwTapVPuFa8LHL/HXbMNh+1lq7OJYf+wd/3bHkOFxymU+1Z/s6KLhOmuzS41Br7aqE3+ve+sAH9vvvdZ5RB6Dl3OuQefNP6P/jV+1YQdgWEVaHGe0XXohZ992HsVdcYccIgiAIwtD46f8+gFnnfQ6tx3wU533+eixauda+0pjggSeg5Zxvofe3n0fyuV/asdvjCKr88t11v/ihEoS9RS1BkjfkvKHnTX81IU/f9Ktrn/1SQPsJrIZj6VTrpp6CLIUD3vhXE04d4YA3/fXaSRGmVjsHW8dVX/LXrIPxFDQZqlltOQJIvTrY/7PVOFCoqDYW5eNdTeBw6vjsF9V474DAsT/9cPXmE39Fcv06HOz32TFDY2YwgOS6tbq8ShLz52P5tdfq/3et+8lP7FhhKFQTFJ35yGu1BEVnrnBdqSYocq5QFG1uNqrWQZz1i4JhtTI4xxzhtdoPQ+V19PdZ87scxxKUoiTLqLY2cX2s1wa9tqlr7Gc1Ebq8DZOmuGqOJdPUGkuu3VzX9DpeZV1xxF+uXbXWRwdHUGXg8f7GM39/Gz/8nz/hzgefRi5vCfru6Ci0nPtN5Na8gb5fX6njBKEcEVaHGZG5c7W4Gpg61Y4RBEEQhB2HoupXfvBLLF+zAbNmTMFjz72Gi7703S1fEgeDb9JhaD3/24j/9UeIP3KTHWshgqogDC94Y18pFpaLG7SGqnbD7QiavKnn4/7VhAWKCrxOcaDaY/S0Grvkcr++8a920+6IiaSaOFHezlqCY6M6HOGVVBuLcoGE/a01Fg37ocombGelwDHY8aY4wnZwrJm+FvvrOvv3e+/FeL/fPts5xvt9eFWV58AnAx1Bla7X+JSgPCm4c3BdqJwLjlhIqgmn5XOllqDIuUJRlLCOSovT8rWJ5VSzuNc/pqj6rfVr+x+GnPWPVJvT7IfThmo/uFS2odaawLFw0tRrwyWX+Xd4LFkeXaA47ai2rrz4/Na1i68ss5L9XVAlX/23X+L0K7+Jb/zof/Cpb/wQcy74whajBJc3iNaz/j+YqR50/+xDMPPbi+jC/ouxoaPDXNnRgXnz5tlRwt6Ei1hi/nzETjlFi6yCIAjCyGXd1WGM/cI99tmehZaqFFVXPnkHxo1q1b++84vizvDFc4/AjV/7kbbuqXeT74nF7KO9w96uf2cZqe0fyeM+0j8zx6r5+JtHouAGLvQF6EBxccHbBXztW0F9fvrxA/iTSsebb8Kbb+52fa+KIxQ8abVFq08HCqmzDvNocYLpTz9uAK8uaLavWjf1LOOJF5r0OTd2mjXbo61THXidbgB4488yrvl8Uj9O79ConYOp48PnxHXfWQdFg8qxKO8HYR1OeaRyLGr1g+ILhYnK9KSyH0fN6se9j0Z1m0ijOsZGe2Gapr4fqLbO0vDimJUr7bN9l2+efAKODYcR2wXial82i5eTSVz/69/qMeVj/5VCqjOuzjXnPoz3ZHwveJ2GL2TVDTfoVz5dyHhed9IwjvlXf/e7Os2U66/XawsF3Ozq1Wi74AJdNst1rGQPvv12/Uqxl3knfPnLum6+95t+/WuE58zBVLtOx2J5+s036/rYlv5nn8WYT31K181yWQ6Z8/TT+pV5KCazLewD+8i6WYdT9+uHH67r5lOTjGe5rJvpWRfzMw3hOLFPLJf9vkXlu3FtDB9R8698PvFz7lhIkvL5SThX+GPJ935guXqotvaUz1HOnaPVfFqs6nKonNNfVusK595g66icjzyvXJsq2125buyONlSuKzx31h1S2aZf/iyLgf7SlnWncl2h+EtLWadfbBN/9HHWx7PUunOH+lzy/ayGMz/42eJn2XnClp8L7g1Dju/t3fK54GeXnzd+bnnMOF5jGsI8zMsyWBbLZNmcG/zc8rP4QkuLTvv+N9/Un8nFV16pP7sTr7lGfyb5WXc+kyerNZPwnPHtJ/ow8xc7vnEdLVUpqrY0RXDnD7+Otxev1N+bzzrxSDz0C2tOO8Rf/j3y3SvR+tm74I5NsGP3XZz3pPx9FLZFhNVhBv9Hxv8ZO4uRIAiCMHLZm8IqH/+npepzd/5Qnz/+whs456pv6+Oh8vmTJ+Hrh03HpgdX6S/FgiAMD85TYVm8Zbubft6QO4ImqbzhrhQCeVNfKZBUipwUDmhV5dzkV97UUwTlzvzOTXw1oYCC4xMvNm0pk9dp4elYZVW2s1odFDQcYaBSeCWVY1HZj8HUMZh+lAunlePNMokjolQb73IhhwLHby+8cFj/cFXO7mrLHQEfPnLgDHjd1udhZ8gXi3j3tTdwVDqjxYFqOMKRI8xQuKGAQyGHgg6FHQo8hOICy6EARCFopIhNu6tP9E77A7X2NBIgKwVFzpVysbByflUKkqRStKyc05V1VIqerKNcnK1WR/naVG1d4bpBC9ZywZjz12lD5Ryv1s/yNbZWG5x1pdq6U20sy9edyrWL4zBpinvLWscyy38gu1itOz9Uc6DW97qRJqz+hzr+o44ZGheePg9//Mm/6uMTL/saFixbjZ5Xtv8un3jzAWSWPY/Wz/wB3gmz7dh9E+c9KX8fhW0RYXWYwQWDvxA6vzwKgiAII5e9KazSpyof///BV/8Rhx08DZd97d/QO6BuEG7/Pk45+jA71eAxC1n0P/kfcLVMRMsVv9ZflvlFuPKLOL+AOzdrewt+ARzJjNT2j+RxH+mfmVHqxnVjvGUbsbDaDTnjyoW8ypt+QrGC13mTzpt+PqbqpCeMK7d2qhRUSLngUSlYkh1tZ606HHGiUR2VFluEcXyk1ulHpThByvtRKZiQcoGjVj/K46rVUS7kNBI49hf+cPCBuHjmQfAYW8dpqFBYvXflKnzr05/VlprVxtYRjijwVFqW8r7M3dy8xeCl0mKV/y9kOoo/FIm4lvB+jlAEIrQEdNKwXLaBcRQpmIewHObldZbLNMzDNIzjNceikOeMZxoGHrNsUp6G8DrzMg3LJSyXOHmYhjANA9MzECePc87rDoxraWnRP+qUz4VqYmHlXCifvw7lgmKlKErK66g2p1lHuXBaPn8dyteSSpGUlIuQ1daV8vWvURt4XPlDFSnvZ602NDW76o6lsz5WjqtD+fhWWz/LRWrHUr7e9zrOj5GCYWx9v4fCCe+fhWd+8+/Y0NmDaaddgemTx2HBQ/9lX92W1MInkXj5d2j9zO/hP9iac/sinOsirNZHhFVBEARB2E3sTWGVPqHoU3Xp6vV2jPoi/ckL8KOvX2WfDZ5Cogt9T9yKwIEnovmjP7ZjLSq/iI+0L+CCsC/AG0kKq+WWnNVEAeLc1Dc1G1VvyMvFi2riBnFu2qsJC6Rc1KwmJpYLA/XaScGRfl8b1VFNOCgfi0b9qGaZRir7UW5VS9gPx/JrqONdLqIMRuDY2z9cleOIbbua6y+7FMc3N6N5JzevIo4rgO8/+7w+p3haKbDK/7eGjrP2lM/pWvPNmQtcNyrFQlI+F6qJoqzDES1r1eEIhlzDqq0b5etbufjoUN6PamsXcfJxzjfHDL0+lOO0oVY/mY8Mpg3VhFdSPpaVP/gQZ+2aNdtddRzKfyhy1h2Hkf69bqjfvbkHAX2q8nvzvLmH4NV3lqBQLOLH37gK//yJC+xU25NZ8TL6Hr8ZrZ/8FYJHftyO3bcQYbUxO/8zoLBL4Yd2d31JEQRBEPYfZk6biLce+E/8+gdf01artFQdkqjatRK9D92I4NyLthNVCa1t+IWbj3Txy7cgCHsPCgDOpkvcJZobMVVCC1VujkI/pM5mT+WUb47i7HZdCeMcyy1nY5dynI2bKFgyVAoTFEsoCPAahYdq7aRYwV31G9XBclheZR3bjkXtflBo4XicdW7tOih0MJSLqoT1Mq5ePyhcsPzyzbXK0bt4q/eqnHrrqmNVOBwC27Y7wgGHzcGm3PY7uA+FTek0pr5vln1mWZFybOlflHUJu4byOV1rvnFOcy5wrlSb05wLvMZyOH/LRVXC81n2fCvfjKkcZ9d8zlv6eK2E6xvnG9evavlZB8t35nzlukIoVnL9ZDmN2lCtn5dc7tP9HEwb9DpdZyz1xlfnbf8DBDfD4kZbzM/2VsIyf/Xz6nNsf/1e5/N6cN/Prtc+Vfn4Py1VG4mqJHDAsWj90HfQd8/XkHiGjgiE/RERVocZ/BWVvwY4j5kIgiAIwlDhl8TLzv8AvvaPHxnS4/+5jrfR8+D/QfSDX0XTOd+yY6vjfBF3NsMQBGHvwBtmWnM5u0RXwpt6CgK8Ia8mBPKmnmIhrapYVqW4QZxd9fWO2+dvLxwwD8UR7uJdTbAkFHh5nQJCtXZqwXEQddz9u607h1fC9tNyq5pIQ9gG1lFLhHbq+NXPMlVFUUJRg0J0rX5QyGEbawmvrINWucxfyf4qcBz9kY9gfXb7Hd6HwoZcXpV3sX22FT7KLwLrroViH9eemvPNFk5riYXlc7qaKEro35l1MG21OpwfhmqJu858qyV6EuajxWi9Oc91hWXVmvNsg1671HElTj9rCcxEr11fT2lRtFo/nR9kqv3gQ5if9ddbd9jOauuOw/74vY5GCdyoij5V+fh/I1HVwTd+FlrP/zaSz/4n4g9tu9GVsH8gwqogCIIgCNuRXvo8eh+5CS2X/yfCJw7e0tXx6yYIwt6BVpY//H6mpqDpCAu1rLGIIxzUu+mnOEJqlUGRhRZR1W7qCQXeRu2k+EDq1UGrrWrCK+FYUCCuJdKwXNbBuobaD4oTrKNeP+gGoJbwShyr1lrsbwLH4ad/EKFx47EoY1lOD5VFqTRC4yfo8mpBgZUb5gg7D8U+WoDXmm/OnOY84HE1OJe1dXcVUZRwvrGOalaaRM/lKa66dVg/qFQXXom26lfXa9XBfFxDq1mCEqcNtconXFfq9ZNrV6N+sg21xF+i21ln3XEsaxsh3+sGh6d1ElrPvQ6ZBY+h9w9X27HC/kL1WSbsNfh4Cn0nycZVgiAIwt4iteBxxF/6LdqufgSBORfZsYIgjAT0Tb26ka4lBBIKefVuyClesIxq1lYOFEBqCSiEIgtv7Gvd1A+2nY3qoLVWvTp4vZ7AwX7UEpAJ63CEmmqwDook9fpBK7tawivheNNXYyP2J4Hjih/9GG+tXYfNqZQds2Mw39vr1+OKH99sxwi7m8HOt3pzmoIi87OsajjrRr06ON/q1cEfderVwfK59lWzBCXMxznbaM43Wlca9XMwY0lxtBasf1esO8LgcYViaDn3myhtXoKe/77UjhX2B2TzKkEQBEHYTezNzauGSuKN+5BZ+QraPns3PGNn2rGCIAxnnA1kHGo9autAa9VargIcaFlWS1ggtISiJWYtYYDQ6queOFvPmoqwnaReHYNpZ6M6Go0FLccontZiV9RBKjeR2d956f778MD3b8L7Y80YHdp2A6B6UFR9va8fF3zzW5h3ofw4uDsZytpDdmZON7q+q9a3evn3xNrVaCwHA9tRrw1kX1t3hst37/7nbkMpnUDb5+6BEdx2g7KRhmxe1RgRVocZ3IWv+4EH0HbBBfqxH0EQBGHkMtKE1YEXf4PiwGa0XnUXXJFRdqwgCMOdSnFDGJmIsLo9f/vdnfjj//t3HDh6FGaHQqgnEXHk3k2msKSzExd/7f/DSZddbl0Qdhuy9ox8RFjdfcRfvQvZde+hTX2v9rQfYMeOPERYbUz9n02FPU5i/nxsvOMO/SoIgiAIe4r+p3+OYiGHtmv+KqKqIAiCMCw46R8uw01PPYPg+w7FI+vW4+3uHnSm0kgX8uC2Oyn1ynPGP7J2PYKHztbpRVQVBGFvEz3qEoSmH4uuW85EbtWrdqywLyLC6jCDvpPoZzUyd64dIwiCIAi7j1I2iZ5HfgBX83i0f/5eGK7aj6UJgiAIwp4m0tKCz9x8C/7xp/+BMWefg7fNEl5fuhx/e+VVPL5xsz5n/D/e+h/49I9v1ukFQRCGA6HZZyPy/g+j69ZzkHn3MTtW2NcQVwCCIAiCsJsY7q4ACv0b0PfkrQgeeg6aLrzJjhUEYaQhj+PuG4grgMHz1gc+oB9P5aa/wt5D1p6Rj7gC2DNkV7+OvsdvQeySmxE69pN27MhAXAE0RixWhxmZVau0GwB+eAVBEARhd5HftAQ9D92I8DGfEFFVEARBGDH0PfOMDrxn4v4UgiAIwx3/lPej9UPfRv+DNyDx1x/ZscK+ggirwwz6V3398MOx9pZb7BhBEARB2LXwV/OeP/8fNJ9/AyIf/KodKwiCIAjDn9Xf/a59tO2xIAjCcMY75iC0nncdkq/8Lwbu/5YdK+wLiLAqCIIgCPsR6cXPou+Jn6L103eOuEeRBEEQhP0bx1rVQaxWBUEYSXiax6H13OuQXfY8+n57lR0rjHREWB1mTL3hBpxsmvpVEARBEHYlqXceReL1P6H96kcQOPQsO1YQBEEQRgbVLFTFalUQhJGEyx9G6znfQKl/Pbp+8WGYpaJ9RRipiLAqCIIgCPsB8VfvQnrFK2i/5nH4ph5lxwqCIAjCyKDSWtVBrFYFQRiJNH/gi3B7fOi+5YMoJTrtWGEkIsLqMIO+Veljlb5WBUEQBGFX0P/cbSj2bcKoa56Ap/0AO1YQBEEQRg71LFPFalUQhJFI03GfhHf0dHTefBoKGxfZscJIQ4TVYUahr0//6ppZtcqOEQRBEIShYZYK6HviJ4Dbh7YvPwYj2GRfEQRBEISRg3N/FJg6VQcH55z3UNWsWQVBEIY7kSMuQmjmqei85Qzte1UYeRgbOjrMlR0dmDdvnh0l7E34pYFfCiJz5yJ2yil2rCAIgjASWXd1GGO/cI99NjT64km8tWgFVq3bhJOPmo2pE8bYV+pTSqmbzCdvhWfKkWj5+K12rCAI+yKGYdhHwkjHNE37SKjHs/ZnnntTCHsPWXv2DfaldWdXfPfeW6SXPo+Bp/4Drf/4GwTmXGjHNoYaEvWj3QV/uHqhpQWeWAzH9/basUI5IqwKgiAIwm5iV3y5+8g//1888ORL9hnw5B0/0AJrPQo9Heh78qcIHfExRM+/3o4VBEEQhH0D5+m+cutVQRCE4SCspjM5BAM++2zHyHW8jb4nbkHTh76H8IlX2bHVoUHeup/8RAurx6xcacfuekRYbYwIq8MMTgrnFwcGfoidLw7OrxC8TpxzXmc6ftCdLxe10vA609UrtzLNYMqtl6ZWuaQyzY6U66QZTLmVaQZTbr00tcollWl2pFwnzWDKrUwzmHLrpalVLqlMsyPlOmkGU25lmsGUWy9NrXJJZZodKddJM5hyK9MMptx6aWqVSyrT7Ei5TprBlFuZZjDl1ktTq1xSmWZHynXSDKbcyjSDKbdeGue8/7bjMfE/Uvp4Z/jyJy/ASUfOxqevuxkXnDYPt914rX1le3LrF6Dvrz9B9OxvIHLKP9mxgiAIgiAIgrBvszeF1Y1dvbj+1v/FHff9FYdMn4wrLzpDf4ffUQpdK9H7xE8RPvZTiJ7zLTt2K46g6mzax/sREVb3MhRWX3zxRVMYHqy8/nrzGcBcds01+rz36af1+fOxmD4nr82dq+M23H67Pu+4+WZ9zngHnjPE33xTn7M8ni+64gp9zngnTb63V8fNP+UUfc7ySOd99+nzl6dO1eeE7WAcrxGnve9eeKE+Z1k8Z0ivXKnjeI3n0ifpE5E+SZ94zrCv94n51v5TiM9W7XToevlus7DgYVN9OTNj0fCW88qQePDb5tqrI2bq1d/rdgiCIAjCvgj/3+78/10QBMGB372rfUfeE+HTF59pGoZhXn35h8xTj52rv8MvevRXVdM2CplXfm1uvPFIs+/ua+2eWfdIzn1TeSi/b9kdOPc65fdmwrbI5lXDjLFXXKF9q7qbm/U5fxWgBZRjBUUqz/kLBc8dyylSmcY/ZYo+Z3mkXrmVaeqVO5i6w3Pm6HPpk/SJSJ+kT5Vp9tU+OdfVl6MhB8cytW8goV9XrduMKRPGIBYN6/NyUgufxMCzv0L7lx5E8MiP27GCIAiCsO+x8Y47dBAE8vzzz4P+XmuFnb1eLfz7v/+7DtWulYcZM2bg4x//uK5D2Hfh4/+0VP2ny87Hzd/8HB6/7UYE/D7MPPuz8Mw6d4dD4JhPYex1r+EbP/sjOn98PhZcdBHe+sAHtliplsOn6WhRurvCK9Om2TUJtRBXAIIgCIKwm9jZx5G4cVX7sR/Tx3NmHqA3sfrOF/8B3/nSZTrOIfHmA8gsfR6tn/0DvBPq+18VBEHYE5x11ln4y1/+Yp9ty7/927/hX/7lX+wzCwoUX//61+2zwWGaphYuGnHmmWfi1FNPxac//Wm0tbXZscJIZvm11g+P02++Wb8KArnuuutw00032Wfqc7J8OQ444AD7DPjDH/6ASy+91D4Dfv/732vRk3At+cUvfoGLL75YrxOVaxLXG0KB9Fr1+fvoRz+6ZR076qij8Nprr+ljrjePPfaYPmZ9X/ziF9FrPz5dXp+we9ibrgDmXvQljG6N4U8//Vfc/ejf8Lnrf2pfGTqfP3kSvnvOLKz8z0VbXJrtLWgEePDtt9tnQjlisSoIgiAIwxRapnKzKoqqq9dt0v5V//mT2+4SGn/5d9qvavs1j4uoKgjCsIHCQldXl31m8YUvfEGLE5WiqsORRx6J+fPn6zSOiOFAMZZxLJPHDox7+OGH7TOL5557TsdTVLnkkku0wEuBhGJvd3e3nUoYyVBQFVFVqKTZfvrJoVxUJRMnTrSPLMrPuT597nOfa/jjywknnICbKz57tfJQRP35z39un0GLrLIG7bvQp+pTL8/HmOM/rkXVpkgIiTfuq/pUWqOQeeXX2HjjkfjBly7G6H95VPtQnXXffds8MefAOPo+3d1BRNXaiLAqCIIgCMOYk4+ajdf/dCu6Xr5b/wJe7gag/9n/QjE9gPZrnoA7NsGOFQRBGB5Uig1Tq9wQlnPXXXdhzpw59ll1WCaFWVqFOTQ1NdlH20JRhRZj06dP1+e0KLvtttv0sTCy4eYtDIKwqygXQBtBcbXWD0SVlIu3tFxduHChfSbsa3Cjqr/f/RN8+uIzceu/fhHr/3andgewo+jNqx66EcG5F6H5oz+2Y4H2Cy+sKbDSndnuDkJtRFgVBEEQhBFGKZ9Gz2P/D65QK9q+9GcY3oB9RRAEYWRCkaLSuqwezqO2g4E+Dh2eeuop+0gYydDXIIMgjDSi0ah9JOyLHDFrhhZVv3DpuUMSVXMdb6Pnwf+D6Ae/iqZzvmXHbks9gVXYO4iwKgiCIAgjiGK8E70PfR++yUcg9il5JEcQBGFHiInVjSAIe5C1a9faR5b/1UZW+cL+S3rp8+h95Ca0XP6fCJ94lR1bG0dglUf09z4irAqCIAjCCKHQuQK9D9+IwBEfQfPFP7JjBUEQhHosW7bMPgL+6Z/+yT4SRjInm6YOglCP8t35GU488UT7yu6HvlSdzasIfbjeeeed+lgQKkkteBzxl36LtqsfQWDORXbs4Iidcop9JOwthp2wyv89amf1KvBfSZ87F6wTJ15fs18ZrBPL2b2d1PlTESx4VFJ/Syjqv4IgCIIwXMl2vIXuB7+H6Jn/gqazv2nHCoIgCLV466239OYx3MSKfla5yRV9IwqCsH+gdYGywI3tdjfcLI8ibnt7Oy699FLtV5Wb8tGHa6ONsYT9E25wlVr0FEZd8zj8M+T/USOR4Wuxapa4EvIAMKwoLYOq+FKJMqiJggrd+SIW9aexNJFBf0ldM5w0TGELplxIVW7+dRZVnqlU+p8+t6sSBEEQhOFGZukL6H3kB2i5/JcIHf8ZO1YQBEGoBq3SKGzMnTtXb4h1ySWXaKvVc845x04hjHReP/xwHQRhuMHH/fljTktLix0DfOYz8t1NqM7Ai79BfvNyjLr2SXjGzrRjhZHGsBNWDVsMLRpsmgGXaemqpvpjqiNqnyV1KaEOFicLeGxVL+55Zz3ue289/rahD6tzJWTh1umLFFkNlZCZdAFWmYxgHSzNreLc8MBgoYIgCIIwzEgt+Avir9yJUf/8KIJzL7RjBUEQhFrQKu1b39q66QfF1UceecQ+E/YFEvPn6yAIwxFuxPfb3/7WPgNee+01XHfddfaZIFj0P/1zFAs5tF3zV7gio+xYYSQy7NREiqclw7AEUfucUqu2VjXUDabhwuqsicfXZ3D7u914byCHww8ah5lj2vDumj488N56vNQTx6YCpVOPyqfKozrLkhyBVXXbUP9cJXVVW7OaKKo0JbtOQRAEQRgOxF//E9KL/4b2Lz8O3/Tj7FhBEIR9D8cP4a7ixhtvxJFHHmmfAZdffjlWrFhhnwkjHW7WIhu2CMMZWsiX/8Bz0003yQ88gqaUTaLnkR/A1Twe7Z+/F4bLbV8RRirDTlilHalRMuApqSMzD9MsqLgSMjCxoVDCC105/G5BAvcsyWJhxg9/LIqZLT6cP6UZH507GS0BL15YvBaPLdmE9/py6C+6kDfduoySUUDRKKKgQlHVZRoUWS0R1zRUXTpWEARBEPY+cT4a1LVS/4rtGXOQHSsIgrDv4WzysquhparzOC79HNIlgLBvMPaKK3QQhOGM/MAjVFLo34Ceh2/UvlRjl//SjhVGOsNMWDVVgwrqtaT+8nF+ugQoordg4p3ePO5ZMoD/XRjHC3EXeoIR5D1RvLcujzc2JJFWuWaEPPjYIeNxwSHTUMyU8MiCDfjLij6sTOSQNFWphirfLMAoFVAyi6oOAwVax9J6Vf+j0CoIgiAIe5e+p36GYrGAUdc8CXek3Y4VBEHYN7nttttw9NFH22e7Dnkcd99l4x136CAI5fT399tHFpUi5tq1a+0ji8rzclatWmUfWfAHoFqUX6tMV+0HHm6sJ+x/5DctQc9DNyJ8zCfQdOFNdqywLzDMhFXrMX1CdwADph8LUy48vroPjy7ajPldeXR6QsiF/IC7BJfLhXTRh66UgWTRRMk04S+ZmN0awIePmIgjD2jDqr5u/HnRajy/fgDrMgZyhg9uww2PSucyi9palfW6Sh4YpphgC4IgCHuPUiaB3odvgrtlEto+90f1vyf5wU8QhJFLpcDwxhtv2EcWFD3+/d//HV//+tftmK1UCiKVgkk5AwMD9pFF+Xm1x3FZpzCyWXzllToIAnn++ef1hnWc3+VMnz5dxzvXuUt/OTx3rjtwfWDcf/7nf9oxFtzl/6yzzrLPLJy0/NHGgceMc9aZaj/wcGO98jqFfZ/s6tfR8+f/g+bzb0Dkg1+1Y4V9BWNDR4e5sqMD8+bNs6P2LJasuRXu9J9RkZ2ZAhZsTuG9DQPwevM4eFI7liXdeGpdEQkjBI9Le0ZFKJ/G+RO8+Mi0MJpdJRRNywKV//F1Y76I1zu6sHBjH4KhIGZPaMXBLSGMdpfgM0souSw/rC7TbolaBC0qWyYIgiAIO8a6q8MY+4V77LP65HvXY+CpWxGYfa78ii0IwoiHAsRf/vIX+6wxX/jCF/Dzn/9cH1OUqAU3pjrhhBPss/ppTf393uKoo47aRvzgzt2PPfaYfSaMNF4//HD9+v4339SvgiAIZEe+e+8p0oufxcDz/4PWK/8XgUO3FeeFfYNdL6za31/0JvzWEQzQXyq/9NhffIySTlCCS6dx8br6l1Ghs1jE0t4k3l3bh2yqgEPGNWPW+AhChqni8/jL2hzeS3qQd/tVMRkcEMziogOiOLYtCF9JlavQm/+z3pKqz+XS5a5J5vDK6o1Y2Z/G2JZmHK7KnR7xolldt2xki6qNqiWGW31BU8f0t6otWN2qJNVKVT/buKUPgiAIgtCAwX65y29ajL4nforIB65G5PSv2LGCIAiCIAiCIAyW4Saspt55FEkVWj/ze/imHmXHCvsau94VgH60niIlxUnTElQtpVNB4bMI/njMI74yeVGd9ZTyeHsgg4eXdOP5pZvREvLhQ3Mm4IRxfjSl4gjkizhiTATnT/PhpNYk5oT7cHwsgzNHGZjuyQO5jK7BNOgpVQUtkqqyVSVuFaaHfbjofZNx1sxJyKezeHzBejy6Jo6F2SIGVBuLWkC1G8W8pkfFUfJlrAosjME+FQRBEIRdQXbVa+h54HuInv9/RFQVBEEQBEEQhH2A+Kt3Ib3iFbRf87iIqvs4u9xiVVt30gJVC5C07mTgi4mSUYKp4rhFFY+p6xaKwJpMAa9u7MWKTQMYFQrjqKltmB5WBSTiupxIJAyvz4uCOu5PpZE1XMipvB51MVQqopRIwOfxIRRpgtvv1YIuoc9VQltTvd+/bpQLcVX1K+sG8GTHAAy/C8dNjOKIWBBjvS742VzTpdMbKr1Lt1P1ynSrnHZfSNmhIAiCIFSj0a/mfDQo/sIdaPnH3yDwvjPtWEEQBEEQ6vGs7QLiZPt+TxAEgQwXi9X+526DmUmi9aq7YQSb7FhhX2WXW6xaIqYbJRW0+KgCDVZN/T8/SpMGr6BolrAhk8Wz6+O4d8EmLO5KY+bkMTj7fWNxkCcNd38PwoEQYq3t8Hh8qgATmXwBpYKJVpcHowsFtKm4ADei8oVQNHwYSGVQKNLG1KqYOiotZtlJj4p1qXO2rFVFjIkFYQajeLvfh98vTuC3S3rxSm8OnUUDeZVdN9coqdSqTgrFFFi1MMt464UwxgmCIAiCMFiSbz+MxBv3oe3qR0RUFQRBEARBEIQRjlkqoO+JnwBuH9q+/JiIqvsJu1xYtSRNS2qkdWpJHXJDqaJ6NUzas5rYXCzh1e4cHlrYhTfWdGF0SwinHTIJs2IRIJFSeV1oam2DPxhQxbAsNtMFM5uF3zDg8XmRR1FbpPp9QXXNrT63LhjuEhKJBIqqMmajM3urPSq3+uNWR3QLkEQBCzozWDVQRDHYjE7vWPytx4tfL+zFfSv78HYii17TdgJgutXkcNP81WpLmahKnNOKaEEQBEGoCR8NoguAUXw0aMr77VhBEARBEAbDnKef1kEQBGG4UEr1offh78M1+iDtU1XYf9gNwio9k+bADaD0dk+mCXcpj5JZQm/Jhbf783hg6Wb8Zfl6BENufPjwyfjglBaMLRXgTWcRcBvwB4MwXT6VhxtcqQ+oy0CeYmmhAJ+7BI8q26XKo9Dp9qg6VCgVsgj7PSqugGQyri5RwqVTAhOlUglFlTefy6CQyyKVyiCRzqFouqCqg9ul0vhj2IBmvLQxgYcXd+Dpjl6sTJnImlqOVeXS1tVxMrAtIqoKgiAIg6X/b/+NwsBmtF/7JNxtU+1YQRAEQRAGS+yUU3QQBEHY1Tz76jv43s/uxK/vf8KOaUyhpwM9D9+IwPvOQsvHb7Vjhf0F99e+8pUb+gYGMGnSJDtq56HQSFEUhgumYSJl0o9qES9t6MeLa/rQVzQwc9IoHD2xBeO8Kl0ug2Ihh4DLQNTngc/n1u5ZuXUU5UyXKieTyaBUKiIQDMLlciNHtwBuN9xeH7zqNZfN65qDgSCSyRSyuaxOl83mkMmmVfosMuk88gUTBZVycyaHNakS0iU3PKYJV7GIFncBp04L4eCWIJati2NJZxpp1Y6g342AV9WlLWAtadWyhbVx1NayKEEQBEGIP3oTIkd9zDrRjwb9FEawGW2fvxcG3dwIgiAIgrDDLL/2WvT+5S9oPessfb7xjjvQ98wzcAUC8I0di8yqVTpu4OWX0XTssduk4XVPLKaPu+6/H7mNGxGaOVOnWXvLLToPz1mWk6ak7kUDU6fWLbey7tSiRYjMnavT1Cq3Wt2NypU+SZ+kT7X7VFx579bv3kPgI//8f/Gtm+/AW4tW4O5H/4bv/fx3mDPzAMw8oLZellu/AL2P/jsip1+L6Jn/YscK+xXcvOrFF180d5SSDvxbtM8siuqQoVQqmslSwVyaLZgPrOs3f/DaKvNf/77avHFJn3nj0pR53dtJ87tv95v/s6zffHZT3OxI58yMylgoFlReK5ilnCoxp0ovmn39fWb/wICuSV01e+Nxs3cgbuZyeTOXzZudnV3mmjVrzN6ebrNz82Zz2bLlZkfHOp0nlUmZ2XzWLBWLZjqTMzv7+81FPf3mrxYPmJ99rtv8xLNd5j8+t8H80bubzaWJrJlXdazIlMzfLVxv3vDce+bP3lprvtiZMjfnS6o17DF7bvVe/3GCIAiCIJSx9p9CZmHBw2b27781N33/GLPvD/9sXxEEQRAEYag8A+iQXrlSn7974YX6fNk11+jz3qef1ufPx2L6nLw2d66O23D77fq84+ab9TnjHXjOEH/zTX3O8ni+6Ior9DnjnTT53l4dN/+UU/Q5yyOd992nz1+eOlWfE7aDcbxGVl5/vT5nuwnL4jmD9En6RKRPQ+sTv3tDHe9MOPyQ6WbXy3ebr//pVnPqhDFmLBrW3+erhcSD3zbXXh0xU6/+XrdF2D8ZksUqXY1yQye9y7/6Z5RKepMoZ/f9ggH0F0t4pz+FZ1duxqquBMaOaUGwJYZFXXkVSujI+LEy7cbSngx6MlmMiXoxLuSFlyWo8ujbtGC4kVchkckhncvB7fbq/aMK+SwS8Tgy6RRMlbZQyMFwqbqLJlxuN1pamhEKR2CqvKbXj4zHj4TpQrZQQDqVRMDlxoRYBJNjAUwMG5gSKuLYcSEc2eJFq6rA7fEg4gHGhj0Y1RxGdzKHt9b2ojNThMvnRtALeFQfOQ5sq9V36A2vrI2zLDcF2q6VpzwkPBYEQRD2G2ixGjjgGPQ9+u8IHXUJmi680b4iCIIgCMJQoRsAb0uLtnKjtRyt1vzjxiE8Z84Wqzbut9F88slbXAbQSi58yCE6D63lmMcdDOo8Tppif7++znOWyzwsN3rMMbpc5uG+H04anpPgtGn6nOUyD8tlnvK6o4cfrstgGsL2s32M43WnXOmT9En6NPQ+mRsewY//zqeZh86Rsw/EFRd9EGPbW7RbgNXrNuFfPvNR++pWUgufRPyFX6Pt839C4LDz7Fhhf8SgxerKjg7MmzfPjhoEWjQs6Mf1TZN+TRlpoOgyMVAysTKRx1vre7CxdwCjAkHMmTgaaZXsoZVxLBjwo+iPwHS5UNJCZAmeTC+OjxVw6YwWTA+6UcwVUDDd6FPlrkwWsLanF1FXCQfHmtDiMeFR9eRyebhUGU1NTfB4VOGKdDql4tUED4dVnB+dmTxe25TC/L4ccqqm2U0eHDcmhLFBF8xSUftxhaHqU3nZhWwuh3gyg5LLq7tomEWEIgEkC0Us7VJt70wiqdp28JgYDh3TjHEBN0JqgaH/WCq+brgopepjXaJ2hyBqqiAIwv7KuqvDcIViaDrvBoRP/KwdKwiCIAiCIAjCrobfvcd+4R77bMfoiyfRfuzHMHXCGHzygtOwev1m7Wf1UxeejttuvNZOZZF48wFklj6P1s/+Ad4Js+1YYX9laBartlZomC6YcKHgMpBECR3pIv6+oR+vru5CNpvH0ZNH4fSDxmBUwINX1w7glR4g640ALpXPxZxE5Te8yOfzGO0pod1tIpdKI10q4O+b4rh3eQIv9QIbMiWMaw5gxqgmhP1+vSEVxU+fz6derQ2qXIaBdDqNoior4Pdgjcrz4NoUXugpYU3KQLGQx4xYAKODPuQp6pY88JhFuI08skWgP6Py593IFF3IlrgFlxvpTE5b0R6o6j1wbIvqr4GFG+JY1p1GTpXh87vh9RjwajGVPaKVarmYqo7LTwVBEIT9BlqstnziVwgdc7kdIwiCIAiCIAjC7mCb/Q12kIDfhwtOm4dNXb346f8+oOM+97Gz8Z0vXaavOcRf/h3ym5ei7Ut/hmf0DDtW2J8ZgsWqdgKAkukC/3EjqE35IhZ2JfDeul7kzSIOm9SGGWE/2j1AcyiInmIRdy8fwEMbPMh5w4DbhOky4CmoEkygYJqIlAZw/rgSzhrtR6yUw4DbjXs6Mnh0kxtZXwyBXBxnjcnhokkBxAygP57QYmwkEtFCJq1GXapNyWQauWwO0aYIFmeAe9bnsLAQUc124UBvHJcfFMYx7U263UaJj/QXkYeJ3pSBnrQBU5XlUnF0clAy3KpcEz7VyLagGzG/qkflW6vK/3tHN1ZuHkB7OIrZE1sxPeZDzO2Cl8arHCZ2zLFaFXFVEARBEARBEARBEARht7EzFquDof/Z/4JZMtF61d0wvJbrAkGwjEZ3CAM0FeWO/S51uCqZxUNLNuG1lZvRFvTi7EMm4sQxUYwppeEr5S0B0+XSPksDBh+6pwsAPn7vQlHVbhq0AS3Ab5QQdBvwqEINlxt+txttIS9ingIC+QG0efMYF/EhFgohFA5rQTUajerQ3NyMWHMMTbEmjBk3GrG2dphuP8aFAzgs6sZEVxpjjBQObfFicjSgO+2BCS+FT1V7RjUrUSih4Cqh5FYtduXgduVV/wpQTQLUNbongJpAPtXy6X4vLpwxDmcfOkWP4GOL1+KZNV3oLxRV21V6BX3P8p91xL+CIAiCIAiCIAiCIAjCSKKUT6Pnsf8HV6hVW6qKqCqUMwRhFdpC1MXH79Xx0u4BdKbyOPF9k3H++8ZinJlDumcA9Djq9/vhUemaVfpJYRdafFmYxQyNXnXevGEiR7G1mMVYfwnjte/TPFIlE/SaeniLBx8YBRwVjeOsScC8MSGE3G4qtXCrVwb6WWWwzl3wetV1Vw4D8W7EfCWcM7MNnzwoiEunenDmxAjG++k6gLXTmpQbT1EvNbTVKuPYL9N0oajaz1RsKa8WiwyqrbRAVRcCKhzWFMAls8fj0LERdPT0oTOR1jlUEdRgLViN/U8YOdC9BIMgCIIgCIIgCIIgCPsnxXgneh/6PnyTj0DsU7fbsYKwlSEJq7TypPRp2Z+aGB3yYnLAi5jLjaDHh0Qig2yhBMPrQ6mQR36gHxO9Jby/zY1xrgQC+SR8+Sy8xQwChRQmepI4epQLU0MGAoYBvyrDKBoIJxM4PmriUwe34INjQmhV9dFylIKX3iSqArNoIplIal+q0ZAPftW70R4XThoVwUltPoxGXiXiQ/4u0JVByaUSuAzdB4+pDimkMd7woWj4UaJdK3VU1a+MupZIZZBJZ7QLgmK+oPqWQ7Mqc0rIj5A3iLztfJZ/KclZwqzD9u0Vhj+irQqCIAiCIAiCIAjC/kehcwV6H74RgSM+guaLf2THCsK2DMHHKqEAaehd/f+6uhMr+ws4c/poTAu7kckV0N3VqWVEPpqfzWVhFExEmpvQa7jx7No+vLE5i96iV7sBoJXqYa0+HB5zY4zPQC6bh5kvIeDzIZNPw+Nxw+fz602k8kVTux8I+Lwo5IsomkWEwyH41fVCsYhUIqlfo81RLehmkmkEwhH4Az4kU2nkc/TJGoLH7dF+MaiDFs08EukMBjJAGj4U3G4VT72ZVrkluFR5HtXOoBcI07q2VEQun4fH5QZUWq/Pg7e6MninJ4Pjp8RwWCyoFdWSy4Sh/b5afltZl6uGuEqhmNawjoWkY4FbDq+VivRtu61c2xirTtUEy9JYtZvHW2Gd1ds1WLhxmLbmtTG0RbHq7bYVNaSynF2BY9VcD9aZU5+NbDarRfNcLodMJqviS/D7fQgGA3qTNJ/63NEKu/K9EQRBEARBEARBEARh77IrfaxmO95C319vRuyimxA6/jN2rCBszxCEVQpxlhhXUOGJNZuxoj+PM6aPwYyQB4lEUotTmUwaqVQKrW1tiDU3WyKbaaIrmUJXvoSU26MtOkeHfNqqNBcfQC5fRCQa1ZajnZ2d6jyHUe3tCIcjlvanqqU4ms0XkEqkkM2lEQoG4fUGkM5k4FHlxJqb4PGqsksm4qpM1tvU1KTFslQqjUDAj4DfjwKF12wW6WwO0CKpF/GCAVWKqoiuAOjMoAR3MY+w14WWSABhVT7hhlalUhFZisAuF17ZlMDC3gxOntqG2bGAymnCoOqs8psqC21iqwmYFBIp+vX29mLpshVIxJN6jCZMGI/pMw7QorIDhb7ly1dg0+bNtmipytRvhSqT/gzKUWUwxqX6rsdd/ceyQqEgmumLVo0HxUKKhlZ6FVQ6S9g1VZvYzlpipPOIvNUXlr9u3VqsWLFKjznF31hLDAceOF37waXbBd3WGvA6y6Pg29GxDqtWrkZRjQv98tIuWrdtCOgy3QamTZuGiRPHq3Mdq+PZdkP1sVgooKenB2vWWO1ft24d0hTgbXGVwirFVAr7waAfEyaOw4wZMzBlyhS0trboNlvl8X1k+fxjjYsgCIIgCIIgCIIgCHuOXSWsZpa+gL6n/gOtV/4awbkX2rGCUJ1dIqyuGsjjg9PHYnrAwObOLmSzOTQ1RbUlIIVDCmwej0cLrflsSluNur0hXVpJ/ytQCUMqzsfsCzpvOp3R6b1enxYFA4GALsNwW+JVvpBX6Sni5pDMpLW+2BSOwq/S06KQghjFMVohhkIhbXHY3z+gLVV9Xo8WfllWKByBz+9XZWQxkM7rx/kLWqx0aUtVVymPoNeF5nAAXgqwrJ1iZRkvbOzHW5sTOHFyK2bHgpTvGgqrliBnlbV48WL8/vd3Y8WqNTBU/KmnnoKPfvQjWgh16O3tw91334OXXn5ZW02yLBqvaj2voj120VvgZbfH0AJhOBxGW1sLZsyYjlmzZmHc+AlaaLbao9qt0lr927bMrVCctI6YjuN7//3344knnlTvtWVJPGbsaFxyyUcxZ85sK2EdLOHVqu+vf30S9/7pAf3eutV7rSuq7MwgKRYL+jNw8Ucv1uNJKGQTfibj8QTmvzUfL7/0Mlav6UAuk9cDpVvCDdTsY6sJliBLr8IU5inWnnDCcTj00EMRDKr3W/eBJVv9EARBEARBEARBEARhz7IrhNXUgr8g+eYDaP307+CbfpwdKwi10brcjmOJcA604KSVYW9/HxLJhBZS9U79sZgWpBKJhBZZaQloer0oen06vckn600DRdWMEtyIhqMI+Pzo7u5BJpNBS0uLCjFdRzwe10JrIZ+DoTK6Vb5sno/lmxjXPgYHTJyIpgjF2pIWTfv7++1HujMYGBjQFp+0WO0fiCNfKCLa1IyWWKt2OZDLZpFNpxH2uhE0ivDkk2jxuzA6GkR7JAhPMY+8uk7xzJLNKLJtK/rVMcosG6nq0KLTq8YlaFvTetyVj+szjQGvh4+iB+HzMfi1xSlFYYrGFKCdYFlZMqhjVS5FStNwI5PNoau7G4sWL8GDDz2K/77tDjzxxFPo7umx+qYrpeVqReXbYIuOdgNXrlyJJYuXwu3yqrYHtWuGvp5eLFZ1ZDM5nWawUDj3+dlH9svqg1cHnrNf7J8TpwKPy4L1uL4VPDqdJbITiqpsM0XV/r4B/OWxx3H33X/CkmXLdX99auz9QT+C6jMUaYpqcZ+WvZFIWIunFPb9voD63AILFy1Wef+I5557Xn++LItchnrjJgiCIAiCIAiCIAjCcCX++p+QXvw3tH/5cRFVhUHj/tpXvnJD38AAJk2aZEcNBkqFFEMNrOhPojOZx1i/F8F8SltEtrZymylbDPR6tVUjhVG3y4VgKAS32wNXiT5MaRVIRc+lSuM/QwtoyVRSi6iOpaljrZrNZLVYSvkqp4774wmEI2E0RSNa3HV7fNqak+lZL0mn09qtQDKZ0gIar0ebouq6R5dDwZfCr9/nhV/F5VR6n9uNSMiHoNcNr8cNs1jQlrEejxd8/JviLR+/t9puYG08g03JHCY3hTCGzlgVhhYn1TjxxfpjpbdxhEm+9vT04r2Fi7RFLWOnTp2CQw6ZuaUPJJ3OYtHCJVi/YT2rRDAYwvjx4zF69Gi0tMb0o+kUovna1taq3wMet6rjpuZmhNT7QjHW8jdqaMGRfmeXLV2GuKp37LhxWkx0cNpXSbmlLa2LX3zxJbzz7ntwqfeU0P0ALU75mP2ECRPQ1s7PAvNUL8/Qbgx4zcCqVau0SFssqfdStS+m+jN6zBjEYs1otgPdDFCwZ6Do7hxbgWI+QxOam5v0uBx88EEYp/pm1UUL2xyeePIJPPXUU+q9N9WYWJ+tUaPaVdqDcdjsQ3WgNe/MQw7GAdOnY9z4cepzG9S+dfn54+eXIv2GDRt0vZMmTdTlC4IgCIIgCIIgCIKwd4g/ehMiR33MPtsx4i/+Bvm+dWj70oPwtO6IPibs7wzNFQCfQYeBguHCE6s3Y2lPGqdOG43JnjxchluLq+VQWKUIxceox4wZrXLSryZlRhfVLhalSqW4ZupH9Sl00hKQ4hrFVQqlFMWo6ZuTmTQAAExdSURBVA3Ek+jq3azOS/AEoohGo4gF/XpTJ4P+UVUi+j+1/LxmtBBIMc2y5LTcAdBHKS0rKQJmVRqXx60tbFPJFLiJUcDerIjiKykVixgY6NfiIa0Y2X9rEymXFiqfX9+Lt7qSOGkyfazy8X3V0DJXAEyn/YXqPm6FbWO/li5dhnv++CesXt2h23/SSSfiwx++QFtKOvT19uPee+/H3197DfT9ecghh+AjF12ICRPGIV+obxlKK8tMOoPevj50qPf6vfcWahGT40O/qKbq31FHHYXzP3QuRo1qs3NVp1xYXbJkGf74x3uxavVqy6LTr96HUkmL2RyXM888E2effQY8arydvm6P9VlieObpZ/HAAw9q6+JAKIhzzz0XJ554nBa1We12uasVZzXPuqYyWe+jV9fPNi1Y8J52qbBx4yb9mfCrdh9++FyceNIJmDRxot4crRpZ9blYsmSpauMzWLJ0qa6H4v7sObPx0Y9+WH2ux9gpBUEQBEEQBEEQBEHY0wzVFUDfUz8DvEG0XaXyVtUtBKE2WvYbCvbD8BoKVxQO6R/US9+YFVDUokDKR6bTqSxKFEANN0wXc7EUyqpFFUrI5ij2lbQlIIXFZDKp/a3SjyXFUm6O1NzUinyoFe/EC5jfn8OmAlAy3SgWijrtwIDlNoCiGi0329raQD+tfMS8paVZC79+fwAJVfZAIq6tVukuIJlOo6DqtjaV2jqZXG7u/u/X9XOTLLoSoNCWzmSRUCGdK9jCX/UJuEumJQuxC+J4Uhz2B/nIu1uPU70QCQfR3t6CA2dMw6kfOAlXXPEJnH76qXqjL/2+qfflzTfn45VX/q6tUBtBgbSgxmDRwsXYsGGjKsPQAvdRRx2JyZMn6/GjZeeSJYuxft36LXkaovtopeMLN4yiJXFEvV/RSFg/lr9NUPHbhfJrkYj+DDiiKlm9eg26u3rgcXthqnbOnXMYLrjgPEyZZImqemMyHdQ4qz/sC/OzHbMPfR8+9KFzMXXKJPWZKWihvWNNBzo61uqyBUEQBEEQBEEQBEEYGZQyCfQ+fBPcLZPQ9rk/btEjBGFHGLKwSjHN+chRfMpkcjDVh5C+LcuhKEXLSAqrseZmLXymUxn7Iv9Q2mMzrJDPWsIeH89mHoqg6XRKW5omEgNg8Ua0CW+k3fjTeuA3S5J4aEUP1vQOYCA+gGzO2qyKYirzsn6Kqpwf2SwFWlWLFtlMLTqOHTcebe1tWiikGEghl4/mJxNJ1c6Utr5MqNciSkhns9i8ebN2a0ArWAqtdG/g83ng0n42dyeWAL01qL/sjMIS/7YNKpYpdDBN1fpS0bqm0kbCIZzxwdNxyiknqbEKaBGZfZ//5ltYsWIli6yJI5BSUFy0aLHKl2UNmDJ1Ck479QOYefBB+r3jGFNwXErrThu212lzNcov8Zj9ss+GFNhfWuQ6babgvmnTJv1es+ympghmzToE0UhEp2VgSloya8mf7h7svE5bpkyZgtmzZ+s+koT6nHRu7tLHgiAIgiAIgiAIgiAMf/K969H78I3wHXgSYpf9wo4VhB1nSGogZStLWqUIZT1m3t+fQCZbQK5YQl4FLUSZlqjK9B6vXz92HQz5kcqmkM6mVf4i5SsUVQLarBZo+Vkq2kKlyqTyBwN8HN5AZ2cXn8BHJBLFplQOL2/KYLkZxSpXDK90ZrG0s1+nD4eC2rqQJoe0PFR/QR01EAyqNvGxeNW+Qh6JgQF43R7dJvr09LgNxKJR7QOWj9on4wls3LgRGzZtQE93NzKpjHZlELA3jNKbczU3waPqGigZSJVcug/EGp/dixYo7YrYP1qdlgfrrWV71PtUfk0lZl6KqUcfdRRmzXqfGhc15qpfGzZuwOIlS/QYEUcI5b9yKFa+t3Ax1q5bp8o00KTG7X0zZ6KtrQUHHDAV48aO0e8FhWlulLVp82Y7J9tpjVEtrNqc+ramrSfI1oJ1lVen3UOoz6olRJv6feR7Sqw6rTpYFfNSLHfay1enDRMnTtA+ibVfV26upq4VCuWWvlvLEgRBEARBEARBEARh+JDftBh9j9yI0LxPoenCm+xYQRgaQxJWy9UqCnSFPB+LdsPj9iART2JgIKktWPlYeTaThtttwOP3omiWtDVp0B9EOplGLldQZXETLMu+ktamLNrv82tdihsgxeMDur72UaNB/60U60pFE0HDg3CpgGAhh9ZACBPGjtUWqAPxuLZspXjqSFv0h6p3x3e5VR059PX1I5vPaZExlU6jp6cP/b2WFSr3UuJj5KNGj8akyZMxfsJEtLa1IeALwqvaUVL54n10NZDR/VmWzOLVzhR68gYMNQa7X1Dj2NuCn974iWx9P6pDgXHbNBQJm5qbcOihs9DU0oyCGsu8Gm/6eaWIbaey69g279qOddoNQDqb0Y/MTz9gGg46aLq+xo2cDjxwhrbk5b9VK1di2fIV+hrZXiB1BGAbXV1ley1x2Eq3Y4HlOGXRZ69laUpXCh69odnmzk5dJT9blvjMtKYWVx0qx27K1Kn46EcvxhVXfgKfuuITeP/7j9AWultx6hcEQRAEQRAEQRAEYbiQXfUaeh74HqLn/x9ETv+KHSsIQ2cIwioFI5d+7J9iKDehoqDq9boRDnjQ3hxBwO9BKptBZ3cnUqkEPEYJrhJ9q5ZQVOkprtIvazyZRiZX0JsrZfMmMgUTpscHw+NFoVBCPJ7QO7FHomE0N0fhcruwYXMnPJl+HD/KjaPCBRzmSePYVgMToh69c3usqRkFVWBfPx/nH0A+V0QuW1R1JZEr5bC5tw8ru1NYXQxiUa6EDarOvNuLSNsoxNpaEY2G0ByLwuPzaBEuoNoTUe1tagoh2hRFU3OrtZlVTze6Eim8uSmBhT15FFz0M8sHyEmleLir2bnyy4XCiRMnYcK48do/Ld/b7u5u9Pb0WhfLKK9x0eLFWLN2jbbo5NjMnDkTo0eP1te4idUhh9B6tY3ypH78fvGiRer96LfrrRQcq/fFshq1T3YSR8yl79X2Ue2WT2BVdiKZwt///jreevsdLbA7LgLYzmp1sxxeD6s+T506GQcffCAOOnCG6nu7/oFBEARBEARBEARBEIThSXrxs+h/6mdoveoPCB97uR0rCDvHkCxWjZIKpvXgOzf44WP2wUgAmQI3dsogHPKjrTkKn5v+Vj3IZotI9A+gyMewtYBnIhiOoOjyoiueRncih854BvECkFXpk/kC+pIJmKqCaDSi8qjz3l79yHosFkXM78JR7V5ceWirCi04ssUDd44WsFn9SLbLo+p1G+jq7MS6teuRUHUUVJv7VHdXGhH83RyF+zt9+N2qHP64JoNXBoCkz6eF34IqgxsT0UkBH3nXfgrUK6U50+VCULUn1h7TIh39yQ7kTKRKPrgMrx6TcrY9G1444iofZ6d1rlv7RTWQVOPe39+vr+k0uhNbe7Ju3Xq899572vcs4ydPsQRG4giYU6dOxfQZ022B0sDy5SuwcuUqfW17wZJ5ykZKXXeETbpkIHyEfyiBm5I5QimhUD5t2lTEWmMo0v2B16fatRq/v/MPuPvuP+Lll/+ud/7fvLkTiURCu7Hg58kpg1apjmUq+2r5cKXvWn6mBUEQBEEQBEEQBEEYjiTffhiJN+5D29WPIPC+M+1YQdh53F/7yldu6BsY0D4jdwRKYQXDwIq+FOL5Eg4aE0MAJSRyJkxvAKlsHm4YaGlugS8Y1o/jU6ji4/bcIijPx/pLLqTyQLZoIgcXCipHrlhUabLwutzwe73IpFNaMKUlZDgcQYg+MU03MokMgvkUWnwuZNMZDPT1wWOa2oqSomgwFEBrSww+T1D7Zs16PZjfX8TjG4p4KxtCR8GLTaqtq5IlrOlPw+MxMSrshq+QV8duuGjVqP65VDBdKlDw4x9VmKlamkpmVR+L2JA3sSyRR3PAg7ltfoz20x2AndZQY8EX9c/Ced0KBbuenh68995CvUEX4QZJtPqkZaUDx27hwkVYt36DFnzHjBm9ZeMli+3LHgwUGzds3Ihly1ZoAZGWq5MmTdCP81tlWuKmI06+/sYbePXVV0E3D3xPTjh+HubOnaOvOWn8fp9+zyimZjJZpDNpRNV7N336dNUna9OnrfCTxHwGVq1ajcWLl+p2uNT7zx39E8mkdk/QsXat3gyrY439Wi2oNHRTwJ3/16tx8vv9aGqK6locotEmLZquVWkp3Hrc6rOkxpabcb3z7rt4990FWLhoIZYsXYrNmzajT32uEomULaRaYavAagnC7LbVd6v/giAIgiAIgiAIgiDsWeKP3oTIUR+zz7YSf/Uu5Na+jfYv/Rnece+zYwVh1zA0YVULhiUUaQHal0J/toRpbREYbi/e6MriVRU25kw0h7xoCdF3pWFtFBT0w6BVZLaAgXQB6ZJbW62WXG5VngrqGkVXa/f6Ik0WEfB5EQoFtS9PinS5TA65nGX1msskUSgWEI40wa/qptAXaYogqOqiP1VaYHJzKopn73X24y9r0liSjyIfCMPwuOBy+1D0BZEwXehPZtHmMzAp7EPA49HXWYdRMlBQ7aH1Y0G1O5NJIZOnC4MiSgV1XaXrzOagWo/D2kPbCKvaMQBFty2C2/bC294WVsm6dRv0Dv+01KUAPmP6dBx88EH6mqWVqn4aLmzcuAnPPPOstlqlsfMB06bhpJNORHNzk06jU7LLKpPfH9DiJkVbWjXzfZo4YTza29u1OMk0VlrmYyWWsMp2WNeBtWs78NZbb2HBggV49933VHh3i/i5fXhXp2OYr/IsXboMk/QmUxN1uwjLpVg7duw4bYna092lLVtZt8tNi12Xdj3R29uHDRs2YPmy5XjnnXe1he6CBQuxbNkydHf3aJcBbo9bvz/022pBK9uhvweCIAiCIAiCIAiCIAydasJq/9/+G8V0P9r/6SG4opYLQ0HYlQzJFQAVMe6BT0mJWhJFs/5cHm/3pvDI2jj+uCaN+1an8FZ/HimKdfks8pksCpk83IYbQX9AP65fNFwosQAGtkSdmy6Piveoc68Wu3KqXG4URb+mFLECoTDaYk0Y296MCWNHIxr0wY88/D4PsoWiFsaowlLiokhL68d8MYd4yY1+dxgljweqBrhVH0xVH8UwlzeAzQUflg8UkTbdemOjUjGPfC6rfW/GEwm9KVYql4HhcSMUjqCttQWeUhFjkMXclgBa3YYWEPnPERlHCvRdS1FRC4Oq6Rwzip4OjmC4ZOkSrFrFR/oNBIIBzJo1S4uXFlYaOyna29vwvvfNRCQSUWW7sXHDJu2blYLmVlHVSluOJUJbF5jPoz4nbjeDRwePx6fjtg++inRe9XFyRM+tUCBvbY3hggs+hI9+7GIcNudQjBrVrjc+c6s8zOv1+uDzBdSrX7chnkhiTUcH3nzzLTz40MO47X/uwG9+cydeevFlvRGabnW1zgiCIAiCIAiCIAiCsOcpFdD311sAbxDtVz8Kw+8YpQnCrmWHhVUtG5ZrSKoEw2UiVyhiXV8GG3IeZEMx9Jb8WN2TRWdfEslkEvF4CgPxNPr7E0imkqBvSlp0sigt0ppaBrXKd3lQKJooqkDLx2AgAJ/Pr60NoeqyNsFSKb0qLtSEdC6PfDGv49OZjCpAlasb6UYynUY+n0PB40XScCPncqNgqPLVMd2nuvjYuao7p5J3pjLoTeaRyeQxMDCg/Y0W1WRkG2LNMbQ2NyHkD6KYLSKTSsHn98Cv2hN1FRDxubUlaTWqxw4fKFprK2GOm2H5Et1WJ3Tp3fMXvPceEomkFkXHjxuHAw+cri2QKZbSipMbjlmvBZ1r2rRp2mKUZRfUe0D/pXwEn7B81rc9fNzecj/Q3NyM8ePHY5yqa9y4sXYYUyfw+jidZ8yYMfpzU85WS1l+rnx6N/9PfeoT+MdPX4kLL7xAW9/OmTMHkydPRmtrqxbQvbbAyuDx+uBxe7Xl9ML3FuHue/6I++//s7bk3Rb2a7i/64IgCIIgCIIgCIKw71FM9qL7oZvgGXsIWj99px0rCLsHY0NHh7myowPz5s2zoxpDq0wDRdCL6l87urGyK41jJrVjY6aAP3dksSlrYLQnh3Mmh/GBiVFEjJJ+pN4wXaAxaipfQFcqi0TRgGn44GI8ZVHDVMEFd7GEEAoIuUvwq0C/psUCNwoytbDKR/v5aDp1OVoUUojLZtLIqnJNjx/haBj+gF/7cy2kMmiO+PFaErh9WQ4r80EYXo+WvVRRKrA3BYQLSRzljeO8cSEc2BqF21eEV2/oRJ+glH6BQjaLZDKlex8KqfIzWQwUing9UcLqRBGnTIxhdsyvR8goUXG2hGJthVnFotER+vjY+j1//JP2JcpOUeD78Icv0FaUDvT1ee+99+Pvr76mxcvZsw/Fxz52McaNHaOuWrUMFkfQdKwsH3/8CTz40COq3LwWVc879xycfbblzJlJmeyll17B/fc9gHg8zkHHzJmH4Kgj3w+/36s3gtqK1Ra+L8VCAa+9/gYWLHhP1+nzeXH+eefgtNNO1ems/jMP/xh4+uln8MCfH9bvGy1iz/jg6Zh37NHIq3btCGyzSxUcDofVGG4rrjYiny8ioTfwGkB3V5cWlHt7etDT06sD+5/NZnTaQrGo+l7CB045Beeffw7CodB2YysIgiAIgiAIgiAIwu5n3dVhtH/sh+j7608QOu5TiJ71TfuKIOw+huRj1ZKMaDnqwvL+NAZyJRw2pgnTYwH4TROjPHkcM96PI0eH0Gr7KqV0SrGL1qOpVAq5Eu1L6ZmU1ylcUmQ1tOWql4/ql/LqmK4DTHi8HgQCIQR18MPr88Ljtnax11avqkFsEzfMWt6XwZt9OSweyKA3nkHE7UbU70JelbkxmUG3CqAwa7rgof9UlTGn6prkzuOEUW7MUW1uivi1eEu/ryXT0P5ds+m0tmDlZk/Rpmakcxlk8nkYoWasThbRl8nhgFgQYwLW5ky0mNVGszxm62oIbRTgdszH6notMFs+Vt+3Uz5WWTfLfXP+W1i5coUWBcOhIA4/fO6WzwObTVH3b88+hxXLV6mx92vXAYyz/Jm+jbfffndLeEuFd95+B2/Nfxvvquv0Scp6nLoo3E6ZPAmRSFTFWZapVtvpY3UVFi1agkK+oEXYI46Yq61iKVjuUAiHEFKv3CjLEm8tK1WOm2OZ6wigzivTELfqGy1dYzFay47DgTOmaxH7sMNmY+bMg9HW1qY/vwPxAZXHpa1zU6k0JkwYj9GjR6k4XcyW8gRBEARBEARBEARB2P3Qx2pm5d8RPftbiJz2ZTtWEHYvO+wKYCuUCw0ticKgeFVELJ/ESTHgEzPbccb4GMZ7DbiLBUtkVCmz+SwSiT54VPr2SBgRj1v7KaX1KzcxcpdK6ryAkMoX9ntUOgO0Vs1m04jH+5FIxvVmQ5SsKHBSAKNVYliVFWtpQdv4cVjrDuCRDQX8cVUWz27OocugR1UTY9wlHNfuwSGRHAKFpKonD6+ZR8DMYIzZj7nNRRzY5IYrn0E+m0MmV0IubyKbKyLeH0c6mdCbaEWbmlR7ssjkC8h7vEiVaO9qjQSHohy2c7jLaxR1N27YqN0umOp9ampq1o/Bl7N06QosW75SC6p8S0pFNTZ0v6DeC4qghZwKfFWhqI6LauwK+aIaRyuOG2IR+shdvXo1li9foc+3//hRgOUwWsInfb1W4oiitYKDc+wInCxr7dp1mD//bR244RX77viWrZa/PI4bo1E8PeWUk3DxxR/B1KnTtKhK8bu/vw+bNm3W6Yb/Oy4IgiAIgiAIgiAI+yaxj9+K8Imftc8EYfczJGGVUpNlgwp4SurIdCNbdCOdKyBomGh2A/5SgU/CqzQe9ceFTDaDRHxAWxFGmyIIqdfWoB/NATd8RgE+M4tAKYeo10BLyIu2aAjRcEhX5lL5g6GQtmLUj9/3x5EYiCOTTqOQz28R4FJFExsKHnR6m9EXGouOQhDdBROBYBgt4QiOGteCc6a24OjmIg7x9OMgbxzHRnL46AQ3jo+VEKAVaq6EZLaIZCKFns2dWL9uPQYScbj9HrCWgb4BHegRtuD2IENRzkW3BBRXa7BVrxsmUMC0WtvRsQ4bN22G22NZd1Kgbo4162tkQI3ze+++h96eXv14P62FW9tatXUmd/hn4OZPo/hqn7e1t22Jb29vRay5Sb/v3Ek/EU9oq1TuvG+JkGWjpg+3tq3auPFaveBQfkz4GXn99ddx++2343/+53/wm9/8r3ZRUE61/NbLtiLrlCmTtbUwxX3GcUOshPqM0EUDqaxbEARBEARBEARBEITdy4Rbkwge/mH7TBD2DEO0WKUFKu1MiQmXYSKVzaLg8sITCmvRsWTyUXqPfhw+m0whnUggEAgiEmkGH6E2TBNBL9DsB6LuIsLIIWimEXYVEeAu9apkbjDEDYy4U3sqmYRZKuld5puiUS1q8RFsPj6fTCT1Dv5eo4R2VWaLmUWkmEC7r4AWPwVgE9lsHvmBJGb4DJwzJYyPHhjBp2c147Pvi+H8qaPQptq9puDGQgSwwetH3udSdRTR3hbB6DGj4PUHkM3lVX39qsuq9y43svT7yk2wthhWbiuoWQK0Yi/rbI4oWC4OEoqbFBf5WL9b9YfC9bSpUzB61Gg7BbB8+XIsXbFMvY90/WBi5iGH4LJLL8UVn7wcn/zEP+CTn7wMnygLn/zU1sDzT33qk7j8sssw65CZVDf1WCxbvgwrVjhWq2Xopm1t49aW7jy0LI2qz42L7h3UBzcRT2Lzpk5teUvKhmULlkDqhK3jSGgt7fV4t7Z1VzZWEARBEARBEARBEARBGPbsuLBqgk/+642f9KkqIV/IoJjLwO8G6FKVYpOhXwtIJQeQTcURCYcQpj9Qww392LzLEquMUhF+PpIP9ari6FPVpPLFy6zL5UYkGkVzc1SLp3zsmpsG0YcmH1lvaorqcvrjcZSSSRwe8+OUVuCEUApnTwpgRpNXi2i0lPSqRuddHqzsziOVKWJiyMBYtwl6Ml2WAe5bV8R/vduP3767GcsSJTS3j0KsOYag14eQL6D9rtKac/TYUdqPp1eVpa1yVVuHu5GiIwBSLGRbc7kCXv3763hvwUItqnLjKu6kf+BBB2o/oySpxpPCa1dXtz6PxZrw/iPm4uCZMzB12hRMn36ACtPqhmkq3cyZB+HwIw633ivTQE9vHxYuWqw3ArM+glsHj83cavFpf8h2EfSbGggGrfJVWL1mNdatW6ev0V+vM0ZWvU7YFual9Wtff/+WTbXoTiASCcPjURNAEARBEARBEARBEARB2C+wFLQdgaqqfije2naKCmo8lUE6m4ZZzCGXzsAsFFHM5zEQjyOTyyHa3IRAKIAShStbqzJt4YrCKnfmd7tc8Pi8KBQLyOWyVhqDtqbqVV33eb1oiTXrXd65idRA3BK2PCq+qakJrS2tiASDmBgATmgBzh7nw/sCBZT6e8BNiygK0tp17UAazy7djHfW9mMgU1ClGxgoFbCwN4MlmTA2esZhadyDTWkTRbeP3l+11S2tY+lTNBC0Nm/iRlw+uOEtAm7dLas/e47yR9+3CpPVYDqKf4SPrdPK929/ew7PPPMsUhQ3VXZadM6dOwfTDzhApyPLl6/EkiXL9PixjAMPmoFpB0y1rzqwz/w8VA/cLIoccMA0HDBdlU1NU/1j2atWr9HXtowbr/E9d0639G+oWG1z2jBu3FiMHTtaf8ZobUp/ry+//Ire7Z9Y9ZW/h1vrLx/DpUuXYuHChdrHKj+bFIzp+oBsFWcFQRAEQRAEQRAEQRCEfZkdFlYpG/GxcAqsOnPJhVC4GbHWNsDjRzJXQG/fANZv2IxEKodAuAkGBUruyG7lZi4Nj7KmgSw8KLo9MPlotYs78VtpuKEVxS3tdoDimAFVVwSRlhbkVO098QRS6bQqm5tfGQj4/Ii6XWguJBHK9MOTy6uSvXCpuvP5AjL5HJo8JRw+JohD28KIebiDvypXtcHrMuB35+D2pOAPFLSbAm7MRWktk8nrEPAH4fP7YRZLMHM5lPJZuFS7nEHc2rNqVLm6RTgsu1a/ENUeCqrWbvSJhOp/KoV4PF430E9qT28v1qzpwN///ir+8Ie78dCDD1suADxu7WJh9uxZOOboo7QvVMId/BctXISuzi4tIjc3N+GQmTMRa6b/VTbSaSj7wBGoFSxL0FgspvIfgqao+jyo8jZv7sTSpcuQ135J7XGwi3SsR+l2obu7R28MtWnTJmzcWB42lx1vrAhW/IYNG7WITGGYjB8/ATNVG7gRFWMY/9JLL+P++/+MZcuWq/FMatcAhUIJRfUeU4QuqvZxrLPZnC7rjTfeUmP3CFavWq034zLNorbKnTRpoq6DaHFVBFZBEARBEARBEARBEIR9GmNDR4e5sqMD8+bNs6MaQ2+b3Om/aBj46+pOrIjnccb0sZgRdCGXz6N/YEBvKkUhEhRUTRNev1dbRXo8BrgZFQXUzkIR73WnkMmbmBIxMDHsA/JFGKUSmiIhbbFK0ZO6GMVcSlX9JWBNMoe+dAF+dT7KXUSrt4SA24NiAXpDK8MoobklBr8/oHenp1iWzmQQ7x+AmxanoQi31EJL0AufakeqCLy0eQAvbUqjp2hiesyDcye2YGrYi0yxiIGBFPxuN6JNIapxyCdVWemkKicEdziC59cOYFlvCqdMbsFhMdUH1VKjpPqohWGKs5bVqz7WBxSL9YuWFGkBec8992LN6rV6rE466UR8+MMXIBgKMJWmt68P9933AP7+99e0oEe3CmNGj1Jj7FPjY70fDpYcrMrmuKny6Bs1XyhoNwq0zqTIzDIYaIU58+ADcf5552DS5Ek6PYXP995biHvuvhfr168H3TYcccRcfOTDF2rLTN32waKHQJWpyqBAyn6+8+4CXQ+tWD/20Y+oV8sK9qmnn8EDf35QvWdsnwdR1cdQOGyVsbV7W1Hx1khWtojCLF1U5HHkUUfgtNNO1T5RyaZNnbj/gQfx1vy3tAsEPT7qPW5uacbUqVMxZcoUtLS06Mf6KShTfGV7urq6sHzFcqxevUZb+brVZ9lU+caOacOHLjwfc+YerltB21gX54ducLVGC4IgCIIgCIIgCIIgCPsCQxBWLYHTElaBx9dsxoqBHM44YCymeYropy9Tn0+LYhToSoUScvkcsrksigU+el9S193ocwfw5Losnl2X09aBx45y4UMzWtFsZrWQFYs2AW5VARUyCmWqrL6Sib9t6Mdf1yTQmTXQHvDgpLEhzPFn4Uv26jr9wbAWvUKhgCVtqewsgpabuWwOfn8QGdWWgWQ/ItEgmkJhZFMFxHMF5ClS+v1wFYsY41Nl+DzoTcS1+NYcDWsRMplIa5E2EvKpOsKqBjee29iLtzcncOKkdsxuoYBnVhFWLZFNR9kHtMylyLxkyVLcc/cftUUp0594MoXVixAKBvU5+09h9U9/uh+vvvoqfD4/6OeTflFL9Eera9C9VcH5a8G2O9CFAcVU57F2bgI2Z+4cnHLySRg1ul1brhoqnuP05wcewlNPPa3riURDuODCC3DiiSeo8igaqjJ1f7bWWR1LVCe0siVPPPEkHnr4YaTSGXg8Xpx37tk4++wzVSkGnnjqKVXvg9pilO2j4FnizmD22PGN3NobhYov05O3gfXms1mc+oGTcPHFH9E+eWldahhuPc4PPfQoFi1aZJWpCi2oz2ZR9ZViKut20V+qyxJWdTvUNdbuiNGUT8e0j8GZZ56OI48+Ah7byrak2sPx4ZZp9cdGEARBEARBEARBEARBGMlYatdOYGmHBjLZHBLJJAJ+P6KRqL5GMcpwG/AH/Fv8oDZFYvB4QljXn8Mb3TmsNaLY4I7gnVQJa9I5ld4S1NLqOJ8vIpPPIKsCha/VfUm8sCGFRbkIugOjsSgfwrPqvN/wYfLkCQgFA+jv7dOWqWZRNUy1i3JYItGPYjGL5tYoos1hjGqJIdYcRU6VuWFzJwYSAwiaRYxCAdNDPrQZQC6bRV8qBRRMNAeCcKuSEgP9up8h1ZcAhTrWodAPvG/R0KqLaVtlNubZ6veTaKGTflvVWHG8eO5g1UBswc9FYc/QFpV8pJ3iLkXDYCioLVytwOOg3qgpFA4h0hRBc3Ozfoyf1piTJ0/GccfNwz9c9g+46MMXaFHVEg4t6Pt0ydKlWnCk0Dpt2jQceOAM+yoj7deGQaHSsuSSGl9y0EEH6sfm3aqPxUIey5Yv1Y/vE4qrVv8oALu0QO9X76lf9ZPWx/pVB3/Fq3PshIAeG/rDpZW0A4VRtmPy5Em45JKP4PTTT0X7qFHaty/ropsHt8er+2wJ1xRUVT9UW91et7rOND71GYpi7uGzcMmlH8KRxxyuPwBF9dkxDO2RV/1znEgIgiAIgiAIgiAIgiAI+yrG+o4Oc9VQLFbVP0plf+3oxKLONE4Y34zDWv3wefmAvqGS0XqPaS2JiekpwlKLLBkG3u1N4q6Vcbyb8MNdMnFgNI+LDohgVqCEZM8AvN4QfEEfcmYKrgItKQN4oz+HP64HVqAZLo8L+byJiUYK/zjdg5PHRrSW193Zpf2Ocvd+iorZbFZbLoYjYXj9Pp2G1pOJVAb9ff3w+1Sbw0HE40k+O46IykODxHhyQFuUtre1w+/1oTcxgGIxj1ikCR51ToFO2yWqvry0sR9vdSZwwsQ2HNbC/m9vsUpxjnVTZuQBLS15xaX+dW7uwltvvat9nnK8Dph2AGYfNgs+n0dn4filVHvfeftdrF27VltN6qIULNt5Ryzs+nik2k/Bln0MBkOIRi2BtbWtBS0tTToNYT+dPGzmkiXL8S4f12dT1flBB8/ArFmHaGtOllku/A4WJx/dELz99jtYsXyljg+psT/88DkYP348lixdgQW2mwBuZrZ1szOnvm37ufW4OvSResD0qTh09vv05mdWeVY7GNi/9es3YfHixVizZg26uruRTCT15lZM6vzqQDcGHMOWlhjGT5iAgw6cgenTp8AXoHVyyRo/9V5bMj4leLfOu+OjJAiCIAiCIAiCIAiCIIwUdl5YXbMZS3uz+OD0cZgZ8ehHt3mNT34znanFJvW3aCKfK2i3APlCFvES8HbSjbd7SvAWi5jb6sX7xzehLehGOp6Cy+VFIOxXufNwqbyFogtv92Vw/8o03k57UfC64M6XcGjEhUsPDGBWs09bGOYyGT7Brd0OJPrj4KPfLW2tCIT8KPJRcNUml4rr74ujv6cXbe0t8DdFEI+n4TMMeD0mksk4NqzfAL8viDETxiOl2m0YHu0OwOdRHStRVDVAxwYUAF/c2I/5nUmcOKkVc2J+3d+awioVO3VI0ZnQn6y+7nLr8y2YLL0C1W6deadRg69VU4Zy7PdW12NtYmXB9JbLAQur7YNri5NWQTWZWbbpB6+rvtIy1MU6HTlzV6HK1W1nPWVtsRqi/rP6WSqqzyQ3Q0ul9MZdFGXZJgqwfr/6LIaC2hKbriasNnJMnPJYljWefLfpHsJxfSAIgiAIgiAIgiAIgiDsmwxdWDUtH6sUVpf35/DBA8bioDB3mGcaWlGWwMfduYlVNqcC/asabni5C30hj6ILKIabkIYbAdOEP5eGV+UJhyOID8RVOSYiTWHkCjlkUxkUVGX5YAjvxgt4fl0cXekCWtwGjp/cjHnjQ4ipvJlsEfFEBqGQX+/y39vVpeUufyQCXzAIn9cNj2Egm8lr8ZbuAYJNQf0YeLIvqTc4CkYC6E8MIJMuIpfMIJlJwB8KojXaAj9dAngM7U+T5remy0RKtf/Jjn4s60ngg1PaMTtGv6gldZmPg1tiniWs6kMrqGM9TOovUxXSCWR7NsLMpqDNZdV4aMmOeTTqjFaR6oj67Jay9CvT28fEOdZCri7Fto7lgfPHCU4m50gnUKgW2xXpWnU5qk0sqBzn1MlWjfI0LEed66K3KYuCJL2S8p+VxsEeiRo4hZHyfHrEVXVsszVu25XijI96I9hXuldwaf+zajy3lEOYk61Qn+hSSYWi/mzqcVcJ9RX1ufbH2lUYpeIo1LItVn8EQRAEQRAEQRAEQRCEfZMhCKvEkqkKMPDEms1Y3JXGB6ePxfua/dpilZsA5fM57ae0VCzA7fHBGwqh6HJhcyKLzqy67nYDPhfafG5M8HtgpBMwKKaGo0gmE8hmMtqvJcvz+vza16bH7UFa1btZ5e/PqNoLOYwJqDLCfmRNA13pPOKZHKIqX7BURFSVHfT7EE8kkc4XEPKH4FFtoFga8ge1xW28WNSP9hfTWRXnB3d0z+Vy8KnrA919gLuAllEtKGYLyKp4w3TD51b9CRooqvJf7crjviX9iKi+XDqzFbOi9OlpamHVGicGQ/1HCY6ndB/AWPVPtZE6as/aFVj14l+Q27RG+/is1C91RgqAPNCPnBMnEV8rM1Sia7OPnbR2u3RgmdZ1CpGMKk9FKDjqVNqK047UOCmqY/VlawYeGVrwZF0sV8Uw0i5mm7LV8dZ216I8QyXMy/aqtuv6nLEj1hH/6hrUH123CnoMVMSW94Gn+iLzO4k4HkxbRN7lw6T3n4QpKsBFlwN0XcFU/AwIgiAIgiAIgiAIgiAI+yJDFFYpSQJ5uPFURzfmb0jhmPExHN7qhVHMaWGSvkP9fp/eQMil0iULRSzrS+K1zgIWJYDufEE/Dj824MGx48OYHSmizWUi6AsiGY8jEeeu/VG0tLSCmwlpUUurYwb0s/6KVDaNXCYNVyiCd/pL+FtHDxL5PA5uCeH4CU2YFlJ1a+tCE6VCAQMDSfSqsoPNITQ3NWMgXUJvDqodblVkEV7Vr4BZQMBtiWdukxalBfgCftWPMErqeiFTRCaTV33PYVMJuL8jj2c3AYeO8uOKGUHMjvp0W3U7bYGOopzlFoCnWnGzj0vqUgmF5ACy3etgZhPq3LlYhl0MRVWNFgjL01nHlgipjvV/Zde3tKNS6FNpdJGW/ayFnVbHq3brLE59tYRCO48tPjp5iW6T05SyQwdaqloHzGRd3ZqcGXRhNuqKTmbH8bJ1VB19UWdQf3li/dVo1VQFZ7x1/UzBV5XO7osVy96rI36W2F59jXnVPKCvXcMLT+t4+FrGqWOPTmsVa5ctCIIgCIIgCIIgCIIg7HMMyRUAxSRKcQXDg6fX9eHR5UlMCntwSruJyTEvosEg/G6fFkQLpRLSqRTWZYp4YnMRf+sy0OcKwXR5Ve2qrEIO44wsTmotYl6rC2P8LnjchraMDKpyuLu7Fr22CGCWrEtdzK3KTqbiWJ0u4c9r83i2B8ireg/yZvGJgyI4dpS1cz+zcmOh/ngSqXQWbq8XedX2lOlF1qAQCrhUn+g11meWEFBpmyM+RAI+JAf6QHebkeYmuG2RkfIcW7OwP4PfLMvg1V5gbruBT80IYXbEr9tOQdJli6t0GUCqWTBqK0qnb/sEVl/Z212FUyIZriNF1xeWOGttaCYIgiAIgiAIgiAIgiDs22yv9DXEsK3yPDpzyWWg0yyhx+2CNxBEyBOCzx1QaVza52lfbwpplXZ1ycD8uIkeVxQFfxAFr4GSx0BRHa9HCK92F7A+byDa0oxYczO8Xi8KBe7Obqoqy4QqderWgYKkC0FvGLk8MFDIweVzw+XxIquuZQt5LcIZLkrAJhLxFMxCCaNGtcEfiSJTcqOoeuAyTB1oBWu63MiqMgteNwyPtZmU10+Zle4BMvqcW0oVqZ+pOsaG/Jja5EXYyMIwWR8vKLY01zrX7dgauQ2OCJdIJjEwEEdJW0Vui9brKuC4cId9btjFcWLedJqbLpV0nG6iHbhrPdOzHB5b+ayx1Qa9LNBOTb+4Oqg+8zWdSWNz52b09/epcy1p69Rb0249d+L4KDxfWQc3gcpmc+jrG0Amk7VSqqRWUOm3lGeVUA7z9vR0Y/26tXozsQ0bNqK3r09vgKZ9nW6p26LynDAunkjoUPX6Ni2wYDIrKf+UBwern7TM7urqVG3qATdn46ZfIqoKgiAIgiAIgiAIgiDsH+ywsEp5qUBhSf3jNj1GyYC3ZGJU0I0xzWGU8kUkk2mkcnn0JxPwBTwIhoPoTBTQk3Xp3f49RVbs1sFtulBy+zHgDiDn9sCtH1c3UCpaguF26MslmAzqn9vrwahIGAeGfZhmpDChNICZTT6MC/pglpjGg1QqjWw+g1DUB7fbhUy+hLzhRsnlVjXR/lSVZKg61XnR40ZG5ctk8yioPnBTIwqraVVGQeWjz9eCWdRWrM0eF44eF8AhbWoMTIqr24pv7IfltqCx2LZxw0asXbsW+XxeC4oU7bg7vTMGFEuz2eyWOKZh+vXr16s4Cnxd2jft5s2bsHr1KmQz9G9rtZeiLjde4nhk0hmsWbMGnZ1dWgSkn1f6s02ns7oOq60qmCqXei82bdyMBe++p8rv1X2mSJpOp7U4y3QsN8fNyVS81VZDddml0pq6no0bNyIeH1DHq9DT06P7R/+7jlBZUOWwPPaN7SuHdaxYsQrvvbdI9XUd1nasxaKFi7BmdYctQBtb2sPxsgRRU7eH48RrjIv3D6C/r0+XyTZSgGZ9bIs1RkV9zDgGiraWPuq8d4bqjxo7PU7qc6DGiWPDMXznnXexYf0mXe9WeFx+LgiCIAiCIAiCIAiCIOxruL/6la/c0DcwgEmTJtlRg8EEn26nMLcplca6eArRoA/jWgLwukro7+9HIZtBS7Mf4ZAfhWIJ724u4L2kF0WPT2uNJW0QasBNLc0w4SlmcEgUODDmh+Wl0rKudLtccHOjKxtLvyqhxDJUOgqfnlIB4WJe5Q3imMnNOGZsEG3FlPa/WiyUkM+mEQwF9a7/qilI5V3IqNeSar8lrCooMqoj/qVbAHc+i3wyiVQqoQW1lOpjMZ8HN7fSwqdqSE+ugM2FAjqTOTSpfLNaw2j1edQRhVpqwLpkdtNmy8F29PX1q7+8bmD9+o1atKOYSCvPcDiMzZs7sXLlKv2qBcGSieXLV+jzUCikxkjL3Fi3bj3WrOlAIBDUAiJFxnA4pMvvV+8zX5cvX66vRaMRLXYuW7YCGzdu0HGsi9bCfG+Tqv/Lli3T8WPHjlVjkVJ5V2oxl8ehUBi9vf1a/Ozs7FTvhUuV2aTb0d3dg4ULF+v6fD6/LiMep/DbqfL06TZRrFy5ciVWrVqjhWHmj0Qium7CfvKzNGHCRMyadYh6Ha/bxvHxeDw6/9Kly/U4JRJJPQ4crxUrVupx6OvrU3Xbrh5cbp2HYu+qVat1uyj4UhBlHevWrVNt26yFYNbBdjhQSOV1jvf69Rv0mHK8Oc4DakzHjh2DWCym278ttd9vQRAEQRAEQRAEQRAEYWQzRGEVWljlhkzBkA8BvxfruuNY3tmPJEraSrPJ70MsGKJMiZzpwqpkCYsTJWRcHphul964iiKllqKKRcSQw6yIC+O8JRiFvBa8KGhSVKXQZVkEbhWqLNHS0AJrIZ9GMZNGS9SPWDRoPe7u8yGZyyPV3Y1IwItIU4tqsFu1B8gWisgUaM1Kq0y7VL6qfIZZQkC1rSUUQEtTRIuHUfpX9XjgUiEYjaDg9mNNysTza3vw+qpNaFXxJ0wZhSlNAXh1eaoPqiL6VLU2f7LrsP9Wo6enV79StKWI19bWipaWGHp7e7WQRwGUwl17e5seFwqGtL6kH9q2tjZ0q35yszAKzRQmx40bp4VFCrCxWLMWOilsRiJhna+1tUXVVdCiZEtLC5qamrFp0yadl+eEYmR//4Cug/XxOts0YcIELULSypVWsr29PRg/fpxuB8VLRxil6BiJRFVdrVp4ZTkTJ07Q7aI1KgVWCpwTJ05S59Zj9RR2mY5Q0GQa9ot9IBQ0E4m4tizlmPGR/IkTJ2phltakFIPj8TjGjBmj+86mWBa2dEfQp9s0ZcpkXQ7b5PX6dDytmqdNm6byp7RIy36yL2Tdug3o6FinBdTRo0frcWDb/D4/XOrzOW7cWC3qOv223ufa77UgCIIgCIIgCIIgCIIw8qk0sRsUfGCcWz3RfLTN5cIpY6K4eNZETIlGsHBtHK91F7EwaWDVQAG9yTzSqSQmN5Uwo6kIbyGjN3ei61ODj3MXs4iU4pgRKWJKzA2/G/qx7kQioa0iKZRRSKQYqP1q8nFxipamAT7Iz0fR4+p1oy+Ip7qK+K93unHrO124fUkCL/X7EA+2IGt4kOSj/EXrsfVSLgs/8vCaBccEVouqtFT1qeBV9aiCtdWp2+OG2+WBLxxGxuPFykwJz3dn8JdlXehOFnHCgZNx8ZzJmN0SgJYDKc5qXc0S1lg6LVgHCwVkCpETJozD6NGjtNDoUW2gRWShkNeWrHzEnZt6UfRtbm7SlqeE6Rjf3NyMpqYmLYw6Wh/HibA85mlujuq4nBoLS7QuIRj06zTWuVVeMMjyoqALhUDAj/Hjx6K9vVULs9lsRr8nFHEpVlIQdfJSaGRdTU0RXS7bOm7cGC0MUyTle8r3mLAdHjW2Pl9AHW87VrzmtJ2wTsePLC1aCV/9fr8W4B1htqurW4u+FJopSFPIpdUsx3bMmNFaCG5vb9fXWO+YMWO1MMxxpihaKGytM6U+v6FQAJMmTcTYsaMRU+NXVO+Fz+fRY8Px3yqqCoIgCIIgCIIgCIIgCPsDQxJWXdrWk2KkCy7ThL9UwvQAcO6B7Tj3sCkIe114Zc1GPLhsA94ayFBlw6HtUXxwXACzfGk0F/oRLGQQUCFWSmB2NI+TR7swNWAiHPBrYXDUqFEIqnwUzWhxSCtEbkCUTGeQz+VRKOZVK0rI5opYEy/i6Y0FPLy2hJf7Q3g7HcYL3Sb+vCaNFxJu9HgjGEglsXHTBgwMJBHx+9AaDsDvtqxmKQbSUpXbWYV8bu3WIJNLoy+ZQMEE6BF0wHDjvXgejyxch/mrNmJyewgXzpmIE8ZGMUrl9aqEHAtVmJZRy2W2baXC2jiipOX6wPLraYmCfPS/pK0waTHJsaAlK0VDPvrubAplYcVZvkJLWywwafFKwZq+QSky0kKTQmQoFNHCIIMlhlqWl05b9GZUqhyKqpZVaZcWRGk1Ggh4bStZ9Ymw028VGHlOq+OsrpfCrDMqFE99Kh9F0FAwhOamGCLhqH78nkJuOSyXAjsf16dVLh+/Z3v4+aCYagnFzbrtfn9Ajxnjpk6drPM6rhPYforOtMDlRl+04KU1LPvndluWy1Z9/Mtzqz+EY8K+08KVFq8J9bnwB1S/1ftEYblc+BUEQRAEQRAEQRAEQRD2D3bYFQD1J0siU0c80c+8qwOKi0YRo3weTB1FkSyEDYkcVvZnkSy5EPJ70O4zMDGqXgNAqyuPaYESjhntxwljwpiEHEKlEuiPEy63LpN72/t8XsSiTTqeG0wVCwVksymkcznk8iV0ZXJ4fmMKL/d40euJweTj2S4DpteNpGppVyKPZlcJE4MmzGIOfr8XTYGALtft52ZaJvyq3JDHQDjgQpNqZ1il8Xq8SGby6CuUsCZTxItrOrF4Qx9GR8P4wIHjcOyYCNrctNvlY/90TGA9gs+hsMRFfaBfiB6vOlD8Y5soAFJYpdBJwZKP64dCQW2xS2Evk0lrIZEWlyyegilfmYaCIoVOCpBsA4XKvr5eLSDy0Xdaa9JalWIrhcq2tnb9KPyGDfRH2qPFSQq3FFwJy6UIyzQUMsnGjZuxadNmbRU6adIU/bi816vGTNVdDuundSnrp+UsXRBY7aNAm0dI5Wd7aJG8YcMG3a+W1hbthmCrOEuR2PLNSjGXfeXYTJkyRbWzXaVzqbhu7UKAedhGtp3jZPlX9erH9NlWCrYUVim00pqV/ae1LttA4dQRadk2j4e+YqO6b4TxdNHAdrJsXps8eYquk4Ixx7ncD7AgCIIgCIIgCIIgCIKw72Os7+gwV3V0YN68eXbUTmDyv6L2n1qEB91F4K31fXhnQ48WRWeOasJhY6NoDvjAh7gpRVGOo6Xn+o2b4XaZGDe6TT/izwf903nu1J5ByOcHfXxysyltUmgW9CZU8YEk3u4ewJ97PFiQi8H0BnTOkotyZ1E/3m9kMjg5lsMnD4piQtCL1EAPipkcAtEYPEFubMTyaINL61Va4jKvC7QBXZ3I4M113Vij6mgOh3HE5FE4pDUIvT1TiRtoqUYYFFVVoIGjowcOAVpXllt9OsdsHwVES6i0rFApAlJ05TGFQF5zxFDmowhLGEf3ATSodDZxYrG00mQaioF83J8WpU65Vho1FrYVqtMM1sHhz+XU2BeLWsClgM2y2UYrWOkcmJ+Wnmy/075ymJb1WhatlkBbjtVWq1ynHbQuJTxnVfl8Ufdxq+Ws5T7AKtOt+u1RcVbb6UZh9eoO3WemoYA6depU7TaB507bWafV363vA485/jznOFltZ7pt0wuCIAiCIAiCIAiCIAj7B7tWWCVmSQXamtK/p4GcCquyBby2thcdmwfQHvLj0IktmBoLotVtwqtSZktubB7IIl0ooLXJi2avoeI9yOYKiKfTCAb82kVAqVTQG2ZRyiwVTWSyebyt8v1uvYEFqRACtjCXM9zaPyof70c2i2ObsvjU9CAOjPq1IpdIJZHK5FSZAYRUoA1uyVBtVnkKpgud+QLe60ni3Y092mJ0zrhROGJMDG2egiqTga1WdVFUs+vRkpoxJM8KW6BAR/GOUAgtF+qcY+txfktIdQS9Wmx/3VRjqHrr2hqnkqg09sl2UFS0LtJylWNBK1BaoFJUzOUyqr1We1gXhU1ah9LSs7Luam1t1H5S2T7mcWDddDXAx/E5HrRItTbPshMoyutgVrpFoLUq20pfr7T0rdZWsjXftueVNLouCIIgCIIgCIIgCIIg7HvssCuARtAylRafNGSkhakBE60eYEZrBK2xCNb1p/HO+l5szhTg9vsQ8HqRyZtI5lwoFF3IF62NjPwuNzwqf7GYBy1PfV66CGBptHA1kEomtauAjDeIBf0FrFf5+fh/idqWysc0tIQ1SnlMDpRwWLMbrW4KgIYWB91uE6lMGqVCER5adqr6aGG7qDeJF1dsxPLOfkwc3YJTD5yIObEgmgzatJagWo2iSktB1apHvahDHttHQ4aWoIsWLcLatWv1I/G0jKRm5/g55WPxvEYrVT6O7sBrFPf4Wihwk69trS9ZLgPho+u8zjIY54isTMe8TjonP32WEj5uv3HjRgQCQd0upl26dClWrlypH9PnI/J8rJ/uBChwMh/LckRHUlm+1TYrHXHawFcGtlPFbmmLdU5h2KX97q5YsUKNRwd6enr1Tv2M52ZZzvvg5OeLVS436Apo61aOJceCfWE6XuerZfW6tX6rbVZ5TtustvOc42pZBzOf005BEARBEATh/2/vzH4bubI7/GNVcWlSOyVKYsvuRUonSJxBMvEYM0AeggxmnvOeP3Ke8pDHIC9OAjiBHRh2DzojtZqSWiu1keJWlfudIt20ppexp+NG2ueTuNStuufeW9LTh1PnOo7jOI7jvP+89YxV9GP+gwQtiEfkYyxUOBpFkdrh25fHHX22faLuYKRHd+f14eKMyuHaeDRSPEy1UI60VC2qGEsXF6FHGml2bkGjgrlVZb0bXV5fKrlT1U25pt88OdU/HQx1XlxQIUoUUfM0DBkNe1rILvXL9Vi/apS0EI7tsfRSpFKxoKGiEKenm0JRx4VEXxycqnVyoeb8jD6+t6rNmZLusJY0De8RNtCEMauhbAAvU8eIttCGVvtj1Br1P588eWLScHNz03baJysTaYm85HF5Nq5C4iHC8535e1ZPNN/Iinqk4b4kie1yTxvHZLkSk3qi7ITP9YhIJCF1SZeW6nYd4/NYPVK30VgJ/QZ2HRmhjI9o3NraskfnGQsJjER9+PChCUjmRwYpgpV5sZs+9WCp20obtVmJubS0YLvvkz16cXEe+hVUq82M+3QsW5c1MB79mc/l5YVdTz+yYvf29vT06VO7D2zq9exZy8ZDOHMNn9RkZZ4hjM2ff8Pl5bqN8fjx17ZG5s5mVmdnpzbm+vp6+EsUbJMqRCrrmZubtYxYNgzLr1mzDF7qzQ4G+XnmMCml4DiO4ziO4ziO4ziO47z/vPWM1fyx+Cx84YH9cdYothEvqZEqStWslvWwMW8Zfl/tn+p3Z9casMHRnZKWKAPQ74bLByqWirYrfzYKbXGiOIkIo+51xzJGKzM13YkiVRLprHOj9s3AJGyUFpSkqeaGV/rZ4ki/vDenD2fuaJgU9TzNdD0cajQY6mqQ6Eglffb8Qp9tH4X5SZ88XNff3l/Rh6VYRcbG0IZ55ht25TI1tpzcsK5winNvQ6ySucmO92yUhExkwyY+9/cPTCIi7xCLCEIkJps9scHVRJJ2wvqRp9VqTcfHpyZTyUolJvITOcq1SEUEKoIQWUi8bvcmjPPcsi+Rg2Sm0sbO+dfXVzYOm0SRoTkRiMwhz1LtmLhst89tHcxxe/t3yuu3JrZxFsKUmMwJKYpgpe3w8LkJTUQtbfRdXFywzbeOj4+/WStCuNXas8xUxOikDAI7/NPGXMmUXVxcsvtFbGQs4pVxWTPzQTQjXxG2CFfkK8KXa9iAiu9IVsZF1LJOxtre3gn9uiZs2fiKNbfbF3Zv2PgKCYsMvr2Bl+M4juM4juM4juM4jvP+8vZLAZhajCyzFLHKj+V4FjgTXmlqj8/PxAXdn6+ouTKn9s1Ijw/aOu8iu0qqlhMNB30lyFQV1e8NxGPbSZG6qz11en2VS5XwKoVYqRYqJdVrJZWzvkqDjuZ0o/XijX6+WtSv789rq0oc6Yujjv55+0S9pKS5uXl9ddbTv24f6LLf11/fW9ffba7q0UxJMynTzdjTKswYQRwIb9RhDQvIRbG95Vmsdjh+fV+QiUhQZCiZktQBJSJSEEm4ufnA5B/ylJ39OUc2K1IRqbdcXzahiHBEECJTySBFZG5tbY775rIUkfno0Z9YJuns7Lxt6nR+3rZ2ZCJxJnVIHzy4Z1mcUZRYtilysVJBrKbWj/GQvsCYkxqw9+/f08pK3eaCgCXDE/nJC6mJMCV7mKzcepg7sZrNZvg/3AjryePPzuabSrHmfN3zlnGLyKWdsgSIZGK3Ws9sfsTnHnI9cyGrl5qwzIM/GWvkXhAfiYsk7Xa6JmWpG0tc1o903dx8aPcaIY3QZU3MnfvOPcizYZlHZXxfqNfrOI7jOI7jOI7jOI7j/Bh4+xmrY8VYYMd8ZVZnFQFpOZ/hO5mmJl+zWEmWWobqg3pN9fkZHbav9d+tY7XTSElcsozRarmoESbNxGqsi86V0ijWbLVm2bEUHYjDdSuVRI/qVf3Z0h19tFTSzzeq+lmjrLViGDZl3Ei/Pe3o0/2OWn1p+7St/bNzba3O6VePmlZHdSHMmQxb5si0EcFUi83zUclZpY5mbGux3FVbW7gu9LFv4+PvCtmkT58+M0nJY+YIRcQjO+oj7hCVCD6yPxGjPL6P2EPEMuTa6qqJSbIt19ebGg2Zcz4Xsn7r9SWThZeX1yYU6YOMRUCSgYmEJLsUgUvWJXKVTMx+v2eZnkjGgwOyYrth7GU7j/zlUXtEJcJxZqZmfRCVnENaAnPON70qmxglFlmgEwHK5lHIX9bFHNhQijkhdrkX4ZZoZ2fHrr97txniFG3+rda+Dg8PtbbWCO3rti7kKfcGobq7+8zWzZj8+zBPxCuSF7ivcVxUlvK3LWi1sRr+54phHbN276jHSn/+DqyTtSFPJ+UFWAeClvns7++bhK7X6xbbcRzHcRzHcRzHcRzHef9562IVn5f7Rd5QVEjW8aEd5YIVNQkIrFL4XK0k2mrMqVYq6cnzth6f3Kg9GCquxOpEiU6HmXppatKwVi6rXIxVyEZikyrbsCrEu5NlWilFWg99lpKCKqENv8uD+1dhuKNw7eEg1fVNV/cXy/r1n67rF415rSBtEWxMKcrnaxsRTb04ttfYuoZ3wz7H13wXkKhATIQoNUQRh83mumWAsskSmZHlclE89r+zs2vlARCdZE8iCqn5iQjd+GBD152Orq4u1ev3TWSm6TAsJ9Pc7JzFGwwHJgTr9QWThTs72zo6OlSSUK/1Q2vjkXjEIWKULFVEJ4/EU2qA+SFBEauIUSDLFaF7cnJmj+AzP9aDtEU6IhuRnYuL82EdpRB/P8SnpMDA5s8tQ9IidMk6ZSzi8UJ8Nptrdg3jLy8vaW1t1e4bYxSLicnX3d2WiU1qxFKmYGOjGdov1G6fWfYrGa2t1q5lnXJPuF8rKw1dhGuurq+0OK5Te3B4ENraqtaqNlfGRfJOMoDpNykdwDnkLeUKkM/cD7J/WbPjOI7jOI7jOI7jOI7z4+Ctb171h4NYzGUm2Z8Yylw1SnujTJ/unenr1r4Ul3QTzavbT1UvDfWLu7P6ZHVW1QLykE6JhjG74qcqjfK6rrhPXmkUqZdl2r8Z6PPDc311eKFqMdHHG8v6qF7TfLgm/CpF1o2FnVmzH4BpsYqkQySS/YiM5BTnyehEbiI6Ly6uTELyuDm1QhGsk8fu6UfGJrVBqYOKAORu8h0ByYv4xCEesRGNfJKFmY+RmpxkzPn5WRuLeSBL2aAJ0ZskRRs3nyPz65v85TqOOYdk5DwxgXiUcaANEcqcySKdZMzymD7zQ5BSS5b19Xo3YjMrSgMcHuY1UtnMCxnLOoB4yGDuC8IYqTk7W7P5PnnyPzYPSiAgSRGn1EEFYrA2sm/zfrM2d0QsWbVz82GMEJcsYmKwNtY5WRtzBsYnJrGJSUat4ziO4ziO4ziO4ziO8+Ph3YnV3CuOP3n8nh33OaCOqdRTolZ3oE932/q35yPtDYsaDHv66Uqsf3y0oEfVWIOUR/PJfu2FnuEzLYVQIU4s9UOMs/5QX51d6b9aJ+r1R/qLjYZ+2lzQ3ShcPaJMAf1HoWcYuUAVVsTsDwMyb4IJ3QBtk3baJu3TcJ7m/DLO/36cvO1F3zwm8SbfX1w7GXN6vLyNa6Zjvug7YfrcNJN4Eyb9ptsQpNPHSE4yU5GvloW7cddqwpJFy+P9PGY/HXfy/fY4ZM4iPJGyZOoyDhJ0mum223ObPp4eDyZznm5zHMdxHMdxHMdxHMdxfpz84GJ17K2Mb/RUaCTDNP+aSz00J4/l9woFfXkx1L+0rvSfx10VleofHs7q7+/OqBQ62ab9Go5lV6JhOD7LUj25uNbnz451fN7VZmNRn9xr6INyrGKaKs2GGkXI1ERJFtlmWhk1A5Bm+cg/CNPibiL03kR+OW/5fbKjb+5d/vmH8G1pSMfp78TMj6dj3haN+fcX8/i+TOJOx77Nq+/PpD2fCxti5TAvzr06JjDmdOzpNU7PZ/qaSfvtOb1u/o7jOI7jOI7jOI7jOM77xbsTqwX7xX/lYEg5yNgqihfwbWiZpSdZrM+Puvps91Sj4UA/aS7oo8aMGsVYxdCXbNdO6PS0M9DnByfaOTxXo1bTJ/fXtDVfVpVYGXVHCxpZhddUkW1WhUiNTawyH6sB+46Ylnq/z/j+vJRJn1f1fRNviv26uK/rC2/q/8fwsrFvj/Xdxn7938BxHMdxHMdxHMdxHMdxct5JKQDk6rfcFW5sIlbzA3uNwvso/NCSKBbqk/qr/7F7rsd7Z5qpxPrJxqI2F6vqptIXR+f6eu9YlSjWxx+s6i8bM5oLoZCuhI+szEBoyNj4iq/U62QUxovzc47jOI7jOI7jOI7jOI7jOG/gHYhVNCnclpg8up1vTDQ5h/xEwlJ/FelJHusofPbCuaedof5950h7Z5eaq1XVH2W66Q/0581F/U1zQasJZQLSsTbNlGTkoo7raka5rOWxf/O5ogwAn7zdnpfjOI7jOI7jOI7jOI7jOM63eQelAHKxyuPW469TiaJZ+EGuRipQdJXz4aIMHxoOLYc1pT3SKC6Ifd5/277WlzsnqpSK+qv7y3pwp6hylpqkJQQ61WKFXxsoxMxswyqCxyEiJxiRIgGQvzuO4ziO4ziO4ziO4ziO47yKd1IK4PtjpvVFFqvYz18a2DfKBUhRlloWaiGaCNLwSbdX+NLXnHIcx3Ecx3Ecx3Ecx3Ecx3kp726npu8FWa55+mr+CD9Zppkq4VvJWnkL72PpOm755uNlvOaU4ziO4ziO4ziO4ziO4zjOS/l/JlbHznSMZaaGTwoI8M7j/FFo8V3dHcdxHMdxHMdxHMdxHMf5v0P6X8UIufTATV4kAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " TerminationCondition,\n", + " value,\n", + " Expression,\n", + " minimize,\n", + " units,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (\n", + " Mixer,\n", + " MomentumMixingType,\n", + " PressureChanger,\n", + " Heater,\n", + " Separator,\n", + " HeatExchanger,\n", + ")\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from properties import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguments, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 729.38\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 729.38 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 535.47 736.02\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 31903. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "667.9424945058901 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory(\"ipopt\")\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.turbine = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.splitter_1 = Separator(\n", + " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", + " )\n", + "\n", + " m.fs.co2_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.main_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(\n", + " dynamic=False,\n", + " property_package=m.fs.properties,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", + " )\n", + "\n", + " m.fs.splitter_2 = Separator(\n", + " property_package=m.fs.properties,\n", + " ideal_separation=False,\n", + " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", + " )\n", + "\n", + " m.fs.FG_cooler = Heater(\n", + " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", + " )\n", + "\n", + " m.fs.mixer = Mixer(\n", + " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", + " )\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(\n", + " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", + " )\n", + " m.fs.s04 = Arc(\n", + " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", + " )\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(\n", + " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", + " )\n", + " m.fs.s07 = Arc(\n", + " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", + " )\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(\n", + " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", + " )\n", + " m.fs.s10 = Arc(\n", + " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", + " )\n", + " m.fs.s11 = Arc(\n", + " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", + " )\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline\n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1 / 3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(\n", + " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(\n", + " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", + " )\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import (\n", + " convert_quantity_to_reporting_units,\n", + " report_quantity,\n", + " )\n", + "\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(\n", + " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", + " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", + " units.kW,\n", + " )\n", + " return m\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "def main():\n", - " # Setup solver and options\n", - " solver = SolverFactory(\"ipopt\")\n", - " outlvl = 0\n", - " tee = True\n", - "\n", - " # Set up concrete model\n", - " m = ConcreteModel()\n", - "\n", - " # Create a flowsheet block\n", - " m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - " # Create the properties param block\n", - " m.fs.properties = SCO2ParameterBlock()\n", - "\n", - " # Add unit models to the flowsheet\n", - " m.fs.boiler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.turbine = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=False,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.HTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_shell = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.LTR_pseudo_tube = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.splitter_1 = Separator(\n", - " property_package=m.fs.properties, outlet_list=[\"bypass\", \"to_cooler\"]\n", - " )\n", - "\n", - " m.fs.co2_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.main_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.bypass_compressor = PressureChanger(\n", - " dynamic=False,\n", - " property_package=m.fs.properties,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isentropic,\n", - " )\n", - "\n", - " m.fs.splitter_2 = Separator(\n", - " property_package=m.fs.properties,\n", - " ideal_separation=False,\n", - " outlet_list=[\"to_FG_cooler\", \"to_LTR\"],\n", - " )\n", - "\n", - " m.fs.FG_cooler = Heater(\n", - " dynamic=False, property_package=m.fs.properties, has_pressure_change=True\n", - " )\n", - "\n", - " m.fs.mixer = Mixer(\n", - " property_package=m.fs.properties, inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"]\n", - " )\n", - "\n", - " # # Connect the flowsheet\n", - " m.fs.s01 = Arc(source=m.fs.boiler.outlet, destination=m.fs.turbine.inlet)\n", - " m.fs.s02 = Arc(source=m.fs.turbine.outlet, destination=m.fs.HTR_pseudo_shell.inlet)\n", - " m.fs.s03 = Arc(\n", - " source=m.fs.HTR_pseudo_shell.outlet, destination=m.fs.LTR_pseudo_shell.inlet\n", - " )\n", - " m.fs.s04 = Arc(\n", - " source=m.fs.LTR_pseudo_shell.outlet, destination=m.fs.splitter_1.inlet\n", - " )\n", - " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, destination=m.fs.co2_cooler.inlet)\n", - " m.fs.s06 = Arc(\n", - " source=m.fs.splitter_1.bypass, destination=m.fs.bypass_compressor.inlet\n", - " )\n", - " m.fs.s07 = Arc(\n", - " source=m.fs.co2_cooler.outlet, destination=m.fs.main_compressor.inlet\n", - " )\n", - " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, destination=m.fs.mixer.bypass)\n", - " m.fs.s09 = Arc(\n", - " source=m.fs.main_compressor.outlet, destination=m.fs.splitter_2.inlet\n", - " )\n", - " m.fs.s10 = Arc(\n", - " source=m.fs.splitter_2.to_FG_cooler, destination=m.fs.FG_cooler.inlet\n", - " )\n", - " m.fs.s11 = Arc(\n", - " source=m.fs.splitter_2.to_LTR, destination=m.fs.LTR_pseudo_tube.inlet\n", - " )\n", - " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, destination=m.fs.mixer.LTR_out)\n", - " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, destination=m.fs.mixer.FG_out)\n", - " m.fs.s14 = Arc(source=m.fs.mixer.outlet, destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", - " # NETL Baseline\n", - " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", - " m.fs.boiler.inlet.temperature.fix(685.15)\n", - " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", - " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", - " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s01)\n", - "\n", - " m.fs.turbine.ratioP.fix(1 / 3.68)\n", - " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", - " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s02)\n", - " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", - " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s03)\n", - "\n", - " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", - " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", - " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s04)\n", - " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", - "\n", - " m.fs.splitter_1.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s05)\n", - " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", - " m.fs.co2_cooler.deltaP.fix(-0.07)\n", - " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s06)\n", - " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.bypass_compressor.ratioP.fix(3.8)\n", - " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s07)\n", - " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", - " m.fs.main_compressor.ratioP.fix(3.8)\n", - " m.fs.main_compressor.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s09)\n", - "\n", - " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", - " m.fs.splitter_2.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s10)\n", - " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", - " m.fs.FG_cooler.deltaP.fix(-0.06)\n", - " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s11)\n", - "\n", - " m.fs.LTR_pseudo_tube.deltaP.fix(0)\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " # Add constraint heats of the LTR_pseudo shell and tube\n", - " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c1 = Constraint(\n", - " expr=m.fs.LTR_pseudo_shell.heat_duty[0] == -m.fs.LTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " propagate_state(m.fs.s08)\n", - " propagate_state(m.fs.s12)\n", - " propagate_state(m.fs.s13)\n", - "\n", - " m.fs.mixer.initialize(outlvl=outlvl)\n", - "\n", - " propagate_state(m.fs.s14)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", - " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", - " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", - "\n", - " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", - " m.fs.c2 = Constraint(\n", - " expr=m.fs.HTR_pseudo_shell.heat_duty[0] == -m.fs.HTR_pseudo_tube.heat_duty[0]\n", - " )\n", - "\n", - " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", - "\n", - " print(\"--------------------------------------------------------------------\")\n", - " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", - " print(\"--------------------------------------------------------------------\")\n", - "\n", - " solver.solve(m, tee=tee)\n", - "\n", - " #\n", - " from idaes.core.util.units_of_measurement import (\n", - " convert_quantity_to_reporting_units,\n", - " report_quantity,\n", - " )\n", - "\n", - " # Print reports\n", - " for i in m.fs.component_objects(Block):\n", - " if isinstance(i, UnitModelBlockData):\n", - " i.report()\n", - "\n", - " # Converting units for readability\n", - " print(\n", - " -1 * value(units.convert(m.fs.turbine.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.main_compressor.work_mechanical[0], units.kW))\n", - " - 1 * value(units.convert(m.fs.bypass_compressor.work_mechanical[0], units.kW)),\n", - " units.kW,\n", - " )\n", - " return m\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " m = main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index 6cd173b7..9c8acbe4 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -1,15 +1,16 @@ -############################################################################## -# Institute for the Design of Advanced Energy Systems Process Systems -# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the -# software owners: The Regents of the University of California, through -# Lawrence Berkeley National Laboratory, National Technology & Engineering -# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia -# University Research Corporation, et al. All rights reserved. +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). # -# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and -# license information, respectively. Both files are also available online -# at the URL "https://github.com/IDAES/idaes-pse". -############################################################################## +# Copyright (c) 2018-2025 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +# +################################################################################# """ Maintainer: Javal Vyas diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json index f7f6e287..17c53879 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json @@ -1 +1 @@ -{"model_encoding": {"enth_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-539145.2641931743], [-1572.9941129612596], [1028.1303702529963], [-41.89265612633253], [-2.854098382160082], [3.1084792045014056], [0.0040249321969904606], [-0.07298691795031877], [-2.7827021177926484e-06], [0.0006559340352560386], [7.62454692622566e-10], [4.50540106476475], [-0.0025967218940188964], [3.27147430041989e-05], [-0.05205092851352775], [149943.17003170087], [-3.5662256522946807]], "final_polynomial_order": 5, "errors": {"MAE": 116.22937611304296, "MSE": 39254.96789837278, "R2": 0.9997117200542968}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}, "entr_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-529.9581296941684], [-5.674476891947422], [3.6251620831469844], [-0.012206052330165947], [-0.010121999171951317], [0.0044164987227566545], [1.4212146246171698e-05], [-0.00012049491972756627], [-9.875650167428602e-09], [1.1673348430972035e-06], [2.72031843813476e-12], [0.010605178085763924], [-6.047902870413699e-06], [6.872924493404928e-08], [-0.00011146830780061758], [437.25207041949056], [0.0015391876304710196]], "final_polynomial_order": 5, "errors": {"MAE": 0.34548912239751245, "MSE": 0.3560561890323906, "R2": 0.9991570382929269}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}}, "input_labels": ["pressure", "temperature"], "output_labels": ["enth_mol", "entr_mol"], "input_bounds": {"pressure": [7, 40], "temperature": [306, 1000]}, "surrogate_type": "poly"} +{"model_encoding": {"enth_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-541092.5943570095], [-3485.037614541703], [1100.5328082296978], [99.44186836309524], [-3.0693992781651227], [-2.9402365083648583], [0.004343168574325156], [0.0588297780356798], [-3.010896457753368e-06], [-0.0004724466679913183], [8.272080563278464e-10], [5.317428139906877], [-0.003219325229480838], [4.438226910352845e-05], [-0.06610226475123816], [195321.10141739884], [-25.31724070025135]], "final_polynomial_order": 5, "errors": {"MAE": 138.65505661875198, "MSE": 55816.77064996388, "R2": 0.9995812255692139}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}, "entr_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-533.1433487146091], [-11.382044986921615], [3.8301849640307815], [0.41278671762658736], [-0.010731511967613915], [-0.013511359842171135], [1.5122818400972181e-05], [0.00026456812655521885], [-1.0534647243397528e-08], [-2.089555841149664e-06], [2.9088290958316355e-12], [0.012751763519948106], [-7.827035760612202e-06], [1.030963694409036e-07], [-0.0001507459196335798], [553.3693572366914], [-0.075583276326012]], "final_polynomial_order": 5, "errors": {"MAE": 0.40682545544444154, "MSE": 0.503090963628653, "R2": 0.9987871803561564}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}}, "input_labels": ["pressure", "temperature"], "output_labels": ["enth_mol", "entr_mol"], "input_bounds": {"pressure": [7, 40], "temperature": [306, 1000]}, "surrogate_type": "poly"} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb index 2a3ddb83..3f182b58 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e8df2ea4", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -659,8 +686,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb index 9dd602c4..690b3dbb 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -62,7 +88,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -122,9 +148,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dang/miniforge3/envs/idaes_examples_py3.11/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ], "source": [ "# Import training data\n", "np.set_printoptions(precision=6, suppress=True)\n", @@ -164,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -174,95 +209,165 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; previous file will be overwritten.\n", - "\n", "No iterations will be run.\n", "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "No iterations will be run.\n", + "No iterations will be run.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 36880.489657\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -534397.59515\n", - "(x_ 1 )^ 1 | -2733.579691\n", - "(x_ 2 )^ 1 | 1036.106357\n", - "(x_ 1 )^ 2 | 32.409203\n", - "(x_ 2 )^ 2 | -2.852387\n", - "(x_ 1 )^ 3 | 0.893563\n", - "(x_ 2 )^ 3 | 0.004018\n", - "(x_ 1 )^ 4 | -0.045284\n", + "k | -541092.594357\n", + "(x_ 1 )^ 1 | -3485.037615\n", + "(x_ 2 )^ 1 | 1100.532808\n", + "(x_ 1 )^ 2 | 99.441868\n", + "(x_ 2 )^ 2 | -3.069399\n", + "(x_ 1 )^ 3 | -2.940237\n", + "(x_ 2 )^ 3 | 0.004343\n", + "(x_ 1 )^ 4 | 0.05883\n", "(x_ 2 )^ 4 | -3e-06\n", - "(x_ 1 )^ 5 | 0.000564\n", + "(x_ 1 )^ 5 | -0.000472\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 4.372684\n", + "x_ 1 .x_ 2 | 5.317428\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -0.002723\n", - "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", - "Coeff. additional_regression_features[ 3 ]: -0.050607\n", - "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", - "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "Coeff. additional_regression_features[ 1 ]: -0.003219\n", + "Coeff. additional_regression_features[ 2 ]: 4.4e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.066102\n", + "Coeff. additional_regression_features[ 4 ]: 195321.101417\n", + "Coeff. additional_regression_features[ 5 ]: -25.317241\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "Order: 5 / MAE: 138.655057 / MSE: 55816.770650 / R^2: 0.999581\n", "\n", "Results saved in solution.pickle\n", - "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "2025-03-17 17:39:25 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "===========================Polynomial Regression===============================================\n", "\n", @@ -272,89 +377,144 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "No iterations will be run.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + "No iterations will be run.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", + " Exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.292645\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -519.862457\n", - "(x_ 1 )^ 1 | -8.820865\n", - "(x_ 2 )^ 1 | 3.676641\n", - "(x_ 1 )^ 2 | 0.18002\n", - "(x_ 2 )^ 2 | -0.010217\n", - "(x_ 1 )^ 3 | -0.000783\n", - "(x_ 2 )^ 3 | 1.4e-05\n", - "(x_ 1 )^ 4 | -6.9e-05\n", + "k | -533.143349\n", + "(x_ 1 )^ 1 | -11.382045\n", + "(x_ 2 )^ 1 | 3.830185\n", + "(x_ 1 )^ 2 | 0.412787\n", + "(x_ 2 )^ 2 | -0.010732\n", + "(x_ 1 )^ 3 | -0.013511\n", + "(x_ 2 )^ 3 | 1.5e-05\n", + "(x_ 1 )^ 4 | 0.000265\n", "(x_ 2 )^ 4 | -0.0\n", - "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 1 )^ 5 | -2e-06\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 0.010367\n", + "x_ 1 .x_ 2 | 0.012752\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 1 ]: -8e-06\n", "Coeff. additional_regression_features[ 2 ]: 0.0\n", - "Coeff. additional_regression_features[ 3 ]: -0.000112\n", - "Coeff. additional_regression_features[ 4 ]: 484.312223\n", - "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "Coeff. additional_regression_features[ 3 ]: -0.000151\n", + "Coeff. additional_regression_features[ 4 ]: 553.369357\n", + "Coeff. additional_regression_features[ 5 ]: -0.075583\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "Order: 5 / MAE: 0.406825 / MSE: 0.503091 / R^2: 0.998787\n", "\n", "Results saved in solution.pickle\n", - "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + "2025-03-17 17:39:32 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" ] } ], @@ -401,12 +561,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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FhYXcuXPn9C4iQRAEYTCkjt9BOmtAWZw/fx4bNmxA9+7dERwcDAD4+OOP0bJlS2zfvh39+vUDx3FIS0vDwoULERkZCQDIzc1FWlqaw7nS09MxefJkAEBlZSUOHDiA2bNnC98HBAQgLS0Nubm5AIADBw6gqqrK4Txt27ZF8+bNkZubi27duiE3NxcdOnRAkyZNHK4zYcIE/Prrr7jppptUqRctKCkpQWVlJQCgsDAABQVBSEq6goSEGgBASEgIoqKi9CyiZEpKSrBixQrYbI2wZMlkcFztbvc1NRZMnx6OP/9cC6v1IrKysgxzT4T32LdpVxipTRMEYSwMJcJmzpyJFStWoLy8HN26dcP27duF7/7v//4Px48fxwcffIC3334b1dXVmDJlCgYPHoy9e/cCAIqLix2EEQA0adIEZWVl+Pvvv3HhwgVUV1e7POb3338XzhESEoKIiIg6xxQXF3u8Dv+dOyoqKlBRUSH8XVZWJqVaNIMXLQDwww834eOP7wbHBcBiqcE992xHp04/AoBhRAs/8J4/HwWOc/TMc1wAzp+PhNV60eMAzSIkKqRj36YBwGZrhPPnoxAZWQKr9aLwuVHaNEEQxkJXETZr1iwsWLDA4zGHDx9G27ZtAQDTp0/H6NGjcfz4ccybNw/Dhg3D9u3bYbFYUFNTg4qKCrz99tu49tprAQBr1qxB586dceTIEbRp00b1+/GV+fPnY968eXoXwy38wG6zNRIEGFArWD7++G4kJ/9hSNESGVkCi6XGQYhZLDWIjDyvY6nkQaLCO+zbqqeJhdHaNME2LE6UWCyTP6CrCJs2bRpGjBjh8ZiWLVsK/46OjkZ0dDSuvfZatGvXDs2aNcM333yD1NRUxMfHIygoSBBgANCuXTsAtSsV27Rpg7i4uDqrGE+fPo3w8HDUr18fgYGBCAwMdHlMXFwcACAuLg6VlZUoLS11sIY5H+O8opI/J3+MK2bPno2pU6cKf5eVlaFZs2Ye60cPxCxHRsNqvYh77tleZwA24r2QqJCH2MSCIJTCeaLkDi0nSiyWyV/QVYTFxMQgJiZG1m9rampjkHj3XY8ePXDlyhXk5+cjOTkZAPDf//4XANCiRQsAQGpqKj755BOH8+Tk5CA1NRVArdLv3Lkz9uzZg4EDBwrX2bNnD7KysgAAnTt3RnBwMPbs2YNBgwYBAI4cOYITJ04I50lNTcULL7yAM2fOIDY2VrhOeHg42rdv7/aeQkNDERoaKqs+tERLy5FWs7NOnX5EcvIfOH8+EpGR53UZeJW8VxIV3mG2iQXBLlInQFpOlJyv5c6CTpM35TFETFheXh6+++473HLLLWjcuDHy8/MxZ84cJCcnC8InLS0NnTp1wqhRo7BkyRLU1NRg4sSJuOOOOwTr2Pjx47FixQrMmDEDo0aNwt69e7F582bs2LFDuNbUqVMxfPhwdOnSBV27dsWSJUtw6dIljBw5EgBgtVoxevRoTJ06FZGRkQgPD8ekSZOQmpqKbt26AQD69u2L9u3b4+GHH8bChQtRXFyMp556ChMnTjSEyBJDK8uRVNfaQw89JAhvX7BaL+o24CrtRiRR4R1mckkbFX91h7l71/XCkwWdUB5DiLCwsDBkZ2fj6aefxqVLlxAfH49+/frhqaeeEkRNQEAAPv74Y0yaNAk9e/ZEgwYNcOedd2LRokXCeZKSkrBjxw5MmTIFS5cuRdOmTfHmm28iPT1dOCYzMxNnz57F3LlzUVxcjBtvvBE7d+50CLRfvHgxAgICMGjQIFRUVCA9PR2vvfaa8H1gYCC2b9+OCRMmIDU1FQ0aNMDw4cPx7LPPalBb2qCF5cidaw2owR137EaPHrUrVt99913Dm8mVdiOSqPAOM7mkjYi/usNYEzxkQdceQ4iwDh06CCscPZGQkICtW7d6PKZ379748UfPjTwrK0twP7qiXr16WLlyJVauXOn2mBYtWtRxfZoNrSxHzh0DEICcnDsAQBBiZjGTK9UJkqjwHhZc0v6KP7rDWBQ8ZEHXHkOIMMK/cdUxABbs3p2G66//RXbnEBISouhxSuBLJ1hSUoJz584Jf5Oo8B49XdJELaxZh9SCRcFDFnTtIRFGSEYv0RIZWQKgBs67bPnaYUVFRSErK4upOBS5naA7dw6JCveUlJSgtLRU0rFaCnF/hkXrkFqwKHjIgq49JMIIyeglWqzWi7jjjt3/c0FahM+V6LBYiy+R2wlKdefw+LuokBqDlJmZiZiYGObaiVlh0TqkFqwKHrKgawuJMMIr9BqM+Niv3bvTmOqw1MBTJ3ju3DlRoevJnZORkYGEhAS/FxVSRavVatW0rvx1hSAPi9YhpbGfAHl617WcKDlfy50FXevJmz+8DyTCCMPQo0curr/+F7+YobnrBLOzswG4XyUm5s6Jjo42fKelNKzEILG8QlDNwdA+lpFV65CSsBgGwWKZWH4flIREGMEsrmZdesQ4aTEbczfD9HaVmD+5c6Ti6vnxgz5LMUisrhBUczB0dW5/cIexKBpYKxOr74PSkAgjmCUqKgoPPfQQ3n33XdFj1TKTazUb42eihYWFgrVLjoXGH9w5rnAnlEtLS7F582a3v2NVtLJknSssLHT4TMnB0N1vWHGHEWzAyvugBiTCCKZJTk7W1Uyu5WwsKipK8ibp7vAHd44zUoUyUPf5sShaWbHOuapXtQdDd+8XxTL6L6y8D2pBIoxQDaXceKx0vFrOxqRaaPg69uf8YFKFsrvnx5poZcU651yvp07FqzoYenq/KJbRf2HlfVALEmGEKpgtqFLr2ZgUC42nOvZXd467gdzT82NNtLJonfvhh5uwbdvdUDpXH4+S75c/rKjzJ1h8H5SERBihCmYLqtR6NibFrSi1jjMyMhAdHW36wcfTQC72/FgSray5lPl6dRZggHKDoVLvl9kmfwR774PSkAgjVMcMQZV6zMa8sdCIuXLi4+NVKycreBrIxZ4fL1Tt0VO0smSdc71tGBQdDJV6v8w2+SNqYel9UBoSYYSqmCWoUqvZmNSkieXl5SgvLwdgnjr2FU8DudjzY0Gospow01W9AjUYPfpNNG1apMg11Hi/1Jz86e3y1Pv6WsDq+6A0JMIIVTFTUKUWszEpSRPLy8sd0naYqY59QWwgZ302zWLCTMB9vToLMDmDoVrZ49WcmOjt8tT7+q5QQxSy+j4oDYkwQlWMHlSpx2xMrFMpKnIc/Ixex0oiJrRYn02zOqB4qldf0keoNdCqOTGR6spUy+Wp9/WdUVMUsvo+KAmJMEJVjB5UqcVsTIlZZGpqLvbvTwVgvDr2FalCOTMzE1ar1eXv/aGz9xap9epr/i416l5sYmKf0sUeOW3BXdwZoI3b0NP1tYA1UWg0SIQRiuDc2Zgpb5XasR2+zCKd415SU79GSkqe4erYF/zFbaE1Rq5XsckfvyuFK7yx2HiKOzt58iQ+/fRT0XP44jZkcdGT3qLQaJAII3xGipBg3Q2kF76s5nIV95Kbm4qUlDyH4/yhjlkUAmbAyPUqd/In1WIjFncmRYB5cz1vr68HLIpC1iERRviM1E7EOQ0Aq7NovfC2AxOLe6GtXtjFH1a36YFUFyrgu8XG27gzpS1ErC3IYVEUGgESYYTiuOtsWEgDwCpyOjCxuBcjbPVSUlKCs2fPoqqqyuX3QUFBiI2NZf4+vIHF1W1mwdmFeu7cOZeuR08THvtQCk9i2JsFMV9/nYrdu9MUtRCxtiCHNVFoFEiEEYpC5mh5yOnAWF70IMXSA0DyxttmEiQUyKwuntqJzdYIJ082c9iCyXnC4yza3LU9qe/f11+nIifnDgAWl9eTC2vvP2ui0CiQCCMUwxdztL+7Z7zpwNTKraQUUi09Q4YMqfOZP2Y4p0BmbbCfIDrjacLj3Pakvn9A7bPNyUkDL8CkXE8MVt9/1kShUSARRiiGXHM0uWe868BYX7UmVTBduXLF4W81XDasQ5ZjbXCeIDpjsdQgOLgSBQWJomJYyvtns9mwadMmnD8fBaX33GTt/ddbFBp9Ak8ijFAMueZoOSsEjf7iucKb1VxGujcplh61XDYsQ4HM2uFu/0ugto+64YZDWLNmjGQxLPX9c73lE4e0tN0+PWOW3n89RaEZJvAkwgjFUMIcLcUyYIYXj8fs+6NJeZ5quGyMgN6BzGacyLjD3QRx0KAtiIgoFQQYoKwYdu4TgRrcccdu9OiRKxxj1HfbHlas7u4mfIWFhS7bOgttnEQY4TNKmaOlWgZ8ya3FGqy5FpRE6vNUw2VjBPQMZDbTREYK7iaI119/GAUFiYqLYal94kMPPWSK+mUBTxO+7Oxst+OE3m2cRBjhM0oJCTmWATPE1Ji1E5b6PNVy2YihtyVIz0Bmf1mhKUUMqSGGjTy50vu9kIPYhM/TOKF3GycRRiiCEi+lt50hxdSwjdTnKcVlozR6WoL0DmR2hVlXaHoSQ3wOMbXEMGtCRQpGtZB6mvABYHqcIBFGMIO3naHeMTWEZ8SeZ3BwsHCs2KIEpQWJni5tNa0kcqwYZrAmy7lvFsWw3hjVQuppwsf6OEEijGAKb1YIUnJANpE6uMXExCArK0v3jPl6iBC9V4rxmMGaLNd6Y2SXoVZoZSH11QUqNuFjeZwgEUZIRq1YAbkrBP0hOaDW8RlKXM/bwU3PQc5XEcJS/Iwc6x7rVgIp+GLV9GeBJYbUrZ0A39q5Ui5QdxM+1scJEmGEJNSMFfBlRuqN5cxoaB2foeT1jDK4+SJCWI6fkWrdM5s12QyuVRYQm5y42o9Tbjv3RURLncCzPE6QCCMkoXasgDcvr9lza/FoHZ9h1HgQX/BFhLBaX95Y91i3EniDGVyrrCAW6K5W/KS3IlrKwgsed+OE3pAII2Sh52oqo8Zy+Oq6UqLOPZXB2cVg1hVz9igpQlipLynWPTMGpZvBtcoK7iYnhYUJePvtYapYGuWKaHd9ptS2q3cbJxFGeA0LJn/WBJYYvrqulKhzqWVQ6npqoXQclhKuCpbqS4p1T4mJDEvxcID5XKt64mpykpa2W9jbFVDe0qi0iDbKZJ1EGOEVYrOVc+fOMdGwWcMX15VSbhapsRdquHWUGrCVisNS0qXNmhtMqnXPl3eUxXg4M7lW9cKThVRtS6NaSXNZh0QY4RViLyLvg2ctmR9reOO6UqPz82S5Ufp6Sg7YSsVhKTlLFquv0tJSxMfHSyq3UqgdiMzq1mEsB2AbAVfvhX1slZqWRn8V0STCCK+QOlsxU/C20njrulJ6hihmuVH6emoGsPsSh6XUJEGsvjZv3qzJpESvBSt6u2L1XqjDmlvWV9yVVQuR5I8imkQY4RWuXsTUVPW2lzEqzh0zH/Qux3WldOcnZrlRu7NVKoBdi8FfykIGKfWlxaREjxgYFlyxesb+sOiWVRM1RJLeIlpvSIQRknAVK5CXl4L9+1Oxf38P5OamMhW8rSeeOmZvXH1qrWCTYulSa8WcUsJJi8Hfm4UMfH2dPNkUgAXNmp1UpAzeovVAz8qKRL0EDqtuWSVRWyQZJYBeLUiEEZLgX5TCwkIhPiA3NxUAG8HILOGpM/HG1adW5yRmucnIyEB0dLRi1+NRUjhpMfhLHWB58vNbMbNCUitoReJV9HbLqoUWIsmsAksKJMIIyURFRQkvIiszYC2RG/vhPHh74+pTq3PyZOmKjo5WJZBcyTaj9eAvNsCy4JbTAzMEUysR02X25+/PIkltSIQRshAbBPl4GXcdmNGCWeVsjgy4H7z1SI6pd+yFksJJy8FfygDrj5MSHiMHUysV0+Xr8zdaf0goB4kwQhZig6D9dhHOHZgRg1nlxH6IDd4jR97h4PZTu6PVwq3gajDxJoBdDD0yvUsZYP3NLae3oFcKpWK6fHn+RuwPCeUgEUZ4hdRB0J6zZ886dB6s7rknFamxH2KDt1puP0+o2YlLGUx8FU56BPFKGWDN4JbzBjMGU/sS0+XL8zdjcD9Z9qRDIozwCvvO9+jRo9i3b5/QUbjrPKqqqjyeU6s997SO/TCjdUSJvSedLYCAd52y1p231AHWLHswSsVMg6gSMV1m2/5KLs6TMXf9AFn2aiERRngN/+LYD7pyOw+tOh09Yj/MZh1Rau9JPSyAvuJpgM3MzITVanX7W7PM+s1s3ZAb02Xm7a/kYt9GPPUDRrLsqQmJMMJn5HYeWnY6SrlApVi39Ihb0gKpdWiWwUTqABsTE2NY8SEVs8ctybVaa7n9ldEwSz+gNiTCCJ+R23no2enIdYFKsW6ZMV7GFe7q0CyDiavnaLPZHNzrQUFBqKysRFFREQB9nqsWFiozxi3Z44vVWqvtr4yGWfoBtSERRvhMcHAFAA6Axe5TDsHB6q0o8gVfXaBSYj+MLrDE8FSHZhpMnFf1btq0SfQ3WlqD9Ii/MUPckiv0TrVhtvAFM/UDakIijPCZqqpQOAowALCgqsqzu02PTkeuiVyp2A8zxNWI1aHZBhMeFq1BWsffmM3FxFqqDb2FoJKYtR9QGhJhhGyCgmqbj9iMhz+OR2rMlM1mc3ttuWJFrolcCRejWeJqpNShmQYTV7BmDdJKHJnNxcRC6ABrQlBJzN4PKAGJMEI2sbGxAMRnPPxxPGIdX2lpKTZv3izq+pEjVnwxkVNcTS1S61CJwYRFyyGL1iCtxJEZXUx6T3i8EYIsvg9iuOsHvMGI9y0VEmGEbJw7j7lzz+LYsSAkJl5BQsLNAG52+3Io8cLIESusmMhZs6R4g1YbgLNqOWTRGqSVOGLl/WERX4SClPbL6vvgjNRJltTjzJ53jEQY4RP2jT4+HujcWflrKJHMlaW0EWKWFOekp/ZlY6Vz9VSHCQkJqohsViyHLFqDtBRH5GKqixZCQer7cPbsWV2tRkq7eM2ed4xEGKEb7maOSiSBdYaF2A8eMUuK/b6bzug529OzDlmyHLJqDVJTHJk5bkkJtBYKnq5hH8ahl9VIjXOzGAagBCTCCF2QYlpX+qVjxVQtxZLCitXHGT3qkMXOl1VrkBLxN65gaRLDMlq0VanXMJvViMUwACUgEUboghTTullfOjFLCktWHxZgpR2waA1SOv7GE/4usKSgRVuVcg25IQ8Au2KaxTAAJSARRuiOO9Eh5aUz6qoZd5YUFq0+esNK56u0NUiJtksWKvWR8px4tGirUq4hNeTBSEHurIYB+AqJMEJXxESHp5fOZrMxl8HcE1IsKaxYfVhCrB1ouZBBqfMpGcjNQts2K1KfU2ZmJgBthIKUa0gRamq5K9WcGLMaBuALJMIIXRETHZ5eOo7jJF2DlfgHT1aLc+fOITs7W7TztBcc/mTh8NQOWF3I4Ak5gdxGtfoaGanPyX4/US2Egtg1xCew6ljctVglqlbco16QCCN0RUx0uMs5FRISInnJNkuIdTxinaez4GBVZCiB1BgsgN2FDGJIHQzNniuJdbwVLWoIBW/eB8CzUFPK4u48MZC6st2b91LLuEc9IBFG6Iq35nv72X5RUZHwuZmC2V11nr6IDL0tKHKvL8VyCBj72UsdDNVOgaB3G2EdsefkvDWbO3wRClLi//jdRnjcCTUlYtfcTwwaAYBiljazxz2SCCN0x1tXk/Ns3wzB7J5mub6IDL2zbPt6fbEyGf3ZezsYit1vaWkp4uPjvSoDWdnEEXtOERERmggFsd9LFXlKxK55mhikpuYqGttq5nZnGBE2YMAAHDx4EGfOnEHjxo2RlpaGBQsWICEhAQDwzDPPYN68eXV+FxYWhkuXLgl/f/DBB5gzZw6OHTuG1q1bY8GCBbjrrruE7zmOw9NPP4033ngDpaWl6NGjB1atWoXWrVsLx5w/fx6TJk3Cxx9/jICAAAwaNAhLly5Fw4YNhWMOHTqEiRMn4rvvvkNMTAwmTZqEGTNmqFE1hkSKaV2q9ccMwezOsz3e0uOryJBqGVHLbad21nujP3tvB0Ox+928ebPXYsnsGcmVQMpzYkEoiFmN7C3ISsWuueqj9u9PZWJFsxEwjAi77bbb8MQTTyA+Ph5//vknHn/8cQwePBj79+8HADz++OMYP368w2/69OmDm2++Wfh7//79uP/++zF//nzcfffd2LhxIwYOHIgffvgB119/PQBg4cKFWLZsGd566y0kJSVhzpw5SE9Px2+//YZ69eoBAB588EEUFRUhJycHVVVVGDlyJMaNG4eNGzcCAMrKytC3b1+kpaVh9erV+PnnnzFq1ChERERg3LhxWlQX87jqLOS6mFhJYeArrjpxpUWGnnFzargNzfDsvRkMpdyvXLFkdKui2hhlZZ43YlCJ2DVXfRQQgNTUr5Gbm2qqdBJqYBgRNmXKFOHfLVq0wKxZszBw4EBUVVUhODgYDRs2dLBE/fTTT/jtt9+wevVq4bOlS5eiX79+mD59OgDgueeeQ05ODlasWIHVq1eD4zgsWbIETz31FO69914AwNtvv40mTZrgo48+wtChQ3H48GHs3LkT3333Hbp06QIAWL58Oe666y688sorSEhIwIYNG1BZWYm1a9ciJCQE1113HQ4ePIhXX32VRBjE40/kBMFKtSYYLfZFSZGhZ+yUWgO8WXIHSR0Mne8XADgOyM9v5fOzNLpVUQuMvjJPjSB3d31USkoeUlLymBetemMYEWbP+fPnsWHDBnTv3h3BwcEuj3nzzTdx7bXX4tZbbxU+y83NxdSpUx2OS09Px0cffQQAKCgoQHFxMdLS0oTvrVYrUlJSkJubi6FDhyI3NxcRERGCAAOAtLQ0BAQEIC8vD//4xz+Qm5uLnj17OjTk9PR0LFiwABcuXEDjxo2VqAbDYC98nANHXSF1MPB2U26946PkoJTI0NvKoeYAbxQLhT2+DIbJyX/AMTuLMs/SDFZFpTHbyjw1gtzF+igjvI96YigRNnPmTKxYsQLl5eXo1q0btm/f7vK4y5cvY8OGDZg1a5bD58XFxWjSpInDZ02aNEFxcbHwPf+Zp2NiY2Mdvg8KCkJkZKTDMUlJSXXOwX/nToRVVFSgoqJC+LusrMzlcUZCqvCxR+pg4G2HonZ8klooITL0tnJ4M8BLsVayuIWQN/gyGJ4/HwVA+WdpFquikphxZZ4aZfW2j2L1vdQDXUXYrFmzsGDBAo/HHD58GG3btgUATJ8+HaNHj8bx48cxb948DBs2DNu3b4fFYnH4zYcffoiLFy9i+PDhqpVdDebPn+9ycYGRERM0roSQN4OB3A6F9bQGSosMva0cUp+pN9ZKow+O3pRNq61xjGhVVBuW2xBLOPdR7ia5Dz30ENWpHbqKsGnTpmHEiBEej2nZsqXw7+joaERHR+Paa69Fu3bt0KxZM3zzzTdITU11+M2bb76Ju+++u45FKy4uDqdPn3b47PTp04iLixO+5z+zX+Z9+vRp3HjjjcIxZ86ccTjHlStXcP78eYfzuLqO/TVcMXv2bAd3aVlZGZo1a+b2eCNi/2Lm57dyK4TUHAz0ds1JQekZOAtWDinP1BtrpbepGIyIvVWwb9++2LVrl+rP0uhxT4Q2eJoAeprkhoWFaVVEQ6CrCIuJiUFMTIys39bU1ACAg/sOqI3r2rdvH7Zt21bnN6mpqdizZw8mT54sfJaTkyOIuKSkJMTFxWHPnj2C6CorK0NeXh4mTJggnKO0tBQHDhxA586dAQB79+5FTU0NUlJShGOefPJJYdEAf502bdp4jAcLDQ1FaGiojNowBs4vZm1ci3shpJaLSW/XnFSUmC16GzenNL5Y9Fi3VqqN6/xdiYiMLFF0kmK2uCdCG9RKq+NvGCImLC8vD9999x1uueUWNG7cGPn5+ZgzZw6Sk5PrWMHWrl2L+Ph43HnnnXXO89hjj6FXr15YtGgR+vfvj/fffx/ff/89Xn/9dQCAxWLB5MmT8fzzz6N169ZCioqEhAQMHDgQANCuXTv069cPY8eOxerVq1FVVYWsrCwMHTpUyFn2wAMPYN68eRg9ejRmzpyJX375BUuXLsXixYvVrSiGcfViOmMvhDxtV+SrONHbNaclese0yL0+deTS8ncpMUnxtY0YbcUxoRxapNUxO4YQYWFhYcjOzsbTTz+NS5cuIT4+Hv369cNTTz3lYDmqqanB+vXrMWLECAQGBtY5T/fu3bFx40Y89dRTeOKJJ9C6dWt89NFHQo4wAJgxYwYuXbqEcePGobS0FLfccgt27twp5AgDgA0bNiArKwt9+vQRkrUuW7ZM+N5qtWLXrl2YOHEiOnfujOjoaMydO9cQ6SnU6lBd55JxxF4IRUdHq+ZuYsE1pyV6D4BKtRd/7cjFBKn9hEXu+ym3jRg52z6JR3Xwp0muEhhChHXo0AF79+4VPS4gIAAnT570eMx9992H++67z+33FosFzz77LJ599lm3x0RGRgqJWd1xww034Msvv/RcYMZQM4WDuxeTd0lqLYQoAJltqCO/ipggFZuwqCk2pGbbLywsdDhWb4FjZPHIOv42yfUVQ4gwQhvU3OLG3YupZYyS0dMa+BPUkV/FF0GqldjwZK0DgEWLfmBK4NBWTerC2iTXfiJSWBiAgoIgJCVdQUJCbWy5npMCEmGEqkgJDLdaLyIzMxNWq1X4jRovhN7xUYR3sNaR64UvglQrseHOWpeXl1Jn6xr+mmfOnNF9YKTYQ/VgZZWt/UTE0zug16SARBjhFiX2GWRN+JDAYhup1srS0lKXvzXr8/VVkKotNlxZ64AaQYC5uia/c4aeAyPFHioHq6ts+bFH7B3Qy+pJIoxwiZLpAcw6MBLK406022w2bNq0Sfjb3dZXZo7h8cWyoLTY4N07586dE8rmbK1LTc3F/v09PF5T74HRaLGHLC8mYG3C7QyrgptEGFEHMtETeiKlkzbKllO+oKRlQUmx4S7OLDn5D0yevESw1gFwsIS5uqbeA6ORYg+NsJiA5QkQq4KbRBhRB707RoLwhFQrLctWAykoaVlQUmxIjTMDIHpNFgZGo8Qe0mIC32BVcJMII+rAQsdIEK6QaqU1gtVACkqWTWmxIfYsbLZGaNz4AkaPfhNVVSEur8nKwMhKELkUjOyp0HtixKLgJhFGCPBuDbGOUaqbxPmFs9lsqKqqEv4OCgpCRESEw/VZHhAJ/ZFqpSWrgWvciQ0+tssZT++kp2fhal/YpKTjLs+jx8DIahC5FLTwVKghlliZGLEmuEmEEQLO7o+5c8/i2LEgJCZeQULCzQBulvzySU386oyWlgm9Z2WE93hrpTWy1UAJpIoIfs8/bwZFd88iOLjS6zrXemBkPYjcE2p7KtQSSzQxcg2JMMIB+5cqPh743x7lXiP2IukdWK3m7gCEenjrvvL3+EZPYoPfcBmQNyi6exZVVaEe6zwjIwPBwcEOq131gNX32t3k0NNKVCVduGqLJa0nRqxbPUmEEZpgL7pcuSrkpr+Qi5q7AxDq4o37iuIbxcWGL4Oiq2dhszXyWOfR0dHMD4x6IdUKpYULVy2xpPXEiHWrJ4kwwgE1XHT2symgBoDlf/+x4x5SIjEtoR5yt5xiJfCbZXwdFJ2fhZQ6Z31g1AsxK1St8LraT6nZjtUSS3pMjFhuRyTCCAE1XHTOsykgoM4x9i92aWmpx82I1UDJxLSEOvgyaLO4IoolvB0UpVinpNQ5ywOj3rizQnEcAHjup+QunOLh3Z5qiSWaGDlCIowQUMNF52o25Yz9i71582ZN47D8PXDbSPjSJlhbEcUS3g6K7gSxfYwZf15v65wWy9Tizgpl/2/7fiojI0Nw8yq1cEpNsUQTo6uQCCPcooSLzt2echYLhBc7LW23blnP/T1w26xQzJF3eDsouhrofa1zqfFQQ4YMcUht43xuM4g01/2mI/b9VHR0tFcehLrbgmkfe0YTo1pIhBEuUcpF52429fff9ZCTkwaOC8Du3WmoX/+yLi5ACtw2J/4ccyTXmuTroOhrnUtdlcfvG2rUBLxScNVv8q5IHqX6KbG+XimxRBMj15AII+qghIvO/kVynk0BwJIlk8F3KHq6ACk+wbwYfSCWgzc5ntQYFJWocyn9jxnyTLkSy/ZJc537TVerytXe9cAT3oolf54YeYJEGFEHJVx0rl64Y8eOYdeuXSgoSJR9fqViRjyJRPsy+NusjDA23uR4io+PZ3JQFOt/zBDH6VksXz3O3gqlhmtQrK75WDNn5LYLfxNYUiARRtRBKReduxdO7vmVXL1JszJCDCMHiUsVKiyW310c6aVLDQSxYvQ4Tm82QbdH6Tgqsb7Y21gzwntIhBF1UNtFJ/f8Sq/eZHEAItjA6DsqGFmoOPcPfG7BLVvuExbymCWOU0ws33HHHcjJyRE9j1yLPYVj6A+JMEJASxedEqZ1d/EucjYjJgiekpISFBYWOnym9zZbrvAUU2T0BSd8/3DyZFNs3ToYHHc1ufPu3WlIS9uN3bvTDC8cxMRyUlKS6hZ7qX2xkS3DLEMijBBQ20UnN+u5KzyZ8O1zFTnDquWCYANXsTp5eSnIzU1lKpmvWEyRGSwcVutFnD//t0uRkpBQiMmTlxg+z5QUsSy1v/JGJHnbF6u1qTdBIoxwQs0XSCmRJyXehUXLBcE+zrE627bdDfu0AKwEgUuJKTJaQkxXky9PIsUMeaaUEsveiiRv+2K1N/X2Z0iEEZqihMgTM+HTNkSEr/BCX2ybLd79V1paiitXrjgcFxwcDOv/lrqp5aoRm5AYSajYCwM++74ckWK0Fc1KiGU5IklOezTDylTWIBFGGA5Ps2PqJAgl8LTdlr27yJXrW0tXjdwAfFaFird7fzqnUGA1LsnZVegct6qUWFa7/zPygg9WIRFGGA5Ps2MWcpARxsfdtjHuLDG88CosjK8TMK6mq0YspshVnidX7Zj1tu9OpBghhYJ7V2EjxROiqi2SjL7gg0VIhBGGQcrqTRZykPkTag3eeosCV2kSunfPRUpKXp3BzN79A3AArq7kU9sKK+aukyJSWAy6NtMWN1JdhUpY9dQWSWZY8MEaJMIIw+ApmNSXGBJA+Rxk/oBawlXN84oJO3ukxOo4u394AcajhavG15giFoOuzZhMWcxVqIRVTwuRZLQFH6xDIowwFFI6XTVzkBFXcR4glVqRqsZ5pVp7MjMzHX7nzg122223Yd++fR5jxwDtXDVKxBSxFk9pJIElBa3iqbQQSUZa8ME6JMIIU6BVDjLCNWrVmVLnlWrt4ThO0vliYmIAuIsdq3VJqumqUcNdR0HX6qJlPJXSIslM7mHWIBFGmAItc5ARjojVmbsdDADPz8Sb80p1TYmdMyIiQlI74r935f5JS9uNhIRCVV01arjrxETCuXPnDOcCZAk1XYVqiyQzuodZgUQYYRq0yEFG1EWszjztYAC4j+3y9rxSYsSkPF8p7aioqEj4t14xMkoPeK4WI6Sm5grf8/VNi1Pko1Zb0UIk0TNXBxJhBGEHLcH2Hql15m1sl7vzBgdXoqAgUVaMmFLPV6r729NvWIQXCfxWTfv390BubiplRVcQteKpSCQZExJhBGEHLcGuxZuVhFLqTE5sl6vz3nDDIaxZM0Z2jJhYWaW63JwtD3plzFcLfq9MoK7LtrS0lPncXCxB8VTeoXd6Gq0hEUYQkJaDDKgdbJ1/Z6YOAZCeIsJ+JaGnOvMlzs7+vMHBlYIA8/Y87s7pXFZvXG723xtZlDgP/mIu282bN5NL0gsonko6/pivkUQYQcB1R2mz2bBp0yaH4zZv3lznt0bvEMS2VHGH80pCd24Wb+Ps3Ln65OyGwN+bu21ibLZGsl2bZoFv+4WFhcjOzna74rOwMAFJSccB+Ff9KIGR+wctUSvtDcuQCCMMg9pmaikrJ83WIUiZebq7b7GVhHwCXW/jsJwFsdzziN0bpSK5SlRUlMOKz7S03cjJuQNXk89asHt3Gq6//he/c81rhb+54cTw5f00Ul2SCCMMgdpmalcvrb31xKwDttjMU+y+pdS1nDg7V+f19jye7g0ApSLxQEJCEfTI/u+vsLh1lJ74EsJgNJcmiTDCEKi5rZDYS+svucOcBVda2m5hM2rAt/tWamm+3PM431tqai6lIvEArRLWFqnJhAsLC132cSxZdpTAl1RBRnNpkggjDImS2wqJvbRiHYJzvJERO0RXQjMnJw2AfKGi1C4Gvp7H1b3l5qYCqIH9/fm7yPB2xSuhPGITvuzsbL+wkik1CTCCB4NEmJ9gJB+5GGq+WK7OnZz8h8cOwVUyUqN1iK73QAzwqSNUalWYr+dxJ6K7d/9aSMWghsgw2jsXFRWFzMxMYTEKbdSsPWITPlY2WFcbJSYBRvFgkAjzA4zmI/eEmi+Wu3NPnrzEbYfAuqlbKu5mnvYuSTkdoVLtyZfzuLu3lJQ8pKTkqSIyjPrO8XnNrv5NGzVriScLkFFEhVL4Ogkwyu4nJML8ADXjqbRGzRfL07lddQhGMHVLxd3Ms1OnH3H99b+47AiNklxSbFYtd6N3TxgtLoWHEovqi6e2KpaiRe4erWJoadFVKoQBME5cI4kwP8TdgCBnQ2StUfPFEju3fYdgxlmpu5mn1XoRI0fegejoaOFYVtuHOzzNqjMyMlS9Nz3EutyBkxKL6o+7tirWP8ndo9UTWlt0lWx/RolrJBHmZ3gaEJxf4iFDhiAiIgIAOx2vmi+W2LkzMjIA1NaTUUzdYkideSYkJDDx/L2BhXvTQ6z7OnAa7TmbEVdtVWrfp6TVVQ+LrpLtzwhxjSTC/AhvBwTn7PCZmZl1YkYAbQSa1G2FfHWTeDq3vbXEG4scywHaZrZ8sHBveoh1o7pC/Rmp/ZaYqNB60RKL4RdKujS1gESYH+FuQDh5sinOn/9b6KTdddrOW/jYo3aAsZoDqjcvrX1WcSmzUiMEaBtRYElF73vTOy7FKAOnv+Opf+N3jOBx1z/psWiJxfALFiZf3kAizI9wvSdcDbZuHSx00jfccAiHDt3gttPWc1at1kvjzUtbVFQkfCbF1E1WCf9GSfe5txZVIw2c/oqUZ5qQkCDpXHotWmKxLbEisKRAIsyPcB4QapNVWsBxtduTcFwAfvqpI/jtSpw7bTPPqqW+tL6Yus1cf4R7lIhL8caiymO0gdNMSBFXACQ/UzX2aPUGPSy6LIdxKAmJMD/AXTzVpUsNsGXLfU5Hu94vDqC99gD5pm6ySjhi9g5W6bgUORZVvV2h/opUwZyZmenwt6dnGh8fL3o+PRctKY0RwjiUgkSYH+AsHPiZk83WyIV7koO9EOM7bZpVX0XOS+8v9aekBYDF1blSUTMuRapF1ShL9M2GVMFcVVUl/FspK7maqwG1XGnoT2EcJML8BFedvatO2lVMGN/oaVYtH3+wSkidvQ4ZMkTS+ZxX5xpt1qtGWb21qBphib6ZkSKupD5TdxMcm83m8LeSqwFZWGlo9jAOEmF+iFi6h9tv3+syYaces2qzuK38wSohdVZ69uxZh7+dZ7lmnvX6ihSLKgsDJyFdXEl5pt5McHjrsT1y+0m9Vxr6KlC1KKOvkAjzQ5xfLJvN5pB+wl2nrfWs2mxxAZ7qzwi7FXiLOzG1b98+4d/Os1yx1bn+jphF9dy5c4iOjsaQIUNw5coV4Zjg4GCHHH9maWMsIzUEQYqVXKp7LiIiQlL8mFFQUqCyOk6QCPNT7BtjfHy829lOaWmpg1tIy1m1GeICpFolnHcrYLXDkIqzuEpL242EhCKHZ+dqlutpdS4hblH1tHWNfbLlyspKlJSUGLqNsY7UEARvreRauuf0FjhyBKo7WB0nSIQRANzHr3gSaDxazKqNGhfgblEEjxGFpRiuxFVOzh0ALA7PztUs193qXBJhV5FrkXaVbNnoYp9lvBFXUp+p1qus9RY4csI43PWprCJZhJWVlUk+aXh4uKzCEGyidydt9PQO7urPqMJSDE/iyv7ZuU4e7Hp1rr8j1aIKeDcIqTV4GjlGR0k8iaugIMfhV8pOHXqvstZD4Hgz6TBinypZhEVERMBisXg8huM4WCwWVFdX+1wwguDRu+NxRokBxujC0hOuxdVV+GeXlHTcq9W5/ozUbW3cDUJaDp56u7D0Rqpgjo2N9XqnDj1XWWspcORsJWfUPlWyCLMPpiUILWEpvYOcVUquRBlrwlJJ6u7M4N66JXV1LiFukXY3CP39dz3s3p2mmXXADLGcvqDmikK9VllrLXDkbCVn1D5Vsgjr1auXmuUgCLewlN5B6gAjluOKJWGpFO5SnxQWJtQRAXxdZWRkIDg4WNLqXEqp4Bl3g1BOThoAfawDRnQPKYGaFj49cr/JETi+egy8rUOj9qmyA/NLS0uxZs0aHD58GABw3XXXYdSoUQ7LoAnjwHoMB4tJJ10NMLVlFJ/1syQslcJ+9sq7yKzWi0hKOo7rr//F5bOLjo5mZvGH0XE3CClpHZC6IwJgXPcQi+id+81bgaOHS9qofaosEfb9998jPT0d9evXR9euXQEAr776Kl544QXs2rULnTp1UrSQhLo4vzDurDtax3Do3fHw2A88fD4vVwPMtm13w2KB5Fk/i8LSV/j24e2zI4HliJxJkatBKC1tt2CF5JFrHfB2T0SjuodYRO+kqd4KHC1XVYolH3d1HEvIEmFTpkzBgAED8MYbbwgrPK5cuYIxY8Zg8uTJ+M9//qNoIQl1sX8RPLkPtI7h0LvjAdwPPK5XAAaA42r/5W7Wz4qwlIpcCykLz86oeGtFEBuE6te/rIh1QOr7z++JaFT3EKvo8a4oJXDUXBhi9L5GtiXMXoABtcttZ8yYgS5duihWOEJbWHQfuHpx7IVBZWWlEJjJo+QL5y4GLDi4wuMKQMD1rN9IHYavLgUW7sEX9HLRe2tFcNWm7JMsq2UdEBtYjeoeIq6iRH+lZFwg62EzcpAlwsLDw3HixAm0bdvW4fOTJ0+iUaNGihSM0B4juA/0dJ162mIHqEHtCkDxHFdG6ST0TtSoJyylWZBiRXAug9pxdlIHVjO63FlFLYHiS/tWcmLvjTvcVWw6qwJNlgjLzMzE6NGj8corr6B79+4AgK+//hrTp0/H/fffr2gBeQYMGICDBw/izJkzaNy4MdLS0rBgwQIkJCQIx3z22Wd4+umn8euvv6JevXro2bMnFi1ahMTEROGYzz//HFOnTsWvv/6KZs2a4amnnsKIESMcrrVy5Uq8/PLLKC4uRseOHbF8+XIh9g0ALl++jGnTpuH9999HRUUF0tPT8dprr6FJkybCMSdOnMCECROwb98+NGzYEMOHD8f8+fPrJOhjCSO4D/RynbrqTA4dugGjR7+JqqoQREaeR35+K1PP+o2WidoXWEmz4IsVQa9Nlb1JQqolZrSi8LA0abBHyYm91HfN1c4QPCzmppOlCF555RVYLBYMGzZM2CQ2ODgYEyZMwEsvvaRoAXluu+02PPHEE4iPj8eff/6Jxx9/HIMHD8b+/fsBAAUFBbj33nsxdepUbNiwATabDVOmTEFGRgZ++OEH4Zj+/ftj/Pjx2LBhA/bs2YMxY8YgPj4e6enpAGof4NSpU7F69WqkpKRgyZIlSE9Px5EjRxAbGwugNiZux44d+OCDD2C1WpGVlYWMjAx8/fXXAIDq6mr0798fcXFx2L9/P4qKijBs2DAEBwfjxRdfVKV+lMBI7gOtXafuOpOqqhAkJR0HYO5Zv1apBlgcKPVKs8BieAAgPrBGREQw53JXQqSw2DZ5WJk0OKPmxN7TpJCV+5eCLBEWEhKCpUuXYv78+cjPzwcAJCcnIywsTNHC2TNlyhTh3y1atMCsWbMwcOBAVFVVITg4GAcOHEB1dTWef/55BATUPvDHH38c9957r3DM6tWrkZSUhEWLFgEA2rVrh6+++gqLFy8WRNirr76KsWPHYuTIkQCA1atXY8eOHVi7di1mzZoFm82GNWvWYOPGjbj99tsBAOvWrUO7du3wzTffoFu3bti1axd+++037N69G02aNMGNN96I5557DjNnzsQzzzzDTNC1K4wiJLR2nXqzGS8Ls34l0UoMsDib11oIlZSUCCtwWQ0PkPIusGZt8NW1zmLbdAdLudnUmth7ukeW7l8KPvnGwsLC0KFDB6XKIpnz589jw4YN6N69O4KDgwEAnTt3RkBAANatW4cRI0bgr7/+wjvvvIO0tDThmNzcXKSlpTmcKz09HZMnTwZQ+wIeOHAAs2fPFr4PCAhAWloacnNzAQAHDhxAVVWVw3natm2L5s2bIzc3F926dUNubi46dOjg4J5MT0/HhAkT8Ouvv+Kmm25yeV8VFRWoqKgQ/vZmv04l8bQvHSto7ToV60wyMjIQHR3t8rdGdnMA2okBvWLQXFk49BBCzgM9q+EBRrKYu8Nb17pR4iNZsZ6qmTbC0z0CYOL+vUGWCLt8+TKWL1+Offv24cyZM6ipqXH4nnf/Kc3MmTOxYsUKlJeXo1u3bti+fbvwXVJSEnbt2oUhQ4bgkUceQXV1NVJTU/HJJ58IxxQXFzsIIwBo0qQJysrK8Pfff+PChQuorq52eczvv/8unCMkJETYksb+mOLiYo/X4b9zx/z58zFv3jyJtaEcUl8Ed8cpbab3dD5+cNRjIPDUmfBC3xWVlZUoKSnRRYgp8WzExAD/TLw5pxS0iEETs3BoKYRcPafU1Fzs358KQFux46rd2Gw24d9GzMfEo4SlhNX4SLmTBqX7cDVXgXu6R8DCpPXYE7JE2OjRo7Fr1y4MHjwYXbt2Fd3Y2x2zZs3CggULPB5z+PBhYRXm9OnTMXr0aBw/fhzz5s3DsGHDsH37dlgsFhQXF2Ps2LEYPnw47r//fly8eBFz587F4MGDkZOTI7uMWjJ79mxMnTpV+LusrAzNmjVT/bq+vDBKr1aUej5AG9ep1LxenoJBebR2VSjlQhETvPzm0d6cUwytXApilgu9rD7O95+a+jVSUvI0ETtS283YsXfWmYyybvVVwlLEsrtLzqRBLVerWu1A7B5ZtB57QpYI2759Oz755BP06NHDp4tPmzatzspEZ1q2bCn8Ozo6GtHR0bj22mvRrl07NGvWDN988w1SU1OxcuVKWK1WLFy4UDj+3XffRbNmzZCXl4du3bohLi4Op0+fdjj/6dOnER4ejvr16yMwMBCBgYEuj4mLiwMAxMXFobKyEqWlpQ4dkPMx3377bZ1z8N+5IzQ0FKGhoR7rQy3kvjBKr1aUej4etV2nUgSqfU4mgJ2gUF9dKFJcCmrcq54uFVf3o3WcpKv7z81NRUpKHoBa13dCQoJqg5zUZxcREYH4+HhVyqAWvrqXWXH3uUPOpMEorlYesXs0mqtclgi75pprFMkHFhMTg5iYGFm/5V2gfAxVeXm5EJDPExgY6HCss3sSAHJycpCamgqgdtDp3Lkz9uzZg4EDBwq/3bNnD7KysgDUxp4FBwdjz549GDRoEADgyJEjOHHihHCe1NRUvPDCCzhz5oywojInJwfh4eFo3769rPtlHaU7J1/Pp6SVwJvBjuVZsrcuFFcClN8TElDvXvUKSPd0P1ouuBC7/+joaE2tTay63uTgq3uZ1cUS9vg6aWD1eUuNMzPK4jIeWSJs0aJFmDlzJlavXo0WLVooXaY65OXl4bvvvsMtt9yCxo0bIz8/H3PmzEFycrIgfPr374/Fixfj2WefFdyRTzzxBFq0aCEEwo8fPx4rVqzAjBkzMGrUKOzduxebN2/Gjh07hGtNnToVw4cPR5cuXdC1a1csWbIEly5dElZLWq1WjB49GlOnTkVkZCTCw8MxadIkpKamolu3bgCAvn37on379nj44YexcOFCFBcX46mnnsLEiRN1s3SpjdKdk9j5WAyEZ3mWLFcwuatHNe9VqTgsNTebVtMaxVJAPsuTCjn46l72Nj6SR+0+Sant0Fh+3lFRURgyZIjgdfDkBTHSKnVZIqxLly64fPkyWrZsibCwsDpByefPK9tZhIWFITs7G08//TQuXbqE+Ph49OvXD0899ZQgam6//XZs3LgRCxcuxMKFCxEWFobU1FTs3LkT9evXB1AbvL9jxw5MmTIFS5cuRdOmTfHmm28K6SmA2kS0Z8+exdy5c1FcXIwbb7wRO3fudAi0X7x4MQICAjBo0CCHZK08gYGB2L59OyZMmIDU1FQ0aNAAw4cPx7PPPqtovbCE0gOH2Pmio6Mlu0K0yu/D6ixZDcGk5r0qEYel1GbTJ082xfnzf9exCqhpjWJl9SHLkwpvUWq1npz4SB41Y0KVCIQ3wvN2jkF0Z7VzNUlnNV5Rlgi7//778eeff+LFF19EkyZNVA9679ChA/bu3St63NChQzF06FCPx/Tu3Rs//uhZ2WdlZQnuR1fUq1cPK1euxMqVK90e06JFizquTzOj9MCh1Pm0zO/DkgXDHm8Fk5TVqWrcq5LL2qUmrxTbbHrLlsGwX5molVWABZcKq5MKOfgqUqS2TU+oHVPla/9ltOftyWrnzSRdb2SJsP379yM3NxcdO3ZUujyEgVF64FDifFpmktbCgiHHqueNYJIqWtW4V7WWtUtd4OF8PxwHANpZBZRyKUlBCaGtl+tNLr6UyVPbtI+TBNiNqRJDjYmVWl4II1jtpCJLhLVt2xZ///230mUhTIDSqxXlno9/+e0HCi3iHdS0YMi16nkjmLwRo2rcq9KDtzf7HNrfz6VLDbBly30O51LbKqBmbiV7lBLaerne9ELK/bAcUyWG0hMrNb0QRrPaeUKWCHvppZcwbdo0vPDCC+jQoUOdmLDw8HBFCkewj6+JXtU4zlWusZMnm6k2c9LKguGtVU8J957YrJ71AFhv9znkrRo2WyNdXMtaCBclhTYr6VhYwKjWGbWy26uZ+oLV0A85yBJh/fr1AwD06dPH4XOO42CxWFBdXe17yQhDoPTsXYnzucs15oxSMyetLBj2SJlx+1ouT9dgcXWqK+Tuc8hKcLwWeBPcrEWKEqNiVOuMVv2Xkm5aM72fskTYvn37lC4HYWCUHnCVOp/zzNQZJWdOWudtkjrjllsusWsYJfDV285azT3vWERucLNRrT5qYmTrjNr9l1KC3YzvpywR1qtXL0nH/fOf/8Szzz7rdsZMEGriambKY+SZkxYzbqPO6l3hTeyaHlZNvfBFSJmpffhKaWkpAHNZZ5REScFuxvdTlgiTyrvvvovHH3+cRBihC65mpkANBg/egmbNThm2c9Rixm3kWT3gW5yekTpwX/BFSLHcPrTKC8hfy37LMtasM1rWhTuUFuyeysvfb1FRkcvvWRRoqoowrnaNN0HogruZ6fXXH65zrJHM11rMuI0+qzfjjFlpfBFSrLYPLfMCAq6Dyl0J/szMTM3bmtZ14Q6tBDsr9+stqoowgtAbTzNTPvDYiIOx0ukh+BmkzWaTdA0jiFajPVOt8VVIsZBQ1hkt8wK6wt31rFarKtfzBCsbc2sl2Fm5X28hEUaYHneuKKMEl/OolQrD0wzS/hpDhgxBRESEIUUrcRVfgpu1TCjrK1qv3mR9tajWSWT1DqI3StJcEmGE6VA6JxkrqOVikzozjIiIMJRo1QoW4m68wZd2pJeb19s61nr1JuurRfUQiN60FaXfIdYFsT0kwgjTYeZ4IC3KbJQZJAtIjUPhrYiu0KMt+rqFj5Z4E+vDo/XqTZZXi4oJRPtdRZRui1LOpXQsF+uC2BlVRdhDDz1E2fMJXTCiwGIBI80gWUBqDJL9CjpXsBYszBJyYn20Xr3J8mpRMYHovP2U1m1R6vMtLCwEIN63syyIXSFbhJWWluLbb7/FmTNnUFNT4/DdsGHDAACrVq3yrXQEQWiG0WaQrOGLgGUtWJhlpFhq5QaDy3WLsbpaFPBeILLaFnmxKCYSWRbErpAlwj7++GM8+OCD+OuvvxAeHg6LxSJ8Z7FYBBFGEIRxEJtBlpaWMhcTxko8llQBq7Wrl5X6UQpvhK63qzfluMX0Dj6XgjuBCAAFBYnMhR2IvSNiIpFlQewKWSJs2rRpGDVqFF588UWEhYUpXSaC0BX7gauwMAAFBUFISrqChIRai6/RBi6piM0gN2/ezJTbjKW8QFJcIFq7elmqHyWQInR9Wb0pJ70Fy/GnngRifn4rLFky2WVbtNlsuk22fHlHpApifocD+9/p2f5libA///wTjz76KAkwwnTYD1yeOgSjDFze4GoGmZa2W5P8SnLQOyeUPWICVg9XL0v1owRShK5SosgbMcBqP+BcF/zm62JtcdOmTbr0b67KtW3b3YiNLUbTpq4z4Nvj6tnbbDZs2rTJ4ThX8Zl69ueyRFh6ejq+//57tGzZUunyEISu8C+wWEfF4sAl1/XkPIP8++96yMlJA8cFYPfuNNSvf1m2xUYrd5jeCwrEXCB6BwvrXT9KIDXWx9f2ZKbYSFd1IaUt6tG/ud7rNwBr1oyR3F7lPns9+3PJImzbtm3Cv/v374/p06fjt99+Q4cOHRAcHOxw7IABA5QrIUHogFhHZZ9Z3hVam7idXU/urB6uZnxRUVHIzMzEpk2bYLM1wu7daQB8H4C0coexMmh6coHoGSysVf2oLbi1ivXRWzCrjbs9dfUOXHddLmXbK4vpdySLsIEDB9b57Nlnn63zmcViQXV1tU+FIohTp4CjR4HWrYGmTbW/vtigaW/i9kbwqIX94OfJ6uFukOS3VVFyANLKHabnoCk1BknPYGEt6kdNwa118LvRVtd5C98Wt227G/xkC7AgP7+VLtZR/rm5LlctSrRXVq3BkkWYcxoKglAafia9cWN9zJhhRU2NBQEBHBYutOGBB/7W1LokddCUI3jUxFerh1oDkJodoJ6DplgMkn1Mil6r57SoHzUFt9bB70ZbXScV+zaWnPwHLBaA4/hPLLq5XPnnW5sHLBuxscVYs2aMou2VFWu5K2TFhL399tvIzMxEaGiow+eVlZV4//33KUUF4TX8TNpma/S/VTu1aU9qaiyYPj0cf/65FlbrRU2tS2JL3Fl8sX21eqgxAKldT3oPmp7aY3x8vO6r57SuHzUEt9ZB0yxuTu4rUVFRGDJkCDZv3sycy9X++TZtWuSxvcqZsLB2v/bIEmEjR45Ev379EBsb6/D5xYsXMXLkSBJhhNfwg5TYy6K1dcmdewlg88VWwuqh9ACkRT2xPGiysHpOq/phcWIiFSNtTi4XfussFl2uzhbPuXPP4tixICQmXkFCws0AbpY9YWHxfnlkiTCO4xwStPKcOnVKiC0hCDmw/LI4w2JZ5Vo91ByA1Konfxg0fUGP+hET3GruU+gr3ro9jZwI1xfrqJr3bf+7+Higc2dZp6mD3tZyT3glwm666SZYLBZYLBb06dMHQUFXf15dXY2CggL069dP8UIS/oPeL4s3A5LeZXWHHKuHmnE3atWTc5lLS0tx5coVh2OCg4NRWVmJoqIipgdFNdAjkaiY4NZ7n0IxpJbFqIlwfV3kYLT7NsKOBl6JMH6F5MGDB5Geno6GDRsK34WEhCAxMRGDBg1StICE/6Gna0nKwFVaWiok/PNUVvtZP49WQsCTG9UdapZLrWdqb5UQ2yQb0HZwYMFSovVA6O0WOSzm25OCnE3FWcBXYa7GfattWdM7JlMMr0TY008/DQBITExEZmYm6tWrp0qhCEKqiFBjiyFX7gb7c4eEOFpb3JV13boczVJXSJ3JaTnj09Idxlp2eKNZDJTEmy1yfIEFkcvDYv4pdyhZJ77etxbvCevvl6yYsOHDhwOo7dDOnDlTJ31F8+bNfS8ZQYig5hZDns+dh06dPP9e69QVLM749CoTC/mAjGopkYs7wa1WoD5LIpeF9qYHSty3v70nrpAlwo4ePYpRo0Zh//79Dp/zAfuUrJXQAjW3GJJ67oyMDERHRwOQvjebUrBkCXAHC/vPsbA6z0iWEjm426dQrZWxrAzerLY3tfHX+1YDWSJsxIgRCAoKwvbt2xEfH+9ypSRBeIMv7jQ1UyCInTs6Ohrx8fGalYdHqiVgyJAhwrJ0Z1gQaUrDYtoQby0GRhDXrnBVJq1WEOslcllsb1og976d27Zz3KzZJyuukCXCDh48iAMHDqBt27ZKl4fwU3xxXanZ0cs5N0sZysWC1c0Wk8Ra2hBvLQYsudmUQIsVxHq6A1lrb1oh577F2raSz9FIExlZIqx9+/YuV34RhC/IfSnU7OjlnJvFDOV6B6prBWtpQ7y1GLDiZlMSNVc76+0WY629aYWc+/bUZpV8jkabyMgSYQsWLMCMGTPw4osvokOHDggODnb4Pjw8XJHCEYRU1Ozo5ZybpQzl/hY4zFL2fF8tJe7E87lz55iazTuj1cpYvdyBRsg/pQZK3rd921byORptIiNLhKWlpQEAbr/9dod4MArMJ/RETm4sNc+tZnl4xDovvS0FWsFq9nxfLCWexDOf9JSV2bwzWq2M1csdyOJqZC1Q6r6d23Za2m7VniPrcWayRNi+ffuULgdBuOXUKeDoUaB1a6BpU71L4x49cnWJDUL+EjjM2qDoq8XAlXjetu1uxMYWo2nTIuE4VmbzrtAqKbFe7kCzCSyp+Hrfrtr27t1pSEvbjd270xTbtBswhhdAlgjr1asXvvzyS/zrX/9Cfn4+tmzZgmuuuQbvvPMOkpKSlC4j4YfwgZUbN9bHjBlW1NRYEBDAYeFCGx544G+EhISoKnrknFsPIeA8CAG1s0q+8/KnwGGWBkVf24Ir8QwEYM2aMUwOJFqjhjvQSMHcRsbdxDAhoRCTJy9BcnI6unWL9nnTbqN4AWSJsK1bt+Lhhx/Ggw8+iB9//BEVFRUAAJvNhhdffBGffPKJooUk1Ecta5Oc8/KBlTZbo/9l2q51edfUWDB9ejj+/HMtrNaLyMrKUlz02HfEQ4YMcbkXIb9Jvatz69FJd+r0I/7+u54wi9y9Ow31619Gp04/iloK9Nxayez4UoeuxDPA7kCiNUpPeIwWzG1kPE0Mazd534LERN/r2ZvN5O3Ruv+TJcKef/55rF69GsOGDcP7778vfN6jRw88//zzihWO0IY1a4Bx44CaGiAgAHj9dWD0aP3Oy3esYi9RZWVlnRxdvuDcEbuLJRDriNXYSskZ+xm+zdZIEGBA3YHak6XAeUNlqffoj2hpKeHF87ZtdwMwvztZDkq2T9a2vWIFNdq8FBeyEvXs7Wby9mjZ/8kSYUeOHEHPnj3rfG61WlFaWuprmQiNKCkpwbFjVzBuXCxqanhrE/DIIxxuvPEMEhODZDVEpc6rtSvN/sWXu+2Qmlsp2RMVFYUhQ4Zg8+bNkuK+3AWq02AjDa0sJc5uttjYYqxZM8Yv3MmsoHUcEatuUKXbvCsX8smTTQFY0KzZSV+K6hIpYo+F/k+WCIuLi8Mff/yBxMREh8+/+uortGzZUolyESpTUlKCF154C7/+eh1qatIdvquutmD58k+RlHTc60GFf3ELChJRUzPcp/PqFXTrSyyBmlspOcNnwhcTq662VgKMEbTKClpZSng3W2FhIbKzs9G0aZFf5qHSC63jiFh2gyqd6sF+4ggA+fmtVOl/pMYLstL/yRJhY8eOxWOPPYa1a9fCYrGgsLAQubm5ePzxxzFnzhyly0iowPr1gf+LtwoAwAG4mmrEfgD3dlDhjxcTBlLP6+ol4gfAwsIAKOiNFFBiRaHUcygxCxYTq662VjJK0CqLqN15R0VFObQJlvKemR2tVxMbKaeVEqke+Imjmv2Pp3hBrff3lYIsETZr1izU1NSgT58+KC8vR8+ePREaGorHH38ckyZNUrqMhILwrsIZM2KFgPdaAVYrxJSaaStpxbJ3pdkPgO+8w2HhwlJhtaRSM0Ul3KBSzqFUDBrg/UDtL6krlEas8y4tLVUkTlHpvGesurxYQ+/VxKzmtFJ64qF2/yPWllnq/2SJMIvFgieffBLTp0/HH3/8gb/++gvt27dHw4YNlS4foSCeXIWABenpO9G+/W+KNUKlZ/DOA6Cr1ZJKDCRKCEhvg0/lxqA5X1NqGfUebIyKWOe9efNmRdqhkqv/WHZ5sYaeecdYcY85o4bVSO/+R+/r2yNLhPGEhISgffv2SpWFUBkxV6GSAoxHyazxUlZLKoUSAlLqOeR2cr7kSfPXPe98RUrnrVQ7VEoQGcnlxQJ6uH9Zco85IzfVA+B+oqB3/6P39e3xSYQRxoSlBugKd+JC69mLEgJSyjnkmsZ9tZZQrJH31E2OC3BcbZAxC1YLKbDq8tITvbe9Ysk95owvqR4ARwsrS3tustL/kQjzU1hpgK5wFhd8MKXa4lGPbYcA38Slt9YSvQcbM5Cc/Ac4zv4T6VYLvWOzWHV56Y3e216x5B5zxtd+175O9a5nFvs/EmF+jBYbTMtFD+uNEh2E1Je3vLwc5eXlALS1TOrdCbKMJ4Fks9mEf58/HwU5CVT1js1iyeWltxh1hZ5tnkXvhFSrFY9UC6ue9cxi/0cijPCItzMCLaxJcsWjFpnspbzk5eXlePfddx0+09Iy6Y8CSwypAgmQb7XQOys7Ky4vvcUoS7DknnNGrC/TOuegUsKdtTZFIoyoA5/cU44oYXGmAWiXyR4Qf8mLiooc/rYfjJOSjvt0bUIe3ggfJawWergFWXF56S1GWYLV/pJHynW1sLCaWbiTCCPq4Cq5pzew+BJomcneG6QMxhSfpT1irhVfLJd6uQXVdHnJtVJQjBqb/aU3+GJhldpuzCzcSYT5EXoFnvuKkuVmxSUDiA/GGRkZSEhIMHwnbTSkCgO5Qb1at0G1XV5yrRQsxagR8pFrYfWm3dhjNuFOIsyPYN307Q4ly82KSwYQH4yjo6OZexZmR0wYZGZmwmq1uv29lHaodRtU+72Xa6VgaUJkFvRY8CDXwiq13RQWFjocYzbhTiLMzzDqoK5UuVlahcSSICRqERMGVqvV522J9GiD9u+Pq4G6srJSiFX0ZaD2xkpB7V9ZtI6bUtLC6qnd2OchM6NwJxFG+B2s5EhjSRAStWglDPRqg2oO1N5aKaj9K4vWcVNKWVjdtZvY2GJUVYU6lN+Mwp1EGMEUp04BR48CrVsDTZuqdx1WcqSxIgiJWtQUBiwkilRzoJZjpaD2rw5axU0pYVFz127efHMMAMfym1G4kwgjdId3j2zcWB8zZlhRU2NBQACHhQtteOCBv5mMU/MFFgZjwhEt8jWxFpOp9EAt1UrhbftnMbEryxgtbspVuwE48AmRnctvNuFOIozQFd49YrM1wpIlk8FxFgBATY0F06eH488/18JqvehzHANLK0NZG4yVQsvBUulrafVMWHmmagzUUq0U3tS1mfNDqYXR4qZctRux8ptp4koijNAVviMW6zh8jWNgTfiYbcDQcrBU61pmeyaeUGuglmqlkFrXZs4PpRZGjJuybzfBwZVYs2aM2/LzycSdMeLEFSARRjCCFh2HEV9QoyB1EFRisNTyWmZFyfdNK/e62fJDqYVR4qY8tRtP5fc1mThrkAgjmMAoHYdR0TquRupmvka7lllQ8n3TwspstDgnpZD73hohbsq53djvRSm1/GaIFyQRRnhEq9WKgDE6DiOidVyNlhYLso7IR8n3Te2BzmhxTkrg7XtrxAU/9u1GzoIN+/pxNxljPV6QRBhRBz1XK7KSOsJMaBlXo6TFwt0s99y5c4pfy18w4kANGDPOyVe8fW9Zi3v1Fm/Lb3+cp8kY62EJJMIIB8RWK37zTQ6aNTuJcePugtVqZfqlJuqituVIKYuFFCuAP1pHfMWoA7W/hytIfW89PTd+UsPvjOAMC89dzvWNPhkjEUY4ILZaccuW+2Cx1CA//2onwLq5V09YilnQorNSymIhZfbqj9YRJTDqu+qv4QpKvLdmTvVh9MlYgPghbDBgwAA0b94c9erVQ3x8PB5++GGHjT0BYPPmzbjxxhsRFhaGFi1a4OWXX65zns8//xydOnVCaGgoWrVqhfXr19c5ZuXKlUhMTES9evWQkpKCb7/91uH7y5cvY+LEiYiKikLDhg0xaNAgnD592uGYEydOoH///ggLC0NsbCymT5+OK1eu+F4RGsEPcK7gOwGbrREAzwNmSUkJioqK3P5XXl4uqTysuUekwHd8r7/+utv/VqxYgZKSEk3K46mzUgreYsG3HaUsFjZbIxQUJAptTs1rEezgyn2alHS8zjM2Yv8gFSXe2zNnzjj87ep9Ath33bnC1VhlpMmYYSxht912G5544gnEx8fjzz//xOOPP47Bgwdj//79AIBPP/0UDz74IJYvX46+ffvi8OHDGDt2LOrXr4+srCwAQEFBAfr374/x48djw4YN2LNnD8aMGYP4+Hikp6cDADZt2oSpU6di9erVSElJwZIlS5Ceno4jR44gNjYWADBlyhTs2LEDH3zwAaxWK7KyspCRkYGvv/4aAFBdXY3+/fsjLi4O+/fvR1FREYYNG4bg4GC8+OKLOtSe9zib/52RMtOQGjj50EMPISwszO15WDCTy4G1VApqWo7UzDjvyRWjVnZ7gg2M6j5VEl/f25KSEmzevFn4W4mQBJYs/EZ3VRtGhE2ZMkX4d4sWLTBr1iwMHDgQVVVVCA4OxjvvvIOBAwdi/PjxAICWLVti9uzZWLBgASZOnAiLxYLVq1cjKSkJixYtAgC0a9cOX331FRYvXiyIsFdffRVjx47FyJEjAQCrV6/Gjh07sHbtWsyaNQs2mw1r1qzBxo0bcfvttwMA1q1bh3bt2uGbb75Bt27dsGvXLvz222/YvXs3mjRpghtvvBHPPfccZs6ciWeeecYwgwM/wJ082RRbtgyGveFUSicgNXAyLCzMVHlf3KF3KgU1Oyu1BksxV4wREzeyNIAZASXrwoh17+t7a3+/ZnVtGtlVbRgRZs/58+exYcMGdO/eHcHBwQCAioqKOtaU+vXr49SpUzh+/DgSExORm5uLtLQ0h2PS09MxefJkALWN9cCBA5g9e7bwfUBAANLS0pCbmwsAOHDgAKqqqhzO07ZtWzRv3hy5ubno1q0bcnNz0aFDBzRp0sThOhMmTMCvv/6Km266yeV9VVRUoKKiQvi7rKxMRu0oS+3qqcOorJTfCRg9cFIJWEmloGZnpUaHKxbvoVXiRqUGbxYHMH/ByHWv1Hubl5fic/wUq7sYGHVlvaFE2MyZM7FixQqUl5ejW7du2L59u/Bdeno6pkyZghEjRuC2227DH3/8IVi8ioqKkJiYiOLiYgdhBABNmjRBWVkZ/v77b1y4cAHV1dUuj/n9998BAMXFxQgJCUFERESdY4qLi4VjXJ2D/84d8+fPx7x587yoEe3wpRMweuCkr+gtQo2algBgI/heycGbNRe1P6GkeNDCoqb0e2uzNcL+/al1PvflfdJzcsnSfsC+oKsImzVrFhYsWODxmMOHD6Nt27YAgOnTp2P06NE4fvw45s2bh2HDhmH79u2wWCwYO3Ys8vPzcffdd6Oqqgrh4eF47LHH8MwzzyAgwBjrD2bPno2pU6cKf5eVlaFZs2Y6lsgRd53AuXPnPHY6LAykeqK3CDVyXA0L8R5qCie9XdT+ii/iQSuLmtLv7fnzUXC1Fi81NVdW29N7cmnkfs0eXUXYtGnTMGLECI/HtGzZUvh3dHQ0oqOjce2116Jdu3Zo1qwZvvnmG6SmpsJisWDBggV48cUXUVxcjJiYGOzZs8fhHHFxcXVWMZ4+fRrh4eGoX78+AgMDERgY6PKYuLg44RyVlZUoLS11sIY5H+O8opI/J3+MK0JDQxEaGuqxPtRGyqzBeeDgt5pw1+mwMJDqCQsilPWOyBk1A/19RSnhpJcVwYhxUUriq3jQ0h2n5HNw1Q8BNUhJyZN1Pr0nl4Dx+jVX6CrCYmJiEBMTI+u3NTW1S1LtY6gAIDAwENdccw0A4L333kNqaqpwjdTUVHzyyScOx+fk5CA1tdZEGxISgs6dO2PPnj0YOHCgcJ09e/YIKyw7d+6M4OBg7NmzB4MGDQIAHDlyBCdOnBDOk5qaihdeeAFnzpwRVlTm5OQgPDwc7du3l3W/WmE/uygtLXVYVQPIz0xs5MBJX/F3ESoHVme5SgknvawIesRFsSb6lBQPSrUH5zqy2WyoqqoS/g4KCnKY9MupMyn9kDeTGhYml2bAEDFheXl5+O6773DLLbegcePGyM/Px5w5c5CcnCwIn3PnzmHLli3o3bs3Ll++jHXr1uGDDz7AF198IZxn/PjxWLFiBWbMmIFRo0Zh79692Lx5M3bs2CEcM3XqVAwfPhxdunRB165dsWTJEly6dElYLWm1WjF69GhMnToVkZGRCA8Px6RJk5Camopu3boBAPr27Yv27dvj4YcfxsKFC1FcXIynnnoKEydO1N3S5Qp3nWRERASGDBmC0tJS7Nq1y+eBw6iBk3Jh2ZpjBFib5Yq1f347JUB8kNTLiqB1UDWLwfBKiQelhLTUOnJGTp156ocyMzO9Oh9NLpXBECIsLCwM2dnZePrpp3Hp0iXEx8ejX79+eOqppxxEzVtvvYXHH38cHMchNTUVn3/+Obp27Sp8n5SUhB07dmDKlClYunQpmjZtijfffFNITwHUNsSzZ89i7ty5KC4uxo033oidO3c6BNovXrwYAQEBGDRoECoqKpCeno7XXntN+D4wMBDbt2/HhAkTkJqaigYNGmD48OF49tlnVa4p7/GmA/B24NA7cFLvGTir1hxCHmLtn3fL83gaJJUQAr62by3coSyupFNKPCglpMXqyNc6kxrgL8cr5c8eDqUwhAjr0KED9u7d6/GY6OhoIY2EJ3r37o0ff/Tc0WRlZQnuR1fUq1cPK1euxMqVK90e06JFizquTxaR2kkC3g8ceooQVmbgLOc40lukGg137T84uBIFBYleDZK+CgFf27ce7lBW0rQAyogHNdxxznV0ww2HcOjQDT7VmdL9sJFXW7OIIUQYoQ1inaScgUOvQdxsqQCUFpWsiFQj4ar933DDIaxZM0byIKmUi9pXC5PW7lC9V9KVlJTAZrM5fOareFDaHeeqjn76qSMAi/C33DpT8h1mxcJvlkkkiTACgPRO0qjmZ6OlAnDuYOzjjTyhtPg0ikhVE3fCKTi4UhBggLRBUo0BTI6FSeugaj1X0kmdcAwZMgSxsbFe1b2S/aGrOuIFGA8r+RX1FjdSt8QzwiSSRJgfYj/A84O7N52k0QLsWXKDSEFuoK4vGE2kaomzcDp37hyys7NRUJAoS1goOSjItTBpHVSt50o6qVbDiIgIl8/GeUJUWlrq8L1S7jjXKSQ42AsxWn1Yi9Qt8YwwiSQR5me4G+CV6CRZjAHQ2w0iBykdh5KiyWgiVQ9cDc5i74wr66XSLhJfLExaWrVZWUnnbVuXOiHKzMyE1WoV/lYqhYSrmDBW+y09MGL/7gyJMD/D3QAv1kkOGTKkzlZN9rDqf2choaCvOAsuJUWTGToxvRB7Z5xXS/Io6SLxdvKkZ1C13qEMctq6VCua1WpVZA9TV3V0++17DRf+oRVm6N9JhBECnjrJiIgITTZKVhqjJxR0Flxpabuxe3eaYqLJDJ2YnsgRFkq6SLy1MGkdVM3SSjpf27paFmOxOqLVh+4xev8OkAgjnDDbC8+KG0QOrmbuOTlpcN7/zRfRZIZOTGukCgut4uy8FYJaWqxZWUkH+NbW1bQYu6ojNTLmmxEj9+88JML8HHcDRUZGBqKjowEY84U3Q7Z616ulAhQVTWboxLTGnbDgA/YB9ePsWLIwiaFl3+EqbQEfm+dLW1fbYuxcR0b0OuiF3m5uXyER5sd4Giiio6MN3RGwNAOXi7uZu71L0nkgkbNLgVFFqjNa5g3ydB4t4uzM0L6VxnPagtrP5A7YZDFmG6Ot2LeHRJif4g8B2UYfgNzN3Dt1+hHXX/8Lrr9+IDp2bICEhJsB3OzVoGu2QZyl5LNaxdkZ5dlohdS0BXKshma2GBsx6aneW+IpCYkwP4UCstlFipXKar2Ie++1+tQ5stax+gJLyWfJaqIvYhNM+1ALHnuh4SqPIqCM24s1wWPUpKdmmkSSCPMz+AFebKAwwgzCF1jrDO0xUwejF3omnzWz1cQIiE0wPYVauBYlV/cF9SX2jkXBY+Skp2bp/0iE+Rn2A/w115Rh5kwrqqstCAzksGBBGR544H7TD/Asua7cYeb6Vxu9ks+aMc7OiPhiiZQqSuQsXGJZ8PhDeAqrkAjzQ/gOY9o0IDMT+OMPoFUrC5o2jQAQoWfRNIEl15URYNlq6IyegwlZMNlACUukWDvyZeESi4KHwlP0g0SYn9O0ae1/BOEKI1gN7dF7MGGhDgjf47fUbEd6t1FXUByjfjgnISIIghAwmtWQH0zsocHEP7FaLyIp6bhPSYztUaodsdhGeeshXy6KY9QOsoQRhAEwkktQTygo3n9RMm2Bmu2I1TZq9KSnRoVEGOH36LmSTgosuQRZrSt/DIonYe6I0jF5aooSVgWPWNJTanPKQyKM8Gv0WknnDay4BFmuK38LimdJmLOE0veqZiZ2FrK8e2M9ZDHFhhkgEUb4LSyuUmIVI9SVP3X8zmLT3YDISqyeUVAzEzuLWd69mbwUFRUJn7GWYsPIkAgj/A6+kxNbpWQm15WvsLiii6iFZQul0qjtDlPTomoWa60RJmRGgkQYIRujxgfwneGxY1fwzjscamoswneBgRwmTboTiYlBTJZdL2gJO5v404ColTtMzfeetT7FmzrloQmZspAII2Rh9JiUqKgoREUBr78OPPIIUF0NBAYC//qXBZ07N9G7eB7RMjietwaKregiq6E+iA2I9nsfsjopkgrLGeeNipw6pQmZspAII2TBSrC4r4weDaSn87sGsJ+4VmvXk7MLZe7cszh2LAiJiVeQkHAzgJsNP7gbGXcDYnBwJQoKErFuXY4hg6ZdWdl5QelP1j+t8KZOWU2xYVRIhBF+j1F2DdBr8LEftOPjgc6dVbsU4SWuBsQbbjiENWvGGNZK5NlFRu4wNfC2TllNsWFESIQRqmLvDgGM7xLRA1pIQHjCfkAMDq4UBBhgTCuRmIssOfkPcocpjBwXIwspNswAiTBCVbKzs+t8ZhSXCCvQQgLCGWfBzQ+IBQWJprESubP8Tp68hNxhCiPFxchiig0zQCKM0BwjuERYw8gLCQjlcY7VO3fuHLKzsxULmmZh5bMnyy+5w5RHrE7NkmKDNUiEEYSBMNpCAkI9XA12SgRNs5IZXUxQkjtMecTqlASW8pAIIwgvYMFCoNRCAhbuhVAeX61ErKSCkCso5bjD7N+FwsIAFBQEISnpChISaoRzmvFdIBej/pAII2Thjy+l0XOj2ePrvZCAYxslrEQspILwJCgzMjIQHR3tcLycdmf/LngSnUZ4r72Bf4czMzNx8iSHEydC0bx5BeLirgAAgoKCEBERQe+yypAII2ThKj6Aj0uRitEGcrPkRgN8uxcziVGzoIZFQ69UEKWlpQ5/uxOU0dHRiI+P9/l6fBsXE51GeK+l4q/Ck0VIhBGy8eXlpIHcePCi2TntiDvMNGixjhpB03pkRi8pKcHmzZuFvz3tDqG0Nd6f8o/5o/BkFRJhhCKcOgV8800IbLZGkjosM1mVnOFFCmuWPF+QKpq1wGgWVK1Q+p71yIwuNR4tMzMTAFBUVARAmTguf9yOx5+EJ6uQCCN8Zs0aYNw4oKYmChbLZNGtdEJCQgwprqRi75I1iiVPbD9KT89Ly70syYKqLXqlgpASj6a0O80ft+PxR+HJGiTCCJ84dYoXYLV/c1wAduy4B3PnpggzUnv42Sk/gzU7RhCbvuxHqfVelma2oLKKHqkgxCw0Fy5cAFAr1rZtuxuAMu40f8s/5o/CkzVIhBE+cfToVQHGU11twcWLTaBAzCzTaGkBUgtfVsCxsHrODM+ANVhIWyBmodm1axcAIC8vBbwA4/HVneZv+cf8TXiyBokwwidatwYCAhyFWGBgbSLRU6dqRVrr1uZLKqq1BUgt5MaE2GyN8Ouv1+kaT2KWZ8AaLGRGl2KhsdkaITc31cWvyZ3mLf4mPFmCRBjhE02butpKB/jss6tuyoCA2mNGj9a7tL7Bz/xZsAD5Cn8vYhYHV9YOe/EDcAAsLn/rDiUC683wDFiGhXg6MQuNqwkEAHTvnutVG2DB8kf4LyTCCJ9x3koHAFq0uGodq6mpFWnp6ca2iPEWgn37gMWLjb2iyN7acc01ZZg504rqagsCAzksWFCGBx6436UYchY/tQKsVog5WytcDVpSA+uHDBmC2NhYt2KAVnX5B54sNO4mECkpeV5dgwXLn9aQ8GQHEmGEIthvpbNvn6s4sVqRxh9j1E4gKioK3boBAQEcamq8swCxBj+oTJsGZGbyItqCpk0jAES4/I1r64MF6ek70b79bxg58g5ER9/sdtCSGijN54pyt7qNVnWZE2/edyWDys0ksKTgj8KTVUiEEYrjKU6Mx8idQNOmwMKFNkyfHi50/mlpu3H+fG1ZjWiJEduPUsx92b79b7BaLyIhIcGrZyY3NQat6jIn9v2CzWbDpk2bhO9ctRUKKpcPi32rVMwUb0wijFAcd3Fizi+LkTuBESOq8eefS3D+fCQKCxOwe3eaywBx1ix5cpHrvvSEY2xZDbp3z0VKSp7HgdS+Pj0NwGapd3/EVRvytAjDk8uS2oF54GNJN26sjxkzrKipsSAggMPChTY88MDfzE7axbBwHMfpXQjCNWVlZbBarbDZbAgPD9e7OF5z6tTVODGx2YoRZzYlJSU4duwKunaNdXBNBgZyyMs7g8TEIEN2ClLw5tnaU1RUhNdffx02WyMsWTK5jmvTeYAdN25cnf0BKWO+f+CprVgsNZg8eYlHwT5kyBC0a9dOi6ISKsPHkoq1BZaSNEsdv8kSRqiGmIuL52rGfWOtpIyKisKhQ+7zpDHSF6iC1GfrDncr26SscmSlkyW0wd0ijLy8FPTtu9vt72JjY9UuGqER/KRLbEGOEZM0kwgjdMU5477SKynVtppIiX8j6uIqtoyHVjkS9rhrK/v3pwru64yMDERHRwvfkTXUnJhxQQ6JMEJXXGfcd1xJKRct9hmUGv9GOMIH1ttvOcNj9E6VUBar9SJSU3Oxf38Pp2+uivXo6Og6bmvCfJhxQQ6JMEJX1LQkabXPoHOeNBJg7nEVWJ+Xl4Lc3FTTdKqE8qSk5GH//lTYC3YS6/6J2VbEkggjdMUsliRfY6T8BX6V5dmzZ7Fp0yZYrRfRt+9upKTk0SpHwi1W60UMGGAuCwghHzNts0QijNAdsiT5F1FRUYbOE0dog7MAN5sFhCAAEmEEI5AlyVgoseDB3wWWfR0WFgagoCAISUlXkJBQ65v3dxEaFRWFzMxMh4StZrKAEARAIowgCC9xXvDgLus9Szl7WMO+Dj0lIvX3OrRarXoXgWAAo25zJwUSYYTfILZFDnEVT5auc+fOCf/2JCCMmLNHK/i6cd4Q3TlPmr/XoZkHX3/G2+TcZg5fIBFG+AWexALhiFRLl5iAIMQRSz7p75h58PU3fN12yKzPmEQYYVr42bGYWDDTLFqJWC3733sSryQgfMeMySeVxqyDrz9Rd9uh2m3eamosmD49HH/+uZa5bYe0gkQYYVr4WfS+fcDixXXFQo8ew9G7t3k6eaVjtcTEq5iAsHdbkrXCNWZMPmkPLT4gAHNvO+QrJMIIUxMVFYVu3VwnhE1JiTLV/o5SLVhSOzqxDtOdgACAgoJErFuXQ4H6EjBr6gVafEA4Q5bfupAII0yPWRLCSkWpWC0pHaazgMjPb/U/dwMF6nuDGVMv0OIDwhmzW37lQCKM8Av8KSGsUrFaUjtMXkBQoD7hCoodJOxR2/JrNBc4iTDCb/CXhLBKmvy96TBpsJWOP6VekBo7yMqAqcTiFsIzall+jegCJxFGECZDaZO/uw4zIyMD0dHROHfuHLKzsynewwv8KfWCWHvMzs5mZsCkRMTGxogucBJhBOEGbxMKsoQvJn+p1peEhASHgYjiPbzDKIO4Eu4dT+2RpQFT6cUtzpCVTRuMZJUnEUYQdviaUJAl5Jr8fbHSmHWln7+ipHvHXXtkccBUI77Rn61sWrvfjWSVJxFGEP/D6AkFve3oPFn6fLk/M67081e0cO+wOGCqIQzVtrKxjNbudyNZ5Q0nwioqKpCSkoKffvoJP/74I2688Ubhu0OHDmHixIn47rvvEBMTg0mTJmHGjBkOv//ggw8wZ84cHDt2DK1bt8aCBQtw1113Cd9zHIenn34ab7zxBkpLS9GjRw+sWrUKrVu3Fo45f/48Jk2ahI8//hgBAQEYNGgQli5dioYNG3pVFoItjJ5QUEpHV15ejsrKSixaVKqYpc+fgsz9FTmiROrzZnHAVFMY+usqYq0nrkaxyhtOhM2YMQMJCQn46aefHD4vKytD3759kZaWhtWrV+Pnn3/GqFGjEBERgXHjxgEA9u/fj/vvvx/z58/H3XffjY0bN2LgwIH44YcfcP311wMAFi5ciGXLluGtt95CUlIS5syZg/T0dPz222+oV68eAODBBx9EUVERcnJyUFVVhZEjR2LcuHHYuHGj5LIQ7MLizFwqnjq6kpISvP7664pb+vwpyNxfkfNOiLULfkEHwN6AqaYwZNH9alaMYJU3lAj79NNPsWvXLmzduhWffvqpw3cbNmxAZWUl1q5di5CQEFx33XU4ePAgXn31VUH4LF26FP369cP06dMBAM899xxycnKwYsUKrF69GhzHYcmSJXjqqadw7733AgDefvttNGnSBB999BGGDh2Kw4cPY+fOnfjuu+/QpUsXAMDy5ctx11134ZVXXkFCQoKksihFTU0Ns5YZo1FVVYUGDRqgQYMajBixE/v29UZ1dQCKi4Nw552fMP8yi6GmpY8ElrmRK0q8aResDZhqCUMjT/II5TGMCDt9+jTGjh2Ljz76CGFhYXW+z83NRc+ePR1M4Onp6ViwYAEuXLiAxo0bIzc3F1OnTnX4XXp6Oj766CMAQEFBAYqLi5GWliZ8b7VakZKSgtzcXAwdOhS5ubmIiIgQBBgApKWlISAgAHl5efjHP/4hqSyuqKioQEVFhfB3WVmZxzqprKxEQUEBauz34yFkU11djR49egAAevQARo/+GTU1Abh8uRw//HAYly/rXECFoEGAkANr1iotUEMYsuh+NQtGDI0whAjjOA4jRozA+PHj0aVLFxw7dqzOMcXFxUhKSnL4rEmTJsJ3jRs3RnFxsfCZ/THFxcXCcfa/c3dMbGysw/dBQUGIjIx0OEasLK6YP38+5s2b57oSnOA4DkVFRQgMDESzZs0QEBAg/iPCI5WVlSgtLXX4jOM4XLhwAW3atKnjAjcKfAB+eHhtG6FBgJCLkqKExQFTqzL5o6DVAiOGRugqwmbNmoUFCxZ4PObw4cPYtWsXLl68iNmzZ2tUMn2YPXu2g6WurKwMzZo1c3nslStXUF5ejoSEBJeWQcJ7AgICEBRU95WwWq2IiYlBSEiI4Vy/a9YA48bVbl4eEBCLu+++CZ06/UiDgA4YbTsVtWFxwNSyTKy5X82C0d4hXUXYtGnTMGLECI/HtGzZEnv37kVubi5CQ0MdvuvSpQsefPBBvPXWW4iLi8Pp06cdvuf/jouLE/7v6hj77/nP4uPjHY7hV2HGxcXhzJkzDue4cuUKzp8/L3od+2u4IjQ0tM49uqO6uhoAW2ZVsxIQEICAgAAEBwf7JMK0TP5aUlKCY8euYNy4WNTUXA3At1+FRYOAdhhxOxVAfcsQS/fKo1aZWLT8EfqjqwiLiYlBTEyM6HHLli3D888/L/xdWFiI9PR0bNq0CSkpKQCA1NRUPPnkk6iqqkJwcDAAICcnB23atBHcf6mpqdizZw8mT54snCsnJwepqakAgKSkJMTFxWHPnj2C6CorK0NeXh4mTJggnKO0tBQHDhxA586dAQB79+5FTU2NV2VRCovFouj55FBZCVy+DNSrBxi5/3Dn0nWuYzmdpKNFCnj99dpNxdWAH/ALChJRUzPc4TtahaUPRtxOBWDTWmVUqC4JVxgiJqx58+YOf/P5uJKTk9H0fyaFBx54APPmzcPo0aMxc+ZM/PLLL1i6dCkWL14s/O6xxx5Dr169sGjRIvTv3x/vv/8+vv/+e7z++usAagfbyZMn4/nnn0fr1q2FFBUJCQkYOHAgAKBdu3bo168fxo4di9WrV6OqqgpZWVkYOnQoEhISJJfFLJw9Cxw/fvXvFi0ACbqaSYKCghAbG1tnocPly5dRVlaGIUOGoEGDBl53kqdOXRVgQO3/H3kESE9XxyLGd/IUgM8eRkxPQKJAOaguCWdME81ttVqxa9cuFBQUoHPnzpg2bRrmzp3rkBKie/fu2LhxI15//XV07NgRW7ZswUcffSTkCANq85BNmjQJ48aNw80334y//voLO3fuFHKEAbXpMNq2bYs+ffrgrrvuwi233CIIOallMQOVlY4CDKj929NkfsSIEbBYLLBYLAgODkaTJk1wxx13YO3atV6t8ly/fj0iIiLkFdwDQUFBCAkJqfNfYGAgYmNjZXWiR49eFWA81dXAH38oVGg38AH4FkvtxaUG4JM7RD14YWwPCWOC8F8MYQlzJjExERzH1fn8hhtuwJdffunxt/fddx/uu+8+t99bLBY8++yzePbZZ90eExkZKSRmdYeUshgddykbKio8uyX79euHdevWobq6GqdPn8bOnTvx2GOPYcuWLdi2bZvL4Hgj07p1rQvSXogFBgKtWql/bU8B+BkZGYiOjnY4Xkt3iJE3SJcLrUwlCMIec412hGTsV2q5QspgbGccdEBsbUFoaKiwQOGaa65Bp06d0K1bN/Tp0wfr16/HmDFj8Oqrr2LdunX4v//7P0RGRuKee+7BwoUL0bBhQ3z++ecYOXIkgKvxWk8//TSeeeYZvPPOO1i6dCmOHDmCBg0a4Pbbb8eSJUvqpBXRipKSEgQGVmLhwvqYOdOK6moLAgM5LFhgQ2Dg3ygpUV/0uAvAj46OdliAoiVaxsixBq1MJQiCh0SYH2K/UssTYiu1QkJqY8CcY8LkeLNuv/12dOzYEdnZ2RgzZgwCAgKwbNkyJCUl4f/+7//wz3/+EzNmzMBrr72G7t27Y8mSJZg7dy6OHDkC4GqcYFVVFZ577jm0adMGZ86cwdSpUzFixAh88skn3hfKR5zr+dFHGwkD719/XQTvwWZtRZyauF61CTzyCIcbbzyDxMQgv6gLWplKENqihOFBDUiE+SFSV2BJOS4mBrBaa12QoaG+rY5s27YtDh06BAAOK1gTExPx/PPPY/z48XjttdcQEhICq9UKi8VSJ+XHqFGjhH+3bNkSy5YtE2L77DdY1wLn+nM38LK2Ik4tPK3arK62YPnyT5GUdNyvRKk/weogqDT+cp9GQinDgxqQCCN8JiREmdQUHMcJ7sXdu3dj/vz5+P3331FWVoYrV67g8uXLKC8v95ic9sCBA3jmmWfw008/4cKFC0Kw/4kTJ9C+fXvfC0nIRuqqTTOKUn/PEcXyIKgk/nKfRkNJw4PSkAgjmOHw4cNISkrCsWPHcPfdd2PChAl44YUXEBkZia+++gqjR49GZWWlWxF26dIlpKenIz09HRs2bEBMTAxOnDiB9PR0Uw7s7mB9wPfH4HR/zxHlfN82WyOcPx+FyMgSh+du9PeU5cGeuIq79qcHJMIIJti7dy9+/vlnTJkyBQcOHEBNTQ0WLVokJFDdvHmzw/EhISHCrgE8v//+O0pKSvDSSy8J2z19//332twAQxhhwGctOF2LLYXMKrC8xdOOAQShNqy1PxJhhOZUVFSguLjYIUXF/Pnzcffdd2PYsGH45ZdfUFVVheXLl+Oee+7B119/jdWrVzucIzExEX/99Rf27NmDjh07IiwsDM2bN0dISAiWL1+O8ePH45dffsFzzz2n013qixEGfFaC0426pZAREdsxwGywZHEh2Gx/JMIIWVy5csVjclV3m2EDwM6dOxEfH4+goCA0btwYHTt2xLJlyzB8+HAEBASgY8eOePXVV7FgwQLMnj0bPXv2xPz58zFs2DDhHN27d8f48eORmZmJkpISIUXF+vXr8cQTT2DZsmXo1KkTXnnlFQwYMEDx+yfMg1G3FHIF60HhRtwxQC6sWVwINtsfiTDCa65cuVJnE3NXxMbG1hFi69evx/r160V/O2XKFEyZMsXhs4cfftjh71WrVmHVqlUOn91///24//77HT5zldiXIJxhsYP2BiMEhYstyrDZbLrlrlMSFi0uBJtbuZlm2yJCOr4GbkvdXsibbYjMiK/1fOoUsG9f7f8J9TH6lkKugt8LChJhszXyeJyWiG2ltWnTJpSUlOhWPqXwJOgJ/ZC7lZuakCXMDzFC4LYZ8KWezZhRnlZtagfLrjCxRRlGcPuKwaLFxZ+x71M8tT89+h4SYX4KCSxtkFPPp05dFWAAn1EeSE839h6LRhD/rK3alIPWrjApcWjOgxsrizKUhr9PMUFv1nxwrMJy30MijCAY4+hRx82+AaC6GvjjD+VEmF6bZxtB/LsTCOfOnZPcUesZIK9lbJs3cWhDhgxxSDVjxpWDzoP93LlncexYEBITryAh4WYAN+s+0fBXWK1zEmEEwRitW9e6IO2FWGAg0KqVMuc3o6tTC7KzswGIB7brHSCvpSvMm+SkERERwt8su0t9xf6ZxscDnTvLOw/rK10JZSARRhAMUVJSgsDASixcWB8zZ1pRXW1BYCCHBQtsCAz8GyUl8jte2jzbNe5cQ3KzuuudHZ712DZaOSiO3kKe0A4SYQTBCM4d76OPNhJik/766yJef732czkdL22e7R7ehVRYWChYu5Sy1Ohl8ZET26aE5UWKi9HoqUC0gLY/8h9IhBFew28lpNRxRC3OHaq72CQ5Ha8/b54thaioKMlJW6WitcVHavC7K8ufEpYXqYKTVg4SxFVIhBFeExQUhNjYWNkZ8wn9YN1VxQJKWWq0tvj4sgLMV8uLFMGp9cpBiqkijACNkoQsWBVYn3/+OW677TZcuHDBIRDYE4mJiZg8eTImT56satlYgbU0DKwNlkpZavSw+OglKqQITi1XDlJMFWEU2BxJCdMyYsQIvPXWW3jkkUfqbMo9ceJEvPbaaxg+fLikrY0I+bCSp4nFwVIpa6E/WR2lCk6lVg6KQTFVhFEgEUZoTrNmzfD+++9j8eLFqF+/PgDg8uXL2LhxI5o3b65z6Qgt0XsloTuUshayZnVUGkpOqg1mzKlG1EIijNCcTp06IT8/H9nZ2XjwwQcB1OZgat68OZKSkoTjKioqMH36dLz//vsoKytDly5dsHjxYtx8883CMZ988gkmT56MkydPolu3bhg+fHid63311VeYPXs2vv/+e0RHR+Mf//gH5s+fjwYNGqh/s4Rk9M4d5Utguxrn0ROpgz4lJ1Ufvd8LQl1IhBG6MGrUKKxbt04QYWvXrsXIkSPx+eefC8fMmDEDW7duxVtvvYUWLVpg4cKFSE9Pxx9//IHIyEicPHkSGRkZmDhxIsaNG4fvv/8e06ZNc7hOfn4++vXrh+effx5r167F2bNnkZWVhaysLKxbt07LWyY8wELuKKW2NmF5ixQpeDvoa+Vi9Cd4gS72XrAs5AlpkAgjdNnC5qGHHsLs2bNx/PhxAMDXX3+N999/XxBhly5dwqpVq7B+/XrceeedAIA33ngDOTk5WLNmDaZPn45Vq1YhOTkZixYtAgC0adMGP//8MxYsWCBcZ/78+XjwwQeFoPvWrVtj2bJl6NWrF1atWoV69eppc8MSUHODa9Y3z2Yld5RSwohVgSUGC2KYuCrk9+0DFi+u+1706DEcvXsbt50RVyER5ufotYVNTEwM+vfvj/Xr14PjOPTv3x/R0dHC9/n5+aiqqkKPHj2Ez4KDg9G1a1ccPnwYAHD48GGkpKQ4nDc1NdXh759++gmHDh3Chg0bhM84jkNNTQ0KCgrQrl07NW5PFmpaUFi3zlDuKH3hxbeYGCbLi3ZERUWhWzfXW5ilpESB9Jc5IBHmx5w6dVWAAfwWNkB6ujYWsVGjRiErKwsAsHLlSlWu8ddff+GRRx7Bo48+Wuc7FhcBqCmCWJ41+9NKQhbhRfqxY1fwzjucsK0VAAQGcpg06U5Db2tl1MD2pk1rJ8aPPAJUV9cKsH/9SzuPBaE+JML8mKNHHWdYQO2L/scf2rzk/fr1Q2VlJSwWC9LT0x2+S05ORkhICL7++mu0aNECAFBVVYXvvvtOcC22a9cO27Ztc/jdN9984/B3p06d8Ntvv6GVUrtfE6ph9pWErBMVVWtdqTvoW9C5cxO9i+cV9hY7TzFuRrDsjR5dOzH+4w+gVSsSYGaDRJgf07q1a1O3VnolMDBQcC0GBgY6fNegQQNMmDAB06dPR2RkJJo3b46FCxeivLwco//nLx0/fjwWLVqE6dOnY8yYMThw4ECd/GIzZ85Et27dkJWVhTFjxqBBgwb47bffkJOTIyk/FaEuZlhJaDbMMOjbW/aefTYWHFdr2eO4AOzYcQ/mzk0xlGWvaVNjPgdCHBJhfgwLpu7w8HC337300kuoqanBww8/jIsXL6JLly747LPP0LhxYwC17sStW7diypQpWL58Obp27YoXX3wRo0aNEs5xww034IsvvsCTTz6JW2+9FRzHITk5GZmZmarfGyEO67Fq/ooZBv2oqCgcOuTK2m/BxYtNKKaKYAILx3Gc3oUgXFNWVgar1QqbzVZHrFy+fBkFBQVISkryeYXfqVPGnvWqjZJ1TRCEdpw6BbRoUdfaf+wY9XWEungav+0JcPsN4Tc0bQr07k2dEkEQ5oK39vPRDhTYTrAGuSMJgiAI02KGGDfCvJAIIwiCIEyNGWLcCHNC7kiCIAiCIAgdIBFGEARBEAShAyTCDA4tblUfqmOCIAhCDUiEGRQ+uamn/EqEMvB17JxQliAIgiB8gQLzDUpQUBDCwsJw9uxZBAcHIyCA9LQa1NTU4OzZswgLC0NQEL0uBEEQhHLQqGJQLBYL4uPjUVBQgOPHj+tdHFMTEBCA5s2bw2KxiB9MEARBEBIhEWZgQkJC0Lp1a3JJqkxISAhZGgmCIAjFIRFmcAICAmgrHYIgCIIwIDS9JwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHKCaMYfgkoWVlZTqXhCAIgiAIqfDjtliybxJhDHPx4kUAQLNmzXQuCUEQBEEQ3nLx4kVYrVa331s42pOFWWpqalBYWIhGjRr5ZY6qsrIyNGvWDCdPnkR4eLjexTEsVI/KQPXoO1SHykD1qAxq1iPHcbh48SISEhI8pjgiSxjDBAQEoGnTpnoXQ3fCw8Opo1EAqkdloHr0HapDZaB6VAa16tGTBYyHAvMJgiAIgiB0gEQYQRAEQRCEDpAII5glNDQUTz/9NEJDQ/UuiqGhelQGqkffoTpUBqpHZWChHikwnyAIgiAIQgfIEkYQBEEQBKEDJMIIgiAIgiB0gEQYQRAEQRCEDpAIIwiCIAiC0AESYYTu/Oc//8E999yDhIQEWCwWfPTRRw7fcxyHuXPnIj4+HvXr10daWhqOHj2qT2EZRawOR4wYAYvF4vBfv3799Cksw8yfPx8333wzGjVqhNjYWAwcOBBHjhxxOOby5cuYOHEioqKi0LBhQwwaNAinT5/WqcRsIqUee/fuXadNjh8/XqcSs8mqVatwww03CMlEU1NT8emnnwrfU1uUhlg96tkWSYQRunPp0iV07NgRK1eudPn9woULsWzZMqxevRp5eXlo0KAB0tPTcfnyZY1Lyi5idQgA/fr1Q1FRkfDfe++9p2EJjcEXX3yBiRMn4ptvvkFOTg6qqqrQt29fXLp0SThmypQp+Pjjj/HBBx/giy++QGFhITIyMnQsNXtIqUcAGDt2rEObXLhwoU4lZpOmTZvipZdewoEDB/D999/j9ttvx7333otff/0VALVFqYjVI6BjW+QIgiEAcB9++KHwd01NDRcXF8e9/PLLwmelpaVcaGgo99577+lQQvZxrkOO47jhw4dz9957ry7lMTJnzpzhAHBffPEFx3G1bS84OJj74IMPhGMOHz7MAeByc3P1KibzONcjx3Fcr169uMcee0y/QhmUxo0bc2+++Sa1RR/h65Hj9G2LZAkjmKagoADFxcVIS0sTPrNarUhJSUFubq6OJTMen3/+OWJjY9GmTRtMmDABJSUleheJeWw2GwAgMjISAHDgwAFUVVU5tMe2bduiefPm1B494FyPPBs2bEB0dDSuv/56zJ49G+Xl5XoUzxBUV1fj/fffx6VLl5CamkptUSbO9cijV1ukDbwJpikuLgYANGnSxOHzJk2aCN8R4vTr1w8ZGRlISkpCfn4+nnjiCdx5553Izc1FYGCg3sVjkpqaGkyePBk9evTA9ddfD6C2PYaEhCAiIsLhWGqP7nFVjwDwwAMPoEWLFkhISMChQ4cwc+ZMHDlyBNnZ2TqWlj1+/vlnpKam4vLly2jYsCE+/PBDtG/fHgcPHqS26AXu6hHQty2SCCMIP2Do0KHCvzt06IAbbrgBycnJ+Pzzz9GnTx8dS8YuEydOxC+//IKvvvpK76IYGnf1OG7cOOHfHTp0QHx8PPr06YP8/HwkJydrXUxmadOmDQ4ePAibzYYtW7Zg+PDh+OKLL/QuluFwV4/t27fXtS2SO5Jgmri4OACos+Ln9OnTwneE97Rs2RLR0dH4448/9C4Kk2RlZWH79u3Yt28fmjZtKnweFxeHyspKlJaWOhxP7dE17urRFSkpKQBAbdKJkJAQtGrVCp07d8b8+fPRsWNHLF26lNqil7irR1do2RZJhBFMk5SUhLi4OOzZs0f4rKysDHl5eQ7+fMI7Tp06hZKSEsTHx+tdFKbgOA5ZWVn48MMPsXfvXiQlJTl837lzZwQHBzu0xyNHjuDEiRPUHu0Qq0dXHDx4EACoTYpQU1ODiooKaos+wtejK7Rsi+SOJHTnr7/+cphxFBQU4ODBg4iMjETz5s0xefJkPP/882jdujWSkpIwZ84cJCQkYODAgfoVmjE81WFkZCTmzZuHQYMGIS4uDvn5+ZgxYwZatWqF9PR0HUvNHhMnTsTGjRvx73//G40aNRJia6xWK+rXrw+r1YrRo0dj6tSpiIyMRHh4OCZNmoTU1FR069ZN59Kzg1g95ufnY+PGjbjrrrsQFRWFQ4cOYcqUKejZsyduuOEGnUvPDrNnz8add96J5s2b4+LFi9i4cSM+//xzfPbZZ9QWvcBTPereFnVZk0kQduzbt48DUOe/4cOHcxxXm6Zizpw5XJMmTbjQ0FCuT58+3JEjR/QtNGN4qsPy8nKub9++XExMDBccHMy1aNGCGzt2LFdcXKx3sZnDVR0C4NatWycc8/fff3P//Oc/ucaNG3NhYWHcP/7xD66oqEi/QjOIWD2eOHGC69mzJxcZGcmFhoZyrVq14qZPn87ZbDZ9C84Yo0aN4lq0aMGFhIRwMTExXJ8+fbhdu3YJ31NblIanetS7LVo4juPUl3oEQRAEQRCEPRQTRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQOkAgjCIIgCILQARJhBEEQBEEQOkAijCAIgiAIQgdIhBEEQRAEQegAiTCCIAiCIAgdIBFGEARBEAShAyTCCIIgZFBZWal3EerAYpkIgnAPiTCCIAgAvXv3RlZWFrKysmC1WhEdHY05c+aA39ktMTERzz33HIYNG4bw8HCMGzcOAPDVV1/h1ltvRf369dGsWTM8+uijuHTpknDe1157Da1bt0a9evXQpEkTDB48WPhuy5Yt6NChA+rXr4+oqCikpaUJv+3duzcmT57sUMaBAwdixIgRwt9yy0QQBBuQCCMIgvgfb731FoKCgvDtt99i6dKlePXVV/Hmm28K37/yyivo2LEjfvzxR8yZMwf5+fno168fBg0ahEOHDmHTpk346quvkJWVBQD4/vvv8eijj+LZZ5/FkSNHsHPnTvTs2RMAUFRUhPvvvx+jRo3C4cOH8fnnnyMjIwPebufrbZkIgmAH2sCbIAgCtZanM2fO4Ndff4XFYgEAzJo1C9u2bcNvv/2GxMRE3HTTTfjwww+F34wZMwaBgYH417/+JXz21VdfoVevXrh06RI++eQTjBw5EqdOnUKjRo0crvfDDz+gc+fOOHbsGFq0aOGyPDfeeCOWLFkifDZw4EBERERg/fr1ACCrTPXq1fOpngiCUA6yhBEEQfyPbt26CQIMAFJTU3H06FFUV1cDALp06eJw/E8//YT169ejYcOGwn/p6emoqalBQUEB7rjjDrRo0QItW7bEww8/jA0bNqC8vBwA0LFjR/Tp0wcdOnTAfffdhzfeeAMXLlzwuszelokgCHYgEUYQBCGRBg0aOPz9119/4ZFHHsHBgweF/3766SccPXoUycnJaNSoEX744Qe89957iI+Px9y5c9GxY0eUlpYiMDAQOTk5+PTTT9G+fXssX74cbdq0EYRSQEBAHddkVVWVz2UiCIIdSIQRBEH8j7y8PIe/v/nmG7Ru3RqBgYEuj+/UqRN+++03tGrVqs5/ISEhAICgoCCkpaVh4cKFOHToEI4dO4a9e/cCACwWC3r06IF58+bhxx9/REhIiOBajImJQVFRkXCt6upq/PLLL6L3IKVMBEGwAYkwgiCI/3HixAlMnToVR44cwXvvvYfly5fjsccec3v8zJkzsX//fmRlZeHgwYM4evQo/v3vfwtB8Nu3b8eyZctw8OBBHD9+HG+//TZqamrQpk0b5OXl4cUXX8T333+PEydOIDs7G2fPnkW7du0AALfffjt27NiBHTt24Pfff8eECRNQWloqeg9iZSIIgh2C9C4AQRAEKwwbNgx///03unbtisDAQDz22GNC2gdX3HDDDfjiiy/w5JNP4tZbbwXHcUhOTkZmZiYAICIiAtnZ2XjmmWdw+fJltG7dGu+99x6uu+46HD58GP/5z3+wZMkSlJWVoUWLFli0aBHuvPNOAMCoUaPw008/YdiwYQgKCsKUKVNw2223id6DWJkIgmAHWh1JEAQB16sRCYIg1ITckQRBEARBEDpAIowgCIIgCEIHyB1JEARBEAShA2QJIwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHSIQRBEEQBEHoAIkwgiAIgiAIHSARRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQOkAgjCIIgCILQgf8HFkwbEKpg6twAAAAASUVORK5CYII=", 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", 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", 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", 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" ] @@ -426,7 +586,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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SnD5R0gr7/H6d1lzqEgQB33//PX777TeEh4dLQg8ANGnSBIaGhjh69Ch69uwJALhx4wbu378PNzc3OVomIiIqs/LW5Dl/PgsTJszCq7NKunTpgkaNGsnX3BtoTfDx9vbGpk2bsGfPHlhaWorzdqytrWFqagpra2sMHDgQY8aMQbly5WBlZYXvv/8ebm5uJXJHFxERkS7z9QUmTLiNXr02SOo//vgjzM3NZerq7bQm+CxfvhwA0KZNG0k9ODhYXCV4wYIF0NPTQ8+ePSULGBIREZF6paRsQK9et8Xt999/H59//rmMHalGa4KPKlORTExMsHTpUixdulQDHREREemelJQULFiwQFL79ttv4ezsLFNHRaM1wYeIiIjkdebMGcmCwnp6evDz84OBgfbECe3plIiIiGSRk5ODoKAgyYr8N254YNOmD2XsqngYfIiIiKhQDx48wOrVqyW1Eyd+wKhRNvI09I4YfIiIiKhAe/bswaVLl8TtqlWron///rI+a+tdMfgQERGRRFpaGubMmSOpVa/eG337vidTR+qjlY+soNLJy8sL3bp1E7fbtGmDUaNGvdM51XEOIiJSTWgo0LPn5XyhZ9YsPyxfrv2hB+CIj07w8vLCunXrAACGhoZwdnZGv379MGHChBKdib9r1y4YGhqqdGx4eDjatm2b75EjRTkHEREVT+7jJwS4uS3FBx88Eevu7u5IT/8/NGmSu2BhWcDgoyM6dOiA4OBgZGRk4Pfff4e3tzcMDQ3h5+cnOS4zMxNGRkZqec1y5cqVinMQEdGbLV78GJ6e0gV/hw8fDjs7OwBAly5ydFUyeKlLRxgbG8PBwQFVq1bFsGHD4OHhgdDQUPHy1MyZM+Ho6AhXV1cAQFxcHL744gvY2NigXLly6Nq1K2JjY8Xz5eTkYMyYMbCxsUH58uUxbty4fItMvn6ZKiMjA+PHj4eTkxOMjY1Rs2ZNrFmzBrGxsWjbti0AwNbWFgqFQlyN+/VzPH36FP369YOtrS3MzMzQsWNH3Lx5U9wfEhICGxsbhIWFoU6dOrCwsECHDh0QHx8vHhMeHo7mzZvD3NwcNjY2+PDDD3Hv3j01/U0TEWmXw4cP46OPXoae1NRymDx5shh6yhoGHx1lamoqrsdw9OhR3LhxA4cPH8a+ffuQlZUFT09PWFpa4tSpUzh9+rQYIPJ+Zt68eQgJCcHatWvxxx9/4L///sNvv/32xtfs168fNm/ejEWLFuH69etYuXIlLCws4OTkhJ07dwLIfbBsfHw8Fi5cWOA5vLy8cO7cOYSGhiIyMhKCIODTTz9FVlaWeExaWhrmzp2L9evX4+TJk7h//z5+/PFHAEB2dja6deuG1q1b4/Lly4iMjMSQIUO0+g4FIqLiyMzMxNSpUxERESHWLl/ujo8++r5M/zeRl7p0jCAIOHr0KMLCwvD999/j8ePHMDc3x+rVq8VLXBs2bIBSqcTq1avFf/iDg4NhY2OD8PBwtG/fHj///DP8/PzQo0cPAMCKFSsQFhZW6Ov+/fff2LZtGw4fPgwPDw8AQPXq1cX9eZe07O3tJXN8XnXz5k2Ehobi9OnTcHd3BwBs3LgRTk5O2L17N3r16gUAyMrKwooVK1CjRg0AwIgRIzBt2jQAuUutJycno1OnTuL+OnXqFP0vkohIi928eRObNm2S1OrXHwt/fzOZOtIcjvjIJDQUcHfP/a4J+/btg4WFBUxMTNCxY0d8+eWXmDJlCgCgfv36knk9UVFRuHXrFiwtLWFhYQELCwuUK1cOL168wO3bt5GcnIz4+Hi0aNFC/BkDAwM0bdq00Ne/dOkS9PX10bp162L/DtevX4eBgYHkdcuXLw9XV1dcv35drJmZmYmhBgAqVaqExMREALkBy8vLC56enujcuTMWLlwouQxGRFSWCYKAdevWSULPlSsNMGWKPyZMKPuhB+CIj2wCA4HIyNzvmpg01rZtWyxfvhxGRkZwdHSU3M1lbm4uOfb58+do0qQJNm7cmO88xb3ma2pqWqyfK47X7wJTKBSS+UfBwcEYOXIkDh48iK1bt2LSpEk4fPgwWrZsqbEeiYg0LSkpKd80gg0bBiI9vQoAoAxf3ZLgiI9MfH0BNzfN3R5obm6OmjVrwtnZ+a23sDdu3Bg3b96Evb09atasKfmytraGtbU1KlWqhDNnzog/k52djfPnzxd6zvr160OpVOLEiRMF7s8bccrJySn0HHXq1EF2drbkdZ88eYIbN26gbt26b/ydXteoUSP4+fkhIiIC9erVyzfkS0RUlkREREhCjyAY4dChSZg3rwqWLcv9PJo9W8YGNYgjPjLp0qX03h749ddfY86cOejatSumTZuGKlWq4N69e9i1axfGjRuHKlWq4IcffkBgYCBq1aqF2rVrY/78+UhKSir0nC4uLujfvz8GDBiARYsWoUGDBrh37x4SExPxxRdfoGrVqlAoFNi3bx8+/fRTmJqawsLCQnKOWrVqoWvXrhg8eDBWrlwJS0tL+Pr6onLlyujatatKv9vdu3exatUqdOnSBY6Ojrhx4wZu3ryJfv36vctfGRFRqbR7dw4uXpwFPT2lWDtwoAOuXm2BjRtffg6V1s+jksARH8rHzMwMJ0+ehLOzM3r06IE6depg4MCBePHiBaysrAAAPj4+6Nu3L/r37w83NzdYWlqie/fubzzv8uXL8fnnn2P48OGoXbs2Bg8ejNTUVABA5cqVMXXqVPj6+qJixYoYMWJEgecIDg5GkyZN0KlTJ7i5uUEQBPz+++8qL3JoZmaGmJgY9OzZE++99x6GDBkCb29vfPfdd0X4GyIiKv3u37+PqKgZktDz/vujcfVqCzx/njvVQhcphNcXX9FxKSkpsLa2RnJysvghDwAvXrzA3bt3Ua1aNZiYmMjYIWka33si0jY7duzA1atXxe1bt2rg9u1vEBGRt0pz7lSLsjTSU9jn9+t4qYuIiKiMSE1Nxdy5cyW1rKxvcPt2DXFOaWmeaqEJDD5ERERlwKVLl7Bnzx5JbebMCWja1BCvrFGo8xh8iIiItJhSqcSiRYuQnJws1i5caIUDB9rB3r7sPFxUXRh8iIiItNSjR4+wYsUKSe3UKW8cPVoBAODsrNuXtQrC4FNEnAuue/ieE1FpdPDgQcm6ZgkJFREe/h1mz1bgwQNAEDjaUxAGHxXl3S6dlpam0VWISX5paWkA8q8ITUQkh4yMDAS+di/6gQM9ceZMPbi6cvLy2zD4qEhfXx82NjbiM5/MzMzK9NNrKXekJy0tDYmJibCxsYG+vr7cLRGRjouJicHWrVsltcDAcQBMNfo0AG3G4FMEDg4OACCGH9INNjY24ntPRCQHQRCwZs0aPHjwQKw1btwYf/7ZGQYGwMiRwMyZMjaoRbiA4WtUWQApJycHWVlZGu6M5GBoaMiRHiKS1dOnT7Fo0SJJbfDgwXB0dJSpo9KJCxiWIH19fX4YEhFRiTt16hSOHTsmbqemmmHfPh/4+/OJU8XF4ENERFTKZGdnY+Zr16727fsU5841g6urTE2VEQw+REREpUhsbCzWrVsnqc2dOwbPn1vCxAQICpKpsTKCwYeIiKiU2Lp1K2JiYsTtGzfew+bNvQEAlSsDy5bxVvV3xeBDREQks+fPn2PevHmSWkhIP2RlVYOFBe/aUicGHyIiIhmdO3cO+/fvl9RmzJiI7GwD7NnDER51Y/AhIiKSgVKpxPz585GamirWjh9vg9OnWyMnB/j8c4aeksDgQ0REpGHx8fFYtWqVpLZly/ewtS2HnTsZeEoSgw8REZEG7d+/H+fOnRO3HzxwxC+/DELt2gpERMjYmI5g8CEiItKAFy9eYPbs2ZLa1q1f4Pr1OgCA13ZRCWHwISIiKmHXrl3D9u3bJbWAgPHIyDCBqSkwejQvb2kKgw8REVEJEQQBq1atQkJCglg7c6YZjhz5FAYGwIQJvE1d0xh8iIiISsCTJ0+wZMkSSS0r6zskJTlgxw6O8MiFwYeIiEjNjh8/jpMnT4rbyclW+PnnH9CypR4nMMuMwYeIiEhNsrKyMGvWLEktNLQzLlxoDADw9ZWjK3oVgw8REZEa3LlzB+vXr5fU5szxQVqaBfT0gB49eHmrNGDwISIiekebNm3CzZs3xe2rV+ti+/ZeADiBubRh8CEiIiqmlJQULFiwQFJbu9YL9+9XBcDQUxox+BARERXD2bNnceDAAXFbqVRg5swJsLY24BPVSzEGHyIioiLIycnBrFlzoFRmiLXDhz1w+vSHMDEBgoM5l6c005O7ASIiIm3x4MEDzJgxQxJ6Vq36AZUqfQg3N2DrVoae0o4jPkRERCrYs2cPLl26JG7fu+eMyEgvPHigkK8pKjIGHyIiojdIT09HUFCQpLZp01eIi3PFli0yNUXFxuBDRERUiJ9+ugIDg12S2gcf+CIszBhz5vCyljZi8CEiInqNIAiYMGEZTEz+FWuRkW4YPrw9unQBuneXsTl6Jww+REREr/j333+xdOlSmJi8rC1bNgyJifZISeEoj7Zj8CEiIvqfI0eO4PTp0+L2f//ZYu3a77FxowKBgXzWVlnA4ENERDrvt98ycflywGu1boiKaoDatXNHeTjSUzYw+BARkU67efMmLl/eJKnNmTMWHTuawcyMozxlDYMPERHppD17BBw9+ivKl48VazdvfoA7d7pj0yaO8JRVDD5ERKRzkpOTcenSzyhf/mVt48YBmDvXiYGnjGPwISIinREaCmzeHInatQ+JtexsAxw75ou5c/UZenQAgw8REemE3btzcP58AGrXzhFrMTHt0bu3G6ZPl7Ex0igGHyIiKvM2bYrDzZtrYfDKp96oUaNgbW0tX1MkCwYfIiIqk0JDgcBAwMNjF/T1r4j1f/6pjqtXv0GjRgpe2tJBDD5ERFQm/fRTKnr0mCupHT/+NRISauLGjdxQxOCje/TkboCIiEjdfvrpUr7Q88EHfggPr4mgIMDNjevz6KoyGXyWLl0KFxcXmJiYoEWLFjh79qzcLRERkQYIgoCFCxfCwGCPWDt58iNs2eKP7t2NAOSO8kREcLRHV5W54LN161aMGTMG/v7+uHDhAho0aABPT08kJibK3RoREZWQiRMBZ+dHmDZtGpKSksT62rXeePjwE8yeLV9vVLooBEEQ5G5CnVq0aIFmzZphyZIlAAClUgknJyd8//338FVhXDMlJQXW1tZITk6GlZVVSbdLRETvKDQUWL48DC1b/inWnj+3w7x5w9CypQIRETI2Rxqj6ud3mRrxyczMxPnz5+Hh4SHW9PT04OHhgcjIyAJ/JiMjAykpKZIvIiIq/UJDgVatMnDx4lRJ6ImI6IlWrYajZUsF5/FQPmUq+Pz777/IyclBxYoVJfWKFSsiISGhwJ8JCAiAtbW1+OXk5KSJVomI6B2EhgITJ96Ah0egpL5kyTh4e9fjPB4qVJkKPsXh5+eH5ORk8SsuLk7uloiIqBATJwKWlgIOHFiLzz/fItbLl2+EsDB/rFljyrBDb1Sm1vGpUKEC9PX18ejRI0n90aNHcHBwKPBnjI2NYWxsrIn2iIjoHYSGAitWPMWPPy6S1N97bxB6966MESNkaoy0Spka8TEyMkKTJk1w9OhRsaZUKnH06FG4ubnJ2BkREb2LiROBuXP/wMiRL0NPZqYpfvrpJ/TuXVnGzkjblKkRHwAYM2YM+vfvj6ZNm6J58+b4+eefkZqaim+//Vbu1oiIqBiys7NhZDQTn3zysnbkyKcYO7YZ9MrU/76TJpS54PPll1/i8ePHmDx5MhISEtCwYUMcPHgw34RnIiIq/TZtuoebN0MktfDwMRg71pJzeahYytw6Pu+K6/gQEZUOCxduQ1LSdXG7Vq1a6NOnj4wdUWmm6ud3mRvxISIi7bZz53NER8+T1Pbv74uzZ6vL1BGVJbw6SkREsgsNBdzdgbVrL+QLPTNmTMCzZww9pB4c8SEiItkFBirh7r4AcXHPxdrx421gZ9cazZrxSeqkPgw+REQki9BQIDAQsLJKgKfnSsm+U6e+x5gx5TiBmdSOwYeIiGQxfjxQvfp+NG9+Tqw9fFgJ69cPRlqaQsbOqCxj8CEiIo2aOBFYuvQFRo+eLalv3doLt27VxdixMjVGOoHBh4iINCY0FNi9+xpGj94uqe/cOR6BgSa8tEUljsGHiIg0QhAEnDixCl98kSDWzp5tiqdPP8PlyzI2RjqFwYeIiErc9u3/4dq1xXh1XbmsrCF4+rQS79gijWLwISKiEvXTT+EwMDghbr94YYGZM0dDjw/aIhkw+BARUYnYvTsLUVGzYPDKJ82BA50wYUITPlyUZMPgQ0REanfnzh1ERa2X1DZs8MG8eRacwEyyYvAhIiK1WrBgE1JSborbd+7Uwbp1X8DfX8amiP6HwYeIiNRi585niI6eL6kFB3vBxKSqTB0R5cerrERE9E5CQ4G2bc/mCz1r1kyEqWlVBAXJ1BhRATjiQ0RExaZUKnHmzBy0afNCrB050g45Oa1w/76MjREVgsGHiIiK5eHDh/jll19gZPSy9ssvI+HsbMu1eajUYvAhIqIiCw0NxcWLF8XthAQnhId/i2XLFLxri0o1Bh8iIlJZeno6gl6btLN581coV84V16/L1BRRETD4EBGRSqKjo7Fz505JbdYsXxgZGXMCM2kNBh8iInojQRAQFLQcL148FmvnzrXEvn2e0NMDtm8HL2+R1uDt7EREVKh///0X06ZNk4SeYcOG4YMPPGFhAfj6MvSQdlEIgiDI3URpkpKSAmtrayQnJ8Pq1ccIExHpmMmTj0Jf/w9xOy3NFu7u36NrV4WMXREVTNXPb17qIiIiUWgoMGdOJjw8AqCv/7J+5UpX7NjRULa+iNSFwYeIiES//HILHh4bJbX163/E/PnmMnVEpF4MPkREBEEQsGDBBjRtekesxcbWR/fuPfhwUSpTGHyIiHRccnIyfv75Z0ltzZoBqFLFiROXqcxh8CEi0mF//vknwsLCxO3sbAOsW+cLc3N9PnaCyiQGHyIiHbR7dw4uXAiEvn62WGvfvj3c3NwwfbqMjRGVMK7jQ0SkY+Li4hAVNUMSelauHAU3NzcZuyLSDI74EBHpkF27duHKlSvi9p071bBhQ1/4+nJtHtINDD5ERDogLS0Nc+bMkdQ2bOiD9PRa+O03rr5MuoPBh4iojIuKisLu3bsltZkz/ZCVZQQ3N4Ye0i0MPkREZZQgCFi8eDGePn0q1k6d+hBHj3rAxASoUQO8c4t0Dic3ExGVAaGhgLt77ncASExMxLRp0yShZ/ny4Th2zAPlygFbtwLXr3O0h3QPR3yIiMqAwEAgMjL3u4nJIURGRor7/vuvAlatGo4xYxSYOVPGJolKgSKP+Ojr6yMxMTFf/cmTJ9B/9Yl2RESkMb6+wEcfZcLTc6ok9OzY0QOLFnmjUSOGHiKgGCM+giAUWM/IyICRkdE7N0REREUTGgqsWXMDHh5bJPWFC8fh6VNTmJpyLg9RHpWDz6JFiwAACoUCq1evhoWFhbgvJycHJ0+eRO3atdXfIRERFUoQBBw7FozGjePE2oULDfH3310REpJ76cvXl3N5iPKoHHwWLFgAIPdfshUrVkguaxkZGcHFxQUrVqxQf4dERFSgpKQkLFy4ELa2L2u//DII8fGVxbV5GHiIpFQOPnfv3gUAtG3bFrt27YLtq/+mERGRRp0+fRpHjhwRt7OyjBEQMBaAPkd4iN6gyHN8jh8/XhJ9EBGRCnbvzsalS7OgULycb3n9ekf06dMcLVrwshbR2xQ5+AwYMOCN+9euXVvsZoiIdF1o6Mt5OYB0js69e/cQFRUCxSuP1QoPH43Ro614WYtIRUUOPq8uhgUAWVlZiI6ORlJSEtq1a6e2xoiIdNGr6/EAL/+ckbEd165dE4+7ebMm9uz5Gs+eydQokZYqcvD57bff8tWUSiWGDRuGGjVqqKUpIiJd8uooj6+vdMRn/vxUtG07F69kHvz6a1/ExlbnLepExaAQCluYp4hu3LiBNm3aID4+Xh2nk01KSgqsra2RnJwMKysrudshIh3g7p47suPmBkREvKxfuHABe/fulRybmTkBx48bci4P0WtU/fxW2yMrbt++jezsbHWdjohIZ7w+yqNUKhEY+DOysl5exwoP/xjh4W3zhSMiKpoiB58xY8ZItgVBQHx8PPbv34/+/furrTEiorLs1ctbeROTQ0MBT88EuLuvlBy7b98InDtXniswE6lBkYPPxYsXJdt6enqws7PDvHnz3nrHFxER5Qacr78Gnj/PDT95l6x27/4d7u5/icfFxzvg11+HYMsWBVdgJlITtc3xKSs4x4eISlrenB4LC2DjRiA7+wWuXJktOWbbtl6IiakLX1/w4aJEKijxOT6JiYm4ceMGAMDV1RX29vbFPRURkU55dU6Pre0pHDt2TLI/MHA8DAxMxMdOEJH6FDn4pKSkwNvbG5s3b4ZSqQQA6Ovr48svv8TSpUthbW2t9iaJiMqSLl2Azp0FTJs2TVK/cKEJzp/vBBcXYPZshh6ikqBX1B8YPHgwzpw5g/379yMpKQlJSUnYt28fzp07h++++64keiQiKlMePHiQL/Rs3NgHoaGd4OwMXL/O0ENUUoo8x8fc3BxhYWH46KOPJPVTp06hQ4cOSE1NVWuDmsY5PkRUkn799Vfxoc95liyZhCdP9GFrCwQHM/QQFUeJzfEpX758gZezrK2t+cR2IiLkv1UdADIyMhCY9xyK/zl3rjGePOmMx49laJJIRxX5UtekSZMwZswYJCQkiLWEhASMHTsWP/30k1qbIyLSRq8/b+vixYv5Qk9m5vd48qQz1+Uh0rAiX+pq1KgRbt26hYyMDDg7OwMA7t+/D2NjY9SqVUty7IULF9TXqYbwUhcRvauJE4FFi4CRIwEjo6n59k+Z4s8VmInUrMQudXXt2hUKheKdmiMiKsuOHweMjZ/AyGiJpH7lSlfs3NkQFhZcgZlILkUOPlOmTCmBNoiIyo7evffgv/8uSWrLl/th4EAjuLlxBWYiORU5+FSvXh1//fUXypcvL6knJSWhcePGuHPnjtqaIyLSFqGhwOzZOWjffoakfvNmTWzc+DWA3JEgXt4ikleRg09sbCxycnLy1TMyMvDPP/+opSkiIm2zZs0NtG+/RVL79dchuHOnEhQKwNGRl7eISgOVg09oaKj457CwMMkt7Tk5OTh69CiqVaum3u6IiLTAzJnz0Ljxc0mtYcPJaNSIDxclKm1UvqtLTy/3zneFQoHXf8TQ0BAuLi6YN28eOnXqpP4uNYh3dRGRqp49e4b58+dLaidOeOD48Q951xaRhqn6+a3yOj5KpRJKpRLOzs5ITEwUt5VKJTIyMnDjxo0SCz2xsbEYOHAgqlWrBlNTU9SoUQP+/v7IzMyUHHf58mW0atUKJiYmcHJyQlBQUIn0Q0R07NixfKEnM3MsRo36UJzATESlT5Hn+Ly+1LomxMTEQKlUYuXKlahZsyaio6MxePBgpKamYu7cuQByk1779u3h4eGBFStW4MqVKxgwYABsbGwwZMgQjfdMRGVP7orMAjw9pc/Zevy4ApYu9YabGzBzJi9rEZVmRV7A8PUH671u8uTJ79SQqubMmYPly5eLd5EtX74cEydOREJCAoyMjAAAvr6+2L17N2JiYlQ+Ly91EdGrXn38xIoVcWjRYq1k//79ffHwYXVYWABBQQw9RHIpsQUMf/vtN8l2VlYW7t69CwMDA9SoUUNjwSc5ORnlypUTtyMjI/Hxxx+LoQcAPD09MXv2bDx9+rTQ54hlZGQgIyND3E5JSSm5polI6+Q9fuLYsbVo0SJOsm/atJ+gVOpxPg+RFily8Ll48WK+WkpKCry8vNC9e3e1NPU2t27dwuLFi8XLXEDu88Jev6usYsWK4r7Cgk9AQACmTs2/pDwREQCMHfsCly/PltTOn2+O06c7QqkETEw4n4dImxT5IaUFsbKywtSpU4v8kFJfX18oFIo3fr1+merBgwfo0KEDevXqhcGDB79z735+fkhOTha/4uLi3v5DRFQmhYYC7u653wHgr7/+yhd69u79AXv3doS9PeDmBmzdystbRNqkyCM+hckLDkXh4+MDLy+vNx5TvXp18c8PHz5E27Zt4e7ujlWrVkmOc3BwwKNHjyS1vG0HB4dCz29sbAxjY+Mi9U1EZdOrT1W/eFE6EpyTo4fp039CuXLgYyeItFiRg8+iRYsk24IgID4+HuvXr0fHjh2LdC47OzvY2dmpdOyDBw/Qtm1bNGnSBMHBweK6Qnnc3NwwceJEZGVlwdDQEABw+PBhuLq6FnqZi4gIeDmBuXJloGrVx/D0XCbZHxXVEwcP1gMApKdzPg+RNivyXV2vz6PR09ODnZ0d2rVrBz8/P1haWqq1QSA39LRp0wZVq1bFunXroK+vL+7LG81JTk6Gq6sr2rdvj/HjxyM6OhoDBgzAggULinQ7O+/qItI97u65Iz1ffrkTdepES/YdPjwB48YZ4swZYNEiYOTI3FvWiah0UfXzu8jBRw4hISH49ttvC9z3avuXL1+Gt7c3/vrrL1SoUAHff/89xo8fX6TXYvAh0g2v3qauVGYjKkqaZh49qoMOHb7g5SwiLVGiwScpKQm3bt0CANSsWRM2NjbFbrS0YfAh0g15ozzdul1Dw4bbJfuWLx+KR48q8jZ1Ii1SIuv4xMbGwtvbG2FhYeJIi0KhQIcOHbBkyRK4uLi8U9NERJri6wucPRsIQ8MMSX3KlMkAFLCw4G3qRGWRysEnLi4OLVu2hKGhIaZPn446deoAAK5du4bly5fDzc0Nf/31F6pUqVJizRIRqUNycjIuXvwZ/7sPAgBw/bon+vRpCVdXQKEAZs/mXVtEZZHKl7oGDhyIW7duISwsDCYmJpJ96enp6NChA2rVqoXVq1eXSKOawktdRGXb4cOHEfHa9aujR8fhxx9NGXSItJjaL3UdPHgQW7duzRd6AMDU1BTTp0/HV199VbxuiYhKmFKpxPTp0yU1U9NK2L17CNfkIdIhKq/c/O+//75xDk/16tXx33//qaMnIiK1io2NzRd6vLy8sHv3EHHBQiLSDSoHn0qVKuHatWuF7o+Ojn7jCslERJqU9/iJoKAVWLdunWRfWNhkVK1aFb6+L1dhJiLdoPKlrm7duuHHH3/E0aNH8622nJiYiPHjx6Nbt27q7o+IqFjmzUuHp2cQ0tNf1q5d+xCXL3tg9v8ev9WlCy9xEekalSc3P336FC1atEBCQgK++eYb1K5dG4Ig4Pr169i0aRMcHBzw559/oly5ciXdc4ni5GYi7RcZGYlDhw5JauHhoxEebsW1eYjKKLVPbra1tcWZM2cwYcIEbNmyBUlJSQAAGxsb9OnTB7NmzdL60ENE2k0QBEybNk1Se/HCBHp64zF6NJCRwctaRLquWCs3C4KAx48fA8h90KhCoVB7Y3LhiA+RdkpISMDKlSsltW3bvsC1a3VgYQE8eyZTY0SkESWycnMehUIBe3v7YjdHRKROW7duRUxMjKQ2d+5EdOhggPv3cx8sSkQEFDP4EBGVBllZWZg1a5akdvlyfRw40ANbtnDiMhHlx+BDRFrpypUr2LVrl6RWp85whIXZMfQQUaEYfIhI60ybNg2vT0+cO9cfz54BX3whU1NEpBUYfIhIazx9+hSLFi2S1I4f/wxnzjTFmDEyNUVEWkWl4PP6f2jeZCRnERJRCVi27AAePz4rqQUE+CIjwxhubsDMmTI1RkRaRaXgs2DBApVOplAoGHyISK0KerjovXvO2LDhW9jbA5aWXJuHiFSnUvC5e/duSfdBRJTP7du3sWHDBkltz54BSEtzwo4dnMBMREVX7Dk+mZmZuHv3LmrUqAEDA04VIiL1WrJkCZ48eSKpNWw4Gf7+ZWfBVCLSPJWfzp4nLS0NAwcOhJmZGd5//33cv38fAPD9998jMDBQ7Q0SkW5JTU3F1KlTJaEnIqI1GjXyR9euDD1E9G6KHHz8/PwQFRWF8PBwmJiYiHUPDw9s3bpVrc0RkW45deoU5s6dK6kdP+4Db+82vKxFRGpR5GtUu3fvxtatW9GyZUvJM7ref/993L59W63NEZFuKOjhoikploiMHMMnqRORWhV5xOfx48cFPqcrNTW1TD2slIg04+HDh/lCT/XqvREZOQZt2wLu7kBoqEzNEVGZU+Tg07RpU+zfv1/czgs7q1evhpubm/o6I6Iyb8OGDfjll18ktUOHJqFv3/cQEQEcPw5ERgKcPkhE6lLkS12zZs1Cx44dce3aNWRnZ2PhwoW4du0aIiIicOLEiZLokYjKmMzMTAQEBEhqt241wrlzXTB79suar29u6OE6PUSkLgrh9QfeqOD27dsIDAxEVFQUnj9/jsaNG2P8+PGoX79+SfSoUSkpKbC2tkZycjKsrKzkboeozLl48SJCX7t2tXr1CPzzT3m4uYFzeoioWFT9/C7WAjw1atTINzxNRPQ2U6dOzVcLDPTHmDG5l7U4skNEJU2l4JOSkqLyCTlKQkSve/LkCZYsWSKp7d7dFbduNcTWrVyBmYg0R6XgY2Njo/IdWzk5Oe/UEBGVLaGhobh48aKk9sEHfggLM8LGjQw9RKRZKgWf48ePi3+OjY2Fr68vvLy8xLu4IiMjsW7dunyTFYlId+Xk5GDGjBmS2q1bNXD79jfw9we6d5epMSLSaUWe3PzJJ59g0KBB6N27t6S+adMmrFq1CuHh4ersT+M4uZno3f3999/YvHmzpObqOhiLFjnC15ejPESkfqp+fhc5+JiZmSEqKgq1atWS1P/++280bNgQaWlpxeu4lGDwIXo38+fPx7NnzyS1hg0n8zlbRFSiVP38LvIChk5OTgXe0bV69Wo4OTkV9XREVEY8f/4cU6dOlYSeo0c/wZQp/pg9m6GHiEqHIt/OvmDBAvTs2RMHDhxAixYtAABnz57FzZs3sXPnTrU3SESlX3h4eL4FTIOCfkTDhuZwc+Nt6kRUehQ5+Hz66ae4efMmli1bhpiYGABA586dMXToUI74EOmYgh8uWh7z54/4334uSEhEpUuxFjCsUqUKZs2ape5eiEiL/PPPP1izZo2k9s033+Dq1Rr4/ffc0MORHiIqbYoVfJKSkrBmzRpcv34dAPD+++9jwIABsLa2VmtzRFQ6jRsXDHPz+5LaTz/9BD09PdSowbu2iKj0KvJdXefOnYOnpydMTU3RvHlzAMBff/2F9PR0HDp0CI0bNy6RRjWFd3URFe7FixeY/epTRAGcOdMMd+58iv9d+SYikkWJ3c7eqlUr1KxZE7/88gsMDHIHjLKzszFo0CDcuXMHJ0+efLfOZcbgQ1Swc+fOYf/+/ZLaqVMjkZZmy7V5iEh2JRZ8TE1NcfHiRdSuXVtSv3btGpo2bcp1fIjKmNBQ4OJF6cNFlUoFpk6dLFNHRET5ldg6PlZWVrh//36+elxcHCwtLYt6OiIqxR4/fpwv9Ozb1wNNmjD0EJF2KvLk5i+//BIDBw7E3Llz4e7uDgA4ffo0xo4dm+8xFkSkvXbt2oUrV65Iatu2TUBAgCEvaxGR1ipy8Jk7dy4UCgX69euH7OxsAIChoSGGDRuGwMBAtTdIRJqVnZ2NmTNnSmqPHrmiQ4ev4O8vU1NERGpS5Dk+edLS0nD79m0AQI0aNWBmZqbWxuTCOT6ky3799Tru3t0mqX333XdwcHCQqSMiItWo+vldrHV8gNyHldavX7+4P05EpcyMGbORk/NCUps8eTIUitznbIWGAoGB4B1cRKTVVA4+AwYMUOm4tWvXFrsZItK8HTtScPXqAkntwgVP7NnTUlILDAQiI3O/M/gQkbZSOfiEhISgatWqaNSoEYp5dYyISpnDhw/j6lXpw7R27RqH6dNN8x3r6/tyxIeISFupPMfH29sbmzdvRtWqVfHtt9/im2++Qbly5Uq6P43jHB/SBQU9XNTUtCLGjRsqU0dERO9G7ev4LF26FPHx8Rg3bhz27t0LJycnfPHFFwgLC+MIEJEWuXfvXr7QU7Nmf4YeItIJxb6r6969ewgJCcGvv/6K7OxsXL16FRYWFuruT+M44kNlWVDQKqSnx0tqU6f+hJYt9RARUcgPERFpgRK/q0tPTw8KhQKCICAnJ6e4pyEiDUhPT0dQUJCk5u7ujvT0/0PLlpy3Q0S6o0jBJyMjA7t27cLatWvxxx9/oFOnTliyZAk6dOgAPb0iP/2CiEpYaCiwadOfqFMnTFJ///1R+L//swbAO7SISLeoHHyGDx+OLVu2wMnJCQMGDMDmzZtRoUKFkuyNiN7Bnj0CLl2ahjp1XtaMjY1Rt64vAgMBIyOGHiLSPSrP8dHT04OzszMaNWokLmhWkF27dqmtOTlwjg+VBY8ePcKKFSsktV69eqFu3bpwd89dj8fNDZzXQ0Rlhtrn+PTr1++NgYeISodt27bh+vXrktrEiRNhYJD7rzvX4yEiXVakBQyJqPTKysrCrFmzJLV69eqhZ8+eAKSPnOBIDxHpqmLf1UVEpUd0dDR27twpqQ0fPhx2dnbiNh85QUTE4EOktUJDgXHjgC++mAF9femSEv7+/vmO5yUuIqJ3WMCwrOLkZtIWjRsnoWvXhZLap59+imbNmsnUERGRfEp8AUMiks/BgwfRtesZSW38+PEwMTGRqSMiIu3A4EOkRZRKJaZPny6pmZs74ccfB8jUERGRduFyy0Ra4s6dO/lCz4ABA9459ISGAu7uud+JiMo6jvgQaYGlS5fi33//ldQmT56slrW1eLcXEekSrRvxycjIQMOGDaFQKHDp0iXJvsuXL6NVq1YwMTGBk5NTvocyEmmbtLQ0TJ06VRJ6bt/+GI0a+attQVFf39xVnHm3FxHpAq0b8Rk3bhwcHR0RFRUlqaekpKB9+/bw8PDAihUrcOXKFQwYMAA2NjYYMmSITN0SFd/p06dx5MgRSW3MmDGwtLRU6+t06cKRHiLSHVoVfA4cOIBDhw5h586dOHDggGTfxo0bkZmZibVr18LIyAjvv/8+Ll26hPnz5zP4kFYRBAHTpk2T1CwsLODj4yNTR0REZYfWBJ9Hjx5h8ODB2L17N8zMzPLtj4yMxMcffwwjIyOx5unpidmzZ+Pp06ewtbUt8LwZGRnIyMgQt1NSUtTfPJEKch8pEQ9Pz1WS+ldffQVXV1eZuiIiKlu0Yo6PIAjw8vLC0KFD0bRp0wKPSUhIQMWKFSW1vO2EhIRCzx0QEABra2vxy8nJSX2NExXBoUMb84WeSZMmMfQQEamRrMHH19cXCoXijV8xMTFYvHgxnj17Bj8/P7X34Ofnh+TkZPErLi5O7a9BVJjQUKBVq0xMnToVdna3xHq5cg3h7+8PfX39dz4/b1UnInpJ1ktdPj4+8PLyeuMx1atXx7FjxxAZGQljY2PJvqZNm+Lrr7/GunXr4ODggEePHkn25207ODgUen5jY+N85yXSlF9/vQQPjz2S2ogRI1C+fHm1nJ+3qhMRSckafOzs7CRPjy7MokWLMGPGDHH74cOH8PT0xNatW9GiRQsAgJubGyZOnIisrCwYGhoCAA4fPgxXV9dC5/cQySF3Lg/g6TkV9etL9xX0cNF3wQeTEhFJaeVDSmNjY1GtWjVcvHgRDRs2BAAkJyfD1dUV7du3x/jx4xEdHY0BAwZgwYIFRbqriw8ppZIUGgp4e/+HQYMWS+qdO3dG48aNZeqKiEj7qfr5rRWTm1VhbW2NQ4cO4e7du2jSpAl8fHwwefJk3spOpUpo6N58ocfPz6/Q0MM5OkRE6qWVIz4liSM+VBJycnIkl2sBoFq1aujXrx+Al5e/fH2lc3Hc3XPn6Li5ARERmuyYiEi76NyID1FptX79zXyhZ9CgQbCx6SeO5rw6CflVfJwEEZF6ac0ChkTaaP/+/bhz55yklvdw0V69XoadwiYh83ESRETqxeBDVAJevHiB2bNnS2qVKrXDkCGtxO1Xww4DDhGRZjD4EKnZtWvXsH37dklt/PjxMDExkdQYdoiINI9zfIjUIPfuKwFBQSsloadZs2bw9/fHoUMmxb47i3d2ERGpD4MP0TvICyXTpj2Bp+c0pKe/fC5cRMR3yM7+FEDBk5dVDTSFTXwmIqKiY/AhegeBgYCxcTg6d14i1iwtLREW9hMOHXIQw0pBd2epGmh4ZxcRkfpwHZ/XcB0fepu828/HjctCVNQsyb68FZgLW5enoPO86RgiIlKNqp/fDD6vYfCht6ldG8jKuoN+/dZL6j4+PrCwsJCpKyIi3abq5zfv6iIqotatN8HR8aa4XbduXfTq1UvGjoiISFUMPkRvkXdJasyYFFy9ugCOji/3nTnjhUaNqsrXHBERFQmDD9FbBAYCOTlncfXqAbGmUCgQFjYBEREGSEriHB0iIm3B4EP0Bjk5OejYcQ6Uygyx9vffn+DLLz9Cw4YFP2aCiIhKLwYfokI8ePAAq1evltROnPgBx4/b4O7d3Kelc6SHiEi7MPgQFWDPnj24dOmSuO3s7AwvLy/s3avAixcc5SEi0lYMPkSvSE9PR1BQkKT21VdfwdXVFQCfr0VEpO0YfIj+58qVK9i1a5ek5uvrC2NjY5k6IiIidWPwIZ2We6u6gC5dliEj41+x7ubmhvbt28vYGRERlQQGH9Jpixf/C0/Ppch4edMWhg0bBnt7e/maIiKiEsOHlJLOOnLkCD76aKm4bWtri8mTJxcaelR9mjoREZVeHPEhnZOZmYmAgABJrVu3bmjQoMEbf+7Vp6lzgjMRkXZi8CGdkPfYiWHDbuLOnU2SfWPHjoWZmdlbz+HrywULiYi0HZ/O/ho+nb1scncX8N57v6JatVix9sEHH6B79+7yNUVERGqj6uc35/hQmfTqfJzk5GR4ek6ThJ4BAwYw9BAR6SBe6qIyKW8+zubNkbh48ZBYNzAwgK+vL/T19WXsjoiI5MLgQ2XSuHE5uHAhAPr6OWItJqY9Nm92k7ErIiKSG4MPlTmbNsXh5s21eHVQ58SJURg1ylq+poiIqFRg8KEyIzQU2L9/Fxwdr4i16tWr45tvvoFCoZCxMyIiKi0YfKhMSE1NxcWLc+Ho+LJWo8bX+OabmvI1RUREpQ6DD2m9S5cuYc+ePZKan58fjIyMZOqIiIhKKwYf0kp5Dxft3HkRMjOTxPqdOx+hZ89PwMxDREQFYfAhrbRkSSI8PZcjM/NlzdvbGxUqVJCvKSIiKvUYfEjrhIWF4cMP/xS37ezsMGzYME5gJiKit2LwIa2RkZGBwMBASa1nz56oV6+eTB0REZG24SMrqNR69bETN27cyBd6xo0bx9BDRERFwhEfKpVCQ4GvvwaePxdw7FgwLl6ME/c1atQIXbp0kbE7IiLSVgw+VCoFBgIGBk8xZcoiSX3QoEGoXLmyTF0REZG2Y/ChUiP3FnXA1xfw8voD8fFHxX0mJiYYO3Ys9PR4dZaIiIqPwYdKjcBA4OzZbFy8OFNS79ixI5o3by5TV0REVJbwf59JFq9OXM4zYsQ9/PSTNPSMGTOGoYeIiNSGIz4ki8BAIDIy93uXLsC2bdtw8+Z1cX+tWrXQp08fGTskIqKyiMGHZOHrmxt6fHyeY+rUeZJ9ffv2RfXq1WXqjIiIyjIGH9K4vEnMgwZdQHT0Xsm+CRMmwNDQUKbOiIiorGPwIY0KDQW++UaJIUMWIC7uuVhv06YNWrduLWNnRESkCzi5WYcVNMG4pF9v1KgE+PhMh6Xly9Dz/fffM/QQEZFGMPjosFcnGJeUV8PVnj370b//SnFfpUqVMHnyZJQrV67kGiAiInoFg48O8/UF3Nxyv7+rwkaPAgOBCxde4OLFqXB2PifWe/XqhSFDhvCJ6kREpFEKQRAEuZsoTVJSUmBtbY3k5GRYWVnJ3Y7WcHfPHT1ycwMiInJroaHAokXX0arVNsmx48ePh4mJiQxdEhFRWaXq5zdHfEglb5sP9ProkSAICA9fJQk9TZs2hb+/P0MPERHJhiM+r+GIT8EKGtEpzH///YfFixdLakOGDEGlSpVKsEMiItJlqn5+83Z2UknegoNvmw8UHh6OEydOiNsWFhYYPXo0Hy5KRESlAoMPqaRLl9yvwmRlZWHWrFmSWqdOndCkSZMS7oyIiEh1DD70zu7cuYP169dLaj4+PrCwsJCpIyIiooIx+FCR5T1ywtcXeP58E27evCnuq1OnDr744gsZuyMiIiocgw8VWWAgEB39DBcvzpfU+/fvDxcXF3maIiIiUgGDDxXZwIF/4Z9/fpfUJk6cCAMD/uNERESlGz+pSGVKpRJz585Fenq6WGvXrh1atWolY1dERESqY/AhlTx8+BC//PKLpDZy5EjY2trK1BEREVHRcXEVkihohebQ0FBJ6HFycsLkyZMZeoiISOtwxIckXn1i+//9XzqCgoIk+7/66iu4urrK1B0REdG7YfAhibwVmr/7LhpBQTtf2+cLY2NjmTojIiJ6dww+JNG5s4B//lmO2NjHYq1ly5bw9PSUsSsiIiL1YPAh0b///oulS5dKakOHDkXFihVl6oiIiEi9tGpy8/79+9GiRQuYmprC1tYW3bp1k+y/f/8+PvvsM5iZmcHe3h5jx45Fdna2PM1qmaNHj0pCj42NDX766SeGHiIiKlO0ZsRn586dGDx4MGbNmoV27dohOzsb0dHR4v6cnBx89tlncHBwQEREBOLj49GvXz8YGhrme3gmvVTQw0W7du2Khg0bytMQERFRCVIIgiDI3cTbZGdnw8XFBVOnTsXAgQMLPObAgQPo1KkTHj58KI5SrFixAuPHj8fjx49hZGSk0mulpKTA2toaycnJsLKyUtvvUBrdunULGzdulNR+/PFHmJuby9QRERFR8aj6+a0Vl7ouXLiABw8eQE9PD40aNUKlSpXQsWNHyYhPZGQk6tevL7k04+npiZSUFFy9erXQc2dkZCAlJUXyVdYJgoD169dLQk/9+vXh7+/P0ENERGWaVgSfO3fuAACmTJmCSZMmYd++fbC1tUWbNm3w33//AQASEhLyzUfJ205ISCj03AEBAbC2tha/nJycSui3kFfewoQ7diRj2rRp4t8pAAwYMAA9evSQsTsiIiLNkDX4+Pr6QqFQvPErJiYGSqUSQO6DMHv27IkmTZogODgYCoUC27dvf6ce/Pz8kJycLH7FxcWp41crdQIDAUH4E1ev/izWDAwMMGnSpDIb9oiIiF4n6+RmHx8feHl5vfGY6tWrIz4+HgBQt25dsW5sbIzq1avj/v37AAAHBwecPXtW8rOPHj0S9xXG2Ni4zC/Kl5OTgw4dAiEIL+9wa9++Pdzc3GTsioiISPNkDT52dnaws7N763FNmjSBsbExbty4gY8++ghA7t1IsbGxqFq1KgDAzc0NM2fORGJiIuzt7QEAhw8fhpWVlSQwabvQ0NzRG19foEuXtx8fFxeHtWvXSmonToyCv791CXVIRERUemnF7exWVlYYOnQo/P394eTkhKpVq2LOnDkAgF69egHIHcGoW7cu+vbti6CgICQkJGDSpEnw9vYuUyM6rz5L623B57fffsPly5fFbQuLati5sy98fRUl3CUREVHppBXBBwDmzJkDAwMD9O3bF+np6WjRogWOHTsmPiFcX18f+/btw7Bhw+Dm5gZzc3P0798f06ZNk7lz9cp7lpavb+HHpKWlicEwT58+fVCrVi34+JRwg0RERKWYVqzjo0navo5PVFQUdu/eLan5+fmpvI4RERGRNlL181trRnzozQRBwOLFi/H06VOxlpPzIY4c8UCjRqrNByIiIirrtGIdH12XtwZPaGjB+xMTEzFt2jRJ6FmyZDgWLPAQ5wMRERERR3y0wpsmNB86dAiRkZHitolJBdSuPRy1ainQti1w/Pib5wMRERHpEgYfLVDQhObMzEwEBARIjtuxowcsLetj/Higa1cNN0lERKQFGHy0QJcu0pGeGzduYMuWLZJj6tcfh7AwU47uEBERvQGDjxYRBAEhISHiatUA0LBhQ3T93/AOH7dFRET0Zgw+WiIpKQkLFy6U1AYNGoTKlSvL1BEREZH2YfDRAqdPn8aRI0fEbX19Yxw4MBaNGumDuYeIiEh1DD6lWHZ2NmbNmoVX15g8f74j/v23ucqPrSAiIqKXGHxKqXv37iEkJERSmzdvNBwdrRAU9PbHVhAREVF+DD6l0Pbt23Ht2jVxu2bNmrC0/Br16r18KjtHeoiIiIqOwacUSU1Nxdy5cyW1vn37Ijq6ujjCw8BDRERUfAw+pcSFCxewd+9eSW3ChAkwNDTEN98UvnIzERERqY7BR2ZKpRI///wznj17JtY+/vhjtG3bVtwuaOVmIiIiKjoGHxlt2ZKAGzdWSmojRoxA+fLlJTXO6SEiIlIPBh+ZHDhwADdunBW3HRwcMGTIECgUChm7IiIiKtsYfDQsIyMDgYGBkpqLSy/0719Xpo6IiIh0B4OPBl2/fh3btm2T1MaPHw8TExOZOiIiItItDD4asnfvXly4cEHcbtq0KT777DMZOyIiItI9enI3oCuuX78u/nnIkCFi6AkNBdzdc78TERFRyVIIrz4IipCSkgJra2skJyfDyspKbeeNjY3Fw4cP0bJlS+jpvcyb7u65a/S4uQEREWp7OSIiIp2i6uc3L3VpiIuLC1xcXPLV27YFrlzJ/U5EREQli5e6ZHb8OPD8ee53IiIiKlkMPjLz9c29zMVVmYmIiEoeL3XJjKsyExERaQ5HfIiIiEhnMPgQERGRzmDw0RCu10NERCQ/Bh8NCQzMXa/ntcd0ERERkQYx+GgI794iIiKSH+/q0hDevUVERCQ/jvgQERGRzmDwISIiIp3B4ENEREQ6g8GHiIiIdAaDDxEREekMBh8iIiLSGQw+REREpDMYfIiIiEhnMPgQERGRzmDwISIiIp3B4ENEREQ6g8GHiIiIdAaDDxEREekMPp39NYIgAABSUlJk7oSIiIhUlfe5nfc5XhgGn9c8e/YMAODk5CRzJ0RERFRUz549g7W1daH7FcLbopGOUSqVePjwISwtLaFQKORup0SlpKTAyckJcXFxsLKykrsdegXfm9KJ70vpxPel9NLkeyMIAp49ewZHR0fo6RU+k4cjPq/R09NDlSpV5G5Do6ysrPgfi1KK703pxPeldOL7Unpp6r1500hPHk5uJiIiIp3B4ENEREQ6g8FHhxkbG8Pf3x/GxsZyt0Kv4XtTOvF9KZ34vpRepfG94eRmIiIi0hkc8SEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfHbZ//360aNECpqamsLW1Rbdu3ST779+/j88++wxmZmawt7fH2LFjkZ2dLU+zOiYjIwMNGzaEQqHApUuXJPsuX76MVq1awcTEBE5OTggKCpKnSR0SGxuLgQMHolq1ajA1NUWNGjXg7++PzMxMyXF8b+SxdOlSuLi4wMTEBC1atMDZs2flbkmnBAQEoFmzZrC0tIS9vT26deuGGzduSI558eIFvL29Ub58eVhYWKBnz5549OiRLP0y+OionTt3om/fvvj2228RFRWF06dPo0+fPuL+nJwcfPbZZ8jMzERERATWrVuHkJAQTJ48Wcaudce4cePg6OiYr56SkoL27dujatWqOH/+PObMmYMpU6Zg1apVMnSpO2JiYqBUKrFy5UpcvXoVCxYswIoVKzBhwgTxGL438ti6dSvGjBkDf39/XLhwAQ0aNICnpycSExPlbk1nnDhxAt7e3vjzzz9x+PBhZGVloX379khNTRWPGT16NPbu3Yvt27fjxIkTePjwIXr06CFPwwLpnKysLKFy5crC6tWrCz3m999/F/T09ISEhASxtnz5csHKykrIyMjQRJs66/fffxdq164tXL16VQAgXLx4Udy3bNkywdbWVvIejB8/XnB1dZWhU90WFBQkVKtWTdzmeyOP5s2bC97e3uJ2Tk6O4OjoKAQEBMjYlW5LTEwUAAgnTpwQBEEQkpKSBENDQ2H79u3iMdevXxcACJGRkRrvjyM+OujChQt48OAB9PT00KhRI1SqVAkdO3ZEdHS0eExkZCTq16+PihUrijVPT0+kpKTg6tWrcrStEx49eoTBgwdj/fr1MDMzy7c/MjISH3/8MYyMjMSap6cnbty4gadPn2qyVZ2XnJyMcuXKidt8bzQvMzMT58+fh4eHh1jT09ODh4cHIiMjZexMtyUnJwOA+O/H+fPnkZWVJXmfateuDWdnZ1neJwYfHXTnzh0AwJQpUzBp0iTs27cPtra2aNOmDf777z8AQEJCgiT0ABC3ExISNNuwjhAEAV5eXhg6dCiaNm1a4DF8X0qHW7duYfHixfjuu+/EGt8bzfv333+Rk5NT4N87/87loVQqMWrUKHz44YeoV68egNx//o2MjGBjYyM5Vq73icGnDPH19YVCoXjjV95cBQCYOHEievbsiSZNmiA4OBgKhQLbt2+X+bcoe1R9XxYvXoxnz57Bz89P7pZ1hqrvzasePHiADh06oFevXhg8eLBMnROVTt7e3oiOjsaWLVvkbqVQBnI3QOrj4+MDLy+vNx5TvXp1xMfHAwDq1q0r1o2NjVG9enXcv38fAODg4JDvzoi8GfgODg5q7LrsU/V9OXbsGCIjI/M906Zp06b4+uuvsW7dOjg4OOS7E4LvS/Gp+t7kefjwIdq2bQt3d/d8k5b53mhehQoVoK+vX+DfO//ONW/EiBHYt28fTp48iSpVqoh1BwcHZGZmIikpSTLqI9v7pPFZRSS75ORkwdjYWDK5OTMzU7C3txdWrlwpCMLLyc2PHj0Sj1m5cqVgZWUlvHjxQuM964J79+4JV65cEb/CwsIEAMKOHTuEuLg4QRBeTqDNzMwUf87Pz48TaDXgn3/+EWrVqiV89dVXQnZ2dr79fG/k0bx5c2HEiBHidk5OjlC5cmVObtYgpVIpeHt7C46OjsLff/+db3/e5OYdO3aItZiYGNkmNzP46KgffvhBqFy5shAWFibExMQIAwcOFOzt7YX//vtPEARByM7OFurVqye0b99euHTpknDw4EHBzs5O8PPzk7lz3XH37t18d3UlJSUJFStWFPr27StER0cLW7ZsEczMzMTASiXjn3/+EWrWrCl88sknwj///CPEx8eLX3n43shjy5YtgrGxsRASEiJcu3ZNGDJkiGBjYyO5I5VK1rBhwwRra2shPDxc8u9GWlqaeMzQoUMFZ2dn4dixY8K5c+cENzc3wc3NTZZ+GXx0VGZmpuDj4yPY29sLlpaWgoeHhxAdHS05JjY2VujYsaNgamoqVKhQQfDx8RGysrJk6lj3FBR8BEEQoqKihI8++kgwNjYWKleuLAQGBsrToA4JDg4WABT49Sq+N/JYvHix4OzsLBgZGQnNmzcX/vzzT7lb0imF/bsRHBwsHpOeni4MHz5csLW1FczMzITu3btL/sdBkxT/a5qIiIiozONdXURERKQzGHyIiIhIZzD4EBERkc5g8CEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfIqISEh4eDoVCgaSkJLlbIaL/YfAhIq01ZcoUNGzYUO42iEiLMPgQUZmXlZUldwtEVEow+BCRbJRKJQICAlCtWjWYmpqiQYMG2LFjB4CXl4mOHj2Kpk2bwszMDO7u7rhx4wYAICQkBFOnTkVUVBQUCgUUCgVCQkIAAAqFAsuXL0eXLl1gbm6OmTNnvrGPvNcKCwtDo0aNYGpqinbt2iExMREHDhxAnTp1YGVlhT59+iAtLU38uYyMDIwcORL29vYwMTHBRx99hL/++qtk/rKISD1keUIYEZEgCDNmzBBq164tHDx4ULh9+7YQHBwsGBsbC+Hh4cLx48cFAEKLFi2E8PBw4erVq0KrVq0Ed3d3QRAEIS0tTfDx8RHef//9fE+DBiDY29sLa9euFW7fvi3cu3fvjX3kvVbLli2FP/74Q7hw4YJQs2ZNoXXr1kL79u2FCxcuCCdPnhTKly8vefDoyJEjBUdHR+H3338Xrl69KvTv31+wtbUVnjx5Ijnv06dPS+YvkIiKjMGHiGTx4sULwczMTIiIiJDUBw4cKPTu3VsMDUeOHBH37d+/XwAgpKenC4IgCP7+/kKDBg3ynRuAMGrUKJV7Kei1AgICBADC7du3xdp3330neHp6CoIgCM+fPxcMDQ2FjRs3ivszMzMFR0dHISgoSHJeBh+i0sNArpEmItJtt27dQlpaGv7v//5PUs/MzESjRo3E7Q8++ED8c6VKlQAAiYmJcHZ2fuP5mzZtWuSeXn2tihUrwszMDNWrV5fUzp49CwC4ffs2srKy8OGHH4r7DQ0N0bx5c1y/fr3Ir01EmsHgQ0SyeP78OQBg//79qFy5smSfsbExbt++DSA3TORRKBQAcucGvY25uXmRe3r9tV7dzqup8tpEVHpxcjMRyaJu3bowNjbG/fv3UbNmTcmXk5OTSucwMjJCTk5OCXdasBo1asDIyAinT58Wa1lZWfjrr79Qt25dWXoiorfjiA8RycLS0hI//vgjRo8eDaVSiY8++gjJyck4ffo0rKysULVq1beew8XFBXfv3sWlS5dQpUoVWFpawtjYWAPd544oDRs2DGPHjkW5cuXg7OyMoKAgpKWlYeDAgRrpgYiKjsGHiGQzffp02NnZISAgAHfu3IGNjQ0aN26MCRMmqHRJqWfPnti1axfatm2LpKQkBAcHw8vLq+Qb/5/AwEAolUr07dsXz549Q9OmTREWFgZbW1uN9UBERaMQBEGQuwkiIiIiTeAcHyIiItIZDD5EVOYNHToUFhYWBX4NHTpU7vaISIN4qYuIyrzExESkpKQUuM/Kygr29vYa7oiI5MLgQ0RERDqDl7qIiIhIZzD4EBERkc5g8CEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfIiIi0hkMPkRERKQz/h8ZE1xgsXhGfQAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[
,\n", + "
,\n", + "
,\n", + "
]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -523,12 +696,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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XgObNm+u2227TLbfcIpfLpcGDB8vpdOqjjz5STEyMOzCcTFpamvLz87V582a1a9dOzZs3V2RkZIOU90RNmzbV9ddfr9tvv12tWrVSamqq/u///k+lpaWaNGmSX8pQiXADAEAN/N0F4L777lN8fLyys7P13XffqUWLFjrjjDN011131ar555JLLtGSJUt0zjnnaN++fXrhhRc0fvz4Bi3z8ebOnSuXy6WrrrpK+/fvV79+/fTBBx+oZcuWfiuDJDmMMebUq9lHSUmJYmNj5XQ6FRMTE+jiAAB87NChQ8rPz1d6erqaNGkS6OKgDk723dXl+s1oKQAAYCuEGwAAQsx1113nMdz8+Md1110X6OLVG31uAAAIMffee69uu+22ap+zQ5cNwg0AACEmISFBCQkJgS5Gg6FZCgAA2ArhBgBgS1acORcn56sB3DRLAQBsJSIiQmFhYfrpp58UHx+viIgI920KYF3GGO3Zs6fa2zzUFeEGAGArYWFhSk9PV2FhoX766adAFwd14HA41K5dO4WHh9frfQg3AADbiYiIUGpqqo4ePRqwey2h7ho3blzvYCMRbgAANlXZvFHfJg4EHzoUAwAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWwlouMnOztaZZ56p5s2bKyEhQaNHj9a2bdtO+brXX39dXbt2VZMmTdSzZ0+9++67figtAAAIBgENN2vWrNGUKVP0n//8R8uXL9eRI0d0/vnn6+DBgzW+Zt26dRo7dqwmTZqkTZs2afTo0Ro9erS2bt3qx5LDTgqdZVqXV6xCZ1mgiwIA8AGHMcYEuhCV9uzZo4SEBK1Zs0ZDhgypdp2srCwdPHhQ77zzjnvZWWedpd69e2v+/Pmn/IySkhLFxsbK6XQqJibGZ2VHcMpZX6CZS7bIZaQwh5Q9pqeyzkwNdLEAACeoy/XbUn1unE6nJKlVq1Y1rpObm6vhw4d7LBsxYoRyc3OrXb+8vFwlJSUeD0A6VmNTGWwkyWWku5ZspQYHAIKcZcKNy+XStGnTNGjQIPXo0aPG9YqKipSYmOixLDExUUVFRdWun52drdjYWPcjJSXFp+VG8MovPugONpUqjNGO4tLAFAgA4BOWCTdTpkzR1q1btWjRIp++78yZM+V0Ot2PnTt3+vT9EbzS45oqzOG5LNzhUFpcdGAKBADwCUuEmxtvvFHvvPOOVq1apXbt2p103aSkJO3atctj2a5du5SUlFTt+pGRkYqJifF4AJKUHBul7DE9Fe44lnDCHQ49OKaHkmOjAlwyAEB9NArkhxtjdNNNN+nNN9/U6tWrlZ6efsrXZGZmasWKFZo2bZp72fLly5WZmdmAJYVdZZ2ZqiGd47WjuFRpcdEEGwCwgYCGmylTpujVV1/VP/7xDzVv3tzdbyY2NlZRUccuMldffbXatm2r7OxsSdLUqVM1dOhQPfLIIxo1apQWLVqkDRs26Nlnnw3YdiC4JcdGEWoAwEYC2iw1b948OZ1ODRs2TMnJye5HTk6Oe52CggIVFha6/x44cKBeffVVPfvss8rIyNDixYv11ltvnbQTMgAACB2WmufGH5jnBgCA4BO089wAAADUF+EGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuHGhwqdZVqXV6xCZ1mgiwIAQMhqFOgC2EXO+gLNXLJFLiOFOaTsMT2VdWZqoIsFAEDIoebGBwqdZe5gI0kuI921ZCs1OAAABADhxgfyiw+6g02lCmO0o7g0MAUCACCEEW58ID2uqcIcnsvCHQ6lxUUHpkAAAIQwwo0PJMdGKXtMT4U7jiWccIdDD47poeTYqACXDACA0EOHYh/JOjNVQzrHa0dxqdLiogk2AAAECOHGh5Jjowg1AAAEGM1SAADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgIabj788ENdeOGFatOmjRwOh956662Trr969Wo5HI4qj6KiIv8UGAAAWF5Aw83BgweVkZGhp59+uk6v27ZtmwoLC92PhISEBiohAAAINo0C+eEjR47UyJEj6/y6hIQEtWjRwvcFAgAAQS8o+9z07t1bycnJ+tWvfqWPPvropOuWl5erpKTE4wEAAOwrqMJNcnKy5s+frzfeeENvvPGGUlJSNGzYMG3cuLHG12RnZys2Ntb9SElJ8WOJAQCAvzmMMSbQhZAkh8OhN998U6NHj67T64YOHarU1FS9/PLL1T5fXl6u8vJy998lJSVKSUmR0+lUTExMfYoMAAD8pKSkRLGxsbW6fge0z40v9O/fX2vXrq3x+cjISEVGRvqxRAAAIJCCqlmqOps3b1ZycnKgiwEAACwioDU3Bw4c0Pbt291/5+fna/PmzWrVqpVSU1M1c+ZM/fjjj3rppZckSY899pjS09N1+umn69ChQ3ruuee0cuVKLVu2LFCbAAAALCag4WbDhg0655xz3H9Pnz5dkjRu3DgtXLhQhYWFKigocD9/+PBh3Xrrrfrxxx8VHR2tXr166V//+pfHewAAgNBmmQ7F/lKXDkkAAMAa6nL9Dvo+N6i/QmeZ1uUVq9BZFuiiAABQb0E/Wgr1k7O+QDOXbJHLSGEOKXtMT2WdmRroYgEA4DVqbkJYobPMHWwkyWWku5ZspQYHABDUCDchLL/4oDvYVKowRjuKSwNTIAAAfIBwE8LS45oqzOG5LNzhUFpcdGAKBACADxBuQlhybJSyx/RUuONYwgl3OPTgmB5Kjo0KcMkAAPAeHYpDXNaZqRrSOV47ikuVFhdNsAEABD3CDZQcG0WoAQDYBs1SAADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVhrVdsWSkpJav2lMTIxXhQEAAKivWoebFi1ayOFwnHQdY4wcDocqKirqXTAAAABv1DrcrFq1qiHLAQAA4BO1DjdDhw5tyHIAAAD4RK3DzYn27dunv/71r/rqq68kSaeffromTpyo2NhYnxUOAACgrrwaLbVhwwZ17NhRjz76qPbu3au9e/fqz3/+szp27KiNGzf6uowAAAC15jDGmLq+6Oyzz1anTp20YMECNWp0rPLn6NGjmjx5sr777jt9+OGHPi+or5SUlCg2NlZOp5NRXQAA+FChs0z5xQeVHtdUybFRPn3vuly/vQo3UVFR2rRpk7p27eqx/Msvv1S/fv1UWlpa17f0G8INAAC+l7O+QDOXbJHLSGEOKXtMT2Wdmeqz96/L9durZqmYmBgVFBRUWb5z5041b97cm7cEAABBqtBZ5g42kuQy0l1LtqrQWRaQ8ngVbrKysjRp0iTl5ORo586d2rlzpxYtWqTJkydr7Nixvi4jAACwsPzig+5gU6nCGO0oDkxLjlejpf70pz/J4XDo6quv1tGjRyVJjRs31vXXX6+5c+f6tIAAAMDa0uOaKswhj4AT7nAoLS46IOXxqs9NpdLSUuXl5UmSOnbsqOjowGxEXdDnBgAA38tZX6C7lmxVhTEKdzj04JgeAetzU69wE4wINwAANIxCZ5l2FJcqLS46oKOlvGqWOnTokJ588kmtWrVKu3fvlsvl8nieuW4AAAg9ybFRPg813vAq3EyaNEnLli3Tb3/7W/Xv3/+UN9QEAADwF6/CzTvvvKN3331XgwYN8nV5AAAA6sWroeBt27ZlPhsbK3SWaV1eccDmJwAAoD68CjePPPKI7rzzTn3//fe+Lg8CLGd9gQbNXanLF3ysQXNXKmd91ckaAQCwMq+apfr166dDhw6pQ4cOio6OVuPGjT2e37t3r08KB/+qaYbJIZ3jLdFBDACA2vAq3IwdO1Y//vijHnzwQSUmJtKhuA4a8qZi9XWyGSatVlYAAGriVbhZt26dcnNzlZGR4evy2FpD31Ssvqw2wyQAAN7wqs9N165dVVZGZ9O6sNpNxaqTHBul7DE9Ff7fmrjKGSaptQEABBOvam7mzp2rW2+9VQ888IB69uxZpc8NM/9WFSxNPllnpmpI5/gGm2ESAICG5lW4ueCCCyRJ5513nsdyY4wcDocqKirqXzKbCaYmH6vMMAkAgDe8CjerVq3ydTlsr7LJ58SbihEiAADwrQa9ceYNN9yge++9V3FxcQ31EXUW6BtnNuRNxQAAsKu6XL+96lBcW6+88opKSkoa8iOCTnJslDI7tibYAADQQBo03DRgpRAAAEC1GjTcAAAA+BvhBgAA2ArhBgAA2Arhxk8KnWVal1dsqRmJAQCwozqHm6NHj+ree+/VDz/8cMp1r7zySmYr1rF7Sg2au1KXL/hYg+auVM76gkAXCQAA2/JqnpvmzZtry5YtSktLa4AiNSx/z3NT6CzToLkrq8xMvHbGOQwHBwCglhp8nptzzz1Xa9as8apwoeZk95QKBjSnAQCCjVe3Xxg5cqRmzJihLVu2qG/fvmratKnH8xdddJFPCmcHwXRPqRPlrC9w38k8zCFlj+mprDNTA10sAABOyqtmqbCwmit8rH7jzEDcfiFnfUGVe0pZPSTQnAYAsJK6XL+9qrlxuVxeFSxUZZ2ZqiGd44PqnlIna04LhvIDAEKXV31uXnrpJZWXl1dZfvjwYb300kv1LpQdBds9pSqb044XLM1pAIDQ5lW4mTBhgpxOZ5Xl+/fv14QJE+pdKARecmyUssf0VLjjWMKpbE4LlnAGAAhdXjVLGWPkcDiqLP/hhx8UGxtb70LBGoKxOQ0AgDqFmz59+sjhcMjhcOi8885To0b/e3lFRYXy8/N1wQUX+LyQCJzk2ChCDQAgqNQp3IwePVqStHnzZo0YMULNmjVzPxcREaG0tDRdcskltX6/Dz/8UA8//LA+/fRTFRYW6s0333R/Rk1Wr16t6dOn64svvlBKSor+8Ic/aPz48XXZDAAAYGN1CjezZ8+WJKWlpSkrK0tNmjSp14cfPHhQGRkZmjhxosaMGXPK9fPz8zVq1Chdd911+tvf/qYVK1Zo8uTJSk5O1ogRI+pVFgAAYA9ezXNT6fDhw9q9e3eVoeGpqXWfw8XhcJyy5ubOO+/U0qVLtXXrVveyyy67TPv27dP7779fq88JxDw3AACgfhp8nptvv/1WEydO1Lp16zyWV3Y0bqhJ/HJzczV8+HCPZSNGjNC0adNqfE15ebnHsPWSkpIGKRsAALAGr8LN+PHj1ahRI73zzjtKTk6uduRUQygqKlJiYqLHssTERJWUlKisrExRUVU7vmZnZ+uee+7xS/kAAEDgeRVuNm/erE8//VRdu3b1dXl8bubMmZo+fbr775KSEqWkpASwRAAAoCF5FW66d++u4uJiX5fllJKSkrRr1y6PZbt27VJMTEy1tTaSFBkZqcjISH8UDwAAWIBXMxQ/9NBDuuOOO7R69Wr9/PPPKikp8Xg0lMzMTK1YscJj2fLly5WZmdlgnwkAAIKLVzU3lZ16zz33XI/+NnXtUHzgwAFt377d/Xd+fr42b96sVq1aKTU1VTNnztSPP/7ovl/Vddddp6eeekp33HGHJk6cqJUrV+rvf/+7li5d6s1mAAAAG/Iq3KxatconH75hwwadc8457r8r+8aMGzdOCxcuVGFhoQoKCtzPp6ena+nSpbrlllv0+OOPq127dnruueeY4wYAALh5Pc/Nv//9bz3zzDPKy8vT4sWL1bZtW7388stKT0/X4MGDfV1On2GeGwAAgk9drt9e9bl54403NGLECEVFRWnTpk3ueWScTqcefPBBb94SAADAJ7wKN/fff7/mz5+vBQsWqHHjxu7lgwYN0saNG31WOAAAgLryKtxs27ZNQ4YMqbI8NjZW+/btq2+ZAAAAvOZVuElKSvIY5VRp7dq16tChQ70LBd8rdJZpXV6xCp1lgS4KAAANyqvRUtdcc42mTp2q559/Xg6HQz/99JNyc3N12223adasWb4uI+opZ32BZi7ZIpeRwhxS9pieyjqz7jc3BQAgGHgVbmbMmCGXy6XzzjtPpaWlGjJkiCIjI3Xbbbfppptu8nUZUQ+FzjJ3sJEkl5HuWrJVQzrHKzm2+lmdAQAIZl6FG4fDobvvvlu33367tm/frgMHDqh79+5q1qyZr8uHesovPugONpUqjNGO4lLCDQDAlrwKN5UiIiLUvXt3X5UFDSA9rqnCHPIIOOEOh9LiogNXKAAAGpBXHYoRPJJjo5Q9pqfC/3ubjHCHQw+O6UGtDQDAtupVc4PgkHVmqoZ0jteO4lKlxUUTbAAAtka4CRHJsVGEGgBASKBZCgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA1Fuhs0zr8opV6CwLdFHUKNAFAAAAwS1nfYFmLtkil5HCHFL2mJ7KOjM1YOWh5gYAAHit0FnmDjaS5DLSXUu2BrQGh3ADAAC8ll980B1sKlUYox3FpYEpkAg3AACgHtLjmirM4bks3OFQWlx0YAokwg0AAKiH5NgoZY/pqXDHsYQT7nDowTE9lBwbFbAy0aEYAADUS9aZqRrSOV47ikuVFhcd0GAjEW5gc4XOMuUXH1R6XNOA/2cDADtLjo2yzHmWcAPbstrQRACAf9DnBrZkxaGJAAD/INzAlqw4NBEA4B+EG9iSFYcmAgD8g3ADW7Li0EQAgH/QodgCGNHTMKw2NBEA4B+EmwBjRE/DstLQRACAf9AsFUCM6AEAwPcINwHEiB4AAHyPcBNAjOgBAMD3CDcBxIgeAAB8jw7FAcaIHgAAfItwYwGM6AEAwHdolvKTQmeZ1uUVMxIKAIAGRs2NHzCXDQAA/kPNTQNjLhsAAPyLcNPA7DyXDU1tAAArolmqgVXOZXN8wLHDXDY0tQEArMoSNTdPP/200tLS1KRJEw0YMECffPJJjesuXLhQDofD49GkSRM/lrZu7DiXDU1tAAArC3jNTU5OjqZPn6758+drwIABeuyxxzRixAht27ZNCQkJ1b4mJiZG27Ztc//tcDiqXc8q7DaXzcma2oJ92wAAwS/gNTd//vOfdc0112jChAnq3r275s+fr+joaD3//PM1vsbhcCgpKcn9SExM9GOJvZMcG6XMjq1tcfHnthEAACsLaLg5fPiwPv30Uw0fPty9LCwsTMOHD1dubm6Nrztw4IDat2+vlJQU/eY3v9EXX3zhj+Liv+zY1Aag9hhMAKsLaLNUcXGxKioqqtS8JCYm6uuvv672NV26dNHzzz+vXr16yel06k9/+pMGDhyoL774Qu3atauyfnl5ucrLy91/l5SU+HYjQpTdmtoA1A6DCRAMAt4sVVeZmZm6+uqr1bt3bw0dOlRLlixRfHy8nnnmmWrXz87OVmxsrPuRkpLi5xI3rED+grJTU5u/8csXwYjBBAgWAa25iYuLU3h4uHbt2uWxfNeuXUpKSqrVezRu3Fh9+vTR9u3bq31+5syZmj59uvvvkpIS2wQcfkEFJ743BCsGEyBYBLTmJiIiQn379tWKFSvcy1wul1asWKHMzMxavUdFRYW2bNmi5OTkap+PjIxUTEyMx8MO+AUVnILte6OGCcdjMAGCRcCbpaZPn64FCxboxRdf1FdffaXrr79eBw8e1IQJEyRJV199tWbOnOle/95779WyZcv03XffaePGjbryyiv1/fffa/LkyYHahICw88zHdhZM31vO+gINmrtSly/4WIPmrlTO+oJAFwl+cLJAy2ACBIuAz3OTlZWlPXv26I9//KOKiorUu3dvvf/+++5OxgUFBQoL+18G++WXX3TNNdeoqKhILVu2VN++fbVu3Tp17949UJsQENXNfCxJn/+4T5kdWwemUDilYJmxuroapplvbNGQzvFcyGyq0Fmm59fm669r80/aZMpgAgQDhzHGnHo1+ygpKVFsbKycTmfQN1E9syZP2e95jioLdzi0dsY5nHAsLGd9ge5aslUVxrh/+Vqtz826vGJdvuDjKsuvPbuD7hrVLQAlQkPKWV+gGW9s0YkXA84nwaXQWab84oNKj2vq/s6qWxas6nL9DnjNDbzXs11slWWB7txnp/9IDSUYfvmmxzWVQ6pysXtu7XeaMDjNkmWGdypr6ar7lRvo8wlqr7qBCpKqHbwQCudpwk0Qs1oTB6OAai85NsrSJ5Xk2Chdc3a6nv13vsdylxEXO5uprh9YJSs2maKqmpqRddz1oXLwwr6yI3rova9tf54OeIdieM9KnfuCbRQQTm3C4HRGxoSA6kZASccufHQWrh9/jTasLqC6VLVPZoUxmvvfYCPZ+zxNzU2Qs0oTB/Nf2E9leD6xfxDfp72c+D2HSZo8JF0TBqVb6ru2YlPKycrkz5rsLT84qywLkzxqbiqXhcp5mnBjA1Zo4rBaExl8wyrhGQ3L6t+zFZu8T1ammmqyG2K0YaGzTA+9X/V2RXf+uqtaRDX2+HFyxwVd9ND7X4fEeZpwA5/gV759WSE8o+FZ9Xv2Z1DwVZn8WZNdU5+pXm1bKLNj6yqhtUV045A4TxNu4DNW//UHIPhYscn7VGXyZ032qT7rxNAaKudpOhTDp7iZJgBfsuItH05VJn8O9vDms0LhPM0kfoCNWLHTJQLHLseDFSe+rE2ZCp1lfqsh8ednBUpdrt+EG8AmrNjpEoFzquMh2IJPQ168vd0XoRAorIRwcxKEG9hRobNMg+aurNLuztT5oelUxwNB+H/YF8GjLtdv+twAQa7QWaZ3Pv8paO42job3wn9vfnm8yuOBCTf/h31hX4yWAoLY8b86TxTucCg6Ikzr8oqDpukB9VfoLNOCE26bIR2rlUiLi7bk6KNAYV/YF+EGCFKFzrJq7+RcaUSPRF38l3VUt4eY/OKD1R4Tkwd3cF+wmXDzGCYftS+apYAg9fza/BqDjSS9t7WI6vYQVN0w5TBJEwanSQrcPen8dZ+lurDS/fngW9Tc2EywjYA4UbCX318KnWX669qqTQ+V7HoPGY6PU6vNbOH+nsjNyp12Q2VSu1BDuLERK59AaiPYy+9PNU25Lsm295Dh+Ki92lyw/XW7BSvePuFEVr31BLxHs5RNBHuv/2Avv79V2/TgkJ4a20drZ5yj3w/taKvqdo6PurPKLLQn67QLNBRqbmwi2Hv9B3v5/a2mpof/l9HGvY6dqts5PoIXnXYRCIQbm7DCCaQ+/SGsUP5gY6Wmh4bG8RG8atMHCPA1Zii2kUDef8UX/SGseP8YWAfHR3Dz9lYF/uxETod1a+P2Cydh53AjBeZeJ76c+p97teBkOD5Ciz87kdNh3frqcv2mWcpmAtEM4cv+EHZpRgl2Vv0Fy/EROvw5yioYRnShbgg3qDf6Q9gLv2BhBf7sRB4MHdat+oPDqhgKjnpjlk/7YMg1rKK66Q4a6keTPz+rrgqdZXpg6ZcaNHelLl/wsQbNXamc9QWBLpblUXMDn7DTsONQFgy/YBEa6jPKqq61HFYd0ZWzvqDK/eNoMqsdwg18xp8zntbmxEU1bt3RxAgr8eZHk7fNqlb7gVZZi1rdiB9+cJwa4QZBpbYnLvqNeMeqv2ARuuryo6m+HYOt1GH9VLdY4QfHyRFuEDRqe+Ji5EP9WO0XLFBbNTWrbvz+F7VsGly1uNXVokrHfqzxg+PUCDcIGrXtD0K/kfqz0i9YoLaqCwQOh3Tjq5tkFFy1uCfWooZJmjwkXRMGpfN/sxYINwgate0PQr+R0EQfK1QJBA7JGLn7rQS6Freuxyi1qN4j3MCSqjsJ1LY/CP1GQo8V+1gRtgLj+EBQfOCQbnpts8fz/q7FrTwOtvzo1EPvfV3nY5RaVO9w+wWbC8YT7KkuVLWdgp+p+kODL2//4StWDFuhqKGOjdqeV48/Dk4U6GM0GHH7BUgKzhNsbToD1/aXDL94QoPV+ljRod06GqIWtzY/vvKLD6ppRHiNwUaiH2BDI9zYVLCeYK12oYL1Wa2PFcewtfiy38qpzqvHBx/Hf/v71IR+gA2L2y/Y1MlOsFZm5WnQ7a7QWaZ1ecVBd6sFq93+g2PYepJjo5TZsXW9j4mTnVdPDD6nCjZ26Ado5XMGNTc2ZbVfs7XlzynXg0lDb1swNmEez0qjSujQbl8nO6/WNOlemCTXf9e744Iu6tWuRa2PUSuf06x+zqBDsY3lrC+ocoK10sF3MnXtDGz1/2j10dDbZsUOuQ3B3xcKOrTbU03n1Zr+Hy25IVOlh111Pg6sfE4L1DmDDsWQZK1fs3VVm87ANXXcC5b+RbXhj75TodBHJBAXCjq021NN59WaauwyUlrW+TOs3mcyGM4ZhBubs+sJ1qPjnlTl5nJW+49WV5XBbe/Bww1+EgnWJszasvqFAsGnpvOqr35QWj08BMM5g3CDoFOl414161jtP1pdnBjcTgxvvt42u/cR8deFwsr9I+A/vvhBafXwEAznDMINgk6NHff+ezKw4n+02qouuDnU8NsWzE2Yp+KPC4WV+0cg+ARDeLD6OYNwg6BT08XK2457VlJdcDOSnrysj1o3i2zQbbNrE+bJLhS+qG2h2QsNwerhQbL2OYNwg6Djy457VlNTcOub1tKyJ5FgUN2Fwle1LVbvH4HgZeXwYHWEGwSlYPhV441gqI4OVsdfKHxZ21KfZi/66YQmvveGR7hB0LLrrxq7Bjcr8WVti7eB1Fc1R1wogwv9s/yDcANYkF2Dm1VUV9sS5pCiI7y7I01dA6mvao64UAYX+mf5D/eWAhByTrwflXTsQnPxX9YpZ32B1+9Z2/sX+eLebzVdKK14nx8cE6z3/AtGhBsAISnrzFQtuSFTx9/j0l8BwRc31+RCaQ11uXkkN1X1H8INgJB18HBFjbNbNyRf3MmcC2Xg5awv0KC5K3X5go81aO7KU9b6We0O9nZGnxsAIWvLD84qy/wVEOrbcZyRdYHlbf8ZBgz4B+EGgN9YaWRPobNMD73/dZXld4zs4rey1bfjOBfKk2vI460+I+4YMNDwCDcA/MJqI3tquo1Hr7YtTvo6fwW02n4OF8rqNfTxVtOIO5oFrfEjhnCDkGOF/3ihxopDYL2ZfM9fAc1qQTDY+ON4q2wWnLFki0zlveCM9OE3e0L6u7LKsUuHYoSUunYAhG9YcWRPXTt3+mvoNUO8689fx9uQzvE6vke6UWh/V1Y6dqm5QciwYu1BqPDHnbm9UZc+K/66hxT3qqo/fx1v+cUHaxxtF4rflZWOXWpuEDKsWHsQKqw8BLa2k+/5a+g1Q7zrz1/HG9+VJyvtD2puEDKsWnsQKoJ9ZI8vhl7Xpr8XQ7x9wx/HG9+VJyvtD4cx5sRaNVsrKSlRbGysnE6nYmJiAl0c+FnO+oIq//Hs2PmPTtMNp9BZ5tUFs64dLb39HPhfMH1X/jg3NNT+qMv1m3CDkFPX/3iFzjJ9+v0vMsaoX1qrk3Y4tUKgsMpoBSuwyndS6CzToLkrq9Qarp1xjuUvhrCPYD831OX6TbMUQk5d5gXJWV+gGW9scXcadEiae0nVE4JVThp0mv4fq3wnkrU6WiI0hdq5gQ7FQA0qTwbHX5OMpJlvbPEY2niy4Y91uameL9Bp+hgrDUmVrNXR8mT8fbzCf0Lt3EDNjYVZpUo9VNU0g61L0tLPCzWqV7KSY6NqPGm8sHaHnlv7nV9rDug0fYzVakqs1NGyJlaq6bKyYD0vh9q5gXBjUZxoAq+6k0Gl+5d+pQff/UrZY3pqSOf4qtOwS+5gI/mvCjgYLqL+YMUTuZVHi4Vak4W3gvm8HGrnBjoUWxCdD60jZ32Bx/TqJ6r8Xj78Zo/HSWPS4DQ9++/8Kuu/ds1ZyuzYuoFLHVyjNxpKMIyMs0otwLq8Yl2+4OMqy/11vAYDu5yXg/ncEHQdip9++mk9/PDDKioqUkZGhp588kn179+/xvVff/11zZo1Szt27NBpp52mhx56SL/+9a/9WOKGZbUq9bqyygnbFyp/bW/8/hdtK9qvJ1Zu93i+8ns58Ve5JD23Nj9gNQfcTNHaNSWStWoBrFjTZTUbduwN6vNypVA5NwS8Q3FOTo6mT5+u2bNna+PGjcrIyNCIESO0e/fuatdft26dxo4dq0mTJmnTpk0aPXq0Ro8era1bt/q55A0nWDofVseO925Kjo3SqF5tNHZA6km/l+NnurXyjLyhpLazD/ub1To8c7yeXM76Ak1dtLnK8mA5L4eigDdLDRgwQGeeeaaeeuopSZLL5VJKSopuuukmzZgxo8r6WVlZOnjwoN555x33srPOOku9e/fW/PnzT/l5wdAsJQVHlfqJ7FJtezJ1/V6CuQoYDceqzUAcr1VVd16TjtUMZFczLQQaTtA0Sx0+fFiffvqpZs6c6V4WFham4cOHKzc3t9rX5Obmavr06R7LRowYobfeeqva9cvLy1VeXu7+u6SkpP4F9wOrV6lXJ9ib02qjrt9LqFQBo26s2gzE8VpVTaMmn7y8j0b1auP/AqFWAtosVVxcrIqKCiUmJnosT0xMVFFRUbWvKSoqqtP62dnZio2NdT9SUlJ8U3g/sGqVek2CuTmtLoLte4H10AwUPGo6r53RvmVgCoRaCXifm4Y2c+ZMOZ1O92Pnzp2BLpJtccIGai/rzFStnXGOXrvmLK2dcQ7NGxbFeS04BbRZKi4uTuHh4dq1a5fH8l27dikpKana1yQlJdVp/cjISEVGRvqmwDilYGxOAwKFZqDgwHkt+AS05iYiIkJ9+/bVihUr3MtcLpdWrFihzMzMal+TmZnpsb4kLV++vMb14X802wCwG85rwSXg89xMnz5d48aNU79+/dS/f3899thjOnjwoCZMmCBJuvrqq9W2bVtlZ2dLkqZOnaqhQ4fqkUce0ahRo7Ro0SJt2LBBzz77bCA3AwAAWETAw01WVpb27NmjP/7xjyoqKlLv3r31/vvvuzsNFxQUKCzsfxVMAwcO1Kuvvqo//OEPuuuuu3TaaafprbfeUo8ePQK1CQAAwEICPs+NvwXLPDcAAOB/6nL9tv1oKQAAEFoINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYCPkOxv1XOWVhSUhLgkgAAgNqqvG7XZu7hkAs3+/fvlySlpKQEuCQAAKCu9u/fr9jY2JOuE3K3X3C5XPrpp5/UvHlzORyOOr22pKREKSkp2rlzZ0jfuoH9cAz74Rj2wzHsh2PYD8ewH47x5X4wxmj//v1q06aNxz0nqxNyNTdhYWFq165dvd4jJiYmpA/WSuyHY9gPx7AfjmE/HMN+OIb9cIyv9sOpamwq0aEYAADYCuEGAADYCuGmDiIjIzV79mxFRkYGuigBxX44hv1wDPvhGPbDMeyHY9gPxwRqP4Rch2IAAGBv1NwAAABbIdwAAABbIdwAAABbIdwAAABbCflwM2/ePPXq1cs9wVBmZqbee+899/OHDh3SlClT1Lp1azVr1kyXXHKJdu3a5fEeBQUFGjVqlKKjo5WQkKDbb79dR48e9fem+MzcuXPlcDg0bdo097JQ2Q9z5syRw+HweHTt2tX9fKjsB0n68ccfdeWVV6p169aKiopSz549tWHDBvfzxhj98Y9/VHJysqKiojR8+HB9++23Hu+xd+9eXXHFFYqJiVGLFi00adIkHThwwN+b4rW0tLQqx4PD4dCUKVMkhc7xUFFRoVmzZik9PV1RUVHq2LGj7rvvPo97/ITC8SAdm/p/2rRpat++vaKiojRw4ECtX7/e/bwd98OHH36oCy+8UG3atJHD4dBbb73l8byvtvnzzz/X2WefrSZNmiglJUX/93//532hTYh7++23zdKlS80333xjtm3bZu666y7TuHFjs3XrVmOMMdddd51JSUkxK1asMBs2bDBnnXWWGThwoPv1R48eNT169DDDhw83mzZtMu+++66Ji4szM2fODNQm1csnn3xi0tLSTK9evczUqVPdy0NlP8yePducfvrpprCw0P3Ys2eP+/lQ2Q979+417du3N+PHjzcff/yx+e6778wHH3xgtm/f7l5n7ty5JjY21rz11lvms88+MxdddJFJT083ZWVl7nUuuOACk5GRYf7zn/+Yf//736ZTp05m7Nixgdgkr+zevdvjWFi+fLmRZFatWmWMCZ3j4YEHHjCtW7c277zzjsnPzzevv/66adasmXn88cfd64TC8WCMMZdeeqnp3r27WbNmjfn222/N7NmzTUxMjPnhhx+MMfbcD++++665++67zZIlS4wk8+abb3o874ttdjqdJjEx0VxxxRVm69at5rXXXjNRUVHmmWee8arMIR9uqtOyZUvz3HPPmX379pnGjRub119/3f3cV199ZSSZ3NxcY8yxLz0sLMwUFRW515k3b56JiYkx5eXlfi97fezfv9+cdtppZvny5Wbo0KHucBNK+2H27NkmIyOj2udCaT/ceeedZvDgwTU+73K5TFJSknn44Yfdy/bt22ciIyPNa6+9Zowx5ssvvzSSzPr1693rvPfee8bhcJgff/yx4QrfgKZOnWo6duxoXC5XSB0Po0aNMhMnTvRYNmbMGHPFFVcYY0LneCgtLTXh4eHmnXfe8Vh+xhlnmLvvvjsk9sOJ4cZX2/yXv/zFtGzZ0uP/xZ133mm6dOniVTlDvlnqeBUVFVq0aJEOHjyozMxMffrppzpy5IiGDx/uXqdr165KTU1Vbm6uJCk3N1c9e/ZUYmKie50RI0aopKREX3zxhd+3oT6mTJmiUaNGeWyvpJDbD99++63atGmjDh066IorrlBBQYGk0NoPb7/9tvr166ff/e53SkhIUJ8+fbRgwQL38/n5+SoqKvLYF7GxsRowYIDHvmjRooX69evnXmf48OEKCwvTxx9/7L+N8ZHDhw/rlVde0cSJE+VwOELqeBg4cKBWrFihb775RpL02Wefae3atRo5cqSk0Dkejh49qoqKCjVp0sRjeVRUlNauXRsy++F4vtrm3NxcDRkyRBEREe51RowYoW3btumXX36pc7lC7saZ1dmyZYsyMzN16NAhNWvWTG+++aa6d++uzZs3KyIiQi1atPBYPzExUUVFRZKkoqIijxNX5fOVzwWLRYsWaePGjR5tx5WKiopCZj8MGDBACxcuVJcuXVRYWKh77rlHZ599trZu3RpS++G7777TvHnzNH36dN11111av369br75ZkVERGjcuHHubaluW4/fFwkJCR7PN2rUSK1atQqqfVHprbfe0r59+zR+/HhJofX/YsaMGSopKVHXrl0VHh6uiooKPfDAA7riiiskKWSOh+bNmyszM1P33XefunXrpsTERL322mvKzc1Vp06dQmY/HM9X21xUVKT09PQq71H5XMuWLetULsKNpC5dumjz5s1yOp1avHixxo0bpzVr1gS6WH6zc+dOTZ06VcuXL6/yiyTUVP4SlaRevXppwIABat++vf7+978rKioqgCXzL5fLpX79+unBBx+UJPXp00dbt27V/PnzNW7cuACXLjD++te/auTIkWrTpk2gi+J3f//73/W3v/1Nr776qk4//XRt3rxZ06ZNU5s2bULueHj55Zc1ceJEtW3bVuHh4TrjjDM0duxYffrpp4EuGo5Ds5SkiIgIderUSX379lV2drYyMjL0+OOPKykpSYcPH9a+ffs81t+1a5eSkpIkSUlJSVVGR1T+XbmO1X366afavXu3zjjjDDVq1EiNGjXSmjVr9MQTT6hRo0ZKTEwMif1QnRYtWqhz587avn17yBwPkpScnKzu3bt7LOvWrZu7ia5yW6rb1uP3xe7duz2eP3r0qPbu3RtU+0KSvv/+e/3rX//S5MmT3ctC6Xi4/fbbNWPGDF122WXq2bOnrrrqKt1yyy3Kzs6WFFrHQ8eOHbVmzRodOHBAO3fu1CeffKIjR46oQ4cOIbUfKvlqm339f4VwUw2Xy6Xy8nL17dtXjRs31ooVK9zPbdu2TQUFBcrMzJQkZWZmasuWLR5f3PLlyxUTE1Pl4mBV5513nrZs2aLNmze7H/369dMVV1zh/nco7IfqHDhwQHl5eUpOTg6Z40GSBg0apG3btnks++abb9S+fXtJUnp6upKSkjz2RUlJiT7++GOPfbFv3z6PX7QrV66Uy+XSgAED/LAVvvPCCy8oISFBo0aNci8LpeOhtLRUYWGel4vw8HC5XC5JoXc8SFLTpk2VnJysX375RR988IF+85vfhOR+8NU2Z2Zm6sMPP9SRI0fc6yxfvlxdunSpc5OUJIaCz5gxw6xZs8bk5+ebzz//3MyYMcM4HA6zbNkyY8yxoZ6pqalm5cqVZsOGDSYzM9NkZma6X1851PP88883mzdvNu+//76Jj48PuqGeJzp+tJQxobMfbr31VrN69WqTn59vPvroIzN8+HATFxdndu/ebYwJnf3wySefmEaNGpkHHnjAfPvtt+Zvf/ubiY6ONq+88op7nblz55oWLVqYf/zjH+bzzz83v/nNb6od/tmnTx/z8ccfm7Vr15rTTjvN0kNeq1NRUWFSU1PNnXfeWeW5UDkexo0bZ9q2beseCr5kyRITFxdn7rjjDvc6oXI8vP/+++a9994z3333nVm2bJnJyMgwAwYMMIcPHzbG2HM/7N+/32zatMls2rTJSDJ//vOfzaZNm8z3339vjPHNNu/bt88kJiaaq666ymzdutUsWrTIREdHMxTcWxMnTjTt27c3ERERJj4+3px33nnuYGOMMWVlZeaGG24wLVu2NNHR0ebiiy82hYWFHu+xY8cOM3LkSBMVFWXi4uLMrbfeao4cOeLvTfGpE8NNqOyHrKwsk5ycbCIiIkzbtm1NVlaWx9wuobIfjDHmn//8p+nRo4eJjIw0Xbt2Nc8++6zH8y6Xy8yaNcskJiaayMhIc95555lt27Z5rPPzzz+bsWPHmmbNmpmYmBgzYcIEs3//fn9uRr198MEHRlKVbTMmdI6HkpISM3XqVJOammqaNGliOnToYO6++26PYbuhcjzk5OSYDh06mIiICJOUlGSmTJli9u3b537ejvth1apVRlKVx7hx44wxvtvmzz77zAwePNhERkaatm3bmrlz53pdZocxx00xCQAAEOTocwMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAOgVoYNG6Zp06YFuhgNbs6cOerdu3egiwGgHgg3AELC4cOH/fp5xhgdPXrUr58J4BjCDYBTGj9+vNasWaPHH39cDodDDodDO3bs0NatWzVy5Eg1a9ZMiYmJuuqqq1RcXOx+3bBhw3TTTTdp2rRpatmypRITE7VgwQIdPHhQEyZMUPPmzdWpUye999577tesXr1aDodDS5cuVa9evdSkSROdddZZ2rp1q0eZ1q5dq7PPPltRUVFKSUnRzTffrIMHD7qfT0tL03333aerr75aMTExuvbaayVJd955pzp37qzo6Gh16NBBs2bNct+JeOHChbrnnnv02Wefubdz4cKF2rFjhxwOhzZv3ux+/3379snhcGj16tUe5X7vvffUt29fRUZGau3atXK5XMrOzlZ6erqioqKUkZGhxYsX+/orAnAcwg2AU3r88ceVmZmpa665RoWFhSosLFTz5s117rnnqk+fPtqwYYPef/997dq1S5deeqnHa1988UXFxcXpk08+0U033aTrr79ev/vd7zRw4EBt3LhR559/vq666iqVlpZ6vO7222/XI488ovXr1ys+Pl4XXnihO4Tk5eXpggsu0CWXXKLPP/9cOTk5Wrt2rW688UaP9/jTn/6kjIwMbdq0SbNmzZIkNW/eXAsXLtSXX36pxx9/XAsWLNCjjz4qScrKytKtt96q008/3b2dWVlZddpXM2bM0Ny5c/XVV1+pV69eys7O1ksvvaT58+friy++0C233KIrr7xSa9asqdP7AqgDr2+5CSCknHin+Pvuu8+cf/75Huvs3LnT4w7aQ4cONYMHD3Y/f/ToUdO0aVNz1VVXuZcVFhYaSSY3N9cY8787EC9atMi9zs8//2yioqJMTk6OMcaYSZMmmWuvvdbjs//973+bsLAwU1ZWZowxpn379mb06NGn3K6HH37Y9O3b1/337NmzTUZGhsc6+fn5RpLZtGmTe9kvv/xiJJlVq1Z5lPutt95yr3Po0CETHR1t1q1b5/F+kyZNMmPHjj1l2QB4p1EggxWA4PXZZ59p1apVatasWZXn8vLy1LlzZ0lSr1693MvDw8PVunVr9ezZ070sMTFRkrR7926P98jMzHT/u1WrVurSpYu++uor92d//vnn+tvf/uZexxgjl8ul/Px8devWTZLUr1+/KmXLycnRE088oby8PB04cEBHjx5VTExMnbe/Jsd/5vbt21VaWqpf/epXHuscPnxYffr08dlnAvBEuAHglQMHDujCCy/UQw89VOW55ORk978bN27s8ZzD4fBY5nA4JEkul6tOn/373/9eN998c5XnUlNT3f9u2rSpx3O5ubm64oordM8992jEiBGKjY3VokWL9Mgjj5z088LCjrXgG2PcyyqbyE50/GceOHBAkrR06VK1bdvWY73IyMiTfiYA7xFuANRKRESEKioq3H+fccYZeuONN5SWlqZGjXx/KvnPf/7jDiq//PKLvvnmG3eNzBlnnKEvv/xSnTp1qtN7rlu3Tu3bt9fdd9/tXvb99997rHPidkpSfHy8JKmwsNBd43J85+KadO/eXZGRkSooKNDQoUPrVFYA3qNDMYBaSUtL08cff6wdO3aouLhYU6ZM0d69ezV27FitX79eeXl5+uCDDzRhwoQq4cAb9957r1asWKGtW7dq/PjxiouL0+jRoyUdG/G0bt063Xjjjdq8ebO+/fZb/eMf/6jSofhEp512mgoKCrRo0SLl5eXpiSee0JtvvlllO/Pz87V582YVFxervLxcUVFROuuss9wdhdesWaM//OEPp9yG5s2b67bbbtMtt9yiF198UXl5edq4caOefPJJvfjii17vGwAnR7gBUCu33XabwsPD1b17d8XHx+vw4cP66KOPVFFRofPPP189e/bUtGnT1KJFC3czTn3MnTtXU6dOVd++fVVUVKR//vOfioiIkHSsH8+aNWv0zTff6Oyzz1afPn30xz/+UW3atDnpe1500UW65ZZbdOONN6p3795at26dexRVpUsuuUQXXHCBzjnnHMXHx+u1116TJD3//PM6evSo+vbtq2nTpun++++v1Xbcd999mjVrlrKzs9WtWzddcMEFWrp0qdLT073YKwBqw2GOb0QGgABbvXq1zjnnHP3yyy9q0aJFoIsDIAhRcwMAAGyFcAMAAGyFZikAAGAr1NwAAABbIdwAAABbIdwAAABbIdwAAABbIdwAAABbIdwAAABbIdwAAABbIdwAAABbIdwAAABb+f95SF+lELeFFQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[
,\n", + "
,\n", + "
,\n", + "
]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -638,7 +824,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_doc.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_doc.md) file." ] } ], @@ -658,10 +844,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb index ed528130..6e55e673 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb @@ -1,667 +1,692 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "## 1. Introduction\n", - "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", - "\n", - "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", - "\n", - "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", - "\n", - "\n", - "### 1.1 Need for ML Surrogates\n", - "\n", - "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", - "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", - "\n", - "### 1.2 Supercritical CO2 cycle process\n", - "\n", - "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", - "\n", - "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "csv_data.columns.values[0:6] = [\n", - " \"pressure\",\n", - " \"temperature\",\n", - " \"enth_mol\",\n", - " \"entr_mol\",\n", - " \"CO2_enthalpy\",\n", - " \"CO2_entropy\",\n", - "]\n", - "data = csv_data.sample(n=500)\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# # Define labels, and split training and validation data\n", - "input_labels = list(input_data.columns)\n", - "output_labels = list(output_data.columns)\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogates with PySMO\n", - "\n", - "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", - "\n", - "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; previous file will be overwritten.\n", - "\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "No iterations will be run.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", - "\n", - "------------------------------------------------------------\n", - "The final coefficients of the regression terms are: \n", - "\n", - "k | -534397.59515\n", - "(x_ 1 )^ 1 | -2733.579691\n", - "(x_ 2 )^ 1 | 1036.106357\n", - "(x_ 1 )^ 2 | 32.409203\n", - "(x_ 2 )^ 2 | -2.852387\n", - "(x_ 1 )^ 3 | 0.893563\n", - "(x_ 2 )^ 3 | 0.004018\n", - "(x_ 1 )^ 4 | -0.045284\n", - "(x_ 2 )^ 4 | -3e-06\n", - "(x_ 1 )^ 5 | 0.000564\n", - "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 4.372684\n", - "\n", - "The coefficients of the extra terms in additional_regression_features are:\n", - "\n", - "Coeff. additional_regression_features[ 1 ]: -0.002723\n", - "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", - "Coeff. additional_regression_features[ 3 ]: -0.050607\n", - "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", - "Coeff. additional_regression_features[ 5 ]: -44.726026\n", - "\n", - "Regression model performance on training data:\n", - "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", - "\n", - "Results saved in solution.pickle\n", - "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; previous file will be overwritten.\n", - "\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "No iterations will be run.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", - "\n", - "------------------------------------------------------------\n", - "The final coefficients of the regression terms are: \n", - "\n", - "k | -519.862457\n", - "(x_ 1 )^ 1 | -8.820865\n", - "(x_ 2 )^ 1 | 3.676641\n", - "(x_ 1 )^ 2 | 0.18002\n", - "(x_ 2 )^ 2 | -0.010217\n", - "(x_ 1 )^ 3 | -0.000783\n", - "(x_ 2 )^ 3 | 1.4e-05\n", - "(x_ 1 )^ 4 | -6.9e-05\n", - "(x_ 2 )^ 4 | -0.0\n", - "(x_ 1 )^ 5 | 1e-06\n", - "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 0.010367\n", - "\n", - "The coefficients of the extra terms in additional_regression_features are:\n", - "\n", - "Coeff. additional_regression_features[ 1 ]: -7e-06\n", - "Coeff. additional_regression_features[ 2 ]: 0.0\n", - "Coeff. additional_regression_features[ 3 ]: -0.000112\n", - "Coeff. additional_regression_features[ 4 ]: 484.312223\n", - "Coeff. additional_regression_features[ 5 ]: -0.1166\n", - "\n", - "Regression model performance on training data:\n", - "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", - "\n", - "Results saved in solution.pickle\n", - "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" - ] - } - ], - "source": [ - "# Create PySMO trainer object\n", - "trainer = PysmoPolyTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - ")\n", - "\n", - "var = output_labels\n", - "trainer.config.extra_features = [\n", - " \"pressure*temperature*temperature\",\n", - " \"pressure*pressure*temperature*temperature\",\n", - " \"pressure*pressure*temperature\",\n", - " \"pressure/temperature\",\n", - " \"temperature/pressure\",\n", - "]\n", - "# Set PySMO options\n", - "trainer.config.maximum_polynomial_order = 5\n", - "trainer.config.multinomials = True\n", - "trainer.config.training_split = 0.8\n", - "trainer.config.number_of_crossvalidations = 10\n", - "\n", - "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", - "poly_train = trainer.train_surrogate()\n", - "\n", - "# create callable surrogate object\n", - "xmin, xmax = [7, 306], [40, 1000]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", - "# save model to JSON\n", - "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Visualizing surrogates\n", - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "## 1. Introduction\n", + "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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rprVEcrRaLaqqqmqieA5JTk6GVqvF7t27kZCQAKB6+o39+/fj+eefr9WyMCAiIiJygTFt4eV1x1ElCDWethAcHIwXXngB06ZNg8FgQM+ePaHT6bB7926EhISIgYUtiYmJyM/PR05ODpo1a4bg4GAEBATUSHltadiwIZ555hm8+OKLCA8PR/PmzbFw4UKUlZVh3LhxtVoWBkREREQuqu20hddffx2RkZHIysrCL7/8grCwMNxzzz14+eWXFTVHDR06FOvWrUPfvn1RXFyMlStXYvTo0TVaZjnz58+HwWDAk08+id9//x1dunTBf//7XzRu3LhWy6ERBEGwvxjp9XqEhoZCp9MhJCTE08UhIiIX3bhxA/n5+UhKSkKDBg08XRxyga3vUun1m73MiIiIyOcxICIiIiK7nn76abNu/qaPp59+2tPFc5lHA6KdO3fiwQcfRFxcnOQMu8YhxS0fixYtEpdJTEy0en3+/Plm6zl69Ch69eqFBg0aID4+HgsXLqyNj0dERFRnzJ07Fzk5OZKPuXPnerp4LvNoUnVpaSlSU1MxduxYDBkyxOr1wsJCs783bdqEcePGYejQoWbPz507FxMmTBD/Dg4OFv+t1+sxYMAA9O/fH8uXL8exY8cwduxYhIWFYeLEiW7+RERERHVTVFQUoqKiPF2MGuPRgCgzMxOZmZmyr8fExJj9/c0336Bv375mI14C1QGQ5bJGn376KSorK/Hhhx9Cq9Xi7rvvRk5ODt58800GRERERATAi3KILl68iI0bN0qOSzB//nw0adIEnTp1wqJFi3Dr1i3xtezsbKSnp0Or1YrPZWRkIDc3F9evX5fdXkVFBfR6vdmDiIjqHjWOmkyOccd36DXjEH300UcIDg62alqbOnUq7rnnHoSHh2PPnj2YNWsWCgsL8eabbwIAioqKkJSUZPae6Oho8TW5cQ6ysrIwZ86cGvgkRESkBlqtFn5+frhw4QIiIyOh1WrFKS7IOwiCgMrKSly+fBl+fn5mlR+O8pqA6MMPP8Tjjz9uNb7A9OnTxX936NABWq0WTz31FLKyslwadXPWrFlm69br9YiPj3d6fUREpC5+fn5ISkpCYWEhLly44OnikAuCgoLQvHlz+Pk53/DlFQHR//73P+Tm5mLt2rV2l+3evTtu3bqFs2fPonXr1oiJicHFixfNljH+LZd3BFRPkOeJYcyJiKj2aLVaNG/eHLdu3VLF3F7kOH9/f9SrV8/l2j2vCIg++OADdO7cGampqXaXzcnJgZ+fn5gJn5aWhr/+9a+4efOmOOPuli1b0Lp161ofFpyIiNTHOCO75azs5Fs8mlRdUlIijmEAQJxorqCgQFxGr9fj888/x/jx463en52djcWLF+PIkSP45Zdf8Omnn2LatGl44oknxGBnxIgR0Gq1GDduHE6cOIG1a9fi7bffNmsOIyIiIt/m0RqiAwcOoG/fvuLfxiBl1KhRWLVqFQDgs88+gyAIGD58uNX7AwIC8Nlnn2H27NmoqKhAUlISpk2bZhbshIaGYvPmzZg0aRI6d+6MiIgIvPrqq+xyT0RERCJO7qoQJ3clIiLyPpzclYiIiEghBkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHP82hAtHPnTjz44IOIi4uDRqPB119/bfb66NGjodFozB4DBw40W+batWt4/PHHERISgrCwMIwbNw4lJSVmyxw9ehS9evVCgwYNEB8fj4ULF9b0RyMiIiIv4tGAqLS0FKmpqVi6dKnsMgMHDkRhYaH4WLNmjdnrjz/+OE6cOIEtW7Zgw4YN2LlzJyZOnCi+rtfrMWDAACQkJODgwYNYtGgRZs+ejX//+9819rmIiIjIu9Tz5MYzMzORmZlpc5mAgADExMRIvvbzzz/j+++/x/79+9GlSxcAwJIlS/DAAw/gH//4B+Li4vDpp5+isrISH374IbRaLe6++27k5OTgzTffNAuciIiIyHepPodo+/btiIqKQuvWrfHMM8/g6tWr4mvZ2dkICwsTgyEA6N+/P/z8/LB3715xmfT0dGi1WnGZjIwM5Obm4vr167LbraiogF6vN3sQERFR3aTqgGjgwIH4+OOP8eOPP2LBggXYsWMHMjMzUVVVBQAoKipCVFSU2Xvq1auH8PBwFBUVictER0ebLWP827iMlKysLISGhoqP+Ph4d340IiIiUhGPNpnZ89hjj4n/TklJQYcOHZCcnIzt27ejX79+NbrtWbNmYfr06eLfer2eQREREVEdpeoaIkstWrRAREQEzpw5AwCIiYnBpUuXzJa5desWrl27JuYdxcTE4OLFi2bLGP+Wy00CqnOXQkJCzB5ERERUN3lVQPTrr7/i6tWriI2NBQCkpaWhuLgYBw8eFJfZunUrDAYDunfvLi6zc+dO3Lx5U1xmy5YtaN26NRo3bly7H4CIiIhUyaMBUUlJCXJycpCTkwMAyM/PR05ODgoKClBSUoIXX3wRP/30E86ePYsff/wRf/rTn9CyZUtkZGQAANq2bYuBAwdiwoQJ2LdvH3bv3o3JkyfjscceQ1xcHABgxIgR0Gq1GDduHE6cOIG1a9fi7bffNmsOIyIiIt+mEQRB8NTGt2/fjr59+1o9P2rUKCxbtgyDBw/G4cOHUVxcjLi4OAwYMACvv/66WZL0tWvXMHnyZHz77bfw8/PD0KFD8c4776BRo0biMkePHsWkSZOwf/9+REREYMqUKZgxY4ZDZdXr9QgNDYVOp2PzGRERkZdQev32aEDkTRgQEREReR+l12+vyiEiIiIiqgkMiIiIiMjnMSAiIiIin8eAiIiIiHweAyIiIiLyeQyIiIiIyOcxICIiIiKfx4CIiIiIfB4DIiIiIvJ5DIiIiIjI5zEgIiIiIp/HgIiIiIh8HgMiIiIi8nkMiIiIiMjnMSAiIiIin8eAiIiIiHweAyIiIiLyeQyIiIiIyOcxICIiIiKfx4DIBxTqyrEn7woKdeWeLgoREZEq1fN0Aahmrd1fgFnrjsEgAH4aIGtICoZ1be7pYhEREakKa4jqsEJduRgMAYBBAF5ed5w1RURERBYYENVh+VdKxWDIqEoQcPZKmWcKREREpFIMiOqwpIiG8NOYP+ev0SAxIsgzBSIiIlIpBkR1WGxoILKGpMBfUx0V+Ws0mDekPWJDA22+j0nYRETka5hUXccN69oc6a0icfZKGRIjguwGQ0zCJiIiX8QaIh8QGxqItOQmimqGmIRNRES+iAERiZiETUREvooBEYmYhE1ERL6KARGJnE3CJiIi8nZMqiYzjiZhExER1QUMiMhKbGggAyEiIvIpbDIjIiIin8eAiIiIiHweAyIiIiLyeR4NiHbu3IkHH3wQcXFx0Gg0+Prrr8XXbt68iRkzZiAlJQUNGzZEXFwcRo4ciQsXLpitIzExERqNxuwxf/58s2WOHj2KXr16oUGDBoiPj8fChQtr4+MRERGRl/BoQFRaWorU1FQsXbrU6rWysjIcOnQIr7zyCg4dOoR169YhNzcXDz30kNWyc+fORWFhofiYMmWK+Jper8eAAQOQkJCAgwcPYtGiRZg9ezb+/e9/1+hnIyIiIu/h0V5mmZmZyMzMlHwtNDQUW7ZsMXvuX//6F7p164aCggI0b35nfq3g4GDExMRIrufTTz9FZWUlPvzwQ2i1Wtx9993IycnBm2++iYkTJ7rvw6hIoa4c+VdKkRTRkL3FiIiIFPCqHCKdTgeNRoOwsDCz5+fPn48mTZqgU6dOWLRoEW7duiW+lp2djfT0dGi1WvG5jIwM5Obm4vr167LbqqiogF6vN3t4g7X7C9Bj/laMWLEXPeZvxdr9BZ4uEhERkep5TUB048YNzJgxA8OHD0dISIj4/NSpU/HZZ59h27ZteOqppzBv3jy89NJL4utFRUWIjo42W5fx76KiItntZWVlITQ0VHzEx8e7+RO5HydnJSIico5XDMx48+ZNPProoxAEAcuWLTN7bfr06eK/O3ToAK1Wi6eeegpZWVkICAhwepuzZs0yW7der1d9UGRrclY2nREREclTfUBkDIbOnTuHrVu3mtUOSenevTtu3bqFs2fPonXr1oiJicHFixfNljH+LZd3BAABAQEuBVSeYJyc1TQo4uSsRERE9qm6ycwYDJ0+fRo//PADmjRpYvc9OTk58PPzQ1RUFAAgLS0NO3fuxM2bN8VltmzZgtatW6Nx48Y1VnZP4OSsREREzvFoDVFJSQnOnDkj/p2fn4+cnByEh4cjNjYWjzzyCA4dOoQNGzagqqpKzPkJDw+HVqtFdnY29u7di759+yI4OBjZ2dmYNm0annjiCTHYGTFiBObMmYNx48ZhxowZOH78ON5++2289dZbHvnMNY2TsxIRETlOIwiCYH+xmrF9+3b07dvX6vlRo0Zh9uzZSEpKknzftm3b0KdPHxw6dAjPPvssTp06hYqKCiQlJeHJJ5/E9OnTzZq7jh49ikmTJmH//v2IiIjAlClTMGPGDIfKqtfrERoaCp1OZ7fZjoiIiNRB6fXbowGRN2FARERE5H2UXr9VnUNEREREVBsYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PPqKV1Qr9crXmlISIhThSEiIiLyBMUBUVhYGDQajc1lBEGARqNBVVWVywUjIiIiqi2KA6Jt27bVZDmIiIiIPEZxQNS7d++aLAcRERGRxygOiCwVFxfjgw8+wM8//wwAuPvuuzF27FiEhoa6rXBEREREtcGpXmYHDhxAcnIy3nrrLVy7dg3Xrl3Dm2++ieTkZBw6dMjdZSQiIiKqURpBEARH39SrVy+0bNkSK1asQL161ZVMt27dwvjx4/HLL79g586dbi+op+n1eoSGhkKn07EXHRERkZdQev12KiAKDAzE4cOH0aZNG7PnT548iS5duqCsrMzxEqscAyIiIiLvo/T67VSTWUhICAoKCqyeP3/+PIKDg51ZJREREZHHOBUQDRs2DOPGjcPatWtx/vx5nD9/Hp999hnGjx+P4cOHu7uMRERERDXKqYDoH//4B4YMGYKRI0ciMTERiYmJGD16NB555BEsWLBA8Xp27tyJBx98EHFxcdBoNPj666/NXhcEAa+++ipiY2MRGBiI/v374/Tp02bLXLt2DY8//jhCQkIQFhaGcePGoaSkxGyZo0ePolevXmjQoAHi4+OxcOFCZz42ERER1VFOBURarRZvv/02rl+/jpycHOTk5ODatWt46623EBAQoHg9paWlSE1NxdKlSyVfX7hwId555x0sX74ce/fuRcOGDZGRkYEbN26Iyzz++OM4ceIEtmzZgg0bNmDnzp2YOHGi+Lper8eAAQOQkJCAgwcPYtGiRZg9ezb+/e9/O/PRiYiIqC4SVAKA8NVXX4l/GwwGISYmRli0aJH4XHFxsRAQECCsWbNGEARBOHnypABA2L9/v7jMpk2bBI1GI/z222+CIAjCu+++KzRu3FioqKgQl5kxY4bQunVrh8qn0+kEAIJOp3Pm4xEREZEHKL1+O1VDdOPGDSxatAgPPPAAunTpgnvuucfs4Q75+fkoKipC//79xedCQ0PRvXt3ZGdnAwCys7MRFhaGLl26iMv0798ffn5+2Lt3r7hMeno6tFqtuExGRgZyc3Nx/fp12e1XVFRAr9ebPYiIiKhucmqk6nHjxmHz5s145JFH0K1bN7uTvjqjqKgIABAdHW32fHR0tPhaUVERoqKizF6vV68ewsPDzZZJSkqyWofxtcaNG0tuPysrC3PmzHH9gxAREZHqORUQbdiwAd999x169Ojh7vKoxqxZszB9+nTxb71ej/j4eA+WiIiIiGqKU01mTZs2rfHxhmJiYgAAFy9eNHv+4sWL4msxMTG4dOmS2eu3bt3CtWvXzJaRWofpNqQEBAQgJCTE7EFERER1k1MB0T//+U/MmDED586dc3d5RElJSYiJicGPP/4oPqfX67F3716kpaUBANLS0lBcXIyDBw+Ky2zduhUGgwHdu3cXl9m5cydu3rwpLrNlyxa0bt1atrmMiIiIfItTAVGXLl1w48YNtGjRAsHBwQgPDzd7KFVSUiJ22weqE6lzcnJQUFAAjUaD559/Hm+88QbWr1+PY8eOYeTIkYiLi8PgwYMBAG3btsXAgQMxYcIE7Nu3D7t378bkyZPx2GOPIS4uDgAwYsQIaLVajBs3DidOnMDatWvx9ttvmzWHERERkW9zKodo+PDh+O233zBv3jxER0c7nVR94MAB9O3bV/zbGKSMGjUKq1atwksvvYTS0lJMnDgRxcXF6NmzJ77//ns0aNBAfM+nn36KyZMno1+/fvDz88PQoUPxzjvviK+HhoZi8+bNmDRpEjp37oyIiAi8+uqrZmMVERERkW9zanLXoKAgZGdnIzU1tSbKpEqc3JWIiMj71Ojkrm3atEF5ebnThSMiIiJSE6cCovnz5+Mvf/kLtm/fjqtXr3IAQyIiIvJqTjWZ+flVx1GWuUOCIECj0aCqqso9pVMRNpkRERF5H6XXb6eSqrdt2+Z0wYiIiIjUxqmAqHfv3oqWe/bZZzF37lxEREQ4sxkiIiKiWuFUDpFSn3zyCXOKiIiISPVqNCByIj2JiIiIqNbVaEBERERE5A0YEBEREZHPY0BEREREPo8BEREREfm8Gg2InnjiCQ5iSKpTqCvHnrwrKNRx+hkiIqrm1DhEAFBcXIx9+/bh0qVLMBgMZq+NHDkSALBs2TLXSkfkZmv3F2DWumMwCICfBsgakoJhXZt7ulhERORhTk3d8e233+Lxxx9HSUkJQkJCzKbw0Gg0uHbtmlsLqQacusP7FerK0WP+VhhMjnh/jQa7ZvZFbGig5wpGREQ1pkZnu//LX/6CsWPHoqSkBMXFxbh+/br4qIvBENUN+VdKzYIhAKgSBJy9UuaZAhERkWo4FRD99ttvmDp1KoKCgtxdHqIakxTREH7m8xHDX6NBYgSPYyIiX+dUQJSRkYEDBw64uyxENSo2NBBZQ1Lgf7uJ11+jwbwh7dlcRkREypOq169fL/570KBBePHFF3Hy5EmkpKSgfv36Zss+9NBD7ishkRsN69oc6a0icfZKGRIjghgMERERAAeSqv38lFUmaTQaVFVVuVQoNWJStW8r1JUj/0opkiIaMogiIvIiSq/fimuILLvWE/kKdtUnIqr7nMoh+vjjj1FRUWH1fGVlJT7++GOXC0WkFoW6cjEYAgCDALy87jgHdSRyEgdGJbVyKiAaM2YMdDqd1fO///47xowZ43KhiNSCXfWJ3Gft/gL0mL8VI1bsRY/5W7F2f4Gni0QkciogEgTBbDBGo19//RWhoaEuF4pILdhVn8g9WNtKaufQ1B2dOnWCRqOBRqNBv379UK/enbdXVVUhPz8fAwcOdHshiTzF2FX/5XXHUSUI7KpP5CRbta38PZEaOBQQDR48GACQk5ODjIwMNGrUSHxNq9UiMTERQ4cOdWsBiTyNXfWJXGesbbWcOoe1raQWDgVEr732GgAgMTERw4YNQ4MGDWqkUERqExsayECIyAWsbSW1c2pyV6PKykrJ2e6bN697XZI5DhERkesKdeWsbaVa5fZxiEydPn0aY8eOxZ49e8yeNyZb18WBGYmIyHWsbSW1ciogGj16NOrVq4cNGzYgNjZWsscZERERkbdwKiDKycnBwYMH0aZNG3eXh4iIiKjWOTUOUbt27XDlyhV3l4WIiIjII5wKiBYsWICXXnoJ27dvx9WrV6HX680eRERERN7EqV5mpjPfm+YP1eWkavYyIyIi8j412sts27ZtTheMqCYU6sqRf6UUSREN2YOFiIgc5lSTWe/eveHn54cVK1Zg5syZaNmyJXr37o2CggL4+/u7u4xENnHCSCIicpVTAdGXX36JjIwMBAYG4vDhw6ioqAAA6HQ6zJs3z60FTExMFOdPM31MmjQJANCnTx+r155++mmzdRQUFGDQoEEICgpCVFQUXnzxRdy6dcut5STP4ISRRFSbCnXl2JN3heeYOsipJrM33ngDy5cvx8iRI/HZZ5+Jz/fo0QNvvPGG2woHAPv37zfLSTp+/Dj+8Ic/4M9//rP43IQJEzB37lzx76CgO3PjVFVVYdCgQYiJicGePXtQWFiIkSNHon79+m4P3qj2ccJIIqota/cXiDdgfhoga0gKhnWtezMz+Cqnaohyc3ORnp5u9XxoaCiKi4tdLZOZyMhIxMTEiI8NGzYgOTkZvXv3FpcJCgoyW8Y0aWrz5s04efIkPvnkE3Ts2BGZmZl4/fXXsXTpUlRWVrq1rFT7jBNGmuKEkUTkbqyNrvucCohiYmJw5swZq+d37dqFFi1auFwoOZWVlfjkk08wduxYs95tn376KSIiItC+fXvMmjULZWVl4mvZ2dlISUlBdHS0+FxGRgb0ej1OnDghu62KigoOJ+AFjBNG+t8+HjhhJBHVBFu10VQ3ONVkNmHCBDz33HP48MMPodFocOHCBWRnZ+OFF17AK6+84u4yir7++msUFxdj9OjR4nMjRoxAQkIC4uLicPToUcyYMQO5ublYt24dAKCoqMgsGAIg/l1UVCS7raysLMyZM8f9H4IAuLdX2LCuzZHeKpITRhJRjTHWRpsGRayNrlucCohmzpwJg8GAfv36oaysDOnp6QgICMALL7yAKVOmuLuMog8++ACZmZmIi4sTn5s4caL475SUFMTGxqJfv37Iy8tDcnKy09uaNWsWpk+fLv6t1+sRHx/v9ProjppohzedMJJd8InI3Yy10S+vO44qQWBtdB3kVECk0Wjw17/+FS+++CLOnDmDkpIStGvXDo0aNXJ3+UTnzp3DDz/8INb8yOnevTsA4MyZM0hOTkZMTAz27dtntszFixcBVDf9yQkICEBAQICLpSZLcu3w6a0i3XJiYdIjEdUU1kbXbU7lEBlptVq0a9cO3bp1q9FgCABWrlyJqKgoDBo0yOZyOTk5AIDY2FgAQFpaGo4dO4ZLly6Jy2zZsgUhISFo165djZWXpNVkOzyTHomopsWGBiItuQmDoTrIpYCothgMBqxcuRKjRo1CvXp3KrXy8vLw+uuv4+DBgzh79izWr1+PkSNHIj09HR06dAAADBgwAO3atcOTTz6JI0eO4L///S/+9re/YdKkSawB8oCa7BXGpEciInKWVwREP/zwAwoKCjB27Fiz57VaLX744QcMGDAAbdq0wV/+8hcMHToU3377rbiMv78/NmzYAH9/f6SlpeGJJ57AyJEjzcYtotpTk73C2AWfiIic5dTkrr6Ik7u6V6GuvEba4dfuL7BKemQOERGR76rRyV2JXGXaK8ydmPRIRETOYEBEdU5NBVtERFR3eUUOEREREVFNYkBEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBHVokJdOfbkXUGhrtzTRSEiIhOc7Z6olqzdX4BZ647BIAB+GiBrSAqGdW3u6WIRERFYQ0RUKwp15WIwBAAGAXh53XHWFBERqQQDIqJakH+lVAyGjKoEAWevlHmmQEREZIYBEVEtSIpoCD+N+XP+Gg0SI4I8UyAiIjLDgEglmGxbt8WGBiJrSAr8NdVRkb9Gg3lD2iM2NNDDJSMiIoBJ1apgmWw7I7MNUpqGIimiIS+Ydciwrs2R3ioSZ6+UITEiiN8tEZGKMCDyMKlk26zvTgFgT6S6KDY0kIEQEZEKscnMw6SSbY3YE4mIiKh2MCDyMKlkW1PsiURERFTzGBB5mGWyrSX2RCIiIqp5zCFSAdNk26O/FmPh97moEgT2RPIRhbpy5F8pZRI9EZEHMSBSCWOybVpyEzzUMY49kXwEp/MgIlIHNpmpkDEwYjBUt3E6DyIi9WBAROQhnM6DiEg9GBAReQin8yAiUg8GREQewuk8iIjUg0nVRB7E6TyIiNSBARGRh3E6DyIiz2OTGalKoa4ce/KusKcVERHVKtYQkWpwTB4iIvIU1dcQzZ49GxqNxuzRpk0b8fUbN25g0qRJaNKkCRo1aoShQ4fi4sWLZusoKCjAoEGDEBQUhKioKLz44ou4detWbX8UsoFj8hARkSd5RQ3R3XffjR9++EH8u169O8WeNm0aNm7ciM8//xyhoaGYPHkyhgwZgt27dwMAqqqqMGjQIMTExGDPnj0oLCzEyJEjUb9+fcybN6/WPwtJszUmD/NryJdxahei2uEVAVG9evUQExNj9bxOp8MHH3yA1atX4/777wcArFy5Em3btsVPP/2Ee++9F5s3b8bJkyfxww8/IDo6Gh07dsTrr7+OGTNmYPbs2dBqtbX9cUiCcUwe06CIY/LUHl501YnNyES1R/VNZgBw+vRpxMXFoUWLFnj88cdRUFAAADh48CBu3ryJ/v37i8u2adMGzZs3R3Z2NgAgOzsbKSkpiI6OFpfJyMiAXq/HiRMnZLdZUVEBvV5v9vBmak9W5pg8nrN2fwF6zN+KESv2osf8rVi7v8DTRSKwGZmotqm+hqh79+5YtWoVWrdujcLCQsyZMwe9evXC8ePHUVRUBK1Wi7CwMLP3REdHo6ioCABQVFRkFgwZXze+JicrKwtz5sxx74fxEG+5y+SYPLVP7qKb3iqS+9/D2IxMVLtUHxBlZmaK/+7QoQO6d++OhIQE/N///R8CA2vupDBr1ixMnz5d/Fuv1yM+Pr7GtldTvO2CxzF5ahcvuurFZmSi2uUVTWamwsLC0KpVK5w5cwYxMTGorKxEcXGx2TIXL14Uc45iYmKsep0Z/5bKSzIKCAhASEiI2cMbcQLRmqP2ZkglOJ+aerEZmah2eV1AVFJSgry8PMTGxqJz586oX78+fvzxR/H13NxcFBQUIC0tDQCQlpaGY8eO4dKlS+IyW7ZsQUhICNq1a1fr5a9tvODVjLqSd8OLrroN69ocu2b2xZoJ92LXzL6qbOomqis0giAI9hfznBdeeAEPPvggEhIScOHCBbz22mvIycnByZMnERkZiWeeeQbfffcdVq1ahZCQEEyZMgUAsGfPHgDV3e47duyIuLg4LFy4EEVFRXjyyScxfvx4h7rd6/V6hIaGQqfTeV1t0dr9BXh53XFUCYJ4weOJ1XmFunL0mL/Vqilj18y+XhtIFOrKmbtFRHWS0uu36nOIfv31VwwfPhxXr15FZGQkevbsiZ9++gmRkZEAgLfeegt+fn4YOnQoKioqkJGRgXfffVd8v7+/PzZs2IBnnnkGaWlpaNiwIUaNGoW5c+d66iPVOiYru1ddzLth7hYR+TrV1xCphTfXEJF71cUaIiKiukrp9dvrcoiIPI15N0REdY/qm8yI1IjNkEREdQsDIiInuTPvhlNnEBF5FgMiIg/zlpHEiYjqMuYQEXkQ56siIlIHBkREHsSRxImI1IEBEZEHSY0k7gfgamkFa4mIiGoRAyIiD7Lswq8BIACYvPqwV08JQkTkbRgQEXmYcb6qfw3vBI2mOiACmE9ERFSbGBARqUBsaCDCG2mZT0RE5CEMiMghhbpy7Mm7wlqLGiCVT+Sv0SAxIsgzBSIi8iEMiEixtfsL0GP+VoxYsZf5LTWAU4IQEXkOJ3dVyNcnd+WEprWnUFfOKUGIiNxE6fWbI1WTIrbGy+FF273cOSUIEREpwyYzUoT5LUREZKku5ZUyICJF5PJbANSZHwMRESlX1/JKmUOkkK/nEBmZ5rfs/H+XOSkpEZEP8qa8UqXXb9YQkUNiQwORltwEADgpKRGRj6qL8zAyIFIRb2qLrYs/BiIiUqYu5pUyIFIJb2uLrekfgzcFh0REvqYujpvGHCKFajKHSO1tsYW6cuRfKUVSREOz8qzdX4CX1x1HlSCIPwZ35BCt3V/A3CQiF8n9boncyRvGTeM4RF5EzWP82ApOhnVtjvRWkW79MRTqyiVzk9JbRXp8XxB5C95UUG2pS+OmsclMBdTaFisXnJg2YxmTrN31g2BuEpFrlPxuicgaAyIVUNoWW9t5NZ4ITtQaHKqR1PHA3CviTQWRc9hkphL2mp88UQVuDE4sc5tqMjgxBoeWuUl1pUrWXSyPh3E9kxARHIAFm06xmcTHeeJ3S1QXMKlaIU8OzOippOtCXTlW7srH+//LhwFwa+K0km2rPVHPU6SOBylqSsyn2lVTHR6IvBGTqusQTyRdW9ZATOzZAmN6Jirenqs9XCwT9dhj5g6p40GKWhLz1a4uHls10eGBqK5jQOQFarsKXCop84Nd+RjTM1HR+93dvMceM+akjgcpbCaxry4fW3Wp9w9RbWBStReo7QGwXEnKdHcPF/aYsWY8HiyTz00x98o+HltEtccbOnywhshL1GYVuCs1Uu5u3lPzGE2eZDweVu7Ox/s77+R4vTSwNTo0C2MziQI8tohqh7fUxDIg8iK1VQXuSk8vdzfvsceMvNjQQLz8QDuM6ZHEXBEn+PKxVRfzpkidvGmwXQZEXqqmT2jO1ki5u9s8u+Hbx1wR53j7seXsOcBb7tapdtT0tcSbamLZ7V4hT3a7t+QNJzR3d5tnN3yqKd54bDl7DlD7vIlUu2rjWqKGY07p9ZtJ1V7GWxJB3T2lh7vXR2TkbceWK+cAubv1Q+eumyW82kuA9YYEWbKttq4ltd0pyBVsMvMy3lT9SETu58o5QCpvSqMBJq8+DAHVtQQPd2qKrw7/Jltr4A011GRfbV5LvGVcLNXXEGVlZaFr164IDg5GVFQUBg8ejNzcXLNl+vTpA41GY/Z4+umnzZYpKCjAoEGDEBQUhKioKLz44ou4detWbX4Ut+BcX+QrWAshzZVzgOXdup8GgAAYr4sGAfjy0G+ytQbeUkPtzWrruK/ta4k31MSqPiDasWMHJk2ahJ9++glbtmzBzZs3MWDAAJSWlpotN2HCBBQWFoqPhQsXiq9VVVVh0KBBqKysxJ49e/DRRx9h1apVePXVV2v747jMm6ofiZy1dn8BeszfihEr9qLH/K1Yu7/A00VSDVfPAcO6NseumX2xZsK9ePuxjrCXRGo6Bhknjq1ZtXnc81pizeuSqi9fvoyoqCjs2LED6enpAKpriDp27IjFixdLvmfTpk344x//iAsXLiA6OhoAsHz5csyYMQOXL1+GVqu1u101JVUD3pkIKoddgMmUGpIwvYE7zgFK5sUz3feFunLcl7XVLIjSANgz635+Ny7y5JyVdeVaIqfOJlXrdDoAQHh4uNnzn376KSIiItC+fXvMmjULZWV37liys7ORkpIiBkMAkJGRAb1ejxMnTkhup6KiAnq93uyhJt5Q/agEawLIEmshlHHHOUCqlmDoPU0dqzWwMWI6Keep476uXEvcwauSqg0GA55//nn06NED7du3F58fMWIEEhISEBcXh6NHj2LGjBnIzc3FunXrAABFRUVmwRAA8e+ioiLJbWVlZWHOnDk19EkI8K4Bu4xYm1XzfHnARE+QSnh9IaO1ZK1B/pVSqyY2QQA7dbgBj3vP86qAaNKkSTh+/Dh27dpl9vzEiRPFf6ekpCA2Nhb9+vVDXl4ekpOTndrWrFmzMH36dPFvvV6P+Ph45wpew7z1Iq20l4NaPh9719QOTw+YqJbjrTZZDu4pN9inrYu2L+43d/L0cU9eFBBNnjwZGzZswM6dO9GsWTOby3bv3h0AcObMGSQnJyMmJgb79u0zW+bixYsAgJiYGMl1BAQEICAgwA0ldw+5k403X6SV3BGp5fN5Y22WN/NUN121HG9qJXfR3vn/LtfKAH91PeDylu7pdZXqAyJBEDBlyhR89dVX2L59O5KSkuy+JycnBwAQGxsLAEhLS8Pf//53XLp0CVFRUQCALVu2ICQkBO3atauxsruL3Em6UFeOmV8eM+syO/PLY2gTE4zU+MYeLbMS9u6I1BSEcPyn2lfbU5Ko6XhTM8uLNgCzZGCDAMxad8yt+82XAlVOxeM5qg+IJk2ahNWrV+Obb75BcHCwmPMTGhqKwMBA5OXlYfXq1XjggQfQpEkTHD16FNOmTUN6ejo6dOgAABgwYADatWuHJ598EgsXLkRRURH+9re/YdKkSaqqBZJi6yR98Nx16/Z8AH9augcLhtbcCcOdd2q27ojUFISwqaDuU9PxpnamF+09eVes9ptBAFbuzsfLD7h2w1moK8eBs9d8MlDleaX2qT4gWrZsGYDqrvWmVq5cidGjR0Or1eKHH37A4sWLUVpaivj4eAwdOhR/+9vfxGX9/f2xYcMGPPPMM0hLS0PDhg0xatQozJ07tzY/ilNsnaRtjZhQUyeMmrhTcyZfobZ5sqmAaoeajjdvIrXfAOD9nfkY0yPJ6XOQ6bnGkjOBqjcFGI6eZ73ps6mZ6gMie8MkxcfHY8eOHXbXk5CQgO+++85dxao1tk7SiRFBuD3QrJWauLOt7SYFtSUZKmkqcHV/8MTmOWo73rxFbGggxvVMwor/5Zs9b4Dzvc8szzWWHA1UvanJzdHzrDd9NktqO9+pPiDydcaTtOkBb3qSnj80xSyPyKgm7mzlaqsOnr2OP6bWzMGstiRDe00FrgSi3nxiqyvUdrypia2L19ieSXj/f/lm5yFXzkFS5xrT9ToSqHpbbpgjTbfe9tlMqfF853UDM/oqY0WZZYXZsK7NsWfW/ZiYniR+mX4aYFi3eBw4e82t8+FIzX0DAFM/O1zjQ8ybDhymljmu3DkXUE3NEaWWfeVNOFCdNXsDqMaGBmL+UPdNAyH12/IDsHREJ+ya2dehC6e3DfTpyHnF2z6bkVrnxGNApHLGA8d4zAuwPnBiQwPx8gPtsPt2YGQQgNV7CzBlTQ7uy3Lf6M/G2irLg6Y2D2Y1jWztzrmAauLEpqZ9Rd5L6cXLdI40R4MWS1K/rayhKRjUIc7h35e3TYjtyHnF2z6bkVoDOTaZqZyjPV/et2jHFwDM+tJ9XWCHdW2OhgH1MHn1YcVlchc1Vg+7q4nF3Qm9atxX5J0cOQe5s8u4u35bSob3UFMeC6D8s3tr3ptaOzAwIFI5Rw4cuXZ3V5IbpXROaOyRg1mt3aLdcRFw94lNrfuKvI8nL17uCrDkAgw15rEYKf3sxs926Nx1GAQBXRLD7b5HCWOg2FDrj9LKKrcGjGoN5BgQqZwjB45c91c/wK0nL08dzGq9q3AXdyb0qmlfqfEOnJRT68XLUZYBhppqUY3jLWk0GnROaOzw9t09/IfUkAfuDhjV2IFBI9jr104AqucyCw0NhU6nQ0hISK1vv1BXrujAWbu/ADPXHROTrzUAZj7QBk+lm8/p5o6LlNIySb3P2W2v3V9gdWJWyx2d2qhhX6n5Dpwc4+zvXa325F3BiBV7rZ5fM+FepCU3qdFtm54Dd/6/y2Y9hTWo7j2s9HdSqCs3G/4DqL752TWzr+z3ZOscLLU+peu1t25PUXr9Zg2Rl3C0+nTJj6exet95CAAWbDqFsMD64g/MXRcpZ6qzXd22Gu8qlKrtE4Wn95Wa7sDJdXVtSglP1aJangMtAw8Bjk194mjzuNQ5OL1VpHhusjXkgb1md2+/AWJAVEd9tv+8+G/TCxEAj12k3HWB9MYTs6dOFI7sK3cHbMxjUg9H8kHs1R6o7e7fWZ5oCpQ6B0oxCMrzPh0J7KS2P3PdMUCoDsT8NMCMgW1kB/y1FTDWhRsgBkQeVhMnGJvTfUDw2EXK1Qukq/vK2fe7Y7tqP1HURMCmpjwmX2F6rALVv7ljv+mwYNMpRfkgto6D2grqazPoqu1aVFu1L6b8NMrzPh0J7KS2b5o0YxCABd+fkgyGLAcFVrJub7sBYkDkQTV1grF3IfLURcqVC6Sr+8rZ97vjO1L7icIdAZvURayuJON6C9Nj1Tg0jdy1V+o7tnUcXNLfMMtNtKx1diUhWO4z1FZNam3WOEudAzUa86BEc/tzx4YGKg4OlQZ2ch1vTMm99s5jnfDH1DiHPpuSCbDVVOvIgMhDarLWwN6FyFMXKWcvkK7uK2ff767vSO01Ja4GbLYuYp7OY/JWjl4kLI9VJT1lLL9jueNg5e58rNiZb7VOqdccTQi29RnUWJPqKrlzYHqrSBw8ex0aDXBPQmMAwLyNJ7Hi9nQoSoJDJYGd5fb9bgdjpt+t1HP+Gg06JzZ26rPZ6gGntpwjBkQeUtO1BrYuRJ68SDmzbVf3lbPvd9d3pPaaElcCNiUXMW/M+aoNckGPMxcJpU0xpiy/Y6njwE8DyWDI6N87883+djQh2JTaa1LdRe4caJwPUqrLu+XvypVaFcvt7/x/l+8ESKjOIQoLqu/UjWt8eBDWPZuGskqD3QmwL+lvmPWuU0MAzIDIQ2qj1sD0zs/0b+O/HT3o3NVV39F1OJo0aLn+Y7/qrJZTsq/d+R2puabElYDNVy5i7iYX9DhbS6KkKcSU1HcsdRyM7ZloNYu9PY4kBJvyZE2qkvOSkmYfpYMYyp1/Lb9/U8bflTvGHDLd/rCuzVFcfhPzb+eZLfj+FLKGpGDXzL6Kz1dSx3NachPZCbBX7jqLFbt+kax19OS5gwGRh9RGrYEzd5ruvGt1R3kA5ftKrjvpgu9PWa3zpczWDlcvu/odqbmmxDRgC9L6obSyCoW6crvlVXtzoJqYXjTlgh65APPQuesY1EG+ZsDyWNXc7iYkoPr7eGlga3RoFoYgrZ949y713VoG7gDwwa58h2qfHEkINuXu35vSmy8l5yVbXdUdSVq3x1ZNn79GgyCtn9PN/7aCuQWbTlnlh+2a2VfReEy2gnjJWkcA7+/6xWqicuNn9OS5gwGRB9VkrYEzd5ruvmt1tTym7O0rufUvfixV8gTToWmYonKruWbH3WJDAx2++1R7c2BNcbSm0yzh2SKJFrhzZyxX0zN59WFsPXUJXx3+Tfa7kQpmnDluLQN30+/Xkq2EYGfY+r0prcU5cPYasvOuYs3tcdik9pWt4HTWl8fQMKCemCBur6u6FHfmGwLVQcS8Ie1RWlnlcI2svYBvpUTAq7SmplBXjg1HL8i+Py25idX5YVzPRPxbotbR+Bk9ee5gQORhNVVr4GhThq2AxR3NIu5Yh619Jbd+P43G5RoMNdfsuJOzQasvBY2A4zWdVgnPNu6MjQGmZbOJAODLQ7+Jf8t9N5bHqrvzEY/+VoyFm3JtJgS7o1OI5Tre25mH+bdrMaQGE4wNDawepd8kJ8XIcl+ZrktqvB0DqgNQ43biw4NsdlWXYzp7u9LgWSrpeXzPFhjTMxGxoYE4cv66VRDq6NhAs9YdQ5DWX5zzTKpJVEktn1Suk1SZpAL19y2CMD8N8NWz9yE13nbidk1jQFRHOdqUYStgcUeziFzV6dXSCkVNM86s31+jwT0JjZ3u2aaWrqC1Re4YOHj2upjwKcfXg8Y2McGyuSNyzSB+qL74Wh6Tw7o2R5DWH1PW5NgsS23mWxi/37TkJngoNU42IdgWZ7tev7cjD1mb7jR7GwRg5pfHoLn9e/fTADMy22D+d9Lj5wB39tX6nAtm67IV1xi/23XPpjmUn2Xkr9Hg6G/FePz9nxxKE0hvFYnFj6XC7/b5y7g/jAGIZTAkdT47cv469p29hsqbBqtyGwRgypoc+GmAcT2TJPfB+J4t7DbByQVDGonxiozHj/F7njGwDRZ+bx5YezoYAhgQqY67LsRKmjJMt2Ur6HFHs4hVjgOqT0amd2KudLe0VUalNRjG/WGaE6CGrqDuZOv4kquun7LmMEorb9WZfeAKuaBx8NI9EFBd4zChVxLG9EwS96/cb8u0N47ld9ElMVx2tGDTdXgi38KZ4NfZAR8LdeWYv8k6B1AAzHJeFmySD4aMdp+5jKXb8iRfkwt4qgQBv14vt9tV3ZIxb8s0t8idaQtAdUC97tk0q0DiL/+XY1abKMcgVOeHWR5nfgDG9Ey0+V5buU4aAeL4VID8eXVGZht0aBqmqlplBkQq4u4xGWwFAlLberhTU7Mf0uBOcWZ3ra42ixjXcfDsdUz97LBLOUmOfl57J3G56l81dAV1F3vHV2xoIGZktkHWd+YXIAHOd6X2NvZuSOSCRsHk///+Xz7e35Uv7l+5YN3WHfHO/3fZ7G8NgCH3NMXXhy94Xa6WrVq189fLbXa9zr9SqmhMJSW1N+9uz5NclwbAipGdUV5pMDsvGU1efRjzh5r3ujLtqi6XtF7TaQsGAGWVBrPnjpy/rigYEtchABPTk/DB/846dFzZ6tVowJ1ehrbOqws35dqdKLa2MSBSiZoalEwqEDhy/rrVSUjqoP368AW8kHGnN5Y7mkViQwMR3qjmumo7U0Zb1b/uKpunph0xfb+S4yulaajk+w0CxJ5OrlJrc6SSGxK5mk5LlvvXkRsK43dlud6+baIwMi0BZZUGh3oCOsvVruhGsrVq7+6RzMUx9qpr3LA66Vnqwmu5341ByXwbNUUGmZwhAcCEjw8ia0iKbP6WZa8rpd+nI6kGB89ddzltYd/ZazKfXpq/RoMxPZIwpkeSQze7Yq7bl8dgsHjNdHTqmj6vuhsDIpWorfFc1u4vqB6C3+J5uerig2evI7zRnROeOy5mauuqbW9QO1fLVlvTjtj6bpQeX0kRDWUv8pNXH0ZJxS2rZFZHKPkscmO6ODN6s+Xycutw5IbEtKZzymeHZdtOLPev0mBdcr4p3GlefrhTU5u9zdzB2a7oxuYd030sNQ4YIJ+YrNFUf1YB1esd2D4G3x8vqt4OqptawoLqm2173pD2GNa1OR7qGIeDZ6+juLwSr3x9wqopaEbmndwVU8bve9fMvnj7sY5W+VtSvxV732dsaKDNWnfLfTnzy2NWz9tKW3hpYGurMea63U6UVsJybjJHf8vG38HKXWfx/q5fYBDMc5qkxiCS+mxqwoBIJWojSBDvPBUmB2oAsQpZyYlY6QXLHTlJ7mSr+leubEo/q9ILrSMX6plfHkNllQH920ZbJVzKfTdKj6/Y0EDMH5oi2VtHgHUyq62AxpmgQ6qK3ZkgQGp/ADB7bsbANkhpFoqkiIYO35AYazpt/Zac/f3abI4QrHubzfryGNrEBLstKVXJ9yS3THHZTSz4/pTZPpYaB0yOZX6OQQC+O1YEoPp8NCOzDZ7qnYy1+wvEfS8IwJlLJWJtmTHBu76/n9lxLAAIC6qPXTP7YuPRQryx8WezbRu/7y6J4W45FxfqyvHVYfPmq68O/4aRaQlm35VcjaBlwGLZ2880H2dczySM7ZmE1PjGGHpPU6tmM9NxqfwAjE+vrhVyR438y4PaYkzPRKsaJmfOq56mEQSll0ffptfrERoaCp1Oh5CQkBrZxtr9BVZBgjvv/PbkXcGIFXsVL68kqdPYBuzsIJBq6aptue+NOQFSZXPks8rt8zUT7hWr322tz9Z3pkH1vFHprSLNhscHzL8bManxV514d2y8W36oY5xs8LJmbwHe2XrG5n4z3Y7UZzGeqI13jLb2RaGu3Opz2NsucKdLs/HfDbX+ePjdPWbrsXUsm164Lbc96/YFWIqt8tr7/doLqE2PRyU0GmC+wppDe9tWcszKLSOVoGvZpCLFT1M9eagAwWbvOmMyuuX3a9y26TxqUt+P6XFj6zfj6LlYap/a2kem5ZRb7l/DzSdTNa05tff5j5y/jgNnryMxIghB2voujUvlCkfOqzVJ6fWbNUQqItUube/k5UgzguRMy7f/b3natRcMAebjbEiNdWEv/8kdOUnuIDUHj62cCUdyvezVzNhbn627LAHVtQNvD+8oW7thOdDiwPYx2HS7+WH+plNizoWx10dK01DxWBrevTmWbD1j8zgwrUU5cv661YzoK/6Xj/f/ly8GblJzZQVp/QA4Nh9XlSBgyY9n8Nn+AjE3xLhP5PJE5BgEYOH3uXi2TzL+ZdELaeH3ubi3Rbhkl3rJZozM1nZ7ztjrVZV/pRTprSKxa2ZfsVnOXlwkCObjy8gFqEpq2pTUJsrNeyaV+Cv1fWgAq27zf0yNQ6Gu3GYX9ypBwP6z1rk2wJ1cH9OEbLnfhdSAgXK1MfYu3nLfp60EfHu/ccvJVM0G9pTYn5brTY1vLFljqPR86648P28bo4wBkcqYBgn2aiIcrZWxPIHL3b0Zaw+k7phNGU+Skj0gBGDl7ny8/EA75R++Flj+0OXm4JHjTNOK1IkXqL4zvFpSYXN9UnkIpgyA7OCTUsP8G5sfAPOTqkGA2LvMtInJlFTwbDwG5AbFMy4/a90xfPXsfVajHhsE4OF394gD7SkJxI1W7yuQ/CzOVHlXCQLCgrSSzxuTfzUAhneLx5R+dzl14QRsB8CmwatGA8zMbIOn0pNx5NdiRfOJGYQ748sY96fltpQO7mivSVsuGLScwsL4/HyTqSGMNRnFZXfmz5r/3SlAAJ7qnWxzZGx/jQZdExvb7CZv/O3YC+zsfXdKbtjs3dBIJWlbltPe/rYa2NNGedyRd+ru3s5Kb3zV0NmCAZFK2fuhuWNU4TOXfscr35ywWmbu4LvxxL2JZomLliyHWZc6Qb2/M19RO7WzPwRHJ1S0/KHPyGzj8DghSmp8LD+L1OzSxup6P410jxnT9VnmIZjyA2QHn5Qa5l8JY14KNObl0txuWrIcUA2AZA6E5ToHL92DmZlt8NpDbfHaNyetulrvmtkXMzPbmA2cZ5TZPgb/PV6kqPnFGbYutILJhWj1vvNYve88JpqMNaT0wpl/pRTXSislL44Hz163Gs0663aQMLZnEt7/n/mM83I1u8Cd/SlVc2hJ7gKqJNCTWiYs0HqW9GFdm+Oh1DgcOncdggCx5qPH/K1m+T1Zm04BGuCp9OQ7uTK/FksO4CcXaJj+dpQGdq5cfO3dIA3r2hwFV8uwdLt5zaNljZut/S07sKfEsepq3qkz1xV3BDLuDsKcxYBIpez90FzplWY8CVwpuSH5elhg9Z3ysK7N0SYmWBx0zshymPXY0ECM65lkdRdrAOyOcuzsD0Eu+dZWDyzLH7rl3Sxgvg+lfui2TrK28meMD6lyaHAnKLI8aUt1xTXSAMgamiKeeKWaWx2pcTFlgPUbDQLQoVmY1SzY9nqTGIkXPQnG/f5U72RAA6sB3J5KT8Z/ss9KBvCuMiavGi+09mpQAeuxhqSY5m6ZdgeXCoAhU+OxYNMpPNQxDvOHmg8MOCOzDQBYjRllVHU7M9neCMu2LqBKggXLZeQu7LGhgWZDNsgdMws2ncJDqXHietOSm+ChjtYjY4s9nHbn4/2d+ZIjftsqjxx7+VaWfyu5QVq2w3owSKnJpeX2t62BPdceOI81e89Lnjuc4eh1xd75W0n+Wk0NOeMMBkQqtfvMFcnnjfkW7uiVJjUargYwa7tOjW9sdjKWG1RO6i4WqO6lJjfKsbM/BLnxLWy9X65ZT652xtYPXS74sPwsxvyZmQ/cyc2R61KtATCx1505iwDrKQuM/DTAnIfuRv920VYnGSWzUyvhB1hdpE27AJvu34Zaf5e3Z3rsPpWebDY9BADM23hSckJIV/mhOriPCmmAPXlXxNyds1fKEKT1kx0rB7B9vNma50nAnUDF+HvqnNBYctJX4yB3w7o2N2tiWrDpFMb1TJL9XMYcFMvgfXCnOIcHd3S0BsD0+JB7r9zwDgYBVhdfuUAhNjQQLz/Qzu4YOkprgezlW0nlX6W3isS4nkn44PbcXJb7VK52R+nk0sbyS92EnSr6HZ/dnsBWA+Clga1drlWRu64Eaf2wJ++K2Hnh4LnruFZagdnrT8qev5Xmr9XWkDNKMCBSoUJdOd7dLj3EvHFkUiXVwfbEhlZ3sTYOruWHOzUOppTcZVmuy8jRIEXJD8FW8q3c++XmUnusWzw+23deTP4c1q0ZLulv2A3ULE+ycmUSYJ6bM2NgG9lEyw925YtD5r+3Uz4YGtczSQyGbAVuSkf5BYAR3eOxdt+vVk1h9o4v4/alKK2dMl23ZXBnK7BwlB+AZ/okY/mOX6wuLKZTb5j2AJopMXK3Kanjzd6AdEB1QNsyKlgcYFFuW6aD3C34/pRZM6PUtAuAeXdtqd/uCxmtFdeYuNKUYeu9saGBks2jzjT5KAnA7JG6obHMt7L823TGew2qR3y2TBFwx42rVKcPwLyXnIDqDgAPdZQe58h0Xbb2j9R1ZXCnOLFXm73ftPH3cEl/w6qDhVz+mprGpWNApEL5V6THN/EDFLc7K6V0HZYBgFyuTMOAepi8+rDZex0JUpT8EOyNbyH1fssfOlB9971633mzfIzVe89j9d7zVu+vEgT8+PNFtIhs5NA8YKYMQvVJyzJ3yXQbxl57UvM3Gdex4n/5+GBXvlVXcYNQnctjHJPG1iCLpvw1Gky5/y5Muf8us1qZ/CulNnve2brwSwVVlvwALBnRSZzA0l6OlytM81meSEsw+5z3ZZnnssz88k4PyafSkwFBvqnPT2M9K7iS3nLhDbUouFZqFTDMMn5mmAeKUk1MBsF82gW58WUsf7umzbfGu36p370rTRlK3ivVPGralORocONK8OZID0cj00NaAPDB/85iTA/zWjtXb1zlOn1IHQ/2biaV7h/Ta0KQ1s+si7+Sc8nR34ptTrRrWt7qlIo41YxLx4BIheQurjMy29gNSpzhaGKh1A/LOHpxfONAxUGO5cnC8oRoq7xSPVH8AIyzMSmh8Ye+5Mczsj2UbPnb19X5K3Ink8z2Mdho0otLSpUgoGloIOY8dLdVPoxprz173azFHCiJ5wcv3SPWckjdhWs01RMwSuVdyNU6GccJMj3e5C4irwxqiwc6xIrrNB1MbuEm8wTZQR2qx1lRmuPlDM3t48q0dsJYtm+P/CbZRd809+2p3smIC2sgOT6O1KzgSoLjBvX9MGWNdcCwa2ZfyZwZuZuHMT2SMCglFvvPXkfXROmu1lLk8t2AO+M6udKUofS9xoDTtCkwLLA+ADgU3Liah6LkO7PHlQR1KbY+k5KmLVu1lvb2j/E3ojQ/0Lh944S2SnejaUqFGrrnMyBSIalAwZhYauTK3ZArgZTcyMmmY4o83Ml8EkqpIeaNpHIjwgLrm43LcvDcdQiCYDa+SnqrSCx+LBV+Gg2aNQ7ExqNFeH/XL4qSXdfsL5B83pKt0YJNRwd2pFlHgzujf5vWTFkGJkpOzgZAMu9EwJ0xaR7qGGd2F27cTpuYYMmLqNIRiI1BsNRYNFEhAWblMUuQTbW+2BfqyrHh6AVFOV72DL2nKUamJZh1BBCE6okkjcm6pjQajfVKcHtkXxNSoxfLzQoeGyrdycDU+I8PWn1vVYKAjUcLMahDrFUumFxNg+U4U0pGj5fLd1vxvztNcHLNu0qbMpTW/ko1BRpH0zd9zl5w42oeitQ5t2tiY+zNvy65vOWI2nKfz3T9jp5r7Y2jNGOgeW2iadOWVPO5M/unodbf7m9QA+Bft2t6bfWIe7hTU3x16DebKRWeCoSMGBCplK2I2ZW7IVe7N8olBZu2FX99+ILY1GI5xLzUCVvqhBik9ccF3Q2zqldjfgdgPQWDcS4d0/3RJibYqju+ktoXoHq9sx9qh1e/OSn5ugHA4Hf3YKbMCMeS67y9r0yrn/00wJLHOomJ7Ma7O3sXVODOGC9SNSkGwXxMmt0z7zfr9u/oidO0l5RpbYbZRKe3LxKm25WaHNX0GLUVTBqDaXF0bQ2Q2T4WG48VWi07pFMcOjYPwx/axUjmTsmd/DsnNLbuWKCpHsrAstxKq/ULdeVIjQ+z/kAm5I7BNzb+jHnf/Sy57yzPCYB5HonleUDut26recj0OzY271rW6jkTYMi9V66zgyV7F29HAjC5m0HTnmsrdubLBkPP3d8Sd8UE47fr5WY3Ce5u5rH1mdbuL6jeNqqP16f7tMCy7Xmyx4Iz6QnG48deMDR/aIpY0wtY38wZOy6kxjdG3zZRilMqPIEBkYrJRczORvvu6N6opGq5ShDEvJPH3//J5vbkTohSzRMCzGujjMtKzXBdJQhiLYGS0WNNaUxqQEx7UViVR5ButpLyyqC2iA5tYHUyMAhAk0YBVnf6MzLbSPYANH5205yYe5PCrYZGMF2/6UzdUseA6ajiSkcgNh5vxovIoXPXxQk5Tbdrb/wSezlIxgk7D527DoMgoHl4EDYdLzR7jwbA1zkXsO7wBcxef9JuzYblRXH+0BSrwEGqzEqq9eVGFHakpsvWvjM9J8jlkVTvq2uSv/U2McG4WlKhqAayShDQoan1MAtKKdlfcsebI7UvgLIATOnNoFRvWSMNgCXbzlh9v3IBriu18XKfCTCfGUAQgGXb8qzOQ44M/ihVblu13hoAc/90p6ervTIba6E7J1iP9aWmSV59KiBaunQpFi1ahKKiIqSmpmLJkiXo1q2bp4vlMGeTkd3RvdHygJc60dsawdpye4623ZvWRpk+J7cscKdHiOnosZbNkQ+lVs+SbawdMJbP1qi5gHyzlSljM5JUfhUA7D5zGe9a3N0t3JSLmZnWgyBKXWAsh0awZG/8KoNwZ1RxqROa3AjExt5P1esUFNfKAPLNZADwXL+WeKxbc/F9lsGiaZOsZa2bvZoNuYui0vwFqZsU0wFCLScTFd3O27L8uBoAU+5vaTVnnJLfpeRUPBqYBaaW6zTtTWcvSJMbZsFIycXeXjOIrYu+o0m27qhVV5JcbVrDayTA9kTFcgGYvX0o9ZkkE+xhfR5yZPBHS7b2g7253YzbOXj2OqCpDoKMXE0yr2k+ExCtXbsW06dPx/Lly9G9e3csXrwYGRkZyM3NRVRUlKeL5xBnDyp3dW80PeCnSsyz9NLAO4nR9rYnlyAtR3P7P0qavUwJAnDo3HUM6iDdFRmA5ACSpsuWVd60yv0wBgymF1/TsV4sm5Ey7q6eS8yUaTBkVCUIkoMgAtLzEcnV1BjLaNzncgGo6aji9kYg9gPwdO8WWLmrOu/EWAtneYGVO7bs5Vy98+MZxIVVl0PqQmbaJHu1tEKyCl6qZsPeRdGZk7KSOaYAiFN/WD0PoFV0sFO/S6ng3rJmRWp7xv/7ARjePR6f7b0z7ITxt2XvnOLOkYXlfo/OJNkav0fLHnRKbwZt3aDZCyAtB3W1F4BZHjszZSYStjw25c7jluchqe9P6XEuV3P3zu3mfXvrsJXbppYEaik+ExC9+eabmDBhAsaMGQMAWL58OTZu3IgPP/wQM2fO9HDpHOfMQeXO6Dw2NBDhjWQGHWsW5tD2bF3MTRnbq89cKlE0t5Ml00DGkQug6bLzJT6PcWoCy7FepJqR/nvcuhea1MXU3t25XDkHdQhEScUt2X0ul/BrHADQdDnT7Q7r2hzF5XeS3y2nIjAI1XeofoJ07zUjJWP0mN5ty13IyioNYhOgXDBh+RncPQCcI3NMyb1uHEDRcr66wZ1sjydjZHoeuFJyQ3ameKmLvAHAgx2aWg23YO+cUhMjC0sd5+4IUm11AJAKOuVu0MQeVDbyBU3XZ+9Ykzp2TKcusUXuvCp1HnKW3Db+mBpn971Kjg9nv9ua5hMBUWVlJQ4ePIhZs2aJz/n5+aF///7Izs6WfE9FRQUqKirEv/V6fY2X01HOHFTujM6V1Dg5Ms6R1MX8pYGt0axxoDgHkvFkYqudX4oG5iNwO8vW1ASWbemNG1on98pVbZsmD7tajWxvn0uNKm6vRqJQV17dndZWICNU9zYJbxgg+10rHe/FePGwd4w5EuS7ewA4R8eukfueAVjNV/f14Qt4IcP+EBSAea2IVELrkhGd0KxxoNl4MsbySAWO9rapppGFLcldjC07ANg6TizH4TEdhyss6E5Nqeb2XYwA6xsAe8ea3LFjOnWJLUrPQ65w9lqh5uPDHp8IiK5cuYKqqipER0ebPR8dHY1Tp6QHXMvKysKcOXNqo3i1zl0/GqUXI0e258io2LamJBjcKU7s4ik3ArezXKl2lqvaNiYPu6sa2VYZpfafvQBMycXfX6Mxy7+S4mgSrZJjzJGg2535C3I5PKajF1s2QUl9z84MsufI5zP2AHLXZ1fTyMKWbF2MXc0VA6R7+kmtz96x5sjUJY6W0Z2c2Yaajw97NILgaDaG97lw4QKaNm2KPXv2IC0tTXz+pZdewo4dO7B3716r90jVEMXHx0On0yEkJKRWyu0tCnXlHmkPttyuvb89Ye3+AsmqbTWUzZEyFOrKzbp4WzIGnUon5lWSRGs5PIO79pc71yX1WZRcMC3LY7lv/TUa7JrZ1+mmPLltuuuzyx3XnubufemO8sjtb6m5Cj1ZVndS2/Gh1+sRGhpq9/rtEwFRZWUlgoKC8MUXX2Dw4MHi86NGjUJxcTG++eYbu+tQukOJLKkh+HEH05OckZ+merRm00lplZDaJ966n9xRbrVdQJRQ6/flTfvyvZ15VoOmqrWsjlLT8cGAyEL37t3RrVs3LFmyBABgMBjQvHlzTJ48WVFSNQMiojsnOcvcCnKdmi4g3s6b9qU3ldVbKb1++0QOEQBMnz4do0aNQpcuXdCtWzcsXrwYpaWlYq8zIrJPrb1D6gLuW/fxpn3pTWWt63wmIBo2bBguX76MV199FUVFRejYsSO+//57q0RrIiIi8j0+02TmKjaZEREReR+l12+/WiwTERERkSoxICIiIiKfx4CIiIiIfB4DIiIiIvJ5DIiIiIjI5zEgIiIiIp/HgIiIiIh8HgMiIiIi8nkMiIiIiMjn+czUHa4yDuit1+s9XBIiIiJSynjdtjcxBwMihX7//XcAQHx8vIdLQkRERI76/fffERoaKvs65zJTyGAw4MKFCwgODoZGo/F0cWqdXq9HfHw8zp8/z7ncXMD96DruQ/fgfnQP7kf3qMn9KAgCfv/9d8TFxcHPTz5TiDVECvn5+aFZs2aeLobHhYSE8EfvBtyPruM+dA/uR/fgfnSPmtqPtmqGjJhUTURERD6PARERERH5PAZEpEhAQABee+01BAQEeLooXo370XXch+7B/ege3I/uoYb9yKRqIiIi8nmsISIiIiKfx4CIiIiIfB4DIiIiIvJ5DIiIiIjI5zEgItHOnTvx4IMPIi4uDhqNBl9//bXZ64Ig4NVXX0VsbCwCAwPRv39/nD592jOFVTF7+3H06NHQaDRmj4EDB3qmsCqWlZWFrl27Ijg4GFFRURg8eDByc3PNlrlx4wYmTZqEJk2aoFGjRhg6dCguXrzooRKrk5L92KdPH6tj8umnn/ZQidVp2bJl6NChgzhwYFpaGjZt2iS+zmPRPnv70NPHIQMiEpWWliI1NRVLly6VfH3hwoV45513sHz5cuzduxcNGzZERkYGbty4UcslVTd7+xEABg4ciMLCQvGxZs2aWiyhd9ixYwcmTZqEn376CVu2bMHNmzcxYMAAlJaWistMmzYN3377LT7//HPs2LEDFy5cwJAhQzxYavVRsh8BYMKECWbH5MKFCz1UYnVq1qwZ5s+fj4MHD+LAgQO4//778ac//QknTpwAwGNRCXv7EPDwcSgQSQAgfPXVV+LfBoNBiImJERYtWiQ+V1xcLAQEBAhr1qzxQAm9g+V+FARBGDVqlPCnP/3JI+XxZpcuXRIACDt27BAEofr4q1+/vvD555+Ly/z8888CACE7O9tTxVQ9y/0oCILQu3dv4bnnnvNcobxU48aNhffff5/HoguM+1AQPH8csoaIFMnPz0dRURH69+8vPhcaGoru3bsjOzvbgyXzTtu3b0dUVBRat26NZ555BlevXvV0kVRPp9MBAMLDwwEABw8exM2bN82OyTZt2qB58+Y8Jm2w3I9Gn376KSIiItC+fXvMmjULZWVlniieV6iqqsJnn32G0tJSpKWl8Vh0guU+NPLkccjJXUmRoqIiAEB0dLTZ89HR0eJrpMzAgQMxZMgQJCUlIS8vDy+//DIyMzORnZ0Nf39/TxdPlQwGA55//nn06NED7du3B1B9TGq1WoSFhZkty2NSntR+BIARI0YgISEBcXFxOHr0KGbMmIHc3FysW7fOg6VVn2PHjiEtLQ03btxAo0aN8NVXX6Fdu3bIycnhsaiQ3D4EPH8cMiAiqmWPPfaY+O+UlBR06NABycnJ2L59O/r16+fBkqnXpEmTcPz4cezatcvTRfFqcvtx4sSJ4r9TUlIQGxuLfv36IS8vD8nJybVdTNVq3bo1cnJyoNPp8MUXX2DUqFHYsWOHp4vlVeT2Ybt27Tx+HLLJjBSJiYkBAKteExcvXhRfI+e0aNECEREROHPmjKeLokqTJ0/Ghg0bsG3bNjRr1kx8PiYmBpWVlSguLjZbnsekNLn9KKV79+4AwGPSglarRcuWLdG5c2dkZWUhNTUVb7/9No9FB8jtQym1fRwyICJFkpKSEBMTgx9//FF8Tq/XY+/evWbtv+S4X3/9FVevXkVsbKyni6IqgiBg8uTJ+Oqrr7B161YkJSWZvd65c2fUr1/f7JjMzc1FQUEBj0kT9vajlJycHADgMWmHwWBARUUFj0UXGPehlNo+DtlkRqKSkhKzSDw/Px85OTkIDw9H8+bN8fzzz+ONN97AXXfdhaSkJLzyyiuIi4vD4MGDPVdoFbK1H8PDwzFnzhwMHToUMTExyMvLw0svvYSWLVsiIyPDg6VWn0mTJmH16tX45ptvEBwcLOZihIaGIjAwEKGhoRg3bhymT5+O8PBwhISEYMqUKUhLS8O9997r4dKrh739mJeXh9WrV+OBBx5AkyZNcPToUUybNg3p6eno0KGDh0uvHrNmzUJmZiaaN2+O33//HatXr8b27dvx3//+l8eiQrb2oSqOQ4/1byPV2bZtmwDA6jFq1ChBEKq73r/yyitCdHS0EBAQIPTr10/Izc31bKFVyNZ+LCsrEwYMGCBERkYK9evXFxISEoQJEyYIRUVFni626kjtQwDCypUrxWXKy8uFZ599VmjcuLEQFBQkPPzww0JhYaHnCq1C9vZjQUGBkJ6eLoSHhwsBAQFCy5YthRdffFHQ6XSeLbjKjB07VkhISBC0Wq0QGRkp9OvXT9i8ebP4Oo9F+2ztQzUchxpBEITaCb2IiIiI1Ik5REREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQEZHXq6ys9HQRrKixTEQkjwEREalOnz59MHnyZEyePBmhoaGIiIjAK6+8AuNMQ4mJiXj99dcxcuRIhISEYOLEiQCAXbt2oVevXggMDER8fDymTp2K0tJScb3vvvsu7rrrLjRo0ADR0dF45JFHxNe++OILpKSkIDAwEE2aNEH//v3F9/bp0wfPP/+8WRkHDx6M0aNHi387WyYiUgcGRESkSh999BHq1auHffv24e2338abb76J999/X3z9H//4B1JTU3H48GG88soryMvLw8CBAzF06FAcPXoUa9euxa5duzB58mQAwIEDBzB16lTMnTsXubm5+P7775Geng4AKCwsxPDhwzF27Fj8/PPP2L59O4YMGQJHp3p0tExEpB6c3JWIVKdPnz64dOkSTpw4AY1GAwCYOXMm1q9fj5MnTyIxMRGdOnXCV199Jb5n/Pjx8Pf3x3vvvSc+t2vXLvTu3RulpaX47rvvMGbMGPz6668IDg42296hQ4fQuXNnnD17FgkJCZLl6dixIxYvXiw+N3jwYISFhWHVqlUA4FSZGjRo4NJ+IiL3YQ0REanSvffeKwZDAJCWlobTp0+jqqoKANClSxez5Y8cOYJVq1ahUaNG4iMjIwMGgwH5+fn4wx/+gISEBLRo0QJPPvkkPv30U5SVlQEAUlNT0a9fP6SkpODPf/4zVqxYgevXrztcZkfLRETqwYCIiLxSw4YNzf4uKSnBU089hZycHPFx5MgRnD59GsnJyQgODsahQ4ewZs0axMbG4tVXX0VqaiqKi4vh7++PLVu2YNOmTWjXrh2WLFmC1q1bi0GLn5+fVfPZzZs3XS4TEakHAyIiUqW9e/ea/f3TTz/hrrvugr+/v+Ty99xzD06ePImWLVtaPbRaLQCgXr166N+/PxYuXIijR4/i7Nmz2Lp1KwBAo9GgR48emDNnDg4fPgytVis2f0VGRqKwsFDcVlVVFY4fP273MygpExGpAwMiIlKlgoICTJ8+Hbm5uVizZg2WLFmC5557Tnb5GTNmYM+ePZg8eTJycnJw+vRpfPPNN2IC84YNG/DOO+8gJycH586dw8cffwyDwYDWrVtj7969mDdvHg4cOICCggKsW7cOly9fRtu2bQEA999/PzZu3IiNGzfi1KlTeOaZZ1BcXGz3M9grExGpRz1PF4CISMrIkSNRXl6Obt26wd/fH88995zYlV1Khw4dsGPHDvz1r39Fr169IAgCkpOTMWzYMABAWFgY1q1bh9mzZ+PGjRu46667sGbNGtx99934+eefsXPnTixevBh6vR4JCQn45z//iczMTADA2LFjceTIEYwcORL16tXDtGnT0LdvX7ufwV6ZiEg92MuMiFRHqlcXEVFNYpMZERER+TwGREREROTz2GRGREREPo81REREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8/4/NXj9NDYxwzoAAAAASUVORK5CYII=", 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" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Model Validation\n", - "\n", - "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] = [\n", + " \"pressure\",\n", + " \"temperature\",\n", + " \"enth_mol\",\n", + " \"entr_mol\",\n", + " \"CO2_enthalpy\",\n", + " \"CO2_entropy\",\n", + "]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns)\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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YtmwZ3N3dAQAJCQmIiIhQXCsyMhKxsbEAilYbnjhxArNnz5bP29jYICIiAgkJCQCAEydOoKCgQHGd5s2bIyAgAAkJCejQoQMSEhLQunVreHt7K77npZdewh9//IF27dpVyHMhIiIi4zIzM7FmzZpi28XExMDDw6MSemRF05EAMHPmTNSuXRseHh64cuUK/ve//8nn/v77b1y+fBnbtm3DZ599hri4OJw4cUIxZZmenq4IjADA29sbOTk5uHfvHjIyMlBYWGi0TXp6unwNe3t7uLm5mW1j7Brac6bk5eUhJydH8apuRo0aBUmSIEkS7Ozs4O3tjR49euCTTz4pVamNuLg4g38HREREpujX1FSrXZCSEgi12sVsu4pk0SBs1qxZ8h9kU6/z58/L7adPn45Tp07hu+++g62tLUaMGAEhBICiUgJ5eXn47LPP0LlzZzz99NP4+OOPcfjwYSQlJVnqFktlyZIlUKlU8svf37/CviszMxNpaWkmX5mZmRX23b169UJaWhouXbqEffv2oVu3bpg8eTL69u1brosXiIiIjDl5sh1WrozFhg0jsXJlLE6etMwMlUWnI6dNm4ZRo0aZbdOoUSP5Z09PT3h6eqJp06Zo0aIF/P398csvvyA8PBy+vr6oVasWmjZtKrdv0aIFgKKVis2aNYOPj4/BKsbr16/D1dUVTk5OsLW1ha2trdE2Pj4+AAAfHx/k5+cjOztbMRKj30Z/RaX2mto2xsyePRtTp06V3+fk5FRIIGbpIVkHBwf5OdSvXx8hISHo0KEDunfvjri4OIwdOxbvvPMOPv30U/z9999wd3dHVFQUli1bhjp16uD777/H888/D+Bh0vz8+fOxYMECfP7551i1ahWSkpJQu3ZtPPPMM1i5ciXq1atX7vdBRETWR612wa5dfSFE0TiUEDbYtasvgoMvQqXKrdS+WHQkzMvLC82bNzf7MlWCQTt1lZeXBwB46qmn8ODBAyQnJ8tt/vrrLwBAw4YNAQDh4eE4ePCg4jrx8fEIDw8HUFSKIDQ0VNFGo9Hg4MGDcpvQ0FDY2dkp2iQlJeHKlStym/DwcPz++++KFZXx8fFwdXVFy5YtTT4PBwcHuLq6Kl4VoaRDrZU5JPvMM8+gTZs22LFjB4CiXLzVq1fjjz/+wIYNG3Do0CHMmDEDANCxY0esXLkSrq6u8sjdyy+/DAAoKCjAokWL8Ntvv+Gbb77BpUuXig30iYioesjMzMT58+fx+++/G7wuXLgAAMjK8pADMC0hbJCV5V7p/bWKxPzExEQcP34cnTp1Qt26dZGcnIy5c+ciODhYDnwiIiIQEhKC0aNHY+XKldBoNJgwYQJ69Oghj46NHz8ea9aswYwZMzB69GgcOnQIW7duxZ49e+Tvmjp1KkaOHIn27dvjySefxMqVK3Hnzh155EWlUmHMmDGYOnUq3N3d4erqiokTJyI8PBwdOnQAAPTs2RMtW7bE8OHDsWzZMqSnp+PVV1/FhAkT4ODgUMlPz3o0b94cZ86cAQB5sQQABAYG4vXXX8f48ePx/vvvw97eHiqVCpIkGYwsjh49Wv65UaNGWL16NZ544gncvn0bderUqZT7ICKiypWZmYmbN29iy5YtxbZ1d8+EJGkUgZgkaeDunlWRXTTKKoIwZ2dn7NixA/Pnz8edO3fg6+uLXr164dVXX5WDGhsbG+zatQsTJ05Ely5dULt2bfTu3RvLly+XrxMUFIQ9e/ZgypQpWLVqFRo0aICPPvoIkZGRcpvBgwfj5s2bmDdvHtLT09G2bVvs379fkWi/YsUK2NjYIDo6Gnl5eYiMjMT7778vn7e1tcXu3bvx0ksvITw8HLVr18bIkSPx2muvVcLTsl5CCHl68cCBA1iyZAnOnz+PnJwcPHjwAPfv38fdu3fN7hBw4sQJLFiwAL/99htu3bolj5heuXLF7CgkERFZJ/0UG7XaBVlZHrCzy0N2dl0AgL//VXmqUaXKRVTUbnlKUpI0iIraXelTkYCVBGGtW7fGoUOHim3n5+eH7du3m23z9NNP49SpU2bbxMTEICYmxuR5R0dHvPfee3jvvfdMtmnYsCH27t1rvsOkcO7cOQQFBeHSpUvo27cvXnrpJSxevBju7u746aefMGbMGOTn55sMwu7cuYPIyEhERkZi48aN8PLywpUrVxAZGVmpU6tERFSxMjMzkZ+fD7VarUj9OXmynU6+lwCgLbYt0K/fLoSEFP39Dwk5heDgi8jKcoe7e5ZFAjDASoIwqv4OHTqE33//HVOmTMGJEyeg0WiwfPlyeWPyrVu3Ktrb29sbbCN0/vx5ZGZmYunSpfKChl9//bVyboCIiCqF8ZGvQNjZ5SkS7h8GYEU/6yffq1S5RoOvytwOkEEYVbq8vDykp6ejsLAQ169fx/79+7FkyRL07dsXI0aMwNmzZ1FQUIB3330XUVFR+Pnnn7Fu3TrFNQIDA3H79m0cPHgQbdq0gbOzMwICAmBvb493330X48ePx9mzZ7Fo0SIL3SUREVUEUyNf+nle+rTJ9ypVLgYMGABPT0+DNvb29pVWqBWwsmKtVD3s378fvr6+CAwMRK9evXD48GGsXr0a//vf/2Bra4s2bdrgnXfewZtvvolWrVph48aNWLJkieIaHTt2xPjx4zF48GB4eXlh2bJl8PLyQlxcHLZt24aWLVti6dKlePvtty10l0REVN4yMzPlmRFjpSaKpiCN002+9/T0hK+vr8GrMgMwgCNhNVJJh1orYkg2Li4OcXFxxbabMmUKpkyZojg2fPhwxfu1a9di7dq1imNDhw7F0KFDFce0BX2JiMi66eb3Gis1UTQFKfT+CYsm35vDIKwG8vDwQExMjNlk9coekiUiIioNY6UmikiQJA2GDPkSBQVF+0v7+1+rcgEYwCCsxmKARURE1iw5uTFMTXQIYQN7+wI0a3bR6PnKTL43h0EYERERWRVtPpip1HZjxVd79OiBunXrol69elVmIIJBGBEREVmUtu6XKfopMsbzwYro538NGDAAfn5+VSbw0sUgjIiIiCxGv+6XKbpF1I3ng2kwcOBXBvlfVTUAAxiEERERkYVkZmYiNTVVcUy77ZC7e6YimNIdKTO19VCrVucAFE09BgUFVflFZgzCiIiIqFKZ2nBbv/hqVNRueashQJlQb27roWbNmlXp4EuLQRgRERFVGlPTj9eu+WLnzofJ9kLYGGw1VN1KLDEIIyIiokqjH0Cp1S5ITAzD0aMdodzvUbnVkJa1BFglwW2LqFr5/vvvIUkSsrOzS/yZwMBArFy5ssL6RERUk2VmZiItLU1+ZWRkyOdOnmyHFSticfToU9APwADjpSaqEwZhVKlGjRoFSZIwfvx4g3MTJkyAJEkYNWpU5XeMiIjKnXbqcf369fJrx44dAIqv9QVUza2GyhODMKp0/v7+2Lx5M+7duycfu3//PjZt2oSAgAAL9oyIiMqTsanHlJRAeQWkuVpfY8d+pEjKr44YhFGlCwkJgb+/v/xfQwCwY8cOBAQEoF27dvKxvLw8TJo0CfXq1YOjoyM6deqE48ePK661d+9eNG3aFE5OTujWrRsuXbpk8H0//fQTOnfuDCcnJ/j7+2PSpEm4c+dOhd0fEREZOnmyHVaujMWGDSOxYkUs/v47EJKkMWinXRXZoEGa4nhV2WqoPDEII1y7Bhw+XPTPyjJ69Gh8+umn8vtPPvkEzz//vKLNjBkzsH37dmzYsAEnT55E48aNERkZiaysovyAq1evYsCAAYiKisLp06cxduxYzJo1S3GN5ORk9OrVC9HR0Thz5gy2bNmCn376SVH0j4iIKpZ26vHhyJcNfvyxC1q0OKcTiGnQsePPiI1dqRgBGzBgAGJiYqpVQr4WV0fWcB9/DIwbB2g0gI0NsH49MGZMxX/vc889h9mzZ+Py5csAgJ9//hmbN2/G999/DwC4c+cO1q5di7i4OPTu3RsA8OGHHyI+Ph4ff/wxpk+fjrVr1yI4OBjLly8HUFQX5vfff8ebb74pf8+SJUswbNgwxMbGAgCaNGmC1atXo2vXrli7di0cHR0r/maJiGo441OPEs6da4ExYz5CQYG9Qa0vrapc8f5RMQirwa5dexiAAUX/fPFFIDISaNCgYr/by8sLffr0QVxcHIQQ6NOnDzw9PeXzycnJKCgowFNPPSUfs7Ozw5NPPolz54oqIp87dw5hYWGK64aHhyve//bbbzhz5gw2btwoHxNCQKPRICUlBS1atKiI2yMiqlFM7f2oXQnp7p4JQAP9CTghbFBQYI+goMsGnx00aFCV2my7IjAIq8EuXHgYgGkVFgIXL1Z8EAYUTUlqpwXfe++9CvmO27dv48UXX8SkSZMMznERABHRoyvJ3o8qVS569DiA+Pge0C1FoVuCYsCAAfJ/jFtTwdVHwSCsBmvSpGgKUjcQs7UFGjeunO/v1asX8vPzIUkSIiMjFeeCg4Nhb2+Pn3/+GQ0bNgQAFBQU4Pjx4/LUYosWLbBz507F53755RfF+5CQEPz5559oXFk3RURUjRkb8dKt+wWY3vvxqacSAAAHDkQotiXStvH09ISvr28F30HVwiCsBmvQoCgH7MUXi0bAbG2BDz6onFEwALC1tZWnFm1tbRXnateujZdeegnTp0+Hu7s7AgICsGzZMty9exdj/i9pbfz48Vi+fDmmT5+OsWPH4sSJE4iLi1NcZ+bMmejQoQNiYmIwduxY1K5dG3/++Sfi4+OL/S83IiJ6GHip1WqDvR71Fbf341NPJaBVq7NG93usiRiE1XBjxhTlgF28WDQCVlkBmJarq6vJc0uXLoVGo8Hw4cORm5uL9u3b49tvv0XdunUBFE0nbt++HVOmTMG7776LJ598Em+88QZGjx4tX+Pxxx/HkSNHMGfOHHTu3BlCCAQHB2Pw4MEVfm9ERNbO3FSj/oiX/gpIY3s/AkVTk8aCr+pYgqI4khBCWLoTZFxOTg5UKhXUarVBsHL//n2kpKQgKCiIK/wqGJ81EdVUaWlpWL9+vcFxYyNedevewoYNIw3ajhwZh6Cgy4qcL33VLQfM3N9vXRwJIyIiohJRq11w9aq/0RGvMWM+giRpFKUodBPva2LOV3EYhBEREVGxdEe/9GlLTURF7TYYIavpeV/mMAgjIiIime4KSO3KR8OK90raEa+goMsIDr7IxPsSYhBGREREAEwn4he32bbuiBcT70uOQZiV47qKisdnTETVUXJyMu7evas4duvWLcV77QpIO7s8g3wvQIOBA7+Cv/81RdBlLAG/uiXelxcGYVZKW1crPz8fTk5OFu5N9aYdltevZUZEZK2Sk5PxxRdfmG2jvwLy8cfP4MyZxxX5Xq1anTP4XHXe67G8MQizUrVq1YKzszNu3rwJOzs72NgYHyamR6PRaHDz5k04OzujVi3+z4WIqgf9ETB9xmp+nTnzuMnNtgcNGgQ3NzeOeJUS/6pYKUmS4Ovri5SUFFy+bLjxKZUfGxsbBAQEQJKk4hsTEVmha9d8ceVKQwQEXEaDBmlGc8B0N9uuifs8VgQGYVbM3t4eTZo0MbpzPZUfe3t7jjQSUbX19df/xm+/tUHRxtoCbdr8hmeeOcSaX5WAQZiVs7GxYRV3IiIqk2vXfHUCMACQ8NtvbfDEE8dY86sSMAgjIiKq5nRrfwEPV0FeudIQDwMwLQlXrwYgPDyRNb8qGIMwIiKiaszcJtwBAZcBCCgDMQF//ysAWPOrojEIIyIismL6o1z61Gq13vui2l/u7plo0CANbdr8ZpAT1qBBGgCgW7duaNKkieLzTMQvPwzCiIiIrJS5US5j9Gt/RUXtxv/7f//DE08cw9WrAfD3vyIHYABQv359JuBXIAZhREREVkJ/1Eu7t2NJGKv9tWtXXwQHX0SDBmkYPrw56tYNl9s7OzsjODi4/DpPBhiEERERWYGSjHrpTjXq53KZqv2VleUOlSoXTZo04ahXJWMQRkREZAWKqwlpbKoxJOSUfN7dPdNs7S+qfKxASUREZOVMTTWq1S5yG5UqF1FRuyFJGgBg7a8qgCNhREREVq64qUatkJBTrP1VhTAIIyIiskK6+V+pqb7Qr/dlaqqRtb+qDgZhREREVVxmZqZiJaRu/hegQVHwpSy4GhFxQA62Bg0aBDc3N5PXZ+0vy2AQRkREVIXpr4rUz/8ynt4twc8vVX5Xr149BllVEBPziYiIqjD9VZFXr/ob5H/p052KHDx4MAOwKoojYURERBZQ3HZDxqYItdOQhjSQJCjKU2inIlUqVXl2m8oRgzAiIqJKVtLthmJiYuSfDachtTTo1283Vz1aIQZhRERElUx/BMxUpXvddsbKUADAwIFfoVWrcwDAVY9WhkEYERGRBZmrdJ+dnS2vajRV8d7f/xoAYMCAAfD09FRcm6seqzYm5hMREVlIcZXut27dCrVaDaD4iveenp7w9fVVvBiAVW0cCSMiIqogppLvtTW/TFW6T0wMQ8+eBwAABQUF8jlWvK9eGIQRERFVgJIk3xubYgSAo0fDERaWCJUqF7VqKf9Us+J99cEgjIiIqAKYKz+hpVLlIjw8AUePPqV35uG+j25uboiJiSl1OQuq+hiEERERVQJTKyDDwhJx9Gg4dNO09fd9ZIBVPTEIIyIiqmD6KyAjIg7gqacSABSNhvXrt9tghSTzvao/BmFEREQVyNgKyPj4HgAgB2JMuK+ZWKKCiIioAhkvsiohPj5CLkUBFI2IBQVdZgBWgzAIIyIiqkDaFZCGipLvi8NVj9UXpyOJiIgqkEqVi4iIA/83BSnJx3WT741Vuwe46rG6YxBGRERkgqliq1rmgiTdESxt7ld8fAQAw+R7bbV7qlkYhBERERlRkmKrADB48GCoVCrFMW1wNnjwYGzZsgVAUSDWqtVZJt+TjEEYERGRESUptgoA69fvNVr/KyYmxiA4M1Xtnmomq0nM79evHwICAuDo6AhfX18MHz4cqamp8vkFCxZAkiSDV+3atRXX2bZtG5o3bw5HR0e0bt0ae/fuVZwXQmDevHnw9fWFk5MTIiIicOHCBUWbrKwsDBs2DK6urnBzc8OYMWNw+/ZtRZszZ86gc+fOcHR0hL+/P5YtW1bOT4SIiMpTZmYm0tLS5Jd2f0cttdoFKSmBihWNJ0+2w8qVsdiwYSRWrozFyZPt5HP5+fklTqpn8n3NZDUjYd26dcMrr7wCX19f/PPPP3j55ZcxcOBAHD16FADw8ssvY/z48YrPdO/eHU888YT8/ujRoxg6dCiWLFmCvn37YtOmTejfvz9OnjyJVq1aAQCWLVuG1atXY8OGDQgKCsLcuXMRGRmJP//8E46OjgCAYcOGIS0tDfHx8SgoKMDzzz+PcePGYdOmTQCAnJwc9OzZExEREVi3bh1+//13jB49Gm5ubhg3blxlPC4iIiqF4qYe9YutRkXtRnDwRYP6X7t29UVw8EV5tMvDw4NbDpFJkhBCWLoTZbFz5070798feXl5sLOzMzj/22+/oW3btvjhhx/QuXNnAEXz9nfu3MHu3bvldh06dEDbtm2xbt06CCHg5+eHadOm4eWXXwYAqNVqeHt7Iy4uDkOGDMG5c+fQsmVLHD9+HO3btwcA7N+/H//6179w7do1+Pn5Ye3atZgzZw7S09Pl/7qZNWsWvvnmG5w/f77E95iTkwOVSgW1Wg1XV9cyPysiIjIvLS0N69evN3pOrXbBypWxilpfkqRBdPR2fPXVfwzajxwZh6Cgyxg3bhyT7Wuokv79tprpSF1ZWVnYuHEjOnbsaDQAA4CPPvoITZs2lQMwAEhISEBERISiXWRkJBISilatpKSkID09XdFGpVIhLCxMbpOQkAA3Nzc5AAOAiIgI2NjYIDExUW7TpUsXxfByZGQkkpKScOvWrUe8eyIiqmhqtQvOnm2JY8dCceJEiEGx1aL3wqD+l/6ej0TmWM10JADMnDkTa9aswd27d9GhQwfFiJau+/fvY+PGjZg1a5bieHp6Ory9vRXHvL29kZ6eLp/XHjPXpl69eorztWrVgru7u6JNUFCQwTW05+rWrWu033l5ecjLy5Pf5+TkGG1HRETlS61Wyz+fPNkOO3dGQbemFyCgX+PL3/8aoqK45yOVnUVHwmbNmmU0mV73pTt9N336dJw6dQrfffcdbG1tMWLECBibTf3666+Rm5uLkSNHVubtPLIlS5ZApVLJL39/f0t3iYio2svMzJTLSKjVLti5sy+UARj+733R3xvdYCsk5BRiY1di5Mg4xMauREjIqUrtO1k3i46ETZs2DaNGjTLbplGjRvLPnp6e8PT0RNOmTdGiRQv4+/vjl19+QXh4uOIzH330Efr27WswouXj44Pr168rjl2/fh0+Pj7yee0x3Xn869evo23btnKbGzduKK7x4MEDZGVlKa5j7Ht0v8OY2bNnY+rUqfL7nJwcBmJERBVMN2k+MTEMpscnJERG7kfLln8qRrtYdoLKyqJBmJeXF7y8vMr0WY2maB5ed/oOKMrrOnz4MHbu3GnwmfDwcBw8eBCxsbHysfj4eDmICwoKgo+PDw4ePCgHXTk5OUhMTMRLL70kXyM7OxsnTpxAaGgoAODQoUPQaDQICwuT28yZMwcFBQVyzlp8fDyaNWtmcioSABwcHODg4FCGp0FERI9KrXZBQkK4yfOSpDEIwMxh2QkqjlXkhCUmJuL48ePo1KkT6tati+TkZMydOxfBwcEGo2CffPIJfH190bt3b4PrTJ48GV27dsXy5cvRp08fbN68Gb/++qu8IkaSJMTGxuL1119HkyZN5BIVfn5+6N+/PwCgRYsW6NWrF1544QWsW7cOBQUFiImJwZAhQ+Dn5wcAePbZZ7Fw4UKMGTMGM2fOxNmzZ7Fq1SqsWLGiYh8UERGVWVaWh0ECvpZ+vpexKvm6WHaCSsIqgjBnZ2fs2LED8+fPx507d+Dr64tevXrh1VdfVYwcaTQaxMXFYdSoUbC1tTW4TseOHbFp0ya8+uqreOWVV9CkSRN88803co0wAJgxYwbu3LmDcePGITs7G506dcL+/fvlGmEAsHHjRsTExKB79+6wsbFBdHQ0Vq9eLZ9XqVT47rvvMGHCBISGhsLT0xPz5s1jjTAiIgsobv9HbVK+u3smJEmjF4hp8K9/7UGzZhf0piBVLD9Bj8xq64TVBKwTRkT0aPSLsKrVLka3GNIyVpTVWLI9a4CROSX9+20VI2FERERloTsCVpIAKyTkFIKDLxa7yTbzvag8MAgjIqJqT612KXaLIS1Tqx0HDBgAT09P5ntRuWEQRkRE1YpuDph2E25jSfdC2CAry73Eqx39/PwYfFG5YhBGRETVhvEcsEDk59cySLrX3WJo0KBBcHNzM3ldjn5RRWAQRkRE1YapHLCH2w4V/VO/5ISbmxsT7anSMQgjIiKrUFypCd1kef0csIfbEEkANBgz5iM0aJBWYX0lKgkGYUREVOXpTzOa0qNHDwDmC68CNigo4OpGsjwGYUREVGVpR7+0CfZa2npfdnZ5KChwkOt+xcfHAzBVeLWIbi6YFktOkCUwCCMioirJ1OiXsVwv/bpfKlUuoqJ2m2ynzQUbMGAAVz2SxTAIIyKiKkk//0utdsHVq/5Gc72M1f3SLbxqZ5ePggJ7gwKsnp6eDMDIYhiEERGRRZlKuNedglSOfhlnrO6XqcKrRFUBgzAiIrKYkiTcG650NM5YrhdRVWb+N5qIiKgCGZtyTEkJhFrtIh8zv9JRAIBBrlfPnj1L9P1MyCdL4kgYERFVCaY22HZ3zwSggXLcQIOBA7+Cm1u20VyvwMBAxMTEFFtXjPlgZEkMwoiIyOLMbbBdRFK0lyTA3/+a2XwvBlhU1TEIIyIiizO3wXZRACYZPWcqCOM0I1kDBmFERGRxxoqr6ibamzvXo0cPBAUFyec4zUjWgon5RERkcdriqpKkAaBMtDd3DgCCgoLg6+srvxiAkbXgSBgREZW7kmy2rR8s6RZX1U+0N3eOyFoxCCMionJV0s22Y2JiDHK3zBVXZeFVqm4YhBERUbkyVvsrK8tD3mRb648//kDdunXRu3dvODk5oVatWnBzc5PPZ2dnY+vWrcV+H5PwyVoxCCMiogqjX/srIuIA/PzS4O6eicOHDxu0j4mJkacpfX19WeuLqjUGYUREVCGM1f6Kj++BonITGnTsmICwsETF6Jh+wMUAi6ozro4kIqIKYXy7IW29LxscPfoUVq6MxcmT7Sq7a0RVAoMwIiIqF5mZmUhLS0NGRgaAh7W/zNFWxtfdK5KopuB0JBERPTJjKyK19b0eTkkK6Fe+B4qvfk9UXTEIIyKiR2ZqRWRw8EXExq5EVpY7UlP9EB8fAf1JGN3q90Q1CYMwIiIqE92CrNopSMBwRWRU1G6EhJxCUNBltGp1FomJYUhICFec5ygY1UQMwoiIqNSSk5PxxRdfyO+LRr4CYWeXZ7AicteuvggOvigXW+3Z8wDCwhJZ/Z5qPAZhRERUKpmZmYoATHfkC9BAf7rRWM6Xqer3LLxKNQmDMCIiKpUbN27IP6vVLti5sy8eBl6GCfi6OV/dunVD3bp1FdfTVspn4VWqaRiEERFRiWVmZiq2Evrhh84wrHYkQZI0RnO+mjRpAl9f38rrMFEVxiCMiKiG0E2kN6YkI1G6n//553CcONHeSCsNxoz5CAUF9sz5IjKDQRgRUQ1grI6XMTExMQAMS05oaVdBqtUu/1duwrDuV8eOCWjQIM3o55nzRfQQgzAiohrA3AiYrqSkJMTHx8vvtfW+3N0zFSNaWVkeMLbpiiRpEBaWKL8fMGAAPD09AXCzbSJ9DMKIiGogU8GVbgBmqt4X8HBLIuXekAIREQcU1/P09GQOGJEJDMKIiKqp0hZT1Q3MABRb70u5JZEGPXocwFNPJSj6wOlHItMYhBERVUOmcsDUahejwdW9e444cCBCDszCwxP0RrkM632FhJxCcPBFk0VXn3vuOU4/EpnBIIyIqBoytZfjnTvORoMrbQCmfZ+QEA79wqvG9ng0VXR18ODBCA4OLp+bIaqmGIQREVVzhhXtlcVUAf3crqJArGPHn8u8x6OXl1d5dZ+o2mIQRkRUjelPPxaNbGkgSUIOriIiDihGwoCHqxxN7fGou+pRH1dBEpUMgzAiomosK8vDYJQLsEF09FbUrn1XDq6cnO4bJOtrgy5jo19c9Uj06BiEERFVI9oVkdrVkMZKSUiSBv7+1xTBVXFJ9vq46pHo0TEIIyKyYrplKNRqNbZs2SKf0ybj6043msvtMpdkr1Kp5PecbiQqHwzCiIislLmtiPRrgUVEHICfX6rBKJd+gKWPARdRxWEQRkRkpUyVobCzyzOoBXbgQARiY1cajHR5eXkxyCKyEAZhRETVgGEZCtOFVrUrGznKRWRZDMKIiKxQZmamnHxvvAyFshaYbqFVrmwkqhoYhBERWRn9XDDjZSgkeVVkaQutElHlYBBGRGRl9HPB7OzyjJahGDPmIxQU2Jeo5AQRVT4GYURElUi3pIQxpc3TUuaCFU1Bake+GjRIe/QOE1GFYRBGRFRJzJWU0BUTE1OiQMwwF0wCUDQCZi4AY6FVoqqBQRgRUSUxVVLC3T1TMV1obqRMl6ktiQoKlEHWoEGD4ObmBoB1v4iqEgZhREQWoF9MNSpqN0JCTpXqGqa2JNKughwwYAD8/PwYdBFVUfr/CUVERBVMfxpRCBvs2tUXarVLqa6jUuUiKmo3JEkDAAarID09PRmAEVVhHAkjIqpkxqYRdYuplkZpN94moqqDQRgRUTkztQJSW1y1uGnE4ugn1pvaeJsJ+ERVG4MwIqJyVJIVkNppRP2csJKOYnl4eCAmJqZcS10QUeUrcRCWk5NT4ou6urqWqTNERNaupCsgH3UakQEWkfUrcRDm5uYGSZLMthFCQJIkFBYWPnLHiIisXXErIE1NI969e7cyu0lEFlLiIOzw4cMV2Q8iomrF1ArI4OCLxY56ffHFFyUu2EpE1qvEQVjXrl0rsh9ERNVKSVdAPmrBViKyXmVOzM/OzsbHH3+Mc+fOAQAee+wxjB49GiqVqtw6R0RkrUqyArI8CrYSkfUqU7HWX3/9FcHBwVixYgWysrKQlZWFd955B8HBwTh58mR595GIyOoUV0i1vAq2EpH1KtNI2JQpU9CvXz98+OGHqFWr6BIPHjzA2LFjERsbix9++KFcO0lEZI3MrYAsz4KtRGSdyjwSNnPmTDkAA4BatWphxowZ+PXXX8utc7r69euHgIAAODo6wtfXF8OHD0dqaqqizbfffosOHTrAxcUFXl5eiI6OxqVLlxRtvv/+e4SEhMDBwQGNGzdGXFycwXe99957CAwMhKOjI8LCwnDs2DHF+fv372PChAnw8PBAnTp1EB0djevXryvaXLlyBX369IGzszPq1auH6dOn48GDB+XyLIio6jJWSDUo6LJBYKWdrtRVmoKtRGT9yjQS5urqiitXrqB58+aK41evXoWLS8UMpXfr1g2vvPIKfH198c8//+Dll1/GwIEDcfToUQBASkoK/v3vf2Pq1KnYuHEj1Go1pkyZggEDBshTpCkpKejTpw/Gjx+PjRs34uDBgxg7dix8fX0RGRkJANiyZQumTp2KdevWISwsDCtXrkRkZCSSkpJQr149AEUjgXv27MG2bdugUqkQExODAQMG4OeffwYAFBYWok+fPvDx8cHRo0eRlpaGESNGwM7ODm+88UaFPB8iqhqKK6SakZGBHTt2PHLBViKyfpIQQpT2Q5MmTcLXX3+Nt99+Gx07dgQA/Pzzz5g+fTqio6OxcuXK8u6ngZ07d6J///7Iy8uDnZ0dvvrqKwwdOhR5eXmwsSka4Nu1axf+/e9/y21mzpyJPXv24OzZs/J1hgwZguzsbOzfvx8AEBYWhieeeEKueK3RaODv74+JEydi1qxZUKvV8PLywqZNmzBw4EAAwPnz59GiRQskJCSgQ4cO2LdvH/r27YvU1FR4e3sDANatW4eZM2fi5s2bJd5KJCcnByqVCmq1mgVwiaqJtLQ0rF+/Xn5ftDrScLpy3Lhx8PX1tUQXiegRlfTvd5mmI99++20MGDAAI0aMQGBgIAIDAzFq1CgMHDgQb775Zpk7XVJZWVnYuHEjOnbsCDs7OwBAaGgobGxs8Omnn6KwsBBqtRqff/45IiIi5DYJCQmIiIhQXCsyMhIJCQkAipaEnzhxQtHGxsYGERERcpsTJ06goKBA0aZ58+YICAiQ2yQkJKB169ZyAKb9npycHPzxxx8m7ysvLw85OTmKFxFVLyWdruS+j0TVX5mmI+3t7bFq1SosWbIEycnJAIDg4GA4OzuXa+f0zZw5E2vWrMHdu3fRoUMH7N69Wz4XFBSE7777DoMGDcKLL76IwsJChIeHY+/evXKb9PR0RWAEAN7e3sjJycG9e/dw69YtFBYWGm1z/vx5+Rr29vZwc3MzaJOenm72e7TnTFmyZAkWLlxYwqdBRNaI+z4SkVaZRsK0nJ2d0bp1a7Ru3bpMAdisWbMgSZLZlzb4AYDp06fj1KlT+O6772Bra4sRI0ZAO5uanp6OF154ASNHjsTx48dx5MgR2NvbY+DAgSjDjKtFzJ49G2q1Wn5dvXrV0l0iogrg4eEBX19fky8GYEQ1Q5lGwu7fv493330Xhw8fxo0bN6DRKFf4lLRW2LRp0zBq1CizbRo1aiT/7OnpCU9PTzRt2hQtWrSAv78/fvnlF4SHh+O9996DSqXCsmXL5PZffPEF/P39kZiYiA4dOsDHx8dgFeP169fh6uoKJycn2NrawtbW1mgbHx8fAICPjw/y8/ORnZ2tGA3Tb6O/olJ7TW0bYxwcHODg4GD2eRAREVH1UKYgbMyYMfjuu+8wcOBAPPnkk8Vu7G2Kl5cXvLy8yvRZbeCXl5cHoGjDW21Cvpatra2irf70JADEx8cjPDwcQNEUQGhoKA4ePIj+/fvLnz148CBiYmIAFOWe2dnZ4eDBg4iOjgYAJCUl4cqVK/J1wsPDsXjxYty4cUNeURkfHw9XV1e0bNmyTPdLRBUnMzOT04NEVOnKtDpSpVJh7969eOqppyqiTwYSExNx/PhxdOrUCXXr1kVycjLmzp2L69ev448//oCDgwMOHTqEiIgILFiwAEOHDkVubi5eeeUVnD9/HufOnYOTkxNSUlLQqlUrTJgwAaNHj8ahQ4cwadIk7NmzR1GiYuTIkfjggw/w5JNPYuXKldi6dSvOnz8v53W99NJL2Lt3L+Li4uDq6oqJEycCgFwuo7CwEG3btoWfnx+WLVuG9PR0DB8+HGPHji1ViQqujiSqeJmZmfJqaHO4oTYRlVSFro6sX79+hdUDM8bZ2Rk7duxA9+7d0axZM4wZMwaPP/44jhw5Ik/fPfPMM9i0aRO++eYbtGvXDr169YKDgwP2798PJycnAEXJ+3v27EF8fDzatGmD5cuX46OPPpIDMAAYPHgw3n77bcybNw9t27bF6dOnsX//fkWi/YoVK9C3b19ER0ejS5cu8PHxwY4dO+Tztra22L17N2xtbREeHo7nnnsOI0aMwGuvvVZJT4yISqqkG2VzQ20iKm9lGgnbt28fVq9ejXXr1qFhw4YV0S8CR8KIKoPxul0ecHfPZN0uIiqTkv79LlNOWPv27XH//n00atQIzs7Och0urawsbrtBRNbn5Ml2BhXsQ0JOWbpbRFRNlSkIGzp0KP755x+88cYb8Pb2LnNiPhFRVaFWu8gBGFC0mfauXX0RHHyRWwkRUYUoUxB29OhRJCQkoE2bNuXdHyIii8jK8pADMC0hbJCV5c4gjIgqRJkS85s3b4579+6Vd1+IiCzG3T0TkqSseShJGri7M72CiCpGmUbCli5dimnTpmHx4sVo3bq1QU4Yk8iJqDKUZ30vlSoXUVG7DXLCOApGRBWlTEFYr169AADdu3dXHBdCQJIkFBYWPnrPiIjMKK/6XrobZYeEnEJw8EVkZbnD3T1LEYBxQ20iKm9lCsIOHz5c3v0gIioV/REwU6UlUlNTFW31R8e4oTYRWUqZgrCuXbuWqN1///tfvPbaa/D09CzL1xARlYi50hK6hZS19EfHGGARkSWUKTG/pL744gvk5ORU5FcQUQ1nqrSEWm16Vw9WvyeiqqBMI2ElVYZi/EREpWKutIT2vP4UJRFRVVChQRgRUUXTlpbQDcQkSYPUVD989tkIVr8noiqrQqcjiYgqmra0hLbGlyRpEBFxAAcORJRqipKIqLJxJIyIqixzdcAyMjLkn/VLS7D6PRFZAwZhRFQllbQOmJZKlasIsIxNUbL6PRFVJRUahD333HOsnk9EZVLSFYwDBgyQy+BkZGRgx44drH5PRFahzEFYdnY2jh07hhs3bkCjUe63NmLECADA2rVrH613RFQjZWZmKqYbAdPFWD09PeHr6wuA1e+JyLqUKQjbtWsXhg0bhtu3b8PV1RWSJMnnJEmSgzAiotIyNg1prhirLla/JyJrUqYgbNq0aRg9ejTeeOMNODs7l3efiKgGM7YdkbFirMHBF41OLzLAIiJrUaYSFf/88w8mTZrEAIyIKtzVq/5mi7ESEVmrMgVhkZGR+PXXX8u7L0RECidPtsP27dEGx7nSkYiqgxJPR+7cuVP+uU+fPpg+fTr+/PNPtG7dGnZ2doq2/fr1K78eElGNpD8N+ZBypaNarZYT84mIrEmJg7D+/fsbHHvttdcMjkmShMLCwkfqFBHVLLpFWbWrIo0VXAWAgQO/QqtW5+T3W7ZsQUxMDHPBiMjqlDgI0y9DQURUHkwVZTW1J6S//zWDtiWtKUZEVJWUKSfss88+Q15ensHx/Px8fPbZZ4/cKSKq2jIzM5GWlmbylZmZWeJrGVsNmZISCAAGe0J26vQjsrI8uAckEVULkhBClPZDtra2SEtLQ7169RTHMzMzUa9ePU5HlpOcnByoVCqo1WruPEBVhv7IlakiqoMGDYKbm5v83lR9rrS0NKxfvx6A8Xpg9+45Ij4+AkX/zSgASAa1wsaNG8e8MCKqMkr697tMdcKEEIoCrVrXrl2DSqUqyyWJyErojlyZK6K6detWg8+ay90yVg9s586+KPq/Gu2gvSSfM1crjIjIGpQqCGvXrh0kSYIkSejevTtq1Xr48cLCQqSkpKBXr17l3kkiqnpKW0QVMJ+7ZTwR3wamxuq1tcIYhBGRtSpVEKZdIXn69GlERkaiTp068jl7e3sEBgYiOtqwpg8RVT/GgibdIqrGpijNMZaIXzT9CGhHwHSxVhgRWbtSBWHz588HAAQGBmLw4MFwdHSskE4RUdVnavViaqofPvtsRLH7POpTqXIREXEA8fE98DDokgBoIEni/75HmRPGUTAismZlygkbOXIkgKKphRs3bhiUrwgICHj0nhFRlaZS5SIqarciJywi4gAOHIgo1RSlLj+/NBiOetkgOnorate+Czu7fBQU2MPdPUtxPXt7+3K8MyKiylGmIOzChQsYPXo0jh49qjiuTdjn6kiimiEk5BSCgy8iK8sd7u5ZZqcoVapcuRAr8HC1pG4AZa42mEqVi27duqFu3boAgFq1asHNzc3kqksioqquTEHYqFGjUKtWLezevRu+vr5GV0oSkXXRrVpvjKlgR6XKVYxKGQuitLlbO3bsUHxWu1py0KBB2Lp1q9HRNd1pxyZNmrAUBRFVG2UKwk6fPo0TJ06gefPm5d0fIrKAktb+iomJMTv1V1wQpU8b9OnWHNQfXeO0IxFVV2UKwlq2bKmYViAi61bS2l/5+fnw9fVFTEyM4jNqtRpbtmwBYDyIMhXUaXl4eBhcUx+nHYmouilTEPbmm29ixowZeOONN9C6dWvY2dkpzrO6O5F1KmntL/1gSDcwy8jIwI4dO+T25oI6XQywiKimKVMQFhERAQB45plnFPlgTMwnsm7FJdabYyyIUqtdsHNnX2gr3rPSPRHRQ2UKwg4fPlze/SCiKsDU6sSyFkVNTAzDwy2HirDSPRFREf09Qkqka9eusLGxwYcffohZs2ahcePG6Nq1K65cuQJbW9vy7iMRVRJtYr0kFdX+00+sz8jIQFpaGjIzM4u9llrtgoSEcCNnWOmeiAgo40jY9u3bMXz4cAwbNgynTp1CXl4egKLk3DfeeAN79+4t104SUcXQlqXQXWhjbnWibokJc5txA6b2ggQ6dkzgKBgREcoYhL3++utYt24dRowYgc2bN8vHn3rqKbz++uvl1jkiqjj6ZSl06db+MrWy0dxKRsD01GZYWKL8niUniKgmK1MQlpSUhC5duhgcV6lUyM7OftQ+EVEl0A+ijAVbJV3ZaExxNcMGDRrEFZFEVKOVKQjz8fHBxYsXERgYqDj+008/oVGjRuXRLyKqRMaCreDgiyUqV6FPd3TL3NSmboFWIqKaqExB2AsvvIDJkyfjk08+gSRJSE1NRUJCAl5++WXMnTu3vPtIRBXIVG2w6OjtZSpXwcKrREQlU6YgbNasWdBoNOjevTvu3r2LLl26wMHBAS+//DImTpxY3n0kogpkqjYYIMpcroIBFhFR8cpUokKSJMyZMwdZWVk4e/YsfvnlF9y8eROLFi0q7/4RUQXTJtDrkiQN/P2vmS1XQUREj6ZMI2Fa9vb2aNmyZXn1hYgswFwCvbmcLiIiejSPFIQRUfVgLtjSLVehi+UliIgeDYMwohpKP4gyFWwNGjQIbm5uBp9l3hcR0aNhEEZUQ3EVIxGRZTEII6oCtNsHAUBqqg1SUmohKOgB/PyKkuIrKhhigEVEZDkMwogsTHf7IHMV6ovbq5GIiKxLmUpUEFH50Y6AmSqaqla7KNoREVH1wCCMqIowVTQ1K8vdQj0iIqKKxCCMqIowVTS1JBXqiYjI+jAII6oitEVTWaGeiKhmYGI+URXCCvVERDUHgzCiKsZU0VQiIqpeGIQRWYmMjAwALKBKRFRdMAgjsrCS7sG4Y8cO+WfWDCMisn4MwogsTHf7oIyMDEWwpVa7ICvLA+7umYopSv2aYboV943h6BkRUdXDIIyoBK5dAy5cAJo0ARo0KP/rGwuQzFXP105NAoBarcaWLVuK/Q6OnhERVS0MwohM0I4ubdrkhBkzVNBoJNjYCCxbpsazz96r0NElU9Xzg4MvQqXKVYyWGftsSUbPiIjIshiEERmh3c9RrXbBypWxEEICAGg0EqZPd8U//3wClSq3wkaXzFXPV6lyTQZa5kbPiIioamEQRmSEdtSouGCookaXtNXzdb9bWz3fVKBV3OgZERFVLayYT2SGpbYSMlU9H4DJTb659yQRkXWxmiCsX79+CAgIgKOjI3x9fTF8+HCkpqYq2mzduhVt27aFs7MzGjZsiLfeesvgOt9//z1CQkLg4OCAxo0bIy4uzqDNe++9h8DAQDg6OiIsLAzHjh1TnL9//z4mTJgADw8P1KlTB9HR0bh+/bqizZUrV9CnTx84OzujXr16mD59Oh48ePDoD4IqVWVvJZSdnS3/HBJyCrGxKzFyZBxiY1ciJOSU2UCLe08SEVkXqwnCunXrhq1btyIpKQnbt29HcnIyBg4cKJ/ft28fhg0bhvHjx+Ps2bN4//33sWLFCqxZs0Zuk5KSgj59+qBbt244ffo0YmNjMXbsWHz77bdymy1btmDq1KmYP38+Tp48iTZt2iAyMhI3btyQ20yZMgW7du3Ctm3bcOTIEaSmpmLAgAHy+cLCQvTp0wf5+fk4evQoNmzYgLi4OMybN6+CnxJVBGPBUEWRJMnYUfknc4EW954kIrIukhBCWLoTZbFz5070798feXl5sLOzw7PPPouCggJs27ZNbvPuu+9i2bJluHLlCiRJwsyZM7Fnzx6cPXtWbjNkyBBkZ2dj//79AICwsDA88cQTcvCm0Wjg7++PiRMnYtasWVCr1fDy8sKmTZvkIPD8+fNo0aIFEhIS0KFDB+zbtw99+/ZFamoqvL29AQDr1q3DzJkzcfPmzRIX58zJyYFKpYJarYarq2u5PDcqmbS0NKxfv77YduPGjYOvr2+FfK+p3K/iku+LpiYN954s774SEZFxJf37bTUjYbqysrKwceNGdOzYEXZ2dgCAvLw8ODo6Kto5OTnh2rVruHz5MgAgISEBERERijaRkZFISEgAUJSMfeLECUUbGxsbREREyG1OnDiBgoICRZvmzZsjICBAbpOQkIDWrVvLAZj2e3JycvDHH3+YvK+8vDzk5OQoXlQzmUqyV6tdih2ZU6lyERR02WAErKTBPxERVQ6rWh05c+ZMrFmzBnfv3kWHDh2we/du+VxkZCSmTJmCUaNGoVu3brh48SKWL18OoGh0ITAwEOnp6YrACAC8vb2Rk5ODe/fu4datWygsLDTa5vz58wCA9PR02Nvbw83NzaBNenq63MbYNbTnTFmyZAkWLlxYiidClqZbNFXXo9YQK25VpqlNvgcNGmTwu1ke/SEiovJn0SBs1qxZePPNN822OXfuHJo3bw4AmD59OsaMGYPLly9j4cKFGDFiBHbv3g1JkvDCCy8gOTkZffv2RUFBAVxdXTF58mQsWLAANjbWMeA3e/ZsTJ06VX6fk5MDf39/C/ao5irLfo76HqWGmLkSFQAwYMAAeHp6Kj7DQIuIyLpYNAibNm0aRo0aZbZNo0aN5J89PT3h6emJpk2bokWLFvD398cvv/yC8PBwSJKEN998E2+88QbS09Ph5eWFgwcPKq7h4+NjsIrx+vXrcHV1hZOTE2xtbWFra2u0jY+Pj3yN/Px8ZGdnK0Yc9Nvor6jUXlPbxhgHBwc4ODiYfR5UOXT3c9Snv7+jKY9SQ0ybZK+f+6Ud/fL09GR+FxGRlbNoEObl5QUvL68yfVajKVoBlpeXpzhua2uL+vXrAwC+/PJLhIeHy98RHh6OvXv3KtrHx8cjPDwcQNFIQmhoKA4ePIj+/fvL33Pw4EHExMQAAEJDQ2FnZ4eDBw8iOjoaAJCUlIQrV67I1wkPD8fixYtx48YN1KtXT/4eV1dXtGzZskz3S5WvpKNKpqrXP6qQkFMIDr5oNMmeiIisn1XkhCUmJuL48ePo1KkT6tati+TkZMydOxfBwcFy4JORkYGvvvoKTz/9NO7fv49PP/1ULiGhNX78eKxZswYzZszA6NGjcejQIWzduhV79uyR20ydOhUjR45E+/bt8eSTT2LlypW4c+cOnn/+eQCASqXCmDFjMHXqVLi7u8PV1RUTJ05EeHg4OnToAADo2bMnWrZsieHDh2PZsmVIT0/Hq6++igkTJnCkq5op722C9KdBTeV+McmeiMj6WUUQ5uzsjB07dmD+/Pm4c+cOfH190atXL7z66quKoGbDhg14+eWXIYRAeHg4vv/+ezz55JPy+aCgIOzZswdTpkzBqlWr0KBBA3z00UeIjIyU2wwePBg3b97EvHnzkJ6ejrZt22L//v2KRPsVK1bAxsYG0dHRyMvLQ2RkJN5//335vK2tLXbv3o2XXnoJ4eHhqF27NkaOHInXXnutgp8UVaaK2CbI3DSoFnO/iIiqB6utE1YTsE5Y1aSt5ZWSEogNG0YanB85Mg5BQZfN1uXKzMxkoEVEVE2V9O+3VYyEEVVFxa1gNCUzM1Oxk4OpnLJHWV1JRERVH4MwojIqbgWjKbojYOZyyh5ldSUREVV9DMKISkk3Kd7cCsbikucrIqeMiIisB4MwolIqr+T54qriExFR9cYgjKgMyiNXq6w5ZUREVD0wCCOrZ+mVhmX9/rLmlBERUfXAIIysin7Ao1arsWXLlmI/Vx4rDY0FW/rfX9qVjqyKT0RUczEIoypLP+jJzs7G1q1by3StR11pqF9WwpiyrnQ0VRWfiIiqNwZhVCWVJOipTIYjYMoRr9KsdCzplkPcmoiIqHpjEEZVTmZmJlJTU8v02YraTFuXsRGvunVvlXilI7cmIiIigEEYVTGPMgJW3ptp6/YpIyMDgOnaXmPGfFSqlY4MsIiIyKb4JkSVpzS5W2q1C1JSAqFWu5gMjtRql0fqjzYo3LFjBwDg6lV/oyNeBQX2iIraDUnSAABXOhIRUbE4EkZWSX/UKzw8oUIKnxrbYkifdsQrKOgyVzoSEVGJMQgjq6Cb6wXAYNTr6NHwCi18qj/S9pByxIsrHYmIqKQYhFGVV5JRL8AG4eE/IyEh3Gjh00ddaWhsiyEAGDjwK7Rqda7Yz3OlIxER6WMQRlWasVyvhIRwABropjRKkgZhYYkIC0tEmzbRaNXKEX5+TwB4olxWGpraYsjf/5qi3aBBg+Dm5qY4xpWORERkDIMwqtJMJcJ37Phw1MvWVuDNN3Pw7LNDKyzgKW6LoQEDBsDPz4/BFhERlRiDMKqyTp5sh507jSfCL1jgDh8fNTIz66JxYwkNGrgBcCv1dxS376NarZZ/NrfFkKenJwMwIiIqFQZhVKVoc6e005D6VVQe1v96Ch4edR/pu/RrkpWk0CsT74mIqLwwCKMqRVtN/vBhYMUKw0T4devUiI5+qlxGnYyVnyhroVcm3hMRUWkxCKMqx8PDAx06ADY2gEbz8LitLfCvf9VFec/6Fbfvo7Fke11MvCciorJgEEZVUoMGwPr1wIsvAoWFRQHYBx8UHS9vxspP6BZ6dXNzg6+vb/l/MRER1WgMwqjKGjMGiIwELl4EGjcufQBWXNJ9dnY2ANPlJ8qr0CsREZExDMKoSmvQoGyjX6XZCLy48hNEREQVgUEYVUv6I2DGVj7qHjNXfoKIiKgiMAijas/YykcARldDMvgiIqLKwiCMqjVjKx937uwLSYLJ1ZD6WH6CiIgqAoMwqtaMb7xtAyGUR3RXQw4YMACenp4AWH6CiIgqDoMwqtaMrXwENIqRMEC5GtLT05MlKYiIqMIZliQnqka0Kx8lqajqqyRp0K/fboNjXA1JRESVjSNhVO2ZWvnI1ZBERGRJDMKoWtJPpje28TY34yYiIktiEEbVknYjcN16YWq1Glu2bCn2s1wNSURElUESQn+dGFUVOTk5UKlUUKvVcHV1tXR3Hllx2whVxkrEqtAHIiKq3kr695sjYVQpSrqNUExMTIUGQQywiIioquDqSKoU5kafytKOiIjI2jEIIyIiIrIABmFEREREFsAgjIiIiMgCmJhPFqFWuyArywPu7pllrtWlu9IxNdUGKSm1EBT0AH5+RZXwudKRiIiqMgZhVOlOnmyHXbv6QggbecugkJBTpbqG7mpLc9er6NWWREREZcXpSKpUarWLHDABRZto79rVF2q1S6muox0BK+56XG1JRERVFYMwqhTaKvRZWR5ywKQlhA2ystwV7UqquOsRERFVVZyOpEqh3Ubo0qUH+PxzAY1Gks/Z2gpMnNgbgYG1Sj116O6eCUnSKAIxSdLA3T2r3PpORERUETgSRpXGw8MDoaHeWL9egq1t0TFbW+CDDySEhnqXKXdLpcpFVNRuSFJRMr42J4wbcxMRUVXHkTCqdGPGAJGRwMWLQOPGQIMGj3a9kJBTCA6+iKwsd7i7ZzEAIyIiq8AgjCyiQQPAyamoxERa2qOXmFCpchl8ERGRVWEQRhZR2hIT164BFy4ATZo8+sgZERFRVcCcMLKIkpaYSE1NxfLl2WjYUOCZZ4CGDQWWL8+GWq0u0feUdrUlERFRZeFIGFmUuRITKlUuPv00HitXxkKIotWUGo2E6dNd8c8/n0ClAgYNGgQ3Nzej12bFfCIiqsoYhJFFFVdiorggzc3NDb6+vpXaZyIiovLA6UiyqOJKTGiDNF2sA0ZERNUBR8LI4syVmNAGafqJ+1wJSURE1o5BGFUJ5kpMsA4YERFVRwzCyCqwDhgREVU3zAkji2DpCCIiquk4EkYWod3QW1svTF9GRgZ27NhRyb0iIiKqPAzCyGLM1fAq6UgZR9SIiMhaMQijKqm4kTKAxViJiMi6MQijKosBFhERVWdMzCciIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC7C6ICwvLw9t27aFJEk4ffq04tyZM2fQuXNnODo6wt/fH8uWLTP4/LZt29C8eXM4OjqidevW2Lt3r+K8EALz5s2Dr68vnJycEBERgQsXLijaZGVlYdiwYXB1dYWbmxvGjBmD27dvl7ovREREVHNZXRA2Y8YM+Pn5GRzPyclBz5490bBhQ5w4cQJvvfUWFixYgPXr18ttjh49iqFDh2LMmDE4deoU+vfvj/79++Ps2bNym2XLlmH16tVYt24dEhMTUbt2bURGRuL+/ftym2HDhuGPP/5AfHw8du/ejR9++AHjxo0rVV+qk2vXgMOHi/5JREREJSSsyN69e0Xz5s3FH3/8IQCIU6dOyefef/99UbduXZGXlycfmzlzpmjWrJn8ftCgQaJPnz6Ka4aFhYkXX3xRCCGERqMRPj4+4q233pLPZ2dnCwcHB/Hll18KIYT4888/BQBx/Phxuc2+ffuEJEnin3/+KXFfSkKtVgsAQq1Wl+pzlSEjI0OkpqaKt9++JWxsNAIQwsZGI95++5ZITU0VGRkZlu4iERGRRZT077fVjIRdv34dL7zwAj7//HM4OzsbnE9ISECXLl0U29hERkYiKSkJt27dkttEREQoPhcZGYmEhAQAQEpKCtLT0xVtVCoVwsLC5DYJCQlwc3ND+/bt5TYRERGwsbFBYmJiiftiTF5eHnJychSvqigzMxNr1qzBW299ienTXaHRSAAAjUbC9OmueOutL7FmzRpkZmZauKdERERVl1UEYUIIjBo1CuPHj1cEP7rS09Ph7e2tOKZ9n56ebraN7nndz5lqU69ePcX5WrVqwd3dvdjv0f0OY5YsWQKVSiW//P39TbatTPrTjdqthLKyPKAfxwthg6wsd0U7IiIiMmTRIGzWrFmQJMns6/z583j33XeRm5uL2bNnW7K7FW727NlQq9Xy6+rVq5buEj7+GGjYEHjmmaJ/fvwxkJ2dDQBwd8+EJGkU7SVJA3f3LAv0lIiIyLpYdO/IadOmYdSoUWbbNGrUCIcOHUJCQgIcHBwU59q3b49hw4Zhw4YN8PHxwfXr1xXnte99fHzkfxpro3tee8zX11fRpm3btnKbGzduKK7x4MEDZGVlFfs9ut9hjIODg8E9WkpmZiYuXXqAcePq6Uw3AuPGCUyevA8qFZCc3BhCPPyMJGkQFbUbKlWuhXpNRERkPSwahHl5ecHLy6vYdqtXr8brr78uv09NTUVkZCS2bNmCsLAwAEB4eDjmzJmDgoIC2NnZAQDi4+PRrFkz1K1bV25z8OBBxMbGyteKj49HeHg4ACAoKAg+Pj44ePCgHHTl5OQgMTERL730knyN7OxsnDhxAqGhoQCAQ4cOQaPRlKovVZk25yslJRAazUjFOY1Gkqcbd+3qC93BVCGA4OCLldlVIiIiq2UVOWEBAQFo1aqV/GratCkAIDg4GA0aNAAAPPvss7C3t8eYMWPwxx9/YMuWLVi1ahWmTp0qX2fy5MnYv38/li9fjvPnz2PBggX49ddfERMTAwCQJAmxsbF4/fXXsXPnTvz+++8YMWIE/Pz80L9/fwBAixYt0KtXL7zwwgs4duwYfv75Z8TExGDIkCFy6YyS9KUq0+ZymZtuNJYPBjzMByMiIiLzrCIIKwmVSoXvvvsOKSkpCA0NxbRp0zBv3jxF/a6OHTti06ZNWL9+Pdq0aYOvvvoK33zzDVq1aiW3mTFjBiZOnIhx48bhiSeewO3bt7F//344OjrKbTZu3IjmzZuje/fu+Ne//oVOnTopaoCVpC/WQKXKRVTUbjkQ051uZD4YERHRo5GE0M3qoaokJycHKpUKarUarq6ulfa9aWlpiqBSrXZBVpY73N2zFPleJ0+2w65dfSGEjRyghYScks+PGzdOkVtHRERUE5T077dFc8LIOqhUuUaT7UNCTiE4+KLRAA2Aok4aERERKTEIo0diKkAbPHgwPDw8LNAjIiIi61BtcsKoalGpVJbuAhERUZXGIIwqBKciiYiIzON0JBkoaQA1ePBgoyNe9vb2nIokIiIqBoMwMuDh4YGYmBizez8y0CIiIno0DMLIKAZYREREFYs5YUREREQWwCCMiIiIyAIYhBERERFZAIMwIiIiIgtgEEZERERkAQzCiIiIiCyAQRgRERGRBTAIIyIiIrIABmFEREREFsCK+TVEZmYmtyEiIiKqQhiE1QCZmZlYs2ZNse1iYmIYiBEREVUSTkfWAOZGwMrSjoiIiB4dgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjIiIisgAGYUREREQWwCCsBrC3t1e8V6tdkJISCLXaxWw7IiIiqjgs1loDeHh4ICYmBvn5+di0yQmvvaaCRiPBxkZg2TI1nn32HivmExERVTJJCCEs3QkyLicnByqVCmq1Gq6uro98vWvXgIYNAY3m4TFbW+DSJaBBg0e+PBEREaHkf785HVmDXLigDMAAoLAQuHjRMv0hIiKqyRiE1SBNmgA2ev/GbW2Bxo0t0x8iIqKajEFYDdKgAbB+fVHgBRT984MPOBVJRERkCUzMr2HGjAEiI4umIBs3ZgBGRERkKQzCaqAGDRh8ERERWRqnI4mIiIgsgEEYERERkQUwCCMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjIiIisgDuHVmFCSEAADk5ORbuCREREZWU9u+29u+4KQzCqrDc3FwAgL+/v4V7QkRERKWVm5sLlUpl8rwkigvTyGI0Gg1SU1Ph4uICSZJK9JmcnBz4+/vj6tWrcHV1reAeVl18DkX4HPgMtPgcivA58BloVeRzEEIgNzcXfn5+sLExnfnFkbAqzMbGBg0aNCjTZ11dXWv0/7i0+ByK8DnwGWjxORThc+Az0Kqo52BuBEyLiflEREREFsAgjIiIiMgCGIRVMw4ODpg/fz4cHBws3RWL4nMowufAZ6DF51CEz4HPQKsqPAcm5hMRERFZAEfCiIiIiCyAQRgRERGRBTAIIyIiIrIABmFEREREFsAgzAqsXbsWjz/+uFxQLjw8HPv27ZPP379/HxMmTICHhwfq1KmD6OhoXL9+XXGNK1euoE+fPnB2dka9evUwffp0PHjwoLJvpdwsXboUkiQhNjZWPlZTnsOCBQsgSZLi1bx5c/l8TXkO//zzD5577jl4eHjAyckJrVu3xq+//iqfF0Jg3rx58PX1hZOTEyIiInDhwgXFNbKysjBs2DC4urrCzc0NY8aMwe3btyv7VsosMDDQ4HdBkiRMmDABQM35XSgsLMTcuXMRFBQEJycnBAcHY9GiRYp9+2rC70Nubi5iY2PRsGFDODk5oWPHjjh+/Lh8vjo+gx9++AFRUVHw8/ODJEn45ptvFOfL657PnDmDzp07w9HREf7+/li2bFn53ICgKm/nzp1iz5494q+//hJJSUnilVdeEXZ2duLs2bNCCCHGjx8v/P39xcGDB8Wvv/4qOnToIDp27Ch//sGDB6JVq1YiIiJCnDp1Suzdu1d4enqK2bNnW+qWHsmxY8dEYGCgePzxx8XkyZPl4zXlOcyfP1889thjIi0tTX7dvHlTPl8TnkNWVpZo2LChGDVqlEhMTBR///23+Pbbb8XFixflNkuXLhUqlUp888034rfffhP9+vUTQUFB4t69e3KbXr16iTZt2ohffvlF/Pjjj6Jx48Zi6NChlrilMrlx44bi9yA+Pl4AEIcPHxZC1IzfBSGEWLx4sfDw8BC7d+8WKSkpYtu2baJOnTpi1apVcpua8PswaNAg0bJlS3HkyBFx4cIFMX/+fOHq6iquXbsmhKiez2Dv3r1izpw5YseOHQKA+PrrrxXny+Oe1Wq18Pb2FsOGDRNnz54VX375pXBychIffPDBI/efQZiVqlu3rvjoo49Edna2sLOzE9u2bZPPnTt3TgAQCQkJQoiiX1IbGxuRnp4ut1m7dq1wdXUVeXl5ld73R5GbmyuaNGki4uPjRdeuXeUgrCY9h/nz54s2bdoYPVdTnsPMmTNFp06dTJ7XaDTCx8dHvPXWW/Kx7Oxs4eDgIL788kshhBB//vmnACCOHz8ut9m3b5+QJEn8888/Fdf5CjR58mQRHBwsNBpNjfldEEKIPn36iNGjRyuODRgwQAwbNkwIUTN+H+7evStsbW3F7t27FcdDQkLEnDlzasQz0A/Cyuue33//fVG3bl3F/yZmzpwpmjVr9sh95nSklSksLMTmzZtx584dhIeH48SJEygoKEBERITcpnnz5ggICEBCQgIAICEhAa1bt4a3t7fcJjIyEjk5Ofjjjz8q/R4exYQJE9CnTx/F/QKocc/hwoUL8PPzQ6NGjTBs2DBcuXIFQM15Djt37kT79u3xn//8B/Xq1UO7du3w4YcfyudTUlKQnp6ueA4qlQphYWGK5+Dm5ob27dvLbSIiImBjY4PExMTKu5lykp+fjy+++AKjR4+GJEk15ncBADp27IiDBw/ir7/+AgD89ttv+Omnn9C7d28ANeP34cGDBygsLISjo6PiuJOTE3766aca8Qz0ldc9JyQkoEuXLrC3t5fbREZGIikpCbdu3XqkPnIDbyvx+++/Izw8HPfv30edOnXw9ddfo2XLljh9+jTs7e3h5uamaO/t7Y309HQAQHp6uuL/ZLXnteesxebNm3Hy5ElFjoNWenp6jXkOYWFhiIuLQ7NmzZCWloaFCxeic+fOOHv2bI15Dn///TfWrl2LqVOn4pVXXsHx48cxadIk2NvbY+TIkfJ9GLtP3edQr149xflatWrB3d3dap6Drm+++QbZ2dkYNWoUgJr1v4lZs2YhJycHzZs3h62tLQoLC7F48WIMGzYMAGrE74OLiwvCw8OxaNEitGjRAt7e3vjyyy+RkJCAxo0b14hnoK+87jk9PR1BQUEG19Ceq1u3bpn7yCDMSjRr1gynT5+GWq3GV199hZEjR+LIkSOW7laluXr1KiZPnoz4+HiD/9KrabT/dQ8Ajz/+OMLCwtCwYUNs3boVTk5OFuxZ5dFoNGjfvj3eeOMNAEC7du1w9uxZrFu3DiNHjrRw7yzj448/Ru/eveHn52fprlS6rVu3YuPGjdi0aRMee+wxnD59GrGxsfDz86tRvw+ff/45Ro8ejfr168PW1hYhISEYOnQoTpw4YemukQmcjrQS9vb2aNy4MUJDQ7FkyRK0adMGq1atgo+PD/Lz85Gdna1of/36dfj4+AAAfHx8DFZEad9r21R1J06cwI0bNxASEoJatWqhVq1aOHLkCFavXo1atWrB29u7RjwHY9zc3NC0aVNcvHixxvw++Pr6omXLlopjLVq0kKdltfdh7D51n8ONGzcU5x88eICsrCyreQ5aly9fxoEDBzB27Fj5WE35XQCA6dOnY9asWRgyZAhat26N4cOHY8qUKViyZAmAmvP7EBwcjCNHjuD27du4evUqjh07hoKCAjRq1KjGPANd5XXPFfm/EwZhVkqj0SAvLw+hoaGws7PDwYMH5XNJSUm4cuUKwsPDAQDh4eH4/fffFb9o8fHxcHV1NfhDVlV1794dv//+O06fPi2/2rdvj2HDhsk/14TnYMzt27eRnJwMX1/fGvP78NRTTyEpKUlx7K+//kLDhg0BAEFBQfDx8VE8h5ycHCQmJiqeQ3Z2tmKU4NChQ9BoNAgLC6uEuyg/n376KerVq4c+ffrIx2rK7wIA3L17FzY2yj9ntra20Gg0AGre70Pt2rXh6+uLW7du4dtvv8W///3vGvcMgPL79x4eHo4ffvgBBQUFcpv4+Hg0a9bskaYiAbBEhTWYNWuWOHLkiEhJSRFnzpwRs2bNEpIkie+++04IUbQMPSAgQBw6dEj8+uuvIjw8XISHh8uf1y5D79mzpzh9+rTYv3+/8PLysrpl6Pp0V0cKUXOew7Rp08T3338vUlJSxM8//ywiIiKEp6enuHHjhhCiZjyHY8eOiVq1aonFixeLCxcuiI0bNwpnZ2fxxRdfyG2WLl0q3NzcxP/+9z9x5swZ8e9//9vo0vR27dqJxMRE8dNPP4kmTZpU6eX4xhQWFoqAgAAxc+ZMg3M14XdBCCFGjhwp6tevL5eo2LFjh/D09BQzZsyQ29SE34f9+/eLffv2ib///lt89913ok2bNiIsLEzk5+cLIarnM8jNzRWnTp0Sp06dEgDEO++8I06dOiUuX74shCife87Ozhbe3t5i+PDh4uzZs2Lz5s3C2dmZJSpqitGjR4uGDRsKe3t74eXlJbp37y4HYEIIce/ePfHf//5X1K1bVzg7O4v/9//+n0hLS1Nc49KlS6J3797CyclJeHp6imnTpomCgoLKvpVypR+E1ZTnMHjwYOHr6yvs7e1F/fr1xeDBgxX1sWrKc9i1a5do1aqVcHBwEM2bNxfr169XnNdoNGLu3LnC29tbODg4iO7du4ukpCRFm8zMTDF06FBRp04d4erqKp5//nmRm5tbmbfxyL799lsBwODehKg5vws5OTli8uTJIiAgQDg6OopGjRqJOXPmKEoK1ITfhy1btohGjRoJe3t74ePjIyZMmCCys7Pl89XxGRw+fFgAMHiNHDlSCFF+9/zbb7+JTp06CQcHB1G/fn2xdOnScum/JIROSWEiIiIiqhTMCSMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjomrl6aefRmxsrKW7UeEWLFiAtm3bWrobRPQIGIQREVUh+fn5lfp9Qgg8ePCgUr+TiIowCCOiamPUqFE4cuQIVq1aBUmSIEkSLl26hLNnz6J3796oU6cOvL29MXz4cGRkZMife/rppzFx4kTExsaibt268Pb2xocffog7d+7g+eefh4uLCxo3box9+/bJn/n+++8hSRL27NmDxx9/HI6OjujQoQPOnj2r6NNPP/2Ezp07w8nJCf7+/pg0aRLu3Lkjnw8MDMSiRYswYsQIuLq6Yty4cQCAmTNnomnTpnB2dkajRo0wd+5cFBQUAADi4uKwcOFC/Pbbb/J9xsXF4dKlS5AkCadPn5avn52dDUmS8P333yv6vW/fPoSGhsLBwQE//fQTNBoNlixZgqCgIDg5OaFNmzb46quvyvtfERHpYBBGRNXGqlWrEB4ejhdeeAFpaWlIS0uDi4sLnnnmGbRr1w6//vor9u/fj+vXr2PQoEGKz27YsAGenp44duwYJk6ciJdeegn/+c9/0LFjR5w8eRI9e/bE8OHDcffuXcXnpk+fjuXLl+P48ePw8vJCVFSUHCwlJyejV69eiI6OxpkzZ7Blyxb89NNPiImJUVzj7bffRps2bXDq1CnMnTsXAODi4oK4uDj8+eefWLVqFT788EOsWLECADB48GBMmzYNjz32mHyfgwcPLtWzmjVrFpYuXYpz587h8ccfx5IlS/DZZ59h3bp1+OOPPzBlyhQ899xzOHLkSKmuS0SlUC7bgBMRVRFdu3YVkydPlt8vWrRI9OzZU9Hm6tWrAoBISkqSP9OpUyf5/IMHD0Tt2rXF8OHD5WNpaWkCgEhISBBCCHH48GEBQGzevFluk5mZKZycnMSWLVuEEEKMGTNGjBs3TvHdP/74o7CxsRH37t0TQgjRsGFD0b9//2Lv66233hKhoaHy+/nz54s2bdoo2qSkpAgA4tSpU/KxW7duCQDi8OHDin5/8803cpv79+8LZ2dncfToUcX1xowZI4YOHVps34iobGpZMgAkIqpov/32Gw4fPow6deoYnEtOTkbTpk0BAI8//rh83NbWFh4eHmjdurV8zNvbGwBw48YNxTXCw8Pln93d3dGsWTOcO3dO/u4zZ85g48aNchshBDQaDVJSUtCiRQsAQPv27Q36tmXLFqxevRrJycm4ffs2Hjx4AFdX11Lfvym633nx4kXcvXsXPXr0ULTJz89Hu3btyu07iUiJQRgRVWu3b99GVFQU3nzzTYNzvr6+8s92dnaKc5IkKY5JkgQA0Gg0pfruF198EZMmTTI4FxAQIP9cu3ZtxbmEhAQMGzYMCxcuRGRkJFQqFTZv3ozly5eb/T4bm6IMEyGEfEw7NapP9ztv374NANizZw/q16+vaOfg4GD2O4mo7BiEEVG1Ym9vj8LCQvl9SEgItm/fjsDAQNSqVf7/l/fLL7/IAdWtW7fw119/ySNcISEh+PPPP9G4ceNSXfPo0aNo2LAh5syZIx+7fPmyoo3+fQKAl5cXACAtLU0ewdJN0jelZcuWcHBwwJUrV9C1a9dS9ZWIyo6J+URUrQQGBiIxMRGXLl1CRkYGJkyYgKysLAwdOhTHjx9HcnIyvv32Wzz//PMGQUxZvPbaazh48CDOnj2LUaNGwdPTE/379wdQtMLx6NGjiImJwenTp3HhwgX873//M0jM19ekSRNcuXIFmzdvRnJyMlavXo2vv/7a4D5TUlJw+vRpZGRkIC8vD05OTujQoYOccH/kyBG8+uqrxd6Di4sLXn75ZUyZMgUbNmxAcnIyTp48iXfffRcbNmwo87MhIvMYhBFRtfLyyy/D1tYWLVu2hJeXF/Lz8/Hzzz+jsLAQPXv2ROvWrREbGws3Nzd5+u5RLF26FJMnT0ZoaCjS09Oxa9cu2NvbAyjKMzty5Aj++usvdO7cGe3atcO8efPg5+dn9pr9+vXDlClTEBMTg7Zt2+Lo0aPyqkmt6Oho9OrVC926dYOXlxe+/PJLAMAnn3yCBw8eIDQ0FLGxsXj99ddLdB+LFi3C3LlzsWTJErRo0QK9evXCnj17EBQUVIanQkQlIQnd5AEiIiqR77//Ht26dcOtW7fg5uZm6e4QkRXiSBgRERGRBTAIIyIiIrIATkcSERERWQBHwoiIiIgsgEEYERERkQUwCCMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILOD/A1M3HSEaHErLAAAAAElFTkSuQmCC", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogates with PySMO\n", + "\n", + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 4.372684\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", + "(x_ 2 )^ 4 | -0.0\n", + "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 0.010367\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 2 ]: 0.0\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + ] + } + ], + "source": [ + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "var = output_labels\n", + "trainer.config.extra_features = [\n", + " \"pressure*temperature*temperature\",\n", + " \"pressure*pressure*temperature*temperature\",\n", + " \"pressure*pressure*temperature\",\n", + " \"pressure/temperature\",\n", + " \"temperature/pressure\",\n", + "]\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 5\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [7, 306], [40, 1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing surrogates\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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AcXFxWLJkCX744Qe8/PLL+PjjjzFr1iwAQK9evbB48WKkpqaGwi/++Mc/AgimnD/88MP47rvv8Pbbb2P//v0YO3aspd9FCMcIn3nz5qFHjx5ISUlBZmYmhg8fjt27d0dcc+bMGUydOhXp6elo0aIFRo4ciSNHjtjUYmNp1coHjyfS5+rxBNCq1XGbWkQQBEG4lYsuugj79+8HAEyfPh1XXXUV8vPz0b9/fzzyyCNYs2YNgKArzuv1wuPxICsrC1lZWWjRogUAYPz48RgyZAjat2+PK664AkuWLMEHH3yAX375xa6vBcBBwufTTz/F1KlT8eWXX6KsrAz19fW4+uqrcfr06dA1d999N9577z28+eab+PTTT1FZWYkRI0bY2Grj8HpPYdiwDSHxw8f4eL2nbG4ZQRAE4TY4jgu5rjZt2oQBAwbgggsuQEpKCm655Rb4fD7U1tZKvse2bdswbNgwtG3bFikpKejbty8A4MCBA6a3XwrHxPh8+OGHEX+vXLkSmZmZ2LZtG/r06QO/34/ly5dj1apV6N+/PwBgxYoV+O1vf4svv/wSV1xxhR3NNpTu3b9FYeFeHD/eCq1aHSfRQxAEQZjCrl27UFBQgP3792Po0KG4/fbb8eijj6JVq1b4/PPPMWHCBNTV1YlWrD59+jQGDRqEQYMG4bXXXkPr1q1x4MABDBo0yPaYVMcIn2j8fj8AoFWrYIzLtm3bUF9fj5KSktA1F110Edq2bYvy8nJR4XP27FmcPXs29PfJkydNbLV+vN5TJHgIgiAI0/j444/x/fff4+6778a2bdsQCATw5JNPIi4u6CTi3Vw8SUlJaGhoiHjsxx9/hM/nw+OPP468vDwAwNdff23NF5DBkcInEAhg+vTp6N27Nzp37gwgeDpuUlJSo1oCbdq0weHDh0Xfa968eZg7d66ZzXUc4emVlZVxqKhIQEHBOeTkBN1sRqZXHjoE7NkDdOwI5OYa8pYEQRCEQs6ePYvDhw9HpLPPmzcPQ4cOxa233oqdO3eivr4eS5cuxbBhw/DFF1/g+eefj3iP/Px8/PLLL/joo4/QrVs3NGvWDG3btkVSUhKWLl2K2267DTt37sTDDz9s07eMxDExPuFMnToVO3fuxBtvvKH7vWbPng2/3x/6d/DgQQNaaBxJSUmGXicHn165bNky3HbbV+jRozWuvz4dl13WGmPGfI9ly5bhmWeewb59+3R/1vLlQLt2QP/+wf8uX27AFyAIgiAU8+GHHyI7Oxv5+fkYPHgwNm/ejCVLluCdd95BfHw8unXrhkWLFmH+/Pno3LkzXnvtNcybNy/iPXr16oXbbrsNpaWlaN26NRYsWIDWrVtj5cqVePPNN9GpUyc8/vjjeOKJJ2z6lpF4OI7j7G6EGqZNm4Z33nkH//znP1FQUBB6/OOPP8aAAQNw4sSJCKtPu3btMH36dNx9992K3v/kyZPwer3w+/1ITU01uvmasLLAVVVVFZYtWwa/PwWLF0+PSqHnMHBgGXr3LgegvbaEz+fD/v3ncPnlmQgEzh84Fx/PYevWo8jPT6BijARBOIIzZ86goqICBQUFaNKkiarXslDHx2lI9bfS9dsxri6O43DHHXfgrbfewieffBIhegCgqKgIiYmJ+OijjzBy5EgAwO7du3HgwAEUFxfb0WTDUDPgjRJJQnWDAA82bSpB5847NRdN5G/0iop8BAJjIp5raPBg6dIPUFDwE93oBEG4nvT0dEybNs3Wys2xiGOEz9SpU7Fq1Sq88847SElJCcXteL1eNG3aFF6vFxMmTMCMGTPQqlUrpKam4o477kBxcbErMrqUYOTuoVUrH4AAor2hfNFErQHW/A3O1yUKF1fhdYnsjvonCIKwAhI11uOYGJ/nnnsOfr8f/fr1Q3Z2dujf6tWrQ9c89dRTGDp0KEaOHIk+ffogKysrVHkyFjCyCqjXewoDB24CEOkJNapoItUlIgiCIOzAMRYfJaFITZo0wV/+8hf85S9/saBF9hPt1qquro543u9PwfHj6WjVyqdJUPCxPJs2lUQcjGqUOKG6RARBEITVOEb4EJHIubWkTnJXQ+/e5ejceadp4oTqEhEEQRBW4hhXFxGJlLtK7CR3vz9F02d5vadQUPATCRSCIAjC8ZDwcSF6TnI3qh4QQRAEQbAIubpcQng8j1zGlBTp6ekYPXo0Xn31Vdlr3SCSrKxSTRAEQdgPCR8XIBTPM2zYhkaPKXVVFRYWmlZbQqlYqq2tRVVVleGfH054nJRUTJSS9H8SUARBEM6AhA/jiBUk5DO4xOJ5pk9fjOnTFwsGJfPiw8qK0DxKCnbV1tZGWJzEstP0Fjnk2yDWh4WFexUVajRSQBEEQbiFTz75BFdddVWjExWkyM/Px/Tp0zF9+nTT2kXCh2GUFCSUiucpKPgJ48YNREZGRug5XszYWSpd7v3CLT1SQsKoIodSfajESmaUgCIIgrCSsWPH4uWXX8aUKVMaHTw6depUPPvssxgzZgxWrlxpTwNNgoKbGUbJQsnH84QTHs+TkZERUfCRFx1GFjs0C6Oz08SQ68Pq6mr4fD7B1/p8vpD1TU9QOUEQhB3k5eXhjTfewK+//hp67MyZM1i1ahXatm1rY8vMg4SPw5GrgKw0psbvT0FFRb6kqPD5fKiqqhL9JyYOtGKVkBDqw+Li8tDz69evxzPPPNPo+/FWM746uJyAIgiCYI3u3bsjLy8v4pSD9evXo23btrj00ktDj509exZ33nknMjMz0aRJE/zud7/DV199FfFef//73/Gb3/wGTZs2xVVXXYX9+/c3+rzPP/8cV155JZo2bYq8vDzceeedOH36tGnfTwhydTkIsVgXvgLyJZdch4svTkZOTg8APRTH6CgpdmiHa0xPdppa+D7curUntmwpxpYtvVFeXizpWhOyhhUXl6O8vNiUStcEQbibQ4eAPXuAjh2B3FzrPnf8+PFYsWIFbr75ZgDASy+9hHHjxuGTTz4JXTNr1iysW7cOL7/8Mtq1a4cFCxZg0KBB2Lt3L1q1aoWDBw9ixIgRmDp1KiZPnoyvv/4a99xzT8Tn7Nu3D4MHD8YjjzyCl156CceOHcO0adMwbdo0rFixwrLvS8LHIciJk+DZWonIzm6j6n3l4lJ47HCN8ZYYrdlpWigvLwZvCBXrCz4oPPyIkPDfBwigV68v0LPnVhI9BEEoYvlyYPJkIBAA4uKAZcuACROs+ezRo0dj9uzZ+OmnnwAAX3zxBd54442Q8Dl9+jSee+45rFy5EkOGDAEA/PWvf0VZWRmWL1+OmTNn4rnnnkNhYSGefPJJAMCFF16I77//HvPnzw99zrx583DzzTeHApc7duyIJUuWoG/fvnjuuefQpEkTS74vCR8HoFScaEFrYK/ec8CUYuV5Xkr6QsjyFf37AHEoLy9Gz55bTWsrQRDu4dCh86IHCP53yhRg0CBrLD+tW7fGNddcg5UrV4LjOFxzzTURSTH79u1DfX09evfuHXosMTERl19+OXbt2gUA2LVrF3r27BnxvsXFxRF/f/fdd9ixYwdee+210GMcxyEQCKCiogK//e1vzfh6jSDh4wDEFuSDB3Ph9e7S9d5a3ElGnQOmFDPO8/L5fPD7/RGPyfWF3+9HfX19o/fSmxUWi1DdI4I4z54950UPT0MDsHevdS6v8ePHY9q0aQBg2kHfv/zyC6ZMmYI777yz0XNWBlKT8HEAQgsyAKxdex3q6s6Ljj179qC6uhrNmjVDYWGhovdW604y0/rEozQgW2vlaLF4Jbm+WL16daPX+P0pOH26GYAAwnMFhMSjGypdGwHVPSKISDp2DLq3wsVPfDzQoYN1bRg8eDDq6urg8XgwaNCgiOcKCwuRlJSEL774Au3atQMA1NfX46uvvgq5rX7729/i3XffjXjdl19+GfF39+7d8e9//xsdrPxiApDwYRh+oYxekM8TKTo2b94cemb06NGS4id8EZZyJ0Uv1lZYN5QUOdRjEZB6XzWutci4Hg68+IkWTCNGjEBOTg4t4v+F6h4RRCS5ucGYnilTgpae+HjghResDXCOj48Pua3i4+MjnmvevDluv/12zJw5E61atULbtm2xYMEC1NbWYsJ/A5Fuu+02PPnkk5g5cyYmTpyIbdu2Nar/c++99+KKK67AtGnTMHHiRDRv3hz//ve/UVZWpih5xihI+DAMLwAqKysBrEdS0lmsXXt9xDVioqO2tlbRe6sVF1ZlWlkpEqLjlZS41g4dysa77w7FeSuPBx4Ph5Ej1yAv71DE6zMyMkj0CEAuQoI4z4QJwZievXuDlh4rRQ9Pamqq6HOPP/44AoEAbrnlFpw6dQqXXXYZ/vGPf6Bly5YAgq6qdevW4e6778bSpUtx+eWX47HHHsP48eND79G1a1d8+umnmDNnDq688kpwHIfCwkKUlpaa/t3CIeHDOOnp6SFxkpd3UJfoMOKICjsyrcxES7wS/5roMlgcF4fmzWsb9QW5uM4TXvDRynIFBOEEcnOtFTxyFZnffvvt0P83adIES5YswZIlS0SvHzp0KIYOHRrx2Lhx4yL+7tGjBzZu3Cj6HkK1f4yGhI8DEHN5eTwB/O53n+GHHy5G27Y/ITdX/FBPvXV4tLrGWEbO1TJixIhQZkNNTQ3WrFkjkMF1nvBFm3+tm4N0pYR0TU0NPB4PvF5v6DG/3x8RJ+U2EU0QhDMg4eMAwt1Se/bsQWFh8PDRf/2rBz77rA8ADwAO3bp9h//5n3cE30NvHR6z427sQM7Vwh/3IfeaIJGLttBr3YRSIS2F35+Cli1PYMKEF1Ffn2R6uQKCIAiAhI9j4AVFdXU1vN5TOHWqBXbt6oSg6AEAD777rht69PiXpOWHR0sdHieJGiVocbWIvWbChBcj+t1Jli8t6A08FnIxFhT8ZFDrCIIgxCHh41AOHGiH86KHx4ODB9vKCh+r6/CwihZXi9hr+D6P1QwuKSEd/ZwVJRHUYETsG0EQzoGEj0Np2/YnBFOow8UPh7y8A5KvY23RMRuhRS28cKFUvFL4dUpfE4uiR0pICz3XsuUJRdlcVljN7DiDjnAnHMfZ3YSYwIh+JuHjMJo1awYAyM2tQrdu3+G777ohPMaHtzzw10UTSynEShe1SZOGIC0tDcD5IGZAuGCh0Gt47LYMKLVc+Hw+HDt2TLAKNQAkJCQgMzNT0XeREtIABJ+bMOFFSRejlVYzO86gI9xFYmIigGAJkaZNm9rcGvfDl2rh+10LJHwcRmFhIUaPHo3a2lqMGAF8//1ebN/eHJdcchpdusQDGCFZuVkurqWiokL0s7Us7HrdCGpeH31t+CGiUqSlpakORNbyGjNRKvJGjx6NV199VdF7KrFySAlpwCP4XH19kqSL0c66R1adQccK5ObTT3x8PNLS0nD06FEAwU2nxxMdhkDoheM41NbW4ujRo0hLS2tUZFENJHwcSLio6dIFuOkm5a+Vi2spKyuTfL0ak79eN4Ka1wOQvVbLouaUhVCpRUKusKXa95QT0mLPFRT8ZNnhs0phLfbNbFFCbj7jyMrKAoCQ+CHMIy0tLdTfWiHhEyMorcPDI7bgqzH5N46tUfeeel8fjp5ChXoXQrkFrLa2VtQ1CWhb4JQKNr8/BQcP5gEIFshUK0DkhLTUc2IVsu3IiGMp9o13RUq5Wnn0iBIj769Yx+PxIDs7G5mZmaIuZEI/iYmJuiw9PCR8YoT09HSUlpaGJlN+Yjt+PD3ib8Ccna/e91T7+vBJHBCONZFa1IxaCI2odwOoW+CU9tU331wadewGh2uvfU/R76JUSEs9N2rUKGbipFiJfVM7XrSKkvAK2gB71i4zsMKtFx8fb8jCTJgLCZ8YIryKrthEZ8bOV+97qn199HcrLi5XvagZtRAatVtW+j5K+8rvT4kSPQDgUfy7yBW0FKrcHA5rsSOsHJ8h1p9GulyjxRVL1i6zILceEQ4JnxhEaqJTsuCr3TnpFRFqXi/03crLi8GfnM6jtVCh3oVQbgHTu8Ap7augpa9xBWo1v4vUAsFS4DeP0LjlrR6sHp/h96dg69aeKC8vNswaE90HrFi7zIRVtx4Fl9sDCR8GsHrwS010cgt+9HlLYvABx4B+EaHm9WLfrVevLxotHloKFepZCOTcCUa4G5T2VdAFGCkGxa41ErsmeiU7ftbOoGvsijTHGpOYeJYJa5dVsOLWIyuUfZDwsRmzBn/0AlNTU4Njx44BkF4c5RZ8pYF74Z+tV0Soeb3Yd+vZcyt69twqu6iZdRirnDvBKHeDXF+dOHEidN21126IWFjNtnLYOdEr3cmPGzcwdDAtjx27bn486LXKyREuAviCqFrGgVMsFyy59Vi1QsUCJHxsxowCanILjNTiqPbgSKWuGSWZZFIofb3cwj98+GVo2bIodH10ESyzDmOVcyfodTcoFWybN29udN3Bg7kAgLy8Q6ZaOVgqFig2blk5XFb8MFzjrDHRIiBYCLXxuXNyOMlywapbjxUrVKxAwseFyO0kxMSNkoMjExLODxm5mzV64VSbvqz19UoX/nDCJ2UzJmc5F5Red2C0YKuursb69etlXxcUgi3/+9dvAQhXbjZjR29XjSQnLDJC4wEw1ionLK6CBSbDkRPALAlaOVgJYg+HJStUrEDChzGMXgyiJ/muXXdgx46ujcSN3M3HHyPAT15Kbla91hO1rxe6NloA2GVOlrNEGRFTJCU8xL53x44dZS0cZuzo9YgPPSLMKYtM9HgAAujVqxw9e241zConJwK0Hh3CctFPFoPYWbVCuRkSPgxh9E5UaJI/f7aXumwu/hiBqqqgCVzpzarXeqLm9XLXSvWv2PEWemMT1BSOlHq+pqZG9DOECiAaWaPF6FgEPeJDrwize5FRUsySR64GktLz1MSQEwFajg4x8x4zyuqo1+1uNCxaodwOCR9GMGMnKmzKjjxDRmk2VzRqrmch8FGuf6XcQnpiE5RYraQqN/NZdPzBqWoxelwZIc71iA+9bhUzFhk1h8MqPU/N6Ere0a/nsTKAX889plfw6nW7mwlrVigW5muzIeHDCGbsRIXjBIKZGzxKs7miUXo9K4GPSvrXLDeYlZNE9HcwclwZJaLkxIeUdSvaaiD2m9XU1Ai68IxeZNSMb6XjqFmzZqYGWNsVwC9FZWVlqD1Cn620744ePYpjx44JZp9effXV4DgOLVu2bFQxXOxzwzFTELBihWJlvjYbEj42w0/yShYDtZOh0CQvFOPD32RKdn9qd4uspGzK9a8TAl4B6fgJoe9QWLjXMAuHUSJKTnwotW5J/WZr1qyJmJzNsnLosUDZGQtjRwA/IP6do61Bcgur2PsoHTtayoMYLQhYtEKxMl+bDQkfm/F4gtYXucWAv06O6DN4hCb5/v0/Fpz0R4wY0aiGCRC5k9GzW7RTXMil8EtZMsL7004zr1T/iX2H6dMXG2bh0OsmUntQrhRKrE/hY9QsK4dQu5SIGacIbTWI3WMAUFGRj8rKbGzaVKLoO0v9Tkb0ndqF24zMNavGpFbcOEZ5SPjYTPhZRlKLgdiZR+GI7UqidxJiOwulGRxabkQWsmnE+lfOkqF2N2oGcv0n9R2MsnDodRMpmeijK4OLCQkt1iezfzOlCwUL94KRSAnaffs6YPHi6f/9rufd7Fq/s9q+M8uqZtT76hmTZrre3DZGoyHhwyTKrDvRaDU/lpaWonXr1rbH2JiBEnOymCUjMbEOFRX5TJh55fpPzhpjVEVivZYaNZ8nJSTE4tcqK3Ma1Z6yAjULhZ57gaXA0/C2lJaWhuJqTpw4gc2bN4sUSDwPx8Xh4MFcHD/+q2IBoabvvviiWLGFSQ0sWELMjsWxO/vRbEj4MIQZ6ezRhQuFdiler9f0ydKulE0pKwNf40csFmr58onMmHnl+k9JarLSGDGh407CsSIWQU5IeL2nUFKyCWVlA3F+QfVg06YSdO680/LJWc1CofVeYCnwVElbpKpPA8HvvHbtdQCU32NK++6LL4ojxoZRFgtWLCFmF410e4o9CR9GMDvtWCiomZ9ktAROq8XOlE21tT0SE+tCogfQ9lsYvTNX0n9GZIYoXVxLS0sj3K9WHqTLf6+cnCqIlWewWvioWSi03gssVUhW8hlSWaXBxwH+LDKl95iSvvP7U1BWVgIzxgarlhCjXXqspdgbDQkfRjA77ViscKHXe6pRBoxZsJKyySPmBquoyNf1Wxi5M1cbEKzXGqM0q8Pr9ZoqlpUICZZ2pUoWCqMzy1iqkCzUFqE+KSnZhJycSpw+3Rxr114f8R5S95ia++D48XQIHe5qxNhgaczxmOV6Y22+NhISPoxg5A2lpnChUAaMkbCYsskjdr6V3t/CyJRQvQUQAe3WGBaz8ABExF3ZvStVI2aMzOJhIc5ESVvE+sTvT1F1j6kJjBezNJWUbNI9NlgYc+EY7Slgeb42EhI+NsMPILkbSs1AU1O40GxYT9kU+lwjJzcjFigtfRPuaqurqwsdNcIj1+d2xTIozRAK70ujavJoQe34NmKcsxJnoqQt0SUyampqsGbNGk3znVLraPR7AwEMHLgJvXuXN7pWKWbVgeLR6ho32vWmdDwDaDSnKGkvK5DwsZnogfbAA8ewf38C8vPPISenB4AehsSDhMf4AADHAfv2dbBklyh3ynf4wszKDWOEmdeuBcoIV5vchGrW2WZiVji5vozOWjN7HNmZXcVSnImSM/7CXaLZ2dmGz3c8/Ng5duwYRoyox7Rpu3HwYDLy8s4iKysbwAgkJCRoOuPMzA2cnvvVDNeb3HdQ2t5Ro0ZpqpBtBSR8GCB8EGRnA0VF2t5HalcCAN991zXsaut3iSxlpQhhtJnXrgXKCFeb3ITKixGh99X7+2nZ2arJWtNL9Dg2qx/4zwoXgQBbcSZa2mLEfCclPL1eL5KSktCli7FziFlzktIwg/BjPfixYIfrTWl7papo233kBQkfFxG9KwkvBldRkY/ogD+rd4msl0M3elfHwgKl1dUmN6FKva8Zvx8LfckT/v3M7AdhgRWMb2IlzkTrwqvHYsb6BkovSo/14NHqejPKaikVZM/aHM9DwsdlRO6mslFaWioa8Kd04TDDrM9ScGY4Rk6UdgdC6nW1SQWmWu3Cs7svhTC7H+QE1vTpi22LbdIT86JXuLC+gdKD2nlR7JghwBrxKNVeVud4gISP6+FrrejZmRm9u2IpONNs7EwJVeNq48Wt3++PeFzI3WeXC8+oOkVGiXir+kHqHLaCgp8iFj+r4if0WEeNFC4sL65qkZoXAQj2kVYXrxG/gVx7WZ7jSfi4HL3ZCGbsrlgKzjQDVlJClVr5lIrbgQMHoqyszFK3k5F9aXRsjlX9wFJ8UzhGCCw9wkXvBoql4z8A8d9569aeKC8vNk3caf0NpMYl4GF6jifh43Ksqh2iJsuHpXgNM2AlhV+plU+puE1JSVH1vkZgZF9qic0RWhytDix16/2iV7joPfOMtTghsd95y5ZiqK1wrRQ9v4HcuGR5zJLwiQGsqB0iFngHNJ48WIzXMBpWgirVuoeU7v6sdOEZ3ZdKJ3tpC1HwMSv6wa33i17Lrx5ByGKckNDvXFxcji1bekdcZ6TlRM9vIDcuWR6zJHxMhDVTqh6U3CBqJg83l0O3G63uIbW7P7H3ZR2lk71SC5EV/eDG+0WvJcsoQWh3nJBcGRLezcVjpOVEy2+gNHyC5TFLwsckWDSl6kHuBlEyebAS++J2tLqH5ARBYmKios9n/fdTO9lrdQfI9YPcxkhJoLmTMUK46F1cWUi0ELpf+cKdgHLLiZaNtpbfQGp+CS+hwr8/i3M8CR+TYOkkZSOQukGUTh6sxL7EAlr6UE4QeL1eV/x+aid7qaDTq6/eBKBxWrFcPygNtFaC3YuIHrQIF6UbqJqaGtnfgZVEi+g2qk1K0bPR1vIbiPVpdGVuIViYI0j42Ex4UDALA0IKsRtEzeTB8veLdeQEQXV1daOaIayPWTHUTPbCZ98BW7YUo2fPrZoyq5S60cTK/vM4sf/1Wn7Dj6YIty5Ei0e+crCUVZ3VwHG1m0S1MUtmWt+dMB5J+FiE0mqcrLm+lNwgrBVHJLQjJQhWrCgz7WgGO5CyFACR2VtCQaaAfsuAnLU0LS3NllR1M1GyqNfW1goersuTlJQUqlEGaK+grcT6Z9ccpfU9lYQdxLr1nYSPBagJngs/j4WFgSd1g/B+aJaKIxLqUSJu9aR/R3+WXb+l0t1r+BlD/IalU6cfItKKAWMsA6y4WqxGzg24bNmy0N9im8bS0tLQ82ZUKA++t1/SqsQzevRoNGvWTPQzrBr3avoiludUEj4mo/amZNECpOTztfiJWUwpjUXExK3Sk9F5rDy8UwtKdrnhC1202OvWbQd27OhqaHouq64WO1HqBqyvrwegTTwqdfVwHKeoLa+++mroOjvHfawKabWQ8DEZsYF48GAujh//VTaYMdwCFA4L1iAj/cR2p5TGOkYEgFp1eKce9ByrsmNHV0yY8CLq65MMS891a40eI1AquLWeEK/E1XP+wGdlbZEa92JzOf9ZRsznRgpplq23eiHhYzLCgZEBrFt3XcTNEbSWKD+RF7BmByE3+I0w8bKQUkqIY1X6N0uIib36+iQUFPwk+3o1qeos1zuxE6WCW6t4VDJ38jFGSuuYKSnyaqZFyCghzbr1Vi+uFD5/+ctfsHDhQhw+fBjdunXD0qVLcfnll1vaBt7KET0QgQCC55h4AARvjnffHQqPB6LWDrtcQFbF4JB5lm2MSv920u+pVuyFWzW1pKqrrdETLqwqK+NQUZGAgoJzyMkJhNrjxAUpHDW/gdniUUlblIx7KyyhRvSFE6y3enCd8Fm9ejVmzJiB559/Hj179sTixYsxaNAg7N69G5mZmZa1I9qUOm3abrz22lacPt0ca9deH3V1HHhXcvQuwU4XkFUxOBTnwD5607+d9nvKiT2pE9GVLhpKEHIThwsrNanwThNDagW3mQUelbRFbtybaQmtra1t1F4j0tPdYL0VwnXCZ9GiRZg0aRLGjRsHAHj++efx/vvv46WXXsJ9991naVuiJ5mCgp/g96cI1gQJh98lAGBm0JkpwJTUjwnHaRO4W1C6sLglbkVK7Cmp2yO3aGit0aMk7gQA5s//l+NdE1K/QUKCsuXLqAKPcuJfbtwbZQmNdqOqyTxT+9u7wXorhKuET11dHbZt24bZs2eHHouLi0NJSQnKy8sFX3P27FmcPXs29PfJkydNaZuc6yv4Lwi/S2Bl0Jml+pXGOQjFOTltAnciShcMoevcEreix4ogd/+qrdHDL3jV1dXw+1Pwww8Xi1aU5s93it6kHD161HEuMrHfIC0tzfJaNHLjQWrcG2EJFXejpsh6CKRiMcVwg/VWCFcJn+rqajQ0NKBNmzYRj7dp0wY//vij4GvmzZuHuXPnmt42MddXq1bHsW9fB9FdAguDzgwB5vP5InYpgPCkQinu9qG3yJmZrgez0CP2opFbNKItmeHvHd2njd1b0//7vhzCN01AIOJQy+hNCl+jSGqBNHNToSQ2Sc1vYLZI02ItEhv3RlhCpdyoJSWbsGlTiaEbVLdYb6NxlfDRwuzZszFjxozQ3ydPnkReXp4pnxV+kyYlJWHLlmB2iNgugZVBZ4bqVxI/RCnu9qNmYTFSNNiFkRVt5e5fvkaSkmwZMfdWUPQExY/HE0D37tuwbVuPiHaozT4ya1OhNDZp2rRpzFQVVlplOryOjxRGWUKFfsOyshKEF9jkH9frIXCL9TYcVwmfjIwMxMfH48iRIxGPHzlyBFlZWYKvSU5ORnJyshXNi0BJRWSAjUFntgATmgQLC/dKTs6xHPfDan0Nt5TBN7J9UvevlmwZIesr4MGgQR8iEOAXv6hnNWQfmYHSmjh1dXVMHdOhZDxIjfvw+RwwxhIqPA7iTPMQONF6K4WrhE9SUhKKiorw0UcfYfjw4QCAQCCAjz76CNOmTbO3cQKI3VBmHiCnFbMEmNgkOHLkOsnJOVbjflg/5kPrZ/Jizu/3hyryhpOQkIC0tDRHCKdoxFy4WuLmxKyveXkHsHz5RETv+LVkH5kNK7GLRiI1Js2whIr9hr/73Wf47LMrwYsgrRtUN1hvpXCV8AGAGTNmYMyYMbjssstw+eWXY/HixTh9+nQoy4slpHbupaWl4DhONOvDDv+2GQJMbBIEONWTcyzE/ZhZYsAuS5KYmHNS0bTwvuMPOZVC6+IvZn2tr08WzBQdOXItOnfepeg9rBIddgsvqzHDEir0G3btugOff35e9JSUbNIcGuAW660YrhM+paWlOHbsGB544AEcPnwYl1xyCT788MNGAc9244TKmFYMfvEd7CHRyVlJQbhYwMgYKDstSUJiTioziTWBK3wv5wseqllfX4/169frWvyFrK9CZTL4+0jpe1iF3cLLDsyYw8N/w8TEOixfPjHCgrhpUwk6d96puV+dKmqU4DrhA5wPjmMZlitjWrnzl5oEhSZntwQ8C/VxuJuHd+2EE25Zk3OViGUM8e8T/fuxcmDsN99cinffHYpwl42YG4iVWCel97LX6w1dp3fxj7a+ank/O+M2WIhdVAor40wI/jesqMiXtSA61S1lBq4UPk6CtcqYduz8pSbB8MmZtb7SitI+FqK0tBSAvKtE6ow3QPr3k1q8wwWVkglfyaLBw/++0XEqQOR3q66uRk1NTSg1WwqjLVRC34fvE7Xj0+jFX+r9RowYgcTExEYlJISwKnHACQGzrFnmxcSLnAVx1KhRrrbgqIWEj82wFuhn1c5fafxQuHtATV+xvEtT0ndi/c5bhJS6StT+fkoPWuSRmvCVCjwpMSf03YQOe+Rfb6aFSnoR1HYvK138le7Wxd4vIyND0Xv4/Sl48slvmHK52wlrlvno8AM+Y0zO4idVIdwKDh0C9uwBOnYEcnNtbQoAEj62w3Kgn5luJTXxQ/wJyUr7ivXMp2iixYmSflfi2tDy+6ldvKV+P6UiWkrMAcKZSeHfLVjHhkN4JosZ7k+5RbCwcK/s+NSaLSN2v0SnSksh9B7hr5caL5WVlairq9NV7ZnlTCGxjZJWa56ZiPU1a+5Dvk9XrWqKWbO8CAQ8iIvjsGCBHzfd9Kutm08SPjbDaqCfFTe62kGvtK+U7r5YCJLVU31VaqIzOl06MbEOFRWNA3a1fk8lYg4IoFevcvTsuVXyu4Uf+WLFgiTWt9OnL5Ydn3oSBoQeUysmxO45oe/07rtDkZR0Fnl5B7F+/Xrd1Z5ZzRRS4s6ywjJvhJWaFfch36d+fwoWL54Ojgven4GABzNnpuLnn1+C13vKts0nCR8GYE2pA2y54MInd6m+ElsEWM0C01J9NfpgRrGJzsh06a5dd4QyRrRYVNSIMCX3gpRLTOn31INU3yppv5ETvVFiQqwg3tq11ysS5Eo3ESxYWKNRYsk7fbqZqZZ5rbFESoUvfy6iVeez8X0qNw/Ztfkk4cMIrCh1HpZccHomd5azwLRUX5U7mJF3XRiVLi2UJitnUQk/TFPse4ZPfqdORb6P3L0g5hJT+z21Ite3Su9lo+LQjFi0pPqUT41mZSNkFmICneOA4GYkEOojoy3zWmOJ5OZGPglg9erVtpzPxtI6Eg4JH5tg2d8NsOeC03JTsuSXF0JsUgjfXYu5S+RQ+/uJBZsrSZMNRyi+Sm7yKysrAxDMPPF4PKKVm8+dOycayMkvTlaMU633Rngfs5Yt1NjNGAnvegy3RrKwgKlFKitPvJgqTxw4LoDrrluDvLxDmsaYGbFESsaHXeezsbaO8JDwsQm9JmorspZYdMGpgSV3nRBik0L37t+ic+eduvtdze8nli2idscmNCaFvmdxcXmj69LS0iTPaOKD3IW+GwBLx6lc6nhGRkbE9dH3I2vZQsD573TwYC7WrbtOtSBnHbmsPDlLYpA4NG9eq1n02BVLZOdcyOI6QsLHRvSca2TWblFpmnl0WX6zgxK1CD25RVttTRqjUBKzJNbvchZAPceMCH1/vTs2fmwWFu7F9OmLsXVrT2zZUowtW3qjvLxYl+sx+rtp6S89SKWOKz1kkzWrZPA77UJdnXmC3C6UiE0xSyKPmOhXMs6UfL5ZriG7XU6shXKQ8HEgZu4WhSxRfr8/ovCZ35+C+fP/ZZlZXmt6utyiraYmjZGIWfuUVG62KnNGb0A5IJyxVl5eDH4hUbvIKxUxpaWl8Hq9hotZM9zTcjvx6upqU0W5WFvVCnInISU2o7/3vn0dROcP3rKn9veRE7tmuIZYdTnZBQkfB2PWblHqJrbDLK+0HgxfayTcGiV2rpEdxzGEI9THSq0EWt5by3voEVBCY1NvgKzd6dBmfL6weyVYPgA4L87NEuXh3ym6JpAbRI4QcmIz/HtLiX5e9NTV1UW4YcMRGg9yn2+Wa4hFl5NdkPBxMEb6baVcSXYU8QpvT7hLSkp4RVtw+J2/0iJtRCR6FlrxQFF9AbJ2p0Mb/fmNg4qDhRiXL58YMTaPHj1qqqDTC4vnQEXPafw8otbtIyYAhSzhSkIOlHy+2GfqPU7EKjHLevIOCR8HY5TfVmnMkFUBcmKuLTHhlZl5GPX1yY3a6/V6I6worMVTuBmtGWt6YfmoEjG6d/8WmZmHJcsGrFmzxnRXrFpXYvjrWOtT8TktRdbtM3DgwFCmoRRcMAAIgDpLuFa3k5OOE7HbOisHCR8HY5TfVmnMkFUBcmKurdOnmwkKL6XF9VjP8tKK0sXeSlFgdsaaEE47qiSc+vpkwbG5dWtPXH31JgDmu2JZX6zUoGROE3P7FBQUKOoH/nktGyolWYFKLdVKDwGWwgzLC8vjhISPwzHSbyt3A9sRIBd5JlMAQVeAJ+wKTvGEY3dmgxkoXeyHDBmCDz74QPa6UaNGhYKqtSxyZmasyWHVAbtGEP1dxVKpt2wpbnRkh5mwvFhpQcmcJoSSfuDjevRUSVeSFajHUu0mMWskJHxcgFF+WyU3sJUBco3PZOKrp3JhQkj5hOPGzAali70S0QMAa9asifhbrXVEbKKtqanBuXPnAACJiYkRrhLA+MmX9Vguvp8qKytDRRmLi8uxZUvvqCudb5G0Ey2iRK0AV7qh0mp90WupjjVRowQSPg7ELPOl0hvYqgA5sSMdRo5cg+bNaxsdpyDW3nDcnNmgZrFXen6Z1pII0RiVsaYEu2K51LoS09PTI67v2XMrtmw5n+4PON8iaTdyc1p0sUktAlzphkqr9cWNlmq7IeHjQMwyX+q1iBjtJxa74cPLxStpr56ifk5B7nTt8O8rdhAjiwe5asGOWC4j4ou83lO49lp3WSTtRm5OU1NsUgqlGyot1hc3WqrthoSPQzHLfKm2FD+PGX5iJTe8VHtramqQnZ0dE35uudO1eeuPmEDyeMCsW0gtduyQtcYXRYttN1sk7cKqPjXTEk7jwlhI+BCNMKIUv1EZREpueLH2hqcAO1nUKEHudG3e1SMmkPjMXDek+Bu5Q9YyjtW4HNPT01FaWhpRD8athQPtRKxPo4/eiUZqnrI6Y4rGhXGQ8CEMv4H1niWm1DUVDssZPGrRstg2LoQXCe/qUXIQoxtS/I3YIatxX/FoiS9q3bq1ovY42RVrNUr7KjqYXwixecpsSzLrRQCdDAkfwrAbmF+wlVZaFvs8Je2pqakJTVqsZ/CoQU+siNzp2rwAiBRIAQTLA3gaXes0jI7l0uK+0hJfFAuuWKtR0qdKKy/zR+FEY/ZvQuPCPEj4EAD0xwwJWXkOHszTVX9CCW6rxqzUQsVfJ7zYC5+uLVaSQOogRidh5kKhVFxrjS+ixct41PSpmqNwwjG7GKZV48KJFc/1QMKHMASxSqnRGO1GcWs1Zh65tPPwxT58Byvl6iktLQUARdc6zYxuxuSsRlxTBo7zUPL7usmVHo3e0AQnQsKHMJTGRQcjMdqNwmKNC6N2T0qtDPx7ZWdnq7J4kBldGXLiOvrgSMrAcRZyv6+bXOlCKD2yyA0ij4eED2EowllDQczY/bK2wzbqvCitLjy1VZYJeeTEtZArRCy+qLa21ryGEpqQ+n3l7sNo0RuO0zYOesMGnOQuI+FDGIpw1lAA1123NqLwoJGwtMNWG6Mjhlorg55JxUkTlh0oEddKK2G/+uqrrnIZuAGp37eiIl/yPpSK/wG0u4fsuCf1hA04zV1GwocwFLFJpHPnXYZ+jpXVmPVMQkoXxGi0WBm0TCqsnWjOqgiTEtdi7gE3x4U4GaHsU7HfV6kr3cjf2q57Uk/YgNPcZSR8CMNRaoHRI0qsSvXUMwnpiQ3Q4sLTMqkYZaEyAtZEmBJxLeYe+PXXJti0qcS1cSEsokQ0AxAdY0K/r5L70OgYIK1VwPViRNiAU7JsSfgQpiBmgeGPvTBClFix+GkVBlongPDFVkxAarUiKcHM95bDbhEmtHCWlpaivr4eAJCQkIC0tDQAQHV1NdavXy/qHigrKwF/2Cirh6S6CaWimc9olOOqq67C5s2bAUhv5Mxe6K0OrNYbNuCULFsSPoQhKLXe5OTkOHryVSoM1E4A4YvWqFGjcO7cudBzJ06cwObNPwEwdyJkLXvFShGm1dok5h5w4iGpTkaplYQXsXLXRVfTFtvImbnQ22U90XM0BotZtkKQ8CEMIRaqjKoRBmomAKWLlpkTodWTrJB1QmnFbzPQ6l4Qcg+UlGwKubl49Ez+Siw5drlHWETp2JG6Li0tDdOmTcOxY8caiSWA34xsNnWht8p6YuTRGKxl2YpBwocIcegQsGcP0LEjkJur/vVOFjVyqBUGaiYApYuRmROhlSZqOaFnd5yAWtEl5B5o2vSMYYekqnXhsGa5sxKlY0fpdeFHWghh5kJvlfUkugiqkNBLSEhAXV0dfD6f7DzPUpatGCR8CADA8uXA5MlAIADExQHLlgETJtjdKnZQKgyUxOhEXxeN2G7dzInQqPfWYp2Ixuo4AZ/PF7I2KV0Q5QKfjZr8lYpifrGyWzTajdKxo+Q6pVY0sxZ6K60n6enp8Pl8skIPUOYu1eMuswISPjGOz+fD/v3nMHlyJgKB4EGVgQAwZQqHSy45ivz8BFdbcpSiVBjodflJ7dbNnAiNeG+tAabRC4qVcQLRbVa6cAr9zuEH5wLmlFiQi3tySnCpWSgdO2rHmJwVzaxyGlZaT/QkFzjtJHkSPjEMP+lXVOQjEBgT8VxDgwdLl36AgoKfLAmIDLcUVFbGoaIiAQUF55CTEwBgf3yQGmGgtZ1KdutGn6tlhIWKR611AhBfUKza6Ua3Wc2CGP07qz0yRC1KXFhOCS41C6X3qZr7We6+5DNVo9H6W0dX97arCria5AKnxXiS8Ilh+EEqN1maHRAZvuuWmtztyEgxUhjIIbdbF5tg+c/X0jdmTlhyE6fYgpKZeRgtW57AhAkvor4+yfKDU4uLy7FlSzEA9aLLrPGp1IXllOBSM5G6TxMSEhRdF+6SlbsvMzIykJ2dbVj7mzVrZuh1WlAisp1cPoGED2H7ZMnfPHKTux0ZKWYKg+gKsnIC1OgJlseMyUnJxCm2oLz44kSEi46CgmAq/4gRI0wthxDd5uLiL9Cz51bL7gOpTDc1LiwnBJcajdJK7pmZmYrv56qqKgD2W9Gsrq2lRGQ7vXwCCR8CABuTJavxCWbcuEITh90C1CiUWieEz3XjIFb4LyMjw1KLSnl5MXr23BpxnVnWJrmFRG7xDbdkAOYe4aIGq6wCZm5Q7Lwv7cjQ0xL4LQar5RNI+BAh7I7El5vc/X5/6HEW44DUIDYhmO1OswKlAlZoQbFL+CpxM5ppbZJbIOQWX77uDEuuB6utAmZ+Nzs2hnZl6GmxcNlZ8V0LJHxsRG/dHBYw8jvITe58qiVrcUBGED1x8N/ZyCM+rEKNdSJ8QUlMrMPy5RNtcSkocTNa2f9CC4mcKGZtfBhhFbAzjsTKg5CF0GIBN6K/1Fq4nFg3ioSPTbihbo4Z30FuZ8ViHJBepCYOs+J6zEStdYI/9wqAbS4FltyMUuNh3LiBjQLcWRQ9Qqi1CtgdR6LEfVZbW4u6urpQPFA0en4btZYXvf2lJpFDaVwmq5DwsZhDh4AtW4BJkwCOCz4WrJsDDBrkDMuP2bV/pFxurMUB6d1hyU0cNTU1jhE+aibO8D6xMnNOT5v1IjVWlBZQdKIQBrRZBViII5G6d30+H5YtWyb7HlqFmVoxrre/1MRJ8UKPtflYKSR8LCTcQhJNQwOwd6+1wkdL0Sm7a//YnWERjhE7UrmJY82aNY5x32kNMLWzBohVn610rMiNh/DzzIxuo1kYZRVgLY7ELGFmlBjX0l9qxxBL87EaSPhYxKFD4qIHAOLjOaSkHIXPZ12lZC2Tvhm1f9TspllySRhxMKSSicNJ7jutY9fORduKz1b6G8qNB94lKASrAtkIq4AT4kiMEmZGiHEj+0uqzAJL87EaSPhYxJ494qLH4wngmms2YMMGYwJ01QQca/0cIwe82CF5hw8n4MCBZLRtexbNm59AWVkZADZS76PROtEI9WNJySYmvhNhHmKLpJ77ilWBrNcq4IQ4EqOFmZ7538j+UmKplJqPza4urRUSPhbg8/mQmnoOcXHnY2KA4M0/cuRa5OUdUmwhkMPKoGkjBUj0IXnCE8n568XigIRcAWa7AbRMNNHm7F9/bYKyshJwXBw2bSpB06ZnmNvRsoSTq8bKLZJKAvxZcvnIoXeTxLr7T6vQMGsMGxl3o3QtEpuPX331VSYtkSR8TCZcMQ8d2njC69x5l2GfFe1O0xM0rdRqZGTtHyMyBVasKBNcFMy8+bRMNOnp6SgtLcXq1avh96dg06YSiBXuU4vZosBu0WF3to8etJz7FJ715gSXD49RsSqsu/+0pp2bNYbNjLvRIrpZtESS8DGZ8B/dTBeNz+fDl18CgUDkTdLQAGzd6kPTpsrNp3an2mvdsUgtCmbefFonGq/XC8DYHZrZooAF0aH0t6ysrBS91i6LkJ5zn5zg8gnHqMBx1t1/Wu5/MzPWzIq7kRPdTrJEkvCxGDOqI/OLkd+fAo9neqMb8IsvXsbOnadkFyMlaepWIDeRhO+K+d2wnYuC3onGyB2aEQHXdr6/FsTaIGUJAOyxCOn5rZ2YOqynf5VajAB7F10jhIYR7TezNIPc/OokSyRAwscV8IuM3A0oVyFVSZr6qFGjzPsi/0Xuewjtiu1eFPRY8+zaoemFhclOTxvsMMHr+a2dmjqsFSmLEQvuP6OEhlHtN7M0g9T8CsBRlkiAhI/r0LoAK01T93g8gq+PRm/BN7Xfw45FQW9Jezt3aHphwe2itA0smOCN+K1ZTh02K+5L7jV2jkMjhIbR7TfLgik1v9q96dQCCR8XosedJje5er1ey4rNqfkediwKeic+u3ZoRvQJC5OdkjawYJUC9P3Wdla2VoIdcV81NTUA7B+Her+PGe03Q4TKza9Os0SS8GEQu0/hFppc+V1zZWUciorYypThsaK+j9GTih07NCe8vxFtsMsaYMYYYe3k9XCsPlrC5/NhzZo1ANgYh3owuv1Gi1Ah0X3wYC4AD/LyDgJg2xIphmLhc/LkScVvmpqaqqkxsQxrp3CHW1vCd82vvMKZluWVlJSkyC0h5w4w64aLnlTE2jpq1CikpaUJvodVv6/ZkxELk51cG+ywBphl/bBjTtAq4Mx2LYa3iYVxqAej22+0CE1PT8eoUaNCQnPfvg6CFlQWLZFSKBY+aWlpsvEdHMfB4/GgoaFBd8PcgtIfPScnhwnBE030rjkQ8Bh2GCkPP8GuWtUUTz99NwIBD+LiODzwwM8YMeIEEhMTQ6nfUgdcSmHEzRc+WUi5UPhJQgyrsonMtoDZVUFbqevHDmsACwdrGoEaAReOHa5Fpy26APvuy3D4TZycBXXcuIGNalCxspGPRrHw2bx5s5ntcC2sm6nlENo1G3kYaXgq/uLF08FxfBq9B3Pn5sDvXwOvVzwV347+1RtUa+aipzfg2qj39/v9oeuNHttyvzmf8cOCNYCFwGotaBFwdrkWxfq4tLSUaUuZFfOWkeNPTw0q1lAsfPr27WtmO1yN2TefmVYPIw8jFYJ/vdxNJfU5/HEX/DWVlXGoqEhAQcE55OQEUFdXB5/PZ9jvwHJQrdkTavj719TUiFq3+KNHAHMsXFLvx8pumpXAaiNQsoBqdS3qiYmS6mPeSmwlLBT45DF6/Dk9nioczcHNNTU1WL58OXbtCh65cPHFF2P8+PG2DLZYx4jFTmwRsGrXrOemCp9spG52oyYbVoNqecyeUNW+v9VuHRasrHaPASNRuoBquYf1CAUW+5gVV6cZfcOCBdUoNAmfr7/+GoMGDULTpk1x+eWXAwAWLVqERx99FBs3bkT38NMkCUvQO4lHLxbhBcKsiOUwoiy93M1u1GQT3VYg8kR1u1Ns7YIlt47drmOrxoDZ56apWUC13MN6hIIT7jO77gkj+0apBZV3bwu93u77MRpNwufuu+/Gtddei7/+9a9ISAi+xblz5zBx4kRMnz4d//znPw1tJGENUoPTzGwpHr0Cy8qJkD9RfdOmxiequ8kkrBRW3Dp2H6DKY8UYsMKtouSessu1yPp9JnVPhJ8ob8aYNLJvpCyo4e7ucPd2NKwdGKzZ4hMuegAgISEBs2bNwmWXXWZY44jYQ4/AsnIi5E9UF9sJS+18wyc9HhZ3RUphxeXAUnyFFW4BK85NU3JPGelaVFPOQq6P7cyIkrsnos+RM2pMqukbNZsEvW1jLYtRk/BJTU3FgQMHcNFFF0U8fvDgQaSkpBjSMIJQi9LFRioQGlBWQVduJyy18xU7PJO1XZFSWHE5sBBfYZf1wyyLm9J7yohxq/Q7RAutBx44hv37E5Cffw45OT0A9LB9I6H2njBqTCrtGwDMbBLsQJPwKS0txYQJE/DEE0+gV69eAIAvvvgCM2fOxI033mhoAwFg//79ePjhh/Hxxx/j8OHDyMnJwejRozFnzpyICWTHjh2YOnUqvvrqK7Ru3Rp33HEHZs2aZXh7CG0cOgTs2QN07Ajk5przGXLuMj2B0OHFvJTshMVTve0/0dxIWHU52BFfwWJ5BS1uFasFnFqrYfh3yM4GiooMaYZhqL0n/H6/YangSvqmqqpK0XtVVlY2ek8pWIrzk0KT8HniiSfg8Xhw66234ty5cwCAxMRE3H777Xj88ccNbSAA/PjjjwgEAnjhhRfQoUMH7Ny5E5MmTcLp06fxxBNPAAhWlr766qtRUlKC559/Ht9//z3Gjx+PtLQ0TJ482fA2uR2jUuTDixPOmuUNFSdcsMCPm276FbW1tYa2B5B2l+kNhOaLecnthPlK3AAbJ0mbCYvZHnb2s9U7ZDnrgha3itUCjhWroVGI3RMAUFGR30gYrF69mknrCj92lLTNSXObJuGTlJSEp59+GvPmzcO+ffsAAIWFhWjWrJmhjeMZPHgwBg8eHPq7ffv22L17N5577rmQ8HnttddQV1eHl156CUlJSbj44ouxfft2LFq0iISPBoyY+KSKE86cmYqff34JXu8pjB49WnLsmGG2NmKildoJCxXzYiUWxihYqZcTjZZ+ZiUoWgtqrQtqjiuwClathmqRuif27evw33lQWBjYafGVs9TItc1pc5uuQ0qbNWuGLl26GNUWVfj9frRq1Sr0d3l5Ofr06RMx8AYNGoT58+fjxIkTaNmypeD7nD17FmfPng39reZMMrcg5oIyKqBNTmQ0a9ZMl5lXjXWKb5NRE62aYGy37WpZqJcjhNp+ZikoWgtqrQss4YRAZTWIlQVhWRgYYalx2tymSficOXMGS5cuxebNm3H06FEEAoGI57/55htDGifG3r17sXTp0pC1BwAOHz6MgoKCiOvatGkTek5M+MybNw9z5841r7GMs3w5MHkyEAgAcXEw5QBSs3dzahZg3ret1T2jxwXoll1tOCwKAb0WECfGYKm1LrCC2kBlJ1jmhD6fVWEgJMjefXcoMjMPIzdXWRwQIH/PRWey2v07aRI+EyZMwMaNG3Hdddfh8ssvlz28VIz77rsP8+fPl7xm165dEdljP//8MwYPHozrr78ekyZN0vS54cyePRszZswI/X3y5Enk5eXpfl89WBEE7PP5sH//OUyenIlAgHdBwfADSAFrYkCUtNXn84VuQL8/BS1bnsCECS+ivj5Jcd0gPVYOFmNh3IiefparvWL3hB2O2LlpZloXzBAeSgOVnWyZExIGAIfKyhwUFPxkW7uEBBkQh+XLJyoSy0otdkKZrHb+TpqEz4YNG/D3v/8dvXv31vXh99xzD8aOHSt5Tfv27UP/X1lZiauuugq9evXCsmXLIq7LysrCkSNHIh7j/87KyhJ9/+TkZCQnJ6tsufHIBQEbOeHyE0hFRT4CgTERzxl5AGk4dp3kzSOXzaVm8tHTJ3b3g5vRG3OktPYKKwurmFvFLOuC3cKDhXIFagkXBiUlm1BWNhAAbyjwYNOmEnTuvNO2eUBYkCkXy0IWu+++O42dO9+W/U52/k6ahM8FF1xgSL2e1q1bo3Xr1oqu/fnnn3HVVVehqKgIK1asQFxc5A9VXFyMOXPmoL6+HomJiQCAsrIyXHjhhaJuLlaQCwL+8ssy5OUdxJw5YwyZUJTGuRg9MK2o/iyG0mwuMzD7xHQ3o7bImp6YIzHBcPBgLrzeXaHHWFpYhb6LWS5VJwoPuwkvgZGTU4XzoieIXe6uaEvNu+8OBaBNLEdb7HJyqnDw4PnXsJjirkn4PPnkk7j33nvx/PPPo127dka3qRE///wz+vXrh3bt2uGJJ57AsWPHQs/x1pybbroJc+fOxYQJE3Dvvfdi586dePrpp/HUU0+Z3j69yAUBr117PTyeAC644CTuuce4z41F14vS3bCRIoTVIGDW0WJh0NOHYrvfdeuuQ10dmzEyQlh1X9u9oNn9+UrhS2DoPYjZyPmDn5OCdXrWIzPzMJYvn2i4WGY1xV2T8Lnssstw5swZtG/fHs2aNQtZWHiOHzc2WLOsrAx79+7F3r17kRsV9MJxHADA6/Vi48aNmDp1KoqKipCRkYEHHnjAUansYhMvEFycZ83yol+/I4bG38Sa60Vu8hkxYgRycnIMFyEkatRjddCx2O6XpQwcKawsL2D3gmb356tBb+aaWS7G8Gtzc6sMz6pjOZNNk/C58cYb8fPPP+Oxxx5DmzZtNAc3K2Xs2LGysUAA0LVrV3z22WemtsVMom+MaAIBc+JvzHJBGVUE0UjkJp+MjAwSKQxi1ULXvfu3SEo6i7Vrr494nIUMHDmssizavaDZ/flq0XvEhpkuRjOP/2A1kw3QKHy2bNmC8vJydOvWzej2xDz8Tu3gwVysXXsdwneeZsbfmAGrLp5Ys3I5HbmFrqamxtA6UHl5Bx1bdsCKe8nuBc3uz9cCa0dsiLnOcnICyMmpM2ReZrl8hybhc9FFF+HXX381ui3EfwlaYHahrs758TesWk/sDLQm1CG30K1Zs0aXBTQ83mH9+vUxGfumBrsXNLs/3270xjZZlZ3H8n2kSfg8/vjjuOeee/Doo4+iS5cujWJ8UlNTDWlcrEOWCYJQttDptYCmp6dHvAfde+LYtaC5rcqzFoxw+ZqdncfqUTbhaBI+/LlZAwYMiHic4zh4PB40NDTobxkBQJtlItyMWVkZh4qKBBQUnENOTrDCthkHg7IOi/FGhDKEYt84Dti3r4OhcT5UdkAaMxY0PeUKjIxHcQJaY5ui+zi6irLRsBriEI4m4bN582aj2xHTGDmRyhXq4xcKOw4GtRMn3IyxiNTCFz5BFxbuxX8TOP+L8QGtRo8RJxyvoAYz+kdPuQIWYmWsREtsk5I+NqMsAOvjWpPw6du3r6Lr/vCHP+DPf/4zMjIytHxMzBA+ofDVV7WitFCf3oNBnQjrN2OsoXThA4KTvtYCa2qtCkZgd5VjszCyrVQQUR1aYpvk+s7IbEknCX1dp7PL8eqrr+KPf/wjCR8FqB0QcucGOTHzgYgtlNbpAbQHtNolQNxw8KnVOKUgodUYGdsU3scADCsL4DShb6rw4SJt04QCpM4RCp8U5M4NivXMB8JZyO08tQbUsiBAnFRszy6s7iMnWSeMim2K7uPi4nLDNsdOs96ZKnwIcaRuvFGjRqGmpgYbN24EID0piL0Hy6mEBBGO0qBNvZlWdggQpxXbswOr+8hp1glAf2yTUB+XlxcDCECsVpybIeFjA2piG/RMCpSSSzgBNW5ZrZlWdgkQcjnLY3UfsW6dMMMaJdbHvXp9gfLyYsPLArDutiThYwNqYhv0TgpUqM8+rDCnO8lkL4acW7a0tBRer1f09Uq+o10ChFzO8lAfnccsa5RYH/fsuRU9e27F8eOtcPPNPdG5s/6yAE5w7ZLwsRm5QUKTgjOxwpzuRJO9EHJuWa/XqzsD0a77iCWXM6si2e4+Ysk6YZY1Sv4eO4XOnQfpvs/kLKtiNYSsHnuqhc+5c+fw2GOPYfz48Y1OSo9m9OjRVMVZAiXmd7WTAhXqY2OCtyKoloXAXaMw2y1r5+LKgsuZRZHMQoVf1q0TekWZUB8fPJgLwIO8vIMGtjSInGVVqlSLlWNPtfBJSEjAwoULceutt8pe+9xzz2lqVKyg1PyuZuKM9UJ90RO82MRh5U1mxeRqxWcYLSitrpRspQCx4rup+T1YFMl2z1WsB54bcU+np6dj1KhRWLNmDYBgtXMz5wklllUWxp4mV1f//v3x6aefIj8/3+DmuJfwSYo396kxv6uJ1XGrqFFC+M2jJRvOaKyYXK34DDMsBlYsfHYdQxH93fx+P+rr6yOuSUhIQF1dHaqqqlR/Tz2/B0tWDjvnKpYDz428p9PS0gx/z2iU1hpiZexpEj5DhgzBfffdh++//x5FRUVo3rx5xPPXXnutIY1zC2KTlBHmdze7rPTAym7OisnVis8wy2Jg9sJnp1WBf0+fz4fVq1fLXq9GNGr9PVi5L1iA5fhJuXta6rwtsfFs5jwhVWsoKakA69d/y9TY0yR8/vCHPwAAFi1a1Og5OqS0MVKTrpT5fdSoUSG1LoSbXVZ6YWU3Z8XkavUEzsquTSl23yNmu5nU/B6s3Bd24oRT3uXuabljjYREtNnzhFitoaqq4OHYLI09TcInEAgY3Y6YRsz8npmZafuk7VRY2c1ZEVRrZeAuS7s2J2K0aFT7e7ByX9iJE05513tPh4toVoQeS2NPk/D529/+htLSUiQnJ0c8XldXhzfeeENR4DPRmBEjRoTONbP7xnM6dqfIhmNFUK1RnyEWMMub1lnatTkNM0Sj2t+DpfvCTlg95V1pphuPkqwvVoQeS2NPk/AZN24cBg8ejMzMzIjHT506hXHjxpHw0UhGRobjT0xnIZWcx840YiuCao3+DCUBsyzt2pyGGaJRy+/BQno9IYxcTFp1dXXIzaXGesiK0GNl7GkSPhzHwePxNHr80KFDkhVWiSAsFcwyEhZrhdhVudqKoFqjP0NJLApLuzanYYZoVPp7qBXJLG1gYg0l/WqVy9mIcWBXZqUUqoTPpZdeCo/HA4/HgwEDBiAh4fzLGxoaUFFRgcGDBxveSDfhtMBQNbBwBg5LBRytWBjM+gypccrKrs1pmCUalfweakQyixsYlrFDJOqxHiptr1HjwO56TUKoEj7Dhw8HAGzfvh2DBg1CixYtQs8lJSUhPz8fI0eONLSBbiLWAkPtsGyxeJM5DaUVxe3etTkRo0Sjll202anysYhdIlGr9VBNe40cB6zNt6qEz4MPPggAyM/PR2lpKZo0aWJKo9wGP/nIqXQ3LRp2WrZYu8mchtw4DQ/CD4cEpTBmmPqtEvhutlAbgV0iUav1UGk7KisrI/522zjQFOMzZswYAMFOPHr0aKP09rZt2+pvmYvgJ6n9+8/hlVc4BALn46Pi4znccccQ5OcnuGbRiDXLltuQ2026IQjfSswSKWbPF3Qfq8MKcWD0+WZiQi28TpAbx4Em4bNnzx6MHz8eW7ZsiXicD3qmAoaNSU9PR3o6sGwZMGUK0NAAxMcDL7zgQVFRG7ubZyiU8uxsKIDZeJy4qaH7WDlWiQMjRbRSoebGcaBJ+IwdOxYJCQnYsGEDsrOzBTO8CGEmTAAGDQL27gU6dABkDrh3JG5KeY7V7BYKYCbcdB+bjZXiwIj5RkyoZWYeRn19coQFyI3jQJPw2b59O7Zt24aLLrrI6PbEBLm57hQ8PE61GESLHL/fb/gZSyzDWtqpkaIzVgWsHpx6H9uB08SBmFB78cWJACItQG4cB5qET6dOnSQPSSNiE6P9z1aiNNtBCDWBiywvwCxlxBmZLUPp2dqJRcuflnvUaeJASKgBHABhV53bxoEm4TN//nzMmjULjz32GLp06YLExMSI51NTUw1pHOEsWFo41aJEvOhNz3fCAszKb2NktgylZ6uDNcuflei5R50kDoSEmpyrzk3jQJPwKSkpAQD0798/Ir6HgpsJVhZOozEiY4OFAo9OxMhsGbel5ZqBkzcwelErkp0mEsWs8omJdVi+fKKoq85tJSw0CZ/Nmzcb3Q6CYIrwCQ+AKRkbbj26xEiMzJZxY1quWThxMTMaJSLZaSIxur3hZ39JuercVsJCk/Dp27cvPvvsM7zwwgvYt28f1q5diwsuuACvvPIKCgoKjG4jQVhK9IRXXFxueMYGWR6UIZctEx5rKLfAuDEtlzAHNSKZFVGjlPD2aonLZDlOUSmahM+6detwyy234Oabb8a3336Ls2fPAghmwTz22GP4+9//bmgjCcIqhCa8LVuKDc3YIMuDcuSyZcILrQHS8VF2Zt64YbHQihO/u1EimfXvrtZiFR0DJWa1Zj1RQJPweeSRR/D888/j1ltvxRtvvBF6vHfv3njkkUcMaxxBWI3QhAfEobj4C5SXFxuSsUGWB+WIZcsAQEVFvqoAZbsyb9yyWGjBCQH9Qhghkvft24dXX31V9jq7v7uazw6/v6Ss1qzHKWoSPrt370afPn0aPe71elFTU6O3TYRLYX33A4hPeD17bkXPnlsNSc93Ws0Pu4k2we/b1wGLF0/X5CbUm3mjZQy7ZbHQglMz6vSKZJ/P10j0OOW7K8HpVmtNwicrKwt79+5Ffn5+xOOff/452rdvb0S7CJfB+s6PFy9yE96kSUOQlpYW8Tq17XVazQ87EMuW0TLhGpV5o3cMW7lYsLjJ0BvXZvV30iOSo9v5xRfF2LSpxDUxfU63WmsSPpMmTcJdd92Fl156CR6PB5WVlSgvL8cf//hH3H///Ua3kXABrKdyR/u6H3jgGPbvT0B+/jnk5PQA0EP3xOrkAo9WI5Z9omXCNSrzRq/1wqrFgkXXml7RZ9XGyYz09C++KEZZ2UAAwdIvWgUvS2LW6VZrTcLnvvvuQyAQwIABA1BbW4s+ffogOTkZf/zjH3HHHXcY3UaCsITwSSM7GygqMv79nZT6ajdC/aB1wjW6T7VYL6xaLFh0rekVfVa5zIy+R/3+FJSVlYAXPTxqBS9rYtbpVmtNwsfj8WDOnDmYOXMm9u7di19++QWdOnVCixYtjG4fQbgKEjX6YGHC1Wq9sLrtLMVhGCn6zC4FYeQ9evx4OvhjIMJR+91ZFLNOqlQdjSbhw5OUlIROnToZ1RaCIAhZpCZcoTMEjbai6bFeWLlYsBSHYZToM1LMWeE6EjsTq6Rkk6bfgCUxC4i7AllHl/AhCIKwAqWxF9F1fXiMdAHotV5YtViwFodhhOgzsr6OkpihUaNGhZIZjEhkAAIYOHATevcuV/U+PHaLWaWxTazHKZLwcRgsBbjpgY5rINQgFnsRXnIfsCZlWK31wq7Fwm63oM/ng9/vb9QmPcHCRok5uZgh/u+//vUD3TE0coJPze9ut5h1S5wiCR8HwXpKuFLouAb3YKUQl3sfK8eVGuuFnYuFXXEYaiwqmZmZir+7GWIuetx07boDO3Z01RVDo9RCOXr0aFW/u91iFnBHnCIJHwfBekq4FPxEIOejZt1ESpyHJSFuReyDnlRnOxcLO+IwlGZhpaWlGW5BUYPQuPnuu27Qm3pupth1clAxK5DwISyBnwg2bwaeeqqxj7p37zHo188duwkjcIJLkyUhbkXsg1PM/KzFYRhhiTOjvg4gdkSNvtRzHjPHgVODilmBhA9hGenp6bjiCiAuDggEzj8eHw/07JkO0jxBWLKkqMHOuC2rYh9Y6m8xWBJoWi1xQsJ/1KhROHfuHAAgMTERXq834nkt30ks6ypc/LBQmI81Met0SPgQlpKbCyxbBkyZAjQ0BEXPCy8EH2cNu6wuLFlSlGJ33BYLsQ8swYpA02KJs7JYn9C4EYrxsXscsSRm3QAJH8JyJkwABg0C9u4FOnRgV/Q40epiB6zUFqHYB/bQYomzulif0Ljp3/9j5sZRrM8zRkLCx4VEF3FjcSeQm8um4OFxotXFLuysLWJW7AdhDHoscWYKarlxQzE0woRbwSsr41BRkYCCgnPIyQnGLrC41ghBwseFCBVxI8uEc2G95pGdtUXIBWA/Qouh11sTel6rJc5MQR09bvx+P1avXi37ulgW0OFWcClLnBPWGhI+DkLPTUeWCWdid+yMEuyOr2F9knUz4othSwwbdmlorGqxxJktqCMPJc4mAS3DeZEobYlzwlpDwsdBCO1uoyvXEu6BldgZMcIXLaldfSzvkt2O0sVwxIgRyMjIiHitnJCwWlDHsqhRg93HZhgBCR+HQTenPdjhbmJ9giE3E8EjN1YzMjKQnZ2t+n0pYJ097D42wwhI+BCEDFa7m3gLidwEw4IlhUQNAehfDMPjhKKTMyjQmC3sdm0bAQkfgpDADndTuCXlggtO4t57vWho8CA+nsP8+Sdx0003kiWFYAo9i6Fw3Z58WeuqUuHvhCroTsPpljgSPgQhAD+pypnwzbK68BPxPfcApaV8zSMPcnPTAKSZ8pkEoQeti6HSuj3hcUJKxYqVxRDV4Ia0cCdb4kj4EIQAvNVl//5zeOUVDoHA+RL28fEc7rhjCPLzEyyZnFiveRRLkPVAGj2LoZx1VUuckNXFEJXgprRwp0LCx+HQGS7mkZ4ePD+s8REbHhQVtbG7eYTFUDVvczEzmJ+lDEmnpoW7aa0h4eNgDh0C9uxJx4gRdyIj44zodeG7UNqxqscJR2wQ5hN934i5TVhbsMzEyMXQzGwhFjMkWWyTFG7K4iTh41CWLwcmTw6ech4X1xLLlgUXaCmcumNlQayRu4kIxwmFJa3AyMXQzGwhFlOwWWyTHCytC3og4eNADh06L3qA4H+nTAlaJaQWZyeeP+VUsUa4F5bcJixg5H1nVrYQiynYLLYpViDh40D27DkvengaGoKuGLdZJZwo1gh34zQXhdMwK1uIxRRsFtsUC8TJX8IWZ8+exSWXXAKPx4Pt27dHPLdjxw5ceeWVaNKkCfLy8rBgwQJ7GmkyHTsCcVG/XHx8MP6EIAhz4V0U4YS7KKqrq1FVVYWqqir4fD47mugorAya9XpPoaDgJ6YEBottcjuOs/jMmjULOTk5+O677yIeP3nyJK6++mqUlJTg+eefx/fff4/x48cjLS0NkydPtqm15pCbK5Rp5D5rjxthIV6J0IeciyL67Dynu2HNHrNmBs26KROJMA5HCZ8PPvgAGzduxLp16/DBBx9EPPfaa6+hrq4OL730EpKSknDxxRdj+/btWLRokeuED0CZRk6E4pXcg5CLwo1ZXlYVADRrvLOYiURizH4cI3yOHDmCSZMm4e2330azZs0aPV9eXo4+ffpEDJZBgwZh/vz5OHHiBFq2bCn4vmfPnsXZs2dDf588edL4xpuEUKZRMMU96A4jMcQWFK/kbKIXovBYFLdmebFYAFAtrG0i1IoxN1R5Zg1HCB+O4zB27FjcdtttuOyyy7B///5G1xw+fBgFBQURj7Vp0yb0nJjwmTdvHubOnWt4m+0gMsUdilLc3QJ/sKGbJwFyk9lL9IJVXV2N9evXx0SWVyx8RytRep9SlWdzsFX43HfffZg/f77kNbt27cLGjRtx6tQpzJ492/A2zJ49GzNmzAj9ffLkSeTl5Rn+OWajJMXdzSbW8LgKN04C5CZjA6G+dUuWl5BlweutAeCe7+g0nFrlmXVsFT733HMPxo4dK3lN+/bt8fHHH6O8vBzJyckRz1122WW4+eab8fLLLyMrKwtHjhyJeJ7/OysrS/T9k5OTG72vE1GS4s6iv1sOLSLMjZMAucnYxYmF6KIRtyy0xLBhl6KwcK/jvyNLqHVfkfA0FluFT+vWrdG6dWvZ65YsWYJHHnkk9HdlZSUGDRqE1atXo2fPngCA4uJizJkzB/X19UhMTAQAlJWV4cILLxR1c7kJPsU9XPwIpbizJGqUEC7WqqursWJFmWBwpVvhJ0jelUewhxsK0clZFqZPX+z478gKatxXPG4Q1yzhiBiftm3bRvzdokULAEBhYSFy/2vOuOmmmzB37lxMmDAB9957L3bu3Imnn34aTz31lOXttQM3p7jzYm3VqqZYvHi6awJIxTJkeJS6twj7MaIQHQsxXFKWBSq2Zwxa3FduENcs4QjhowSv14uNGzdi6tSpKCoqQkZGBh544AFXprKL4eYU90OHgFmzvOA4DwDnB1cqyQKSWgTlRBNhPlJZXlLXCWFV2rgccpYFs6oqxyJq3VckPI3DkcInPz8fHMc1erxr16747LPPbGgRO7j1MM1gDJMn4jGn+bj5BVBupye3UNqROs2CNYI1jIyZYyVtXKtlwYkJEXajxX1FwtMYHCl8iNgjGMPERYgfp/m4+YVy82bgqaca7/R69x6Dfv2k47DsSCumjDJxjP6+LKSNS1kWRowYgYyMjIjrY1H0GgG5r+yDhA/hCHJzgQUL/Jg5M9XRk0R6ejquuEI4EL1nz3TIrR92ZHdQRpl1sJK9I2ZZyMjIQHZ2tmXtcDty7is3lyCxExI+MYzTqjyPHduAn39eLOvjZn0S0BqI7ven4PTpZpLmcda/OyFNLGXvUEXiIFLuKyeWIHECJHxiFCdWeU5PT8ecOWNcMVmqDUQPj/sAAqHF0eMJ4MEHKzF58o2WfXcKrDYPu9wftbW1iq4zSlhTRWLlxPr3NwMSPjGIkirPrMJPAk4UbtEoDUSPjvsA4sBxAVx33Rrk5R3C5Mk3yrofjApOduuZVCxhdfaOz+fDq6++GvpbTNiOHj3asEU4lisSk/vKfkj4xCBKqjyzjJOFmxr4iU8o7gOIQ/PmtYqywIwKTmYh8DZWsDJ7R2lGmdDh0HphJabJSnj31f795xpZrHmcYrl2KiR8YhClVZ5ZxenCTSnhE+Qrr0RmtMXHc7jjjiHIz0+QnSCNCk6OxUXKKliwAigRtkbH5cRSTBNwvv9WrWqKWbNaIRDwIC6Ow4IFftx0068keCyChE8MYnaVZ7NrvjhduKkhPT2Y6dX49/KgqKiNoZ9VXV0t+dvE2iJlJSwEscoJW7/fj9WrVwMwLi4nllK6ecur35/y3wr0wY1MIODBzJmp+Pnnl+D1nqK4Jgsg4ROjmFXl2YqaL24+nkMMI38vsRgO/oT76N+GtzLILVIUk6APuxc7OWFbX18PIDh+3n13KABj4nJipSIx3y9yAtONcU2sQcInhjGjyrNVNV/cfDyHGEb8XlqOyoi2RjzwwDHs35+A/PxzyMnpAaAHmehdgJyw/eWXXwAAW7f2BC96ePS6PGOpIjFZTu2HhA/hWNx6PIdZCMVwvPvuUGRmHkZubpXka8NFTXY2UFRkalMJm5CyvmzcuBF+fwrKy4sFXkkLt1Jiyb3HKiR8CCJGEMsOW758IqWlEyGkrC/CYwjo1atc1cLNQjC3ncSKe49VSPgQRIwgZGIHKC091lEjLsTcND17blX1mSwEc9tNLLn3WIOED0G4nOjg5PDAVB5KS49dlIiQmpoarFmzxlA3jZtFDcE2JHwIwuXwC1tlZSWA9cjMPIzlyydScCURQo0IITcN4XRI+BBEDJCenh7a0efmVlFwJSGKUJFCr7cm4hopN41b43L0EutxTSxBwocwFLq52SW8z6V27fTbxC7ih4e2xLBhl8oGwI8aNYpcWCJQXBM7kPAhDIVubnah34aQQ+nhoWKkpaVZ0UzHQvcWG5DwIQwn/OY+dCh4tlbHjlRzhwVo4iWUoPVcNrIWEk6AhA9hGsuXnz9FPS4ueMzEhAl2t4ogCDnkqgsPHDgQBQUFEa8hayHhFBpXoiIIAzh06LzoAYL/nTIl+DihDZ/Ph6qqKtF/Pp/P7iYSLoFPW/d4zp8EzHHAvn3Bk4DLysqQlJSE7Ozs0D8SPYRTIIsPYQp79kSeng4EDxTdu5dcXlqw4vBXgginsHAvOC78EX2HkRLOxk1hC2TxIUyhY8egeyuc+PjggaKEeqIXGb8/BRUV+fD7UySvIwitHD+eDrFCl0RswFuZn3yyBu3acejfH2jXjsOTT9Y42spMFh/CFHJzgzE9U6YELT3x8cALLzh/p2Am4fVToqmurg79v5IT1glCL3SKeGzDW5n9/hQsXjwdHOcBAAQCHsycmYqff34JXu8pR1qZSfgQpjFhAjBoUNC91aEDiR4plLqytKYZE4QSwrOy6BTx2IbfhMll+DnRykzChzCV3Fz3Cx4pSw2gLNtFyJV1/Hg6WrXyRSw0WtOMiSBCVYkLCs4hJycYkBbrmUnp6ekoLS3F6tWrAdDxFIQ7LX8kfAhCB9GWGjHBosYcLOXKcuMkZBXiVYkj+9iJpnsj8Xq9UX/TKeKxjBstfyR8CEIH4ZYaqcVUqTlYzpXlxknIKpRWJXai6Z4gzMRtlj8SPgRhAEbF3ihxZUlNQuFB0LHuthGD3IXS0Hl7hBBusvyR8CEIAzBqMVXqyoqehHgX24oVZZpdbLECuQuloTPdCLdDwocgDMCoxVTOlTVixAhkZGSguroa69evB2CMiy2WIHehPCRqCDdDwocgDMDIxVTKlZWRkYHs7OzQ35Terg23xSwQhNG42eVJwodgCieXRdezmEZPHmL+9OjrKF5FO26KWSAIo3Gzy5OED2E7fG2VVauaYtYsLwIBD+LiOCxY4MdNN/3qqJtL62KqdZKheBWCIMzCKfOuWkj4ELbi9LLoRpqDtXw/ildRjptN9+FQkUaCkIaED2ErTi+LzoI5mOJVlMHCb2U2VKSREMPJYQRGQ8KHYAInu2xYWEAoXkUZLPxWZkJFGolw3BRGYCQkfAgmiBWXjVG7rlhx2xDaoKB3wulhBGZCwodgBre7bJYvByZPBgIBIC4OWLYseIK9FmLBbUNox8kWVMIYnB5GYCYkfAimcKPLxufzYf/+c5g8OROBAL/rAqZM4XDJJUeRn5+gSaCQqCHEiBULKiEPieDGkPAhCBPhzc0VFfkIBMZEPNfQ4MHSpR+goOCnmDQ3E+ai5Ew3yvpyPySCG0PChyBMhDcjy+26YtHcTIhjVEq6mAV1/fr1lPUVQ7g9jEAtJHwciltSE2MlSJd2XYRSrEhJZy3ri2oPmY8bwwi0QsLHgRgZJGs3sRSkS7suQgl6UtKVbhBYyvqi2kPuwgmbchI+DuPQofOiB+CDZIFBg9gdZHK4cTLjb/7U1MjFhXZdhFK0iBO5jUR1dTXWr1/PVMAr1R5yPk6rF0TCx2Hs2XNe9PA0NAB79zpX+LiNSItcJoYOvTS0ayUIpWgVJ0pjf1hzvZplhYpVN5pVYQROrBdEwsdhdOwYdG+Fi5/4eKBDB/vaxDJWm10bW+Q8EbtWglCK2eKENderGVaoWHajWRVG4MR6QSR8HEZubjCmZ8qUoKUnPh544QWy9ghhRyyUkEWOKuYSWjFbnLDkejVD6MW6G81KMceS+1QOEj4OZMKEYEzP3r1BSw+JnsbYFQslZJFTcvM7PWuNMA+WxInZmCX0WArmdissuk/FIOHjUHJzSfBIYUcslM/nQ3x8HRYsaIp77/WiocGD+HgODz10GL///UAkJibC6/U2ep1bYwwI9nBC+QgzhJ6TrBFOhjX3qRgkfAhXYnUsVHgsAQDceWdK6OZvaDiF9euDj7sxloAwFjPFSSyVjwjHSdYIp+MECyUJH8KVWB0LFb2QiN38bo0lcAJOqC8CmC9OWBM1VlmhnGKNIMyHhA/hWigWinBafREeFttkFlZaoZxgjSDMh4QP4WooFip2cWJ9kViF+t+5OCFuLBoSPoTrCC9YJgSru3zCWJxYX4QwFicuyk7DiXFjJHwIVxEdZCwG7fJjB8roiV2cuCg7Eaf1HwkfwlUo3b3H6i7fKQG+RkIZPbGN0xZlwnxI+BBEjGBHJWtWoIwegiB44uQvIQhCDtZjCcQqWR86ZEtzbMHrPYWCgp9I9BBEjEMWH4IwAJZjCXw+H778EggEIj+7oQHYutWHpk3JHUAQROxAwocgDIJF8RCe0u3xTG8U4PvFFy9j505K6SYIInYgVxdBuBjeAsUH+Ho8QV9XdICvG4O9WXc/EgRhD2TxcRBUn4bQQ6wF+LLsfiQIwj4cJXzef/99/PnPf8aOHTvQpEkT9O3bF2+//Xbo+QMHDuD222/H5s2b0aJFC4wZMwbz5s1DQoKjvqYgVJ9GGbTLlybWSvbH8r1AEIQwjlEE69atw6RJk/DYY4+hf//+OHfuHHbu3Bl6vqGhAddccw2ysrKwZcsWVFVV4dZbb0ViYiIee+wxG1tuDFSfRhm0yycIgiCkcITwOXfuHO666y4sXLgQE8IKj3Tq1Cn0/xs3bsS///1vbNq0CW3atMEll1yChx9+GPfeey8eeuihmN3hxyIkapxHLBZWtAJyjxNEYxwhfL755hv8/PPPiIuLw6WXXorDhw/jkksuwcKFC9G5c2cAQHl5Obp06YI2bdqEXjdo0CDcfvvt+OGHH3DppZcKvvfZs2dx9uzZ0N8nT54098sQBAHAuSenOwVyjxOEMI7I6vq///s/AMBDDz2EP/3pT9iwYQNatmyJfv364fjx4Hk7hw8fjhA9AEJ/Hz58WPS9582bB6/XG/qXl5dn0rcgCIKHX5QXLnwdM2emIhCIPDl94cLX8cwzz8Dn89ncUudC7nGCEMZW4XPffffB4/FI/vvxxx8R+G+52Tlz5mDkyJEoKirCihUr4PF48Oabb+pqw+zZs+H3+0P/Dh48aMRXIxzKoUPA5s3uqWjMarC3kpPTw68j9OP3p6CiIh9+f4rdTTEcn8+Hqqoq0X8koIlwbHV13XPPPRg7dqzkNe3bt0dVVRWAyJie5ORktG/fHgcOHAAAZGVl4V//+lfEa48cORJ6Tozk5GQkJydraT7hMtx4lhXrwd50cro1fPPNpY0Oae3e/Vu7m2UI5NJjF1ZjzGwVPq1bt0br1q1lrysqKkJycjJ2796N3/3udwCA+vp67N+/H+3atQMAFBcX49FHH8XRo0eRmZkJACgrK0NqamqEYCIIIcTOsho0yPnBtixP9nRyuvn4/Smh/gWCFrX33huKwsK9ruhncumxCcuC1BHBzampqbjtttvw4IMPIi8vD+3atcPChQsBANdffz0A4Oqrr0anTp1wyy23YMGCBTh8+DD+9Kc/YerUqa6w6LDqsnALe/acFz08DQ3A3r3OFz6s4/TCiqzuanmk3IlO62vCObAsSB0hfABg4cKFSEhIwC233IJff/0VPXv2xMcff4yWLVsCAOLj47FhwwbcfvvtKC4uRvPmzTFmzBj8+c9/trnlxsC6y8LpdOwYdG+Fi5/4eKBDB/vaFEs4tbAiy7tanlhzJ/r9KTh+PB2tWvkcOaYI83GM8ElMTMQTTzyBJ554QvSadu3a4e9//7uFrbIWEjXmkZsbjOmZMiVo6YmPB154gaw9hDQs72p5Ysmd6OZYJqfDkiB1jPAhCLOZMCEY07N3b9DSY7TooSJ97oelyT3c7S3lTnSLe9ztsUxOhjVBSsKHIMLIzTVHlLgxY0wPboxZY21y593jx44dQ319veA1bjjHkIdimdiERUHqnlFPEBoxOzjVzRljWnFbzBqLkzvP6tWrZa9xQ6p3rMUyOQUWBSkJHyKmsSI4lTLGhOH7U0x41tXVoaqqSrEAsjO7isXJHXBGDJJeeKugXCyTk6yHboJFQUrCh4hpzF4YfD4fUlPPIS4uM3QsAwDEx3NISTkKny/B8TttPRglPO3OrrJ6cmc9hd5Koq2HDzxwDPv3JyA//xxycnoA6BFT/cEKLAtSEj4EYRLhi/HQoZHxH9dcswEbNgTjP9zgZtBK9OItFhwsJzzttmxYmTmlRuRFw1LwtZGE3z/Z2UBRkY2NIQCwLUhJ+BCESYQvslJZNU52MxiJkcHBVi3wdmROaRV5rAVfE+6HVUFKwocgLMKpRfqswMjgYCsXeKcEabMcfE0QVkPChyAI2zEqONiOBd5uUaMEVoOvCcIO4uQvIQiCMBc+ODgcLcHBUgu8W/H7U1BRkQ+/P0X0GqP6lyDcAFl8CIKwHaOCg1lMnZVCb3aWnFuP5cwaFqFsudiAhA8R07ixgrASWJzgjTil3UnnUulNwVfi1mM5s4Y17C6JQFgHCR8ipnFKcKqRRE/wYhlQVkzw0YJSLABcTng68VwqvSn4SuN2WM2sYQ27SyIQ1kHCh4h5zFrcWbUmhU/cUq4SKyZ4o4RnLAlYfrzIufVYEnkEwRIkfAjCJFhfjFlJcTbq+7tB1CghfFxdcMFJ3HuvFw0NHsTHc5g//yRuuulG14g8gjADEj4EYSIsLz6U4uxc+HF1zz1AaWnw3LcOHTzIzU0DkGZbu1iMHSOIaEj4EESM4rQMKEKY3Fw2Drul4GDCKVAdH4KIUfgMKL6+C8sZULGAkno8LEPBwYRTIIsPQcQwRqSQE/qhc7QIwjrI4kMQMY7XewoFBT+R6LEYPutKLMict/xQdpY1RPezmAWOfg/nQxYfgogxWE2zjzX47KzNm4GnnmocZN679xj068d2gLybCM+WW7WqKf78Zy8CAQ/i4jgsWODHTTf9SsHZLoGED0HEGKyn2ccS6enpuOIKIC4OCIQdpRUfD/TsmQ76CawlPT0dhw4Bs2ad/z0CAQ/uvTcNpaVp9Hu4BBI+BBGDkKhhh9xcYNkyYMoUoKEhKHpeeIGNTK1YZM+eSBEKBH+XvXvpN3ELJHwIgiBsZsIEYNAgvh4PLbB20rGjsAWuQwf72kQYCwU3EwRBMEBuLtCvn3NFj1tix3gLXHx88G+ywLkPD8dxnN2NYImTJ0/C6/XC7/cjNTXV7uYQBEE4hvDKzZWVcaioSEBBwTnk5ATNJ06KHTt0iCxwTkPp+k2uLoIgCMIQeFGzfDkweXLQXRQXF7SgTJhgc+NUwkpFbMJ4yNVFEARBGMahQ+dFDxD875QpwccJggVI+BAEQRCGIZUVRRAsQMKHIAiCMAw+KyocyooiWIKED0EQBGEYlBVFsA4FNxMEQRCGQnWJCJYh4UMQBEEYDmVFEaxCri6CIAiCIGIGEj4EQRAEQcQMJHwIgiAIgogZSPgQBEEQBBEzkPAhCIIgCCJmIOFDEARBEETMQMKHIAiCIIiYgYQPQRAEQRAxAwkfgiAIgiBiBhI+BEEQBEHEDCR8CIIgCIKIGeisrig4jgMAnDx50uaWEARBEAShFH7d5tdxMUj4RHHq1CkAQF5ens0tIQiCIAhCLadOnYLX6xV93sPJSaMYIxAIoLKyEikpKfB4PHY3x3JOnjyJvLw8HDx4EKmpqXY3x7FQPxoD9aN+qA+NgfrRGMzsR47jcOrUKeTk5CAuTjyShyw+UcTFxSE3N9fuZthOamoq3dwGQP1oDNSP+qE+NAbqR2Mwqx+lLD08FNxMEARBEETMQMKHIAiCIIiYgYQPEUFycjIefPBBJCcn290UR0P9aAzUj/qhPjQG6kdjYKEfKbiZIAiCIIiYgSw+BEEQBEHEDCR8CIIgCIKIGUj4EARBEAQRM5DwIQiCIAgiZiDhE6P885//xLBhw5CTkwOPx4O333474nmO4/DAAw8gOzsbTZs2RUlJCfbs2WNPYxlFrg/Hjh0Lj8cT8W/w4MH2NJZh5s2bhx49eiAlJQWZmZkYPnw4du/eHXHNmTNnMHXqVKSnp6NFixYYOXIkjhw5YlOL2URJP/br16/RmLzttttsajGbPPfcc+jatWuowF5xcTE++OCD0PM0FpUh1492jkUSPjHK6dOn0a1bN/zlL38RfH7BggVYsmQJnn/+eWzduhXNmzfHoEGDcObMGYtbyi5yfQgAgwcPRlVVVejf66+/bmELncGnn36KqVOn4ssvv0RZWRnq6+tx9dVX4/Tp06Fr7r77brz33nt488038emnn6KyshIjRoywsdXsoaQfAWDSpEkRY3LBggU2tZhNcnNz8fjjj2Pbtm34+uuv0b9/f/z+97/HDz/8AIDGolLk+hGwcSxyRMwDgHvrrbdCfwcCAS4rK4tbuHBh6LGamhouOTmZe/31121oIftE9yHHcdyYMWO43//+97a0x8kcPXqUA8B9+umnHMcFx15iYiL35ptvhq7ZtWsXB4ArLy+3q5nME92PHMdxffv25e666y77GuVQWrZsyb344os0FnXC9yPH2TsWyeJDNKKiogKHDx9GSUlJ6DGv14uePXuivLzcxpY5j08++QSZmZm48MILcfvtt8Pn89ndJObx+/0AgFatWgEAtm3bhvr6+ojxeNFFF6Ft27Y0HiWI7kee1157DRkZGejcuTNmz56N2tpaO5rnCBoaGvDGG2/g9OnTKC4uprGokeh+5LFrLNIhpUQjDh8+DABo06ZNxONt2rQJPUfIM3jwYIwYMQIFBQXYt28f/vd//xdDhgxBeXk54uPj7W4ekwQCAUyfPh29e/dG586dAQTHY1JSEtLS0iKupfEojlA/AsBNN92Edu3aIScnBzt27MC9996L3bt3Y/369Ta2lj2+//57FBcX48yZM2jRogXeeustdOrUCdu3b6exqAKxfgTsHYskfAjCJG644YbQ/3fp0gVdu3ZFYWEhPvnkEwwYMMDGlrHL1KlTsXPnTnz++ed2N8XRiPXj5MmTQ//fpUsXZGdnY8CAAdi3bx8KCwutbiazXHjhhdi+fTv8fj/Wrl2LMWPG4NNPP7W7WY5DrB87depk61gkVxfRiKysLABolKlw5MiR0HOEetq3b4+MjAzs3bvX7qYwybRp07BhwwZs3rwZubm5ocezsrJQV1eHmpqaiOtpPAoj1o9C9OzZEwBoTEaRlJSEDh06oKioCPPmzUO3bt3w9NNP01hUiVg/CmHlWCThQzSioKAAWVlZ+Oijj0KPnTx5Elu3bo3wzxLqOHToEHw+H7Kzs+1uClNwHIdp06bhrbfewscff4yCgoKI54uKipCYmBgxHnfv3o0DBw7QeAxDrh+F2L59OwDQmJQhEAjg7NmzNBZ1wvejEFaORXJ1xSi//PJLhLKuqKjA9u3b0apVK7Rt2xbTp0/HI488go4dO6KgoAD3338/cnJyMHz4cPsazRhSfdiqVSvMnTsXI0eORFZWFvbt24dZs2ahQ4cOGDRokI2tZo+pU6di1apVeOedd5CSkhKKlfB6vWjatCm8Xi8mTJiAGTNmoFWrVkhNTcUdd9yB4uJiXHHFFTa3nh3k+nHfvn1YtWoV/t//+39IT0/Hjh07cPfdd6NPnz7o2rWrza1nh9mzZ2PIkCFo27YtTp06hVWrVuGTTz7BP/7xDxqLKpDqR9vHoi25ZITtbN68mQPQ6N+YMWM4jgumtN9///1cmzZtuOTkZG7AgAHc7t277W00Y0j1YW1tLXf11VdzrVu35hITE7l27dpxkyZN4g4fPmx3s5lDqA8BcCtWrAhd8+uvv3J/+MMfuJYtW3LNmjXj/ud//oerqqqyr9EMItePBw4c4Pr06cO1atWKS05O5jp06MDNnDmT8/v99jacMcaPH8+1a9eOS0pK4lq3bs0NGDCA27hxY+h5GovKkOpHu8eih+M4znx5RRAEQRAEYT8U40MQBEEQRMxAwocgCIIgiJiBhA9BEARBEDEDCR+CIAiCIGIGEj4EQRAEQcQMJHwIgiAIgogZSPgQBEEQBBEzkPAhCIIgCCJmIOFDEARBEETMQMKHIAjHUFdXZ3cTGsFimwiCEIeED0EQttGvXz9MmzYN06ZNg9frRUZGBu6//37wJ+nk5+fj4Ycfxq233orU1FRMnjwZAPD555/jyiuvRNOmTZGXl4c777wTp0+fDr3vs88+i44dO6JJkyZo06YNrrvuutBza9euRZcuXdC0aVOkp6ejpKQk9Np+/fph+vTpEW0cPnw4xo4dG/pba5sIgmADEj4EQdjKyy+/jISEBPzrX//C008/jUWLFuHFF18MPf/EE0+gW7du+Pbbb3H//fdj3759GDx4MEaOHIkdO3Zg9erV+PzzzzFt2jQAwNdff40777wTf/7zn7F79258+OGH6NOnDwCgqqoKN954I8aPH49du3bhk08+wYgRI6D2yEK1bSIIgh3okFKCIGyjX79+OHr0KH744Qd4PB4AwH333Yd3330X//73v5Gfn49LL70Ub731Vug1EydORHx8PF544YXQY59//jn69u2L06dP4+9//zvGjRuHQ4cOISUlJeLzvvnmGxQVFWH//v1o166dYHsuueQSLF68OPTY8OHDkZaWhpUrVwKApjY1adJEVz8RBGEcZPEhCMJWrrjiipDoAYDi4mLs2bMHDQ0NAIDLLrss4vrvvvsOK1euRIsWLUL/Bg0ahEAggIqKCgwcOBDt2rVD+/btccstt+C1115DbW0tAKBbt24YMGAAunTpguuvvx5//etfceLECdVtVtsmgiDYgYQPQRBM07x584i/f/nlF0yZMgXbt28P/fvuu++wZ88eFBYWIiUlBd988w1ef/11ZGdn44EHHkC3bt1QU1OD+Ph4lJWV4YMPPkCnTp2wdOlSXHjhhSFxEhcX18jtVV9fr7tNBEGwAwkfgiBsZevWrRF/f/nll+jYsSPi4+MFr+/evTv+/e9/o0OHDo3+JSUlAQASEhJQUlKCBQsWYMeOHdi/fz8+/vhjAIDH40Hv3r0xd+5cfPvtt0hKSgq5rVq3bo2qqqrQZzU0NGDnzp2y30FJmwiCYAMSPgRB2MqBAwcwY8YM7N69G6+//jqWLl2Ku+66S/T6e++9F1u2bMG0adOwfft27NmzB++8804okHjDhg1YsmQJtm/fjp9++gl/+9vfEAgEcOGFF2Lr1q147LHH8PXXX+PAgQNYv349jh07ht/+9rcAgP79++P999/H+++/jx9//BG33347ampqZL+DXJsIgmCHBLsbQBBEbHPrrbfi119/xeWXX474+HjcddddoRRxIbp27YpPP/0Uc+bMwZVXXgmO41BYWIjS0lIAQFpaGtavX4+HHnoIZ86cQceOHfH666/j4osvxq5du/DPf/4TixcvxsmTJ9GuXTs8+eSTGDJkCABg/Pjx+O6773DrrbciISEBd999N6666irZ7yDXJoIg2IGyugiCsA2hLCqCIAgzIVcXQRAEQRAxAwkfgiAIgiBiBnJ1EQRBEAQRM5DFhyAIgiCImIGED0EQBEEQMQMJH4IgCIIgYgYSPgRBEARBxAwkfAiCIAiCiBlI+BAEQRAEETOQ8CEIgiAIImYg4UMQBEEQRMxAwocgCIIgiJjh/wN9s77nJ4/qUAAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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rnh8iIlInBh+R1B5asYVDK95VUhJuCT1mguCDkpIwBh8iIg3grC6RODpkwqEV7woLM0CnM1ndptOZEBZWIlGLiIjImxh8SFP0+jL077/NEn7MNT7s7SEi0gYOdZHmpKUdRnJyPkpKwhAWVsLQQ0SkIQw+pEl6fRkDDxGRBnGoy0uMxhAUFCTCaAyRuimaFBAQ4NHtiIhImdjj4wX21o0h57myTEB4eDhycnK4vIBIuHQDESkFg4/IuG6MZ7mzTAAPvOLgqthEpCQMPiIxD5k0tG4Mh1acw2UC5IerYhORkjD4iMQ8tFJYeBPr1gkwmXSW+3x9BYwf3weJiX48A3aTrd4F8j72bhKREjD4iCg8PBzh4cBbbwFPPAFUVQG+vsCbb+qQnh4ldfMUz1u1U1qrX3H19XJVbCJSAgYfLxg1CsjMBPLzgZQUIC5O6hYpn7d6F7R26RF3Xq95Veya4YerYhOR3DD4eElcHAOPJ3mrd0HpNUXO9t6483rNq2LX7oVjbw8RyQmDDymSVL0LSqop8kRvlbOvl6tiE5HcMfiQIknRu6C09Zjc7a1y9fXaWhX78uXLNn9GbXVSRCRfDD6kKDWn/9vrXfD0MgFqmLHkTO+NM6/X0fd68+bNdu9XS52UXHmjSF9rEwFImRh8yGlS7txqrsBsNBpRWVlZZxs/Pz9UVFTAYDB4rB1Kn7HkbO+NM6+3oVWxL1++3GDoAeRbJ6UG3ijS50KWpBQMPuQUOcxyCg8Ph8FgwMaNG73WDiXPWHKlt8rZ1+vMe6ykOim1qB0qbf0O3AmfXMiSlILBh5wil1lO3tiR16TkGUuu9FaJ9XqVVielRmL/DtQwLEzqxuBDbpHD2buYO3KpaopscWWY0ZneGzFfLw+I0vPG70Dpw8Kkfgw+5DI5nL2LvSOX01XdXR1mdKb3RszXywOiY8SsofPG70DJw8KkDQw+KiBFsbFczt69sSOXSyGms8OMrvbeiPV6eUBsmNg1dN74HSh5WJi0gcFH4aQqNpbL2TsPprbJqbcK4AHREWLX0HnrdyDXhSw53Z4ABh/Fk6rYWC6BQ8sHU0fqq9zZiXvqICG3OiklEaOGzluhxNZCllKRw4xUkgcGH5URq9jYfBA0r74rp8Ah17NLMYldX+XJg4Tcep6UwpO/49qh0lYocSd8OvqzUgVcucxIJekx+KiIWAdDWwdBKc/evbEjlytv1Fd5+iDBUOMcT/+OvRE+lRZw5TAjlaTB4KMSYh4M7e3IagaOgQMHIiIiwis7N6XtZD1JivoqHiS8S4zfsTf+FpTy9yaHGalKoNaaKAYflfDmwdDWQTAiIgIxMTEefS57lPgH5wnerq/iQcL75FJDp0ZymZHqSWIEFDVfgoTBRyW8taPkQVA65mG7huqrPDm8p8aDhBLIqYZOap4+qMtlRqqniFW0reZLkDD4qIQ3dpQ8CEqr9vDezJmXUFjoh8TEm4iN7Qygs8e7ntV2kJA7zoCzJsZBXW29aWIXbatxv8/go3De3FHyIPgHqca+az5mTAyQnu7xp7CitoOE3Gm5dq0+YhzU1d6b5ul6PDXu9xl8FM6bO0oeBKtpaT0QtR8k5Ejpnxm50kJvmhilCGrc7zP4qABX3vUub18ZXgpaOEiojVpn4NTkTm+G2nvTxBqSUuN+n8GHGsSDoG1qLfZW+0FCbbTQC+mJvzWlvnZHiDkkpbZFYhl8qEE8CNZPjUV/NWnt96lkau+FVPvfmieIPSQlt0uQuIPBhxzCg2Bdaiz6I+VTYy8k/9Ya5ukhKblfgsQdDD5ELlJj0R8pm1p7Rvi3Vj+DwQCj0Wj53pOlCGru6WfwIXKRGov+SNnU1jMixaKdSmGrrqv2kNRDDz2E5s2buxRQlBhqHMHgQ+QGtRX9kbKprWdEikU7lcLReq2mTZtq8v2xh8GHyEnuXBleC1OOSTpq7IX09qKdpH4MPiQLSgoEro59a2HKMUmPvZDa5OkVm9WMwYckp8RA4Eo71D7lmKTjTi8kKZ8aZ/KJicGHPM7Z3hstBgLuqMiT1DwDh+xT60w+MTH4kEe523ujhUDAHRWJgaFGm9Q2k88bfBrehMhx9fXeFBQkwmgMsbudedv6AkHtn1U6ezsqIiJnmGfy1aTkmXzewB4fEo2zvTdaOXNR25RjIvI+rnHkOgYfmVDSrCZHuDKco5VAoMYpx0TkXa6ucaS2Y40rGHxkQImzmhriSu+N1IHAmzsETjkmUjY5BAhn1zhS47HGFQw+MuDobCUlzWpytfdGqkDgjR0CpxwTqYNSA4QWZ9DWh8GHROFM740cAoE3dgicckxaJ4deEk9QQ4DQwgxaWxh8ZEgtK3A62nsjt0Ag5g5BCTt1IjHU7iWxtZ+TWy9JQ5QYILS+pAaDj8wo8Y+oJld7b+Syo9P6DoFILDVPbOzt5+TcS1Kbt/cXnuox08oMWlsYfGREDQddufXeOEvrOwStceZAUnPb8+d9UFDgh6Skm4iNNdXZlmxTw37OzJv7C0/WFWllBq0tDD4yopaDrpx3/rYOdJcvXwbAHYJceKMWxJmhFwCWbe31VihtmEYKatnPAd7dX3iyrkjqGbRSY/CRER50xeXIGZPWdwhy4K1aEFeGXhrqrVDSMI1UvLWf80Z4lmp/4YmSCC0vqcHgIwNcgdM7HD1j0vIOQQ68XQvizNCLmnorpOKNsODNQmpv7y/cGSqUwwxaOWDwkQFXV+Ak1zV0xqTVHYKceKsWxJkww15ZzxA7LIgdnqUMEO6Eb6XXYHoKg49MOLsCJ7muoQPqwIEDERERUefntLBDkBNv9a44E2Y4FOo5tsKCJ4kVnqUMEO6Gb+7DGHxIgxo6oEZERCAmJkai1pGZt3pXnA0zHAp1jaO9H57sJREzPEsVIBi+3afK4PP666/j1VdfRXFxMTp06IBly5bhtttuk7pZJBMcrlAGb+7gnQ0z3uitUBspeknU+rfO8O0e1QWfjRs3YtKkSVi5ciW6dOmCxYsXIzMzE3l5eWjevLnUzbNJLUu5KwHPmJRDrB28wWCwLGFgxjAjPm/vw9T0t87CZM9RXfBZtGgRRo8ejREjRgAAVq5cie3bt2P16tWYNm2axK2rn1IveOcoOYY6njEph6cDiaMzfsycOZDwoCM/Svlbd2Q/ycJkz1BV8KmoqMDBgwcxffp0y20+Pj7o3bs39u/fX+/PlJeXo7y83PJ9aWmp6O2sTY1XZzeTU6jjGZMyiF0L4uiMn4EDByI2NtbyueRBR7nk3pvnzPR71h+6T1XB5/Lly6iqqkJUVJTV7VFRUThx4kS9PzN37lzMnj3bG81zmFouUgrI6yrGnMqpDN76PTU04yciIsLqOfi5UA4pCqndocbrmMmZqoKPK6ZPn45JkyZZvi8tLUV8fLxk7VH6RUrtkcNr48FLGbzxe+JihOql1JMcNV3HTM5UFXwiIiLg6+uLCxcuWN1+4cIFREdH1/szgYGBCAwM9EbzGqTmD72aXxspk1pn/FA1uYUaRzCMe4dPw5soR0BAANLT0/HFF19YbjOZTPjiiy+QkZEhYcscY+9Dr3Rqfm2kTOYZPzpd9dXVlTzjh9TBHMZrYhj3PFX1+ADApEmTMGzYMHTq1Am33XYbFi9ejGvXrllmecmZms9A1fzaSLmUMuNHa2rOcDp/3gcFBX5ISrqJ2NjqUCDHYSpPUNP0ezlTXfAZPHgwLl26hJkzZ6K4uBh/+ctf8Omnn9YpeJYjNX/o1fzaSNnkPuNHa2rOcLJXF6jU5T0awjAuPtUFH6D6DyInJ0fqZjis5swCex96ucxAcBX/oEkOlDbjR2vMPT0N1QWqeYYTw7i4VBl8lEapMxAcwbVzSG7U/PemJloq9GUY9y4GH5lQ606WBxmSI37e5E9LdYHcT3oXgw+Jjn+sROQsrdUFcj/pPQw+REQkS6wLJDEw+JDL5HjxUSJSFxb6yosalhpg8CGXyOnio0REJD61LDXA4EMukdPFR4nIu8Ts7VXLDCc19IzUppalBhh8yG1yuPgoEXlH7d5eWyc9rp71q2GGk1p6RmxR+lIDDD7kFl58lEhbagYSewd1d876lRgGalJLz4gtSl9qgMGH3KL05E/qxeJ7cfGkp2Fq3T8qfakBBh9yi9KTP6kzILD4XnxqPah7kpr3j0peaoDBh9yi9OSvdWoNCCy+F19DB/UrV64gJiZGqubJgtr3j0pdaoDBh9ym5OSvdY4e+JUcEFh8L476Duq9e39u+fv/17/+pbjALAbuH+WHwYdcwouPqpOtnhGlYh2KuNLSDuP33xth167eEAQffP55bwQF3fBIgbOr5DiNXKk9I7WpZakBBh9yiRqmnJI1JfeM1K5Tunz5MgDWoYjNaAzB55/3BiCPYKn2aeTO8nT9nlr2+ww+5DK5f7jJcUruGbFXp6Tm4lKp1Dybl1uwlMs0cjn0jIi13pIa9vsMPkQkuwOYMxoqZFZzcakUwsPDMXjwYGzcuFG2wVLqz7Mceka8sd6SUjH4EJFsD2DOsrWDZ3GpZ+n1+v/963yw9MbyCWJ+nh1tv1x6RpTcmysWBh8iUkXPSEM7eBbfi8OZYCn25S7MxPo8e6v9niR175ccMfgQaVjNA7+9A5gSAkJDO/iBAwciIiLCcp8SijCVwtFZS94cfhGjp0+Jw0dq6c31JAYfIg2TQy2CpzS0g4+IiND8gnqe4m7xrreGX8SaRq6k4SM19OZ6GoMPkcYpIdQ4gjt473E3MCt9+EVp7bfX+2Ve+sFMKSc67mDwISLVYCGz97hzcBRr+MVb08iVOHxUX++X0RiChQsPybpGSQwMPkSkaFxFXHnE6p3z1tCtEnoXG/q8K6VGSQwMPkSkaGqqU9ISsXrnvPV7lnvvYn1/F5cvX8bmzZsVVaMkBgYfIlI8hhrXeWNdHVuUfg0rubdfrTVW7mLwISLSKHuX+6jJUzUfcriUgzuU3n4zJdYoeRKDDxGRRjV0uQ9b27lK6cOSSm+/mRJqlMTE4ENERHaLXT1J7qGgIUpvv5nca5TE5NPwJkREpGa2il2NxhCJW0Zi0uvLkJT0q6ZCD8DgQ0SkefaKXUk91FKj5C4OdZFqSTlbhUhJtF7sqhVqqVFyF4MPqZISr6JMJBWtF7tqCfd3DD6kUkq8ijKRlLRc7ErawhofUjUWbRLZVt/lPuordlV7zQdpC3t8SNW0vkKpt7CeSpnEqvng54HkjMGHVI1Fm+Lz9uq/5Fme/p3w80Byx6EuUjVz0aZOZwIAFm2KwNE6KdZTaQM/DyR37PEh1WPRpnfZmkFH2sTPA8kNgw9pgtyvoqwW3rrsASkDPw8kRxzqIlXiCqXexxl0VBM/DyRX7PEhVeIKpd7HGXRUEz8PJFcMPqRaDDXexRl0VBM/DyRXHOoiIo/gDDqqiZ8Hkiv2+JDX1Vzc7Px5HxQU+CEp6SZiY6t3kByCUpaadVL2ZtCxnkob+HkgudMJgiBI3Qg5KS0thV6vh9FoRGhoqNTNUZ2ai5vZm/HBxc2UhSv1Uk38PJAUHD1+s8eHvMq8M7Q14yM5OR96fRkXN1MYHsSoJn4eSM4YfEgSnPFBYmKPAxHZwuBDkuCMD+WSe6jgtaKIyB4GH5KEecZH7Rof9vbImxJCBa8VRUT2MPiQZHgNLeVRYqjgtaKIqCYGH5IUr6GlbHIPFbxWFBHVxuBDRC6Re6hoaOYgEWkTV24mr+LFQ9VBCRegtDdzkIi0iz0+5FViXTxU7jON1EYuyxHU93u/fPkyAM4cJKL6MfiQ13k6gNSeaWSr7oTTlz1HDqGioRlmnDlIRPVh8CHFq3nGb6/uRE4zjdwldQ+XHEKFI79PXiuKiGpj8CHV0Eoxq5Rr6cj5ApS2evpGjLgHERERVtty6JNIuxh8SDXkUncitto9HbYO+GL0cIlVo+Uuez19ERERiImJ8Wp7iEi+GHxINeRQd+JtUkwpl1tPiVZ6+ojIMzidnVTDXHei05kAQPXFrEqYUu4NnLZORM5gjw+pipYug6GVob2GaLGnj4hcxx4fUh29vgxJSb+q/uBvPuDXpMUDvtZ6+ojIPezxIcXT6mrQcphSLiU5zzAjIvli8CHFk+tMI2/Q0tBebVr+vROR6xh8SBW0dHCr3YNh6wr3Wujp0NLvnYg8g8GHSGHY00GeIPXq30RSUUTwKSwsxIsvvogvv/wSxcXFiI2NxaOPPooZM2ZYndUeOXIE2dnZ+OGHHxAZGYnx48djypQpErZcW86eBU6eBFJTgbg4qVujbjwgkTukXP2bSGqKCD4nTpyAyWTCm2++iZSUFBw7dgyjR4/GtWvXsGDBAgBAaWkp7r33XvTu3RsrV67E0aNHMXLkSDRt2hRjxoyR+BWol/msccOGIEyZoofJpIOPj4BXXjFiyJDfedZIJEOOruqtpuvbEZkpIvhkZWUhKyvL8n3Lli2Rl5eHN954wxJ81q9fj4qKCqxevRoBAQFo27YtcnNzsWjRIgYfkZjPGo3GECxePBGCoAMAmEw6TJ4cinPnVkOvL+NZI5HM2brsCZEaORx8SktLHX7Q0NBQlxrjDKPRiLCwP1Zm3b9/P7p162Y19JWZmYn58+fjt99+Q7Nmzep9nPLycpSXl1u+d+Z1ukMNw0Lms8GGFtLjWSORfElx2RM1YI2UcjkcfJo2bQqdTmd3G0EQoNPpUFVV5XbD7MnPz8eyZcssvT0AUFxcjKSkJKvtoqKiLPfZCj5z587F7NmzxWtsPVatAsaMAUwmwMcHeOstYNQorzbBo7hyLpEy8TpnrmGNlLI5HHx2797t8SefNm0a5s+fb3eb48ePo3Xr1pbvz507h6ysLDz44IMYPXq0222YPn06Jk2aZPm+tLQU8fHxbj9ufQwGAwoLb2LMmOYwmczDQsATTwj4y18uIjHRT5F/JFpfSI9IqXjZE9ewRkrZHA4+3bt39/iTP/PMMxg+fLjdbVq2bGn5//nz53H33XfjjjvuwFtvvWW1XXR0NC5cuGB1m/n76Ohom48fGBiIwMBAJ1vuPPMZQkFBIkymYVb3VVXpsGzZDiQl/arYMwQtL6RHtnE4QN7YWyuuy5cv17lN7p95LfzNulzcfOXKFaxatQrHjx8HALRt2xYjR46EXq93+DEiIyMRGRnp0Lbnzp3D3XffjfT0dKxZswY+PtZnKRkZGZgxYwYqKyvh7+8PANi1axdatWplc5jLm8wfpIZ2NEo+Q7C1kB5pE4cD5I+9tZ5hqzh88+bN9W4v18+8Vv5mXQo+P/74IzIzMxEUFITbbrsNALBo0SK8/PLL2LlzJ9LS0jzayHPnzqFHjx5ISEjAggULcOnSJct95t6cIUOGYPbs2Rg1ahSmTp2KY8eOYcmSJXjttdc82hZ3cUdDWsHhAPnidc48x5XicLl+5rXyN+tS8Hn66adx33334e2334afX/VD3Lx5E48//jgmTpyIr7/+2qON3LVrF/Lz85Gfn4+4WlOgBEEAAOj1euzcuRPZ2dlIT09HREQEZs6cKcup7BwWIi3ilGn54OrfnsHicGVyucenZugBAD8/P0yZMgWdOnXyWOPMhg8f3mAtEAC0b98e33zzjcefXwxqGBbS6lXRyXlKnDKt9loHJbddLhoqDld62Fd6+21xKfiEhobi9OnTVrOtAODMmTMICQnxSMNI/njWSI5Q4lmxVmodyD32ajaVGPZrUnr77XEp+AwePBijRo3CggULcMcddwAAvv32W0yePBmPPPKIRxtI8sadPjVEiVOma4d5W2e+Sq91oGrO9u6Ze7Ft1WwCUFzYr0mJJyvOcCn4LFiwADqdDkOHDsXNmzcBAP7+/hg3bhzmzZvn0QYSkbIpfcq0ms98ybXevdq93TNnXkJhoR9CQy9i377DKChIVFzYr0mJJyvO8Gl4k7oCAgKwZMkS/Pbbb8jNzUVubi5KSkrw2muveWVNHCViPQxplfmsWKczAYCiZjLaOvM1GjmkrxauzmQKDw9HTEwMYmJikJ4ehUGDwnHrrU0B/BH2a6oZ9o1Go/sNF1FD7Vc6ty5S2rhxY7Rr185TbVE11sOQ1qhhyrTaz3zJsxoaAjN/ZjZu3CjL+jBH2y/nv1lHuBR8bty4gWXLlmH37t24ePEiTCbrZHjo0CGPNE5t5PYhJxKTGsK+0ofpyHnuzGQKDw/HQw89hH/9618NLlsix/owW0N4iYk3ERvbGUBn2f/NOsKl4DNq1Cjs3LkTDzzwAG677bYGL15KRNqk9B0kFxzVFk/UczVt2tTyfyUuW1LzbzYmBkhPl7AxInEp+Gzbtg2ffPIJunbt6un2EBHJChcc1QaxZjKpdS0cJXMp+Nxyyy1cr4eIVKt2DYOtM3el1zrQH8So5+KMQHlyKfgsXLgQU6dOxcqVK5GQkODpNhERSUoN9UnkHE/Xc6l9LRwlcyn4dOrUCTdu3EDLli3RuHFjy9XQzUpKWPhHRMrGUKMNYs1k4oxA+XIp+DzyyCM4d+4c5syZg6ioKBY3ExGRIok1k4kzAuXLpeCzb98+7N+/Hx06dPB0e4iIiLzKkzOZtLIWjpK5FHxat26N33//3dNtISIiUjStrIWjZC4Fn3nz5uGZZ57Byy+/jHbt2tWp8QkNDfVI44hImZy96CORmmhhLRwl0wmCIDj7Qz4+1WOWtWt7BEGATqdDVVWVZ1ongdLSUuj1ehiNRgY4Ihe4ctFHIiJ3OXr8dqnHZ/fu3S43jIjUrXZPj60F3OS4ZD8RqZ9Lwad79+4Obffkk0/ihRdeQEREhCtPQ0QKxwXciEhufBrexHX//Oc/UVpaKuZTEJFM2VrAzWjkqu9EJB1Rg48L5UNEpBL2FnAjIpKKqMGHiLTLvIBbTVzAjYikxuBDRKIwL+BmDj+1F3AjIpKCS8XNRESOSEs7jOTkfJSUhCEsrIShh4gkx+BDRB5Veyl+vb6s3sDDJfuJSApOB5+bN29izpw5GDlyJOLi4uxu++ijj3IRQCIXKHnl49pL9tdHzu0nInVzaeXmkJAQHD16FImJiSI0SVpcuZmkxpWPiYicJ+rKzT179sSePXtUGXzUTsk9CVrBlY+JiMTjUvDp06cPpk2bhqNHjyI9PR3BwcFW9993330eaRzZ52yIYU+C8nDlYyIiz3Ip+Dz55JMAgEWLFtW5T+kXKVUKV0KMoz0EzvYksBdJHLZWPk5OzufsKCIiF7kUfEwmU8MbkajECjHOYi+SeOytfMzgQ1rCkyvyJJeCz7vvvovBgwcjMDDQ6vaKigq8//77GDp0qEcaR+67fPlyvf/3FLkEMDUyr3xcM/xw5WPSGp5ckae5FHxGjBiBrKwsNG/e3Or2srIyjBgxgsFHRjZv3ix1E8hF5pWPa9f4sLeHtITF/uRpLgUfQRCg0+nq3H727Fno9Xq3G0VE1bjyMdEfWOzvOA4P2uZU8OnYsSN0Oh10Oh169eoFP78/fryqqgoFBQXIysryeCNJHLbOnOT2mFrDlY9Jy2ofsM1D9Cz2dxyHB+1zKvgMGDAAAJCbm4vMzEw0adLEcl9AQAASExMxaNAgjzaQHONs4BDjzIlnY57BlY9Jq+wdsFns7zgOD9rnVPB5/vnnAQCJiYkYPHgwGjVqJEqjyDnOBo6Gzpxc6Ung2ZhnMdSQFtk7YLPY3zU8Ia3LpRqfYcOGAaj+kF68eLHO9PYWLVq43zKyyxxOXAkcts6cunYdhh49XDvo8myMiDypvgM2i/2dwxPS+rkUfE6ePImRI0di3759Vrebi565gKH4zMMhu3cDr73mXOCo78zJ1xfo0iUczmYecwBr6GyM9ShE5ChbB+yJExdj4sTFLPZ3EE9I6+dS8Bk+fDj8/Pywbds2xMTE1DvDi8QXHh6O228HfHyAmp1uvr6C3e5f8zTp7dv7o6pKB19f4M03gbg419pgrke55ZZSTJ2q/99jCpg/vxRDhjzCehQicoq9A3ZS0q8s9ncQhwfr51Lwyc3NxcGDB9G6dWtPt4ecFBcHvPUW8MQTQFVVdc/NwoXXcOXKHzuG+grb0tIO46WX7oLB0AwpKa6FHjNzqHnmGWDwYCA/H0hJ0SEurimApq4/MBFpUkMH7IEDByIiIsJyH0+u6se1wOrnUvBp06aNKKsAk2tGjQIyM82BA4iLawKDoboXZsOGILzwgh4mkw4+PgJeecWIIUN+/9+OopnH2xIX516IIiJq6IAdERGBmJgYiVupDFwLrC6Xgs/8+fMxZcoUzJkzB+3atYO/v7/V/aGhoR5pHDmuduAIDw/H2bPAlCl/DIOZTDpMndoUgwc3dbqWh4jIm3jAdh3XArPPpeDTu3dvAEDPnj2t6ntY3CwvJ09a1/4A1cNh+fnslSEi+eEB2zO4Fph9LgWf3bt3e7odJILU1PoKn6uHw4iI5IYHbM/he2SbS8Gne/fu+Oabb/Dmm2/i1KlT+PDDD3HLLbdg3bp1SEpK8nQbyUX1FT67OnuLiMgbeMAmsfk0vEldmzZtQmZmJoKCgnD48GGUl5cDAIxGI+bMmePRBpJ7Ro0CCguB3bur/x01SuoWERERScel4PPSSy9h5cqVePvtt60Km7t27YpDhw55rHHkGXFxQI8e7OkhIiJyKfjk5eWhW7dudW7X6/W4cuWKu20iIiIiEoVLwSc6Ohr5+fl1bt+7dy9atmzpdqOIiIiIxOBS8Bk9ejSeeuopHDhwADqdDufPn8f69evxt7/9DePGjfN0G4mIiIg8wqVZXdOmTYPJZEKvXr1w/fp1dOvWDYGBgfjb3/6G8ePHe7qNRET0PwaDgdO9idygEwRBcPWHKyoqkJ+fj6tXr6JNmzZo0qSJJ9smidLSUuj1ehiNRq5ATUSyYjAYsHz5csv39V2HDwBycnIYfkhzHD1+u9TjYxYQEIA2bdq48xBEROSgmj09hw51rHMtq7S0w3W2I/K2mr2S58/7oKDAD0lJNxEbW72artS9km4FHyIi8j6jMcQSegBAEHywdWs/JCfna/KaVhz+k4+avZL2wrmUvZIMPkREClNSEm4JPWaC4IOSkjDNBR8O/8mLOYA2FM6l7JVk8CEiUpiwMAN0OpNV+NHpTAgLK5GwVdLg8J88yTmcuzSdnYiIpKPXl6F//23Q6aprJswHeakPKFKy1cNgNIZI3DJtMofzmuQSztnjQ0SkQGlph5GcnI+SkjCEhZVIFnrkUl8j5x4GLTKH89o9cHL4XTD4EBEplF5fJumBRE71NRz+kx+5hPPaGHyIiBQiICDAo9u5S071NXLuYdAyqcN5fRh8iIgUIjw8HDk5ObIYWqpJLtPr5drDQPLC4ENEpCBynJItp/oaOfYwaInceiXrw+BDRERukbK+RgkHWi2Ra69kTQw+REQq5o1ZV1LW1yjhQKs1cn+vGXyIiFTKm7OupKyvkfuBluSFwYeISKW8PeuK9TWkBFy5mYhI5cRa1Zj1NaREiuvxKS8vR5cuXfDTTz/h8OHD+Mtf/mK578iRI8jOzsYPP/yAyMhIjB8/HlOmTJGusUREMiDWrCvW15ASKS74TJkyBbGxsfjpp5+sbi8tLcW9996L3r17Y+XKlTh69ChGjhyJpk2bYsyYMRK1lohIemLOumKoIaVRVPDZsWMHdu7ciU2bNmHHjh1W961fvx4VFRVYvXo1AgIC0LZtW+Tm5mLRokUMPkQkCblcx4qrGhP9QTHB58KFCxg9ejQ++ugjNG7cuM79+/fvR7du3azGkjMzMzF//nz89ttvaNasmTebS0QaJ6frWAFc1ZjITBHBRxAEDB8+HGPHjkWnTp1QWFhYZ5vi4mIkJSVZ3RYVFWW5z1bwKS8vR3l5ueX70tJSzzWciDRLTtexMuOsKyKJZ3VNmzYNOp3O7teJEyewbNkylJWVYfr06R5vw9y5c6HX6y1f8fHxHn8OItIusWZUOYKzrojqkrTH55lnnsHw4cPtbtOyZUt8+eWX2L9/PwIDA63u69SpE/7617/inXfeQXR0NC5cuGB1v/n76Ohom48/ffp0TJo0yfJ9aWkpww8BkE99BimblNex4qwrorokDT6RkZGIjIxscLulS5fipZdesnx//vx5ZGZmYuPGjejSpQsAICMjAzNmzEBlZSX8/f0BALt27UKrVq3s1vcEBgbWCVREteszbPFWfQYpl5TXsQLUM+uq5onI+fM+KCjwQ1LSTcTGmgAwwJHjFFHj06JFC6vvmzRpAgBITk5GXFwcAGDIkCGYPXs2Ro0ahalTp+LYsWNYsmQJXnvtNa+3l5TP0boLb9ZnkDJxRpX7ap6I2KuX4okIOUIRwccRer0eO3fuRHZ2NtLT0xEREYGZM2dyKjsRSY4zqtxjPsGwVS+VnJwPvb6MJyLkEEUGn8TERAiCUOf29u3b45tvvpGgRURE9nFGlfukrJci9eC1uoiIRMAZVZ5nrpeqyZv1UqQOiuzxISKSO86o8jzWS5EnMPgQEYmEocbzWC9F7mLwIXKArcsNEJH3sV6K3MHgQ1SPmnUX9qbPsj6DiEhZGHyI6mGuzygsvIkXXmgOQdABqJ5Bsn17f8yc2QWJiX4cyiDyAhaKkycx+BDZEB4ejiNHAJP1JBJUVelQVhYFZh4i72ChOHkSgw+RHampgI+Pdfjx9QVSUqRrE5EWMdSQp3AdHyI74uKAt96qDjtA9b9vvll9OxERKQ97fIgaMGoUkJkJ5OdX9/Qw9BARKReDD5ED4uIYeIiI1IBDXURERKQZDD5ERESkGQw+REREpBkMPkRERKQZDD5ERESkGQw+REREpBkMPkRERKQZDD5EREQydPYssHt39b/kOQw+REREMmEwGFBUVISFC68gIUFAz55AQoKAhQuvoKioCAaDQeomKh5XbiYizTEYDLzSN8mOwWDA8uXLYTSGYPHiiRAEHQDAZNJh8uRQnDu3Gnp9GXJycvj5dAODDxFpivngYmY0hqCkJBxhYQbo9WWW23lwIW8zh/GSknAIgvWAjCD4oKQkDHp9md3QTg1j8CEiTal50Dh0qCO2bu0HQfCBTmdC//7bkJZ2uM52RN4UFmaATmeyCj86nQlhYSUStko9WONDRJpkNIZYQg9QfUa9dWs/GI0hEreMtE6vL0P//tug05kAwBLKa/ZIkuvY40NEmtTQcAKRlNLSDiM5OR8lJWEICyvhZ9KDGHyISJM4nEByp9eXMfCIgENdRKRJHE4g0ib2+BCRZnE4gUh7GHyISNM4nEByERAQ4NHtqH4MPkSkKTy4kFyFh4cjJyeHi2uKTCcIgiB1I+SktLQUer0eRqMRoaGhUjeHiETAlZuJ1MfR4zd7fIhIcxhqiKRx9ixw8iSQmgrExUnTBs7qIiIiItGtWgUkJOB/F16t/l4KDD5EREQkGoPBgIMHL2DMGAGm6tUjYDIBTzwh4ODBC16/4jyHuoiIiEgU5osCFxQkwmQaZnVfVZUOy5btQFLSr169KDB7fIiIiEgU5kkE5pXSa6q5Uro3LwrM4ENERESiktNK6RzqIiIiItHJZaV0Bh8iIiLyCjmslM6hLiIiItIMBh8iIiLSDAYfIiIi0gwGHyIiIhKFHC8KzOJmIiIiEoUcrzjP4ENERESikdtFgTnURURERJrB4ENERESaweBDREREmsHgQ0RERJrB4EMkY2fPArt3V/9LRETuY/AhkqlVq4CEBKBnz+p/V62SukXaxPBJpC4MPkQydPYsMGYMYDJVf28yAU88wYOvtxgMBhQVFWHhwitISBD+Fz4FLFx4BUVFRTAYDFI3kYhcxHV8iGTo5Mk/Qo9ZVRWQnw/ExUnTJq0wGAxYvnw5jMYQLF48EYKgAwCYTDpMnhyKc+dWQ68vQ05OjuzWJyGihrHHh0hmDAYDQkMvwMdHsLrd11dASMgF9jaIzLzCbElJOATBehcpCD4oKQmz2o6IlIU9PkQyYu5tAIB+/Tpi69Z+EAQf6HQm9O27Ddu2HQYA9jZ4QViYATqdySr86HQmhIWVSNgqInIXgw+RjNTsRUhLO4zk5HyUlIQhLKwEen1ZvduROPT6MvTvv80qfPbvv83q90BEysPgQyRjen0ZD7QSshc+HWUwGCxB9fx5HxQU+CEp6SZiY6uLuLx9gUYirWPwISKyw53wWXPo8tChjnV6j9LSOHRJ5G0sbiYiEom5p8doDLGEHqC6SHrr1n4wGkOstiMi8TH4EBGJrKEZYkTkPQw+REQ1BAQEeHQ74I8ZYjVxhhiRNFjjQ0RUQ3h4OHJycuwOPzlbkMwZYkTyweBDJCNi9DaQ88QoNPbEDDEich+DD5GMiNHbQPLB5QmIpMfgQyQzDDVEROJhcTMRkUg4dEkkP+zxISISCYcuieRHUT0+27dvR5cuXRAUFIRmzZphwIABVvefPn0affv2RePGjdG8eXNMnjwZN2/elKaxRESoDj8xMTE2vxh6iLxLMT0+mzZtwujRozFnzhz07NkTN2/exLFjxyz3V1VVoW/fvoiOjsa+fftQVFSEoUOHwt/fH3PmzJGw5URERCQXOkEQBKkb0ZCbN28iMTERs2fPxqhRo+rdZseOHejXrx/Onz+PqKgoAMDKlSsxdepUXLp0yeEx9NLSUuj1ehiNRoSGhnrsNRAREZF4HD1+K2Ko69ChQzh37hx8fHzQsWNHxMTEoE+fPlY9Pvv370e7du0soQcAMjMzUVpaiv/85z9SNJuIiIhkRhHB57///S8AYNasWfj73/+Obdu2oVmzZujRowdKSqqXfC8uLrYKPQAs3xcXF9t87PLycpSWllp9ERERkTpJGnymTZsGnU5n9+vEiRMwmaqvcTNjxgwMGjQI6enpWLNmDXQ6HT744AO32jB37lzo9XrLV3x8vCdeGhEREcmQpMXNzzzzDIYPH253m5YtW6KoqAgA0KZNG8vtgYGBaNmyJU6fPg0AiI6Oxvfff2/1sxcuXLDcZ8v06dMxadIky/elpaUNhh+TyWR3eiq5LyAgAD4+iuiQJCIiBZE0+ERGRiIyMrLB7dLT0xEYGIi8vDzceeedAIDKykoUFhYiISEBAJCRkYGXX34ZFy9eRPPmzQEAu3btQmhoqFVgqi0wMBCBgYEOt7miogIFBQWWXigSh4+PD5KSkriwGxEReZQiprOHhoZi7NixeP755xEfH4+EhAS8+uqrAIAHH3wQAHDvvfeiTZs2eOyxx/DKK6+guLgYf//735Gdne1UsLFHEAQUFRXB19cX8fHx7JEQiclkwvnz51FUVIQWLVpAp9NJ3SQiIlIJRQQfAHj11Vfh5+eHxx57DL///ju6dOmCL7/8Es2aNQMA+Pr6Ytu2bRg3bhwyMjIQHByMYcOG4YUXXvBYG27evInr168jNjYWjRs39tjjUl2RkZE4f/48bt68CX9/f6mbQ0REKqGIdXy8yd46ADdu3EBBQQESExMRFBQkUQu14ffff0dhYSGSkpLQqFEjqZtDREQyp6p1fOSGQy/i43tMRERiYPAhIiIizWDw0YDhw4db1kXy9/dHVFQU7rnnHqxevdqp2Wlr165F06ZNxWsoERGRyBh8NCIrKwtFRUUoLCzEjh07cPfdd+Opp55Cv379eAV7IiLSDAYfLzIYDCgqKrL5ZTAYRHvuwMBAREdH45ZbbkFaWhqeffZZfPzxx9ixYwfWrl0LAFi0aBHatWuH4OBgxMfH48knn8TVq1cBAF999RVGjBgBo9Fo6T2aNWsWAGDdunXo1KkTQkJCEB0djSFDhuDixYuivRYiIiJXKWY6u9IZDAYsX768we1ycnIQHh7uhRYBPXv2RIcOHbB582Y8/vjj8PHxwdKlS5GUlIT//ve/ePLJJzFlyhSsWLECd9xxBxYvXoyZM2ciLy8PANCkSRMA1YtJvvjii2jVqhUuXryISZMmYfjw4fjkk0+88jqIiIgcxeDjJY5e4sLbl8Jo3bo1jhw5AgCYOHGi5fbExES89NJLGDt2LFasWIGAgADo9XrodLo6lwAZOXKk5f8tW7bE0qVL0blzZ1y9etUSjoiIiOSAQ10aJwiCZer4559/jl69euGWW25BSEgIHnvsMRgMBly/ft3uYxw8eBD9+/dHixYtEBISgu7duwOA5TpqREREcsHgo3HHjx9HUlISCgsL0a9fP7Rv3x6bNm3CwYMH8frrrwOw3wt17do1ZGZmIjQ0FOvXr8cPP/yALVu2NPhzREREUuBQl4Z9+eWXOHr0KJ5++mkcPHgQJpMJCxcutFyD7F//+pfV9gEBAaiqqrK67cSJEzAYDJg3b57lqvY//vijd14AERGRk9jjoxHl5eUoLi7GuXPncOjQIcyZMwf3338/+vXrh6FDhyIlJQWVlZVYtmwZ/vvf/2LdunVYuXKl1WMkJibi6tWr+OKLL3D58mVcv34dLVq0QEBAgOXn/v3vf+PFF1+U6FUSERHZx+CjEZ9++iliYmKQmJiIrKws7N69G0uXLsXHH38MX19fdOjQAYsWLcL8+fNx6623Yv369Zg7d67VY9xxxx0YO3YsBg8ejMjISLzyyiuIjIzE2rVr8cEHH6BNmzaYN28eFixYINGrJCIiso8XKa3FkYuUunLhzKKiIrz11lsNbjdmzBjExMQ49dhq5M57TURE2sOLlMpMQECAR7cjIiIi57G42UvCw8ORk5Njd6ZTQECA1xYvJCIi0iIGHy9iqCEiIpIWh7qIiIhIM9jjQ0RE5ACDwcByBRVg8CEiImqAHC80Ta7hUBcREVED5HqhaXIegw8RERFpBoMPERERaQaDD7ntq6++gk6nw5UrVxz+mcTERCxevFi0NhEREdWHwUcDhg8fDp1Oh7Fjx9a5Lzs7GzqdDsOHD/d+w4iIiLyMwUcj4uPj8f777+P333+33Hbjxg1s2LABLVq0kLBlRERE3sPgoxFpaWmIj4/H5s2bLbdt3rwZLVq0QMeOHS23lZeXY8KECWjevDkaNWqEO++8Ez/88IPVY33yySf405/+hKCgINx9990oLCys83x79+7FXXfdhaCgIMTHx2PChAm4du2aaK+PiIjIEQw+GjJy5EisWbPG8v3q1asxYsQIq22mTJmCTZs24Z133sGhQ4eQkpKCzMxMlJSUAADOnDmDgQMHon///sjNzcXjjz+OadOmWT3GqVOnkJWVhUGDBuHIkSPYuHEj9u7di5ycHPFfJBGRCHihafXgAoYSOXsWOHkSSE0F4uK885yPPvoopk+fjl9//RUA8O233+L999/HV199BQC4du0a3njjDaxduxZ9+vQBALz99tvYtWsXVq1ahcmTJ+ONN95AcnIyFi5cCABo1aoVjh49ivnz51ueZ+7cufjrX/+KiRMnAgBSU1OxdOlSdO/eHW+88QYaNWrknRdMROQhvNC0ejD4SGDVKmDMGMBkAnx8gLfeAkaNEv95IyMj0bdvX6xduxaCIKBv376IiIiw3H/q1ClUVlaia9eultv8/f1x22234fjx4wCA48ePo0uXLlaPm5GRYfX9Tz/9hCNHjmD9+vWW2wRBgMlkQkFBAf785z+L8fKIiETFUKMODD5edvbsH6EHqP73iSeAzEzv9PyMHDnSMuT0+uuvi/IcV69exRNPPIEJEybUuY+F1ESkBlL02pNnsMbHy06e/CP0mFVVAfn53nn+rKwsVFRUoLKyEpmZmVb3JScnIyAgAN9++63ltsrKSvzwww9o06YNAODPf/4zvv/+e6uf++6776y+T0tLw88//4yUlJQ6Xxz/JiKlW7UKSEgAevas/nfVKqlbRM5g8PGy1NTq4a2afH2BlBTvPL+vry+OHz+On3/+Gb6+vlb3BQcHY9y4cZg8eTI+/fRT/Pzzzxg9ejSuX7+OUf8bixs7dixOnjyJyZMnIy8vDxs2bMDatWutHmfq1KnYt28fcnJykJubi5MnT+Ljjz9mcTMRKZ6tXvuzZ6VtFzmOwcfL4uKqa3rMmcPXF3jzTe92lYaGhiI0NLTe++bNm4dBgwbhscceQ1paGvLz8/HZZ5+hWbNmAKqHqjZt2oSPPvoIHTp0wMqVKzFnzhyrx2jfvj327NmDX375BXfddRc6duyImTNnIjY2VvTXRkQkJql77cl9OkEQBKkbISelpaXQ6/UwGo11wsGNGzdQUFCApKQkt2cmnT1b/YeSksLx4fp48r0mIvKUs2erh7dqhh9fX6CwkPtyqdk7ftfEHh+JxMUBPXrwD4WISEnk0GtP7uGsLiIiIieMGlU9E5e99srE4ENEROSkuDgGHqXiUBcRERFpBoMPERERaQaDjws4EU58fI+JiEgMDD5OMC/4Z+8ideQZ5ve49iKLRERE7mBxsxP8/PzQuHFjXLp0Cf7+/vCpvQQzeYTJZMKlS5fQuHFj+PnxI0pERJ7Do4oTdDodYmJiUFBQgF9//VXq5qiaj48PWrRoAZ1OJ3VTiIhIRRh8nBQQEIDU1FQOd4ksICCAPWpERORxDD4u8PHx4WUUiIiIFIin1ERERKQZDD5ERESkGQw+REREpBms8anFvHBeaWmpxC0hIiIiR5mP2w0tgMvgU0tZWRkAID4+XuKWEBERkbPKysqg1+tt3q8TeG0AKyaTCefPn0dISIjm1pApLS1FfHw8zpw5g9DQUKmbo1h8Hz2H76Vn8H30HL6XniHG+ygIAsrKyhAbG2t3ORT2+NTi4+ODuLg4qZshqdDQUP5BewDfR8/he+kZfB89h++lZ3j6fbTX02PG4mYiIiLSDAYfIiIi0gwGH7IIDAzE888/j8DAQKmbomh8Hz2H76Vn8H30HL6XniHl+8jiZiIiItIM9vgQERGRZjD4EBERkWYw+BAREZFmMPgQERGRZjD4aMzXX3+N/v37IzY2FjqdDh999JHV/YIgYObMmYiJiUFQUBB69+6NkydPStNYmWvovRw+fDh0Op3VV1ZWljSNlbG5c+eic+fOCAkJQfPmzTFgwADk5eVZbXPjxg1kZ2cjPDwcTZo0waBBg3DhwgWJWixPjryPPXr0qPOZHDt2rEQtlq833ngD7du3tyyul5GRgR07dlju5+fRcQ29l1J8Jhl8NObatWvo0KEDXn/99Xrvf+WVV7B06VKsXLkSBw4cQHBwMDIzM3Hjxg0vt1T+GnovASArKwtFRUWWr/fee8+LLVSGPXv2IDs7G9999x127dqFyspK3Hvvvbh27Zplm6effhpbt27FBx98gD179uD8+fMYOHCghK2WH0feRwAYPXq01WfylVdekajF8hUXF4d58+bh4MGD+PHHH9GzZ0/cf//9+M9//gOAn0dnNPReAhJ8JgXSLADCli1bLN+bTCYhOjpaePXVVy23XblyRQgMDBTee+89CVqoHLXfS0EQhGHDhgn333+/JO1RsosXLwoAhD179giCUP0Z9Pf3Fz744APLNsePHxcACPv375eqmbJX+30UBEHo3r278NRTT0nXKAVr1qyZ8I9//IOfRw8wv5eCIM1nkj0+ZFFQUIDi4mL07t3bcpter0eXLl2wf/9+CVumXF999RWaN2+OVq1aYdy4cTAYDFI3SfaMRiMAICwsDABw8OBBVFZWWn0uW7dujRYtWvBzaUft99Fs/fr1iIiIwK233orp06fj+vXrUjRPMaqqqvD+++/j2rVryMjI4OfRDbXfSzNvfyZ5kVKyKC4uBgBERUVZ3R4VFWW5jxyXlZWFgQMHIikpCadOncKzzz6LPn36YP/+/fD19ZW6ebJkMpkwceJEdO3aFbfeeiuA6s9lQEAAmjZtarUtP5e21fc+AsCQIUOQkJCA2NhYHDlyBFOnTkVeXh42b94sYWvl6ejRo8jIyMCNGzfQpEkTbNmyBW3atEFubi4/j06y9V4C0nwmGXyIRPLwww9b/t+uXTu0b98eycnJ+Oqrr9CrVy8JWyZf2dnZOHbsGPbu3St1UxTN1vs4ZswYy//btWuHmJgY9OrVC6dOnUJycrK3mylrrVq1Qm5uLoxGIz788EMMGzYMe/bskbpZimTrvWzTpo0kn0kOdZFFdHQ0ANSZnXDhwgXLfeS6li1bIiIiAvn5+VI3RZZycnKwbds27N69G3FxcZbbo6OjUVFRgStXrlhtz89l/Wy9j/Xp0qULAPAzWY+AgACkpKQgPT0dc+fORYcOHbBkyRJ+Hl1g672sjzc+kww+ZJGUlITo6Gh88cUXlttKS0tx4MABq/FYcs3Zs2dhMBgQExMjdVNkRRAE5OTkYMuWLfjyyy+RlJRkdX96ejr8/f2tPpd5eXk4ffo0P5c1NPQ+1ic3NxcA+Jl0gMlkQnl5OT+PHmB+L+vjjc8kh7o05urVq1ZJuqCgALm5uQgLC0OLFi0wceJEvPTSS0hNTUVSUhKee+45xMbGYsCAAdI1WqbsvZdhYWGYPXs2Bg0ahOjoaJw6dQpTpkxBSkoKMjMzJWy1/GRnZ2PDhg34+OOPERISYqmT0Ov1CAoKgl6vx6hRozBp0iSEhYUhNDQU48ePR0ZGBm6//XaJWy8fDb2Pp06dwoYNG/B///d/CA8Px5EjR/D000+jW7duaN++vcStl5fp06ejT58+aNGiBcrKyrBhwwZ89dVX+Oyzz/h5dJK991Kyz6RX55CR5Hbv3i0AqPM1bNgwQRCqp7Q/99xzQlRUlBAYGCj06tVLyMvLk7bRMmXvvbx+/bpw7733CpGRkYK/v7+QkJAgjB49WiguLpa62bJT33sIQFizZo1lm99//1148sknhWbNmgmNGzcW/t//+39CUVGRdI2WoYbex9OnTwvdunUTwsLChMDAQCElJUWYPHmyYDQapW24DI0cOVJISEgQAgIChMjISKFXr17Czp07Lffz8+g4e++lVJ9JnSAIgnixioiIiEg+WONDREREmsHgQ0RERJrB4ENERESaweBDREREmsHgQ0RERJrB4ENERESaweBDREREmsHgQ0RERJrB4ENERESaweBDRIpRUVEhdRPqkGObiMg2Bh8ikkyPHj2Qk5ODnJwc6PV6RERE4LnnnoP5SjqJiYl48cUXMXToUISGhmLMmDEAgL179+Kuu+5CUFAQ4uPjMWHCBFy7ds3yuCtWrEBqaioaNWqEqKgoPPDAA5b7PvzwQ7Rr1w5BQUEIDw9H7969LT/bo0cPTJw40aqNAwYMwPDhwy3fu9omIpIHBh8iktQ777wDPz8/fP/991iyZAkWLVqEf/zjH5b7FyxYgA4dOuDw4cN47rnncOrUKWRlZWHQoEE4cuQINm7ciL179yInJwcA8OOPP2LChAl44YUXkJeXh08//RTdunUDABQVFeGRRx7ByJEjcfz4cXz11VcYOHAgnL1kobNtIiL54EVKiUgyPXr0wMWLF/Gf//wHOp0OADBt2jT8+9//xs8//4zExER07NgRW7ZssfzM448/Dl9fX7z55puW2/bu3Yvu3bvj2rVr+OSTTzBixAicPXsWISEhVs936NAhpKeno7CwEAkJCfW25y9/+QsWL15suW3AgAFo2rQp1q5dCwAutalRo0ZuvU9E5Dns8SEiSd1+++2W0AMAGRkZOHnyJKqqqgAAnTp1str+p59+wtq1a9GkSRPLV2ZmJkwmEwoKCnDPPfcgISEBLVu2xGOPPYb169fj+vXrAIAOHTqgV69eaNeuHR588EG8/fbb+O2335xus7NtIiL5YPAhIlkLDg62+v7q1at44oknkJuba/n66aefcPLkSSQnJyMkJASHDh3Ce++9h5iYGMycORMdOnTAlStX4Ovri127dmHHjh1o06YNli1bhlatWlnCiY+PT51hr8rKSrfbRETyweBDRJI6cOCA1fffffcdUlNT4evrW+/2aWlp+Pnnn5GSklLnKyAgAADg5+eH3r1745VXXsGRI0dQWFiIL7/8EgCg0+nQtWtXzJ49G4cPH0ZAQIBl2CoyMhJFRUWW56qqqsKxY8cafA2OtImI5IHBh4gkdfr0aUyaNAl5eXl47733sGzZMjz11FM2t586dSr27duHnJwc5Obm4uTJk/j4448thcTbtm3D0qVLkZubi19//RXvvvsuTCYTWrVqhQMHDmDOnDn48ccfcfr0aWzevBmXLl3Cn//8ZwBAz549sX37dmzfvh0nTpzAuHHjcOXKlQZfQ0NtIiL58JO6AUSkbUOHDsXvv/+O2267Db6+vnjqqacsU8Tr0759e+zZswczZszAXXfdBUEQkJycjMGDBwMAmjZtis2bN2PWrFm4ceMGUlNT8d5776Ft27Y4fvw4vv76ayxevBilpaVISEjAwoUL0adPHwDAyJEj8dNPP2Ho0KHw8/PD008/jbvvvrvB19BQm4hIPjiri4gkU98sKiIiMXGoi4iIiDSDwYeIiIg0g0NdREREpBns8SEiIiLNYPAhIiIizWDwISIiIs1g8CEiIiLNYPAhIiIizWDwISIiIs1g8CEiIiLNYPAhIiIizWDwISIiIs34/2GiY/EoyBLqAAAAAElFTkSuQmCC", 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", 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zEB7++r/DqpJOLMB7+vRpnDlzBp06dUKNGjWQnJyMGTNmoGHDhlJY8vHxQdu2bTF8+HAsXrwYxcXFGD16NN59911pFC4wMBDLly/HlClTMHz4cBw5cgRbt27Fvn37pPeaOHEihg0bhnbt2qF9+/ZYvHgxcnJypBEeW1tbBAQEYOLEiahZsyZsbGwwduxYyOVyeHt7AwC6deuG5s2bY8iQIYiIiEBaWhqmT5+O0aNHV9qIWkFBAebOnVsp+36VkJCQUgN2eTRr1gwXLlwAAIwfP15qr1+/Pr7++msEBgZi5cqVMDMzg62tLWQymdII5vDhw6W/N2jQAEuXLsUbb7yBx48fo1q1aq9dIxER6aZLly5h+/btCm2HD09DcLBuLbOkE8HNysoKO3bsQGhoKHJycuDi4oLu3btj+vTpUhAyMjLCnj17MHbsWHTp0gXW1tbo0aMHFixYIO3H3d0d+/btw4QJE7BkyRLUrVsXa9asga+vr9RnwIABuH//PmbOnIm0tDS0adMGBw8eVLjYYNGiRTAyMkK/fv2Ql5cHX19frFy5UtpubGyMvXv3YtSoUZDL5bC2tsawYcMwe/bsKvi2dJcQQpr6/PXXXzF37lwkJiYiOzsbhYWFyM3NxZMnT1668HB8fDxmzZqF8+fP4+HDh9LIbGpqaqWNdhIRkfYSQuA///mPwsWBHTt2xLvvvovQUA0WVkEyoY55LqoU2dnZsLW1RVZWFmxsbKT23NxcpKSkwN3dHRYWFgCe/YepqZMsTU1Ny7wwrL+/PzIzM7Fr1y6lbf/3f/8HNzc3LF++HM2aNcOoUaMwYMAA1KxZE7/99hsCAgLw8OFD2NnZITIyEuPHj0dmZqb0+pycHNSrVw++vr4IDAyEg4MDUlNT4evri4SEBLRp00Y9H1iDVB17IiJSLT09HatXr1ZoCwwMVFr5Qd1K+/2tDjox4kavJpPJ1DJdqSlHjhzBxYsXMWHCBMTHx6O4uBgLFiyAkdGz0zC3bt2q0N/MzAxFRUUKbYmJifjnn38QHh4uLaVy9uzZqvkARESkVcLCwhSeV69eHePHj5d+r+gqBjeqcnl5eUhLS0NRURHS09Nx8OBBzJ07F7169cLQoUNx6dIlFBQUYNmyZejduzdOnTql9H9M9evXx+PHjxETE4PWrVvDysoKbm5uMDMzw7JlyxAYGIhLly7hq6++0tCnJCIiTcjJycH8+fMV2o4f98GRI//SUEXqpduxk3TSwYMH4eLigvr166N79+44evQoli5dit27d8PY2BitW7fGwoUL8e2336Jly5bYsGGD0oUXHTt2RGBgIAYMGAAHBwdERETAwcEBkZGR2LZtG5o3b47w8HClf7xERKS/wsLClH7u79rVH3K5foQ2gOe4abXynONGhoHHnohItRenRoFna3xqAs9xIyIiIlLhypUr2LZtm1K7pkJbZWNwIyIiIp2kapTtp5+GwMmpgU4u9VEWPMeNiIiIdIoQQmVo8/QMhZNTAwQHa6CoKsIRNyIiItIZkZGRuHXrllJ7dHQoQkOhU/cdrQgGNyIiItIJqkbZWrachAULqun1KNvzGNyIiIhIqz169AgLFy5Uai+5AKFfv6quSHMY3IiIiEhrqRplq1+/PoYNG6aBajSPwY2IiIi0kqrQNnPmzDLfH1sfMbgRERGRVjlz5gz279+v1K6va7OVB5cDIb3k7++Pvn37Ss/feustjB8//rX2qY59EBHRy4WFhSmFtqtXu8PTk6EN4IgbVTF/f3+sW7cOAGBqago3NzcMHToU06ZNg4lJ5f3nuGPHDpiampap77Fjx9C1a1c8fPgQdnZ2FdoHERGVn6qp0ejoUMTGaqAYLcXgRlWue/fuWLt2LfLy8rB//36MHj0apqamCAkJUeiXn58PMzMztbxnzZo1tWIfRESkbOnSpXj48KFS+/z5odiwQQMFaTFOlVKVMzc3h7OzM+rVq4dRo0bBx8cHUVFR0vTmN998g9q1a6Np06YAgNu3b+PDDz+EnZ0datasiT59+uDmzZvS/oqKijBx4kTY2dmhVq1amDJlCoQQCu/54jRnXl4epk6dCldXV5ibm6NRo0b44YcfcPPmTXTt2hUAUKNGDchkMvj7+6vcx8OHDzF06FDUqFEDVlZW6NGjB65fvy5tj4yMhJ2dHaKjo+Hh4YFq1aqhe/fuuHfvntTn2LFjaN++PaytrWFnZ4d//etfKheWJCLSV2FhYUqhrVmzUYiOfhba9H1B3fJicCONs7S0RH5+PgAgJiYGSUlJOHz4MPbu3YuCggL4+vqievXqOHnyJE6dOiUFoJLXLFiwAJGRkfjxxx/x22+/4cGDB9i5c+dL33Po0KHYtGkTli5diqtXr+K7775DtWrV4Orqiu3btwMAkpKScO/ePSxZskTlPvz9/XH27FlERUUhLi4OQgi89957KCgokPo8efIE8+fPx/r163HixAmkpqZi8uTJAIDCwkL07dsXb775Ji5cuIC4uDh8+umnBn21FBEZjsePH6ucGg0NDcWAAY6IjWVoU4VTpaQxQgjExMQgOjoaY8eOxf3792FtbY01a9ZIU6Q///wziouLsWbNGinQrF27FnZ2djh27Bi6deuGxYsXIyQkBB988AEAYPXq1YiOji71fa9du4atW7fi8OHD8PHxAQA0aNBA2l4yJero6Khwjtvzrl+/jqioKJw6dQodO3YEAGzYsAGurq7YtWsX/v3vfwMACgoKsHr1ajRs2BAAMGbMGMyePRsAkJ2djaysLPTq1Uva7uHhUf4vkohIx6gKbACvGi0LjrgRoqKAjh2f/VkV9u7di2rVqsHCwgI9evTAgAEDMGvWLABAq1atFM5rO3/+PP766y9Ur14d1apVQ7Vq1VCzZk3k5uYiOTkZWVlZuHfvHjp06CC9xsTEBO3atSv1/c+dOwdjY2O8+eabFf4MV69ehYmJicL71qpVC02bNsXVq1elNisrKymUAYCLiwsyMjIAPAuI/v7+8PX1Re/evbFkyRKFaVQiIn2kKrQtXDidV42WEUfcCOHhQFzcsz+rYli6a9euWLVqFczMzFC7dm2Fq0mtra0V+j5+/BheXl7YoOLsVAcHhwq9v6WlZYVeVxEvXoUqk8kUzr9bu3Ytxo0bh4MHD2LLli2YPn06Dh8+DG9v7yqrkYioKpw7dw67d+9Wao+ODsX69ZwWLSuOuBGCgwG5HFV2g15ra2s0atQIbm5ur1wCpG3btrh+/TocHR3RqFEjhYetrS1sbW3h4uKC06dPS68pLCxEfHx8qfts1aoViouLcfz4cZXbS0b8ioqKSt2Hh4cHCgsLFd73n3/+QVJSEpo3b/7Sz/QiT09PhISEIDY2Fi1btsTGjRvL9XoiIm0XFhamFNpatmyJ0NBQnstWTgxuBD8/aO0/nMGDB8Pe3h59+vTByZMnkZKSgmPHjmHcuHG4c+cOAOCLL75AeHg4du3ahcTERHz++efIzMwsdZ8l97gbPnw4du3aJe1z69atAIB69epBJpNh7969uH//Ph4/fqy0j8aNG6NPnz4YOXIkfvvtN5w/fx4ff/wx6tSpgz59+pTps6WkpCAkJARxcXG4desWDh06hOvXr/M8NyLSK6VdgNDPkO4Mr0YMbqTVrKyscOLECbi5ueGDDz6Ah4cHAgICkJubCxsbGwDApEmTMGTIEAwbNgxyuRzVq1fH+++//9L9rlq1Cv3798fnn3+OZs2aYeTIkcjJyQEA1KlTB2FhYQgODoaTkxPGjBmjch9r166Fl5cXevXqBblcDiEE9u/fX+ZFeq2srJCYmIh+/fqhSZMm+PTTTzF69Gh89tln5fiGiIi0U0RERKmhjSpOJl5c8Iq0RnZ2NmxtbZGVlSWFFADIzc1FSkoK3N3dYWFhocEKqarx2BORLlAV2P74Yxj27atf9cVoQGm/v9WBFycQERGRWjx9+hQRERFK7dHRoVV2HrW+Y3AjIiKi1/aytdk4O6o+PMeNiIiIXouq0Pbrr8Fcm60ScMSNiIiIKuTixYvYsWOHUrunJ0fZKguDmw7jdSWGh8eciLSFqlE2KysrBAUFaaAaw8HgpoNKlpt48uRJld4FgDTvyZMnAJTvyEBEVJW4zIfmMLjpIGNjY9jZ2Un3vLSyspJuwE76SQiBJ0+eICMjA3Z2djA2NtZ0SURkgL799lvk5uYqtTO0VR0GNx3l7OwMAFJ4I8NgZ2cnHXsioqqkapTt8uVe2LrVSwPVGC4GNx0lk8ng4uICR0dHFBQUaLocqgKmpqYcaSOiKldQUIA5c+YotXNtNs1gcNNxxsbG/GVORESVgmuzaR+u40ZERERKVIW2+fMnIjqaiU2TOOJGREREksTERGzZskWpfdOmUNStC06PahiDGxEREQHg1KguYHAjIiIirs2mIxjciIiIDNjy5cvxzz//KLUztGknBjciIiIDpWqULTW1HX74oacGqqGy4FWlREREBqa4uFhlaIuODkWfPgxt2owjbkRERAaEFyDoNo64ERERGQhVoW3lykCuzaZDOOJGRESk527cuIH169crtXt6hqJhQ67NpksY3IiIiPTYy6ZGAcDPryqrodfFqVIiIiI9pSq0tWkzE9HRoYiK0kBB9No44kZERKRnVq9ejfT0dKV2T89QhIcDcXFAeDhH23QRgxsREZEeUTXK5uTkhMDAQOl5eDjPa9NVDG5ERER6QAiB2bNnK7W/eAcEPz+OtOkyBjciIiId96oLEEh/8OIEIiIiHaYqtJ05MxSengxt+ogjbkRERDooLS0N3333nVI7R9n0G4MbERGRjuHUqOHiVCkREZEOURXaDh2azqlRA8ERNyIiIh2wceNGXL9+XamdN4c3LAxuREREWo5To1SCwY2IiEiLqQptDGyGi8GNiIhIC3GUjVRhcCMiItIyqkLbpUu9sW1bWw1UQ9qEwY2IiEhLZGZmYsmSJUrt0dGhvLcoAWBwIyIi0govmxrl7CiVYHAjIiLSMFWhLTg4GObm5hqohrQZgxsREZGGHDhwAH/88YdSOy9AoNIwuBEREWkArxqlitCZW175+fnBzc0NFhYWcHFxwZAhQ3D37l1p+6xZsyCTyZQe1tbWCvvZtm0bmjVrBgsLC7Rq1Qr79+9X2C6EwMyZM+Hi4gJLS0v4+PgorVT94MEDDB48GDY2NrCzs0NAQAAeP36s0OfChQvo3LkzLCws4OrqioiICDV/I0REpKtKW5uNoY1eRWeCW9euXbF161YkJSVh+/btSE5ORv/+/aXtkydPxr179xQezZs3x7///W+pT2xsLAYNGoSAgAAkJCSgb9++6Nu3Ly5duiT1iYiIwNKlS7F69WqcPn0a1tbW8PX1RW5urtRn8ODBuHz5Mg4fPoy9e/fixIkT+PTTT6Xt2dnZ6NatG+rVq4f4+HjMmzcPs2bNwn/+859K/paIiEibhYWFcUFdei0yIYTQdBEVERUVhb59+yIvLw+mpqZK28+fP482bdrgxIkT6Ny5MwBgwIAByMnJwd69e6V+3t7eaNOmDVavXg0hBGrXro1JkyZh8uTJAICsrCw4OTkhMjISAwcOxNWrV9G8eXOcOXMG7dq1AwAcPHgQ7733Hu7cuYPatWtj1apV+PLLL5GWlgYzMzMAz04y3bVrFxITE8v8GbOzs2Fra4usrCzY2NhU+LsiIiLNUxXYunTpgq5du2qgGqpMlfn7W2dG3J734MEDbNiwAR07dlQZ2gBgzZo1aNKkiRTaACAuLg4+Pj4K/Xx9fREXFwcASElJQVpamkIfW1tbdOjQQeoTFxcHOzs7KbQBgI+PD4yMjHD69GmpT5cuXaTQVvI+SUlJePjwYamfKy8vD9nZ2QoPIiLSbU+ePFEZ2qKjQxnaqNx0KrhNnToV1tbWqFWrFlJTU7F7926V/XJzc7FhwwYEBAQotKelpcHJyUmhzcnJCWlpadL2kraX9XF0dFTYbmJigpo1ayr0UbWP599Dlblz58LW1lZ6uLq6ltqXiIi0X1hYGObNm6fUzgV1qaI0GtyCg4NVXlDw/OP5qcWgoCAkJCTg0KFDMDY2xtChQ6Fqpnfnzp149OgRhg0bVpUf57WFhIQgKytLety+fVvTJRERUQWpGmVr2XIiQkNDERsL+PlpoCjSeRpdDmTSpEnw9/d/aZ8GDRpIf7e3t4e9vT2aNGkCDw8PuLq64vfff4dcLld4zZo1a9CrVy+lUS9nZ2ekp6crtKWnp8PZ2VnaXtLm4uKi0KdNmzZSn4yMDIV9FBYW4sGDBwr7UfU+z7+HKubm5lxskYhIx504cQJHjx5Vap81KxRyOdCvnwaKIr2h0eDm4OAABweHCr22uLgYwLPzwp6XkpKCo0ePIioqSuk1crkcMTExGD9+vNR2+PBhKfi5u7vD2dkZMTExUlDLzs7G6dOnMWrUKGkfmZmZiI+Ph5eXFwDgyJEjKC4uRocOHaQ+X375JQoKCqRz8A4fPoymTZuiRo0aFfq8RESk/Upbmy0//1lo4/QovS6dWID39OnTOHPmDDp16oQaNWogOTkZM2bMQMOGDZVG23788Ue4uLigR48eSvv54osv8Oabb2LBggXo2bMnNm/ejLNnz0rLdMhkMowfPx5ff/01GjduDHd3d8yYMQO1a9dG3759AQAeHh7o3r07Ro4cidWrV6OgoABjxozBwIEDUbt2bQDARx99hLCwMAQEBGDq1Km4dOkSlixZgkWLFlXuF0VERBrDZT6oKuhEcLOyssKOHTsQGhqKnJwcuLi4oHv37pg+fbrC1GJxcTEiIyPh7+8PY2Njpf107NgRGzduxPTp0zFt2jQ0btwYu3btQsuWLaU+U6ZMQU5ODj799FNkZmaiU6dOOHjwICwsLKQ+GzZswJgxY/DOO+/AyMgI/fr1w9KlS6Xttra2OHToEEaPHg0vLy/Y29tj5syZCmu9ERGRfuAdEKgq6ew6boaA67gREWk3VaHtxcXfyfBU5u9vnRhxIyIi0iaFhYX45ptvlNo5ykaVjcGNiIioHDg1SprE4EZERFRGqkLb559/XuEVEojKi8GNiIjoFS5fvoxffvlFqZ2jbFTVGNyIiIheglOjpE0Y3IiIiFT48kvAzIxrs5F2YXAjIiJ6QVhYGMzMlNsZ2kjTGNyIiIieo2pq1NHRUbr1IZEmMbgREREBEEJg9uzZSu0cZSNtwuBGREQGjxcgkK5gcCMiIoOmKrSdOTMEe/c20EA1RC/H4EZERAbpxo0bWL9+vVJ7dHQogoM1UBBRGTC4ERGRwXnZ1ChnR0mbGWm6ACIioqqkKrRFR8+ApycTG2k/jrgREZFBmDNnIQoKHim1c5SNdAmDGxER6T1eNUr6gsGNiIj0mqrQ5ukZCj8/DRRD9JoY3IiISC9xlI30EYMbERHpHVWhrXfv3mjbtq0GqiFSHwY3IiLSG+np6Vi9erVSO0fZSF8wuBERkV7g1CgZAgY3IiLSeapC27Rp02BqaqqBaogqD4MbERHprJ9//hnJyclK7RxlI33F4EZERDqJU6NkiHjLKyIi0jmqb1sVyttWkd7jiBsREekM3hyeDB1H3IiISCeoCm3JyZ04ykYGhSNuRESk1bKzs7Fo0SKldp7LRoaIwY2IiLQWL0AgUsTgRkREWklVaJs8eTKsra01UA2RdmBwIyIirbJnzx78+eefSu2enqFgZiNDx+BGRERag1OjRC/H4EZERFpBVWjz9AyFn58GiiHSUgxuRESkURxlIyo7ruNGREQaoyq0ZWQ04dpsRKXgiBsREVW5vLw8hIeHK7VzapTo5RjciIioSnFqlKjiGNyIiKjKqAptHh6j8eGH9hqohkj3MLgREVGli42NxeHDh5XaZ80KhVwOfPihBooi0kEMbkREVKlKmxr19HwW2oKDq7ggIh3G4EZERJXmVWuz8UIEovJhcCMiIrXjBQhElaPc67gZGxsjIyNDqf2ff/6BsbGxWooiIiLdpSq05eVZcW02IjUo94ibEEJle15eHszMzF67ICIi0k1FRUX4+uuvldo5ykakPmUObkuXLgUAyGQyrFmzBtWqVZO2FRUV4cSJE2jWrJn6KyQiIq3HqVGiqlHm4LZo0SIAz0bcVq9erTAtamZmhvr162P16tXqr5CIiLSaqtA2ZMgQNGjQQAPVEOm3Mge3lJQUAEDXrl2xY8cO1KhRo9KKIiIi7Xfp0iVs375dqZ2jbESVp9znuB09erQy6iAiIh3CqVEizSh3cBs+fPhLt//4448VLoaIiLSfqtA2c+ZMyGQyDVRDZFjKHdwePnyo8LygoACXLl1CZmYm3n77bbUVRkRE2uVld0BgZiOqGuUObjt37lRqKy4uxqhRo9CwYUO1FEVERNqFU6NE2kEmSluYrZySkpLw1ltv4d69e+rYHQHIzs6Gra0tsrKyYGNjo+lyiMgACSEwe/ZspXYGNqLSVebvb7Xd8io5ORmFhYXq2h0REWkYR9mItE+5g9vEiRMVngshcO/ePezbtw/Dhg1TW2FERKQ5qkJbr1694OXlpYFqiKhEuYNbQkKCwnMjIyM4ODhgwYIFr7zilIiItNuNGzewfv16pXaOshFpB67jRkREADg1SqQLKnyOW0ZGBpKSkgAATZs2haOjo9qKIiKiqqUqtB0+/CWmTFHbqdBEpAbl/heZnZ2N0aNHY9OmTSguLgYAGBsbY8CAAVixYgVsbW3VXiQREVWOb775RuWFZaGhoeBAG5H2MSrvC0aOHInTp09j3759yMzMRGZmJvbu3YuzZ8/is88+q4waiYioEoSFhZUa2ohIO5V7HTdra2tER0ejU6dOCu0nT55E9+7dkZOTo9YCDRnXcSOiyqJqapSBjUg9tGodt1q1aqmcDrW1tUWNGjXUUhQREVUOXoBApNvKPVU6ffp0TJw4EWlpaVJbWloagoKCMGPGDLUWR0RE6qMqtDk4dGBoI9Ih5Z4q9fT0xF9//YW8vDy4ubkBAFJTU2Fubo7GjRsr9P3zzz/VV6kB4lQpEanD33//jRUrVii1M7ARVQ6tmirt06cPZDKZWosgIqLKwalRIv2itpvMk/pxxI2IXoeq0HbkSBCOH7fSQDVEhqMyf3+X+xy3Bg0a4J9//lFqz8zMRIMGDdRSlCp+fn5wc3ODhYUFXFxcMGTIENy9e1ehT3R0NLy9vVG9enU4ODigX79+uHnzpkKfY8eOoW3btjA3N0ejRo0QGRmp9F4rVqxA/fr1YWFhgQ4dOuCPP/5Q2J6bm4vRo0ejVq1aqFatGvr164f09HSFPqmpqejZsyesrKzg6OiIoKAglZfdExGp28aNG1WGtujoUEyaxNBGpMvKHdxu3ryJoqIipfa8vDzcuXNHLUWp0rVrV2zduhVJSUnYvn07kpOT0b9/f2l7SkoK+vTpg7fffhvnzp1DdHQ0/v77b3zwwQcKfXr27ImuXbvi3LlzGD9+PEaMGIHo6Gipz5YtWzBx4kSEhobizz//ROvWreHr64uMjAypz4QJE7Bnzx5s27YNx48fx927dxXep6ioCD179kR+fj5iY2Oxbt06REZGYubMmZX2/RARAc9G2a5fv67UHhoaithYwM9PA0URkdqUeao0KioKANC3b1+sW7dOYUmQoqIixMTE4PDhw9JtsCpbVFQU+vbti7y8PJiamuKXX37BoEGDkJeXByOjZ3l0z5496NOnj9Rn6tSp2LdvHy5duiTtZ+DAgcjMzMTBgwcBAB06dMAbb7yB5cuXAwCKi4vh6uqKsWPHIjg4GFlZWXBwcMDGjRul4JiYmAgPDw/ExcXB29sbBw4cQK9evXD37l04OTkBAFavXo2pU6fi/v37MDMzK9Nn5FQpEZUH12Yj0g5acXFC3759AQAymQzDhg1T2GZqaor69etjwYIFai2uNA8ePMCGDRvQsWNHmJqaAgC8vLxgZGSEtWvXwt/fH48fP8b69evh4+Mj9YmLi4OPj4/Cvnx9fTF+/HgAQH5+PuLj4xESEiJtNzIygo+PD+Li4gAA8fHxKCgoUNhPs2bN4ObmJgW3uLg4tGrVSgptJe8zatQoXL58GZ6enio/V15eHvLy8qTn2dnZr/EtEZGh4AUIRIajzFOlxcXFKC4uhpubGzIyMqTnxcXFyMvLQ1JSEnr16lWZtWLq1KmwtrZGrVq1kJqait27d0vb3N3dcejQIUybNg3m5uaws7PDnTt3sHXrVqlPWlqaQpgCACcnJ2RnZ+Pp06f4+++/UVRUpLJPybp1aWlpMDMzg52d3Uv7qNpHybbSzJ07F7a2ttLD1dW1jN8MERkqVaGtRYsWDG1Eeqrc57ilpKTA3t5eLW8eHBwMmUz20kdiYqLUPygoCAkJCTh06BCMjY0xdOhQlMz0pqWlYeTIkRg2bBjOnDmD48ePw8zMDP3794euXDgbEhKCrKws6XH79m1Nl0REWionJ6fUqdHnz/8lIv1S7nXcZs+e/dLt5TkBf9KkSfD3939pn+evVLW3t4e9vT2aNGkCDw8PuLq64vfff4dcLseKFStga2uLiIgIqf/PP/8MV1dXnD59Gt7e3nB2dla6+jM9PR02NjawtLSEsbExjI2NVfZxdnYGADg7OyM/Px+ZmZkKo24v9nnxStSSfZb0UcXc3Bzm5uYv/T6IiDg1SmS4yh3cdu7cqfC8oKAAKSkpMDExQcOGDcsV3BwcHODg4FDeEgA8m7oFIJ0T9uTJE+mihBLGxsYKfeVyOfbv36/Q5/Dhw5DL5QAAMzMzeHl5ISYmRjqnr7i4GDExMRgzZgyAZ+fSmZqaIiYmBv369QMAJCUlITU1VdqPXC7HN998g4yMDDg6OkrvY2Njg+bNm1fo8xIRAapD27hx43ivaCIDUe7glpCQoNSWnZ0Nf39/vP/++2op6kWnT5/GmTNn0KlTJ9SoUQPJycmYMWMGGjZsKIWlnj17YtGiRZg9ezYGDRqER48eYdq0aahXr550MUBgYCCWL1+OKVOmYPjw4Thy5Ai2bt2Kffv2Se81ceJEDBs2DO3atUP79u2xePFi5OTk4JNPPgEA2NraIiAgABMnTkTNmjVhY2ODsWPHQi6Xw9vbGwDQrVs3NG/eHEOGDEFERATS0tIwffp0jB49miNqRFQhhw4dki6Seh5H2YgMi9runHDx4kX07t1bacFbde37iy++wPnz55GTkwMXFxd0794d06dPR506daR+mzdvRkREBK5duwYrKyvI5XJ8++23aNasmdTn2LFjmDBhAq5cuYK6detixowZStO1y5cvx7x585CWloY2bdpg6dKl6NChg7Q9NzcXkyZNwqZNm5CXlwdfX1+sXLlSYRr01q1bGDVqFI4dOwZra2sMGzYM4eHhMDEpe1bmciBEBHBqlEjXVObvb7UFt99++w29e/fGw4cP1bE7AoMbEXFtNiJdpBXruJVYunSpwnMhBO7du4f169ejR48eaiuMiMiQcZSNiFQpd3BbtGiRwnMjIyM4ODhg2LBhCgvXEhFRxagKbRYWDpg69XMNVENE2qTcwS0lJaUy6iAiMngFBQWYM2eOUvusWaGQy4GpUzVQFBFplXIHNwDIzMzEX3/9BQBo1KiR0l0EiIiofEqbGvX0fBbagoOruCAi0krlCm43b97E6NGjER0dLd2NQCaToXv37li+fDnq169fGTUSEek1VaEtICAAdevWBQD4+VV1RUSkrcoc3G7fvg1vb2+Ympriq6++goeHBwDgypUrWLVqFeRyOc6cOSP9oCEiopeLj4/H3r17ldp5AQIRlabMy4EEBATgr7/+QnR0NCwsLBS2PX36FN27d0fjxo2xZs2aSinUEHE5ECL9xatGifSXViwHcvDgQWzZskUptAGApaUlvvrqKwwcOFCtxRER6SNVoW3mzJmQyWQaqIaIdEmZg9vff//90nPYGjRogAcPHqijJiIivcRRNiJ6XUav7vKMi4sLrly5Uur2S5cuKdzyiYiI/oehjYjUoczBrW/fvpg8eTLu37+vtC0jIwNTp05F37591VkbEZHOE0KoDG3R0aHw9GRoI6LyKfPFCQ8fPkSHDh2QlpaGjz/+GM2aNYMQAlevXsXGjRvh7OyM33//HTVr1qzsmg0GL04g0m0cZSMyTFpxcUKNGjVw+vRpTJs2DZs3b0ZmZiYAwM7ODh999BHmzJnD0EZE9P+pCm0JCQMwfHgzDVRDRPqizCNuzxNCSFOmDg4OvBKqknDEjUj3XL9+HRs3blRq5ygbkeHQihG358lkMjg6Oqq1ECIiXcepUSKqbBUKbkREpEhVaJsxYwaMjMp8DRgR0SsxuBERvYaIiAg8ffpUqZ2jbERUGRjciIgqiFOjRFTVGNyIiCqgtLXZYmM1UAwRGYwyBbelS5eWeYfjxo2rcDFERNqutFG26OhQBAdXcTFEZHDKtByIu7t72XYmk+HGjRuvXRQ9w+VAiLSLqtB29Wp3bN7cQQPVEJG20vhyICkpKWp9UyIiXZKRkYFVq1YptXOUjYiqWoXPccvPz0dKSgoaNmwIExOeKkdE+ullFyDwGgQiqmrlXmDoyZMnCAgIgJWVFVq0aIHU1FQAwNixYxEeHq72AomINEVVaAsJCeFVo0SkMeUObiEhITh//jyOHTsGCwsLqd3HxwdbtmxRa3FERJqwc+dOlaEtNDQUZmZmGqiIiOiZcs9x7tq1C1u2bIG3t7fCPUpbtGiB5ORktRZHRFSVoqKAhASuzUZE2qvcwe3+/fsq71Oak5PDm80TkU5TFdpmzQpFtWrg+WxEpBXKHdzatWuHffv2YezYsQAghbU1a9ZALpertzoioipQ2gUI338fCktLgMtTEpG2KHdwmzNnDnr06IErV66gsLAQS5YswZUrVxAbG4vjx49XRo1ERJVGVWhzcemKTz/twlE2ItI65b44oVOnTjh37hwKCwvRqlUrHDp0CI6OjoiLi4OXl1dl1EhEpHY5OTkqQ9usWaGIjOyigYqIiF6tQguwNWzYEN9//726ayEiqhKlTY1u3hwKuRxcVJeItFaZglt2dnaZd8hbMxGRNlMV2lq1CsL8+Vb49lvAz08DRRERlVGZgpudnV2ZrxgtKip6rYKIiCrDyZMnceTIEaX2kmU+PvigqisiIiq/MgW3o0ePSn+/efMmgoOD4e/vL11FGhcXh3Xr1mHu3LmVUyUR0Wt42W2riIh0iUwIIcrzgnfeeQcjRozAoEGDFNo3btyI//znPzh27Jg66zNo2dnZsLW1RVZWFqegiSqotDsgEBFVlsr8/V3u4GZlZYXz58+jcePGCu3Xrl1DmzZt8OTJE7UWaMgY3IgqjqNsRKQplfn7u9zLgbi6uqq8onTNmjVwdXVVS1FERK9DVWhLSmrP0EZEOq/cy4EsWrQI/fr1w4EDB9ChQwcAwB9//IHr169j+/btai+QiKis8vPzVZ5ru2lTKCIiNFAQEZGalXuqFADu3LmDlStXIjExEQDg4eGBwMBAjripGadKicqutKnRWbOerc0WG1vFBRGRwarM398VWoC3bt26mDNnjloLISKqKFWhbdy4cTh5sgYX1CUivVKh4JaZmYkffvgBV69eBQC0aNECw4cPh62trVqLIyJ6mQsXLmDnzp1K7SXnsvn5cUFdItIv5Z4qPXv2LHx9fWFpaYn27dsDAM6cOYOnT5/i0KFDaNu2baUUaog4VUpUOl41SkTaSquWA+ncuTMaNWqE77//HiYmzwbsCgsLMWLECNy4cQMnTpxQa4GGjMGNSDWuzUZE2kyrznE7e/asQmgDABMTE0yZMgXt2rVTa3FERM8rbZTN05OhjYgMQ7nXcbOxsUFqaqpS++3bt1G9enW1FEVE9CJVoe3Bg3rw9AzleWxEZDDKPeI2YMAABAQEYP78+ejYsSMA4NSpUwgKClK6DRYR0esqLi7GV199pdTOqVEiMkTlDm7z58+HTCbD0KFDUVhYCAAwNTXFqFGjEB4ervYCichw8QIEIiJFFVqAFwCePHmC5ORkAEDDhg1hZWWl1sKIFyeQYVMV2gICAlC3bl0NVENEVHZadXFCCSsrK7Rq1UqdtRARITk5GT///LNSO0fZiIjKEdyGDx9epn4//vhjhYshIsPGqVEiopcrc3CLjIxEvXr14OnpiQrOrhIRlUpVaJs5cyZkMpkGqiEi0k5lDm6jRo3Cpk2bkJKSgk8++QQff/wxatasWZm1EZGei4oCTp1aCiurh0rbOMpGRKSszOu4rVixAvfu3cOUKVOwZ88euLq64sMPP0R0dDRH4IioQhISwpRCm4WFBUMbEVEpKnxV6a1btxAZGYmffvoJhYWFuHz5MqpVq6bu+gwaryolfSWEwOzZs5XaGdiISB9o5VWlRkZGkMlkEEKgqKhInTURkR7jBQhERBVXrlte5eXlYdOmTXj33XfRpEkTXLx4EcuXL0dqaipH24jolVSFtgEDBjC0ERGVUZlH3D7//HNs3rwZrq6uGD58ODZt2gR7e/vKrI2I9ERaWhq+++47pXYGNiKi8inzOW5GRkZwc3ODp6fnSy/P37Fjh9qKM3Q8x430AadGicjQaMU5bkOHDuV6SkRULqpC2/Tp02FsbKyBaoiIdF+5FuAlIiqLjRs34vr160rtHGUjIno9Fb6qlIhIFU6NEhFVHgY3IlIbVaGNgY2ISH0Y3IiowqKigPBwwNdX9ShbdHQomNuIiNSHwY2IKqy00Fa3bg/88EN7BAdroCgiIj3G4EZEFZKVlQVf38VK7dHRoYiNBQICqr4mIiJ9V647J2iSn58f3NzcYGFhARcXFwwZMgR3795V6LN161a0adMGVlZWqFevHubNm6e0n2PHjqFt27YwNzdHo0aNVF4tu2LFCtSvXx8WFhbo0KED/vjjD4Xtubm5GD16NGrVqoVq1aqhX79+SE9PV+iTmpqKnj17wsrKCo6OjggKCkJhYeHrfxFEWiAsLAyLFy9Wao+ODuUoGxFRJdKZ4Na1a1ds3boVSUlJ2L59O5KTk9G/f39p+4EDBzB48GAEBgbi0qVLWLlyJRYtWoTly5dLfVJSUtCzZ0907doV586dw/jx4zFixAhER0dLfbZs2YKJEyciNDQUf/75J1q3bg1fX19kZGRIfSZMmIA9e/Zg27ZtOH78OO7evYsPPvhA2l5UVISePXsiPz8fsbGxWLduHSIjIzFz5sxK/paIKp+qCxD+7/+mIjT02Uibn58GiiIiMhBlvnOCtomKikLfvn2Rl5cHU1NTfPTRRygoKMC2bdukPsuWLUNERARSU1Mhk8kwdepU7Nu3D5cuXZL6DBw4EJmZmTh48CAAoEOHDnjjjTekwFdcXAxXV1eMHTsWwcHByMrKgoODAzZu3CgFx8TERHh4eCAuLg7e3t44cOAAevXqhbt378LJyQkAsHr1akydOhX379+HmZlZmT4j75xA2uTXX3/FqVOnlNp51SgRkaLK/P2tMyNuz3vw4AE2bNiAjh07wtTUFACQl5cHCwsLhX6Wlpa4c+cObt26BQCIi4uDj4+PQh9fX1/ExcUBAPLz8xEfH6/Qx8jICD4+PlKf+Ph4FBQUKPRp1qwZ3NzcpD5xcXFo1aqVFNpK3ic7OxuXL18u9XPl5eUhOztb4UGkDcLCwhjaiIi0gE4Ft6lTp8La2hq1atVCamoqdu/eLW3z9fXFjh07EBMTg+LiYly7dg0LFiwAANy7dw/AsxtdPx+mAMDJyQnZ2dl4+vQp/v77bxQVFansk5aWJu3DzMwMdnZ2L+2jah8l20ozd+5c2NraSg9XV9eyfjVElaa0tdkY2oiIqp5Gg1twcDBkMtlLH4mJiVL/oKAgJCQk4NChQzA2NsbQoUNRMtM7cuRIjBkzBr169YKZmRm8vb0xcOBAAM9GzXRBSEgIsrKypMft27c1XRIZsLCwMJWhzdOTgY2ISFM0uhzIpEmT4O/v/9I+DRo0kP5ub28Pe3t7NGnSBB4eHnB1dcXvv/8OuVwOmUyGb7/9FnPmzEFaWhocHBwQExOjsA9nZ2elqz/T09NhY2MDS0tLGBsbw9jYWGUfZ2dnaR/5+fnIzMxUGHV7sc+LV6KW7LOkjyrm5uYwNzd/6fdBVBVUBbauXbuiS5cuGqiGiIhKaDS4OTg4wMHBoUKvLS4uBvDsvLDnGRsbo06dOgCATZs2QS6XS+8hl8uxf/9+hf6HDx+GXC4HAJiZmcHLywsxMTHo27ev9D4xMTEYM2YMAMDLywumpqaIiYlBv379AABJSUlITU2V9iOXy/HNN98gIyMDjo6O0vvY2NigefPmFfq8RFUhNzcX3377rVI7p0WJiLSDTizAe/r0aZw5cwadOnVCjRo1kJycjBkzZqBhw4ZSWPr777/xyy+/4K233kJubi7Wrl0rLddRIjAwEMuXL8eUKVMwfPhwHDlyBFu3bsW+ffukPhMnTsSwYcPQrl07tG/fHosXL0ZOTg4++eQTAICtrS0CAgIwceJE1KxZEzY2Nhg7dizkcjm8vb0BAN26dUPz5s0xZMgQREREIC0tDdOnT8fo0aM5okZaizeHJyLSfjoR3KysrLBjxw6EhoYiJycHLi4u6N69O6ZPn64QhNatW4fJkydDCAG5XI5jx46hffv20nZ3d3fs27cPEyZMwJIlS1C3bl2sWbMGvr6+Up8BAwbg/v37mDlzJtLS0tCmTRscPHhQ4WKDRYsWwcjICP369UNeXh58fX2xcuVKabuxsTH27t2LUaNGQS6Xw9raGsOGDcPs2bMr+ZsiqhhVoe3YsYk4erS6BqohIqLS6Ow6boaA67hRZUtISEBUVJRSe8kdELiYLhFR+VXm72+dGHEjIvV72dQoZ0eJiLQTgxuRASptbTYiItJuDG5EBoQXIBAR6TYGNyIDoXoxXU/48UQ2IiKdweBGpOeKiorw9ddfK7VzlI2ISPcwuBHpMU6NEhHpFwY3Ij2lKrSNGTMGtWrV0kA1RESkDgxuRHrmxo0bWL9+vVI7R9mIiHQfgxuRHuHUKBGRfmNwI9ITqkLbzJkzIZPJNFANERFVBgY3Ih33888/Izk5Wamdo2xERPqHwY1Ih6kaZbOxaYJt2wbB05P3GiUi0jcMbkQ6SAiB2bNnK7Vv2hQKmQxITATCwxnciIj0DYMbkY4p7QKE6OhQJCUBTZsCcjkQHFzFhRERUaVjcCPSIapC22effQZnZ2d4ej4bZQsO5kgbEZG+MtJ0AUT0av/884/K0BYaGoo//nBGx47PnsfGMrQREekzjrgRablXrc0WHg7ExfGcNiIiQ8DgRqTFyrI2W3Dw/6ZIiYhIvzG4EWmhY8eO4fjx40rtJaNsUVGK57NxpI2IyDAwuBFpGVWjbC1atED//v2l55weJSIyTAxuRFqktAsQXsTpUSIiw8TgRqQFvv76axQVFSm1l3bbKk6PEhEZJgY3Ig1TNcrWuLE/PvqongaqISIibcbgRqQhOTk5mD9/vlI7bw5PRESlYXAj0oBXrc1GRESkCoMbURVTFdqmT58OY2NjDVRDRES6hMGNqIqsW3cFN29uU2rnKBsREZUVgxtRFVA1ytawYUN8/PHHGqiGiIh0FYMbUSVTFdo8PUO5nAcREZUbgxtRJdm2bRuuXLmi1M6pUSIiqigGN6JKoGqU7ZNPPoGbm5sGqiEiIn3B4EakRgUFBZgzZ45SO0fZiIhIHRjciNSEa7MREVFlY3AjUgNVoS0kJARmZmYaqIaIiPQVgxvRa0hNTcXatWuV2jnKRkRElYHBjaiCVI2yeXh44MMPP9RANUREZAgY3IgqQFVo4ygbERFVNgY3onI4duwYjh8/rtTO0EZERFWBwY2ojFSNsg0aNAhNmjTRQDVERGSIGNyIXqGoqAhff/21UjtH2YiIqKoxuBG9RHh4OPLy8pTaGdqIiEgTGNyIXhAVBYSHA76+ylOjU6dOhYWFhdQnOBi8WTwREVUZI00XQKRtli79W2VoCw0NhYWFBYBnoS0u7tmfREREVYUjbkTPCQsLQ+fOim3NmjXDgAEDFNqCg/834kZERFRVGNyI/r/yrM3m58cpUiIiqnoMbmTwzp8/j127dim18wIEIiLSNgxuZNBUjbJ9/PHHaNiwoQaqISIiejkGNzJIQgjMnj1bqZ2jbEREpM0Y3MigREUBhw5tgIPDX0rbGNqIiEjbMbiR3nt+zbWEhDA4OChunzx5MqytrTVTHBERUTkwuJHeCw8Hzp9/goSEeUrbOMpGRES6hMGN9J6v71fw9S1WaGvevDn+/e9/a6giIiKiimFwI72m6qrRmTNnQiaTaaAaIiKi18PgRnrp7t27+P7775XaOTVKRES6jMGN9I6qUbbhw4fD1dVVA9UQERGpD4Mb6Q2uzUZERPqOwY30wpkzZ7B//36FNgcHB3z++ecaqoiIiEj9GNxI56maGg0KCoKVlZUGqiEiIqo8DG6ks/Lz8zF37lyldk6NEhGRvmJwI520ceNGXL9+XaGtc+fOePvttzVUERERUeVjcCOdw7XZiIjIUDG4kc64f/8+Vq5cqdTOqVEiIjIUDG6kE1SNsg0ZMgQNGjTQQDVERESaweBGWk9VaOMoGxERGSIGN9JaFy5cwM6dOxXarK2tMXnyZA1VREREpFlGmi6gvPLy8tCmTRvIZDKcO3dOYduFCxfQuXNnWFhYwNXVFREREUqv37ZtG5o1awYLCwu0atVKadFWIQRmzpwJFxcXWFpawsfHR+nqxQcPHmDw4MGwsbGBnZ0dAgIC8Pjx43LXQqULCwtTCm0TJkxgaCMiIoOmc8FtypQpqF27tlJ7dnY2unXrhnr16iE+Ph7z5s3DrFmz8J///EfqExsbi0GDBiEgIAAJCQno27cv+vbti0uXLkl9IiIisHTpUqxevRqnT5+GtbU1fH19kZubK/UZPHgwLl++jMOHD2Pv3r04ceIEPv3003LVQqoVFhaWOjVqY2OjgYqIiIi0h0wIITRdRFkdOHAAEydOxPbt29GiRQskJCSgTZs2AIBVq1bhyy+/RFpaGszMzAAAwcHB2LVrFxITEwEAAwYMQE5ODvbu3Svt09vbG23atMHq1ashhEDt2rUxadIkaWQnKysLTk5OiIyMxMCBA3H16lU0b94cZ86cQbt27QAABw8exHvvvYc7d+6gdu3aZaqlLLKzs2Fra4usrCyDCC07d+7EhQsXFNreeOMNvPfeexqqiIiIqPwq8/e3zoy4paenY+TIkVi/fr3KWxnFxcWhS5cuUlACAF9fXyQlJeHhw4dSHx8fH4XX+fr6Ii4uDgCQkpKCtLQ0hT62trbo0KGD1CcuLg52dnZSaAMAHx8fGBkZ4fTp02WuRZW8vDxkZ2crPPRdVBTQseOzqdEXQ9uMGTMY2oiIiJ6jE8FNCAF/f38EBgYqBKbnpaWlwcnJSaGt5HlaWtpL+zy//fnXldbH0dFRYbuJiQlq1qz5yvd5/j1UmTt3LmxtbaWHq6trqX31xZIlD+Hrq3pq1MhIJ/7zJCIiqjIa/c0YHBwMmUz20kdiYiKWLVuGR48eISQkRJPlVrqQkBBkZWVJj9u3b2u6pEq1ZMkSdOmyVKHtww8/5FIfREREpdDociCTJk2Cv7//S/s0aNAAR44cQVxcHMzNzRW2tWvXDoMHD8a6devg7OyM9PR0he0lz52dnaU/VfV5fntJm4uLi0KfknPpnJ2dkZGRobCPwsJCPHjw4JXv8/x7qGJubq70GfVFVBQQHg4EBwN+flybjYiIqCI0GtwcHBzg4ODwyn5Lly7F119/LT2/e/cufH19sWXLFnTo0AEAIJfL8eWXX6KgoACmpqYAgMOHD6Np06aoUaOG1CcmJgbjx4+X9nX48GHI5XIAgLu7O5ydnRETEyMFtezsbJw+fRqjRo2S9pGZmYn4+Hh4eXkBAI4cOYLi4uJy1WJowsOBuDggKCgVCQlrFbbJZDLMnDlTQ5URERHpDp26qrTEzZs34e7urnBVaVZWFpo2bYpu3bph6tSpuHTpEoYPH45FixZJS3XExsbizTffRHh4OHr27InNmzdjzpw5+PPPP9GyZUsAwLfffovw8HCsW7cO7u7umDFjBi5cuIArV67AwsICANCjRw+kp6dj9erVKCgowCeffIJ27dph48aNZa6lLPTpqtKoKOC33+bD2jpHoX3ixImoXr26hqoiIiJSv8r8/a03d06wtbXFoUOHMHr0aHh5ecHe3h4zZ85UCEodO3bExo0bMX36dEybNg2NGzfGrl27pNAGPFsnLicnB59++ikyMzPRqVMnHDx4UAptALBhwwaMGTMG77zzDoyMjNCvXz8sXbq0XLUYEiEEEhJmw9pasZ1To0REROWjkyNuhkIfRtwePHiAZcuWKbR17doVXbp00VBFRERElYsjbqSTTpw4gaNHjyq0zZgxg8t8EBERVRCDG6ldYWEhvvnmG4W23r17o23bthqqiIiISD8wuJFa3b59Gz/++KNC26RJk1CtWjUNVURERKQ/GNxIbXbt2oXz589Lzxs3boyPPvpIgxURERHpFwY3em1Pnz5FRESEQtvHH3+Mhg0baqgiIiIi/cTgRq/lypUr2LZtm0JbSEgIzMzMNFQRERGR/mJwowoRQmDNmjW4e/eu1CaXy9GtWzcNVkVERKTfGNyo3DIzM7FkyRKFtsDAQDg5OWmoIiIiIsPA4EblEhsbi8OHD0vPra2tMXHiRK7NRkREVAUY3KhMioqKEB4ejsLCQqntvffewxtvvKHBqoiIiAwLgxu90n//+1+sWbNGoW3ChAk6exsuIiIiXcXgRi+1d+9exMfHS8/d3d0xZMgQyGQyDVZFRERkmBjcSKW8vDyEh4crtA0aNAhNmjTRUEVERETE4EZKkpKSsHnzZoW24OBgmJuba6giIiIiAhjc6DlCCKxbtw63bt2S2tq1a4eePXtqsCoiIiIqweBGAICsrCwsXrxYoW3kyJGoXbu2ZgoiIiIiJQxuhD/++AMHDhyQnpubmyMoKAjGxsYarIqIiIhexOBmwIqLizF//nw8ffpUauvWrRvkcrkGqyIiIqLSMLgZqLS0NHz33XcKbV988QXs7Ow0UxARERG9EoObATp58iSOHDkiPa9bty6GDx/OtdmIiIi0HIObATp58qT09w8//BAeHh4arIaIiIjKisHNAPXu3Rt//fUXevToAQsLC02XQ0RERGXE4GaAWrVqhVatWmm6DCIiIionI00XQERERERlw+BGREREpCMY3IiIiIh0BIMbERERkY5gcDNAUVFAx47P/iQiIiLdweBmgMLDgbi4Z38SERGR7mBwM0DBwYBc/uxPIiIi0h1cx80A+fk9exAREZFu4YgbERERkY5gcCMiIiLSEQxuRERERDqCwY2IiIhIRzC4EREREekIBjciIiIiHcHgRkRERKQjGNyIiIiIdASDGxEREZGOYHAjIiIi0hEMbkREREQ6gsGNiIiISEcwuBERERHpCBNNF0ClE0IAALKzszVcCREREZVVye/tkt/j6sTgpsUePXoEAHB1ddVwJURERFRejx49gq2trVr3KROVEQdJLYqLi3H37l1Ur14dMplM0+VUuuzsbLi6uuL27duwsbHRdDn0Ah4f7cbjo914fLSbuo+PEAKPHj1C7dq1YWSk3rPSOOKmxYyMjFC3bl1Nl1HlbGxs+INNi/H4aDceH+3G46Pd1Hl81D3SVoIXJxARERHpCAY3IiIiIh3B4EZaw9zcHKGhoTA3N9d0KaQCj4924/HRbjw+2k2Xjg8vTiAiIiLSERxxIyIiItIRDG5EREREOoLBjYiIiEhHMLgRERER6QgGN1KLvLw8tGnTBjKZDOfOnVPYduHCBXTu3BkWFhZwdXVFRESE0uu3bduGZs2awcLCAq1atcL+/fsVtgshMHPmTLi4uMDS0hI+Pj64fv26Qp8HDx5g8ODBsLGxgZ2dHQICAvD48eNy16JP/Pz84ObmBgsLC7i4uGDIkCG4e/euQp+tW7eiTZs2sLKyQr169TBv3jyl/Rw7dgxt27aFubk5GjVqhMjISKU+K1asQP369WFhYYEOHTrgjz/+UNiem5uL0aNHo1atWqhWrRr69euH9PR0hT6pqano2bMnrKys4OjoiKCgIBQWFr7+F6GlynJ8oqOj4e3tjerVq8PBwQH9+vXDzZs3Ffrw+FSeVx2jWbNmQSaTKT2sra0V9sOfcZWjLP+GhBCYP38+mjRpAnNzc9SpUwfffPONQh+d+jckiNRg3LhxokePHgKASEhIkNqzsrKEk5OTGDx4sLh06ZLYtGmTsLS0FN99953U59SpU8LY2FhERESIK1euiOnTpwtTU1Nx8eJFqU94eLiwtbUVu3btEufPnxd+fn7C3d1dPH36VOrTvXt30bp1a/H777+LkydPikaNGolBgwaVqxZ9s3DhQhEXFydu3rwpTp06JeRyuZDL5dL2/fv3CxMTE7Fq1SqRnJws9u7dK1xcXMSyZcukPjdu3BBWVlZi4sSJ4sqVK2LZsmXC2NhYHDx4UOqzefNmYWZmJn788Udx+fJlMXLkSGFnZyfS09OlPoGBgcLV1VXExMSIs2fPCm9vb9GxY0dpe2FhoWjZsqXw8fERCQkJYv/+/cLe3l6EhIRU8rekOa86Pjdu3BDm5uYiJCRE/PXXXyI+Pl506dJFeHp6KvTh8ak8rzpGjx49Evfu3VN4NG/eXAwbNkzqw59xledVx0cIIcaOHSuaNm0qdu/eLW7cuCHOnj0rDh06JG3XtX9DDG702vbv3y+aNWsmLl++rBTcVq5cKWrUqCHy8vKktqlTp4qmTZtKzz/88EPRs2dPhX126NBBfPbZZ0IIIYqLi4Wzs7OYN2+etD0zM1OYm5uLTZs2CSGEuHLligAgzpw5I/U5cOCAkMlk4r///W+Za9F3u3fvFjKZTOTn5wshhBg0aJDo37+/Qp+lS5eKunXriuLiYiGEEFOmTBEtWrRQ6DNgwADh6+srPW/fvr0YPXq09LyoqEjUrl1bzJ07Vwjx7HiZmpqKbdu2SX2uXr0qAIi4uDghxLP/joyMjERaWprUZ9WqVcLGxkbhmOmzF4/Ptm3bhImJiSgqKpL6REVFKfTh8alaLx6jF507d04AECdOnJDa+DOu6rx4fK5cuSJMTExEYmJiqa/RtX9DnCql15Keno6RI0di/fr1sLKyUtoeFxeHLl26wMzMTGrz9fVFUlISHj58KPXx8fFReJ2vry/i4uIAACkpKUhLS1PoY2triw4dOkh94uLiYGdnh3bt2kl9fHx8YGRkhNOnT5e5Fn324MEDbNiwAR07doSpqSmAZ1PcFhYWCv0sLS1x584d3Lp1C8Crj09+fj7i4+MV+hgZGcHHx0fqEx8fj4KCAoU+zZo1g5ubm8IxbNWqFZycnBTeJzs7G5cvX1bX16C1VB0fLy8vGBkZYe3atSgqKkJWVhbWr18PHx8fqQ+PT9VRdYxetGbNGjRp0gSdO3eW2vgzrmqoOj579uxBgwYNsHfvXri7u6N+/foYMWIEHjx4IL1O1/4NMbhRhQkh4O/vj8DAQIUfJs9LS0tT+I8UgPQ8LS3tpX2e3/7860rr4+joqLDdxMQENWvWfOX7PP8e+mjq1KmwtrZGrVq1kJqait27d0vbfH19sWPHDsTExKC4uBjXrl3DggULAAD37t0DUPr3lp2djadPn+Lvv/9GUVHRK4+PmZkZ7OzsXtqHx0fx+Li7u+PQoUOYNm0azM3NYWdnhzt37mDr1q1SHx6fyveyY/S83NxcbNiwAQEBAQrt/BlXuV52fG7cuIFbt25h27Zt+OmnnxAZGYn4+Hj0799f6qNr/4YY3EhJcHCwypNtn38kJiZi2bJlePToEUJCQjRdskEp6/EpERQUhISEBBw6dAjGxsYYOnQoxP+/YcrIkSMxZswY9OrVC2ZmZvD29sbAgQMBPPs/Sio/dR6ftLQ0jBw5EsOGDcOZM2dw/PhxmJmZoX///lIfKj91HqPn7dy5E48ePcKwYcOq8uPoHXUen+LiYuTl5eGnn35C586d8dZbb+GHH37A0aNHkZSUpKmP+FpMNF0AaZ9JkybB39//pX0aNGiAI0eOIC4uTunebu3atcPgwYOxbt06ODs7K11VU/Lc2dlZ+lNVn+e3l7S5uLgo9GnTpo3UJyMjQ2EfhYWFePDgwSvf5/n30AVlPT4l7O3tYW9vjyZNmsDDwwOurq74/fffIZfLIZPJ8O2332LOnDlIS0uDg4MDYmJiFPZR2vdmY2MDS0tLGBsbw9jY+JXHMD8/H5mZmQr/R/pinxev0jL047NixQrY2toqXBn4888/w9XVFadPn4a3tzePTwWo8xg9b82aNejVq5fSqAp/xpWPOo+Pi4sLTExM0KRJE6m/h4cHgGdXeDZt2lT3/g2V+Ww4ohfcunVLXLx4UXpER0cLAOKXX34Rt2/fFkL872TZ50/kDQkJUbo4oVevXgr7lsvlSifuzp8/X9qelZWl8sTds2fPSn2io6NVnrj7slr03a1btwQAcfTo0VL7DBkyROGqrClTpoiWLVsq9Bk0aJDSibtjxoyRnhcVFYk6deoonbj7yy+/SH0SExNVnrj7/FVa3333nbCxsRG5ubkV+8A65sXjM3HiRNG+fXuFPnfv3hUAxKlTp4QQPD5VrbR/Qzdu3BAymUzs2bNH6TX8GVd1Xjw+Jb+X/vrrL6lPyQUkSUlJQgjd+zfE4EZqk5KSonRVaWZmpnBychJDhgwRly5dEps3bxZWVlZKy4GYmJiI+fPni6tXr4rQ0FCVl8rb2dmJ3bt3iwsXLog+ffqovFTe09NTnD59Wvz222+icePGCpfKl6UWffL777+LZcuWiYSEBHHz5k0RExMjOnbsKBo2bCj9kLh//75YtWqVuHr1qkhISBDjxo0TFhYW4vTp09J+Si6VDwoKElevXhUrVqxQeam8ubm5iIyMFFeuXBGffvqpsLOzU7h6KjAwULi5uYkjR46Is2fPKl22X3KpfLdu3cS5c+fEwYMHhYODg94uN1GW4xMTEyNkMpkICwsT165dE/Hx8cLX11fUq1dPPHnyRAjB41OZynKMSkyfPl3Url1bFBYWKu2HP+MqR1mOT1FRkWjbtq3o0qWL+PPPP8XZs2dFhw4dxLvvvivtR9f+DTG4kdqoCm5CCHH+/HnRqVMnYW5uLurUqSPCw8OVXrt161bRpEkTYWZmJlq0aCH27dunsL24uFjMmDFDODk5CXNzc/HOO+9I/7dU4p9//hGDBg0S1apVEzY2NuKTTz4Rjx49Knct+uLChQuia9euombNmsLc3FzUr19fBAYGijt37kh97t+/L7y9vYW1tbWwsrIS77zzjvj999+V9nX06FHRpk0bYWZmJho0aCDWrl2r1GfZsmXCzc1NmJmZifbt2yvt5+nTp+Lzzz8XNWrUEFZWVuL9998X9+7dU+hz8+ZN0aNHD2FpaSns7e3FpEmTREFBgXq+EC1TluMjhBCbNm0Snp6ewtraWjg4OAg/Pz9x9epVhT48PpWjrMeoqKhI1K1bV0ybNq3UffFnnPqV9fj897//FR988IGoVq2acHJyEv7+/uKff/5R6KNL/4ZkQvAMVyIiIiJdwMvGiIiIiHQEgxsRERGRjmBwIyIiItIRDG5EREREOoLBjYiIiEhHMLgRERER6QgGNyIiIiIdweBGRFQJZDIZdu3apekyFBw7dgwymQyZmZmaLoWIKojBjYjoNcyaNUu6ETgRUWVjcCMiIiLSEQxuRGTQiouLMXfuXLi7u8PS0hKtW7fGL7/8AuB/U4sxMTFo164drKys0LFjRyQlJQEAIiMjERYWhvPnz0Mmk0EmkyEyMlLa999//433338fVlZWaNy4MaKiospUU8n7RkdHw9PTE5aWlnj77beRkZGBAwcOwMPDAzY2Nvjoo4/w5MkT6XV5eXkYN24cHB0dYWFhgU6dOuHMmTPq+7KISOMY3IjIoM2dOxc//fQTVq9ejcuXL2PChAn4+OOPcfz4canPl19+iQULFuDs2bMwMTHB8OHDAQADBgzApEmT0KJFC9y7dw/37t3DgAEDpNeFhYXhww8/xIULF/Dee+9h8ODBePDgQZlrmzVrFpYvX47Y2Fjcvn0bH374IRYvXoyNGzdi3759OHToEJYtWyb1nzJlCrZv345169bhzz//RKNGjeDr61uu9yQiLVeuW9ITEemR3NxcYWVlJWJjYxXaAwICxKBBg8TRo0cFAPHrr79K2/bt2ycAiKdPnwohhAgNDRWtW7dW2jcAMX36dOn548ePBQBx4MCBV9al6n3nzp0rAIjk5GSp7bPPPhO+vr7S/k1NTcWGDRuk7fn5+aJ27doiIiJCYb8PHz58ZQ1EpJ1MNJgZiYg06q+//sKTJ0/w7rvvKrTn5+fD09NTev5///d/0t9dXFwAABkZGXBzc3vp/p9/nbW1NWxsbJCRkVHm+p5/vZOTE6ysrNCgQQOFtj/++AMAkJycjIKCAvzrX/+StpuamqJ9+/a4evVqmd+TiLQbgxsRGazHjx8DAPbt24c6deoobDM3N0dycjKAZwGohEwmA/Ds3LhXef51Ja8ty+tUvV4mk732/ohI9/EcNyIyWM2bN4e5uTlSU1PRqFEjhYerq2uZ9mFmZoaioqJKrvTVGjZsCDMzM5w6dUpqKygowJkzZ9C8eXMNVkZE6sQRNyIyWNWrV8fkyZMxYcIEFBcXo1OnTsjKysKpU6dgY2ODevXqvXIf9evXR0pKCs6dO4e6deuievXqMDc3r4LqFVlbW2PUqFEICgpCzZo14ebmhoiICDx58gQBAQFVXg8RVQ4GNyIyaF999RUcHBwwd+5c3LhxA3Z2dmjbti2mTZtWpmnIfv36YceOHejatSsyMzOxdu1a+Pv7V37hKoSHh6O4uBhDhgzBo0eP0K5dO0RHR6NGjRoaqYeI1E8mhBCaLoKIiIiIXo3nuBERERHpCAY3IqIqFhgYiGrVqql8BAYGaro8ItJinColIqpiGRkZyM7OVrnNxsYGjo6OVVwREekKBjciIiIiHcGpUiIiIiIdweBGREREpCMY3IiIiIh0BIMbERERkY5gcCMiIiLSEQxuRERERDqCwY2IiIhIRzC4EREREemI/wfIwUKhYZuBRgAAAABJRU5ErkJggg==", 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YtWqV6tWrp8aNG6tnz55at26dZs+erQ8++ED+/v5q27atZs6cqaeeekpt2rTR4sWL7SaWJyQkaPjw4frnP/+pqKgoTZ8+XVFRUVq4cKHefvtttW7dWmlpaZoxY4ZJ3xIAYIbCwkKlpqbahKKwsDClpKR4VCiSJIvhjPu+vUheXp4iIiKUm5ur8PBwa/uZM2e0b98+NWnSRMHBwSZWCFfj2gNA2b788kulp6fbtN15552Ki4vT8uVSWpo0bpzUu3fV1lHW7++KYigNAABUyp8nWEvShAkTrHc7p6VJGRlnf1Z1MHIWghEAAKiQJUsOa/fuV2zaOnXqpF69etm0jRv3vx4jT0EwAgAA5eaol+js2kTV7dp79/acnqJzCEYAAOAvFRUVacqUKXbtKSkpJlRTdQhGAADgvJYsWaLdu3fbtH35ZSfl5vaSl+UighEAACibo6Gzdu0eV3q6v0fNHSovghEAALCTlZWlRYsW2bWfGzrr08fVFbkGwQgAANhw1EtUWDhQTz7Z1IRqXIuVrwEAgCTJMAyHoWjixBTNmuX9oUiixwhOlJSUpJycHC1btkySdO2116pdu3Z67rnnKn1OZ5wDAPDXZsyYoVOnTtm1FxSkqEYNadQoE4oyAcHIByQlJVnHiQMCAhQXF6dBgwbpscceU7VqVfd/gffee08BAQHlOnb9+vXq2rWrTpw4ocjIyEqdAwBQOY56iZ5++iH93/9V18aNkoO79L0WwchH9OzZUwsWLFB+fr4+/PBDjRgxQgEBAUpOTrY5rqCgwGkP/KtVq5ZbnAMA4Nju3bu1ZMkSu/aCghT93/951orVzsIcIx8RFBSkmJgYNWrUSPfee6+6d++u5cuXKykpSX379tWUKVNUv359tWjRQpJ04MAB3XrrrYqMjFStWrXUp08fZWVlWc9XXFysMWPGKDIyUrVr19YjjzyiPz+P+Nprr9Xo0aOt2/n5+Xr00UcVGxuroKAgNW/eXK+++qqysrLUtWtXSVLNmjVlsViUlJTk8BwnTpzQoEGDVLNmTYWGhqpXr1764YcfrPsXLlyoyMhIpaenq1WrVqpRo4Z69uypI0eOWI9Zv369OnXqpOrVqysyMlJXXnmlfvrpJyf9SQOAZ0hNTbULRfv3t9LEiSlat07auNHzVq12BoKRjwoJCVFBQYEkac2aNdq9e7dWr16tFStWqLCwUImJiQoLC9Nnn32mL774whowzr3nmWee0cKFCzV//nx9/vnn+vXXX/X++++f9zMHDRqkN998U7Nnz9auXbv08ssvq0aNGoqNjdW7774r6ey/Xo4cOaJZs2Y5PEdSUpK++eYbLV++XBkZGTIMQ3//+99VWFhoPeb06dOaMWOGXnvtNW3YsEH79+/XQw89JOnsyq19+/ZVly5dtG3bNmVkZOjuu++2PvAQAHyBo6Gzp55KUZ8+tyo+3jd7is5hKM3HGIahNWvWKD09Xffff7+OHz+u6tWra968edYhtNdff10lJSWaN2+eNTAsWLBAkZGRWr9+vXr06KHnnntOycnJuummmyRJc+fOVXp6epmf+/333+utt97S6tWr1b17d0lS06b/u8Ph3JBZdHS0zRyjP/rhhx+0fPlyffHFF0pISJAkLV68WLGxsVq2bJluueUWSVJhYaHmzp2rZs2aSZJGjhypSZMmSZLy8vKUm5urG264wbq/VatWFf+DBAAP5CgQSdKMGSl68EHPfLaZs9FjZJLly6WEhLM/XWHFihWqUaOGgoOD1atXL/3zn//UxIkTJUmXXnqpzbyirVu3as+ePQoLC1ONGjVUo0YN1apVS2fOnNHevXuVm5urI0eOqHPnztb3VKtWTR07dizz87ds2SJ/f3916dKl0t9h165dqlatms3n1q5dWy1atNCuXbusbaGhodbQI0n16tXTsWPHJJ0NYElJSUpMTNSNN96oWbNm2QyzAYC3chSKbrnlFqWkpOi333xrgvX50GNkkrQ0KSPj7E9XpPOuXbtqzpw5CgwMVP369W3uRvvzE5FPnjypDh06aPHixXbniYqKqtTnh4SEVOp9lfHnu9gsFovN/KcFCxZo1KhRWrVqlZYuXarHH39cq1ev1hVXXOGyGgHAVY4ePaq5c+fatXvbw1+dhR4jk4wbJ5eO41avXl3NmzdXXFzcX96if9lll+mHH35QdHS0mjdvbvOKiIhQRESE6tWrp//+97/W9xQVFWnTpk1lnvPSSy9VSUmJPv30U4f7z/VYFRcXl3mOVq1aqaioyOZzf/nlF+3evVutW7c+73f6s/bt2ys5OVkbN25UmzZt9MYbb1To/QDgCVJTUx2GovR0QlFZCEYm6d3bfWf833777apTp4769Omjzz77TPv27dP69es1atQoHTx4UJL0wAMPKC0tTcuWLVNmZqbuu+8+5eTklHnOxo0ba/DgwRoyZIiWLVtmPedbb70lSWrUqJEsFotWrFih48eP6+TJk3bnuOiii9SnTx8NGzZMn3/+ubZu3ao77rhDDRo0UJ9yPrRn3759Sk5OVkZGhn766Sd9/PHH+uGHH5hnBMDrOBo6mzfvCaWnp/j05Oq/QjCCndDQUG3YsEFxcXG66aab1KpVKw0dOlRnzpxReHi4JGns2LEaOHCgBg8erPj4eIWFhekf//jHec87Z84c3XzzzbrvvvvUsmVLDRs2zLrKaoMGDZSamqpx48apbt26GjlypMNzLFiwQB06dNANN9yg+Ph4GYahDz/8sNyLQIaGhiozM1P9+vXTxRdfrLvvvlsjRozQPffcU4E/IQBwX9OnTy/zsR7Vq/u57T/K3YXF+PPiMz4uLy9PERERys3NtYYASTpz5oz27dunJk2aKDg42MQK4WpcewCewlEgysxsoS1b+qtmzbPTN7w1FJX1+7uimHwNAICHO3PmjJ566im79okTzz7nbPFi7w1EzkYwAgDAg5W1NlFBQYr1Jh9CUfkRjAAA8FCOQtEll4zWzTdHmFCNdyAYAQDgYT744ANt2bLFrp21iS4cd6VVEHPVfQ/XHIA7SU1NdRiKWJvIOegxKqdzt4OfPn3apas4w3ynT5+WZL+iNgC4kmEY1uc+/lFBQYrWrfPtB786E8GonPz9/RUZGWl95lZoaChPZPdyhmHo9OnTOnbsmCIjI+Xv7292SQB8VFkTrCdOPDvBeuNGFxfkxQhGFRATEyNJ1nAE3xAZGWm99gDgao5C0ebN/9Qll7R06aOlfAXBqAIsFovq1aun6OhoFRYWml0OXCAgIICeIgCm2LZtm95//3279vT0FHqIqhDBqBL8/f35ZQkAqDJlDZ3xnLOqRzACAMCNOApFEyZMkMViEXfjVz2CEQAAbqCsXiLWJnItghEAACZzFIratWunPn36mFCNbyMYAQBgkp9//lkvvviiXXt6egrDZiYhGAEAYAImWLsnghEAAC7mKBQ9+uijCg4OpqfIZAQjAABcZMqUKSoqKrJrb98+RcHBJhQEOwQjAABcoKyhs/btU9S7t4uLQZkIRgAAVKGioiJNmTLFrp0VrN0TwQgAgCrCBGvPQzACAKAKOApFQ4cOVcOGDZlg7cYIRgAAONHq1au10cEYGStYewaCEQAATsJjPTwfwQgAACdwFIomTkzRBx+YUAwqjWAEAMAFKKuXaNKkFN18s7gV38MQjAAAqITx46XAQPtQVL/+dRo2LIEJ1h6KYAQAQAUtXrxPgYH/tmtnLpHnIxgBAFBOy5dLmzczwdqb+ZldAAAAnsJRKEpPf0Lt2xOKvAU9RgAA/IXz3YZPR5F38coeoxdffFGNGzdWcHCwOnfurK+++srskgAAHspRKPr110b0Enkpr+sxWrp0qcaMGaO5c+eqc+fOeu6555SYmKjdu3crOjra7PIAAB7i1KlTmjFjhl07c4m8m8UwDMPsIpypc+fOuvzyy/XCCy9IkkpKShQbG6v7779f48rxxL68vDxFREQoNzdX4eHhVV0uAMANsYK153HW72+v6jEqKCjQpk2blJycbG3z8/NT9+7dlZGR4fA9+fn5ys/Pt27n5eVVeZ0AAPflKBSNGTNGYWFhJlQDV/OqYPTzzz+ruLhYdevWtWmvW7euMjMzHb5n2rRpZf7LAADgO5599k3l5X1v104vkW/xysnXFZGcnKzc3Fzr68CBA2aXBABwsdTUVEIRJHlZj1GdOnXk7++vo0eP2rQfPXpUMTExDt8TFBSkoKAgV5QHAHAzhmFo0qRJdu0EIt/lVT1GgYGB6tChg9asWWNtKykp0Zo1axQfH29iZQAAd5OamuowFM2YQSjyZV7VYySdnSA3ePBgdezYUZ06ddJzzz2nU6dO6c477zS7NACAm3A0t7So6CY999ylGjXKhILgNrwuGP3zn//U8ePHNWHCBGVnZ6tdu3ZatWqV3YRsAIDv2bBhg9atW2fXfm7obPJkV1cEd+N16xhdKNYxAgDvw8NfvR/rGAEAUE6OQhGBCI4QjAAAXosVrFFRBCMAgFdyFIpatGih/v37m1ANPAXBCADgVQ4dOqR58+bZtdNLhPIgGAEAvAZDZ7hQBCMAgFdwFIqSk5MVGBhoQjXwVAQjAIDH4jZ8OJtXPRIEAOBbCEVwNnqMAAAeZfly6emn89W9e5rdPgIRLhTBCADgUTZvTlX37vbthCI4A0NpAACP4WiCdUbGXWrfnlAE56DHCADg9pYsWaLdu3fbtdNLBGcjGAEA3BprE8GVCEYAALflKBQRiFCVCEYAALdDLxHMQjACALiNshZs3LChmx544GoTKoKvIRgBANzCt99+q82b/2PXnp6eonHjpN69TSgKPodgBAAw3fmGzhg9gysRjAAApnIUiiZMmCCLxWJCNfB1BCMAgCmYYA13xMrXAACXcxSKiooCWMEapqPHCADgMidOnNDs2bPt2uklgrsgGAEAXKKsoTN6ieBOCEYAgCrnKBQ9+OCDCg8PN6EaoGwEIwBAlUlNnSTJsGtn6AzuimAEAKgS3HUGT0QwAgA4VUlJiSZPnmzXTiCCJyAYAQCchl4ieDqCEQDAKRyFoltvvVWtWrUyoRqgcghGAIALsmLFCm3atMmunV4ieCKCEQCg0hg6g7chGAEAKsVRKCIQwdMRjAAAFUIvEbwZwQgAUG6OQlGLFi3Uv39/E6oBnI9gBAD4S3v37tXrr79u104vEbwNwQgAcF4MncGXEIwAAGVyFIratn1MffsGmFANUPUIRgAAO/QSwVf5mV0AAMC9EIrgy+gxAgBIkn7//XdNnz7drp1ABF9CMAIA0EsElGIoDQB8nKNQNHfuPUpPJxTB99BjBAA+6pVXXtHhw4ft2pcsSVFEhDRunAlFASYjGAGADzrf0BmjZ/BlBCMA8CGGYWjSpEl27cwlAs4iGAGAj2CCNfDXCEYA4MWWL5fS0qTERPtQdN111ykhIcGEqgD3RTACAC/2xhtfKTHxI7t2eokAxwhGAOClUlNT1aqVfTuhCCgbwQgAvJCj+UQTJkyQxWIxoRrAcxCMAMCLMMEauDAEIwDwEo5CUZ06dTRixAgTqgE8U4WDkb+/v44cOaLo6Gib9l9++UXR0dEqLi52WnEAgL/2888/68UXX7Rrb98+Rb17m1AQ4MEqHIwMw3DYnp+fr8DAwAsuCABQfmUNnU2cmKL4eBGMgAoqdzCaPXu2JMlisWjevHmqUaOGdV9xcbE2bNigli1bOr9CAIBDjkLR9OkPKzY2VPHxPOsMqIxyB6Nnn31W0tkeo7lz58rf39+6LzAwUI0bN9bcuXOdXyEAwEZZvUTt26eobduzgYieIqByyh2M9u3bJ0nq2rWr3nvvPdWsWbPKigIAOPZXd50RiIALU+E5RuvWrauKOgAA57FsWbG2bn3Srp3b8AHnqnAwGjJkyHn3z58/v9LFAADssTYR4DoVDkYnTpyw2S4sLNT27duVk5Ojbt26Oa0wAIDjUNSs2UDdcUdTE6oBvF+Fg9H7779v11ZSUqJ7771XzZo1c0pRAODrVq5cqW+++cau/dxt+HfcYUJRgA/wc8pJ/Pw0ZswY651rAIDKS01NdRiK2rdP4TZ8oIo57ZEge/fuVVFRkbNOBwA+ydHQWXp6ivUWfO46A6pWhYPRmDFjbLYNw9CRI0e0cuVKDR482GmFAYAvOd8Ea+ZYA65T4WC0efNmm20/Pz9FRUXpmWee+cs71gAA9hyFok6dOqlXr14mVAP4NtYxAgCT7NmzR4sXL7Zr5zZ8wDyVnmN07Ngx7d69W5LUokULRUdHO60oAPB2rE0EuKcK35WWl5engQMHqn79+urSpYu6dOmiBg0a6I477lBubm5V1KisrCwNHTpUTZo0UUhIiJo1a6aUlBQVFBTYHLdt2zZdffXVCg4OVmxsrKZPn14l9QDAhXAUih5//HFCEeAGKtxjNGzYMG3evFkrV65UfHy8JCkjI0MPPPCA7rnnHi1ZssTpRWZmZqqkpEQvv/yymjdvru3bt2vYsGE6deqUZsyYIelsYOvRo4e6d++uuXPn6rvvvtOQIUMUGRmpu+++2+k1AUBF0UsEuD+LYRhGRd5QvXp1paen66qrrrJp/+yzz9SzZ0+dOnXKqQWW5emnn9acOXP0448/SpLmzJmj8ePHKzs7W4GBgZKkcePGadmyZcrMzCz3efPy8hQREaHc3FyFh4dXSe0AfMPy5VJa2tl1hzZvJhQBVclZv78r3GNUu3ZtRURE2LVHRESoZs2alS6konJzc1WrVi3rdkZGhq655hprKJKkxMREPfXUUzpx4kSZteXn5ys/P9+6nZeXV3VFA/ApaWnStm2ntHnzDLt9BCLAPVV4jtHjjz+uMWPGKDs729qWnZ2thx9+WE888YRTiyvLnj179Pzzz+uee+6xqaFu3bo2x53b/mOtfzZt2jRFRERYX7GxsVVTNACfk5iYqocftg9F6emEIsBdVTgYzZkzR19++aXi4uLUvHlzNW/eXHFxcdq4caNefvllXXbZZdbXXxk3bpwsFst5X38eBjt06JB69uypW265RcOGDato+XaSk5OVm5trfR04cOCCzwnA9yxfLiUknP0pOZ5P1KrVSOsq1gDcU4WH0vr06SOLxeKUDx87dqySkpLOe0zTpv97gvThw4fVtWtXJSQk6F//+pfNcTExMTp69KhN27ntmJiYMs8fFBSkoKCgClYOALbS0qSMDGnDhjnavPmY3f5zQ2e33urqygBURIWD0cSJE5324VFRUYqKiirXsYcOHVLXrl3VoUMHLViwQH5+tp1d8fHxGj9+vAoLCxUQECBJWr16tVq0aOHSuU8AfBMTrAHvUOGhtKZNm+qXX36xa8/JybHp3XGmQ4cO6dprr1VcXJxmzJih48ePKzs722bu0IABAxQYGKihQ4dqx44dWrp0qWbNmmX3bDcAcDbDMByGorPPOSMUAZ6kwj1GWVlZKi4utmvPz8/XwYMHnVLUn61evVp79uzRnj171LBhQ5t951YbiIiI0Mcff6wRI0aoQ4cOqlOnjiZMmMAaRgCqVFlrE7VvTyACPFG51zFaXjqjsG/fvlq0aJHNLfvFxcVas2aNVq9ebX1MiKdiHSMA5eUoFG3b1lfvvddW8fHSxo0mFAX4KJevY9S3b19JksVi0eDBg232BQQEqHHjxnrmmWcqXQgAeIpvvvlGK1eutGtPSUnR8uXSkSPizjPAQ5U7GJWUlEiSmjRpoq+//lp16tSpsqIAwF2VNXS2ffvZobPevc++AHimCs8x2rdvX1XUAQBuz1EomjEjRSdPSjVqmFAQAKercDCaNGnSefdPmDCh0sUAgDs638NfCwqk2bOlUaNcXBSAKlHhh8i2b9/eZruwsFD79u1TtWrV1KxZM3377bdOLdDVmHwN4I8chaIjRy7R3Lk3m1ANgLKY9hDZzZs3OywmKSlJ//jHPypdCAC4k6NHj2ru3Ll27TzSA/BuFe4xKst3332nG2+8UVlZWc44nWnoMQJwvqEzAO7JWb+/K7zydVnOPYQVADyZo1A0bdo4pacTigBfUOGhtNmzZ9tsG4ahI0eO6LXXXlOvXr2cVhgAuNKkSZPkqAO9ffsUXXYZ6xIBvqLCQ2lNmjSx2fbz81NUVJS6deum5ORkhYWFObVAV2MoDfA9DJ0Bns+0ydesYwTAWxQWFmrq1Kl27RMnpig+XiIXAb6nwsFIknJycrRnzx5JUvPmzRUZGenMmgCgyp3v4a/x8QydAb6qQsEoKytLI0aMUHp6unUs3mKxqGfPnnrhhRfUuHHjqqgRAJzKUSi666671KBBA0k80gPwZeUORgcOHNAVV1yhgIAATZ48Wa1atZIk7dy5U3PmzFF8fLy+/vprNWzYsMqKBYALsXLlSn3zzTd27cwlAnBOuSdfDx06VHv27FF6erqCg4Nt9v3+++/q2bOnLrroIs2bN69KCnUVJl8D3okJ1oB3c/nk61WrVmnp0qV2oUiSQkJCNHnyZPXv37/ShQBAVXEUighEABwpdzD6+eefzzuHqGnTpvr111+dURMAOAW9RAAqqtzBqF69etq5c2eZc4i2b9+umJgYpxUGABfCUSjq3r27rrzyShOqAeApyh2M+vbtq4ceekhr1qxRVFSUzb5jx47p0UcfVd++fZ1dHwBUyJ49e7R48WK7dnqJAJRHuSdfnzhxQp07d1Z2drbuuOMOtWzZUoZhaNeuXXrjjTcUExOjL7/8UrVq1arqmqsUk68Bz8XQGeC7XD75umbNmvrvf/+rxx57TEuWLFFOTo4kKTIyUgMGDNDUqVM9PhQB8FyOQlG7dk+oTx+nPSsbgA+o8LPSpLMPjj1+/LgkKSoqShaLxemFmYUeI8Cz0EsEQDLxWWnS2dWuo6OjK/2hAOAMjkJRw4YNNXToUBOqAeANKhWMAMBMJ0+e1DPPPGPXTi8RgAtFMALgURg6A1CVCEYAPIajUDRmzBiFhYWZUA0Ab0QwAuD25s+frwMHDti100sEwNnKFYxmz55d7hOOGjWq0sUAwJ8xdAbAlcp1u36TJk3KdzKLRT/++OMFF2UmbtcH3INhGJo0aZJdO4EIgCMuvV1/3759lf4AAKiosnqJ0tNTRC4CUJUqvSRsQUGBdu/eraKiImfWA8DHOQpFTZsOUHp6isaNM6EgAD6lwpOvT58+rfvvv1+LFi2SJH3//fdq2rSp7r//fjVo0EDj+C8XgErYtGmTVqxYYdd+buhs4EBXVwTAF1W4xyg5OVlbt27V+vXrFRwcbG3v3r27li5d6tTiAPiG1NTU84YiAHCVCvcYLVu2TEuXLtUVV1xh84y0Sy65RHv37nVqcQC8n6OhMwIRALNUOBgdP37c4XPSTp065VUPkwVQtc53G/7y5VJamjRunNS7t4sLA+DTKjyU1rFjR61cudK6fS4MzZs3T/Hx8c6rDIDXchSKduy4SkuW/C8UZWSc/QkArlThHqOpU6eqV69e2rlzp4qKijRr1izt3LlTGzdu1KeffloVNQLwEj///LNefPFFu/aUlBQlJEiZmdIjj0gWi9SihbgLDYDLVbjH6KqrrtKWLVtUVFSkSy+9VB9//LGio6OVkZGhDh06VEWNALxAampqmaFIOhuC4uPPhqLMTKlWLYbRALheuVa+9iWsfA04n6Ohs9Wrx+uRR6rZhR/mFwGoDGf9/i5XMMrLyyv3CT09TBCMAOcp6+Gv7dunEHoAOJVLHwkSGRlZ7jvOiouLK10MAO/hqJeoWbNmuuOOO0yoBgDKp1zBaN26ddb/nZWVpXHjxikpKcl6F1pGRoYWLVqkadOmVU2VADxGYWGhpk6datfuaG0ihs0AuJsKzzH629/+prvuuku33XabTfsbb7yhf/3rX1q/fr0z63M5htKAyjvf2kSOJCScvS0/Pl7auLEqKwPg7Zz1+7vCd6VlZGSoY8eOdu0dO3bUV199VelCAHg2R6HogQceOO8q1ufuROO2fADuosLBKDY2Vq+88opd+7x58xQbG+uUogB4js8//7zMx3pERkae9729e5/tKWIYDYC7qPACj88++6z69eunjz76SJ07d5YkffXVV/rhhx/07rvvOr1AAO6rokNnAODuKrWO0cGDB/XSSy8pMzNTktSqVSsNHz7cK3qMmGMElA8PfwXgTly6jpEvIRgB50cvEQB35NJ1jP4sJydHr776qnbt2iVJuuSSSzRkyBBFRERUuhAA7s9RKOrfv79atGhhQjUA4HwV7jH65ptvlJiYqJCQEHXq1EmS9PXXX+v333/Xxx9/rMsuu6xKCnUVeowAez/99JMWLlxo104vEQB3YdpQ2tVXX63mzZvrlVdeUbVqZzucioqKdNddd+nHH3/Uhg0bKl2MOyAYAbYYOgPgCUwLRiEhIdq8ebNatmxp075z50517NhRp0+frnQx7oBgBPyPo1A0YcKEcj8iCABcxbQ5RuHh4dq/f79dMDpw4IDCwsIqXQgA9zF58mSVlJTYtdNLBMDbVTgY/fOf/9TQoUM1Y8YMJSQkSJK++OILPfzww3aPCQHgOc49tywx0b6XKD4+Xj169DChKgBwrQoHoxkzZshisWjQoEEqKiqSJAUEBOjee+9VWlqa0wsE4BozZ55UYuIzdu30EgHwJZVex+j06dPau3evJKlZs2YKDQ11amFmYY4RfM3y5dLmzUywBuDZTF3HSJJCQ0N16aWXVvqDAbgHR6Ho0UcfVXBwsAnVAIC5yh2MhgwZUq7j5s+fX+liALjO6tWrtXHjRrt2eokA+LJyB6OFCxeqUaNGat++vXiKCODZHN2GHxMTo3vuuceEagDAfZQ7GN1777168803tW/fPt1555264447VKtWraqsDYATnb3rrESJiZPt9tFLBABn+ZX3wBdffFFHjhzRI488ov/85z+KjY3VrbfeqvT0dHqQAA+weXMqoQgA/kKl70o79+ykf//73yoqKtKOHTtUo0YNZ9fnctyVBm/kaOjsvvvuU1RUlAnVAIDzmX5Xmp+fnywWiwzDUHFxcaULAFB1MjMztXTpUrt2eokAwLFyD6VJUn5+vt58801dd911uvjii/Xdd9/phRde0P79+72itwjwJqmpqYQiAKigcvcY3XfffVqyZIliY2M1ZMgQvfnmm6pTp05V1gagkhwNnRGIAOCvlXuOkZ+fn+Li4tS+ffvzPln7vffec1pxZmCOETyZo0AkEYoAeD+XzzEaNGjQeQMRAPOU9ViPm2++WZdccokJFQGAZ6rQAo/uID8/X507d9bWrVu1efNmtWvXzrpv27ZtGjFihL7++mtFRUXp/vvv1yOPPGJesYALnDhxQps3z7Zrp5cIACqu0nelmeWRRx5R/fr1tXXrVpv2vLw89ejRQ927d9fcuXP13XffaciQIYqMjNTdd99tUrVA1WLoDACcy6OC0UcffaSPP/5Y7777rj766CObfYsXL1ZBQYHmz5+vwMBAXXLJJdqyZYtmzpxJMIJXchSKnnjiCfn5VehmUwDAH3hMMDp69KiGDRumZcuWKTQ01G5/RkaGrrnmGgUGBlrbEhMT9dRTT+nEiROqWbOmw/Pm5+crPz/fup2Xl+f84gEnev/997Vt2za7dnqJAODCecQ/LQ3DUFJSkoYPH66OHTs6PCY7O1t169a1aTu3nZ2dXea5p02bpoiICOsrNjbWeYUDTpaammoXiq666ipCEQA4ianBaNy4cbJYLOd9ZWZm6vnnn9dvv/2m5ORkp9eQnJys3Nxc6+vAgQNO/wzgQhUWFpa5NtHf/vY3EyoCAO9k6lDa2LFjlZSUdN5jmjZtqrVr1yojI0NBQUE2+zp27Kjbb79dixYtUkxMjI4ePWqz/9x2TExMmecPCgqyOy/gTphgDQCuY2owioqKKtdDLGfPnq0nn3zSun348GElJiZq6dKl6ty5syQpPj5e48ePV2FhoQICAiRJq1evVosWLcqcXwS4O0ehqE2bh9SvX3UTqgEA7+cRk6/j4uJsts89l61Zs2Zq2LChJGnAgAFKTU3V0KFD9eijj2r79u2aNWuWnn32WZfXC1yo7777zuEq8vQSAUDV8ohgVB4RERH6+OOPNWLECHXo0EF16tTRhAkTuFUfHmP5ciktTUpMtO8lat68uW6//XYTqgIA31LuZ6X5Cp6VBrMkJBhKTJxk104vEQD8NZc/Kw1A1Zk3b54SEw/ZtROKAMC1CEaAyRxNsL7vvvvKdWMCAMC5CEaASX755Re98MILdu30EgGAeQhGgAkc9RI1aNBAd911lwnVAADOIRgBLuYoFE2YMEEWi8WEagAAf+QRz0oDvEF6enqZj/X4YyhavlxKSDj7EwDgWvQYAS7gKBDdcccdatasmV17WpqUkXH2Z+/erqgOAHAOwQioQmfOnNFTTz1l136+Cdbjxp0NRePGVWVlAABHCEZAFansw19796anCADMQjACqoCjUDR+/HhVq8ZfOQBwZ/xXGnCirVu3atmyZXbtrE0EAJ6BYAQ4iaNeosTERF1xxRUmVAMAqAyCEXCBSkpKNHnyZLt2eokAwPMQjIALkJaWpvz8fLt2QhEAeCaCEVBJjobOxo4dqxo1aphQDQDAGQhGQAUdPHhQr776ql07vUQA4PkIRkAFOOolatOmjfr162dCNQAAZyMYAeVU1nPOAADeg2AE/IWlS5cqMzPTrp1QBADeh2AEnIejXqK7775b9erVM6EaAEBV8zO7AMBdLF8uJSSc/ZmXl1fm0BmhCAC8Fz1GQKm0NCkjQ9q8OVWbN9vuCwsL05gxY8wpDADgMgQjQGd7iX79VZo40b6XaMKECbJYLCZUBQBwNYIRIOn117/RbbettGtngjUA+BaCEXxeamqqLrnEtu3mm2/WJX9uBAB4PYIRfFZxcbGefPJJu3Z6iQDAdxGM4JPmzZunQ4cO2bTVrl1bI0eONKkiAIA7IBjB5zi6Df+xxx5TQECACdUAANwJwQg+49ChQ5o3b55dO0NnAIBzCEbwCY56ifr06aN27dq5vhgAgNsiGMGrGYahSZMm2bXTSwQAcIRgBK+1YsUKbdq0ya6dUAQAKAvBCF7J0dDZ2LFjVaNGDROqAQB4Ch4iC4/2xwe/SlJubm6ZD38lFAEA/go9RvBo5x78mpZ29uGvf3bllVeqe/fuJlQGAPBEBCN4tHHjzoaixETHvUQAAFQEwQgeLSbmKyUmfmTXTigCAFQGwQgey9FcouHDh6tu3bomVAMA8AYEI3icwsJCTZ061a6dXiIAwIUiGMGj/Pvf/9a+ffts2i6//HL9/e9/N6kiAIA3IRjBYzgaOnviiSfk58eqEwAA5yAYwe0dOHBA8+fPt2tn6AwA4GwEI7g1R71Ed955p+Li4kyoBgDg7QhGcEslJSWaPHmyXTu9RACAqkQwgttZtWqV/vvf/9q0NWrUSElJSeYUBADwGQQjuBVHQ2fJyckKDAw0oRoAgK8hGMEtnDhxQrNnz7ZrZ+gMAOBKBCOYzlEv0U033aRLL73UhGoAAL6MYARTOQpF9BIBAMxCMIIpvv76a3344Yc2bYGBgUpOTjapIgAACEYwgaNeojFjxigsLMyEagAA+B+epQCnW75cSkg4+/OPfv/99zKHzghFAAB3QI8RnC4tTcrIOPvz3HafPi/qzJmfbY7r2rWrrrnmGhMqBADAMYIRnG7cuLNh6NzPxMRUnTlje8yECRNksVjMKRAAgDIQjOB0vXuffR08eFCJia/a7eeuMwCAuyIYoUo4mks0fPhw1a1b14RqAAAoH4IRnIqHvwIAPBnBCE6zc+dOvf322zZtTLAGAHgSghEuyPLl/5tg/WdPPPGE/PxYEQIA4DkIRrggM2eeVGLiMzZtF110kQYMGGBSRQAAVB7BCJX2/fffq2vXN23a7r//ftWqVcukigAAuDAEI1SYYRhauHCh9u/fb9POBGsAgKcjGKFCcnJyNGvWLJu2u+66Sw0aNDCpIgAAnIdghHL7/PPPtWbNGut2cHCwHnroIfn7+5tYFQAAzkMwwl8qKirSlClTbNp69eqlTp06mVQRAABVg2CE8/rpp5+0cOFCm7YxY8YoLCzMnIIAAKhCBCOU6a233tKuXbus2xdffLFuu+02EysCAKBqedTqeytXrlTnzp0VEhKimjVrqm/fvjb79+/fr+uvv16hoaGKjo7Www8/rKKiInOK9WAnT55UamqqTSgaNGgQoQgA4PU8psfo3Xff1bBhwzR16lR169ZNRUVF2r59u3V/cXGxrr/+esXExGjjxo06cuSIBg0apICAAE2dOtXEyj3Lpk2btGLFCpu2xx57TAEBASZVBACA61gMwzDMLuKvFBUVqXHjxkpNTdXQoUMdHvPRRx/phhtu0OHDh61PcJ87d64effRRHT9+XIGBgeX6rLy8PEVERCg3N1fh4eFO+w7urqSkRDNnztSpU6esbddee626dOliYlUAAJSPs35/e8RQ2rfffqtDhw7Jz89P7du3V7169dSrVy+bHqOMjAxdeuml1lAkSYmJicrLy9OOHTvMKNtjHDlyRJMnT7YJRffffz+hCADgczxiKO3HH3+UJE2cOFEzZ85U48aN9cwzz+jaa6/V999/r1q1aik7O9smFEmybmdnZ5d57vz8fOXn51u38/LyquAbuK+VK1fqm2++sW7Xr19fd911lywWi4lVAQBgDlN7jMaNGyeLxXLeV2ZmpkpKSiRJ48ePV79+/dShQwctWLBAFotFb7/99gXVMG3aNEVERFhfsbGxzvhqbu/MmTNKTU21CUW33nqrhg0bRigCAPgsU3uMxo4dq6SkpPMe07RpUx05ckSS1Lp1a2t7UFCQmjZtan1eV0xMjL766iub9x49etS6ryzJyckaM2aMdTsvL8/rw9HOnTvtAuW4ceMUFBRkUkUAALgHU4NRVFSUoqKi/vK4Dh06KCgoSLt379ZVV10lSSosLFRWVpYaNWokSYqPj9eUKVN07NgxRUdHS5JWr16t8PBwm0D1Z0FBQT4TCAzD0Msvv2wNjJLUqVMn9erVy8SqAABwHx4xxyg8PFzDhw9XSkqKYmNj1ahRIz399NOSpFtuuUWS1KNHD7Vu3VoDBw7U9OnTlZ2drccff1wjRozwmeBzPr/88oteeOEFm7bhw4fbzcsCAMCXeUQwkqSnn35a1apV08CBA/X777+rc+fOWrt2rWrWrClJ8vf314oVK3TvvfcqPj5e1atX1+DBgzVp0iSTKzffunXrtGHDBut2RESERo0aJT8/j7gpEQAAl/GIdYxcyZvWMSosLLRb3LJ3795q3769SRUBAFA1nPX722N6jFAxe/fu1euvv27T9tBDD6l69eomVQQAgPsjGHmh119/XXv37rVut2nTRv369TOxIgAAPAPByIvk5eXp2WeftWkbMmSI1y8/AACAsxCMvMSXX36p9PR067a/v7+Sk5Pl7+9vYlUAAHgWgpGHKy4u1lNPPaXCwkJr23XXXaeEhAQTqwIAwDMRjDzYwYMH9eqrr9q0jR49WhERESZVBACAZyMYeaj3339f27Zts243adJEAwcO5DlnAABcAIKRhzl9+rR11e9zbr/9djVv3tykigAA8B4EIw+ydetWLVu2zKYtOTlZgYGB5hQEAICXIRh5AMMwNHv2bOXk5FjbrrrqKv3tb38zrygAALwQwcjNHTt2THPmzLFpGzFihOrUqWNSRQAAeC+CkRtLT0/Xl19+ad2Ojo7W8OHDmWANAEAVIRi5ofz8fKWlpdm09evXT23atDGpIgAAfAPByM3s3r1bS5YssWl75JFHFBISYlJFAAD4DoKRmzAMQ/Pnz9fBgwetbZdddpluvPFGE6sCAMC3EIzcwIkTJzR79mybtmHDhql+/fomVQQAgG8iGJnss88+09q1a63boaGhGjt2rPz8/EysCgAA30QwMklRUZGmTJli03b99derY8eOJlUEAAAIRibIysrSokWLbNrGjBmjsLAwkyoCAAASwcjllixZot27d1u3W7Roof79+5tYEQAAOIdg5CK//fabZs6cadM2ePBgNW7c2JyCAACAHYKRi/w5FI0fP17VqvHHDwCAO+E3s4u0atVKu3btUrdu3XT11VebXQ4AAHCAYOQit956q9klAACAv8BiOQAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRi6yfLmUkHD2JwAAcE8EIxdJS5MyMs7+BAAA7olg5CLjxknx8Wd/AgAA98QjQVykd++zLwAA4L7oMQIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChVzewC3I1hGJKkvLw8kysBAADlde739rnf45VFMPqT3377TZIUGxtrciUAAKCifvvtN0VERFT6/RbjQqOVlykpKdHhw4cVFhYmi8VidjkukZeXp9jYWB04cEDh4eFml4Ny4Jp5Fq6X5+GaeZZz12vnzp1q0aKF/PwqP1OIHqM/8fPzU8OGDc0uwxTh4eH8B8DDcM08C9fL83DNPEuDBg0uKBRJTL4GAACwIhgBAACUIhhBQUFBSklJUVBQkNmloJy4Zp6F6+V5uGaexZnXi8nXAAAApegxAgAAKEUwAgAAKEUwAgAAKEUwAgAAKEUwglauXKnOnTsrJCRENWvWVN++fW3279+/X9dff71CQ0MVHR2thx9+WEVFReYUC6v8/Hy1a9dOFotFW7Zssdm3bds2XX311QoODlZsbKymT59uTpE+LisrS0OHDlWTJk0UEhKiZs2aKSUlRQUFBTbHcb3cy4svvqjGjRsrODhYnTt31ldffWV2SZA0bdo0XX755QoLC1N0dLT69u2r3bt32xxz5swZjRgxQrVr11aNGjXUr18/HT16tEKfQzDyce+++64GDhyoO++8U1u3btUXX3yhAQMGWPcXFxfr+uuvV0FBgTZu3KhFixZp4cKFmjBhgolVQ5IeeeQR1a9f3649Ly9PPXr0UKNGjbRp0yY9/fTTmjhxov71r3+ZUKVvy8zMVElJiV5++WXt2LFDzz77rObOnavHHnvMegzXy70sXbpUY8aMUUpKir799lu1bdtWiYmJOnbsmNml+bxPP/1UI0aM0JdffqnVq1ersLBQPXr00KlTp6zHPPjgg/rPf/6jt99+W59++qkOHz6sm266qWIfZMBnFRYWGg0aNDDmzZtX5jEffvih4efnZ2RnZ1vb5syZY4SHhxv5+fmuKBMOfPjhh0bLli2NHTt2GJKMzZs3W/e99NJLRs2aNW2uz6OPPmq0aNHChErxZ9OnTzeaNGli3eZ6uZdOnToZI0aMsG4XFxcb9evXN6ZNm2ZiVXDk2LFjhiTj008/NQzDMHJycoyAgADj7bffth6za9cuQ5KRkZFR7vPSY+TDvv32Wx06dEh+fn5q37696tWrp169emn79u3WYzIyMnTppZeqbt261rbExETl5eVpx44dZpTt844ePaphw4bptddeU2hoqN3+jIwMXXPNNQoMDLS2JSYmavfu3Tpx4oQrS4UDubm5qlWrlnWb6+U+CgoKtGnTJnXv3t3a5ufnp+7duysjI8PEyuBIbm6uJFn/Pm3atEmFhYU2169ly5aKi4ur0PUjGPmwH3/8UZI0ceJEPf7441qxYoVq1qypa6+9Vr/++qskKTs72yYUSbJuZ2dnu7ZgyDAMJSUlafjw4erYsaPDY7hm7mvPnj16/vnndc8991jbuF7u4+eff1ZxcbHD68G1cC8lJSUaPXq0rrzySrVp00bS2b8vgYGBioyMtDm2otePYOSFxo0bJ4vFct7XubkPkjR+/Hj169dPHTp00IIFC2SxWPT222+b/C18S3mv2fPPP6/ffvtNycnJZpfs08p7vf7o0KFD6tmzp2655RYNGzbMpMoB7zBixAht375dS5Yscfq5qzn9jDDd2LFjlZSUdN5jmjZtqiNHjkiSWrdubW0PCgpS06ZNtX//fklSTEyM3R0Z52b4x8TEOLFq31bea7Z27VplZGTYPQ+oY8eOuv3227Vo0SLFxMTY3YXBNXOu8l6vcw4fPqyuXbsqISHBblI118t91KlTR/7+/g6vB9fCfYwcOVIrVqzQhg0b1LBhQ2t7TEyMCgoKlJOTY9NrVOHr5+zJUPAcubm5RlBQkM3k64KCAiM6Otp4+eWXDcP43+Tro0ePWo95+eWXjfDwcOPMmTMur9nX/fTTT8Z3331nfaWnpxuSjHfeecc4cOCAYRj/m8xbUFBgfV9ycjKTeU1y8OBB46KLLjL69+9vFBUV2e3nermXTp06GSNHjrRuFxcXGw0aNGDytRsoKSkxRowYYdSvX9/4/vvv7fafm3z9zjvvWNsyMzMrPPmaYOTjHnjgAaNBgwZGenq6kZmZaQwdOtSIjo42fv31V8MwDKOoqMho06aN0aNHD2PLli3GqlWrjKioKCM5OdnkymEYhrFv3z67u9JycnKMunXrGgMHDjS2b99uLFmyxAgNDbWGXbjOwYMHjebNmxt/+9vfjIMHDxpHjhyxvs7hermXJUuWGEFBQcbChQuNnTt3GnfffbcRGRlpc2cuzHHvvfcaERERxvr1623+Lp0+fdp6zPDhw424uDhj7dq1xjfffGPEx8cb8fHxFfocgpGPKygoMMaOHWtER0cbYWFhRvfu3Y3t27fbHJOVlWX06tXLCAkJMerUqWOMHTvWKCwsNKli/JGjYGQYhrF161bjqquuMoKCgowGDRoYaWlp5hTo4xYsWGBIcvj6I66Xe3n++eeNuLg4IzAw0OjUqZPx5Zdfml0SDKPMv0sLFiywHvP7778b9913n1GzZk0jNDTU+Mc//mHzD5HysJR+GAAAgM/jrjQAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAqCLr16+XxWJRTk6O2aUAKCeCEQCPNXHiRLVr187sMgB4EYIRAK9XWFhodgkAPATBCIBpSkpKNG3aNDVp0kQhISFq27at3nnnHUn/G4Zas2aNOnbsqNDQUCUkJGj37t2SpIULFyo1NVVbt26VxWKRxWLRwoULJUkWi0Vz5sxR7969Vb16dU2ZMuW8dZz7rPT0dLVv314hISHq1q2bjh07po8++kitWrVSeHi4BgwYoNOnT1vfl5+fr1GjRik6OlrBwcG66qqr9PXXX1fNHxYA13DqE94AoAKefPJJo2XLlsaqVauMvXv3GgsWLDCCgoKM9evXG+vWrTMkGZ07dzbWr19v7Nixw7j66quNhIQEwzAM4/Tp08bYsWONSy65xO4p25KM6OhoY/78+cbevXuNn3766bx1nPusK664wvj888+Nb7/91mjevLnRpUsXo0ePHsa3335rbNiwwahdu7bNA15HjRpl1K9f3/jwww+NHTt2GIMHDzZq1qxp/PLLLzbnPXHiRNX8AQJwOoIRAFOcOXPGCA0NNTZu3GjTPnToUOO2226zhopPPvnEum/lypWGJOP33383DMMwUlJSjLZt29qdW5IxevToctfi6LOmTZtmSDL27t1rbbvnnnuMxMREwzAM4+TJk0ZAQICxePFi6/6CggKjfv36xvTp023OSzACPEc1s3qqAPi2PXv26PTp07ruuuts2gsKCtS+fXvr9v/93/9Z/3e9evUkSceOHVNcXNx5z9+xY8cK1/THz6pbt65CQ0PVtGlTm7avvvpKkrR3714VFhbqyiuvtO4PCAhQp06dtGvXrgp/NgD3QDACYIqTJ09KklauXKkGDRrY7AsKCtLevXslnQ0b51gsFkln5yb9lerVq1e4pj9/1h+3z7WV57MBeC4mXwMwRevWrRUUFKT9+/erefPmNq/Y2NhynSMwMFDFxcVVXKljzZo1U2BgoL744gtrW2Fhob7++mu1bt3alJoAXDh6jACYIiwsTA899JAefPBBlZSU6KqrrlJubq6++OILhYeHq1GjRn95jsaNG2vfvn3asmWLGjZsqLCwMAUFBbmg+rM9Uvfee68efvhh1apVS3FxcZo+fbpOnz6toUOHuqQGAM5HMAJgmsmTJysqKkrTpk3Tjz/+qMjISF122WV67LHHyjVk1a9fP7333nvq2rWrcnJytGDBAiUlJVV94aXS0tJUUlKigQMH6rffflPHjh2Vnp6umjVruqwGAM5lMQzDMLsIAAAAd8AcIwAAgFIEIwBeb/jw4apRo4bD1/Dhw80uD4AbYSgNgNc7duyY8vLyHO4LDw9XdHS0iysC4K4IRgAAAKUYSgMAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAACj1/1j+WUwWRiX3AAAAAElFTkSuQmCC", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_test.ipynb) file." ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_test.ipynb) file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb index 73934a11..c49826a8 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb @@ -1,667 +1,692 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "## 1. Introduction\n", - "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", - "\n", - "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", - "\n", - "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", - "\n", - "\n", - "### 1.1 Need for ML Surrogates\n", - "\n", - "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", - "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", - "\n", - "### 1.2 Supercritical CO2 cycle process\n", - "\n", - "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", - "\n", - "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "from pathlib import Path\n", - "\n", - "\n", - "def datafile_path(name):\n", - " return Path(\"..\") / name\n", - "\n", - "\n", - "Image(datafile_path(\"CO2_flowsheet.png\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Training and Validating Surrogate\n", - "\n", - "First, let's import the required Python and IDAES modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import statements\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Import IDAES libraries\n", - "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", - "from idaes.core.surrogate.plotting.sm_plotter import (\n", - " surrogate_scatter2D,\n", - " surrogate_parity,\n", - " surrogate_residual,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Importing Training and Validation Datasets\n", - "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", - "\n", - "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import training data\n", - "np.set_printoptions(precision=6, suppress=True)\n", - "\n", - "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", - "csv_data.columns.values[0:6] = [\n", - " \"pressure\",\n", - " \"temperature\",\n", - " \"enth_mol\",\n", - " \"entr_mol\",\n", - " \"CO2_enthalpy\",\n", - " \"CO2_entropy\",\n", - "]\n", - "data = csv_data.sample(n=500)\n", - "\n", - "input_data = data.iloc[:, :2]\n", - "output_data = data.iloc[:, 2:4]\n", - "\n", - "# # Define labels, and split training and validation data\n", - "input_labels = list(input_data.columns)\n", - "output_labels = list(output_data.columns)\n", - "\n", - "n_data = data[input_labels[0]].size\n", - "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Training Surrogates with PySMO\n", - "\n", - "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", - "\n", - "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; previous file will be overwritten.\n", - "\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "No iterations will be run.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", - "\n", - "------------------------------------------------------------\n", - "The final coefficients of the regression terms are: \n", - "\n", - "k | -534397.59515\n", - "(x_ 1 )^ 1 | -2733.579691\n", - "(x_ 2 )^ 1 | 1036.106357\n", - "(x_ 1 )^ 2 | 32.409203\n", - "(x_ 2 )^ 2 | -2.852387\n", - "(x_ 1 )^ 3 | 0.893563\n", - "(x_ 2 )^ 3 | 0.004018\n", - "(x_ 1 )^ 4 | -0.045284\n", - "(x_ 2 )^ 4 | -3e-06\n", - "(x_ 1 )^ 5 | 0.000564\n", - "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 4.372684\n", - "\n", - "The coefficients of the extra terms in additional_regression_features are:\n", - "\n", - "Coeff. additional_regression_features[ 1 ]: -0.002723\n", - "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", - "Coeff. additional_regression_features[ 3 ]: -0.050607\n", - "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", - "Coeff. additional_regression_features[ 5 ]: -44.726026\n", - "\n", - "Regression model performance on training data:\n", - "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", - "\n", - "Results saved in solution.pickle\n", - "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", - "\n", - "===========================Polynomial Regression===============================================\n", - "\n", - "Warning: solution.pickle already exists; previous file will be overwritten.\n", - "\n", - "No iterations will be run.\n", - "Default parameter estimation method is used.\n", - "Parameter estimation method: pyomo \n", - "\n", - "No iterations will be run.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: maxIterations\n", - " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", - " Exceeded.\n", - "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", - "\n", - "------------------------------------------------------------\n", - "The final coefficients of the regression terms are: \n", - "\n", - "k | -519.862457\n", - "(x_ 1 )^ 1 | -8.820865\n", - "(x_ 2 )^ 1 | 3.676641\n", - "(x_ 1 )^ 2 | 0.18002\n", - "(x_ 2 )^ 2 | -0.010217\n", - "(x_ 1 )^ 3 | -0.000783\n", - "(x_ 2 )^ 3 | 1.4e-05\n", - "(x_ 1 )^ 4 | -6.9e-05\n", - "(x_ 2 )^ 4 | -0.0\n", - "(x_ 1 )^ 5 | 1e-06\n", - "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 0.010367\n", - "\n", - "The coefficients of the extra terms in additional_regression_features are:\n", - "\n", - "Coeff. additional_regression_features[ 1 ]: -7e-06\n", - "Coeff. additional_regression_features[ 2 ]: 0.0\n", - "Coeff. additional_regression_features[ 3 ]: -0.000112\n", - "Coeff. additional_regression_features[ 4 ]: 484.312223\n", - "Coeff. additional_regression_features[ 5 ]: -0.1166\n", - "\n", - "Regression model performance on training data:\n", - "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", - "\n", - "Results saved in solution.pickle\n", - "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" - ] - } - ], - "source": [ - "# Create PySMO trainer object\n", - "trainer = PysmoPolyTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - ")\n", - "\n", - "var = output_labels\n", - "trainer.config.extra_features = [\n", - " \"pressure*temperature*temperature\",\n", - " \"pressure*pressure*temperature*temperature\",\n", - " \"pressure*pressure*temperature\",\n", - " \"pressure/temperature\",\n", - " \"temperature/pressure\",\n", - "]\n", - "# Set PySMO options\n", - "trainer.config.maximum_polynomial_order = 5\n", - "trainer.config.multinomials = True\n", - "trainer.config.training_split = 0.8\n", - "trainer.config.number_of_crossvalidations = 10\n", - "\n", - "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", - "poly_train = trainer.train_surrogate()\n", - "\n", - "# create callable surrogate object\n", - "xmin, xmax = [7, 306], [40, 1000]\n", - "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", - "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", - "# save model to JSON\n", - "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Visualizing surrogates\n", - "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "## 1. Introduction\n", + "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebraic, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebraic equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsequent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.4 Model Validation\n", - "\n", - "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] = [\n", + " \"pressure\",\n", + " \"temperature\",\n", + " \"enth_mol\",\n", + " \"entr_mol\",\n", + " \"CO2_enthalpy\",\n", + " \"CO2_entropy\",\n", + "]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns)\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(data, 0.8, seed=n_data)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogates with PySMO\n", + "\n", + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", 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" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 4.372684\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", + "(x_ 2 )^ 4 | -0.0\n", + "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 0.010367\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 2 ]: 0.0\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + ] + } + ], + "source": [ + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "var = output_labels\n", + "trainer.config.extra_features = [\n", + " \"pressure*temperature*temperature\",\n", + " \"pressure*pressure*temperature*temperature\",\n", + " \"pressure*pressure*temperature\",\n", + " \"pressure/temperature\",\n", + " \"temperature/pressure\",\n", + "]\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 5\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [7, 306], [40, 1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing surrogates\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FhYXcuXPn9C4iQRAEYTCkjt9BOmtAWZw/fx4bNmxA9+7dERwcDAD4+OOP0bJlS2zfvh39+vUDx3FIS0vDwoULERkZCQDIzc1FWlqaw7nS09MxefJkAEBlZSUOHDiA2bNnC98HBAQgLS0Nubm5AIADBw6gqqrK4Txt27ZF8+bNkZubi27duiE3NxcdOnRAkyZNHK4zYcIE/Prrr7jppptUqRctKCkpQWVlJQCgsDAABQVBSEq6goSEGgBASEgIoqKi9CyiZEpKSrBixQrYbI2wZMlkcFztbvc1NRZMnx6OP/9cC6v1IrKysgxzT4T32LdpVxipTRMEYSwMJcJmzpyJFStWoLy8HN26dcP27duF7/7v//4Px48fxwcffIC3334b1dXVmDJlCgYPHoy9e/cCAIqLix2EEQA0adIEZWVl+Pvvv3HhwgVUV1e7POb3338XzhESEoKIiIg6xxQXF3u8Dv+dOyoqKlBRUSH8XVZWJqVaNIMXLQDwww834eOP7wbHBcBiqcE992xHp04/AoBhRAs/8J4/HwWOc/TMc1wAzp+PhNV60eMAzSIkKqRj36YBwGZrhPPnoxAZWQKr9aLwuVHaNEEQxkJXETZr1iwsWLDA4zGHDx9G27ZtAQDTp0/H6NGjcfz4ccybNw/Dhg3D9u3bYbFYUFNTg4qKCrz99tu49tprAQBr1qxB586dceTIEbRp00b1+/GV+fPnY968eXoXwy38wG6zNRIEGFArWD7++G4kJ/9hSNESGVkCi6XGQYhZLDWIjDyvY6nkQaLCO+zbqqeJhdHaNME2LE6UWCyTP6CrCJs2bRpGjBjh8ZiWLVsK/46OjkZ0dDSuvfZatGvXDs2aNcM333yD1NRUxMfHIygoSBBgANCuXTsAtSsV27Rpg7i4uDqrGE+fPo3w8HDUr18fgYGBCAwMdHlMXFwcACAuLg6VlZUoLS11sIY5H+O8opI/J3+MK2bPno2pU6cKf5eVlaFZs2Ye60cPxCxHRsNqvYh77tleZwA24r2QqJCH2MSCIJTCeaLkDi0nSiyWyV/QVYTFxMQgJiZG1m9rampjkHj3XY8ePXDlyhXk5+cjOTkZAPDf//4XANCiRQsAQGpqKj755BOH8+Tk5CA1NRVArdLv3Lkz9uzZg4EDBwrX2bNnD7KysgAAnTt3RnBwMPbs2YNBgwYBAI4cOYITJ04I50lNTcULL7yAM2fOIDY2VrhOeHg42rdv7/aeQkNDERoaKqs+tERLy5FWs7NOnX5EcvIfOH8+EpGR53UZeJW8VxIV3mG2iQXBLlInQFpOlJyv5c6CTpM35TFETFheXh6+++473HLLLWjcuDHy8/MxZ84cJCcnC8InLS0NnTp1wqhRo7BkyRLU1NRg4sSJuOOOOwTr2Pjx47FixQrMmDEDo0aNwt69e7F582bs2LFDuNbUqVMxfPhwdOnSBV27dsWSJUtw6dIljBw5EgBgtVoxevRoTJ06FZGRkQgPD8ekSZOQmpqKbt26AQD69u2L9u3b4+GHH8bChQtRXFyMp556ChMnTjSEyBJDK8uRVNfaQw89JAhvX7BaL+o24CrtRiRR4R1mckkbFX91h7l71/XCkwWdUB5DiLCwsDBkZ2fj6aefxqVLlxAfH49+/frhqaeeEkRNQEAAPv74Y0yaNAk9e/ZEgwYNcOedd2LRokXCeZKSkrBjxw5MmTIFS5cuRdOmTfHmm28iPT1dOCYzMxNnz57F3LlzUVxcjBtvvBE7d+50CLRfvHgxAgICMGjQIFRUVCA9PR2vvfaa8H1gYCC2b9+OCRMmIDU1FQ0aNMDw4cPx7LPPalBb2qCF5cidaw2owR137EaPHrUrVt99913Dm8mVdiOSqPAOM7mkjYi/usNYEzxkQdceQ4iwDh06CCscPZGQkICtW7d6PKZ379748UfPjTwrK0twP7qiXr16WLlyJVauXOn2mBYtWtRxfZoNrSxHzh0DEICcnDsAQBBiZjGTK9UJkqjwHhZc0v6KP7rDWBQ8ZEHXHkOIMMK/cdUxABbs3p2G66//RXbnEBISouhxSuBLJ1hSUoJz584Jf5Oo8B49XdJELaxZh9SCRcFDFnTtIRFGSEYv0RIZWQKgBs67bPnaYUVFRSErK4upOBS5naA7dw6JCveUlJSgtLRU0rFaCnF/hkXrkFqwKHjIgq49JMIIyeglWqzWi7jjjt3/c0FahM+V6LBYiy+R2wlKdefw+LuokBqDlJmZiZiYGObaiVlh0TqkFqwKHrKgawuJMMIr9BqM+Niv3bvTmOqw1MBTJ3ju3DlRoevJnZORkYGEhAS/FxVSRavVatW0rvx1hSAPi9YhpbGfAHl617WcKDlfy50FXevJmz+8DyTCCMPQo0curr/+F7+YobnrBLOzswG4XyUm5s6Jjo42fKelNKzEILG8QlDNwdA+lpFV65CSsBgGwWKZWH4flIREGMEsrmZdesQ4aTEbczfD9HaVmD+5c6Ti6vnxgz5LMUisrhBUczB0dW5/cIexKBpYKxOr74PSkAgjmCUqKgoPPfQQ3n33XdFj1TKTazUb42eihYWFgrVLjoXGH9w5rnAnlEtLS7F582a3v2NVtLJknSssLHT4TMnB0N1vWHGHEWzAyvugBiTCCKZJTk7W1Uyu5WwsKipK8ibp7vAHd44zUoUyUPf5sShaWbHOuapXtQdDd+8XxTL6L6y8D2pBIoxQDaXceKx0vFrOxqRaaPg69uf8YFKFsrvnx5poZcU651yvp07FqzoYenq/KJbRf2HlfVALEmGEKpgtqFLr2ZgUC42nOvZXd467gdzT82NNtLJonfvhh5uwbdvdUDpXH4+S75c/rKjzJ1h8H5SERBihCmYLqtR6NibFrSi1jjMyMhAdHW36wcfTQC72/FgSray5lPl6dRZggHKDoVLvl9kmfwR774PSkAgjVMcMQZV6zMa8sdCIuXLi4+NVKycreBrIxZ4fL1Tt0VO0smSdc71tGBQdDJV6v8w2+SNqYel9UBoSYYSqmCWoUqvZmNSkieXl5SgvLwdgnjr2FU8DudjzY0Gospow01W9AjUYPfpNNG1apMg11Hi/1Jz86e3y1Pv6WsDq+6A0JMIIVTFTUKUWszEpSRPLy8sd0naYqY59QWwgZ302zWLCTMB9vToLMDmDoVrZ49WcmOjt8tT7+q5QQxSy+j4oDYkwQlWMHlSpx2xMrFMpKnIc/Ixex0oiJrRYn02zOqB4qldf0keoNdCqOTGR6spUy+Wp9/WdUVMUsvo+KAmJMEJVjB5UqcVsTIlZZGpqLvbvTwVgvDr2FalCOTMzE1ar1eXv/aGz9xap9epr/i416l5sYmKf0sUeOW3BXdwZoI3b0NP1tYA1UWg0SIQRiuDc2Zgpb5XasR2+zCKd415SU79GSkqe4erYF/zFbaE1Rq5XsckfvyuFK7yx2HiKOzt58iQ+/fRT0XP44jZkcdGT3qLQaJAII3xGipBg3Q2kF76s5nIV95Kbm4qUlDyH4/yhjlkUAmbAyPUqd/In1WIjFncmRYB5cz1vr68HLIpC1iERRviM1E7EOQ0Aq7NovfC2AxOLe6GtXtjFH1a36YFUFyrgu8XG27gzpS1ErC3IYVEUGgESYYTiuOtsWEgDwCpyOjCxuBcjbPVSUlKCs2fPoqqqyuX3QUFBiI2NZf4+vIHF1W1mwdmFeu7cOZeuR08THvtQCk9i2JsFMV9/nYrdu9MUtRCxtiCHNVFoFEiEEYpC5mh5yOnAWF70IMXSA0DyxttmEiQUyKwuntqJzdYIJ082c9iCyXnC4yza3LU9qe/f11+nIifnDgAWl9eTC2vvP2ui0CiQCCMUwxdztL+7Z7zpwNTKraQUUi09Q4YMqfOZP2Y4p0BmbbCfIDrjacLj3Pakvn9A7bPNyUkDL8CkXE8MVt9/1kShUSARRiiGXHM0uWe868BYX7UmVTBduXLF4W81XDasQ5ZjbXCeIDpjsdQgOLgSBQWJomJYyvtns9mwadMmnD8fBaX33GTt/ddbFBp9Ak8ijFAMueZoOSsEjf7iucKb1VxGujcplh61XDYsQ4HM2uFu/0ugto+64YZDWLNmjGQxLPX9c73lE4e0tN0+PWOW3n89RaEZJvAkwgjFUMIcLcUyYIYXj8fs+6NJeZ5quGyMgN6BzGacyLjD3QRx0KAtiIgoFQQYoKwYdu4TgRrcccdu9OiRKxxj1HfbHlas7u4mfIWFhS7bOgttnEQY4TNKmaOlWgZ8ya3FGqy5FpRE6vNUw2VjBPQMZDbTREYK7iaI119/GAUFiYqLYal94kMPPWSK+mUBTxO+7Oxst+OE3m2cRBjhM0oJCTmWATPE1Ji1E5b6PNVy2YihtyVIz0Bmf1mhKUUMqSGGjTy50vu9kIPYhM/TOKF3GycRRiiCEi+lt50hxdSwjdTnKcVlozR6WoL0DmR2hVlXaHoSQ3wOMbXEMGtCRQpGtZB6mvABYHqcIBFGMIO3naHeMTWEZ8SeZ3BwsHCs2KIEpQWJni5tNa0kcqwYZrAmy7lvFsWw3hjVQuppwsf6OEEijGAKb1YIUnJANpE6uMXExCArK0v3jPl6iBC9V4rxmMGaLNd6Y2SXoVZoZSH11QUqNuFjeZwgEUZIRq1YAbkrBP0hOaDW8RlKXM/bwU3PQc5XEcJS/Iwc6x7rVgIp+GLV9GeBJYbUrZ0A39q5Ui5QdxM+1scJEmGEJNSMFfBlRuqN5cxoaB2foeT1jDK4+SJCWI6fkWrdM5s12QyuVRYQm5y42o9Tbjv3RURLncCzPE6QCCMkoXasgDcvr9lza/FoHZ9h1HgQX/BFhLBaX95Y91i3EniDGVyrrCAW6K5W/KS3IlrKwgsed+OE3pAII2Sh52oqo8Zy+Oq6UqLOPZXB2cVg1hVz9igpQlipLynWPTMGpZvBtcoK7iYnhYUJePvtYapYGuWKaHd9ptS2q3cbJxFGeA0LJn/WBJYYvrqulKhzqWVQ6npqoXQclhKuCpbqS4p1T4mJDEvxcID5XKt64mpykpa2W9jbFVDe0qi0iDbKZJ1EGOEVYrOVc+fOMdGwWcMX15VSbhapsRdquHWUGrCVisNS0qXNmhtMqnXPl3eUxXg4M7lW9cKThVRtS6NaSXNZh0QY4RViLyLvg2ctmR9reOO6UqPz82S5Ufp6Sg7YSsVhKTlLFquv0tJSxMfHSyq3UqgdiMzq1mEsB2AbAVfvhX1slZqWRn8V0STCCK+QOlsxU/C20njrulJ6hihmuVH6emoGsPsSh6XUJEGsvjZv3qzJpESvBSt6u2L1XqjDmlvWV9yVVQuR5I8imkQY4RWuXsTUVPW2lzEqzh0zH/Qux3WldOcnZrlRu7NVKoBdi8FfykIGKfWlxaREjxgYFlyxesb+sOiWVRM1RJLeIlpvSIQRknAVK5CXl4L9+1Oxf38P5OamMhW8rSeeOmZvXH1qrWCTYulSa8WcUsJJi8Hfm4UMfH2dPNkUgAXNmp1UpAzeovVAz8qKRL0EDqtuWSVRWyQZJYBeLUiEEZLgX5TCwkIhPiA3NxUAG8HILOGpM/HG1adW5yRmucnIyEB0dLRi1+NRUjhpMfhLHWB58vNbMbNCUitoReJV9HbLqoUWIsmsAksKJMIIyURFRQkvIiszYC2RG/vhPHh74+pTq3PyZOmKjo5WJZBcyTaj9eAvNsCy4JbTAzMEUysR02X25+/PIkltSIQRshAbBPl4GXcdmNGCWeVsjgy4H7z1SI6pd+yFksJJy8FfygDrj5MSHiMHUysV0+Xr8zdaf0goB4kwQhZig6D9dhHOHZgRg1nlxH6IDd4jR97h4PZTu6PVwq3gajDxJoBdDD0yvUsZYP3NLae3oFcKpWK6fHn+RuwPCeUgEUZ4hdRB0J6zZ886dB6s7rknFamxH2KDt1puP0+o2YlLGUx8FU56BPFKGWDN4JbzBjMGU/sS0+XL8zdjcD9Z9qRDIozwCvvO9+jRo9i3b5/QUbjrPKqqqjyeU6s997SO/TCjdUSJvSedLYCAd52y1p231AHWLHswSsVMg6gSMV1m2/5KLs6TMXf9AFn2aiERRngN/+LYD7pyOw+tOh09Yj/MZh1Rau9JPSyAvuJpgM3MzITVanX7W7PM+s1s3ZAb02Xm7a/kYt9GPPUDRrLsqQmJMMJn5HYeWnY6SrlApVi39Ihb0gKpdWiWwUTqABsTE2NY8SEVs8ctybVaa7n9ldEwSz+gNiTCCJ+R23no2enIdYFKsW6ZMV7GFe7q0CyDiavnaLPZHNzrQUFBqKysRFFREQB9nqsWFiozxi3Z44vVWqvtr4yGWfoBtSERRvhMcHAFAA6Axe5TDsHB6q0o8gVfXaBSYj+MLrDE8FSHZhpMnFf1btq0SfQ3WlqD9Ii/MUPckiv0TrVhtvAFM/UDakIijPCZqqpQOAowALCgqsqzu02PTkeuiVyp2A8zxNWI1aHZBhMeFq1BWsffmM3FxFqqDb2FoJKYtR9QGhJhhGyCgmqbj9iMhz+OR2rMlM1mc3ttuWJFrolcCRejWeJqpNShmQYTV7BmDdJKHJnNxcRC6ABrQlBJzN4PKAGJMEI2sbGxAMRnPPxxPGIdX2lpKTZv3izq+pEjVnwxkVNcTS1S61CJwYRFyyGL1iCtxJEZXUx6T3i8EYIsvg9iuOsHvMGI9y0VEmGEbJw7j7lzz+LYsSAkJl5BQsLNAG52+3Io8cLIESusmMhZs6R4g1YbgLNqOWTRGqSVOGLl/WERX4SClPbL6vvgjNRJltTjzJ53jEQY4RP2jT4+HujcWflrKJHMlaW0EWKWFOekp/ZlY6Vz9VSHCQkJqohsViyHLFqDtBRH5GKqixZCQer7cPbsWV2tRkq7eM2ed4xEGKEb7maOSiSBdYaF2A8eMUuK/b6bzug529OzDlmyHLJqDVJTHJk5bkkJtBYKnq5hH8ahl9VIjXOzGAagBCTCCF2QYlpX+qVjxVQtxZLCitXHGT3qkMXOl1VrkBLxN65gaRLDMlq0VanXMJvViMUwACUgEUboghTTullfOjFLCktWHxZgpR2waA1SOv7GE/4usKSgRVuVcg25IQ8Au2KaxTAAJSARRuiOO9Eh5aUz6qoZd5YUFq0+esNK56u0NUiJtksWKvWR8px4tGirUq4hNeTBSEHurIYB+AqJMEJXxESHp5fOZrMxl8HcE1IsKaxYfVhCrB1ouZBBqfMpGcjNQts2K1KfU2ZmJgBthIKUa0gRamq5K9WcGLMaBuALJMIIXRETHZ5eOo7jJF2DlfgHT1aLc+fOITs7W7TztBcc/mTh8NQOWF3I4Ak5gdxGtfoaGanPyX4/US2Egtg1xCew6ljctVglqlbco16QCCN0RUx0uMs5FRISInnJNkuIdTxinaez4GBVZCiB1BgsgN2FDGJIHQzNniuJdbwVLWoIBW/eB8CzUFPK4u48MZC6st2b91LLuEc9IBFG6Iq35nv72X5RUZHwuZmC2V11nr6IDL0tKHKvL8VyCBj72UsdDNVOgaB3G2EdsefkvDWbO3wRClLi//jdRnjcCTUlYtfcTwwaAYBiljazxz2SCCN0x1tXk/Ns3wzB7J5mub6IDL2zbPt6fbEyGf3ZezsYit1vaWkp4uPjvSoDWdnEEXtOERERmggFsd9LFXlKxK55mhikpuYqGttq5nZnGBE2YMAAHDx4EGfOnEHjxo2RlpaGBQsWICEhAQDwzDPPYN68eXV+FxYWhkuXLgl/f/DBB5gzZw6OHTuG1q1bY8GCBbjrrruE7zmOw9NPP4033ngDpaWl6NGjB1atWoXWrVsLx5w/fx6TJk3Cxx9/jICAAAwaNAhLly5Fw4YNhWMOHTqEiRMn4rvvvkNMTAwmTZqEGTNmqFE1hkSKaV2q9ccMwezOsz3e0uOryJBqGVHLbad21nujP3tvB0Ox+928ebPXYsnsGcmVQMpzYkEoiFmN7C3ISsWuueqj9u9PZWJFsxEwjAi77bbb8MQTTyA+Ph5//vknHn/8cQwePBj79+8HADz++OMYP368w2/69OmDm2++Wfh7//79uP/++zF//nzcfffd2LhxIwYOHIgffvgB119/PQBg4cKFWLZsGd566y0kJSVhzpw5SE9Px2+//YZ69eoBAB588EEUFRUhJycHVVVVGDlyJMaNG4eNGzcCAMrKytC3b1+kpaVh9erV+PnnnzFq1ChERERg3LhxWlQX87jqLOS6mFhJYeArrjpxpUWGnnFzargNzfDsvRkMpdyvXLFkdKui2hhlZZ43YlCJ2DVXfRQQgNTUr5Gbm2qqdBJqYBgRNmXKFOHfLVq0wKxZszBw4EBUVVUhODgYDRs2dLBE/fTTT/jtt9+wevVq4bOlS5eiX79+mD59OgDgueeeQ05ODlasWIHVq1eD4zgsWbIETz31FO69914AwNtvv40mTZrgo48+wtChQ3H48GHs3LkT3333Hbp06QIAWL58Oe666y688sorSEhIwIYNG1BZWYm1a9ciJCQE1113HQ4ePIhXX32VRBjE40/kBMFKtSYYLfZFSZGhZ+yUWgO8WXIHSR0Mne8XADgOyM9v5fOzNLpVUQuMvjJPjSB3d31USkoeUlLymBetemMYEWbP+fPnsWHDBnTv3h3BwcEuj3nzzTdx7bXX4tZbbxU+y83NxdSpUx2OS09Px0cffQQAKCgoQHFxMdLS0oTvrVYrUlJSkJubi6FDhyI3NxcRERGCAAOAtLQ0BAQEIC8vD//4xz+Qm5uLnj17OjTk9PR0LFiwABcuXEDjxo2VqAbDYC98nANHXSF1MPB2U26946PkoJTI0NvKoeYAbxQLhT2+DIbJyX/AMTuLMs/SDFZFpTHbyjw1gtzF+igjvI96YigRNnPmTKxYsQLl5eXo1q0btm/f7vK4y5cvY8OGDZg1a5bD58XFxWjSpInDZ02aNEFxcbHwPf+Zp2NiY2Mdvg8KCkJkZKTDMUlJSXXOwX/nToRVVFSgoqJC+LusrMzlcUZCqvCxR+pg4G2HonZ8klooITL0tnJ4M8BLsVayuIWQN/gyGJ4/HwVA+WdpFquikphxZZ4aZfW2j2L1vdQDXUXYrFmzsGDBAo/HHD58GG3btgUATJ8+HaNHj8bx48cxb948DBs2DNu3b4fFYnH4zYcffoiLFy9i+PDhqpVdDebPn+9ycYGRERM0roSQN4OB3A6F9bQGSosMva0cUp+pN9ZKow+O3pRNq61xjGhVVBuW2xBLOPdR7ia5Dz30ENWpHbqKsGnTpmHEiBEej2nZsqXw7+joaERHR+Paa69Fu3bt0KxZM3zzzTdITU11+M2bb76Ju+++u45FKy4uDqdPn3b47PTp04iLixO+5z+zX+Z9+vRp3HjjjcIxZ86ccTjHlStXcP78eYfzuLqO/TVcMXv2bAd3aVlZGZo1a+b2eCNi/2Lm57dyK4TUHAz0ds1JQekZOAtWDinP1BtrpbepGIyIvVWwb9++2LVrl+rP0uhxT4Q2eJoAeprkhoWFaVVEQ6CrCIuJiUFMTIys39bU1ACAg/sOqI3r2rdvH7Zt21bnN6mpqdizZw8mT54sfJaTkyOIuKSkJMTFxWHPnj2C6CorK0NeXh4mTJggnKO0tBQHDhxA586dAQB79+5FTU0NUlJShGOefPJJYdEAf502bdp4jAcLDQ1FaGiojNowBs4vZm1ci3shpJaLSW/XnFSUmC16GzenNL5Y9Fi3VqqN6/xdiYiMLFF0kmK2uCdCG9RKq+NvGCImLC8vD9999x1uueUWNG7cGPn5+ZgzZw6Sk5PrWMHWrl2L+Ph43HnnnXXO89hjj6FXr15YtGgR+vfvj/fffx/ff/89Xn/9dQCAxWLB5MmT8fzzz6N169ZCioqEhAQMHDgQANCuXTv069cPY8eOxerVq1FVVYWsrCwMHTpUyFn2wAMPYN68eRg9ejRmzpyJX375BUuXLsXixYvVrSiGcfViOmMvhDxtV+SrONHbNaclese0yL0+deTS8ncpMUnxtY0YbcUxoRxapNUxO4YQYWFhYcjOzsbTTz+NS5cuIT4+Hv369cNTTz3lYDmqqanB+vXrMWLECAQGBtY5T/fu3bFx40Y89dRTeOKJJ9C6dWt89NFHQo4wAJgxYwYuXbqEcePGobS0FLfccgt27twp5AgDgA0bNiArKwt9+vQRkrUuW7ZM+N5qtWLXrl2YOHEiOnfujOjoaMydO9cQ6SnU6lBd55JxxF4IRUdHq+ZuYsE1pyV6D4BKtRd/7cjFBKn9hEXu+ym3jRg52z6JR3Xwp0muEhhChHXo0AF79+4VPS4gIAAnT570eMx9992H++67z+33FosFzz77LJ599lm3x0RGRgqJWd1xww034Msvv/RcYMZQM4WDuxeTd0lqLYQoAJltqCO/ipggFZuwqCk2pGbbLywsdDhWb4FjZPHIOv42yfUVQ4gwQhvU3OLG3YupZYyS0dMa+BPUkV/FF0GqldjwZK0DgEWLfmBK4NBWTerC2iTXfiJSWBiAgoIgJCVdQUJCbWy5npMCEmGEqkgJDLdaLyIzMxNWq1X4jRovhN7xUYR3sNaR64UvglQrseHOWpeXl1Jn6xr+mmfOnNF9YKTYQ/VgZZWt/UTE0zug16SARBjhFiX2GWRN+JDAYhup1srS0lKXvzXr8/VVkKotNlxZ64AaQYC5uia/c4aeAyPFHioHq6ts+bFH7B3Qy+pJIoxwiZLpAcw6MBLK406022w2bNq0Sfjb3dZXZo7h8cWyoLTY4N07586dE8rmbK1LTc3F/v09PF5T74HRaLGHLC8mYG3C7QyrgptEGFEHMtETeiKlkzbKllO+oKRlQUmx4S7OLDn5D0yevESw1gFwsIS5uqbeA6ORYg+NsJiA5QkQq4KbRBhRB707RoLwhFQrLctWAykoaVlQUmxIjTMDIHpNFgZGo8Qe0mIC32BVcJMII+rAQsdIEK6QaqU1gtVACkqWTWmxIfYsbLZGaNz4AkaPfhNVVSEur8nKwMhKELkUjOyp0HtixKLgJhFGCPBuDbGOUaqbxPmFs9lsqKqqEv4OCgpCRESEw/VZHhAJ/ZFqpSWrgWvciQ0+tssZT++kp2fhal/YpKTjLs+jx8DIahC5FLTwVKghlliZGLEmuEmEEQLO7o+5c8/i2LEgJCZeQULCzQBulvzySU386oyWlgm9Z2WE93hrpTWy1UAJpIoIfs8/bwZFd88iOLjS6zrXemBkPYjcE2p7KtQSSzQxcg2JMMIB+5cqPh743x7lXiP2IukdWK3m7gCEenjrvvL3+EZPYoPfcBmQNyi6exZVVaEe6zwjIwPBwcEOq131gNX32t3k0NNKVCVduGqLJa0nRqxbPUmEEZpgL7pcuSrkpr+Qi5q7AxDq4o37iuIbxcWGL4Oiq2dhszXyWOfR0dHMD4x6IdUKpYULVy2xpPXEiHWrJ4kwwgE1XHT2symgBoDlf/+x4x5SIjEtoR5yt5xiJfCbZXwdFJ2fhZQ6Z31g1AsxK1St8LraT6nZjtUSS3pMjFhuRyTCCAE1XHTOsykgoM4x9i92aWmpx82I1UDJxLSEOvgyaLO4IoolvB0UpVinpNQ5ywOj3rizQnEcAHjup+QunOLh3Z5qiSWaGDlCIowQUMNF52o25Yz9i71582ZN47D8PXDbSPjSJlhbEcUS3g6K7gSxfYwZf15v65wWy9Tizgpl/2/7fiojI0Nw8yq1cEpNsUQTo6uQCCPcooSLzt2echYLhBc7LW23blnP/T1w26xQzJF3eDsouhrofa1zqfFQQ4YMcUht43xuM4g01/2mI/b9VHR0tFcehLrbgmkfe0YTo1pIhBEuUcpF52429fff9ZCTkwaOC8Du3WmoX/+yLi5ACtw2J/4ccyTXmuTroOhrnUtdlcfvG2rUBLxScNVv8q5IHqX6KbG+XimxRBMj15AII+qghIvO/kVynk0BwJIlk8F3KHq6ACk+wbwYfSCWgzc5ntQYFJWocyn9jxnyTLkSy/ZJc537TVerytXe9cAT3oolf54YeYJEGFEHJVx0rl64Y8eOYdeuXSgoSJR9fqViRjyJRPsy+NusjDA23uR4io+PZ3JQFOt/zBDH6VksXz3O3gqlhmtQrK75WDNn5LYLfxNYUiARRtRBKReduxdO7vmVXL1JszJCDCMHiUsVKiyW310c6aVLDQSxYvQ4Tm82QbdH6Tgqsb7Y21gzwntIhBF1UNtFJ/f8Sq/eZHEAItjA6DsqGFmoOPcPfG7BLVvuExbymCWOU0ws33HHHcjJyRE9j1yLPYVj6A+JMEJASxedEqZ1d/EucjYjJgiekpISFBYWOnym9zZbrvAUU2T0BSd8/3DyZFNs3ToYHHc1ufPu3WlIS9uN3bvTDC8cxMRyUlKS6hZ7qX2xkS3DLEMijBBQ20UnN+u5KzyZ8O1zFTnDquWCYANXsTp5eSnIzU1lKpmvWEyRGSwcVutFnD//t0uRkpBQiMmTlxg+z5QUsSy1v/JGJHnbF6u1qTdBIoxwQs0XSCmRJyXehUXLBcE+zrE627bdDfu0AKwEgUuJKTJaQkxXky9PIsUMeaaUEsveiiRv+2K1N/X2Z0iEEZqihMgTM+HTNkSEr/BCX2ybLd79V1paiitXrjgcFxwcDOv/lrqp5aoRm5AYSajYCwM++74ckWK0Fc1KiGU5IklOezTDylTWIBFGGA5Ps2PqJAgl8LTdlr27yJXrW0tXjdwAfFaFird7fzqnUGA1LsnZVegct6qUWFa7/zPygg9WIRFGGA5Ps2MWcpARxsfdtjHuLDG88CosjK8TMK6mq0YspshVnidX7Zj1tu9OpBghhYJ7V2EjxROiqi2SjL7gg0VIhBGGQcrqTRZykPkTag3eeosCV2kSunfPRUpKXp3BzN79A3AArq7kU9sKK+aukyJSWAy6NtMWN1JdhUpY9dQWSWZY8MEaJMIIw+ApmNSXGBJA+Rxk/oBawlXN84oJO3ukxOo4u394AcajhavG15giFoOuzZhMWcxVqIRVTwuRZLQFH6xDIowwFFI6XTVzkBFXcR4glVqRqsZ5pVp7MjMzHX7nzg122223Yd++fR5jxwDtXDVKxBSxFk9pJIElBa3iqbQQSUZa8ME6JMIIU6BVDjLCNWrVmVLnlWrt4ThO0vliYmIAuIsdq3VJqumqUcNdR0HX6qJlPJXSIslM7mHWIBFGmAItc5ARjojVmbsdDADPz8Sb80p1TYmdMyIiQlI74r935f5JS9uNhIRCVV01arjrxETCuXPnDOcCZAk1XYVqiyQzuodZgUQYYRq0yEFG1EWszjztYAC4j+3y9rxSYsSkPF8p7aioqEj4t14xMkoPeK4WI6Sm5grf8/VNi1Pko1Zb0UIk0TNXBxJhBGEHLcH2Hql15m1sl7vzBgdXoqAgUVaMmFLPV6r729NvWIQXCfxWTfv390BubiplRVcQteKpSCQZExJhBGEHLcGuxZuVhFLqTE5sl6vz3nDDIaxZM0Z2jJhYWaW63JwtD3plzFcLfq9MoK7LtrS0lPncXCxB8VTeoXd6Gq0hEUYQkJaDDKgdbJ1/Z6YOAZCeIsJ+JaGnOvMlzs7+vMHBlYIA8/Y87s7pXFZvXG723xtZlDgP/mIu282bN5NL0gsonko6/pivkUQYQcB1R2mz2bBp0yaH4zZv3lznt0bvEMS2VHGH80pCd24Wb+Ps3Ln65OyGwN+bu21ibLZGsl2bZoFv+4WFhcjOzna74rOwMAFJSccB+Ff9KIGR+wctUSvtDcuQCCMMg9pmaikrJ83WIUiZebq7b7GVhHwCXW/jsJwFsdzziN0bpSK5SlRUlMOKz7S03cjJuQNXk89asHt3Gq6//he/c81rhb+54cTw5f00Ul2SCCMMgdpmalcvrb31xKwDttjMU+y+pdS1nDg7V+f19jye7g0ApSLxQEJCEfTI/u+vsLh1lJ74EsJgNJcmiTDCEKi5rZDYS+svucOcBVda2m5hM2rAt/tWamm+3PM431tqai6lIvEArRLWFqnJhAsLC132cSxZdpTAl1RBRnNpkggjDImS2wqJvbRiHYJzvJERO0RXQjMnJw2AfKGi1C4Gvp7H1b3l5qYCqIH9/fm7yPB2xSuhPGITvuzsbL+wkik1CTCCB4NEmJ9gJB+5GGq+WK7OnZz8h8cOwVUyUqN1iK73QAzwqSNUalWYr+dxJ6K7d/9aSMWghsgw2jsXFRWFzMxMYTEKbdSsPWITPlY2WFcbJSYBRvFgkAjzA4zmI/eEmi+Wu3NPnrzEbYfAuqlbKu5mnvYuSTkdoVLtyZfzuLu3lJQ8pKTkqSIyjPrO8XnNrv5NGzVriScLkFFEhVL4Ogkwyu4nJML8ADXjqbRGzRfL07lddQhGMHVLxd3Ms1OnH3H99b+47AiNklxSbFYtd6N3TxgtLoWHEovqi6e2KpaiRe4erWJoadFVKoQBME5cI4kwP8TdgCBnQ2StUfPFEju3fYdgxlmpu5mn1XoRI0fegejoaOFYVtuHOzzNqjMyMlS9Nz3EutyBkxKL6o+7tirWP8ndo9UTWlt0lWx/RolrJBHmZ3gaEJxf4iFDhiAiIgIAOx2vmi+W2LkzMjIA1NaTUUzdYkideSYkJDDx/L2BhXvTQ6z7OnAa7TmbEVdtVWrfp6TVVQ+LrpLtzwhxjSTC/AhvBwTn7PCZmZl1YkYAbQSa1G2FfHWTeDq3vbXEG4scywHaZrZ8sHBveoh1o7pC/Rmp/ZaYqNB60RKL4RdKujS1gESYH+FuQDh5sinOn/9b6KTdddrOW/jYo3aAsZoDqjcvrX1WcSmzUiMEaBtRYElF73vTOy7FKAOnv+Opf+N3jOBx1z/psWiJxfALFiZf3kAizI9wvSdcDbZuHSx00jfccAiHDt3gttPWc1at1kvjzUtbVFQkfCbF1E1WCf9GSfe5txZVIw2c/oqUZ5qQkCDpXHotWmKxLbEisKRAIsyPcB4QapNVWsBxtduTcFwAfvqpI/jtSpw7bTPPqqW+tL6Yus1cf4R7lIhL8caiymO0gdNMSBFXACQ/UzX2aPUGPSy6LIdxKAmJMD/AXTzVpUsNsGXLfU5Hu94vDqC99gD5pm6ySjhi9g5W6bgUORZVvV2h/opUwZyZmenwt6dnGh8fL3o+PRctKY0RwjiUgkSYH+AsHPiZk83WyIV7koO9EOM7bZpVX0XOS+8v9aekBYDF1blSUTMuRapF1ShL9M2GVMFcVVUl/FspK7maqwG1XGnoT2EcJML8BFedvatO2lVMGN/oaVYtH3+wSkidvQ4ZMkTS+ZxX5xpt1qtGWb21qBphib6ZkSKupD5TdxMcm83m8LeSqwFZWGlo9jAOEmF+iFi6h9tv3+syYaces2qzuK38wSohdVZ69uxZh7+dZ7lmnvX6ihSLKgsDJyFdXEl5pt5McHjrsT1y+0m9Vxr6KlC1KKOvkAjzQ5xfLJvN5pB+wl2nrfWs2mxxAZ7qzwi7FXiLOzG1b98+4d/Os1yx1bn+jphF9dy5c4iOjsaQIUNw5coV4Zjg4GCHHH9maWMsIzUEQYqVXKp7LiIiQlL8mFFQUqCyOk6QCPNT7BtjfHy829lOaWmpg1tIy1m1GeICpFolnHcrYLXDkIqzuEpL242EhCKHZ+dqlutpdS4hblH1tHWNfbLlyspKlJSUGLqNsY7UEARvreRauuf0FjhyBKo7WB0nSIQRANzHr3gSaDxazKqNGhfgblEEjxGFpRiuxFVOzh0ALA7PztUs193qXBJhV5FrkXaVbNnoYp9lvBFXUp+p1qus9RY4csI43PWprCJZhJWVlUk+aXh4uKzCEGyidydt9PQO7urPqMJSDE/iyv7ZuU4e7Hp1rr8j1aIKeDcIqTV4GjlGR0k8iaugIMfhV8pOHXqvstZD4Hgz6TBinypZhEVERMBisXg8huM4WCwWVFdX+1wwguDRu+NxRokBxujC0hOuxdVV+GeXlHTcq9W5/ozUbW3cDUJaDp56u7D0Rqpgjo2N9XqnDj1XWWspcORsJWfUPlWyCLMPpiUILWEpvYOcVUquRBlrwlJJ6u7M4N66JXV1LiFukXY3CP39dz3s3p2mmXXADLGcvqDmikK9VllrLXDkbCVn1D5Vsgjr1auXmuUgCLewlN5B6gAjluOKJWGpFO5SnxQWJtQRAXxdZWRkIDg4WNLqXEqp4Bl3g1BOThoAfawDRnQPKYGaFj49cr/JETi+egy8rUOj9qmyA/NLS0uxZs0aHD58GABw3XXXYdSoUQ7LoAnjwHoMB4tJJ10NMLVlFJ/1syQslcJ+9sq7yKzWi0hKOo7rr//F5bOLjo5mZvGH0XE3CClpHZC6IwJgXPcQi+id+81bgaOHS9qofaosEfb9998jPT0d9evXR9euXQEAr776Kl544QXs2rULnTp1UrSQhLo4vzDurDtax3Do3fHw2A88fD4vVwPMtm13w2KB5Fk/i8LSV/j24e2zI4HliJxJkatBKC1tt2CF5JFrHfB2T0SjuodYRO+kqd4KHC1XVYolH3d1HEvIEmFTpkzBgAED8MYbbwgrPK5cuYIxY8Zg8uTJ+M9//qNoIQl1sX8RPLkPtI7h0LvjAdwPPK5XAAaA42r/5W7Wz4qwlIpcCykLz86oeGtFEBuE6te/rIh1QOr7z++JaFT3EKvo8a4oJXDUXBhi9L5GtiXMXoABtcttZ8yYgS5duihWOEJbWHQfuHpx7IVBZWWlEJjJo+QL5y4GLDi4wuMKQMD1rN9IHYavLgUW7sEX9HLRe2tFcNWm7JMsq2UdEBtYjeoeIq6iRH+lZFwg62EzcpAlwsLDw3HixAm0bdvW4fOTJ0+iUaNGihSM0B4juA/0dJ162mIHqEHtCkDxHFdG6ST0TtSoJyylWZBiRXAug9pxdlIHVjO63FlFLYHiS/tWcmLvjTvcVWw6qwJNlgjLzMzE6NGj8corr6B79+4AgK+//hrTp0/H/fffr2gBeQYMGICDBw/izJkzaNy4MdLS0rBgwQIkJCQIx3z22Wd4+umn8euvv6JevXro2bMnFi1ahMTEROGYzz//HFOnTsWvv/6KZs2a4amnnsKIESMcrrVy5Uq8/PLLKC4uRseOHbF8+XIh9g0ALl++jGnTpuH9999HRUUF0tPT8dprr6FJkybCMSdOnMCECROwb98+NGzYEMOHD8f8+fPrJOhjCSO4D/RynbrqTA4dugGjR7+JqqoQREaeR35+K1PP+o2WidoXWEmz4IsVQa9Nlb1JQqolZrSi8LA0abBHyYm91HfN1c4QPCzmppOlCF555RVYLBYMGzZM2CQ2ODgYEyZMwEsvvaRoAXluu+02PPHEE4iPj8eff/6Jxx9/HIMHD8b+/fsBAAUFBbj33nsxdepUbNiwATabDVOmTEFGRgZ++OEH4Zj+/ftj/Pjx2LBhA/bs2YMxY8YgPj4e6enpAGof4NSpU7F69WqkpKRgyZIlSE9Px5EjRxAbGwugNiZux44d+OCDD2C1WpGVlYWMjAx8/fXXAIDq6mr0798fcXFx2L9/P4qKijBs2DAEBwfjxRdfVKV+lMBI7gOtXafuOpOqqhAkJR0HYO5Zv1apBlgcKPVKs8BieAAgPrBGREQw53JXQqSw2DZ5WJk0OKPmxN7TpJCV+5eCLBEWEhKCpUuXYv78+cjPzwcAJCcnIywsTNHC2TNlyhTh3y1atMCsWbMwcOBAVFVVITg4GAcOHEB1dTWef/55BATUPvDHH38c9957r3DM6tWrkZSUhEWLFgEA2rVrh6+++gqLFy8WRNirr76KsWPHYuTIkQCA1atXY8eOHVi7di1mzZoFm82GNWvWYOPGjbj99tsBAOvWrUO7du3wzTffoFu3bti1axd+++037N69G02aNMGNN96I5557DjNnzsQzzzzDTNC1K4wiJLR2nXqzGS8Ls34l0UoMsDib11oIlZSUCCtwWQ0PkPIusGZt8NW1zmLbdAdLudnUmth7ukeW7l8KPvnGwsLC0KFDB6XKIpnz589jw4YN6N69O4KDgwEAnTt3RkBAANatW4cRI0bgr7/+wjvvvIO0tDThmNzcXKSlpTmcKz09HZMnTwZQ+wIeOHAAs2fPFr4PCAhAWloacnNzAQAHDhxAVVWVw3natm2L5s2bIzc3F926dUNubi46dOjg4J5MT0/HhAkT8Ouvv+Kmm25yeV8VFRWoqKgQ/vZmv04l8bQvHSto7ToV60wyMjIQHR3t8rdGdnMA2okBvWLQXFk49BBCzgM9q+EBRrKYu8Nb17pR4iNZsZ6qmTbC0z0CYOL+vUGWCLt8+TKWL1+Offv24cyZM6ipqXH4nnf/Kc3MmTOxYsUKlJeXo1u3bti+fbvwXVJSEnbt2oUhQ4bgkUceQXV1NVJTU/HJJ58IxxQXFzsIIwBo0qQJysrK8Pfff+PChQuorq52eczvv/8unCMkJETYksb+mOLiYo/X4b9zx/z58zFv3jyJtaEcUl8Ed8cpbab3dD5+cNRjIPDUmfBC3xWVlZUoKSnRRYgp8WzExAD/TLw5pxS0iEETs3BoKYRcPafU1Fzs358KQFux46rd2Gw24d9GzMfEo4SlhNX4SLmTBqX7cDVXgXu6R8DCpPXYE7JE2OjRo7Fr1y4MHjwYXbt2Fd3Y2x2zZs3CggULPB5z+PBhYRXm9OnTMXr0aBw/fhzz5s3DsGHDsH37dlgsFhQXF2Ps2LEYPnw47r//fly8eBFz587F4MGDkZOTI7uMWjJ79mxMnTpV+LusrAzNmjVT/bq+vDBKr1aUej5AG9ep1LxenoJBebR2VSjlQhETvPzm0d6cUwytXApilgu9rD7O95+a+jVSUvI0ETtS283YsXfWmYyybvVVwlLEsrtLzqRBLVerWu1A7B5ZtB57QpYI2759Oz755BP06NHDp4tPmzatzspEZ1q2bCn8Ozo6GtHR0bj22mvRrl07NGvWDN988w1SU1OxcuVKWK1WLFy4UDj+3XffRbNmzZCXl4du3bohLi4Op0+fdjj/6dOnER4ejvr16yMwMBCBgYEuj4mLiwMAxMXFobKyEqWlpQ4dkPMx3377bZ1z8N+5IzQ0FKGhoR7rQy3kvjBKr1aUej4etV2nUgSqfU4mgJ2gUF9dKFJcCmrcq54uFVf3o3WcpKv7z81NRUpKHoBa13dCQoJqg5zUZxcREYH4+HhVyqAWvrqXWXH3uUPOpMEorlYesXs0mqtclgi75pprFMkHFhMTg5iYGFm/5V2gfAxVeXm5EJDPExgY6HCss3sSAHJycpCamgqgdtDp3Lkz9uzZg4EDBwq/3bNnD7KysgDUxp4FBwdjz549GDRoEADgyJEjOHHihHCe1NRUvPDCCzhz5oywojInJwfh4eFo3769rPtlHaU7J1/Pp6SVwJvBjuVZsrcuFFcClN8TElDvXvUKSPd0P1ouuBC7/+joaE2tTay63uTgq3uZ1cUS9vg6aWD1eUuNMzPK4jIeWSJs0aJFmDlzJlavXo0WLVooXaY65OXl4bvvvsMtt9yCxo0bIz8/H3PmzEFycrIgfPr374/Fixfj2WefFdyRTzzxBFq0aCEEwo8fPx4rVqzAjBkzMGrUKOzduxebN2/Gjh07hGtNnToVw4cPR5cuXdC1a1csWbIEly5dElZLWq1WjB49GlOnTkVkZCTCw8MxadIkpKamolu3bgCAvn37on379nj44YexcOFCFBcX46mnnsLEiRN1s3SpjdKdk9j5WAyEZ3mWLFcwuatHNe9VqTgsNTebVtMaxVJAPsuTCjn46l72Nj6SR+0+Sant0Fh+3lFRURgyZIjgdfDkBTHSKnVZIqxLly64fPkyWrZsibCwsDpByefPK9tZhIWFITs7G08//TQuXbqE+Ph49OvXD0899ZQgam6//XZs3LgRCxcuxMKFCxEWFobU1FTs3LkT9evXB1AbvL9jxw5MmTIFS5cuRdOmTfHmm28K6SmA2kS0Z8+exdy5c1FcXIwbb7wRO3fudAi0X7x4MQICAjBo0CCHZK08gYGB2L59OyZMmIDU1FQ0aNAAw4cPx7PPPqtovbCE0gOH2Pmio6Mlu0K0yu/D6ixZDcGk5r0qEYel1GbTJ082xfnzf9exCqhpjWJl9SHLkwpvUWq1npz4SB41Y0KVCIQ3wvN2jkF0Z7VzNUlnNV5Rlgi7//778eeff+LFF19EkyZNVA9679ChA/bu3St63NChQzF06FCPx/Tu3Rs//uhZ2WdlZQnuR1fUq1cPK1euxMqVK90e06JFizquTzOj9MCh1Pm0zO/DkgXDHm8Fk5TVqWrcq5LL2qUmrxTbbHrLlsGwX5molVWABZcKq5MKOfgqUqS2TU+oHVPla/9ltOftyWrnzSRdb2SJsP379yM3NxcdO3ZUujyEgVF64FDifFpmktbCgiHHqueNYJIqWtW4V7WWtUtd4OF8PxwHANpZBZRyKUlBCaGtl+tNLr6UyVPbtI+TBNiNqRJDjYmVWl4II1jtpCJLhLVt2xZ///230mUhTIDSqxXlno9/+e0HCi3iHdS0YMi16nkjmLwRo2rcq9KDtzf7HNrfz6VLDbBly30O51LbKqBmbiV7lBLaerne9ELK/bAcUyWG0hMrNb0QRrPaeUKWCHvppZcwbdo0vPDCC+jQoUOdmLDw8HBFCkewj6+JXtU4zlWusZMnm6k2c9LKguGtVU8J957YrJ71AFhv9znkrRo2WyNdXMtaCBclhTYr6VhYwKjWGbWy26uZ+oLV0A85yBJh/fr1AwD06dPH4XOO42CxWFBdXe17yQhDoPTsXYnzucs15oxSMyetLBj2SJlx+1ouT9dgcXWqK+Tuc8hKcLwWeBPcrEWKEqNiVOuMVv2Xkm5aM72fskTYvn37lC4HYWCUHnCVOp/zzNQZJWdOWudtkjrjllsusWsYJfDV285azT3vWERucLNRrT5qYmTrjNr9l1KC3YzvpywR1qtXL0nH/fOf/8Szzz7rdsZMEGriambKY+SZkxYzbqPO6l3hTeyaHlZNvfBFSJmpffhKaWkpAHNZZ5REScFuxvdTlgiTyrvvvovHH3+cRBihC65mpkANBg/egmbNThm2c9Rixm3kWT3gW5yekTpwX/BFSLHcPrTKC8hfy37LMtasM1rWhTuUFuyeysvfb1FRkcvvWRRoqoowrnaNN0HogruZ6fXXH65zrJHM11rMuI0+qzfjjFlpfBFSrLYPLfMCAq6Dyl0J/szMTM3bmtZ14Q6tBDsr9+stqoowgtAbTzNTPvDYiIOx0ukh+BmkzWaTdA0jiFajPVOt8VVIsZBQ1hkt8wK6wt31rFarKtfzBCsbc2sl2Fm5X28hEUaYHneuKKMEl/OolQrD0wzS/hpDhgxBRESEIUUrcRVfgpu1TCjrK1qv3mR9tajWSWT1DqI3StJcEmGE6VA6JxkrqOVikzozjIiIMJRo1QoW4m68wZd2pJeb19s61nr1JuurRfUQiN60FaXfIdYFsT0kwgjTYeZ4IC3KbJQZJAtIjUPhrYiu0KMt+rqFj5Z4E+vDo/XqTZZXi4oJRPtdRZRui1LOpXQsF+uC2BlVRdhDDz1E2fMJXTCiwGIBI80gWUBqDJL9CjpXsBYszBJyYn20Xr3J8mpRMYHovP2U1m1R6vMtLCwEIN63syyIXSFbhJWWluLbb7/FmTNnUFNT4/DdsGHDAACrVq3yrXQEQWiG0WaQrOGLgGUtWJhlpFhq5QaDy3WLsbpaFPBeILLaFnmxKCYSWRbErpAlwj7++GM8+OCD+OuvvxAeHg6LxSJ8Z7FYBBFGEIRxEJtBlpaWMhcTxko8llQBq7Wrl5X6UQpvhK63qzfluMX0Dj6XgjuBCAAFBYnMhR2IvSNiIpFlQewKWSJs2rRpGDVqFF588UWEhYUpXSaC0BX7gauwMAAFBUFISrqChIRai6/RBi6piM0gN2/ezJTbjKW8QFJcIFq7elmqHyWQInR9Wb0pJ70Fy/GnngRifn4rLFky2WVbtNlsuk22fHlHpApifocD+9/p2f5libA///wTjz76KAkwwnTYD1yeOgSjDFze4GoGmZa2W5P8SnLQOyeUPWICVg9XL0v1owRShK5SosgbMcBqP+BcF/zm62JtcdOmTbr0b67KtW3b3YiNLUbTpq4z4Nvj6tnbbDZs2rTJ4ThX8Zl69ueyRFh6ejq+//57tGzZUunyEISu8C+wWEfF4sAl1/XkPIP8++96yMlJA8cFYPfuNNSvf1m2xUYrd5jeCwrEXCB6BwvrXT9KIDXWx9f2ZKbYSFd1IaUt6tG/ud7rNwBr1oyR3F7lPns9+3PJImzbtm3Cv/v374/p06fjt99+Q4cOHRAcHOxw7IABA5QrIUHogFhHZZ9Z3hVam7idXU/urB6uZnxRUVHIzMzEpk2bYLM1wu7daQB8H4C0coexMmh6coHoGSysVf2oLbi1ivXRWzCrjbs9dfUOXHddLmXbK4vpdySLsIEDB9b57Nlnn63zmcViQXV1tU+FIohTp4CjR4HWrYGmTbW/vtigaW/i9kbwqIX94OfJ6uFukOS3VVFyANLKHabnoCk1BknPYGEt6kdNwa118LvRVtd5C98Wt227G/xkC7AgP7+VLtZR/rm5LlctSrRXVq3BkkWYcxoKglAafia9cWN9zJhhRU2NBQEBHBYutOGBB/7W1LokddCUI3jUxFerh1oDkJodoJ6DplgMkn1Mil6r57SoHzUFt9bB70ZbXScV+zaWnPwHLBaA4/hPLLq5XPnnW5sHLBuxscVYs2aMou2VFWu5K2TFhL399tvIzMxEaGiow+eVlZV4//33KUUF4TX8TNpma/S/VTu1aU9qaiyYPj0cf/65FlbrRU2tS2JL3Fl8sX21eqgxAKldT3oPmp7aY3x8vO6r57SuHzUEt9ZB0yxuTu4rUVFRGDJkCDZv3sycy9X++TZtWuSxvcqZsLB2v/bIEmEjR45Ev379EBsb6/D5xYsXMXLkSBJhhNfwg5TYy6K1dcmdewlg88VWwuqh9ACkRT2xPGiysHpOq/phcWIiFSNtTi4XfussFl2uzhbPuXPP4tixICQmXkFCws0AbpY9YWHxfnlkiTCO4xwStPKcOnVKiC0hCDmw/LI4w2JZ5Vo91ByA1Konfxg0fUGP+hET3GruU+gr3ro9jZwI1xfrqJr3bf+7+Higc2dZp6mD3tZyT3glwm666SZYLBZYLBb06dMHQUFXf15dXY2CggL069dP8UIS/oPeL4s3A5LeZXWHHKuHmnE3atWTc5lLS0tx5coVh2OCg4NRWVmJoqIipgdFNdAjkaiY4NZ7n0IxpJbFqIlwfV3kYLT7NsKOBl6JMH6F5MGDB5Geno6GDRsK34WEhCAxMRGDBg1StICE/6Gna0nKwFVaWiok/PNUVvtZP49WQsCTG9UdapZLrWdqb5UQ2yQb0HZwYMFSovVA6O0WOSzm25OCnE3FWcBXYa7GfattWdM7JlMMr0TY008/DQBITExEZmYm6tWrp0qhCEKqiFBjiyFX7gb7c4eEOFpb3JV13boczVJXSJ3JaTnj09Idxlp2eKNZDJTEmy1yfIEFkcvDYv4pdyhZJ77etxbvCevvl6yYsOHDhwOo7dDOnDlTJ31F8+bNfS8ZQYig5hZDns+dh06dPP9e69QVLM749CoTC/mAjGopkYs7wa1WoD5LIpeF9qYHSty3v70nrpAlwo4ePYpRo0Zh//79Dp/zAfuUrJXQAjW3GJJ67oyMDERHRwOQvjebUrBkCXAHC/vPsbA6z0iWEjm426dQrZWxrAzerLY3tfHX+1YDWSJsxIgRCAoKwvbt2xEfH+9ypSRBeIMv7jQ1UyCInTs6Ohrx8fGalYdHqiVgyJAhwrJ0Z1gQaUrDYtoQby0GRhDXrnBVJq1WEOslcllsb1og976d27Zz3KzZJyuukCXCDh48iAMHDqBt27ZKl4fwU3xxXanZ0cs5N0sZysWC1c0Wk8Ra2hBvLQYsudmUQIsVxHq6A1lrb1oh577F2raSz9FIExlZIqx9+/YuV34RhC/IfSnU7OjlnJvFDOV6B6prBWtpQ7y1GLDiZlMSNVc76+0WY629aYWc+/bUZpV8jkabyMgSYQsWLMCMGTPw4osvokOHDggODnb4Pjw8XJHCEYRU1Ozo5ZybpQzl/hY4zFL2fF8tJe7E87lz55iazTuj1cpYvdyBRsg/pQZK3rd921byORptIiNLhKWlpQEAbr/9dod4MArMJ/RETm4sNc+tZnl4xDovvS0FWsFq9nxfLCWexDOf9JSV2bwzWq2M1csdyOJqZC1Q6r6d23Za2m7VniPrcWayRNi+ffuULgdBuOXUKeDoUaB1a6BpU71L4x49cnWJDUL+EjjM2qDoq8XAlXjetu1uxMYWo2nTIuE4VmbzrtAqKbFe7kCzCSyp+Hrfrtr27t1pSEvbjd270xTbtBswhhdAlgjr1asXvvzyS/zrX/9Cfn4+tmzZgmuuuQbvvPMOkpKSlC4j4YfwgZUbN9bHjBlW1NRYEBDAYeFCGx544G+EhISoKnrknFsPIeA8CAG1s0q+8/KnwGGWBkVf24Ir8QwEYM2aMUwOJFqjhjvQSMHcRsbdxDAhoRCTJy9BcnI6unWL9nnTbqN4AWSJsK1bt+Lhhx/Ggw8+iB9//BEVFRUAAJvNhhdffBGffPKJooUk1Ecta5Oc8/KBlTZbo/9l2q51edfUWDB9ejj+/HMtrNaLyMrKUlz02HfEQ4YMcbkXIb9Jvatz69FJd+r0I/7+u54wi9y9Ow31619Gp04/iloK9Nxayez4UoeuxDPA7kCiNUpPeIwWzG1kPE0Mazd534LERN/r2ZvN5O3Ruv+TJcKef/55rF69GsOGDcP7778vfN6jRw88//zzihWO0IY1a4Bx44CaGiAgAHj9dWD0aP3Oy3esYi9RZWVlnRxdvuDcEbuLJRDriNXYSskZ+xm+zdZIEGBA3YHak6XAeUNlqffoj2hpKeHF87ZtdwMwvztZDkq2T9a2vWIFNdq8FBeyEvXs7Wby9mjZ/8kSYUeOHEHPnj3rfG61WlFaWuprmQiNKCkpwbFjVzBuXCxqanhrE/DIIxxuvPEMEhODZDVEpc6rtSvN/sWXu+2Qmlsp2RMVFYUhQ4Zg8+bNkuK+3AWq02AjDa0sJc5uttjYYqxZM8Yv3MmsoHUcEatuUKXbvCsX8smTTQFY0KzZSV+K6hIpYo+F/k+WCIuLi8Mff/yBxMREh8+/+uortGzZUolyESpTUlKCF154C7/+eh1qatIdvquutmD58k+RlHTc60GFf3ELChJRUzPcp/PqFXTrSyyBmlspOcNnwhcTq662VgKMEbTKClpZSng3W2FhIbKzs9G0aZFf5qHSC63jiFh2gyqd6sF+4ggA+fmtVOl/pMYLstL/yRJhY8eOxWOPPYa1a9fCYrGgsLAQubm5ePzxxzFnzhyly0iowPr1gf+LtwoAwAG4mmrEfgD3dlDhjxcTBlLP6+ol4gfAwsIAKOiNFFBiRaHUcygxCxYTq662VjJK0CqLqN15R0VFObQJlvKemR2tVxMbKaeVEqke+Imjmv2Pp3hBrff3lYIsETZr1izU1NSgT58+KC8vR8+ePREaGorHH38ckyZNUrqMhILwrsIZM2KFgPdaAVYrxJSaaStpxbJ3pdkPgO+8w2HhwlJhtaRSM0Ul3KBSzqFUDBrg/UDtL6krlEas8y4tLVUkTlHpvGesurxYQ+/VxKzmtFJ64qF2/yPWllnq/2SJMIvFgieffBLTp0/HH3/8gb/++gvt27dHw4YNlS4foSCeXIWABenpO9G+/W+KNUKlZ/DOA6Cr1ZJKDCRKCEhvg0/lxqA5X1NqGfUebIyKWOe9efNmRdqhkqv/WHZ5sYaeecdYcY85o4bVSO/+R+/r2yNLhPGEhISgffv2SpWFUBkxV6GSAoxHyazxUlZLKoUSAlLqOeR2cr7kSfPXPe98RUrnrVQ7VEoQGcnlxQJ6uH9Zco85IzfVA+B+oqB3/6P39e3xSYQRxoSlBugKd+JC69mLEgJSyjnkmsZ9tZZQrJH31E2OC3BcbZAxC1YLKbDq8tITvbe9Ysk95owvqR4ARwsrS3tustL/kQjzU1hpgK5wFhd8MKXa4lGPbYcA38Slt9YSvQcbM5Cc/Ac4zv4T6VYLvWOzWHV56Y3e216x5B5zxtd+175O9a5nFvs/EmF+jBYbTMtFD+uNEh2E1Je3vLwc5eXlALS1TOrdCbKMJ4Fks9mEf58/HwU5CVT1js1iyeWltxh1hZ5tnkXvhFSrFY9UC6ue9cxi/0cijPCItzMCLaxJcsWjFpnspbzk5eXlePfddx0+09Iy6Y8CSwypAgmQb7XQOys7Ky4vvcUoS7DknnNGrC/TOuegUsKdtTZFIoyoA5/cU44oYXGmAWiXyR4Qf8mLiooc/rYfjJOSjvt0bUIe3ggfJawWergFWXF56S1GWYLV/pJHynW1sLCaWbiTCCPq4Cq5pzew+BJomcneG6QMxhSfpT1irhVfLJd6uQXVdHnJtVJQjBqb/aU3+GJhldpuzCzcSYT5EXoFnvuKkuVmxSUDiA/GGRkZSEhIMHwnbTSkCgO5Qb1at0G1XV5yrRQsxagR8pFrYfWm3dhjNuFOIsyPYN307Q4ly82KSwYQH4yjo6OZexZmR0wYZGZmwmq1uv29lHaodRtU+72Xa6VgaUJkFvRY8CDXwiq13RQWFjocYzbhTiLMzzDqoK5UuVlahcSSICRqERMGVqvV522J9GiD9u+Pq4G6srJSiFX0ZaD2xkpB7V9ZtI6bUtLC6qnd2OchM6NwJxFG+B2s5EhjSRAStWglDPRqg2oO1N5aKaj9K4vWcVNKWVjdtZvY2GJUVYU6lN+Mwp1EGMEUp04BR48CrVsDTZuqdx1WcqSxIgiJWtQUBiwkilRzoJZjpaD2rw5axU0pYVFz127efHMMAMfym1G4kwgjdId3j2zcWB8zZlhRU2NBQACHhQtteOCBv5mMU/MFFgZjwhEt8jWxFpOp9EAt1UrhbftnMbEryxgtbspVuwE48AmRnctvNuFOIozQFd49YrM1wpIlk8FxFgBATY0F06eH488/18JqvehzHANLK0NZG4yVQsvBUulrafVMWHmmagzUUq0U3tS1mfNDqYXR4qZctRux8ptp4koijNAVviMW6zh8jWNgTfiYbcDQcrBU61pmeyaeUGuglmqlkFrXZs4PpRZGjJuybzfBwZVYs2aM2/LzycSdMeLEFSARRjCCFh2HEV9QoyB1EFRisNTyWmZFyfdNK/e62fJDqYVR4qY8tRtP5fc1mThrkAgjmMAoHYdR0TquRupmvka7lllQ8n3TwspstDgnpZD73hohbsq53djvRSm1/GaIFyQRRnhEq9WKgDE6DiOidVyNlhYLso7IR8n3Te2BzmhxTkrg7XtrxAU/9u1GzoIN+/pxNxljPV6QRBhRBz1XK7KSOsJMaBlXo6TFwt0s99y5c4pfy18w4kANGDPOyVe8fW9Zi3v1Fm/Lb3+cp8kY62EJJMIIB8RWK37zTQ6aNTuJcePugtVqZfqlJuqituVIKYuFFCuAP1pHfMWoA7W/hytIfW89PTd+UsPvjOAMC89dzvWNPhkjEUY4ILZaccuW+2Cx1CA//2onwLq5V09YilnQorNSymIhZfbqj9YRJTDqu+qv4QpKvLdmTvVh9MlYgPghbDBgwAA0b94c9erVQ3x8PB5++GGHjT0BYPPmzbjxxhsRFhaGFi1a4OWXX65zns8//xydOnVCaGgoWrVqhfXr19c5ZuXKlUhMTES9evWQkpKCb7/91uH7y5cvY+LEiYiKikLDhg0xaNAgnD592uGYEydOoH///ggLC0NsbCymT5+OK1eu+F4RGsEPcK7gOwGbrREAzwNmSUkJioqK3P5XXl4uqTysuUekwHd8r7/+utv/VqxYgZKSEk3K46mzUgreYsG3HaUsFjZbIxQUJAptTs1rEezgyn2alHS8zjM2Yv8gFSXe2zNnzjj87ep9Ath33bnC1VhlpMmYYSxht912G5544gnEx8fjzz//xOOPP47Bgwdj//79AIBPP/0UDz74IJYvX46+ffvi8OHDGDt2LOrXr4+srCwAQEFBAfr374/x48djw4YN2LNnD8aMGYP4+Hikp6cDADZt2oSpU6di9erVSElJwZIlS5Ceno4jR44gNjYWADBlyhTs2LEDH3zwAaxWK7KyspCRkYGvv/4aAFBdXY3+/fsjLi4O+/fvR1FREYYNG4bg4GC8+OKLOtSe9zib/52RMtOQGjj50EMPISwszO15WDCTy4G1VApqWo7UzDjvyRWjVnZ7gg2M6j5VEl/f25KSEmzevFn4W4mQBJYs/EZ3VRtGhE2ZMkX4d4sWLTBr1iwMHDgQVVVVCA4OxjvvvIOBAwdi/PjxAICWLVti9uzZWLBgASZOnAiLxYLVq1cjKSkJixYtAgC0a9cOX331FRYvXiyIsFdffRVjx47FyJEjAQCrV6/Gjh07sHbtWsyaNQs2mw1r1qzBxo0bcfvttwMA1q1bh3bt2uGbb75Bt27dsGvXLvz222/YvXs3mjRpghtvvBHPPfccZs6ciWeeecYwgwM/wJ082RRbtgyGveFUSicgNXAyLCzMVHlf3KF3KgU1Oyu1BksxV4wREzeyNIAZASXrwoh17+t7a3+/ZnVtGtlVbRgRZs/58+exYcMGdO/eHcHBwQCAioqKOtaU+vXr49SpUzh+/DgSExORm5uLtLQ0h2PS09MxefJkALWN9cCBA5g9e7bwfUBAANLS0pCbmwsAOHDgAKqqqhzO07ZtWzRv3hy5ubno1q0bcnNz0aFDBzRp0sThOhMmTMCvv/6Km266yeV9VVRUoKKiQvi7rKxMRu0oS+3qqcOorJTfCRg9cFIJWEmloGZnpUaHKxbvoVXiRqUGbxYHMH/ByHWv1Hubl5fic/wUq7sYGHVlvaFE2MyZM7FixQqUl5ejW7du2L59u/Bdeno6pkyZghEjRuC2227DH3/8IVi8ioqKkJiYiOLiYgdhBABNmjRBWVkZ/v77b1y4cAHV1dUuj/n9998BAMXFxQgJCUFERESdY4qLi4VjXJ2D/84d8+fPx7x587yoEe3wpRMweuCkr+gtQo2algBgI/heycGbNRe1P6GkeNDCoqb0e2uzNcL+/al1PvflfdJzcsnSfsC+oKsImzVrFhYsWODxmMOHD6Nt27YAgOnTp2P06NE4fvw45s2bh2HDhmH79u2wWCwYO3Ys8vPzcffdd6Oqqgrh4eF47LHH8MwzzyAgwBjrD2bPno2pU6cKf5eVlaFZs2Y6lsgRd53AuXPnPHY6LAykeqK3CDVyXA0L8R5qCie9XdT+ii/iQSuLmtLv7fnzUXC1Fi81NVdW29N7cmnkfs0eXUXYtGnTMGLECI/HtGzZUvh3dHQ0oqOjce2116Jdu3Zo1qwZvvnmG6SmpsJisWDBggV48cUXUVxcjJiYGOzZs8fhHHFxcXVWMZ4+fRrh4eGoX78+AgMDERgY6PKYuLg44RyVlZUoLS11sIY5H+O8opI/J3+MK0JDQxEaGuqxPtRGyqzBeeDgt5pw1+mwMJDqCQsilPWOyBk1A/19RSnhpJcVwYhxUUriq3jQ0h2n5HNw1Q8BNUhJyZN1Pr0nl4Dx+jVX6CrCYmJiEBMTI+u3NTW1S1LtY6gAIDAwENdccw0A4L333kNqaqpwjdTUVHzyyScOx+fk5CA1tdZEGxISgs6dO2PPnj0YOHCgcJ09e/YIKyw7d+6M4OBg7NmzB4MGDQIAHDlyBCdOnBDOk5qaihdeeAFnzpwRVlTm5OQgPDwc7du3l3W/WmE/uygtLXVYVQPIz0xs5MBJX/F3ESoHVme5SgknvawIesRFsSb6lBQPSrUH5zqy2WyoqqoS/g4KCnKY9MupMyn9kDeTGhYml2bAEDFheXl5+O6773DLLbegcePGyM/Px5w5c5CcnCwIn3PnzmHLli3o3bs3Ll++jHXr1uGDDz7AF198IZxn/PjxWLFiBWbMmIFRo0Zh79692Lx5M3bs2CEcM3XqVAwfPhxdunRB165dsWTJEly6dElYLWm1WjF69GhMnToVkZGRCA8Px6RJk5Camopu3boBAPr27Yv27dvj4YcfxsKFC1FcXIynnnoKEydO1N3S5Qp3nWRERASGDBmC0tJS7Nq1y+eBw6iBk3Jh2ZpjBFib5Yq1f347JUB8kNTLiqB1UDWLwfBKiQelhLTUOnJGTp156ocyMzO9Oh9NLpXBECIsLCwM2dnZePrpp3Hp0iXEx8ejX79+eOqppxxEzVtvvYXHH38cHMchNTUVn3/+Obp27Sp8n5SUhB07dmDKlClYunQpmjZtijfffFNITwHUNsSzZ89i7ty5KC4uxo033oidO3c6BNovXrwYAQEBGDRoECoqKpCeno7XXntN+D4wMBDbt2/HhAkTkJqaigYNGmD48OF49tlnVa4p7/GmA/B24NA7cFLvGTir1hxCHmLtn3fL83gaJJUQAr62by3coSyupFNKPCglpMXqyNc6kxrgL8cr5c8eDqUwhAjr0KED9u7d6/GY6OhoIY2EJ3r37o0ff/Tc0WRlZQnuR1fUq1cPK1euxMqVK90e06JFizquTxaR2kkC3g8ceooQVmbgLOc40lukGg137T84uBIFBYleDZK+CgFf27ce7lBW0rQAyogHNdxxznV0ww2HcOjQDT7VmdL9sJFXW7OIIUQYoQ1inaScgUOvQdxsqQCUFpWsiFQj4ar933DDIaxZM0byIKmUi9pXC5PW7lC9V9KVlJTAZrM5fOareFDaHeeqjn76qSMAi/C33DpT8h1mxcJvlkkkiTACgPRO0qjmZ6OlAnDuYOzjjTyhtPg0ikhVE3fCKTi4UhBggLRBUo0BTI6FSeugaj1X0kmdcAwZMgSxsbFe1b2S/aGrOuIFGA8r+RX1FjdSt8QzwiSSRJgfYj/A84O7N52k0QLsWXKDSEFuoK4vGE2kaomzcDp37hyys7NRUJAoS1goOSjItTBpHVSt50o6qVbDiIgIl8/GeUJUWlrq8L1S7jjXKSQ42AsxWn1Yi9Qt8YwwiSQR5me4G+CV6CRZjAHQ2w0iBykdh5KiyWgiVQ9cDc5i74wr66XSLhJfLExaWrVZWUnnbVuXOiHKzMyE1WoV/lYqhYSrmDBW+y09MGL/7gyJMD/D3QAv1kkOGTKkzlZN9rDqf2choaCvOAsuJUWTGToxvRB7Z5xXS/Io6SLxdvKkZ1C13qEMctq6VCua1WpVZA9TV3V0++17DRf+oRVm6N9JhBECnjrJiIgITTZKVhqjJxR0Flxpabuxe3eaYqLJDJ2YnsgRFkq6SLy1MGkdVM3SSjpf27paFmOxOqLVh+4xev8OkAgjnDDbC8+KG0QOrmbuOTlpcN7/zRfRZIZOTGukCgut4uy8FYJaWqxZWUkH+NbW1bQYu6ojNTLmmxEj9+88JML8HHcDRUZGBqKjowEY84U3Q7Z616ulAhQVTWboxLTGnbDgA/YB9ePsWLIwiaFl3+EqbQEfm+dLW1fbYuxcR0b0OuiF3m5uXyER5sd4Giiio6MN3RGwNAOXi7uZu71L0nkgkbNLgVFFqjNa5g3ydB4t4uzM0L6VxnPagtrP5A7YZDFmG6Ot2LeHRJif4g8B2UYfgNzN3Dt1+hHXX/8Lrr9+IDp2bICEhJsB3OzVoGu2QZyl5LNaxdkZ5dlohdS0BXKshma2GBsx6aneW+IpCYkwP4UCstlFipXKar2Ie++1+tQ5stax+gJLyWfJaqIvYhNM+1ALHnuh4SqPIqCM24s1wWPUpKdmmkSSCPMz+AFebKAwwgzCF1jrDO0xUwejF3omnzWz1cQIiE0wPYVauBYlV/cF9SX2jkXBY+Skp2bp/0iE+Rn2A/w115Rh5kwrqqstCAzksGBBGR544H7TD/Asua7cYeb6Vxu9ks+aMc7OiPhiiZQqSuQsXGJZ8PhDeAqrkAjzQ/gOY9o0IDMT+OMPoFUrC5o2jQAQoWfRNIEl15URYNlq6IyegwlZMNlACUukWDvyZeESi4KHwlP0g0SYn9O0ae1/BOEKI1gN7dF7MGGhDgjf47fUbEd6t1FXUByjfjgnISIIghAwmtWQH0zsocHEP7FaLyIp6bhPSYztUaodsdhGeeshXy6KY9QOsoQRhAEwkktQTygo3n9RMm2Bmu2I1TZq9KSnRoVEGOH36LmSTgosuQRZrSt/DIonYe6I0jF5aooSVgWPWNJTanPKQyKM8Gv0WknnDay4BFmuK38LimdJmLOE0veqZiZ2FrK8e2M9ZDHFhhkgEUb4LSyuUmIVI9SVP3X8zmLT3YDISqyeUVAzEzuLWd69mbwUFRUJn7GWYsPIkAgj/A6+kxNbpWQm15WvsLiii6iFZQul0qjtDlPTomoWa60RJmRGgkQYIRujxgfwneGxY1fwzjscamoswneBgRwmTboTiYlBTJZdL2gJO5v404ColTtMzfeetT7FmzrloQmZspAII2Rh9JiUqKgoREUBr78OPPIIUF0NBAYC//qXBZ07N9G7eB7RMjietwaKregiq6E+iA2I9nsfsjopkgrLGeeNipw6pQmZspAII2TBSrC4r4weDaSn87sGsJ+4VmvXk7MLZe7cszh2LAiJiVeQkHAzgJsNP7gbGXcDYnBwJQoKErFuXY4hg6ZdWdl5QelP1j+t8KZOWU2xYVRIhBF+j1F2DdBr8LEftOPjgc6dVbsU4SWuBsQbbjiENWvGGNZK5NlFRu4wNfC2TllNsWFESIQRqmLvDgGM7xLRA1pIQHjCfkAMDq4UBBhgTCuRmIssOfkPcocpjBwXIwspNswAiTBCVbKzs+t8ZhSXCCvQQgLCGWfBzQ+IBQWJprESubP8Tp68hNxhCiPFxchiig0zQCKM0BwjuERYw8gLCQjlcY7VO3fuHLKzsxULmmZh5bMnyy+5w5RHrE7NkmKDNUiEEYSBMNpCAkI9XA12SgRNs5IZXUxQkjtMecTqlASW8pAIIwgvYMFCoNRCAhbuhVAeX61ErKSCkCso5bjD7N+FwsIAFBQEISnpChISaoRzmvFdIBej/pAII2Thjy+l0XOj2ePrvZCAYxslrEQspILwJCgzMjIQHR3tcLycdmf/LngSnUZ4r72Bf4czMzNx8iSHEydC0bx5BeLirgAAgoKCEBERQe+yypAII2ThKj6Aj0uRitEGcrPkRgN8uxcziVGzoIZFQ69UEKWlpQ5/uxOU0dHRiI+P9/l6fBsXE51GeK+l4q/Ck0VIhBGy8eXlpIHcePCi2TntiDvMNGixjhpB03pkRi8pKcHmzZuFvz3tDqG0Nd6f8o/5o/BkFRJhhCKcOgV8800IbLZGkjosM1mVnOFFCmuWPF+QKpq1wGgWVK1Q+p71yIwuNR4tMzMTAFBUVARAmTguf9yOx5+EJ6uQCCN8Zs0aYNw4oKYmChbLZNGtdEJCQgwprqRi75I1iiVPbD9KT89Ly70syYKqLXqlgpASj6a0O80ft+PxR+HJGiTCCJ84dYoXYLV/c1wAduy4B3PnpggzUnv42Sk/gzU7RhCbvuxHqfVelma2oLKKHqkgxCw0Fy5cAFAr1rZtuxuAMu40f8s/5o/CkzVIhBE+cfToVQHGU11twcWLTaBAzCzTaGkBUgtfVsCxsHrODM+ANVhIWyBmodm1axcAIC8vBbwA4/HVneZv+cf8TXiyBokwwidatwYCAhyFWGBgbSLRU6dqRVrr1uZLKqq1BUgt5MaE2GyN8Ouv1+kaT2KWZ8AaLGRGl2KhsdkaITc31cWvyZ3mLf4mPFmCRBjhE02butpKB/jss6tuyoCA2mNGj9a7tL7Bz/xZsAD5Cn8vYhYHV9YOe/EDcAAsLn/rDiUC683wDFiGhXg6MQuNqwkEAHTvnutVG2DB8kf4LyTCCJ9x3koHAFq0uGodq6mpFWnp6ca2iPEWgn37gMWLjb2iyN7acc01ZZg504rqagsCAzksWFCGBx6436UYchY/tQKsVog5WytcDVpSA+uHDBmC2NhYt2KAVnX5B54sNO4mECkpeV5dgwXLn9aQ8GQHEmGEIthvpbNvn6s4sVqRxh9j1E4gKioK3boBAQEcamq8swCxBj+oTJsGZGbyItqCpk0jAES4/I1r64MF6ek70b79bxg58g5ER9/sdtCSGijN54pyt7qNVnWZE2/edyWDys0ksKTgj8KTVUiEEYrjKU6Mx8idQNOmwMKFNkyfHi50/mlpu3H+fG1ZjWiJEduPUsx92b79b7BaLyIhIcGrZyY3NQat6jIn9v2CzWbDpk2bhO9ctRUKKpcPi32rVMwUb0wijFAcd3Fizi+LkTuBESOq8eefS3D+fCQKCxOwe3eaywBx1ix5cpHrvvSEY2xZDbp3z0VKSp7HgdS+Pj0NwGapd3/EVRvytAjDk8uS2oF54GNJN26sjxkzrKipsSAggMPChTY88MDfzE7axbBwHMfpXQjCNWVlZbBarbDZbAgPD9e7OF5z6tTVODGx2YoRZzYlJSU4duwKunaNdXBNBgZyyMs7g8TEIEN2ClLw5tnaU1RUhNdffx02WyMsWTK5jmvTeYAdN25cnf0BKWO+f+CprVgsNZg8eYlHwT5kyBC0a9dOi6ISKsPHkoq1BZaSNEsdv8kSRqiGmIuL52rGfWOtpIyKisKhQ+7zpDHSF6iC1GfrDncr26SscmSlkyW0wd0ijLy8FPTtu9vt72JjY9UuGqER/KRLbEGOEZM0kwgjdMU5477SKynVtppIiX8j6uIqtoyHVjkS9rhrK/v3pwru64yMDERHRwvfkTXUnJhxQQ6JMEJXXGfcd1xJKRct9hmUGv9GOMIH1ttvOcNj9E6VUBar9SJSU3Oxf38Pp2+uivXo6Og6bmvCfJhxQQ6JMEJX1LQkabXPoHOeNBJg7nEVWJ+Xl4Lc3FTTdKqE8qSk5GH//lTYC3YS6/6J2VbEkggjdMUsliRfY6T8BX6V5dmzZ7Fp0yZYrRfRt+9upKTk0SpHwi1W60UMGGAuCwghHzNts0QijNAdsiT5F1FRUYbOE0dog7MAN5sFhCAAEmEEI5AlyVgoseDB3wWWfR0WFgagoCAISUlXkJBQ65v3dxEaFRWFzMxMh4StZrKAEARAIowgCC9xXvDgLus9Szl7WMO+Dj0lIvX3OrRarXoXgWAAo25zJwUSYYTfILZFDnEVT5auc+fOCf/2JCCMmLNHK/i6cd4Q3TlPmr/XoZkHX3/G2+TcZg5fIBFG+AWexALhiFRLl5iAIMQRSz7p75h58PU3fN12yKzPmEQYYVr42bGYWDDTLFqJWC3733sSryQgfMeMySeVxqyDrz9Rd9uh2m3eamosmD49HH/+uZa5bYe0gkQYYVr4WfS+fcDixXXFQo8ew9G7t3k6eaVjtcTEq5iAsHdbkrXCNWZMPmkPLT4gAHNvO+QrJMIIUxMVFYVu3VwnhE1JiTLV/o5SLVhSOzqxDtOdgACAgoJErFuXQ4H6EjBr6gVafEA4Q5bfupAII0yPWRLCSkWpWC0pHaazgMjPb/U/dwMF6nuDGVMv0OIDwhmzW37lQCKM8Av8KSGsUrFaUjtMXkBQoD7hCoodJOxR2/JrNBc4iTDCb/CXhLBKmvy96TBpsJWOP6VekBo7yMqAqcTiFsIzall+jegCJxFGECZDaZO/uw4zIyMD0dHROHfuHLKzsynewwv8KfWCWHvMzs5mZsCkRMTGxogucBJhBOEGbxMKsoQvJn+p1peEhASHgYjiPbzDKIO4Eu4dT+2RpQFT6cUtzpCVTRuMZJUnEUYQdviaUJAl5Jr8fbHSmHWln7+ipHvHXXtkccBUI77Rn61sWrvfjWSVJxFGEP/D6AkFve3oPFn6fLk/M67081e0cO+wOGCqIQzVtrKxjNbudyNZ5Q0nwioqKpCSkoKffvoJP/74I2688Ubhu0OHDmHixIn47rvvEBMTg0mTJmHGjBkOv//ggw8wZ84cHDt2DK1bt8aCBQtw1113Cd9zHIenn34ab7zxBkpLS9GjRw+sWrUKrVu3Fo45f/48Jk2ahI8//hgBAQEYNGgQli5dioYNG3pVFoItjJ5QUEpHV15ejsrKSixaVKqYpc+fgsz9FTmiROrzZnHAVFMY+usqYq0nrkaxyhtOhM2YMQMJCQn46aefHD4vKytD3759kZaWhtWrV+Pnn3/GqFGjEBERgXHjxgEA9u/fj/vvvx/z58/H3XffjY0bN2LgwIH44YcfcP311wMAFi5ciGXLluGtt95CUlIS5syZg/T0dPz222+oV68eAODBBx9EUVERcnJyUFVVhZEjR2LcuHHYuHGj5LIQ7MLizFwqnjq6kpISvP7664pb+vwpyNxfkfNOiLULfkEHwN6AqaYwZNH9alaMYJU3lAj79NNPsWvXLmzduhWffvqpw3cbNmxAZWUl1q5di5CQEFx33XU4ePAgXn31VUH4LF26FP369cP06dMBAM899xxycnKwYsUKrF69GhzHYcmSJXjqqadw7733AgDefvttNGnSBB999BGGDh2Kw4cPY+fOnfjuu+/QpUsXAMDy5ctx11134ZVXXkFCQoKksihFTU0Ns5YZo1FVVYUGDRqgQYMajBixE/v29UZ1dQCKi4Nw552fMP8yi6GmpY8ElrmRK0q8aResDZhqCUMjT/II5TGMCDt9+jTGjh2Ljz76CGFhYXW+z83NRc+ePR1M4Onp6ViwYAEuXLiAxo0bIzc3F1OnTnX4XXp6Oj766CMAQEFBAYqLi5GWliZ8b7VakZKSgtzcXAwdOhS5ubmIiIgQBBgApKWlISAgAHl5efjHP/4hqSyuqKioQEVFhfB3WVmZxzqprKxEQUEBauz34yFkU11djR49egAAevQARo/+GTU1Abh8uRw//HAYly/rXECFoEGAkANr1iotUEMYsuh+NQtGDI0whAjjOA4jRozA+PHj0aVLFxw7dqzOMcXFxUhKSnL4rEmTJsJ3jRs3RnFxsfCZ/THFxcXCcfa/c3dMbGysw/dBQUGIjIx0OEasLK6YP38+5s2b57oSnOA4DkVFRQgMDESzZs0QEBAg/iPCI5WVlSgtLXX4jOM4XLhwAW3atKnjAjcKfAB+eHhtG6FBgJCLkqKExQFTqzL5o6DVAiOGRugqwmbNmoUFCxZ4PObw4cPYtWsXLl68iNmzZ2tUMn2YPXu2g6WurKwMzZo1c3nslStXUF5ejoSEBJeWQcJ7AgICEBRU95WwWq2IiYlBSEiI4Vy/a9YA48bVbl4eEBCLu+++CZ06/UiDgA4YbTsVtWFxwNSyTKy5X82C0d4hXUXYtGnTMGLECI/HtGzZEnv37kVubi5CQ0MdvuvSpQsefPBBvPXWW4iLi8Pp06cdvuf/jouLE/7v6hj77/nP4uPjHY7hV2HGxcXhzJkzDue4cuUKzp8/L3od+2u4IjQ0tM49uqO6uhoAW2ZVsxIQEICAgAAEBwf7JMK0TP5aUlKCY8euYNy4WNTUXA3At1+FRYOAdhhxOxVAfcsQS/fKo1aZWLT8EfqjqwiLiYlBTEyM6HHLli3D888/L/xdWFiI9PR0bNq0CSkpKQCA1NRUPPnkk6iqqkJwcDAAICcnB23atBHcf6mpqdizZw8mT54snCsnJwepqakAgKSkJMTFxWHPnj2C6CorK0NeXh4mTJggnKO0tBQHDhxA586dAQB79+5FTU2NV2VRCovFouj55FBZCVy+DNSrBxi5/3Dn0nWuYzmdpKNFCnj99dpNxdWAH/ALChJRUzPc4TtahaUPRtxOBWDTWmVUqC4JVxgiJqx58+YOf/P5uJKTk9H0fyaFBx54APPmzcPo0aMxc+ZM/PLLL1i6dCkWL14s/O6xxx5Dr169sGjRIvTv3x/vv/8+vv/+e7z++usAagfbyZMn4/nnn0fr1q2FFBUJCQkYOHAgAKBdu3bo168fxo4di9WrV6OqqgpZWVkYOnQoEhISJJfFLJw9Cxw/fvXvFi0ACbqaSYKCghAbG1tnocPly5dRVlaGIUOGoEGDBl53kqdOXRVgQO3/H3kESE9XxyLGd/IUgM8eRkxPQKJAOaguCWdME81ttVqxa9cuFBQUoHPnzpg2bRrmzp3rkBKie/fu2LhxI15//XV07NgRW7ZswUcffSTkCANq85BNmjQJ48aNw80334y//voLO3fuFHKEAbXpMNq2bYs+ffrgrrvuwi233CIIOallMQOVlY4CDKj929NkfsSIEbBYLLBYLAgODkaTJk1wxx13YO3atV6t8ly/fj0iIiLkFdwDQUFBCAkJqfNfYGAgYmNjZXWiR49eFWA81dXAH38oVGg38AH4FkvtxaUG4JM7RD14YWwPCWOC8F8MYQlzJjExERzH1fn8hhtuwJdffunxt/fddx/uu+8+t99bLBY8++yzePbZZ90eExkZKSRmdYeUshgddykbKio8uyX79euHdevWobq6GqdPn8bOnTvx2GOPYcuWLdi2bZvL4Hgj07p1rQvSXogFBgKtWql/bU8B+BkZGYiOjnY4Xkt3iJE3SJcLrUwlCMIec412hGTsV2q5QspgbGccdEBsbUFoaKiwQOGaa65Bp06d0K1bN/Tp0wfr16/HmDFj8Oqrr2LdunX4v//7P0RGRuKee+7BwoUL0bBhQ3z++ecYOXIkgKvxWk8//TSeeeYZvPPOO1i6dCmOHDmCBg0a4Pbbb8eSJUvqpBXRipKSEgQGVmLhwvqYOdOK6moLAgM5LFhgQ2Dg3ygpUV/0uAvAj46OdliAoiVaxsixBq1MJQiCh0SYH2K/UssTYiu1QkJqY8CcY8LkeLNuv/12dOzYEdnZ2RgzZgwCAgKwbNkyJCUl4f/+7//wz3/+EzNmzMBrr72G7t27Y8mSJZg7dy6OHDkC4GqcYFVVFZ577jm0adMGZ86cwdSpUzFixAh88skn3hfKR5zr+dFHGwkD719/XQTvwWZtRZyauF61CTzyCIcbbzyDxMQgv6gLWplKENqihOFBDUiE+SFSV2BJOS4mBrBaa12QoaG+rY5s27YtDh06BAAOK1gTExPx/PPPY/z48XjttdcQEhICq9UKi8VSJ+XHqFGjhH+3bNkSy5YtE2L77DdY1wLn+nM38LK2Ik4tPK3arK62YPnyT5GUdNyvRKk/weogqDT+cp9GQinDgxqQCCN8JiREmdQUHMcJ7sXdu3dj/vz5+P3331FWVoYrV67g8uXLKC8v95ic9sCBA3jmmWfw008/4cKFC0Kw/4kTJ9C+fXvfC0nIRuqqTTOKUn/PEcXyIKgk/nKfRkNJw4PSkAgjmOHw4cNISkrCsWPHcPfdd2PChAl44YUXEBkZia+++gqjR49GZWWlWxF26dIlpKenIz09HRs2bEBMTAxOnDiB9PR0Uw7s7mB9wPfH4HR/zxHlfN82WyOcPx+FyMgSh+du9PeU5cGeuIq79qcHJMIIJti7dy9+/vlnTJkyBQcOHEBNTQ0WLVokJFDdvHmzw/EhISHCrgE8v//+O0pKSvDSSy8J2z19//332twAQxhhwGctOF2LLYXMKrC8xdOOAQShNqy1PxJhhOZUVFSguLjYIUXF/Pnzcffdd2PYsGH45ZdfUFVVheXLl+Oee+7B119/jdWrVzucIzExEX/99Rf27NmDjh07IiwsDM2bN0dISAiWL1+O8ePH45dffsFzzz2n013qixEGfFaC0426pZAREdsxwGywZHEh2Gx/JMIIWVy5csVjclV3m2EDwM6dOxEfH4+goCA0btwYHTt2xLJlyzB8+HAEBASgY8eOePXVV7FgwQLMnj0bPXv2xPz58zFs2DDhHN27d8f48eORmZmJkpISIUXF+vXr8cQTT2DZsmXo1KkTXnnlFQwYMEDx+yfMg1G3FHIF60HhRtwxQC6sWVwINtsfiTDCa65cuVJnE3NXxMbG1hFi69evx/r160V/O2XKFEyZMsXhs4cfftjh71WrVmHVqlUOn91///24//77HT5zldiXIJxhsYP2BiMEhYstyrDZbLrlrlMSFi0uBJtbuZlm2yJCOr4GbkvdXsibbYjMiK/1fOoUsG9f7f8J9TH6lkKugt8LChJhszXyeJyWiG2ltWnTJpSUlOhWPqXwJOgJ/ZC7lZuakCXMDzFC4LYZ8KWezZhRnlZtagfLrjCxRRlGcPuKwaLFxZ+x71M8tT89+h4SYX4KCSxtkFPPp05dFWAAn1EeSE839h6LRhD/rK3alIPWrjApcWjOgxsrizKUhr9PMUFv1nxwrMJy30MijCAY4+hRx82+AaC6GvjjD+VEmF6bZxtB/LsTCOfOnZPcUesZIK9lbJs3cWhDhgxxSDVjxpWDzoP93LlncexYEBITryAh4WYAN+s+0fBXWK1zEmEEwRitW9e6IO2FWGAg0KqVMuc3o6tTC7KzswGIB7brHSCvpSvMm+SkERERwt8su0t9xf6ZxscDnTvLOw/rK10JZSARRhAMUVJSgsDASixcWB8zZ1pRXW1BYCCHBQtsCAz8GyUl8jte2jzbNe5cQ3KzuuudHZ712DZaOSiO3kKe0A4SYQTBCM4d76OPNhJik/766yJef732czkdL22e7R7ehVRYWChYu5Sy1Ohl8ZET26aE5UWKi9HoqUC0gLY/8h9IhBFew28lpNRxRC3OHaq72CQ5Ha8/b54thaioKMlJW6WitcVHavC7K8ufEpYXqYKTVg4SxFVIhBFeExQUhNjYWNkZ8wn9YN1VxQJKWWq0tvj4sgLMV8uLFMGp9cpBiqkijACNkoQsWBVYn3/+OW677TZcuHDBIRDYE4mJiZg8eTImT56satlYgbU0DKwNlkpZavSw+OglKqQITi1XDlJMFWEU2BxJCdMyYsQIvPXWW3jkkUfqbMo9ceJEvPbaaxg+fLikrY0I+bCSp4nFwVIpa6E/WR2lCk6lVg6KQTFVhFEgEUZoTrNmzfD+++9j8eLFqF+/PgDg8uXL2LhxI5o3b65z6Qgt0XsloTuUshayZnVUGkpOqg1mzKlG1EIijNCcTp06IT8/H9nZ2XjwwQcB1OZgat68OZKSkoTjKioqMH36dLz//vsoKytDly5dsHjxYtx8883CMZ988gkmT56MkydPolu3bhg+fHid63311VeYPXs2vv/+e0RHR+Mf//gH5s+fjwYNGqh/s4Rk9M4d5Utguxrn0ROpgz4lJ1Ufvd8LQl1IhBG6MGrUKKxbt04QYWvXrsXIkSPx+eefC8fMmDEDW7duxVtvvYUWLVpg4cKFSE9Pxx9//IHIyEicPHkSGRkZmDhxIsaNG4fvv/8e06ZNc7hOfn4++vXrh+effx5r167F2bNnkZWVhaysLKxbt07LWyY8wELuKKW2NmF5ixQpeDvoa+Vi9Cd4gS72XrAs5AlpkAgjdNnC5qGHHsLs2bNx/PhxAMDXX3+N999/XxBhly5dwqpVq7B+/XrceeedAIA33ngDOTk5WLNmDaZPn45Vq1YhOTkZixYtAgC0adMGP//8MxYsWCBcZ/78+XjwwQeFoPvWrVtj2bJl6NWrF1atWoV69eppc8MSUHODa9Y3z2Yld5RSwohVgSUGC2KYuCrk9+0DFi+u+1706DEcvXsbt50RVyER5ufotYVNTEwM+vfvj/Xr14PjOPTv3x/R0dHC9/n5+aiqqkKPHj2Ez4KDg9G1a1ccPnwYAHD48GGkpKQ4nDc1NdXh759++gmHDh3Chg0bhM84jkNNTQ0KCgrQrl07NW5PFmpaUFi3zlDuKH3hxbeYGCbLi3ZERUWhWzfXW5ilpESB9Jc5IBHmx5w6dVWAAfwWNkB6ujYWsVGjRiErKwsAsHLlSlWu8ddff+GRRx7Bo48+Wuc7FhcBqCmCWJ41+9NKQhbhRfqxY1fwzjucsK0VAAQGcpg06U5Db2tl1MD2pk1rJ8aPPAJUV9cKsH/9SzuPBaE+JML8mKNHHWdYQO2L/scf2rzk/fr1Q2VlJSwWC9LT0x2+S05ORkhICL7++mu0aNECAFBVVYXvvvtOcC22a9cO27Ztc/jdN9984/B3p06d8Ntvv6GVUrtfE6ph9pWErBMVVWtdqTvoW9C5cxO9i+cV9hY7TzFuRrDsjR5dOzH+4w+gVSsSYGaDRJgf07q1a1O3VnolMDBQcC0GBgY6fNegQQNMmDAB06dPR2RkJJo3b46FCxeivLwco//nLx0/fjwWLVqE6dOnY8yYMThw4ECd/GIzZ85Et27dkJWVhTFjxqBBgwb47bffkJOTIyk/FaEuZlhJaDbMMOjbW/aefTYWHFdr2eO4AOzYcQ/mzk0xlGWvaVNjPgdCHBJhfgwLpu7w8HC337300kuoqanBww8/jIsXL6JLly747LPP0LhxYwC17sStW7diypQpWL58Obp27YoXX3wRo0aNEs5xww034IsvvsCTTz6JW2+9FRzHITk5GZmZmarfGyEO67Fq/ooZBv2oqCgcOuTK2m/BxYtNKKaKYAILx3Gc3oUgXFNWVgar1QqbzVZHrFy+fBkFBQVISkryeYXfqVPGnvWqjZJ1TRCEdpw6BbRoUdfaf+wY9XWEungav+0JcPsN4Tc0bQr07k2dEkEQ5oK39vPRDhTYTrAGuSMJgiAI02KGGDfCvJAIIwiCIEyNGWLcCHNC7kiCIAiCIAgdIBFGEARBEAShAyTCDA4tblUfqmOCIAhCDUiEGRQ+uamn/EqEMvB17JxQliAIgiB8gQLzDUpQUBDCwsJw9uxZBAcHIyCA9LQa1NTU4OzZswgLC0NQEL0uBEEQhHLQqGJQLBYL4uPjUVBQgOPHj+tdHFMTEBCA5s2bw2KxiB9MEARBEBIhEWZgQkJC0Lp1a3JJqkxISAhZGgmCIAjFIRFmcAICAmgrHYIgCIIwIDS9JwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHKCaMYfgkoWVlZTqXhCAIgiAIqfDjtliybxJhDHPx4kUAQLNmzXQuCUEQBEEQ3nLx4kVYrVa331s42pOFWWpqalBYWIhGjRr5ZY6qsrIyNGvWDCdPnkR4eLjexTEsVI/KQPXoO1SHykD1qAxq1iPHcbh48SISEhI8pjgiSxjDBAQEoGnTpnoXQ3fCw8Opo1EAqkdloHr0HapDZaB6VAa16tGTBYyHAvMJgiAIgiB0gEQYQRAEQRCEDpAII5glNDQUTz/9NEJDQ/UuiqGhelQGqkffoTpUBqpHZWChHikwnyAIgiAIQgfIEkYQBEEQBKEDJMIIgiAIgiB0gEQYQRAEQRCEDpAIIwiCIAiC0AESYYTu/Oc//8E999yDhIQEWCwWfPTRRw7fcxyHuXPnIj4+HvXr10daWhqOHj2qT2EZRawOR4wYAYvF4vBfv3799Cksw8yfPx8333wzGjVqhNjYWAwcOBBHjhxxOOby5cuYOHEioqKi0LBhQwwaNAinT5/WqcRsIqUee/fuXadNjh8/XqcSs8mqVatwww03CMlEU1NT8emnnwrfU1uUhlg96tkWSYQRunPp0iV07NgRK1eudPn9woULsWzZMqxevRp5eXlo0KAB0tPTcfnyZY1Lyi5idQgA/fr1Q1FRkfDfe++9p2EJjcEXX3yBiRMn4ptvvkFOTg6qqqrQt29fXLp0SThmypQp+Pjjj/HBBx/giy++QGFhITIyMnQsNXtIqUcAGDt2rEObXLhwoU4lZpOmTZvipZdewoEDB/D999/j9ttvx7333otff/0VALVFqYjVI6BjW+QIgiEAcB9++KHwd01NDRcXF8e9/PLLwmelpaVcaGgo99577+lQQvZxrkOO47jhw4dz9957ry7lMTJnzpzhAHBffPEFx3G1bS84OJj74IMPhGMOHz7MAeByc3P1KibzONcjx3Fcr169uMcee0y/QhmUxo0bc2+++Sa1RR/h65Hj9G2LZAkjmKagoADFxcVIS0sTPrNarUhJSUFubq6OJTMen3/+OWJjY9GmTRtMmDABJSUleheJeWw2GwAgMjISAHDgwAFUVVU5tMe2bduiefPm1B494FyPPBs2bEB0dDSuv/56zJ49G+Xl5XoUzxBUV1fj/fffx6VLl5CamkptUSbO9cijV1ukDbwJpikuLgYANGnSxOHzJk2aCN8R4vTr1w8ZGRlISkpCfn4+nnjiCdx5553Izc1FYGCg3sVjkpqaGkyePBk9evTA9ddfD6C2PYaEhCAiIsLhWGqP7nFVjwDwwAMPoEWLFkhISMChQ4cwc+ZMHDlyBNnZ2TqWlj1+/vlnpKam4vLly2jYsCE+/PBDtG/fHgcPHqS26AXu6hHQty2SCCMIP2Do0KHCvzt06IAbbrgBycnJ+Pzzz9GnTx8dS8YuEydOxC+//IKvvvpK76IYGnf1OG7cOOHfHTp0QHx8PPr06YP8/HwkJydrXUxmadOmDQ4ePAibzYYtW7Zg+PDh+OKLL/QuluFwV4/t27fXtS2SO5Jgmri4OACos+Ln9OnTwneE97Rs2RLR0dH4448/9C4Kk2RlZWH79u3Yt28fmjZtKnweFxeHyspKlJaWOhxP7dE17urRFSkpKQBAbdKJkJAQtGrVCp07d8b8+fPRsWNHLF26lNqil7irR1do2RZJhBFMk5SUhLi4OOzZs0f4rKysDHl5eQ7+fMI7Tp06hZKSEsTHx+tdFKbgOA5ZWVn48MMPsXfvXiQlJTl837lzZwQHBzu0xyNHjuDEiRPUHu0Qq0dXHDx4EACoTYpQU1ODiooKaos+wtejK7Rsi+SOJHTnr7/+cphxFBQU4ODBg4iMjETz5s0xefJkPP/882jdujWSkpIwZ84cJCQkYODAgfoVmjE81WFkZCTmzZuHQYMGIS4uDvn5+ZgxYwZatWqF9PR0HUvNHhMnTsTGjRvx73//G40aNRJia6xWK+rXrw+r1YrRo0dj6tSpiIyMRHh4OCZNmoTU1FR069ZN59Kzg1g95ufnY+PGjbjrrrsQFRWFQ4cOYcqUKejZsyduuOEGnUvPDrNnz8add96J5s2b4+LFi9i4cSM+//xzfPbZZ9QWvcBTPereFnVZk0kQduzbt48DUOe/4cOHcxxXm6Zizpw5XJMmTbjQ0FCuT58+3JEjR/QtNGN4qsPy8nKub9++XExMDBccHMy1aNGCGzt2LFdcXKx3sZnDVR0C4NatWycc8/fff3P//Oc/ucaNG3NhYWHcP/7xD66oqEi/QjOIWD2eOHGC69mzJxcZGcmFhoZyrVq14qZPn87ZbDZ9C84Yo0aN4lq0aMGFhIRwMTExXJ8+fbhdu3YJ31NblIanetS7LVo4juPUl3oEQRAEQRCEPRQTRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQOkAgjCIIgCILQARJhBEEQBEEQOkAijCAIgiAIQgdIhBEEQRAEQegAiTCCIAiCIAgdIBFGEARBEAShAyTCCIIgZFBZWal3EerAYpkIgnAPiTCCIAgAvXv3RlZWFrKysmC1WhEdHY05c+aA39ktMTERzz33HIYNG4bw8HCMGzcOAPDVV1/h1ltvRf369dGsWTM8+uijuHTpknDe1157Da1bt0a9evXQpEkTDB48WPhuy5Yt6NChA+rXr4+oqCikpaUJv+3duzcmT57sUMaBAwdixIgRwt9yy0QQBBuQCCMIgvgfb731FoKCgvDtt99i6dKlePXVV/Hmm28K37/yyivo2LEjfvzxR8yZMwf5+fno168fBg0ahEOHDmHTpk346quvkJWVBQD4/vvv8eijj+LZZ5/FkSNHsHPnTvTs2RMAUFRUhPvvvx+jRo3C4cOH8fnnnyMjIwPebufrbZkIgmAH2sCbIAgCtZanM2fO4Ndff4XFYgEAzJo1C9u2bcNvv/2GxMRE3HTTTfjwww+F34wZMwaBgYH417/+JXz21VdfoVevXrh06RI++eQTjBw5EqdOnUKjRo0crvfDDz+gc+fOOHbsGFq0aOGyPDfeeCOWLFkifDZw4EBERERg/fr1ACCrTPXq1fOpngiCUA6yhBEEQfyPbt26CQIMAFJTU3H06FFUV1cDALp06eJw/E8//YT169ejYcOGwn/p6emoqalBQUEB7rjjDrRo0QItW7bEww8/jA0bNqC8vBwA0LFjR/Tp0wcdOnTAfffdhzfeeAMXLlzwuszelokgCHYgEUYQBCGRBg0aOPz9119/4ZFHHsHBgweF/3766SccPXoUycnJaNSoEX744Qe89957iI+Px9y5c9GxY0eUlpYiMDAQOTk5+PTTT9G+fXssX74cbdq0EYRSQEBAHddkVVWVz2UiCIIdSIQRBEH8j7y8PIe/v/nmG7Ru3RqBgYEuj+/UqRN+++03tGrVqs5/ISEhAICgoCCkpaVh4cKFOHToEI4dO4a9e/cCACwWC3r06IF58+bhxx9/REhIiOBajImJQVFRkXCt6upq/PLLL6L3IKVMBEGwAYkwgiCI/3HixAlMnToVR44cwXvvvYfly5fjsccec3v8zJkzsX//fmRlZeHgwYM4evQo/v3vfwtB8Nu3b8eyZctw8OBBHD9+HG+//TZqamrQpk0b5OXl4cUXX8T333+PEydOIDs7G2fPnkW7du0AALfffjt27NiBHTt24Pfff8eECRNQWloqeg9iZSIIgh2C9C4AQRAEKwwbNgx///03unbtisDAQDz22GNC2gdX3HDDDfjiiy/w5JNP4tZbbwXHcUhOTkZmZiYAICIiAtnZ2XjmmWdw+fJltG7dGu+99x6uu+46HD58GP/5z3+wZMkSlJWVoUWLFli0aBHuvPNOAMCoUaPw008/YdiwYQgKCsKUKVNw2223id6DWJkIgmAHWh1JEAQB16sRCYIg1ITckQRBEARBEDpAIowgCIIgCEIHyB1JEARBEAShA2QJIwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHSIQRBEEQBEHoAIkwgiAIgiAIHSARRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQOkAgjCIIgCILQgf8HFkwbEKpg6twAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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YtWqV6tWrp8aNG6tnz55at26dZs+erQ8++ED+/v5q27atZs6cqaeeekpt2rTR4sWL7SaWJyQkaPjw4frnP/+pqKgoTZ8+XVFRUVq4cKHefvtttW7dWmlpaZoxY4ZJ3xIAYIbCwkKlpqbahKKwsDClpKR4VCiSJIvhjPu+vUheXp4iIiKUm5ur8PBwa/uZM2e0b98+NWnSRMHBwSZWCFfj2gNA2b788kulp6fbtN15552Ki4vT8uVSWpo0bpzUu3fV1lHW7++KYigNAABUyp8nWEvShAkTrHc7p6VJGRlnf1Z1MHIWghEAAKiQJUsOa/fuV2zaOnXqpF69etm0jRv3vx4jT0EwAgAA5eaol+js2kTV7dp79/acnqJzCEYAAOAvFRUVacqUKXbtKSkpJlRTdQhGAADgvJYsWaLdu3fbtH35ZSfl5vaSl+UighEAACibo6Gzdu0eV3q6v0fNHSovghEAALCTlZWlRYsW2bWfGzrr08fVFbkGwQgAANhw1EtUWDhQTz7Z1IRqXIuVrwEAgCTJMAyHoWjixBTNmuX9oUiixwhOlJSUpJycHC1btkySdO2116pdu3Z67rnnKn1OZ5wDAPDXZsyYoVOnTtm1FxSkqEYNadQoE4oyAcHIByQlJVnHiQMCAhQXF6dBgwbpscceU7VqVfd/gffee08BAQHlOnb9+vXq2rWrTpw4ocjIyEqdAwBQOY56iZ5++iH93/9V18aNkoO79L0WwchH9OzZUwsWLFB+fr4+/PBDjRgxQgEBAUpOTrY5rqCgwGkP/KtVq5ZbnAMA4Nju3bu1ZMkSu/aCghT93/951orVzsIcIx8RFBSkmJgYNWrUSPfee6+6d++u5cuXKykpSX379tWUKVNUv359tWjRQpJ04MAB3XrrrYqMjFStWrXUp08fZWVlWc9XXFysMWPGKDIyUrVr19YjjzyiPz+P+Nprr9Xo0aOt2/n5+Xr00UcVGxuroKAgNW/eXK+++qqysrLUtWtXSVLNmjVlsViUlJTk8BwnTpzQoEGDVLNmTYWGhqpXr1764YcfrPsXLlyoyMhIpaenq1WrVqpRo4Z69uypI0eOWI9Zv369OnXqpOrVqysyMlJXXnmlfvrpJyf9SQOAZ0hNTbULRfv3t9LEiSlat07auNHzVq12BoKRjwoJCVFBQYEkac2aNdq9e7dWr16tFStWqLCwUImJiQoLC9Nnn32mL774whowzr3nmWee0cKFCzV//nx9/vnn+vXXX/X++++f9zMHDRqkN998U7Nnz9auXbv08ssvq0aNGoqNjdW7774r6ey/Xo4cOaJZs2Y5PEdSUpK++eYbLV++XBkZGTIMQ3//+99VWFhoPeb06dOaMWOGXnvtNW3YsEH79+/XQw89JOnsyq19+/ZVly5dtG3bNmVkZOjuu++2PvAQAHyBo6Gzp55KUZ8+tyo+3jd7is5hKM3HGIahNWvWKD09Xffff7+OHz+u6tWra968edYhtNdff10lJSWaN2+eNTAsWLBAkZGRWr9+vXr06KHnnntOycnJuummmyRJc+fOVXp6epmf+/333+utt97S6tWr1b17d0lS06b/u8Ph3JBZdHS0zRyjP/rhhx+0fPlyffHFF0pISJAkLV68WLGxsVq2bJluueUWSVJhYaHmzp2rZs2aSZJGjhypSZMmSZLy8vKUm5urG264wbq/VatWFf+DBAAP5CgQSdKMGSl68EHPfLaZs9FjZJLly6WEhLM/XWHFihWqUaOGgoOD1atXL/3zn//UxIkTJUmXXnqpzbyirVu3as+ePQoLC1ONGjVUo0YN1apVS2fOnNHevXuVm5urI0eOqHPnztb3VKtWTR07dizz87ds2SJ/f3916dKl0t9h165dqlatms3n1q5dWy1atNCuXbusbaGhodbQI0n16tXTsWPHJJ0NYElJSUpMTNSNN96oWbNm2QyzAYC3chSKbrnlFqWkpOi333xrgvX50GNkkrQ0KSPj7E9XpPOuXbtqzpw5CgwMVP369W3uRvvzE5FPnjypDh06aPHixXbniYqKqtTnh4SEVOp9lfHnu9gsFovN/KcFCxZo1KhRWrVqlZYuXarHH39cq1ev1hVXXOGyGgHAVY4ePaq5c+fatXvbw1+dhR4jk4wbJ5eO41avXl3NmzdXXFzcX96if9lll+mHH35QdHS0mjdvbvOKiIhQRESE6tWrp//+97/W9xQVFWnTpk1lnvPSSy9VSUmJPv30U4f7z/VYFRcXl3mOVq1aqaioyOZzf/nlF+3evVutW7c+73f6s/bt2ys5OVkbN25UmzZt9MYbb1To/QDgCVJTUx2GovR0QlFZCEYm6d3bfWf833777apTp4769Omjzz77TPv27dP69es1atQoHTx4UJL0wAMPKC0tTcuWLVNmZqbuu+8+5eTklHnOxo0ba/DgwRoyZIiWLVtmPedbb70lSWrUqJEsFotWrFih48eP6+TJk3bnuOiii9SnTx8NGzZMn3/+ubZu3ao77rhDDRo0UJ9yPrRn3759Sk5OVkZGhn766Sd9/PHH+uGHH5hnBMDrOBo6mzfvCaWnp/j05Oq/QjCCndDQUG3YsEFxcXG66aab1KpVKw0dOlRnzpxReHi4JGns2LEaOHCgBg8erPj4eIWFhekf//jHec87Z84c3XzzzbrvvvvUsmVLDRs2zLrKaoMGDZSamqpx48apbt26GjlypMNzLFiwQB06dNANN9yg+Ph4GYahDz/8sNyLQIaGhiozM1P9+vXTxRdfrLvvvlsjRozQPffcU4E/IQBwX9OnTy/zsR7Vq/u57T/K3YXF+PPiMz4uLy9PERERys3NtYYASTpz5oz27dunJk2aKDg42MQK4WpcewCewlEgysxsoS1b+qtmzbPTN7w1FJX1+7uimHwNAICHO3PmjJ566im79okTzz7nbPFi7w1EzkYwAgDAg5W1NlFBQYr1Jh9CUfkRjAAA8FCOQtEll4zWzTdHmFCNdyAYAQDgYT744ANt2bLFrp21iS4cd6VVEHPVfQ/XHIA7SU1NdRiKWJvIOegxKqdzt4OfPn3apas4w3ynT5+WZL+iNgC4kmEY1uc+/lFBQYrWrfPtB786E8GonPz9/RUZGWl95lZoaChPZPdyhmHo9OnTOnbsmCIjI+Xv7292SQB8VFkTrCdOPDvBeuNGFxfkxQhGFRATEyNJ1nAE3xAZGWm99gDgao5C0ebN/9Qll7R06aOlfAXBqAIsFovq1aun6OhoFRYWml0OXCAgIICeIgCm2LZtm95//3279vT0FHqIqhDBqBL8/f35ZQkAqDJlDZ3xnLOqRzACAMCNOApFEyZMkMViEXfjVz2CEQAAbqCsXiLWJnItghEAACZzFIratWunPn36mFCNbyMYAQBgkp9//lkvvviiXXt6egrDZiYhGAEAYAImWLsnghEAAC7mKBQ9+uijCg4OpqfIZAQjAABcZMqUKSoqKrJrb98+RcHBJhQEOwQjAABcoKyhs/btU9S7t4uLQZkIRgAAVKGioiJNmTLFrp0VrN0TwQgAgCrCBGvPQzACAKAKOApFQ4cOVcOGDZlg7cYIRgAAONHq1au10cEYGStYewaCEQAATsJjPTwfwQgAACdwFIomTkzRBx+YUAwqjWAEAMAFKKuXaNKkFN18s7gV38MQjAAAqITx46XAQPtQVL/+dRo2LIEJ1h6KYAQAQAUtXrxPgYH/tmtnLpHnIxgBAFBOy5dLmzczwdqb+ZldAAAAnsJRKEpPf0Lt2xOKvAU9RgAA/IXz3YZPR5F38coeoxdffFGNGzdWcHCwOnfurK+++srskgAAHspRKPr110b0Enkpr+sxWrp0qcaMGaO5c+eqc+fOeu6555SYmKjdu3crOjra7PIAAB7i1KlTmjFjhl07c4m8m8UwDMPsIpypc+fOuvzyy/XCCy9IkkpKShQbG6v7779f48rxxL68vDxFREQoNzdX4eHhVV0uAMANsYK153HW72+v6jEqKCjQpk2blJycbG3z8/NT9+7dlZGR4fA9+fn5ys/Pt27n5eVVeZ0AAPflKBSNGTNGYWFhJlQDV/OqYPTzzz+ruLhYdevWtWmvW7euMjMzHb5n2rRpZf7LAADgO5599k3l5X1v104vkW/xysnXFZGcnKzc3Fzr68CBA2aXBABwsdTUVEIRJHlZj1GdOnXk7++vo0eP2rQfPXpUMTExDt8TFBSkoKAgV5QHAHAzhmFo0qRJdu0EIt/lVT1GgYGB6tChg9asWWNtKykp0Zo1axQfH29iZQAAd5OamuowFM2YQSjyZV7VYySdnSA3ePBgdezYUZ06ddJzzz2nU6dO6c477zS7NACAm3A0t7So6CY999ylGjXKhILgNrwuGP3zn//U8ePHNWHCBGVnZ6tdu3ZatWqV3YRsAIDv2bBhg9atW2fXfm7obPJkV1cEd+N16xhdKNYxAgDvw8NfvR/rGAEAUE6OQhGBCI4QjAAAXosVrFFRBCMAgFdyFIpatGih/v37m1ANPAXBCADgVQ4dOqR58+bZtdNLhPIgGAEAvAZDZ7hQBCMAgFdwFIqSk5MVGBhoQjXwVAQjAIDH4jZ8OJtXPRIEAOBbCEVwNnqMAAAeZfly6emn89W9e5rdPgIRLhTBCADgUTZvTlX37vbthCI4A0NpAACP4WiCdUbGXWrfnlAE56DHCADg9pYsWaLdu3fbtdNLBGcjGAEA3BprE8GVCEYAALflKBQRiFCVCEYAALdDLxHMQjACALiNshZs3LChmx544GoTKoKvIRgBANzCt99+q82b/2PXnp6eonHjpN69TSgKPodgBAAw3fmGzhg9gysRjAAApnIUiiZMmCCLxWJCNfB1BCMAgCmYYA13xMrXAACXcxSKiooCWMEapqPHCADgMidOnNDs2bPt2uklgrsgGAEAXKKsoTN6ieBOCEYAgCrnKBQ9+OCDCg8PN6EaoGwEIwBAlUlNnSTJsGtn6AzuimAEAKgS3HUGT0QwAgA4VUlJiSZPnmzXTiCCJyAYAQCchl4ieDqCEQDAKRyFoltvvVWtWrUyoRqgcghGAIALsmLFCm3atMmunV4ieCKCEQCg0hg6g7chGAEAKsVRKCIQwdMRjAAAFUIvEbwZwQgAUG6OQlGLFi3Uv39/E6oBnI9gBAD4S3v37tXrr79u104vEbwNwQgAcF4MncGXEIwAAGVyFIratn1MffsGmFANUPUIRgAAO/QSwVf5mV0AAMC9EIrgy+gxAgBIkn7//XdNnz7drp1ABF9CMAIA0EsElGIoDQB8nKNQNHfuPUpPJxTB99BjBAA+6pVXXtHhw4ft2pcsSVFEhDRunAlFASYjGAGADzrf0BmjZ/BlBCMA8CGGYWjSpEl27cwlAs4iGAGAj2CCNfDXCEYA4MWWL5fS0qTERPtQdN111ykhIcGEqgD3RTACAC/2xhtfKTHxI7t2eokAxwhGAOClUlNT1aqVfTuhCCgbwQgAvJCj+UQTJkyQxWIxoRrAcxCMAMCLMMEauDAEIwDwEo5CUZ06dTRixAgTqgE8U4WDkb+/v44cOaLo6Gib9l9++UXR0dEqLi52WnEAgL/2888/68UXX7Rrb98+Rb17m1AQ4MEqHIwMw3DYnp+fr8DAwAsuCABQfmUNnU2cmKL4eBGMgAoqdzCaPXu2JMlisWjevHmqUaOGdV9xcbE2bNigli1bOr9CAIBDjkLR9OkPKzY2VPHxPOsMqIxyB6Nnn31W0tkeo7lz58rf39+6LzAwUI0bN9bcuXOdXyEAwEZZvUTt26eobduzgYieIqByyh2M9u3bJ0nq2rWr3nvvPdWsWbPKigIAOPZXd50RiIALU+E5RuvWrauKOgAA57FsWbG2bn3Srp3b8AHnqnAwGjJkyHn3z58/v9LFAADssTYR4DoVDkYnTpyw2S4sLNT27duVk5Ojbt26Oa0wAIDjUNSs2UDdcUdTE6oBvF+Fg9H7779v11ZSUqJ7771XzZo1c0pRAODrVq5cqW+++cau/dxt+HfcYUJRgA/wc8pJ/Pw0ZswY651rAIDKS01NdRiK2rdP4TZ8oIo57ZEge/fuVVFRkbNOBwA+ydHQWXp6ivUWfO46A6pWhYPRmDFjbLYNw9CRI0e0cuVKDR482GmFAYAvOd8Ea+ZYA65T4WC0efNmm20/Pz9FRUXpmWee+cs71gAA9hyFok6dOqlXr14mVAP4NtYxAgCT7NmzR4sXL7Zr5zZ8wDyVnmN07Ngx7d69W5LUokULRUdHO60oAPB2rE0EuKcK35WWl5engQMHqn79+urSpYu6dOmiBg0a6I477lBubm5V1KisrCwNHTpUTZo0UUhIiJo1a6aUlBQVFBTYHLdt2zZdffXVCg4OVmxsrKZPn14l9QDAhXAUih5//HFCEeAGKtxjNGzYMG3evFkrV65UfHy8JCkjI0MPPPCA7rnnHi1ZssTpRWZmZqqkpEQvv/yymjdvru3bt2vYsGE6deqUZsyYIelsYOvRo4e6d++uuXPn6rvvvtOQIUMUGRmpu+++2+k1AUBF0UsEuD+LYRhGRd5QvXp1paen66qrrrJp/+yzz9SzZ0+dOnXKqQWW5emnn9acOXP0448/SpLmzJmj8ePHKzs7W4GBgZKkcePGadmyZcrMzCz3efPy8hQREaHc3FyFh4dXSe0AfMPy5VJa2tl1hzZvJhQBVclZv78r3GNUu3ZtRURE2LVHRESoZs2alS6konJzc1WrVi3rdkZGhq655hprKJKkxMREPfXUUzpx4kSZteXn5ys/P9+6nZeXV3VFA/ApaWnStm2ntHnzDLt9BCLAPVV4jtHjjz+uMWPGKDs729qWnZ2thx9+WE888YRTiyvLnj179Pzzz+uee+6xqaFu3bo2x53b/mOtfzZt2jRFRERYX7GxsVVTNACfk5iYqocftg9F6emEIsBdVTgYzZkzR19++aXi4uLUvHlzNW/eXHFxcdq4caNefvllXXbZZdbXXxk3bpwsFst5X38eBjt06JB69uypW265RcOGDato+XaSk5OVm5trfR04cOCCzwnA9yxfLiUknP0pOZ5P1KrVSOsq1gDcU4WH0vr06SOLxeKUDx87dqySkpLOe0zTpv97gvThw4fVtWtXJSQk6F//+pfNcTExMTp69KhN27ntmJiYMs8fFBSkoKCgClYOALbS0qSMDGnDhjnavPmY3f5zQ2e33urqygBURIWD0cSJE5324VFRUYqKiirXsYcOHVLXrl3VoUMHLViwQH5+tp1d8fHxGj9+vAoLCxUQECBJWr16tVq0aOHSuU8AfBMTrAHvUOGhtKZNm+qXX36xa8/JybHp3XGmQ4cO6dprr1VcXJxmzJih48ePKzs722bu0IABAxQYGKihQ4dqx44dWrp0qWbNmmX3bDcAcDbDMByGorPPOSMUAZ6kwj1GWVlZKi4utmvPz8/XwYMHnVLUn61evVp79uzRnj171LBhQ5t951YbiIiI0Mcff6wRI0aoQ4cOqlOnjiZMmMAaRgCqVFlrE7VvTyACPFG51zFaXjqjsG/fvlq0aJHNLfvFxcVas2aNVq9ebX1MiKdiHSMA5eUoFG3b1lfvvddW8fHSxo0mFAX4KJevY9S3b19JksVi0eDBg232BQQEqHHjxnrmmWcqXQgAeIpvvvlGK1eutGtPSUnR8uXSkSPizjPAQ5U7GJWUlEiSmjRpoq+//lp16tSpsqIAwF2VNXS2ffvZobPevc++AHimCs8x2rdvX1XUAQBuz1EomjEjRSdPSjVqmFAQAKercDCaNGnSefdPmDCh0sUAgDs638NfCwqk2bOlUaNcXBSAKlHhh8i2b9/eZruwsFD79u1TtWrV1KxZM3377bdOLdDVmHwN4I8chaIjRy7R3Lk3m1ANgLKY9hDZzZs3OywmKSlJ//jHPypdCAC4k6NHj2ru3Ll27TzSA/BuFe4xKst3332nG2+8UVlZWc44nWnoMQJwvqEzAO7JWb+/K7zydVnOPYQVADyZo1A0bdo4pacTigBfUOGhtNmzZ9tsG4ahI0eO6LXXXlOvXr2cVhgAuNKkSZPkqAO9ffsUXXYZ6xIBvqLCQ2lNmjSx2fbz81NUVJS6deum5ORkhYWFObVAV2MoDfA9DJ0Bns+0ydesYwTAWxQWFmrq1Kl27RMnpig+XiIXAb6nwsFIknJycrRnzx5JUvPmzRUZGenMmgCgyp3v4a/x8QydAb6qQsEoKytLI0aMUHp6unUs3mKxqGfPnnrhhRfUuHHjqqgRAJzKUSi666671KBBA0k80gPwZeUORgcOHNAVV1yhgIAATZ48Wa1atZIk7dy5U3PmzFF8fLy+/vprNWzYsMqKBYALsXLlSn3zzTd27cwlAnBOuSdfDx06VHv27FF6erqCg4Nt9v3+++/q2bOnLrroIs2bN69KCnUVJl8D3okJ1oB3c/nk61WrVmnp0qV2oUiSQkJCNHnyZPXv37/ShQBAVXEUighEABwpdzD6+eefzzuHqGnTpvr111+dURMAOAW9RAAqqtzBqF69etq5c2eZc4i2b9+umJgYpxUGABfCUSjq3r27rrzyShOqAeApyh2M+vbtq4ceekhr1qxRVFSUzb5jx47p0UcfVd++fZ1dHwBUyJ49e7R48WK7dnqJAJRHuSdfnzhxQp07d1Z2drbuuOMOtWzZUoZhaNeuXXrjjTcUExOjL7/8UrVq1arqmqsUk68Bz8XQGeC7XD75umbNmvrvf/+rxx57TEuWLFFOTo4kKTIyUgMGDNDUqVM9PhQB8FyOQlG7dk+oTx+nPSsbgA+o8LPSpLMPjj1+/LgkKSoqShaLxemFmYUeI8Cz0EsEQDLxWWnS2dWuo6OjK/2hAOAMjkJRw4YNNXToUBOqAeANKhWMAMBMJ0+e1DPPPGPXTi8RgAtFMALgURg6A1CVCEYAPIajUDRmzBiFhYWZUA0Ab0QwAuD25s+frwMHDti100sEwNnKFYxmz55d7hOOGjWq0sUAwJ8xdAbAlcp1u36TJk3KdzKLRT/++OMFF2UmbtcH3INhGJo0aZJdO4EIgCMuvV1/3759lf4AAKiosnqJ0tNTRC4CUJUqvSRsQUGBdu/eraKiImfWA8DHOQpFTZsOUHp6isaNM6EgAD6lwpOvT58+rfvvv1+LFi2SJH3//fdq2rSp7r//fjVo0EDj+C8XgErYtGmTVqxYYdd+buhs4EBXVwTAF1W4xyg5OVlbt27V+vXrFRwcbG3v3r27li5d6tTiAPiG1NTU84YiAHCVCvcYLVu2TEuXLtUVV1xh84y0Sy65RHv37nVqcQC8n6OhMwIRALNUOBgdP37c4XPSTp065VUPkwVQtc53G/7y5VJamjRunNS7t4sLA+DTKjyU1rFjR61cudK6fS4MzZs3T/Hx8c6rDIDXchSKduy4SkuW/C8UZWSc/QkArlThHqOpU6eqV69e2rlzp4qKijRr1izt3LlTGzdu1KeffloVNQLwEj///LNefPFFu/aUlBQlJEiZmdIjj0gWi9SihbgLDYDLVbjH6KqrrtKWLVtUVFSkSy+9VB9//LGio6OVkZGhDh06VEWNALxAampqmaFIOhuC4uPPhqLMTKlWLYbRALheuVa+9iWsfA04n6Ohs9Wrx+uRR6rZhR/mFwGoDGf9/i5XMMrLyyv3CT09TBCMAOcp6+Gv7dunEHoAOJVLHwkSGRlZ7jvOiouLK10MAO/hqJeoWbNmuuOOO0yoBgDKp1zBaN26ddb/nZWVpXHjxikpKcl6F1pGRoYWLVqkadOmVU2VADxGYWGhpk6datfuaG0ihs0AuJsKzzH629/+prvuuku33XabTfsbb7yhf/3rX1q/fr0z63M5htKAyjvf2kSOJCScvS0/Pl7auLEqKwPg7Zz1+7vCd6VlZGSoY8eOdu0dO3bUV199VelCAHg2R6HogQceOO8q1ufuROO2fADuosLBKDY2Vq+88opd+7x58xQbG+uUogB4js8//7zMx3pERkae9729e5/tKWIYDYC7qPACj88++6z69eunjz76SJ07d5YkffXVV/rhhx/07rvvOr1AAO6rokNnAODuKrWO0cGDB/XSSy8pMzNTktSqVSsNHz7cK3qMmGMElA8PfwXgTly6jpEvIRgB50cvEQB35NJ1jP4sJydHr776qnbt2iVJuuSSSzRkyBBFRERUuhAA7s9RKOrfv79atGhhQjUA4HwV7jH65ptvlJiYqJCQEHXq1EmS9PXXX+v333/Xxx9/rMsuu6xKCnUVeowAez/99JMWLlxo104vEQB3YdpQ2tVXX63mzZvrlVdeUbVqZzucioqKdNddd+nHH3/Uhg0bKl2MOyAYAbYYOgPgCUwLRiEhIdq8ebNatmxp075z50517NhRp0+frnQx7oBgBPyPo1A0YcKEcj8iCABcxbQ5RuHh4dq/f79dMDpw4IDCwsIqXQgA9zF58mSVlJTYtdNLBMDbVTgY/fOf/9TQoUM1Y8YMJSQkSJK++OILPfzww3aPCQHgOc49tywx0b6XKD4+Xj169DChKgBwrQoHoxkzZshisWjQoEEqKiqSJAUEBOjee+9VWlqa0wsE4BozZ55UYuIzdu30EgHwJZVex+j06dPau3evJKlZs2YKDQ11amFmYY4RfM3y5dLmzUywBuDZTF3HSJJCQ0N16aWXVvqDAbgHR6Ho0UcfVXBwsAnVAIC5yh2MhgwZUq7j5s+fX+liALjO6tWrtXHjRrt2eokA+LJyB6OFCxeqUaNGat++vXiKCODZHN2GHxMTo3vuuceEagDAfZQ7GN1777168803tW/fPt1555264447VKtWraqsDYATnb3rrESJiZPt9tFLBABn+ZX3wBdffFFHjhzRI488ov/85z+KjY3VrbfeqvT0dHqQAA+weXMqoQgA/kKl70o79+ykf//73yoqKtKOHTtUo0YNZ9fnctyVBm/kaOjsvvvuU1RUlAnVAIDzmX5Xmp+fnywWiwzDUHFxcaULAFB1MjMztXTpUrt2eokAwLFyD6VJUn5+vt58801dd911uvjii/Xdd9/phRde0P79+72itwjwJqmpqYQiAKigcvcY3XfffVqyZIliY2M1ZMgQvfnmm6pTp05V1gagkhwNnRGIAOCvlXuOkZ+fn+Li4tS+ffvzPln7vffec1pxZmCOETyZo0AkEYoAeD+XzzEaNGjQeQMRAPOU9ViPm2++WZdccokJFQGAZ6rQAo/uID8/X507d9bWrVu1efNmtWvXzrpv27ZtGjFihL7++mtFRUXp/vvv1yOPPGJesYALnDhxQps3z7Zrp5cIACqu0nelmeWRRx5R/fr1tXXrVpv2vLw89ejRQ927d9fcuXP13XffaciQIYqMjNTdd99tUrVA1WLoDACcy6OC0UcffaSPP/5Y7777rj766CObfYsXL1ZBQYHmz5+vwMBAXXLJJdqyZYtmzpxJMIJXchSKnnjiCfn5VehmUwDAH3hMMDp69KiGDRumZcuWKTQ01G5/RkaGrrnmGgUGBlrbEhMT9dRTT+nEiROqWbOmw/Pm5+crPz/fup2Xl+f84gEnev/997Vt2za7dnqJAODCecQ/LQ3DUFJSkoYPH66OHTs6PCY7O1t169a1aTu3nZ2dXea5p02bpoiICOsrNjbWeYUDTpaammoXiq666ipCEQA4ianBaNy4cbJYLOd9ZWZm6vnnn9dvv/2m5ORkp9eQnJys3Nxc6+vAgQNO/wzgQhUWFpa5NtHf/vY3EyoCAO9k6lDa2LFjlZSUdN5jmjZtqrVr1yojI0NBQUE2+zp27Kjbb79dixYtUkxMjI4ePWqz/9x2TExMmecPCgqyOy/gTphgDQCuY2owioqKKtdDLGfPnq0nn3zSun348GElJiZq6dKl6ty5syQpPj5e48ePV2FhoQICAiRJq1evVosWLcqcXwS4O0ehqE2bh9SvX3UTqgEA7+cRk6/j4uJsts89l61Zs2Zq2LChJGnAgAFKTU3V0KFD9eijj2r79u2aNWuWnn32WZfXC1yo7777zuEq8vQSAUDV8ohgVB4RERH6+OOPNWLECHXo0EF16tTRhAkTuFUfHmP5ciktTUpMtO8lat68uW6//XYTqgIA31LuZ6X5Cp6VBrMkJBhKTJxk104vEQD8NZc/Kw1A1Zk3b54SEw/ZtROKAMC1CEaAyRxNsL7vvvvKdWMCAMC5CEaASX755Re98MILdu30EgGAeQhGgAkc9RI1aNBAd911lwnVAADOIRgBLuYoFE2YMEEWi8WEagAAf+QRz0oDvEF6enqZj/X4YyhavlxKSDj7EwDgWvQYAS7gKBDdcccdatasmV17WpqUkXH2Z+/erqgOAHAOwQioQmfOnNFTTz1l136+Cdbjxp0NRePGVWVlAABHCEZAFansw19796anCADMQjACqoCjUDR+/HhVq8ZfOQBwZ/xXGnCirVu3atmyZXbtrE0EAJ6BYAQ4iaNeosTERF1xxRUmVAMAqAyCEXCBSkpKNHnyZLt2eokAwPMQjIALkJaWpvz8fLt2QhEAeCaCEVBJjobOxo4dqxo1aphQDQDAGQhGQAUdPHhQr776ql07vUQA4PkIRkAFOOolatOmjfr162dCNQAAZyMYAeVU1nPOAADeg2AE/IWlS5cqMzPTrp1QBADeh2AEnIejXqK7775b9erVM6EaAEBV8zO7AMBdLF8uJSSc/ZmXl1fm0BmhCAC8Fz1GQKm0NCkjQ9q8OVWbN9vuCwsL05gxY8wpDADgMgQjQGd7iX79VZo40b6XaMKECbJYLCZUBQBwNYIRIOn117/RbbettGtngjUA+BaCEXxeamqqLrnEtu3mm2/WJX9uBAB4PYIRfFZxcbGefPJJu3Z6iQDAdxGM4JPmzZunQ4cO2bTVrl1bI0eONKkiAIA7IBjB5zi6Df+xxx5TQECACdUAANwJwQg+49ChQ5o3b55dO0NnAIBzCEbwCY56ifr06aN27dq5vhgAgNsiGMGrGYahSZMm2bXTSwQAcIRgBK+1YsUKbdq0ya6dUAQAKAvBCF7J0dDZ2LFjVaNGDROqAQB4Ch4iC4/2xwe/SlJubm6ZD38lFAEA/go9RvBo5x78mpZ29uGvf3bllVeqe/fuJlQGAPBEBCN4tHHjzoaixETHvUQAAFQEwQgeLSbmKyUmfmTXTigCAFQGwQgey9FcouHDh6tu3bomVAMA8AYEI3icwsJCTZ061a6dXiIAwIUiGMGj/Pvf/9a+ffts2i6//HL9/e9/N6kiAIA3IRjBYzgaOnviiSfk58eqEwAA5yAYwe0dOHBA8+fPt2tn6AwA4GwEI7g1R71Ed955p+Li4kyoBgDg7QhGcEslJSWaPHmyXTu9RACAqkQwgttZtWqV/vvf/9q0NWrUSElJSeYUBADwGQQjuBVHQ2fJyckKDAw0oRoAgK8hGMEtnDhxQrNnz7ZrZ+gMAOBKBCOYzlEv0U033aRLL73UhGoAAL6MYARTOQpF9BIBAMxCMIIpvv76a3344Yc2bYGBgUpOTjapIgAACEYwgaNeojFjxigsLMyEagAA+B+epQCnW75cSkg4+/OPfv/99zKHzghFAAB3QI8RnC4tTcrIOPvz3HafPi/qzJmfbY7r2rWrrrnmGhMqBADAMYIRnG7cuLNh6NzPxMRUnTlje8yECRNksVjMKRAAgDIQjOB0vXuffR08eFCJia/a7eeuMwCAuyIYoUo4mks0fPhw1a1b14RqAAAoH4IRnIqHvwIAPBnBCE6zc+dOvf322zZtTLAGAHgSghEuyPLl/5tg/WdPPPGE/PxYEQIA4DkIRrggM2eeVGLiMzZtF110kQYMGGBSRQAAVB7BCJX2/fffq2vXN23a7r//ftWqVcukigAAuDAEI1SYYRhauHCh9u/fb9POBGsAgKcjGKFCcnJyNGvWLJu2u+66Sw0aNDCpIgAAnIdghHL7/PPPtWbNGut2cHCwHnroIfn7+5tYFQAAzkMwwl8qKirSlClTbNp69eqlTp06mVQRAABVg2CE8/rpp5+0cOFCm7YxY8YoLCzMnIIAAKhCBCOU6a233tKuXbus2xdffLFuu+02EysCAKBqedTqeytXrlTnzp0VEhKimjVrqm/fvjb79+/fr+uvv16hoaGKjo7Www8/rKKiInOK9WAnT55UamqqTSgaNGgQoQgA4PU8psfo3Xff1bBhwzR16lR169ZNRUVF2r59u3V/cXGxrr/+esXExGjjxo06cuSIBg0apICAAE2dOtXEyj3Lpk2btGLFCpu2xx57TAEBASZVBACA61gMwzDMLuKvFBUVqXHjxkpNTdXQoUMdHvPRRx/phhtu0OHDh61PcJ87d64effRRHT9+XIGBgeX6rLy8PEVERCg3N1fh4eFO+w7urqSkRDNnztSpU6esbddee626dOliYlUAAJSPs35/e8RQ2rfffqtDhw7Jz89P7du3V7169dSrVy+bHqOMjAxdeuml1lAkSYmJicrLy9OOHTvMKNtjHDlyRJMnT7YJRffffz+hCADgczxiKO3HH3+UJE2cOFEzZ85U48aN9cwzz+jaa6/V999/r1q1aik7O9smFEmybmdnZ5d57vz8fOXn51u38/LyquAbuK+VK1fqm2++sW7Xr19fd911lywWi4lVAQBgDlN7jMaNGyeLxXLeV2ZmpkpKSiRJ48ePV79+/dShQwctWLBAFotFb7/99gXVMG3aNEVERFhfsbGxzvhqbu/MmTNKTU21CUW33nqrhg0bRigCAPgsU3uMxo4dq6SkpPMe07RpUx05ckSS1Lp1a2t7UFCQmjZtan1eV0xMjL766iub9x49etS6ryzJyckaM2aMdTsvL8/rw9HOnTvtAuW4ceMUFBRkUkUAALgHU4NRVFSUoqKi/vK4Dh06KCgoSLt379ZVV10lSSosLFRWVpYaNWokSYqPj9eUKVN07NgxRUdHS5JWr16t8PBwm0D1Z0FBQT4TCAzD0Msvv2wNjJLUqVMn9erVy8SqAABwHx4xxyg8PFzDhw9XSkqKYmNj1ahRIz399NOSpFtuuUWS1KNHD7Vu3VoDBw7U9OnTlZ2drccff1wjRozwmeBzPr/88oteeOEFm7bhw4fbzcsCAMCXeUQwkqSnn35a1apV08CBA/X777+rc+fOWrt2rWrWrClJ8vf314oVK3TvvfcqPj5e1atX1+DBgzVp0iSTKzffunXrtGHDBut2RESERo0aJT8/j7gpEQAAl/GIdYxcyZvWMSosLLRb3LJ3795q3769SRUBAFA1nPX722N6jFAxe/fu1euvv27T9tBDD6l69eomVQQAgPsjGHmh119/XXv37rVut2nTRv369TOxIgAAPAPByIvk5eXp2WeftWkbMmSI1y8/AACAsxCMvMSXX36p9PR067a/v7+Sk5Pl7+9vYlUAAHgWgpGHKy4u1lNPPaXCwkJr23XXXaeEhAQTqwIAwDMRjDzYwYMH9eqrr9q0jR49WhERESZVBACAZyMYeaj3339f27Zts243adJEAwcO5DlnAABcAIKRhzl9+rR11e9zbr/9djVv3tykigAA8B4EIw+ydetWLVu2zKYtOTlZgYGB5hQEAICXIRh5AMMwNHv2bOXk5FjbrrrqKv3tb38zrygAALwQwcjNHTt2THPmzLFpGzFihOrUqWNSRQAAeC+CkRtLT0/Xl19+ad2Ojo7W8OHDmWANAEAVIRi5ofz8fKWlpdm09evXT23atDGpIgAAfAPByM3s3r1bS5YssWl75JFHFBISYlJFAAD4DoKRmzAMQ/Pnz9fBgwetbZdddpluvPFGE6sCAMC3EIzcwIkTJzR79mybtmHDhql+/fomVQQAgG8iGJnss88+09q1a63boaGhGjt2rPz8/EysCgAA30QwMklRUZGmTJli03b99derY8eOJlUEAAAIRibIysrSokWLbNrGjBmjsLAwkyoCAAASwcjllixZot27d1u3W7Roof79+5tYEQAAOIdg5CK//fabZs6cadM2ePBgNW7c2JyCAACAHYKRi/w5FI0fP17VqvHHDwCAO+E3s4u0atVKu3btUrdu3XT11VebXQ4AAHCAYOQit956q9klAACAv8BiOQAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRgAAAKUIRi6yfLmUkHD2JwAAcE8EIxdJS5MyMs7+BAAA7olg5CLjxknx8Wd/AgAA98QjQVykd++zLwAA4L7oMQIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChVzewC3I1hGJKkvLw8kysBAADlde739rnf45VFMPqT3377TZIUGxtrciUAAKCifvvtN0VERFT6/RbjQqOVlykpKdHhw4cVFhYmi8VidjkukZeXp9jYWB04cEDh4eFml4Ny4Jp5Fq6X5+GaeZZz12vnzp1q0aKF/PwqP1OIHqM/8fPzU8OGDc0uwxTh4eH8B8DDcM08C9fL83DNPEuDBg0uKBRJTL4GAACwIhgBAACUIhhBQUFBSklJUVBQkNmloJy4Zp6F6+V5uGaexZnXi8nXAAAApegxAgAAKEUwAgAAKEUwAgAAKEUwAgAAKEUwglauXKnOnTsrJCRENWvWVN++fW3279+/X9dff71CQ0MVHR2thx9+WEVFReYUC6v8/Hy1a9dOFotFW7Zssdm3bds2XX311QoODlZsbKymT59uTpE+LisrS0OHDlWTJk0UEhKiZs2aKSUlRQUFBTbHcb3cy4svvqjGjRsrODhYnTt31ldffWV2SZA0bdo0XX755QoLC1N0dLT69u2r3bt32xxz5swZjRgxQrVr11aNGjXUr18/HT16tEKfQzDyce+++64GDhyoO++8U1u3btUXX3yhAQMGWPcXFxfr+uuvV0FBgTZu3KhFixZp4cKFmjBhgolVQ5IeeeQR1a9f3649Ly9PPXr0UKNGjbRp0yY9/fTTmjhxov71r3+ZUKVvy8zMVElJiV5++WXt2LFDzz77rObOnavHHnvMegzXy70sXbpUY8aMUUpKir799lu1bdtWiYmJOnbsmNml+bxPP/1UI0aM0JdffqnVq1ersLBQPXr00KlTp6zHPPjgg/rPf/6jt99+W59++qkOHz6sm266qWIfZMBnFRYWGg0aNDDmzZtX5jEffvih4efnZ2RnZ1vb5syZY4SHhxv5+fmuKBMOfPjhh0bLli2NHTt2GJKMzZs3W/e99NJLRs2aNW2uz6OPPmq0aNHChErxZ9OnTzeaNGli3eZ6uZdOnToZI0aMsG4XFxcb9evXN6ZNm2ZiVXDk2LFjhiTj008/NQzDMHJycoyAgADj7bffth6za9cuQ5KRkZFR7vPSY+TDvv32Wx06dEh+fn5q37696tWrp169emn79u3WYzIyMnTppZeqbt261rbExETl5eVpx44dZpTt844ePaphw4bptddeU2hoqN3+jIwMXXPNNQoMDLS2JSYmavfu3Tpx4oQrS4UDubm5qlWrlnWb6+U+CgoKtGnTJnXv3t3a5ufnp+7duysjI8PEyuBIbm6uJFn/Pm3atEmFhYU2169ly5aKi4ur0PUjGPmwH3/8UZI0ceJEPf7441qxYoVq1qypa6+9Vr/++qskKTs72yYUSbJuZ2dnu7ZgyDAMJSUlafjw4erYsaPDY7hm7mvPnj16/vnndc8991jbuF7u4+eff1ZxcbHD68G1cC8lJSUaPXq0rrzySrVp00bS2b8vgYGBioyMtDm2otePYOSFxo0bJ4vFct7XubkPkjR+/Hj169dPHTp00IIFC2SxWPT222+b/C18S3mv2fPPP6/ffvtNycnJZpfs08p7vf7o0KFD6tmzp2655RYNGzbMpMoB7zBixAht375dS5Yscfq5qzn9jDDd2LFjlZSUdN5jmjZtqiNHjkiSWrdubW0PCgpS06ZNtX//fklSTEyM3R0Z52b4x8TEOLFq31bea7Z27VplZGTYPQ+oY8eOuv3227Vo0SLFxMTY3YXBNXOu8l6vcw4fPqyuXbsqISHBblI118t91KlTR/7+/g6vB9fCfYwcOVIrVqzQhg0b1LBhQ2t7TEyMCgoKlJOTY9NrVOHr5+zJUPAcubm5RlBQkM3k64KCAiM6Otp4+eWXDcP43+Tro0ePWo95+eWXjfDwcOPMmTMur9nX/fTTT8Z3331nfaWnpxuSjHfeecc4cOCAYRj/m8xbUFBgfV9ycjKTeU1y8OBB46KLLjL69+9vFBUV2e3nermXTp06GSNHjrRuFxcXGw0aNGDytRsoKSkxRowYYdSvX9/4/vvv7fafm3z9zjvvWNsyMzMrPPmaYOTjHnjgAaNBgwZGenq6kZmZaQwdOtSIjo42fv31V8MwDKOoqMho06aN0aNHD2PLli3GqlWrjKioKCM5OdnkymEYhrFv3z67u9JycnKMunXrGgMHDjS2b99uLFmyxAgNDbWGXbjOwYMHjebNmxt/+9vfjIMHDxpHjhyxvs7hermXJUuWGEFBQcbChQuNnTt3GnfffbcRGRlpc2cuzHHvvfcaERERxvr1623+Lp0+fdp6zPDhw424uDhj7dq1xjfffGPEx8cb8fHxFfocgpGPKygoMMaOHWtER0cbYWFhRvfu3Y3t27fbHJOVlWX06tXLCAkJMerUqWOMHTvWKCwsNKli/JGjYGQYhrF161bjqquuMoKCgowGDRoYaWlp5hTo4xYsWGBIcvj6I66Xe3n++eeNuLg4IzAw0OjUqZPx5Zdfml0SDKPMv0sLFiywHvP7778b9913n1GzZk0jNDTU+Mc//mHzD5HysJR+GAAAgM/jrjQAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAAIBSBCMAqCLr16+XxWJRTk6O2aUAKCeCEQCPNXHiRLVr187sMgB4EYIRAK9XWFhodgkAPATBCIBpSkpKNG3aNDVp0kQhISFq27at3nnnHUn/G4Zas2aNOnbsqNDQUCUkJGj37t2SpIULFyo1NVVbt26VxWKRxWLRwoULJUkWi0Vz5sxR7969Vb16dU2ZMuW8dZz7rPT0dLVv314hISHq1q2bjh07po8++kitWrVSeHi4BgwYoNOnT1vfl5+fr1GjRik6OlrBwcG66qqr9PXXX1fNHxYA13DqE94AoAKefPJJo2XLlsaqVauMvXv3GgsWLDCCgoKM9evXG+vWrTMkGZ07dzbWr19v7Nixw7j66quNhIQEwzAM4/Tp08bYsWONSy65xO4p25KM6OhoY/78+cbevXuNn3766bx1nPusK664wvj888+Nb7/91mjevLnRpUsXo0ePHsa3335rbNiwwahdu7bNA15HjRpl1K9f3/jwww+NHTt2GIMHDzZq1qxp/PLLLzbnPXHiRNX8AQJwOoIRAFOcOXPGCA0NNTZu3GjTPnToUOO2226zhopPPvnEum/lypWGJOP33383DMMwUlJSjLZt29qdW5IxevToctfi6LOmTZtmSDL27t1rbbvnnnuMxMREwzAM4+TJk0ZAQICxePFi6/6CggKjfv36xvTp023OSzACPEc1s3qqAPi2PXv26PTp07ruuuts2gsKCtS+fXvr9v/93/9Z/3e9evUkSceOHVNcXNx5z9+xY8cK1/THz6pbt65CQ0PVtGlTm7avvvpKkrR3714VFhbqyiuvtO4PCAhQp06dtGvXrgp/NgD3QDACYIqTJ09KklauXKkGDRrY7AsKCtLevXslnQ0b51gsFkln5yb9lerVq1e4pj9/1h+3z7WV57MBeC4mXwMwRevWrRUUFKT9+/erefPmNq/Y2NhynSMwMFDFxcVVXKljzZo1U2BgoL744gtrW2Fhob7++mu1bt3alJoAXDh6jACYIiwsTA899JAefPBBlZSU6KqrrlJubq6++OILhYeHq1GjRn95jsaNG2vfvn3asmWLGjZsqLCwMAUFBbmg+rM9Uvfee68efvhh1apVS3FxcZo+fbpOnz6toUOHuqQGAM5HMAJgmsmTJysqKkrTpk3Tjz/+qMjISF122WV67LHHyjVk1a9fP7333nvq2rWrcnJytGDBAiUlJVV94aXS0tJUUlKigQMH6rffflPHjh2Vnp6umjVruqwGAM5lMQzDMLsIAAAAd8AcIwAAgFIEIwBeb/jw4apRo4bD1/Dhw80uD4AbYSgNgNc7duyY8vLyHO4LDw9XdHS0iysC4K4IRgAAAKUYSgMAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAAChFMAIAACj1/1j+WUwWRiX3AAAAAElFTkSuQmCC", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_usr.ipynb) file." ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } - ], - "source": [ - "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_usr.ipynb) file." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb index 18c47c59..6d115998 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "f51679f9", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": null, @@ -488,8 +515,7 @@ "metadata": { "language_info": { "name": "python" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb index aac02d9c..f29ac8d3 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb @@ -2,7 +2,33 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -481,16 +507,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_doc.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_doc.md). " ] } ], "metadata": { "language_info": { - "name": "python" - }, - "orig_nbformat": 4 + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb index e2b362b7..a3ee9ef5 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb @@ -1,498 +1,521 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################################################################\n", - "# Institute for the Design of Advanced Energy Systems Process Systems\n", - "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", - "# software owners: The Regents of the University of California, through\n", - "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", - "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", - "# University Research Corporation, et al. All rights reserved.\n", - "#\n", - "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", - "# license information, respectively. Both files are also available online\n", - "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", - "##############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Constraint,\n", + " Param,\n", + " Reals,\n", + " Set,\n", + " value,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + ")\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component,\n", + ")\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(\n", + " {\n", + " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", + " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", + " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", + " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", + " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", + " }\n", + " )\n", + "\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.s,\n", + " \"length\": units.m,\n", + " \"mass\": units.kg,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_test.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + "\n", + " self.flow_mol = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol / units.s,\n", + " doc=\"Total molar flowrate [kmol/s]\",\n", + " )\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc=\"State pressure [MPa]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760 + 273.15),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " self.entr_mol = Var(\n", + " domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ / units.kmol / units.K,\n", + " doc=\"Entropy [kJ/ kmol / K]\",\n", + " )\n", + "\n", + " self.enth_mol = Var(\n", + " domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ / units.kmol,\n", + " doc=\"Enthalpy [kJ/ kmol]\",\n", + " )\n", + "\n", + " inputs = [self.pressure, self.temperature]\n", + " outputs = [self.enth_mol, self.entr_mol]\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\n", + " \"pysmo_poly_surrogate.json\"\n", + " )\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.pysmo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol * self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would define them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def initialize(\n", + " blk,\n", + " state_args=None,\n", + " hold_state=False,\n", + " outlvl=1,\n", + " state_vars_fixed=False,\n", + " solver=\"ipopt\",\n", + " optarg={\"tol\": 1e-8},\n", + " ):\n", + " \"\"\"\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output information (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating which solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states variables are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " release_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " \"\"\"\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\n", + " \"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\"\n", + " )\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " \"\"\"\n", + " Method to release state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " \"\"\"\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags[\"Fcflag\"][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags[\"Pflag\"][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags[\"Tflag\"][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info(\"{} State Released.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "## 1. Integration of Surrogate into Custom Property Package\n", - "\n", - "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", - "\n", - "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", - "\n", - "### 1.1 Steps in Creating a Property Package\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **phases and components** of interest.\n", - "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", - "5. Declaring the **state variables** to be used for the property package.\n", - "6. Creating **variables and constraints** to describe the properties of interest.\n", - "7. Creating an **initialization routine** for the property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Importing libraries for making Property Package\n", - "\n", - "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Constraint,\n", - " Param,\n", - " Reals,\n", - " Set,\n", - " value,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - ")\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " LiquidPhase,\n", - " Component,\n", - ")\n", - "from idaes.core.util.initialization import solve_indexed_blocks\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.misc import extract_data\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", - "\n", - "import os\n", - "\n", - "from pyomo.util.model_size import build_model_size_report\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Defining Classes\n", - "\n", - "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", - "\n", - "## 3.1 Physical Parameter Block\n", - "\n", - "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What properties are supported and how they are implemented\n", - "* What components and phases are included in the packages\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To assemble the above mentioned things in a class we need to follow the following steps:\n", - "\n", - "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", - "* Declaring any necessary configuration arguments\n", - "* Writing the build method for our class\n", - "* Creating a define_metadata method for the class.\n", - "\n", - "The code below follows the above mentioned steps. \n", - "\n", - "*NOTE*: The SCO2StateBlock will be discussed in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2ParameterBlock\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " supercritical CO2.\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(PhysicalParameterData, self).build()\n", - "\n", - " self._state_block_class = SCO2StateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Liq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.CO2 = Component()\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(\n", - " {\n", - " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", - " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", - " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", - " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", - " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", - " }\n", - " )\n", - "\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.s,\n", - " \"length\": units.m,\n", - " \"mass\": units.kg,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 State Block\n", - "\n", - "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", - "\n", - "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", - "\n", - "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", - "\n", - "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_test.ipynb file) using the PySMO Surrogate API of IDAES package\n", - "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", - "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", - "class SCO2StateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super(SCO2StateBlockData, self).build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - "\n", - " self.flow_mol = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1.0,\n", - " units=units.kmol / units.s,\n", - " doc=\"Total molar flowrate [kmol/s]\",\n", - " )\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=8,\n", - " bounds=(7.38, 40),\n", - " units=units.MPa,\n", - " doc=\"State pressure [MPa]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=350,\n", - " bounds=(304.2, 760 + 273.15),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " self.entr_mol = Var(\n", - " domain=Reals,\n", - " initialize=10,\n", - " units=units.kJ / units.kmol / units.K,\n", - " doc=\"Entropy [kJ/ kmol / K]\",\n", - " )\n", - "\n", - " self.enth_mol = Var(\n", - " domain=Reals,\n", - " initialize=1,\n", - " units=units.kJ / units.kmol,\n", - " doc=\"Enthalpy [kJ/ kmol]\",\n", - " )\n", - "\n", - " inputs = [self.pressure, self.temperature]\n", - " outputs = [self.enth_mol, self.entr_mol]\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\n", - " \"pysmo_poly_surrogate.json\"\n", - " )\n", - " self.surrogate_enth = SurrogateBlock()\n", - " self.surrogate_enth.build_model(\n", - " self.pysmo_surrogate,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.flow_mol\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.flow_mol * self.enth_mol\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_mol\": self.flow_mol,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def model_check(blk):\n", - " \"\"\"\n", - " Model checks for property block\n", - " \"\"\"\n", - " # Check temperature bounds\n", - " if value(blk.temperature) < blk.temperature.lb:\n", - " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", - " if value(blk.temperature) > blk.temperature.ub:\n", - " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", - "\n", - " # Check pressure bounds\n", - " if value(blk.pressure) < blk.pressure.lb:\n", - " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", - " if value(blk.pressure) > blk.pressure.ub:\n", - " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Define Initialization Routine\n", - "\n", - "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", - "\n", - "Any initialization routine can be written by following a 3 step process:\n", - "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", - "\n", - "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", - "\n", - "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", - "\n", - "\n", - "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would define them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _StateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def initialize(\n", - " blk,\n", - " state_args=None,\n", - " hold_state=False,\n", - " outlvl=1,\n", - " state_vars_fixed=False,\n", - " solver=\"ipopt\",\n", - " optarg={\"tol\": 1e-8},\n", - " ):\n", - " \"\"\"\n", - " Initialisation routine for property package.\n", - "\n", - " Keyword Arguments:\n", - " flow_mol : value at which to initialize component flows\n", - " (default=None)\n", - " pressure : value at which to initialize pressure (default=None)\n", - " temperature : value at which to initialize temperature\n", - " (default=None)\n", - " outlvl : sets output level of initialisation routine\n", - "\n", - " * 0 = no output (default)\n", - " * 1 = return solver state for each step in routine\n", - " * 2 = include solver output information (tee=True)\n", - " state_vars_fixed: Flag to denote if state vars have already been\n", - " fixed.\n", - " - True - states have already been fixed by the\n", - " control volume 1D. Control volume 0D\n", - " does not fix the state vars, so will\n", - " be False if this state block is used\n", - " with 0D blocks.\n", - " - False - states have not been fixed. The state\n", - " block will deal with fixing/unfixing.\n", - " optarg : solver options dictionary object (default=None)\n", - " solver : str indicating which solver to use during\n", - " initialization (default = 'ipopt')\n", - " hold_state : flag indicating whether the initialization routine\n", - " should unfix any state variables fixed during\n", - " initialization (default=False).\n", - " - True - states variables are not unfixed, and\n", - " a dict of returned containing flags for\n", - " which states were fixed during\n", - " initialization.\n", - " - False - state variables are unfixed after\n", - " initialization by calling the\n", - " release_state method\n", - "\n", - " Returns:\n", - " If hold_states is True, returns a dict containing flags for\n", - " which states were fixed during initialization.\n", - " \"\"\"\n", - " if state_vars_fixed is False:\n", - " # Fix state variables if not already fixed\n", - " Fcflag = {}\n", - " Pflag = {}\n", - " Tflag = {}\n", - "\n", - " for k in blk.keys():\n", - " if blk[k].flow_mol.fixed is True:\n", - " Fcflag[k] = True\n", - " else:\n", - " Fcflag[k] = False\n", - " if state_args is None:\n", - " blk[k].flow_mol.fix()\n", - " else:\n", - " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", - "\n", - " if blk[k].pressure.fixed is True:\n", - " Pflag[k] = True\n", - " else:\n", - " Pflag[k] = False\n", - " if state_args is None:\n", - " blk[k].pressure.fix()\n", - " else:\n", - " blk[k].pressure.fix(state_args[\"pressure\"])\n", - "\n", - " if blk[k].temperature.fixed is True:\n", - " Tflag[k] = True\n", - " else:\n", - " Tflag[k] = False\n", - " if state_args is None:\n", - " blk[k].temperature.fix()\n", - " else:\n", - " blk[k].temperature.fix(state_args[\"temperature\"])\n", - "\n", - " # If input block, return flags, else release state\n", - " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", - "\n", - " else:\n", - " # Check when the state vars are fixed already result in dof 0\n", - " for k in blk.keys():\n", - " if degrees_of_freedom(blk[k]) != 0:\n", - " raise Exception(\n", - " \"State vars fixed but degrees of freedom \"\n", - " \"for state block is not zero during \"\n", - " \"initialization.\"\n", - " )\n", - "\n", - " if state_vars_fixed is False:\n", - " if hold_state is True:\n", - " return flags\n", - " else:\n", - " blk.release_state(flags)\n", - "\n", - " def release_state(blk, flags, outlvl=0):\n", - " \"\"\"\n", - " Method to release state variables fixed during initialisation.\n", - "\n", - " Keyword Arguments:\n", - " flags : dict containing information of which state variables\n", - " were fixed during initialization, and should now be\n", - " unfixed. This dict is returned by initialize if\n", - " hold_state=True.\n", - " outlvl : sets output level of of logging\n", - " \"\"\"\n", - " if flags is None:\n", - " return\n", - "\n", - " # Unfix state variables\n", - " for k in blk.keys():\n", - " if flags[\"Fcflag\"][k] is False:\n", - " blk[k].flow_mol.unfix()\n", - " if flags[\"Pflag\"][k] is False:\n", - " blk[k].pressure.unfix()\n", - " if flags[\"Tflag\"][k] is False:\n", - " blk[k].temperature.unfix()\n", - "\n", - " if outlvl > 0:\n", - " if outlvl > 0:\n", - " _log.info(\"{} State Released.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb index 5ac4559d..c70306f3 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb @@ -1,496 +1,521 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################################################################\n", - "# Institute for the Design of Advanced Energy Systems Process Systems\n", - "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", - "# software owners: The Regents of the University of California, through\n", - "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", - "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", - "# University Research Corporation, et al. All rights reserved.\n", - "#\n", - "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", - "# license information, respectively. Both files are also available online\n", - "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", - "##############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Constraint,\n", + " Param,\n", + " Reals,\n", + " Set,\n", + " value,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + ")\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component,\n", + ")\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties(\n", + " {\n", + " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", + " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", + " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", + " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", + " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", + " }\n", + " )\n", + "\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.s,\n", + " \"length\": units.m,\n", + " \"mass\": units.kg,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_usr.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + "\n", + " self.flow_mol = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol / units.s,\n", + " doc=\"Total molar flowrate [kmol/s]\",\n", + " )\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc=\"State pressure [MPa]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760 + 273.15),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " self.entr_mol = Var(\n", + " domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ / units.kmol / units.K,\n", + " doc=\"Entropy [kJ/ kmol / K]\",\n", + " )\n", + "\n", + " self.enth_mol = Var(\n", + " domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ / units.kmol,\n", + " doc=\"Enthalpy [kJ/ kmol]\",\n", + " )\n", + "\n", + " inputs = [self.pressure, self.temperature]\n", + " outputs = [self.enth_mol, self.entr_mol]\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\n", + " \"pysmo_poly_surrogate.json\"\n", + " )\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.pysmo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol * self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would define them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def initialize(\n", + " blk,\n", + " state_args=None,\n", + " hold_state=False,\n", + " outlvl=1,\n", + " state_vars_fixed=False,\n", + " solver=\"ipopt\",\n", + " optarg={\"tol\": 1e-8},\n", + " ):\n", + " \"\"\"\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output information (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating which solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states variables are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " release_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " \"\"\"\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\n", + " \"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\"\n", + " )\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " \"\"\"\n", + " Method to release state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " \"\"\"\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags[\"Fcflag\"][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags[\"Pflag\"][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags[\"Tflag\"][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info(\"{} State Released.\".format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", - "Maintainer: Javal Vyas\n", - "\n", - "Author: Javal Vyas\n", - "\n", - "Updated: 2024-01-24\n", - "## 1. Integration of Surrogate into Custom Property Package\n", - "\n", - "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical parameter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", - "\n", - "*NOTE:* For ease of explanation the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practice all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", - "\n", - "### 1.1 Steps in Creating a Property Package\n", - "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", - "\n", - "1. Defining the **units of measurement** for the property package.\n", - "2. Defining the **properties supported** by the property package and the associated metadata.\n", - "3. Defining the **phases and components** of interest.\n", - "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", - "5. Declaring the **state variables** to be used for the property package.\n", - "6. Creating **variables and constraints** to describe the properties of interest.\n", - "7. Creating an **initialization routine** for the property package.\n", - "8. Defining **interface methods** used to couple the property package with unit models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Importing libraries for making Property Package\n", - "\n", - "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Constraint,\n", - " Param,\n", - " Reals,\n", - " Set,\n", - " value,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - ")\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " LiquidPhase,\n", - " Component,\n", - ")\n", - "from idaes.core.util.initialization import solve_indexed_blocks\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.misc import extract_data\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", - "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", - "\n", - "from pyomo.util.model_size import build_model_size_report\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Defining Classes\n", - "\n", - "We shall be going through each class of the property package in detail. Since there are not reactions occurring in the flowsheet we shall only write the Physical Parameter Block.\n", - "\n", - "## 3.1 Physical Parameter Block\n", - "\n", - "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", - "\n", - "* Units of measurement\n", - "* What properties are supported and how they are implemented\n", - "* What components and phases are included in the packages\n", - "* All the global parameters necessary for calculating properties\n", - "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To assemble the above mentioned things in a class we need to follow the following steps:\n", - "\n", - "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", - "* Declaring any necessary configuration arguments\n", - "* Writing the build method for our class\n", - "* Creating a define_metadata method for the class.\n", - "\n", - "The code below follows the above mentioned steps. \n", - "\n", - "*NOTE*: The SCO2StateBlock will be discussed in the next section." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2ParameterBlock\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " supercritical CO2.\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super(PhysicalParameterData, self).build()\n", - "\n", - " self._state_block_class = SCO2StateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Liq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.CO2 = Component()\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_properties(\n", - " {\n", - " \"flow_mol\": {\"method\": None, \"units\": \"kmol/s\"},\n", - " \"pressure\": {\"method\": None, \"units\": \"MPa\"},\n", - " \"temperature\": {\"method\": None, \"units\": \"K\"},\n", - " \"enth_mol\": {\"method\": None, \"units\": \"kJ/kmol\"},\n", - " \"entr_mol\": {\"method\": None, \"units\": \"kJ/kmol/K\"},\n", - " }\n", - " )\n", - "\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.s,\n", - " \"length\": units.m,\n", - " \"mass\": units.kg,\n", - " \"amount\": units.mol,\n", - " \"temperatureo\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 State Block\n", - "\n", - "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", - "\n", - "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", - "\n", - "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", - "\n", - "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_usr.ipynb file) using the PySMO Surrogate API of IDAES package\n", - "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguments. \n", - "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"SCO2StateBlock\", block_class=StateBlock)\n", - "class SCO2StateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super(SCO2StateBlockData, self).build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - "\n", - " self.flow_mol = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1.0,\n", - " units=units.kmol / units.s,\n", - " doc=\"Total molar flowrate [kmol/s]\",\n", - " )\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=8,\n", - " bounds=(7.38, 40),\n", - " units=units.MPa,\n", - " doc=\"State pressure [MPa]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=350,\n", - " bounds=(304.2, 760 + 273.15),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " self.entr_mol = Var(\n", - " domain=Reals,\n", - " initialize=10,\n", - " units=units.kJ / units.kmol / units.K,\n", - " doc=\"Entropy [kJ/ kmol / K]\",\n", - " )\n", - "\n", - " self.enth_mol = Var(\n", - " domain=Reals,\n", - " initialize=1,\n", - " units=units.kJ / units.kmol,\n", - " doc=\"Enthalpy [kJ/ kmol]\",\n", - " )\n", - "\n", - " inputs = [self.pressure, self.temperature]\n", - " outputs = [self.enth_mol, self.entr_mol]\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\n", - " \"pysmo_poly_surrogate.json\"\n", - " )\n", - " self.surrogate_enth = SurrogateBlock()\n", - " self.surrogate_enth.build_model(\n", - " self.pysmo_surrogate,\n", - " input_vars=inputs,\n", - " output_vars=outputs,\n", - " )\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.flow_mol\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.flow_mol * self.enth_mol\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_mol\": self.flow_mol,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def model_check(blk):\n", - " \"\"\"\n", - " Model checks for property block\n", - " \"\"\"\n", - " # Check temperature bounds\n", - " if value(blk.temperature) < blk.temperature.lb:\n", - " _log.error(\"{} Temperature set below lower bound.\".format(blk.name))\n", - " if value(blk.temperature) > blk.temperature.ub:\n", - " _log.error(\"{} Temperature set above upper bound.\".format(blk.name))\n", - "\n", - " # Check pressure bounds\n", - " if value(blk.pressure) < blk.pressure.lb:\n", - " _log.error(\"{} Pressure set below lower bound.\".format(blk.name))\n", - " if value(blk.pressure) > blk.pressure.ub:\n", - " _log.error(\"{} Pressure set above upper bound.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Define Initialization Routine\n", - "\n", - "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", - "\n", - "Any initialization routine can be written by following a 3 step process:\n", - "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", - "\n", - "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", - "\n", - "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", - "\n", - "\n", - "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would define them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the variable with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _StateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def initialize(\n", - " blk,\n", - " state_args=None,\n", - " hold_state=False,\n", - " outlvl=1,\n", - " state_vars_fixed=False,\n", - " solver=\"ipopt\",\n", - " optarg={\"tol\": 1e-8},\n", - " ):\n", - " \"\"\"\n", - " Initialisation routine for property package.\n", - "\n", - " Keyword Arguments:\n", - " flow_mol : value at which to initialize component flows\n", - " (default=None)\n", - " pressure : value at which to initialize pressure (default=None)\n", - " temperature : value at which to initialize temperature\n", - " (default=None)\n", - " outlvl : sets output level of initialisation routine\n", - "\n", - " * 0 = no output (default)\n", - " * 1 = return solver state for each step in routine\n", - " * 2 = include solver output information (tee=True)\n", - " state_vars_fixed: Flag to denote if state vars have already been\n", - " fixed.\n", - " - True - states have already been fixed by the\n", - " control volume 1D. Control volume 0D\n", - " does not fix the state vars, so will\n", - " be False if this state block is used\n", - " with 0D blocks.\n", - " - False - states have not been fixed. The state\n", - " block will deal with fixing/unfixing.\n", - " optarg : solver options dictionary object (default=None)\n", - " solver : str indicating which solver to use during\n", - " initialization (default = 'ipopt')\n", - " hold_state : flag indicating whether the initialization routine\n", - " should unfix any state variables fixed during\n", - " initialization (default=False).\n", - " - True - states variables are not unfixed, and\n", - " a dict of returned containing flags for\n", - " which states were fixed during\n", - " initialization.\n", - " - False - state variables are unfixed after\n", - " initialization by calling the\n", - " release_state method\n", - "\n", - " Returns:\n", - " If hold_states is True, returns a dict containing flags for\n", - " which states were fixed during initialization.\n", - " \"\"\"\n", - " if state_vars_fixed is False:\n", - " # Fix state variables if not already fixed\n", - " Fcflag = {}\n", - " Pflag = {}\n", - " Tflag = {}\n", - "\n", - " for k in blk.keys():\n", - " if blk[k].flow_mol.fixed is True:\n", - " Fcflag[k] = True\n", - " else:\n", - " Fcflag[k] = False\n", - " if state_args is None:\n", - " blk[k].flow_mol.fix()\n", - " else:\n", - " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", - "\n", - " if blk[k].pressure.fixed is True:\n", - " Pflag[k] = True\n", - " else:\n", - " Pflag[k] = False\n", - " if state_args is None:\n", - " blk[k].pressure.fix()\n", - " else:\n", - " blk[k].pressure.fix(state_args[\"pressure\"])\n", - "\n", - " if blk[k].temperature.fixed is True:\n", - " Tflag[k] = True\n", - " else:\n", - " Tflag[k] = False\n", - " if state_args is None:\n", - " blk[k].temperature.fix()\n", - " else:\n", - " blk[k].temperature.fix(state_args[\"temperature\"])\n", - "\n", - " # If input block, return flags, else release state\n", - " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag, \"Tflag\": Tflag}\n", - "\n", - " else:\n", - " # Check when the state vars are fixed already result in dof 0\n", - " for k in blk.keys():\n", - " if degrees_of_freedom(blk[k]) != 0:\n", - " raise Exception(\n", - " \"State vars fixed but degrees of freedom \"\n", - " \"for state block is not zero during \"\n", - " \"initialization.\"\n", - " )\n", - "\n", - " if state_vars_fixed is False:\n", - " if hold_state is True:\n", - " return flags\n", - " else:\n", - " blk.release_state(flags)\n", - "\n", - " def release_state(blk, flags, outlvl=0):\n", - " \"\"\"\n", - " Method to release state variables fixed during initialisation.\n", - "\n", - " Keyword Arguments:\n", - " flags : dict containing information of which state variables\n", - " were fixed during initialization, and should now be\n", - " unfixed. This dict is returned by initialize if\n", - " hold_state=True.\n", - " outlvl : sets output level of of logging\n", - " \"\"\"\n", - " if flags is None:\n", - " return\n", - "\n", - " # Unfix state variables\n", - " for k in blk.keys():\n", - " if flags[\"Fcflag\"][k] is False:\n", - " blk[k].flow_mol.unfix()\n", - " if flags[\"Pflag\"][k] is False:\n", - " blk[k].pressure.unfix()\n", - " if flags[\"Tflag\"][k] is False:\n", - " blk[k].temperature.unfix()\n", - "\n", - " if outlvl > 0:\n", - " if outlvl > 0:\n", - " _log.info(\"{} State Released.\".format(blk.name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/solution.pickle b/idaes_examples/notebooks/docs/surrogates/solution.pickle index 260d20b2..3305e66d 100644 Binary files a/idaes_examples/notebooks/docs/surrogates/solution.pickle and b/idaes_examples/notebooks/docs/surrogates/solution.pickle differ diff --git a/idaes_examples/notebooks/docs/tut/core/flash_unit.ipynb b/idaes_examples/notebooks/docs/tut/core/flash_unit.ipynb index 6dc88109..f2e10b28 100644 --- a/idaes_examples/notebooks/docs/tut/core/flash_unit.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/flash_unit.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/core/flash_unit_doc.ipynb b/idaes_examples/notebooks/docs/tut/core/flash_unit_doc.ipynb index 9432ccbe..91dfd48a 100644 --- a/idaes_examples/notebooks/docs/tut/core/flash_unit_doc.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/flash_unit_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -448,98 +449,98 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_out: State Released.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_out: State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume: Initialization Complete\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash.control_volume.properties_in: State Released.\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash.control_volume.properties_in: State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:25 [INFO] idaes.init.fs.flash: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:39:41 [INFO] idaes.init.fs.flash: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -682,14 +683,14 @@ "Output from display:\n", "vap_outlet : Size=1\n", " Key : Name : Value\n", - " None : flow_mol : {0.0: 0.39611817487741735}\n", - " : mole_frac_comp : {(0.0, 'benzene'): 0.6339766485081294, (0.0, 'toluene'): 0.36602335149187054}\n", + " None : flow_mol : {0.0: 0.3961181748774178}\n", + " : mole_frac_comp : {(0.0, 'benzene'): 0.6339766485081293, (0.0, 'toluene'): 0.3660233514918707}\n", " : pressure : {0.0: 101325.0}\n", " : temperature : {0.0: 368.0}\n", "liq_outlet : Size=1\n", " Key : Name : Value\n", - " None : flow_mol : {0.0: 0.6038818251225827}\n", - " : mole_frac_comp : {(0.0, 'benzene'): 0.4121175977229309, (0.0, 'toluene'): 0.587882402277069}\n", + " None : flow_mol : {0.0: 0.6038818251225821}\n", + " : mole_frac_comp : {(0.0, 'benzene'): 0.41211759772293083, (0.0, 'toluene'): 0.5878824022770692}\n", " : pressure : {0.0: 101325.0}\n", " : temperature : {0.0: 368.0}\n" ] @@ -740,7 +741,13 @@ " Pressure Change : 0.0000 : pascal : True : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", - " Stream Table\n", + " Stream Table\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Units Inlet Vapor Outlet Liquid Outlet\n", " flow_mol mole / second 1.0000 0.39612 0.60388 \n", " mole_frac_comp benzene dimensionless 0.50000 0.63398 0.41212 \n", @@ -990,7 +997,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "... solve successful.\n", + "... solve successful." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "Simulating with Q = 2714.2857142857138\n" ] }, @@ -1158,7 +1172,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "... solve successful.\n", + "... solve successful." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "Simulating with Q = 20714.28571428571\n" ] }, @@ -1211,16 +1232,12 @@ }, { "data": { - "image/png": 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UCgVycnKqbM/JyYG7u3uNt3nvvfcQFhaGiRMnAgC6du2K4uJivPTSS/jXv/4FuVzyk7+IiBq9dYmXMHNLKoQAnu7WHPNH+EFpwfdnajiS/bYplUoEBARg9+7dum1arRa7d+9GSEhIjbcpKSmpVmAUijsrxgoh6i8sERE9kBACi/aex79+vFNsxgR74YsX/VlsqMFJuo8wOjoaERERCAwMRFBQEBYsWIDi4mLd2VPh4eHw8PBATEwMAGDo0KGYP38+/P39dR9Lvffeexg6dKiu5BARUcMTQuCj7WfwzW8XAQBRj/rgzcHtuQAmSULScjNy5Ehcv34ds2bNQnZ2Nvz8/LBjxw7dQcaZmZlV9tTMnDkTMpkMM2fORFZWFpo1a4ahQ4fiww8/lOpHICJq9Co1Wvzzx5PYdOQKAGDmkE6Y2LeNxKmoMZP86K6oqChERUXVeN2+ffuqXLawsMDs2bMxe/bsBkhGREQPUqbW4PUNx/DrqRzIZcC857thRKCn1LGokZO83BARkWkqKq/E5DVH8Mf5G1Aq5Fg4yh9P+NZ8QghRQ2K5ISIig90qrsC4uMM4fjkftkoFloYHoo+Pi9SxiACw3BARkYGyb5chbHkizuUWoYmNJeIig9Dd00nqWEQ6LDdERKS3i3nFGLssEVn5pXB3sMKaCUFo52YvdSyiKlhuiIhIL6evFiB8RRLyisrR2sUWayYEoWUTrtFHxoflhoiIHuhIxk1Exh1GYVklOjd3wKrxQWhmX31pGyJjwHJDRET3tTc9Fy+vTUaZWoue3k2wfFxPOFhxLSQyXiw3RER0Tz8dv4rojSmo1Ao82qEZvh4TAGslvxGejBvLDRER1WjtoUt4b+uddaL+4dcCnw3vDksF14ki48dyQ0REVQgh8PW+C/j013QAQHhIK7w/tAvkcq4TRaaB5YaIiHT+vgDm1Md8MG0QF8Ak08JyQ0REAKovgPne050x4ZHWEqciMhzLDRERVVkAUyGX4ePnu+GFgJZSxyKqFZYbIqJGrri8Ei/9ZQHML0f7I7QLF8Ak08VyQ0TUiP19AcxvwgPRmwtgkoljuSEiaqS4ACaZK5YbIqJGKCOvGGOXJ+LKrTsLYK6dGAQfVy6ASeaB5YaIqJHhAphk7lhuiIgaES6ASY0Byw0RUSOxLz0XU/6yAOayiJ5wtOYCmGR+WG6IiBqBn49fxbT/XwBzQIdmWMwFMMmMsdwQEZm5dYmXMHPLnQUwh3W/swCm0oILYJL5YrkhIjJTQggs3n8Bn+y4swDm2F5e+GCYLxfAJLPHckNEZIaEEIj5bxqWHvgTABD1qA/eHMwFMKlxYLkhIjIzWgH8a+tpbE7OAgDMHNIJE/u2kTgVUcNhuSEiMiPllVrEnZXj+M0syGXAvOe7YUSgp9SxiBoUyw0RkZkoLq/E5LXHcPymHJYKGb4c1QNP+HIBTGp8WG6IiMxAfkkFxq08jJTL+VDKBb4JC0D/jiw21Dix3BARmbicgjKEL09Cek4hHK0tML5tGXq3bSp1LCLJ8IsOiIhM2KUbxXgh9iDScwrh5qDCtxOC4M31L6mR454bIiITlZZdgLDlSbheWI5WTW2wdkIw3O0tcU7qYEQSY7khIjJByZduYXzcYdwuVaOjuz1Wjw+Cq4MV1Gq11NGIJMdyQ0RkYg6cvY7Ja5JRqtagh5cTVo4LgqMNF8AkuovlhojIhGw/eQ2vbzgGtUagbzsXLAkLgI2Sb+VEf8VXBBGRidh4OBMzfjgJrQCGdG2O+SO7Q2XBlb2J/k6vcnPixAmD77hz586wsGB3IiKqC0sPXMBH29MAAC/29MSHz3aFggtgEtVIr/bh5+cHmUwGIYRedyqXy3H27Fm0acO1TIiIHoYQAp/+mo6v910AAEzu1wbTn+zIBTCJ7kPvXSuJiYlo1qzZA8cJIeDr6/tQoYiICNBoBWZtTcW6xEwAwLtPdMTLA9pKnIrI+OlVbvr37w8fHx84OTnpdaf9+vWDtbX1w+QiImrUKiq1iN6Ugl9OXINMBnz4TFeMDvaSOhaRSdCr3Ozdu9egO92+fXutwhAREVBaocHL65KxL/06LBUyzB/hh6HdW0gdi8hkPNTyC3/88QfKy8vrKgsRUaN3u1SNsOWJ2Jd+HVaWcnwTHshiQ2Sghyo3Tz75JLKysuoqCxFRo3a9sByjlh7CkUu3YG9lgTUTgjGgg6vUsYhMzkOdq63v2VNERHR/V26VIGx5Ei7mFcPFTolV44PQpYWj1LGITBK/iIaISGLnc4sQtjwR126XwcPJGmsmBKFNMzupYxGZrIcqN0uWLIGbm1tdZSEianROXrmNiJVJuFlcgbbNbLF2YjCaO/JsU6KH8VDlZvTo0XWVg4io0Um4cAOTVh9BUXkluno4YtX4IDjbKqWORWTy9Dqg+LnnnkNBQYHedzpmzBjk5ubWOhQRkbnbdToHESuTUFReieDWzlg/KZjFhqiO6LXnZuvWrbh+/bpedyiEwM8//4y5c+fC1ZVH+RMR/d2Px67grc0noNEKDOzkiq9G94CVJRfAJKorepUbIQTat29f31mIiMzeqoMZmP3TKQDAs/4e+OSFbrBUPNS3chDR39TLNxQDgIeHh8G3ISIyV0IIfLnnPObHnwUARIS0wuyhXSDnyt5EdU7vtaWIiKh2tFqBD7efwfLfLwIAXn+8Hd4Y2I4rexPVE37PDRFRParUaDH9h5P4LvkKAGDW050x/pHWEqciMm8sN0RE9aS8UoPXv03BjlPZkMuAj5/vhuGBnlLHIjJ7LDdERPWguLwSL605gj/O34BSIceXo/0R2sVd6lhEjQLLDRFRHcsvqcC4lYeRcjkfNkoFvgkPRB8fF6ljETUaLDdERHUot6AMYcuTkJ5TCCcbS8RFBsHP00nqWESNisFfrpCTk4OwsDC0aNECFhYWUCgUVf4ZatGiRfD29oaVlRWCg4ORlJR03/H5+fl49dVX0bx5c6hUKrRv3x7bt283+HGJiOpa5o0SvBCbgPScQrjaq7DxpRAWGyIJGLznZty4ccjMzMR7772H5s2bP9SpjBs3bkR0dDRiY2MRHByMBQsWIDQ0FOnp6TV+u3FFRQUGDRoEV1dXfPfdd/Dw8MClS5fg5ORU6wxERHUhPbsQYcsTkVtYDi9nG6ybGAxPZxupYxE1SgaXm99//x2//fYb/Pz8HvrB58+fj0mTJiEyMhIAEBsbi23btmHFihWYPn16tfErVqzAzZs3cfDgQVhaWgIAvL297/sY5eXlKC8v112+u0aWWq2GWq1+6J+Baufuc885MH6cqwdLuZyPiWuO4nZpJdq72mHluAC42ltK8pxxvkwH58owhjxPMiGEMOTOO3fujHXr1sHf39/gYH9VUVEBGxsbfPfdd3jmmWd02yMiIpCfn4+tW7dWu81TTz0FZ2dn2NjYYOvWrWjWrBlGjx6Nd999954fib3//vuYM2dOte3r16+HjQ3/q4qIHk76bRmWpclRoZXB207gpY4a2FpKnYrI/JSUlGD06NG4ffs2HBwc7jvW4D03CxYswPTp07FkyZIH7jW5n7y8PGg0Gri5uVXZ7ubmhrS0tBpv8+eff2LPnj0YM2YMtm/fjvPnz+OVV16BWq3G7Nmza7zNjBkzEB0drbtcUFAAT09PDB48+IFPDtUftVqN+Ph4DBo0SLcXjowT5+re4k/n4ptNx6HWCvRu64yvR/nBViXteRqcL9PBuTLM3U9e9GHwq3DkyJEoKSlB27ZtYWNjU21Cbt68aehd6k2r1cLV1RVLly6FQqFAQEAAsrKy8Omnn96z3KhUKqhUqmrbLS0t+ctkBDgPpoNzVdV3yVfwznfHoRXAE13c8cUoP6gsjGdlb86X6eBc6ceQ56hWe27qgouLCxQKBXJycqpsz8nJgbt7zV901bx5c1haWlb5CKpTp07Izs5GRUUFlEplnWQjIrqflX9cxJyfTwMAhge0RMxzXWHBlb2JjIbB5SYiIqJOHlipVCIgIAC7d+/WHXOj1Wqxe/duREVF1XibPn36YP369dBqtZDL77yRnD17Fs2bN2exIaJ6J4TAF7vPYcGucwCACY+0xr+e6sSVvYmMTK0+HNZoNNiyZQvOnDkDAOjSpQuGDRtm8PfcREdHIyIiAoGBgQgKCsKCBQtQXFysO3sqPDwcHh4eiImJAQC8/PLL+Oqrr/D666/jtddew7lz5/DRRx9h6tSptfkxiIj0ptUKfPDLacQdzAAAvDmoPaIe8+HK3kRGyOByc/78eTz11FPIyspChw4dAAAxMTHw9PTEtm3b0LZtW73va+TIkbh+/TpmzZqF7Oxs+Pn5YceOHbqDjDMzM3V7aADA09MTv/76K6ZNm4Zu3brBw8MDr7/+Ot59911DfwwiIr1VarR49/uT+P7onZW95wzrgoje3tKGIqJ7MrjcTJ06FW3btsWhQ4fg7OwMALhx4wbGjh2LqVOnYtu2bQbdX1RU1D0/htq3b1+1bSEhITh06JChsYmIaqVMrcHUb49h5+kcKOQyfPpCNzzXo6XUsYjoPgwuN/v3769SbACgadOmmDdvHvr06VOn4YiIpFRUXonJd1f2tpBj0egeGNTZ7cE3JCJJGVxuVCoVCgsLq20vKiriQb1EZDbySyoQsfIwjl/Oh61SgW8iAtG7LVf2JjIFBp+7+PTTT+Oll15CYmIihBAQQuDQoUOYMmUKhg0bVh8ZiYgaVE5BGUYsScDxy/lwsrHE+km9WGyITIjB5WbhwoVo27YtQkJCYGVlBSsrK/Tp0wc+Pj744osv6iMjEVGDybxRguGxCTibUwQ3BxU2TQ5Bd67sTWRSDP5YysnJCVu3bsW5c+d0yyR06tQJPj4+dR6OiKghcWVvIvNQ60VQ2rVrh3bt2tVlFiIiyRzLvIVxKw/jdqkaHd3tsXp8EFwdrKSORUS1oFe5iY6Oxty5c2Fra1tlEcqazJ8/v06CERE1lIPn8zBx9RGUVGjg7+WEleN6wsmGJ0gQmSq9ys2xY8egVqt1/5+IyFz8eiobr60/hgqNFo/4uGBJWIDkK3sT0cPR6xW8d+/eGv8/EZEp+z75Ct75/gQ0WoHQLm5YOMrfqFb2JqLaMfhsqfHjx9f4PTfFxcUYP358nYQiIqpvcX9cxJubj0OjFXghoCUWje7BYkNkJgwuN6tWrUJpaWm17aWlpVi9enWdhCIiqi9CCHyx6xze//k0AGB8n9b45PlusFAY/HZIREZK7w+WCwoKdF/aV1hYCCur/51FoNFosH37dri6utZLSCKiuqDVCvx72xms+OMiAGDawPaY+jhX9iYyN3qXGycnJ8hkMshkMrRv377a9TKZDHPmzKnTcEREdaVSo8X0H07iu+Q7K3vPHtoZkX1aS5yKiOqD3uVm7969EELgsccew/fff19l4UylUolWrVqhRYsW9RKSiOhhlFdq8Pq3KdhxKhtyGfDpC93xfABX9iYyV3qXm/79+wMALl68CC8vL+7GJSKTUFxeiSlrk/HbuTwoFXIsHOWPJ3zdpY5FRPXI4CPo9uzZg++++67a9s2bN2PVqlV1EoqIqC7cLlEjbHkifjuXBxulAivG9WSxIWoEDC43MTExcHGpvjquq6srPvroozoJRUT0sHILyzByaQKOZubD0doSaycG45F2XNmbqDEw+Gs4MzMz0bp19YPwWrVqhczMzDoJRUT0MC7fLEHY8kRk3ChBM3sV1kwIQkd3B6ljEVEDMXjPjaurK06cOFFt+/Hjx9G0adM6CUVEVFvncwsxPDYBGTdK4Olsje+mhLDYEDUyBu+5GTVqFKZOnQp7e3v069cPALB//368/vrrePHFF+s8IBGRvk5euY2IlUm4WVyBdq52WDMhGO6OXNmbqLExuNzMnTsXGRkZePzxx2FhcefmWq0W4eHhPOaGiCST+OcNTFh1BEXllejW0hFxkUFwtuXK3kSNkcHlRqlUYuPGjZg7dy6OHz8Oa2trdO3aFa1ataqPfERED7QnLQcvrz2K8koterVxxjfhgbC3spQ6FhFJxOByc1f79u1r/KZiIqKG9NPxq4jemIJKrcDATq74anQPWFlyAUyixqxW5ebKlSv46aefkJmZiYqKiirXzZ8/v06CERE9yLrES5i5JRVCAM/4tcCnw7vDkgtgEjV6Bpeb3bt3Y9iwYWjTpg3S0tLg6+uLjIwMCCHQo0eP+shIRFRN7P4LmPffNABAWK9WmDOsC+RyfnM6EdXiVPAZM2bgrbfewsmTJ2FlZYXvv/8ely9fRv/+/TF8+PD6yEhEpCOEwMc70nTF5tVH2+KDf7DYENH/GFxuzpw5g/DwcACAhYUFSktLYWdnhw8++AAff/xxnQckIrpLqxWYuSUVi/ddAADMeLIj3g7tyLXuiKgKg8uNra2t7jib5s2b48KFC7rr8vLy6i4ZEdFfqDVaTNuUgnWJmZDJgJjnumJy/7ZSxyIiI2TwMTe9evXC77//jk6dOuGpp57Cm2++iZMnT+KHH35Ar1696iMjETVyZWoNXl13FLvTcmEhl+E/I/0wtHsLqWMRkZEyuNzMnz8fRUVFAIA5c+agqKgIGzduRLt27XimFBHVucIyNSauOoLEizehspAjdmwAHu3oKnUsIjJiBpUbjUaDK1euoFu3bgDufEQVGxtbL8GIiG4WV2DcyiScuHIbdioLLI8IRHAbrmFHRPdn0DE3CoUCgwcPxq1bt+orDxERACD7dhlGLEnAiSu34WyrxLeTerHYEJFeDD6g2NfXF3/++Wd9ZCEiAgBculGMF2IP4nxuEdwdrLBpcgi6tnSUOhYRmQiDy82///1vvPXWW/jll19w7do1FBQUVPlHRPQw0rML8UJsAq7cKoV3UxtsnhICH1c7qWMRkQkx+IDip556CgAwbNiwKt8tIYSATCaDRqOpu3RE1Kgcy7yFcSsP43apGh3d7bF6QhBc7a2kjkVEJsbgcrN37976yEFEjdzB83mYuPoISio08PdyQty4IDjacGVvIjKc3uUmPDwcixYtQv/+/QEAx48fR+fOnWFpyTcfIno4O09lI+rbY6io1KKPT1MsDQuErapW6/oSEel/zM26detQWlqqu9y3b19cvny5XkIRUePx47EreHndUVRUajG4sxuWR/RksSGih6L3O4gQ4r6XiYgMtSYhA+9tPQUAeK6HBz55vhssFAaf50BEVAX/84iIGpwQAl/vu4BPf00HAIzr7Y1ZT3fmyt5EVCcMKjenT59GdnY2gDtvTmlpabqlGO66++3FREQ1EUJg3n/TsOTAne/LmvqYD6YNas+VvYmozhhUbh5//PEqH0c9/fTTAACZTMZTwYnogTRagZlbUvFtUiYAYOaQTpjYt43EqYjI3Ohdbi5evFifOYjIzKk1WkzbmIJfTlyDXAbEPNcVI3t6SR2LiMyQ3uWmVatW9ZmDiMxYmVqDV9YdxZ60XFgqZFgw0h9DujWXOhYRmSkeUExE9aqwTI0Jq44g6eJNWFnKETs2AAM6uEodi4jMGMsNEdWbm8UViFiRhJNZt2GvssDycT0R1NpZ6lhEZOZYboioXmTfLsPY5Yk4n1sEZ1slVo8Pgq8HV/Ymovpn0LdlCSGQmZmJsrKy+spDRGbg0o1ivBB7EOdzi+DuYIVNk0NYbIiowRhcbnx8fLjsAhHdU3p2IV6ITcCVW6XwbmqDzVNC4ONqJ3UsImpEDCo3crkc7dq1w40bN+orDxGZsJTL+RixJAHXC8vR0d0em6aEwNPZRupYRNTIGLyIy7x58/D2228jNTW1PvIQkYk6eCEPY745hNulavh7OWHDS73gam8ldSwiaoQMPqA4PDwcJSUl6N69O5RKJaytratcf/PmzToLR0SmYdfpHLyy/s7K3n18mmJpWCBX9iYiyRj87rNgwYJ6iEFEpmprShaiNx2HRiswqLMbvhzlDytLhdSxiKgRM7jcRERE1EcOIjJBaw9dwntbUyEE8Ky/Bz55oRssFQZ/2k1EVKdqtd9Yo9Fgy5YtOHPmDACgS5cuGDZsGBQK/tcaUWOxeN8FfLwjDQAQ1qsV5gzrArmcK3sTkfQMLjfnz5/HU089haysLHTo0AEAEBMTA09PT2zbtg1t27at85BEZDyEEPj013R8ve8CAOCVAW3xdmgHyGQsNkRkHAzefzx16lS0bdsWly9fxtGjR3H06FFkZmaidevWmDp1aq1CLFq0CN7e3rCyskJwcDCSkpL0ut2GDRsgk8nwzDPP1OpxicgwWq3ArK2ndMXm3Sc64p0nOrLYEJFRMbjc7N+/H5988gmcnf+3PkzTpk0xb9487N+/3+AAGzduRHR0NGbPno2jR4+ie/fuCA0NRW5u7n1vl5GRgbfeegt9+/Y1+DGJyHCVGi3e3Hwcaw5dgkwG/PsZX7w8gHtqicj4GFxuVCoVCgsLq20vKiqCUqk0OMD8+fMxadIkREZGonPnzoiNjYWNjQ1WrFhxz9toNBqMGTMGc+bMQZs2bQx+TCIyjFoLTN14Aj8ey4JCLsOCkX4Y26uV1LGIiGpk8DE3Tz/9NF566SUsX74cQUFBAIDExERMmTIFw4YNM+i+KioqkJycjBkzZui2yeVyDBw4EAkJCfe83QcffABXV1dMmDABv/32230fo7y8HOXl5brLBQUFAAC1Wg21Wm1QXqo7d597zoHxyy8uxdI0Oc7ezoXSQo6FI7vh8Y6unDsjxdeW6eBcGcaQ58ngcrNw4UJEREQgJCQElpaWAIDKykoMGzYMX3zxhUH3lZeXB41GAzc3tyrb3dzckJaWVuNtfv/9dyxfvhwpKSl6PUZMTAzmzJlTbfvOnTthY8OvhZdafHy81BHoPkoqgSVnFMgokkMpF5jUXo3yP49g+59SJ6MH4WvLdHCu9FNSUqL3WIPLjZOTE7Zu3Ypz587hzJkzkMlk6NSpE3x8fAy9K4MVFhYiLCwM33zzDVxcXPS6zYwZMxAdHa27XFBQAE9PTwwePBgODg71FZUeQK1WIz4+HoMGDdKVZDIueUXliIxLRkZREWwUAssjAhDYWr/XHUmHry3TwbkyzN1PXvRR6+9Hb9euna7Q1PZMCRcXFygUCuTk5FTZnpOTA3d392rjL1y4gIyMDAwdOlS3TavVAgAsLCyQnp5e7VR0lUoFlUpV7b4sLS35y2QEOA/G6cqtEoQtP4KLecVoZqfE+LYlCGztwrkyIXxtmQ7OlX4MeY5q9VWiy5cvh6+vL6ysrGBlZQVfX18sW7bM4PtRKpUICAjA7t27ddu0Wi12796NkJCQauM7duyIkydPIiUlRfdv2LBhePTRR5GSkgJPT8/a/DhE9BcXrhdhRGwCLuYVw8PJGt9ODEILfoJLRCbE4D03s2bNwvz58/Haa6/pCkhCQgKmTZuGzMxMfPDBBwbdX3R0NCIiIhAYGIigoCAsWLAAxcXFiIyMBHBnoU4PDw/ExMToitRfOTk5AUC17URkuFNXbyN8eRJuFFegbTNbrJ0YDBcbC5ySOhgRkQEMLjeLFy/GN998g1GjRum2DRs2DN26dcNrr71mcLkZOXIkrl+/jlmzZiE7Oxt+fn7YsWOH7iDjzMxMyOVcq4aoviVfuolxKw+jsKwSXVo4YPX4IDS1U/FMDiIyOQaXG7VajcDAwGrbAwICUFlZWasQUVFRiIqKqvG6ffv23fe2cXFxtXpMIvqf385dx0urk1Gq1qCndxMsH9cTDlY8BoCITJPBu0TCwsKwePHiatuXLl2KMWPG1EkoImo4O1KzMSHuCErVGvRr3wyrxwez2BCRSavV2VLLly/Hzp070atXLwB3vsQvMzMT4eHhVU67nj9/ft2kJKJ68X3yFbzz/QlotAJP+rrjixf9obTgx8BEZNoMLjepqano0aMHgDunZgN3Tul2cXFBamqqbhwX0iMybqsTMjBr651DhYcHtETMc11hoWCxISLTZ3C52bt3b33kIKIGIoTA1/su4NNf0wEAkX288d6QzpDL+R8kRGQeav0lfkRkeoQQmLcjDUv231k/Yerj7TBtYDvuaSUis1KrcnPkyBFs2rQJmZmZqKioqHLdDz/8UCfBiKhuabQC721NxfrETADAzCGdMLFvG4lTERHVPYM/YN+wYQN69+6NM2fO4Mcff4RarcapU6ewZ88eODo61kdGInpIao0W0ZtSsD4xEzIZMO+5riw2RGS2DC43H330Ef7zn//g559/hlKpxBdffIG0tDSMGDECXl5e9ZGRiB5CmVqDl9cmY2vKVVjIZVj4oj9eDOJrlYjMl8Hl5sKFCxgyZAiAO2tDFRcXQyaTYdq0aVi6dGmdBySi2isur8T4uMPYdSYXKgs5loYHYGj3FlLHIiKqVwaXmyZNmqCwsBAA4OHhoTv9Oz8/HyUlJXWbjohqLb+kAmOWJeLghRuwVSqwanwQHuvoJnUsIqJ6Z/ABxf369UN8fDy6du2K4cOH4/XXX8eePXsQHx+Pxx9/vD4yEpGBcgvLEL48CWnZhXCyscSqyCB093SSOhYRUYPQu9ykpqbC19cXX331FcrKygAA//rXv2BpaYmDBw/i+eefx8yZM+stKBHpJyu/FGOXJeJiXjFc7VVYMyEYHdztpY5FRNRg9C433bp1Q8+ePTFx4kS8+OKLAAC5XI7p06fXWzgiMsyf14swdlkirt4uQ8sm1lg3MRitmtpKHYuIqEHpfczN/v370aVLF7z55pto3rw5IiIi8Ntvv9VnNiIywOmrBRixJAFXb5ehbTNbbJ4SwmJDRI2S3uWmb9++WLFiBa5du4Yvv/wSGRkZ6N+/P9q3b4+PP/4Y2dnZ9ZmTiO4j+dItvLg0AXlFFejSwgGbJoeguaO11LGIiCRh8NlStra2iIyMxP79+3H27FkMHz4cixYtgpeXF4YNG1YfGYnoPv44n4ew5YkoKKtEYKsmWD+pF5raqaSORUQkmYdaAtjHxwf//Oc/MXPmTNjb22Pbtm11lYuI9LDzVDYiVx5GSYUGfdu5YPWEIDhaW0odi4hIUrVeOPPAgQNYsWIFvv/+e8jlcowYMQITJkyoy2xEdB9bjmXhzc3HodEKhHZxw8JR/lBZKKSORUQkOYPKzdWrVxEXF4e4uDicP38evXv3xsKFCzFixAjY2vLARaKGsvbQJby3NRVCAM/18MAnz3eDheKhdsQSEZkNvcvNk08+iV27dsHFxQXh4eEYP348OnToUJ/ZiKgGsfsvYN5/0wAA4SGt8P7QLpDLZRKnIiIyHnqXG0tLS3z33Xd4+umnoVBw1zdRQxNC4POdZ/HV3vMAgFcfbYu3BneATMZiQ0T0V3qXm59++qk+cxDRfWi1Ah/8chpxBzMAAO8+0REvD2grbSgiIiNV6wOKiahhVGq0ePf7k/j+6BXIZMAH//BFWK9WUsciIjJaLDdERqy8UoM3NqTgv6nZUMhl+Gx4Nzzr31LqWERERo3lhshIlVZoMHltMg6cvQ6lQo4vR/sjtIu71LGIiIweyw2RESooU2NC3GEczrgFa0sFloYHoG+7ZlLHIiIyCSw3REbmZnEFIlYk4WTWbdhbWSAusicCWjlLHYuIyGSw3BAZkZyCMoxdlohzuUVwtlVi9fgg+Ho4Sh2LiMiksNwQGYnLN0swZlkiMm+WwN3BCmsnBsPH1U7qWEREJoflhsgInM8txJhlicgpKIeXsw3WTQyGp7ON1LGIiEwSyw2RxFKzbiN8RRJuFlegvZsd1kwIhpuDldSxiIhMFssNkYSOZNxE5MrDKCyvRLeWjlgVGYQmtkqpYxERmTSWGyKJHDh7HZPXJKNUrUFQa2csjwiEvZWl1LGIiEweyw2RBHakZmPqt8dQodGif/tmiB0bAGslF6QlIqoLLDdEDeyHo1fw9ncnoNEKPNXVHQtG+kNpIZc6FhGR2WC5IWpAaxIy8N7WUwCAFwJaYt5zXWGhYLEhIqpLLDdEDWTxvgv4eEcaAGBcb2/Meroz5HKZxKmIiMwPyw1RPRNC4LOd6Vi09wIA4LXHfBA9qD1kMhYbIqL6wHJDVI+0WoE5P5/CqoRLAIDpT3bElP5tJU5FRGTeWG6I6kmlRovpP5zEd8lXIJMBH/zDF2G9Wkkdi4jI7LHcENWDikot3th4DNtPZkMhl+Gz4d3wrH9LqWMRETUKLDdEday0QoMpa5Ox/+x1KBVyLBzljyd83aWORUTUaLDcENWhwjI1Jqw6gqSLN2FlKcfSsED0a99M6lhERI0Kyw1RHblVXIGIlUk4ceU27FUWWBHZEz29naWORUTU6LDcENWB3IIyjF2eiLM5RXC2VWL1+CD4ejhKHYuIqFFiuSF6SFdulWDsskRk3CiBm4MKaycEo52bvdSxiIgaLZYboofw5/UijFmWiGu3y9CyiTXWT+wFr6Y2UsciImrUWG6IaunMtQKELU9EXlEF2jazxbqJveDuaCV1LCKiRo/lhqgWjmXeQsSKJBSUVaJzcwesmRCEpnYqqWMRERFYbogMdvBCHiauOoKSCg16eDlhZWQQHK0tpY5FRET/j+WGyAB70nLw8tqjKK/Uoo9PUywNC4Stii8jIiJjwndlIj39cuIq3tiQgkqtwMBObvhqtD+sLBVSxyIior9huSHSw6YjlzH9+xPQCmBY9xb4fER3WCrkUsciIqIasNwQPcDKPy5izs+nAQCjgjzx72e6QiGXSZyKiIjuheWG6B6EEFi09zw+23kWADCpb2v886lOkMlYbIiIjBnLDVENhBD4eEc6YvdfAAC8MbAdXn+8HYsNEZEJMIqDBhYtWgRvb29YWVkhODgYSUlJ9xz7zTffoG/fvmjSpAmaNGmCgQMH3nc8kaG0WoFZW0/pis3MIZ3wxsD2LDZERCZC8nKzceNGREdHY/bs2Th69Ci6d++O0NBQ5Obm1jh+3759GDVqFPbu3YuEhAR4enpi8ODByMrKauDkZI4qNVq89d1xrDl0CTIZ8NGzXTGxbxupYxERkQEkLzfz58/HpEmTEBkZic6dOyM2NhY2NjZYsWJFjePXrVuHV155BX5+fujYsSOWLVsGrVaL3bt3N3ByMjfllRpErT+GH45mQSGXYcFIP4wO9pI6FhERGUjSY24qKiqQnJyMGTNm6LbJ5XIMHDgQCQkJet1HSUkJ1Go1nJ2da7y+vLwc5eXlussFBQUAALVaDbVa/RDp6WHcfe6NZQ5KKzR49dsU/Hb+BiwVMiwc2R0DO7kaTT4pGdtc0f1xvkwH58owhjxPkpabvLw8aDQauLm5Vdnu5uaGtLQ0ve7j3XffRYsWLTBw4MAar4+JicGcOXOqbd+5cydsbLh6s9Ti4+OljoCySmBpmgIXCmVQygUmtNeg4uIRbL8odTLjYgxzRfrjfJkOzpV+SkpK9B5r0mdLzZs3Dxs2bMC+fftgZVXzaswzZsxAdHS07nJBQYHuOB0HB4eGikp/o1arER8fj0GDBsHSUrp1mW6VVGDC6qO4UFgAO5UFloX5I6BVE8nyGCNjmSvSD+fLdHCuDHP3kxd9SFpuXFxcoFAokJOTU2V7Tk4O3N3d73vbzz77DPPmzcOuXbvQrVu3e45TqVRQqaqv1mxpaclfJiMg5TzkFpYhbEUy0nMK0cTGEmsmBMPXw1GSLKaArxnTwvkyHZwr/RjyHEl6QLFSqURAQECVg4HvHhwcEhJyz9t98sknmDt3Lnbs2IHAwMCGiEpm5sqtEoyITUB6TiFc7VXYNDmExYaIyExI/rFUdHQ0IiIiEBgYiKCgICxYsADFxcWIjIwEAISHh8PDwwMxMTEAgI8//hizZs3C+vXr4e3tjezsbACAnZ0d7OzsJPs5yHRczCvGmG8O4ertMrRsYo31E3vBqymPvyIiMheSl5uRI0fi+vXrmDVrFrKzs+Hn54cdO3boDjLOzMyEXP6/HUyLFy9GRUUFXnjhhSr3M3v2bLz//vsNGZ1MUFp2AcYuS0JeUTnaNLPFuonBaO5oLXUsIiKqQ5KXGwCIiopCVFRUjdft27evyuWMjIz6D0Rm6cSVfISvSEJ+iRqdmjtgzYQguNhVPx6LiIhMm1GUG6L6lnTxJsbHHUZReSX8PJ2wKjIIjjY8gI+IyByx3JDZ23/2OiavOYIytRYhbZrim4hA2Kn4q09EZK74Dk9mbUdqNl779ijUGoHHOrri6zE9YGWpkDoWERHVI5YbMls/HruCtzafgEYrMKRrc/xnpB+UFpIvp0ZERPWM5YbM0rrES5i5JRVCAC8EtMTHz3eDQi6TOhYRETUAlhsyO0sPXMBH2++sTTautzdmPd0ZchYbIqJGg+WGzIYQAgt2ncMXu88BAF4Z0BZvh3aATMZiQ0TUmLDckFkQQuDDbWew7Pc7S3m/HdoBrz7qI3EqIiKSAssNmTyNVmDmllR8m5QJAHh/aGeM69Na4lRERCQVlhsyaWqNFm9tPo6tKVchlwHznuuGET09pY5FREQSYrkhk1VeqUHU+mOIP50DC7kMC170w9PdWkgdi4iIJMZyQyappKISk9ck47dzeVBayLF4TA883slN6lhERGQEWG7I5BSUqTEh7jAOZ9yCjVKBZeGB6O3jInUsIiIyEiw3ZFJuFVcgfEUSTmbdhr2VBeIigxDQqonUsYiIyIiw3JDJyC0sQ9iyJKTnFMLZVonV44Pg6+EodSwiIjIyLDdkErLySzHmm0PIuFECV3sV1k0MRjs3e6ljERGREWK5IaN3Ma8YY5clIiu/FB5O1lg/KRitmtpKHYuIiIwUyw0ZtfTsQoxdnojrheVo42KLtROD0cLJWupYRERkxFhuyGiduJKP8BVJyC9Ro6O7PdZMCEYze5XUsYiIyMix3JBROpxxE5ErD6OovBLdPZ2wKrInnGyUUsciIiITwHJDRue3c9cxafURlKm1CGrtjBXjesJOxV9VIiLSD/9ikFGJP52DV9cdRYVGi37tm2HJ2ABYKxVSxyIiIhPCckNG46fjVzFtYwo0WoHQLm5YOMofKgsWGyIiMgzLDRmFjYczMf2HkxACeNbfA5++0A0WCrnUsYiIyASx3JDkVvx+ER/8choAMDrYC//+hy/kcpnEqYiIyFSx3JCkFu//E/N3nQcATOrbGv98qhNkMhYbIiKqPZYbkoQQAj9nyrEr606xeWNgO7z+eDsWGyIiemgsN9TgtFqBudvTsSvrzjE1/3qqEyb1ayNxKiIiMhcsN9SgNFqB6d+fwObkKwCAOUM7IaIPiw0REdUdlhtqMGqNFtM2puCXE9cglwGj2mowOshT6lhERGRmWG6oQZSpNYhafxS7zuTCUiHD/OHdoL2ULHUsIiIyQ/wiEap3JRWVmLjqCHadyYXKQo6lYYF4ooub1LGIiMhMcc8N1auCMjXGrzyMI5duwUapwLKIQPRu6wK1Wi11NCIiMlMsN1RvbhVXIHxFEk5m3Ya9lQVWjQ9CD68mUsciIiIzx3JD9SK3sAxhy5KQnlMIZ1slVo8Pgq+Ho9SxiIioEWC5oTqXlV+KscsScTGvGK72KqyfFAwfV3upYxERUSPBckN1KiOvGGOWJSIrvxQeTtZYPykYrZraSh2LiIgaEZYbqjPncgoxZlkicgvL0cbFFmsnBqOFk7XUsYiIqJFhuaE6kZp1G2HLE3GrRI0ObvZYOzEYzexVUsciIqJGiOWGHlrypVsYtzIJhWWV6NbSEasig9DEVil1LCIiaqRYbuihHLyQh4mrjqCkQoOe3k2wYlxP2FtZSh2LiIgaMZYbqrW9abmYsjYZ5ZVa9G3ngiVhAbBR8leKiIikxb9EVCv/PXkNUzccg1ojMLCTG74a7Q8rS4XUsYiIiFhuyHDfJ1/B298dh1YAT3drjv+M9IOlgsuUERGRcWC5IYOsPXQJM7ekAgCGB7TEvOe7QSGXSZyKiIjof1huSG/LfvsT/952BgAQEdIKs4d2gZzFhoiIjAzLDT2QEAILd5/Hf3adBQBM6d8W7z7RATIZiw0RERkflhu6LyEE5u1Iw5L9fwIA3hzUHlGP+bDYEBGR0WK5oXvSagXe//kUVidcAgDMHNIJE/u2kTgVERHR/bHcUI00WoF3vz+B75KvQCYDPnymK0YHe0kdi4iI6IFYbqgatUaLaRtT8MuJa5DLgM9HdMez/i2ljkVERKQXlhuqokytQdT6o9h1JheWChkWvuiPJ7s2lzoWERGR3lhuSKekohKT1yTjt3N5UFnIETs2AI92dJU6FhERkUFYbggAUFimxvi4wziccQs2SgWWRQSid1sXqWMREREZjOWGkF9SgfAVSThx5TbsrSwQFxmEgFZNpI5FRERUKyw3jViZWoMdqdn4au95nM8tgrOtEqvHB8HXw1HqaERERLVmFKsdLlq0CN7e3rCyskJwcDCSkpLuO37z5s3o2LEjrKys0LVrV2zfvr2BkpqH87lFmPvLafSK2Y03NqbgfG4RmtmrsPGlXiw2RERk8iTfc7Nx40ZER0cjNjYWwcHBWLBgAUJDQ5Geng5X1+oHsx48eBCjRo1CTEwMnn76aaxfvx7PPPMMjh49Cl9fXwl+AtNwdy/N+qRMJF28qdvewtEKI3t6YXSwF5rZqyRMSEREVDckLzfz58/HpEmTEBkZCQCIjY3Ftm3bsGLFCkyfPr3a+C+++AJPPPEE3n77bQDA3LlzER8fj6+++gqxsbENmv2vyis1uF5YLtnj38vtUjV+PJqF749ewa0SNQBALgMe6+iG0cGe6N/elat6ExGRWZG03FRUVCA5ORkzZszQbZPL5Rg4cCASEhJqvE1CQgKio6OrbAsNDcWWLVtqHF9eXo7y8v+VjoKCAgCAWq2GWq1+yJ/gf45fzseIpff/OE1q7g4qjAhoiRcCPNDc0QoAoNVUQqtp+Cx3n/u6nAOqH5wr08L5Mh2cK8MY8jxJWm7y8vKg0Wjg5uZWZbubmxvS0tJqvE12dnaN47Ozs2scHxMTgzlz5lTbvnPnTtjY2NQyeXUZhYClTFFn91dXZDKgnaNAbzeBzk7FkJel49gf6TgmdbD/Fx8fL3UE0hPnyrRwvkwH50o/JSUleo+V/GOp+jZjxowqe3oKCgrg6emJwYMHw8HBoU4f65U6vTfzplarER8fj0GDBsHS0lLqOHQfnCvTwvkyHZwrw9z95EUfkpYbFxcXKBQK5OTkVNmek5MDd3f3Gm/j7u5u0HiVSgWVqvqBspaWlvxlMgKcB9PBuTItnC/TwbnSjyHPkaSngiuVSgQEBGD37t26bVqtFrt370ZISEiNtwkJCakyHrizS+9e44mIiKhxkfxjqejoaERERCAwMBBBQUFYsGABiouLdWdPhYeHw8PDAzExMQCA119/Hf3798fnn3+OIUOGYMOGDThy5AiWLl0q5Y9BRERERkLycjNy5Ehcv34ds2bNQnZ2Nvz8/LBjxw7dQcOZmZmQy/+3g6l3795Yv349Zs6ciX/+859o164dtmzZwu+4ISIiIgBGUG4AICoqClFRUTVet2/fvmrbhg8fjuHDh9dzKiIiIjJFRrH8AhEREVFdYbkhIiIis8JyQ0RERGaF5YaIiIjMCssNERERmRWWGyIiIjIrLDdERERkVlhuiIiIyKyw3BAREZFZMYpvKG5IQggAhi2dTnVPrVajpKQEBQUFXA3XyHGuTAvny3Rwrgxz9+/23b/j99Poyk1hYSEAwNPTU+IkREREZKjCwkI4Ojred4xM6FOBzIhWq8XVq1dhb28PmUwmdZxGq6CgAJ6enrh8+TIcHBykjkP3wbkyLZwv08G5MowQAoWFhWjRokWVBbVr0uj23MjlcrRs2VLqGPT/HBwc+KI2EZwr08L5Mh2cK/09aI/NXTygmIiIiMwKyw0RERGZFZYbkoRKpcLs2bOhUqmkjkIPwLkyLZwv08G5qj+N7oBiIiIiMm/cc0NERERmheWGiIiIzArLDREREZkVlhsiIiIyKyw39FA+/PBD9O7dGzY2NnBycqpxTGZmJoYMGQIbGxu4urri7bffRmVlZZUx+/btQ48ePaBSqeDj44O4uLhq97No0SJ4e3vDysoKwcHBSEpKqnJ9WVkZXn31VTRt2hR2dnZ4/vnnkZOTU1c/aqP1oOedHs6BAwcwdOhQtGjRAjKZDFu2bKlyvRACs2bNQvPmzWFtbY2BAwfi3LlzVcbcvHkTY8aMgYODA5ycnDBhwgQUFRVVGXPixAn07dsXVlZW8PT0xCeffFIty+bNm9GxY0dYWVmha9eu2L59e53/vKYsJiYGPXv2hL29PVxdXfHMM88gPT29yhh93oca6j2xURNED2HWrFli/vz5Ijo6Wjg6Ola7vrKyUvj6+oqBAweKY8eOie3btwsXFxcxY8YM3Zg///xT2NjYiOjoaHH69Gnx5ZdfCoVCIXbs2KEbs2HDBqFUKsWKFSvEqVOnxKRJk4STk5PIycnRjZkyZYrw9PQUu3fvFkeOHBG9evUSvXv3rtef39zp87zTw9m+fbv417/+JX744QcBQPz4449Vrp83b55wdHQUW7ZsEcePHxfDhg0TrVu3FqWlpboxTzzxhOjevbs4dOiQ+O2334SPj48YNWqU7vrbt28LNzc3MWbMGJGamiq+/fZbYW1tLZYsWaIb88cffwiFQiE++eQTcfr0aTFz5kxhaWkpTp48We/PgakIDQ0VK1euFKmpqSIlJUU89dRTwsvLSxQVFenGPOh9qCHfExszlhuqEytXrqyx3Gzfvl3I5XKRnZ2t27Z48WLh4OAgysvLhRBCvPPOO6JLly5Vbjdy5EgRGhqquxwUFCReffVV3WWNRiNatGghYmJihBBC5OfnC0tLS7F582bdmDNnzggAIiEhoU5+xsboQc871a2/lxutVivc3d3Fp59+qtuWn58vVCqV+Pbbb4UQQpw+fVoAEIcPH9aN+e9//ytkMpnIysoSQgjx9ddfiyZNmuhec0II8e6774oOHTroLo8YMUIMGTKkSp7g4GAxefLkOv0ZzUlubq4AIPbv3y+E0O99qKHeExs7fixF9SohIQFdu3aFm5ubbltoaCgKCgpw6tQp3ZiBAwdWuV1oaCgSEhIAABUVFUhOTq4yRi6XY+DAgboxycnJUKvVVcZ07NgRXl5eujFkGH2ed6pfFy9eRHZ2dpU5cHR0RHBwsG4OEhIS4OTkhMDAQN2YgQMHQi6XIzExUTemX79+UCqVujGhoaFIT0/HrVu3dGPu9zqk6m7fvg0AcHZ2BqDf+1BDvSc2diw3VK+ys7OrvIgB6C5nZ2ffd0xBQQFKS0uRl5cHjUZT45i/3odSqax23M9fx5Bh9HneqX7dfZ4f9Lvv6upa5XoLCws4Ozs/8DX218e41xjOdc20Wi3eeOMN9OnTB76+vgD0ex9qqPfExo7lhqqZPn06ZDLZff+lpaVJHZOISDKvvvoqUlNTsWHDBqmjUA0spA5AxufNN9/EuHHj7jumTZs2et2Xu7t7tSP475454O7urvvfv59NkJOTAwcHB1hbW0OhUEChUNQ45q/3UVFRgfz8/Cr/1fTXMWQYFxeXBz7vVL/uPs85OTlo3ry5bntOTg78/Px0Y3Jzc6vcrrKyEjdv3nzga+yvj3GvMZzr6qKiovDLL7/gwIEDaNmypW67Pu9DDfWe2Nhxzw1V06xZM3Ts2PG+//762f39hISE4OTJk1XefOPj4+Hg4IDOnTvrxuzevbvK7eLj4xESEgIAUCqVCAgIqDJGq9Vi9+7dujEBAQGwtLSsMiY9PR2ZmZm6MWQYfZ53ql+tW7eGu7t7lTkoKChAYmKibg5CQkKQn5+P5ORk3Zg9e/ZAq9UiODhYN+bAgQNQq9W6MfHx8ejQoQOaNGmiG3O/1yHdOS0/KioKP/74I/bs2YPWrVtXuV6f96GGek9s9KQ+oplM26VLl8SxY8fEnDlzhJ2dnTh27Jg4duyYKCwsFEL877THwYMHi5SUFLFjxw7RrFmzGk97fPvtt8WZM2fEokWLajztUaVSibi4OHH69Gnx0ksvCScnpypnHEyZMkV4eXmJPXv2iCNHjoiQkBAREhLScE+GGdLneaeHU1hYqHvdABDz588Xx44dE5cuXRJC3DkV3MnJSWzdulWcOHFC/OMf/6jxVHB/f3+RmJgofv/9d9GuXbsqp4Ln5+cLNzc3ERYWJlJTU8WGDRuEjY1NtVPBLSwsxGeffSbOnDkjZs+ezVPB/+bll18Wjo6OYt++feLatWu6fyUlJboxD3ofasj3xMaM5YYeSkREhABQ7d/evXt1YzIyMsSTTz4prK2thYuLi3jzzTeFWq2ucj979+4Vfn5+QqlUijZt2oiVK1dWe6wvv/xSeHl5CaVSKYKCgsShQ4eqXF9aWipeeeUV0aRJE2FjYyOeffZZce3atfr4sRuVBz3v9HD27t1b42soIiJCCHHndPD33ntPuLm5CZVKJR5//HGRnp5e5T5u3LghRo0aJezs7ISDg4OIjIzU/QfGXcePHxePPPKIUKlUwsPDQ8ybN69alk2bNon27dsLpVIpunTpIrZt21ZvP7cpqmmeAFR5v9Lnfaih3hMbM5kQQjT47iIiIiKiesJjboiIiMissNwQERGRWWG5ISIiIrPCckNERERmheWGiIiIzArLDREREZkVlhsiIiIyKyw3REREZFZYboiI6siAAQMgk8kgk8mQkpJS45iMjAzdmLuLXxJR3WK5IaL7GjduHJ555plq2/ft2weZTIb8/Pw6eyx97/PuOJlMBrlcDkdHR/j7++Odd97BtWvXDH5cb29vLFiwoHah/2bSpEm4du0afH19AfyvzNwtO56enrh27RrefPPNOnk8IqqO5YaITFZ6ejquXr2Kw4cP491338WuXbvg6+uLkydPSpbJxsYG7u7usLCwqPF6hUIBd3d32NnZNXAyosaD5YaI6szvv/+Ovn37wtraGp6enpg6dSqKi4t1169ZswaBgYGwt7eHu7s7Ro8ejdzcXAB39nA8+uijAIAmTZpAJpNh3Lhx9308V1dXuLu7o3379njxxRfxxx9/oFmzZnj55Zd1YwYMGIA33nijyu2eeeYZ3X0PGDAAly5dwrRp03R7g4qLi+Hg4IDvvvuuyu22bNkCW1tbFBYW1vIZIqKGwHJDRHXiwoULeOKJJ/D888/jxIkT2LhxI37//XdERUXpxqjVasydOxfHjx/Hli1bkJGRoSsZnp6e+P777wHc2SNz7do1fPHFFwZlsLa2xpQpU/DHH3/oStOD/PDDD2jZsiU++OADXLt2DdeuXYOtrS1efPFFrFy5ssrYlStX4oUXXoC9vb1BuYioYdW835SI6C9++eWXah+jaDSaKpdjYmIwZswY3V6Sdu3aYeHChejfvz8WL14MKysrjB8/Xje+TZs2WLhwIXr27ImioiLY2dnB2dkZwJ09Mk5OTrXK2rFjRwB39gS5uro+cLyzszMUCoVub9JdEydORO/evXHt2jU0b94cubm52L59O3bt2lWrXETUcLjnhoge6NFHH0VKSkqVf8uWLasy5vjx44iLi4OdnZ3uX2hoKLRaLS5evAgASE5OxtChQ+Hl5QV7e3v0798fAJCZmVlnWYUQAACZTPZQ9xMUFIQuXbpg1apVAIC1a9eiVatW6Nev30NnJKL6xT03RPRAtra28PHxqbLtypUrVS4XFRVh8uTJmDp1arXbe3l5obi4GKGhoQgNDcW6devQrFkzZGZmIjQ0FBUVFXWW9cyZMwDunAEFAHK5XFd47lKr1Xrd18SJE7Fo0SJMnz4dK1euRGRk5EOXJiKqfyw3RFQnevTogdOnT1crQXedPHkSN27cwLx58+Dp6QkAOHLkSJUxSqUSQPWPvPRVWlqKpUuXol+/fmjWrBkAoFmzZlVOD9doNEhNTdUdvHz3cWt6zLFjx+Kdd97BwoULcfr0aURERNQqFxE1LH4sRUR14t1338XBgwcRFRWFlJQUnDt3Dlu3btUdUOzl5QWlUokvv/wSf/75J3766SfMnTu3yn20atUKMpkMv/zyC65fv46ioqL7PmZubi6ys7Nx7tw5bNiwAX369EFeXh4WL16sG/PYY49h27Zt2LZtG9LS0vDyyy9X+x4db29vHDhwAFlZWcjLy9Ntb9KkCZ577jm8/fbbGDx4MFq2bPmQzxIRNQSWGyKqE926dcP+/ftx9uxZ9O3bF/7+/pg1axZatGgB4M4elLi4OGzevBmdO3fGvHnz8Nlnn1W5Dw8PD8yZMwfTp0+Hm5tblTOtatKhQwe0aNECAQEBmDdvHgYOHIjU1FR07txZN2b8+PGIiIhAeHg4+vfvjzZt2lTZawMAH3zwATIyMtC2bVvdHp+7JkyYgIqKiioHQxtCq9UCwD2/94aI6p5M/P3DaCIi0lmzZg2mTZuGq1ev6j42u5cBAwbAz8+vyrcdHzp0CCEhIbh+/TpcXFx0299//31s2bLlnss0EFHtcc8NEVENSkpKcOHCBcybNw+TJ09+YLG56+uvv4adnR1OnjyJ8+fP49NPP0X37t11xSYzMxN2dnb46KOP6jM+UaPGPTdERDV4//338eGHH6Jfv37YunWrXsslZGVlobS0FABw8+ZN3Z6c2NhYdOvWDQBQWVmJjIwMAIBKpdIdXE1EdYflhoiIiMwKP5YiIiIis8JyQ0RERGaF5YaIiIjMCssNERERmRWWGyIiIjIrLDdERERkVlhuiIiIyKyw3BAREZFZ+T9nfJu7QnQLtQAAAABJRU5ErkJggg==", + "image/png": 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", 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", + "image/png": 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"text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\tut\\core\\flash_unit_doc_35_51.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1812,16 +1825,16 @@ " 0 0.0000000e+00 3.40e-02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", " 1 0.0000000e+00 1.64e+02 7.01e-02 -1.0 5.15e+03 - 9.87e-01 1.00e+00h 1\n", " 2 0.0000000e+00 9.59e-02 2.03e-03 -1.0 7.07e+01 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 6.95e-08 2.50e-06 -1.0 4.13e-01 - 9.98e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 6.96e-08 2.50e-06 -1.0 4.13e-01 - 9.98e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 3\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 8.9144743362344083e-11 6.9545421865768731e-08\n", + "Constraint violation....: 8.9151738215745240e-11 6.9550878833979368e-08\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 8.9144743362344083e-11 6.9545421865768731e-08\n", + "Overall NLP error.......: 8.9151738215745240e-11 6.9550878833979368e-08\n", "\n", "\n", "Number of objective function evaluations = 4\n", @@ -1843,7 +1856,13 @@ "output_type": "stream", "text": [ "\n", - "====================================================================================\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Unit : fs.flash Time: 0.0\n", "------------------------------------------------------------------------------------\n", " Unit Performance\n", @@ -1904,9 +1923,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/flash_unit_exercise.ipynb b/idaes_examples/notebooks/docs/tut/core/flash_unit_exercise.ipynb index 19e3b691..ad695f94 100644 --- a/idaes_examples/notebooks/docs/tut/core/flash_unit_exercise.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/flash_unit_exercise.ipynb @@ -1,657 +1,658 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Flash Unit Model\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", - "\n", - "Inlet Specifications:\n", - "* Mole fraction (Benzene) = 0.5\n", - "* Mole fraction (Toluene) = 0.5\n", - "* Pressure = 101325 Pa\n", - "* Temperature = 368 K\n", - "\n", - "We will complete the following tasks:\n", - "* Create the model and the IDAES Flowsheet object\n", - "* Import the appropriate property packages\n", - "* Create the flash unit and set the operating conditions\n", - "* Initialize the model and simulate the system\n", - "* Demonstrate analyses on this model through some examples and exercises\n", - "\n", - "## Key links to documentation\n", - "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", - "\n", - "## Create the Model and the IDAES Flowsheet\n", - "\n", - "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", - "\n", - "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to create the objects\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: call pprint on the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Properties\n", - "\n", - "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "\n", - "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", - "\n", - "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import and create the properties block.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Flash Unit\n", - "\n", - "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", - "\n", - "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", - "\n", - "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", - "\n", - "Some of the IDAES pre-written unit models:\n", - "* Mixer / Splitter\n", - "* Heater / Cooler\n", - "* Heat Exchangers (simple and 1D discretized)\n", - "* Flash\n", - "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", - "* Pressure changing equipment (compressors, expanders, pumps)\n", - "* Feed and Product (source / sink) components\n", - "\n", - "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash = Flash(property_package=m.fs.properties)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Operating Conditions\n", - "\n", - "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", - "\n", - "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "\n", - "\n", - "# Todo: Call the python help on the degrees_of_freedom function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now print the degrees of freedom for your model. The result should be 7.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", - "\n", - "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", - "\n", - "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.flow_mol.fix(1.0)\n", - "```\n", - "\n", - "To specify variables that are indexed by components, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "```\n", - "\n", - "
\n", - "Note:\n", - "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", - "
\n", - "\n", - "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", - "\n", - "\n", - "To specify the value of a variable on the unit itself, use the following notation.\n", - "\n", - "```python\n", - "m.fs.flash.heat_duty.fix(0)\n", - "```\n", - "\n", - "For this module, we will use the following specifications:\n", - "* inlet overall molar flow = 1.0 (`flow_mol`)\n", - "* inlet temperature = 368 K (`temperature`)\n", - "* inlet pressure = 101325 Pa (`pressure`)\n", - "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", - "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", - "* The heat duty on the flash set to 0 (`heat_duty`)\n", - "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Write the code below to specify the inlet conditions and unit specifications described above\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add inlet specifications given above\n", - "\n", - "\n", - "# Todo: Add 2 flash unit specifications given above" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing the Model\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the initialize method on the flash unit to initialize the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: initialize the flash unit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model has been defined and initialized, we can solve the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: create the ipopt solver\n", - "\n", - "# Todo: solve the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing the Results\n", - "\n", - "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", - "```python\n", - "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", - "```\n", - "\n", - "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", - "```python\n", - "m.fs.flash.vap_outlet.temperature.display()\n", - "m.fs.flash.vap_outlet.display()\n", - "```\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the pressure of the flash vapor outlet\n", - "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", - "\n", - "print()\n", - "print(\"Output from display:\")\n", - "# Call display on vap_outlet and liq_outlet of the flash\n", - "m.fs.flash.vap_outlet.display()\n", - "m.fs.flash.liq_outlet.display()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Studying Purity as a Function of Heat Duty\n", - "\n", - "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", - "\n", - "First, let's import the matplotlib package for plotting as we did in the previous module.\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to import matplotlib appropriately.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exercise specifications:\n", - "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", - "* Specify the heat duty from -17000 to 25000 over 50 steps\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# import the solve_successful checking function from workshop tools\n", - "from idaes_examples.mod.tut.workshoptools import solve_successful\n", - "\n", - "# Todo: import numpy as np\n", - "\n", - "\n", - "# create the empty lists to store the results that will be plotted\n", - "Q = []\n", - "V = []\n", - "\n", - "# re-initialize model\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Todo: Write the for loop specification using numpy's linspace\n", - "\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - " \n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - " \n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # Solve the model\n", - " status = solver.solve(m)\n", - " \n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", - " print('... solve successful.')\n", - " else:\n", - " V.append(0.0)\n", - " print('... solve failed.')\n", - " \n", - "# Create and show the figure\n", - "plt.figure(\"Vapor Fraction\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", - "\n", - "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", - "\n", - "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Unfix the heat_duty variable\n", - "m.fs.flash.heat_duty.unfix()\n", - "\n", - "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", - "\n", - "# solve the problem\n", - "status = solver.solve(m, tee=True)\n", - "\n", - "# Check stream condition\n", - "m.fs.flash.report()" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flash Unit Model\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", + "\n", + "Inlet Specifications:\n", + "* Mole fraction (Benzene) = 0.5\n", + "* Mole fraction (Toluene) = 0.5\n", + "* Pressure = 101325 Pa\n", + "* Temperature = 368 K\n", + "\n", + "We will complete the following tasks:\n", + "* Create the model and the IDAES Flowsheet object\n", + "* Import the appropriate property packages\n", + "* Create the flash unit and set the operating conditions\n", + "* Initialize the model and simulate the system\n", + "* Demonstrate analyses on this model through some examples and exercises\n", + "\n", + "## Key links to documentation\n", + "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", + "\n", + "## Create the Model and the IDAES Flowsheet\n", + "\n", + "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", + "\n", + "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to create the objects\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: call pprint on the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Properties\n", + "\n", + "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "\n", + "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", + "\n", + "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import and create the properties block.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Flash Unit\n", + "\n", + "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", + "\n", + "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", + "\n", + "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", + "\n", + "Some of the IDAES pre-written unit models:\n", + "* Mixer / Splitter\n", + "* Heater / Cooler\n", + "* Heat Exchangers (simple and 1D discretized)\n", + "* Flash\n", + "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", + "* Pressure changing equipment (compressors, expanders, pumps)\n", + "* Feed and Product (source / sink) components\n", + "\n", + "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash = Flash(property_package=m.fs.properties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Operating Conditions\n", + "\n", + "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", + "\n", + "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "\n", + "\n", + "# Todo: Call the python help on the degrees_of_freedom function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now print the degrees of freedom for your model. The result should be 7.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", + "\n", + "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", + "\n", + "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.flow_mol.fix(1.0)\n", + "```\n", + "\n", + "To specify variables that are indexed by components, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "```\n", + "\n", + "
\n", + "Note:\n", + "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", + "
\n", + "\n", + "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", + "\n", + "\n", + "To specify the value of a variable on the unit itself, use the following notation.\n", + "\n", + "```python\n", + "m.fs.flash.heat_duty.fix(0)\n", + "```\n", + "\n", + "For this module, we will use the following specifications:\n", + "* inlet overall molar flow = 1.0 (`flow_mol`)\n", + "* inlet temperature = 368 K (`temperature`)\n", + "* inlet pressure = 101325 Pa (`pressure`)\n", + "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", + "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", + "* The heat duty on the flash set to 0 (`heat_duty`)\n", + "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Write the code below to specify the inlet conditions and unit specifications described above\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add inlet specifications given above\n", + "\n", + "\n", + "# Todo: Add 2 flash unit specifications given above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the Model\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the initialize method on the flash unit to initialize the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: initialize the flash unit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model has been defined and initialized, we can solve the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: create the ipopt solver\n", + "\n", + "# Todo: solve the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing the Results\n", + "\n", + "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", + "```python\n", + "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", + "```\n", + "\n", + "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", + "```python\n", + "m.fs.flash.vap_outlet.temperature.display()\n", + "m.fs.flash.vap_outlet.display()\n", + "```\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the pressure of the flash vapor outlet\n", + "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", + "\n", + "print()\n", + "print(\"Output from display:\")\n", + "# Call display on vap_outlet and liq_outlet of the flash\n", + "m.fs.flash.vap_outlet.display()\n", + "m.fs.flash.liq_outlet.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Studying Purity as a Function of Heat Duty\n", + "\n", + "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", + "\n", + "First, let's import the matplotlib package for plotting as we did in the previous module.\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to import matplotlib appropriately.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise specifications:\n", + "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", + "* Specify the heat duty from -17000 to 25000 over 50 steps\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# import the solve_successful checking function from workshop tools\n", + "from idaes_examples.mod.tut.workshoptools import solve_successful\n", + "\n", + "# Todo: import numpy as np\n", + "\n", + "\n", + "# create the empty lists to store the results that will be plotted\n", + "Q = []\n", + "V = []\n", + "\n", + "# re-initialize model\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Todo: Write the for loop specification using numpy's linspace\n", + "\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + " \n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + " \n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # Solve the model\n", + " status = solver.solve(m)\n", + " \n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", + " print('... solve successful.')\n", + " else:\n", + " V.append(0.0)\n", + " print('... solve failed.')\n", + " \n", + "# Create and show the figure\n", + "plt.figure(\"Vapor Fraction\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", + "\n", + "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", + "\n", + "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Unfix the heat_duty variable\n", + "m.fs.flash.heat_duty.unfix()\n", + "\n", + "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", + "\n", + "# solve the problem\n", + "status = solver.solve(m, tee=True)\n", + "\n", + "# Check stream condition\n", + "m.fs.flash.report()" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/tut/core/flash_unit_solution.ipynb b/idaes_examples/notebooks/docs/tut/core/flash_unit_solution.ipynb index 6b4b3752..a986e1df 100644 --- a/idaes_examples/notebooks/docs/tut/core/flash_unit_solution.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/flash_unit_solution.ipynb @@ -1,896 +1,897 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Flash Unit Model\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", - "\n", - "Inlet Specifications:\n", - "* Mole fraction (Benzene) = 0.5\n", - "* Mole fraction (Toluene) = 0.5\n", - "* Pressure = 101325 Pa\n", - "* Temperature = 368 K\n", - "\n", - "We will complete the following tasks:\n", - "* Create the model and the IDAES Flowsheet object\n", - "* Import the appropriate property packages\n", - "* Create the flash unit and set the operating conditions\n", - "* Initialize the model and simulate the system\n", - "* Demonstrate analyses on this model through some examples and exercises\n", - "\n", - "## Key links to documentation\n", - "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", - "\n", - "## Create the Model and the IDAES Flowsheet\n", - "\n", - "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", - "\n", - "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to create the objects\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: call pprint on the model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: call pprint on the model\n", - "m.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Properties\n", - "\n", - "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "\n", - "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", - "\n", - "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import and create the properties block.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Flash Unit\n", - "\n", - "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", - "\n", - "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", - "\n", - "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", - "\n", - "Some of the IDAES pre-written unit models:\n", - "* Mixer / Splitter\n", - "* Heater / Cooler\n", - "* Heat Exchangers (simple and 1D discretized)\n", - "* Flash\n", - "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", - "* Pressure changing equipment (compressors, expanders, pumps)\n", - "* Feed and Product (source / sink) components\n", - "\n", - "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash = Flash(property_package=m.fs.properties)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Operating Conditions\n", - "\n", - "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", - "\n", - "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "\n", - "\n", - "# Todo: Call the python help on the degrees_of_freedom function" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Todo: Call the python help on the degrees_of_freedom function\n", - "help(degrees_of_freedom)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now print the degrees of freedom for your model. The result should be 7.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model\n", - "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", - "\n", - "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", - "\n", - "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.flow_mol.fix(1.0)\n", - "```\n", - "\n", - "To specify variables that are indexed by components, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "```\n", - "\n", - "
\n", - "Note:\n", - "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", - "
\n", - "\n", - "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", - "\n", - "\n", - "To specify the value of a variable on the unit itself, use the following notation.\n", - "\n", - "```python\n", - "m.fs.flash.heat_duty.fix(0)\n", - "```\n", - "\n", - "For this module, we will use the following specifications:\n", - "* inlet overall molar flow = 1.0 (`flow_mol`)\n", - "* inlet temperature = 368 K (`temperature`)\n", - "* inlet pressure = 101325 Pa (`pressure`)\n", - "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", - "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", - "* The heat duty on the flash set to 0 (`heat_duty`)\n", - "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Write the code below to specify the inlet conditions and unit specifications described above\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add inlet specifications given above\n", - "\n", - "\n", - "# Todo: Add 2 flash unit specifications given above" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add inlet specifications given above\n", - "m.fs.flash.inlet.flow_mol.fix(1)\n", - "m.fs.flash.inlet.temperature.fix(368)\n", - "m.fs.flash.inlet.pressure.fix(101325)\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"toluene\"].fix(0.5)\n", - "\n", - "# Todo: Add 2 flash unit specifications given above\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model\n", - "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing the Model\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the initialize method on the flash unit to initialize the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: initialize the flash unit" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: initialize the flash unit\n", - "m.fs.flash.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model has been defined and initialized, we can solve the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: create the ipopt solver\n", - "\n", - "# Todo: solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: create the ipopt solver\n", - "solver = SolverFactory(\"ipopt\")\n", - "\n", - "# Todo: solve the model\n", - "status = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing the Results\n", - "\n", - "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", - "```python\n", - "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", - "```\n", - "\n", - "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", - "```python\n", - "m.fs.flash.vap_outlet.temperature.display()\n", - "m.fs.flash.vap_outlet.display()\n", - "```\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the pressure of the flash vapor outlet\n", - "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", - "\n", - "print()\n", - "print(\"Output from display:\")\n", - "# Call display on vap_outlet and liq_outlet of the flash\n", - "m.fs.flash.vap_outlet.display()\n", - "m.fs.flash.liq_outlet.display()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Studying Purity as a Function of Heat Duty\n", - "\n", - "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", - "\n", - "First, let's import the matplotlib package for plotting as we did in the previous module.\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to import matplotlib appropriately.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exercise specifications:\n", - "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", - "* Specify the heat duty from -17000 to 25000 over 50 steps\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# import the solve_successful checking function from workshop tools\n", - "from idaes_examples.mod.tut.workshoptools import solve_successful\n", - "\n", - "# Todo: import numpy as np\n", - "\n", - "\n", - "# create the empty lists to store the results that will be plotted\n", - "Q = []\n", - "V = []\n", - "\n", - "# re-initialize model\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Todo: Write the for loop specification using numpy's linspace\n", - "\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - " \n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - " \n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # Solve the model\n", - " status = solver.solve(m)\n", - " \n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", - " print('... solve successful.')\n", - " else:\n", - " V.append(0.0)\n", - " print('... solve failed.')\n", - " \n", - "# Create and show the figure\n", - "plt.figure(\"Vapor Fraction\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# import the solve_successful checking function from workshop tools\n", - "from idaes_examples.mod.tut.workshoptools import solve_successful\n", - "\n", - "# Todo: import numpy\n", - "import numpy as np\n", - "\n", - "# create the empty lists to store the results that will be plotted\n", - "Q = []\n", - "V = []\n", - "\n", - "# re-initialize model\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Todo: Write the for loop specification using numpy's linspace\n", - "for duty in np.linspace(-17000, 25000, 50):\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - "\n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - "\n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # Solve the model\n", - " status = solver.solve(m)\n", - "\n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", - " print(\"... solve successful.\")\n", - " else:\n", - " V.append(0.0)\n", - " print(\"... solve failed.\")\n", - "\n", - "# Create and show the figure\n", - "plt.figure(\"Vapor Fraction\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor\n", - "Q = []\n", - "V = []\n", - "\n", - "for duty in np.linspace(-17000, 25000, 50):\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - "\n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - "\n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # solve the model\n", - " status = solver.solve(m)\n", - "\n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]))\n", - " print(\"... solve successful.\")\n", - " else:\n", - " V.append(0.0)\n", - " print(\"... solve failed.\")\n", - "\n", - "plt.figure(\"Purity\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Benzene Mole Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", - "\n", - "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", - "\n", - "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Unfix the heat_duty variable\n", - "m.fs.flash.heat_duty.unfix()\n", - "\n", - "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", - "\n", - "# solve the problem\n", - "status = solver.solve(m, tee=True)\n", - "\n", - "# Check stream condition\n", - "m.fs.flash.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Unfix the heat_duty variable\n", - "m.fs.flash.heat_duty.unfix()\n", - "\n", - "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", - "m.benz_purity_con = Constraint(\n", - " expr=m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"] == 0.6\n", - ")\n", - "\n", - "# solve the problem\n", - "status = solver.solve(m, tee=True)\n", - "\n", - "# Check stream condition\n", - "m.fs.flash.report()" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flash Unit Model\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", + "\n", + "Inlet Specifications:\n", + "* Mole fraction (Benzene) = 0.5\n", + "* Mole fraction (Toluene) = 0.5\n", + "* Pressure = 101325 Pa\n", + "* Temperature = 368 K\n", + "\n", + "We will complete the following tasks:\n", + "* Create the model and the IDAES Flowsheet object\n", + "* Import the appropriate property packages\n", + "* Create the flash unit and set the operating conditions\n", + "* Initialize the model and simulate the system\n", + "* Demonstrate analyses on this model through some examples and exercises\n", + "\n", + "## Key links to documentation\n", + "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", + "\n", + "## Create the Model and the IDAES Flowsheet\n", + "\n", + "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", + "\n", + "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to create the objects\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: call pprint on the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: call pprint on the model\n", + "m.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Properties\n", + "\n", + "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "\n", + "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", + "\n", + "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import and create the properties block.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Flash Unit\n", + "\n", + "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", + "\n", + "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", + "\n", + "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", + "\n", + "Some of the IDAES pre-written unit models:\n", + "* Mixer / Splitter\n", + "* Heater / Cooler\n", + "* Heat Exchangers (simple and 1D discretized)\n", + "* Flash\n", + "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", + "* Pressure changing equipment (compressors, expanders, pumps)\n", + "* Feed and Product (source / sink) components\n", + "\n", + "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash = Flash(property_package=m.fs.properties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Operating Conditions\n", + "\n", + "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", + "\n", + "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "\n", + "\n", + "# Todo: Call the python help on the degrees_of_freedom function" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Todo: Call the python help on the degrees_of_freedom function\n", + "help(degrees_of_freedom)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now print the degrees of freedom for your model. The result should be 7.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model\n", + "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", + "\n", + "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", + "\n", + "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.flow_mol.fix(1.0)\n", + "```\n", + "\n", + "To specify variables that are indexed by components, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "```\n", + "\n", + "
\n", + "Note:\n", + "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", + "
\n", + "\n", + "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", + "\n", + "\n", + "To specify the value of a variable on the unit itself, use the following notation.\n", + "\n", + "```python\n", + "m.fs.flash.heat_duty.fix(0)\n", + "```\n", + "\n", + "For this module, we will use the following specifications:\n", + "* inlet overall molar flow = 1.0 (`flow_mol`)\n", + "* inlet temperature = 368 K (`temperature`)\n", + "* inlet pressure = 101325 Pa (`pressure`)\n", + "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", + "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", + "* The heat duty on the flash set to 0 (`heat_duty`)\n", + "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Write the code below to specify the inlet conditions and unit specifications described above\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add inlet specifications given above\n", + "\n", + "\n", + "# Todo: Add 2 flash unit specifications given above" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add inlet specifications given above\n", + "m.fs.flash.inlet.flow_mol.fix(1)\n", + "m.fs.flash.inlet.temperature.fix(368)\n", + "m.fs.flash.inlet.pressure.fix(101325)\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"toluene\"].fix(0.5)\n", + "\n", + "# Todo: Add 2 flash unit specifications given above\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model\n", + "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the Model\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the initialize method on the flash unit to initialize the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: initialize the flash unit" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: initialize the flash unit\n", + "m.fs.flash.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model has been defined and initialized, we can solve the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: create the ipopt solver\n", + "\n", + "# Todo: solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: create the ipopt solver\n", + "solver = SolverFactory(\"ipopt\")\n", + "\n", + "# Todo: solve the model\n", + "status = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing the Results\n", + "\n", + "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", + "```python\n", + "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", + "```\n", + "\n", + "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", + "```python\n", + "m.fs.flash.vap_outlet.temperature.display()\n", + "m.fs.flash.vap_outlet.display()\n", + "```\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the pressure of the flash vapor outlet\n", + "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", + "\n", + "print()\n", + "print(\"Output from display:\")\n", + "# Call display on vap_outlet and liq_outlet of the flash\n", + "m.fs.flash.vap_outlet.display()\n", + "m.fs.flash.liq_outlet.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Studying Purity as a Function of Heat Duty\n", + "\n", + "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", + "\n", + "First, let's import the matplotlib package for plotting as we did in the previous module.\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to import matplotlib appropriately.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise specifications:\n", + "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", + "* Specify the heat duty from -17000 to 25000 over 50 steps\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# import the solve_successful checking function from workshop tools\n", + "from idaes_examples.mod.tut.workshoptools import solve_successful\n", + "\n", + "# Todo: import numpy as np\n", + "\n", + "\n", + "# create the empty lists to store the results that will be plotted\n", + "Q = []\n", + "V = []\n", + "\n", + "# re-initialize model\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Todo: Write the for loop specification using numpy's linspace\n", + "\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + " \n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + " \n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # Solve the model\n", + " status = solver.solve(m)\n", + " \n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", + " print('... solve successful.')\n", + " else:\n", + " V.append(0.0)\n", + " print('... solve failed.')\n", + " \n", + "# Create and show the figure\n", + "plt.figure(\"Vapor Fraction\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# import the solve_successful checking function from workshop tools\n", + "from idaes_examples.mod.tut.workshoptools import solve_successful\n", + "\n", + "# Todo: import numpy\n", + "import numpy as np\n", + "\n", + "# create the empty lists to store the results that will be plotted\n", + "Q = []\n", + "V = []\n", + "\n", + "# re-initialize model\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Todo: Write the for loop specification using numpy's linspace\n", + "for duty in np.linspace(-17000, 25000, 50):\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + "\n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + "\n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # Solve the model\n", + " status = solver.solve(m)\n", + "\n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", + " print(\"... solve successful.\")\n", + " else:\n", + " V.append(0.0)\n", + " print(\"... solve failed.\")\n", + "\n", + "# Create and show the figure\n", + "plt.figure(\"Vapor Fraction\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor\n", + "Q = []\n", + "V = []\n", + "\n", + "for duty in np.linspace(-17000, 25000, 50):\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + "\n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + "\n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # solve the model\n", + " status = solver.solve(m)\n", + "\n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]))\n", + " print(\"... solve successful.\")\n", + " else:\n", + " V.append(0.0)\n", + " print(\"... solve failed.\")\n", + "\n", + "plt.figure(\"Purity\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Benzene Mole Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", + "\n", + "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", + "\n", + "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Unfix the heat_duty variable\n", + "m.fs.flash.heat_duty.unfix()\n", + "\n", + "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", + "\n", + "# solve the problem\n", + "status = solver.solve(m, tee=True)\n", + "\n", + "# Check stream condition\n", + "m.fs.flash.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Unfix the heat_duty variable\n", + "m.fs.flash.heat_duty.unfix()\n", + "\n", + "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", + "m.benz_purity_con = Constraint(\n", + " expr=m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"] == 0.6\n", + ")\n", + "\n", + "# solve the problem\n", + "status = solver.solve(m, tee=True)\n", + "\n", + "# Check stream condition\n", + "m.fs.flash.report()" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/tut/core/flash_unit_test.ipynb b/idaes_examples/notebooks/docs/tut/core/flash_unit_test.ipynb index 562af431..8136aac7 100644 --- a/idaes_examples/notebooks/docs/tut/core/flash_unit_test.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/flash_unit_test.ipynb @@ -1,817 +1,818 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Flash Unit Model\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", - "\n", - "Inlet Specifications:\n", - "* Mole fraction (Benzene) = 0.5\n", - "* Mole fraction (Toluene) = 0.5\n", - "* Pressure = 101325 Pa\n", - "* Temperature = 368 K\n", - "\n", - "We will complete the following tasks:\n", - "* Create the model and the IDAES Flowsheet object\n", - "* Import the appropriate property packages\n", - "* Create the flash unit and set the operating conditions\n", - "* Initialize the model and simulate the system\n", - "* Demonstrate analyses on this model through some examples and exercises\n", - "\n", - "## Key links to documentation\n", - "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", - "\n", - "## Create the Model and the IDAES Flowsheet\n", - "\n", - "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", - "\n", - "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to create the objects\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: call pprint on the model\n", - "m.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Properties\n", - "\n", - "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "\n", - "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", - "\n", - "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import and create the properties block.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Flash Unit\n", - "\n", - "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", - "\n", - "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", - "\n", - "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", - "\n", - "Some of the IDAES pre-written unit models:\n", - "* Mixer / Splitter\n", - "* Heater / Cooler\n", - "* Heat Exchangers (simple and 1D discretized)\n", - "* Flash\n", - "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", - "* Pressure changing equipment (compressors, expanders, pumps)\n", - "* Feed and Product (source / sink) components\n", - "\n", - "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash = Flash(property_package=m.fs.properties)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Operating Conditions\n", - "\n", - "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", - "\n", - "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Todo: Call the python help on the degrees_of_freedom function\n", - "help(degrees_of_freedom)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now print the degrees of freedom for your model. The result should be 7.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model\n", - "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 7" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", - "\n", - "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", - "\n", - "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.flow_mol.fix(1.0)\n", - "```\n", - "\n", - "To specify variables that are indexed by components, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "```\n", - "\n", - "
\n", - "Note:\n", - "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", - "
\n", - "\n", - "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", - "\n", - "\n", - "To specify the value of a variable on the unit itself, use the following notation.\n", - "\n", - "```python\n", - "m.fs.flash.heat_duty.fix(0)\n", - "```\n", - "\n", - "For this module, we will use the following specifications:\n", - "* inlet overall molar flow = 1.0 (`flow_mol`)\n", - "* inlet temperature = 368 K (`temperature`)\n", - "* inlet pressure = 101325 Pa (`pressure`)\n", - "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", - "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", - "* The heat duty on the flash set to 0 (`heat_duty`)\n", - "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Write the code below to specify the inlet conditions and unit specifications described above\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add inlet specifications given above\n", - "m.fs.flash.inlet.flow_mol.fix(1)\n", - "m.fs.flash.inlet.temperature.fix(368)\n", - "m.fs.flash.inlet.pressure.fix(101325)\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"toluene\"].fix(0.5)\n", - "\n", - "# Todo: Add 2 flash unit specifications given above\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model\n", - "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing the Model\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the initialize method on the flash unit to initialize the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: initialize the flash unit\n", - "m.fs.flash.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model has been defined and initialized, we can solve the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: create the ipopt solver\n", - "solver = SolverFactory(\"ipopt\")\n", - "\n", - "# Todo: solve the model\n", - "status = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for optimal solution\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert status.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing the Results\n", - "\n", - "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", - "```python\n", - "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", - "```\n", - "\n", - "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", - "```python\n", - "m.fs.flash.vap_outlet.temperature.display()\n", - "m.fs.flash.vap_outlet.display()\n", - "```\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the pressure of the flash vapor outlet\n", - "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", - "\n", - "print()\n", - "print(\"Output from display:\")\n", - "# Call display on vap_outlet and liq_outlet of the flash\n", - "m.fs.flash.vap_outlet.display()\n", - "m.fs.flash.liq_outlet.display()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash.report()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check optimal solution values\n", - "import pytest\n", - "\n", - "assert value(m.fs.flash.liq_outlet.flow_mol[0]) == pytest.approx(0.6038, abs=1e-3)\n", - "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", - " 0.4121, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", - " 0.5878, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.liq_outlet.temperature[0]) == pytest.approx(368, abs=1e-3)\n", - "assert value(m.fs.flash.liq_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)\n", - "\n", - "assert value(m.fs.flash.vap_outlet.flow_mol[0]) == pytest.approx(0.3961, abs=1e-3)\n", - "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", - " 0.6339, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", - " 0.3660, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.vap_outlet.temperature[0]) == pytest.approx(368, abs=1e-3)\n", - "assert value(m.fs.flash.vap_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Studying Purity as a Function of Heat Duty\n", - "\n", - "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", - "\n", - "First, let's import the matplotlib package for plotting as we did in the previous module.\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to import matplotlib appropriately.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exercise specifications:\n", - "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", - "* Specify the heat duty from -17000 to 25000 over 50 steps\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# import the solve_successful checking function from workshop tools\n", - "from idaes_examples.mod.tut.workshoptools import solve_successful\n", - "\n", - "# Todo: import numpy\n", - "import numpy as np\n", - "\n", - "# create the empty lists to store the results that will be plotted\n", - "Q = []\n", - "V = []\n", - "\n", - "# re-initialize model\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Todo: Write the for loop specification using numpy's linspace\n", - "for duty in np.linspace(-17000, 25000, 50):\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - "\n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - "\n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # Solve the model\n", - " status = solver.solve(m)\n", - "\n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", - " print(\"... solve successful.\")\n", - " else:\n", - " V.append(0.0)\n", - " print(\"... solve failed.\")\n", - "\n", - "# Create and show the figure\n", - "plt.figure(\"Vapor Fraction\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor\n", - "Q = []\n", - "V = []\n", - "\n", - "for duty in np.linspace(-17000, 25000, 50):\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - "\n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - "\n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # solve the model\n", - " status = solver.solve(m)\n", - "\n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]))\n", - " print(\"... solve successful.\")\n", - " else:\n", - " V.append(0.0)\n", - " print(\"... solve failed.\")\n", - "\n", - "plt.figure(\"Purity\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Benzene Mole Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", - "\n", - "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", - "\n", - "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Unfix the heat_duty variable\n", - "m.fs.flash.heat_duty.unfix()\n", - "\n", - "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", - "m.benz_purity_con = Constraint(\n", - " expr=m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"] == 0.6\n", - ")\n", - "\n", - "# solve the problem\n", - "status = solver.solve(m, tee=True)\n", - "\n", - "# Check stream condition\n", - "m.fs.flash.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for solver status\n", - "assert status.solver.termination_condition == TerminationCondition.optimal\n", - "\n", - "# Check for optimal values\n", - "assert value(m.fs.flash.liq_outlet.flow_mol[0]) == pytest.approx(0.4516, abs=1e-3)\n", - "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", - " 0.3786, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", - " 0.6214, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.liq_outlet.temperature[0]) == pytest.approx(369.07, abs=1e-2)\n", - "assert value(m.fs.flash.liq_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)\n", - "\n", - "assert value(m.fs.flash.vap_outlet.flow_mol[0]) == pytest.approx(0.5483, abs=1e-3)\n", - "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", - " 0.6, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", - " 0.4, abs=1e-3\n", - ")\n", - "assert value(m.fs.flash.vap_outlet.temperature[0]) == pytest.approx(369.07, abs=1e-2)\n", - "assert value(m.fs.flash.vap_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flash Unit Model\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", + "\n", + "Inlet Specifications:\n", + "* Mole fraction (Benzene) = 0.5\n", + "* Mole fraction (Toluene) = 0.5\n", + "* Pressure = 101325 Pa\n", + "* Temperature = 368 K\n", + "\n", + "We will complete the following tasks:\n", + "* Create the model and the IDAES Flowsheet object\n", + "* Import the appropriate property packages\n", + "* Create the flash unit and set the operating conditions\n", + "* Initialize the model and simulate the system\n", + "* Demonstrate analyses on this model through some examples and exercises\n", + "\n", + "## Key links to documentation\n", + "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", + "\n", + "## Create the Model and the IDAES Flowsheet\n", + "\n", + "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", + "\n", + "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to create the objects\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: call pprint on the model\n", + "m.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Properties\n", + "\n", + "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "\n", + "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", + "\n", + "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import and create the properties block.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Flash Unit\n", + "\n", + "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", + "\n", + "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", + "\n", + "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", + "\n", + "Some of the IDAES pre-written unit models:\n", + "* Mixer / Splitter\n", + "* Heater / Cooler\n", + "* Heat Exchangers (simple and 1D discretized)\n", + "* Flash\n", + "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", + "* Pressure changing equipment (compressors, expanders, pumps)\n", + "* Feed and Product (source / sink) components\n", + "\n", + "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash = Flash(property_package=m.fs.properties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Operating Conditions\n", + "\n", + "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", + "\n", + "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Todo: Call the python help on the degrees_of_freedom function\n", + "help(degrees_of_freedom)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now print the degrees of freedom for your model. The result should be 7.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model\n", + "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", + "\n", + "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", + "\n", + "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.flow_mol.fix(1.0)\n", + "```\n", + "\n", + "To specify variables that are indexed by components, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "```\n", + "\n", + "
\n", + "Note:\n", + "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", + "
\n", + "\n", + "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", + "\n", + "\n", + "To specify the value of a variable on the unit itself, use the following notation.\n", + "\n", + "```python\n", + "m.fs.flash.heat_duty.fix(0)\n", + "```\n", + "\n", + "For this module, we will use the following specifications:\n", + "* inlet overall molar flow = 1.0 (`flow_mol`)\n", + "* inlet temperature = 368 K (`temperature`)\n", + "* inlet pressure = 101325 Pa (`pressure`)\n", + "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", + "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", + "* The heat duty on the flash set to 0 (`heat_duty`)\n", + "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Write the code below to specify the inlet conditions and unit specifications described above\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add inlet specifications given above\n", + "m.fs.flash.inlet.flow_mol.fix(1)\n", + "m.fs.flash.inlet.temperature.fix(368)\n", + "m.fs.flash.inlet.pressure.fix(101325)\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"toluene\"].fix(0.5)\n", + "\n", + "# Todo: Add 2 flash unit specifications given above\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model\n", + "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the Model\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the initialize method on the flash unit to initialize the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: initialize the flash unit\n", + "m.fs.flash.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model has been defined and initialized, we can solve the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: create the ipopt solver\n", + "solver = SolverFactory(\"ipopt\")\n", + "\n", + "# Todo: solve the model\n", + "status = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for optimal solution\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert status.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing the Results\n", + "\n", + "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", + "```python\n", + "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", + "```\n", + "\n", + "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", + "```python\n", + "m.fs.flash.vap_outlet.temperature.display()\n", + "m.fs.flash.vap_outlet.display()\n", + "```\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the pressure of the flash vapor outlet\n", + "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", + "\n", + "print()\n", + "print(\"Output from display:\")\n", + "# Call display on vap_outlet and liq_outlet of the flash\n", + "m.fs.flash.vap_outlet.display()\n", + "m.fs.flash.liq_outlet.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash.report()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check optimal solution values\n", + "import pytest\n", + "\n", + "assert value(m.fs.flash.liq_outlet.flow_mol[0]) == pytest.approx(0.6038, abs=1e-3)\n", + "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", + " 0.4121, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", + " 0.5878, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.liq_outlet.temperature[0]) == pytest.approx(368, abs=1e-3)\n", + "assert value(m.fs.flash.liq_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)\n", + "\n", + "assert value(m.fs.flash.vap_outlet.flow_mol[0]) == pytest.approx(0.3961, abs=1e-3)\n", + "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", + " 0.6339, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", + " 0.3660, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.vap_outlet.temperature[0]) == pytest.approx(368, abs=1e-3)\n", + "assert value(m.fs.flash.vap_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Studying Purity as a Function of Heat Duty\n", + "\n", + "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", + "\n", + "First, let's import the matplotlib package for plotting as we did in the previous module.\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to import matplotlib appropriately.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise specifications:\n", + "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", + "* Specify the heat duty from -17000 to 25000 over 50 steps\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# import the solve_successful checking function from workshop tools\n", + "from idaes_examples.mod.tut.workshoptools import solve_successful\n", + "\n", + "# Todo: import numpy\n", + "import numpy as np\n", + "\n", + "# create the empty lists to store the results that will be plotted\n", + "Q = []\n", + "V = []\n", + "\n", + "# re-initialize model\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Todo: Write the for loop specification using numpy's linspace\n", + "for duty in np.linspace(-17000, 25000, 50):\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + "\n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + "\n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # Solve the model\n", + " status = solver.solve(m)\n", + "\n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", + " print(\"... solve successful.\")\n", + " else:\n", + " V.append(0.0)\n", + " print(\"... solve failed.\")\n", + "\n", + "# Create and show the figure\n", + "plt.figure(\"Vapor Fraction\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor\n", + "Q = []\n", + "V = []\n", + "\n", + "for duty in np.linspace(-17000, 25000, 50):\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + "\n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + "\n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # solve the model\n", + " status = solver.solve(m)\n", + "\n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]))\n", + " print(\"... solve successful.\")\n", + " else:\n", + " V.append(0.0)\n", + " print(\"... solve failed.\")\n", + "\n", + "plt.figure(\"Purity\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Benzene Mole Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", + "\n", + "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", + "\n", + "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Unfix the heat_duty variable\n", + "m.fs.flash.heat_duty.unfix()\n", + "\n", + "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", + "m.benz_purity_con = Constraint(\n", + " expr=m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"] == 0.6\n", + ")\n", + "\n", + "# solve the problem\n", + "status = solver.solve(m, tee=True)\n", + "\n", + "# Check stream condition\n", + "m.fs.flash.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for solver status\n", + "assert status.solver.termination_condition == TerminationCondition.optimal\n", + "\n", + "# Check for optimal values\n", + "assert value(m.fs.flash.liq_outlet.flow_mol[0]) == pytest.approx(0.4516, abs=1e-3)\n", + "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", + " 0.3786, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.liq_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", + " 0.6214, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.liq_outlet.temperature[0]) == pytest.approx(369.07, abs=1e-2)\n", + "assert value(m.fs.flash.liq_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)\n", + "\n", + "assert value(m.fs.flash.vap_outlet.flow_mol[0]) == pytest.approx(0.5483, abs=1e-3)\n", + "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]) == pytest.approx(\n", + " 0.6, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"toluene\"]) == pytest.approx(\n", + " 0.4, abs=1e-3\n", + ")\n", + "assert value(m.fs.flash.vap_outlet.temperature[0]) == pytest.approx(369.07, abs=1e-2)\n", + "assert value(m.fs.flash.vap_outlet.pressure[0]) == pytest.approx(101325, abs=1e-3)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/tut/core/flash_unit_usr.ipynb b/idaes_examples/notebooks/docs/tut/core/flash_unit_usr.ipynb index 6b4b3752..a986e1df 100644 --- a/idaes_examples/notebooks/docs/tut/core/flash_unit_usr.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/flash_unit_usr.ipynb @@ -1,896 +1,897 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Flash Unit Model\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", - "\n", - "Inlet Specifications:\n", - "* Mole fraction (Benzene) = 0.5\n", - "* Mole fraction (Toluene) = 0.5\n", - "* Pressure = 101325 Pa\n", - "* Temperature = 368 K\n", - "\n", - "We will complete the following tasks:\n", - "* Create the model and the IDAES Flowsheet object\n", - "* Import the appropriate property packages\n", - "* Create the flash unit and set the operating conditions\n", - "* Initialize the model and simulate the system\n", - "* Demonstrate analyses on this model through some examples and exercises\n", - "\n", - "## Key links to documentation\n", - "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", - "\n", - "## Create the Model and the IDAES Flowsheet\n", - "\n", - "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", - "\n", - "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to create the objects\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: call pprint on the model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: call pprint on the model\n", - "m.pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Properties\n", - "\n", - "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", - "\n", - "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", - "\n", - "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import and create the properties block.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", - " BTXParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.properties = BTXParameterBlock(\n", - " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Flash Unit\n", - "\n", - "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", - "\n", - "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", - "\n", - "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", - "\n", - "Some of the IDAES pre-written unit models:\n", - "* Mixer / Splitter\n", - "* Heater / Cooler\n", - "* Heat Exchangers (simple and 1D discretized)\n", - "* Flash\n", - "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", - "* Pressure changing equipment (compressors, expanders, pumps)\n", - "* Feed and Product (source / sink) components\n", - "\n", - "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash = Flash(property_package=m.fs.properties)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set Operating Conditions\n", - "\n", - "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", - "\n", - "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "\n", - "\n", - "# Todo: Call the python help on the degrees_of_freedom function" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Todo: Call the python help on the degrees_of_freedom function\n", - "help(degrees_of_freedom)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now print the degrees of freedom for your model. The result should be 7.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model\n", - "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", - "\n", - "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", - "\n", - "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.flow_mol.fix(1.0)\n", - "```\n", - "\n", - "To specify variables that are indexed by components, you can use the following notation:\n", - "```python\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "```\n", - "\n", - "
\n", - "Note:\n", - "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", - "
\n", - "\n", - "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", - "\n", - "\n", - "To specify the value of a variable on the unit itself, use the following notation.\n", - "\n", - "```python\n", - "m.fs.flash.heat_duty.fix(0)\n", - "```\n", - "\n", - "For this module, we will use the following specifications:\n", - "* inlet overall molar flow = 1.0 (`flow_mol`)\n", - "* inlet temperature = 368 K (`temperature`)\n", - "* inlet pressure = 101325 Pa (`pressure`)\n", - "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", - "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", - "* The heat duty on the flash set to 0 (`heat_duty`)\n", - "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Write the code below to specify the inlet conditions and unit specifications described above\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add inlet specifications given above\n", - "\n", - "\n", - "# Todo: Add 2 flash unit specifications given above" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add inlet specifications given above\n", - "m.fs.flash.inlet.flow_mol.fix(1)\n", - "m.fs.flash.inlet.temperature.fix(368)\n", - "m.fs.flash.inlet.pressure.fix(101325)\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", - "m.fs.flash.inlet.mole_frac_comp[0, \"toluene\"].fix(0.5)\n", - "\n", - "# Todo: Add 2 flash unit specifications given above\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom for your model\n", - "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initializing the Model\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Call the initialize method on the flash unit to initialize the model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: initialize the flash unit" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: initialize the flash unit\n", - "m.fs.flash.initialize(outlvl=idaeslog.INFO)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the model has been defined and initialized, we can solve the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: create the ipopt solver\n", - "\n", - "# Todo: solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: create the ipopt solver\n", - "solver = SolverFactory(\"ipopt\")\n", - "\n", - "# Todo: solve the model\n", - "status = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing the Results\n", - "\n", - "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", - "```python\n", - "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", - "```\n", - "\n", - "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", - "```python\n", - "m.fs.flash.vap_outlet.temperature.display()\n", - "m.fs.flash.vap_outlet.display()\n", - "```\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the pressure of the flash vapor outlet\n", - "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", - "\n", - "print()\n", - "print(\"Output from display:\")\n", - "# Call display on vap_outlet and liq_outlet of the flash\n", - "m.fs.flash.vap_outlet.display()\n", - "m.fs.flash.liq_outlet.display()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.flash.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Studying Purity as a Function of Heat Duty\n", - "\n", - "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", - "\n", - "First, let's import the matplotlib package for plotting as we did in the previous module.\n", - "
\n", - "Inline Exercise:\n", - "Execute the cell below to import matplotlib appropriately.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Exercise specifications:\n", - "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", - "* Specify the heat duty from -17000 to 25000 over 50 steps\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# import the solve_successful checking function from workshop tools\n", - "from idaes_examples.mod.tut.workshoptools import solve_successful\n", - "\n", - "# Todo: import numpy as np\n", - "\n", - "\n", - "# create the empty lists to store the results that will be plotted\n", - "Q = []\n", - "V = []\n", - "\n", - "# re-initialize model\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Todo: Write the for loop specification using numpy's linspace\n", - "\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - " \n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - " \n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # Solve the model\n", - " status = solver.solve(m)\n", - " \n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", - " print('... solve successful.')\n", - " else:\n", - " V.append(0.0)\n", - " print('... solve failed.')\n", - " \n", - "# Create and show the figure\n", - "plt.figure(\"Vapor Fraction\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true, - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# import the solve_successful checking function from workshop tools\n", - "from idaes_examples.mod.tut.workshoptools import solve_successful\n", - "\n", - "# Todo: import numpy\n", - "import numpy as np\n", - "\n", - "# create the empty lists to store the results that will be plotted\n", - "Q = []\n", - "V = []\n", - "\n", - "# re-initialize model\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Todo: Write the for loop specification using numpy's linspace\n", - "for duty in np.linspace(-17000, 25000, 50):\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - "\n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - "\n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # Solve the model\n", - " status = solver.solve(m)\n", - "\n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", - " print(\"... solve successful.\")\n", - " else:\n", - " V.append(0.0)\n", - " print(\"... solve failed.\")\n", - "\n", - "# Create and show the figure\n", - "plt.figure(\"Vapor Fraction\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor\n", - "Q = []\n", - "V = []\n", - "\n", - "for duty in np.linspace(-17000, 25000, 50):\n", - " # fix the heat duty\n", - " m.fs.flash.heat_duty.fix(duty)\n", - "\n", - " # append the value of the duty to the Q list\n", - " Q.append(duty)\n", - "\n", - " # print the current simulation\n", - " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", - "\n", - " # solve the model\n", - " status = solver.solve(m)\n", - "\n", - " # append the value for vapor fraction if the solve was successful\n", - " if solve_successful(status):\n", - " V.append(value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]))\n", - " print(\"... solve successful.\")\n", - " else:\n", - " V.append(0.0)\n", - " print(\"... solve failed.\")\n", - "\n", - "plt.figure(\"Purity\")\n", - "plt.plot(Q, V)\n", - "plt.grid()\n", - "plt.xlabel(\"Heat Duty [J]\")\n", - "plt.ylabel(\"Vapor Benzene Mole Fraction [-]\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", - "\n", - "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", - "\n", - "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Unfix the heat_duty variable\n", - "m.fs.flash.heat_duty.unfix()\n", - "\n", - "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", - "\n", - "# solve the problem\n", - "status = solver.solve(m, tee=True)\n", - "\n", - "# Check stream condition\n", - "m.fs.flash.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", - "m.fs.flash.heat_duty.fix(0)\n", - "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", - "\n", - "# Unfix the heat_duty variable\n", - "m.fs.flash.heat_duty.unfix()\n", - "\n", - "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", - "m.benz_purity_con = Constraint(\n", - " expr=m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"] == 0.6\n", - ")\n", - "\n", - "# solve the problem\n", - "status = solver.solve(m, tee=True)\n", - "\n", - "# Check stream condition\n", - "m.fs.flash.report()" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flash Unit Model\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "In this module, we will familiarize ourselves with the IDAES framework by creating and working with a flowsheet that contains a single flash tank. The flash tank will be used to perform separation of Benzene and Toluene. The inlet specifications for this flash tank are:\n", + "\n", + "Inlet Specifications:\n", + "* Mole fraction (Benzene) = 0.5\n", + "* Mole fraction (Toluene) = 0.5\n", + "* Pressure = 101325 Pa\n", + "* Temperature = 368 K\n", + "\n", + "We will complete the following tasks:\n", + "* Create the model and the IDAES Flowsheet object\n", + "* Import the appropriate property packages\n", + "* Create the flash unit and set the operating conditions\n", + "* Initialize the model and simulate the system\n", + "* Demonstrate analyses on this model through some examples and exercises\n", + "\n", + "## Key links to documentation\n", + "* Main IDAES online documentation page: https://idaes-pse.readthedocs.io/en/stable/\n", + "\n", + "## Create the Model and the IDAES Flowsheet\n", + "\n", + "In the next cell, we will perform the necessary imports to get us started. From `pyomo.environ` (a standard import for the Pyomo package), we are importing `ConcreteModel` (to create the Pyomo model that will contain the IDAES flowsheet) and `SolverFactory` (to create the object we will use to solve the equations). We will also import `Constraint` as we will be adding a constraint to the model later in the module. Lastly, we also import `value` from Pyomo. This is a function that can be used to return the current numerical value for variables and parameters in the model. These are all part of Pyomo.\n", + "\n", + "We will also import the main `FlowsheetBlock` from IDAES. The flowsheet block will contain our unit model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to perform the imports. Let a workshop organizer know if you see any errors.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we will create the `ConcreteModel` and the `FlowsheetBlock`, and attach the flowsheet block to the Pyomo model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to create the objects\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have a single Pyomo model that contains an (almost) empty flowsheet block.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Use the pprint method on the model, i.e. m.pprint(), to see what is currently contained in the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: call pprint on the model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: call pprint on the model\n", + "m.pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Properties\n", + "\n", + "We need to define the property package for our flowsheet. In this example, we will be using the ideal property package that is available as part of the IDAES framework. This property package supports ideal gas - ideal liquid, ideal gas - NRTL, and ideal gas - Wilson models for VLE. More details on this property package can be found at: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/property_models/activity_coefficient.html\n", + "\n", + "IDAES also supports creation of your own property packages that allow for specification of the fluid using any set of valid state variables (e.g., component molar flows vs overall flow and mole fractions). This flexibility is designed to support advanced modeling needs that may rely on specific formulations. To learn about creating your own property package, please consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html and look at examples within IDAES\n", + "\n", + "For this workshop, we will import the BTX_activity_coeff_VLE property parameter block to be used in the flowsheet. This properties block will be passed to our unit model to define the appropriate state variables and equations for performing thermodynamic calculations.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import and create the properties block.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE import (\n", + " BTXParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.properties = BTXParameterBlock(\n", + " valid_phase=(\"Liq\", \"Vap\"), activity_coeff_model=\"Ideal\", state_vars=\"FTPz\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Flash Unit\n", + "\n", + "Now that we have the flowsheet and the properties defined, we can create the flash unit and add it to the flowsheet. \n", + "\n", + "**The Unit Model Library within IDAES includes a large set of common unit operations (see the online documentation for details: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html**\n", + "\n", + "IDAES also fully supports the development of customized unit models (which we will see in a later module).\n", + "\n", + "Some of the IDAES pre-written unit models:\n", + "* Mixer / Splitter\n", + "* Heater / Cooler\n", + "* Heat Exchangers (simple and 1D discretized)\n", + "* Flash\n", + "* Reactors (kinetic, equilibrium, gibbs, stoichiometric conversion)\n", + "* Pressure changing equipment (compressors, expanders, pumps)\n", + "* Feed and Product (source / sink) components\n", + "\n", + "In this module, we will import the `Flash` unit model from `idaes.models.unit_models` and create an instance of the flash unit, attaching it to the flowsheet. Each IDAES unit model has several configurable options to customize the model behavior, but also includes defaults for these options. In this example, we will specify that the property package to be used with the Flash is the one we created earlier.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following two cells to import the Flash and create an instance of the unit model, attaching it to the flowsheet object.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash = Flash(property_package=m.fs.properties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we have created a flowsheet and a properties block. We have also created a flash unit and added it to the flowsheet. Under the hood, IDAES has created the required state variables and model equations. Everything is open. You can see these variables and equations by calling the Pyomo method `pprint` on the model, flowsheet, or flash tank objects. Note that this output is very exhaustive, and is not intended to provide any summary information about the model, but rather a complete picture of all of the variables and equations in the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Operating Conditions\n", + "\n", + "Now that we have created our unit model, we can specify the necessary operating conditions. It is often very useful to determine the degrees of freedom before we specify any conditions.\n", + "\n", + "The `idaes.core.util.model_statistics` package has a function `degrees_of_freedom`. To see how to use this function, we can make use of the Python function `help(func)`. This function prints the appropriate documentation string for the function.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Import the degrees_of_freedom function and print the help for the function by calling the Python help function.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "\n", + "\n", + "# Todo: Call the python help on the degrees_of_freedom function" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Todo: Call the python help on the degrees_of_freedom function\n", + "help(degrees_of_freedom)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now print the degrees of freedom for your model. The result should be 7.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model\n", + "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To satisfy our degrees of freedom, we will first specify the inlet conditions. We can specify these values through the `inlet` port of the flash unit.\n", + "\n", + "**To see the list of naming conventions for variables within the IDAES framework, consult the online documentation at: https://idaes-pse.readthedocs.io/en/stable/explanations/conventions.html#standard-naming-format**\n", + "\n", + "As an example, to fix the molar flow of the inlet to be 1.0, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.flow_mol.fix(1.0)\n", + "```\n", + "\n", + "To specify variables that are indexed by components, you can use the following notation:\n", + "```python\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "```\n", + "\n", + "
\n", + "Note:\n", + "The \"0\" in the indexing of the component mole fraction is present because IDAES models support both dynamic and steady state simulation, and the \"0\" refers to a timestep. Dynamic modeling is beyond the scope of this workshop. Since we are performing steady state modeling, there is only a single timestep in the model.\n", + "
\n", + "\n", + "In the next cell, we will specify the inlet conditions. To satisfy the remaining degrees of freedom, we will make two additional specifications on the flash tank itself. The names of the key variables within the Flash unit model can also be found in the online documentation: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/flash.html#variables.\n", + "\n", + "\n", + "To specify the value of a variable on the unit itself, use the following notation.\n", + "\n", + "```python\n", + "m.fs.flash.heat_duty.fix(0)\n", + "```\n", + "\n", + "For this module, we will use the following specifications:\n", + "* inlet overall molar flow = 1.0 (`flow_mol`)\n", + "* inlet temperature = 368 K (`temperature`)\n", + "* inlet pressure = 101325 Pa (`pressure`)\n", + "* inlet mole fraction (benzene) = 0.5 (`mole_frac_comp[0, \"benzene\"]`)\n", + "* inlet mole fraction (toluene) = 0.5 (`mole_frac_comp[0, \"toluene\"]`)\n", + "* The heat duty on the flash set to 0 (`heat_duty`)\n", + "* The pressure drop across the flash tank set to 0 (`deltaP`)\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Write the code below to specify the inlet conditions and unit specifications described above\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add inlet specifications given above\n", + "\n", + "\n", + "# Todo: Add 2 flash unit specifications given above" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add inlet specifications given above\n", + "m.fs.flash.inlet.flow_mol.fix(1)\n", + "m.fs.flash.inlet.temperature.fix(368)\n", + "m.fs.flash.inlet.pressure.fix(101325)\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"benzene\"].fix(0.5)\n", + "m.fs.flash.inlet.mole_frac_comp[0, \"toluene\"].fix(0.5)\n", + "\n", + "# Todo: Add 2 flash unit specifications given above\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Check the degrees of freedom again to ensure that the system is now square. You should see that the degrees of freedom is now 0.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom for your model\n", + "print(\"Degrees of Freedom =\", degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initializing the Model\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models. You can call this initialize method on the units. In the next module, we will demonstrate the use of a sequential modular solve cycle to initialize flowsheets.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Call the initialize method on the flash unit to initialize the model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: initialize the flash unit" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: initialize the flash unit\n", + "m.fs.flash.initialize(outlvl=idaeslog.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the model has been defined and initialized, we can solve the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using the notation described in the previous model, create an instance of the \"ipopt\" solver and use it to solve the model. Set the tee option to True to see the log output.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: create the ipopt solver\n", + "\n", + "# Todo: solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: create the ipopt solver\n", + "solver = SolverFactory(\"ipopt\")\n", + "\n", + "# Todo: solve the model\n", + "status = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing the Results\n", + "\n", + "Once a model is solved, the values returned by the solver are loaded into the model object itself. We can access the value of any variable in the model with the `value` function. For example:\n", + "```python\n", + "print('Vap. Outlet Temperature = ', value(m.fs.flash.vap_outlet.temperature[0]))\n", + "```\n", + "\n", + "You can also find more information about a variable or an entire port using the `display` method from Pyomo:\n", + "```python\n", + "m.fs.flash.vap_outlet.temperature.display()\n", + "m.fs.flash.vap_outlet.display()\n", + "```\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cells below to show the current value of the flash vapor outlet pressure. This cell also shows use of the display function to see the values of the variables in the vap_outlet and the liq_outlet.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the pressure of the flash vapor outlet\n", + "print(\"Pressure =\", value(m.fs.flash.vap_outlet.pressure[0]))\n", + "\n", + "print()\n", + "print(\"Output from display:\")\n", + "# Call display on vap_outlet and liq_outlet of the flash\n", + "m.fs.flash.vap_outlet.display()\n", + "m.fs.flash.liq_outlet.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output from `display` is quite exhaustive and not really intended to provide quick summary information. Because Pyomo is built on Python, there are opportunities to format the output any way we like. Most IDAES models have a `report` method which provides a summary of the results for the model.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below which uses the function above to print a summary of the key variables in the flash model, including the inlet, the vapor, and the liquid ports. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.flash.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Studying Purity as a Function of Heat Duty\n", + "\n", + "Since the entire modeling framework is built upon Python, it includes a complete programming environment for whatever analysis we may want to perform. In this next exercise, we will make use of what we learned in this and the previous module to generate a figure showing some output variables as a function of the heat duty in the flash tank.\n", + "\n", + "First, let's import the matplotlib package for plotting as we did in the previous module.\n", + "
\n", + "Inline Exercise:\n", + "Execute the cell below to import matplotlib appropriately.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise specifications:\n", + "* Generate a figure showing the flash tank heat duty (`m.fs.flash.heat_duty[0]`) vs. the vapor flowrate (`m.fs.flash.vap_outlet.flow_mol[0]`)\n", + "* Specify the heat duty from -17000 to 25000 over 50 steps\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Using what you have learned so far, fill in the missing code below to generate the figure specified above. (Hint: import numpy and use the linspace function from the previous module)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# import the solve_successful checking function from workshop tools\n", + "from idaes_examples.mod.tut.workshoptools import solve_successful\n", + "\n", + "# Todo: import numpy as np\n", + "\n", + "\n", + "# create the empty lists to store the results that will be plotted\n", + "Q = []\n", + "V = []\n", + "\n", + "# re-initialize model\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Todo: Write the for loop specification using numpy's linspace\n", + "\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + " \n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + " \n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # Solve the model\n", + " status = solver.solve(m)\n", + " \n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", + " print('... solve successful.')\n", + " else:\n", + " V.append(0.0)\n", + " print('... solve failed.')\n", + " \n", + "# Create and show the figure\n", + "plt.figure(\"Vapor Fraction\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true, + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# import the solve_successful checking function from workshop tools\n", + "from idaes_examples.mod.tut.workshoptools import solve_successful\n", + "\n", + "# Todo: import numpy\n", + "import numpy as np\n", + "\n", + "# create the empty lists to store the results that will be plotted\n", + "Q = []\n", + "V = []\n", + "\n", + "# re-initialize model\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Todo: Write the for loop specification using numpy's linspace\n", + "for duty in np.linspace(-17000, 25000, 50):\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + "\n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + "\n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # Solve the model\n", + " status = solver.solve(m)\n", + "\n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.flow_mol[0]))\n", + " print(\"... solve successful.\")\n", + " else:\n", + " V.append(0.0)\n", + " print(\"... solve failed.\")\n", + "\n", + "# Create and show the figure\n", + "plt.figure(\"Vapor Fraction\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Repeat the exercise above, but create a figure showing the heat duty vs. the mole fraction of Benzene in the vapor outlet. Remove any unnecessary printing to create cleaner results.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: generate a figure of heat duty vs. mole fraction of Benzene in the vapor\n", + "Q = []\n", + "V = []\n", + "\n", + "for duty in np.linspace(-17000, 25000, 50):\n", + " # fix the heat duty\n", + " m.fs.flash.heat_duty.fix(duty)\n", + "\n", + " # append the value of the duty to the Q list\n", + " Q.append(duty)\n", + "\n", + " # print the current simulation\n", + " print(\"Simulating with Q = \", value(m.fs.flash.heat_duty[0]))\n", + "\n", + " # solve the model\n", + " status = solver.solve(m)\n", + "\n", + " # append the value for vapor fraction if the solve was successful\n", + " if solve_successful(status):\n", + " V.append(value(m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]))\n", + " print(\"... solve successful.\")\n", + " else:\n", + " V.append(0.0)\n", + " print(\"... solve failed.\")\n", + "\n", + "plt.figure(\"Purity\")\n", + "plt.plot(Q, V)\n", + "plt.grid()\n", + "plt.xlabel(\"Heat Duty [J]\")\n", + "plt.ylabel(\"Vapor Benzene Mole Fraction [-]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that the IDAES framework is an equation-oriented modeling environment. This means that we can specify \"design\" problems natively. That is, there is no need to have our specifications on the inlet alone. We can put specifications on the outlet as long as we retain a well-posed, square system of equations.\n", + "\n", + "For example, we can remove the specification on heat duty and instead specify that we want the mole fraction of Benzene in the vapor outlet to be equal to 0.6. The mole fraction is not a native variable in the property block, so we cannot use \"fix\". We can, however, add a constraint to the model.\n", + "\n", + "Note that we have been executing a number of solves on the problem, and may not be sure of the current state. To help convergence, therefore, we will first call initialize, then add the new constraint and solve the problem. Note that the reference for the mole fraction of Benzene in the vapor outlet is `m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"]`.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Fill in the missing code below and add a constraint on the mole fraction of Benzene (to a value of 0.6) to find the required heat duty.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Unfix the heat_duty variable\n", + "m.fs.flash.heat_duty.unfix()\n", + "\n", + "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", + "\n", + "# solve the problem\n", + "status = solver.solve(m, tee=True)\n", + "\n", + "# Check stream condition\n", + "m.fs.flash.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# re-initialize the model - this may or may not be required depending on current state but safe to initialize\n", + "m.fs.flash.heat_duty.fix(0)\n", + "m.fs.flash.initialize(outlvl=idaeslog.WARNING)\n", + "\n", + "# Unfix the heat_duty variable\n", + "m.fs.flash.heat_duty.unfix()\n", + "\n", + "# Todo: Add a new constraint (benzene mole fraction to 0.6)\n", + "m.benz_purity_con = Constraint(\n", + " expr=m.fs.flash.vap_outlet.mole_frac_comp[0, \"benzene\"] == 0.6\n", + ")\n", + "\n", + "# solve the problem\n", + "status = solver.solve(m, tee=True)\n", + "\n", + "# Check stream condition\n", + "m.fs.flash.report()" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb index a7645f92..4f913d3f 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb index 2d862e0d..78b08fd8 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_doc.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -100,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "tags": [ "solution" @@ -187,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -312,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "tags": [ "solution" @@ -344,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -399,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "tags": [ "solution" @@ -443,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -490,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -512,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -536,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -554,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -574,9 +575,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -590,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -624,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -651,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -667,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -720,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "tags": [ "solution" @@ -741,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -763,13 +772,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "tags": [ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -795,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -819,9 +836,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" @@ -836,11 +861,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], "source": [ "for o in order:\n", " print(o[0].name)" @@ -860,7 +898,7 @@ }, { "cell_type": "code", - 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\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Wegstein failed to converge in 3 iterations\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:48 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:39:49 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Complete\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -938,13 +1607,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "tags": [ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1031\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 934\n", + "\n", + "Total number of variables............................: 340\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 146\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 340\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 6.60e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.42e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 0.0000000e+00 3.05e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.58e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.49e+02 8.87e+08 -1.0 8.42e+03 - 1.00e+00 9.57e-01h 1\n", + " 5 0.0000000e+00 4.25e+03 2.87e+10 -1.0 8.02e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 2.25e+03 1.51e+10 -1.0 8.39e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 2.27e+01 1.40e+08 -1.0 2.45e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 2.45e-03 1.23e+04 -1.0 2.38e-05 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 3.73e-08 3.38e-01 -2.5 1.06e-07 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 2.8284425441187131e+05 2.8284425441187131e+05\n", + "Constraint violation....: 5.8207660913467407e-11 3.7252902984619141e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 2.8284425441187131e+05\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Create the solver object\n", "from idaes.core.solvers import get_solver\n", @@ -967,9 +1722,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $ 419122.3387677973\n" + ] + } + ], "source": [ "print(\"operating cost = $\", value(m.fs.operating_cost))" ] @@ -983,9 +1746,50 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F102 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 7352.5 : watt : False : (None, None)\n", + " Pressure Change : -2.0000e+05 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 0.20460 1.0000e-08 0.062620 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 0.062520 1.0000e-08 0.032257 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 2.6712e-07 1.0000e-08 9.4877e-08 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 2.6712e-07 1.0000e-08 9.4877e-08 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 1.0000e-08 0.14198 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 1.0000e-08 0.030264 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0000e-08 1.8224e-07 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 1.0000e-08 1.8224e-07 1.0000e-08 \n", + " temperature kelvin 325.00 375.00 375.00 \n", + " pressure pascal 3.5000e+05 1.5000e+05 1.5000e+05 \n", + "====================================================================================\n", + "\n", + "benzene purity = 0.8242962943918926\n" + ] + } + ], "source": [ "m.fs.F102.report()\n", "\n", @@ -1007,9 +1811,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -1063,7 +1885,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1079,7 +1901,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1104,7 +1926,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "tags": [ "solution" @@ -1133,7 +1955,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1155,7 +1977,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { "tags": [ "solution" @@ -1177,7 +1999,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1198,7 +2020,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1222,7 +2044,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": { "tags": [ "solution" @@ -1245,7 +2067,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1264,9 +2086,107 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1057\n", + "Number of nonzeros in inequality constraint Jacobian.: 5\n", + "Number of nonzeros in Lagrangian Hessian.............: 937\n", + "\n", + "Total number of variables............................: 345\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 149\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 340\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.1912234e+05 2.99e+05 6.94e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.1628385e+05 2.99e+05 6.94e+00 -1.0 4.82e+09 - 1.80e-05 5.83e-06f 1\n", + " 2 4.1616723e+05 2.99e+05 1.59e+02 -1.0 1.46e+09 - 5.83e-04 1.47e-05f 1\n", + " 3 4.0789953e+05 2.94e+05 4.83e+02 -1.0 1.36e+09 - 2.64e-04 9.30e-04f 1\n", + " 4 2.9668590e+05 2.83e+06 6.97e+02 -1.0 4.80e+08 - 7.26e-05 1.50e-03f 1\n", + " 5 2.9555461e+05 2.83e+06 4.95e+04 -1.0 1.90e+08 - 1.88e-01 1.04e-03f 1\n", + " 6 2.9451022e+05 2.73e+06 4.60e+05 -1.0 4.43e+07 - 1.87e-01 3.43e-02f 1\n", + " 7 2.9628497e+05 2.13e+06 4.43e+05 -1.0 1.48e+07 - 7.40e-02 2.18e-01h 1\n", + " 8 2.9632658e+05 2.13e+06 4.41e+05 -1.0 5.91e+06 - 6.37e-01 3.36e-03h 1\n", + " 9 2.9642679e+05 2.11e+06 4.39e+05 -1.0 6.54e+06 - 7.26e-01 7.12e-03h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 2.9954735e+05 1.64e+06 4.13e+05 -1.0 6.57e+06 - 3.57e-02 2.24e-01h 1\n", + " 11 3.0435085e+05 9.50e+05 6.95e+05 -1.0 5.56e+06 - 9.46e-01 4.20e-01h 1\n", + " 12 3.0895827e+05 3.69e+05 1.22e+07 -1.0 4.03e+06 - 9.90e-01 6.11e-01h 1\n", + " 13 3.1246277e+05 1.42e+06 1.80e+10 -1.0 2.25e+06 - 9.95e-01 9.65e-01h 1\n", + " 14 3.1266092e+05 5.66e+05 7.10e+10 -1.0 2.77e+05 - 4.14e-01 6.11e-01h 1\n", + " 15 3.1266072e+05 5.65e+05 7.08e+10 -1.0 1.18e+06 - 1.09e-02 2.60e-04h 1\n", + " 16 3.1266230e+05 5.58e+05 7.01e+10 -1.0 1.08e+05 - 1.00e+00 1.26e-02h 1\n", + " 17 3.1271669e+05 3.14e+05 7.23e+10 -1.0 1.07e+05 - 4.05e-01 4.39e-01h 1\n", + " 18 3.1278583e+05 3.89e+03 1.58e+10 -1.0 6.01e+04 - 7.76e-03 9.91e-01h 1\n", + " 19 3.1278664e+05 1.57e+03 6.81e+10 -1.0 5.59e+02 - 9.87e-01 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 3.1278678e+05 2.39e+01 1.24e+09 -1.0 1.96e+02 - 1.00e+00 1.00e+00f 1\n", + " 21 3.1278674e+05 1.19e+01 6.32e+08 -1.0 1.30e+02 - 1.00e+00 5.00e-01f 2\n", + " 22 3.1278674e+05 1.21e-02 9.82e+04 -1.0 2.70e+00 - 1.00e+00 1.00e+00f 1\n", + " 23 3.1278642e+05 2.23e-05 2.00e+05 -1.7 1.62e+02 - 1.00e+00 1.00e+00f 1\n", + " 24 3.1278642e+05 7.45e-09 2.01e-03 -1.7 6.37e-01 - 1.00e+00 1.00e+00h 1\n", + " 25 3.1278634e+05 1.39e-06 1.26e+04 -7.0 4.04e+01 - 1.00e+00 1.00e+00f 1\n", + " 26 3.1278634e+05 1.49e-08 3.18e-05 -7.0 6.55e-03 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 26\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 3.1278633834102680e+05 3.1278633834102680e+05\n", + "Dual infeasibility......: 3.1783416921129853e-05 3.1783416921129853e-05\n", + "Constraint violation....: 2.9103830456733704e-11 1.4901161193847656e-08\n", + "Complementarity.........: 9.0926527280252930e-08 9.0926527280252930e-08\n", + "Overall NLP error.......: 6.6903080882733604e-09 3.1783416921129853e-05\n", + "\n", + "\n", + "Number of objective function evaluations = 28\n", + "Number of objective gradient evaluations = 27\n", + "Number of equality constraint evaluations = 28\n", + "Number of inequality constraint evaluations = 28\n", + "Number of equality constraint Jacobian evaluations = 27\n", + "Number of inequality constraint Jacobian evaluations = 27\n", + "Number of Lagrangian Hessian evaluations = 26\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.018\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1282,9 +2202,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $ 312786.3383410268\n", + "\n", + "Product flow rate and purity in F102\n", + "\n", + "====================================================================================\n", + "Unit : fs.F102 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 8377.0 : watt : False : (None, None)\n", + " Pressure Change : -2.4500e+05 : pascal : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 0.21743 1.0000e-08 0.067425 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 0.070695 1.0000e-08 0.037507 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 2.8812e-07 1.0000e-08 1.0493e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 2.8812e-07 1.0000e-08 1.0493e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 1.0000e-08 0.15000 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 1.0000e-08 0.033189 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0000e-08 1.9319e-07 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 1.0000e-08 1.9319e-07 1.0000e-08 \n", + " temperature kelvin 301.88 362.93 362.93 \n", + " pressure pascal 3.5000e+05 1.0500e+05 1.0500e+05 \n", + "====================================================================================\n", + "\n", + "benzene purity = 0.8188276578112275\n", + "\n", + "Overhead loss in F101\n", + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -56353. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 4.3534e-08 1.0000e-08 0.21743 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 7.5866e-07 1.0000e-08 0.070695 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.8812e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.8812e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.27178 0.054356 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.076085 0.0053908 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2414 1.2414 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.35887 0.35887 1.0000e-08 \n", + " temperature kelvin 696.11 301.88 301.88 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], "source": [ "print(\"operating cost = $\", value(m.fs.operating_cost))\n", "\n", @@ -1310,9 +2296,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 500.0 K\n", + "\n", + "R101 outlet temperature = 696.1117584980856 K\n", + "\n", + "F101 outlet temperature = 301.8784760569282 K\n", + "\n", + "F102 outlet temperature = 362.93476830548985 K\n", + "F102 outlet pressure = 105000.0 Pa\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1355,7 +2358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb index bcdc90bb..1bb4ae0e 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_exercise.ipynb @@ -1,1344 +1,1345 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", - "\n", - "- hda_ideal_VLE.py\n", - "- hda_reaction.py\n", - "\n", - "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](HDA_flowsheet.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " value,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "- **Flash**\n", - "- Separator (splitter) \n", - "- PressureChanger" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " StoichiometricReactor,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.exceptions import InitializationError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction package\n", - "\n", - "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", - "\n", - "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", - "\n", - "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", - "
    \n", - "
  • hda_ideal_VLE as thermo_props
    • \n", - "
  • hda_reaction as reaction_props
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", - "from idaes_examples.mod.hda import hda_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.thermo_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.thermo_params,\n", - " ideal_separation=False,\n", - " outlet_list=[\"purge\", \"recycle\"],\n", - ")\n", - "\n", - "\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.thermo_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")\n", - "\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute purity and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", - "\n", - "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", - "
    \n", - "
  • 2.2E-4 dollars/kW for H101
    • \n", - "
  • 1.9E-4 dollars/kW for F102
    • \n", - "
\n", - "Note that the heat duty is in units of watt (J/s). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.75)\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for Flash F102 to the following conditions:\n", - "
    \n", - "
  • T = 375 K
    • \n", - "
  • deltaP = -200000
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set conditions for Flash F102" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "\n", - "\n", - "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "![](HDA_flowsheet.png) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first create an object for the SequentialDecomposition and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "\n", - "![](HDA_tear_stream.png) \n", - "\n", - "\n", - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", - " \n", - "results = solver.solve(m, tee=True)\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "\n", - "\n", - "# Solve the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the results of the square problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 cooling duty provided\n", - "- F101 outlet temperature\n", - "- F102 outlet temperature\n", - "- F102 deltaP in the flash tank\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.heat_duty.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.F102.vap_outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 800] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - F102 outlet temperature [298, 450] K\n", - " - F102 outlet pressure [105000, 110000] Pa\n", - "\n", - "Let us first set the variable bound for the H101 outlet temperature as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the variable bound for the R101 outlet temperature.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the bounds for the rest of the decision variables. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", - "m.fs.F102.vap_outlet.pressure[0].setub(110000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "\n", - "print()\n", - "print(\"Product flow rate and purity in F102\")\n", - "\n", - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))\n", - "\n", - "print()\n", - "print(\"Overhead loss in F101\")\n", - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", - "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", + "\n", + "- hda_ideal_VLE.py\n", + "- hda_reaction.py\n", + "\n", + "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](HDA_flowsheet.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " value,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "- **Flash**\n", + "- Separator (splitter) \n", + "- PressureChanger" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " StoichiometricReactor,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction package\n", + "\n", + "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", + "\n", + "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", + "\n", + "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", + "
    \n", + "
  • hda_ideal_VLE as thermo_props
    • \n", + "
  • hda_reaction as reaction_props
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", + "from idaes_examples.mod.hda import hda_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.thermo_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.thermo_params,\n", + " ideal_separation=False,\n", + " outlet_list=[\"purge\", \"recycle\"],\n", + ")\n", + "\n", + "\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.thermo_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")\n", + "\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute purity and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", + "\n", + "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", + "
    \n", + "
  • 2.2E-4 dollars/kW for H101
    • \n", + "
  • 1.9E-4 dollars/kW for F102
    • \n", + "
\n", + "Note that the heat duty is in units of watt (J/s). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.75)\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for Flash F102 to the following conditions:\n", + "
    \n", + "
  • T = 375 K
    • \n", + "
  • deltaP = -200000
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set conditions for Flash F102" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "\n", + "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "![](HDA_flowsheet.png) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first create an object for the SequentialDecomposition and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "![](HDA_tear_stream.png) \n", + "\n", + "\n", + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", + " \n", + "results = solver.solve(m, tee=True)\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "\n", + "\n", + "# Solve the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the results of the square problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 cooling duty provided\n", + "- F101 outlet temperature\n", + "- F102 outlet temperature\n", + "- F102 deltaP in the flash tank\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.heat_duty.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.F102.vap_outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 800] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - F102 outlet temperature [298, 450] K\n", + " - F102 outlet pressure [105000, 110000] Pa\n", + "\n", + "Let us first set the variable bound for the H101 outlet temperature as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the variable bound for the R101 outlet temperature.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the bounds for the rest of the decision variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", + "m.fs.F102.vap_outlet.pressure[0].setub(110000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "\n", + "print()\n", + "print(\"Product flow rate and purity in F102\")\n", + "\n", + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))\n", + "\n", + "print()\n", + "print(\"Overhead loss in F101\")\n", + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", + "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb index 6f8b47f4..6a8537b3 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_solution.ipynb @@ -1,1483 +1,1484 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", - "\n", - "- hda_ideal_VLE.py\n", - "- hda_reaction.py\n", - "\n", - "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](HDA_flowsheet.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " value,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "- **Flash**\n", - "- Separator (splitter) \n", - "- PressureChanger" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " StoichiometricReactor,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models\n", - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.exceptions import InitializationError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction package\n", - "\n", - "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", - "\n", - "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", - "\n", - "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", - "
    \n", - "
  • hda_ideal_VLE as thermo_props
    • \n", - "
  • hda_reaction as reaction_props
    • \n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", - "from idaes_examples.mod.hda import hda_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.thermo_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = StoichiometricReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.thermo_params,\n", - " ideal_separation=False,\n", - " outlet_list=[\"purge\", \"recycle\"],\n", - ")\n", - "\n", - "\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.thermo_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")\n", - "\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute purity and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", - "\n", - "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", - "
    \n", - "
  • 2.2E-4 dollars/kW for H101
    • \n", - "
  • 1.9E-4 dollars/kW for F102
    • \n", - "
\n", - "Note that the heat duty is in units of watt (J/s). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.75)\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for Flash F102 to the following conditions:\n", - "
    \n", - "
  • T = 375 K
    • \n", - "
  • deltaP = -200000
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set conditions for Flash F102" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "\n", - "\n", - "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "![](HDA_flowsheet.png) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first create an object for the SequentialDecomposition and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "\n", - "![](HDA_tear_stream.png) \n", - "\n", - "\n", - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", - " \n", - "results = solver.solve(m, tee=True)\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "\n", - "\n", - "# Solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the results of the square problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 cooling duty provided\n", - "- F101 outlet temperature\n", - "- F102 outlet temperature\n", - "- F102 deltaP in the flash tank\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.heat_duty.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.F102.vap_outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102\n", - "m.fs.F102.deltaP.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 800] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - F102 outlet temperature [298, 450] K\n", - " - F102 outlet pressure [105000, 110000] Pa\n", - "\n", - "Let us first set the variable bound for the H101 outlet temperature as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the variable bound for the R101 outlet temperature.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the bounds for the rest of the decision variables. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", - "m.fs.F102.vap_outlet.pressure[0].setub(110000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "\n", - "print()\n", - "print(\"Product flow rate and purity in F102\")\n", - "\n", - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))\n", - "\n", - "print()\n", - "print(\"Overhead loss in F101\")\n", - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", - "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", + "\n", + "- hda_ideal_VLE.py\n", + "- hda_reaction.py\n", + "\n", + "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](HDA_flowsheet.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " value,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "- **Flash**\n", + "- Separator (splitter) \n", + "- PressureChanger" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " StoichiometricReactor,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models\n", + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction package\n", + "\n", + "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", + "\n", + "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", + "\n", + "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", + "
    \n", + "
  • hda_ideal_VLE as thermo_props
    • \n", + "
  • hda_reaction as reaction_props
    • \n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", + "from idaes_examples.mod.hda import hda_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.thermo_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = StoichiometricReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.thermo_params,\n", + " ideal_separation=False,\n", + " outlet_list=[\"purge\", \"recycle\"],\n", + ")\n", + "\n", + "\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.thermo_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")\n", + "\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute purity and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", + "\n", + "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", + "
    \n", + "
  • 2.2E-4 dollars/kW for H101
    • \n", + "
  • 1.9E-4 dollars/kW for F102
    • \n", + "
\n", + "Note that the heat duty is in units of watt (J/s). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.75)\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for Flash F102 to the following conditions:\n", + "
    \n", + "
  • T = 375 K
    • \n", + "
  • deltaP = -200000
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set conditions for Flash F102" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "\n", + "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "![](HDA_flowsheet.png) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first create an object for the SequentialDecomposition and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "![](HDA_tear_stream.png) \n", + "\n", + "\n", + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", + " \n", + "results = solver.solve(m, tee=True)\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "\n", + "\n", + "# Solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the results of the square problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 cooling duty provided\n", + "- F101 outlet temperature\n", + "- F102 outlet temperature\n", + "- F102 deltaP in the flash tank\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.heat_duty.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.F102.vap_outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102\n", + "m.fs.F102.deltaP.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 800] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - F102 outlet temperature [298, 450] K\n", + " - F102 outlet pressure [105000, 110000] Pa\n", + "\n", + "Let us first set the variable bound for the H101 outlet temperature as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the variable bound for the R101 outlet temperature.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the bounds for the rest of the decision variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", + "m.fs.F102.vap_outlet.pressure[0].setub(110000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "\n", + "print()\n", + "print(\"Product flow rate and purity in F102\")\n", + "\n", + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))\n", + "\n", + "print()\n", + "print(\"Overhead loss in F101\")\n", + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", + "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb index 415cf48c..b532c485 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_test.ipynb @@ -1,1498 +1,1499 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", - "\n", - "- hda_ideal_VLE.py\n", - "- hda_reaction.py\n", - "\n", - "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](HDA_flowsheet.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " value,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "- **Flash**\n", - "- Separator (splitter) \n", - "- PressureChanger" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " StoichiometricReactor,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models\n", - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.exceptions import InitializationError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction package\n", - "\n", - "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", - "\n", - "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", - "\n", - "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", - "
    \n", - "
  • hda_ideal_VLE as thermo_props
    • \n", - "
  • hda_reaction as reaction_props
    • \n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", - "from idaes_examples.mod.hda import hda_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.thermo_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = StoichiometricReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.thermo_params,\n", - " ideal_separation=False,\n", - " outlet_list=[\"purge\", \"recycle\"],\n", - ")\n", - "\n", - "\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.thermo_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")\n", - "\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute purity and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", - "\n", - "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", - "
    \n", - "
  • 2.2E-4 dollars/kW for H101
    • \n", - "
  • 1.9E-4 dollars/kW for F102
    • \n", - "
\n", - "Note that the heat duty is in units of watt (J/s). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 29" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.75)\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for Flash F102 to the following conditions:\n", - "
    \n", - "
  • T = 375 K
    • \n", - "
  • deltaP = -200000
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "\n", - "\n", - "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "![](HDA_flowsheet.png) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first create an object for the SequentialDecomposition and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "\n", - "![](HDA_tear_stream.png) \n", - "\n", - "\n", - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", - " \n", - "results = solver.solve(m, tee=True)\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the results of the square problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "assert value(m.fs.operating_cost) == pytest.approx(419122.3387, abs=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.purity) == pytest.approx(0.82429, abs=1e-3)\n", - "\n", - "assert value(m.fs.F102.heat_duty[0]) == pytest.approx(7352.4828, abs=1e-3)\n", - "assert value(m.fs.F102.vap_outlet.pressure[0]) == pytest.approx(1.5000e05, abs=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 cooling duty provided\n", - "- F101 outlet temperature\n", - "- F102 outlet temperature\n", - "- F102 deltaP in the flash tank\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.heat_duty.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.F102.vap_outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102\n", - "m.fs.F102.deltaP.unfix()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert degrees_of_freedom(m) == 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 800] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - F102 outlet temperature [298, 450] K\n", - " - F102 outlet pressure [105000, 110000] Pa\n", - "\n", - "Let us first set the variable bound for the H101 outlet temperature as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the variable bound for the R101 outlet temperature.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the bounds for the rest of the decision variables. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", - "m.fs.F102.vap_outlet.pressure[0].setub(110000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "\n", - "print()\n", - "print(\"Product flow rate and purity in F102\")\n", - "\n", - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))\n", - "\n", - "print()\n", - "print(\"Overhead loss in F101\")\n", - "m.fs.F101.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.operating_cost) == pytest.approx(312786.338, abs=1e-3)\n", - "assert value(m.fs.purity) == pytest.approx(0.818827, abs=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", - "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) == pytest.approx(500, abs=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) == pytest.approx(696.112, abs=1e-3)\n", - "assert value(m.fs.F101.vap_outlet.temperature[0]) == pytest.approx(301.878, abs=1e-3)\n", - "assert value(m.fs.F102.vap_outlet.temperature[0]) == pytest.approx(362.935, abs=1e-3)\n", - "assert value(m.fs.F102.vap_outlet.pressure[0]) == pytest.approx(105000, abs=1e-2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", + "\n", + "- hda_ideal_VLE.py\n", + "- hda_reaction.py\n", + "\n", + "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](HDA_flowsheet.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " value,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "- **Flash**\n", + "- Separator (splitter) \n", + "- PressureChanger" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " StoichiometricReactor,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models\n", + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction package\n", + "\n", + "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", + "\n", + "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", + "\n", + "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", + "
    \n", + "
  • hda_ideal_VLE as thermo_props
    • \n", + "
  • hda_reaction as reaction_props
    • \n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", + "from idaes_examples.mod.hda import hda_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.thermo_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = StoichiometricReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.thermo_params,\n", + " ideal_separation=False,\n", + " outlet_list=[\"purge\", \"recycle\"],\n", + ")\n", + "\n", + "\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.thermo_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")\n", + "\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute purity and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", + "\n", + "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", + "
    \n", + "
  • 2.2E-4 dollars/kW for H101
    • \n", + "
  • 1.9E-4 dollars/kW for F102
    • \n", + "
\n", + "Note that the heat duty is in units of watt (J/s). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 29" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.75)\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for Flash F102 to the following conditions:\n", + "
    \n", + "
  • T = 375 K
    • \n", + "
  • deltaP = -200000
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "\n", + "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "![](HDA_flowsheet.png) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first create an object for the SequentialDecomposition and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "![](HDA_tear_stream.png) \n", + "\n", + "\n", + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", + " \n", + "results = solver.solve(m, tee=True)\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the results of the square problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "assert value(m.fs.operating_cost) == pytest.approx(419122.3387, abs=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.purity) == pytest.approx(0.82429, abs=1e-3)\n", + "\n", + "assert value(m.fs.F102.heat_duty[0]) == pytest.approx(7352.4828, abs=1e-3)\n", + "assert value(m.fs.F102.vap_outlet.pressure[0]) == pytest.approx(1.5000e05, abs=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 cooling duty provided\n", + "- F101 outlet temperature\n", + "- F102 outlet temperature\n", + "- F102 deltaP in the flash tank\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.heat_duty.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.F102.vap_outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102\n", + "m.fs.F102.deltaP.unfix()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert degrees_of_freedom(m) == 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 800] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - F102 outlet temperature [298, 450] K\n", + " - F102 outlet pressure [105000, 110000] Pa\n", + "\n", + "Let us first set the variable bound for the H101 outlet temperature as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the variable bound for the R101 outlet temperature.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the bounds for the rest of the decision variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", + "m.fs.F102.vap_outlet.pressure[0].setub(110000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "\n", + "print()\n", + "print(\"Product flow rate and purity in F102\")\n", + "\n", + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))\n", + "\n", + "print()\n", + "print(\"Overhead loss in F101\")\n", + "m.fs.F101.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.operating_cost) == pytest.approx(312786.338, abs=1e-3)\n", + "assert value(m.fs.purity) == pytest.approx(0.818827, abs=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", + "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.H101.outlet.temperature[0]) == pytest.approx(500, abs=1e-3)\n", + "assert value(m.fs.R101.outlet.temperature[0]) == pytest.approx(696.112, abs=1e-3)\n", + "assert value(m.fs.F101.vap_outlet.temperature[0]) == pytest.approx(301.878, abs=1e-3)\n", + "assert value(m.fs.F102.vap_outlet.temperature[0]) == pytest.approx(362.935, abs=1e-3)\n", + "assert value(m.fs.F102.vap_outlet.pressure[0]) == pytest.approx(105000, abs=1e-2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb index 6f8b47f4..6a8537b3 100644 --- a/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb +++ b/idaes_examples/notebooks/docs/tut/core/hda_flowsheet_usr.ipynb @@ -1,1483 +1,1484 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Simulation and Optimization\n", - "\n", - "Author: Jaffer Ghouse \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning outcomes\n", - "\n", - "\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", - "\n", - "- hda_ideal_VLE.py\n", - "- hda_reaction.py\n", - "\n", - "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](HDA_flowsheet.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required pyomo and idaes components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- SolverFactory (to solve the problem)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " SolverFactory,\n", - " TransformationFactory,\n", - " value,\n", - ")\n", - "from pyomo.network import Arc, SequentialDecomposition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "- **Flash**\n", - "- Separator (splitter) \n", - "- PressureChanger" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models import (\n", - " PressureChanger,\n", - " Mixer,\n", - " Separator as Splitter,\n", - " Heater,\n", - " StoichiometricReactor,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: import flash model from idaes.models.unit_models\n", - "from idaes.models.unit_models import Flash" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.exceptions import InitializationError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing required thermo and reaction package\n", - "\n", - "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", - "\n", - "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", - "\n", - "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", - "
    \n", - "
  • hda_ideal_VLE as thermo_props
    • \n", - "
  • hda_reaction as reaction_props
    • \n", - "
\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", - "from idaes_examples.mod.hda import hda_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", - "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", - " property_package=m.fs.thermo_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params,\n", - " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", - ")\n", - "\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"reaction_package\": m.fs.reaction_params
    • \n", - "
  • \"has_heat_of_reaction\": True
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add reactor with the specifications above\n", - "m.fs.R101 = StoichiometricReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", - "
    \n", - "
  • \"property_package\": m.fs.thermo_params
    • \n", - "
  • \"has_heat_transfer\": True
    • \n", - "
  • \"has_pressure_change\": False
    • \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101 = Splitter(\n", - " property_package=m.fs.thermo_params,\n", - " ideal_separation=False,\n", - " outlet_list=[\"purge\", \"recycle\"],\n", - ")\n", - "\n", - "\n", - "m.fs.C101 = PressureChanger(\n", - " property_package=m.fs.thermo_params,\n", - " compressor=True,\n", - " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", - ")\n", - "\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![](HDA_flowsheet.png) \n", - "\n", - "
\n", - "Inline Exercise:\n", - "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Connect the H101 outlet to R101 inlet\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", - "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", - "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding expressions to compute purity and operating costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", - "\n", - "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", - "
    \n", - "
  • 2.2E-4 dollars/kW for H101
    • \n", - "
  • 1.9E-4 dollars/kW for F102
    • \n", - "
\n", - "Note that the heat duty is in units of watt (J/s). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing feed conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", - "m.fs.M101.toluene_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", - "
    \n", - "
  • FH2 = 0.30 mol/s
    • \n", - "
  • FCH4 = 0.02 mol/s
    • \n", - "
  • Remaining components = 1e-5 mol/s
    • \n", - "
  • T = 303.2 K
    • \n", - "
  • P = 350000 Pa
    • \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", - "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", - "m.fs.M101.hydrogen_feed.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing unit model specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.75)\n", - "m.fs.R101.heat_duty.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Flash conditions for F101 can be set as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", - "m.fs.F101.deltaP.fix(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Set the conditions for Flash F102 to the following conditions:\n", - "
    \n", - "
  • T = 375 K
    • \n", - "
  • deltaP = -200000
    • \n", - "
\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set conditions for Flash F102" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", - "m.fs.C101.outlet.pressure.fix(350000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the degrees of freedom" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "\n", - "\n", - "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", - "\n", - "![](HDA_flowsheet.png) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first create an object for the SequentialDecomposition and specify our options for this. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq = SequentialDecomposition()\n", - "seq.options.select_tear_method = \"heuristic\"\n", - "seq.options.tear_method = \"Wegstein\"\n", - "seq.options.iterLim = 3\n", - "\n", - "# Using the SD tool\n", - "G = seq.create_graph(m)\n", - "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", - "order = seq.calculation_order(G)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which is the tear stream? Display tear set and order" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for o in heuristic_tear_set:\n", - " print(o.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "for o in order:\n", - " print(o[0].name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "\n", - "![](HDA_tear_stream.png) \n", - "\n", - "\n", - "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tear_guesses = {\n", - " \"flow_mol_phase_comp\": {\n", - " (0, \"Vap\", \"benzene\"): 1e-5,\n", - " (0, \"Vap\", \"toluene\"): 1e-5,\n", - " (0, \"Vap\", \"hydrogen\"): 0.30,\n", - " (0, \"Vap\", \"methane\"): 0.02,\n", - " (0, \"Liq\", \"benzene\"): 1e-5,\n", - " (0, \"Liq\", \"toluene\"): 0.30,\n", - " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", - " (0, \"Liq\", \"methane\"): 1e-5,\n", - " },\n", - " \"temperature\": {0: 303},\n", - " \"pressure\": {0: 350000},\n", - "}\n", - "\n", - "# Pass the tear_guess to the SD tool\n", - "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def function(unit):\n", - " try:\n", - " initializer = unit.default_initializer()\n", - " initializer.initialize(unit, output_level=idaeslog.INFO)\n", - " except InitializationError:\n", - " solver = get_solver()\n", - " solver.solve(unit)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "seq.run(m, function)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", - " \n", - "results = solver.solve(m, tee=True)\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "\n", - "\n", - "# Solve the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the results of the square problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "How much benzene are we losing in the F101 vapor outlet stream?\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util.tables import (\n", - " create_stream_table_dataframe,\n", - " stream_table_dataframe_to_string,\n", - ")\n", - "\n", - "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", - "print(stream_table_dataframe_to_string(st))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "You can query additional variables here if you like. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization\n", - "\n", - "\n", - "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", - "\n", - "Let us try to minimize this cost such that:\n", - "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", - "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", - "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", - "\n", - "For this problem, our decision variables are as follows:\n", - "- H101 outlet temperature\n", - "- R101 cooling duty provided\n", - "- F101 outlet temperature\n", - "- F102 outlet temperature\n", - "- F102 deltaP in the flash tank\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.R101.heat_duty.unfix()\n", - "m.fs.F101.vap_outlet.temperature.unfix()\n", - "m.fs.F102.vap_outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Unfix deltaP for F102\n", - "m.fs.F102.deltaP.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to set bounds on these decision variables to values shown below:\n", - "\n", - " - H101 outlet temperature [500, 600] K\n", - " - R101 outlet temperature [600, 800] K\n", - " - F101 outlet temperature [298, 450] K\n", - " - F102 outlet temperature [298, 450] K\n", - " - F102 outlet pressure [105000, 110000] Pa\n", - "\n", - "Let us first set the variable bound for the H101 outlet temperature as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature[0].setlb(500)\n", - "m.fs.H101.outlet.temperature[0].setub(600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, set the variable bound for the R101 outlet temperature.\n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Set the bounds for reactor outlet temperature\n", - "m.fs.R101.outlet.temperature[0].setlb(600)\n", - "m.fs.R101.outlet.temperature[0].setub(800)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us fix the bounds for the rest of the decision variables. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", - "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", - "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", - "m.fs.F102.vap_outlet.pressure[0].setub(110000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.overhead_loss = Constraint(\n", - " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", - "\n", - "Use Shift+Enter to run the cell once you have typed in your code. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Add minimum product flow constraint\n", - "m.fs.product_flow = Constraint(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimization Results\n", - "\n", - "Display the results and product specifications" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"operating cost = $\", value(m.fs.operating_cost))\n", - "\n", - "print()\n", - "print(\"Product flow rate and purity in F102\")\n", - "\n", - "m.fs.F102.report()\n", - "\n", - "print()\n", - "print(\"benzene purity = \", value(m.fs.purity))\n", - "\n", - "print()\n", - "print(\"Overhead loss in F101\")\n", - "m.fs.F101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", - "\n", - "print()\n", - "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", - "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Simulation and Optimization\n", + "\n", + "Author: Jaffer Ghouse \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning outcomes\n", + "\n", + "\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Using the SequentialDecomposition tool to initialize a flowsheet with recycle\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing toluene and hydrogen to produce at least 370 TPY of benzene. As shown in the flowsheet, there are two flash tanks, F101 to separate out the non-condensibles and F102 to further separate the benzene-toluene mixture to improve the benzene purity. Note that typically a distillation column is required to obtain high purity benzene but that is beyond the scope of this workshop. The non-condensibles separated out in F101 will be partially recycled back to M101 and the rest will be either purged or combusted for power generation.We will assume ideal gas for this flowsheet. The properties required for this module are available in the same directory:\n", + "\n", + "- hda_ideal_VLE.py\n", + "- hda_reaction.py\n", + "\n", + "The state variables chosen for the property package are **flows of component by phase, temperature and pressure**. The components considered are: **toluene, hydrogen, benzene and methane**. Therefore, every stream has 8 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](HDA_flowsheet.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required pyomo and idaes components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the pyomo and idaes package. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- SolverFactory (to solve the problem)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "- SequentialDecomposition (to initialize the flowsheet in a sequential mode)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " SolverFactory,\n", + " TransformationFactory,\n", + " value,\n", + ")\n", + "from pyomo.network import Arc, SequentialDecomposition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From idaes, we will be needing the FlowsheetBlock and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "- **Flash**\n", + "- Separator (splitter) \n", + "- PressureChanger" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models import (\n", + " PressureChanger,\n", + " Mixer,\n", + " Separator as Splitter,\n", + " Heater,\n", + " StoichiometricReactor,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, import the remaining unit models highlighted in blue above and run the cell using `Shift+Enter` after typing in the code. \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: import flash model from idaes.models.unit_models\n", + "from idaes.models.unit_models import Flash" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.exceptions import InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing required thermo and reaction package\n", + "\n", + "The final set of imports are to import the thermo and reaction package for the HDA process. We have created a custom thermo package that assumes Ideal Gas with support for VLE. \n", + "\n", + "The reaction package here is very simple as we will be using only a StochiometricReactor and the reaction package consists of the stochiometric coefficients for the reaction and the parameter for the heat of reaction. \n", + "\n", + "Let us import the following modules and they are in the same directory as this jupyter notebook:\n", + "
    \n", + "
  • hda_ideal_VLE as thermo_props
    • \n", + "
  • hda_reaction as reaction_props
    • \n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.hda import hda_ideal_VLE as thermo_props\n", + "from idaes_examples.mod.hda import hda_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block as we did in module 1. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike Module 1, where we only had a thermo property package, for this flowsheet we will also need to add a reaction property package. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.thermo_params = thermo_props.HDAParameterBlock()\n", + "m.fs.reaction_params = reaction_props.HDAReactionParameterBlock(\n", + " property_package=m.fs.thermo_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding the Mixer (assigned a name M101) and a Heater (assigned a name H101). Note that, all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details (https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/index.html). For example, the Mixer unit model here is given a `list` consisting of names to the three inlets. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params,\n", + " inlet_list=[\"toluene_feed\", \"hydrogen_feed\", \"vapor_recycle\"],\n", + ")\n", + "\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now add the StoichiometricReactor(assign the name R101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"reaction_package\": m.fs.reaction_params
    • \n", + "
  • \"has_heat_of_reaction\": True
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add reactor with the specifications above\n", + "m.fs.R101 = StoichiometricReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Flash(assign the name F101) and pass the following arguments:\n", + "
    \n", + "
  • \"property_package\": m.fs.thermo_params
    • \n", + "
  • \"has_heat_transfer\": True
    • \n", + "
  • \"has_pressure_change\": False
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add the Splitter(S101), PressureChanger(C101) and the second Flash(F102). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101 = Splitter(\n", + " property_package=m.fs.thermo_params,\n", + " ideal_separation=False,\n", + " outlet_list=[\"purge\", \"recycle\"],\n", + ")\n", + "\n", + "\n", + "m.fs.C101 = PressureChanger(\n", + " property_package=m.fs.thermo_params,\n", + " compressor=True,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isothermal,\n", + ")\n", + "\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the mixer(M101) to the inlet of the heater(H101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![](HDA_flowsheet.png) \n", + "\n", + "
\n", + "Inline Exercise:\n", + "Now, connect the H101 outlet to the R101 inlet using the cell above as a guide. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Connect the H101 outlet to R101 inlet\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be connecting the rest of the flowsheet as shown below. Notice how the outlet names are different for the flash tanks F101 and F102 as they have a vapor and a liquid outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.F101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.F101.vap_outlet, destination=m.fs.S101.inlet)\n", + "m.fs.s08 = Arc(source=m.fs.S101.recycle, destination=m.fs.C101.inlet)\n", + "m.fs.s09 = Arc(source=m.fs.C101.outlet, destination=m.fs.M101.vapor_recycle)\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, each of these arcs link to ports on the two unit models that are connected. In this case, the ports consist of the state variables that need to be linked between the unit models. Pyomo provides a convenient method to write these equality constraints for us between two ports and this is done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding expressions to compute purity and operating costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. Expressions provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on Expressions, please refer to: https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html\n", + "\n", + "For this flowsheet, we are interested in computing the purity of the product Benzene stream (i.e. the mole fraction) and the operating cost which is a sum of the cooling and heating cost. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an Expression to compute the mole fraction of benzene in the `vap_outlet` of F102 which is our product stream. Please note that the var flow_mol_phase_comp has the index - [time, phase, component]. As this is a steady-state flowsheet, the time index by default is 0. The valid phases are [\"Liq\", \"Vap\"]. Similarly the valid component list is [\"benzene\", \"toluene\", \"hydrogen\", \"methane\"]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add an expression to compute the cooling cost assuming a cost of 0.212E-4 $/kW. Note that cooling utility is required for the reactor (R101) and the first flash (F101). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, let us add an expression to compute the heating cost assuming the utility cost as follows:\n", + "
    \n", + "
  • 2.2E-4 dollars/kW for H101
    • \n", + "
  • 1.9E-4 dollars/kW for F102
    • \n", + "
\n", + "Note that the heat duty is in units of watt (J/s). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now add an expression to compute the total operating cost per year which is basically the sum of the cooling and heating cost we defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing feed conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the toluene feed stream to the conditions shown in the flowsheet above. Please note that though this is a pure toluene feed, the remaining components are still assigned a very small non-zero value to help with convergence and initializing. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(0.30)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.toluene_feed.temperature.fix(303.2)\n", + "m.fs.M101.toluene_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Similarly, let us fix the hydrogen feed to the following conditions in the next cell:\n", + "
    \n", + "
  • FH2 = 0.30 mol/s
    • \n", + "
  • FCH4 = 0.02 mol/s
    • \n", + "
  • Remaining components = 1e-5 mol/s
    • \n", + "
  • T = 303.2 K
    • \n", + "
  • P = 350000 Pa
    • \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"hydrogen\"].fix(0.30)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Vap\", \"methane\"].fix(0.02)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"benzene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"toluene\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"hydrogen\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.flow_mol_phase_comp[0, \"Liq\", \"methane\"].fix(1e-5)\n", + "m.fs.M101.hydrogen_feed.temperature.fix(303.2)\n", + "m.fs.M101.hydrogen_feed.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing unit model specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us set set the H101 outlet temperature to 600 K. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the StoichiometricReactor, we have to define the conversion in terms of toluene. This requires us to create a new variable for specifying the conversion and adding a Constraint that defines the conversion with respect to toluene. The second degree of freedom for the reactor is to define the heat duty. In this case, let us assume the reactor to be adiabatic i.e. Q = 0. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(initialize=0.75, bounds=(0, 1))\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.75)\n", + "m.fs.R101.heat_duty.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Flash conditions for F101 can be set as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature.fix(325.0)\n", + "m.fs.F101.deltaP.fix(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Set the conditions for Flash F102 to the following conditions:\n", + "
    \n", + "
  • T = 375 K
    • \n", + "
  • deltaP = -200000
    • \n", + "
\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set conditions for Flash F102" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the purge split fraction to 20% and the outlet pressure of the compressor is set to 350000 Pa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.S101.split_fraction[0, \"purge\"].fix(0.2)\n", + "m.fs.C101.outlet.pressure.fix(350000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now defined all the feed conditions and the inputs required for the unit models. The system should now have 0 degrees of freedom i.e. should be a square problem. Please check that the degrees of freedom is 0. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the degrees of freedom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "\n", + "This section will demonstrate how to use the built-in sequential decomposition tool to initialize our flowsheet.\n", + "\n", + "![](HDA_flowsheet.png) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first create an object for the SequentialDecomposition and specify our options for this. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq = SequentialDecomposition()\n", + "seq.options.select_tear_method = \"heuristic\"\n", + "seq.options.tear_method = \"Wegstein\"\n", + "seq.options.iterLim = 3\n", + "\n", + "# Using the SD tool\n", + "G = seq.create_graph(m)\n", + "heuristic_tear_set = seq.tear_set_arcs(G, method=\"heuristic\")\n", + "order = seq.calculation_order(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which is the tear stream? Display tear set and order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for o in heuristic_tear_set:\n", + " print(o.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sequence did the SD tool determine to solve this flowsheet with the least number of tears? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for o in order:\n", + " print(o[0].name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "![](HDA_tear_stream.png) \n", + "\n", + "\n", + "The SequentialDecomposition tool has determined that the tear stream is the mixer outlet. We will need to provide a reasonable guess for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tear_guesses = {\n", + " \"flow_mol_phase_comp\": {\n", + " (0, \"Vap\", \"benzene\"): 1e-5,\n", + " (0, \"Vap\", \"toluene\"): 1e-5,\n", + " (0, \"Vap\", \"hydrogen\"): 0.30,\n", + " (0, \"Vap\", \"methane\"): 0.02,\n", + " (0, \"Liq\", \"benzene\"): 1e-5,\n", + " (0, \"Liq\", \"toluene\"): 0.30,\n", + " (0, \"Liq\", \"hydrogen\"): 1e-5,\n", + " (0, \"Liq\", \"methane\"): 1e-5,\n", + " },\n", + " \"temperature\": {0: 303},\n", + " \"pressure\": {0: 350000},\n", + "}\n", + "\n", + "# Pass the tear_guess to the SD tool\n", + "seq.set_guesses_for(m.fs.H101.inlet, tear_guesses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to tell the tool how to initialize a particular unit. We will be writing a python function which takes in a \"unit\" and calls the initialize method on that unit. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function(unit):\n", + " try:\n", + " initializer = unit.default_initializer()\n", + " initializer.initialize(unit, output_level=idaeslog.INFO)\n", + " except InitializationError:\n", + " solver = get_solver()\n", + " solver.solve(unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to initialize our flowsheet in a sequential mode. Note that we specifically set the iteration limit to be 5 as we are trying to use this tool only to get a good set of initial values such that IPOPT can then take over and solve this flowsheet for us. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "seq.run(m, function)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "We have now initialized the flowsheet. Let us run the flowsheet in a simulation mode to look at the results. To do this, complete the last line of code where we pass the model to the solver. You will need to type the following:\n", + " \n", + "results = solver.solve(m, tee=True)\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "\n", + "\n", + "# Solve the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the results of the square problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what is the amount of benzene we are able to produce and what purity we are able to achieve? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's look at how much benzene we are losing with the light gases out of F101. IDAES has tools for creating stream tables based on the `Arcs` and/or `Ports` in a flowsheet. Let us create and print a simple stream table showing the stream leaving the reactor and the vapor stream from F101.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "How much benzene are we losing in the F101 vapor outlet stream?\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util.tables import (\n", + " create_stream_table_dataframe,\n", + " stream_table_dataframe_to_string,\n", + ")\n", + "\n", + "st = create_stream_table_dataframe({\"Reactor\": m.fs.s05, \"Light Gases\": m.fs.s06})\n", + "print(stream_table_dataframe_to_string(st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "You can query additional variables here if you like. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization\n", + "\n", + "\n", + "We saw from the results above that the total operating cost for the base case was $419,122 per year. We are producing 0.142 mol/s of benzene at a purity of 82\\%. However, we are losing around 42\\% of benzene in F101 vapor outlet stream. \n", + "\n", + "Let us try to minimize this cost such that:\n", + "- we are producing at least 0.15 mol/s of benzene in F102 vapor outlet i.e. our product stream\n", + "- purity of benzene i.e. the mole fraction of benzene in F102 vapor outlet is at least 80%\n", + "- restricting the benzene loss in F101 vapor outlet to less than 20%\n", + "\n", + "For this problem, our decision variables are as follows:\n", + "- H101 outlet temperature\n", + "- R101 cooling duty provided\n", + "- F101 outlet temperature\n", + "- F102 outlet temperature\n", + "- F102 deltaP in the flash tank\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.R101.heat_duty.unfix()\n", + "m.fs.F101.vap_outlet.temperature.unfix()\n", + "m.fs.F102.vap_outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Let us now unfix the remaining variable which is F102 pressure drop (F102.deltaP) \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Unfix deltaP for F102\n", + "m.fs.F102.deltaP.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to set bounds on these decision variables to values shown below:\n", + "\n", + " - H101 outlet temperature [500, 600] K\n", + " - R101 outlet temperature [600, 800] K\n", + " - F101 outlet temperature [298, 450] K\n", + " - F102 outlet temperature [298, 450] K\n", + " - F102 outlet pressure [105000, 110000] Pa\n", + "\n", + "Let us first set the variable bound for the H101 outlet temperature as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature[0].setlb(500)\n", + "m.fs.H101.outlet.temperature[0].setub(600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, set the variable bound for the R101 outlet temperature.\n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Set the bounds for reactor outlet temperature\n", + "m.fs.R101.outlet.temperature[0].setlb(600)\n", + "m.fs.R101.outlet.temperature[0].setub(800)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us fix the bounds for the rest of the decision variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.F101.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F101.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setlb(298.0)\n", + "m.fs.F102.vap_outlet.temperature[0].setub(450.0)\n", + "m.fs.F102.vap_outlet.pressure[0].setlb(105000)\n", + "m.fs.F102.vap_outlet.pressure[0].setub(110000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the only things left to define are our constraints on overhead loss in F101, product flow rate and purity in F102. Let us first look at defining a constraint for the overhead loss in F101 where we are restricting the benzene leaving the vapor stream to less than 20 \\% of the benzene available in the reactor outlet. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.overhead_loss = Constraint(\n", + " expr=m.fs.F101.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " <= 0.20 * m.fs.R101.outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now, add the constraint such that we are producing at least 0.15 mol/s of benzene in the product stream which is the vapor outlet of F102. Let us name this constraint as m.fs.product_flow. \n", + "\n", + "Use Shift+Enter to run the cell once you have typed in your code. \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Add minimum product flow constraint\n", + "m.fs.product_flow = Constraint(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] >= 0.15\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add the final constraint on product purity or the mole fraction of benzene in the product stream such that it is at least greater than 80%. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.product_purity = Constraint(expr=m.fs.purity >= 0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization Results\n", + "\n", + "Display the results and product specifications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"operating cost = $\", value(m.fs.operating_cost))\n", + "\n", + "print()\n", + "print(\"Product flow rate and purity in F102\")\n", + "\n", + "m.fs.F102.report()\n", + "\n", + "print()\n", + "print(\"benzene purity = \", value(m.fs.purity))\n", + "\n", + "print()\n", + "print(\"Overhead loss in F101\")\n", + "m.fs.F101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(\"H101 outlet temperature = \", value(m.fs.H101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"R101 outlet temperature = \", value(m.fs.R101.outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F101 outlet temperature = \", value(m.fs.F101.vap_outlet.temperature[0]), \"K\")\n", + "\n", + "print()\n", + "print(\"F102 outlet temperature = \", value(m.fs.F102.vap_outlet.temperature[0]), \"K\")\n", + "print(\"F102 outlet pressure = \", value(m.fs.F102.vap_outlet.pressure[0]), \"Pa\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/tut/introduction.ipynb b/idaes_examples/notebooks/docs/tut/introduction.ipynb index 62978e37..c03c3a9b 100644 --- a/idaes_examples/notebooks/docs/tut/introduction.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_doc.ipynb b/idaes_examples/notebooks/docs/tut/introduction_doc.ipynb index 4ac33b41..184b3745 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_doc.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -699,8 +700,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.0, 3.3333333333333335, 6.666666666666667, 10.0, 13.333333333333334, 16.666666666666668, 20.0, 23.333333333333336, 26.666666666666668, 30.0, 33.333333333333336, 36.66666666666667, 40.0, 43.333333333333336, 46.66666666666667, 50.0]\n", - "[0.0, 11.111111111111112, 44.44444444444445, 100.0, 177.7777777777778, 277.7777777777778, 400.0, 544.4444444444446, 711.1111111111112, 900.0, 1111.1111111111113, 1344.4444444444448, 1600.0, 1877.777777777778, 2177.7777777777783, 2500.0]\n" + "[np.float64(0.0), np.float64(3.3333333333333335), np.float64(6.666666666666667), np.float64(10.0), np.float64(13.333333333333334), np.float64(16.666666666666668), np.float64(20.0), np.float64(23.333333333333336), np.float64(26.666666666666668), np.float64(30.0), np.float64(33.333333333333336), np.float64(36.66666666666667), np.float64(40.0), np.float64(43.333333333333336), np.float64(46.66666666666667), np.float64(50.0)]\n", + "[np.float64(0.0), np.float64(11.111111111111112), np.float64(44.44444444444445), np.float64(100.0), np.float64(177.7777777777778), np.float64(277.7777777777778), np.float64(400.0), np.float64(544.4444444444446), np.float64(711.1111111111112), np.float64(900.0), np.float64(1111.1111111111113), np.float64(1344.4444444444448), np.float64(1600.0), np.float64(1877.777777777778), np.float64(2177.7777777777783), np.float64(2500.0)]\n" ] } ], @@ -739,16 +740,12 @@ "outputs": [ { "data": { - "image/png": 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", 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LIcTu3btF69athVwuFw0aNBBLly4Vb7/9trCystJrl5+fL8aOHSuUSqWwt7cXgwcPFhkZGfe87T4zM7PS2n7//XfRqVMnYWtrK2xtbUVAQIAIDw8X8fHxD318RNVBJsRjjLgkIqIaMXXqVPz8889IS0uDjY1NjXzmgAEDEBcXV+mYL6LaimOIiIgMVGFhIX755RcMGjSo2sJQQUGB3vuEhARs27YNzzzzTLV8HpGh4hgiIiIDk5GRgV27dmHDhg24devWPR/zURX8/f0xZswY+Pv749q1a/juu+8gl8vvOeUCUW3FQEREZGDOnTuHESNGwM3NDYsWLUJQUFC1fVbPnj2xdu1apKWlQaFQICQkBPPnz7/nBIpEtRXHEBEREZHJ4xgiIiIiMnkMRERERGTyOIboIWi1WqSkpMDe3r5KnjJNRERE1U8IgZycHHh5ecHM7P7ngBiIHkJKSkqFJ4kTERGRcUhOToa3t/d92zAQPQR7e3sAZR3q4OAgcTVERET0MNRqNXx8fHTf4/fDQPQQyi+TOTg4MBAREREZmYcZ7sJB1URERGTyGIiIiIjI5DEQERERkcnjGKIqpNFoUFJSInUZRsfS0hLm5uZSl0FERCaMgagKCCGQlpaG7OxsqUsxWo6OjvDw8OA8T0REJAkGoipQHobc3NxgY2PDL/VHIIRAfn4+MjIyAACenp4SV0RERKaIgegJaTQaXRhydnaWuhyjZG1tDQDIyMiAm5sbL58REVGN46DqJ1Q+ZsjGxkbiSoxbef9xDBYREUmBgaiK8DLZk2H/ERGRlBiIiIiIyORJGogiIiLQvn172Nvbw83NDQMGDEB8fLxem2eeeQYymUzv9frrr+u1SUpKQp8+fWBjYwM3NzdMnz4dpaWlem2ioqLQpk0bKBQKNGzYECtWrKjuwzNazzzzDKZMmSJ1GURERDVG0kC0b98+hIeH48iRI4iMjERJSQl69OiBvLw8vXavvfYaUlNTda8FCxbo1mk0GvTp0wfFxcU4dOgQVq5ciRUrVmDWrFm6NomJiejTpw+6deuGmJgYTJkyBePGjcOOHTtq7Fhrq6ioKMhkMk45QERERk3Su8z+/vtvvfcrVqyAm5sboqOj0aVLF91yGxsbeHh4VLqPnTt34ty5c9i1axfc3d0RFBSEefPmYcaMGZgzZw7kcjm+//57+Pn54T//+Q8AoGnTpjhw4AC+/PJLhIWFVd8BEhER0QMdunQTgT6OsFVIF0sMagyRSqUCADg5OektX716NVxcXNCiRQvMnDkT+fn5unWHDx9Gy5Yt4e7urlsWFhYGtVqNuLg4XZvQ0FC9fYaFheHw4cOV1lFUVAS1Wq33qq3y8vIwatQo2NnZwdPTUxcay61atQrt2rWDvb09PDw8MHz4cN2cQVevXkW3bt0AAHXq1IFMJsOYMWMAlIXdTp06wdHREc7Ozujbty8uX75co8dGRESG71JGDl5ZcRy9F/2DDHWhZHUYzDxEWq0WU6ZMQceOHdGiRQvd8uHDh8PX1xdeXl44c+YMZsyYgfj4eGzcuBFA2aSId4YhALr3aWlp922jVqtRUFCgmwenXEREBObOnfvYxyKEQEGJ5rG3fxLWluaPdMfW9OnTsW/fPvzxxx9wc3PD+++/j5MnTyIoKAhA2W3w8+bNQ5MmTZCRkYFp06ZhzJgx2LZtG3x8fPD7779j0KBBiI+Ph4ODg64v8/LyMG3aNLRq1Qq5ubmYNWsWXnjhBcTExMDMzKByOBERSaS4VIsp62JQVKqFr7MtXO0VktViMIEoPDwcZ8+exYEDB/SWjx8/Xvfnli1bwtPTE927d8fly5fRoEGDaqll5syZmDZtmu69Wq2Gj4/PQ29fUKJBs1nSjE8691EYbOQP92PNzc3Fzz//jF9++QXdu3cHAKxcuRLe3t66Nq+++qruz/7+/li0aBHat2+P3Nxc2NnZ6c7mubm5wdHRUdd20KBBep+1bNkyuLq64ty5c3qBl4iITNfiPQk4e0MNpbUlFr7YStIpWAzif9UnTZqErVu3Yu/evXpfxpUJDg4GAFy6dAkA4OHhgfT0dL025e/Lxx3dq82dZzTupFAo4ODgoPeqjS5fvozi4mJdnwJllyubNGmiex8dHY1+/fqhXr16sLe3R9euXQGU3dl3PwkJCRg2bBj8/f3h4OCA+vXrP9R2RERkGk4m3caSvWXf5Z+80ALuDlaS1iPpGSIhBCZPnoxNmzYhKioKfn5+D9wmJiYGwP+eeRUSEoJPPvlE99gHAIiMjISDgwOaNWuma7Nt2za9/URGRiIkJKQKj+Z/rC3Nce4jaQZrW1tW3WMv8vLyEBYWhrCwMKxevRqurq5ISkpCWFgYiouL77ttv3794Ovri59++gleXl7QarVo0aLFA7cjIqLaL6+oFNPWxUArgAFBXujbykvqkqQNROHh4VizZg3++OMP2Nvb68b8KJVKWFtb4/Lly1izZg169+4NZ2dnnDlzBlOnTkWXLl3QqlUrAECPHj3QrFkzjBw5EgsWLEBaWho++OADhIeHQ6Eouxb5+uuv45tvvsG7776LV199FXv27MFvv/2Gv/76q1qOSyaTPfRlKyk1aNAAlpaWOHr0KOrVqwcAuH37Ni5evIiuXbviwoULuHXrFj799FPdJcMTJ07o7UMulwMom/6g3K1btxAfH4+ffvoJnTt3BoAKl0KJiMh0fbLtPK7eyoen0gpz+xvGMApJL5l99913UKlUeOaZZ+Dp6al7rVu3DkDZl+2uXbvQo0cPBAQE4O2338agQYOwZcsW3T7Mzc2xdetWmJubIyQkBC+//DJGjRqFjz76SNfGz88Pf/31FyIjIxEYGIj//Oc/WLp0qcnfcm9nZ4exY8di+vTp2LNnD86ePYsxY8boBj3Xq1cPcrkcixcvxpUrV/Dnn39i3rx5evvw9fWFTCbD1q1bkZmZidzcXNSpUwfOzs748ccfcenSJezZs0dvTBYREZmuvRcysOZo2fCJz18KhNLaUuKK/iXogVQqlQAgVCpVhXUFBQXi3LlzoqCgQILKnlxOTo54+eWXhY2NjXB3dxcLFiwQXbt2FW+99ZYQQog1a9aI+vXrC4VCIUJCQsSff/4pAIhTp07p9vHRRx8JDw8PIZPJxOjRo4UQQkRGRoqmTZsKhUIhWrVqJaKiogQAsWnTpkrrMPZ+JCKiB7uVWyTazosUvjO2irl/xlX7593v+/tuMiGEkDaSGT61Wg2lUgmVSlVhgHVhYSESExPh5+cHKytpB4QZM/YjEVHtJoTAxF9O4u+4NDRys8OWyZ1gVYXjXitzv+/vuxnEXWZERERUu208eQN/x6XBwkyGL4cEVXsYelQMRERERFStkrPyMfvPsqdHTH2uMVrUVUpcUUUMRERERFRtNFqBt9efRm5RKdr61sGELv5Sl1QpBiIiIiKqNj8fuIJjiVmwkZvji8GBsDA3zOhhmFUZIY5NfzLsPyKi2udCmhqf77gIAPiwbzP4OttKXNG9MRA9IUvLsvkT8vPzJa7EuJX3X3l/EhGRcSsq1WDKrzEo1mjRPcANQ9s//DNBpWD40ykbOHNzczg6OiIjIwMAYGNjI+nD6YyNEAL5+fnIyMiAo6MjzM0N664DIiJ6PF9EXsSFtBw42crx6SBpH9z6MBiIqkD5Q2TLQxE9OkdHR10/EhGRcTuWmIUf918BAEQMbAlXe4XEFT0YA1EVkMlk8PT0hJubG0pKSqQux+hYWlryzBARUS2RU1iCab/FQAjgpbbeCGtuHP+zy0BUhczNzfnFTkREJu2jLedw/XYBvOtYY1a/ZlKX89A4qJqIiIiqxI64NKyPvg6ZDPhicBDsrYznRhkGIiIiInpimTlFmLkxFgAwvos/Ovg5SVzRo2EgIiIioicihMB7v59BVl4xAjzsMe25xlKX9MgYiIiIiOiJ/Ho8GbsvZEBuboavhgZBYWF842kZiIiIiOixXbuVh3lbzwEA3glrjAAPB4krejwMRERERPRYSjVaTF0Xg/xiDYL9nDC2k2E+uPVhMBARERHRY/lh/xWcTMqGncIC/xkcCHMzw56N+n4YiIiIiOiRnb2hwpeRZQ9unft8c3jXsZG4oifDQERERESPpLBEg6nrYlCqFejZ3AMD29SVuqQnxkBEREREj2TB3/FIyMiFi50C8we2NPgHtz4MBiIiIiJ6aAcv3cSyg4kAgIUvtoKTrVziiqoGAxERERE9FFVBCd5ZfxoAMCK4HroFuElcUdVhICIiIqKHMvuPs0hVFaK+sw3+r09TqcupUgxERERE9EBbz6Rgc0wKzGTAF0OCYCO3kLqkKsVARERERPeVpirE/206CwCY1K0h2tSrI3FFVY+BiIiIiO5JCIHpG05DVVCClnWVmNy9kdQlVQsGIiIiIrqnVUeu4Z+Em1BYmOHLIYGwNK+d0aF2HhURERE9scuZuZi/7TwAYGavADR0s5e4ourDQEREREQVlGi0mLYuBoUlWnRu5IJRIfWlLqlaMRARERFRBd/suYTT11VwsLLAwhcDYWbED259GAxEREREpCcmORvf7L0EAPj4hZbwUFpJXFH1YyAiIiIinfziUkxdFwONVuD5QC88H+gldUk1goGIiIiIdCK2XUDizTx4OFhhXv8WUpdTYxiIiIiICAAQFZ+BVUeuAQAWvtQKShtLiSuqOQxEREREhKy8Yry74QwAYMzT9dG5kavEFdUsBiIiIiITp9UKvP1bDDJyitDA1Rbv9QqQuqQax0BERERk4n765wr2xmdCYWGGb4a3gZWludQl1TgGIiIiIhMWfe02Fu6IBwDM7tccTT0dJK5IGgxEREREJio7vxhvrj2FUq1Av0AvDOvgI3VJkmEgIiIiMkFCCLyz/gxuZBegvrMN5r/QAjJZ7Z6N+n4YiIiIiEzQsoNXset8OuTmZeOG7K1M5xb7yjAQERERmZjTydn4dHvZU+w/6NsULeoqJa5IegxEREREJkRVUILwNSdRohHo3dIDI5/ylbokg8BAREREZCKEEHjv9zO4frsAPk7W+HRQK5MeN3QnBiIiIiITserINWw/mwZLcxm+GdYGDiY+buhODEREREQm4OwNFT7eWjZu6L1eTRHo4yhtQQaGgYiIiKiWyyksGzdUrNHiuWbueLVjfalLMjgMRERERLWYEAIzN8bi2q181HW0xsIXOW6oMgxEREREtdiaY0nYeiYVFmYyLB7eGo42cqlLMkgMRERERLXUuRQ15m45BwB4t2cTtKlXR+KKDBcDERERUS2UV1SKSWtOorhUi2cD3DCuk7/UJRk0BiIiIqJaRgiBDzafxZWbefBUWuE/LwXCzIzjhu6HgYiIiKiWWX/iOjadugFzMxkWDWuNOrYcN/QgDERERES1yMX0HMz68ywAYNpzjdG+vpPEFRkHBiIiIqJaIr+4FG+sPonCEi26NHbFxK4NpC7JaEgaiCIiItC+fXvY29vDzc0NAwYMQHx8vF6bwsJChIeHw9nZGXZ2dhg0aBDS09P12iQlJaFPnz6wsbGBm5sbpk+fjtLSUr02UVFRaNOmDRQKBRo2bIgVK1ZU9+ERERHVqFl/xOFSRi7c7BX4YjDHDT0KSQPRvn37EB4ejiNHjiAyMhIlJSXo0aMH8vLydG2mTp2KLVu2YP369di3bx9SUlIwcOBA3XqNRoM+ffqguLgYhw4dwsqVK7FixQrMmjVL1yYxMRF9+vRBt27dEBMTgylTpmDcuHHYsWNHjR4vERFRdfk9+jo2RF+HmQxYNKw1XOwUUpdkVGRCCCF1EeUyMzPh5uaGffv2oUuXLlCpVHB1dcWaNWvw4osvAgAuXLiApk2b4vDhw3jqqaewfft29O3bFykpKXB3dwcAfP/995gxYwYyMzMhl8sxY8YM/PXXXzh79qzus4YOHYrs7Gz8/fffD6xLrVZDqVRCpVLBwcGheg6eiIjoMV3KyMXz3xxAfrEG055rjDe7N5K6JIPwKN/fBjWGSKVSAQCcnMoGgEVHR6OkpAShoaG6NgEBAahXrx4OHz4MADh8+DBatmypC0MAEBYWBrVajbi4OF2bO/dR3qZ8H3crKiqCWq3WexERERmiwhINJq05ifxiDZ5u4Izwbg2lLskoGUwg0mq1mDJlCjp27IgWLVoAANLS0iCXy+Ho6KjX1t3dHWlpabo2d4ah8vXl6+7XRq1Wo6CgoEItERERUCqVupePj0+VHCMREVFVm7slDhfScuBip8BXQ4NgznFDj8VgAlF4eDjOnj2LX3/9VepSMHPmTKhUKt0rOTlZ6pKIiIgq+CPmBtYeS4ZMBnw9NAhu9lZSl2S0LKQuAAAmTZqErVu3Yv/+/fD29tYt9/DwQHFxMbKzs/XOEqWnp8PDw0PX5tixY3r7K78L7c42d9+Zlp6eDgcHB1hbW1eoR6FQQKHgYDQiIjJcVzJz8f7GWADA5G4N0bGhi8QVGTdJzxAJITBp0iRs2rQJe/bsgZ+fn976tm3bwtLSErt379Yti4+PR1JSEkJCQgAAISEhiI2NRUZGhq5NZGQkHBwc0KxZM12bO/dR3qZ8H0RERMakbNzQKeQVaxDs54S3QhtLXZLRk/QMUXh4ONasWYM//vgD9vb2ujE/SqUS1tbWUCqVGDt2LKZNmwYnJyc4ODhg8uTJCAkJwVNPPQUA6NGjB5o1a4aRI0diwYIFSEtLwwcffIDw8HDdWZ7XX38d33zzDd599128+uqr2LNnD3777Tf89ddfkh07ERHR4/rkr/M4l6qGs60ci4a15rihKiDpbfcyWeU/wOXLl2PMmDEAyiZmfPvtt7F27VoUFRUhLCwM3377re5yGABcu3YNEydORFRUFGxtbTF69Gh8+umnsLD4X96LiorC1KlTce7cOXh7e+PDDz/UfcaD8LZ7IiIyFNtiU/HG6pMAgJWvdkDXxq4SV2S4HuX726DmITJUDERERGQIrt3KQ99FB5BTVIqJzzTAjJ4BUpdk0Ix2HiIiIiKqXFFp2bihnKJStPOtg7ef47ihqsRAREREZAQitl1A7A0VHG0ssWhYa1iY8yu8KrE3iYiIDNzfZ9Ow4tBVAMAXgwPh5Vhxyhh6MgxEREREBiw5Kx/vbjgNABjfxR/PBrg/YAt6HAxEREREBqq4VIvJa09BXViK1vUcMT2sidQl1VoMRERERAZq4Y4LiEnOhoOVBRYNbQ1LjhuqNuxZIiIiA7T7fDp++icRALDwpUD4ONlIXFHtxkBERERkYFKyC/D2+rJxQ690rI+w5h4P2IKeFAMRERGRASnRlI0bys4vQStvJWb2aip1SSaBgYiIiMiAzN92HtHXbsNeYYFvhrWB3IJf1TWBvUxERGQgNkRfx/KDVwEAnw8ORD1njhuqKQxEREREBuB0cjbe3xQLAHireyOOG6phDEREREQSy8gpxIRV0Sgu1SK0qTve6t5I6pJMDgMRERGRhIpLtXjjl5NIUxeigastvhwSCDMzmdRlmRwGIiIiIgnN3RKHE9duw97KAj+Nagd7K0upSzJJDEREREQSWXM0CauPJkEmAxYNbQ1/VzupSzJZDEREREQSiL6Whdl/ngUAvNOjCboFuElckWljICIiIqphaapCvP7LSZRoBHq39MAbzzSQuiSTx0BERERUgwpLNJjwSzQyc4oQ4GGPhS8GQibjIGqpMRARERHVECEEPtx8FqeTs6G0tsSPI9vBVmEhdVkEBiIiIqIa89/D17A++jrMZMA3w1tzJmoDwkBERERUAw5fvoWPtp4DAMzs1RSdG7lKXBHdiYGIiIiomt3ILkD4mpPQaAUGBHlhXGc/qUuiuzAQERERVaOCYg3G//cEsvKK0aKuAz4d1IqDqA0QAxEREVE1EUJg5sYziEtRw9lWjh9GtoOVpbnUZVElGIiIiIiqyc8HErE5JgXmZjIsGdEGdR2tpS6J7oGBiIiIqBocSLiJ+dvOAwBm9W2Gp/ydJa6I7oeBiIiIqIol3crHpLUnoRXAS229MSrEV+qS6AEYiIiIiKpQXlEpxq86gez8EgT6OGLegBYcRG0EGIiIiIiqiBAC0zecxoW0HLjaK/DDy205iNpIMBARERFVkW+jLmNbbBoszWX4/uU28FBaSV0SPSQGIiIioiqw90IGPt8ZDwD4qH8LtPV1krgiehQMRERERE/oSmYu3vz1FIQARgTXw7AO9aQuiR4RAxEREdETyCkswfhV0cgpLEU73zqY3a+51CXRY2AgIiIiekxarcDUdadxKSMXHg5W+PblNpBb8KvVGPGnRkRE9Ji+3p2AXefTIbcwww8j28LNnoOojRUDERER0WPYEZeGr3cnAADmv9ASgT6O0hZET4SBiIiI6BElpOdg2roYAMArHevjxbbe0hZET4yBiIiI6BGoCkrw2n9PIK9YgxB/Z7zfu6nUJVEVYCAiIiJ6SBqtwFu/nsLVW/mo62iNb4a3hqU5v0prA/4UiYiIHtJ/dsYjKj4TVpZlg6id7RRSl0RVhIGIiIjoIWw9k4Jvoy4DAD4b1Aot6iolroiqEgMRERHRA5xLUWP6+jMAgAld/NE/qK7EFVFVYyAiIiK6j9t5xRi/6gQKSjTo3MgF7/YMkLokqgYMRERERPdQqtFi0tqTuH67AL7ONlg8rDXMzWRSl0XVgIGIiIjoHj7dfgEHL92CjdwcP45sB0cbudQlUTVhICIiIqrEbyeSsfRAIgDgi8GBaOJhL3FFVJ0YiIiIiO6y/2Im3t8YCwB489mG6NnCU+KKqLoxEBEREd3hXIoab6w+iVKtwAut62Lqc42lLolqAAMRERHRv1JVBXh1xXHkFpUixN8Znw1qBZmMg6hNAQMRERERAHVhCcYsO440dSEaudnh+5FtIbfg16Sp4E+aiIhMXnGpFhN/iUZ8eg7c7BVY8WoHKK0tpS6LahADERERmTQhBN7beEZ3e/2yMe1R19Fa6rKohjEQERGRSftyVwI2nrwBczMZloxow2eUmSgGIiIiMlm/HU/Got0JAICPB7RAtyZuEldEUmEgIiIik7TvYiZmbiqba2hSt4YY1qGexBWRlCQNRPv370e/fv3g5eUFmUyGzZs3660fM2YMZDKZ3qtnz556bbKysjBixAg4ODjA0dERY8eORW5url6bM2fOoHPnzrCysoKPjw8WLFhQ3YdGREQGLC5FhTd+iYbm37mG3u7BuYZMnaSBKC8vD4GBgViyZMk92/Ts2ROpqam619q1a/XWjxgxAnFxcYiMjMTWrVuxf/9+jB8/XrderVajR48e8PX1RXR0NBYuXIg5c+bgxx9/rLbjIiIiw5WSXTbXUF6xhnMNkY6FlB/eq1cv9OrV675tFAoFPDw8Kl13/vx5/P333zh+/DjatWsHAFi8eDF69+6Nzz//HF5eXli9ejWKi4uxbNkyyOVyNG/eHDExMfjiiy/0ghMREdV+6sISvLL8ONLVRWjszrmG6H8M/rcgKioKbm5uaNKkCSZOnIhbt27p1h0+fBiOjo66MAQAoaGhMDMzw9GjR3VtunTpArn8f08oDgsLQ3x8PG7fvl3pZxYVFUGtVuu9iIjIuN0919DyVzjXEP2PQQeinj174r///S92796Nzz77DPv27UOvXr2g0WgAAGlpaXBz078jwMLCAk5OTkhLS9O1cXd312tT/r68zd0iIiKgVCp1Lx8fn6o+NCIiqkF3zjVky7mGqBKSXjJ7kKFDh+r+3LJlS7Rq1QoNGjRAVFQUunfvXm2fO3PmTEybNk33Xq1WMxQRERkxzjVED2LQZ4ju5u/vDxcXF1y6dAkA4OHhgYyMDL02paWlyMrK0o078vDwQHp6ul6b8vf3GpukUCjg4OCg9yIiIuN051xDnwxogWc41xBVwqgC0fXr13Hr1i14enoCAEJCQpCdnY3o6Ghdmz179kCr1SI4OFjXZv/+/SgpKdG1iYyMRJMmTVCnTp2aPQAiIqpRd841NPnZhhjKuYboHiQNRLm5uYiJiUFMTAwAIDExETExMUhKSkJubi6mT5+OI0eO4OrVq9i9ezf69++Phg0bIiwsDADQtGlT9OzZE6+99hqOHTuGgwcPYtKkSRg6dCi8vLwAAMOHD4dcLsfYsWMRFxeHdevW4euvv9a7JEZERLXPnXMNDWxdF9Oe41xDdG8yIYSQ6sOjoqLQrVu3CstHjx6N7777DgMGDMCpU6eQnZ0NLy8v9OjRA/PmzdMbJJ2VlYVJkyZhy5YtMDMzw6BBg7Bo0SLY2dnp2pw5cwbh4eE4fvw4XFxcMHnyZMyYMeOh61Sr1VAqlVCpVLx8RkRkBFKyC/DCtweRri5CiL8zVr7agbfXm6BH+f6WNBAZCwYiIiLjoS4swUvfHUZ8eg4au9th/etP8/Z6E/Uo39+My0REVGtwriF6XAxERERUKwgh8N7vnGuIHg8DERER1QpfRl7ExlOca4geDwMREREZvXXHk7BoT9kcdZxriB4HAxERERm1fRcz8f6mswA41xA9PgYiIiIyWpxriKoKAxERERmlG9kFeGX5ceQVaxDi74xPB7WCTCaTuiwyUgxERERkdFQFJXhl+TFk5BShsbsdvh/ZlhMv0hPhbw8RERmV8rmGLqbncq4hqjIMREREZDTK5xo6dJlzDVHVYiAiIiKjwbmGqLowEBERkVHgXENUnRiIiIjI4HGuIapuDERERGTQzt7gXENU/RiIiIjIYMWn5WDkz0c51xBVOwYiIiIySFcyczFi6VHczi9BoI8jfhzFuYao+vA3i4iIDE5yVj5GLD2Km7lFaOrpgJWvtIe9FecaourDQERERAYlTVWI4UuPIFVViAautlg1tgMcbeRSl0W1HAMREREZjJu5RRix9AiSswpQz8kGq8c9BRc7hdRlkQlgICIiIoOQnV+Ml5cexeXMPHgprbB6XDA8lFZSl0UmgoGIiIgkl1NYgtHLjuFCWg5c7RVY/dpT8HGykbosMiEMREREJKn84lK8uuI4Tl9XoY6NJVaPC4afi63UZZGJYSAiIiLJFJZoMP6/0Th+9TbsrSywamwwGrvbS10WmSAGIiIikkRxqRbhq0/iwKWbsJGbY8UrHfiwVpLMIwei0aNHY//+/dVRCxERmYhSjRZT18Vg94UMKCzM8PPo9mjrW0fqssiEPXIgUqlUCA0NRaNGjTB//nzcuHGjOuoiIqJaSqsVeHfDGfwVmwpLcxl+GNkWIQ2cpS6LTNwjB6LNmzfjxo0bmDhxItatW4f69eujV69e2LBhA0pKSqqjRiIiqiWEEPjgj7PYeOoGzM1k+GZ4GzzTxE3qsogebwyRq6srpk2bhtOnT+Po0aNo2LAhRo4cCS8vL0ydOhUJCQlVXScRERk5IQQ+/us81hxNgkwGfDE4EGHNPaQuiwjAEw6qTk1NRWRkJCIjI2Fubo7evXsjNjYWzZo1w5dffllVNRIRUS3wReRF/HwgEQDw2cBW6B9UV+KKiP7nkQNRSUkJfv/9d/Tt2xe+vr5Yv349pkyZgpSUFKxcuRK7du3Cb7/9ho8++qg66iUiIiO0ZO8lLN5zCQAw9/nmGNzeR+KKiPRZPOoGnp6e0Gq1GDZsGI4dO4agoKAKbbp16wZHR8cqKI+IiIzdsgOJWLgjHgDwXq8AjH66vrQFEVXikQPRl19+iZdeeglWVvd+voyjoyMSExOfqDAiIjJ+a48l4aOt5wAAb3ZvhNe7NpC4IqLKPXIgGjlyZHXUQUREtcymU9fx/qZYAMD4Lv6YGtpI4oqI7o0zVRMRUZX7+2wq3ll/BkIAI5/yxcxeAZDJZFKXRXRPDERERFSl9l7IwOS1p6DRCrzY1htzn2/OMEQGj4GIiIiqzKFLNzHhl2iUaAT6tvLEZ4NawcyMYYgMHwMRERFViRNXszDuvydQXKpFaFN3fDkkCOYMQ2QkGIiIiOiJnbmejVeWH0d+sQadG7ngm+GtYWnOrxgyHvxtJSKiJ3IhTY1Ry44hp6gUHfyc8OPIdrCyNJe6LKJHwkBERESP7XJmLl5eegzZ+SUI8nHEsjHtYS1nGCLjw0BERESPJTkrHyN+OoqbuUVo5umAla90gJ3ikae3IzIIDERERPTIUlUFGL70CNLUhWjoZodVYztAaWMpdVlEj42BiIiIHklmThFG/HQUyVkF8HW2wepxwXC2U0hdFtETYSAiIqKHdjuvGCN/PoorN/PgpbTC6nHBcHe497MtiYwFAxERET0UdWEJRi8/hgtpOXCzV2DNa0/Bu46N1GURVQmOfiMiogfKyivGqGVHcfaGGk62cqweF4z6LrZSl0VUZRiIiIjovtLVhXh56VEkZOTCyVaOVWM7oJG7vdRlEVUpBiIiIrqn5Kx8jFh6FElZ+fBwsMIv44LR0M1O6rKIqhwDERERVSohPQcv/3wU6eoi1HMqu5vMx4ljhqh2YiAiIqIKzt5QYdSyY8jKK0YjNzv8wrvJqJZjICIiIj3Hr2bh1eXHkVNUilbeSqx4pQOcbOVSl0VUrRiIiIhIZ//FTIxfdQKFJVp08HPCz6Pbwd6KM1BT7cdAREREAIC/z6bizbUxKNZo0bWxK75/uS0f1Eomg4GIiIjwe/R1TN9wGloB9G7pga+GtIbcgnP3kulgICIiMnH/PXwVs/6IAwC81NYbEQNbwsKcYYhMCwMREZEJW7L3EhbuiAcAjHm6Pmb1bQYzM5nEVRHVPEn/F2D//v3o168fvLy8IJPJsHnzZr31QgjMmjULnp6esLa2RmhoKBISEvTaZGVlYcSIEXBwcICjoyPGjh2L3NxcvTZnzpxB586dYWVlBR8fHyxYsKC6D42IyKAJIfDZ3xd0YejNZxtidj+GITJdkgaivLw8BAYGYsmSJZWuX7BgARYtWoTvv/8eR48eha2tLcLCwlBYWKhrM2LECMTFxSEyMhJbt27F/v37MX78eN16tVqNHj16wNfXF9HR0Vi4cCHmzJmDH3/8sdqPj4jIEGm1ArP+iMN3UZcBADN7BWBajyaQyRiGyIQJAwFAbNq0Sfdeq9UKDw8PsXDhQt2y7OxsoVAoxNq1a4UQQpw7d04AEMePH9e12b59u5DJZOLGjRtCCCG+/fZbUadOHVFUVKRrM2PGDNGkSZOHrk2lUgkAQqVSPe7hEREZhJJSjZj66ynhO2OrqP/eVvHLkatSl0RUbR7l+9tgR80lJiYiLS0NoaGhumVKpRLBwcE4fPgwAODw4cNwdHREu3btdG1CQ0NhZmaGo0eP6tp06dIFcvn/JhULCwtDfHw8bt++XUNHQ0QkvaJSDcLXnMTGUzdgbibDV0OCMCLYV+qyiAyCwQ6qTktLAwC4u7vrLXd3d9etS0tLg5ubm956CwsLODk56bXx8/OrsI/ydXXq1Knw2UVFRSgqKtK9V6vVT3g0RETSyi8uxYRV0fgn4SbkFmZYMrwNnmvm/uANiUyEwZ4hklJERASUSqXu5ePjI3VJRESPTVVQglE/H8M/CTdhIzfH8jHtGYaI7mKwgcjDwwMAkJ6errc8PT1dt87DwwMZGRl660tLS5GVlaXXprJ93PkZd5s5cyZUKpXulZyc/OQHREQkgVu5RRj+0xGcuHYbDlYWWDU2GB0bukhdFpHBMdhA5OfnBw8PD+zevVu3TK1W4+jRowgJCQEAhISEIDs7G9HR0bo2e/bsgVarRXBwsK7N/v37UVJSomsTGRmJJk2aVHq5DAAUCgUcHBz0XkRExiZVVYDBPxxGXIoaLnZy/Do+BG19K/93j8jUSRqIcnNzERMTg5iYGABlA6ljYmKQlJQEmUyGKVOm4OOPP8aff/6J2NhYjBo1Cl5eXhgwYAAAoGnTpujZsydee+01HDt2DAcPHsSkSZMwdOhQeHl5AQCGDx8OuVyOsWPHIi4uDuvWrcPXX3+NadOmSXTURETV79qtPLz0/WFczsyDp9IK6yaEoJkX/+eO6F5kQggh1YdHRUWhW7duFZaPHj0aK1asgBACs2fPxo8//ojs7Gx06tQJ3377LRo3bqxrm5WVhUmTJmHLli0wMzPDoEGDsGjRItjZ2enanDlzBuHh4Th+/DhcXFwwefJkzJgx46HrVKvVUCqVUKlUPFtERAbvYnoOXl56FBk5RajvbINfxgXDu46N1GUR1bhH+f6WNBAZCwYiIjIWsddVGLXsKG7nl6CJuz1WjesAN3srqcsiksSjfH8b7G33RET0aI5euYWxK08gt6gUgT6OWPlKezjayB+8IRExEBER1QZR8RmYsCoaRaVaPOXvhKWj28NOwX/iiR4W/7YQERm5bbGpeOvXUyjRCDwb4IZvR7SBlaW51GURGRUGIiIiI7b+RDJm/H4GWgH0beWJLwYHQW5hsDOqEBksBiIiIiO14mAi5mw5BwAY2t4Hn7zQEuZmfGI90eNgICIiMjJCCCzZewmf77wIABjbyQ8f9GkKmYxhiOhxMRARERmRUo0WH/91HisOXQUATAlthLe6N2IYInpCDEREREYip7AEk9eeQlR8JgDggz5NMa6zv8RVEdUODEREREYgOSsf41aeQHx6DqwszfDl4CD0aukpdVlEtQYDERGRgYu+dhsTVp3AzdxiuNkrsHR0O7TydpS6LKJahYGIiMiA/RFzA9M3nEFxqRbNPB3w85h28FRaS10WUa3DQEREZICEEPhqVwK+3p0AAAht6o6vhwbBlrNPE1UL/s0iIjIwhSUaTN9wBltOpwAAJnTxx7s9AzjHEFE1YiAiIjIgmTlFGL/qBE4lZcPCTIZPXmiBIe3rSV0WUa3HQEREZCAupKkxdsUJ3MgugNLaEt+/3BYhDZylLovIJDAQEREZgL0XMjBpzUnkFWvg52KLZWPaw8/FVuqyiEwGAxERkYSEEFhx6CrmbT0HrQBC/J3x3ctt4Ggjl7o0IpPCQEREJJESjRZz/ozD6qNJAIAh7Xwwb0ALPq2eSAIMREREElAVlGDSmpP4J+EmZDLg/V5NMa6zH59JRiQRBiIiohp27VYeXl1xHJcz82BtaY6vhwahR3MPqcsiMmkMRERENehYYhYmrDqB2/kl8HCwwtLR7dCirlLqsohMHgMREVEN+T36Ot7beAYlGoFW3kr8NKod3B2spC6LiMBARERU7bRagf9ExmPJ3ssAgN4tPfCfl4JgLTeXuDIiKsdARERUjQqKNZj2Wwy2n00DAEzq1hDTnmsMMz6Gg8igMBAREVWTdHUhXvvvCZy5roKluQyfDmyFQW29pS6LiCrBQEREVA3O3lBh3MoTSFMXoo6NJX4Y2Q4d/JykLouI7oGBiIioiu2MS8Nbv8agoESDBq5lj+HwdeZjOIgMGQMREVEVEULgp3+uIGL7BQgBdG7kgm+Gt4HS2lLq0ojoARiIiIiqQHGpFh9uPot1J5IBACOC62HO881hac7HcBAZAwYiIqInlJ1fjNd/icaRK1kwkwEf9m2GMU/X52M4iIwIAxER0RO4kpmLsStPIPFmHuwUFlg8rDW6BbhJXRYRPSIGIiKix/RPQiYmrTkFVUEJ6jpa4+cx7RDg4SB1WUT0GBiIiIgeUalGi692JWBJ1CUIAbSu54gfR7aDq71C6tKI6DExEBERPYJUVQHeWhuDY1ezAADDg+thVt9msLLkYziIjBkDERHRQ9obn4Fp62JwO78EdgoLzB/YEs8HekldFhFVAQYiIqIHKNFo8fnOePyw7woAoLmXA74Z3gZ+Lpxskai2YCAiIrqPG9kFmLzmJE4mZQMARoX44v3eTXmJjKiWYSAiIrqHyHPpeGf9aagKSmBvZYEFg1qhV0tPqcsiomrAQEREdJfiUi0++/sCfj6QCAAI9FZi8bA2qOdsI3FlRFRdGIiIiO6QnJWPSWtP4XRyNgDg1Y5+eK9XAOQWfAQHUW3GQERE9K+/z6Zi+oYzyCkshYOVBT5/KRA9mntIXRYR1QAGIiIyeUWlGsz/6zxWHr4GoGyixcXDWsO7Di+REZkKBiIiMmlXb+Zh0tqTOHtDDQCY0MUf74Q14VPqiUwMAxERmaytZ1Lw3u+xyC0qRR0bS/xncCCeDXCXuiwikgADERGZnMISDT7aeg5rjiYBANrXr4NFw1rDU2ktcWVEJBUGIiIyKZczcxG++iQupOVAJgPeeKYBpoY2hgUvkRGZNAYiIjIZm0/dwPubYpFfrIGzrRxfDglCl8auUpdFRAaAgYiIar2CYg3m/BmHdSeSAQBP+Tvh66Gt4e5gJXFlRGQoGIiIqFZLSM9B+JqTuJieC5kMePPZRnizeyOYm8mkLo2IDAgDERHVWutPJGPWH3EoKNHA1V6Br4cE4emGLlKXRUQGiIGIiGqdvKJSfPjHWWw8eQMA0KmhC74cEgRXe4XElRGRoWIgIqJa5UKaGuGrT+JyZh7MZMC05xpj4jMNeYmMiO6LgYiIagUhBH49now5f8ahqFQLdwcFFg1tjWB/Z6lLIyIjwEBEREYvp7AE/7fpLP48nQIA6NrYFV8MDoSzHS+REdHDYSAiIqO2Nz4D/7cxFimqQpibyTA9rAnGd/aHGS+REdEjYCAiIqOUlVeMj7bEYXNM2VkhHydrfDUkCG19nSSujIiMkUHPVT9nzhzIZDK9V0BAgG59YWEhwsPD4ezsDDs7OwwaNAjp6el6+0hKSkKfPn1gY2MDNzc3TJ8+HaWlpTV9KERURYQQ+CPmBkK/2IfNMSkwkwHjOvlhx5QuDENE9NgM/gxR8+bNsWvXLt17C4v/lTx16lT89ddfWL9+PZRKJSZNmoSBAwfi4MGDAACNRoM+ffrAw8MDhw4dQmpqKkaNGgVLS0vMnz+/xo+FiJ5MSnYBPth8FnsuZAAAmrjb47MXWyHIx1HawojI6Bl8ILKwsICHh0eF5SqVCj///DPWrFmDZ599FgCwfPlyNG3aFEeOHMFTTz2FnTt34ty5c9i1axfc3d0RFBSEefPmYcaMGZgzZw7kcnlNHw4RPQatVmD1sSR8tv0CcotKYWkuw+RnG+H1rg0gtzDoE91EZCQM/l+ShIQEeHl5wd/fHyNGjEBSUhIAIDo6GiUlJQgNDdW1DQgIQL169XD48GEAwOHDh9GyZUu4u7vr2oSFhUGtViMuLq5mD4SIHsvlzFwM/fEIPtx8FrlFpWhTzxHb3uyMN7s3Yhgioipj0GeIgoODsWLFCjRp0gSpqamYO3cuOnfujLNnzyItLQ1yuRyOjo5627i7uyMtLQ0AkJaWpheGyteXr7uXoqIiFBUV6d6r1eoqOiIielglGi1+3H8FX+9OQHGpFjZyc7wb1gQjQ+pzkkUiqnIGHYh69eql+3OrVq0QHBwMX19f/Pbbb7C2tq62z42IiMDcuXOrbf9EdH+x11V49/czOJ9a9j8jXRq7Yv4LLeBdx0biyoiotjKq882Ojo5o3LgxLl26BA8PDxQXFyM7O1uvTXp6um7MkYeHR4W7zsrfVzYuqdzMmTOhUql0r+Tk5Ko9ECKqVEGxBhHbzmPAtwdxPlUNRxtLfDE4ECtfac8wRETVyqgCUW5uLi5fvgxPT0+0bdsWlpaW2L17t259fHw8kpKSEBISAgAICQlBbGwsMjIydG0iIyPh4OCAZs2a3fNzFAoFHBwc9F5EVL0OX76FXl/vxw/7r0CjFejbyhO7pnXFwDbekMl4iYyIqpdBXzJ755130K9fP/j6+iIlJQWzZ8+Gubk5hg0bBqVSibFjx2LatGlwcnKCg4MDJk+ejJCQEDz11FMAgB49eqBZs2YYOXIkFixYgLS0NHzwwQcIDw+HQsEp/YkMgbqwBBHbLmDtsbIbJjwcrDBvQAs818z9AVsSEVUdgw5E169fx7Bhw3Dr1i24urqiU6dOOHLkCFxdXQEAX375JczMzDBo0CAUFRUhLCwM3377rW57c3NzbN26FRMnTkRISAhsbW0xevRofPTRR1IdEhHdYWdcGj784yzS1WU3MQwProf3egXAwcpS4sqIyNTIhBBC6iIMnVqthlKphEql4uUzoiqQmVOEOVvi8NeZVACAn4stIga2xFN8Mj0RVaFH+f426DNERFS7CCHw+8kbmLf1HFQFJTA3k+G1zv6YEtoIVpbmUpdHRCaMgYiIakRyVj7e3xSLfxJuAgCaezngs0Gt0KKuUuLKiIgYiIiommm0AisPXcXnO+ORX6yB3MIMU0MbY1xnP1iaG9WNrkRUizEQEVG1uZieg3c3nEFMcjYAoIOfEz4d2BL+rnbSFkZEdBcGIiKqcsWlWnwbdQlL9l5CiUbATmGBmb0DMKx9PZjxsRtEZIAYiIioSp1Kuo0Zv5/BxfRcAED3ADd8/EILeCqr73E7RERPioGIiKpEZk4Rvtp1EWuOJUEIwNlWjtnPN0e/Vp6caZqIDB4DERE9kbyiUiz9JxE/7r+MvGINAGBg67r4sG8z1LGVS1wdEdHDYSAiosdSqtHitxPX8eWui8jMKZtpOtBbiZm9m3KCRSIyOgxERPRIhBCIPJeOz/6+gMuZeQCAek42eLdnE/RpyctjRGScGIiI6KGdTLqNiG3ncfzqbQBAHRtLvNm9EUYE+0JuwTmFiMh4MRAR0QMl3szDwh0XsC02DQCgsDDD2E5+eP2ZBnwQKxHVCgxERHRPN3OLsHh3AlYfTUKpVkAmA15s441pPRrzNnoiqlUYiIiogoJiDX4+cAXf77uC3KJSAEC3Jq6Y0SsAAR73f2I0EZExYiAiIh2NVmBDdDK+iLyIdHXZnWMt6jrg/V5N8XRDF4mrIyKqPgxERAQhBPbGZ+DT7Rd0M0x717HG9LAm6NfKi4/bIKJaj4GIyMSdTs5GxPbzOHIlCwCgtLbE5GcbYmSILxQW5hJXR0RUMxiIiEzUtVt5WLgjHlvPpAIA5BZmeKVjfbzRtSGUNrxzjIhMCwMRkYnJyivG4j0J+OXINZRoyu4cG9i67M6xuo68c4yITBMDEZGJKCzRYNnBRHy39zJy/r1zrEtjV7zXMwDNvHjnGBGZNgYiolpOoxXYePI6voi8iFRVIQCgmacDZvYOQOdGrhJXR0RkGBiIiGopIQT2XczEp9sv4EJaDgCgrqM13glrjP6BdXnnGBHRHRiIiGqhk0m38Z+d8Th46RYAwMHKApOebYhRIfVhZck7x4iI7sZARFRLaLQCO+PSsPRAIqKvlT18VW5uhtFP+yK8W0M42sglrpCIyHAxEBEZudyiUqw/kYxlBxORnFUAoCwI9Q/ywpvdG8HHyUbiComIDB8DEZGRSlUVYMWhq1hzNAk5hWV3jdWxscTLT/liZIgv3OytJK6QiMh4MBARGZmzN1T46Z8r+OtMKkq1AgDg72KLVzv5YVAbb1jLOUaIiOhRMRARGQGtVmDPhQwsPXBF94gNAHjK3wnjOvnj2QA33jVGRPQEGIiIDFhBsQa/n7yOZQcSceVmHgDAwkyGvq08MbaTP1p6KyWukIiodmAgIjJAGTmF+O+ha/jl6DVk55cAAOytLDA8uB7GPF0fnko+YoOIqCoxEBEZkAtpaiz9JxF/xqSgWKMFAPg4WePVjn54qZ0P7BT8K0tEVB34ryuRxIQQ2J9wE0v/uYJ/Em7qlrep54jXOvujR3MPmHN8EBFRtWIgIpJIYYkGf8TcwM8HEnExPRcAYCYDerXwxKud/NDWt47EFRIRmQ4GIqIadiu3CL8cScKqI1dxM7cYAGArN8eQ9vXwSsf6nEiRiEgCDERENeRSRi5+PpCIjSevo6i0bHyQp9IKr3SsjyHt60FpbSlxhUREpouBiKgaCSFw+MotLP0nEXsuZOiWt6yrxLjOfujd0hOW5mYSVkhERAADEVG1SLyZhz9jUvDn6Ru4nFk2f5BMBnQPcMdrnf3Qwc8JMhkHShMRGQoGIqIqkqoqwNbTqfjzdApib6h0y60szfBiW2+82tEP/q52ElZIRET3wkBE9ASy8oqxLbYsBB2/mgVR9mgxmJvJ0LGhC54P9EKP5u5wsOL4ICIiQ8ZARPSIcotKEXkuDX/GpOCfhJu6B6wCQPv6dfB8oBd6tfSEi51CwiqJiOhRMBARPYTCEg2i4jOx5XQKdp1P190lBgDNvRzwfKAX+gZ6oa4jH6lBRGSMGIiI7qFUo8Why7fw5+kU7DibhpyiUt06fxdb9Av0Qr9ALzR047ggIiJjx0BEdAetVuBk0m38eToF22JTdRMnAmVzBvUL9MLzgV5o7uXAu8SIiGoRBiIyeUIInEtV48/TKdh6OhU3sgt065xs5ejd0gPPB9ZFO986MOMzxYiIaiUGIjJZlc0VBAB2Cgv0aO6O5wO90LGhCydOJCIyAQxEZFLuNVeQ3MIMzzZxQ/8gL3QLcIOVpbmEVRIRUU1jIKJaTasVOJ+mxpErWdgZl4Zjd80V1OnfuYKe41xBREQmjYGIahWNVuB8qhpHrtzCkStZOJZ4C+rCUr02Heo7oV+QF3q38IAz5woiIiIwEJGRK9VocS5VjaNXsnDkyi0cu5qFnLsCkJ3CAu3q10HHBi7o3cqTcwUREVEFDERkVEo1WsSllJ0BOpqYheOJWXrzAwGAvcIC7f2cEOznhKf8ndHcywEWHBhNRET3wUBEBq1Eo8XZGyocTSw7A3Ti6m3k3h2ArCwQ7OeEYD9nPOXvjGZeDjDn7fFERPQIGIjIoJRotDhzXYWjiWVjgKKvZiGvWKPXxsHKAh38nPGUf9kZoKaeDEBERPRkGIhIUsWlWsTeyMaRf8cARV+7jfy7ApDS2rLsDJB/WQgK8GAAIiKiqsVARDWqsESD2BsqHLlcNgboxLUsFJZo9drUsbFEh3/H/wT7OSPAw54zRBMRUbViIKIqp9EKpGQX4MrNPCRm5iLxZl7Zn2/m4UZ2gW4eoHJOtvJ/xwA54akGzmjsxgBEREQ1i4GIHosQArfyipF4Mw+JmeWBpyz8XL2Vj+JS7T23dbaVI/jf8T9P+TujoasdAxAREUmKgYjuK6+otCz03PEqP/Nz94SHd5Kbm8HX2QZ+Lrbwc7VFAxc7+Lnaws/FFs62cj4pnoiIDIpJBaIlS5Zg4cKFSEtLQ2BgIBYvXowOHTpIXZbkSjRaJGfl3xV48nDlZi7S1UX33E4mA7yU1vD/N+j4udjC39UO/i628HK05sBnIiIyGiYTiNatW4dp06bh+++/R3BwML766iuEhYUhPj4ebm5uUpf3xIQQyC/WIK+oFHn//je3qBT5xaXILfp3eVEp8oo0yCsuW5euKkTizTwkZeWjVCvuuW8nW7ku8Pi52KKBqy38XOzg62zDh6ASEVGtIBPi7iGutVNwcDDat2+Pb775BgCg1Wrh4+ODyZMn47333rvvtmq1GkqlEiqVCg4ODlVWk0YrcDO3CLl3hpWiUuQV/+/PunXFd4Sau9bnF5eFnCf5SVpZmsHPpezszv/O9pT919FGXmXHTEREVFMe5fvbJM4QFRcXIzo6GjNnztQtMzMzQ2hoKA4fPlyhfVFREYqK/nepSK1WV0tdqaoCdPpsb5XuUyYDbOUWsFWYw1ZhATuFBWzk5rBTWMC2/CUvW+dsp4D/v8HH3d6KA5uJiMhkmUQgunnzJjQaDdzd3fWWu7u748KFCxXaR0REYO7cudVel53CAmYy/BtSykJMeXCxkVvATi/U6K+3VZj/u82/6/9dZ21pzgHLREREj8gkAtGjmjlzJqZNm6Z7r1ar4ePjU+Wfo7S2xOX5vRlgiIiIJGYSgcjFxQXm5uZIT0/XW56eng4PD48K7RUKBRQKRbXXxSBERERkGMykLqAmyOVytG3bFrt379Yt02q12L17N0JCQiSsjIiIiAyBSZwhAoBp06Zh9OjRaNeuHTp06ICvvvoKeXl5eOWVV6QujYiIiCRmMoFoyJAhyMzMxKxZs5CWloagoCD8/fffFQZaExERkekxmXmInkR1zUNERERE1edRvr9NYgwRERER0f0wEBEREZHJYyAiIiIik8dARERERCaPgYiIiIhMHgMRERERmTwGIiIiIjJ5DERERERk8hiIiIiIyOSZzKM7nkT5ZN5qtVriSoiIiOhhlX9vP8xDORiIHkJOTg4AwMfHR+JKiIiI6FHl5ORAqVTetw2fZfYQtFotUlJSYG9vD5lMVqX7VqvV8PHxQXJyMp+TVo3YzzWD/Vwz2M81h31dM6qrn4UQyMnJgZeXF8zM7j9KiGeIHoKZmRm8vb2r9TMcHBz4l60GsJ9rBvu5ZrCfaw77umZURz8/6MxQOQ6qJiIiIpPHQEREREQmj4FIYgqFArNnz4ZCoZC6lFqN/Vwz2M81g/1cc9jXNcMQ+pmDqomIiMjk8QwRERERmTwGIiIiIjJ5DERERERk8hiIiIiIyOQxEEloyZIlqF+/PqysrBAcHIxjx45JXZLR279/P/r16wcvLy/IZDJs3rxZb70QArNmzYKnpyesra0RGhqKhIQEaYo1YhEREWjfvj3s7e3h5uaGAQMGID4+Xq9NYWEhwsPD4ezsDDs7OwwaNAjp6ekSVWycvvvuO7Rq1Uo3WV1ISAi2b9+uW88+rh6ffvopZDIZpkyZolvGvn5yc+bMgUwm03sFBATo1kvdxwxEElm3bh2mTZuG2bNn4+TJkwgMDERYWBgyMjKkLs2o5eXlITAwEEuWLKl0/YIFC7Bo0SJ8//33OHr0KGxtbREWFobCwsIartS47du3D+Hh4Thy5AgiIyNRUlKCHj16IC8vT9dm6tSp2LJlC9avX499+/YhJSUFAwcOlLBq4+Pt7Y1PP/0U0dHROHHiBJ599ln0798fcXFxANjH1eH48eP44Ycf0KpVK73l7Ouq0bx5c6SmpupeBw4c0K2TvI8FSaJDhw4iPDxc916j0QgvLy8REREhYVW1CwCxadMm3XutVis8PDzEwoULdcuys7OFQqEQa9eulaDC2iMjI0MAEPv27RNClPWrpaWlWL9+va7N+fPnBQBx+PBhqcqsFerUqSOWLl3KPq4GOTk5olGjRiIyMlJ07dpVvPXWW0II/j5XldmzZ4vAwMBK1xlCH/MMkQSKi4sRHR2N0NBQ3TIzMzOEhobi8OHDElZWuyUmJiItLU2v35VKJYKDg9nvT0ilUgEAnJycAADR0dEoKSnR6+uAgADUq1ePff2YNBoNfv31V+Tl5SEkJIR9XA3Cw8PRp08fvT4F+PtclRISEuDl5QV/f3+MGDECSUlJAAyjj/lwVwncvHkTGo0G7u7uesvd3d1x4cIFiaqq/dLS0gCg0n4vX0ePTqvVYsqUKejYsSNatGgBoKyv5XI5HB0d9dqyrx9dbGwsQkJCUFhYCDs7O2zatAnNmjVDTEwM+7gK/frrrzh58iSOHz9eYR1/n6tGcHAwVqxYgSZNmiA1NRVz585F586dcfbsWYPoYwYiInoi4eHhOHv2rN5YAKo6TZo0QUxMDFQqFTZs2IDRo0dj3759UpdVqyQnJ+Ott95CZGQkrKyspC6n1urVq5fuz61atUJwcDB8fX3x22+/wdraWsLKyvCSmQRcXFxgbm5eYfR8eno6PDw8JKqq9ivvW/Z71Zk0aRK2bt2KvXv3wtvbW7fcw8MDxcXFyM7O1mvPvn50crkcDRs2RNu2bREREYHAwEB8/fXX7OMqFB0djYyMDLRp0wYWFhawsLDAvn37sGjRIlhYWMDd3Z19XQ0cHR3RuHFjXLp0ySB+nxmIJCCXy9G2bVvs3r1bt0yr1WL37t0ICQmRsLLazc/PDx4eHnr9rlarcfToUfb7IxJCYNKkSdi0aRP27NkDPz8/vfVt27aFpaWlXl/Hx8cjKSmJff2EtFotioqK2MdVqHv37oiNjUVMTIzu1a5dO4wYMUL3Z/Z11cvNzcXly5fh6elpGL/PNTJ0myr49ddfhUKhECtWrBDnzp0T48ePF46OjiItLU3q0oxaTk6OOHXqlDh16pQAIL744gtx6tQpce3aNSGEEJ9++qlwdHQUf/zxhzhz5ozo37+/8PPzEwUFBRJXblwmTpwolEqliIqKEqmpqbpXfn6+rs3rr78u6tWrJ/bs2SNOnDghQkJCREhIiIRVG5/33ntP7Nu3TyQmJoozZ86I9957T8hkMrFz504hBPu4Ot15l5kQ7Ouq8Pbbb4uoqCiRmJgoDh48KEJDQ4WLi4vIyMgQQkjfxwxEElq8eLGoV6+ekMvlokOHDuLIkSNSl2T09u7dKwBUeI0ePVoIUXbr/Ycffijc3d2FQqEQ3bt3F/Hx8dIWbYQq62MAYvny5bo2BQUF4o033hB16tQRNjY24oUXXhCpqanSFW2EXn31VeHr6yvkcrlwdXUV3bt314UhIdjH1enuQMS+fnJDhgwRnp6eQi6Xi7p164ohQ4aIS5cu6dZL3ccyIYSomXNRRERERIaJY4iIiIjI5DEQERERkcljICIiIiKTx0BEREREJo+BiIiIiEweAxERERGZPAYiIiIiMnkMRERERGTyGIiIiIjI5DEQERERkcljICIik5SZmQkPDw/Mnz9ft+zQoUOQy+V6T9wmItPAZ5kRkcnatm0bBgwYgEOHDqFJkyYICgpC//798cUXX0hdGhHVMAYiIjJp4eHh2LVrF9q1a4fY2FgcP34cCoVC6rKIqIYxEBGRSSsoKECLFi2QnJyM6OhotGzZUuqSiEgCHENERCbt8uXLSElJgVarxdWrV6Uuh4gkwjNERGSyiouL0aFDBwQFBaFJkyb46quvEBsbCzc3N6lLI6IaxkBERCZr+vTp2LBhA06fPg07Ozt07doVSqUSW7dulbo0IqphvGRGRCYpKioKX331FVatWgUHBweYmZlh1apV+Oeff/Ddd99JXR4R1TCeISIiIiKTxzNEREREZPIYiIiIiMjkMRARERGRyWMgIiIiIpPHQEREREQmj4GIiIiITB4DEREREZk8BiIiIiIyeQxEREREZPIYiIiIiMjkMRARERGRyWMgIiIiIpP3/9vknaHnESkoAAAAAElFTkSuQmCC", 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", 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", "text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\tut\\introduction_doc_41_0.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1154,7 +1147,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/tut/introduction_exercise.ipynb b/idaes_examples/notebooks/docs/tut/introduction_exercise.ipynb index 251e8e94..6f36a3a6 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_exercise.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_exercise.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_short.ipynb b/idaes_examples/notebooks/docs/tut/introduction_short.ipynb index 4122c058..2f6a0961 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_short.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_short.ipynb @@ -1,930 +1,931 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to IDAES (short)\n", - "Author: Jaffer Ghouse, Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01 \n", - "\n", - "The fundamentals of working with the IDAES process modeling toolset, and how these tools can be applied for optimization applications.\n", - "\n", - "This material was originally presented in an IDAES workshop." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "run \"notebook_test_script.py\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.notebook_checks import run_checks\n", - "\n", - "assert run_checks() == 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Welcome and thank you for taking the time to attend today's workshop. Today we will introduce you to the fundamentals of working with the IDAES process modeling toolset, and we will demonstrate how these tools can be applied for optimization applications.\n", - "\n", - "Today's workshop will be conducted using Jupyter Notebooks which provide an online, interactive Python environment for you to use (without the need for installing anything).\n", - "\n", - "Before we get started on some actual examples, let's make sure that everything is working correctly. The cell below contains a command to run a simple test script that will test that everything we will need for today is working properly.\n", - "\n", - "You can execute a cell by pressing `Shift+Enter`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything worked properly, you should see a message saying `All good!` and a summary of all the checks that were run. If you don't see this, please contact someone for assistance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Outline of Workshop\n", - "\n", - "Today's workshop is divided into four modules which will take you through the steps of setting up a flowsheet within the IDAES framework.\n", - "\n", - "Welcome Module (this one):\n", - "\n", - "* Introduction to Jupyter notebooks and Python\n", - "* Introduction to Pyomo\n", - "\n", - "Module 1 will cover:\n", - "\n", - "* how to import models from the core IDAES model library,\n", - "* how to create a model for a single unit operation,\n", - "* how to define feed and operating conditions,\n", - "* how to initialize and solve a single unit model,\n", - "* some ways we can manipulate the model and examine the results.\n", - "\n", - "Module 2 will demonstrate:\n", - "\n", - "* how to combine unit models together to form flowsheets,\n", - "* tools to initialize and solve flowsheets with recycle loops,\n", - "* how to optimize process operating conditions to meet product specifications.\n", - "\n", - "Module 3 will demonstrate:\n", - "\n", - "* how to build new unit models using the IDAES tools,\n", - "* how to include new unit models into flowsheets.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to Jupyter Notebooks and Python\n", - "\n", - "In this short notebook, we will briefly describe the uses of Jupyter notebooks like this one, and provide you with the necessary background in Python for this workshop. We will cover `if` statements, looping, array-like containers called lists and dictionaries, as well as the use of some external packages for working with data. \n", - "\n", - "There are many additional tutorials online to learn more about the Python syntax. One recommended by the IDAES team is https://www.coursera.org/learn/python." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Python, variables do not need to be declared before they are used. You can simply define a new variable using `x = 5`.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "In the cell below, assign a value of 5 to the variable x. Don't forget to type Shift+Enter to execute the line.
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Assign a value of 5 to the variable x" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Assign a value of 5 to the variable x\n", - "x = 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can easily see the value of a variable using the built-in `print` function. For example, to print the value of `x` use `print(x)`.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Write the code to print the value of x. Don't forget to hit Shift+Enter to execute the cell.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the value of x" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the value of x\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Inline Exercise:\n", - "Now change the value of the x variable to 8 and execute the cell.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: Assign a value of 8 to the variable x" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Todo: Assign a value of 8 to the variable x\n", - "x = 8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Jupyter notebooks and execution order\n", - "\n", - "
\n", - "Note:\n", - "When using Jupyter notebooks, it is very important to know that the cells can be executed out of order (intentionally or not). The state of the environment (e.g., values of variables, imports, etc.) is defined by the execution order.\n", - "
\n", - "\n", - "
\n", - "Inline Exercise:\n", - "To see this concept, select the cell above that contained the print statement and execute the cell again using Shift+Enter.\n", - "
\n", - "\n", - "You should see that the value `8` is now printed. This may seem problematic if you are used to programming in environments where the state is linked to the order of the commands as *written*, not as *executed*.\n", - "\n", - "**Again, notice that the state of the environment is determined by the execution order.**\n", - "\n", - "Note also that the square brackets to the left of the cell show the order that cells were executed. If you scroll to the top, you should see that the code cells show an execution order of `[1]`, `[2]`, `[5]`, and `[4]`, indicating the actual execution order.\n", - "\n", - "There are some useful menu commands at the top of the Jupyter notebook to help with these problems and make sure you retain the execution order as expected.\n", - "\n", - "Some important commands to remember:\n", - "* You can clear the current state with the menu item `Kernel | Restart & Clear Output`\n", - "* It is often useful to clear the state using the menu command just described, and then execute all the lines **above the currently selected cell** using `Cell | Run All Above`.\n", - "* You can clear all the state and re-run the entire notebook using `Kernel | Restart & Run All`.\n", - "\n", - "To show the use of these commands, complete the following.\n", - "
\n", - "Inline Exercise:\n", - "
    \n", - "
  • Clear the current state (using Kernel | Restart & Clear Output). You should notice that the square brackets that listed the execution order are all now empty.
    • \n", - "
  • Select the cell immediately below this text\n", - "
  • Re-run all the code up to this point (Cell | Run All Above). You should now see that the square brackets indicate the expected execution order.
    • \n", - "
  • Print the value of x again using the print function. You should see the value 8 printed, while the earlier cell printing x shows the value of 5 as expected.
    • \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Python `if` statements\n", - "\n", - "In the code below, we show an example of an `if` statement in Python.\n", - "\n", - "```python\n", - "temp = 325\n", - "# some other code\n", - "if temp > 320:\n", - " print('temperature is too high')\n", - "elif x < 290:\n", - " print('temperature is too low')\n", - "else:\n", - " print('temperature is just right')\n", - "```\n", - "\n", - "
\n", - "Note:\n", - "You will notice that there are no braces to separate blocks in the if-else tree. In Python, indentation is used to delineate blocks of code throughout Python (e.g., if statements, for loops, functions, etc.). The indentation in the above example is not only to improve legibility of the code. It is necessary for the code to run correctly. As well, the number of spaces required to define the indentation is arbitrary, but it must be consistent throughout the code. For example, we could use 3 spaces (instead of the 4 used in the example above, but we could not use 3 for one of the blocks and 4 for another).\n", - "
\n", - "\n", - "Using the syntax above for the `if` statement, write the following code.\n", - "
\n", - "Inline Exercise:\n", - "
    \n", - "
  • set the value of the variable T_degC to 20
    • \n", - "
  • convert this from degrees Celsius to degrees Fahrenheit (use variable name T_degF)
    • \n", - "
  • write an `if` statement that prints a message if the degrees Fahrenheit are below 70
    • \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "T_degC = 20\n", - "# some other code\n", - "T_degF = (T_degC * 9.0 / 5.0) + 32.0\n", - "\n", - "# Todo: put the if statement here" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "T_degC = 20\n", - "# some other code\n", - "T_degF = (T_degC * 9.0 / 5.0) + 32.0\n", - "\n", - "# Todo: put the if statement here\n", - "if T_degF < 70:\n", - " print(\"The room is too cold.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Python list containers\n", - "\n", - "Now we will illustrate the use of lists in Python. Lists are similar to vectors or arrays in other languages. A list in Python is indexed by integers from 0 up to the length of the array minus 1. The list can contain standard types (int, float, string), or other objects.\n", - "\n", - "In the next inline exercise, we will create a list that contains the values from 0 to 50 by steps of 5 using a for loop. Note that the python function `range(n)` can be used to iterate from 0 to (n-1) in a for loop. Also note that lists have an `append` method which adds an entry to the end of the list (e.g., if the list `l` currently has 5 elements, then `l.append('temp')` will add the string \"temp\" as the sixth element). Print the new list after the for loop. If this is done correctly, you should see:\n", - "`[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]` printed after the cell.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Complete the code block below to create the desired list and print the result.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Create a list with the values 0 to 50 with steps of 5.\n", - "xlist = list()\n", - "for i in range(11):\n", - " # Todo: use the append method of list to append the correct value\n", - "\n", - "# Todo: print the value of xlist to verify the results\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "# Create a list with the values 0 to 50 with steps of 5.\n", - "xlist = list()\n", - "for i in range(11):\n", - " # Todo: use the append method of list to append the correct value\n", - " xlist.append(i * 5)\n", - "\n", - "print(xlist) # Todo: print the value of xlist to verify the results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can easily check the length of a list using the python `len(l)` function.\n", - "
\n", - "Inline Exercise:\n", - "Print the length of `xlist`. It should be 11.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# Todo: print the len of the list" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "print(len(xlist)) # Todo: print the len of the list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you have a list of values or objects, it is easy to iterate through that list in a for loop. In the next inline exercise, we will create another list, `ylist` where each of the values is equal to the corresponding value in `xlist` squared. That is, $y_i = x_i^2$.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Modify the code below to create ylist as described above. Print the values in ylist to check the result.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "ylist = list()\n", - "\n", - "# Todo: define the for loop to add elements to ylist using the values in xlist\n", - "\n", - "print(ylist)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "ylist = list()\n", - "\n", - "# Todo: define the for loop to add elements to ylist using the values in xlist\n", - "for x in xlist:\n", - " ylist.append(x**2)\n", - "\n", - "print(ylist)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Python dictionary containers\n", - "\n", - "Another valuable data structure in Python are *dictionaries*. Dictionaries are an associative array; that is, a map from keys to values or objects. The keys can be *almost* anything, including floats, integers, and strings. The code below shows an example of creating a dictionary (here, to store the areas of some of the states).\n", - "
\n", - "Inline Exercise:\n", - "Execute the lines below to see the areas dictionary.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "areas = dict()\n", - "areas[\"South Dakota\"] = 199742\n", - "areas[\"Oklahoma\"] = 181035\n", - "print(areas)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dictionaries can contain mixed types (i.e., it is valid to add `areas['Texas'] = 'Really big!'`) but this may lead to unpredictable behavior if the different types are unexpected in other parts of the code.\n", - "\n", - "You can loop through dictionaries in different ways. For example,\n", - "```python\n", - "d = {'A': 2, 'B': 4, 'D': 16}\n", - "for k in d.keys():\n", - " # loop through the keys in the dictionary\n", - " # access the value with d[k]\n", - " print('key=', k, 'value=', d[k])\n", - " \n", - "for v in d.values():\n", - " # loop through the values in the dictionary, ignoring the keys\n", - " print('value=', v)\n", - " \n", - "for k,v in d.items():\n", - " # loop through the entries in the dictionary, retrieving both\n", - " # the key and the value\n", - " print('key=', k, 'value=', v)\n", - "```\n", - "\n", - "
\n", - "Inline Exercise:\n", - "The areas listed above for the two states are in square kilometers. Modify the loop below to create a new dictionary that contains the areas in square miles. Print the new dictionary to verify the correct behavior. Note that 1 kilometer is equal to 0.62137 miles.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "areas_mi = dict()\n", - "for state_name, area in areas.items():\n", - " # Todo: convert the area to sq. mi and assign to the areas_mi dict.\n", - "\n", - "print(areas_mi)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "areas_mi = dict()\n", - "for state_name, area in areas.items():\n", - " # Todo: convert the area to sq. mi and assign to the areas_mi dict.\n", - " areas_mi[state_name] = area * (0.62137**2)\n", - "print(areas_mi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Matplotlib for generating figures\n", - "\n", - "We will now briefly explore the use of the `matplotlib` package to generate figures. Before we do this, we will introduce some other helpful tools.\n", - "\n", - "Another effective way to create a list of evenly spaced numbers (e.g., for plotting or other computation) is to use the `linspace` function from the `numpy` package (more information [here](https://numpy.org/devdocs/)). Let's import the `numpy` package and use linspace function to create a list of 15 evenly spaced intervals (that is, 16 points) from 0 to 50 and store this in `xlist`. We will also create the `ylist` that corresponds to the square of the values in `xlist`. Note, we must first import the `numpy` package.\n", - "
\n", - "Inline Exercise:\n", - "Execute the next two cells to see the output.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "xlist = list(np.linspace(0, 50, 16))\n", - "ylist = [x**2 for x in xlist]\n", - "print(xlist)\n", - "print(ylist)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This printed output is not a very effective way to communicate these results. Let's use matplotlib to create a figure of x versus y. A full treatment of the `matplotlib` package is beyond the scope of this tutorial, and further documentation can be found [here](https://matplotlib.org/). For now, we will import the plotting capability and show how to generate a straightforward figure. You can consult the documentation for matplotlib for further details.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the next two cells to see the output.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(xlist, ylist)\n", - "plt.title(\"Embedded x vs y figure\")\n", - "plt.xlabel(\"x\")\n", - "plt.ylabel(\"y\")\n", - "plt.legend([\"data\"])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will use what you have learned so far to create a plot of `sin(x)` for `x` from 0 to $2 \\pi$ with 100 points. Note, you can get the `sin` function and the value for $\\pi$ from the `math` package.\n", - "
\n", - "Inline Exercise:\n", - "Execute the import statement in the next cell, and then complete the missing code in the following cell to create the figure discussed above.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "x = list(np.linspace(0, 2 * math.pi, 100))\n", - "\n", - "# Todo: create the list for y\n", - "\n", - "for xv in x:\n", - " y.append(math.sin(xv))\n", - "\n", - "# Todo: Generate the figure" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "x = list(np.linspace(0, 2 * math.pi, 100))\n", - "\n", - "# Todo: create the list for y\n", - "y = []\n", - "for xv in x:\n", - " y.append(math.sin(xv))\n", - "\n", - "# Todo: Generate the figure\n", - "plt.plot(x, y)\n", - "plt.title(\"Trig: sin function\")\n", - "plt.xlabel(\"x in radians\")\n", - "plt.ylabel(\"sin(x)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Further Information\n", - "\n", - "Further information of the packages mentioned above can be found using the following links:\n", - "\n", - "* [numpy](https://numpy.org/devdocs/)\n", - "* [matplotlib](https://matplotlib.org/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to Pyomo\n", - "\n", - "[Pyomo](https://www.pyomo.org) is an object-oriented, python-based package for equation-oriented (or *algebraic*) modeling and optimization, and the IDAES framework is built upon the Pyomo package. IDAES extends the Pyomo package and defines a class hierarchy for flowsheet based modeling, including definition of property packages, unit models, and flowsheets.\n", - "\n", - "The use of IDAES does not require extensive knowledge about Pyomo, however, it can be beneficial to have some familiarity with the Pyomo package for certain tasks:\n", - "* IDAES models are open, and you can interrogating the underlying Pyomo model to view the variables, constraints, and objective functions defined in the model.\n", - "* You can use Pyomo components to define your objective function or to create additional constraints.\n", - "* Since IDAES models **are** Pyomo models, any advanced meta-algorithms or analysis tools that can be developed and/or used on a Pyomo model can also be used on an IDAES model.\n", - "\n", - "A full tutorial on Pyomo is beyond the scope of this workshop, however in this section we will briefly cover the commands required to specify an objective function or add a constraint to an existing model.\n", - "\n", - "In the next cell, we will create a Pyomo model, and add a couple of variables to that model. When using IDAES, you will define a flowsheet and the addition of variables and model equations will be handled by the IDAES framework.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the following cell to create a Pyomo model with some variables that will be used later.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import ConcreteModel, Var\n", - "\n", - "model = ConcreteModel()\n", - "model.x = Var()\n", - "model.y = Var()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Pyomo syntax to define a scalar objective function is shown below. This defines the objective function as $x^2$. By default Pyomo models (and IDAES models) seek to *minimize* the objective function.\n", - "```python\n", - "model.obj = Objective(expr=model.x**2)\n", - "```\n", - "To maximize a quantity, include the keyword argument `sense=maximize` as in the following:\n", - "```python\n", - "model.obj = Objective(expr=model.y, sense=maximize)\n", - "```\n", - "Note that `Objective` and `maximize` would need to be imported from `pyomo.environ`.\n", - "\n", - "The Pyomo syntax to define a scalar constraint is shown below. This code defines the equality constraint $x^2 + y^2 = 1$.\n", - "```python\n", - "model.on_unit_circle_con = Constraint(expr=model.x**2 + model.y**2 == 1)\n", - "```\n", - "Pyomo also supports inequalities. For example, the code for the inequality constraint $x^2 + y^2 \\le 1$ is given as the following.\n", - "```python\n", - "model.inside_unit_circle_con = Constraint(expr=model.x**2 + model.y**2 <= 1)\n", - "```\n", - "Note that, as before, we would need to include the appropriate imports. In this case `Constraint` would need to be imported from `pyomo.environ`.\n", - "\n", - "Using the syntax shown above, we will now add the objective function: $\\min x^2 + y^2$ and the constraint $x + y = 1$.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Complete the missing code in the cell below. If this is done correctly, after executing the cell, you should see the log output from the solver and the printed solution should show that x, y, and the objective value are all equal to 0.5.\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "from pyomo.environ import Objective, Constraint, value, SolverFactory\n", - "\n", - "# Todo: add the objective function here\n", - "\n", - "\n", - "# Todo: add the constraint here\n", - "\n", - "\n", - "# now solve the problem\n", - "status = SolverFactory(\"ipopt\").solve(model, tee=True) # tee=True shows the solver log\n", - "\n", - "# print the values of x, y, and the objective function at the solution\n", - "# Note that the results are automatically stored in the model variables\n", - "print(\"x =\", value(model.x))\n", - "print(\"y =\", value(model.y))\n", - "print(\"obj =\", value(model.obj))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [ - "solution" - ] - }, - "outputs": [], - "source": [ - "from pyomo.environ import Objective, Constraint, value, SolverFactory\n", - "\n", - "# Todo: add the objective function here\n", - "model.obj = Objective(expr=model.x**2 + model.y**2)\n", - "\n", - "# Todo: add the constraint here\n", - "model.con = Constraint(expr=model.x + model.y == 1)\n", - "\n", - "# now solve the problem\n", - "status = SolverFactory(\"ipopt\").solve(model, tee=True) # tee=True shows the solver log\n", - "\n", - "# print the values of x, y, and the objective function at the solution\n", - "# Note that the results are automatically stored in the model variables\n", - "print(\"x =\", value(model.x))\n", - "print(\"y =\", value(model.y))\n", - "print(\"obj =\", value(model.obj))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that the code above also imported the `value` function. This is a Pyomo function that should be used to retrieve the value of variables in Pyomo (or IDAES) models. Note that you can display the complete list of all variables, objectives, and constraints (with their expressions) using `model.pprint()`. The `display` method is similar to the `pprint` method except that is shows the *values* of the constraints and objectives instead of the underlying expressions. The `pprint` and `display` methods can also be used on individual components.\n", - "\n", - "
\n", - "Inline Exercise:\n", - "Execute the lines of code below to see the output from pprint and display for a Pyomo model.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Check the solution\n", - "\n", - "assert value(model.obj) == 0.5\n", - "assert value(model.x) == 0.5\n", - "assert value(model.y) == 0.5" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"*** Output from model.pprint():\")\n", - "model.pprint()\n", - "\n", - "print()\n", - "print(\"*** Output from model.display():\")\n", - "model.display()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.11" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to IDAES (short)\n", + "Author: Jaffer Ghouse, Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01 \n", + "\n", + "The fundamentals of working with the IDAES process modeling toolset, and how these tools can be applied for optimization applications.\n", + "\n", + "This material was originally presented in an IDAES workshop." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "run \"notebook_test_script.py\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.notebook_checks import run_checks\n", + "\n", + "assert run_checks() == 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Welcome and thank you for taking the time to attend today's workshop. Today we will introduce you to the fundamentals of working with the IDAES process modeling toolset, and we will demonstrate how these tools can be applied for optimization applications.\n", + "\n", + "Today's workshop will be conducted using Jupyter Notebooks which provide an online, interactive Python environment for you to use (without the need for installing anything).\n", + "\n", + "Before we get started on some actual examples, let's make sure that everything is working correctly. The cell below contains a command to run a simple test script that will test that everything we will need for today is working properly.\n", + "\n", + "You can execute a cell by pressing `Shift+Enter`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything worked properly, you should see a message saying `All good!` and a summary of all the checks that were run. If you don't see this, please contact someone for assistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline of Workshop\n", + "\n", + "Today's workshop is divided into four modules which will take you through the steps of setting up a flowsheet within the IDAES framework.\n", + "\n", + "Welcome Module (this one):\n", + "\n", + "* Introduction to Jupyter notebooks and Python\n", + "* Introduction to Pyomo\n", + "\n", + "Module 1 will cover:\n", + "\n", + "* how to import models from the core IDAES model library,\n", + "* how to create a model for a single unit operation,\n", + "* how to define feed and operating conditions,\n", + "* how to initialize and solve a single unit model,\n", + "* some ways we can manipulate the model and examine the results.\n", + "\n", + "Module 2 will demonstrate:\n", + "\n", + "* how to combine unit models together to form flowsheets,\n", + "* tools to initialize and solve flowsheets with recycle loops,\n", + "* how to optimize process operating conditions to meet product specifications.\n", + "\n", + "Module 3 will demonstrate:\n", + "\n", + "* how to build new unit models using the IDAES tools,\n", + "* how to include new unit models into flowsheets.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Jupyter Notebooks and Python\n", + "\n", + "In this short notebook, we will briefly describe the uses of Jupyter notebooks like this one, and provide you with the necessary background in Python for this workshop. We will cover `if` statements, looping, array-like containers called lists and dictionaries, as well as the use of some external packages for working with data. \n", + "\n", + "There are many additional tutorials online to learn more about the Python syntax. One recommended by the IDAES team is https://www.coursera.org/learn/python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, variables do not need to be declared before they are used. You can simply define a new variable using `x = 5`.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "In the cell below, assign a value of 5 to the variable x. Don't forget to type Shift+Enter to execute the line.
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Assign a value of 5 to the variable x" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Assign a value of 5 to the variable x\n", + "x = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can easily see the value of a variable using the built-in `print` function. For example, to print the value of `x` use `print(x)`.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Write the code to print the value of x. Don't forget to hit Shift+Enter to execute the cell.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the value of x" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the value of x\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Inline Exercise:\n", + "Now change the value of the x variable to 8 and execute the cell.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: Assign a value of 8 to the variable x" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Todo: Assign a value of 8 to the variable x\n", + "x = 8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jupyter notebooks and execution order\n", + "\n", + "
\n", + "Note:\n", + "When using Jupyter notebooks, it is very important to know that the cells can be executed out of order (intentionally or not). The state of the environment (e.g., values of variables, imports, etc.) is defined by the execution order.\n", + "
\n", + "\n", + "
\n", + "Inline Exercise:\n", + "To see this concept, select the cell above that contained the print statement and execute the cell again using Shift+Enter.\n", + "
\n", + "\n", + "You should see that the value `8` is now printed. This may seem problematic if you are used to programming in environments where the state is linked to the order of the commands as *written*, not as *executed*.\n", + "\n", + "**Again, notice that the state of the environment is determined by the execution order.**\n", + "\n", + "Note also that the square brackets to the left of the cell show the order that cells were executed. If you scroll to the top, you should see that the code cells show an execution order of `[1]`, `[2]`, `[5]`, and `[4]`, indicating the actual execution order.\n", + "\n", + "There are some useful menu commands at the top of the Jupyter notebook to help with these problems and make sure you retain the execution order as expected.\n", + "\n", + "Some important commands to remember:\n", + "* You can clear the current state with the menu item `Kernel | Restart & Clear Output`\n", + "* It is often useful to clear the state using the menu command just described, and then execute all the lines **above the currently selected cell** using `Cell | Run All Above`.\n", + "* You can clear all the state and re-run the entire notebook using `Kernel | Restart & Run All`.\n", + "\n", + "To show the use of these commands, complete the following.\n", + "
\n", + "Inline Exercise:\n", + "
    \n", + "
  • Clear the current state (using Kernel | Restart & Clear Output). You should notice that the square brackets that listed the execution order are all now empty.
    • \n", + "
  • Select the cell immediately below this text\n", + "
  • Re-run all the code up to this point (Cell | Run All Above). You should now see that the square brackets indicate the expected execution order.
    • \n", + "
  • Print the value of x again using the print function. You should see the value 8 printed, while the earlier cell printing x shows the value of 5 as expected.
    • \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python `if` statements\n", + "\n", + "In the code below, we show an example of an `if` statement in Python.\n", + "\n", + "```python\n", + "temp = 325\n", + "# some other code\n", + "if temp > 320:\n", + " print('temperature is too high')\n", + "elif x < 290:\n", + " print('temperature is too low')\n", + "else:\n", + " print('temperature is just right')\n", + "```\n", + "\n", + "
\n", + "Note:\n", + "You will notice that there are no braces to separate blocks in the if-else tree. In Python, indentation is used to delineate blocks of code throughout Python (e.g., if statements, for loops, functions, etc.). The indentation in the above example is not only to improve legibility of the code. It is necessary for the code to run correctly. As well, the number of spaces required to define the indentation is arbitrary, but it must be consistent throughout the code. For example, we could use 3 spaces (instead of the 4 used in the example above, but we could not use 3 for one of the blocks and 4 for another).\n", + "
\n", + "\n", + "Using the syntax above for the `if` statement, write the following code.\n", + "
\n", + "Inline Exercise:\n", + "
    \n", + "
  • set the value of the variable T_degC to 20
    • \n", + "
  • convert this from degrees Celsius to degrees Fahrenheit (use variable name T_degF)
    • \n", + "
  • write an `if` statement that prints a message if the degrees Fahrenheit are below 70
    • \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "T_degC = 20\n", + "# some other code\n", + "T_degF = (T_degC * 9.0 / 5.0) + 32.0\n", + "\n", + "# Todo: put the if statement here" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "T_degC = 20\n", + "# some other code\n", + "T_degF = (T_degC * 9.0 / 5.0) + 32.0\n", + "\n", + "# Todo: put the if statement here\n", + "if T_degF < 70:\n", + " print(\"The room is too cold.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python list containers\n", + "\n", + "Now we will illustrate the use of lists in Python. Lists are similar to vectors or arrays in other languages. A list in Python is indexed by integers from 0 up to the length of the array minus 1. The list can contain standard types (int, float, string), or other objects.\n", + "\n", + "In the next inline exercise, we will create a list that contains the values from 0 to 50 by steps of 5 using a for loop. Note that the python function `range(n)` can be used to iterate from 0 to (n-1) in a for loop. Also note that lists have an `append` method which adds an entry to the end of the list (e.g., if the list `l` currently has 5 elements, then `l.append('temp')` will add the string \"temp\" as the sixth element). Print the new list after the for loop. If this is done correctly, you should see:\n", + "`[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]` printed after the cell.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Complete the code block below to create the desired list and print the result.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Create a list with the values 0 to 50 with steps of 5.\n", + "xlist = list()\n", + "for i in range(11):\n", + " # Todo: use the append method of list to append the correct value\n", + "\n", + "# Todo: print the value of xlist to verify the results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "# Create a list with the values 0 to 50 with steps of 5.\n", + "xlist = list()\n", + "for i in range(11):\n", + " # Todo: use the append method of list to append the correct value\n", + " xlist.append(i * 5)\n", + "\n", + "print(xlist) # Todo: print the value of xlist to verify the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can easily check the length of a list using the python `len(l)` function.\n", + "
\n", + "Inline Exercise:\n", + "Print the length of `xlist`. It should be 11.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# Todo: print the len of the list" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "print(len(xlist)) # Todo: print the len of the list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have a list of values or objects, it is easy to iterate through that list in a for loop. In the next inline exercise, we will create another list, `ylist` where each of the values is equal to the corresponding value in `xlist` squared. That is, $y_i = x_i^2$.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Modify the code below to create ylist as described above. Print the values in ylist to check the result.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "ylist = list()\n", + "\n", + "# Todo: define the for loop to add elements to ylist using the values in xlist\n", + "\n", + "print(ylist)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "ylist = list()\n", + "\n", + "# Todo: define the for loop to add elements to ylist using the values in xlist\n", + "for x in xlist:\n", + " ylist.append(x**2)\n", + "\n", + "print(ylist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python dictionary containers\n", + "\n", + "Another valuable data structure in Python are *dictionaries*. Dictionaries are an associative array; that is, a map from keys to values or objects. The keys can be *almost* anything, including floats, integers, and strings. The code below shows an example of creating a dictionary (here, to store the areas of some of the states).\n", + "
\n", + "Inline Exercise:\n", + "Execute the lines below to see the areas dictionary.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "areas = dict()\n", + "areas[\"South Dakota\"] = 199742\n", + "areas[\"Oklahoma\"] = 181035\n", + "print(areas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries can contain mixed types (i.e., it is valid to add `areas['Texas'] = 'Really big!'`) but this may lead to unpredictable behavior if the different types are unexpected in other parts of the code.\n", + "\n", + "You can loop through dictionaries in different ways. For example,\n", + "```python\n", + "d = {'A': 2, 'B': 4, 'D': 16}\n", + "for k in d.keys():\n", + " # loop through the keys in the dictionary\n", + " # access the value with d[k]\n", + " print('key=', k, 'value=', d[k])\n", + " \n", + "for v in d.values():\n", + " # loop through the values in the dictionary, ignoring the keys\n", + " print('value=', v)\n", + " \n", + "for k,v in d.items():\n", + " # loop through the entries in the dictionary, retrieving both\n", + " # the key and the value\n", + " print('key=', k, 'value=', v)\n", + "```\n", + "\n", + "
\n", + "Inline Exercise:\n", + "The areas listed above for the two states are in square kilometers. Modify the loop below to create a new dictionary that contains the areas in square miles. Print the new dictionary to verify the correct behavior. Note that 1 kilometer is equal to 0.62137 miles.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "areas_mi = dict()\n", + "for state_name, area in areas.items():\n", + " # Todo: convert the area to sq. mi and assign to the areas_mi dict.\n", + "\n", + "print(areas_mi)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "areas_mi = dict()\n", + "for state_name, area in areas.items():\n", + " # Todo: convert the area to sq. mi and assign to the areas_mi dict.\n", + " areas_mi[state_name] = area * (0.62137**2)\n", + "print(areas_mi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matplotlib for generating figures\n", + "\n", + "We will now briefly explore the use of the `matplotlib` package to generate figures. Before we do this, we will introduce some other helpful tools.\n", + "\n", + "Another effective way to create a list of evenly spaced numbers (e.g., for plotting or other computation) is to use the `linspace` function from the `numpy` package (more information [here](https://numpy.org/devdocs/)). Let's import the `numpy` package and use linspace function to create a list of 15 evenly spaced intervals (that is, 16 points) from 0 to 50 and store this in `xlist`. We will also create the `ylist` that corresponds to the square of the values in `xlist`. Note, we must first import the `numpy` package.\n", + "
\n", + "Inline Exercise:\n", + "Execute the next two cells to see the output.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "xlist = list(np.linspace(0, 50, 16))\n", + "ylist = [x**2 for x in xlist]\n", + "print(xlist)\n", + "print(ylist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This printed output is not a very effective way to communicate these results. Let's use matplotlib to create a figure of x versus y. A full treatment of the `matplotlib` package is beyond the scope of this tutorial, and further documentation can be found [here](https://matplotlib.org/). For now, we will import the plotting capability and show how to generate a straightforward figure. You can consult the documentation for matplotlib for further details.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the next two cells to see the output.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(xlist, ylist)\n", + "plt.title(\"Embedded x vs y figure\")\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"y\")\n", + "plt.legend([\"data\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will use what you have learned so far to create a plot of `sin(x)` for `x` from 0 to $2 \\pi$ with 100 points. Note, you can get the `sin` function and the value for $\\pi$ from the `math` package.\n", + "
\n", + "Inline Exercise:\n", + "Execute the import statement in the next cell, and then complete the missing code in the following cell to create the figure discussed above.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "x = list(np.linspace(0, 2 * math.pi, 100))\n", + "\n", + "# Todo: create the list for y\n", + "\n", + "for xv in x:\n", + " y.append(math.sin(xv))\n", + "\n", + "# Todo: Generate the figure" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "x = list(np.linspace(0, 2 * math.pi, 100))\n", + "\n", + "# Todo: create the list for y\n", + "y = []\n", + "for xv in x:\n", + " y.append(math.sin(xv))\n", + "\n", + "# Todo: Generate the figure\n", + "plt.plot(x, y)\n", + "plt.title(\"Trig: sin function\")\n", + "plt.xlabel(\"x in radians\")\n", + "plt.ylabel(\"sin(x)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Further Information\n", + "\n", + "Further information of the packages mentioned above can be found using the following links:\n", + "\n", + "* [numpy](https://numpy.org/devdocs/)\n", + "* [matplotlib](https://matplotlib.org/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Pyomo\n", + "\n", + "[Pyomo](https://www.pyomo.org) is an object-oriented, python-based package for equation-oriented (or *algebraic*) modeling and optimization, and the IDAES framework is built upon the Pyomo package. IDAES extends the Pyomo package and defines a class hierarchy for flowsheet based modeling, including definition of property packages, unit models, and flowsheets.\n", + "\n", + "The use of IDAES does not require extensive knowledge about Pyomo, however, it can be beneficial to have some familiarity with the Pyomo package for certain tasks:\n", + "* IDAES models are open, and you can interrogating the underlying Pyomo model to view the variables, constraints, and objective functions defined in the model.\n", + "* You can use Pyomo components to define your objective function or to create additional constraints.\n", + "* Since IDAES models **are** Pyomo models, any advanced meta-algorithms or analysis tools that can be developed and/or used on a Pyomo model can also be used on an IDAES model.\n", + "\n", + "A full tutorial on Pyomo is beyond the scope of this workshop, however in this section we will briefly cover the commands required to specify an objective function or add a constraint to an existing model.\n", + "\n", + "In the next cell, we will create a Pyomo model, and add a couple of variables to that model. When using IDAES, you will define a flowsheet and the addition of variables and model equations will be handled by the IDAES framework.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the following cell to create a Pyomo model with some variables that will be used later.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import ConcreteModel, Var\n", + "\n", + "model = ConcreteModel()\n", + "model.x = Var()\n", + "model.y = Var()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Pyomo syntax to define a scalar objective function is shown below. This defines the objective function as $x^2$. By default Pyomo models (and IDAES models) seek to *minimize* the objective function.\n", + "```python\n", + "model.obj = Objective(expr=model.x**2)\n", + "```\n", + "To maximize a quantity, include the keyword argument `sense=maximize` as in the following:\n", + "```python\n", + "model.obj = Objective(expr=model.y, sense=maximize)\n", + "```\n", + "Note that `Objective` and `maximize` would need to be imported from `pyomo.environ`.\n", + "\n", + "The Pyomo syntax to define a scalar constraint is shown below. This code defines the equality constraint $x^2 + y^2 = 1$.\n", + "```python\n", + "model.on_unit_circle_con = Constraint(expr=model.x**2 + model.y**2 == 1)\n", + "```\n", + "Pyomo also supports inequalities. For example, the code for the inequality constraint $x^2 + y^2 \\le 1$ is given as the following.\n", + "```python\n", + "model.inside_unit_circle_con = Constraint(expr=model.x**2 + model.y**2 <= 1)\n", + "```\n", + "Note that, as before, we would need to include the appropriate imports. In this case `Constraint` would need to be imported from `pyomo.environ`.\n", + "\n", + "Using the syntax shown above, we will now add the objective function: $\\min x^2 + y^2$ and the constraint $x + y = 1$.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Complete the missing code in the cell below. If this is done correctly, after executing the cell, you should see the log output from the solver and the printed solution should show that x, y, and the objective value are all equal to 0.5.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "from pyomo.environ import Objective, Constraint, value, SolverFactory\n", + "\n", + "# Todo: add the objective function here\n", + "\n", + "\n", + "# Todo: add the constraint here\n", + "\n", + "\n", + "# now solve the problem\n", + "status = SolverFactory(\"ipopt\").solve(model, tee=True) # tee=True shows the solver log\n", + "\n", + "# print the values of x, y, and the objective function at the solution\n", + "# Note that the results are automatically stored in the model variables\n", + "print(\"x =\", value(model.x))\n", + "print(\"y =\", value(model.y))\n", + "print(\"obj =\", value(model.obj))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "solution" + ] + }, + "outputs": [], + "source": [ + "from pyomo.environ import Objective, Constraint, value, SolverFactory\n", + "\n", + "# Todo: add the objective function here\n", + "model.obj = Objective(expr=model.x**2 + model.y**2)\n", + "\n", + "# Todo: add the constraint here\n", + "model.con = Constraint(expr=model.x + model.y == 1)\n", + "\n", + "# now solve the problem\n", + "status = SolverFactory(\"ipopt\").solve(model, tee=True) # tee=True shows the solver log\n", + "\n", + "# print the values of x, y, and the objective function at the solution\n", + "# Note that the results are automatically stored in the model variables\n", + "print(\"x =\", value(model.x))\n", + "print(\"y =\", value(model.y))\n", + "print(\"obj =\", value(model.obj))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the code above also imported the `value` function. This is a Pyomo function that should be used to retrieve the value of variables in Pyomo (or IDAES) models. Note that you can display the complete list of all variables, objectives, and constraints (with their expressions) using `model.pprint()`. The `display` method is similar to the `pprint` method except that is shows the *values* of the constraints and objectives instead of the underlying expressions. The `pprint` and `display` methods can also be used on individual components.\n", + "\n", + "
\n", + "Inline Exercise:\n", + "Execute the lines of code below to see the output from pprint and display for a Pyomo model.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Check the solution\n", + "\n", + "assert value(model.obj) == 0.5\n", + "assert value(model.x) == 0.5\n", + "assert value(model.y) == 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"*** Output from model.pprint():\")\n", + "model.pprint()\n", + "\n", + "print()\n", + "print(\"*** Output from model.display():\")\n", + "model.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/idaes_examples/notebooks/docs/tut/introduction_short_doc.ipynb b/idaes_examples/notebooks/docs/tut/introduction_short_doc.ipynb index 98b8ece2..f234c964 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_short_doc.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_short_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -598,8 +599,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.0, 3.3333333333333335, 6.666666666666667, 10.0, 13.333333333333334, 16.666666666666668, 20.0, 23.333333333333336, 26.666666666666668, 30.0, 33.333333333333336, 36.66666666666667, 40.0, 43.333333333333336, 46.66666666666667, 50.0]\n", - "[0.0, 11.111111111111112, 44.44444444444445, 100.0, 177.7777777777778, 277.7777777777778, 400.0, 544.4444444444446, 711.1111111111112, 900.0, 1111.1111111111113, 1344.4444444444448, 1600.0, 1877.777777777778, 2177.7777777777783, 2500.0]\n" + "[np.float64(0.0), np.float64(3.3333333333333335), np.float64(6.666666666666667), np.float64(10.0), np.float64(13.333333333333334), np.float64(16.666666666666668), np.float64(20.0), np.float64(23.333333333333336), np.float64(26.666666666666668), np.float64(30.0), np.float64(33.333333333333336), np.float64(36.66666666666667), np.float64(40.0), np.float64(43.333333333333336), np.float64(46.66666666666667), np.float64(50.0)]\n", + "[np.float64(0.0), np.float64(11.111111111111112), np.float64(44.44444444444445), np.float64(100.0), np.float64(177.7777777777778), np.float64(277.7777777777778), np.float64(400.0), np.float64(544.4444444444446), np.float64(711.1111111111112), np.float64(900.0), np.float64(1111.1111111111113), np.float64(1344.4444444444448), np.float64(1600.0), np.float64(1877.777777777778), np.float64(2177.7777777777783), np.float64(2500.0)]\n" ] } ], @@ -638,16 +639,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\tut\\introduction_short_doc_36_0.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -870,7 +863,7 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n", @@ -1018,7 +1011,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/tut/introduction_short_exercise.ipynb b/idaes_examples/notebooks/docs/tut/introduction_short_exercise.ipynb index ae8806cf..972f6e44 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_short_exercise.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_short_exercise.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_short_solution.ipynb b/idaes_examples/notebooks/docs/tut/introduction_short_solution.ipynb index 4ee4acc7..144a5c4b 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_short_solution.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_short_solution.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_short_test.ipynb b/idaes_examples/notebooks/docs/tut/introduction_short_test.ipynb index 37262790..ad5bb664 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_short_test.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_short_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_short_usr.ipynb b/idaes_examples/notebooks/docs/tut/introduction_short_usr.ipynb index 4ee4acc7..144a5c4b 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_short_usr.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_short_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_solution.ipynb b/idaes_examples/notebooks/docs/tut/introduction_solution.ipynb index e88a5a6a..278bd54b 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_solution.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_solution.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_test.ipynb b/idaes_examples/notebooks/docs/tut/introduction_test.ipynb index 632f8150..895f5053 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_test.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/introduction_usr.ipynb b/idaes_examples/notebooks/docs/tut/introduction_usr.ipynb index e88a5a6a..278bd54b 100644 --- a/idaes_examples/notebooks/docs/tut/introduction_usr.ipynb +++ b/idaes_examples/notebooks/docs/tut/introduction_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/tut/notebook_test_script.py b/idaes_examples/notebooks/docs/tut/notebook_test_script.py index 05585fe7..f59bf587 100644 --- a/idaes_examples/notebooks/docs/tut/notebook_test_script.py +++ b/idaes_examples/notebooks/docs/tut/notebook_test_script.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Script to test functionality of key components for use in workshops. diff --git a/idaes_examples/notebooks/docs/tut/sin_data.csv b/idaes_examples/notebooks/docs/tut/sin_data.csv index 9a49e32e..630b25ae 100644 --- a/idaes_examples/notebooks/docs/tut/sin_data.csv +++ b/idaes_examples/notebooks/docs/tut/sin_data.csv @@ -32,13 +32,13 @@ 30,1.9039955476301778,0.9450008187146685 31,1.967462065884517,0.9223542941045814 32,2.0309285841388562,0.8959937742913359 -33,2.0943951023931957,0.8660254037844386 +33,2.0943951023931957,0.8660254037844385 34,2.1578616206475347,0.8325698546347714 35,2.221328138901874,0.795761840530832 36,2.284794657156213,0.7557495743542583 37,2.3482611754105527,0.7126941713788627 -38,2.4117276936648917,0.6667690005162916 -39,2.475194211919231,0.6181589862206052 +38,2.4117276936648917,0.6667690005162917 +39,2.475194211919231,0.6181589862206051 40,2.53866073017357,0.5670598638627709 41,2.6021272484279097,0.5136773915734063 42,2.6655937666822487,0.4582265217274105 @@ -85,7 +85,7 @@ 83,5.267721015110158,-0.8497254299495144 84,5.331187533364497,-0.8145759520503358 85,5.394654051618837,-0.7761464642917566 -86,5.458120569873176,-0.7345917086575331 +86,5.458120569873176,-0.7345917086575332 87,5.521587088127515,-0.690079011482112 88,5.585053606381854,-0.6427876096865396 89,5.648520124636194,-0.5929079290546402 diff --git a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial.ipynb b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial.ipynb index 78c7ac2d..07cd8161 100644 --- a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial.ipynb +++ b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial.ipynb @@ -1,498 +1,499 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "18d74e8e", - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "id": "e0e52d92", - "metadata": {}, - "source": [ - "# Flowsheet Visualizer Tutorial\n", - "\n", - "Author: Dan Gunter \n", - "Maintainer: Dan Gunter \n", - "Updated: 2023-06-01 \n", - "\n", - "The IDAES Flowsheet Visualizer provides a web-based UI for visualization and inspection of an existing IDAES model.\n", - "## Outline\n", - "\n", - "- Introduction\n", - "- Example flowsheet\n", - "- Running the Flowsheet Visualizer\n", - "- Running from a script\n", - "- Further reading" - ] - }, - { - "cell_type": "markdown", - "id": "84316245", - "metadata": {}, - "source": [ - "## Introduction\n", - "The IDAES Flowsheet Visualizer (FV) is a Python tool that provides a web-based visualization of any existing IDAES model or flowsheet. The visualization shows a diagram of the\n", - "flowsheet as well as a stream table. You can interact with the diagram and export\n", - "it as an image for inclusion in presentations or publications.\n", - "\n", - "This tutorial will show the basic steps of running the FV on an example\n", - "flowsheet, interacting with the resulting GUI, saving your work, and exporting\n", - "the diagram as an image. It will also show how the Visualizer can be updated\n", - "to reflect changes in the model components and/or variable values. The tutorial\n", - "will also show how to run the Visualizer from a Python script." - ] - }, - { - "cell_type": "markdown", - "id": "be083d68", - "metadata": {}, - "source": [ - "## Example flowsheet\n", - "This initial section creates an example flowsheet.\n", - "\n", - "### Setup\n", - "Module imports and any additional housekeeping needed\n", - "to initialize the code." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c740809a", - "metadata": {}, - "outputs": [], - "source": [ - "import idaes_examples.mod.tut.visualizer_tutorial as vistut\n", - "\n", - "vistut.quiet() # turn off default logging and most warnings\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from IPython.display import Markdown" - ] - }, - { - "cell_type": "markdown", - "id": "52ee0d6c", - "metadata": {}, - "source": [ - "### Create the flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "414d3072", - "metadata": {}, - "outputs": [], - "source": [ - "# use the pre-defined function to create the flowsheet\n", - "model = vistut.create_model()\n", - "\n", - "# description of the flowsheet we created\n", - "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", - "\n", - "vistut.quiet()\n", - "\n", - "# initialize the flowsheet as a square problem (dof=0)\n", - "vistut.initialize_model(model)\n", - "\n", - "# verify that there are zero degrees of freedom\n", - "print(f\"DOF = {degrees_of_freedom(model)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "8e88c1e9", - "metadata": {}, - "source": [ - "## Running the Flowsheet Visualizer\n", - "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", - "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", - "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", - "\n", - "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", - "\n", - "
\n", - "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f05ab3ef", - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "if os.path.exists(\"Hydrodealkylation.json\"):\n", - " os.remove(\"Hydrodealkylation.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "91758e92", - "metadata": { - "tags": [ - "noauto" - ] - }, - "outputs": [], - "source": [ - "model.fs.visualize(\"Hydrodealkylation\")" - ] - }, - { - "cell_type": "markdown", - "id": "1174e60c", - "metadata": {}, - "source": [ - "### Optional arguments\n", - "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", - "\n", - " * name: Name of flowsheet to display as the title of the visualization\n", - " * load_from_saved: If True load from saved file if any. Otherwise create\n", - " a new file or overwrite it (depending on 'overwrite' flag).\n", - " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", - " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", - " between the name and the extension). If the value given is the boolean 'False', then nothing\n", - " will be saved. The boolean 'True' value is treated the same as unspecified.\n", - " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", - " be used as the directory to save the default or given file. The current working directory is\n", - " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", - " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", - " in the graph for it to save the model. Default is 5 seconds\n", - " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", - " numbered file.\n", - " * browser: If true, open a browser\n", - " * port: Start listening on this port. If not given, find an open port.\n", - " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", - " See the `idaes.logger` module for details\n", - " * quiet: If True, suppress printing any messages to standard output (console)\n", - " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", - " invoking this function at the end of a script." - ] - }, - { - "cell_type": "markdown", - "id": "bd82cda1", - "metadata": {}, - "source": [ - "## Interacting with the visualizer\n", - "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", - "The UI should initially look something like the screenshot below:\n", - "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", - "\n", - "
\n", - " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9f186a8a", - "metadata": {}, - "source": [ - "### View controls\n", - "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", - "\n", - "| Control | Description | Illustration |\n", - "|:----|:---------------------|:----:|\n", - "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", - "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "59a33cbd", - "metadata": {}, - "source": [ - "### Rearranging the diagram\n", - "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", - "\n", - "|   |   |\n", - "|:--:|:--:|\n", - "| ![](fvr1.png)   |   ![](fvr2.png) |\n", - "| ![](fvr3.png)   |   ![](fvr4.png) |\n" - ] - }, - { - "cell_type": "markdown", - "id": "8ab50bbc", - "metadata": {}, - "source": [ - "### Stream table\n", - "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", - "\n", - "### Stream table \"brushing\"\n", - "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", - "#### Controls\n", - "\n", - "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", - "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", - "\n", - "![Illustration of stream table brushing](fvb1.png)\n", - " \n", - "#### Example\n", - "Stream table brushing is useful for answering questions like:\n", - "> How much benzene are we losing in the F101 vapor outlet stream?\n", - "\n", - "To answer this question, we will use some interactive elements of the stream table.\n", - "\n", - "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", - "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", - "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "297779ac", - "metadata": {}, - "source": [ - "### Showing and hiding streams\n", - "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", - "\n", - "* Click on the \"Hide Fields\" menu and select which fields to hide\n", - "* The mark will toggle between a check (shown) and open circle (hidden)\n", - "\n", - "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", - "\n", - "![Illustration of stream table field hiding](fvst1.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3b0f16ea", - "metadata": {}, - "source": [ - "### Saving and loading\n", - "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", - "\n", - "#### File name\n", - "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", - "\n", - "You can select a different filename and location when you start the visualization, e.g.\n", - "\n", - " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", - "\n", - "#### Reloading\n", - "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." - ] - }, - { - "cell_type": "markdown", - "id": "db0e5eaf", - "metadata": {}, - "source": [ - "### Exporting\n", - "\n", - "#### Exporting the diagram as an image\n", - "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", - "\n", - "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", - "#### Exporting the stream table as CSV\n", - "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." - ] - }, - { - "cell_type": "markdown", - "id": "68477e99", - "metadata": {}, - "source": [ - "### Updating when the flowsheet changes\n", - "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", - "\n", - "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", - "\n", - "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8d7976f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Add a second flash unit\n", - "from idaes.models.unit_models import Flash\n", - "from pyomo.network import Arc\n", - "from pyomo.environ import Expression, TransformationFactory\n", - "\n", - "m = model # alias\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")\n", - "# connect to 1st flash unit\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)\n", - "# update expressions for purity and cost\n", - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "# fix unit output and pressure drop\n", - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)\n", - "\n", - "# expand arcs\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "# re-initialize\n", - "_ = vistut.initialize_model(m)" - ] - }, - { - "cell_type": "markdown", - "id": "5726059b", - "metadata": {}, - "source": [ - "
Since the FV is connected to the current state of the model in memory, simply hitting \"Refresh\" in the FV window will show the new flash unit in the diagram, and the new stream (liquid) in the stream table. We can then interactively rearrange the unit to be in the position we want in the diagram.
" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "42245268", - "metadata": {}, - "outputs": [], - "source": [ - "# v model.fs.visualize(\"Hydrodealkylation-new\")" - ] - }, - { - "cell_type": "markdown", - "id": "e75bfc03", - "metadata": {}, - "source": [ - "### Showing new values\n", - "The previous step showed how a new unit in the flowsheet will be automatically added to the diagram. Similarly, if the values in the flowsheet change these will be reflected in the stream table. Below, we solve the initialized flowsheet.\n", - "To make comparison a little easier, we will open a second UI window with the new values (the old values will not be updated unless we decide to hit the \"Refresh\" button)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "81633474", - "metadata": { - "tags": [ - "noauto" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "from pyomo.environ import SolverFactory\n", - "\n", - "solver = SolverFactory(\"ipopt\")\n", - "solver.options = {\"tol\": 1e-6, \"max_iter\": 5000}\n", - "\n", - "# Solve the model\n", - "results = solver.solve(model, tee=False)\n", - "\n", - "# Open a second window\n", - "model.fs.visualize(\"HDA_solved\")" - ] - }, - { - "cell_type": "markdown", - "id": "350e8705", - "metadata": {}, - "source": [ - "When we look at the stream table, we can see the values in the stream between the first and second flash unit changing.\n" - ] - }, - { - "cell_type": "markdown", - "id": "ec68d7ef", - "metadata": {}, - "source": [ - "## Running from a script\n", - "Finally, although all examples have been shown in a Jupyter Notebook, there is nothing preventing the use of the FV from within a plain Python script (or module).\n", - "\n", - "For example, the code to run this same tutorial as a Python script is also in a module.\n", - "If you have installed the IDAES examples, then you can do the following to import and run the module:\n", - "```\n", - "from idaes_examples.mod.tut import visualizer_tutorial\n", - "visualizer_tutorial.main()\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "beef5b6f", - "metadata": {}, - "source": [ - "# Further reading\n", - "\n", - "Reference documentation for the FV is available in the IDAES main documentation, online at https://idaes-pse.readthedocs.io/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "930eb620", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "18d74e8e", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 5 + { + "cell_type": "markdown", + "id": "e0e52d92", + "metadata": {}, + "source": [ + "# Flowsheet Visualizer Tutorial\n", + "\n", + "Author: Dan Gunter \n", + "Maintainer: Dan Gunter \n", + "Updated: 2023-06-01 \n", + "\n", + "The IDAES Flowsheet Visualizer provides a web-based UI for visualization and inspection of an existing IDAES model.\n", + "## Outline\n", + "\n", + "- Introduction\n", + "- Example flowsheet\n", + "- Running the Flowsheet Visualizer\n", + "- Running from a script\n", + "- Further reading" + ] + }, + { + "cell_type": "markdown", + "id": "84316245", + "metadata": {}, + "source": [ + "## Introduction\n", + "The IDAES Flowsheet Visualizer (FV) is a Python tool that provides a web-based visualization of any existing IDAES model or flowsheet. The visualization shows a diagram of the\n", + "flowsheet as well as a stream table. You can interact with the diagram and export\n", + "it as an image for inclusion in presentations or publications.\n", + "\n", + "This tutorial will show the basic steps of running the FV on an example\n", + "flowsheet, interacting with the resulting GUI, saving your work, and exporting\n", + "the diagram as an image. It will also show how the Visualizer can be updated\n", + "to reflect changes in the model components and/or variable values. The tutorial\n", + "will also show how to run the Visualizer from a Python script." + ] + }, + { + "cell_type": "markdown", + "id": "be083d68", + "metadata": {}, + "source": [ + "## Example flowsheet\n", + "This initial section creates an example flowsheet.\n", + "\n", + "### Setup\n", + "Module imports and any additional housekeeping needed\n", + "to initialize the code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c740809a", + "metadata": {}, + "outputs": [], + "source": [ + "import idaes_examples.mod.tut.visualizer_tutorial as vistut\n", + "\n", + "vistut.quiet() # turn off default logging and most warnings\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from IPython.display import Markdown" + ] + }, + { + "cell_type": "markdown", + "id": "52ee0d6c", + "metadata": {}, + "source": [ + "### Create the flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "414d3072", + "metadata": {}, + "outputs": [], + "source": [ + "# use the pre-defined function to create the flowsheet\n", + "model = vistut.create_model()\n", + "\n", + "# description of the flowsheet we created\n", + "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", + "\n", + "vistut.quiet()\n", + "\n", + "# initialize the flowsheet as a square problem (dof=0)\n", + "vistut.initialize_model(model)\n", + "\n", + "# verify that there are zero degrees of freedom\n", + "print(f\"DOF = {degrees_of_freedom(model)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8e88c1e9", + "metadata": {}, + "source": [ + "## Running the Flowsheet Visualizer\n", + "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", + "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", + "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", + "\n", + "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", + "\n", + "
\n", + "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f05ab3ef", + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "if os.path.exists(\"Hydrodealkylation.json\"):\n", + " os.remove(\"Hydrodealkylation.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "91758e92", + "metadata": { + "tags": [ + "noauto" + ] + }, + "outputs": [], + "source": [ + "model.fs.visualize(\"Hydrodealkylation\")" + ] + }, + { + "cell_type": "markdown", + "id": "1174e60c", + "metadata": {}, + "source": [ + "### Optional arguments\n", + "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", + "\n", + " * name: Name of flowsheet to display as the title of the visualization\n", + " * load_from_saved: If True load from saved file if any. Otherwise create\n", + " a new file or overwrite it (depending on 'overwrite' flag).\n", + " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", + " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", + " between the name and the extension). If the value given is the boolean 'False', then nothing\n", + " will be saved. The boolean 'True' value is treated the same as unspecified.\n", + " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", + " be used as the directory to save the default or given file. The current working directory is\n", + " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", + " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", + " in the graph for it to save the model. Default is 5 seconds\n", + " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", + " numbered file.\n", + " * browser: If true, open a browser\n", + " * port: Start listening on this port. If not given, find an open port.\n", + " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", + " See the `idaes.logger` module for details\n", + " * quiet: If True, suppress printing any messages to standard output (console)\n", + " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", + " invoking this function at the end of a script." + ] + }, + { + "cell_type": "markdown", + "id": "bd82cda1", + "metadata": {}, + "source": [ + "## Interacting with the visualizer\n", + "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", + "The UI should initially look something like the screenshot below:\n", + "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", + "\n", + "
\n", + " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9f186a8a", + "metadata": {}, + "source": [ + "### View controls\n", + "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", + "\n", + "| Control | Description | Illustration |\n", + "|:----|:---------------------|:----:|\n", + "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", + "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "59a33cbd", + "metadata": {}, + "source": [ + "### Rearranging the diagram\n", + "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", + "\n", + "|   |   |\n", + "|:--:|:--:|\n", + "| ![](fvr1.png)   |   ![](fvr2.png) |\n", + "| ![](fvr3.png)   |   ![](fvr4.png) |\n" + ] + }, + { + "cell_type": "markdown", + "id": "8ab50bbc", + "metadata": {}, + "source": [ + "### Stream table\n", + "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", + "\n", + "### Stream table \"brushing\"\n", + "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", + "#### Controls\n", + "\n", + "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", + "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", + "\n", + "![Illustration of stream table brushing](fvb1.png)\n", + " \n", + "#### Example\n", + "Stream table brushing is useful for answering questions like:\n", + "> How much benzene are we losing in the F101 vapor outlet stream?\n", + "\n", + "To answer this question, we will use some interactive elements of the stream table.\n", + "\n", + "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", + "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", + "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "297779ac", + "metadata": {}, + "source": [ + "### Showing and hiding streams\n", + "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", + "\n", + "* Click on the \"Hide Fields\" menu and select which fields to hide\n", + "* The mark will toggle between a check (shown) and open circle (hidden)\n", + "\n", + "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", + "\n", + "![Illustration of stream table field hiding](fvst1.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3b0f16ea", + "metadata": {}, + "source": [ + "### Saving and loading\n", + "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", + "\n", + "#### File name\n", + "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", + "\n", + "You can select a different filename and location when you start the visualization, e.g.\n", + "\n", + " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", + "\n", + "#### Reloading\n", + "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." + ] + }, + { + "cell_type": "markdown", + "id": "db0e5eaf", + "metadata": {}, + "source": [ + "### Exporting\n", + "\n", + "#### Exporting the diagram as an image\n", + "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", + "\n", + "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", + "#### Exporting the stream table as CSV\n", + "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." + ] + }, + { + "cell_type": "markdown", + "id": "68477e99", + "metadata": {}, + "source": [ + "### Updating when the flowsheet changes\n", + "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", + "\n", + "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", + "\n", + "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8d7976f9", + "metadata": {}, + "outputs": [], + "source": [ + "# Add a second flash unit\n", + "from idaes.models.unit_models import Flash\n", + "from pyomo.network import Arc\n", + "from pyomo.environ import Expression, TransformationFactory\n", + "\n", + "m = model # alias\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")\n", + "# connect to 1st flash unit\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)\n", + "# update expressions for purity and cost\n", + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "# fix unit output and pressure drop\n", + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)\n", + "\n", + "# expand arcs\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "# re-initialize\n", + "_ = vistut.initialize_model(m)" + ] + }, + { + "cell_type": "markdown", + "id": "5726059b", + "metadata": {}, + "source": [ + "
Since the FV is connected to the current state of the model in memory, simply hitting \"Refresh\" in the FV window will show the new flash unit in the diagram, and the new stream (liquid) in the stream table. We can then interactively rearrange the unit to be in the position we want in the diagram.
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "42245268", + "metadata": {}, + "outputs": [], + "source": [ + "# v model.fs.visualize(\"Hydrodealkylation-new\")" + ] + }, + { + "cell_type": "markdown", + "id": "e75bfc03", + "metadata": {}, + "source": [ + "### Showing new values\n", + "The previous step showed how a new unit in the flowsheet will be automatically added to the diagram. Similarly, if the values in the flowsheet change these will be reflected in the stream table. Below, we solve the initialized flowsheet.\n", + "To make comparison a little easier, we will open a second UI window with the new values (the old values will not be updated unless we decide to hit the \"Refresh\" button)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "81633474", + "metadata": { + "tags": [ + "noauto" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "from pyomo.environ import SolverFactory\n", + "\n", + "solver = SolverFactory(\"ipopt\")\n", + "solver.options = {\"tol\": 1e-6, \"max_iter\": 5000}\n", + "\n", + "# Solve the model\n", + "results = solver.solve(model, tee=False)\n", + "\n", + "# Open a second window\n", + "model.fs.visualize(\"HDA_solved\")" + ] + }, + { + "cell_type": "markdown", + "id": "350e8705", + "metadata": {}, + "source": [ + "When we look at the stream table, we can see the values in the stream between the first and second flash unit changing.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec68d7ef", + "metadata": {}, + "source": [ + "## Running from a script\n", + "Finally, although all examples have been shown in a Jupyter Notebook, there is nothing preventing the use of the FV from within a plain Python script (or module).\n", + "\n", + "For example, the code to run this same tutorial as a Python script is also in a module.\n", + "If you have installed the IDAES examples, then you can do the following to import and run the module:\n", + "```\n", + "from idaes_examples.mod.tut import visualizer_tutorial\n", + "visualizer_tutorial.main()\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "beef5b6f", + "metadata": {}, + "source": [ + "# Further reading\n", + "\n", + "Reference documentation for the FV is available in the IDAES main documentation, online at https://idaes-pse.readthedocs.io/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "930eb620", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_doc.ipynb b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_doc.ipynb index 635edcca..7ac4500a 100644 --- a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_doc.ipynb +++ b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -144,127815 +145,1934 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 1.94e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 1.9441358745098113e-09 1.9441358745098111e-08\n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 1.9441358745098113e-09 1.9441358745098111e-08\n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" + "2025-03-17 17:39:58 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:00 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:39:59 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 9.70e+02 0.00e+00 -1.0 7.69e+05 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 6.38e+02 0.00e+00 -1.0 4.73e+06 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 3 0.0000000e+00 3.67e+02 0.00e+00 -1.0 2.00e+07 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 4 0.0000000e+00 1.67e+02 0.00e+00 -1.0 5.27e+07 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 5 0.0000000e+00 4.93e+01 0.00e+00 -1.0 7.34e+07 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 6 0.0000000e+00 5.77e+00 0.00e+00 -1.0 4.19e+07 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 7 0.0000000e+00 9.14e-02 0.00e+00 -1.0 6.27e+06 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 8 0.0000000e+00 2.34e-05 0.00e+00 -2.5 1.02e+05 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 9 0.0000000e+00 1.62e-12 0.00e+00 -5.7 2.62e+01 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 9\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 1.6200374375330284e-12 1.6200374375330284e-12\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 1.6200374375330284e-12 1.6200374375330284e-12\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 10\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 10\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 10\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 10\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 9\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" + "2025-03-17 17:40:00 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" + "WARNING: Wegstein failed to converge in 5 iterations\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" + "DOF = 0\n" ] - }, + } + ], + "source": [ + "# use the pre-defined function to create the flowsheet\n", + "model = vistut.create_model()\n", + "\n", + "# description of the flowsheet we created\n", + "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", + "\n", + "vistut.quiet()\n", + "\n", + "# initialize the flowsheet as a square problem (dof=0)\n", + "vistut.initialize_model(model)\n", + "\n", + "# verify that there are zero degrees of freedom\n", + "print(f\"DOF = {degrees_of_freedom(model)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Flowsheet Visualizer\n", + "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", + "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", + "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", + "\n", + "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", + "\n", + "
\n", + "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional arguments\n", + "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", + "\n", + " * name: Name of flowsheet to display as the title of the visualization\n", + " * load_from_saved: If True load from saved file if any. Otherwise create\n", + " a new file or overwrite it (depending on 'overwrite' flag).\n", + " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", + " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", + " between the name and the extension). If the value given is the boolean 'False', then nothing\n", + " will be saved. The boolean 'True' value is treated the same as unspecified.\n", + " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", + " be used as the directory to save the default or given file. The current working directory is\n", + " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", + " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", + " in the graph for it to save the model. Default is 5 seconds\n", + " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", + " numbered file.\n", + " * browser: If true, open a browser\n", + " * port: Start listening on this port. If not given, find an open port.\n", + " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", + " See the `idaes.logger` module for details\n", + " * quiet: If True, suppress printing any messages to standard output (console)\n", + " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", + " invoking this function at the end of a script." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interacting with the visualizer\n", + "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", + "The UI should initially look something like the screenshot below:\n", + "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", + "\n", + "
\n", + " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View controls\n", + "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", + "\n", + "| Control | Description | Illustration |\n", + "|:----|:---------------------|:----:|\n", + "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", + "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rearranging the diagram\n", + "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", + "\n", + "|   |   |\n", + "|:--:|:--:|\n", + "| ![](fvr1.png)   |   ![](fvr2.png) |\n", + "| ![](fvr3.png)   |   ![](fvr4.png) |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stream table\n", + "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", + "\n", + "### Stream table \"brushing\"\n", + "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", + "#### Controls\n", + "\n", + "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", + "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", + "\n", + "![Illustration of stream table brushing](fvb1.png)\n", + " \n", + "#### Example\n", + "Stream table brushing is useful for answering questions like:\n", + "> How much benzene are we losing in the F101 vapor outlet stream?\n", + "\n", + "To answer this question, we will use some interactive elements of the stream table.\n", + "\n", + "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", + "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", + "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing and hiding streams\n", + "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", + "\n", + "* Click on the \"Hide Fields\" menu and select which fields to hide\n", + "* The mark will toggle between a check (shown) and open circle (hidden)\n", + "\n", + "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", + "\n", + "![Illustration of stream table field hiding](fvst1.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving and loading\n", + "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", + "\n", + "#### File name\n", + "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", + "\n", + "You can select a different filename and location when you start the visualization, e.g.\n", + "\n", + " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", + "\n", + "#### Reloading\n", + "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exporting\n", + "\n", + "#### Exporting the diagram as an image\n", + "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", + "\n", + "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", + "#### Exporting the stream table as CSV\n", + "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating when the flowsheet changes\n", + "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", + "\n", + "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", + "\n", + "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "WARNING: Implicitly replacing the Component attribute purity (type=) on block fs with a new\n", + "Component (type=). This\n", + "is usually indicative of a modelling error. To avoid this warning, use\n", + "block.del_component() and block.add_component().\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" + "WARNING: Implicitly replacing the Component attribute heating_cost\n", + "(type=) on block fs with\n", + "a new Component (type=).\n", + "This is usually indicative of a modelling error. To avoid this warning, use\n", + "block.del_component() and block.add_component().\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 1.44e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 8.53e+04 1.03e+01 -1.0 3.65e+04 - 1.44e-01 5.96e-01h 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 5.59e+04 4.56e+02 -1.0 1.46e+04 - 9.90e-01 3.84e-01h 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.46e+04 2.28e+04 -1.0 9.01e+03 - 9.64e-01 2.49e-02h 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 5.45e+04 8.49e+07 -1.0 8.79e+03 - 9.90e-01 2.77e-04h 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 5r 0.0000000e+00 5.45e+04 1.00e+03 1.1 0.00e+00 - 0.00e+00 3.46e-07R 4\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 6r 0.0000000e+00 4.25e+04 4.01e+03 1.1 1.87e+04 - 1.19e-02 4.12e-03f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 7r 0.0000000e+00 4.16e+04 3.60e+03 1.1 4.84e+04 - 1.94e-03 2.54e-03f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 8r 0.0000000e+00 4.11e+04 3.15e+03 1.1 3.96e+04 - 5.35e-03 5.52e-03f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 9r 0.0000000e+00 4.07e+04 7.89e+04 1.1 1.47e+04 - 5.89e-01 6.35e-03f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 10r 0.0000000e+00 2.23e+04 9.22e+04 1.1 1.48e+04 - 1.63e-01 6.48e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 11r 0.0000000e+00 2.54e+03 3.49e+04 1.1 5.74e+03 - 5.05e-01 7.97e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 12r 0.0000000e+00 3.06e+03 1.01e+05 1.1 3.80e+02 - 7.25e-01 3.18e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 13r 0.0000000e+00 5.72e+03 8.17e+03 1.1 4.83e+02 - 9.84e-01 5.00e-01h 2\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 14r 0.0000000e+00 1.84e+03 4.63e+04 1.1 1.39e+03 - 1.00e+00 4.11e-01h 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 15r 0.0000000e+00 1.98e+03 1.27e+04 1.1 1.43e+02 - 1.00e+00 1.00e+00f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 16r 0.0000000e+00 6.07e+03 2.97e+03 1.1 6.22e+01 - 1.00e+00 1.00e+00H 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 17r 0.0000000e+00 5.46e+03 3.14e+01 1.1 2.15e+01 - 1.00e+00 1.00e+00f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 18r 0.0000000e+00 5.04e+03 3.10e+03 -1.0 1.25e+03 - 9.65e-01 7.22e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 19r 0.0000000e+00 3.83e+03 1.04e+04 -1.0 4.22e+04 - 8.76e-02 1.55e-02f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 20r 0.0000000e+00 5.65e+01 1.01e+05 -1.0 2.25e+04 - 1.87e-01 2.36e-02f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 21r 0.0000000e+00 4.94e+01 5.35e+04 -1.0 1.28e+04 - 6.97e-01 9.72e-02f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 22r 0.0000000e+00 6.91e+02 7.70e+03 -1.0 1.09e+04 - 1.00e+00 7.42e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 23r 0.0000000e+00 1.26e+02 1.82e+03 -1.0 3.19e+02 - 1.00e+00 8.54e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 24r 0.0000000e+00 1.12e+00 1.83e+02 -1.0 5.04e+01 - 1.00e+00 1.00e+00f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 25r 0.0000000e+00 2.83e-01 9.28e-02 -1.0 6.94e-01 - 1.00e+00 1.00e+00h 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 26r 0.0000000e+00 5.61e-01 8.91e+00 -3.9 6.16e+01 - 9.98e-01 9.48e-01f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 27r 0.0000000e+00 5.14e+03 1.52e+05 -3.9 8.37e+05 - 3.17e-01 3.42e-02f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 28r 0.0000000e+00 5.13e+03 3.10e+05 -3.9 5.26e+05 - 1.38e-01 1.45e-03f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 29r 0.0000000e+00 4.66e+03 2.85e+05 -3.9 2.85e+05 - 8.58e-02 9.92e-02f 1\n" + "2025-03-17 17:40:01 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 30r 0.0000000e+00 4.60e+03 2.87e+05 -3.9 6.24e+05 - 1.38e-05 1.22e-02f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 31r 0.0000000e+00 4.32e+03 2.91e+05 -3.9 1.68e+03 -4.0 6.44e-03 6.09e-02f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 32r 0.0000000e+00 4.32e+03 2.91e+05 -3.9 5.39e+05 - 4.58e-04 2.64e-04f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 33r 0.0000000e+00 4.32e+03 2.91e+05 -3.9 1.11e+03 -4.5 2.08e-05 3.37e-04f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 34r 0.0000000e+00 4.32e+03 2.91e+05 -3.9 4.06e+06 - 2.47e-08 1.04e-05f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 35r 0.0000000e+00 4.32e+03 2.75e+05 -3.9 6.60e+04 -5.0 6.33e-03 9.06e-07f 4\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 36r 0.0000000e+00 4.32e+03 2.75e+05 -3.9 1.97e+06 - 2.55e-05 1.48e-03f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 37r 0.0000000e+00 4.29e+03 2.75e+05 -3.9 3.44e+03 -5.4 3.12e-03 6.04e-03f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 38r 0.0000000e+00 4.29e+03 4.56e+04 -3.9 9.63e+03 -5.9 1.00e+00 6.91e-04f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 39r 0.0000000e+00 4.25e+03 4.71e+04 -3.9 2.91e+05 - 6.20e-02 8.82e-03f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 40r 0.0000000e+00 4.20e+03 6.91e+04 -3.9 2.00e+05 - 8.94e-01 1.13e-02f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 41r 0.0000000e+00 4.11e+03 6.67e+04 -3.9 1.49e+05 - 1.38e-01 2.22e-02f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 42r 0.0000000e+00 3.20e+03 4.92e+04 -3.9 1.45e+05 - 1.53e-01 2.23e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 43r 0.0000000e+00 1.51e+03 9.84e+03 -3.9 1.13e+05 - 1.00e+00 5.35e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 44r 0.0000000e+00 9.72e+02 4.26e+04 -3.9 2.82e+04 - 3.37e-01 3.59e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 45r 0.0000000e+00 8.62e+02 6.68e+04 -3.9 5.81e+02 - 2.73e-02 1.13e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 46r 0.0000000e+00 4.48e+02 1.74e+04 -3.9 8.71e+02 - 1.00e+00 4.85e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 47r 0.0000000e+00 1.56e+03 1.21e+08 -3.9 3.99e+02 - 1.00e+00 9.88e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 48r 0.0000000e+00 1.56e+03 1.21e+08 -3.9 1.17e+01 - 6.91e-04 1.79e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 49r 0.0000000e+00 1.55e+03 1.17e+08 -3.9 1.47e+01 - 5.76e-01 5.05e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 50r 0.0000000e+00 1.54e+03 1.16e+08 -3.9 2.27e+02 - 3.93e-02 8.92e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 51r 0.0000000e+00 1.02e+03 7.71e+07 -3.9 5.86e+01 - 4.77e-02 5.12e-01h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 52r 0.0000000e+00 1.02e+03 7.56e+07 -3.9 3.15e+04 - 8.10e-03 6.82e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 53r 0.0000000e+00 8.08e+02 3.75e+07 -3.9 9.39e+02 - 3.94e-01 2.56e-01h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 54r 0.0000000e+00 5.20e+02 7.83e+07 -3.9 4.67e+02 - 1.57e-03 5.70e-01h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 55r 0.0000000e+00 4.57e+02 6.97e+07 -3.9 4.79e+02 -6.4 4.40e-02 1.41e-01h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 56r 0.0000000e+00 3.94e+02 5.93e+07 -3.9 1.48e+01 -2.3 1.55e-01 1.71e-01h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 57r 0.0000000e+00 3.73e+02 7.03e+07 -3.9 1.07e+01 -1.9 6.30e-01 5.61e-02h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 58r 0.0000000e+00 3.10e+02 5.83e+07 -3.9 8.41e+01 -2.4 1.46e-02 3.92e-01h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 59r 0.0000000e+00 2.96e+02 6.27e+07 -3.9 2.32e+01 -1.1 3.57e-02 5.65e-02h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 60r 0.0000000e+00 2.62e+02 6.00e+07 -3.9 1.51e+01 -0.6 9.19e-02 1.95e-01f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 61r 0.0000000e+00 2.57e+02 6.07e+07 -3.9 3.06e-03 7.2 1.23e-02 1.83e-02f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 62r 0.0000000e+00 2.52e+02 1.16e+08 -3.9 1.06e-02 6.7 5.76e-05 2.54e-02h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 63r 0.0000000e+00 2.52e+02 1.35e+08 -3.9 7.74e-03 7.1 1.29e-02 2.50e-04h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 64r 0.0000000e+00 2.52e+02 1.70e+08 -3.9 6.87e-02 6.7 8.75e-06 3.40e-03h 2\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 65r 0.0000000e+00 2.50e+02 1.68e+08 -3.9 7.22e-03 7.1 6.82e-03 8.93e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 66r 0.0000000e+00 2.50e+02 1.71e+08 -3.9 9.04e-03 7.5 1.87e-04 1.18e-05h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 67r 0.0000000e+00 2.37e+02 5.99e+07 -3.9 1.50e-02 7.0 8.12e-03 6.89e-02h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 68r 0.0000000e+00 2.37e+02 2.18e+08 -3.9 1.65e-02 6.6 4.95e-02 2.65e-05h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 69r 0.0000000e+00 2.35e+02 1.89e+08 -3.9 1.63e-02 7.0 1.42e-05 4.62e-03f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 70r 0.0000000e+00 2.35e+02 2.09e+08 -3.9 1.65e-02 6.5 1.08e-02 3.70e-03f 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 71r 0.0000000e+00 2.33e+02 1.85e+08 -3.9 1.24e-02 6.9 1.72e-06 5.77e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 72r 0.0000000e+00 2.33e+02 2.05e+08 -3.9 1.05e-02 6.5 1.80e-02 3.01e-03h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 73r 0.0000000e+00 2.16e+02 8.46e+07 -3.9 4.90e-03 6.9 1.99e-06 8.32e-02h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 74r 0.0000000e+00 2.01e+02 7.95e+07 -3.9 8.40e-03 6.4 6.24e-02 7.85e-02h 1\n" + "2025-03-17 17:40:02 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 75r 0.0000000e+00 2.01e+02 7.94e+07 -3.9 2.04e-03 6.8 1.84e-01 3.25e-04h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 76r 0.0000000e+00 1.40e+02 6.05e+07 -3.9 6.90e-03 6.4 1.61e-02 5.44e-01h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 77r 0.0000000e+00 1.25e+02 5.38e+07 -3.9 1.35e-03 6.8 1.75e-01 1.12e-01h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 78r 0.0000000e+00 8.55e+01 3.74e+07 -3.9 3.25e-03 6.3 3.52e-02 3.57e-01h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 79r 0.0000000e+00 7.37e+01 3.21e+07 -3.9 5.54e-03 5.8 6.95e-01 1.56e-01h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 80r 0.0000000e+00 5.85e+01 2.55e+07 -3.9 8.61e-04 6.3 3.05e-01 2.10e-01h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 81r 0.0000000e+00 4.94e+01 2.15e+07 -3.9 1.32e-03 5.8 1.00e+00 1.60e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 82r 0.0000000e+00 5.83e+00 2.96e+05 -3.9 5.86e-04 6.2 1.27e-01 1.00e+00f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 83r 0.0000000e+00 5.28e+00 2.56e+05 -3.9 1.88e-03 5.7 8.30e-01 1.33e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 84r 0.0000000e+00 3.21e+00 1.98e+05 -3.9 5.75e-03 5.3 5.98e-01 2.00e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 85r 0.0000000e+00 5.15e+00 1.81e+05 -3.9 1.75e-02 4.8 5.73e-02 7.58e-02f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 86r 0.0000000e+00 5.15e+00 1.81e+05 -3.9 4.01e-02 4.3 8.87e-03 6.82e-04f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 87r 0.0000000e+00 4.63e+00 1.79e+05 -3.9 1.43e-01 3.8 9.35e-04 8.37e-03f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 88r 0.0000000e+00 4.62e+00 1.71e+05 -3.9 3.17e-01 3.3 1.44e-02 3.65e-04f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 89r 0.0000000e+00 1.26e+01 1.72e+05 -3.9 7.65e-01 2.9 1.33e-02 5.14e-03f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 90r 0.0000000e+00 1.26e+01 4.11e+05 -3.9 1.44e-01 2.4 2.29e-01 2.36e-04f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 91r 0.0000000e+00 1.44e+01 3.89e+05 -3.9 1.77e-02 1.9 3.61e-05 6.69e-02f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 92r 0.0000000e+00 1.43e+01 2.21e+05 -3.9 1.77e-03 1.4 9.97e-01 9.35e-03h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 93r 0.0000000e+00 1.18e+01 2.15e+05 -3.9 5.39e-03 1.0 2.84e-02 1.72e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 94r 0.0000000e+00 2.71e+00 4.94e+04 -3.9 1.59e-02 0.5 1.00e+00 7.65e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 95r 0.0000000e+00 5.22e-01 1.04e+04 -3.9 4.79e-02 0.0 1.00e+00 8.05e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 96r 0.0000000e+00 4.94e-03 1.79e+01 -3.9 1.43e-01 -0.5 1.00e+00 1.00e+00f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 97r 0.0000000e+00 6.07e+01 1.30e+06 -3.9 2.64e+04 - 1.00e+00 1.16e-03f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 98r 0.0000000e+00 7.25e+01 5.06e+04 -3.9 2.57e+03 - 1.00e+00 3.50e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 99r 0.0000000e+00 6.75e+01 1.65e+06 -3.9 5.60e+02 - 1.48e-01 6.64e-01h 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 100r 0.0000000e+00 5.42e+01 2.75e+06 -3.9 2.27e+02 - 1.00e+00 2.18e-01f 1\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 101r 0.0000000e+00 5.15e+01 5.37e+06 -3.9 1.72e+02 - 1.67e-01 1.58e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 102r 0.0000000e+00 3.28e+02 1.83e+08 -3.9 1.61e+02 - 4.21e-01 1.79e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 103r 0.0000000e+00 3.28e+02 1.81e+08 -3.9 5.02e+01 - 2.51e-02 3.70e-04h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 104r 0.0000000e+00 3.26e+02 1.81e+08 -3.9 6.06e+02 - 1.69e-02 4.20e-03h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 105r 0.0000000e+00 3.18e+02 1.73e+08 -3.9 1.61e+03 - 9.71e-02 1.27e-02f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 106r 0.0000000e+00 1.95e+02 1.05e+08 -3.9 1.95e+02 - 6.07e-01 6.53e-02h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 107r 0.0000000e+00 1.94e+02 1.06e+08 -3.9 1.52e+03 - 7.28e-02 1.84e-03h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 108r 0.0000000e+00 2.32e+02 1.41e+08 -3.9 1.13e+02 - 1.00e+00 8.76e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 109r 0.0000000e+00 8.57e+00 5.25e+06 -3.9 3.18e+01 - 1.00e+00 3.53e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 110r 0.0000000e+00 1.06e+00 6.48e+05 -3.9 1.15e+01 - 9.81e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 111r 0.0000000e+00 8.06e-01 6.48e+05 -3.9 9.04e+01 - 1.00e+00 1.16e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 112r 0.0000000e+00 8.94e-02 7.38e+04 -3.9 2.94e+00 - 1.00e+00 1.00e+00f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 113r 0.0000000e+00 1.04e-02 6.72e+03 -3.9 1.01e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: 114r 0.0000000e+00 2.21e-05 1.30e+01 -3.9 4.77e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 114\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 2.5693904537327228e-07 2.2066466213388480e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 2.5693904537327228e-07 2.2066466213388480e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 132\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 132\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 116\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 114\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.031\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 2.98e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 2.98e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.50e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 1.27e+07 1.40e+03 -1.0 1.10e+05 - 7.92e-02 4.95e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 1.13e+07 1.23e+03 -1.0 8.57e+04 - 7.40e-01 1.24e-01h 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 1.13e+07 1.25e+03 -1.0 7.81e+04 - 7.86e-01 1.93e-03h 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 1.13e+07 1.27e+03 -1.0 7.79e+04 - 9.90e-01 4.83e-04h 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 1.13e+07 1.29e+03 -1.0 7.79e+04 - 9.90e-01 2.42e-04h 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 1.13e+07 1.31e+03 -1.0 7.79e+04 - 1.00e+00 6.04e-05h 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 1.04e+07 1.31e+05 -1.0 7.79e+04 - 1.00e+00 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 1.08e+05 8.15e+04 -1.0 1.24e+04 - 1.00e+00 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.91e+04 8.98e+04 -1.0 7.64e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 5.55e-04 8.87e-02 -1.0 3.91e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 5.22e-08 9.54e-10 -7.0 9.96e-07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 2.0037676417114954e-11 5.2154064178466803e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 2.0037676417114954e-11 5.2154064178466803e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 5.96e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 5.9604644775390628e-09 5.9604644775390625e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 5.9604644775390628e-09 5.9604644775390625e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Error in an AMPL evaluation. Run with \"halt_on_ampl_error yes\" to see details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Warning: Cutting back alpha due to evaluation error\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Error in an AMPL evaluation. Run with \"halt_on_ampl_error yes\" to see details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Warning: Cutting back alpha due to evaluation error\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 5.25e+08 0.00e+00 -1.0 7.56e+05 - 1.00e+00 2.50e-01h 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Error in an AMPL evaluation. Run with \"halt_on_ampl_error yes\" to see details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Warning: Cutting back alpha due to evaluation error\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 2 0.0000000e+00 2.62e+08 0.00e+00 -1.0 1.86e+06 - 1.00e+00 5.00e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 3 0.0000000e+00 4.60e+02 0.00e+00 -1.0 6.06e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 4 0.0000000e+00 2.53e+02 0.00e+00 -1.0 2.32e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 5 0.0000000e+00 1.07e+02 0.00e+00 -1.0 5.33e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 6 0.0000000e+00 2.69e+01 0.00e+00 -1.0 6.19e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 7 0.0000000e+00 2.22e+00 0.00e+00 -1.0 2.71e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 8 0.0000000e+00 1.69e-02 0.00e+00 -1.0 2.66e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 9 0.0000000e+00 9.93e-07 0.00e+00 -3.8 2.06e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 9.9315880675021617e-07 9.9315880675021617e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 9.9315880675021617e-07 9.9315880675021617e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 1.17e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 1.28e+05 8.02e+01 -1.0 8.01e+04 - 1.39e-02 1.19e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 1.05e+05 2.50e+03 -1.0 2.81e+04 - 3.47e-03 2.64e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 4.52e+04 1.99e+03 -1.0 3.48e+04 - 9.89e-01 5.59e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 6.23e+03 3.63e+02 -1.0 1.39e+04 - 8.95e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 3.17e+02 1.80e+01 -1.0 3.02e+02 - 9.90e-01 9.66e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 6 0.0000000e+00 2.76e-01 3.87e+01 -1.0 9.51e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: 7 0.0000000e+00 2.04e-07 3.21e-02 -3.8 1.64e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 6.0661898475542513e-11 2.0431252778507769e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 6.0661898475542513e-11 2.0431252778507769e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 3.00e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 3.00e-03 1.14e-05 -1.0 3.00e-01 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 2.96e-05 1.04e-01 -1.0 3.00e-03 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: 3 0.0000000e+00 6.94e-18 8.47e+02 -1.0 2.96e-05 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 6.9388939039072284e-18 6.9388939039072284e-18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 6.9388939039072284e-18 6.9388939039072284e-18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 3.63e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 3.63e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 9.18e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 1.31e+01 1.59e-03 -1.0 2.28e+01 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 1.31e-01 9.82e+00 -1.0 2.25e+01 - 1.00e+00 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: 3 0.0000000e+00 2.31e-07 7.90e-13 -1.0 2.24e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.7594960952141457e-09 2.3093889467418194e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.7594960952141457e-09 2.3093889467418194e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 1 0.0000000e+00 1.05e-07 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.0512303560972215e-08 1.0512303560972215e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.0512303560972215e-08 1.0512303560972215e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 1 0.0000000e+00 1.05e-07 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 1.0512303560972215e-08 1.0512303560972215e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 1.0512303560972215e-08 1.0512303560972215e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 3.63e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:01 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 1.72e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 1.7229467630386353e-09 1.7229467630386353e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 1.7229467630386353e-09 1.7229467630386353e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.50e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 2.80e+03 2.76e+00 -1.0 3.50e+05 - 9.85e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 3.49e+00 1.01e+01 -1.0 2.80e+03 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 5.90e-05 3.59e+01 -1.0 3.24e-02 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 4 0.0000000e+00 7.45e-09 7.01e-13 -1.0 5.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 2.9103830456733704e-11 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 2.9103830456733704e-11 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 6.00e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 5.68e-14 0.00e+00 -1.0 1.61e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 3.24e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 1.09e+01 0.00e+00 -1.0 6.35e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 3.73e-01 0.00e+00 -1.0 2.52e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 3 0.0000000e+00 4.59e-04 0.00e+00 -1.7 4.46e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 4 0.0000000e+00 7.00e-10 0.00e+00 -5.7 3.70e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 6.9962879933882505e-10 6.9962879933882505e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 6.9962879933882505e-10 6.9962879933882505e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 1.05e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 5.93e+04 1.13e+01 -1.0 2.36e+04 - 9.10e-02 5.64e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 2.98e+04 3.85e+00 -1.0 1.03e+04 - 9.90e-01 5.47e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 1.39e+04 1.30e+04 -1.0 4.72e+03 - 9.90e-01 5.60e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 1.33e+03 1.60e+08 -1.0 2.08e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 6.98e+02 8.18e+07 -1.0 2.38e+01 - 1.00e+00 6.05e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 1.49e+02 3.71e+08 -1.0 9.38e+00 - 4.26e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 7.19e+01 6.66e+06 -1.0 4.73e+00 - 1.00e+00 4.96e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 2.79e+01 7.00e+08 -1.0 2.38e+00 - 1.00e+00 9.94e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 7.04e+00 1.76e+08 -1.0 1.50e-02 - 1.00e+00 5.00e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 6.93e+00 1.74e+08 -1.0 7.52e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 11 0.0000000e+00 1.47e-04 3.72e+03 -1.0 7.73e-07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: 12 0.0000000e+00 2.91e-11 1.82e-03 -1.7 1.65e-07 - 1.00e+00 1.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 1.7053025658242404e-13 2.9103830456733704e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 1.7053025658242404e-13 2.9103830456733704e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 8.69e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 2.73e-12 0.00e+00 -1.0 8.69e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 2.7284841053187847e-12 2.7284841053187847e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 2.7284841053187847e-12 2.7284841053187847e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 1.55e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.45e-09 0.00e+00 -1.0 1.55e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.47e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 5.18e+05 3.27e+01 -1.0 9.30e+04 - 1.51e-01 1.24e-01h 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 5.16e+05 3.91e+01 -1.0 8.67e+04 - 5.58e-01 1.55e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 5.14e+05 4.24e+01 -1.0 8.59e+04 - 3.40e-01 1.55e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 5.12e+05 4.89e+01 -1.0 8.51e+04 - 6.07e-01 1.55e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.05e+04 1.27e+04 -1.0 8.43e+04 - 3.85e-01 9.90e-01H 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 2.19e+04 3.31e+04 -1.0 1.35e+03 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 5.80e+04 2.14e+04 -1.0 3.92e+04 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 2.71e-04 2.73e-02 -1.0 5.80e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 2.24e-08 9.54e-10 -7.0 1.92e-07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 9.2564253581013618e-12 2.2351741790771484e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 9.2564253581013618e-12 2.2351741790771484e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 3.51e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 2.91e-11 0.00e+00 -1.0 3.59e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 2.9103830456733704e-11 2.9103830456733704e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 2.9103830456733704e-11 2.9103830456733704e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 9.90e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 8.81e-13 0.00e+00 -1.0 9.90e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 8.8107299234252423e-13 8.8107299234252423e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 8.8107299234252423e-13 8.8107299234252423e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 8.74e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 7.30e+04 3.10e+01 -1.0 1.08e+04 - 4.52e-02 3.85e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 3.99e+04 3.26e+01 -1.0 7.60e+03 - 2.91e-01 4.83e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 2.87e+03 1.84e+01 -1.0 3.50e+03 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.31e+02 8.00e-01 -1.0 2.46e+02 - 9.90e-01 9.65e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 5.89e-02 2.42e+02 -1.0 7.54e+00 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: 6 0.0000000e+00 1.09e-08 1.29e-02 -3.8 1.80e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 5.2093202509210664e-12 1.0943040251731873e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 5.2093202509210664e-12 1.0943040251731873e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 1.73e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 2.84e-14 0.00e+00 -1.0 1.73e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 1.73e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 2.27e-13 0.00e+00 -1.0 1.73e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 3.63e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 9.87e-01 5.18e-04 -1.0 3.56e+00 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 3.56e-04 9.67e-14 -1.0 3.52e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: 3 0.0000000e+00 5.82e-11 3.47e-13 -2.5 3.56e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 1.73e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 2.84e-14 0.00e+00 -1.0 1.73e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.85e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 1.71e-13 0.00e+00 -1.0 6.05e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 1.7053025658242404e-13 1.7053025658242404e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 1.7053025658242404e-13 1.7053025658242404e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 2.24e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 2.98e+02 5.66e-01 -1.0 2.35e+02 - 9.88e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 9.87e-01 9.99e+00 -1.0 1.69e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.71e-07 3.28e+01 -1.0 1.13e-02 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 2.0915153662155086e-10 1.7136335372924805e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 2.0915153662155086e-10 1.7136335372924805e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 7.74e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.43e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 2.04e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 6.24e+01 0.00e+00 -1.0 1.17e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 1.07e+01 0.00e+00 -1.0 8.96e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 3 0.0000000e+00 3.90e-01 0.00e+00 -1.0 6.73e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 4 0.0000000e+00 5.39e-04 0.00e+00 -1.7 1.71e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 5 0.0000000e+00 1.03e-09 0.00e+00 -5.7 2.37e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 1.0325038601877168e-09 1.0325038601877168e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 1.0325038601877168e-09 1.0325038601877168e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:02 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.41e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.71e+04 1.18e+01 -1.0 1.40e+04 - 6.37e-02 5.29e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.27e+04 5.09e+00 -1.0 8.31e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.78e+03 1.16e+04 -1.0 4.22e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.65e+02 4.37e+08 -1.0 2.01e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.26e+02 2.40e+08 -1.0 1.89e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.80e+01 4.07e+08 -1.0 7.37e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.17e+01 6.12e+06 -1.0 3.72e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.74e+01 6.04e+08 -1.0 1.87e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.15e-02 2.83e+06 -1.0 1.66e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.57e-08 1.27e+00 -1.0 7.28e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.1026156709134016e-12 3.5717675928026438e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.1026156709134016e-12 3.5717675928026438e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 1.04e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.04e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 1.20e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.45e-09 0.00e+00 -1.0 1.20e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.37e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.37e+04 5.26e+00 -1.0 5.46e+04 - 4.79e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.18e+01 -1.0 5.45e+04 - 6.18e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.36e+04 1.87e+01 -1.0 5.45e+04 - 6.78e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.36e+04 2.86e+01 -1.0 5.45e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.49e+01 -1.0 5.44e+04 - 6.24e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.35e+04 4.48e+01 -1.0 5.44e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.35e+04 5.02e+01 -1.0 5.43e+04 - 5.46e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 6.02e+01 -1.0 5.43e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.34e+04 6.57e+01 -1.0 5.43e+04 - 5.48e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.34e+04 7.57e+01 -1.0 5.42e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.22e+07 3.70e+00 -1.0 5.42e+04 - 5.51e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.69e+05 7.90e+01 -1.0 7.67e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.20e+04 6.56e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.71e-05 4.35e-02 -1.7 4.20e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.2867050784990244e-08 3.7141144275665283e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.2867050784990244e-08 3.7141144275665283e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 5.79e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 1.49e-08 0.00e+00 -1.0 5.94e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.45e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 5.40e-13 0.00e+00 -1.0 4.45e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 5.4001247917767614e-13 5.4001247917767614e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 5.4001247917767614e-13 5.4001247917767614e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.81e+04 6.10e+00 -1.0 1.66e+04 - 1.31e-01 6.60e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.85e+03 2.82e+01 -1.0 5.30e+03 - 8.83e-01 6.24e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.72e+02 1.96e+01 -1.0 2.30e+03 - 8.30e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.40e+00 2.52e+00 -1.0 1.54e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.60e-06 7.26e+02 -1.0 1.27e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 2.3443628029030458e-09 2.6049783627968282e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 2.3443628029030458e-09 2.6049783627968282e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 7.35e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 7.35e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 7.35e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 2.47e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.33e-10 0.00e+00 -1.0 7.72e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.58e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.84e+03 3.49e+00 -1.0 5.83e+02 - 7.71e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.22e-01 1.01e+01 -1.0 5.51e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.45e-06 3.74e+01 -1.0 2.11e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6366937707051519e-09 1.4454126358032227e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6366937707051519e-09 1.4454126358032227e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 3.96e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.66e-10 0.00e+00 -1.0 3.96e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.6566128730773928e-11 4.6566128730773926e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.6566128730773928e-11 4.6566128730773926e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.04e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 9.31e-04 0.00e+00 -1.0 5.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 3.07e-09 0.00e+00 -5.7 2.33e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 3.0722731025889516e-09 3.0722731025889516e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 3.0722731025889516e-09 3.0722731025889516e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.31e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.73e+04 1.13e+01 -1.0 9.78e+03 - 9.29e-02 5.26e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.30e+04 5.18e+00 -1.0 4.93e+03 - 9.90e-01 5.20e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.95e+03 1.15e+04 -1.0 2.43e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.00e+02 4.35e+08 -1.0 1.13e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 4.96e+02 2.42e+08 -1.0 1.74e+01 - 1.00e+00 6.09e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.84e+01 4.10e+08 -1.0 6.78e+00 - 3.95e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.21e+01 6.19e+06 -1.0 3.42e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.65e+01 6.23e+08 -1.0 1.72e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 7.93e-02 2.99e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.32e-08 1.28e+00 -1.0 6.80e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 6.5958889984627885e-12 3.3225660445168614e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 6.5958889984627885e-12 3.3225660445168614e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 2.53e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 1.36e-12 0.00e+00 -1.0 2.53e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 1.3642420526593924e-12 1.3642420526593924e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 1.3642420526593924e-12 1.3642420526593924e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.19e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 2.27e-12 0.00e+00 -1.0 6.19e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 2.2737367544323206e-12 2.2737367544323206e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 2.2737367544323206e-12 2.2737367544323206e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.39e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.39e+04 4.69e+00 -1.0 5.65e+04 - 4.27e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.39e+04 1.11e+01 -1.0 5.64e+04 - 6.10e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.38e+04 1.74e+01 -1.0 5.64e+04 - 6.20e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.38e+04 2.74e+01 -1.0 5.64e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.38e+04 3.19e+01 -1.0 5.63e+04 - 4.45e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.37e+04 4.18e+01 -1.0 5.63e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.37e+04 4.62e+01 -1.0 5.62e+04 - 4.49e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.37e+04 5.63e+01 -1.0 5.62e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.36e+04 6.07e+01 -1.0 5.62e+04 - 4.50e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.36e+04 7.07e+01 -1.0 5.61e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.32e+07 5.73e+01 -1.0 5.61e+04 - 4.53e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.83e+05 1.50e+02 -1.0 8.34e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.35e+04 8.07e+01 -1.0 2.35e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.89e-05 4.87e-02 -1.7 4.35e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3482493703515918e-08 3.8929283618927002e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3482493703515918e-08 3.8929283618927002e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 2.57e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 1.49e-08 0.00e+00 -1.0 2.90e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.21e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 1.45e-12 0.00e+00 -1.0 4.21e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 1.4495071809506044e-12 1.4495071809506044e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 1.4495071809506044e-12 1.4495071809506044e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:03 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 4.96e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.54e+04 5.11e+00 -1.0 3.23e+03 - 1.46e-01 6.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.50e+03 2.82e+01 -1.0 2.15e+03 - 8.67e-01 6.00e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.87e+02 2.36e+01 -1.0 7.00e+02 - 7.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.45e+00 2.85e+00 -1.0 1.47e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 4.74e-07 8.13e+02 -1.0 1.14e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 3.9519780131877856e-10 4.7424327931366861e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 3.9519780131877856e-10 4.7424327931366861e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 3.19e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 1.42e-13 0.00e+00 -1.0 3.19e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 3.19e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 1.42e-13 0.00e+00 -1.0 3.19e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 3.19e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 1.42e-13 0.00e+00 -1.0 3.19e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.85e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.33e-10 0.00e+00 -1.0 5.85e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.52e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.80e+03 3.46e+00 -1.0 5.77e+02 - 7.73e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.17e-01 1.01e+01 -1.0 5.46e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.43e-06 3.74e+01 -1.0 2.10e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6234143080625137e-09 1.4305114746093750e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6234143080625137e-09 1.4305114746093750e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 6.38e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.82e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 9.84e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 3.92e-04 0.00e+00 -1.0 3.45e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 5.45e-10 0.00e+00 -5.7 9.79e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 5.4455995268654078e-10 5.4455995268654078e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 5.4455995268654078e-10 5.4455995268654078e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.39e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.72e+04 1.17e+01 -1.0 9.60e+03 - 6.94e-02 5.28e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.28e+04 5.11e+00 -1.0 4.88e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.83e+03 1.16e+04 -1.0 2.41e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.55e+02 4.36e+08 -1.0 1.12e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.22e+02 2.40e+08 -1.0 1.85e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.82e+01 4.08e+08 -1.0 7.23e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.18e+01 6.14e+06 -1.0 3.64e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.74e+01 6.12e+08 -1.0 1.83e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.26e-02 2.90e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.65e-08 1.31e+00 -1.0 9.97e-09 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.2342399038511207e-12 3.6536221159622073e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.2342399038511207e-12 3.6536221159622073e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 1.94e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 1.36e-12 0.00e+00 -1.0 1.94e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 1.3642420526593924e-12 1.3642420526593924e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 1.3642420526593924e-12 1.3642420526593924e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.44e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.45e-09 0.00e+00 -1.0 6.44e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.38e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.38e+04 5.12e+00 -1.0 5.51e+04 - 4.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.16e+01 -1.0 5.50e+04 - 6.16e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.37e+04 1.84e+01 -1.0 5.50e+04 - 6.71e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.37e+04 2.84e+01 -1.0 5.49e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.41e+01 -1.0 5.49e+04 - 5.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.36e+04 4.40e+01 -1.0 5.49e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.36e+04 4.92e+01 -1.0 5.48e+04 - 5.21e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 5.92e+01 -1.0 5.48e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.35e+04 6.44e+01 -1.0 5.47e+04 - 5.22e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.35e+04 7.44e+01 -1.0 5.47e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.25e+07 1.48e+01 -1.0 5.47e+04 - 5.25e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.73e+05 9.39e+01 -1.0 7.84e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.24e+04 6.91e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.76e-05 4.49e-02 -1.7 4.24e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3035939771934499e-08 3.7617981433868408e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3035939771934499e-08 3.7617981433868408e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 1.91e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 3.64e-12 0.00e+00 -1.0 2.16e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 3.6379788070917130e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 3.6379788070917130e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.37e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 1.22e-12 0.00e+00 -1.0 4.37e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 1.2221335055073723e-12 1.2221335055073723e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 1.2221335055073723e-12 1.2221335055073723e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.12e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.74e+04 5.83e+00 -1.0 3.26e+03 - 1.35e-01 6.68e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.79e+03 2.83e+01 -1.0 2.31e+03 - 8.78e-01 6.18e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.77e+02 2.07e+01 -1.0 7.29e+02 - 8.20e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.43e+00 2.59e+00 -1.0 1.53e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.02e-06 7.46e+02 -1.0 1.25e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 1.7885068906748583e-09 2.0238840079400688e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 1.7885068906748583e-09 2.0238840079400688e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 2.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 2.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:04 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 2.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.78e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 5.68e-14 0.00e+00 -1.0 5.78e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.55e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.82e+03 3.47e+00 -1.0 5.80e+02 - 7.72e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.20e-01 1.01e+01 -1.0 5.49e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.44e-06 3.74e+01 -1.0 2.11e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6300540393838327e-09 1.4379620552062988e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6300540393838327e-09 1.4379620552062988e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 3.34e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 3.34e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.00e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 1.28e-05 0.00e+00 -1.0 1.00e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 1.82e-12 0.00e+00 -7.0 3.20e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 1.8189894035458565e-12 1.8189894035458565e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 1.8189894035458565e-12 1.8189894035458565e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.39e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.72e+04 1.17e+01 -1.0 9.61e+03 - 6.95e-02 5.28e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.28e+04 5.11e+00 -1.0 4.88e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.84e+03 1.16e+04 -1.0 2.41e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.56e+02 4.35e+08 -1.0 1.12e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.23e+02 2.40e+08 -1.0 1.85e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.83e+01 4.08e+08 -1.0 7.22e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.18e+01 6.15e+06 -1.0 3.64e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.74e+01 6.12e+08 -1.0 1.83e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.29e-02 2.91e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.67e-08 1.32e+00 -1.0 3.06e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.2839166460891293e-12 3.6718120099976659e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.2839166460891293e-12 3.6718120099976659e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 2.73e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 7.28e-12 0.00e+00 -1.0 2.73e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.25e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.28e-12 0.00e+00 -1.0 6.25e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.38e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.38e+04 5.12e+00 -1.0 5.51e+04 - 4.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.16e+01 -1.0 5.51e+04 - 6.16e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.37e+04 1.84e+01 -1.0 5.50e+04 - 6.71e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.37e+04 2.84e+01 -1.0 5.50e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.40e+01 -1.0 5.49e+04 - 5.65e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.36e+04 4.39e+01 -1.0 5.49e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.36e+04 4.91e+01 -1.0 5.49e+04 - 5.20e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 5.91e+01 -1.0 5.48e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.35e+04 6.43e+01 -1.0 5.48e+04 - 5.21e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.35e+04 7.43e+01 -1.0 5.47e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.25e+07 1.53e+01 -1.0 5.47e+04 - 5.24e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.73e+05 9.46e+01 -1.0 7.85e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.24e+04 6.93e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.77e-05 4.50e-02 -1.7 4.24e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3055977448351614e-08 3.7685036659240723e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3055977448351614e-08 3.7685036659240723e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 4.26e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 6.37e-12 0.00e+00 -1.0 4.49e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 6.3664629124104977e-12 6.3664629124104977e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 6.3664629124104977e-12 6.3664629124104977e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.35e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 2.84e-13 0.00e+00 -1.0 4.35e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 2.8421709430404007e-13 2.8421709430404007e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 2.8421709430404007e-13 2.8421709430404007e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.12e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.74e+04 5.83e+00 -1.0 3.26e+03 - 1.35e-01 6.68e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.80e+03 2.83e+01 -1.0 2.31e+03 - 8.78e-01 6.17e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.77e+02 2.07e+01 -1.0 7.30e+02 - 8.20e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.43e+00 2.59e+00 -1.0 1.53e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.03e-06 7.46e+02 -1.0 1.25e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 1.7946090848256982e-09 2.0297338778618723e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 1.7946090848256982e-09 2.0297338778618723e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 2.21e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.7 2.21e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 2.21e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.7 2.21e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 2.21e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.7 2.21e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.82e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.33e-10 0.00e+00 -1.0 5.82e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:05 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.55e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.82e+03 3.47e+00 -1.0 5.79e+02 - 7.72e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.19e-01 1.01e+01 -1.0 5.48e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.43e-06 3.74e+01 -1.0 2.10e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6234143080625137e-09 1.4305114746093750e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6234143080625137e-09 1.4305114746093750e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 5.83e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 5.68e-14 0.00e+00 -1.0 5.83e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.00e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 4.68e-05 0.00e+00 -1.0 1.19e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 7.73e-12 0.00e+00 -5.7 1.17e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 7.7307049650698900e-12 7.7307049650698900e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 7.7307049650698900e-12 7.7307049650698900e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.40e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.72e+04 1.17e+01 -1.0 9.62e+03 - 6.95e-02 5.28e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.28e+04 5.11e+00 -1.0 4.89e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.84e+03 1.16e+04 -1.0 2.42e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.57e+02 4.35e+08 -1.0 1.12e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.23e+02 2.40e+08 -1.0 1.85e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.83e+01 4.08e+08 -1.0 7.23e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.19e+01 6.15e+06 -1.0 3.64e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.75e+01 6.13e+08 -1.0 1.83e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.34e-02 2.93e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.71e-08 1.33e+00 -1.0 7.32e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.3552916719798679e-12 3.7125573726370931e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.3552916719798679e-12 3.7125573726370931e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 8.44e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 1.82e-12 0.00e+00 -1.0 8.44e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 1.8189894035458565e-12 1.8189894035458565e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 1.8189894035458565e-12 1.8189894035458565e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.26e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.28e-12 0.00e+00 -1.0 6.26e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.38e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.38e+04 5.11e+00 -1.0 5.51e+04 - 4.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.16e+01 -1.0 5.51e+04 - 6.16e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.37e+04 1.84e+01 -1.0 5.51e+04 - 6.71e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.37e+04 2.84e+01 -1.0 5.50e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.40e+01 -1.0 5.50e+04 - 5.64e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.36e+04 4.39e+01 -1.0 5.49e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.36e+04 4.91e+01 -1.0 5.49e+04 - 5.20e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 5.91e+01 -1.0 5.49e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.35e+04 6.43e+01 -1.0 5.48e+04 - 5.21e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.35e+04 7.43e+01 -1.0 5.48e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.25e+07 1.59e+01 -1.0 5.47e+04 - 5.24e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.73e+05 9.54e+01 -1.0 7.86e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.24e+04 6.95e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.78e-05 4.51e-02 -1.7 4.24e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3084602700376065e-08 3.7789344787597656e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3084602700376065e-08 3.7789344787597656e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 5.60e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 5.69e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.35e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 5.26e-13 0.00e+00 -1.0 4.35e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 5.2580162446247414e-13 5.2580162446247414e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 5.2580162446247414e-13 5.2580162446247414e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.13e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.75e+04 5.85e+00 -1.0 3.26e+03 - 1.34e-01 6.67e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.81e+03 2.83e+01 -1.0 2.31e+03 - 8.78e-01 6.17e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.77e+02 2.07e+01 -1.0 7.31e+02 - 8.20e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.43e+00 2.58e+00 -1.0 1.54e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.07e-06 7.45e+02 -1.0 1.25e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 1.8323701215288182e-09 2.0685693016275764e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 1.8323701215288182e-09 2.0685693016275764e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 7.91e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 8.53e-14 0.00e+00 -2.5 7.91e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 7.91e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 8.53e-14 0.00e+00 -2.5 7.91e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 7.91e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 8.53e-14 0.00e+00 -2.5 7.91e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.81e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.33e-10 0.00e+00 -1.0 5.81e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.54e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.81e+03 3.47e+00 -1.0 5.78e+02 - 7.72e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.19e-01 1.01e+01 -1.0 5.47e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.44e-06 3.74e+01 -1.0 2.10e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6267341737231732e-09 1.4379620552062988e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:06 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6267341737231732e-09 1.4379620552062988e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Wegstein failed to converge in 5 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DOF = 0\n" - ] - } - ], - "source": [ - "# use the pre-defined function to create the flowsheet\n", - "model = vistut.create_model()\n", - "\n", - "# description of the flowsheet we created\n", - "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", - "\n", - "vistut.quiet()\n", - "\n", - "# initialize the flowsheet as a square problem (dof=0)\n", - "vistut.initialize_model(model)\n", - "\n", - "# verify that there are zero degrees of freedom\n", - "print(f\"DOF = {degrees_of_freedom(model)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the Flowsheet Visualizer\n", - "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", - "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", - "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", - "\n", - "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", - "\n", - "
\n", - "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional arguments\n", - "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", - "\n", - " * name: Name of flowsheet to display as the title of the visualization\n", - " * load_from_saved: If True load from saved file if any. Otherwise create\n", - " a new file or overwrite it (depending on 'overwrite' flag).\n", - " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", - " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", - " between the name and the extension). If the value given is the boolean 'False', then nothing\n", - " will be saved. The boolean 'True' value is treated the same as unspecified.\n", - " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", - " be used as the directory to save the default or given file. The current working directory is\n", - " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", - " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", - " in the graph for it to save the model. Default is 5 seconds\n", - " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", - " numbered file.\n", - " * browser: If true, open a browser\n", - " * port: Start listening on this port. If not given, find an open port.\n", - " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", - " See the `idaes.logger` module for details\n", - " * quiet: If True, suppress printing any messages to standard output (console)\n", - " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", - " invoking this function at the end of a script." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interacting with the visualizer\n", - "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", - "The UI should initially look something like the screenshot below:\n", - "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", - "\n", - "
\n", - " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View controls\n", - "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", - "\n", - "| Control | Description | Illustration |\n", - "|:----|:---------------------|:----:|\n", - "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", - "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rearranging the diagram\n", - "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", - "\n", - "|   |   |\n", - "|:--:|:--:|\n", - "| ![](fvr1.png)   |   ![](fvr2.png) |\n", - "| ![](fvr3.png)   |   ![](fvr4.png) |\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stream table\n", - "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", - "\n", - "### Stream table \"brushing\"\n", - "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", - "#### Controls\n", - "\n", - "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", - "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", - "\n", - "![Illustration of stream table brushing](fvb1.png)\n", - " \n", - "#### Example\n", - "Stream table brushing is useful for answering questions like:\n", - "> How much benzene are we losing in the F101 vapor outlet stream?\n", - "\n", - "To answer this question, we will use some interactive elements of the stream table.\n", - "\n", - "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", - "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", - "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Showing and hiding streams\n", - "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", - "\n", - "* Click on the \"Hide Fields\" menu and select which fields to hide\n", - "* The mark will toggle between a check (shown) and open circle (hidden)\n", - "\n", - "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", - "\n", - "![Illustration of stream table field hiding](fvst1.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Saving and loading\n", - "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", - "\n", - "#### File name\n", - "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", - "\n", - "You can select a different filename and location when you start the visualization, e.g.\n", - "\n", - " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", - "\n", - "#### Reloading\n", - "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exporting\n", - "\n", - "#### Exporting the diagram as an image\n", - "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", - "\n", - "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", - "#### Exporting the stream table as CSV\n", - "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Updating when the flowsheet changes\n", - "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", - "\n", - "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", - "\n", - "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Implicitly replacing the Component attribute purity (type=) on block fs with a new\n", - "Component (type=). This\n", - "is usually indicative of a modelling error. To avoid this warning, use\n", - "block.del_component() and block.add_component().\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Implicitly replacing the Component attribute heating_cost\n", - "(type=) on block fs with\n", - "a new Component (type=).\n", - "This is usually indicative of a modelling error. To avoid this warning, use\n", - "block.del_component() and block.add_component().\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - 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"name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 1.77e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 3.49e-10 0.00e+00 -1.0 3.07e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 3.4924596548080450e-11 3.4924596548080450e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 3.4924596548080450e-11 3.4924596548080450e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.73e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 4.82e+01 0.00e+00 -1.0 1.49e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 6.18e+00 0.00e+00 -1.0 1.95e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 3 0.0000000e+00 1.19e-01 0.00e+00 -1.0 2.53e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 4 0.0000000e+00 4.53e-05 0.00e+00 -2.5 1.49e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 5 0.0000000e+00 6.37e-12 0.00e+00 -5.7 4.91e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 6.3664629124104977e-12 6.3664629124104977e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 6.3664629124104977e-12 6.3664629124104977e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 1.44e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 8.53e+04 1.03e+01 -1.0 3.65e+04 - 1.44e-01 5.96e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 5.59e+04 4.56e+02 -1.0 1.46e+04 - 9.90e-01 3.84e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.46e+04 2.28e+04 -1.0 9.01e+03 - 9.64e-01 2.49e-02h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 5.45e+04 8.49e+07 -1.0 8.79e+03 - 9.90e-01 2.77e-04h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 5r 0.0000000e+00 5.45e+04 1.00e+03 0.8 0.00e+00 - 0.00e+00 3.46e-07R 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 6r 0.0000000e+00 4.52e+04 1.72e+03 0.8 4.86e+04 - 3.39e-03 5.74e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 7r 0.0000000e+00 4.54e+04 2.22e+03 0.8 2.56e+04 - 7.23e-03 2.69e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 8r 0.0000000e+00 4.37e+04 2.43e+03 0.8 2.00e+04 - 1.36e-02 6.74e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 9r 0.0000000e+00 4.06e+04 4.57e+03 0.8 2.05e+04 - 7.28e-02 2.92e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 10r 0.0000000e+00 3.45e+04 7.90e+03 0.8 2.00e+04 - 2.01e-01 1.27e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 11r 0.0000000e+00 1.21e+04 2.39e+04 0.8 1.70e+04 - 2.59e-01 5.42e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 12r 0.0000000e+00 2.72e+03 1.77e+04 0.8 6.44e+03 - 1.00e+00 5.14e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 13r 0.0000000e+00 6.69e+02 6.62e+03 0.8 2.22e+03 - 1.00e+00 4.48e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 14r 0.0000000e+00 1.14e+03 6.61e+03 0.8 3.49e+02 - 1.00e+00 7.81e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 15r 0.0000000e+00 5.91e+02 1.93e+01 0.8 9.22e+01 - 1.00e+00 1.00e+00f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 16r 0.0000000e+00 2.19e+02 3.80e+02 -1.3 8.85e+02 - 9.43e-01 7.97e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 17r 0.0000000e+00 1.17e+02 2.34e+03 -1.3 8.99e+03 - 3.28e-01 1.18e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 18r 0.0000000e+00 2.25e+02 3.17e+03 -1.3 1.46e+04 - 2.67e-02 1.27e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 19r 0.0000000e+00 3.95e+02 1.19e+04 -1.3 4.01e+03 - 7.78e-01 1.99e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 20r 0.0000000e+00 2.40e+03 7.42e+03 -1.3 1.98e+03 - 1.00e+00 7.03e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 21r 0.0000000e+00 3.00e+02 1.47e+04 -1.3 3.39e+03 - 6.90e-01 1.00e+00f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 22r 0.0000000e+00 2.71e-01 4.33e+03 -1.3 2.52e+02 - 4.82e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 23r 0.0000000e+00 3.15e-01 5.75e+00 -1.3 2.74e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 24r 0.0000000e+00 1.02e-01 5.63e-02 -1.3 3.50e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 25r 0.0000000e+00 3.63e+00 8.07e+01 -4.5 6.57e+01 - 8.61e-01 9.36e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 26r 0.0000000e+00 1.31e+03 5.28e+03 -4.5 1.63e+06 - 1.55e-02 6.19e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 27r 0.0000000e+00 1.31e+03 5.27e+05 -4.5 6.86e+05 - 3.68e-01 5.27e-04f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 28r 0.0000000e+00 1.87e+03 4.39e+05 -4.5 1.60e+05 - 3.22e-02 1.15e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 29r 0.0000000e+00 1.78e+03 1.38e+05 -4.5 1.44e+05 - 1.00e+00 5.04e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 30r 0.0000000e+00 6.19e+02 1.35e+05 -4.5 3.83e+04 - 1.00e+00 6.55e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 31r 0.0000000e+00 4.41e+02 2.68e+05 -4.5 1.22e+04 - 1.00e+00 2.88e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 32r 0.0000000e+00 1.86e+02 4.62e+07 -4.5 9.50e+03 - 1.00e+00 9.78e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 33r 0.0000000e+00 1.84e+02 3.37e+07 -4.5 2.29e+02 - 4.62e-01 1.41e-02h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 34r 0.0000000e+00 1.76e+02 3.09e+07 -4.5 2.51e+02 - 4.97e-02 4.70e-02h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 35r 0.0000000e+00 9.49e+01 1.37e+07 -4.5 2.16e+02 - 6.25e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 36r 0.0000000e+00 8.67e+01 1.24e+07 -4.5 7.70e+00 -4.0 9.10e-02 1.02e-01h 1\n" - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 41r 0.0000000e+00 8.12e+01 1.05e+07 -4.5 4.67e+01 -2.3 2.31e-02 1.18e-01h 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 42r 0.0000000e+00 7.88e+01 1.01e+07 -4.5 3.70e+01 -1.9 3.66e-02 4.33e-02h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 43r 0.0000000e+00 7.55e+01 3.32e+07 -4.5 1.51e+01 -1.4 2.07e-01 5.71e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 44r 0.0000000e+00 7.10e+01 2.94e+07 -4.5 7.40e+00 -1.0 5.30e-02 8.31e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 45r 0.0000000e+00 7.10e+01 1.00e+08 -4.5 4.33e+02 -1.5 1.89e-02 5.90e-04h 3\n" - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 96r 0.0000000e+00 4.40e+02 6.23e+07 -4.5 3.06e-04 6.6 1.31e-01 1.06e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 97r 0.0000000e+00 4.39e+02 5.85e+07 -4.5 8.53e-04 6.1 3.89e-01 5.90e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 98r 0.0000000e+00 4.39e+02 5.80e+07 -4.5 2.96e-03 5.6 9.68e-03 8.82e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 99r 0.0000000e+00 4.25e+02 4.95e+07 -4.5 1.13e-03 6.0 3.50e-02 1.00e+00f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 100r 0.0000000e+00 4.23e+02 1.52e+07 -4.5 3.81e-04 6.5 2.85e-01 1.00e+00f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 101r 0.0000000e+00 4.22e+02 1.50e+07 -4.5 1.03e-03 6.0 5.68e-01 1.11e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 102r 0.0000000e+00 4.21e+02 1.45e+07 -4.5 3.61e-03 5.5 2.88e-01 3.15e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 103r 0.0000000e+00 4.13e+02 1.45e+07 -4.5 1.59e-01 5.0 1.42e-03 3.88e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 104r 0.0000000e+00 4.13e+02 1.45e+07 -4.5 4.42e-04 6.3 9.74e-01 9.03e-04f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 105r 0.0000000e+00 4.01e+02 4.54e+06 -4.5 1.56e-03 5.9 2.61e-01 1.00e+00f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 106r 0.0000000e+00 3.99e+02 2.25e+06 -4.5 3.76e-03 5.4 7.08e-01 1.76e-01F 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 107r 0.0000000e+00 3.74e+02 1.90e+06 -4.5 1.31e-02 4.9 3.64e-01 2.05e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 108r 0.0000000e+00 3.42e+02 1.78e+06 -4.5 3.87e-02 4.4 2.09e-01 8.37e-02f 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 109r 0.0000000e+00 3.06e+02 1.75e+06 -4.5 1.39e-01 4.0 1.67e-02 2.31e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 110r 0.0000000e+00 3.04e+02 1.74e+06 -4.5 6.29e-02 4.4 3.47e-03 4.51e-03f 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 111r 0.0000000e+00 3.01e+02 1.70e+06 -4.5 1.41e-02 4.8 1.55e-02 1.89e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 112r 0.0000000e+00 3.01e+02 1.70e+06 -4.5 5.68e-02 4.3 3.23e-06 5.96e-05f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 113r 0.0000000e+00 3.01e+02 1.70e+06 -4.5 1.09e-01 4.8 7.36e-03 5.36e-06f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 114r 0.0000000e+00 2.83e+02 1.65e+06 -4.5 5.50e-02 4.3 7.29e-04 3.24e-02f 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 115r 0.0000000e+00 2.83e+02 1.65e+06 -4.5 1.79e-02 4.7 1.35e-01 3.48e-04f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 116r 0.0000000e+00 2.82e+02 1.65e+06 -4.5 6.16e-02 4.2 1.98e-03 2.04e-04f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 117r 0.0000000e+00 2.82e+02 1.65e+06 -4.5 9.60e-02 4.7 5.15e-03 2.06e-04h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 118r 0.0000000e+00 2.74e+02 1.63e+06 -4.5 7.03e-02 4.2 3.52e-02 1.16e-02f 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 119r 0.0000000e+00 2.72e+02 1.62e+06 -4.5 2.29e-02 4.6 1.26e-01 4.99e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 120r 0.0000000e+00 2.60e+02 1.60e+06 -4.5 7.89e-02 4.1 1.83e-04 1.45e-02f 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 121r 0.0000000e+00 2.60e+02 1.60e+06 -4.5 3.48e-03 5.5 1.00e+00 1.34e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 122r 0.0000000e+00 2.26e+02 1.13e+06 -4.5 1.11e-02 5.0 7.20e-01 3.27e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 123r 0.0000000e+00 1.98e+02 1.05e+06 -4.5 3.33e-02 4.5 4.09e-02 8.65e-02f 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 124r 0.0000000e+00 1.70e+02 1.02e+06 -4.5 9.90e-02 4.0 2.62e-01 2.71e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 125r 0.0000000e+00 1.60e+02 1.02e+06 -4.5 3.70e-01 3.6 3.45e-02 2.58e-03f 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 126r 0.0000000e+00 1.55e+02 1.02e+06 -4.5 3.14e+00 3.1 4.96e-04 1.42e-04f 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 127r 0.0000000e+00 1.55e+02 1.02e+06 -4.5 3.67e-02 4.4 1.77e-01 1.29e-04f 1\n" - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 146r 0.0000000e+00 1.05e+02 9.26e+05 -4.5 3.54e-02 4.4 8.94e-03 3.89e-06f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 147r 0.0000000e+00 1.03e+02 9.20e+05 -4.5 1.34e-01 3.9 1.57e-03 6.76e-03f 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 148r 0.0000000e+00 1.03e+02 9.20e+05 -4.5 5.08e-02 4.3 7.88e-01 1.75e-04f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 149r 0.0000000e+00 7.72e+01 8.46e+05 -4.5 1.45e-01 3.8 1.60e-01 7.86e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 150r 0.0000000e+00 5.06e+01 8.20e+05 -4.5 3.95e-01 3.4 1.69e-06 2.98e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 151r 0.0000000e+00 4.84e+01 7.78e+05 -4.5 1.87e-02 4.7 1.12e-01 3.37e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 152r 0.0000000e+00 2.27e+01 6.38e+05 -4.5 6.44e-02 4.2 3.65e-01 2.10e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 153r 0.0000000e+00 1.68e+01 6.38e+05 -4.5 5.31e+05 - 3.98e-02 4.51e-05f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 154r 0.0000000e+00 1.61e+01 6.09e+05 -4.5 3.54e-03 3.7 2.13e-02 3.70e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 155r 0.0000000e+00 1.65e+01 6.06e+05 -4.5 1.89e-01 3.3 1.31e-06 1.92e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 156r 0.0000000e+00 1.65e+01 6.06e+05 -4.5 6.90e+04 - 4.47e-06 7.87e-05f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 157r 0.0000000e+00 1.65e+01 6.06e+05 -4.5 2.08e+00 2.8 5.85e-04 2.48e-07f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 158r 0.0000000e+00 1.52e+01 8.19e+05 -4.5 1.57e+05 - 1.00e+00 7.37e-03f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 159r 0.0000000e+00 5.60e+01 1.03e+06 -4.5 7.41e+02 - 1.00e+00 1.29e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 160r 0.0000000e+00 5.54e+01 1.03e+06 -4.5 9.99e+02 - 9.92e-04 1.01e-02h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 161r 0.0000000e+00 1.06e+02 1.63e+06 -4.5 1.24e+03 - 1.00e+00 5.59e-02f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 162r 0.0000000e+00 2.66e+01 1.87e+06 -4.5 1.08e+03 - 6.59e-01 5.75e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 163r 0.0000000e+00 2.25e+01 4.38e+05 -4.5 4.42e+02 - 1.00e+00 9.71e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 164r 0.0000000e+00 2.05e-01 4.07e+03 -4.5 1.12e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: 165r 0.0000000e+00 7.88e-05 1.61e+00 -4.5 1.24e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 165\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 1.4182702752789231e-08 7.8801036579534411e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 1.4182702752789231e-08 7.8801036579534411e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 299\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 301\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 169\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 165\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.069\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 1.92e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 1.46e-11 0.00e+00 -1.0 1.92e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 1.4551915228366852e-11 1.4551915228366852e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 1.4551915228366852e-11 1.4551915228366852e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.26e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 2.91e-11 0.00e+00 -1.0 6.26e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 2.9103830456733704e-11 2.9103830456733704e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 2.9103830456733704e-11 2.9103830456733704e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.50e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 1.27e+07 1.40e+03 -1.0 1.10e+05 - 7.92e-02 4.95e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 1.13e+07 1.23e+03 -1.0 8.57e+04 - 7.40e-01 1.24e-01h 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 1.13e+07 1.25e+03 -1.0 7.81e+04 - 7.86e-01 1.93e-03h 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 1.13e+07 1.27e+03 -1.0 7.79e+04 - 9.90e-01 4.83e-04h 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 1.13e+07 1.29e+03 -1.0 7.79e+04 - 9.90e-01 2.42e-04h 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 1.13e+07 1.31e+03 -1.0 7.79e+04 - 1.00e+00 6.04e-05h 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 1.04e+07 1.31e+05 -1.0 7.79e+04 - 1.00e+00 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 1.08e+05 7.84e+04 -1.0 1.24e+04 - 1.00e+00 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 4.16e+04 9.36e+04 -1.0 7.40e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 5.09e-04 9.33e-02 -1.0 4.16e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 5.22e-08 2.29e-09 -7.0 9.13e-07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 2.0037676417114954e-11 5.2154064178466803e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 2.0037676417114954e-11 5.2154064178466803e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 66\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 9.22e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 2.98e-08 0.00e+00 -1.0 9.45e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 1.43e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 2.98e-13 0.00e+00 -1.0 1.43e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 2.9842794901924208e-13 2.9842794901924208e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 2.9842794901924208e-13 2.9842794901924208e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 1.17e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 1.28e+05 8.02e+01 -1.0 9.79e+03 - 1.39e-02 1.19e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 1.05e+05 2.50e+03 -1.0 3.38e+04 - 3.47e-03 2.64e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 4.52e+04 1.99e+03 -1.0 1.08e+04 - 9.89e-01 5.59e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 6.23e+03 3.63e+02 -1.0 6.26e+03 - 8.95e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 3.17e+02 1.80e+01 -1.0 3.02e+02 - 9.90e-01 9.66e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 6 0.0000000e+00 2.76e-01 3.87e+01 -1.0 9.51e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: 7 0.0000000e+00 2.04e-07 3.21e-02 -3.8 1.64e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 6.0647087810765159e-11 2.0424340618774292e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 6.0647087810765159e-11 2.0424340618774292e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 8\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: 1 0.0000000e+00 3.63e-08 0.00e+00 -1.0 7.00e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Constraint violation....: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Overall NLP error.......: 3.6321580410003665e-09 3.6321580410003662e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 0 0.0000000e+00 7.00e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 1 0.0000000e+00 8.98e+02 0.00e+00 -1.0 7.65e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 2 0.0000000e+00 5.89e+02 0.00e+00 -1.0 4.68e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 3 0.0000000e+00 3.38e+02 0.00e+00 -1.0 1.97e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 4 0.0000000e+00 1.53e+02 0.00e+00 -1.0 5.12e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 5 0.0000000e+00 4.47e+01 0.00e+00 -1.0 7.04e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 6 0.0000000e+00 5.10e+00 0.00e+00 -1.0 3.94e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 7 0.0000000e+00 7.67e-02 0.00e+00 -1.0 5.71e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 8 0.0000000e+00 1.77e-05 0.00e+00 -2.5 8.86e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 9 0.0000000e+00 9.95e-13 0.00e+00 -7.0 2.04e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of Iterations....: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Constraint violation....: 9.9475983006414026e-13 9.9475983006414026e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Overall NLP error.......: 9.9475983006414026e-13 9.9475983006414026e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of objective function evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of objective gradient evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of equality constraint evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 0 0.0000000e+00 9.03e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 1 0.0000000e+00 3.70e+04 6.71e+00 -1.0 2.03e+04 - 9.90e-01 7.88e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 2 0.0000000e+00 7.96e+03 1.00e+02 -1.0 3.88e+03 - 8.89e-01 9.03e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 3 0.0000000e+00 1.10e+03 2.77e+04 -1.0 1.55e+03 - 9.90e-01 9.82e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 4 0.0000000e+00 2.97e+02 7.45e+05 -1.0 3.70e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 5 0.0000000e+00 4.45e-01 2.09e+04 -1.0 5.52e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: 6 0.0000000e+00 2.57e-05 1.41e+01 -1.0 1.21e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of Iterations....: 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Constraint violation....: 1.5226407084512139e-10 2.5742469006218020e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Overall NLP error.......: 1.5226407084512139e-10 2.5742469006218020e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of objective function evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of objective gradient evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of equality constraint evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of equality constraint Jacobian evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Number of Lagrangian Hessian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.F102: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.F102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 8.25e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 5.68e-14 0.00e+00 -1.0 8.25e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 8.25e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 5.68e-14 0.00e+00 -1.0 8.25e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 9.18e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 1.32e+01 5.18e-04 -1.0 2.28e+01 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 2.35e-03 9.67e-14 -1.0 2.25e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: 3 0.0000000e+00 3.64e-12 3.47e-13 -2.5 2.35e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:07 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 8.25e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 5.68e-14 0.00e+00 -1.0 8.25e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 5.6843418860808015e-14 5.6843418860808015e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.80e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 3.13e-13 0.00e+00 -1.0 1.33e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 3.1263880373444408e-13 3.1263880373444408e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 3.1263880373444408e-13 3.1263880373444408e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 4.42e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.72e+03 2.76e+00 -1.0 5.66e+02 - 9.85e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 3.48e+00 1.01e+01 -1.0 4.85e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 2.84e-06 3.38e+01 -1.0 3.19e-02 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 3.3630239142481114e-09 2.8386712074279785e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 3.3630239142481114e-09 2.8386712074279785e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 6.00e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 2.27e-13 0.00e+00 -1.0 1.61e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 3.24e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 1.09e+01 0.00e+00 -1.0 6.35e+06 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 3.73e-01 0.00e+00 -1.0 2.52e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 3 0.0000000e+00 4.59e-04 0.00e+00 -1.7 4.46e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 4 0.0000000e+00 7.00e-10 0.00e+00 -5.7 3.70e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 6.9962879933882505e-10 6.9962879933882505e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 6.9962879933882505e-10 6.9962879933882505e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 1.05e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 5.93e+04 1.13e+01 -1.0 2.36e+04 - 9.10e-02 5.64e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 2.98e+04 3.85e+00 -1.0 1.03e+04 - 9.90e-01 5.47e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 1.39e+04 1.30e+04 -1.0 4.72e+03 - 9.90e-01 5.60e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 1.33e+03 1.60e+08 -1.0 2.08e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 6.98e+02 8.17e+07 -1.0 2.38e+01 - 1.00e+00 6.05e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 1.49e+02 3.71e+08 -1.0 9.38e+00 - 4.26e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 7.19e+01 6.66e+06 -1.0 4.73e+00 - 1.00e+00 4.96e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 2.79e+01 7.00e+08 -1.0 2.38e+00 - 1.00e+00 9.94e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 7.04e+00 1.76e+08 -1.0 1.50e-02 - 1.00e+00 5.00e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 6.93e+00 1.74e+08 -1.0 7.52e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 11 0.0000000e+00 1.47e-04 3.72e+03 -1.0 7.73e-07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: 12 0.0000000e+00 4.37e-11 8.57e-03 -1.7 1.58e-07 - 1.00e+00 1.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 2.2737367544323206e-13 4.3655745685100555e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 2.2737367544323206e-13 4.3655745685100555e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 8.69e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 3.64e-12 0.00e+00 -1.0 8.69e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 3.6379788070917130e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 3.6379788070917130e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 1.55e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 2.98e-08 0.00e+00 -1.0 1.55e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.47e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 5.18e+05 3.27e+01 -1.0 9.30e+04 - 1.51e-01 1.24e-01h 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 5.16e+05 3.91e+01 -1.0 8.67e+04 - 5.58e-01 1.55e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 5.14e+05 4.24e+01 -1.0 8.59e+04 - 3.40e-01 1.55e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 5.12e+05 4.89e+01 -1.0 8.51e+04 - 6.07e-01 1.55e-02h 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.05e+04 1.27e+04 -1.0 8.43e+04 - 3.85e-01 9.90e-01H 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 2.19e+04 3.31e+04 -1.0 1.35e+03 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 5.80e+04 2.14e+04 -1.0 3.92e+04 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 2.71e-04 2.73e-02 -1.0 5.80e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 5.22e-08 9.54e-10 -7.0 1.92e-07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 2.8097163774505327e-11 5.2154064178466797e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 2.8097163774505327e-11 5.2154064178466797e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 3.51e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 2.98e-08 0.00e+00 -1.0 3.59e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 2.9802322387695314e-09 2.9802322387695312e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 9.90e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 2.27e-13 0.00e+00 -1.0 9.90e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 8.74e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 7.30e+04 3.10e+01 -1.0 1.08e+04 - 4.52e-02 3.85e-01f 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 3.99e+04 3.26e+01 -1.0 7.60e+03 - 2.91e-01 4.83e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 2.87e+03 1.84e+01 -1.0 3.50e+03 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.31e+02 8.00e-01 -1.0 2.46e+02 - 9.90e-01 9.65e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 5.89e-02 2.42e+02 -1.0 7.54e+00 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: 6 0.0000000e+00 1.09e-08 1.29e-02 -3.8 1.80e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 5.2154013264316498e-12 1.0946678230538964e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 5.2154013264316498e-12 1.0946678230538964e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 1.72e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.72e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 1.72e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.72e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 3.63e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 9.87e-01 5.18e-04 -1.0 3.56e+00 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 3.56e-04 9.67e-14 -1.0 3.52e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: 3 0.0000000e+00 5.82e-11 3.47e-13 -2.5 3.56e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:08 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 1.72e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.72e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.85e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.0 6.05e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 2.24e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 2.98e+02 5.66e-01 -1.0 2.35e+02 - 9.88e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 9.87e-01 9.99e+00 -1.0 1.69e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.71e-07 3.28e+01 -1.0 1.13e-02 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 2.0251180530023178e-10 1.7136335372924805e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 2.0251180530023178e-10 1.7136335372924805e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 7.74e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.69e-13 0.00e+00 -1.0 1.43e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.6895820560166612e-13 4.6895820560166612e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.6895820560166612e-13 4.6895820560166612e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 2.04e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 6.24e+01 0.00e+00 -1.0 1.17e+07 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 1.08e+01 0.00e+00 -1.0 8.96e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 3 0.0000000e+00 3.91e-01 0.00e+00 -1.0 6.74e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 4 0.0000000e+00 5.40e-04 0.00e+00 -1.7 1.71e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 5 0.0000000e+00 1.04e-09 0.00e+00 -5.7 2.38e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 1.0363692126702517e-09 1.0363692126702517e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 1.0363692126702517e-09 1.0363692126702517e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.41e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.71e+04 1.18e+01 -1.0 1.40e+04 - 6.36e-02 5.29e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.27e+04 5.09e+00 -1.0 8.31e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.78e+03 1.16e+04 -1.0 4.22e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.65e+02 4.37e+08 -1.0 2.01e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.26e+02 2.40e+08 -1.0 1.89e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.80e+01 4.07e+08 -1.0 7.37e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.17e+01 6.12e+06 -1.0 3.72e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.74e+01 6.04e+08 -1.0 1.87e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.15e-02 2.83e+06 -1.0 1.66e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.57e-08 1.27e+00 -1.0 1.54e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.0889160467221185e-12 3.5739503800868988e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.0889160467221185e-12 3.5739503800868988e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 1.04e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 4.09e-12 0.00e+00 -1.0 1.04e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 4.0927261579781771e-12 4.0927261579781771e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 4.0927261579781771e-12 4.0927261579781771e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 1.20e+08 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 2.24e-08 0.00e+00 -1.0 1.20e+05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 2.2351741790771488e-09 2.2351741790771488e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 2.2351741790771488e-09 2.2351741790771488e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.37e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.37e+04 5.26e+00 -1.0 5.46e+04 - 4.79e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.18e+01 -1.0 5.45e+04 - 6.18e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.36e+04 1.87e+01 -1.0 5.45e+04 - 6.78e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.36e+04 2.86e+01 -1.0 5.45e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.49e+01 -1.0 5.44e+04 - 6.24e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.35e+04 4.48e+01 -1.0 5.44e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.35e+04 5.02e+01 -1.0 5.43e+04 - 5.46e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 6.02e+01 -1.0 5.43e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.34e+04 6.57e+01 -1.0 5.43e+04 - 5.48e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.34e+04 7.57e+01 -1.0 5.42e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.22e+07 3.74e+00 -1.0 5.42e+04 - 5.51e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.69e+05 7.89e+01 -1.0 7.67e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.20e+04 6.56e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.71e-05 4.35e-02 -1.7 4.20e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.2852738158978019e-08 3.7133693695068359e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.2852738158978019e-08 3.7133693695068359e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 5.79e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 1.49e-08 0.00e+00 -1.0 5.94e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.45e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 1.14e-12 0.00e+00 -1.0 4.45e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 1.1368683772161603e-12 1.1368683772161603e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 1.1368683772161603e-12 1.1368683772161603e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.18e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.81e+04 6.09e+00 -1.0 1.66e+04 - 1.31e-01 6.60e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.85e+03 2.82e+01 -1.0 5.30e+03 - 8.83e-01 6.24e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.72e+02 1.97e+01 -1.0 2.29e+03 - 8.30e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.40e+00 2.53e+00 -1.0 1.54e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.60e-06 7.26e+02 -1.0 1.27e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 2.3414288502824821e-09 2.6022753445431590e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 2.3414288502824821e-09 2.6022753445431590e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 7.35e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 1.42e-13 0.00e+00 -1.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 7.35e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 1.42e-13 0.00e+00 -1.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:09 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 7.35e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 1.42e-13 0.00e+00 -1.0 7.35e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 2.47e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 5.12e-13 0.00e+00 -1.0 7.72e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 5.1159076974727213e-13 5.1159076974727213e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 5.1159076974727213e-13 5.1159076974727213e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.58e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.84e+03 3.49e+00 -1.0 5.83e+02 - 7.71e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.22e-01 1.01e+01 -1.0 5.51e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.45e-06 3.74e+01 -1.0 2.11e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6400136363658114e-09 1.4528632164001465e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6400136363658114e-09 1.4528632164001465e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 3.96e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.66e-10 0.00e+00 -1.0 3.96e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.6566128730773928e-11 4.6566128730773926e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.6566128730773928e-11 4.6566128730773926e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.04e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 1.07e-03 0.00e+00 -1.0 5.71e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 4.05e-09 0.00e+00 -5.7 2.68e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 4.0522536437492818e-09 4.0522536437492818e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 4.0522536437492818e-09 4.0522536437492818e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.31e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.73e+04 1.13e+01 -1.0 9.78e+03 - 9.34e-02 5.26e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.30e+04 5.18e+00 -1.0 4.93e+03 - 9.90e-01 5.20e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.95e+03 1.15e+04 -1.0 2.43e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 8.99e+02 4.35e+08 -1.0 1.13e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 4.96e+02 2.42e+08 -1.0 1.74e+01 - 1.00e+00 6.09e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.84e+01 4.10e+08 -1.0 6.78e+00 - 3.95e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.20e+01 6.19e+06 -1.0 3.42e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.65e+01 6.23e+08 -1.0 1.72e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 7.91e-02 2.99e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.28e-08 1.27e+00 -1.0 3.98e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 6.5032986331031271e-12 3.2840034691616893e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 6.5032986331031271e-12 3.2840034691616893e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 2.50e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 9.09e-13 0.00e+00 -1.0 2.50e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 9.0949470177292824e-13 9.0949470177292824e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 9.0949470177292824e-13 9.0949470177292824e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.19e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.28e-12 0.00e+00 -1.0 6.19e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.2759576141834259e-12 7.2759576141834259e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.39e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.39e+04 4.68e+00 -1.0 5.65e+04 - 4.27e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.39e+04 1.11e+01 -1.0 5.65e+04 - 6.10e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.38e+04 1.74e+01 -1.0 5.64e+04 - 6.19e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.38e+04 2.74e+01 -1.0 5.64e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.38e+04 3.19e+01 -1.0 5.63e+04 - 4.45e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.37e+04 4.18e+01 -1.0 5.63e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.37e+04 4.62e+01 -1.0 5.62e+04 - 4.49e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.37e+04 5.62e+01 -1.0 5.62e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.36e+04 6.07e+01 -1.0 5.62e+04 - 4.50e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.36e+04 7.07e+01 -1.0 5.61e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.32e+07 5.75e+01 -1.0 5.61e+04 - 4.53e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.83e+05 1.50e+02 -1.0 8.34e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.35e+04 8.07e+01 -1.0 2.35e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.90e-05 4.87e-02 -1.7 4.35e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3508256430337923e-08 3.8973987102508545e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3508256430337923e-08 3.8973987102508545e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 2.59e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 1.46e-11 0.00e+00 -1.0 2.92e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 1.4551915228366852e-11 1.4551915228366852e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 1.4551915228366852e-11 1.4551915228366852e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.21e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 4.21e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 4.96e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.54e+04 5.12e+00 -1.0 3.23e+03 - 1.46e-01 6.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.51e+03 2.82e+01 -1.0 2.15e+03 - 8.67e-01 6.00e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.87e+02 2.35e+01 -1.0 7.01e+02 - 7.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.45e+00 2.84e+00 -1.0 1.47e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 4.82e-07 8.12e+02 -1.0 1.14e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 4.0148485686193228e-10 4.8150832299143076e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 4.0148485686193228e-10 4.8150832299143076e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 3.19e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 8.53e-14 0.00e+00 -1.0 3.19e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 3.19e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 8.53e-14 0.00e+00 -1.0 3.19e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:10 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 3.19e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 8.53e-14 0.00e+00 -1.0 3.19e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 8.5265128291212022e-14 8.5265128291212022e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.85e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.33e-10 0.00e+00 -1.0 5.85e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.52e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.80e+03 3.46e+00 -1.0 5.77e+02 - 7.73e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.17e-01 1.01e+01 -1.0 5.46e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.42e-06 3.74e+01 -1.0 2.10e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6267341737231732e-09 1.4230608940124512e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6267341737231732e-09 1.4230608940124512e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 6.40e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 2.27e-13 0.00e+00 -1.0 1.81e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 9.84e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 4.65e-04 0.00e+00 -1.0 3.76e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 7.66e-10 0.00e+00 -5.7 1.16e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 7.6579453889280558e-10 7.6579453889280558e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 7.6579453889280558e-10 7.6579453889280558e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.39e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.72e+04 1.17e+01 -1.0 9.60e+03 - 6.94e-02 5.28e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.28e+04 5.11e+00 -1.0 4.88e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.83e+03 1.16e+04 -1.0 2.41e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.55e+02 4.36e+08 -1.0 1.12e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.22e+02 2.40e+08 -1.0 1.85e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.82e+01 4.08e+08 -1.0 7.23e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.18e+01 6.14e+06 -1.0 3.64e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.74e+01 6.12e+08 -1.0 1.83e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.26e-02 2.90e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.65e-08 1.31e+00 -1.0 2.08e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.2342852904786382e-12 3.6496203392744064e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.2342852904786382e-12 3.6496203392744064e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 1.91e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 1.91e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.44e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 6.44e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.38e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.38e+04 5.12e+00 -1.0 5.51e+04 - 4.67e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.16e+01 -1.0 5.50e+04 - 6.16e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.37e+04 1.84e+01 -1.0 5.50e+04 - 6.71e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.37e+04 2.84e+01 -1.0 5.49e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.41e+01 -1.0 5.49e+04 - 5.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.36e+04 4.40e+01 -1.0 5.49e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.36e+04 4.92e+01 -1.0 5.48e+04 - 5.21e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 5.92e+01 -1.0 5.48e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.35e+04 6.44e+01 -1.0 5.47e+04 - 5.22e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.35e+04 7.44e+01 -1.0 5.47e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.25e+07 1.48e+01 -1.0 5.47e+04 - 5.25e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.73e+05 9.39e+01 -1.0 7.84e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.24e+04 6.91e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.77e-05 4.49e-02 -1.7 4.24e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3044527347541834e-08 3.7662684917449951e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3044527347541834e-08 3.7662684917449951e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 1.92e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 1.46e-11 0.00e+00 -1.0 2.17e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 1.4551915228366852e-11 1.4551915228366852e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 1.4551915228366852e-11 1.4551915228366852e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.37e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 7.39e-13 0.00e+00 -1.0 4.37e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 7.3896444519050419e-13 7.3896444519050419e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 7.3896444519050419e-13 7.3896444519050419e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.12e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.74e+04 5.83e+00 -1.0 3.26e+03 - 1.35e-01 6.68e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.79e+03 2.83e+01 -1.0 2.31e+03 - 8.78e-01 6.18e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.77e+02 2.07e+01 -1.0 7.29e+02 - 8.20e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.43e+00 2.59e+00 -1.0 1.53e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.02e-06 7.46e+02 -1.0 1.25e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 1.7885121523301191e-09 2.0237566786818206e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 1.7885121523301191e-09 2.0237566786818206e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 2.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 2.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 2.40e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.0 2.40e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:11 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.78e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.27e-13 0.00e+00 -1.0 5.78e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.2737367544323206e-13 2.2737367544323206e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.55e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.82e+03 3.47e+00 -1.0 5.80e+02 - 7.72e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.20e-01 1.01e+01 -1.0 5.49e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.43e-06 3.74e+01 -1.0 2.11e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6333739050444924e-09 1.4305114746093750e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6333739050444924e-09 1.4305114746093750e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 3.35e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 1.99e-13 0.00e+00 -1.0 3.35e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 1.9895196601282805e-13 1.9895196601282805e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.00e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 1.28e-05 0.00e+00 -1.0 1.00e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 9.09e-13 0.00e+00 -7.0 3.21e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 9.0949470177292824e-13 9.0949470177292824e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 9.0949470177292824e-13 9.0949470177292824e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.39e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.72e+04 1.17e+01 -1.0 9.61e+03 - 6.95e-02 5.28e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.28e+04 5.11e+00 -1.0 4.88e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.84e+03 1.16e+04 -1.0 2.41e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.56e+02 4.35e+08 -1.0 1.12e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.23e+02 2.40e+08 -1.0 1.85e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.83e+01 4.08e+08 -1.0 7.22e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.18e+01 6.15e+06 -1.0 3.64e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.74e+01 6.12e+08 -1.0 1.83e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.29e-02 2.91e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.67e-08 1.32e+00 -1.0 8.16e-09 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.2734060488714093e-12 3.6714482121169567e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.2734060488714093e-12 3.6714482121169567e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 2.71e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 2.71e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.25e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 3.18e-12 0.00e+00 -1.0 6.25e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 3.1832314562052488e-12 3.1832314562052488e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 3.1832314562052488e-12 3.1832314562052488e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.38e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.38e+04 5.12e+00 -1.0 5.51e+04 - 4.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.16e+01 -1.0 5.51e+04 - 6.16e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.37e+04 1.84e+01 -1.0 5.50e+04 - 6.71e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.37e+04 2.84e+01 -1.0 5.50e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.40e+01 -1.0 5.49e+04 - 5.65e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.36e+04 4.39e+01 -1.0 5.49e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.36e+04 4.91e+01 -1.0 5.49e+04 - 5.20e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 5.91e+01 -1.0 5.48e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.35e+04 6.43e+01 -1.0 5.48e+04 - 5.21e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.35e+04 7.43e+01 -1.0 5.47e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.25e+07 1.53e+01 -1.0 5.47e+04 - 5.24e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.73e+05 9.46e+01 -1.0 7.85e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.24e+04 6.93e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.77e-05 4.50e-02 -1.7 4.24e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3047389872744279e-08 3.7692487239837646e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3047389872744279e-08 3.7692487239837646e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 4.28e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 1.49e-08 0.00e+00 -1.0 4.51e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 1.4901161193847657e-09 1.4901161193847656e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.35e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 4.55e-13 0.00e+00 -1.0 4.35e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 4.5474735088646412e-13 4.5474735088646412e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.12e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.74e+04 5.83e+00 -1.0 3.26e+03 - 1.35e-01 6.68e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.80e+03 2.83e+01 -1.0 2.31e+03 - 8.78e-01 6.17e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.77e+02 2.07e+01 -1.0 7.30e+02 - 8.20e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.43e+00 2.59e+00 -1.0 1.53e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.03e-06 7.46e+02 -1.0 1.25e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 1.7944467911229722e-09 2.0295010472182184e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 1.7944467911229722e-09 2.0295010472182184e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 2.23e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.7 2.23e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 2.23e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.7 2.23e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 2.23e-01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 3.98e-13 0.00e+00 -1.7 2.23e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 3.9790393202565610e-13 3.9790393202565610e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.82e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 2.33e-10 0.00e+00 -1.0 5.82e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 2.3283064365386964e-11 2.3283064365386963e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:12 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.55e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.82e+03 3.47e+00 -1.0 5.79e+02 - 7.72e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.19e-01 1.01e+01 -1.0 5.48e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.45e-06 3.74e+01 -1.0 2.10e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6300540393838327e-09 1.4454126358032227e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6300540393838327e-09 1.4454126358032227e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 0 0.0000000e+00 5.85e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: 1 0.0000000e+00 4.66e-10 0.00e+00 -1.0 5.85e-01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Constraint violation....: 4.6566128730773928e-11 4.6566128730773926e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Overall NLP error.......: 4.6566128730773928e-11 4.6566128730773926e-10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 0 0.0000000e+00 1.00e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 1 0.0000000e+00 4.77e-05 0.00e+00 -1.0 1.20e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: 2 0.0000000e+00 7.96e-12 0.00e+00 -5.7 1.19e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Constraint violation....: 7.9580786405131221e-12 7.9580786405131221e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Overall NLP error.......: 7.9580786405131221e-12 7.9580786405131221e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 0 0.0000000e+00 5.40e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 1 0.0000000e+00 2.72e+04 1.17e+01 -1.0 9.62e+03 - 6.95e-02 5.28e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 2 0.0000000e+00 1.28e+04 5.11e+00 -1.0 4.89e+03 - 9.90e-01 5.21e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 3 0.0000000e+00 5.84e+03 1.16e+04 -1.0 2.42e+03 - 9.90e-01 5.38e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 4 0.0000000e+00 9.57e+02 4.35e+08 -1.0 1.12e+03 - 9.92e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 5 0.0000000e+00 5.23e+02 2.40e+08 -1.0 1.85e+01 - 1.00e+00 6.08e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 6 0.0000000e+00 2.83e+01 4.08e+08 -1.0 7.23e+00 - 3.97e-02 4.96e-01f 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 7 0.0000000e+00 1.19e+01 6.15e+06 -1.0 3.64e+00 - 1.00e+00 4.98e-01h 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 8 0.0000000e+00 1.75e+01 6.13e+08 -1.0 1.83e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 9 0.0000000e+00 8.34e-02 2.93e+06 -1.0 1.68e-04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: 10 0.0000000e+00 3.72e-08 1.33e+00 -1.0 8.11e-08 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of Iterations....: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Constraint violation....: 7.3728511770380485e-12 3.7191057344898582e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Overall NLP error.......: 7.3728511770380485e-12 3.7191057344898582e-08\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of objective function evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of objective gradient evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of equality constraint evaluations = 16\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of equality constraint Jacobian evaluations = 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Number of Lagrangian Hessian evaluations = 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.H101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 0 0.0000000e+00 8.52e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: 1 0.0000000e+00 1.36e-12 0.00e+00 -1.0 8.52e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Constraint violation....: 1.3642420526593924e-12 1.3642420526593924e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Overall NLP error.......: 1.3642420526593924e-12 1.3642420526593924e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 0 0.0000000e+00 6.26e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: 1 0.0000000e+00 7.45e-09 0.00e+00 -1.0 6.26e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Constraint violation....: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Overall NLP error.......: 7.4505805969238285e-10 7.4505805969238281e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in equality constraint Jacobian...: 102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of nonzeros in Lagrangian Hessian.............: 72\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 0 0.0000000e+00 3.38e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 1 0.0000000e+00 3.38e+04 5.11e+00 -1.0 5.51e+04 - 4.66e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 2 0.0000000e+00 3.37e+04 1.16e+01 -1.0 5.51e+04 - 6.16e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 3 0.0000000e+00 3.37e+04 1.84e+01 -1.0 5.51e+04 - 6.71e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 4 0.0000000e+00 3.37e+04 2.84e+01 -1.0 5.50e+04 - 9.90e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 5 0.0000000e+00 3.36e+04 3.40e+01 -1.0 5.50e+04 - 5.64e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 6 0.0000000e+00 3.36e+04 4.39e+01 -1.0 5.49e+04 - 9.91e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 7 0.0000000e+00 3.36e+04 4.91e+01 -1.0 5.49e+04 - 5.20e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 8 0.0000000e+00 3.35e+04 5.91e+01 -1.0 5.49e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 9 0.0000000e+00 3.35e+04 6.43e+01 -1.0 5.48e+04 - 5.21e-01 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 10 0.0000000e+00 3.35e+04 7.43e+01 -1.0 5.48e+04 - 1.00e+00 9.67e-04h 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 11 0.0000000e+00 1.25e+07 1.59e+01 -1.0 5.47e+04 - 5.24e-01 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 12 0.0000000e+00 1.73e+05 9.54e+01 -1.0 7.86e+03 - 1.00e+00 9.90e-01w 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 13 0.0000000e+00 4.24e+04 6.95e+01 -1.0 2.33e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: 14 0.0000000e+00 3.78e-05 4.51e-02 -1.7 4.24e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of Iterations....: 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Constraint violation....: 1.3093190275983399e-08 3.7796795368194580e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Overall NLP error.......: 1.3093190275983399e-08 3.7796795368194580e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of objective function evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of objective gradient evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of equality constraint evaluations = 155\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of equality constraint Jacobian evaluations = 15\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Number of Lagrangian Hessian evaluations = 14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.R101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 0 0.0000000e+00 5.65e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: 1 0.0000000e+00 3.64e-12 0.00e+00 -1.0 5.73e+01 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Constraint violation....: 3.6379788070917130e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Overall NLP error.......: 3.6379788070917130e-12 3.6379788070917130e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 0 0.0000000e+00 4.35e+03 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: 1 0.0000000e+00 6.39e-13 0.00e+00 -1.0 4.35e+03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Constraint violation....: 6.3948846218409017e-13 6.3948846218409017e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Overall NLP error.......: 6.3948846218409017e-13 6.3948846218409017e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: 0 0.0000000e+00 5.13e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: 1 0.0000000e+00 3.75e+04 5.85e+00 -1.0 3.26e+03 - 1.34e-01 6.67e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: 2 0.0000000e+00 9.81e+03 2.83e+01 -1.0 2.31e+03 - 8.78e-01 6.17e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: 3 0.0000000e+00 1.77e+02 2.07e+01 -1.0 7.31e+02 - 8.20e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: 4 0.0000000e+00 1.43e+00 2.58e+00 -1.0 1.54e+01 - 9.90e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: 5 0.0000000e+00 2.07e-06 7.45e+02 -1.0 1.25e-01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of Iterations....: 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Constraint violation....: 1.8325340066076841e-09 2.0688094082288444e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Overall NLP error.......: 1.8325340066076841e-09 2.0688094082288444e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of objective function evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of objective gradient evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of equality constraint evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of equality constraint Jacobian evaluations = 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Number of Lagrangian Hessian evaluations = 5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.F101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.purge_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.purge_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in equality constraint Jacobian...: 29\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Total number of variables............................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: variables with lower and upper bounds: 20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Total number of equality constraints.................: 21\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: 0 0.0000000e+00 1.00e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: 1 0.0000000e+00 1.00e-04 1.14e-05 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: 2 0.0000000e+00 9.88e-07 1.04e-01 -1.0 1.00e-04 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Constraint violation....: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Overall NLP error.......: 9.8800099999998489e-07 9.8800099999998489e-07\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.S101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.S101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 0 0.0000000e+00 7.88e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: 1 0.0000000e+00 1.42e-13 0.00e+00 -2.5 7.88e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 0 0.0000000e+00 7.88e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: 1 0.0000000e+00 1.42e-13 0.00e+00 -2.5 7.88e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in equality constraint Jacobian...: 74\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of nonzeros in Lagrangian Hessian.............: 63\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Total number of variables............................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Total number of equality constraints.................: 32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: 0 0.0000000e+00 8.03e+01 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: 1 0.0000000e+00 7.95e-01 5.18e-04 -1.0 1.00e-02 - 9.90e-01 9.90e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: 2 0.0000000e+00 5.82e-11 9.67e-14 -1.0 9.90e-05 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of Iterations....: 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Constraint violation....: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Overall NLP error.......: 3.1719263458133377e-12 5.8207660913467407e-11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of objective function evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of objective gradient evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of equality constraint evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of equality constraint Jacobian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Number of Lagrangian Hessian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.C101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101: Initialization Step 2 optimal - Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101.control_volume.properties_in: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.toluene_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: 0 0.0000000e+00 1.78e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Constraint violation....: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Overall NLP error.......: 1.7763568394002505e-12 1.7763568394002505e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.toluene_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.hydrogen_feed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: 0 0.0000000e+00 2.84e-14 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Overall NLP error.......: 2.8421709430404007e-14 2.8421709430404007e-14\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.hydrogen_feed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.vapor_recycle_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 0 0.0000000e+00 7.88e-02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: 1 0.0000000e+00 1.42e-13 0.00e+00 -2.5 7.88e-02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Constraint violation....: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Overall NLP error.......: 1.4210854715202004e-13 1.4210854715202004e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.vapor_recycle_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.mixed_state: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: 0 0.0000000e+00 5.81e+05 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: 1 0.0000000e+00 1.56e-13 0.00e+00 -1.0 5.81e+02 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Constraint violation....: 1.5631940186722204e-13 1.5631940186722204e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Overall NLP error.......: 1.5631940186722204e-13 1.5631940186722204e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101.mixed_state: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.mixed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in equality constraint Jacobian...: 117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of nonzeros in Lagrangian Hessian.............: 65\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total number of variables............................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: variables with lower and upper bounds: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total number of equality constraints.................: 53\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 0 0.0000000e+00 3.54e+02 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 1 0.0000000e+00 1.81e+03 3.47e+00 -1.0 5.78e+02 - 7.72e-01 9.92e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 2 0.0000000e+00 1.19e-01 1.01e+01 -1.0 5.47e+01 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: 3 0.0000000e+00 1.44e-06 3.74e+01 -1.0 2.10e-02 - 9.90e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of Iterations....: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Constraint violation....: 1.6267341737231732e-09 1.4379620552062988e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Overall NLP error.......: 1.6267341737231732e-09 1.4379620552062988e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of objective function evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of objective gradient evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of equality constraint evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of equality constraint Jacobian evaluations = 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Number of Lagrangian Hessian evaluations = 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:13 [DEBUG] idaes.solve.fs.M101: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.M101.toluene_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.M101.hydrogen_feed_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.M101.vapor_recycle_state: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: Wegstein failed to converge in 5 iterations\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of nonzeros in equality constraint Jacobian...: 18\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of nonzeros in Lagrangian Hessian.............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total number of variables............................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total number of equality constraints.................: 10\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: 0 0.0000000e+00 1.79e-12 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of Iterations....: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Constraint violation....: 1.7905676941154525e-12 1.7905676941154525e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Overall NLP error.......: 1.7905676941154525e-12 1.7905676941154525e-12\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of objective function evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of objective gradient evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of equality constraint evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of equality constraint Jacobian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Number of Lagrangian Hessian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_in: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Step 1 - Dew and bubble points calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Step 2 - Equilibrium temperature calculation completed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of nonzeros in equality constraint Jacobian...: 31\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of nonzeros in Lagrangian Hessian.............: 11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total number of variables............................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: variables with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total number of equality constraints.................: 17\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 0 0.0000000e+00 1.88e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: 1 0.0000000e+00 3.41e-13 0.00e+00 -1.0 1.88e+04 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of Iterations....: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Constraint violation....: 3.4106051316484809e-13 3.4106051316484809e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Overall NLP error.......: 3.4106051316484809e-13 3.4106051316484809e-13\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of objective function evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of objective gradient evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of equality constraint evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of inequality constraint evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of equality constraint Jacobian evaluations = 2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of inequality constraint Jacobian evaluations = 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Number of Lagrangian Hessian evaluations = 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: Total CPU secs in NLP function evaluations = 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102.control_volume.properties_out: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume.properties_out: State Released.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume: Initialization Complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102: Initialization Step 1 Complete.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Ipopt 3.13.2: nlp_scaling_method=gradient-based\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: tol=1e-06\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: This program contains Ipopt, a library for large-scale nonlinear optimization.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Ipopt is released as open source code under the Eclipse Public License (EPL).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: For more information visit http://projects.coin-or.org/Ipopt\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: This version of Ipopt was compiled from source code available at\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: This version of Ipopt was compiled using HSL, a collection of Fortran codes\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: for large-scale scientific computation. All technical papers, sales and\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: publicity material resulting from use of the HSL codes within IPOPT must\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: contain the following acknowledgement:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: HSL, a collection of Fortran codes for large-scale scientific\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: computation. See http://www.hsl.rl.ac.uk.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: ******************************************************************************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: This is Ipopt version 3.13.2, running with linear solver ma27.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Number of nonzeros in equality constraint Jacobian...: 124\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Number of nonzeros in inequality constraint Jacobian.: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Number of nonzeros in Lagrangian Hessian.............: 115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Total number of variables............................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: variables with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: variables with lower and upper bounds: 9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: variables with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Total number of equality constraints.................: 41\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Total number of inequality constraints...............: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: inequality constraints with only lower bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: inequality constraints with lower and upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: inequality constraints with only upper bounds: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 0 0.0000000e+00 9.03e+04 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 1 0.0000000e+00 3.70e+04 6.71e+00 -1.0 2.03e+04 - 9.90e-01 7.88e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 2 0.0000000e+00 7.96e+03 1.00e+02 -1.0 3.88e+03 - 8.89e-01 9.03e-01h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 3 0.0000000e+00 4.57e+02 4.52e+04 -1.0 1.43e+03 - 9.12e-01 9.90e-01H 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 4 0.0000000e+00 1.81e+01 1.55e+04 -1.0 9.26e+01 - 9.91e-01 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 5 0.0000000e+00 6.31e-03 2.66e+02 -1.0 1.60e+00 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: 6 0.0000000e+00 2.56e-09 1.82e-02 -1.7 1.19e-03 - 1.00e+00 1.00e+00h 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Number of Iterations....: 6\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: (scaled) (unscaled)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Constraint violation....: 1.7905676941154525e-12 2.5611370801925659e-09\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [DEBUG] idaes.solve.fs.F102: EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102: Initialization Step 2 optimal - Optimal Solution Found.\n" + "WARNING: Wegstein failed to converge in 5 iterations\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102.control_volume.properties_in: State Released.\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.F102.control_volume.properties_in: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:28:14 [INFO] idaes.init.fs.F102: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:03 [INFO] idaes.init.fs.F102.control_volume.properties_out: Initialization Complete\n" ] } ], @@ -128072,7 +2192,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_test.ipynb b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_test.ipynb index 41e38f50..6f19c2b2 100644 --- a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_test.ipynb +++ b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_test.ipynb @@ -1,434 +1,435 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Flowsheet Visualizer Tutorial\n", - "\n", - "Author: Dan Gunter \n", - "Maintainer: Dan Gunter \n", - "Updated: 2023-06-01 \n", - "\n", - "The IDAES Flowsheet Visualizer provides a web-based UI for visualization and inspection of an existing IDAES model.\n", - "## Outline\n", - "\n", - "- Introduction\n", - "- Example flowsheet\n", - "- Running the Flowsheet Visualizer\n", - "- Running from a script\n", - "- Further reading" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "The IDAES Flowsheet Visualizer (FV) is a Python tool that provides a web-based visualization of any existing IDAES model or flowsheet. The visualization shows a diagram of the\n", - "flowsheet as well as a stream table. You can interact with the diagram and export\n", - "it as an image for inclusion in presentations or publications.\n", - "\n", - "This tutorial will show the basic steps of running the FV on an example\n", - "flowsheet, interacting with the resulting GUI, saving your work, and exporting\n", - "the diagram as an image. It will also show how the Visualizer can be updated\n", - "to reflect changes in the model components and/or variable values. The tutorial\n", - "will also show how to run the Visualizer from a Python script." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example flowsheet\n", - "This initial section creates an example flowsheet.\n", - "\n", - "### Setup\n", - "Module imports and any additional housekeeping needed\n", - "to initialize the code." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import idaes_examples.mod.tut.visualizer_tutorial as vistut\n", - "\n", - "vistut.quiet() # turn off default logging and most warnings\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from IPython.display import Markdown" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# use the pre-defined function to create the flowsheet\n", - "model = vistut.create_model()\n", - "\n", - "# description of the flowsheet we created\n", - "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", - "\n", - "vistut.quiet()\n", - "\n", - "# initialize the flowsheet as a square problem (dof=0)\n", - "vistut.initialize_model(model)\n", - "\n", - "# verify that there are zero degrees of freedom\n", - "print(f\"DOF = {degrees_of_freedom(model)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the Flowsheet Visualizer\n", - "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", - "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", - "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", - "\n", - "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", - "\n", - "
\n", - "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "if os.path.exists(\"Hydrodealkylation.json\"):\n", - " os.remove(\"Hydrodealkylation.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional arguments\n", - "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", - "\n", - " * name: Name of flowsheet to display as the title of the visualization\n", - " * load_from_saved: If True load from saved file if any. Otherwise create\n", - " a new file or overwrite it (depending on 'overwrite' flag).\n", - " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", - " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", - " between the name and the extension). If the value given is the boolean 'False', then nothing\n", - " will be saved. The boolean 'True' value is treated the same as unspecified.\n", - " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", - " be used as the directory to save the default or given file. The current working directory is\n", - " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", - " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", - " in the graph for it to save the model. Default is 5 seconds\n", - " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", - " numbered file.\n", - " * browser: If true, open a browser\n", - " * port: Start listening on this port. If not given, find an open port.\n", - " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", - " See the `idaes.logger` module for details\n", - " * quiet: If True, suppress printing any messages to standard output (console)\n", - " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", - " invoking this function at the end of a script." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interacting with the visualizer\n", - "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", - "The UI should initially look something like the screenshot below:\n", - "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", - "\n", - "
\n", - " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View controls\n", - "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", - "\n", - "| Control | Description | Illustration |\n", - "|:----|:---------------------|:----:|\n", - "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", - "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rearranging the diagram\n", - "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", - "\n", - "|   |   |\n", - "|:--:|:--:|\n", - "| ![](fvr1.png)   |   ![](fvr2.png) |\n", - "| ![](fvr3.png)   |   ![](fvr4.png) |\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stream table\n", - "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", - "\n", - "### Stream table \"brushing\"\n", - "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", - "#### Controls\n", - "\n", - "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", - "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", - "\n", - "![Illustration of stream table brushing](fvb1.png)\n", - " \n", - "#### Example\n", - "Stream table brushing is useful for answering questions like:\n", - "> How much benzene are we losing in the F101 vapor outlet stream?\n", - "\n", - "To answer this question, we will use some interactive elements of the stream table.\n", - "\n", - "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", - "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", - "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Showing and hiding streams\n", - "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", - "\n", - "* Click on the \"Hide Fields\" menu and select which fields to hide\n", - "* The mark will toggle between a check (shown) and open circle (hidden)\n", - "\n", - "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", - "\n", - "![Illustration of stream table field hiding](fvst1.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Saving and loading\n", - "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", - "\n", - "#### File name\n", - "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", - "\n", - "You can select a different filename and location when you start the visualization, e.g.\n", - "\n", - " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", - "\n", - "#### Reloading\n", - "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exporting\n", - "\n", - "#### Exporting the diagram as an image\n", - "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", - "\n", - "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", - "#### Exporting the stream table as CSV\n", - "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Updating when the flowsheet changes\n", - "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", - "\n", - "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", - "\n", - "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a second flash unit\n", - "from idaes.models.unit_models import Flash\n", - "from pyomo.network import Arc\n", - "from pyomo.environ import Expression, TransformationFactory\n", - "\n", - "m = model # alias\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")\n", - "# connect to 1st flash unit\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)\n", - "# update expressions for purity and cost\n", - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "# fix unit output and pressure drop\n", - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)\n", - "\n", - "# expand arcs\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "# re-initialize\n", - "_ = vistut.initialize_model(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Since the FV is connected to the current state of the model in memory, simply hitting \"Refresh\" in the FV window will show the new flash unit in the diagram, and the new stream (liquid) in the stream table. We can then interactively rearrange the unit to be in the position we want in the diagram.
" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# v model.fs.visualize(\"Hydrodealkylation-new\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Showing new values\n", - "The previous step showed how a new unit in the flowsheet will be automatically added to the diagram. Similarly, if the values in the flowsheet change these will be reflected in the stream table. Below, we solve the initialized flowsheet.\n", - "To make comparison a little easier, we will open a second UI window with the new values (the old values will not be updated unless we decide to hit the \"Refresh\" button)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When we look at the stream table, we can see the values in the stream between the first and second flash unit changing.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running from a script\n", - "Finally, although all examples have been shown in a Jupyter Notebook, there is nothing preventing the use of the FV from within a plain Python script (or module).\n", - "\n", - "For example, the code to run this same tutorial as a Python script is also in a module.\n", - "If you have installed the IDAES examples, then you can do the following to import and run the module:\n", - "```\n", - "from idaes_examples.mod.tut import visualizer_tutorial\n", - "visualizer_tutorial.main()\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Further reading\n", - "\n", - "Reference documentation for the FV is available in the IDAES main documentation, online at https://idaes-pse.readthedocs.io/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flowsheet Visualizer Tutorial\n", + "\n", + "Author: Dan Gunter \n", + "Maintainer: Dan Gunter \n", + "Updated: 2023-06-01 \n", + "\n", + "The IDAES Flowsheet Visualizer provides a web-based UI for visualization and inspection of an existing IDAES model.\n", + "## Outline\n", + "\n", + "- Introduction\n", + "- Example flowsheet\n", + "- Running the Flowsheet Visualizer\n", + "- Running from a script\n", + "- Further reading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "The IDAES Flowsheet Visualizer (FV) is a Python tool that provides a web-based visualization of any existing IDAES model or flowsheet. The visualization shows a diagram of the\n", + "flowsheet as well as a stream table. You can interact with the diagram and export\n", + "it as an image for inclusion in presentations or publications.\n", + "\n", + "This tutorial will show the basic steps of running the FV on an example\n", + "flowsheet, interacting with the resulting GUI, saving your work, and exporting\n", + "the diagram as an image. It will also show how the Visualizer can be updated\n", + "to reflect changes in the model components and/or variable values. The tutorial\n", + "will also show how to run the Visualizer from a Python script." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example flowsheet\n", + "This initial section creates an example flowsheet.\n", + "\n", + "### Setup\n", + "Module imports and any additional housekeeping needed\n", + "to initialize the code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import idaes_examples.mod.tut.visualizer_tutorial as vistut\n", + "\n", + "vistut.quiet() # turn off default logging and most warnings\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from IPython.display import Markdown" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# use the pre-defined function to create the flowsheet\n", + "model = vistut.create_model()\n", + "\n", + "# description of the flowsheet we created\n", + "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", + "\n", + "vistut.quiet()\n", + "\n", + "# initialize the flowsheet as a square problem (dof=0)\n", + "vistut.initialize_model(model)\n", + "\n", + "# verify that there are zero degrees of freedom\n", + "print(f\"DOF = {degrees_of_freedom(model)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Flowsheet Visualizer\n", + "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", + "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", + "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", + "\n", + "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", + "\n", + "
\n", + "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "if os.path.exists(\"Hydrodealkylation.json\"):\n", + " os.remove(\"Hydrodealkylation.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional arguments\n", + "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", + "\n", + " * name: Name of flowsheet to display as the title of the visualization\n", + " * load_from_saved: If True load from saved file if any. Otherwise create\n", + " a new file or overwrite it (depending on 'overwrite' flag).\n", + " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", + " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", + " between the name and the extension). If the value given is the boolean 'False', then nothing\n", + " will be saved. The boolean 'True' value is treated the same as unspecified.\n", + " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", + " be used as the directory to save the default or given file. The current working directory is\n", + " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", + " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", + " in the graph for it to save the model. Default is 5 seconds\n", + " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", + " numbered file.\n", + " * browser: If true, open a browser\n", + " * port: Start listening on this port. If not given, find an open port.\n", + " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", + " See the `idaes.logger` module for details\n", + " * quiet: If True, suppress printing any messages to standard output (console)\n", + " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", + " invoking this function at the end of a script." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interacting with the visualizer\n", + "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", + "The UI should initially look something like the screenshot below:\n", + "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", + "\n", + "
\n", + " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View controls\n", + "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", + "\n", + "| Control | Description | Illustration |\n", + "|:----|:---------------------|:----:|\n", + "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", + "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rearranging the diagram\n", + "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", + "\n", + "|   |   |\n", + "|:--:|:--:|\n", + "| ![](fvr1.png)   |   ![](fvr2.png) |\n", + "| ![](fvr3.png)   |   ![](fvr4.png) |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stream table\n", + "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", + "\n", + "### Stream table \"brushing\"\n", + "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", + "#### Controls\n", + "\n", + "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", + "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", + "\n", + "![Illustration of stream table brushing](fvb1.png)\n", + " \n", + "#### Example\n", + "Stream table brushing is useful for answering questions like:\n", + "> How much benzene are we losing in the F101 vapor outlet stream?\n", + "\n", + "To answer this question, we will use some interactive elements of the stream table.\n", + "\n", + "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", + "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", + "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing and hiding streams\n", + "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", + "\n", + "* Click on the \"Hide Fields\" menu and select which fields to hide\n", + "* The mark will toggle between a check (shown) and open circle (hidden)\n", + "\n", + "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", + "\n", + "![Illustration of stream table field hiding](fvst1.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving and loading\n", + "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", + "\n", + "#### File name\n", + "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", + "\n", + "You can select a different filename and location when you start the visualization, e.g.\n", + "\n", + " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", + "\n", + "#### Reloading\n", + "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exporting\n", + "\n", + "#### Exporting the diagram as an image\n", + "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", + "\n", + "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", + "#### Exporting the stream table as CSV\n", + "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating when the flowsheet changes\n", + "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", + "\n", + "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", + "\n", + "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a second flash unit\n", + "from idaes.models.unit_models import Flash\n", + "from pyomo.network import Arc\n", + "from pyomo.environ import Expression, TransformationFactory\n", + "\n", + "m = model # alias\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")\n", + "# connect to 1st flash unit\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)\n", + "# update expressions for purity and cost\n", + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "# fix unit output and pressure drop\n", + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)\n", + "\n", + "# expand arcs\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "# re-initialize\n", + "_ = vistut.initialize_model(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Since the FV is connected to the current state of the model in memory, simply hitting \"Refresh\" in the FV window will show the new flash unit in the diagram, and the new stream (liquid) in the stream table. We can then interactively rearrange the unit to be in the position we want in the diagram.
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# v model.fs.visualize(\"Hydrodealkylation-new\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing new values\n", + "The previous step showed how a new unit in the flowsheet will be automatically added to the diagram. Similarly, if the values in the flowsheet change these will be reflected in the stream table. Below, we solve the initialized flowsheet.\n", + "To make comparison a little easier, we will open a second UI window with the new values (the old values will not be updated unless we decide to hit the \"Refresh\" button)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we look at the stream table, we can see the values in the stream between the first and second flash unit changing.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running from a script\n", + "Finally, although all examples have been shown in a Jupyter Notebook, there is nothing preventing the use of the FV from within a plain Python script (or module).\n", + "\n", + "For example, the code to run this same tutorial as a Python script is also in a module.\n", + "If you have installed the IDAES examples, then you can do the following to import and run the module:\n", + "```\n", + "from idaes_examples.mod.tut import visualizer_tutorial\n", + "visualizer_tutorial.main()\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Further reading\n", + "\n", + "Reference documentation for the FV is available in the IDAES main documentation, online at https://idaes-pse.readthedocs.io/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_usr.ipynb b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_usr.ipynb index 1893363a..5c404a1d 100644 --- a/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_usr.ipynb +++ b/idaes_examples/notebooks/docs/tut/ui/visualizer_tutorial_usr.ipynb @@ -1,454 +1,455 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Flowsheet Visualizer Tutorial\n", - "\n", - "Author: Dan Gunter \n", - "Maintainer: Dan Gunter \n", - "Updated: 2023-06-01 \n", - "\n", - "The IDAES Flowsheet Visualizer provides a web-based UI for visualization and inspection of an existing IDAES model.\n", - "## Outline\n", - "\n", - "- Introduction\n", - "- Example flowsheet\n", - "- Running the Flowsheet Visualizer\n", - "- Running from a script\n", - "- Further reading" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "The IDAES Flowsheet Visualizer (FV) is a Python tool that provides a web-based visualization of any existing IDAES model or flowsheet. The visualization shows a diagram of the\n", - "flowsheet as well as a stream table. You can interact with the diagram and export\n", - "it as an image for inclusion in presentations or publications.\n", - "\n", - "This tutorial will show the basic steps of running the FV on an example\n", - "flowsheet, interacting with the resulting GUI, saving your work, and exporting\n", - "the diagram as an image. It will also show how the Visualizer can be updated\n", - "to reflect changes in the model components and/or variable values. The tutorial\n", - "will also show how to run the Visualizer from a Python script." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example flowsheet\n", - "This initial section creates an example flowsheet.\n", - "\n", - "### Setup\n", - "Module imports and any additional housekeeping needed\n", - "to initialize the code." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import idaes_examples.mod.tut.visualizer_tutorial as vistut\n", - "\n", - "vistut.quiet() # turn off default logging and most warnings\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from IPython.display import Markdown" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the flowsheet" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# use the pre-defined function to create the flowsheet\n", - "model = vistut.create_model()\n", - "\n", - "# description of the flowsheet we created\n", - "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", - "\n", - "vistut.quiet()\n", - "\n", - "# initialize the flowsheet as a square problem (dof=0)\n", - "vistut.initialize_model(model)\n", - "\n", - "# verify that there are zero degrees of freedom\n", - "print(f\"DOF = {degrees_of_freedom(model)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the Flowsheet Visualizer\n", - "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", - "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", - "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", - "\n", - "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", - "\n", - "
\n", - "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [ - "noauto" - ] - }, - "outputs": [], - "source": [ - "model.fs.visualize(\"Hydrodealkylation\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional arguments\n", - "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", - "\n", - " * name: Name of flowsheet to display as the title of the visualization\n", - " * load_from_saved: If True load from saved file if any. Otherwise create\n", - " a new file or overwrite it (depending on 'overwrite' flag).\n", - " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", - " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", - " between the name and the extension). If the value given is the boolean 'False', then nothing\n", - " will be saved. The boolean 'True' value is treated the same as unspecified.\n", - " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", - " be used as the directory to save the default or given file. The current working directory is\n", - " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", - " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", - " in the graph for it to save the model. Default is 5 seconds\n", - " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", - " numbered file.\n", - " * browser: If true, open a browser\n", - " * port: Start listening on this port. If not given, find an open port.\n", - " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", - " See the `idaes.logger` module for details\n", - " * quiet: If True, suppress printing any messages to standard output (console)\n", - " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", - " invoking this function at the end of a script." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interacting with the visualizer\n", - "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", - "The UI should initially look something like the screenshot below:\n", - "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", - "\n", - "
\n", - " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View controls\n", - "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", - "\n", - "| Control | Description | Illustration |\n", - "|:----|:---------------------|:----:|\n", - "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", - "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rearranging the diagram\n", - "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", - "\n", - "|   |   |\n", - "|:--:|:--:|\n", - "| ![](fvr1.png)   |   ![](fvr2.png) |\n", - "| ![](fvr3.png)   |   ![](fvr4.png) |\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Stream table\n", - "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", - "\n", - "### Stream table \"brushing\"\n", - "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", - "#### Controls\n", - "\n", - "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", - "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", - "\n", - "![Illustration of stream table brushing](fvb1.png)\n", - " \n", - "#### Example\n", - "Stream table brushing is useful for answering questions like:\n", - "> How much benzene are we losing in the F101 vapor outlet stream?\n", - "\n", - "To answer this question, we will use some interactive elements of the stream table.\n", - "\n", - "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", - "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", - "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Showing and hiding streams\n", - "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", - "\n", - "* Click on the \"Hide Fields\" menu and select which fields to hide\n", - "* The mark will toggle between a check (shown) and open circle (hidden)\n", - "\n", - "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", - "\n", - "![Illustration of stream table field hiding](fvst1.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Saving and loading\n", - "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", - "\n", - "#### File name\n", - "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", - "\n", - "You can select a different filename and location when you start the visualization, e.g.\n", - "\n", - " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", - "\n", - "#### Reloading\n", - "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exporting\n", - "\n", - "#### Exporting the diagram as an image\n", - "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", - "\n", - "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", - "#### Exporting the stream table as CSV\n", - "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Updating when the flowsheet changes\n", - "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", - "\n", - "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", - "\n", - "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a second flash unit\n", - "from idaes.models.unit_models import Flash\n", - "from pyomo.network import Arc\n", - "from pyomo.environ import Expression, TransformationFactory\n", - "\n", - "m = model # alias\n", - "m.fs.F102 = Flash(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=True,\n", - ")\n", - "# connect to 1st flash unit\n", - "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)\n", - "# update expressions for purity and cost\n", - "m.fs.purity = Expression(\n", - " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " / (\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", - " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", - " )\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "# fix unit output and pressure drop\n", - "m.fs.F102.vap_outlet.temperature.fix(375)\n", - "m.fs.F102.deltaP.fix(-200000)\n", - "\n", - "# expand arcs\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", - "# re-initialize\n", - "_ = vistut.initialize_model(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Since the FV is connected to the current state of the model in memory, simply hitting \"Refresh\" in the FV window will show the new flash unit in the diagram, and the new stream (liquid) in the stream table. We can then interactively rearrange the unit to be in the position we want in the diagram.
" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# v model.fs.visualize(\"Hydrodealkylation-new\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Showing new values\n", - "The previous step showed how a new unit in the flowsheet will be automatically added to the diagram. Similarly, if the values in the flowsheet change these will be reflected in the stream table. Below, we solve the initialized flowsheet.\n", - "To make comparison a little easier, we will open a second UI window with the new values (the old values will not be updated unless we decide to hit the \"Refresh\" button)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [ - "noauto" - ] - }, - "outputs": [], - "source": [ - "# Create the solver object\n", - "from pyomo.environ import SolverFactory\n", - "\n", - "solver = SolverFactory(\"ipopt\")\n", - "solver.options = {\"tol\": 1e-6, \"max_iter\": 5000}\n", - "\n", - "# Solve the model\n", - "results = solver.solve(model, tee=False)\n", - "\n", - "# Open a second window\n", - "model.fs.visualize(\"HDA_solved\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When we look at the stream table, we can see the values in the stream between the first and second flash unit changing.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running from a script\n", - "Finally, although all examples have been shown in a Jupyter Notebook, there is nothing preventing the use of the FV from within a plain Python script (or module).\n", - "\n", - "For example, the code to run this same tutorial as a Python script is also in a module.\n", - "If you have installed the IDAES examples, then you can do the following to import and run the module:\n", - "```\n", - "from idaes_examples.mod.tut import visualizer_tutorial\n", - "visualizer_tutorial.main()\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Further reading\n", - "\n", - "Reference documentation for the FV is available in the IDAES main documentation, online at https://idaes-pse.readthedocs.io/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flowsheet Visualizer Tutorial\n", + "\n", + "Author: Dan Gunter \n", + "Maintainer: Dan Gunter \n", + "Updated: 2023-06-01 \n", + "\n", + "The IDAES Flowsheet Visualizer provides a web-based UI for visualization and inspection of an existing IDAES model.\n", + "## Outline\n", + "\n", + "- Introduction\n", + "- Example flowsheet\n", + "- Running the Flowsheet Visualizer\n", + "- Running from a script\n", + "- Further reading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "The IDAES Flowsheet Visualizer (FV) is a Python tool that provides a web-based visualization of any existing IDAES model or flowsheet. The visualization shows a diagram of the\n", + "flowsheet as well as a stream table. You can interact with the diagram and export\n", + "it as an image for inclusion in presentations or publications.\n", + "\n", + "This tutorial will show the basic steps of running the FV on an example\n", + "flowsheet, interacting with the resulting GUI, saving your work, and exporting\n", + "the diagram as an image. It will also show how the Visualizer can be updated\n", + "to reflect changes in the model components and/or variable values. The tutorial\n", + "will also show how to run the Visualizer from a Python script." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example flowsheet\n", + "This initial section creates an example flowsheet.\n", + "\n", + "### Setup\n", + "Module imports and any additional housekeeping needed\n", + "to initialize the code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import idaes_examples.mod.tut.visualizer_tutorial as vistut\n", + "\n", + "vistut.quiet() # turn off default logging and most warnings\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from IPython.display import Markdown" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the flowsheet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# use the pre-defined function to create the flowsheet\n", + "model = vistut.create_model()\n", + "\n", + "# description of the flowsheet we created\n", + "display(Markdown(vistut.function_markdown(vistut.create_model)))\n", + "\n", + "vistut.quiet()\n", + "\n", + "# initialize the flowsheet as a square problem (dof=0)\n", + "vistut.initialize_model(model)\n", + "\n", + "# verify that there are zero degrees of freedom\n", + "print(f\"DOF = {degrees_of_freedom(model)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Flowsheet Visualizer\n", + "In most cases, you will run the FV by calling the `visualize()` method attached to your flowsheet.\n", + "This function takes a number of optional arguments, which we will look at briefly later, and one required argument:\n", + "the **title** to give the visualization. Unless you give more information, this title also is used as the filename in which to save its current state.\n", + "\n", + "In the following, we start the FV with the title \"Hydrodealkylation\". This will pop up a new browser tab (and save the status in a file called _Hydrodealkylation.json_).\n", + "\n", + "
\n", + "After the visualizer starts, we recommend making its tab into its own browser window and viewing it side-by-side with this notebook window.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "noauto" + ] + }, + "outputs": [], + "source": [ + "model.fs.visualize(\"Hydrodealkylation\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional arguments\n", + "The optional (keyword) arguments are documented in the base function, which can be found in `idaes.core.ui.fsvis.visualize`:\n", + "\n", + " * name: Name of flowsheet to display as the title of the visualization\n", + " * load_from_saved: If True load from saved file if any. Otherwise create\n", + " a new file or overwrite it (depending on 'overwrite' flag).\n", + " * save: Where to save the current flowsheet layout and values. If this argument is not specified,\n", + " \"``name``.json\" will be used (if this file already exists, a \"-``\" number will be added\n", + " between the name and the extension). If the value given is the boolean 'False', then nothing\n", + " will be saved. The boolean 'True' value is treated the same as unspecified.\n", + " * save_dir: If this argument is given, and ``save`` is not given or a relative path, then it will\n", + " be used as the directory to save the default or given file. The current working directory is\n", + " the default. If ``save`` is given and an absolute path, this argument is ignored.\n", + " * save_time_interval: The time interval that the UI application checks if any changes has occurred\n", + " in the graph for it to save the model. Default is 5 seconds\n", + " * overwrite: If True, and the file given by ``save`` exists, overwrite instead of creating a new\n", + " numbered file.\n", + " * browser: If true, open a browser\n", + " * port: Start listening on this port. If not given, find an open port.\n", + " * log_level: An IDAES logging level, which is a superset of the built-in `logging` module levels.\n", + " See the `idaes.logger` module for details\n", + " * quiet: If True, suppress printing any messages to standard output (console)\n", + " * loop_forever: If True, don't return but instead loop until a Control-C is received. Useful when\n", + " invoking this function at the end of a script." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interacting with the visualizer\n", + "The first things you need to learn about the FV are how to manipulate the overall layout and control the view.\n", + "The UI should initially look something like the screenshot below:\n", + "![](\"fv1.png\") alt=\"Screenshot of Flowsheet Visualizer\"> \n", + "\n", + "
\n", + " As you can see, the FV has two main panels. We will call the top panel the diagram and the bottom panel the stream table.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View controls\n", + "Before looking at the two panels in detail, it helps to know some basic controls for making them easier to view.\n", + "\n", + "| Control | Description | Illustration |\n", + "|:----|:---------------------|:----:|\n", + "| Panel height | Change the height of the panels by grabbing the small handle in the lower right corner with your mouse. | ![](fv2.png) |\n", + "| Diagram size | Zoom in/out on the diagram with the magnifying glass \"+\" and \"-\" buttons in the upper-right corner of the top panel. The button labeled with two crossing arrows fits the diagram into the current panel height and width. | ![](fv3.png) |" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rearranging the diagram\n", + "The diagram shown in the top panel is interactive. You can move the units shown there into different positions. Whatever arrangement you end up with will be saved for next time. The arcs (i.e., lines representing streams) connecting the units will automatically re-route themselves as you move them. Below is a summary of the different actions you can take when rearranging the diagram.\n", + "\n", + "|   |   |\n", + "|:--:|:--:|\n", + "| ![](fvr1.png)   |   ![](fvr2.png) |\n", + "| ![](fvr3.png)   |   ![](fvr4.png) |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stream table\n", + "The stream table panel shows the values of variables on all the streams between units, and also from units to outlets.\n", + "\n", + "### Stream table \"brushing\"\n", + "Brushing refers to the ability to have actions in one visual area influence the display in another. It is commonly used in statistics to show how points in one scatterplot correspond to their points in another, for the same samples. Here, we use it to link the position of a stream in the diagram with its variable values in the stream table.\n", + "#### Controls\n", + "\n", + "* Moving the mouse over an **arc** in the diagram → highlights the corresponding **column** in the stream table\n", + "* Moving the mouse over a **column** in the stream table → highlights the corresponding **arc** in the diagram\n", + "\n", + "![Illustration of stream table brushing](fvb1.png)\n", + " \n", + "#### Example\n", + "Stream table brushing is useful for answering questions like:\n", + "> How much benzene are we losing in the F101 vapor outlet stream?\n", + "\n", + "To answer this question, we will use some interactive elements of the stream table.\n", + "\n", + "1. Find the inlet of F101 on the diagram. Mouse over this to see the values for that stream highlighted in the stream table below. This is stream `s05`. Look across at the row for Benzene vapor (`flow_mol_phase_comp('Vap', 'benzene')`) and see that the value is $0.35384$\n", + "2. Find the vapor outlet of F101 by looking for the arc connecting to the splitter and compressor feedback loop. This is stream `s06`. Then look at the same row for the Benzene vapor mol fraction and see that the value is $0.14916$\n", + "3. Thus the amount of benzene lost is (in mole fractions) about $0.2$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing and hiding streams\n", + "For complex diagrams, there are a lot of streams and the stream table does not fit in the window. To avoid having to scroll back and forth, there is the ability to \"hide\" selected streams in the stream table. \n", + "\n", + "* Click on the \"Hide Fields\" menu and select which fields to hide\n", + "* The mark will toggle between a check (shown) and open circle (hidden)\n", + "\n", + "For example, we can hide all the streams except the feeds and the flash inlets and outlets.\n", + "\n", + "![Illustration of stream table field hiding](fvst1.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving and loading\n", + "The current layout and status can be saved to a file, and this file can then be loaded when the model is viewed again. The main benefit is that interactive layout of the diagram is saved for re-use.\n", + "\n", + "#### File name\n", + "This file is named, by default, for the title of the visualizer (e.g., \"Hydrodealkylation\") with a \".json\" extension to indicate the data format and saved in the same directory as the Jupyter notebook. \n", + "\n", + "You can select a different filename and location when you start the visualization, e.g.\n", + "\n", + " model.fs.visualize(\"The Title\", save=\"thefilename.json\", save_dir=\"/path/to/save/the/file\")\n", + "\n", + "#### Reloading\n", + "To reload the saved layout, simply choose the same title (since the filename, by default, matches the title) or explicitly use the `save` and `save_dir` keywords for the `visualize()` function to select a previously saved file. This means you only need to manually lay out the diagram once. Of course, if you add new pieces to the flowsheet you will need to position them correctly (as discussed below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exporting\n", + "\n", + "#### Exporting the diagram as an image\n", + "You can export an image of the flowsheet diagram in the [Scalable Vector Graphics (SVG)](https://www.w3.org/Graphics/SVG/) format, which can render without fuzziness at arbitrary sizes. Almost all presentation and drawing programs, including MS Word and Powerpoint, can use SVG images.\n", + "\n", + "From the top menu select _Export -> Flowsheet_. You will get a preview of the flowsheet that you can then download to a file.\n", + "#### Exporting the stream table as CSV\n", + "You can export the stream table as comma-separated values. From the top menu select _Export -> Stream Table_." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating when the flowsheet changes\n", + "The FV has a connection to the Python program that has the flowsheet (model) in memory. Therefore, when the underlying flowsheet changes, the visualization can be quickly updated to show the new state. This feature is particularly useful for interactive flowsheet creation and debugging in Jupyter Notebooks.\n", + "\n", + "To illustrate the feature, below is some IDAES modeling code that adds another Flash unit to the model, connecting the liquid outlet of the first flash unit to its inlet. There is a little more code that updates some of the output values of the model and sets initial values for this new unit, and then re-initializes the model.\n", + "\n", + "**After this code executes, the model will have a unit called \"F102\" connected to \"F101\".**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a second flash unit\n", + "from idaes.models.unit_models import Flash\n", + "from pyomo.network import Arc\n", + "from pyomo.environ import Expression, TransformationFactory\n", + "\n", + "m = model # alias\n", + "m.fs.F102 = Flash(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=True,\n", + ")\n", + "# connect to 1st flash unit\n", + "m.fs.s10 = Arc(source=m.fs.F101.liq_outlet, destination=m.fs.F102.inlet)\n", + "# update expressions for purity and cost\n", + "m.fs.purity = Expression(\n", + " expr=m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " / (\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"]\n", + " + m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"toluene\"]\n", + " )\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "# fix unit output and pressure drop\n", + "m.fs.F102.vap_outlet.temperature.fix(375)\n", + "m.fs.F102.deltaP.fix(-200000)\n", + "\n", + "# expand arcs\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)\n", + "# re-initialize\n", + "_ = vistut.initialize_model(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Since the FV is connected to the current state of the model in memory, simply hitting \"Refresh\" in the FV window will show the new flash unit in the diagram, and the new stream (liquid) in the stream table. We can then interactively rearrange the unit to be in the position we want in the diagram.
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# v model.fs.visualize(\"Hydrodealkylation-new\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing new values\n", + "The previous step showed how a new unit in the flowsheet will be automatically added to the diagram. Similarly, if the values in the flowsheet change these will be reflected in the stream table. Below, we solve the initialized flowsheet.\n", + "To make comparison a little easier, we will open a second UI window with the new values (the old values will not be updated unless we decide to hit the \"Refresh\" button)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [ + "noauto" + ] + }, + "outputs": [], + "source": [ + "# Create the solver object\n", + "from pyomo.environ import SolverFactory\n", + "\n", + "solver = SolverFactory(\"ipopt\")\n", + "solver.options = {\"tol\": 1e-6, \"max_iter\": 5000}\n", + "\n", + "# Solve the model\n", + "results = solver.solve(model, tee=False)\n", + "\n", + "# Open a second window\n", + "model.fs.visualize(\"HDA_solved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we look at the stream table, we can see the values in the stream between the first and second flash unit changing.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running from a script\n", + "Finally, although all examples have been shown in a Jupyter Notebook, there is nothing preventing the use of the FV from within a plain Python script (or module).\n", + "\n", + "For example, the code to run this same tutorial as a Python script is also in a module.\n", + "If you have installed the IDAES examples, then you can do the following to import and run the module:\n", + "```\n", + "from idaes_examples.mod.tut import visualizer_tutorial\n", + "visualizer_tutorial.main()\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Further reading\n", + "\n", + "Reference documentation for the FV is available in the IDAES main documentation, online at https://idaes-pse.readthedocs.io/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model.ipynb index 7465fdda..1027ee5e 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model.ipynb @@ -1,5 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "3633308e", + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_doc.ipynb index cdbbb12c..0dcaaf77 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_doc.ipynb @@ -3,6 +3,32 @@ { "cell_type": "code", "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +53,6 @@ "# Creating Custom Unit Model\n", "Author: Javal Vyas \n", "Maintainer: Javal Vyas \n", - "Updated: 2023-02-20\n", "\n", "This tutorial is a comprehensive step-wise procedure to build a custom unit model from scratch. This tutorial will include creating a property package, a custom unit model and testing them. For this tutorial we shall create a custom unit model for Liquid - Liquid Extraction. \n", "\n", @@ -57,12 +82,12 @@ "- Define State Block Data\n", "\n", "# 1.1 Importing necessary packages \n", - "Let us begin with the importing the necessary libraries where we will be using functionalities from IDAES and Pyomo. " + "Let us begin with importing the necessary libraries where we will be using functionalities from IDAES and Pyomo. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -115,7 +140,7 @@ "\n", "To construct this block, we begin by declaring a process block class using a Python decorator. One can learn more about `declare_process_block_class` [here](https://github.com/IDAES/idaes-pse/blob/eea1209077b75f7d940d8958362e69d4650c079d/idaes/core/base/process_block.py#L173). After constructing the process block, we define a build function which contains all the components that the property package would have. `super` function here is used to give access to methods and properties of a parent or sibling class and since this is used on the class `PhysicalParameterData` class, build has access to all the parent and sibling class methods. \n", "\n", - "The `PhysicalParameterBlock` then refers to the `state block`, in this case `OrgPhaseStateBlock` (which will be declared later), so that we can build a state block instance by only knowing the `PhysicalParameterBlock` we wish to use. Then we move on to list the number of phases in this property package. Then we assign the variable to the phase which follows a naming convention. Like here since the solvent is in the Organic phase, we will assign the Phase as OrganicPhase and the variable will be named Org as per the naming convention. The details of naming conventions can be found [here](https://github.com/IDAES/idaes-pse/blob/main/docs/explanations/conventions.rst). We will be following the same convention throughout the example. \n", + "The `PhysicalParameterBlock` then refers to the `state block`, in this case `OrgPhaseStateBlock` (which will be declared later), so that we can build a state block instance by only knowing the `PhysicalParameterBlock` we wish to use. Then we list the number of phases in this property package. Then we assign the variable to the phase which follows a naming convention. The solvent is in the Organic phase; we will assign the Phase as OrganicPhase, and the variable will be named Org as per the naming convention. The details of naming conventions can be found [here](https://github.com/IDAES/idaes-pse/blob/main/docs/explanations/conventions.rst). We will be following the same convention throughout the example. \n", " \n", "After defining the list of the phases, we move on to list the components and their type in the phase. It can be a solute or a solvent in the Organic phase. Thus, we define the component and assign it to either being a solute or a solvent. In this case, the salts are the solutes and Ethylene dibromide is the solvent. Next, we define the physical properties involved in the package, like the heat capacity and density of the solvent, the reference temperature, and the distribution factor that would govern the mass transfer from one phase into another. Additionally, a parameter, the `diffusion_factor`, is introduced. This factor plays a crucial role in governing mass transfer between phases, necessitating its definition within the state block.\n", "\n", @@ -126,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -210,14 +235,14 @@ "\n", "Creating a State Block requires us to write two classes. The reason we write two classes is because of the inherent nature of how `declare_process_block_data` works. `declare_process_block_data` facilitates creating an `IndexedComponent` object which can handle multiple `ComponentData` objects which represent the component at each point in the indexing set. This makes it easier to build an instance of the model at each indexed point. However, State Blocks are slightly different, as they are always indexed (at least by time). Due to this, we often want to perform actions on all the elements of the indexed StateBlock all at once (rather than element by element).\n", "\n", - "The class `_OrganicStateBlock` is defined without the `declare_process_block_data` decorator and thus works as a traditional class and this facilitates performing a method on the class as a whole rather than individual elements of the indexed property blocks. In this class we define the `fix_initialization_states` function. `fix_initialization_states` function is to used to fix the state variable within the state block with the provided initial values (usually inlet conditions). It takes a `block` as the argument in which the state variables are to be fixed. It also takes `state_args` as an optional argument. `state_args` is a dictionary with the value for the state variables to be fixed. This function returns a dictionary indexed by the block, state variables and variable index indicating the fixed status of each variable before applying the function. \n", + "The class `_OrganicStateBlock` is defined without the `declare_process_block_data` decorator and thus works as a traditional class and this facilitates performing a method on the class as a whole rather than individual elements of the indexed property blocks. In this class we define the `fix_initialization_states` function. `fix_initialization_states` function is used to fix the state variable within the state block with the provided initial values (usually inlet conditions). It takes a `block` as the argument in which the state variables are to be fixed. It also takes `state_args` as an optional argument. `state_args` is a dictionary with the value for the state variables to be fixed. This function returns a dictionary indexed by the block, state variables and variable index indicating the fixed status of each variable before applying the function. \n", "\n", - "The above function comprise of the _OrganicStateBlock, next we shall see the construction of the OrgPhaseStateBlockData class." + "The above function comprise of the _OrganicStateBlock. Next, we shall see the construction of the OrgPhaseStateBlockData class." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -260,14 +285,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "@declare_process_block_class(\"OrgPhaseStateBlock\", block_class=_OrganicStateBlock)\n", "class OrgPhaseStateBlockData(StateBlockData):\n", " \"\"\"\n", - " An example property package for Organic phzase for liquid liquid extraction\n", + " An example property package for Organic phase for liquid liquid extraction\n", " \"\"\"\n", "\n", " def build(self):\n", @@ -364,12 +389,12 @@ "source": [ "# 2. Creating Aqueous Property Package\n", "\n", - "The structure of Aqueous Property Package mirrors that of the Organic Property Package we previously developed. We'll commence with an overview, importing the required libraries, followed by the creation of the physical property block and two state blocks. The distinctions in this package lie in the physical parameter values, and notably, the absence of the diffusion factor term, differentiating it from the prior package. The following code snippet should provide clarity on these distinctions." + "The structure of the Aqueous Property Package mirrors that of the Organic Property Package we previously developed. We'll commence with an overview, importing the required libraries, followed by the creation of the physical property block and two state blocks. The distinctions in this package lie in the physical parameter values, and notably, the absence of the diffusion factor term, differentiating it from the prior package. The following code snippet should provide clarity on these distinctions." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -593,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -633,26 +658,26 @@ "source": [ "## 3.2 Creating the unit model\n", "\n", - "Creating a unit model starts by creating a class called `LiqExtractionData` and use the `declare_process_block_class` decorator. The `LiqExtractionData` inherits the properties of `UnitModelBlockData` class, which allows us to create a control volume which is necessary for the unit model. After declaration of the class we proceed to define the relevant config arguments for the control volume. The config arguments includes the following properties:\n", + "Creating a unit model starts by creating a class called `LiqExtractionData` and using the `declare_process_block_class` decorator. The `LiqExtractionData` inherits the properties of the `UnitModelBlockData` class, which allows us to create a control volume that is necessary for the unit model. After declaration of the class we proceed to define the relevant config arguments for the control volume. The config arguments include the following properties:\n", "\n", "- `material_balance_type` - Indicates what type of mass balance should be constructed\n", "- `has_pressure_change` - Indicates whether terms for pressure change should be\n", "constructed\n", "- `has_phase_equilibrium` - Indicates whether terms for phase equilibrium should be\n", "constructed\n", - "- `Organic Property` - Property parameter object used to define property calculations\n", + "- `organic_property_package` - Property parameter object used to define property calculations\n", "for the Organic phase\n", - "- `Organic Property Arguments` - Arguments to use for constructing Organic phase properties\n", - "- `Aqueous Property` - Property parameter object used to define property calculations\n", + "- `organic_property_package_args` - Arguments to use for constructing Organic phase properties\n", + "- `aqueous_property_package` - Property parameter object used to define property calculations\n", "for the aqueous phase\n", - "- `Aqueous Property Arguments` - Arguments to use for constructing aqueous phase properties\n", + "- `aqueous_property_package_args` - Arguments to use for constructing aqueous phase properties\n", "\n", "As there are no pressure changes or reactions in this scenario, configuration arguments for these aspects are not included. However, additional details on configuration arguments can be found [here](https://github.com/IDAES/idaes-pse/blob/8948c6ce27d4c7f2c06b377a173f413599091998/idaes/models/unit_models/cstr.py)." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -770,17 +795,17 @@ "source": [ "### Building the model\n", "\n", - "After constructing the `LiqExtractionData` block and defining the config arguments for the control block, the next step is to write a build function that incorporates control volume and establishes constraints on the control volume to achieve the desired mass transfer. The control volume serves as a pivotal component in the unit model construction, representing the volume in which the process unfolds.\n", + "After constructing the `LiqExtractionData` block and defining the config arguments for the control block, the next step is to write a build function that incorporates the control volume and establishes constraints on the control volume to achieve the desired mass transfer. The control volume serves as a pivotal component in the unit model construction, representing the volume in which the process unfolds.\n", "\n", "IDAES provides flexibility in choosing control volumes based on geometry, with options including 0D or 1D. In this instance, we opt for a 0D control volume, the most commonly used control volume. This choice is suitable for systems where there is a well-mixed volume of fluid or where spatial variations are deemed negligible.\n", "\n", "The control volume encompasses parameters from (1-8), and its equations are configured to satisfy the specified config arguments. For a more in-depth understanding, users are encouraged to refer to [this resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst). \n", "\n", - "The `build` function is initiated using the `super` function to gain access to methods and properties of a parent or sibling class, in this case, the `LiqExtractionData` class. Following the `super` function, checks are performed on the property packages to ensure the appropriate names for the solvents, such as 'Aq' for the aqueous phase and 'Org' for the Organic phase. An error is raised if these conditions are not met. Subsequently, a check is performed to ensure there is at least one common component between the two property packages that can be transferred from one phase to another.\n", + "The `build` function is initiated using the `super` function to gain access to methods and properties of a parent or sibling class, in this case, the `LiqExtractionData` class. Following the `super` function, checks are performed on the property packages to ensure the appropriate names for the solvents, such as 'Aq' for the aqueous phase and 'Org' for the organic phase. An error is raised if these conditions are not met. Subsequently, a check is performed to ensure there is at least one common component between the two property packages that can be transferred from one phase to another.\n", "\n", - "After these checks are completed without any exceptions raised, it is ensured that the property packages have the desired components with appropriate names. The next step is to create a control volume and assign it to a property package. Here, we initiate with the Organic phase and attach a 0D control volume to it. The control volume takes arguments about the dynamics of the block, and the property package, along with property package arguments. \n", + "After these checks are completed without any exceptions raised, it is ensured that the property packages have the desired components with appropriate names. The next step is to create a control volume and assign it to a property package. Here, we initiate with the organic phase and attach a 0D control volume to it. The control volume takes arguments about the dynamics of the block, and the property package, along with property package arguments. \n", "\n", - "The subsequent steps involve adding inlet and outlet state blocks to the control volume using the `add_state_blocks` function. This function takes arguments about the flow direction (defaulted to forward) and a flag for `has_phase_equilibrium`, which is read from the config. The control volume is now equipped with the inlet and outlet state blocks and has access to the Organic property package\n", + "The subsequent steps involve adding inlet and outlet state blocks to the control volume using the `add_state_blocks` function. This function takes arguments about the flow direction (defaulted to forward) and a flag for `has_phase_equilibrium`, which is read from the config. The control volume is now equipped with the inlet and outlet state blocks and has access to the organic property package\n", "\n", "Next, material balance equations are added to the control volume using the `add_material_balance` function, taking into account the type of material balance, `has_phase_equilibrium`, and the presence of `has_mass_transfer`. To understand this arguments further let us have a look at the material balance equation and how it is implemented in control volume. \n", "\n", @@ -801,9 +826,9 @@ "- e indicates element index\n", "- r indicates reaction name index\n", "\n", - "Here we shall see that $N_{transfer, t, p, j}$ is the term in the equation which is responsible for the mass transfer and the `mass_transfer_term` should only be equal to the amount being transferred and not include a material balance on our own. For a detailed description of the terms one should refer to the following [resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst)\n", + "Here we shall see that $N_{transfer, t, p, j}$ is the term in the equation which is responsible for the mass transfer and the `mass_transfer_term` should only be equal to the amount being transferred and not include a material balance on our own. For a detailed description of the terms one should refer to the following [resource.](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst)\n", "\n", - "This concludes the creation of organic phase control volume. Similar procedure is done for the aqueous phase control volume with aqueous property package. \n", + "This concludes the creation of the organic phase control volume. A similar procedure is done for the aqueous phase control volume with aqueous property package. \n", "\n", "Now, the unit model has two control volumes with appropriate configurations and material, momentum and energy balances. The next step is to check the basis of the two property packages. They should both have the same flow basis, and an error is raised if this is not the case.\n", "\n", @@ -811,14 +836,14 @@ "\n", "The subsequent steps involve writing unit-level constraints. A check if the basis is either molar or mass, and unit-level constraints are written accordingly. The first constraint pertains to the mass transfer term for the aqueous phase. The mass transfer term is equal to $mass\\_transfer\\_term_{aq} = (D_{i})\\frac{mass_{i}~in~aq~phase}{flowrate~of~aq~phase}$. The second constraint relates to the mass transfer term in the organic phase, which is the negative of the mass transfer term in the aqueous phase: $mass\\_transfer\\_term_{org} = - mass\\_transfer\\_term_{aq} $\n", "\n", - "Here $mass\\_transfer\\_term_{p}$ is the term indicating the amount of material being transferred from/to the phase and $D_{i}$ is the Distribution co-efficient for component i. \n", + "Here $mass\\_transfer\\_term_{p}$ is the term indicating the amount of material being transferred from/to the phase and $D_{i}$ is the Distribution coefficient for component i. \n", "\n", "This marks the completion of the build function, and the unit model is now equipped with the necessary process constraints. The subsequent steps involve writing the initialization routine." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -836,7 +861,7 @@ " # Check phase lists match assumptions\n", " if self.config.aqueous_property_package.phase_list != [\"Aq\"]:\n", " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the aquoues \"\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the aqueous \"\n", " f\"phase property package have a single phase named 'Aq'\"\n", " )\n", " if self.config.organic_property_package.phase_list != [\"Org\"]:\n", @@ -949,8 +974,6 @@ "\n", " if flow_basis == MaterialFlowBasis.mass:\n", " fb = \"flow_mass\"\n", - " elif flow_basis == MaterialFlowBasis.molar:\n", - " fb = \"flow_mole\"\n", " else:\n", " raise ConfigurationError(\n", " f\"{self.name} Liquid-Liquid Extractor only supports mass \"\n", @@ -1015,13 +1038,13 @@ "\n", "- Have precheck for structural singularity\n", "- Run incidence analysis on given block data and check matching.\n", - "- Call Block Triangularization solver on model.\n", + "- Call Block Triangularization solver on the model.\n", "- Call solve_strongly_connected_components on a given BlockData.\n", "\n", - "For more details about this initialization routine can be found [here](https://github.com/IDAES/idaes-pse/blob/c09433b9afed5ae2fe25c0ccdc732783324f0101/idaes/core/initialization/block_triangularization.py). \n", + "More details about this initialization routine can be found [here](https://github.com/IDAES/idaes-pse/blob/c09433b9afed5ae2fe25c0ccdc732783324f0101/idaes/core/initialization/block_triangularization.py). \n", "\n", "\n", - "This marks the conclusion of creating a custom unit model, for a more detailed explanation on creating a unit model refer [this resource](../../unit_models/custom_unit_models/custom_compressor_doc.md). The next sections will deal with the diagonistics and testing of the property package and unit model. " + "This marks the conclusion of creating a custom unit model, for a more detailed explanation on creating a unit model refer [this resource](../../unit_models/custom_unit_models/custom_compressor_doc.md). The next sections will deal with the diagnostics and testing of the property package and unit model. " ] }, { @@ -1046,18 +1069,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import pyomo.environ as pyo\n", - "import idaes.core\n", - "import idaes.models.unit_models\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.logger as idaeslog\n", - "from pyomo.network import Arc\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.initialization import InitializationStatus\n", + "from idaes.core import FlowsheetBlock\n", + "\n", "from idaes.core.initialization.block_triangularization import (\n", " BlockTriangularizationInitializer,\n", ")\n", @@ -1068,7 +1086,7 @@ "\n", "def build_model():\n", " m = pyo.ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", " m.fs.org_properties = OrgPhase()\n", " m.fs.aq_properties = AqPhase()\n", "\n", @@ -1141,7 +1159,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1156,7 +1174,7 @@ "\n", "Here's a breakdown of the steps to start with:\n", "\n", - "- `Instantiate Model:` Ensure you have an instance of the model with a degrees of freedom equal to 0.\n", + "- `Instantiate Model:` Ensure you have an instance of the model with degrees of freedom equal to 0.\n", "\n", "- `Create DiagnosticsToolbox Instance:` Next, instantiate a DiagnosticsToolbox object.\n", "\n", @@ -1169,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1179,15 +1197,33 @@ "WARNING (W1001): Setting Var\n", "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]' to a value\n", "`-0.1725` (float) not in domain NonNegativeReals.\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING (W1001): Setting Var\n", "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3]' to a value\n", "`-0.4` (float) not in domain NonNegativeReals.\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING (W1001): Setting Var\n", "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4]' to a value\n", "`-0.05` (float) not in domain NonNegativeReals.\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "====================================================================================\n", "Model Statistics\n", "\n", @@ -1235,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1310,10 +1346,17 @@ "Number of equality constraint Jacobian evaluations = 14\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: infeasible\n", @@ -1324,10 +1367,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.06552338600158691}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.0067138671875}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1341,12 +1384,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The model is probably infeasible thus indicating numerical issues with the model. We should call the `report_numerical_issues()` function and check what the constraints/variables causing this issue. " + "The model is probably infeasible, indicating numerical issues with the model. We should call the `report_numerical_issues()` function and check the constraints/variables causing this issue. " ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1375,6 +1418,7 @@ "Suggested next steps:\n", "\n", " display_constraints_with_large_residuals()\n", + " compute_infeasibility_explanation()\n", " display_variables_at_or_outside_bounds()\n", "\n", "====================================================================================\n" @@ -1402,7 +1446,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1439,7 +1483,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1468,12 +1512,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As expected there are convergence issues with the constraints which have `conc_mass_comp` variable in them specifically in the aqeous phase. Now, let us investigate further by printing this constraints and checking the value of each term. Since this is an persistent issue across the components, we can focus on just one of the component to identify the issue. " + "As expected there are convergence issues with the constraints which have `conc_mass_comp` variable in them specifically in the aqueous phase. Now, let us investigate further by printing this constraints and checking the value of each term. Since this is an persistent issue across the components, we can focus on just one of the component to identify the issue. " ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1493,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1501,7 +1545,7 @@ "output_type": "stream", "text": [ "{Member of conc_mass_comp} : Component mass concentrations\n", - " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", + " Size=3, Index=fs.aq_properties.solute_set, Units=g/l\n", " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", " NaCl : 0 : 0.15 : None : True : True : NonNegativeReals\n", "flow_vol : Total volumetric flowrate\n", @@ -1509,7 +1553,7 @@ " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", " None : 0 : 100.0 : None : True : True : NonNegativeReals\n", "{Member of conc_mass_comp} : Component mass concentrations\n", - " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", + " Size=3, Index=fs.aq_properties.solute_set, Units=g/l\n", " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", " NaCl : 0 : 0.0 : None : False : False : NonNegativeReals\n", "flow_vol : Total volumetric flowrate\n", @@ -1542,7 +1586,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1569,7 +1613,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1591,12 +1635,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After the corrective actions, we should check if this have made any structural issues, for this we would call `report_structural_issues()`" + "After the corrective actions, we should check if this has made any structural issues, for this we would call `report_structural_issues()`" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1648,7 +1692,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1715,16 +1759,17 @@ "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", - "EXIT: Optimal Solution Found.\n" + "EXIT: Optimal Solution Found.\n", + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] }, { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.07779264450073242}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.008719205856323242}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1752,7 +1797,7 @@ "\n", "Testing is a crucial part of model development to ensure that the model works as expected, and remains reliable. Here's an overview of why we conduct testing:\n", "\n", - "1. `Verify Correctness`: Testing ensure that the model works as expected and meets the specified requirements. \n", + "1. `Verify Correctness`: Testing ensures that the model works as expected and meets the specified requirements. \n", "2. `Detect Bugs and Issues`: Testing helps in identifying bugs, errors, or unexpected behaviors in the code or model, allowing for timely fixes.\n", "3. `Ensure Reliability`: Testing improves the reliability and robustness of the software, reducing the risk of failures when the user uses it.\n", "4. `Support Changes`: Tests provide confidence when making changes or adding new features, ensuring that existing functionalities are not affected and work as they should.\n", @@ -1786,7 +1831,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1844,12 +1889,12 @@ "\n", "2. Initialization Function Test: Check that state variables are not fixed before initialization and are released after initialization. This test ensures that the initialization process occurs as expected and that the state variables are appropriately managed throughout.\n", "\n", - "These unit tests provide comprehensive coverage for validating the functionality and behavior of the state block in the Aqueous property phase package. Similar tests can be written for the organic property package to ensure consistency and reliability across both packages." + "These unit tests provide comprehensive coverage for validating the functionality and behavior of the state block in the aqueous property phase package. Similar tests can be written for the organic property package to ensure consistency and reliability across both packages." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1905,7 +1950,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1927,13 +1972,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import pytest\n", "\n", - "import idaes.core\n", + "from idaes.core import FlowsheetBlock\n", "import idaes.models.unit_models\n", "from idaes.core.solvers import get_solver\n", "import idaes.logger as idaeslog\n", @@ -1956,7 +2001,7 @@ "@pytest.mark.unit\n", "def test_config():\n", " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", " m.fs.org_properties = OrgPhase()\n", " m.fs.aq_properties = AqPhase()\n", "\n", @@ -1990,7 +2035,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1998,7 +2043,7 @@ " @pytest.fixture(scope=\"class\")\n", " def model(self):\n", " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", " m.fs.org_properties = OrgPhase()\n", " m.fs.aq_properties = AqPhase()\n", "\n", @@ -2087,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -2217,7 +2262,7 @@ "- Debugging the model using DiagnosticsToolbox\n", "- Writing tests for the unit model\n", "\n", - "By following the aforementioned procedure, one can create their own custom unit model. This would conclude the tutorial on creating custom unit model. " + "By following the aforementioned procedure, one can create their own custom unit model. This concludes the tutorial on creating a custom unit model. " ] } ], @@ -2237,9 +2282,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_test.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_test.ipynb index f11bab48..5fdd9b0e 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_test.ipynb @@ -1,1964 +1,2263 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating Custom Unit Model\n", - "Author: Javal Vyas \n", - "Maintainer: Javal Vyas \n", - "Updated: 2023-02-20\n", - "\n", - "This tutorial is a comprehensive step-wise procedure to build a custom unit model from scratch. This tutorial will include creating a property package, a custom unit model and testing them. For this tutorial we shall create a custom unit model for Liquid - Liquid Extraction. \n", - "\n", - "The Liquid - Liquid Extractor model contains two immiscible fluids forming the two phases. One of the phases, say phase_1 has a high concentration of solutes which is to be separated. A mass transfer happens between the two phases and the solute is transferred from phase_1 to phase_2. This mass transfer is governed by a parameter called the distribution coefficient.\n", - "\n", - "After reviewing the working principles of the Liquid - Liquid Extractor, we shall proceed to create a custom unit model. We will require a property package for each phase, a custom unit model class and tests for the model and property packages.\n", - "\n", - "Before commencing the development of the model, we need to state some assumptions which the following unit model will be using. \n", - "- Steady-state only\n", - "- Organic phase property package has a single phase named Org\n", - "- Aqueous phase property package has a single phase named Aq\n", - "- Organic and Aqueous phase properties need not have the same component list. \n", - "\n", - "Thus as per the assumptions, we will be creating one property package for the aqueous phase (Aq), and the other for the Organic phase (Org). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Creating Organic Property Package\n", - "\n", - "Creating a property package is a 4 step process\n", - "- Import necessary libraries \n", - "- Creating Physical Parameter Data Block\n", - "- Define State Block\n", - "- Define State Block Data\n", - "\n", - "# 1.1 Importing necessary packages \n", - "Let us begin with the importing the necessary libraries where we will be using functionalities from IDAES and Pyomo. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.util.initialization import fix_state_vars\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Param,\n", - " Set,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - " Expression,\n", - " PositiveReals,\n", - ")\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " MaterialFlowBasis,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " Solute,\n", - " Solvent,\n", - " LiquidPhase,\n", - ")\n", - "from idaes.core.util.model_statistics import degrees_of_freedom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1.2 Physical Parameter Data Block\n", - "\n", - "A `PhysicalParameterBlock` serves as the central point of reference for all aspects of the property package and needs to define several things about the package. These are summarized below:\n", - "\n", - "- Units of measurement\n", - "- What properties are supported and how they are implemented\n", - "- What components and phases are included in the packages\n", - "- All the global parameters necessary for calculating properties\n", - "- A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To construct this block, we begin by declaring a process block class using a Python decorator. One can learn more about `declare_process_block_class` [here](https://github.com/IDAES/idaes-pse/blob/eea1209077b75f7d940d8958362e69d4650c079d/idaes/core/base/process_block.py#L173). After constructing the process block, we define a build function which contains all the components that the property package would have. `super` function here is used to give access to methods and properties of a parent or sibling class and since this is used on the class `PhysicalParameterData` class, build has access to all the parent and sibling class methods. \n", - "\n", - "The `PhysicalParameterBlock` then refers to the `state block`, in this case `OrgPhaseStateBlock` (which will be declared later), so that we can build a state block instance by only knowing the `PhysicalParameterBlock` we wish to use. Then we move on to list the number of phases in this property package. Then we assign the variable to the phase which follows a naming convention. Like here since the solvent is in the Organic phase, we will assign the Phase as OrganicPhase and the variable will be named Org as per the naming convention. The details of naming conventions can be found [here](https://github.com/IDAES/idaes-pse/blob/main/docs/explanations/conventions.rst). We will be following the same convention throughout the example. \n", - " \n", - "After defining the list of the phases, we move on to list the components and their type in the phase. It can be a solute or a solvent in the Organic phase. Thus, we define the component and assign it to either being a solute or a solvent. In this case, the salts are the solutes and Ethylene dibromide is the solvent. Next, we define the physical properties involved in the package, like the heat capacity and density of the solvent, the reference temperature, and the distribution factor that would govern the mass transfer from one phase into another. Additionally, a parameter, the `diffusion_factor`, is introduced. This factor plays a crucial role in governing mass transfer between phases, necessitating its definition within the state block.\n", - "\n", - "The final step in creating the Physical Parameter Block is to declare a `classmethod` named `define_metadata`, which takes two arguments: a class (cls) and an instance of that class (obj). In this method, we will call the predefined method `add_default_units()`.\n", - "\n", - "- `obj.add_default_units()` sets the default units metadata for the property package, and here we define units to be used with this property package as default. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"OrgPhase\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " organic Phase\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super().build()\n", - "\n", - " self._state_block_class = OrgPhaseStateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Org = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.NaCl = Solute()\n", - " self.KNO3 = Solute()\n", - " self.CaSO4 = Solute()\n", - " self.solvent = (\n", - " Solvent()\n", - " ) # Solvent used here is ethylene dibromide (Organic Polar)\n", - "\n", - " # Heat capacity of solvent\n", - " self.cp_mass = Param(\n", - " mutable=True,\n", - " initialize=717.01,\n", - " doc=\"Specific heat capacity of solvent\",\n", - " units=units.J / units.kg / units.K,\n", - " )\n", - "\n", - " self.dens_mass = Param(\n", - " mutable=True,\n", - " initialize=2170,\n", - " doc=\"Density of ethylene dibromide\",\n", - " units=units.kg / units.m**3,\n", - " )\n", - " self.temperature_ref = Param(\n", - " within=PositiveReals,\n", - " mutable=True,\n", - " default=298.15,\n", - " doc=\"Reference temperature\",\n", - " units=units.K,\n", - " )\n", - " self.diffusion_factor = Param(\n", - " self.solute_set,\n", - " initialize={\"NaCl\": 2.15, \"KNO3\": 3, \"CaSO4\": 1.5},\n", - " within=PositiveReals,\n", - " mutable=True,\n", - " )\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.hour,\n", - " \"length\": units.m,\n", - " \"mass\": units.g,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1.3 State Block\n", - "\n", - "After the `PhysicalParameterBlock` class has been created, the next step is to write the code necessary to create the State Blocks that will be used throughout the flowsheet. `StateBlock` contains all the information necessary to define the state of the system. This includes the state variables and constraints on those variables which are used to describe a state property like the enthalpy, material balance, etc.\n", - "\n", - "Creating a State Block requires us to write two classes. The reason we write two classes is because of the inherent nature of how `declare_process_block_data` works. `declare_process_block_data` facilitates creating an `IndexedComponent` object which can handle multiple `ComponentData` objects which represent the component at each point in the indexing set. This makes it easier to build an instance of the model at each indexed point. However, State Blocks are slightly different, as they are always indexed (at least by time). Due to this, we often want to perform actions on all the elements of the indexed StateBlock all at once (rather than element by element).\n", - "\n", - "The class `_OrganicStateBlock` is defined without the `declare_process_block_data` decorator and thus works as a traditional class and this facilitates performing a method on the class as a whole rather than individual elements of the indexed property blocks. In this class we define the `fix_initialization_states` function. `fix_initialization_states` function is to used to fix the state variable within the state block with the provided initial values (usually inlet conditions). It takes a `block` as the argument in which the state variables are to be fixed. It also takes `state_args` as an optional argument. `state_args` is a dictionary with the value for the state variables to be fixed. This function returns a dictionary indexed by the block, state variables and variable index indicating the fixed status of each variable before applying the function. \n", - "\n", - "The above function comprise of the _OrganicStateBlock, next we shall see the construction of the OrgPhaseStateBlockData class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class _OrganicStateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def fix_initialization_states(self):\n", - " fix_state_vars(self)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The class `OrgPhaseStateBlockData` is designated with the `declare_process_block_class` decorator, named `OrgPhaseStateBlock`, and inherits the block class from `_OrganicStateBlock`. This inheritance allows `OrgPhaseStateBlockData` to leverage functions from `_OrganicStateBlock`. Following the class definition, a build function similar to the one used in the `PhysicalParameterData` block is employed. The super function is utilized to enable the utilization of functions from the parent or sibling class.\n", - "\n", - "The subsequent objective is to delineate the state variables, accomplished through the `_make_state_vars` method. This method encompasses all the essential state variables and associated data. For this particular property package, the required state variables are:\n", - "\n", - "- `flow_vol` - volumetric flow rate\n", - "- `conc_mass_comp` - mass fractions\n", - "- `pressure` - state pressure\n", - "- `temperature` - state temperature\n", - "\n", - "After establishing the state variables, the subsequent step involves setting up state properties as constraints. This includes specifying the relationships and limitations that dictate the system's behavior. The following properties need to be articulated:\n", - "\n", - "-`get_material_flow_terms`: quantifies the amount of material flow.\n", - "- `get_enthalpy_flow_terms`: quantifies the amount of enthalpy flow.\n", - "- `get_flow_rate`: details volumetric flow rates.\n", - "- `default_material_balance_type`: defines the kind of material balance to be used.\n", - "- `default_energy_balance_type`: defines the kind of energy balance to be used.\n", - "- `define_state_vars`: involves defining state variables with units, akin to the define_metadata function in the PhysicalParameterData block.\n", - "- `get_material_flow_basis`: establishes the basis on which state variables are measured, whether in mass or molar terms.\n", - "\n", - "These definitions mark the conclusion of the state block construction and thus the property package. For additional details on creating a property package, please refer to this [resource](../../properties/custom/custom_physical_property_packages_test.ipynb ).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"OrgPhaseStateBlock\", block_class=_OrganicStateBlock)\n", - "class OrgPhaseStateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for Organic phzase for liquid liquid extraction\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super().build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " self.flow_vol = Var(\n", - " initialize=1,\n", - " domain=NonNegativeReals,\n", - " doc=\"Total volumetric flowrate\",\n", - " units=units.L / units.hour,\n", - " )\n", - " self.conc_mass_comp = Var(\n", - " self.params.solute_set,\n", - " domain=NonNegativeReals,\n", - " initialize=1,\n", - " doc=\"Component mass concentrations\",\n", - " units=units.g / units.L,\n", - " )\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1,\n", - " bounds=(1, 5),\n", - " units=units.atm,\n", - " doc=\"State pressure [atm]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=300,\n", - " bounds=(273, 373),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " def material_flow_expression(self, j):\n", - " if j == \"solvent\":\n", - " return self.flow_vol * self.params.dens_mass\n", - " else:\n", - " return self.flow_vol * self.conc_mass_comp[j]\n", - "\n", - " self.material_flow_expression = Expression(\n", - " self.component_list,\n", - " rule=material_flow_expression,\n", - " doc=\"Material flow terms\",\n", - " )\n", - "\n", - " def enthalpy_flow_expression(self):\n", - " return (\n", - " self.flow_vol\n", - " * self.params.dens_mass\n", - " * self.params.cp_mass\n", - " * (self.temperature - self.params.temperature_ref)\n", - " )\n", - "\n", - " self.enthalpy_flow_expression = Expression(\n", - " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", - " )\n", - "\n", - " def get_flow_rate(self):\n", - " return self.flow_vol\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.material_flow_expression[j]\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.enthalpy_flow_expression\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_vol\": self.flow_vol,\n", - " \"conc_mass_comp\": self.conc_mass_comp,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def get_material_flow_basis(self):\n", - " return MaterialFlowBasis.mass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Creating Aqueous Property Package\n", - "\n", - "The structure of Aqueous Property Package mirrors that of the Organic Property Package we previously developed. We'll commence with an overview, importing the required libraries, followed by the creation of the physical property block and two state blocks. The distinctions in this package lie in the physical parameter values, and notably, the absence of the diffusion factor term, differentiating it from the prior package. The following code snippet should provide clarity on these distinctions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "from idaes.core.util.initialization import fix_state_vars\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Param,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - " Expression,\n", - " PositiveReals,\n", - ")\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " MaterialFlowBasis,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " Solute,\n", - " Solvent,\n", - " LiquidPhase,\n", - ")\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)\n", - "\n", - "\n", - "@declare_process_block_class(\"AqPhase\")\n", - "class AqPhaseData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " aqueous Phase\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super().build()\n", - "\n", - " self._state_block_class = AqPhaseStateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Aq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.NaCl = Solute()\n", - " self.KNO3 = Solute()\n", - " self.CaSO4 = Solute()\n", - " self.H2O = Solvent()\n", - "\n", - " # Heat capacity of solvent\n", - " self.cp_mass = Param(\n", - " mutable=True,\n", - " initialize=4182,\n", - " doc=\"Specific heat capacity of solvent\",\n", - " units=units.J / units.kg / units.K,\n", - " )\n", - "\n", - " self.dens_mass = Param(\n", - " mutable=True,\n", - " initialize=997,\n", - " doc=\"Density of ethylene dibromide\",\n", - " units=units.kg / units.m**3,\n", - " )\n", - " self.temperature_ref = Param(\n", - " within=PositiveReals,\n", - " mutable=True,\n", - " default=298.15,\n", - " doc=\"Reference temperature\",\n", - " units=units.K,\n", - " )\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.hour,\n", - " \"length\": units.m,\n", - " \"mass\": units.g,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )\n", - "\n", - "\n", - "class _AqueousStateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def fix_initialization_states(self):\n", - " fix_state_vars(self)\n", - "\n", - "\n", - "@declare_process_block_class(\"AqPhaseStateBlock\", block_class=_AqueousStateBlock)\n", - "class AqPhaseStateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super().build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " self.flow_vol = Var(\n", - " initialize=1,\n", - " domain=NonNegativeReals,\n", - " doc=\"Total volumetric flowrate\",\n", - " units=units.L / units.hour,\n", - " )\n", - "\n", - " self.conc_mass_comp = Var(\n", - " self.params.solute_set,\n", - " domain=NonNegativeReals,\n", - " initialize={\"NaCl\": 0.15, \"KNO3\": 0.2, \"CaSO4\": 0.1},\n", - " doc=\"Component mass concentrations\",\n", - " units=units.g / units.L,\n", - " )\n", - "\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1,\n", - " bounds=(1, 5),\n", - " units=units.atm,\n", - " doc=\"State pressure [atm]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=300,\n", - " bounds=(273, 373),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " def material_flow_expression(self, j):\n", - " if j == \"H2O\":\n", - " return self.flow_vol * self.params.dens_mass\n", - " else:\n", - " return self.conc_mass_comp[j] * self.flow_vol\n", - "\n", - " self.material_flow_expression = Expression(\n", - " self.component_list,\n", - " rule=material_flow_expression,\n", - " doc=\"Material flow terms\",\n", - " )\n", - "\n", - " def enthalpy_flow_expression(self):\n", - " return (\n", - " self.flow_vol\n", - " * self.params.dens_mass\n", - " * self.params.cp_mass\n", - " * (self.temperature - self.params.temperature_ref)\n", - " )\n", - "\n", - " self.enthalpy_flow_expression = Expression(\n", - " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", - " )\n", - "\n", - " def get_flow_rate(self):\n", - " return self.flow_vol\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.material_flow_expression[j]\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.enthalpy_flow_expression\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_vol\": self.flow_vol,\n", - " \"conc_mass_comp\": self.conc_mass_comp,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def get_material_flow_basis(self):\n", - " return MaterialFlowBasis.mass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Liquid Liquid Extractor Unit Model\n", - "\n", - "Following the creation of property packages, our next step is to develop a unit model that facilitates the mass transfer of solutes between phases. This involves importing necessary libraries, building the unit model, defining auxiliary functions, and establishing the initialization routine for the unit model.\n", - "\n", - "## 3.1 Importing necessary libraries\n", - "\n", - "Let's commence by importing the essential libraries from Pyomo and IDAES." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Pyomo libraries\n", - "from pyomo.common.config import ConfigBlock, ConfigValue, In, Bool\n", - "from pyomo.environ import (\n", - " value,\n", - " Constraint,\n", - " check_optimal_termination,\n", - ")\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " ControlVolume0DBlock,\n", - " declare_process_block_class,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " MaterialFlowBasis,\n", - " MomentumBalanceType,\n", - " UnitModelBlockData,\n", - " useDefault,\n", - ")\n", - "from idaes.core.util.config import (\n", - " is_physical_parameter_block,\n", - " is_reaction_parameter_block,\n", - ")\n", - "\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.exceptions import ConfigurationError, InitializationError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Creating the unit model\n", - "\n", - "Creating a unit model starts by creating a class called `LiqExtractionData` and use the `declare_process_block_class` decorator. The `LiqExtractionData` inherits the properties of `UnitModelBlockData` class, which allows us to create a control volume which is necessary for the unit model. After declaration of the class we proceed to define the relevant config arguments for the control volume. The config arguments includes the following properties:\n", - "\n", - "- `material_balance_type` - Indicates what type of mass balance should be constructed\n", - "- `has_pressure_change` - Indicates whether terms for pressure change should be\n", - "constructed\n", - "- `has_phase_equilibrium` - Indicates whether terms for phase equilibrium should be\n", - "constructed\n", - "- `Organic Property` - Property parameter object used to define property calculations\n", - "for the Organic phase\n", - "- `Organic Property Arguments` - Arguments to use for constructing Organic phase properties\n", - "- `Aqueous Property` - Property parameter object used to define property calculations\n", - "for the aqueous phase\n", - "- `Aqueous Property Arguments` - Arguments to use for constructing aqueous phase properties\n", - "\n", - "As there are no pressure changes or reactions in this scenario, configuration arguments for these aspects are not included. However, additional details on configuration arguments can be found [here](https://github.com/IDAES/idaes-pse/blob/8948c6ce27d4c7f2c06b377a173f413599091998/idaes/models/unit_models/cstr.py)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"LiqExtraction\")\n", - "class LiqExtractionData(UnitModelBlockData):\n", - " \"\"\"\n", - " LiqExtraction Unit Model Class\n", - " \"\"\"\n", - "\n", - " CONFIG = UnitModelBlockData.CONFIG()\n", - "\n", - " CONFIG.declare(\n", - " \"material_balance_type\",\n", - " ConfigValue(\n", - " default=MaterialBalanceType.useDefault,\n", - " domain=In(MaterialBalanceType),\n", - " description=\"Material balance construction flag\",\n", - " doc=\"\"\"Indicates what type of mass balance should be constructed,\n", - " **default** - MaterialBalanceType.useDefault.\n", - " **Valid values:** {\n", - " **MaterialBalanceType.useDefault - refer to property package for default\n", - " balance type\n", - " **MaterialBalanceType.none** - exclude material balances,\n", - " **MaterialBalanceType.componentPhase** - use phase component balances,\n", - " **MaterialBalanceType.componentTotal** - use total component balances,\n", - " **MaterialBalanceType.elementTotal** - use total element balances,\n", - " **MaterialBalanceType.total** - use total material balance.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"has_pressure_change\",\n", - " ConfigValue(\n", - " default=False,\n", - " domain=Bool,\n", - " description=\"Pressure change term construction flag\",\n", - " doc=\"\"\"Indicates whether terms for pressure change should be\n", - " constructed,\n", - " **default** - False.\n", - " **Valid values:** {\n", - " **True** - include pressure change terms,\n", - " **False** - exclude pressure change terms.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"has_phase_equilibrium\",\n", - " ConfigValue(\n", - " default=False,\n", - " domain=Bool,\n", - " description=\"Phase equilibrium construction flag\",\n", - " doc=\"\"\"Indicates whether terms for phase equilibrium should be\n", - " constructed,\n", - " **default** = False.\n", - " **Valid values:** {\n", - " **True** - include phase equilibrium terms\n", - " **False** - exclude phase equilibrium terms.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"organic_property_package\",\n", - " ConfigValue(\n", - " default=useDefault,\n", - " domain=is_physical_parameter_block,\n", - " description=\"Property package to use for organic phase\",\n", - " doc=\"\"\"Property parameter object used to define property calculations\n", - " for the organic phase,\n", - " **default** - useDefault.\n", - " **Valid values:** {\n", - " **useDefault** - use default package from parent model or flowsheet,\n", - " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"organic_property_package_args\",\n", - " ConfigBlock(\n", - " implicit=True,\n", - " description=\"Arguments to use for constructing organic phase properties\",\n", - " doc=\"\"\"A ConfigBlock with arguments to be passed to organic phase\n", - " property block(s) and used when constructing these,\n", - " **default** - None.\n", - " **Valid values:** {\n", - " see property package for documentation.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"aqueous_property_package\",\n", - " ConfigValue(\n", - " default=useDefault,\n", - " domain=is_physical_parameter_block,\n", - " description=\"Property package to use for aqueous phase\",\n", - " doc=\"\"\"Property parameter object used to define property calculations\n", - " for the aqueous phase,\n", - " **default** - useDefault.\n", - " **Valid values:** {\n", - " **useDefault** - use default package from parent model or flowsheet,\n", - " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"aqueous_property_package_args\",\n", - " ConfigBlock(\n", - " implicit=True,\n", - " description=\"Arguments to use for constructing aqueous phase properties\",\n", - " doc=\"\"\"A ConfigBlock with arguments to be passed to aqueous phase\n", - " property block(s) and used when constructing these,\n", - " **default** - None.\n", - " **Valid values:** {\n", - " see property package for documentation.}\"\"\",\n", - " ),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Building the model\n", - "\n", - "After constructing the `LiqExtractionData` block and defining the config arguments for the control block, the next step is to write a build function that incorporates control volume and establishes constraints on the control volume to achieve the desired mass transfer. The control volume serves as a pivotal component in the unit model construction, representing the volume in which the process unfolds.\n", - "\n", - "IDAES provides flexibility in choosing control volumes based on geometry, with options including 0D or 1D. In this instance, we opt for a 0D control volume, the most commonly used control volume. This choice is suitable for systems where there is a well-mixed volume of fluid or where spatial variations are deemed negligible.\n", - "\n", - "The control volume encompasses parameters from (1-8), and its equations are configured to satisfy the specified config arguments. For a more in-depth understanding, users are encouraged to refer to [this resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst). \n", - "\n", - "The `build` function is initiated using the `super` function to gain access to methods and properties of a parent or sibling class, in this case, the `LiqExtractionData` class. Following the `super` function, checks are performed on the property packages to ensure the appropriate names for the solvents, such as 'Aq' for the aqueous phase and 'Org' for the Organic phase. An error is raised if these conditions are not met. Subsequently, a check is performed to ensure there is at least one common component between the two property packages that can be transferred from one phase to another.\n", - "\n", - "After these checks are completed without any exceptions raised, it is ensured that the property packages have the desired components with appropriate names. The next step is to create a control volume and assign it to a property package. Here, we initiate with the Organic phase and attach a 0D control volume to it. The control volume takes arguments about the dynamics of the block, and the property package, along with property package arguments. \n", - "\n", - "The subsequent steps involve adding inlet and outlet state blocks to the control volume using the `add_state_blocks` function. This function takes arguments about the flow direction (defaulted to forward) and a flag for `has_phase_equilibrium`, which is read from the config. The control volume is now equipped with the inlet and outlet state blocks and has access to the Organic property package\n", - "\n", - "Next, material balance equations are added to the control volume using the `add_material_balance` function, taking into account the type of material balance, `has_phase_equilibrium`, and the presence of `has_mass_transfer`. To understand this arguments further let us have a look at the material balance equation and how it is implemented in control volume. \n", - "\n", - "$\\frac{\\partial M_{t, p, j}}{\\partial t} = F_{in, t, p, j} - F_{out, t, p, j} + N_{kinetic, t, p, j} + N_{equilibrium, t, p, j} + N_{pe, t, p, j} + N_{transfer, t, p, j} + N_{custom, t, p, j}$\n", - "\n", - "- $\\frac{\\partial M_{t, p, j}}{\\partial t}$ - Material accumulation\n", - "- $F_{in, t, p, j}$ - Flow into the control volume\n", - "- $F_{out, t, p, j}$ - Flow out of the control volume\n", - "- $N_{kinetic, t, p, j}$ - Rate of reaction generation\n", - "- $N_{equilibrium, t, p, j}$ - Equilibrium reaction generation\n", - "- $N_{pe, t, p, j}$ - Equilibrium reaction extent\n", - "- $N_{transfer, t, p, j}$ - Mass transfer\n", - "- $N_{custom, t, p, j}$ - User defined terms in material balance\n", - "\n", - "- t indicates time index\n", - "- p indicates phase index\n", - "- j indicates component index\n", - "- e indicates element index\n", - "- r indicates reaction name index\n", - "\n", - "Here we shall see that $N_{transfer, t, p, j}$ is the term in the equation which is responsible for the mass transfer and the `mass_transfer_term` should only be equal to the amount being transferred and not include a material balance on our own. For a detailed description of the terms one should refer to the following [resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst)\n", - "\n", - "This concludes the creation of organic phase control volume. Similar procedure is done for the aqueous phase control volume with aqueous property package. \n", - "\n", - "Now, the unit model has two control volumes with appropriate configurations and material, momentum and energy balances. The next step is to check the basis of the two property packages. They should both have the same flow basis, and an error is raised if this is not the case.\n", - "\n", - "Following this, the `add_inlet_ports` and `add_outlet_ports` functions are used to create inlet and outlet ports. These ports are named and assigned to each control volume, resulting in labeled inlet and outlet ports for each control volume.\n", - "\n", - "The subsequent steps involve writing unit-level constraints. A check if the basis is either molar or mass, and unit-level constraints are written accordingly. The first constraint pertains to the mass transfer term for the aqueous phase. The mass transfer term is equal to $mass\\_transfer\\_term_{aq} = (D_{i})\\frac{mass_{i}~in~aq~phase}{flowrate~of~aq~phase}$. The second constraint relates to the mass transfer term in the organic phase, which is the negative of the mass transfer term in the aqueous phase: $mass\\_transfer\\_term_{org} = - mass\\_transfer\\_term_{aq} $\n", - "\n", - "Here $mass\\_transfer\\_term_{p}$ is the term indicating the amount of material being transferred from/to the phase and $D_{i}$ is the Distribution co-efficient for component i. \n", - "\n", - "This marks the completion of the build function, and the unit model is now equipped with the necessary process constraints. The subsequent steps involve writing the initialization routine." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def build(self):\n", - " \"\"\"\n", - " Begin building model (pre-DAE transformation).\n", - " Args:\n", - " None\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - " # Call UnitModel.build to setup dynamics\n", - " super().build()\n", - "\n", - " # Check phase lists match assumptions\n", - " if self.config.aqueous_property_package.phase_list != [\"Aq\"]:\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the aquoues \"\n", - " f\"phase property package have a single phase named 'Aq'\"\n", - " )\n", - " if self.config.organic_property_package.phase_list != [\"Org\"]:\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", - " f\"phase property package have a single phase named 'Org'\"\n", - " )\n", - "\n", - " # Check for at least one common component in component lists\n", - " if not any(\n", - " j in self.config.aqueous_property_package.component_list\n", - " for j in self.config.organic_property_package.component_list\n", - " ):\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", - " f\"and aqueous phase property packages have at least one \"\n", - " f\"common component.\"\n", - " )\n", - "\n", - " self.organic_phase = ControlVolume0DBlock(\n", - " dynamic=self.config.dynamic,\n", - " property_package=self.config.organic_property_package,\n", - " property_package_args=self.config.organic_property_package_args,\n", - " )\n", - "\n", - " self.organic_phase.add_state_blocks(\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium\n", - " )\n", - "\n", - " # Separate organic and aqueous phases means that phase equilibrium will\n", - " # be handled at the unit model level, thus has_phase_equilibrium is\n", - " # False, but has_mass_transfer is True.\n", - "\n", - " self.organic_phase.add_material_balances(\n", - " balance_type=self.config.material_balance_type,\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", - " has_mass_transfer=True,\n", - " )\n", - " # ---------------------------------------------------------------------\n", - "\n", - " self.aqueous_phase = ControlVolume0DBlock(\n", - " dynamic=self.config.dynamic,\n", - " property_package=self.config.aqueous_property_package,\n", - " property_package_args=self.config.aqueous_property_package_args,\n", - " )\n", - "\n", - " self.aqueous_phase.add_state_blocks(\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium\n", - " )\n", - "\n", - " # Separate liquid and aqueous phases means that phase equilibrium will\n", - " # be handled at the unit model level, thus has_phase_equilibrium is\n", - " # False, but has_mass_transfer is True.\n", - "\n", - " self.aqueous_phase.add_material_balances(\n", - " balance_type=self.config.material_balance_type,\n", - " # has_rate_reactions=False,\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", - " has_mass_transfer=True,\n", - " )\n", - "\n", - " self.aqueous_phase.add_geometry()\n", - "\n", - " # ---------------------------------------------------------------------\n", - " # Check flow basis is compatible\n", - " t_init = self.flowsheet().time.first()\n", - " if (\n", - " self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", - " != self.organic_phase.properties_out[t_init].get_material_flow_basis()\n", - " ):\n", - " raise ConfigurationError(\n", - " f\"{self.name} aqueous and organic property packages must use the \"\n", - " f\"same material flow basis.\"\n", - " )\n", - "\n", - " self.organic_phase.add_geometry()\n", - "\n", - " # Add Ports\n", - " self.add_inlet_port(\n", - " name=\"organic_inlet\", block=self.organic_phase, doc=\"Organic feed\"\n", - " )\n", - " self.add_inlet_port(\n", - " name=\"aqueous_inlet\", block=self.aqueous_phase, doc=\"Aqueous feed\"\n", - " )\n", - " self.add_outlet_port(\n", - " name=\"organic_outlet\", block=self.organic_phase, doc=\"Organic outlet\"\n", - " )\n", - " self.add_outlet_port(\n", - " name=\"aqueous_outlet\",\n", - " block=self.aqueous_phase,\n", - " doc=\"Aqueous outlet\",\n", - " )\n", - "\n", - " # ---------------------------------------------------------------------\n", - " # Add unit level constraints\n", - " # First, need the union and intersection of component lists\n", - " all_comps = (\n", - " self.aqueous_phase.properties_out.component_list\n", - " | self.organic_phase.properties_out.component_list\n", - " )\n", - " common_comps = (\n", - " self.aqueous_phase.properties_out.component_list\n", - " & self.organic_phase.properties_out.component_list\n", - " )\n", - "\n", - " # Get units for unit conversion\n", - " aunits = self.config.aqueous_property_package.get_metadata().get_derived_units\n", - " lunits = self.config.organic_property_package.get_metadata().get_derived_units\n", - " flow_basis = self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", - "\n", - " if flow_basis == MaterialFlowBasis.mass:\n", - " fb = \"flow_mass\"\n", - " elif flow_basis == MaterialFlowBasis.molar:\n", - " fb = \"flow_mole\"\n", - " else:\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor only supports mass \"\n", - " f\"basis for MaterialFlowBasis.\"\n", - " )\n", - "\n", - " # Material balances\n", - " def rule_material_aq_balance(self, t, j):\n", - " if j in common_comps:\n", - " return self.aqueous_phase.mass_transfer_term[\n", - " t, \"Aq\", j\n", - " ] == -self.organic_phase.config.property_package.diffusion_factor[j] * (\n", - " self.aqueous_phase.properties_in[t].get_material_flow_terms(\"Aq\", j)\n", - " )\n", - " elif j in self.organic_phase.properties_out.component_list:\n", - " # No mass transfer term\n", - " # Set organic flowrate to an arbitrary small value\n", - " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * lunits(fb)\n", - " elif j in self.aqueous_phase.properties_out.component_list:\n", - " # No mass transfer term\n", - " # Set aqueous flowrate to an arbitrary small value\n", - " return self.aqueous_phase.mass_transfer_term[t, \"Aq\", j] == 0 * aunits(fb)\n", - "\n", - " self.material_aq_balance = Constraint(\n", - " self.flowsheet().time,\n", - " self.aqueous_phase.properties_out.component_list,\n", - " rule=rule_material_aq_balance,\n", - " doc=\"Unit level material balances for Aq\",\n", - " )\n", - "\n", - " def rule_material_liq_balance(self, t, j):\n", - " if j in common_comps:\n", - " return (\n", - " self.organic_phase.mass_transfer_term[t, \"Org\", j]\n", - " == -self.aqueous_phase.mass_transfer_term[t, \"Aq\", j]\n", - " )\n", - " else:\n", - " # No mass transfer term\n", - " # Set organic flowrate to an arbitrary small value\n", - " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * aunits(fb)\n", - "\n", - " self.material_org_balance = Constraint(\n", - " self.flowsheet().time,\n", - " self.organic_phase.properties_out.component_list,\n", - " rule=rule_material_liq_balance,\n", - " doc=\"Unit level material balances Org\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization Routine\n", - "\n", - "After writing the unit model it is crucial to initialize the model properly, as non-linear models may encounter local minima or infeasibility if not initialized properly. IDAES provides us with a few initialization routines which may not work for all the models, and in such cases the developer will have to define their own initialization routines. \n", - "\n", - "To create a custom initialization routine, model developers must create an initialize method as part of their model, and provide a sequence of steps intended to build up a feasible solution. Initialization routines generally make use of Pyomo’s tools for activating and deactivating constraints and often involve solving multiple sub-problems whilst building up an initial state.\n", - "\n", - "For this tutorial we would use the pre-defined initialization routine of `BlockTriangularizationInitializer` when initializing the model in the flowsheet. This Initializer should be suitable for most models, but may struggle to initialize\n", - "tightly coupled systems of equations. This method of initialization will follow the following workflow. \n", - "\n", - "- Have precheck for structural singularity\n", - "- Run incidence analysis on given block data and check matching.\n", - "- Call Block Triangularization solver on model.\n", - "- Call solve_strongly_connected_components on a given BlockData.\n", - "\n", - "For more details about this initialization routine can be found [here](https://github.com/IDAES/idaes-pse/blob/c09433b9afed5ae2fe25c0ccdc732783324f0101/idaes/core/initialization/block_triangularization.py). \n", - "\n", - "\n", - "This marks the conclusion of creating a custom unit model, for a more detailed explanation on creating a unit model refer [this resource](../../unit_models/custom_unit_models/custom_compressor_test.ipynb). The next sections will deal with the diagonistics and testing of the property package and unit model. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Building a Flowsheet\n", - "\n", - "Once we have set up the unit model and its property packages, we can start building a flowsheet using them. In this tutorial, we're focusing on a simple flowsheet with just a liquid-liquid extractor. To create the flowsheet we follow the following steps:\n", - "\n", - "- Import necessary libraries\n", - "- Create a Pyomo model.\n", - "- Inside the model, create a flowsheet block.\n", - "- Assign property packages to the flowsheet block.\n", - "- Add the liquid-liquid extractor to the flowsheet block.\n", - "- Fix variable to make it a square problem\n", - "- Run an initialization process.\n", - "- Solve the flowsheet.\n", - "\n", - "Following these steps, we've built a basic flowsheet using Pyomo. For more details, refer to the [documentation](../../flowsheets/hda_flowsheet_with_distillation_test.ipynb).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "import idaes.core\n", - "import idaes.models.unit_models\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.logger as idaeslog\n", - "from pyomo.network import Arc\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.initialization import InitializationStatus\n", - "from idaes.core.initialization.block_triangularization import (\n", - " BlockTriangularizationInitializer,\n", - ")\n", - "from liquid_extraction.organic_property import OrgPhase\n", - "from liquid_extraction.aqueous_property import AqPhase\n", - "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", - "\n", - "\n", - "def build_model():\n", - " m = pyo.ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.lex = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - " return m\n", - "\n", - "\n", - "def fix_state_variables(m):\n", - " m.fs.lex.organic_inlet.flow_vol.fix(80 * pyo.units.L / pyo.units.hour)\n", - " m.fs.lex.organic_inlet.temperature.fix(300 * pyo.units.K)\n", - " m.fs.lex.organic_inlet.pressure.fix(1 * pyo.units.atm)\n", - " m.fs.lex.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", - " 1e-5 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", - " 1e-5 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", - " 1e-5 * pyo.units.g / pyo.units.L\n", - " )\n", - "\n", - " m.fs.lex.aqueous_inlet.flow_vol.fix(100 * pyo.units.L / pyo.units.hour)\n", - " m.fs.lex.aqueous_inlet.temperature.fix(300 * pyo.units.K)\n", - " m.fs.lex.aqueous_inlet.pressure.fix(1 * pyo.units.atm)\n", - " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", - " 0.15 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", - " 0.2 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", - " 0.1 * pyo.units.g / pyo.units.L\n", - " )\n", - "\n", - " return m\n", - "\n", - "\n", - "def initialize_model(m):\n", - " initializer = BlockTriangularizationInitializer()\n", - " initializer.initialize(m.fs.lex)\n", - " return m\n", - "\n", - "\n", - "def main():\n", - " m = build_model()\n", - " m = fix_state_variables(m)\n", - " m = initialize_model(m)\n", - " return m\n", - "\n", - "\n", - "if __name__ == main:\n", - " main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Model Diagnostics using DiagnosticsToolbox\n", - "\n", - "Here, during initialization, we encounter warnings indicating that variables are being set to negative values, which is not expected behavior. These warnings suggest that there may be flaws in the model that require further investigation using the DiagnosticsToolbox from IDAES. A detailed notebook on using `DiagnosticsToolbox` can be found [here](../../diagnostics/degeneracy_hunter_test.ipynb).\n", - "\n", - "To proceed with investigating these issues, we need to import the DiagnosticsToolbox. We can gain a better understanding of its functionality by running the help function on it. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util import DiagnosticsToolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The help() function provides comprehensive information on the DiagnosticsToolbox and all its supported methods. However, it's essential to focus on the initial steps outlined at the beginning of the docstring to get started effectively.\n", - "\n", - "Here's a breakdown of the steps to start with:\n", - "\n", - "- `Instantiate Model:` Ensure you have an instance of the model with a degrees of freedom equal to 0.\n", - "\n", - "- `Create DiagnosticsToolbox Instance:` Next, instantiate a DiagnosticsToolbox object.\n", - "\n", - "- `Provide Model to DiagnosticsToolbox:` Pass the model instance to the DiagnosticsToolbox.\n", - "\n", - "- `Call report_structural_issues() Function:` Finally, call the report_structural_issues() function. This function will highlight any warnings in the model's structure, such as unit inconsistencies or other issues related to variables in the caution section.\n", - "\n", - "By following these steps, you can efficiently utilize the DiagnosticsToolbox to identify and address any structural issues or warnings in your model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = main()\n", - "dt = DiagnosticsToolbox(m)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although no warnings were reported, it's important to note that there are 3 variables fixed to 0 and 10 unused variables, out of which 4 are fixed. As indicated in the output, the next step is to solve the model. After solving, you should call the report_numerical_issues() function. This function will help identify any numerical issues that may arise during the solution process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model is probably infeasible thus indicating numerical issues with the model. We should call the `report_numerical_issues()` function and check what the constraints/variables causing this issue. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this scenario, it's observed that the condition number of the Jacobian is high, indicating that the Jacobian is ill-conditioned. Additionally, there are 2 warnings related to constraints with large residuals and variables at or outside the bounds. The cautions mentioned in the output are also related to these warnings.\n", - "\n", - "As suggested, the next steps would be to:\n", - "\n", - "- Call the `display_variables_at_or_outside_bounds()` function to investigate variables at or outside the bounds.\n", - "\n", - "- Call the `display_constraints_with_large_residuals()` function to examine constraints with large residuals.\n", - "\n", - "These steps will help identify the underlying causes of the numerical issues and constraints violations, allowing for further analysis and potential resolution. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.display_variables_at_or_outside_bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this scenario, there are a couple of issues to address:\n", - "\n", - "- The pressure variable is fixed to 1, which is its lower bound. This could potentially lead to numerical issues, although it may not affect the model significantly since there is no pressure change in the model. To mitigate this, consider adjusting the lower bound of the pressure variable to avoid having its value at or outside the bounds.\n", - "\n", - "- The more concerning issue is with the `conc_mass_comp` variable attempting to go below 0 in the output. This suggests that there may be constraints involving `conc_mass_comp` in the aqueous phase causing this behavior. To investigate further, it's recommended to call the `display_constraints_with_large_residuals()` function. This will provide insights into whether constraints involving `conc_mass_comp` are contributing to the convergence issue." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.display_constraints_with_large_residuals()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected there are convergence issues with the constraints which have `conc_mass_comp` variable in them specifically in the aqeous phase. Now, let us investigate further by printing this constraints and checking the value of each term. Since this is an persistent issue across the components, we can focus on just one of the component to identify the issue. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.lex.aqueous_phase.material_balances[0.0, \"NaCl\"].pprint()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", - "m.fs.lex.aqueous_phase.properties_in[0.0].flow_vol.pprint()\n", - "m.fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", - "m.fs.lex.aqueous_phase.properties_out[0.0].flow_vol.pprint()\n", - "m.fs.lex.aqueous_phase.mass_transfer_term[0.0, \"Aq\", \"NaCl\"].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It seems there is a discrepancy between the mass transfer term and the amount of input of NaCl. This can be inferred from the values where the input equals 15g/h and the `mass_transfer_term` equals -31.706g/h.\n", - "\n", - "To further investigate this issue, it's advisable to examine the `material_aq_balance` constraint within the unit model where the `mass_transfer_term` is defined. By printing out this constraint and analyzing its components, you can gain a better understanding of the discrepancy and take appropriate corrective actions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.lex.material_aq_balance[0.0, \"NaCl\"].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the problem can be tracked down easily as there being a typing error while recording the distribution factor. The distribution factor here was wrongly written ignoring its magnitude which should have been 1e-2, but that was missed, thus adjusting the distribution factor parameter we should have this issue resolved. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", - ")\n", - "m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", - ")\n", - "m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", - ")\n", - "\n", - "m.fs.lex.organic_phase.properties_in[0.0].pressure.setlb(0.5)\n", - "m.fs.lex.organic_phase.properties_out[0.0].pressure.setlb(0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the corrective actions, we should check if this have made any structural issues, for this we would call `report_structural_issues()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now since there are no warnings we can go ahead and solve the model and see if the results are optimal. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a good sign that the model solved optimally and a solution was found. \n", - "\n", - "**NOTE:** It is a good practice to run the model through DiagnosticsToolbox regardless of the solver termination status. \n", - "\n", - "The next section we shall focus on testing the unit model. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Testing\n", - "\n", - "Testing is a crucial part of model development to ensure that the model works as expected, and remains reliable. Here's an overview of why we conduct testing:\n", - "\n", - "1. `Verify Correctness`: Testing ensure that the model works as expected and meets the specified requirements. \n", - "2. `Detect Bugs and Issues`: Testing helps in identifying bugs, errors, or unexpected behaviors in the code or model, allowing for timely fixes.\n", - "3. `Ensure Reliability`: Testing improves the reliability and robustness of the software, reducing the risk of failures when the user uses it.\n", - "4. `Support Changes`: Tests provide confidence when making changes or adding new features, ensuring that existing functionalities are not affected and work as they should.\n", - "\n", - "There are typically 3 types of tests:\n", - "\n", - "1. `Unit tests`: Test runs quickly (under 2 seconds) and has no network/system dependencies. Uses only libraries installed by default with the software\n", - "2. `Component test`: Test may run more slowly (under 10 seconds, or so), e.g. it may run a solver or create a bunch of files. Like unit tests, it still shouldn't depend on special libraries or dependencies.\n", - "3. `Integration test`: Test may take a long time to run, and may have complex dependencies.\n", - "\n", - "The expectation is that unit tests should be run by developers rather frequently, component tests should be run by the continuous integration system before running code, and integration tests are run across the codebase regularly, but infrequently (e.g. daily).\n", - "\n", - "\n", - "As a developer, testing is a crucial aspect of ensuring the reliability and correctness of the unit model. The testing process involves both Unit tests and Component tests, and pytest is used as the testing framework. A typical test is marked with @pytest.mark.level, where the level indicates the depth or specificity of the testing. This is written in a file usually named as test_*.py or *_test.py. The test files have functions written in them with the appropriate level of test being conducted. \n", - "\n", - "For more detailed information on testing methodologies and procedures, developers are encouraged to refer to [this resource](https://idaes-pse.readthedocs.io/en/stable/reference_guides/developer/testing.html). The resource provides comprehensive guidance on the testing process and ensures that the unit model meets the required standards and functionality.\n", - "\n", - "## 5.1 Property package\n", - "### Unit Tests\n", - "\n", - "When writing tests for the Aqueous property phase package, it's essential to focus on key aspects to ensure the correctness and robustness of the implementation. Here are the areas to cover in the unit tests:\n", - "\n", - "1. Number of Config Dictionaries: Verify that the property phase package has the expected number of configuration dictionaries.\n", - "\n", - "2. State Block Class Name: Confirm that the correct state block class is associated with the Aqueous property phase package.\n", - "\n", - "3. Number of Phases: Check that the Aqueous property phase package defines the expected number of phases.\n", - "\n", - "4. Components in the Phase and Physical Parameter Values: Test that the components present in the Aqueous phase match the anticipated list. Additionally, validate that the physical parameter values (such as density, viscosity, etc.) are correctly defined.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import ConcreteModel, Param, value, Var\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core import MaterialBalanceType, EnergyBalanceType\n", - "\n", - "from liquid_extraction.organic_property import OrgPhase\n", - "from liquid_extraction.aqueous_property import AqPhase\n", - "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "\n", - "class TestParamBlock(object):\n", - " @pytest.fixture(scope=\"class\")\n", - " def model(self):\n", - " model = ConcreteModel()\n", - " model.params = AqPhase()\n", - " return model\n", - "\n", - " @pytest.mark.unit\n", - " def test_config(self, model):\n", - " assert len(model.params.config) == 1\n", - "\n", - " @pytest.mark.unit\n", - " def test_build(self, model):\n", - " assert len(model.params.phase_list) == 1\n", - " for i in model.params.phase_list:\n", - " assert i == \"Aq\"\n", - "\n", - " assert len(model.params.component_list) == 4\n", - " for i in model.params.component_list:\n", - " assert i in [\"H2O\", \"NaCl\", \"KNO3\", \"CaSO4\"]\n", - "\n", - " assert isinstance(model.params.cp_mass, Param)\n", - " assert value(model.params.cp_mass) == 4182\n", - "\n", - " assert isinstance(model.params.dens_mass, Param)\n", - " assert value(model.params.dens_mass) == 997\n", - "\n", - " assert isinstance(model.params.temperature_ref, Param)\n", - " assert value(model.params.temperature_ref) == 298.15" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next set of unit tests focuses on testing the build function in the state block. Here are the key aspects to cover in these tests:\n", - "\n", - "1. Existence and Initialized Values of State Variables: Verify that the state variables are correctly defined and initialized within the state block. This ensures that the state block is properly constructed and ready for initialization.\n", - "\n", - "2. Initialization Function Test: Check that state variables are not fixed before initialization and are released after initialization. This test ensures that the initialization process occurs as expected and that the state variables are appropriately managed throughout.\n", - "\n", - "These unit tests provide comprehensive coverage for validating the functionality and behavior of the state block in the Aqueous property phase package. Similar tests can be written for the organic property package to ensure consistency and reliability across both packages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class TestStateBlock(object):\n", - " @pytest.fixture(scope=\"class\")\n", - " def model(self):\n", - " model = ConcreteModel()\n", - " model.params = AqPhase()\n", - "\n", - " model.props = model.params.build_state_block([1])\n", - "\n", - " return model\n", - "\n", - " @pytest.mark.unit\n", - " def test_build(self, model):\n", - " assert isinstance(model.props[1].flow_vol, Var)\n", - " assert value(model.props[1].flow_vol) == 1\n", - "\n", - " assert isinstance(model.props[1].temperature, Var)\n", - " assert value(model.props[1].temperature) == 300\n", - "\n", - " assert isinstance(model.props[1].conc_mass_comp, Var)\n", - " assert len(model.props[1].conc_mass_comp) == 3\n", - "\n", - " @pytest.mark.unit\n", - " def test_initialize(self, model):\n", - " assert not model.props[1].flow_vol.fixed\n", - " assert not model.props[1].temperature.fixed\n", - " assert not model.props[1].pressure.fixed\n", - " for i in model.props[1].conc_mass_comp:\n", - " assert not model.props[1].conc_mass_comp[i].fixed\n", - "\n", - " model.props.initialize(hold_state=False, outlvl=1)\n", - "\n", - " assert not model.props[1].flow_vol.fixed\n", - " assert not model.props[1].temperature.fixed\n", - " assert not model.props[1].pressure.fixed\n", - " for i in model.props[1].conc_mass_comp:\n", - " assert not model.props[1].conc_mass_comp[i].fixed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Component Tests\n", - "In the component test, we aim to ensure unit consistency across the entire property package. Unlike unit tests that focus on individual functions, component tests assess the coherence and consistency of the entire package. Here's what the component test will entail:\n", - "\n", - "Unit Consistency Check: Verify that all units used within the property package are consistent throughout. This involves checking that all parameters, variables, and equations within the package adhere to the same unit system, ensuring compatibility.\n", - "\n", - "By conducting a comprehensive component test, we can ensure that the property package functions as a cohesive unit, maintaining consistency and reliability across its entirety. This concludes our tests on the property package. Next we shall test the unit model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@pytest.mark.component\n", - "def check_units(model):\n", - " model = ConcreteModel()\n", - " model.params = AqPhase()\n", - " assert_units_consistent(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Testing the property package without the triggering pytest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "m = ConcreteModel()\n", - "m.params = AqPhase()\n", - "m.props = m.params.build_state_block([1])\n", - "assert_units_consistent(m)\n", - "\n", - "assert len(m.props[1].conc_mass_comp) == 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar tests are done for the Organic Phase as well" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "m = ConcreteModel()\n", - "m.params = OrgPhase()\n", - "m.props = m.params.build_state_block([1])\n", - "assert_units_consistent(m)\n", - "\n", - "assert len(m.props[1].conc_mass_comp) == 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5.2 Unit Model\n", - "### Unit tests\n", - "Unit tests for the unit model encompass verifying the configuration arguments and the build function, similar to the approach taken for the property package. When testing the config arguments, we ensure that the correct number of arguments is provided and then match each argument with the expected one. This ensures that the unit model is properly configured and ready to operate as intended." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "import idaes.core\n", - "import idaes.models.unit_models\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.logger as idaeslog\n", - "\n", - "\n", - "from pyomo.environ import value, check_optimal_termination, units\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.util.model_statistics import (\n", - " number_variables,\n", - " number_total_constraints,\n", - ")\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.initialization import (\n", - " SingleControlVolumeUnitInitializer,\n", - ")\n", - "\n", - "solver = get_solver()\n", - "\n", - "\n", - "@pytest.mark.unit\n", - "def test_config():\n", - " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.unit = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - "\n", - " # Check unit config arguments\n", - " assert len(m.fs.unit.config) == 9\n", - "\n", - " # Check for config arguments\n", - " assert m.fs.unit.config.material_balance_type == MaterialBalanceType.useDefault\n", - " assert not m.fs.unit.config.has_pressure_change\n", - " assert not m.fs.unit.config.has_phase_equilibrium\n", - " assert m.fs.unit.config.organic_property_package is m.fs.org_properties\n", - " assert m.fs.unit.config.aqueous_property_package is m.fs.aq_properties\n", - "\n", - " # Check for unit initializer\n", - " assert m.fs.unit.default_initializer is SingleControlVolumeUnitInitializer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Testing the config arguments for the flowsheet\n", - "test_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In testing the build function, we verify whether the number of variables aligns with the intended values and also check for the existence of desired constraints within the unit model. This ensures that the unit model is constructed accurately and includes all the necessary variables and constraints required for its proper functioning." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class TestBuild(object):\n", - " @pytest.fixture(scope=\"class\")\n", - " def model(self):\n", - " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.unit = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - "\n", - " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.l / units.h)\n", - " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.l)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.l)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.l)\n", - "\n", - " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.l / units.h)\n", - " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.l)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.l)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.l)\n", - "\n", - " return m\n", - "\n", - " @pytest.mark.build\n", - " @pytest.mark.unit\n", - " def test_build(self, model):\n", - "\n", - " assert hasattr(model.fs.unit, \"aqueous_inlet\")\n", - " assert len(model.fs.unit.aqueous_inlet.vars) == 4\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"organic_inlet\")\n", - " assert len(model.fs.unit.organic_inlet.vars) == 4\n", - " assert hasattr(model.fs.unit.organic_inlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.organic_inlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.organic_inlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.organic_inlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"aqueous_outlet\")\n", - " assert len(model.fs.unit.aqueous_outlet.vars) == 4\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"organic_outlet\")\n", - " assert len(model.fs.unit.organic_outlet.vars) == 4\n", - " assert hasattr(model.fs.unit.organic_outlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.organic_outlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.organic_outlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.organic_outlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"material_aq_balance\")\n", - " assert hasattr(model.fs.unit, \"material_org_balance\")\n", - "\n", - " assert number_variables(model) == 34\n", - " assert number_total_constraints(model) == 16" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Component tests\n", - "\n", - "During the component tests, we evaluate the performance of the unit model when integrated with the property package. This evaluation process typically involves several steps:\n", - "\n", - "1. Unit Consistency Check: Verify that the unit model maintains consistency in its units throughout the model. This ensures that all variables and constraints within the model adhere to the same unit system, guaranteeing compatibility.\n", - "\n", - "2. Termination Condition Verification: This involves checking whether the model terminates optimally with the given inlet conditions.\n", - "\n", - "3. Variable Value Assessment: Check the values of outlet variables against the expected values. To account for the numerical tolerance of the solvers, the values are compared using the approx function with a relative tolerance.\n", - "\n", - "4. Input Variable Stability Test: Verify that input variables, which should remain fixed during model operation, are not inadvertently unfixed or altered.\n", - "\n", - "5. Structural Issues: Verify that there are no structural issues with the model. \n", - "\n", - "By performing these checks, we conclude the testing for the unit model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class TestFlowsheet:\n", - " @pytest.fixture\n", - " def model(self):\n", - " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.unit = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - " m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", - " )\n", - " m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", - " )\n", - " m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", - " )\n", - "\n", - " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.ml / units.min)\n", - " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.kg)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.kg)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.kg)\n", - "\n", - " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.ml / units.min)\n", - " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.kg)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.kg)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.kg)\n", - "\n", - " return m\n", - "\n", - " @pytest.mark.component\n", - " def test_unit_model(self, model):\n", - " assert_units_consistent(model)\n", - " solver = get_solver()\n", - " results = solver.solve(model, tee=False)\n", - "\n", - " # Check for optimal termination\n", - " assert check_optimal_termination(results)\n", - "\n", - " # Checking for outlet flows\n", - " assert value(model.fs.unit.organic_outlet.flow_vol[0]) == pytest.approx(\n", - " 80.0, rel=1e-5\n", - " )\n", - " assert value(model.fs.unit.aqueous_outlet.flow_vol[0]) == pytest.approx(\n", - " 10.0, rel=1e-5\n", - " )\n", - "\n", - " # Checking for outlet mass_comp\n", - " assert value(\n", - " model.fs.unit.organic_outlet.conc_mass_comp[0, \"CaSO4\"]\n", - " ) == pytest.approx(0.000187499, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.organic_outlet.conc_mass_comp[0, \"KNO3\"]\n", - " ) == pytest.approx(0.000749999, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.organic_outlet.conc_mass_comp[0, \"NaCl\"]\n", - " ) == pytest.approx(0.000403124, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"CaSO4\"]\n", - " ) == pytest.approx(0.0985, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"KNO3\"]\n", - " ) == pytest.approx(0.194, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"NaCl\"]\n", - " ) == pytest.approx(0.146775, rel=1e-5)\n", - "\n", - " # Checking for outlet temperature\n", - " assert value(model.fs.unit.organic_outlet.temperature[0]) == pytest.approx(\n", - " 300, rel=1e-5\n", - " )\n", - " assert value(model.fs.unit.aqueous_outlet.temperature[0]) == pytest.approx(\n", - " 300, rel=1e-5\n", - " )\n", - "\n", - " # Checking for outlet pressure\n", - " assert value(model.fs.unit.organic_outlet.pressure[0]) == pytest.approx(\n", - " 1, rel=1e-5\n", - " )\n", - " assert value(model.fs.unit.aqueous_outlet.pressure[0]) == pytest.approx(\n", - " 1, rel=1e-5\n", - " )\n", - "\n", - " # Fixed state variables\n", - " assert model.fs.unit.organic_inlet.flow_vol[0].fixed\n", - " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", - " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", - " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", - " assert model.fs.unit.organic_inlet.temperature[0].fixed\n", - " assert model.fs.unit.organic_inlet.pressure[0].fixed\n", - "\n", - " assert model.fs.unit.aqueous_inlet.flow_vol[0].fixed\n", - " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", - " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", - " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", - " assert model.fs.unit.aqueous_inlet.temperature[0].fixed\n", - " assert model.fs.unit.aqueous_inlet.pressure[0].fixed\n", - "\n", - " @pytest.mark.component\n", - " def test_structural_issues(self, model):\n", - " dt = DiagnosticsToolbox(model)\n", - " dt.assert_no_structural_warnings()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Testing the consolidated flowsheet. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from liquid_extraction.liq_liq_extractor_flowsheet import (\n", - " build_model,\n", - " fix_initial_state,\n", - " initialize_model,\n", - " solve_model,\n", - ")\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.core.util import DiagnosticsToolbox\n", - "\n", - "m = pyo.ConcreteModel(name=\"NGFC no CCS\")\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "build_model(m)\n", - "fix_initial_state(m)\n", - "initialize_model(m)\n", - "solve_model(m)\n", - "\n", - "assert_units_consistent(m)\n", - "assert value(m.fs.lex.organic_outlet.temperature[0]) == pytest.approx(300, rel=1e-5)\n", - "dt = DiagnosticsToolbox(m)\n", - "dt.assert_no_numerical_warnings()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, we have covered the comprehensive process of creating a custom unit model from scratch. Let's recap the key steps we have undertaken:\n", - "\n", - "- Developing property package\n", - "- Constructing the unit model \n", - "- Creating a Flowsheet\n", - "- Debugging the model using DiagnosticsToolbox\n", - "- Writing tests for the unit model\n", - "\n", - "By following the aforementioned procedure, one can create their own custom unit model. This would conclude the tutorial on creating custom unit model. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "idaes-pse", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating Custom Unit Model\n", + "Author: Javal Vyas \n", + "Maintainer: Javal Vyas \n", + "\n", + "This tutorial is a comprehensive step-wise procedure to build a custom unit model from scratch. This tutorial will include creating a property package, a custom unit model and testing them. For this tutorial we shall create a custom unit model for Liquid - Liquid Extraction. \n", + "\n", + "The Liquid - Liquid Extractor model contains two immiscible fluids forming the two phases. One of the phases, say phase_1 has a high concentration of solutes which is to be separated. A mass transfer happens between the two phases and the solute is transferred from phase_1 to phase_2. This mass transfer is governed by a parameter called the distribution coefficient.\n", + "\n", + "After reviewing the working principles of the Liquid - Liquid Extractor, we shall proceed to create a custom unit model. We will require a property package for each phase, a custom unit model class and tests for the model and property packages.\n", + "\n", + "Before commencing the development of the model, we need to state some assumptions which the following unit model will be using. \n", + "- Steady-state only\n", + "- Organic phase property package has a single phase named Org\n", + "- Aqueous phase property package has a single phase named Aq\n", + "- Organic and Aqueous phase properties need not have the same component list. \n", + "\n", + "Thus as per the assumptions, we will be creating one property package for the aqueous phase (Aq), and the other for the Organic phase (Org). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Creating Organic Property Package\n", + "\n", + "Creating a property package is a 4 step process\n", + "- Import necessary libraries \n", + "- Creating Physical Parameter Data Block\n", + "- Define State Block\n", + "- Define State Block Data\n", + "\n", + "# 1.1 Importing necessary packages \n", + "Let us begin with importing the necessary libraries where we will be using functionalities from IDAES and Pyomo. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.util.initialization import fix_state_vars\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Param,\n", + " Set,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + " Expression,\n", + " PositiveReals,\n", + ")\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " MaterialFlowBasis,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " Solute,\n", + " Solvent,\n", + " LiquidPhase,\n", + ")\n", + "from idaes.core.util.model_statistics import degrees_of_freedom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1.2 Physical Parameter Data Block\n", + "\n", + "A `PhysicalParameterBlock` serves as the central point of reference for all aspects of the property package and needs to define several things about the package. These are summarized below:\n", + "\n", + "- Units of measurement\n", + "- What properties are supported and how they are implemented\n", + "- What components and phases are included in the packages\n", + "- All the global parameters necessary for calculating properties\n", + "- A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To construct this block, we begin by declaring a process block class using a Python decorator. One can learn more about `declare_process_block_class` [here](https://github.com/IDAES/idaes-pse/blob/eea1209077b75f7d940d8958362e69d4650c079d/idaes/core/base/process_block.py#L173). After constructing the process block, we define a build function which contains all the components that the property package would have. `super` function here is used to give access to methods and properties of a parent or sibling class and since this is used on the class `PhysicalParameterData` class, build has access to all the parent and sibling class methods. \n", + "\n", + "The `PhysicalParameterBlock` then refers to the `state block`, in this case `OrgPhaseStateBlock` (which will be declared later), so that we can build a state block instance by only knowing the `PhysicalParameterBlock` we wish to use. Then we list the number of phases in this property package. Then we assign the variable to the phase which follows a naming convention. The solvent is in the Organic phase; we will assign the Phase as OrganicPhase, and the variable will be named Org as per the naming convention. The details of naming conventions can be found [here](https://github.com/IDAES/idaes-pse/blob/main/docs/explanations/conventions.rst). We will be following the same convention throughout the example. \n", + " \n", + "After defining the list of the phases, we move on to list the components and their type in the phase. It can be a solute or a solvent in the Organic phase. Thus, we define the component and assign it to either being a solute or a solvent. In this case, the salts are the solutes and Ethylene dibromide is the solvent. Next, we define the physical properties involved in the package, like the heat capacity and density of the solvent, the reference temperature, and the distribution factor that would govern the mass transfer from one phase into another. Additionally, a parameter, the `diffusion_factor`, is introduced. This factor plays a crucial role in governing mass transfer between phases, necessitating its definition within the state block.\n", + "\n", + "The final step in creating the Physical Parameter Block is to declare a `classmethod` named `define_metadata`, which takes two arguments: a class (cls) and an instance of that class (obj). In this method, we will call the predefined method `add_default_units()`.\n", + "\n", + "- `obj.add_default_units()` sets the default units metadata for the property package, and here we define units to be used with this property package as default. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"OrgPhase\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " organic Phase\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super().build()\n", + "\n", + " self._state_block_class = OrgPhaseStateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Org = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.NaCl = Solute()\n", + " self.KNO3 = Solute()\n", + " self.CaSO4 = Solute()\n", + " self.solvent = (\n", + " Solvent()\n", + " ) # Solvent used here is ethylene dibromide (Organic Polar)\n", + "\n", + " # Heat capacity of solvent\n", + " self.cp_mass = Param(\n", + " mutable=True,\n", + " initialize=717.01,\n", + " doc=\"Specific heat capacity of solvent\",\n", + " units=units.J / units.kg / units.K,\n", + " )\n", + "\n", + " self.dens_mass = Param(\n", + " mutable=True,\n", + " initialize=2170,\n", + " doc=\"Density of ethylene dibromide\",\n", + " units=units.kg / units.m**3,\n", + " )\n", + " self.temperature_ref = Param(\n", + " within=PositiveReals,\n", + " mutable=True,\n", + " default=298.15,\n", + " doc=\"Reference temperature\",\n", + " units=units.K,\n", + " )\n", + " self.diffusion_factor = Param(\n", + " self.solute_set,\n", + " initialize={\"NaCl\": 2.15, \"KNO3\": 3, \"CaSO4\": 1.5},\n", + " within=PositiveReals,\n", + " mutable=True,\n", + " )\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.hour,\n", + " \"length\": units.m,\n", + " \"mass\": units.g,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1.3 State Block\n", + "\n", + "After the `PhysicalParameterBlock` class has been created, the next step is to write the code necessary to create the State Blocks that will be used throughout the flowsheet. `StateBlock` contains all the information necessary to define the state of the system. This includes the state variables and constraints on those variables which are used to describe a state property like the enthalpy, material balance, etc.\n", + "\n", + "Creating a State Block requires us to write two classes. The reason we write two classes is because of the inherent nature of how `declare_process_block_data` works. `declare_process_block_data` facilitates creating an `IndexedComponent` object which can handle multiple `ComponentData` objects which represent the component at each point in the indexing set. This makes it easier to build an instance of the model at each indexed point. However, State Blocks are slightly different, as they are always indexed (at least by time). Due to this, we often want to perform actions on all the elements of the indexed StateBlock all at once (rather than element by element).\n", + "\n", + "The class `_OrganicStateBlock` is defined without the `declare_process_block_data` decorator and thus works as a traditional class and this facilitates performing a method on the class as a whole rather than individual elements of the indexed property blocks. In this class we define the `fix_initialization_states` function. `fix_initialization_states` function is used to fix the state variable within the state block with the provided initial values (usually inlet conditions). It takes a `block` as the argument in which the state variables are to be fixed. It also takes `state_args` as an optional argument. `state_args` is a dictionary with the value for the state variables to be fixed. This function returns a dictionary indexed by the block, state variables and variable index indicating the fixed status of each variable before applying the function. \n", + "\n", + "The above function comprise of the _OrganicStateBlock. Next, we shall see the construction of the OrgPhaseStateBlockData class." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class _OrganicStateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def fix_initialization_states(self):\n", + " fix_state_vars(self)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class `OrgPhaseStateBlockData` is designated with the `declare_process_block_class` decorator, named `OrgPhaseStateBlock`, and inherits the block class from `_OrganicStateBlock`. This inheritance allows `OrgPhaseStateBlockData` to leverage functions from `_OrganicStateBlock`. Following the class definition, a build function similar to the one used in the `PhysicalParameterData` block is employed. The super function is utilized to enable the utilization of functions from the parent or sibling class.\n", + "\n", + "The subsequent objective is to delineate the state variables, accomplished through the `_make_state_vars` method. This method encompasses all the essential state variables and associated data. For this particular property package, the required state variables are:\n", + "\n", + "- `flow_vol` - volumetric flow rate\n", + "- `conc_mass_comp` - mass fractions\n", + "- `pressure` - state pressure\n", + "- `temperature` - state temperature\n", + "\n", + "After establishing the state variables, the subsequent step involves setting up state properties as constraints. This includes specifying the relationships and limitations that dictate the system's behavior. The following properties need to be articulated:\n", + "\n", + "-`get_material_flow_terms`: quantifies the amount of material flow.\n", + "- `get_enthalpy_flow_terms`: quantifies the amount of enthalpy flow.\n", + "- `get_flow_rate`: details volumetric flow rates.\n", + "- `default_material_balance_type`: defines the kind of material balance to be used.\n", + "- `default_energy_balance_type`: defines the kind of energy balance to be used.\n", + "- `define_state_vars`: involves defining state variables with units, akin to the define_metadata function in the PhysicalParameterData block.\n", + "- `get_material_flow_basis`: establishes the basis on which state variables are measured, whether in mass or molar terms.\n", + "\n", + "These definitions mark the conclusion of the state block construction and thus the property package. For additional details on creating a property package, please refer to this [resource](../../properties/custom/custom_physical_property_packages_test.ipynb ).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"OrgPhaseStateBlock\", block_class=_OrganicStateBlock)\n", + "class OrgPhaseStateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for Organic phase for liquid liquid extraction\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super().build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " self.flow_vol = Var(\n", + " initialize=1,\n", + " domain=NonNegativeReals,\n", + " doc=\"Total volumetric flowrate\",\n", + " units=units.L / units.hour,\n", + " )\n", + " self.conc_mass_comp = Var(\n", + " self.params.solute_set,\n", + " domain=NonNegativeReals,\n", + " initialize=1,\n", + " doc=\"Component mass concentrations\",\n", + " units=units.g / units.L,\n", + " )\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1,\n", + " bounds=(1, 5),\n", + " units=units.atm,\n", + " doc=\"State pressure [atm]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=300,\n", + " bounds=(273, 373),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " def material_flow_expression(self, j):\n", + " if j == \"solvent\":\n", + " return self.flow_vol * self.params.dens_mass\n", + " else:\n", + " return self.flow_vol * self.conc_mass_comp[j]\n", + "\n", + " self.material_flow_expression = Expression(\n", + " self.component_list,\n", + " rule=material_flow_expression,\n", + " doc=\"Material flow terms\",\n", + " )\n", + "\n", + " def enthalpy_flow_expression(self):\n", + " return (\n", + " self.flow_vol\n", + " * self.params.dens_mass\n", + " * self.params.cp_mass\n", + " * (self.temperature - self.params.temperature_ref)\n", + " )\n", + "\n", + " self.enthalpy_flow_expression = Expression(\n", + " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", + " )\n", + "\n", + " def get_flow_rate(self):\n", + " return self.flow_vol\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.material_flow_expression[j]\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.enthalpy_flow_expression\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_vol\": self.flow_vol,\n", + " \"conc_mass_comp\": self.conc_mass_comp,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def get_material_flow_basis(self):\n", + " return MaterialFlowBasis.mass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Creating Aqueous Property Package\n", + "\n", + "The structure of the Aqueous Property Package mirrors that of the Organic Property Package we previously developed. We'll commence with an overview, importing the required libraries, followed by the creation of the physical property block and two state blocks. The distinctions in this package lie in the physical parameter values, and notably, the absence of the diffusion factor term, differentiating it from the prior package. The following code snippet should provide clarity on these distinctions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "from idaes.core.util.initialization import fix_state_vars\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Param,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + " Expression,\n", + " PositiveReals,\n", + ")\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " MaterialFlowBasis,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " Solute,\n", + " Solvent,\n", + " LiquidPhase,\n", + ")\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)\n", + "\n", + "\n", + "@declare_process_block_class(\"AqPhase\")\n", + "class AqPhaseData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " aqueous Phase\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super().build()\n", + "\n", + " self._state_block_class = AqPhaseStateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Aq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.NaCl = Solute()\n", + " self.KNO3 = Solute()\n", + " self.CaSO4 = Solute()\n", + " self.H2O = Solvent()\n", + "\n", + " # Heat capacity of solvent\n", + " self.cp_mass = Param(\n", + " mutable=True,\n", + " initialize=4182,\n", + " doc=\"Specific heat capacity of solvent\",\n", + " units=units.J / units.kg / units.K,\n", + " )\n", + "\n", + " self.dens_mass = Param(\n", + " mutable=True,\n", + " initialize=997,\n", + " doc=\"Density of ethylene dibromide\",\n", + " units=units.kg / units.m**3,\n", + " )\n", + " self.temperature_ref = Param(\n", + " within=PositiveReals,\n", + " mutable=True,\n", + " default=298.15,\n", + " doc=\"Reference temperature\",\n", + " units=units.K,\n", + " )\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.hour,\n", + " \"length\": units.m,\n", + " \"mass\": units.g,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )\n", + "\n", + "\n", + "class _AqueousStateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def fix_initialization_states(self):\n", + " fix_state_vars(self)\n", + "\n", + "\n", + "@declare_process_block_class(\"AqPhaseStateBlock\", block_class=_AqueousStateBlock)\n", + "class AqPhaseStateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super().build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " self.flow_vol = Var(\n", + " initialize=1,\n", + " domain=NonNegativeReals,\n", + " doc=\"Total volumetric flowrate\",\n", + " units=units.L / units.hour,\n", + " )\n", + "\n", + " self.conc_mass_comp = Var(\n", + " self.params.solute_set,\n", + " domain=NonNegativeReals,\n", + " initialize={\"NaCl\": 0.15, \"KNO3\": 0.2, \"CaSO4\": 0.1},\n", + " doc=\"Component mass concentrations\",\n", + " units=units.g / units.L,\n", + " )\n", + "\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1,\n", + " bounds=(1, 5),\n", + " units=units.atm,\n", + " doc=\"State pressure [atm]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=300,\n", + " bounds=(273, 373),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " def material_flow_expression(self, j):\n", + " if j == \"H2O\":\n", + " return self.flow_vol * self.params.dens_mass\n", + " else:\n", + " return self.conc_mass_comp[j] * self.flow_vol\n", + "\n", + " self.material_flow_expression = Expression(\n", + " self.component_list,\n", + " rule=material_flow_expression,\n", + " doc=\"Material flow terms\",\n", + " )\n", + "\n", + " def enthalpy_flow_expression(self):\n", + " return (\n", + " self.flow_vol\n", + " * self.params.dens_mass\n", + " * self.params.cp_mass\n", + " * (self.temperature - self.params.temperature_ref)\n", + " )\n", + "\n", + " self.enthalpy_flow_expression = Expression(\n", + " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", + " )\n", + "\n", + " def get_flow_rate(self):\n", + " return self.flow_vol\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.material_flow_expression[j]\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.enthalpy_flow_expression\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_vol\": self.flow_vol,\n", + " \"conc_mass_comp\": self.conc_mass_comp,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def get_material_flow_basis(self):\n", + " return MaterialFlowBasis.mass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Liquid Liquid Extractor Unit Model\n", + "\n", + "Following the creation of property packages, our next step is to develop a unit model that facilitates the mass transfer of solutes between phases. This involves importing necessary libraries, building the unit model, defining auxiliary functions, and establishing the initialization routine for the unit model.\n", + "\n", + "## 3.1 Importing necessary libraries\n", + "\n", + "Let's commence by importing the essential libraries from Pyomo and IDAES." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Pyomo libraries\n", + "from pyomo.common.config import ConfigBlock, ConfigValue, In, Bool\n", + "from pyomo.environ import (\n", + " value,\n", + " Constraint,\n", + " check_optimal_termination,\n", + ")\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " ControlVolume0DBlock,\n", + " declare_process_block_class,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " MaterialFlowBasis,\n", + " MomentumBalanceType,\n", + " UnitModelBlockData,\n", + " useDefault,\n", + ")\n", + "from idaes.core.util.config import (\n", + " is_physical_parameter_block,\n", + " is_reaction_parameter_block,\n", + ")\n", + "\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.exceptions import ConfigurationError, InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Creating the unit model\n", + "\n", + "Creating a unit model starts by creating a class called `LiqExtractionData` and using the `declare_process_block_class` decorator. The `LiqExtractionData` inherits the properties of the `UnitModelBlockData` class, which allows us to create a control volume that is necessary for the unit model. After declaration of the class we proceed to define the relevant config arguments for the control volume. The config arguments include the following properties:\n", + "\n", + "- `material_balance_type` - Indicates what type of mass balance should be constructed\n", + "- `has_pressure_change` - Indicates whether terms for pressure change should be\n", + "constructed\n", + "- `has_phase_equilibrium` - Indicates whether terms for phase equilibrium should be\n", + "constructed\n", + "- `organic_property_package` - Property parameter object used to define property calculations\n", + "for the Organic phase\n", + "- `organic_property_package_args` - Arguments to use for constructing Organic phase properties\n", + "- `aqueous_property_package` - Property parameter object used to define property calculations\n", + "for the aqueous phase\n", + "- `aqueous_property_package_args` - Arguments to use for constructing aqueous phase properties\n", + "\n", + "As there are no pressure changes or reactions in this scenario, configuration arguments for these aspects are not included. However, additional details on configuration arguments can be found [here](https://github.com/IDAES/idaes-pse/blob/8948c6ce27d4c7f2c06b377a173f413599091998/idaes/models/unit_models/cstr.py)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"LiqExtraction\")\n", + "class LiqExtractionData(UnitModelBlockData):\n", + " \"\"\"\n", + " LiqExtraction Unit Model Class\n", + " \"\"\"\n", + "\n", + " CONFIG = UnitModelBlockData.CONFIG()\n", + "\n", + " CONFIG.declare(\n", + " \"material_balance_type\",\n", + " ConfigValue(\n", + " default=MaterialBalanceType.useDefault,\n", + " domain=In(MaterialBalanceType),\n", + " description=\"Material balance construction flag\",\n", + " doc=\"\"\"Indicates what type of mass balance should be constructed,\n", + " **default** - MaterialBalanceType.useDefault.\n", + " **Valid values:** {\n", + " **MaterialBalanceType.useDefault - refer to property package for default\n", + " balance type\n", + " **MaterialBalanceType.none** - exclude material balances,\n", + " **MaterialBalanceType.componentPhase** - use phase component balances,\n", + " **MaterialBalanceType.componentTotal** - use total component balances,\n", + " **MaterialBalanceType.elementTotal** - use total element balances,\n", + " **MaterialBalanceType.total** - use total material balance.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"has_pressure_change\",\n", + " ConfigValue(\n", + " default=False,\n", + " domain=Bool,\n", + " description=\"Pressure change term construction flag\",\n", + " doc=\"\"\"Indicates whether terms for pressure change should be\n", + " constructed,\n", + " **default** - False.\n", + " **Valid values:** {\n", + " **True** - include pressure change terms,\n", + " **False** - exclude pressure change terms.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"has_phase_equilibrium\",\n", + " ConfigValue(\n", + " default=False,\n", + " domain=Bool,\n", + " description=\"Phase equilibrium construction flag\",\n", + " doc=\"\"\"Indicates whether terms for phase equilibrium should be\n", + " constructed,\n", + " **default** = False.\n", + " **Valid values:** {\n", + " **True** - include phase equilibrium terms\n", + " **False** - exclude phase equilibrium terms.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"organic_property_package\",\n", + " ConfigValue(\n", + " default=useDefault,\n", + " domain=is_physical_parameter_block,\n", + " description=\"Property package to use for organic phase\",\n", + " doc=\"\"\"Property parameter object used to define property calculations\n", + " for the organic phase,\n", + " **default** - useDefault.\n", + " **Valid values:** {\n", + " **useDefault** - use default package from parent model or flowsheet,\n", + " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"organic_property_package_args\",\n", + " ConfigBlock(\n", + " implicit=True,\n", + " description=\"Arguments to use for constructing organic phase properties\",\n", + " doc=\"\"\"A ConfigBlock with arguments to be passed to organic phase\n", + " property block(s) and used when constructing these,\n", + " **default** - None.\n", + " **Valid values:** {\n", + " see property package for documentation.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"aqueous_property_package\",\n", + " ConfigValue(\n", + " default=useDefault,\n", + " domain=is_physical_parameter_block,\n", + " description=\"Property package to use for aqueous phase\",\n", + " doc=\"\"\"Property parameter object used to define property calculations\n", + " for the aqueous phase,\n", + " **default** - useDefault.\n", + " **Valid values:** {\n", + " **useDefault** - use default package from parent model or flowsheet,\n", + " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"aqueous_property_package_args\",\n", + " ConfigBlock(\n", + " implicit=True,\n", + " description=\"Arguments to use for constructing aqueous phase properties\",\n", + " doc=\"\"\"A ConfigBlock with arguments to be passed to aqueous phase\n", + " property block(s) and used when constructing these,\n", + " **default** - None.\n", + " **Valid values:** {\n", + " see property package for documentation.}\"\"\",\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building the model\n", + "\n", + "After constructing the `LiqExtractionData` block and defining the config arguments for the control block, the next step is to write a build function that incorporates the control volume and establishes constraints on the control volume to achieve the desired mass transfer. The control volume serves as a pivotal component in the unit model construction, representing the volume in which the process unfolds.\n", + "\n", + "IDAES provides flexibility in choosing control volumes based on geometry, with options including 0D or 1D. In this instance, we opt for a 0D control volume, the most commonly used control volume. This choice is suitable for systems where there is a well-mixed volume of fluid or where spatial variations are deemed negligible.\n", + "\n", + "The control volume encompasses parameters from (1-8), and its equations are configured to satisfy the specified config arguments. For a more in-depth understanding, users are encouraged to refer to [this resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst). \n", + "\n", + "The `build` function is initiated using the `super` function to gain access to methods and properties of a parent or sibling class, in this case, the `LiqExtractionData` class. Following the `super` function, checks are performed on the property packages to ensure the appropriate names for the solvents, such as 'Aq' for the aqueous phase and 'Org' for the organic phase. An error is raised if these conditions are not met. Subsequently, a check is performed to ensure there is at least one common component between the two property packages that can be transferred from one phase to another.\n", + "\n", + "After these checks are completed without any exceptions raised, it is ensured that the property packages have the desired components with appropriate names. The next step is to create a control volume and assign it to a property package. Here, we initiate with the organic phase and attach a 0D control volume to it. The control volume takes arguments about the dynamics of the block, and the property package, along with property package arguments. \n", + "\n", + "The subsequent steps involve adding inlet and outlet state blocks to the control volume using the `add_state_blocks` function. This function takes arguments about the flow direction (defaulted to forward) and a flag for `has_phase_equilibrium`, which is read from the config. The control volume is now equipped with the inlet and outlet state blocks and has access to the organic property package\n", + "\n", + "Next, material balance equations are added to the control volume using the `add_material_balance` function, taking into account the type of material balance, `has_phase_equilibrium`, and the presence of `has_mass_transfer`. To understand this arguments further let us have a look at the material balance equation and how it is implemented in control volume. \n", + "\n", + "$\\frac{\\partial M_{t, p, j}}{\\partial t} = F_{in, t, p, j} - F_{out, t, p, j} + N_{kinetic, t, p, j} + N_{equilibrium, t, p, j} + N_{pe, t, p, j} + N_{transfer, t, p, j} + N_{custom, t, p, j}$\n", + "\n", + "- $\\frac{\\partial M_{t, p, j}}{\\partial t}$ - Material accumulation\n", + "- $F_{in, t, p, j}$ - Flow into the control volume\n", + "- $F_{out, t, p, j}$ - Flow out of the control volume\n", + "- $N_{kinetic, t, p, j}$ - Rate of reaction generation\n", + "- $N_{equilibrium, t, p, j}$ - Equilibrium reaction generation\n", + "- $N_{pe, t, p, j}$ - Equilibrium reaction extent\n", + "- $N_{transfer, t, p, j}$ - Mass transfer\n", + "- $N_{custom, t, p, j}$ - User defined terms in material balance\n", + "\n", + "- t indicates time index\n", + "- p indicates phase index\n", + "- j indicates component index\n", + "- e indicates element index\n", + "- r indicates reaction name index\n", + "\n", + "Here we shall see that $N_{transfer, t, p, j}$ is the term in the equation which is responsible for the mass transfer and the `mass_transfer_term` should only be equal to the amount being transferred and not include a material balance on our own. For a detailed description of the terms one should refer to the following [resource.](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst)\n", + "\n", + "This concludes the creation of the organic phase control volume. A similar procedure is done for the aqueous phase control volume with aqueous property package. \n", + "\n", + "Now, the unit model has two control volumes with appropriate configurations and material, momentum and energy balances. The next step is to check the basis of the two property packages. They should both have the same flow basis, and an error is raised if this is not the case.\n", + "\n", + "Following this, the `add_inlet_ports` and `add_outlet_ports` functions are used to create inlet and outlet ports. These ports are named and assigned to each control volume, resulting in labeled inlet and outlet ports for each control volume.\n", + "\n", + "The subsequent steps involve writing unit-level constraints. A check if the basis is either molar or mass, and unit-level constraints are written accordingly. The first constraint pertains to the mass transfer term for the aqueous phase. The mass transfer term is equal to $mass\\_transfer\\_term_{aq} = (D_{i})\\frac{mass_{i}~in~aq~phase}{flowrate~of~aq~phase}$. The second constraint relates to the mass transfer term in the organic phase, which is the negative of the mass transfer term in the aqueous phase: $mass\\_transfer\\_term_{org} = - mass\\_transfer\\_term_{aq} $\n", + "\n", + "Here $mass\\_transfer\\_term_{p}$ is the term indicating the amount of material being transferred from/to the phase and $D_{i}$ is the Distribution coefficient for component i. \n", + "\n", + "This marks the completion of the build function, and the unit model is now equipped with the necessary process constraints. The subsequent steps involve writing the initialization routine." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def build(self):\n", + " \"\"\"\n", + " Begin building model (pre-DAE transformation).\n", + " Args:\n", + " None\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " # Call UnitModel.build to setup dynamics\n", + " super().build()\n", + "\n", + " # Check phase lists match assumptions\n", + " if self.config.aqueous_property_package.phase_list != [\"Aq\"]:\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the aqueous \"\n", + " f\"phase property package have a single phase named 'Aq'\"\n", + " )\n", + " if self.config.organic_property_package.phase_list != [\"Org\"]:\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", + " f\"phase property package have a single phase named 'Org'\"\n", + " )\n", + "\n", + " # Check for at least one common component in component lists\n", + " if not any(\n", + " j in self.config.aqueous_property_package.component_list\n", + " for j in self.config.organic_property_package.component_list\n", + " ):\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", + " f\"and aqueous phase property packages have at least one \"\n", + " f\"common component.\"\n", + " )\n", + "\n", + " self.organic_phase = ControlVolume0DBlock(\n", + " dynamic=self.config.dynamic,\n", + " property_package=self.config.organic_property_package,\n", + " property_package_args=self.config.organic_property_package_args,\n", + " )\n", + "\n", + " self.organic_phase.add_state_blocks(\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium\n", + " )\n", + "\n", + " # Separate organic and aqueous phases means that phase equilibrium will\n", + " # be handled at the unit model level, thus has_phase_equilibrium is\n", + " # False, but has_mass_transfer is True.\n", + "\n", + " self.organic_phase.add_material_balances(\n", + " balance_type=self.config.material_balance_type,\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", + " has_mass_transfer=True,\n", + " )\n", + " # ---------------------------------------------------------------------\n", + "\n", + " self.aqueous_phase = ControlVolume0DBlock(\n", + " dynamic=self.config.dynamic,\n", + " property_package=self.config.aqueous_property_package,\n", + " property_package_args=self.config.aqueous_property_package_args,\n", + " )\n", + "\n", + " self.aqueous_phase.add_state_blocks(\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium\n", + " )\n", + "\n", + " # Separate liquid and aqueous phases means that phase equilibrium will\n", + " # be handled at the unit model level, thus has_phase_equilibrium is\n", + " # False, but has_mass_transfer is True.\n", + "\n", + " self.aqueous_phase.add_material_balances(\n", + " balance_type=self.config.material_balance_type,\n", + " # has_rate_reactions=False,\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", + " has_mass_transfer=True,\n", + " )\n", + "\n", + " self.aqueous_phase.add_geometry()\n", + "\n", + " # ---------------------------------------------------------------------\n", + " # Check flow basis is compatible\n", + " t_init = self.flowsheet().time.first()\n", + " if (\n", + " self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", + " != self.organic_phase.properties_out[t_init].get_material_flow_basis()\n", + " ):\n", + " raise ConfigurationError(\n", + " f\"{self.name} aqueous and organic property packages must use the \"\n", + " f\"same material flow basis.\"\n", + " )\n", + "\n", + " self.organic_phase.add_geometry()\n", + "\n", + " # Add Ports\n", + " self.add_inlet_port(\n", + " name=\"organic_inlet\", block=self.organic_phase, doc=\"Organic feed\"\n", + " )\n", + " self.add_inlet_port(\n", + " name=\"aqueous_inlet\", block=self.aqueous_phase, doc=\"Aqueous feed\"\n", + " )\n", + " self.add_outlet_port(\n", + " name=\"organic_outlet\", block=self.organic_phase, doc=\"Organic outlet\"\n", + " )\n", + " self.add_outlet_port(\n", + " name=\"aqueous_outlet\",\n", + " block=self.aqueous_phase,\n", + " doc=\"Aqueous outlet\",\n", + " )\n", + "\n", + " # ---------------------------------------------------------------------\n", + " # Add unit level constraints\n", + " # First, need the union and intersection of component lists\n", + " all_comps = (\n", + " self.aqueous_phase.properties_out.component_list\n", + " | self.organic_phase.properties_out.component_list\n", + " )\n", + " common_comps = (\n", + " self.aqueous_phase.properties_out.component_list\n", + " & self.organic_phase.properties_out.component_list\n", + " )\n", + "\n", + " # Get units for unit conversion\n", + " aunits = self.config.aqueous_property_package.get_metadata().get_derived_units\n", + " lunits = self.config.organic_property_package.get_metadata().get_derived_units\n", + " flow_basis = self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", + "\n", + " if flow_basis == MaterialFlowBasis.mass:\n", + " fb = \"flow_mass\"\n", + " else:\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor only supports mass \"\n", + " f\"basis for MaterialFlowBasis.\"\n", + " )\n", + "\n", + " # Material balances\n", + " def rule_material_aq_balance(self, t, j):\n", + " if j in common_comps:\n", + " return self.aqueous_phase.mass_transfer_term[\n", + " t, \"Aq\", j\n", + " ] == -self.organic_phase.config.property_package.diffusion_factor[j] * (\n", + " self.aqueous_phase.properties_in[t].get_material_flow_terms(\"Aq\", j)\n", + " )\n", + " elif j in self.organic_phase.properties_out.component_list:\n", + " # No mass transfer term\n", + " # Set organic flowrate to an arbitrary small value\n", + " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * lunits(fb)\n", + " elif j in self.aqueous_phase.properties_out.component_list:\n", + " # No mass transfer term\n", + " # Set aqueous flowrate to an arbitrary small value\n", + " return self.aqueous_phase.mass_transfer_term[t, \"Aq\", j] == 0 * aunits(fb)\n", + "\n", + " self.material_aq_balance = Constraint(\n", + " self.flowsheet().time,\n", + " self.aqueous_phase.properties_out.component_list,\n", + " rule=rule_material_aq_balance,\n", + " doc=\"Unit level material balances for Aq\",\n", + " )\n", + "\n", + " def rule_material_liq_balance(self, t, j):\n", + " if j in common_comps:\n", + " return (\n", + " self.organic_phase.mass_transfer_term[t, \"Org\", j]\n", + " == -self.aqueous_phase.mass_transfer_term[t, \"Aq\", j]\n", + " )\n", + " else:\n", + " # No mass transfer term\n", + " # Set organic flowrate to an arbitrary small value\n", + " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * aunits(fb)\n", + "\n", + " self.material_org_balance = Constraint(\n", + " self.flowsheet().time,\n", + " self.organic_phase.properties_out.component_list,\n", + " rule=rule_material_liq_balance,\n", + " doc=\"Unit level material balances Org\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization Routine\n", + "\n", + "After writing the unit model it is crucial to initialize the model properly, as non-linear models may encounter local minima or infeasibility if not initialized properly. IDAES provides us with a few initialization routines which may not work for all the models, and in such cases the developer will have to define their own initialization routines. \n", + "\n", + "To create a custom initialization routine, model developers must create an initialize method as part of their model, and provide a sequence of steps intended to build up a feasible solution. Initialization routines generally make use of Pyomo\u2019s tools for activating and deactivating constraints and often involve solving multiple sub-problems whilst building up an initial state.\n", + "\n", + "For this tutorial we would use the pre-defined initialization routine of `BlockTriangularizationInitializer` when initializing the model in the flowsheet. This Initializer should be suitable for most models, but may struggle to initialize\n", + "tightly coupled systems of equations. This method of initialization will follow the following workflow. \n", + "\n", + "- Have precheck for structural singularity\n", + "- Run incidence analysis on given block data and check matching.\n", + "- Call Block Triangularization solver on the model.\n", + "- Call solve_strongly_connected_components on a given BlockData.\n", + "\n", + "More details about this initialization routine can be found [here](https://github.com/IDAES/idaes-pse/blob/c09433b9afed5ae2fe25c0ccdc732783324f0101/idaes/core/initialization/block_triangularization.py). \n", + "\n", + "\n", + "This marks the conclusion of creating a custom unit model, for a more detailed explanation on creating a unit model refer [this resource](../../unit_models/custom_unit_models/custom_compressor_test.ipynb). The next sections will deal with the diagnostics and testing of the property package and unit model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Building a Flowsheet\n", + "\n", + "Once we have set up the unit model and its property packages, we can start building a flowsheet using them. In this tutorial, we're focusing on a simple flowsheet with just a liquid-liquid extractor. To create the flowsheet we follow the following steps:\n", + "\n", + "- Import necessary libraries\n", + "- Create a Pyomo model.\n", + "- Inside the model, create a flowsheet block.\n", + "- Assign property packages to the flowsheet block.\n", + "- Add the liquid-liquid extractor to the flowsheet block.\n", + "- Fix variable to make it a square problem\n", + "- Run an initialization process.\n", + "- Solve the flowsheet.\n", + "\n", + "Following these steps, we've built a basic flowsheet using Pyomo. For more details, refer to the [documentation](../../flowsheets/hda_flowsheet_with_distillation_test.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "from idaes.core.initialization.block_triangularization import (\n", + " BlockTriangularizationInitializer,\n", + ")\n", + "from liquid_extraction.organic_property import OrgPhase\n", + "from liquid_extraction.aqueous_property import AqPhase\n", + "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", + "\n", + "\n", + "def build_model():\n", + " m = pyo.ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.lex = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + " return m\n", + "\n", + "\n", + "def fix_state_variables(m):\n", + " m.fs.lex.organic_inlet.flow_vol.fix(80 * pyo.units.L / pyo.units.hour)\n", + " m.fs.lex.organic_inlet.temperature.fix(300 * pyo.units.K)\n", + " m.fs.lex.organic_inlet.pressure.fix(1 * pyo.units.atm)\n", + " m.fs.lex.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", + " 1e-5 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", + " 1e-5 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", + " 1e-5 * pyo.units.g / pyo.units.L\n", + " )\n", + "\n", + " m.fs.lex.aqueous_inlet.flow_vol.fix(100 * pyo.units.L / pyo.units.hour)\n", + " m.fs.lex.aqueous_inlet.temperature.fix(300 * pyo.units.K)\n", + " m.fs.lex.aqueous_inlet.pressure.fix(1 * pyo.units.atm)\n", + " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", + " 0.15 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", + " 0.2 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", + " 0.1 * pyo.units.g / pyo.units.L\n", + " )\n", + "\n", + " return m\n", + "\n", + "\n", + "def initialize_model(m):\n", + " initializer = BlockTriangularizationInitializer()\n", + " initializer.initialize(m.fs.lex)\n", + " return m\n", + "\n", + "\n", + "def main():\n", + " m = build_model()\n", + " m = fix_state_variables(m)\n", + " m = initialize_model(m)\n", + " return m\n", + "\n", + "\n", + "if __name__ == main:\n", + " main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Model Diagnostics using DiagnosticsToolbox\n", + "\n", + "Here, during initialization, we encounter warnings indicating that variables are being set to negative values, which is not expected behavior. These warnings suggest that there may be flaws in the model that require further investigation using the DiagnosticsToolbox from IDAES. A detailed notebook on using `DiagnosticsToolbox` can be found [here](../../diagnostics/degeneracy_hunter_test.ipynb).\n", + "\n", + "To proceed with investigating these issues, we need to import the DiagnosticsToolbox. We can gain a better understanding of its functionality by running the help function on it. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util import DiagnosticsToolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The help() function provides comprehensive information on the DiagnosticsToolbox and all its supported methods. However, it's essential to focus on the initial steps outlined at the beginning of the docstring to get started effectively.\n", + "\n", + "Here's a breakdown of the steps to start with:\n", + "\n", + "- `Instantiate Model:` Ensure you have an instance of the model with degrees of freedom equal to 0.\n", + "\n", + "- `Create DiagnosticsToolbox Instance:` Next, instantiate a DiagnosticsToolbox object.\n", + "\n", + "- `Provide Model to DiagnosticsToolbox:` Pass the model instance to the DiagnosticsToolbox.\n", + "\n", + "- `Call report_structural_issues() Function:` Finally, call the report_structural_issues() function. This function will highlight any warnings in the model's structure, such as unit inconsistencies or other issues related to variables in the caution section.\n", + "\n", + "By following these steps, you can efficiently utilize the DiagnosticsToolbox to identify and address any structural issues or warnings in your model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING (W1001): Setting Var\n", + "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]' to a value\n", + "`-0.1725` (float) not in domain NonNegativeReals.\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + "WARNING (W1001): Setting Var\n", + "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3]' to a value\n", + "`-0.4` (float) not in domain NonNegativeReals.\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + "WARNING (W1001): Setting Var\n", + "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4]' to a value\n", + "`-0.05` (float) not in domain NonNegativeReals.\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 21 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 16 (External: 0)\n", + " Free Variables with only lower bounds: 8\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 8 (External: 0)\n", + " Activated Equality Constraints: 16 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 10 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m = main()\n", + "dt = DiagnosticsToolbox(m)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although no warnings were reported, it's important to note that there are 3 variables fixed to 0 and 10 unused variables, out of which 4 are fixed. As indicated in the output, the next step is to solve the model. After solving, you should call the report_numerical_issues() function. This function will help identify any numerical issues that may arise during the solution process." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 33\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 14\n", + "\n", + "Total number of variables............................: 16\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 16\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 4.10e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 4.00e+01 4.93e+01 -1.0 4.10e-01 - 9.91e-01 2.41e-02h 1\n", + " 2 0.0000000e+00 4.00e+01 2.03e+05 -1.0 4.00e-01 - 1.00e+00 2.47e-04h 1\n", + " 3r 0.0000000e+00 4.00e+01 1.00e+03 1.6 0.00e+00 - 0.00e+00 3.09e-07R 4\n", + " 4r 0.0000000e+00 4.00e+01 9.88e+04 1.6 3.68e+02 - 9.92e-01 2.29e-03f 1\n", + " 5r 0.0000000e+00 3.60e+01 3.03e+00 1.6 4.01e+00 - 1.00e+00 1.00e+00f 1\n", + " 6r 0.0000000e+00 3.69e+01 1.21e+01 -1.2 9.24e-01 - 9.69e-01 9.78e-01f 1\n", + " 7r 0.0000000e+00 3.70e+01 2.11e-01 -1.9 1.00e-01 - 9.97e-01 1.00e+00f 1\n", + " 8r 0.0000000e+00 3.78e+01 2.03e-02 -4.3 8.71e-01 - 9.71e-01 1.00e+00f 1\n", + " 9r 0.0000000e+00 3.80e+01 2.62e-04 -6.4 1.24e-01 - 9.99e-01 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 0.0000000e+00 3.81e+01 5.87e-09 -6.4 1.58e-01 - 1.00e+00 1.00e+00f 1\n", + " 11r 0.0000000e+00 3.91e+01 1.09e-05 -9.0 9.35e-01 - 9.68e-01 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 11\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 5.1393961893966849e-07 5.1393961893966849e-07\n", + "Constraint violation....: 3.9105165554489545e+01 3.9105165554489545e+01\n", + "Complementarity.........: 9.0909090910996620e-10 9.0909090910996620e-10\n", + "Overall NLP error.......: 3.9105165554489545e+01 3.9105165554489545e+01\n", + "\n", + "\n", + "Number of objective function evaluations = 17\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 17\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 14\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: infeasible\n", + " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", + " point. Problem may be infeasible.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.06552338600158691}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is probably infeasible, indicating numerical issues with the model. We should call the `report_numerical_issues()` function and check the constraints/variables causing this issue. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 7.955E+03\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 6 Constraints with large residuals (>1.0E-05)\n", + " WARNING: 5 Variables at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 Cautions\n", + "\n", + " Caution: 8 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 5 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 3 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this scenario, it's observed that the condition number of the Jacobian is high, indicating that the Jacobian is ill-conditioned. Additionally, there are 2 warnings related to constraints with large residuals and variables at or outside the bounds. The cautions mentioned in the output are also related to these warnings.\n", + "\n", + "As suggested, the next steps would be to:\n", + "\n", + "- Call the `display_variables_at_or_outside_bounds()` function to investigate variables at or outside the bounds.\n", + "\n", + "- Call the `display_constraints_with_large_residuals()` function to examine constraints with large residuals.\n", + "\n", + "These steps will help identify the underlying causes of the numerical issues and constraints violations, allowing for further analysis and potential resolution. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", + "\n", + " fs.lex.organic_phase.properties_in[0.0].pressure (fixed): value=1.0 bounds=(1, 5)\n", + " fs.lex.organic_phase.properties_out[0.0].pressure (free): value=1 bounds=(1, 5)\n", + " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl] (free): value=0.0 bounds=(0, None)\n", + " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3] (free): value=0.0 bounds=(0, None)\n", + " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4] (free): value=0.0 bounds=(0, None)\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_at_or_outside_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this scenario, there are a couple of issues to address:\n", + "\n", + "- The pressure variable is fixed to 1, which is its lower bound. This could potentially lead to numerical issues, although it may not affect the model significantly since there is no pressure change in the model. To mitigate this, consider adjusting the lower bound of the pressure variable to avoid having its value at or outside the bounds.\n", + "\n", + "- The more concerning issue is with the `conc_mass_comp` variable attempting to go below 0 in the output. This suggests that there may be constraints involving `conc_mass_comp` in the aqueous phase causing this behavior. To investigate further, it's recommended to call the `display_constraints_with_large_residuals()` function. This will provide insights into whether constraints involving `conc_mass_comp` are contributing to the convergence issue." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following constraint(s) have large residuals (>1.0E-05):\n", + "\n", + " fs.lex.material_aq_balance[0.0,NaCl]: 5.49716E-01\n", + " fs.lex.material_aq_balance[0.0,KNO3]: 8.94833E-01\n", + " fs.lex.material_aq_balance[0.0,CaSO4]: 5.48843E-02\n", + " fs.lex.aqueous_phase.material_balances[0.0,NaCl]: 1.67003E+01\n", + " fs.lex.aqueous_phase.material_balances[0.0,KNO3]: 3.91052E+01\n", + " fs.lex.aqueous_phase.material_balances[0.0,CaSO4]: 4.94512E+00\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_constraints_with_large_residuals()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected there are convergence issues with the constraints which have `conc_mass_comp` variable in them specifically in the aqueous phase. Now, let us investigate further by printing this constraints and checking the value of each term. Since this is an persistent issue across the components, we can focus on just one of the component to identify the issue. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of material_balances} : Material balances\n", + " Size=4, Index=fs._time*fs.aq_properties.component_list, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " (0.0, 'NaCl') : 0.0 : (fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_in[0.0].flow_vol) - (fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_out[0.0].flow_vol) + fs.lex.aqueous_phase.mass_transfer_term[0.0,Aq,NaCl] : 0.0 : True\n" + ] + } + ], + "source": [ + "m.fs.lex.aqueous_phase.material_balances[0.0, \"NaCl\"].pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of conc_mass_comp} : Component mass concentrations\n", + " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " NaCl : 0 : 0.15 : None : True : True : NonNegativeReals\n", + "flow_vol : Total volumetric flowrate\n", + " Size=1, Index=None, Units=l/h\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " None : 0 : 100.0 : None : True : True : NonNegativeReals\n", + "{Member of conc_mass_comp} : Component mass concentrations\n", + " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " NaCl : 0 : 0.0 : None : False : False : NonNegativeReals\n", + "flow_vol : Total volumetric flowrate\n", + " Size=1, Index=None, Units=l/h\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " None : 0 : 100.0 : None : False : False : NonNegativeReals\n", + "{Member of mass_transfer_term} : Component material transfer into unit\n", + " Size=4, Index=fs._time*fs.aq_properties._phase_component_set, Units=g/h\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " (0.0, 'Aq', 'NaCl') : None : -31.700284300098897 : None : False : False : Reals\n" + ] + } + ], + "source": [ + "m.fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", + "m.fs.lex.aqueous_phase.properties_in[0.0].flow_vol.pprint()\n", + "m.fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", + "m.fs.lex.aqueous_phase.properties_out[0.0].flow_vol.pprint()\n", + "m.fs.lex.aqueous_phase.mass_transfer_term[0.0, \"Aq\", \"NaCl\"].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems there is a discrepancy between the mass transfer term and the amount of input of NaCl. This can be inferred from the values where the input equals 15g/h and the `mass_transfer_term` equals -31.706g/h.\n", + "\n", + "To further investigate this issue, it's advisable to examine the `material_aq_balance` constraint within the unit model where the `mass_transfer_term` is defined. By printing out this constraint and analyzing its components, you can gain a better understanding of the discrepancy and take appropriate corrective actions." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of material_aq_balance} : Unit level material balances for Aq\n", + " Size=4, Index=fs._time*fs.aq_properties.component_list, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " (0.0, 'NaCl') : 0.0 : fs.lex.aqueous_phase.mass_transfer_term[0.0,Aq,NaCl] + fs.org_properties.diffusion_factor[NaCl]*(fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_in[0.0].flow_vol) : 0.0 : True\n" + ] + } + ], + "source": [ + "m.fs.lex.material_aq_balance[0.0, \"NaCl\"].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the problem can be tracked down easily as there being a typing error while recording the distribution factor. The distribution factor here was wrongly written ignoring its magnitude which should have been 1e-2, but that was missed, thus adjusting the distribution factor parameter we should have this issue resolved. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", + ")\n", + "m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", + ")\n", + "m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", + ")\n", + "\n", + "m.fs.lex.organic_phase.properties_in[0.0].pressure.setlb(0.5)\n", + "m.fs.lex.organic_phase.properties_out[0.0].pressure.setlb(0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the corrective actions, we should check if this has made any structural issues, for this we would call `report_structural_issues()`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 21 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 16 (External: 0)\n", + " Free Variables with only lower bounds: 8\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 8 (External: 0)\n", + " Activated Equality Constraints: 16 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 10 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now since there are no warnings we can go ahead and solve the model and see if the results are optimal. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 33\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 14\n", + "\n", + "Total number of variables............................: 16\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 16\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.85e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.55e-15 8.41e+00 -1.0 5.85e+01 - 1.05e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.5527136788005009e-15 3.5527136788005009e-15\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.5527136788005009e-15 3.5527136788005009e-15\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.07779264450073242}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a good sign that the model solved optimally and a solution was found. \n", + "\n", + "**NOTE:** It is a good practice to run the model through DiagnosticsToolbox regardless of the solver termination status. \n", + "\n", + "The next section we shall focus on testing the unit model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Testing\n", + "\n", + "Testing is a crucial part of model development to ensure that the model works as expected, and remains reliable. Here's an overview of why we conduct testing:\n", + "\n", + "1. `Verify Correctness`: Testing ensures that the model works as expected and meets the specified requirements. \n", + "2. `Detect Bugs and Issues`: Testing helps in identifying bugs, errors, or unexpected behaviors in the code or model, allowing for timely fixes.\n", + "3. `Ensure Reliability`: Testing improves the reliability and robustness of the software, reducing the risk of failures when the user uses it.\n", + "4. `Support Changes`: Tests provide confidence when making changes or adding new features, ensuring that existing functionalities are not affected and work as they should.\n", + "\n", + "There are typically 3 types of tests:\n", + "\n", + "1. `Unit tests`: Test runs quickly (under 2 seconds) and has no network/system dependencies. Uses only libraries installed by default with the software\n", + "2. `Component test`: Test may run more slowly (under 10 seconds, or so), e.g. it may run a solver or create a bunch of files. Like unit tests, it still shouldn't depend on special libraries or dependencies.\n", + "3. `Integration test`: Test may take a long time to run, and may have complex dependencies.\n", + "\n", + "The expectation is that unit tests should be run by developers rather frequently, component tests should be run by the continuous integration system before running code, and integration tests are run across the codebase regularly, but infrequently (e.g. daily).\n", + "\n", + "\n", + "As a developer, testing is a crucial aspect of ensuring the reliability and correctness of the unit model. The testing process involves both Unit tests and Component tests, and pytest is used as the testing framework. A typical test is marked with @pytest.mark.level, where the level indicates the depth or specificity of the testing. This is written in a file usually named as test_*.py or *_test.py. The test files have functions written in them with the appropriate level of test being conducted. \n", + "\n", + "For more detailed information on testing methodologies and procedures, developers are encouraged to refer to [this resource](https://idaes-pse.readthedocs.io/en/stable/reference_guides/developer/testing.html). The resource provides comprehensive guidance on the testing process and ensures that the unit model meets the required standards and functionality.\n", + "\n", + "## 5.1 Property package\n", + "### Unit Tests\n", + "\n", + "When writing tests for the Aqueous property phase package, it's essential to focus on key aspects to ensure the correctness and robustness of the implementation. Here are the areas to cover in the unit tests:\n", + "\n", + "1. Number of Config Dictionaries: Verify that the property phase package has the expected number of configuration dictionaries.\n", + "\n", + "2. State Block Class Name: Confirm that the correct state block class is associated with the Aqueous property phase package.\n", + "\n", + "3. Number of Phases: Check that the Aqueous property phase package defines the expected number of phases.\n", + "\n", + "4. Components in the Phase and Physical Parameter Values: Test that the components present in the Aqueous phase match the anticipated list. Additionally, validate that the physical parameter values (such as density, viscosity, etc.) are correctly defined.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import ConcreteModel, Param, value, Var\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core import MaterialBalanceType, EnergyBalanceType\n", + "\n", + "from liquid_extraction.organic_property import OrgPhase\n", + "from liquid_extraction.aqueous_property import AqPhase\n", + "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "\n", + "class TestParamBlock(object):\n", + " @pytest.fixture(scope=\"class\")\n", + " def model(self):\n", + " model = ConcreteModel()\n", + " model.params = AqPhase()\n", + " return model\n", + "\n", + " @pytest.mark.unit\n", + " def test_config(self, model):\n", + " assert len(model.params.config) == 1\n", + "\n", + " @pytest.mark.unit\n", + " def test_build(self, model):\n", + " assert len(model.params.phase_list) == 1\n", + " for i in model.params.phase_list:\n", + " assert i == \"Aq\"\n", + "\n", + " assert len(model.params.component_list) == 4\n", + " for i in model.params.component_list:\n", + " assert i in [\"H2O\", \"NaCl\", \"KNO3\", \"CaSO4\"]\n", + "\n", + " assert isinstance(model.params.cp_mass, Param)\n", + " assert value(model.params.cp_mass) == 4182\n", + "\n", + " assert isinstance(model.params.dens_mass, Param)\n", + " assert value(model.params.dens_mass) == 997\n", + "\n", + " assert isinstance(model.params.temperature_ref, Param)\n", + " assert value(model.params.temperature_ref) == 298.15" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next set of unit tests focuses on testing the build function in the state block. Here are the key aspects to cover in these tests:\n", + "\n", + "1. Existence and Initialized Values of State Variables: Verify that the state variables are correctly defined and initialized within the state block. This ensures that the state block is properly constructed and ready for initialization.\n", + "\n", + "2. Initialization Function Test: Check that state variables are not fixed before initialization and are released after initialization. This test ensures that the initialization process occurs as expected and that the state variables are appropriately managed throughout.\n", + "\n", + "These unit tests provide comprehensive coverage for validating the functionality and behavior of the state block in the aqueous property phase package. Similar tests can be written for the organic property package to ensure consistency and reliability across both packages." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "class TestStateBlock(object):\n", + " @pytest.fixture(scope=\"class\")\n", + " def model(self):\n", + " model = ConcreteModel()\n", + " model.params = AqPhase()\n", + "\n", + " model.props = model.params.build_state_block([1])\n", + "\n", + " return model\n", + "\n", + " @pytest.mark.unit\n", + " def test_build(self, model):\n", + " assert isinstance(model.props[1].flow_vol, Var)\n", + " assert value(model.props[1].flow_vol) == 1\n", + "\n", + " assert isinstance(model.props[1].temperature, Var)\n", + " assert value(model.props[1].temperature) == 300\n", + "\n", + " assert isinstance(model.props[1].conc_mass_comp, Var)\n", + " assert len(model.props[1].conc_mass_comp) == 3\n", + "\n", + " @pytest.mark.unit\n", + " def test_initialize(self, model):\n", + " assert not model.props[1].flow_vol.fixed\n", + " assert not model.props[1].temperature.fixed\n", + " assert not model.props[1].pressure.fixed\n", + " for i in model.props[1].conc_mass_comp:\n", + " assert not model.props[1].conc_mass_comp[i].fixed\n", + "\n", + " model.props.initialize(hold_state=False, outlvl=1)\n", + "\n", + " assert not model.props[1].flow_vol.fixed\n", + " assert not model.props[1].temperature.fixed\n", + " assert not model.props[1].pressure.fixed\n", + " for i in model.props[1].conc_mass_comp:\n", + " assert not model.props[1].conc_mass_comp[i].fixed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Component Tests\n", + "In the component test, we aim to ensure unit consistency across the entire property package. Unlike unit tests that focus on individual functions, component tests assess the coherence and consistency of the entire package. Here's what the component test will entail:\n", + "\n", + "Unit Consistency Check: Verify that all units used within the property package are consistent throughout. This involves checking that all parameters, variables, and equations within the package adhere to the same unit system, ensuring compatibility.\n", + "\n", + "By conducting a comprehensive component test, we can ensure that the property package functions as a cohesive unit, maintaining consistency and reliability across its entirety. This concludes our tests on the property package. Next we shall test the unit model. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "@pytest.mark.component\n", + "def check_units(model):\n", + " model = ConcreteModel()\n", + " model.params = AqPhase()\n", + " assert_units_consistent(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.2 Unit Model\n", + "### Unit tests\n", + "Unit tests for the unit model encompass verifying the configuration arguments and the build function, similar to the approach taken for the property package. When testing the config arguments, we ensure that the correct number of arguments is provided and then match each argument with the expected one. This ensures that the unit model is properly configured and ready to operate as intended." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "import idaes.models.unit_models\n", + "from idaes.core.solvers import get_solver\n", + "import idaes.logger as idaeslog\n", + "\n", + "\n", + "from pyomo.environ import value, check_optimal_termination, units\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.util.model_statistics import (\n", + " number_variables,\n", + " number_total_constraints,\n", + ")\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.initialization import (\n", + " SingleControlVolumeUnitInitializer,\n", + ")\n", + "\n", + "solver = get_solver()\n", + "\n", + "\n", + "@pytest.mark.unit\n", + "def test_config():\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.unit = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + "\n", + " # Check unit config arguments\n", + " assert len(m.fs.unit.config) == 9\n", + "\n", + " # Check for config arguments\n", + " assert m.fs.unit.config.material_balance_type == MaterialBalanceType.useDefault\n", + " assert not m.fs.unit.config.has_pressure_change\n", + " assert not m.fs.unit.config.has_phase_equilibrium\n", + " assert m.fs.unit.config.organic_property_package is m.fs.org_properties\n", + " assert m.fs.unit.config.aqueous_property_package is m.fs.aq_properties\n", + "\n", + " # Check for unit initializer\n", + " assert m.fs.unit.default_initializer is SingleControlVolumeUnitInitializer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In testing the build function, we verify whether the number of variables aligns with the intended values and also check for the existence of desired constraints within the unit model. This ensures that the unit model is constructed accurately and includes all the necessary variables and constraints required for its proper functioning." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "class TestBuild(object):\n", + " @pytest.fixture(scope=\"class\")\n", + " def model(self):\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.unit = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + "\n", + " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.l / units.h)\n", + " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.l)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.l)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.l)\n", + "\n", + " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.l / units.h)\n", + " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.l)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.l)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.l)\n", + "\n", + " return m\n", + "\n", + " @pytest.mark.build\n", + " @pytest.mark.unit\n", + " def test_build(self, model):\n", + "\n", + " assert hasattr(model.fs.unit, \"aqueous_inlet\")\n", + " assert len(model.fs.unit.aqueous_inlet.vars) == 4\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"organic_inlet\")\n", + " assert len(model.fs.unit.organic_inlet.vars) == 4\n", + " assert hasattr(model.fs.unit.organic_inlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.organic_inlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.organic_inlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.organic_inlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"aqueous_outlet\")\n", + " assert len(model.fs.unit.aqueous_outlet.vars) == 4\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"organic_outlet\")\n", + " assert len(model.fs.unit.organic_outlet.vars) == 4\n", + " assert hasattr(model.fs.unit.organic_outlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.organic_outlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.organic_outlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.organic_outlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"material_aq_balance\")\n", + " assert hasattr(model.fs.unit, \"material_org_balance\")\n", + "\n", + " assert number_variables(model) == 34\n", + " assert number_total_constraints(model) == 16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Component tests\n", + "\n", + "During the component tests, we evaluate the performance of the unit model when integrated with the property package. This evaluation process typically involves several steps:\n", + "\n", + "1. Unit Consistency Check: Verify that the unit model maintains consistency in its units throughout the model. This ensures that all variables and constraints within the model adhere to the same unit system, guaranteeing compatibility.\n", + "\n", + "2. Termination Condition Verification: This involves checking whether the model terminates optimally with the given inlet conditions.\n", + "\n", + "3. Variable Value Assessment: Check the values of outlet variables against the expected values. To account for the numerical tolerance of the solvers, the values are compared using the approx function with a relative tolerance.\n", + "\n", + "4. Input Variable Stability Test: Verify that input variables, which should remain fixed during model operation, are not inadvertently unfixed or altered.\n", + "\n", + "5. Structural Issues: Verify that there are no structural issues with the model. \n", + "\n", + "By performing these checks, we conclude the testing for the unit model. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "class TestFlowsheet:\n", + " @pytest.fixture\n", + " def model(self):\n", + " m = ConcreteModel()\n", + " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.unit = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + " m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", + " )\n", + " m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", + " )\n", + " m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", + " )\n", + "\n", + " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.ml / units.min)\n", + " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.kg)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.kg)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.kg)\n", + "\n", + " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.ml / units.min)\n", + " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.kg)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.kg)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.kg)\n", + "\n", + " return m\n", + "\n", + " @pytest.mark.component\n", + " def test_unit_model(self, model):\n", + " assert_units_consistent(model)\n", + " solver = get_solver()\n", + " results = solver.solve(model, tee=False)\n", + "\n", + " # Check for optimal termination\n", + " assert check_optimal_termination(results)\n", + "\n", + " # Checking for outlet flows\n", + " assert value(model.fs.unit.organic_outlet.flow_vol[0]) == pytest.approx(\n", + " 80.0, rel=1e-5\n", + " )\n", + " assert value(model.fs.unit.aqueous_outlet.flow_vol[0]) == pytest.approx(\n", + " 10.0, rel=1e-5\n", + " )\n", + "\n", + " # Checking for outlet mass_comp\n", + " assert value(\n", + " model.fs.unit.organic_outlet.conc_mass_comp[0, \"CaSO4\"]\n", + " ) == pytest.approx(0.000187499, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.organic_outlet.conc_mass_comp[0, \"KNO3\"]\n", + " ) == pytest.approx(0.000749999, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.organic_outlet.conc_mass_comp[0, \"NaCl\"]\n", + " ) == pytest.approx(0.000403124, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"CaSO4\"]\n", + " ) == pytest.approx(0.0985, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"KNO3\"]\n", + " ) == pytest.approx(0.194, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"NaCl\"]\n", + " ) == pytest.approx(0.146775, rel=1e-5)\n", + "\n", + " # Checking for outlet temperature\n", + " assert value(model.fs.unit.organic_outlet.temperature[0]) == pytest.approx(\n", + " 300, rel=1e-5\n", + " )\n", + " assert value(model.fs.unit.aqueous_outlet.temperature[0]) == pytest.approx(\n", + " 300, rel=1e-5\n", + " )\n", + "\n", + " # Checking for outlet pressure\n", + " assert value(model.fs.unit.organic_outlet.pressure[0]) == pytest.approx(\n", + " 1, rel=1e-5\n", + " )\n", + " assert value(model.fs.unit.aqueous_outlet.pressure[0]) == pytest.approx(\n", + " 1, rel=1e-5\n", + " )\n", + "\n", + " # Fixed state variables\n", + " assert model.fs.unit.organic_inlet.flow_vol[0].fixed\n", + " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", + " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", + " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", + " assert model.fs.unit.organic_inlet.temperature[0].fixed\n", + " assert model.fs.unit.organic_inlet.pressure[0].fixed\n", + "\n", + " assert model.fs.unit.aqueous_inlet.flow_vol[0].fixed\n", + " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", + " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", + " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", + " assert model.fs.unit.aqueous_inlet.temperature[0].fixed\n", + " assert model.fs.unit.aqueous_inlet.pressure[0].fixed\n", + "\n", + " @pytest.mark.component\n", + " def test_structural_issues(self, model):\n", + " dt = DiagnosticsToolbox(model)\n", + " dt.assert_no_structural_warnings()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we have covered the comprehensive process of creating a custom unit model from scratch. Let's recap the key steps we have undertaken:\n", + "\n", + "- Developing property package\n", + "- Constructing the unit model \n", + "- Creating a Flowsheet\n", + "- Debugging the model using DiagnosticsToolbox\n", + "- Writing tests for the unit model\n", + "\n", + "By following the aforementioned procedure, one can create their own custom unit model. This concludes the tutorial on creating a custom unit model. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_usr.ipynb index 211052dd..9b790938 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/creating_unit_model_usr.ipynb @@ -1,2245 +1,2263 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating Custom Unit Model\n", - "Author: Javal Vyas \n", - "Maintainer: Javal Vyas \n", - "Updated: 2023-02-20\n", - "\n", - "This tutorial is a comprehensive step-wise procedure to build a custom unit model from scratch. This tutorial will include creating a property package, a custom unit model and testing them. For this tutorial we shall create a custom unit model for Liquid - Liquid Extraction. \n", - "\n", - "The Liquid - Liquid Extractor model contains two immiscible fluids forming the two phases. One of the phases, say phase_1 has a high concentration of solutes which is to be separated. A mass transfer happens between the two phases and the solute is transferred from phase_1 to phase_2. This mass transfer is governed by a parameter called the distribution coefficient.\n", - "\n", - "After reviewing the working principles of the Liquid - Liquid Extractor, we shall proceed to create a custom unit model. We will require a property package for each phase, a custom unit model class and tests for the model and property packages.\n", - "\n", - "Before commencing the development of the model, we need to state some assumptions which the following unit model will be using. \n", - "- Steady-state only\n", - "- Organic phase property package has a single phase named Org\n", - "- Aqueous phase property package has a single phase named Aq\n", - "- Organic and Aqueous phase properties need not have the same component list. \n", - "\n", - "Thus as per the assumptions, we will be creating one property package for the aqueous phase (Aq), and the other for the Organic phase (Org). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Creating Organic Property Package\n", - "\n", - "Creating a property package is a 4 step process\n", - "- Import necessary libraries \n", - "- Creating Physical Parameter Data Block\n", - "- Define State Block\n", - "- Define State Block Data\n", - "\n", - "# 1.1 Importing necessary packages \n", - "Let us begin with the importing the necessary libraries where we will be using functionalities from IDAES and Pyomo. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.util.initialization import fix_state_vars\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Param,\n", - " Set,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - " Expression,\n", - " PositiveReals,\n", - ")\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " MaterialFlowBasis,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " Solute,\n", - " Solvent,\n", - " LiquidPhase,\n", - ")\n", - "from idaes.core.util.model_statistics import degrees_of_freedom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1.2 Physical Parameter Data Block\n", - "\n", - "A `PhysicalParameterBlock` serves as the central point of reference for all aspects of the property package and needs to define several things about the package. These are summarized below:\n", - "\n", - "- Units of measurement\n", - "- What properties are supported and how they are implemented\n", - "- What components and phases are included in the packages\n", - "- All the global parameters necessary for calculating properties\n", - "- A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", - "\n", - "To construct this block, we begin by declaring a process block class using a Python decorator. One can learn more about `declare_process_block_class` [here](https://github.com/IDAES/idaes-pse/blob/eea1209077b75f7d940d8958362e69d4650c079d/idaes/core/base/process_block.py#L173). After constructing the process block, we define a build function which contains all the components that the property package would have. `super` function here is used to give access to methods and properties of a parent or sibling class and since this is used on the class `PhysicalParameterData` class, build has access to all the parent and sibling class methods. \n", - "\n", - "The `PhysicalParameterBlock` then refers to the `state block`, in this case `OrgPhaseStateBlock` (which will be declared later), so that we can build a state block instance by only knowing the `PhysicalParameterBlock` we wish to use. Then we move on to list the number of phases in this property package. Then we assign the variable to the phase which follows a naming convention. Like here since the solvent is in the Organic phase, we will assign the Phase as OrganicPhase and the variable will be named Org as per the naming convention. The details of naming conventions can be found [here](https://github.com/IDAES/idaes-pse/blob/main/docs/explanations/conventions.rst). We will be following the same convention throughout the example. \n", - " \n", - "After defining the list of the phases, we move on to list the components and their type in the phase. It can be a solute or a solvent in the Organic phase. Thus, we define the component and assign it to either being a solute or a solvent. In this case, the salts are the solutes and Ethylene dibromide is the solvent. Next, we define the physical properties involved in the package, like the heat capacity and density of the solvent, the reference temperature, and the distribution factor that would govern the mass transfer from one phase into another. Additionally, a parameter, the `diffusion_factor`, is introduced. This factor plays a crucial role in governing mass transfer between phases, necessitating its definition within the state block.\n", - "\n", - "The final step in creating the Physical Parameter Block is to declare a `classmethod` named `define_metadata`, which takes two arguments: a class (cls) and an instance of that class (obj). In this method, we will call the predefined method `add_default_units()`.\n", - "\n", - "- `obj.add_default_units()` sets the default units metadata for the property package, and here we define units to be used with this property package as default. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"OrgPhase\")\n", - "class PhysicalParameterData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " organic Phase\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super().build()\n", - "\n", - " self._state_block_class = OrgPhaseStateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Org = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.NaCl = Solute()\n", - " self.KNO3 = Solute()\n", - " self.CaSO4 = Solute()\n", - " self.solvent = (\n", - " Solvent()\n", - " ) # Solvent used here is ethylene dibromide (Organic Polar)\n", - "\n", - " # Heat capacity of solvent\n", - " self.cp_mass = Param(\n", - " mutable=True,\n", - " initialize=717.01,\n", - " doc=\"Specific heat capacity of solvent\",\n", - " units=units.J / units.kg / units.K,\n", - " )\n", - "\n", - " self.dens_mass = Param(\n", - " mutable=True,\n", - " initialize=2170,\n", - " doc=\"Density of ethylene dibromide\",\n", - " units=units.kg / units.m**3,\n", - " )\n", - " self.temperature_ref = Param(\n", - " within=PositiveReals,\n", - " mutable=True,\n", - " default=298.15,\n", - " doc=\"Reference temperature\",\n", - " units=units.K,\n", - " )\n", - " self.diffusion_factor = Param(\n", - " self.solute_set,\n", - " initialize={\"NaCl\": 2.15, \"KNO3\": 3, \"CaSO4\": 1.5},\n", - " within=PositiveReals,\n", - " mutable=True,\n", - " )\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.hour,\n", - " \"length\": units.m,\n", - " \"mass\": units.g,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1.3 State Block\n", - "\n", - "After the `PhysicalParameterBlock` class has been created, the next step is to write the code necessary to create the State Blocks that will be used throughout the flowsheet. `StateBlock` contains all the information necessary to define the state of the system. This includes the state variables and constraints on those variables which are used to describe a state property like the enthalpy, material balance, etc.\n", - "\n", - "Creating a State Block requires us to write two classes. The reason we write two classes is because of the inherent nature of how `declare_process_block_data` works. `declare_process_block_data` facilitates creating an `IndexedComponent` object which can handle multiple `ComponentData` objects which represent the component at each point in the indexing set. This makes it easier to build an instance of the model at each indexed point. However, State Blocks are slightly different, as they are always indexed (at least by time). Due to this, we often want to perform actions on all the elements of the indexed StateBlock all at once (rather than element by element).\n", - "\n", - "The class `_OrganicStateBlock` is defined without the `declare_process_block_data` decorator and thus works as a traditional class and this facilitates performing a method on the class as a whole rather than individual elements of the indexed property blocks. In this class we define the `fix_initialization_states` function. `fix_initialization_states` function is to used to fix the state variable within the state block with the provided initial values (usually inlet conditions). It takes a `block` as the argument in which the state variables are to be fixed. It also takes `state_args` as an optional argument. `state_args` is a dictionary with the value for the state variables to be fixed. This function returns a dictionary indexed by the block, state variables and variable index indicating the fixed status of each variable before applying the function. \n", - "\n", - "The above function comprise of the _OrganicStateBlock, next we shall see the construction of the OrgPhaseStateBlockData class." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class _OrganicStateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def fix_initialization_states(self):\n", - " fix_state_vars(self)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The class `OrgPhaseStateBlockData` is designated with the `declare_process_block_class` decorator, named `OrgPhaseStateBlock`, and inherits the block class from `_OrganicStateBlock`. This inheritance allows `OrgPhaseStateBlockData` to leverage functions from `_OrganicStateBlock`. Following the class definition, a build function similar to the one used in the `PhysicalParameterData` block is employed. The super function is utilized to enable the utilization of functions from the parent or sibling class.\n", - "\n", - "The subsequent objective is to delineate the state variables, accomplished through the `_make_state_vars` method. This method encompasses all the essential state variables and associated data. For this particular property package, the required state variables are:\n", - "\n", - "- `flow_vol` - volumetric flow rate\n", - "- `conc_mass_comp` - mass fractions\n", - "- `pressure` - state pressure\n", - "- `temperature` - state temperature\n", - "\n", - "After establishing the state variables, the subsequent step involves setting up state properties as constraints. This includes specifying the relationships and limitations that dictate the system's behavior. The following properties need to be articulated:\n", - "\n", - "-`get_material_flow_terms`: quantifies the amount of material flow.\n", - "- `get_enthalpy_flow_terms`: quantifies the amount of enthalpy flow.\n", - "- `get_flow_rate`: details volumetric flow rates.\n", - "- `default_material_balance_type`: defines the kind of material balance to be used.\n", - "- `default_energy_balance_type`: defines the kind of energy balance to be used.\n", - "- `define_state_vars`: involves defining state variables with units, akin to the define_metadata function in the PhysicalParameterData block.\n", - "- `get_material_flow_basis`: establishes the basis on which state variables are measured, whether in mass or molar terms.\n", - "\n", - "These definitions mark the conclusion of the state block construction and thus the property package. For additional details on creating a property package, please refer to this [resource](../../properties/custom/custom_physical_property_packages_usr.ipynb ).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"OrgPhaseStateBlock\", block_class=_OrganicStateBlock)\n", - "class OrgPhaseStateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for Organic phzase for liquid liquid extraction\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super().build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " self.flow_vol = Var(\n", - " initialize=1,\n", - " domain=NonNegativeReals,\n", - " doc=\"Total volumetric flowrate\",\n", - " units=units.L / units.hour,\n", - " )\n", - " self.conc_mass_comp = Var(\n", - " self.params.solute_set,\n", - " domain=NonNegativeReals,\n", - " initialize=1,\n", - " doc=\"Component mass concentrations\",\n", - " units=units.g / units.L,\n", - " )\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1,\n", - " bounds=(1, 5),\n", - " units=units.atm,\n", - " doc=\"State pressure [atm]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=300,\n", - " bounds=(273, 373),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " def material_flow_expression(self, j):\n", - " if j == \"solvent\":\n", - " return self.flow_vol * self.params.dens_mass\n", - " else:\n", - " return self.flow_vol * self.conc_mass_comp[j]\n", - "\n", - " self.material_flow_expression = Expression(\n", - " self.component_list,\n", - " rule=material_flow_expression,\n", - " doc=\"Material flow terms\",\n", - " )\n", - "\n", - " def enthalpy_flow_expression(self):\n", - " return (\n", - " self.flow_vol\n", - " * self.params.dens_mass\n", - " * self.params.cp_mass\n", - " * (self.temperature - self.params.temperature_ref)\n", - " )\n", - "\n", - " self.enthalpy_flow_expression = Expression(\n", - " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", - " )\n", - "\n", - " def get_flow_rate(self):\n", - " return self.flow_vol\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.material_flow_expression[j]\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.enthalpy_flow_expression\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_vol\": self.flow_vol,\n", - " \"conc_mass_comp\": self.conc_mass_comp,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def get_material_flow_basis(self):\n", - " return MaterialFlowBasis.mass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Creating Aqueous Property Package\n", - "\n", - "The structure of Aqueous Property Package mirrors that of the Organic Property Package we previously developed. We'll commence with an overview, importing the required libraries, followed by the creation of the physical property block and two state blocks. The distinctions in this package lie in the physical parameter values, and notably, the absence of the diffusion factor term, differentiating it from the prior package. The following code snippet should provide clarity on these distinctions." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Python libraries\n", - "import logging\n", - "\n", - "from idaes.core.util.initialization import fix_state_vars\n", - "\n", - "# Import Pyomo libraries\n", - "from pyomo.environ import (\n", - " Param,\n", - " Var,\n", - " NonNegativeReals,\n", - " units,\n", - " Expression,\n", - " PositiveReals,\n", - ")\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " declare_process_block_class,\n", - " MaterialFlowBasis,\n", - " PhysicalParameterBlock,\n", - " StateBlockData,\n", - " StateBlock,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " Solute,\n", - " Solvent,\n", - " LiquidPhase,\n", - ")\n", - "\n", - "# Some more information about this module\n", - "__author__ = \"Javal Vyas\"\n", - "\n", - "\n", - "# Set up logger\n", - "_log = logging.getLogger(__name__)\n", - "\n", - "\n", - "@declare_process_block_class(\"AqPhase\")\n", - "class AqPhaseData(PhysicalParameterBlock):\n", - " \"\"\"\n", - " Property Parameter Block Class\n", - "\n", - " Contains parameters and indexing sets associated with properties for\n", - " aqueous Phase\n", - "\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction.\n", - " \"\"\"\n", - " super().build()\n", - "\n", - " self._state_block_class = AqPhaseStateBlock\n", - "\n", - " # List of valid phases in property package\n", - " self.Aq = LiquidPhase()\n", - "\n", - " # Component list - a list of component identifiers\n", - " self.NaCl = Solute()\n", - " self.KNO3 = Solute()\n", - " self.CaSO4 = Solute()\n", - " self.H2O = Solvent()\n", - "\n", - " # Heat capacity of solvent\n", - " self.cp_mass = Param(\n", - " mutable=True,\n", - " initialize=4182,\n", - " doc=\"Specific heat capacity of solvent\",\n", - " units=units.J / units.kg / units.K,\n", - " )\n", - "\n", - " self.dens_mass = Param(\n", - " mutable=True,\n", - " initialize=997,\n", - " doc=\"Density of ethylene dibromide\",\n", - " units=units.kg / units.m**3,\n", - " )\n", - " self.temperature_ref = Param(\n", - " within=PositiveReals,\n", - " mutable=True,\n", - " default=298.15,\n", - " doc=\"Reference temperature\",\n", - " units=units.K,\n", - " )\n", - "\n", - " @classmethod\n", - " def define_metadata(cls, obj):\n", - " obj.add_default_units(\n", - " {\n", - " \"time\": units.hour,\n", - " \"length\": units.m,\n", - " \"mass\": units.g,\n", - " \"amount\": units.mol,\n", - " \"temperature\": units.K,\n", - " }\n", - " )\n", - "\n", - "\n", - "class _AqueousStateBlock(StateBlock):\n", - " \"\"\"\n", - " This Class contains methods which should be applied to Property Blocks as a\n", - " whole, rather than individual elements of indexed Property Blocks.\n", - " \"\"\"\n", - "\n", - " def fix_initialization_states(self):\n", - " fix_state_vars(self)\n", - "\n", - "\n", - "@declare_process_block_class(\"AqPhaseStateBlock\", block_class=_AqueousStateBlock)\n", - "class AqPhaseStateBlockData(StateBlockData):\n", - " \"\"\"\n", - " An example property package for ideal gas properties with Gibbs energy\n", - " \"\"\"\n", - "\n", - " def build(self):\n", - " \"\"\"\n", - " Callable method for Block construction\n", - " \"\"\"\n", - " super().build()\n", - " self._make_state_vars()\n", - "\n", - " def _make_state_vars(self):\n", - " self.flow_vol = Var(\n", - " initialize=1,\n", - " domain=NonNegativeReals,\n", - " doc=\"Total volumetric flowrate\",\n", - " units=units.L / units.hour,\n", - " )\n", - "\n", - " self.conc_mass_comp = Var(\n", - " self.params.solute_set,\n", - " domain=NonNegativeReals,\n", - " initialize={\"NaCl\": 0.15, \"KNO3\": 0.2, \"CaSO4\": 0.1},\n", - " doc=\"Component mass concentrations\",\n", - " units=units.g / units.L,\n", - " )\n", - "\n", - " self.pressure = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=1,\n", - " bounds=(1, 5),\n", - " units=units.atm,\n", - " doc=\"State pressure [atm]\",\n", - " )\n", - "\n", - " self.temperature = Var(\n", - " domain=NonNegativeReals,\n", - " initialize=300,\n", - " bounds=(273, 373),\n", - " units=units.K,\n", - " doc=\"State temperature [K]\",\n", - " )\n", - "\n", - " def material_flow_expression(self, j):\n", - " if j == \"H2O\":\n", - " return self.flow_vol * self.params.dens_mass\n", - " else:\n", - " return self.conc_mass_comp[j] * self.flow_vol\n", - "\n", - " self.material_flow_expression = Expression(\n", - " self.component_list,\n", - " rule=material_flow_expression,\n", - " doc=\"Material flow terms\",\n", - " )\n", - "\n", - " def enthalpy_flow_expression(self):\n", - " return (\n", - " self.flow_vol\n", - " * self.params.dens_mass\n", - " * self.params.cp_mass\n", - " * (self.temperature - self.params.temperature_ref)\n", - " )\n", - "\n", - " self.enthalpy_flow_expression = Expression(\n", - " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", - " )\n", - "\n", - " def get_flow_rate(self):\n", - " return self.flow_vol\n", - "\n", - " def get_material_flow_terms(self, p, j):\n", - " return self.material_flow_expression[j]\n", - "\n", - " def get_enthalpy_flow_terms(self, p):\n", - " return self.enthalpy_flow_expression\n", - "\n", - " def default_material_balance_type(self):\n", - " return MaterialBalanceType.componentTotal\n", - "\n", - " def default_energy_balance_type(self):\n", - " return EnergyBalanceType.enthalpyTotal\n", - "\n", - " def define_state_vars(self):\n", - " return {\n", - " \"flow_vol\": self.flow_vol,\n", - " \"conc_mass_comp\": self.conc_mass_comp,\n", - " \"temperature\": self.temperature,\n", - " \"pressure\": self.pressure,\n", - " }\n", - "\n", - " def get_material_flow_basis(self):\n", - " return MaterialFlowBasis.mass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Liquid Liquid Extractor Unit Model\n", - "\n", - "Following the creation of property packages, our next step is to develop a unit model that facilitates the mass transfer of solutes between phases. This involves importing necessary libraries, building the unit model, defining auxiliary functions, and establishing the initialization routine for the unit model.\n", - "\n", - "## 3.1 Importing necessary libraries\n", - "\n", - "Let's commence by importing the essential libraries from Pyomo and IDAES." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Pyomo libraries\n", - "from pyomo.common.config import ConfigBlock, ConfigValue, In, Bool\n", - "from pyomo.environ import (\n", - " value,\n", - " Constraint,\n", - " check_optimal_termination,\n", - ")\n", - "\n", - "# Import IDAES cores\n", - "from idaes.core import (\n", - " ControlVolume0DBlock,\n", - " declare_process_block_class,\n", - " MaterialBalanceType,\n", - " EnergyBalanceType,\n", - " MaterialFlowBasis,\n", - " MomentumBalanceType,\n", - " UnitModelBlockData,\n", - " useDefault,\n", - ")\n", - "from idaes.core.util.config import (\n", - " is_physical_parameter_block,\n", - " is_reaction_parameter_block,\n", - ")\n", - "\n", - "import idaes.logger as idaeslog\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.exceptions import ConfigurationError, InitializationError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2 Creating the unit model\n", - "\n", - "Creating a unit model starts by creating a class called `LiqExtractionData` and use the `declare_process_block_class` decorator. The `LiqExtractionData` inherits the properties of `UnitModelBlockData` class, which allows us to create a control volume which is necessary for the unit model. After declaration of the class we proceed to define the relevant config arguments for the control volume. The config arguments includes the following properties:\n", - "\n", - "- `material_balance_type` - Indicates what type of mass balance should be constructed\n", - "- `has_pressure_change` - Indicates whether terms for pressure change should be\n", - "constructed\n", - "- `has_phase_equilibrium` - Indicates whether terms for phase equilibrium should be\n", - "constructed\n", - "- `Organic Property` - Property parameter object used to define property calculations\n", - "for the Organic phase\n", - "- `Organic Property Arguments` - Arguments to use for constructing Organic phase properties\n", - "- `Aqueous Property` - Property parameter object used to define property calculations\n", - "for the aqueous phase\n", - "- `Aqueous Property Arguments` - Arguments to use for constructing aqueous phase properties\n", - "\n", - "As there are no pressure changes or reactions in this scenario, configuration arguments for these aspects are not included. However, additional details on configuration arguments can be found [here](https://github.com/IDAES/idaes-pse/blob/8948c6ce27d4c7f2c06b377a173f413599091998/idaes/models/unit_models/cstr.py)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"LiqExtraction\")\n", - "class LiqExtractionData(UnitModelBlockData):\n", - " \"\"\"\n", - " LiqExtraction Unit Model Class\n", - " \"\"\"\n", - "\n", - " CONFIG = UnitModelBlockData.CONFIG()\n", - "\n", - " CONFIG.declare(\n", - " \"material_balance_type\",\n", - " ConfigValue(\n", - " default=MaterialBalanceType.useDefault,\n", - " domain=In(MaterialBalanceType),\n", - " description=\"Material balance construction flag\",\n", - " doc=\"\"\"Indicates what type of mass balance should be constructed,\n", - " **default** - MaterialBalanceType.useDefault.\n", - " **Valid values:** {\n", - " **MaterialBalanceType.useDefault - refer to property package for default\n", - " balance type\n", - " **MaterialBalanceType.none** - exclude material balances,\n", - " **MaterialBalanceType.componentPhase** - use phase component balances,\n", - " **MaterialBalanceType.componentTotal** - use total component balances,\n", - " **MaterialBalanceType.elementTotal** - use total element balances,\n", - " **MaterialBalanceType.total** - use total material balance.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"has_pressure_change\",\n", - " ConfigValue(\n", - " default=False,\n", - " domain=Bool,\n", - " description=\"Pressure change term construction flag\",\n", - " doc=\"\"\"Indicates whether terms for pressure change should be\n", - " constructed,\n", - " **default** - False.\n", - " **Valid values:** {\n", - " **True** - include pressure change terms,\n", - " **False** - exclude pressure change terms.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"has_phase_equilibrium\",\n", - " ConfigValue(\n", - " default=False,\n", - " domain=Bool,\n", - " description=\"Phase equilibrium construction flag\",\n", - " doc=\"\"\"Indicates whether terms for phase equilibrium should be\n", - " constructed,\n", - " **default** = False.\n", - " **Valid values:** {\n", - " **True** - include phase equilibrium terms\n", - " **False** - exclude phase equilibrium terms.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"organic_property_package\",\n", - " ConfigValue(\n", - " default=useDefault,\n", - " domain=is_physical_parameter_block,\n", - " description=\"Property package to use for organic phase\",\n", - " doc=\"\"\"Property parameter object used to define property calculations\n", - " for the organic phase,\n", - " **default** - useDefault.\n", - " **Valid values:** {\n", - " **useDefault** - use default package from parent model or flowsheet,\n", - " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"organic_property_package_args\",\n", - " ConfigBlock(\n", - " implicit=True,\n", - " description=\"Arguments to use for constructing organic phase properties\",\n", - " doc=\"\"\"A ConfigBlock with arguments to be passed to organic phase\n", - " property block(s) and used when constructing these,\n", - " **default** - None.\n", - " **Valid values:** {\n", - " see property package for documentation.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"aqueous_property_package\",\n", - " ConfigValue(\n", - " default=useDefault,\n", - " domain=is_physical_parameter_block,\n", - " description=\"Property package to use for aqueous phase\",\n", - " doc=\"\"\"Property parameter object used to define property calculations\n", - " for the aqueous phase,\n", - " **default** - useDefault.\n", - " **Valid values:** {\n", - " **useDefault** - use default package from parent model or flowsheet,\n", - " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", - " ),\n", - " )\n", - " CONFIG.declare(\n", - " \"aqueous_property_package_args\",\n", - " ConfigBlock(\n", - " implicit=True,\n", - " description=\"Arguments to use for constructing aqueous phase properties\",\n", - " doc=\"\"\"A ConfigBlock with arguments to be passed to aqueous phase\n", - " property block(s) and used when constructing these,\n", - " **default** - None.\n", - " **Valid values:** {\n", - " see property package for documentation.}\"\"\",\n", - " ),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Building the model\n", - "\n", - "After constructing the `LiqExtractionData` block and defining the config arguments for the control block, the next step is to write a build function that incorporates control volume and establishes constraints on the control volume to achieve the desired mass transfer. The control volume serves as a pivotal component in the unit model construction, representing the volume in which the process unfolds.\n", - "\n", - "IDAES provides flexibility in choosing control volumes based on geometry, with options including 0D or 1D. In this instance, we opt for a 0D control volume, the most commonly used control volume. This choice is suitable for systems where there is a well-mixed volume of fluid or where spatial variations are deemed negligible.\n", - "\n", - "The control volume encompasses parameters from (1-8), and its equations are configured to satisfy the specified config arguments. For a more in-depth understanding, users are encouraged to refer to [this resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst). \n", - "\n", - "The `build` function is initiated using the `super` function to gain access to methods and properties of a parent or sibling class, in this case, the `LiqExtractionData` class. Following the `super` function, checks are performed on the property packages to ensure the appropriate names for the solvents, such as 'Aq' for the aqueous phase and 'Org' for the Organic phase. An error is raised if these conditions are not met. Subsequently, a check is performed to ensure there is at least one common component between the two property packages that can be transferred from one phase to another.\n", - "\n", - "After these checks are completed without any exceptions raised, it is ensured that the property packages have the desired components with appropriate names. The next step is to create a control volume and assign it to a property package. Here, we initiate with the Organic phase and attach a 0D control volume to it. The control volume takes arguments about the dynamics of the block, and the property package, along with property package arguments. \n", - "\n", - "The subsequent steps involve adding inlet and outlet state blocks to the control volume using the `add_state_blocks` function. This function takes arguments about the flow direction (defaulted to forward) and a flag for `has_phase_equilibrium`, which is read from the config. The control volume is now equipped with the inlet and outlet state blocks and has access to the Organic property package\n", - "\n", - "Next, material balance equations are added to the control volume using the `add_material_balance` function, taking into account the type of material balance, `has_phase_equilibrium`, and the presence of `has_mass_transfer`. To understand this arguments further let us have a look at the material balance equation and how it is implemented in control volume. \n", - "\n", - "$\\frac{\\partial M_{t, p, j}}{\\partial t} = F_{in, t, p, j} - F_{out, t, p, j} + N_{kinetic, t, p, j} + N_{equilibrium, t, p, j} + N_{pe, t, p, j} + N_{transfer, t, p, j} + N_{custom, t, p, j}$\n", - "\n", - "- $\\frac{\\partial M_{t, p, j}}{\\partial t}$ - Material accumulation\n", - "- $F_{in, t, p, j}$ - Flow into the control volume\n", - "- $F_{out, t, p, j}$ - Flow out of the control volume\n", - "- $N_{kinetic, t, p, j}$ - Rate of reaction generation\n", - "- $N_{equilibrium, t, p, j}$ - Equilibrium reaction generation\n", - "- $N_{pe, t, p, j}$ - Equilibrium reaction extent\n", - "- $N_{transfer, t, p, j}$ - Mass transfer\n", - "- $N_{custom, t, p, j}$ - User defined terms in material balance\n", - "\n", - "- t indicates time index\n", - "- p indicates phase index\n", - "- j indicates component index\n", - "- e indicates element index\n", - "- r indicates reaction name index\n", - "\n", - "Here we shall see that $N_{transfer, t, p, j}$ is the term in the equation which is responsible for the mass transfer and the `mass_transfer_term` should only be equal to the amount being transferred and not include a material balance on our own. For a detailed description of the terms one should refer to the following [resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst)\n", - "\n", - "This concludes the creation of organic phase control volume. Similar procedure is done for the aqueous phase control volume with aqueous property package. \n", - "\n", - "Now, the unit model has two control volumes with appropriate configurations and material, momentum and energy balances. The next step is to check the basis of the two property packages. They should both have the same flow basis, and an error is raised if this is not the case.\n", - "\n", - "Following this, the `add_inlet_ports` and `add_outlet_ports` functions are used to create inlet and outlet ports. These ports are named and assigned to each control volume, resulting in labeled inlet and outlet ports for each control volume.\n", - "\n", - "The subsequent steps involve writing unit-level constraints. A check if the basis is either molar or mass, and unit-level constraints are written accordingly. The first constraint pertains to the mass transfer term for the aqueous phase. The mass transfer term is equal to $mass\\_transfer\\_term_{aq} = (D_{i})\\frac{mass_{i}~in~aq~phase}{flowrate~of~aq~phase}$. The second constraint relates to the mass transfer term in the organic phase, which is the negative of the mass transfer term in the aqueous phase: $mass\\_transfer\\_term_{org} = - mass\\_transfer\\_term_{aq} $\n", - "\n", - "Here $mass\\_transfer\\_term_{p}$ is the term indicating the amount of material being transferred from/to the phase and $D_{i}$ is the Distribution co-efficient for component i. \n", - "\n", - "This marks the completion of the build function, and the unit model is now equipped with the necessary process constraints. The subsequent steps involve writing the initialization routine." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def build(self):\n", - " \"\"\"\n", - " Begin building model (pre-DAE transformation).\n", - " Args:\n", - " None\n", - " Returns:\n", - " None\n", - " \"\"\"\n", - " # Call UnitModel.build to setup dynamics\n", - " super().build()\n", - "\n", - " # Check phase lists match assumptions\n", - " if self.config.aqueous_property_package.phase_list != [\"Aq\"]:\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the aquoues \"\n", - " f\"phase property package have a single phase named 'Aq'\"\n", - " )\n", - " if self.config.organic_property_package.phase_list != [\"Org\"]:\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", - " f\"phase property package have a single phase named 'Org'\"\n", - " )\n", - "\n", - " # Check for at least one common component in component lists\n", - " if not any(\n", - " j in self.config.aqueous_property_package.component_list\n", - " for j in self.config.organic_property_package.component_list\n", - " ):\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", - " f\"and aqueous phase property packages have at least one \"\n", - " f\"common component.\"\n", - " )\n", - "\n", - " self.organic_phase = ControlVolume0DBlock(\n", - " dynamic=self.config.dynamic,\n", - " property_package=self.config.organic_property_package,\n", - " property_package_args=self.config.organic_property_package_args,\n", - " )\n", - "\n", - " self.organic_phase.add_state_blocks(\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium\n", - " )\n", - "\n", - " # Separate organic and aqueous phases means that phase equilibrium will\n", - " # be handled at the unit model level, thus has_phase_equilibrium is\n", - " # False, but has_mass_transfer is True.\n", - "\n", - " self.organic_phase.add_material_balances(\n", - " balance_type=self.config.material_balance_type,\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", - " has_mass_transfer=True,\n", - " )\n", - " # ---------------------------------------------------------------------\n", - "\n", - " self.aqueous_phase = ControlVolume0DBlock(\n", - " dynamic=self.config.dynamic,\n", - " property_package=self.config.aqueous_property_package,\n", - " property_package_args=self.config.aqueous_property_package_args,\n", - " )\n", - "\n", - " self.aqueous_phase.add_state_blocks(\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium\n", - " )\n", - "\n", - " # Separate liquid and aqueous phases means that phase equilibrium will\n", - " # be handled at the unit model level, thus has_phase_equilibrium is\n", - " # False, but has_mass_transfer is True.\n", - "\n", - " self.aqueous_phase.add_material_balances(\n", - " balance_type=self.config.material_balance_type,\n", - " # has_rate_reactions=False,\n", - " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", - " has_mass_transfer=True,\n", - " )\n", - "\n", - " self.aqueous_phase.add_geometry()\n", - "\n", - " # ---------------------------------------------------------------------\n", - " # Check flow basis is compatible\n", - " t_init = self.flowsheet().time.first()\n", - " if (\n", - " self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", - " != self.organic_phase.properties_out[t_init].get_material_flow_basis()\n", - " ):\n", - " raise ConfigurationError(\n", - " f\"{self.name} aqueous and organic property packages must use the \"\n", - " f\"same material flow basis.\"\n", - " )\n", - "\n", - " self.organic_phase.add_geometry()\n", - "\n", - " # Add Ports\n", - " self.add_inlet_port(\n", - " name=\"organic_inlet\", block=self.organic_phase, doc=\"Organic feed\"\n", - " )\n", - " self.add_inlet_port(\n", - " name=\"aqueous_inlet\", block=self.aqueous_phase, doc=\"Aqueous feed\"\n", - " )\n", - " self.add_outlet_port(\n", - " name=\"organic_outlet\", block=self.organic_phase, doc=\"Organic outlet\"\n", - " )\n", - " self.add_outlet_port(\n", - " name=\"aqueous_outlet\",\n", - " block=self.aqueous_phase,\n", - " doc=\"Aqueous outlet\",\n", - " )\n", - "\n", - " # ---------------------------------------------------------------------\n", - " # Add unit level constraints\n", - " # First, need the union and intersection of component lists\n", - " all_comps = (\n", - " self.aqueous_phase.properties_out.component_list\n", - " | self.organic_phase.properties_out.component_list\n", - " )\n", - " common_comps = (\n", - " self.aqueous_phase.properties_out.component_list\n", - " & self.organic_phase.properties_out.component_list\n", - " )\n", - "\n", - " # Get units for unit conversion\n", - " aunits = self.config.aqueous_property_package.get_metadata().get_derived_units\n", - " lunits = self.config.organic_property_package.get_metadata().get_derived_units\n", - " flow_basis = self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", - "\n", - " if flow_basis == MaterialFlowBasis.mass:\n", - " fb = \"flow_mass\"\n", - " elif flow_basis == MaterialFlowBasis.molar:\n", - " fb = \"flow_mole\"\n", - " else:\n", - " raise ConfigurationError(\n", - " f\"{self.name} Liquid-Liquid Extractor only supports mass \"\n", - " f\"basis for MaterialFlowBasis.\"\n", - " )\n", - "\n", - " # Material balances\n", - " def rule_material_aq_balance(self, t, j):\n", - " if j in common_comps:\n", - " return self.aqueous_phase.mass_transfer_term[\n", - " t, \"Aq\", j\n", - " ] == -self.organic_phase.config.property_package.diffusion_factor[j] * (\n", - " self.aqueous_phase.properties_in[t].get_material_flow_terms(\"Aq\", j)\n", - " )\n", - " elif j in self.organic_phase.properties_out.component_list:\n", - " # No mass transfer term\n", - " # Set organic flowrate to an arbitrary small value\n", - " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * lunits(fb)\n", - " elif j in self.aqueous_phase.properties_out.component_list:\n", - " # No mass transfer term\n", - " # Set aqueous flowrate to an arbitrary small value\n", - " return self.aqueous_phase.mass_transfer_term[t, \"Aq\", j] == 0 * aunits(fb)\n", - "\n", - " self.material_aq_balance = Constraint(\n", - " self.flowsheet().time,\n", - " self.aqueous_phase.properties_out.component_list,\n", - " rule=rule_material_aq_balance,\n", - " doc=\"Unit level material balances for Aq\",\n", - " )\n", - "\n", - " def rule_material_liq_balance(self, t, j):\n", - " if j in common_comps:\n", - " return (\n", - " self.organic_phase.mass_transfer_term[t, \"Org\", j]\n", - " == -self.aqueous_phase.mass_transfer_term[t, \"Aq\", j]\n", - " )\n", - " else:\n", - " # No mass transfer term\n", - " # Set organic flowrate to an arbitrary small value\n", - " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * aunits(fb)\n", - "\n", - " self.material_org_balance = Constraint(\n", - " self.flowsheet().time,\n", - " self.organic_phase.properties_out.component_list,\n", - " rule=rule_material_liq_balance,\n", - " doc=\"Unit level material balances Org\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization Routine\n", - "\n", - "After writing the unit model it is crucial to initialize the model properly, as non-linear models may encounter local minima or infeasibility if not initialized properly. IDAES provides us with a few initialization routines which may not work for all the models, and in such cases the developer will have to define their own initialization routines. \n", - "\n", - "To create a custom initialization routine, model developers must create an initialize method as part of their model, and provide a sequence of steps intended to build up a feasible solution. Initialization routines generally make use of Pyomo’s tools for activating and deactivating constraints and often involve solving multiple sub-problems whilst building up an initial state.\n", - "\n", - "For this tutorial we would use the pre-defined initialization routine of `BlockTriangularizationInitializer` when initializing the model in the flowsheet. This Initializer should be suitable for most models, but may struggle to initialize\n", - "tightly coupled systems of equations. This method of initialization will follow the following workflow. \n", - "\n", - "- Have precheck for structural singularity\n", - "- Run incidence analysis on given block data and check matching.\n", - "- Call Block Triangularization solver on model.\n", - "- Call solve_strongly_connected_components on a given BlockData.\n", - "\n", - "For more details about this initialization routine can be found [here](https://github.com/IDAES/idaes-pse/blob/c09433b9afed5ae2fe25c0ccdc732783324f0101/idaes/core/initialization/block_triangularization.py). \n", - "\n", - "\n", - "This marks the conclusion of creating a custom unit model, for a more detailed explanation on creating a unit model refer [this resource](../../unit_models/custom_unit_models/custom_compressor_usr.ipynb). The next sections will deal with the diagonistics and testing of the property package and unit model. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.3 Building a Flowsheet\n", - "\n", - "Once we have set up the unit model and its property packages, we can start building a flowsheet using them. In this tutorial, we're focusing on a simple flowsheet with just a liquid-liquid extractor. To create the flowsheet we follow the following steps:\n", - "\n", - "- Import necessary libraries\n", - "- Create a Pyomo model.\n", - "- Inside the model, create a flowsheet block.\n", - "- Assign property packages to the flowsheet block.\n", - "- Add the liquid-liquid extractor to the flowsheet block.\n", - "- Fix variable to make it a square problem\n", - "- Run an initialization process.\n", - "- Solve the flowsheet.\n", - "\n", - "Following these steps, we've built a basic flowsheet using Pyomo. For more details, refer to the [documentation](../../flowsheets/hda_flowsheet_with_distillation_usr.ipynb).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "import idaes.core\n", - "import idaes.models.unit_models\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.logger as idaeslog\n", - "from pyomo.network import Arc\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.initialization import InitializationStatus\n", - "from idaes.core.initialization.block_triangularization import (\n", - " BlockTriangularizationInitializer,\n", - ")\n", - "from liquid_extraction.organic_property import OrgPhase\n", - "from liquid_extraction.aqueous_property import AqPhase\n", - "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", - "\n", - "\n", - "def build_model():\n", - " m = pyo.ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.lex = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - " return m\n", - "\n", - "\n", - "def fix_state_variables(m):\n", - " m.fs.lex.organic_inlet.flow_vol.fix(80 * pyo.units.L / pyo.units.hour)\n", - " m.fs.lex.organic_inlet.temperature.fix(300 * pyo.units.K)\n", - " m.fs.lex.organic_inlet.pressure.fix(1 * pyo.units.atm)\n", - " m.fs.lex.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", - " 1e-5 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", - " 1e-5 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", - " 1e-5 * pyo.units.g / pyo.units.L\n", - " )\n", - "\n", - " m.fs.lex.aqueous_inlet.flow_vol.fix(100 * pyo.units.L / pyo.units.hour)\n", - " m.fs.lex.aqueous_inlet.temperature.fix(300 * pyo.units.K)\n", - " m.fs.lex.aqueous_inlet.pressure.fix(1 * pyo.units.atm)\n", - " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", - " 0.15 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", - " 0.2 * pyo.units.g / pyo.units.L\n", - " )\n", - " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", - " 0.1 * pyo.units.g / pyo.units.L\n", - " )\n", - "\n", - " return m\n", - "\n", - "\n", - "def initialize_model(m):\n", - " initializer = BlockTriangularizationInitializer()\n", - " initializer.initialize(m.fs.lex)\n", - " return m\n", - "\n", - "\n", - "def main():\n", - " m = build_model()\n", - " m = fix_state_variables(m)\n", - " m = initialize_model(m)\n", - " return m\n", - "\n", - "\n", - "if __name__ == main:\n", - " main()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Model Diagnostics using DiagnosticsToolbox\n", - "\n", - "Here, during initialization, we encounter warnings indicating that variables are being set to negative values, which is not expected behavior. These warnings suggest that there may be flaws in the model that require further investigation using the DiagnosticsToolbox from IDAES. A detailed notebook on using `DiagnosticsToolbox` can be found [here](../../diagnostics/degeneracy_hunter_usr.ipynb).\n", - "\n", - "To proceed with investigating these issues, we need to import the DiagnosticsToolbox. We can gain a better understanding of its functionality by running the help function on it. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core.util import DiagnosticsToolbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The help() function provides comprehensive information on the DiagnosticsToolbox and all its supported methods. However, it's essential to focus on the initial steps outlined at the beginning of the docstring to get started effectively.\n", - "\n", - "Here's a breakdown of the steps to start with:\n", - "\n", - "- `Instantiate Model:` Ensure you have an instance of the model with a degrees of freedom equal to 0.\n", - "\n", - "- `Create DiagnosticsToolbox Instance:` Next, instantiate a DiagnosticsToolbox object.\n", - "\n", - "- `Provide Model to DiagnosticsToolbox:` Pass the model instance to the DiagnosticsToolbox.\n", - "\n", - "- `Call report_structural_issues() Function:` Finally, call the report_structural_issues() function. This function will highlight any warnings in the model's structure, such as unit inconsistencies or other issues related to variables in the caution section.\n", - "\n", - "By following these steps, you can efficiently utilize the DiagnosticsToolbox to identify and address any structural issues or warnings in your model." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING (W1001): Setting Var\n", - "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]' to a value\n", - "`-0.1725` (float) not in domain NonNegativeReals.\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", - "WARNING (W1001): Setting Var\n", - "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3]' to a value\n", - "`-0.4` (float) not in domain NonNegativeReals.\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", - "WARNING (W1001): Setting Var\n", - "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4]' to a value\n", - "`-0.05` (float) not in domain NonNegativeReals.\n", - " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 21 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 16 (External: 0)\n", - " Free Variables with only lower bounds: 8\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 0\n", - " Fixed Variables in Activated Constraints: 8 (External: 0)\n", - " Activated Equality Constraints: 16 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 Cautions\n", - "\n", - " Caution: 10 unused variables (4 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "m = main()\n", - "dt = DiagnosticsToolbox(m)\n", - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although no warnings were reported, it's important to note that there are 3 variables fixed to 0 and 10 unused variables, out of which 4 are fixed. As indicated in the output, the next step is to solve the model. After solving, you should call the report_numerical_issues() function. This function will help identify any numerical issues that may arise during the solution process." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 33\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 14\n", - "\n", - "Total number of variables............................: 16\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 16\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 4.10e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 4.00e+01 4.93e+01 -1.0 4.10e-01 - 9.91e-01 2.41e-02h 1\n", - " 2 0.0000000e+00 4.00e+01 2.03e+05 -1.0 4.00e-01 - 1.00e+00 2.47e-04h 1\n", - " 3r 0.0000000e+00 4.00e+01 1.00e+03 1.6 0.00e+00 - 0.00e+00 3.09e-07R 4\n", - " 4r 0.0000000e+00 4.00e+01 9.88e+04 1.6 3.68e+02 - 9.92e-01 2.29e-03f 1\n", - " 5r 0.0000000e+00 3.60e+01 3.03e+00 1.6 4.01e+00 - 1.00e+00 1.00e+00f 1\n", - " 6r 0.0000000e+00 3.69e+01 1.21e+01 -1.2 9.24e-01 - 9.69e-01 9.78e-01f 1\n", - " 7r 0.0000000e+00 3.70e+01 2.11e-01 -1.9 1.00e-01 - 9.97e-01 1.00e+00f 1\n", - " 8r 0.0000000e+00 3.78e+01 2.03e-02 -4.3 8.71e-01 - 9.71e-01 1.00e+00f 1\n", - " 9r 0.0000000e+00 3.80e+01 2.62e-04 -6.4 1.24e-01 - 9.99e-01 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10r 0.0000000e+00 3.81e+01 5.87e-09 -6.4 1.58e-01 - 1.00e+00 1.00e+00f 1\n", - " 11r 0.0000000e+00 3.91e+01 1.09e-05 -9.0 9.35e-01 - 9.68e-01 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 11\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 5.1393961893966849e-07 5.1393961893966849e-07\n", - "Constraint violation....: 3.9105165554489545e+01 3.9105165554489545e+01\n", - "Complementarity.........: 9.0909090910996620e-10 9.0909090910996620e-10\n", - "Overall NLP error.......: 3.9105165554489545e+01 3.9105165554489545e+01\n", - "\n", - "\n", - "Number of objective function evaluations = 17\n", - "Number of objective gradient evaluations = 5\n", - "Number of equality constraint evaluations = 17\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 14\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", - "WARNING: Loading a SolverResults object with a warning status into\n", - "model.name=\"unknown\";\n", - " - termination condition: infeasible\n", - " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", - " point. Problem may be infeasible.\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] }, { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.06552338600158691}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating Custom Unit Model\n", + "Author: Javal Vyas \n", + "Maintainer: Javal Vyas \n", + "\n", + "This tutorial is a comprehensive step-wise procedure to build a custom unit model from scratch. This tutorial will include creating a property package, a custom unit model and testing them. For this tutorial we shall create a custom unit model for Liquid - Liquid Extraction. \n", + "\n", + "The Liquid - Liquid Extractor model contains two immiscible fluids forming the two phases. One of the phases, say phase_1 has a high concentration of solutes which is to be separated. A mass transfer happens between the two phases and the solute is transferred from phase_1 to phase_2. This mass transfer is governed by a parameter called the distribution coefficient.\n", + "\n", + "After reviewing the working principles of the Liquid - Liquid Extractor, we shall proceed to create a custom unit model. We will require a property package for each phase, a custom unit model class and tests for the model and property packages.\n", + "\n", + "Before commencing the development of the model, we need to state some assumptions which the following unit model will be using. \n", + "- Steady-state only\n", + "- Organic phase property package has a single phase named Org\n", + "- Aqueous phase property package has a single phase named Aq\n", + "- Organic and Aqueous phase properties need not have the same component list. \n", + "\n", + "Thus as per the assumptions, we will be creating one property package for the aqueous phase (Aq), and the other for the Organic phase (Org). " ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver = pyo.SolverFactory(\"ipopt\")\n", - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model is probably infeasible thus indicating numerical issues with the model. We should call the `report_numerical_issues()` function and check what the constraints/variables causing this issue. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Jacobian Condition Number: 7.955E+03\n", - "\n", - "------------------------------------------------------------------------------------\n", - "2 WARNINGS\n", - "\n", - " WARNING: 6 Constraints with large residuals (>1.0E-05)\n", - " WARNING: 5 Variables at or outside bounds (tol=0.0E+00)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "3 Cautions\n", - "\n", - " Caution: 8 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", - " Caution: 5 Variables with value close to zero (tol=1.0E-08)\n", - " Caution: 3 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " display_constraints_with_large_residuals()\n", - " display_variables_at_or_outside_bounds()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_numerical_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this scenario, it's observed that the condition number of the Jacobian is high, indicating that the Jacobian is ill-conditioned. Additionally, there are 2 warnings related to constraints with large residuals and variables at or outside the bounds. The cautions mentioned in the output are also related to these warnings.\n", - "\n", - "As suggested, the next steps would be to:\n", - "\n", - "- Call the `display_variables_at_or_outside_bounds()` function to investigate variables at or outside the bounds.\n", - "\n", - "- Call the `display_constraints_with_large_residuals()` function to examine constraints with large residuals.\n", - "\n", - "These steps will help identify the underlying causes of the numerical issues and constraints violations, allowing for further analysis and potential resolution. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Creating Organic Property Package\n", + "\n", + "Creating a property package is a 4 step process\n", + "- Import necessary libraries \n", + "- Creating Physical Parameter Data Block\n", + "- Define State Block\n", + "- Define State Block Data\n", + "\n", + "# 1.1 Importing necessary packages \n", + "Let us begin with importing the necessary libraries where we will be using functionalities from IDAES and Pyomo. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", - "\n", - " fs.lex.organic_phase.properties_in[0.0].pressure (fixed): value=1.0 bounds=(1, 5)\n", - " fs.lex.organic_phase.properties_out[0.0].pressure (free): value=1 bounds=(1, 5)\n", - " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl] (free): value=0.0 bounds=(0, None)\n", - " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3] (free): value=0.0 bounds=(0, None)\n", - " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4] (free): value=0.0 bounds=(0, None)\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_variables_at_or_outside_bounds()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this scenario, there are a couple of issues to address:\n", - "\n", - "- The pressure variable is fixed to 1, which is its lower bound. This could potentially lead to numerical issues, although it may not affect the model significantly since there is no pressure change in the model. To mitigate this, consider adjusting the lower bound of the pressure variable to avoid having its value at or outside the bounds.\n", - "\n", - "- The more concerning issue is with the `conc_mass_comp` variable attempting to go below 0 in the output. This suggests that there may be constraints involving `conc_mass_comp` in the aqueous phase causing this behavior. To investigate further, it's recommended to call the `display_constraints_with_large_residuals()` function. This will provide insights into whether constraints involving `conc_mass_comp` are contributing to the convergence issue." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.util.initialization import fix_state_vars\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Param,\n", + " Set,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + " Expression,\n", + " PositiveReals,\n", + ")\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " MaterialFlowBasis,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " Solute,\n", + " Solvent,\n", + " LiquidPhase,\n", + ")\n", + "from idaes.core.util.model_statistics import degrees_of_freedom" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "The following constraint(s) have large residuals (>1.0E-05):\n", - "\n", - " fs.lex.material_aq_balance[0.0,NaCl]: 5.49716E-01\n", - " fs.lex.material_aq_balance[0.0,KNO3]: 8.94833E-01\n", - " fs.lex.material_aq_balance[0.0,CaSO4]: 5.48843E-02\n", - " fs.lex.aqueous_phase.material_balances[0.0,NaCl]: 1.67003E+01\n", - " fs.lex.aqueous_phase.material_balances[0.0,KNO3]: 3.91052E+01\n", - " fs.lex.aqueous_phase.material_balances[0.0,CaSO4]: 4.94512E+00\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.display_constraints_with_large_residuals()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected there are convergence issues with the constraints which have `conc_mass_comp` variable in them specifically in the aqeous phase. Now, let us investigate further by printing this constraints and checking the value of each term. Since this is an persistent issue across the components, we can focus on just one of the component to identify the issue. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1.2 Physical Parameter Data Block\n", + "\n", + "A `PhysicalParameterBlock` serves as the central point of reference for all aspects of the property package and needs to define several things about the package. These are summarized below:\n", + "\n", + "- Units of measurement\n", + "- What properties are supported and how they are implemented\n", + "- What components and phases are included in the packages\n", + "- All the global parameters necessary for calculating properties\n", + "- A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To construct this block, we begin by declaring a process block class using a Python decorator. One can learn more about `declare_process_block_class` [here](https://github.com/IDAES/idaes-pse/blob/eea1209077b75f7d940d8958362e69d4650c079d/idaes/core/base/process_block.py#L173). After constructing the process block, we define a build function which contains all the components that the property package would have. `super` function here is used to give access to methods and properties of a parent or sibling class and since this is used on the class `PhysicalParameterData` class, build has access to all the parent and sibling class methods. \n", + "\n", + "The `PhysicalParameterBlock` then refers to the `state block`, in this case `OrgPhaseStateBlock` (which will be declared later), so that we can build a state block instance by only knowing the `PhysicalParameterBlock` we wish to use. Then we list the number of phases in this property package. Then we assign the variable to the phase which follows a naming convention. The solvent is in the Organic phase; we will assign the Phase as OrganicPhase, and the variable will be named Org as per the naming convention. The details of naming conventions can be found [here](https://github.com/IDAES/idaes-pse/blob/main/docs/explanations/conventions.rst). We will be following the same convention throughout the example. \n", + " \n", + "After defining the list of the phases, we move on to list the components and their type in the phase. It can be a solute or a solvent in the Organic phase. Thus, we define the component and assign it to either being a solute or a solvent. In this case, the salts are the solutes and Ethylene dibromide is the solvent. Next, we define the physical properties involved in the package, like the heat capacity and density of the solvent, the reference temperature, and the distribution factor that would govern the mass transfer from one phase into another. Additionally, a parameter, the `diffusion_factor`, is introduced. This factor plays a crucial role in governing mass transfer between phases, necessitating its definition within the state block.\n", + "\n", + "The final step in creating the Physical Parameter Block is to declare a `classmethod` named `define_metadata`, which takes two arguments: a class (cls) and an instance of that class (obj). In this method, we will call the predefined method `add_default_units()`.\n", + "\n", + "- `obj.add_default_units()` sets the default units metadata for the property package, and here we define units to be used with this property package as default. " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{Member of material_balances} : Material balances\n", - " Size=4, Index=fs._time*fs.aq_properties.component_list, Active=True\n", - " Key : Lower : Body : Upper : Active\n", - " (0.0, 'NaCl') : 0.0 : (fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_in[0.0].flow_vol) - (fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_out[0.0].flow_vol) + fs.lex.aqueous_phase.mass_transfer_term[0.0,Aq,NaCl] : 0.0 : True\n" - ] - } - ], - "source": [ - "m.fs.lex.aqueous_phase.material_balances[0.0, \"NaCl\"].pprint()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"OrgPhase\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " organic Phase\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super().build()\n", + "\n", + " self._state_block_class = OrgPhaseStateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Org = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.NaCl = Solute()\n", + " self.KNO3 = Solute()\n", + " self.CaSO4 = Solute()\n", + " self.solvent = (\n", + " Solvent()\n", + " ) # Solvent used here is ethylene dibromide (Organic Polar)\n", + "\n", + " # Heat capacity of solvent\n", + " self.cp_mass = Param(\n", + " mutable=True,\n", + " initialize=717.01,\n", + " doc=\"Specific heat capacity of solvent\",\n", + " units=units.J / units.kg / units.K,\n", + " )\n", + "\n", + " self.dens_mass = Param(\n", + " mutable=True,\n", + " initialize=2170,\n", + " doc=\"Density of ethylene dibromide\",\n", + " units=units.kg / units.m**3,\n", + " )\n", + " self.temperature_ref = Param(\n", + " within=PositiveReals,\n", + " mutable=True,\n", + " default=298.15,\n", + " doc=\"Reference temperature\",\n", + " units=units.K,\n", + " )\n", + " self.diffusion_factor = Param(\n", + " self.solute_set,\n", + " initialize={\"NaCl\": 2.15, \"KNO3\": 3, \"CaSO4\": 1.5},\n", + " within=PositiveReals,\n", + " mutable=True,\n", + " )\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.hour,\n", + " \"length\": units.m,\n", + " \"mass\": units.g,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{Member of conc_mass_comp} : Component mass concentrations\n", - " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " NaCl : 0 : 0.15 : None : True : True : NonNegativeReals\n", - "flow_vol : Total volumetric flowrate\n", - " Size=1, Index=None, Units=l/h\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " None : 0 : 100.0 : None : True : True : NonNegativeReals\n", - "{Member of conc_mass_comp} : Component mass concentrations\n", - " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " NaCl : 0 : 0.0 : None : False : False : NonNegativeReals\n", - "flow_vol : Total volumetric flowrate\n", - " Size=1, Index=None, Units=l/h\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " None : 0 : 100.0 : None : False : False : NonNegativeReals\n", - "{Member of mass_transfer_term} : Component material transfer into unit\n", - " Size=4, Index=fs._time*fs.aq_properties._phase_component_set, Units=g/h\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " (0.0, 'Aq', 'NaCl') : None : -31.700284300098897 : None : False : False : Reals\n" - ] - } - ], - "source": [ - "m.fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", - "m.fs.lex.aqueous_phase.properties_in[0.0].flow_vol.pprint()\n", - "m.fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", - "m.fs.lex.aqueous_phase.properties_out[0.0].flow_vol.pprint()\n", - "m.fs.lex.aqueous_phase.mass_transfer_term[0.0, \"Aq\", \"NaCl\"].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It seems there is a discrepancy between the mass transfer term and the amount of input of NaCl. This can be inferred from the values where the input equals 15g/h and the `mass_transfer_term` equals -31.706g/h.\n", - "\n", - "To further investigate this issue, it's advisable to examine the `material_aq_balance` constraint within the unit model where the `mass_transfer_term` is defined. By printing out this constraint and analyzing its components, you can gain a better understanding of the discrepancy and take appropriate corrective actions." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1.3 State Block\n", + "\n", + "After the `PhysicalParameterBlock` class has been created, the next step is to write the code necessary to create the State Blocks that will be used throughout the flowsheet. `StateBlock` contains all the information necessary to define the state of the system. This includes the state variables and constraints on those variables which are used to describe a state property like the enthalpy, material balance, etc.\n", + "\n", + "Creating a State Block requires us to write two classes. The reason we write two classes is because of the inherent nature of how `declare_process_block_data` works. `declare_process_block_data` facilitates creating an `IndexedComponent` object which can handle multiple `ComponentData` objects which represent the component at each point in the indexing set. This makes it easier to build an instance of the model at each indexed point. However, State Blocks are slightly different, as they are always indexed (at least by time). Due to this, we often want to perform actions on all the elements of the indexed StateBlock all at once (rather than element by element).\n", + "\n", + "The class `_OrganicStateBlock` is defined without the `declare_process_block_data` decorator and thus works as a traditional class and this facilitates performing a method on the class as a whole rather than individual elements of the indexed property blocks. In this class we define the `fix_initialization_states` function. `fix_initialization_states` function is used to fix the state variable within the state block with the provided initial values (usually inlet conditions). It takes a `block` as the argument in which the state variables are to be fixed. It also takes `state_args` as an optional argument. `state_args` is a dictionary with the value for the state variables to be fixed. This function returns a dictionary indexed by the block, state variables and variable index indicating the fixed status of each variable before applying the function. \n", + "\n", + "The above function comprise of the _OrganicStateBlock. Next, we shall see the construction of the OrgPhaseStateBlockData class." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{Member of material_aq_balance} : Unit level material balances for Aq\n", - " Size=4, Index=fs._time*fs.aq_properties.component_list, Active=True\n", - " Key : Lower : Body : Upper : Active\n", - " (0.0, 'NaCl') : 0.0 : fs.lex.aqueous_phase.mass_transfer_term[0.0,Aq,NaCl] + fs.org_properties.diffusion_factor[NaCl]*(fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_in[0.0].flow_vol) : 0.0 : True\n" - ] - } - ], - "source": [ - "m.fs.lex.material_aq_balance[0.0, \"NaCl\"].pprint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the problem can be tracked down easily as there being a typing error while recording the distribution factor. The distribution factor here was wrongly written ignoring its magnitude which should have been 1e-2, but that was missed, thus adjusting the distribution factor parameter we should have this issue resolved. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", - ")\n", - "m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", - ")\n", - "m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", - ")\n", - "\n", - "m.fs.lex.organic_phase.properties_in[0.0].pressure.setlb(0.5)\n", - "m.fs.lex.organic_phase.properties_out[0.0].pressure.setlb(0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the corrective actions, we should check if this have made any structural issues, for this we would call `report_structural_issues()`" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class _OrganicStateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def fix_initialization_states(self):\n", + " fix_state_vars(self)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================\n", - "Model Statistics\n", - "\n", - " Activated Blocks: 21 (Deactivated: 0)\n", - " Free Variables in Activated Constraints: 16 (External: 0)\n", - " Free Variables with only lower bounds: 8\n", - " Free Variables with only upper bounds: 0\n", - " Free Variables with upper and lower bounds: 0\n", - " Fixed Variables in Activated Constraints: 8 (External: 0)\n", - " Activated Equality Constraints: 16 (Deactivated: 0)\n", - " Activated Inequality Constraints: 0 (Deactivated: 0)\n", - " Activated Objectives: 0 (Deactivated: 0)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "0 WARNINGS\n", - "\n", - " No warnings found!\n", - "\n", - "------------------------------------------------------------------------------------\n", - "1 Cautions\n", - "\n", - " Caution: 10 unused variables (4 fixed)\n", - "\n", - "------------------------------------------------------------------------------------\n", - "Suggested next steps:\n", - "\n", - " Try to initialize/solve your model and then call report_numerical_issues()\n", - "\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "dt.report_structural_issues()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now since there are no warnings we can go ahead and solve the model and see if the results are optimal. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class `OrgPhaseStateBlockData` is designated with the `declare_process_block_class` decorator, named `OrgPhaseStateBlock`, and inherits the block class from `_OrganicStateBlock`. This inheritance allows `OrgPhaseStateBlockData` to leverage functions from `_OrganicStateBlock`. Following the class definition, a build function similar to the one used in the `PhysicalParameterData` block is employed. The super function is utilized to enable the utilization of functions from the parent or sibling class.\n", + "\n", + "The subsequent objective is to delineate the state variables, accomplished through the `_make_state_vars` method. This method encompasses all the essential state variables and associated data. For this particular property package, the required state variables are:\n", + "\n", + "- `flow_vol` - volumetric flow rate\n", + "- `conc_mass_comp` - mass fractions\n", + "- `pressure` - state pressure\n", + "- `temperature` - state temperature\n", + "\n", + "After establishing the state variables, the subsequent step involves setting up state properties as constraints. This includes specifying the relationships and limitations that dictate the system's behavior. The following properties need to be articulated:\n", + "\n", + "-`get_material_flow_terms`: quantifies the amount of material flow.\n", + "- `get_enthalpy_flow_terms`: quantifies the amount of enthalpy flow.\n", + "- `get_flow_rate`: details volumetric flow rates.\n", + "- `default_material_balance_type`: defines the kind of material balance to be used.\n", + "- `default_energy_balance_type`: defines the kind of energy balance to be used.\n", + "- `define_state_vars`: involves defining state variables with units, akin to the define_metadata function in the PhysicalParameterData block.\n", + "- `get_material_flow_basis`: establishes the basis on which state variables are measured, whether in mass or molar terms.\n", + "\n", + "These definitions mark the conclusion of the state block construction and thus the property package. For additional details on creating a property package, please refer to this [resource](../../properties/custom/custom_physical_property_packages_usr.ipynb ).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"OrgPhaseStateBlock\", block_class=_OrganicStateBlock)\n", + "class OrgPhaseStateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for Organic phase for liquid liquid extraction\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super().build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " self.flow_vol = Var(\n", + " initialize=1,\n", + " domain=NonNegativeReals,\n", + " doc=\"Total volumetric flowrate\",\n", + " units=units.L / units.hour,\n", + " )\n", + " self.conc_mass_comp = Var(\n", + " self.params.solute_set,\n", + " domain=NonNegativeReals,\n", + " initialize=1,\n", + " doc=\"Component mass concentrations\",\n", + " units=units.g / units.L,\n", + " )\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1,\n", + " bounds=(1, 5),\n", + " units=units.atm,\n", + " doc=\"State pressure [atm]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=300,\n", + " bounds=(273, 373),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " def material_flow_expression(self, j):\n", + " if j == \"solvent\":\n", + " return self.flow_vol * self.params.dens_mass\n", + " else:\n", + " return self.flow_vol * self.conc_mass_comp[j]\n", + "\n", + " self.material_flow_expression = Expression(\n", + " self.component_list,\n", + " rule=material_flow_expression,\n", + " doc=\"Material flow terms\",\n", + " )\n", + "\n", + " def enthalpy_flow_expression(self):\n", + " return (\n", + " self.flow_vol\n", + " * self.params.dens_mass\n", + " * self.params.cp_mass\n", + " * (self.temperature - self.params.temperature_ref)\n", + " )\n", + "\n", + " self.enthalpy_flow_expression = Expression(\n", + " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", + " )\n", + "\n", + " def get_flow_rate(self):\n", + " return self.flow_vol\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.material_flow_expression[j]\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.enthalpy_flow_expression\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_vol\": self.flow_vol,\n", + " \"conc_mass_comp\": self.conc_mass_comp,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def get_material_flow_basis(self):\n", + " return MaterialFlowBasis.mass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Creating Aqueous Property Package\n", + "\n", + "The structure of the Aqueous Property Package mirrors that of the Organic Property Package we previously developed. We'll commence with an overview, importing the required libraries, followed by the creation of the physical property block and two state blocks. The distinctions in this package lie in the physical parameter values, and notably, the absence of the diffusion factor term, differentiating it from the prior package. The following code snippet should provide clarity on these distinctions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Python libraries\n", + "import logging\n", + "\n", + "from idaes.core.util.initialization import fix_state_vars\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import (\n", + " Param,\n", + " Var,\n", + " NonNegativeReals,\n", + " units,\n", + " Expression,\n", + " PositiveReals,\n", + ")\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " declare_process_block_class,\n", + " MaterialFlowBasis,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " Solute,\n", + " Solvent,\n", + " LiquidPhase,\n", + ")\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)\n", + "\n", + "\n", + "@declare_process_block_class(\"AqPhase\")\n", + "class AqPhaseData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " aqueous Phase\n", + "\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction.\n", + " \"\"\"\n", + " super().build()\n", + "\n", + " self._state_block_class = AqPhaseStateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Aq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.NaCl = Solute()\n", + " self.KNO3 = Solute()\n", + " self.CaSO4 = Solute()\n", + " self.H2O = Solvent()\n", + "\n", + " # Heat capacity of solvent\n", + " self.cp_mass = Param(\n", + " mutable=True,\n", + " initialize=4182,\n", + " doc=\"Specific heat capacity of solvent\",\n", + " units=units.J / units.kg / units.K,\n", + " )\n", + "\n", + " self.dens_mass = Param(\n", + " mutable=True,\n", + " initialize=997,\n", + " doc=\"Density of ethylene dibromide\",\n", + " units=units.kg / units.m**3,\n", + " )\n", + " self.temperature_ref = Param(\n", + " within=PositiveReals,\n", + " mutable=True,\n", + " default=298.15,\n", + " doc=\"Reference temperature\",\n", + " units=units.K,\n", + " )\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_default_units(\n", + " {\n", + " \"time\": units.hour,\n", + " \"length\": units.m,\n", + " \"mass\": units.g,\n", + " \"amount\": units.mol,\n", + " \"temperature\": units.K,\n", + " }\n", + " )\n", + "\n", + "\n", + "class _AqueousStateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + "\n", + " def fix_initialization_states(self):\n", + " fix_state_vars(self)\n", + "\n", + "\n", + "@declare_process_block_class(\"AqPhaseStateBlock\", block_class=_AqueousStateBlock)\n", + "class AqPhaseStateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super().build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " self.flow_vol = Var(\n", + " initialize=1,\n", + " domain=NonNegativeReals,\n", + " doc=\"Total volumetric flowrate\",\n", + " units=units.L / units.hour,\n", + " )\n", + "\n", + " self.conc_mass_comp = Var(\n", + " self.params.solute_set,\n", + " domain=NonNegativeReals,\n", + " initialize={\"NaCl\": 0.15, \"KNO3\": 0.2, \"CaSO4\": 0.1},\n", + " doc=\"Component mass concentrations\",\n", + " units=units.g / units.L,\n", + " )\n", + "\n", + " self.pressure = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=1,\n", + " bounds=(1, 5),\n", + " units=units.atm,\n", + " doc=\"State pressure [atm]\",\n", + " )\n", + "\n", + " self.temperature = Var(\n", + " domain=NonNegativeReals,\n", + " initialize=300,\n", + " bounds=(273, 373),\n", + " units=units.K,\n", + " doc=\"State temperature [K]\",\n", + " )\n", + "\n", + " def material_flow_expression(self, j):\n", + " if j == \"H2O\":\n", + " return self.flow_vol * self.params.dens_mass\n", + " else:\n", + " return self.conc_mass_comp[j] * self.flow_vol\n", + "\n", + " self.material_flow_expression = Expression(\n", + " self.component_list,\n", + " rule=material_flow_expression,\n", + " doc=\"Material flow terms\",\n", + " )\n", + "\n", + " def enthalpy_flow_expression(self):\n", + " return (\n", + " self.flow_vol\n", + " * self.params.dens_mass\n", + " * self.params.cp_mass\n", + " * (self.temperature - self.params.temperature_ref)\n", + " )\n", + "\n", + " self.enthalpy_flow_expression = Expression(\n", + " rule=enthalpy_flow_expression, doc=\"Enthalpy flow term\"\n", + " )\n", + "\n", + " def get_flow_rate(self):\n", + " return self.flow_vol\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.material_flow_expression[j]\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.enthalpy_flow_expression\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\n", + " \"flow_vol\": self.flow_vol,\n", + " \"conc_mass_comp\": self.conc_mass_comp,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure,\n", + " }\n", + "\n", + " def get_material_flow_basis(self):\n", + " return MaterialFlowBasis.mass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Liquid Liquid Extractor Unit Model\n", + "\n", + "Following the creation of property packages, our next step is to develop a unit model that facilitates the mass transfer of solutes between phases. This involves importing necessary libraries, building the unit model, defining auxiliary functions, and establishing the initialization routine for the unit model.\n", + "\n", + "## 3.1 Importing necessary libraries\n", + "\n", + "Let's commence by importing the essential libraries from Pyomo and IDAES." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Import Pyomo libraries\n", + "from pyomo.common.config import ConfigBlock, ConfigValue, In, Bool\n", + "from pyomo.environ import (\n", + " value,\n", + " Constraint,\n", + " check_optimal_termination,\n", + ")\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (\n", + " ControlVolume0DBlock,\n", + " declare_process_block_class,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " MaterialFlowBasis,\n", + " MomentumBalanceType,\n", + " UnitModelBlockData,\n", + " useDefault,\n", + ")\n", + "from idaes.core.util.config import (\n", + " is_physical_parameter_block,\n", + " is_reaction_parameter_block,\n", + ")\n", + "\n", + "import idaes.logger as idaeslog\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.exceptions import ConfigurationError, InitializationError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Creating the unit model\n", + "\n", + "Creating a unit model starts by creating a class called `LiqExtractionData` and using the `declare_process_block_class` decorator. The `LiqExtractionData` inherits the properties of the `UnitModelBlockData` class, which allows us to create a control volume that is necessary for the unit model. After declaration of the class we proceed to define the relevant config arguments for the control volume. The config arguments include the following properties:\n", + "\n", + "- `material_balance_type` - Indicates what type of mass balance should be constructed\n", + "- `has_pressure_change` - Indicates whether terms for pressure change should be\n", + "constructed\n", + "- `has_phase_equilibrium` - Indicates whether terms for phase equilibrium should be\n", + "constructed\n", + "- `organic_property_package` - Property parameter object used to define property calculations\n", + "for the Organic phase\n", + "- `organic_property_package_args` - Arguments to use for constructing Organic phase properties\n", + "- `aqueous_property_package` - Property parameter object used to define property calculations\n", + "for the aqueous phase\n", + "- `aqueous_property_package_args` - Arguments to use for constructing aqueous phase properties\n", + "\n", + "As there are no pressure changes or reactions in this scenario, configuration arguments for these aspects are not included. However, additional details on configuration arguments can be found [here](https://github.com/IDAES/idaes-pse/blob/8948c6ce27d4c7f2c06b377a173f413599091998/idaes/models/unit_models/cstr.py)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"LiqExtraction\")\n", + "class LiqExtractionData(UnitModelBlockData):\n", + " \"\"\"\n", + " LiqExtraction Unit Model Class\n", + " \"\"\"\n", + "\n", + " CONFIG = UnitModelBlockData.CONFIG()\n", + "\n", + " CONFIG.declare(\n", + " \"material_balance_type\",\n", + " ConfigValue(\n", + " default=MaterialBalanceType.useDefault,\n", + " domain=In(MaterialBalanceType),\n", + " description=\"Material balance construction flag\",\n", + " doc=\"\"\"Indicates what type of mass balance should be constructed,\n", + " **default** - MaterialBalanceType.useDefault.\n", + " **Valid values:** {\n", + " **MaterialBalanceType.useDefault - refer to property package for default\n", + " balance type\n", + " **MaterialBalanceType.none** - exclude material balances,\n", + " **MaterialBalanceType.componentPhase** - use phase component balances,\n", + " **MaterialBalanceType.componentTotal** - use total component balances,\n", + " **MaterialBalanceType.elementTotal** - use total element balances,\n", + " **MaterialBalanceType.total** - use total material balance.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"has_pressure_change\",\n", + " ConfigValue(\n", + " default=False,\n", + " domain=Bool,\n", + " description=\"Pressure change term construction flag\",\n", + " doc=\"\"\"Indicates whether terms for pressure change should be\n", + " constructed,\n", + " **default** - False.\n", + " **Valid values:** {\n", + " **True** - include pressure change terms,\n", + " **False** - exclude pressure change terms.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"has_phase_equilibrium\",\n", + " ConfigValue(\n", + " default=False,\n", + " domain=Bool,\n", + " description=\"Phase equilibrium construction flag\",\n", + " doc=\"\"\"Indicates whether terms for phase equilibrium should be\n", + " constructed,\n", + " **default** = False.\n", + " **Valid values:** {\n", + " **True** - include phase equilibrium terms\n", + " **False** - exclude phase equilibrium terms.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"organic_property_package\",\n", + " ConfigValue(\n", + " default=useDefault,\n", + " domain=is_physical_parameter_block,\n", + " description=\"Property package to use for organic phase\",\n", + " doc=\"\"\"Property parameter object used to define property calculations\n", + " for the organic phase,\n", + " **default** - useDefault.\n", + " **Valid values:** {\n", + " **useDefault** - use default package from parent model or flowsheet,\n", + " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"organic_property_package_args\",\n", + " ConfigBlock(\n", + " implicit=True,\n", + " description=\"Arguments to use for constructing organic phase properties\",\n", + " doc=\"\"\"A ConfigBlock with arguments to be passed to organic phase\n", + " property block(s) and used when constructing these,\n", + " **default** - None.\n", + " **Valid values:** {\n", + " see property package for documentation.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"aqueous_property_package\",\n", + " ConfigValue(\n", + " default=useDefault,\n", + " domain=is_physical_parameter_block,\n", + " description=\"Property package to use for aqueous phase\",\n", + " doc=\"\"\"Property parameter object used to define property calculations\n", + " for the aqueous phase,\n", + " **default** - useDefault.\n", + " **Valid values:** {\n", + " **useDefault** - use default package from parent model or flowsheet,\n", + " **PropertyParameterObject** - a PropertyParameterBlock object.}\"\"\",\n", + " ),\n", + " )\n", + " CONFIG.declare(\n", + " \"aqueous_property_package_args\",\n", + " ConfigBlock(\n", + " implicit=True,\n", + " description=\"Arguments to use for constructing aqueous phase properties\",\n", + " doc=\"\"\"A ConfigBlock with arguments to be passed to aqueous phase\n", + " property block(s) and used when constructing these,\n", + " **default** - None.\n", + " **Valid values:** {\n", + " see property package for documentation.}\"\"\",\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building the model\n", + "\n", + "After constructing the `LiqExtractionData` block and defining the config arguments for the control block, the next step is to write a build function that incorporates the control volume and establishes constraints on the control volume to achieve the desired mass transfer. The control volume serves as a pivotal component in the unit model construction, representing the volume in which the process unfolds.\n", + "\n", + "IDAES provides flexibility in choosing control volumes based on geometry, with options including 0D or 1D. In this instance, we opt for a 0D control volume, the most commonly used control volume. This choice is suitable for systems where there is a well-mixed volume of fluid or where spatial variations are deemed negligible.\n", + "\n", + "The control volume encompasses parameters from (1-8), and its equations are configured to satisfy the specified config arguments. For a more in-depth understanding, users are encouraged to refer to [this resource](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst). \n", + "\n", + "The `build` function is initiated using the `super` function to gain access to methods and properties of a parent or sibling class, in this case, the `LiqExtractionData` class. Following the `super` function, checks are performed on the property packages to ensure the appropriate names for the solvents, such as 'Aq' for the aqueous phase and 'Org' for the organic phase. An error is raised if these conditions are not met. Subsequently, a check is performed to ensure there is at least one common component between the two property packages that can be transferred from one phase to another.\n", + "\n", + "After these checks are completed without any exceptions raised, it is ensured that the property packages have the desired components with appropriate names. The next step is to create a control volume and assign it to a property package. Here, we initiate with the organic phase and attach a 0D control volume to it. The control volume takes arguments about the dynamics of the block, and the property package, along with property package arguments. \n", + "\n", + "The subsequent steps involve adding inlet and outlet state blocks to the control volume using the `add_state_blocks` function. This function takes arguments about the flow direction (defaulted to forward) and a flag for `has_phase_equilibrium`, which is read from the config. The control volume is now equipped with the inlet and outlet state blocks and has access to the organic property package\n", + "\n", + "Next, material balance equations are added to the control volume using the `add_material_balance` function, taking into account the type of material balance, `has_phase_equilibrium`, and the presence of `has_mass_transfer`. To understand this arguments further let us have a look at the material balance equation and how it is implemented in control volume. \n", + "\n", + "$\\frac{\\partial M_{t, p, j}}{\\partial t} = F_{in, t, p, j} - F_{out, t, p, j} + N_{kinetic, t, p, j} + N_{equilibrium, t, p, j} + N_{pe, t, p, j} + N_{transfer, t, p, j} + N_{custom, t, p, j}$\n", + "\n", + "- $\\frac{\\partial M_{t, p, j}}{\\partial t}$ - Material accumulation\n", + "- $F_{in, t, p, j}$ - Flow into the control volume\n", + "- $F_{out, t, p, j}$ - Flow out of the control volume\n", + "- $N_{kinetic, t, p, j}$ - Rate of reaction generation\n", + "- $N_{equilibrium, t, p, j}$ - Equilibrium reaction generation\n", + "- $N_{pe, t, p, j}$ - Equilibrium reaction extent\n", + "- $N_{transfer, t, p, j}$ - Mass transfer\n", + "- $N_{custom, t, p, j}$ - User defined terms in material balance\n", + "\n", + "- t indicates time index\n", + "- p indicates phase index\n", + "- j indicates component index\n", + "- e indicates element index\n", + "- r indicates reaction name index\n", + "\n", + "Here we shall see that $N_{transfer, t, p, j}$ is the term in the equation which is responsible for the mass transfer and the `mass_transfer_term` should only be equal to the amount being transferred and not include a material balance on our own. For a detailed description of the terms one should refer to the following [resource.](https://github.com/IDAES/idaes-pse/blob/2f34dd3abc1bce5ba17c80939a01f9034e4fbeef/docs/reference_guides/core/control_volume_0d.rst)\n", + "\n", + "This concludes the creation of the organic phase control volume. A similar procedure is done for the aqueous phase control volume with aqueous property package. \n", + "\n", + "Now, the unit model has two control volumes with appropriate configurations and material, momentum and energy balances. The next step is to check the basis of the two property packages. They should both have the same flow basis, and an error is raised if this is not the case.\n", + "\n", + "Following this, the `add_inlet_ports` and `add_outlet_ports` functions are used to create inlet and outlet ports. These ports are named and assigned to each control volume, resulting in labeled inlet and outlet ports for each control volume.\n", + "\n", + "The subsequent steps involve writing unit-level constraints. A check if the basis is either molar or mass, and unit-level constraints are written accordingly. The first constraint pertains to the mass transfer term for the aqueous phase. The mass transfer term is equal to $mass\\_transfer\\_term_{aq} = (D_{i})\\frac{mass_{i}~in~aq~phase}{flowrate~of~aq~phase}$. The second constraint relates to the mass transfer term in the organic phase, which is the negative of the mass transfer term in the aqueous phase: $mass\\_transfer\\_term_{org} = - mass\\_transfer\\_term_{aq} $\n", + "\n", + "Here $mass\\_transfer\\_term_{p}$ is the term indicating the amount of material being transferred from/to the phase and $D_{i}$ is the Distribution coefficient for component i. \n", + "\n", + "This marks the completion of the build function, and the unit model is now equipped with the necessary process constraints. The subsequent steps involve writing the initialization routine." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def build(self):\n", + " \"\"\"\n", + " Begin building model (pre-DAE transformation).\n", + " Args:\n", + " None\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " # Call UnitModel.build to setup dynamics\n", + " super().build()\n", + "\n", + " # Check phase lists match assumptions\n", + " if self.config.aqueous_property_package.phase_list != [\"Aq\"]:\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the aqueous \"\n", + " f\"phase property package have a single phase named 'Aq'\"\n", + " )\n", + " if self.config.organic_property_package.phase_list != [\"Org\"]:\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", + " f\"phase property package have a single phase named 'Org'\"\n", + " )\n", + "\n", + " # Check for at least one common component in component lists\n", + " if not any(\n", + " j in self.config.aqueous_property_package.component_list\n", + " for j in self.config.organic_property_package.component_list\n", + " ):\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor model requires that the organic \"\n", + " f\"and aqueous phase property packages have at least one \"\n", + " f\"common component.\"\n", + " )\n", + "\n", + " self.organic_phase = ControlVolume0DBlock(\n", + " dynamic=self.config.dynamic,\n", + " property_package=self.config.organic_property_package,\n", + " property_package_args=self.config.organic_property_package_args,\n", + " )\n", + "\n", + " self.organic_phase.add_state_blocks(\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium\n", + " )\n", + "\n", + " # Separate organic and aqueous phases means that phase equilibrium will\n", + " # be handled at the unit model level, thus has_phase_equilibrium is\n", + " # False, but has_mass_transfer is True.\n", + "\n", + " self.organic_phase.add_material_balances(\n", + " balance_type=self.config.material_balance_type,\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", + " has_mass_transfer=True,\n", + " )\n", + " # ---------------------------------------------------------------------\n", + "\n", + " self.aqueous_phase = ControlVolume0DBlock(\n", + " dynamic=self.config.dynamic,\n", + " property_package=self.config.aqueous_property_package,\n", + " property_package_args=self.config.aqueous_property_package_args,\n", + " )\n", + "\n", + " self.aqueous_phase.add_state_blocks(\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium\n", + " )\n", + "\n", + " # Separate liquid and aqueous phases means that phase equilibrium will\n", + " # be handled at the unit model level, thus has_phase_equilibrium is\n", + " # False, but has_mass_transfer is True.\n", + "\n", + " self.aqueous_phase.add_material_balances(\n", + " balance_type=self.config.material_balance_type,\n", + " # has_rate_reactions=False,\n", + " has_phase_equilibrium=self.config.has_phase_equilibrium,\n", + " has_mass_transfer=True,\n", + " )\n", + "\n", + " self.aqueous_phase.add_geometry()\n", + "\n", + " # ---------------------------------------------------------------------\n", + " # Check flow basis is compatible\n", + " t_init = self.flowsheet().time.first()\n", + " if (\n", + " self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", + " != self.organic_phase.properties_out[t_init].get_material_flow_basis()\n", + " ):\n", + " raise ConfigurationError(\n", + " f\"{self.name} aqueous and organic property packages must use the \"\n", + " f\"same material flow basis.\"\n", + " )\n", + "\n", + " self.organic_phase.add_geometry()\n", + "\n", + " # Add Ports\n", + " self.add_inlet_port(\n", + " name=\"organic_inlet\", block=self.organic_phase, doc=\"Organic feed\"\n", + " )\n", + " self.add_inlet_port(\n", + " name=\"aqueous_inlet\", block=self.aqueous_phase, doc=\"Aqueous feed\"\n", + " )\n", + " self.add_outlet_port(\n", + " name=\"organic_outlet\", block=self.organic_phase, doc=\"Organic outlet\"\n", + " )\n", + " self.add_outlet_port(\n", + " name=\"aqueous_outlet\",\n", + " block=self.aqueous_phase,\n", + " doc=\"Aqueous outlet\",\n", + " )\n", + "\n", + " # ---------------------------------------------------------------------\n", + " # Add unit level constraints\n", + " # First, need the union and intersection of component lists\n", + " all_comps = (\n", + " self.aqueous_phase.properties_out.component_list\n", + " | self.organic_phase.properties_out.component_list\n", + " )\n", + " common_comps = (\n", + " self.aqueous_phase.properties_out.component_list\n", + " & self.organic_phase.properties_out.component_list\n", + " )\n", + "\n", + " # Get units for unit conversion\n", + " aunits = self.config.aqueous_property_package.get_metadata().get_derived_units\n", + " lunits = self.config.organic_property_package.get_metadata().get_derived_units\n", + " flow_basis = self.aqueous_phase.properties_out[t_init].get_material_flow_basis()\n", + "\n", + " if flow_basis == MaterialFlowBasis.mass:\n", + " fb = \"flow_mass\"\n", + " else:\n", + " raise ConfigurationError(\n", + " f\"{self.name} Liquid-Liquid Extractor only supports mass \"\n", + " f\"basis for MaterialFlowBasis.\"\n", + " )\n", + "\n", + " # Material balances\n", + " def rule_material_aq_balance(self, t, j):\n", + " if j in common_comps:\n", + " return self.aqueous_phase.mass_transfer_term[\n", + " t, \"Aq\", j\n", + " ] == -self.organic_phase.config.property_package.diffusion_factor[j] * (\n", + " self.aqueous_phase.properties_in[t].get_material_flow_terms(\"Aq\", j)\n", + " )\n", + " elif j in self.organic_phase.properties_out.component_list:\n", + " # No mass transfer term\n", + " # Set organic flowrate to an arbitrary small value\n", + " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * lunits(fb)\n", + " elif j in self.aqueous_phase.properties_out.component_list:\n", + " # No mass transfer term\n", + " # Set aqueous flowrate to an arbitrary small value\n", + " return self.aqueous_phase.mass_transfer_term[t, \"Aq\", j] == 0 * aunits(fb)\n", + "\n", + " self.material_aq_balance = Constraint(\n", + " self.flowsheet().time,\n", + " self.aqueous_phase.properties_out.component_list,\n", + " rule=rule_material_aq_balance,\n", + " doc=\"Unit level material balances for Aq\",\n", + " )\n", + "\n", + " def rule_material_liq_balance(self, t, j):\n", + " if j in common_comps:\n", + " return (\n", + " self.organic_phase.mass_transfer_term[t, \"Org\", j]\n", + " == -self.aqueous_phase.mass_transfer_term[t, \"Aq\", j]\n", + " )\n", + " else:\n", + " # No mass transfer term\n", + " # Set organic flowrate to an arbitrary small value\n", + " return self.organic_phase.mass_transfer_term[t, \"Org\", j] == 0 * aunits(fb)\n", + "\n", + " self.material_org_balance = Constraint(\n", + " self.flowsheet().time,\n", + " self.organic_phase.properties_out.component_list,\n", + " rule=rule_material_liq_balance,\n", + " doc=\"Unit level material balances Org\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialization Routine\n", + "\n", + "After writing the unit model it is crucial to initialize the model properly, as non-linear models may encounter local minima or infeasibility if not initialized properly. IDAES provides us with a few initialization routines which may not work for all the models, and in such cases the developer will have to define their own initialization routines. \n", + "\n", + "To create a custom initialization routine, model developers must create an initialize method as part of their model, and provide a sequence of steps intended to build up a feasible solution. Initialization routines generally make use of Pyomo\u2019s tools for activating and deactivating constraints and often involve solving multiple sub-problems whilst building up an initial state.\n", + "\n", + "For this tutorial we would use the pre-defined initialization routine of `BlockTriangularizationInitializer` when initializing the model in the flowsheet. This Initializer should be suitable for most models, but may struggle to initialize\n", + "tightly coupled systems of equations. This method of initialization will follow the following workflow. \n", + "\n", + "- Have precheck for structural singularity\n", + "- Run incidence analysis on given block data and check matching.\n", + "- Call Block Triangularization solver on the model.\n", + "- Call solve_strongly_connected_components on a given BlockData.\n", + "\n", + "More details about this initialization routine can be found [here](https://github.com/IDAES/idaes-pse/blob/c09433b9afed5ae2fe25c0ccdc732783324f0101/idaes/core/initialization/block_triangularization.py). \n", + "\n", + "\n", + "This marks the conclusion of creating a custom unit model, for a more detailed explanation on creating a unit model refer [this resource](../../unit_models/custom_unit_models/custom_compressor_usr.ipynb). The next sections will deal with the diagnostics and testing of the property package and unit model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Building a Flowsheet\n", + "\n", + "Once we have set up the unit model and its property packages, we can start building a flowsheet using them. In this tutorial, we're focusing on a simple flowsheet with just a liquid-liquid extractor. To create the flowsheet we follow the following steps:\n", + "\n", + "- Import necessary libraries\n", + "- Create a Pyomo model.\n", + "- Inside the model, create a flowsheet block.\n", + "- Assign property packages to the flowsheet block.\n", + "- Add the liquid-liquid extractor to the flowsheet block.\n", + "- Fix variable to make it a square problem\n", + "- Run an initialization process.\n", + "- Solve the flowsheet.\n", + "\n", + "Following these steps, we've built a basic flowsheet using Pyomo. For more details, refer to the [documentation](../../flowsheets/hda_flowsheet_with_distillation_usr.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "from idaes.core.initialization.block_triangularization import (\n", + " BlockTriangularizationInitializer,\n", + ")\n", + "from liquid_extraction.organic_property import OrgPhase\n", + "from liquid_extraction.aqueous_property import AqPhase\n", + "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", + "\n", + "\n", + "def build_model():\n", + " m = pyo.ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.lex = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + " return m\n", + "\n", + "\n", + "def fix_state_variables(m):\n", + " m.fs.lex.organic_inlet.flow_vol.fix(80 * pyo.units.L / pyo.units.hour)\n", + " m.fs.lex.organic_inlet.temperature.fix(300 * pyo.units.K)\n", + " m.fs.lex.organic_inlet.pressure.fix(1 * pyo.units.atm)\n", + " m.fs.lex.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", + " 1e-5 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", + " 1e-5 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", + " 1e-5 * pyo.units.g / pyo.units.L\n", + " )\n", + "\n", + " m.fs.lex.aqueous_inlet.flow_vol.fix(100 * pyo.units.L / pyo.units.hour)\n", + " m.fs.lex.aqueous_inlet.temperature.fix(300 * pyo.units.K)\n", + " m.fs.lex.aqueous_inlet.pressure.fix(1 * pyo.units.atm)\n", + " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(\n", + " 0.15 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(\n", + " 0.2 * pyo.units.g / pyo.units.L\n", + " )\n", + " m.fs.lex.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(\n", + " 0.1 * pyo.units.g / pyo.units.L\n", + " )\n", + "\n", + " return m\n", + "\n", + "\n", + "def initialize_model(m):\n", + " initializer = BlockTriangularizationInitializer()\n", + " initializer.initialize(m.fs.lex)\n", + " return m\n", + "\n", + "\n", + "def main():\n", + " m = build_model()\n", + " m = fix_state_variables(m)\n", + " m = initialize_model(m)\n", + " return m\n", + "\n", + "\n", + "if __name__ == main:\n", + " main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Model Diagnostics using DiagnosticsToolbox\n", + "\n", + "Here, during initialization, we encounter warnings indicating that variables are being set to negative values, which is not expected behavior. These warnings suggest that there may be flaws in the model that require further investigation using the DiagnosticsToolbox from IDAES. A detailed notebook on using `DiagnosticsToolbox` can be found [here](../../diagnostics/degeneracy_hunter_usr.ipynb).\n", + "\n", + "To proceed with investigating these issues, we need to import the DiagnosticsToolbox. We can gain a better understanding of its functionality by running the help function on it. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core.util import DiagnosticsToolbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The help() function provides comprehensive information on the DiagnosticsToolbox and all its supported methods. However, it's essential to focus on the initial steps outlined at the beginning of the docstring to get started effectively.\n", + "\n", + "Here's a breakdown of the steps to start with:\n", + "\n", + "- `Instantiate Model:` Ensure you have an instance of the model with degrees of freedom equal to 0.\n", + "\n", + "- `Create DiagnosticsToolbox Instance:` Next, instantiate a DiagnosticsToolbox object.\n", + "\n", + "- `Provide Model to DiagnosticsToolbox:` Pass the model instance to the DiagnosticsToolbox.\n", + "\n", + "- `Call report_structural_issues() Function:` Finally, call the report_structural_issues() function. This function will highlight any warnings in the model's structure, such as unit inconsistencies or other issues related to variables in the caution section.\n", + "\n", + "By following these steps, you can efficiently utilize the DiagnosticsToolbox to identify and address any structural issues or warnings in your model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING (W1001): Setting Var\n", + "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]' to a value\n", + "`-0.1725` (float) not in domain NonNegativeReals.\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + "WARNING (W1001): Setting Var\n", + "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3]' to a value\n", + "`-0.4` (float) not in domain NonNegativeReals.\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + "WARNING (W1001): Setting Var\n", + "'fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4]' to a value\n", + "`-0.05` (float) not in domain NonNegativeReals.\n", + " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1001\n", + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 21 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 16 (External: 0)\n", + " Free Variables with only lower bounds: 8\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 8 (External: 0)\n", + " Activated Equality Constraints: 16 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 10 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "m = main()\n", + "dt = DiagnosticsToolbox(m)\n", + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although no warnings were reported, it's important to note that there are 3 variables fixed to 0 and 10 unused variables, out of which 4 are fixed. As indicated in the output, the next step is to solve the model. After solving, you should call the report_numerical_issues() function. This function will help identify any numerical issues that may arise during the solution process." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 33\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 14\n", + "\n", + "Total number of variables............................: 16\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 16\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 4.10e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 4.00e+01 4.93e+01 -1.0 4.10e-01 - 9.91e-01 2.41e-02h 1\n", + " 2 0.0000000e+00 4.00e+01 2.03e+05 -1.0 4.00e-01 - 1.00e+00 2.47e-04h 1\n", + " 3r 0.0000000e+00 4.00e+01 1.00e+03 1.6 0.00e+00 - 0.00e+00 3.09e-07R 4\n", + " 4r 0.0000000e+00 4.00e+01 9.88e+04 1.6 3.68e+02 - 9.92e-01 2.29e-03f 1\n", + " 5r 0.0000000e+00 3.60e+01 3.03e+00 1.6 4.01e+00 - 1.00e+00 1.00e+00f 1\n", + " 6r 0.0000000e+00 3.69e+01 1.21e+01 -1.2 9.24e-01 - 9.69e-01 9.78e-01f 1\n", + " 7r 0.0000000e+00 3.70e+01 2.11e-01 -1.9 1.00e-01 - 9.97e-01 1.00e+00f 1\n", + " 8r 0.0000000e+00 3.78e+01 2.03e-02 -4.3 8.71e-01 - 9.71e-01 1.00e+00f 1\n", + " 9r 0.0000000e+00 3.80e+01 2.62e-04 -6.4 1.24e-01 - 9.99e-01 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10r 0.0000000e+00 3.81e+01 5.87e-09 -6.4 1.58e-01 - 1.00e+00 1.00e+00f 1\n", + " 11r 0.0000000e+00 3.91e+01 1.09e-05 -9.0 9.35e-01 - 9.68e-01 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 11\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 5.1393961893966849e-07 5.1393961893966849e-07\n", + "Constraint violation....: 3.9105165554489545e+01 3.9105165554489545e+01\n", + "Complementarity.........: 9.0909090910996620e-10 9.0909090910996620e-10\n", + "Overall NLP error.......: 3.9105165554489545e+01 3.9105165554489545e+01\n", + "\n", + "\n", + "Number of objective function evaluations = 17\n", + "Number of objective gradient evaluations = 5\n", + "Number of equality constraint evaluations = 17\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 14\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: infeasible\n", + " - message from solver: Ipopt 3.13.2\\x3a Converged to a locally infeasible\n", + " point. Problem may be infeasible.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'warning', 'Message': 'Ipopt 3.13.2\\\\x3a Converged to a locally infeasible point. Problem may be infeasible.', 'Termination condition': 'infeasible', 'Id': 200, 'Error rc': 0, 'Time': 0.06552338600158691}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver = pyo.SolverFactory(\"ipopt\")\n", + "solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is probably infeasible, indicating numerical issues with the model. We should call the `report_numerical_issues()` function and check the constraints/variables causing this issue. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Jacobian Condition Number: 7.955E+03\n", + "\n", + "------------------------------------------------------------------------------------\n", + "2 WARNINGS\n", + "\n", + " WARNING: 6 Constraints with large residuals (>1.0E-05)\n", + " WARNING: 5 Variables at or outside bounds (tol=0.0E+00)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "3 Cautions\n", + "\n", + " Caution: 8 Variables with value close to their bounds (abs=1.0E-04, rel=1.0E-04)\n", + " Caution: 5 Variables with value close to zero (tol=1.0E-08)\n", + " Caution: 3 Variables with extreme value (<1.0E-04 or >1.0E+04)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " display_constraints_with_large_residuals()\n", + " display_variables_at_or_outside_bounds()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_numerical_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this scenario, it's observed that the condition number of the Jacobian is high, indicating that the Jacobian is ill-conditioned. Additionally, there are 2 warnings related to constraints with large residuals and variables at or outside the bounds. The cautions mentioned in the output are also related to these warnings.\n", + "\n", + "As suggested, the next steps would be to:\n", + "\n", + "- Call the `display_variables_at_or_outside_bounds()` function to investigate variables at or outside the bounds.\n", + "\n", + "- Call the `display_constraints_with_large_residuals()` function to examine constraints with large residuals.\n", + "\n", + "These steps will help identify the underlying causes of the numerical issues and constraints violations, allowing for further analysis and potential resolution. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following variable(s) have values at or outside their bounds (tol=0.0E+00):\n", + "\n", + " fs.lex.organic_phase.properties_in[0.0].pressure (fixed): value=1.0 bounds=(1, 5)\n", + " fs.lex.organic_phase.properties_out[0.0].pressure (free): value=1 bounds=(1, 5)\n", + " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl] (free): value=0.0 bounds=(0, None)\n", + " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[KNO3] (free): value=0.0 bounds=(0, None)\n", + " fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[CaSO4] (free): value=0.0 bounds=(0, None)\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_variables_at_or_outside_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this scenario, there are a couple of issues to address:\n", + "\n", + "- The pressure variable is fixed to 1, which is its lower bound. This could potentially lead to numerical issues, although it may not affect the model significantly since there is no pressure change in the model. To mitigate this, consider adjusting the lower bound of the pressure variable to avoid having its value at or outside the bounds.\n", + "\n", + "- The more concerning issue is with the `conc_mass_comp` variable attempting to go below 0 in the output. This suggests that there may be constraints involving `conc_mass_comp` in the aqueous phase causing this behavior. To investigate further, it's recommended to call the `display_constraints_with_large_residuals()` function. This will provide insights into whether constraints involving `conc_mass_comp` are contributing to the convergence issue." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "The following constraint(s) have large residuals (>1.0E-05):\n", + "\n", + " fs.lex.material_aq_balance[0.0,NaCl]: 5.49716E-01\n", + " fs.lex.material_aq_balance[0.0,KNO3]: 8.94833E-01\n", + " fs.lex.material_aq_balance[0.0,CaSO4]: 5.48843E-02\n", + " fs.lex.aqueous_phase.material_balances[0.0,NaCl]: 1.67003E+01\n", + " fs.lex.aqueous_phase.material_balances[0.0,KNO3]: 3.91052E+01\n", + " fs.lex.aqueous_phase.material_balances[0.0,CaSO4]: 4.94512E+00\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.display_constraints_with_large_residuals()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected there are convergence issues with the constraints which have `conc_mass_comp` variable in them specifically in the aqueous phase. Now, let us investigate further by printing this constraints and checking the value of each term. Since this is an persistent issue across the components, we can focus on just one of the component to identify the issue. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of material_balances} : Material balances\n", + " Size=4, Index=fs._time*fs.aq_properties.component_list, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " (0.0, 'NaCl') : 0.0 : (fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_in[0.0].flow_vol) - (fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_out[0.0].flow_vol) + fs.lex.aqueous_phase.mass_transfer_term[0.0,Aq,NaCl] : 0.0 : True\n" + ] + } + ], + "source": [ + "m.fs.lex.aqueous_phase.material_balances[0.0, \"NaCl\"].pprint()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of conc_mass_comp} : Component mass concentrations\n", + " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " NaCl : 0 : 0.15 : None : True : True : NonNegativeReals\n", + "flow_vol : Total volumetric flowrate\n", + " Size=1, Index=None, Units=l/h\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " None : 0 : 100.0 : None : True : True : NonNegativeReals\n", + "{Member of conc_mass_comp} : Component mass concentrations\n", + " Size=3, Index=fs.aq_properties.solutes, Units=g/l\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " NaCl : 0 : 0.0 : None : False : False : NonNegativeReals\n", + "flow_vol : Total volumetric flowrate\n", + " Size=1, Index=None, Units=l/h\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " None : 0 : 100.0 : None : False : False : NonNegativeReals\n", + "{Member of mass_transfer_term} : Component material transfer into unit\n", + " Size=4, Index=fs._time*fs.aq_properties._phase_component_set, Units=g/h\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " (0.0, 'Aq', 'NaCl') : None : -31.700284300098897 : None : False : False : Reals\n" + ] + } + ], + "source": [ + "m.fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", + "m.fs.lex.aqueous_phase.properties_in[0.0].flow_vol.pprint()\n", + "m.fs.lex.aqueous_phase.properties_out[0.0].conc_mass_comp[\"NaCl\"].pprint()\n", + "m.fs.lex.aqueous_phase.properties_out[0.0].flow_vol.pprint()\n", + "m.fs.lex.aqueous_phase.mass_transfer_term[0.0, \"Aq\", \"NaCl\"].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems there is a discrepancy between the mass transfer term and the amount of input of NaCl. This can be inferred from the values where the input equals 15g/h and the `mass_transfer_term` equals -31.706g/h.\n", + "\n", + "To further investigate this issue, it's advisable to examine the `material_aq_balance` constraint within the unit model where the `mass_transfer_term` is defined. By printing out this constraint and analyzing its components, you can gain a better understanding of the discrepancy and take appropriate corrective actions." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{Member of material_aq_balance} : Unit level material balances for Aq\n", + " Size=4, Index=fs._time*fs.aq_properties.component_list, Active=True\n", + " Key : Lower : Body : Upper : Active\n", + " (0.0, 'NaCl') : 0.0 : fs.lex.aqueous_phase.mass_transfer_term[0.0,Aq,NaCl] + fs.org_properties.diffusion_factor[NaCl]*(fs.lex.aqueous_phase.properties_in[0.0].conc_mass_comp[NaCl]*fs.lex.aqueous_phase.properties_in[0.0].flow_vol) : 0.0 : True\n" + ] + } + ], + "source": [ + "m.fs.lex.material_aq_balance[0.0, \"NaCl\"].pprint()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the problem can be tracked down easily as there being a typing error while recording the distribution factor. The distribution factor here was wrongly written ignoring its magnitude which should have been 1e-2, but that was missed, thus adjusting the distribution factor parameter we should have this issue resolved. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", + ")\n", + "m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", + ")\n", + "m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", + ")\n", + "\n", + "m.fs.lex.organic_phase.properties_in[0.0].pressure.setlb(0.5)\n", + "m.fs.lex.organic_phase.properties_out[0.0].pressure.setlb(0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the corrective actions, we should check if this has made any structural issues, for this we would call `report_structural_issues()`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "====================================================================================\n", + "Model Statistics\n", + "\n", + " Activated Blocks: 21 (Deactivated: 0)\n", + " Free Variables in Activated Constraints: 16 (External: 0)\n", + " Free Variables with only lower bounds: 8\n", + " Free Variables with only upper bounds: 0\n", + " Free Variables with upper and lower bounds: 0\n", + " Fixed Variables in Activated Constraints: 8 (External: 0)\n", + " Activated Equality Constraints: 16 (Deactivated: 0)\n", + " Activated Inequality Constraints: 0 (Deactivated: 0)\n", + " Activated Objectives: 0 (Deactivated: 0)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "0 WARNINGS\n", + "\n", + " No warnings found!\n", + "\n", + "------------------------------------------------------------------------------------\n", + "1 Cautions\n", + "\n", + " Caution: 10 unused variables (4 fixed)\n", + "\n", + "------------------------------------------------------------------------------------\n", + "Suggested next steps:\n", + "\n", + " Try to initialize/solve your model and then call report_numerical_issues()\n", + "\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "dt.report_structural_issues()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now since there are no warnings we can go ahead and solve the model and see if the results are optimal. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 33\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 14\n", + "\n", + "Total number of variables............................: 16\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 16\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 5.85e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.55e-15 8.41e+00 -1.0 5.85e+01 - 1.05e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.5527136788005009e-15 3.5527136788005009e-15\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.5527136788005009e-15 3.5527136788005009e-15\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.07779264450073242}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.solve(m, tee=True)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 33\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 14\n", - "\n", - "Total number of variables............................: 16\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 0\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 16\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 5.85e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 3.55e-15 8.41e+00 -1.0 5.85e+01 - 1.05e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 1\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 3.5527136788005009e-15 3.5527136788005009e-15\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.5527136788005009e-15 3.5527136788005009e-15\n", - "\n", - "\n", - "Number of objective function evaluations = 2\n", - "Number of objective gradient evaluations = 2\n", - "Number of equality constraint evaluations = 2\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 2\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a good sign that the model solved optimally and a solution was found. \n", + "\n", + "**NOTE:** It is a good practice to run the model through DiagnosticsToolbox regardless of the solver termination status. \n", + "\n", + "The next section we shall focus on testing the unit model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Testing\n", + "\n", + "Testing is a crucial part of model development to ensure that the model works as expected, and remains reliable. Here's an overview of why we conduct testing:\n", + "\n", + "1. `Verify Correctness`: Testing ensures that the model works as expected and meets the specified requirements. \n", + "2. `Detect Bugs and Issues`: Testing helps in identifying bugs, errors, or unexpected behaviors in the code or model, allowing for timely fixes.\n", + "3. `Ensure Reliability`: Testing improves the reliability and robustness of the software, reducing the risk of failures when the user uses it.\n", + "4. `Support Changes`: Tests provide confidence when making changes or adding new features, ensuring that existing functionalities are not affected and work as they should.\n", + "\n", + "There are typically 3 types of tests:\n", + "\n", + "1. `Unit tests`: Test runs quickly (under 2 seconds) and has no network/system dependencies. Uses only libraries installed by default with the software\n", + "2. `Component test`: Test may run more slowly (under 10 seconds, or so), e.g. it may run a solver or create a bunch of files. Like unit tests, it still shouldn't depend on special libraries or dependencies.\n", + "3. `Integration test`: Test may take a long time to run, and may have complex dependencies.\n", + "\n", + "The expectation is that unit tests should be run by developers rather frequently, component tests should be run by the continuous integration system before running code, and integration tests are run across the codebase regularly, but infrequently (e.g. daily).\n", + "\n", + "\n", + "As a developer, testing is a crucial aspect of ensuring the reliability and correctness of the unit model. The testing process involves both Unit tests and Component tests, and pytest is used as the testing framework. A typical test is marked with @pytest.mark.level, where the level indicates the depth or specificity of the testing. This is written in a file usually named as test_*.py or *_test.py. The test files have functions written in them with the appropriate level of test being conducted. \n", + "\n", + "For more detailed information on testing methodologies and procedures, developers are encouraged to refer to [this resource](https://idaes-pse.readthedocs.io/en/stable/reference_guides/developer/testing.html). The resource provides comprehensive guidance on the testing process and ensures that the unit model meets the required standards and functionality.\n", + "\n", + "## 5.1 Property package\n", + "### Unit Tests\n", + "\n", + "When writing tests for the Aqueous property phase package, it's essential to focus on key aspects to ensure the correctness and robustness of the implementation. Here are the areas to cover in the unit tests:\n", + "\n", + "1. Number of Config Dictionaries: Verify that the property phase package has the expected number of configuration dictionaries.\n", + "\n", + "2. State Block Class Name: Confirm that the correct state block class is associated with the Aqueous property phase package.\n", + "\n", + "3. Number of Phases: Check that the Aqueous property phase package defines the expected number of phases.\n", + "\n", + "4. Components in the Phase and Physical Parameter Values: Test that the components present in the Aqueous phase match the anticipated list. Additionally, validate that the physical parameter values (such as density, viscosity, etc.) are correctly defined.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import ConcreteModel, Param, value, Var\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core import MaterialBalanceType, EnergyBalanceType\n", + "\n", + "from liquid_extraction.organic_property import OrgPhase\n", + "from liquid_extraction.aqueous_property import AqPhase\n", + "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "\n", + "class TestParamBlock(object):\n", + " @pytest.fixture(scope=\"class\")\n", + " def model(self):\n", + " model = ConcreteModel()\n", + " model.params = AqPhase()\n", + " return model\n", + "\n", + " @pytest.mark.unit\n", + " def test_config(self, model):\n", + " assert len(model.params.config) == 1\n", + "\n", + " @pytest.mark.unit\n", + " def test_build(self, model):\n", + " assert len(model.params.phase_list) == 1\n", + " for i in model.params.phase_list:\n", + " assert i == \"Aq\"\n", + "\n", + " assert len(model.params.component_list) == 4\n", + " for i in model.params.component_list:\n", + " assert i in [\"H2O\", \"NaCl\", \"KNO3\", \"CaSO4\"]\n", + "\n", + " assert isinstance(model.params.cp_mass, Param)\n", + " assert value(model.params.cp_mass) == 4182\n", + "\n", + " assert isinstance(model.params.dens_mass, Param)\n", + " assert value(model.params.dens_mass) == 997\n", + "\n", + " assert isinstance(model.params.temperature_ref, Param)\n", + " assert value(model.params.temperature_ref) == 298.15" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next set of unit tests focuses on testing the build function in the state block. Here are the key aspects to cover in these tests:\n", + "\n", + "1. Existence and Initialized Values of State Variables: Verify that the state variables are correctly defined and initialized within the state block. This ensures that the state block is properly constructed and ready for initialization.\n", + "\n", + "2. Initialization Function Test: Check that state variables are not fixed before initialization and are released after initialization. This test ensures that the initialization process occurs as expected and that the state variables are appropriately managed throughout.\n", + "\n", + "These unit tests provide comprehensive coverage for validating the functionality and behavior of the state block in the aqueous property phase package. Similar tests can be written for the organic property package to ensure consistency and reliability across both packages." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "class TestStateBlock(object):\n", + " @pytest.fixture(scope=\"class\")\n", + " def model(self):\n", + " model = ConcreteModel()\n", + " model.params = AqPhase()\n", + "\n", + " model.props = model.params.build_state_block([1])\n", + "\n", + " return model\n", + "\n", + " @pytest.mark.unit\n", + " def test_build(self, model):\n", + " assert isinstance(model.props[1].flow_vol, Var)\n", + " assert value(model.props[1].flow_vol) == 1\n", + "\n", + " assert isinstance(model.props[1].temperature, Var)\n", + " assert value(model.props[1].temperature) == 300\n", + "\n", + " assert isinstance(model.props[1].conc_mass_comp, Var)\n", + " assert len(model.props[1].conc_mass_comp) == 3\n", + "\n", + " @pytest.mark.unit\n", + " def test_initialize(self, model):\n", + " assert not model.props[1].flow_vol.fixed\n", + " assert not model.props[1].temperature.fixed\n", + " assert not model.props[1].pressure.fixed\n", + " for i in model.props[1].conc_mass_comp:\n", + " assert not model.props[1].conc_mass_comp[i].fixed\n", + "\n", + " model.props.initialize(hold_state=False, outlvl=1)\n", + "\n", + " assert not model.props[1].flow_vol.fixed\n", + " assert not model.props[1].temperature.fixed\n", + " assert not model.props[1].pressure.fixed\n", + " for i in model.props[1].conc_mass_comp:\n", + " assert not model.props[1].conc_mass_comp[i].fixed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Component Tests\n", + "In the component test, we aim to ensure unit consistency across the entire property package. Unlike unit tests that focus on individual functions, component tests assess the coherence and consistency of the entire package. Here's what the component test will entail:\n", + "\n", + "Unit Consistency Check: Verify that all units used within the property package are consistent throughout. This involves checking that all parameters, variables, and equations within the package adhere to the same unit system, ensuring compatibility.\n", + "\n", + "By conducting a comprehensive component test, we can ensure that the property package functions as a cohesive unit, maintaining consistency and reliability across its entirety. This concludes our tests on the property package. Next we shall test the unit model. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "@pytest.mark.component\n", + "def check_units(model):\n", + " model = ConcreteModel()\n", + " model.params = AqPhase()\n", + " assert_units_consistent(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.2 Unit Model\n", + "### Unit tests\n", + "Unit tests for the unit model encompass verifying the configuration arguments and the build function, similar to the approach taken for the property package. When testing the config arguments, we ensure that the correct number of arguments is provided and then match each argument with the expected one. This ensures that the unit model is properly configured and ready to operate as intended." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "import idaes.models.unit_models\n", + "from idaes.core.solvers import get_solver\n", + "import idaes.logger as idaeslog\n", + "\n", + "\n", + "from pyomo.environ import value, check_optimal_termination, units\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.util.model_statistics import (\n", + " number_variables,\n", + " number_total_constraints,\n", + ")\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.initialization import (\n", + " SingleControlVolumeUnitInitializer,\n", + ")\n", + "\n", + "solver = get_solver()\n", + "\n", + "\n", + "@pytest.mark.unit\n", + "def test_config():\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.unit = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + "\n", + " # Check unit config arguments\n", + " assert len(m.fs.unit.config) == 9\n", + "\n", + " # Check for config arguments\n", + " assert m.fs.unit.config.material_balance_type == MaterialBalanceType.useDefault\n", + " assert not m.fs.unit.config.has_pressure_change\n", + " assert not m.fs.unit.config.has_phase_equilibrium\n", + " assert m.fs.unit.config.organic_property_package is m.fs.org_properties\n", + " assert m.fs.unit.config.aqueous_property_package is m.fs.aq_properties\n", + "\n", + " # Check for unit initializer\n", + " assert m.fs.unit.default_initializer is SingleControlVolumeUnitInitializer" + ] }, { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 16, 'Number of variables': 16, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.07779264450073242}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In testing the build function, we verify whether the number of variables aligns with the intended values and also check for the existence of desired constraints within the unit model. This ensures that the unit model is constructed accurately and includes all the necessary variables and constraints required for its proper functioning." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "class TestBuild(object):\n", + " @pytest.fixture(scope=\"class\")\n", + " def model(self):\n", + " m = ConcreteModel()\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.unit = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + "\n", + " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.l / units.h)\n", + " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.l)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.l)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.l)\n", + "\n", + " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.l / units.h)\n", + " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.l)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.l)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.l)\n", + "\n", + " return m\n", + "\n", + " @pytest.mark.build\n", + " @pytest.mark.unit\n", + " def test_build(self, model):\n", + "\n", + " assert hasattr(model.fs.unit, \"aqueous_inlet\")\n", + " assert len(model.fs.unit.aqueous_inlet.vars) == 4\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.aqueous_inlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"organic_inlet\")\n", + " assert len(model.fs.unit.organic_inlet.vars) == 4\n", + " assert hasattr(model.fs.unit.organic_inlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.organic_inlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.organic_inlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.organic_inlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"aqueous_outlet\")\n", + " assert len(model.fs.unit.aqueous_outlet.vars) == 4\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.aqueous_outlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"organic_outlet\")\n", + " assert len(model.fs.unit.organic_outlet.vars) == 4\n", + " assert hasattr(model.fs.unit.organic_outlet, \"flow_vol\")\n", + " assert hasattr(model.fs.unit.organic_outlet, \"conc_mass_comp\")\n", + " assert hasattr(model.fs.unit.organic_outlet, \"temperature\")\n", + " assert hasattr(model.fs.unit.organic_outlet, \"pressure\")\n", + "\n", + " assert hasattr(model.fs.unit, \"material_aq_balance\")\n", + " assert hasattr(model.fs.unit, \"material_org_balance\")\n", + "\n", + " assert number_variables(model) == 34\n", + " assert number_total_constraints(model) == 16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Component tests\n", + "\n", + "During the component tests, we evaluate the performance of the unit model when integrated with the property package. This evaluation process typically involves several steps:\n", + "\n", + "1. Unit Consistency Check: Verify that the unit model maintains consistency in its units throughout the model. This ensures that all variables and constraints within the model adhere to the same unit system, guaranteeing compatibility.\n", + "\n", + "2. Termination Condition Verification: This involves checking whether the model terminates optimally with the given inlet conditions.\n", + "\n", + "3. Variable Value Assessment: Check the values of outlet variables against the expected values. To account for the numerical tolerance of the solvers, the values are compared using the approx function with a relative tolerance.\n", + "\n", + "4. Input Variable Stability Test: Verify that input variables, which should remain fixed during model operation, are not inadvertently unfixed or altered.\n", + "\n", + "5. Structural Issues: Verify that there are no structural issues with the model. \n", + "\n", + "By performing these checks, we conclude the testing for the unit model. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "class TestFlowsheet:\n", + " @pytest.fixture\n", + " def model(self):\n", + " m = ConcreteModel()\n", + " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", + " m.fs.org_properties = OrgPhase()\n", + " m.fs.aq_properties = AqPhase()\n", + "\n", + " m.fs.unit = LiqExtraction(\n", + " dynamic=False,\n", + " has_pressure_change=False,\n", + " organic_property_package=m.fs.org_properties,\n", + " aqueous_property_package=m.fs.aq_properties,\n", + " )\n", + " m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", + " )\n", + " m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", + " )\n", + " m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", + " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", + " )\n", + "\n", + " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.ml / units.min)\n", + " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.kg)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.kg)\n", + " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.kg)\n", + "\n", + " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.ml / units.min)\n", + " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", + " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.kg)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.kg)\n", + " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.kg)\n", + "\n", + " return m\n", + "\n", + " @pytest.mark.component\n", + " def test_unit_model(self, model):\n", + " assert_units_consistent(model)\n", + " solver = get_solver()\n", + " results = solver.solve(model, tee=False)\n", + "\n", + " # Check for optimal termination\n", + " assert check_optimal_termination(results)\n", + "\n", + " # Checking for outlet flows\n", + " assert value(model.fs.unit.organic_outlet.flow_vol[0]) == pytest.approx(\n", + " 80.0, rel=1e-5\n", + " )\n", + " assert value(model.fs.unit.aqueous_outlet.flow_vol[0]) == pytest.approx(\n", + " 10.0, rel=1e-5\n", + " )\n", + "\n", + " # Checking for outlet mass_comp\n", + " assert value(\n", + " model.fs.unit.organic_outlet.conc_mass_comp[0, \"CaSO4\"]\n", + " ) == pytest.approx(0.000187499, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.organic_outlet.conc_mass_comp[0, \"KNO3\"]\n", + " ) == pytest.approx(0.000749999, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.organic_outlet.conc_mass_comp[0, \"NaCl\"]\n", + " ) == pytest.approx(0.000403124, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"CaSO4\"]\n", + " ) == pytest.approx(0.0985, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"KNO3\"]\n", + " ) == pytest.approx(0.194, rel=1e-5)\n", + " assert value(\n", + " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"NaCl\"]\n", + " ) == pytest.approx(0.146775, rel=1e-5)\n", + "\n", + " # Checking for outlet temperature\n", + " assert value(model.fs.unit.organic_outlet.temperature[0]) == pytest.approx(\n", + " 300, rel=1e-5\n", + " )\n", + " assert value(model.fs.unit.aqueous_outlet.temperature[0]) == pytest.approx(\n", + " 300, rel=1e-5\n", + " )\n", + "\n", + " # Checking for outlet pressure\n", + " assert value(model.fs.unit.organic_outlet.pressure[0]) == pytest.approx(\n", + " 1, rel=1e-5\n", + " )\n", + " assert value(model.fs.unit.aqueous_outlet.pressure[0]) == pytest.approx(\n", + " 1, rel=1e-5\n", + " )\n", + "\n", + " # Fixed state variables\n", + " assert model.fs.unit.organic_inlet.flow_vol[0].fixed\n", + " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", + " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", + " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", + " assert model.fs.unit.organic_inlet.temperature[0].fixed\n", + " assert model.fs.unit.organic_inlet.pressure[0].fixed\n", + "\n", + " assert model.fs.unit.aqueous_inlet.flow_vol[0].fixed\n", + " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", + " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", + " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", + " assert model.fs.unit.aqueous_inlet.temperature[0].fixed\n", + " assert model.fs.unit.aqueous_inlet.pressure[0].fixed\n", + "\n", + " @pytest.mark.component\n", + " def test_structural_issues(self, model):\n", + " dt = DiagnosticsToolbox(model)\n", + " dt.assert_no_structural_warnings()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we have covered the comprehensive process of creating a custom unit model from scratch. Let's recap the key steps we have undertaken:\n", + "\n", + "- Developing property package\n", + "- Constructing the unit model \n", + "- Creating a Flowsheet\n", + "- Debugging the model using DiagnosticsToolbox\n", + "- Writing tests for the unit model\n", + "\n", + "By following the aforementioned procedure, one can create their own custom unit model. This concludes the tutorial on creating a custom unit model. " ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a good sign that the model solved optimally and a solution was found. \n", - "\n", - "**NOTE:** It is a good practice to run the model through DiagnosticsToolbox regardless of the solver termination status. \n", - "\n", - "The next section we shall focus on testing the unit model. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Testing\n", - "\n", - "Testing is a crucial part of model development to ensure that the model works as expected, and remains reliable. Here's an overview of why we conduct testing:\n", - "\n", - "1. `Verify Correctness`: Testing ensure that the model works as expected and meets the specified requirements. \n", - "2. `Detect Bugs and Issues`: Testing helps in identifying bugs, errors, or unexpected behaviors in the code or model, allowing for timely fixes.\n", - "3. `Ensure Reliability`: Testing improves the reliability and robustness of the software, reducing the risk of failures when the user uses it.\n", - "4. `Support Changes`: Tests provide confidence when making changes or adding new features, ensuring that existing functionalities are not affected and work as they should.\n", - "\n", - "There are typically 3 types of tests:\n", - "\n", - "1. `Unit tests`: Test runs quickly (under 2 seconds) and has no network/system dependencies. Uses only libraries installed by default with the software\n", - "2. `Component test`: Test may run more slowly (under 10 seconds, or so), e.g. it may run a solver or create a bunch of files. Like unit tests, it still shouldn't depend on special libraries or dependencies.\n", - "3. `Integration test`: Test may take a long time to run, and may have complex dependencies.\n", - "\n", - "The expectation is that unit tests should be run by developers rather frequently, component tests should be run by the continuous integration system before running code, and integration tests are run across the codebase regularly, but infrequently (e.g. daily).\n", - "\n", - "\n", - "As a developer, testing is a crucial aspect of ensuring the reliability and correctness of the unit model. The testing process involves both Unit tests and Component tests, and pytest is used as the testing framework. A typical test is marked with @pytest.mark.level, where the level indicates the depth or specificity of the testing. This is written in a file usually named as test_*.py or *_test.py. The test files have functions written in them with the appropriate level of test being conducted. \n", - "\n", - "For more detailed information on testing methodologies and procedures, developers are encouraged to refer to [this resource](https://idaes-pse.readthedocs.io/en/stable/reference_guides/developer/testing.html). The resource provides comprehensive guidance on the testing process and ensures that the unit model meets the required standards and functionality.\n", - "\n", - "## 5.1 Property package\n", - "### Unit Tests\n", - "\n", - "When writing tests for the Aqueous property phase package, it's essential to focus on key aspects to ensure the correctness and robustness of the implementation. Here are the areas to cover in the unit tests:\n", - "\n", - "1. Number of Config Dictionaries: Verify that the property phase package has the expected number of configuration dictionaries.\n", - "\n", - "2. State Block Class Name: Confirm that the correct state block class is associated with the Aqueous property phase package.\n", - "\n", - "3. Number of Phases: Check that the Aqueous property phase package defines the expected number of phases.\n", - "\n", - "4. Components in the Phase and Physical Parameter Values: Test that the components present in the Aqueous phase match the anticipated list. Additionally, validate that the physical parameter values (such as density, viscosity, etc.) are correctly defined.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import ConcreteModel, Param, value, Var\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core import MaterialBalanceType, EnergyBalanceType\n", - "\n", - "from liquid_extraction.organic_property import OrgPhase\n", - "from liquid_extraction.aqueous_property import AqPhase\n", - "from liquid_extraction.liquid_liquid_extractor import LiqExtraction\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "\n", - "class TestParamBlock(object):\n", - " @pytest.fixture(scope=\"class\")\n", - " def model(self):\n", - " model = ConcreteModel()\n", - " model.params = AqPhase()\n", - " return model\n", - "\n", - " @pytest.mark.unit\n", - " def test_config(self, model):\n", - " assert len(model.params.config) == 1\n", - "\n", - " @pytest.mark.unit\n", - " def test_build(self, model):\n", - " assert len(model.params.phase_list) == 1\n", - " for i in model.params.phase_list:\n", - " assert i == \"Aq\"\n", - "\n", - " assert len(model.params.component_list) == 4\n", - " for i in model.params.component_list:\n", - " assert i in [\"H2O\", \"NaCl\", \"KNO3\", \"CaSO4\"]\n", - "\n", - " assert isinstance(model.params.cp_mass, Param)\n", - " assert value(model.params.cp_mass) == 4182\n", - "\n", - " assert isinstance(model.params.dens_mass, Param)\n", - " assert value(model.params.dens_mass) == 997\n", - "\n", - " assert isinstance(model.params.temperature_ref, Param)\n", - " assert value(model.params.temperature_ref) == 298.15" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next set of unit tests focuses on testing the build function in the state block. Here are the key aspects to cover in these tests:\n", - "\n", - "1. Existence and Initialized Values of State Variables: Verify that the state variables are correctly defined and initialized within the state block. This ensures that the state block is properly constructed and ready for initialization.\n", - "\n", - "2. Initialization Function Test: Check that state variables are not fixed before initialization and are released after initialization. This test ensures that the initialization process occurs as expected and that the state variables are appropriately managed throughout.\n", - "\n", - "These unit tests provide comprehensive coverage for validating the functionality and behavior of the state block in the Aqueous property phase package. Similar tests can be written for the organic property package to ensure consistency and reliability across both packages." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "class TestStateBlock(object):\n", - " @pytest.fixture(scope=\"class\")\n", - " def model(self):\n", - " model = ConcreteModel()\n", - " model.params = AqPhase()\n", - "\n", - " model.props = model.params.build_state_block([1])\n", - "\n", - " return model\n", - "\n", - " @pytest.mark.unit\n", - " def test_build(self, model):\n", - " assert isinstance(model.props[1].flow_vol, Var)\n", - " assert value(model.props[1].flow_vol) == 1\n", - "\n", - " assert isinstance(model.props[1].temperature, Var)\n", - " assert value(model.props[1].temperature) == 300\n", - "\n", - " assert isinstance(model.props[1].conc_mass_comp, Var)\n", - " assert len(model.props[1].conc_mass_comp) == 3\n", - "\n", - " @pytest.mark.unit\n", - " def test_initialize(self, model):\n", - " assert not model.props[1].flow_vol.fixed\n", - " assert not model.props[1].temperature.fixed\n", - " assert not model.props[1].pressure.fixed\n", - " for i in model.props[1].conc_mass_comp:\n", - " assert not model.props[1].conc_mass_comp[i].fixed\n", - "\n", - " model.props.initialize(hold_state=False, outlvl=1)\n", - "\n", - " assert not model.props[1].flow_vol.fixed\n", - " assert not model.props[1].temperature.fixed\n", - " assert not model.props[1].pressure.fixed\n", - " for i in model.props[1].conc_mass_comp:\n", - " assert not model.props[1].conc_mass_comp[i].fixed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Component Tests\n", - "In the component test, we aim to ensure unit consistency across the entire property package. Unlike unit tests that focus on individual functions, component tests assess the coherence and consistency of the entire package. Here's what the component test will entail:\n", - "\n", - "Unit Consistency Check: Verify that all units used within the property package are consistent throughout. This involves checking that all parameters, variables, and equations within the package adhere to the same unit system, ensuring compatibility.\n", - "\n", - "By conducting a comprehensive component test, we can ensure that the property package functions as a cohesive unit, maintaining consistency and reliability across its entirety. This concludes our tests on the property package. Next we shall test the unit model. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "@pytest.mark.component\n", - "def check_units(model):\n", - " model = ConcreteModel()\n", - " model.params = AqPhase()\n", - " assert_units_consistent(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5.2 Unit Model\n", - "### Unit tests\n", - "Unit tests for the unit model encompass verifying the configuration arguments and the build function, similar to the approach taken for the property package. When testing the config arguments, we ensure that the correct number of arguments is provided and then match each argument with the expected one. This ensures that the unit model is properly configured and ready to operate as intended." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "import idaes.core\n", - "import idaes.models.unit_models\n", - "from idaes.core.solvers import get_solver\n", - "import idaes.logger as idaeslog\n", - "\n", - "\n", - "from pyomo.environ import value, check_optimal_termination, units\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "from idaes.core.util.model_statistics import (\n", - " number_variables,\n", - " number_total_constraints,\n", - ")\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.initialization import (\n", - " SingleControlVolumeUnitInitializer,\n", - ")\n", - "\n", - "solver = get_solver()\n", - "\n", - "\n", - "@pytest.mark.unit\n", - "def test_config():\n", - " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.unit = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - "\n", - " # Check unit config arguments\n", - " assert len(m.fs.unit.config) == 9\n", - "\n", - " # Check for config arguments\n", - " assert m.fs.unit.config.material_balance_type == MaterialBalanceType.useDefault\n", - " assert not m.fs.unit.config.has_pressure_change\n", - " assert not m.fs.unit.config.has_phase_equilibrium\n", - " assert m.fs.unit.config.organic_property_package is m.fs.org_properties\n", - " assert m.fs.unit.config.aqueous_property_package is m.fs.aq_properties\n", - "\n", - " # Check for unit initializer\n", - " assert m.fs.unit.default_initializer is SingleControlVolumeUnitInitializer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In testing the build function, we verify whether the number of variables aligns with the intended values and also check for the existence of desired constraints within the unit model. This ensures that the unit model is constructed accurately and includes all the necessary variables and constraints required for its proper functioning." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "class TestBuild(object):\n", - " @pytest.fixture(scope=\"class\")\n", - " def model(self):\n", - " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.unit = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - "\n", - " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.l / units.h)\n", - " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.l)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.l)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.l)\n", - "\n", - " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.l / units.h)\n", - " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.l)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.l)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.l)\n", - "\n", - " return m\n", - "\n", - " @pytest.mark.build\n", - " @pytest.mark.unit\n", - " def test_build(self, model):\n", - "\n", - " assert hasattr(model.fs.unit, \"aqueous_inlet\")\n", - " assert len(model.fs.unit.aqueous_inlet.vars) == 4\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.aqueous_inlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"organic_inlet\")\n", - " assert len(model.fs.unit.organic_inlet.vars) == 4\n", - " assert hasattr(model.fs.unit.organic_inlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.organic_inlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.organic_inlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.organic_inlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"aqueous_outlet\")\n", - " assert len(model.fs.unit.aqueous_outlet.vars) == 4\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.aqueous_outlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"organic_outlet\")\n", - " assert len(model.fs.unit.organic_outlet.vars) == 4\n", - " assert hasattr(model.fs.unit.organic_outlet, \"flow_vol\")\n", - " assert hasattr(model.fs.unit.organic_outlet, \"conc_mass_comp\")\n", - " assert hasattr(model.fs.unit.organic_outlet, \"temperature\")\n", - " assert hasattr(model.fs.unit.organic_outlet, \"pressure\")\n", - "\n", - " assert hasattr(model.fs.unit, \"material_aq_balance\")\n", - " assert hasattr(model.fs.unit, \"material_org_balance\")\n", - "\n", - " assert number_variables(model) == 34\n", - " assert number_total_constraints(model) == 16" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Component tests\n", - "\n", - "During the component tests, we evaluate the performance of the unit model when integrated with the property package. This evaluation process typically involves several steps:\n", - "\n", - "1. Unit Consistency Check: Verify that the unit model maintains consistency in its units throughout the model. This ensures that all variables and constraints within the model adhere to the same unit system, guaranteeing compatibility.\n", - "\n", - "2. Termination Condition Verification: This involves checking whether the model terminates optimally with the given inlet conditions.\n", - "\n", - "3. Variable Value Assessment: Check the values of outlet variables against the expected values. To account for the numerical tolerance of the solvers, the values are compared using the approx function with a relative tolerance.\n", - "\n", - "4. Input Variable Stability Test: Verify that input variables, which should remain fixed during model operation, are not inadvertently unfixed or altered.\n", - "\n", - "5. Structural Issues: Verify that there are no structural issues with the model. \n", - "\n", - "By performing these checks, we conclude the testing for the unit model. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "class TestFlowsheet:\n", - " @pytest.fixture\n", - " def model(self):\n", - " m = ConcreteModel()\n", - " m.fs = idaes.core.FlowsheetBlock(dynamic=False)\n", - " m.fs.org_properties = OrgPhase()\n", - " m.fs.aq_properties = AqPhase()\n", - "\n", - " m.fs.unit = LiqExtraction(\n", - " dynamic=False,\n", - " has_pressure_change=False,\n", - " organic_property_package=m.fs.org_properties,\n", - " aqueous_property_package=m.fs.aq_properties,\n", - " )\n", - " m.fs.org_properties.diffusion_factor[\"NaCl\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"NaCl\"] / 100\n", - " )\n", - " m.fs.org_properties.diffusion_factor[\"KNO3\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"KNO3\"] / 100\n", - " )\n", - " m.fs.org_properties.diffusion_factor[\"CaSO4\"] = (\n", - " m.fs.org_properties.diffusion_factor[\"CaSO4\"] / 100\n", - " )\n", - "\n", - " m.fs.unit.organic_inlet.flow_vol.fix(80 * units.ml / units.min)\n", - " m.fs.unit.organic_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.organic_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fix(0 * units.g / units.kg)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fix(0 * units.g / units.kg)\n", - " m.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0 * units.g / units.kg)\n", - "\n", - " m.fs.unit.aqueous_inlet.flow_vol.fix(10 * units.ml / units.min)\n", - " m.fs.unit.aqueous_inlet.temperature.fix(300 * units.K)\n", - " m.fs.unit.aqueous_inlet.pressure.fix(1 * units.atm)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fix(0.15 * units.g / units.kg)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fix(0.2 * units.g / units.kg)\n", - " m.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fix(0.1 * units.g / units.kg)\n", - "\n", - " return m\n", - "\n", - " @pytest.mark.component\n", - " def test_unit_model(self, model):\n", - " assert_units_consistent(model)\n", - " solver = get_solver()\n", - " results = solver.solve(model, tee=False)\n", - "\n", - " # Check for optimal termination\n", - " assert check_optimal_termination(results)\n", - "\n", - " # Checking for outlet flows\n", - " assert value(model.fs.unit.organic_outlet.flow_vol[0]) == pytest.approx(\n", - " 80.0, rel=1e-5\n", - " )\n", - " assert value(model.fs.unit.aqueous_outlet.flow_vol[0]) == pytest.approx(\n", - " 10.0, rel=1e-5\n", - " )\n", - "\n", - " # Checking for outlet mass_comp\n", - " assert value(\n", - " model.fs.unit.organic_outlet.conc_mass_comp[0, \"CaSO4\"]\n", - " ) == pytest.approx(0.000187499, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.organic_outlet.conc_mass_comp[0, \"KNO3\"]\n", - " ) == pytest.approx(0.000749999, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.organic_outlet.conc_mass_comp[0, \"NaCl\"]\n", - " ) == pytest.approx(0.000403124, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"CaSO4\"]\n", - " ) == pytest.approx(0.0985, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"KNO3\"]\n", - " ) == pytest.approx(0.194, rel=1e-5)\n", - " assert value(\n", - " model.fs.unit.aqueous_outlet.conc_mass_comp[0, \"NaCl\"]\n", - " ) == pytest.approx(0.146775, rel=1e-5)\n", - "\n", - " # Checking for outlet temperature\n", - " assert value(model.fs.unit.organic_outlet.temperature[0]) == pytest.approx(\n", - " 300, rel=1e-5\n", - " )\n", - " assert value(model.fs.unit.aqueous_outlet.temperature[0]) == pytest.approx(\n", - " 300, rel=1e-5\n", - " )\n", - "\n", - " # Checking for outlet pressure\n", - " assert value(model.fs.unit.organic_outlet.pressure[0]) == pytest.approx(\n", - " 1, rel=1e-5\n", - " )\n", - " assert value(model.fs.unit.aqueous_outlet.pressure[0]) == pytest.approx(\n", - " 1, rel=1e-5\n", - " )\n", - "\n", - " # Fixed state variables\n", - " assert model.fs.unit.organic_inlet.flow_vol[0].fixed\n", - " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", - " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", - " assert model.fs.unit.organic_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", - " assert model.fs.unit.organic_inlet.temperature[0].fixed\n", - " assert model.fs.unit.organic_inlet.pressure[0].fixed\n", - "\n", - " assert model.fs.unit.aqueous_inlet.flow_vol[0].fixed\n", - " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"NaCl\"].fixed\n", - " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"KNO3\"].fixed\n", - " assert model.fs.unit.aqueous_inlet.conc_mass_comp[0, \"CaSO4\"].fixed\n", - " assert model.fs.unit.aqueous_inlet.temperature[0].fixed\n", - " assert model.fs.unit.aqueous_inlet.pressure[0].fixed\n", - "\n", - " @pytest.mark.component\n", - " def test_structural_issues(self, model):\n", - " dt = DiagnosticsToolbox(model)\n", - " dt.assert_no_structural_warnings()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, we have covered the comprehensive process of creating a custom unit model from scratch. Let's recap the key steps we have undertaken:\n", - "\n", - "- Developing property package\n", - "- Constructing the unit model \n", - "- Creating a Flowsheet\n", - "- Debugging the model using DiagnosticsToolbox\n", - "- Writing tests for the unit model\n", - "\n", - "By following the aforementioned procedure, one can create their own custom unit model. This would conclude the tutorial on creating custom unit model. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "idaes-pse", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor.ipynb index 47f203dd..295a4a3b 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_doc.ipynb index 88f645ec..bc60cce2 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -391,9 +392,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_test.ipynb index 2aa3d42f..c6cdf932 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_test.ipynb @@ -1,347 +1,348 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Custom Compressor Unit Model\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01\n", - "\n", - "To demonstrate creation of a new unit model, we will create a constant-heat-capacity ideal-gas isentropic compressor. This will be a simple textbook model. We will utilize the mass and energy balances provided by IDAES control volumes, but we will write our own isentropic constraint based off of equations 7.18 and 7.23 from \"Introduction to Chemical Engineering Thermodynamics\" by J.M. Smith, H.C. Van Ness, and M.M. Abbott. \n", - "\n", - "The outlet temperature of an ideal gas undergoing isentropic compression is given by \n", - "$$\n", - "t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} \\left(\\frac{p_{out}}{p_{in}}\\right)^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", - "$$\n", - "where $p$ is pressure, $t$ is temperature, and $\\gamma$ is the ratio of constant pressure heat capacity to constant volume heat capacity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will begin with relevant imports. We will need\n", - "\n", - "- Pyomo for writing our energy balance constraints\n", - "- ConfigBlocks for specifying options for our compressor\n", - "- ControlVolume0DBlocks for creating the appropriate state blocks for the inlet and outlet and for defining mas balances\n", - "- IdealParameterBlock which provides a simple ideal-gas property package.\n", - "- A few other helpful functions and enums from IDAES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pe\n", - "from pyomo.common.config import ConfigBlock, ConfigValue, In\n", - "from idaes.core import (\n", - " ControlVolume0DBlock,\n", - " declare_process_block_class,\n", - " EnergyBalanceType,\n", - " MomentumBalanceType,\n", - " MaterialBalanceType,\n", - " UnitModelBlockData,\n", - " useDefault,\n", - " FlowsheetBlock,\n", - ")\n", - "from idaes.core.util.config import is_physical_parameter_block\n", - "from idaes_examples.mod.methanol.methanol_param_VLE import PhysicalParameterBlock\n", - "from idaes.core.util.misc import add_object_reference" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can write a function to create a control volume for our compressor. The control volume will define the inlet and outlet streams along with the appropriate state variables (specified by the property package). We will also use the control volume to create mass and energy balance constraints. \n", - "\n", - "Our function will take the compressor unit model object, the name of the control volume, and configuration options as arguments. Our compressor will only support steady-state models, so we will first ensure that ``dynamic`` and ``has_holdup`` are both ``False``.\n", - "\n", - "Next, we will create a 0D control volume. We are using a 0D control volume because our model does not depend on space. We then\n", - "\n", - "1. Attach the control volume to the compressor\n", - "2. Create the appropriate state blocks with the control volume (for the inlet and outlet streams)\n", - "3. Use the control volume to add mass balance constraints\n", - "4. Use the control volume to add energy balance constraints" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def make_control_volume(unit, name, config):\n", - " if config.dynamic is not False:\n", - " raise ValueError(\"IdealGasIsentropcCompressor does not support dynamics\")\n", - " if config.has_holdup is not False:\n", - " raise ValueError(\"IdealGasIsentropcCompressor does not support holdup\")\n", - "\n", - " control_volume = ControlVolume0DBlock(\n", - " property_package=config.property_package,\n", - " property_package_args=config.property_package_args,\n", - " )\n", - "\n", - " setattr(unit, name, control_volume)\n", - "\n", - " control_volume.add_state_blocks(has_phase_equilibrium=config.has_phase_equilibrium)\n", - " control_volume.add_material_balances(\n", - " balance_type=config.material_balance_type,\n", - " has_phase_equilibrium=config.has_phase_equilibrium,\n", - " )\n", - " control_volume.add_total_enthalpy_balances(\n", - " has_heat_of_reaction=False, has_heat_transfer=False, has_work_transfer=True\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will write a function to add constraints to specify that the compressor is isentropic. \n", - "1. Create a ``pressure_ratio`` variable to represent $p_{out}/p_{in}$. The lower bound is $1$, because we only want to allow compression (and not expansion).\n", - "2. Create a ``ConstraintList`` to hold the constraints.\n", - "3. Add the ``ConstraintList`` to the compressor\n", - "4. Create the local variables ``inlet`` and ``outlet`` to reference the inlet and outlet state blocks.\n", - "5. Add a constraint relating the inlet pressure, outlet pressure, and pressure ratio variables:\n", - "\\begin{align}\n", - "p_{in} p_{ratio} = p_{out}\n", - "\\end{align}\n", - "6. Add a constraint relating the inlet and outlet temperatures:\n", - "\\begin{align}\n", - "& t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} p_{ratio}^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def add_isentropic(unit, name, config):\n", - " unit.pressure_ratio = pe.Var(initialize=1.0, bounds=(1, None))\n", - " cons = pe.ConstraintList()\n", - " setattr(unit, name, cons)\n", - " inlet = unit.control_volume.properties_in[0.0]\n", - " outlet = unit.control_volume.properties_out[0.0]\n", - " gamma = inlet.params.gamma\n", - " cons.add(inlet.pressure * unit.pressure_ratio == outlet.pressure)\n", - " cons.add(\n", - " outlet.temperature\n", - " == (\n", - " inlet.temperature\n", - " + 1\n", - " / config.compressor_efficiency\n", - " * (\n", - " inlet.temperature * unit.pressure_ratio ** ((gamma - 1) / gamma)\n", - " - inlet.temperature\n", - " )\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also need a function to specify configuration options for the compressor. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def make_compressor_config_block(config):\n", - " config.declare(\n", - " \"material_balance_type\",\n", - " ConfigValue(\n", - " default=MaterialBalanceType.componentPhase, domain=In(MaterialBalanceType)\n", - " ),\n", - " )\n", - " config.declare(\n", - " \"energy_balance_type\",\n", - " ConfigValue(\n", - " default=EnergyBalanceType.enthalpyTotal,\n", - " domain=In([EnergyBalanceType.enthalpyTotal]),\n", - " ),\n", - " )\n", - " config.declare(\n", - " \"momentum_balance_type\",\n", - " ConfigValue(\n", - " default=MomentumBalanceType.none, domain=In([MomentumBalanceType.none])\n", - " ),\n", - " )\n", - " config.declare(\n", - " \"has_phase_equilibrium\", ConfigValue(default=False, domain=In([False]))\n", - " )\n", - " config.declare(\n", - " \"has_pressure_change\", ConfigValue(default=False, domain=In([False]))\n", - " )\n", - " config.declare(\n", - " \"property_package\",\n", - " ConfigValue(default=useDefault, domain=is_physical_parameter_block),\n", - " )\n", - " config.declare(\"property_package_args\", ConfigBlock(implicit=True))\n", - " config.declare(\"compressor_efficiency\", ConfigValue(default=0.75, domain=float))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can define the ideal-gas isentropic compressor. To do so, we create a class called ``IdealGasIsentropicCompressorData`` and use the ``declare_process_block_class`` decorator. For now, just consider the decorator to be boiler-plate. We then need to define the config block and write the ``build`` method. The ``build`` method should always call ``super``. Next, we simply call the functions we wrote to build the control volume, energy balance, and electricity requirement performance equation. Finally, we need to call ``self.add_inlet_port()`` and ``self.add_outlet_port()``. These methods need to be called in order to create the ports which are used for connecting the unit to other units." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"IdealGasIsentropicCompressor\")\n", - "class IdealGasIsentropicCompressorData(UnitModelBlockData):\n", - " CONFIG = UnitModelBlockData.CONFIG()\n", - " make_compressor_config_block(CONFIG)\n", - "\n", - " def build(self):\n", - " super(IdealGasIsentropicCompressorData, self).build()\n", - "\n", - " make_control_volume(self, \"control_volume\", self.config)\n", - " add_isentropic(self, \"isentropic\", self.config)\n", - "\n", - " self.add_inlet_port()\n", - " self.add_outlet_port()\n", - "\n", - " add_object_reference(self, \"work\", self.control_volume.work[0.0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The compressor model is complete and can now be used like other IDAES unit models. Note that the input temperature is in hectoKelvin, the input pressure is in MPa and energy units are in MJ. This is to simplify user input and is accounted for in the property package files; the standard unit definitions may be found in the metadata section at the end of the main parameter property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m = pe.ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "m.fs.properties = props = PhysicalParameterBlock(\n", - " Cp=0.038056, valid_phase=\"Vap\"\n", - ") # MJ/kmol-K\n", - "\n", - "m.fs.compressor = IdealGasIsentropicCompressor(\n", - " property_package=props, has_phase_equilibrium=False\n", - ")\n", - "m.fs.compressor.inlet.flow_mol.fix(1) # kmol\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"CH3OH\"].fix(0.25)\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"CH4\"].fix(0.25)\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"H2\"].fix(0.25)\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"CO\"].fix(0.25)\n", - "m.fs.compressor.inlet.pressure.fix(0.14) # MPa\n", - "m.fs.compressor.inlet.temperature.fix(2.9315) # hK [100K]\n", - "m.fs.compressor.outlet.pressure.fix(0.56) # MPa\n", - "\n", - "opt = pe.SolverFactory(\"ipopt\")\n", - "opt.options[\"linear_solver\"] = \"ma27\"\n", - "res = opt.solve(m, tee=True)\n", - "print(res.solver.termination_condition)\n", - "m.fs.compressor.outlet.display()\n", - "print(\"work: \", round(m.fs.compressor.work.value, 2), \" MJ\") # MJ" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import TerminationCondition, value\n", - "\n", - "assert res.solver.termination_condition == TerminationCondition.optimal\n", - "assert value(m.fs.compressor.work) == pytest.approx(5.2616, abs=1e-2)" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Custom Compressor Unit Model\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01\n", + "\n", + "To demonstrate creation of a new unit model, we will create a constant-heat-capacity ideal-gas isentropic compressor. This will be a simple textbook model. We will utilize the mass and energy balances provided by IDAES control volumes, but we will write our own isentropic constraint based off of equations 7.18 and 7.23 from \"Introduction to Chemical Engineering Thermodynamics\" by J.M. Smith, H.C. Van Ness, and M.M. Abbott. \n", + "\n", + "The outlet temperature of an ideal gas undergoing isentropic compression is given by \n", + "$$\n", + "t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} \\left(\\frac{p_{out}}{p_{in}}\\right)^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", + "$$\n", + "where $p$ is pressure, $t$ is temperature, and $\\gamma$ is the ratio of constant pressure heat capacity to constant volume heat capacity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will begin with relevant imports. We will need\n", + "\n", + "- Pyomo for writing our energy balance constraints\n", + "- ConfigBlocks for specifying options for our compressor\n", + "- ControlVolume0DBlocks for creating the appropriate state blocks for the inlet and outlet and for defining mas balances\n", + "- IdealParameterBlock which provides a simple ideal-gas property package.\n", + "- A few other helpful functions and enums from IDAES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pe\n", + "from pyomo.common.config import ConfigBlock, ConfigValue, In\n", + "from idaes.core import (\n", + " ControlVolume0DBlock,\n", + " declare_process_block_class,\n", + " EnergyBalanceType,\n", + " MomentumBalanceType,\n", + " MaterialBalanceType,\n", + " UnitModelBlockData,\n", + " useDefault,\n", + " FlowsheetBlock,\n", + ")\n", + "from idaes.core.util.config import is_physical_parameter_block\n", + "from idaes_examples.mod.methanol.methanol_param_VLE import PhysicalParameterBlock\n", + "from idaes.core.util.misc import add_object_reference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can write a function to create a control volume for our compressor. The control volume will define the inlet and outlet streams along with the appropriate state variables (specified by the property package). We will also use the control volume to create mass and energy balance constraints. \n", + "\n", + "Our function will take the compressor unit model object, the name of the control volume, and configuration options as arguments. Our compressor will only support steady-state models, so we will first ensure that ``dynamic`` and ``has_holdup`` are both ``False``.\n", + "\n", + "Next, we will create a 0D control volume. We are using a 0D control volume because our model does not depend on space. We then\n", + "\n", + "1. Attach the control volume to the compressor\n", + "2. Create the appropriate state blocks with the control volume (for the inlet and outlet streams)\n", + "3. Use the control volume to add mass balance constraints\n", + "4. Use the control volume to add energy balance constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_control_volume(unit, name, config):\n", + " if config.dynamic is not False:\n", + " raise ValueError(\"IdealGasIsentropcCompressor does not support dynamics\")\n", + " if config.has_holdup is not False:\n", + " raise ValueError(\"IdealGasIsentropcCompressor does not support holdup\")\n", + "\n", + " control_volume = ControlVolume0DBlock(\n", + " property_package=config.property_package,\n", + " property_package_args=config.property_package_args,\n", + " )\n", + "\n", + " setattr(unit, name, control_volume)\n", + "\n", + " control_volume.add_state_blocks(has_phase_equilibrium=config.has_phase_equilibrium)\n", + " control_volume.add_material_balances(\n", + " balance_type=config.material_balance_type,\n", + " has_phase_equilibrium=config.has_phase_equilibrium,\n", + " )\n", + " control_volume.add_total_enthalpy_balances(\n", + " has_heat_of_reaction=False, has_heat_transfer=False, has_work_transfer=True\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will write a function to add constraints to specify that the compressor is isentropic. \n", + "1. Create a ``pressure_ratio`` variable to represent $p_{out}/p_{in}$. The lower bound is $1$, because we only want to allow compression (and not expansion).\n", + "2. Create a ``ConstraintList`` to hold the constraints.\n", + "3. Add the ``ConstraintList`` to the compressor\n", + "4. Create the local variables ``inlet`` and ``outlet`` to reference the inlet and outlet state blocks.\n", + "5. Add a constraint relating the inlet pressure, outlet pressure, and pressure ratio variables:\n", + "\\begin{align}\n", + "p_{in} p_{ratio} = p_{out}\n", + "\\end{align}\n", + "6. Add a constraint relating the inlet and outlet temperatures:\n", + "\\begin{align}\n", + "& t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} p_{ratio}^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add_isentropic(unit, name, config):\n", + " unit.pressure_ratio = pe.Var(initialize=1.0, bounds=(1, None))\n", + " cons = pe.ConstraintList()\n", + " setattr(unit, name, cons)\n", + " inlet = unit.control_volume.properties_in[0.0]\n", + " outlet = unit.control_volume.properties_out[0.0]\n", + " gamma = inlet.params.gamma\n", + " cons.add(inlet.pressure * unit.pressure_ratio == outlet.pressure)\n", + " cons.add(\n", + " outlet.temperature\n", + " == (\n", + " inlet.temperature\n", + " + 1\n", + " / config.compressor_efficiency\n", + " * (\n", + " inlet.temperature * unit.pressure_ratio ** ((gamma - 1) / gamma)\n", + " - inlet.temperature\n", + " )\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need a function to specify configuration options for the compressor. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_compressor_config_block(config):\n", + " config.declare(\n", + " \"material_balance_type\",\n", + " ConfigValue(\n", + " default=MaterialBalanceType.componentPhase, domain=In(MaterialBalanceType)\n", + " ),\n", + " )\n", + " config.declare(\n", + " \"energy_balance_type\",\n", + " ConfigValue(\n", + " default=EnergyBalanceType.enthalpyTotal,\n", + " domain=In([EnergyBalanceType.enthalpyTotal]),\n", + " ),\n", + " )\n", + " config.declare(\n", + " \"momentum_balance_type\",\n", + " ConfigValue(\n", + " default=MomentumBalanceType.none, domain=In([MomentumBalanceType.none])\n", + " ),\n", + " )\n", + " config.declare(\n", + " \"has_phase_equilibrium\", ConfigValue(default=False, domain=In([False]))\n", + " )\n", + " config.declare(\n", + " \"has_pressure_change\", ConfigValue(default=False, domain=In([False]))\n", + " )\n", + " config.declare(\n", + " \"property_package\",\n", + " ConfigValue(default=useDefault, domain=is_physical_parameter_block),\n", + " )\n", + " config.declare(\"property_package_args\", ConfigBlock(implicit=True))\n", + " config.declare(\"compressor_efficiency\", ConfigValue(default=0.75, domain=float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can define the ideal-gas isentropic compressor. To do so, we create a class called ``IdealGasIsentropicCompressorData`` and use the ``declare_process_block_class`` decorator. For now, just consider the decorator to be boiler-plate. We then need to define the config block and write the ``build`` method. The ``build`` method should always call ``super``. Next, we simply call the functions we wrote to build the control volume, energy balance, and electricity requirement performance equation. Finally, we need to call ``self.add_inlet_port()`` and ``self.add_outlet_port()``. These methods need to be called in order to create the ports which are used for connecting the unit to other units." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"IdealGasIsentropicCompressor\")\n", + "class IdealGasIsentropicCompressorData(UnitModelBlockData):\n", + " CONFIG = UnitModelBlockData.CONFIG()\n", + " make_compressor_config_block(CONFIG)\n", + "\n", + " def build(self):\n", + " super(IdealGasIsentropicCompressorData, self).build()\n", + "\n", + " make_control_volume(self, \"control_volume\", self.config)\n", + " add_isentropic(self, \"isentropic\", self.config)\n", + "\n", + " self.add_inlet_port()\n", + " self.add_outlet_port()\n", + "\n", + " add_object_reference(self, \"work\", self.control_volume.work[0.0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The compressor model is complete and can now be used like other IDAES unit models. Note that the input temperature is in hectoKelvin, the input pressure is in MPa and energy units are in MJ. This is to simplify user input and is accounted for in the property package files; the standard unit definitions may be found in the metadata section at the end of the main parameter property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m = pe.ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "m.fs.properties = props = PhysicalParameterBlock(\n", + " Cp=0.038056, valid_phase=\"Vap\"\n", + ") # MJ/kmol-K\n", + "\n", + "m.fs.compressor = IdealGasIsentropicCompressor(\n", + " property_package=props, has_phase_equilibrium=False\n", + ")\n", + "m.fs.compressor.inlet.flow_mol.fix(1) # kmol\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"CH3OH\"].fix(0.25)\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"CH4\"].fix(0.25)\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"H2\"].fix(0.25)\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"CO\"].fix(0.25)\n", + "m.fs.compressor.inlet.pressure.fix(0.14) # MPa\n", + "m.fs.compressor.inlet.temperature.fix(2.9315) # hK [100K]\n", + "m.fs.compressor.outlet.pressure.fix(0.56) # MPa\n", + "\n", + "opt = pe.SolverFactory(\"ipopt\")\n", + "opt.options[\"linear_solver\"] = \"ma27\"\n", + "res = opt.solve(m, tee=True)\n", + "print(res.solver.termination_condition)\n", + "m.fs.compressor.outlet.display()\n", + "print(\"work: \", round(m.fs.compressor.work.value, 2), \" MJ\") # MJ" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import TerminationCondition, value\n", + "\n", + "assert res.solver.termination_condition == TerminationCondition.optimal\n", + "assert value(m.fs.compressor.work) == pytest.approx(5.2616, abs=1e-2)" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_usr.ipynb index 7441542a..098d5a99 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_compressor_usr.ipynb @@ -1,315 +1,316 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Custom Compressor Unit Model\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "Updated: 2023-06-01\n", - "\n", - "To demonstrate creation of a new unit model, we will create a constant-heat-capacity ideal-gas isentropic compressor. This will be a simple textbook model. We will utilize the mass and energy balances provided by IDAES control volumes, but we will write our own isentropic constraint based off of equations 7.18 and 7.23 from \"Introduction to Chemical Engineering Thermodynamics\" by J.M. Smith, H.C. Van Ness, and M.M. Abbott. \n", - "\n", - "The outlet temperature of an ideal gas undergoing isentropic compression is given by \n", - "$$\n", - "t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} \\left(\\frac{p_{out}}{p_{in}}\\right)^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", - "$$\n", - "where $p$ is pressure, $t$ is temperature, and $\\gamma$ is the ratio of constant pressure heat capacity to constant volume heat capacity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will begin with relevant imports. We will need\n", - "\n", - "- Pyomo for writing our energy balance constraints\n", - "- ConfigBlocks for specifying options for our compressor\n", - "- ControlVolume0DBlocks for creating the appropriate state blocks for the inlet and outlet and for defining mas balances\n", - "- IdealParameterBlock which provides a simple ideal-gas property package.\n", - "- A few other helpful functions and enums from IDAES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pe\n", - "from pyomo.common.config import ConfigBlock, ConfigValue, In\n", - "from idaes.core import (\n", - " ControlVolume0DBlock,\n", - " declare_process_block_class,\n", - " EnergyBalanceType,\n", - " MomentumBalanceType,\n", - " MaterialBalanceType,\n", - " UnitModelBlockData,\n", - " useDefault,\n", - " FlowsheetBlock,\n", - ")\n", - "from idaes.core.util.config import is_physical_parameter_block\n", - "from idaes_examples.mod.methanol.methanol_param_VLE import PhysicalParameterBlock\n", - "from idaes.core.util.misc import add_object_reference" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can write a function to create a control volume for our compressor. The control volume will define the inlet and outlet streams along with the appropriate state variables (specified by the property package). We will also use the control volume to create mass and energy balance constraints. \n", - "\n", - "Our function will take the compressor unit model object, the name of the control volume, and configuration options as arguments. Our compressor will only support steady-state models, so we will first ensure that ``dynamic`` and ``has_holdup`` are both ``False``.\n", - "\n", - "Next, we will create a 0D control volume. We are using a 0D control volume because our model does not depend on space. We then\n", - "\n", - "1. Attach the control volume to the compressor\n", - "2. Create the appropriate state blocks with the control volume (for the inlet and outlet streams)\n", - "3. Use the control volume to add mass balance constraints\n", - "4. Use the control volume to add energy balance constraints" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def make_control_volume(unit, name, config):\n", - " if config.dynamic is not False:\n", - " raise ValueError(\"IdealGasIsentropcCompressor does not support dynamics\")\n", - " if config.has_holdup is not False:\n", - " raise ValueError(\"IdealGasIsentropcCompressor does not support holdup\")\n", - "\n", - " control_volume = ControlVolume0DBlock(\n", - " property_package=config.property_package,\n", - " property_package_args=config.property_package_args,\n", - " )\n", - "\n", - " setattr(unit, name, control_volume)\n", - "\n", - " control_volume.add_state_blocks(has_phase_equilibrium=config.has_phase_equilibrium)\n", - " control_volume.add_material_balances(\n", - " balance_type=config.material_balance_type,\n", - " has_phase_equilibrium=config.has_phase_equilibrium,\n", - " )\n", - " control_volume.add_total_enthalpy_balances(\n", - " has_heat_of_reaction=False, has_heat_transfer=False, has_work_transfer=True\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will write a function to add constraints to specify that the compressor is isentropic. \n", - "1. Create a ``pressure_ratio`` variable to represent $p_{out}/p_{in}$. The lower bound is $1$, because we only want to allow compression (and not expansion).\n", - "2. Create a ``ConstraintList`` to hold the constraints.\n", - "3. Add the ``ConstraintList`` to the compressor\n", - "4. Create the local variables ``inlet`` and ``outlet`` to reference the inlet and outlet state blocks.\n", - "5. Add a constraint relating the inlet pressure, outlet pressure, and pressure ratio variables:\n", - "\\begin{align}\n", - "p_{in} p_{ratio} = p_{out}\n", - "\\end{align}\n", - "6. Add a constraint relating the inlet and outlet temperatures:\n", - "\\begin{align}\n", - "& t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} p_{ratio}^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", - "\\end{align}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def add_isentropic(unit, name, config):\n", - " unit.pressure_ratio = pe.Var(initialize=1.0, bounds=(1, None))\n", - " cons = pe.ConstraintList()\n", - " setattr(unit, name, cons)\n", - " inlet = unit.control_volume.properties_in[0.0]\n", - " outlet = unit.control_volume.properties_out[0.0]\n", - " gamma = inlet.params.gamma\n", - " cons.add(inlet.pressure * unit.pressure_ratio == outlet.pressure)\n", - " cons.add(\n", - " outlet.temperature\n", - " == (\n", - " inlet.temperature\n", - " + 1\n", - " / config.compressor_efficiency\n", - " * (\n", - " inlet.temperature * unit.pressure_ratio ** ((gamma - 1) / gamma)\n", - " - inlet.temperature\n", - " )\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also need a function to specify configuration options for the compressor. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def make_compressor_config_block(config):\n", - " config.declare(\n", - " \"material_balance_type\",\n", - " ConfigValue(\n", - " default=MaterialBalanceType.componentPhase, domain=In(MaterialBalanceType)\n", - " ),\n", - " )\n", - " config.declare(\n", - " \"energy_balance_type\",\n", - " ConfigValue(\n", - " default=EnergyBalanceType.enthalpyTotal,\n", - " domain=In([EnergyBalanceType.enthalpyTotal]),\n", - " ),\n", - " )\n", - " config.declare(\n", - " \"momentum_balance_type\",\n", - " ConfigValue(\n", - " default=MomentumBalanceType.none, domain=In([MomentumBalanceType.none])\n", - " ),\n", - " )\n", - " config.declare(\n", - " \"has_phase_equilibrium\", ConfigValue(default=False, domain=In([False]))\n", - " )\n", - " config.declare(\n", - " \"has_pressure_change\", ConfigValue(default=False, domain=In([False]))\n", - " )\n", - " config.declare(\n", - " \"property_package\",\n", - " ConfigValue(default=useDefault, domain=is_physical_parameter_block),\n", - " )\n", - " config.declare(\"property_package_args\", ConfigBlock(implicit=True))\n", - " config.declare(\"compressor_efficiency\", ConfigValue(default=0.75, domain=float))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can define the ideal-gas isentropic compressor. To do so, we create a class called ``IdealGasIsentropicCompressorData`` and use the ``declare_process_block_class`` decorator. For now, just consider the decorator to be boiler-plate. We then need to define the config block and write the ``build`` method. The ``build`` method should always call ``super``. Next, we simply call the functions we wrote to build the control volume, energy balance, and electricity requirement performance equation. Finally, we need to call ``self.add_inlet_port()`` and ``self.add_outlet_port()``. These methods need to be called in order to create the ports which are used for connecting the unit to other units." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@declare_process_block_class(\"IdealGasIsentropicCompressor\")\n", - "class IdealGasIsentropicCompressorData(UnitModelBlockData):\n", - " CONFIG = UnitModelBlockData.CONFIG()\n", - " make_compressor_config_block(CONFIG)\n", - "\n", - " def build(self):\n", - " super(IdealGasIsentropicCompressorData, self).build()\n", - "\n", - " make_control_volume(self, \"control_volume\", self.config)\n", - " add_isentropic(self, \"isentropic\", self.config)\n", - "\n", - " self.add_inlet_port()\n", - " self.add_outlet_port()\n", - "\n", - " add_object_reference(self, \"work\", self.control_volume.work[0.0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The compressor model is complete and can now be used like other IDAES unit models. Note that the input temperature is in hectoKelvin, the input pressure is in MPa and energy units are in MJ. This is to simplify user input and is accounted for in the property package files; the standard unit definitions may be found in the metadata section at the end of the main parameter property package." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m = pe.ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "m.fs.properties = props = PhysicalParameterBlock(\n", - " Cp=0.038056, valid_phase=\"Vap\"\n", - ") # MJ/kmol-K\n", - "\n", - "m.fs.compressor = IdealGasIsentropicCompressor(\n", - " property_package=props, has_phase_equilibrium=False\n", - ")\n", - "m.fs.compressor.inlet.flow_mol.fix(1) # kmol\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"CH3OH\"].fix(0.25)\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"CH4\"].fix(0.25)\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"H2\"].fix(0.25)\n", - "m.fs.compressor.inlet.mole_frac_comp[0, \"CO\"].fix(0.25)\n", - "m.fs.compressor.inlet.pressure.fix(0.14) # MPa\n", - "m.fs.compressor.inlet.temperature.fix(2.9315) # hK [100K]\n", - "m.fs.compressor.outlet.pressure.fix(0.56) # MPa\n", - "\n", - "opt = pe.SolverFactory(\"ipopt\")\n", - "opt.options[\"linear_solver\"] = \"ma27\"\n", - "res = opt.solve(m, tee=True)\n", - "print(res.solver.termination_condition)\n", - "m.fs.compressor.outlet.display()\n", - "print(\"work: \", round(m.fs.compressor.work.value, 2), \" MJ\") # MJ" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Custom Compressor Unit Model\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "Updated: 2023-06-01\n", + "\n", + "To demonstrate creation of a new unit model, we will create a constant-heat-capacity ideal-gas isentropic compressor. This will be a simple textbook model. We will utilize the mass and energy balances provided by IDAES control volumes, but we will write our own isentropic constraint based off of equations 7.18 and 7.23 from \"Introduction to Chemical Engineering Thermodynamics\" by J.M. Smith, H.C. Van Ness, and M.M. Abbott. \n", + "\n", + "The outlet temperature of an ideal gas undergoing isentropic compression is given by \n", + "$$\n", + "t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} \\left(\\frac{p_{out}}{p_{in}}\\right)^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", + "$$\n", + "where $p$ is pressure, $t$ is temperature, and $\\gamma$ is the ratio of constant pressure heat capacity to constant volume heat capacity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will begin with relevant imports. We will need\n", + "\n", + "- Pyomo for writing our energy balance constraints\n", + "- ConfigBlocks for specifying options for our compressor\n", + "- ControlVolume0DBlocks for creating the appropriate state blocks for the inlet and outlet and for defining mas balances\n", + "- IdealParameterBlock which provides a simple ideal-gas property package.\n", + "- A few other helpful functions and enums from IDAES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pyomo.environ as pe\n", + "from pyomo.common.config import ConfigBlock, ConfigValue, In\n", + "from idaes.core import (\n", + " ControlVolume0DBlock,\n", + " declare_process_block_class,\n", + " EnergyBalanceType,\n", + " MomentumBalanceType,\n", + " MaterialBalanceType,\n", + " UnitModelBlockData,\n", + " useDefault,\n", + " FlowsheetBlock,\n", + ")\n", + "from idaes.core.util.config import is_physical_parameter_block\n", + "from idaes_examples.mod.methanol.methanol_param_VLE import PhysicalParameterBlock\n", + "from idaes.core.util.misc import add_object_reference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can write a function to create a control volume for our compressor. The control volume will define the inlet and outlet streams along with the appropriate state variables (specified by the property package). We will also use the control volume to create mass and energy balance constraints. \n", + "\n", + "Our function will take the compressor unit model object, the name of the control volume, and configuration options as arguments. Our compressor will only support steady-state models, so we will first ensure that ``dynamic`` and ``has_holdup`` are both ``False``.\n", + "\n", + "Next, we will create a 0D control volume. We are using a 0D control volume because our model does not depend on space. We then\n", + "\n", + "1. Attach the control volume to the compressor\n", + "2. Create the appropriate state blocks with the control volume (for the inlet and outlet streams)\n", + "3. Use the control volume to add mass balance constraints\n", + "4. Use the control volume to add energy balance constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_control_volume(unit, name, config):\n", + " if config.dynamic is not False:\n", + " raise ValueError(\"IdealGasIsentropcCompressor does not support dynamics\")\n", + " if config.has_holdup is not False:\n", + " raise ValueError(\"IdealGasIsentropcCompressor does not support holdup\")\n", + "\n", + " control_volume = ControlVolume0DBlock(\n", + " property_package=config.property_package,\n", + " property_package_args=config.property_package_args,\n", + " )\n", + "\n", + " setattr(unit, name, control_volume)\n", + "\n", + " control_volume.add_state_blocks(has_phase_equilibrium=config.has_phase_equilibrium)\n", + " control_volume.add_material_balances(\n", + " balance_type=config.material_balance_type,\n", + " has_phase_equilibrium=config.has_phase_equilibrium,\n", + " )\n", + " control_volume.add_total_enthalpy_balances(\n", + " has_heat_of_reaction=False, has_heat_transfer=False, has_work_transfer=True\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will write a function to add constraints to specify that the compressor is isentropic. \n", + "1. Create a ``pressure_ratio`` variable to represent $p_{out}/p_{in}$. The lower bound is $1$, because we only want to allow compression (and not expansion).\n", + "2. Create a ``ConstraintList`` to hold the constraints.\n", + "3. Add the ``ConstraintList`` to the compressor\n", + "4. Create the local variables ``inlet`` and ``outlet`` to reference the inlet and outlet state blocks.\n", + "5. Add a constraint relating the inlet pressure, outlet pressure, and pressure ratio variables:\n", + "\\begin{align}\n", + "p_{in} p_{ratio} = p_{out}\n", + "\\end{align}\n", + "6. Add a constraint relating the inlet and outlet temperatures:\n", + "\\begin{align}\n", + "& t_{out} = t_{in} + \\frac{1}{\\eta} \\left(t_{in} p_{ratio}^{\\frac{\\gamma - 1}{\\gamma}} - t_{in}\\right)\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add_isentropic(unit, name, config):\n", + " unit.pressure_ratio = pe.Var(initialize=1.0, bounds=(1, None))\n", + " cons = pe.ConstraintList()\n", + " setattr(unit, name, cons)\n", + " inlet = unit.control_volume.properties_in[0.0]\n", + " outlet = unit.control_volume.properties_out[0.0]\n", + " gamma = inlet.params.gamma\n", + " cons.add(inlet.pressure * unit.pressure_ratio == outlet.pressure)\n", + " cons.add(\n", + " outlet.temperature\n", + " == (\n", + " inlet.temperature\n", + " + 1\n", + " / config.compressor_efficiency\n", + " * (\n", + " inlet.temperature * unit.pressure_ratio ** ((gamma - 1) / gamma)\n", + " - inlet.temperature\n", + " )\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need a function to specify configuration options for the compressor. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_compressor_config_block(config):\n", + " config.declare(\n", + " \"material_balance_type\",\n", + " ConfigValue(\n", + " default=MaterialBalanceType.componentPhase, domain=In(MaterialBalanceType)\n", + " ),\n", + " )\n", + " config.declare(\n", + " \"energy_balance_type\",\n", + " ConfigValue(\n", + " default=EnergyBalanceType.enthalpyTotal,\n", + " domain=In([EnergyBalanceType.enthalpyTotal]),\n", + " ),\n", + " )\n", + " config.declare(\n", + " \"momentum_balance_type\",\n", + " ConfigValue(\n", + " default=MomentumBalanceType.none, domain=In([MomentumBalanceType.none])\n", + " ),\n", + " )\n", + " config.declare(\n", + " \"has_phase_equilibrium\", ConfigValue(default=False, domain=In([False]))\n", + " )\n", + " config.declare(\n", + " \"has_pressure_change\", ConfigValue(default=False, domain=In([False]))\n", + " )\n", + " config.declare(\n", + " \"property_package\",\n", + " ConfigValue(default=useDefault, domain=is_physical_parameter_block),\n", + " )\n", + " config.declare(\"property_package_args\", ConfigBlock(implicit=True))\n", + " config.declare(\"compressor_efficiency\", ConfigValue(default=0.75, domain=float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can define the ideal-gas isentropic compressor. To do so, we create a class called ``IdealGasIsentropicCompressorData`` and use the ``declare_process_block_class`` decorator. For now, just consider the decorator to be boiler-plate. We then need to define the config block and write the ``build`` method. The ``build`` method should always call ``super``. Next, we simply call the functions we wrote to build the control volume, energy balance, and electricity requirement performance equation. Finally, we need to call ``self.add_inlet_port()`` and ``self.add_outlet_port()``. These methods need to be called in order to create the ports which are used for connecting the unit to other units." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"IdealGasIsentropicCompressor\")\n", + "class IdealGasIsentropicCompressorData(UnitModelBlockData):\n", + " CONFIG = UnitModelBlockData.CONFIG()\n", + " make_compressor_config_block(CONFIG)\n", + "\n", + " def build(self):\n", + " super(IdealGasIsentropicCompressorData, self).build()\n", + "\n", + " make_control_volume(self, \"control_volume\", self.config)\n", + " add_isentropic(self, \"isentropic\", self.config)\n", + "\n", + " self.add_inlet_port()\n", + " self.add_outlet_port()\n", + "\n", + " add_object_reference(self, \"work\", self.control_volume.work[0.0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The compressor model is complete and can now be used like other IDAES unit models. Note that the input temperature is in hectoKelvin, the input pressure is in MPa and energy units are in MJ. This is to simplify user input and is accounted for in the property package files; the standard unit definitions may be found in the metadata section at the end of the main parameter property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m = pe.ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "m.fs.properties = props = PhysicalParameterBlock(\n", + " Cp=0.038056, valid_phase=\"Vap\"\n", + ") # MJ/kmol-K\n", + "\n", + "m.fs.compressor = IdealGasIsentropicCompressor(\n", + " property_package=props, has_phase_equilibrium=False\n", + ")\n", + "m.fs.compressor.inlet.flow_mol.fix(1) # kmol\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"CH3OH\"].fix(0.25)\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"CH4\"].fix(0.25)\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"H2\"].fix(0.25)\n", + "m.fs.compressor.inlet.mole_frac_comp[0, \"CO\"].fix(0.25)\n", + "m.fs.compressor.inlet.pressure.fix(0.14) # MPa\n", + "m.fs.compressor.inlet.temperature.fix(2.9315) # hK [100K]\n", + "m.fs.compressor.outlet.pressure.fix(0.56) # MPa\n", + "\n", + "opt = pe.SolverFactory(\"ipopt\")\n", + "opt.options[\"linear_solver\"] = \"ma27\"\n", + "res = opt.solve(m, tee=True)\n", + "print(res.solver.termination_condition)\n", + "m.fs.compressor.outlet.display()\n", + "print(\"work: \", round(m.fs.compressor.work.value, 2), \" MJ\") # MJ" + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater.ipynb index 746d13a5..66087c9d 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_doc.ipynb index fc220837..6161e8bc 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -212,7 +213,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_test.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_test.ipynb index 899650a2..7ae398c4 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_usr.ipynb index 6cb98088..be6a2de6 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/custom_heater_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/aqueous_property.py b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/aqueous_property.py index c3b62e90..636ee763 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/aqueous_property.py +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/aqueous_property.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# # Import Python libraries diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liq_liq_extractor_flowsheet.py b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liq_liq_extractor_flowsheet.py index 2c611928..e939b28b 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liq_liq_extractor_flowsheet.py +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liq_liq_extractor_flowsheet.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ The below is an implementation of a flowsheet for liquid liquid extractor. diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liquid_liquid_extractor.py b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liquid_liquid_extractor.py index 99385b27..930d79ca 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liquid_liquid_extractor.py +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/liquid_liquid_extractor.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ diff --git a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/organic_property.py b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/organic_property.py index 4c9102f9..750354c4 100644 --- a/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/organic_property.py +++ b/idaes_examples/notebooks/docs/unit_models/custom_unit_models/liquid_extraction/organic_property.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# # Import Python libraries diff --git a/idaes_examples/notebooks/docs/unit_models/operations/compressor.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/compressor.ipynb index f4e07086..1f59506f 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/compressor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/compressor.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/compressor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/compressor_doc.ipynb index d3f073fb..b3c9f520 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/compressor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/compressor_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -232,7 +233,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:30 [INFO] idaes.init.fs.compr_case_1: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:14 [INFO] idaes.init.fs.compr_case_1: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -294,16 +295,16 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 2.38e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 0 0.0000000e+00 3.12e-11 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", "\n", "Number of Iterations....: 0\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.6180568054461275e-10 2.3841857910156250e-07\n", + "Constraint violation....: 3.1228353236656403e-11 3.1228353236656403e-11\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.6180568054461275e-10 2.3841857910156250e-07\n", + "Overall NLP error.......: 3.1228353236656403e-11 3.1228353236656403e-11\n", "\n", "\n", "Number of objective function evaluations = 1\n", @@ -313,16 +314,10 @@ "Number of equality constraint Jacobian evaluations = 1\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] } @@ -385,13 +380,7 @@ " Pressure Ratio : 3.8000 : dimensionless : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", - " Stream Table\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " Stream Table\n", " Units Inlet Outlet \n", " Molar Flow mole / second 91067. 91067.\n", " Mass Flow kilogram / second 4007.8 4007.8\n", @@ -523,7 +512,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:26:30 [INFO] idaes.init.fs.compr_case_2: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:14 [INFO] idaes.init.fs.compr_case_2: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -604,7 +593,7 @@ "Number of equality constraint Jacobian evaluations = 1\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 0\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n", @@ -705,9 +694,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/compressor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/compressor_test.ipynb index 7d354e12..270622a5 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/compressor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/compressor_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -557,4 +558,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/compressor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/compressor_usr.ipynb index 097aedb3..6b1843f3 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/compressor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/compressor_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -431,4 +432,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py b/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py index 40607b83..14541461 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py +++ b/idaes_examples/notebooks/docs/unit_models/operations/eg_h2o_ideal.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Phase equilibrium package for Ethylene Oxide hydrolysis to Ethylene Glycol diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d.ipynb index d7f22f19..97e8e315 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_doc.ipynb index bd16e69e..046f1321 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -245,105 +246,105 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:29 [INFO] idaes.init.fs.heat_exchanger.hot_side: Initialization Complete\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.hot_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:29 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Starting initialization\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: State variable initialization completed.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_in: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Starting initialization\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Starting initialization\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Dew and bubble point initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Bubble, dew, and critical point initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Equilibrium temperature initialization completed.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Equilibrium temperature initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: State variable initialization completed.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: State variable initialization completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Phase equilibrium initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side.properties_out: Property initialization: optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger.cold_side: Initialization Complete\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger.cold_side: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:25:30 [INFO] idaes.init.fs.heat_exchanger: Initialization Completed, optimal - Optimal Solution Found\n" + "2025-03-17 17:40:17 [INFO] idaes.init.fs.heat_exchanger: Initialization Completed, optimal - Optimal Solution Found\n" ] }, { @@ -351,7 +352,13 @@ "output_type": "stream", "text": [ "\n", - "====================================================================================\n", + "====================================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Unit : fs.heat_exchanger Time: 0.0\n", "------------------------------------------------------------------------------------\n", " Unit Performance\n", @@ -359,6 +366,8 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", + " Delta T In : 80.757 : kelvin : False : (None, None)\n", + " Delta T Out : 23.124 : kelvin : False : (None, None)\n", " HX Area : 50.000 : meter ** 2 : True : (0, None)\n", " HX Coefficient : 500.00 : kilogram / kelvin / second ** 3 : True : (0, None)\n", " Heat Duty : 1.2985e+06 : watt : False : (None, None)\n", @@ -367,8 +376,6 @@ "\n", " Key : Value : Units\n", " Delta T Driving : 51.940 : kelvin\n", - " Delta T In : 80.757 : kelvin\n", - " Delta T Out : 23.124 : kelvin\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", @@ -452,11 +459,17 @@ " Termination condition: optimal\n", " Id: 0\n", " Error rc: 0\n", - " Time: 0.05035805702209473\n", + " Time: 0.011766672134399414\n", "Solution: \n", "- number of solutions: 0\n", " number of solutions displayed: 0\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "====================================================================================\n", "Unit : fs.heat_exchanger Time: 0.0\n", @@ -466,6 +479,8 @@ " Variables: \n", "\n", " Key : Value : Units : Fixed : Bounds\n", + " Delta T In : 78.730 : kelvin : False : (None, None)\n", + " Delta T Out : 10.000 : kelvin : False : (None, None)\n", " HX Area : 200.26 : meter ** 2 : False : (0, None)\n", " HX Coefficient : 500.00 : kilogram / kelvin / second ** 3 : True : (0, None)\n", " Heat Duty : 4.4423e+06 : watt : False : (None, None)\n", @@ -474,8 +489,6 @@ "\n", " Key : Value : Units\n", " Delta T Driving : 44.365 : kelvin\n", - " Delta T In : 78.730 : kelvin\n", - " Delta T Out : 10.000 : kelvin\n", "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", @@ -529,9 +542,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_test.ipynb index 573ea6df..050ed7cc 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_test.ipynb @@ -1,360 +1,361 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Heat Exchanger 0D Unit Model with Ideal & IAPWS Property Package\n", - "Author: Anuja Deshpande \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "![](heat_exchanger_4.svg)\n", - "\n", - "**Problem Statement**: In this example, we will be heating a benzene-toluene mixture using steam. \n", - "\n", - "**Tube Side Inlet**\n", - "\n", - "Flow Rate = 250 mol/s\n", - "\n", - "Mole fraction (Benzene) = 0.4\n", - "\n", - "Mole fraction (Toluene) = 0.6\n", - "\n", - "Pressure = 101325 Pa\n", - "\n", - "Temperature = 350 K\n", - "\n", - "**Shell Side Inlet**\n", - "\n", - "Flow Rate = 100 mol/s\n", - "\n", - "Mole fraction (Steam) = 1\n", - "\n", - "Pressure = 101325 Pa\n", - "\n", - "Temperature = 450 K\n", - "\n", - "This example will demonstrate the simulation of the 0D heat exchanger by fixing any 2 of the following degrees of freedom:\n", - "- heat transfer area\n", - "- overall heat transfer coefficient\n", - "- minimum approach temperature\n", - "\n", - "\n", - "IDAES documentation reference for heat exchanger 0D model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Setting up the problem in IDAES**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Import pyomo package\n", - "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value, units\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "\n", - "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# import the BTX property package to create a properties block for the flowsheet\n", - "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", - "\n", - "# Import the IAPWS property package to create a properties block for the flowsheet\n", - "from idaes.models.properties import iapws95\n", - "\n", - "from idaes.models.properties.iapws95 import htpx\n", - "\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "\n", - "from idaes.models.properties.modular_properties.examples.BT_ideal import configuration\n", - "\n", - "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import a heat exchanger unit\n", - "from idaes.models.unit_models.heat_exchanger import (\n", - " HeatExchanger,\n", - " delta_temperature_amtd_callback,\n", - ")\n", - "\n", - "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", - "m = ConcreteModel()\n", - "\n", - "# Steady State Model\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "# Setup property packages for shell and tube side\n", - "# Steam property package\n", - "m.fs.properties_shell = iapws95.Iapws95ParameterBlock()\n", - "\n", - "# BT ideal property package\n", - "m.fs.properties_tube = GenericParameterBlock(**configuration)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the heat exchanger unit, attaching it to the flowsheet\n", - "# Specify that the property package to be used with the heater is the one we created earlier.\n", - "m.fs.heat_exchanger = HeatExchanger(\n", - " delta_temperature_callback=delta_temperature_amtd_callback,\n", - " hot_side_name=\"shell\",\n", - " cold_side_name=\"tube\",\n", - " shell={\"property_package\": m.fs.properties_shell},\n", - " tube={\"property_package\": m.fs.properties_tube},\n", - ")\n", - "\n", - "# Call the degrees_of_freedom function, get initial DOF\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The initial DOF is {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_initial == 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "h = htpx(450 * units.K, P=101325 * units.Pa)\n", - "\n", - "# Fix the inlet conditions\n", - "m.fs.heat_exchanger.shell_inlet.flow_mol.fix(100) # mol/s\n", - "m.fs.heat_exchanger.shell_inlet.pressure.fix(101325)\n", - "m.fs.heat_exchanger.shell_inlet.enth_mol.fix(h) # J/mol\n", - "\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The DOF is {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heat_exchanger.tube_inlet.flow_mol.fix(250) # mol/s\n", - "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", - "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", - "m.fs.heat_exchanger.tube_inlet.pressure.fix(101325) # Pa\n", - "m.fs.heat_exchanger.tube_inlet.temperature[0].fix(350) # K\n", - "\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The DOF is {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 1: Fix overall HTC and the heat transfer area\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heat_exchanger.area.fix(50) # m2\n", - "m.fs.heat_exchanger.overall_heat_transfer_coefficient[0].fix(500) # W/m2/K\n", - "\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The DOF is {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_final == 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "m.fs.heat_exchanger.initialize(outlvl=idaeslog.INFO)\n", - "\n", - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "solve_status = opt.solve(m)\n", - "\n", - "# Display a readable report\n", - "m.fs.heat_exchanger.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from pyomo.opt import TerminationCondition, SolverStatus\n", - "import pytest\n", - "\n", - "# Check if termination condition is optimal\n", - "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", - "assert solve_status.solver.status == SolverStatus.ok\n", - "\n", - "assert value(m.fs.heat_exchanger.shell.properties_out[0].temperature) == pytest.approx(\n", - " 373.13, abs=1e-2, rel=1e-5\n", - ")\n", - "assert value(m.fs.heat_exchanger.tube.properties_out[0].temperature) == pytest.approx(\n", - " 369.24, abs=1e-2, rel=1e-5\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 2: Unfix area and fix shell side outlet temperature\n", - "\n", - "In the previous example, we fixed the heat exchanger area and overall heat transfer coefficient. However, given that the models in IDAES are equation oriented, we can fix the outlet variables. For example, we can fix the outlet temperature for the shell side and solve for the heat exchanger area that will satisfy that condition. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heat_exchanger.area.unfix()\n", - "m.fs.heat_exchanger.shell_outlet.enth_mol.fix(htpx(360 * units.K, P=101325 * units.Pa))\n", - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result = opt.solve(m)\n", - "\n", - "print(result)\n", - "\n", - "# Display a readable report\n", - "m.fs.heat_exchanger.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check if termination condition is optimal\n", - "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", - "assert solve_status.solver.status == SolverStatus.ok\n", - "\n", - "assert value(m.fs.heat_exchanger.area) == pytest.approx(200.26, abs=1e-2)\n", - "assert value(m.fs.heat_exchanger.tube.properties_out[0].temperature) == pytest.approx(\n", - " 371.27, abs=1e-2, rel=1e-5\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Heat Exchanger 0D Unit Model with Ideal & IAPWS Property Package\n", + "Author: Anuja Deshpande \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "![](heat_exchanger_4.svg)\n", + "\n", + "**Problem Statement**: In this example, we will be heating a benzene-toluene mixture using steam. \n", + "\n", + "**Tube Side Inlet**\n", + "\n", + "Flow Rate = 250 mol/s\n", + "\n", + "Mole fraction (Benzene) = 0.4\n", + "\n", + "Mole fraction (Toluene) = 0.6\n", + "\n", + "Pressure = 101325 Pa\n", + "\n", + "Temperature = 350 K\n", + "\n", + "**Shell Side Inlet**\n", + "\n", + "Flow Rate = 100 mol/s\n", + "\n", + "Mole fraction (Steam) = 1\n", + "\n", + "Pressure = 101325 Pa\n", + "\n", + "Temperature = 450 K\n", + "\n", + "This example will demonstrate the simulation of the 0D heat exchanger by fixing any 2 of the following degrees of freedom:\n", + "- heat transfer area\n", + "- overall heat transfer coefficient\n", + "- minimum approach temperature\n", + "\n", + "\n", + "IDAES documentation reference for heat exchanger 0D model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Setting up the problem in IDAES**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import pyomo package\n", + "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value, units\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "\n", + "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# import the BTX property package to create a properties block for the flowsheet\n", + "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", + "\n", + "# Import the IAPWS property package to create a properties block for the flowsheet\n", + "from idaes.models.properties import iapws95\n", + "\n", + "from idaes.models.properties.iapws95 import htpx\n", + "\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "\n", + "from idaes.models.properties.modular_properties.examples.BT_ideal import configuration\n", + "\n", + "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import a heat exchanger unit\n", + "from idaes.models.unit_models.heat_exchanger import (\n", + " HeatExchanger,\n", + " delta_temperature_amtd_callback,\n", + ")\n", + "\n", + "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", + "m = ConcreteModel()\n", + "\n", + "# Steady State Model\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "# Setup property packages for shell and tube side\n", + "# Steam property package\n", + "m.fs.properties_shell = iapws95.Iapws95ParameterBlock()\n", + "\n", + "# BT ideal property package\n", + "m.fs.properties_tube = GenericParameterBlock(**configuration)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an instance of the heat exchanger unit, attaching it to the flowsheet\n", + "# Specify that the property package to be used with the heater is the one we created earlier.\n", + "m.fs.heat_exchanger = HeatExchanger(\n", + " delta_temperature_callback=delta_temperature_amtd_callback,\n", + " hot_side_name=\"shell\",\n", + " cold_side_name=\"tube\",\n", + " shell={\"property_package\": m.fs.properties_shell},\n", + " tube={\"property_package\": m.fs.properties_tube},\n", + ")\n", + "\n", + "# Call the degrees_of_freedom function, get initial DOF\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The initial DOF is {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_initial == 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "h = htpx(450 * units.K, P=101325 * units.Pa)\n", + "\n", + "# Fix the inlet conditions\n", + "m.fs.heat_exchanger.shell_inlet.flow_mol.fix(100) # mol/s\n", + "m.fs.heat_exchanger.shell_inlet.pressure.fix(101325)\n", + "m.fs.heat_exchanger.shell_inlet.enth_mol.fix(h) # J/mol\n", + "\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The DOF is {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heat_exchanger.tube_inlet.flow_mol.fix(250) # mol/s\n", + "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", + "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", + "m.fs.heat_exchanger.tube_inlet.pressure.fix(101325) # Pa\n", + "m.fs.heat_exchanger.tube_inlet.temperature[0].fix(350) # K\n", + "\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The DOF is {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Option 1: Fix overall HTC and the heat transfer area\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heat_exchanger.area.fix(50) # m2\n", + "m.fs.heat_exchanger.overall_heat_transfer_coefficient[0].fix(500) # W/m2/K\n", + "\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The DOF is {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_final == 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "m.fs.heat_exchanger.initialize(outlvl=idaeslog.INFO)\n", + "\n", + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "solve_status = opt.solve(m)\n", + "\n", + "# Display a readable report\n", + "m.fs.heat_exchanger.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from pyomo.opt import TerminationCondition, SolverStatus\n", + "import pytest\n", + "\n", + "# Check if termination condition is optimal\n", + "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", + "assert solve_status.solver.status == SolverStatus.ok\n", + "\n", + "assert value(m.fs.heat_exchanger.shell.properties_out[0].temperature) == pytest.approx(\n", + " 373.13, abs=1e-2, rel=1e-5\n", + ")\n", + "assert value(m.fs.heat_exchanger.tube.properties_out[0].temperature) == pytest.approx(\n", + " 369.24, abs=1e-2, rel=1e-5\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Option 2: Unfix area and fix shell side outlet temperature\n", + "\n", + "In the previous example, we fixed the heat exchanger area and overall heat transfer coefficient. However, given that the models in IDAES are equation oriented, we can fix the outlet variables. For example, we can fix the outlet temperature for the shell side and solve for the heat exchanger area that will satisfy that condition. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heat_exchanger.area.unfix()\n", + "m.fs.heat_exchanger.shell_outlet.enth_mol.fix(htpx(360 * units.K, P=101325 * units.Pa))\n", + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = opt.solve(m)\n", + "\n", + "print(result)\n", + "\n", + "# Display a readable report\n", + "m.fs.heat_exchanger.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check if termination condition is optimal\n", + "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", + "assert solve_status.solver.status == SolverStatus.ok\n", + "\n", + "assert value(m.fs.heat_exchanger.area) == pytest.approx(200.26, abs=1e-2)\n", + "assert value(m.fs.heat_exchanger.tube.properties_out[0].temperature) == pytest.approx(\n", + " 371.27, abs=1e-2, rel=1e-5\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_usr.ipynb index 03106f87..381bf0d4 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heat_exchanger_0d_usr.ipynb @@ -1,289 +1,290 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Heat Exchanger 0D Unit Model with Ideal & IAPWS Property Package\n", - "Author: Anuja Deshpande \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "![](heat_exchanger_4.svg)\n", - "\n", - "**Problem Statement**: In this example, we will be heating a benzene-toluene mixture using steam. \n", - "\n", - "**Tube Side Inlet**\n", - "\n", - "Flow Rate = 250 mol/s\n", - "\n", - "Mole fraction (Benzene) = 0.4\n", - "\n", - "Mole fraction (Toluene) = 0.6\n", - "\n", - "Pressure = 101325 Pa\n", - "\n", - "Temperature = 350 K\n", - "\n", - "**Shell Side Inlet**\n", - "\n", - "Flow Rate = 100 mol/s\n", - "\n", - "Mole fraction (Steam) = 1\n", - "\n", - "Pressure = 101325 Pa\n", - "\n", - "Temperature = 450 K\n", - "\n", - "This example will demonstrate the simulation of the 0D heat exchanger by fixing any 2 of the following degrees of freedom:\n", - "- heat transfer area\n", - "- overall heat transfer coefficient\n", - "- minimum approach temperature\n", - "\n", - "\n", - "IDAES documentation reference for heat exchanger 0D model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Setting up the problem in IDAES**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Import pyomo package\n", - "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value, units\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "\n", - "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# import the BTX property package to create a properties block for the flowsheet\n", - "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", - "\n", - "# Import the IAPWS property package to create a properties block for the flowsheet\n", - "from idaes.models.properties import iapws95\n", - "\n", - "from idaes.models.properties.iapws95 import htpx\n", - "\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "\n", - "from idaes.models.properties.modular_properties.examples.BT_ideal import configuration\n", - "\n", - "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Import a heat exchanger unit\n", - "from idaes.models.unit_models.heat_exchanger import (\n", - " HeatExchanger,\n", - " delta_temperature_amtd_callback,\n", - ")\n", - "\n", - "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", - "m = ConcreteModel()\n", - "\n", - "# Steady State Model\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "# Setup property packages for shell and tube side\n", - "# Steam property package\n", - "m.fs.properties_shell = iapws95.Iapws95ParameterBlock()\n", - "\n", - "# BT ideal property package\n", - "m.fs.properties_tube = GenericParameterBlock(**configuration)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the heat exchanger unit, attaching it to the flowsheet\n", - "# Specify that the property package to be used with the heater is the one we created earlier.\n", - "m.fs.heat_exchanger = HeatExchanger(\n", - " delta_temperature_callback=delta_temperature_amtd_callback,\n", - " hot_side_name=\"shell\",\n", - " cold_side_name=\"tube\",\n", - " shell={\"property_package\": m.fs.properties_shell},\n", - " tube={\"property_package\": m.fs.properties_tube},\n", - ")\n", - "\n", - "# Call the degrees_of_freedom function, get initial DOF\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The initial DOF is {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "h = htpx(450 * units.K, P=101325 * units.Pa)\n", - "\n", - "# Fix the inlet conditions\n", - "m.fs.heat_exchanger.shell_inlet.flow_mol.fix(100) # mol/s\n", - "m.fs.heat_exchanger.shell_inlet.pressure.fix(101325)\n", - "m.fs.heat_exchanger.shell_inlet.enth_mol.fix(h) # J/mol\n", - "\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The DOF is {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heat_exchanger.tube_inlet.flow_mol.fix(250) # mol/s\n", - "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", - "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", - "m.fs.heat_exchanger.tube_inlet.pressure.fix(101325) # Pa\n", - "m.fs.heat_exchanger.tube_inlet.temperature[0].fix(350) # K\n", - "\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The DOF is {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 1: Fix overall HTC and the heat transfer area\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heat_exchanger.area.fix(50) # m2\n", - "m.fs.heat_exchanger.overall_heat_transfer_coefficient[0].fix(500) # W/m2/K\n", - "\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The DOF is {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "m.fs.heat_exchanger.initialize(outlvl=idaeslog.INFO)\n", - "\n", - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "solve_status = opt.solve(m)\n", - "\n", - "# Display a readable report\n", - "m.fs.heat_exchanger.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 2: Unfix area and fix shell side outlet temperature\n", - "\n", - "In the previous example, we fixed the heat exchanger area and overall heat transfer coefficient. However, given that the models in IDAES are equation oriented, we can fix the outlet variables. For example, we can fix the outlet temperature for the shell side and solve for the heat exchanger area that will satisfy that condition. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heat_exchanger.area.unfix()\n", - "m.fs.heat_exchanger.shell_outlet.enth_mol.fix(htpx(360 * units.K, P=101325 * units.Pa))\n", - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "result = opt.solve(m)\n", - "\n", - "print(result)\n", - "\n", - "# Display a readable report\n", - "m.fs.heat_exchanger.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Heat Exchanger 0D Unit Model with Ideal & IAPWS Property Package\n", + "Author: Anuja Deshpande \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "![](heat_exchanger_4.svg)\n", + "\n", + "**Problem Statement**: In this example, we will be heating a benzene-toluene mixture using steam. \n", + "\n", + "**Tube Side Inlet**\n", + "\n", + "Flow Rate = 250 mol/s\n", + "\n", + "Mole fraction (Benzene) = 0.4\n", + "\n", + "Mole fraction (Toluene) = 0.6\n", + "\n", + "Pressure = 101325 Pa\n", + "\n", + "Temperature = 350 K\n", + "\n", + "**Shell Side Inlet**\n", + "\n", + "Flow Rate = 100 mol/s\n", + "\n", + "Mole fraction (Steam) = 1\n", + "\n", + "Pressure = 101325 Pa\n", + "\n", + "Temperature = 450 K\n", + "\n", + "This example will demonstrate the simulation of the 0D heat exchanger by fixing any 2 of the following degrees of freedom:\n", + "- heat transfer area\n", + "- overall heat transfer coefficient\n", + "- minimum approach temperature\n", + "\n", + "\n", + "IDAES documentation reference for heat exchanger 0D model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Setting up the problem in IDAES**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import pyomo package\n", + "from pyomo.environ import ConcreteModel, SolverFactory, Constraint, value, units\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "\n", + "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# import the BTX property package to create a properties block for the flowsheet\n", + "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", + "\n", + "# Import the IAPWS property package to create a properties block for the flowsheet\n", + "from idaes.models.properties import iapws95\n", + "\n", + "from idaes.models.properties.iapws95 import htpx\n", + "\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "\n", + "from idaes.models.properties.modular_properties.examples.BT_ideal import configuration\n", + "\n", + "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Import a heat exchanger unit\n", + "from idaes.models.unit_models.heat_exchanger import (\n", + " HeatExchanger,\n", + " delta_temperature_amtd_callback,\n", + ")\n", + "\n", + "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", + "m = ConcreteModel()\n", + "\n", + "# Steady State Model\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "# Setup property packages for shell and tube side\n", + "# Steam property package\n", + "m.fs.properties_shell = iapws95.Iapws95ParameterBlock()\n", + "\n", + "# BT ideal property package\n", + "m.fs.properties_tube = GenericParameterBlock(**configuration)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an instance of the heat exchanger unit, attaching it to the flowsheet\n", + "# Specify that the property package to be used with the heater is the one we created earlier.\n", + "m.fs.heat_exchanger = HeatExchanger(\n", + " delta_temperature_callback=delta_temperature_amtd_callback,\n", + " hot_side_name=\"shell\",\n", + " cold_side_name=\"tube\",\n", + " shell={\"property_package\": m.fs.properties_shell},\n", + " tube={\"property_package\": m.fs.properties_tube},\n", + ")\n", + "\n", + "# Call the degrees_of_freedom function, get initial DOF\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The initial DOF is {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "h = htpx(450 * units.K, P=101325 * units.Pa)\n", + "\n", + "# Fix the inlet conditions\n", + "m.fs.heat_exchanger.shell_inlet.flow_mol.fix(100) # mol/s\n", + "m.fs.heat_exchanger.shell_inlet.pressure.fix(101325)\n", + "m.fs.heat_exchanger.shell_inlet.enth_mol.fix(h) # J/mol\n", + "\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The DOF is {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heat_exchanger.tube_inlet.flow_mol.fix(250) # mol/s\n", + "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", + "m.fs.heat_exchanger.tube_inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", + "m.fs.heat_exchanger.tube_inlet.pressure.fix(101325) # Pa\n", + "m.fs.heat_exchanger.tube_inlet.temperature[0].fix(350) # K\n", + "\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The DOF is {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Option 1: Fix overall HTC and the heat transfer area\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heat_exchanger.area.fix(50) # m2\n", + "m.fs.heat_exchanger.overall_heat_transfer_coefficient[0].fix(500) # W/m2/K\n", + "\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The DOF is {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "m.fs.heat_exchanger.initialize(outlvl=idaeslog.INFO)\n", + "\n", + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "solve_status = opt.solve(m)\n", + "\n", + "# Display a readable report\n", + "m.fs.heat_exchanger.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Option 2: Unfix area and fix shell side outlet temperature\n", + "\n", + "In the previous example, we fixed the heat exchanger area and overall heat transfer coefficient. However, given that the models in IDAES are equation oriented, we can fix the outlet variables. For example, we can fix the outlet temperature for the shell side and solve for the heat exchanger area that will satisfy that condition. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heat_exchanger.area.unfix()\n", + "m.fs.heat_exchanger.shell_outlet.enth_mol.fix(htpx(360 * units.K, P=101325 * units.Pa))\n", + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = opt.solve(m)\n", + "\n", + "print(result)\n", + "\n", + "# Display a readable report\n", + "m.fs.heat_exchanger.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heater.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heater.ipynb index 5774adcb..f3e08dee 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heater.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heater.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heater_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heater_doc.ipynb index 65fc12f9..cec833a5 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heater_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heater_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -205,112 +206,112 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 1 skipped.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 1 skipped.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 1 skipped.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 1 skipped.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_out: State Released.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_out: State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume: Initialization Complete\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater: Initialization Step 1 Complete.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater: Initialization Step 1 Complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater: Initialization Step 2 optimal - Optimal Solution Found.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater: Initialization Step 2 optimal - Optimal Solution Found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater.control_volume.properties_in: State Released.\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater.control_volume.properties_in: State Released.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:10 [INFO] idaes.init.fs.heater: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:20 [INFO] idaes.init.fs.heater: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -546,13 +547,7 @@ "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] } @@ -671,16 +666,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "filenames": { - "image/png": "C:\\Users\\dkgun\\src\\dangunter\\examples\\idaes_examples\\notebooks\\_build\\jupyter_execute\\docs\\unit_models\\operations\\heater_doc_22_0.png" - } - }, + "metadata": {}, "output_type": "display_data" } ], @@ -721,9 +712,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heater_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heater_test.ipynb index c845805c..5631fe61 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heater_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heater_test.ipynb @@ -1,475 +1,476 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Heater Unit Model with Ideal Property Package\n", - "Author: Anuja Deshpande \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "\n", - "![](heater_2.svg)\n", - "\n", - "In this tutorial, we will heat a liquid mixture of benzene-toluene using a simple heater unit model, and an ideal property package with the phases specified to liquid apriori. The inlet specifications are as follows:\n", - "\n", - "* Flow Rate = 1 kmol/hr\n", - "* Mole fraction (Benzene) = 0.4\n", - "* Mole fraction (Toluene) = 0.6\n", - "* Pressure = 101325 Pa\n", - "* Temperature = 353 K\n", - "\n", - "In addition to the inlet specifications, there is one additional unit level specification that needs to be set:\n", - "* Option 1: Specify the outlet temperature\n", - "* Option 2: Specify the heat duty\n", - "\n", - "Therefore, in this tutorial, we will simulate the following cases:\n", - "\n", - "* Case 1: Compute the heat duty (J/s) required to heat the mixture to 363 K.\n", - "* Case 2: Compute the outlet temperature of the mixture when fixing the heat duty to 2 J/s. \n", - "\n", - "IDAES documentation reference for heater model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heater.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the problem in IDAES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import objects from pyomo package\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value\n", - "from pyomo.opt import TerminationCondition, SolverStatus\n", - "\n", - "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "\n", - "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", - "m = ConcreteModel()\n", - "\n", - "m.fs = FlowsheetBlock(\n", - " dynamic=False\n", - ") # dynamic or ss flowsheet needs to be specified here\n", - "\n", - "\n", - "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", - "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", - "\n", - "# Add properties parameter block to the flowsheet with specifications\n", - "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", - " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import heater unit model from the model library\n", - "from idaes.models.unit_models.heater import Heater\n", - "\n", - "# Create an instance of the heater unit, attaching it to the flowsheet\n", - "# Specify that the property package to be used with the heater is the one we created earlier.\n", - "m.fs.heater = Heater(property_package=m.fs.properties)\n", - "\n", - "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "# DOF = Number of Model Variables - Number of Model Constraints\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "\n", - "# Call the degrees_of_freedom function, get initial DOF\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The initial DOF is {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_initial == 6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the BT stream inlet conditions\n", - "m.fs.heater.inlet.flow_mol.fix(\n", - " 1 * 1000 / 3600\n", - ") # converting to mol/s as unit basis is mol/s\n", - "m.fs.heater.inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", - "m.fs.heater.inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", - "m.fs.heater.inlet.pressure.fix(101325) # Pa\n", - "m.fs.heater.inlet.temperature.fix(353) # K" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 1: Fix Outlet Temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heater.outlet.temperature.fix(363)\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The final DOF is {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_final == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flowsheet Initialization\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models.\n", - "The output from initialization can be set to 7 different levels depending on the details required by the user.\n", - "In general, when a particular output level is set, any information at that level and above gets picked up by logger. The default level taken by the logger is INFO. \n", - "More information on these levels can be found in the IDAES documentation: \n", - "https://idaes-pse.readthedocs.io/en/stable/reference_guides/logging.html" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "m.fs.heater.initialize(outlvl=idaeslog.WARNING)\n", - "# From the output it can be inferred that since there are no errors or warnings encountered during initialization, nothing is displayed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at INFO_HIGH\n", - "m.fs.heater.initialize(outlvl=idaeslog.INFO_HIGH)\n", - "# At INFO_HIGH level, details of all the initialization steps are displayed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining Simulation Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "solve_status = opt.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check if termination condition is optimal\n", - "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", - "assert solve_status.solver.status == SolverStatus.ok" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display Heat Duty only\n", - "m.fs.heater.heat_duty.display()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display a readable report\n", - "m.fs.heater.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "# Check results\n", - "assert m.fs.heater.heat_duty[0].value == pytest.approx(459.10, abs=1e-2)\n", - "\n", - "assert m.fs.heater.outlet.flow_mol[0].value == pytest.approx(0.27778, abs=1e-2)\n", - "assert m.fs.heater.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", - " 0.4, abs=1e-3\n", - ")\n", - "assert m.fs.heater.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", - " 0.6, abs=1e-3\n", - ")\n", - "assert m.fs.heater.outlet.temperature[0].value == pytest.approx(363, abs=1e-2)\n", - "assert m.fs.heater.outlet.pressure[0].value == pytest.approx(101325, abs=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 2: Fix Heat Duty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix heat duty and solve the model\n", - "m.fs.heater.outlet.temperature.unfix()\n", - "m.fs.heater.heat_duty.fix(459.10147722222354)\n", - "solve_status = opt.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check if termination condition is optimal\n", - "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", - "assert solve_status.solver.status == SolverStatus.ok" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display outlet temperature only\n", - "m.fs.heater.outlet.temperature.display()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display a readable report\n", - "m.fs.heater.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check results\n", - "assert m.fs.heater.heat_duty[0].value == pytest.approx(459.10, abs=1e-2)\n", - "\n", - "assert m.fs.heater.outlet.flow_mol[0].value == pytest.approx(0.27778, abs=1e-2)\n", - "assert m.fs.heater.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", - " 0.4, abs=1e-3\n", - ")\n", - "assert m.fs.heater.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", - " 0.6, abs=1e-3\n", - ")\n", - "assert m.fs.heater.outlet.temperature[0].value == pytest.approx(363, abs=1e-2)\n", - "assert m.fs.heater.outlet.pressure[0].value == pytest.approx(101325, abs=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting Q vs. Outlet Temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Heat Duty vs Outlet Temperature\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Unfix the heat duty from case 2\n", - "m.fs.heater.heat_duty.unfix()\n", - "\n", - "# Create a list of outlet temperatures for which corresponding heat duty values need to be obtained\n", - "outlet_temp_fixed = [\n", - " 91.256405 + 273.15,\n", - " 90.828456 + 273.15,\n", - " 86.535145 + 273.15,\n", - " 89.383218 + 273.15,\n", - " 93.973657 + 273.15,\n", - " 85.377274 + 273.15,\n", - " 92.399101 + 273.15,\n", - " 94.151562 + 273.15,\n", - " 87.564579 + 273.15,\n", - " 88.767855 + 273.15,\n", - "]\n", - "\n", - "# Fix the outlet temperature values and solve the model to obtain the heat duties\n", - "heat_duty = []\n", - "for temp in outlet_temp_fixed:\n", - " m.fs.heater.outlet.temperature.fix(temp)\n", - " solve_status = opt.solve(m)\n", - " if solve_status.solver.termination_condition == TerminationCondition.optimal:\n", - " heat_duty.append(m.fs.heater.heat_duty[0].value)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plotting the results\n", - "\n", - "plt.figure(\"Q vs. Temperature\")\n", - "plt.plot(outlet_temp_fixed, heat_duty, \"bo\")\n", - "plt.xlim(358.15, 368.15)\n", - "plt.ylim(250, 700)\n", - "plt.xlabel(\"Outlet Temperature (K)\")\n", - "plt.ylabel(\"Heat Duty (W)\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Heater Unit Model with Ideal Property Package\n", + "Author: Anuja Deshpande \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "\n", + "![](heater_2.svg)\n", + "\n", + "In this tutorial, we will heat a liquid mixture of benzene-toluene using a simple heater unit model, and an ideal property package with the phases specified to liquid apriori. The inlet specifications are as follows:\n", + "\n", + "* Flow Rate = 1 kmol/hr\n", + "* Mole fraction (Benzene) = 0.4\n", + "* Mole fraction (Toluene) = 0.6\n", + "* Pressure = 101325 Pa\n", + "* Temperature = 353 K\n", + "\n", + "In addition to the inlet specifications, there is one additional unit level specification that needs to be set:\n", + "* Option 1: Specify the outlet temperature\n", + "* Option 2: Specify the heat duty\n", + "\n", + "Therefore, in this tutorial, we will simulate the following cases:\n", + "\n", + "* Case 1: Compute the heat duty (J/s) required to heat the mixture to 363 K.\n", + "* Case 2: Compute the outlet temperature of the mixture when fixing the heat duty to 2 J/s. \n", + "\n", + "IDAES documentation reference for heater model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heater.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the problem in IDAES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import objects from pyomo package\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value\n", + "from pyomo.opt import TerminationCondition, SolverStatus\n", + "\n", + "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "\n", + "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", + "m = ConcreteModel()\n", + "\n", + "m.fs = FlowsheetBlock(\n", + " dynamic=False\n", + ") # dynamic or ss flowsheet needs to be specified here\n", + "\n", + "\n", + "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", + "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", + "\n", + "# Add properties parameter block to the flowsheet with specifications\n", + "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", + " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import heater unit model from the model library\n", + "from idaes.models.unit_models.heater import Heater\n", + "\n", + "# Create an instance of the heater unit, attaching it to the flowsheet\n", + "# Specify that the property package to be used with the heater is the one we created earlier.\n", + "m.fs.heater = Heater(property_package=m.fs.properties)\n", + "\n", + "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "# DOF = Number of Model Variables - Number of Model Constraints\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "\n", + "# Call the degrees_of_freedom function, get initial DOF\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The initial DOF is {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_initial == 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the BT stream inlet conditions\n", + "m.fs.heater.inlet.flow_mol.fix(\n", + " 1 * 1000 / 3600\n", + ") # converting to mol/s as unit basis is mol/s\n", + "m.fs.heater.inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", + "m.fs.heater.inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", + "m.fs.heater.inlet.pressure.fix(101325) # Pa\n", + "m.fs.heater.inlet.temperature.fix(353) # K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 1: Fix Outlet Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heater.outlet.temperature.fix(363)\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The final DOF is {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_final == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flowsheet Initialization\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models.\n", + "The output from initialization can be set to 7 different levels depending on the details required by the user.\n", + "In general, when a particular output level is set, any information at that level and above gets picked up by logger. The default level taken by the logger is INFO. \n", + "More information on these levels can be found in the IDAES documentation: \n", + "https://idaes-pse.readthedocs.io/en/stable/reference_guides/logging.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "m.fs.heater.initialize(outlvl=idaeslog.WARNING)\n", + "# From the output it can be inferred that since there are no errors or warnings encountered during initialization, nothing is displayed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at INFO_HIGH\n", + "m.fs.heater.initialize(outlvl=idaeslog.INFO_HIGH)\n", + "# At INFO_HIGH level, details of all the initialization steps are displayed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining Simulation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "solve_status = opt.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check if termination condition is optimal\n", + "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", + "assert solve_status.solver.status == SolverStatus.ok" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display Heat Duty only\n", + "m.fs.heater.heat_duty.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a readable report\n", + "m.fs.heater.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "# Check results\n", + "assert m.fs.heater.heat_duty[0].value == pytest.approx(459.10, abs=1e-2)\n", + "\n", + "assert m.fs.heater.outlet.flow_mol[0].value == pytest.approx(0.27778, abs=1e-2)\n", + "assert m.fs.heater.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", + " 0.4, abs=1e-3\n", + ")\n", + "assert m.fs.heater.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", + " 0.6, abs=1e-3\n", + ")\n", + "assert m.fs.heater.outlet.temperature[0].value == pytest.approx(363, abs=1e-2)\n", + "assert m.fs.heater.outlet.pressure[0].value == pytest.approx(101325, abs=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 2: Fix Heat Duty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix heat duty and solve the model\n", + "m.fs.heater.outlet.temperature.unfix()\n", + "m.fs.heater.heat_duty.fix(459.10147722222354)\n", + "solve_status = opt.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check if termination condition is optimal\n", + "assert solve_status.solver.termination_condition == TerminationCondition.optimal\n", + "assert solve_status.solver.status == SolverStatus.ok" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display outlet temperature only\n", + "m.fs.heater.outlet.temperature.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a readable report\n", + "m.fs.heater.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check results\n", + "assert m.fs.heater.heat_duty[0].value == pytest.approx(459.10, abs=1e-2)\n", + "\n", + "assert m.fs.heater.outlet.flow_mol[0].value == pytest.approx(0.27778, abs=1e-2)\n", + "assert m.fs.heater.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", + " 0.4, abs=1e-3\n", + ")\n", + "assert m.fs.heater.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", + " 0.6, abs=1e-3\n", + ")\n", + "assert m.fs.heater.outlet.temperature[0].value == pytest.approx(363, abs=1e-2)\n", + "assert m.fs.heater.outlet.pressure[0].value == pytest.approx(101325, abs=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Q vs. Outlet Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Heat Duty vs Outlet Temperature\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Unfix the heat duty from case 2\n", + "m.fs.heater.heat_duty.unfix()\n", + "\n", + "# Create a list of outlet temperatures for which corresponding heat duty values need to be obtained\n", + "outlet_temp_fixed = [\n", + " 91.256405 + 273.15,\n", + " 90.828456 + 273.15,\n", + " 86.535145 + 273.15,\n", + " 89.383218 + 273.15,\n", + " 93.973657 + 273.15,\n", + " 85.377274 + 273.15,\n", + " 92.399101 + 273.15,\n", + " 94.151562 + 273.15,\n", + " 87.564579 + 273.15,\n", + " 88.767855 + 273.15,\n", + "]\n", + "\n", + "# Fix the outlet temperature values and solve the model to obtain the heat duties\n", + "heat_duty = []\n", + "for temp in outlet_temp_fixed:\n", + " m.fs.heater.outlet.temperature.fix(temp)\n", + " solve_status = opt.solve(m)\n", + " if solve_status.solver.termination_condition == TerminationCondition.optimal:\n", + " heat_duty.append(m.fs.heater.heat_duty[0].value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting the results\n", + "\n", + "plt.figure(\"Q vs. Temperature\")\n", + "plt.plot(outlet_temp_fixed, heat_duty, \"bo\")\n", + "plt.xlim(358.15, 368.15)\n", + "plt.ylim(250, 700)\n", + "plt.xlabel(\"Outlet Temperature (K)\")\n", + "plt.ylabel(\"Heat Duty (W)\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/unit_models/operations/heater_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/heater_usr.ipynb index ea1b4363..14c72d6a 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/heater_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/heater_usr.ipynb @@ -1,369 +1,370 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Heater Unit Model with Ideal Property Package\n", - "Author: Anuja Deshpande \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "\n", - "![](heater_2.svg)\n", - "\n", - "In this tutorial, we will heat a liquid mixture of benzene-toluene using a simple heater unit model, and an ideal property package with the phases specified to liquid apriori. The inlet specifications are as follows:\n", - "\n", - "* Flow Rate = 1 kmol/hr\n", - "* Mole fraction (Benzene) = 0.4\n", - "* Mole fraction (Toluene) = 0.6\n", - "* Pressure = 101325 Pa\n", - "* Temperature = 353 K\n", - "\n", - "In addition to the inlet specifications, there is one additional unit level specification that needs to be set:\n", - "* Option 1: Specify the outlet temperature\n", - "* Option 2: Specify the heat duty\n", - "\n", - "Therefore, in this tutorial, we will simulate the following cases:\n", - "\n", - "* Case 1: Compute the heat duty (J/s) required to heat the mixture to 363 K.\n", - "* Case 2: Compute the outlet temperature of the mixture when fixing the heat duty to 2 J/s. \n", - "\n", - "IDAES documentation reference for heater model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heater.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the problem in IDAES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import objects from pyomo package\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value\n", - "from pyomo.opt import TerminationCondition, SolverStatus\n", - "\n", - "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "\n", - "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", - "m = ConcreteModel()\n", - "\n", - "m.fs = FlowsheetBlock(\n", - " dynamic=False\n", - ") # dynamic or ss flowsheet needs to be specified here\n", - "\n", - "\n", - "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", - "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", - "\n", - "# Add properties parameter block to the flowsheet with specifications\n", - "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", - " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import heater unit model from the model library\n", - "from idaes.models.unit_models.heater import Heater\n", - "\n", - "# Create an instance of the heater unit, attaching it to the flowsheet\n", - "# Specify that the property package to be used with the heater is the one we created earlier.\n", - "m.fs.heater = Heater(property_package=m.fs.properties)\n", - "\n", - "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "# DOF = Number of Model Variables - Number of Model Constraints\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "\n", - "# Call the degrees_of_freedom function, get initial DOF\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The initial DOF is {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the BT stream inlet conditions\n", - "m.fs.heater.inlet.flow_mol.fix(\n", - " 1 * 1000 / 3600\n", - ") # converting to mol/s as unit basis is mol/s\n", - "m.fs.heater.inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", - "m.fs.heater.inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", - "m.fs.heater.inlet.pressure.fix(101325) # Pa\n", - "m.fs.heater.inlet.temperature.fix(353) # K" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 1: Fix Outlet Temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.heater.outlet.temperature.fix(363)\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The final DOF is {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flowsheet Initialization\n", - "\n", - "IDAES includes pre-written initialization routines for all unit models.\n", - "The output from initialization can be set to 7 different levels depending on the details required by the user.\n", - "In general, when a particular output level is set, any information at that level and above gets picked up by logger. The default level taken by the logger is INFO. \n", - "More information on these levels can be found in the IDAES documentation: \n", - "https://idaes-pse.readthedocs.io/en/stable/reference_guides/logging.html" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "m.fs.heater.initialize(outlvl=idaeslog.WARNING)\n", - "# From the output it can be inferred that since there are no errors or warnings encountered during initialization, nothing is displayed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at INFO_HIGH\n", - "m.fs.heater.initialize(outlvl=idaeslog.INFO_HIGH)\n", - "# At INFO_HIGH level, details of all the initialization steps are displayed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining Simulation Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "solve_status = opt.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display Heat Duty only\n", - "m.fs.heater.heat_duty.display()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display a readable report\n", - "m.fs.heater.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 2: Fix Heat Duty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix heat duty and solve the model\n", - "m.fs.heater.outlet.temperature.unfix()\n", - "m.fs.heater.heat_duty.fix(459.10147722222354)\n", - "solve_status = opt.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display outlet temperature only\n", - "m.fs.heater.outlet.temperature.display()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display a readable report\n", - "m.fs.heater.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting Q vs. Outlet Temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Heat Duty vs Outlet Temperature\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Unfix the heat duty from case 2\n", - "m.fs.heater.heat_duty.unfix()\n", - "\n", - "# Create a list of outlet temperatures for which corresponding heat duty values need to be obtained\n", - "outlet_temp_fixed = [\n", - " 91.256405 + 273.15,\n", - " 90.828456 + 273.15,\n", - " 86.535145 + 273.15,\n", - " 89.383218 + 273.15,\n", - " 93.973657 + 273.15,\n", - " 85.377274 + 273.15,\n", - " 92.399101 + 273.15,\n", - " 94.151562 + 273.15,\n", - " 87.564579 + 273.15,\n", - " 88.767855 + 273.15,\n", - "]\n", - "\n", - "# Fix the outlet temperature values and solve the model to obtain the heat duties\n", - "heat_duty = []\n", - "for temp in outlet_temp_fixed:\n", - " m.fs.heater.outlet.temperature.fix(temp)\n", - " solve_status = opt.solve(m)\n", - " if solve_status.solver.termination_condition == TerminationCondition.optimal:\n", - " heat_duty.append(m.fs.heater.heat_duty[0].value)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plotting the results\n", - "\n", - "plt.figure(\"Q vs. Temperature\")\n", - "plt.plot(outlet_temp_fixed, heat_duty, \"bo\")\n", - "plt.xlim(358.15, 368.15)\n", - "plt.ylim(250, 700)\n", - "plt.xlabel(\"Outlet Temperature (K)\")\n", - "plt.ylabel(\"Heat Duty (W)\")\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Heater Unit Model with Ideal Property Package\n", + "Author: Anuja Deshpande \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "\n", + "![](heater_2.svg)\n", + "\n", + "In this tutorial, we will heat a liquid mixture of benzene-toluene using a simple heater unit model, and an ideal property package with the phases specified to liquid apriori. The inlet specifications are as follows:\n", + "\n", + "* Flow Rate = 1 kmol/hr\n", + "* Mole fraction (Benzene) = 0.4\n", + "* Mole fraction (Toluene) = 0.6\n", + "* Pressure = 101325 Pa\n", + "* Temperature = 353 K\n", + "\n", + "In addition to the inlet specifications, there is one additional unit level specification that needs to be set:\n", + "* Option 1: Specify the outlet temperature\n", + "* Option 2: Specify the heat duty\n", + "\n", + "Therefore, in this tutorial, we will simulate the following cases:\n", + "\n", + "* Case 1: Compute the heat duty (J/s) required to heat the mixture to 363 K.\n", + "* Case 2: Compute the outlet temperature of the mixture when fixing the heat duty to 2 J/s. \n", + "\n", + "IDAES documentation reference for heater model: https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heater.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the problem in IDAES" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import objects from pyomo package\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value\n", + "from pyomo.opt import TerminationCondition, SolverStatus\n", + "\n", + "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "\n", + "# Create the ConcreteModel and the FlowsheetBlock, and attach the flowsheet block to it.\n", + "m = ConcreteModel()\n", + "\n", + "m.fs = FlowsheetBlock(\n", + " dynamic=False\n", + ") # dynamic or ss flowsheet needs to be specified here\n", + "\n", + "\n", + "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", + "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", + "\n", + "# Add properties parameter block to the flowsheet with specifications\n", + "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", + " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import heater unit model from the model library\n", + "from idaes.models.unit_models.heater import Heater\n", + "\n", + "# Create an instance of the heater unit, attaching it to the flowsheet\n", + "# Specify that the property package to be used with the heater is the one we created earlier.\n", + "m.fs.heater = Heater(property_package=m.fs.properties)\n", + "\n", + "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "# DOF = Number of Model Variables - Number of Model Constraints\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "\n", + "# Call the degrees_of_freedom function, get initial DOF\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The initial DOF is {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the BT stream inlet conditions\n", + "m.fs.heater.inlet.flow_mol.fix(\n", + " 1 * 1000 / 3600\n", + ") # converting to mol/s as unit basis is mol/s\n", + "m.fs.heater.inlet.mole_frac_comp[0, \"benzene\"].fix(0.4)\n", + "m.fs.heater.inlet.mole_frac_comp[0, \"toluene\"].fix(0.6)\n", + "m.fs.heater.inlet.pressure.fix(101325) # Pa\n", + "m.fs.heater.inlet.temperature.fix(353) # K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 1: Fix Outlet Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.heater.outlet.temperature.fix(363)\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The final DOF is {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flowsheet Initialization\n", + "\n", + "IDAES includes pre-written initialization routines for all unit models.\n", + "The output from initialization can be set to 7 different levels depending on the details required by the user.\n", + "In general, when a particular output level is set, any information at that level and above gets picked up by logger. The default level taken by the logger is INFO. \n", + "More information on these levels can be found in the IDAES documentation: \n", + "https://idaes-pse.readthedocs.io/en/stable/reference_guides/logging.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "m.fs.heater.initialize(outlvl=idaeslog.WARNING)\n", + "# From the output it can be inferred that since there are no errors or warnings encountered during initialization, nothing is displayed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at INFO_HIGH\n", + "m.fs.heater.initialize(outlvl=idaeslog.INFO_HIGH)\n", + "# At INFO_HIGH level, details of all the initialization steps are displayed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining Simulation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "solve_status = opt.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display Heat Duty only\n", + "m.fs.heater.heat_duty.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a readable report\n", + "m.fs.heater.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 2: Fix Heat Duty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix heat duty and solve the model\n", + "m.fs.heater.outlet.temperature.unfix()\n", + "m.fs.heater.heat_duty.fix(459.10147722222354)\n", + "solve_status = opt.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display outlet temperature only\n", + "m.fs.heater.outlet.temperature.display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a readable report\n", + "m.fs.heater.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Q vs. Outlet Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Heat Duty vs Outlet Temperature\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Unfix the heat duty from case 2\n", + "m.fs.heater.heat_duty.unfix()\n", + "\n", + "# Create a list of outlet temperatures for which corresponding heat duty values need to be obtained\n", + "outlet_temp_fixed = [\n", + " 91.256405 + 273.15,\n", + " 90.828456 + 273.15,\n", + " 86.535145 + 273.15,\n", + " 89.383218 + 273.15,\n", + " 93.973657 + 273.15,\n", + " 85.377274 + 273.15,\n", + " 92.399101 + 273.15,\n", + " 94.151562 + 273.15,\n", + " 87.564579 + 273.15,\n", + " 88.767855 + 273.15,\n", + "]\n", + "\n", + "# Fix the outlet temperature values and solve the model to obtain the heat duties\n", + "heat_duty = []\n", + "for temp in outlet_temp_fixed:\n", + " m.fs.heater.outlet.temperature.fix(temp)\n", + " solve_status = opt.solve(m)\n", + " if solve_status.solver.termination_condition == TerminationCondition.optimal:\n", + " heat_duty.append(m.fs.heater.heat_duty[0].value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting the results\n", + "\n", + "plt.figure(\"Q vs. Temperature\")\n", + "plt.plot(outlet_temp_fixed, heat_duty, \"bo\")\n", + "plt.xlim(358.15, 368.15)\n", + "plt.ylim(250, 700)\n", + "plt.xlabel(\"Outlet Temperature (K)\")\n", + "plt.ylabel(\"Heat Duty (W)\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/docs/unit_models/operations/mixer.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/mixer.ipynb index c7a7e2a2..96cade8e 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/mixer.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/mixer.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/mixer_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/mixer_doc.ipynb index 463d11e8..4d9b1705 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/mixer_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/mixer_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -302,7 +303,7 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n", @@ -613,7 +614,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/mixer_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/mixer_test.ipynb index 9bb28c24..d39b8092 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/mixer_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/mixer_test.ipynb @@ -1,561 +1,562 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mixer Unit Model with Ideal Property Package\n", - "Author: Anuja Deshpande \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "![](mixer.svg)\n", - "\n", - "## Learning Outcomes\n", - "\n", - "- Demonstrate use of the Mixer unit model in IDAES\n", - "- Demonstrate different options available\n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "In this example, we will be mixing liquid benzene and liquid toluene streams to form a mixture. The inlet conditions are as follows:\n", - "\n", - "**Stream 1:**\n", - "\n", - "Benzene Flow Rate = 100 mol/s\n", - "\n", - "Pressure = 101325 Pa \n", - "\n", - "Temperature = 353 K\n", - "\n", - "**Stream 2**\n", - "\n", - "Toluene Flow Rate = 100 mol/s\n", - "\n", - "Pressure = 202650 Pa \n", - "\n", - "Temperature = 356 K\n", - "\n", - "We will look at two cases in this tutorial:\n", - "\n", - "* Case 1: Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\"\n", - "\n", - "* Case 2: Specify the inlet names, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream)\n", - "\n", - "**Note: \n", - "When the momentum mixing type is set to 'minimize', the mixed stream pressure takes the minimum value among all inlet stream pressures.\n", - "When the momentum mixing type is set to 'equality', the mixed stream, along with all inlet streams have the same value of pressure.**\n", - "\n", - "\n", - "For more details, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/stable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the problem in IDAES" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the following cell, we will be importing the necessary components from Pyomo and IDAES." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import objects from pyomo package\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value\n", - "\n", - "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import the mixer unit model\n", - "from idaes.models.unit_models import Mixer, MomentumMixingType\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "\n", - "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", - "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", - "\n", - "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "# DOF = Number of Model Variables - Number of Model Constraints\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Create the ConcreteModel and the FlowsheetBlock objects, and attach the flowsheet block to it.\n", - "m = ConcreteModel()\n", - "\n", - "m.fs = FlowsheetBlock(\n", - " dynamic=False\n", - ") # dynamic or ss flowsheet needs to be specified here\n", - "\n", - "# Add properties parameter block to the flowsheet with specifications\n", - "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", - " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 1:\n", - "\n", - "Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\". " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the mixer unit, attaching it to the flowsheet\n", - "# Specify that the property package to be used with the mixer is the\n", - "# one we created earlier, the number of mixer inlets is 2, and momentum\n", - "# mixing type is minimize\n", - "\n", - "m.fs.mixer_1 = Mixer(\n", - " property_package=m.fs.properties,\n", - " num_inlets=2,\n", - " momentum_mixing_type=MomentumMixingType.minimize,\n", - ")\n", - "\n", - "# Call the degrees_of_freedom function, get initial DOF\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The initial degrees of freedom is: {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_initial == 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For case 1, we chose to specify only the number of inlets and names were not specified. When this option is selected, the inlets are named as \"inlet_1\", \"inlet_2\" and so on depending on the number of inlets specified. In the following cell, we will use this naming convention to specify the inlet conditions. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the inlet conditions\n", - "\n", - "# Benzene stream\n", - "m.fs.mixer_1.inlet_1.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"benzene\"].fix(1)\n", - "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"toluene\"].fix(0)\n", - "m.fs.mixer_1.inlet_1.pressure.fix(101325) # Pa\n", - "m.fs.mixer_1.inlet_1.temperature.fix(353) # K\n", - "\n", - "# Toluene stream\n", - "m.fs.mixer_1.inlet_2.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"benzene\"].fix(0)\n", - "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"toluene\"].fix(1)\n", - "m.fs.mixer_1.inlet_2.pressure.fix(202650) # Pa\n", - "m.fs.mixer_1.inlet_2.temperature.fix(356) # K\n", - "\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_final == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flowsheet Initialization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "m.fs.mixer_1.initialize(outlvl=idaeslog.WARNING)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining Simulation Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "result = opt.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from pyomo.opt import TerminationCondition, SolverStatus\n", - "\n", - "# Check if termination condition is optimal\n", - "assert result.solver.termination_condition == TerminationCondition.optimal\n", - "assert result.solver.status == SolverStatus.ok" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display output report\n", - "m.fs.mixer_1.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Energy Balance Validation\n", - "\n", - "molflow_1 = m.fs.mixer_1.inlet_1.flow_mol[0].value\n", - "molflow_2 = m.fs.mixer_1.inlet_2.flow_mol[0].value\n", - "benzene_1 = m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"benzene\"].value\n", - "toluene_2 = m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"toluene\"].value\n", - "\n", - "# total_enthalpy_in = molflow_1*benzene_1*molar_enthalpy_1 + molflow_2*toluene_2*molar_enthalpy_2\n", - "total_enthalpy_in1 = (\n", - " molflow_1 * m.fs.mixer_1.inlet_1_state[0].enth_mol_phase[\"Liq\"].value\n", - ")\n", - "total_enthalpy_in2 = (\n", - " molflow_2 * m.fs.mixer_1.inlet_2_state[0].enth_mol_phase[\"Liq\"].value\n", - ")\n", - "\n", - "molar_enthalpy_out = m.fs.mixer_1.mixed_state[0].enth_mol_phase[\"Liq\"].value\n", - "mole_flow_out = m.fs.mixer_1.outlet.flow_mol[0].value\n", - "total_enthalpy_out = mole_flow_out * molar_enthalpy_out" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "# Check results\n", - "assert mole_flow_out == pytest.approx(200, abs=1e-3)\n", - "assert m.fs.mixer_1.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", - " 0.5, abs=1e-3\n", - ")\n", - "assert m.fs.mixer_1.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", - " 0.5, abs=1e-3\n", - ")\n", - "assert m.fs.mixer_1.outlet.temperature[0].value == pytest.approx(354.61, abs=1e-2)\n", - "assert m.fs.mixer_1.outlet.pressure[0].value == pytest.approx(101325, abs=1)\n", - "assert total_enthalpy_out - total_enthalpy_in1 - total_enthalpy_in2 == pytest.approx(\n", - " 0, abs=1e-2\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 2\n", - "\n", - "For case 2, we will specify the inlet names for the two inlets, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream). We will name the 2 inlets as \"benzene_inlet\" and \"toluene_inlet\". " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of another mixer unit, attaching it to the same flowsheet.\n", - "# Specify that the property package to be used with the mixer is the one we created earlier,\n", - "# inlet list is specified but names are specified, and momentum mixing type is equality\n", - "\n", - "m.fs.mixer_2 = Mixer(\n", - " property_package=m.fs.properties,\n", - " inlet_list=[\"benzene_inlet\", \"toluene_inlet\"],\n", - " momentum_mixing_type=MomentumMixingType.equality,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check the required degrees of freedom\n", - "DOF_init = degrees_of_freedom(m.fs.mixer_2)\n", - "print(\"The initial degrees of freedom is: {0}\".format(DOF_init))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the degrees of freedom has dropped by 1 to 9 when compared with case 1. This is because we selected the `momentum_mixing_type` as `MomentumMixingType.equality` which basically adds a constraint that equates the pressure between all inlets and the outlet. Therefore, when we specify the inlet confitions in the next cell, we will define the pressure for only the `benzene_inlet` stream. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert DOF_init == 9" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the stream inlet conditions\n", - "\n", - "# Benzene stream\n", - "m.fs.mixer_2.benzene_inlet.flow_mol.fix(\n", - " 100\n", - ") # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"benzene\"].fix(1)\n", - "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"toluene\"].fix(0)\n", - "m.fs.mixer_2.benzene_inlet.pressure.fix(\n", - " 101325\n", - ") # Pa , Another option is m1.fs.mixer2.inlet2.pressure.fix(202650)\n", - "m.fs.mixer_2.benzene_inlet.temperature.fix(353) # K\n", - "\n", - "# Toluene stream\n", - "m.fs.mixer_2.toluene_inlet.flow_mol.fix(\n", - " 100\n", - ") # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"benzene\"].fix(0)\n", - "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"toluene\"].fix(1)\n", - "m.fs.mixer_2.toluene_inlet.temperature.fix(356) # K\n", - "\n", - "DOF_final = degrees_of_freedom(m.fs.mixer_2)\n", - "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flowsheet Initialization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "\n", - "m.fs.mixer_2.initialize(outlvl=idaeslog.WARNING)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining Simulation Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "result = opt.solve(m.fs.mixer_2, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "from pyomo.opt import TerminationCondition, SolverStatus\n", - "\n", - "# Check if termination condition is optimal\n", - "assert result.solver.termination_condition == TerminationCondition.optimal\n", - "assert result.solver.status == SolverStatus.ok" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display a readable report\n", - "m.fs.mixer_2.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check results\n", - "assert m.fs.mixer_2.outlet.flow_mol[0].value == pytest.approx(200, abs=1e-2)\n", - "assert m.fs.mixer_2.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", - " 0.5, abs=1e-3\n", - ")\n", - "assert m.fs.mixer_2.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", - " 0.5, abs=1e-3\n", - ")\n", - "assert m.fs.mixer_2.outlet.temperature[0].value == pytest.approx(354.61, abs=1e-2)\n", - "assert m.fs.mixer_2.outlet.pressure[0].value == pytest.approx(101325, abs=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mixer Unit Model with Ideal Property Package\n", + "Author: Anuja Deshpande \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "![](mixer.svg)\n", + "\n", + "## Learning Outcomes\n", + "\n", + "- Demonstrate use of the Mixer unit model in IDAES\n", + "- Demonstrate different options available\n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "In this example, we will be mixing liquid benzene and liquid toluene streams to form a mixture. The inlet conditions are as follows:\n", + "\n", + "**Stream 1:**\n", + "\n", + "Benzene Flow Rate = 100 mol/s\n", + "\n", + "Pressure = 101325 Pa \n", + "\n", + "Temperature = 353 K\n", + "\n", + "**Stream 2**\n", + "\n", + "Toluene Flow Rate = 100 mol/s\n", + "\n", + "Pressure = 202650 Pa \n", + "\n", + "Temperature = 356 K\n", + "\n", + "We will look at two cases in this tutorial:\n", + "\n", + "* Case 1: Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\"\n", + "\n", + "* Case 2: Specify the inlet names, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream)\n", + "\n", + "**Note: \n", + "When the momentum mixing type is set to 'minimize', the mixed stream pressure takes the minimum value among all inlet stream pressures.\n", + "When the momentum mixing type is set to 'equality', the mixed stream, along with all inlet streams have the same value of pressure.**\n", + "\n", + "\n", + "For more details, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/stable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the problem in IDAES" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following cell, we will be importing the necessary components from Pyomo and IDAES." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import objects from pyomo package\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value\n", + "\n", + "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import the mixer unit model\n", + "from idaes.models.unit_models import Mixer, MomentumMixingType\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "\n", + "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", + "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", + "\n", + "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "# DOF = Number of Model Variables - Number of Model Constraints\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Create the ConcreteModel and the FlowsheetBlock objects, and attach the flowsheet block to it.\n", + "m = ConcreteModel()\n", + "\n", + "m.fs = FlowsheetBlock(\n", + " dynamic=False\n", + ") # dynamic or ss flowsheet needs to be specified here\n", + "\n", + "# Add properties parameter block to the flowsheet with specifications\n", + "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", + " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 1:\n", + "\n", + "Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\". " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an instance of the mixer unit, attaching it to the flowsheet\n", + "# Specify that the property package to be used with the mixer is the\n", + "# one we created earlier, the number of mixer inlets is 2, and momentum\n", + "# mixing type is minimize\n", + "\n", + "m.fs.mixer_1 = Mixer(\n", + " property_package=m.fs.properties,\n", + " num_inlets=2,\n", + " momentum_mixing_type=MomentumMixingType.minimize,\n", + ")\n", + "\n", + "# Call the degrees_of_freedom function, get initial DOF\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The initial degrees of freedom is: {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_initial == 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For case 1, we chose to specify only the number of inlets and names were not specified. When this option is selected, the inlets are named as \"inlet_1\", \"inlet_2\" and so on depending on the number of inlets specified. In the following cell, we will use this naming convention to specify the inlet conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the inlet conditions\n", + "\n", + "# Benzene stream\n", + "m.fs.mixer_1.inlet_1.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"benzene\"].fix(1)\n", + "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"toluene\"].fix(0)\n", + "m.fs.mixer_1.inlet_1.pressure.fix(101325) # Pa\n", + "m.fs.mixer_1.inlet_1.temperature.fix(353) # K\n", + "\n", + "# Toluene stream\n", + "m.fs.mixer_1.inlet_2.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"benzene\"].fix(0)\n", + "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"toluene\"].fix(1)\n", + "m.fs.mixer_1.inlet_2.pressure.fix(202650) # Pa\n", + "m.fs.mixer_1.inlet_2.temperature.fix(356) # K\n", + "\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_final == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flowsheet Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "m.fs.mixer_1.initialize(outlvl=idaeslog.WARNING)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining Simulation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "result = opt.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from pyomo.opt import TerminationCondition, SolverStatus\n", + "\n", + "# Check if termination condition is optimal\n", + "assert result.solver.termination_condition == TerminationCondition.optimal\n", + "assert result.solver.status == SolverStatus.ok" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display output report\n", + "m.fs.mixer_1.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Energy Balance Validation\n", + "\n", + "molflow_1 = m.fs.mixer_1.inlet_1.flow_mol[0].value\n", + "molflow_2 = m.fs.mixer_1.inlet_2.flow_mol[0].value\n", + "benzene_1 = m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"benzene\"].value\n", + "toluene_2 = m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"toluene\"].value\n", + "\n", + "# total_enthalpy_in = molflow_1*benzene_1*molar_enthalpy_1 + molflow_2*toluene_2*molar_enthalpy_2\n", + "total_enthalpy_in1 = (\n", + " molflow_1 * m.fs.mixer_1.inlet_1_state[0].enth_mol_phase[\"Liq\"].value\n", + ")\n", + "total_enthalpy_in2 = (\n", + " molflow_2 * m.fs.mixer_1.inlet_2_state[0].enth_mol_phase[\"Liq\"].value\n", + ")\n", + "\n", + "molar_enthalpy_out = m.fs.mixer_1.mixed_state[0].enth_mol_phase[\"Liq\"].value\n", + "mole_flow_out = m.fs.mixer_1.outlet.flow_mol[0].value\n", + "total_enthalpy_out = mole_flow_out * molar_enthalpy_out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "# Check results\n", + "assert mole_flow_out == pytest.approx(200, abs=1e-3)\n", + "assert m.fs.mixer_1.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", + " 0.5, abs=1e-3\n", + ")\n", + "assert m.fs.mixer_1.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", + " 0.5, abs=1e-3\n", + ")\n", + "assert m.fs.mixer_1.outlet.temperature[0].value == pytest.approx(354.61, abs=1e-2)\n", + "assert m.fs.mixer_1.outlet.pressure[0].value == pytest.approx(101325, abs=1)\n", + "assert total_enthalpy_out - total_enthalpy_in1 - total_enthalpy_in2 == pytest.approx(\n", + " 0, abs=1e-2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 2\n", + "\n", + "For case 2, we will specify the inlet names for the two inlets, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream). We will name the 2 inlets as \"benzene_inlet\" and \"toluene_inlet\". " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an instance of another mixer unit, attaching it to the same flowsheet.\n", + "# Specify that the property package to be used with the mixer is the one we created earlier,\n", + "# inlet list is specified but names are specified, and momentum mixing type is equality\n", + "\n", + "m.fs.mixer_2 = Mixer(\n", + " property_package=m.fs.properties,\n", + " inlet_list=[\"benzene_inlet\", \"toluene_inlet\"],\n", + " momentum_mixing_type=MomentumMixingType.equality,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check the required degrees of freedom\n", + "DOF_init = degrees_of_freedom(m.fs.mixer_2)\n", + "print(\"The initial degrees of freedom is: {0}\".format(DOF_init))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the degrees of freedom has dropped by 1 to 9 when compared with case 1. This is because we selected the `momentum_mixing_type` as `MomentumMixingType.equality` which basically adds a constraint that equates the pressure between all inlets and the outlet. Therefore, when we specify the inlet confitions in the next cell, we will define the pressure for only the `benzene_inlet` stream. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert DOF_init == 9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the stream inlet conditions\n", + "\n", + "# Benzene stream\n", + "m.fs.mixer_2.benzene_inlet.flow_mol.fix(\n", + " 100\n", + ") # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"benzene\"].fix(1)\n", + "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"toluene\"].fix(0)\n", + "m.fs.mixer_2.benzene_inlet.pressure.fix(\n", + " 101325\n", + ") # Pa , Another option is m1.fs.mixer2.inlet2.pressure.fix(202650)\n", + "m.fs.mixer_2.benzene_inlet.temperature.fix(353) # K\n", + "\n", + "# Toluene stream\n", + "m.fs.mixer_2.toluene_inlet.flow_mol.fix(\n", + " 100\n", + ") # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"benzene\"].fix(0)\n", + "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"toluene\"].fix(1)\n", + "m.fs.mixer_2.toluene_inlet.temperature.fix(356) # K\n", + "\n", + "DOF_final = degrees_of_freedom(m.fs.mixer_2)\n", + "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flowsheet Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "\n", + "m.fs.mixer_2.initialize(outlvl=idaeslog.WARNING)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining Simulation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "result = opt.solve(m.fs.mixer_2, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "from pyomo.opt import TerminationCondition, SolverStatus\n", + "\n", + "# Check if termination condition is optimal\n", + "assert result.solver.termination_condition == TerminationCondition.optimal\n", + "assert result.solver.status == SolverStatus.ok" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a readable report\n", + "m.fs.mixer_2.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check results\n", + "assert m.fs.mixer_2.outlet.flow_mol[0].value == pytest.approx(200, abs=1e-2)\n", + "assert m.fs.mixer_2.outlet.mole_frac_comp[0, \"benzene\"].value == pytest.approx(\n", + " 0.5, abs=1e-3\n", + ")\n", + "assert m.fs.mixer_2.outlet.mole_frac_comp[0, \"toluene\"].value == pytest.approx(\n", + " 0.5, abs=1e-3\n", + ")\n", + "assert m.fs.mixer_2.outlet.temperature[0].value == pytest.approx(354.61, abs=1e-2)\n", + "assert m.fs.mixer_2.outlet.pressure[0].value == pytest.approx(101325, abs=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/operations/mixer_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/mixer_usr.ipynb index 051e8bf0..972e47fa 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/mixer_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/mixer_usr.ipynb @@ -1,409 +1,410 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mixer Unit Model with Ideal Property Package\n", - "Author: Anuja Deshpande \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "![](mixer.svg)\n", - "\n", - "## Learning Outcomes\n", - "\n", - "- Demonstrate use of the Mixer unit model in IDAES\n", - "- Demonstrate different options available\n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "In this example, we will be mixing liquid benzene and liquid toluene streams to form a mixture. The inlet conditions are as follows:\n", - "\n", - "**Stream 1:**\n", - "\n", - "Benzene Flow Rate = 100 mol/s\n", - "\n", - "Pressure = 101325 Pa \n", - "\n", - "Temperature = 353 K\n", - "\n", - "**Stream 2**\n", - "\n", - "Toluene Flow Rate = 100 mol/s\n", - "\n", - "Pressure = 202650 Pa \n", - "\n", - "Temperature = 356 K\n", - "\n", - "We will look at two cases in this tutorial:\n", - "\n", - "* Case 1: Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\"\n", - "\n", - "* Case 2: Specify the inlet names, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream)\n", - "\n", - "**Note: \n", - "When the momentum mixing type is set to 'minimize', the mixed stream pressure takes the minimum value among all inlet stream pressures.\n", - "When the momentum mixing type is set to 'equality', the mixed stream, along with all inlet streams have the same value of pressure.**\n", - "\n", - "\n", - "For more details, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/stable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the problem in IDAES" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the following cell, we will be importing the necessary components from Pyomo and IDAES." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import objects from pyomo package\n", - "from pyomo.environ import ConcreteModel, SolverFactory, value\n", - "\n", - "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", - "from idaes.core import FlowsheetBlock\n", - "\n", - "# Import the mixer unit model\n", - "from idaes.models.unit_models import Mixer, MomentumMixingType\n", - "\n", - "# Import idaes logger to set output levels\n", - "import idaes.logger as idaeslog\n", - "\n", - "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", - "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", - "\n", - "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", - "# DOF = Number of Model Variables - Number of Model Constraints\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "# Create the ConcreteModel and the FlowsheetBlock objects, and attach the flowsheet block to it.\n", - "m = ConcreteModel()\n", - "\n", - "m.fs = FlowsheetBlock(\n", - " dynamic=False\n", - ") # dynamic or ss flowsheet needs to be specified here\n", - "\n", - "# Add properties parameter block to the flowsheet with specifications\n", - "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", - " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 1:\n", - "\n", - "Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\". " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the mixer unit, attaching it to the flowsheet\n", - "# Specify that the property package to be used with the mixer is the\n", - "# one we created earlier, the number of mixer inlets is 2, and momentum\n", - "# mixing type is minimize\n", - "\n", - "m.fs.mixer_1 = Mixer(\n", - " property_package=m.fs.properties,\n", - " num_inlets=2,\n", - " momentum_mixing_type=MomentumMixingType.minimize,\n", - ")\n", - "\n", - "# Call the degrees_of_freedom function, get initial DOF\n", - "DOF_initial = degrees_of_freedom(m)\n", - "print(\"The initial degrees of freedom is: {0}\".format(DOF_initial))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For case 1, we chose to specify only the number of inlets and names were not specified. When this option is selected, the inlets are named as \"inlet_1\", \"inlet_2\" and so on depending on the number of inlets specified. In the following cell, we will use this naming convention to specify the inlet conditions. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the inlet conditions\n", - "\n", - "# Benzene stream\n", - "m.fs.mixer_1.inlet_1.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"benzene\"].fix(1)\n", - "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"toluene\"].fix(0)\n", - "m.fs.mixer_1.inlet_1.pressure.fix(101325) # Pa\n", - "m.fs.mixer_1.inlet_1.temperature.fix(353) # K\n", - "\n", - "# Toluene stream\n", - "m.fs.mixer_1.inlet_2.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"benzene\"].fix(0)\n", - "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"toluene\"].fix(1)\n", - "m.fs.mixer_1.inlet_2.pressure.fix(202650) # Pa\n", - "m.fs.mixer_1.inlet_2.temperature.fix(356) # K\n", - "\n", - "# Call the degrees_of_freedom function, get final DOF\n", - "DOF_final = degrees_of_freedom(m)\n", - "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flowsheet Initialization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "m.fs.mixer_1.initialize(outlvl=idaeslog.WARNING)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining Simulation Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "result = opt.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display output report\n", - "m.fs.mixer_1.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Case 2\n", - "\n", - "For case 2, we will specify the inlet names for the two inlets, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream). We will name the 2 inlets as \"benzene_inlet\" and \"toluene_inlet\". " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of another mixer unit, attaching it to the same flowsheet.\n", - "# Specify that the property package to be used with the mixer is the one we created earlier,\n", - "# inlet list is specified but names are specified, and momentum mixing type is equality\n", - "\n", - "m.fs.mixer_2 = Mixer(\n", - " property_package=m.fs.properties,\n", - " inlet_list=[\"benzene_inlet\", \"toluene_inlet\"],\n", - " momentum_mixing_type=MomentumMixingType.equality,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check the required degrees of freedom\n", - "DOF_init = degrees_of_freedom(m.fs.mixer_2)\n", - "print(\"The initial degrees of freedom is: {0}\".format(DOF_init))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the degrees of freedom has dropped by 1 to 9 when compared with case 1. This is because we selected the `momentum_mixing_type` as `MomentumMixingType.equality` which basically adds a constraint that equates the pressure between all inlets and the outlet. Therefore, when we specify the inlet confitions in the next cell, we will define the pressure for only the `benzene_inlet` stream. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the stream inlet conditions\n", - "\n", - "# Benzene stream\n", - "m.fs.mixer_2.benzene_inlet.flow_mol.fix(\n", - " 100\n", - ") # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"benzene\"].fix(1)\n", - "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"toluene\"].fix(0)\n", - "m.fs.mixer_2.benzene_inlet.pressure.fix(\n", - " 101325\n", - ") # Pa , Another option is m1.fs.mixer2.inlet2.pressure.fix(202650)\n", - "m.fs.mixer_2.benzene_inlet.temperature.fix(353) # K\n", - "\n", - "# Toluene stream\n", - "m.fs.mixer_2.toluene_inlet.flow_mol.fix(\n", - " 100\n", - ") # converting to mol/s as unit basis is mol/s\n", - "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"benzene\"].fix(0)\n", - "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"toluene\"].fix(1)\n", - "m.fs.mixer_2.toluene_inlet.temperature.fix(356) # K\n", - "\n", - "DOF_final = degrees_of_freedom(m.fs.mixer_2)\n", - "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flowsheet Initialization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the flowsheet, and set the output at WARNING\n", - "\n", - "m.fs.mixer_2.initialize(outlvl=idaeslog.WARNING)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining Simulation Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the simulation using ipopt\n", - "# Note: If the degrees of freedom = 0, we have a square problem\n", - "opt = SolverFactory(\"ipopt\")\n", - "result = opt.solve(m.fs.mixer_2, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Display a readable report\n", - "m.fs.mixer_2.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mixer Unit Model with Ideal Property Package\n", + "Author: Anuja Deshpande \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "![](mixer.svg)\n", + "\n", + "## Learning Outcomes\n", + "\n", + "- Demonstrate use of the Mixer unit model in IDAES\n", + "- Demonstrate different options available\n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "In this example, we will be mixing liquid benzene and liquid toluene streams to form a mixture. The inlet conditions are as follows:\n", + "\n", + "**Stream 1:**\n", + "\n", + "Benzene Flow Rate = 100 mol/s\n", + "\n", + "Pressure = 101325 Pa \n", + "\n", + "Temperature = 353 K\n", + "\n", + "**Stream 2**\n", + "\n", + "Toluene Flow Rate = 100 mol/s\n", + "\n", + "Pressure = 202650 Pa \n", + "\n", + "Temperature = 356 K\n", + "\n", + "We will look at two cases in this tutorial:\n", + "\n", + "* Case 1: Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\"\n", + "\n", + "* Case 2: Specify the inlet names, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream)\n", + "\n", + "**Note: \n", + "When the momentum mixing type is set to 'minimize', the mixed stream pressure takes the minimum value among all inlet stream pressures.\n", + "When the momentum mixing type is set to 'equality', the mixed stream, along with all inlet streams have the same value of pressure.**\n", + "\n", + "\n", + "For more details, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/stable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the problem in IDAES" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following cell, we will be importing the necessary components from Pyomo and IDAES." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import objects from pyomo package\n", + "from pyomo.environ import ConcreteModel, SolverFactory, value\n", + "\n", + "# Import the main FlowsheetBlock from IDAES. The flowsheet block will contain the unit model\n", + "from idaes.core import FlowsheetBlock\n", + "\n", + "# Import the mixer unit model\n", + "from idaes.models.unit_models import Mixer, MomentumMixingType\n", + "\n", + "# Import idaes logger to set output levels\n", + "import idaes.logger as idaeslog\n", + "\n", + "# Import the BTX_ideal property package to create a properties block for the flowsheet\n", + "from idaes.models.properties.activity_coeff_models import BTX_activity_coeff_VLE\n", + "\n", + "# Import the degrees_of_freedom function from the idaes.core.util.model_statistics package\n", + "# DOF = Number of Model Variables - Number of Model Constraints\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "# Create the ConcreteModel and the FlowsheetBlock objects, and attach the flowsheet block to it.\n", + "m = ConcreteModel()\n", + "\n", + "m.fs = FlowsheetBlock(\n", + " dynamic=False\n", + ") # dynamic or ss flowsheet needs to be specified here\n", + "\n", + "# Add properties parameter block to the flowsheet with specifications\n", + "m.fs.properties = BTX_activity_coeff_VLE.BTXParameterBlock(\n", + " valid_phase=\"Liq\", activity_coeff_model=\"Ideal\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 1:\n", + "\n", + "Specify the number of inlets to the mixer, and set the `momentum_mixing` type set to \"minimize\". " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an instance of the mixer unit, attaching it to the flowsheet\n", + "# Specify that the property package to be used with the mixer is the\n", + "# one we created earlier, the number of mixer inlets is 2, and momentum\n", + "# mixing type is minimize\n", + "\n", + "m.fs.mixer_1 = Mixer(\n", + " property_package=m.fs.properties,\n", + " num_inlets=2,\n", + " momentum_mixing_type=MomentumMixingType.minimize,\n", + ")\n", + "\n", + "# Call the degrees_of_freedom function, get initial DOF\n", + "DOF_initial = degrees_of_freedom(m)\n", + "print(\"The initial degrees of freedom is: {0}\".format(DOF_initial))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For case 1, we chose to specify only the number of inlets and names were not specified. When this option is selected, the inlets are named as \"inlet_1\", \"inlet_2\" and so on depending on the number of inlets specified. In the following cell, we will use this naming convention to specify the inlet conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the inlet conditions\n", + "\n", + "# Benzene stream\n", + "m.fs.mixer_1.inlet_1.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"benzene\"].fix(1)\n", + "m.fs.mixer_1.inlet_1.mole_frac_comp[0, \"toluene\"].fix(0)\n", + "m.fs.mixer_1.inlet_1.pressure.fix(101325) # Pa\n", + "m.fs.mixer_1.inlet_1.temperature.fix(353) # K\n", + "\n", + "# Toluene stream\n", + "m.fs.mixer_1.inlet_2.flow_mol.fix(100) # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"benzene\"].fix(0)\n", + "m.fs.mixer_1.inlet_2.mole_frac_comp[0, \"toluene\"].fix(1)\n", + "m.fs.mixer_1.inlet_2.pressure.fix(202650) # Pa\n", + "m.fs.mixer_1.inlet_2.temperature.fix(356) # K\n", + "\n", + "# Call the degrees_of_freedom function, get final DOF\n", + "DOF_final = degrees_of_freedom(m)\n", + "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flowsheet Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "m.fs.mixer_1.initialize(outlvl=idaeslog.WARNING)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining Simulation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "result = opt.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display output report\n", + "m.fs.mixer_1.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Case 2\n", + "\n", + "For case 2, we will specify the inlet names for the two inlets, and set `momentum_mixing` type set to \"equality\" (in this case, pressure will be specified for only one inlet stream). We will name the 2 inlets as \"benzene_inlet\" and \"toluene_inlet\". " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an instance of another mixer unit, attaching it to the same flowsheet.\n", + "# Specify that the property package to be used with the mixer is the one we created earlier,\n", + "# inlet list is specified but names are specified, and momentum mixing type is equality\n", + "\n", + "m.fs.mixer_2 = Mixer(\n", + " property_package=m.fs.properties,\n", + " inlet_list=[\"benzene_inlet\", \"toluene_inlet\"],\n", + " momentum_mixing_type=MomentumMixingType.equality,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check the required degrees of freedom\n", + "DOF_init = degrees_of_freedom(m.fs.mixer_2)\n", + "print(\"The initial degrees of freedom is: {0}\".format(DOF_init))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the degrees of freedom has dropped by 1 to 9 when compared with case 1. This is because we selected the `momentum_mixing_type` as `MomentumMixingType.equality` which basically adds a constraint that equates the pressure between all inlets and the outlet. Therefore, when we specify the inlet confitions in the next cell, we will define the pressure for only the `benzene_inlet` stream. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the stream inlet conditions\n", + "\n", + "# Benzene stream\n", + "m.fs.mixer_2.benzene_inlet.flow_mol.fix(\n", + " 100\n", + ") # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"benzene\"].fix(1)\n", + "m.fs.mixer_2.benzene_inlet.mole_frac_comp[0, \"toluene\"].fix(0)\n", + "m.fs.mixer_2.benzene_inlet.pressure.fix(\n", + " 101325\n", + ") # Pa , Another option is m1.fs.mixer2.inlet2.pressure.fix(202650)\n", + "m.fs.mixer_2.benzene_inlet.temperature.fix(353) # K\n", + "\n", + "# Toluene stream\n", + "m.fs.mixer_2.toluene_inlet.flow_mol.fix(\n", + " 100\n", + ") # converting to mol/s as unit basis is mol/s\n", + "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"benzene\"].fix(0)\n", + "m.fs.mixer_2.toluene_inlet.mole_frac_comp[0, \"toluene\"].fix(1)\n", + "m.fs.mixer_2.toluene_inlet.temperature.fix(356) # K\n", + "\n", + "DOF_final = degrees_of_freedom(m.fs.mixer_2)\n", + "print(\"The final degrees of freedom is: {0}\".format(DOF_final))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flowsheet Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the flowsheet, and set the output at WARNING\n", + "\n", + "m.fs.mixer_2.initialize(outlvl=idaeslog.WARNING)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtaining Simulation Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solve the simulation using ipopt\n", + "# Note: If the degrees of freedom = 0, we have a square problem\n", + "opt = SolverFactory(\"ipopt\")\n", + "result = opt.solve(m.fs.mixer_2, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display a readable report\n", + "m.fs.mixer_2.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/operations/pump.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/pump.ipynb index 1fa67e77..8084266a 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/pump.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/pump.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/pump_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/pump_doc.ipynb index a03e1227..8e9f109d 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/pump_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/pump_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -231,14 +232,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:15 [INFO] idaes.init.fs.pump_case_1.control_volume: Initialization Complete\n" + "2025-03-17 17:40:26 [INFO] idaes.init.fs.pump_case_1.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:15 [INFO] idaes.init.fs.pump_case_1: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:26 [INFO] idaes.init.fs.pump_case_1: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -301,16 +302,16 @@ "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 0.0000000e+00 7.89e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 6.55e-09 5.16e-07 -1.0 9.86e-07 - 9.90e-01 1.00e+00h 1\n", + " 1 0.0000000e+00 3.33e-08 5.16e-07 -1.0 9.86e-07 - 9.90e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 1\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 3.4603063846230464e-10 6.5483618527650833e-09\n", + "Constraint violation....: 1.7593735573373009e-09 3.3294782042503357e-08\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.4603063846230464e-10 6.5483618527650833e-09\n", + "Overall NLP error.......: 1.7593735573373009e-09 3.3294782042503357e-08\n", "\n", "\n", "Number of objective function evaluations = 2\n", @@ -320,16 +321,10 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.001\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", + "Total CPU secs in NLP function evaluations = 0.002\n", "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] } @@ -379,13 +374,7 @@ " Pressure Ratio : 1.9869 : dimensionless : False : (None, None)\n", "\n", "------------------------------------------------------------------------------------\n", - " Stream Table\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " Stream Table\n", " Units Inlet Outlet \n", " Molar Flow mole / second 100.00 100.00\n", " Mass Flow kilogram / second 1.8015 1.8015\n", @@ -516,14 +505,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:15 [INFO] idaes.init.fs.pump_case_2.control_volume: Initialization Complete\n" + "2025-03-17 17:40:26 [INFO] idaes.init.fs.pump_case_2.control_volume: Initialization Complete\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:30:15 [INFO] idaes.init.fs.pump_case_2: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:26 [INFO] idaes.init.fs.pump_case_2: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -586,16 +575,16 @@ "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", " 0 0.0000000e+00 7.89e-07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 6.55e-09 5.16e-07 -1.0 9.86e-07 - 9.90e-01 1.00e+00h 1\n", + " 1 0.0000000e+00 3.33e-08 5.16e-07 -1.0 9.86e-07 - 9.90e-01 1.00e+00h 1\n", "\n", "Number of Iterations....: 1\n", "\n", " (scaled) (unscaled)\n", "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 3.4603063846230464e-10 6.5483618527650833e-09\n", + "Constraint violation....: 1.7593735573373009e-09 3.3294782042503357e-08\n", "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 3.4603063846230464e-10 6.5483618527650833e-09\n", + "Overall NLP error.......: 1.7593735573373009e-09 3.3294782042503357e-08\n", "\n", "\n", "Number of objective function evaluations = 2\n", @@ -605,8 +594,8 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", + "Total CPU secs in NLP function evaluations = 0.003\n", "\n", "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" @@ -686,9 +675,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/pump_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/pump_test.ipynb index f1ba0ff3..8dc44df4 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/pump_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/pump_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -535,4 +536,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/pump_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/pump_usr.ipynb index 15e6db9a..18360051 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/pump_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/pump_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -417,4 +418,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb index 83a73858..09e0ff72 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb index 4a3fab04..238767f7 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_doc.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -75,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -351,9 +352,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -369,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -435,9 +444,267 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.WATER.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.WATER.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.WATER.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.WATER: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.GLYCOL.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.GLYCOL.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.GLYCOL.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.GLYCOL: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 11\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 0\n", + "\n", + "Total number of variables............................: 7\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 0\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 7\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.13e-16 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + "\n", + "Number of Iterations....: 0\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1275702593849246e-16 1.1275702593849246e-16\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1275702593849246e-16 1.1275702593849246e-16\n", + "\n", + "\n", + "Number of objective function evaluations = 1\n", + "Number of objective gradient evaluations = 1\n", + "Number of equality constraint evaluations = 1\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 1\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 0\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom initialization routine complete: Ipopt 3.13.2\\x3a Optimal Solution Found\n", + "2025-03-17 17:40:29 [INFO] idaes.init.fs.PERMEATE.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.PERMEATE.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.PERMEATE.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.PERMEATE: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.RETENTATE.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.RETENTATE.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.RETENTATE.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:29 [INFO] idaes.init.fs.RETENTATE: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 113\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 74\n", + "\n", + "Total number of variables............................: 47\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 33\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 47\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 7.42e+07 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.10e+05 2.45e+01 -1.0 1.01e+05 - 5.55e-01 9.90e-01h 1\n", + " 2 0.0000000e+00 2.30e+05 1.02e+02 -1.0 1.00e+03 - 7.67e-01 6.65e-01h 1\n", + " 3 0.0000000e+00 5.49e+03 6.43e+01 -1.0 1.57e+03 - 9.90e-01 9.90e-01h 1\n", + " 4 0.0000000e+00 7.38e+01 7.18e+02 -1.0 1.92e+03 - 9.90e-01 1.00e+00h 1\n", + " 5 0.0000000e+00 1.39e-03 1.95e+00 -1.0 1.76e+02 - 1.00e+00 1.00e+00h 1\n", + " 6 0.0000000e+00 3.38e-10 5.22e-03 -3.8 2.31e-01 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.6906692712481686e-10 3.3813385424963371e-10\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.6906692712481686e-10 3.3813385424963371e-10\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Add this to the imports\n", "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", @@ -521,9 +788,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.WATER Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Outlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 0.34000\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 1.0000e-06\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1.0132e+05\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.GLYCOL Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Outlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 1.0000e-06\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.66000\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1.0132e+05\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.PERMEATE Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 0.14259\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.00026675\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1300.0\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.RETENTATE Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 0.19742\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.65973\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1.0132e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "# print results\n", "\n", @@ -535,9 +853,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inlet water mole fraction: 0.34000031999936\n", + "Permeate water mole fraction: 0.998132696792035\n", + "Separation factor: 1037.6188153503224\n", + "Condensation duty: 5.812711092458081 kW\n", + "Duty per mole water recovered: 0.011324013423286048 kW-h / mol\n" + ] + } + ], "source": [ "# separation factor for results analysis\n", "m.fs.inlet_water_frac = Expression(\n", @@ -574,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -609,9 +939,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 121\n", + "Number of nonzeros in inequality constraint Jacobian.: 4\n", + "Number of nonzeros in Lagrangian Hessian.............: 88\n", + "\n", + "Total number of variables............................: 49\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 35\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 48\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 1\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 -3.4000032e-01 7.26e+03 6.14e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 -3.7417153e-01 7.06e+02 3.96e+01 -1.0 4.48e-02 - 9.90e-01 8.90e-01h 1\n", + " 2 -5.0289931e-01 2.22e+02 1.14e+02 -1.0 2.30e-01 - 9.90e-01 6.53e-01h 1\n", + " 3 -6.1963465e-01 9.31e+00 1.14e+04 -1.0 1.37e-01 - 9.91e-01 9.93e-01h 1\n", + " 4 -6.1883656e-01 1.48e-02 1.01e+02 -1.0 1.25e-03 - 1.00e+00 9.99e-01h 1\n", + " 5 -6.1153811e-01 2.14e-04 7.97e+02 -1.0 8.51e-03 - 1.00e+00 1.00e+00f 1\n", + " 6 -6.1019822e-01 1.82e-06 1.48e+01 -1.0 1.56e-03 - 1.00e+00 1.00e+00h 1\n", + " 7 -7.3642396e-01 1.07e-02 2.29e+04 -2.5 1.47e-01 - 7.70e-01 1.00e+00f 1\n", + " 8 -8.1765759e-01 2.06e-02 9.69e+02 -2.5 1.47e-01 - 1.00e+00 6.43e-01f 1\n", + " 9 -8.3576879e-01 3.92e-03 4.60e+01 -2.5 2.11e-02 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 -8.3954010e-01 1.75e-04 1.05e+00 -2.5 4.40e-03 - 1.00e+00 1.00e+00h 1\n", + " 11 -8.3930724e-01 8.95e-08 2.93e-03 -2.5 2.72e-04 - 1.00e+00 1.00e+00h 1\n", + " 12 -8.4239161e-01 6.13e-05 9.69e+01 -3.8 3.73e-03 - 1.00e+00 9.63e-01f 1\n", + " 13 -8.4225198e-01 7.39e-08 1.37e-02 -3.8 1.63e-04 - 1.00e+00 1.00e+00f 1\n", + " 14 -8.4225232e-01 5.82e-11 3.86e-08 -3.8 3.92e-07 - 1.00e+00 1.00e+00h 1\n", + " 15 -8.4240230e-01 1.51e-07 2.22e-02 -5.7 1.75e-04 - 1.00e+00 1.00e+00f 1\n", + " 16 -8.4240161e-01 9.09e-13 1.67e-08 -5.7 8.12e-07 - 1.00e+00 1.00e+00h 1\n", + " 17 -8.4240336e-01 9.09e-13 3.07e-06 -7.0 2.05e-06 - 1.00e+00 1.00e+00f 1\n", + " 18 -8.4240336e-01 5.82e-11 1.08e-12 -7.0 1.12e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 18\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: -8.4240336294854945e-01 -8.4240336294854945e-01\n", + "Dual infeasibility......: 1.0800249583553523e-12 1.0800249583553523e-12\n", + "Constraint violation....: 1.2738317640843951e-14 5.8207660913467407e-11\n", + "Complementarity.........: 9.0909090917798691e-08 9.0909090917798691e-08\n", + "Overall NLP error.......: 9.0909090917798691e-08 9.0909090917798691e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 19\n", + "Number of objective gradient evaluations = 19\n", + "Number of equality constraint evaluations = 19\n", + "Number of inequality constraint evaluations = 19\n", + "Number of equality constraint Jacobian evaluations = 19\n", + "Number of inequality constraint Jacobian evaluations = 19\n", + "Number of Lagrangian Hessian evaluations = 18\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# unfix inlet flows but fix total to prevent divergence during solve\n", "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", @@ -633,9 +1052,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.WATER Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Outlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 0.84240\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 1.0000e-06\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1.0132e+05\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.GLYCOL Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Outlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 1.0000e-06\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.15760\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1.0132e+05\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.PERMEATE Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 0.14259\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.00026675\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1300.0\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.RETENTATE Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet \n", + " Molar Flowrate ('Liq', 'water') mole / second 0.69982\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 0.15733\n", + " Temperature kelvin 318.15\n", + " Pressure pascal 1.0132e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "# print results\n", "\n", @@ -647,9 +1117,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inlet water mole fraction: 0.8424033629485495\n", + "Permeate water mole fraction: 0.9981326967920352\n", + "Separation factor: 100.00006747653238\n", + "Condensation duty: 5.812711092447902 kW\n", + "Duty per mole water recovered: 0.011324013423286044 kW-h / mol\n" + ] + } + ], "source": [ "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", @@ -662,7 +1144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -720,7 +1202,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb index 4a3fab04..d20631c8 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_test.ipynb @@ -1,728 +1,729 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IDAES Skeleton Unit Model\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "This notebook demonstrates usage of the IDAES Skeleton Unit Model, which provides a generic \"bare bones\" unit for user-defined models and custom variable and constraint sets. To allow maximum versatility, this unit may be defined as a surrogate model or a custom equation-oriented model. Users must add ports and variables that match connected models, and this is facilitated through a provided method to add port-variable sets.\n", - "\n", - "For users who wish to train surrogates with IDAES tools and insert obtained models into a flowsheet, see more detailed information on [IDAES Surrogate Tools](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/index.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Motivation\n", - "\n", - "In many cases, a specific application requires a unique unit operation that does not exist in the IDAES repository. Custom user models may source from external scripts, import surrogate equations or use first-principles calculations. However, IDAES flowsheets adhere to a standardized modeling hierarchy and simple Pyomo models do not always follow these conventions. Additionally, simple flowsheet submodels often require integration with other IDAES unit models which requires consistency between corresponding port variables, stream properties and physical unit sets, as well as proper usage of `ControlVolume` blocks.\n", - "\n", - "The IDAES `SkeletonUnitModel` allows custom creation of user models blocks that do not require `ControlVolume` blocks, and enabling connection with standard IDAES unit models that do contain `ControlVolume` blocks. To motivate the usefulness and versatility of this tool, we will consider a simple pervaporation unit. The custom model does not require rigorous thermodynamic calculations contained in adjacent unit models, and using a Skeleton model allows definition of only required variables and constraints. The new block does require state variable connections for the inlet and outlet streams. We will demonstrate this scenario below to highlight the usage and benefits of the Skeleton model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Example - Pervaporation\n", - "\n", - "Pervaporation is a low-energy separation process, and is particularly advantageous over distillation for azeotropic solutions or aqueous mixtures of heavy alcohols. Ethylene glycol is more environmentally friendly than typical chloride- and bromide-based dessicants, and is a common choice for commercial recovery of water from flue gas via liquid spray columns. Due to ethylene glycol's high boiling point, diffusion-based water recovery is economically favorable compared to distillation-based processes. The following example and flux correlation are taken from the literature source below:\n", - "\n", - "Jennifer Runhong Du, Amit Chakma, X. Feng, Dehydration of ethylene glycol by pervaporation using poly(N,N-dimethylaminoethyl methacrylate)/polysulfone composite membranes, Separation and Purification Technology, Volume 64, Issue 1, 2008, Pages 63-70, ISSN 1383-5866, https://doi.org/10.1016/j.seppur.2008.08.004.\n", - "\n", - "The process is adapted from the literature, utilizing an inlet aqueous glycol feed circulated through a feed tank-membrane-feed tank recycle loop while permeate is continuously extracted by the membrane. To demonstrate the usefulness of the Skeleton model, we will model this system as a Mixer and custom Pervaporation unit per the diagram below and define the flux as an empirical custom mass balance term rather than requiring rigorous diffusion calculations. We will also circumvent the need for a vapor phase and VLE calculations by manually calculating the duty to condense and collect permeate vapor, and use correlations for steady-state fluxes to avoid a recycle requiring tear calculations.\n", - "\n", - "![](pervaporation_process.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.1 Pyomo and IDAES Imports\n", - "We will begin with relevant imports. We will need basic Pyomo and IDAES components:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import (\n", - " check_optimal_termination,\n", - " ConcreteModel,\n", - " Constraint,\n", - " Expression,\n", - " Objective,\n", - " maximize,\n", - " Var,\n", - " Set,\n", - " TransformationFactory,\n", - " value,\n", - " exp,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.unit_models import Feed, SkeletonUnitModel, Mixer, Product\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "# import thermophysical properties\n", - "import eg_h2o_ideal as thermo_props\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.core.util.constants import Constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Build Flowsheet\n", - "\n", - "We will build a simple model manually defining state variables relations entering and exiting the pervaporation unit. As shown below, we may define our pre-separation mixer as usual:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# build the flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "\n", - "m.fs.WATER = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.GLYCOL = Feed(property_package=m.fs.thermo_params)\n", - "\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"water_feed\", \"glycol_feed\"]\n", - ")\n", - "\n", - "m.fs.RETENTATE = Product(property_package=m.fs.thermo_params)\n", - "m.fs.PERMEATE = Product(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Defining Skeleton Model and Connections\n", - "\n", - "Now that our flowsheet exists, we can manually define variables, units, constraints and ports for our custom pervaporation unit model. By using a Skeleton model, we avoid rigorous mass and energy balances and phase equilibrium which impact model tractability. Instead, we define state variable relations as below - note that we include the fluxes as outlet flow terms. In this model, the variables specify an `FpcTP` system where molar flow of each component, temperature and pressure are selected as state variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define Skeleton model for pervaporation unit\n", - "m.fs.pervap = SkeletonUnitModel(dynamic=False)\n", - "m.fs.pervap.comp_list = Set(initialize=[\"water\", \"ethylene_glycol\"])\n", - "m.fs.pervap.phase_list = Set(initialize=[\"Liq\"])\n", - "\n", - "# input vars for skeleton\n", - "# m.fs.time is a pre-initialized Set belonging to the FlowsheetBlock; for dynamic=False, time=[0]\n", - "m.fs.pervap.flow_in = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.temperature_in = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", - "m.fs.pervap.pressure_in = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", - "\n", - "# output vars for skeleton\n", - "m.fs.pervap.perm_flow = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.ret_flow = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.temperature_out = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", - "m.fs.pervap.pressure_out = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", - "m.fs.pervap.vacuum = Var(m.fs.time, initialize=1.3e3, units=pyunits.Pa)\n", - "\n", - "# dictionaries relating state properties to custom variables\n", - "inlet_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.flow_in,\n", - " \"temperature\": m.fs.pervap.temperature_in,\n", - " \"pressure\": m.fs.pervap.pressure_in,\n", - "}\n", - "retentate_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.ret_flow,\n", - " \"temperature\": m.fs.pervap.temperature_out,\n", - " \"pressure\": m.fs.pervap.pressure_out,\n", - "}\n", - "permeate_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.perm_flow,\n", - " \"temperature\": m.fs.pervap.temperature_out,\n", - " \"pressure\": m.fs.pervap.vacuum,\n", - "}\n", - "\n", - "m.fs.pervap.add_ports(name=\"inlet\", member_dict=inlet_dict)\n", - "m.fs.pervap.add_ports(name=\"retentate\", member_dict=retentate_dict)\n", - "m.fs.pervap.add_ports(name=\"permeate\", member_dict=permeate_dict)\n", - "\n", - "# internal vars for skeleton\n", - "energy_activation_dict = {\n", - " (0, \"Liq\", \"water\"): 51e3,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 53e3,\n", - "}\n", - "m.fs.pervap.energy_activation = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=energy_activation_dict,\n", - " units=pyunits.J / pyunits.mol,\n", - ")\n", - "m.fs.pervap.energy_activation.fix()\n", - "\n", - "permeance_dict = {\n", - " (0, \"Liq\", \"water\"): 5611320,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 22358.88,\n", - "} # calculated from literature data\n", - "m.fs.pervap.permeance = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=permeance_dict,\n", - " units=pyunits.mol / pyunits.s / pyunits.m**2,\n", - ")\n", - "m.fs.pervap.permeance.fix()\n", - "\n", - "m.fs.pervap.area = Var(m.fs.time, initialize=6, units=pyunits.m**2)\n", - "m.fs.pervap.area.fix()\n", - "\n", - "latent_heat_dict = {\n", - " (0, \"Liq\", \"water\"): 40.660e3,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 56.9e3,\n", - "}\n", - "m.fs.pervap.latent_heat_of_vaporization = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=latent_heat_dict,\n", - " units=pyunits.J / pyunits.mol,\n", - ")\n", - "m.fs.pervap.latent_heat_of_vaporization.fix()\n", - "m.fs.pervap.heat_duty = Var(\n", - " m.fs.time, initialize=1, units=pyunits.J / pyunits.s\n", - ") # we will calculate this later" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define our surrogate equations for flux and permeance, and link them to the port variables. Users can use this structure to write custom relations between inlet and outlet streams; for example, here we define the outlet flow of the pervaporation unit as a sum of the inlet flow and calculated recovery fluxes. By defining model constraints in lieu of rigorous mass balances, we add the flux as a custom mass balance term via an empirical correlation and calculate only the condensation duty rather than implementing full energy balance calculations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Surrogate and first principles model equations\n", - "\n", - "# flux equation (gas constant is defined as J/mol-K)\n", - "\n", - "\n", - "def rule_permeate_flux(pervap, t, p, i):\n", - " return pervap.permeate.flow_mol_phase_comp[t, p, i] / pervap.area[t] == (\n", - " pervap.permeance[t, p, i]\n", - " * exp(\n", - " -pervap.energy_activation[t, p, i]\n", - " / (Constants.gas_constant * pervap.inlet.temperature[t])\n", - " )\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_permeate_flux = Constraint(\n", - " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_permeate_flux\n", - ")\n", - "\n", - "# permeate condensation equation\n", - "# heat duty based on condensing all of permeate product vapor\n", - "# avoids the need for a Heater or HeatExchanger unit model\n", - "\n", - "\n", - "def rule_duty(pervap, t):\n", - " return pervap.heat_duty[t] == sum(\n", - " pervap.latent_heat_of_vaporization[t, p, i]\n", - " * pervap.permeate.flow_mol_phase_comp[t, p, i]\n", - " for p in pervap.phase_list\n", - " for i in pervap.comp_list\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_duty = Constraint(m.fs.time, rule=rule_duty)\n", - "\n", - "\n", - "# flow equation adding total recovery as a custom mass balance term\n", - "def rule_retentate_flow(pervap, t, p, i):\n", - " return pervap.retentate.flow_mol_phase_comp[t, p, i] == (\n", - " pervap.inlet.flow_mol_phase_comp[t, p, i]\n", - " - pervap.permeate.flow_mol_phase_comp[t, p, i]\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_retentate_flow = Constraint(\n", - " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_retentate_flow\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's define the Arc connecting our two models (IDAES Mixer and custom Pervaporation) and build the flowsheet network:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.WATER.outlet, destination=m.fs.M101.water_feed)\n", - "m.fs.s02 = Arc(source=m.fs.GLYCOL.outlet, destination=m.fs.M101.glycol_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.pervap.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.pervap.permeate, destination=m.fs.PERMEATE.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.pervap.retentate, destination=m.fs.RETENTATE.inlet)\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how many degrees of freedom the flowsheet has:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.3 Inlet Specifications\n", - "\n", - "To obtain a square problem with zero degrees of freedom, we specify the inlet water flow, ethylene glycol flow, temperature and pressure for each feed stream, as well as the permeate stream pressure:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(0.34) # mol/s\n", - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(1e-6) # mol/s\n", - "m.fs.WATER.outlet.temperature.fix(318.15) # K\n", - "m.fs.WATER.outlet.pressure.fix(101.325e3) # Pa\n", - "\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(1e-6) # mol/s\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(0.66) # mol/s\n", - "m.fs.GLYCOL.outlet.temperature.fix(318.15) # K\n", - "m.fs.GLYCOL.outlet.pressure.fix(101.325e3) # Pa" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, we need to pass rules defining the temperature and pressure outlets of the pervaporation unit:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a constraint to calculate the outlet temperature.\n", - "# Here, assume outlet temperature is the same as inlet temperature for illustration\n", - "# in reality, temperature change from latent heat loss through membrane is negligible\n", - "\n", - "\n", - "def rule_temp_out(pervap, t):\n", - " return pervap.inlet.temperature[t] == pervap.retentate.temperature[t]\n", - "\n", - "\n", - "m.fs.pervap.temperature_out_calculation = Constraint(m.fs.time, rule=rule_temp_out)\n", - "\n", - "# Add a constraint to calculate the retentate pressure\n", - "# Here, assume the retentate pressure is the same as the inlet pressure for illustration\n", - "# in reality, pressure change from mass loss through membrane is negligible\n", - "\n", - "\n", - "def rule_pres_out(pervap, t):\n", - " return pervap.inlet.pressure[t] == pervap.retentate.pressure[t]\n", - "\n", - "\n", - "m.fs.pervap.pressure_out_calculation = Constraint(m.fs.time, rule=rule_pres_out)\n", - "\n", - "# fix permeate vacuum pressure\n", - "m.fs.PERMEATE.inlet.pressure.fix(1.3e3)\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.4 Custom Initialization\n", - "In addition to allowing custom variable and constraint definitions, the Skeleton model enables implementation of a custom initialization scheme. Complex unit operations may present unique tractability issues, and users have precise control over piecewise unit model solving." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add this to the imports\n", - "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", - "\n", - "\n", - "def my_initialize(unit, **kwargs):\n", - " # Callback for user provided initialization sequence\n", - " # Fix the inlet state\n", - " unit.inlet.flow_mol_phase_comp.fix()\n", - " unit.inlet.pressure.fix()\n", - " unit.inlet.temperature.fix()\n", - "\n", - " # Calculate the values of the remaining variables\n", - " for t in m.fs.time:\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", - " unit.eq_permeate_flux[t, \"Liq\", \"water\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", - " unit.eq_permeate_flux[t, \"Liq\", \"ethylene_glycol\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(unit.heat_duty[t], unit.eq_duty[t])\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", - " unit.eq_retentate_flow[t, \"Liq\", \"water\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", - " unit.eq_retentate_flow[t, \"Liq\", \"ethylene_glycol\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.temperature[t], unit.temperature_out_calculation[t]\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.pressure[t], unit.pressure_out_calculation[t]\n", - " )\n", - "\n", - " assert degrees_of_freedom(unit) == 0\n", - " if degrees_of_freedom(unit) == 0:\n", - " res = solver.solve(unit, tee=True)\n", - " unit.inlet.flow_mol_phase_comp.unfix()\n", - " unit.inlet.temperature.unfix()\n", - " unit.inlet.pressure.unfix()\n", - " print(\"Custom initialization routine complete: \", res.solver.message)\n", - "\n", - "\n", - "solver = get_solver()\n", - "\n", - "m.fs.WATER.initialize()\n", - "propagate_state(m.fs.s01)\n", - "\n", - "m.fs.GLYCOL.initialize()\n", - "propagate_state(m.fs.s02)\n", - "\n", - "m.fs.pervap.config.initializer = my_initialize\n", - "my_initialize(m.fs.pervap)\n", - "propagate_state(m.fs.s03)\n", - "\n", - "m.fs.PERMEATE.initialize()\n", - "propagate_state(m.fs.s04)\n", - "\n", - "m.fs.RETENTATE.initialize()\n", - "\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print results\n", - "\n", - "m.fs.WATER.report()\n", - "m.fs.GLYCOL.report()\n", - "m.fs.PERMEATE.report()\n", - "m.fs.RETENTATE.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# separation factor for results analysis\n", - "m.fs.inlet_water_frac = Expression(\n", - " expr=(\n", - " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " / sum(\n", - " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", i]\n", - " for i in m.fs.pervap.comp_list\n", - " )\n", - " )\n", - ")\n", - "m.fs.permeate_water_frac = Expression(\n", - " expr=(\n", - " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " / sum(\n", - " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", i]\n", - " for i in m.fs.pervap.comp_list\n", - " )\n", - " )\n", - ")\n", - "m.fs.separation_factor = Expression(\n", - " expr=(m.fs.permeate_water_frac / (1 - m.fs.permeate_water_frac))\n", - " / (m.fs.inlet_water_frac / (1 - m.fs.inlet_water_frac))\n", - ")\n", - "\n", - "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", - "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", - "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", - "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", - "print(\n", - " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# check results\n", - "assert check_optimal_termination(results)\n", - "assert_units_consistent(m)\n", - "\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.14258566, rel=1e-5)\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.000266748768, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.19741534, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.65973425, rel=1e-5)\n", - "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Optimization\n", - "\n", - "Suppose we wish to characterize the membrane behavior by calculating the maximum inlet water mole fraction allowing a separation factor of at least 100 (typical value for high-efficiency separation processes such as gas separation of CO2/N2). We need to fix total inlet flow to ensure physically-sound solutions. We can quickly modify and resolve the model, and check some key results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# unfix inlet flows but fix total to prevent divergence during solve\n", - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].unfix()\n", - "m.fs.total_flow = Constraint(\n", - " expr=m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " + m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " == 1 * pyunits.mol / pyunits.s\n", - ")\n", - "\n", - "# set criteria for separation factor\n", - "m.fs.sep_min = Constraint(expr=m.fs.separation_factor >= 100)\n", - "\n", - "# set objective - defaults to minimization\n", - "m.fs.obj = Objective(expr=m.fs.inlet_water_frac, sense=maximize)\n", - "\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print results\n", - "\n", - "m.fs.WATER.report()\n", - "m.fs.GLYCOL.report()\n", - "m.fs.PERMEATE.report()\n", - "m.fs.RETENTATE.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", - "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", - "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", - "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", - "print(\n", - " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# check results\n", - "assert check_optimal_termination(results)\n", - "assert_units_consistent(m)\n", - "\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.14258566, rel=1e-5)\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.000266748768, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.69981938, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.15733020, rel=1e-5)\n", - "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Summary\n", - "\n", - "The IDAES Skeleton Unit Model is a powerful tool for implementing relatively simple first-princples, surrogate-based or empirical unit operations. More crucially, users can add their own custom models and integrate them into a larger IDAES flowsheet without adding control volumes or rigorous flow balance and equilibrium calculations when not required. The pervaporation example displays a case where all model equations are empirical correlations or simple manual calculations, with a small number of state variable and port connections, and the Skeleton model avoids complex calculations that impact model tractability. The example also demonstrates adding a custom initialization scheme to handle internally model degrees of freedom, a feature providing greater user control than with most IDAES unit models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Skeleton Unit Model\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "This notebook demonstrates usage of the IDAES Skeleton Unit Model, which provides a generic \"bare bones\" unit for user-defined models and custom variable and constraint sets. To allow maximum versatility, this unit may be defined as a surrogate model or a custom equation-oriented model. Users must add ports and variables that match connected models, and this is facilitated through a provided method to add port-variable sets.\n", + "\n", + "For users who wish to train surrogates with IDAES tools and insert obtained models into a flowsheet, see more detailed information on [IDAES Surrogate Tools](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/index.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Motivation\n", + "\n", + "In many cases, a specific application requires a unique unit operation that does not exist in the IDAES repository. Custom user models may source from external scripts, import surrogate equations or use first-principles calculations. However, IDAES flowsheets adhere to a standardized modeling hierarchy and simple Pyomo models do not always follow these conventions. Additionally, simple flowsheet submodels often require integration with other IDAES unit models which requires consistency between corresponding port variables, stream properties and physical unit sets, as well as proper usage of `ControlVolume` blocks.\n", + "\n", + "The IDAES `SkeletonUnitModel` allows custom creation of user models blocks that do not require `ControlVolume` blocks, and enabling connection with standard IDAES unit models that do contain `ControlVolume` blocks. To motivate the usefulness and versatility of this tool, we will consider a simple pervaporation unit. The custom model does not require rigorous thermodynamic calculations contained in adjacent unit models, and using a Skeleton model allows definition of only required variables and constraints. The new block does require state variable connections for the inlet and outlet streams. We will demonstrate this scenario below to highlight the usage and benefits of the Skeleton model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Example - Pervaporation\n", + "\n", + "Pervaporation is a low-energy separation process, and is particularly advantageous over distillation for azeotropic solutions or aqueous mixtures of heavy alcohols. Ethylene glycol is more environmentally friendly than typical chloride- and bromide-based dessicants, and is a common choice for commercial recovery of water from flue gas via liquid spray columns. Due to ethylene glycol's high boiling point, diffusion-based water recovery is economically favorable compared to distillation-based processes. The following example and flux correlation are taken from the literature source below:\n", + "\n", + "Jennifer Runhong Du, Amit Chakma, X. Feng, Dehydration of ethylene glycol by pervaporation using poly(N,N-dimethylaminoethyl methacrylate)/polysulfone composite membranes, Separation and Purification Technology, Volume 64, Issue 1, 2008, Pages 63-70, ISSN 1383-5866, https://doi.org/10.1016/j.seppur.2008.08.004.\n", + "\n", + "The process is adapted from the literature, utilizing an inlet aqueous glycol feed circulated through a feed tank-membrane-feed tank recycle loop while permeate is continuously extracted by the membrane. To demonstrate the usefulness of the Skeleton model, we will model this system as a Mixer and custom Pervaporation unit per the diagram below and define the flux as an empirical custom mass balance term rather than requiring rigorous diffusion calculations. We will also circumvent the need for a vapor phase and VLE calculations by manually calculating the duty to condense and collect permeate vapor, and use correlations for steady-state fluxes to avoid a recycle requiring tear calculations.\n", + "\n", + "![](pervaporation_process.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Pyomo and IDAES Imports\n", + "We will begin with relevant imports. We will need basic Pyomo and IDAES components:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import (\n", + " check_optimal_termination,\n", + " ConcreteModel,\n", + " Constraint,\n", + " Expression,\n", + " Objective,\n", + " maximize,\n", + " Var,\n", + " Set,\n", + " TransformationFactory,\n", + " value,\n", + " exp,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.unit_models import Feed, SkeletonUnitModel, Mixer, Product\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "# import thermophysical properties\n", + "import eg_h2o_ideal as thermo_props\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.core.util.constants import Constants" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Build Flowsheet\n", + "\n", + "We will build a simple model manually defining state variables relations entering and exiting the pervaporation unit. As shown below, we may define our pre-separation mixer as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# build the flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "\n", + "m.fs.WATER = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.GLYCOL = Feed(property_package=m.fs.thermo_params)\n", + "\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"water_feed\", \"glycol_feed\"]\n", + ")\n", + "\n", + "m.fs.RETENTATE = Product(property_package=m.fs.thermo_params)\n", + "m.fs.PERMEATE = Product(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Defining Skeleton Model and Connections\n", + "\n", + "Now that our flowsheet exists, we can manually define variables, units, constraints and ports for our custom pervaporation unit model. By using a Skeleton model, we avoid rigorous mass and energy balances and phase equilibrium which impact model tractability. Instead, we define state variable relations as below - note that we include the fluxes as outlet flow terms. In this model, the variables specify an `FpcTP` system where molar flow of each component, temperature and pressure are selected as state variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define Skeleton model for pervaporation unit\n", + "m.fs.pervap = SkeletonUnitModel(dynamic=False)\n", + "m.fs.pervap.comp_list = Set(initialize=[\"water\", \"ethylene_glycol\"])\n", + "m.fs.pervap.phase_list = Set(initialize=[\"Liq\"])\n", + "\n", + "# input vars for skeleton\n", + "# m.fs.time is a pre-initialized Set belonging to the FlowsheetBlock; for dynamic=False, time=[0]\n", + "m.fs.pervap.flow_in = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.temperature_in = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", + "m.fs.pervap.pressure_in = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", + "\n", + "# output vars for skeleton\n", + "m.fs.pervap.perm_flow = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.ret_flow = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.temperature_out = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", + "m.fs.pervap.pressure_out = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", + "m.fs.pervap.vacuum = Var(m.fs.time, initialize=1.3e3, units=pyunits.Pa)\n", + "\n", + "# dictionaries relating state properties to custom variables\n", + "inlet_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.flow_in,\n", + " \"temperature\": m.fs.pervap.temperature_in,\n", + " \"pressure\": m.fs.pervap.pressure_in,\n", + "}\n", + "retentate_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.ret_flow,\n", + " \"temperature\": m.fs.pervap.temperature_out,\n", + " \"pressure\": m.fs.pervap.pressure_out,\n", + "}\n", + "permeate_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.perm_flow,\n", + " \"temperature\": m.fs.pervap.temperature_out,\n", + " \"pressure\": m.fs.pervap.vacuum,\n", + "}\n", + "\n", + "m.fs.pervap.add_ports(name=\"inlet\", member_dict=inlet_dict)\n", + "m.fs.pervap.add_ports(name=\"retentate\", member_dict=retentate_dict)\n", + "m.fs.pervap.add_ports(name=\"permeate\", member_dict=permeate_dict)\n", + "\n", + "# internal vars for skeleton\n", + "energy_activation_dict = {\n", + " (0, \"Liq\", \"water\"): 51e3,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 53e3,\n", + "}\n", + "m.fs.pervap.energy_activation = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=energy_activation_dict,\n", + " units=pyunits.J / pyunits.mol,\n", + ")\n", + "m.fs.pervap.energy_activation.fix()\n", + "\n", + "permeance_dict = {\n", + " (0, \"Liq\", \"water\"): 5611320,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 22358.88,\n", + "} # calculated from literature data\n", + "m.fs.pervap.permeance = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=permeance_dict,\n", + " units=pyunits.mol / pyunits.s / pyunits.m**2,\n", + ")\n", + "m.fs.pervap.permeance.fix()\n", + "\n", + "m.fs.pervap.area = Var(m.fs.time, initialize=6, units=pyunits.m**2)\n", + "m.fs.pervap.area.fix()\n", + "\n", + "latent_heat_dict = {\n", + " (0, \"Liq\", \"water\"): 40.660e3,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 56.9e3,\n", + "}\n", + "m.fs.pervap.latent_heat_of_vaporization = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=latent_heat_dict,\n", + " units=pyunits.J / pyunits.mol,\n", + ")\n", + "m.fs.pervap.latent_heat_of_vaporization.fix()\n", + "m.fs.pervap.heat_duty = Var(\n", + " m.fs.time, initialize=1, units=pyunits.J / pyunits.s\n", + ") # we will calculate this later" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define our surrogate equations for flux and permeance, and link them to the port variables. Users can use this structure to write custom relations between inlet and outlet streams; for example, here we define the outlet flow of the pervaporation unit as a sum of the inlet flow and calculated recovery fluxes. By defining model constraints in lieu of rigorous mass balances, we add the flux as a custom mass balance term via an empirical correlation and calculate only the condensation duty rather than implementing full energy balance calculations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Surrogate and first principles model equations\n", + "\n", + "# flux equation (gas constant is defined as J/mol-K)\n", + "\n", + "\n", + "def rule_permeate_flux(pervap, t, p, i):\n", + " return pervap.permeate.flow_mol_phase_comp[t, p, i] / pervap.area[t] == (\n", + " pervap.permeance[t, p, i]\n", + " * exp(\n", + " -pervap.energy_activation[t, p, i]\n", + " / (Constants.gas_constant * pervap.inlet.temperature[t])\n", + " )\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_permeate_flux = Constraint(\n", + " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_permeate_flux\n", + ")\n", + "\n", + "# permeate condensation equation\n", + "# heat duty based on condensing all of permeate product vapor\n", + "# avoids the need for a Heater or HeatExchanger unit model\n", + "\n", + "\n", + "def rule_duty(pervap, t):\n", + " return pervap.heat_duty[t] == sum(\n", + " pervap.latent_heat_of_vaporization[t, p, i]\n", + " * pervap.permeate.flow_mol_phase_comp[t, p, i]\n", + " for p in pervap.phase_list\n", + " for i in pervap.comp_list\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_duty = Constraint(m.fs.time, rule=rule_duty)\n", + "\n", + "\n", + "# flow equation adding total recovery as a custom mass balance term\n", + "def rule_retentate_flow(pervap, t, p, i):\n", + " return pervap.retentate.flow_mol_phase_comp[t, p, i] == (\n", + " pervap.inlet.flow_mol_phase_comp[t, p, i]\n", + " - pervap.permeate.flow_mol_phase_comp[t, p, i]\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_retentate_flow = Constraint(\n", + " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_retentate_flow\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's define the Arc connecting our two models (IDAES Mixer and custom Pervaporation) and build the flowsheet network:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.WATER.outlet, destination=m.fs.M101.water_feed)\n", + "m.fs.s02 = Arc(source=m.fs.GLYCOL.outlet, destination=m.fs.M101.glycol_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.pervap.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.pervap.permeate, destination=m.fs.PERMEATE.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.pervap.retentate, destination=m.fs.RETENTATE.inlet)\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how many degrees of freedom the flowsheet has:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Inlet Specifications\n", + "\n", + "To obtain a square problem with zero degrees of freedom, we specify the inlet water flow, ethylene glycol flow, temperature and pressure for each feed stream, as well as the permeate stream pressure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(0.34) # mol/s\n", + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(1e-6) # mol/s\n", + "m.fs.WATER.outlet.temperature.fix(318.15) # K\n", + "m.fs.WATER.outlet.pressure.fix(101.325e3) # Pa\n", + "\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(1e-6) # mol/s\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(0.66) # mol/s\n", + "m.fs.GLYCOL.outlet.temperature.fix(318.15) # K\n", + "m.fs.GLYCOL.outlet.pressure.fix(101.325e3) # Pa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we need to pass rules defining the temperature and pressure outlets of the pervaporation unit:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a constraint to calculate the outlet temperature.\n", + "# Here, assume outlet temperature is the same as inlet temperature for illustration\n", + "# in reality, temperature change from latent heat loss through membrane is negligible\n", + "\n", + "\n", + "def rule_temp_out(pervap, t):\n", + " return pervap.inlet.temperature[t] == pervap.retentate.temperature[t]\n", + "\n", + "\n", + "m.fs.pervap.temperature_out_calculation = Constraint(m.fs.time, rule=rule_temp_out)\n", + "\n", + "# Add a constraint to calculate the retentate pressure\n", + "# Here, assume the retentate pressure is the same as the inlet pressure for illustration\n", + "# in reality, pressure change from mass loss through membrane is negligible\n", + "\n", + "\n", + "def rule_pres_out(pervap, t):\n", + " return pervap.inlet.pressure[t] == pervap.retentate.pressure[t]\n", + "\n", + "\n", + "m.fs.pervap.pressure_out_calculation = Constraint(m.fs.time, rule=rule_pres_out)\n", + "\n", + "# fix permeate vacuum pressure\n", + "m.fs.PERMEATE.inlet.pressure.fix(1.3e3)\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Custom Initialization\n", + "In addition to allowing custom variable and constraint definitions, the Skeleton model enables implementation of a custom initialization scheme. Complex unit operations may present unique tractability issues, and users have precise control over piecewise unit model solving." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add this to the imports\n", + "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", + "\n", + "\n", + "def my_initialize(unit, **kwargs):\n", + " # Callback for user provided initialization sequence\n", + " # Fix the inlet state\n", + " unit.inlet.flow_mol_phase_comp.fix()\n", + " unit.inlet.pressure.fix()\n", + " unit.inlet.temperature.fix()\n", + "\n", + " # Calculate the values of the remaining variables\n", + " for t in m.fs.time:\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", + " unit.eq_permeate_flux[t, \"Liq\", \"water\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", + " unit.eq_permeate_flux[t, \"Liq\", \"ethylene_glycol\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(unit.heat_duty[t], unit.eq_duty[t])\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", + " unit.eq_retentate_flow[t, \"Liq\", \"water\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", + " unit.eq_retentate_flow[t, \"Liq\", \"ethylene_glycol\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.temperature[t], unit.temperature_out_calculation[t]\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.pressure[t], unit.pressure_out_calculation[t]\n", + " )\n", + "\n", + " assert degrees_of_freedom(unit) == 0\n", + " if degrees_of_freedom(unit) == 0:\n", + " res = solver.solve(unit, tee=True)\n", + " unit.inlet.flow_mol_phase_comp.unfix()\n", + " unit.inlet.temperature.unfix()\n", + " unit.inlet.pressure.unfix()\n", + " print(\"Custom initialization routine complete: \", res.solver.message)\n", + "\n", + "\n", + "solver = get_solver()\n", + "\n", + "m.fs.WATER.initialize()\n", + "propagate_state(m.fs.s01)\n", + "\n", + "m.fs.GLYCOL.initialize()\n", + "propagate_state(m.fs.s02)\n", + "\n", + "m.fs.pervap.config.initializer = my_initialize\n", + "my_initialize(m.fs.pervap)\n", + "propagate_state(m.fs.s03)\n", + "\n", + "m.fs.PERMEATE.initialize()\n", + "propagate_state(m.fs.s04)\n", + "\n", + "m.fs.RETENTATE.initialize()\n", + "\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print results\n", + "\n", + "m.fs.WATER.report()\n", + "m.fs.GLYCOL.report()\n", + "m.fs.PERMEATE.report()\n", + "m.fs.RETENTATE.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# separation factor for results analysis\n", + "m.fs.inlet_water_frac = Expression(\n", + " expr=(\n", + " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " / sum(\n", + " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", i]\n", + " for i in m.fs.pervap.comp_list\n", + " )\n", + " )\n", + ")\n", + "m.fs.permeate_water_frac = Expression(\n", + " expr=(\n", + " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " / sum(\n", + " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", i]\n", + " for i in m.fs.pervap.comp_list\n", + " )\n", + " )\n", + ")\n", + "m.fs.separation_factor = Expression(\n", + " expr=(m.fs.permeate_water_frac / (1 - m.fs.permeate_water_frac))\n", + " / (m.fs.inlet_water_frac / (1 - m.fs.inlet_water_frac))\n", + ")\n", + "\n", + "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", + "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", + "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", + "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", + "print(\n", + " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check results\n", + "assert check_optimal_termination(results)\n", + "assert_units_consistent(m)\n", + "\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.19741534, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.65973425, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Optimization\n", + "\n", + "Suppose we wish to characterize the membrane behavior by calculating the maximum inlet water mole fraction allowing a separation factor of at least 100 (typical value for high-efficiency separation processes such as gas separation of CO2/N2). We need to fix total inlet flow to ensure physically-sound solutions. We can quickly modify and resolve the model, and check some key results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# unfix inlet flows but fix total to prevent divergence during solve\n", + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].unfix()\n", + "m.fs.total_flow = Constraint(\n", + " expr=m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " + m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " == 1 * pyunits.mol / pyunits.s\n", + ")\n", + "\n", + "# set criteria for separation factor\n", + "m.fs.sep_min = Constraint(expr=m.fs.separation_factor >= 100)\n", + "\n", + "# set objective - defaults to minimization\n", + "m.fs.obj = Objective(expr=m.fs.inlet_water_frac, sense=maximize)\n", + "\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print results\n", + "\n", + "m.fs.WATER.report()\n", + "m.fs.GLYCOL.report()\n", + "m.fs.PERMEATE.report()\n", + "m.fs.RETENTATE.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", + "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", + "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", + "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", + "print(\n", + " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check results\n", + "assert check_optimal_termination(results)\n", + "assert_units_consistent(m)\n", + "\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.69981938, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.15733020, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Summary\n", + "\n", + "The IDAES Skeleton Unit Model is a powerful tool for implementing relatively simple first-princples, surrogate-based or empirical unit operations. More crucially, users can add their own custom models and integrate them into a larger IDAES flowsheet without adding control volumes or rigorous flow balance and equilibrium calculations when not required. The pervaporation example displays a case where all model equations are empirical correlations or simple manual calculations, with a small number of state variable and port connections, and the Skeleton model avoids complex calculations that impact model tractability. The example also demonstrates adding a custom initialization scheme to handle internally model degrees of freedom, a feature providing greater user control than with most IDAES unit models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb index 4a3fab04..d20631c8 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/skeleton_unit_usr.ipynb @@ -1,728 +1,729 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# IDAES Skeleton Unit Model\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "This notebook demonstrates usage of the IDAES Skeleton Unit Model, which provides a generic \"bare bones\" unit for user-defined models and custom variable and constraint sets. To allow maximum versatility, this unit may be defined as a surrogate model or a custom equation-oriented model. Users must add ports and variables that match connected models, and this is facilitated through a provided method to add port-variable sets.\n", - "\n", - "For users who wish to train surrogates with IDAES tools and insert obtained models into a flowsheet, see more detailed information on [IDAES Surrogate Tools](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/index.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Motivation\n", - "\n", - "In many cases, a specific application requires a unique unit operation that does not exist in the IDAES repository. Custom user models may source from external scripts, import surrogate equations or use first-principles calculations. However, IDAES flowsheets adhere to a standardized modeling hierarchy and simple Pyomo models do not always follow these conventions. Additionally, simple flowsheet submodels often require integration with other IDAES unit models which requires consistency between corresponding port variables, stream properties and physical unit sets, as well as proper usage of `ControlVolume` blocks.\n", - "\n", - "The IDAES `SkeletonUnitModel` allows custom creation of user models blocks that do not require `ControlVolume` blocks, and enabling connection with standard IDAES unit models that do contain `ControlVolume` blocks. To motivate the usefulness and versatility of this tool, we will consider a simple pervaporation unit. The custom model does not require rigorous thermodynamic calculations contained in adjacent unit models, and using a Skeleton model allows definition of only required variables and constraints. The new block does require state variable connections for the inlet and outlet streams. We will demonstrate this scenario below to highlight the usage and benefits of the Skeleton model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Example - Pervaporation\n", - "\n", - "Pervaporation is a low-energy separation process, and is particularly advantageous over distillation for azeotropic solutions or aqueous mixtures of heavy alcohols. Ethylene glycol is more environmentally friendly than typical chloride- and bromide-based dessicants, and is a common choice for commercial recovery of water from flue gas via liquid spray columns. Due to ethylene glycol's high boiling point, diffusion-based water recovery is economically favorable compared to distillation-based processes. The following example and flux correlation are taken from the literature source below:\n", - "\n", - "Jennifer Runhong Du, Amit Chakma, X. Feng, Dehydration of ethylene glycol by pervaporation using poly(N,N-dimethylaminoethyl methacrylate)/polysulfone composite membranes, Separation and Purification Technology, Volume 64, Issue 1, 2008, Pages 63-70, ISSN 1383-5866, https://doi.org/10.1016/j.seppur.2008.08.004.\n", - "\n", - "The process is adapted from the literature, utilizing an inlet aqueous glycol feed circulated through a feed tank-membrane-feed tank recycle loop while permeate is continuously extracted by the membrane. To demonstrate the usefulness of the Skeleton model, we will model this system as a Mixer and custom Pervaporation unit per the diagram below and define the flux as an empirical custom mass balance term rather than requiring rigorous diffusion calculations. We will also circumvent the need for a vapor phase and VLE calculations by manually calculating the duty to condense and collect permeate vapor, and use correlations for steady-state fluxes to avoid a recycle requiring tear calculations.\n", - "\n", - "![](pervaporation_process.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.1 Pyomo and IDAES Imports\n", - "We will begin with relevant imports. We will need basic Pyomo and IDAES components:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "from pyomo.environ import (\n", - " check_optimal_termination,\n", - " ConcreteModel,\n", - " Constraint,\n", - " Expression,\n", - " Objective,\n", - " maximize,\n", - " Var,\n", - " Set,\n", - " TransformationFactory,\n", - " value,\n", - " exp,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.unit_models import Feed, SkeletonUnitModel, Mixer, Product\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state\n", - "from idaes.core.solvers import get_solver\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "# import thermophysical properties\n", - "import eg_h2o_ideal as thermo_props\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.core.util.constants import Constants" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Build Flowsheet\n", - "\n", - "We will build a simple model manually defining state variables relations entering and exiting the pervaporation unit. As shown below, we may define our pre-separation mixer as usual:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# build the flowsheet\n", - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)\n", - "\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "\n", - "m.fs.WATER = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.GLYCOL = Feed(property_package=m.fs.thermo_params)\n", - "\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"water_feed\", \"glycol_feed\"]\n", - ")\n", - "\n", - "m.fs.RETENTATE = Product(property_package=m.fs.thermo_params)\n", - "m.fs.PERMEATE = Product(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Defining Skeleton Model and Connections\n", - "\n", - "Now that our flowsheet exists, we can manually define variables, units, constraints and ports for our custom pervaporation unit model. By using a Skeleton model, we avoid rigorous mass and energy balances and phase equilibrium which impact model tractability. Instead, we define state variable relations as below - note that we include the fluxes as outlet flow terms. In this model, the variables specify an `FpcTP` system where molar flow of each component, temperature and pressure are selected as state variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define Skeleton model for pervaporation unit\n", - "m.fs.pervap = SkeletonUnitModel(dynamic=False)\n", - "m.fs.pervap.comp_list = Set(initialize=[\"water\", \"ethylene_glycol\"])\n", - "m.fs.pervap.phase_list = Set(initialize=[\"Liq\"])\n", - "\n", - "# input vars for skeleton\n", - "# m.fs.time is a pre-initialized Set belonging to the FlowsheetBlock; for dynamic=False, time=[0]\n", - "m.fs.pervap.flow_in = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.temperature_in = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", - "m.fs.pervap.pressure_in = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", - "\n", - "# output vars for skeleton\n", - "m.fs.pervap.perm_flow = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.ret_flow = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=1.0,\n", - " units=pyunits.mol / pyunits.s,\n", - ")\n", - "m.fs.pervap.temperature_out = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", - "m.fs.pervap.pressure_out = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", - "m.fs.pervap.vacuum = Var(m.fs.time, initialize=1.3e3, units=pyunits.Pa)\n", - "\n", - "# dictionaries relating state properties to custom variables\n", - "inlet_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.flow_in,\n", - " \"temperature\": m.fs.pervap.temperature_in,\n", - " \"pressure\": m.fs.pervap.pressure_in,\n", - "}\n", - "retentate_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.ret_flow,\n", - " \"temperature\": m.fs.pervap.temperature_out,\n", - " \"pressure\": m.fs.pervap.pressure_out,\n", - "}\n", - "permeate_dict = {\n", - " \"flow_mol_phase_comp\": m.fs.pervap.perm_flow,\n", - " \"temperature\": m.fs.pervap.temperature_out,\n", - " \"pressure\": m.fs.pervap.vacuum,\n", - "}\n", - "\n", - "m.fs.pervap.add_ports(name=\"inlet\", member_dict=inlet_dict)\n", - "m.fs.pervap.add_ports(name=\"retentate\", member_dict=retentate_dict)\n", - "m.fs.pervap.add_ports(name=\"permeate\", member_dict=permeate_dict)\n", - "\n", - "# internal vars for skeleton\n", - "energy_activation_dict = {\n", - " (0, \"Liq\", \"water\"): 51e3,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 53e3,\n", - "}\n", - "m.fs.pervap.energy_activation = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=energy_activation_dict,\n", - " units=pyunits.J / pyunits.mol,\n", - ")\n", - "m.fs.pervap.energy_activation.fix()\n", - "\n", - "permeance_dict = {\n", - " (0, \"Liq\", \"water\"): 5611320,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 22358.88,\n", - "} # calculated from literature data\n", - "m.fs.pervap.permeance = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=permeance_dict,\n", - " units=pyunits.mol / pyunits.s / pyunits.m**2,\n", - ")\n", - "m.fs.pervap.permeance.fix()\n", - "\n", - "m.fs.pervap.area = Var(m.fs.time, initialize=6, units=pyunits.m**2)\n", - "m.fs.pervap.area.fix()\n", - "\n", - "latent_heat_dict = {\n", - " (0, \"Liq\", \"water\"): 40.660e3,\n", - " (0, \"Liq\", \"ethylene_glycol\"): 56.9e3,\n", - "}\n", - "m.fs.pervap.latent_heat_of_vaporization = Var(\n", - " m.fs.time,\n", - " m.fs.pervap.phase_list,\n", - " m.fs.pervap.comp_list,\n", - " initialize=latent_heat_dict,\n", - " units=pyunits.J / pyunits.mol,\n", - ")\n", - "m.fs.pervap.latent_heat_of_vaporization.fix()\n", - "m.fs.pervap.heat_duty = Var(\n", - " m.fs.time, initialize=1, units=pyunits.J / pyunits.s\n", - ") # we will calculate this later" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define our surrogate equations for flux and permeance, and link them to the port variables. Users can use this structure to write custom relations between inlet and outlet streams; for example, here we define the outlet flow of the pervaporation unit as a sum of the inlet flow and calculated recovery fluxes. By defining model constraints in lieu of rigorous mass balances, we add the flux as a custom mass balance term via an empirical correlation and calculate only the condensation duty rather than implementing full energy balance calculations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Surrogate and first principles model equations\n", - "\n", - "# flux equation (gas constant is defined as J/mol-K)\n", - "\n", - "\n", - "def rule_permeate_flux(pervap, t, p, i):\n", - " return pervap.permeate.flow_mol_phase_comp[t, p, i] / pervap.area[t] == (\n", - " pervap.permeance[t, p, i]\n", - " * exp(\n", - " -pervap.energy_activation[t, p, i]\n", - " / (Constants.gas_constant * pervap.inlet.temperature[t])\n", - " )\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_permeate_flux = Constraint(\n", - " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_permeate_flux\n", - ")\n", - "\n", - "# permeate condensation equation\n", - "# heat duty based on condensing all of permeate product vapor\n", - "# avoids the need for a Heater or HeatExchanger unit model\n", - "\n", - "\n", - "def rule_duty(pervap, t):\n", - " return pervap.heat_duty[t] == sum(\n", - " pervap.latent_heat_of_vaporization[t, p, i]\n", - " * pervap.permeate.flow_mol_phase_comp[t, p, i]\n", - " for p in pervap.phase_list\n", - " for i in pervap.comp_list\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_duty = Constraint(m.fs.time, rule=rule_duty)\n", - "\n", - "\n", - "# flow equation adding total recovery as a custom mass balance term\n", - "def rule_retentate_flow(pervap, t, p, i):\n", - " return pervap.retentate.flow_mol_phase_comp[t, p, i] == (\n", - " pervap.inlet.flow_mol_phase_comp[t, p, i]\n", - " - pervap.permeate.flow_mol_phase_comp[t, p, i]\n", - " )\n", - "\n", - "\n", - "m.fs.pervap.eq_retentate_flow = Constraint(\n", - " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_retentate_flow\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's define the Arc connecting our two models (IDAES Mixer and custom Pervaporation) and build the flowsheet network:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.WATER.outlet, destination=m.fs.M101.water_feed)\n", - "m.fs.s02 = Arc(source=m.fs.GLYCOL.outlet, destination=m.fs.M101.glycol_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.pervap.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.pervap.permeate, destination=m.fs.PERMEATE.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.pervap.retentate, destination=m.fs.RETENTATE.inlet)\n", - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how many degrees of freedom the flowsheet has:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.3 Inlet Specifications\n", - "\n", - "To obtain a square problem with zero degrees of freedom, we specify the inlet water flow, ethylene glycol flow, temperature and pressure for each feed stream, as well as the permeate stream pressure:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(0.34) # mol/s\n", - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(1e-6) # mol/s\n", - "m.fs.WATER.outlet.temperature.fix(318.15) # K\n", - "m.fs.WATER.outlet.pressure.fix(101.325e3) # Pa\n", - "\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(1e-6) # mol/s\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(0.66) # mol/s\n", - "m.fs.GLYCOL.outlet.temperature.fix(318.15) # K\n", - "m.fs.GLYCOL.outlet.pressure.fix(101.325e3) # Pa" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, we need to pass rules defining the temperature and pressure outlets of the pervaporation unit:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a constraint to calculate the outlet temperature.\n", - "# Here, assume outlet temperature is the same as inlet temperature for illustration\n", - "# in reality, temperature change from latent heat loss through membrane is negligible\n", - "\n", - "\n", - "def rule_temp_out(pervap, t):\n", - " return pervap.inlet.temperature[t] == pervap.retentate.temperature[t]\n", - "\n", - "\n", - "m.fs.pervap.temperature_out_calculation = Constraint(m.fs.time, rule=rule_temp_out)\n", - "\n", - "# Add a constraint to calculate the retentate pressure\n", - "# Here, assume the retentate pressure is the same as the inlet pressure for illustration\n", - "# in reality, pressure change from mass loss through membrane is negligible\n", - "\n", - "\n", - "def rule_pres_out(pervap, t):\n", - " return pervap.inlet.pressure[t] == pervap.retentate.pressure[t]\n", - "\n", - "\n", - "m.fs.pervap.pressure_out_calculation = Constraint(m.fs.time, rule=rule_pres_out)\n", - "\n", - "# fix permeate vacuum pressure\n", - "m.fs.PERMEATE.inlet.pressure.fix(1.3e3)\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.4 Custom Initialization\n", - "In addition to allowing custom variable and constraint definitions, the Skeleton model enables implementation of a custom initialization scheme. Complex unit operations may present unique tractability issues, and users have precise control over piecewise unit model solving." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add this to the imports\n", - "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", - "\n", - "\n", - "def my_initialize(unit, **kwargs):\n", - " # Callback for user provided initialization sequence\n", - " # Fix the inlet state\n", - " unit.inlet.flow_mol_phase_comp.fix()\n", - " unit.inlet.pressure.fix()\n", - " unit.inlet.temperature.fix()\n", - "\n", - " # Calculate the values of the remaining variables\n", - " for t in m.fs.time:\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", - " unit.eq_permeate_flux[t, \"Liq\", \"water\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", - " unit.eq_permeate_flux[t, \"Liq\", \"ethylene_glycol\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(unit.heat_duty[t], unit.eq_duty[t])\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", - " unit.eq_retentate_flow[t, \"Liq\", \"water\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", - " unit.eq_retentate_flow[t, \"Liq\", \"ethylene_glycol\"],\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.temperature[t], unit.temperature_out_calculation[t]\n", - " )\n", - "\n", - " calculate_variable_from_constraint(\n", - " unit.retentate.pressure[t], unit.pressure_out_calculation[t]\n", - " )\n", - "\n", - " assert degrees_of_freedom(unit) == 0\n", - " if degrees_of_freedom(unit) == 0:\n", - " res = solver.solve(unit, tee=True)\n", - " unit.inlet.flow_mol_phase_comp.unfix()\n", - " unit.inlet.temperature.unfix()\n", - " unit.inlet.pressure.unfix()\n", - " print(\"Custom initialization routine complete: \", res.solver.message)\n", - "\n", - "\n", - "solver = get_solver()\n", - "\n", - "m.fs.WATER.initialize()\n", - "propagate_state(m.fs.s01)\n", - "\n", - "m.fs.GLYCOL.initialize()\n", - "propagate_state(m.fs.s02)\n", - "\n", - "m.fs.pervap.config.initializer = my_initialize\n", - "my_initialize(m.fs.pervap)\n", - "propagate_state(m.fs.s03)\n", - "\n", - "m.fs.PERMEATE.initialize()\n", - "propagate_state(m.fs.s04)\n", - "\n", - "m.fs.RETENTATE.initialize()\n", - "\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print results\n", - "\n", - "m.fs.WATER.report()\n", - "m.fs.GLYCOL.report()\n", - "m.fs.PERMEATE.report()\n", - "m.fs.RETENTATE.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# separation factor for results analysis\n", - "m.fs.inlet_water_frac = Expression(\n", - " expr=(\n", - " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " / sum(\n", - " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", i]\n", - " for i in m.fs.pervap.comp_list\n", - " )\n", - " )\n", - ")\n", - "m.fs.permeate_water_frac = Expression(\n", - " expr=(\n", - " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " / sum(\n", - " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", i]\n", - " for i in m.fs.pervap.comp_list\n", - " )\n", - " )\n", - ")\n", - "m.fs.separation_factor = Expression(\n", - " expr=(m.fs.permeate_water_frac / (1 - m.fs.permeate_water_frac))\n", - " / (m.fs.inlet_water_frac / (1 - m.fs.inlet_water_frac))\n", - ")\n", - "\n", - "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", - "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", - "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", - "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", - "print(\n", - " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# check results\n", - "assert check_optimal_termination(results)\n", - "assert_units_consistent(m)\n", - "\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.14258566, rel=1e-5)\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.000266748768, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.19741534, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.65973425, rel=1e-5)\n", - "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Optimization\n", - "\n", - "Suppose we wish to characterize the membrane behavior by calculating the maximum inlet water mole fraction allowing a separation factor of at least 100 (typical value for high-efficiency separation processes such as gas separation of CO2/N2). We need to fix total inlet flow to ensure physically-sound solutions. We can quickly modify and resolve the model, and check some key results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# unfix inlet flows but fix total to prevent divergence during solve\n", - "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", - "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].unfix()\n", - "m.fs.total_flow = Constraint(\n", - " expr=m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - " + m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " == 1 * pyunits.mol / pyunits.s\n", - ")\n", - "\n", - "# set criteria for separation factor\n", - "m.fs.sep_min = Constraint(expr=m.fs.separation_factor >= 100)\n", - "\n", - "# set objective - defaults to minimization\n", - "m.fs.obj = Objective(expr=m.fs.inlet_water_frac, sense=maximize)\n", - "\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print results\n", - "\n", - "m.fs.WATER.report()\n", - "m.fs.GLYCOL.report()\n", - "m.fs.PERMEATE.report()\n", - "m.fs.RETENTATE.report()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", - "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", - "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", - "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", - "print(\n", - " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# check results\n", - "assert check_optimal_termination(results)\n", - "assert_units_consistent(m)\n", - "\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.14258566, rel=1e-5)\n", - "assert value(\n", - " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.000266748768, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", - ") == pytest.approx(0.69981938, rel=1e-5)\n", - "assert value(\n", - " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - ") == pytest.approx(0.15733020, rel=1e-5)\n", - "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", - "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Summary\n", - "\n", - "The IDAES Skeleton Unit Model is a powerful tool for implementing relatively simple first-princples, surrogate-based or empirical unit operations. More crucially, users can add their own custom models and integrate them into a larger IDAES flowsheet without adding control volumes or rigorous flow balance and equilibrium calculations when not required. The pervaporation example displays a case where all model equations are empirical correlations or simple manual calculations, with a small number of state variable and port connections, and the Skeleton model avoids complex calculations that impact model tractability. The example also demonstrates adding a custom initialization scheme to handle internally model degrees of freedom, a feature providing greater user control than with most IDAES unit models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IDAES Skeleton Unit Model\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "This notebook demonstrates usage of the IDAES Skeleton Unit Model, which provides a generic \"bare bones\" unit for user-defined models and custom variable and constraint sets. To allow maximum versatility, this unit may be defined as a surrogate model or a custom equation-oriented model. Users must add ports and variables that match connected models, and this is facilitated through a provided method to add port-variable sets.\n", + "\n", + "For users who wish to train surrogates with IDAES tools and insert obtained models into a flowsheet, see more detailed information on [IDAES Surrogate Tools](https://idaes-pse.readthedocs.io/en/stable/explanations/modeling_extensions/surrogate/index.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Motivation\n", + "\n", + "In many cases, a specific application requires a unique unit operation that does not exist in the IDAES repository. Custom user models may source from external scripts, import surrogate equations or use first-principles calculations. However, IDAES flowsheets adhere to a standardized modeling hierarchy and simple Pyomo models do not always follow these conventions. Additionally, simple flowsheet submodels often require integration with other IDAES unit models which requires consistency between corresponding port variables, stream properties and physical unit sets, as well as proper usage of `ControlVolume` blocks.\n", + "\n", + "The IDAES `SkeletonUnitModel` allows custom creation of user models blocks that do not require `ControlVolume` blocks, and enabling connection with standard IDAES unit models that do contain `ControlVolume` blocks. To motivate the usefulness and versatility of this tool, we will consider a simple pervaporation unit. The custom model does not require rigorous thermodynamic calculations contained in adjacent unit models, and using a Skeleton model allows definition of only required variables and constraints. The new block does require state variable connections for the inlet and outlet streams. We will demonstrate this scenario below to highlight the usage and benefits of the Skeleton model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Example - Pervaporation\n", + "\n", + "Pervaporation is a low-energy separation process, and is particularly advantageous over distillation for azeotropic solutions or aqueous mixtures of heavy alcohols. Ethylene glycol is more environmentally friendly than typical chloride- and bromide-based dessicants, and is a common choice for commercial recovery of water from flue gas via liquid spray columns. Due to ethylene glycol's high boiling point, diffusion-based water recovery is economically favorable compared to distillation-based processes. The following example and flux correlation are taken from the literature source below:\n", + "\n", + "Jennifer Runhong Du, Amit Chakma, X. Feng, Dehydration of ethylene glycol by pervaporation using poly(N,N-dimethylaminoethyl methacrylate)/polysulfone composite membranes, Separation and Purification Technology, Volume 64, Issue 1, 2008, Pages 63-70, ISSN 1383-5866, https://doi.org/10.1016/j.seppur.2008.08.004.\n", + "\n", + "The process is adapted from the literature, utilizing an inlet aqueous glycol feed circulated through a feed tank-membrane-feed tank recycle loop while permeate is continuously extracted by the membrane. To demonstrate the usefulness of the Skeleton model, we will model this system as a Mixer and custom Pervaporation unit per the diagram below and define the flux as an empirical custom mass balance term rather than requiring rigorous diffusion calculations. We will also circumvent the need for a vapor phase and VLE calculations by manually calculating the duty to condense and collect permeate vapor, and use correlations for steady-state fluxes to avoid a recycle requiring tear calculations.\n", + "\n", + "![](pervaporation_process.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Pyomo and IDAES Imports\n", + "We will begin with relevant imports. We will need basic Pyomo and IDAES components:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "from pyomo.environ import (\n", + " check_optimal_termination,\n", + " ConcreteModel,\n", + " Constraint,\n", + " Expression,\n", + " Objective,\n", + " maximize,\n", + " Var,\n", + " Set,\n", + " TransformationFactory,\n", + " value,\n", + " exp,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.unit_models import Feed, SkeletonUnitModel, Mixer, Product\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "# import thermophysical properties\n", + "import eg_h2o_ideal as thermo_props\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.core.util.constants import Constants" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Build Flowsheet\n", + "\n", + "We will build a simple model manually defining state variables relations entering and exiting the pervaporation unit. As shown below, we may define our pre-separation mixer as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# build the flowsheet\n", + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "\n", + "m.fs.WATER = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.GLYCOL = Feed(property_package=m.fs.thermo_params)\n", + "\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"water_feed\", \"glycol_feed\"]\n", + ")\n", + "\n", + "m.fs.RETENTATE = Product(property_package=m.fs.thermo_params)\n", + "m.fs.PERMEATE = Product(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Defining Skeleton Model and Connections\n", + "\n", + "Now that our flowsheet exists, we can manually define variables, units, constraints and ports for our custom pervaporation unit model. By using a Skeleton model, we avoid rigorous mass and energy balances and phase equilibrium which impact model tractability. Instead, we define state variable relations as below - note that we include the fluxes as outlet flow terms. In this model, the variables specify an `FpcTP` system where molar flow of each component, temperature and pressure are selected as state variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define Skeleton model for pervaporation unit\n", + "m.fs.pervap = SkeletonUnitModel(dynamic=False)\n", + "m.fs.pervap.comp_list = Set(initialize=[\"water\", \"ethylene_glycol\"])\n", + "m.fs.pervap.phase_list = Set(initialize=[\"Liq\"])\n", + "\n", + "# input vars for skeleton\n", + "# m.fs.time is a pre-initialized Set belonging to the FlowsheetBlock; for dynamic=False, time=[0]\n", + "m.fs.pervap.flow_in = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.temperature_in = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", + "m.fs.pervap.pressure_in = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", + "\n", + "# output vars for skeleton\n", + "m.fs.pervap.perm_flow = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.ret_flow = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=1.0,\n", + " units=pyunits.mol / pyunits.s,\n", + ")\n", + "m.fs.pervap.temperature_out = Var(m.fs.time, initialize=298.15, units=pyunits.K)\n", + "m.fs.pervap.pressure_out = Var(m.fs.time, initialize=101e3, units=pyunits.Pa)\n", + "m.fs.pervap.vacuum = Var(m.fs.time, initialize=1.3e3, units=pyunits.Pa)\n", + "\n", + "# dictionaries relating state properties to custom variables\n", + "inlet_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.flow_in,\n", + " \"temperature\": m.fs.pervap.temperature_in,\n", + " \"pressure\": m.fs.pervap.pressure_in,\n", + "}\n", + "retentate_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.ret_flow,\n", + " \"temperature\": m.fs.pervap.temperature_out,\n", + " \"pressure\": m.fs.pervap.pressure_out,\n", + "}\n", + "permeate_dict = {\n", + " \"flow_mol_phase_comp\": m.fs.pervap.perm_flow,\n", + " \"temperature\": m.fs.pervap.temperature_out,\n", + " \"pressure\": m.fs.pervap.vacuum,\n", + "}\n", + "\n", + "m.fs.pervap.add_ports(name=\"inlet\", member_dict=inlet_dict)\n", + "m.fs.pervap.add_ports(name=\"retentate\", member_dict=retentate_dict)\n", + "m.fs.pervap.add_ports(name=\"permeate\", member_dict=permeate_dict)\n", + "\n", + "# internal vars for skeleton\n", + "energy_activation_dict = {\n", + " (0, \"Liq\", \"water\"): 51e3,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 53e3,\n", + "}\n", + "m.fs.pervap.energy_activation = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=energy_activation_dict,\n", + " units=pyunits.J / pyunits.mol,\n", + ")\n", + "m.fs.pervap.energy_activation.fix()\n", + "\n", + "permeance_dict = {\n", + " (0, \"Liq\", \"water\"): 5611320,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 22358.88,\n", + "} # calculated from literature data\n", + "m.fs.pervap.permeance = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=permeance_dict,\n", + " units=pyunits.mol / pyunits.s / pyunits.m**2,\n", + ")\n", + "m.fs.pervap.permeance.fix()\n", + "\n", + "m.fs.pervap.area = Var(m.fs.time, initialize=6, units=pyunits.m**2)\n", + "m.fs.pervap.area.fix()\n", + "\n", + "latent_heat_dict = {\n", + " (0, \"Liq\", \"water\"): 40.660e3,\n", + " (0, \"Liq\", \"ethylene_glycol\"): 56.9e3,\n", + "}\n", + "m.fs.pervap.latent_heat_of_vaporization = Var(\n", + " m.fs.time,\n", + " m.fs.pervap.phase_list,\n", + " m.fs.pervap.comp_list,\n", + " initialize=latent_heat_dict,\n", + " units=pyunits.J / pyunits.mol,\n", + ")\n", + "m.fs.pervap.latent_heat_of_vaporization.fix()\n", + "m.fs.pervap.heat_duty = Var(\n", + " m.fs.time, initialize=1, units=pyunits.J / pyunits.s\n", + ") # we will calculate this later" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define our surrogate equations for flux and permeance, and link them to the port variables. Users can use this structure to write custom relations between inlet and outlet streams; for example, here we define the outlet flow of the pervaporation unit as a sum of the inlet flow and calculated recovery fluxes. By defining model constraints in lieu of rigorous mass balances, we add the flux as a custom mass balance term via an empirical correlation and calculate only the condensation duty rather than implementing full energy balance calculations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Surrogate and first principles model equations\n", + "\n", + "# flux equation (gas constant is defined as J/mol-K)\n", + "\n", + "\n", + "def rule_permeate_flux(pervap, t, p, i):\n", + " return pervap.permeate.flow_mol_phase_comp[t, p, i] / pervap.area[t] == (\n", + " pervap.permeance[t, p, i]\n", + " * exp(\n", + " -pervap.energy_activation[t, p, i]\n", + " / (Constants.gas_constant * pervap.inlet.temperature[t])\n", + " )\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_permeate_flux = Constraint(\n", + " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_permeate_flux\n", + ")\n", + "\n", + "# permeate condensation equation\n", + "# heat duty based on condensing all of permeate product vapor\n", + "# avoids the need for a Heater or HeatExchanger unit model\n", + "\n", + "\n", + "def rule_duty(pervap, t):\n", + " return pervap.heat_duty[t] == sum(\n", + " pervap.latent_heat_of_vaporization[t, p, i]\n", + " * pervap.permeate.flow_mol_phase_comp[t, p, i]\n", + " for p in pervap.phase_list\n", + " for i in pervap.comp_list\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_duty = Constraint(m.fs.time, rule=rule_duty)\n", + "\n", + "\n", + "# flow equation adding total recovery as a custom mass balance term\n", + "def rule_retentate_flow(pervap, t, p, i):\n", + " return pervap.retentate.flow_mol_phase_comp[t, p, i] == (\n", + " pervap.inlet.flow_mol_phase_comp[t, p, i]\n", + " - pervap.permeate.flow_mol_phase_comp[t, p, i]\n", + " )\n", + "\n", + "\n", + "m.fs.pervap.eq_retentate_flow = Constraint(\n", + " m.fs.time, m.fs.pervap.phase_list, m.fs.pervap.comp_list, rule=rule_retentate_flow\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's define the Arc connecting our two models (IDAES Mixer and custom Pervaporation) and build the flowsheet network:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.WATER.outlet, destination=m.fs.M101.water_feed)\n", + "m.fs.s02 = Arc(source=m.fs.GLYCOL.outlet, destination=m.fs.M101.glycol_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.pervap.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.pervap.permeate, destination=m.fs.PERMEATE.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.pervap.retentate, destination=m.fs.RETENTATE.inlet)\n", + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how many degrees of freedom the flowsheet has:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Inlet Specifications\n", + "\n", + "To obtain a square problem with zero degrees of freedom, we specify the inlet water flow, ethylene glycol flow, temperature and pressure for each feed stream, as well as the permeate stream pressure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(0.34) # mol/s\n", + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(1e-6) # mol/s\n", + "m.fs.WATER.outlet.temperature.fix(318.15) # K\n", + "m.fs.WATER.outlet.pressure.fix(101.325e3) # Pa\n", + "\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(1e-6) # mol/s\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(0.66) # mol/s\n", + "m.fs.GLYCOL.outlet.temperature.fix(318.15) # K\n", + "m.fs.GLYCOL.outlet.pressure.fix(101.325e3) # Pa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we need to pass rules defining the temperature and pressure outlets of the pervaporation unit:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a constraint to calculate the outlet temperature.\n", + "# Here, assume outlet temperature is the same as inlet temperature for illustration\n", + "# in reality, temperature change from latent heat loss through membrane is negligible\n", + "\n", + "\n", + "def rule_temp_out(pervap, t):\n", + " return pervap.inlet.temperature[t] == pervap.retentate.temperature[t]\n", + "\n", + "\n", + "m.fs.pervap.temperature_out_calculation = Constraint(m.fs.time, rule=rule_temp_out)\n", + "\n", + "# Add a constraint to calculate the retentate pressure\n", + "# Here, assume the retentate pressure is the same as the inlet pressure for illustration\n", + "# in reality, pressure change from mass loss through membrane is negligible\n", + "\n", + "\n", + "def rule_pres_out(pervap, t):\n", + " return pervap.inlet.pressure[t] == pervap.retentate.pressure[t]\n", + "\n", + "\n", + "m.fs.pervap.pressure_out_calculation = Constraint(m.fs.time, rule=rule_pres_out)\n", + "\n", + "# fix permeate vacuum pressure\n", + "m.fs.PERMEATE.inlet.pressure.fix(1.3e3)\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Custom Initialization\n", + "In addition to allowing custom variable and constraint definitions, the Skeleton model enables implementation of a custom initialization scheme. Complex unit operations may present unique tractability issues, and users have precise control over piecewise unit model solving." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add this to the imports\n", + "from pyomo.util.calc_var_value import calculate_variable_from_constraint\n", + "\n", + "\n", + "def my_initialize(unit, **kwargs):\n", + " # Callback for user provided initialization sequence\n", + " # Fix the inlet state\n", + " unit.inlet.flow_mol_phase_comp.fix()\n", + " unit.inlet.pressure.fix()\n", + " unit.inlet.temperature.fix()\n", + "\n", + " # Calculate the values of the remaining variables\n", + " for t in m.fs.time:\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", + " unit.eq_permeate_flux[t, \"Liq\", \"water\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.permeate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", + " unit.eq_permeate_flux[t, \"Liq\", \"ethylene_glycol\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(unit.heat_duty[t], unit.eq_duty[t])\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"water\"],\n", + " unit.eq_retentate_flow[t, \"Liq\", \"water\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.flow_mol_phase_comp[t, \"Liq\", \"ethylene_glycol\"],\n", + " unit.eq_retentate_flow[t, \"Liq\", \"ethylene_glycol\"],\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.temperature[t], unit.temperature_out_calculation[t]\n", + " )\n", + "\n", + " calculate_variable_from_constraint(\n", + " unit.retentate.pressure[t], unit.pressure_out_calculation[t]\n", + " )\n", + "\n", + " assert degrees_of_freedom(unit) == 0\n", + " if degrees_of_freedom(unit) == 0:\n", + " res = solver.solve(unit, tee=True)\n", + " unit.inlet.flow_mol_phase_comp.unfix()\n", + " unit.inlet.temperature.unfix()\n", + " unit.inlet.pressure.unfix()\n", + " print(\"Custom initialization routine complete: \", res.solver.message)\n", + "\n", + "\n", + "solver = get_solver()\n", + "\n", + "m.fs.WATER.initialize()\n", + "propagate_state(m.fs.s01)\n", + "\n", + "m.fs.GLYCOL.initialize()\n", + "propagate_state(m.fs.s02)\n", + "\n", + "m.fs.pervap.config.initializer = my_initialize\n", + "my_initialize(m.fs.pervap)\n", + "propagate_state(m.fs.s03)\n", + "\n", + "m.fs.PERMEATE.initialize()\n", + "propagate_state(m.fs.s04)\n", + "\n", + "m.fs.RETENTATE.initialize()\n", + "\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print results\n", + "\n", + "m.fs.WATER.report()\n", + "m.fs.GLYCOL.report()\n", + "m.fs.PERMEATE.report()\n", + "m.fs.RETENTATE.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# separation factor for results analysis\n", + "m.fs.inlet_water_frac = Expression(\n", + " expr=(\n", + " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " / sum(\n", + " m.fs.pervap.inlet.flow_mol_phase_comp[0, \"Liq\", i]\n", + " for i in m.fs.pervap.comp_list\n", + " )\n", + " )\n", + ")\n", + "m.fs.permeate_water_frac = Expression(\n", + " expr=(\n", + " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " / sum(\n", + " m.fs.pervap.permeate.flow_mol_phase_comp[0, \"Liq\", i]\n", + " for i in m.fs.pervap.comp_list\n", + " )\n", + " )\n", + ")\n", + "m.fs.separation_factor = Expression(\n", + " expr=(m.fs.permeate_water_frac / (1 - m.fs.permeate_water_frac))\n", + " / (m.fs.inlet_water_frac / (1 - m.fs.inlet_water_frac))\n", + ")\n", + "\n", + "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", + "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", + "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", + "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", + "print(\n", + " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check results\n", + "assert check_optimal_termination(results)\n", + "assert_units_consistent(m)\n", + "\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.19741534, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.65973425, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(1037.6188, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Optimization\n", + "\n", + "Suppose we wish to characterize the membrane behavior by calculating the maximum inlet water mole fraction allowing a separation factor of at least 100 (typical value for high-efficiency separation processes such as gas separation of CO2/N2). We need to fix total inlet flow to ensure physically-sound solutions. We can quickly modify and resolve the model, and check some key results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# unfix inlet flows but fix total to prevent divergence during solve\n", + "m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].unfix()\n", + "m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].unfix()\n", + "m.fs.total_flow = Constraint(\n", + " expr=m.fs.WATER.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + " + m.fs.GLYCOL.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " == 1 * pyunits.mol / pyunits.s\n", + ")\n", + "\n", + "# set criteria for separation factor\n", + "m.fs.sep_min = Constraint(expr=m.fs.separation_factor >= 100)\n", + "\n", + "# set objective - defaults to minimization\n", + "m.fs.obj = Objective(expr=m.fs.inlet_water_frac, sense=maximize)\n", + "\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print results\n", + "\n", + "m.fs.WATER.report()\n", + "m.fs.GLYCOL.report()\n", + "m.fs.PERMEATE.report()\n", + "m.fs.RETENTATE.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Inlet water mole fraction: {value(m.fs.inlet_water_frac)}\")\n", + "print(f\"Permeate water mole fraction: {value(m.fs.permeate_water_frac)}\")\n", + "print(f\"Separation factor: {value(m.fs.separation_factor)}\")\n", + "print(f\"Condensation duty: {value(m.fs.pervap.heat_duty[0]/1000)} kW\")\n", + "print(\n", + " f\"Duty per mole water recovered: {value(m.fs.pervap.heat_duty[0]/(1000*m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, 'Liq', 'water']*3600))} kW-h / mol\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check results\n", + "assert check_optimal_termination(results)\n", + "assert_units_consistent(m)\n", + "\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.14258566, rel=1e-5)\n", + "assert value(\n", + " m.fs.PERMEATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.000266748768, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"water\"]\n", + ") == pytest.approx(0.69981938, rel=1e-5)\n", + "assert value(\n", + " m.fs.RETENTATE.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + ") == pytest.approx(0.15733020, rel=1e-5)\n", + "assert value(m.fs.separation_factor) == pytest.approx(100.000067, rel=1e-5)\n", + "assert value(m.fs.pervap.heat_duty[0]) == pytest.approx(5812.7111, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Summary\n", + "\n", + "The IDAES Skeleton Unit Model is a powerful tool for implementing relatively simple first-princples, surrogate-based or empirical unit operations. More crucially, users can add their own custom models and integrate them into a larger IDAES flowsheet without adding control volumes or rigorous flow balance and equilibrium calculations when not required. The pervaporation example displays a case where all model equations are empirical correlations or simple manual calculations, with a small number of state variable and port connections, and the Skeleton model avoids complex calculations that impact model tractability. The example also demonstrates adding a custom initialization scheme to handle internally model degrees of freedom, a feature providing greater user control than with most IDAES unit models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py b/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py index 99bbcd25..578ab356 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py +++ b/idaes_examples/notebooks/docs/unit_models/operations/tests/test_eg_h2o_ideal.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Author: Brandon Paul diff --git a/idaes_examples/notebooks/docs/unit_models/operations/turbine.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/turbine.ipynb index 3979dd95..747d49ff 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/turbine.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/turbine.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/operations/turbine_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/turbine_doc.ipynb index e2f39fcc..6299a9fa 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/turbine_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/turbine_doc.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -232,7 +233,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:24:37 [INFO] idaes.init.fs.turbine_case_1: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:33 [INFO] idaes.init.fs.turbine_case_1: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -314,16 +315,10 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", "Total CPU secs in NLP function evaluations = 0.002\n", "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] } @@ -526,7 +521,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-11-02 10:24:38 [INFO] idaes.init.fs.turbine_case_2: Initialization Complete: optimal - Optimal Solution Found\n" + "2025-03-17 17:40:33 [INFO] idaes.init.fs.turbine_case_2: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -608,16 +603,10 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.007\n", "Total CPU secs in NLP function evaluations = 0.001\n", "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "EXIT: Optimal Solution Found.\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] } @@ -716,9 +705,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/turbine_test.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/turbine_test.ipynb index d3fff7d0..3ff7dcca 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/turbine_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/turbine_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -549,4 +550,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/operations/turbine_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/operations/turbine_usr.ipynb index bcebdfef..a7cd873e 100644 --- a/idaes_examples/notebooks/docs/unit_models/operations/turbine_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/operations/turbine_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -440,4 +441,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb index 0d0fc742..a6606783 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb index 891c3495..e3fd5ed2 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_doc.ipynb @@ -1,723 +1,1254 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Continuous Stirred Tank Reactor (CSTR) Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES CSTR unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160.\n", - "\n", - "Ethylene glycol (EG) is a high-demand chemical, with billions of pounds produced every year for applications such as vehicle anti-freeze. EG may be readily obtained from the hydrolysis of ethylene oxide in the presence of a catalytic intermediate. In this example, an aqueous solution of ethylene oxide hydrolizes after mixing with an aqueous solution of sulfuric acid catalyst:\n", - "\n", - "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", - "\n", - "This reaction often occurs by two mechanisms, as the catalyst may bind to either reactant before the final hydrolysis step; we will simplify the reaction to a single step for this example.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing ethylene oxide and catalyst solutions of fixed concentrations to produce 200 MM lb/year of EG. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a heater H101 to preheat the feed to the reaction temperature, and a CSTR unit R101 with an external cooling system to remove heat generated by the exothermic reaction. We will assume ideal solutions and thermodynamics for this flowsheet, as well as well-mixed liquid behavior (no vapor phase) in the reactor. The properties required for this module are available in the same directory:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](egprod_flowsheet.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- CSTR\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import (\n", - " GenericParameterBlock,\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, CSTR, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", - "\n", - "The reaction package here assumes Arrhenius kinetic behavior for the CSTR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", - "\n", - "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", - "\n", - "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", - "$k$ - reaction rate constant per second \n", - "$V$ - volume of CSTR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", - "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", - "$k_0$ - pre-exponential Arrhenius factor per second \n", - "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", - "$R$ - gas constant in J/mol-K \n", - "$T$ - reactor temperature in K\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "ParamEst parameter estimation: \n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `CSTR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = CSTR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `CSTR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom (reactor conversion or reactor volume) since we have not yet defined the CSTR performance equation. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and volume." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll add constraints defining the reactor volume and conversion in relation to the stream properties. Particularly, we want to use our CSTR performance relation: \n", - "\n", - "$V = \\frac{v_0 X} {k(1-X)}$, where the `CSTR` reaction volume $V$ will be specified, the inlet volumetric flow $v_0$ is determined by stream properties, $k$ is calculated by the reaction package, and $X$ will be calculated. Reactor volume is commonly selected as a specification in simulation problems, and choosing conversion is often to perform reactor design.\n", - "\n", - "For the `CSTR`, we have to define the conversion in terms of ethylene oxide as well as the `CSTR` reaction volume. This requires us to create new variables and constraints relating reactor properties to stream properties. Note that the `CSTR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `CSTR` model. Additionally, the heat duty is not fixed, since the heat of reaction depends on the reactor conversion (through the extent of reaction and heat of reaction). We'll estimate 80% conversion for our initial flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)\n", - "\n", - "m.fs.R101.volume.fix(5.538 * pyunits.m**3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.R101.volume.unfix()\n", - "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", - "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"CSTR reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", - "\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", - ")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Continuous Stirred Tank Reactor (CSTR) Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES CSTR unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160.\n", + "\n", + "Ethylene glycol (EG) is a high-demand chemical, with billions of pounds produced every year for applications such as vehicle anti-freeze. EG may be readily obtained from the hydrolysis of ethylene oxide in the presence of a catalytic intermediate. In this example, an aqueous solution of ethylene oxide hydrolizes after mixing with an aqueous solution of sulfuric acid catalyst:\n", + "\n", + "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "\n", + "This reaction often occurs by two mechanisms, as the catalyst may bind to either reactant before the final hydrolysis step; we will simplify the reaction to a single step for this example.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing ethylene oxide and catalyst solutions of fixed concentrations to produce 200 MM lb/year of EG. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a heater H101 to preheat the feed to the reaction temperature, and a CSTR unit R101 with an external cooling system to remove heat generated by the exothermic reaction. We will assume ideal solutions and thermodynamics for this flowsheet, as well as well-mixed liquid behavior (no vapor phase) in the reactor. The properties required for this module are available in the same directory:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](egprod_flowsheet.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- CSTR\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import (\n", + " GenericParameterBlock,\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, CSTR, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", + "\n", + "The reaction package here assumes Arrhenius kinetic behavior for the CSTR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", + "\n", + "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", + "\n", + "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", + "$k$ - reaction rate constant per second \n", + "$V$ - volume of CSTR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", + "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", + "$k_0$ - pre-exponential Arrhenius factor per second \n", + "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", + "$R$ - gas constant in J/mol-K \n", + "$T$ - reactor temperature in K\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "ParamEst parameter estimation: \n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `CSTR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = CSTR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `CSTR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom (reactor conversion or reactor volume) since we have not yet defined the CSTR performance equation. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and volume." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll add constraints defining the reactor volume and conversion in relation to the stream properties. Particularly, we want to use our CSTR performance relation: \n", + "\n", + "$V = \\frac{v_0 X} {k(1-X)}$, where the `CSTR` reaction volume $V$ will be specified, the inlet volumetric flow $v_0$ is determined by stream properties, $k$ is calculated by the reaction package, and $X$ will be calculated. Reactor volume is commonly selected as a specification in simulation problems, and choosing conversion is often to perform reactor design.\n", + "\n", + "For the `CSTR`, we have to define the conversion in terms of ethylene oxide as well as the `CSTR` reaction volume. This requires us to create new variables and constraints relating reactor properties to stream properties. Note that the `CSTR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `CSTR` model. Additionally, the heat duty is not fixed, since the heat of reaction depends on the reactor conversion (through the extent of reaction and heat of reaction). We'll estimate 80% conversion for our initial flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)\n", + "\n", + "m.fs.R101.volume.fix(5.538 * pyunits.m**3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.OXIDE.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.OXIDE.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.OXIDE.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.OXIDE: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.ACID.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.ACID.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.ACID.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.ACID: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.reagent_feed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.reagent_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.catalyst_feed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.catalyst_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:36 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 345\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 393\n", + "\n", + "Total number of variables............................: 96\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 87\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 96\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.24e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.01e+06 2.25e+01 -1.0 1.10e+07 - 6.77e-02 9.90e-01h 1\n", + " 2 0.0000000e+00 2.70e+04 2.90e+02 -1.0 9.65e+04 - 7.00e-01 9.90e-01h 1\n", + " 3 0.0000000e+00 2.70e+02 2.42e+03 -1.0 3.57e+04 - 9.65e-01 9.90e-01h 1\n", + " 4 0.0000000e+00 1.93e+00 3.24e+03 -1.0 2.93e+03 - 9.90e-01 9.92e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 5 0.0000000e+00 1.62e-05 4.91e+03 -1.0 3.29e+01 - 9.91e-01 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 5\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.6686893433829877e+06 1.6686893433829877e+06\n", + "Constraint violation....: 8.7029969081382703e-09 1.6207806766033173e-05\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 8.7029969081382703e-09 1.6686893433829877e+06\n", + "\n", + "\n", + "Number of objective function evaluations = 6\n", + "Number of objective gradient evaluations = 6\n", + "Number of equality constraint evaluations = 6\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 6\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 5\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $8.004012 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -5.8675e+06 : watt : False : (None, None)\n", + " Volume : 5.5380 : meter ** 3 : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 11.600\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 193.20\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 46.400\n", + " Temperature kelvin 328.15 329.26\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n", + "\n", + "Assuming a 20% design factor for reactor volume,total CSTR volume required = 6.645600 m^3 = 1755.581791 gal\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.R101.volume.unfix()\n", + "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", + "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 348\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 408\n", + "\n", + "Total number of variables............................: 98\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 89\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 96\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 1\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 8.0040115e+06 1.74e+06 6.34e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 7.7527391e+06 1.74e+06 1.58e+01 -1.0 1.01e+10 - 1.05e-04 6.26e-06f 1\n", + " 2 7.3318298e+06 1.74e+06 8.38e+01 -1.0 6.50e+09 - 7.68e-05 1.06e-04f 1\n", + " 3 7.1963002e+06 1.74e+06 1.13e+02 -1.0 2.14e+09 - 2.88e-04 1.04e-04f 1\n", + " 4 7.1956623e+06 1.74e+06 1.30e+04 -1.0 3.21e+06 - 7.84e-02 3.62e-04f 1\n", + " 5 7.2878860e+06 1.61e+06 6.46e+04 -1.0 1.57e+06 - 1.30e-01 7.88e-02h 1\n", + " 6 8.3387605e+06 3.53e+04 1.73e+07 -1.0 1.41e+06 - 7.27e-01 9.90e-01h 1\n", + " 7 8.3196702e+06 3.65e+02 8.13e+05 -1.0 3.48e+04 - 9.84e-01 9.91e-01f 1\n", + " 8 8.3181815e+06 1.14e-01 5.33e+03 -1.0 2.47e+03 - 9.90e-01 1.00e+00f 1\n", + " 9 8.3181770e+06 8.75e-07 2.12e+03 -1.7 7.11e+00 - 9.91e-01 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 8.3181770e+06 1.49e-08 2.81e-07 -1.7 6.61e-05 - 1.00e+00 1.00e+00h 1\n", + " 11 8.3181770e+06 1.68e-08 3.47e-07 -5.7 3.56e-02 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 11\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 8.3181770063406546e+06 8.3181770063406546e+06\n", + "Dual infeasibility......: 3.4654545178966489e-07 3.4654545178966489e-07\n", + "Constraint violation....: 1.1657620996360177e-14 1.6763806343078613e-08\n", + "Complementarity.........: 1.8475611193718096e-06 1.8475611193718096e-06\n", + "Overall NLP error.......: 1.8402625221080294e-07 1.8475611193718096e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 12\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 12\n", + "Number of equality constraint Jacobian evaluations = 12\n", + "Number of inequality constraint Jacobian evaluations = 12\n", + "Number of Lagrangian Hessian evaluations = 11\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $8.318177 million per year\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 6.9784e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 58.000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 239.60\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 2.0000e-05\n", + " Temperature kelvin 298.15 328.15\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n", + "\n", + "CSTR reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -6.3821e+06 : watt : False : (None, None)\n", + " Volume : 18.927 : meter ** 3 : False : (0.0, 18.927058919999997)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 5.8000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 187.40\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 52.200\n", + " Temperature kelvin 328.15 338.90\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"CSTR reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 328.150000 K\n", + "\n", + "Assuming a 20% design factor for reactor volume,total CSTR volume required = 22.712471 m^3 = 6000.000000 gal\n", + "\n", + "Ethylene glycol produced = 225.415471 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume[0]):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume[0], to_units=pyunits.gal)):0.6f} gal\"\n", + ")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb index a77d3809..6c6b7cb0 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb index 946169b9..fb9b0cbe 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/cstr_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py b/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py index 1c18b1fc..402be7c0 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py +++ b/idaes_examples/notebooks/docs/unit_models/reactors/egprod_ideal.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Phase equilibrium package for Ethylene Oxide hydrolysis to Ethylene Glycol diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/egprod_reaction.py b/idaes_examples/notebooks/docs/unit_models/reactors/egprod_reaction.py index 47e15c10..208ff30c 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/egprod_reaction.py +++ b/idaes_examples/notebooks/docs/unit_models/reactors/egprod_reaction.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Phase equilibrium package for Ethylene Oxide hydrolysis to Ethylene Glycol diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor.ipynb index e8b02ceb..06e3f97b 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb index bafa95cf..49f978cc 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_doc.ipynb @@ -1,1098 +1,1357 @@ { - "cells": [ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES EquilibriumReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", + "\n", + "**CH4 + H2O → CO + 3H2** \n", + "**CO + H2O → CO2 + H2**\n", + "\n", + "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- EquilibriumReactor\n", + "- Product\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " EquilibriumReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", + "\n", + "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", + "\n", + "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", + "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", + "\n", + "The correlations are taken from the following literature: \n", + "\n", + "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "### Determining $k_{eq}^{ref}$\n", + "\n", + "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", + "\n", + "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", + "- msr_reaction as reaction_props (contains configuration dictionary)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", + "import msr_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = EquilibriumReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=True,\n", + " has_rate_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", + "\n", + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", + "\n", + "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_doc.md) demonstrates adding a check before writing an additional constraint that may overspecify the system." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES EquilibriumReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", - "\n", - "**CH4 + H2O \u2192 CO + 3H2** \n", - "**CO + H2O \u2192 CO2 + H2**\n", - "\n", - "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- EquilibriumReactor\n", - "- Product\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties.base.generic_property import (\n", - " GenericParameterBlock,\n", - ")\n", - "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " EquilibriumReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", - "\n", - "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", - "\n", - "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", - "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", - "\n", - "The correlations are taken from the following literature: \n", - "\n", - "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "### Determining $k_{eq}^{ref}$\n", - "\n", - "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", - "\n", - "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", - "- msr_reaction as reaction_props (contains configuration dictionary)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", - "import msr_reaction as reaction_props" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.CH4: Initialization Complete.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.H2O: Initialization Complete.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = EquilibriumReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=True,\n", - " has_rate_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:39 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", - "\n", - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", - "\n", - "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_doc.md) demonstrates adding a check before writing an additional constraint that may overspecify the system." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + ] }, { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 562\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 477\n", - "\n", - "Total number of variables............................: 204\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 174\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.8421709430404007e-14 2.1187588572502136e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $45.933 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 2.7605e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", - " Temperature kelvin 500.00 868.56\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # pressurize to at least 2 bar\n", - "m.fs.C101.outlet.pressure[0].setub(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 10 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # outlet temperature is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:40 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 569\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 493\n", - "\n", - "Total number of variables............................: 206\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 176\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", - " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", - " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", - " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", - " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", - " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", - " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", - " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", - " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", - "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", - "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", - "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", - "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 10\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", - "Total CPU secs in NLP function evaluations = 0.003\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 562\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 477\n", + "\n", + "Total number of variables............................: 204\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 174\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.24e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 2.2351741790771484e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 2.2351741790771484e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $43.309 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", - " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 423.34\n", - " Pressure pascal 1.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 423.34 423.34\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Equilibrium reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 3.2486e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", - " Temperature kelvin 423.34 910.04\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Equilibrium reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $45.933 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 2.7605e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n" + ] }, { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 0.200 MPa\n", - "\n", - "C101 outlet temperature = 423.345 K\n", - "\n", - "H101 outlet temperature = 423.345 K\n", - "\n", - "R101 outlet temperature = 910.044 K\n", - "\n", - "Hydrogen produced = 33.648 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", + " Temperature kelvin 500.00 868.56\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # pressurize to at least 2 bar\n", + "m.fs.C101.outlet.pressure[0].setub(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 10 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # outlet temperature is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 569\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 493\n", + "\n", + "Total number of variables............................: 206\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 176\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", + " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", + " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", + " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", + " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", + " 6 4.3309131e+07 5.78e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", + " 7 4.3309131e+07 1.12e-08 9.40e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", + " 8 4.3309131e+07 2.94e-08 8.99e-07 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 4.3309131e+07 3.59e-08 1.18e-06 -5.7 3.08e-05 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.3309131e+07 7.22e-09 8.49e-07 -7.0 3.42e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", + "Dual infeasibility......: 8.4896141664767067e-07 8.4896141664767067e-07\n", + "Constraint violation....: 1.4551915228366852e-11 7.2177499532699585e-09\n", + "Complementarity.........: 9.0909090913936446e-08 9.0909090913936446e-08\n", + "Overall NLP error.......: 9.0909090913936446e-08 8.4896141664767067e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.004\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $43.309 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", + " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 423.34\n", + " Pressure pascal 1.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 423.34 423.34\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Equilibrium reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 3.2486e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", + " Temperature kelvin 423.34 910.04\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Equilibrium reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 0.200 MPa\n", + "\n", + "C101 outlet temperature = 423.345 K\n", + "\n", + "H101 outlet temperature = 423.345 K\n", + "\n", + "R101 outlet temperature = 910.044 K\n", + "\n", + "Hydrogen produced = 33.648 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb index a845483e..2c9dfece 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_test.ipynb @@ -1,1232 +1,1233 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES EquilibriumReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", - "\n", - "**CH4 + H2O \u2192 CO + 3H2** \n", - "**CO + H2O \u2192 CO2 + H2**\n", - "\n", - "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- EquilibriumReactor\n", - "- Product\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties.base.generic_property import (\n", - " GenericParameterBlock,\n", - ")\n", - "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " EquilibriumReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", - "\n", - "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", - "\n", - "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", - "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", - "\n", - "The correlations are taken from the following literature: \n", - "\n", - "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "### Determining $k_{eq}^{ref}$\n", - "\n", - "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", - "\n", - "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", - "- msr_reaction as reaction_props (contains configuration dictionary)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", - "import msr_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_test.ipynb), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = EquilibriumReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=True,\n", - " has_rate_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", - "\n", - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", - "\n", - "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_test.ipynb) demonstrates adding a check before writing an additional constraint that may overspecify the system." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 562\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 477\n", - "\n", - "Total number of variables............................: 204\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 174\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.8421709430404007e-14 2.1187588572502136e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $45.933 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(45.933, rel=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 2.7605e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", - " Temperature kelvin 500.00 868.56\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.800, rel=1e-3)\n", - "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(27.605, rel=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(8.6856, rel=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # pressurize to at least 2 bar\n", - "m.fs.C101.outlet.pressure[0].setub(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 10 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # outlet temperature is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert degrees_of_freedom(m) == 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES EquilibriumReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", + "\n", + "**CH4 + H2O → CO + 3H2** \n", + "**CO + H2O → CO2 + H2**\n", + "\n", + "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- EquilibriumReactor\n", + "- Product\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " EquilibriumReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", + "\n", + "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", + "\n", + "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", + "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", + "\n", + "The correlations are taken from the following literature: \n", + "\n", + "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "### Determining $k_{eq}^{ref}$\n", + "\n", + "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", + "\n", + "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", + "- msr_reaction as reaction_props (contains configuration dictionary)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", + "import msr_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_test.ipynb), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = EquilibriumReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=True,\n", + " has_rate_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", + "\n", + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", + "\n", + "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_test.ipynb) demonstrates adding a check before writing an additional constraint that may overspecify the system." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 569\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 493\n", - "\n", - "Total number of variables............................: 206\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 176\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", - " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", - " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", - " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", - " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", - " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", - " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", - " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", - " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", - "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", - "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", - "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", - "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 10\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", - "Total CPU secs in NLP function evaluations = 0.003\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $43.309 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", - " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 423.34\n", - " Pressure pascal 1.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 423.34 423.34\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Equilibrium reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 3.2486e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", - " Temperature kelvin 423.34 910.04\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Equilibrium reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(43.309, rel=1e-3)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 562\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 477\n", + "\n", + "Total number of variables............................: 204\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 174\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $45.933 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(45.933, rel=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 0.200 MPa\n", - "\n", - "C101 outlet temperature = 423.345 K\n", - "\n", - "H101 outlet temperature = 423.345 K\n", - "\n", - "R101 outlet temperature = 910.044 K\n", - "\n", - "Hydrogen produced = 33.648 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 2.7605e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", + " Temperature kelvin 500.00 868.56\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.R101.conversion) == pytest.approx(0.800, rel=1e-3)\n", + "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(27.605, rel=1e-3)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(8.6856, rel=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # pressurize to at least 2 bar\n", + "m.fs.C101.outlet.pressure[0].setub(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 10 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # outlet temperature is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert degrees_of_freedom(m) == 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.C101.outlet.pressure[0]) / 1e6 == pytest.approx(0.2000, rel=1e-3)\n", - "assert value(m.fs.C101.outlet.temperature[0]) / 100 == pytest.approx(4.23345, rel=1e-3)\n", - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(4.23345, rel=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(9.10044, rel=1e-3)\n", - "assert value(m.fs.hyd_prod) == pytest.approx(33.648, rel=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-3)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 569\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 493\n", + "\n", + "Total number of variables............................: 206\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 176\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", + " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", + " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", + " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", + " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", + " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", + " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", + " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", + "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", + "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", + "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", + "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $43.309 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", + " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 423.34\n", + " Pressure pascal 1.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 423.34 423.34\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Equilibrium reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 3.2486e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", + " Temperature kelvin 423.34 910.04\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Equilibrium reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(43.309, rel=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 0.200 MPa\n", + "\n", + "C101 outlet temperature = 423.345 K\n", + "\n", + "H101 outlet temperature = 423.345 K\n", + "\n", + "R101 outlet temperature = 910.044 K\n", + "\n", + "Hydrogen produced = 33.648 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.C101.outlet.pressure[0]) / 1e6 == pytest.approx(0.2000, rel=1e-3)\n", + "assert value(m.fs.C101.outlet.temperature[0]) / 100 == pytest.approx(4.23345, rel=1e-3)\n", + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(4.23345, rel=1e-3)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(9.10044, rel=1e-3)\n", + "assert value(m.fs.hyd_prod) == pytest.approx(33.648, rel=1e-3)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb index 6185b931..939883ac 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/equilibrium_reactor_usr.ipynb @@ -1,1098 +1,1099 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Maintainer: Brandon Paul \n", - "Author: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES EquilibriumReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", - "\n", - "**CH4 + H2O \u2192 CO + 3H2** \n", - "**CO + H2O \u2192 CO2 + H2**\n", - "\n", - "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- EquilibriumReactor\n", - "- Product\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties.base.generic_property import (\n", - " GenericParameterBlock,\n", - ")\n", - "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " EquilibriumReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", - "\n", - "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", - "\n", - "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", - "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", - "\n", - "The correlations are taken from the following literature: \n", - "\n", - "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", - "\n", - "### Determining $k_{eq}^{ref}$\n", - "\n", - "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", - "\n", - "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", - "- msr_reaction as reaction_props (contains configuration dictionary)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", - "import msr_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_usr.ipynb), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = EquilibriumReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=True,\n", - " has_rate_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", - "\n", - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", - "\n", - "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_usr.ipynb) demonstrates adding a check before writing an additional constraint that may overspecify the system." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 562\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 477\n", - "\n", - "Total number of variables............................: 204\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 174\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 2.8421709430404007e-14 2.1187588572502136e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $45.933 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 2.7605e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", - " Temperature kelvin 500.00 868.56\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Equilibrium Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Maintainer: Brandon Paul \n", + "Author: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES EquilibriumReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "This example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "Steam methane reforming (SMR) is one of the most common pathways for hydrogen production, taking advantage of chemical equilibria in natural gas systems. The process is typically done in two steps: methane reformation at a high temperature to partially oxidize methane, and water gas shift at a low temperature to complete the oxidation reaction:\n", + "\n", + "**CH4 + H2O → CO + 3H2** \n", + "**CO + H2O → CO2 + H2**\n", + "\n", + "This reaction is often carried out in two separate reactors to allow for different reaction temperatures and pressures; in this example, we will minimize operating cost for a single reactor.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. We will be processing natural gas and steam feeds of fixed composition to produce hydrogen. As shown in the flowsheet, the process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a EquilibriumReactor unit R101. We will use thermodynamic properties from the Peng-Robinson equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- EquilibriumReactor\n", + "- Product\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " EquilibriumReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We will import natural gas properties from an existing IDAES module, and reaction properties from a custom module to describe equilibrium behavior. These configuration dictionaries provide parameter data that we will pass to the Modular Property Framework.\n", + "\n", + "The reaction package here assumes all reactions reach chemical equilibrium at the given conditions. \n", + "\n", + "${K_{eq}^{MSR}} = \\exp\\left(\\frac {-26830} {T} + 30.114\\right)$, ${K_{eq}^{WGS}} = \\exp\\left(\\frac {4400} {T} - 4.036\\right)$ with the reactor temperature $T$ in K. \n", + "The total reaction equilibrium constant is given by $K_{eq} = {K_{eq}^{MSR}}{K_{eq}^{WGS}}$.\n", + "\n", + "The correlations are taken from the following literature: \n", + "\n", + "Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903\n", + "\n", + "### Determining $k_{eq}^{ref}$\n", + "\n", + "As part of the parameter dictionary, users may define equilibrium reactions using a constant coefficient or built-in correlations for van't Hoff and Gibbs formulations. Using the literature correlations above for $k_{eq}$, we can easily calculate the necessary parameters to use the van't Hoff equilibrium constant form:\n", + "\n", + "For an empirical correlation $ln(k_{eq}) = f(T)$ for a catalyst (reaction) temperature $T$, we obtain $k_{eq}^{ref} = \\exp\\left({f(T_{eq}^{ref})}\\right)$. From the paper, we obtain a reference catalyst temperature of 973.15 K and reaction energies for the two reaction steps; these values exist in the reaction property parameter module in this same directory.\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)\n", + "- msr_reaction as reaction_props (contains configuration dictionary)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop\n", + "import msr_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_usr.ipynb), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The `get_prop` method for the natural gas property module automatically returns the correct dictionary using a component list argument. The `GenericParameterBlock` and `GenericReactionParameterBlock` methods build states blocks from passed parameter data; the reaction block unpacks using `**reaction_props.config_dict` to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Compressor`, a `Heater` and an `EquilibriumReactor`. Note that all unit models should be explicitly defined with a given property package. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets. Note that the `Compressor` is a `PressureChanger` assuming compression operation and with a fixed isentropic compressor efficiency as the default thermodynamic behavior." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = EquilibriumReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=True,\n", + " has_rate_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a Pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Compressor`, the outlet of the compressor `Compressor` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `EquilibriumReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few `Expressions` that allow us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities.\n", + "\n", + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of hydrogen. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, the compression cost (\\\\$/s) assuming 1.2E-3 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of Watt (J/s). The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=1.2e-6 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion.\n", + "\n", + "Although the model has eight degrees of freedom per stream, the mole fractions are not all independent and the physical system only has seven. Each `StateBlock` sets a flag `defined_state` based on any remaining degrees of freedom; if this flag is set to `False` a `Constraint` is written to ensure all mole fractions sum to one. However, a fully specified system with `defined_state` set to `True` will not create this constraint and it is the responsibility of the user to set physically meaningful values, i.e. that all mole fractions are nonnegative and sum to one. While not necessary in this example, the [Custom Thermophysical Property Package Example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/Advanced/CustomProperties/custom_physical_property_packages_testing_usr.ipynb) demonstrates adding a check before writing an additional constraint that may overspecify the system." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on the literature source, we will initialize our simulation with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `EquilibriumReactor` unit model calculates the amount of product and reactant based on the calculated equilibrium constant; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # pressurize to at least 2 bar\n", - "m.fs.C101.outlet.pressure[0].setub(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 10 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # outlet temperature is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:03 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:04 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:08:05 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 569\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 493\n", - "\n", - "Total number of variables............................: 206\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 176\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 204\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", - " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", - " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", - " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", - " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", - " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", - " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", - " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", - " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", - "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", - "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", - "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", - "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 10\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", - "Total CPU secs in NLP function evaluations = 0.003\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 562\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 477\n", + "\n", + "Total number of variables............................: 204\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 174\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.8421709430404007e-14 2.1187588572502136e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $43.309 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", - " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 423.34\n", - " Pressure pascal 1.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 423.34 423.34\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Equilibrium reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 3.2486e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", - " Temperature kelvin 423.34 910.04\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Equilibrium reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $45.933 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 2.7605e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31487\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51029\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.049157\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.090717\n", + " Temperature kelvin 500.00 868.56\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. As mentioned earlier, the two reactions have competing equilibria - steam methane reformation occurs more readily at higher temperatures (500-700 C) while water gas shift occurs more readily at lower temperatures (300-400 C). We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # pressurize to at least 2 bar\n", + "m.fs.C101.outlet.pressure[0].setub(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 10 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # outlet temperature is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 0.200 MPa\n", - "\n", - "C101 outlet temperature = 423.345 K\n", - "\n", - "H101 outlet temperature = 423.345 K\n", - "\n", - "R101 outlet temperature = 910.044 K\n", - "\n", - "Hydrogen produced = 33.648 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 569\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 493\n", + "\n", + "Total number of variables............................: 206\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 176\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 204\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.5933014e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.5420427e+07 1.49e+06 1.33e+03 -1.0 1.08e+07 - 4.58e-01 5.96e-03f 1\n", + " 2 4.2830345e+07 8.68e+05 6.47e+06 -1.0 5.32e+06 - 8.03e-01 4.18e-01f 1\n", + " 3 4.3111576e+07 1.26e+05 1.06e+07 -1.0 2.54e+06 - 9.54e-01 8.85e-01h 1\n", + " 4 4.3307552e+07 2.24e+03 3.12e+05 -1.0 3.51e+05 - 9.89e-01 9.86e-01h 1\n", + " 5 4.3309118e+07 2.20e+01 3.08e+03 -1.0 2.69e+03 - 9.90e-01 9.90e-01h 1\n", + " 6 4.3309131e+07 5.77e-06 3.84e+01 -1.0 2.31e+01 - 9.92e-01 1.00e+00h 1\n", + " 7 4.3309131e+07 7.77e-09 4.84e-07 -2.5 1.97e-02 - 1.00e+00 1.00e+00f 1\n", + " 8 4.3309131e+07 1.63e-08 1.71e-06 -3.8 5.56e-04 - 1.00e+00 1.00e+00f 1\n", + " 9 4.3309131e+07 1.72e-08 1.31e-06 -5.7 3.08e-05 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.3309131e+07 2.20e-08 8.55e-07 -7.0 3.59e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3309130854568794e+07 4.3309130854568794e+07\n", + "Dual infeasibility......: 8.5488594337511447e-07 8.5488594337511447e-07\n", + "Constraint violation....: 1.4551915228366852e-11 2.2002495825290680e-08\n", + "Complementarity.........: 9.0909090913936433e-08 9.0909090913936433e-08\n", + "Overall NLP error.......: 9.0909090913936433e-08 8.5488594337511447e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $43.309 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 7.5471e+05 : watt : False : (None, None)\n", + " Pressure Change : 1.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 2.0000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 423.34\n", + " Pressure pascal 1.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 423.34 423.34\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Equilibrium reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 3.2486e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.29075\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.54032\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.067801\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.084239\n", + " Temperature kelvin 423.34 910.04\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Equilibrium reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 0.200 MPa\n", + "\n", + "C101 outlet temperature = 423.345 K\n", + "\n", + "H101 outlet temperature = 423.345 K\n", + "\n", + "R101 outlet temperature = 910.044 K\n", + "\n", + "Hydrogen produced = 33.648 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor.ipynb index da52dcfb..4b7109b3 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, @@ -61,8 +62,7 @@ "\n", "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", "\n", - "![](msr_flowsheet.png)\n", - "" + "![](msr_flowsheet.png)\n" ] }, { diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb index 069a9a84..254789c7 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_doc.ipynb @@ -1,1049 +1,1296 @@ { - "cells": [ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES GibbsReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_doc.md), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", + "\n", + "**CH4 + H2O → CO + 3H2** \n", + "**CO + H2O → CO2 + H2**\n", + "\n", + "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- GibbsReactor\n", + "- Product" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " GibbsReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical Package\n", + "\n", + "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", + "\n", + "Let us import the following module from the IDAES library:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = GibbsReactor(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the literature source, we will initialize our simulation with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES GibbsReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_doc.md), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", - "\n", - "**CH4 + H2O \u2192 CO + 3H2** \n", - "**CO + H2O \u2192 CO2 + H2**\n", - "\n", - "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- GibbsReactor\n", - "- Product" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " GibbsReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical Package\n", - "\n", - "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", - "\n", - "Let us import the following module from the IDAES library:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.CH4: Initialization Complete.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "Let us create a `ConcreteModel` and add the flowsheet block. " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.H2O: Initialization Complete.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = GibbsReactor(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the literature source, we will initialize our simulation with the following values:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:43 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + ] }, { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 591\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 490\n", - "\n", - "Total number of variables............................: 203\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 179\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 7.6029602259665645e-13 2.5960616767406464e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $39.958 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.7819e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", - " Temperature kelvin 500.00 920.80\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" + ] }, { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] }, { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # equals inlet pressure\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 1 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # ensures outlet is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:44 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 591\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 490\n", + "\n", + "Total number of variables............................: 203\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 179\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.12e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1404440338949848e-12 2.1187588572502136e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1404440338949848e-12 2.1187588572502136e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 598\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 506\n", - "\n", - "Total number of variables............................: 205\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 181\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", - " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", - " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", - " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", - " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", - " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", - " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", - " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", - " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", - " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", - " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 12\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", - "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", - "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", - "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", - "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", - "\n", - "\n", - "Number of objective function evaluations = 13\n", - "Number of objective gradient evaluations = 13\n", - "Number of equality constraint evaluations = 13\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 13\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.011\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $39.958 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $107.218 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", - " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 619.25\n", - " Pressure pascal 1.0000e+05 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 619.25 619.25\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Gibbs reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 2.1076e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", - " Temperature kelvin 619.25 1087.4\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Gibbs reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.7819e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", + " Temperature kelvin 500.00 920.80\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # equals inlet pressure\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 1 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # ensures outlet is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" + ] }, { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 1.000 MPa\n", - "\n", - "C101 outlet temperature = 619.248 K\n", - "\n", - "H101 outlet temperature = 619.248 K\n", - "\n", - "R101 outlet temperature = 1087.385 K\n", - "\n", - "Hydrogen produced = 32.070 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 598\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 506\n", + "\n", + "Total number of variables............................: 205\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 181\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", + " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", + " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", + " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", + " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", + " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", + " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", + " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", + " 9 1.0721794e+08 1.77e-08 9.40e-07 -2.5 1.72e-02 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 1.0721794e+08 1.21e-08 1.13e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", + " 11 1.0721794e+08 1.07e-08 2.07e-06 -5.7 2.62e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 1.0721794e+08 1.65e-08 7.36e-07 -7.0 3.26e-07 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 12\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.0721793780338211e+08 1.0721793780338211e+08\n", + "Dual infeasibility......: 7.3567665382433322e-07 7.3567665382433322e-07\n", + "Constraint violation....: 1.5205920451933327e-12 1.6473644925842347e-08\n", + "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", + "Overall NLP error.......: 9.0909090914354020e-08 7.3567665382433322e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 13\n", + "Number of objective gradient evaluations = 13\n", + "Number of equality constraint evaluations = 13\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 13\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.008\n", + "Total CPU secs in NLP function evaluations = 0.007\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $107.218 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", + " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 619.25\n", + " Pressure pascal 1.0000e+05 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 619.25 619.25\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Gibbs reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 2.1076e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", + " Temperature kelvin 619.25 1087.4\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Gibbs reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 1.000 MPa\n", + "\n", + "C101 outlet temperature = 619.248 K\n", + "\n", + "H101 outlet temperature = 619.248 K\n", + "\n", + "R101 outlet temperature = 1087.385 K\n", + "\n", + "Hydrogen produced = 32.070 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb index 7a9cba50..2dd90dfe 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_test.ipynb @@ -1,1183 +1,1184 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES GibbsReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_test.ipynb), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", - "\n", - "**CH4 + H2O \u2192 CO + 3H2** \n", - "**CO + H2O \u2192 CO2 + H2**\n", - "\n", - "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- GibbsReactor\n", - "- Product" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " GibbsReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical Package\n", - "\n", - "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", - "\n", - "Let us import the following module from the IDAES library:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = GibbsReactor(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the literature source, we will initialize our simulation with the following values:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 591\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 490\n", - "\n", - "Total number of variables............................: 203\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 179\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 7.6029602259665645e-13 2.5960616767406464e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $39.958 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(39.958, rel=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.7819e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", - " Temperature kelvin 500.00 920.80\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.800, rel=1e-3)\n", - "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(17.819, rel=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(9.208, rel=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # equals inlet pressure\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 1 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # ensures outlet is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert degrees_of_freedom(m) == 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES GibbsReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_test.ipynb), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", + "\n", + "**CH4 + H2O → CO + 3H2** \n", + "**CO + H2O → CO2 + H2**\n", + "\n", + "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- GibbsReactor\n", + "- Product" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " GibbsReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical Package\n", + "\n", + "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", + "\n", + "Let us import the following module from the IDAES library:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = GibbsReactor(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 598\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 506\n", - "\n", - "Total number of variables............................: 205\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 181\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", - " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", - " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", - " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", - " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", - " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", - " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", - " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", - " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", - " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", - " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 12\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", - "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", - "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", - "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", - "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", - "\n", - "\n", - "Number of objective function evaluations = 13\n", - "Number of objective gradient evaluations = 13\n", - "Number of equality constraint evaluations = 13\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 13\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.011\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the literature source, we will initialize our simulation with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $107.218 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", - " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 619.25\n", - " Pressure pascal 1.0000e+05 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 619.25 619.25\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Gibbs reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 2.1076e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", - " Temperature kelvin 619.25 1087.4\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Gibbs reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(107.218, rel=1e-3)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 591\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 490\n", + "\n", + "Total number of variables............................: 203\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 179\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $39.958 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(39.958, rel=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 1.000 MPa\n", - "\n", - "C101 outlet temperature = 619.248 K\n", - "\n", - "H101 outlet temperature = 619.248 K\n", - "\n", - "R101 outlet temperature = 1087.385 K\n", - "\n", - "Hydrogen produced = 32.070 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.7819e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", + " Temperature kelvin 500.00 920.80\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.R101.conversion) == pytest.approx(0.800, rel=1e-3)\n", + "assert value(m.fs.R101.heat_duty[0]) / 1e6 == pytest.approx(17.819, rel=1e-3)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(9.208, rel=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # equals inlet pressure\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 1 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # ensures outlet is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert degrees_of_freedom(m) == 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.C101.outlet.pressure[0]) / 1e6 == pytest.approx(1.000, rel=1e-3)\n", - "assert value(m.fs.C101.outlet.temperature[0]) / 100 == pytest.approx(6.19248, rel=1e-3)\n", - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(6.19248, rel=1e-3)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(10.8738, rel=1e-3)\n", - "assert value(m.fs.hyd_prod) == pytest.approx(32.070, rel=1e-3)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-3)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 598\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 506\n", + "\n", + "Total number of variables............................: 205\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 181\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", + " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", + " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", + " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", + " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", + " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", + " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", + " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", + " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", + " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 12\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", + "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", + "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", + "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", + "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 13\n", + "Number of objective gradient evaluations = 13\n", + "Number of equality constraint evaluations = 13\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 13\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.011\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $107.218 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", + " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 619.25\n", + " Pressure pascal 1.0000e+05 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 619.25 619.25\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Gibbs reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 2.1076e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", + " Temperature kelvin 619.25 1087.4\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Gibbs reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(107.218, rel=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 1.000 MPa\n", + "\n", + "C101 outlet temperature = 619.248 K\n", + "\n", + "H101 outlet temperature = 619.248 K\n", + "\n", + "R101 outlet temperature = 1087.385 K\n", + "\n", + "Hydrogen produced = 32.070 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.C101.outlet.pressure[0]) / 1e6 == pytest.approx(1.000, rel=1e-3)\n", + "assert value(m.fs.C101.outlet.temperature[0]) / 100 == pytest.approx(6.19248, rel=1e-3)\n", + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(6.19248, rel=1e-3)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 100 == pytest.approx(10.8738, rel=1e-3)\n", + "assert value(m.fs.hyd_prod) == pytest.approx(32.070, rel=1e-3)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.0, rel=1e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb index b7984618..25a67591 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/gibbs_reactor_usr.ipynb @@ -1,1049 +1,1050 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES GibbsReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_usr.ipynb), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", - "\n", - "**CH4 + H2O \u2192 CO + 3H2** \n", - "**CO + H2O \u2192 CO2 + H2**\n", - "\n", - "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", - "\n", - "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", - "\n", - "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", - "\n", - "![](msr_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES Components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", - "\n", - "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Feed\n", - "- Mixer\n", - "- Compressor\n", - "- Heater\n", - "- GibbsReactor\n", - "- Product" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import GenericParameterBlock\n", - "from idaes.models.unit_models import (\n", - " Feed,\n", - " Mixer,\n", - " Compressor,\n", - " Heater,\n", - " GibbsReactor,\n", - " Product,\n", - ")\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical Package\n", - "\n", - "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", - "\n", - "Let us import the following module from the IDAES library:\n", - "- natural_gas_PR as get_prop (method to get configuration dictionary)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "Let us create a `ConcreteModel` and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", - "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")\n", - "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = GibbsReactor(\n", - " property_package=m.fs.thermo_params,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", - "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.hyd_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol[0]\n", - " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", - " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", - ") # the reaction is endothermic, so R101 duty is positive\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.compression_cost = Expression(\n", - " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", - ") # the stream must be pressurized, so the C101 work is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the literature source, we will initialize our simulation with the following values:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", - "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", - "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", - "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", - "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", - "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", - "m.fs.C101.efficiency_isentropic.fix(0.90)\n", - "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol[0]\n", - " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", - " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", - "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", - "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" - ] - } - ], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.CH4.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.H2O.initialize()\n", - "propagate_state(arc=m.fs.s02)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.C101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s06)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 591\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 490\n", - "\n", - "Total number of variables............................: 203\n", - " variables with only lower bounds: 13\n", - " variables with lower and upper bounds: 179\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", - " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", - " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 3\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Constraint violation....: 7.6029602259665645e-13 2.5960616767406464e-08\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-08\n", - "\n", - "\n", - "Number of objective function evaluations = 4\n", - "Number of objective gradient evaluations = 4\n", - "Number of equality constraint evaluations = 4\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 4\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $39.958 million per year\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of methane to hydrogen?" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 1.7819e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 429.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", - " Temperature kelvin 500.00 920.80\n", - " Pressure pascal 2.0000e+05 2.0000e+05\n", - "====================================================================================\n", - "\n", - "Conversion achieved = 80.0%\n" - ] - } - ], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Hydrogen Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Gibbs Reactor Simulation and Optimization of Steam Methane Reforming\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES GibbsReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example of [Steam Methane Reformation in an Equilibrium Reactor](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/equilibrium_reactor_testing_usr.ipynb), this example solves the flowsheet using a Gibbs Reactor instead. The steam methane reformation example is adapted from S.Z. Abbas, V. Dupont, T. Mahmud, Kinetics study and modelling of steam methane reforming process over a NiO/Al2O3 catalyst in an adiabatic packed bed reactor. Int. J. Hydrogen Energy, 42 (2017), pp. 2889-2903. Typically, the process follows the chemical equations below:\n", + "\n", + "**CH4 + H2O → CO + 3H2** \n", + "**CO + H2O → CO2 + H2**\n", + "\n", + "However, the GibbsReactor unit model solves the equilibrium by minimizing Gibbs free energy. Conveniently, this eliminates the need for a reaction package although a thermophysical package is still required.\n", + "\n", + "The flowsheet that we will be using for this module is shown below with the stream conditions. As in the prior example, we will be processing natural gas and steam feeds of fixed composition to produce hydrogen. The process consists of a mixer M101 for the two inlet streams, a compressor to compress the feed to the reaction pressure, a heater H101 to heat the feed to the reaction temperature, and a GibbsReactor unit R101. We will use thermophysical properties following the Peng-Robinsion cubic equation of state for this flowsheet.\n", + "\n", + "The state variables chosen for the property package are **total molar flows of each stream, temperature of each stream and pressure of each stream, and mole fractions of each component in each stream**. The components considered are: **CH4, H2O, CO, CO2, and H2** and the process occurs in vapor phase only. Therefore, every stream has 1 flow variable, 5 mole fraction variables, 1 temperature and 1 pressure variable. \n", + "\n", + "![](msr_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES Components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages, as well as some utility tools to build the flowsheet. For further details on these components, please refer to the [Pyomo documentation]( https://pyomo.readthedocs.io/en/stable/).\n", + "\n", + "From IDAES, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Feed\n", + "- Mixer\n", + "- Compressor\n", + "- Heater\n", + "- GibbsReactor\n", + "- Product" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import GenericParameterBlock\n", + "from idaes.models.unit_models import (\n", + " Feed,\n", + " Mixer,\n", + " Compressor,\n", + " Heater,\n", + " GibbsReactor,\n", + " Product,\n", + ")\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical Package\n", + "\n", + "As mentioned earlier, the `GibbsReactor` does not require a reaction package.\n", + "\n", + "Let us import the following module from the IDAES library:\n", + "- natural_gas_PR as get_prop (method to get configuration dictionary)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.power_generation.properties.natural_gas_PR import get_prop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "Let us create a `ConcreteModel` and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the previous example, we do not need to add a reaction package for the reactor model to calculate results. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) to build a state block for the parameter dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "thermo_props_config_dict = get_prop(components=[\"CH4\", \"H2O\", \"H2\", \"CO\", \"CO2\"])\n", + "m.fs.thermo_params = GenericParameterBlock(**thermo_props_config_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.CH4 = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.H2O = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"methane_feed\", \"steam_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")\n", + "m.fs.C101 = Compressor(property_package=m.fs.thermo_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = GibbsReactor(\n", + " property_package=m.fs.thermo_params,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. Let us connect the unit models by defining and building each `Arc`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.CH4.outlet, destination=m.fs.M101.methane_feed)\n", + "m.fs.s02 = Arc(source=m.fs.H2O.outlet, destination=m.fs.M101.steam_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.C101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.C101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s06 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use Pyomo's `TransformationFactory` to write the equality constraints on each Arc:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "For this flowsheet, we are interested in computing hydrogen production in millions of pounds per year, as well as the total costs due to pressurizing, cooling, and heating utilities:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add `Expressions` to convert the product flow from mol/s to MM lb/year of hydrogen, and to calculate the cooling, heating and compression operating costs. The total operating cost will be the sum of the costs, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.hyd_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol[0]\n", + " * m.fs.PROD.inlet.mole_frac_comp[0, \"H2\"]\n", + " * m.fs.thermo_params.H2.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (m.fs.R101.heat_duty[0])\n", + ") # the reaction is endothermic, so R101 duty is positive\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.compression_cost = Expression(\n", + " expr=0.12e-5 * m.fs.C101.work_isentropic[0]\n", + ") # the stream must be pressurized, so the C101 work is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost + m.fs.compression_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "We expect each stream to have 8 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the compressor to have 2 (the pressure change and efficiency), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (conversion). Therefore, we have 20 degrees of freedom to specify: temperature, pressure, flow and mole fractions of all five components on both streams; compressor pressure change and efficiency; outlet heater temperature; and reactor conversion." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the literature source, we will initialize our simulation with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.CH4.outlet.mole_frac_comp[0, \"CH4\"].fix(1)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2O\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.CH4.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.CH4.outlet.flow_mol.fix(75 * pyunits.mol / pyunits.s)\n", + "m.fs.CH4.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.CH4.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CH4\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2O\"].fix(1)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"H2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO\"].fix(1e-5)\n", + "m.fs.H2O.outlet.mole_frac_comp[0, \"CO2\"].fix(1e-5)\n", + "m.fs.H2O.outlet.flow_mol.fix(234 * pyunits.mol / pyunits.s)\n", + "m.fs.H2O.outlet.temperature.fix(373.15 * pyunits.K)\n", + "m.fs.H2O.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "For the initial problem, let us fix the compressor outlet pressure to 2 bar for now, the efficiency to 0.90 (a common assumption for compressor units), and the heater outlet temperature to 500 K. We will unfix these values later to optimize the flowsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.C101.outlet.pressure.fix(pyunits.convert(2 * pyunits.bar, to_units=pyunits.Pa))\n", + "m.fs.C101.efficiency_isentropic.fix(0.90)\n", + "m.fs.H101.outlet.temperature.fix(500 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `GibbsReactor` unit model calculates the amount of product and reactant based on the free energy minimization; therefore, we will specify a desired conversion and let the solver determine the reactor duty and heat transfer. For convenience, we will define the reactor conversion as the amount of methane that is converted." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol[0]\n", + " * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol[0] * m.fs.R101.inlet.mole_frac_comp[0, \"CH4\"]\n", + " - m.fs.R101.outlet.flow_mol[0] * m.fs.R101.outlet.mole_frac_comp[0, \"CH4\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.C101.outlet.pressure.unfix()\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # equals inlet pressure\n", - "m.fs.C101.outlet.pressure[0].setlb(\n", - " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", - ") # at most, pressurize to 1 bar\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.heat_duty[0].setlb(\n", - " 0 * pyunits.J / pyunits.s\n", - ") # ensures outlet is equal to or greater than inlet temperature\n", - "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation and propagate the outlet results in sequence to solve the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Starting initialization\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.CH4: Initialization Complete.\n", + "2024-05-23 06:07:53 [INFO] idaes.init.fs.H2O.properties: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.H2O: Initialization Complete.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.methane_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.steam_feed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.control_volume.properties_out: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Starting initialization\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:54 [INFO] idaes.init.fs.C101.properties_isentropic: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:55 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n", + "2024-05-23 06:07:56 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.CH4.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.H2O.initialize()\n", + "propagate_state(arc=m.fs.s02)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.C101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s06)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 598\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 506\n", - "\n", - "Total number of variables............................: 205\n", - " variables with only lower bounds: 14\n", - " variables with lower and upper bounds: 181\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 203\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", - " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", - " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", - " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", - " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", - " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", - " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", - " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", - " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", - " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", - " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 12\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", - "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", - "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", - "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", - "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", - "\n", - "\n", - "Number of objective function evaluations = 13\n", - "Number of objective gradient evaluations = 13\n", - "Number of equality constraint evaluations = 13\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 13\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 12\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.011\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 591\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 490\n", + "\n", + "Total number of variables............................: 203\n", + " variables with only lower bounds: 13\n", + " variables with lower and upper bounds: 179\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.49e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.35e+04 2.00e-01 -1.0 3.59e+00 - 9.90e-01 9.91e-01h 1\n", + " 2 0.0000000e+00 3.59e-04 9.99e+00 -1.0 3.56e+00 - 9.90e-01 1.00e+00h 1\n", + " 3 0.0000000e+00 2.60e-08 8.98e+01 -1.0 2.91e-04 - 9.90e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 7.6029602259665645e-13 2.5960616767406464e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "operating cost = $107.218 million per year\n", - "\n", - "Compressor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.C101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", - " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", - " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", - " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 353.80 619.25\n", - " Pressure pascal 1.0000e+05 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Heater results\n", - "\n", - "====================================================================================\n", - "Unit : fs.H101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 309.01\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", - " Temperature kelvin 619.25 619.25\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n", - "\n", - "Gibbs reactor results\n", - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 2.1076e+07 : watt : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " Total Molar Flowrate mole / second 309.01 444.02\n", - " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", - " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", - " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", - " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", - " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", - " Temperature kelvin 619.25 1087.4\n", - " Pressure pascal 1.0000e+06 1.0000e+06\n", - "====================================================================================\n" - ] - } - ], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", - "\n", - "print()\n", - "print(\"Compressor results\")\n", - "\n", - "m.fs.C101.report()\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Gibbs reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $39.958 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of methane to hydrogen?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.7819e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 429.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.034965\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.32532\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.49984\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.059609\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.080265\n", + " Temperature kelvin 500.00 920.80\n", + " Pressure pascal 2.0000e+05 2.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Hydrogen Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to determine optimal conditions for producing hydrogen. Suppose we wish to find ideal conditions for the competing reactions. The GibbsReactor does not drive equilibrium forward based on temperature, so we will see small amounts of intermediate components present in the product stream. We will allow for variable reactor temperature and pressure by freeing our heater and compressor specifications, and minimize cost to achieve 90% methane conversion. Since we assume an isentopic compressor, allowing compression will heat our feed stream and reduce or eliminate the required heater duty." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.C101.outlet.pressure.unfix()\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(1 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # equals inlet pressure\n", + "m.fs.C101.outlet.pressure[0].setlb(\n", + " pyunits.convert(10 * pyunits.bar, to_units=pyunits.Pa)\n", + ") # at most, pressurize to 1 bar\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.heat_duty[0].setlb(\n", + " 0 * pyunits.J / pyunits.s\n", + ") # ensures outlet is equal to or greater than inlet temperature\n", + "m.fs.H101.outlet.temperature[0].setub(1000 * pyunits.K) # at most, heat to 1000 K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "C101 outlet pressure = 1.000 MPa\n", - "\n", - "C101 outlet temperature = 619.248 K\n", - "\n", - "H101 outlet temperature = 619.248 K\n", - "\n", - "R101 outlet temperature = 1087.385 K\n", - "\n", - "Hydrogen produced = 32.070 MM lb/year\n", - "\n", - "Conversion achieved = 90.0%\n" - ] - } - ], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", - "print()\n", - "\n", - "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", - "\n", - "print()\n", - "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 598\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 506\n", + "\n", + "Total number of variables............................: 205\n", + " variables with only lower bounds: 14\n", + " variables with lower and upper bounds: 181\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 203\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 3.9958388e+07 1.49e+06 3.46e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.8920063e+07 1.48e+06 1.52e+03 -1.0 7.19e+06 - 3.91e-01 6.43e-03f 1\n", + " 2 7.0948609e+07 1.15e+06 1.86e+06 -1.0 4.83e+06 - 1.51e-01 2.26e-01h 1\n", + " 3 1.0553921e+08 5.23e+05 1.04e+07 -1.0 2.42e+06 - 3.41e-01 5.67e-01h 1\n", + " 4 1.0874890e+08 1.58e+05 7.64e+06 -1.0 8.45e+05 - 7.09e-01 7.11e-01h 1\n", + " 5 1.0751027e+08 1.51e+04 1.67e+06 -1.0 2.97e+05 - 9.49e-01 9.09e-01f 1\n", + " 6 1.0721898e+08 5.95e+00 9.98e+03 -1.0 3.47e+04 - 9.90e-01 1.00e+00f 1\n", + " 7 1.0721794e+08 3.43e-05 8.84e+01 -1.0 1.59e+02 - 9.90e-01 1.00e+00f 1\n", + " 8 1.0721794e+08 1.90e-08 7.14e-01 -1.0 1.43e-02 - 9.92e-01 1.00e+00h 1\n", + " 9 1.0721794e+08 7.55e-09 1.53e-06 -2.5 1.72e-02 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 1.0721794e+08 2.10e-08 1.59e-06 -3.8 4.73e-04 - 1.00e+00 1.00e+00f 1\n", + " 11 1.0721794e+08 1.12e-08 2.07e-06 -5.7 2.63e-05 - 1.00e+00 1.00e+00f 1\n", + " 12 1.0721794e+08 3.57e-08 1.65e-06 -7.0 3.14e-07 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 12\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.0721793780338226e+08 1.0721793780338226e+08\n", + "Dual infeasibility......: 1.6485091371912918e-06 1.6485091371912918e-06\n", + "Constraint violation....: 4.6566128730773926e-10 3.5680419252624450e-08\n", + "Complementarity.........: 9.0909090914354020e-08 9.0909090914354020e-08\n", + "Overall NLP error.......: 9.0909090914354020e-08 1.6485091371912918e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 13\n", + "Number of objective gradient evaluations = 13\n", + "Number of equality constraint evaluations = 13\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 13\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 12\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.011\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $107.218 million per year\n", + "\n", + "Compressor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.C101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.90000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.0334e+06 : watt : False : (None, None)\n", + " Pressure Change : 9.0000e+05 : pascal : False : (None, None)\n", + " Pressure Ratio : 10.000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 353.80 619.25\n", + " Pressure pascal 1.0000e+05 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 5.8781e-09 : watt : False : (0.0, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 309.01\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.24272\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.75725\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 9.9996e-06\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 9.9996e-06\n", + " Temperature kelvin 619.25 619.25\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n", + "\n", + "Gibbs reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 2.1076e+07 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Total Molar Flowrate mole / second 309.01 444.02\n", + " Total Mole Fraction CH4 dimensionless 0.24272 0.016892\n", + " Total Mole Fraction H2O dimensionless 0.75725 0.31609\n", + " Total Mole Fraction H2 dimensionless 9.9996e-06 0.51498\n", + " Total Mole Fraction CO dimensionless 9.9996e-06 0.093140\n", + " Total Mole Fraction CO2 dimensionless 9.9996e-06 0.058900\n", + " Temperature kelvin 619.25 1087.4\n", + " Pressure pascal 1.0000e+06 1.0000e+06\n", + "====================================================================================\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.3f} million per year\")\n", + "\n", + "print()\n", + "print(\"Compressor results\")\n", + "\n", + "m.fs.C101.report()\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Gibbs reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "C101 outlet pressure = 1.000 MPa\n", + "\n", + "C101 outlet temperature = 619.248 K\n", + "\n", + "H101 outlet temperature = 619.248 K\n", + "\n", + "R101 outlet temperature = 1087.385 K\n", + "\n", + "Hydrogen produced = 32.070 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet pressure = {value(m.fs.C101.outlet.pressure[0])/1E6:0.3f} MPa\")\n", + "print()\n", + "\n", + "print(f\"C101 outlet temperature = {value(m.fs.C101.outlet.temperature[0]):0.3f} K\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.3f} K\")\n", + "\n", + "print()\n", + "print(f\"Hydrogen produced = {value(m.fs.hyd_prod):0.3f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/msr_reaction.py b/idaes_examples/notebooks/docs/unit_models/reactors/msr_reaction.py index e8a70eba..7a265ac2 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/msr_reaction.py +++ b/idaes_examples/notebooks/docs/unit_models/reactors/msr_reaction.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Property package for Methane Steam Reforming and Water Gas Shift diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb index 989a3458..c3277436 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor.ipynb @@ -1,980 +1,981 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Plug Flow Reactor (PFR) Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES PFR unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing.ipynb), we can alter the flowsheet to use a plug flow reactor (PFR). As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike a CSTR which assumes well-mixed liquid behavior, the concentration profiles will vary spatially in one dimension. In actuality, following start-up flow reactor exhibit dynamic behavior as they approach a steady-state equilibrium; we will assume our system has already achieved steady-state behavior. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", - "\n", - "Chemical reaction:\n", - "\n", - "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", - "\n", - "Property Packages:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "Flowsheet\n", - "\n", - "![](egprod_flowsheet.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- PFR\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import (\n", - " GenericParameterBlock,\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, PFR, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", - "\n", - "The reaction package here assumes Arrhenius kinetic behavior for the PFR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", - "\n", - "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", - "\n", - "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", - "$k$ - reaction rate constant per second \n", - "$V$ - volume of PFR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", - "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", - "$k_0$ - pre-exponential Arrhenius factor per second \n", - "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", - "$R$ - gas constant in J/mol-K \n", - "$T$ - reactor temperature in K\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing.ipynb), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `PFR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = PFR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - " transformation_method=\"dae.finite_difference\",\n", - " transformation_scheme=\"BACKWARD\",\n", - " finite_elements=20,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `PFR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. To calculate cooling cost, it is important to note that the heat duty is not constant throughout the reactor's length and is expressed in terms of heat per length (J/m/s). This is why we utilize the trapezoid rule to calculate the total heat duty of the reactor:$Q=\\Delta x\\big(\\sum_{k=1}^{N-1}(Q_k)+\\frac{Q_N+Q_0}{2}\\big)$ \n", - "where k is the subinterval in the length domain, N is the number of intervals, and $\\Delta x$ is the length of the interval.\n", - "Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year, assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8\n", - " * m.fs.R101.length\n", - " / m.fs.R101.config.finite_elements\n", - " * (\n", - " -sum(\n", - " m.fs.R101.heat_duty[0, k]\n", - " for k in m.fs.R101.control_volume.length_domain\n", - " if 0.0 <= k < 1.0\n", - " )\n", - " )\n", - " - (\n", - " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " - value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", - " )\n", - " / 2\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 2 unit specifications and 1 specification for each finite element. Therefore, we have 35 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; a reactor property such as conversion or heat duty at each finite element; reactor volume and reactor length." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 35" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the `PFR`, we have to define the conversion in terms of ethylene oxide. Note that the `PFR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `PFR` model. We'll estimate 50% conversion for our initial flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " bounds=(0, 1), initialize=0.80, units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "for x in m.fs.R101.control_volume.length_domain:\n", - " if x == 0:\n", - " continue\n", - " m.fs.R101.control_volume.properties[0, x].temperature.fix(\n", - " 328.15 * pyunits.K\n", - " ) # equal inlet reactor temperature\n", - "\n", - "m.fs.R101.conversion.fix(0.5)\n", - "\n", - "m.fs.R101.length.fix(1 * pyunits.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we did not place a specification on reactor duty, the solver may try positive values to increase the reaction temperature and rate. To prevent the optimization from diverging, we need to set an upper bound restricting heat flow to cooling only:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.heat_duty.setub(\n", - " 0 * pyunits.J / pyunits.m / pyunits.s\n", - ") # heat duty is only used for cooling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check the degrees of freedom\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(6.589, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", - "print()\n", - "print(\n", - " f\"Total heat duty required = \"\n", - " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", - " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", - " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", - " f\" MJ\"\n", - ")\n", - "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", - "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", - "print()\n", - "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.R101.conversion) == pytest.approx(0.5000, rel=1e-5)\n", - "assert value(m.fs.R101.area) == pytest.approx(0.987071, rel=1e-5)\n", - "assert (\n", - " value(m.fs.R101.length)\n", - " / value(m.fs.R101.config.finite_elements)\n", - " * value(\n", - " sum(\n", - " m.fs.R101.heat_duty[0, k]\n", - " for k in m.fs.R101.control_volume.length_domain\n", - " if 0.0 <= k < 1.0\n", - " )\n", - " )\n", - " + (\n", - " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", - " )\n", - " / 2\n", - ") / 1e6 == pytest.approx(-4.881815, rel=1e-5)\n", - "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", - "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", - "\n", - "m.fs.R101.length.unfix()\n", - "m.fs.R101.length.setlb(0 * pyunits.m)\n", - "m.fs.R101.length.setub(5 * pyunits.m)\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)\n", - "\n", - "for x in m.fs.R101.control_volume.length_domain:\n", - " if x == 0:\n", - " continue\n", - " m.fs.R101.control_volume.properties[\n", - " 0, x\n", - " ].temperature.unfix() # allow for temperature change in each finite element" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert degrees_of_freedom(m) == 22 # 2 unit variables and 20 finite element variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Check for solver solve status\n", - "from pyomo.environ import TerminationCondition\n", - "\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"PFR reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(4.421530, rel=1e-5)\n", - "assert value(m.fs.R101.area) == pytest.approx(2.9300, rel=1e-5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", - "\n", - "print()\n", - "print(\n", - " \"Total heat duty required = \",\n", - " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", - " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", - " f\" MJ\",\n", - ")\n", - "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", - "\n", - "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", - "\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", - ")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", - "assert (\n", - " value(m.fs.R101.length)\n", - " / value(m.fs.R101.config.finite_elements)\n", - " * (\n", - " value(\n", - " sum(\n", - " m.fs.R101.heat_duty[0, k]\n", - " for k in m.fs.R101.control_volume.length_domain\n", - " if 0.0 <= k < 1.0\n", - " )\n", - " )\n", - " + (\n", - " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(\n", - " m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]\n", - " )\n", - " )\n", - " / 2\n", - " )\n", - ") / 1e6 == pytest.approx(-3.789565, rel=1e-5)\n", - "assert value(m.fs.R101.area) == pytest.approx(2.930001, rel=1e-5)\n", - "assert value(m.fs.R101.control_volume.length) == pytest.approx(4.982470, rel=1e-5)\n", - "assert value(m.fs.R101.volume * 1.2) == pytest.approx(17.518369, rel=1e-5)\n", - "assert value(m.fs.eg_prod) == pytest.approx(225.415471, rel=1e-5)\n", - "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.000, rel=1e-5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Plug Flow Reactor (PFR) Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES PFR unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing.ipynb), we can alter the flowsheet to use a plug flow reactor (PFR). As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike a CSTR which assumes well-mixed liquid behavior, the concentration profiles will vary spatially in one dimension. In actuality, following start-up flow reactor exhibit dynamic behavior as they approach a steady-state equilibrium; we will assume our system has already achieved steady-state behavior. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", + "\n", + "Chemical reaction:\n", + "\n", + "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "\n", + "Property Packages:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "Flowsheet\n", + "\n", + "![](egprod_flowsheet.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- PFR\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import (\n", + " GenericParameterBlock,\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, PFR, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", + "\n", + "The reaction package here assumes Arrhenius kinetic behavior for the PFR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", + "\n", + "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", + "\n", + "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", + "$k$ - reaction rate constant per second \n", + "$V$ - volume of PFR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", + "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", + "$k_0$ - pre-exponential Arrhenius factor per second \n", + "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", + "$R$ - gas constant in J/mol-K \n", + "$T$ - reactor temperature in K\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing.ipynb), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `PFR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m.fs.R101 = PFR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + " transformation_method=\"dae.finite_difference\",\n", + " transformation_scheme=\"BACKWARD\",\n", + " finite_elements=20,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `PFR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. To calculate cooling cost, it is important to note that the heat duty is not constant throughout the reactor's length and is expressed in terms of heat per length (J/m/s). This is why we utilize the trapezoid rule to calculate the total heat duty of the reactor:$Q=\\Delta x\\big(\\sum_{k=1}^{N-1}(Q_k)+\\frac{Q_N+Q_0}{2}\\big)$ \n", + "where k is the subinterval in the length domain, N is the number of intervals, and $\\Delta x$ is the length of the interval.\n", + "Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year, assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8\n", + " * m.fs.R101.length\n", + " / m.fs.R101.config.finite_elements\n", + " * (\n", + " -sum(\n", + " m.fs.R101.heat_duty[0, k]\n", + " for k in m.fs.R101.control_volume.length_domain\n", + " if 0.0 <= k < 1.0\n", + " )\n", + " )\n", + " - (\n", + " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " - value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", + " )\n", + " / 2\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 2 unit specifications and 1 specification for each finite element. Therefore, we have 35 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; a reactor property such as conversion or heat duty at each finite element; reactor volume and reactor length." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 35" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the `PFR`, we have to define the conversion in terms of ethylene oxide. Note that the `PFR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `PFR` model. We'll estimate 50% conversion for our initial flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " bounds=(0, 1), initialize=0.80, units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "for x in m.fs.R101.control_volume.length_domain:\n", + " if x == 0:\n", + " continue\n", + " m.fs.R101.control_volume.properties[0, x].temperature.fix(\n", + " 328.15 * pyunits.K\n", + " ) # equal inlet reactor temperature\n", + "\n", + "m.fs.R101.conversion.fix(0.5)\n", + "\n", + "m.fs.R101.length.fix(1 * pyunits.m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we did not place a specification on reactor duty, the solver may try positive values to increase the reaction temperature and rate. To prevent the optimization from diverging, we need to set an upper bound restricting heat flow to cooling only:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.heat_duty.setub(\n", + " 0 * pyunits.J / pyunits.m / pyunits.s\n", + ") # heat duty is only used for cooling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check the degrees of freedom\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(6.589, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", + "print()\n", + "print(\n", + " f\"Total heat duty required = \"\n", + " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", + " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", + " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", + " f\" MJ\"\n", + ")\n", + "print()\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", + "print()\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", + "print()\n", + "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.R101.conversion) == pytest.approx(0.5000, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(0.987071, rel=1e-5)\n", + "assert (\n", + " value(m.fs.R101.length)\n", + " / value(m.fs.R101.config.finite_elements)\n", + " * value(\n", + " sum(\n", + " m.fs.R101.heat_duty[0, k]\n", + " for k in m.fs.R101.control_volume.length_domain\n", + " if 0.0 <= k < 1.0\n", + " )\n", + " )\n", + " + (\n", + " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", + " )\n", + " / 2\n", + ") / 1e6 == pytest.approx(-4.881815, rel=1e-5)\n", + "assert value(m.fs.R101.outlet.temperature[0]) / 1e2 == pytest.approx(3.2815, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", + "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", + "\n", + "m.fs.R101.length.unfix()\n", + "m.fs.R101.length.setlb(0 * pyunits.m)\n", + "m.fs.R101.length.setub(5 * pyunits.m)\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)\n", + "\n", + "for x in m.fs.R101.control_volume.length_domain:\n", + " if x == 0:\n", + " continue\n", + " m.fs.R101.control_volume.properties[\n", + " 0, x\n", + " ].temperature.unfix() # allow for temperature change in each finite element" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert degrees_of_freedom(m) == 22 # 2 unit variables and 20 finite element variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Check for solver solve status\n", + "from pyomo.environ import TerminationCondition\n", + "\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"PFR reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.operating_cost) / 1e6 == pytest.approx(4.421530, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(2.9300, rel=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(\n", + " \"Total heat duty required = \",\n", + " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", + " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", + " f\" MJ\",\n", + ")\n", + "print()\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", + "\n", + "print()\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", + "\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", + ")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "assert value(m.fs.H101.outlet.temperature[0]) / 100 == pytest.approx(3.2815, rel=1e-5)\n", + "assert (\n", + " value(m.fs.R101.length)\n", + " / value(m.fs.R101.config.finite_elements)\n", + " * (\n", + " value(\n", + " sum(\n", + " m.fs.R101.heat_duty[0, k]\n", + " for k in m.fs.R101.control_volume.length_domain\n", + " if 0.0 <= k < 1.0\n", + " )\n", + " )\n", + " + (\n", + " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(\n", + " m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]\n", + " )\n", + " )\n", + " / 2\n", + " )\n", + ") / 1e6 == pytest.approx(-3.789565, rel=1e-5)\n", + "assert value(m.fs.R101.area) == pytest.approx(2.930001, rel=1e-5)\n", + "assert value(m.fs.R101.control_volume.length) == pytest.approx(4.982470, rel=1e-5)\n", + "assert value(m.fs.R101.volume * 1.2) == pytest.approx(17.518369, rel=1e-5)\n", + "assert value(m.fs.eg_prod) == pytest.approx(225.415471, rel=1e-5)\n", + "assert value(m.fs.R101.conversion) * 100 == pytest.approx(90.000, rel=1e-5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb index 05620386..3d9622f9 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_doc.ipynb @@ -1,809 +1,1495 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Plug Flow Reactor (PFR) Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Andrew Lee \n", - "Maintainer: Andrew Lee \n", - "\n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES PFR unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a plug flow reactor (PFR). As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike a CSTR which assumes well-mixed liquid behavior, the concentration profiles will vary spatially in one dimension. In actuality, following start-up flow reactor exhibit dynamic behavior as they approach a steady-state equilibrium; we will assume our system has already achieved steady-state behavior. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", - "\n", - "Chemical reaction:\n", - "\n", - "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", - "\n", - "Property Packages:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "Flowsheet\n", - "\n", - "![](egprod_flowsheet.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- PFR\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties import (\n", - " GenericParameterBlock,\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, PFR, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", - "\n", - "The reaction package here assumes Arrhenius kinetic behavior for the PFR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", - "\n", - "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", - "\n", - "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", - "$k$ - reaction rate constant per second \n", - "$V$ - volume of PFR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", - "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", - "$k_0$ - pre-exponential Arrhenius factor per second \n", - "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", - "$R$ - gas constant in J/mol-K \n", - "$T$ - reactor temperature in K\n", - "\n", - "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `PFR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101 = PFR(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_equilibrium_reactions=False,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - " transformation_method=\"dae.finite_difference\",\n", - " transformation_scheme=\"BACKWARD\",\n", - " finite_elements=20,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `PFR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. To calculate cooling cost, it is important to note that the heat duty is not constant throughout the reactor's length and is expressed in terms of heat per length (J/m/s). This is why we utilize the trapezoid rule to calculate the total heat duty of the reactor:$Q=\\Delta x\\big(\\sum_{k=1}^{N-1}(Q_k)+\\frac{Q_N+Q_0}{2}\\big)$ \n", - "where k is the subinterval in the length domain, N is the number of intervals, and $\\Delta x$ is the length of the interval.\n", - "Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year, assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8\n", - " * m.fs.R101.length\n", - " / m.fs.R101.config.finite_elements\n", - " * (\n", - " -sum(\n", - " m.fs.R101.heat_duty[0, k]\n", - " for k in m.fs.R101.control_volume.length_domain\n", - " if 0.0 <= k < 1.0\n", - " )\n", - " )\n", - " - (\n", - " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " - value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", - " )\n", - " / 2\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 2 unit specifications and 1 specification for each finite element. Therefore, we have 35 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; a reactor property such as conversion or heat duty at each finite element; reactor volume and reactor length." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the `PFR`, we have to define the conversion in terms of ethylene oxide. Note that the `PFR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `PFR` model. We'll estimate 50% conversion for our initial flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " bounds=(0, 1), initialize=0.80, units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "for x in m.fs.R101.control_volume.length_domain:\n", - " if x == 0:\n", - " continue\n", - " m.fs.R101.control_volume.properties[0, x].temperature.fix(\n", - " 328.15 * pyunits.K\n", - " ) # equal inlet reactor temperature\n", - "\n", - "m.fs.R101.conversion.fix(0.5)\n", - "\n", - "m.fs.R101.length.fix(1 * pyunits.m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we did not place a specification on reactor duty, the solver may try positive values to increase the reaction temperature and rate. To prevent the optimization from diverging, we need to set an upper bound restricting heat flow to cooling only:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.heat_duty.setub(\n", - " 0 * pyunits.J / pyunits.m / pyunits.s\n", - ") # heat duty is only used for cooling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", - "print()\n", - "print(\n", - " f\"Total heat duty required = \"\n", - " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", - " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", - " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", - " f\" MJ\"\n", - ")\n", - "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", - "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", - "print()\n", - "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", - "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", - "\n", - "m.fs.R101.length.unfix()\n", - "m.fs.R101.length.setlb(0 * pyunits.m)\n", - "m.fs.R101.length.setub(5 * pyunits.m)\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)\n", - "\n", - "for x in m.fs.R101.control_volume.length_domain:\n", - " if x == 0:\n", - " continue\n", - " m.fs.R101.control_volume.properties[\n", - " 0, x\n", - " ].temperature.unfix() # allow for temperature change in each finite element" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"PFR reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", - "\n", - "print()\n", - "print(\n", - " \"Total heat duty required = \",\n", - " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", - " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", - " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", - " f\" MJ\",\n", - ")\n", - "print()\n", - "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", - "\n", - "print()\n", - "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", - "\n", - "print()\n", - "print(\n", - " f\"Assuming a 20% design factor for reactor volume,\"\n", - " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", - " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", - ")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Plug Flow Reactor (PFR) Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Andrew Lee \n", + "Maintainer: Andrew Lee \n", + "\n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES PFR unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a plug flow reactor (PFR). As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike a CSTR which assumes well-mixed liquid behavior, the concentration profiles will vary spatially in one dimension. In actuality, following start-up flow reactor exhibit dynamic behavior as they approach a steady-state equilibrium; we will assume our system has already achieved steady-state behavior. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", + "\n", + "Chemical reaction:\n", + "\n", + "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "\n", + "Property Packages:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "Flowsheet\n", + "\n", + "![](egprod_flowsheet.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- PFR\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties import (\n", + " GenericParameterBlock,\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, PFR, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only.\n", + "\n", + "The reaction package here assumes Arrhenius kinetic behavior for the PFR, for which $k_0$ and $E_a$ are known *a priori* (if unknown, they may be obtained using one of the parameter estimation tools within IDAES).\n", + "\n", + "$ r = -kVC_{EO} $, $ k = k_0 e^{(-E_a/RT)}$, with the variables as follows:\n", + "\n", + "$r$ - reaction rate extent in moles of ethylene oxide consumed per second; note that the traditional reaction rate would be given by $rate = r/V$ in moles per $m^3$ per second \n", + "$k$ - reaction rate constant per second \n", + "$V$ - volume of PFR in $m^3$, note that this is *liquid volume* and not the *total volume* of the reactor itself \n", + "$C_{EO}$ - bulk concentration of ethylene oxide in moles per $m^3$ (the limiting reagent, since we assume excess catalyst and water) \n", + "$k_0$ - pre-exponential Arrhenius factor per second \n", + "$E_a$ - reaction activation energy in kJ per mole of ethylene oxide consumed \n", + "$R$ - gas constant in J/mol-K \n", + "$T$ - reactor temperature in K\n", + "\n", + "These calculations are contained within the property, reaction and unit model packages, and do not need to be entered into the flowsheet. More information on property estimation may be found in the IDAES documentation on [Parameter Estimation](https://idaes-pse.readthedocs.io/en/stable/how_to_guides/workflow/data_rec_parmest.html).\n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `PFR`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m.fs.R101 = PFR(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_equilibrium_reactions=False,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + " transformation_method=\"dae.finite_difference\",\n", + " transformation_scheme=\"BACKWARD\",\n", + " finite_elements=20,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `PFR`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. To calculate cooling cost, it is important to note that the heat duty is not constant throughout the reactor's length and is expressed in terms of heat per length (J/m/s). This is why we utilize the trapezoid rule to calculate the total heat duty of the reactor:$Q=\\Delta x\\big(\\sum_{k=1}^{N-1}(Q_k)+\\frac{Q_N+Q_0}{2}\\big)$ \n", + "where k is the subinterval in the length domain, N is the number of intervals, and $\\Delta x$ is the length of the interval.\n", + "Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year, assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8\n", + " * m.fs.R101.length\n", + " / m.fs.R101.config.finite_elements\n", + " * (\n", + " -sum(\n", + " m.fs.R101.heat_duty[0, k]\n", + " for k in m.fs.R101.control_volume.length_domain\n", + " if 0.0 <= k < 1.0\n", + " )\n", + " )\n", + " - (\n", + " value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " - value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)])\n", + " )\n", + " / 2\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 2 unit specifications and 1 specification for each finite element. Therefore, we have 35 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; a reactor property such as conversion or heat duty at each finite element; reactor volume and reactor length." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "35\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the `PFR`, we have to define the conversion in terms of ethylene oxide. Note that the `PFR` reaction volume variable (m.fs.R101.volume) does not need to be defined here since it is internally defined by the `PFR` model. We'll estimate 50% conversion for our initial flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " bounds=(0, 1), initialize=0.80, units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "for x in m.fs.R101.control_volume.length_domain:\n", + " if x == 0:\n", + " continue\n", + " m.fs.R101.control_volume.properties[0, x].temperature.fix(\n", + " 328.15 * pyunits.K\n", + " ) # equal inlet reactor temperature\n", + "\n", + "m.fs.R101.conversion.fix(0.5)\n", + "\n", + "m.fs.R101.length.fix(1 * pyunits.m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we did not place a specification on reactor duty, the solver may try positive values to increase the reaction temperature and rate. To prevent the optimization from diverging, we need to set an upper bound restricting heat flow to cooling only:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.heat_duty.setub(\n", + " 0 * pyunits.J / pyunits.m / pyunits.s\n", + ") # heat duty is only used for cooling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.OXIDE.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.OXIDE.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.OXIDE.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.OXIDE: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.ACID.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.ACID.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.ACID.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.ACID: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.reagent_feed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.reagent_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.catalyst_feed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.catalyst_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.R101.control_volume.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.R101.control_volume.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:47 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1923\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1323\n", + "\n", + "Total number of variables............................: 608\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 257\n", + " variables with only upper bounds: 20\n", + "Total number of equality constraints.................: 608\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.24e+06 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 0.0000000e+00 1.55e+07 2.12e+05 -1.0 1.11e+08 - 2.85e-01 9.90e-01f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 0.0000000e+00 2.43e+05 5.37e+03 -1.0 1.11e+06 - 8.25e-01 9.90e-01h 1\n", + " 3 0.0000000e+00 2.41e+03 5.69e+01 -1.0 1.11e+04 - 9.90e-01 9.90e-01h 1\n", + " 4 0.0000000e+00 1.83e+01 3.21e+03 -1.0 1.10e+02 - 9.90e-01 9.92e-01h 1\n", + " 5 0.0000000e+00 3.09e-07 4.89e+03 -1.0 8.35e-01 - 9.91e-01 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 5\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.6686898244612168e+06 1.6686898244612168e+06\n", + "Constraint violation....: 3.0919909477233887e-07 3.0919909477233887e-07\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.0919909477233887e-07 1.6686898244612168e+06\n", + "\n", + "\n", + "Number of objective function evaluations = 6\n", + "Number of objective gradient evaluations = 6\n", + "Number of equality constraint evaluations = 6\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 6\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 5\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $6.589014 million per year\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Area : 0.98707 : meter ** 2 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 29.000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 210.60\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 29.000\n", + " Temperature kelvin 328.15 328.15\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 50.0%\n", + "\n", + "Total heat duty required = -3.616582 MJ\n", + "\n", + "Tube area required = 0.987071 m^2\n", + "\n", + "Tube length required = 1.000000 m\n", + "\n", + "Tube volume required = 0.987071 m^3\n" + ] } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")\n", + "print()\n", + "print(\n", + " f\"Total heat duty required = \"\n", + " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * \n", + " (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", + " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", + " f\" MJ\"\n", + ")\n", + "print()\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", + "print()\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", + "print()\n", + "print(f\"Tube volume required = {value(m.fs.R101.volume):0.6f} m^3\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable reactor volume (considering operating/non-capital costs only) and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.R101.volume.setlb(0 * pyunits.m**3)\n", + "m.fs.R101.volume.setub(pyunits.convert(5000 * pyunits.gal, to_units=pyunits.m**3))\n", + "\n", + "m.fs.R101.length.unfix()\n", + "m.fs.R101.length.setlb(0 * pyunits.m)\n", + "m.fs.R101.length.setub(5 * pyunits.m)\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)\n", + "\n", + "for x in m.fs.R101.control_volume.length_domain:\n", + " if x == 0:\n", + " continue\n", + " m.fs.R101.control_volume.properties[\n", + " 0, x\n", + " ].temperature.unfix() # allow for temperature change in each finite element" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 2067\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 1886\n", + "\n", + "Total number of variables............................: 631\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 280\n", + " variables with only upper bounds: 21\n", + "Total number of equality constraints.................: 608\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 1\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 6.5890144e+06 3.50e+06 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 6.7059537e+06 2.91e+06 8.32e+01 -1.0 2.08e+06 - 7.82e-02 1.70e-01h 1\n", + " 2 8.4754506e+06 8.20e+04 2.59e+02 -1.0 2.00e+06 - 3.84e-01 9.90e-01H 1\n", + " 3 8.5040982e+06 3.11e+03 7.65e+01 -1.0 3.92e+05 - 9.34e-01 9.91e-01h 1\n", + " 4 8.3384488e+06 4.84e+04 1.42e+04 -1.0 2.10e+07 - 1.88e-01 6.90e-02f 4\n", + " 5 8.3415720e+06 5.62e+00 1.74e+06 -1.0 1.23e+04 -4.0 4.92e-01 1.00e+00h 1\n", + " 6 8.3410277e+06 1.47e-02 1.60e+04 -1.0 1.35e+03 -4.5 9.90e-01 1.00e+00h 1\n", + " 7 8.3410335e+06 4.53e-07 1.58e+02 -1.0 2.07e+02 -5.0 9.90e-01 1.00e+00f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8 8.1642024e+06 5.58e+04 1.51e+02 -1.0 5.96e+07 - 4.33e-02 2.38e-02f 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 9 7.9601638e+06 1.53e+05 1.47e+02 -1.0 2.01e+08 - 3.09e-02 6.55e-03f 2\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 7.9944681e+06 7.58e+02 5.54e+05 -1.0 3.55e+04 -5.4 1.98e-01 1.00e+00h 1\n", + " 11 7.9909260e+06 6.67e-01 8.30e+03 -1.0 5.71e+03 -5.9 1.00e+00 1.00e+00h 1\n", + " 12 7.9907050e+06 7.35e-05 2.17e+00 -1.0 5.29e+02 -6.4 1.00e+00 1.00e+00f 1\n", + " 13 7.9898995e+06 1.05e-03 6.64e-03 -3.8 1.52e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 14 7.9874742e+06 9.47e-03 6.47e-04 -3.8 4.59e+02 -7.3 1.00e+00 1.00e+00f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 15 7.9801953e+06 8.53e-02 1.91e-04 -5.7 1.38e+03 -7.8 1.00e+00 1.00e+00f 1\n", + " 16 7.9681969e+06 2.70e-01 5.59e-04 -5.7 4.13e+03 -8.3 1.00e+00 5.50e-01f 1\n", + " 17 7.9681103e+06 1.90e-01 4.78e+00 -5.7 1.25e+03 -8.8 1.00e+00 3.20e-01h 1\n", + " 18 7.9673275e+06 5.67e-01 7.69e-02 -5.7 3.24e+03 -9.2 1.00e+00 1.00e+00f 1\n", + " 19 7.9650200e+06 5.09e+00 1.18e-03 -5.7 9.70e+03 -9.7 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 7.9581018e+06 4.59e+01 1.06e-02 -5.7 2.91e+04 -10.2 1.00e+00 1.00e+00f 1\n", + " 21 7.9373665e+06 4.13e+02 9.20e-02 -5.7 8.75e+04 -10.7 1.00e+00 1.00e+00f 1\n", + " 22 7.9053826e+06 1.19e+03 2.54e-01 -5.7 2.64e+05 -11.2 1.00e+00 5.15e-01f 1\n", + " 23 7.9053825e+06 1.19e+03 1.01e+00 -5.7 2.12e+05 -11.6 1.00e+00 1.51e-05h 1\n", + " 24 7.8793665e+06 3.15e+03 6.40e+00 -5.7 6.33e+05 -12.1 1.00e+00 1.00e+00f 1\n", + " 25 7.7729606e+06 6.83e+04 2.55e+00 -5.7 2.26e+06 -12.6 1.00e+00 1.00e+00f 1\n", + " 26 7.7234737e+06 9.24e+04 3.17e+00 -5.7 2.68e+07 -13.1 6.92e-01 2.72e-02f 1\n", + " 27 7.6836742e+06 7.11e+04 2.33e+00 -5.7 2.89e+06 -12.6 1.00e+00 2.90e-01h 1\n", + " 28 7.6836472e+06 7.11e+04 2.33e+00 -5.7 1.68e+07 -13.1 1.00e+00 4.18e-05h 1\n", + " 29 7.6441379e+06 5.91e+04 2.13e+00 -5.7 2.77e+06 -12.7 1.00e+00 3.53e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 7.6438379e+06 5.91e+04 2.13e+00 -5.7 1.56e+07 -13.2 1.00e+00 6.15e-04h 1\n", + " 31 7.6055360e+06 5.59e+04 1.63e+00 -5.7 2.67e+06 -12.7 1.00e+00 4.17e-01h 1\n", + " 32 7.6047544e+06 5.58e+04 1.63e+00 -5.7 1.42e+07 -13.2 1.00e+00 2.13e-03h 1\n", + " 33 7.5683106e+06 5.88e+04 1.18e+00 -5.7 2.59e+06 -12.8 1.00e+00 4.80e-01h 1\n", + " 34 7.5676139e+06 5.87e+04 1.17e+00 -5.7 1.28e+07 -13.3 1.00e+00 2.56e-03h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 35 7.5324643e+06 6.64e+04 1.06e+00 -5.7 2.50e+06 -12.8 1.00e+00 5.62e-01h 1\n", + " 36 7.5321898e+06 6.63e+04 1.05e+00 -5.7 1.14e+07 -13.3 1.00e+00 1.37e-03h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 37 7.4975271e+06 7.83e+04 1.12e+00 -5.7 2.40e+06 -12.9 1.00e+00 6.73e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 38 7.4974468e+06 7.82e+04 1.12e+00 -5.7 1.00e+07 -13.4 1.00e+00 5.40e-04h 1\n", + " 39 7.4626011e+06 9.39e+04 1.23e+00 -5.7 2.28e+06 -12.9 1.00e+00 8.13e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 40 7.4625682e+06 9.38e+04 1.23e+00 -5.7 8.67e+06 -13.4 1.00e+00 2.86e-04h 1\n", + " 41 7.4257032e+06 1.16e+05 1.36e+00 -5.7 2.15e+06 -13.0 1.00e+00 9.91e-01h 1\n", + " 42 7.4256642e+06 1.16e+05 1.36e+00 -5.7 9.51e+06 -13.5 1.00e+00 3.92e-04h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 43 7.3923708e+06 1.13e+05 1.09e+00 -5.7 2.63e+06 -13.0 1.00e+00 1.00e+00h 1\n", + " 44 7.3835672e+06 1.18e+05 1.09e+00 -5.7 1.39e+07 -13.5 1.00e+00 3.68e-02h 1\n", + " 45 7.3447536e+06 1.73e+05 1.89e+00 -5.7 3.69e+06 -13.1 1.00e+00 1.00e+00h 1\n", + " 46 7.2794144e+06 3.97e+05 5.08e+00 -5.7 3.42e+07 -13.6 8.37e-01 1.28e-01h 1\n", + " 47 7.1836803e+06 5.67e+05 1.18e+01 -5.7 7.77e+06 -13.2 1.00e+00 1.00e+00h 1\n", + " 48 7.1642452e+06 9.81e+04 8.21e-01 -5.7 3.48e+06 -12.7 1.00e+00 9.29e-01h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 49 6.8513444e+06 9.88e+05 1.56e+01 -5.7 1.69e+07 -13.2 1.00e+00 6.85e-01f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 50 6.7933193e+06 9.39e+04 5.66e+00 -5.7 6.24e+06 -12.8 1.00e+00 1.00e+00h 1\n", + " 51 6.4367558e+06 4.74e+05 7.96e+00 -5.7 2.76e+07 -13.3 1.00e+00 3.62e-01f 1\n", + " 52 6.4367197e+06 4.74e+05 7.96e+00 -5.7 1.95e+08 -13.7 1.23e-02 6.50e-06h 1\n", + " 53 5.7085573e+06 1.56e+05 5.82e+02 -5.7 3.26e+07 -13.3 3.83e-02 8.77e-01f 1\n", + " 54 5.6258506e+06 1.54e+05 5.68e+02 -5.7 8.59e+07 -13.8 2.52e-01 2.34e-02f 1\n", + " 55 5.0191648e+06 2.10e+05 5.30e+02 -5.7 2.78e+08 -14.3 1.70e-02 5.35e-02f 1\n", + " 56 5.0191357e+06 2.10e+05 5.30e+02 -5.7 1.72e+10 - 3.83e-03 4.84e-08f 1\n", + " 57 4.9655024e+06 2.10e+05 5.30e+02 -5.7 1.35e+10 - 5.69e-03 1.12e-04f 1\n", + " 58 4.8687934e+06 2.10e+05 5.29e+02 -5.7 1.62e+09 - 7.98e-02 1.60e-03f 1\n", + " 59 4.8687391e+06 2.10e+05 5.29e+02 -5.7 9.14e+08 - 1.24e-01 1.66e-06h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 60 4.7738071e+06 2.11e+05 5.27e+02 -5.7 9.41e+08 - 1.33e-08 2.92e-03f 1\n", + " 61 4.7737999e+06 2.11e+05 5.27e+02 -5.7 9.46e+05 -12.0 5.86e-01 5.36e-05h 1\n", + " 62 4.7641413e+06 1.83e+05 4.56e+02 -5.7 4.23e+05 -11.6 6.87e-01 1.36e-01h 1\n", + " 63 4.7406167e+06 1.49e+05 3.75e+02 -5.7 1.07e+06 -12.1 7.76e-01 1.82e-01h 1\n", + " 64 4.7078824e+06 6.14e+04 1.55e+02 -5.7 5.52e+05 -11.7 7.68e-01 5.90e-01h 1\n", + " 65 4.6930261e+06 5.24e+04 1.33e+02 -5.7 1.15e+06 -12.1 9.03e-01 1.47e-01h 1\n", + " 66 4.6703240e+06 5.24e+04 1.33e+02 -5.7 2.77e+11 - 8.46e-09 2.52e-06f 1\n", + " 67 4.6703200e+06 5.24e+04 1.33e+02 -5.7 6.58e+06 -12.6 3.59e-06 1.04e-05h 1\n", + " 68 4.6703175e+06 5.24e+04 1.33e+02 -5.7 1.38e+07 -13.1 1.03e-01 5.51e-06f 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 69 4.6315831e+06 5.24e+04 1.32e+02 -5.7 1.07e+09 - 8.92e-02 1.11e-03f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 70 4.6315829e+06 5.24e+04 1.32e+02 -5.7 4.58e+06 -12.7 5.74e-01 8.86e-07h 2\n", + " 71 4.5662699e+06 5.23e+04 1.32e+02 -5.7 8.61e+08 - 1.26e-01 2.33e-03f 1\n", + " 72 4.4663666e+06 5.07e+04 1.28e+02 -5.7 1.04e+08 - 2.48e-03 3.07e-02f 1\n", + " 73 4.4482155e+06 5.06e+04 1.28e+02 -5.7 1.79e+08 - 4.82e-03 3.05e-03f 1\n", + " 74 4.4215601e+06 4.95e+04 1.25e+02 -5.7 4.38e+07 - 5.72e-03 2.40e-02f 1\n", + " 75 4.4215563e+06 4.54e+04 1.14e+02 -5.7 6.87e+05 - 9.99e-01 8.35e-02h 1\n", + " 76 4.4215319e+06 2.27e+05 1.60e+00 -5.7 1.25e+06 - 1.00e+00 1.00e+00f 1\n", + " 77 4.4215311e+06 1.50e+01 1.04e-02 -5.7 5.87e+04 - 9.99e-01 1.00e+00h 1\n", + " 78 4.4215311e+06 4.71e+02 1.42e-02 -5.7 1.68e+05 -13.1 1.00e+00 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 79 4.4215310e+06 3.62e-03 4.02e-03 -5.7 4.58e+02 -12.7 9.96e-01 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 80 4.4215310e+06 2.46e+05 3.71e-03 -5.7 1.51e+07 - 9.95e-01 9.26e-02h 1\n", + " 81 4.4215310e+06 3.27e+03 4.82e-04 -5.7 2.76e+05 - 8.11e-01 1.00e+00h 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 82 4.4215310e+06 2.85e+02 1.58e-05 -5.7 2.70e+05 - 1.00e+00 1.00e+00h 1\n", + " 83 4.4215310e+06 3.75e-01 7.37e-08 -5.7 7.05e+03 - 1.00e+00 1.00e+00h 1\n", + " 84 4.4215310e+06 5.32e-03 6.75e-11 -5.7 1.14e+03 - 1.00e+00 1.00e+00h 1\n", + " 85 4.4215302e+06 3.10e-07 3.75e-10 -8.6 2.39e+01 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 85\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.0399370128875694e+02 4.4215302056112001e+06\n", + "Dual infeasibility......: 3.7511146302236229e-10 8.1304797831804192e-06\n", + "Constraint violation....: 3.0989758670330048e-07 3.0989758670330048e-07\n", + "Complementarity.........: 2.5107403638322330e-09 5.4419887903388504e-05\n", + "Overall NLP error.......: 3.0989758670330048e-07 5.4419887903388504e-05\n", + "\n", + "\n", + "Number of objective function evaluations = 100\n", + "Number of objective gradient evaluations = 86\n", + "Number of equality constraint evaluations = 100\n", + "Number of inequality constraint evaluations = 100\n", + "Number of equality constraint Jacobian evaluations = 86\n", + "Number of inequality constraint Jacobian evaluations = 86\n", + "Number of Lagrangian Hessian evaluations = 85\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.220\n", + "Total CPU secs in NLP function evaluations = 0.022\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $4.421530 million per year\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 6.9784e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 58.000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 239.60\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 2.0000e-05\n", + " Temperature kelvin 298.15 328.15\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n", + "\n", + "PFR reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Area : 2.9300 : meter ** 2 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 5.8000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 187.40\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 52.200\n", + " Temperature kelvin 328.15 286.11\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"PFR reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 328.150000 K\n", + "\n", + "Total heat duty required = -3.789584 MJ\n", + "\n", + "Tube area required = 2.930000 m^2\n", + "\n", + "Tube length required = 4.982471 m\n", + "\n", + "Assuming a 20% design factor for reactor volume,total CSTR volume required = 17.518364 m^3 = 4627.862142 gal\n", + "\n", + "Ethylene glycol produced = 225.415471 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(\n", + " \"Total heat duty required = \",\n", + " f\"\"\"{(value(m.fs.R101.length) / value(m.fs.R101.config.finite_elements) * (value(sum(m.fs.R101.heat_duty[0, k] for k in m.fs.R101.control_volume.length_domain if 0.0 <= k < 1.0))\n", + " + (value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(1)])\n", + " + value(m.fs.R101.heat_duty[0, m.fs.R101.control_volume.length_domain.at(-1)]))/2))/1e6:0.6f}\"\"\"\n", + " f\" MJ\",\n", + ")\n", + "print()\n", + "print(f\"Tube area required = {value(m.fs.R101.area):0.6f} m^2\")\n", + "\n", + "print()\n", + "print(f\"Tube length required = {value(m.fs.R101.length):0.6f} m\")\n", + "\n", + "print()\n", + "print(\n", + " f\"Assuming a 20% design factor for reactor volume,\"\n", + " f\"total CSTR volume required = {value(1.2*m.fs.R101.volume):0.6f}\"\n", + " f\" m^3 = {value(pyunits.convert(1.2*m.fs.R101.volume, to_units=pyunits.gal)):0.6f} gal\"\n", + ")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb index 76d32312..bfcc247e 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb index 30984465..db4aeb49 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/plug_flow_reactor_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb index b0ed3611..b250015c 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb index 99443416..a3276966 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_doc.ipynb @@ -1,693 +1,1216 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Flowsheet Stoichiometric Reactor Simulation and Optimization of Ethylene Glycol Production\n", - "Author: Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "## Learning Outcomes\n", - "\n", - "\n", - "- Call and implement the IDAES StochiometricReactor unit model\n", - "- Construct a steady-state flowsheet using the IDAES unit model library\n", - "- Connecting unit models in a flowsheet using Arcs\n", - "- Fomulate and solve an optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a stochiometric (or yield) reactor. As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike the previous two reactors which apply performance equations to calculate reaction extent, this simplified reactor model neglects all geometric properties and allows the user to specify a yield per reaction. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", - "\n", - "Chemical reaction:\n", - "\n", - "**C2H4O + H2O + H2SO4 \u2192 C2H6O2 + H2SO4**\n", - "\n", - "Property Packages:\n", - "\n", - "- egprod_ideal.py\n", - "- egprod_reaction.py\n", - "\n", - "Flowsheet:\n", - "\n", - "![](egprod_flowsheet.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Pyomo and IDAES components\n", - "\n", - "\n", - "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", - "- Constraint (to write constraints)\n", - "- Var (to declare variables)\n", - "- ConcreteModel (to create the concrete model object)\n", - "- Expression (to evaluate values as a function of variables defined in the model)\n", - "- Objective (to define an objective function for optimization)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- Arc (to connect two unit models)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", - "\n", - "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", - "- Mixer\n", - "- Heater\n", - "- StoichiometricReactor\n", - "\n", - "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " Constraint,\n", - " Var,\n", - " ConcreteModel,\n", - " Expression,\n", - " Objective,\n", - " TransformationFactory,\n", - " value,\n", - " units as pyunits,\n", - ")\n", - "from pyomo.network import Arc\n", - "\n", - "from idaes.core import FlowsheetBlock\n", - "from idaes.models.properties.modular_properties.base.generic_property import (\n", - " GenericParameterBlock,\n", - ")\n", - "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", - " GenericReactionParameterBlock,\n", - ")\n", - "from idaes.models.unit_models import Feed, Mixer, Heater, StoichiometricReactor, Product\n", - "\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "from idaes.core.util.initialization import propagate_state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Required Thermophysical and Reaction Packages\n", - "\n", - "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only. \n", - "\n", - "Let us import the following modules from the same directory as this Jupyter notebook:\n", - "- egprod_ideal as thermo_props\n", - "- egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import egprod_ideal as thermo_props\n", - "import egprod_reaction as reaction_props" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Flowsheet\n", - "\n", - "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()\n", - "m.fs = FlowsheetBlock(dynamic=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", - "m.fs.reaction_params = GenericReactionParameterBlock(\n", - " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Unit Models\n", - "\n", - "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `StoichiometricReactor`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", - "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", - "m.fs.M101 = Mixer(\n", - " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", - ")\n", - "m.fs.H101 = Heater(\n", - " property_package=m.fs.thermo_params,\n", - " has_pressure_change=False,\n", - " has_phase_equilibrium=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101 = StoichiometricReactor(\n", - " property_package=m.fs.thermo_params,\n", - " reaction_package=m.fs.reaction_params,\n", - " has_heat_of_reaction=True,\n", - " has_heat_transfer=True,\n", - " has_pressure_change=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connecting Unit Models Using Arcs\n", - "\n", - "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `StoichiometricReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", - "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", - "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", - "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", - "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TransformationFactory(\"network.expand_arcs\").apply_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adding Expressions to Compute Operating Costs\n", - "\n", - "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", - "\n", - "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod = Expression(\n", - " expr=pyunits.convert(\n", - " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", - " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", - " to_units=pyunits.Mlb / pyunits.yr,\n", - " )\n", - ") # converting kg/s to MM lb/year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.cooling_cost = Expression(\n", - " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", - ") # the reaction is exothermic, so R101 duty is negative\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", - ") # the stream must be heated to T_rxn, so H101 duty is positive\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Feed Conditions\n", - "\n", - "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or overall conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom since we have not yet defined the yield of the sole rate-kinetics reaction. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and duty." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 58.0 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 39.6 * pyunits.mol / pyunits.s\n", - ") # calculated from 16.1 mol EO / cudm in stream\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", - "\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", - " 200 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", - " 0.334 * pyunits.mol / pyunits.s\n", - ") # calculated from 0.9 wt% SA in stream\n", - "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", - " 1e-5 * pyunits.mol / pyunits.s\n", - ")\n", - "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", - "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing Unit Model Specifications\n", - "\n", - "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will need to specify both initial reactant extent (conversion or yield) and heat duty values (these are the only two free variables to choose from). The reaction extent can be specified directly, as a molar or mass yield ratio of product to a particular reactant, or fractional conversion of a particular reactant. Here, we choose fractional conversion in terms of ethylene oxide. Since heat duty and the outlet reactor temperature are interdependent, we can choose to specify this quantity instead. While the reaction kinetic parameters exist in the property package, we also do not need to add a rate constant expression since generation is explicitly defined through the conversion/yield. Note that our initial problem will solve with zero *temperature change* but will be infeasible with zero *heat duty*; this is due to the heat of reaction enforced by allowing heat transfer and mandating a non-zero conversion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.conversion = Var(\n", - " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", - ") # fraction\n", - "\n", - "m.fs.R101.conv_constraint = Constraint(\n", - " expr=m.fs.R101.conversion\n", - " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " == (\n", - " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", - " )\n", - ")\n", - "\n", - "m.fs.R101.conversion.fix(0.80)\n", - "\n", - "m.fs.R101.outlet.temperature.fix(328.15 * pyunits.K) # equal inlet reactor temperature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(degrees_of_freedom(m))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize and solve each unit operation\n", - "m.fs.OXIDE.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.ACID.initialize()\n", - "propagate_state(arc=m.fs.s01)\n", - "\n", - "m.fs.M101.initialize()\n", - "propagate_state(arc=m.fs.s03)\n", - "\n", - "m.fs.H101.initialize()\n", - "propagate_state(arc=m.fs.s04)\n", - "\n", - "m.fs.R101.initialize()\n", - "propagate_state(arc=m.fs.s05)\n", - "\n", - "m.fs.PROD.initialize()\n", - "\n", - "# set solver\n", - "solver = get_solver()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Solve the model\n", - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze the Results of the Square Problem\n", - "\n", - "\n", - "What is the total operating cost? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.R101.report()\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing Ethylene Glycol Production\n", - "\n", - "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable and reactor temperature (heater outlet)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us declare our objective function for this problem. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.objective = Objective(expr=m.fs.operating_cost)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m.fs.eg_prod_con = Constraint(\n", - " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", - ") # MM lb/year\n", - "m.fs.R101.conversion.fix(0.90)\n", - "\n", - "m.fs.H101.outlet.temperature.unfix()\n", - "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", - "m.fs.H101.outlet.temperature[0].setub(\n", - " 470.45 * pyunits.K\n", - ") # highest component boiling point (ethylene glycol)\n", - "\n", - "m.fs.R101.outlet.temperature.unfix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "We have now defined the optimization problem and we are now ready to solve this problem. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "results = solver.solve(m, tee=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", - "\n", - "print()\n", - "print(\"Heater results\")\n", - "\n", - "m.fs.H101.report()\n", - "\n", - "print()\n", - "print(\"Stoichiometric reactor results\")\n", - "\n", - "m.fs.R101.report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Display optimal values for the decision variables and design variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Optimal Values\")\n", - "print()\n", - "\n", - "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", - "\n", - "print()\n", - "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.6f} K\")\n", - "\n", - "print()\n", - "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", - "\n", - "print()\n", - "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Flowsheet Stoichiometric Reactor Simulation and Optimization of Ethylene Glycol Production\n", + "Author: Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "## Learning Outcomes\n", + "\n", + "\n", + "- Call and implement the IDAES StochiometricReactor unit model\n", + "- Construct a steady-state flowsheet using the IDAES unit model library\n", + "- Connecting unit models in a flowsheet using Arcs\n", + "- Fomulate and solve an optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Following the previous example implementing a [Continuous Stirred Tank Reactor (CSTR) unit model](http://localhost:8888/notebooks/GitHub/examples-pse/src/Examples/UnitModels/Reactors/cstr_testing_doc.md), we can alter the flowsheet to use a stochiometric (or yield) reactor. As before, this example is adapted from Fogler, H.S., Elements of Chemical Reaction Engineering 5th ed., 2016, Prentice Hall, p. 157-160 with the following chemical reaction, property packages and flowsheet. Unlike the previous two reactors which apply performance equations to calculate reaction extent, this simplified reactor model neglects all geometric properties and allows the user to specify a yield per reaction. The state variables chosen for the property package are **molar flows of each component by phase in each stream, temperature of each stream and pressure of each stream**. The components considered are: **ethylene oxide, water, sulfuric acid and ethylene glycol** and the process occurs in liquid phase only. Therefore, every stream has 4 flow variables, 1 temperature and 1 pressure variable.\n", + "\n", + "Chemical reaction:\n", + "\n", + "**C2H4O + H2O + H2SO4 → C2H6O2 + H2SO4**\n", + "\n", + "Property Packages:\n", + "\n", + "- egprod_ideal.py\n", + "- egprod_reaction.py\n", + "\n", + "Flowsheet:\n", + "\n", + "![](egprod_flowsheet.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Pyomo and IDAES components\n", + "\n", + "\n", + "To construct a flowsheet, we will need several components from the Pyomo and IDAES packages. Let us first import the following components from Pyomo:\n", + "- Constraint (to write constraints)\n", + "- Var (to declare variables)\n", + "- ConcreteModel (to create the concrete model object)\n", + "- Expression (to evaluate values as a function of variables defined in the model)\n", + "- Objective (to define an objective function for optimization)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- Arc (to connect two unit models)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/stable/\n", + "\n", + "From idaes, we will be needing the `FlowsheetBlock` and the following unit models:\n", + "- Mixer\n", + "- Heater\n", + "- StoichiometricReactor\n", + "\n", + "We will also be needing some utility tools to put together the flowsheet and calculate the degrees of freedom, tools for model expressions and calling variable values, and built-in functions to define property packages, add unit containers to objects and define our initialization scheme.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " Constraint,\n", + " Var,\n", + " ConcreteModel,\n", + " Expression,\n", + " Objective,\n", + " TransformationFactory,\n", + " value,\n", + " units as pyunits,\n", + ")\n", + "from pyomo.network import Arc\n", + "\n", + "from idaes.core import FlowsheetBlock\n", + "from idaes.models.properties.modular_properties.base.generic_property import (\n", + " GenericParameterBlock,\n", + ")\n", + "from idaes.models.properties.modular_properties.base.generic_reaction import (\n", + " GenericReactionParameterBlock,\n", + ")\n", + "from idaes.models.unit_models import Feed, Mixer, Heater, StoichiometricReactor, Product\n", + "\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing Required Thermophysical and Reaction Packages\n", + "\n", + "The final step is to import the thermophysical and reaction packages. We have created a custom thermophysical package that support ideal vapor and liquid behavior for this system, and in this case we will restrict it to ideal liquid behavior only. \n", + "\n", + "Let us import the following modules from the same directory as this Jupyter notebook:\n", + "- egprod_ideal as thermo_props\n", + "- egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import egprod_ideal as thermo_props\n", + "import egprod_reaction as reaction_props" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Flowsheet\n", + "\n", + "We have now imported all the components, unit models, and property modules we need to construct a flowsheet. Let us create a ConcreteModel and add the flowsheet block. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()\n", + "m.fs = FlowsheetBlock(dynamic=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to add the property packages to the flowsheet. Unlike the basic [Flash unit model example](http://localhost:8888/notebooks/GitHub/examples-pse/src/Tutorials/Basics/flash_unit_solution_testing_doc.md), where we only had a thermophysical property package, for this flowsheet we will also need to add a reaction property package. We will use the [Modular Property Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-property-package-framework) and [Modular Reaction Framework](https://idaes-pse.readthedocs.io/en/stable/explanations/components/property_package/index.html#generic-reaction-package-framework). The get_prop method for the natural gas property module automatically returns the correct dictionary using a component list argument. The GenericParameterBlock and GenericReactionParameterBlock methods build states blocks from passed parameter data; the reaction block unpacks using **reaction_props.config_dict to allow for optional or empty keyword arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.thermo_params = GenericParameterBlock(**thermo_props.config_dict)\n", + "m.fs.reaction_params = GenericReactionParameterBlock(\n", + " property_package=m.fs.thermo_params, **reaction_props.config_dict\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Unit Models\n", + "\n", + "Let us start adding the unit models we have imported to the flowsheet. Here, we are adding a `Mixer`, a `Heater` and a `StoichiometricReactor`. Note that all unit models need to be given a property package argument. In addition to that, there are several arguments depending on the unit model, please refer to the documentation for more details on [IDAES Unit Models](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/index.html). For example, the `Mixer` is given a `list` consisting of names to the two inlets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "m.fs.OXIDE = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.ACID = Feed(property_package=m.fs.thermo_params)\n", + "m.fs.PROD = Product(property_package=m.fs.thermo_params)\n", + "m.fs.M101 = Mixer(\n", + " property_package=m.fs.thermo_params, inlet_list=[\"reagent_feed\", \"catalyst_feed\"]\n", + ")\n", + "m.fs.H101 = Heater(\n", + " property_package=m.fs.thermo_params,\n", + " has_pressure_change=False,\n", + " has_phase_equilibrium=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101 = StoichiometricReactor(\n", + " property_package=m.fs.thermo_params,\n", + " reaction_package=m.fs.reaction_params,\n", + " has_heat_of_reaction=True,\n", + " has_heat_transfer=True,\n", + " has_pressure_change=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Unit Models Using Arcs\n", + "\n", + "We have now added all the unit models we need to the flowsheet. However, we have not yet specified how the units are to be connected. To do this, we will be using the `Arc` which is a pyomo component that takes in two arguments: `source` and `destination`. Let us connect the outlet of the `Mixer` to the inlet of the `Heater`, and the outlet of the `Heater` to the inlet of the `StoichiometricReactor`. Additionally, we will connect the `Feed` and `Product` blocks to the flowsheet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.s01 = Arc(source=m.fs.OXIDE.outlet, destination=m.fs.M101.reagent_feed)\n", + "m.fs.s02 = Arc(source=m.fs.ACID.outlet, destination=m.fs.M101.catalyst_feed)\n", + "m.fs.s03 = Arc(source=m.fs.M101.outlet, destination=m.fs.H101.inlet)\n", + "m.fs.s04 = Arc(source=m.fs.H101.outlet, destination=m.fs.R101.inlet)\n", + "m.fs.s05 = Arc(source=m.fs.R101.outlet, destination=m.fs.PROD.inlet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now connected the unit model block using the arcs. However, we also need to link the state variables on connected ports. Pyomo provides a convenient method `TransformationFactory` to write these equality constraints for us between two ports:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "TransformationFactory(\"network.expand_arcs\").apply_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Expressions to Compute Operating Costs\n", + "\n", + "In this section, we will add a few Expressions that allows us to evaluate the performance. `Expressions` provide a convenient way of calculating certain values that are a function of the variables defined in the model. For more details on `Expressions`, please refer to the [Pyomo Expression documentation]( https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Expressions.html).\n", + "\n", + "For this flowsheet, we are interested in computing ethylene glycol production in millions of pounds per year, as well as the total costs due to cooling and heating utilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first add an `Expression` to convert the product flow from mol/s to MM lb/year of ethylene glycol. We see that the molecular weight exists in the thermophysical property package, so we may use that value for our calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod = Expression(\n", + " expr=pyunits.convert(\n", + " m.fs.PROD.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"]\n", + " * m.fs.thermo_params.ethylene_glycol.mw, # MW defined in properties as kg/mol\n", + " to_units=pyunits.Mlb / pyunits.yr,\n", + " )\n", + ") # converting kg/s to MM lb/year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us add expressions to compute the reactor cooling cost (\\\\$/s) assuming a cost of 2.12E-5 \\\\$/kW, and the heating utility cost (\\\\$/s) assuming 2.2E-4 \\\\$/kW. Note that the heat duty is in units of watt (J/s). The total operating cost will be the sum of the two, expressed in \\\\$/year assuming 8000 operating hours per year (~10\\% downtime, which is fairly common for small scale chemical plants):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.cooling_cost = Expression(\n", + " expr=2.12e-8 * (-m.fs.R101.heat_duty[0])\n", + ") # the reaction is exothermic, so R101 duty is negative\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0]\n", + ") # the stream must be heated to T_rxn, so H101 duty is positive\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 8000 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Feed Conditions\n", + "\n", + "Let us first check how many degrees of freedom exist for this flowsheet using the `degrees_of_freedom` tool we imported earlier. We expect each stream to have 6 degrees of freedom, the mixer to have 0 (after both streams are accounted for), the heater to have 1 (just the duty, since the inlet is also the outlet of M101), and the reactor to have 1 (duty or overall conversion, since the inlet is also the outlet of H101). In this case, the reactor has an extra degree of freedom since we have not yet defined the yield of the sole rate-kinetics reaction. Therefore, we have 15 degrees of freedom to specify: temperature, pressure and flow of all four components on both streams; outlet heater temperature; reactor conversion and duty." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n" + ] } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now be fixing the feed stream to the conditions shown in the flowsheet above. As mentioned in other tutorials, the IDAES framework expects a time index value for every referenced internal stream or unit variable, even in steady-state systems with a single time point $ t = 0 $ (`t = [0]` is the default when creating a `FlowsheetBlock` without passing a `time_set` argument). The non-present components in each stream are assigned a very small non-zero value to help with convergence and initializing. Based on stoichiometric ratios for the reaction, 80% conversion and 200 MM lb/year (46.4 mol/s) of ethylene glycol, we will initialize our simulation with the following calculated values:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 58.0 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 39.6 * pyunits.mol / pyunits.s\n", + ") # calculated from 16.1 mol EO / cudm in stream\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.OXIDE.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.OXIDE.outlet.pressure.fix(1e5 * pyunits.Pa)\n", + "\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"water\"].fix(\n", + " 200 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"sulfuric_acid\"].fix(\n", + " 0.334 * pyunits.mol / pyunits.s\n", + ") # calculated from 0.9 wt% SA in stream\n", + "m.fs.ACID.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_glycol\"].fix(\n", + " 1e-5 * pyunits.mol / pyunits.s\n", + ")\n", + "m.fs.ACID.outlet.temperature.fix(298.15 * pyunits.K)\n", + "m.fs.ACID.outlet.pressure.fix(1e5 * pyunits.Pa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing Unit Model Specifications\n", + "\n", + "Now that we have fixed our inlet feed conditions, we will now be fixing the operating conditions for the unit models in the flowsheet. Let us fix the outlet temperature of H101 to 328.15 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.H101.outlet.temperature.fix(328.15 * pyunits.K)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will need to specify both initial reactant extent (conversion or yield) and heat duty values (these are the only two free variables to choose from). The reaction extent can be specified directly, as a molar or mass yield ratio of product to a particular reactant, or fractional conversion of a particular reactant. Here, we choose fractional conversion in terms of ethylene oxide. Since heat duty and the outlet reactor temperature are interdependent, we can choose to specify this quantity instead. While the reaction kinetic parameters exist in the property package, we also do not need to add a rate constant expression since generation is explicitly defined through the conversion/yield. Note that our initial problem will solve with zero *temperature change* but will be infeasible with zero *heat duty*; this is due to the heat of reaction enforced by allowing heat transfer and mandating a non-zero conversion." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.R101.conversion = Var(\n", + " initialize=0.80, bounds=(0, 1), units=pyunits.dimensionless\n", + ") # fraction\n", + "\n", + "m.fs.R101.conv_constraint = Constraint(\n", + " expr=m.fs.R101.conversion\n", + " * m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " == (\n", + " m.fs.R101.inlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " - m.fs.R101.outlet.flow_mol_phase_comp[0, \"Liq\", \"ethylene_oxide\"]\n", + " )\n", + ")\n", + "\n", + "m.fs.R101.conversion.fix(0.80)\n", + "\n", + "m.fs.R101.outlet.temperature.fix(328.15 * pyunits.K) # equal inlet reactor temperature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For initialization, we solve a square problem (degrees of freedom = 0). Let's check the degrees of freedom below:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] } + ], + "source": [ + "print(degrees_of_freedom(m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we need to initialize the each unit operation in sequence to solve the flowsheet. As in best practice, unit operations are initialized or solved, and outlet properties are propagated to connected inlet streams via arc definitions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.OXIDE.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.OXIDE.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.OXIDE.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.OXIDE: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.ACID.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.ACID.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.ACID.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.ACID: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.M101.reagent_feed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.M101.reagent_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:50 [INFO] idaes.init.fs.M101.catalyst_feed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.M101.catalyst_feed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.M101.mixed_state: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.M101.mixed_state: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.M101.mixed_state: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.H101.control_volume.properties_in: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.H101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.H101.control_volume.properties_out: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.H101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101.control_volume.properties_in: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101.control_volume.properties_in: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101.control_volume.properties_out: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101.control_volume.properties_out: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101.control_volume.reactions: Initialization Complete.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.PROD.properties: Starting initialization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.PROD.properties: Property initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.PROD.properties: Property package initialization: optimal - Optimal Solution Found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-17 17:40:51 [INFO] idaes.init.fs.PROD: Initialization Complete.\n" + ] + } + ], + "source": [ + "# Initialize and solve each unit operation\n", + "m.fs.OXIDE.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.ACID.initialize()\n", + "propagate_state(arc=m.fs.s01)\n", + "\n", + "m.fs.M101.initialize()\n", + "propagate_state(arc=m.fs.s03)\n", + "\n", + "m.fs.H101.initialize()\n", + "propagate_state(arc=m.fs.s04)\n", + "\n", + "m.fs.R101.initialize()\n", + "propagate_state(arc=m.fs.s05)\n", + "\n", + "m.fs.PROD.initialize()\n", + "\n", + "# set solver\n", + "solver = get_solver()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 337\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 383\n", + "\n", + "Total number of variables............................: 95\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 86\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 95\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.24e+06 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 2.80e+06 1.28e+01 -1.0 1.02e+07 - 6.77e-02 9.90e-01h 1\n", + " 2 0.0000000e+00 1.86e+04 2.90e+02 -1.0 1.02e+05 - 7.00e-01 9.90e-01h 1\n", + " 3 0.0000000e+00 1.93e+02 1.44e+01 -1.0 1.02e+03 - 9.90e-01 9.90e-01h 1\n", + " 4 0.0000000e+00 1.47e+00 3.18e+03 -1.0 1.00e+01 - 9.90e-01 9.92e-01h 1\n", + " 5 0.0000000e+00 5.96e-08 3.34e+03 -1.0 7.63e-02 - 9.94e-01 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 5\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.6686898422600192e+06 1.6686898422600192e+06\n", + "Constraint violation....: 1.9895196601282805e-13 5.9604644775390625e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.9895196601282805e-13 1.6686898422600192e+06\n", + "\n", + "\n", + "Number of objective function evaluations = 6\n", + "Number of objective gradient evaluations = 6\n", + "Number of equality constraint evaluations = 6\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 6\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 5\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "# Solve the model\n", + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the Results of the Square Problem\n", + "\n", + "\n", + "What is the total operating cost? " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $8.019605 million per year\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this operating cost, what conversion did we achieve of ethylene oxide to ethylene glycol? " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -5.8931e+06 : watt : False : (None, None)\n", + " Reaction Extent [R1] : 46.400 : mole / second : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 11.600\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 193.20\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 46.400\n", + " Temperature kelvin 328.15 328.15\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n", + "\n", + "Conversion achieved = 80.0%\n" + ] + } + ], + "source": [ + "m.fs.R101.report()\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing Ethylene Glycol Production\n", + "\n", + "Now that the flowsheet has been squared and solved, we can run a small optimization problem to minimize our production costs. Suppose we require at least 200 million pounds/year of ethylene glycol produced and 90% conversion of ethylene oxide, allowing for variable and reactor temperature (heater outlet)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us declare our objective function for this problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.objective = Objective(expr=m.fs.operating_cost)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to add the design constraints and unfix the decision variables as we had solved a square problem (degrees of freedom = 0) until now, as well as set bounds for the design variables (reactor outlet temperature is set by state variable bounds in property package):" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "m.fs.eg_prod_con = Constraint(\n", + " expr=m.fs.eg_prod >= 200 * pyunits.Mlb / pyunits.yr\n", + ") # MM lb/year\n", + "m.fs.R101.conversion.fix(0.90)\n", + "\n", + "m.fs.H101.outlet.temperature.unfix()\n", + "m.fs.H101.outlet.temperature[0].setlb(328.15 * pyunits.K)\n", + "m.fs.H101.outlet.temperature[0].setub(\n", + " 470.45 * pyunits.K\n", + ") # highest component boiling point (ethylene glycol)\n", + "\n", + "m.fs.R101.outlet.temperature.unfix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We have now defined the optimization problem and we are now ready to solve this problem. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 341\n", + "Number of nonzeros in inequality constraint Jacobian.: 1\n", + "Number of nonzeros in Lagrangian Hessian.............: 403\n", + "\n", + "Total number of variables............................: 97\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 88\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 95\n", + "Total number of inequality constraints...............: 1\n", + " inequality constraints with only lower bounds: 1\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 8.0196054e+06 1.74e+06 6.34e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 7.7629238e+06 1.74e+06 3.17e+01 -1.0 1.15e+10 - 2.05e-04 6.26e-06f 1\n", + " 2 6.1434461e+06 1.86e+06 6.26e+02 -1.0 1.33e+10 - 9.73e-05 2.00e-04f 1\n", + " 3 6.0659620e+06 1.86e+06 1.56e+03 -1.0 4.69e+08 - 9.60e-03 2.71e-04f 1\n", + " 4 6.0710959e+06 1.84e+06 1.28e+04 -1.0 5.10e+05 - 7.87e-02 9.76e-03h 1\n", + " 5 6.1245821e+06 1.68e+06 2.22e+04 -1.0 6.34e+05 - 1.58e-01 8.92e-02h 1\n", + " 6 6.6655990e+06 2.55e+04 3.27e+04 -1.0 5.78e+05 - 8.93e-01 9.90e-01h 1\n", + " 7 6.6706813e+06 2.41e+02 3.10e+02 -1.0 5.23e+03 - 9.90e-01 9.91e-01h 1\n", + " 8 6.6707292e+06 2.37e-04 1.09e+03 -1.0 4.89e+01 - 9.90e-01 1.00e+00h 1\n", + " 9 6.6707289e+06 2.98e-08 1.95e+02 -2.5 3.32e-01 - 9.98e-01 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 6.6707289e+06 2.42e-08 1.87e-07 -2.5 2.78e-06 - 1.00e+00 1.00e+00h 1\n", + " 11 6.6707289e+06 2.79e-09 3.69e-07 -5.7 9.67e-03 - 1.00e+00 1.00e+00f 1\n", + "\n", + "Number of Iterations....: 11\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 6.6707289104388300e+06 6.6707289104388300e+06\n", + "Dual infeasibility......: 3.6871028773460129e-07 3.6871028773460129e-07\n", + "Constraint violation....: 7.1054273576010019e-15 2.7939677238464355e-09\n", + "Complementarity.........: 1.8450200502283614e-06 1.8450200502283614e-06\n", + "Overall NLP error.......: 1.8334287948259664e-07 1.8450200502283614e-06\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 12\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 12\n", + "Number of equality constraint Jacobian evaluations = 12\n", + "Number of inequality constraint Jacobian evaluations = 12\n", + "Number of Lagrangian Hessian evaluations = 11\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], + "source": [ + "results = solver.solve(m, tee=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "operating cost = $6.670729 million per year\n", + "\n", + "Heater results\n", + "\n", + "====================================================================================\n", + "Unit : fs.H101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 6.9784e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 58.000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 239.60\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 2.0000e-05\n", + " Temperature kelvin 298.15 328.15\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n", + "\n", + "Stoichiometric reactor results\n", + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.6838e+06 : watt : False : (None, None)\n", + " Reaction Extent [R1] : 52.200 : mole / second : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " Molar Flowrate ('Liq', 'ethylene_oxide') mole / second 58.000 5.8000\n", + " Molar Flowrate ('Liq', 'water') mole / second 239.60 187.40\n", + " Molar Flowrate ('Liq', 'sulfuric_acid') mole / second 0.33401 0.33401\n", + " Molar Flowrate ('Liq', 'ethylene_glycol') mole / second 2.0000e-05 52.200\n", + " Temperature kelvin 328.15 450.00\n", + " Pressure pascal 1.0000e+05 1.0000e+05\n", + "====================================================================================\n" + ] + } + ], + "source": [ + "print(f\"operating cost = ${value(m.fs.operating_cost)/1e6:0.6f} million per year\")\n", + "\n", + "print()\n", + "print(\"Heater results\")\n", + "\n", + "m.fs.H101.report()\n", + "\n", + "print()\n", + "print(\"Stoichiometric reactor results\")\n", + "\n", + "m.fs.R101.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display optimal values for the decision variables and design variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 328.150000 K\n", + "\n", + "R101 outlet temperature = 450.000000 K\n", + "\n", + "Ethylene glycol produced = 225.415471 MM lb/year\n", + "\n", + "Conversion achieved = 90.0%\n" + ] + } + ], + "source": [ + "print(\"Optimal Values\")\n", + "print()\n", + "\n", + "print(f\"H101 outlet temperature = {value(m.fs.H101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(f\"R101 outlet temperature = {value(m.fs.R101.outlet.temperature[0]):0.6f} K\")\n", + "\n", + "print()\n", + "print(f\"Ethylene glycol produced = {value(m.fs.eg_prod):0.6f} MM lb/year\")\n", + "\n", + "print()\n", + "print(f\"Conversion achieved = {value(m.fs.R101.conversion):.1%}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 3 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb index 675c7fea..419c61a7 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_test.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb index 5660e55a..9fd66919 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb +++ b/idaes_examples/notebooks/docs/unit_models/reactors/stoichiometric_reactor_usr.ipynb @@ -16,12 +16,13 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, diff --git a/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py b/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py index 7b9ca054..09a26d2c 100644 --- a/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py +++ b/idaes_examples/notebooks/docs/unit_models/reactors/tests/test_egprod_ideal.py @@ -3,12 +3,13 @@ # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES). # -# Copyright (c) 2018-2023 by the software owners: The Regents of the +# Copyright (c) 2018-2025 by the software owners: The Regents of the # University of California, through Lawrence Berkeley National Laboratory, # National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon # University, West Virginia University Research Corporation, et al. # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. +# ################################################################################# """ Author: Brandon Paul diff --git a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed.ipynb b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed.ipynb index f90e547b..136d2230 100644 --- a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed.ipynb +++ b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed.ipynb @@ -3,6 +3,7 @@ { "cell_type": "code", "execution_count": null, + "id": "481b550a", "metadata": { "tags": [ "header", @@ -16,17 +17,19 @@ "# Framework (IDAES IP) was produced under the DOE Institute for the\n", "# Design of Advanced Energy Systems (IDAES).\n", "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", "# University of California, through Lawrence Berkeley National Laboratory,\n", "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", "# University, West Virginia University Research Corporation, et al.\n", "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", "# for full copyright and license information.\n", + "#\n", "###############################################################################" ] }, { "cell_type": "markdown", + "id": "05567d94", "metadata": {}, "source": [ "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", @@ -46,6 +49,7 @@ }, { "cell_type": "markdown", + "id": "88cfff26", "metadata": {}, "source": [ "## Cycle details\n", @@ -65,6 +69,7 @@ }, { "cell_type": "markdown", + "id": "56f43c68", "metadata": {}, "source": [ "## Notes\n", @@ -77,6 +82,7 @@ }, { "cell_type": "markdown", + "id": "fbd1f882", "metadata": {}, "source": [ "# Step 1: Import relevant libraries and packages" @@ -84,6 +90,7 @@ }, { "cell_type": "markdown", + "id": "b597bf97", "metadata": {}, "source": [ "### Import python libraries\n", @@ -95,6 +102,7 @@ { "cell_type": "code", "execution_count": 1, + "id": "e98d4d62", "metadata": {}, "outputs": [], "source": [ @@ -104,6 +112,7 @@ }, { "cell_type": "markdown", + "id": "fdbfcdeb", "metadata": {}, "source": [ "\n", @@ -123,6 +132,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "78d543e6", "metadata": {}, "outputs": [], "source": [ @@ -138,6 +148,7 @@ }, { "cell_type": "markdown", + "id": "7d9f3a59", "metadata": {}, "source": [ "### Import IDAES core components\n", @@ -159,6 +170,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "ed955425", "metadata": {}, "outputs": [], "source": [ @@ -173,6 +185,7 @@ }, { "cell_type": "markdown", + "id": "f93fe356", "metadata": {}, "source": [ "### Import IDAES unit models and NETL 32D property packages\n", @@ -185,6 +198,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "fbdb0eb1", "metadata": {}, "outputs": [], "source": [ @@ -202,6 +216,7 @@ }, { "cell_type": "markdown", + "id": "053bbc78", "metadata": {}, "source": [ "### Import custom libraries and functions\n", @@ -219,6 +234,7 @@ { "cell_type": "code", "execution_count": 5, + "id": "31dae66f", "metadata": {}, "outputs": [], "source": [ @@ -233,6 +249,7 @@ }, { "cell_type": "markdown", + "id": "61b42aa6", "metadata": {}, "source": [ "#### Create a Function to Build the Flowsheet\n", @@ -248,6 +265,7 @@ { "cell_type": "code", "execution_count": 6, + "id": "f74bda4b", "metadata": {}, "outputs": [], "source": [ @@ -287,6 +305,7 @@ }, { "cell_type": "markdown", + "id": "6ba1b0f5", "metadata": {}, "source": [ "#### Create a Function to Fix the Flowsheet Conditions\n", @@ -304,6 +323,7 @@ { "cell_type": "code", "execution_count": 7, + "id": "6cd31b89", "metadata": {}, "outputs": [], "source": [ @@ -387,6 +407,7 @@ }, { "cell_type": "markdown", + "id": "2e807852", "metadata": {}, "source": [ "# Step 2: Setup and run simulation\n", @@ -395,6 +416,7 @@ }, { "cell_type": "markdown", + "id": "45ad2103", "metadata": {}, "source": [ "#### Create concrete model\n", @@ -404,6 +426,7 @@ { "cell_type": "code", "execution_count": 8, + "id": "119ab860", "metadata": {}, "outputs": [], "source": [ @@ -412,6 +435,7 @@ }, { "cell_type": "markdown", + "id": "89645871", "metadata": {}, "source": [ "#### Setup solver for initialization\n", @@ -421,6 +445,7 @@ { "cell_type": "code", "execution_count": 9, + "id": "9d5ffd80", "metadata": {}, "outputs": [], "source": [ @@ -438,6 +463,7 @@ }, { "cell_type": "markdown", + "id": "59bfe6a2", "metadata": {}, "source": [ "#### Set spatial elements and design variables for adsorption and desorption simulations\n", @@ -447,6 +473,7 @@ { "cell_type": "code", "execution_count": 10, + "id": "ea0d8523", "metadata": {}, "outputs": [], "source": [ @@ -460,6 +487,7 @@ }, { "cell_type": "markdown", + "id": "59ca3c96", "metadata": {}, "source": [ "## Adsorption simulation\n", @@ -468,6 +496,7 @@ }, { "cell_type": "markdown", + "id": "265c3bb3", "metadata": {}, "source": [ "### Setup simulation horizon and create time set\n", @@ -477,6 +506,7 @@ { "cell_type": "code", "execution_count": 11, + "id": "545640af", "metadata": {}, "outputs": [], "source": [ @@ -492,6 +522,7 @@ { "cell_type": "code", "execution_count": 11, + "id": "91dd7499", "metadata": { "tags": [ "testing" @@ -505,6 +536,7 @@ }, { "cell_type": "markdown", + "id": "544b63c3", "metadata": {}, "source": [ "### Initial conditions for gas and solid phases\n", @@ -514,6 +546,7 @@ { "cell_type": "code", "execution_count": 12, + "id": "650fba0e", "metadata": {}, "outputs": [], "source": [ @@ -549,6 +582,7 @@ }, { "cell_type": "markdown", + "id": "988437df", "metadata": {}, "source": [ "### Setup the adsorption model\n", @@ -558,6 +592,7 @@ { "cell_type": "code", "execution_count": 13, + "id": "9296c7c0", "metadata": {}, "outputs": [], "source": [ @@ -579,6 +614,7 @@ }, { "cell_type": "markdown", + "id": "e37d6deb", "metadata": {}, "source": [ "### Adsorption model - initialize and solve\n", @@ -587,6 +623,7 @@ }, { "cell_type": "markdown", + "id": "03cf66de", "metadata": {}, "source": [ "#### Apply scaling transformation\n", @@ -596,6 +633,7 @@ { "cell_type": "code", "execution_count": 14, + "id": "e871cd0f", "metadata": {}, "outputs": [], "source": [ @@ -604,6 +642,7 @@ }, { "cell_type": "markdown", + "id": "8e2d9ce8", "metadata": {}, "source": [ "#### Initialize model\n", @@ -613,6 +652,7 @@ { "cell_type": "code", "execution_count": 15, + "id": "396f52ce", "metadata": {}, "outputs": [], "source": [ @@ -638,6 +678,7 @@ }, { "cell_type": "markdown", + "id": "0358562f", "metadata": {}, "source": [ "#### PETSc integrator\n", @@ -647,6 +688,7 @@ { "cell_type": "code", "execution_count": 16, + "id": "1f67cb73", "metadata": { "scrolled": true }, @@ -693,6 +735,7 @@ }, { "cell_type": "markdown", + "id": "717938a9", "metadata": {}, "source": [ "### Plot the adsorption simulation results\n", @@ -701,6 +744,7 @@ }, { "cell_type": "markdown", + "id": "edabf0bf", "metadata": {}, "source": [ "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", @@ -717,6 +761,7 @@ { "cell_type": "code", "execution_count": 17, + "id": "da090871", "metadata": {}, "outputs": [], "source": [ @@ -727,6 +772,7 @@ { "cell_type": "code", "execution_count": 18, + "id": "f239c25c", "metadata": {}, "outputs": [], "source": [ @@ -736,6 +782,7 @@ }, { "cell_type": "markdown", + "id": "4d82b006", "metadata": {}, "source": [ "## Desorption simulation\n", @@ -744,6 +791,7 @@ }, { "cell_type": "markdown", + "id": "42848d1d", "metadata": {}, "source": [ "### Setup simulation horizon and create time set\n", @@ -753,6 +801,7 @@ { "cell_type": "code", "execution_count": 19, + "id": "5c34482e", "metadata": {}, "outputs": [], "source": [ @@ -767,6 +816,7 @@ }, { "cell_type": "markdown", + "id": "eb1c4558", "metadata": {}, "source": [ "### Initial and boundary conditions for gas phase\n", @@ -776,6 +826,7 @@ { "cell_type": "code", "execution_count": 20, + "id": "59d85b8f", "metadata": {}, "outputs": [], "source": [ @@ -792,6 +843,7 @@ }, { "cell_type": "markdown", + "id": "505dd3dc", "metadata": {}, "source": [ "### Setup the desorption model\n", @@ -801,6 +853,7 @@ { "cell_type": "code", "execution_count": 21, + "id": "57b65754", "metadata": {}, "outputs": [], "source": [ @@ -811,6 +864,7 @@ { "cell_type": "code", "execution_count": 22, + "id": "2b501245", "metadata": {}, "outputs": [], "source": [ @@ -824,6 +878,7 @@ }, { "cell_type": "markdown", + "id": "7c48cc94", "metadata": {}, "source": [ "### Desorption model - initialize and solve\n", @@ -832,6 +887,7 @@ }, { "cell_type": "markdown", + "id": "735277bd", "metadata": {}, "source": [ "##### Copy values from adsorption model to desorption model\n", @@ -841,6 +897,7 @@ { "cell_type": "code", "execution_count": 23, + "id": "f924efd2", "metadata": {}, "outputs": [], "source": [ @@ -869,6 +926,7 @@ }, { "cell_type": "markdown", + "id": "877f239c", "metadata": {}, "source": [ "#### Fix initial and boundary conditions\n", @@ -878,6 +936,7 @@ { "cell_type": "code", "execution_count": 24, + "id": "f5968589", "metadata": {}, "outputs": [], "source": [ @@ -891,6 +950,7 @@ }, { "cell_type": "markdown", + "id": "ecdf94af", "metadata": {}, "source": [ "#### Calculate scaling of desorption model\n", @@ -900,6 +960,7 @@ { "cell_type": "code", "execution_count": 25, + "id": "3a486a39", "metadata": {}, "outputs": [], "source": [ @@ -908,6 +969,7 @@ }, { "cell_type": "markdown", + "id": "0c3da468", "metadata": {}, "source": [ "#### Initialize desorption model\n", @@ -917,6 +979,7 @@ { "cell_type": "code", "execution_count": 26, + "id": "4b6e53d2", "metadata": {}, "outputs": [], "source": [ @@ -940,6 +1003,7 @@ }, { "cell_type": "markdown", + "id": "f788f262", "metadata": {}, "source": [ "#### Setup PETSc integrator and simulate desorption step\n", @@ -949,6 +1013,7 @@ { "cell_type": "code", "execution_count": 27, + "id": "45eb63d6", "metadata": {}, "outputs": [], "source": [ @@ -990,6 +1055,7 @@ }, { "cell_type": "markdown", + "id": "578acb67", "metadata": {}, "source": [ "### Plot the desorption simulation results\n", @@ -1007,6 +1073,7 @@ { "cell_type": "code", "execution_count": 28, + "id": "24d1e750", "metadata": {}, "outputs": [], "source": [ @@ -1017,6 +1084,7 @@ { "cell_type": "code", "execution_count": 29, + "id": "67a1ca0d", "metadata": {}, "outputs": [], "source": [ @@ -1026,6 +1094,7 @@ }, { "cell_type": "markdown", + "id": "885a1f4c", "metadata": {}, "source": [ "## Step 3: Generate performance results" @@ -1034,6 +1103,7 @@ { "cell_type": "code", "execution_count": 30, + "id": "c97aadd6", "metadata": {}, "outputs": [], "source": [ @@ -1046,6 +1116,7 @@ { "cell_type": "code", "execution_count": 31, + "id": "79c69d29", "metadata": {}, "outputs": [], "source": [ @@ -1059,6 +1130,7 @@ }, { "cell_type": "markdown", + "id": "399c7878", "metadata": {}, "source": [ "## Simulation time results" @@ -1067,6 +1139,7 @@ { "cell_type": "code", "execution_count": 32, + "id": "2eab1bfb", "metadata": {}, "outputs": [], "source": [ @@ -1087,6 +1160,7 @@ }, { "cell_type": "markdown", + "id": "31cb38a0", "metadata": {}, "source": [ "# Summary\n", @@ -1096,6 +1170,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4a5885bf", "metadata": {}, "outputs": [], "source": [] diff --git a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_doc.ipynb b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_doc.ipynb index 8440fb43..d9db9cc5 100644 --- a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_doc.ipynb +++ b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_doc.ipynb @@ -1,1112 +1,1113 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", - "Author: Chinedu Okoli, Anca Ostace, Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "This jupyter notebook shows an example of a CO2 Adsorption Desorption cycle with the IDAES 1D FixedBed model. The IDAES 1D FixedBed model is a dynamic and axially varying reactor/adsorption model which is able to model the gas and solid interactions of the modeled species in detail. The sorbent used for this example is the NETL_32D sorbent with its details and parameters obtained from the following references: \n", - "- A. Lee, D.C. Miller, A One-Dimensional (1-D) Three-Region Model for a Bubbling Fluidized-Bed Adsorber, Ind. Eng. Chem. Res. 52 (2013) 469–484\n", - "- Lee, A.; Mebane, D.; Fauth, D. J.; Miller, D. C. A Model for the Adsorption Kinetics of CO2 on Amine-Impregnated Mesoporous Sorbents in the Presence of Water. Presented at the 28th International Pittsburgh Coal Conference, Pittsburgh, PA, 2011.\n", - "\n", - "The notebook demonstrates how to use the IDAES 1DFixedBed model for an adsorption/desorption application with distinct adsorption and desorption steps. This example leverages custom libraries and functions specific to the NETL_32D solid sorbent and associated gas phase properties and surface reactions. In this system, the silicon monoxide (SiO(s)) sorbent reduces carbon dioxide (CO2(g)) to carbamate (denoted Car(s)) while simultaneously absorbing water vapor (H2O(g)) to produce a solid solution-state hydrate (H2O(s)). The solid phase is considered a one-dimensional domain with three regions for bubble, cloud wake and emulsion properties for bubbling bed systems; in the case of a fixed bed reactor non-bulk gas behavior is neglected and assumed to be homogeneous everywhere not near the solid surface.\n", - "\n", - "The notebook also shows how to simulate the adsorption and desorption steps using the PETSc integrator. PETSc leverages nonlinear equation and differential algebraic equation solvers to solve time-trajectory problems. These solvers are applicable for systems with zero degrees of freedom, such as a fully-specified bed reactor model. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cycle details\n", - "The system contains the following equipment and stream properties:\n", - "- Bed height: 9 m\n", - "- Bed diameter: 1 m\n", - "- Adsorption time: 30 hrs\n", - "- Desorption time: 2 hrs\n", - "- Adsorption temperature: 303.15 K\n", - "- Desorption temperature: 470 K\n", - "- Flue gas inlet conditions (Temperature and mole fractions obtained from NETL baseline report. Exhibit 5-22 B31B case)\n", - " - Temperature: 315 K\n", - " - Pressure: 106.5 kPa\n", - " - Flowrate: 3.544 mol/s\n", - " - Mole fractions: CO2: 0.0408, H2O: 0.0875, N2: 0.7517, O2: 0.12" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notes\n", - "Some additional information regarding the problem:\n", - "- Isothermal conditions: heat duty requirements of each step are calculated from gas and sorbent properties, i.e., loading, density, heat of adsorption, and heat capacity\n", - "- Heating and cooling modes not modeled in detail: cycle times are assumed negligible in comparison to adsorption and desorption\n", - "- Adsorption and desorption steps are modeled in different flowsheets\n", - "- Initial condition (except temperature) of sorbent in desorption step is set to final condition of sorbent in adsorption step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Import relevant libraries and packages" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import python libraries\n", - "\n", - "- numpy (numerical python library which provides numerical computing tools)\n", - "- time (time python library which will be used to track the simulation time)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Import Pyomo packages\n", - "For the flowsheet, we will need several components from the pyomo libraries.\n", - "\n", - "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- SolverFactory (to solve the problem)\n", - "- Var (to create a Pyomo variable)\n", - "- value (to return the numerical value of Pyomo objects such as variables, constraints or expressions)\n", - "- units (to handle units in Pyomo and IDAES)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/latest/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " TransformationFactory,\n", - " SolverFactory,\n", - " Var,\n", - " value,\n", - " units as pyunits,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES core components\n", - "\n", - "To build, initialize, and solve IDAES flowsheets we will need several core components/utilities:\n", - "\n", - "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", - "- EnergyBalanceType (to specify the energy balance type)\n", - "- petsc (PETSc integrator)\n", - "- get_solver (IDAES solver utility)\n", - "- iscale (is used to apply scaling factors in variables and constraints)\n", - "- propagate_state (is used to initialize models, propagating the state variables from one unit model to another)\n", - "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", - "- idaeslog (is used to set output messages like warnings or errors)\n", - "\n", - "For further details on these components, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/latest/" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock, EnergyBalanceType\n", - "import idaes.core.solvers.petsc as petsc # PETSc utilities module\n", - "from idaes.core.util import scaling as iscale\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "import idaes.logger as idaeslog\n", - "import logging" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES unit models and NETL 32D property packages\n", - "\n", - "To build the IDAES flowsheet for the CO2 Adsorption Desorption example, we will need the following: \n", - "1) the 1D Fixed Bed unit model \n", - "2) the NETL 32D property package" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.gas_solid_contactors.unit_models.fixed_bed_1D import FixedBed1D\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_gas_phase_thermo import (\n", - " GasPhaseParameterBlock,\n", - ")\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_solid_phase_thermo import (\n", - " SolidPhaseParameterBlock,\n", - ")\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_adsorption_reactions import (\n", - " HeteroReactionParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import custom libraries and functions\n", - "To simplify and automate this example at the flowsheet level, several custom methods were defined in an external script. The methods are custom, and as such are imported separately from the set of IDAES and Pyomo methods above. These utility functions perform a variety of numerical and reporting tasks:\n", - "\n", - "- heat_computation (used to calculate the heat requirements of the CO2 Adsorption Desorption cycle)\n", - "- performance_results (used to evaluate the performance of the CO2 Adsorption Desorption cycle)\n", - "- results_summary (provides summarized results of the CO2 Adsorption Desorption cycle)\n", - "- plot_results_temporal (plots the temporal profiles of the flowsheet simulation)\n", - "- plot_results_spatial (plots the spatial profiles of the flowsheet simulation)\n", - "- fb_model_setup (function that builds the 1D FixedBed model)\n", - "- fb_fixed_conditions (function that fixes the initial and boundary conditions of the 1D FixedBed model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.co2_adsorption_desorption.simulation_utilities import (\n", - " heat_computation,\n", - " performance_results,\n", - " results_summary,\n", - " plot_results_temporal,\n", - " plot_results_spatial,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a Function to Build the Flowsheet\n", - "\n", - "The function \"fb_model_setup\" below builds instances of the 1D FixedBed model for the adsorption and desorption simulations. While the effort required to define the reactor is low in terms of lines of code, we will need to do this for both the adsorption and desorption steps. Therefore, defining this setup as a single function will make later steps easier to follow. As arguments it takes the following:\n", - "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", - "2) The number of time finite elements \n", - "3) The number of spatial finite elements \n", - "\n", - "It returns a flowsheet object which contains an instance of the 1D FixedBed model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def fb_model_setup(\n", - " fs,\n", - " ntfe, # number of time finite elements\n", - " nxfe, # number of space finite elements\n", - "):\n", - "\n", - " # Set up thermo props and reaction props\n", - " fs.gas_properties = GasPhaseParameterBlock()\n", - " fs.solid_properties = SolidPhaseParameterBlock()\n", - "\n", - " fs.hetero_reactions = HeteroReactionParameterBlock(\n", - " solid_property_package=fs.solid_properties,\n", - " gas_property_package=fs.gas_properties,\n", - " )\n", - "\n", - " fs.FB = FixedBed1D(\n", - " finite_elements=nxfe,\n", - " transformation_method=\"dae.finite_difference\",\n", - " energy_balance_type=EnergyBalanceType.none,\n", - " pressure_drop_type=\"ergun_correlation\",\n", - " gas_phase_config={\"property_package\": fs.gas_properties},\n", - " solid_phase_config={\n", - " \"property_package\": fs.solid_properties,\n", - " \"reaction_package\": fs.hetero_reactions,\n", - " },\n", - " )\n", - "\n", - " # Discretize time domain\n", - " fs.discretizer = TransformationFactory(\"dae.finite_difference\")\n", - " fs.discretizer.apply_to(fs, nfe=ntfe, wrt=fs.time, scheme=\"BACKWARD\")\n", - "\n", - " return fs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a Function to Fix the Flowsheet Conditions\n", - "\n", - "The function \"fb_fix_conditions\" fixes the initial and boundary conditions of an instance of the 1D FixedBed model. It also has checks to ensure that the degrees of freedom are zero, and that the velocity into the bed is less than the minimum fluidization velocity of the bed. Similarly to the function above, we will need to run this for the adsorption and desorption steps, and it is clearer to define the code once as a single function here. As arguments it takes the following:\n", - "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", - "2) The reactor bed diameter \n", - "3) The reactor bed height \n", - "4) A dictionary of gas phase state data for which the gas phase state variables of the model should be fixed to \n", - "5) A dictionary of solid phase state data for which the solid phase state variables of the model should be fixed to. If None, the solid phase state variables are fixed to their current values. \n", - "\n", - "No object is returned by this function." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def fb_fix_conditions(\n", - " fs, bed_diameter, bed_height, gas_phase_state_dict, solid_phase_state_dict=None\n", - "):\n", - " # Fix bed geometry variables\n", - " fs.FB.bed_diameter.fix(bed_diameter) # m\n", - " fs.FB.bed_height.fix(bed_height) # m\n", - "\n", - " # Fix boundary values for gas for all time\n", - " blk = fs.FB\n", - " for t in fs.time:\n", - " # Gas values\n", - " blk.gas_inlet.flow_mol[t].fix(gas_phase_state_dict[\"flow_mol\"])\n", - " blk.gas_inlet.temperature[t].fix(gas_phase_state_dict[\"temperature\"])\n", - " blk.gas_inlet.pressure[t].fix(gas_phase_state_dict[\"pressure\"])\n", - " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", - " blk.gas_inlet.mole_frac_comp[t, j].fix(val)\n", - "\n", - " # Specify gas phase and solid phase initial conditions for all space\n", - " t0 = fs.time.first()\n", - " for x in blk.length_domain:\n", - " blk.gas_phase.properties[t0, x].flow_mol.fix(gas_phase_state_dict[\"flow_mol\"])\n", - " blk.gas_phase.properties[t0, x].temperature.fix(\n", - " gas_phase_state_dict[\"temperature\"]\n", - " ) # K\n", - " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", - " blk.gas_phase.properties[t0, x].mole_frac_comp[j].fix(val)\n", - "\n", - " if solid_phase_state_dict is None:\n", - " # Fix to existing values if dict is empty\n", - " blk.solid_properties[t0, x].dens_mass_particle.fix()\n", - " blk.solid_properties[t0, x].temperature.fix()\n", - " blk.solid_properties[t0, x].mass_frac_comp[:].fix()\n", - " else:\n", - " blk.solid_properties[t0, x].dens_mass_particle.fix(\n", - " solid_phase_state_dict[\"dens_mass_particle\"]\n", - " )\n", - " blk.solid_properties[t0, x].temperature.fix(\n", - " solid_phase_state_dict[\"temperature\"]\n", - " )\n", - " for j, val in solid_phase_state_dict[\"mass_frac_comp\"].items():\n", - " blk.solid_properties[t0, x].mass_frac_comp[j].fix(val)\n", - "\n", - " dof = degrees_of_freedom(fs)\n", - "\n", - " print(\"degrees of freedom = \", dof)\n", - " try:\n", - " assert degrees_of_freedom(fs) == 0\n", - " except AssertionError:\n", - " print(\"Degrees of freedom is not equal to zero. This is unexpected.\")\n", - " raise\n", - "\n", - " # Assert that inlet gas velocity is less than v_mf\n", - " # Use solid temperature as the thermal mass of solid >> than that of gas\n", - " pi = 3.14 # [-]\n", - " R = 8.314 # Gas constant [J/mol/K]\n", - "\n", - " @blk.Expression(doc=\"gas inlet velocity, m/s\")\n", - " def gas_inlet_velocity(blk):\n", - " v_gas_inlet = (\n", - " blk.gas_inlet.flow_mol[0] / (pi * value(blk.bed_diameter**2) / 4)\n", - " ) * (R * blk.solid_properties[0, 0].temperature / blk.gas_inlet.pressure[0])\n", - " return v_gas_inlet\n", - "\n", - " v_mf = value( # minimum fluidization velocity [m/s]\n", - " blk.solid_properties[t0, 0]._params.velocity_mf\n", - " )\n", - " print(\"inlet gas velocity = \", value(blk.gas_inlet_velocity), \" m/s\")\n", - " print(\"min. fluid velocity = \", v_mf, \" m/s\")\n", - " try:\n", - " assert value(blk.gas_inlet_velocity) <= v_mf\n", - " except AssertionError:\n", - " print(\n", - " \"The inlet gas velocity is greater than the minimum fluidization velocity. \"\n", - " \"This is unexpected for a Fixed Bed.\"\n", - " )\n", - " raise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Setup and run simulation\n", - "Now that the system properties and all required utility functions are defined, we will build the model itself." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create concrete model\n", - "First, create the model object:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup solver for initialization\n", - "We limit the maximum number of iterations and apply appropriate scaling for the linear solver for initialization:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Solver arguments\n", - "optarg = {\n", - " \"max_iter\": 100,\n", - " \"nlp_scaling_method\": \"user-scaling\",\n", - " \"linear_solver\": \"ma27\",\n", - "}\n", - "\n", - "# Create a solver\n", - "solver = get_solver(\"ipopt\")\n", - "solver.options = optarg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set spatial elements and design variables for adsorption and desorption simulations\n", - "Let's define the size of the system:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of spatial elements\n", - "nxfe = 50\n", - "\n", - "# Design variables for static and dynamic models\n", - "bed_diameter = 9 # m\n", - "bed_height = 1 # m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adsorption simulation\n", - "First, we will run the adsorption step by defining the simulation domain, add initial conditions, and set up and solve the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup simulation horizon and create time set\n", - "We specify the time discretization to an exact set of temporal points according to the problem horizon:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Time horizon\n", - "horizon = 108000 # s\n", - "\n", - "# Create time_set list\n", - "t_element_size = horizon / 4 # s\n", - "ntfe = int(horizon / t_element_size)\n", - "time_set = list(np.linspace(0, horizon, ntfe + 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial conditions for gas and solid phases\n", - "Next, we set reasoanble initial conditions for the gas and solid phases at time = 0, with a constant adsorption temperature:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Flue gas inlet conditions to adsorption system (flue gas stream) -\n", - "# Temperature and mole fractions obtained from NETL baseline report.\n", - "# Exhibit 5-22 B31B case.\n", - "adsorption_temperature = 303.15 # K\n", - "\n", - "# Dictionary of initial and boundary conditions for gas phase\n", - "gas_phase_state_dict_ads = {\n", - " \"flow_mol\": 3.544, # mol/s\n", - " \"temperature\": adsorption_temperature, # K\n", - " \"pressure\": 1.2452e5, # Pa\n", - " \"mole_frac_comp\": { # [-]\n", - " \"CO2\": 0.0408,\n", - " \"H2O\": 0.0875,\n", - " \"N2\": 0.7517,\n", - " \"O2\": 0.12,\n", - " },\n", - "}\n", - "\n", - "# Dictionary of initial conditions for solid phase\n", - "solid_phase_state_dict_ads = {\n", - " \"dens_mass_particle\": 442, # kg/m3\n", - " \"temperature\": adsorption_temperature, # K\n", - " \"mass_frac_comp\": { # [-]\n", - " \"H2O_s\": 1e-8,\n", - " \"Car\": 1e-8,\n", - " \"SiO\": 1,\n", - " },\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the adsorption model\n", - "Then, we call our previously defined methods to build the model and fix initial conditions:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the adsorption flowsheet\n", - "m.fs_ads = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)\n", - "\n", - "# Setup an instance of the 1D FixedBed model for the adsorption simulation\n", - "m.fs_ads = fb_model_setup(m.fs_ads, ntfe, nxfe)\n", - "\n", - "# Fix initial and boundary conditions\n", - "fb_fix_conditions(\n", - " m.fs_ads,\n", - " bed_diameter,\n", - " bed_height,\n", - " gas_phase_state_dict_ads,\n", - " solid_phase_state_dict_ads,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adsorption model - initialize and solve\n", - "Finally for the adsorption model, we initialize and solve." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Apply scaling transformation\n", - "We first scale the model variables and equations to reduce ill-conditioning and improve its convergence properties:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "iscale.calculate_scaling_factors(m.fs_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize model\n", - "The model initialization is done with the block triangularization initialization method in the 1D FixedBed model as this is faster than using the traditional sequential heirarchichal initialization approach. See the 1D FixedBed model and documentation for more details on this method." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_ads = time.time()\n", - "\n", - "# Initialize model\n", - "calc_var_kwds = {\"eps\": 1e-5}\n", - "m.fs_ads.FB.block_triangularization_initialize(\n", - " gas_phase_state_args=gas_phase_state_dict_ads,\n", - " solid_phase_state_args=solid_phase_state_dict_ads,\n", - " outlvl=idaeslog.DEBUG,\n", - " solver=solver,\n", - " calc_var_kwds=calc_var_kwds,\n", - ")\n", - "\n", - "# Run end time\n", - "t_end_ads = time.time()\n", - "\n", - "# Initialization time\n", - "adsorption_initialization_time = value(t_end_ads - t_start_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### PETSc integrator\n", - "As mentioned earlier, the PETSc integrator is used for simulating the time trajectory. After starting the clock and instantiating the time variables, PETSc performs and element-wise discretization to define and solve the dynamic system of equations. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "petscsolvelog = idaeslog.getSolveLogger(\"petsc-dae\")\n", - "petscsolvelog.setLevel(logging.WARNING) # comment this line to see PETSc solver output\n", - "\n", - "# Run start time\n", - "t_start_ads = time.time()\n", - "\n", - "# Setup PETSc integrator and simulate adsorption flowsheet\n", - "m.fs_ads.time_var = Var(m.fs_ads.time)\n", - "m.fs_ads.time_var[0].fix(m.fs_ads.time.first())\n", - "\n", - "result_ads = petsc.petsc_dae_by_time_element(\n", - " m.fs_ads,\n", - " time=m.fs_ads.time,\n", - " timevar=m.fs_ads.time_var,\n", - " keepfiles=True,\n", - " symbolic_solver_labels=True,\n", - " skip_initial=False,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\", # backward euler integration\n", - " \"--ts_dt\": 200, # set initial step to 200\n", - " \"--ts_rtol\": 20,\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ksp_rtol\": 1e-10,\n", - " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ts_max_snes_failures\": 1000,\n", - " },\n", - ")\n", - "tj_ads = result_ads.trajectory # trajectory data\n", - "\n", - "# Run end time\n", - "t_end_ads = time.time()\n", - "\n", - "# Initialization time\n", - "adsorption_simulation_time = value(t_end_ads - t_start_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the adsorption simulation results\n", - "After running the adsorption simulation, the dynamic profiles are plotted." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", - "\n", - "The gas flowrate is constant at the inlet and decreases over the length of the bed as CO2 is removed from the gas. At each spatial point, the CO2 content of the gas increases over time as the bed becomes more saturated with CO2 and the mass transfer driving force decreases. Assessing the two gas flowrate plots, adsorption occurs steadily over time with a much larger capture rate in the initial spatial region; this region grows from 10% to 25% of the bed length as the bed becomes more saturated with CO2 over time.\n", - "\n", - "The bed pressure stays relatively constant over time, and exhibits a linear drop over the length of the reactor. There are no temporal or spatial gradients in gas or solid temperature.\n", - "\n", - "The saturation trend is strongly apparent in the composition trends. The CO2 content of the gas quickly drops from the initial concentration over the length of the bed, and steadily rises over time as the driving force of mass transfer decreases. Water vapor concentration in the gas follows a similar trend.\n", - "\n", - "Following the solid-state reaction, the concentration of carbmate steadily increases over time with formation skewing heavily towards the initial spatial region of the bed. The spatial contours of the temporal carbamate composition plot cross as a result of the solid reaction rate heavily favoring the initial section of the bed and suddenly dropping about at about 20% of the reactor length. The mass fraction and mol/kg plots differ slightly due to water and CO2 absorbing at different rates, but otherwise demonstrate similar trends." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot temporal result profiles\n", - "plot_results_temporal(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot spatial result profiles\n", - "plot_results_spatial(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Desorption simulation\n", - "The desorption simulation begins when the adsorption simulation ends. It uses the final state of the sorbent (besides temperature which is set at the desorption temperature) in the adsorption mode as the initial state of the sorbent in the desorption mode." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup simulation horizon and create time set\n", - "As in the adsorption simulation, we first specify the time discretization:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Space and time discretization arguments\n", - "horizon = 7200 # s\n", - "\n", - "# Setup for PETSc run\n", - "t_element_size = horizon # s\n", - "ntfe = int(horizon / t_element_size)\n", - "time_set = list(np.linspace(0, horizon, ntfe + 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial and boundary conditions for gas phase\n", - "- The initial conditions of the solid phase will be copied from the final conditions of the model in the adsorption simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Desorption operating conditions\n", - "desorption_temperature = 470 # K\n", - "\n", - "gas_phase_state_dict_des = {\n", - " \"flow_mol\": 10, # mol/s\n", - " \"temperature\": desorption_temperature, # K\n", - " \"pressure\": 1.06525e5, # Pa\n", - " \"mole_frac_comp\": {\"CO2\": 1e-8, \"H2O\": 1, \"N2\": 1e-8, \"O2\": 1e-8}, # [-]\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the desorption model\n", - "Then, we build the model and fix initial conditions:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the desorption flowsheet\n", - "m.fs_des = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup an instance of the 1D FixedBed model for the desorption simulation\n", - "m.fs_des = fb_model_setup(\n", - " m.fs_des,\n", - " ntfe,\n", - " nxfe,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Desorption model - initialize and solve\n", - "Finally for the desorption model, we initialize and solve." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Copy values from adsorption model to desorption model\n", - "Copy state values of solid phase (besides temperature) from last time point in adsorption model to all time point in desorption model. Also set temperature state variable of desorption model to desorption temperature." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "blk_des = m.fs_des.FB\n", - "tf_ads = tj_ads.time[-1] # Get final time from adsorption trajectory results\n", - "tf_ads_index = tj_ads.time.index(tf_ads) # Get index at final time\n", - "component_list = blk_des.config.solid_phase_config.property_package.component_list\n", - "\n", - "for t in m.fs_des.time:\n", - " for x in blk_des.length_domain:\n", - " blk_des.solid_properties[t, x].temperature.set_value(desorption_temperature)\n", - " blk_des.solid_properties[t, x].dens_mass_particle.set_value(\n", - " tj_ads.get_vec(m.fs_ads.FB.solid_properties[tf_ads, x].dens_mass_particle)[\n", - " tf_ads_index\n", - " ]\n", - " )\n", - " for j in component_list:\n", - " blk_des.solid_properties[t, x].mass_frac_comp[j].set_value(\n", - " value(\n", - " tj_ads.get_vec(\n", - " m.fs_ads.FB.solid_properties[tf_ads, x].mass_frac_comp[j]\n", - " )[tf_ads_index]\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Fix initial and boundary conditions\n", - "We fix the conditions from the final adsorption model state as our new initial conditions for the desorption model:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "fb_fix_conditions(\n", - " m.fs_des,\n", - " bed_diameter,\n", - " bed_height,\n", - " gas_phase_state_dict_des,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate scaling of desorption model\n", - "Then, we apply scaling to the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "iscale.calculate_scaling_factors(m.fs_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize desorption model\n", - "The desorption model initialization is done with the block triangularization initialization method, as was done with the adsorption model." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_des = time.time()\n", - "\n", - "# Initialize model\n", - "m.fs_des.FB.block_triangularization_initialize(\n", - " gas_phase_state_args=gas_phase_state_dict_des,\n", - " outlvl=idaeslog.DEBUG,\n", - " solver=solver,\n", - " calc_var_kwds=calc_var_kwds,\n", - ")\n", - "\n", - "# Run end time\n", - "t_end_des = time.time()\n", - "\n", - "# Initialization time\n", - "desorption_initialization_time = value(t_end_des - t_start_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup PETSc integrator and simulate desorption step\n", - "Now that the model is fully defined, we can call the PETSc integrator to simulate the dynamics of the problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_des = time.time()\n", - "\n", - "# Setup PETSc integrator and simulate desorption flowsheet\n", - "m.fs_des.time_var = Var(m.fs_des.time)\n", - "m.fs_des.time_var[0].fix(m.fs_des.time.first())\n", - "\n", - "result_des = petsc.petsc_dae_by_time_element(\n", - " m.fs_des,\n", - " time=m.fs_des.time,\n", - " timevar=m.fs_des.time_var,\n", - " keepfiles=True,\n", - " symbolic_solver_labels=True,\n", - " skip_initial=False,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\", # backward euler integration\n", - " \"--ts_dt\": 200, # set initial step to 200\n", - " \"--ts_rtol\": 20,\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ksp_rtol\": 1e-10,\n", - " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ts_max_snes_failures\": 1000,\n", - " },\n", - ")\n", - "tj_des = result_des.trajectory # trajectory data\n", - "\n", - "# Run end time\n", - "t_end_des = time.time()\n", - "\n", - "# Initialization time\n", - "desorption_simulation_time = value(t_end_des - t_start_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the desorption simulation results\n", - "Similar to the results of the adsorption model, the plots below are presented as temporal plots with spatial contours and spatial plots with time contours to fully capture the model trends.\n", - "\n", - "The gas flowrate spikes initially as large quantities of CO2 are recovered, and then the flowrate drops rapidly as the CO2 is carried away. An interesting note is that this occurs over the same time period everywhere across the length of the bed, peaking around 1000 seconds into the simulation. In the spatial plot, the peak is missed between the first and second contours; instead, the spatial plots captures a similar shifting inflection point around 10-25% of the way into the bed as the mass transfer driving force suddenly decreases.\n", - "\n", - "The bed pressure drops suddenly as CO2 is initially desorbed from the bed, and a steady state is reached at larger time points. As in the adsorption case, the desorption results show no temporal or spatial gradients in gas or solid temperature.\n", - "\n", - "The CO2 content of the gas sharply rises initially, and steadily decreases as CO2 is carried away in the sweep gas. A greater amount of CO2 is recovered closer to the reactor inlet. As the sweep gas is nearly pure water vapor, the water concentration in the gas sharply drops as CO2 is desorbed from the bed and recovers to near unity towards the temporal end of the simulation.\n", - "\n", - "Carbamate disappears quickly as CO2 is recovered and the hydrate is broken down, occurring evenly across the length of the reactor bed." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot temporal result profiles\n", - "plot_results_temporal(m.fs_des, tj_des)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot spatial result profiles\n", - "plot_results_spatial(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Generate performance results" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "###########################################################################\n", - "# Heat requirements\n", - "###########################################################################\n", - "heat_computation(m, tj_ads, tj_des, adsorption_temperature, desorption_temperature)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "###########################################################################\n", - "# Performance results\n", - "###########################################################################\n", - "performance_results(m, tj_ads, tj_des)\n", - "\n", - "results_summary(m, adsorption_temperature, desorption_temperature)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation time results" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Adsorption initialization time: \", adsorption_initialization_time, \" s\")\n", - "print(\"Adsorption simulation time: \", adsorption_simulation_time, \" s\")\n", - "print()\n", - "print(\"Desorption initialization time: \", desorption_initialization_time, \" s\")\n", - "print(\"Desorption simulation time: \", desorption_simulation_time, \" s\")\n", - "print()\n", - "total_simulation_time = (\n", - " adsorption_initialization_time\n", - " + adsorption_simulation_time\n", - " + desorption_initialization_time\n", - " + desorption_simulation_time\n", - ")\n", - "print(\"Total simulation time: \", total_simulation_time, \" s\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "This example demonstrates implementation of the 1D FixedBed model within IDAES. External property packages for multiple phase domains and interfaces, as well as mass transfer and reaction properties, were integrated into two simulations. The simulations are connected via their final (adsorption) and initial (desorption) states, and each utilizes the PETSc integrator to calculate temporal profiles over the spatial domains." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", + "Author: Chinedu Okoli, Anca Ostace, Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "This jupyter notebook shows an example of a CO2 Adsorption Desorption cycle with the IDAES 1D FixedBed model. The IDAES 1D FixedBed model is a dynamic and axially varying reactor/adsorption model which is able to model the gas and solid interactions of the modeled species in detail. The sorbent used for this example is the NETL_32D sorbent with its details and parameters obtained from the following references: \n", + "- A. Lee, D.C. Miller, A One-Dimensional (1-D) Three-Region Model for a Bubbling Fluidized-Bed Adsorber, Ind. Eng. Chem. Res. 52 (2013) 469–484\n", + "- Lee, A.; Mebane, D.; Fauth, D. J.; Miller, D. C. A Model for the Adsorption Kinetics of CO2 on Amine-Impregnated Mesoporous Sorbents in the Presence of Water. Presented at the 28th International Pittsburgh Coal Conference, Pittsburgh, PA, 2011.\n", + "\n", + "The notebook demonstrates how to use the IDAES 1DFixedBed model for an adsorption/desorption application with distinct adsorption and desorption steps. This example leverages custom libraries and functions specific to the NETL_32D solid sorbent and associated gas phase properties and surface reactions. In this system, the silicon monoxide (SiO(s)) sorbent reduces carbon dioxide (CO2(g)) to carbamate (denoted Car(s)) while simultaneously absorbing water vapor (H2O(g)) to produce a solid solution-state hydrate (H2O(s)). The solid phase is considered a one-dimensional domain with three regions for bubble, cloud wake and emulsion properties for bubbling bed systems; in the case of a fixed bed reactor non-bulk gas behavior is neglected and assumed to be homogeneous everywhere not near the solid surface.\n", + "\n", + "The notebook also shows how to simulate the adsorption and desorption steps using the PETSc integrator. PETSc leverages nonlinear equation and differential algebraic equation solvers to solve time-trajectory problems. These solvers are applicable for systems with zero degrees of freedom, such as a fully-specified bed reactor model. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cycle details\n", + "The system contains the following equipment and stream properties:\n", + "- Bed height: 9 m\n", + "- Bed diameter: 1 m\n", + "- Adsorption time: 30 hrs\n", + "- Desorption time: 2 hrs\n", + "- Adsorption temperature: 303.15 K\n", + "- Desorption temperature: 470 K\n", + "- Flue gas inlet conditions (Temperature and mole fractions obtained from NETL baseline report. Exhibit 5-22 B31B case)\n", + " - Temperature: 315 K\n", + " - Pressure: 106.5 kPa\n", + " - Flowrate: 3.544 mol/s\n", + " - Mole fractions: CO2: 0.0408, H2O: 0.0875, N2: 0.7517, O2: 0.12" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes\n", + "Some additional information regarding the problem:\n", + "- Isothermal conditions: heat duty requirements of each step are calculated from gas and sorbent properties, i.e., loading, density, heat of adsorption, and heat capacity\n", + "- Heating and cooling modes not modeled in detail: cycle times are assumed negligible in comparison to adsorption and desorption\n", + "- Adsorption and desorption steps are modeled in different flowsheets\n", + "- Initial condition (except temperature) of sorbent in desorption step is set to final condition of sorbent in adsorption step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Import relevant libraries and packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import python libraries\n", + "\n", + "- numpy (numerical python library which provides numerical computing tools)\n", + "- time (time python library which will be used to track the simulation time)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Import Pyomo packages\n", + "For the flowsheet, we will need several components from the pyomo libraries.\n", + "\n", + "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- SolverFactory (to solve the problem)\n", + "- Var (to create a Pyomo variable)\n", + "- value (to return the numerical value of Pyomo objects such as variables, constraints or expressions)\n", + "- units (to handle units in Pyomo and IDAES)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/latest/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " TransformationFactory,\n", + " SolverFactory,\n", + " Var,\n", + " value,\n", + " units as pyunits,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES core components\n", + "\n", + "To build, initialize, and solve IDAES flowsheets we will need several core components/utilities:\n", + "\n", + "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", + "- EnergyBalanceType (to specify the energy balance type)\n", + "- petsc (PETSc integrator)\n", + "- get_solver (IDAES solver utility)\n", + "- iscale (is used to apply scaling factors in variables and constraints)\n", + "- propagate_state (is used to initialize models, propagating the state variables from one unit model to another)\n", + "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", + "- idaeslog (is used to set output messages like warnings or errors)\n", + "\n", + "For further details on these components, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/latest/" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock, EnergyBalanceType\n", + "import idaes.core.solvers.petsc as petsc # PETSc utilities module\n", + "from idaes.core.util import scaling as iscale\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import idaes.logger as idaeslog\n", + "import logging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES unit models and NETL 32D property packages\n", + "\n", + "To build the IDAES flowsheet for the CO2 Adsorption Desorption example, we will need the following: \n", + "1) the 1D Fixed Bed unit model \n", + "2) the NETL 32D property package" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.gas_solid_contactors.unit_models.fixed_bed_1D import FixedBed1D\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_gas_phase_thermo import (\n", + " GasPhaseParameterBlock,\n", + ")\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_solid_phase_thermo import (\n", + " SolidPhaseParameterBlock,\n", + ")\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_adsorption_reactions import (\n", + " HeteroReactionParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import custom libraries and functions\n", + "To simplify and automate this example at the flowsheet level, several custom methods were defined in an external script. The methods are custom, and as such are imported separately from the set of IDAES and Pyomo methods above. These utility functions perform a variety of numerical and reporting tasks:\n", + "\n", + "- heat_computation (used to calculate the heat requirements of the CO2 Adsorption Desorption cycle)\n", + "- performance_results (used to evaluate the performance of the CO2 Adsorption Desorption cycle)\n", + "- results_summary (provides summarized results of the CO2 Adsorption Desorption cycle)\n", + "- plot_results_temporal (plots the temporal profiles of the flowsheet simulation)\n", + "- plot_results_spatial (plots the spatial profiles of the flowsheet simulation)\n", + "- fb_model_setup (function that builds the 1D FixedBed model)\n", + "- fb_fixed_conditions (function that fixes the initial and boundary conditions of the 1D FixedBed model)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.co2_adsorption_desorption.simulation_utilities import (\n", + " heat_computation,\n", + " performance_results,\n", + " results_summary,\n", + " plot_results_temporal,\n", + " plot_results_spatial,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Function to Build the Flowsheet\n", + "\n", + "The function \"fb_model_setup\" below builds instances of the 1D FixedBed model for the adsorption and desorption simulations. While the effort required to define the reactor is low in terms of lines of code, we will need to do this for both the adsorption and desorption steps. Therefore, defining this setup as a single function will make later steps easier to follow. As arguments it takes the following:\n", + "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", + "2) The number of time finite elements \n", + "3) The number of spatial finite elements \n", + "\n", + "It returns a flowsheet object which contains an instance of the 1D FixedBed model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def fb_model_setup(\n", + " fs,\n", + " ntfe, # number of time finite elements\n", + " nxfe, # number of space finite elements\n", + "):\n", + "\n", + " # Set up thermo props and reaction props\n", + " fs.gas_properties = GasPhaseParameterBlock()\n", + " fs.solid_properties = SolidPhaseParameterBlock()\n", + "\n", + " fs.hetero_reactions = HeteroReactionParameterBlock(\n", + " solid_property_package=fs.solid_properties,\n", + " gas_property_package=fs.gas_properties,\n", + " )\n", + "\n", + " fs.FB = FixedBed1D(\n", + " finite_elements=nxfe,\n", + " transformation_method=\"dae.finite_difference\",\n", + " energy_balance_type=EnergyBalanceType.none,\n", + " pressure_drop_type=\"ergun_correlation\",\n", + " gas_phase_config={\"property_package\": fs.gas_properties},\n", + " solid_phase_config={\n", + " \"property_package\": fs.solid_properties,\n", + " \"reaction_package\": fs.hetero_reactions,\n", + " },\n", + " )\n", + "\n", + " # Discretize time domain\n", + " fs.discretizer = TransformationFactory(\"dae.finite_difference\")\n", + " fs.discretizer.apply_to(fs, nfe=ntfe, wrt=fs.time, scheme=\"BACKWARD\")\n", + "\n", + " return fs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Function to Fix the Flowsheet Conditions\n", + "\n", + "The function \"fb_fix_conditions\" fixes the initial and boundary conditions of an instance of the 1D FixedBed model. It also has checks to ensure that the degrees of freedom are zero, and that the velocity into the bed is less than the minimum fluidization velocity of the bed. Similarly to the function above, we will need to run this for the adsorption and desorption steps, and it is clearer to define the code once as a single function here. As arguments it takes the following:\n", + "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", + "2) The reactor bed diameter \n", + "3) The reactor bed height \n", + "4) A dictionary of gas phase state data for which the gas phase state variables of the model should be fixed to \n", + "5) A dictionary of solid phase state data for which the solid phase state variables of the model should be fixed to. If None, the solid phase state variables are fixed to their current values. \n", + "\n", + "No object is returned by this function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def fb_fix_conditions(\n", + " fs, bed_diameter, bed_height, gas_phase_state_dict, solid_phase_state_dict=None\n", + "):\n", + " # Fix bed geometry variables\n", + " fs.FB.bed_diameter.fix(bed_diameter) # m\n", + " fs.FB.bed_height.fix(bed_height) # m\n", + "\n", + " # Fix boundary values for gas for all time\n", + " blk = fs.FB\n", + " for t in fs.time:\n", + " # Gas values\n", + " blk.gas_inlet.flow_mol[t].fix(gas_phase_state_dict[\"flow_mol\"])\n", + " blk.gas_inlet.temperature[t].fix(gas_phase_state_dict[\"temperature\"])\n", + " blk.gas_inlet.pressure[t].fix(gas_phase_state_dict[\"pressure\"])\n", + " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", + " blk.gas_inlet.mole_frac_comp[t, j].fix(val)\n", + "\n", + " # Specify gas phase and solid phase initial conditions for all space\n", + " t0 = fs.time.first()\n", + " for x in blk.length_domain:\n", + " blk.gas_phase.properties[t0, x].flow_mol.fix(gas_phase_state_dict[\"flow_mol\"])\n", + " blk.gas_phase.properties[t0, x].temperature.fix(\n", + " gas_phase_state_dict[\"temperature\"]\n", + " ) # K\n", + " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", + " blk.gas_phase.properties[t0, x].mole_frac_comp[j].fix(val)\n", + "\n", + " if solid_phase_state_dict is None:\n", + " # Fix to existing values if dict is empty\n", + " blk.solid_properties[t0, x].dens_mass_particle.fix()\n", + " blk.solid_properties[t0, x].temperature.fix()\n", + " blk.solid_properties[t0, x].mass_frac_comp[:].fix()\n", + " else:\n", + " blk.solid_properties[t0, x].dens_mass_particle.fix(\n", + " solid_phase_state_dict[\"dens_mass_particle\"]\n", + " )\n", + " blk.solid_properties[t0, x].temperature.fix(\n", + " solid_phase_state_dict[\"temperature\"]\n", + " )\n", + " for j, val in solid_phase_state_dict[\"mass_frac_comp\"].items():\n", + " blk.solid_properties[t0, x].mass_frac_comp[j].fix(val)\n", + "\n", + " dof = degrees_of_freedom(fs)\n", + "\n", + " print(\"degrees of freedom = \", dof)\n", + " try:\n", + " assert degrees_of_freedom(fs) == 0\n", + " except AssertionError:\n", + " print(\"Degrees of freedom is not equal to zero. This is unexpected.\")\n", + " raise\n", + "\n", + " # Assert that inlet gas velocity is less than v_mf\n", + " # Use solid temperature as the thermal mass of solid >> than that of gas\n", + " pi = 3.14 # [-]\n", + " R = 8.314 # Gas constant [J/mol/K]\n", + "\n", + " @blk.Expression(doc=\"gas inlet velocity, m/s\")\n", + " def gas_inlet_velocity(blk):\n", + " v_gas_inlet = (\n", + " blk.gas_inlet.flow_mol[0] / (pi * value(blk.bed_diameter**2) / 4)\n", + " ) * (R * blk.solid_properties[0, 0].temperature / blk.gas_inlet.pressure[0])\n", + " return v_gas_inlet\n", + "\n", + " v_mf = value( # minimum fluidization velocity [m/s]\n", + " blk.solid_properties[t0, 0]._params.velocity_mf\n", + " )\n", + " print(\"inlet gas velocity = \", value(blk.gas_inlet_velocity), \" m/s\")\n", + " print(\"min. fluid velocity = \", v_mf, \" m/s\")\n", + " try:\n", + " assert value(blk.gas_inlet_velocity) <= v_mf\n", + " except AssertionError:\n", + " print(\n", + " \"The inlet gas velocity is greater than the minimum fluidization velocity. \"\n", + " \"This is unexpected for a Fixed Bed.\"\n", + " )\n", + " raise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2: Setup and run simulation\n", + "Now that the system properties and all required utility functions are defined, we will build the model itself." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create concrete model\n", + "First, create the model object:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup solver for initialization\n", + "We limit the maximum number of iterations and apply appropriate scaling for the linear solver for initialization:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Solver arguments\n", + "optarg = {\n", + " \"max_iter\": 100,\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + " \"linear_solver\": \"ma27\",\n", + "}\n", + "\n", + "# Create a solver\n", + "solver = get_solver(\"ipopt\")\n", + "solver.options = optarg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set spatial elements and design variables for adsorption and desorption simulations\n", + "Let's define the size of the system:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Number of spatial elements\n", + "nxfe = 50\n", + "\n", + "# Design variables for static and dynamic models\n", + "bed_diameter = 9 # m\n", + "bed_height = 1 # m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adsorption simulation\n", + "First, we will run the adsorption step by defining the simulation domain, add initial conditions, and set up and solve the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup simulation horizon and create time set\n", + "We specify the time discretization to an exact set of temporal points according to the problem horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Time horizon\n", + "horizon = 108000 # s\n", + "\n", + "# Create time_set list\n", + "t_element_size = horizon / 4 # s\n", + "ntfe = int(horizon / t_element_size)\n", + "time_set = list(np.linspace(0, horizon, ntfe + 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial conditions for gas and solid phases\n", + "Next, we set reasoanble initial conditions for the gas and solid phases at time = 0, with a constant adsorption temperature:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Flue gas inlet conditions to adsorption system (flue gas stream) -\n", + "# Temperature and mole fractions obtained from NETL baseline report.\n", + "# Exhibit 5-22 B31B case.\n", + "adsorption_temperature = 303.15 # K\n", + "\n", + "# Dictionary of initial and boundary conditions for gas phase\n", + "gas_phase_state_dict_ads = {\n", + " \"flow_mol\": 3.544, # mol/s\n", + " \"temperature\": adsorption_temperature, # K\n", + " \"pressure\": 1.2452e5, # Pa\n", + " \"mole_frac_comp\": { # [-]\n", + " \"CO2\": 0.0408,\n", + " \"H2O\": 0.0875,\n", + " \"N2\": 0.7517,\n", + " \"O2\": 0.12,\n", + " },\n", + "}\n", + "\n", + "# Dictionary of initial conditions for solid phase\n", + "solid_phase_state_dict_ads = {\n", + " \"dens_mass_particle\": 442, # kg/m3\n", + " \"temperature\": adsorption_temperature, # K\n", + " \"mass_frac_comp\": { # [-]\n", + " \"H2O_s\": 1e-8,\n", + " \"Car\": 1e-8,\n", + " \"SiO\": 1,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the adsorption model\n", + "Then, we call our previously defined methods to build the model and fix initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the adsorption flowsheet\n", + "m.fs_ads = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)\n", + "\n", + "# Setup an instance of the 1D FixedBed model for the adsorption simulation\n", + "m.fs_ads = fb_model_setup(m.fs_ads, ntfe, nxfe)\n", + "\n", + "# Fix initial and boundary conditions\n", + "fb_fix_conditions(\n", + " m.fs_ads,\n", + " bed_diameter,\n", + " bed_height,\n", + " gas_phase_state_dict_ads,\n", + " solid_phase_state_dict_ads,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adsorption model - initialize and solve\n", + "Finally for the adsorption model, we initialize and solve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Apply scaling transformation\n", + "We first scale the model variables and equations to reduce ill-conditioning and improve its convergence properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "iscale.calculate_scaling_factors(m.fs_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize model\n", + "The model initialization is done with the block triangularization initialization method in the 1D FixedBed model as this is faster than using the traditional sequential heirarchichal initialization approach. See the 1D FixedBed model and documentation for more details on this method." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_ads = time.time()\n", + "\n", + "# Initialize model\n", + "calc_var_kwds = {\"eps\": 1e-5}\n", + "m.fs_ads.FB.block_triangularization_initialize(\n", + " gas_phase_state_args=gas_phase_state_dict_ads,\n", + " solid_phase_state_args=solid_phase_state_dict_ads,\n", + " outlvl=idaeslog.DEBUG,\n", + " solver=solver,\n", + " calc_var_kwds=calc_var_kwds,\n", + ")\n", + "\n", + "# Run end time\n", + "t_end_ads = time.time()\n", + "\n", + "# Initialization time\n", + "adsorption_initialization_time = value(t_end_ads - t_start_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PETSc integrator\n", + "As mentioned earlier, the PETSc integrator is used for simulating the time trajectory. After starting the clock and instantiating the time variables, PETSc performs and element-wise discretization to define and solve the dynamic system of equations. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "petscsolvelog = idaeslog.getSolveLogger(\"petsc-dae\")\n", + "petscsolvelog.setLevel(logging.WARNING) # comment this line to see PETSc solver output\n", + "\n", + "# Run start time\n", + "t_start_ads = time.time()\n", + "\n", + "# Setup PETSc integrator and simulate adsorption flowsheet\n", + "m.fs_ads.time_var = Var(m.fs_ads.time)\n", + "m.fs_ads.time_var[0].fix(m.fs_ads.time.first())\n", + "\n", + "result_ads = petsc.petsc_dae_by_time_element(\n", + " m.fs_ads,\n", + " time=m.fs_ads.time,\n", + " timevar=m.fs_ads.time_var,\n", + " keepfiles=True,\n", + " symbolic_solver_labels=True,\n", + " skip_initial=False,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\", # backward euler integration\n", + " \"--ts_dt\": 200, # set initial step to 200\n", + " \"--ts_rtol\": 20,\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ksp_rtol\": 1e-10,\n", + " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ts_max_snes_failures\": 1000,\n", + " },\n", + ")\n", + "tj_ads = result_ads.trajectory # trajectory data\n", + "\n", + "# Run end time\n", + "t_end_ads = time.time()\n", + "\n", + "# Initialization time\n", + "adsorption_simulation_time = value(t_end_ads - t_start_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the adsorption simulation results\n", + "After running the adsorption simulation, the dynamic profiles are plotted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", + "\n", + "The gas flowrate is constant at the inlet and decreases over the length of the bed as CO2 is removed from the gas. At each spatial point, the CO2 content of the gas increases over time as the bed becomes more saturated with CO2 and the mass transfer driving force decreases. Assessing the two gas flowrate plots, adsorption occurs steadily over time with a much larger capture rate in the initial spatial region; this region grows from 10% to 25% of the bed length as the bed becomes more saturated with CO2 over time.\n", + "\n", + "The bed pressure stays relatively constant over time, and exhibits a linear drop over the length of the reactor. There are no temporal or spatial gradients in gas or solid temperature.\n", + "\n", + "The saturation trend is strongly apparent in the composition trends. The CO2 content of the gas quickly drops from the initial concentration over the length of the bed, and steadily rises over time as the driving force of mass transfer decreases. Water vapor concentration in the gas follows a similar trend.\n", + "\n", + "Following the solid-state reaction, the concentration of carbmate steadily increases over time with formation skewing heavily towards the initial spatial region of the bed. The spatial contours of the temporal carbamate composition plot cross as a result of the solid reaction rate heavily favoring the initial section of the bed and suddenly dropping about at about 20% of the reactor length. The mass fraction and mol/kg plots differ slightly due to water and CO2 absorbing at different rates, but otherwise demonstrate similar trends." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot temporal result profiles\n", + "plot_results_temporal(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot spatial result profiles\n", + "plot_results_spatial(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Desorption simulation\n", + "The desorption simulation begins when the adsorption simulation ends. It uses the final state of the sorbent (besides temperature which is set at the desorption temperature) in the adsorption mode as the initial state of the sorbent in the desorption mode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup simulation horizon and create time set\n", + "As in the adsorption simulation, we first specify the time discretization:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Space and time discretization arguments\n", + "horizon = 7200 # s\n", + "\n", + "# Setup for PETSc run\n", + "t_element_size = horizon # s\n", + "ntfe = int(horizon / t_element_size)\n", + "time_set = list(np.linspace(0, horizon, ntfe + 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial and boundary conditions for gas phase\n", + "- The initial conditions of the solid phase will be copied from the final conditions of the model in the adsorption simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Desorption operating conditions\n", + "desorption_temperature = 470 # K\n", + "\n", + "gas_phase_state_dict_des = {\n", + " \"flow_mol\": 10, # mol/s\n", + " \"temperature\": desorption_temperature, # K\n", + " \"pressure\": 1.06525e5, # Pa\n", + " \"mole_frac_comp\": {\"CO2\": 1e-8, \"H2O\": 1, \"N2\": 1e-8, \"O2\": 1e-8}, # [-]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the desorption model\n", + "Then, we build the model and fix initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the desorption flowsheet\n", + "m.fs_des = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup an instance of the 1D FixedBed model for the desorption simulation\n", + "m.fs_des = fb_model_setup(\n", + " m.fs_des,\n", + " ntfe,\n", + " nxfe,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Desorption model - initialize and solve\n", + "Finally for the desorption model, we initialize and solve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Copy values from adsorption model to desorption model\n", + "Copy state values of solid phase (besides temperature) from last time point in adsorption model to all time point in desorption model. Also set temperature state variable of desorption model to desorption temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "blk_des = m.fs_des.FB\n", + "tf_ads = tj_ads.time[-1] # Get final time from adsorption trajectory results\n", + "tf_ads_index = tj_ads.time.index(tf_ads) # Get index at final time\n", + "component_list = blk_des.config.solid_phase_config.property_package.component_list\n", + "\n", + "for t in m.fs_des.time:\n", + " for x in blk_des.length_domain:\n", + " blk_des.solid_properties[t, x].temperature.set_value(desorption_temperature)\n", + " blk_des.solid_properties[t, x].dens_mass_particle.set_value(\n", + " tj_ads.get_vec(m.fs_ads.FB.solid_properties[tf_ads, x].dens_mass_particle)[\n", + " tf_ads_index\n", + " ]\n", + " )\n", + " for j in component_list:\n", + " blk_des.solid_properties[t, x].mass_frac_comp[j].set_value(\n", + " value(\n", + " tj_ads.get_vec(\n", + " m.fs_ads.FB.solid_properties[tf_ads, x].mass_frac_comp[j]\n", + " )[tf_ads_index]\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fix initial and boundary conditions\n", + "We fix the conditions from the final adsorption model state as our new initial conditions for the desorption model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "fb_fix_conditions(\n", + " m.fs_des,\n", + " bed_diameter,\n", + " bed_height,\n", + " gas_phase_state_dict_des,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate scaling of desorption model\n", + "Then, we apply scaling to the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "iscale.calculate_scaling_factors(m.fs_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize desorption model\n", + "The desorption model initialization is done with the block triangularization initialization method, as was done with the adsorption model." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_des = time.time()\n", + "\n", + "# Initialize model\n", + "m.fs_des.FB.block_triangularization_initialize(\n", + " gas_phase_state_args=gas_phase_state_dict_des,\n", + " outlvl=idaeslog.DEBUG,\n", + " solver=solver,\n", + " calc_var_kwds=calc_var_kwds,\n", + ")\n", + "\n", + "# Run end time\n", + "t_end_des = time.time()\n", + "\n", + "# Initialization time\n", + "desorption_initialization_time = value(t_end_des - t_start_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup PETSc integrator and simulate desorption step\n", + "Now that the model is fully defined, we can call the PETSc integrator to simulate the dynamics of the problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_des = time.time()\n", + "\n", + "# Setup PETSc integrator and simulate desorption flowsheet\n", + "m.fs_des.time_var = Var(m.fs_des.time)\n", + "m.fs_des.time_var[0].fix(m.fs_des.time.first())\n", + "\n", + "result_des = petsc.petsc_dae_by_time_element(\n", + " m.fs_des,\n", + " time=m.fs_des.time,\n", + " timevar=m.fs_des.time_var,\n", + " keepfiles=True,\n", + " symbolic_solver_labels=True,\n", + " skip_initial=False,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\", # backward euler integration\n", + " \"--ts_dt\": 200, # set initial step to 200\n", + " \"--ts_rtol\": 20,\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ksp_rtol\": 1e-10,\n", + " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ts_max_snes_failures\": 1000,\n", + " },\n", + ")\n", + "tj_des = result_des.trajectory # trajectory data\n", + "\n", + "# Run end time\n", + "t_end_des = time.time()\n", + "\n", + "# Initialization time\n", + "desorption_simulation_time = value(t_end_des - t_start_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the desorption simulation results\n", + "Similar to the results of the adsorption model, the plots below are presented as temporal plots with spatial contours and spatial plots with time contours to fully capture the model trends.\n", + "\n", + "The gas flowrate spikes initially as large quantities of CO2 are recovered, and then the flowrate drops rapidly as the CO2 is carried away. An interesting note is that this occurs over the same time period everywhere across the length of the bed, peaking around 1000 seconds into the simulation. In the spatial plot, the peak is missed between the first and second contours; instead, the spatial plots captures a similar shifting inflection point around 10-25% of the way into the bed as the mass transfer driving force suddenly decreases.\n", + "\n", + "The bed pressure drops suddenly as CO2 is initially desorbed from the bed, and a steady state is reached at larger time points. As in the adsorption case, the desorption results show no temporal or spatial gradients in gas or solid temperature.\n", + "\n", + "The CO2 content of the gas sharply rises initially, and steadily decreases as CO2 is carried away in the sweep gas. A greater amount of CO2 is recovered closer to the reactor inlet. As the sweep gas is nearly pure water vapor, the water concentration in the gas sharply drops as CO2 is desorbed from the bed and recovers to near unity towards the temporal end of the simulation.\n", + "\n", + "Carbamate disappears quickly as CO2 is recovered and the hydrate is broken down, occurring evenly across the length of the reactor bed." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot temporal result profiles\n", + "plot_results_temporal(m.fs_des, tj_des)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot spatial result profiles\n", + "plot_results_spatial(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Generate performance results" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# Heat requirements\n", + "###########################################################################\n", + "heat_computation(m, tj_ads, tj_des, adsorption_temperature, desorption_temperature)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# Performance results\n", + "###########################################################################\n", + "performance_results(m, tj_ads, tj_des)\n", + "\n", + "results_summary(m, adsorption_temperature, desorption_temperature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulation time results" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Adsorption initialization time: \", adsorption_initialization_time, \" s\")\n", + "print(\"Adsorption simulation time: \", adsorption_simulation_time, \" s\")\n", + "print()\n", + "print(\"Desorption initialization time: \", desorption_initialization_time, \" s\")\n", + "print(\"Desorption simulation time: \", desorption_simulation_time, \" s\")\n", + "print()\n", + "total_simulation_time = (\n", + " adsorption_initialization_time\n", + " + adsorption_simulation_time\n", + " + desorption_initialization_time\n", + " + desorption_simulation_time\n", + ")\n", + "print(\"Total simulation time: \", total_simulation_time, \" s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "This example demonstrates implementation of the 1D FixedBed model within IDAES. External property packages for multiple phase domains and interfaces, as well as mass transfer and reaction properties, were integrated into two simulations. The simulations are connected via their final (adsorption) and initial (desorption) states, and each utilizes the PETSc integrator to calculate temporal profiles over the spatial domains." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_test.ipynb b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_test.ipynb index 0b57769a..bdfe15f8 100644 --- a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_test.ipynb +++ b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_test.ipynb @@ -1,1126 +1,1127 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", - "Author: Chinedu Okoli, Anca Ostace, Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "This jupyter notebook shows an example of a CO2 Adsorption Desorption cycle with the IDAES 1D FixedBed model. The IDAES 1D FixedBed model is a dynamic and axially varying reactor/adsorption model which is able to model the gas and solid interactions of the modeled species in detail. The sorbent used for this example is the NETL_32D sorbent with its details and parameters obtained from the following references: \n", - "- A. Lee, D.C. Miller, A One-Dimensional (1-D) Three-Region Model for a Bubbling Fluidized-Bed Adsorber, Ind. Eng. Chem. Res. 52 (2013) 469–484\n", - "- Lee, A.; Mebane, D.; Fauth, D. J.; Miller, D. C. A Model for the Adsorption Kinetics of CO2 on Amine-Impregnated Mesoporous Sorbents in the Presence of Water. Presented at the 28th International Pittsburgh Coal Conference, Pittsburgh, PA, 2011.\n", - "\n", - "The notebook demonstrates how to use the IDAES 1DFixedBed model for an adsorption/desorption application with distinct adsorption and desorption steps. This example leverages custom libraries and functions specific to the NETL_32D solid sorbent and associated gas phase properties and surface reactions. In this system, the silicon monoxide (SiO(s)) sorbent reduces carbon dioxide (CO2(g)) to carbamate (denoted Car(s)) while simultaneously absorbing water vapor (H2O(g)) to produce a solid solution-state hydrate (H2O(s)). The solid phase is considered a one-dimensional domain with three regions for bubble, cloud wake and emulsion properties for bubbling bed systems; in the case of a fixed bed reactor non-bulk gas behavior is neglected and assumed to be homogeneous everywhere not near the solid surface.\n", - "\n", - "The notebook also shows how to simulate the adsorption and desorption steps using the PETSc integrator. PETSc leverages nonlinear equation and differential algebraic equation solvers to solve time-trajectory problems. These solvers are applicable for systems with zero degrees of freedom, such as a fully-specified bed reactor model. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cycle details\n", - "The system contains the following equipment and stream properties:\n", - "- Bed height: 9 m\n", - "- Bed diameter: 1 m\n", - "- Adsorption time: 30 hrs\n", - "- Desorption time: 2 hrs\n", - "- Adsorption temperature: 303.15 K\n", - "- Desorption temperature: 470 K\n", - "- Flue gas inlet conditions (Temperature and mole fractions obtained from NETL baseline report. Exhibit 5-22 B31B case)\n", - " - Temperature: 315 K\n", - " - Pressure: 106.5 kPa\n", - " - Flowrate: 3.544 mol/s\n", - " - Mole fractions: CO2: 0.0408, H2O: 0.0875, N2: 0.7517, O2: 0.12" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notes\n", - "Some additional information regarding the problem:\n", - "- Isothermal conditions: heat duty requirements of each step are calculated from gas and sorbent properties, i.e., loading, density, heat of adsorption, and heat capacity\n", - "- Heating and cooling modes not modeled in detail: cycle times are assumed negligible in comparison to adsorption and desorption\n", - "- Adsorption and desorption steps are modeled in different flowsheets\n", - "- Initial condition (except temperature) of sorbent in desorption step is set to final condition of sorbent in adsorption step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Import relevant libraries and packages" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import python libraries\n", - "\n", - "- numpy (numerical python library which provides numerical computing tools)\n", - "- time (time python library which will be used to track the simulation time)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Import Pyomo packages\n", - "For the flowsheet, we will need several components from the pyomo libraries.\n", - "\n", - "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- SolverFactory (to solve the problem)\n", - "- Var (to create a Pyomo variable)\n", - "- value (to return the numerical value of Pyomo objects such as variables, constraints or expressions)\n", - "- units (to handle units in Pyomo and IDAES)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/latest/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " TransformationFactory,\n", - " SolverFactory,\n", - " Var,\n", - " value,\n", - " units as pyunits,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES core components\n", - "\n", - "To build, initialize, and solve IDAES flowsheets we will need several core components/utilities:\n", - "\n", - "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", - "- EnergyBalanceType (to specify the energy balance type)\n", - "- petsc (PETSc integrator)\n", - "- get_solver (IDAES solver utility)\n", - "- iscale (is used to apply scaling factors in variables and constraints)\n", - "- propagate_state (is used to initialize models, propagating the state variables from one unit model to another)\n", - "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", - "- idaeslog (is used to set output messages like warnings or errors)\n", - "\n", - "For further details on these components, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/latest/" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock, EnergyBalanceType\n", - "import idaes.core.solvers.petsc as petsc # PETSc utilities module\n", - "from idaes.core.util import scaling as iscale\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "import idaes.logger as idaeslog\n", - "import logging" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES unit models and NETL 32D property packages\n", - "\n", - "To build the IDAES flowsheet for the CO2 Adsorption Desorption example, we will need the following: \n", - "1) the 1D Fixed Bed unit model \n", - "2) the NETL 32D property package" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.gas_solid_contactors.unit_models.fixed_bed_1D import FixedBed1D\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_gas_phase_thermo import (\n", - " GasPhaseParameterBlock,\n", - ")\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_solid_phase_thermo import (\n", - " SolidPhaseParameterBlock,\n", - ")\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_adsorption_reactions import (\n", - " HeteroReactionParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import custom libraries and functions\n", - "To simplify and automate this example at the flowsheet level, several custom methods were defined in an external script. The methods are custom, and as such are imported separately from the set of IDAES and Pyomo methods above. These utility functions perform a variety of numerical and reporting tasks:\n", - "\n", - "- heat_computation (used to calculate the heat requirements of the CO2 Adsorption Desorption cycle)\n", - "- performance_results (used to evaluate the performance of the CO2 Adsorption Desorption cycle)\n", - "- results_summary (provides summarized results of the CO2 Adsorption Desorption cycle)\n", - "- plot_results_temporal (plots the temporal profiles of the flowsheet simulation)\n", - "- plot_results_spatial (plots the spatial profiles of the flowsheet simulation)\n", - "- fb_model_setup (function that builds the 1D FixedBed model)\n", - "- fb_fixed_conditions (function that fixes the initial and boundary conditions of the 1D FixedBed model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.co2_adsorption_desorption.simulation_utilities import (\n", - " heat_computation,\n", - " performance_results,\n", - " results_summary,\n", - " plot_results_temporal,\n", - " plot_results_spatial,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a Function to Build the Flowsheet\n", - "\n", - "The function \"fb_model_setup\" below builds instances of the 1D FixedBed model for the adsorption and desorption simulations. While the effort required to define the reactor is low in terms of lines of code, we will need to do this for both the adsorption and desorption steps. Therefore, defining this setup as a single function will make later steps easier to follow. As arguments it takes the following:\n", - "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", - "2) The number of time finite elements \n", - "3) The number of spatial finite elements \n", - "\n", - "It returns a flowsheet object which contains an instance of the 1D FixedBed model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def fb_model_setup(\n", - " fs,\n", - " ntfe, # number of time finite elements\n", - " nxfe, # number of space finite elements\n", - "):\n", - "\n", - " # Set up thermo props and reaction props\n", - " fs.gas_properties = GasPhaseParameterBlock()\n", - " fs.solid_properties = SolidPhaseParameterBlock()\n", - "\n", - " fs.hetero_reactions = HeteroReactionParameterBlock(\n", - " solid_property_package=fs.solid_properties,\n", - " gas_property_package=fs.gas_properties,\n", - " )\n", - "\n", - " fs.FB = FixedBed1D(\n", - " finite_elements=nxfe,\n", - " transformation_method=\"dae.finite_difference\",\n", - " energy_balance_type=EnergyBalanceType.none,\n", - " pressure_drop_type=\"ergun_correlation\",\n", - " gas_phase_config={\"property_package\": fs.gas_properties},\n", - " solid_phase_config={\n", - " \"property_package\": fs.solid_properties,\n", - " \"reaction_package\": fs.hetero_reactions,\n", - " },\n", - " )\n", - "\n", - " # Discretize time domain\n", - " fs.discretizer = TransformationFactory(\"dae.finite_difference\")\n", - " fs.discretizer.apply_to(fs, nfe=ntfe, wrt=fs.time, scheme=\"BACKWARD\")\n", - "\n", - " return fs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a Function to Fix the Flowsheet Conditions\n", - "\n", - "The function \"fb_fix_conditions\" fixes the initial and boundary conditions of an instance of the 1D FixedBed model. It also has checks to ensure that the degrees of freedom are zero, and that the velocity into the bed is less than the minimum fluidization velocity of the bed. Similarly to the function above, we will need to run this for the adsorption and desorption steps, and it is clearer to define the code once as a single function here. As arguments it takes the following:\n", - "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", - "2) The reactor bed diameter \n", - "3) The reactor bed height \n", - "4) A dictionary of gas phase state data for which the gas phase state variables of the model should be fixed to \n", - "5) A dictionary of solid phase state data for which the solid phase state variables of the model should be fixed to. If None, the solid phase state variables are fixed to their current values. \n", - "\n", - "No object is returned by this function." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def fb_fix_conditions(\n", - " fs, bed_diameter, bed_height, gas_phase_state_dict, solid_phase_state_dict=None\n", - "):\n", - " # Fix bed geometry variables\n", - " fs.FB.bed_diameter.fix(bed_diameter) # m\n", - " fs.FB.bed_height.fix(bed_height) # m\n", - "\n", - " # Fix boundary values for gas for all time\n", - " blk = fs.FB\n", - " for t in fs.time:\n", - " # Gas values\n", - " blk.gas_inlet.flow_mol[t].fix(gas_phase_state_dict[\"flow_mol\"])\n", - " blk.gas_inlet.temperature[t].fix(gas_phase_state_dict[\"temperature\"])\n", - " blk.gas_inlet.pressure[t].fix(gas_phase_state_dict[\"pressure\"])\n", - " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", - " blk.gas_inlet.mole_frac_comp[t, j].fix(val)\n", - "\n", - " # Specify gas phase and solid phase initial conditions for all space\n", - " t0 = fs.time.first()\n", - " for x in blk.length_domain:\n", - " blk.gas_phase.properties[t0, x].flow_mol.fix(gas_phase_state_dict[\"flow_mol\"])\n", - " blk.gas_phase.properties[t0, x].temperature.fix(\n", - " gas_phase_state_dict[\"temperature\"]\n", - " ) # K\n", - " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", - " blk.gas_phase.properties[t0, x].mole_frac_comp[j].fix(val)\n", - "\n", - " if solid_phase_state_dict is None:\n", - " # Fix to existing values if dict is empty\n", - " blk.solid_properties[t0, x].dens_mass_particle.fix()\n", - " blk.solid_properties[t0, x].temperature.fix()\n", - " blk.solid_properties[t0, x].mass_frac_comp[:].fix()\n", - " else:\n", - " blk.solid_properties[t0, x].dens_mass_particle.fix(\n", - " solid_phase_state_dict[\"dens_mass_particle\"]\n", - " )\n", - " blk.solid_properties[t0, x].temperature.fix(\n", - " solid_phase_state_dict[\"temperature\"]\n", - " )\n", - " for j, val in solid_phase_state_dict[\"mass_frac_comp\"].items():\n", - " blk.solid_properties[t0, x].mass_frac_comp[j].fix(val)\n", - "\n", - " dof = degrees_of_freedom(fs)\n", - "\n", - " print(\"degrees of freedom = \", dof)\n", - " try:\n", - " assert degrees_of_freedom(fs) == 0\n", - " except AssertionError:\n", - " print(\"Degrees of freedom is not equal to zero. This is unexpected.\")\n", - " raise\n", - "\n", - " # Assert that inlet gas velocity is less than v_mf\n", - " # Use solid temperature as the thermal mass of solid >> than that of gas\n", - " pi = 3.14 # [-]\n", - " R = 8.314 # Gas constant [J/mol/K]\n", - "\n", - " @blk.Expression(doc=\"gas inlet velocity, m/s\")\n", - " def gas_inlet_velocity(blk):\n", - " v_gas_inlet = (\n", - " blk.gas_inlet.flow_mol[0] / (pi * value(blk.bed_diameter**2) / 4)\n", - " ) * (R * blk.solid_properties[0, 0].temperature / blk.gas_inlet.pressure[0])\n", - " return v_gas_inlet\n", - "\n", - " v_mf = value( # minimum fluidization velocity [m/s]\n", - " blk.solid_properties[t0, 0]._params.velocity_mf\n", - " )\n", - " print(\"inlet gas velocity = \", value(blk.gas_inlet_velocity), \" m/s\")\n", - " print(\"min. fluid velocity = \", v_mf, \" m/s\")\n", - " try:\n", - " assert value(blk.gas_inlet_velocity) <= v_mf\n", - " except AssertionError:\n", - " print(\n", - " \"The inlet gas velocity is greater than the minimum fluidization velocity. \"\n", - " \"This is unexpected for a Fixed Bed.\"\n", - " )\n", - " raise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Setup and run simulation\n", - "Now that the system properties and all required utility functions are defined, we will build the model itself." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create concrete model\n", - "First, create the model object:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup solver for initialization\n", - "We limit the maximum number of iterations and apply appropriate scaling for the linear solver for initialization:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Solver arguments\n", - "optarg = {\n", - " \"max_iter\": 100,\n", - " \"nlp_scaling_method\": \"user-scaling\",\n", - " \"linear_solver\": \"ma27\",\n", - "}\n", - "\n", - "# Create a solver\n", - "solver = get_solver(\"ipopt\")\n", - "solver.options = optarg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set spatial elements and design variables for adsorption and desorption simulations\n", - "Let's define the size of the system:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of spatial elements\n", - "nxfe = 50\n", - "\n", - "# Design variables for static and dynamic models\n", - "bed_diameter = 9 # m\n", - "bed_height = 1 # m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adsorption simulation\n", - "First, we will run the adsorption step by defining the simulation domain, add initial conditions, and set up and solve the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup simulation horizon and create time set\n", - "We specify the time discretization to an exact set of temporal points according to the problem horizon:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Time horizon\n", - "horizon = 108000 # s\n", - "\n", - "# Create time_set list\n", - "t_element_size = horizon / 4 # s\n", - "ntfe = int(horizon / t_element_size)\n", - "time_set = list(np.linspace(0, horizon, ntfe + 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "tags": [ - "testing" - ] - }, - "outputs": [], - "source": [ - "# Reduce time horizon to speed up testing\n", - "horizon /= 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial conditions for gas and solid phases\n", - "Next, we set reasoanble initial conditions for the gas and solid phases at time = 0, with a constant adsorption temperature:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Flue gas inlet conditions to adsorption system (flue gas stream) -\n", - "# Temperature and mole fractions obtained from NETL baseline report.\n", - "# Exhibit 5-22 B31B case.\n", - "adsorption_temperature = 303.15 # K\n", - "\n", - "# Dictionary of initial and boundary conditions for gas phase\n", - "gas_phase_state_dict_ads = {\n", - " \"flow_mol\": 3.544, # mol/s\n", - " \"temperature\": adsorption_temperature, # K\n", - " \"pressure\": 1.2452e5, # Pa\n", - " \"mole_frac_comp\": { # [-]\n", - " \"CO2\": 0.0408,\n", - " \"H2O\": 0.0875,\n", - " \"N2\": 0.7517,\n", - " \"O2\": 0.12,\n", - " },\n", - "}\n", - "\n", - "# Dictionary of initial conditions for solid phase\n", - "solid_phase_state_dict_ads = {\n", - " \"dens_mass_particle\": 442, # kg/m3\n", - " \"temperature\": adsorption_temperature, # K\n", - " \"mass_frac_comp\": { # [-]\n", - " \"H2O_s\": 1e-8,\n", - " \"Car\": 1e-8,\n", - " \"SiO\": 1,\n", - " },\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the adsorption model\n", - "Then, we call our previously defined methods to build the model and fix initial conditions:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the adsorption flowsheet\n", - "m.fs_ads = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)\n", - "\n", - "# Setup an instance of the 1D FixedBed model for the adsorption simulation\n", - "m.fs_ads = fb_model_setup(m.fs_ads, ntfe, nxfe)\n", - "\n", - "# Fix initial and boundary conditions\n", - "fb_fix_conditions(\n", - " m.fs_ads,\n", - " bed_diameter,\n", - " bed_height,\n", - " gas_phase_state_dict_ads,\n", - " solid_phase_state_dict_ads,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adsorption model - initialize and solve\n", - "Finally for the adsorption model, we initialize and solve." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Apply scaling transformation\n", - "We first scale the model variables and equations to reduce ill-conditioning and improve its convergence properties:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "iscale.calculate_scaling_factors(m.fs_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize model\n", - "The model initialization is done with the block triangularization initialization method in the 1D FixedBed model as this is faster than using the traditional sequential heirarchichal initialization approach. See the 1D FixedBed model and documentation for more details on this method." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_ads = time.time()\n", - "\n", - "# Initialize model\n", - "calc_var_kwds = {\"eps\": 1e-5}\n", - "m.fs_ads.FB.block_triangularization_initialize(\n", - " gas_phase_state_args=gas_phase_state_dict_ads,\n", - " solid_phase_state_args=solid_phase_state_dict_ads,\n", - " outlvl=idaeslog.DEBUG,\n", - " solver=solver,\n", - " calc_var_kwds=calc_var_kwds,\n", - ")\n", - "\n", - "# Run end time\n", - "t_end_ads = time.time()\n", - "\n", - "# Initialization time\n", - "adsorption_initialization_time = value(t_end_ads - t_start_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### PETSc integrator\n", - "As mentioned earlier, the PETSc integrator is used for simulating the time trajectory. After starting the clock and instantiating the time variables, PETSc performs and element-wise discretization to define and solve the dynamic system of equations. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "petscsolvelog = idaeslog.getSolveLogger(\"petsc-dae\")\n", - "petscsolvelog.setLevel(logging.WARNING) # comment this line to see PETSc solver output\n", - "\n", - "# Run start time\n", - "t_start_ads = time.time()\n", - "\n", - "# Setup PETSc integrator and simulate adsorption flowsheet\n", - "m.fs_ads.time_var = Var(m.fs_ads.time)\n", - "m.fs_ads.time_var[0].fix(m.fs_ads.time.first())\n", - "\n", - "result_ads = petsc.petsc_dae_by_time_element(\n", - " m.fs_ads,\n", - " time=m.fs_ads.time,\n", - " timevar=m.fs_ads.time_var,\n", - " keepfiles=True,\n", - " symbolic_solver_labels=True,\n", - " skip_initial=False,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\", # backward euler integration\n", - " \"--ts_dt\": 200, # set initial step to 200\n", - " \"--ts_rtol\": 20,\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ksp_rtol\": 1e-10,\n", - " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ts_max_snes_failures\": 1000,\n", - " },\n", - ")\n", - "tj_ads = result_ads.trajectory # trajectory data\n", - "\n", - "# Run end time\n", - "t_end_ads = time.time()\n", - "\n", - "# Initialization time\n", - "adsorption_simulation_time = value(t_end_ads - t_start_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the adsorption simulation results\n", - "After running the adsorption simulation, the dynamic profiles are plotted." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", - "\n", - "The gas flowrate is constant at the inlet and decreases over the length of the bed as CO2 is removed from the gas. At each spatial point, the CO2 content of the gas increases over time as the bed becomes more saturated with CO2 and the mass transfer driving force decreases. Assessing the two gas flowrate plots, adsorption occurs steadily over time with a much larger capture rate in the initial spatial region; this region grows from 10% to 25% of the bed length as the bed becomes more saturated with CO2 over time.\n", - "\n", - "The bed pressure stays relatively constant over time, and exhibits a linear drop over the length of the reactor. There are no temporal or spatial gradients in gas or solid temperature.\n", - "\n", - "The saturation trend is strongly apparent in the composition trends. The CO2 content of the gas quickly drops from the initial concentration over the length of the bed, and steadily rises over time as the driving force of mass transfer decreases. Water vapor concentration in the gas follows a similar trend.\n", - "\n", - "Following the solid-state reaction, the concentration of carbmate steadily increases over time with formation skewing heavily towards the initial spatial region of the bed. The spatial contours of the temporal carbamate composition plot cross as a result of the solid reaction rate heavily favoring the initial section of the bed and suddenly dropping about at about 20% of the reactor length. The mass fraction and mol/kg plots differ slightly due to water and CO2 absorbing at different rates, but otherwise demonstrate similar trends." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot temporal result profiles\n", - "plot_results_temporal(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot spatial result profiles\n", - "plot_results_spatial(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Desorption simulation\n", - "The desorption simulation begins when the adsorption simulation ends. It uses the final state of the sorbent (besides temperature which is set at the desorption temperature) in the adsorption mode as the initial state of the sorbent in the desorption mode." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup simulation horizon and create time set\n", - "As in the adsorption simulation, we first specify the time discretization:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Space and time discretization arguments\n", - "horizon = 7200 # s\n", - "\n", - "# Setup for PETSc run\n", - "t_element_size = horizon # s\n", - "ntfe = int(horizon / t_element_size)\n", - "time_set = list(np.linspace(0, horizon, ntfe + 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial and boundary conditions for gas phase\n", - "- The initial conditions of the solid phase will be copied from the final conditions of the model in the adsorption simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Desorption operating conditions\n", - "desorption_temperature = 470 # K\n", - "\n", - "gas_phase_state_dict_des = {\n", - " \"flow_mol\": 10, # mol/s\n", - " \"temperature\": desorption_temperature, # K\n", - " \"pressure\": 1.06525e5, # Pa\n", - " \"mole_frac_comp\": {\"CO2\": 1e-8, \"H2O\": 1, \"N2\": 1e-8, \"O2\": 1e-8}, # [-]\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the desorption model\n", - "Then, we build the model and fix initial conditions:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the desorption flowsheet\n", - "m.fs_des = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup an instance of the 1D FixedBed model for the desorption simulation\n", - "m.fs_des = fb_model_setup(\n", - " m.fs_des,\n", - " ntfe,\n", - " nxfe,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Desorption model - initialize and solve\n", - "Finally for the desorption model, we initialize and solve." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Copy values from adsorption model to desorption model\n", - "Copy state values of solid phase (besides temperature) from last time point in adsorption model to all time point in desorption model. Also set temperature state variable of desorption model to desorption temperature." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "blk_des = m.fs_des.FB\n", - "tf_ads = tj_ads.time[-1] # Get final time from adsorption trajectory results\n", - "tf_ads_index = tj_ads.time.index(tf_ads) # Get index at final time\n", - "component_list = blk_des.config.solid_phase_config.property_package.component_list\n", - "\n", - "for t in m.fs_des.time:\n", - " for x in blk_des.length_domain:\n", - " blk_des.solid_properties[t, x].temperature.set_value(desorption_temperature)\n", - " blk_des.solid_properties[t, x].dens_mass_particle.set_value(\n", - " tj_ads.get_vec(m.fs_ads.FB.solid_properties[tf_ads, x].dens_mass_particle)[\n", - " tf_ads_index\n", - " ]\n", - " )\n", - " for j in component_list:\n", - " blk_des.solid_properties[t, x].mass_frac_comp[j].set_value(\n", - " value(\n", - " tj_ads.get_vec(\n", - " m.fs_ads.FB.solid_properties[tf_ads, x].mass_frac_comp[j]\n", - " )[tf_ads_index]\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Fix initial and boundary conditions\n", - "We fix the conditions from the final adsorption model state as our new initial conditions for the desorption model:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "fb_fix_conditions(\n", - " m.fs_des,\n", - " bed_diameter,\n", - " bed_height,\n", - " gas_phase_state_dict_des,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate scaling of desorption model\n", - "Then, we apply scaling to the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "iscale.calculate_scaling_factors(m.fs_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize desorption model\n", - "The desorption model initialization is done with the block triangularization initialization method, as was done with the adsorption model." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_des = time.time()\n", - "\n", - "# Initialize model\n", - "m.fs_des.FB.block_triangularization_initialize(\n", - " gas_phase_state_args=gas_phase_state_dict_des,\n", - " outlvl=idaeslog.DEBUG,\n", - " solver=solver,\n", - " calc_var_kwds=calc_var_kwds,\n", - ")\n", - "\n", - "# Run end time\n", - "t_end_des = time.time()\n", - "\n", - "# Initialization time\n", - "desorption_initialization_time = value(t_end_des - t_start_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup PETSc integrator and simulate desorption step\n", - "Now that the model is fully defined, we can call the PETSc integrator to simulate the dynamics of the problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_des = time.time()\n", - "\n", - "# Setup PETSc integrator and simulate desorption flowsheet\n", - "m.fs_des.time_var = Var(m.fs_des.time)\n", - "m.fs_des.time_var[0].fix(m.fs_des.time.first())\n", - "\n", - "result_des = petsc.petsc_dae_by_time_element(\n", - " m.fs_des,\n", - " time=m.fs_des.time,\n", - " timevar=m.fs_des.time_var,\n", - " keepfiles=True,\n", - " symbolic_solver_labels=True,\n", - " skip_initial=False,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\", # backward euler integration\n", - " \"--ts_dt\": 200, # set initial step to 200\n", - " \"--ts_rtol\": 20,\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ksp_rtol\": 1e-10,\n", - " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ts_max_snes_failures\": 1000,\n", - " },\n", - ")\n", - "tj_des = result_des.trajectory # trajectory data\n", - "\n", - "# Run end time\n", - "t_end_des = time.time()\n", - "\n", - "# Initialization time\n", - "desorption_simulation_time = value(t_end_des - t_start_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the desorption simulation results\n", - "Similar to the results of the adsorption model, the plots below are presented as temporal plots with spatial contours and spatial plots with time contours to fully capture the model trends.\n", - "\n", - "The gas flowrate spikes initially as large quantities of CO2 are recovered, and then the flowrate drops rapidly as the CO2 is carried away. An interesting note is that this occurs over the same time period everywhere across the length of the bed, peaking around 1000 seconds into the simulation. In the spatial plot, the peak is missed between the first and second contours; instead, the spatial plots captures a similar shifting inflection point around 10-25% of the way into the bed as the mass transfer driving force suddenly decreases.\n", - "\n", - "The bed pressure drops suddenly as CO2 is initially desorbed from the bed, and a steady state is reached at larger time points. As in the adsorption case, the desorption results show no temporal or spatial gradients in gas or solid temperature.\n", - "\n", - "The CO2 content of the gas sharply rises initially, and steadily decreases as CO2 is carried away in the sweep gas. A greater amount of CO2 is recovered closer to the reactor inlet. As the sweep gas is nearly pure water vapor, the water concentration in the gas sharply drops as CO2 is desorbed from the bed and recovers to near unity towards the temporal end of the simulation.\n", - "\n", - "Carbamate disappears quickly as CO2 is recovered and the hydrate is broken down, occurring evenly across the length of the reactor bed." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot temporal result profiles\n", - "plot_results_temporal(m.fs_des, tj_des)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot spatial result profiles\n", - "plot_results_spatial(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Generate performance results" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "###########################################################################\n", - "# Heat requirements\n", - "###########################################################################\n", - "heat_computation(m, tj_ads, tj_des, adsorption_temperature, desorption_temperature)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "###########################################################################\n", - "# Performance results\n", - "###########################################################################\n", - "performance_results(m, tj_ads, tj_des)\n", - "\n", - "results_summary(m, adsorption_temperature, desorption_temperature)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation time results" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Adsorption initialization time: \", adsorption_initialization_time, \" s\")\n", - "print(\"Adsorption simulation time: \", adsorption_simulation_time, \" s\")\n", - "print()\n", - "print(\"Desorption initialization time: \", desorption_initialization_time, \" s\")\n", - "print(\"Desorption simulation time: \", desorption_simulation_time, \" s\")\n", - "print()\n", - "total_simulation_time = (\n", - " adsorption_initialization_time\n", - " + adsorption_simulation_time\n", - " + desorption_initialization_time\n", - " + desorption_simulation_time\n", - ")\n", - "print(\"Total simulation time: \", total_simulation_time, \" s\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "This example demonstrates implementation of the 1D FixedBed model within IDAES. External property packages for multiple phase domains and interfaces, as well as mass transfer and reaction properties, were integrated into two simulations. The simulations are connected via their final (adsorption) and initial (desorption) states, and each utilizes the PETSc integrator to calculate temporal profiles over the spatial domains." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", + "Author: Chinedu Okoli, Anca Ostace, Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "This jupyter notebook shows an example of a CO2 Adsorption Desorption cycle with the IDAES 1D FixedBed model. The IDAES 1D FixedBed model is a dynamic and axially varying reactor/adsorption model which is able to model the gas and solid interactions of the modeled species in detail. The sorbent used for this example is the NETL_32D sorbent with its details and parameters obtained from the following references: \n", + "- A. Lee, D.C. Miller, A One-Dimensional (1-D) Three-Region Model for a Bubbling Fluidized-Bed Adsorber, Ind. Eng. Chem. Res. 52 (2013) 469–484\n", + "- Lee, A.; Mebane, D.; Fauth, D. J.; Miller, D. C. A Model for the Adsorption Kinetics of CO2 on Amine-Impregnated Mesoporous Sorbents in the Presence of Water. Presented at the 28th International Pittsburgh Coal Conference, Pittsburgh, PA, 2011.\n", + "\n", + "The notebook demonstrates how to use the IDAES 1DFixedBed model for an adsorption/desorption application with distinct adsorption and desorption steps. This example leverages custom libraries and functions specific to the NETL_32D solid sorbent and associated gas phase properties and surface reactions. In this system, the silicon monoxide (SiO(s)) sorbent reduces carbon dioxide (CO2(g)) to carbamate (denoted Car(s)) while simultaneously absorbing water vapor (H2O(g)) to produce a solid solution-state hydrate (H2O(s)). The solid phase is considered a one-dimensional domain with three regions for bubble, cloud wake and emulsion properties for bubbling bed systems; in the case of a fixed bed reactor non-bulk gas behavior is neglected and assumed to be homogeneous everywhere not near the solid surface.\n", + "\n", + "The notebook also shows how to simulate the adsorption and desorption steps using the PETSc integrator. PETSc leverages nonlinear equation and differential algebraic equation solvers to solve time-trajectory problems. These solvers are applicable for systems with zero degrees of freedom, such as a fully-specified bed reactor model. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cycle details\n", + "The system contains the following equipment and stream properties:\n", + "- Bed height: 9 m\n", + "- Bed diameter: 1 m\n", + "- Adsorption time: 30 hrs\n", + "- Desorption time: 2 hrs\n", + "- Adsorption temperature: 303.15 K\n", + "- Desorption temperature: 470 K\n", + "- Flue gas inlet conditions (Temperature and mole fractions obtained from NETL baseline report. Exhibit 5-22 B31B case)\n", + " - Temperature: 315 K\n", + " - Pressure: 106.5 kPa\n", + " - Flowrate: 3.544 mol/s\n", + " - Mole fractions: CO2: 0.0408, H2O: 0.0875, N2: 0.7517, O2: 0.12" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes\n", + "Some additional information regarding the problem:\n", + "- Isothermal conditions: heat duty requirements of each step are calculated from gas and sorbent properties, i.e., loading, density, heat of adsorption, and heat capacity\n", + "- Heating and cooling modes not modeled in detail: cycle times are assumed negligible in comparison to adsorption and desorption\n", + "- Adsorption and desorption steps are modeled in different flowsheets\n", + "- Initial condition (except temperature) of sorbent in desorption step is set to final condition of sorbent in adsorption step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Import relevant libraries and packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import python libraries\n", + "\n", + "- numpy (numerical python library which provides numerical computing tools)\n", + "- time (time python library which will be used to track the simulation time)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Import Pyomo packages\n", + "For the flowsheet, we will need several components from the pyomo libraries.\n", + "\n", + "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- SolverFactory (to solve the problem)\n", + "- Var (to create a Pyomo variable)\n", + "- value (to return the numerical value of Pyomo objects such as variables, constraints or expressions)\n", + "- units (to handle units in Pyomo and IDAES)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/latest/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " TransformationFactory,\n", + " SolverFactory,\n", + " Var,\n", + " value,\n", + " units as pyunits,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES core components\n", + "\n", + "To build, initialize, and solve IDAES flowsheets we will need several core components/utilities:\n", + "\n", + "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", + "- EnergyBalanceType (to specify the energy balance type)\n", + "- petsc (PETSc integrator)\n", + "- get_solver (IDAES solver utility)\n", + "- iscale (is used to apply scaling factors in variables and constraints)\n", + "- propagate_state (is used to initialize models, propagating the state variables from one unit model to another)\n", + "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", + "- idaeslog (is used to set output messages like warnings or errors)\n", + "\n", + "For further details on these components, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/latest/" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock, EnergyBalanceType\n", + "import idaes.core.solvers.petsc as petsc # PETSc utilities module\n", + "from idaes.core.util import scaling as iscale\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import idaes.logger as idaeslog\n", + "import logging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES unit models and NETL 32D property packages\n", + "\n", + "To build the IDAES flowsheet for the CO2 Adsorption Desorption example, we will need the following: \n", + "1) the 1D Fixed Bed unit model \n", + "2) the NETL 32D property package" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.gas_solid_contactors.unit_models.fixed_bed_1D import FixedBed1D\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_gas_phase_thermo import (\n", + " GasPhaseParameterBlock,\n", + ")\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_solid_phase_thermo import (\n", + " SolidPhaseParameterBlock,\n", + ")\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_adsorption_reactions import (\n", + " HeteroReactionParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import custom libraries and functions\n", + "To simplify and automate this example at the flowsheet level, several custom methods were defined in an external script. The methods are custom, and as such are imported separately from the set of IDAES and Pyomo methods above. These utility functions perform a variety of numerical and reporting tasks:\n", + "\n", + "- heat_computation (used to calculate the heat requirements of the CO2 Adsorption Desorption cycle)\n", + "- performance_results (used to evaluate the performance of the CO2 Adsorption Desorption cycle)\n", + "- results_summary (provides summarized results of the CO2 Adsorption Desorption cycle)\n", + "- plot_results_temporal (plots the temporal profiles of the flowsheet simulation)\n", + "- plot_results_spatial (plots the spatial profiles of the flowsheet simulation)\n", + "- fb_model_setup (function that builds the 1D FixedBed model)\n", + "- fb_fixed_conditions (function that fixes the initial and boundary conditions of the 1D FixedBed model)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.co2_adsorption_desorption.simulation_utilities import (\n", + " heat_computation,\n", + " performance_results,\n", + " results_summary,\n", + " plot_results_temporal,\n", + " plot_results_spatial,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Function to Build the Flowsheet\n", + "\n", + "The function \"fb_model_setup\" below builds instances of the 1D FixedBed model for the adsorption and desorption simulations. While the effort required to define the reactor is low in terms of lines of code, we will need to do this for both the adsorption and desorption steps. Therefore, defining this setup as a single function will make later steps easier to follow. As arguments it takes the following:\n", + "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", + "2) The number of time finite elements \n", + "3) The number of spatial finite elements \n", + "\n", + "It returns a flowsheet object which contains an instance of the 1D FixedBed model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def fb_model_setup(\n", + " fs,\n", + " ntfe, # number of time finite elements\n", + " nxfe, # number of space finite elements\n", + "):\n", + "\n", + " # Set up thermo props and reaction props\n", + " fs.gas_properties = GasPhaseParameterBlock()\n", + " fs.solid_properties = SolidPhaseParameterBlock()\n", + "\n", + " fs.hetero_reactions = HeteroReactionParameterBlock(\n", + " solid_property_package=fs.solid_properties,\n", + " gas_property_package=fs.gas_properties,\n", + " )\n", + "\n", + " fs.FB = FixedBed1D(\n", + " finite_elements=nxfe,\n", + " transformation_method=\"dae.finite_difference\",\n", + " energy_balance_type=EnergyBalanceType.none,\n", + " pressure_drop_type=\"ergun_correlation\",\n", + " gas_phase_config={\"property_package\": fs.gas_properties},\n", + " solid_phase_config={\n", + " \"property_package\": fs.solid_properties,\n", + " \"reaction_package\": fs.hetero_reactions,\n", + " },\n", + " )\n", + "\n", + " # Discretize time domain\n", + " fs.discretizer = TransformationFactory(\"dae.finite_difference\")\n", + " fs.discretizer.apply_to(fs, nfe=ntfe, wrt=fs.time, scheme=\"BACKWARD\")\n", + "\n", + " return fs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Function to Fix the Flowsheet Conditions\n", + "\n", + "The function \"fb_fix_conditions\" fixes the initial and boundary conditions of an instance of the 1D FixedBed model. It also has checks to ensure that the degrees of freedom are zero, and that the velocity into the bed is less than the minimum fluidization velocity of the bed. Similarly to the function above, we will need to run this for the adsorption and desorption steps, and it is clearer to define the code once as a single function here. As arguments it takes the following:\n", + "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", + "2) The reactor bed diameter \n", + "3) The reactor bed height \n", + "4) A dictionary of gas phase state data for which the gas phase state variables of the model should be fixed to \n", + "5) A dictionary of solid phase state data for which the solid phase state variables of the model should be fixed to. If None, the solid phase state variables are fixed to their current values. \n", + "\n", + "No object is returned by this function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def fb_fix_conditions(\n", + " fs, bed_diameter, bed_height, gas_phase_state_dict, solid_phase_state_dict=None\n", + "):\n", + " # Fix bed geometry variables\n", + " fs.FB.bed_diameter.fix(bed_diameter) # m\n", + " fs.FB.bed_height.fix(bed_height) # m\n", + "\n", + " # Fix boundary values for gas for all time\n", + " blk = fs.FB\n", + " for t in fs.time:\n", + " # Gas values\n", + " blk.gas_inlet.flow_mol[t].fix(gas_phase_state_dict[\"flow_mol\"])\n", + " blk.gas_inlet.temperature[t].fix(gas_phase_state_dict[\"temperature\"])\n", + " blk.gas_inlet.pressure[t].fix(gas_phase_state_dict[\"pressure\"])\n", + " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", + " blk.gas_inlet.mole_frac_comp[t, j].fix(val)\n", + "\n", + " # Specify gas phase and solid phase initial conditions for all space\n", + " t0 = fs.time.first()\n", + " for x in blk.length_domain:\n", + " blk.gas_phase.properties[t0, x].flow_mol.fix(gas_phase_state_dict[\"flow_mol\"])\n", + " blk.gas_phase.properties[t0, x].temperature.fix(\n", + " gas_phase_state_dict[\"temperature\"]\n", + " ) # K\n", + " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", + " blk.gas_phase.properties[t0, x].mole_frac_comp[j].fix(val)\n", + "\n", + " if solid_phase_state_dict is None:\n", + " # Fix to existing values if dict is empty\n", + " blk.solid_properties[t0, x].dens_mass_particle.fix()\n", + " blk.solid_properties[t0, x].temperature.fix()\n", + " blk.solid_properties[t0, x].mass_frac_comp[:].fix()\n", + " else:\n", + " blk.solid_properties[t0, x].dens_mass_particle.fix(\n", + " solid_phase_state_dict[\"dens_mass_particle\"]\n", + " )\n", + " blk.solid_properties[t0, x].temperature.fix(\n", + " solid_phase_state_dict[\"temperature\"]\n", + " )\n", + " for j, val in solid_phase_state_dict[\"mass_frac_comp\"].items():\n", + " blk.solid_properties[t0, x].mass_frac_comp[j].fix(val)\n", + "\n", + " dof = degrees_of_freedom(fs)\n", + "\n", + " print(\"degrees of freedom = \", dof)\n", + " try:\n", + " assert degrees_of_freedom(fs) == 0\n", + " except AssertionError:\n", + " print(\"Degrees of freedom is not equal to zero. This is unexpected.\")\n", + " raise\n", + "\n", + " # Assert that inlet gas velocity is less than v_mf\n", + " # Use solid temperature as the thermal mass of solid >> than that of gas\n", + " pi = 3.14 # [-]\n", + " R = 8.314 # Gas constant [J/mol/K]\n", + "\n", + " @blk.Expression(doc=\"gas inlet velocity, m/s\")\n", + " def gas_inlet_velocity(blk):\n", + " v_gas_inlet = (\n", + " blk.gas_inlet.flow_mol[0] / (pi * value(blk.bed_diameter**2) / 4)\n", + " ) * (R * blk.solid_properties[0, 0].temperature / blk.gas_inlet.pressure[0])\n", + " return v_gas_inlet\n", + "\n", + " v_mf = value( # minimum fluidization velocity [m/s]\n", + " blk.solid_properties[t0, 0]._params.velocity_mf\n", + " )\n", + " print(\"inlet gas velocity = \", value(blk.gas_inlet_velocity), \" m/s\")\n", + " print(\"min. fluid velocity = \", v_mf, \" m/s\")\n", + " try:\n", + " assert value(blk.gas_inlet_velocity) <= v_mf\n", + " except AssertionError:\n", + " print(\n", + " \"The inlet gas velocity is greater than the minimum fluidization velocity. \"\n", + " \"This is unexpected for a Fixed Bed.\"\n", + " )\n", + " raise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2: Setup and run simulation\n", + "Now that the system properties and all required utility functions are defined, we will build the model itself." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create concrete model\n", + "First, create the model object:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup solver for initialization\n", + "We limit the maximum number of iterations and apply appropriate scaling for the linear solver for initialization:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Solver arguments\n", + "optarg = {\n", + " \"max_iter\": 100,\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + " \"linear_solver\": \"ma27\",\n", + "}\n", + "\n", + "# Create a solver\n", + "solver = get_solver(\"ipopt\")\n", + "solver.options = optarg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set spatial elements and design variables for adsorption and desorption simulations\n", + "Let's define the size of the system:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Number of spatial elements\n", + "nxfe = 50\n", + "\n", + "# Design variables for static and dynamic models\n", + "bed_diameter = 9 # m\n", + "bed_height = 1 # m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adsorption simulation\n", + "First, we will run the adsorption step by defining the simulation domain, add initial conditions, and set up and solve the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup simulation horizon and create time set\n", + "We specify the time discretization to an exact set of temporal points according to the problem horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Time horizon\n", + "horizon = 108000 # s\n", + "\n", + "# Create time_set list\n", + "t_element_size = horizon / 4 # s\n", + "ntfe = int(horizon / t_element_size)\n", + "time_set = list(np.linspace(0, horizon, ntfe + 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "testing" + ] + }, + "outputs": [], + "source": [ + "# Reduce time horizon to speed up testing\n", + "horizon /= 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial conditions for gas and solid phases\n", + "Next, we set reasoanble initial conditions for the gas and solid phases at time = 0, with a constant adsorption temperature:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Flue gas inlet conditions to adsorption system (flue gas stream) -\n", + "# Temperature and mole fractions obtained from NETL baseline report.\n", + "# Exhibit 5-22 B31B case.\n", + "adsorption_temperature = 303.15 # K\n", + "\n", + "# Dictionary of initial and boundary conditions for gas phase\n", + "gas_phase_state_dict_ads = {\n", + " \"flow_mol\": 3.544, # mol/s\n", + " \"temperature\": adsorption_temperature, # K\n", + " \"pressure\": 1.2452e5, # Pa\n", + " \"mole_frac_comp\": { # [-]\n", + " \"CO2\": 0.0408,\n", + " \"H2O\": 0.0875,\n", + " \"N2\": 0.7517,\n", + " \"O2\": 0.12,\n", + " },\n", + "}\n", + "\n", + "# Dictionary of initial conditions for solid phase\n", + "solid_phase_state_dict_ads = {\n", + " \"dens_mass_particle\": 442, # kg/m3\n", + " \"temperature\": adsorption_temperature, # K\n", + " \"mass_frac_comp\": { # [-]\n", + " \"H2O_s\": 1e-8,\n", + " \"Car\": 1e-8,\n", + " \"SiO\": 1,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the adsorption model\n", + "Then, we call our previously defined methods to build the model and fix initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the adsorption flowsheet\n", + "m.fs_ads = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)\n", + "\n", + "# Setup an instance of the 1D FixedBed model for the adsorption simulation\n", + "m.fs_ads = fb_model_setup(m.fs_ads, ntfe, nxfe)\n", + "\n", + "# Fix initial and boundary conditions\n", + "fb_fix_conditions(\n", + " m.fs_ads,\n", + " bed_diameter,\n", + " bed_height,\n", + " gas_phase_state_dict_ads,\n", + " solid_phase_state_dict_ads,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adsorption model - initialize and solve\n", + "Finally for the adsorption model, we initialize and solve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Apply scaling transformation\n", + "We first scale the model variables and equations to reduce ill-conditioning and improve its convergence properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "iscale.calculate_scaling_factors(m.fs_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize model\n", + "The model initialization is done with the block triangularization initialization method in the 1D FixedBed model as this is faster than using the traditional sequential heirarchichal initialization approach. See the 1D FixedBed model and documentation for more details on this method." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_ads = time.time()\n", + "\n", + "# Initialize model\n", + "calc_var_kwds = {\"eps\": 1e-5}\n", + "m.fs_ads.FB.block_triangularization_initialize(\n", + " gas_phase_state_args=gas_phase_state_dict_ads,\n", + " solid_phase_state_args=solid_phase_state_dict_ads,\n", + " outlvl=idaeslog.DEBUG,\n", + " solver=solver,\n", + " calc_var_kwds=calc_var_kwds,\n", + ")\n", + "\n", + "# Run end time\n", + "t_end_ads = time.time()\n", + "\n", + "# Initialization time\n", + "adsorption_initialization_time = value(t_end_ads - t_start_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PETSc integrator\n", + "As mentioned earlier, the PETSc integrator is used for simulating the time trajectory. After starting the clock and instantiating the time variables, PETSc performs and element-wise discretization to define and solve the dynamic system of equations. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "petscsolvelog = idaeslog.getSolveLogger(\"petsc-dae\")\n", + "petscsolvelog.setLevel(logging.WARNING) # comment this line to see PETSc solver output\n", + "\n", + "# Run start time\n", + "t_start_ads = time.time()\n", + "\n", + "# Setup PETSc integrator and simulate adsorption flowsheet\n", + "m.fs_ads.time_var = Var(m.fs_ads.time)\n", + "m.fs_ads.time_var[0].fix(m.fs_ads.time.first())\n", + "\n", + "result_ads = petsc.petsc_dae_by_time_element(\n", + " m.fs_ads,\n", + " time=m.fs_ads.time,\n", + " timevar=m.fs_ads.time_var,\n", + " keepfiles=True,\n", + " symbolic_solver_labels=True,\n", + " skip_initial=False,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\", # backward euler integration\n", + " \"--ts_dt\": 200, # set initial step to 200\n", + " \"--ts_rtol\": 20,\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ksp_rtol\": 1e-10,\n", + " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ts_max_snes_failures\": 1000,\n", + " },\n", + ")\n", + "tj_ads = result_ads.trajectory # trajectory data\n", + "\n", + "# Run end time\n", + "t_end_ads = time.time()\n", + "\n", + "# Initialization time\n", + "adsorption_simulation_time = value(t_end_ads - t_start_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the adsorption simulation results\n", + "After running the adsorption simulation, the dynamic profiles are plotted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", + "\n", + "The gas flowrate is constant at the inlet and decreases over the length of the bed as CO2 is removed from the gas. At each spatial point, the CO2 content of the gas increases over time as the bed becomes more saturated with CO2 and the mass transfer driving force decreases. Assessing the two gas flowrate plots, adsorption occurs steadily over time with a much larger capture rate in the initial spatial region; this region grows from 10% to 25% of the bed length as the bed becomes more saturated with CO2 over time.\n", + "\n", + "The bed pressure stays relatively constant over time, and exhibits a linear drop over the length of the reactor. There are no temporal or spatial gradients in gas or solid temperature.\n", + "\n", + "The saturation trend is strongly apparent in the composition trends. The CO2 content of the gas quickly drops from the initial concentration over the length of the bed, and steadily rises over time as the driving force of mass transfer decreases. Water vapor concentration in the gas follows a similar trend.\n", + "\n", + "Following the solid-state reaction, the concentration of carbmate steadily increases over time with formation skewing heavily towards the initial spatial region of the bed. The spatial contours of the temporal carbamate composition plot cross as a result of the solid reaction rate heavily favoring the initial section of the bed and suddenly dropping about at about 20% of the reactor length. The mass fraction and mol/kg plots differ slightly due to water and CO2 absorbing at different rates, but otherwise demonstrate similar trends." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot temporal result profiles\n", + "plot_results_temporal(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot spatial result profiles\n", + "plot_results_spatial(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Desorption simulation\n", + "The desorption simulation begins when the adsorption simulation ends. It uses the final state of the sorbent (besides temperature which is set at the desorption temperature) in the adsorption mode as the initial state of the sorbent in the desorption mode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup simulation horizon and create time set\n", + "As in the adsorption simulation, we first specify the time discretization:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Space and time discretization arguments\n", + "horizon = 7200 # s\n", + "\n", + "# Setup for PETSc run\n", + "t_element_size = horizon # s\n", + "ntfe = int(horizon / t_element_size)\n", + "time_set = list(np.linspace(0, horizon, ntfe + 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial and boundary conditions for gas phase\n", + "- The initial conditions of the solid phase will be copied from the final conditions of the model in the adsorption simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Desorption operating conditions\n", + "desorption_temperature = 470 # K\n", + "\n", + "gas_phase_state_dict_des = {\n", + " \"flow_mol\": 10, # mol/s\n", + " \"temperature\": desorption_temperature, # K\n", + " \"pressure\": 1.06525e5, # Pa\n", + " \"mole_frac_comp\": {\"CO2\": 1e-8, \"H2O\": 1, \"N2\": 1e-8, \"O2\": 1e-8}, # [-]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the desorption model\n", + "Then, we build the model and fix initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the desorption flowsheet\n", + "m.fs_des = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup an instance of the 1D FixedBed model for the desorption simulation\n", + "m.fs_des = fb_model_setup(\n", + " m.fs_des,\n", + " ntfe,\n", + " nxfe,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Desorption model - initialize and solve\n", + "Finally for the desorption model, we initialize and solve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Copy values from adsorption model to desorption model\n", + "Copy state values of solid phase (besides temperature) from last time point in adsorption model to all time point in desorption model. Also set temperature state variable of desorption model to desorption temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "blk_des = m.fs_des.FB\n", + "tf_ads = tj_ads.time[-1] # Get final time from adsorption trajectory results\n", + "tf_ads_index = tj_ads.time.index(tf_ads) # Get index at final time\n", + "component_list = blk_des.config.solid_phase_config.property_package.component_list\n", + "\n", + "for t in m.fs_des.time:\n", + " for x in blk_des.length_domain:\n", + " blk_des.solid_properties[t, x].temperature.set_value(desorption_temperature)\n", + " blk_des.solid_properties[t, x].dens_mass_particle.set_value(\n", + " tj_ads.get_vec(m.fs_ads.FB.solid_properties[tf_ads, x].dens_mass_particle)[\n", + " tf_ads_index\n", + " ]\n", + " )\n", + " for j in component_list:\n", + " blk_des.solid_properties[t, x].mass_frac_comp[j].set_value(\n", + " value(\n", + " tj_ads.get_vec(\n", + " m.fs_ads.FB.solid_properties[tf_ads, x].mass_frac_comp[j]\n", + " )[tf_ads_index]\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fix initial and boundary conditions\n", + "We fix the conditions from the final adsorption model state as our new initial conditions for the desorption model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "fb_fix_conditions(\n", + " m.fs_des,\n", + " bed_diameter,\n", + " bed_height,\n", + " gas_phase_state_dict_des,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate scaling of desorption model\n", + "Then, we apply scaling to the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "iscale.calculate_scaling_factors(m.fs_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize desorption model\n", + "The desorption model initialization is done with the block triangularization initialization method, as was done with the adsorption model." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_des = time.time()\n", + "\n", + "# Initialize model\n", + "m.fs_des.FB.block_triangularization_initialize(\n", + " gas_phase_state_args=gas_phase_state_dict_des,\n", + " outlvl=idaeslog.DEBUG,\n", + " solver=solver,\n", + " calc_var_kwds=calc_var_kwds,\n", + ")\n", + "\n", + "# Run end time\n", + "t_end_des = time.time()\n", + "\n", + "# Initialization time\n", + "desorption_initialization_time = value(t_end_des - t_start_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup PETSc integrator and simulate desorption step\n", + "Now that the model is fully defined, we can call the PETSc integrator to simulate the dynamics of the problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_des = time.time()\n", + "\n", + "# Setup PETSc integrator and simulate desorption flowsheet\n", + "m.fs_des.time_var = Var(m.fs_des.time)\n", + "m.fs_des.time_var[0].fix(m.fs_des.time.first())\n", + "\n", + "result_des = petsc.petsc_dae_by_time_element(\n", + " m.fs_des,\n", + " time=m.fs_des.time,\n", + " timevar=m.fs_des.time_var,\n", + " keepfiles=True,\n", + " symbolic_solver_labels=True,\n", + " skip_initial=False,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\", # backward euler integration\n", + " \"--ts_dt\": 200, # set initial step to 200\n", + " \"--ts_rtol\": 20,\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ksp_rtol\": 1e-10,\n", + " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ts_max_snes_failures\": 1000,\n", + " },\n", + ")\n", + "tj_des = result_des.trajectory # trajectory data\n", + "\n", + "# Run end time\n", + "t_end_des = time.time()\n", + "\n", + "# Initialization time\n", + "desorption_simulation_time = value(t_end_des - t_start_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the desorption simulation results\n", + "Similar to the results of the adsorption model, the plots below are presented as temporal plots with spatial contours and spatial plots with time contours to fully capture the model trends.\n", + "\n", + "The gas flowrate spikes initially as large quantities of CO2 are recovered, and then the flowrate drops rapidly as the CO2 is carried away. An interesting note is that this occurs over the same time period everywhere across the length of the bed, peaking around 1000 seconds into the simulation. In the spatial plot, the peak is missed between the first and second contours; instead, the spatial plots captures a similar shifting inflection point around 10-25% of the way into the bed as the mass transfer driving force suddenly decreases.\n", + "\n", + "The bed pressure drops suddenly as CO2 is initially desorbed from the bed, and a steady state is reached at larger time points. As in the adsorption case, the desorption results show no temporal or spatial gradients in gas or solid temperature.\n", + "\n", + "The CO2 content of the gas sharply rises initially, and steadily decreases as CO2 is carried away in the sweep gas. A greater amount of CO2 is recovered closer to the reactor inlet. As the sweep gas is nearly pure water vapor, the water concentration in the gas sharply drops as CO2 is desorbed from the bed and recovers to near unity towards the temporal end of the simulation.\n", + "\n", + "Carbamate disappears quickly as CO2 is recovered and the hydrate is broken down, occurring evenly across the length of the reactor bed." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot temporal result profiles\n", + "plot_results_temporal(m.fs_des, tj_des)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot spatial result profiles\n", + "plot_results_spatial(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Generate performance results" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# Heat requirements\n", + "###########################################################################\n", + "heat_computation(m, tj_ads, tj_des, adsorption_temperature, desorption_temperature)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# Performance results\n", + "###########################################################################\n", + "performance_results(m, tj_ads, tj_des)\n", + "\n", + "results_summary(m, adsorption_temperature, desorption_temperature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulation time results" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Adsorption initialization time: \", adsorption_initialization_time, \" s\")\n", + "print(\"Adsorption simulation time: \", adsorption_simulation_time, \" s\")\n", + "print()\n", + "print(\"Desorption initialization time: \", desorption_initialization_time, \" s\")\n", + "print(\"Desorption simulation time: \", desorption_simulation_time, \" s\")\n", + "print()\n", + "total_simulation_time = (\n", + " adsorption_initialization_time\n", + " + adsorption_simulation_time\n", + " + desorption_initialization_time\n", + " + desorption_simulation_time\n", + ")\n", + "print(\"Total simulation time: \", total_simulation_time, \" s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "This example demonstrates implementation of the 1D FixedBed model within IDAES. External property packages for multiple phase domains and interfaces, as well as mass transfer and reaction properties, were integrated into two simulations. The simulations are connected via their final (adsorption) and initial (desorption) states, and each utilizes the PETSc integrator to calculate temporal profiles over the spatial domains." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 } diff --git a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_usr.ipynb b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_usr.ipynb index 8440fb43..d9db9cc5 100644 --- a/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_usr.ipynb +++ b/idaes_examples/notebooks/held/flowsheets/CO2_adsorption_desorption/CO2_Adsorption_Desorption_1DFixedBed_usr.ipynb @@ -1,1112 +1,1113 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", - "Author: Chinedu Okoli, Anca Ostace, Brandon Paul \n", - "Maintainer: Brandon Paul \n", - "Updated: 2023-06-01 \n", - "\n", - "\n", - "This jupyter notebook shows an example of a CO2 Adsorption Desorption cycle with the IDAES 1D FixedBed model. The IDAES 1D FixedBed model is a dynamic and axially varying reactor/adsorption model which is able to model the gas and solid interactions of the modeled species in detail. The sorbent used for this example is the NETL_32D sorbent with its details and parameters obtained from the following references: \n", - "- A. Lee, D.C. Miller, A One-Dimensional (1-D) Three-Region Model for a Bubbling Fluidized-Bed Adsorber, Ind. Eng. Chem. Res. 52 (2013) 469–484\n", - "- Lee, A.; Mebane, D.; Fauth, D. J.; Miller, D. C. A Model for the Adsorption Kinetics of CO2 on Amine-Impregnated Mesoporous Sorbents in the Presence of Water. Presented at the 28th International Pittsburgh Coal Conference, Pittsburgh, PA, 2011.\n", - "\n", - "The notebook demonstrates how to use the IDAES 1DFixedBed model for an adsorption/desorption application with distinct adsorption and desorption steps. This example leverages custom libraries and functions specific to the NETL_32D solid sorbent and associated gas phase properties and surface reactions. In this system, the silicon monoxide (SiO(s)) sorbent reduces carbon dioxide (CO2(g)) to carbamate (denoted Car(s)) while simultaneously absorbing water vapor (H2O(g)) to produce a solid solution-state hydrate (H2O(s)). The solid phase is considered a one-dimensional domain with three regions for bubble, cloud wake and emulsion properties for bubbling bed systems; in the case of a fixed bed reactor non-bulk gas behavior is neglected and assumed to be homogeneous everywhere not near the solid surface.\n", - "\n", - "The notebook also shows how to simulate the adsorption and desorption steps using the PETSc integrator. PETSc leverages nonlinear equation and differential algebraic equation solvers to solve time-trajectory problems. These solvers are applicable for systems with zero degrees of freedom, such as a fully-specified bed reactor model. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cycle details\n", - "The system contains the following equipment and stream properties:\n", - "- Bed height: 9 m\n", - "- Bed diameter: 1 m\n", - "- Adsorption time: 30 hrs\n", - "- Desorption time: 2 hrs\n", - "- Adsorption temperature: 303.15 K\n", - "- Desorption temperature: 470 K\n", - "- Flue gas inlet conditions (Temperature and mole fractions obtained from NETL baseline report. Exhibit 5-22 B31B case)\n", - " - Temperature: 315 K\n", - " - Pressure: 106.5 kPa\n", - " - Flowrate: 3.544 mol/s\n", - " - Mole fractions: CO2: 0.0408, H2O: 0.0875, N2: 0.7517, O2: 0.12" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notes\n", - "Some additional information regarding the problem:\n", - "- Isothermal conditions: heat duty requirements of each step are calculated from gas and sorbent properties, i.e., loading, density, heat of adsorption, and heat capacity\n", - "- Heating and cooling modes not modeled in detail: cycle times are assumed negligible in comparison to adsorption and desorption\n", - "- Adsorption and desorption steps are modeled in different flowsheets\n", - "- Initial condition (except temperature) of sorbent in desorption step is set to final condition of sorbent in adsorption step" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Import relevant libraries and packages" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import python libraries\n", - "\n", - "- numpy (numerical python library which provides numerical computing tools)\n", - "- time (time python library which will be used to track the simulation time)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Import Pyomo packages\n", - "For the flowsheet, we will need several components from the pyomo libraries.\n", - "\n", - "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", - "- TransformationFactory (to apply certain transformations)\n", - "- SolverFactory (to solve the problem)\n", - "- Var (to create a Pyomo variable)\n", - "- value (to return the numerical value of Pyomo objects such as variables, constraints or expressions)\n", - "- units (to handle units in Pyomo and IDAES)\n", - "\n", - "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/latest/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyomo.environ import (\n", - " ConcreteModel,\n", - " TransformationFactory,\n", - " SolverFactory,\n", - " Var,\n", - " value,\n", - " units as pyunits,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES core components\n", - "\n", - "To build, initialize, and solve IDAES flowsheets we will need several core components/utilities:\n", - "\n", - "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", - "- EnergyBalanceType (to specify the energy balance type)\n", - "- petsc (PETSc integrator)\n", - "- get_solver (IDAES solver utility)\n", - "- iscale (is used to apply scaling factors in variables and constraints)\n", - "- propagate_state (is used to initialize models, propagating the state variables from one unit model to another)\n", - "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", - "- idaeslog (is used to set output messages like warnings or errors)\n", - "\n", - "For further details on these components, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/latest/" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.core import FlowsheetBlock, EnergyBalanceType\n", - "import idaes.core.solvers.petsc as petsc # PETSc utilities module\n", - "from idaes.core.util import scaling as iscale\n", - "from idaes.core.solvers import get_solver\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "import idaes.logger as idaeslog\n", - "import logging" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import IDAES unit models and NETL 32D property packages\n", - "\n", - "To build the IDAES flowsheet for the CO2 Adsorption Desorption example, we will need the following: \n", - "1) the 1D Fixed Bed unit model \n", - "2) the NETL 32D property package" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models_extra.gas_solid_contactors.unit_models.fixed_bed_1D import FixedBed1D\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_gas_phase_thermo import (\n", - " GasPhaseParameterBlock,\n", - ")\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_solid_phase_thermo import (\n", - " SolidPhaseParameterBlock,\n", - ")\n", - "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_adsorption_reactions import (\n", - " HeteroReactionParameterBlock,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import custom libraries and functions\n", - "To simplify and automate this example at the flowsheet level, several custom methods were defined in an external script. The methods are custom, and as such are imported separately from the set of IDAES and Pyomo methods above. These utility functions perform a variety of numerical and reporting tasks:\n", - "\n", - "- heat_computation (used to calculate the heat requirements of the CO2 Adsorption Desorption cycle)\n", - "- performance_results (used to evaluate the performance of the CO2 Adsorption Desorption cycle)\n", - "- results_summary (provides summarized results of the CO2 Adsorption Desorption cycle)\n", - "- plot_results_temporal (plots the temporal profiles of the flowsheet simulation)\n", - "- plot_results_spatial (plots the spatial profiles of the flowsheet simulation)\n", - "- fb_model_setup (function that builds the 1D FixedBed model)\n", - "- fb_fixed_conditions (function that fixes the initial and boundary conditions of the 1D FixedBed model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes_examples.mod.co2_adsorption_desorption.simulation_utilities import (\n", - " heat_computation,\n", - " performance_results,\n", - " results_summary,\n", - " plot_results_temporal,\n", - " plot_results_spatial,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a Function to Build the Flowsheet\n", - "\n", - "The function \"fb_model_setup\" below builds instances of the 1D FixedBed model for the adsorption and desorption simulations. While the effort required to define the reactor is low in terms of lines of code, we will need to do this for both the adsorption and desorption steps. Therefore, defining this setup as a single function will make later steps easier to follow. As arguments it takes the following:\n", - "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", - "2) The number of time finite elements \n", - "3) The number of spatial finite elements \n", - "\n", - "It returns a flowsheet object which contains an instance of the 1D FixedBed model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def fb_model_setup(\n", - " fs,\n", - " ntfe, # number of time finite elements\n", - " nxfe, # number of space finite elements\n", - "):\n", - "\n", - " # Set up thermo props and reaction props\n", - " fs.gas_properties = GasPhaseParameterBlock()\n", - " fs.solid_properties = SolidPhaseParameterBlock()\n", - "\n", - " fs.hetero_reactions = HeteroReactionParameterBlock(\n", - " solid_property_package=fs.solid_properties,\n", - " gas_property_package=fs.gas_properties,\n", - " )\n", - "\n", - " fs.FB = FixedBed1D(\n", - " finite_elements=nxfe,\n", - " transformation_method=\"dae.finite_difference\",\n", - " energy_balance_type=EnergyBalanceType.none,\n", - " pressure_drop_type=\"ergun_correlation\",\n", - " gas_phase_config={\"property_package\": fs.gas_properties},\n", - " solid_phase_config={\n", - " \"property_package\": fs.solid_properties,\n", - " \"reaction_package\": fs.hetero_reactions,\n", - " },\n", - " )\n", - "\n", - " # Discretize time domain\n", - " fs.discretizer = TransformationFactory(\"dae.finite_difference\")\n", - " fs.discretizer.apply_to(fs, nfe=ntfe, wrt=fs.time, scheme=\"BACKWARD\")\n", - "\n", - " return fs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a Function to Fix the Flowsheet Conditions\n", - "\n", - "The function \"fb_fix_conditions\" fixes the initial and boundary conditions of an instance of the 1D FixedBed model. It also has checks to ensure that the degrees of freedom are zero, and that the velocity into the bed is less than the minimum fluidization velocity of the bed. Similarly to the function above, we will need to run this for the adsorption and desorption steps, and it is clearer to define the code once as a single function here. As arguments it takes the following:\n", - "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", - "2) The reactor bed diameter \n", - "3) The reactor bed height \n", - "4) A dictionary of gas phase state data for which the gas phase state variables of the model should be fixed to \n", - "5) A dictionary of solid phase state data for which the solid phase state variables of the model should be fixed to. If None, the solid phase state variables are fixed to their current values. \n", - "\n", - "No object is returned by this function." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def fb_fix_conditions(\n", - " fs, bed_diameter, bed_height, gas_phase_state_dict, solid_phase_state_dict=None\n", - "):\n", - " # Fix bed geometry variables\n", - " fs.FB.bed_diameter.fix(bed_diameter) # m\n", - " fs.FB.bed_height.fix(bed_height) # m\n", - "\n", - " # Fix boundary values for gas for all time\n", - " blk = fs.FB\n", - " for t in fs.time:\n", - " # Gas values\n", - " blk.gas_inlet.flow_mol[t].fix(gas_phase_state_dict[\"flow_mol\"])\n", - " blk.gas_inlet.temperature[t].fix(gas_phase_state_dict[\"temperature\"])\n", - " blk.gas_inlet.pressure[t].fix(gas_phase_state_dict[\"pressure\"])\n", - " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", - " blk.gas_inlet.mole_frac_comp[t, j].fix(val)\n", - "\n", - " # Specify gas phase and solid phase initial conditions for all space\n", - " t0 = fs.time.first()\n", - " for x in blk.length_domain:\n", - " blk.gas_phase.properties[t0, x].flow_mol.fix(gas_phase_state_dict[\"flow_mol\"])\n", - " blk.gas_phase.properties[t0, x].temperature.fix(\n", - " gas_phase_state_dict[\"temperature\"]\n", - " ) # K\n", - " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", - " blk.gas_phase.properties[t0, x].mole_frac_comp[j].fix(val)\n", - "\n", - " if solid_phase_state_dict is None:\n", - " # Fix to existing values if dict is empty\n", - " blk.solid_properties[t0, x].dens_mass_particle.fix()\n", - " blk.solid_properties[t0, x].temperature.fix()\n", - " blk.solid_properties[t0, x].mass_frac_comp[:].fix()\n", - " else:\n", - " blk.solid_properties[t0, x].dens_mass_particle.fix(\n", - " solid_phase_state_dict[\"dens_mass_particle\"]\n", - " )\n", - " blk.solid_properties[t0, x].temperature.fix(\n", - " solid_phase_state_dict[\"temperature\"]\n", - " )\n", - " for j, val in solid_phase_state_dict[\"mass_frac_comp\"].items():\n", - " blk.solid_properties[t0, x].mass_frac_comp[j].fix(val)\n", - "\n", - " dof = degrees_of_freedom(fs)\n", - "\n", - " print(\"degrees of freedom = \", dof)\n", - " try:\n", - " assert degrees_of_freedom(fs) == 0\n", - " except AssertionError:\n", - " print(\"Degrees of freedom is not equal to zero. This is unexpected.\")\n", - " raise\n", - "\n", - " # Assert that inlet gas velocity is less than v_mf\n", - " # Use solid temperature as the thermal mass of solid >> than that of gas\n", - " pi = 3.14 # [-]\n", - " R = 8.314 # Gas constant [J/mol/K]\n", - "\n", - " @blk.Expression(doc=\"gas inlet velocity, m/s\")\n", - " def gas_inlet_velocity(blk):\n", - " v_gas_inlet = (\n", - " blk.gas_inlet.flow_mol[0] / (pi * value(blk.bed_diameter**2) / 4)\n", - " ) * (R * blk.solid_properties[0, 0].temperature / blk.gas_inlet.pressure[0])\n", - " return v_gas_inlet\n", - "\n", - " v_mf = value( # minimum fluidization velocity [m/s]\n", - " blk.solid_properties[t0, 0]._params.velocity_mf\n", - " )\n", - " print(\"inlet gas velocity = \", value(blk.gas_inlet_velocity), \" m/s\")\n", - " print(\"min. fluid velocity = \", v_mf, \" m/s\")\n", - " try:\n", - " assert value(blk.gas_inlet_velocity) <= v_mf\n", - " except AssertionError:\n", - " print(\n", - " \"The inlet gas velocity is greater than the minimum fluidization velocity. \"\n", - " \"This is unexpected for a Fixed Bed.\"\n", - " )\n", - " raise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 2: Setup and run simulation\n", - "Now that the system properties and all required utility functions are defined, we will build the model itself." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create concrete model\n", - "First, create the model object:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "m = ConcreteModel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup solver for initialization\n", - "We limit the maximum number of iterations and apply appropriate scaling for the linear solver for initialization:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Solver arguments\n", - "optarg = {\n", - " \"max_iter\": 100,\n", - " \"nlp_scaling_method\": \"user-scaling\",\n", - " \"linear_solver\": \"ma27\",\n", - "}\n", - "\n", - "# Create a solver\n", - "solver = get_solver(\"ipopt\")\n", - "solver.options = optarg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set spatial elements and design variables for adsorption and desorption simulations\n", - "Let's define the size of the system:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Number of spatial elements\n", - "nxfe = 50\n", - "\n", - "# Design variables for static and dynamic models\n", - "bed_diameter = 9 # m\n", - "bed_height = 1 # m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Adsorption simulation\n", - "First, we will run the adsorption step by defining the simulation domain, add initial conditions, and set up and solve the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup simulation horizon and create time set\n", - "We specify the time discretization to an exact set of temporal points according to the problem horizon:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Time horizon\n", - "horizon = 108000 # s\n", - "\n", - "# Create time_set list\n", - "t_element_size = horizon / 4 # s\n", - "ntfe = int(horizon / t_element_size)\n", - "time_set = list(np.linspace(0, horizon, ntfe + 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial conditions for gas and solid phases\n", - "Next, we set reasoanble initial conditions for the gas and solid phases at time = 0, with a constant adsorption temperature:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Flue gas inlet conditions to adsorption system (flue gas stream) -\n", - "# Temperature and mole fractions obtained from NETL baseline report.\n", - "# Exhibit 5-22 B31B case.\n", - "adsorption_temperature = 303.15 # K\n", - "\n", - "# Dictionary of initial and boundary conditions for gas phase\n", - "gas_phase_state_dict_ads = {\n", - " \"flow_mol\": 3.544, # mol/s\n", - " \"temperature\": adsorption_temperature, # K\n", - " \"pressure\": 1.2452e5, # Pa\n", - " \"mole_frac_comp\": { # [-]\n", - " \"CO2\": 0.0408,\n", - " \"H2O\": 0.0875,\n", - " \"N2\": 0.7517,\n", - " \"O2\": 0.12,\n", - " },\n", - "}\n", - "\n", - "# Dictionary of initial conditions for solid phase\n", - "solid_phase_state_dict_ads = {\n", - " \"dens_mass_particle\": 442, # kg/m3\n", - " \"temperature\": adsorption_temperature, # K\n", - " \"mass_frac_comp\": { # [-]\n", - " \"H2O_s\": 1e-8,\n", - " \"Car\": 1e-8,\n", - " \"SiO\": 1,\n", - " },\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the adsorption model\n", - "Then, we call our previously defined methods to build the model and fix initial conditions:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the adsorption flowsheet\n", - "m.fs_ads = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)\n", - "\n", - "# Setup an instance of the 1D FixedBed model for the adsorption simulation\n", - "m.fs_ads = fb_model_setup(m.fs_ads, ntfe, nxfe)\n", - "\n", - "# Fix initial and boundary conditions\n", - "fb_fix_conditions(\n", - " m.fs_ads,\n", - " bed_diameter,\n", - " bed_height,\n", - " gas_phase_state_dict_ads,\n", - " solid_phase_state_dict_ads,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adsorption model - initialize and solve\n", - "Finally for the adsorption model, we initialize and solve." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Apply scaling transformation\n", - "We first scale the model variables and equations to reduce ill-conditioning and improve its convergence properties:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "iscale.calculate_scaling_factors(m.fs_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize model\n", - "The model initialization is done with the block triangularization initialization method in the 1D FixedBed model as this is faster than using the traditional sequential heirarchichal initialization approach. See the 1D FixedBed model and documentation for more details on this method." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_ads = time.time()\n", - "\n", - "# Initialize model\n", - "calc_var_kwds = {\"eps\": 1e-5}\n", - "m.fs_ads.FB.block_triangularization_initialize(\n", - " gas_phase_state_args=gas_phase_state_dict_ads,\n", - " solid_phase_state_args=solid_phase_state_dict_ads,\n", - " outlvl=idaeslog.DEBUG,\n", - " solver=solver,\n", - " calc_var_kwds=calc_var_kwds,\n", - ")\n", - "\n", - "# Run end time\n", - "t_end_ads = time.time()\n", - "\n", - "# Initialization time\n", - "adsorption_initialization_time = value(t_end_ads - t_start_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### PETSc integrator\n", - "As mentioned earlier, the PETSc integrator is used for simulating the time trajectory. After starting the clock and instantiating the time variables, PETSc performs and element-wise discretization to define and solve the dynamic system of equations. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "petscsolvelog = idaeslog.getSolveLogger(\"petsc-dae\")\n", - "petscsolvelog.setLevel(logging.WARNING) # comment this line to see PETSc solver output\n", - "\n", - "# Run start time\n", - "t_start_ads = time.time()\n", - "\n", - "# Setup PETSc integrator and simulate adsorption flowsheet\n", - "m.fs_ads.time_var = Var(m.fs_ads.time)\n", - "m.fs_ads.time_var[0].fix(m.fs_ads.time.first())\n", - "\n", - "result_ads = petsc.petsc_dae_by_time_element(\n", - " m.fs_ads,\n", - " time=m.fs_ads.time,\n", - " timevar=m.fs_ads.time_var,\n", - " keepfiles=True,\n", - " symbolic_solver_labels=True,\n", - " skip_initial=False,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\", # backward euler integration\n", - " \"--ts_dt\": 200, # set initial step to 200\n", - " \"--ts_rtol\": 20,\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ksp_rtol\": 1e-10,\n", - " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ts_max_snes_failures\": 1000,\n", - " },\n", - ")\n", - "tj_ads = result_ads.trajectory # trajectory data\n", - "\n", - "# Run end time\n", - "t_end_ads = time.time()\n", - "\n", - "# Initialization time\n", - "adsorption_simulation_time = value(t_end_ads - t_start_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the adsorption simulation results\n", - "After running the adsorption simulation, the dynamic profiles are plotted." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", - "\n", - "The gas flowrate is constant at the inlet and decreases over the length of the bed as CO2 is removed from the gas. At each spatial point, the CO2 content of the gas increases over time as the bed becomes more saturated with CO2 and the mass transfer driving force decreases. Assessing the two gas flowrate plots, adsorption occurs steadily over time with a much larger capture rate in the initial spatial region; this region grows from 10% to 25% of the bed length as the bed becomes more saturated with CO2 over time.\n", - "\n", - "The bed pressure stays relatively constant over time, and exhibits a linear drop over the length of the reactor. There are no temporal or spatial gradients in gas or solid temperature.\n", - "\n", - "The saturation trend is strongly apparent in the composition trends. The CO2 content of the gas quickly drops from the initial concentration over the length of the bed, and steadily rises over time as the driving force of mass transfer decreases. Water vapor concentration in the gas follows a similar trend.\n", - "\n", - "Following the solid-state reaction, the concentration of carbmate steadily increases over time with formation skewing heavily towards the initial spatial region of the bed. The spatial contours of the temporal carbamate composition plot cross as a result of the solid reaction rate heavily favoring the initial section of the bed and suddenly dropping about at about 20% of the reactor length. The mass fraction and mol/kg plots differ slightly due to water and CO2 absorbing at different rates, but otherwise demonstrate similar trends." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot temporal result profiles\n", - "plot_results_temporal(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot spatial result profiles\n", - "plot_results_spatial(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Desorption simulation\n", - "The desorption simulation begins when the adsorption simulation ends. It uses the final state of the sorbent (besides temperature which is set at the desorption temperature) in the adsorption mode as the initial state of the sorbent in the desorption mode." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup simulation horizon and create time set\n", - "As in the adsorption simulation, we first specify the time discretization:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Space and time discretization arguments\n", - "horizon = 7200 # s\n", - "\n", - "# Setup for PETSc run\n", - "t_element_size = horizon # s\n", - "ntfe = int(horizon / t_element_size)\n", - "time_set = list(np.linspace(0, horizon, ntfe + 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial and boundary conditions for gas phase\n", - "- The initial conditions of the solid phase will be copied from the final conditions of the model in the adsorption simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Desorption operating conditions\n", - "desorption_temperature = 470 # K\n", - "\n", - "gas_phase_state_dict_des = {\n", - " \"flow_mol\": 10, # mol/s\n", - " \"temperature\": desorption_temperature, # K\n", - " \"pressure\": 1.06525e5, # Pa\n", - " \"mole_frac_comp\": {\"CO2\": 1e-8, \"H2O\": 1, \"N2\": 1e-8, \"O2\": 1e-8}, # [-]\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup the desorption model\n", - "Then, we build the model and fix initial conditions:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the desorption flowsheet\n", - "m.fs_des = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup an instance of the 1D FixedBed model for the desorption simulation\n", - "m.fs_des = fb_model_setup(\n", - " m.fs_des,\n", - " ntfe,\n", - " nxfe,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Desorption model - initialize and solve\n", - "Finally for the desorption model, we initialize and solve." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Copy values from adsorption model to desorption model\n", - "Copy state values of solid phase (besides temperature) from last time point in adsorption model to all time point in desorption model. Also set temperature state variable of desorption model to desorption temperature." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "blk_des = m.fs_des.FB\n", - "tf_ads = tj_ads.time[-1] # Get final time from adsorption trajectory results\n", - "tf_ads_index = tj_ads.time.index(tf_ads) # Get index at final time\n", - "component_list = blk_des.config.solid_phase_config.property_package.component_list\n", - "\n", - "for t in m.fs_des.time:\n", - " for x in blk_des.length_domain:\n", - " blk_des.solid_properties[t, x].temperature.set_value(desorption_temperature)\n", - " blk_des.solid_properties[t, x].dens_mass_particle.set_value(\n", - " tj_ads.get_vec(m.fs_ads.FB.solid_properties[tf_ads, x].dens_mass_particle)[\n", - " tf_ads_index\n", - " ]\n", - " )\n", - " for j in component_list:\n", - " blk_des.solid_properties[t, x].mass_frac_comp[j].set_value(\n", - " value(\n", - " tj_ads.get_vec(\n", - " m.fs_ads.FB.solid_properties[tf_ads, x].mass_frac_comp[j]\n", - " )[tf_ads_index]\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Fix initial and boundary conditions\n", - "We fix the conditions from the final adsorption model state as our new initial conditions for the desorption model:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "fb_fix_conditions(\n", - " m.fs_des,\n", - " bed_diameter,\n", - " bed_height,\n", - " gas_phase_state_dict_des,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate scaling of desorption model\n", - "Then, we apply scaling to the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "iscale.calculate_scaling_factors(m.fs_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize desorption model\n", - "The desorption model initialization is done with the block triangularization initialization method, as was done with the adsorption model." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_des = time.time()\n", - "\n", - "# Initialize model\n", - "m.fs_des.FB.block_triangularization_initialize(\n", - " gas_phase_state_args=gas_phase_state_dict_des,\n", - " outlvl=idaeslog.DEBUG,\n", - " solver=solver,\n", - " calc_var_kwds=calc_var_kwds,\n", - ")\n", - "\n", - "# Run end time\n", - "t_end_des = time.time()\n", - "\n", - "# Initialization time\n", - "desorption_initialization_time = value(t_end_des - t_start_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Setup PETSc integrator and simulate desorption step\n", - "Now that the model is fully defined, we can call the PETSc integrator to simulate the dynamics of the problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Run start time\n", - "t_start_des = time.time()\n", - "\n", - "# Setup PETSc integrator and simulate desorption flowsheet\n", - "m.fs_des.time_var = Var(m.fs_des.time)\n", - "m.fs_des.time_var[0].fix(m.fs_des.time.first())\n", - "\n", - "result_des = petsc.petsc_dae_by_time_element(\n", - " m.fs_des,\n", - " time=m.fs_des.time,\n", - " timevar=m.fs_des.time_var,\n", - " keepfiles=True,\n", - " symbolic_solver_labels=True,\n", - " skip_initial=False,\n", - " ts_options={\n", - " \"--ts_type\": \"beuler\", # backward euler integration\n", - " \"--ts_dt\": 200, # set initial step to 200\n", - " \"--ts_rtol\": 20,\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ksp_rtol\": 1e-10,\n", - " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", - " \"--ts_monitor\": \"\",\n", - " \"--ts_save_trajectory\": 1,\n", - " \"--ts_max_snes_failures\": 1000,\n", - " },\n", - ")\n", - "tj_des = result_des.trajectory # trajectory data\n", - "\n", - "# Run end time\n", - "t_end_des = time.time()\n", - "\n", - "# Initialization time\n", - "desorption_simulation_time = value(t_end_des - t_start_des)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the desorption simulation results\n", - "Similar to the results of the adsorption model, the plots below are presented as temporal plots with spatial contours and spatial plots with time contours to fully capture the model trends.\n", - "\n", - "The gas flowrate spikes initially as large quantities of CO2 are recovered, and then the flowrate drops rapidly as the CO2 is carried away. An interesting note is that this occurs over the same time period everywhere across the length of the bed, peaking around 1000 seconds into the simulation. In the spatial plot, the peak is missed between the first and second contours; instead, the spatial plots captures a similar shifting inflection point around 10-25% of the way into the bed as the mass transfer driving force suddenly decreases.\n", - "\n", - "The bed pressure drops suddenly as CO2 is initially desorbed from the bed, and a steady state is reached at larger time points. As in the adsorption case, the desorption results show no temporal or spatial gradients in gas or solid temperature.\n", - "\n", - "The CO2 content of the gas sharply rises initially, and steadily decreases as CO2 is carried away in the sweep gas. A greater amount of CO2 is recovered closer to the reactor inlet. As the sweep gas is nearly pure water vapor, the water concentration in the gas sharply drops as CO2 is desorbed from the bed and recovers to near unity towards the temporal end of the simulation.\n", - "\n", - "Carbamate disappears quickly as CO2 is recovered and the hydrate is broken down, occurring evenly across the length of the reactor bed." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot temporal result profiles\n", - "plot_results_temporal(m.fs_des, tj_des)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot spatial result profiles\n", - "plot_results_spatial(m.fs_ads, tj_ads)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Generate performance results" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "###########################################################################\n", - "# Heat requirements\n", - "###########################################################################\n", - "heat_computation(m, tj_ads, tj_des, adsorption_temperature, desorption_temperature)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "###########################################################################\n", - "# Performance results\n", - "###########################################################################\n", - "performance_results(m, tj_ads, tj_des)\n", - "\n", - "results_summary(m, adsorption_temperature, desorption_temperature)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation time results" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Adsorption initialization time: \", adsorption_initialization_time, \" s\")\n", - "print(\"Adsorption simulation time: \", adsorption_simulation_time, \" s\")\n", - "print()\n", - "print(\"Desorption initialization time: \", desorption_initialization_time, \" s\")\n", - "print(\"Desorption simulation time: \", desorption_simulation_time, \" s\")\n", - "print()\n", - "total_simulation_time = (\n", - " adsorption_initialization_time\n", - " + adsorption_simulation_time\n", - " + desorption_initialization_time\n", - " + desorption_simulation_time\n", - ")\n", - "print(\"Total simulation time: \", total_simulation_time, \" s\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "This example demonstrates implementation of the 1D FixedBed model within IDAES. External property packages for multiple phase domains and interfaces, as well as mass transfer and reaction properties, were integrated into two simulations. The simulations are connected via their final (adsorption) and initial (desorption) states, and each utilizes the PETSc integrator to calculate temporal profiles over the spatial domains." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2025 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "#\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CO2 Adsorption Desorption simulation example with a 1D Fixed Bed model\n", + "Author: Chinedu Okoli, Anca Ostace, Brandon Paul \n", + "Maintainer: Brandon Paul \n", + "Updated: 2023-06-01 \n", + "\n", + "\n", + "This jupyter notebook shows an example of a CO2 Adsorption Desorption cycle with the IDAES 1D FixedBed model. The IDAES 1D FixedBed model is a dynamic and axially varying reactor/adsorption model which is able to model the gas and solid interactions of the modeled species in detail. The sorbent used for this example is the NETL_32D sorbent with its details and parameters obtained from the following references: \n", + "- A. Lee, D.C. Miller, A One-Dimensional (1-D) Three-Region Model for a Bubbling Fluidized-Bed Adsorber, Ind. Eng. Chem. Res. 52 (2013) 469–484\n", + "- Lee, A.; Mebane, D.; Fauth, D. J.; Miller, D. C. A Model for the Adsorption Kinetics of CO2 on Amine-Impregnated Mesoporous Sorbents in the Presence of Water. Presented at the 28th International Pittsburgh Coal Conference, Pittsburgh, PA, 2011.\n", + "\n", + "The notebook demonstrates how to use the IDAES 1DFixedBed model for an adsorption/desorption application with distinct adsorption and desorption steps. This example leverages custom libraries and functions specific to the NETL_32D solid sorbent and associated gas phase properties and surface reactions. In this system, the silicon monoxide (SiO(s)) sorbent reduces carbon dioxide (CO2(g)) to carbamate (denoted Car(s)) while simultaneously absorbing water vapor (H2O(g)) to produce a solid solution-state hydrate (H2O(s)). The solid phase is considered a one-dimensional domain with three regions for bubble, cloud wake and emulsion properties for bubbling bed systems; in the case of a fixed bed reactor non-bulk gas behavior is neglected and assumed to be homogeneous everywhere not near the solid surface.\n", + "\n", + "The notebook also shows how to simulate the adsorption and desorption steps using the PETSc integrator. PETSc leverages nonlinear equation and differential algebraic equation solvers to solve time-trajectory problems. These solvers are applicable for systems with zero degrees of freedom, such as a fully-specified bed reactor model. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cycle details\n", + "The system contains the following equipment and stream properties:\n", + "- Bed height: 9 m\n", + "- Bed diameter: 1 m\n", + "- Adsorption time: 30 hrs\n", + "- Desorption time: 2 hrs\n", + "- Adsorption temperature: 303.15 K\n", + "- Desorption temperature: 470 K\n", + "- Flue gas inlet conditions (Temperature and mole fractions obtained from NETL baseline report. Exhibit 5-22 B31B case)\n", + " - Temperature: 315 K\n", + " - Pressure: 106.5 kPa\n", + " - Flowrate: 3.544 mol/s\n", + " - Mole fractions: CO2: 0.0408, H2O: 0.0875, N2: 0.7517, O2: 0.12" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes\n", + "Some additional information regarding the problem:\n", + "- Isothermal conditions: heat duty requirements of each step are calculated from gas and sorbent properties, i.e., loading, density, heat of adsorption, and heat capacity\n", + "- Heating and cooling modes not modeled in detail: cycle times are assumed negligible in comparison to adsorption and desorption\n", + "- Adsorption and desorption steps are modeled in different flowsheets\n", + "- Initial condition (except temperature) of sorbent in desorption step is set to final condition of sorbent in adsorption step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Import relevant libraries and packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import python libraries\n", + "\n", + "- numpy (numerical python library which provides numerical computing tools)\n", + "- time (time python library which will be used to track the simulation time)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Import Pyomo packages\n", + "For the flowsheet, we will need several components from the pyomo libraries.\n", + "\n", + "- ConcreteModel (to create the Pyomo model that will contain the IDAES flowsheet)\n", + "- TransformationFactory (to apply certain transformations)\n", + "- SolverFactory (to solve the problem)\n", + "- Var (to create a Pyomo variable)\n", + "- value (to return the numerical value of Pyomo objects such as variables, constraints or expressions)\n", + "- units (to handle units in Pyomo and IDAES)\n", + "\n", + "For further details on these components, please refer to the pyomo documentation: https://pyomo.readthedocs.io/en/latest/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (\n", + " ConcreteModel,\n", + " TransformationFactory,\n", + " SolverFactory,\n", + " Var,\n", + " value,\n", + " units as pyunits,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES core components\n", + "\n", + "To build, initialize, and solve IDAES flowsheets we will need several core components/utilities:\n", + "\n", + "- FlowsheetBlock (the flowsheet block contains idaes properties, time, and unit models)\n", + "- EnergyBalanceType (to specify the energy balance type)\n", + "- petsc (PETSc integrator)\n", + "- get_solver (IDAES solver utility)\n", + "- iscale (is used to apply scaling factors in variables and constraints)\n", + "- propagate_state (is used to initialize models, propagating the state variables from one unit model to another)\n", + "- degrees_of_freedom (useful for debugging, this method returns the DOF of the model)\n", + "- idaeslog (is used to set output messages like warnings or errors)\n", + "\n", + "For further details on these components, please refer to the IDAES documentation: https://idaes-pse.readthedocs.io/en/latest/" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.core import FlowsheetBlock, EnergyBalanceType\n", + "import idaes.core.solvers.petsc as petsc # PETSc utilities module\n", + "from idaes.core.util import scaling as iscale\n", + "from idaes.core.solvers import get_solver\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "import idaes.logger as idaeslog\n", + "import logging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import IDAES unit models and NETL 32D property packages\n", + "\n", + "To build the IDAES flowsheet for the CO2 Adsorption Desorption example, we will need the following: \n", + "1) the 1D Fixed Bed unit model \n", + "2) the NETL 32D property package" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models_extra.gas_solid_contactors.unit_models.fixed_bed_1D import FixedBed1D\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_gas_phase_thermo import (\n", + " GasPhaseParameterBlock,\n", + ")\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_solid_phase_thermo import (\n", + " SolidPhaseParameterBlock,\n", + ")\n", + "from idaes_examples.mod.co2_adsorption_desorption.NETL_32D_adsorption_reactions import (\n", + " HeteroReactionParameterBlock,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import custom libraries and functions\n", + "To simplify and automate this example at the flowsheet level, several custom methods were defined in an external script. The methods are custom, and as such are imported separately from the set of IDAES and Pyomo methods above. These utility functions perform a variety of numerical and reporting tasks:\n", + "\n", + "- heat_computation (used to calculate the heat requirements of the CO2 Adsorption Desorption cycle)\n", + "- performance_results (used to evaluate the performance of the CO2 Adsorption Desorption cycle)\n", + "- results_summary (provides summarized results of the CO2 Adsorption Desorption cycle)\n", + "- plot_results_temporal (plots the temporal profiles of the flowsheet simulation)\n", + "- plot_results_spatial (plots the spatial profiles of the flowsheet simulation)\n", + "- fb_model_setup (function that builds the 1D FixedBed model)\n", + "- fb_fixed_conditions (function that fixes the initial and boundary conditions of the 1D FixedBed model)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes_examples.mod.co2_adsorption_desorption.simulation_utilities import (\n", + " heat_computation,\n", + " performance_results,\n", + " results_summary,\n", + " plot_results_temporal,\n", + " plot_results_spatial,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Function to Build the Flowsheet\n", + "\n", + "The function \"fb_model_setup\" below builds instances of the 1D FixedBed model for the adsorption and desorption simulations. While the effort required to define the reactor is low in terms of lines of code, we will need to do this for both the adsorption and desorption steps. Therefore, defining this setup as a single function will make later steps easier to follow. As arguments it takes the following:\n", + "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", + "2) The number of time finite elements \n", + "3) The number of spatial finite elements \n", + "\n", + "It returns a flowsheet object which contains an instance of the 1D FixedBed model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def fb_model_setup(\n", + " fs,\n", + " ntfe, # number of time finite elements\n", + " nxfe, # number of space finite elements\n", + "):\n", + "\n", + " # Set up thermo props and reaction props\n", + " fs.gas_properties = GasPhaseParameterBlock()\n", + " fs.solid_properties = SolidPhaseParameterBlock()\n", + "\n", + " fs.hetero_reactions = HeteroReactionParameterBlock(\n", + " solid_property_package=fs.solid_properties,\n", + " gas_property_package=fs.gas_properties,\n", + " )\n", + "\n", + " fs.FB = FixedBed1D(\n", + " finite_elements=nxfe,\n", + " transformation_method=\"dae.finite_difference\",\n", + " energy_balance_type=EnergyBalanceType.none,\n", + " pressure_drop_type=\"ergun_correlation\",\n", + " gas_phase_config={\"property_package\": fs.gas_properties},\n", + " solid_phase_config={\n", + " \"property_package\": fs.solid_properties,\n", + " \"reaction_package\": fs.hetero_reactions,\n", + " },\n", + " )\n", + "\n", + " # Discretize time domain\n", + " fs.discretizer = TransformationFactory(\"dae.finite_difference\")\n", + " fs.discretizer.apply_to(fs, nfe=ntfe, wrt=fs.time, scheme=\"BACKWARD\")\n", + "\n", + " return fs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a Function to Fix the Flowsheet Conditions\n", + "\n", + "The function \"fb_fix_conditions\" fixes the initial and boundary conditions of an instance of the 1D FixedBed model. It also has checks to ensure that the degrees of freedom are zero, and that the velocity into the bed is less than the minimum fluidization velocity of the bed. Similarly to the function above, we will need to run this for the adsorption and desorption steps, and it is clearer to define the code once as a single function here. As arguments it takes the following:\n", + "1) The flowsheet block of the simulation, i.e., the adsorption flowsheet \n", + "2) The reactor bed diameter \n", + "3) The reactor bed height \n", + "4) A dictionary of gas phase state data for which the gas phase state variables of the model should be fixed to \n", + "5) A dictionary of solid phase state data for which the solid phase state variables of the model should be fixed to. If None, the solid phase state variables are fixed to their current values. \n", + "\n", + "No object is returned by this function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def fb_fix_conditions(\n", + " fs, bed_diameter, bed_height, gas_phase_state_dict, solid_phase_state_dict=None\n", + "):\n", + " # Fix bed geometry variables\n", + " fs.FB.bed_diameter.fix(bed_diameter) # m\n", + " fs.FB.bed_height.fix(bed_height) # m\n", + "\n", + " # Fix boundary values for gas for all time\n", + " blk = fs.FB\n", + " for t in fs.time:\n", + " # Gas values\n", + " blk.gas_inlet.flow_mol[t].fix(gas_phase_state_dict[\"flow_mol\"])\n", + " blk.gas_inlet.temperature[t].fix(gas_phase_state_dict[\"temperature\"])\n", + " blk.gas_inlet.pressure[t].fix(gas_phase_state_dict[\"pressure\"])\n", + " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", + " blk.gas_inlet.mole_frac_comp[t, j].fix(val)\n", + "\n", + " # Specify gas phase and solid phase initial conditions for all space\n", + " t0 = fs.time.first()\n", + " for x in blk.length_domain:\n", + " blk.gas_phase.properties[t0, x].flow_mol.fix(gas_phase_state_dict[\"flow_mol\"])\n", + " blk.gas_phase.properties[t0, x].temperature.fix(\n", + " gas_phase_state_dict[\"temperature\"]\n", + " ) # K\n", + " for j, val in gas_phase_state_dict[\"mole_frac_comp\"].items():\n", + " blk.gas_phase.properties[t0, x].mole_frac_comp[j].fix(val)\n", + "\n", + " if solid_phase_state_dict is None:\n", + " # Fix to existing values if dict is empty\n", + " blk.solid_properties[t0, x].dens_mass_particle.fix()\n", + " blk.solid_properties[t0, x].temperature.fix()\n", + " blk.solid_properties[t0, x].mass_frac_comp[:].fix()\n", + " else:\n", + " blk.solid_properties[t0, x].dens_mass_particle.fix(\n", + " solid_phase_state_dict[\"dens_mass_particle\"]\n", + " )\n", + " blk.solid_properties[t0, x].temperature.fix(\n", + " solid_phase_state_dict[\"temperature\"]\n", + " )\n", + " for j, val in solid_phase_state_dict[\"mass_frac_comp\"].items():\n", + " blk.solid_properties[t0, x].mass_frac_comp[j].fix(val)\n", + "\n", + " dof = degrees_of_freedom(fs)\n", + "\n", + " print(\"degrees of freedom = \", dof)\n", + " try:\n", + " assert degrees_of_freedom(fs) == 0\n", + " except AssertionError:\n", + " print(\"Degrees of freedom is not equal to zero. This is unexpected.\")\n", + " raise\n", + "\n", + " # Assert that inlet gas velocity is less than v_mf\n", + " # Use solid temperature as the thermal mass of solid >> than that of gas\n", + " pi = 3.14 # [-]\n", + " R = 8.314 # Gas constant [J/mol/K]\n", + "\n", + " @blk.Expression(doc=\"gas inlet velocity, m/s\")\n", + " def gas_inlet_velocity(blk):\n", + " v_gas_inlet = (\n", + " blk.gas_inlet.flow_mol[0] / (pi * value(blk.bed_diameter**2) / 4)\n", + " ) * (R * blk.solid_properties[0, 0].temperature / blk.gas_inlet.pressure[0])\n", + " return v_gas_inlet\n", + "\n", + " v_mf = value( # minimum fluidization velocity [m/s]\n", + " blk.solid_properties[t0, 0]._params.velocity_mf\n", + " )\n", + " print(\"inlet gas velocity = \", value(blk.gas_inlet_velocity), \" m/s\")\n", + " print(\"min. fluid velocity = \", v_mf, \" m/s\")\n", + " try:\n", + " assert value(blk.gas_inlet_velocity) <= v_mf\n", + " except AssertionError:\n", + " print(\n", + " \"The inlet gas velocity is greater than the minimum fluidization velocity. \"\n", + " \"This is unexpected for a Fixed Bed.\"\n", + " )\n", + " raise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2: Setup and run simulation\n", + "Now that the system properties and all required utility functions are defined, we will build the model itself." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create concrete model\n", + "First, create the model object:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "m = ConcreteModel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup solver for initialization\n", + "We limit the maximum number of iterations and apply appropriate scaling for the linear solver for initialization:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Solver arguments\n", + "optarg = {\n", + " \"max_iter\": 100,\n", + " \"nlp_scaling_method\": \"user-scaling\",\n", + " \"linear_solver\": \"ma27\",\n", + "}\n", + "\n", + "# Create a solver\n", + "solver = get_solver(\"ipopt\")\n", + "solver.options = optarg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set spatial elements and design variables for adsorption and desorption simulations\n", + "Let's define the size of the system:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Number of spatial elements\n", + "nxfe = 50\n", + "\n", + "# Design variables for static and dynamic models\n", + "bed_diameter = 9 # m\n", + "bed_height = 1 # m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adsorption simulation\n", + "First, we will run the adsorption step by defining the simulation domain, add initial conditions, and set up and solve the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup simulation horizon and create time set\n", + "We specify the time discretization to an exact set of temporal points according to the problem horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Time horizon\n", + "horizon = 108000 # s\n", + "\n", + "# Create time_set list\n", + "t_element_size = horizon / 4 # s\n", + "ntfe = int(horizon / t_element_size)\n", + "time_set = list(np.linspace(0, horizon, ntfe + 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial conditions for gas and solid phases\n", + "Next, we set reasoanble initial conditions for the gas and solid phases at time = 0, with a constant adsorption temperature:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Flue gas inlet conditions to adsorption system (flue gas stream) -\n", + "# Temperature and mole fractions obtained from NETL baseline report.\n", + "# Exhibit 5-22 B31B case.\n", + "adsorption_temperature = 303.15 # K\n", + "\n", + "# Dictionary of initial and boundary conditions for gas phase\n", + "gas_phase_state_dict_ads = {\n", + " \"flow_mol\": 3.544, # mol/s\n", + " \"temperature\": adsorption_temperature, # K\n", + " \"pressure\": 1.2452e5, # Pa\n", + " \"mole_frac_comp\": { # [-]\n", + " \"CO2\": 0.0408,\n", + " \"H2O\": 0.0875,\n", + " \"N2\": 0.7517,\n", + " \"O2\": 0.12,\n", + " },\n", + "}\n", + "\n", + "# Dictionary of initial conditions for solid phase\n", + "solid_phase_state_dict_ads = {\n", + " \"dens_mass_particle\": 442, # kg/m3\n", + " \"temperature\": adsorption_temperature, # K\n", + " \"mass_frac_comp\": { # [-]\n", + " \"H2O_s\": 1e-8,\n", + " \"Car\": 1e-8,\n", + " \"SiO\": 1,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the adsorption model\n", + "Then, we call our previously defined methods to build the model and fix initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the adsorption flowsheet\n", + "m.fs_ads = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)\n", + "\n", + "# Setup an instance of the 1D FixedBed model for the adsorption simulation\n", + "m.fs_ads = fb_model_setup(m.fs_ads, ntfe, nxfe)\n", + "\n", + "# Fix initial and boundary conditions\n", + "fb_fix_conditions(\n", + " m.fs_ads,\n", + " bed_diameter,\n", + " bed_height,\n", + " gas_phase_state_dict_ads,\n", + " solid_phase_state_dict_ads,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adsorption model - initialize and solve\n", + "Finally for the adsorption model, we initialize and solve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Apply scaling transformation\n", + "We first scale the model variables and equations to reduce ill-conditioning and improve its convergence properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "iscale.calculate_scaling_factors(m.fs_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize model\n", + "The model initialization is done with the block triangularization initialization method in the 1D FixedBed model as this is faster than using the traditional sequential heirarchichal initialization approach. See the 1D FixedBed model and documentation for more details on this method." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_ads = time.time()\n", + "\n", + "# Initialize model\n", + "calc_var_kwds = {\"eps\": 1e-5}\n", + "m.fs_ads.FB.block_triangularization_initialize(\n", + " gas_phase_state_args=gas_phase_state_dict_ads,\n", + " solid_phase_state_args=solid_phase_state_dict_ads,\n", + " outlvl=idaeslog.DEBUG,\n", + " solver=solver,\n", + " calc_var_kwds=calc_var_kwds,\n", + ")\n", + "\n", + "# Run end time\n", + "t_end_ads = time.time()\n", + "\n", + "# Initialization time\n", + "adsorption_initialization_time = value(t_end_ads - t_start_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PETSc integrator\n", + "As mentioned earlier, the PETSc integrator is used for simulating the time trajectory. After starting the clock and instantiating the time variables, PETSc performs and element-wise discretization to define and solve the dynamic system of equations. See https://idaes-pse.readthedocs.io/en/stable/reference_guides/core/solvers.html#petsc-utilities for more details.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "petscsolvelog = idaeslog.getSolveLogger(\"petsc-dae\")\n", + "petscsolvelog.setLevel(logging.WARNING) # comment this line to see PETSc solver output\n", + "\n", + "# Run start time\n", + "t_start_ads = time.time()\n", + "\n", + "# Setup PETSc integrator and simulate adsorption flowsheet\n", + "m.fs_ads.time_var = Var(m.fs_ads.time)\n", + "m.fs_ads.time_var[0].fix(m.fs_ads.time.first())\n", + "\n", + "result_ads = petsc.petsc_dae_by_time_element(\n", + " m.fs_ads,\n", + " time=m.fs_ads.time,\n", + " timevar=m.fs_ads.time_var,\n", + " keepfiles=True,\n", + " symbolic_solver_labels=True,\n", + " skip_initial=False,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\", # backward euler integration\n", + " \"--ts_dt\": 200, # set initial step to 200\n", + " \"--ts_rtol\": 20,\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ksp_rtol\": 1e-10,\n", + " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ts_max_snes_failures\": 1000,\n", + " },\n", + ")\n", + "tj_ads = result_ads.trajectory # trajectory data\n", + "\n", + "# Run end time\n", + "t_end_ads = time.time()\n", + "\n", + "# Initialization time\n", + "adsorption_simulation_time = value(t_end_ads - t_start_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the adsorption simulation results\n", + "After running the adsorption simulation, the dynamic profiles are plotted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the temporal plots below, quantities are reported as functions of time and contoured for six points along the length of the bed. The time and spatial dimensions are exchanged in the spatial plots, with quantities reported as functions of length and contoured for five time points.\n", + "\n", + "The gas flowrate is constant at the inlet and decreases over the length of the bed as CO2 is removed from the gas. At each spatial point, the CO2 content of the gas increases over time as the bed becomes more saturated with CO2 and the mass transfer driving force decreases. Assessing the two gas flowrate plots, adsorption occurs steadily over time with a much larger capture rate in the initial spatial region; this region grows from 10% to 25% of the bed length as the bed becomes more saturated with CO2 over time.\n", + "\n", + "The bed pressure stays relatively constant over time, and exhibits a linear drop over the length of the reactor. There are no temporal or spatial gradients in gas or solid temperature.\n", + "\n", + "The saturation trend is strongly apparent in the composition trends. The CO2 content of the gas quickly drops from the initial concentration over the length of the bed, and steadily rises over time as the driving force of mass transfer decreases. Water vapor concentration in the gas follows a similar trend.\n", + "\n", + "Following the solid-state reaction, the concentration of carbmate steadily increases over time with formation skewing heavily towards the initial spatial region of the bed. The spatial contours of the temporal carbamate composition plot cross as a result of the solid reaction rate heavily favoring the initial section of the bed and suddenly dropping about at about 20% of the reactor length. The mass fraction and mol/kg plots differ slightly due to water and CO2 absorbing at different rates, but otherwise demonstrate similar trends." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot temporal result profiles\n", + "plot_results_temporal(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot spatial result profiles\n", + "plot_results_spatial(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Desorption simulation\n", + "The desorption simulation begins when the adsorption simulation ends. It uses the final state of the sorbent (besides temperature which is set at the desorption temperature) in the adsorption mode as the initial state of the sorbent in the desorption mode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup simulation horizon and create time set\n", + "As in the adsorption simulation, we first specify the time discretization:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Space and time discretization arguments\n", + "horizon = 7200 # s\n", + "\n", + "# Setup for PETSc run\n", + "t_element_size = horizon # s\n", + "ntfe = int(horizon / t_element_size)\n", + "time_set = list(np.linspace(0, horizon, ntfe + 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial and boundary conditions for gas phase\n", + "- The initial conditions of the solid phase will be copied from the final conditions of the model in the adsorption simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Desorption operating conditions\n", + "desorption_temperature = 470 # K\n", + "\n", + "gas_phase_state_dict_des = {\n", + " \"flow_mol\": 10, # mol/s\n", + " \"temperature\": desorption_temperature, # K\n", + " \"pressure\": 1.06525e5, # Pa\n", + " \"mole_frac_comp\": {\"CO2\": 1e-8, \"H2O\": 1, \"N2\": 1e-8, \"O2\": 1e-8}, # [-]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup the desorption model\n", + "Then, we build the model and fix initial conditions:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the desorption flowsheet\n", + "m.fs_des = FlowsheetBlock(dynamic=True, time_set=time_set, time_units=pyunits.s)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup an instance of the 1D FixedBed model for the desorption simulation\n", + "m.fs_des = fb_model_setup(\n", + " m.fs_des,\n", + " ntfe,\n", + " nxfe,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Desorption model - initialize and solve\n", + "Finally for the desorption model, we initialize and solve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Copy values from adsorption model to desorption model\n", + "Copy state values of solid phase (besides temperature) from last time point in adsorption model to all time point in desorption model. Also set temperature state variable of desorption model to desorption temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "blk_des = m.fs_des.FB\n", + "tf_ads = tj_ads.time[-1] # Get final time from adsorption trajectory results\n", + "tf_ads_index = tj_ads.time.index(tf_ads) # Get index at final time\n", + "component_list = blk_des.config.solid_phase_config.property_package.component_list\n", + "\n", + "for t in m.fs_des.time:\n", + " for x in blk_des.length_domain:\n", + " blk_des.solid_properties[t, x].temperature.set_value(desorption_temperature)\n", + " blk_des.solid_properties[t, x].dens_mass_particle.set_value(\n", + " tj_ads.get_vec(m.fs_ads.FB.solid_properties[tf_ads, x].dens_mass_particle)[\n", + " tf_ads_index\n", + " ]\n", + " )\n", + " for j in component_list:\n", + " blk_des.solid_properties[t, x].mass_frac_comp[j].set_value(\n", + " value(\n", + " tj_ads.get_vec(\n", + " m.fs_ads.FB.solid_properties[tf_ads, x].mass_frac_comp[j]\n", + " )[tf_ads_index]\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fix initial and boundary conditions\n", + "We fix the conditions from the final adsorption model state as our new initial conditions for the desorption model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "fb_fix_conditions(\n", + " m.fs_des,\n", + " bed_diameter,\n", + " bed_height,\n", + " gas_phase_state_dict_des,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate scaling of desorption model\n", + "Then, we apply scaling to the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "iscale.calculate_scaling_factors(m.fs_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize desorption model\n", + "The desorption model initialization is done with the block triangularization initialization method, as was done with the adsorption model." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_des = time.time()\n", + "\n", + "# Initialize model\n", + "m.fs_des.FB.block_triangularization_initialize(\n", + " gas_phase_state_args=gas_phase_state_dict_des,\n", + " outlvl=idaeslog.DEBUG,\n", + " solver=solver,\n", + " calc_var_kwds=calc_var_kwds,\n", + ")\n", + "\n", + "# Run end time\n", + "t_end_des = time.time()\n", + "\n", + "# Initialization time\n", + "desorption_initialization_time = value(t_end_des - t_start_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup PETSc integrator and simulate desorption step\n", + "Now that the model is fully defined, we can call the PETSc integrator to simulate the dynamics of the problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Run start time\n", + "t_start_des = time.time()\n", + "\n", + "# Setup PETSc integrator and simulate desorption flowsheet\n", + "m.fs_des.time_var = Var(m.fs_des.time)\n", + "m.fs_des.time_var[0].fix(m.fs_des.time.first())\n", + "\n", + "result_des = petsc.petsc_dae_by_time_element(\n", + " m.fs_des,\n", + " time=m.fs_des.time,\n", + " timevar=m.fs_des.time_var,\n", + " keepfiles=True,\n", + " symbolic_solver_labels=True,\n", + " skip_initial=False,\n", + " ts_options={\n", + " \"--ts_type\": \"beuler\", # backward euler integration\n", + " \"--ts_dt\": 200, # set initial step to 200\n", + " \"--ts_rtol\": 20,\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ksp_rtol\": 1e-10,\n", + " \"--snes_type\": \"newtontr\", # newton trust region non-linear solver\n", + " \"--ts_monitor\": \"\",\n", + " \"--ts_save_trajectory\": 1,\n", + " \"--ts_max_snes_failures\": 1000,\n", + " },\n", + ")\n", + "tj_des = result_des.trajectory # trajectory data\n", + "\n", + "# Run end time\n", + "t_end_des = time.time()\n", + "\n", + "# Initialization time\n", + "desorption_simulation_time = value(t_end_des - t_start_des)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the desorption simulation results\n", + "Similar to the results of the adsorption model, the plots below are presented as temporal plots with spatial contours and spatial plots with time contours to fully capture the model trends.\n", + "\n", + "The gas flowrate spikes initially as large quantities of CO2 are recovered, and then the flowrate drops rapidly as the CO2 is carried away. An interesting note is that this occurs over the same time period everywhere across the length of the bed, peaking around 1000 seconds into the simulation. In the spatial plot, the peak is missed between the first and second contours; instead, the spatial plots captures a similar shifting inflection point around 10-25% of the way into the bed as the mass transfer driving force suddenly decreases.\n", + "\n", + "The bed pressure drops suddenly as CO2 is initially desorbed from the bed, and a steady state is reached at larger time points. As in the adsorption case, the desorption results show no temporal or spatial gradients in gas or solid temperature.\n", + "\n", + "The CO2 content of the gas sharply rises initially, and steadily decreases as CO2 is carried away in the sweep gas. A greater amount of CO2 is recovered closer to the reactor inlet. As the sweep gas is nearly pure water vapor, the water concentration in the gas sharply drops as CO2 is desorbed from the bed and recovers to near unity towards the temporal end of the simulation.\n", + "\n", + "Carbamate disappears quickly as CO2 is recovered and the hydrate is broken down, occurring evenly across the length of the reactor bed." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot temporal result profiles\n", + "plot_results_temporal(m.fs_des, tj_des)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot spatial result profiles\n", + "plot_results_spatial(m.fs_ads, tj_ads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Generate performance results" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# Heat requirements\n", + "###########################################################################\n", + "heat_computation(m, tj_ads, tj_des, adsorption_temperature, desorption_temperature)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# Performance results\n", + "###########################################################################\n", + "performance_results(m, tj_ads, tj_des)\n", + "\n", + "results_summary(m, adsorption_temperature, desorption_temperature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulation time results" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Adsorption initialization time: \", adsorption_initialization_time, \" s\")\n", + "print(\"Adsorption simulation time: \", adsorption_simulation_time, \" s\")\n", + "print()\n", + "print(\"Desorption initialization time: \", desorption_initialization_time, \" s\")\n", + "print(\"Desorption simulation time: \", desorption_simulation_time, \" s\")\n", + "print()\n", + "total_simulation_time = (\n", + " adsorption_initialization_time\n", + " + adsorption_simulation_time\n", + " + desorption_initialization_time\n", + " + desorption_simulation_time\n", + ")\n", + "print(\"Total simulation time: \", total_simulation_time, \" s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "This example demonstrates implementation of the 1D FixedBed model within IDAES. External property packages for multiple phase domains and interfaces, as well as mass transfer and reaction properties, were integrated into two simulations. The simulations are connected via their final (adsorption) and initial (desorption) states, and each utilizes the PETSc integrator to calculate temporal profiles over the spatial domains." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 }